[
  {
    "path": ".gitignore",
    "content": "__pychache__/\n__pycache__\n.ipynb_checkpoints/\n.nfs*\n"
  },
  {
    "path": "ADNI Training.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext autoreload\\n\",\n    \"%autoreload 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/fabiane/anaconda2/envs/mort1/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n      \"  from ._conv import register_converters as _register_converters\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import glob\\n\",\n    \"import h5py\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import time\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"# pytorch\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler\\n\",\n    \"\\n\",\n    \"# sklearn functions\\n\",\n    \"from sklearn.metrics import accuracy_score\\n\",\n    \"from sklearn.model_selection import train_test_split, KFold, GroupShuffleSplit\\n\",\n    \"\\n\",\n    \"# load functions from nitorch\\n\",\n    \"from nitorch.data import load_nifti\\n\",\n    \"from nitorch.transforms import  ToTensor, SagittalTranslate, SagittalFlip, \\\\\\n\",\n    \"                                AxialTranslate, normalization_factors, Normalize, \\\\\\n\",\n    \"                                IntensityRescale\\n\",\n    \"from nitorch.callbacks import EarlyStopping, ModelCheckpoint\\n\",\n    \"from nitorch.trainer import Trainer\\n\",\n    \"from nitorch.initialization import weights_init\\n\",\n    \"from nitorch.metrics import balanced_accuracy, sensitivity, specificity\\n\",\n    \"from nitorch.utils import count_parameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from settings import settings\\n\",\n    \"for k in settings.keys():\\n\",\n    \"    print(\\\"Adding \\\" + k + \\\" to namespace\\\")\\n\",\n    \"    globals()[k] = settings[k]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.0.0'\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"torch.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gpu = 5\\n\",\n    \"b = 4\\n\",\n    \"num_classes = 2\\n\",\n    \"\\n\",\n    \"dtype = np.float64\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_h5_ = h5py.File(train_h5, 'r')\\n\",\n    \"val_h5_ = h5py.File(val_h5, 'r')\\n\",\n    \"holdout_h5_ = h5py.File(holdout_h5, 'r')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X_train, y_train = train_h5_['X'], train_h5_['y']\\n\",\n    \"X_val, y_val = val_h5_['X'], val_h5_['y']\\n\",\n    \"X_holdout, y_holdout = holdout_h5_['X'], holdout_h5_['y']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"mean_std_normalization = False\\n\",\n    \"min_max_normalization = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# normalize min-max\\n\",\n    \"X_train = np.array(X_train)\\n\",\n    \"X_val = np.array(X_val)\\n\",\n    \"X_holdout = np.array(X_holdout)\\n\",\n    \"\\n\",\n    \"y_train = np.array(y_train)\\n\",\n    \"y_val = np.array(y_val)\\n\",\n    \"y_holdout = np.array(y_holdout)\\n\",\n    \"\\n\",\n    \"if mean_std_normalization:\\n\",\n    \"    mean = np.mean(X_train)\\n\",\n    \"    std = np.std(X_train)\\n\",\n    \"    X_train = (X_train - mean) / std\\n\",\n    \"    X_val = (X_val - mean) / std\\n\",\n    \"    X_holdout = (X_holdout - mean) / std\\n\",\n    \"    \\n\",\n    \"if min_max_normalization:\\n\",\n    \"    for i in range(len(X_train)):\\n\",\n    \"        X_train[i] -= np.min(X_train[i])\\n\",\n    \"        X_train[i] /= np.max(X_train[i])\\n\",\n    \"\\n\",\n    \"    for i in range(len(X_val)):\\n\",\n    \"        X_val[i] -= np.min(X_val[i])\\n\",\n    \"        X_val[i] /= np.max(X_val[i])\\n\",\n    \"\\n\",\n    \"    for i in range(len(X_holdout)):\\n\",\n    \"        X_holdout[i] -= np.min(X_holdout[i])\\n\",\n    \"        X_holdout[i] /= np.max(X_holdout[i])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class ADNIDataset(Dataset):\\n\",\n    \"    def __init__(self, X, y, transform=None, target_transform=None, mask=None, z_factor=None, dtype=np.float32, num_classes=2):\\n\",\n    \"        self.X = np.copy(X)\\n\",\n    \"        self.y = np.copy(y)\\n\",\n    \"        self.X = X\\n\",\n    \"        self.y = y\\n\",\n    \"        self.transform = transform\\n\",\n    \"        self.target_transform = target_transform\\n\",\n    \"        self.mask = mask\\n\",\n    \"        self.z_factor = z_factor\\n\",\n    \"        self.dtype = dtype\\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"        \\n\",\n    \"    def __len__(self):\\n\",\n    \"        return len(self.X)\\n\",\n    \"    \\n\",\n    \"    def __getitem__(self, idx):\\n\",\n    \"        image = self.X[idx]\\n\",\n    \"        label_tensor = np.zeros(shape=(self.num_classes,))\\n\",\n    \"        label = self.y[idx] >= 0.5\\n\",\n    \"        label = torch.LongTensor([label])\\n\",\n    \"        \\n\",\n    \"        if self.transform:\\n\",\n    \"            image = self.transform(image)\\n\",\n    \"            \\n\",\n    \"        sample = {\\\"image\\\" : image,\\n\",\n    \"                 \\\"label\\\" : label}\\n\",\n    \"        return sample\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"augmentations = [SagittalFlip(), SagittalTranslate(dist=(-2, 3))]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"adni_data_train = ADNIDataset(X_train, y_train, transform=transforms.Compose(augmentations + [ToTensor()]), dtype=dtype)\\n\",\n    \"adni_data_val = ADNIDataset(X_val, y_val, transform=transforms.Compose([ToTensor()]), dtype=dtype)\\n\",\n    \"adni_data_test = ADNIDataset(X_holdout, y_holdout, transform=transforms.Compose([ToTensor()]), dtype=dtype)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sample = adni_data_train[400]\\n\",\n    \"img = sample[\\\"image\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x7ff090007b00>\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.imshow(img[0][:,:,70], cmap='gray')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Define the classifier\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class ClassificationModel3D(nn.Module):\\n\",\n    \"    \\\"\\\"\\\"The model we use in the paper.\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    def __init__(self, dropout=0.4, dropout2=0.4):\\n\",\n    \"        nn.Module.__init__(self)\\n\",\n    \"        self.Conv_1 = nn.Conv3d(1, 8, 3)\\n\",\n    \"        self.Conv_1_bn = nn.BatchNorm3d(8)\\n\",\n    \"        self.Conv_1_mp = nn.MaxPool3d(2)\\n\",\n    \"        self.Conv_2 = nn.Conv3d(8, 16, 3)\\n\",\n    \"        self.Conv_2_bn = nn.BatchNorm3d(16)\\n\",\n    \"        self.Conv_2_mp = nn.MaxPool3d(3)\\n\",\n    \"        self.Conv_3 = nn.Conv3d(16, 32, 3)\\n\",\n    \"        self.Conv_3_bn = nn.BatchNorm3d(32)\\n\",\n    \"        self.Conv_3_mp = nn.MaxPool3d(2)\\n\",\n    \"        self.Conv_4 = nn.Conv3d(32, 64, 3)\\n\",\n    \"        self.Conv_4_bn = nn.BatchNorm3d(64)\\n\",\n    \"        self.Conv_4_mp = nn.MaxPool3d(3)\\n\",\n    \"        self.dense_1 = nn.Linear(5120, 128)\\n\",\n    \"        self.dense_2 = nn.Linear(128, 2)\\n\",\n    \"        self.relu = nn.ReLU()\\n\",\n    \"        self.dropout = nn.Dropout(dropout)\\n\",\n    \"        self.dropout2 = nn.Dropout(dropout2)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.relu(self.Conv_1_bn(self.Conv_1(x)))\\n\",\n    \"        x = self.Conv_1_mp(x)\\n\",\n    \"        x = self.relu(self.Conv_2_bn(self.Conv_2(x)))\\n\",\n    \"        x = self.Conv_2_mp(x)\\n\",\n    \"        x = self.relu(self.Conv_3_bn(self.Conv_3(x)))\\n\",\n    \"        x = self.Conv_3_mp(x)\\n\",\n    \"        x = self.relu(self.Conv_4_bn(self.Conv_4(x)))\\n\",\n    \"        x = self.Conv_4_mp(x)\\n\",\n    \"        x = x.view(x.size(0), -1)\\n\",\n    \"        x = self.dropout(x)\\n\",\n    \"        x = self.relu(self.dense_1(x))\\n\",\n    \"        x = self.dropout2(x)\\n\",\n    \"        x = self.dense_2(x)\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"net = ClassificationModel3D().cuda(gpu)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Trainable model parameters: 728898\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Trainable model parameters: {}\\\".format(count_parameters(net)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def run(\\n\",\n    \"    net,\\n\",\n    \"    data,\\n\",\n    \"    shape,\\n\",\n    \"    callbacks=[],\\n\",\n    \"    augmentations=[],\\n\",\n    \"    masked=False,\\n\",\n    \"    metrics=[],\\n\",\n    \"    k_folds=None,\\n\",\n    \"    b=4,\\n\",\n    \"    num_epochs=35,\\n\",\n    \"    retain_metric=None\\n\",\n    \"):      \\n\",\n    \"   \\n\",\n    \"    fold_metric = []\\n\",\n    \"    models = []\\n\",\n    \"    fold = 0\\n\",\n    \"    initial_prepend = None\\n\",\n    \"    \\n\",\n    \"    for trial in range(4):\\n\",\n    \"        print(\\\"Starting trial {}\\\".format(trial))\\n\",\n    \"\\n\",\n    \"        # add current fold number to model checkpoint path\\n\",\n    \"        if callbacks is not None:\\n\",\n    \"            for idx, callback in enumerate(callbacks):\\n\",\n    \"                if isinstance(callback, ModelCheckpoint):\\n\",\n    \"                    if initial_prepend is None:\\n\",\n    \"                        initial_prepend = callbacks[idx].prepend\\n\",\n    \"                    callbacks[idx].prepend = initial_prepend + \\\"cv_fold_{}_\\\".format(fold)\\n\",\n    \"        fold += 1\\n\",\n    \"\\n\",\n    \"        # restart model\\n\",\n    \"        del net\\n\",\n    \"        net = ClassificationModel3D().cuda(gpu)\\n\",\n    \"        \\n\",\n    \"        # reset hyperparameters\\n\",\n    \"        lr = 1e-4\\n\",\n    \"        wd = 1e-4\\n\",\n    \"        criterion = nn.CrossEntropyLoss().cuda(gpu)\\n\",\n    \"        optimizer = optim.Adam(net.parameters(), lr=lr, weight_decay=wd)\\n\",\n    \"\\n\",\n    \"        train_loader = DataLoader(\\n\",\n    \"            adni_data_train, batch_size=b, num_workers=4, shuffle=True\\n\",\n    \"        )\\n\",\n    \"        val_loader = DataLoader(\\n\",\n    \"            adni_data_val, batch_size=1, num_workers=1, shuffle=True\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        sample = next(iter(train_loader))\\n\",\n    \"        img = sample[\\\"image\\\"][0]\\n\",\n    \"        lbl = sample[\\\"label\\\"][0]\\n\",\n    \"        plt.imshow(img.squeeze()[:,:,70], cmap='gray')\\n\",\n    \"        plt.title(lbl.item())\\n\",\n    \"        plt.show()\\n\",\n    \"        trainer = Trainer(\\n\",\n    \"            net,\\n\",\n    \"            criterion,\\n\",\n    \"            optimizer,\\n\",\n    \"            metrics=metrics,\\n\",\n    \"            callbacks=callbacks,\\n\",\n    \"            device=gpu,\\n\",\n    \"            prediction_type=\\\"classification\\\"\\n\",\n    \"        )\\n\",\n    \"        # train model and store results\\n\",\n    \"        net, report = trainer.train_model(\\n\",\n    \"            train_loader,\\n\",\n    \"            val_loader,\\n\",\n    \"            num_epochs=num_epochs,\\n\",\n    \"            show_train_steps=60,\\n\",\n    \"            show_validation_epochs=1,\\n\",\n    \"        )\\n\",\n    \"        # append validation score of the retain metric\\n\",\n    \"        if isinstance(retain_metric, str):\\n\",\n    \"            fold_metric.append(report[\\\"val_metrics\\\"][retain_metric][-1])\\n\",\n    \"        else:\\n\",\n    \"            fold_metric.append(report[\\\"val_metrics\\\"][retain_metric.__name__][-1])\\n\",\n    \"\\n\",\n    \"        models.append(net)\\n\",\n    \"        print(\\\"Finished fold.\\\")\\n\",\n    \"\\n\",\n    \"        # visualize result\\n\",\n    \"        trainer.visualize_training(report, metrics)\\n\",\n    \"        trainer.evaluate_model(val_loader, gpu)\\n\",\n    \"\\n\",\n    \"    print(\\\"################################\\\")\\n\",\n    \"    print(\\\"################################\\\")\\n\",\n    \"    print(\\\"All accuracies: {}\\\".format(fold_metric))\\n\",\n    \"    return fold_metric, models\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_epochs = 200\\n\",\n    \"min_iters = 3\\n\",\n    \"ignore_epochs = 15\\n\",\n    \"normalize = False\\n\",\n    \"retain_metric = accuracy_score\\n\",\n    \"metrics = [accuracy_score]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"r = 0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1_OHE\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"check = ModelCheckpoint(path=model_path,\\n\",\n    \"                             prepend=\\\"repeat_{}\\\".format(r),\\n\",\n    \"                             store_best=True,\\n\",\n    \"                             ignore_before=ignore_epochs,\\n\",\n    \"                             retain_metric=retain_metric)\\n\",\n    \"callbacks = [check, EarlyStopping(patience=8, ignore_before=ignore_epochs, retain_metric=\\\"loss\\\", mode='min')]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Starting trial 0\\n\",\n      \"torch.Size([1, 193, 229, 193])\\n\",\n      \"175\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0,    60] loss: 0.77046\\n\",\n      \"[0,   120] loss: 0.72945\\n\",\n      \"Time elapsed: 0h:1m:43s\\n\",\n      \"train accuracy_score: 50.93 %\\n\",\n      \"tensor([[[[[ 2.7221e-03, -2.3426e-03, -4.9928e-03],\\n\",\n      \"           [ 7.2687e-03,  2.1895e-03, -9.9231e-04],\\n\",\n      \"           [ 5.0693e-03,  1.1742e-03, -9.7307e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0745e-02, -4.3186e-04,  1.5243e-03],\\n\",\n      \"           [ 3.8719e-03, -5.3556e-03, -7.9576e-03],\\n\",\n      \"           [-8.2282e-03, -8.7681e-03, -2.9832e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 7.3162e-03, -4.6082e-04, -4.1793e-03],\\n\",\n      \"           [ 1.3357e-03, -1.0075e-02,  9.4900e-04],\\n\",\n      \"           [-1.2312e-03, -1.2214e-02, -6.5313e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.7730e-03, -7.8781e-04, -6.2785e-04],\\n\",\n      \"           [ 7.9231e-04, -1.3995e-03, -9.6758e-04],\\n\",\n      \"           [ 9.1635e-04,  3.7616e-04, -6.7217e-05]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5894e-03,  8.4799e-04, -2.4266e-04],\\n\",\n      \"           [ 7.8274e-04, -1.5266e-04,  8.1652e-05],\\n\",\n      \"           [ 9.0051e-04,  1.2808e-03,  6.6106e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 6.8734e-04,  4.8240e-04,  1.0291e-03],\\n\",\n      \"           [ 1.1977e-03,  1.3444e-03,  7.4090e-04],\\n\",\n      \"           [ 3.5483e-03,  2.8902e-03,  1.5653e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.1590e-02,  1.0071e-01,  1.2848e-02],\\n\",\n      \"           [ 8.7946e-02,  6.1027e-02, -3.2667e-02],\\n\",\n      \"           [ 7.7709e-02,  3.1118e-02,  4.4848e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0502e-02,  4.9018e-02,  1.5662e-02],\\n\",\n      \"           [ 4.1684e-02,  6.3206e-02, -1.8465e-03],\\n\",\n      \"           [ 7.2267e-02, -4.5830e-03,  4.5449e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.1092e-02, -2.0349e-02,  1.6682e-02],\\n\",\n      \"           [-1.4339e-02, -1.7723e-03,  2.5060e-02],\\n\",\n      \"           [ 4.8745e-02,  1.4286e-02,  2.5077e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.4239e-02, -9.8297e-04, -2.0611e-02],\\n\",\n      \"           [ 1.2665e-02,  1.1523e-02, -1.9523e-02],\\n\",\n      \"           [ 2.7978e-02,  2.5733e-03,  3.8068e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.2117e-03, -1.6788e-02, -4.0979e-02],\\n\",\n      \"           [-2.7235e-03, -1.1259e-02, -2.5511e-02],\\n\",\n      \"           [ 1.6184e-02,  9.2201e-03, -7.6581e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.6789e-02, -4.2164e-02, -5.0115e-02],\\n\",\n      \"           [-1.8809e-02, -2.9938e-02, -3.0981e-02],\\n\",\n      \"           [-2.3406e-02, -2.1684e-02, -2.5703e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1711e-02, -1.0396e-02,  1.5377e-02],\\n\",\n      \"           [ 1.9324e-03,  1.8760e-02,  1.7304e-02],\\n\",\n      \"           [ 3.0152e-02,  4.0763e-02,  3.6730e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.9967e-02,  1.9211e-02,  1.0357e-02],\\n\",\n      \"           [ 4.4292e-04,  1.7039e-02,  9.6121e-03],\\n\",\n      \"           [ 3.0547e-02,  1.2460e-02,  2.4193e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4929e-02,  2.1474e-02, -6.7426e-03],\\n\",\n      \"           [ 1.3854e-02,  9.7724e-03,  2.5532e-02],\\n\",\n      \"           [ 2.6497e-02,  1.1244e-02,  2.0984e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.0217e-02, -9.3339e-02, -8.9514e-02],\\n\",\n      \"           [-6.5392e-02, -1.0298e-01, -1.5707e-01],\\n\",\n      \"           [-1.4591e-01, -1.9476e-01, -1.3713e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.1004e-02, -3.3931e-02, -1.1022e-01],\\n\",\n      \"           [-9.5908e-02, -7.4950e-02, -1.1220e-01],\\n\",\n      \"           [-1.0737e-01, -1.3857e-01, -1.4344e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.6006e-02, -7.2949e-03, -6.0536e-02],\\n\",\n      \"           [-5.9262e-02, -5.5727e-02, -4.4026e-02],\\n\",\n      \"           [-9.4763e-02, -7.9315e-02, -1.0301e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1356e-02, -2.1935e-02, -2.9925e-02],\\n\",\n      \"           [-1.3265e-02, -2.2284e-02, -1.8857e-02],\\n\",\n      \"           [-1.9964e-02, -8.8339e-03, -4.7833e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.7302e-03, -2.3389e-02, -2.7019e-02],\\n\",\n      \"           [-1.2288e-02, -1.4467e-02, -1.9736e-02],\\n\",\n      \"           [-3.2509e-02, -1.1792e-02, -1.9762e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.2540e-03, -1.7294e-02, -3.3219e-02],\\n\",\n      \"           [-1.2043e-02, -2.9645e-02, -2.6166e-02],\\n\",\n      \"           [-2.8043e-02, -2.7313e-02, -3.1156e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.1544e-03, -7.8267e-03, -6.1361e-03],\\n\",\n      \"           [-1.1465e-02, -2.1223e-02, -2.5235e-02],\\n\",\n      \"           [-1.0276e-02, -1.3119e-02, -2.8322e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1968e-02, -2.4747e-03,  1.0112e-02],\\n\",\n      \"           [ 6.3849e-03,  1.3388e-03,  1.7962e-02],\\n\",\n      \"           [-8.3232e-03, -1.7708e-02, -1.0786e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9424e-02,  1.3540e-02,  1.9060e-02],\\n\",\n      \"           [-5.3721e-03,  2.8265e-03,  1.2461e-02],\\n\",\n      \"           [-7.7020e-03, -1.4549e-02, -7.5710e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 40.00 %\\n\",\n      \"Val loss: 0.708377\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[1,    60] loss: 0.69267\\n\",\n      \"[1,   120] loss: 0.71795\\n\",\n      \"Time elapsed: 0h:3m:35s\\n\",\n      \"train accuracy_score: 51.65 %\\n\",\n      \"tensor([[[[[-3.0643e-03, -3.4232e-03, -1.5428e-03],\\n\",\n      \"           [-4.6975e-04, -2.2131e-04, -1.9041e-03],\\n\",\n      \"           [ 3.4050e-03,  3.1443e-04, -2.9809e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0919e-03, -5.9018e-04, -3.2822e-04],\\n\",\n      \"           [ 7.7742e-04, -1.4528e-03, -3.9635e-03],\\n\",\n      \"           [ 3.8027e-04, -5.8253e-04, -3.4171e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.2975e-04,  5.5857e-04,  1.0652e-03],\\n\",\n      \"           [ 7.5994e-04, -1.0174e-03, -9.5373e-05],\\n\",\n      \"           [ 2.4613e-03, -2.7814e-03, -1.7514e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.0200e-04,  4.9445e-04,  1.6841e-04],\\n\",\n      \"           [ 1.7736e-04,  2.5019e-04,  1.9399e-08],\\n\",\n      \"           [ 1.8798e-04,  1.2850e-05,  1.6936e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 3.8709e-04,  2.2361e-04,  2.0229e-04],\\n\",\n      \"           [ 4.6828e-04,  3.8873e-04,  2.2356e-04],\\n\",\n      \"           [ 2.6167e-04,  4.0541e-05,  3.0819e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9713e-04,  2.5289e-04,  3.7217e-04],\\n\",\n      \"           [ 1.0014e-03,  3.4756e-04, -7.7678e-05],\\n\",\n      \"           [ 7.3152e-04,  3.6794e-04,  3.5350e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.9940e-03, -6.7362e-03,  8.2507e-05],\\n\",\n      \"           [-1.4954e-02,  8.4101e-03,  3.1206e-02],\\n\",\n      \"           [-9.1263e-03,  1.9130e-02,  3.3406e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.2706e-02, -2.6493e-03,  1.2737e-02],\\n\",\n      \"           [-3.1115e-03,  2.0911e-02,  1.1576e-02],\\n\",\n      \"           [ 1.1409e-02,  1.9569e-02,  3.4068e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.3846e-02, -6.9486e-03,  1.4660e-02],\\n\",\n      \"           [ 2.7466e-03,  1.7902e-02,  2.2535e-03],\\n\",\n      \"           [ 1.4570e-02,  2.0452e-02,  2.5962e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.5239e-03, -1.7384e-02, -1.3855e-02],\\n\",\n      \"           [-6.0046e-03, -1.0328e-02, -9.2352e-03],\\n\",\n      \"           [-6.1330e-03, -5.5843e-03, -3.9912e-03]],\\n\",\n      \"\\n\",\n      \"          [[-9.8116e-03, -8.0060e-03, -8.8921e-03],\\n\",\n      \"           [-8.0380e-03, -1.4394e-03, -3.2713e-03],\\n\",\n      \"           [-1.6386e-03,  4.4082e-03, -1.1490e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.0167e-03, -7.5728e-03, -8.7922e-03],\\n\",\n      \"           [ 6.1013e-04,  3.1107e-03,  4.1427e-04],\\n\",\n      \"           [ 2.3568e-03,  5.2986e-03,  3.7772e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.7361e-03,  3.5806e-03,  4.0739e-03],\\n\",\n      \"           [ 1.3802e-02,  1.7588e-03,  7.7251e-03],\\n\",\n      \"           [ 2.2180e-02,  9.3035e-03,  4.3664e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.2377e-03,  6.8819e-03, -2.3888e-05],\\n\",\n      \"           [ 6.9914e-03,  9.1079e-03,  2.2931e-03],\\n\",\n      \"           [ 1.7430e-02,  1.3805e-02,  1.2205e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.8800e-03,  6.0421e-04,  1.6836e-03],\\n\",\n      \"           [-1.4376e-03,  9.2180e-03,  4.8607e-03],\\n\",\n      \"           [ 1.8131e-02,  1.6860e-02,  5.7582e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.2562e-02,  2.0626e-02,  2.7309e-02],\\n\",\n      \"           [ 2.8939e-02,  2.5483e-02,  2.7293e-02],\\n\",\n      \"           [ 1.0897e-02,  1.6417e-02,  2.7047e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4365e-02,  2.6811e-02,  2.7645e-02],\\n\",\n      \"           [ 2.3141e-02,  2.2036e-02,  3.6239e-02],\\n\",\n      \"           [ 4.0527e-02,  2.1517e-02,  2.5407e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7887e-02,  8.0259e-03,  1.5803e-02],\\n\",\n      \"           [ 2.6385e-02,  2.7975e-03,  1.4330e-02],\\n\",\n      \"           [ 5.3272e-02,  1.1136e-02,  1.3186e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.5161e-05, -2.8969e-03, -4.1544e-03],\\n\",\n      \"           [ 8.0516e-04, -1.2242e-03, -4.9403e-03],\\n\",\n      \"           [ 1.8416e-03, -1.9994e-03, -4.0912e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8330e-03, -3.0297e-03, -3.2453e-03],\\n\",\n      \"           [ 3.5643e-03, -2.3876e-03, -1.4953e-03],\\n\",\n      \"           [ 1.4824e-03, -3.5125e-03, -3.7619e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.8243e-03,  3.5364e-04,  1.9288e-05],\\n\",\n      \"           [ 2.3075e-03, -1.5177e-03, -3.8834e-03],\\n\",\n      \"           [ 5.1646e-04, -3.5586e-03, -3.3649e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.9836e-03, -1.8373e-03, -6.0961e-03],\\n\",\n      \"           [ 6.5237e-03,  1.6906e-03, -2.2735e-03],\\n\",\n      \"           [ 9.0623e-03,  6.4753e-03, -1.2113e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 8.5333e-03, -2.8434e-06, -4.6472e-03],\\n\",\n      \"           [ 1.0829e-02,  9.8649e-03,  2.4547e-03],\\n\",\n      \"           [ 1.3366e-02,  1.1223e-02,  4.3164e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2623e-02,  2.9697e-03, -3.7423e-04],\\n\",\n      \"           [ 9.0934e-03,  7.0207e-03,  4.5313e-03],\\n\",\n      \"           [ 9.5970e-03,  1.1337e-02,  4.5281e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 40.00 %\\n\",\n      \"Val loss: 0.707614\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[2,    60] loss: 0.69596\\n\",\n      \"[2,   120] loss: 0.69488\\n\",\n      \"Time elapsed: 0h:5m:28s\\n\",\n      \"train accuracy_score: 52.51 %\\n\",\n      \"tensor([[[[[-4.7889e-03, -6.8642e-03, -8.8356e-03],\\n\",\n      \"           [ 1.1909e-03,  1.7182e-03, -6.2580e-04],\\n\",\n      \"           [ 1.3776e-02,  2.2064e-03,  1.1468e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1604e-03,  2.5393e-03,  8.4712e-04],\\n\",\n      \"           [ 3.8908e-03, -7.7131e-04,  1.1780e-03],\\n\",\n      \"           [ 2.2896e-03, -3.8589e-03, -2.2979e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.6228e-04,  1.2628e-03,  2.8336e-04],\\n\",\n      \"           [-2.1825e-04, -3.0103e-04, -4.4355e-04],\\n\",\n      \"           [ 4.3467e-03, -1.4258e-03, -3.7024e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.0758e-05, -1.9076e-04,  6.9687e-05],\\n\",\n      \"           [ 3.7954e-06, -6.0695e-04, -8.0562e-04],\\n\",\n      \"           [-1.9615e-04, -9.4320e-04, -5.3425e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 5.1386e-04, -4.7264e-05,  1.5397e-04],\\n\",\n      \"           [-4.0732e-04, -8.8119e-04, -6.0483e-04],\\n\",\n      \"           [-1.2373e-04, -6.2761e-04, -7.8477e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3501e-03,  9.4062e-04,  1.2626e-03],\\n\",\n      \"           [ 1.4083e-03,  1.2859e-04, -3.3909e-04],\\n\",\n      \"           [ 5.3555e-05, -3.1547e-04,  3.9614e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.5510e-01,  1.3898e-01,  7.6121e-02],\\n\",\n      \"           [ 8.7152e-02,  1.1798e-01,  7.3193e-02],\\n\",\n      \"           [ 7.7615e-02,  1.0866e-01,  6.7264e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.6566e-02,  8.3916e-02,  8.5857e-02],\\n\",\n      \"           [ 6.1114e-02,  9.7261e-02,  4.8895e-02],\\n\",\n      \"           [ 9.4726e-02,  1.0568e-01,  5.5040e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.8103e-02,  3.6260e-02,  6.5627e-02],\\n\",\n      \"           [ 3.2092e-02,  7.0826e-02,  4.1163e-02],\\n\",\n      \"           [ 6.7846e-02,  7.3731e-02,  5.3278e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.9547e-02, -3.8764e-02, -2.2894e-02],\\n\",\n      \"           [-1.6970e-02, -2.3596e-02, -1.5129e-02],\\n\",\n      \"           [-4.5921e-03, -6.3794e-03,  9.2934e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.4283e-02, -1.5407e-02, -1.6685e-02],\\n\",\n      \"           [-1.0233e-02, -1.2537e-02, -7.2433e-03],\\n\",\n      \"           [-8.0866e-04,  5.6191e-03,  1.4749e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.5473e-03, -2.2745e-02, -6.8571e-03],\\n\",\n      \"           [-4.7022e-03, -2.3025e-03, -1.0918e-02],\\n\",\n      \"           [ 1.6963e-03,  8.4551e-03,  1.2557e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.3699e-03,  2.4319e-03,  6.3655e-03],\\n\",\n      \"           [ 1.0169e-02,  6.7785e-03,  1.9842e-02],\\n\",\n      \"           [ 6.7228e-02,  5.3797e-02,  3.1331e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.7711e-03,  1.6589e-02,  1.1435e-02],\\n\",\n      \"           [ 1.0278e-02,  2.2775e-02,  1.8144e-02],\\n\",\n      \"           [ 4.2112e-02,  3.6141e-02,  2.8930e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.8154e-02,  1.3968e-02,  1.4532e-02],\\n\",\n      \"           [ 1.2338e-02,  8.3529e-03,  1.0795e-02],\\n\",\n      \"           [ 3.9892e-02,  2.5574e-02,  2.1718e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.4988e-02, -3.7754e-02,  1.8564e-02],\\n\",\n      \"           [-5.1372e-02, -4.9353e-02,  8.6507e-03],\\n\",\n      \"           [-2.8160e-02, -3.6715e-02,  1.2111e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.2682e-02, -5.5172e-02,  6.5142e-03],\\n\",\n      \"           [-6.4713e-02, -2.2049e-02,  3.5704e-02],\\n\",\n      \"           [-4.7282e-03,  1.4865e-02,  2.2121e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.8737e-03, -8.5682e-03, -6.4683e-04],\\n\",\n      \"           [-4.4351e-02, -5.3044e-03, -1.3699e-03],\\n\",\n      \"           [-1.7656e-02, -2.6655e-03,  1.6255e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.0089e-03, -1.0175e-02, -1.1623e-02],\\n\",\n      \"           [-5.3850e-03, -6.2567e-03, -1.9123e-03],\\n\",\n      \"           [-1.2584e-02, -5.8470e-03, -2.0001e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.1409e-03, -1.5422e-02, -1.4552e-02],\\n\",\n      \"           [-7.2859e-03, -1.5395e-02, -1.0837e-02],\\n\",\n      \"           [-1.4204e-02, -1.7336e-02, -6.8284e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.6307e-03, -1.2640e-02, -2.0409e-02],\\n\",\n      \"           [-1.0298e-02, -1.3491e-02, -1.2077e-02],\\n\",\n      \"           [-9.9675e-03, -4.1597e-03, -9.0865e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.7445e-03, -1.4249e-03,  5.2351e-03],\\n\",\n      \"           [ 1.1575e-02,  1.4338e-02,  3.3071e-04],\\n\",\n      \"           [ 1.6313e-02,  3.2700e-03,  7.2611e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.8977e-03, -1.0806e-02, -6.9727e-03],\\n\",\n      \"           [ 1.4080e-02,  5.2365e-03,  9.4517e-03],\\n\",\n      \"           [ 2.0211e-02,  9.5190e-03,  9.0600e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.2078e-03, -5.3508e-03,  8.7414e-03],\\n\",\n      \"           [ 1.1903e-02, -5.5527e-03,  9.4370e-03],\\n\",\n      \"           [ 1.2612e-02,  7.5377e-03,  1.2523e-02]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 60.00 %\\n\",\n      \"Val loss: 0.676732\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[3,    60] loss: 0.69582\\n\",\n      \"[3,   120] loss: 0.69822\\n\",\n      \"Time elapsed: 0h:7m:18s\\n\",\n      \"train accuracy_score: 47.78 %\\n\",\n      \"tensor([[[[[ 4.4019e-04, -4.2944e-04, -1.1478e-04],\\n\",\n      \"           [ 2.1512e-04, -3.5999e-04, -6.5329e-04],\\n\",\n      \"           [-1.8476e-03, -3.9075e-04, -5.5727e-04]],\\n\",\n      \"\\n\",\n      \"          [[-5.1329e-04, -2.8179e-04, -1.5882e-05],\\n\",\n      \"           [-1.0250e-03, -1.3188e-03, -2.5725e-04],\\n\",\n      \"           [-1.2628e-03, -2.2454e-04,  3.5691e-04]],\\n\",\n      \"\\n\",\n      \"          [[-8.8981e-04, -1.0456e-05,  8.2314e-05],\\n\",\n      \"           [-2.5531e-04, -3.5256e-04, -4.5992e-04],\\n\",\n      \"           [-2.5773e-03, -8.2342e-04, -1.2640e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.4555e-04, -1.1651e-04, -3.2884e-04],\\n\",\n      \"           [-4.5790e-05, -1.5493e-04, -1.5467e-04],\\n\",\n      \"           [-1.5763e-04, -3.7271e-04, -3.6286e-04]],\\n\",\n      \"\\n\",\n      \"          [[-4.2965e-05, -1.1277e-04, -1.5334e-04],\\n\",\n      \"           [-1.1808e-04, -1.9941e-04,  4.9272e-05],\\n\",\n      \"           [-2.4667e-04, -2.8065e-04, -3.2343e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.4522e-04, -3.3435e-05,  1.0754e-04],\\n\",\n      \"           [-2.8871e-04,  7.7554e-06,  9.0195e-05],\\n\",\n      \"           [-2.2841e-04,  2.3080e-05,  1.5253e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0924e-02,  1.2836e-02,  6.9168e-03],\\n\",\n      \"           [ 1.1311e-02,  5.6721e-03, -2.1746e-03],\\n\",\n      \"           [ 8.3137e-03,  5.9176e-04, -3.2183e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8159e-02,  1.3473e-02,  6.9647e-03],\\n\",\n      \"           [ 3.2979e-03,  2.4507e-03,  2.9591e-03],\\n\",\n      \"           [ 3.5422e-03, -7.4805e-05,  7.7009e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1731e-02,  4.4810e-03, -7.0474e-05],\\n\",\n      \"           [ 9.2783e-04,  4.0191e-03, -2.3924e-03],\\n\",\n      \"           [ 4.0947e-04,  1.7004e-03,  3.0982e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.5642e-03,  1.5433e-02,  1.4194e-02],\\n\",\n      \"           [ 2.7835e-03,  8.9831e-03,  9.6239e-03],\\n\",\n      \"           [ 1.4922e-03,  3.5595e-03,  2.2295e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.2137e-03,  1.0437e-02,  1.4108e-02],\\n\",\n      \"           [ 4.1122e-03,  5.2131e-03,  6.4943e-03],\\n\",\n      \"           [ 2.4065e-03, -1.9502e-03, -2.3372e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.5028e-03,  8.8749e-03,  1.2921e-02],\\n\",\n      \"           [ 2.0176e-03,  1.8053e-03,  5.0122e-03],\\n\",\n      \"           [-1.4906e-03, -2.9350e-03, -3.4809e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.4171e-03, -1.4029e-03, -4.8981e-03],\\n\",\n      \"           [-1.4092e-02, -4.4773e-03, -3.2137e-03],\\n\",\n      \"           [-2.1552e-02, -9.6872e-03, -7.9258e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.0865e-03, -1.4979e-03, -3.8350e-03],\\n\",\n      \"           [-4.3817e-03, -3.8538e-03, -3.1937e-04],\\n\",\n      \"           [-1.4130e-02, -8.6425e-03, -3.7363e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1666e-03, -9.8099e-04, -4.4336e-03],\\n\",\n      \"           [-3.7579e-03, -3.9377e-03, -5.0782e-03],\\n\",\n      \"           [-9.0301e-03, -1.0447e-02, -5.2353e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.7833e-02,  1.0163e-02,  1.2145e-02],\\n\",\n      \"           [ 1.4365e-02,  8.2042e-03,  8.7606e-04],\\n\",\n      \"           [ 6.8522e-03, -2.6457e-03, -7.4796e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1842e-02,  1.0085e-02,  7.0517e-03],\\n\",\n      \"           [ 1.4107e-02,  8.6131e-03,  7.5126e-04],\\n\",\n      \"           [-2.6897e-03, -3.8516e-03, -8.2974e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0278e-02,  4.4808e-03, -2.2588e-03],\\n\",\n      \"           [ 7.5593e-03,  6.0422e-04,  4.6099e-04],\\n\",\n      \"           [-1.2540e-02,  1.0250e-03, -4.0721e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.4327e-04,  2.9005e-05,  9.0832e-04],\\n\",\n      \"           [-9.2082e-04,  9.6568e-04,  1.3426e-03],\\n\",\n      \"           [ 1.0881e-03,  2.3686e-04,  2.3674e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 7.7389e-04,  2.1145e-03,  1.7745e-03],\\n\",\n      \"           [ 2.2937e-03,  2.4870e-03,  9.4858e-04],\\n\",\n      \"           [ 2.5304e-03,  1.8989e-03, -3.7167e-05]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4983e-03,  1.9848e-03,  2.3454e-03],\\n\",\n      \"           [ 3.5752e-03,  2.5948e-03,  2.2234e-03],\\n\",\n      \"           [ 4.0814e-03,  3.4959e-03,  7.4313e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.8852e-03, -3.3066e-03, -3.8020e-03],\\n\",\n      \"           [-3.3156e-04, -4.3269e-03, -1.7014e-03],\\n\",\n      \"           [-8.7485e-04, -1.7034e-03, -2.4976e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7330e-03,  1.3304e-03, -1.7470e-03],\\n\",\n      \"           [ 4.5326e-03,  1.0225e-03, -2.8449e-03],\\n\",\n      \"           [-7.4504e-04, -1.9768e-03, -8.0871e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1184e-03,  3.5353e-04, -1.6857e-03],\\n\",\n      \"           [ 5.2518e-03,  2.6567e-03,  8.6996e-04],\\n\",\n      \"           [-3.2011e-04,  2.4172e-03,  2.9924e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 40.00 %\\n\",\n      \"Val loss: 0.695125\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[4,    60] loss: 0.69211\\n\",\n      \"[4,   120] loss: 0.69528\\n\",\n      \"Time elapsed: 0h:9m:11s\\n\",\n      \"train accuracy_score: 51.79 %\\n\",\n      \"tensor([[[[[-9.1483e-04,  1.0130e-03,  2.0885e-03],\\n\",\n      \"           [-1.0456e-04,  5.2086e-04,  1.7278e-03],\\n\",\n      \"           [ 3.3424e-03,  2.3985e-03,  2.0810e-03]],\\n\",\n      \"\\n\",\n      \"          [[-6.9126e-05,  3.6494e-05,  8.9620e-04],\\n\",\n      \"           [ 1.2307e-03,  3.5878e-04, -1.3666e-04],\\n\",\n      \"           [ 1.7964e-03,  1.8796e-03,  2.7707e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.9528e-04,  7.3827e-04,  6.5463e-04],\\n\",\n      \"           [ 1.1875e-03,  3.7712e-04,  1.1606e-03],\\n\",\n      \"           [ 2.4690e-03, -5.9478e-05,  1.2871e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.1116e-04,  7.3721e-05,  2.6062e-04],\\n\",\n      \"           [-2.2378e-04,  2.3757e-04,  2.1290e-04],\\n\",\n      \"           [-2.0948e-04, -1.4088e-04, -3.8991e-05]],\\n\",\n      \"\\n\",\n      \"          [[-4.2391e-04,  2.1899e-05, -4.7339e-05],\\n\",\n      \"           [-2.6913e-04, -1.3225e-04, -1.0837e-04],\\n\",\n      \"           [-1.5478e-04, -3.4089e-04, -3.0203e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.9156e-04, -3.8606e-04, -1.6940e-04],\\n\",\n      \"           [-4.5269e-04, -1.3355e-04, -4.5415e-05],\\n\",\n      \"           [-4.0368e-04,  3.8209e-05, -1.8526e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.4843e-03, -1.5820e-03,  9.7852e-03],\\n\",\n      \"           [-4.1019e-03,  5.9944e-03,  1.4167e-02],\\n\",\n      \"           [-3.5052e-03,  7.7615e-05,  6.4824e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.8198e-02, -2.5539e-03,  5.7338e-03],\\n\",\n      \"           [-1.3658e-02, -5.8082e-03,  6.7681e-04],\\n\",\n      \"           [-2.7181e-03, -7.5489e-03, -6.2978e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.4461e-02, -1.2606e-02, -6.8112e-03],\\n\",\n      \"           [-1.2524e-02, -1.3599e-02, -2.8553e-03],\\n\",\n      \"           [-1.4206e-02, -1.4466e-02, -4.6733e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2371e-02, -1.8140e-02, -1.3686e-02],\\n\",\n      \"           [-5.3452e-03, -1.0185e-02, -1.1613e-02],\\n\",\n      \"           [-2.0909e-03, -5.1929e-03, -6.2243e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.2576e-03, -1.0448e-02, -1.0443e-02],\\n\",\n      \"           [-4.7717e-03, -2.3643e-03, -2.6727e-03],\\n\",\n      \"           [-1.4445e-03,  9.6224e-04,  2.8172e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.9512e-03, -6.2983e-03, -5.1193e-03],\\n\",\n      \"           [-8.0445e-04, -1.4516e-03, -3.5437e-04],\\n\",\n      \"           [-4.7877e-03,  2.3412e-03,  4.1287e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.7661e-03, -1.7604e-03, -3.2349e-03],\\n\",\n      \"           [ 9.7415e-03,  5.5630e-03, -1.6011e-03],\\n\",\n      \"           [ 1.9796e-02,  1.7295e-02,  5.3030e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.8359e-04, -1.9509e-05, -2.9921e-03],\\n\",\n      \"           [ 9.9979e-03,  6.4710e-03, -1.6979e-04],\\n\",\n      \"           [ 1.2415e-02,  1.8096e-02,  8.1845e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.2532e-03, -8.2229e-03, -7.8411e-03],\\n\",\n      \"           [ 5.8375e-04,  5.5899e-03, -1.0920e-03],\\n\",\n      \"           [ 9.4636e-03,  1.1787e-02,  6.5794e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.5650e-03,  1.6651e-02,  2.1703e-02],\\n\",\n      \"           [-7.9639e-04,  1.2271e-02,  1.5969e-02],\\n\",\n      \"           [-5.7837e-03, -7.2351e-03,  5.2111e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.5036e-03,  1.1590e-02,  1.8634e-02],\\n\",\n      \"           [ 4.8538e-05,  3.9872e-03,  1.0971e-02],\\n\",\n      \"           [-7.5141e-03,  1.6277e-04,  5.0108e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.2170e-03,  1.3011e-02,  1.6977e-02],\\n\",\n      \"           [ 1.1268e-02,  1.0971e-02,  8.0232e-03],\\n\",\n      \"           [ 2.5902e-03,  2.2402e-03,  2.7450e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.0144e-03,  9.8982e-04,  3.3139e-04],\\n\",\n      \"           [ 1.4059e-03, -3.3757e-04, -4.9246e-04],\\n\",\n      \"           [ 1.4725e-03, -3.3786e-04, -1.2718e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 8.1488e-04, -6.1564e-04, -1.0028e-03],\\n\",\n      \"           [-5.5333e-04, -5.3942e-04, -2.0900e-03],\\n\",\n      \"           [-1.9473e-03, -1.9691e-03, -6.9688e-05]],\\n\",\n      \"\\n\",\n      \"          [[-1.2703e-03, -2.3918e-04, -3.5519e-04],\\n\",\n      \"           [-3.5456e-03, -1.8605e-03, -9.1988e-04],\\n\",\n      \"           [-3.0625e-03, -3.7058e-03, -4.1097e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.7093e-03,  3.8942e-03,  2.9159e-03],\\n\",\n      \"           [ 2.8259e-03,  3.8373e-04,  9.1826e-04],\\n\",\n      \"           [ 4.4682e-03,  2.2085e-03,  1.6051e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.6394e-03, -1.3622e-03, -8.4814e-04],\\n\",\n      \"           [ 2.7824e-05, -3.1131e-03, -2.5998e-03],\\n\",\n      \"           [-4.4810e-04, -1.4194e-03, -1.9084e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.1098e-03, -1.4685e-03, -2.9335e-03],\\n\",\n      \"           [-2.6580e-03, -2.0653e-03, -1.7103e-03],\\n\",\n      \"           [-2.8861e-03, -1.9730e-03, -2.6718e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 40.00 %\\n\",\n      \"Val loss: 0.695751\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[5,    60] loss: 0.69449\\n\",\n      \"[5,   120] loss: 0.69399\\n\",\n      \"Time elapsed: 0h:11m:1s\\n\",\n      \"train accuracy_score: 54.81 %\\n\",\n      \"tensor([[[[[ 4.1890e-03,  3.4730e-04, -4.0182e-03],\\n\",\n      \"           [ 1.3071e-03, -5.3122e-03, -4.5729e-03],\\n\",\n      \"           [-5.6348e-03,  2.5529e-03, -2.4043e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.2350e-03, -3.2579e-03, -2.0125e-03],\\n\",\n      \"           [ 9.2520e-04, -1.6700e-03, -5.2942e-03],\\n\",\n      \"           [-2.3441e-03, -2.3440e-04, -2.7797e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.5546e-03, -3.9639e-03,  5.1559e-04],\\n\",\n      \"           [-2.3595e-03, -6.5492e-04, -3.6344e-03],\\n\",\n      \"           [-1.0179e-02, -3.7451e-03, -2.6258e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.7772e-03,  2.4010e-03, -6.6054e-04],\\n\",\n      \"           [ 3.3175e-03,  1.9078e-03,  1.9663e-04],\\n\",\n      \"           [ 1.6383e-03,  1.0777e-03,  6.3847e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 5.1384e-03,  1.9757e-03,  4.6205e-04],\\n\",\n      \"           [ 2.1185e-03,  1.8283e-03,  1.0592e-04],\\n\",\n      \"           [ 7.5035e-04,  1.3728e-03,  4.5401e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2856e-03,  6.4405e-04, -1.2596e-04],\\n\",\n      \"           [ 1.4583e-03,  9.7416e-04,  3.3437e-04],\\n\",\n      \"           [ 8.7211e-04, -5.9296e-04, -1.0638e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.8687e-02, -4.3717e-02, -4.3768e-02],\\n\",\n      \"           [-4.8859e-02, -7.5722e-02, -2.0636e-02],\\n\",\n      \"           [-9.1666e-02, -7.3670e-02, -4.0221e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.1700e-02, -6.7575e-03, -3.0237e-02],\\n\",\n      \"           [-1.9689e-02, -2.9343e-02, -2.3020e-02],\\n\",\n      \"           [-5.8315e-02, -3.0836e-02, -2.8252e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.0846e-02, -1.1091e-02, -2.7290e-02],\\n\",\n      \"           [ 3.2620e-03, -5.5599e-03, -2.1699e-02],\\n\",\n      \"           [ 2.4267e-04, -1.4705e-02, -1.0429e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.9573e-02,  4.7572e-02,  3.9129e-02],\\n\",\n      \"           [ 2.4181e-02,  2.9711e-02,  3.5489e-02],\\n\",\n      \"           [ 2.4595e-02,  2.1633e-02,  1.8755e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1495e-02,  2.8376e-02,  3.3914e-02],\\n\",\n      \"           [ 1.6962e-02,  1.1587e-02,  1.0053e-02],\\n\",\n      \"           [ 7.5354e-03,  1.3709e-03, -3.5722e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.8363e-02,  3.9243e-02,  3.0398e-02],\\n\",\n      \"           [ 1.0287e-02,  1.3669e-02,  5.8003e-03],\\n\",\n      \"           [ 7.6141e-03, -1.9927e-02, -2.2643e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.1375e-02, -2.2474e-02, -2.2500e-02],\\n\",\n      \"           [-6.6349e-02, -2.5742e-02, -2.7280e-02],\\n\",\n      \"           [-1.0672e-01, -4.6575e-02, -2.7713e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.7764e-02, -3.9653e-02, -2.8093e-02],\\n\",\n      \"           [-6.9982e-02, -6.5793e-02, -4.9277e-02],\\n\",\n      \"           [-6.9470e-02, -6.8494e-02, -5.1658e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.1533e-02, -4.6229e-02, -3.6598e-02],\\n\",\n      \"           [-5.5139e-02, -5.9954e-02, -6.3197e-02],\\n\",\n      \"           [-7.0339e-02, -9.4175e-02, -5.1070e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.0871e-02, -5.7010e-02, -4.5943e-02],\\n\",\n      \"           [-9.9685e-02, -9.1444e-02, -8.2518e-02],\\n\",\n      \"           [-1.0356e-01, -9.1660e-02, -1.2460e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.8787e-02, -4.7555e-02, -6.6022e-02],\\n\",\n      \"           [-8.8936e-02, -1.7913e-02, -1.0608e-01],\\n\",\n      \"           [-6.5336e-02, -8.3607e-02, -8.8802e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.2916e-02, -2.2256e-02, -2.4411e-02],\\n\",\n      \"           [-9.7354e-02,  2.0095e-03, -5.9550e-02],\\n\",\n      \"           [-1.5521e-01, -2.9469e-02, -7.2321e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2571e-02, -6.9242e-03,  5.4950e-03],\\n\",\n      \"           [ 1.2584e-03,  1.2249e-02,  7.2653e-03],\\n\",\n      \"           [ 9.2706e-03,  1.7766e-02,  1.4462e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.5392e-03,  1.0506e-02,  1.1706e-02],\\n\",\n      \"           [ 5.6925e-03,  1.3744e-02,  9.7675e-03],\\n\",\n      \"           [ 1.1268e-02,  1.3064e-02,  9.1983e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1318e-02,  1.3174e-02,  1.3035e-02],\\n\",\n      \"           [ 1.2855e-02,  8.0451e-03,  5.0978e-03],\\n\",\n      \"           [ 1.3931e-02,  1.3838e-02,  1.3268e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3994e-02, -1.2014e-02, -1.0937e-02],\\n\",\n      \"           [-2.4122e-02, -1.2498e-02, -6.7546e-03],\\n\",\n      \"           [-2.8147e-02, -2.6769e-02, -1.3895e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0754e-02, -5.7895e-03,  5.2691e-03],\\n\",\n      \"           [-1.9472e-02,  3.3613e-03,  7.4988e-03],\\n\",\n      \"           [-2.6184e-02, -9.2692e-03, -2.6054e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.1521e-02,  4.9818e-04,  1.2930e-02],\\n\",\n      \"           [-1.9322e-02,  1.6696e-02,  2.1713e-02],\\n\",\n      \"           [-1.1728e-02,  3.3053e-03,  1.0775e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 68.00 %\\n\",\n      \"Val loss: 0.670632\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[6,    60] loss: 0.68246\\n\",\n      \"[6,   120] loss: 0.70781\\n\",\n      \"Time elapsed: 0h:12m:52s\\n\",\n      \"train accuracy_score: 54.23 %\\n\",\n      \"tensor([[[[[ 2.9743e-03,  2.2253e-03,  2.5064e-03],\\n\",\n      \"           [ 1.4774e-03,  3.4152e-03,  6.0238e-03],\\n\",\n      \"           [ 7.8410e-03,  4.6897e-03,  4.6147e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3186e-03,  3.1189e-03,  8.5141e-04],\\n\",\n      \"           [ 4.9721e-03,  9.2222e-04,  1.5894e-04],\\n\",\n      \"           [ 3.4512e-03,  1.0151e-03,  3.3934e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7931e-03,  1.0666e-03,  5.1474e-04],\\n\",\n      \"           [ 5.1265e-03,  2.1061e-03,  1.7002e-03],\\n\",\n      \"           [ 5.0802e-03,  2.7956e-04, -1.8503e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.5370e-03, -1.2696e-03, -4.9553e-04],\\n\",\n      \"           [-6.1986e-04, -1.4088e-03, -2.6599e-04],\\n\",\n      \"           [-1.3973e-03, -1.1964e-03,  2.2600e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.5692e-03, -1.8340e-03,  1.8877e-04],\\n\",\n      \"           [-1.0318e-03, -1.0954e-03,  1.1322e-04],\\n\",\n      \"           [-7.3908e-04, -6.2206e-04,  1.7974e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.5243e-03, -3.2500e-04, -4.4769e-04],\\n\",\n      \"           [-1.9821e-03, -1.2821e-03,  1.9359e-04],\\n\",\n      \"           [-2.0729e-03, -1.3695e-04,  7.6027e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.7084e-02, -1.6455e-02,  4.6419e-03],\\n\",\n      \"           [-4.1819e-02, -1.6691e-02,  2.2570e-03],\\n\",\n      \"           [-4.2413e-02, -5.1351e-02, -3.9442e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.3758e-02, -3.1217e-02, -2.8745e-03],\\n\",\n      \"           [-5.8534e-02, -3.4079e-02, -4.8619e-03],\\n\",\n      \"           [-5.9181e-02, -3.3372e-02, -4.3313e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.3356e-02, -4.2115e-02, -9.7526e-03],\\n\",\n      \"           [-7.5663e-02, -5.0472e-02, -1.8698e-02],\\n\",\n      \"           [-5.6644e-02, -3.1783e-02, -1.7341e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2246e-02, -2.8007e-02, -1.1566e-02],\\n\",\n      \"           [-3.4825e-03, -3.3394e-03, -1.2314e-02],\\n\",\n      \"           [-1.2044e-02,  4.5655e-03,  1.4759e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.2876e-02, -1.4421e-02, -1.4930e-03],\\n\",\n      \"           [-1.0373e-02,  1.3252e-03, -2.6345e-03],\\n\",\n      \"           [-1.1936e-02,  1.7768e-03,  1.5287e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.2115e-02, -7.4168e-03,  5.0435e-03],\\n\",\n      \"           [-1.8168e-02, -2.1342e-03,  2.0188e-03],\\n\",\n      \"           [-2.3480e-02, -1.7699e-03,  1.9325e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.7324e-04, -2.7629e-03,  5.8669e-03],\\n\",\n      \"           [ 1.8583e-02, -2.8341e-03, -1.0317e-02],\\n\",\n      \"           [ 3.4931e-02,  1.8365e-02,  1.0104e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.5548e-03, -1.1159e-03, -4.5970e-03],\\n\",\n      \"           [ 1.8107e-02, -7.2319e-03, -1.9589e-02],\\n\",\n      \"           [ 1.3354e-02,  8.8485e-03, -5.1537e-04]],\\n\",\n      \"\\n\",\n      \"          [[-9.4944e-03, -7.7017e-03, -9.9150e-03],\\n\",\n      \"           [-3.6273e-03,  3.4189e-04, -1.5577e-03],\\n\",\n      \"           [ 1.5962e-03, -6.5279e-03, -9.9764e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.3139e-02,  1.1928e-01,  1.9533e-01],\\n\",\n      \"           [ 6.9394e-02,  1.1618e-01,  1.9433e-01],\\n\",\n      \"           [ 3.8001e-02,  9.1382e-02,  1.7241e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.0397e-02,  7.9071e-02,  2.1579e-01],\\n\",\n      \"           [ 5.9392e-02,  8.4949e-02,  2.0661e-01],\\n\",\n      \"           [ 5.5841e-02,  8.7295e-02,  1.8475e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.9964e-02,  9.1218e-02,  1.9032e-01],\\n\",\n      \"           [ 7.1388e-02,  8.6135e-02,  1.9876e-01],\\n\",\n      \"           [ 5.7400e-02,  9.8254e-02,  1.4991e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0375e-02,  9.2043e-03,  1.0158e-02],\\n\",\n      \"           [ 6.2809e-03,  7.5945e-03,  1.1520e-02],\\n\",\n      \"           [ 8.0889e-03,  1.0346e-02,  4.6247e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.4661e-03,  1.2149e-02,  1.1193e-02],\\n\",\n      \"           [ 6.9586e-03,  8.9404e-03,  8.4748e-03],\\n\",\n      \"           [ 3.1607e-03,  7.0145e-03,  7.1960e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 9.6856e-03,  3.6855e-03,  7.1725e-03],\\n\",\n      \"           [ 1.5505e-03,  5.3456e-03,  1.0203e-02],\\n\",\n      \"           [ 2.7809e-04,  9.0216e-03,  1.3501e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.7468e-02,  1.7032e-02,  6.3799e-03],\\n\",\n      \"           [ 3.3492e-03, -1.2080e-03, -1.1584e-03],\\n\",\n      \"           [-3.4806e-04, -9.2058e-03,  9.7391e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.6632e-03, -1.2088e-03, -1.3380e-03],\\n\",\n      \"           [-8.9645e-03, -1.4944e-02, -7.9418e-03],\\n\",\n      \"           [-1.8060e-02, -2.5317e-02, -7.0856e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.9402e-02, -1.5392e-02, -5.6944e-03],\\n\",\n      \"           [-1.9043e-02, -1.5165e-02, -6.3237e-03],\\n\",\n      \"           [-2.6128e-02, -2.1354e-02, -1.4057e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 47.00 %\\n\",\n      \"Val loss: 0.686003\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[7,    60] loss: 0.67969\\n\",\n      \"[7,   120] loss: 0.67345\\n\",\n      \"Time elapsed: 0h:14m:42s\\n\",\n      \"train accuracy_score: 58.54 %\\n\",\n      \"tensor([[[[[-2.2860e-03, -4.7019e-03, -1.6897e-03],\\n\",\n      \"           [-5.5692e-03, -3.1104e-03, -8.7784e-04],\\n\",\n      \"           [-9.8300e-03, -3.4627e-03,  1.4488e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 7.7160e-04,  6.1571e-04,  1.1596e-03],\\n\",\n      \"           [-2.1766e-03, -2.6597e-03,  2.1402e-03],\\n\",\n      \"           [-6.7162e-03, -1.7218e-03,  4.9066e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2164e-04,  2.1122e-03,  2.5152e-03],\\n\",\n      \"           [ 1.6871e-03, -1.2992e-03,  1.9791e-05],\\n\",\n      \"           [-2.5708e-03,  4.7192e-04,  1.1739e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0136e-03,  5.6309e-04, -4.0109e-04],\\n\",\n      \"           [ 5.6325e-04,  7.0088e-04, -1.6704e-04],\\n\",\n      \"           [ 1.5523e-03,  6.5003e-05, -2.3459e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 4.9614e-04,  5.7932e-04, -7.9001e-04],\\n\",\n      \"           [-3.0129e-04,  1.8082e-04, -5.0975e-05],\\n\",\n      \"           [ 1.4037e-03,  5.6511e-04, -1.5225e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 7.0164e-05, -4.7928e-04, -6.7093e-04],\\n\",\n      \"           [ 2.2223e-04, -7.9735e-04, -3.3912e-04],\\n\",\n      \"           [ 1.1193e-04,  5.1054e-04,  2.1776e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.1447e-02,  2.7262e-02, -4.5564e-03],\\n\",\n      \"           [ 5.3847e-02,  4.2850e-02, -1.5848e-02],\\n\",\n      \"           [ 7.7687e-02,  3.7728e-02, -3.0432e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.8182e-02,  4.1646e-04,  8.8307e-03],\\n\",\n      \"           [ 4.5752e-02,  2.4758e-02,  7.5316e-03],\\n\",\n      \"           [ 3.6090e-02,  3.6984e-02,  3.9801e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6294e-02,  1.4853e-02,  5.3654e-03],\\n\",\n      \"           [ 3.0788e-02,  2.2481e-02,  1.1612e-02],\\n\",\n      \"           [ 2.0630e-02,  2.2694e-02,  2.0392e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.0102e-02,  2.6539e-02,  2.2244e-02],\\n\",\n      \"           [ 2.3276e-02,  2.3033e-02,  2.3264e-02],\\n\",\n      \"           [ 1.0427e-02,  6.8091e-03,  1.0193e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5070e-02,  1.5369e-02,  8.7243e-03],\\n\",\n      \"           [ 2.1743e-02,  3.2913e-03, -3.2669e-03],\\n\",\n      \"           [ 9.4077e-03, -3.3664e-03, -1.2012e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6847e-02,  1.8207e-02,  1.8261e-03],\\n\",\n      \"           [ 1.5566e-02, -3.8004e-03, -1.2716e-02],\\n\",\n      \"           [ 1.5581e-02, -5.5748e-03, -2.4263e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2903e-02, -1.3174e-02, -1.9156e-02],\\n\",\n      \"           [-2.7172e-02, -1.8345e-02, -1.7874e-02],\\n\",\n      \"           [-4.2299e-02, -3.9084e-02, -1.7047e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.9756e-02, -1.1014e-02, -2.3187e-02],\\n\",\n      \"           [-2.8654e-02, -3.4406e-02, -2.7585e-02],\\n\",\n      \"           [-4.5787e-02, -3.8397e-02, -4.3587e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.3156e-02, -3.7502e-02, -3.2945e-02],\\n\",\n      \"           [-3.0781e-02, -2.7658e-02, -3.5828e-02],\\n\",\n      \"           [-2.9666e-02, -2.9969e-02, -3.6069e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.0842e-02, -1.0320e-01, -1.6448e-01],\\n\",\n      \"           [-4.6323e-02, -1.1387e-01, -1.7775e-01],\\n\",\n      \"           [-2.9147e-02, -8.9223e-02, -1.7070e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.7147e-02, -8.2329e-02, -1.6951e-01],\\n\",\n      \"           [-3.4585e-02, -9.8864e-02, -1.6706e-01],\\n\",\n      \"           [-5.7459e-02, -8.6816e-02, -1.6945e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.3677e-02, -4.1861e-02, -1.1347e-01],\\n\",\n      \"           [-5.5575e-02, -4.4080e-02, -1.3192e-01],\\n\",\n      \"           [-5.8188e-02, -7.1382e-02, -1.1716e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.9742e-03, -5.2791e-03, -7.2511e-03],\\n\",\n      \"           [-5.1375e-03, -4.6051e-03, -4.1697e-03],\\n\",\n      \"           [ 2.7464e-03,  1.2012e-03,  2.2475e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.0348e-02, -8.1312e-03, -6.0552e-03],\\n\",\n      \"           [-7.4751e-03, -4.6174e-03, -6.5145e-03],\\n\",\n      \"           [-1.3262e-03, -1.7997e-04,  8.8732e-04]],\\n\",\n      \"\\n\",\n      \"          [[-7.8940e-03, -6.8707e-03, -1.0612e-02],\\n\",\n      \"           [-4.6978e-03, -6.1815e-03, -1.2905e-02],\\n\",\n      \"           [-4.3564e-03, -4.9690e-03, -5.7857e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3150e-02, -9.1216e-03,  1.1238e-03],\\n\",\n      \"           [-1.0783e-02, -4.3695e-03, -7.1029e-04],\\n\",\n      \"           [-4.7406e-04,  5.7465e-03,  6.6232e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.0437e-02, -5.1992e-03, -9.4575e-04],\\n\",\n      \"           [-4.7965e-03,  6.2423e-03, -5.4447e-03],\\n\",\n      \"           [ 3.8350e-04,  1.8814e-03,  5.5792e-04]],\\n\",\n      \"\\n\",\n      \"          [[-7.9671e-03,  1.8396e-03,  1.0676e-02],\\n\",\n      \"           [-2.4442e-03,  6.8426e-03,  5.9161e-03],\\n\",\n      \"           [ 7.0462e-03,  2.3222e-03, -4.2909e-05]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 40.00 %\\n\",\n      \"Val loss: 0.718239\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[8,    60] loss: 0.66700\\n\",\n      \"[8,   120] loss: 0.64206\\n\",\n      \"Time elapsed: 0h:16m:32s\\n\",\n      \"train accuracy_score: 61.41 %\\n\",\n      \"tensor([[[[[-3.3230e-03, -2.1179e-03,  3.2768e-03],\\n\",\n      \"           [-6.6261e-03, -3.7610e-03,  1.2782e-03],\\n\",\n      \"           [-1.7262e-02, -6.4756e-03, -3.6899e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.1094e-03, -2.0204e-03,  4.2462e-03],\\n\",\n      \"           [-3.8529e-03, -3.9512e-03, -1.4300e-04],\\n\",\n      \"           [-4.0657e-03, -1.3106e-03, -1.7339e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3362e-04, -3.5785e-04,  4.1645e-03],\\n\",\n      \"           [ 4.4598e-03,  6.9674e-04,  4.5031e-03],\\n\",\n      \"           [-6.1116e-03,  9.3723e-04,  1.8389e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.4189e-03,  4.3774e-04, -4.2303e-04],\\n\",\n      \"           [ 9.5557e-04,  4.6352e-05, -7.0420e-04],\\n\",\n      \"           [ 6.5462e-04, -1.7121e-04, -1.1346e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5522e-03,  1.6883e-03, -4.5254e-05],\\n\",\n      \"           [ 1.0842e-03,  7.4115e-06, -1.3027e-04],\\n\",\n      \"           [-1.9596e-04, -1.4639e-04, -1.6228e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.3456e-04,  1.6704e-03,  1.8836e-06],\\n\",\n      \"           [-7.9884e-04, -1.9059e-04, -1.0682e-03],\\n\",\n      \"           [-5.9006e-04, -1.6461e-03, -1.7049e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.5626e-03, -1.0985e-02, -2.8114e-02],\\n\",\n      \"           [ 2.2536e-02,  1.3236e-02, -2.8365e-02],\\n\",\n      \"           [ 5.3743e-02,  3.8474e-02,  7.7436e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.4372e-03, -2.9682e-02, -8.9258e-03],\\n\",\n      \"           [ 3.3208e-03,  1.8527e-02,  9.9082e-03],\\n\",\n      \"           [ 5.3232e-02,  5.4251e-02,  2.4644e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.9941e-02, -5.3446e-02, -3.6064e-02],\\n\",\n      \"           [-1.3862e-02, -1.4612e-03, -4.5047e-03],\\n\",\n      \"           [ 2.3175e-02,  4.4012e-02,  9.8861e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.9348e-02,  5.9185e-02,  6.7211e-02],\\n\",\n      \"           [ 1.5645e-02,  3.8796e-02,  6.0204e-02],\\n\",\n      \"           [ 7.7486e-03,  2.0118e-02,  1.6191e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.3798e-02,  4.4255e-02,  4.0724e-02],\\n\",\n      \"           [ 1.5891e-02,  2.0700e-02,  3.2391e-02],\\n\",\n      \"           [ 6.7751e-03,  6.2354e-03,  3.1836e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.4324e-02,  4.1764e-02,  2.2895e-02],\\n\",\n      \"           [ 4.8168e-03,  5.6607e-03,  8.6634e-03],\\n\",\n      \"           [-8.7891e-03, -1.2025e-02, -1.9058e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.5440e-02, -1.5902e-02, -1.4206e-02],\\n\",\n      \"           [-5.0024e-02, -2.0728e-02, -9.2128e-03],\\n\",\n      \"           [-9.0590e-02, -6.1769e-02, -2.4870e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.6220e-02, -2.5299e-02, -1.8751e-02],\\n\",\n      \"           [-5.6984e-02, -4.7421e-02, -3.0628e-02],\\n\",\n      \"           [-8.0719e-02, -6.2002e-02, -5.4591e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.8787e-02, -4.9930e-02, -5.3262e-02],\\n\",\n      \"           [-5.2916e-02, -6.4486e-02, -7.2916e-02],\\n\",\n      \"           [-7.8019e-02, -8.9733e-02, -6.4202e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1371e-01, -1.1104e-01, -1.1155e-01],\\n\",\n      \"           [-5.3496e-02, -5.4015e-02, -9.7734e-02],\\n\",\n      \"           [-1.2734e-03, -1.0890e-02, -6.2810e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.8111e-02, -7.4114e-02, -1.0744e-01],\\n\",\n      \"           [-5.0377e-02, -6.8016e-02, -1.0400e-01],\\n\",\n      \"           [ 3.5827e-03, -3.4836e-02, -6.8578e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.2317e-01, -6.3997e-02, -8.6983e-02],\\n\",\n      \"           [-4.7948e-02, -3.7671e-02, -1.0192e-01],\\n\",\n      \"           [-5.8789e-03, -2.1709e-02, -6.7750e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.9028e-02, -1.5022e-02, -1.2059e-02],\\n\",\n      \"           [-5.4795e-03, -1.9775e-04,  1.0576e-03],\\n\",\n      \"           [ 2.2777e-03,  4.7773e-03,  4.8706e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.8770e-02, -1.2987e-02, -8.4685e-03],\\n\",\n      \"           [-2.6365e-03, -1.5353e-03, -5.2690e-03],\\n\",\n      \"           [ 3.8204e-03,  6.1228e-03,  5.9664e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.0668e-02, -2.1487e-02, -1.6139e-02],\\n\",\n      \"           [-5.1543e-03, -8.1799e-03, -9.1575e-03],\\n\",\n      \"           [ 3.7938e-03,  4.3494e-03,  4.6384e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.8051e-02, -2.6906e-02, -1.1822e-02],\\n\",\n      \"           [-8.8388e-03,  4.5348e-03,  7.2788e-03],\\n\",\n      \"           [ 8.1720e-03,  1.5072e-02,  2.2994e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1485e-02, -1.1182e-02, -6.5638e-03],\\n\",\n      \"           [ 1.3240e-02,  1.9352e-02,  1.9975e-02],\\n\",\n      \"           [ 3.4068e-02,  4.0409e-02,  3.5675e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8514e-02,  1.4743e-02,  1.0314e-02],\\n\",\n      \"           [ 2.6768e-02,  3.6633e-02,  2.9264e-02],\\n\",\n      \"           [ 4.0764e-02,  3.8391e-02,  3.6145e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 61.00 %\\n\",\n      \"Val loss: 0.638837\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[9,    60] loss: 0.65549\\n\",\n      \"[9,   120] loss: 0.63233\\n\",\n      \"Time elapsed: 0h:18m:25s\\n\",\n      \"train accuracy_score: 64.99 %\\n\",\n      \"tensor([[[[[-1.1875e-02, -1.3798e-02, -1.7340e-02],\\n\",\n      \"           [-1.1217e-02, -1.0786e-02, -1.9816e-02],\\n\",\n      \"           [-3.2616e-02, -1.3682e-02, -2.3199e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.4101e-04, -3.0685e-03, -4.3214e-03],\\n\",\n      \"           [-5.2864e-03, -6.3344e-03, -5.1559e-03],\\n\",\n      \"           [-1.7605e-02, -7.2476e-03, -1.0335e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.8382e-03, -3.5173e-03,  3.0387e-04],\\n\",\n      \"           [-3.9603e-03, -4.5746e-03, -1.5137e-03],\\n\",\n      \"           [-2.4382e-02, -4.6955e-03,  1.0323e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.9552e-04,  7.2889e-05,  3.6631e-04],\\n\",\n      \"           [ 1.1550e-03, -4.5865e-04,  9.4429e-05],\\n\",\n      \"           [ 2.2119e-03, -1.5561e-04,  9.7163e-05]],\\n\",\n      \"\\n\",\n      \"          [[-2.2622e-03, -1.0965e-03, -8.3765e-04],\\n\",\n      \"           [ 5.9413e-05, -8.2973e-04, -2.9927e-04],\\n\",\n      \"           [ 1.1715e-03, -1.4280e-03, -1.4545e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.1935e-03, -1.6650e-03, -6.1216e-04],\\n\",\n      \"           [-9.3899e-04, -1.7683e-03, -9.7531e-04],\\n\",\n      \"           [ 5.7092e-04, -1.5941e-03, -1.3504e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1733e-01, -1.3911e-01, -1.8872e-01],\\n\",\n      \"           [ 7.0310e-03, -3.6998e-02, -1.3122e-01],\\n\",\n      \"           [ 1.6605e-01,  1.0061e-01, -1.5454e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.6461e-02, -1.3699e-01, -1.1473e-01],\\n\",\n      \"           [ 4.2384e-02,  2.6738e-02, -4.1885e-02],\\n\",\n      \"           [ 1.4445e-01,  1.2564e-01,  4.4564e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.8699e-02, -1.1124e-01, -9.4732e-02],\\n\",\n      \"           [ 6.9817e-03,  1.2308e-02, -2.6662e-02],\\n\",\n      \"           [ 8.6062e-02,  8.8561e-02,  2.0742e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.0020e-02,  7.2759e-02,  6.3386e-02],\\n\",\n      \"           [ 3.2824e-02,  5.2788e-02,  4.6914e-02],\\n\",\n      \"           [ 2.1263e-02,  1.5793e-02,  2.6325e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.2301e-02,  5.3348e-02,  4.6390e-02],\\n\",\n      \"           [ 1.3461e-02,  1.7955e-02,  1.3157e-02],\\n\",\n      \"           [ 7.4201e-03, -3.2678e-03, -1.8107e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9778e-02,  3.6948e-02,  1.6227e-02],\\n\",\n      \"           [-8.8687e-03, -3.7407e-03, -1.0039e-03],\\n\",\n      \"           [-7.3828e-03, -2.0660e-02, -4.2850e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.6627e-02, -1.7707e-02, -1.8453e-02],\\n\",\n      \"           [-4.6194e-02, -1.7968e-02, -1.6262e-03],\\n\",\n      \"           [-6.7160e-02, -5.2860e-02, -1.7219e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.9976e-02, -3.5375e-02, -2.8342e-02],\\n\",\n      \"           [-6.9990e-02, -7.2824e-02, -4.1285e-02],\\n\",\n      \"           [-5.8329e-02, -7.2869e-02, -6.8963e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.2904e-02, -7.7915e-02, -5.0887e-02],\\n\",\n      \"           [-7.6255e-02, -8.5963e-02, -7.5971e-02],\\n\",\n      \"           [-6.6203e-02, -6.4420e-02, -6.4937e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2741e-01, -1.8645e-01, -3.0418e-01],\\n\",\n      \"           [-6.5276e-02, -1.6768e-01, -2.7621e-01],\\n\",\n      \"           [-1.8381e-02, -1.1208e-01, -2.1499e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.3409e-01, -1.1504e-01, -2.4495e-01],\\n\",\n      \"           [-1.3974e-02, -9.6938e-02, -2.2977e-01],\\n\",\n      \"           [-6.8792e-03, -1.0325e-01, -1.7491e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.6598e-01, -9.5355e-02, -1.6653e-01],\\n\",\n      \"           [-5.8449e-02, -6.4733e-02, -1.7345e-01],\\n\",\n      \"           [ 3.3056e-02, -6.2323e-02, -1.0464e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.1542e-02, -2.2863e-02, -2.7420e-02],\\n\",\n      \"           [-8.3025e-03, -2.1138e-03, -1.3339e-02],\\n\",\n      \"           [ 7.2577e-03,  6.1900e-03,  6.7058e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.6975e-02, -2.0770e-02, -2.0000e-02],\\n\",\n      \"           [-7.6878e-03, -8.4838e-03, -1.4614e-02],\\n\",\n      \"           [ 1.7247e-02,  3.5255e-03, -5.7970e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.0833e-02, -3.0330e-02, -2.7912e-02],\\n\",\n      \"           [-7.5224e-03, -1.1759e-02, -2.6029e-02],\\n\",\n      \"           [ 9.8327e-03,  4.6246e-03, -1.1117e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.7369e-02, -2.8650e-02,  9.4269e-03],\\n\",\n      \"           [-4.0941e-02, -7.6621e-03,  9.3678e-03],\\n\",\n      \"           [-2.6352e-02,  1.9074e-03,  3.1488e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.2349e-02, -9.2552e-03,  1.7255e-03],\\n\",\n      \"           [-1.4653e-02,  9.5931e-03,  5.5084e-03],\\n\",\n      \"           [ 5.5219e-04,  2.5810e-02,  4.9486e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.2546e-02, -1.1158e-03, -1.0741e-03],\\n\",\n      \"           [ 5.3192e-03,  2.5224e-02,  2.1049e-02],\\n\",\n      \"           [ 2.6425e-02,  2.6619e-02,  4.3143e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 72.00 %\\n\",\n      \"Val loss: 0.549941\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[10,    60] loss: 0.62608\\n\",\n      \"[10,   120] loss: 0.52780\\n\",\n      \"Time elapsed: 0h:20m:17s\\n\",\n      \"train accuracy_score: 69.15 %\\n\",\n      \"tensor([[[[[-7.8442e-03, -7.5800e-03, -6.0120e-03],\\n\",\n      \"           [-7.5438e-03, -8.4542e-03, -7.5224e-03],\\n\",\n      \"           [-2.3689e-02, -9.0542e-03, -1.1572e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.1442e-03,  7.1297e-04,  2.0211e-03],\\n\",\n      \"           [-1.0603e-02, -3.7958e-03, -1.4106e-03],\\n\",\n      \"           [-1.7586e-02, -4.4745e-03, -8.3326e-03]],\\n\",\n      \"\\n\",\n      \"          [[-6.9455e-03, -1.3424e-04,  2.5356e-03],\\n\",\n      \"           [-8.1952e-03, -5.7179e-04,  2.0566e-03],\\n\",\n      \"           [-2.2202e-02, -4.7169e-03, -1.7792e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.3675e-03,  4.3633e-04, -3.8590e-04],\\n\",\n      \"           [ 2.3384e-03,  1.2285e-04, -1.0624e-03],\\n\",\n      \"           [ 2.0995e-03,  2.0949e-04, -9.3895e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 3.4216e-03,  1.4590e-03, -4.2090e-04],\\n\",\n      \"           [ 2.8394e-03, -5.8007e-04, -3.0996e-04],\\n\",\n      \"           [ 1.6010e-03, -5.5935e-04, -1.2499e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7457e-03,  2.4912e-03,  4.2288e-04],\\n\",\n      \"           [ 1.8624e-03, -3.9087e-05, -5.4606e-04],\\n\",\n      \"           [ 2.0508e-03, -8.0931e-04, -7.1488e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.6260e-02, -3.6761e-02, -1.3350e-01],\\n\",\n      \"           [ 6.4673e-02,  5.2888e-02, -8.0993e-02],\\n\",\n      \"           [ 2.0009e-01,  1.8200e-01,  4.8602e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.8218e-02, -9.0141e-02, -7.2361e-02],\\n\",\n      \"           [ 8.5981e-02,  5.9137e-02, -2.8532e-02],\\n\",\n      \"           [ 2.1060e-01,  1.5545e-01,  6.3101e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.2192e-02, -8.6666e-02, -7.3816e-02],\\n\",\n      \"           [ 4.5309e-02,  2.5311e-02,  1.3154e-02],\\n\",\n      \"           [ 1.4219e-01,  1.1780e-01,  6.2764e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.0587e-02,  4.9030e-02,  6.4167e-02],\\n\",\n      \"           [ 1.4072e-02,  3.1294e-02,  4.7668e-02],\\n\",\n      \"           [ 1.8012e-02,  1.2955e-02,  1.0223e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9866e-02,  3.2902e-02,  4.1064e-02],\\n\",\n      \"           [ 5.0500e-04,  6.6920e-03,  2.8556e-02],\\n\",\n      \"           [ 6.8280e-04,  2.6472e-03,  7.3978e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 3.2058e-02,  3.8660e-02,  2.4039e-02],\\n\",\n      \"           [ 8.9900e-03, -8.9389e-04, -2.1875e-03],\\n\",\n      \"           [-1.2064e-03, -4.9634e-03, -2.2956e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.1789e-03, -5.6533e-03, -1.8477e-02],\\n\",\n      \"           [-1.2098e-02, -3.3930e-03, -4.7061e-03],\\n\",\n      \"           [-2.9890e-02, -3.8531e-02, -9.7459e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.2161e-02, -9.8703e-03, -1.1247e-02],\\n\",\n      \"           [-1.8293e-02, -3.0437e-02, -2.0823e-02],\\n\",\n      \"           [-1.8867e-02, -3.9747e-02, -2.7572e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.7451e-02, -2.7385e-02, -3.5322e-02],\\n\",\n      \"           [-2.5240e-02, -4.2444e-02, -3.5092e-02],\\n\",\n      \"           [-2.3711e-02, -3.5214e-02, -3.3298e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.7967e-02, -6.7411e-02, -1.2973e-01],\\n\",\n      \"           [-3.5744e-03, -6.0018e-02, -9.5368e-02],\\n\",\n      \"           [ 8.6871e-02,  2.0095e-02, -8.6257e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.4058e-02, -6.6000e-02, -9.1339e-02],\\n\",\n      \"           [ 3.1989e-02, -3.2363e-02, -7.1533e-02],\\n\",\n      \"           [ 7.6841e-02, -2.7452e-02, -7.8230e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1605e-01, -1.5264e-02, -3.4891e-02],\\n\",\n      \"           [ 2.4623e-02, -1.3130e-02, -4.7921e-02],\\n\",\n      \"           [ 9.7059e-02, -1.5762e-02, -5.0733e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.6203e-02, -2.0784e-02, -1.7733e-02],\\n\",\n      \"           [-1.1999e-02, -2.7022e-04, -4.4991e-04],\\n\",\n      \"           [ 1.7706e-03,  9.4830e-03,  9.9197e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.8833e-02, -2.1722e-02, -2.5576e-02],\\n\",\n      \"           [-1.2875e-02, -5.4768e-03, -1.1366e-02],\\n\",\n      \"           [ 1.0915e-03,  6.5753e-03,  4.9746e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.3468e-02, -3.3117e-02, -3.0572e-02],\\n\",\n      \"           [-1.4019e-02, -1.5352e-02, -2.1978e-02],\\n\",\n      \"           [-2.8944e-03, -4.0353e-03, -4.1970e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.1789e-02, -4.0861e-02, -1.9077e-02],\\n\",\n      \"           [-4.2012e-02, -1.6173e-02, -8.6406e-03],\\n\",\n      \"           [-1.4734e-02, -6.9508e-04,  1.0346e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.3460e-02, -1.4173e-02, -1.1671e-02],\\n\",\n      \"           [-8.7091e-03,  1.6651e-02,  2.1864e-02],\\n\",\n      \"           [ 1.2260e-02,  3.1968e-02,  4.8633e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.9365e-03,  1.2018e-02,  2.5318e-02],\\n\",\n      \"           [ 2.8402e-02,  4.2021e-02,  3.7011e-02],\\n\",\n      \"           [ 3.6271e-02,  4.5083e-02,  5.1590e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 82.00 %\\n\",\n      \"Val loss: 0.526765\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[11,    60] loss: 0.55397\\n\",\n      \"[11,   120] loss: 0.55571\\n\",\n      \"Time elapsed: 0h:22m:8s\\n\",\n      \"train accuracy_score: 71.88 %\\n\",\n      \"tensor([[[[[-2.0102e-02, -3.1127e-02, -2.7042e-02],\\n\",\n      \"           [-1.1710e-02, -1.3953e-02, -2.1197e-02],\\n\",\n      \"           [-3.8521e-02, -4.0745e-03, -1.3400e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.8715e-03,  4.4080e-03, -4.1721e-03],\\n\",\n      \"           [-5.9447e-03,  3.7317e-03, -3.4111e-03],\\n\",\n      \"           [-3.4505e-02, -5.6556e-03,  7.2885e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1764e-03,  5.7504e-03, -8.7102e-04],\\n\",\n      \"           [-5.9943e-03,  2.1816e-06,  1.0611e-03],\\n\",\n      \"           [-2.7397e-02, -1.0157e-02,  5.1086e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.8962e-03,  4.7313e-03,  3.1387e-03],\\n\",\n      \"           [ 5.4145e-03,  1.8732e-03,  2.9944e-03],\\n\",\n      \"           [ 6.8539e-03,  1.6999e-03,  9.8368e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6538e-03,  2.8342e-03, -5.1917e-04],\\n\",\n      \"           [ 4.7203e-03, -8.8787e-04, -2.0561e-03],\\n\",\n      \"           [ 5.2888e-03,  4.0366e-04, -3.2593e-05]],\\n\",\n      \"\\n\",\n      \"          [[-1.5723e-03, -1.1682e-03,  2.1164e-05],\\n\",\n      \"           [ 2.4466e-03, -2.3170e-03,  6.9886e-04],\\n\",\n      \"           [ 2.9730e-03, -4.2859e-04, -8.5260e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.2566e-02, -2.0547e-01, -5.0695e-01],\\n\",\n      \"           [ 2.3929e-01,  4.0691e-02, -2.8283e-01],\\n\",\n      \"           [ 4.4046e-01,  2.8553e-01, -1.9308e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 9.6635e-02, -2.3424e-01, -2.3241e-01],\\n\",\n      \"           [ 2.6565e-01,  1.2782e-01, -6.7226e-02],\\n\",\n      \"           [ 4.4433e-01,  3.5111e-01,  1.3545e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.9670e-02, -1.6841e-01, -1.5861e-01],\\n\",\n      \"           [ 1.8299e-01,  8.7905e-02,  6.1588e-02],\\n\",\n      \"           [ 3.2768e-01,  2.8323e-01,  1.5297e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.5626e-02,  1.6655e-01,  1.8680e-01],\\n\",\n      \"           [ 1.0332e-01,  1.0128e-01,  1.1786e-01],\\n\",\n      \"           [ 1.2868e-01,  4.0277e-02,  7.1994e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.8880e-02,  1.2244e-01,  1.3184e-01],\\n\",\n      \"           [ 2.5713e-02,  4.4049e-02,  4.1648e-02],\\n\",\n      \"           [ 2.3450e-02, -2.6358e-02, -6.9008e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.3991e-02,  9.3134e-02,  7.9256e-02],\\n\",\n      \"           [ 2.4047e-02, -1.4797e-02, -2.0832e-02],\\n\",\n      \"           [ 6.7032e-03, -5.6644e-02, -1.2948e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.1143e-02, -1.1324e-02, -2.2667e-02],\\n\",\n      \"           [ 2.5422e-02, -8.4523e-03, -1.5747e-02],\\n\",\n      \"           [ 3.1303e-02, -4.3053e-02, -3.8040e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.2537e-02, -2.8064e-02, -1.9742e-02],\\n\",\n      \"           [-1.2661e-02, -4.6979e-02, -5.6415e-02],\\n\",\n      \"           [-7.3106e-03, -7.8286e-02, -8.5008e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.4791e-02, -1.0924e-01, -1.5179e-01],\\n\",\n      \"           [-3.6145e-02, -6.4255e-02, -1.2781e-01],\\n\",\n      \"           [-1.4067e-02, -5.4750e-02, -8.8030e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.9574e-01, -3.3457e-01, -5.4594e-01],\\n\",\n      \"           [-1.6962e-01, -2.8734e-01, -5.7595e-01],\\n\",\n      \"           [-7.0664e-02, -2.0989e-01, -4.8147e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.3643e-01, -3.5068e-01, -5.5486e-01],\\n\",\n      \"           [-1.1234e-01, -2.2591e-01, -4.8166e-01],\\n\",\n      \"           [ 2.9076e-02, -2.2113e-01, -4.8781e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.1031e-01, -1.6721e-01, -3.2938e-01],\\n\",\n      \"           [-7.0580e-02, -1.2306e-01, -3.6303e-01],\\n\",\n      \"           [ 6.5312e-02, -1.1347e-01, -3.4952e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.0362e-02, -7.9327e-03, -1.2488e-02],\\n\",\n      \"           [-5.5099e-03,  1.3430e-02, -9.2769e-03],\\n\",\n      \"           [ 3.9757e-02,  2.7125e-02, -1.7521e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.1699e-02, -1.0296e-02, -1.4569e-02],\\n\",\n      \"           [ 1.5893e-02,  1.3447e-02, -1.9925e-02],\\n\",\n      \"           [ 3.6009e-02,  1.5822e-02, -2.5337e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.3597e-02, -1.2178e-02, -1.1676e-02],\\n\",\n      \"           [ 3.1116e-02,  1.1637e-02, -2.1305e-02],\\n\",\n      \"           [ 3.6503e-02,  2.4462e-02, -1.2976e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.9420e-01, -1.1265e-01, -6.2191e-02],\\n\",\n      \"           [-1.1148e-01, -1.0596e-02, -1.1581e-02],\\n\",\n      \"           [-1.1586e-02,  3.6877e-02,  1.9451e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.4292e-01, -7.4587e-02, -9.3438e-03],\\n\",\n      \"           [ 3.1059e-03,  4.9568e-02,  4.0465e-02],\\n\",\n      \"           [ 1.1417e-01,  1.6041e-01,  1.1554e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.8879e-02,  6.3131e-03,  5.1365e-02],\\n\",\n      \"           [ 4.8605e-02,  1.1757e-01,  1.3410e-01],\\n\",\n      \"           [ 2.2983e-01,  2.3055e-01,  1.6364e-01]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 50.00 %\\n\",\n      \"Val loss: 0.792585\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[12,    60] loss: 0.57776\\n\",\n      \"[12,   120] loss: 0.52578\\n\",\n      \"Time elapsed: 0h:24m:1s\\n\",\n      \"train accuracy_score: 71.02 %\\n\",\n      \"tensor([[[[[ 6.6102e-03,  1.6117e-02,  8.5778e-03],\\n\",\n      \"           [ 2.6800e-02,  2.6391e-02,  3.4738e-02],\\n\",\n      \"           [ 8.8031e-02,  3.7606e-02,  5.2210e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4852e-02, -2.3735e-03, -3.6609e-04],\\n\",\n      \"           [ 2.9608e-02,  9.8912e-03,  1.1231e-02],\\n\",\n      \"           [ 4.7787e-02,  2.2050e-02,  2.3713e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.4611e-04,  1.1803e-03, -5.8640e-04],\\n\",\n      \"           [ 2.1407e-02,  1.0696e-03, -1.9092e-03],\\n\",\n      \"           [ 7.3955e-02,  1.0961e-02, -7.9223e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.4647e-03, -4.1938e-03,  6.4250e-04],\\n\",\n      \"           [-3.0273e-03, -3.3874e-04,  2.9833e-03],\\n\",\n      \"           [-2.0828e-03,  1.5066e-03,  3.8177e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.5445e-03, -1.7769e-03,  2.7012e-03],\\n\",\n      \"           [ 6.2212e-05,  1.5917e-03,  2.7107e-03],\\n\",\n      \"           [-4.3282e-04,  5.8317e-04,  3.2325e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1044e-03,  4.1887e-04,  1.6005e-03],\\n\",\n      \"           [ 3.0848e-03,  3.4380e-03,  5.5176e-04],\\n\",\n      \"           [ 2.2066e-03,  2.8594e-03,  1.3841e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.8781e-01,  4.6643e-01,  5.7051e-01],\\n\",\n      \"           [ 3.3479e-03,  1.6318e-01,  3.4246e-01],\\n\",\n      \"           [-2.5313e-01, -2.0583e-01,  3.8048e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.3946e-01,  3.7224e-01,  3.3782e-01],\\n\",\n      \"           [-4.0421e-02,  3.0333e-02,  1.5977e-01],\\n\",\n      \"           [-2.7708e-01, -2.3016e-01, -2.5643e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.9583e-01,  3.2099e-01,  2.9130e-01],\\n\",\n      \"           [ 3.8781e-02,  1.0068e-02,  1.1965e-01],\\n\",\n      \"           [-2.3431e-01, -1.3144e-01, -4.3216e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.6368e-02, -1.9505e-01, -2.2133e-01],\\n\",\n      \"           [-5.6675e-02, -1.2923e-01, -1.0930e-01],\\n\",\n      \"           [-3.6224e-02, -3.8094e-02,  7.5704e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.5493e-02, -1.7957e-01, -2.2014e-01],\\n\",\n      \"           [-3.2920e-02, -6.8484e-02, -6.1554e-02],\\n\",\n      \"           [-8.5492e-04,  2.1504e-02,  9.1583e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.5273e-02, -1.5180e-01, -1.7250e-01],\\n\",\n      \"           [-2.6480e-02, -3.9877e-02, -6.5132e-02],\\n\",\n      \"           [-1.1933e-02,  3.6915e-02,  1.1313e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.8394e-02, -1.2541e-03,  2.6082e-03],\\n\",\n      \"           [ 1.4880e-01,  1.3477e-02, -3.2828e-03],\\n\",\n      \"           [ 1.0072e-01,  8.8034e-02,  1.6566e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0230e-01,  1.4349e-02, -2.4286e-02],\\n\",\n      \"           [ 1.5112e-01,  8.6688e-02,  4.2822e-02],\\n\",\n      \"           [ 8.3265e-02,  1.1876e-01,  9.4451e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.2617e-02,  8.2883e-02,  7.3735e-02],\\n\",\n      \"           [ 1.1853e-01,  1.0287e-01,  1.1707e-01],\\n\",\n      \"           [ 1.0571e-01,  1.2004e-01,  9.6639e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.5262e-01,  4.9984e-01,  4.7741e-01],\\n\",\n      \"           [ 2.3236e-01,  3.0056e-01,  3.1701e-01],\\n\",\n      \"           [-8.0918e-02,  1.5192e-03,  1.6625e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.5206e-01,  3.2581e-01,  3.3201e-01],\\n\",\n      \"           [-1.9660e-02,  2.2651e-01,  2.1179e-01],\\n\",\n      \"           [-1.7523e-01,  1.0053e-01,  1.3325e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0677e-01,  9.4328e-02,  3.1785e-02],\\n\",\n      \"           [-2.2359e-02,  4.1593e-02,  5.1331e-02],\\n\",\n      \"           [-4.1938e-01,  4.3719e-02,  4.2788e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.4842e-02,  7.0518e-02,  8.7544e-02],\\n\",\n      \"           [ 2.6951e-02,  4.2025e-03,  6.8218e-02],\\n\",\n      \"           [-2.8738e-02, -1.0374e-02,  6.2489e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.1792e-02,  4.6137e-02,  7.5462e-02],\\n\",\n      \"           [ 2.0981e-03,  4.4945e-03,  6.3018e-02],\\n\",\n      \"           [-3.2892e-02, -3.0088e-03,  4.5307e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.8727e-02,  3.6318e-02,  4.2639e-02],\\n\",\n      \"           [-1.5613e-02,  1.2855e-04,  4.0622e-02],\\n\",\n      \"           [-4.9180e-02, -1.2347e-02,  2.8374e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.7283e-01,  4.4036e-02, -3.5749e-02],\\n\",\n      \"           [ 6.0855e-02, -1.7467e-02, -8.8909e-04],\\n\",\n      \"           [-1.9997e-02, -5.2736e-02,  1.9943e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2761e-01,  4.0096e-02,  8.9877e-05],\\n\",\n      \"           [ 2.0918e-02, -5.6545e-02, -2.6444e-02],\\n\",\n      \"           [-6.8433e-02, -6.6600e-02,  1.7548e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.1889e-02,  5.6956e-03, -3.6534e-02],\\n\",\n      \"           [ 3.0162e-03, -2.8144e-02, -2.8976e-02],\\n\",\n      \"           [-9.1556e-02, -7.5330e-02,  7.6890e-03]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 46.00 %\\n\",\n      \"Val loss: 0.731752\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[13,    60] loss: 0.59713\\n\",\n      \"[13,   120] loss: 0.49862\\n\",\n      \"Time elapsed: 0h:25m:51s\\n\",\n      \"train accuracy_score: 74.03 %\\n\",\n      \"tensor([[[[[ 3.9524e-03, -1.1814e-03, -6.6409e-03],\\n\",\n      \"           [-2.0280e-03, -9.9965e-03, -1.3072e-02],\\n\",\n      \"           [-3.1198e-02, -1.7588e-02, -2.1124e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1956e-03,  4.9903e-03, -1.3160e-03],\\n\",\n      \"           [-6.7924e-03, -3.8680e-03, -3.3000e-03],\\n\",\n      \"           [-2.4515e-02, -7.5290e-03, -4.3900e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.3711e-03, -2.3282e-03,  1.1947e-03],\\n\",\n      \"           [-4.1594e-03,  5.1957e-04,  1.5001e-03],\\n\",\n      \"           [-2.5921e-02, -2.4753e-03,  2.9956e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.3177e-03,  2.7530e-03,  1.0965e-03],\\n\",\n      \"           [ 1.3159e-03, -7.0661e-04,  1.7182e-03],\\n\",\n      \"           [ 5.5312e-04,  6.3897e-05,  7.0922e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 4.8790e-03,  3.3424e-03,  3.2084e-04],\\n\",\n      \"           [ 1.8929e-03,  2.9079e-04,  1.5296e-04],\\n\",\n      \"           [ 1.2802e-03,  4.3311e-04,  3.0715e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4867e-03,  3.0985e-03,  8.5570e-04],\\n\",\n      \"           [ 2.1430e-03,  1.3777e-03,  1.9448e-05],\\n\",\n      \"           [ 2.2473e-03,  1.3113e-03,  1.6865e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.1206e-01, -1.3790e-01, -1.3163e-01],\\n\",\n      \"           [ 7.4941e-03,  6.7068e-03, -4.6869e-02],\\n\",\n      \"           [ 1.4879e-01,  1.7972e-01,  9.2008e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.5831e-02, -2.0663e-01, -1.0978e-01],\\n\",\n      \"           [ 2.0384e-02,  2.6361e-02, -3.2889e-02],\\n\",\n      \"           [ 1.7333e-01,  1.2463e-01,  1.0931e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.7956e-01, -2.0479e-01, -1.2323e-01],\\n\",\n      \"           [ 1.0184e-02, -8.8918e-03, -9.1282e-03],\\n\",\n      \"           [ 1.2318e-01,  1.3390e-01,  1.0483e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.5099e-02,  7.6541e-02,  1.2274e-01],\\n\",\n      \"           [ 1.6391e-02,  4.2343e-02,  9.6068e-02],\\n\",\n      \"           [ 1.2719e-02,  2.8281e-02,  2.3559e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.9189e-02,  6.1256e-02,  9.3854e-02],\\n\",\n      \"           [ 1.8492e-03,  2.3052e-02,  6.0270e-02],\\n\",\n      \"           [-1.1619e-02,  7.3297e-03,  2.5050e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6682e-02,  6.0285e-02,  6.8486e-02],\\n\",\n      \"           [ 1.7770e-04,  1.8264e-02,  1.6403e-02],\\n\",\n      \"           [-1.3367e-02, -2.1419e-02, -1.0060e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3044e-02,  3.3280e-03,  9.6184e-03],\\n\",\n      \"           [ 8.6513e-03, -8.3620e-03,  7.6622e-03],\\n\",\n      \"           [ 9.7748e-03, -5.6313e-02, -1.2292e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.9681e-03, -3.2402e-03,  7.8269e-03],\\n\",\n      \"           [-1.1411e-03, -1.4358e-02, -1.7936e-02],\\n\",\n      \"           [-3.3444e-02, -5.2238e-02, -4.5798e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2914e-02, -1.1613e-02, -1.2500e-02],\\n\",\n      \"           [-2.2303e-02, -9.2246e-03, -3.1612e-02],\\n\",\n      \"           [-3.1706e-02, -3.7203e-02, -3.5826e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2246e-01, -6.9549e-02,  8.0387e-02],\\n\",\n      \"           [-1.1023e-01,  2.2288e-02,  9.7478e-02],\\n\",\n      \"           [ 3.4059e-02,  5.8301e-02,  7.9659e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1255e-01,  4.6467e-02,  1.0138e-01],\\n\",\n      \"           [-1.3659e-02, -1.0402e-03,  9.1203e-02],\\n\",\n      \"           [ 5.0261e-02, -2.9902e-02,  1.0235e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.5679e-01,  5.6648e-02,  1.1152e-01],\\n\",\n      \"           [ 4.7582e-02,  3.1533e-02,  1.5075e-01],\\n\",\n      \"           [ 5.6261e-02,  2.2220e-03,  1.3231e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.4780e-02, -3.6219e-02, -3.4122e-02],\\n\",\n      \"           [-2.3186e-02, -1.3574e-02, -1.7657e-02],\\n\",\n      \"           [ 7.6138e-03, -2.2071e-04, -7.5491e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.2957e-02, -2.7063e-02, -3.4804e-02],\\n\",\n      \"           [-6.3822e-03, -1.1006e-02, -2.8008e-02],\\n\",\n      \"           [ 1.0629e-02,  8.9562e-04, -1.2347e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.7869e-02, -3.5700e-02, -3.7213e-02],\\n\",\n      \"           [-7.6619e-03, -1.1479e-02, -1.7579e-02],\\n\",\n      \"           [ 1.7107e-02,  5.0621e-03,  2.6114e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.3200e-02, -2.0990e-02, -8.6724e-03],\\n\",\n      \"           [ 9.9224e-03,  1.7767e-02,  2.6387e-02],\\n\",\n      \"           [ 4.3258e-02,  2.9657e-02,  2.5215e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.0164e-03, -1.5703e-02,  5.8766e-04],\\n\",\n      \"           [ 3.1200e-02,  1.4468e-02,  1.5106e-02],\\n\",\n      \"           [ 5.6255e-02,  5.2327e-02,  5.6911e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 8.3297e-03, -1.9373e-02, -3.9826e-03],\\n\",\n      \"           [ 4.7981e-02,  2.5845e-02,  1.7229e-02],\\n\",\n      \"           [ 8.5092e-02,  5.9457e-02,  5.4428e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 82.00 %\\n\",\n      \"Val loss: 0.474213\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[14,    60] loss: 0.56561\\n\",\n      \"[14,   120] loss: 0.56568\\n\",\n      \"Time elapsed: 0h:27m:43s\\n\",\n      \"train accuracy_score: 75.61 %\\n\",\n      \"tensor([[[[[-2.6677e-03, -1.9123e-03, -3.2809e-03],\\n\",\n      \"           [-1.1519e-03, -2.8198e-03, -4.4151e-03],\\n\",\n      \"           [-8.4176e-03, -4.4273e-03, -4.7847e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.8635e-04,  1.0962e-04, -6.3407e-04],\\n\",\n      \"           [-1.0031e-03, -2.0675e-04, -6.8393e-04],\\n\",\n      \"           [-5.8464e-03, -2.6807e-03, -3.5811e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.9088e-04,  1.7654e-04,  1.6767e-03],\\n\",\n      \"           [-2.2260e-03, -5.5834e-04,  2.2401e-05],\\n\",\n      \"           [-1.0280e-02, -2.8230e-03, -1.6545e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.8955e-04,  4.3480e-04,  6.8314e-04],\\n\",\n      \"           [ 7.8470e-04,  4.2940e-04,  1.0815e-03],\\n\",\n      \"           [-8.5186e-05,  1.5760e-04,  2.1825e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 4.2747e-04,  6.4043e-04,  1.2311e-04],\\n\",\n      \"           [ 8.8043e-05, -1.7439e-04, -4.7096e-05],\\n\",\n      \"           [-2.9993e-05, -2.5078e-04,  8.2564e-05]],\\n\",\n      \"\\n\",\n      \"          [[-3.5320e-05,  1.9894e-04,  5.0769e-05],\\n\",\n      \"           [-5.7745e-05, -2.5918e-04,  9.7204e-05],\\n\",\n      \"           [ 4.2759e-05, -2.3298e-04, -2.9986e-06]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.6082e-02, -8.6794e-02, -1.2559e-01],\\n\",\n      \"           [-3.5045e-02, -6.3657e-02, -1.1409e-01],\\n\",\n      \"           [ 1.4620e-03, -3.9664e-02, -6.8720e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.1161e-02, -8.3196e-02, -8.0180e-02],\\n\",\n      \"           [-1.6827e-02, -3.2296e-02, -5.4988e-02],\\n\",\n      \"           [ 2.8545e-02,  1.7831e-02, -1.5294e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.7546e-02, -6.5470e-02, -4.1968e-02],\\n\",\n      \"           [-6.2876e-03, -1.0467e-02, -1.6133e-02],\\n\",\n      \"           [ 2.8686e-02,  2.5530e-02,  7.9341e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.1986e-03,  1.9734e-02,  2.6568e-02],\\n\",\n      \"           [ 6.2208e-03,  9.1738e-03,  1.5080e-02],\\n\",\n      \"           [ 8.1271e-03,  4.0345e-03, -6.0674e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2900e-02,  2.1757e-02,  2.6666e-02],\\n\",\n      \"           [ 5.3347e-03,  1.2477e-02,  4.6895e-03],\\n\",\n      \"           [ 3.3505e-03, -2.5936e-03, -1.3891e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3013e-02,  2.4870e-02,  2.0056e-02],\\n\",\n      \"           [ 6.0659e-03,  7.6570e-03,  7.5969e-04],\\n\",\n      \"           [ 6.9322e-04, -4.6689e-03, -1.6530e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.2250e-04,  8.6975e-04, -4.1275e-03],\\n\",\n      \"           [-7.1651e-03, -4.8289e-03,  1.5785e-03],\\n\",\n      \"           [-3.8871e-03, -1.1813e-02,  1.2672e-03]],\\n\",\n      \"\\n\",\n      \"          [[-9.0882e-03, -1.4733e-03,  1.8281e-04],\\n\",\n      \"           [-5.9035e-03, -9.6309e-03, -3.7108e-03],\\n\",\n      \"           [-3.5028e-03, -1.1304e-02, -6.6108e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.4371e-03, -1.8357e-02, -5.3884e-03],\\n\",\n      \"           [-1.3758e-02, -9.9234e-03, -7.0080e-03],\\n\",\n      \"           [-5.6772e-03, -1.0172e-02, -1.2193e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3731e-02, -2.7298e-03,  3.4178e-03],\\n\",\n      \"           [-9.0402e-03, -1.6537e-02, -1.2881e-02],\\n\",\n      \"           [ 1.0096e-02,  1.8539e-03, -6.5074e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.4090e-02, -9.1970e-03,  1.6211e-02],\\n\",\n      \"           [ 9.5708e-03, -1.1998e-02, -1.3780e-02],\\n\",\n      \"           [ 1.8015e-02, -2.0247e-02, -1.3778e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.8088e-02,  1.9077e-02,  4.3103e-02],\\n\",\n      \"           [ 9.7016e-03,  5.6921e-03,  2.5643e-02],\\n\",\n      \"           [ 4.3283e-02,  2.0336e-03,  1.1383e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.0782e-02, -1.4846e-02, -1.2036e-02],\\n\",\n      \"           [-9.7770e-03, -2.9267e-03, -3.2811e-03],\\n\",\n      \"           [ 1.3934e-03,  1.2717e-03,  9.6661e-05]],\\n\",\n      \"\\n\",\n      \"          [[-1.8676e-02, -1.3540e-02, -9.2647e-03],\\n\",\n      \"           [-5.7781e-03, -2.9168e-03, -2.4563e-03],\\n\",\n      \"           [ 3.9984e-03,  4.6607e-03,  5.8984e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.1480e-02, -9.2248e-03, -8.1338e-03],\\n\",\n      \"           [-1.1367e-03, -5.5716e-04, -2.0560e-03],\\n\",\n      \"           [ 9.5196e-03,  7.6466e-03,  5.5164e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.0803e-02, -1.1919e-02, -7.2512e-03],\\n\",\n      \"           [-1.0021e-02,  7.0402e-04, -3.9924e-04],\\n\",\n      \"           [ 1.6416e-02,  1.5175e-02,  1.8400e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.2842e-02, -4.6010e-03,  4.4886e-04],\\n\",\n      \"           [ 4.0287e-03,  1.5370e-02,  9.4408e-03],\\n\",\n      \"           [ 3.3128e-02,  2.4354e-02,  2.2464e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.6103e-02, -8.3269e-03,  2.0237e-03],\\n\",\n      \"           [-6.7874e-04,  1.1918e-02,  1.9059e-02],\\n\",\n      \"           [ 3.8680e-02,  3.0007e-02,  2.4765e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 80.00 %\\n\",\n      \"Val loss: 0.430308\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[15,    60] loss: 0.49564\\n\",\n      \"[15,   120] loss: 0.49114\\n\",\n      \"Time elapsed: 0h:29m:33s\\n\",\n      \"train accuracy_score: 76.61 %\\n\",\n      \"tensor([[[[[-4.4310e-03, -5.4018e-03, -2.0447e-03],\\n\",\n      \"           [ 1.0691e-03, -6.3921e-03, -1.2628e-02],\\n\",\n      \"           [-2.2706e-02, -1.6648e-02, -1.1367e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.8217e-03,  1.8668e-03,  2.4184e-03],\\n\",\n      \"           [-2.0148e-03, -5.7040e-03, -2.8411e-03],\\n\",\n      \"           [-2.3495e-02, -1.2155e-02, -1.1211e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.4477e-03,  3.9530e-03,  5.7132e-03],\\n\",\n      \"           [-5.2641e-03, -1.7469e-03,  3.3454e-03],\\n\",\n      \"           [-2.9896e-02, -8.7367e-03, -1.1623e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.8551e-03,  2.1471e-03,  1.4077e-03],\\n\",\n      \"           [ 2.1390e-03,  5.1975e-04,  1.5250e-03],\\n\",\n      \"           [ 5.7212e-04,  4.5943e-04,  3.7278e-07]],\\n\",\n      \"\\n\",\n      \"          [[ 3.8663e-03,  3.2953e-03,  5.6848e-04],\\n\",\n      \"           [ 2.5604e-03,  4.5185e-04, -2.8656e-04],\\n\",\n      \"           [ 1.3321e-03,  1.9120e-04,  2.9830e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 4.1393e-03,  2.1255e-03,  4.4084e-04],\\n\",\n      \"           [ 3.1083e-03,  6.1848e-04,  3.9677e-04],\\n\",\n      \"           [ 3.8085e-03,  5.5615e-04,  3.3125e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.9673e-01, -1.5697e-01, -1.9584e-01],\\n\",\n      \"           [-9.8281e-02, -1.2485e-01, -1.3961e-01],\\n\",\n      \"           [-5.8287e-02, -6.4935e-02, -3.5061e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.4052e-01, -2.0895e-01, -1.3994e-01],\\n\",\n      \"           [-5.2813e-02, -2.0738e-02, -5.6852e-02],\\n\",\n      \"           [ 1.1070e-02,  1.6831e-02,  4.7468e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.6194e-01, -1.6882e-01, -7.0502e-02],\\n\",\n      \"           [-9.7862e-03, -1.5316e-02, -2.4442e-02],\\n\",\n      \"           [ 7.1654e-02,  1.2006e-01,  5.8239e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.6192e-02,  8.7873e-02,  1.0718e-01],\\n\",\n      \"           [ 5.3192e-02,  3.4350e-02,  4.6395e-02],\\n\",\n      \"           [ 3.6660e-02,  4.0534e-03, -3.6974e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.7649e-02,  7.0036e-02,  7.6810e-02],\\n\",\n      \"           [ 3.0564e-02,  6.9608e-03,  3.5901e-02],\\n\",\n      \"           [ 2.0377e-02, -7.6313e-03, -3.7520e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.2241e-02,  7.6620e-02,  7.7455e-02],\\n\",\n      \"           [ 1.4658e-02,  1.5788e-03,  9.8660e-03],\\n\",\n      \"           [ 7.2396e-03, -2.0327e-02, -5.6383e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.9945e-04,  2.8130e-03,  1.8226e-04],\\n\",\n      \"           [-1.3309e-02, -7.3574e-03, -2.0651e-03],\\n\",\n      \"           [ 5.9940e-03, -2.7374e-02, -2.5272e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.1683e-02, -3.4666e-03, -3.8193e-03],\\n\",\n      \"           [-3.2720e-02, -2.5974e-02, -1.0161e-02],\\n\",\n      \"           [-3.7670e-03, -2.3204e-02, -1.1715e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.1271e-02,  2.0224e-03, -1.2556e-02],\\n\",\n      \"           [-1.3175e-02, -6.0140e-03, -1.7776e-02],\\n\",\n      \"           [-1.0781e-02, -1.1789e-02, -2.8599e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.7212e-01, -1.1655e-01, -5.4032e-02],\\n\",\n      \"           [-1.8962e-01, -1.0968e-01, -6.4676e-02],\\n\",\n      \"           [-1.0415e-01, -1.1914e-01, -8.6793e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.3611e-01, -4.0508e-02, -3.4548e-02],\\n\",\n      \"           [-5.6709e-02, -8.4234e-02, -5.4278e-02],\\n\",\n      \"           [ 1.5953e-02, -8.3013e-02, -4.3533e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0320e-01, -1.5214e-04,  6.8421e-02],\\n\",\n      \"           [-1.2945e-02, -6.5757e-03, -6.1069e-04],\\n\",\n      \"           [ 6.5941e-02, -6.0883e-03, -2.9475e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.6097e-02, -2.1279e-02, -2.5315e-02],\\n\",\n      \"           [-1.9718e-02,  8.4306e-04, -1.3736e-02],\\n\",\n      \"           [ 3.6324e-03,  3.9898e-04, -1.3073e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.7824e-02, -1.9520e-02, -2.1397e-02],\\n\",\n      \"           [ 4.2102e-03,  1.0934e-03, -1.1103e-02],\\n\",\n      \"           [ 1.9743e-02,  1.3874e-02, -3.4727e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.9513e-02, -2.1649e-02, -2.4796e-02],\\n\",\n      \"           [ 1.3804e-02, -3.6165e-03, -1.3007e-02],\\n\",\n      \"           [ 3.2790e-02,  1.9289e-02,  4.3666e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.5262e-02, -3.1065e-02,  1.4307e-02],\\n\",\n      \"           [-1.4288e-04,  2.0568e-02,  2.0392e-02],\\n\",\n      \"           [ 4.9778e-02,  2.8977e-02,  1.0382e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.0668e-02, -1.8555e-02,  1.2650e-03],\\n\",\n      \"           [ 2.9889e-02,  3.3176e-02,  3.2189e-02],\\n\",\n      \"           [ 6.5128e-02,  6.6034e-02,  6.4121e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.2392e-02, -9.8070e-03,  5.5336e-03],\\n\",\n      \"           [ 1.8697e-02,  2.7200e-02,  3.8042e-02],\\n\",\n      \"           [ 8.0379e-02,  3.2224e-02,  4.4771e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 77.00 %\\n\",\n      \"Val loss: 0.483063\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[16,    60] loss: 0.46117\\n\",\n      \"[16,   120] loss: 0.45714\\n\",\n      \"Time elapsed: 0h:31m:23s\\n\",\n      \"train accuracy_score: 79.34 %\\n\",\n      \"tensor([[[[[ 2.0803e-03,  1.9638e-05, -6.2774e-04],\\n\",\n      \"           [ 4.2979e-03,  8.6259e-03,  1.0037e-02],\\n\",\n      \"           [ 3.3204e-02,  2.2081e-02,  3.1293e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.0371e-03, -5.8374e-03, -8.2603e-03],\\n\",\n      \"           [ 2.4022e-03,  6.6689e-03, -3.0603e-04],\\n\",\n      \"           [ 1.1943e-02,  1.0445e-02,  1.8714e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.7956e-03, -1.2662e-03, -9.7169e-03],\\n\",\n      \"           [ 1.0887e-03,  7.0281e-03,  1.3466e-03],\\n\",\n      \"           [ 2.4426e-02,  8.9337e-03,  1.0920e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.8137e-03, -1.3619e-03, -1.2981e-03],\\n\",\n      \"           [-9.0332e-04,  2.9876e-04, -1.1719e-03],\\n\",\n      \"           [-1.5099e-03,  2.4184e-04,  3.9136e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.0541e-03, -1.6546e-03, -1.0526e-05],\\n\",\n      \"           [-6.0179e-04,  6.0731e-04,  8.2271e-05],\\n\",\n      \"           [ 3.9065e-04,  8.9735e-04,  1.1661e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.0220e-04, -6.4062e-04, -1.7581e-04],\\n\",\n      \"           [-5.2277e-04,  5.5975e-04,  2.7156e-04],\\n\",\n      \"           [ 3.4245e-05,  1.5338e-03,  1.2386e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.2023e-01,  2.3569e-01,  2.9565e-01],\\n\",\n      \"           [ 5.2307e-02,  1.0042e-01,  1.8784e-01],\\n\",\n      \"           [-1.0866e-01, -6.6740e-02,  1.0499e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5916e-01,  2.1408e-01,  1.7051e-01],\\n\",\n      \"           [ 1.3911e-02,  2.2013e-02,  1.0877e-01],\\n\",\n      \"           [-1.5423e-01, -9.1361e-02,  1.6366e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4870e-01,  1.5918e-01,  7.9894e-02],\\n\",\n      \"           [ 1.9903e-02,  2.1240e-02,  5.1758e-02],\\n\",\n      \"           [-8.4523e-02, -7.7156e-02, -1.1133e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.1929e-02, -7.6852e-02, -9.8913e-02],\\n\",\n      \"           [-2.2512e-02, -3.4311e-02, -7.7754e-02],\\n\",\n      \"           [-6.9693e-03, -1.5959e-02, -2.0846e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.8048e-03, -4.9596e-02, -7.0659e-02],\\n\",\n      \"           [-2.6354e-04, -3.6577e-02, -4.1879e-02],\\n\",\n      \"           [ 4.7076e-03, -1.1247e-02, -3.1479e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9194e-03, -4.4407e-02, -5.1563e-02],\\n\",\n      \"           [ 5.0963e-03, -1.7398e-02, -2.0241e-02],\\n\",\n      \"           [ 8.5578e-04, -6.5821e-04,  1.5761e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.8285e-03,  7.3138e-03,  1.9003e-02],\\n\",\n      \"           [-8.2498e-03,  1.0539e-04, -9.1331e-03],\\n\",\n      \"           [-2.2616e-03,  1.8559e-02,  4.7243e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.9744e-02,  1.9122e-02,  1.1036e-02],\\n\",\n      \"           [ 5.3389e-03,  3.1808e-02,  3.6427e-02],\\n\",\n      \"           [-6.3272e-03,  2.4510e-02,  4.1839e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0223e-02,  4.5735e-02,  4.0793e-02],\\n\",\n      \"           [ 4.6124e-02,  3.7634e-02,  4.0928e-02],\\n\",\n      \"           [ 2.6244e-02,  4.9426e-02,  5.3808e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.5861e-01,  4.0204e-02,  2.2120e-02],\\n\",\n      \"           [ 4.9073e-02,  1.8662e-02, -4.8757e-02],\\n\",\n      \"           [-1.0583e-01, -7.9054e-02, -7.2227e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2461e-01,  9.5125e-02, -1.1910e-02],\\n\",\n      \"           [-5.6678e-02,  2.7943e-02,  1.9883e-02],\\n\",\n      \"           [-1.2428e-01, -3.9684e-02, -6.1683e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2245e-01, -1.5445e-02, -6.0682e-02],\\n\",\n      \"           [-9.2076e-02, -2.5982e-02, -5.6405e-02],\\n\",\n      \"           [-8.7310e-02, -5.7445e-02, -4.3587e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.2318e-02,  2.2495e-02,  1.6239e-02],\\n\",\n      \"           [ 1.9358e-02,  8.0761e-03,  3.5039e-03],\\n\",\n      \"           [-1.1475e-03, -3.9789e-03,  5.1089e-05]],\\n\",\n      \"\\n\",\n      \"          [[ 3.0358e-02,  1.5669e-02,  1.2647e-02],\\n\",\n      \"           [ 6.5719e-03,  2.0617e-03,  9.8711e-03],\\n\",\n      \"           [-1.2817e-02, -1.1580e-02, -1.1758e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1974e-02,  1.3576e-02,  7.5307e-03],\\n\",\n      \"           [-8.2584e-03,  8.3541e-05,  5.3043e-03],\\n\",\n      \"           [-2.5961e-02, -1.7213e-02, -1.0198e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.9719e-02,  1.8038e-02, -4.1539e-03],\\n\",\n      \"           [-2.3288e-02, -7.6536e-03,  1.7498e-02],\\n\",\n      \"           [-5.0444e-02, -1.2070e-02,  2.4294e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.2099e-04,  9.2864e-03,  9.7343e-03],\\n\",\n      \"           [-5.5156e-02, -3.9632e-02, -4.9386e-03],\\n\",\n      \"           [-1.0151e-01, -6.6941e-02, -2.5392e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.8975e-02,  1.6839e-02,  7.0473e-03],\\n\",\n      \"           [-6.1041e-02, -3.6538e-02, -2.8396e-02],\\n\",\n      \"           [-1.1537e-01, -7.2179e-02, -7.4070e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 74.00 %\\n\",\n      \"Val loss: 0.518982\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[17,    60] loss: 0.49296\\n\",\n      \"[17,   120] loss: 0.45795\\n\",\n      \"Time elapsed: 0h:33m:15s\\n\",\n      \"train accuracy_score: 78.77 %\\n\",\n      \"tensor([[[[[ 5.4109e-04,  2.6816e-05,  7.9062e-04],\\n\",\n      \"           [ 6.6088e-04,  1.4090e-03,  1.5776e-03],\\n\",\n      \"           [ 6.6722e-03,  1.3329e-03,  8.3254e-05]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2324e-03,  6.2889e-04,  9.7687e-05],\\n\",\n      \"           [ 2.0972e-03,  1.5764e-03,  9.9829e-04],\\n\",\n      \"           [ 3.4595e-03,  1.7611e-03,  1.5623e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1723e-03,  1.4792e-03,  4.2551e-04],\\n\",\n      \"           [ 2.5548e-03,  2.0198e-03,  1.1379e-03],\\n\",\n      \"           [ 8.4095e-03,  1.5520e-03,  5.0323e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.5494e-04, -5.7059e-05, -2.7511e-04],\\n\",\n      \"           [-1.9164e-04,  1.2838e-05, -2.2806e-04],\\n\",\n      \"           [-6.2179e-05,  4.5619e-05, -4.1492e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.5318e-04, -7.4453e-05,  8.3739e-05],\\n\",\n      \"           [-5.5749e-05, -4.5232e-06, -7.8957e-05],\\n\",\n      \"           [-8.7001e-05,  6.8526e-07, -1.6515e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.2896e-04, -5.7423e-05,  5.9401e-05],\\n\",\n      \"           [-1.9511e-04,  9.8991e-06, -2.6962e-05],\\n\",\n      \"           [ 7.7999e-05,  1.2920e-04, -5.4816e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 9.6452e-02,  8.9832e-02,  9.2176e-02],\\n\",\n      \"           [ 6.6878e-02,  6.9017e-02,  9.3985e-02],\\n\",\n      \"           [ 4.7402e-02,  5.8570e-02,  9.0503e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 8.3817e-02,  6.3902e-02,  5.4410e-02],\\n\",\n      \"           [ 4.8180e-02,  3.1872e-02,  3.8708e-02],\\n\",\n      \"           [ 1.5391e-02,  2.5184e-02,  4.4112e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.5054e-02,  5.4398e-02,  3.1701e-02],\\n\",\n      \"           [ 3.0335e-02,  3.0190e-02,  3.1908e-02],\\n\",\n      \"           [ 1.4191e-02,  1.1241e-02,  2.6749e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.3515e-03, -1.1039e-02, -2.4672e-02],\\n\",\n      \"           [-2.3635e-04, -1.0177e-02, -2.0634e-02],\\n\",\n      \"           [-2.5763e-03, -3.9837e-03, -7.6431e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.5316e-03, -9.9476e-03, -2.6208e-02],\\n\",\n      \"           [ 1.4246e-03, -7.3440e-03, -1.7168e-02],\\n\",\n      \"           [ 7.5382e-04, -2.0413e-03, -5.0756e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.1907e-03, -1.1637e-02, -2.4605e-02],\\n\",\n      \"           [ 1.5746e-03, -6.5866e-03, -1.5296e-02],\\n\",\n      \"           [ 7.3320e-04, -3.0227e-03, -4.2471e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.5378e-03,  3.6433e-03,  4.7762e-03],\\n\",\n      \"           [ 9.4455e-03,  7.9177e-03,  5.3192e-03],\\n\",\n      \"           [ 1.5908e-02,  1.4557e-02,  7.4412e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.7132e-03,  2.9590e-03,  2.8185e-03],\\n\",\n      \"           [ 5.2466e-03,  1.0087e-02,  8.4506e-03],\\n\",\n      \"           [ 9.1096e-03,  1.0304e-02,  1.2280e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1633e-02,  1.2926e-02,  6.1858e-03],\\n\",\n      \"           [ 1.2278e-02,  1.1296e-02,  1.1920e-02],\\n\",\n      \"           [ 5.1147e-03,  9.2655e-03,  1.0964e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.3210e-02,  1.0587e-02, -5.6106e-03],\\n\",\n      \"           [ 2.7730e-02,  1.7637e-02,  1.4454e-03],\\n\",\n      \"           [ 2.0729e-02,  1.6860e-02,  1.4928e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8153e-02, -3.3071e-03, -3.3867e-02],\\n\",\n      \"           [-2.7532e-03,  2.1490e-03, -1.8782e-02],\\n\",\n      \"           [-1.4521e-02,  1.5942e-02,  6.3698e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3202e-02, -3.1091e-02, -5.6727e-02],\\n\",\n      \"           [-1.7644e-02, -1.9048e-02, -4.2461e-02],\\n\",\n      \"           [-3.5916e-02, -1.4883e-02, -1.2418e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.0720e-03,  4.3781e-03,  3.4098e-03],\\n\",\n      \"           [-4.9325e-04, -1.7749e-03,  3.4276e-03],\\n\",\n      \"           [-4.4584e-03, -3.0494e-03,  3.9617e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.8560e-03,  4.7218e-03,  4.2036e-03],\\n\",\n      \"           [ 7.3845e-04, -1.2887e-04,  3.3711e-03],\\n\",\n      \"           [-3.0293e-03, -2.3602e-03,  3.9486e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 7.4561e-03,  4.9531e-03,  4.2603e-03],\\n\",\n      \"           [ 1.3740e-04,  1.8802e-04,  4.0970e-03],\\n\",\n      \"           [-4.4385e-03, -9.2593e-05,  2.4106e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.9468e-03,  1.3800e-03, -6.9374e-03],\\n\",\n      \"           [-4.3805e-03, -3.3756e-03,  1.8415e-03],\\n\",\n      \"           [-1.5037e-02, -7.8221e-03,  7.7617e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 8.7048e-03,  2.3750e-03, -4.4722e-03],\\n\",\n      \"           [-5.3058e-03, -6.8094e-03,  9.0955e-04],\\n\",\n      \"           [-2.0810e-02, -1.0682e-02,  4.1196e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2160e-02,  3.3229e-03,  1.0157e-03],\\n\",\n      \"           [-2.4714e-03, -2.3164e-03,  2.4985e-03],\\n\",\n      \"           [-2.1124e-02, -6.6957e-03,  2.4670e-03]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 83.00 %\\n\",\n      \"Val loss: 0.445741\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[18,    60] loss: 0.48623\\n\",\n      \"[18,   120] loss: 0.47283\\n\",\n      \"Time elapsed: 0h:35m:6s\\n\",\n      \"train accuracy_score: 81.06 %\\n\",\n      \"tensor([[[[[ 3.2179e-02,  4.0175e-02, -1.8388e-04],\\n\",\n      \"           [ 3.7922e-02,  3.3751e-02,  2.1505e-02],\\n\",\n      \"           [ 1.5178e-01,  5.6349e-02,  6.8306e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.9430e-04, -6.3880e-03, -2.0131e-02],\\n\",\n      \"           [ 2.2175e-02,  1.4068e-02, -3.0826e-02],\\n\",\n      \"           [ 7.6525e-02,  3.2160e-02,  1.3711e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.3519e-02, -1.6771e-03, -1.8521e-02],\\n\",\n      \"           [ 8.3468e-03,  7.6979e-03, -1.8942e-02],\\n\",\n      \"           [ 1.0108e-01,  2.3220e-02, -2.1254e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.0995e-03, -4.0485e-03, -3.1838e-03],\\n\",\n      \"           [-1.5256e-03, -2.3152e-03, -9.9626e-03],\\n\",\n      \"           [-1.4287e-03,  1.5359e-03, -1.0488e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.3094e-03, -3.6553e-04, -2.1299e-04],\\n\",\n      \"           [ 2.2427e-04,  6.9265e-04, -1.3514e-04],\\n\",\n      \"           [ 3.3043e-04,  2.5315e-03, -7.6942e-03]],\\n\",\n      \"\\n\",\n      \"          [[-6.3389e-04, -1.0307e-04,  6.4361e-04],\\n\",\n      \"           [ 2.7235e-03,  6.2631e-04, -1.0770e-03],\\n\",\n      \"           [ 1.2823e-03,  1.7039e-03, -4.2173e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.7858e-01,  7.2242e-01,  1.0671e+00],\\n\",\n      \"           [ 3.0217e-01,  3.4506e-01,  7.6375e-01],\\n\",\n      \"           [-1.1957e-01,  1.8361e-01,  5.2557e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.3166e-01,  6.2462e-01,  4.5287e-01],\\n\",\n      \"           [-2.6557e-02,  1.7472e-01,  3.2242e-01],\\n\",\n      \"           [-3.6231e-01, -2.3554e-01,  1.2498e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.0939e-01,  4.9966e-01,  2.5758e-01],\\n\",\n      \"           [-6.8517e-02,  5.5961e-02,  1.1923e-01],\\n\",\n      \"           [-3.3846e-01, -2.0494e-01,  2.1016e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.2654e-02, -2.3089e-01, -3.2075e-01],\\n\",\n      \"           [-1.0389e-01, -1.4631e-01, -1.9164e-01],\\n\",\n      \"           [-8.6708e-02, -4.3496e-02, -1.5592e-03]],\\n\",\n      \"\\n\",\n      \"          [[-6.8921e-02, -2.5492e-01, -3.3254e-01],\\n\",\n      \"           [-8.0493e-02, -1.6469e-01, -1.1648e-01],\\n\",\n      \"           [-3.8611e-02,  2.8122e-02,  1.0417e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.8138e-02, -2.7241e-01, -2.7158e-01],\\n\",\n      \"           [-5.2424e-02, -1.1714e-01, -5.4006e-02],\\n\",\n      \"           [-1.6729e-02,  8.9919e-02,  2.0362e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.3211e-02,  1.2654e-02,  4.1798e-03],\\n\",\n      \"           [ 7.4893e-02,  1.0784e-02, -9.1762e-03],\\n\",\n      \"           [ 2.0628e-02,  1.2238e-01,  5.7718e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.8511e-02,  9.6401e-02, -6.2155e-03],\\n\",\n      \"           [ 6.2622e-02,  7.1535e-02,  1.2681e-01],\\n\",\n      \"           [ 4.6892e-02,  1.3201e-01,  1.8049e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5288e-01,  1.2599e-01,  1.3607e-01],\\n\",\n      \"           [ 1.2682e-01,  1.0322e-01,  1.7415e-01],\\n\",\n      \"           [ 6.9498e-02,  2.1011e-01,  2.0766e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.4252e-01,  3.6313e-01,  2.9509e-01],\\n\",\n      \"           [ 1.8505e-01,  2.0440e-01,  1.8425e-01],\\n\",\n      \"           [-1.9936e-01, -1.2039e-01,  3.4152e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.3587e-01,  1.7480e-01,  6.2178e-02],\\n\",\n      \"           [-1.1451e-02,  8.2260e-02,  1.8894e-01],\\n\",\n      \"           [-4.1373e-01,  1.1514e-01,  2.4096e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7808e-01, -1.2031e-01, -3.1232e-01],\\n\",\n      \"           [-1.0557e-01,  5.5015e-02, -8.8008e-02],\\n\",\n      \"           [-7.4897e-01, -3.7265e-02, -1.4904e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.5993e-01,  1.1442e-01,  1.1268e-01],\\n\",\n      \"           [ 6.7666e-02,  1.2208e-02,  1.8410e-02],\\n\",\n      \"           [-2.2963e-02, -4.2643e-02,  2.0138e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4391e-01,  7.7744e-02,  8.3702e-02],\\n\",\n      \"           [ 6.1477e-02,  2.2800e-02,  4.2844e-02],\\n\",\n      \"           [-4.2662e-02, -6.3524e-02, -6.2837e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0203e-01,  7.8412e-02,  4.8693e-02],\\n\",\n      \"           [-1.6174e-02,  2.4554e-03,  3.0260e-02],\\n\",\n      \"           [-8.3623e-02, -7.5775e-02, -1.1415e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.2426e-01,  1.5636e-02, -9.0145e-02],\\n\",\n      \"           [ 1.4506e-01, -5.2604e-02, -5.9568e-02],\\n\",\n      \"           [ 1.7425e-02, -4.9579e-04, -5.7229e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8347e-01,  4.3284e-02, -3.7528e-02],\\n\",\n      \"           [ 7.8105e-02, -4.8495e-02,  2.2902e-02],\\n\",\n      \"           [-1.1567e-01, -9.6482e-02, -3.3976e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 9.2118e-02, -3.1312e-02, -1.0131e-01],\\n\",\n      \"           [-3.4513e-02, -1.2416e-01, -3.9268e-02],\\n\",\n      \"           [-2.9912e-01, -1.2226e-01, -8.2456e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 77.00 %\\n\",\n      \"Val loss: 0.469427\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[19,    60] loss: 0.49115\\n\",\n      \"[19,   120] loss: 0.45892\\n\",\n      \"Time elapsed: 0h:36m:57s\\n\",\n      \"train accuracy_score: 79.05 %\\n\",\n      \"tensor([[[[[ 1.0765e-04,  1.2710e-02, -2.6621e-03],\\n\",\n      \"           [ 6.1754e-03,  2.5047e-02,  2.0877e-02],\\n\",\n      \"           [ 8.2923e-02,  4.5289e-02,  6.3141e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0034e-02, -8.3428e-03, -4.7839e-03],\\n\",\n      \"           [ 1.9675e-03,  7.8179e-03,  1.6545e-02],\\n\",\n      \"           [ 3.3783e-02,  1.2287e-02,  4.1158e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.3633e-02, -4.5611e-03, -1.1049e-02],\\n\",\n      \"           [ 1.6466e-03,  6.6533e-03,  2.4644e-03],\\n\",\n      \"           [ 4.3613e-02,  1.0704e-02,  1.4452e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.6689e-03, -1.6893e-03, -2.3793e-03],\\n\",\n      \"           [-1.8281e-03,  5.0648e-04,  1.1299e-04],\\n\",\n      \"           [-9.5496e-06,  8.9240e-04,  2.5881e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.3917e-03, -1.1395e-03,  1.8199e-03],\\n\",\n      \"           [-1.0791e-03,  1.1856e-04,  1.1868e-03],\\n\",\n      \"           [ 3.3222e-04,  1.2549e-03,  3.9463e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5288e-04,  9.1840e-04,  1.0494e-03],\\n\",\n      \"           [ 3.8266e-05,  1.8558e-03,  6.8970e-04],\\n\",\n      \"           [ 7.1673e-04,  1.7598e-03,  7.9441e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.9551e-01,  4.6558e-01,  5.1805e-01],\\n\",\n      \"           [ 3.4627e-01,  3.0161e-01,  3.6169e-01],\\n\",\n      \"           [ 9.6390e-03, -4.0409e-02,  1.0775e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.1681e-01,  2.6568e-01,  1.5083e-01],\\n\",\n      \"           [ 1.4101e-01,  9.1877e-02,  7.2757e-02],\\n\",\n      \"           [-1.2663e-02, -6.7819e-02,  3.3580e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.8774e-01,  2.3254e-01,  6.7142e-02],\\n\",\n      \"           [-2.0979e-06, -6.4997e-02,  4.5146e-02],\\n\",\n      \"           [-1.7160e-01, -6.8783e-02,  3.0465e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.8007e-02, -1.4161e-01, -1.9973e-01],\\n\",\n      \"           [-1.1509e-01, -9.8647e-02, -1.2381e-01],\\n\",\n      \"           [-1.2359e-01, -7.9613e-02, -3.9549e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.5804e-03, -1.0615e-01, -1.6755e-01],\\n\",\n      \"           [ 6.4486e-03, -4.0531e-02, -6.0513e-02],\\n\",\n      \"           [-6.5977e-02, -2.2110e-03,  6.5191e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.0717e-02, -9.6603e-02, -1.4786e-01],\\n\",\n      \"           [ 7.7675e-03, -7.7474e-03, -2.9959e-02],\\n\",\n      \"           [ 7.4298e-03,  4.5821e-02,  1.1472e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.6594e-03, -1.4074e-02, -2.8241e-02],\\n\",\n      \"           [ 7.4019e-02,  2.7835e-02, -1.0939e-02],\\n\",\n      \"           [ 1.6637e-02,  9.4199e-02,  8.0120e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.4376e-02, -3.2618e-03, -3.3400e-02],\\n\",\n      \"           [ 1.0211e-01,  4.0565e-02,  5.0384e-02],\\n\",\n      \"           [ 1.6327e-02,  3.4138e-02,  8.6467e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1816e-01,  1.0618e-01,  7.4590e-02],\\n\",\n      \"           [ 1.3596e-01,  7.1370e-02,  9.8675e-02],\\n\",\n      \"           [ 8.6404e-02,  4.7066e-02,  8.7451e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.7902e-01,  1.4208e-01,  3.7845e-02],\\n\",\n      \"           [ 3.5686e-01,  1.1426e-01,  2.7581e-02],\\n\",\n      \"           [ 1.2335e-01,  1.4977e-01,  8.4499e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0180e-01, -9.9290e-03,  5.3734e-02],\\n\",\n      \"           [ 5.5192e-02,  1.0900e-01,  1.0517e-01],\\n\",\n      \"           [-4.0362e-02,  2.8006e-01,  6.6003e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0876e-01, -1.2320e-01, -2.5305e-01],\\n\",\n      \"           [-8.0960e-02, -1.1384e-01, -1.6758e-01],\\n\",\n      \"           [-2.8440e-01,  6.9659e-02, -2.8453e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.8730e-02,  7.9487e-03,  2.1595e-03],\\n\",\n      \"           [-1.2071e-02, -3.7184e-02, -1.9438e-02],\\n\",\n      \"           [-4.4957e-02, -5.2478e-02, -1.3983e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.8516e-02,  1.1915e-02, -1.1759e-02],\\n\",\n      \"           [ 3.6705e-05, -4.0889e-02, -1.4698e-02],\\n\",\n      \"           [-6.0082e-02, -5.5626e-02, -4.8332e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.2863e-02,  1.3194e-02, -9.6777e-03],\\n\",\n      \"           [-5.1117e-03, -2.8898e-02, -1.6311e-02],\\n\",\n      \"           [-6.1935e-02, -6.7522e-02, -3.4355e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.8040e-01,  1.2629e-01, -1.6134e-02],\\n\",\n      \"           [ 8.2403e-02,  7.8321e-02, -1.6087e-02],\\n\",\n      \"           [-2.8953e-02,  2.9059e-02,  4.2575e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2234e-01,  7.8637e-02, -5.2415e-02],\\n\",\n      \"           [-1.6798e-02,  1.1325e-02, -2.6601e-02],\\n\",\n      \"           [-1.4512e-01, -3.6096e-02, -1.8992e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.4637e-02, -4.7202e-02, -1.5376e-01],\\n\",\n      \"           [-8.9081e-02, -1.0476e-01, -1.1082e-01],\\n\",\n      \"           [-2.9000e-01, -1.2403e-01, -8.1966e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 78.00 %\\n\",\n      \"Val loss: 0.429490\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[20,    60] loss: 0.39468\\n\",\n      \"[20,   120] loss: 0.44995\\n\",\n      \"Time elapsed: 0h:38m:47s\\n\",\n      \"train accuracy_score: 85.80 %\\n\",\n      \"tensor([[[[[ 1.0863e-02,  1.0240e-02,  6.0351e-03],\\n\",\n      \"           [ 1.0206e-02, -4.3572e-03, -1.1469e-02],\\n\",\n      \"           [-2.0451e-02, -2.4615e-02, -2.3985e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3073e-03,  5.4707e-03,  3.8237e-03],\\n\",\n      \"           [-5.4613e-03, -3.1730e-03, -3.4409e-03],\\n\",\n      \"           [-1.2939e-02, -7.9779e-03, -1.9159e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.1879e-03, -2.5703e-03, -3.6179e-04],\\n\",\n      \"           [-5.4516e-03, -5.3986e-03, -2.9813e-03],\\n\",\n      \"           [-2.0759e-02, -5.8613e-04, -7.4648e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.8141e-03,  8.6252e-04,  3.3237e-05],\\n\",\n      \"           [ 1.9612e-03,  9.1121e-05, -5.4584e-04],\\n\",\n      \"           [ 1.0557e-04, -6.6297e-04, -1.0852e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.0407e-03,  2.3646e-03,  1.6593e-04],\\n\",\n      \"           [ 3.0291e-03, -8.0862e-05,  1.5074e-05],\\n\",\n      \"           [ 8.8539e-04, -4.2409e-04, -6.5206e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.9733e-03,  1.5124e-03,  1.2694e-04],\\n\",\n      \"           [ 2.3306e-03,  2.2500e-04,  1.8498e-04],\\n\",\n      \"           [ 1.6308e-03,  5.8576e-05,  4.9833e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.2622e-01, -1.7005e-01, -1.1550e-01],\\n\",\n      \"           [-1.9822e-01, -1.3954e-01, -8.1874e-02],\\n\",\n      \"           [-2.9042e-03,  4.2482e-02,  4.3932e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.4439e-01, -1.3287e-01, -5.1614e-02],\\n\",\n      \"           [-1.4722e-01, -8.2672e-02, -4.9781e-02],\\n\",\n      \"           [ 5.6565e-03, -1.0789e-02,  4.9771e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.1089e-01, -1.5557e-01, -1.2744e-01],\\n\",\n      \"           [-1.3653e-01, -8.3461e-03, -3.5454e-02],\\n\",\n      \"           [-3.3722e-02,  1.9686e-02,  1.2702e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.8013e-02,  4.2007e-02,  4.4672e-02],\\n\",\n      \"           [ 2.9058e-02,  2.6415e-02,  2.8577e-02],\\n\",\n      \"           [ 3.4065e-02,  1.5577e-02, -6.1921e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5617e-02,  3.7501e-02,  3.9232e-02],\\n\",\n      \"           [ 1.4381e-02,  7.6068e-03,  2.0146e-02],\\n\",\n      \"           [ 6.2277e-03, -6.4056e-03, -1.3487e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3424e-02,  2.1141e-02,  3.8491e-02],\\n\",\n      \"           [ 6.4459e-03, -5.0742e-03,  6.6810e-03],\\n\",\n      \"           [-8.2893e-03, -3.3542e-02, -3.0879e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.3365e-02, -1.6932e-02, -6.5288e-03],\\n\",\n      \"           [-6.5621e-02, -1.8928e-02,  1.0485e-02],\\n\",\n      \"           [-5.8786e-02, -3.8648e-02,  6.8699e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.7395e-02, -2.9105e-02, -1.3124e-02],\\n\",\n      \"           [-6.3133e-02, -5.9255e-02, -2.7132e-02],\\n\",\n      \"           [-6.5143e-02, -5.5089e-02, -3.7171e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.1604e-02, -6.4007e-02, -6.4161e-02],\\n\",\n      \"           [-7.2438e-02, -5.8483e-02, -6.9241e-02],\\n\",\n      \"           [-6.4463e-02, -4.1144e-02, -5.7233e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.0863e-02,  1.8447e-01,  2.0897e-01],\\n\",\n      \"           [ 5.5250e-02,  1.5093e-01,  1.9174e-01],\\n\",\n      \"           [ 1.3728e-01,  1.2013e-01,  1.2678e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3819e-02,  1.2052e-01,  1.9051e-01],\\n\",\n      \"           [ 6.5130e-02,  1.0820e-01,  6.6631e-02],\\n\",\n      \"           [ 8.2352e-02,  8.3600e-02,  1.1657e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.5946e-02,  8.0975e-02,  9.9586e-02],\\n\",\n      \"           [ 2.3982e-02,  3.6997e-02,  7.7472e-02],\\n\",\n      \"           [ 1.4165e-01,  7.3353e-02,  4.3107e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.1375e-02, -2.0358e-02, -3.0420e-03],\\n\",\n      \"           [-1.0381e-02,  4.7532e-03,  3.1589e-04],\\n\",\n      \"           [ 5.6482e-03,  1.3257e-02, -4.1096e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.8009e-02, -9.7148e-04,  6.7642e-03],\\n\",\n      \"           [ 6.2376e-03,  1.8884e-02,  1.8652e-02],\\n\",\n      \"           [ 2.3999e-02,  2.8236e-02,  1.5563e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.3679e-02, -1.3835e-03, -1.5413e-03],\\n\",\n      \"           [ 1.5745e-02,  1.3653e-02,  1.3368e-02],\\n\",\n      \"           [ 3.5413e-02,  3.4298e-02,  2.1418e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.8522e-02, -3.1957e-02, -2.9769e-03],\\n\",\n      \"           [-3.0382e-02, -1.7152e-02, -5.0225e-03],\\n\",\n      \"           [ 2.1731e-03, -4.7242e-03, -1.3290e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0751e-02, -1.8003e-02,  1.3129e-02],\\n\",\n      \"           [-4.0922e-03,  1.6901e-02,  3.2072e-02],\\n\",\n      \"           [ 3.0410e-02,  3.4541e-02,  3.1431e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.2730e-02,  2.8929e-02,  2.7451e-02],\\n\",\n      \"           [ 3.7918e-02,  3.2390e-02,  4.3058e-02],\\n\",\n      \"           [ 6.9040e-02,  3.1893e-02,  4.0280e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 82.00 %\\n\",\n      \"Val loss: 0.409000\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[21,    60] loss: 0.44574\\n\",\n      \"[21,   120] loss: 0.40725\\n\",\n      \"Time elapsed: 0h:40m:39s\\n\",\n      \"train accuracy_score: 82.50 %\\n\",\n      \"tensor([[[[[-2.5905e-03, -3.4861e-03, -8.7973e-03],\\n\",\n      \"           [ 1.9933e-03,  1.6684e-04, -8.1708e-03],\\n\",\n      \"           [ 7.9051e-03,  2.5831e-03, -6.2729e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.7361e-03, -1.8189e-03, -5.7789e-03],\\n\",\n      \"           [ 5.7942e-04,  4.7621e-04, -5.3589e-03],\\n\",\n      \"           [ 5.7914e-03,  1.1067e-03, -6.6705e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.8765e-03, -1.4911e-03, -2.8971e-03],\\n\",\n      \"           [ 4.6282e-04, -9.9490e-04, -3.9841e-03],\\n\",\n      \"           [ 5.2333e-03,  6.6692e-05, -4.0205e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.6376e-03,  7.6505e-05,  1.3095e-04],\\n\",\n      \"           [-5.9332e-04,  1.2742e-04, -2.7075e-04],\\n\",\n      \"           [-2.4094e-04,  9.8213e-06, -2.7240e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.7067e-03, -1.1745e-03,  3.5999e-05],\\n\",\n      \"           [-1.1212e-03,  2.1342e-06,  7.5590e-05],\\n\",\n      \"           [-2.3424e-04,  7.9675e-05, -4.6895e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.6704e-03, -1.3801e-03, -4.2898e-04],\\n\",\n      \"           [-1.5547e-03, -5.6071e-04, -2.4766e-04],\\n\",\n      \"           [-9.5127e-04, -4.8060e-04, -8.9165e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.1855e-01,  3.4645e-02,  1.5006e-02],\\n\",\n      \"           [-2.0852e-03, -2.8795e-02,  9.3080e-03],\\n\",\n      \"           [-6.6647e-02, -5.4513e-02, -7.6440e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5201e-01,  8.4664e-02,  2.0905e-02],\\n\",\n      \"           [ 6.4643e-02,  1.2504e-02,  2.2265e-02],\\n\",\n      \"           [-2.6845e-02, -1.1749e-02,  2.7405e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4485e-01,  1.0207e-01,  4.0993e-02],\\n\",\n      \"           [ 7.2061e-02,  2.0012e-02,  1.5871e-02],\\n\",\n      \"           [ 2.3240e-02, -4.5353e-03,  2.5845e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.9555e-02, -4.3639e-02, -5.5552e-02],\\n\",\n      \"           [-2.2484e-02, -3.3929e-02, -2.7039e-02],\\n\",\n      \"           [-1.5310e-02, -9.9954e-03,  1.5645e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.2854e-03, -3.3013e-02, -4.6440e-02],\\n\",\n      \"           [-1.3777e-03, -1.4545e-02, -1.7361e-02],\\n\",\n      \"           [-1.0838e-03,  3.5714e-03,  1.9626e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.7643e-03, -2.3291e-02, -4.7786e-02],\\n\",\n      \"           [ 6.6492e-03, -6.0834e-03, -8.6110e-03],\\n\",\n      \"           [ 1.3517e-02,  1.5347e-02,  2.0877e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.4798e-02,  1.2621e-02,  9.0460e-03],\\n\",\n      \"           [ 1.9484e-02,  1.3672e-02,  8.2239e-03],\\n\",\n      \"           [ 1.8271e-02,  2.9717e-02,  1.0511e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0225e-02,  1.3617e-02,  4.1987e-03],\\n\",\n      \"           [ 2.5434e-02,  2.7180e-02,  2.0667e-02],\\n\",\n      \"           [ 2.6104e-02,  1.9769e-02,  2.1290e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.7998e-02,  3.0311e-02,  2.4171e-02],\\n\",\n      \"           [ 4.8683e-02,  3.0880e-02,  3.1986e-02],\\n\",\n      \"           [ 2.9126e-02,  2.8557e-02,  2.3621e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.9945e-02, -1.9513e-02, -5.4488e-02],\\n\",\n      \"           [ 1.7037e-02, -4.2871e-03, -2.4278e-02],\\n\",\n      \"           [-3.2441e-02,  1.5438e-02,  1.8963e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.6165e-02, -2.9543e-02, -7.0823e-02],\\n\",\n      \"           [ 4.1043e-03, -6.7694e-03, -2.2575e-02],\\n\",\n      \"           [-6.4496e-03,  3.5894e-02,  4.6744e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.2428e-02, -3.4110e-02, -1.0955e-01],\\n\",\n      \"           [-1.3326e-02, -2.8908e-02, -4.6196e-02],\\n\",\n      \"           [-3.8194e-02, -4.7726e-04,  2.2232e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3518e-02,  1.0084e-03, -1.0294e-03],\\n\",\n      \"           [-1.4855e-03, -9.6666e-03, -5.2336e-03],\\n\",\n      \"           [-1.2931e-02, -1.6475e-02, -8.1625e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8244e-02,  7.9934e-03,  5.1188e-03],\\n\",\n      \"           [ 5.2845e-03, -2.4019e-03,  7.9490e-04],\\n\",\n      \"           [-9.0345e-03, -8.5603e-03, -5.6142e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3019e-02,  7.9760e-03,  4.7024e-03],\\n\",\n      \"           [ 1.6081e-03,  5.1465e-04,  1.6898e-03],\\n\",\n      \"           [-9.1487e-03, -8.9295e-03, -6.7068e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.2352e-02,  2.3910e-02, -6.0746e-03],\\n\",\n      \"           [ 1.3792e-02, -2.8295e-03, -1.4208e-02],\\n\",\n      \"           [-2.7735e-02, -2.6302e-02, -1.0164e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.6174e-02,  1.9042e-02, -2.0540e-03],\\n\",\n      \"           [ 2.7286e-03, -1.7554e-02, -8.1830e-03],\\n\",\n      \"           [-4.1476e-02, -4.1233e-02, -2.6115e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0087e-02,  8.9404e-03, -8.2644e-03],\\n\",\n      \"           [-1.6107e-02, -2.6164e-02, -1.3623e-02],\\n\",\n      \"           [-6.7641e-02, -5.1812e-02, -3.4339e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 80.00 %\\n\",\n      \"Val loss: 0.449154\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[22,    60] loss: 0.45823\\n\",\n      \"[22,   120] loss: 0.37312\\n\",\n      \"Time elapsed: 0h:42m:29s\\n\",\n      \"train accuracy_score: 83.36 %\\n\",\n      \"tensor([[[[[-5.7748e-02,  7.4885e-03, -1.3936e-02],\\n\",\n      \"           [-3.9434e-03,  7.0262e-02,  1.3325e-01],\\n\",\n      \"           [ 1.2366e-01,  1.4521e-01,  1.4355e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0906e-02, -3.0148e-02, -4.1806e-02],\\n\",\n      \"           [ 6.7774e-02,  3.2112e-02,  5.5345e-02],\\n\",\n      \"           [ 1.8039e-01,  8.6916e-02,  1.3340e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5499e-02, -1.7190e-02, -4.0349e-02],\\n\",\n      \"           [ 5.7514e-02,  7.6633e-03, -1.7445e-02],\\n\",\n      \"           [ 1.7101e-01,  6.5968e-02,  1.3110e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.3783e-02, -7.5620e-03, -1.5957e-02],\\n\",\n      \"           [-6.6095e-03,  2.2197e-05, -2.8914e-02],\\n\",\n      \"           [ 5.7882e-03,  1.7315e-03, -1.8963e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.8549e-02, -4.8459e-03, -5.4762e-03],\\n\",\n      \"           [-3.2027e-03,  6.6756e-04, -2.5282e-03],\\n\",\n      \"           [ 3.3181e-04,  7.2957e-03, -1.1263e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.4226e-02, -6.2254e-03, -2.5353e-03],\\n\",\n      \"           [-9.2126e-03, -2.5273e-03, -3.2811e-03],\\n\",\n      \"           [-3.6862e-03,  7.9805e-03, -1.9488e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 9.8960e+00,  9.1212e+00,  8.0249e+00],\\n\",\n      \"           [ 7.1997e+00,  7.6786e+00,  8.2489e+00],\\n\",\n      \"           [ 6.0684e+00,  7.3927e+00,  7.8558e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 7.1821e+00,  6.2072e+00,  5.3350e+00],\\n\",\n      \"           [ 5.2318e+00,  4.0051e+00,  4.7259e+00],\\n\",\n      \"           [ 3.0994e+00,  3.6685e+00,  4.6693e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 5.0186e+00,  4.7007e+00,  4.4831e+00],\\n\",\n      \"           [ 3.7563e+00,  3.2175e+00,  3.2490e+00],\\n\",\n      \"           [ 2.1311e+00,  2.2355e+00,  2.9704e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.5621e-01, -5.8842e-01, -9.2343e-01],\\n\",\n      \"           [-2.2093e-01, -3.6554e-01, -5.1886e-01],\\n\",\n      \"           [-3.5625e-01, -2.1100e-01, -1.5728e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.4275e-01, -3.5458e-01, -6.4139e-01],\\n\",\n      \"           [-1.2708e-01, -1.7422e-01, -2.0670e-01],\\n\",\n      \"           [-1.8825e-01, -4.3789e-02,  1.8048e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.1921e-02, -2.5698e-01, -4.9510e-01],\\n\",\n      \"           [-6.6980e-02,  4.6217e-02,  5.5432e-03],\\n\",\n      \"           [-6.3099e-02,  1.8510e-01,  3.2121e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.8491e-01,  1.9538e-01,  1.5172e-01],\\n\",\n      \"           [-1.3077e-01,  1.6051e-01,  7.1904e-02],\\n\",\n      \"           [ 1.3830e-02,  3.8989e-01,  3.1491e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7159e-01,  2.3516e-01,  2.5591e-01],\\n\",\n      \"           [ 1.3013e-01,  3.1153e-01,  5.3427e-01],\\n\",\n      \"           [-1.7741e-01,  2.6757e-01,  6.4977e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 8.6736e-01,  9.2240e-01,  8.5038e-01],\\n\",\n      \"           [ 4.6923e-01,  4.1670e-01,  8.3100e-01],\\n\",\n      \"           [ 3.3460e-02,  3.3554e-01,  5.4403e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.6514e+00,  2.3188e+00,  1.6203e+00],\\n\",\n      \"           [ 2.6310e+00,  2.0708e+00,  1.8208e+00],\\n\",\n      \"           [ 1.9657e+00,  2.2269e+00,  1.6456e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8066e+00,  8.2075e-01,  5.4655e-01],\\n\",\n      \"           [ 8.5863e-01,  1.1194e+00,  6.0234e-01],\\n\",\n      \"           [ 3.7392e-01,  1.5006e+00,  9.4935e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 9.1705e-01, -3.9476e-01, -1.4267e+00],\\n\",\n      \"           [-1.8997e-01, -1.5226e-01, -1.2200e+00],\\n\",\n      \"           [-7.6167e-01, -1.2001e-01, -1.8417e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.6157e-01,  2.6266e-01,  1.1362e-01],\\n\",\n      \"           [ 2.3193e-01,  3.3004e-02,  3.1809e-02],\\n\",\n      \"           [-7.1568e-02, -7.3502e-02,  6.7153e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1426e-01,  1.7191e-01,  8.1920e-02],\\n\",\n      \"           [-5.6567e-02, -1.3638e-01, -1.7565e-02],\\n\",\n      \"           [-2.0947e-01, -2.1953e-01, -1.6220e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1113e-01,  8.3742e-02,  6.1183e-02],\\n\",\n      \"           [-2.0356e-01, -2.0203e-01, -8.9569e-02],\\n\",\n      \"           [-4.1428e-01, -3.2550e-01, -1.5287e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.9275e-01,  2.4016e-01,  3.3386e-02],\\n\",\n      \"           [ 1.2122e-01,  1.7771e-02,  6.2614e-02],\\n\",\n      \"           [-4.0505e-01, -4.2011e-01, -9.2701e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7974e-01,  1.1788e-02, -7.8266e-02],\\n\",\n      \"           [-1.6186e-01, -3.4118e-01, -2.8327e-01],\\n\",\n      \"           [-7.7768e-01, -5.4752e-01, -4.3728e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.7086e-01,  9.9127e-02, -3.6619e-02],\\n\",\n      \"           [-9.9638e-02, -2.1672e-01, -2.5615e-01],\\n\",\n      \"           [-1.0175e+00, -5.9009e-01, -2.7395e-01]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 79.00 %\\n\",\n      \"Val loss: 0.401098\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[23,    60] loss: 0.44645\\n\",\n      \"[23,   120] loss: 0.41398\\n\",\n      \"Time elapsed: 0h:44m:21s\\n\",\n      \"train accuracy_score: 82.35 %\\n\",\n      \"tensor([[[[[ 4.5612e-03,  1.3636e-02,  1.8102e-02],\\n\",\n      \"           [ 1.3681e-02, -1.3895e-02,  3.9396e-03],\\n\",\n      \"           [ 8.4335e-04, -3.9053e-02, -2.7929e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1316e-03,  9.4710e-03,  1.9425e-02],\\n\",\n      \"           [-7.4045e-03, -2.6541e-03,  1.6471e-03],\\n\",\n      \"           [-1.9192e-02, -1.0040e-04, -9.8670e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.8687e-03, -1.8147e-03,  2.0569e-02],\\n\",\n      \"           [-1.5812e-02, -1.1863e-02,  9.3908e-03],\\n\",\n      \"           [-3.7489e-02, -1.2178e-02,  1.6022e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.1107e-03, -4.4062e-04,  2.1478e-03],\\n\",\n      \"           [-9.1445e-04, -1.2502e-03,  3.1879e-03],\\n\",\n      \"           [-1.7417e-03, -8.3627e-04,  3.2717e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1213e-03, -8.2961e-04, -4.9026e-04],\\n\",\n      \"           [ 4.7038e-04, -2.3634e-04, -1.8946e-04],\\n\",\n      \"           [-5.3329e-04, -1.0773e-03,  1.6153e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.2499e-03,  2.3065e-03,  1.6090e-03],\\n\",\n      \"           [ 2.5228e-03, -5.1537e-05,  1.7113e-03],\\n\",\n      \"           [ 4.5488e-03,  2.2759e-03,  1.5165e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.7110e-01, -6.6219e-01, -4.4529e-01],\\n\",\n      \"           [-6.0525e-01, -5.5395e-01, -3.6471e-01],\\n\",\n      \"           [-5.1830e-01, -3.0424e-01, -2.8457e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.6114e-01, -4.4146e-01, -2.4507e-01],\\n\",\n      \"           [-3.7944e-01, -1.8582e-01, -1.8618e-01],\\n\",\n      \"           [-1.6807e-01, -1.4328e-01, -1.1836e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.7216e-01, -3.9246e-01,  8.4880e-02],\\n\",\n      \"           [-2.1632e-01, -5.8862e-02, -9.2397e-02],\\n\",\n      \"           [-4.4350e-02, -1.6305e-03, -6.2238e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.3224e-02,  5.5495e-02,  6.9438e-02],\\n\",\n      \"           [ 2.9524e-02,  1.6856e-02,  2.9355e-02],\\n\",\n      \"           [-4.0947e-02, -2.2333e-02, -3.9080e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.7225e-02,  4.4718e-02,  7.2438e-02],\\n\",\n      \"           [ 5.5410e-02,  3.2626e-02,  6.4856e-02],\\n\",\n      \"           [-9.9200e-03, -1.1894e-02, -5.7357e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.3467e-02,  2.6660e-02,  5.2915e-02],\\n\",\n      \"           [ 1.5588e-02, -1.8034e-02, -1.3875e-02],\\n\",\n      \"           [-4.0737e-02, -2.3617e-02, -2.1593e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.0721e-02,  2.9261e-02,  6.1753e-02],\\n\",\n      \"           [-8.4423e-03, -1.1320e-02,  2.3383e-02],\\n\",\n      \"           [ 2.7016e-02, -3.2278e-02, -6.8135e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.2963e-02, -1.7157e-03,  6.6268e-02],\\n\",\n      \"           [-2.1718e-02, -2.4272e-02, -3.3060e-02],\\n\",\n      \"           [ 3.2014e-02, -5.0351e-03, -7.7230e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.4123e-03,  4.5398e-02,  7.3534e-02],\\n\",\n      \"           [-1.5229e-02, -5.9303e-03, -4.8437e-02],\\n\",\n      \"           [ 3.5948e-03, -3.5587e-02, -6.9886e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.9085e-01,  5.7177e-01,  8.3562e-01],\\n\",\n      \"           [ 2.7180e-01,  4.8125e-01,  7.7713e-01],\\n\",\n      \"           [ 4.2175e-01,  4.6954e-01,  5.4159e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7200e-01,  6.2752e-01,  1.0510e+00],\\n\",\n      \"           [ 3.6490e-01,  4.1665e-01,  7.6495e-01],\\n\",\n      \"           [ 3.6719e-01,  3.3882e-01,  6.4070e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3826e-01,  5.5012e-01,  1.0880e+00],\\n\",\n      \"           [ 4.1524e-01,  3.7769e-01,  9.7847e-01],\\n\",\n      \"           [ 5.2050e-01,  2.9153e-01,  6.3438e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.0945e-01, -7.1529e-02, -4.1931e-02],\\n\",\n      \"           [-4.2844e-02,  2.5599e-02,  2.5780e-03],\\n\",\n      \"           [ 2.4313e-02,  3.2892e-02, -1.7678e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.8827e-02, -2.4192e-02, -1.1997e-02],\\n\",\n      \"           [ 2.8264e-02,  5.4761e-02,  1.1857e-02],\\n\",\n      \"           [ 7.8760e-02,  8.1472e-02,  3.7478e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.8174e-03, -1.1957e-02,  2.5438e-02],\\n\",\n      \"           [ 5.3338e-02,  4.3372e-02,  4.8815e-02],\\n\",\n      \"           [ 1.0836e-01,  9.0245e-02,  5.9632e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.1530e-02, -5.9217e-02, -5.1838e-03],\\n\",\n      \"           [-3.4063e-02,  8.3037e-03, -1.0395e-02],\\n\",\n      \"           [ 1.1021e-01,  7.4542e-02,  1.9984e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.1506e-03, -2.1607e-02,  2.5879e-02],\\n\",\n      \"           [ 7.5496e-02,  5.9787e-02,  5.6763e-02],\\n\",\n      \"           [ 2.1732e-01,  1.4867e-01,  1.0453e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2407e-01,  5.7229e-02,  5.4379e-02],\\n\",\n      \"           [ 1.5903e-01,  1.2815e-01,  1.4270e-01],\\n\",\n      \"           [ 3.5690e-01,  1.7661e-01,  1.3618e-01]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 83.00 %\\n\",\n      \"Val loss: 0.374477\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[24,    60] loss: 0.38576\\n\",\n      \"[24,   120] loss: 0.35815\\n\",\n      \"Time elapsed: 0h:46m:13s\\n\",\n      \"train accuracy_score: 85.37 %\\n\",\n      \"tensor([[[[[ 1.0364e-02, -9.0973e-02, -7.3199e-02],\\n\",\n      \"           [-3.9678e-02, -1.8485e-01, -3.1066e-01],\\n\",\n      \"           [-3.3148e-01, -2.8838e-01, -4.0788e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0736e-02, -5.5503e-02,  3.7846e-02],\\n\",\n      \"           [-4.9099e-02, -1.1071e-01, -1.2277e-01],\\n\",\n      \"           [-2.1264e-01, -1.2382e-01, -2.5634e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0406e-04, -6.5560e-02,  6.2099e-02],\\n\",\n      \"           [-1.1505e-01, -7.1137e-02,  2.7920e-03],\\n\",\n      \"           [-3.2346e-01, -9.4097e-02, -3.6352e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.0691e-02,  4.8507e-03,  4.0198e-03],\\n\",\n      \"           [ 1.4783e-02,  3.7360e-03,  1.8774e-02],\\n\",\n      \"           [ 2.0973e-03,  4.0025e-03,  1.3255e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6544e-02,  2.8404e-03, -5.0001e-03],\\n\",\n      \"           [-2.6720e-03, -5.6568e-03,  2.2697e-04],\\n\",\n      \"           [-4.2526e-03, -7.3220e-03,  1.1850e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2859e-02,  6.9937e-03,  6.5668e-04],\\n\",\n      \"           [ 4.8386e-03,  2.1558e-03, -3.0791e-03],\\n\",\n      \"           [ 7.2439e-03, -4.2373e-03,  4.8142e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.7512e+00, -2.5934e+00, -2.9140e+00],\\n\",\n      \"           [-1.2589e+00, -2.0502e+00, -2.8860e+00],\\n\",\n      \"           [-2.8645e-01, -1.5664e+00, -3.1702e+00]],\\n\",\n      \"\\n\",\n      \"          [[-2.1030e+00, -2.3145e+00, -1.7735e+00],\\n\",\n      \"           [-8.1026e-01, -7.8579e-01, -1.7895e+00],\\n\",\n      \"           [ 2.4547e-01, -5.2696e-01, -1.6850e+00]],\\n\",\n      \"\\n\",\n      \"          [[-2.1935e+00, -2.1711e+00, -1.0079e+00],\\n\",\n      \"           [-8.1437e-01, -1.0818e+00, -5.1443e-01],\\n\",\n      \"           [-3.2809e-01, -9.0009e-02, -7.9815e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.8581e-01,  4.9964e-01,  7.0298e-01],\\n\",\n      \"           [ 1.6044e-01,  2.5048e-01,  2.8466e-01],\\n\",\n      \"           [ 2.1418e-01,  1.6121e-01, -2.1638e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3398e-01,  5.0814e-01,  5.8074e-01],\\n\",\n      \"           [ 8.2822e-02,  1.5252e-01,  2.9893e-01],\\n\",\n      \"           [ 3.6901e-02, -6.1097e-02, -1.0347e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7486e-01,  3.9998e-01,  4.8459e-01],\\n\",\n      \"           [ 2.7803e-02, -8.1930e-02, -4.8357e-03],\\n\",\n      \"           [ 4.1381e-02, -1.2200e-01, -3.9592e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.5705e-02, -3.0604e-02,  9.7555e-03],\\n\",\n      \"           [ 1.9392e-02, -1.3191e-01, -1.1512e-01],\\n\",\n      \"           [ 3.4736e-02, -1.8903e-01, -2.2441e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.1730e-01, -2.9854e-01, -2.1226e-01],\\n\",\n      \"           [-1.1249e-01, -3.3715e-01, -2.2999e-01],\\n\",\n      \"           [-2.3726e-02, -1.6304e-01, -4.8629e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.7032e-01, -5.2177e-01, -4.3199e-01],\\n\",\n      \"           [-3.2323e-01, -3.5586e-01, -4.8915e-01],\\n\",\n      \"           [-3.8310e-02, -2.2470e-01, -2.1616e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.5664e+00, -2.2213e-01,  4.3686e-01],\\n\",\n      \"           [-1.6013e+00, -1.1412e+00, -1.0725e+00],\\n\",\n      \"           [-6.8897e-01, -1.6405e+00, -1.5184e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.2235e+00,  3.1764e-01,  9.8934e-01],\\n\",\n      \"           [-7.8124e-01, -5.5686e-01, -5.4983e-01],\\n\",\n      \"           [-1.0252e-01, -1.5130e+00, -1.6892e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.5129e+00,  6.0193e-01,  1.4709e+00],\\n\",\n      \"           [ 3.7602e-02, -5.1223e-02, -3.4193e-01],\\n\",\n      \"           [ 7.0073e-01, -7.9577e-01, -1.1258e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.8814e-01, -2.0357e-01, -1.2014e-01],\\n\",\n      \"           [-6.0441e-02,  2.1687e-01,  3.5087e-02],\\n\",\n      \"           [ 3.5792e-01,  3.5981e-01, -1.9807e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.6224e-01, -1.4170e-01, -9.8238e-02],\\n\",\n      \"           [ 6.8376e-02,  2.0071e-01,  1.2132e-02],\\n\",\n      \"           [ 2.6753e-01,  3.1850e-01,  9.6524e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.0352e-01, -5.4325e-02,  1.6639e-02],\\n\",\n      \"           [ 8.5479e-02,  1.2557e-01, -1.1434e-01],\\n\",\n      \"           [ 5.0108e-01,  2.1144e-01, -3.0518e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.0923e+00, -7.4433e-01, -1.8953e-01],\\n\",\n      \"           [-5.5806e-01, -1.1274e-01, -1.6270e-01],\\n\",\n      \"           [ 4.4454e-01,  3.5599e-01,  1.5111e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.0578e-01, -2.2462e-01,  3.2705e-02],\\n\",\n      \"           [ 8.5195e-02,  3.3091e-01,  3.6564e-01],\\n\",\n      \"           [ 9.0414e-01,  5.7980e-01,  4.7605e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.5380e-01,  3.4720e-01,  4.4385e-01],\\n\",\n      \"           [ 3.9960e-01,  7.1344e-01,  5.1979e-01],\\n\",\n      \"           [ 1.4970e+00,  9.8151e-01,  7.1786e-01]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 80.00 %\\n\",\n      \"Val loss: 0.441053\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[25,    60] loss: 0.36188\\n\",\n      \"[25,   120] loss: 0.38150\\n\",\n      \"Time elapsed: 0h:48m:5s\\n\",\n      \"train accuracy_score: 85.22 %\\n\",\n      \"tensor([[[[[-1.0788e-02, -6.7830e-02, -9.1959e-02],\\n\",\n      \"           [ 3.4753e-02, -6.8146e-02, -1.0332e-01],\\n\",\n      \"           [-1.8384e-01, -1.7443e-01, -1.6408e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.6734e-02, -2.0065e-03,  9.1853e-03],\\n\",\n      \"           [-2.6463e-02, -5.3687e-02, -1.0842e-01],\\n\",\n      \"           [-7.2601e-02, -5.4966e-02, -1.1843e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.3744e-03, -1.7838e-02, -1.6847e-03],\\n\",\n      \"           [-4.7276e-02, -2.7065e-02, -1.3082e-02],\\n\",\n      \"           [-1.3934e-01, -5.8033e-02, -4.5263e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 9.1953e-03,  3.2365e-03,  4.9539e-03],\\n\",\n      \"           [ 9.3655e-04, -1.1367e-03, -2.2482e-03],\\n\",\n      \"           [-9.5626e-04, -7.7132e-03, -8.5131e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.0874e-03,  1.2793e-03, -3.3958e-03],\\n\",\n      \"           [-2.3743e-03, -3.7508e-03, -2.3319e-03],\\n\",\n      \"           [-3.8304e-03, -4.6323e-03, -8.1269e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2423e-03, -3.0989e-04, -3.4416e-04],\\n\",\n      \"           [-1.1817e-03, -2.3557e-04, -2.6373e-03],\\n\",\n      \"           [ 3.4119e-03, -6.8711e-03,  2.3928e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.7945e+00, -4.7121e+00, -4.4707e+00],\\n\",\n      \"           [-3.2035e+00, -3.8677e+00, -3.8261e+00],\\n\",\n      \"           [-2.3855e+00, -2.9786e+00, -3.6796e+00]],\\n\",\n      \"\\n\",\n      \"          [[-3.5642e+00, -3.1801e+00, -2.7940e+00],\\n\",\n      \"           [-2.3735e+00, -1.8025e+00, -2.1789e+00],\\n\",\n      \"           [-1.0822e+00, -1.4875e+00, -2.1074e+00]],\\n\",\n      \"\\n\",\n      \"          [[-2.5615e+00, -2.3164e+00, -2.0689e+00],\\n\",\n      \"           [-1.8125e+00, -1.5076e+00, -1.4073e+00],\\n\",\n      \"           [-1.0513e+00, -9.5588e-01, -1.6156e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.8936e-01,  4.4041e-01,  5.2993e-01],\\n\",\n      \"           [ 3.5473e-01,  3.9615e-01,  3.9223e-01],\\n\",\n      \"           [ 3.0172e-01,  1.9815e-01, -5.7374e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.8536e-02,  3.2087e-01,  3.6631e-01],\\n\",\n      \"           [ 2.3528e-01,  1.9581e-01,  6.8568e-02],\\n\",\n      \"           [ 1.3052e-01, -2.2621e-02, -2.9334e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5891e-02,  3.4957e-01,  3.0847e-01],\\n\",\n      \"           [ 9.0060e-02,  1.0890e-01,  5.5571e-02],\\n\",\n      \"           [ 4.1465e-02, -1.0278e-01, -4.0095e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2174e-01,  1.2611e-02,  7.5403e-03],\\n\",\n      \"           [-1.6202e-01, -7.3795e-02, -6.2085e-02],\\n\",\n      \"           [-1.6060e-01, -1.2515e-01, -1.9716e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.8800e-02, -4.0756e-02, -5.2966e-03],\\n\",\n      \"           [-5.8593e-02, -3.1328e-02, -1.8467e-01],\\n\",\n      \"           [-6.2471e-02, -2.1575e-01, -3.3807e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.0510e-01, -1.6525e-01, -1.3026e-01],\\n\",\n      \"           [-2.7175e-01, -1.3912e-01, -3.4815e-01],\\n\",\n      \"           [-9.9494e-02, -1.3613e-01, -3.0663e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.5520e+00, -1.2154e+00, -8.8054e-01],\\n\",\n      \"           [-1.4238e+00, -1.4086e+00, -1.2349e+00],\\n\",\n      \"           [-1.0261e+00, -1.3020e+00, -1.2535e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.2487e+00, -5.6072e-01, -4.0078e-01],\\n\",\n      \"           [-4.7768e-01, -5.0514e-01, -8.9593e-01],\\n\",\n      \"           [-2.6801e-01, -8.6900e-01, -1.1508e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.2243e+00,  2.8399e-03,  4.9658e-01],\\n\",\n      \"           [-3.5439e-01, -7.5164e-03, -7.7598e-02],\\n\",\n      \"           [ 1.1038e-01, -2.4833e-01, -6.9063e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.7300e-01, -2.9991e-02,  1.2923e-02],\\n\",\n      \"           [ 7.3226e-02,  1.4321e-01,  7.5364e-02],\\n\",\n      \"           [ 2.2128e-01,  1.5845e-01, -7.1644e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.3686e-01,  5.5976e-02,  9.0342e-02],\\n\",\n      \"           [ 7.0447e-02,  1.4858e-01,  1.0403e-01],\\n\",\n      \"           [ 2.4455e-01,  2.4768e-01,  9.8559e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.4088e-02,  7.5612e-02,  1.3497e-01],\\n\",\n      \"           [ 1.6438e-01,  1.9286e-01,  1.2671e-01],\\n\",\n      \"           [ 3.0106e-01,  2.2183e-01,  1.3439e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.6139e-01, -3.1144e-01, -8.6972e-02],\\n\",\n      \"           [-3.6550e-01, -1.4761e-01, -1.1507e-01],\\n\",\n      \"           [-1.9849e-02,  1.2294e-02, -1.1430e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.6802e-01, -7.7431e-02,  5.1984e-02],\\n\",\n      \"           [-9.8264e-02,  1.1535e-01,  1.5782e-01],\\n\",\n      \"           [ 3.5663e-01,  3.0429e-01,  2.7233e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.7794e-01,  9.8724e-02,  3.3532e-01],\\n\",\n      \"           [ 2.7026e-02,  2.6806e-01,  3.8812e-01],\\n\",\n      \"           [ 4.9971e-01,  3.5761e-01,  3.1512e-01]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 79.00 %\\n\",\n      \"Val loss: 0.479837\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[26,    60] loss: 0.39508\\n\",\n      \"[26,   120] loss: 0.33188\\n\",\n      \"Time elapsed: 0h:49m:59s\\n\",\n      \"train accuracy_score: 87.52 %\\n\",\n      \"tensor([[[[[ 2.9214e-03, -7.3231e-03, -1.4968e-02],\\n\",\n      \"           [ 5.4239e-03, -5.0855e-04, -2.1129e-02],\\n\",\n      \"           [-9.7434e-02, -3.7332e-02, -4.5176e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.6433e-03, -1.8235e-03,  9.9754e-03],\\n\",\n      \"           [-1.3212e-02, -1.0511e-02, -1.1174e-02],\\n\",\n      \"           [-4.9358e-02, -3.6725e-02,  1.8168e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.3922e-03, -1.8860e-04, -4.2068e-03],\\n\",\n      \"           [-1.6257e-02, -4.3351e-03,  4.6674e-03],\\n\",\n      \"           [-7.9496e-02, -1.3586e-02,  6.9263e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 9.6502e-03,  3.4788e-03,  4.0999e-03],\\n\",\n      \"           [ 3.1242e-03,  4.5620e-04,  6.4004e-03],\\n\",\n      \"           [ 5.1631e-04, -2.4686e-03,  2.2452e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1593e-02,  6.8929e-03, -5.9381e-04],\\n\",\n      \"           [ 5.2047e-03, -4.6772e-04, -1.1063e-03],\\n\",\n      \"           [-6.5899e-04, -1.8605e-03,  4.4764e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 9.5664e-03,  4.9963e-03, -1.2004e-03],\\n\",\n      \"           [ 4.1900e-03,  1.4587e-03, -2.4039e-03],\\n\",\n      \"           [-1.2649e-03, -1.0797e-03,  1.1980e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.5600e-01, -4.1949e-01, -4.4204e-01],\\n\",\n      \"           [-2.2058e-01, -1.6252e-01, -3.8700e-01],\\n\",\n      \"           [ 1.9116e-01, -4.3086e-02, -4.4431e-01]],\\n\",\n      \"\\n\",\n      \"          [[-7.2327e-01, -5.4385e-01, -1.7782e-01],\\n\",\n      \"           [-1.8012e-01,  3.7876e-02, -1.9569e-01],\\n\",\n      \"           [ 1.8859e-01, -5.3727e-04, -2.3362e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.5798e-01, -5.9350e-01, -9.6770e-02],\\n\",\n      \"           [-1.5182e-01, -8.6331e-02, -3.2461e-01],\\n\",\n      \"           [ 3.1227e-02, -1.0568e-01, -2.7587e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.0122e-03,  1.0633e-01,  1.8479e-01],\\n\",\n      \"           [ 3.0920e-02,  4.4069e-02,  7.5224e-02],\\n\",\n      \"           [ 6.6534e-02, -3.3056e-02, -7.0040e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.1861e-03,  1.1209e-01,  1.6746e-01],\\n\",\n      \"           [ 1.3051e-02,  3.6343e-02,  7.1036e-02],\\n\",\n      \"           [ 6.7153e-02, -9.7295e-03, -5.6630e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.5761e-03,  8.0191e-02,  1.2693e-01],\\n\",\n      \"           [ 5.5758e-02,  1.3723e-02,  4.6101e-02],\\n\",\n      \"           [ 8.1505e-02, -8.5896e-02, -1.3280e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.8306e-02, -5.9169e-02, -3.5126e-02],\\n\",\n      \"           [-4.2151e-02,  1.6398e-02,  8.1333e-02],\\n\",\n      \"           [ 1.8910e-02,  4.0316e-02,  1.0348e-01]],\\n\",\n      \"\\n\",\n      \"          [[-6.1555e-02, -8.8207e-02,  6.1890e-02],\\n\",\n      \"           [-6.1914e-02, -2.6963e-02, -6.9452e-03],\\n\",\n      \"           [-5.5874e-02,  5.7123e-02, -3.9359e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.5940e-02,  1.3818e-02,  4.9011e-03],\\n\",\n      \"           [-8.5431e-02,  3.8445e-03, -1.9201e-03],\\n\",\n      \"           [-1.7050e-02, -1.1872e-01, -9.2902e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.5160e-01,  5.2633e-01,  4.4418e-01],\\n\",\n      \"           [ 2.9222e-01,  4.4378e-01,  1.8052e-01],\\n\",\n      \"           [ 5.4324e-01,  5.0701e-01,  1.6390e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2892e-01,  3.2776e-01,  5.9659e-01],\\n\",\n      \"           [ 3.2822e-01,  2.5793e-01,  1.0698e-01],\\n\",\n      \"           [ 4.3879e-01,  1.2192e-02, -3.0617e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5120e-02,  2.6804e-01,  6.7710e-01],\\n\",\n      \"           [ 2.0891e-01,  1.4112e-01,  3.9252e-01],\\n\",\n      \"           [ 4.2020e-01,  6.0248e-02,  5.1477e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.9132e-01, -2.6188e-01, -2.5264e-01],\\n\",\n      \"           [-1.2116e-01, -9.7332e-02, -1.5391e-01],\\n\",\n      \"           [-4.3114e-02, -4.5079e-02, -1.2821e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.9381e-01, -2.0816e-01, -1.6934e-01],\\n\",\n      \"           [-1.4428e-01, -9.5682e-02, -1.3887e-01],\\n\",\n      \"           [-4.2419e-02, -3.4561e-02, -8.0254e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.4678e-01, -1.5614e-01, -1.8515e-01],\\n\",\n      \"           [-5.9134e-02, -3.2481e-02, -7.1762e-02],\\n\",\n      \"           [ 3.2370e-02,  3.4870e-02, -5.8719e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3164e-01, -1.4726e-01, -2.3606e-02],\\n\",\n      \"           [ 1.9012e-02, -4.1899e-02, -4.6944e-02],\\n\",\n      \"           [ 1.9921e-01,  4.3221e-02, -1.1518e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.7406e-02, -4.3295e-02,  8.5505e-02],\\n\",\n      \"           [ 1.5555e-01,  1.3171e-01,  5.8215e-02],\\n\",\n      \"           [ 3.1529e-01,  1.6175e-01,  9.2885e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5854e-02,  1.2988e-02,  1.2564e-01],\\n\",\n      \"           [ 2.0865e-01,  1.6967e-01,  1.4358e-01],\\n\",\n      \"           [ 4.9650e-01,  2.0124e-01,  1.0502e-01]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 78.00 %\\n\",\n      \"Val loss: 0.410046\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[27,    60] loss: 0.39896\\n\",\n      \"[27,   120] loss: 0.40795\\n\",\n      \"Time elapsed: 0h:51m:56s\\n\",\n      \"train accuracy_score: 85.22 %\\n\",\n      \"tensor([[[[[ 1.9271e-02, -3.1929e-03, -5.1293e-02],\\n\",\n      \"           [-5.0391e-03, -1.4982e-02, -4.6965e-02],\\n\",\n      \"           [-6.6752e-02, -4.9797e-02, -5.6761e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.6870e-03, -3.1380e-03, -7.9609e-03],\\n\",\n      \"           [-2.9739e-02, -2.4801e-03, -2.2068e-02],\\n\",\n      \"           [-4.5595e-02, -2.4475e-02, -2.5067e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.3965e-02, -8.5884e-03, -6.0926e-03],\\n\",\n      \"           [-3.8997e-02, -1.5543e-02,  6.3680e-03],\\n\",\n      \"           [-6.2626e-02, -2.0396e-02,  9.5390e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.6801e-03,  1.7422e-03,  1.6113e-03],\\n\",\n      \"           [ 1.0551e-03,  1.3841e-03,  4.0541e-03],\\n\",\n      \"           [ 6.1606e-04,  1.5584e-03,  5.3712e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.2593e-04,  2.6157e-03, -9.6971e-04],\\n\",\n      \"           [ 2.0757e-03, -1.0406e-04,  1.0048e-03],\\n\",\n      \"           [ 2.1273e-03,  1.0749e-03,  7.1204e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.0327e-03, -1.7468e-04, -1.5363e-03],\\n\",\n      \"           [-1.7283e-03, -9.3865e-04, -2.9447e-04],\\n\",\n      \"           [-9.8798e-04, -1.2325e-03,  1.8969e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.7715e-01, -3.8967e-02, -3.0081e-01],\\n\",\n      \"           [ 3.4637e-01,  9.6299e-02, -1.6084e-01],\\n\",\n      \"           [ 3.3817e-01,  1.2631e-02, -2.9546e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1957e-01, -2.3206e-01, -1.3672e-01],\\n\",\n      \"           [ 3.6647e-01,  2.2683e-01, -4.8510e-02],\\n\",\n      \"           [ 4.1782e-01,  1.7011e-01, -1.4884e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.9732e-02, -2.5230e-01,  3.4568e-02],\\n\",\n      \"           [ 2.5195e-01,  1.1515e-01, -5.0012e-02],\\n\",\n      \"           [ 3.4529e-01,  2.3259e-01, -2.2255e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.5614e-02,  1.1714e-01,  1.6677e-01],\\n\",\n      \"           [ 5.1028e-02,  4.9083e-02,  9.0834e-02],\\n\",\n      \"           [-1.7660e-03,  1.4891e-03, -3.3927e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.1553e-02,  1.3036e-01,  2.1293e-01],\\n\",\n      \"           [ 3.0983e-02,  7.4745e-02,  1.0582e-01],\\n\",\n      \"           [-2.7993e-03, -2.1971e-02, -5.0079e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.3960e-02,  1.5800e-01,  2.0573e-01],\\n\",\n      \"           [ 1.1546e-02,  4.9866e-02,  8.3681e-02],\\n\",\n      \"           [-7.3856e-03, -4.3945e-02, -6.7313e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.2936e-02,  6.2909e-02,  2.2240e-02],\\n\",\n      \"           [ 4.7667e-02,  2.2276e-02,  4.2285e-02],\\n\",\n      \"           [ 8.8553e-02, -2.2297e-02, -8.1409e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.9212e-02,  3.7611e-02,  1.4693e-02],\\n\",\n      \"           [ 6.2290e-02,  2.9188e-02, -1.0472e-01],\\n\",\n      \"           [ 1.0065e-01,  2.7811e-03, -1.6148e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 7.9045e-02, -4.8042e-03,  1.7652e-02],\\n\",\n      \"           [ 4.7472e-02, -7.1633e-03, -1.4910e-01],\\n\",\n      \"           [ 6.7340e-02, -8.5596e-02, -1.6208e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.4149e-01,  3.9451e-01,  4.0264e-01],\\n\",\n      \"           [ 1.1406e-01,  2.9628e-01,  1.3082e-01],\\n\",\n      \"           [ 2.3606e-01, -5.5930e-02, -7.7833e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3389e-01,  3.3804e-01,  5.2396e-01],\\n\",\n      \"           [ 2.5361e-01,  1.5349e-01,  2.6141e-01],\\n\",\n      \"           [ 1.7570e-01,  3.4423e-02, -7.0507e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7514e-02,  2.7743e-01,  7.4437e-01],\\n\",\n      \"           [ 1.6841e-01,  1.1286e-01,  5.3820e-01],\\n\",\n      \"           [ 2.7268e-01,  4.5979e-02,  2.6875e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1666e-01, -8.3946e-02, -8.9487e-02],\\n\",\n      \"           [-2.9468e-02, -7.4840e-03, -8.6493e-02],\\n\",\n      \"           [-8.0597e-03, -2.7011e-02, -1.3010e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.3636e-02, -6.9092e-02, -5.0509e-02],\\n\",\n      \"           [-5.5808e-03,  1.8237e-02, -5.0544e-02],\\n\",\n      \"           [ 3.1489e-02,  4.6222e-02, -3.9926e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.2358e-02, -1.2303e-02, -1.7880e-02],\\n\",\n      \"           [ 3.9066e-02,  6.6211e-02,  4.9603e-03],\\n\",\n      \"           [ 9.1109e-02,  7.9075e-02,  2.1381e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.5532e-01, -1.1178e-01, -5.0506e-02],\\n\",\n      \"           [-9.3695e-02, -9.5237e-02, -2.0426e-02],\\n\",\n      \"           [ 6.6979e-02,  4.2454e-02,  2.3693e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0868e-01, -5.1957e-02, -1.9102e-02],\\n\",\n      \"           [ 3.4121e-02,  6.5141e-02,  7.9955e-02],\\n\",\n      \"           [ 2.0867e-01,  1.3984e-01,  1.3182e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.2577e-02, -4.0893e-03,  8.3077e-02],\\n\",\n      \"           [ 9.7075e-02,  1.3684e-01,  1.7928e-01],\\n\",\n      \"           [ 3.6601e-01,  2.7234e-01,  2.0698e-01]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 81.00 %\\n\",\n      \"Val loss: 0.386431\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[28,    60] loss: 0.35563\\n\",\n      \"[28,   120] loss: 0.31672\\n\",\n      \"Time elapsed: 0h:53m:51s\\n\",\n      \"train accuracy_score: 85.80 %\\n\",\n      \"tensor([[[[[ 1.3117e-01,  6.9735e-02,  3.7899e-02],\\n\",\n      \"           [ 2.8865e-02, -1.9349e-02, -1.3241e-01],\\n\",\n      \"           [ 3.3720e-02,  7.2970e-04, -2.1263e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 8.4488e-02, -2.5208e-02, -1.1385e-01],\\n\",\n      \"           [ 8.7103e-02, -1.0751e-02, -4.9294e-02],\\n\",\n      \"           [ 1.8793e-01,  4.4804e-02, -7.7191e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.1249e-02,  9.2782e-03, -4.1587e-02],\\n\",\n      \"           [ 1.4888e-01,  2.1127e-02,  1.5287e-04],\\n\",\n      \"           [ 3.6979e-01,  1.1578e-01,  2.5566e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.3357e-03,  6.3930e-03, -1.8242e-02],\\n\",\n      \"           [-2.9983e-03, -1.1843e-03, -1.4221e-02],\\n\",\n      \"           [-2.1332e-03,  3.7241e-03, -1.1370e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1507e-02, -1.1271e-02, -4.8432e-03],\\n\",\n      \"           [-1.0183e-03,  7.2574e-03,  4.4345e-03],\\n\",\n      \"           [ 1.9689e-02,  1.0339e-02,  5.5052e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.9960e-03, -1.1902e-02, -8.2108e-03],\\n\",\n      \"           [ 2.7616e-03,  5.3444e-03,  3.3524e-03],\\n\",\n      \"           [ 5.5175e-03,  2.6620e-02,  1.1815e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.8167e-02, -2.3684e-01,  5.6789e-01],\\n\",\n      \"           [-6.1990e-01,  2.0910e-02,  2.1754e+00],\\n\",\n      \"           [-4.9868e-01,  7.0718e-01,  2.3931e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 4.2071e-01,  7.8349e-01,  3.5346e-01],\\n\",\n      \"           [-2.3781e-01, -1.7707e-01,  5.5316e-01],\\n\",\n      \"           [-7.0169e-01,  1.2039e-02,  3.6954e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9159e+00,  2.1299e+00,  8.0163e-01],\\n\",\n      \"           [ 5.0955e-01,  3.0968e-01,  3.4804e-01],\\n\",\n      \"           [-7.5781e-01, -9.0820e-01,  4.5935e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.5571e-01, -1.3812e+00, -1.8954e+00],\\n\",\n      \"           [-4.9851e-01, -8.5675e-01, -1.3269e+00],\\n\",\n      \"           [-2.7694e-01, -2.3920e-01, -3.0419e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.1432e-01, -1.0420e+00, -1.8062e+00],\\n\",\n      \"           [-1.4090e-01, -3.5805e-01, -9.4641e-01],\\n\",\n      \"           [ 2.1240e-02,  1.5415e-01,  3.9833e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.4133e-01, -7.8293e-01, -1.2501e+00],\\n\",\n      \"           [-1.4907e-01, -2.1145e-01, -8.1057e-01],\\n\",\n      \"           [-6.1683e-02,  1.6427e-01, -6.6361e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.0122e-01,  1.2153e-01,  2.3310e-01],\\n\",\n      \"           [ 2.7494e-01,  8.7635e-02, -4.7275e-02],\\n\",\n      \"           [ 2.9740e-01,  8.6878e-01,  4.6536e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.7378e-01,  1.6029e-01,  1.0558e-01],\\n\",\n      \"           [ 6.8895e-01,  7.6618e-01,  6.0737e-01],\\n\",\n      \"           [ 4.5192e-01,  7.6858e-01,  7.2270e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.9526e-01,  6.7593e-01,  5.5896e-01],\\n\",\n      \"           [ 7.7972e-01,  7.4992e-01,  7.1099e-01],\\n\",\n      \"           [ 4.7266e-01,  8.8141e-01,  5.8031e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.0379e-01, -1.4039e+00, -1.4384e+00],\\n\",\n      \"           [ 4.5459e-01,  6.7528e-01, -3.3446e-01],\\n\",\n      \"           [ 2.7444e-01,  1.0212e+00, -3.8005e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.9025e-01, -4.6785e-01, -1.5867e+00],\\n\",\n      \"           [-4.6240e-01,  4.3634e-01, -3.6942e-01],\\n\",\n      \"           [-1.1265e+00,  4.5949e-01,  9.1496e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.9050e-01, -1.0021e+00, -2.8478e+00],\\n\",\n      \"           [-9.1987e-01, -5.0532e-01, -2.1333e+00],\\n\",\n      \"           [-1.3561e+00, -2.9738e-01, -9.2850e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.0578e-01,  4.1127e-01,  5.9538e-01],\\n\",\n      \"           [ 2.6580e-01,  3.8241e-02,  2.6531e-01],\\n\",\n      \"           [-2.1510e-01,  6.9963e-02,  5.8977e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.5654e-01,  5.0527e-01,  6.2972e-01],\\n\",\n      \"           [ 2.4292e-01,  4.4768e-02,  2.5165e-01],\\n\",\n      \"           [-2.2850e-01, -1.0551e-01,  2.2785e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.2902e-01,  4.0386e-01,  1.8325e-01],\\n\",\n      \"           [-3.2154e-02, -2.3032e-01,  2.2420e-02],\\n\",\n      \"           [-3.8620e-01, -2.5020e-01, -8.3390e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.9316e-01,  5.2440e-01, -1.5638e-02],\\n\",\n      \"           [ 9.4391e-02, -3.9623e-02, -1.7987e-01],\\n\",\n      \"           [-8.4322e-01, -4.3585e-01, -3.2478e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.7581e-01,  3.0949e-01, -2.5050e-01],\\n\",\n      \"           [-1.9081e-01, -4.5018e-01, -5.4980e-01],\\n\",\n      \"           [-1.0894e+00, -5.5995e-01, -6.1967e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.2059e-01,  1.8869e-01, -3.9537e-01],\\n\",\n      \"           [-2.2757e-01, -5.4740e-01, -5.5086e-01],\\n\",\n      \"           [-1.5083e+00, -6.9751e-01, -7.2688e-01]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 84.00 %\\n\",\n      \"Val loss: 0.365563\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[29,    60] loss: 0.30718\\n\",\n      \"[29,   120] loss: 0.30966\\n\",\n      \"Time elapsed: 0h:55m:45s\\n\",\n      \"train accuracy_score: 87.23 %\\n\",\n      \"tensor([[[[[-1.6169e-02,  2.5965e-03,  2.2546e-02],\\n\",\n      \"           [-3.8216e-03,  6.6338e-03, -4.3363e-03],\\n\",\n      \"           [-4.3445e-02, -2.8919e-02, -3.0608e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.9919e-03,  1.4710e-02,  5.2641e-02],\\n\",\n      \"           [ 5.2223e-04, -1.0916e-02,  1.8936e-02],\\n\",\n      \"           [-1.7312e-02, -2.2947e-02, -2.0182e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.3811e-03,  9.3176e-03,  2.7324e-02],\\n\",\n      \"           [-2.6255e-02, -1.6557e-02, -1.7957e-02],\\n\",\n      \"           [-5.8234e-02, -3.0388e-02, -2.2161e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0156e-02,  3.0292e-03,  4.3859e-03],\\n\",\n      \"           [ 3.2744e-03, -1.5333e-03,  1.0260e-02],\\n\",\n      \"           [ 2.5173e-03,  2.4651e-03,  8.2175e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6276e-02,  5.5527e-03,  6.1913e-05],\\n\",\n      \"           [ 8.0946e-03, -1.9520e-04,  1.7660e-03],\\n\",\n      \"           [ 4.8023e-04,  1.5022e-03,  7.2660e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2884e-02,  4.3102e-03,  4.0546e-03],\\n\",\n      \"           [ 1.1518e-02,  4.1509e-03, -6.4798e-04],\\n\",\n      \"           [ 9.2101e-03, -9.4395e-04,  6.0897e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.8951e-01, -4.1206e-01, -5.2420e-01],\\n\",\n      \"           [-2.8555e-01, -3.6054e-01, -5.1470e-01],\\n\",\n      \"           [-1.2256e-01, -4.0316e-01, -8.2796e-01]],\\n\",\n      \"\\n\",\n      \"          [[-7.7114e-01, -6.0177e-01, -2.2853e-01],\\n\",\n      \"           [-3.5067e-01, -1.5190e-01, -2.6872e-01],\\n\",\n      \"           [-4.4510e-03, -1.5190e-01, -5.2605e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.0649e-01, -8.0239e-01, -3.9003e-01],\\n\",\n      \"           [-3.8808e-01, -3.7454e-01, -2.6370e-01],\\n\",\n      \"           [-1.0473e-01, -1.5218e-01, -1.8188e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.8061e-02,  1.2236e-01,  2.3432e-01],\\n\",\n      \"           [ 6.7488e-02,  6.7269e-02,  4.7881e-02],\\n\",\n      \"           [ 5.3464e-02, -2.1341e-02, -1.5616e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1700e-02,  1.2290e-01,  2.8916e-01],\\n\",\n      \"           [ 1.4593e-02,  4.3892e-02,  1.3251e-01],\\n\",\n      \"           [-7.6968e-03, -4.0350e-02, -9.9874e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.2403e-02,  9.3443e-02,  2.0431e-01],\\n\",\n      \"           [-6.3760e-03, -1.2063e-02,  6.2451e-02],\\n\",\n      \"           [-1.0430e-01, -1.5036e-01, -7.4798e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.8158e-03,  4.2282e-02,  5.8545e-02],\\n\",\n      \"           [ 1.7340e-01,  1.3057e-01,  8.1915e-02],\\n\",\n      \"           [ 3.5366e-01,  2.2394e-01,  1.5548e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3897e-01, -1.4045e-02,  4.0983e-02],\\n\",\n      \"           [ 9.4450e-02,  1.3014e-01,  6.1753e-02],\\n\",\n      \"           [ 3.0605e-01,  2.4367e-01,  1.4615e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 8.9275e-02, -1.9371e-04, -7.0530e-02],\\n\",\n      \"           [ 5.8686e-02,  5.0594e-02, -1.9858e-02],\\n\",\n      \"           [ 1.9697e-01,  1.2039e-01,  9.0007e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.7463e-01,  1.0763e+00,  1.2729e+00],\\n\",\n      \"           [ 3.4215e-01,  1.0041e+00,  1.1448e+00],\\n\",\n      \"           [ 5.2545e-01,  6.6012e-01,  8.2188e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.6098e-01,  6.0779e-01,  1.3479e+00],\\n\",\n      \"           [ 3.7180e-01,  5.1160e-01,  1.1037e+00],\\n\",\n      \"           [ 5.3213e-01,  3.6270e-01,  8.9939e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8806e-01,  6.9180e-01,  1.3800e+00],\\n\",\n      \"           [ 6.0458e-01,  5.8786e-01,  1.1705e+00],\\n\",\n      \"           [ 9.6362e-01,  5.7571e-01,  1.0617e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.9624e-01, -1.2104e-01, -1.2821e-01],\\n\",\n      \"           [-4.0004e-02,  2.2778e-02, -4.2017e-02],\\n\",\n      \"           [ 6.3282e-02,  3.0983e-02, -1.2196e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.6042e-02, -5.1713e-02, -5.7144e-02],\\n\",\n      \"           [ 2.3489e-02,  7.8677e-02, -3.1098e-02],\\n\",\n      \"           [ 1.1952e-01,  8.1453e-02, -6.7999e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.9377e-02,  8.2840e-02,  5.8336e-02],\\n\",\n      \"           [ 1.1241e-01,  1.1151e-01,  2.1753e-02],\\n\",\n      \"           [ 1.6655e-01,  1.2733e-01,  7.9628e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.1105e-01, -1.2086e-01, -3.2405e-02],\\n\",\n      \"           [-1.5774e-01, -1.0054e-01, -9.3067e-02],\\n\",\n      \"           [-9.8534e-03, -5.3948e-02, -1.7044e-01]],\\n\",\n      \"\\n\",\n      \"          [[-8.5979e-02, -1.3844e-02,  7.2783e-02],\\n\",\n      \"           [ 1.4409e-02,  5.5062e-02,  3.6667e-02],\\n\",\n      \"           [ 1.8838e-01,  1.4638e-01,  1.7606e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 8.4255e-02,  1.0444e-01,  2.0334e-01],\\n\",\n      \"           [ 2.2004e-01,  1.8661e-01,  2.6973e-01],\\n\",\n      \"           [ 4.4470e-01,  1.7828e-01,  1.7570e-01]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 83.00 %\\n\",\n      \"Val loss: 0.376354\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[30,    60] loss: 0.28193\\n\",\n      \"[30,   120] loss: 0.28301\\n\",\n      \"Time elapsed: 0h:57m:40s\\n\",\n      \"train accuracy_score: 89.10 %\\n\",\n      \"tensor([[[[[ 5.6447e-03,  6.0866e-03,  7.7223e-03],\\n\",\n      \"           [ 3.5553e-03, -5.3188e-03,  1.3891e-03],\\n\",\n      \"           [ 1.2932e-03, -9.6720e-03, -8.3572e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.8318e-03,  5.6150e-03,  1.2869e-02],\\n\",\n      \"           [ 5.7196e-03,  1.3717e-03,  4.3417e-03],\\n\",\n      \"           [-2.8557e-03, -1.8845e-03, -5.4569e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.6055e-03,  5.7273e-04,  4.9413e-03],\\n\",\n      \"           [ 1.4376e-03,  2.2734e-03,  4.9461e-03],\\n\",\n      \"           [ 3.9562e-03, -2.6496e-03,  2.9569e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.3815e-04, -4.2855e-05, -6.3662e-05],\\n\",\n      \"           [ 6.0304e-04,  1.8337e-04, -9.9521e-05],\\n\",\n      \"           [-1.3484e-05, -3.8009e-04,  2.2625e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 9.9782e-04,  1.0208e-03,  1.1774e-04],\\n\",\n      \"           [ 9.9931e-04,  1.0501e-04,  3.6487e-04],\\n\",\n      \"           [-2.8184e-04, -3.1621e-04, -2.3211e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3920e-03,  1.1054e-03, -3.5278e-04],\\n\",\n      \"           [ 1.2948e-03,  5.0717e-04,  1.4981e-04],\\n\",\n      \"           [ 2.0512e-04,  1.2292e-04, -1.0836e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.6003e-01, -4.2405e-02, -2.6915e-02],\\n\",\n      \"           [-7.6571e-02, -5.1417e-02, -8.1418e-02],\\n\",\n      \"           [-1.0766e-01, -1.4354e-01, -1.6355e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.6943e-01, -1.1038e-01, -5.7348e-03],\\n\",\n      \"           [-1.4045e-01, -6.5670e-02, -4.5133e-02],\\n\",\n      \"           [-7.0928e-02, -9.7819e-02, -1.2076e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.9529e-01, -1.6474e-01, -4.6701e-02],\\n\",\n      \"           [-1.2340e-01, -9.1434e-02, -4.6116e-02],\\n\",\n      \"           [-8.5005e-02, -3.8461e-02, -7.8770e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.1831e-02, -9.9364e-03, -4.8735e-03],\\n\",\n      \"           [ 1.0290e-03,  8.2494e-03, -2.2181e-02],\\n\",\n      \"           [-7.1394e-03, -1.2943e-02, -4.7921e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.1279e-02, -9.5411e-03,  4.3025e-03],\\n\",\n      \"           [-5.0978e-03,  7.0711e-03, -7.1631e-03],\\n\",\n      \"           [ 2.4448e-03, -2.8682e-03, -2.4198e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.5535e-02, -1.9899e-02,  5.3657e-03],\\n\",\n      \"           [-5.1677e-03, -3.3683e-03, -1.9312e-02],\\n\",\n      \"           [-1.4850e-04, -1.4018e-02, -2.9496e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.0366e-03,  4.0486e-03,  9.9673e-03],\\n\",\n      \"           [ 8.3368e-03, -2.9707e-03, -5.3505e-03],\\n\",\n      \"           [ 2.2273e-03, -1.9514e-02, -3.4355e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.8082e-03,  4.9613e-03,  2.4536e-04],\\n\",\n      \"           [ 3.9989e-04,  3.6869e-03, -9.4689e-03],\\n\",\n      \"           [-8.1685e-03, -1.0151e-02, -2.8929e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0608e-02, -1.4870e-02, -4.6192e-03],\\n\",\n      \"           [-1.6356e-02,  9.5757e-03, -3.6779e-03],\\n\",\n      \"           [-1.3189e-02, -1.5431e-02, -1.2865e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.8135e-02,  1.1106e-01,  1.0581e-01],\\n\",\n      \"           [ 6.9304e-03,  6.0588e-02,  7.6104e-02],\\n\",\n      \"           [ 3.6914e-02,  1.3573e-02,  4.4373e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6850e-02,  5.4393e-02,  1.1343e-01],\\n\",\n      \"           [ 1.2741e-02,  4.7323e-02,  4.9725e-02],\\n\",\n      \"           [ 3.7190e-02,  2.1204e-02,  5.6665e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.4854e-02,  5.3931e-02,  1.1688e-01],\\n\",\n      \"           [ 1.8914e-02,  3.2831e-02,  1.0328e-01],\\n\",\n      \"           [ 3.9342e-02,  3.2467e-02,  5.6928e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2241e-02, -9.9963e-03,  5.6653e-03],\\n\",\n      \"           [ 5.1511e-03,  2.0584e-02,  5.2744e-03],\\n\",\n      \"           [ 2.2126e-02,  1.7978e-02, -1.7701e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.6187e-03,  2.0655e-03,  1.8752e-02],\\n\",\n      \"           [ 2.1604e-02,  3.1284e-02,  1.1824e-02],\\n\",\n      \"           [ 3.5103e-02,  3.3011e-02,  1.2749e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.5089e-02,  1.1766e-02,  2.0410e-02],\\n\",\n      \"           [ 1.7474e-02,  3.5779e-02,  3.1803e-02],\\n\",\n      \"           [ 4.2926e-02,  4.6675e-02,  2.7466e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3911e-02, -1.5729e-02, -3.9988e-03],\\n\",\n      \"           [ 8.0527e-03, -4.2267e-03, -7.4542e-03],\\n\",\n      \"           [ 5.6947e-02,  8.7051e-03, -4.7161e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2615e-04,  5.3358e-03,  1.2651e-02],\\n\",\n      \"           [ 4.1241e-02,  2.2434e-02,  1.3669e-02],\\n\",\n      \"           [ 9.6928e-02,  3.9123e-02, -1.0061e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0950e-02,  1.6776e-03,  9.6815e-03],\\n\",\n      \"           [ 5.4326e-02,  2.6808e-02,  1.1338e-02],\\n\",\n      \"           [ 1.2620e-01,  4.9860e-02,  1.3225e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 83.00 %\\n\",\n      \"Val loss: 0.358354\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[31,    60] loss: 0.25273\\n\",\n      \"[31,   120] loss: 0.27968\\n\",\n      \"Time elapsed: 0h:59m:34s\\n\",\n      \"train accuracy_score: 90.24 %\\n\",\n      \"tensor([[[[[-6.3863e-03,  4.2241e-03,  1.3857e-02],\\n\",\n      \"           [-1.1989e-02, -2.5483e-03,  5.0358e-03],\\n\",\n      \"           [ 2.1537e-02,  1.1182e-02,  8.4727e-03]],\\n\",\n      \"\\n\",\n      \"          [[-6.9754e-03,  5.2025e-04,  1.8663e-02],\\n\",\n      \"           [-1.5421e-03,  7.4484e-03,  6.2982e-03],\\n\",\n      \"           [ 1.3037e-02,  6.3312e-03, -4.0139e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.7596e-03,  4.0232e-03,  1.5036e-02],\\n\",\n      \"           [ 8.7974e-03,  5.5561e-03,  6.0917e-04],\\n\",\n      \"           [ 1.4937e-02, -2.4415e-03, -7.9289e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.5814e-03, -3.7302e-05,  4.8451e-04],\\n\",\n      \"           [-1.0361e-03,  4.6099e-04, -2.9617e-04],\\n\",\n      \"           [-7.2089e-04, -5.8953e-04, -4.7910e-04]],\\n\",\n      \"\\n\",\n      \"          [[-5.5179e-04, -1.9500e-04, -3.3675e-04],\\n\",\n      \"           [-2.8263e-04,  1.0434e-03,  1.0910e-03],\\n\",\n      \"           [-6.3699e-04,  6.5148e-05, -3.5788e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.2302e-03, -4.1222e-04, -3.1722e-04],\\n\",\n      \"           [-6.0395e-04,  9.1057e-04,  4.0266e-04],\\n\",\n      \"           [-1.8355e-03, -4.3430e-04, -1.3362e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.8928e-01,  3.7818e-01,  3.3173e-01],\\n\",\n      \"           [ 1.2614e-01,  1.8232e-01,  2.5239e-01],\\n\",\n      \"           [ 2.6443e-02,  1.2043e-01,  2.8282e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4306e-01,  2.8859e-01,  1.9979e-01],\\n\",\n      \"           [ 1.8417e-02,  2.9886e-02,  1.3371e-01],\\n\",\n      \"           [-7.5520e-03, -4.2199e-03,  1.7151e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.8236e-01,  3.0912e-01,  1.0573e-01],\\n\",\n      \"           [ 5.5938e-02,  5.6270e-02,  6.4356e-02],\\n\",\n      \"           [-6.6891e-02, -2.4094e-02,  5.6749e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.8204e-02, -9.8896e-02, -1.0825e-01],\\n\",\n      \"           [-5.7415e-02, -4.5681e-02, -5.3003e-02],\\n\",\n      \"           [-4.9631e-02, -1.7973e-02,  5.4980e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.6329e-02, -6.3095e-02, -7.5142e-02],\\n\",\n      \"           [-2.4124e-02, -2.1588e-02, -1.8791e-02],\\n\",\n      \"           [-1.3286e-02,  2.1651e-02,  4.3038e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.3517e-03, -4.1957e-02, -5.7017e-02],\\n\",\n      \"           [-4.5833e-03,  1.3596e-02,  1.0919e-02],\\n\",\n      \"           [ 1.2745e-02,  4.7661e-02,  6.4072e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.4032e-02,  5.9068e-03, -2.0216e-03],\\n\",\n      \"           [-7.5680e-03, -1.2838e-02, -9.6232e-03],\\n\",\n      \"           [-8.0535e-02, -3.2111e-02,  9.2060e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.9731e-02,  4.2219e-02,  4.0558e-02],\\n\",\n      \"           [ 2.5190e-02,  5.8587e-02,  6.7251e-02],\\n\",\n      \"           [-4.8020e-02,  3.5858e-02,  9.9394e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.9406e-02,  1.1873e-01,  7.1481e-02],\\n\",\n      \"           [ 7.3360e-02,  8.3940e-02,  1.1086e-01],\\n\",\n      \"           [-8.5147e-03,  6.9976e-02,  1.1804e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.1315e-02, -1.3403e-02, -4.6661e-02],\\n\",\n      \"           [ 7.8541e-02,  7.8160e-02, -5.6129e-02],\\n\",\n      \"           [-1.7375e-02,  1.8085e-02,  9.0207e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.8166e-02, -4.4137e-02, -6.0256e-02],\\n\",\n      \"           [-3.0285e-02, -1.8075e-02, -4.8563e-02],\\n\",\n      \"           [-1.0146e-01,  2.0135e-02,  3.2632e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0278e-02, -3.5589e-02, -2.1438e-01],\\n\",\n      \"           [-3.9899e-02, -6.6173e-02, -1.5620e-01],\\n\",\n      \"           [-1.3663e-01, -2.5529e-03, -1.8515e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.2736e-02,  2.6389e-03, -1.8080e-02],\\n\",\n      \"           [-2.4509e-02, -4.4349e-02, -3.7801e-02],\\n\",\n      \"           [-6.2651e-02, -5.2454e-02, -5.7052e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.3908e-02,  2.0598e-02,  3.8177e-03],\\n\",\n      \"           [ 5.8131e-03, -2.7179e-02, -1.8082e-02],\\n\",\n      \"           [-6.5254e-02, -4.8689e-02, -1.7709e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.4933e-02,  2.6465e-02,  7.1954e-03],\\n\",\n      \"           [-9.8230e-04, -3.5504e-02, -3.8614e-02],\\n\",\n      \"           [-7.0536e-02, -6.9572e-02, -5.8730e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.0484e-02,  2.5406e-02,  1.4451e-02],\\n\",\n      \"           [-9.1833e-03, -1.5310e-02, -4.8798e-04],\\n\",\n      \"           [-6.7097e-02, -4.1993e-02,  1.6169e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.9451e-02,  2.3526e-02,  3.2962e-03],\\n\",\n      \"           [-1.8426e-02, -1.6623e-03, -1.7256e-02],\\n\",\n      \"           [-7.9038e-02, -4.3450e-02, -1.5728e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.4426e-03, -1.5033e-02, -3.1871e-02],\\n\",\n      \"           [-1.6451e-02, -3.2444e-02, -3.6854e-02],\\n\",\n      \"           [-1.1713e-01, -7.3326e-02, -4.4170e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 82.00 %\\n\",\n      \"Val loss: 0.344108\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[32,    60] loss: 0.23335\\n\",\n      \"[32,   120] loss: 0.25778\\n\",\n      \"Time elapsed: 1h:1m:28s\\n\",\n      \"train accuracy_score: 90.96 %\\n\",\n      \"tensor([[[[[ 5.4687e-04,  2.7796e-03,  4.4870e-03],\\n\",\n      \"           [ 2.6311e-03,  1.9168e-03,  2.3381e-03],\\n\",\n      \"           [ 3.3799e-03, -2.2109e-04,  2.1395e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1504e-04,  2.1893e-03,  2.4444e-03],\\n\",\n      \"           [ 2.1761e-03,  1.0286e-03,  2.0280e-03],\\n\",\n      \"           [ 7.8183e-04, -7.7656e-04,  1.3872e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.7133e-04,  1.1430e-03,  1.2472e-03],\\n\",\n      \"           [ 2.1112e-03,  5.3332e-04,  1.9252e-03],\\n\",\n      \"           [ 2.2953e-03,  8.3619e-04, -1.0041e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.2581e-05,  8.8184e-05,  5.0432e-05],\\n\",\n      \"           [ 9.2855e-05,  1.4895e-04,  2.2674e-04],\\n\",\n      \"           [ 1.9350e-04,  2.4037e-04,  2.9457e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0225e-04,  7.6233e-06, -2.2043e-04],\\n\",\n      \"           [ 2.0016e-04, -1.4980e-05,  5.4883e-05],\\n\",\n      \"           [ 1.8455e-04,  5.8784e-05,  3.5545e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3924e-04,  1.7050e-04, -3.0486e-04],\\n\",\n      \"           [ 4.1741e-04,  7.2751e-05, -2.8108e-06],\\n\",\n      \"           [ 3.6277e-04,  2.0899e-04,  9.5344e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.3033e-02,  4.1924e-02,  3.9007e-02],\\n\",\n      \"           [ 3.7586e-02,  2.1095e-02,  1.1043e-02],\\n\",\n      \"           [ 1.4978e-02, -1.1650e-02, -3.0784e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.3062e-02,  1.7024e-02,  2.2002e-02],\\n\",\n      \"           [ 1.9234e-02,  4.0184e-03,  4.1634e-03],\\n\",\n      \"           [ 2.8208e-03, -1.1946e-02, -3.5343e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5115e-02,  3.4369e-03,  1.2687e-02],\\n\",\n      \"           [ 8.8585e-03,  7.2063e-04,  8.2230e-03],\\n\",\n      \"           [-1.3540e-03, -3.3589e-03, -1.1601e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.8060e-03, -2.5707e-04,  6.7965e-03],\\n\",\n      \"           [-6.6716e-04,  1.2271e-04,  5.3234e-03],\\n\",\n      \"           [-3.4737e-03, -1.4528e-03,  3.3030e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.3288e-03,  2.6380e-03,  8.5504e-03],\\n\",\n      \"           [-1.1680e-03,  1.3275e-03,  8.4601e-03],\\n\",\n      \"           [-2.0378e-03, -1.4633e-03,  5.8992e-03]],\\n\",\n      \"\\n\",\n      \"          [[-9.5485e-04,  2.9005e-04,  5.7092e-03],\\n\",\n      \"           [-5.8281e-04, -1.5669e-03,  4.9698e-03],\\n\",\n      \"           [-4.3520e-04,  1.2514e-05,  5.6262e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.0902e-02, -2.4663e-03, -3.6706e-03],\\n\",\n      \"           [-1.1231e-02,  7.2187e-04,  1.8004e-03],\\n\",\n      \"           [-1.0644e-02, -5.0871e-03, -4.6491e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.7050e-03, -1.6616e-04, -5.3144e-05],\\n\",\n      \"           [-9.5345e-03, -2.7914e-03, -7.4193e-04],\\n\",\n      \"           [-1.0105e-02, -4.9585e-03, -3.5453e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.1620e-02,  5.9068e-04,  1.0807e-02],\\n\",\n      \"           [-9.1386e-03,  6.0013e-04,  4.3479e-03],\\n\",\n      \"           [-9.8096e-03, -3.2510e-03, -1.3589e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.9692e-02,  3.3828e-02,  4.3503e-02],\\n\",\n      \"           [ 1.0531e-02,  7.9064e-03,  1.7985e-02],\\n\",\n      \"           [-1.9805e-03, -1.6267e-02, -3.8741e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4872e-02,  2.4171e-02,  3.6440e-02],\\n\",\n      \"           [ 9.9683e-03,  7.2821e-04,  1.0446e-02],\\n\",\n      \"           [-1.4819e-03, -1.2159e-02, -1.8300e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4462e-02,  1.3396e-02,  3.4025e-02],\\n\",\n      \"           [ 5.8448e-03,  2.4157e-03,  9.2110e-03],\\n\",\n      \"           [-1.0188e-03, -7.8596e-03, -4.9475e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.7543e-03,  3.2304e-03,  1.6461e-03],\\n\",\n      \"           [ 3.0058e-03,  6.8636e-04,  3.6269e-03],\\n\",\n      \"           [ 1.6799e-03, -1.6071e-04, -5.4247e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.2042e-03,  4.9399e-03,  8.5830e-03],\\n\",\n      \"           [ 3.1572e-03,  5.6321e-03,  6.7477e-03],\\n\",\n      \"           [ 4.0270e-03,  5.8602e-03,  1.1536e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0073e-02,  1.0466e-02,  1.3493e-02],\\n\",\n      \"           [ 1.0031e-02,  1.1555e-02,  1.6100e-02],\\n\",\n      \"           [ 9.1698e-03,  1.0859e-02,  1.0096e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.8065e-03, -3.4019e-03,  1.2317e-04],\\n\",\n      \"           [-7.5236e-03, -4.4811e-03,  4.0031e-04],\\n\",\n      \"           [-1.4550e-03, -2.1467e-03,  2.8660e-03]],\\n\",\n      \"\\n\",\n      \"          [[-6.8152e-03, -9.2824e-04,  4.0828e-03],\\n\",\n      \"           [-2.2008e-03, -2.9237e-03,  4.6675e-03],\\n\",\n      \"           [ 3.5099e-03,  3.9255e-03,  5.3162e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.3570e-03,  3.3141e-03,  8.3026e-03],\\n\",\n      \"           [ 3.9597e-03,  5.7499e-03,  1.2741e-02],\\n\",\n      \"           [ 1.5391e-02,  7.1687e-03,  1.0144e-02]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 81.00 %\\n\",\n      \"Val loss: 0.391441\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[33,    60] loss: 0.24296\\n\",\n      \"[33,   120] loss: 0.27252\\n\",\n      \"Time elapsed: 1h:3m:24s\\n\",\n      \"train accuracy_score: 90.39 %\\n\",\n      \"tensor([[[[[ 2.2956e-05,  3.3611e-05,  1.9384e-05],\\n\",\n      \"           [-1.3605e-05, -1.1414e-05, -4.7072e-06],\\n\",\n      \"           [ 5.0106e-05,  9.2067e-06, -1.9435e-05]],\\n\",\n      \"\\n\",\n      \"          [[-2.0239e-05, -2.5024e-05, -3.5404e-05],\\n\",\n      \"           [ 6.0537e-06, -1.9538e-06, -1.9490e-05],\\n\",\n      \"           [-2.6565e-06, -1.7996e-06,  1.6981e-05]],\\n\",\n      \"\\n\",\n      \"          [[-3.4575e-05, -3.1742e-05, -4.0329e-05],\\n\",\n      \"           [-2.0005e-05, -1.2658e-05, -2.7483e-05],\\n\",\n      \"           [ 4.4157e-06, -1.2067e-05, -2.2002e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.0606e-05, -9.4794e-06, -1.8830e-05],\\n\",\n      \"           [-4.2537e-06, -2.2244e-06,  6.7626e-06],\\n\",\n      \"           [-1.0381e-05, -1.7120e-05, -9.6012e-06]],\\n\",\n      \"\\n\",\n      \"          [[ 4.4073e-06,  9.6272e-06, -1.4490e-06],\\n\",\n      \"           [-2.5407e-06, -1.1578e-05, -1.4044e-05],\\n\",\n      \"           [-1.0477e-05, -8.9327e-06,  1.5602e-05]],\\n\",\n      \"\\n\",\n      \"          [[-1.9920e-05, -2.0969e-05, -1.0379e-05],\\n\",\n      \"           [ 1.0142e-05, -1.5625e-05, -8.3629e-06],\\n\",\n      \"           [ 3.9754e-06,  3.8698e-06, -1.3560e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 9.7570e-04,  7.3280e-04,  5.8339e-04],\\n\",\n      \"           [ 5.2418e-04,  4.4687e-04,  5.6917e-04],\\n\",\n      \"           [ 2.3411e-04,  3.0360e-04,  5.4458e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 8.0055e-04,  5.0927e-04,  2.9009e-04],\\n\",\n      \"           [ 4.5570e-04,  2.2127e-04,  1.8688e-04],\\n\",\n      \"           [ 1.8008e-04,  1.6007e-04,  1.8693e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 7.5270e-04,  5.7451e-04,  3.1624e-04],\\n\",\n      \"           [ 4.2858e-04,  2.7639e-04,  2.3295e-04],\\n\",\n      \"           [ 1.5177e-04,  5.9996e-05,  1.3828e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.9302e-06, -1.1698e-04, -2.2721e-04],\\n\",\n      \"           [-2.5654e-05, -6.9665e-05, -1.3493e-04],\\n\",\n      \"           [ 2.2041e-05,  2.7987e-05, -1.8331e-05]],\\n\",\n      \"\\n\",\n      \"          [[ 6.6963e-06, -9.9157e-05, -2.0802e-04],\\n\",\n      \"           [-2.4142e-05, -2.6990e-05, -1.2139e-04],\\n\",\n      \"           [ 5.0895e-05,  4.5896e-05, -2.4924e-05]],\\n\",\n      \"\\n\",\n      \"          [[-8.1322e-07, -1.0441e-04, -2.0530e-04],\\n\",\n      \"           [ 7.9895e-06, -3.8609e-05, -1.1864e-04],\\n\",\n      \"           [ 5.2419e-05,  3.9669e-05, -1.5097e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.0432e-05,  3.5629e-05,  1.5327e-05],\\n\",\n      \"           [ 1.0164e-05,  9.2370e-06,  3.9097e-05],\\n\",\n      \"           [ 1.0399e-04,  3.2874e-05,  2.6774e-05]],\\n\",\n      \"\\n\",\n      \"          [[-4.0880e-06, -4.4717e-07,  3.0281e-05],\\n\",\n      \"           [ 3.3980e-05,  5.0448e-05,  1.3963e-04],\\n\",\n      \"           [ 8.4419e-05,  8.4602e-05,  1.5493e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1583e-04,  1.0529e-04,  1.4363e-04],\\n\",\n      \"           [ 1.6716e-04,  9.1946e-05,  1.1977e-04],\\n\",\n      \"           [ 1.3006e-04,  1.4435e-04,  1.2369e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.1574e-04, -5.0026e-04, -8.3604e-04],\\n\",\n      \"           [-2.2265e-04, -4.0282e-04, -7.5878e-04],\\n\",\n      \"           [-3.3160e-04, -2.4490e-04, -6.3122e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.4922e-04, -3.8691e-04, -9.0692e-04],\\n\",\n      \"           [-2.5581e-04, -2.7868e-04, -7.1521e-04],\\n\",\n      \"           [-3.4784e-04, -2.0973e-04, -5.5898e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.3928e-04, -3.3987e-04, -1.0437e-03],\\n\",\n      \"           [-2.4175e-04, -3.0459e-04, -8.2643e-04],\\n\",\n      \"           [-1.0309e-04, -3.3076e-04, -7.2884e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.9980e-05, -1.7549e-05, -7.5470e-05],\\n\",\n      \"           [-5.0188e-05, -1.6043e-04, -1.3320e-04],\\n\",\n      \"           [-2.0586e-04, -1.8629e-04, -6.8312e-05]],\\n\",\n      \"\\n\",\n      \"          [[ 9.8064e-05,  2.9565e-05, -5.2809e-05],\\n\",\n      \"           [-7.3036e-05, -9.2357e-05, -8.1960e-05],\\n\",\n      \"           [-1.5611e-04, -1.8209e-04, -9.9346e-05]],\\n\",\n      \"\\n\",\n      \"          [[ 6.5934e-05,  4.6014e-05, -4.4304e-05],\\n\",\n      \"           [-3.8059e-05, -5.7544e-05, -4.8615e-05],\\n\",\n      \"           [-1.1660e-04, -1.6555e-04, -1.2428e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3117e-04,  5.3702e-05,  1.9102e-05],\\n\",\n      \"           [-1.8245e-05,  2.7031e-06, -6.8105e-06],\\n\",\n      \"           [-1.8320e-04, -6.0545e-05,  4.1011e-06]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2977e-04,  1.6508e-05, -7.6186e-06],\\n\",\n      \"           [-1.3023e-05, -4.0918e-05, -1.1129e-04],\\n\",\n      \"           [-1.7679e-04, -1.7205e-04, -1.0732e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 5.4707e-05,  5.1760e-05, -1.7458e-05],\\n\",\n      \"           [-6.8121e-05, -8.2542e-05, -1.0792e-04],\\n\",\n      \"           [-3.0051e-04, -1.2884e-04, -1.5993e-04]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 86.00 %\\n\",\n      \"Val loss: 0.393134\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[34,    60] loss: 0.22335\\n\",\n      \"[34,   120] loss: 0.18967\\n\",\n      \"Time elapsed: 1h:5m:19s\\n\",\n      \"train accuracy_score: 93.11 %\\n\",\n      \"tensor([[[[[-4.9799e-04, -9.4251e-04, -1.1482e-03],\\n\",\n      \"           [-1.5897e-03, -7.3544e-04, -2.1561e-03],\\n\",\n      \"           [ 1.3199e-03,  1.6362e-03, -1.3310e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.6208e-03, -1.0354e-03, -2.3795e-03],\\n\",\n      \"           [-1.5447e-03, -8.9285e-04, -2.1357e-03],\\n\",\n      \"           [ 4.1944e-04,  1.7571e-04,  7.4312e-06]],\\n\",\n      \"\\n\",\n      \"          [[-7.7792e-04, -1.0098e-04, -1.2982e-03],\\n\",\n      \"           [-1.0022e-03, -4.1889e-04, -3.1764e-05],\\n\",\n      \"           [-1.2155e-03, -1.7352e-05,  2.4136e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.6702e-04, -6.1752e-05, -2.5500e-04],\\n\",\n      \"           [-2.9460e-05,  1.5716e-05, -1.4931e-04],\\n\",\n      \"           [-5.9618e-05,  9.1417e-05,  1.0367e-05]],\\n\",\n      \"\\n\",\n      \"          [[-5.8913e-04, -2.8697e-04, -6.4089e-05],\\n\",\n      \"           [-2.7351e-04,  3.4245e-05,  1.5181e-05],\\n\",\n      \"           [ 1.0616e-04,  2.3195e-04, -7.9349e-06]],\\n\",\n      \"\\n\",\n      \"          [[-5.5020e-04, -4.0183e-04, -1.1605e-04],\\n\",\n      \"           [-3.3659e-04, -1.0638e-04,  5.8715e-06],\\n\",\n      \"           [-5.0835e-05,  2.7166e-04,  5.8137e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.4592e-02,  4.1210e-02,  3.1176e-02],\\n\",\n      \"           [ 2.0711e-02,  2.4619e-02,  2.9464e-02],\\n\",\n      \"           [ 1.0949e-02,  2.3917e-02,  4.0034e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.3274e-02,  3.3727e-02,  2.4032e-02],\\n\",\n      \"           [ 3.1729e-02,  1.6020e-02,  1.5300e-02],\\n\",\n      \"           [ 1.1335e-02,  2.4111e-02,  3.0205e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.3826e-02,  4.3530e-02,  2.1390e-02],\\n\",\n      \"           [ 3.7447e-02,  2.0766e-02,  1.0379e-02],\\n\",\n      \"           [ 1.7550e-02,  9.6246e-03,  1.7108e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.4429e-03, -5.2125e-03, -9.9557e-03],\\n\",\n      \"           [ 3.6511e-04, -4.4533e-03, -8.4250e-03],\\n\",\n      \"           [-3.5799e-04, -3.6597e-04,  3.3177e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.2509e-03, -4.9249e-03, -1.3695e-02],\\n\",\n      \"           [ 2.1108e-03, -4.8372e-03, -7.4484e-03],\\n\",\n      \"           [ 9.7706e-04,  9.9179e-05, -1.3701e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.9779e-03, -7.2900e-03, -9.3606e-03],\\n\",\n      \"           [ 2.9441e-03, -2.2315e-03, -5.9013e-03],\\n\",\n      \"           [-2.3821e-03,  2.0007e-03,  3.1672e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.8548e-03,  4.1102e-03,  9.8745e-03],\\n\",\n      \"           [ 7.3677e-03,  1.7985e-03,  3.0465e-03],\\n\",\n      \"           [ 6.5663e-03,  4.4794e-03,  5.1027e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.1442e-03,  5.9510e-03,  6.4250e-03],\\n\",\n      \"           [ 1.0039e-02,  1.1390e-02,  1.3969e-02],\\n\",\n      \"           [ 7.3556e-03,  8.2702e-03,  1.4672e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3674e-02,  1.4331e-02,  1.3692e-02],\\n\",\n      \"           [ 1.9521e-02,  1.3565e-02,  1.8713e-02],\\n\",\n      \"           [ 8.6493e-03,  1.5317e-02,  1.7354e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.0721e-03, -2.3514e-02, -2.6693e-02],\\n\",\n      \"           [-6.0140e-03, -1.5639e-02, -1.6181e-02],\\n\",\n      \"           [-1.0933e-02, -1.5318e-02, -1.2685e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.2527e-03, -8.5228e-03, -2.6834e-02],\\n\",\n      \"           [-8.6690e-03, -1.1304e-02, -1.0984e-02],\\n\",\n      \"           [-9.7476e-03, -9.5818e-03, -2.7840e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.5029e-03, -7.7786e-03, -2.5582e-02],\\n\",\n      \"           [-9.6899e-03, -9.9999e-03, -1.7199e-02],\\n\",\n      \"           [-1.4871e-03, -5.6397e-03, -4.2691e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.6561e-03,  3.2354e-03, -3.0324e-03],\\n\",\n      \"           [-2.8482e-03, -1.0392e-02, -5.2177e-03],\\n\",\n      \"           [-1.1062e-02, -8.7309e-03, -4.4231e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0898e-03, -4.5041e-04, -5.7888e-03],\\n\",\n      \"           [-3.3077e-03, -8.1510e-03, -7.3895e-03],\\n\",\n      \"           [-1.1966e-02, -1.1866e-02, -6.3871e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.8123e-04, -4.1421e-03, -8.0203e-03],\\n\",\n      \"           [-8.4184e-03, -1.1775e-02, -1.1813e-02],\\n\",\n      \"           [-1.4254e-02, -1.6725e-02, -1.6632e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.2417e-02,  5.0178e-03,  3.0132e-03],\\n\",\n      \"           [ 2.9609e-03,  1.9159e-03,  3.1959e-04],\\n\",\n      \"           [-8.0277e-03, -3.2618e-03,  3.7868e-05]],\\n\",\n      \"\\n\",\n      \"          [[ 7.2021e-03,  2.7774e-03, -5.4046e-04],\\n\",\n      \"           [-4.2442e-03, -6.2643e-03, -4.7477e-03],\\n\",\n      \"           [-1.4985e-02, -7.6936e-03, -5.4381e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.1674e-03, -1.9311e-03, -3.7744e-03],\\n\",\n      \"           [-3.0633e-03, -8.0874e-03, -8.7539e-03],\\n\",\n      \"           [-2.2904e-02, -1.1866e-02, -8.5948e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 83.00 %\\n\",\n      \"Val loss: 0.378834\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[35,    60] loss: 0.28776\\n\",\n      \"[35,   120] loss: 0.17337\\n\",\n      \"Time elapsed: 1h:7m:15s\\n\",\n      \"train accuracy_score: 91.39 %\\n\",\n      \"tensor([[[[[ 1.1233e-02,  9.2883e-03,  5.2940e-03],\\n\",\n      \"           [ 7.5966e-03, -3.5930e-04, -9.0133e-03],\\n\",\n      \"           [-4.6379e-03, -1.3468e-02, -1.2158e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.7821e-03,  3.4885e-03,  4.2570e-03],\\n\",\n      \"           [ 3.9755e-03, -2.6351e-03, -8.5706e-04],\\n\",\n      \"           [-6.4587e-03, -5.7604e-03, -1.1698e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.2070e-03,  3.4238e-04,  4.0409e-03],\\n\",\n      \"           [-3.3427e-03, -2.7576e-03,  1.2693e-04],\\n\",\n      \"           [-6.7612e-03, -5.5286e-03, -4.5891e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.5993e-03,  3.4662e-04,  7.0815e-05],\\n\",\n      \"           [ 5.6714e-04,  6.9414e-06,  7.4285e-04],\\n\",\n      \"           [-2.5505e-05, -3.0829e-04,  7.0599e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 5.9634e-04,  6.9641e-05, -5.4648e-04],\\n\",\n      \"           [-1.5379e-04, -6.0563e-04, -8.1938e-05],\\n\",\n      \"           [-6.5197e-04, -5.4057e-04,  5.3281e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1627e-03,  4.8168e-04, -5.1266e-04],\\n\",\n      \"           [ 4.7741e-04,  4.0318e-04, -3.7716e-04],\\n\",\n      \"           [-4.0466e-04, -8.3573e-04, -7.2395e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.4960e-01, -3.3833e-01, -2.4295e-01],\\n\",\n      \"           [-2.5108e-01, -2.8478e-01, -2.7130e-01],\\n\",\n      \"           [-2.6358e-01, -3.1597e-01, -3.7313e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.4277e-01, -2.3557e-01, -1.8871e-01],\\n\",\n      \"           [-2.1217e-01, -1.5287e-01, -1.6686e-01],\\n\",\n      \"           [-1.6313e-01, -1.8736e-01, -2.0869e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.4703e-01, -2.1202e-01, -1.5030e-01],\\n\",\n      \"           [-1.9075e-01, -1.8136e-01, -9.7822e-02],\\n\",\n      \"           [-1.5593e-01, -1.2828e-01, -1.3732e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.4545e-02,  2.3503e-02,  2.6732e-02],\\n\",\n      \"           [ 2.5348e-03,  1.3707e-02,  1.5288e-02],\\n\",\n      \"           [ 1.7281e-03,  6.9447e-03, -1.1470e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.7057e-03,  2.0152e-02,  1.7819e-02],\\n\",\n      \"           [ 8.5755e-04,  1.5144e-02,  1.5676e-02],\\n\",\n      \"           [-6.9933e-03, -9.5543e-04, -5.6294e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.0706e-02,  4.2006e-03, -8.7167e-04],\\n\",\n      \"           [-1.1565e-02, -6.5162e-03, -6.0510e-03],\\n\",\n      \"           [-1.3118e-02, -9.4297e-03, -1.9400e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.2171e-02, -3.0786e-02, -2.5543e-02],\\n\",\n      \"           [-3.1527e-02, -2.5652e-02, -3.1310e-02],\\n\",\n      \"           [-6.8237e-02, -3.4279e-02, -1.6616e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.7697e-02, -5.2195e-02, -4.9389e-02],\\n\",\n      \"           [-4.1653e-02, -5.8167e-02, -5.9439e-02],\\n\",\n      \"           [-5.6408e-02, -5.7051e-02, -7.0827e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.7893e-02, -8.6485e-02, -8.2688e-02],\\n\",\n      \"           [-7.0592e-02, -5.6609e-02, -8.7011e-02],\\n\",\n      \"           [-4.8044e-02, -6.8171e-02, -7.2177e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.2619e-02, -1.4449e-02, -1.2199e-02],\\n\",\n      \"           [-1.1009e-01, -5.0006e-02, -6.7879e-02],\\n\",\n      \"           [-6.8689e-02, -1.3133e-01, -8.2011e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.7975e-02,  2.4786e-02,  2.9091e-02],\\n\",\n      \"           [-5.1349e-02, -3.0178e-02, -5.6209e-02],\\n\",\n      \"           [-4.7214e-02, -1.1213e-01, -8.5106e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.3893e-02,  3.8238e-02,  9.8116e-02],\\n\",\n      \"           [ 1.9634e-03, -9.5125e-03,  2.5931e-02],\\n\",\n      \"           [-6.8450e-02, -6.6953e-02, -1.7853e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.8869e-02,  4.4638e-02,  5.6402e-02],\\n\",\n      \"           [ 6.1620e-02,  7.3245e-02,  5.6543e-02],\\n\",\n      \"           [ 8.3526e-02,  6.5450e-02,  2.4016e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8001e-02,  5.5104e-02,  7.2007e-02],\\n\",\n      \"           [ 5.9341e-02,  8.3600e-02,  7.2529e-02],\\n\",\n      \"           [ 7.5274e-02,  7.7246e-02,  5.1267e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7147e-02,  4.9462e-02,  7.5600e-02],\\n\",\n      \"           [ 5.0899e-02,  6.7078e-02,  7.1006e-02],\\n\",\n      \"           [ 8.2134e-02,  6.9638e-02,  5.2224e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.7451e-02, -2.2891e-02, -2.7330e-02],\\n\",\n      \"           [-1.3704e-03,  7.1064e-03, -5.7891e-03],\\n\",\n      \"           [ 5.1437e-02,  3.6910e-02,  1.5126e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.9368e-03, -1.1522e-02, -2.0115e-02],\\n\",\n      \"           [ 1.2627e-02,  3.0464e-02,  2.3166e-02],\\n\",\n      \"           [ 6.4576e-02,  5.8143e-02,  4.8693e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.2684e-04,  1.4782e-02,  1.0730e-03],\\n\",\n      \"           [ 3.1015e-02,  4.8382e-02,  3.1088e-02],\\n\",\n      \"           [ 9.8751e-02,  6.9682e-02,  5.4016e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 81.00 %\\n\",\n      \"Val loss: 0.463464\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[36,    60] loss: 0.23855\\n\",\n      \"[36,   120] loss: 0.21054\\n\",\n      \"Time elapsed: 1h:9m:10s\\n\",\n      \"train accuracy_score: 93.26 %\\n\",\n      \"tensor([[[[[-3.3068e-04, -2.9252e-04, -1.1765e-04],\\n\",\n      \"           [ 3.0876e-05, -1.7637e-05,  2.0234e-04],\\n\",\n      \"           [ 5.3032e-04,  7.5279e-04,  2.5067e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0352e-06, -2.0175e-04, -1.7613e-04],\\n\",\n      \"           [ 2.3435e-04,  1.5208e-04,  4.1409e-05],\\n\",\n      \"           [ 2.5945e-04,  1.3863e-04,  2.3219e-04]],\\n\",\n      \"\\n\",\n      \"          [[-3.1501e-04, -2.1485e-04, -4.5497e-04],\\n\",\n      \"           [ 1.8747e-04, -4.7663e-05, -1.5731e-04],\\n\",\n      \"           [ 7.4586e-04,  2.2406e-04,  3.1167e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.9442e-05, -1.4643e-05, -2.9244e-05],\\n\",\n      \"           [-2.7195e-05, -8.5498e-06,  2.3899e-05],\\n\",\n      \"           [ 1.0305e-05, -1.0375e-05,  3.4297e-05]],\\n\",\n      \"\\n\",\n      \"          [[-4.3875e-05, -5.2767e-05, -3.5898e-06],\\n\",\n      \"           [-6.7292e-05, -4.4230e-05, -3.6485e-05],\\n\",\n      \"           [-1.9519e-05, -1.6932e-05, -2.5410e-05]],\\n\",\n      \"\\n\",\n      \"          [[-1.2786e-04, -1.0466e-04, -4.2508e-05],\\n\",\n      \"           [-6.2578e-05, -1.1137e-05, -2.4885e-06],\\n\",\n      \"           [-8.0204e-05,  7.7739e-05,  8.0658e-06]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.8849e-03,  1.3201e-02,  1.2454e-02],\\n\",\n      \"           [ 2.4699e-03,  5.6120e-03,  1.0630e-02],\\n\",\n      \"           [-1.1174e-03,  4.3828e-03,  1.1112e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0695e-02,  1.0897e-02,  6.1484e-03],\\n\",\n      \"           [ 3.5577e-03,  4.1193e-04,  3.8592e-03],\\n\",\n      \"           [-8.5849e-04,  1.3535e-03,  2.3436e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3040e-02,  1.5551e-02,  9.0471e-03],\\n\",\n      \"           [ 6.9409e-03,  6.0656e-03,  5.3397e-03],\\n\",\n      \"           [-2.1135e-04,  1.3299e-03,  2.2969e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.2343e-04, -2.5478e-03, -4.1800e-03],\\n\",\n      \"           [-2.5431e-04, -1.3589e-03, -2.9176e-03],\\n\",\n      \"           [-1.2841e-03, -1.2191e-03,  1.0999e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1186e-03, -1.3089e-03, -3.2125e-03],\\n\",\n      \"           [ 6.6466e-04, -7.3053e-04, -1.8633e-03],\\n\",\n      \"           [-9.6383e-04, -9.8635e-04,  7.5384e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5047e-03, -8.4535e-04, -3.5438e-03],\\n\",\n      \"           [ 1.7853e-03,  2.4535e-04, -1.7217e-03],\\n\",\n      \"           [ 2.1275e-05,  2.8031e-05, -2.1631e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.5337e-03,  1.9177e-03,  1.9027e-03],\\n\",\n      \"           [ 1.7834e-03,  1.9315e-03,  2.0036e-03],\\n\",\n      \"           [ 1.3251e-03,  2.7372e-03,  1.5819e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.1352e-03,  3.1505e-03,  2.9288e-03],\\n\",\n      \"           [ 2.8846e-03,  4.9784e-03,  4.1959e-03],\\n\",\n      \"           [ 1.2602e-03,  5.2866e-03,  5.3003e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 7.6000e-03,  6.9714e-03,  6.0814e-03],\\n\",\n      \"           [ 6.9820e-03,  5.2356e-03,  6.4613e-03],\\n\",\n      \"           [ 2.5897e-03,  5.9098e-03,  5.0872e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.0208e-03,  2.7839e-03,  5.3578e-04],\\n\",\n      \"           [ 4.9723e-03,  3.5667e-03,  2.3128e-03],\\n\",\n      \"           [ 1.5019e-03,  2.1869e-03,  1.6168e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.8387e-03,  4.3858e-04, -2.5447e-03],\\n\",\n      \"           [ 2.3093e-03,  1.0675e-03,  1.6083e-03],\\n\",\n      \"           [ 8.7375e-04,  2.2609e-03, -1.1554e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5928e-03, -9.8369e-04, -8.0946e-03],\\n\",\n      \"           [ 4.0244e-04, -1.2777e-03, -5.0476e-03],\\n\",\n      \"           [-1.0599e-03, -2.9724e-03, -4.8948e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.9439e-03,  1.6547e-03,  2.6437e-03],\\n\",\n      \"           [ 4.9309e-05, -5.2141e-05,  1.9393e-03],\\n\",\n      \"           [-2.5004e-03, -3.9806e-04,  3.6366e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7296e-03,  3.5386e-03,  4.0165e-03],\\n\",\n      \"           [ 4.5858e-04,  4.2522e-04,  3.3794e-03],\\n\",\n      \"           [-1.6704e-03, -2.6211e-04,  2.9804e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.7331e-03,  5.0657e-03,  5.1745e-03],\\n\",\n      \"           [ 1.5032e-03,  1.9806e-03,  3.5529e-03],\\n\",\n      \"           [-8.3578e-04,  7.5193e-04,  2.2852e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.5386e-03,  3.2024e-03,  1.3078e-03],\\n\",\n      \"           [ 1.9512e-03,  6.5145e-04,  8.4345e-04],\\n\",\n      \"           [-1.5120e-03, -1.1222e-03,  1.9051e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.6650e-03,  1.8629e-03,  8.8539e-04],\\n\",\n      \"           [ 7.5695e-05, -2.2399e-03, -9.7365e-04],\\n\",\n      \"           [-4.5581e-03, -3.4455e-03, -1.5868e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.7638e-03,  1.0938e-03, -7.8981e-04],\\n\",\n      \"           [ 7.6959e-04, -1.4259e-03, -3.1454e-03],\\n\",\n      \"           [-3.4058e-03, -4.3152e-03, -3.9413e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 83.00 %\\n\",\n      \"Val loss: 0.433019\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[37,    60] loss: 0.19964\\n\",\n      \"[37,   120] loss: 0.29205\\n\",\n      \"Time elapsed: 1h:11m:5s\\n\",\n      \"train accuracy_score: 91.68 %\\n\",\n      \"tensor([[[[[-6.7913e-04, -1.3783e-03, -5.6345e-04],\\n\",\n      \"           [-4.6543e-04, -1.8957e-04, -3.8389e-04],\\n\",\n      \"           [ 1.3781e-03,  9.1847e-04,  3.1291e-04]],\\n\",\n      \"\\n\",\n      \"          [[-3.2518e-04, -9.7798e-04, -7.2668e-04],\\n\",\n      \"           [-8.9886e-04, -2.2700e-04, -6.3745e-04],\\n\",\n      \"           [ 6.4108e-04,  2.5916e-04,  1.9683e-04]],\\n\",\n      \"\\n\",\n      \"          [[-6.6365e-04, -8.4160e-04, -8.5085e-04],\\n\",\n      \"           [-1.0748e-03, -2.9932e-05, -7.0334e-04],\\n\",\n      \"           [-6.6910e-04, -3.4926e-05, -7.3912e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.4822e-04,  2.3218e-05, -1.7227e-05],\\n\",\n      \"           [-7.6647e-05, -2.4681e-05, -1.0444e-04],\\n\",\n      \"           [-1.5543e-05, -2.2504e-05, -8.1213e-05]],\\n\",\n      \"\\n\",\n      \"          [[-1.5881e-04, -1.8948e-04, -4.2115e-05],\\n\",\n      \"           [-7.1764e-05, -3.6223e-05, -1.0565e-04],\\n\",\n      \"           [-5.3971e-05, -6.2650e-05, -5.5730e-05]],\\n\",\n      \"\\n\",\n      \"          [[-2.1578e-04, -1.7585e-04, -9.0015e-05],\\n\",\n      \"           [-2.2906e-04, -1.0393e-04, -9.7804e-05],\\n\",\n      \"           [-2.7004e-04, -8.5552e-05, -8.8440e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.2222e-02,  2.6785e-02,  1.7557e-02],\\n\",\n      \"           [ 1.0715e-02,  9.1075e-03,  1.0582e-02],\\n\",\n      \"           [-4.9934e-03,  5.7846e-04,  5.1440e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.6625e-02,  2.0808e-02,  1.2521e-02],\\n\",\n      \"           [ 1.6920e-02,  9.0651e-03,  1.1591e-02],\\n\",\n      \"           [ 1.5879e-03, -2.2327e-03,  8.8225e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.9852e-02,  2.5000e-02,  1.1793e-02],\\n\",\n      \"           [ 1.8664e-02,  1.2258e-02,  1.1308e-02],\\n\",\n      \"           [ 6.3296e-03,  2.4481e-03,  1.0155e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.4817e-03, -2.3357e-03, -6.8316e-03],\\n\",\n      \"           [ 3.5303e-04, -1.3961e-03, -5.2854e-03],\\n\",\n      \"           [-1.4714e-03, -9.0579e-04, -9.5965e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7508e-03, -2.1425e-03, -7.4859e-03],\\n\",\n      \"           [-3.8926e-04, -1.7669e-03, -3.0839e-03],\\n\",\n      \"           [-1.1266e-03, -1.2198e-03, -8.3084e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1658e-03, -2.7214e-03, -7.2912e-03],\\n\",\n      \"           [ 9.5411e-04, -1.4223e-03, -2.2431e-03],\\n\",\n      \"           [ 9.7491e-04,  3.8734e-04,  9.9167e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.6864e-03,  3.7490e-03,  2.5477e-03],\\n\",\n      \"           [ 2.5043e-03,  2.0626e-03,  2.3361e-03],\\n\",\n      \"           [ 1.0641e-03,  2.7545e-03,  2.3701e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.5390e-03,  3.9391e-03,  2.8334e-03],\\n\",\n      \"           [ 7.6574e-03,  8.1979e-03,  6.6787e-03],\\n\",\n      \"           [ 6.5173e-03,  7.0112e-03,  8.5378e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1442e-02,  7.6962e-03,  4.7876e-03],\\n\",\n      \"           [ 1.5820e-02,  7.5727e-03,  7.8703e-03],\\n\",\n      \"           [ 7.2898e-03,  1.0044e-02,  9.5947e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0478e-02, -2.2886e-03, -1.0064e-02],\\n\",\n      \"           [-2.2986e-04, -7.8348e-03, -5.8403e-03],\\n\",\n      \"           [-7.8756e-03, -8.8285e-03, -2.8383e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.4550e-03, -1.7320e-03, -1.3232e-02],\\n\",\n      \"           [-6.5056e-03, -7.1939e-03, -4.7069e-03],\\n\",\n      \"           [-9.3533e-03, -3.9803e-03,  6.0797e-06]],\\n\",\n      \"\\n\",\n      \"          [[ 3.2919e-03, -5.2762e-03, -1.6764e-02],\\n\",\n      \"           [-4.3694e-03, -6.2301e-03, -1.0617e-02],\\n\",\n      \"           [-9.1650e-03, -2.6321e-03, -4.1604e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.2565e-03, -3.3452e-03, -8.1193e-03],\\n\",\n      \"           [-4.7433e-03, -9.3104e-03, -9.7068e-03],\\n\",\n      \"           [-1.3404e-02, -1.2741e-02, -8.0193e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.5733e-03, -1.2196e-03, -5.3510e-03],\\n\",\n      \"           [-5.3920e-03, -7.7116e-03, -7.3011e-03],\\n\",\n      \"           [-1.1864e-02, -1.2652e-02, -6.8654e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4894e-04, -2.3283e-03, -2.8452e-03],\\n\",\n      \"           [-6.1163e-03, -8.3582e-03, -7.0216e-03],\\n\",\n      \"           [-1.0784e-02, -1.1213e-02, -1.1240e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.6403e-03,  5.2192e-03, -1.8482e-03],\\n\",\n      \"           [ 3.2750e-03,  1.3640e-03,  6.9925e-05],\\n\",\n      \"           [-2.2795e-03, -2.0662e-03, -8.3338e-05]],\\n\",\n      \"\\n\",\n      \"          [[ 4.3953e-03,  2.2609e-03, -1.2361e-03],\\n\",\n      \"           [ 9.4624e-04, -2.5655e-03, -1.5534e-03],\\n\",\n      \"           [-3.9709e-03, -4.6410e-03, -1.5241e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5375e-03,  1.2138e-04, -1.8319e-03],\\n\",\n      \"           [-1.3672e-03, -4.4149e-03, -3.9159e-03],\\n\",\n      \"           [-8.2697e-03, -5.1126e-03, -4.5934e-03]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 84.00 %\\n\",\n      \"Val loss: 0.412112\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[38,    60] loss: 0.23698\\n\",\n      \"[38,   120] loss: 0.13524\\n\",\n      \"Time elapsed: 1h:12m:59s\\n\",\n      \"train accuracy_score: 92.54 %\\n\",\n      \"tensor([[[[[-4.3865e-04, -1.2428e-03, -4.9397e-04],\\n\",\n      \"           [-4.1403e-04, -3.5279e-04, -7.6335e-04],\\n\",\n      \"           [ 1.5404e-03,  9.2791e-04,  1.6095e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.8819e-03, -2.5095e-04, -3.1489e-03],\\n\",\n      \"           [-9.7815e-04,  4.0830e-04, -2.0341e-03],\\n\",\n      \"           [ 1.1310e-04,  5.7119e-04, -4.3742e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.4444e-03, -9.5835e-04, -1.6102e-03],\\n\",\n      \"           [-2.9083e-03,  1.7344e-03, -1.0529e-03],\\n\",\n      \"           [-6.0811e-03, -1.4497e-03, -7.8773e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.6560e-04, -1.2031e-04, -1.6702e-04],\\n\",\n      \"           [-1.9016e-04,  2.1860e-04, -3.2679e-06],\\n\",\n      \"           [ 2.4887e-04,  1.1813e-04,  2.1575e-05]],\\n\",\n      \"\\n\",\n      \"          [[-8.3671e-04, -3.5868e-04,  1.2426e-05],\\n\",\n      \"           [-6.1354e-05,  7.2981e-05, -4.6435e-05],\\n\",\n      \"           [ 1.4875e-04, -1.0815e-04,  1.4329e-05]],\\n\",\n      \"\\n\",\n      \"          [[-9.4565e-04, -3.8830e-04, -2.4660e-04],\\n\",\n      \"           [-6.7994e-04, -3.0780e-04, -2.2153e-04],\\n\",\n      \"           [-4.8630e-04, -2.5970e-04,  1.5248e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3496e-01,  1.0958e-01,  9.6510e-02],\\n\",\n      \"           [ 6.8471e-02,  5.8194e-02,  6.7614e-02],\\n\",\n      \"           [-6.3962e-04,  1.7075e-02,  5.9680e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3546e-01,  9.7681e-02,  6.5444e-02],\\n\",\n      \"           [ 6.0684e-02,  3.1536e-02,  3.7529e-02],\\n\",\n      \"           [ 9.7343e-03,  2.4092e-03,  2.7981e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2220e-01,  1.0213e-01,  5.3530e-02],\\n\",\n      \"           [ 6.4128e-02,  4.5330e-02,  2.2272e-02],\\n\",\n      \"           [ 1.3886e-02,  1.3030e-02,  2.2154e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3668e-03, -5.8089e-03, -1.3791e-02],\\n\",\n      \"           [ 3.9899e-03, -2.1419e-03, -5.9775e-03],\\n\",\n      \"           [ 5.5202e-03,  7.1321e-03,  1.8574e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 9.3689e-03, -4.2993e-03, -1.6503e-02],\\n\",\n      \"           [ 4.7171e-03, -2.1719e-03, -6.3843e-03],\\n\",\n      \"           [-3.6594e-03, -2.6586e-03,  1.1118e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1949e-02, -1.6005e-03, -1.0218e-02],\\n\",\n      \"           [ 8.0500e-05, -4.7157e-03, -3.9627e-03],\\n\",\n      \"           [-9.0521e-04,  8.8233e-04,  7.9574e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.2793e-03, -6.9324e-03, -4.1215e-03],\\n\",\n      \"           [-1.5370e-02, -1.3129e-02, -6.8236e-03],\\n\",\n      \"           [-3.3062e-02, -1.6662e-02, -5.2560e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.3663e-03, -8.9193e-04, -3.6515e-03],\\n\",\n      \"           [-3.6121e-05, -3.9842e-03,  4.1263e-03],\\n\",\n      \"           [-2.4359e-02, -1.0156e-02,  4.2668e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 8.2651e-03,  7.8188e-03,  2.9555e-05],\\n\",\n      \"           [ 9.2870e-03,  5.4399e-03,  9.0087e-03],\\n\",\n      \"           [ 4.9063e-03,  1.2077e-02,  9.6092e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.9001e-04, -1.3377e-02, -3.0085e-02],\\n\",\n      \"           [-4.3386e-03, -7.2097e-03, -1.8162e-02],\\n\",\n      \"           [-2.9283e-02, -8.3441e-03, -1.5832e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 8.7741e-03, -1.1123e-02, -2.9637e-02],\\n\",\n      \"           [-1.4361e-02, -6.2087e-03, -1.9594e-02],\\n\",\n      \"           [-1.8604e-02,  1.2844e-03, -8.0761e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5670e-02, -1.7184e-02, -5.9512e-02],\\n\",\n      \"           [ 1.7644e-03, -6.9410e-03, -4.4119e-02],\\n\",\n      \"           [-2.0305e-02, -4.8401e-03, -1.6196e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.4291e-03, -1.8898e-02, -2.6745e-02],\\n\",\n      \"           [-2.0074e-02, -3.3878e-02, -2.8705e-02],\\n\",\n      \"           [-3.4817e-02, -2.7938e-02, -9.5840e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.1685e-02, -1.3212e-02, -2.5652e-02],\\n\",\n      \"           [-3.1695e-02, -2.9937e-02, -2.4468e-02],\\n\",\n      \"           [-4.0313e-02, -3.1240e-02, -1.6814e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.0477e-03, -1.9133e-02, -1.9307e-02],\\n\",\n      \"           [-2.6530e-02, -3.4694e-02, -2.5473e-02],\\n\",\n      \"           [-3.6030e-02, -3.4264e-02, -2.6480e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3080e-02,  3.5984e-03, -7.6489e-03],\\n\",\n      \"           [ 1.7096e-03,  7.6041e-04,  4.3169e-03],\\n\",\n      \"           [ 1.3152e-03,  1.6443e-02,  1.6504e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.5899e-03,  1.3176e-03, -5.6010e-03],\\n\",\n      \"           [-4.9483e-03, -2.9357e-03,  1.7792e-03],\\n\",\n      \"           [-1.2260e-02,  3.3387e-04,  3.6848e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 9.3435e-03,  4.3217e-03, -3.3670e-03],\\n\",\n      \"           [-4.3428e-03, -7.7152e-03, -2.5296e-03],\\n\",\n      \"           [-1.6441e-02, -7.7809e-03, -9.4496e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 83.00 %\\n\",\n      \"Val loss: 0.407189\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[39,    60] loss: 0.19454\\n\",\n      \"[39,   120] loss: 0.18436\\n\",\n      \"Time elapsed: 1h:14m:54s\\n\",\n      \"train accuracy_score: 93.11 %\\n\",\n      \"tensor([[[[[-2.8442e-01, -1.3975e-02, -2.9489e-02],\\n\",\n      \"           [-1.0181e-02, -6.7873e-02, -6.6051e-02],\\n\",\n      \"           [ 1.9412e-01,  4.1628e-02, -1.7228e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.4816e-02, -1.2716e-01, -9.2678e-02],\\n\",\n      \"           [-4.6845e-02, -1.0227e-01, -2.0617e-01],\\n\",\n      \"           [ 2.5395e-01,  6.0522e-02, -5.3009e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.9686e-02,  8.4286e-02, -9.4913e-02],\\n\",\n      \"           [ 4.2860e-02, -2.9120e-02, -7.5595e-02],\\n\",\n      \"           [ 2.3770e-01,  1.5946e-02, -2.2666e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.7493e-02, -1.8422e-02, -2.3161e-02],\\n\",\n      \"           [-3.1121e-02,  5.9400e-04, -1.5529e-02],\\n\",\n      \"           [-1.1923e-03,  2.5183e-03, -8.0944e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.2649e-01, -4.8214e-02, -1.3040e-02],\\n\",\n      \"           [-4.1680e-02,  5.0720e-03,  8.0435e-03],\\n\",\n      \"           [ 4.9652e-03,  5.9469e-03, -6.3225e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.3322e-01, -6.8218e-02, -1.7611e-02],\\n\",\n      \"           [-1.1851e-01, -1.7005e-02, -1.2635e-02],\\n\",\n      \"           [-7.7576e-02,  1.2032e-02,  1.2060e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.6564e+00,  7.3770e+00,  6.6252e+00],\\n\",\n      \"           [ 4.1883e+00,  5.8681e+00,  7.4050e+00],\\n\",\n      \"           [ 4.7242e+00,  6.8339e+00,  8.0894e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 6.4328e+00,  6.5009e+00,  4.5255e+00],\\n\",\n      \"           [ 4.7976e+00,  3.5645e+00,  3.8114e+00],\\n\",\n      \"           [ 2.0726e+00,  3.4606e+00,  4.7924e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 6.5399e+00,  5.7474e+00,  4.1120e+00],\\n\",\n      \"           [ 4.4484e+00,  3.2054e+00,  2.9666e+00],\\n\",\n      \"           [ 2.2727e+00,  2.6747e+00,  4.6631e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.7928e-01, -1.1689e+00, -1.4230e+00],\\n\",\n      \"           [-7.2452e-01, -8.6993e-01, -9.1029e-01],\\n\",\n      \"           [-5.6544e-01, -1.0276e-01,  7.8499e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.2788e-01, -8.0335e-01, -1.4885e+00],\\n\",\n      \"           [-9.7945e-02, -7.7910e-01, -5.6108e-01],\\n\",\n      \"           [-9.3496e-02,  8.2738e-02,  7.8376e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.9006e-01,  1.6076e-01, -1.0898e+00],\\n\",\n      \"           [ 6.8075e-01,  3.5831e-01, -8.4660e-02],\\n\",\n      \"           [ 4.8143e-01,  7.6516e-01,  1.0158e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.8049e-02,  2.0916e-01,  1.8177e-02],\\n\",\n      \"           [ 5.9470e-01, -2.9126e-01, -1.5111e-01],\\n\",\n      \"           [ 4.4170e-01,  1.1343e+00,  9.1017e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0970e-01,  5.8355e-01,  2.5902e-01],\\n\",\n      \"           [ 9.7020e-01,  2.9671e-01,  1.0900e+00],\\n\",\n      \"           [ 6.3070e-01,  1.1026e+00,  1.5591e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8353e+00,  1.2533e+00,  9.7545e-01],\\n\",\n      \"           [ 1.2445e+00,  1.3308e+00,  1.7513e+00],\\n\",\n      \"           [ 1.0603e+00,  1.7102e+00,  1.5554e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.2282e+00, -2.8107e+00, -3.8523e+00],\\n\",\n      \"           [-1.1452e+00, -2.6356e+00, -3.1805e+00],\\n\",\n      \"           [-1.9854e+00, -4.5291e-01, -1.1634e+00]],\\n\",\n      \"\\n\",\n      \"          [[-2.4266e+00, -2.3613e+00, -4.3569e+00],\\n\",\n      \"           [-2.2987e+00, -1.7615e+00, -2.5681e+00],\\n\",\n      \"           [-1.2646e+00, -5.6840e-01, -1.9605e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.6204e+00, -2.0428e+00, -4.4254e+00],\\n\",\n      \"           [-1.9133e+00, -1.6194e+00, -3.9930e+00],\\n\",\n      \"           [-2.2629e+00, -1.2651e+00, -2.3331e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.0843e-03, -5.3974e-01, -1.4084e+00],\\n\",\n      \"           [-1.0900e+00, -1.9674e+00, -1.4642e+00],\\n\",\n      \"           [-1.9661e+00, -1.7079e+00, -2.9381e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.8907e-01, -5.7309e-01, -1.0505e+00],\\n\",\n      \"           [-1.2980e+00, -1.5524e+00, -1.5216e+00],\\n\",\n      \"           [-1.9125e+00, -1.9858e+00, -7.9155e-01]],\\n\",\n      \"\\n\",\n      \"          [[-6.2817e-01, -7.1761e-01, -1.1756e+00],\\n\",\n      \"           [-1.5918e+00, -1.8411e+00, -1.8344e+00],\\n\",\n      \"           [-2.4644e+00, -2.5796e+00, -1.7520e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 9.4983e-01,  2.7230e-01, -3.7905e-01],\\n\",\n      \"           [-1.8091e-01, -4.1397e-01, -3.0047e-01],\\n\",\n      \"           [-9.0070e-02,  3.6585e-01,  8.6050e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.8291e-01,  8.8064e-02, -4.9757e-01],\\n\",\n      \"           [-7.2649e-01, -6.0743e-01, -3.0357e-01],\\n\",\n      \"           [-6.5665e-01, -4.4624e-02, -6.2730e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.7234e-01,  7.9549e-01, -1.2222e-01],\\n\",\n      \"           [-1.1299e-01, -3.8401e-01, -4.3217e-01],\\n\",\n      \"           [-1.1390e+00, -2.3973e-01, -1.4769e-01]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 87.00 %\\n\",\n      \"Val loss: 0.434723\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"Early stopping in epoch 40\\n\",\n      \"Total time elapsed: 1h:15m:1s\\n\",\n      \"Writing model to disk...\\n\",\n      \"Best result during training: 0.87. Saving model..\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Finished fold.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Starting trial 1\\n\",\n      \"torch.Size([1, 193, 229, 193])\\n\",\n      \"175\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0,    60] loss: 0.75976\\n\",\n      \"[0,   120] loss: 0.73436\\n\",\n      \"Time elapsed: 0h:1m:47s\\n\",\n      \"train accuracy_score: 51.65 %\\n\",\n      \"tensor([[[[[-5.1819e-03, -9.7396e-03, -1.2974e-02],\\n\",\n      \"           [-3.3770e-03, -1.0226e-02, -1.0170e-02],\\n\",\n      \"           [-4.8642e-03, -6.3232e-03, -7.5892e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.9342e-03, -6.1747e-03, -1.1854e-02],\\n\",\n      \"           [-3.9194e-03, -7.6393e-03, -1.1057e-02],\\n\",\n      \"           [-5.1562e-03, -6.7267e-03, -7.3693e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1568e-03, -4.5528e-03, -9.3306e-03],\\n\",\n      \"           [-3.3827e-03, -6.8181e-03, -7.2822e-03],\\n\",\n      \"           [-1.0988e-03, -3.8309e-03, -6.0653e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.1114e-04, -1.2383e-04,  8.2875e-04],\\n\",\n      \"           [-3.6071e-04,  5.9362e-04,  1.4375e-03],\\n\",\n      \"           [ 2.7390e-04,  1.9585e-04,  4.3127e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9604e-04, -9.8328e-04,  8.0266e-04],\\n\",\n      \"           [-1.2820e-03, -3.8575e-05,  1.8540e-03],\\n\",\n      \"           [-1.0055e-03, -1.7318e-05,  1.2630e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.8612e-04, -1.5552e-04,  8.9186e-04],\\n\",\n      \"           [-1.4166e-03, -2.8529e-03,  1.7938e-03],\\n\",\n      \"           [-2.8828e-03, -2.3677e-03, -1.4443e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.0910e-03,  4.9462e-04, -2.0604e-03],\\n\",\n      \"           [ 3.9227e-03,  3.2053e-03,  1.0822e-04],\\n\",\n      \"           [ 2.7263e-03,  2.3311e-03,  3.3978e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9090e-03,  4.6544e-05,  4.4512e-04],\\n\",\n      \"           [ 1.7548e-03,  1.0903e-03, -8.9809e-04],\\n\",\n      \"           [ 1.9754e-03,  7.2584e-04, -2.5376e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7550e-03,  1.4479e-03,  1.2308e-03],\\n\",\n      \"           [ 1.1343e-04,  5.1472e-04, -2.1700e-03],\\n\",\n      \"           [-3.9800e-04,  1.7317e-03,  5.2325e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.2351e-02,  4.4141e-02,  6.3176e-02],\\n\",\n      \"           [ 7.7882e-02,  5.9803e-02,  7.1679e-02],\\n\",\n      \"           [ 7.5781e-02,  7.5819e-02,  7.8560e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.7349e-02,  5.6727e-02,  7.4104e-02],\\n\",\n      \"           [ 7.3109e-02,  7.4905e-02,  8.1908e-02],\\n\",\n      \"           [ 9.0420e-02,  9.3675e-02,  9.4581e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.0135e-02,  6.2252e-02,  8.9394e-02],\\n\",\n      \"           [ 9.2018e-02,  8.8814e-02,  9.0715e-02],\\n\",\n      \"           [ 1.0156e-01,  1.0045e-01,  1.0674e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3368e-02,  2.1011e-02,  5.7114e-02],\\n\",\n      \"           [ 5.7339e-02,  5.0903e-02,  4.5718e-02],\\n\",\n      \"           [ 1.0652e-01,  8.1038e-02,  6.4481e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.7162e-02,  5.6323e-02,  6.4640e-02],\\n\",\n      \"           [ 1.4381e-01,  1.3389e-01,  1.0499e-01],\\n\",\n      \"           [ 1.6240e-01,  1.6659e-01,  1.2356e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0100e-02,  9.1722e-02,  7.4598e-02],\\n\",\n      \"           [ 9.6098e-02,  1.6098e-01,  1.4701e-01],\\n\",\n      \"           [ 2.0646e-01,  1.9662e-01,  1.5820e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2993e-02, -7.9555e-03, -5.4090e-03],\\n\",\n      \"           [-6.6396e-03, -8.3412e-03, -1.7798e-02],\\n\",\n      \"           [-1.1520e-02, -1.1693e-02, -1.6196e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.2363e-03, -4.2014e-04, -1.0451e-02],\\n\",\n      \"           [-7.7132e-03, -7.3019e-03, -1.2364e-02],\\n\",\n      \"           [-1.1008e-02, -1.3702e-02, -6.6561e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.2214e-03, -6.0469e-03, -5.4167e-03],\\n\",\n      \"           [-1.1090e-02, -7.1243e-03, -8.0499e-03],\\n\",\n      \"           [-1.3394e-02, -5.0341e-03, -3.9867e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.3526e-02,  7.7560e-02,  9.4474e-02],\\n\",\n      \"           [ 6.4567e-02,  8.4830e-02,  9.5201e-02],\\n\",\n      \"           [ 9.8544e-02,  1.2005e-01,  1.2538e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 8.9065e-02,  7.3480e-02,  6.9488e-02],\\n\",\n      \"           [ 7.5189e-02,  9.5636e-02,  1.0354e-01],\\n\",\n      \"           [ 8.1875e-02,  1.2489e-01,  1.4254e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0333e-01,  9.1456e-02,  8.9479e-02],\\n\",\n      \"           [ 8.3984e-02,  1.0224e-01,  9.8260e-02],\\n\",\n      \"           [ 9.4033e-02,  9.9623e-02,  1.2510e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.0117e-05, -1.7170e-04, -3.2106e-04],\\n\",\n      \"           [ 1.0435e-03,  3.8570e-04,  8.1939e-05],\\n\",\n      \"           [ 1.3377e-03,  5.0190e-04,  4.1093e-04]],\\n\",\n      \"\\n\",\n      \"          [[-3.3709e-05, -1.3728e-04, -1.6450e-04],\\n\",\n      \"           [ 1.5878e-04,  1.9555e-04, -1.7763e-04],\\n\",\n      \"           [ 3.6249e-04,  1.9795e-04,  1.3533e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 4.6092e-05,  2.2522e-04,  1.0488e-04],\\n\",\n      \"           [ 5.6524e-04,  4.8641e-05,  1.0506e-04],\\n\",\n      \"           [ 3.7805e-04, -3.3909e-07,  1.3195e-04]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 60.00 %\\n\",\n      \"Val loss: 0.672947\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[1,    60] loss: 0.70482\\n\",\n      \"[1,   120] loss: 0.69743\\n\",\n      \"Time elapsed: 0h:3m:42s\\n\",\n      \"train accuracy_score: 49.07 %\\n\",\n      \"tensor([[[[[-1.2871e-03, -2.4293e-05,  8.4085e-04],\\n\",\n      \"           [ 2.1517e-03,  3.1929e-03,  1.4349e-04],\\n\",\n      \"           [ 1.1875e-03,  5.9938e-03,  5.1766e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.5241e-04, -1.9644e-04,  1.5899e-03],\\n\",\n      \"           [ 1.3215e-03,  9.8839e-04,  2.0855e-03],\\n\",\n      \"           [-9.6447e-04,  7.7102e-04,  4.1094e-03]],\\n\",\n      \"\\n\",\n      \"          [[-6.1079e-04,  4.3991e-04,  2.9455e-03],\\n\",\n      \"           [ 5.9630e-04,  1.1331e-03,  1.2872e-03],\\n\",\n      \"           [-1.1479e-04, -3.7657e-04,  2.3547e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 9.0382e-05,  1.4585e-03, -1.1981e-03],\\n\",\n      \"           [-6.4206e-04,  1.4975e-04,  1.8896e-04],\\n\",\n      \"           [-2.9710e-04,  1.3674e-03,  1.0374e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.3015e-03,  2.7000e-04, -1.5282e-04],\\n\",\n      \"           [-1.1237e-03, -1.0948e-04,  8.4135e-05],\\n\",\n      \"           [-8.8789e-04, -1.4455e-04,  4.7874e-05]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3290e-04,  9.2145e-05, -1.8781e-03],\\n\",\n      \"           [ 1.6759e-03,  5.3380e-04, -2.7933e-03],\\n\",\n      \"           [-4.7978e-04,  2.5600e-03, -1.1497e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3768e-02,  1.2223e-02,  1.2241e-02],\\n\",\n      \"           [ 1.0986e-02,  9.9975e-03,  1.2466e-02],\\n\",\n      \"           [ 9.8088e-03,  9.4526e-03,  1.1665e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4590e-02,  1.6165e-02,  1.6778e-02],\\n\",\n      \"           [ 1.1731e-02,  1.2872e-02,  1.5286e-02],\\n\",\n      \"           [ 1.4737e-02,  1.5027e-02,  1.3186e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4236e-02,  1.3631e-02,  1.3982e-02],\\n\",\n      \"           [ 1.5266e-02,  1.5688e-02,  1.5559e-02],\\n\",\n      \"           [ 1.7104e-02,  1.6808e-02,  1.6157e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.1881e-03,  1.0880e-02,  3.3340e-02],\\n\",\n      \"           [ 5.7577e-03,  1.3530e-03,  2.5911e-02],\\n\",\n      \"           [-2.2563e-02, -3.3362e-02,  1.5260e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9642e-02,  1.7031e-02,  2.5426e-02],\\n\",\n      \"           [-5.7014e-03,  1.5765e-02,  1.3292e-02],\\n\",\n      \"           [-9.7416e-03,  1.2768e-02,  2.9309e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1474e-02,  2.9427e-02,  3.5385e-03],\\n\",\n      \"           [-2.8432e-03,  1.3400e-02,  2.7853e-02],\\n\",\n      \"           [-3.8561e-02,  7.7992e-03,  1.2981e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.2609e-01,  2.0410e-01,  1.3752e-01],\\n\",\n      \"           [ 1.8560e-01,  1.4444e-01,  9.2243e-02],\\n\",\n      \"           [ 1.1756e-01,  4.5075e-02,  7.9392e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0310e-01,  1.6366e-01,  6.1395e-02],\\n\",\n      \"           [ 1.8080e-01,  7.4805e-02,  4.0311e-02],\\n\",\n      \"           [ 1.2888e-01,  7.4117e-02,  5.7405e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5706e-01,  7.8715e-02,  2.4722e-02],\\n\",\n      \"           [ 1.4824e-01,  4.8659e-02,  1.8950e-02],\\n\",\n      \"           [ 1.3528e-01,  6.0173e-02,  4.6061e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.2831e-02, -1.1136e-02, -1.1981e-02],\\n\",\n      \"           [-2.3031e-02, -2.5512e-02, -1.4138e-02],\\n\",\n      \"           [-1.7910e-02, -1.7673e-02, -1.5461e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0259e-02, -1.5847e-02, -2.3116e-02],\\n\",\n      \"           [-1.1743e-02, -8.4124e-03, -2.0164e-02],\\n\",\n      \"           [-1.6124e-02, -1.5834e-02, -1.4842e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.8007e-03, -9.7019e-03, -2.3180e-02],\\n\",\n      \"           [-9.5584e-03, -7.1307e-03, -1.9996e-02],\\n\",\n      \"           [-8.6780e-03, -1.2212e-02, -1.2883e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.2735e-02,  6.5852e-02,  9.1155e-02],\\n\",\n      \"           [ 1.9328e-02,  2.5000e-02,  4.3467e-02],\\n\",\n      \"           [ 2.0548e-02,  2.7522e-02,  3.8185e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.4429e-02,  7.5065e-02,  5.1166e-02],\\n\",\n      \"           [ 3.4296e-02,  5.2957e-02,  3.3202e-02],\\n\",\n      \"           [ 4.2597e-02,  2.6200e-02,  4.3123e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6103e-02,  3.9459e-02,  3.4156e-02],\\n\",\n      \"           [ 4.1756e-02,  5.1159e-02,  3.5902e-02],\\n\",\n      \"           [ 4.1051e-02,  4.0687e-02,  3.8897e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.2442e-05,  6.3997e-05,  1.8688e-04],\\n\",\n      \"           [ 1.7580e-04,  2.3675e-04,  1.1635e-04],\\n\",\n      \"           [ 8.5047e-05, -2.7620e-05,  1.3238e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 6.9972e-05, -4.8735e-05, -3.5141e-04],\\n\",\n      \"           [ 1.9915e-04,  5.3932e-04,  3.5308e-04],\\n\",\n      \"           [ 3.1824e-04,  3.2151e-04,  1.1494e-05]],\\n\",\n      \"\\n\",\n      \"          [[-6.2822e-06,  7.9617e-05, -1.6220e-04],\\n\",\n      \"           [ 3.7083e-04,  2.1313e-04,  4.5651e-05],\\n\",\n      \"           [ 3.8426e-04, -1.8244e-04,  4.7464e-05]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 60.00 %\\n\",\n      \"Val loss: 0.669581\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[2,    60] loss: 0.71097\\n\",\n      \"[2,   120] loss: 0.68105\\n\",\n      \"Time elapsed: 0h:5m:35s\\n\",\n      \"train accuracy_score: 52.65 %\\n\",\n      \"tensor([[[[[ 8.7943e-03,  5.8359e-03,  3.4609e-03],\\n\",\n      \"           [ 7.4431e-03,  5.4747e-03,  1.8846e-03],\\n\",\n      \"           [ 6.3986e-03,  3.1012e-03,  1.8000e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.6533e-03,  3.4519e-03,  1.9520e-03],\\n\",\n      \"           [ 6.9021e-03,  2.5160e-03,  5.1114e-03],\\n\",\n      \"           [ 6.7228e-03,  4.0274e-03,  3.7882e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.4943e-03,  9.6068e-04,  4.2375e-03],\\n\",\n      \"           [ 5.6390e-03,  4.8942e-03,  3.4721e-03],\\n\",\n      \"           [ 5.8851e-03,  4.9373e-03,  2.4567e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.2861e-07,  8.2507e-04,  9.5193e-04],\\n\",\n      \"           [ 4.6371e-04,  1.1485e-03,  2.3804e-03],\\n\",\n      \"           [ 6.6438e-04,  9.3910e-04,  1.6538e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2737e-03, -3.9912e-04, -2.1994e-03],\\n\",\n      \"           [ 2.1689e-03,  4.4050e-05, -2.1945e-03],\\n\",\n      \"           [ 2.4572e-03, -2.3742e-04, -3.5843e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1958e-03,  5.7730e-04, -4.2676e-03],\\n\",\n      \"           [ 1.4641e-03, -4.2085e-04, -2.1592e-03],\\n\",\n      \"           [ 3.2694e-03, -5.9774e-05, -1.1220e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.9310e-03, -8.0547e-03, -7.8800e-03],\\n\",\n      \"           [-7.0553e-03, -1.0346e-02, -7.7390e-03],\\n\",\n      \"           [-7.4327e-03, -8.2548e-03, -8.2294e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.8244e-03, -1.1171e-02, -1.1878e-02],\\n\",\n      \"           [-8.5396e-03, -1.2234e-02, -1.1389e-02],\\n\",\n      \"           [-9.6845e-03, -1.2219e-02, -1.1527e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.0486e-03, -1.0586e-02, -1.1041e-02],\\n\",\n      \"           [-8.8581e-03, -9.8739e-03, -1.1726e-02],\\n\",\n      \"           [-9.2449e-03, -1.0388e-02, -1.1766e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.5364e-02, -7.7495e-02, -6.3047e-02],\\n\",\n      \"           [-6.0851e-02, -1.0005e-01, -8.2516e-02],\\n\",\n      \"           [-6.4659e-02, -1.0139e-01, -1.0841e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.7285e-02, -5.5432e-02, -4.4017e-02],\\n\",\n      \"           [-4.5908e-02, -6.1568e-02, -6.1733e-02],\\n\",\n      \"           [-5.2641e-02, -8.4881e-02, -8.2455e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.4045e-02, -2.3630e-02, -1.7648e-02],\\n\",\n      \"           [-1.8868e-02, -4.4997e-02, -4.0885e-02],\\n\",\n      \"           [-1.5023e-02, -7.1086e-02, -7.2389e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.2574e-01, -1.3423e-01, -7.0153e-02],\\n\",\n      \"           [-1.5009e-01, -8.9320e-02, -1.6084e-02],\\n\",\n      \"           [-6.9353e-02, -1.5029e-02, -1.7243e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.9239e-01, -7.9006e-02, -4.3380e-02],\\n\",\n      \"           [-1.1730e-01, -2.3642e-02,  2.6219e-03],\\n\",\n      \"           [-3.4185e-02,  5.6135e-02,  3.1743e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.6392e-01, -8.3342e-02,  1.5596e-02],\\n\",\n      \"           [-1.3754e-01,  9.0916e-03,  7.5447e-02],\\n\",\n      \"           [-6.3132e-02,  2.2192e-02,  7.6276e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.9214e-02,  1.8046e-02,  1.6031e-02],\\n\",\n      \"           [ 2.1513e-02,  1.7281e-02,  1.8015e-02],\\n\",\n      \"           [ 2.0789e-02,  1.6548e-02,  1.3227e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.5465e-03,  1.8899e-02,  2.2651e-02],\\n\",\n      \"           [ 1.6705e-02,  1.4974e-02,  1.2690e-02],\\n\",\n      \"           [ 1.6401e-02,  1.5122e-02,  8.6401e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 9.3092e-03,  1.5936e-02,  1.6652e-02],\\n\",\n      \"           [ 1.1166e-02,  8.4516e-03,  9.1349e-03],\\n\",\n      \"           [ 1.0285e-02,  9.5822e-03,  5.2170e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.4267e-02, -2.2042e-02, -2.1086e-02],\\n\",\n      \"           [-3.4736e-02, -3.7168e-02, -3.5033e-02],\\n\",\n      \"           [-1.9446e-02, -4.0941e-02, -3.0835e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.7537e-03, -1.1444e-02,  7.1122e-03],\\n\",\n      \"           [-2.1159e-02, -2.8852e-02, -3.6337e-02],\\n\",\n      \"           [-2.0392e-02, -4.2094e-02, -3.1200e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.8602e-03,  9.7003e-03,  9.7595e-03],\\n\",\n      \"           [-8.6082e-03, -2.0555e-02,  9.5674e-03],\\n\",\n      \"           [ 6.1170e-04, -7.0053e-03, -1.7779e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.3965e-04, -1.2055e-03, -1.0127e-03],\\n\",\n      \"           [-5.9059e-04, -1.0861e-03, -9.2446e-04],\\n\",\n      \"           [-7.4456e-04, -9.8182e-04, -1.1633e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.8113e-04, -8.0361e-04, -1.0136e-03],\\n\",\n      \"           [-5.6770e-04, -8.3457e-04, -4.7579e-04],\\n\",\n      \"           [-3.8837e-04, -4.2151e-04, -8.2179e-04]],\\n\",\n      \"\\n\",\n      \"          [[-3.3950e-04, -4.0503e-04, -9.1300e-04],\\n\",\n      \"           [ 1.9295e-06, -8.7265e-05, -4.2784e-04],\\n\",\n      \"           [-6.4121e-05, -5.9617e-05, -1.4850e-04]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 74.00 %\\n\",\n      \"Val loss: 0.680240\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[3,    60] loss: 0.68079\\n\",\n      \"[3,   120] loss: 0.69228\\n\",\n      \"Time elapsed: 0h:7m:30s\\n\",\n      \"train accuracy_score: 53.66 %\\n\",\n      \"tensor([[[[[ 3.4760e-03,  2.3528e-03,  1.8862e-03],\\n\",\n      \"           [ 1.7893e-03,  1.4760e-03,  1.0293e-03],\\n\",\n      \"           [ 8.4064e-04,  6.0094e-04, -1.0175e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6674e-03,  1.8172e-03,  1.4465e-03],\\n\",\n      \"           [ 2.0995e-03,  1.3727e-03,  7.4528e-04],\\n\",\n      \"           [ 1.4647e-03,  1.2923e-03,  1.6700e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 3.4531e-03,  2.1794e-03,  1.7080e-03],\\n\",\n      \"           [ 2.4645e-03,  1.4882e-03,  7.6327e-04],\\n\",\n      \"           [ 2.1396e-03,  1.3495e-03,  4.5421e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.0228e-04, -4.5929e-04, -8.6964e-05],\\n\",\n      \"           [-7.5257e-04, -4.3849e-04,  3.7792e-04],\\n\",\n      \"           [-4.3780e-04,  5.3679e-04,  6.3969e-05]],\\n\",\n      \"\\n\",\n      \"          [[-1.1913e-03, -9.0567e-04,  4.2771e-05],\\n\",\n      \"           [-3.7784e-04,  1.0224e-05, -9.8502e-05],\\n\",\n      \"           [-1.2560e-04,  6.3725e-05,  4.4876e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.2203e-03, -5.9086e-04, -3.2366e-04],\\n\",\n      \"           [-7.4011e-04, -8.9142e-04, -6.2836e-04],\\n\",\n      \"           [-5.8416e-05, -3.6056e-04, -2.9861e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.7673e-03, -1.8069e-03, -2.2653e-03],\\n\",\n      \"           [-1.6477e-03, -2.1068e-03, -2.2545e-03],\\n\",\n      \"           [-2.7593e-03, -2.2324e-03, -2.3110e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.9010e-03, -3.7714e-03, -3.3976e-03],\\n\",\n      \"           [-2.3792e-03, -2.8026e-03, -2.8314e-03],\\n\",\n      \"           [-2.4036e-03, -2.6581e-03, -2.6352e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.3971e-03, -4.0614e-03, -4.1077e-03],\\n\",\n      \"           [-3.4331e-03, -3.4022e-03, -3.3748e-03],\\n\",\n      \"           [-3.0777e-03, -2.9125e-03, -2.9792e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.4170e-03, -1.5363e-02, -1.6514e-02],\\n\",\n      \"           [-9.5383e-03, -1.6434e-02, -2.4650e-02],\\n\",\n      \"           [-1.2315e-02, -2.2316e-02, -2.7054e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0787e-02, -1.1262e-02, -1.0165e-02],\\n\",\n      \"           [-1.5043e-02, -1.6527e-02, -1.7656e-02],\\n\",\n      \"           [-1.0601e-02, -1.5254e-02, -1.7341e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.2230e-02, -8.7882e-03, -1.5338e-02],\\n\",\n      \"           [-8.1525e-03, -9.9363e-03, -8.4067e-03],\\n\",\n      \"           [-3.5347e-03, -9.1821e-03, -8.1839e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.5332e-02, -6.4172e-02, -4.5932e-02],\\n\",\n      \"           [-7.3465e-02, -6.0685e-02, -3.8617e-02],\\n\",\n      \"           [-5.9356e-02, -4.5288e-02, -4.8634e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.6234e-02, -4.2360e-02, -3.2338e-02],\\n\",\n      \"           [-4.9732e-02, -2.6446e-02, -2.1176e-02],\\n\",\n      \"           [-4.3415e-02, -1.9156e-02, -2.1942e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.6211e-02, -3.9645e-02, -3.2678e-02],\\n\",\n      \"           [-4.4954e-02, -1.6986e-02, -2.0830e-02],\\n\",\n      \"           [-3.7982e-02, -1.1730e-02, -1.2680e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.4538e-03, -1.0619e-03, -4.0743e-03],\\n\",\n      \"           [-4.1882e-03, -6.9228e-03, -8.9728e-03],\\n\",\n      \"           [-9.2069e-03, -8.9921e-03, -9.3423e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.1944e-03, -3.6717e-03, -7.7071e-03],\\n\",\n      \"           [-7.3256e-03, -1.0506e-02, -1.0879e-02],\\n\",\n      \"           [-9.7664e-03, -1.2574e-02, -1.2248e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.3032e-03, -5.9764e-03, -7.0032e-03],\\n\",\n      \"           [-8.1495e-03, -1.0227e-02, -1.0720e-02],\\n\",\n      \"           [-1.1241e-02, -1.3275e-02, -1.1514e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.1359e-02,  2.1892e-02,  2.1155e-02],\\n\",\n      \"           [ 2.1405e-02,  2.4536e-02,  3.2973e-02],\\n\",\n      \"           [ 3.0849e-02,  3.0388e-02,  3.4738e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1759e-02,  2.9117e-02,  3.3068e-02],\\n\",\n      \"           [ 2.5115e-02,  3.4514e-02,  3.9020e-02],\\n\",\n      \"           [ 3.7836e-02,  3.8345e-02,  4.4375e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.9849e-02,  3.1363e-02,  4.1293e-02],\\n\",\n      \"           [ 3.5862e-02,  3.8530e-02,  4.6070e-02],\\n\",\n      \"           [ 5.1164e-02,  4.9279e-02,  4.7807e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.9467e-04,  7.5497e-05,  1.7006e-04],\\n\",\n      \"           [ 1.6118e-04,  1.7643e-04,  2.9770e-04],\\n\",\n      \"           [ 2.9365e-04,  4.2101e-04,  4.1720e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 7.6716e-05, -2.6512e-05, -1.2322e-05],\\n\",\n      \"           [ 1.3325e-04,  1.7278e-04,  2.3095e-04],\\n\",\n      \"           [ 1.9162e-04,  2.9903e-04,  3.9790e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2965e-04,  1.3865e-04,  1.7741e-04],\\n\",\n      \"           [ 2.2491e-04,  2.4009e-04,  2.3593e-04],\\n\",\n      \"           [ 8.6925e-05,  2.0275e-04,  3.4584e-04]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 76.00 %\\n\",\n      \"Val loss: 0.684078\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[4,    60] loss: 0.68260\\n\",\n      \"[4,   120] loss: 0.67241\\n\",\n      \"Time elapsed: 0h:9m:24s\\n\",\n      \"train accuracy_score: 57.25 %\\n\",\n      \"tensor([[[[[-2.1379e-02, -1.5586e-02, -1.0827e-02],\\n\",\n      \"           [-1.7190e-02, -1.3736e-02, -7.7558e-03],\\n\",\n      \"           [-1.3875e-02, -1.0593e-02, -7.2013e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.6465e-02, -1.3933e-02, -8.0092e-03],\\n\",\n      \"           [-1.5193e-02, -1.2690e-02, -7.4053e-03],\\n\",\n      \"           [-1.1579e-02, -8.5753e-03, -7.2536e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.3494e-02, -1.1155e-02, -7.0696e-03],\\n\",\n      \"           [-1.1056e-02, -9.8790e-03, -3.9161e-03],\\n\",\n      \"           [-5.6299e-03, -3.8181e-03, -2.6180e-06]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.9778e-03, -5.6965e-04,  2.0020e-03],\\n\",\n      \"           [ 9.7438e-04,  3.5712e-05,  1.1213e-06],\\n\",\n      \"           [-9.4541e-05, -1.9794e-03, -8.1836e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.7036e-03, -1.2216e-03,  2.8981e-04],\\n\",\n      \"           [ 7.0124e-04, -3.3605e-04,  1.6147e-03],\\n\",\n      \"           [ 5.7074e-04, -5.0118e-04,  3.4011e-04]],\\n\",\n      \"\\n\",\n      \"          [[-6.5733e-04,  2.3563e-03,  1.9953e-03],\\n\",\n      \"           [ 2.9366e-03,  3.3610e-03,  2.2103e-03],\\n\",\n      \"           [ 1.1999e-03,  1.0200e-03,  3.1011e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 9.6042e-06,  1.1969e-03,  3.6550e-03],\\n\",\n      \"           [ 1.1484e-03,  3.0280e-03,  6.1742e-03],\\n\",\n      \"           [ 4.0331e-03,  5.7208e-03,  6.1652e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.5046e-03,  5.9637e-03,  6.8852e-03],\\n\",\n      \"           [ 3.8129e-03,  6.1441e-03,  7.3664e-03],\\n\",\n      \"           [ 6.7931e-03,  7.2735e-03,  1.0064e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.9638e-03,  6.8357e-03,  6.7420e-03],\\n\",\n      \"           [ 7.5541e-03,  6.5314e-03,  7.0908e-03],\\n\",\n      \"           [ 8.4296e-03,  8.5053e-03,  1.1162e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.8897e-01,  1.6049e-01,  1.7339e-01],\\n\",\n      \"           [ 1.7074e-01,  1.6111e-01,  2.0211e-01],\\n\",\n      \"           [ 2.4457e-01,  2.4494e-01,  2.3715e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8450e-01,  1.4931e-01,  1.4467e-01],\\n\",\n      \"           [ 1.4786e-01,  1.4670e-01,  1.5504e-01],\\n\",\n      \"           [ 1.8315e-01,  2.1462e-01,  1.8456e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7650e-01,  1.5470e-01,  1.4289e-01],\\n\",\n      \"           [ 1.6632e-01,  1.4841e-01,  1.3741e-01],\\n\",\n      \"           [ 1.6978e-01,  1.7709e-01,  1.5012e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.0606e-01,  4.8052e-01,  4.0935e-01],\\n\",\n      \"           [ 5.2013e-01,  4.5791e-01,  3.8229e-01],\\n\",\n      \"           [ 4.6418e-01,  4.2286e-01,  4.2034e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.9415e-01,  4.0907e-01,  3.9735e-01],\\n\",\n      \"           [ 4.6141e-01,  3.6990e-01,  3.3443e-01],\\n\",\n      \"           [ 4.3705e-01,  3.7430e-01,  3.3443e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.8721e-01,  3.9203e-01,  3.1805e-01],\\n\",\n      \"           [ 4.9607e-01,  3.9599e-01,  3.1640e-01],\\n\",\n      \"           [ 4.0007e-01,  3.6270e-01,  3.2335e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 9.7476e-03,  1.8485e-02,  1.4493e-02],\\n\",\n      \"           [ 1.4156e-02,  2.3501e-02,  2.4543e-02],\\n\",\n      \"           [ 2.5325e-02,  2.5409e-02,  2.1702e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1279e-02,  1.7432e-02,  2.7794e-02],\\n\",\n      \"           [ 2.3661e-02,  2.5353e-02,  2.5138e-02],\\n\",\n      \"           [ 3.2780e-02,  3.4173e-02,  2.4071e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7588e-02,  1.7118e-02,  2.4647e-02],\\n\",\n      \"           [ 2.6430e-02,  2.6476e-02,  2.8891e-02],\\n\",\n      \"           [ 2.3740e-02,  2.4666e-02,  2.8464e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2555e-01, -1.1407e-01, -6.9306e-02],\\n\",\n      \"           [-1.5851e-01, -1.5606e-01, -1.2864e-01],\\n\",\n      \"           [-1.8207e-01, -1.8809e-01, -2.1277e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.4271e-01, -1.4419e-01, -1.3576e-01],\\n\",\n      \"           [-1.8308e-01, -1.8663e-01, -2.0267e-01],\\n\",\n      \"           [-2.1358e-01, -2.4686e-01, -2.4606e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.5845e-01, -1.9539e-01, -2.0031e-01],\\n\",\n      \"           [-1.9118e-01, -2.1090e-01, -2.3444e-01],\\n\",\n      \"           [-2.6450e-01, -2.7147e-01, -2.7008e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.7126e-04,  4.0586e-04,  5.9088e-04],\\n\",\n      \"           [ 4.1922e-04,  3.7074e-04,  5.9347e-04],\\n\",\n      \"           [ 9.0270e-05,  6.4570e-05,  2.5185e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 9.1809e-05,  2.2639e-04,  5.0051e-04],\\n\",\n      \"           [ 3.0841e-04,  2.4310e-04,  2.7819e-04],\\n\",\n      \"           [ 6.3801e-05, -2.2909e-04, -1.5601e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 4.3182e-06, -1.1264e-04,  2.2965e-04],\\n\",\n      \"           [-9.8327e-05, -1.8242e-04,  2.2930e-05],\\n\",\n      \"           [ 7.9759e-05,  6.7713e-05,  4.6264e-06]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 61.00 %\\n\",\n      \"Val loss: 0.654488\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[5,    60] loss: 0.65797\\n\",\n      \"[5,   120] loss: 0.67308\\n\",\n      \"Time elapsed: 0h:11m:19s\\n\",\n      \"train accuracy_score: 58.82 %\\n\",\n      \"tensor([[[[[ 5.7214e-02,  3.9410e-02,  1.6706e-02],\\n\",\n      \"           [ 4.6563e-02,  3.7701e-02,  1.2369e-02],\\n\",\n      \"           [ 4.4629e-02,  3.0462e-02,  9.4139e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.6637e-02,  4.5918e-02,  3.4928e-02],\\n\",\n      \"           [ 6.3118e-02,  3.9273e-02,  2.9693e-02],\\n\",\n      \"           [ 5.0981e-02,  3.4879e-02,  1.9596e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.3703e-02,  4.4835e-02,  3.2167e-02],\\n\",\n      \"           [ 6.4590e-02,  3.8086e-02,  2.7903e-02],\\n\",\n      \"           [ 4.9110e-02,  2.7930e-02,  1.9936e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0278e-02,  8.6329e-03,  6.8224e-03],\\n\",\n      \"           [ 1.2447e-02,  5.3211e-03,  4.9688e-03],\\n\",\n      \"           [ 1.4285e-02,  6.8202e-03,  4.1548e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 7.8538e-03,  4.3267e-03,  3.0748e-03],\\n\",\n      \"           [ 1.0823e-02,  5.4190e-03,  5.0287e-03],\\n\",\n      \"           [ 7.9911e-03,  7.1929e-03,  4.8815e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 8.6718e-04, -2.4163e-04, -1.7275e-03],\\n\",\n      \"           [ 5.1771e-03,  2.0569e-03, -4.6809e-03],\\n\",\n      \"           [ 1.0831e-02,  6.6248e-04, -3.9629e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.0302e-03,  2.7281e-03, -2.8057e-03],\\n\",\n      \"           [ 2.7990e-03, -1.3165e-03, -6.5540e-03],\\n\",\n      \"           [-3.7586e-03, -2.3153e-03, -8.7455e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 7.7051e-04, -4.9978e-03, -8.3929e-03],\\n\",\n      \"           [-5.8667e-03, -4.7283e-03, -7.8691e-03],\\n\",\n      \"           [-8.4784e-03, -6.2220e-03, -7.3556e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.4455e-03, -4.3834e-03, -6.8514e-03],\\n\",\n      \"           [-6.7055e-03, -5.0945e-03, -7.2831e-03],\\n\",\n      \"           [-9.7018e-03, -5.9079e-03, -7.4225e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.1260e-02, -2.4735e-03, -4.8623e-02],\\n\",\n      \"           [-1.2589e-02, -5.9105e-02, -5.9656e-02],\\n\",\n      \"           [-1.4948e-01, -2.2925e-01, -1.7757e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.1119e-03,  2.7742e-02,  6.6471e-02],\\n\",\n      \"           [ 8.3256e-02, -5.2507e-02,  4.2309e-02],\\n\",\n      \"           [ 1.1134e-02, -1.7968e-01, -7.6431e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.2852e-02,  4.1468e-03,  6.6549e-02],\\n\",\n      \"           [-2.7780e-02, -1.1501e-01,  3.2194e-02],\\n\",\n      \"           [-3.6163e-02, -2.2294e-01, -7.1169e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.2405e-01, -4.3298e-01, -2.5305e-01],\\n\",\n      \"           [-6.0693e-01, -4.7740e-01, -2.6952e-01],\\n\",\n      \"           [-4.9339e-01, -2.6944e-01, -4.3487e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.0483e-01, -4.1587e-01, -1.4051e-01],\\n\",\n      \"           [-4.8226e-01, -1.8413e-01, -1.5607e-01],\\n\",\n      \"           [-4.3841e-01, -2.0674e-01, -1.1230e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.5500e-01, -3.5417e-01, -1.0292e-01],\\n\",\n      \"           [-5.3165e-01, -1.8501e-01,  3.0410e-02],\\n\",\n      \"           [-2.7757e-01, -1.2723e-01,  2.4382e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 9.5439e-03, -1.3112e-02, -2.4049e-02],\\n\",\n      \"           [-1.6469e-02, -1.5519e-02, -3.0836e-02],\\n\",\n      \"           [-1.8271e-02, -1.3607e-02, -2.2673e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.0158e-03,  5.4891e-03, -1.5025e-02],\\n\",\n      \"           [-1.6657e-02, -2.5497e-02, -2.6862e-02],\\n\",\n      \"           [-1.8374e-02, -2.2704e-02, -3.0843e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.8743e-03,  1.4257e-02,  6.3549e-03],\\n\",\n      \"           [-8.9191e-03, -4.6438e-03, -6.1619e-03],\\n\",\n      \"           [-3.1342e-02, -2.8193e-02, -1.7530e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.5423e-02, -4.0840e-03,  1.6315e-02],\\n\",\n      \"           [-1.6794e-02, -2.3776e-02,  8.9761e-03],\\n\",\n      \"           [-1.1124e-02, -2.4142e-02,  9.6546e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1925e-02,  7.7167e-02,  6.0030e-02],\\n\",\n      \"           [-2.5787e-02,  5.0962e-02,  1.2857e-01],\\n\",\n      \"           [-5.2647e-02,  3.3318e-02,  1.5553e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 8.9255e-02,  1.4801e-01,  1.2501e-01],\\n\",\n      \"           [ 2.5126e-05,  9.4230e-02,  1.9999e-01],\\n\",\n      \"           [ 3.8083e-03,  1.3556e-01,  2.4455e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.4637e-04, -1.1760e-03, -1.8649e-03],\\n\",\n      \"           [-1.3515e-03, -6.9095e-04, -8.3846e-04],\\n\",\n      \"           [-2.6092e-04, -1.5200e-04, -6.7225e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.1385e-03, -9.0825e-04, -1.3194e-03],\\n\",\n      \"           [-9.8282e-04, -9.0915e-04, -1.4842e-03],\\n\",\n      \"           [ 1.2883e-04, -6.3447e-04, -9.9069e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 3.9634e-04, -2.8568e-04, -1.1508e-03],\\n\",\n      \"           [ 3.3423e-04, -1.5007e-04, -1.3348e-03],\\n\",\n      \"           [-2.2486e-04, -4.5629e-04, -1.4105e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 73.00 %\\n\",\n      \"Val loss: 0.633099\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[6,    60] loss: 0.67347\\n\",\n      \"[6,   120] loss: 0.65730\\n\",\n      \"Time elapsed: 0h:13m:13s\\n\",\n      \"train accuracy_score: 58.25 %\\n\",\n      \"tensor([[[[[ 2.7865e-02,  2.0125e-02,  5.5760e-03],\\n\",\n      \"           [ 2.0906e-02,  2.0268e-02,  1.1132e-02],\\n\",\n      \"           [ 2.6506e-02,  2.3415e-02,  1.5560e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6668e-02,  1.2567e-02,  1.0293e-02],\\n\",\n      \"           [ 1.7752e-02,  1.2229e-02,  8.6884e-03],\\n\",\n      \"           [ 2.0428e-02,  1.7433e-02,  1.2492e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6718e-02,  8.7420e-03,  3.3947e-03],\\n\",\n      \"           [ 1.5551e-02,  6.5395e-03,  4.5289e-03],\\n\",\n      \"           [ 1.2978e-02,  9.8725e-03,  3.2794e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.8841e-03,  4.8707e-03,  6.1820e-03],\\n\",\n      \"           [ 4.2563e-03,  5.3756e-03,  5.9959e-03],\\n\",\n      \"           [ 6.1510e-03,  6.9831e-03,  7.7140e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3623e-03, -3.7310e-04,  6.3496e-04],\\n\",\n      \"           [ 2.7212e-04,  1.5738e-03,  3.4249e-03],\\n\",\n      \"           [ 4.5918e-04,  3.0466e-03,  3.7224e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.2573e-03, -2.8504e-03, -2.0514e-03],\\n\",\n      \"           [-3.8722e-03, -6.4874e-04, -1.9344e-03],\\n\",\n      \"           [-3.3286e-03,  1.9203e-04,  4.2574e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.3100e-03, -5.2487e-03, -7.8215e-03],\\n\",\n      \"           [-6.0563e-03, -7.3452e-03, -7.1885e-03],\\n\",\n      \"           [-6.9037e-03, -5.6283e-03, -3.2915e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.8494e-03, -7.4023e-03, -1.0306e-02],\\n\",\n      \"           [-8.6462e-03, -8.7620e-03, -1.0227e-02],\\n\",\n      \"           [-1.0087e-02, -9.3193e-03, -5.6557e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.5772e-03, -7.9264e-03, -8.0371e-03],\\n\",\n      \"           [-7.9311e-03, -9.1178e-03, -9.0287e-03],\\n\",\n      \"           [-8.5292e-03, -8.1014e-03, -7.7083e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.5644e-01, -2.4983e-01, -2.5911e-01],\\n\",\n      \"           [-2.7950e-01, -3.0977e-01, -3.3760e-01],\\n\",\n      \"           [-3.4091e-01, -3.9448e-01, -4.1176e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.5096e-01, -2.4864e-01, -2.1049e-01],\\n\",\n      \"           [-2.5065e-01, -3.2916e-01, -2.9079e-01],\\n\",\n      \"           [-2.8489e-01, -4.1824e-01, -3.8399e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.6244e-01, -2.4945e-01, -2.4702e-01],\\n\",\n      \"           [-2.6540e-01, -3.1549e-01, -3.0037e-01],\\n\",\n      \"           [-2.8889e-01, -3.9899e-01, -3.6599e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.2052e-01, -4.8597e-01, -4.1684e-01],\\n\",\n      \"           [-5.1347e-01, -4.7211e-01, -3.6681e-01],\\n\",\n      \"           [-4.6357e-01, -4.6563e-01, -4.6990e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.3478e-01, -4.7502e-01, -4.3577e-01],\\n\",\n      \"           [-5.0093e-01, -3.7453e-01, -3.6728e-01],\\n\",\n      \"           [-4.8804e-01, -4.1577e-01, -4.0053e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.4458e-01, -4.6208e-01, -4.1084e-01],\\n\",\n      \"           [-5.2088e-01, -4.2239e-01, -3.9126e-01],\\n\",\n      \"           [-4.9120e-01, -4.3357e-01, -4.0627e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.5842e-03, -7.7194e-03, -1.4326e-02],\\n\",\n      \"           [-1.4686e-02, -1.4891e-02, -1.4585e-02],\\n\",\n      \"           [-2.2955e-02, -2.3333e-02, -1.7626e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.7675e-03, -6.1659e-03, -1.2759e-02],\\n\",\n      \"           [-1.9292e-02, -1.7461e-02, -1.2940e-02],\\n\",\n      \"           [-2.8650e-02, -2.6100e-02, -1.4946e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.4997e-02, -6.5742e-03,  4.1905e-03],\\n\",\n      \"           [-1.7070e-02, -1.4352e-02, -7.8805e-03],\\n\",\n      \"           [-2.2546e-02, -1.4802e-02, -1.0134e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.0459e-02,  4.9469e-02,  5.9578e-02],\\n\",\n      \"           [ 5.2999e-02,  6.3108e-02,  7.9108e-02],\\n\",\n      \"           [ 7.1653e-02,  1.0312e-01,  1.4299e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2802e-02,  6.5764e-02,  1.2292e-01],\\n\",\n      \"           [ 6.1165e-02,  8.7741e-02,  1.3306e-01],\\n\",\n      \"           [ 8.7527e-02,  1.1186e-01,  1.8534e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.9962e-02,  1.2613e-01,  1.8814e-01],\\n\",\n      \"           [ 5.7401e-02,  1.0300e-01,  1.9695e-01],\\n\",\n      \"           [ 1.2796e-01,  1.5160e-01,  2.1765e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.7886e-04, -6.1146e-04, -5.9066e-04],\\n\",\n      \"           [-8.0732e-04, -3.8627e-04,  1.2463e-04],\\n\",\n      \"           [-1.0931e-03, -3.5092e-04,  9.9307e-06]],\\n\",\n      \"\\n\",\n      \"          [[-6.9394e-04, -2.0068e-04,  3.6659e-04],\\n\",\n      \"           [-1.3670e-04, -7.0259e-05,  2.5896e-04],\\n\",\n      \"           [-3.4560e-04, -2.2757e-04,  1.8101e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.5971e-04, -2.8565e-04, -7.1031e-05],\\n\",\n      \"           [-2.3540e-04, -2.3652e-04, -2.2860e-05],\\n\",\n      \"           [-1.1156e-04, -9.2155e-05,  6.1423e-04]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 82.00 %\\n\",\n      \"Val loss: 0.624326\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[7,    60] loss: 0.66380\\n\",\n      \"[7,   120] loss: 0.65868\\n\",\n      \"Time elapsed: 0h:15m:7s\\n\",\n      \"train accuracy_score: 60.55 %\\n\",\n      \"tensor([[[[[ 4.1943e-02,  3.3613e-02,  2.2853e-02],\\n\",\n      \"           [ 3.6296e-02,  3.4945e-02,  2.3894e-02],\\n\",\n      \"           [ 3.3377e-02,  3.3112e-02,  2.2090e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0596e-02,  3.2764e-02,  2.7759e-02],\\n\",\n      \"           [ 3.8158e-02,  3.0747e-02,  2.5956e-02],\\n\",\n      \"           [ 3.4291e-02,  2.9480e-02,  2.2996e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.9294e-02,  3.1645e-02,  2.5213e-02],\\n\",\n      \"           [ 3.8640e-02,  3.3418e-02,  2.1898e-02],\\n\",\n      \"           [ 3.2790e-02,  2.6885e-02,  1.8924e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.5225e-03,  3.1658e-03,  2.3476e-03],\\n\",\n      \"           [ 3.3625e-03,  2.7908e-03,  2.6088e-03],\\n\",\n      \"           [ 2.3131e-03,  3.4942e-03,  2.9115e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6074e-04,  1.4613e-03,  2.6211e-04],\\n\",\n      \"           [ 8.3341e-04,  1.0393e-03,  1.2591e-03],\\n\",\n      \"           [ 1.1827e-03,  1.7633e-03,  2.0910e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.2136e-03, -1.0391e-03, -1.3647e-04],\\n\",\n      \"           [-6.9743e-04,  2.7603e-05,  1.3126e-03],\\n\",\n      \"           [ 4.5811e-04,  1.4602e-03,  1.5763e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.1546e-03,  2.9138e-03,  1.2794e-03],\\n\",\n      \"           [ 2.5906e-03,  2.0598e-03,  4.4444e-04],\\n\",\n      \"           [ 2.5531e-04,  4.8763e-05, -2.6775e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 4.5684e-03,  2.8073e-03,  1.0434e-03],\\n\",\n      \"           [ 2.3832e-03,  3.1230e-03,  1.4586e-03],\\n\",\n      \"           [ 1.5467e-03,  1.7418e-03,  1.8445e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.1420e-03,  3.6733e-03,  1.3589e-03],\\n\",\n      \"           [ 2.9881e-03,  3.2913e-03,  2.5407e-03],\\n\",\n      \"           [ 2.3627e-03,  3.1842e-03,  3.7548e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1092e-01, -1.3726e-01, -1.3771e-01],\\n\",\n      \"           [-1.0691e-01, -1.4297e-01, -1.5338e-01],\\n\",\n      \"           [-1.4613e-01, -1.7044e-01, -1.9752e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.0965e-01, -1.1253e-01, -1.0661e-01],\\n\",\n      \"           [-1.0643e-01, -1.4348e-01, -1.4168e-01],\\n\",\n      \"           [-1.2589e-01, -1.8371e-01, -1.7950e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.3560e-01, -1.2555e-01, -1.1668e-01],\\n\",\n      \"           [-1.1633e-01, -1.3479e-01, -1.4060e-01],\\n\",\n      \"           [-1.1963e-01, -1.7816e-01, -1.8740e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.8712e-01, -1.8432e-01, -9.8730e-02],\\n\",\n      \"           [-2.3273e-01, -1.4956e-01, -5.7144e-02],\\n\",\n      \"           [-1.6531e-01, -1.1619e-01, -6.6924e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.9136e-01, -1.8131e-01, -9.8851e-02],\\n\",\n      \"           [-2.1121e-01, -1.5228e-01, -7.2582e-02],\\n\",\n      \"           [-1.5444e-01, -8.5161e-02, -4.6423e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.2262e-01, -1.7377e-01, -8.9752e-02],\\n\",\n      \"           [-2.1082e-01, -1.3655e-01, -7.0275e-02],\\n\",\n      \"           [-1.5603e-01, -1.0091e-01, -4.6660e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.8285e-04, -4.4184e-03, -1.1832e-02],\\n\",\n      \"           [-4.0320e-03, -8.7227e-03, -1.1927e-02],\\n\",\n      \"           [-6.6023e-03, -1.2548e-02, -1.3995e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.5958e-03, -4.3220e-03, -9.4499e-03],\\n\",\n      \"           [-6.7605e-03, -1.1768e-02, -1.4010e-02],\\n\",\n      \"           [-9.9705e-03, -1.3831e-02, -1.3374e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.7568e-03, -7.1984e-03, -1.0630e-02],\\n\",\n      \"           [-8.0140e-03, -9.4383e-03, -8.4532e-03],\\n\",\n      \"           [-9.2346e-03, -1.1092e-02, -1.3659e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.3265e-02,  4.2544e-02,  5.7314e-03],\\n\",\n      \"           [ 2.8085e-02,  2.9446e-02,  1.8869e-02],\\n\",\n      \"           [ 2.8534e-02,  3.1737e-02,  4.2582e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.8338e-02,  4.1323e-02,  4.0883e-02],\\n\",\n      \"           [ 3.8327e-02,  4.3245e-02,  4.1983e-02],\\n\",\n      \"           [ 5.2167e-02,  5.9616e-02,  6.8861e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.6303e-02,  5.9957e-02,  6.2585e-02],\\n\",\n      \"           [ 6.1333e-02,  6.3413e-02,  6.8577e-02],\\n\",\n      \"           [ 8.1170e-02,  7.3729e-02,  7.3355e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.7443e-04, -3.6279e-04, -9.3962e-04],\\n\",\n      \"           [-7.7443e-04, -5.2477e-04, -8.3869e-04],\\n\",\n      \"           [-1.1621e-03, -7.5558e-04, -1.0244e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.2775e-04, -2.5536e-04, -5.9492e-04],\\n\",\n      \"           [-4.3291e-04, -2.3189e-04, -4.7715e-04],\\n\",\n      \"           [-7.6139e-04, -4.7009e-04, -7.6154e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.6951e-04, -4.0030e-04, -8.0694e-04],\\n\",\n      \"           [-3.4863e-04, -3.5593e-04, -6.8879e-04],\\n\",\n      \"           [-8.4779e-04, -5.6085e-04, -8.1881e-04]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 81.00 %\\n\",\n      \"Val loss: 0.601197\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[8,    60] loss: 0.61550\\n\",\n      \"[8,   120] loss: 0.63276\\n\",\n      \"Time elapsed: 0h:17m:1s\\n\",\n      \"train accuracy_score: 66.71 %\\n\",\n      \"tensor([[[[[-2.2205e-01, -2.1481e-01, -2.0090e-01],\\n\",\n      \"           [-2.0324e-01, -1.9844e-01, -1.7808e-01],\\n\",\n      \"           [-1.9012e-01, -1.9269e-01, -1.7204e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.9794e-01, -1.8938e-01, -2.0151e-01],\\n\",\n      \"           [-1.8065e-01, -1.6410e-01, -1.8172e-01],\\n\",\n      \"           [-1.7868e-01, -1.6737e-01, -1.7304e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.7701e-01, -1.6773e-01, -1.9516e-01],\\n\",\n      \"           [-1.5688e-01, -1.4249e-01, -1.6806e-01],\\n\",\n      \"           [-1.4620e-01, -1.3799e-01, -1.6409e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1510e-02, -5.0570e-03, -7.1432e-03],\\n\",\n      \"           [-5.6038e-03, -3.5146e-03, -1.1682e-03],\\n\",\n      \"           [-4.0314e-03, -4.8182e-03, -1.2915e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.7954e-03, -1.4984e-06, -9.5558e-05],\\n\",\n      \"           [-8.3891e-03, -2.8192e-04,  2.0458e-03],\\n\",\n      \"           [-6.7814e-03, -1.0478e-03, -1.8311e-04]],\\n\",\n      \"\\n\",\n      \"          [[-3.8187e-03,  3.9860e-03,  2.5714e-03],\\n\",\n      \"           [-4.7952e-03, -7.1752e-04,  2.1147e-03],\\n\",\n      \"           [-6.1610e-03, -7.2398e-04,  2.1937e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.2644e-03,  6.5011e-03,  9.5246e-03],\\n\",\n      \"           [ 9.0504e-03,  1.2719e-02,  1.1871e-02],\\n\",\n      \"           [ 1.6137e-02,  1.7073e-02,  1.7675e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1044e-02,  1.5447e-02,  1.8383e-02],\\n\",\n      \"           [ 1.4634e-02,  1.6620e-02,  2.1763e-02],\\n\",\n      \"           [ 1.9656e-02,  2.1031e-02,  1.8434e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7206e-02,  1.7926e-02,  2.1411e-02],\\n\",\n      \"           [ 2.3366e-02,  2.0530e-02,  2.3955e-02],\\n\",\n      \"           [ 2.1548e-02,  1.9962e-02,  2.3800e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.7560e-01,  5.0876e-01,  4.7027e-01],\\n\",\n      \"           [ 4.9280e-01,  6.2271e-01,  6.2164e-01],\\n\",\n      \"           [ 6.6237e-01,  7.8341e-01,  8.3717e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.9946e-01,  4.5404e-01,  4.5198e-01],\\n\",\n      \"           [ 4.0433e-01,  5.7225e-01,  5.5304e-01],\\n\",\n      \"           [ 5.9045e-01,  7.6583e-01,  7.1427e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0608e-01,  3.7756e-01,  4.9149e-01],\\n\",\n      \"           [ 4.4615e-01,  5.0251e-01,  4.9731e-01],\\n\",\n      \"           [ 5.1367e-01,  6.4417e-01,  6.1340e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0716e+00,  7.0081e-01,  4.0109e-01],\\n\",\n      \"           [ 6.9782e-01,  4.9208e-01,  2.8380e-01],\\n\",\n      \"           [ 4.5549e-01,  3.5574e-01,  2.7411e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 7.4101e-01,  5.1209e-01,  2.9081e-01],\\n\",\n      \"           [ 6.6552e-01,  3.4554e-01,  1.9162e-01],\\n\",\n      \"           [ 4.8189e-01,  2.1923e-01,  1.1038e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 7.1439e-01,  4.5797e-01,  1.9582e-01],\\n\",\n      \"           [ 5.6355e-01,  2.4136e-01,  1.6647e-01],\\n\",\n      \"           [ 3.8451e-01,  1.8958e-01,  1.1934e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.1509e-02,  4.4082e-02,  6.3006e-02],\\n\",\n      \"           [ 4.1579e-02,  7.2039e-02,  7.2504e-02],\\n\",\n      \"           [ 7.9095e-02,  9.7786e-02,  7.8707e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.4710e-02,  3.4861e-02,  7.2839e-02],\\n\",\n      \"           [ 6.0982e-02,  7.1841e-02,  7.0919e-02],\\n\",\n      \"           [ 8.9003e-02,  1.0551e-01,  8.6867e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.7836e-02,  5.7237e-02,  9.1718e-02],\\n\",\n      \"           [ 7.2993e-02,  7.8697e-02,  9.1618e-02],\\n\",\n      \"           [ 1.0877e-01,  1.0632e-01,  1.0546e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.8114e-02,  8.9116e-03, -9.9068e-03],\\n\",\n      \"           [ 1.0972e-02, -1.5918e-02, -1.1391e-01],\\n\",\n      \"           [-8.4554e-02, -1.1122e-01, -1.8795e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.6616e-02,  1.8837e-02, -2.9835e-02],\\n\",\n      \"           [ 5.0512e-02,  6.8973e-03, -8.7853e-02],\\n\",\n      \"           [-6.7538e-02, -9.4990e-02, -1.8517e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.9593e-02, -3.8563e-02, -2.6780e-02],\\n\",\n      \"           [ 1.3692e-03,  1.6149e-02, -9.7517e-02],\\n\",\n      \"           [-1.0338e-01, -1.3448e-01, -2.5132e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1293e-04,  2.2439e-04, -6.7360e-05],\\n\",\n      \"           [ 5.8882e-04,  2.9445e-04, -1.0630e-03],\\n\",\n      \"           [ 8.3084e-04,  5.1599e-04, -5.1113e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.2644e-04, -4.9026e-04,  1.6465e-04],\\n\",\n      \"           [ 1.3049e-04, -9.0120e-05, -9.0740e-04],\\n\",\n      \"           [-3.0462e-04, -4.3802e-04, -2.3728e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3842e-04, -8.1235e-05,  3.1871e-05],\\n\",\n      \"           [ 9.6805e-05, -3.9618e-04, -8.2240e-04],\\n\",\n      \"           [ 3.3891e-04, -4.7631e-04, -1.3432e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 45.00 %\\n\",\n      \"Val loss: 0.704238\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[9,    60] loss: 0.64267\\n\",\n      \"[9,   120] loss: 0.59621\\n\",\n      \"Time elapsed: 0h:18m:55s\\n\",\n      \"train accuracy_score: 66.14 %\\n\",\n      \"tensor([[[[[ 4.3738e-01,  3.2756e-01,  2.8729e-01],\\n\",\n      \"           [ 3.5312e-01,  3.1116e-01,  2.3785e-01],\\n\",\n      \"           [ 3.1718e-01,  3.0067e-01,  2.3894e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.8435e-01,  3.0778e-01,  3.0442e-01],\\n\",\n      \"           [ 3.3283e-01,  2.6474e-01,  2.7524e-01],\\n\",\n      \"           [ 3.0319e-01,  2.5705e-01,  2.4761e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.7589e-01,  3.0674e-01,  2.8806e-01],\\n\",\n      \"           [ 3.1843e-01,  2.6458e-01,  2.4156e-01],\\n\",\n      \"           [ 2.9990e-01,  2.4005e-01,  2.0649e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.2030e-02,  3.4816e-02,  3.9984e-02],\\n\",\n      \"           [ 4.4164e-02,  3.5590e-02,  3.4354e-02],\\n\",\n      \"           [ 3.7717e-02,  4.1240e-02,  4.6042e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0724e-02,  2.0946e-02,  2.6212e-02],\\n\",\n      \"           [ 1.6846e-02,  1.8631e-02,  1.6145e-02],\\n\",\n      \"           [ 2.0879e-02,  2.0224e-02,  2.5785e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0947e-02, -1.3404e-03,  1.7000e-03],\\n\",\n      \"           [ 3.5663e-03, -6.9843e-03,  1.8560e-03],\\n\",\n      \"           [ 3.6354e-03, -2.6281e-03,  1.1873e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.4647e-02, -2.4076e-02, -4.2971e-02],\\n\",\n      \"           [-2.3968e-02, -3.0805e-02, -3.5594e-02],\\n\",\n      \"           [-2.3105e-02, -2.8146e-02, -2.1371e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.3805e-02, -3.7811e-02, -4.1341e-02],\\n\",\n      \"           [-2.9551e-02, -3.2479e-02, -3.6994e-02],\\n\",\n      \"           [-3.8597e-02, -3.7134e-02, -2.7184e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.6441e-02, -3.4660e-02, -4.5117e-02],\\n\",\n      \"           [-3.8533e-02, -3.2983e-02, -4.4661e-02],\\n\",\n      \"           [-3.6654e-02, -3.6559e-02, -4.2755e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.4247e-01,  3.5348e-01,  3.5714e-01],\\n\",\n      \"           [ 7.3667e-02,  2.6135e-02, -7.6264e-02],\\n\",\n      \"           [-2.4954e-01, -3.5317e-01, -4.6578e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4949e-01,  3.3836e-01,  4.1550e-01],\\n\",\n      \"           [ 1.0799e-01, -2.2092e-01, -6.8282e-03],\\n\",\n      \"           [-2.2465e-01, -6.6564e-01, -4.2263e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6505e-01,  1.7289e-01,  1.4691e-01],\\n\",\n      \"           [ 1.2278e-01, -2.8200e-01, -1.4903e-01],\\n\",\n      \"           [-1.1045e-01, -6.5679e-01, -5.5943e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.0171e+00, -1.3570e+00, -7.5325e-01],\\n\",\n      \"           [-1.5406e+00, -9.8215e-01, -4.8360e-01],\\n\",\n      \"           [-1.1577e+00, -6.0608e-01, -3.9317e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.3468e+00, -8.7317e-01, -4.3684e-01],\\n\",\n      \"           [-1.2627e+00, -6.4673e-01, -2.6177e-01],\\n\",\n      \"           [-1.0120e+00, -4.5521e-01, -3.0887e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.1980e+00, -8.9219e-01, -4.6937e-01],\\n\",\n      \"           [-1.0411e+00, -4.9293e-01, -1.3707e-01],\\n\",\n      \"           [-8.8463e-01, -3.7563e-01, -1.2521e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.8355e-03, -5.3477e-02, -8.6022e-02],\\n\",\n      \"           [-4.0008e-02, -8.5322e-02, -7.6141e-02],\\n\",\n      \"           [-6.1245e-02, -7.7779e-02, -8.0857e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.8625e-02, -4.3591e-02, -1.1487e-01],\\n\",\n      \"           [-5.1178e-02, -7.0631e-02, -7.0735e-02],\\n\",\n      \"           [-8.2813e-02, -7.2703e-02, -6.9818e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.4605e-02, -6.0436e-02, -1.0931e-01],\\n\",\n      \"           [-5.6665e-02, -6.8461e-02, -7.1082e-02],\\n\",\n      \"           [-6.7108e-02, -8.7786e-02, -8.1329e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.1423e-02,  1.7933e-02,  1.5531e-01],\\n\",\n      \"           [-3.2136e-02,  1.4359e-01,  1.9062e-01],\\n\",\n      \"           [ 9.9767e-02,  2.8928e-01,  4.4662e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.5043e-02,  1.3651e-01,  2.5692e-01],\\n\",\n      \"           [-3.1084e-02,  9.9614e-02,  3.4159e-01],\\n\",\n      \"           [ 2.3432e-02,  2.4420e-01,  4.7263e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0848e-01,  3.1099e-01,  5.1846e-01],\\n\",\n      \"           [ 3.2165e-02,  1.9854e-01,  4.4752e-01],\\n\",\n      \"           [ 1.7241e-01,  3.4860e-01,  5.7386e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.0142e-03, -3.8532e-03, -5.2857e-03],\\n\",\n      \"           [-4.8167e-03, -3.4478e-03, -3.1858e-03],\\n\",\n      \"           [-4.5112e-03, -4.3157e-03, -5.8970e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.7077e-03, -3.4946e-03, -3.4823e-03],\\n\",\n      \"           [-2.1402e-03, -1.2679e-03, -2.3950e-03],\\n\",\n      \"           [-3.2752e-03, -9.9891e-04, -2.4244e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.0416e-04, -1.2934e-03, -3.4734e-03],\\n\",\n      \"           [-1.5206e-03, -2.6539e-04, -1.6452e-03],\\n\",\n      \"           [-3.3321e-03, -7.9937e-04, -2.7626e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 80.00 %\\n\",\n      \"Val loss: 0.549116\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[10,    60] loss: 0.58335\\n\",\n      \"[10,   120] loss: 0.59307\\n\",\n      \"Time elapsed: 0h:20m:48s\\n\",\n      \"train accuracy_score: 70.16 %\\n\",\n      \"tensor([[[[[ 2.7193e-02,  1.9239e-02,  1.2867e-02],\\n\",\n      \"           [ 2.0342e-02,  1.9438e-02,  1.5431e-02],\\n\",\n      \"           [ 1.8836e-02,  2.2781e-02,  1.7847e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5588e-02,  1.5457e-02,  1.3632e-02],\\n\",\n      \"           [ 2.2100e-02,  1.2587e-02,  1.2751e-02],\\n\",\n      \"           [ 1.8007e-02,  1.8419e-02,  1.6704e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5943e-02,  1.8149e-02,  1.1999e-02],\\n\",\n      \"           [ 2.1544e-02,  1.4067e-02,  8.6183e-03],\\n\",\n      \"           [ 1.8928e-02,  1.4224e-02,  1.1030e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.8928e-04, -1.5427e-03, -2.5997e-03],\\n\",\n      \"           [-1.3312e-03, -1.9996e-03, -2.2607e-03],\\n\",\n      \"           [-1.2781e-03, -1.9697e-03, -1.6699e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2094e-03, -1.7227e-03, -2.5736e-03],\\n\",\n      \"           [ 1.1604e-03, -1.9069e-03, -2.5168e-03],\\n\",\n      \"           [ 7.1756e-04, -1.1724e-03, -1.0942e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0491e-03, -5.1931e-04, -2.0348e-03],\\n\",\n      \"           [ 1.4431e-03, -6.2142e-04, -1.7833e-03],\\n\",\n      \"           [ 1.0342e-03, -1.0570e-04, -6.8302e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.1163e-03, -2.0102e-03, -2.9290e-03],\\n\",\n      \"           [-2.6315e-03, -2.6868e-03, -2.6094e-03],\\n\",\n      \"           [-2.3999e-03, -2.3789e-03, -2.1195e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.6962e-03, -3.7563e-03, -3.5501e-03],\\n\",\n      \"           [-2.8154e-03, -2.3150e-03, -2.6961e-03],\\n\",\n      \"           [-3.6277e-03, -2.3275e-03, -1.2229e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.1861e-03, -4.2734e-03, -3.4447e-03],\\n\",\n      \"           [-4.0815e-03, -3.3970e-03, -3.1349e-03],\\n\",\n      \"           [-3.6184e-03, -2.9796e-03, -2.4006e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.6847e-02, -6.5336e-02, -7.4312e-02],\\n\",\n      \"           [-1.0162e-01, -1.1243e-01, -1.2919e-01],\\n\",\n      \"           [-1.4798e-01, -1.6717e-01, -1.9079e-01]],\\n\",\n      \"\\n\",\n      \"          [[-8.5285e-02, -7.2552e-02, -7.5301e-02],\\n\",\n      \"           [-1.0758e-01, -1.3980e-01, -1.3381e-01],\\n\",\n      \"           [-1.5393e-01, -2.0113e-01, -1.8941e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.9201e-02, -8.9340e-02, -1.0520e-01],\\n\",\n      \"           [-1.1598e-01, -1.5084e-01, -1.4881e-01],\\n\",\n      \"           [-1.4663e-01, -2.0883e-01, -2.0745e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.0783e-01, -1.4790e-01, -1.0036e-01],\\n\",\n      \"           [-1.9281e-01, -1.4323e-01, -9.1331e-02],\\n\",\n      \"           [-1.5755e-01, -1.2507e-01, -9.3232e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.6347e-01, -1.2191e-01, -6.2790e-02],\\n\",\n      \"           [-1.5218e-01, -9.5732e-02, -7.7961e-02],\\n\",\n      \"           [-1.3825e-01, -8.6425e-02, -7.6783e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.3277e-01, -9.9250e-02, -5.6840e-02],\\n\",\n      \"           [-1.4029e-01, -7.5698e-02, -4.6958e-02],\\n\",\n      \"           [-1.1046e-01, -8.1843e-02, -5.4344e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3063e-02, -8.4217e-03, -1.1536e-02],\\n\",\n      \"           [-1.2091e-02, -1.4791e-02, -1.3023e-02],\\n\",\n      \"           [-2.0760e-02, -1.8299e-02, -1.2939e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0463e-02, -1.2443e-02, -1.7368e-02],\\n\",\n      \"           [-2.0256e-02, -1.5895e-02, -1.5988e-02],\\n\",\n      \"           [-2.6797e-02, -2.4124e-02, -1.5028e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.6313e-02, -1.6358e-02, -1.9090e-02],\\n\",\n      \"           [-2.0771e-02, -1.8371e-02, -1.5792e-02],\\n\",\n      \"           [-2.8951e-02, -2.5094e-02, -2.1673e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.3211e-03, -8.1885e-04,  6.5208e-04],\\n\",\n      \"           [ 2.8555e-03,  6.8418e-03, -1.9087e-03],\\n\",\n      \"           [ 1.7385e-02,  1.6122e-02,  2.5356e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.3560e-04,  1.6248e-04,  4.4277e-03],\\n\",\n      \"           [-9.9620e-03, -8.7151e-03,  1.4655e-02],\\n\",\n      \"           [-8.3967e-03,  8.6566e-03,  3.0965e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.2743e-03, -1.4534e-02, -3.0655e-03],\\n\",\n      \"           [-5.9943e-03, -7.2652e-03,  1.2265e-02],\\n\",\n      \"           [-2.0496e-03,  1.1370e-02,  3.3572e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.6701e-04, -2.6392e-04, -6.0601e-04],\\n\",\n      \"           [-1.1113e-04, -1.2513e-04, -3.1843e-04],\\n\",\n      \"           [-2.4308e-04, -8.9254e-05, -3.6525e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.3693e-04, -1.8052e-04, -7.0145e-04],\\n\",\n      \"           [-1.8479e-04, -8.8645e-05, -5.1422e-04],\\n\",\n      \"           [-2.0010e-04, -1.8733e-04, -4.5031e-04]],\\n\",\n      \"\\n\",\n      \"          [[-9.9203e-05, -1.2631e-04, -6.3957e-04],\\n\",\n      \"           [-1.0169e-04, -1.7522e-04, -4.1337e-04],\\n\",\n      \"           [-3.3915e-04, -1.1682e-04, -9.7205e-05]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 45.00 %\\n\",\n      \"Val loss: 0.732540\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[11,    60] loss: 0.57218\\n\",\n      \"[11,   120] loss: 0.56092\\n\",\n      \"Time elapsed: 0h:22m:41s\\n\",\n      \"train accuracy_score: 72.45 %\\n\",\n      \"tensor([[[[[ 2.1586e-02, -1.7638e-02, -6.2647e-02],\\n\",\n      \"           [ 5.0224e-03, -6.7043e-03, -2.3919e-02],\\n\",\n      \"           [ 1.5426e-02,  3.0865e-02,  1.7698e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8264e-02, -3.5760e-02, -6.6449e-02],\\n\",\n      \"           [ 1.5890e-03, -3.9217e-02, -3.4017e-02],\\n\",\n      \"           [ 2.8584e-03, -1.3479e-02, -6.2297e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8640e-02, -5.0498e-02, -7.1105e-02],\\n\",\n      \"           [ 2.6559e-03, -3.6937e-02, -5.1688e-02],\\n\",\n      \"           [-8.2906e-03, -3.4481e-02, -5.9312e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.1334e-02,  7.7640e-03,  1.5627e-03],\\n\",\n      \"           [ 9.8677e-03,  4.9391e-03,  4.3157e-04],\\n\",\n      \"           [ 1.1913e-02,  5.8014e-03,  2.9702e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.9133e-03,  8.2129e-05, -4.0617e-04],\\n\",\n      \"           [ 9.1119e-03,  1.9048e-03,  7.8274e-04],\\n\",\n      \"           [ 1.0480e-02,  2.7085e-03,  1.9690e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.2090e-04, -3.5876e-03, -2.0360e-03],\\n\",\n      \"           [ 8.8444e-04, -2.9604e-05, -1.6092e-03],\\n\",\n      \"           [ 2.4087e-03, -1.3622e-04, -4.2649e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.7615e-02, -3.7208e-02, -3.8667e-02],\\n\",\n      \"           [-3.1468e-02, -4.1543e-02, -4.0545e-02],\\n\",\n      \"           [-2.7965e-02, -3.7635e-02, -3.5811e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.4733e-02, -4.7700e-02, -5.4282e-02],\\n\",\n      \"           [-4.1110e-02, -4.6804e-02, -5.3546e-02],\\n\",\n      \"           [-4.5459e-02, -4.2366e-02, -4.1205e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.5918e-02, -4.8260e-02, -5.9239e-02],\\n\",\n      \"           [-4.2792e-02, -4.7446e-02, -5.5189e-02],\\n\",\n      \"           [-4.4351e-02, -4.7646e-02, -4.9796e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.9707e-01, -1.9916e-01, -1.9834e-01],\\n\",\n      \"           [-2.2842e-01, -3.1549e-01, -3.3490e-01],\\n\",\n      \"           [-4.1736e-01, -5.5532e-01, -5.5480e-01]],\\n\",\n      \"\\n\",\n      \"          [[-8.2997e-02, -1.3866e-01, -7.1576e-02],\\n\",\n      \"           [-1.5014e-01, -3.5031e-01, -2.8299e-01],\\n\",\n      \"           [-3.6204e-01, -6.3784e-01, -4.9493e-01]],\\n\",\n      \"\\n\",\n      \"          [[-6.9466e-02, -1.3382e-01, -1.4020e-01],\\n\",\n      \"           [-1.2056e-01, -3.0147e-01, -2.6919e-01],\\n\",\n      \"           [-2.7296e-01, -5.4273e-01, -4.4598e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.8171e-02,  1.2735e-01,  1.6846e-01],\\n\",\n      \"           [-5.1091e-02,  8.3745e-02,  1.5348e-01],\\n\",\n      \"           [-3.6342e-03,  8.9411e-02,  1.1949e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0229e-01,  2.0993e-01,  3.8266e-01],\\n\",\n      \"           [ 2.1740e-02,  1.9918e-01,  3.1502e-01],\\n\",\n      \"           [-2.8886e-02,  2.0441e-01,  2.1178e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9897e-01,  2.8814e-01,  3.9766e-01],\\n\",\n      \"           [ 6.3472e-02,  3.3276e-01,  4.3741e-01],\\n\",\n      \"           [ 1.7582e-01,  2.3807e-01,  3.4020e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.9267e-02,  2.1164e-02, -1.9396e-03],\\n\",\n      \"           [ 4.3017e-02,  1.7749e-02, -5.9844e-04],\\n\",\n      \"           [ 2.6296e-02,  1.8122e-02,  1.8827e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3157e-03,  9.0254e-03, -5.0524e-02],\\n\",\n      \"           [-1.4726e-02,  1.3771e-03, -1.7068e-02],\\n\",\n      \"           [-1.1931e-02, -2.3776e-02, -1.0166e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.4854e-02, -2.9713e-02, -5.8287e-02],\\n\",\n      \"           [-4.8305e-02, -4.0736e-02, -2.6204e-02],\\n\",\n      \"           [-5.4056e-02, -5.1046e-02, -3.7087e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3078e-01, -6.8474e-02,  2.7609e-02],\\n\",\n      \"           [-1.4917e-03,  4.8126e-02,  1.4111e-01],\\n\",\n      \"           [ 7.3124e-02,  1.3477e-01,  2.3334e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.4492e-01, -3.4365e-02,  2.2519e-02],\\n\",\n      \"           [-5.7719e-02,  1.6661e-02,  1.0667e-01],\\n\",\n      \"           [ 8.7549e-04,  8.2478e-02,  2.3799e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.9461e-02,  1.5709e-02,  9.1058e-02],\\n\",\n      \"           [-8.2889e-02,  2.0979e-04,  1.2812e-01],\\n\",\n      \"           [-6.2939e-04,  7.0379e-02,  2.2025e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.1266e-04,  5.4263e-04, -1.1001e-03],\\n\",\n      \"           [ 9.2428e-04,  2.8966e-03,  2.9770e-03],\\n\",\n      \"           [ 3.9635e-03,  5.4950e-03,  5.4254e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.6618e-05,  1.2600e-04, -1.0080e-03],\\n\",\n      \"           [ 1.0914e-03,  1.5875e-03,  1.1200e-03],\\n\",\n      \"           [ 2.7745e-03,  4.1179e-03,  3.2663e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5930e-03,  4.9029e-04, -5.9518e-04],\\n\",\n      \"           [ 7.2958e-04,  1.1483e-03,  5.1396e-04],\\n\",\n      \"           [-1.8985e-03,  2.2001e-03,  1.5308e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 84.00 %\\n\",\n      \"Val loss: 0.483705\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[12,    60] loss: 0.57261\\n\",\n      \"[12,   120] loss: 0.54806\\n\",\n      \"Time elapsed: 0h:24m:33s\\n\",\n      \"train accuracy_score: 72.60 %\\n\",\n      \"tensor([[[[[-2.8694e-01, -1.6319e-01, -1.0775e-01],\\n\",\n      \"           [-1.8434e-01, -1.5567e-01, -1.3276e-01],\\n\",\n      \"           [-2.1121e-01, -2.2135e-01, -1.8274e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.3494e-01, -1.6182e-01, -1.3870e-01],\\n\",\n      \"           [-1.6257e-01, -1.5811e-01, -1.7513e-01],\\n\",\n      \"           [-1.7931e-01, -1.8615e-01, -2.0175e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.7446e-01, -1.4544e-01, -1.5671e-01],\\n\",\n      \"           [-1.5506e-01, -1.5244e-01, -1.7427e-01],\\n\",\n      \"           [-1.3755e-01, -1.7467e-01, -1.7711e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.6709e-02, -2.4426e-02, -9.4286e-04],\\n\",\n      \"           [-3.2191e-02, -1.9954e-02, -3.1103e-03],\\n\",\n      \"           [-7.1571e-03, -5.8168e-03,  8.5871e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.5908e-02, -1.7411e-02, -8.3811e-03],\\n\",\n      \"           [-3.2525e-02, -1.7100e-02, -1.9877e-03],\\n\",\n      \"           [-8.4290e-03, -4.1634e-03,  7.4304e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.6878e-02, -6.8661e-03,  1.0003e-02],\\n\",\n      \"           [-7.5125e-03, -1.3668e-03,  1.0760e-02],\\n\",\n      \"           [-3.6360e-03,  3.1381e-03,  1.6607e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.7817e-02,  8.5150e-02,  7.2628e-02],\\n\",\n      \"           [ 7.0394e-02,  7.2453e-02,  6.9268e-02],\\n\",\n      \"           [ 5.6824e-02,  5.4419e-02,  4.1841e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0608e-01,  1.0967e-01,  9.6748e-02],\\n\",\n      \"           [ 8.2544e-02,  7.5254e-02,  7.6345e-02],\\n\",\n      \"           [ 8.2552e-02,  6.8988e-02,  4.1611e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2187e-01,  1.0836e-01,  1.0066e-01],\\n\",\n      \"           [ 1.0376e-01,  8.1111e-02,  8.4000e-02],\\n\",\n      \"           [ 7.8730e-02,  6.4305e-02,  5.6115e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.7346e-01,  4.7366e-01,  5.6442e-01],\\n\",\n      \"           [ 6.7291e-01,  8.4129e-01,  8.9774e-01],\\n\",\n      \"           [ 1.0159e+00,  1.2141e+00,  1.3170e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 4.6996e-01,  4.3542e-01,  4.3120e-01],\\n\",\n      \"           [ 5.8676e-01,  9.8386e-01,  9.3724e-01],\\n\",\n      \"           [ 1.0102e+00,  1.4521e+00,  1.3707e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 4.3481e-01,  4.2395e-01,  5.7786e-01],\\n\",\n      \"           [ 5.2299e-01,  8.2802e-01,  8.6694e-01],\\n\",\n      \"           [ 8.0731e-01,  1.3100e+00,  1.3211e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3829e+00,  9.2532e-01,  3.5858e-01],\\n\",\n      \"           [ 1.1248e+00,  6.3739e-01,  1.5055e-01],\\n\",\n      \"           [ 8.0928e-01,  3.7365e-01,  1.7493e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 9.0003e-01,  6.2895e-01,  1.4365e-01],\\n\",\n      \"           [ 1.1880e+00,  6.8326e-01,  3.0561e-01],\\n\",\n      \"           [ 8.7201e-01,  4.1618e-01,  3.1979e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 8.2589e-01,  5.4019e-01,  2.2458e-01],\\n\",\n      \"           [ 8.9701e-01,  4.3472e-01,  2.7071e-01],\\n\",\n      \"           [ 8.1222e-01,  5.6512e-01,  3.7905e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.4134e-02,  7.2881e-02,  8.0307e-02],\\n\",\n      \"           [ 7.6969e-02,  1.0470e-01,  8.2829e-02],\\n\",\n      \"           [ 1.6365e-01,  1.5209e-01,  1.0581e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 9.3353e-02,  1.2911e-01,  1.5971e-01],\\n\",\n      \"           [ 1.5848e-01,  1.5162e-01,  1.0827e-01],\\n\",\n      \"           [ 2.4351e-01,  2.1934e-01,  1.4325e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8366e-01,  1.8900e-01,  2.2381e-01],\\n\",\n      \"           [ 2.0812e-01,  1.5843e-01,  1.3958e-01],\\n\",\n      \"           [ 3.1116e-01,  2.3062e-01,  2.4220e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.7940e-01, -1.0060e-01, -1.6866e-01],\\n\",\n      \"           [-3.2273e-01, -2.9082e-01, -2.6571e-01],\\n\",\n      \"           [-3.9153e-01, -4.0009e-01, -5.1799e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.2837e-01, -1.1301e-01, -2.4862e-01],\\n\",\n      \"           [-1.8765e-01, -2.3849e-01, -3.7810e-01],\\n\",\n      \"           [-3.3375e-01, -4.0962e-01, -5.9299e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.2806e-01, -1.8280e-01, -3.8391e-01],\\n\",\n      \"           [-1.5112e-01, -2.4583e-01, -4.6029e-01],\\n\",\n      \"           [-3.0123e-01, -4.4377e-01, -6.7747e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.1149e-03, -2.9347e-03, -3.7116e-04],\\n\",\n      \"           [-4.8057e-03, -6.1652e-03, -3.1929e-03],\\n\",\n      \"           [-3.8063e-03, -8.2948e-03, -7.5555e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.7517e-03, -1.9161e-03,  4.6892e-04],\\n\",\n      \"           [-3.0231e-03, -4.3274e-03, -5.2871e-03],\\n\",\n      \"           [-1.8559e-04, -7.9057e-03, -8.6338e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.9690e-03, -8.3031e-04,  7.6197e-04],\\n\",\n      \"           [-7.9404e-05, -1.5520e-03, -5.5984e-03],\\n\",\n      \"           [ 6.2195e-03, -4.0406e-03, -1.1251e-02]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 59.00 %\\n\",\n      \"Val loss: 0.624904\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[13,    60] loss: 0.53958\\n\",\n      \"[13,   120] loss: 0.55674\\n\",\n      \"Time elapsed: 0h:26m:25s\\n\",\n      \"train accuracy_score: 73.31 %\\n\",\n      \"tensor([[[[[ 4.8158e-01,  4.5472e-01,  4.4938e-01],\\n\",\n      \"           [ 3.0833e-01,  4.0439e-01,  4.6269e-01],\\n\",\n      \"           [ 2.8186e-01,  4.0196e-01,  4.8183e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.9126e-01,  4.6085e-01,  5.2596e-01],\\n\",\n      \"           [ 3.2491e-01,  3.6539e-01,  5.1747e-01],\\n\",\n      \"           [ 2.8092e-01,  3.7272e-01,  4.9934e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.2149e-01,  4.8417e-01,  5.9211e-01],\\n\",\n      \"           [ 3.8629e-01,  4.1415e-01,  5.1000e-01],\\n\",\n      \"           [ 2.9899e-01,  3.7204e-01,  5.1815e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.1250e-02,  2.3867e-02,  1.7800e-02],\\n\",\n      \"           [ 2.6738e-02,  2.2194e-02,  1.2448e-02],\\n\",\n      \"           [ 2.8974e-02,  2.2351e-02,  1.2461e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2878e-02, -1.2946e-03, -7.2650e-03],\\n\",\n      \"           [ 2.4971e-02,  8.0235e-03,  2.1135e-03],\\n\",\n      \"           [ 2.4511e-02,  1.0128e-02,  2.6216e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.8149e-03, -1.5092e-02, -2.1035e-02],\\n\",\n      \"           [ 8.0927e-03,  3.7762e-03, -7.9922e-03],\\n\",\n      \"           [ 9.4606e-03,  6.9588e-04,  4.6352e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.3095e-02,  2.8252e-02,  2.8141e-02],\\n\",\n      \"           [ 2.9181e-02,  2.2142e-02,  3.5257e-02],\\n\",\n      \"           [ 1.7839e-02,  1.9778e-02,  4.8592e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.7636e-02,  3.2349e-02,  3.8120e-02],\\n\",\n      \"           [ 3.8275e-02,  4.1011e-02,  5.9912e-02],\\n\",\n      \"           [ 2.4918e-02,  4.8236e-02,  7.1946e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.6873e-02,  5.2365e-02,  6.5892e-02],\\n\",\n      \"           [ 4.4237e-02,  7.0178e-02,  7.8054e-02],\\n\",\n      \"           [ 5.5631e-02,  7.3360e-02,  9.0331e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.0874e-01, -4.4296e-01, -4.5421e-01],\\n\",\n      \"           [-6.6133e-01, -8.5011e-01, -1.0131e+00],\\n\",\n      \"           [-1.0974e+00, -1.3034e+00, -1.5779e+00]],\\n\",\n      \"\\n\",\n      \"          [[-5.8801e-01, -4.9587e-01, -3.8801e-01],\\n\",\n      \"           [-7.5451e-01, -9.9883e-01, -1.0244e+00],\\n\",\n      \"           [-1.1652e+00, -1.5663e+00, -1.5339e+00]],\\n\",\n      \"\\n\",\n      \"          [[-6.4444e-01, -7.1676e-01, -6.8129e-01],\\n\",\n      \"           [-8.7929e-01, -1.1254e+00, -1.0036e+00],\\n\",\n      \"           [-1.0973e+00, -1.5658e+00, -1.4535e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.6338e-01, -2.2864e-01, -1.2932e-01],\\n\",\n      \"           [-5.2616e-01, -5.3764e-02, -5.5622e-02],\\n\",\n      \"           [-1.0711e-01,  4.2737e-02, -7.3437e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.7694e-01, -1.3548e-01, -6.8459e-02],\\n\",\n      \"           [-5.0974e-01, -1.0587e-01, -1.5141e-01],\\n\",\n      \"           [-3.8868e-01, -8.0019e-02, -2.7968e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.1910e-01, -4.2493e-01, -2.7715e-01],\\n\",\n      \"           [-4.9748e-01, -4.4817e-01, -4.1660e-01],\\n\",\n      \"           [-6.1532e-01, -5.2032e-01, -4.9992e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.3406e-02, -6.8042e-02, -9.5796e-02],\\n\",\n      \"           [-7.1205e-02, -9.6215e-02, -5.9329e-02],\\n\",\n      \"           [-1.3337e-01, -1.0769e-01, -3.8513e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0923e-01, -1.0348e-01, -1.5032e-01],\\n\",\n      \"           [-1.5759e-01, -1.1542e-01, -8.9703e-02],\\n\",\n      \"           [-2.1903e-01, -1.9709e-01, -6.1416e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.7436e-01, -1.8983e-01, -1.7085e-01],\\n\",\n      \"           [-2.0894e-01, -1.5036e-01, -7.5608e-02],\\n\",\n      \"           [-2.8282e-01, -2.1233e-01, -1.3062e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.6297e-01, -3.1202e-02, -8.9252e-02],\\n\",\n      \"           [-1.7153e-01, -9.1842e-02, -1.1686e-01],\\n\",\n      \"           [-2.6704e-01, -1.6035e-01, -1.2309e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.5520e-01,  1.3752e-02,  2.1095e-02],\\n\",\n      \"           [-1.8991e-01,  1.2494e-02,  3.0028e-02],\\n\",\n      \"           [-2.4156e-01, -1.0842e-01,  4.3431e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 8.0239e-02,  1.1639e-01,  7.8071e-02],\\n\",\n      \"           [-1.0100e-01,  6.3798e-02,  1.4145e-01],\\n\",\n      \"           [-7.7464e-02,  1.1968e-01,  1.2627e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.4186e-03,  3.5467e-03,  1.5564e-03],\\n\",\n      \"           [ 3.3383e-04,  4.2444e-03,  7.4541e-03],\\n\",\n      \"           [ 2.0184e-03,  8.2747e-03,  1.1364e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0057e-03,  2.7004e-03, -7.0261e-04],\\n\",\n      \"           [ 1.5388e-03,  4.9427e-03,  7.7669e-03],\\n\",\n      \"           [ 5.0193e-03,  7.4886e-03,  1.3129e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.6256e-03,  8.7744e-04, -7.2710e-05],\\n\",\n      \"           [ 2.2651e-05,  3.4937e-03,  7.2714e-03],\\n\",\n      \"           [-3.6532e-03,  9.5146e-03,  1.8765e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 64.00 %\\n\",\n      \"Val loss: 0.612121\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[14,    60] loss: 0.50741\\n\",\n      \"[14,   120] loss: 0.52425\\n\",\n      \"Time elapsed: 0h:28m:17s\\n\",\n      \"train accuracy_score: 76.61 %\\n\",\n      \"tensor([[[[[-1.8420e-01, -1.0005e-01, -8.4526e-02],\\n\",\n      \"           [-9.9340e-02, -1.1392e-01, -9.8585e-02],\\n\",\n      \"           [-1.2728e-01, -1.7002e-01, -1.4839e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.9707e-01, -1.1245e-01, -1.1602e-01],\\n\",\n      \"           [-1.3530e-01, -8.1062e-02, -9.3185e-02],\\n\",\n      \"           [-1.3042e-01, -1.2800e-01, -1.3439e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.7876e-01, -1.7454e-01, -1.4129e-01],\\n\",\n      \"           [-1.9297e-01, -1.5065e-01, -1.1441e-01],\\n\",\n      \"           [-1.4897e-01, -1.2737e-01, -1.4005e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.2050e-03, -3.1147e-03, -3.0769e-03],\\n\",\n      \"           [-6.1371e-03, -2.9703e-03,  1.8114e-03],\\n\",\n      \"           [-3.7569e-03, -7.4086e-04, -2.7696e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.4881e-03, -2.4840e-03, -2.9048e-04],\\n\",\n      \"           [-5.9709e-03,  1.9482e-03,  1.3161e-03],\\n\",\n      \"           [-2.3419e-03,  2.7644e-03, -7.6672e-04]],\\n\",\n      \"\\n\",\n      \"          [[-8.7224e-03, -4.8812e-03, -2.5346e-03],\\n\",\n      \"           [-3.7924e-03, -3.0752e-03, -3.4610e-03],\\n\",\n      \"           [-1.4741e-03,  1.0764e-03, -3.9806e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.4537e-02,  2.6196e-02,  3.8788e-02],\\n\",\n      \"           [ 1.9580e-02,  3.3449e-02,  4.0341e-02],\\n\",\n      \"           [ 2.4458e-02,  3.2173e-02,  3.1987e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2883e-02,  3.6489e-02,  5.0558e-02],\\n\",\n      \"           [ 1.8961e-02,  2.6022e-02,  4.2376e-02],\\n\",\n      \"           [ 3.0631e-02,  3.4778e-02,  3.6021e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2696e-02,  3.7333e-02,  4.9287e-02],\\n\",\n      \"           [ 3.1480e-02,  3.5027e-02,  4.7917e-02],\\n\",\n      \"           [ 3.0608e-02,  3.3934e-02,  4.7462e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.9031e-01,  2.9653e-01,  3.0332e-01],\\n\",\n      \"           [ 3.5205e-01,  4.7366e-01,  5.1832e-01],\\n\",\n      \"           [ 5.4403e-01,  6.8046e-01,  7.3564e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5540e-01,  2.2808e-01,  2.3518e-01],\\n\",\n      \"           [ 3.2456e-01,  5.3628e-01,  5.3219e-01],\\n\",\n      \"           [ 5.9751e-01,  7.8424e-01,  7.5247e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8251e-01,  2.4937e-01,  3.1790e-01],\\n\",\n      \"           [ 3.3723e-01,  5.1427e-01,  5.1577e-01],\\n\",\n      \"           [ 4.5990e-01,  7.5266e-01,  7.3324e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.1916e-01,  2.8897e-01,  1.1604e-01],\\n\",\n      \"           [ 4.6412e-01,  2.2649e-01,  3.2409e-03],\\n\",\n      \"           [ 3.8522e-01,  1.8564e-01,  6.9152e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1371e-01,  1.5562e-01, -4.7046e-02],\\n\",\n      \"           [ 2.9129e-01,  1.1975e-01, -1.9237e-02],\\n\",\n      \"           [ 2.8714e-01,  9.0846e-02, -6.2953e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5163e-01,  2.2804e-02, -1.0316e-01],\\n\",\n      \"           [ 2.4727e-01, -8.0834e-04, -1.7042e-01],\\n\",\n      \"           [ 1.8289e-01,  3.0936e-02, -1.0093e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.3990e-02,  7.4283e-03,  4.4037e-02],\\n\",\n      \"           [-1.8109e-02,  1.5127e-02,  2.1898e-02],\\n\",\n      \"           [ 4.1759e-03,  1.1295e-02,  1.5005e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 9.5144e-03,  3.1935e-02,  8.2635e-02],\\n\",\n      \"           [ 2.2113e-02,  4.2397e-02,  5.0950e-02],\\n\",\n      \"           [ 3.0957e-02,  4.8282e-02,  4.5675e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.8383e-02,  6.6631e-02,  1.3299e-01],\\n\",\n      \"           [ 5.4088e-02,  8.4628e-02,  7.3405e-02],\\n\",\n      \"           [ 7.6042e-02,  6.8216e-02,  9.0568e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.9869e-02, -9.7449e-02, -7.6008e-02],\\n\",\n      \"           [-1.3222e-01, -1.7237e-01, -1.7715e-01],\\n\",\n      \"           [-2.1217e-01, -2.5941e-01, -3.0842e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.4327e-02, -4.8073e-03, -2.0051e-02],\\n\",\n      \"           [-7.2370e-02, -1.2936e-01, -1.9359e-01],\\n\",\n      \"           [-1.6929e-01, -2.6484e-01, -3.5949e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.8885e-01, -1.3116e-01, -1.3678e-01],\\n\",\n      \"           [-1.2133e-01, -1.1369e-01, -2.2029e-01],\\n\",\n      \"           [-2.4155e-01, -3.0604e-01, -3.9246e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3429e-03,  3.1347e-03,  8.8812e-03],\\n\",\n      \"           [ 3.6928e-03,  2.2487e-03,  4.1748e-03],\\n\",\n      \"           [ 4.8128e-03,  2.0757e-03,  5.8928e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0752e-03,  2.3353e-03,  6.0192e-03],\\n\",\n      \"           [ 3.6306e-03,  3.5023e-03,  4.7360e-03],\\n\",\n      \"           [ 3.1245e-03,  3.9667e-04,  3.6665e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7760e-03,  3.4437e-03,  7.0085e-03],\\n\",\n      \"           [ 3.5213e-03,  3.4899e-03,  3.0088e-03],\\n\",\n      \"           [ 8.6695e-03,  1.4301e-03,  2.4212e-04]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 80.00 %\\n\",\n      \"Val loss: 0.486392\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[15,    60] loss: 0.46643\\n\",\n      \"[15,   120] loss: 0.48999\\n\",\n      \"Time elapsed: 0h:30m:8s\\n\",\n      \"train accuracy_score: 78.62 %\\n\",\n      \"tensor([[[[[ 8.6800e-01,  9.4584e-01,  7.8376e-01],\\n\",\n      \"           [ 1.4496e+00,  1.1130e+00,  4.8987e-01],\\n\",\n      \"           [ 1.5801e+00,  6.3387e-01,  1.7046e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 7.6044e-01,  8.3411e-01,  3.1512e-01],\\n\",\n      \"           [ 1.1842e+00,  9.3586e-01,  1.0517e-01],\\n\",\n      \"           [ 1.1231e+00,  5.3153e-01,  2.1700e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.8359e-01,  7.0632e-01,  3.9931e-02],\\n\",\n      \"           [ 9.4849e-01,  6.7099e-01, -1.1444e-02],\\n\",\n      \"           [ 1.0022e+00,  4.3789e-01,  2.2217e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2364e-01, -7.0717e-02, -1.4473e-02],\\n\",\n      \"           [-1.2931e-01, -3.3880e-02, -6.1238e-03],\\n\",\n      \"           [-1.2787e-01, -7.5200e-02, -2.6632e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.1983e-02,  9.4146e-03,  2.7786e-02],\\n\",\n      \"           [-8.3233e-02,  7.6460e-03,  2.2621e-02],\\n\",\n      \"           [-7.4082e-02, -2.8790e-02, -2.7309e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.1029e-02,  2.2033e-02,  4.7685e-02],\\n\",\n      \"           [-3.7447e-02,  2.1228e-03,  6.6452e-03],\\n\",\n      \"           [-2.5696e-02, -2.9019e-02, -2.1610e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.3505e-03,  3.2987e-02,  1.8146e-02],\\n\",\n      \"           [ 9.7968e-04,  1.9632e-02, -7.8292e-02],\\n\",\n      \"           [-2.4872e-02, -6.1081e-02, -2.1768e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 9.2458e-03,  1.3214e-02, -3.9795e-02],\\n\",\n      \"           [-6.1253e-02, -1.0563e-01, -1.8482e-01],\\n\",\n      \"           [-9.3928e-03, -1.7731e-01, -3.3222e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.5217e-02, -1.1768e-01, -1.5605e-01],\\n\",\n      \"           [-1.1342e-01, -2.2086e-01, -3.0085e-01],\\n\",\n      \"           [-1.6772e-01, -3.0301e-01, -3.7301e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.9586e+00,  3.5548e+00,  3.8026e+00],\\n\",\n      \"           [ 4.4437e+00,  5.1339e+00,  5.6468e+00],\\n\",\n      \"           [ 5.6642e+00,  6.6328e+00,  7.3089e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 3.7529e+00,  3.8501e+00,  4.1401e+00],\\n\",\n      \"           [ 4.7481e+00,  5.9839e+00,  6.3503e+00],\\n\",\n      \"           [ 6.3259e+00,  8.1880e+00,  8.1184e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 4.4393e+00,  4.6692e+00,  5.4897e+00],\\n\",\n      \"           [ 5.4352e+00,  6.7486e+00,  6.8463e+00],\\n\",\n      \"           [ 6.6364e+00,  9.0089e+00,  8.6419e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.1677e+00,  2.1105e+00,  1.0877e+00],\\n\",\n      \"           [ 2.0168e+00,  7.2782e-01,  9.7862e-02],\\n\",\n      \"           [ 1.1357e+00,  3.5179e-01, -1.2403e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8143e+00,  1.3946e+00,  3.8318e-01],\\n\",\n      \"           [ 1.1050e+00,  4.6674e-01,  7.1110e-02],\\n\",\n      \"           [ 1.0887e+00, -7.5293e-02, -7.5127e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1874e+00,  1.1334e+00,  8.1280e-01],\\n\",\n      \"           [ 7.6426e-01,  5.3819e-01, -2.7224e-01],\\n\",\n      \"           [ 3.9606e-01,  3.0718e-02, -1.1099e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.2010e-02, -1.0053e-01,  4.9851e-02],\\n\",\n      \"           [-4.4878e-02, -1.9960e-01, -2.2232e-01],\\n\",\n      \"           [ 2.5185e-01, -1.2591e-01, -3.2184e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8334e-01, -7.2272e-02,  1.3912e-01],\\n\",\n      \"           [ 3.5806e-01,  4.3001e-02, -1.4320e-01],\\n\",\n      \"           [ 5.2282e-01,  2.2253e-01, -1.0528e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.1224e-01,  2.4703e-01,  2.9927e-01],\\n\",\n      \"           [ 5.8216e-01,  2.6939e-01, -4.3041e-02],\\n\",\n      \"           [ 8.4171e-01,  4.8454e-01,  1.6782e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.1278e-01,  1.7925e-01,  5.3516e-01],\\n\",\n      \"           [ 1.3001e-01,  3.4685e-01,  6.6255e-01],\\n\",\n      \"           [ 4.5906e-01,  8.5373e-01,  6.5530e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0452e-01,  2.8581e-01,  6.9847e-01],\\n\",\n      \"           [ 2.9832e-01,  4.7010e-01,  6.6822e-01],\\n\",\n      \"           [ 6.8072e-01,  6.8671e-01,  4.3719e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.2258e-01,  2.0149e-01,  1.3080e-01],\\n\",\n      \"           [ 3.5066e-01,  4.2451e-01,  1.1759e-01],\\n\",\n      \"           [ 6.9325e-01,  2.3494e-01, -1.3219e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.1834e-02, -7.2678e-03,  3.0827e-02],\\n\",\n      \"           [-9.5100e-03, -1.8995e-02, -3.4228e-02],\\n\",\n      \"           [-8.9569e-03, -4.8914e-02, -7.6650e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.6114e-02, -9.9412e-03,  8.7162e-03],\\n\",\n      \"           [-7.4035e-03, -2.8896e-02, -4.6038e-02],\\n\",\n      \"           [-1.4361e-02, -6.5148e-02, -9.3375e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.5856e-02, -7.9161e-03,  2.6889e-03],\\n\",\n      \"           [-9.8590e-03, -2.9631e-02, -6.5147e-02],\\n\",\n      \"           [ 1.0804e-02, -7.5806e-02, -1.1326e-01]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 85.00 %\\n\",\n      \"Val loss: 0.419818\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[16,    60] loss: 0.48524\\n\",\n      \"[16,   120] loss: 0.51656\\n\",\n      \"Time elapsed: 0h:32m:1s\\n\",\n      \"train accuracy_score: 78.34 %\\n\",\n      \"tensor([[[[[ 6.7610e-01,  5.6420e-01,  5.6609e-01],\\n\",\n      \"           [ 4.5459e-01,  5.1149e-01,  5.5357e-01],\\n\",\n      \"           [ 4.9093e-01,  6.1578e-01,  6.8113e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.1102e-01,  5.4855e-01,  5.4677e-01],\\n\",\n      \"           [ 5.4140e-01,  4.0910e-01,  4.7232e-01],\\n\",\n      \"           [ 4.9405e-01,  5.4780e-01,  5.5247e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.3892e-01,  5.4917e-01,  5.8223e-01],\\n\",\n      \"           [ 5.1963e-01,  5.1649e-01,  4.8805e-01],\\n\",\n      \"           [ 4.8049e-01,  5.1064e-01,  5.6949e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.4643e-03, -7.5236e-03, -2.8658e-02],\\n\",\n      \"           [ 2.9117e-03, -7.4313e-03, -2.1333e-02],\\n\",\n      \"           [ 6.3919e-03, -9.1696e-03, -2.2428e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6243e-02, -8.4251e-03, -2.6967e-02],\\n\",\n      \"           [ 1.4641e-02, -6.1066e-03, -1.7908e-02],\\n\",\n      \"           [ 5.3983e-03, -5.9530e-03, -1.5340e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2885e-02, -2.2372e-03, -2.0039e-02],\\n\",\n      \"           [ 2.1580e-02,  6.6624e-03, -7.4196e-03],\\n\",\n      \"           [ 4.2635e-03,  1.2509e-03, -3.2153e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.5638e-02, -2.8421e-02, -3.1723e-02],\\n\",\n      \"           [-2.8162e-02, -3.0622e-02, -2.4198e-02],\\n\",\n      \"           [-3.2883e-02, -1.9885e-02, -1.3886e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.7169e-02, -5.1043e-02, -4.9285e-02],\\n\",\n      \"           [-2.3683e-02, -2.1515e-02, -2.3145e-02],\\n\",\n      \"           [-3.4400e-02, -1.7527e-02, -1.3298e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.2183e-02, -4.7499e-02, -4.9477e-02],\\n\",\n      \"           [-4.0808e-02, -3.5652e-02, -3.3855e-02],\\n\",\n      \"           [-4.0371e-02, -2.3731e-02, -3.3678e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.4398e-01, -4.1167e-01, -4.9512e-01],\\n\",\n      \"           [-7.2239e-01, -9.1680e-01, -1.0156e+00],\\n\",\n      \"           [-1.1625e+00, -1.3367e+00, -1.5087e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.8045e-01, -1.8196e-01, -3.1090e-01],\\n\",\n      \"           [-7.0156e-01, -8.8448e-01, -9.2879e-01],\\n\",\n      \"           [-1.2114e+00, -1.5346e+00, -1.4656e+00]],\\n\",\n      \"\\n\",\n      \"          [[-3.7382e-01, -2.6937e-01, -3.0362e-01],\\n\",\n      \"           [-6.2328e-01, -8.9285e-01, -8.0814e-01],\\n\",\n      \"           [-1.0218e+00, -1.3958e+00, -1.3815e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.3980e-01, -3.8496e-01,  1.0720e-01],\\n\",\n      \"           [-2.8684e-01,  1.6517e-01,  6.1949e-01],\\n\",\n      \"           [ 2.8792e-01,  7.1784e-01,  1.0148e+00]],\\n\",\n      \"\\n\",\n      \"          [[-3.6156e-01, -8.4364e-02,  4.0819e-01],\\n\",\n      \"           [-3.3765e-01,  1.6006e-01,  5.7851e-01],\\n\",\n      \"           [ 3.7723e-01,  7.5369e-01,  9.0656e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.6949e-01,  9.0720e-02,  4.9987e-01],\\n\",\n      \"           [-5.6026e-02,  3.8927e-01,  7.0662e-01],\\n\",\n      \"           [ 3.8044e-01,  6.9534e-01,  1.0456e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.8634e-02, -4.8164e-03,  1.8864e-02],\\n\",\n      \"           [ 3.3219e-02,  4.2763e-02,  8.7075e-02],\\n\",\n      \"           [-7.1602e-02,  4.7905e-02,  1.2322e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.5501e-02,  3.5470e-02, -1.8888e-02],\\n\",\n      \"           [-5.7055e-02,  3.7327e-02,  9.5419e-02],\\n\",\n      \"           [-1.0987e-01,  1.1597e-03,  1.5576e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.5392e-01, -4.6483e-02, -5.5527e-02],\\n\",\n      \"           [-1.8644e-01, -2.8704e-02,  8.0822e-02],\\n\",\n      \"           [-2.2462e-01, -7.6118e-02,  9.0368e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.0122e-03, -1.7332e-01, -3.0202e-01],\\n\",\n      \"           [ 1.4579e-02, -8.8562e-02, -6.4308e-02],\\n\",\n      \"           [-9.1256e-02,  8.1406e-02,  1.5854e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.8906e-01, -2.8181e-01, -3.1118e-01],\\n\",\n      \"           [-1.7534e-01, -1.9421e-01, -1.7481e-02],\\n\",\n      \"           [-2.7282e-01, -1.6196e-01,  8.8393e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.7790e-01, -3.1427e-01, -2.9090e-01],\\n\",\n      \"           [-3.8871e-01, -4.2860e-01, -2.1029e-01],\\n\",\n      \"           [-3.3820e-01, -2.0941e-01,  1.0520e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.6829e-03,  1.4972e-02,  1.0377e-02],\\n\",\n      \"           [ 1.1363e-02,  1.8044e-02,  2.3949e-02],\\n\",\n      \"           [ 1.4842e-02,  2.1903e-02,  2.7397e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 9.2908e-03,  6.3983e-03,  7.6392e-03],\\n\",\n      \"           [ 1.1700e-02,  1.7169e-02,  1.8728e-02],\\n\",\n      \"           [ 1.3390e-02,  2.3435e-02,  2.9656e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1478e-02,  3.8078e-03,  3.6733e-03],\\n\",\n      \"           [ 7.4001e-03,  9.1057e-03,  1.6993e-02],\\n\",\n      \"           [ 5.2869e-03,  1.6570e-02,  2.7010e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 81.00 %\\n\",\n      \"Val loss: 0.470479\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[17,    60] loss: 0.48687\\n\",\n      \"[17,   120] loss: 0.41175\\n\",\n      \"Time elapsed: 0h:33m:53s\\n\",\n      \"train accuracy_score: 81.21 %\\n\",\n      \"tensor([[[[[-7.1307e-01, -8.2218e-01, -7.9040e-01],\\n\",\n      \"           [-8.0152e-01, -7.9991e-01, -7.4410e-01],\\n\",\n      \"           [-7.3784e-01, -5.2922e-01, -5.0033e-01]],\\n\",\n      \"\\n\",\n      \"          [[-7.6922e-01, -7.5777e-01, -7.1195e-01],\\n\",\n      \"           [-8.1860e-01, -7.6493e-01, -6.0621e-01],\\n\",\n      \"           [-7.5232e-01, -6.1512e-01, -4.9129e-01]],\\n\",\n      \"\\n\",\n      \"          [[-7.9441e-01, -7.9829e-01, -6.8647e-01],\\n\",\n      \"           [-8.0807e-01, -7.0944e-01, -5.5724e-01],\\n\",\n      \"           [-7.2009e-01, -5.8442e-01, -4.5666e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.7002e-04, -1.0256e-02, -7.2970e-03],\\n\",\n      \"           [-1.4671e-02, -2.5530e-02, -1.3660e-02],\\n\",\n      \"           [-4.4443e-02, -2.9976e-02, -4.0576e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2269e-02,  1.2426e-03, -4.8919e-03],\\n\",\n      \"           [-3.9010e-03, -1.3231e-02, -9.7979e-03],\\n\",\n      \"           [-2.2309e-02, -2.0060e-02, -1.1763e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.9989e-02,  1.4294e-02, -5.5535e-03],\\n\",\n      \"           [ 6.5087e-03, -7.3339e-03, -1.0077e-02],\\n\",\n      \"           [-2.2781e-02, -2.2766e-02, -1.0395e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.3438e-02, -4.6638e-02, -6.0550e-02],\\n\",\n      \"           [-4.7306e-02, -4.6705e-02, -4.6869e-02],\\n\",\n      \"           [-5.1326e-02, -3.1845e-02, -2.4139e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.2223e-02, -8.6657e-02, -8.5074e-02],\\n\",\n      \"           [-7.2266e-02, -7.4767e-02, -7.5845e-02],\\n\",\n      \"           [-9.0298e-02, -5.6986e-02, -4.4030e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.4482e-02, -1.1066e-01, -1.1084e-01],\\n\",\n      \"           [-9.7159e-02, -9.2193e-02, -1.0481e-01],\\n\",\n      \"           [-9.5066e-02, -8.2118e-02, -7.3605e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.1595e-01,  5.9424e-01,  7.2946e-01],\\n\",\n      \"           [ 9.0364e-02,  3.8999e-03,  4.0937e-02],\\n\",\n      \"           [-4.3757e-01, -5.0376e-01, -5.5711e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 7.0149e-01,  6.9760e-01,  8.4741e-01],\\n\",\n      \"           [ 1.6744e-01, -1.3484e-01,  1.5314e-01],\\n\",\n      \"           [-3.3199e-01, -6.0852e-01, -4.2857e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.5171e-01,  6.8808e-01,  6.7482e-01],\\n\",\n      \"           [ 2.7488e-01, -8.5248e-02,  1.6409e-01],\\n\",\n      \"           [-4.8946e-02, -5.1292e-01, -3.6892e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.9727e-01, -5.7103e-01, -1.5321e-01],\\n\",\n      \"           [-2.7464e-01, -4.3689e-02,  2.7598e-01],\\n\",\n      \"           [ 2.9390e-01,  4.3667e-01,  8.1930e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.4082e-01, -1.0828e-01,  4.7476e-02],\\n\",\n      \"           [-7.6521e-03,  1.4591e-01,  2.1072e-01],\\n\",\n      \"           [ 3.2100e-01,  5.0601e-01,  6.7890e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.2362e-01,  1.3302e-01,  3.1209e-01],\\n\",\n      \"           [ 1.0799e-01,  3.5493e-01,  3.1547e-01],\\n\",\n      \"           [ 4.3277e-01,  5.6168e-01,  4.5953e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.2636e-01, -6.2689e-02, -1.4929e-01],\\n\",\n      \"           [ 1.6954e-01, -9.0087e-02, -8.9666e-02],\\n\",\n      \"           [ 8.7642e-02, -6.1515e-02, -5.4999e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.0365e-02, -6.5820e-02, -2.0735e-01],\\n\",\n      \"           [-4.5577e-03, -1.1937e-01, -1.8472e-01],\\n\",\n      \"           [ 1.3194e-02, -1.6340e-01, -1.7493e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.1494e-01, -1.9451e-01, -3.3408e-01],\\n\",\n      \"           [-1.3555e-01, -1.7575e-01, -2.3126e-01],\\n\",\n      \"           [-1.2600e-01, -2.4381e-01, -2.0791e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3376e-01,  1.8462e-02, -2.1443e-01],\\n\",\n      \"           [ 1.0691e-02,  1.0697e-02, -1.5479e-01],\\n\",\n      \"           [-1.8401e-02, -8.2443e-02, -7.9719e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4884e-01, -1.2760e-01, -4.1109e-01],\\n\",\n      \"           [ 8.8091e-02, -1.0716e-02, -2.1165e-01],\\n\",\n      \"           [-1.1807e-01, -1.3083e-01, -1.0324e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3042e-01, -2.1242e-01, -3.0304e-01],\\n\",\n      \"           [-1.3731e-01, -2.6536e-01, -2.4895e-01],\\n\",\n      \"           [-2.9603e-01, -2.1494e-01, -8.6669e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.0622e-03, -4.9624e-03, -2.5616e-02],\\n\",\n      \"           [ 7.4663e-03,  3.7259e-03, -6.9517e-03],\\n\",\n      \"           [ 1.4057e-02,  1.1308e-02,  8.0846e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.5943e-03, -9.0733e-03, -2.6871e-02],\\n\",\n      \"           [ 6.2985e-03,  5.0178e-03, -6.5110e-03],\\n\",\n      \"           [ 1.5093e-02,  1.5552e-02,  1.2946e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2877e-03, -7.3957e-03, -1.3314e-02],\\n\",\n      \"           [-1.2512e-04,  4.3257e-03,  1.3368e-03],\\n\",\n      \"           [ 7.5801e-03,  1.9028e-02,  1.9059e-02]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 89.00 %\\n\",\n      \"Val loss: 0.385951\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[18,    60] loss: 0.42497\\n\",\n      \"[18,   120] loss: 0.43877\\n\",\n      \"Time elapsed: 0h:35m:44s\\n\",\n      \"train accuracy_score: 82.35 %\\n\",\n      \"tensor([[[[[-1.0288e-01, -7.1672e-02, -3.3593e-02],\\n\",\n      \"           [-8.4329e-02, -6.0219e-02, -3.2145e-02],\\n\",\n      \"           [-8.7016e-02, -7.6803e-02, -6.0026e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0161e-01, -7.4528e-02, -3.9063e-02],\\n\",\n      \"           [-8.7729e-02, -5.7470e-02, -3.2046e-02],\\n\",\n      \"           [-8.0719e-02, -6.9139e-02, -3.7768e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.9953e-02, -7.6214e-02, -5.7293e-02],\\n\",\n      \"           [-8.5609e-02, -5.7779e-02, -5.1665e-02],\\n\",\n      \"           [-7.8522e-02, -6.1526e-02, -5.5628e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.9604e-03, -2.2222e-03, -1.9651e-03],\\n\",\n      \"           [-1.0059e-03, -1.2223e-03, -9.2962e-04],\\n\",\n      \"           [-1.2418e-03, -1.6983e-03, -2.5559e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.3441e-04, -1.2848e-03, -2.0659e-03],\\n\",\n      \"           [-7.0718e-04, -1.1140e-03, -8.9195e-04],\\n\",\n      \"           [ 1.0050e-03, -9.6858e-04, -1.6598e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.9659e-04, -5.7442e-04, -4.8434e-04],\\n\",\n      \"           [-7.1323e-04, -1.3482e-03, -1.9533e-03],\\n\",\n      \"           [ 1.7430e-03, -6.6961e-04, -4.7782e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.6726e-03,  3.5605e-03,  9.8854e-03],\\n\",\n      \"           [-4.5367e-04,  4.8319e-03,  7.0806e-03],\\n\",\n      \"           [ 3.0421e-03,  3.1118e-03,  6.2711e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.1414e-03,  4.7853e-03,  1.0858e-02],\\n\",\n      \"           [-1.9262e-04,  2.6709e-03,  6.1259e-03],\\n\",\n      \"           [ 2.2510e-03,  2.9859e-03,  4.7883e-03]],\\n\",\n      \"\\n\",\n      \"          [[-6.7506e-04,  4.2257e-03,  1.0168e-02],\\n\",\n      \"           [ 2.5635e-04,  1.5531e-03,  8.2798e-03],\\n\",\n      \"           [ 1.3690e-03,  1.3095e-03,  5.7461e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.8942e-02,  3.5318e-02,  4.7982e-02],\\n\",\n      \"           [ 1.0070e-01,  1.1287e-01,  1.3498e-01],\\n\",\n      \"           [ 1.5505e-01,  1.7684e-01,  2.2809e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.8212e-02,  3.8626e-02,  5.1234e-03],\\n\",\n      \"           [ 8.3579e-02,  1.1433e-01,  1.1827e-01],\\n\",\n      \"           [ 1.4996e-01,  2.2040e-01,  2.1216e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 7.4957e-02,  7.1031e-02,  5.8403e-02],\\n\",\n      \"           [ 8.3965e-02,  1.5026e-01,  1.2407e-01],\\n\",\n      \"           [ 1.2431e-01,  2.2479e-01,  2.3002e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.1946e-02, -3.1122e-02, -6.1545e-02],\\n\",\n      \"           [ 5.7783e-02, -4.9665e-02, -9.6323e-02],\\n\",\n      \"           [ 2.6913e-02, -7.5318e-02, -4.6712e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.5822e-04, -8.8790e-02, -1.0959e-01],\\n\",\n      \"           [ 2.4964e-02, -8.8767e-02, -9.5432e-02],\\n\",\n      \"           [ 7.5978e-03, -1.0573e-01, -9.7605e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.7269e-02, -1.1020e-01, -1.2213e-01],\\n\",\n      \"           [-9.3122e-03, -1.2567e-01, -5.9192e-02],\\n\",\n      \"           [-8.7686e-03, -8.0707e-02, -1.6561e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.4430e-02,  8.3027e-02,  9.2712e-02],\\n\",\n      \"           [ 5.6627e-02,  9.1800e-02,  9.6955e-02],\\n\",\n      \"           [ 7.9451e-02,  9.2764e-02,  8.7737e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.5829e-02,  6.9625e-02,  1.0306e-01],\\n\",\n      \"           [ 6.8258e-02,  9.4672e-02,  1.0323e-01],\\n\",\n      \"           [ 8.2378e-02,  1.0806e-01,  9.7712e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.4980e-02,  7.6369e-02,  9.0548e-02],\\n\",\n      \"           [ 6.4506e-02,  8.2069e-02,  8.3932e-02],\\n\",\n      \"           [ 8.7198e-02,  1.0245e-01,  9.9304e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.7590e-02, -4.7496e-02, -3.6608e-02],\\n\",\n      \"           [-1.0554e-01, -5.5563e-02, -5.0829e-02],\\n\",\n      \"           [-8.4058e-02, -7.3962e-02, -9.0612e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.1189e-02, -6.0730e-02, -5.9481e-02],\\n\",\n      \"           [-1.3076e-01, -5.9302e-02, -9.2078e-02],\\n\",\n      \"           [-1.1491e-01, -1.0574e-01, -1.1194e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.2962e-01, -8.5719e-02, -1.0275e-01],\\n\",\n      \"           [-1.3350e-01, -8.1777e-02, -1.2092e-01],\\n\",\n      \"           [-1.5531e-01, -1.5587e-01, -1.4524e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.1690e-03,  3.0110e-04,  4.0091e-03],\\n\",\n      \"           [-1.3982e-03, -4.7702e-04,  2.9528e-03],\\n\",\n      \"           [-2.0432e-03, -1.2243e-03,  2.0759e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.5355e-04,  9.2192e-04,  3.5952e-03],\\n\",\n      \"           [-2.4570e-04, -5.7196e-04,  2.7442e-03],\\n\",\n      \"           [-3.9554e-04, -1.3154e-03,  1.3597e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 7.4997e-04,  1.8186e-03,  3.5409e-03],\\n\",\n      \"           [ 1.0613e-03,  9.0974e-04,  1.7988e-03],\\n\",\n      \"           [ 2.1560e-04, -7.2397e-04,  7.7763e-04]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 83.00 %\\n\",\n      \"Val loss: 0.433629\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[19,    60] loss: 0.36389\\n\",\n      \"[19,   120] loss: 0.43072\\n\",\n      \"Time elapsed: 0h:37m:36s\\n\",\n      \"train accuracy_score: 82.21 %\\n\",\n      \"tensor([[[[[ 2.0187e-01,  2.2356e-01,  2.3868e-01],\\n\",\n      \"           [ 1.8859e-01,  1.9779e-01,  2.0372e-01],\\n\",\n      \"           [ 1.6944e-01,  1.6013e-01,  1.6545e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1653e-01,  2.4230e-01,  2.5205e-01],\\n\",\n      \"           [ 2.0656e-01,  2.2962e-01,  2.2885e-01],\\n\",\n      \"           [ 1.8504e-01,  1.9366e-01,  1.8928e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3035e-01,  2.6253e-01,  2.6506e-01],\\n\",\n      \"           [ 2.4208e-01,  2.4906e-01,  2.3204e-01],\\n\",\n      \"           [ 2.1597e-01,  2.0939e-01,  2.1080e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.3284e-04,  1.4958e-03,  8.8933e-04],\\n\",\n      \"           [ 1.4222e-03,  2.8979e-03,  2.3752e-03],\\n\",\n      \"           [ 2.3749e-03,  2.4647e-03,  2.5431e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.1041e-04, -4.4075e-04,  6.4582e-04],\\n\",\n      \"           [-1.2022e-03,  4.2361e-04,  1.4679e-03],\\n\",\n      \"           [ 1.6908e-03,  9.7815e-04,  8.9502e-04]],\\n\",\n      \"\\n\",\n      \"          [[-3.1602e-03, -1.7410e-03, -5.5237e-04],\\n\",\n      \"           [-1.9535e-03, -1.7727e-03,  2.0677e-04],\\n\",\n      \"           [ 1.3166e-04,  4.5825e-04, -9.5270e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0783e-02,  1.1751e-02,  1.4556e-02],\\n\",\n      \"           [ 7.1097e-03,  7.8207e-03,  1.0371e-02],\\n\",\n      \"           [ 4.8619e-03,  4.9112e-03,  6.7754e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0476e-02,  1.4383e-02,  1.6122e-02],\\n\",\n      \"           [ 7.0224e-03,  6.1390e-03,  1.0199e-02],\\n\",\n      \"           [ 3.7880e-03,  2.9247e-03,  4.6243e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 8.8084e-03,  1.3325e-02,  1.7071e-02],\\n\",\n      \"           [ 6.3610e-03,  6.8493e-03,  1.1990e-02],\\n\",\n      \"           [ 4.3719e-03,  3.8460e-03,  7.1952e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.2172e-01, -3.6271e-01, -3.9609e-01],\\n\",\n      \"           [-2.4261e-01, -2.8161e-01, -3.3105e-01],\\n\",\n      \"           [-1.5928e-01, -1.6940e-01, -2.3107e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.4950e-01, -3.9017e-01, -4.5453e-01],\\n\",\n      \"           [-2.7147e-01, -2.7064e-01, -3.5644e-01],\\n\",\n      \"           [-1.7835e-01, -1.6395e-01, -2.3953e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.4012e-01, -4.0184e-01, -4.5430e-01],\\n\",\n      \"           [-2.9598e-01, -2.7735e-01, -3.6496e-01],\\n\",\n      \"           [-2.2987e-01, -1.8082e-01, -2.3549e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.2853e-02, -3.1522e-02, -5.0254e-02],\\n\",\n      \"           [-1.8535e-02, -9.5694e-02, -8.4873e-02],\\n\",\n      \"           [-2.0066e-02, -1.3000e-01, -1.2283e-01]],\\n\",\n      \"\\n\",\n      \"          [[-6.1574e-02, -1.1859e-01, -1.3964e-01],\\n\",\n      \"           [-8.5124e-02, -1.2018e-01, -1.2345e-01],\\n\",\n      \"           [-6.2899e-02, -1.2798e-01, -1.1808e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.3535e-01, -1.4612e-01, -1.4515e-01],\\n\",\n      \"           [-1.1748e-01, -1.8813e-01, -1.4371e-01],\\n\",\n      \"           [-1.2767e-01, -1.4851e-01, -1.4728e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.0247e-02, -1.6750e-02,  1.0546e-03],\\n\",\n      \"           [-2.9539e-02, -2.4687e-02, -1.4343e-02],\\n\",\n      \"           [-2.2490e-02, -2.5191e-02, -2.2411e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.7037e-02, -1.1659e-02,  1.1186e-02],\\n\",\n      \"           [-1.0379e-02, -1.1863e-02, -5.6968e-03],\\n\",\n      \"           [-1.0918e-02, -1.2464e-02, -1.0220e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.3745e-03, -1.7676e-03,  1.8298e-02],\\n\",\n      \"           [ 1.1918e-02,  2.3615e-03,  3.5594e-03],\\n\",\n      \"           [ 1.5647e-02,  2.7522e-03, -3.9451e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.3597e-03,  3.0034e-02,  2.1214e-02],\\n\",\n      \"           [-1.1066e-02,  2.9840e-02,  1.9607e-02],\\n\",\n      \"           [-6.1602e-03,  3.7597e-03, -6.8888e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.2070e-03,  3.1170e-02,  4.0378e-02],\\n\",\n      \"           [-9.1577e-03,  2.0709e-02,  7.5679e-03],\\n\",\n      \"           [ 2.9063e-02,  1.5234e-02,  3.5382e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0865e-03,  4.2503e-02,  4.4723e-02],\\n\",\n      \"           [ 4.6765e-02,  4.4941e-02,  1.8324e-02],\\n\",\n      \"           [ 4.2265e-02,  2.2241e-03, -2.2842e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.5895e-04,  1.7003e-03,  3.8307e-03],\\n\",\n      \"           [-3.0612e-04, -7.3583e-04, -7.0258e-04],\\n\",\n      \"           [-2.7189e-03, -2.9989e-03, -2.3099e-03]],\\n\",\n      \"\\n\",\n      \"          [[-6.2799e-04,  5.2117e-04,  3.0597e-03],\\n\",\n      \"           [-1.3067e-03, -1.2474e-03,  3.6370e-04],\\n\",\n      \"           [-1.8272e-03, -2.8984e-03, -2.7646e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.2432e-03, -1.5082e-04,  2.3274e-03],\\n\",\n      \"           [-1.0166e-03, -8.8934e-04,  4.3736e-04],\\n\",\n      \"           [-1.8915e-03, -1.6030e-03, -2.8206e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 79.00 %\\n\",\n      \"Val loss: 0.366630\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[20,    60] loss: 0.36152\\n\",\n      \"[20,   120] loss: 0.35807\\n\",\n      \"Time elapsed: 0h:39m:27s\\n\",\n      \"train accuracy_score: 84.65 %\\n\",\n      \"tensor([[[[[-6.2431e-01, -8.3786e-01, -7.5304e-01],\\n\",\n      \"           [-8.2202e-01, -5.2156e-01, -3.4728e-01],\\n\",\n      \"           [-4.9851e-01, -1.6767e-02,  2.0183e-01]],\\n\",\n      \"\\n\",\n      \"          [[-8.2991e-01, -9.4498e-01, -5.3617e-01],\\n\",\n      \"           [-7.7849e-01, -7.7823e-01, -2.5505e-01],\\n\",\n      \"           [-4.7871e-01, -2.4863e-01,  1.2740e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.7628e-01, -9.2238e-01, -6.3234e-01],\\n\",\n      \"           [-7.0778e-01, -6.9772e-01, -4.7260e-01],\\n\",\n      \"           [-6.5353e-01, -4.0642e-01,  1.1543e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.1359e-02,  1.8087e-02,  1.0321e-03],\\n\",\n      \"           [-1.2620e-02, -1.3601e-02, -2.0250e-03],\\n\",\n      \"           [-4.7839e-02, -1.9223e-02, -1.4613e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0666e-02,  9.1463e-03, -7.6115e-03],\\n\",\n      \"           [ 1.0370e-03, -1.9238e-02, -8.3181e-03],\\n\",\n      \"           [-4.3517e-02, -2.3204e-02, -7.2010e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.0956e-02,  1.0784e-02, -5.4189e-03],\\n\",\n      \"           [ 1.3363e-03, -8.3831e-03, -1.2202e-02],\\n\",\n      \"           [-4.0576e-02, -1.4581e-02, -6.0831e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.4483e-02, -7.0508e-02, -7.2361e-02],\\n\",\n      \"           [-2.3013e-02, -4.6983e-02, -1.5973e-02],\\n\",\n      \"           [-1.2319e-02,  1.7691e-02,  3.3276e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.3053e-02, -7.9598e-02, -5.9877e-02],\\n\",\n      \"           [-2.4758e-02, -4.1736e-03,  8.8996e-03],\\n\",\n      \"           [-2.1372e-02,  3.0231e-02,  7.1421e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.6436e-02, -4.1496e-02, -3.5110e-02],\\n\",\n      \"           [-3.1825e-02,  6.1610e-03,  2.8680e-02],\\n\",\n      \"           [ 8.2489e-03,  4.7053e-02,  7.7308e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.7079e+00, -1.8791e+00, -2.1029e+00],\\n\",\n      \"           [-2.3095e+00, -2.7603e+00, -3.1264e+00],\\n\",\n      \"           [-3.2153e+00, -3.9104e+00, -4.1685e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.6693e+00, -1.7917e+00, -1.9274e+00],\\n\",\n      \"           [-2.2705e+00, -2.9159e+00, -3.0715e+00],\\n\",\n      \"           [-3.2157e+00, -4.3616e+00, -4.2368e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.9006e+00, -2.0413e+00, -2.3064e+00],\\n\",\n      \"           [-2.4248e+00, -3.2967e+00, -3.1785e+00],\\n\",\n      \"           [-3.2860e+00, -4.4085e+00, -4.4329e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3616e+00, -1.0446e+00, -8.7633e-01],\\n\",\n      \"           [-7.1965e-01, -1.2263e-01, -2.9665e-01],\\n\",\n      \"           [-2.0089e-01,  4.6585e-02,  2.8850e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.9047e-01, -7.7659e-01, -1.1000e+00],\\n\",\n      \"           [-5.6211e-01, -6.1078e-01, -8.1693e-01],\\n\",\n      \"           [-2.7165e-02, -4.1277e-02, -6.7592e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.8427e-01, -1.0111e+00, -8.3002e-01],\\n\",\n      \"           [-8.2952e-01, -5.4353e-01, -7.1388e-01],\\n\",\n      \"           [-3.6231e-01, -6.0008e-01, -9.6860e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.7156e-01, -2.2856e-01, -1.5203e-01],\\n\",\n      \"           [-1.4159e-02, -7.7831e-02,  7.9145e-02],\\n\",\n      \"           [ 3.5823e-02,  1.6450e-01,  2.8748e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.2122e-01, -9.9113e-02, -1.9917e-01],\\n\",\n      \"           [-1.4830e-01, -1.7498e-02,  1.2518e-01],\\n\",\n      \"           [-1.3746e-02,  2.5361e-01,  2.7842e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.9992e-01, -1.0659e-01, -1.8167e-01],\\n\",\n      \"           [-4.4947e-02,  1.1845e-01,  2.8519e-01],\\n\",\n      \"           [ 4.0239e-02,  2.2006e-01,  3.6217e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.8881e-01,  6.8500e-02,  4.0904e-01],\\n\",\n      \"           [-1.2151e-01,  1.2111e-01,  2.6601e-01],\\n\",\n      \"           [-2.1787e-01,  1.0135e-01,  1.7305e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.1151e-02, -2.3243e-02, -4.3906e-02],\\n\",\n      \"           [-1.8032e-01,  7.8300e-02,  3.0551e-01],\\n\",\n      \"           [-3.0483e-01, -1.7973e-01,  2.6963e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3030e-01,  1.5827e-02, -4.3948e-02],\\n\",\n      \"           [-1.0242e-01, -5.5664e-02,  6.6495e-02],\\n\",\n      \"           [-3.5428e-01, -9.0373e-02,  2.3803e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.4380e-02,  4.0289e-02,  1.5731e-02],\\n\",\n      \"           [ 4.5163e-02,  4.9483e-02,  5.7411e-02],\\n\",\n      \"           [ 5.8962e-02,  8.2851e-02,  9.9404e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.3030e-02,  2.3806e-02,  1.9175e-02],\\n\",\n      \"           [ 3.2377e-02,  4.8333e-02,  5.6620e-02],\\n\",\n      \"           [ 5.7515e-02,  7.9832e-02,  9.9536e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1970e-02,  1.7139e-02,  1.1734e-02],\\n\",\n      \"           [ 2.3912e-02,  4.1247e-02,  5.4850e-02],\\n\",\n      \"           [ 5.0416e-02,  7.6723e-02,  1.0190e-01]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 80.00 %\\n\",\n      \"Val loss: 0.416734\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[21,    60] loss: 0.35707\\n\",\n      \"[21,   120] loss: 0.35496\\n\",\n      \"Time elapsed: 0h:41m:19s\\n\",\n      \"train accuracy_score: 84.65 %\\n\",\n      \"tensor([[[[[ 3.7032e-01,  4.5676e-01,  4.8814e-01],\\n\",\n      \"           [ 4.0827e-01,  3.7723e-01,  3.9870e-01],\\n\",\n      \"           [ 3.0836e-01,  1.7258e-01,  1.9951e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.7119e-01,  5.1777e-01,  3.9845e-01],\\n\",\n      \"           [ 4.4254e-01,  4.8809e-01,  3.1587e-01],\\n\",\n      \"           [ 3.0691e-01,  3.2060e-01,  2.0716e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.5915e-01,  4.3505e-01,  3.7729e-01],\\n\",\n      \"           [ 3.3257e-01,  3.4101e-01,  2.1372e-01],\\n\",\n      \"           [ 3.3453e-01,  2.2778e-01,  2.1217e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.7400e-03,  4.0205e-03,  2.6982e-04],\\n\",\n      \"           [-1.9831e-03,  3.8050e-03, -2.5775e-03],\\n\",\n      \"           [ 8.3183e-03,  3.8962e-03, -5.5697e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.4330e-03, -1.3643e-03, -4.5792e-03],\\n\",\n      \"           [-1.0687e-03, -4.3866e-03, -7.8071e-03],\\n\",\n      \"           [ 1.1379e-02,  1.1294e-02, -5.1337e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.1655e-02, -5.5767e-03, -1.3252e-03],\\n\",\n      \"           [-8.0804e-03, -5.8417e-03, -7.9837e-03],\\n\",\n      \"           [ 6.1008e-03,  4.1920e-03, -6.7964e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.9761e-02,  5.3239e-02,  7.1625e-02],\\n\",\n      \"           [ 2.4969e-02,  4.3384e-02,  6.0665e-02],\\n\",\n      \"           [ 3.6616e-02,  3.0121e-02,  2.9051e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5553e-02,  5.0966e-02,  7.9252e-02],\\n\",\n      \"           [ 1.4622e-02,  2.4637e-02,  5.6296e-02],\\n\",\n      \"           [ 1.5173e-02,  8.1647e-03,  1.3739e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4202e-02,  3.0730e-02,  6.5628e-02],\\n\",\n      \"           [ 1.2736e-02,  1.0256e-02,  3.3716e-02],\\n\",\n      \"           [-8.3495e-03, -3.7473e-03,  2.4147e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.2009e-01, -5.1578e-01, -6.3088e-01],\\n\",\n      \"           [-1.9415e-01, -1.7892e-01, -2.1601e-01],\\n\",\n      \"           [ 1.7888e-01,  2.2892e-01,  1.8789e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.7200e-01, -6.9920e-01, -7.7343e-01],\\n\",\n      \"           [-2.2106e-01, -8.3929e-02, -2.5515e-01],\\n\",\n      \"           [ 1.9242e-01,  4.4052e-01,  2.1089e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.8270e-01, -7.5096e-01, -5.7007e-01],\\n\",\n      \"           [-1.7254e-01, -5.7057e-02, -1.8255e-01],\\n\",\n      \"           [ 9.1958e-02,  4.5760e-01,  3.3111e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.9729e-01,  1.5188e-01,  7.2171e-02],\\n\",\n      \"           [ 5.6044e-02, -1.6165e-01, -2.1173e-01],\\n\",\n      \"           [-7.7701e-02, -1.3591e-01, -3.0996e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2120e-01,  1.6263e-02, -2.0826e-01],\\n\",\n      \"           [-1.6991e-02, -1.0870e-01, -2.6025e-01],\\n\",\n      \"           [-3.0220e-02, -2.5082e-01, -2.7775e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.5648e-01, -2.0361e-01, -3.4253e-01],\\n\",\n      \"           [-2.6106e-01, -1.9726e-01, -1.6506e-01],\\n\",\n      \"           [-2.5196e-01, -2.9862e-01, -7.4484e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.0979e-01, -5.1485e-02,  1.7667e-02],\\n\",\n      \"           [-1.0427e-01, -3.9638e-02, -4.4275e-02],\\n\",\n      \"           [-2.8542e-02, -5.1414e-02, -6.2282e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.9157e-02,  7.4437e-03,  9.8214e-02],\\n\",\n      \"           [ 2.9822e-02,  2.1165e-02,  3.3665e-02],\\n\",\n      \"           [ 2.2805e-02, -2.0523e-03,  1.9259e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 8.5149e-02,  9.0932e-02,  1.5309e-01],\\n\",\n      \"           [ 1.1524e-01,  8.3232e-02,  9.0150e-02],\\n\",\n      \"           [ 1.3419e-01,  1.1318e-01,  1.0023e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.8578e-02,  2.0399e-01,  3.5843e-01],\\n\",\n      \"           [ 2.0443e-01,  2.3859e-01,  3.9203e-01],\\n\",\n      \"           [ 3.0240e-01,  3.1122e-01,  4.6195e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0925e-02,  2.3933e-01,  4.3323e-01],\\n\",\n      \"           [ 9.6507e-02,  3.0099e-01,  5.1192e-01],\\n\",\n      \"           [ 3.5482e-01,  4.0764e-01,  4.9380e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.4867e-02,  2.9845e-01,  4.4706e-01],\\n\",\n      \"           [ 2.7243e-01,  3.9948e-01,  5.5173e-01],\\n\",\n      \"           [ 4.0613e-01,  3.9528e-01,  5.0083e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.5801e-02, -2.1538e-02, -3.4263e-03],\\n\",\n      \"           [-2.1418e-02, -2.3791e-02, -1.6186e-02],\\n\",\n      \"           [-2.7624e-02, -2.9594e-02, -2.4925e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.7139e-02, -1.0544e-02,  1.3858e-03],\\n\",\n      \"           [-1.1598e-02, -1.7571e-02, -1.3091e-02],\\n\",\n      \"           [-1.8251e-02, -2.9930e-02, -2.6916e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.3608e-02, -3.9878e-03,  3.2865e-03],\\n\",\n      \"           [-4.7740e-03, -9.6267e-03, -9.7134e-03],\\n\",\n      \"           [-2.5285e-03, -2.0410e-02, -3.0209e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 87.00 %\\n\",\n      \"Val loss: 0.303808\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[22,    60] loss: 0.37192\\n\",\n      \"[22,   120] loss: 0.33600\\n\",\n      \"Time elapsed: 0h:43m:11s\\n\",\n      \"train accuracy_score: 86.80 %\\n\",\n      \"tensor([[[[[ 9.0806e-02,  1.1695e-01,  1.1397e-01],\\n\",\n      \"           [ 1.0908e-01,  1.1204e-01,  9.8379e-02],\\n\",\n      \"           [ 9.5392e-02,  7.3852e-02,  7.1674e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 9.8298e-02,  1.0864e-01,  9.3952e-02],\\n\",\n      \"           [ 1.0074e-01,  1.1077e-01,  8.2910e-02],\\n\",\n      \"           [ 9.6416e-02,  8.5591e-02,  7.4961e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 8.4147e-02,  9.1896e-02,  8.4163e-02],\\n\",\n      \"           [ 8.2138e-02,  8.8117e-02,  6.0220e-02],\\n\",\n      \"           [ 7.3172e-02,  7.9290e-02,  7.3144e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.9485e-03, -4.2175e-04,  1.5523e-03],\\n\",\n      \"           [-2.1985e-03,  1.2530e-03,  2.9807e-03],\\n\",\n      \"           [ 6.8647e-05,  2.6096e-03,  3.8876e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.5036e-03,  1.6434e-03,  2.6287e-03],\\n\",\n      \"           [-1.5084e-03,  2.5660e-04,  2.1896e-03],\\n\",\n      \"           [ 1.4701e-03,  4.6757e-04,  4.3302e-04]],\\n\",\n      \"\\n\",\n      \"          [[-5.5660e-03,  6.5905e-04,  3.4571e-03],\\n\",\n      \"           [-3.4568e-03, -9.8031e-05,  2.6254e-04],\\n\",\n      \"           [ 9.7455e-04, -2.8802e-04, -1.8538e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.6073e-03,  2.3057e-03,  2.4879e-03],\\n\",\n      \"           [-7.7923e-04, -1.6811e-03, -3.0258e-03],\\n\",\n      \"           [-3.4266e-03, -9.6168e-03, -1.0092e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.2637e-05,  3.8213e-03,  1.2034e-03],\\n\",\n      \"           [-4.0169e-04, -1.4621e-03, -8.2602e-03],\\n\",\n      \"           [-9.4158e-04, -1.0710e-02, -1.3814e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0450e-03,  9.3410e-04, -2.5073e-04],\\n\",\n      \"           [ 2.7402e-03, -2.6935e-03, -7.1932e-03],\\n\",\n      \"           [-3.1499e-03, -1.0669e-02, -1.0750e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.7817e-02,  2.7275e-02,  6.2620e-02],\\n\",\n      \"           [ 1.5652e-01,  1.8295e-01,  2.1521e-01],\\n\",\n      \"           [ 2.6546e-01,  3.0482e-01,  3.1839e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.3809e-02,  5.4476e-03, -2.5011e-02],\\n\",\n      \"           [ 1.6494e-01,  1.6260e-01,  1.7599e-01],\\n\",\n      \"           [ 2.6114e-01,  3.2513e-01,  3.2683e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.0001e-02,  2.6988e-02,  1.2667e-02],\\n\",\n      \"           [ 1.1905e-01,  1.8448e-01,  1.3729e-01],\\n\",\n      \"           [ 2.3555e-01,  3.2012e-01,  3.0402e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.6950e-01,  9.5027e-02,  7.3109e-02],\\n\",\n      \"           [ 7.0053e-02,  3.9561e-02, -3.4639e-02],\\n\",\n      \"           [ 2.3108e-02, -8.1061e-02, -1.2550e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 8.6113e-02,  5.7809e-02,  5.4639e-02],\\n\",\n      \"           [ 9.7799e-02,  3.7020e-02, -2.7838e-03],\\n\",\n      \"           [ 1.7688e-02, -7.8658e-02, -9.8895e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.2569e-02,  6.3759e-02,  5.8497e-02],\\n\",\n      \"           [ 3.8161e-02,  3.6890e-02,  1.3368e-02],\\n\",\n      \"           [-3.7548e-02, -2.3157e-02, -3.6911e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.6809e-02, -1.6374e-02, -1.8537e-02],\\n\",\n      \"           [-1.3520e-02, -1.7471e-02, -1.6848e-02],\\n\",\n      \"           [-4.2798e-03, -2.4735e-02, -2.1435e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.8023e-03, -1.2854e-02, -1.0876e-02],\\n\",\n      \"           [-1.7084e-03, -1.7208e-02, -2.7990e-02],\\n\",\n      \"           [ 7.6142e-03, -1.3651e-02, -3.2856e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2280e-02, -1.0834e-03, -4.5796e-03],\\n\",\n      \"           [ 1.7232e-02, -5.5522e-03, -2.2783e-02],\\n\",\n      \"           [ 1.6796e-02, -5.7613e-03, -2.0281e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0597e-02,  1.2447e-02,  8.1036e-02],\\n\",\n      \"           [ 1.5443e-02,  3.6906e-02,  4.5602e-02],\\n\",\n      \"           [ 5.4130e-02,  5.6481e-02,  1.5391e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.6160e-03,  4.0042e-03,  3.3855e-02],\\n\",\n      \"           [-1.4454e-02,  8.5283e-03,  2.8962e-02],\\n\",\n      \"           [ 4.8426e-02,  3.3531e-02,  1.5380e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.3885e-02, -4.6591e-03, -7.1564e-03],\\n\",\n      \"           [ 2.2247e-02, -5.2851e-03, -6.2402e-03],\\n\",\n      \"           [ 5.0735e-02,  3.1676e-03, -2.0525e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.4178e-03, -8.7710e-03, -8.8685e-03],\\n\",\n      \"           [-8.5870e-03, -1.0993e-02, -1.2989e-02],\\n\",\n      \"           [-9.5972e-03, -1.4144e-02, -1.5921e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.5204e-03, -6.7822e-03, -6.1258e-03],\\n\",\n      \"           [-6.1530e-03, -8.8266e-03, -1.0590e-02],\\n\",\n      \"           [-7.5009e-03, -1.2036e-02, -1.4175e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.5725e-03, -6.2415e-03, -5.7308e-03],\\n\",\n      \"           [-5.6794e-03, -7.1569e-03, -1.0883e-02],\\n\",\n      \"           [-6.8427e-03, -1.2278e-02, -1.5086e-02]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 90.00 %\\n\",\n      \"Val loss: 0.331052\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[23,    60] loss: 0.34400\\n\",\n      \"[23,   120] loss: 0.33474\\n\",\n      \"Time elapsed: 0h:45m:4s\\n\",\n      \"train accuracy_score: 88.38 %\\n\",\n      \"tensor([[[[[-2.6938e-01, -2.4852e-01, -1.4689e-01],\\n\",\n      \"           [-3.2330e-01, -2.3967e-01, -9.9087e-02],\\n\",\n      \"           [-2.8309e-01, -7.5300e-02,  5.4653e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.3217e-01, -2.1281e-01, -2.2252e-02],\\n\",\n      \"           [-2.9418e-01, -2.1115e-01, -2.6866e-02],\\n\",\n      \"           [-2.2196e-01, -1.3010e-01,  5.8492e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.3541e-01, -1.6422e-01,  1.1941e-02],\\n\",\n      \"           [-1.6847e-01, -1.4949e-01, -2.4440e-02],\\n\",\n      \"           [-1.7338e-01, -9.1062e-02,  9.9261e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.5648e-02, -7.3985e-04, -1.7669e-02],\\n\",\n      \"           [ 1.5236e-02,  9.2823e-04, -1.1000e-02],\\n\",\n      \"           [ 1.1077e-02,  5.3680e-03, -6.8247e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9080e-02, -5.2256e-03, -2.0420e-02],\\n\",\n      \"           [ 1.6285e-02,  1.4972e-03, -9.7456e-03],\\n\",\n      \"           [ 6.6380e-03,  1.6732e-03, -7.9273e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0561e-02, -4.1411e-03, -2.2242e-02],\\n\",\n      \"           [ 1.0710e-02, -4.8571e-05, -1.5444e-02],\\n\",\n      \"           [ 4.3355e-05, -4.1411e-03, -1.3951e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.2988e-03, -1.3092e-02, -1.3827e-02],\\n\",\n      \"           [ 6.8524e-03, -5.2783e-03,  8.0679e-04],\\n\",\n      \"           [ 4.5315e-03,  1.5473e-02,  3.2890e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.6279e-03, -7.1022e-03,  2.0989e-03],\\n\",\n      \"           [ 1.4765e-02,  1.8024e-02,  2.2395e-02],\\n\",\n      \"           [ 1.1324e-02,  3.0531e-02,  4.5312e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5322e-02,  1.0703e-02,  1.6234e-02],\\n\",\n      \"           [ 2.2745e-02,  3.4859e-02,  3.2259e-02],\\n\",\n      \"           [ 2.6695e-02,  4.2631e-02,  4.5550e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.5487e-01, -9.9977e-01, -1.0974e+00],\\n\",\n      \"           [-1.1886e+00, -1.3886e+00, -1.4786e+00],\\n\",\n      \"           [-1.4839e+00, -1.7372e+00, -1.9298e+00]],\\n\",\n      \"\\n\",\n      \"          [[-9.2127e-01, -9.0328e-01, -1.0483e+00],\\n\",\n      \"           [-1.1943e+00, -1.3975e+00, -1.5124e+00],\\n\",\n      \"           [-1.5439e+00, -1.8297e+00, -1.9570e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.0798e+00, -1.1537e+00, -1.2643e+00],\\n\",\n      \"           [-1.2994e+00, -1.5723e+00, -1.6332e+00],\\n\",\n      \"           [-1.5115e+00, -1.9307e+00, -2.0139e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.4191e-01, -5.7005e-01, -3.6076e-01],\\n\",\n      \"           [-5.5139e-01, -3.0900e-01, -5.6563e-02],\\n\",\n      \"           [-3.4503e-01, -6.7749e-02,  5.2589e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.5334e-01, -3.7421e-01, -3.3255e-01],\\n\",\n      \"           [-4.7297e-01, -2.7307e-01, -2.7468e-01],\\n\",\n      \"           [-2.8274e-01, -6.5716e-02, -6.1993e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.0263e-01, -3.3541e-01, -2.1686e-01],\\n\",\n      \"           [-3.7252e-01, -2.5345e-01, -2.0606e-01],\\n\",\n      \"           [-1.9028e-01, -7.2299e-02, -2.6700e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.9840e-03,  5.2995e-02,  1.2634e-01],\\n\",\n      \"           [ 5.4326e-02,  6.8709e-02,  1.7651e-01],\\n\",\n      \"           [ 6.4635e-02,  1.4041e-01,  2.4638e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.1010e-02, -5.8348e-03,  4.3679e-02],\\n\",\n      \"           [-3.0670e-02,  4.9847e-02,  1.6839e-01],\\n\",\n      \"           [ 1.9170e-02,  1.2251e-01,  2.7836e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.0532e-01, -6.5809e-02,  1.8692e-02],\\n\",\n      \"           [-9.4128e-02,  9.5718e-03,  1.6341e-01],\\n\",\n      \"           [-3.8693e-02,  1.0150e-01,  2.2178e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.0607e-02, -1.5486e-01, -1.3516e-01],\\n\",\n      \"           [-1.6275e-01, -1.9598e-01, -1.7351e-01],\\n\",\n      \"           [-2.6078e-01, -2.0962e-01, -1.7007e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.8793e-02, -1.5848e-01, -1.3715e-01],\\n\",\n      \"           [-2.5848e-01, -2.8739e-01, -1.6158e-01],\\n\",\n      \"           [-3.4049e-01, -2.9326e-01, -1.2232e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.3196e-02, -7.2705e-02, -1.1678e-01],\\n\",\n      \"           [-2.1532e-01, -1.4660e-01, -1.0154e-01],\\n\",\n      \"           [-3.7142e-01, -2.1527e-01, -8.5242e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3918e-02,  9.5500e-03, -9.8546e-04],\\n\",\n      \"           [ 1.6894e-02,  2.1995e-02,  1.8483e-02],\\n\",\n      \"           [ 2.1763e-02,  3.4207e-02,  3.4460e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5135e-02,  6.7193e-03, -3.4358e-03],\\n\",\n      \"           [ 1.2186e-02,  1.5391e-02,  1.5091e-02],\\n\",\n      \"           [ 1.6929e-02,  2.8741e-02,  3.1898e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7107e-02,  4.5757e-03,  2.2127e-04],\\n\",\n      \"           [ 9.6338e-03,  1.4819e-02,  1.7419e-02],\\n\",\n      \"           [ 1.1588e-02,  2.7438e-02,  3.4310e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 80.00 %\\n\",\n      \"Val loss: 0.527531\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[24,    60] loss: 0.30276\\n\",\n      \"[24,   120] loss: 0.31221\\n\",\n      \"Time elapsed: 0h:46m:57s\\n\",\n      \"train accuracy_score: 89.38 %\\n\",\n      \"tensor([[[[[-8.8401e-02, -7.3262e-02, -5.7374e-02],\\n\",\n      \"           [-1.0290e-01, -7.8917e-02, -5.8080e-02],\\n\",\n      \"           [-1.0280e-01, -6.4195e-02, -3.3921e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0574e-01, -9.3079e-02, -7.1603e-02],\\n\",\n      \"           [-9.9927e-02, -8.1815e-02, -6.4322e-02],\\n\",\n      \"           [-8.8224e-02, -6.0267e-02, -5.8642e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0814e-01, -8.6875e-02, -7.3772e-02],\\n\",\n      \"           [-1.0522e-01, -7.4320e-02, -6.2779e-02],\\n\",\n      \"           [-1.0667e-01, -6.2175e-02, -5.8407e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.8423e-03, -5.9154e-03, -8.0360e-03],\\n\",\n      \"           [-5.5873e-03, -6.0225e-03, -6.1060e-03],\\n\",\n      \"           [-5.6633e-03, -5.3265e-03, -5.2649e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 8.6621e-04, -2.3903e-03, -5.9830e-03],\\n\",\n      \"           [-5.3086e-04, -1.7434e-03, -5.2518e-03],\\n\",\n      \"           [-2.0277e-03, -2.1844e-03, -1.6276e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6556e-03,  5.3032e-04, -2.9974e-03],\\n\",\n      \"           [ 3.2358e-03,  1.2777e-03, -1.3765e-03],\\n\",\n      \"           [ 3.3053e-04,  1.8778e-04, -1.0099e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.4462e-03,  6.1763e-03,  5.9959e-03],\\n\",\n      \"           [ 5.3241e-03,  7.0956e-03,  7.6606e-03],\\n\",\n      \"           [ 3.3102e-03,  8.1444e-03,  9.2017e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.7414e-03,  4.4712e-03,  7.9934e-03],\\n\",\n      \"           [ 3.5014e-03,  8.3670e-03,  1.1158e-02],\\n\",\n      \"           [ 4.2418e-03,  1.1470e-02,  1.4015e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.5698e-03,  6.1841e-03,  8.9652e-03],\\n\",\n      \"           [ 1.6055e-03,  6.6784e-03,  1.0667e-02],\\n\",\n      \"           [ 2.7056e-03,  1.0310e-02,  1.0783e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1507e-01, -1.2087e-01, -1.2549e-01],\\n\",\n      \"           [-1.5235e-01, -1.6205e-01, -1.9444e-01],\\n\",\n      \"           [-1.8029e-01, -2.4549e-01, -2.4262e-01]],\\n\",\n      \"\\n\",\n      \"          [[-6.4265e-02, -8.4308e-02, -9.0436e-02],\\n\",\n      \"           [-1.1830e-01, -1.7662e-01, -1.7831e-01],\\n\",\n      \"           [-1.7549e-01, -2.5350e-01, -2.3234e-01]],\\n\",\n      \"\\n\",\n      \"          [[-8.1543e-02, -1.0388e-01, -1.1717e-01],\\n\",\n      \"           [-1.1799e-01, -1.7525e-01, -1.6265e-01],\\n\",\n      \"           [-1.8123e-01, -2.3759e-01, -2.2315e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.3536e-01, -1.7499e-01, -1.3749e-01],\\n\",\n      \"           [-1.9997e-01, -1.4025e-01, -9.4140e-02],\\n\",\n      \"           [-1.1680e-01, -6.4305e-02, -6.0293e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.7903e-01, -1.6039e-01, -1.5685e-01],\\n\",\n      \"           [-8.4791e-02, -1.4034e-01, -1.1589e-01],\\n\",\n      \"           [-8.3464e-02, -5.8842e-02, -7.6646e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.7191e-01, -1.6715e-01, -1.6009e-01],\\n\",\n      \"           [-1.1969e-01, -1.1961e-01, -1.1496e-01],\\n\",\n      \"           [-7.6546e-02, -6.3557e-02, -8.4631e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.9423e-02,  6.3896e-02,  9.1039e-02],\\n\",\n      \"           [ 9.9322e-02,  1.0582e-01,  1.3284e-01],\\n\",\n      \"           [ 1.2831e-01,  1.4390e-01,  1.4468e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.9675e-02,  6.1577e-02,  8.1215e-02],\\n\",\n      \"           [ 7.9338e-02,  9.6830e-02,  1.1967e-01],\\n\",\n      \"           [ 1.1524e-01,  1.2565e-01,  1.4047e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.4759e-02,  7.1884e-02,  7.9021e-02],\\n\",\n      \"           [ 7.3301e-02,  9.2777e-02,  1.0722e-01],\\n\",\n      \"           [ 8.7869e-02,  9.9327e-02,  1.2403e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1825e-01, -1.0736e-01, -7.7990e-02],\\n\",\n      \"           [-1.1596e-01, -8.4931e-02, -8.0081e-02],\\n\",\n      \"           [-1.1392e-01, -1.1220e-01, -8.4085e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.2967e-01, -1.2893e-01, -1.1884e-01],\\n\",\n      \"           [-1.5656e-01, -1.2907e-01, -9.2094e-02],\\n\",\n      \"           [-1.7803e-01, -1.6134e-01, -1.3162e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.3323e-01, -1.4505e-01, -1.5819e-01],\\n\",\n      \"           [-1.9591e-01, -1.8642e-01, -1.4426e-01],\\n\",\n      \"           [-2.1738e-01, -2.0127e-01, -1.6895e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.6425e-02,  1.4198e-02,  1.6225e-02],\\n\",\n      \"           [ 1.4493e-02,  1.6995e-02,  2.2760e-02],\\n\",\n      \"           [ 1.7396e-02,  2.3250e-02,  2.7410e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3115e-02,  1.2256e-02,  1.4276e-02],\\n\",\n      \"           [ 1.1875e-02,  1.4848e-02,  2.0831e-02],\\n\",\n      \"           [ 1.3766e-02,  2.0763e-02,  2.6810e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0984e-02,  1.1065e-02,  1.3864e-02],\\n\",\n      \"           [ 1.0940e-02,  1.4381e-02,  1.9864e-02],\\n\",\n      \"           [ 1.2523e-02,  1.8850e-02,  2.6446e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 86.00 %\\n\",\n      \"Val loss: 0.308637\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[25,    60] loss: 0.24906\\n\",\n      \"[25,   120] loss: 0.30885\\n\",\n      \"Time elapsed: 0h:48m:51s\\n\",\n      \"train accuracy_score: 88.38 %\\n\",\n      \"tensor([[[[[-3.2483e-01, -4.0767e-01, -4.6229e-01],\\n\",\n      \"           [-3.6581e-01, -3.6520e-01, -3.4491e-01],\\n\",\n      \"           [-3.3541e-01, -2.5688e-01, -2.2953e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.1844e-01, -3.8807e-01, -3.8330e-01],\\n\",\n      \"           [-3.4693e-01, -3.8593e-01, -3.5261e-01],\\n\",\n      \"           [-3.4155e-01, -3.2388e-01, -2.8151e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.3234e-01, -3.8496e-01, -3.7591e-01],\\n\",\n      \"           [-3.5220e-01, -3.7590e-01, -3.1351e-01],\\n\",\n      \"           [-3.5577e-01, -3.2354e-01, -3.0577e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.7328e-02,  8.8448e-03,  2.1977e-03],\\n\",\n      \"           [ 1.6373e-02,  9.8908e-03,  2.0716e-03],\\n\",\n      \"           [ 1.1066e-02,  7.5001e-03,  9.3633e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5674e-02,  7.4819e-03, -1.1381e-03],\\n\",\n      \"           [ 8.8992e-03,  2.2094e-03, -8.9042e-04],\\n\",\n      \"           [ 3.6681e-03,  3.2317e-03,  3.6935e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1265e-02,  2.5935e-03, -6.9727e-03],\\n\",\n      \"           [ 4.3853e-03,  3.4317e-03, -2.5905e-03],\\n\",\n      \"           [-1.0186e-03,  9.2609e-04,  5.1400e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.3796e-02,  3.3299e-02,  2.3479e-02],\\n\",\n      \"           [ 4.1938e-02,  3.2567e-02,  2.2711e-02],\\n\",\n      \"           [ 3.4444e-02,  3.6997e-02,  3.1230e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.8850e-02,  4.9520e-02,  5.0141e-02],\\n\",\n      \"           [ 5.4784e-02,  5.3053e-02,  4.8572e-02],\\n\",\n      \"           [ 4.3860e-02,  5.2681e-02,  4.6679e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.6198e-02,  6.9623e-02,  7.0794e-02],\\n\",\n      \"           [ 7.4769e-02,  7.5434e-02,  7.4168e-02],\\n\",\n      \"           [ 6.8929e-02,  7.5060e-02,  6.6058e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.8043e-01, -6.7726e-01, -7.0962e-01],\\n\",\n      \"           [-7.1908e-01, -9.1651e-01, -1.0371e+00],\\n\",\n      \"           [-1.0178e+00, -1.2546e+00, -1.3374e+00]],\\n\",\n      \"\\n\",\n      \"          [[-6.0088e-01, -5.9015e-01, -6.4499e-01],\\n\",\n      \"           [-7.0351e-01, -8.7004e-01, -9.7553e-01],\\n\",\n      \"           [-9.5174e-01, -1.1941e+00, -1.2778e+00]],\\n\",\n      \"\\n\",\n      \"          [[-6.8628e-01, -6.6826e-01, -7.1180e-01],\\n\",\n      \"           [-7.0434e-01, -8.6804e-01, -9.3128e-01],\\n\",\n      \"           [-8.0733e-01, -1.1068e+00, -1.1600e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.6737e-01, -1.9232e-01, -7.3817e-02],\\n\",\n      \"           [-9.0061e-03,  1.9600e-02,  6.4325e-02],\\n\",\n      \"           [ 1.1473e-01,  1.8370e-01,  1.8134e-01]],\\n\",\n      \"\\n\",\n      \"          [[-6.0798e-02, -9.2834e-02, -8.3386e-02],\\n\",\n      \"           [ 5.0160e-02,  1.5060e-01,  1.6292e-01],\\n\",\n      \"           [ 2.3659e-02,  2.0700e-01,  1.9726e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.3759e-02, -7.4202e-02, -1.0358e-01],\\n\",\n      \"           [-4.3322e-02,  1.3526e-02, -1.6666e-02],\\n\",\n      \"           [-1.8271e-04,  4.9069e-02,  8.8111e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.9199e-01,  3.4147e-01,  3.7465e-01],\\n\",\n      \"           [ 4.1585e-01,  4.8384e-01,  4.8984e-01],\\n\",\n      \"           [ 4.7213e-01,  4.8720e-01,  4.8911e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6094e-01,  2.9261e-01,  3.1878e-01],\\n\",\n      \"           [ 3.4285e-01,  3.9998e-01,  4.3738e-01],\\n\",\n      \"           [ 3.6242e-01,  4.2375e-01,  4.6782e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9974e-01,  2.7090e-01,  2.9924e-01],\\n\",\n      \"           [ 2.2850e-01,  2.8985e-01,  3.8172e-01],\\n\",\n      \"           [ 2.4458e-01,  3.2623e-01,  4.0608e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.2253e-01, -1.6865e-01, -1.8934e-01],\\n\",\n      \"           [-2.3144e-01, -2.4824e-01, -1.6707e-01],\\n\",\n      \"           [-2.8941e-01, -2.6460e-01, -1.3049e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.7234e-01, -2.0140e-01, -2.0886e-01],\\n\",\n      \"           [-3.2765e-01, -2.8497e-01, -2.1908e-01],\\n\",\n      \"           [-3.4748e-01, -3.2221e-01, -2.1013e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.8751e-01, -2.3441e-01, -2.3689e-01],\\n\",\n      \"           [-4.0008e-01, -3.1129e-01, -2.1807e-01],\\n\",\n      \"           [-4.0734e-01, -3.0970e-01, -2.2373e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.7019e-02,  3.5337e-02,  3.0086e-02],\\n\",\n      \"           [ 4.0421e-02,  4.5231e-02,  4.9616e-02],\\n\",\n      \"           [ 5.0798e-02,  6.0531e-02,  6.9751e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.0078e-02,  2.5643e-02,  2.0863e-02],\\n\",\n      \"           [ 3.1015e-02,  3.4808e-02,  3.9497e-02],\\n\",\n      \"           [ 4.3272e-02,  5.7566e-02,  6.3404e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6804e-02,  1.9706e-02,  1.9710e-02],\\n\",\n      \"           [ 2.6599e-02,  3.0799e-02,  3.7765e-02],\\n\",\n      \"           [ 3.5535e-02,  5.3521e-02,  6.2463e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 88.00 %\\n\",\n      \"Val loss: 0.318689\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[26,    60] loss: 0.27969\\n\",\n      \"[26,   120] loss: 0.26182\\n\",\n      \"Time elapsed: 0h:50m:46s\\n\",\n      \"train accuracy_score: 89.96 %\\n\",\n      \"tensor([[[[[-6.0581e-02,  3.3146e-02,  3.0867e-02],\\n\",\n      \"           [ 9.9902e-03,  1.0201e-03, -2.9079e-02],\\n\",\n      \"           [-5.7123e-02, -1.6783e-01, -1.0930e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9822e-02,  5.1585e-02,  2.3848e-03],\\n\",\n      \"           [ 2.9822e-02,  3.6626e-02, -5.9186e-03],\\n\",\n      \"           [-3.8658e-02, -4.7186e-02, -6.0700e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.4742e-02,  7.3241e-02,  2.5895e-02],\\n\",\n      \"           [ 3.9945e-02,  2.5355e-02, -1.2422e-04],\\n\",\n      \"           [-5.3849e-03, -4.0550e-02, -8.2056e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.5082e-02,  1.5344e-02,  1.7592e-02],\\n\",\n      \"           [ 2.0088e-02,  1.6250e-02,  7.9720e-03],\\n\",\n      \"           [ 2.0356e-02,  8.1316e-03,  2.4837e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.5814e-03,  1.2400e-02,  1.0117e-02],\\n\",\n      \"           [ 9.6278e-03,  4.9397e-03,  4.2902e-03],\\n\",\n      \"           [ 1.3852e-02,  3.6032e-03, -4.3058e-03]],\\n\",\n      \"\\n\",\n      \"          [[-9.0730e-04,  5.9583e-03,  8.8457e-03],\\n\",\n      \"           [ 5.2007e-03,  2.3425e-03, -3.8357e-04],\\n\",\n      \"           [ 1.1351e-02,  1.6864e-03, -4.3890e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.5539e-02,  3.3333e-02,  3.6883e-02],\\n\",\n      \"           [ 1.8009e-02,  2.3761e-02,  1.9115e-02],\\n\",\n      \"           [ 1.9447e-02,  1.3299e-02,  1.3625e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.8933e-02,  3.8475e-02,  4.2047e-02],\\n\",\n      \"           [ 2.2514e-02,  2.1895e-02,  3.0508e-02],\\n\",\n      \"           [ 2.9433e-02,  1.4235e-02,  1.4016e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.5198e-02,  4.4222e-02,  4.8959e-02],\\n\",\n      \"           [ 3.5400e-02,  3.5870e-02,  4.0738e-02],\\n\",\n      \"           [ 3.3628e-02,  2.5426e-02,  3.0617e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.1495e-01, -3.2752e-01, -3.4914e-01],\\n\",\n      \"           [-9.6010e-02, -1.3417e-01, -9.8150e-02],\\n\",\n      \"           [ 1.4250e-02,  8.0732e-02,  1.0818e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.9586e-01, -3.9056e-01, -4.3274e-01],\\n\",\n      \"           [-1.4188e-01, -1.1448e-01, -1.4868e-01],\\n\",\n      \"           [-2.4217e-02,  1.0979e-01,  2.9954e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.4706e-01, -4.5730e-01, -4.4032e-01],\\n\",\n      \"           [-3.5100e-01, -2.0192e-01, -2.7832e-01],\\n\",\n      \"           [-3.2105e-01, -1.7202e-02, -2.0153e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.6826e-01,  1.5375e-01,  2.3660e-02],\\n\",\n      \"           [ 1.0972e-01, -1.3627e-01, -2.0670e-01],\\n\",\n      \"           [-1.9837e-01, -3.7040e-01, -3.3919e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0341e-01, -1.7839e-02, -1.9471e-01],\\n\",\n      \"           [-1.0857e-01, -2.7952e-01, -2.8107e-01],\\n\",\n      \"           [-1.9489e-01, -4.8065e-01, -4.9159e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.2037e-04, -1.7852e-01, -2.2978e-01],\\n\",\n      \"           [-6.2405e-02, -3.1929e-01, -3.1159e-01],\\n\",\n      \"           [-2.6798e-01, -3.9827e-01, -4.2077e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.1127e-02, -2.9879e-02,  8.7427e-03],\\n\",\n      \"           [-7.6920e-02, -3.2759e-02, -1.8937e-02],\\n\",\n      \"           [-2.3326e-02, -4.2366e-02, -8.0245e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.2971e-03,  2.1866e-02,  6.2312e-02],\\n\",\n      \"           [-1.5557e-02,  6.0537e-03, -1.4936e-02],\\n\",\n      \"           [ 1.5342e-02,  1.0746e-02, -4.0048e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.8094e-02,  9.0491e-02,  5.5343e-02],\\n\",\n      \"           [ 7.1729e-02,  9.0892e-02,  3.5579e-03],\\n\",\n      \"           [ 8.9079e-02,  6.1346e-02,  2.0703e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.2494e-01,  1.5031e-01,  2.6490e-01],\\n\",\n      \"           [ 1.2695e-01,  1.5482e-01,  2.2863e-01],\\n\",\n      \"           [ 1.8469e-01,  1.8700e-01,  1.7043e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7056e-01,  2.3920e-01,  2.8233e-01],\\n\",\n      \"           [ 2.4518e-01,  2.7043e-01,  2.5163e-01],\\n\",\n      \"           [ 3.0053e-01,  2.3807e-01,  2.2413e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4598e-01,  2.6444e-01,  2.3383e-01],\\n\",\n      \"           [ 3.3237e-01,  2.7985e-01,  3.1671e-01],\\n\",\n      \"           [ 3.4317e-01,  2.3979e-01,  2.3743e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.8983e-03,  7.6167e-03,  1.6963e-02],\\n\",\n      \"           [ 1.1025e-03, -2.8368e-04,  6.1481e-03],\\n\",\n      \"           [-2.6449e-04, -2.1839e-03,  1.3606e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.0821e-04,  3.6592e-03,  1.3258e-02],\\n\",\n      \"           [ 2.0191e-03, -1.8727e-03,  6.8223e-03],\\n\",\n      \"           [ 1.2092e-03, -3.5938e-03,  3.2197e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3267e-03,  3.7699e-03,  1.3510e-02],\\n\",\n      \"           [ 3.8982e-03,  1.0570e-03,  5.1013e-03],\\n\",\n      \"           [ 2.4477e-03, -2.0633e-03, -7.2119e-04]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 81.00 %\\n\",\n      \"Val loss: 0.498559\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[27,    60] loss: 0.23186\\n\",\n      \"[27,   120] loss: 0.25120\\n\",\n      \"Time elapsed: 0h:52m:40s\\n\",\n      \"train accuracy_score: 91.97 %\\n\",\n      \"tensor([[[[[-8.6480e-03, -1.0286e-02, -7.4473e-03],\\n\",\n      \"           [-1.4817e-02, -7.6966e-03, -3.6291e-03],\\n\",\n      \"           [-1.1840e-02, -4.2747e-03,  4.4457e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.0112e-02, -6.1534e-03, -2.4736e-03],\\n\",\n      \"           [-1.1892e-02, -8.5163e-03, -1.1835e-03],\\n\",\n      \"           [-1.2212e-02, -4.8423e-03, -6.7048e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.1060e-02, -5.5076e-03,  2.5925e-04],\\n\",\n      \"           [-9.3103e-03, -6.0614e-03,  1.8620e-03],\\n\",\n      \"           [-1.0612e-02, -2.2706e-03,  1.3469e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.6276e-03,  9.2332e-04, -7.7246e-05],\\n\",\n      \"           [ 1.5623e-03,  9.0481e-04,  5.4298e-05],\\n\",\n      \"           [ 1.2016e-03,  8.9580e-04,  3.0905e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3722e-03, -5.5148e-05, -1.0110e-03],\\n\",\n      \"           [ 1.0926e-03,  3.2709e-04, -3.9002e-04],\\n\",\n      \"           [ 5.1324e-04,  6.6788e-04,  3.0029e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 3.8364e-04, -8.0721e-04, -1.6647e-03],\\n\",\n      \"           [ 6.9851e-04, -1.0338e-04, -7.8784e-04],\\n\",\n      \"           [ 5.9205e-05,  2.3056e-04, -1.9462e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.6166e-03,  1.3208e-03,  1.7697e-03],\\n\",\n      \"           [ 1.1519e-03,  1.6879e-03,  2.3096e-03],\\n\",\n      \"           [ 1.3419e-03,  1.9777e-03,  2.7278e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0877e-03,  1.6482e-03,  2.9230e-03],\\n\",\n      \"           [ 1.6144e-03,  2.6714e-03,  3.5301e-03],\\n\",\n      \"           [ 1.0706e-03,  2.9480e-03,  4.2685e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3237e-03,  3.2262e-03,  3.8559e-03],\\n\",\n      \"           [ 1.9051e-03,  2.8382e-03,  4.2699e-03],\\n\",\n      \"           [ 1.8516e-03,  3.3287e-03,  4.4804e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1558e-02, -7.1599e-03, -8.2255e-03],\\n\",\n      \"           [-1.3283e-02, -1.7735e-02, -1.9727e-02],\\n\",\n      \"           [-2.1420e-02, -2.7465e-02, -3.5091e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.1309e-03, -3.5026e-03, -2.7995e-03],\\n\",\n      \"           [-1.4973e-02, -2.3810e-02, -1.9945e-02],\\n\",\n      \"           [-2.4026e-02, -3.4910e-02, -3.6163e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.1121e-02, -1.7533e-02, -1.7471e-02],\\n\",\n      \"           [-2.2817e-02, -3.1567e-02, -2.7877e-02],\\n\",\n      \"           [-2.5825e-02, -4.3046e-02, -4.3449e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.5980e-02, -6.2969e-02, -4.9719e-02],\\n\",\n      \"           [-5.7599e-02, -4.8028e-02, -3.9740e-02],\\n\",\n      \"           [-3.9894e-02, -3.8259e-02, -4.4832e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.1170e-02, -5.5901e-02, -5.2141e-02],\\n\",\n      \"           [-4.7143e-02, -3.6972e-02, -3.4767e-02],\\n\",\n      \"           [-3.6552e-02, -3.3341e-02, -3.8396e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.6687e-02, -4.9296e-02, -4.9190e-02],\\n\",\n      \"           [-4.1429e-02, -3.9950e-02, -4.1751e-02],\\n\",\n      \"           [-3.4383e-02, -3.4970e-02, -4.0868e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.8407e-03,  3.0143e-03,  4.0098e-03],\\n\",\n      \"           [ 8.2443e-03,  1.0243e-02,  1.0153e-02],\\n\",\n      \"           [ 4.7159e-03,  1.1772e-02,  1.6313e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0247e-03,  2.4156e-04,  2.5836e-03],\\n\",\n      \"           [ 4.3544e-04,  5.5360e-03,  1.2220e-02],\\n\",\n      \"           [ 2.0194e-03,  8.3877e-03,  1.5478e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.9701e-03,  8.0658e-04,  4.3622e-03],\\n\",\n      \"           [-1.3755e-03,  4.0035e-03,  1.2476e-02],\\n\",\n      \"           [-3.0515e-03,  5.9349e-03,  1.2943e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.1178e-03,  9.5202e-04, -1.9991e-03],\\n\",\n      \"           [-9.5288e-03, -2.7421e-03, -4.8816e-03],\\n\",\n      \"           [-1.4848e-02, -1.0395e-02, -6.2771e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.7765e-03, -3.8957e-03, -4.6052e-03],\\n\",\n      \"           [-8.1688e-03, -4.0783e-03, -3.1322e-03],\\n\",\n      \"           [-9.2546e-03, -5.6960e-03, -1.1364e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.0897e-03, -5.3152e-04, -5.9653e-03],\\n\",\n      \"           [-5.2246e-03, -2.0561e-03, -5.9372e-03],\\n\",\n      \"           [-9.9286e-03, -5.8701e-03, -1.1497e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.2032e-03,  1.9202e-03,  2.3216e-03],\\n\",\n      \"           [ 1.8450e-03,  2.2008e-03,  2.7956e-03],\\n\",\n      \"           [ 2.1387e-03,  2.6941e-03,  3.2494e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8204e-03,  1.5399e-03,  1.4670e-03],\\n\",\n      \"           [ 1.3825e-03,  1.8037e-03,  2.1242e-03],\\n\",\n      \"           [ 1.4795e-03,  2.1163e-03,  2.6736e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5097e-03,  1.2377e-03,  1.2783e-03],\\n\",\n      \"           [ 1.1642e-03,  1.3599e-03,  1.8899e-03],\\n\",\n      \"           [ 5.4311e-04,  1.6697e-03,  2.4815e-03]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 91.00 %\\n\",\n      \"Val loss: 0.334379\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[28,    60] loss: 0.24697\\n\",\n      \"[28,   120] loss: 0.19208\\n\",\n      \"Time elapsed: 0h:54m:34s\\n\",\n      \"train accuracy_score: 91.68 %\\n\",\n      \"tensor([[[[[-4.9989e+00, -5.2356e+00, -4.9553e+00],\\n\",\n      \"           [-5.7747e+00, -4.8998e+00, -3.6678e+00],\\n\",\n      \"           [-5.2300e+00, -3.3534e+00, -2.3363e+00]],\\n\",\n      \"\\n\",\n      \"          [[-5.7141e+00, -5.3960e+00, -4.9021e+00],\\n\",\n      \"           [-5.7372e+00, -5.6443e+00, -4.1501e+00],\\n\",\n      \"           [-5.3693e+00, -3.9988e+00, -3.4563e+00]],\\n\",\n      \"\\n\",\n      \"          [[-5.6180e+00, -5.6205e+00, -5.0067e+00],\\n\",\n      \"           [-5.1706e+00, -5.0833e+00, -4.1838e+00],\\n\",\n      \"           [-4.9575e+00, -4.3185e+00, -2.9482e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.3604e-01,  8.7970e-02, -1.0086e-02],\\n\",\n      \"           [ 1.6599e-01,  4.0650e-02, -6.1224e-02],\\n\",\n      \"           [ 4.2771e-02, -2.1487e-03, -4.8526e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4672e-01,  1.1008e-01,  4.3143e-02],\\n\",\n      \"           [ 1.7317e-01,  5.5815e-02, -4.8973e-02],\\n\",\n      \"           [-6.8226e-03, -1.7616e-02, -6.1813e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1326e-01,  1.3712e-01, -1.6471e-03],\\n\",\n      \"           [ 1.0278e-01,  5.7305e-02, -6.7338e-02],\\n\",\n      \"           [-8.4707e-02, -1.0033e-01, -7.9143e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3121e-01, -3.3076e-01, -3.8343e-01],\\n\",\n      \"           [-7.5205e-02, -9.0136e-02, -1.3762e-01],\\n\",\n      \"           [-6.5640e-03,  2.9760e-02,  1.4825e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.1231e-01, -2.9787e-01, -3.2279e-01],\\n\",\n      \"           [-5.4809e-02, -3.6141e-02, -4.6571e-02],\\n\",\n      \"           [ 1.3610e-01,  2.7283e-01,  2.5835e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.8323e-02, -1.9338e-01, -2.5070e-01],\\n\",\n      \"           [ 1.6713e-02,  2.6206e-02, -2.9191e-02],\\n\",\n      \"           [ 2.4532e-01,  3.7028e-01,  2.6887e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.9404e+01, -2.0806e+01, -2.3135e+01],\\n\",\n      \"           [-2.2717e+01, -2.5589e+01, -2.7939e+01],\\n\",\n      \"           [-2.7191e+01, -3.0919e+01, -3.3123e+01]],\\n\",\n      \"\\n\",\n      \"          [[-1.8746e+01, -1.9018e+01, -2.1629e+01],\\n\",\n      \"           [-2.2346e+01, -2.5389e+01, -2.8010e+01],\\n\",\n      \"           [-2.5943e+01, -3.0844e+01, -3.2965e+01]],\\n\",\n      \"\\n\",\n      \"          [[-1.9948e+01, -2.0348e+01, -2.2851e+01],\\n\",\n      \"           [-2.2199e+01, -2.5407e+01, -2.7775e+01],\\n\",\n      \"           [-2.5591e+01, -3.0681e+01, -3.2891e+01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.5566e+00, -1.8256e+00,  3.6701e-01],\\n\",\n      \"           [-3.9260e-01,  1.0182e+00,  2.7003e+00],\\n\",\n      \"           [ 3.2728e-01,  1.7052e+00,  6.3671e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 7.8699e-01,  1.5555e+00,  1.2707e+00],\\n\",\n      \"           [ 2.6793e+00,  2.7035e+00,  2.8254e+00],\\n\",\n      \"           [ 3.0299e+00,  3.3556e+00,  5.6704e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1299e+00,  2.6244e+00,  1.4294e+00],\\n\",\n      \"           [ 3.6100e+00,  3.9023e+00,  2.2403e+00],\\n\",\n      \"           [ 6.2410e+00,  4.3882e+00,  4.1505e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.5672e+00,  2.2833e+00,  2.1986e+00],\\n\",\n      \"           [ 4.0736e+00,  3.8676e+00,  3.5464e+00],\\n\",\n      \"           [ 5.3481e+00,  4.7824e+00,  4.5428e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2924e+00,  2.1606e+00,  2.2707e+00],\\n\",\n      \"           [ 3.6899e+00,  3.7641e+00,  3.7293e+00],\\n\",\n      \"           [ 4.9946e+00,  4.9340e+00,  5.1070e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4638e+00,  2.7207e+00,  2.7709e+00],\\n\",\n      \"           [ 3.0317e+00,  3.9011e+00,  4.1020e+00],\\n\",\n      \"           [ 4.3282e+00,  4.6166e+00,  5.0510e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.5856e-01, -6.2803e-01, -1.3708e+00],\\n\",\n      \"           [-4.9590e-01, -1.4810e+00, -1.0298e+00],\\n\",\n      \"           [-1.1278e+00, -1.9613e+00, -1.2212e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.3330e+00, -8.2549e-01, -1.3635e+00],\\n\",\n      \"           [-1.2608e+00, -1.5589e+00, -1.5864e+00],\\n\",\n      \"           [-1.8678e+00, -1.9636e+00, -9.1725e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.3574e+00, -1.3391e+00, -1.2626e+00],\\n\",\n      \"           [-1.6403e+00, -1.8192e+00, -1.9723e+00],\\n\",\n      \"           [-2.5130e+00, -2.1827e+00, -1.5796e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.6650e-01,  2.7921e-01,  3.1555e-01],\\n\",\n      \"           [ 3.0655e-01,  4.1443e-01,  5.0528e-01],\\n\",\n      \"           [ 4.6302e-01,  5.8486e-01,  6.6483e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.8485e-01,  2.0836e-01,  2.1296e-01],\\n\",\n      \"           [ 2.8177e-01,  3.4276e-01,  4.1431e-01],\\n\",\n      \"           [ 3.5355e-01,  5.1782e-01,  6.2717e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.3997e-01,  2.2894e-01,  2.2361e-01],\\n\",\n      \"           [ 2.6957e-01,  3.3683e-01,  4.2005e-01],\\n\",\n      \"           [ 3.5095e-01,  4.8930e-01,  6.2400e-01]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 63.00 %\\n\",\n      \"Val loss: 0.881810\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[29,    60] loss: 0.27077\\n\",\n      \"[29,   120] loss: 0.27959\\n\",\n      \"Time elapsed: 0h:56m:29s\\n\",\n      \"train accuracy_score: 90.10 %\\n\",\n      \"tensor([[[[[-1.1722e-01, -2.0725e-01, -2.6736e-01],\\n\",\n      \"           [-2.1900e-01, -2.3676e-01, -2.1102e-01],\\n\",\n      \"           [-1.8330e-01, -8.1210e-02, -5.8150e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.5692e-02, -6.1311e-02, -6.7122e-02],\\n\",\n      \"           [-1.0122e-01, -1.4423e-01, -1.1279e-01],\\n\",\n      \"           [-1.6896e-01, -7.0429e-02, -6.4177e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7560e-01,  7.8659e-02,  8.3234e-02],\\n\",\n      \"           [ 4.7807e-02,  1.6723e-02,  3.9044e-02],\\n\",\n      \"           [-6.4336e-02,  8.1507e-03,  7.8771e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.2581e-02,  6.5317e-03,  4.9124e-03],\\n\",\n      \"           [ 1.6580e-02,  6.3605e-03,  1.2080e-02],\\n\",\n      \"           [ 1.0368e-02,  8.5682e-03,  1.1669e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0523e-02, -2.8253e-04,  1.8156e-03],\\n\",\n      \"           [ 8.5093e-03,  3.6635e-03,  5.5392e-03],\\n\",\n      \"           [-2.1452e-03,  3.0491e-03,  9.9942e-03]],\\n\",\n      \"\\n\",\n      \"          [[-9.2937e-03, -8.3174e-03,  3.8722e-03],\\n\",\n      \"           [-2.2296e-03,  1.0504e-04,  7.9006e-03],\\n\",\n      \"           [-5.8703e-03, -3.9079e-03,  1.0659e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.4439e-02,  9.1854e-03,  1.8360e-03],\\n\",\n      \"           [ 2.0745e-02,  1.9281e-03,  1.9393e-03],\\n\",\n      \"           [ 4.8237e-03,  1.0483e-02,  1.5546e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.8903e-02,  1.3473e-02,  1.9370e-03],\\n\",\n      \"           [ 3.2038e-02,  1.9541e-02,  1.2451e-02],\\n\",\n      \"           [ 2.1568e-02,  2.6150e-02,  1.7199e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.9648e-02,  3.2726e-02,  1.3831e-02],\\n\",\n      \"           [ 3.5484e-02,  3.8849e-02,  2.7109e-02],\\n\",\n      \"           [ 3.1116e-02,  3.9188e-02,  1.5138e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.2807e-01, -5.5131e-01, -5.8100e-01],\\n\",\n      \"           [-7.9403e-01, -9.3205e-01, -1.0369e+00],\\n\",\n      \"           [-1.1680e+00, -1.2574e+00, -1.4372e+00]],\\n\",\n      \"\\n\",\n      \"          [[-6.8594e-01, -6.1120e-01, -7.6204e-01],\\n\",\n      \"           [-9.5207e-01, -1.1370e+00, -1.2627e+00],\\n\",\n      \"           [-1.3114e+00, -1.6342e+00, -1.6046e+00]],\\n\",\n      \"\\n\",\n      \"          [[-8.3613e-01, -9.7396e-01, -1.1028e+00],\\n\",\n      \"           [-1.0357e+00, -1.4208e+00, -1.4476e+00],\\n\",\n      \"           [-1.2814e+00, -1.7866e+00, -1.8625e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.6750e-01,  6.3106e-01,  6.7032e-01],\\n\",\n      \"           [ 5.2291e-01,  8.1549e-01,  9.1803e-01],\\n\",\n      \"           [ 6.0128e-01,  8.1856e-01,  1.1077e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 5.7899e-01,  8.1241e-01,  9.6378e-01],\\n\",\n      \"           [ 6.3056e-01,  8.6788e-01,  1.0510e+00],\\n\",\n      \"           [ 6.8689e-01,  9.8331e-01,  1.0702e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 9.6644e-01,  9.7341e-01,  1.0232e+00],\\n\",\n      \"           [ 9.7082e-01,  1.1063e+00,  1.1265e+00],\\n\",\n      \"           [ 1.0404e+00,  1.2322e+00,  1.1907e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.0125e-02,  9.4724e-03, -2.3965e-02],\\n\",\n      \"           [ 2.4948e-01,  1.1837e-01,  1.0653e-01],\\n\",\n      \"           [ 2.8882e-01,  2.4222e-01,  2.1241e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.8108e-02,  2.7340e-02, -3.3716e-02],\\n\",\n      \"           [ 1.7517e-01,  9.5500e-02,  1.1423e-01],\\n\",\n      \"           [ 2.7897e-01,  1.8799e-01,  1.8721e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.7970e-02,  1.3984e-02,  3.5199e-02],\\n\",\n      \"           [ 1.5710e-01,  1.2302e-01,  1.3356e-01],\\n\",\n      \"           [ 2.3684e-01,  2.3713e-01,  2.1877e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.7775e-02, -2.0287e-01, -2.1306e-01],\\n\",\n      \"           [-7.2904e-02, -1.4764e-01, -3.5913e-01],\\n\",\n      \"           [-2.2453e-01, -2.5784e-01, -4.4925e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.6118e-02, -1.5622e-01, -3.1393e-01],\\n\",\n      \"           [-1.2231e-01, -2.1233e-01, -3.5931e-01],\\n\",\n      \"           [-2.4789e-01, -3.2709e-01, -3.3600e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2102e-01, -1.3841e-01, -2.7732e-01],\\n\",\n      \"           [-1.6342e-01, -3.3452e-01, -3.0745e-01],\\n\",\n      \"           [-3.7085e-01, -3.1927e-01, -3.0907e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.3472e-02,  1.0170e-02, -4.3253e-03],\\n\",\n      \"           [ 2.0804e-02,  1.8813e-02,  1.5737e-02],\\n\",\n      \"           [ 2.8304e-02,  4.0826e-02,  3.6982e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0032e-02,  6.7876e-03, -2.6775e-03],\\n\",\n      \"           [ 1.3648e-02,  1.9714e-02,  1.6179e-02],\\n\",\n      \"           [ 2.7494e-02,  4.2639e-02,  3.9012e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5101e-02,  6.7960e-03, -2.9332e-03],\\n\",\n      \"           [ 6.7803e-03,  1.8394e-02,  1.6726e-02],\\n\",\n      \"           [ 1.8988e-02,  4.0000e-02,  4.7423e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 85.00 %\\n\",\n      \"Val loss: 0.297195\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"Early stopping in epoch 30\\n\",\n      \"Total time elapsed: 0h:56m:37s\\n\",\n      \"Writing model to disk...\\n\",\n      \"Best result during training: 0.91. Saving model..\\n\",\n      \"Finished fold.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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92YmqOwkNDePTavqTERvD8zM2UHDzKY9f1IzIsNNBNU7tW2ts9Xm+jq1TLUbOWu1Nk86jp3mKDO4CI8PuLe5IcG8HDM9ay79BRnr8pl9jIFn1ZLdvBEti/3f6+V3vuKoiVl0JE3PFa7k7NpOfeItMyNY07pzP/ubYf8zbvYcykuewuOxLoJp26djlSMqGRmpZRwa1m6QGnZrJhR1AEd4BrBqbz4s25bCwqY/Rzc9i251Cgm3Rqcubbu5ynaRkV3Gpu1OEUFQ+mKuCzxYImuAOM6NGGN24/g72HKrj6uTms2Rn4r0annMIVENcB0nLgYBEcCfyONEo1irp67hDwGTMeBXcRuVBE1onIRhG5z83zvxGRpY6flSJSJSJJ/m9u/QZ2as2744cQFiJc9/xc3l60jaID5YFoSstgDOz143qBwhXQrg8kZdv7e/P8d26lmpO6eu4Q8Lx7vcFdREKBZ4CLgF7AWBHp5XqMMebfxpj+xpj+wO+B74wxexqjwZ7o2jaO9342lPYJUfz23eUM/vtXjHrsOx76cBWfryqk9HBFoJrW/PzwGjw1wD9BuKIcitc5gnuWfUxTMypY1dpzdzwW4J67J9NKBgMbjTGbAURkKnAFsLqW48cCb/mneb7rkBjNJ3efw6odpczZVMLsjbuZunArU+bkESLQJy2BoV1SOLNzCrmZrYkKP0WnUC5/2+YH8+dC68yGnat4jT1Xuz7Q2hHcdcaMCla1Bfeo5lHT3ZPgngZsc7lfAJzu7kARaQVcCEyo5flxwDiAjh07etVQX4SGCH3TE+mbnsj4YZ05UlnF0q37mL2phDkbd/PCzM089+0mIkJDyOmUyJDsFM7ITqJ/x8RTY758WRHkz7a/FyyA/mMbdj7nYGq7PhCdCNFJOmNGBafqapt2qSvn3gKCu7h5rLY5PpcBs2tLyRhjJgGTAHJzc5t8nlBkWCinZydzenYy94zqxsEjlSzI28OcjbuZs6mEJ75aj/kSIsNCyOnYmjOyk4M72K/50NajTugIBQsbfr7CFRARe7zXnpSlaRkVnI4eAEzdPfcWkJYpADJc7qcDO2o5dgzNICXjqZjIMEZ0b8OI7m0AKD1UwYK8PczbXMK8zadAsF81HZK7Qu8r4fv/2JktkbG+n69wBbQ9DUIcQzmts+w3AqWCTW2lB6DZbNjhSXBfCHQVkSxgOzaA31DzIBFJAIYBP/JrC5tQQqtwRvVqy6hebYHjwX7upuPBPuLLo4SER3FGdjLndEvl7K6pdE6NQcTdF5xmzJmSOfteSB9ke/A7lkDWOb6dr7oaCldCvzHHH0vKhlXvQ+VRCIvwT7uVag7qCu7NZMOOeoO7MaZSRCYAnwGhwGRjzCoRGe94fqLj0KuAz40xLbfOa8Vh2L/DLp8v3U7C/u2McvwQsYPqhAJCyveSF9OPX+7+NX9eVwxAh4Qozu6aytnd7ABt65gWEMicKZneV0Jce/tYwULfg/u+PPtVtV2f448lZdn32LcVUro0uMlKNRt1BfdmsmGHR0VYjDEzgBk1HptY4/4UYIq/GtYkykthwxew9v8g73s4WHzyMdFJkJAGCWmEZAyGyDgy509ketyD7LzjVb7Zncj3G4qZsXIn/1u0DRHom5bAOd1SOatLCv0yEpvnTBxnSqZNL/uPMbkrbGtA3t11MNXp2Fz3LRrcVXBxBvfoRPfPR8U3/5570CndDutmOAL6LKiugJhU6DIKkrMhPh3iO0BCuu3RRrQ6+Rw9LoW3xtD+ncu4Ycyb3HD6mVRWVbN8eynfr9/N9xuKefbbTfz3642Ehwq9OySQ26k1Ax0/beKjmv66XbmmZJzppIzBsP5Tu6jJlxRT4Qr7VbRNz+OPOQdWdcaMCjZ19dzBznVvCT33ZqVgEcx+EuLaQWxbG4Dj2kJsO/tYdNLxAT2wwapojQ3m6/7P7hIEkNwFhtwJ3S+B9FwI8aJ3nTEI7vgK3rgWXrsSrniGsL7XkdOxNTkdW3P3eV3ZX17Bgs17WJS/lyX5e3ltXj4vzrJBLiMpmoEdWzMwM4mBHVvTvV0coSFNmLN3Tck4pefC0jdsL9vZ4/ZG4UpI6Qbh0ccfi20D4TE6Y0YFn/qCu/bcfXB4LxSvhc3fua+ZHBJmg77zp3jN8dWX6YPg3Adtzzu1W8Pa0ToTfvI5/O8meP8O2zsd9ttjvd74qHDO69WW8xyDs0crq1m5o5Ql+XtZlLeXWRtLmL7UTjqKjQwjKyWGlNgIUmIjSY6NJCU2gtS4SJJjIkmJs4+3bhXhnw8B15SMU/pge7ttoY/BfQV0GnLiYyI2764LmVSwqa2Wu1NkPJQWNF173Gh5wb3rKPsDcPQQlO2yPwd2woFdUFZ4/HbfVhvEzvwldL/I9uz9Kbo1/Oh9+Ohu+PZhG8Que8rtzJAIx3TKnI6tuf1sMMawbc9hFm/dw+L8vWzbc5iiA0dYs/MAJQePUFF18jKAEIG01tGMH9aZ63MzCAv1oe6bu5QM2HRKRKwdVO13vXfnPLQH9hecmG93ap0Juzd4306lmrPyUhvAa/vGHxUPRc1/EVPzFdHK9gyddUwCISwCrnzWtuGbv9tP6+tfs4G/DiJCx+RWdExuxVUD0k94zhhD6eEKdpcdZXfZEXaXHaHE8fvcTSX8YdpKXp2Tzx8u6ck53VK9a6+7lAzYf6RpOb7NS3c3mOqUlGUHraurT0yXKdWS1VZ6wCky8Bt2tOzg3lyI2JRM60z44Ofw4ii48R2fP3REhMRWESS2iqBLmxMXFRlj+GxVIQ/PWMvNkxcwrFsqf7ikJ93axnl2cncpGaf0wTDrcVuHOiLG8wY7g3tbd8E9G6qOwIEddpBaqWBQX3CPctmwI0BrYLQr5U99r4ObpsOh3fDieQ2bWlgLEeHC09rzxT3n8MAlPVmydS8XPjGTP0xbUf8OVM6UTO8r3f+DyxhsC3/tWOpdowpX2IHtWDffInTGjApGnvTcA7xhhwZ3f8s8E37ypV3EMOUSmPlvu0LTzyLDQrn97Gy++80Ibh6SydSF2xj+72957ttNlFdUuX/RsZTMVe6fT8u1t96mZpw13N3R0r8qGB2upZa7U1Tgy/5qcG8MKV3g9q+g+4Xw9d9g4lmQP6dR3iopJoKHLu/NZ788hzOyk/jnp2s59z/f8dGyHZiaezjWlZIBiEmGpM7efeOoKIfd62oP7vHpEBKuM2ZUcPEkLQMBzbtrcG8sMclw3atwwzu2rMHLF8EHE+zMkkbQpU0sL/54EG/efjrx0eHc9dYPXPXsHOZtLrEHlBXXnZJxyhhsZ8x4urlv8Vqorqw9uIeGQWJHTcuo4FJvWkZ77sGv2/nw83lw5t2w9E14OheWvtVoO6MP7ZLCx3edxb9G92XX/nLGTJrHrS8vYOf8t+tOyTil59q9T/d5uPXesZkyfWs/Rkv/qmBSXW2DtvbcFRExMOov8NOZdvbI9PHw6uWwe2OjvF1oiHBdbgbf3Duc31/Ug8X5e9ny3evsiuhIQXhm3S92XczkicIVdhVq6zpmBiVl24VkjfSBplSTOrKfWmu5Ox3bJDtwc901uDeldqfBbZ/DpY/DjmXw3BD49hGorGeWi4+iwkP56bDOzPp5H84IWct75QMZ+Z+Z/PXj1ew5WMsgb5teNlh7unlH4Qp7XXXNYW+dZf9DNFJKSqkmVV/pAdCe+ykpJARyb4MJC6Hn5fDtP+C5oTZlU7q9Ud4yPu8TQqjm2h//gqsGpPHy7C0M+9c3PP31Bg4drTzx4NAwzxczVVfXPVPGSWfMqGDiSXCPDPxuTBrcAyWuLYx+yZYvqK6C6T+Dx3vBUwNsOYMV79p56f6wahokdyU1ewD/HN3XzqzpnMyjn69n2L+/ZcrsLeTtPnh8dk36IBu0Kw7Xfd59+SfXcHfHtfSvUi2dJ8HduWFHAHvuukI10LqcC3ctgV0rbU35Ld/Dyvdh8RT7fGoPyDzbbqKReRa0SvLu/M5ZMmf/+tgsma5t43jh5lwW5e3hkU/W8tBHq+Gj1STFRDAgI5GrYtK5tLqSQ3mLaNX17NrPXdfKVFeJnQDRGTMqOHgS3J0bdgSw567BvTkICYH2fe3PkJ9DVSUULoMtM22wX/oGLHwBEOgwAC78B3Q8w7NzOxcu9brypKdyM5N4Z/wQ1u06wA9b97Ekfy8/bNvHg2ujuTQKnnzlDb5NNgzomOj4aU2X1FhCnJUpd60ECTmxhrs74VG2Rn5D0jKbv4X37oCu50P/sdBxqNaqUYFxLLjXslGHU1Rg68tocG+OQsMgbaD9OetXdoXrjiU20P/wKky+0H4IjHzgxPrp7jhSMrTt7fZpEaFHu3h6tItn7OCOAJQeruDwM//g2shC1sVG8cnKQqYu3AbAoMzW/HdsDu0SomzPPbmr+w1NakrKblhaZtV0+59q9Qew9HU7d77fDXbP1kAWjlOnHk967mDnumvOXdUpLML21If9Bn42B3JvhblPw/Pn2M1LauPpwqUaEqLDic4eQpcjq5lyyyCW/mkUX/96GA9d1otVO/Zz8VPf8/2GYs8GU51aZzYsLZM/B7KHwb3r4eoX7IfFd/+Ep/rD5ItgyWsBr8KnThHlpYDUXsvdKcA9dw3uLU1knJ1KedM0W8/+pVHw5Z/dT6esIyVTr/RBtk5+6TZEhOzUWG45M4sPJ5xFSmwEd03+Gkq3UV1fvt0pKcsujjpywPu2lBXbEgedhtpvCX2vg5s/gF+thJF/tOf9cAI82s2mbjZ9bWfyKNUYjtVyryd8RsbrPHflg84j4c450P8GmPUYTBp+cjXHelIydUofZG+3nTglskubWKb//EzGdbPV7v69PLz+apTgMmMmz/u2bJ1rbzudeeLjCelwzr0wYZEt1tZvDKz/DF67Cr75m/fvo5Qn6is94BQVfzyFEwAeBXcRuVBE1onIRhG5r5ZjhovIUhFZJSLf+beZyq2oBLjiGVu/5vBeeGEkfPOwzdH7mJI5pu1pEBbtNu3TKiKMn3U/BMAHO5O45KnvWZhXzwKlhpT+zZ9t29K+v/vnRey+tpc9YdM2WcPsjCOlGoOnwT3AG3bUG9xFJBR4BrgI6AWMFZFeNY5JBJ4FLjfG9AaubYS2qtp0Ox/unAt9rrV56BdHwvf/8T0lA/UuZpJdKyG2HS/eeQnR4aGMmTSPSTM3nVyJ0qkhC5nyZ9uCZm62LzxJeJTdI3fvFl00pRqHNz1354YdAeBJz30wsNEYs9kYcxSYClxR45gbgPeNMVsBjDF+Wn2jPBbdGq5+Hsa8CQcKYf5zvqdknNIHwc7ltqxvTY7B1F4d4vnwrrM4v1dbHp6xlnGvLab0UMXJx0clQKtk72fMHN4HhStPTsnUpfNIe7vpG+/eSylPeNNzD+CGHZ4E9zRgm8v9AsdjrroBrUXkWxFZLCI3uzuRiIwTkUUisqi4uNi3Fqu69bgE7pwPg+6wUyUbssVXxmCoroCdy058vPKILfXrmCkTHxXOszfm8OBlvfhmbRGXPv09Kwrc5BpbZ3mfltk2HzB2MNVTyZ0hoaMdWFXK38rr2ajDKSqwJQg8Ce7uokPN7xlhwEDgEuAC4I8i0u2kFxkzyRiTa4zJTU31cmNn5bmYZLjk0ZM3wfaWc1C1ZmrGTQ13EeHWM7N4e/wQqqoM10ycw9xNJSe+LsmH4J43C0IjbCliT4lA5+F2EVhVZb2HK+UVb3ruELC8uyfBvQDIcLmfDuxwc8ynxpiDxpjdwEygn3+aqAImto0tHVBjxkxdNdxzOrbm41+cTcekVox7bRFrC13+YSdlw/4C77YdzJ9jF3PVt1irps4jbY9p+2LvXqdUXaqr6q/l7hTgrfY8Ce4Lga4ikiUiEcAY4MMax3wAnC0iYSLSCjgdWOPfpqqAyBh88oyZwhUQ3qrWlaFJMRG8cttgosNDuWXyQnaWOgqQtc6yg7z7tnr23kfKYOdS71IyTlnDANHUjPIvZ6AOhp67MaYSmAB8hg3YbxtjVonIeBEZ7zhmDfApsBxYALxojFnZeM1WTSZ9EBzYAaUFxx8rXGEHakNCa31ZWmI0U24dTNmRSm6ZvJDSwxXez5gpWGjTP74E91ZJdraPBnflT56WHgCXnHtg5rp7NM/dGDNsCL63AAAgAElEQVTDGNPNGNPZGPN3x2MTjTETXY75tzGmlzHmNGPME43VYNXEai5mMsbOXvGg7ECvDvE8f9NANu8u46evLeJIfCf7hKczZvJn27KpGaf70HBsamb7IjvjRil/8Ca4N/eeuzrFtetz4mKmfVttT8TDmjJndknh36P7MW/zHu6dsRMTHuP5oGr+HGjfz5Zc8EXnkTYNtGWmb69Xqiafeu4a3FVzFBpuyww7Z8x4siF2DVcOSON3F/bgo+U7KQpr71lapqLcfqD4kpJxSh8EEbGwWee7Kz/xJrhHxNqS2NpzV81Weq6d6155xAZ3CbF7rXph/LBsbh7SiSVlrdm3fX39L9ixBKqOeLd4qabQcLvJiebdlb94E9wDvGGHBndVv4zBUHXUrlYtXAHJXTyr4e5CRHjwst6EJGcRfXAbM5YX1P2CvNmAQKchvrcbbGpmb56WIlD+4Qzu0fVs1OEUlaA9d9WMuS5m8qaGew2hIcLIIWcQKZX88+1vWLCljmJj+bPtjJzo1pQdqWTe5hK+Xrur9to1tTlWikB778oPnLXcIzwcBwrghh0a3FX94trZ5fwbPofSrT4Hd4Dw1M4ADIzby+2vLGTDrhPru5dXVLEkr4iK/Hl8f7Qr5z32HX0e+owxk+Zx25RF/P79FVRVexHgk7Ltrk1aZ0b5Q3mpHSj1dIvHAG7YodvsKc9kDIKV79nfGxDcnaV/HxgSzcyZofx48gJ+NqILq3eUsmxbKet3HaC32cAHkYf5uDSbTh1bcVnfDvRNT2BR/h6e+WYTZUcqeey6/kSEefAfTMT23le+D1UVNg+vlK88LT3gFBlvV2UHgAZ35Zn0wS7B3fOZMidJSIeQcJKOFDDl1uu4/vm5/HH6ShKiw+mbnsBPe2Rz+cElsBwe+dVPkbi2x146okcb4qPC+ccnazl4pJLnfjSQqPDaF1Id03kkLJ5iSxF4urG4Uu54G9yj4qFIe+6qOXPm3WPb2pozvgoJhdadYO8WTktLYOZvR1B2pJKOSa0QZwXLN+3G266B3emnwzoTGxXGA9NX8uPJC3jxx7nERdXTG886x87w2fS1BnfVMOWlEOXhYCo4ttrTnLtqztr1gbCohqVknFxK/ybHRtIpOeZ4YK+ugvy5dc5vv/H0TjxxfX8W5e/lRy/OZ+/BegqRRbeGDlqKQPmBLz338v0B2bBDg7vyTFgEXPB3GHpXw8/lLP3r7h980Wq7Arae+e1X9E/j+R8NZE3hAa6fNJei/W42FHHVeaRNyxze24CGq1OeLzl3UwUVhxqvTbXQ4K48N+h2yB7e8PMkZcPRA3Co5OTn8mbb28z6Fy+d16stU24ZRMHew1z7/Fy27anjP5CWIlD+cNjDjTqcogJXX0aDu2p6dW2WnT/bTl1MSPfoVEO7pPDG7aez71AF106cy8aiMvcHpufaucmamlG+qqq0nRJve+4QkLy7BnfV9Jylf2tWhzTGFgvzsuTAgI6tmTruDCqrDdc/P5eV292UWHUtRRCgDYtVC+dNLXcn57Hac1enhMROgJxcEmD3Bji026diYT3bx/PO+CFEhYcy9oV5LMxzs/q18whb1VJLEShfeFNXxikycDXdNbirphceBfFpJ6dl8mfZWx+LhWWlxPD2+CGkxkZy4wvzmfZDjcUjWoqg+SgrspU/WxJfgrvm3NUpJynr5LRM/hyIbWcHXH2UlhjN+3cOJadTIr/63zL+8/k6qp3lCpKy7bcGLUUQWNXVMPEsmPmvQLfEOw3quWtwV6eKpKwT0yPG2JkynYbakgENkNgqgldvO53rctP579cbueutHzh8tOp4KYItM20pAhUYpdugbBfs+CHQLfFOg3rumpZRp4rWWXCwGI44CoftzbN7tTZkcw4XEWEh/POavtx/cQ9mrNzJGOdc+M4j7YyHmpt+q6ZTtNre7t4Q2HZ4y5fgHsANOzS4q8A4NmMmz97mz7G3mWf57S1EhHHndOb5Hw1k/a4yrnxmNmujBxwvRaACY9cqe1u6DY4eDGxbvOFLcA/ghh0a3FVgOPPqztRM/hyIToKU7n5/q/N7t+Od8UOoNnD1y6vYl9RPg3sgOXvuACUbA9cOb5WX2o6Bp7XcnSIDs2GHBncVGDUXMuU78u2e1sn20mlpCXww4Uy6tIllyq4sqrcvwRyqY7MQ1XiK1tiFatCyUjPlpXaA1Nt/o1GBKR7mUStF5EIRWSciG0XkPjfPDxeRUhFZ6vj5k/+bqoJKVDy0SrYzZvbvsLcN2S/VA23jo/jfuCFUZg4nhGremPoaRyurG/U9VQ2VR2H3euh5ue0F7/ZgP93mwtu6Mk6Rgdmwo97gLiKhwDPARUAvYKyIuNsd+XtjTH/Hz1/83E4VjJKybVrGmW/302BqXaIjQrnnx2M5EhpDyOZvuHny/Lpr0ij/KtkA1ZXQvj+0zjw1gntUfLNdxDQY2GiM2WyMOQpMBa5o3GapU0LrLNiTZ1MyEXH+KSfsgZDwCCK7juCKuHUsL9jHeY99x+NfrKe8oqpJ3v+UVrTG3rbtBSndoDgAwf1IGWxb4P3rgq3nDqQB21zuFzgeq2mIiCwTkU9EpLe7E4nIOBFZJCKLiouLfWiuCipJWXYLss3f2k00QjzYVclfOo8g5vAOvr2tI6N6teXJrzZw3mPf8fmqQu834Vae27UKQsIguSukdLUDqtVN/KE6/zmYfIFdJeuNBvXcm2dwd7eipOa//iVAJ2NMP+C/wHR3JzLGTDLG5BpjclNTU71rqQo+Sdm2DO+ezR6V+PUrRymCNkVzePqGHN6843Siw0MZ99pibp2ykC27W9AUvZakaLUN7GERtudedcTW+2lK23+w/+62L/Hudd7uwuQUGZgNOzwJ7gVAhsv9dGCH6wHGmP3GmDLH7zOAcBFJ8VsrVXByzpiBRh9MPUlSts35OqZEDu2cwoy7z+aBS3qyKG8vFzw+k39/tpZDRyu9Oq0xhsoqHaSt1a7VNiUDNrhD08+YKVxhb7cv9u51Dem5B2DDDk/2UF0IdBWRLGA7MAa4wfUAEWkH7DLGGBEZjP3QcLMTg1IunAuZwqLtAFtT6zwSlr9tZ3CERRAeGsLtZ2dzef8OPDJjLc98s4lpS7bzwKW9uOi0dse3AgQqq6rJ33OITUVlbCwuY1PRQTYWl7G5qIywUOG1n5zOaWk+BIJgVr4fSrfCwB/b+8eC+3rodn7TtOHQHtsGgB1e9Nx9qeXu5Fr2NyLG+9f7qN7gboypFJEJwGdAKDDZGLNKRMY7np8IjAZ+JiKVwGFgjNHEpapPTKpdnp2WY7+mN7Wel8OiybD0dci97djDbeKieOz6/ow9vSN/+mAVd76xhLO6pNA/I5GNRWVsKi4jr+QgFVXH/4m3jY+kc2osV+Wk8cXqXfz0tcV8OOFMkmMjm/66mqvitfa2rWNIrlUStEpp2hkzzl57QkfbczfGs1pGvtRydzqheFh771/vI0967s5Uy4waj010+f1p4Gn/Nk0FPRE4/682BxsI2cMh4wz47t/Q7wZbitjFoMwkPppwJm8u2Mqjn61j7uYSOiW1onObWM7r1ZbOqbF0aRNLdmoM8VHhx143emA6oyfOZcKbP/DaTwYTFqprBYHjZQfa9Dz+WEq3Jg7uy+1tzs3wzd9s+YukrDpfAkD5Pnvb0J57E/IouCvVaFx6zE1OBEY+AK9cCotfhjN+dtIhYaEh3Dwkk+sHZSAIEWH1B+q+6Yn846o+/PqdZTw8Yy1/uszdspBTUNFq+00toePxx1K6wtqPm64NO5dDXAfoOsoG9+2LPQzuPtSVcQrQhh3apVCntqyzIWsYfP+fOotYRYaFehTYna4ZmM4tQzOZPHsL7y8pqP8Fp4KiNbbX7rp8P6Wb3Sj9YBMN0RUuh/Z9bWooNNLzssMNCe4B2rBDg7tSIx+w5YcXTGr4ubYthOl3QsVh/nBJT87ITuL3769gRUHTr1BsVoyxaZk2Nb7FOAdVS5pgxszRQzYF1K6v3VO3fT/PZ8z4peeuwV2pppUxGLpeALOeaNimCuX74d1bYekbsGoa4aEhPHNDDimxkfz0tUXsLjvivza3NGW74PAeN8HdMd7SFHn3otV2fnv7vvZ+Wg7sXGZnwtRHe+5KtVAj7reDZvOe8/0cn90P+7dDbFs7CwdIjo3k+ZsGUnLwKD9/YwkVp+oceOdgatsawT2xo02PNEVw37nM3rZzBveBdu65cxZPXRoS3J0bdmjPXakA6NDfTo2c+4ydC+2t9Z/BD6/BmXfDmb+EgoV28A5bbviRa/owf8se/v5/a/zc8BbCWVOmZs89JNT23puixkzhCrvC1FluuEOOvfUkNeOs5R7pZS13OL5hh/bclQqQEffbbf/mPOXd6w7tgQ9/YQPX8N9DvzEQFmVn4DhcNSCd287MYsqcPN5dfAoOsBathpg2EONm4XpK16bpuRcut8XpnPPak7JtT9yTxUzO1am+7u8bmaA9d6UCpk1P6HMtzH/eu6JSn/wWDu2GqyZCWKRdnNP7arv61blHLHD/xT0Y2jmZ+6etYNm2fY1wAc3YrlUnp2ScUrrBvnyoKG+896+qtG1wpmTAztrpMMDznrsvKRmnqKavDKnBXSlXw++DyiMw63HPjl/9Aax4B875rZ194ZR7Gxwts885hIWG8PQNOaTGRjL+9cUUHzhFBlirq2xeu43bYrE2uDsLyDWWkg1QWX58MNUpbaCtd1NxuO7XNzS4RzZ9ZUgN7kq5Su4M/W+AhS9B6fa6jy0rho9/ZevinH3Pic+l50LbPrBw8gnVAJNiInj+poHsPXSUn795igyw7s2zgbXWnnsTzJhxjH+c0HMHG9xN1fHna+OXnrsuYlIqsIb91vYkv3+09mOMgY9/adMuV02086ZdiUDurbBrxUlf+09LS+Cf1/RlwZY93PHqInbsq6fX2NK5KzvgKrmLvW3M6pCFy+04iHNevZNzULW+vLv23JUKAokdYeAtsORV2+t0Z8U7dtn8yAdqD1p9r7PT4BzTIl1d0T+NP1/em/mb93DeY9/x0qwtVFUHaa29otWAQGotf04RMbYkwe51jdeGncvsgHdojYor8e1tOYL68u6ac1cqSJz9a7tj0Lf/PPm5/Ttgxr2QcToMmVD7OSLj7ADtyvfg8N6Tnv7x0Ew+/9U5DM5K4q8fr+bKZ2azcnsQrmTdtcrWb4loVfsxjTljxpjjZQfcScupf+MOXzfqcHL23JuwWK4Gd6XciW8Pg26H5VNPnINtjJ32WHkUrnyu/q0Bc2+1+eZlU90+nZHUipdvGcR/xw5gZ2k5lz89i799vJqDR1xWTR7YBUvfbPKdfPymaM3J89trSulm0zLVjTAGUbrNBuea+XantBzYs8ntBzDgqOVe1vCee3Vlk27YocFdqdqc9Su7kci3/zj+2JJXYeMXMOrPdvC1Pu37QVquTc3UEpxFhMv6deCre4Zx/aAMXpy1hfMfn8k3a4ugqgL+dyNM/1nTVk/0l4rDNnDWG9y72sB3YEfdx/mitsFUp2N591qKiDVkdapTZNOXINDgrlRtYlJsGeBV70PhStibb0sMZJ4Ng+7w/Dy5t9mUQ/7sOg9LaBXOP67uy9s/HUJ0RCi3TlnIJ8/8yq52jUqEbx5unJ5tYypeZwena5sp4+S6K5O/FS63q0vb1jIVs8MAe1tb3r0htdydnK9twkFVDe5K1WXoBLu68Ou/wQc/t49d8cyJZWvr0/sq+5/bzcCqO4Ozkvi/X5zFfwYd4IKS15nGcGZ1/70dmFz1vg8XEUDHyg7UElidGnM/1Z3L7YYwteX8oxPtjJ3a8u7ac1cqCEW3hqF3wfpPIO97uOBhaN3Ju3NEtLI7Pa3+0M6N90Dk0VKuyf8LlYmZTG93NzfNTyMvtBPlXz7sWRXD5qJolS0MlpRd93GxbeyHaHEjzJipazDVKW3g8W33avJHcHdWhmzCDTs0uCtVnzPGQ2w76Hah3Z7NF7m3QnWF3a+1PsbAR7+AsiIirn+ZKT8dwePX5/As1xFVuom3pzzG3oNHfWuH09GDkFd3msgvdq2G1G4nT0GsScQe5++0zMESW6mztny7U9pAW5Z4v5ucv/bclQpSkXEwYQGMedP3wlGp3aHTWbDo5frz5ktegTUfwbl/hA4DEBGuHJDGH+/9LTtbdeP0rS8w6tEveWN+vm9z442xA7RTLoaSTb5dj6eKVtefknFyzpjxp0JHmd/6eu51LWbya89dg7tSzUtUQv3THusz6DZbIGvz17UfU7wePrnPbt495K4TnoqLjqD9lX+jkxRxR/w8/jBtJVc9O5ul3hYhW/GurYkDsO4T717rjUN74MDO+gdTnVK6Qlmhf5fp1zdTxqldH7uuwd2gajD33EXkQhFZJyIbReS+Oo4bJCJVIjLaf01UKkj0uAxapdjeuzuVR+C922yO/qrn3Q/adj0f0nIZZ97jv9f2YmdpOVc9O5v73lvOHk9SNft3wIxfQ/pgOz2xMYN7bTXca3NsUHWj/9pQuALi022lzrqER9nZNO4GVctLQULtamNfRcQC0rx67iISCjwDXAT0AsaKyEl/W47j/gl85u9GKhUUwiIg5yYbUN0VJfvyzzYYXfEsxLVzfw4RGPkHZH8Bl1V9yde/HsZPzszincUFjHj0W16fV0eqxhj4YIKdO3/VROhxCWyd69vmJJ4oWm1vvQ7ufsy7ezKY6pQ20M51r5k2a2gtd7Af1E1cgsCTnvtgYKMxZrMx5igwFbjCzXF3Ae8BXhTCVuoUk/NjO+/7h9dOfHzDlzDvGRg8DrpfWPc5skdAx6Ew81HiQit54NJezPjF2fRoF8cD01fym3eXYdzN+lg0GTZ9BaP+YhdgdbvIVkTc8IX/rs/VrlU2KMZ38Oz41pk2NeKv4H70oM3h15eSceqQY3vWe2qMQzS0roxTE2/Y4UlwTwO2udwvcDx2jIikAVcBE+s6kYiME5FFIrKouNizKWFKBZWkLOhyLix+5fiUxrIimD7e9nBH/bX+czh675QV2tLEQPd2cUwddwY/H9GZ95ds570lNb4ZlGyCzx+wHwyDbrePdRhg93tdN8OPF+iiaI0dTPW0xxsaDkmd/Rfcd60CjHc9dzg57+6v4N4Me+7u/mZqdgueAH5njKmq60TGmEnGmFxjTG5qaqqnbVQquOTeZpfZb/jMpgCm/8yWDh492eZ+PZF5lh10nfU4HCkDbBmDe0Z15/SsJP44fSUbi+zjVFfZ9wgJtwuwnME2JMRO79z4la2V40/GOIJ7LZUga+PPAmI1N8SuT2p3CI85Oe/ut55705b99SS4FwAZLvfTgZqTQXOBqSKSB4wGnhWRK/3SQqWCTdcLbJnZRZNh/kTY+CVc8HfvA+GIB+z2fgsmHXsoNER4cswAoiNCmfDmEsorquyesNvmw8X/hoS0E8/R/WI4egDyZ/nhwlyUFtgFO57OlHFK6WZ3ZKqqaHgbCpfbRWgJ6Z4dHxJqN0pv1J5781rEtBDoKiJZIhIBjAE+dD3AGJNljMk0xmQC7wJ3GmOm+721SgWD0DAY+GPbY/7yQeh+CeT+xPvzZAyys2dmP3lC0GiXEMWj1/ZlbeEBXnrvI1uTpufltr58TdnDbHE0f8+aOTaY6uEcd6eUbrZ6Ym119L2xc7nttXszEJqWYwe1Xb/JBGvP3RhTCUzAzoJZA7xtjFklIuNFZHxjN1CpoJRzsy1m1SoZLv+v7zMxRtxvC1vNe+6Eh0f2aMu4oemMWP0njoTFwaWPu3+P8GjoPNIGd3+WFD4W3L1Ny/hpxkxVhW1Duz7eva5DDlQdsWUTnII4544xZoYxppsxprMx5u+OxyYaY04aQDXG3GKMedffDVUqqMR3gOtfg5umQUyy7+fpMAB6XApznzlpSuPvoqfTKySf3xz5CQVH69goo/uFtub5rpW+t6OmXashPs0W5fJGinPLvQYG993roeroiZuWe6LmoGpVBVQcbNhGHU5NvGGHrlBVKlB6XOJ9z9adEffbAdm5Tx9/bNsCQuc8QVnP6/na5PKLt36ofTPubhcC4t/UTNFqz+e3u4pKsHV8ihsY3D1dmVpTYkf7bWq7o7a7s6ftr557daWtcd8ENLgr1dK17W3LCs+bCAd32/nd08ZDfDqxVzzKw1f3YcnWfTz+RS0BM7YNpOf6L7hXVdjqjt4Opjr5o4BY4XI7lpDS1bvXiRyvEAn+qeXuFNm09WU0uCsVDIb/HioP26mRXzxoF+Jc+QxExXN5vw5cn5vBc99t4vsNtawv6X6RLZq1f2fD21KyyVbA9HYw1clZQKwh6Yudy+2Hni/1gDrkQPFa+23In8HdeY4myrtrcFcqGKR2gz7X2WmRC1+AM+6ErHOOPf3Q5b3pnBrLr/63jOIDR05+ffeL7e36TxveFudgpK8pp5RudhplmY+L3Y2xM148XbxUU9pAwNh58v4oGuakPXellE+G/dYuWErpBuf+6YSnoiNCeeaGHA6UV3DP20uprll/JrWHXf7vSM0YY1hXeIBJMzdxy8sL+MO0Fawo8HCO9q7VttBWanffrsOZSvE1NbM3z344eJtvd0pzlP/dvsS/wd1Z9reJ5rrXU0FfKdViJHeGWz6GxE52imMN3dvF8eBlvbl/2gomztzEncO7HH9ShKOdLyB0ycv8+d35fLmhjB2l5QBkp8Ywd1MJb8zfSu8O8YwZlMHl/dNIiA53346i1XbburBI367DdTpk1tnev77Qx8FUp5gUO7C6ffHxgNwCe+4a3JUKJp2G1vn02MEZzN64m/98vp7Ts5KICg/lu/XFfLuumIitKbwefpTSFZ/Tt8vF/OLcVM7plkqHxGhKD1XwwbLtvLVgG3/8YBV/n7GGi/u0Z8ygjgzKbI24zqEvWg3t+/t+DfFptgyArz33ncvtNwdfB3Th+KCqsxfv1567BnellJ+JCA9f3YdlBfsYPXHusTHLXu3jGXnmxVT+8BSP9y4k5MqBJ7wuoVU4Nw/J5KYzOrFy+36mLtzKB0t38P6S7WSnxjBmUAZX56STEl5h0yL9b2xIIxtWY6Zwhe39u/n24rEOObBqmh0cllCIiPH9XE7ac1dKNaaE6HCev2kgU2bnMTgriWHdUmkT7yhYdugCO6haXeV2pomI0Cc9gT7pffjDJT35v+U7+d/CbTw8Yy3/+nQd47vs5V7wbY67q5Rutta8LwqXnzCY7BPnYqbN3zS8lruTc8MO7bkrpRpL7w4J/PtaN6s3u18EK9+1KYmMwXWeo1VEGNfmZnBtbgYbiw4wdcE29sz/BkJhdXUGDQrvKd1gxdt2zr43veayYru1n6/5dqf2/Wx5iH1bISm7YedyCglp0voyOltGKXVcl/Pshhle1njv0iaOBy7txT39KjlMJFe+uY3X5+W73zTEE84ZMyVebrnn6YbY9YmMtTOIwD/5dqcmrAypwV0pdVx0oh2U9XG1asrBjYS368WQzm14YPpKfv3OMg4frXObh1pO5Jwxs8G71x0rO+BlwTB3OvhxMNUpsumKh2lwV0qdqPvFdoXmns3ev3bXasLa9+blWwZx97ldmfbDdq5+bg75JQe9O09Stk2LFK/z7nWFyyGho63j3lD+nCnjFKVpGaVUoHRz7OG6zsvVqmXFdvOQNr0JCRF+Naobk28ZxI59h7n0v7P4as0uz88VHmXn63s7Y2anFxti16cxgnukpmWUUoGSlGVnu3i7t6qbsgMjurfh47vOomNSK37yyiL+8/k6qmqujq1Nanfv0jJHDtiaOg0dTHVq09sG47j2/jkfaM9dKRVg3S+C/DlweK9nxx/aAzMftamUtqed8FRGUive+9lQrh2Yzn+/3sgtLy9g70EP9mxN6WoHVKs9zNkXOurR+6vnHhYB476FoXf553ygOXelVIB1vxhMFWz4sv5jdy6D54fZfVovfxpiU086JCo8lH+N7ss/ru7D/M17uPS/s5i/uaTu86Z0s7si7dvqWZsLV9hbf/XcwZZ0iIzz3/mimm7DDg3uSqmTdciBmDb1p2aWTYWXzrcfBLd9CgNqX5kqIowd3JF3xg8B4PpJ87jj1UVsKi5z/wJvZ8wULrMbbcR38Oz4QIhsug07NLgrpU4WEgLdLoCNX564WbRTVQXM+C1M+ymkD4Jx3x1f1VmPfhmJfHnPMO49vxtzN5Vw/uMzeWD6ipNLER8L7h7MmDm8FwoWeb8hdlNzDs42Qd5dg7tSyr3uF9sgtHXOiY8f2AWvXA4Lnoczfg43TXebiqlLdEQoE0Z25dvfDOfG0zsydcE2hv/7G576agOHjlbag1ol2Z64uxkzxsCuVfD9YzD5QvhXtp2+WU/htIBrwg07tPyAUsq97OEQFmUXNGUPt49tWwhv3wSH98HVL0Lfaxv0FimxkfzlitO4ZWgm//p0HY99sZ435udzz6hujB6YQWiKy4yZo4dgy0zY8Bls+MJu6g22t372r6HrBR5/ewiYJiwepsFdKeVeRCvIHmHz7hc+AounwIzf2Jz27V/4ZxWoQ3ZqLBNvGsiivD08PGMNv3tvBS/N2sKU5AzaF8xAXh8Ned9DZbktB9x5BJzzG+h6PsT7capiY2vCDTs8Cu4iciHwJBAKvGiMeaTG81cAfwWqgUrgl8aYWX5uq1KqqXW/CNZ/Am+NtbedR8I1L9mUSSPIzUzivZ8N5dOVhfzz07U8tS6OR8IPULZzHVEDfkxYjwuh05m+bwQSaM2p5y4iocAzwCigAFgoIh8aY1a7HPYV8KExxohIX+BtoEdjNFgp1YS6XWBv138CZ90DIx/wbdNpL4gIF/Vpz7k92/LmvI5c+v2ZrCyJJG5hGJcdTWV0+CEGZEScuEFIS9GEG3Z40nMfDGw0xmwGEJGpwBXAseBujHGdyxQDNP4kTqVU44trBxc/CgkZ0P3CJn3riLAQbjmrMzcPzWbe5psCLlAAAAiFSURBVBLeXVzA+0sKeHP+VrJTYxg9MJ2rB6TTLiGqSdvVIM2p5w6kAdtc7hcAp9c8SESuAv4BtAEucXciERkHjAPo2LGjt21VSgXC4DsC+vYhIcLQLikM7ZLCn6/ozScrCnl3cQH/+nQdj362jrO6pjJ6YDrn92pLVHjjfqtosCbcsMOT4O7uu89JPXNjzDRgmoicg82/n+fmmEnAJIDc3Fzt3SulvBIXFc51gzK4blAGebsP8v6SAt5bsp1fvPUDcVFh3H9xT8YObsYdx5AQOO0au4F4I/MkuBcAGS7304EdtR1sjJkpIp1FJMUYs7uhDVRKKXcyU2K45/zu/PK8bszbXMLT32zk9++vIDREuC43o/4TBMrol5rkbTxZxLQQ6CoiWSISAYwBPnQ9QES6iGN0Q0RygAignsIRSinVcM60zeRbBnF21xTue285Hy2rtf95yqg3uBtjKoEJwGfAGuBtY8wqERkvIuMdh10DrBSRpdiZNdcbn/fXUkop70WFhzLpplxyOyXxq/8t5cvVXtSPD0ISqBicm5trFi1aFJD3VkoFrwPlFfzoxfms2XmAl27J5eyu3pVGaO5EZLExJre+47S2jFIqqMRFhfPKbYPJTo3hjlcXsWDLnkA3KSA0uCulgk5iqwhev/10OiRGc9uUhSzbti/QTWpyGtyVUkEpJTaSN28/g9Yx4dw8eQFrdjbNDkjNhQZ3pVTQapcQxZu3n0F0eCg3vTSfjUW1bAwShDS4K6WCWkZSK964wy6q/9GL89m251CAW9Q0NLgrpYJe59RYXvvJ6RyuqOKGF+exs7Txt7lzp7Kqmse/WN8kYwAa3JVSp4Se7eN59bbB7D1YwZhJ8/hqzS6acir4jn2HueGF+Tz51QY+X13Y6O+nwV0pdcrol5HIK7cNAuAnryzimufmMGdT41dJ+WL1Li5+6ntW7ijlsev68ZsLGr8iui5iUkqdciqqqnlnUQFPfbWBwv3lnNUlhXsv6E7/jES/vk95RRWPfLKWKXPyOC0tnv+OzSErJaZB5/R0EZMGd6XUKau8oorX5+Xz7Leb2HPwKKN6teXX53ejR7v4Bp97U3EZE978gTU793PbmVn87qLuRIY1vCSxBnellPJQ2ZFKXp61hUnfb6bsSCWX9e3Ar0Z186mXbYzh3cUFPPjhKiLDQnj02n6c27Ot39qqwV0ppby079BRnp+5mSmz8zhaVc21A9O5rF8HMlNiaB8fRUhI3Vv7lR2p5IFpK5i+dAdnZCfxxPUD/L5TlAZ3pZTyUdGBcp79ZhNvzt/K0apqACLDQuiU3IrM5BiyUmLITIk59nvb+EhWbt/PXW8tYeueQ/zyvG78fEQXQuv5MPCFBnellGqgPQePsrZwP3m7D7Fldxlbdh8ir+QgW0sOHQv6ANHhoVRUVZMaF8mTYwYwOCup0drkaXD3ZCcmpZQ6JSXFRDC0cwpDO5/4eFW1Yce+w+SVHGTLbvsTIsJdI7uQ2CoiMI2tQYO7Ukp5KTREyEhqRUZSq2ZbL14XMSmlVBDS4K6UUkFIg7tSSgUhDe5KKRWENLgrpVQQ0uCulFJBSIO7UkoFIQ3uSikVhAJWfkBEioF8H1+eAjR+hf2mFWzXFGzXA8F3TcF2PRB81+TuejoZY+pdORWw4N4QIrLIk9oKLUmwXVOwXQ8E3zUF2/VA8F1TQ65H0zJKKRWENLgrpVQQaqnBfVKgG9AIgu2agu16IPiuKdiuB4Lvmny+nhaZc1dKKVW3ltpzV0opVQcN7kopFYRaXHAXkQtFZJ2IbBSR+wLdHn8QkTwRWSEiS0Wkxe09KCKTRaRIRFa6PJYkIl+IyAbHbetAttFbtVzTQyKy3fH3tFRELg5kG70hIhki8o2IrBGRVSJyt+PxFvn3VMf1tOS/oygRWSAiyxzX9GfH4z79HbWonLuIhALrgVFAAbAQGGuMWR3QhjWQiOQBucaYFrn4QkTOAcqAV40xpzke+xewxxjziONDuLUx5neBbKc3armmh4AyY8yjgWybL0SkPdDeGLNEROKAxcCVwC20wL+nOq7nOlru35EAMcaYMhEJB2YBdwNX48PfUUvruQ8GNhpjNhtjjgJTgSsC3KZTnjFmJrCnxsNXAK84fn8F+x+vxajlmlosY8xOY8wSx+8HgDVAGi3076mO62mxjFXmuBvu+DH4+HfU0oJ7GrDN5X4BLfwv1MEAn4vIYhEZF+jG+ElbY8xOsP8RgTYBbo+/TBCR5Y60TYtIYdQkIpnAAOD/27l/0KaiOIrj30NEkDoI4lYFFTeR4uCiQwcRHR0EnTp2cHF2EQRHxc1BdFOhUP90dRGcxEVQ0FGkVJJJ3O1xeDdQJKn6Uni9j/NZkryQcH8c3o+X333kHT3I6Y96oOKMJA0kfQBGwGvbrTOqrblrwrF65krTnbV9GrgEXC8jgdh9HgDHgQXgO3C32+X8P0n7gVXghu2fXa9nVhPqqToj279sLwDzwBlJJ9t+V23NfR04vOX1PLDR0Vp2jO2N8jgCXtCMn2o3LHPR8Xx01PF6ZmZ7WE6+TeAhleVU5rirwBPbz8vhanOaVE/tGY3Z/gG8AS7SMqPamvt74ISko5L2AleBtY7XNBNJc2VDCElzwAXg0/afqsIasFSeLwGvOlzLjhifYMVlKsqpbNY9Aj7bvrflrSpzmlZP5RkdknSgPN8HnAe+0DKjqu6WASi3Nt0HBsBj23c6XtJMJB2juVoH2AM8ra0mSc+ARZq/Jx0Ct4CXwApwBPgGXLFdzQbllJoWaX7uG/gKLI9nobudpHPAW+AjsFkO36SZU1eX0zb1XKPejE7RbJgOaC68V2zflnSQFhlV19wjIuLvahvLRETEP0hzj4jooTT3iIgeSnOPiOihNPeIiB5Kc4+I6KE094iIHvoNpCDkmtQW6okAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Starting trial 2\\n\",\n      \"torch.Size([1, 193, 229, 193])\\n\",\n      \"175\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0,    60] loss: 0.82498\\n\",\n      \"[0,   120] loss: 0.69393\\n\",\n      \"Time elapsed: 0h:1m:46s\\n\",\n      \"train accuracy_score: 52.65 %\\n\",\n      \"tensor([[[[[ 6.5374e-02,  7.0022e-02,  6.3031e-02],\\n\",\n      \"           [ 1.0931e-01,  4.0493e-02, -2.1070e-03],\\n\",\n      \"           [ 7.8754e-02,  6.7159e-02, -5.2747e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.3309e-02,  5.8323e-03, -1.8290e-02],\\n\",\n      \"           [ 5.9207e-02,  1.1269e-03, -4.4969e-02],\\n\",\n      \"           [ 3.4563e-02, -3.4254e-02, -8.5974e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6501e-02, -9.8744e-03, -6.1281e-03],\\n\",\n      \"           [ 3.5750e-02, -5.0067e-02, -8.7114e-02],\\n\",\n      \"           [-2.0383e-02, -8.2927e-02, -9.0094e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2689e-02, -6.1403e-02, -8.6672e-04],\\n\",\n      \"           [-3.9109e-02, -4.4803e-02, -1.3689e-02],\\n\",\n      \"           [-5.3649e-02, -1.5826e-02, -1.7580e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2296e-03, -4.9410e-02, -5.8227e-02],\\n\",\n      \"           [-4.6874e-02, -3.0509e-02, -1.3298e-02],\\n\",\n      \"           [-3.7052e-02, -2.4884e-02, -3.1101e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1723e-02,  2.8891e-03, -1.5653e-02],\\n\",\n      \"           [-3.4684e-02, -1.9601e-02, -8.8572e-03],\\n\",\n      \"           [-4.1778e-02, -1.0874e-02,  4.4830e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0893e-01,  1.0851e-01, -6.6652e-03],\\n\",\n      \"           [ 1.3414e-01,  1.9316e-02,  8.6398e-03],\\n\",\n      \"           [ 7.5183e-02,  1.8212e-02, -4.2075e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.9939e-02,  1.4113e-02, -7.2245e-02],\\n\",\n      \"           [ 5.1214e-02, -4.5472e-02, -8.6998e-02],\\n\",\n      \"           [ 3.8900e-02, -2.1839e-02, -7.7067e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.1651e-02, -2.0761e-02, -1.2082e-01],\\n\",\n      \"           [ 3.8763e-02, -5.2612e-02, -1.5041e-01],\\n\",\n      \"           [ 1.5278e-02, -3.1548e-02, -6.8271e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.9647e-03,  2.0763e-02, -5.9155e-03],\\n\",\n      \"           [ 2.0058e-02,  3.3860e-02, -3.5286e-03],\\n\",\n      \"           [ 3.8300e-02,  2.8980e-02,  3.3679e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.7286e-02,  1.5765e-02,  1.2434e-02],\\n\",\n      \"           [ 5.7406e-02,  1.8389e-02, -6.3643e-03],\\n\",\n      \"           [ 4.1539e-02,  3.4441e-02,  4.3698e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.0590e-02,  1.2472e-02, -6.0614e-02],\\n\",\n      \"           [ 2.5820e-02,  1.0814e-02, -4.3004e-02],\\n\",\n      \"           [ 3.8511e-02,  4.3198e-02,  3.4768e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.3752e-03, -4.7503e-03, -2.5935e-02],\\n\",\n      \"           [-1.3368e-02, -2.3125e-02, -3.8903e-02],\\n\",\n      \"           [-2.2681e-02, -3.9192e-02, -3.3975e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.3189e-03, -1.0319e-02, -3.0788e-02],\\n\",\n      \"           [-3.0146e-02, -2.9287e-02, -3.5183e-02],\\n\",\n      \"           [-1.0422e-02, -4.1240e-02, -4.2556e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.1312e-03, -1.4911e-02, -3.4471e-02],\\n\",\n      \"           [-2.5399e-02, -2.7249e-02, -2.4376e-02],\\n\",\n      \"           [-1.2437e-02, -1.2530e-02, -2.0479e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.1622e-01, -3.7751e-01, -5.2713e-01],\\n\",\n      \"           [-3.7726e-01, -4.1928e-01, -4.2751e-01],\\n\",\n      \"           [-5.1654e-01, -4.2688e-01, -6.2248e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.8063e-01, -4.9053e-01, -6.5973e-01],\\n\",\n      \"           [-5.3780e-01, -4.5547e-01, -5.8703e-01],\\n\",\n      \"           [-5.6272e-01, -5.4998e-01, -5.9264e-01]],\\n\",\n      \"\\n\",\n      \"          [[-6.7729e-01, -5.1116e-01, -6.6215e-01],\\n\",\n      \"           [-5.7877e-01, -6.9504e-01, -8.4213e-01],\\n\",\n      \"           [-6.4663e-01, -8.2718e-01, -8.5434e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3109e-03, -6.4133e-03, -9.3187e-03],\\n\",\n      \"           [-5.7684e-03,  1.3835e-02,  7.4846e-03],\\n\",\n      \"           [ 1.1947e-02,  2.5395e-02,  2.2019e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.0832e-04, -1.4327e-02, -1.3787e-02],\\n\",\n      \"           [ 7.6923e-03,  7.5488e-03,  1.0819e-02],\\n\",\n      \"           [ 1.1222e-02,  2.4144e-02,  2.2031e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.7547e-03, -5.7817e-03, -1.9444e-02],\\n\",\n      \"           [-3.7590e-03,  3.4440e-03, -5.1935e-03],\\n\",\n      \"           [-1.1357e-02, -8.3282e-03, -5.4359e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.2331e-04, -5.7664e-03, -3.9624e-03],\\n\",\n      \"           [-4.5745e-03, -5.0446e-03, -6.1431e-03],\\n\",\n      \"           [-8.9527e-03, -5.9989e-03, -8.4865e-04]],\\n\",\n      \"\\n\",\n      \"          [[-7.7445e-04, -3.4917e-03, -7.2340e-03],\\n\",\n      \"           [-6.5562e-03, -7.6085e-03, -1.0484e-02],\\n\",\n      \"           [-1.3363e-02, -1.1419e-02, -8.0848e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.8493e-03, -6.7308e-03, -8.7636e-03],\\n\",\n      \"           [-9.5697e-03, -1.2087e-02, -1.2128e-02],\\n\",\n      \"           [-1.2015e-02, -1.5242e-02, -1.6533e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 60.00 %\\n\",\n      \"Val loss: 0.654021\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[1,    60] loss: 0.70007\\n\",\n      \"[1,   120] loss: 0.69318\\n\",\n      \"Time elapsed: 0h:3m:40s\\n\",\n      \"train accuracy_score: 53.08 %\\n\",\n      \"tensor([[[[[ 9.2909e-02,  9.4983e-02,  5.8698e-02],\\n\",\n      \"           [ 1.3008e-01,  1.4298e-01,  9.4106e-02],\\n\",\n      \"           [ 1.3308e-01,  1.3086e-01,  8.1844e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.5929e-02,  4.3650e-02,  1.4444e-02],\\n\",\n      \"           [ 9.1258e-02,  5.3522e-02,  1.9484e-02],\\n\",\n      \"           [ 8.5133e-02,  7.8682e-02,  2.1354e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7221e-03,  1.8139e-02,  4.1388e-03],\\n\",\n      \"           [ 2.3714e-02,  1.3549e-02, -6.6946e-03],\\n\",\n      \"           [ 3.4212e-02, -1.5000e-02, -8.2555e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.9068e-02,  5.5592e-02,  3.5031e-02],\\n\",\n      \"           [ 8.0595e-02,  5.6354e-02,  3.2601e-02],\\n\",\n      \"           [ 8.9938e-02,  5.0946e-02,  4.4725e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.1178e-02,  5.2983e-02,  3.3263e-02],\\n\",\n      \"           [ 5.0989e-02,  5.6158e-02,  4.2817e-02],\\n\",\n      \"           [ 6.1964e-02,  3.3095e-02,  2.2268e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.1487e-02,  4.1795e-02,  2.6011e-02],\\n\",\n      \"           [ 2.5214e-02,  3.9900e-02,  2.8799e-02],\\n\",\n      \"           [ 1.9332e-02,  1.4965e-02, -3.4800e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.4720e-02,  5.6065e-03,  4.2190e-03],\\n\",\n      \"           [-6.8991e-03,  4.4558e-03,  1.5312e-02],\\n\",\n      \"           [-4.0633e-02,  3.9005e-03,  2.7450e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.8802e-03, -7.0962e-03,  6.8231e-04],\\n\",\n      \"           [-2.0985e-02, -9.0913e-03, -1.3640e-02],\\n\",\n      \"           [-2.4453e-02, -1.3737e-02, -1.6447e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.3064e-02, -2.4494e-02, -1.0586e-02],\\n\",\n      \"           [-1.9472e-03,  7.8847e-03,  1.7965e-02],\\n\",\n      \"           [ 2.9892e-02,  9.3130e-03,  2.2769e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.6891e-03,  9.1540e-03,  3.4562e-03],\\n\",\n      \"           [ 1.2952e-02,  1.9613e-02,  1.4462e-02],\\n\",\n      \"           [ 1.8358e-02,  1.8579e-02, -1.8414e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.4063e-02, -3.5305e-03,  3.4052e-02],\\n\",\n      \"           [-1.3824e-02,  9.6677e-03,  6.2760e-03],\\n\",\n      \"           [ 9.6272e-03, -9.9097e-03,  9.5613e-04]],\\n\",\n      \"\\n\",\n      \"          [[-4.1881e-02, -9.9882e-03,  9.9755e-03],\\n\",\n      \"           [-1.4316e-02,  7.2765e-05,  7.5201e-03],\\n\",\n      \"           [-1.0528e-02, -2.0505e-03, -1.8963e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.8578e-02, -1.7545e-02, -8.9639e-03],\\n\",\n      \"           [-1.3403e-02, -2.2125e-02, -1.2222e-02],\\n\",\n      \"           [-1.4399e-02, -1.3085e-02, -1.2359e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.8655e-02, -1.5549e-02,  3.2383e-04],\\n\",\n      \"           [-9.5853e-03, -2.8313e-02, -3.9473e-03],\\n\",\n      \"           [-1.2866e-02, -1.9852e-02, -1.9281e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1910e-02,  8.1912e-03,  4.0652e-03],\\n\",\n      \"           [ 1.1339e-03, -1.8685e-03, -3.1589e-03],\\n\",\n      \"           [-6.5471e-03, -1.5096e-02, -5.2108e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.0887e-01, -2.5458e-01, -2.4540e-01],\\n\",\n      \"           [-2.0795e-01, -1.6609e-01, -1.9876e-01],\\n\",\n      \"           [-1.3495e-01, -8.3743e-02, -1.0952e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.2404e-01, -2.9508e-01, -2.5168e-01],\\n\",\n      \"           [-2.0805e-01, -1.6121e-01, -2.7674e-01],\\n\",\n      \"           [-1.6709e-01, -1.4050e-01, -2.5946e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.9813e-01, -3.4492e-01, -3.7196e-01],\\n\",\n      \"           [-2.3820e-01, -2.5570e-01, -3.4749e-01],\\n\",\n      \"           [-1.8082e-01, -3.0106e-01, -2.7265e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.6430e-02, -1.2346e-02, -7.6171e-03],\\n\",\n      \"           [-1.0452e-02, -8.9056e-03, -1.8772e-03],\\n\",\n      \"           [-5.2235e-03, -5.8956e-03,  5.5375e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.7641e-02, -6.7260e-03, -1.0781e-02],\\n\",\n      \"           [-7.4250e-03, -3.2911e-03,  3.7554e-03],\\n\",\n      \"           [-7.3332e-03,  3.6068e-03,  1.6423e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.2554e-02,  1.2176e-04,  3.7295e-03],\\n\",\n      \"           [-3.1353e-03,  2.8201e-03,  3.4155e-03],\\n\",\n      \"           [-4.8993e-03,  5.2585e-03,  2.0720e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.2633e-03,  3.1571e-04,  3.3150e-03],\\n\",\n      \"           [ 6.0829e-03,  4.5830e-03,  6.3757e-03],\\n\",\n      \"           [ 5.8624e-03,  5.3087e-03,  7.2089e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.6624e-04, -1.2759e-03, -1.7290e-03],\\n\",\n      \"           [ 3.5198e-03,  3.1338e-04, -1.0344e-04],\\n\",\n      \"           [ 3.6930e-03,  2.0152e-03,  4.7419e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.6979e-03, -4.5596e-03, -4.1216e-03],\\n\",\n      \"           [-6.8491e-04, -2.3521e-03, -6.4105e-04],\\n\",\n      \"           [ 1.0562e-03,  4.3659e-04,  2.9037e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 40.00 %\\n\",\n      \"Val loss: 0.746670\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[2,    60] loss: 0.67502\\n\",\n      \"[2,   120] loss: 0.66049\\n\",\n      \"Time elapsed: 0h:5m:35s\\n\",\n      \"train accuracy_score: 58.68 %\\n\",\n      \"tensor([[[[[-5.2558e-02, -7.6014e-02, -3.3179e-02],\\n\",\n      \"           [-9.1460e-02, -1.4048e-01, -5.1874e-02],\\n\",\n      \"           [-9.6186e-02, -1.0099e-01, -1.0220e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.0252e-02, -1.2955e-02, -1.5019e-02],\\n\",\n      \"           [-7.2308e-02, -6.7445e-02, -8.3730e-03],\\n\",\n      \"           [-1.1805e-01, -1.1072e-01, -5.5550e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0832e-02, -1.4414e-02,  9.3924e-03],\\n\",\n      \"           [-4.7552e-02, -4.3903e-02,  1.1992e-02],\\n\",\n      \"           [-1.0016e-01, -7.5496e-02, -3.7735e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.7556e-02, -9.0556e-02, -6.9980e-02],\\n\",\n      \"           [-9.2790e-02, -7.0146e-02, -5.9216e-02],\\n\",\n      \"           [-7.5825e-02, -6.7119e-02, -5.3976e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.4334e-02, -7.2987e-02, -4.4349e-02],\\n\",\n      \"           [-8.2783e-02, -7.8362e-02, -2.5643e-02],\\n\",\n      \"           [-6.4017e-02, -4.4811e-02, -3.6222e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.8591e-02, -4.6216e-02, -1.5718e-02],\\n\",\n      \"           [-4.9356e-02, -5.4436e-02,  3.7573e-03],\\n\",\n      \"           [-4.7107e-02, -3.3258e-02,  1.9027e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.3460e-03,  3.9675e-02,  8.1556e-02],\\n\",\n      \"           [ 6.5339e-02,  6.4604e-02,  1.0114e-01],\\n\",\n      \"           [ 8.6904e-02,  1.0595e-01,  9.4473e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.5390e-02, -3.9186e-02,  3.3504e-02],\\n\",\n      \"           [-1.9071e-02,  5.1641e-03,  1.3573e-02],\\n\",\n      \"           [ 6.8681e-02,  1.1482e-02,  5.1056e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.9262e-02, -3.3531e-02,  4.2080e-02],\\n\",\n      \"           [-4.7077e-02, -5.4311e-02,  2.8556e-03],\\n\",\n      \"           [-1.0064e-02, -5.3819e-02, -3.1984e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.1779e-02, -2.9527e-02, -5.8165e-02],\\n\",\n      \"           [-1.7833e-03, -1.8165e-02, -5.9242e-02],\\n\",\n      \"           [-2.5200e-02, -2.5033e-02, -3.4387e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8294e-02, -1.0017e-02, -3.7041e-02],\\n\",\n      \"           [ 1.2104e-02, -1.0173e-03, -9.3092e-03],\\n\",\n      \"           [-1.9113e-02,  1.2677e-03, -2.5951e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.2691e-03,  2.3686e-02,  1.7692e-02],\\n\",\n      \"           [ 7.1645e-03,  1.0528e-02,  2.2031e-02],\\n\",\n      \"           [ 2.7498e-03, -1.1272e-02,  1.3392e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.0227e-02,  4.1971e-02,  4.3612e-02],\\n\",\n      \"           [ 5.0372e-02,  6.6503e-02,  5.3704e-02],\\n\",\n      \"           [ 6.3837e-02,  7.2428e-02,  6.2633e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.1494e-02,  2.8604e-02,  1.7966e-02],\\n\",\n      \"           [ 3.3191e-02,  6.0732e-02,  3.2254e-02],\\n\",\n      \"           [ 4.4515e-02,  4.7600e-02,  3.3505e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.4023e-03, -2.2087e-02, -2.3572e-02],\\n\",\n      \"           [ 4.5087e-03,  4.6709e-03, -6.3354e-03],\\n\",\n      \"           [ 8.4022e-03,  1.5783e-02,  1.5617e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.6680e-01,  5.9477e-01,  5.7477e-01],\\n\",\n      \"           [ 6.0648e-01,  5.3817e-01,  5.5682e-01],\\n\",\n      \"           [ 5.3318e-01,  4.9140e-01,  5.6183e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.3458e-01,  6.7237e-01,  7.1089e-01],\\n\",\n      \"           [ 5.8260e-01,  6.3884e-01,  7.7055e-01],\\n\",\n      \"           [ 6.4524e-01,  6.3599e-01,  7.9880e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.7095e-01,  9.1378e-01,  9.6829e-01],\\n\",\n      \"           [ 7.6421e-01,  8.1139e-01,  9.3070e-01],\\n\",\n      \"           [ 6.6536e-01,  7.7233e-01,  7.8645e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.7430e-02,  8.9987e-03, -6.5826e-03],\\n\",\n      \"           [ 9.5424e-03, -4.4317e-03, -1.4443e-02],\\n\",\n      \"           [ 8.0434e-03, -4.6404e-03, -1.1538e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2532e-02,  2.9323e-03, -1.2179e-02],\\n\",\n      \"           [ 3.2267e-03,  4.8744e-04, -8.3638e-03],\\n\",\n      \"           [ 1.5280e-02, -6.4382e-03, -1.5404e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4238e-02,  3.9378e-03, -8.5894e-03],\\n\",\n      \"           [ 1.0983e-02, -1.5400e-02, -1.1981e-02],\\n\",\n      \"           [ 6.1390e-03, -1.2833e-02, -2.7211e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.2597e-03, -6.6755e-03, -4.5599e-03],\\n\",\n      \"           [-2.7836e-03, -3.3157e-03, -5.4913e-03],\\n\",\n      \"           [-5.3457e-05,  2.2354e-04, -1.4480e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.2002e-03, -9.7138e-04,  1.4132e-03],\\n\",\n      \"           [ 1.0863e-03,  2.8987e-03,  2.6498e-03],\\n\",\n      \"           [ 3.7669e-03,  7.7253e-04,  3.1411e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 7.7185e-04,  3.2383e-03,  6.0545e-03],\\n\",\n      \"           [ 2.2414e-03,  4.6942e-03,  7.8897e-03],\\n\",\n      \"           [ 7.9260e-03,  5.6615e-03,  5.8772e-03]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 76.00 %\\n\",\n      \"Val loss: 0.636735\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[3,    60] loss: 0.64519\\n\",\n      \"[3,   120] loss: 0.67857\\n\",\n      \"Time elapsed: 0h:7m:30s\\n\",\n      \"train accuracy_score: 59.11 %\\n\",\n      \"tensor([[[[[-1.6166e-02,  7.1877e-03,  3.3278e-03],\\n\",\n      \"           [-2.9594e-02,  3.7473e-02,  7.9373e-03],\\n\",\n      \"           [-4.0335e-02,  4.4335e-02,  4.9750e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.3371e-02, -2.9292e-02,  2.2537e-02],\\n\",\n      \"           [-2.9452e-02,  1.0615e-02, -6.6769e-03],\\n\",\n      \"           [-2.1227e-02,  4.9050e-02,  1.3020e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.3085e-03, -4.3128e-02,  3.1908e-03],\\n\",\n      \"           [-3.5037e-02,  2.3451e-03, -8.5855e-03],\\n\",\n      \"           [-3.4987e-03, -1.0582e-02, -3.9257e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1953e-01, -1.0412e-01, -6.1885e-02],\\n\",\n      \"           [-1.1559e-01, -8.4671e-02, -5.2380e-02],\\n\",\n      \"           [-1.1083e-01, -2.1802e-02,  1.2313e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.2979e-01, -8.9824e-02, -4.8177e-02],\\n\",\n      \"           [-1.2413e-01, -1.0152e-01, -4.4478e-02],\\n\",\n      \"           [-1.0411e-01, -4.2674e-02, -1.0304e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.4653e-01, -1.2973e-01, -6.8751e-02],\\n\",\n      \"           [-1.1840e-01, -1.0499e-01, -5.5804e-02],\\n\",\n      \"           [-1.1426e-01, -6.3586e-02, -2.7240e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.7560e-02, -4.6690e-03,  1.1471e-02],\\n\",\n      \"           [-4.3192e-02,  7.3588e-02,  9.2002e-02],\\n\",\n      \"           [ 1.7165e-02,  6.5474e-02,  3.2483e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.3298e-02, -1.2553e-02,  6.1745e-02],\\n\",\n      \"           [-4.3451e-02,  3.3554e-02,  8.0490e-02],\\n\",\n      \"           [ 2.6036e-02,  9.2953e-02,  3.7297e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.4683e-01, -4.5981e-02,  1.0113e-01],\\n\",\n      \"           [-9.3004e-02, -2.1214e-02,  6.1798e-02],\\n\",\n      \"           [ 4.0335e-02,  5.8091e-02,  4.9274e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.6785e-02,  3.8041e-02,  7.5090e-02],\\n\",\n      \"           [ 2.5807e-03,  4.0816e-02,  6.9230e-02],\\n\",\n      \"           [-1.4220e-02,  7.9838e-03,  1.8997e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.7608e-02,  2.3415e-02,  7.4144e-02],\\n\",\n      \"           [-2.6192e-02, -1.2840e-02,  8.0457e-02],\\n\",\n      \"           [-1.7545e-02, -3.1419e-02,  6.8013e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.6725e-02, -3.6801e-02, -6.2455e-03],\\n\",\n      \"           [-5.5103e-02, -1.6098e-02, -9.9963e-03],\\n\",\n      \"           [-4.9116e-02, -1.8737e-02, -1.3616e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.7018e-02,  4.0288e-02,  2.0866e-02],\\n\",\n      \"           [ 6.6134e-02,  3.6919e-02,  4.7677e-02],\\n\",\n      \"           [ 9.5541e-02,  8.7056e-02,  6.3431e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7082e-02,  1.8421e-02,  3.9075e-02],\\n\",\n      \"           [ 7.3612e-02,  8.6777e-03,  2.4217e-02],\\n\",\n      \"           [ 6.2915e-02,  3.0186e-02,  4.6306e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.2809e-02,  3.8176e-02,  3.1660e-02],\\n\",\n      \"           [ 2.4745e-02,  2.2173e-02,  3.4258e-02],\\n\",\n      \"           [ 3.5353e-02,  3.5540e-02,  4.5789e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.3464e-01, -6.0277e-01, -5.9064e-01],\\n\",\n      \"           [-3.0884e-01, -3.3381e-01, -3.7410e-01],\\n\",\n      \"           [-2.0502e-01, -2.5797e-01, -2.9688e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.8497e-01, -7.0935e-01, -6.4715e-01],\\n\",\n      \"           [-4.2052e-01, -4.7450e-01, -5.7097e-01],\\n\",\n      \"           [-4.2732e-01, -3.7553e-01, -6.1889e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.8004e-01, -8.4643e-01, -8.0677e-01],\\n\",\n      \"           [-6.8551e-01, -7.8648e-01, -8.2007e-01],\\n\",\n      \"           [-5.4828e-01, -6.7429e-01, -7.3287e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.7954e-02, -1.5138e-02, -1.2688e-02],\\n\",\n      \"           [-1.8270e-02, -6.9777e-03, -1.6417e-02],\\n\",\n      \"           [-1.8069e-02, -1.1893e-02, -1.3234e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.8324e-02, -1.5479e-02, -1.0236e-02],\\n\",\n      \"           [-1.5016e-02, -7.0051e-03, -7.2618e-03],\\n\",\n      \"           [-1.0642e-02,  2.5835e-03, -1.6665e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.3955e-02, -2.0411e-02,  4.1544e-03],\\n\",\n      \"           [-3.0815e-03,  9.5649e-04,  9.7669e-03],\\n\",\n      \"           [ 3.7855e-03,  1.5137e-03, -5.0352e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.2573e-03, -4.3438e-03, -5.9123e-03],\\n\",\n      \"           [-6.9130e-03, -3.5055e-03, -2.1134e-03],\\n\",\n      \"           [-9.1272e-04,  1.6700e-03,  9.7058e-04]],\\n\",\n      \"\\n\",\n      \"          [[-3.7427e-03, -1.2680e-03,  5.7125e-04],\\n\",\n      \"           [-4.2436e-03,  2.2091e-03,  3.1657e-04],\\n\",\n      \"           [ 1.6501e-03,  4.6478e-03,  3.4306e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5313e-03,  1.8852e-03,  1.1299e-04],\\n\",\n      \"           [ 3.4270e-03,  3.1577e-03,  3.1594e-03],\\n\",\n      \"           [ 2.7305e-03,  4.8824e-03,  5.6243e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 79.00 %\\n\",\n      \"Val loss: 0.583109\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[4,    60] loss: 0.62076\\n\",\n      \"[4,   120] loss: 0.63804\\n\",\n      \"Time elapsed: 0h:9m:24s\\n\",\n      \"train accuracy_score: 65.14 %\\n\",\n      \"tensor([[[[[-8.8713e-02, -6.3724e-02, -1.8902e-02],\\n\",\n      \"           [-8.8825e-02, -1.1684e-01, -2.6027e-02],\\n\",\n      \"           [-7.9924e-02, -8.8336e-02, -2.9216e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.4615e-02, -5.5150e-02, -6.4943e-02],\\n\",\n      \"           [-9.1793e-02, -7.1015e-02, -4.6074e-02],\\n\",\n      \"           [-9.0596e-02, -7.8456e-02, -2.3713e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.9503e-03, -8.1449e-02, -7.5314e-02],\\n\",\n      \"           [-3.3891e-02, -6.4185e-02, -4.8330e-02],\\n\",\n      \"           [-9.6148e-02, -5.7236e-02, -1.7898e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.3466e-03, -1.4372e-02,  1.5743e-02],\\n\",\n      \"           [-1.5948e-02, -9.5614e-03,  3.2359e-02],\\n\",\n      \"           [-1.9206e-02,  2.4630e-03,  5.2474e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.7737e-04, -3.8353e-02, -6.4048e-03],\\n\",\n      \"           [-2.2410e-03, -2.7675e-02, -5.3157e-03],\\n\",\n      \"           [-3.7651e-03, -1.4863e-03,  3.0373e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.9184e-03, -3.1479e-02, -2.3483e-02],\\n\",\n      \"           [ 7.6579e-03, -3.0316e-02, -1.9058e-02],\\n\",\n      \"           [ 3.2092e-02,  2.0102e-02,  3.8280e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0152e-01,  7.2828e-02,  1.2872e-01],\\n\",\n      \"           [ 5.1490e-02,  6.3094e-02,  9.0089e-02],\\n\",\n      \"           [ 9.8395e-02,  9.1835e-02,  1.3808e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2234e-02, -7.3504e-03, -5.8893e-04],\\n\",\n      \"           [ 4.0346e-04, -4.1442e-02, -1.7569e-02],\\n\",\n      \"           [ 3.1350e-02,  3.0004e-02,  3.2812e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.9176e-02, -5.3990e-02, -4.9202e-02],\\n\",\n      \"           [ 1.2361e-03, -7.5022e-02, -9.3467e-02],\\n\",\n      \"           [ 2.8271e-02, -1.6627e-02, -1.1868e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.1468e-02,  1.5002e-04, -4.6587e-03],\\n\",\n      \"           [-7.8370e-03, -1.7008e-02,  1.3420e-03],\\n\",\n      \"           [-2.9272e-02, -4.2742e-03, -7.6759e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.4540e-02,  1.0957e-02, -8.5657e-03],\\n\",\n      \"           [ 6.7534e-03, -1.7700e-02, -1.7910e-02],\\n\",\n      \"           [-7.5679e-03, -1.4874e-03, -2.6219e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1782e-02,  1.0654e-02, -1.6580e-03],\\n\",\n      \"           [-2.4935e-03,  4.1499e-03, -2.2908e-02],\\n\",\n      \"           [ 2.0262e-02, -2.2324e-02, -3.9093e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.2443e-02,  3.4586e-02,  4.1208e-02],\\n\",\n      \"           [-8.1596e-03,  4.3537e-02,  1.8192e-02],\\n\",\n      \"           [-1.0915e-02, -6.4528e-04,  1.2345e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.4459e-02,  3.4288e-03, -2.2606e-02],\\n\",\n      \"           [-2.2906e-02, -1.9136e-02, -3.5419e-03],\\n\",\n      \"           [-3.5797e-02, -3.0905e-02, -3.1696e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 8.7074e-03, -2.9178e-02, -6.2385e-02],\\n\",\n      \"           [-3.4446e-02, -5.9599e-02, -5.4373e-02],\\n\",\n      \"           [-5.2524e-02, -5.0405e-02, -2.6750e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.7147e-01,  7.5367e-01,  6.2272e-01],\\n\",\n      \"           [ 7.3234e-01,  6.3706e-01,  6.5934e-01],\\n\",\n      \"           [ 7.0247e-01,  5.5406e-01,  5.3076e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 7.1517e-01,  7.5984e-01,  7.2546e-01],\\n\",\n      \"           [ 6.8543e-01,  6.0982e-01,  8.6057e-01],\\n\",\n      \"           [ 6.9545e-01,  6.0137e-01,  8.6584e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.8492e-01,  9.6669e-01,  1.0462e+00],\\n\",\n      \"           [ 8.4665e-01,  8.4077e-01,  1.0842e+00],\\n\",\n      \"           [ 7.2852e-01,  9.3473e-01,  1.0005e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0689e-02,  1.0424e-02,  1.8387e-03],\\n\",\n      \"           [-3.2155e-03, -9.4867e-03, -2.8780e-02],\\n\",\n      \"           [-1.4371e-02, -2.6847e-02, -4.4628e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1885e-02,  1.6098e-02,  3.7215e-03],\\n\",\n      \"           [ 4.4331e-03,  5.5847e-03, -1.2895e-02],\\n\",\n      \"           [-9.6848e-04, -1.7777e-02, -3.4356e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8197e-02,  1.0896e-02, -1.3436e-03],\\n\",\n      \"           [ 8.6186e-03,  1.6915e-03, -4.9132e-03],\\n\",\n      \"           [-1.1019e-04, -5.3140e-03, -7.8587e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.6316e-03, -3.3345e-03, -1.0598e-03],\\n\",\n      \"           [-6.1706e-03, -3.4766e-03, -3.2260e-03],\\n\",\n      \"           [-5.5857e-03, -3.7303e-03, -3.1237e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4212e-03,  6.3022e-04,  3.9354e-04],\\n\",\n      \"           [-4.7471e-04,  5.5270e-04,  1.8052e-03],\\n\",\n      \"           [-6.2311e-04, -5.3942e-04,  7.6689e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6968e-03,  5.5361e-03,  4.5363e-03],\\n\",\n      \"           [ 3.3456e-03,  5.9611e-03,  4.0363e-03],\\n\",\n      \"           [ 1.9089e-03,  3.7131e-03,  5.8516e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 73.00 %\\n\",\n      \"Val loss: 0.573952\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[5,    60] loss: 0.62259\\n\",\n      \"[5,   120] loss: 0.56802\\n\",\n      \"Time elapsed: 0h:11m:17s\\n\",\n      \"train accuracy_score: 68.58 %\\n\",\n      \"tensor([[[[[ 1.1782e-02,  1.0296e-02,  7.2157e-03],\\n\",\n      \"           [ 1.9729e-02,  2.4970e-02,  1.6662e-02],\\n\",\n      \"           [ 2.0813e-02,  3.1708e-02,  2.7842e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.3803e-04, -4.6578e-03,  2.0822e-03],\\n\",\n      \"           [ 1.0345e-02,  1.3408e-02,  1.0068e-02],\\n\",\n      \"           [ 9.9154e-03,  1.7095e-02,  1.6347e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0565e-02, -3.3144e-03, -1.1872e-02],\\n\",\n      \"           [-6.3595e-03,  6.1936e-03, -4.2601e-03],\\n\",\n      \"           [ 8.8378e-03,  9.2453e-03,  3.3973e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.0362e-03, -3.5267e-03, -6.3389e-03],\\n\",\n      \"           [ 1.0093e-02,  5.5680e-03, -1.0825e-03],\\n\",\n      \"           [ 7.9862e-03,  3.5785e-03, -3.9404e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.5072e-04, -3.8757e-03, -1.5108e-03],\\n\",\n      \"           [ 2.4915e-03, -1.1986e-03, -3.3929e-03],\\n\",\n      \"           [ 1.5832e-03, -3.4091e-03, -4.0790e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.3258e-03, -1.5042e-03, -2.4394e-03],\\n\",\n      \"           [ 1.9444e-04,  1.5334e-03,  3.0636e-03],\\n\",\n      \"           [-8.4060e-03, -7.9235e-03, -3.0745e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.0977e-02,  1.8912e-02,  2.1740e-02],\\n\",\n      \"           [-9.7378e-03,  2.8378e-02,  4.7834e-02],\\n\",\n      \"           [ 6.7148e-03,  3.4695e-02,  3.9062e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.4568e-03,  1.2127e-02,  3.1893e-02],\\n\",\n      \"           [-1.0933e-02,  3.7714e-02,  3.4258e-02],\\n\",\n      \"           [ 1.2196e-02,  3.0455e-02,  4.8991e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7310e-03,  1.0146e-02,  1.1732e-02],\\n\",\n      \"           [ 1.6807e-03,  2.1383e-02,  3.7558e-02],\\n\",\n      \"           [ 8.5793e-03,  3.3163e-02,  2.6730e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.8747e-03,  1.2851e-03,  2.5544e-03],\\n\",\n      \"           [ 8.1038e-03,  3.2194e-04,  7.0838e-03],\\n\",\n      \"           [ 2.5625e-03, -1.0146e-03,  3.2855e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.3706e-03, -3.4921e-04,  7.1530e-03],\\n\",\n      \"           [ 2.7478e-03,  4.4932e-04,  1.1920e-02],\\n\",\n      \"           [ 2.2190e-04,  2.8065e-03,  5.3427e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.1531e-03,  2.0998e-03,  1.3557e-03],\\n\",\n      \"           [ 1.3011e-02,  3.1380e-03,  1.8127e-03],\\n\",\n      \"           [-2.8864e-04,  1.9066e-03,  6.9661e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.4632e-03,  5.9097e-04,  8.7448e-03],\\n\",\n      \"           [ 1.2718e-02,  1.2846e-02,  3.2225e-02],\\n\",\n      \"           [ 1.7999e-02,  3.4725e-02,  4.8792e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.6619e-03, -5.8607e-03,  3.6074e-03],\\n\",\n      \"           [ 1.2024e-02,  8.7330e-03,  2.2784e-02],\\n\",\n      \"           [ 2.3619e-02,  3.2652e-02,  4.8166e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.9123e-03, -6.9544e-04,  9.5749e-03],\\n\",\n      \"           [ 1.3099e-02,  1.8033e-02,  2.7581e-02],\\n\",\n      \"           [ 2.9281e-02,  2.9996e-02,  4.0988e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3610e-01, -1.6025e-01, -1.5579e-01],\\n\",\n      \"           [-1.4621e-01, -1.3098e-01, -1.8658e-01],\\n\",\n      \"           [-1.5370e-01, -1.5733e-01, -1.9347e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.5170e-01, -1.6511e-01, -1.5928e-01],\\n\",\n      \"           [-1.4522e-01, -1.2703e-01, -1.7671e-01],\\n\",\n      \"           [-1.5410e-01, -1.5342e-01, -2.0468e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.4508e-01, -1.7944e-01, -1.7333e-01],\\n\",\n      \"           [-1.6469e-01, -1.6463e-01, -1.7233e-01],\\n\",\n      \"           [-1.4242e-01, -1.8725e-01, -1.9797e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.3849e-03, -4.0420e-03, -4.6816e-03],\\n\",\n      \"           [-3.1551e-03, -3.3462e-03, -3.6688e-03],\\n\",\n      \"           [-4.9246e-03, -6.0074e-03, -6.0117e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4732e-03, -9.8284e-04, -2.9654e-03],\\n\",\n      \"           [-1.6522e-03, -2.9966e-03, -3.8749e-03],\\n\",\n      \"           [-3.7106e-03, -4.5586e-03, -3.8232e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.8103e-03,  4.7795e-03,  4.2750e-03],\\n\",\n      \"           [ 2.7895e-03,  3.1033e-03,  1.9822e-03],\\n\",\n      \"           [ 1.5048e-03, -9.4830e-04, -3.2089e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.2289e-04,  1.0994e-03,  3.7036e-04],\\n\",\n      \"           [ 3.1824e-04,  6.6469e-04, -3.9668e-04],\\n\",\n      \"           [ 2.9636e-04,  9.9258e-05, -9.6498e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 6.3736e-04,  3.4096e-04,  4.8049e-04],\\n\",\n      \"           [ 4.2735e-04,  1.1193e-03, -4.8689e-04],\\n\",\n      \"           [ 4.4099e-04,  9.9634e-05, -7.0485e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 6.4165e-05,  6.5876e-04,  2.8297e-04],\\n\",\n      \"           [ 2.5905e-04,  8.7509e-04, -9.9433e-05],\\n\",\n      \"           [ 9.1409e-04,  1.9268e-04, -8.0602e-04]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 64.00 %\\n\",\n      \"Val loss: 0.581172\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[6,    60] loss: 0.58351\\n\",\n      \"[6,   120] loss: 0.61773\\n\",\n      \"Time elapsed: 0h:13m:10s\\n\",\n      \"train accuracy_score: 69.73 %\\n\",\n      \"tensor([[[[[ 1.9745e-02,  2.2606e-02,  1.9613e-02],\\n\",\n      \"           [ 2.5890e-02,  3.7130e-02,  2.3212e-02],\\n\",\n      \"           [ 2.9255e-02,  3.5167e-02,  1.7106e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4815e-03,  3.7736e-03,  1.3001e-02],\\n\",\n      \"           [ 1.6589e-02,  1.5452e-02,  5.4922e-03],\\n\",\n      \"           [ 2.1867e-02,  2.8612e-02,  1.8305e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.0181e-02,  4.7915e-03,  4.8098e-03],\\n\",\n      \"           [-6.0735e-03,  6.8383e-03,  5.5674e-03],\\n\",\n      \"           [ 9.2722e-03,  9.9163e-03,  4.4662e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.6205e-03,  1.0892e-02,  3.6331e-03],\\n\",\n      \"           [ 8.2300e-03,  8.2696e-03, -9.3297e-03],\\n\",\n      \"           [ 3.2711e-03, -6.2193e-03, -2.5889e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6099e-03,  1.6088e-02,  9.2716e-03],\\n\",\n      \"           [ 8.0938e-03,  1.1024e-02, -4.9518e-03],\\n\",\n      \"           [ 9.5689e-04,  1.5020e-03, -2.2319e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.9399e-03,  8.1558e-03,  4.6495e-03],\\n\",\n      \"           [ 1.7053e-03,  7.0851e-03, -6.2433e-04],\\n\",\n      \"           [-1.3015e-02, -1.4241e-02, -2.6739e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.6768e-02, -3.9976e-03, -2.5721e-02],\\n\",\n      \"           [-2.1157e-02, -1.2372e-03, -2.7909e-02],\\n\",\n      \"           [-4.0039e-03, -3.3350e-02, -5.1368e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.2202e-04,  5.3830e-03, -1.2352e-03],\\n\",\n      \"           [-2.8063e-03,  1.4536e-02, -6.3184e-03],\\n\",\n      \"           [ 9.1768e-03, -1.4666e-03, -1.8717e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.9253e-03,  7.8292e-03,  1.1000e-02],\\n\",\n      \"           [-2.1840e-04,  1.8389e-02,  2.3502e-02],\\n\",\n      \"           [ 7.6957e-03,  1.4840e-02,  5.4312e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2885e-02, -8.6408e-03,  4.7092e-03],\\n\",\n      \"           [-3.2848e-03,  4.8322e-03,  1.7678e-03],\\n\",\n      \"           [ 6.3444e-03, -1.3038e-03,  6.9525e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.1493e-02, -9.0083e-03,  1.0029e-02],\\n\",\n      \"           [-6.0030e-03, -5.8399e-03,  6.8468e-03],\\n\",\n      \"           [ 4.8105e-03,  8.9358e-04,  3.4559e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.6476e-02, -6.3809e-03,  7.6131e-03],\\n\",\n      \"           [-1.2859e-02, -2.8329e-03,  1.4085e-02],\\n\",\n      \"           [-7.9658e-03,  1.6717e-03,  1.2085e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.3349e-03, -5.7489e-03, -2.4358e-03],\\n\",\n      \"           [ 2.5635e-03, -9.4246e-03, -1.0894e-02],\\n\",\n      \"           [ 2.3719e-04, -4.6058e-03, -1.2937e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.3953e-02, -7.1279e-03,  3.7509e-03],\\n\",\n      \"           [ 3.8614e-03, -4.3098e-03, -1.1894e-02],\\n\",\n      \"           [ 1.1207e-02, -5.3526e-03, -3.9389e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.7571e-02, -9.6095e-03,  5.1644e-03],\\n\",\n      \"           [ 5.3479e-04,  2.4388e-03,  6.6373e-03],\\n\",\n      \"           [ 1.4634e-02,  7.3802e-03,  4.6355e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.6569e-01, -2.1489e-01, -2.2717e-01],\\n\",\n      \"           [-1.6696e-01, -1.4424e-01, -1.9732e-01],\\n\",\n      \"           [-1.2575e-01, -1.1139e-01, -1.4895e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.9009e-01, -2.1530e-01, -2.4476e-01],\\n\",\n      \"           [-1.6266e-01, -1.6560e-01, -2.5867e-01],\\n\",\n      \"           [-1.5542e-01, -1.4746e-01, -2.6635e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.9302e-01, -2.8529e-01, -3.3485e-01],\\n\",\n      \"           [-2.1307e-01, -2.4320e-01, -3.1653e-01],\\n\",\n      \"           [-1.5705e-01, -2.4263e-01, -2.9323e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.2397e-03, -3.9308e-03,  5.4538e-04],\\n\",\n      \"           [-2.4441e-03,  2.3667e-03,  9.2181e-03],\\n\",\n      \"           [ 7.9438e-04,  4.6731e-03,  1.1332e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.3603e-03, -5.9977e-03,  1.5916e-03],\\n\",\n      \"           [-3.5089e-03, -1.1719e-03,  5.2350e-03],\\n\",\n      \"           [-1.3190e-03,  5.3284e-03,  8.6117e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.8771e-03, -1.5435e-03,  7.8832e-04],\\n\",\n      \"           [-3.2531e-03,  1.8354e-03,  4.5925e-03],\\n\",\n      \"           [-2.2352e-04,  2.7485e-03,  4.5913e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.7959e-04,  1.1678e-03,  2.0356e-04],\\n\",\n      \"           [ 1.3235e-03,  7.1650e-04,  3.7177e-04],\\n\",\n      \"           [ 6.4169e-04,  6.5348e-04,  1.3156e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.9925e-04,  6.5093e-04, -3.3194e-04],\\n\",\n      \"           [ 8.4276e-04,  1.0611e-03,  1.1231e-04],\\n\",\n      \"           [ 1.0091e-03,  1.1063e-03,  8.1974e-05]],\\n\",\n      \"\\n\",\n      \"          [[-4.3720e-04, -8.1911e-05,  1.7399e-04],\\n\",\n      \"           [-5.2232e-05,  3.6535e-04, -2.5854e-04],\\n\",\n      \"           [ 3.4530e-04, -7.4021e-05, -9.6396e-04]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 79.00 %\\n\",\n      \"Val loss: 0.509815\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[7,    60] loss: 0.53940\\n\",\n      \"[7,   120] loss: 0.56754\\n\",\n      \"Time elapsed: 0h:15m:4s\\n\",\n      \"train accuracy_score: 69.30 %\\n\",\n      \"tensor([[[[[-1.1930e-01, -1.4246e-01, -8.3164e-02],\\n\",\n      \"           [-2.6521e-01, -3.4615e-01, -1.4073e-01],\\n\",\n      \"           [-3.6279e-01, -3.3847e-01, -1.6139e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.1428e-02, -2.9033e-02,  3.4587e-03],\\n\",\n      \"           [-2.8039e-01, -1.5900e-01, -7.8288e-02],\\n\",\n      \"           [-3.6615e-01, -2.8260e-01, -1.3974e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1572e-01, -1.1550e-02, -5.1939e-02],\\n\",\n      \"           [-8.7387e-02, -1.4410e-01, -1.1366e-01],\\n\",\n      \"           [-3.0630e-01, -2.1125e-01, -1.1345e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.6178e-02, -1.0913e-01, -4.0230e-02],\\n\",\n      \"           [-1.3772e-01, -1.2277e-01,  3.5811e-02],\\n\",\n      \"           [-1.2737e-01, -5.3604e-02,  1.3705e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.3026e-02, -3.9669e-02, -3.9113e-02],\\n\",\n      \"           [-1.1088e-01, -1.0665e-01, -1.2408e-02],\\n\",\n      \"           [-1.7673e-01, -1.2260e-01, -1.8466e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1825e-01,  3.2200e-03, -6.2865e-02],\\n\",\n      \"           [-4.0020e-02, -9.6318e-02, -6.1863e-02],\\n\",\n      \"           [-1.3613e-01, -8.9328e-02,  1.3012e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.2413e-01,  2.6124e-01,  6.6729e-01],\\n\",\n      \"           [ 2.3285e-01,  2.7898e-01,  5.2106e-01],\\n\",\n      \"           [ 2.3509e-01,  4.1210e-01,  7.4075e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5641e-01,  2.4479e-01,  4.0327e-01],\\n\",\n      \"           [ 1.9805e-01,  1.7835e-01,  3.2431e-01],\\n\",\n      \"           [ 7.5418e-02,  2.0463e-01,  3.1608e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0040e-01,  2.0686e-01,  1.9840e-01],\\n\",\n      \"           [ 8.5341e-02,  8.5180e-02,  6.8561e-02],\\n\",\n      \"           [-1.8960e-02, -5.5425e-02,  2.0320e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0960e-01, -6.8627e-03, -7.8314e-02],\\n\",\n      \"           [ 1.0729e-02, -3.5122e-02, -6.9072e-02],\\n\",\n      \"           [-7.7531e-02, -5.9001e-02, -1.0986e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4505e-01,  6.8206e-02, -8.5846e-02],\\n\",\n      \"           [ 2.1712e-02,  1.6475e-02, -6.2718e-02],\\n\",\n      \"           [-6.8990e-02, -4.8027e-02, -6.1855e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5409e-01,  1.2904e-01,  2.6277e-02],\\n\",\n      \"           [ 4.1941e-02,  7.7682e-02, -3.9185e-02],\\n\",\n      \"           [ 1.5324e-02, -6.9499e-02, -8.6000e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0576e-01,  9.8987e-02,  2.2824e-01],\\n\",\n      \"           [ 4.3614e-02,  2.5165e-01,  2.7724e-01],\\n\",\n      \"           [ 1.3869e-01,  2.4173e-01,  3.8122e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5986e-01,  8.0424e-02,  5.6095e-02],\\n\",\n      \"           [-2.4992e-02,  6.2975e-02,  2.4375e-01],\\n\",\n      \"           [ 3.7773e-02,  1.4533e-01,  1.5988e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8589e-01,  8.1271e-02, -3.3486e-02],\\n\",\n      \"           [-6.3945e-03, -1.4250e-02, -6.6028e-02],\\n\",\n      \"           [-6.0252e-02, -2.7175e-02,  5.8592e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 9.1652e-01,  1.0937e+00,  8.2777e-01],\\n\",\n      \"           [ 1.0355e+00,  6.2487e-01,  6.7345e-01],\\n\",\n      \"           [ 7.4125e-01,  5.2509e-01,  3.9244e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 8.4427e-01,  9.7949e-01,  7.7401e-01],\\n\",\n      \"           [ 8.0442e-01,  7.6435e-01,  9.3230e-01],\\n\",\n      \"           [ 8.8523e-01,  7.1549e-01,  1.1022e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 5.8114e-01,  1.0507e+00,  1.4132e+00],\\n\",\n      \"           [ 9.2776e-01,  8.3911e-01,  1.2983e+00],\\n\",\n      \"           [ 6.6114e-01,  1.0471e+00,  1.0748e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.5744e-02,  2.2622e-02, -6.8294e-03],\\n\",\n      \"           [-7.7796e-04, -2.0755e-02, -4.7625e-02],\\n\",\n      \"           [-2.4198e-02, -3.0170e-02, -6.9355e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.5062e-02,  2.9269e-02,  8.5099e-04],\\n\",\n      \"           [ 8.5008e-03,  9.4073e-03, -2.7634e-02],\\n\",\n      \"           [-1.5315e-02, -4.0768e-02, -5.0607e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.1453e-02,  6.2855e-03,  2.6602e-03],\\n\",\n      \"           [ 2.9384e-03, -1.5280e-02, -3.3675e-02],\\n\",\n      \"           [-3.1293e-02, -5.8991e-02, -5.2794e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1192e-03, -5.6944e-03, -6.9838e-03],\\n\",\n      \"           [-7.2152e-03, -7.5625e-03, -1.0297e-02],\\n\",\n      \"           [-7.5761e-03, -7.7292e-03, -5.8935e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.5968e-03,  4.2474e-03,  2.6960e-03],\\n\",\n      \"           [-1.1717e-03,  2.3986e-03,  2.6198e-03],\\n\",\n      \"           [-4.0735e-03, -6.3118e-03,  8.2202e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 9.1619e-03,  1.0827e-02,  9.5288e-03],\\n\",\n      \"           [ 8.9941e-03,  6.0004e-03,  7.6504e-03],\\n\",\n      \"           [ 3.5532e-03,  7.1342e-04,  7.6432e-03]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 75.00 %\\n\",\n      \"Val loss: 0.527375\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[8,    60] loss: 0.55933\\n\",\n      \"[8,   120] loss: 0.57097\\n\",\n      \"Time elapsed: 0h:16m:58s\\n\",\n      \"train accuracy_score: 71.88 %\\n\",\n      \"tensor([[[[[-1.6066e-02, -3.5359e-02, -8.8214e-03],\\n\",\n      \"           [-4.4719e-02, -7.4703e-02, -2.8084e-02],\\n\",\n      \"           [-5.8423e-02, -7.6305e-02, -4.3251e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.1449e-03, -1.3089e-02, -3.6203e-03],\\n\",\n      \"           [-4.1722e-02, -3.6999e-02, -2.1972e-02],\\n\",\n      \"           [-5.5807e-02, -6.5512e-02, -2.6683e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4955e-02, -3.5538e-03,  4.0311e-03],\\n\",\n      \"           [-8.8048e-03, -3.5699e-02, -1.9378e-02],\\n\",\n      \"           [-6.1246e-02, -4.4420e-02, -1.7347e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.0826e-02, -4.3846e-02, -3.2491e-02],\\n\",\n      \"           [-3.8770e-02, -3.5317e-02,  1.6340e-03],\\n\",\n      \"           [-3.5909e-02, -2.9161e-03,  4.5669e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.3428e-02, -4.6915e-02, -3.5781e-02],\\n\",\n      \"           [-4.7141e-02, -3.5138e-02, -1.0081e-02],\\n\",\n      \"           [-4.1321e-02, -1.4710e-02,  2.2423e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.8153e-02, -4.4013e-02, -2.9279e-02],\\n\",\n      \"           [-4.1570e-02, -4.5913e-02, -1.8236e-02],\\n\",\n      \"           [-3.7087e-02, -1.3372e-02,  2.4393e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.2990e-02,  4.1962e-02,  1.7745e-01],\\n\",\n      \"           [ 1.2385e-01,  8.3645e-02,  2.0971e-01],\\n\",\n      \"           [ 1.8500e-01,  2.0434e-01,  3.5205e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.1590e-02, -3.5738e-02,  6.6605e-02],\\n\",\n      \"           [ 4.3558e-02,  1.7027e-02,  1.4395e-01],\\n\",\n      \"           [ 1.0950e-01,  1.4335e-01,  2.0522e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.2364e-02, -7.4378e-02, -1.6811e-02],\\n\",\n      \"           [-2.5250e-02, -4.1413e-02,  4.9846e-02],\\n\",\n      \"           [ 5.7861e-02,  3.9470e-02,  1.0132e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.6852e-02,  6.9788e-03, -1.8583e-02],\\n\",\n      \"           [ 8.4346e-03, -1.4177e-02, -2.1991e-02],\\n\",\n      \"           [-1.5604e-02, -2.3740e-02, -3.2672e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.8289e-02,  2.5570e-02,  8.5102e-04],\\n\",\n      \"           [ 2.0080e-02,  2.3121e-03, -2.2775e-02],\\n\",\n      \"           [-5.8873e-03, -2.1678e-02, -2.7721e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.5645e-02,  4.3243e-02,  1.5645e-02],\\n\",\n      \"           [ 3.5628e-02,  2.2079e-02, -1.3544e-03],\\n\",\n      \"           [ 2.1120e-02, -8.3929e-03, -1.5138e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.2386e-01,  1.1972e-01,  1.2100e-01],\\n\",\n      \"           [ 1.5357e-01,  1.8216e-01,  1.6624e-01],\\n\",\n      \"           [ 2.1731e-01,  2.3635e-01,  2.2115e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 9.9620e-02,  8.9076e-02,  5.0699e-02],\\n\",\n      \"           [ 9.6297e-02,  1.1444e-01,  1.2381e-01],\\n\",\n      \"           [ 1.6059e-01,  1.8095e-01,  1.3662e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 7.0662e-02,  5.7425e-02,  7.2614e-03],\\n\",\n      \"           [ 6.8600e-02,  6.3815e-02,  4.0338e-02],\\n\",\n      \"           [ 1.0532e-01,  9.6882e-02,  9.8156e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.6182e-01,  2.6185e-01,  2.1560e-01],\\n\",\n      \"           [ 2.4348e-01,  1.4141e-01,  1.9023e-01],\\n\",\n      \"           [ 1.8460e-01,  1.0170e-01,  1.1012e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.0186e-01,  2.4096e-01,  2.4070e-01],\\n\",\n      \"           [ 2.3093e-01,  1.6058e-01,  2.8160e-01],\\n\",\n      \"           [ 2.0490e-01,  1.3083e-01,  2.6516e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5394e-01,  3.8340e-01,  4.2320e-01],\\n\",\n      \"           [ 3.0279e-01,  2.9685e-01,  3.7340e-01],\\n\",\n      \"           [ 2.3387e-01,  2.9922e-01,  3.1103e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0920e-02,  7.1487e-03, -4.6719e-04],\\n\",\n      \"           [ 5.7365e-03,  1.0831e-03, -1.3793e-02],\\n\",\n      \"           [ 1.2522e-03, -8.0105e-03, -2.3528e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2574e-02,  7.1791e-03, -6.8874e-04],\\n\",\n      \"           [ 6.8686e-03,  4.3535e-03, -6.9481e-03],\\n\",\n      \"           [ 2.4832e-03, -5.8747e-03, -1.7408e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.2136e-03,  5.0290e-03,  2.0901e-03],\\n\",\n      \"           [ 4.6483e-03,  1.0006e-03, -4.6365e-03],\\n\",\n      \"           [ 5.4259e-04, -2.4953e-03, -1.3653e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2415e-03, -1.9228e-03, -1.6376e-03],\\n\",\n      \"           [-3.5933e-03, -2.4496e-03, -2.5017e-03],\\n\",\n      \"           [-3.1879e-03, -2.6516e-03, -1.8617e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.6648e-04,  5.7850e-04,  5.5523e-04],\\n\",\n      \"           [-1.5339e-03, -5.2173e-04, -3.4248e-04],\\n\",\n      \"           [-1.7910e-03, -1.3346e-03, -2.7461e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4735e-04,  1.8788e-03,  2.4867e-03],\\n\",\n      \"           [-5.2888e-04,  7.9711e-04,  6.4384e-04],\\n\",\n      \"           [-1.0139e-03, -2.1006e-04,  2.3845e-04]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 79.00 %\\n\",\n      \"Val loss: 0.513758\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[9,    60] loss: 0.52493\\n\",\n      \"[9,   120] loss: 0.60044\\n\",\n      \"Time elapsed: 0h:18m:52s\\n\",\n      \"train accuracy_score: 74.46 %\\n\",\n      \"tensor([[[[[ 5.4433e-02,  3.2345e-02,  2.8079e-02],\\n\",\n      \"           [ 1.5855e-01,  2.0475e-01,  6.8276e-02],\\n\",\n      \"           [ 2.2673e-01,  2.3520e-01,  1.2422e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7263e-02,  2.2487e-03, -2.7877e-03],\\n\",\n      \"           [ 1.4753e-01,  9.4316e-02,  4.4618e-02],\\n\",\n      \"           [ 2.2391e-01,  1.8675e-01,  3.9426e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.8304e-02,  1.8303e-02,  1.0511e-02],\\n\",\n      \"           [ 8.5474e-02,  8.9610e-02,  2.0281e-02],\\n\",\n      \"           [ 1.9290e-01,  1.0733e-01,  3.3717e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.6700e-02, -1.9577e-03, -2.8847e-02],\\n\",\n      \"           [ 3.5750e-02,  6.5957e-04, -9.9537e-02],\\n\",\n      \"           [ 6.1089e-02,  5.4298e-03, -1.3604e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0774e-02,  2.5736e-02,  1.1432e-02],\\n\",\n      \"           [ 8.5372e-02,  3.1975e-02, -4.0443e-02],\\n\",\n      \"           [ 8.2818e-02,  5.0727e-02, -7.6414e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.4640e-03,  5.5417e-02,  3.2247e-02],\\n\",\n      \"           [ 6.9833e-02,  6.3410e-02,  2.7149e-02],\\n\",\n      \"           [ 4.4317e-02,  5.2022e-03, -2.8915e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.2280e-01, -5.1805e-01, -7.5885e-01],\\n\",\n      \"           [-4.0131e-01, -4.1604e-01, -6.2357e-01],\\n\",\n      \"           [-2.4437e-01, -4.0708e-01, -8.0688e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.0243e-01, -2.3938e-01, -4.3617e-01],\\n\",\n      \"           [-1.9207e-01, -1.6399e-01, -3.9274e-01],\\n\",\n      \"           [-1.2230e-01, -1.7189e-01, -3.8444e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.8181e-01, -1.2750e-01, -1.5781e-01],\\n\",\n      \"           [-8.1597e-03, -1.2718e-03, -1.8947e-02],\\n\",\n      \"           [-2.1422e-02,  2.2772e-02, -1.4104e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.0367e-03,  5.6391e-02,  1.0673e-01],\\n\",\n      \"           [ 3.9634e-02,  1.0173e-01,  9.9801e-02],\\n\",\n      \"           [ 5.9290e-02,  4.9909e-02,  7.7824e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.2793e-02, -3.0124e-02,  6.4364e-02],\\n\",\n      \"           [-5.5026e-02,  1.6747e-02,  5.4696e-02],\\n\",\n      \"           [ 1.0893e-02,  7.9088e-03,  2.0724e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1589e-01, -6.3895e-02, -1.3991e-02],\\n\",\n      \"           [-7.1997e-02, -5.9668e-02,  4.4140e-03],\\n\",\n      \"           [-8.7684e-02, -3.6938e-02,  4.3231e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.2765e-02,  1.0844e-01,  1.0111e-01],\\n\",\n      \"           [ 2.2307e-01,  1.0594e-01,  1.4352e-01],\\n\",\n      \"           [ 2.2459e-01,  1.8029e-01,  1.6352e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 7.1953e-02,  7.4872e-02,  1.7181e-01],\\n\",\n      \"           [ 2.6794e-01,  1.2824e-01,  1.0989e-01],\\n\",\n      \"           [ 2.3326e-01,  1.4571e-01,  2.7832e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 7.0344e-02,  7.8656e-02,  1.6079e-01],\\n\",\n      \"           [ 2.2157e-01,  1.8742e-01,  2.4410e-01],\\n\",\n      \"           [ 2.5637e-01,  2.4356e-01,  2.5475e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.0637e-01, -7.9714e-01, -7.0941e-01],\\n\",\n      \"           [-7.4469e-01, -4.5286e-01, -6.0722e-01],\\n\",\n      \"           [-6.2714e-01, -4.2364e-01, -3.9733e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.9139e-01, -7.1054e-01, -7.2524e-01],\\n\",\n      \"           [-6.3944e-01, -4.3665e-01, -7.5873e-01],\\n\",\n      \"           [-7.1181e-01, -4.1652e-01, -8.2081e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.1686e-01, -7.5231e-01, -9.8062e-01],\\n\",\n      \"           [-6.9348e-01, -6.5253e-01, -9.5354e-01],\\n\",\n      \"           [-5.4722e-01, -8.9516e-01, -9.2015e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.5723e-02, -1.8659e-02, -1.2835e-02],\\n\",\n      \"           [-5.9979e-03, -7.2095e-05,  1.2744e-02],\\n\",\n      \"           [ 1.3459e-03,  7.4240e-03,  2.5386e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.8014e-02, -1.3106e-02, -5.9365e-03],\\n\",\n      \"           [-5.0389e-03, -2.7578e-04,  1.0720e-02],\\n\",\n      \"           [ 1.4896e-03,  1.5292e-02,  2.7843e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1466e-02,  2.9836e-03,  2.5066e-03],\\n\",\n      \"           [ 3.5170e-03,  6.6906e-03,  1.0463e-02],\\n\",\n      \"           [ 1.5047e-02,  2.4525e-02,  2.2504e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.0440e-05,  2.3548e-03,  1.0573e-03],\\n\",\n      \"           [ 3.7258e-03,  3.1711e-03,  1.8542e-03],\\n\",\n      \"           [ 3.3348e-03,  1.5432e-03, -9.1657e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1898e-04,  5.1831e-04, -4.8494e-04],\\n\",\n      \"           [ 4.2152e-03,  2.0286e-03, -3.1286e-03],\\n\",\n      \"           [ 1.6530e-03, -4.6567e-04, -6.5176e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2909e-03, -3.0901e-04, -2.8746e-03],\\n\",\n      \"           [-9.5879e-04,  1.6426e-03, -2.3451e-03],\\n\",\n      \"           [-1.0507e-03, -2.3696e-03, -5.1001e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 83.00 %\\n\",\n      \"Val loss: 0.468392\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[10,    60] loss: 0.51907\\n\",\n      \"[10,   120] loss: 0.59264\\n\",\n      \"Time elapsed: 0h:20m:45s\\n\",\n      \"train accuracy_score: 73.89 %\\n\",\n      \"tensor([[[[[ 2.2634e-01,  1.3771e-01,  3.5990e-02],\\n\",\n      \"           [ 3.7204e-01,  4.0042e-01,  1.4059e-01],\\n\",\n      \"           [ 4.5567e-01,  4.0042e-01,  2.2203e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.6994e-02,  8.1004e-03, -6.3029e-05],\\n\",\n      \"           [ 2.9532e-01,  1.7624e-01,  1.4892e-01],\\n\",\n      \"           [ 3.8586e-01,  3.3471e-01,  1.9603e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.3556e-01,  1.8500e-02,  4.0302e-02],\\n\",\n      \"           [ 8.1996e-02,  1.2775e-01,  1.4757e-01],\\n\",\n      \"           [ 2.8703e-01,  1.8279e-01,  1.7834e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3625e-01, -5.1816e-02, -9.3012e-02],\\n\",\n      \"           [ 2.7118e-02, -4.6865e-02, -1.3692e-01],\\n\",\n      \"           [ 9.2961e-02, -4.0244e-02, -2.1624e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.0899e-02, -4.5547e-02, -6.7191e-02],\\n\",\n      \"           [ 1.2795e-02,  4.1679e-02, -1.9377e-02],\\n\",\n      \"           [ 7.6215e-02,  4.2709e-02, -9.6624e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0120e-01, -3.0952e-02, -2.6811e-02],\\n\",\n      \"           [ 1.0269e-02,  6.1910e-02,  5.7075e-02],\\n\",\n      \"           [-6.2669e-02, -8.4839e-02, -6.8114e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2326e+00, -8.1109e-01, -1.1892e+00],\\n\",\n      \"           [-8.6445e-01, -4.2688e-01, -6.1698e-01],\\n\",\n      \"           [-1.2588e+00, -8.9089e-01, -9.4109e-01]],\\n\",\n      \"\\n\",\n      \"          [[-6.9325e-01, -2.8469e-01, -4.5757e-01],\\n\",\n      \"           [-5.8093e-01,  1.2724e-01, -1.0146e-01],\\n\",\n      \"           [-6.3559e-01, -2.5094e-01, -2.0352e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.4860e-01,  4.2307e-03, -3.8305e-02],\\n\",\n      \"           [-7.8728e-02,  4.5690e-01,  4.8424e-01],\\n\",\n      \"           [ 5.9582e-02,  4.9739e-01,  3.7992e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.0421e-02,  8.4556e-02,  1.3110e-01],\\n\",\n      \"           [ 1.9954e-02,  1.3433e-01,  1.3287e-01],\\n\",\n      \"           [ 6.3538e-02,  7.3507e-02,  1.0477e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.9140e-02, -1.4891e-02,  9.0306e-02],\\n\",\n      \"           [-2.3847e-02, -1.5467e-03,  4.8114e-02],\\n\",\n      \"           [ 7.9963e-03, -3.3057e-02, -2.2728e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1303e-01, -4.1466e-02,  3.3632e-02],\\n\",\n      \"           [-4.2796e-02, -4.4049e-02,  3.0380e-03],\\n\",\n      \"           [-8.3234e-02, -8.0265e-02, -2.9394e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.4112e-02, -4.8024e-02, -1.4830e-01],\\n\",\n      \"           [ 4.0075e-02, -2.0143e-01, -1.9330e-01],\\n\",\n      \"           [ 3.2826e-02, -1.3299e-01, -2.9895e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.5271e-02, -4.0581e-02,  8.8980e-02],\\n\",\n      \"           [ 2.9793e-01,  4.0862e-02, -1.6542e-01],\\n\",\n      \"           [ 2.3444e-01, -2.6315e-02, -3.5045e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1027e-01,  7.8606e-02,  2.0247e-01],\\n\",\n      \"           [ 3.0541e-01,  2.2862e-01,  2.9159e-01],\\n\",\n      \"           [ 3.3183e-01,  2.2848e-01,  1.8756e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3979e+00, -1.3983e+00, -1.1405e+00],\\n\",\n      \"           [-1.4291e+00, -9.8096e-01, -9.6243e-01],\\n\",\n      \"           [-1.2260e+00, -7.9066e-01, -8.2106e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.5535e+00, -1.7252e+00, -1.5325e+00],\\n\",\n      \"           [-1.4032e+00, -1.1910e+00, -1.6938e+00],\\n\",\n      \"           [-1.4419e+00, -1.1477e+00, -1.6003e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.3905e+00, -2.0851e+00, -2.2604e+00],\\n\",\n      \"           [-1.8278e+00, -1.8513e+00, -2.1449e+00],\\n\",\n      \"           [-1.5351e+00, -1.9332e+00, -1.8527e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.2572e-03,  2.3226e-03,  2.8444e-02],\\n\",\n      \"           [ 2.5178e-02,  3.8317e-02,  7.6264e-02],\\n\",\n      \"           [ 3.1880e-02,  4.5454e-02,  6.1584e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.0086e-02, -2.2017e-02,  4.1621e-03],\\n\",\n      \"           [-1.0987e-03,  6.7129e-03,  2.3809e-02],\\n\",\n      \"           [ 1.3960e-02,  3.7488e-02,  4.8797e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.6663e-02, -1.1247e-02,  1.0864e-03],\\n\",\n      \"           [-7.4345e-03,  3.4395e-03,  2.7302e-02],\\n\",\n      \"           [ 2.8136e-03,  3.2344e-02,  2.8365e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.4867e-04,  7.3395e-03,  6.4823e-03],\\n\",\n      \"           [ 5.0173e-03,  8.0181e-03,  9.0658e-03],\\n\",\n      \"           [ 5.3079e-03,  2.7955e-03, -5.6232e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.8412e-03,  7.9327e-04,  1.0529e-03],\\n\",\n      \"           [ 1.0127e-03,  2.1133e-03, -3.2740e-03],\\n\",\n      \"           [-3.7285e-03, -2.7354e-03, -6.0962e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.1075e-03, -1.1258e-02, -6.8736e-03],\\n\",\n      \"           [-1.0310e-02, -6.2631e-03, -5.8120e-03],\\n\",\n      \"           [-8.1456e-03, -3.3881e-03, -7.4508e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 54.00 %\\n\",\n      \"Val loss: 0.805283\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[11,    60] loss: 0.48732\\n\",\n      \"[11,   120] loss: 0.53338\\n\",\n      \"Time elapsed: 0h:22m:39s\\n\",\n      \"train accuracy_score: 73.74 %\\n\",\n      \"tensor([[[[[-5.4504e-02, -4.2899e-02, -1.6116e-02],\\n\",\n      \"           [-9.2994e-02, -1.0159e-01, -3.5360e-02],\\n\",\n      \"           [-1.1155e-01, -1.0913e-01, -5.9162e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1418e-02, -1.4791e-02, -2.2071e-02],\\n\",\n      \"           [-8.1409e-02, -5.4662e-02, -1.6801e-02],\\n\",\n      \"           [-1.0131e-01, -9.0840e-02, -3.1715e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6623e-02, -2.7068e-02, -4.3893e-02],\\n\",\n      \"           [-3.1718e-02, -5.1012e-02, -3.7392e-02],\\n\",\n      \"           [-9.8680e-02, -4.9737e-02, -7.3879e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.3239e-02, -4.8255e-02, -6.4122e-03],\\n\",\n      \"           [-6.8377e-02, -4.7567e-02,  1.3109e-02],\\n\",\n      \"           [-6.2742e-02, -2.2064e-02,  5.5701e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.8778e-02, -4.0818e-02, -2.0182e-02],\\n\",\n      \"           [-6.1434e-02, -4.2699e-02,  6.7351e-03],\\n\",\n      \"           [-6.1984e-02, -2.0756e-02,  3.1571e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.1856e-02, -4.7056e-02, -1.7091e-02],\\n\",\n      \"           [-4.3921e-02, -4.0314e-02, -2.5343e-02],\\n\",\n      \"           [-2.9946e-02, -1.4716e-02,  1.3278e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.0021e-01,  2.0518e-01,  3.4914e-01],\\n\",\n      \"           [ 2.5111e-01,  2.0224e-01,  2.8773e-01],\\n\",\n      \"           [ 3.0895e-01,  3.4362e-01,  4.2137e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 8.3151e-02,  3.0205e-02,  1.7993e-01],\\n\",\n      \"           [ 9.6194e-02,  6.9740e-02,  1.9536e-01],\\n\",\n      \"           [ 1.1434e-01,  1.5988e-01,  1.9313e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.2170e-03, -1.0649e-01, -2.4359e-02],\\n\",\n      \"           [-1.0802e-01, -1.2387e-01, -1.0918e-01],\\n\",\n      \"           [-4.0751e-02, -5.9762e-02, -1.9122e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.9811e-02,  1.1433e-03, -2.6931e-02],\\n\",\n      \"           [ 1.6392e-03, -1.5336e-02, -1.9947e-02],\\n\",\n      \"           [-2.3106e-02, -1.3873e-02, -1.4493e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.9456e-02,  2.2786e-02, -2.3324e-02],\\n\",\n      \"           [ 2.0840e-02,  1.3821e-02, -1.8472e-02],\\n\",\n      \"           [-1.8511e-02, -5.9122e-03, -1.5556e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.5728e-02,  1.9368e-02, -5.0270e-03],\\n\",\n      \"           [ 1.7088e-02,  7.7062e-03, -1.4945e-02],\\n\",\n      \"           [ 4.8515e-03, -1.6994e-02, -1.3130e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.5231e-02,  4.1794e-02,  1.1499e-01],\\n\",\n      \"           [-1.5732e-02,  9.1699e-02,  1.1080e-01],\\n\",\n      \"           [-2.2339e-02,  4.5394e-02,  1.1804e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3231e-02, -2.9676e-02, -3.4110e-02],\\n\",\n      \"           [-1.0076e-01, -3.0090e-02,  6.6696e-02],\\n\",\n      \"           [-1.1766e-01, -1.7663e-02, -1.5419e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.4402e-02, -6.7053e-02, -8.3603e-02],\\n\",\n      \"           [-9.0734e-02, -9.4457e-02, -1.1073e-01],\\n\",\n      \"           [-1.1037e-01, -1.1527e-01, -5.5213e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.0876e-01,  5.9274e-01,  4.4305e-01],\\n\",\n      \"           [ 5.5491e-01,  4.1920e-01,  4.5708e-01],\\n\",\n      \"           [ 4.9700e-01,  3.9515e-01,  3.6570e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.8156e-01,  6.5925e-01,  5.3260e-01],\\n\",\n      \"           [ 5.6819e-01,  4.5506e-01,  5.9717e-01],\\n\",\n      \"           [ 5.9932e-01,  4.8427e-01,  6.4225e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.0701e-01,  7.4026e-01,  8.0180e-01],\\n\",\n      \"           [ 6.7121e-01,  6.2781e-01,  7.5760e-01],\\n\",\n      \"           [ 5.2906e-01,  7.1430e-01,  6.9929e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.6565e-03,  2.2791e-03, -3.8855e-03],\\n\",\n      \"           [-4.4333e-03, -1.6566e-02, -2.4574e-02],\\n\",\n      \"           [-1.8324e-02, -2.3746e-02, -3.4141e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7575e-02,  9.7555e-03, -2.2877e-03],\\n\",\n      \"           [ 8.4269e-03,  2.3475e-03, -1.3289e-02],\\n\",\n      \"           [-8.5204e-03, -2.3858e-02, -3.1936e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7063e-02,  1.0879e-02, -6.0598e-04],\\n\",\n      \"           [ 1.1319e-02,  1.8284e-03, -1.2620e-02],\\n\",\n      \"           [-2.6508e-03, -1.2288e-02, -2.9319e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3357e-03, -1.0860e-03, -2.7102e-03],\\n\",\n      \"           [-5.4189e-04, -1.3783e-03, -1.8181e-03],\\n\",\n      \"           [-2.2347e-03, -7.9279e-04,  3.6625e-05]],\\n\",\n      \"\\n\",\n      \"          [[ 3.8081e-03,  2.5599e-03,  2.9023e-04],\\n\",\n      \"           [ 1.6843e-03,  8.4990e-04,  1.1182e-05],\\n\",\n      \"           [ 5.1054e-04, -1.4910e-04,  2.8352e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 4.2284e-03,  4.3334e-03,  3.6120e-03],\\n\",\n      \"           [ 4.0669e-03,  2.7149e-03,  6.7439e-04],\\n\",\n      \"           [ 2.3088e-03,  1.9484e-03,  2.3231e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 63.00 %\\n\",\n      \"Val loss: 0.659416\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[12,    60] loss: 0.52334\\n\",\n      \"[12,   120] loss: 0.47250\\n\",\n      \"Time elapsed: 0h:24m:33s\\n\",\n      \"train accuracy_score: 77.62 %\\n\",\n      \"tensor([[[[[ 2.4488e-01,  1.8614e-01,  4.2645e-02],\\n\",\n      \"           [ 3.4641e-01,  3.1164e-01,  8.3037e-02],\\n\",\n      \"           [ 3.2916e-01,  2.3169e-01,  7.6036e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2052e-01,  6.1400e-02,  4.0267e-03],\\n\",\n      \"           [ 2.5193e-01,  1.7177e-01,  5.0003e-02],\\n\",\n      \"           [ 2.6808e-01,  1.7952e-01,  3.7621e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.6963e-04,  4.3063e-02,  2.9237e-02],\\n\",\n      \"           [ 1.4433e-01,  1.0902e-01,  5.4250e-02],\\n\",\n      \"           [ 2.4397e-01,  1.1294e-01,  3.8606e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.7611e-02,  3.8112e-03, -8.9325e-02],\\n\",\n      \"           [-1.3168e-02, -1.4472e-02, -1.3646e-01],\\n\",\n      \"           [-3.4622e-02, -1.0792e-01, -2.5613e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.0294e-01, -1.5101e-02, -8.5101e-03],\\n\",\n      \"           [-1.4133e-02,  1.7978e-02, -5.6041e-02],\\n\",\n      \"           [-2.1676e-02, -2.7750e-02, -1.5508e-01]],\\n\",\n      \"\\n\",\n      \"          [[-7.7720e-02,  2.0171e-02,  4.1411e-02],\\n\",\n      \"           [-3.1459e-03,  7.6563e-02,  3.4392e-02],\\n\",\n      \"           [-5.7937e-02, -4.0740e-02, -8.9208e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.9104e-01, -6.3515e-01, -9.9329e-01],\\n\",\n      \"           [-6.6113e-01, -5.0856e-01, -6.5753e-01],\\n\",\n      \"           [-6.7770e-01, -6.3681e-01, -9.4547e-01]],\\n\",\n      \"\\n\",\n      \"          [[-6.7189e-01, -3.7093e-01, -4.0669e-01],\\n\",\n      \"           [-3.6469e-01, -2.7695e-01, -3.6495e-01],\\n\",\n      \"           [-3.1996e-01, -3.1662e-01, -2.7101e-01]],\\n\",\n      \"\\n\",\n      \"          [[-8.4716e-01, -4.6090e-01, -2.3405e-01],\\n\",\n      \"           [-4.3499e-01, -2.9301e-01, -6.8569e-03],\\n\",\n      \"           [-2.5408e-01, -9.2858e-02, -1.2328e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.7661e-02,  2.0589e-02,  6.0383e-02],\\n\",\n      \"           [ 5.5298e-02,  4.2706e-02,  1.4077e-02],\\n\",\n      \"           [ 8.2301e-02,  1.5570e-02, -2.6717e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.6554e-03, -1.6424e-02,  2.6878e-02],\\n\",\n      \"           [ 2.6643e-02,  6.0446e-04,  2.7470e-02],\\n\",\n      \"           [ 4.4427e-02, -1.8039e-02, -1.9934e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.0040e-02, -5.7578e-02, -6.7915e-03],\\n\",\n      \"           [ 1.6041e-02, -3.1073e-02,  4.9899e-03],\\n\",\n      \"           [ 1.0772e-02, -1.6702e-02, -3.5981e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.4454e-02, -1.0111e-01, -8.7935e-02],\\n\",\n      \"           [ 2.6666e-03, -2.3104e-01, -1.6453e-01],\\n\",\n      \"           [-9.2987e-02, -2.2762e-01, -2.4036e-01]],\\n\",\n      \"\\n\",\n      \"          [[-8.2223e-02, -7.2597e-02,  1.0597e-01],\\n\",\n      \"           [ 9.8615e-02, -8.7390e-02, -6.4549e-02],\\n\",\n      \"           [-9.5734e-03, -1.3062e-01, -8.2076e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.1344e-02, -4.1492e-02,  1.2819e-01],\\n\",\n      \"           [ 5.4024e-02,  9.1914e-02,  2.4238e-01],\\n\",\n      \"           [ 6.5440e-02,  6.1612e-02,  9.9161e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.1179e-01, -1.0402e+00, -9.1604e-01],\\n\",\n      \"           [-8.8290e-01, -7.5297e-01, -7.9375e-01],\\n\",\n      \"           [-7.1021e-01, -5.5788e-01, -6.3150e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.9059e-01, -1.1947e+00, -1.2821e+00],\\n\",\n      \"           [-8.9954e-01, -9.4938e-01, -1.3232e+00],\\n\",\n      \"           [-8.8131e-01, -9.2570e-01, -1.2736e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.0363e+00, -1.5170e+00, -1.7278e+00],\\n\",\n      \"           [-1.2289e+00, -1.3743e+00, -1.7784e+00],\\n\",\n      \"           [-1.0373e+00, -1.4972e+00, -1.4882e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.9666e-02, -2.7420e-02, -2.4824e-02],\\n\",\n      \"           [ 1.4698e-02,  1.1423e-02,  9.8694e-03],\\n\",\n      \"           [ 4.1423e-02,  4.4310e-02,  4.5195e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.0735e-02, -3.1975e-02, -2.7532e-02],\\n\",\n      \"           [-5.8998e-03, -8.2927e-03,  4.6603e-03],\\n\",\n      \"           [ 1.3638e-02,  3.3028e-02,  4.3458e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.0589e-02, -1.7768e-02, -6.8631e-03],\\n\",\n      \"           [ 1.1184e-02,  1.3406e-02,  2.0287e-02],\\n\",\n      \"           [ 2.5558e-02,  3.6386e-02,  4.5829e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.4127e-03,  2.6213e-03,  7.3358e-04],\\n\",\n      \"           [ 9.8574e-03,  5.6721e-03,  2.4164e-03],\\n\",\n      \"           [ 9.4605e-03,  5.8746e-03,  1.3864e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.9288e-04, -2.7521e-03, -2.4700e-03],\\n\",\n      \"           [ 3.2496e-03, -1.8759e-03, -2.0550e-03],\\n\",\n      \"           [ 1.5715e-03,  2.3987e-03, -3.7303e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.2301e-03, -8.7936e-03, -7.1429e-03],\\n\",\n      \"           [-2.8949e-03, -4.7936e-03, -7.7571e-03],\\n\",\n      \"           [-4.1793e-03, -6.1728e-03, -9.0317e-03]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 83.00 %\\n\",\n      \"Val loss: 0.429474\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[13,    60] loss: 0.48845\\n\",\n      \"[13,   120] loss: 0.50640\\n\",\n      \"Time elapsed: 0h:26m:26s\\n\",\n      \"train accuracy_score: 76.90 %\\n\",\n      \"tensor([[[[[-4.3760e-01, -1.7375e-01, -1.0077e-02],\\n\",\n      \"           [-6.4106e-01, -5.5076e-01, -1.8234e-01],\\n\",\n      \"           [-8.1775e-01, -6.0468e-01, -3.1888e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.0742e-01, -1.4575e-02,  1.4172e-01],\\n\",\n      \"           [-5.7720e-01, -1.9084e-01, -7.6715e-02],\\n\",\n      \"           [-7.2837e-01, -4.4193e-01, -6.8076e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.5034e-01, -7.4246e-02,  6.0988e-02],\\n\",\n      \"           [-3.9622e-01, -1.9595e-01, -5.3114e-02],\\n\",\n      \"           [-6.4662e-01, -3.3559e-01, -1.4122e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.5598e-01,  2.5596e-01,  2.5544e-01],\\n\",\n      \"           [ 3.3516e-01,  2.6231e-01,  4.2080e-01],\\n\",\n      \"           [ 3.7679e-01,  4.2568e-01,  7.1625e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8280e-01,  1.4641e-01,  6.8734e-02],\\n\",\n      \"           [ 1.4376e-01,  1.1646e-01,  1.8291e-01],\\n\",\n      \"           [ 2.4874e-01,  2.4050e-01,  3.3633e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.8237e-02, -1.3476e-01, -1.5316e-01],\\n\",\n      \"           [ 4.7117e-03,  2.7537e-02,  2.3790e-02],\\n\",\n      \"           [ 2.2042e-01,  2.3573e-01,  2.3684e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.9959e+00,  2.4957e+00,  2.4901e+00],\\n\",\n      \"           [ 2.7209e+00,  2.5593e+00,  2.4754e+00],\\n\",\n      \"           [ 3.3975e+00,  2.8787e+00,  2.8880e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8470e+00,  1.2657e+00,  1.0201e+00],\\n\",\n      \"           [ 1.7440e+00,  1.5024e+00,  1.5053e+00],\\n\",\n      \"           [ 2.3066e+00,  1.8046e+00,  1.6797e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3070e+00,  4.6315e-01, -5.2353e-02],\\n\",\n      \"           [ 8.7270e-01,  5.6669e-01,  1.5326e-01],\\n\",\n      \"           [ 1.4907e+00,  1.2918e+00,  8.5015e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.2177e-01, -1.8820e-01, -2.0947e-01],\\n\",\n      \"           [-2.7384e-01, -2.2681e-01, -1.2265e-01],\\n\",\n      \"           [-3.3744e-01, -2.4030e-01, -1.6923e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.5616e-02,  8.1383e-03, -3.8299e-02],\\n\",\n      \"           [-1.2698e-01, -1.3424e-02, -2.9763e-02],\\n\",\n      \"           [-1.0082e-01,  3.3865e-02,  7.1351e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 8.2193e-02,  1.3740e-01,  8.7101e-02],\\n\",\n      \"           [-3.9938e-02,  1.5423e-01,  1.1065e-01],\\n\",\n      \"           [ 2.4155e-03,  1.3713e-01,  1.1676e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.3512e-01, -4.8089e-01, -7.4187e-01],\\n\",\n      \"           [-7.7781e-01, -6.6542e-01, -1.0050e+00],\\n\",\n      \"           [-8.7719e-01, -9.3828e-01, -1.1482e+00]],\\n\",\n      \"\\n\",\n      \"          [[-2.5270e-01, -3.7758e-01, -9.5900e-01],\\n\",\n      \"           [-7.8249e-01, -5.8544e-01, -8.8957e-01],\\n\",\n      \"           [-8.5845e-01, -7.2511e-01, -1.3810e+00]],\\n\",\n      \"\\n\",\n      \"          [[-3.1909e-01, -3.6559e-01, -9.0562e-01],\\n\",\n      \"           [-8.0045e-01, -6.6197e-01, -1.2831e+00],\\n\",\n      \"           [-7.8305e-01, -8.4956e-01, -1.2308e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.8737e+00,  3.9351e+00,  3.5686e+00],\\n\",\n      \"           [ 3.7035e+00,  3.2657e+00,  3.8769e+00],\\n\",\n      \"           [ 3.4420e+00,  3.2247e+00,  3.8561e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 3.9440e+00,  3.9917e+00,  4.2969e+00],\\n\",\n      \"           [ 3.5590e+00,  3.6936e+00,  5.0339e+00],\\n\",\n      \"           [ 3.7183e+00,  3.9206e+00,  5.2581e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 4.2194e+00,  5.2813e+00,  5.7110e+00],\\n\",\n      \"           [ 4.3962e+00,  4.7110e+00,  5.6509e+00],\\n\",\n      \"           [ 3.8800e+00,  5.1429e+00,  5.4661e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.3014e-02,  2.1099e-02,  1.6767e-02],\\n\",\n      \"           [-1.1230e-01, -6.5849e-02, -9.1964e-02],\\n\",\n      \"           [-1.4849e-01, -1.2218e-01, -1.3668e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.9361e-03,  6.1584e-02,  6.4898e-02],\\n\",\n      \"           [-6.0537e-02, -5.8381e-03, -1.7444e-02],\\n\",\n      \"           [-1.0580e-01, -9.1944e-02, -8.9297e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.1198e-03,  1.1895e-02,  3.8421e-02],\\n\",\n      \"           [-3.3043e-02, -1.2003e-02,  4.0244e-03],\\n\",\n      \"           [-1.3296e-01, -6.6697e-02, -4.3952e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.6515e-02, -7.4091e-03,  8.1717e-03],\\n\",\n      \"           [-2.9941e-02, -1.4327e-02,  1.2528e-03],\\n\",\n      \"           [-2.8198e-02, -1.6214e-02, -1.1431e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.3221e-03,  3.5143e-03,  7.8278e-03],\\n\",\n      \"           [-1.7277e-02, -3.3858e-03,  1.5590e-03],\\n\",\n      \"           [-1.6339e-02, -1.5652e-02,  2.1802e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.1312e-03,  7.4854e-03,  9.2404e-03],\\n\",\n      \"           [-1.8511e-03, -5.5911e-04,  6.5207e-03],\\n\",\n      \"           [-9.8246e-03,  1.5698e-03,  1.5672e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 80.00 %\\n\",\n      \"Val loss: 0.391787\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[14,    60] loss: 0.52828\\n\",\n      \"[14,   120] loss: 0.50323\\n\",\n      \"Time elapsed: 0h:28m:21s\\n\",\n      \"train accuracy_score: 76.33 %\\n\",\n      \"tensor([[[[[ 2.1668e-01,  1.5589e-01,  7.9365e-02],\\n\",\n      \"           [ 3.7697e-01,  3.5243e-01,  1.0940e-01],\\n\",\n      \"           [ 3.6946e-01,  3.1270e-01,  1.4495e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8976e-01,  5.6729e-02,  8.1002e-03],\\n\",\n      \"           [ 4.1023e-01,  1.6508e-01, -3.4012e-03],\\n\",\n      \"           [ 4.0751e-01,  2.6870e-01,  2.2640e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0676e-01,  1.2622e-01, -5.7292e-03],\\n\",\n      \"           [ 3.0980e-01,  1.9144e-01,  4.5288e-02],\\n\",\n      \"           [ 3.8072e-01,  1.5708e-01,  3.4327e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.9077e-01, -1.3898e-01, -2.0783e-02],\\n\",\n      \"           [-2.6841e-01, -2.2181e-01, -2.3592e-01],\\n\",\n      \"           [-4.5659e-01, -4.9189e-01, -5.5130e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.7175e-01, -1.1433e-01, -8.7569e-02],\\n\",\n      \"           [-3.3980e-01, -2.1161e-01, -2.4258e-01],\\n\",\n      \"           [-3.9175e-01, -3.9838e-01, -4.1935e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.4908e-01, -5.0713e-02, -3.1093e-02],\\n\",\n      \"           [-2.3149e-01, -9.9399e-02, -1.1021e-01],\\n\",\n      \"           [-4.2638e-01, -3.9462e-01, -2.9611e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.9284e+00, -1.5134e+00, -1.6531e+00],\\n\",\n      \"           [-1.8476e+00, -1.6462e+00, -1.5108e+00],\\n\",\n      \"           [-2.4669e+00, -1.8664e+00, -2.5772e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.8154e+00, -1.5291e+00, -1.0886e+00],\\n\",\n      \"           [-1.7569e+00, -1.3981e+00, -1.3043e+00],\\n\",\n      \"           [-2.2419e+00, -1.6662e+00, -1.3766e+00]],\\n\",\n      \"\\n\",\n      \"          [[-2.0068e+00, -1.7494e+00, -9.6109e-01],\\n\",\n      \"           [-1.7720e+00, -1.4777e+00, -6.8232e-01],\\n\",\n      \"           [-2.0318e+00, -1.4725e+00, -1.0928e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.8088e-02, -7.4635e-02,  5.8654e-03],\\n\",\n      \"           [ 1.0275e-01,  6.0212e-02,  1.7814e-02],\\n\",\n      \"           [ 2.3733e-01,  3.3540e-02, -4.1071e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.3404e-02, -9.3488e-02,  4.8220e-02],\\n\",\n      \"           [ 1.1931e-01, -1.9779e-02,  2.8618e-02],\\n\",\n      \"           [ 3.0939e-01,  8.4340e-02,  9.9795e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 8.1211e-03, -2.9885e-02,  2.2081e-02],\\n\",\n      \"           [ 2.0369e-01,  5.2803e-02,  5.3329e-02],\\n\",\n      \"           [ 3.1928e-01,  2.0247e-01,  7.7047e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.4409e-01,  3.7808e-04,  3.1897e-02],\\n\",\n      \"           [-7.2062e-02, -2.7436e-01, -1.4385e-01],\\n\",\n      \"           [-2.7417e-01, -3.3621e-01, -3.1492e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.8450e-01, -5.1150e-02,  2.0584e-01],\\n\",\n      \"           [ 1.1142e-01, -1.2322e-01, -1.1990e-01],\\n\",\n      \"           [-2.3851e-02, -3.0772e-01,  8.5851e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0983e-01, -3.6141e-02,  2.0861e-01],\\n\",\n      \"           [ 2.3155e-01, -1.8045e-02,  2.9140e-01],\\n\",\n      \"           [ 1.0007e-01, -6.5705e-02,  6.9212e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.1167e-01, -9.8668e-01, -7.7098e-01],\\n\",\n      \"           [-6.9717e-01, -3.7236e-01, -5.3303e-01],\\n\",\n      \"           [-1.5803e-01, -3.7205e-02, -2.3539e-01]],\\n\",\n      \"\\n\",\n      \"          [[-6.2690e-01, -9.9983e-01, -9.2016e-01],\\n\",\n      \"           [-5.3393e-01, -3.0641e-01, -8.9340e-01],\\n\",\n      \"           [-1.5661e-01, -1.8067e-01, -9.7133e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.9597e-01, -1.1336e+00, -1.4247e+00],\\n\",\n      \"           [-4.0087e-01, -5.7169e-01, -1.3077e+00],\\n\",\n      \"           [ 7.4838e-02, -7.4447e-01, -1.0430e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.6624e-02, -1.1195e-02, -2.2854e-02],\\n\",\n      \"           [ 7.0836e-02,  3.8873e-02,  2.6173e-02],\\n\",\n      \"           [ 7.8481e-02,  5.6049e-02,  6.0045e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.3831e-03, -5.9003e-03, -2.8232e-02],\\n\",\n      \"           [ 6.7188e-02,  2.4162e-02,  5.4130e-03],\\n\",\n      \"           [ 8.2582e-02,  5.5243e-02,  4.3046e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.4081e-03,  3.7534e-03, -3.7772e-02],\\n\",\n      \"           [ 4.9428e-02,  1.3269e-02, -7.3994e-04],\\n\",\n      \"           [ 5.9046e-02,  5.2747e-02,  4.7568e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2868e-03,  1.5056e-03, -1.8167e-03],\\n\",\n      \"           [ 2.9666e-03,  2.7605e-03, -1.6110e-04],\\n\",\n      \"           [ 6.3003e-03,  5.0669e-03,  3.3920e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.2536e-04, -2.7707e-04, -5.8791e-03],\\n\",\n      \"           [ 2.2719e-03,  2.7757e-04, -6.0547e-03],\\n\",\n      \"           [ 4.3403e-03,  1.2246e-03, -6.2537e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.9050e-03, -6.4977e-03, -6.2234e-03],\\n\",\n      \"           [ 4.0401e-04, -5.5098e-03, -8.5126e-03],\\n\",\n      \"           [ 2.2496e-03, -6.5975e-03, -1.1453e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 82.00 %\\n\",\n      \"Val loss: 0.432591\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[15,    60] loss: 0.41765\\n\",\n      \"[15,   120] loss: 0.47934\\n\",\n      \"Time elapsed: 0h:30m:16s\\n\",\n      \"train accuracy_score: 81.64 %\\n\",\n      \"tensor([[[[[ 9.1245e-02,  3.9880e-02,  3.8219e-03],\\n\",\n      \"           [ 1.0094e-01,  6.4803e-02,  5.9633e-03],\\n\",\n      \"           [ 1.0085e-01,  5.3013e-02,  8.4915e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 7.5753e-02,  3.0751e-02,  3.5051e-03],\\n\",\n      \"           [ 8.9022e-02,  3.6943e-02,  2.0019e-03],\\n\",\n      \"           [ 8.1930e-02,  3.5783e-02,  5.7216e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.9260e-02,  2.9997e-02,  2.7201e-04],\\n\",\n      \"           [ 5.7999e-02,  3.0603e-02,  1.1769e-02],\\n\",\n      \"           [ 6.7281e-02,  2.8079e-02,  1.9169e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.0216e-01, -1.0696e-01, -9.1159e-02],\\n\",\n      \"           [-1.0253e-01, -1.0285e-01, -9.3439e-02],\\n\",\n      \"           [-1.1027e-01, -8.1984e-02, -1.0514e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.0344e-01, -9.4861e-02, -7.4082e-02],\\n\",\n      \"           [-8.0339e-02, -7.9511e-02, -8.2957e-02],\\n\",\n      \"           [-9.4421e-02, -6.4914e-02, -7.5921e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.3269e-02, -7.1301e-02, -4.9822e-02],\\n\",\n      \"           [-7.0976e-02, -5.9367e-02, -3.5731e-02],\\n\",\n      \"           [-8.9097e-02, -7.2383e-02, -4.6504e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.1462e-01, -4.4233e-01, -3.9862e-01],\\n\",\n      \"           [-4.8327e-01, -4.5201e-01, -4.1567e-01],\\n\",\n      \"           [-5.4608e-01, -4.7079e-01, -4.4449e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.0623e-01, -4.4963e-01, -2.6762e-01],\\n\",\n      \"           [-4.3199e-01, -3.7449e-01, -3.7430e-01],\\n\",\n      \"           [-4.7726e-01, -3.7491e-01, -3.2835e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.6923e-01, -4.2369e-01, -1.8031e-01],\\n\",\n      \"           [-3.2319e-01, -3.1894e-01, -1.4190e-01],\\n\",\n      \"           [-3.5098e-01, -2.7645e-01, -1.7529e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.0755e-02,  9.9049e-03, -6.1829e-03],\\n\",\n      \"           [ 3.4925e-02,  1.2354e-02, -7.0206e-03],\\n\",\n      \"           [ 3.9856e-02,  5.6466e-03, -1.1800e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.9807e-02, -1.2285e-04, -5.9011e-03],\\n\",\n      \"           [ 3.9522e-02,  1.0661e-02, -7.8065e-03],\\n\",\n      \"           [ 5.1803e-02,  1.3415e-02, -1.5286e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7563e-02,  4.2364e-03, -2.6026e-03],\\n\",\n      \"           [ 4.2685e-02,  6.7639e-03, -1.2534e-02],\\n\",\n      \"           [ 4.5511e-02,  2.2980e-02, -5.8918e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0369e-01,  1.1910e-01,  1.6214e-01],\\n\",\n      \"           [ 1.6686e-01,  1.4717e-01,  1.8420e-01],\\n\",\n      \"           [ 1.9834e-01,  1.9926e-01,  2.2751e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 8.8985e-02,  8.5459e-02,  1.6468e-01],\\n\",\n      \"           [ 1.5011e-01,  1.1504e-01,  1.5548e-01],\\n\",\n      \"           [ 1.6836e-01,  1.3686e-01,  2.3822e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 7.4010e-02,  6.8137e-02,  1.4395e-01],\\n\",\n      \"           [ 1.2639e-01,  9.9017e-02,  2.0487e-01],\\n\",\n      \"           [ 1.4290e-01,  1.5514e-01,  2.1781e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.3656e-01, -3.5356e-01, -3.8497e-01],\\n\",\n      \"           [-3.1727e-01, -3.2018e-01, -4.0072e-01],\\n\",\n      \"           [-3.3834e-01, -3.6092e-01, -4.3718e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.8595e-01, -3.7930e-01, -4.4309e-01],\\n\",\n      \"           [-3.3958e-01, -3.8836e-01, -4.9300e-01],\\n\",\n      \"           [-3.5036e-01, -4.4404e-01, -5.4738e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.6038e-01, -4.0838e-01, -4.8266e-01],\\n\",\n      \"           [-3.6885e-01, -4.3492e-01, -5.5391e-01],\\n\",\n      \"           [-3.7291e-01, -5.1012e-01, -5.7916e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.9168e-02,  1.3942e-04, -8.5263e-03],\\n\",\n      \"           [ 1.8081e-02,  1.7375e-03, -2.9230e-03],\\n\",\n      \"           [ 1.5758e-02,  4.2303e-03, -9.8601e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6088e-02, -6.8908e-04, -8.6345e-03],\\n\",\n      \"           [ 1.3607e-02,  2.1554e-03, -6.7919e-03],\\n\",\n      \"           [ 1.6038e-02,  4.0574e-03, -6.7939e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9281e-02,  5.8706e-03, -4.9986e-03],\\n\",\n      \"           [ 1.6964e-02,  4.1147e-03, -4.9453e-03],\\n\",\n      \"           [ 1.6195e-02,  9.7561e-03, -8.3554e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.0154e-03,  9.0280e-04, -3.8116e-04],\\n\",\n      \"           [ 1.6943e-03,  1.9645e-04, -4.5795e-04],\\n\",\n      \"           [ 1.5168e-03,  2.5031e-04,  2.6140e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 9.9795e-04, -3.7819e-04, -4.8123e-04],\\n\",\n      \"           [ 3.7207e-04,  2.0788e-04,  3.3621e-04],\\n\",\n      \"           [ 7.4659e-04,  2.9099e-04, -2.4317e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3971e-04, -9.9377e-04, -1.5972e-03],\\n\",\n      \"           [-5.2251e-04, -1.4108e-03, -1.4448e-03],\\n\",\n      \"           [-4.7764e-04, -1.4824e-03, -2.2664e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 82.00 %\\n\",\n      \"Val loss: 0.399524\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[16,    60] loss: 0.40677\\n\",\n      \"[16,   120] loss: 0.53533\\n\",\n      \"Time elapsed: 0h:32m:12s\\n\",\n      \"train accuracy_score: 79.34 %\\n\",\n      \"tensor([[[[[ 1.9113e-01,  9.4712e-02,  4.3575e-02],\\n\",\n      \"           [ 2.4828e-01,  1.5278e-01,  2.6907e-03],\\n\",\n      \"           [ 2.6676e-01,  1.5366e-01,  1.5390e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 9.9236e-02, -1.3340e-03,  8.2047e-04],\\n\",\n      \"           [ 1.8711e-01,  8.4403e-02,  4.1474e-03],\\n\",\n      \"           [ 2.4733e-01,  1.3748e-01, -1.3841e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.3438e-03, -4.3761e-03,  2.7753e-02],\\n\",\n      \"           [ 1.0914e-01,  4.5819e-02,  1.3918e-02],\\n\",\n      \"           [ 1.9805e-01,  8.6530e-02,  2.9959e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.4146e-02,  3.9867e-02,  3.7678e-02],\\n\",\n      \"           [ 7.1962e-02, -6.7701e-02, -1.5597e-01],\\n\",\n      \"           [-1.9877e-02, -1.2378e-01, -2.5605e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6277e-01,  1.5994e-01,  1.7689e-01],\\n\",\n      \"           [ 1.6534e-01,  8.9566e-02,  7.2002e-02],\\n\",\n      \"           [ 1.0532e-01,  1.8883e-02, -8.6036e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8314e-01,  2.6267e-01,  3.0223e-01],\\n\",\n      \"           [ 2.0032e-01,  2.5060e-01,  2.0858e-01],\\n\",\n      \"           [ 1.4997e-01,  7.2821e-02,  3.0471e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.7309e+00, -1.5938e+00, -1.9165e+00],\\n\",\n      \"           [-1.4879e+00, -1.7022e+00, -2.1298e+00],\\n\",\n      \"           [-1.6788e+00, -1.6486e+00, -2.1475e+00]],\\n\",\n      \"\\n\",\n      \"          [[-9.9255e-01, -9.4299e-01, -1.1034e+00],\\n\",\n      \"           [-1.0353e+00, -9.5777e-01, -1.1790e+00],\\n\",\n      \"           [-1.0341e+00, -1.0811e+00, -1.3223e+00]],\\n\",\n      \"\\n\",\n      \"          [[-8.6447e-01, -7.8284e-01, -5.8352e-01],\\n\",\n      \"           [-7.2825e-01, -9.4428e-01, -4.9298e-01],\\n\",\n      \"           [-8.5380e-01, -9.1340e-01, -6.1866e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.3961e-02, -2.8842e-02,  3.1710e-02],\\n\",\n      \"           [ 1.2756e-02, -4.2556e-02,  8.9466e-03],\\n\",\n      \"           [ 7.7962e-02,  2.1432e-02, -8.0872e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8964e-03, -4.6798e-02,  8.6201e-03],\\n\",\n      \"           [ 3.7510e-02,  1.0280e-03,  1.7099e-02],\\n\",\n      \"           [ 1.0783e-01, -5.3098e-03, -3.4579e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.2884e-03, -5.1430e-02, -2.9150e-02],\\n\",\n      \"           [ 7.6347e-02,  2.0141e-02,  1.7211e-02],\\n\",\n      \"           [ 1.1823e-01,  3.6986e-02,  1.4031e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3819e-01,  1.7804e-01,  1.6105e-01],\\n\",\n      \"           [ 3.4164e-01,  2.5704e-01,  2.9588e-01],\\n\",\n      \"           [ 4.5239e-01,  4.5558e-01,  4.7948e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3769e-01,  2.3059e-01,  3.4755e-01],\\n\",\n      \"           [ 4.9115e-01,  3.4372e-01,  3.5924e-01],\\n\",\n      \"           [ 4.5511e-01,  4.4288e-01,  5.4445e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5325e-01,  2.0796e-01,  2.6410e-01],\\n\",\n      \"           [ 4.1052e-01,  3.3353e-01,  4.1214e-01],\\n\",\n      \"           [ 5.1074e-01,  4.9541e-01,  5.0803e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.7043e-01, -8.6459e-01, -7.5188e-01],\\n\",\n      \"           [-1.0810e+00, -8.4133e-01, -7.9561e-01],\\n\",\n      \"           [-1.0881e+00, -9.5437e-01, -6.9767e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.0161e+00, -9.9985e-01, -1.0110e+00],\\n\",\n      \"           [-1.0727e+00, -9.5617e-01, -1.1214e+00],\\n\",\n      \"           [-1.1668e+00, -1.0389e+00, -1.1625e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.0248e+00, -1.2225e+00, -1.3587e+00],\\n\",\n      \"           [-1.1395e+00, -1.3034e+00, -1.4857e+00],\\n\",\n      \"           [-1.0109e+00, -1.4027e+00, -1.4334e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.0039e-02, -1.6614e-02, -3.8353e-03],\\n\",\n      \"           [ 1.6517e-02,  8.1369e-03,  2.5580e-02],\\n\",\n      \"           [ 2.9293e-02,  5.1557e-02,  6.8080e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.7216e-02, -3.6816e-02, -2.2662e-02],\\n\",\n      \"           [ 1.6708e-02, -5.5928e-03,  8.1042e-03],\\n\",\n      \"           [ 3.8019e-02,  4.7978e-02,  6.1209e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.6938e-02, -1.5780e-02, -1.9993e-02],\\n\",\n      \"           [ 1.0625e-02, -2.7651e-03,  6.4640e-03],\\n\",\n      \"           [ 3.5399e-02,  3.5824e-02,  5.6695e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.8356e-03, -5.2018e-03, -2.4251e-03],\\n\",\n      \"           [ 4.4070e-03,  2.4876e-03,  3.0540e-03],\\n\",\n      \"           [ 1.1235e-02,  8.7592e-03,  9.3926e-03]],\\n\",\n      \"\\n\",\n      \"          [[-6.1010e-03, -1.0805e-02, -5.8183e-03],\\n\",\n      \"           [-1.4168e-04, -1.5444e-03,  2.0288e-03],\\n\",\n      \"           [ 5.3986e-03,  4.4750e-03,  5.0928e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.0057e-03, -1.2135e-02, -8.5104e-03],\\n\",\n      \"           [-2.3085e-03, -4.9575e-03, -4.9174e-03],\\n\",\n      \"           [ 4.9045e-03,  1.0058e-03, -3.3106e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 82.00 %\\n\",\n      \"Val loss: 0.472492\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[17,    60] loss: 0.41061\\n\",\n      \"[17,   120] loss: 0.46229\\n\",\n      \"Time elapsed: 0h:34m:9s\\n\",\n      \"train accuracy_score: 80.77 %\\n\",\n      \"tensor([[[[[ 3.8275e-01,  2.0547e-01,  9.6456e-02],\\n\",\n      \"           [ 6.6021e-01,  4.7871e-01,  1.2807e-01],\\n\",\n      \"           [ 7.5763e-01,  4.6123e-01,  1.1210e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7247e-01,  8.5383e-02,  5.6068e-02],\\n\",\n      \"           [ 5.4693e-01,  2.4326e-01,  5.0595e-02],\\n\",\n      \"           [ 6.6194e-01,  3.5553e-01,  5.5444e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.1648e-02, -1.7864e-02, -1.8221e-02],\\n\",\n      \"           [ 2.4055e-01,  1.5852e-01,  1.3612e-01],\\n\",\n      \"           [ 4.9500e-01,  2.1990e-01,  8.7148e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.5866e-02,  3.0584e-01,  4.2599e-01],\\n\",\n      \"           [ 3.0802e-01,  1.2189e-01,  5.8003e-02],\\n\",\n      \"           [-2.2707e-02, -7.8105e-02, -3.1426e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4018e-01,  2.5455e-01,  2.3662e-01],\\n\",\n      \"           [ 7.3907e-02,  1.6574e-02, -8.2678e-02],\\n\",\n      \"           [-1.1219e-01, -4.8985e-02, -2.9699e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.1335e-02,  9.5356e-02,  1.6143e-01],\\n\",\n      \"           [-5.1049e-02,  9.5358e-03,  1.0359e-01],\\n\",\n      \"           [-2.5479e-01, -2.3888e-01, -2.3564e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.6934e+00, -2.5856e+00, -2.6305e+00],\\n\",\n      \"           [-2.5567e+00, -2.6623e+00, -2.9531e+00],\\n\",\n      \"           [-3.0861e+00, -3.2174e+00, -3.9272e+00]],\\n\",\n      \"\\n\",\n      \"          [[-2.0256e+00, -2.2572e+00, -1.4654e+00],\\n\",\n      \"           [-2.2615e+00, -2.1777e+00, -2.9904e+00],\\n\",\n      \"           [-2.6667e+00, -2.7279e+00, -3.4068e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.4875e+00, -1.8167e+00, -9.2618e-01],\\n\",\n      \"           [-1.6055e+00, -1.9795e+00, -1.6394e+00],\\n\",\n      \"           [-2.5262e+00, -2.4577e+00, -2.6003e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.8741e-01,  1.1792e-01,  1.1244e-01],\\n\",\n      \"           [ 3.1899e-01,  1.9985e-01,  7.4911e-02],\\n\",\n      \"           [ 4.7876e-01,  1.9428e-01,  6.0657e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3215e-01, -6.5654e-02, -1.1660e-02],\\n\",\n      \"           [ 2.3780e-01, -7.2369e-03, -5.4169e-02],\\n\",\n      \"           [ 5.3789e-01,  1.2055e-01, -5.3188e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.3194e-02, -9.0746e-02, -7.7084e-02],\\n\",\n      \"           [ 2.7673e-01, -4.7149e-02, -1.3370e-01],\\n\",\n      \"           [ 3.6401e-01,  1.3541e-01, -4.0945e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.5257e-01,  6.4518e-01,  7.5550e-01],\\n\",\n      \"           [ 1.2870e+00,  8.3475e-01,  8.9756e-01],\\n\",\n      \"           [ 1.3962e+00,  1.0790e+00,  1.0386e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 5.7117e-01,  4.2058e-01,  8.0667e-01],\\n\",\n      \"           [ 1.2571e+00,  6.5225e-01,  6.2112e-01],\\n\",\n      \"           [ 1.3997e+00,  9.2464e-01,  1.0627e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 4.2973e-01,  3.1274e-01,  7.3497e-01],\\n\",\n      \"           [ 1.1109e+00,  5.4287e-01,  1.1844e+00],\\n\",\n      \"           [ 1.1393e+00,  9.2724e-01,  1.1742e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.6100e-01, -8.8842e-01, -6.8552e-01],\\n\",\n      \"           [-6.1934e-01, -4.0970e-01, -3.9465e-01],\\n\",\n      \"           [-3.7774e-01, -1.1003e-02, -1.7366e-01]],\\n\",\n      \"\\n\",\n      \"          [[-8.8343e-01, -8.4253e-01, -9.4227e-01],\\n\",\n      \"           [-5.6453e-01, -2.2780e-01, -8.1324e-01],\\n\",\n      \"           [-5.9535e-01, -5.5918e-01, -9.4310e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.8742e-01, -7.3853e-01, -8.5224e-01],\\n\",\n      \"           [-6.2757e-01, -5.7854e-01, -9.2052e-01],\\n\",\n      \"           [-1.5648e-01, -1.0614e+00, -8.6822e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.8966e-02, -8.9702e-02, -5.2430e-02],\\n\",\n      \"           [-2.6840e-02, -4.6818e-03,  1.3093e-02],\\n\",\n      \"           [ 1.0027e-02,  6.1612e-02,  8.9392e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.1373e-02, -9.5956e-02, -4.3921e-02],\\n\",\n      \"           [-3.8436e-02, -2.7655e-02, -7.8974e-03],\\n\",\n      \"           [ 4.9427e-02,  6.3560e-02,  7.8949e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.0590e-02, -5.7476e-02, -3.7384e-02],\\n\",\n      \"           [ 3.2755e-02,  1.6935e-02,  2.8150e-02],\\n\",\n      \"           [ 8.2279e-02,  1.0068e-01,  5.6087e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.9877e-03, -2.2175e-03, -6.9796e-03],\\n\",\n      \"           [ 4.8605e-03,  1.1319e-02,  1.1027e-02],\\n\",\n      \"           [ 1.5586e-02,  1.9334e-02,  1.1905e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.5432e-02, -1.4382e-02, -1.3331e-02],\\n\",\n      \"           [-4.4628e-03, -4.1606e-03, -3.9509e-03],\\n\",\n      \"           [ 1.1101e-02,  1.2127e-02,  8.4261e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.8183e-02, -2.2993e-02, -2.1111e-02],\\n\",\n      \"           [-1.0696e-02, -1.3229e-02, -1.2433e-02],\\n\",\n      \"           [-4.0909e-03, -8.0781e-03, -1.7976e-02]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 84.00 %\\n\",\n      \"Val loss: 0.382647\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[18,    60] loss: 0.42463\\n\",\n      \"[18,   120] loss: 0.38701\\n\",\n      \"Time elapsed: 0h:36m:5s\\n\",\n      \"train accuracy_score: 82.21 %\\n\",\n      \"tensor([[[[[-3.5816e-01, -1.4703e-01,  5.6622e-02],\\n\",\n      \"           [-5.9326e-01, -4.8162e-01, -4.1469e-02],\\n\",\n      \"           [-7.4747e-01, -5.0128e-01, -1.1295e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.7179e-01, -8.3938e-02,  8.1248e-02],\\n\",\n      \"           [-4.6068e-01, -2.7401e-01, -4.0990e-02],\\n\",\n      \"           [-5.8019e-01, -4.5488e-01, -9.2183e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.7308e-01, -1.3861e-01, -1.1717e-02],\\n\",\n      \"           [-3.5149e-01, -3.1522e-01, -1.4739e-01],\\n\",\n      \"           [-5.7736e-01, -3.6389e-01, -1.5567e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.5219e-01,  1.3863e-01,  1.2501e-02],\\n\",\n      \"           [ 4.3966e-01,  1.9864e-01,  2.0125e-01],\\n\",\n      \"           [ 6.2045e-01,  4.5582e-01,  5.1321e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.9993e-01,  6.3704e-02, -3.3105e-02],\\n\",\n      \"           [ 3.7755e-01,  2.4634e-01,  2.0080e-01],\\n\",\n      \"           [ 6.7419e-01,  4.6541e-01,  4.9095e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.3102e-01,  3.0171e-01,  1.6009e-01],\\n\",\n      \"           [ 4.7775e-01,  2.5455e-01,  2.1391e-01],\\n\",\n      \"           [ 5.8592e-01,  3.4946e-01,  4.1631e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.9441e+00,  3.0143e+00,  2.2909e+00],\\n\",\n      \"           [ 2.4525e+00,  2.4931e+00,  1.8680e+00],\\n\",\n      \"           [ 2.7182e+00,  2.5379e+00,  2.0231e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6624e+00,  2.5806e+00,  1.1946e+00],\\n\",\n      \"           [ 2.0227e+00,  1.9989e+00,  1.2524e+00],\\n\",\n      \"           [ 1.7451e+00,  2.0469e+00,  1.5886e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7384e+00,  2.7937e+00,  1.4631e+00],\\n\",\n      \"           [ 1.4861e+00,  2.4234e+00,  1.0345e+00],\\n\",\n      \"           [ 1.3671e+00,  2.0202e+00,  1.2443e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.8692e-02,  3.6449e-02,  4.4082e-02],\\n\",\n      \"           [-1.6566e-01, -8.7423e-02,  3.9178e-02],\\n\",\n      \"           [-3.8115e-01, -1.9522e-01, -5.7458e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.2803e-02, -7.1400e-03,  3.3355e-02],\\n\",\n      \"           [-1.6832e-01, -4.0618e-02,  6.0303e-02],\\n\",\n      \"           [-3.5589e-01, -1.2144e-01,  2.6020e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.3488e-02,  2.1638e-02,  3.7144e-02],\\n\",\n      \"           [-1.8644e-01, -4.4636e-02,  3.9451e-02],\\n\",\n      \"           [-2.4674e-01, -1.1278e-01,  8.0990e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.7703e-01, -6.6286e-01, -7.7894e-01],\\n\",\n      \"           [-1.1901e+00, -7.5047e-01, -8.5385e-01],\\n\",\n      \"           [-1.2280e+00, -9.7567e-01, -1.0013e+00]],\\n\",\n      \"\\n\",\n      \"          [[-5.4763e-01, -3.2407e-01, -7.3300e-01],\\n\",\n      \"           [-1.0408e+00, -5.4718e-01, -7.2808e-01],\\n\",\n      \"           [-1.1105e+00, -7.8850e-01, -1.1391e+00]],\\n\",\n      \"\\n\",\n      \"          [[-4.9544e-01, -2.7765e-01, -7.3298e-01],\\n\",\n      \"           [-8.4902e-01, -5.0737e-01, -1.0637e+00],\\n\",\n      \"           [-9.9906e-01, -7.5467e-01, -1.0473e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 9.3001e-01,  1.4392e+00,  1.7572e+00],\\n\",\n      \"           [ 1.1858e+00,  1.2430e+00,  1.6501e+00],\\n\",\n      \"           [ 1.1486e+00,  1.1290e+00,  1.5477e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0870e+00,  1.3799e+00,  1.9071e+00],\\n\",\n      \"           [ 1.0231e+00,  1.1771e+00,  2.0918e+00],\\n\",\n      \"           [ 7.3147e-01,  1.2595e+00,  2.0355e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2574e+00,  1.6789e+00,  2.0924e+00],\\n\",\n      \"           [ 1.1772e+00,  1.3685e+00,  2.0929e+00],\\n\",\n      \"           [ 9.1744e-01,  1.7824e+00,  2.0930e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.6361e-02,  6.6810e-02,  5.6287e-02],\\n\",\n      \"           [-2.2590e-02, -2.4959e-02, -1.8937e-02],\\n\",\n      \"           [-9.0930e-02, -7.7822e-02, -7.5624e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.9533e-02,  8.7296e-02,  5.6600e-02],\\n\",\n      \"           [-5.0133e-03, -6.3797e-03, -1.7344e-02],\\n\",\n      \"           [-8.2397e-02, -6.1889e-02, -6.6108e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.7362e-02,  5.7670e-02,  5.1041e-02],\\n\",\n      \"           [-2.9493e-02, -9.5194e-03,  3.9380e-03],\\n\",\n      \"           [-9.1813e-02, -8.0897e-02, -6.2654e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.2128e-03, -5.6628e-03, -5.0986e-03],\\n\",\n      \"           [-1.3171e-02, -1.3664e-02, -9.8024e-03],\\n\",\n      \"           [-2.1619e-02, -1.5287e-02, -1.1589e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.6831e-03,  2.7514e-03,  1.6207e-03],\\n\",\n      \"           [-9.0590e-03, -6.5300e-03, -4.0906e-03],\\n\",\n      \"           [-1.7690e-02, -1.7503e-02, -7.2480e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.5500e-03,  7.4783e-03,  7.9576e-03],\\n\",\n      \"           [-7.2860e-03, -8.8748e-03, -1.2793e-03],\\n\",\n      \"           [-1.7278e-02, -1.3526e-02,  1.0122e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 87.00 %\\n\",\n      \"Val loss: 0.403539\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[19,    60] loss: 0.41442\\n\",\n      \"[19,   120] loss: 0.42076\\n\",\n      \"Time elapsed: 0h:38m:0s\\n\",\n      \"train accuracy_score: 83.50 %\\n\",\n      \"tensor([[[[[ 1.8068e-01,  8.9007e-02, -9.4467e-03],\\n\",\n      \"           [ 3.2083e-01,  2.8066e-01,  4.5636e-02],\\n\",\n      \"           [ 3.6993e-01,  2.5965e-01,  5.0272e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6833e-01,  7.8603e-02, -2.8849e-02],\\n\",\n      \"           [ 3.4842e-01,  1.8176e-01,  1.2707e-02],\\n\",\n      \"           [ 4.3365e-01,  2.4103e-01, -1.9496e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 8.6236e-02,  1.3374e-01,  3.8270e-02],\\n\",\n      \"           [ 2.0760e-01,  1.6162e-01,  7.6361e-02],\\n\",\n      \"           [ 3.6468e-01,  1.9781e-01,  7.4701e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.1531e-02,  2.2489e-01,  2.8601e-01],\\n\",\n      \"           [-3.7399e-02,  1.7085e-02, -2.6036e-02],\\n\",\n      \"           [-2.3996e-01, -2.4234e-01, -2.9430e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 7.7705e-02,  2.6276e-01,  1.6959e-01],\\n\",\n      \"           [-5.0847e-02,  7.8979e-02, -7.8171e-02],\\n\",\n      \"           [-2.4070e-01, -1.7670e-01, -2.2149e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.4246e-02,  1.7274e-01,  1.1603e-01],\\n\",\n      \"           [-1.1273e-01,  6.1309e-02, -4.0083e-02],\\n\",\n      \"           [-3.1729e-01, -1.5056e-01, -1.8293e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.2630e-01, -1.5696e-01, -1.3248e-01],\\n\",\n      \"           [-3.4891e-01, -3.6930e-02,  3.5064e-02],\\n\",\n      \"           [-4.3155e-01, -2.9998e-01, -4.2820e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.0363e-01,  2.0008e-02,  9.3247e-02],\\n\",\n      \"           [-7.6030e-02,  1.3155e-01,  2.3791e-01],\\n\",\n      \"           [-1.0500e-01, -7.2100e-02,  7.6025e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.0128e-01,  1.0493e-01,  6.3190e-01],\\n\",\n      \"           [-4.9883e-02,  2.6479e-01,  5.6446e-01],\\n\",\n      \"           [-2.1065e-01, -6.9878e-02,  1.5687e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3334e-02,  1.3621e-02,  3.9656e-02],\\n\",\n      \"           [ 4.0184e-02,  5.0420e-02,  1.9311e-02],\\n\",\n      \"           [ 1.1657e-01,  4.8398e-02, -6.6551e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.1119e-02, -5.1941e-02,  5.5205e-02],\\n\",\n      \"           [ 1.3969e-02, -2.2128e-02, -3.1283e-02],\\n\",\n      \"           [ 7.0270e-02, -1.6078e-02, -2.2314e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.0443e-02, -5.0544e-02,  8.8304e-03],\\n\",\n      \"           [ 2.0878e-04, -4.8961e-03,  1.7324e-02],\\n\",\n      \"           [ 5.2882e-02,  7.9686e-02,  5.0937e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.8932e-01, -6.9002e-01, -6.9760e-01],\\n\",\n      \"           [-7.2813e-01, -9.1433e-01, -7.9170e-01],\\n\",\n      \"           [-9.3803e-01, -1.0060e+00, -9.0035e-01]],\\n\",\n      \"\\n\",\n      \"          [[-6.8445e-01, -5.2870e-01, -2.8580e-01],\\n\",\n      \"           [-5.3857e-01, -6.1286e-01, -5.4661e-01],\\n\",\n      \"           [-6.9499e-01, -8.2336e-01, -5.2201e-01]],\\n\",\n      \"\\n\",\n      \"          [[-6.8989e-01, -2.9599e-01, -2.7544e-02],\\n\",\n      \"           [-2.4499e-01, -2.9545e-01, -9.7779e-02],\\n\",\n      \"           [-3.9264e-01, -4.5046e-01, -3.4214e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.3347e-01, -4.5753e-01, -1.9509e-01],\\n\",\n      \"           [-2.9458e-01,  2.9939e-02, -2.4415e-01],\\n\",\n      \"           [-3.1877e-02,  6.1519e-02, -1.1032e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.0978e-01, -4.2963e-01, -2.8926e-01],\\n\",\n      \"           [-3.3482e-01, -9.1701e-02, -4.1842e-01],\\n\",\n      \"           [-9.7152e-02,  4.4853e-02, -3.4383e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.6954e-03, -3.1083e-01, -5.0967e-01],\\n\",\n      \"           [-2.5524e-02, -1.5891e-01, -5.7480e-01],\\n\",\n      \"           [ 1.0311e-02, -3.9541e-01, -3.4588e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.2124e-02, -4.5879e-02, -4.6049e-02],\\n\",\n      \"           [-3.4618e-02, -3.1714e-02, -2.0366e-02],\\n\",\n      \"           [-2.3198e-02, -1.3240e-02,  1.6433e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.3270e-02, -3.4758e-02, -2.2314e-02],\\n\",\n      \"           [-2.0401e-02, -3.7211e-03,  1.0388e-02],\\n\",\n      \"           [ 1.6003e-03,  1.5153e-02,  3.7803e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.1408e-02, -1.5244e-02,  9.8945e-03],\\n\",\n      \"           [-8.5211e-03,  1.7063e-02,  4.1047e-02],\\n\",\n      \"           [ 2.0587e-02,  4.3595e-02,  5.4873e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.4097e-03, -1.0308e-03, -7.9230e-04],\\n\",\n      \"           [-2.8163e-03, -2.9322e-03, -1.5112e-03],\\n\",\n      \"           [-8.5284e-04, -2.3686e-03, -5.9237e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.8274e-03, -2.5307e-03, -1.4167e-03],\\n\",\n      \"           [-2.6850e-03, -2.8474e-03, -4.5345e-03],\\n\",\n      \"           [ 1.0325e-04, -1.6528e-03, -1.2075e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.5776e-03, -6.8090e-04,  1.5887e-03],\\n\",\n      \"           [-2.6895e-03, -3.8547e-03, -5.5757e-03],\\n\",\n      \"           [-3.1752e-03, -5.3817e-03, -1.1771e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 82.00 %\\n\",\n      \"Val loss: 0.425512\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[20,    60] loss: 0.37014\\n\",\n      \"[20,   120] loss: 0.39787\\n\",\n      \"Time elapsed: 0h:39m:56s\\n\",\n      \"train accuracy_score: 84.79 %\\n\",\n      \"tensor([[[[[-3.8911e-02, -2.0600e-02, -5.7400e-03],\\n\",\n      \"           [-9.3719e-02, -6.7179e-02, -4.3002e-02],\\n\",\n      \"           [-1.1660e-01, -9.2918e-02, -5.1542e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.2136e-02,  1.0673e-02,  2.0864e-02],\\n\",\n      \"           [-5.6917e-02, -3.0294e-02, -1.0285e-02],\\n\",\n      \"           [-1.0815e-01, -7.0678e-02, -2.9441e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.9945e-03,  1.4983e-02,  1.7094e-02],\\n\",\n      \"           [-2.5552e-02, -2.4465e-02, -9.4068e-03],\\n\",\n      \"           [-8.9193e-02, -4.3784e-02, -7.6615e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3552e-01,  5.6941e-02,  2.0795e-02],\\n\",\n      \"           [ 1.0149e-01,  5.7181e-02,  1.5696e-02],\\n\",\n      \"           [ 1.0377e-01,  7.4764e-02,  3.9262e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4201e-01,  6.2027e-02,  3.9749e-02],\\n\",\n      \"           [ 1.3055e-01,  6.0206e-02,  2.6423e-02],\\n\",\n      \"           [ 1.1328e-01,  7.7545e-02,  3.4141e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4727e-01,  9.0841e-02,  4.6142e-02],\\n\",\n      \"           [ 1.2267e-01,  6.7012e-02,  3.8988e-02],\\n\",\n      \"           [ 1.0602e-01,  5.4673e-02,  4.0391e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.4503e-01,  4.1474e-01,  3.0209e-01],\\n\",\n      \"           [ 4.8222e-01,  3.7511e-01,  2.1791e-01],\\n\",\n      \"           [ 4.9503e-01,  2.8315e-01,  2.7229e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.6614e-01,  4.3250e-01,  2.3385e-01],\\n\",\n      \"           [ 4.6340e-01,  3.3137e-01,  1.5214e-01],\\n\",\n      \"           [ 3.5949e-01,  1.8965e-01,  8.6645e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.5992e-01,  5.0563e-01,  1.8119e-01],\\n\",\n      \"           [ 4.4452e-01,  3.4594e-01,  6.0494e-02],\\n\",\n      \"           [ 3.1482e-01,  1.8431e-01, -1.1488e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.8776e-02, -1.3554e-02, -9.2107e-03],\\n\",\n      \"           [-3.3046e-02, -1.1348e-02, -9.9259e-03],\\n\",\n      \"           [-5.4496e-02, -1.7460e-02, -1.7755e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0090e-02,  1.4058e-03,  5.8885e-03],\\n\",\n      \"           [-2.6911e-02, -8.1280e-04,  4.7659e-03],\\n\",\n      \"           [-5.1561e-02, -4.4493e-03, -1.1293e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.3527e-03,  1.5081e-02,  2.1152e-02],\\n\",\n      \"           [-2.6136e-02,  6.5660e-03,  1.3955e-02],\\n\",\n      \"           [-3.9490e-02, -8.2705e-03,  4.4997e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.9014e-02, -8.7484e-02, -1.1392e-01],\\n\",\n      \"           [-6.9329e-02, -5.0307e-02, -8.1984e-02],\\n\",\n      \"           [-2.2723e-02, -2.1366e-02, -5.0532e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.8611e-02, -4.6747e-02, -1.3654e-01],\\n\",\n      \"           [-6.4794e-02, -4.4383e-02, -5.1795e-02],\\n\",\n      \"           [-2.2366e-02,  1.2628e-02, -7.5450e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7843e-02,  1.1933e-02, -9.3524e-02],\\n\",\n      \"           [-2.4007e-02, -1.4190e-02, -1.0856e-01],\\n\",\n      \"           [ 1.0058e-02, -2.6177e-03, -6.9841e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 9.9207e-02,  1.6088e-01,  7.0698e-02],\\n\",\n      \"           [ 6.1331e-02,  4.8953e-02,  2.2647e-02],\\n\",\n      \"           [-3.4129e-02, -1.8270e-02,  8.1729e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 7.8147e-02,  1.5200e-01,  1.4879e-01],\\n\",\n      \"           [ 4.2597e-02,  8.3954e-02,  1.4427e-01],\\n\",\n      \"           [ 2.6466e-03,  4.7284e-02,  1.2189e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.9786e-03,  1.4703e-01,  2.1416e-01],\\n\",\n      \"           [ 3.0703e-02,  9.7498e-02,  1.8127e-01],\\n\",\n      \"           [ 7.9647e-03,  9.3534e-02,  1.5024e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.5043e-03,  9.1388e-03,  1.6190e-02],\\n\",\n      \"           [-6.8334e-03,  7.3510e-04,  3.8479e-03],\\n\",\n      \"           [-1.6029e-02, -9.9886e-03, -1.7220e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.9966e-04,  6.9689e-03,  1.2723e-02],\\n\",\n      \"           [-8.9501e-03, -1.6259e-03,  5.5903e-03],\\n\",\n      \"           [-1.6106e-02, -8.7434e-03, -2.5112e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 8.9103e-04,  4.0344e-03,  8.4567e-03],\\n\",\n      \"           [-1.3444e-02, -2.6980e-03,  3.9001e-03],\\n\",\n      \"           [-1.8931e-02, -9.9767e-03,  5.5549e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.9266e-03, -4.3504e-03, -1.9502e-03],\\n\",\n      \"           [-4.8829e-03, -4.2049e-03, -2.1760e-03],\\n\",\n      \"           [-3.7847e-03, -3.0702e-03, -6.5788e-04]],\\n\",\n      \"\\n\",\n      \"          [[-3.8420e-03, -3.6372e-03, -1.2216e-03],\\n\",\n      \"           [-6.3751e-03, -4.8080e-03, -1.1505e-03],\\n\",\n      \"           [-5.3066e-03, -3.0884e-03, -1.0970e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.1640e-03, -2.0313e-03,  2.7373e-04],\\n\",\n      \"           [-5.2653e-03, -3.6884e-03, -1.6317e-03],\\n\",\n      \"           [-5.5490e-03, -3.5013e-03, -9.5372e-04]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 80.00 %\\n\",\n      \"Val loss: 0.544816\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[21,    60] loss: 0.38251\\n\",\n      \"[21,   120] loss: 0.35179\\n\",\n      \"Time elapsed: 0h:41m:52s\\n\",\n      \"train accuracy_score: 85.94 %\\n\",\n      \"tensor([[[[[-8.8176e-02, -1.8502e-03,  6.0066e-02],\\n\",\n      \"           [-2.3217e-01, -1.3868e-01,  5.2048e-03],\\n\",\n      \"           [-3.0352e-01, -1.5463e-01,  4.8412e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.2329e-01, -8.9894e-03,  2.3120e-02],\\n\",\n      \"           [-2.5158e-01, -1.1976e-01,  1.7494e-03],\\n\",\n      \"           [-3.2390e-01, -2.1689e-01, -1.9792e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0918e-01, -1.9358e-02,  1.0975e-02],\\n\",\n      \"           [-2.2082e-01, -1.2698e-01, -4.8267e-02],\\n\",\n      \"           [-3.3003e-01, -1.6825e-01, -6.4062e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.0701e-01, -2.3024e-01, -3.6219e-01],\\n\",\n      \"           [-1.0052e-01, -7.0101e-02, -3.9973e-03],\\n\",\n      \"           [ 2.3777e-01,  2.9371e-01,  4.9487e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.4933e-01, -2.0995e-01, -2.6705e-01],\\n\",\n      \"           [-2.2969e-02, -1.6228e-02,  5.0981e-02],\\n\",\n      \"           [ 3.2362e-01,  3.8287e-01,  3.5291e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.0908e-01, -1.5265e-01, -1.3801e-01],\\n\",\n      \"           [ 1.0349e-01,  1.2817e-01,  9.6939e-02],\\n\",\n      \"           [ 3.7931e-01,  3.5076e-01,  3.1016e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.1612e+00,  6.2770e-01,  2.6849e-01],\\n\",\n      \"           [ 1.2454e+00,  9.7054e-01,  4.6868e-01],\\n\",\n      \"           [ 1.7188e+00,  1.2359e+00,  1.0882e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 8.9596e-01,  6.6772e-01,  3.2828e-02],\\n\",\n      \"           [ 1.2089e+00,  8.2623e-01,  3.5561e-01],\\n\",\n      \"           [ 1.5416e+00,  1.1792e+00,  7.5667e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 8.0117e-01,  8.3042e-01,  1.2312e-01],\\n\",\n      \"           [ 8.0234e-01,  9.0987e-01,  1.2004e-01],\\n\",\n      \"           [ 1.4212e+00,  1.0124e+00,  4.9524e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.7507e-02,  6.4374e-02,  2.6683e-04],\\n\",\n      \"           [-1.0051e-01, -1.3562e-02, -9.8886e-03],\\n\",\n      \"           [-2.0750e-01, -7.0569e-02, -3.8258e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.9444e-03,  7.4085e-02, -6.3448e-03],\\n\",\n      \"           [-1.0396e-01,  6.4296e-03, -1.3454e-02],\\n\",\n      \"           [-2.3070e-01, -5.9938e-02,  6.2414e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.1045e-02,  4.9905e-02, -2.1420e-02],\\n\",\n      \"           [-1.2226e-01, -2.1273e-03, -1.6512e-03],\\n\",\n      \"           [-2.2235e-01, -7.5054e-02,  1.6854e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1467e-01, -1.3779e-01, -3.6100e-01],\\n\",\n      \"           [-1.6386e-01,  5.7779e-02, -2.0836e-01],\\n\",\n      \"           [ 7.3223e-02,  1.9680e-01,  1.2001e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.7719e-02,  7.5331e-02, -2.4317e-01],\\n\",\n      \"           [-3.3700e-02,  1.0732e-01,  1.7112e-02],\\n\",\n      \"           [ 1.4335e-01,  2.7152e-01, -4.1985e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4287e-01,  1.7565e-01, -1.3769e-01],\\n\",\n      \"           [ 1.0660e-01,  1.9357e-01, -1.4486e-01],\\n\",\n      \"           [ 2.0817e-01,  2.8018e-01, -6.8285e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.2280e-01,  7.1427e-01,  6.7912e-01],\\n\",\n      \"           [ 6.9015e-01,  5.3231e-01,  8.6792e-01],\\n\",\n      \"           [ 4.8866e-01,  5.8856e-01,  7.8303e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.1048e-01,  7.6431e-01,  7.8264e-01],\\n\",\n      \"           [ 6.1761e-01,  5.8456e-01,  1.0412e+00],\\n\",\n      \"           [ 5.0435e-01,  6.5600e-01,  1.0987e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 3.9399e-01,  6.0896e-01,  8.7368e-01],\\n\",\n      \"           [ 5.1908e-01,  6.9309e-01,  1.0074e+00],\\n\",\n      \"           [ 3.5717e-01,  7.6399e-01,  1.0517e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.7274e-02,  1.1764e-04,  1.9845e-02],\\n\",\n      \"           [-6.3706e-02, -3.0038e-02, -1.4042e-02],\\n\",\n      \"           [-7.2804e-02, -4.5170e-02, -2.2633e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.3676e-03,  1.5202e-02,  3.3292e-02],\\n\",\n      \"           [-4.9587e-02, -2.2187e-02,  7.2109e-03],\\n\",\n      \"           [-7.5187e-02, -3.6014e-02, -2.0559e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.4596e-03,  2.2971e-02,  3.3108e-02],\\n\",\n      \"           [-5.1033e-02, -7.3033e-03,  6.6296e-03],\\n\",\n      \"           [-5.4849e-02, -4.6517e-02, -2.7913e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.6767e-02, -1.1919e-02, -6.0896e-03],\\n\",\n      \"           [-2.0437e-02, -1.2966e-02, -9.5253e-03],\\n\",\n      \"           [-2.1392e-02, -1.7892e-02, -1.3283e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.2203e-02, -5.9407e-03, -2.6719e-03],\\n\",\n      \"           [-1.5997e-02, -8.2026e-03, -9.6555e-03],\\n\",\n      \"           [-2.0283e-02, -1.9628e-02, -1.2011e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.4727e-02, -5.5664e-03, -3.6837e-03],\\n\",\n      \"           [-1.5846e-02, -1.1330e-02, -9.9978e-03],\\n\",\n      \"           [-2.0912e-02, -2.2930e-02, -1.6692e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 77.00 %\\n\",\n      \"Val loss: 0.584343\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[22,    60] loss: 0.28646\\n\",\n      \"[22,   120] loss: 0.35674\\n\",\n      \"Time elapsed: 0h:43m:48s\\n\",\n      \"train accuracy_score: 84.22 %\\n\",\n      \"tensor([[[[[ 2.2856e-01,  8.9665e-02,  1.4362e-02],\\n\",\n      \"           [ 3.0080e-01,  1.8983e-01,  5.5768e-02],\\n\",\n      \"           [ 3.4345e-01,  2.1117e-01,  7.3303e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8815e-01,  4.6710e-02,  5.1771e-03],\\n\",\n      \"           [ 2.8451e-01,  1.0682e-01,  3.9047e-02],\\n\",\n      \"           [ 3.2034e-01,  1.7227e-01,  5.0265e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.7402e-02,  5.3209e-02,  1.0109e-02],\\n\",\n      \"           [ 1.9355e-01,  1.0885e-01,  5.0709e-02],\\n\",\n      \"           [ 3.2404e-01,  1.1547e-01,  7.0986e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.2058e-02,  6.5301e-03,  4.9762e-02],\\n\",\n      \"           [ 7.2536e-02,  4.2326e-02,  5.7289e-03],\\n\",\n      \"           [-3.5125e-02, -5.4501e-02, -8.9439e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0381e-02,  5.5296e-02,  3.9013e-02],\\n\",\n      \"           [ 6.9844e-02,  6.2885e-02, -1.3049e-02],\\n\",\n      \"           [-1.7196e-02, -2.4343e-02, -3.8889e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6464e-03,  6.7099e-02,  3.4977e-02],\\n\",\n      \"           [ 5.4326e-04,  5.0995e-02,  4.4389e-02],\\n\",\n      \"           [-2.5645e-02, -3.9409e-03, -9.4917e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3176e+00, -1.0980e+00, -6.9613e-01],\\n\",\n      \"           [-1.1240e+00, -8.8490e-01, -7.6689e-01],\\n\",\n      \"           [-1.0905e+00, -9.3999e-01, -9.7404e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.1820e+00, -9.8164e-01, -3.6977e-01],\\n\",\n      \"           [-9.6229e-01, -7.5219e-01, -5.9222e-01],\\n\",\n      \"           [-8.7608e-01, -8.0041e-01, -5.1957e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.1189e+00, -1.0897e+00, -2.4312e-01],\\n\",\n      \"           [-9.2133e-01, -9.1575e-01, -3.2684e-01],\\n\",\n      \"           [-1.0244e+00, -9.5116e-01, -4.5126e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.8616e-02, -1.5038e-02, -6.7846e-03],\\n\",\n      \"           [ 9.1452e-02,  4.6169e-02,  1.8272e-02],\\n\",\n      \"           [ 1.3746e-01,  4.7827e-02,  1.1859e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2370e-02, -3.5807e-02, -2.0078e-02],\\n\",\n      \"           [ 8.4031e-02,  8.8578e-03, -7.6033e-03],\\n\",\n      \"           [ 1.6388e-01,  3.6990e-02, -3.8311e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.4185e-02, -2.4833e-02, -8.1205e-03],\\n\",\n      \"           [ 1.2937e-01,  1.4423e-02,  1.0999e-02],\\n\",\n      \"           [ 1.4159e-01,  7.0783e-02,  2.5336e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.8032e-02,  3.7212e-02,  1.1546e-01],\\n\",\n      \"           [ 3.7742e-02, -8.3926e-02, -4.8749e-04],\\n\",\n      \"           [-1.5791e-01, -1.6380e-01, -1.3355e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.2156e-01, -1.0507e-01,  1.7370e-01],\\n\",\n      \"           [-2.5918e-02, -1.3068e-01, -2.2000e-02],\\n\",\n      \"           [-1.2802e-01, -1.7903e-01,  4.3822e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.4880e-01, -1.3581e-01,  9.4451e-02],\\n\",\n      \"           [-5.1380e-02, -9.7991e-02,  1.2197e-01],\\n\",\n      \"           [-1.2039e-01, -1.3755e-01,  2.3149e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.4507e-01, -5.3813e-01, -6.2533e-01],\\n\",\n      \"           [-3.2209e-01, -3.5436e-01, -6.6451e-01],\\n\",\n      \"           [-2.3090e-01, -2.7575e-01, -5.7998e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.6090e-01, -6.0254e-01, -8.2008e-01],\\n\",\n      \"           [-4.2692e-01, -5.1001e-01, -8.1347e-01],\\n\",\n      \"           [-2.9992e-01, -4.9172e-01, -7.9625e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.0271e-01, -6.8108e-01, -8.8849e-01],\\n\",\n      \"           [-4.2532e-01, -6.4178e-01, -8.5787e-01],\\n\",\n      \"           [-2.9405e-01, -6.5609e-01, -7.7183e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 9.8185e-03, -1.3146e-02, -1.8098e-02],\\n\",\n      \"           [ 2.3177e-02,  7.2592e-03, -1.3859e-03],\\n\",\n      \"           [ 3.6151e-02,  2.7064e-02,  1.8843e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.9528e-04, -7.9585e-03, -1.4330e-02],\\n\",\n      \"           [ 1.9709e-02,  5.2585e-03, -4.8768e-03],\\n\",\n      \"           [ 3.0055e-02,  2.7349e-02,  1.7025e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.5155e-03, -3.8549e-03, -5.5052e-03],\\n\",\n      \"           [ 1.1500e-02,  8.5218e-03, -1.4559e-03],\\n\",\n      \"           [ 2.7282e-02,  2.4584e-02,  1.4793e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.0951e-03,  2.7453e-03,  3.1628e-05],\\n\",\n      \"           [ 1.3981e-02,  8.9829e-03,  2.4968e-03],\\n\",\n      \"           [ 1.6746e-02,  9.5280e-03,  1.6266e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 9.2984e-03,  2.5457e-03, -9.5608e-04],\\n\",\n      \"           [ 1.4449e-02,  7.3642e-03,  1.2229e-03],\\n\",\n      \"           [ 1.4585e-02,  7.1023e-03,  6.4430e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0031e-02,  1.9556e-03,  1.0147e-03],\\n\",\n      \"           [ 1.4257e-02,  7.5646e-03,  1.7628e-03],\\n\",\n      \"           [ 1.3083e-02,  6.8607e-03, -1.7152e-03]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 82.00 %\\n\",\n      \"Val loss: 0.380025\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[23,    60] loss: 0.36493\\n\",\n      \"[23,   120] loss: 0.31667\\n\",\n      \"Time elapsed: 0h:45m:44s\\n\",\n      \"train accuracy_score: 88.09 %\\n\",\n      \"tensor([[[[[-2.8603e-03, -1.8314e-03, -4.4120e-04],\\n\",\n      \"           [-1.0751e-02, -9.9003e-03, -4.9250e-03],\\n\",\n      \"           [-1.1926e-02, -9.3467e-03, -4.2658e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5266e-03,  7.0476e-03,  7.7336e-03],\\n\",\n      \"           [-2.1214e-03,  5.9394e-04, -3.1079e-03],\\n\",\n      \"           [-4.1953e-03, -9.8709e-04, -3.6706e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 7.7102e-03,  3.9005e-03,  1.6108e-03],\\n\",\n      \"           [ 3.0434e-03, -1.3762e-03, -8.3091e-03],\\n\",\n      \"           [-1.8703e-03, -2.7922e-03, -1.1936e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.0420e-04, -5.7356e-03, -6.4352e-03],\\n\",\n      \"           [ 7.3124e-03,  1.2464e-02,  1.4748e-02],\\n\",\n      \"           [ 2.6517e-02,  3.0438e-02,  2.6655e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.1014e-03, -1.0143e-02, -1.3358e-02],\\n\",\n      \"           [ 6.5264e-03,  1.2368e-04, -7.5424e-04],\\n\",\n      \"           [ 1.4832e-02,  1.5257e-02,  9.7826e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.5891e-02, -2.8194e-02, -3.1357e-02],\\n\",\n      \"           [-1.0380e-02, -2.1508e-02, -2.6977e-02],\\n\",\n      \"           [-6.2185e-03, -1.1621e-02, -6.0417e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.9348e-02,  7.1595e-02,  5.1710e-02],\\n\",\n      \"           [ 8.4716e-02,  5.2001e-02,  5.0009e-02],\\n\",\n      \"           [ 7.5469e-02,  6.6299e-02,  6.1932e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.0300e-02,  1.6566e-02, -4.1507e-03],\\n\",\n      \"           [ 5.6704e-02,  1.8004e-02, -1.6519e-02],\\n\",\n      \"           [ 2.5473e-02,  1.3189e-02, -1.1314e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.1209e-02, -1.0699e-02, -2.3489e-02],\\n\",\n      \"           [ 1.0959e-02,  3.4637e-04, -2.7615e-02],\\n\",\n      \"           [ 3.0254e-03, -1.3009e-02, -3.8208e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.6563e-03,  3.2640e-03, -2.0413e-03],\\n\",\n      \"           [-5.7833e-04, -1.8861e-03, -4.3695e-03],\\n\",\n      \"           [-4.2930e-03, -5.6979e-03, -5.0983e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 9.4933e-03,  7.6026e-03, -4.2905e-03],\\n\",\n      \"           [ 6.2889e-03,  1.4406e-03, -4.9419e-03],\\n\",\n      \"           [ 2.7807e-04, -1.3104e-03, -2.0544e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0943e-02,  6.4175e-03, -2.8278e-03],\\n\",\n      \"           [ 7.8723e-03,  3.1464e-03, -4.4610e-03],\\n\",\n      \"           [ 2.8073e-03,  1.0888e-03,  1.3702e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.0620e-02,  1.8480e-02,  3.4723e-02],\\n\",\n      \"           [ 1.4682e-02,  4.0971e-02,  5.5910e-02],\\n\",\n      \"           [ 3.9658e-02,  6.8539e-02,  8.1417e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0224e-02,  2.1885e-02,  2.6134e-02],\\n\",\n      \"           [ 2.3596e-02,  3.6139e-02,  5.4477e-02],\\n\",\n      \"           [ 4.5776e-02,  6.6083e-02,  7.3446e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.7444e-02,  3.4900e-02,  2.6999e-02],\\n\",\n      \"           [ 3.6676e-02,  3.7577e-02,  4.5926e-02],\\n\",\n      \"           [ 4.6730e-02,  6.4388e-02,  7.2541e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.6466e-02,  9.0801e-02,  8.9828e-02],\\n\",\n      \"           [ 9.5435e-02,  7.8821e-02,  9.2190e-02],\\n\",\n      \"           [ 8.6684e-02,  7.4473e-02,  8.9853e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 9.5650e-02,  1.1273e-01,  1.1747e-01],\\n\",\n      \"           [ 1.0269e-01,  1.0392e-01,  1.2321e-01],\\n\",\n      \"           [ 1.0760e-01,  1.0537e-01,  1.2809e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0262e-01,  1.3297e-01,  1.6508e-01],\\n\",\n      \"           [ 1.2592e-01,  1.4081e-01,  1.5676e-01],\\n\",\n      \"           [ 1.3260e-01,  1.4575e-01,  1.4374e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.7413e-03, -3.0559e-03, -1.1987e-03],\\n\",\n      \"           [-7.4762e-03, -5.0388e-03, -2.9381e-03],\\n\",\n      \"           [-5.3692e-03, -4.2471e-03, -3.2530e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.8374e-03,  1.2016e-04,  1.8298e-03],\\n\",\n      \"           [-3.8815e-03, -2.1139e-03, -3.8451e-04],\\n\",\n      \"           [-3.4411e-03, -1.8226e-03, -1.2348e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3733e-04,  1.9740e-03,  2.5168e-03],\\n\",\n      \"           [-8.4399e-04,  1.4481e-04, -3.9557e-04],\\n\",\n      \"           [-1.0337e-03,  3.9539e-05, -3.1048e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.1790e-04, -5.4398e-04, -2.0116e-04],\\n\",\n      \"           [-1.1412e-03, -4.6443e-04, -1.9872e-04],\\n\",\n      \"           [-8.0309e-04, -1.8170e-04,  2.9285e-04]],\\n\",\n      \"\\n\",\n      \"          [[-6.3320e-04, -3.1184e-04, -9.1290e-04],\\n\",\n      \"           [-7.8854e-04, -6.2491e-04, -7.1157e-04],\\n\",\n      \"           [-1.0790e-03, -9.4504e-04, -2.4852e-04]],\\n\",\n      \"\\n\",\n      \"          [[-7.8932e-04, -7.5274e-04, -9.6700e-04],\\n\",\n      \"           [-1.1122e-03, -1.0774e-03, -8.4909e-04],\\n\",\n      \"           [-1.2062e-03, -9.6858e-04, -4.6687e-04]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 83.00 %\\n\",\n      \"Val loss: 0.392642\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[24,    60] loss: 0.31516\\n\",\n      \"[24,   120] loss: 0.33440\\n\",\n      \"Time elapsed: 0h:47m:39s\\n\",\n      \"train accuracy_score: 88.67 %\\n\",\n      \"tensor([[[[[ 5.9483e-03, -1.2281e-02, -8.3361e-03],\\n\",\n      \"           [ 4.5704e-02,  3.1132e-02,  4.0375e-03],\\n\",\n      \"           [ 6.0110e-02,  4.3037e-02,  1.5946e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6541e-02, -9.0816e-03, -5.6713e-03],\\n\",\n      \"           [ 5.0828e-02,  2.3769e-02,  1.8742e-03],\\n\",\n      \"           [ 6.9894e-02,  4.0414e-02,  8.7098e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0050e-02, -1.7469e-03, -2.6020e-03],\\n\",\n      \"           [ 3.6957e-02,  3.1153e-02,  1.1885e-02],\\n\",\n      \"           [ 7.0648e-02,  4.1981e-02,  1.9053e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.3755e-02, -1.1988e-02,  2.2157e-02],\\n\",\n      \"           [-8.4109e-03,  3.3073e-03, -2.4144e-03],\\n\",\n      \"           [-9.5142e-03, -1.6129e-02, -2.4853e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.0263e-02, -3.2900e-03,  7.0014e-03],\\n\",\n      \"           [ 2.2065e-03, -4.5498e-03, -1.7289e-02],\\n\",\n      \"           [-1.1716e-02, -1.4560e-02, -3.5572e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.5660e-02, -1.3387e-02,  2.7568e-03],\\n\",\n      \"           [-1.6232e-02, -3.4783e-03, -1.0704e-02],\\n\",\n      \"           [-1.4891e-02, -2.1313e-02, -4.4689e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.8324e-01, -1.1832e-01, -5.8805e-04],\\n\",\n      \"           [-2.2297e-01, -1.6878e-01, -1.1487e-01],\\n\",\n      \"           [-2.4475e-01, -2.2432e-01, -1.6128e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.9084e-01, -9.9289e-02,  3.4350e-02],\\n\",\n      \"           [-1.5285e-01, -1.5601e-01, -1.3673e-01],\\n\",\n      \"           [-1.8845e-01, -2.1368e-01, -1.7186e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.4307e-01, -9.3115e-02, -6.1874e-04],\\n\",\n      \"           [-9.6780e-02, -1.2259e-01, -6.0820e-02],\\n\",\n      \"           [-1.5561e-01, -1.7736e-01, -1.1375e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.9416e-03, -3.7600e-03, -5.0054e-03],\\n\",\n      \"           [ 2.2104e-02,  4.8198e-03, -3.4988e-03],\\n\",\n      \"           [ 3.8296e-02,  2.2792e-02,  3.3001e-04]],\\n\",\n      \"\\n\",\n      \"          [[-8.2540e-03, -1.7580e-02, -2.7407e-03],\\n\",\n      \"           [ 1.7175e-02,  4.1921e-03, -7.7293e-03],\\n\",\n      \"           [ 3.9071e-02,  1.2106e-02, -7.7377e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.7134e-03, -1.1849e-02, -4.6853e-03],\\n\",\n      \"           [ 2.3871e-02,  7.9999e-03, -2.4270e-03],\\n\",\n      \"           [ 4.4319e-02,  2.2401e-02, -4.5641e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.2211e-01,  1.2396e-01,  1.2200e-01],\\n\",\n      \"           [ 1.6916e-01,  1.2914e-01,  9.0354e-02],\\n\",\n      \"           [ 1.7323e-01,  1.0930e-01,  7.4465e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0426e-01,  8.8394e-02,  1.1052e-01],\\n\",\n      \"           [ 1.5743e-01,  8.6978e-02,  4.7515e-02],\\n\",\n      \"           [ 1.4029e-01,  5.9044e-02,  4.5937e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.6513e-02,  2.4318e-02,  6.2948e-02],\\n\",\n      \"           [ 8.0659e-02,  4.5114e-02,  5.9202e-02],\\n\",\n      \"           [ 7.7165e-02,  2.4822e-02,  3.0446e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.3048e-02, -1.1341e-02, -2.8242e-02],\\n\",\n      \"           [ 4.3449e-03,  2.0605e-02,  1.6300e-02],\\n\",\n      \"           [-1.0468e-02,  1.7388e-02,  1.8205e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.0380e-02, -1.3592e-02, -2.5406e-02],\\n\",\n      \"           [ 7.1122e-03,  9.1458e-03, -6.3087e-03],\\n\",\n      \"           [ 2.3237e-03, -8.7374e-03, -3.2060e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9661e-02, -2.6772e-02, -3.1306e-02],\\n\",\n      \"           [-2.0551e-02, -9.7798e-03, -3.2200e-03],\\n\",\n      \"           [ 6.1210e-03, -1.4295e-03,  3.2298e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.7947e-03, -6.3651e-03, -6.5304e-03],\\n\",\n      \"           [ 2.2784e-03,  3.7113e-03,  3.4227e-03],\\n\",\n      \"           [ 7.4844e-03,  9.1976e-03,  5.7218e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.1235e-03, -7.5281e-03, -5.3601e-03],\\n\",\n      \"           [ 1.0568e-03,  2.7399e-03,  1.2944e-03],\\n\",\n      \"           [ 6.9614e-03,  9.0277e-03,  4.2536e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.2696e-03, -2.4373e-03, -1.5362e-03],\\n\",\n      \"           [ 1.5219e-03,  3.3891e-03,  2.8007e-03],\\n\",\n      \"           [ 1.0132e-02,  1.0173e-02,  7.3712e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.8244e-03,  7.1539e-04, -1.4823e-04],\\n\",\n      \"           [ 3.0547e-03,  2.9615e-03,  1.1321e-03],\\n\",\n      \"           [ 4.3794e-03,  3.9505e-03,  2.0744e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2245e-03, -4.0433e-05, -7.8631e-04],\\n\",\n      \"           [ 1.9607e-03,  2.0167e-03,  1.1551e-03],\\n\",\n      \"           [ 3.1452e-03,  3.7145e-03,  1.4618e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.9093e-04, -5.7023e-04, -6.3443e-04],\\n\",\n      \"           [ 5.9901e-04,  1.3901e-03,  1.2380e-03],\\n\",\n      \"           [ 2.6968e-03,  2.6460e-03,  1.6743e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 79.00 %\\n\",\n      \"Val loss: 0.393716\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[25,    60] loss: 0.33720\\n\",\n      \"[25,   120] loss: 0.31437\\n\",\n      \"Time elapsed: 0h:49m:34s\\n\",\n      \"train accuracy_score: 85.65 %\\n\",\n      \"tensor([[[[[-3.4861e-01, -1.2359e-01,  4.4553e-02],\\n\",\n      \"           [-6.8330e-01, -6.3192e-01, -1.5016e-01],\\n\",\n      \"           [-9.9738e-01, -6.9590e-01, -2.0080e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.3504e-01, -4.9699e-02,  1.1773e-01],\\n\",\n      \"           [-7.1989e-01, -3.9787e-01, -1.1164e-01],\\n\",\n      \"           [-9.0794e-01, -6.2368e-01, -1.7902e-01]],\\n\",\n      \"\\n\",\n      \"          [[-7.4558e-02,  1.5816e-02,  7.1278e-02],\\n\",\n      \"           [-4.6294e-01, -2.5521e-01, -4.5830e-02],\\n\",\n      \"           [-9.1631e-01, -4.8182e-01, -1.5366e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.0137e-02, -4.7212e-01, -5.8125e-01],\\n\",\n      \"           [-4.7105e-01, -1.9752e-01,  4.2150e-02],\\n\",\n      \"           [ 6.9638e-02,  4.1597e-01,  5.9460e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.2155e-01, -6.8052e-01, -7.7634e-01],\\n\",\n      \"           [-1.9885e-01, -4.4053e-01, -3.3028e-01],\\n\",\n      \"           [ 8.3543e-02,  2.7145e-01,  2.3362e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.0275e-01, -6.0563e-01, -1.0835e+00],\\n\",\n      \"           [-1.6903e-02, -5.8900e-01, -6.8801e-01],\\n\",\n      \"           [ 1.1588e-01, -2.0413e-01, -1.3990e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.0728e+00,  1.6170e+00,  6.7088e-01],\\n\",\n      \"           [ 2.3226e+00,  8.8680e-01,  9.2604e-01],\\n\",\n      \"           [ 1.9677e+00,  1.5071e+00,  2.4810e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5595e+00,  1.7544e+00,  5.9985e-01],\\n\",\n      \"           [ 1.9078e+00,  1.1572e+00,  6.1360e-01],\\n\",\n      \"           [ 1.2524e+00,  1.2036e+00,  9.3369e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6127e+00,  1.9060e+00, -4.3545e-01],\\n\",\n      \"           [ 1.6084e+00,  1.6051e+00, -4.5091e-01],\\n\",\n      \"           [ 1.5513e+00,  8.5209e-01,  4.8315e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.3310e-02,  5.8579e-02, -1.6931e-02],\\n\",\n      \"           [-1.9656e-01,  8.1245e-03,  6.2206e-02],\\n\",\n      \"           [-3.3890e-01, -2.4212e-02,  9.3812e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.4493e-02,  1.0241e-01, -1.1693e-01],\\n\",\n      \"           [-1.5319e-01, -4.2451e-03, -4.8485e-02],\\n\",\n      \"           [-3.6308e-01, -5.1847e-02,  1.7434e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.4493e-02,  7.2138e-02, -1.4595e-01],\\n\",\n      \"           [-1.8903e-01, -6.5838e-02, -6.4190e-02],\\n\",\n      \"           [-3.8219e-01, -7.6260e-02,  1.2964e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.2013e-01,  3.4190e-01,  1.6714e-01],\\n\",\n      \"           [ 9.0462e-02,  6.4018e-01,  5.3499e-01],\\n\",\n      \"           [ 6.9860e-01,  1.1101e+00,  8.3096e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.0824e-01,  4.8681e-01,  1.0688e-01],\\n\",\n      \"           [ 2.3349e-02,  6.8199e-01,  6.2381e-01],\\n\",\n      \"           [ 3.3890e-01,  1.0119e+00,  5.4908e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 9.2959e-01,  5.2682e-01,  6.8905e-02],\\n\",\n      \"           [ 3.0791e-01,  5.1463e-01, -1.6177e-01],\\n\",\n      \"           [ 3.6014e-01,  6.6762e-01,  4.0788e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.8240e-01,  1.1604e+00,  7.8467e-01],\\n\",\n      \"           [ 3.9592e-01,  1.9307e-02,  5.8198e-01],\\n\",\n      \"           [-3.4883e-01, -1.7351e-01,  7.9668e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.0851e-01,  1.0052e+00,  8.3528e-01],\\n\",\n      \"           [ 2.0839e-01,  1.9755e-01,  9.1867e-01],\\n\",\n      \"           [-4.3005e-01, -5.4869e-02,  8.1845e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3767e-01,  8.1512e-01,  7.5367e-01],\\n\",\n      \"           [ 7.1101e-02,  1.8296e-02,  8.7920e-01],\\n\",\n      \"           [-6.5877e-01,  3.6927e-01,  7.2343e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.2764e-01,  1.7544e-01,  1.9351e-01],\\n\",\n      \"           [ 7.3253e-03,  1.4167e-02,  3.3125e-02],\\n\",\n      \"           [-9.6966e-02, -1.4711e-01, -1.3500e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0486e-01,  1.2825e-01,  1.6602e-01],\\n\",\n      \"           [-4.9396e-02, -2.3390e-02,  1.2737e-02],\\n\",\n      \"           [-1.5082e-01, -1.5958e-01, -1.4058e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 7.0640e-02,  8.8477e-02,  8.8186e-02],\\n\",\n      \"           [-6.5576e-02, -7.8761e-02, -6.6909e-02],\\n\",\n      \"           [-1.9783e-01, -2.0354e-01, -2.0654e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.1279e-03, -1.0124e-02, -3.2320e-03],\\n\",\n      \"           [-2.7995e-02, -2.7849e-02, -9.9683e-03],\\n\",\n      \"           [-2.8287e-02, -2.2230e-02, -7.8261e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.1547e-02,  7.6494e-03,  1.0723e-02],\\n\",\n      \"           [-1.6932e-02, -1.0365e-02,  8.0889e-04],\\n\",\n      \"           [-2.0263e-02, -5.8152e-03,  9.3310e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.0713e-02,  1.4089e-02,  3.0878e-02],\\n\",\n      \"           [-2.1274e-02,  4.3231e-03,  2.5459e-02],\\n\",\n      \"           [-2.7406e-02,  3.7476e-03,  2.5480e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 84.00 %\\n\",\n      \"Val loss: 0.360410\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[26,    60] loss: 0.29857\\n\",\n      \"[26,   120] loss: 0.24820\\n\",\n      \"Time elapsed: 0h:51m:31s\\n\",\n      \"train accuracy_score: 87.95 %\\n\",\n      \"tensor([[[[[-3.4417e-04, -7.6107e-05, -5.6854e-04],\\n\",\n      \"           [-3.4912e-03, -3.6189e-03, -2.3915e-03],\\n\",\n      \"           [-6.4909e-03, -5.7047e-03, -3.5270e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3974e-03,  2.2814e-03,  8.8251e-04],\\n\",\n      \"           [-2.1995e-03, -9.1085e-04, -1.5108e-03],\\n\",\n      \"           [-5.4024e-03, -3.6193e-03, -2.9736e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.2982e-03,  2.8153e-03,  1.9561e-03],\\n\",\n      \"           [ 9.0142e-04, -2.3208e-04, -1.3242e-03],\\n\",\n      \"           [-4.2081e-03, -2.6026e-03, -2.2307e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.9429e-03, -7.0966e-03, -8.0371e-03],\\n\",\n      \"           [-6.9408e-03, -5.8760e-03, -3.6476e-03],\\n\",\n      \"           [-3.0315e-03, -1.1028e-03,  1.5460e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.7796e-03, -3.8674e-03, -4.3287e-03],\\n\",\n      \"           [-3.5283e-03, -2.5495e-03, -2.0937e-03],\\n\",\n      \"           [-8.2482e-04,  1.4919e-03,  2.4634e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.7422e-03, -3.5058e-03, -3.6738e-03],\\n\",\n      \"           [-1.4295e-03, -1.6149e-03, -7.0431e-04],\\n\",\n      \"           [-1.6855e-04,  2.4594e-04,  7.0542e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.1370e-02, -1.5459e-03, -1.2412e-02],\\n\",\n      \"           [ 1.9281e-02,  8.3246e-04, -5.9654e-03],\\n\",\n      \"           [ 2.3857e-02,  8.2860e-03,  1.1456e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1441e-02, -1.7436e-03, -1.3711e-02],\\n\",\n      \"           [ 1.5649e-02,  3.5286e-03, -8.3130e-04],\\n\",\n      \"           [ 1.9952e-02,  1.0226e-02,  7.0782e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7961e-02,  6.4111e-03, -1.0815e-02],\\n\",\n      \"           [ 1.9473e-02,  8.2426e-03, -5.5958e-04],\\n\",\n      \"           [ 2.3471e-02,  1.6459e-02,  1.0311e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.2340e-04, -2.6910e-04, -1.3890e-03],\\n\",\n      \"           [-9.2672e-04, -1.6420e-03, -1.3376e-03],\\n\",\n      \"           [-3.1273e-03, -2.3560e-03, -1.5539e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.6811e-04,  8.2887e-04,  6.0575e-05],\\n\",\n      \"           [-2.4554e-04,  6.7381e-04, -6.7744e-04],\\n\",\n      \"           [-2.0964e-03, -1.4758e-04, -4.3854e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 4.4777e-04,  1.6329e-03,  2.9009e-04],\\n\",\n      \"           [-8.4531e-04,  1.0135e-03, -6.7745e-04],\\n\",\n      \"           [-1.3513e-03, -6.0777e-04, -5.0331e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.2594e-03, -6.4987e-03, -8.2153e-03],\\n\",\n      \"           [-1.0258e-02, -6.5696e-03, -8.0907e-03],\\n\",\n      \"           [-9.2695e-03, -7.0844e-03, -6.9564e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.9351e-03, -4.7002e-03, -1.0180e-02],\\n\",\n      \"           [-8.3309e-03, -6.3454e-03, -6.2636e-03],\\n\",\n      \"           [-8.5460e-03, -6.5266e-03, -9.2777e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.8728e-03, -2.9294e-03, -7.8737e-03],\\n\",\n      \"           [-4.7404e-03, -5.7211e-03, -1.1284e-02],\\n\",\n      \"           [-7.2149e-03, -7.4356e-03, -9.4220e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.9396e-03,  5.7908e-03, -7.1134e-04],\\n\",\n      \"           [ 4.5027e-03,  8.5387e-04, -4.0154e-04],\\n\",\n      \"           [-1.3160e-03, -3.1619e-03, -3.5642e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4022e-03,  4.5970e-03,  6.5766e-04],\\n\",\n      \"           [ 5.3814e-04, -1.9526e-03,  1.0551e-03],\\n\",\n      \"           [-3.1398e-03, -2.9367e-03,  2.5377e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.3210e-03,  2.8333e-03,  5.2439e-03],\\n\",\n      \"           [ 4.3763e-04,  5.4693e-04,  5.0841e-03],\\n\",\n      \"           [-5.0422e-03,  1.6639e-03,  4.6071e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.8903e-04,  1.0500e-03,  1.0102e-03],\\n\",\n      \"           [ 3.1304e-04,  4.2008e-04,  4.3557e-04],\\n\",\n      \"           [-3.4124e-04, -3.9751e-04, -3.9851e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 4.5975e-04,  7.3213e-04,  7.1298e-04],\\n\",\n      \"           [-5.3192e-05,  1.8408e-04,  2.2555e-04],\\n\",\n      \"           [-7.8387e-04, -6.6233e-04, -5.0532e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8009e-04,  9.3171e-05,  1.1666e-04],\\n\",\n      \"           [-3.3337e-04, -4.2964e-04, -2.0671e-04],\\n\",\n      \"           [-9.4720e-04, -9.6632e-04, -1.0759e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.3254e-04, -3.3969e-04, -8.4455e-05],\\n\",\n      \"           [-4.3840e-04, -2.1073e-04, -1.5677e-04],\\n\",\n      \"           [-3.0861e-04, -2.3706e-04, -1.0038e-04]],\\n\",\n      \"\\n\",\n      \"          [[-4.0069e-04, -3.3693e-04, -1.0119e-04],\\n\",\n      \"           [-4.7055e-04, -2.5333e-04, -7.3421e-06],\\n\",\n      \"           [-3.9957e-04, -2.6434e-04,  1.5482e-05]],\\n\",\n      \"\\n\",\n      \"          [[-3.9689e-04, -2.1349e-04, -2.4215e-05],\\n\",\n      \"           [-4.0705e-04, -3.2343e-04, -4.9617e-05],\\n\",\n      \"           [-3.0678e-04, -2.6000e-04,  3.2819e-05]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 64.00 %\\n\",\n      \"Val loss: 0.813321\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[27,    60] loss: 0.27671\\n\",\n      \"[27,   120] loss: 0.26087\\n\",\n      \"Time elapsed: 0h:53m:27s\\n\",\n      \"train accuracy_score: 90.39 %\\n\",\n      \"tensor([[[[[ 1.1384e-01,  5.0645e-02, -1.7577e-02],\\n\",\n      \"           [ 1.6318e-01,  1.2600e-01,  1.6021e-02],\\n\",\n      \"           [ 1.9240e-01,  1.4168e-01,  4.8782e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1747e-01,  4.3875e-02, -1.9977e-02],\\n\",\n      \"           [ 1.9348e-01,  1.0496e-01,  7.4838e-03],\\n\",\n      \"           [ 2.0015e-01,  1.3392e-01,  2.1185e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.8309e-02,  6.4963e-02, -4.0893e-03],\\n\",\n      \"           [ 1.1555e-01,  1.0095e-01,  1.5560e-02],\\n\",\n      \"           [ 1.8106e-01,  9.5526e-02,  3.5869e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.0804e-01, -1.1601e-02,  5.2665e-02],\\n\",\n      \"           [-1.6756e-02, -1.8764e-03, -9.1348e-04],\\n\",\n      \"           [-8.9995e-02, -6.2970e-02, -6.8312e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1840e-02,  3.8454e-02,  9.6937e-02],\\n\",\n      \"           [-2.4219e-02,  4.3345e-02,  7.1508e-03],\\n\",\n      \"           [-8.3874e-02, -4.8964e-02, -3.1876e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.3036e-02,  2.3954e-02,  5.3065e-02],\\n\",\n      \"           [-2.6460e-02,  2.2023e-02,  4.7527e-02],\\n\",\n      \"           [-4.4187e-02,  1.6041e-02,  2.9501e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.4706e-01, -4.5868e-01, -9.8325e-02],\\n\",\n      \"           [-5.0111e-01, -1.5658e-01, -3.8739e-02],\\n\",\n      \"           [-4.4639e-01, -2.3246e-01, -7.3270e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.2540e-01, -3.3310e-01,  4.8574e-02],\\n\",\n      \"           [-4.1572e-01, -7.3124e-02,  3.8524e-02],\\n\",\n      \"           [-3.1426e-01, -1.5707e-01,  2.7579e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.3316e-01, -3.3799e-01,  1.6133e-01],\\n\",\n      \"           [-2.1738e-01, -1.2763e-01,  2.2033e-01],\\n\",\n      \"           [-2.9326e-01, -9.0602e-02,  2.8168e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.5866e-02, -2.4531e-02,  1.6694e-02],\\n\",\n      \"           [ 4.6746e-02,  1.6485e-02,  3.3098e-02],\\n\",\n      \"           [ 8.0806e-02,  5.1047e-02,  5.1511e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.5406e-03, -3.3539e-02, -9.1793e-03],\\n\",\n      \"           [ 2.6744e-02,  1.7731e-03,  2.1231e-03],\\n\",\n      \"           [ 8.0188e-02,  1.7571e-02,  8.4170e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.3512e-03, -2.8483e-02, -1.5329e-02],\\n\",\n      \"           [ 3.9774e-02,  1.3987e-02, -2.0473e-03],\\n\",\n      \"           [ 5.6433e-02,  2.5292e-02, -2.4490e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.2402e-01,  1.4772e-01,  1.3698e-01],\\n\",\n      \"           [ 2.0447e-01,  9.8402e-02,  4.2615e-02],\\n\",\n      \"           [ 9.3449e-02,  7.7223e-03, -1.3314e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 8.4268e-03,  2.6518e-02,  9.3996e-02],\\n\",\n      \"           [ 9.8908e-02,  1.2597e-02, -4.3950e-02],\\n\",\n      \"           [ 3.3407e-02, -7.9805e-02,  1.9512e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.9799e-02, -2.9202e-02, -3.0503e-04],\\n\",\n      \"           [ 7.0523e-02,  1.5975e-03,  1.9604e-02],\\n\",\n      \"           [ 6.0273e-03, -7.8129e-02, -4.8853e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.4188e-01, -2.6594e-01, -2.8095e-01],\\n\",\n      \"           [-2.0244e-01, -1.5568e-01, -3.6133e-01],\\n\",\n      \"           [-2.1512e-01, -2.5537e-01, -3.4059e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.2448e-01, -2.5233e-01, -3.2585e-01],\\n\",\n      \"           [-2.0107e-01, -2.3903e-01, -3.5001e-01],\\n\",\n      \"           [-1.5903e-01, -2.5401e-01, -3.5818e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.8334e-01, -3.2620e-01, -3.8900e-01],\\n\",\n      \"           [-1.8953e-01, -2.5908e-01, -3.5674e-01],\\n\",\n      \"           [-1.0190e-01, -2.4544e-01, -2.4690e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.3009e-03, -1.0472e-02, -2.0996e-02],\\n\",\n      \"           [ 1.7433e-03,  9.5864e-04,  8.5023e-03],\\n\",\n      \"           [ 5.4523e-03,  9.5937e-03,  1.7540e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0731e-02, -1.0770e-02, -1.1550e-02],\\n\",\n      \"           [ 3.8903e-03,  7.0242e-03,  7.4784e-03],\\n\",\n      \"           [ 8.3085e-03,  1.0306e-02,  1.9045e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1941e-03, -5.2769e-03, -8.6951e-03],\\n\",\n      \"           [ 4.9401e-03,  1.1129e-02,  6.6678e-03],\\n\",\n      \"           [ 1.1578e-02,  1.4076e-02,  1.7997e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3362e-02,  8.9266e-03,  3.3409e-03],\\n\",\n      \"           [ 1.4399e-02,  8.0487e-03,  3.2677e-03],\\n\",\n      \"           [ 1.1029e-02,  8.4200e-03,  3.4406e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 9.6104e-03,  3.8580e-03,  7.0895e-04],\\n\",\n      \"           [ 9.1973e-03,  5.3574e-03,  5.5483e-05],\\n\",\n      \"           [ 6.6975e-03,  3.6289e-03,  5.0570e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1638e-03, -1.9277e-03, -2.4998e-03],\\n\",\n      \"           [ 1.2996e-03,  1.1883e-03, -1.0038e-03],\\n\",\n      \"           [ 5.2225e-03,  1.5282e-03, -2.1043e-03]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 80.00 %\\n\",\n      \"Val loss: 0.428375\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[28,    60] loss: 0.26912\\n\",\n      \"[28,   120] loss: 0.25171\\n\",\n      \"Time elapsed: 0h:55m:22s\\n\",\n      \"train accuracy_score: 90.39 %\\n\",\n      \"tensor([[[[[-9.9146e-02, -4.8664e-02, -3.4199e-02],\\n\",\n      \"           [-2.1547e-01, -1.8753e-01, -9.2216e-02],\\n\",\n      \"           [-3.1921e-01, -2.4354e-01, -7.9880e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.0937e-02, -1.1549e-03,  3.3227e-02],\\n\",\n      \"           [-2.0033e-01, -5.9507e-02,  5.5711e-03],\\n\",\n      \"           [-3.2122e-01, -1.3380e-01,  2.7910e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.5260e-02,  3.1704e-02,  3.9852e-02],\\n\",\n      \"           [-1.4768e-01, -3.5582e-02,  1.0963e-02],\\n\",\n      \"           [-2.7307e-01, -7.3442e-02,  3.6066e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.3916e-02, -5.6968e-02, -1.3196e-01],\\n\",\n      \"           [ 7.4451e-02, -2.2161e-02,  2.3112e-02],\\n\",\n      \"           [ 1.7439e-01,  1.5987e-01,  1.7985e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3442e-01,  8.2873e-02,  3.0956e-02],\\n\",\n      \"           [ 1.3582e-01,  1.7834e-01,  1.3946e-01],\\n\",\n      \"           [ 2.4168e-01,  3.4951e-01,  3.4366e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4888e-01,  1.4947e-01,  7.4307e-02],\\n\",\n      \"           [ 2.2685e-01,  2.3868e-01,  1.3346e-01],\\n\",\n      \"           [ 2.8668e-01,  3.3440e-01,  2.4460e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.3749e-01,  6.8705e-02, -2.1787e-01],\\n\",\n      \"           [ 3.1550e-01, -2.5989e-01, -4.0585e-01],\\n\",\n      \"           [ 3.4101e-01, -6.8416e-02, -2.2910e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 7.6402e-01,  3.9857e-01, -1.9461e-01],\\n\",\n      \"           [ 4.7141e-01,  7.2584e-02, -2.7173e-01],\\n\",\n      \"           [ 4.2689e-01,  1.5471e-01, -3.4076e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1123e+00,  7.5816e-01, -3.6340e-01],\\n\",\n      \"           [ 7.6646e-01,  5.4260e-01, -3.2784e-01],\\n\",\n      \"           [ 5.5769e-01,  3.9607e-01,  4.5418e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.0574e-02,  3.7051e-02,  7.7292e-02],\\n\",\n      \"           [-8.9852e-02, -1.0970e-02,  6.5922e-02],\\n\",\n      \"           [-1.4052e-01, -3.9510e-02,  4.5790e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.2619e-02,  4.4481e-03,  3.6329e-02],\\n\",\n      \"           [-1.1070e-01,  1.0896e-02,  3.3118e-02],\\n\",\n      \"           [-1.8233e-01, -5.4145e-02,  4.0041e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.2712e-01, -2.7785e-02, -2.0499e-02],\\n\",\n      \"           [-1.3810e-01, -6.3711e-02, -2.7450e-02],\\n\",\n      \"           [-2.2252e-01, -8.4936e-02,  2.2804e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.4835e-01,  7.1353e-03, -7.7980e-02],\\n\",\n      \"           [ 1.0383e-01,  2.4527e-01,  1.5335e-01],\\n\",\n      \"           [ 3.4829e-01,  4.9513e-01,  3.9401e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.9737e-01,  2.4018e-01, -2.9752e-02],\\n\",\n      \"           [ 1.8347e-01,  3.1405e-01,  1.5167e-01],\\n\",\n      \"           [ 4.3830e-01,  5.4926e-01,  3.4845e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.7480e-01,  3.1758e-01,  7.9081e-02],\\n\",\n      \"           [ 3.0804e-01,  3.1511e-01,  4.7413e-02],\\n\",\n      \"           [ 3.6630e-01,  4.1362e-01,  3.0437e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.1904e-01,  5.7072e-01,  5.6711e-01],\\n\",\n      \"           [ 2.3932e-01,  2.5611e-01,  4.6456e-01],\\n\",\n      \"           [ 2.0987e-01,  2.0809e-01,  2.8978e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7853e-01,  4.6161e-01,  6.1860e-01],\\n\",\n      \"           [ 1.5150e-01,  2.1548e-01,  6.8729e-01],\\n\",\n      \"           [ 1.2478e-01,  3.2423e-01,  5.6908e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 9.9848e-02,  4.1467e-01,  8.0495e-01],\\n\",\n      \"           [ 1.2777e-01,  4.2017e-01,  6.9095e-01],\\n\",\n      \"           [ 1.8550e-02,  4.0485e-01,  5.1973e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.2546e-02,  2.8965e-02,  5.9785e-02],\\n\",\n      \"           [-2.7927e-02, -1.6859e-02,  2.0252e-02],\\n\",\n      \"           [-5.7360e-02, -2.9085e-02, -3.4543e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.8394e-03,  2.8516e-02,  6.5317e-02],\\n\",\n      \"           [-3.8040e-02, -1.8804e-02,  3.1926e-02],\\n\",\n      \"           [-6.9900e-02, -3.5889e-02,  6.0028e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.7542e-02,  4.0035e-03,  3.9944e-02],\\n\",\n      \"           [-4.7601e-02, -2.7950e-02,  2.0320e-02],\\n\",\n      \"           [-7.3572e-02, -3.9361e-02,  3.4612e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.5362e-02, -2.0179e-02, -8.0009e-03],\\n\",\n      \"           [-2.7607e-02, -1.4792e-02, -2.9111e-03],\\n\",\n      \"           [-1.9675e-02, -1.0723e-02, -1.1336e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.5726e-02, -1.6344e-02, -3.8055e-03],\\n\",\n      \"           [-2.5559e-02, -1.2663e-02,  6.0535e-04],\\n\",\n      \"           [-2.4351e-02, -8.7060e-03,  1.6711e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.3951e-02, -1.5223e-02, -6.6456e-04],\\n\",\n      \"           [-2.2473e-02, -1.1480e-02,  2.5332e-03],\\n\",\n      \"           [-2.3731e-02, -5.9785e-03,  7.5867e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 83.00 %\\n\",\n      \"Val loss: 0.381049\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[29,    60] loss: 0.27527\\n\",\n      \"[29,   120] loss: 0.22724\\n\",\n      \"Time elapsed: 0h:57m:19s\\n\",\n      \"train accuracy_score: 89.96 %\\n\",\n      \"tensor([[[[[-4.2467e-02, -3.1397e-02, -1.4779e-02],\\n\",\n      \"           [ 4.7306e-03,  1.2676e-02, -1.0260e-03],\\n\",\n      \"           [ 4.0149e-02,  3.9582e-02,  6.9772e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.7314e-02, -2.6833e-02, -9.1163e-03],\\n\",\n      \"           [ 2.0016e-02,  1.1449e-02,  1.3482e-02],\\n\",\n      \"           [ 5.6196e-02,  4.7798e-02,  2.1395e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.9950e-02, -3.1551e-02, -4.7794e-03],\\n\",\n      \"           [-7.2374e-04,  2.3409e-02,  1.7215e-02],\\n\",\n      \"           [ 4.9115e-02,  3.7804e-02,  2.7516e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.4974e-02, -2.3248e-02,  3.1027e-03],\\n\",\n      \"           [-6.0955e-02, -3.2808e-02, -8.3976e-02],\\n\",\n      \"           [-8.4889e-02, -1.0767e-01, -1.5786e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.0549e-01, -2.9111e-02, -3.3488e-03],\\n\",\n      \"           [-8.2352e-02, -4.4607e-02, -6.0623e-02],\\n\",\n      \"           [-1.0071e-01, -9.2943e-02, -1.3467e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.0343e-01, -6.8270e-02, -3.7693e-02],\\n\",\n      \"           [-1.0078e-01, -4.3111e-02, -3.5656e-02],\\n\",\n      \"           [-1.2468e-01, -1.1156e-01, -1.2561e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.7779e-01, -7.7708e-02,  9.6904e-02],\\n\",\n      \"           [-1.4647e-01, -4.8810e-02,  6.1250e-03],\\n\",\n      \"           [-1.2206e-01, -1.2319e-01, -1.6037e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.4659e-01, -2.6622e-02,  1.5284e-01],\\n\",\n      \"           [-1.1857e-01,  1.0886e-03,  7.0714e-02],\\n\",\n      \"           [-1.1018e-01, -1.3652e-01, -7.1949e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.7031e-01, -6.0810e-02,  1.1786e-01],\\n\",\n      \"           [-7.1102e-02, -3.9008e-02,  2.3945e-02],\\n\",\n      \"           [-1.4454e-01, -1.3456e-01, -1.1803e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.6969e-02, -2.7178e-02, -8.5520e-03],\\n\",\n      \"           [ 5.7721e-04,  7.9416e-04,  1.0056e-03],\\n\",\n      \"           [ 3.8083e-02,  2.5631e-02,  1.3647e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.5031e-02, -3.8913e-02, -2.4892e-02],\\n\",\n      \"           [ 6.9629e-03, -1.4135e-03,  5.6642e-03],\\n\",\n      \"           [ 4.5709e-02,  3.2757e-02,  2.1097e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.0686e-02, -3.2019e-02, -1.3877e-02],\\n\",\n      \"           [ 1.6939e-02,  8.9725e-03,  1.4080e-02],\\n\",\n      \"           [ 4.5749e-02,  3.5367e-02,  1.9856e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.0289e-01, -9.1720e-02, -1.2352e-01],\\n\",\n      \"           [-9.4442e-02, -1.6179e-01, -1.9978e-01],\\n\",\n      \"           [-1.3954e-01, -2.2653e-01, -2.7071e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.0079e-01, -9.9771e-02, -8.3856e-02],\\n\",\n      \"           [-5.3925e-02, -1.1638e-01, -1.7847e-01],\\n\",\n      \"           [-9.8351e-02, -1.9974e-01, -2.3736e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.0143e-01, -8.0974e-02, -6.6232e-02],\\n\",\n      \"           [-4.7512e-02, -1.0146e-01, -1.2259e-01],\\n\",\n      \"           [-7.0489e-02, -1.4538e-01, -1.9699e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.6814e-02, -3.4758e-02,  1.5309e-02],\\n\",\n      \"           [-4.1551e-02,  2.2640e-02,  7.3929e-03],\\n\",\n      \"           [-6.1142e-03,  5.1421e-02,  2.6832e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.5455e-02, -2.6319e-02, -3.6270e-02],\\n\",\n      \"           [-2.3530e-02, -1.9903e-03, -2.4084e-02],\\n\",\n      \"           [ 7.1212e-03,  7.4568e-03, -2.3289e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7722e-02, -5.8393e-02, -1.2533e-02],\\n\",\n      \"           [-3.3392e-02, -1.9095e-02, -9.7914e-03],\\n\",\n      \"           [-1.8602e-03, -3.2658e-02, -5.9225e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3108e-02, -1.5952e-02, -1.7014e-02],\\n\",\n      \"           [ 2.5779e-03, -5.0487e-04,  2.2766e-04],\\n\",\n      \"           [ 1.1832e-02,  1.5862e-02,  1.5783e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0355e-02, -1.3472e-02, -1.2228e-02],\\n\",\n      \"           [ 1.9551e-03,  1.2308e-03,  7.8573e-04],\\n\",\n      \"           [ 1.1057e-02,  1.4545e-02,  1.4581e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.5926e-03, -1.3974e-02, -1.0838e-02],\\n\",\n      \"           [ 1.7068e-03,  2.9465e-03,  2.3496e-03],\\n\",\n      \"           [ 1.3212e-02,  1.2826e-02,  1.3588e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.7108e-03,  8.4007e-04,  1.1196e-03],\\n\",\n      \"           [ 6.5713e-03,  6.5969e-03,  5.5089e-03],\\n\",\n      \"           [ 9.2475e-03,  1.0698e-02,  8.8623e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2566e-03,  2.2292e-03,  6.0573e-04],\\n\",\n      \"           [ 6.5199e-03,  5.9430e-03,  5.3875e-03],\\n\",\n      \"           [ 9.3314e-03,  1.1405e-02,  8.8196e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.0702e-03,  1.1835e-03,  1.1918e-03],\\n\",\n      \"           [ 7.0353e-03,  6.7461e-03,  5.3259e-03],\\n\",\n      \"           [ 9.9229e-03,  1.0645e-02,  8.9742e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 81.00 %\\n\",\n      \"Val loss: 0.442068\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[30,    60] loss: 0.32270\\n\",\n      \"[30,   120] loss: 0.21263\\n\",\n      \"Time elapsed: 0h:59m:16s\\n\",\n      \"train accuracy_score: 91.25 %\\n\",\n      \"tensor([[[[[ 4.2794e-03,  1.0711e-03, -9.8515e-04],\\n\",\n      \"           [ 5.5334e-03,  2.5713e-03,  4.1102e-04],\\n\",\n      \"           [ 5.5503e-03,  2.9318e-03,  1.6767e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1448e-03, -1.6097e-05, -8.4887e-04],\\n\",\n      \"           [ 5.3599e-03,  1.4101e-03,  4.9337e-04],\\n\",\n      \"           [ 5.8758e-03,  2.9318e-03,  1.8284e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9567e-03, -3.5312e-05, -4.4152e-04],\\n\",\n      \"           [ 3.6896e-03,  1.4970e-03,  1.2253e-03],\\n\",\n      \"           [ 5.1014e-03,  2.4874e-03,  2.0188e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.7397e-03, -5.5329e-03, -2.3215e-03],\\n\",\n      \"           [-7.6623e-03, -6.1899e-03, -5.5412e-03],\\n\",\n      \"           [-1.0781e-02, -8.7878e-03, -8.9728e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.2282e-03, -6.1226e-03, -3.2045e-03],\\n\",\n      \"           [-8.7309e-03, -7.6615e-03, -5.9293e-03],\\n\",\n      \"           [-1.2427e-02, -1.0353e-02, -8.3590e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.4261e-03, -6.6465e-03, -4.3954e-03],\\n\",\n      \"           [-9.9897e-03, -7.9926e-03, -7.0821e-03],\\n\",\n      \"           [-1.3576e-02, -1.2208e-02, -8.4209e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.0827e-02, -1.4242e-02, -6.4187e-03],\\n\",\n      \"           [-2.2098e-02, -1.6417e-02, -9.4030e-03],\\n\",\n      \"           [-2.4880e-02, -1.6824e-02, -1.6953e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.7383e-02, -1.2058e-02, -2.0443e-03],\\n\",\n      \"           [-1.8786e-02, -1.1944e-02, -3.0330e-03],\\n\",\n      \"           [-2.1523e-02, -1.3920e-02, -6.7246e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.8443e-02, -1.5464e-02, -1.8302e-03],\\n\",\n      \"           [-1.6092e-02, -1.4501e-02, -1.2676e-03],\\n\",\n      \"           [-2.3094e-02, -1.4690e-02, -4.5256e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.7119e-03, -1.4357e-04, -1.0769e-03],\\n\",\n      \"           [ 2.3244e-03, -1.2971e-04, -1.2962e-03],\\n\",\n      \"           [ 3.3424e-03,  1.0019e-03, -7.4646e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7019e-03, -8.5134e-05, -1.0839e-03],\\n\",\n      \"           [ 2.4680e-03,  3.1717e-04, -9.5200e-04],\\n\",\n      \"           [ 3.1070e-03,  6.4145e-04, -3.3661e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1144e-03,  2.1505e-04, -5.5955e-04],\\n\",\n      \"           [ 2.4916e-03,  7.3156e-04, -5.8000e-05],\\n\",\n      \"           [ 2.8064e-03,  1.1812e-03,  1.1523e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.6528e-03,  5.2022e-03,  7.8010e-03],\\n\",\n      \"           [ 1.8012e-03,  3.7238e-03,  7.6274e-03],\\n\",\n      \"           [ 1.8912e-04,  2.8758e-03,  7.2148e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3872e-03,  1.4708e-03,  6.4731e-03],\\n\",\n      \"           [ 1.1815e-03,  1.4451e-03,  5.5977e-03],\\n\",\n      \"           [-1.0843e-03,  4.1026e-04,  5.5329e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.1940e-05,  1.1864e-04,  3.2620e-03],\\n\",\n      \"           [-4.4085e-04, -5.0140e-04,  6.3142e-03],\\n\",\n      \"           [-1.7024e-03,  3.2761e-04,  5.3828e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.3802e-03, -9.8910e-03, -7.7195e-03],\\n\",\n      \"           [-9.4327e-03, -7.3643e-03, -1.2392e-02],\\n\",\n      \"           [-7.1524e-03, -1.0030e-02, -1.0445e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1483e-02, -1.2338e-02, -9.9472e-03],\\n\",\n      \"           [-9.4944e-03, -1.0706e-02, -1.2960e-02],\\n\",\n      \"           [-5.6880e-03, -9.5905e-03, -1.2353e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1880e-02, -1.4419e-02, -1.3707e-02],\\n\",\n      \"           [-1.0987e-02, -1.2225e-02, -1.4382e-02],\\n\",\n      \"           [-7.1462e-03, -1.1327e-02, -1.1525e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.2779e-04, -4.1602e-04, -6.3569e-04],\\n\",\n      \"           [ 2.3034e-04, -2.7922e-04, -1.5793e-04],\\n\",\n      \"           [ 5.2967e-04,  2.7854e-04,  4.1874e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.9954e-04, -5.3817e-04, -5.9853e-04],\\n\",\n      \"           [ 3.7294e-04, -4.8788e-05, -1.3705e-04],\\n\",\n      \"           [ 5.6884e-04,  6.1488e-04,  4.7818e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1007e-04,  3.7482e-05, -2.1651e-04],\\n\",\n      \"           [ 5.5039e-04,  5.0847e-04,  3.9697e-04],\\n\",\n      \"           [ 9.0865e-04,  8.5781e-04,  9.5369e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.6352e-04,  7.6311e-05,  3.9620e-05],\\n\",\n      \"           [ 2.6658e-04,  1.8010e-04,  5.7627e-05],\\n\",\n      \"           [ 3.0774e-04,  1.5406e-04,  1.3507e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4455e-04,  2.5346e-05, -1.0476e-05],\\n\",\n      \"           [ 2.2859e-04,  6.0107e-05,  1.3242e-05],\\n\",\n      \"           [ 2.5822e-04,  9.4583e-05,  2.4841e-05]],\\n\",\n      \"\\n\",\n      \"          [[ 3.3132e-04,  1.5837e-04,  6.7122e-05],\\n\",\n      \"           [ 2.7486e-04,  1.4082e-04, -2.6514e-05],\\n\",\n      \"           [ 2.4054e-04,  7.9461e-05, -4.6719e-05]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 82.00 %\\n\",\n      \"Val loss: 0.417651\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[31,    60] loss: 0.28740\\n\",\n      \"[31,   120] loss: 0.23527\\n\",\n      \"Time elapsed: 1h:1m:13s\\n\",\n      \"train accuracy_score: 91.54 %\\n\",\n      \"tensor([[[[[ 4.0899e-02, -2.7648e-02, -6.2628e-02],\\n\",\n      \"           [ 2.5011e-01,  1.4323e-01, -2.1349e-03],\\n\",\n      \"           [ 3.1221e-01,  2.0287e-01,  6.5861e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9677e-02, -5.0444e-02, -9.7343e-02],\\n\",\n      \"           [ 2.6937e-01,  1.7472e-01,  2.0849e-02],\\n\",\n      \"           [ 3.7684e-01,  2.7850e-01,  6.8680e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.2091e-02, -2.2602e-02, -8.4768e-02],\\n\",\n      \"           [ 2.0656e-01,  1.3055e-01,  3.1487e-02],\\n\",\n      \"           [ 3.5418e-01,  2.2013e-01,  8.3404e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.2206e-01,  7.5906e-01,  8.3835e-01],\\n\",\n      \"           [ 4.1502e-01,  4.9361e-01,  3.8313e-01],\\n\",\n      \"           [ 2.1559e-01,  9.0084e-02,  1.9814e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.4218e-01,  5.9857e-01,  5.2643e-01],\\n\",\n      \"           [ 4.1528e-01,  4.7964e-01,  2.2334e-01],\\n\",\n      \"           [ 2.1068e-01,  8.9134e-02, -1.0322e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5215e-01,  4.4276e-01,  4.0559e-01],\\n\",\n      \"           [ 1.8136e-01,  2.7482e-01,  1.4453e-01],\\n\",\n      \"           [ 1.5972e-01,  8.4646e-02, -9.3013e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.6805e-01,  5.4930e-02,  5.9580e-01],\\n\",\n      \"           [-4.8392e-01,  2.2987e-01,  1.5290e-01],\\n\",\n      \"           [-4.9894e-01, -7.1123e-02, -1.9035e-01]],\\n\",\n      \"\\n\",\n      \"          [[-6.3005e-01,  1.4291e-02,  5.7557e-01],\\n\",\n      \"           [-3.8353e-01,  1.5864e-01,  4.2721e-01],\\n\",\n      \"           [-2.0018e-01, -2.2102e-02, -1.8165e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.4891e-01, -2.5168e-01,  5.6051e-01],\\n\",\n      \"           [-3.7362e-01,  3.1543e-02,  2.4195e-01],\\n\",\n      \"           [-3.2751e-01, -1.0844e-01, -2.4209e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.1235e-02, -6.8549e-02,  9.2530e-03],\\n\",\n      \"           [ 4.1963e-02,  2.1712e-02,  6.3338e-02],\\n\",\n      \"           [ 1.5382e-01,  1.1670e-01,  1.2033e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.6498e-02, -1.0925e-01, -3.1385e-02],\\n\",\n      \"           [ 6.3270e-02, -1.4670e-02,  4.2129e-02],\\n\",\n      \"           [ 1.6677e-01,  6.2827e-02,  5.8113e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5273e-02, -6.8537e-02, -3.7275e-02],\\n\",\n      \"           [ 9.5575e-02,  2.9303e-02,  3.6024e-03],\\n\",\n      \"           [ 1.7188e-01,  1.2239e-01,  6.3867e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.4420e-01, -2.7067e-01, -2.6597e-01],\\n\",\n      \"           [-2.2923e-01, -3.7237e-01, -5.6025e-01],\\n\",\n      \"           [-4.0362e-01, -6.6015e-01, -8.7013e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.8216e-01, -3.0259e-01, -1.8957e-01],\\n\",\n      \"           [-3.0200e-01, -3.8998e-01, -6.2415e-01],\\n\",\n      \"           [-3.9819e-01, -7.0667e-01, -8.0969e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.8901e-01, -2.8972e-01, -2.1285e-01],\\n\",\n      \"           [-3.6150e-01, -3.9113e-01, -4.5470e-01],\\n\",\n      \"           [-3.7869e-01, -6.1662e-01, -7.5726e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.5085e-01, -3.2288e-01, -2.3905e-01],\\n\",\n      \"           [-2.5326e-01, -1.5761e-01, -2.2200e-01],\\n\",\n      \"           [-2.4807e-01, -5.8031e-02, -1.0467e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.3612e-01, -2.8244e-01, -3.5621e-01],\\n\",\n      \"           [-2.7671e-01, -2.9158e-01, -4.1526e-01],\\n\",\n      \"           [-2.3215e-01, -3.1465e-01, -4.5147e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.3581e-01, -2.9922e-01, -4.2828e-01],\\n\",\n      \"           [-2.3135e-01, -2.8355e-01, -4.7887e-01],\\n\",\n      \"           [-7.8895e-02, -3.6013e-01, -4.3370e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.7124e-02, -4.2285e-02, -4.8383e-02],\\n\",\n      \"           [ 3.6625e-03, -1.0853e-02, -1.4171e-02],\\n\",\n      \"           [ 4.4019e-02,  4.1583e-02,  2.8781e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.7896e-02, -4.6702e-02, -4.0414e-02],\\n\",\n      \"           [ 1.3830e-02,  8.2204e-03, -4.1794e-03],\\n\",\n      \"           [ 6.0750e-02,  5.0408e-02,  1.9458e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.1200e-02, -4.1727e-02, -3.3444e-02],\\n\",\n      \"           [ 1.1435e-02,  6.7883e-03,  8.5283e-04],\\n\",\n      \"           [ 5.9893e-02,  4.9003e-02,  3.0058e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.2154e-02,  1.5293e-02,  5.2647e-03],\\n\",\n      \"           [ 2.8849e-02,  1.8493e-02,  9.4079e-03],\\n\",\n      \"           [ 2.6021e-02,  2.2667e-02,  1.6842e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7985e-02,  2.1107e-02,  8.0601e-03],\\n\",\n      \"           [ 3.9341e-02,  2.8863e-02,  8.6160e-03],\\n\",\n      \"           [ 3.5561e-02,  2.7291e-02,  1.0039e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.4415e-02,  2.6044e-02,  7.6474e-03],\\n\",\n      \"           [ 4.2190e-02,  2.8212e-02,  3.3120e-03],\\n\",\n      \"           [ 4.2025e-02,  3.0157e-02,  4.7432e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 84.00 %\\n\",\n      \"Val loss: 0.391675\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[32,    60] loss: 0.24329\\n\",\n      \"[32,   120] loss: 0.17323\\n\",\n      \"Time elapsed: 1h:3m:9s\\n\",\n      \"train accuracy_score: 92.25 %\\n\",\n      \"tensor([[[[[ 1.9755e-01,  6.7835e-02, -8.8472e-03],\\n\",\n      \"           [ 2.6490e-01,  1.8803e-01,  1.2543e-02],\\n\",\n      \"           [ 3.6770e-01,  2.2113e-01,  6.9633e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 9.6956e-02,  2.6435e-02, -4.7987e-02],\\n\",\n      \"           [ 2.1541e-01,  8.0609e-02, -5.3341e-03],\\n\",\n      \"           [ 2.9494e-01,  1.4311e-01, -1.4478e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2403e-02,  7.5628e-03,  5.2254e-02],\\n\",\n      \"           [ 1.3502e-01,  7.5818e-02,  4.9258e-02],\\n\",\n      \"           [ 2.6043e-01,  1.5338e-01,  5.8084e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.9657e-02,  1.4706e-02,  1.5915e-01],\\n\",\n      \"           [-1.4425e-01,  1.4083e-02, -1.6269e-02],\\n\",\n      \"           [-2.8636e-01, -1.9978e-01, -2.0461e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.0820e-01, -1.4288e-02,  1.4908e-01],\\n\",\n      \"           [-2.0585e-01, -8.0429e-02,  8.1375e-03],\\n\",\n      \"           [-3.6596e-01, -2.0990e-01, -1.8145e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.5903e-01, -1.1536e-01,  9.3254e-02],\\n\",\n      \"           [-2.2408e-01, -7.5072e-02,  1.6299e-02],\\n\",\n      \"           [-1.7543e-01,  3.6646e-02, -5.7458e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.7460e-01, -4.5372e-01, -2.0525e-01],\\n\",\n      \"           [-9.0530e-01, -4.3238e-01, -2.0269e-01],\\n\",\n      \"           [-1.2550e+00, -8.0351e-01, -5.4314e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.2631e-01, -5.8733e-01, -5.8792e-02],\\n\",\n      \"           [-8.1323e-01, -6.0619e-01, -2.7947e-01],\\n\",\n      \"           [-1.1269e+00, -9.0443e-01, -5.1774e-01]],\\n\",\n      \"\\n\",\n      \"          [[-8.8417e-01, -8.8347e-01, -1.5511e-01],\\n\",\n      \"           [-7.5150e-01, -7.3515e-01, -1.8210e-01],\\n\",\n      \"           [-9.4543e-01, -1.0253e+00, -5.1173e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.3391e-02, -4.3133e-03, -1.1814e-02],\\n\",\n      \"           [ 1.2011e-01,  4.5476e-02, -6.3729e-03],\\n\",\n      \"           [ 1.9525e-01,  5.6087e-02, -1.0683e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7367e-02, -5.7472e-03,  8.6623e-03],\\n\",\n      \"           [ 1.3527e-01,  4.0839e-02, -2.5631e-02],\\n\",\n      \"           [ 1.5004e-01,  4.5866e-02, -3.2121e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.0151e-02, -1.3592e-02, -1.0387e-03],\\n\",\n      \"           [ 1.1289e-01,  7.8573e-03, -4.1026e-02],\\n\",\n      \"           [ 1.2884e-01,  4.3084e-02, -8.0272e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.8153e-01,  4.7654e-01,  2.8582e-01],\\n\",\n      \"           [ 6.6707e-01,  3.3220e-01,  2.3338e-01],\\n\",\n      \"           [ 5.1414e-01,  2.9231e-01,  2.0460e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2643e-01,  1.8671e-01,  2.3816e-01],\\n\",\n      \"           [ 4.4650e-01,  1.5872e-01, -9.1463e-03],\\n\",\n      \"           [ 3.4786e-01,  1.7866e-02,  7.1348e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2994e-01,  1.6896e-02,  9.6749e-02],\\n\",\n      \"           [ 2.5364e-01,  7.0075e-02,  1.1308e-01],\\n\",\n      \"           [ 1.8513e-01, -1.4214e-02, -6.5005e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.8881e-01, -2.8971e-01, -3.1923e-01],\\n\",\n      \"           [-2.9721e-01, -2.5397e-01, -2.6164e-01],\\n\",\n      \"           [-3.8694e-01, -2.2179e-01, -3.4252e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.0635e-01, -3.0028e-01, -5.2549e-01],\\n\",\n      \"           [-3.1235e-01, -3.3450e-01, -4.6756e-01],\\n\",\n      \"           [-3.0163e-01, -4.6567e-01, -5.1752e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.8817e-02, -3.4361e-01, -5.9176e-01],\\n\",\n      \"           [-3.1861e-01, -4.5147e-01, -5.1041e-01],\\n\",\n      \"           [-3.9902e-01, -5.4140e-01, -3.9532e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.9808e-02, -1.9553e-02, -3.1954e-02],\\n\",\n      \"           [ 1.6297e-02, -1.0172e-02,  8.8444e-03],\\n\",\n      \"           [ 3.4575e-02,  2.5102e-02,  1.8106e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.5899e-02, -2.7806e-02, -2.9625e-02],\\n\",\n      \"           [ 1.2280e-02,  2.7691e-03,  3.6806e-03],\\n\",\n      \"           [ 2.1386e-02,  2.2324e-02,  2.0004e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.2929e-02, -2.9470e-02, -1.9784e-02],\\n\",\n      \"           [ 1.3050e-02,  7.2002e-03,  8.2500e-03],\\n\",\n      \"           [ 2.2794e-02,  1.6560e-02,  2.3637e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.4212e-02,  2.7901e-02,  1.6216e-02],\\n\",\n      \"           [ 4.2482e-02,  2.7616e-02,  2.3369e-02],\\n\",\n      \"           [ 3.8719e-02,  3.1176e-02,  2.3635e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1692e-02,  2.3239e-02,  1.7097e-02],\\n\",\n      \"           [ 3.9702e-02,  3.0045e-02,  2.5478e-02],\\n\",\n      \"           [ 4.2440e-02,  3.6898e-02,  2.8639e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4456e-02,  1.9810e-02,  8.8173e-03],\\n\",\n      \"           [ 3.0641e-02,  3.1558e-02,  2.4281e-02],\\n\",\n      \"           [ 3.8651e-02,  4.2242e-02,  2.4523e-02]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 81.00 %\\n\",\n      \"Val loss: 0.420683\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[33,    60] loss: 0.18964\\n\",\n      \"[33,   120] loss: 0.19281\\n\",\n      \"Time elapsed: 1h:5m:5s\\n\",\n      \"train accuracy_score: 92.11 %\\n\",\n      \"tensor([[[[[-5.9957e-05,  2.0986e-04,  1.6914e-06],\\n\",\n      \"           [-7.5097e-04, -9.2210e-04, -3.3144e-04],\\n\",\n      \"           [-1.7537e-03, -1.1539e-03, -4.7887e-04]],\\n\",\n      \"\\n\",\n      \"          [[-8.5451e-05,  3.9474e-04,  8.4848e-05],\\n\",\n      \"           [-8.4542e-04, -3.3832e-04, -4.3501e-06],\\n\",\n      \"           [-1.5934e-03, -8.5446e-04,  1.2985e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4588e-04,  3.8150e-04, -1.3732e-04],\\n\",\n      \"           [-5.2034e-04, -2.2333e-04, -1.9434e-04],\\n\",\n      \"           [-1.2523e-03, -3.5707e-04,  2.6299e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.0208e-04, -4.8565e-04, -8.5622e-04],\\n\",\n      \"           [ 1.0802e-03,  4.1824e-04,  1.4855e-03],\\n\",\n      \"           [ 2.6808e-03,  2.5476e-03,  2.8335e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7040e-03,  6.6358e-04, -5.3355e-05],\\n\",\n      \"           [ 1.4033e-03,  1.5544e-03,  1.6027e-03],\\n\",\n      \"           [ 2.8925e-03,  3.1464e-03,  3.5800e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5387e-03,  1.1828e-03,  1.0757e-03],\\n\",\n      \"           [ 2.9101e-03,  2.0338e-03,  1.6219e-03],\\n\",\n      \"           [ 3.7509e-03,  3.0559e-03,  2.6884e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.3654e-03,  1.9748e-03,  8.0106e-04],\\n\",\n      \"           [ 3.4211e-03, -5.7291e-04,  9.0359e-04],\\n\",\n      \"           [ 3.2353e-03,  6.6258e-04,  2.9742e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.6321e-03,  2.8513e-03,  3.7493e-04],\\n\",\n      \"           [ 4.3562e-03,  1.6437e-03,  4.1476e-04],\\n\",\n      \"           [ 3.5781e-03,  1.8617e-03,  2.3276e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 5.9761e-03,  5.2341e-03, -7.8907e-04],\\n\",\n      \"           [ 4.2682e-03,  3.5795e-03, -3.2240e-04],\\n\",\n      \"           [ 4.5393e-03,  3.2814e-03, -4.2220e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.5093e-04, -3.0560e-04, -4.3917e-04],\\n\",\n      \"           [-6.7576e-04, -3.0228e-04, -1.8151e-05],\\n\",\n      \"           [-1.3175e-03, -3.1067e-04, -5.8210e-05]],\\n\",\n      \"\\n\",\n      \"          [[-3.7419e-04,  1.1365e-05, -4.4019e-04],\\n\",\n      \"           [-6.5305e-04,  8.0272e-05,  1.3291e-04],\\n\",\n      \"           [-1.1034e-03, -1.1699e-04,  4.6836e-04]],\\n\",\n      \"\\n\",\n      \"          [[-4.4568e-04, -7.4665e-05, -4.3868e-04],\\n\",\n      \"           [-7.5980e-04,  3.6144e-05,  1.3932e-04],\\n\",\n      \"           [-8.3514e-04,  1.8095e-04,  7.1747e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.4423e-03,  1.8933e-03,  1.8782e-03],\\n\",\n      \"           [ 9.2591e-04,  2.0215e-03,  2.6693e-03],\\n\",\n      \"           [ 1.0991e-03,  2.6009e-03,  3.1491e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4675e-03,  2.1790e-03,  8.2318e-04],\\n\",\n      \"           [ 9.1823e-04,  1.8186e-03,  2.4746e-03],\\n\",\n      \"           [ 1.4475e-04,  1.8608e-03,  2.6306e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7369e-03,  1.4331e-03,  5.1521e-04],\\n\",\n      \"           [ 3.8100e-05,  5.0208e-04,  8.4525e-04],\\n\",\n      \"           [-6.8488e-04,  4.9657e-04,  1.8529e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.6498e-03,  3.8776e-03,  3.7523e-03],\\n\",\n      \"           [ 4.3642e-03,  3.6497e-03,  3.4348e-03],\\n\",\n      \"           [ 5.5659e-03,  4.4673e-03,  3.8066e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.0768e-03,  3.2529e-03,  3.9521e-03],\\n\",\n      \"           [ 3.8541e-03,  3.9375e-03,  4.7475e-03],\\n\",\n      \"           [ 5.0305e-03,  4.8145e-03,  5.0732e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1261e-03,  3.5177e-03,  5.0935e-03],\\n\",\n      \"           [ 3.1970e-03,  4.7575e-03,  5.2194e-03],\\n\",\n      \"           [ 4.0083e-03,  5.6814e-03,  5.0983e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.3020e-04,  2.8490e-04,  3.6743e-04],\\n\",\n      \"           [-1.1911e-04,  5.0377e-07,  1.3431e-04],\\n\",\n      \"           [-2.9257e-04, -1.0706e-04,  5.3625e-06]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4902e-04,  1.9671e-04,  2.5370e-04],\\n\",\n      \"           [-2.2618e-04, -7.2882e-05,  1.8397e-04],\\n\",\n      \"           [-4.2040e-04, -2.6740e-04,  3.1840e-05]],\\n\",\n      \"\\n\",\n      \"          [[-1.8034e-05,  6.6574e-05,  2.4262e-04],\\n\",\n      \"           [-2.1928e-04, -1.0042e-04,  1.4179e-04],\\n\",\n      \"           [-3.7238e-04, -2.5465e-04,  6.6088e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1136e-04, -8.2768e-05, -6.4278e-06],\\n\",\n      \"           [-1.4325e-04, -1.9547e-05, -1.1825e-05],\\n\",\n      \"           [-5.7277e-05,  2.3448e-05,  5.9612e-05]],\\n\",\n      \"\\n\",\n      \"          [[-1.2622e-04, -8.4126e-05,  3.0443e-05],\\n\",\n      \"           [-1.1597e-04, -1.5812e-05,  7.2904e-05],\\n\",\n      \"           [-6.0777e-05, -6.9283e-06,  1.0949e-04]],\\n\",\n      \"\\n\",\n      \"          [[-8.9947e-05, -4.3113e-05,  3.2312e-05],\\n\",\n      \"           [-9.2226e-05,  1.4396e-05,  8.5736e-05],\\n\",\n      \"           [-4.6904e-05,  4.8792e-05,  1.1310e-04]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 81.00 %\\n\",\n      \"Val loss: 0.500183\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"Early stopping in epoch 34\\n\",\n      \"Total time elapsed: 1h:5m:12s\\n\",\n      \"Writing model to disk...\\n\",\n      \"Best result during training: 0.87. Saving model..\\n\",\n      \"Finished fold.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Starting trial 3\\n\",\n      \"torch.Size([1, 193, 229, 193])\\n\",\n      \"175\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0,    60] loss: 0.75768\\n\",\n      \"[0,   120] loss: 0.75380\\n\",\n      \"Time elapsed: 0h:1m:47s\\n\",\n      \"train accuracy_score: 48.78 %\\n\",\n      \"tensor([[[[[ 1.5031e-02, -1.7786e-02, -2.9507e-02],\\n\",\n      \"           [-1.3737e-02, -1.7560e-02, -2.7379e-02],\\n\",\n      \"           [ 1.7342e-03,  7.9124e-04, -2.7345e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7553e-02,  5.2148e-03,  3.1958e-03],\\n\",\n      \"           [ 4.1035e-02,  3.1895e-02, -5.0155e-03],\\n\",\n      \"           [ 3.3984e-03,  3.4218e-04,  2.8400e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2390e-02,  3.6042e-02,  3.9147e-02],\\n\",\n      \"           [ 4.4755e-02,  6.5505e-04,  1.4321e-02],\\n\",\n      \"           [ 2.0333e-02,  2.5171e-04, -1.2182e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.4393e-01,  1.5527e-01,  1.7893e-01],\\n\",\n      \"           [ 1.2992e-01,  1.4508e-01,  1.5688e-01],\\n\",\n      \"           [ 1.4010e-01,  1.4299e-01,  1.6126e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3935e-01,  1.2394e-01,  1.5161e-01],\\n\",\n      \"           [ 1.1848e-01,  1.1292e-01,  1.1977e-01],\\n\",\n      \"           [ 1.2482e-01,  1.1960e-01,  1.2390e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1216e-01,  1.1244e-01,  9.3472e-02],\\n\",\n      \"           [ 9.4796e-02,  1.1072e-01,  8.0141e-02],\\n\",\n      \"           [ 1.1321e-01,  1.1930e-01,  9.5128e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.3172e-02,  3.7233e-02,  5.7235e-04],\\n\",\n      \"           [ 1.8680e-02, -3.4365e-02, -1.8846e-02],\\n\",\n      \"           [-8.6813e-03,  7.2216e-03,  4.2442e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0751e-01, -1.3163e-01, -1.3351e-01],\\n\",\n      \"           [-1.4812e-01, -8.4109e-02,  1.6417e-02],\\n\",\n      \"           [-7.5815e-02, -4.9066e-02,  8.7241e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.2694e-02, -7.7823e-02, -7.7418e-02],\\n\",\n      \"           [-2.1071e-01, -1.5326e-01, -9.0137e-02],\\n\",\n      \"           [-1.8229e-01, -1.9289e-01, -7.4044e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.4650e-01, -1.3796e-01, -2.1959e-01],\\n\",\n      \"           [-5.9827e-02, -1.3875e-01, -1.5970e-01],\\n\",\n      \"           [-1.5116e-01, -2.3870e-01, -2.3987e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.8984e-01, -3.1149e-01, -2.5290e-01],\\n\",\n      \"           [-1.4585e-01, -2.0675e-01, -1.8590e-01],\\n\",\n      \"           [-1.7036e-01, -2.3803e-01, -2.5478e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.6437e-01, -2.3417e-01, -3.0686e-01],\\n\",\n      \"           [-2.0458e-01, -1.9737e-01, -3.0960e-01],\\n\",\n      \"           [-2.4079e-01, -2.6981e-01, -3.5358e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.1325e-02, -3.3756e-02,  9.0687e-03],\\n\",\n      \"           [-3.2897e-02, -8.0066e-03,  6.1088e-03],\\n\",\n      \"           [-9.5972e-03, -1.2618e-02, -5.5996e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.6411e-02, -6.2450e-03,  4.7590e-03],\\n\",\n      \"           [-1.6719e-02, -1.7544e-02,  1.1160e-03],\\n\",\n      \"           [ 2.3193e-03,  1.9411e-03,  4.2495e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.9302e-02,  4.8050e-03,  7.7072e-03],\\n\",\n      \"           [-3.9866e-03, -5.2058e-03,  5.5339e-03],\\n\",\n      \"           [ 3.1139e-03, -1.1279e-02,  7.5397e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.7223e-03, -1.4786e-02, -9.9029e-03],\\n\",\n      \"           [-3.5537e-03, -5.0004e-03, -7.5080e-03],\\n\",\n      \"           [-1.2480e-02, -3.1822e-04, -3.6578e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.3330e-03, -9.2153e-04, -8.9797e-04],\\n\",\n      \"           [-8.0659e-03,  2.7567e-03,  1.1012e-03],\\n\",\n      \"           [ 3.5779e-03,  8.7536e-03,  1.6347e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.1863e-02, -1.5966e-02,  2.7898e-03],\\n\",\n      \"           [-6.9786e-03, -2.5651e-03,  2.9438e-03],\\n\",\n      \"           [-6.3272e-03,  6.9033e-03,  1.1353e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.2194e-03, -1.0302e-02, -7.1594e-03],\\n\",\n      \"           [-1.7208e-02, -1.8454e-02, -1.2508e-02],\\n\",\n      \"           [-2.0294e-02, -1.2948e-02, -2.0660e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.5007e-03, -4.0791e-03, -2.2441e-03],\\n\",\n      \"           [-9.4684e-03, -1.1994e-02, -7.0941e-03],\\n\",\n      \"           [-1.3483e-02, -1.5117e-02, -1.7382e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7902e-03,  2.5004e-03,  3.3997e-04],\\n\",\n      \"           [ 2.5395e-03, -2.0113e-03, -2.7696e-03],\\n\",\n      \"           [-1.0043e-02, -1.1970e-02, -2.3666e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.7341e-01,  1.3084e-01,  8.1934e-02],\\n\",\n      \"           [ 1.3784e-01,  1.1410e-01,  7.7583e-02],\\n\",\n      \"           [ 1.6003e-01,  1.4727e-01,  1.0873e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.7285e-02,  7.2303e-02,  5.9076e-02],\\n\",\n      \"           [ 8.2594e-02,  5.3522e-02,  2.3847e-02],\\n\",\n      \"           [ 1.6077e-01,  1.0316e-01,  1.1896e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.0087e-02,  1.3164e-02,  2.2900e-02],\\n\",\n      \"           [ 1.0416e-01,  6.5634e-02,  3.8733e-02],\\n\",\n      \"           [ 1.4231e-01,  1.1798e-01,  8.0319e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 40.00 %\\n\",\n      \"Val loss: 0.724820\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[1,    60] loss: 0.69001\\n\",\n      \"[1,   120] loss: 0.70524\\n\",\n      \"Time elapsed: 0h:3m:42s\\n\",\n      \"train accuracy_score: 51.51 %\\n\",\n      \"tensor([[[[[ 7.6720e-02,  6.0913e-02,  5.2704e-02],\\n\",\n      \"           [ 5.9847e-02,  5.9979e-02,  5.1579e-02],\\n\",\n      \"           [ 6.3344e-02,  6.5033e-02,  7.6664e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 9.6149e-02,  7.5444e-02,  7.3944e-02],\\n\",\n      \"           [ 8.2643e-02,  7.3518e-02,  6.4896e-02],\\n\",\n      \"           [ 7.8237e-02,  6.9769e-02,  8.1254e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1398e-01,  9.0985e-02,  6.9213e-02],\\n\",\n      \"           [ 9.7716e-02,  9.2086e-02,  7.8338e-02],\\n\",\n      \"           [ 7.5296e-02,  7.6560e-02,  8.0289e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.5121e-02,  5.7867e-02,  5.5142e-02],\\n\",\n      \"           [ 6.0982e-02,  6.9789e-02,  7.3224e-02],\\n\",\n      \"           [ 5.9038e-02,  6.6964e-02,  6.3361e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.3307e-02,  5.3985e-02,  6.1881e-02],\\n\",\n      \"           [ 5.1597e-02,  5.3116e-02,  6.3198e-02],\\n\",\n      \"           [ 5.2208e-02,  5.4251e-02,  5.8614e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.5165e-02,  3.8286e-02,  4.5982e-02],\\n\",\n      \"           [ 4.2866e-02,  3.6750e-02,  4.7074e-02],\\n\",\n      \"           [ 5.1233e-02,  4.9107e-02,  4.8352e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.4060e-01,  3.4982e-01,  3.3762e-01],\\n\",\n      \"           [ 2.9413e-01,  2.9021e-01,  3.0110e-01],\\n\",\n      \"           [ 2.8392e-01,  2.3825e-01,  2.7485e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6987e-01,  2.6286e-01,  2.7888e-01],\\n\",\n      \"           [ 2.9464e-01,  2.7694e-01,  3.1640e-01],\\n\",\n      \"           [ 2.6218e-01,  2.7967e-01,  2.4347e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1946e-01,  2.6668e-01,  2.6784e-01],\\n\",\n      \"           [ 2.3763e-01,  2.4776e-01,  2.3487e-01],\\n\",\n      \"           [ 2.2791e-01,  2.3153e-01,  2.3536e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.8490e-02, -5.4022e-02, -8.9285e-02],\\n\",\n      \"           [-1.4260e-03,  2.6833e-04, -2.1000e-02],\\n\",\n      \"           [ 4.9344e-02,  3.0340e-02, -4.7604e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.9985e-02, -6.0905e-02, -8.9123e-02],\\n\",\n      \"           [-3.4571e-03, -2.3041e-02, -3.8489e-02],\\n\",\n      \"           [ 2.2445e-02,  1.2349e-02, -3.1956e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.8607e-02, -7.0135e-02, -9.4652e-02],\\n\",\n      \"           [-2.0748e-02, -2.2596e-02, -6.4358e-02],\\n\",\n      \"           [-1.7888e-02, -5.0729e-02, -8.6230e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.5259e-02, -1.0272e-02, -6.0850e-03],\\n\",\n      \"           [ 7.7939e-05,  3.0593e-03,  1.2636e-03],\\n\",\n      \"           [-2.1404e-03, -3.0962e-04,  9.3940e-03]],\\n\",\n      \"\\n\",\n      \"          [[-9.2821e-03, -6.0736e-03, -8.6907e-03],\\n\",\n      \"           [ 1.4499e-03,  7.0557e-03,  1.0337e-02],\\n\",\n      \"           [ 4.1987e-03,  1.5091e-02,  1.0127e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.6303e-04,  1.7672e-03, -2.2220e-04],\\n\",\n      \"           [ 1.0120e-02,  9.3010e-03,  1.8658e-03],\\n\",\n      \"           [ 8.8846e-03,  8.9948e-03,  7.4851e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.7811e-03, -1.0106e-02, -6.0701e-03],\\n\",\n      \"           [-6.1380e-03, -5.4152e-03, -2.2450e-03],\\n\",\n      \"           [-6.1919e-03, -3.9475e-03, -2.4445e-03]],\\n\",\n      \"\\n\",\n      \"          [[-6.7434e-03, -5.1484e-03, -2.7531e-03],\\n\",\n      \"           [-4.5228e-03, -2.9647e-03, -7.5163e-04],\\n\",\n      \"           [-5.7122e-03, -3.1105e-03, -1.0921e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.3268e-03, -4.0621e-03, -3.1478e-03],\\n\",\n      \"           [-2.8279e-03, -3.4279e-03, -7.0221e-04],\\n\",\n      \"           [-7.9743e-03, -4.6837e-03, -3.2420e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.0473e-03,  4.1464e-03,  6.8409e-03],\\n\",\n      \"           [ 5.3017e-04, -2.0753e-03, -4.1245e-03],\\n\",\n      \"           [-7.6141e-03, -9.4045e-03, -7.9009e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.0606e-03,  4.9995e-03,  5.3872e-03],\\n\",\n      \"           [ 2.2851e-03, -2.6559e-04,  9.3029e-04],\\n\",\n      \"           [-5.3149e-03, -7.6897e-03, -7.1654e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.7634e-03,  4.0334e-03,  3.9868e-03],\\n\",\n      \"           [ 1.5871e-03,  3.5436e-04,  2.5159e-03],\\n\",\n      \"           [-5.6039e-03, -8.7178e-03, -7.5183e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.2828e-02, -4.5088e-02, -5.9736e-02],\\n\",\n      \"           [-8.7556e-03,  1.0061e-03, -4.0143e-02],\\n\",\n      \"           [ 3.3241e-02,  1.8987e-02, -1.6647e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.8446e-02, -4.2183e-02, -5.3551e-02],\\n\",\n      \"           [-2.1117e-02, -3.6967e-02, -6.0691e-02],\\n\",\n      \"           [ 2.1038e-02, -5.2517e-03, -3.2494e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.2597e-02, -4.0042e-02, -6.0828e-02],\\n\",\n      \"           [ 3.2852e-03, -2.6213e-02, -5.3227e-02],\\n\",\n      \"           [ 1.6427e-02,  1.1224e-03, -3.7236e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 49.00 %\\n\",\n      \"Val loss: 0.690932\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[2,    60] loss: 0.69615\\n\",\n      \"[2,   120] loss: 0.69612\\n\",\n      \"Time elapsed: 0h:5m:36s\\n\",\n      \"train accuracy_score: 52.51 %\\n\",\n      \"tensor([[[[[-3.8795e-02, -2.9400e-02, -1.6036e-02],\\n\",\n      \"           [-3.1571e-02, -3.1844e-02, -2.0536e-02],\\n\",\n      \"           [-3.7567e-02, -3.7932e-02, -3.1910e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.7675e-02, -3.6274e-02, -1.6239e-02],\\n\",\n      \"           [-3.7323e-02, -3.6043e-02, -2.7039e-02],\\n\",\n      \"           [-2.8206e-02, -3.2601e-02, -3.0249e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.4664e-02, -3.6831e-02, -7.9594e-03],\\n\",\n      \"           [-4.1951e-02, -3.5617e-02, -2.0854e-02],\\n\",\n      \"           [-2.5517e-02, -2.3257e-02, -2.7956e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.3149e-05,  6.9698e-03,  1.0467e-02],\\n\",\n      \"           [ 1.3215e-02,  1.1415e-02,  1.4323e-02],\\n\",\n      \"           [ 2.9070e-02,  3.0100e-02,  2.5823e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8396e-02,  2.2917e-02,  2.0338e-02],\\n\",\n      \"           [ 1.7672e-02,  2.3337e-02,  2.3362e-02],\\n\",\n      \"           [ 2.1183e-02,  2.3734e-02,  2.5958e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4028e-02,  2.7148e-02,  2.0580e-02],\\n\",\n      \"           [ 2.2952e-02,  2.5857e-02,  2.3614e-02],\\n\",\n      \"           [ 2.2643e-02,  2.2139e-02,  1.2652e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.4212e-03, -6.6120e-03,  1.6887e-02],\\n\",\n      \"           [ 7.0900e-02,  5.0920e-02,  5.1643e-02],\\n\",\n      \"           [ 7.7572e-02,  8.7671e-02,  7.9482e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0020e-02,  1.5102e-02,  1.7268e-02],\\n\",\n      \"           [ 3.2175e-02,  4.8017e-02,  6.3539e-02],\\n\",\n      \"           [ 3.7286e-02,  4.6413e-02,  2.7370e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.6499e-03, -1.0555e-02, -6.1202e-03],\\n\",\n      \"           [ 7.3783e-03, -2.2174e-03,  4.6929e-03],\\n\",\n      \"           [ 3.1024e-02,  2.1116e-02,  2.3248e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.7030e-03, -2.1970e-03,  2.6013e-03],\\n\",\n      \"           [-4.6702e-03,  7.9938e-04,  1.3135e-02],\\n\",\n      \"           [ 2.8260e-02, -2.4837e-03,  1.6458e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.4218e-02,  5.7554e-03,  1.4715e-02],\\n\",\n      \"           [-1.2475e-02, -4.1519e-03,  2.3024e-02],\\n\",\n      \"           [-1.9393e-02, -2.4702e-02, -1.0087e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4420e-02,  4.2418e-03,  1.6029e-02],\\n\",\n      \"           [ 5.0346e-03, -3.4088e-03,  1.5800e-03],\\n\",\n      \"           [-1.9133e-02, -2.3639e-02,  5.3544e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1324e-02, -4.1122e-03,  9.1950e-04],\\n\",\n      \"           [-9.1410e-03, -5.5596e-03,  4.2732e-03],\\n\",\n      \"           [-7.2232e-03,  1.5401e-03,  6.5472e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.6850e-03, -2.7081e-03,  5.1847e-04],\\n\",\n      \"           [-5.4681e-03, -9.6210e-05, -8.2410e-04],\\n\",\n      \"           [-7.4081e-03, -2.5044e-03,  4.8334e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.4954e-03, -2.0463e-03, -1.4198e-03],\\n\",\n      \"           [-2.9427e-03, -3.0980e-03,  2.1563e-03],\\n\",\n      \"           [ 5.6422e-05,  2.8683e-03,  3.7024e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.9403e-03, -8.8407e-04,  3.3996e-03],\\n\",\n      \"           [ 9.2033e-04,  2.3503e-03, -1.7458e-04],\\n\",\n      \"           [ 2.5256e-03,  2.6204e-04, -2.1436e-04]],\\n\",\n      \"\\n\",\n      \"          [[-4.4066e-04,  1.0599e-03,  1.0764e-04],\\n\",\n      \"           [-1.4425e-03, -1.9858e-03, -3.1552e-04],\\n\",\n      \"           [ 1.2565e-03, -1.3169e-03,  1.4356e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.5561e-04,  1.3074e-03,  5.3636e-04],\\n\",\n      \"           [ 2.3327e-04,  1.0245e-03, -7.3240e-04],\\n\",\n      \"           [ 2.0623e-03,  7.2007e-04,  6.7184e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2721e-03,  1.6177e-05, -1.2276e-03],\\n\",\n      \"           [-1.5624e-04,  4.0701e-04,  5.6184e-04],\\n\",\n      \"           [ 1.5705e-03,  2.0052e-03, -1.6870e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.0804e-04,  3.3769e-04, -1.5462e-03],\\n\",\n      \"           [ 1.0943e-04, -7.1335e-05, -9.0081e-04],\\n\",\n      \"           [ 3.9907e-04,  1.8166e-03,  1.2309e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7715e-04, -5.2117e-04,  1.3630e-03],\\n\",\n      \"           [-6.6469e-04, -1.7774e-04, -1.1021e-03],\\n\",\n      \"           [-1.3491e-03, -1.4035e-03, -2.3225e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.0010e-02,  4.6301e-02,  4.3155e-02],\\n\",\n      \"           [ 4.4654e-03,  1.8034e-02,  2.9018e-02],\\n\",\n      \"           [-8.0656e-04,  1.5758e-02,  1.7587e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7331e-02,  2.9267e-02,  3.0496e-02],\\n\",\n      \"           [ 1.2184e-02,  9.6651e-03,  1.3917e-02],\\n\",\n      \"           [ 7.0348e-03,  9.0785e-03,  2.1405e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5518e-02,  2.3089e-02,  3.0785e-02],\\n\",\n      \"           [ 1.3935e-02,  5.7747e-03,  2.0095e-02],\\n\",\n      \"           [ 2.1885e-03,  1.4158e-02,  1.9238e-02]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 60.00 %\\n\",\n      \"Val loss: 0.665261\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[3,    60] loss: 0.70593\\n\",\n      \"[3,   120] loss: 0.68629\\n\",\n      \"Time elapsed: 0h:7m:31s\\n\",\n      \"train accuracy_score: 52.37 %\\n\",\n      \"tensor([[[[[-5.9853e-02, -4.1473e-02, -3.9030e-03],\\n\",\n      \"           [-5.8350e-02, -3.4855e-02, -6.5389e-03],\\n\",\n      \"           [-3.8765e-02, -2.8278e-02,  1.0173e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.5926e-02, -2.6780e-02,  1.2303e-02],\\n\",\n      \"           [-4.4808e-02, -1.5655e-02, -3.1169e-03],\\n\",\n      \"           [-2.3303e-02, -1.5360e-02,  1.7294e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.5712e-02, -3.3814e-02,  7.1761e-03],\\n\",\n      \"           [-7.0690e-02, -8.9736e-03,  2.4732e-02],\\n\",\n      \"           [-4.7760e-02, -2.6354e-02,  1.3410e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.7482e-02, -6.4985e-02, -5.3910e-02],\\n\",\n      \"           [-7.0032e-02, -4.0290e-02, -4.0229e-02],\\n\",\n      \"           [-6.1010e-02, -5.1729e-02, -3.4273e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.7121e-02, -5.9651e-02, -6.7994e-02],\\n\",\n      \"           [-4.7204e-02, -5.6469e-02, -4.1806e-02],\\n\",\n      \"           [-5.5784e-02, -4.5947e-02, -3.0677e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.5537e-02, -5.1005e-02, -5.3144e-02],\\n\",\n      \"           [-7.0735e-02, -6.2218e-02, -4.8400e-02],\\n\",\n      \"           [-6.4287e-02, -4.5296e-02, -3.8319e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.4160e-04,  7.8305e-02,  9.5123e-02],\\n\",\n      \"           [ 2.3797e-02,  1.3989e-01,  1.7883e-01],\\n\",\n      \"           [ 1.5134e-01,  2.5141e-01,  2.0245e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.5387e-02,  1.2847e-01,  1.2777e-01],\\n\",\n      \"           [ 1.3186e-01,  1.4235e-01,  1.3450e-01],\\n\",\n      \"           [ 2.0935e-01,  2.2315e-01,  1.7990e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.2572e-02,  1.1342e-01,  1.4686e-01],\\n\",\n      \"           [ 6.7002e-02,  2.0135e-01,  1.9706e-01],\\n\",\n      \"           [ 2.0703e-01,  1.9145e-01,  3.2116e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0142e-01,  1.0796e-01,  1.3062e-01],\\n\",\n      \"           [ 7.2609e-02,  1.2044e-01,  1.4003e-01],\\n\",\n      \"           [ 1.0931e-01,  1.4654e-01,  2.3685e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1611e-01,  9.4743e-02,  1.6931e-01],\\n\",\n      \"           [ 9.4514e-02,  1.3166e-01,  1.8448e-01],\\n\",\n      \"           [ 1.0623e-01,  1.8441e-01,  2.8581e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4488e-01,  1.1814e-01,  1.8714e-01],\\n\",\n      \"           [ 1.3570e-01,  1.5927e-01,  2.0536e-01],\\n\",\n      \"           [ 1.5398e-01,  2.0992e-01,  2.6182e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.1937e-02,  5.9780e-03,  9.6380e-03],\\n\",\n      \"           [ 1.1161e-02,  1.3884e-02,  7.9326e-03],\\n\",\n      \"           [ 8.3623e-03,  6.3222e-03,  1.0949e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8648e-03, -1.6192e-06,  4.3694e-03],\\n\",\n      \"           [ 1.0287e-02,  9.4387e-03, -1.2579e-03],\\n\",\n      \"           [ 9.8157e-03, -6.3877e-03, -4.0455e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.1682e-03, -4.4154e-03,  1.6690e-03],\\n\",\n      \"           [-2.3167e-03,  9.5009e-04, -5.5406e-03],\\n\",\n      \"           [ 8.7408e-04, -3.2410e-03, -6.1024e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.7058e-03,  2.0164e-04, -1.4415e-03],\\n\",\n      \"           [-1.5687e-02, -1.2194e-02, -2.6006e-03],\\n\",\n      \"           [-1.8178e-02, -1.5188e-02, -4.9918e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.7629e-04, -5.2850e-03, -2.7546e-03],\\n\",\n      \"           [-1.3701e-02, -5.1964e-03, -3.1777e-03],\\n\",\n      \"           [-9.5327e-03, -8.5428e-03, -2.9858e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.2425e-03, -5.0063e-03, -3.6253e-03],\\n\",\n      \"           [-6.1724e-03, -3.1941e-03, -5.5134e-03],\\n\",\n      \"           [-1.0163e-02, -5.9151e-03, -4.4767e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.6218e-04, -3.4808e-03, -5.0401e-03],\\n\",\n      \"           [ 7.1795e-03,  5.7134e-03, -1.1556e-03],\\n\",\n      \"           [ 1.2933e-02,  1.1314e-02,  1.1358e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1491e-03, -5.8302e-03, -4.1872e-03],\\n\",\n      \"           [ 8.2893e-04, -4.8501e-04, -3.1025e-03],\\n\",\n      \"           [ 9.6299e-03,  7.7020e-03,  5.6209e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.4337e-03, -8.5846e-03, -5.5139e-03],\\n\",\n      \"           [ 4.6746e-03, -1.2861e-03, -3.3856e-03],\\n\",\n      \"           [ 4.2980e-03,  2.5385e-03,  3.0103e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.3598e-02, -2.1811e-02, -1.5285e-02],\\n\",\n      \"           [-5.8508e-02, -3.6545e-02, -3.0290e-03],\\n\",\n      \"           [-5.3372e-02, -3.9167e-02, -2.1164e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.8109e-02, -8.5333e-03, -2.4535e-02],\\n\",\n      \"           [-5.5437e-02, -4.0126e-02, -3.3209e-02],\\n\",\n      \"           [-7.1760e-02, -7.1855e-02, -4.5423e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.1541e-02, -3.8443e-02, -4.1359e-02],\\n\",\n      \"           [-6.4939e-02, -7.5145e-02, -5.8942e-02],\\n\",\n      \"           [-9.4938e-02, -9.5941e-02, -6.8571e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 65.00 %\\n\",\n      \"Val loss: 0.679575\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[4,    60] loss: 0.69971\\n\",\n      \"[4,   120] loss: 0.69384\\n\",\n      \"Time elapsed: 0h:9m:26s\\n\",\n      \"train accuracy_score: 54.09 %\\n\",\n      \"tensor([[[[[-1.3574e-02, -1.0599e-02, -1.7774e-02],\\n\",\n      \"           [-6.5317e-03, -1.3302e-02, -1.5102e-02],\\n\",\n      \"           [-2.7934e-03, -7.2156e-03, -6.1238e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.2610e-02, -1.5149e-02, -1.3792e-02],\\n\",\n      \"           [-1.6693e-03, -1.5558e-03, -7.5910e-03],\\n\",\n      \"           [-1.0852e-03,  3.9487e-03,  9.7129e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.2113e-02, -7.0910e-03, -1.0392e-02],\\n\",\n      \"           [-3.4143e-03, -1.5179e-03,  1.2892e-04],\\n\",\n      \"           [-4.7077e-03,  3.8794e-03,  8.4555e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.9337e-02,  2.2926e-02,  1.9186e-02],\\n\",\n      \"           [ 2.4138e-02,  1.7020e-02,  1.5578e-02],\\n\",\n      \"           [ 1.8206e-02,  1.3431e-02,  1.4545e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.0871e-02,  2.1139e-02,  1.4506e-02],\\n\",\n      \"           [ 2.2067e-02,  1.6097e-02,  1.4401e-02],\\n\",\n      \"           [ 1.6517e-02,  1.1930e-02,  1.4818e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.0664e-02,  2.2992e-02,  1.1858e-02],\\n\",\n      \"           [ 2.2533e-02,  1.8193e-02,  1.1179e-02],\\n\",\n      \"           [ 1.7164e-02,  1.6235e-02,  1.2388e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.2461e-01,  1.2295e-01,  8.2432e-02],\\n\",\n      \"           [ 8.1696e-02,  9.6610e-02,  8.2482e-02],\\n\",\n      \"           [ 6.5006e-02,  9.1279e-02,  6.9270e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.9590e-02,  7.5441e-02,  5.9256e-02],\\n\",\n      \"           [ 6.9250e-02,  6.7447e-02,  6.0072e-02],\\n\",\n      \"           [ 4.3538e-02,  5.7187e-02,  5.8054e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.6349e-02,  7.1630e-02,  4.2229e-02],\\n\",\n      \"           [ 4.1957e-02,  4.0637e-02,  6.3072e-02],\\n\",\n      \"           [ 1.2997e-02,  3.3544e-02,  2.2727e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.2856e-02, -3.1031e-02, -1.6796e-02],\\n\",\n      \"           [-3.1379e-02, -4.9634e-03, -6.2928e-03],\\n\",\n      \"           [-3.5238e-03, -9.1404e-03, -1.9533e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.7194e-03, -3.9985e-03, -5.1000e-03],\\n\",\n      \"           [-1.1761e-02,  1.6651e-03, -1.1865e-02],\\n\",\n      \"           [-2.9254e-03, -4.3307e-03,  4.2577e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4770e-03,  1.3877e-02,  5.9085e-03],\\n\",\n      \"           [ 2.0845e-03,  1.2903e-02,  2.5788e-03],\\n\",\n      \"           [ 1.3576e-02,  1.0944e-02,  1.1341e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.5003e-03, -4.9271e-03, -5.0475e-03],\\n\",\n      \"           [-3.7129e-03, -2.4601e-03, -1.6470e-03],\\n\",\n      \"           [-2.6922e-03, -2.5399e-03, -1.2070e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.0146e-03, -2.7102e-03, -3.1661e-03],\\n\",\n      \"           [-5.2272e-04, -2.2800e-03, -4.4976e-04],\\n\",\n      \"           [-7.9488e-04, -1.2764e-03,  1.3144e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.7123e-03, -3.4559e-03, -3.6382e-03],\\n\",\n      \"           [ 9.7942e-04,  6.9827e-04,  3.1988e-03],\\n\",\n      \"           [-5.3874e-04,  1.6147e-03,  1.4510e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.9235e-03, -4.6268e-03, -1.4106e-03],\\n\",\n      \"           [-4.5861e-03, -3.3833e-03, -1.1689e-03],\\n\",\n      \"           [-2.7242e-03, -2.5312e-03,  2.5496e-04]],\\n\",\n      \"\\n\",\n      \"          [[-4.5594e-03, -3.1093e-03, -4.9346e-04],\\n\",\n      \"           [-3.0616e-03, -1.9740e-03, -5.1987e-04],\\n\",\n      \"           [-4.6525e-04, -1.0618e-03, -4.3250e-04]],\\n\",\n      \"\\n\",\n      \"          [[-3.0063e-03, -2.4688e-03, -3.2069e-04],\\n\",\n      \"           [-3.0518e-03, -9.7586e-04, -3.0419e-05],\\n\",\n      \"           [-1.0144e-03, -8.9023e-05, -1.2719e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.6987e-04,  1.4145e-03,  1.9087e-03],\\n\",\n      \"           [-8.4807e-04, -9.3839e-04,  9.5843e-04],\\n\",\n      \"           [-1.8499e-03, -1.8621e-03, -8.3348e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 9.1636e-04,  2.2045e-03,  9.7227e-04],\\n\",\n      \"           [-6.1792e-05, -8.5334e-04, -8.5722e-04],\\n\",\n      \"           [-3.0097e-03, -3.0900e-03, -2.2263e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.4895e-04,  6.8381e-04, -7.4548e-04],\\n\",\n      \"           [-9.3816e-04, -3.7688e-04, -2.1357e-03],\\n\",\n      \"           [-4.7974e-03, -2.4680e-03, -3.8455e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1854e-02, -2.1496e-02, -2.3883e-02],\\n\",\n      \"           [ 4.7205e-05, -1.0485e-02, -1.2653e-02],\\n\",\n      \"           [ 4.0821e-03,  1.4570e-03, -3.4266e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.4858e-03, -8.6847e-03, -1.8484e-02],\\n\",\n      \"           [ 3.0569e-03, -7.5464e-03, -1.5554e-02],\\n\",\n      \"           [-1.1340e-03, -9.9196e-04, -1.3669e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.5354e-03, -1.2006e-02, -9.3270e-03],\\n\",\n      \"           [-3.3686e-03, -1.5899e-03, -1.0833e-02],\\n\",\n      \"           [-2.6073e-03,  3.0913e-03, -4.5777e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 68.00 %\\n\",\n      \"Val loss: 0.678553\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[5,    60] loss: 0.69589\\n\",\n      \"[5,   120] loss: 0.67778\\n\",\n      \"Time elapsed: 0h:11m:20s\\n\",\n      \"train accuracy_score: 54.81 %\\n\",\n      \"tensor([[[[[-1.2456e-01, -9.3121e-02, -7.8887e-02],\\n\",\n      \"           [-1.0528e-01, -9.6217e-02, -7.2365e-02],\\n\",\n      \"           [-1.1271e-01, -9.5606e-02, -1.0308e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.2483e-01, -9.4978e-02, -6.3485e-02],\\n\",\n      \"           [-1.0258e-01, -9.5920e-02, -7.2034e-02],\\n\",\n      \"           [-1.0710e-01, -1.0280e-01, -9.2641e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1060e-01, -9.9966e-02, -7.8371e-02],\\n\",\n      \"           [-9.7526e-02, -9.3620e-02, -8.4558e-02],\\n\",\n      \"           [-1.0036e-01, -1.0230e-01, -9.9996e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.1637e-02, -6.3869e-02, -6.4250e-02],\\n\",\n      \"           [-6.1224e-02, -6.1802e-02, -6.1332e-02],\\n\",\n      \"           [-4.4851e-02, -5.8088e-02, -4.3456e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.1539e-02, -4.6799e-02, -6.3703e-02],\\n\",\n      \"           [-5.4984e-02, -4.9487e-02, -4.6020e-02],\\n\",\n      \"           [-4.9026e-02, -5.5027e-02, -4.1894e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.6819e-02, -5.2385e-02, -5.1539e-02],\\n\",\n      \"           [-5.9789e-02, -5.3642e-02, -4.3300e-02],\\n\",\n      \"           [-5.6325e-02, -5.7783e-02, -4.5353e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.2674e-02, -7.7940e-02, -1.3299e-01],\\n\",\n      \"           [-2.0939e-02, -5.3629e-02, -1.0501e-01],\\n\",\n      \"           [ 9.5265e-03, -2.6330e-02, -2.0841e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7074e-02,  2.4031e-02, -1.7912e-02],\\n\",\n      \"           [ 1.0231e-03,  7.6597e-03, -2.7258e-02],\\n\",\n      \"           [ 2.7753e-02,  1.9205e-02,  3.2168e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1393e-02,  5.8412e-02,  1.4565e-02],\\n\",\n      \"           [ 5.7430e-02,  1.4678e-01,  2.8268e-02],\\n\",\n      \"           [ 8.7565e-02,  6.0090e-02,  6.4418e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.2144e-02,  2.0205e-02,  1.7509e-02],\\n\",\n      \"           [-1.8755e-02, -1.9974e-02,  3.6699e-02],\\n\",\n      \"           [-1.7952e-02,  3.3678e-02,  7.0892e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1801e-02, -2.1059e-02,  1.6031e-02],\\n\",\n      \"           [-2.3697e-02, -3.4469e-03,  4.6348e-02],\\n\",\n      \"           [-2.9282e-02, -4.9168e-04,  6.3527e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0980e-02,  4.8972e-03,  1.1690e-02],\\n\",\n      \"           [-1.1924e-02, -7.5737e-03,  2.8621e-02],\\n\",\n      \"           [-2.1878e-02,  4.3766e-02,  9.9871e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 9.5086e-03,  1.5254e-02,  1.8027e-02],\\n\",\n      \"           [-5.4120e-03, -9.5788e-03, -6.5164e-03],\\n\",\n      \"           [-6.5328e-03, -1.6390e-02, -1.4299e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.7452e-03,  1.1562e-02,  1.0838e-02],\\n\",\n      \"           [-6.7489e-03, -5.9786e-03, -7.7111e-03],\\n\",\n      \"           [-7.1134e-03, -1.6064e-02, -1.8488e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.4371e-03, -4.0525e-05,  4.3266e-03],\\n\",\n      \"           [-7.1356e-03, -6.6839e-03, -1.4287e-02],\\n\",\n      \"           [-1.2139e-02, -2.1270e-02, -1.8131e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.5138e-04,  3.9084e-03, -4.9815e-04],\\n\",\n      \"           [-1.2686e-03, -1.5299e-03,  1.3545e-04],\\n\",\n      \"           [-5.9484e-04, -2.6496e-03,  6.9159e-04]],\\n\",\n      \"\\n\",\n      \"          [[-2.0389e-03, -2.4236e-03,  1.3236e-03],\\n\",\n      \"           [-3.1989e-03, -4.2834e-04,  1.2050e-04],\\n\",\n      \"           [-2.2352e-03, -1.3287e-03,  1.3475e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.9631e-03, -4.1411e-03,  1.9729e-05],\\n\",\n      \"           [-4.6323e-03, -3.5937e-03, -3.2626e-04],\\n\",\n      \"           [-6.1282e-03, -4.9659e-03,  8.9333e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3093e-04, -4.1637e-03, -6.3636e-03],\\n\",\n      \"           [-1.8635e-03, -3.5544e-03, -3.6271e-03],\\n\",\n      \"           [ 1.9676e-03, -1.1377e-03, -2.0021e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.7682e-04, -3.6721e-03, -2.8962e-03],\\n\",\n      \"           [ 2.4042e-03,  1.2508e-03,  8.5692e-04],\\n\",\n      \"           [ 7.3102e-03,  6.4453e-03,  2.0774e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6155e-03,  1.1061e-03,  2.2534e-04],\\n\",\n      \"           [ 5.0831e-03,  4.3784e-03,  2.5508e-03],\\n\",\n      \"           [ 1.1263e-02,  9.8712e-03,  7.1852e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.1331e-02,  9.2932e-02,  9.8702e-02],\\n\",\n      \"           [ 4.1514e-02,  5.5675e-02,  6.6216e-02],\\n\",\n      \"           [ 1.2198e-02,  2.8379e-02,  4.7966e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.5884e-02,  9.4208e-02,  9.4464e-02],\\n\",\n      \"           [ 4.9052e-02,  7.9597e-02,  7.5956e-02],\\n\",\n      \"           [ 3.1335e-02,  4.8313e-02,  6.5908e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.5554e-02,  9.4885e-02,  1.0635e-01],\\n\",\n      \"           [ 6.1940e-02,  8.3798e-02,  7.6761e-02],\\n\",\n      \"           [ 3.9690e-02,  7.0238e-02,  7.0330e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 73.00 %\\n\",\n      \"Val loss: 0.664026\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[6,    60] loss: 0.67298\\n\",\n      \"[6,   120] loss: 0.66728\\n\",\n      \"Time elapsed: 0h:13m:14s\\n\",\n      \"train accuracy_score: 57.39 %\\n\",\n      \"tensor([[[[[ 5.1870e-02,  3.1361e-02,  1.3491e-02],\\n\",\n      \"           [ 3.8304e-02,  2.3714e-02,  1.6824e-02],\\n\",\n      \"           [ 1.8859e-02,  3.1489e-02,  1.5576e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.1186e-02,  3.5330e-02,  1.6530e-02],\\n\",\n      \"           [ 5.6015e-02,  4.1515e-02,  1.3578e-02],\\n\",\n      \"           [ 4.1319e-02,  2.2220e-02,  1.2944e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 4.2946e-02,  3.5852e-02,  1.4051e-02],\\n\",\n      \"           [ 5.2649e-02,  4.5639e-02,  6.5201e-03],\\n\",\n      \"           [ 5.0016e-02,  4.1672e-02,  1.3650e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.4548e-02,  6.4756e-02,  6.3319e-02],\\n\",\n      \"           [ 7.7364e-02,  6.0599e-02,  4.9339e-02],\\n\",\n      \"           [ 5.9467e-02,  4.9416e-02,  3.8273e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.8189e-02,  5.9238e-02,  5.7406e-02],\\n\",\n      \"           [ 5.9539e-02,  5.7866e-02,  4.9415e-02],\\n\",\n      \"           [ 5.1423e-02,  4.4649e-02,  2.9592e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 8.2260e-02,  6.9865e-02,  5.9341e-02],\\n\",\n      \"           [ 6.7378e-02,  5.5935e-02,  4.7365e-02],\\n\",\n      \"           [ 5.5660e-02,  4.3041e-02,  2.8093e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.5497e-01,  1.1835e-01,  1.0994e-01],\\n\",\n      \"           [ 1.3798e-01,  8.3667e-02,  6.0017e-02],\\n\",\n      \"           [ 7.3776e-02,  6.0396e-02,  6.8819e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1978e-01,  8.7351e-02,  6.1429e-02],\\n\",\n      \"           [ 1.0915e-01,  6.0484e-02,  4.8877e-02],\\n\",\n      \"           [ 3.3907e-02,  3.7878e-02,  5.7551e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2980e-01,  9.5713e-02,  5.7593e-02],\\n\",\n      \"           [ 7.6907e-02,  2.9392e-02,  1.9192e-02],\\n\",\n      \"           [ 7.4914e-03,  3.8370e-02, -1.0934e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.3931e-02, -3.3735e-02, -3.1642e-02],\\n\",\n      \"           [-4.2219e-02, -3.2115e-02, -4.0146e-02],\\n\",\n      \"           [-3.3062e-02, -4.8870e-02, -5.6167e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.0878e-02, -3.8266e-02, -5.0834e-02],\\n\",\n      \"           [-2.4704e-02, -3.8469e-02, -4.0920e-02],\\n\",\n      \"           [-2.9951e-02, -4.9618e-02, -5.8997e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.0913e-02, -2.1200e-02, -6.3478e-02],\\n\",\n      \"           [-2.4614e-02, -5.0578e-02, -5.8713e-02],\\n\",\n      \"           [-3.7523e-02, -6.7552e-02, -8.8519e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.7881e-03, -5.6012e-03, -1.2998e-03],\\n\",\n      \"           [-2.5001e-03,  4.7637e-03,  2.8240e-03],\\n\",\n      \"           [-2.2920e-03,  4.4383e-03,  4.8151e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.1568e-03, -1.5737e-03,  3.9988e-03],\\n\",\n      \"           [-4.8384e-03,  2.9088e-03,  9.4546e-03],\\n\",\n      \"           [ 2.5848e-03,  7.9615e-03,  1.0275e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.1718e-03,  5.2010e-03,  4.3704e-03],\\n\",\n      \"           [ 1.4069e-04,  5.7622e-03,  6.2410e-03],\\n\",\n      \"           [ 1.5703e-02,  1.5423e-02,  1.3719e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.7179e-03, -6.6356e-03, -6.9427e-04],\\n\",\n      \"           [-7.9395e-03, -5.6443e-03, -2.1499e-03],\\n\",\n      \"           [-8.1285e-03, -4.1874e-03, -1.9604e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.4076e-03, -2.5222e-03, -1.2868e-03],\\n\",\n      \"           [-3.1841e-03, -2.5728e-03,  1.2178e-04],\\n\",\n      \"           [-3.0015e-03, -2.6520e-03, -1.8483e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.4754e-03,  4.8205e-04,  4.4331e-04],\\n\",\n      \"           [-2.7284e-03, -2.0645e-03, -1.7414e-04],\\n\",\n      \"           [-2.6807e-04, -2.1702e-03, -3.2477e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.2554e-03, -4.3617e-03,  9.9463e-04],\\n\",\n      \"           [-1.2623e-02, -7.1647e-03, -2.3682e-03],\\n\",\n      \"           [-1.5370e-02, -8.6157e-03, -4.2287e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.1698e-03, -2.9424e-03,  1.1131e-03],\\n\",\n      \"           [-1.2486e-02, -7.8468e-03, -2.8435e-03],\\n\",\n      \"           [-1.6448e-02, -1.1890e-02, -7.9467e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.7274e-03, -6.1790e-03, -2.5695e-03],\\n\",\n      \"           [-1.2895e-02, -9.0242e-03, -6.7790e-03],\\n\",\n      \"           [-1.7141e-02, -1.2407e-02, -9.3987e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.9216e-02, -3.8373e-02, -4.8773e-02],\\n\",\n      \"           [-1.3516e-02, -1.9170e-02, -2.5479e-02],\\n\",\n      \"           [ 1.2195e-02, -8.8758e-03, -1.3710e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.7500e-02, -3.0216e-02, -3.7883e-02],\\n\",\n      \"           [-8.8112e-03, -2.2570e-02, -3.3818e-02],\\n\",\n      \"           [ 7.6414e-03, -1.1064e-02, -2.3871e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.2537e-02, -2.9687e-02, -4.0097e-02],\\n\",\n      \"           [ 1.8862e-03, -1.8069e-02, -2.7820e-02],\\n\",\n      \"           [ 9.0372e-03, -4.4404e-03, -1.7193e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 77.00 %\\n\",\n      \"Val loss: 0.657248\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[7,    60] loss: 0.67728\\n\",\n      \"[7,   120] loss: 0.66295\\n\",\n      \"Time elapsed: 0h:15m:9s\\n\",\n      \"train accuracy_score: 58.25 %\\n\",\n      \"tensor([[[[[ 8.0890e-03,  8.4579e-03, -7.3589e-04],\\n\",\n      \"           [ 8.6427e-04,  1.3666e-02,  1.4253e-02],\\n\",\n      \"           [ 1.0335e-02,  6.6749e-03, -1.7185e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2628e-02,  2.4683e-02,  8.9435e-03],\\n\",\n      \"           [ 1.8254e-02,  2.2132e-02,  1.1847e-02],\\n\",\n      \"           [ 3.3633e-02,  3.6456e-02,  9.5635e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1677e-02,  2.1421e-02,  1.1663e-02],\\n\",\n      \"           [ 1.5139e-02,  1.9442e-02,  5.2209e-03],\\n\",\n      \"           [ 2.2657e-02,  2.0714e-02,  1.7745e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.7436e-02, -9.6982e-03, -4.8694e-03],\\n\",\n      \"           [-1.9230e-02, -6.9839e-03,  2.4895e-03],\\n\",\n      \"           [-1.3178e-02, -3.1630e-03,  6.6100e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.0176e-02, -1.5276e-02, -1.7479e-02],\\n\",\n      \"           [-2.4488e-02, -1.3680e-02, -2.4676e-03],\\n\",\n      \"           [-1.6927e-02, -1.2908e-02,  3.4983e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.0071e-02, -3.1599e-02, -2.5216e-02],\\n\",\n      \"           [-2.9497e-02, -2.4196e-02, -7.4991e-03],\\n\",\n      \"           [-2.4413e-02, -1.4057e-02, -3.2525e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.4736e-01, -2.6540e-01, -2.9283e-01],\\n\",\n      \"           [-2.4664e-01, -2.6135e-01, -2.9562e-01],\\n\",\n      \"           [-2.3224e-01, -2.4940e-01, -2.4829e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.1702e-01, -2.0143e-01, -2.5881e-01],\\n\",\n      \"           [-2.0722e-01, -2.2408e-01, -2.6201e-01],\\n\",\n      \"           [-1.8809e-01, -2.2988e-01, -2.4492e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.8816e-01, -1.8419e-01, -2.1750e-01],\\n\",\n      \"           [-1.6489e-01, -1.5382e-01, -1.9859e-01],\\n\",\n      \"           [-1.4548e-01, -1.8739e-01, -2.0240e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.0108e-02,  8.1011e-02,  7.5914e-02],\\n\",\n      \"           [ 5.7697e-02,  5.6823e-02,  7.5227e-02],\\n\",\n      \"           [ 3.5152e-02,  3.4259e-02,  9.3468e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.2482e-02,  5.7869e-02,  5.9468e-02],\\n\",\n      \"           [ 3.2845e-02,  2.5028e-02,  5.0243e-02],\\n\",\n      \"           [ 4.5394e-02,  5.8267e-02,  8.8397e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.6246e-02,  5.3780e-02,  4.6469e-02],\\n\",\n      \"           [ 2.3036e-02,  1.9456e-02,  6.3645e-02],\\n\",\n      \"           [ 6.0739e-02,  6.4352e-02,  8.7521e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.9778e-03, -3.8481e-03, -3.1977e-03],\\n\",\n      \"           [-6.4316e-03, -7.1607e-03, -4.6643e-03],\\n\",\n      \"           [-9.8435e-03, -1.5210e-02, -5.7487e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.2230e-04,  5.3797e-04,  3.2470e-04],\\n\",\n      \"           [-2.3892e-03, -6.5117e-03, -1.5639e-03],\\n\",\n      \"           [-1.0454e-02, -1.1464e-02, -8.5508e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6644e-03,  1.6928e-03, -8.8777e-04],\\n\",\n      \"           [-5.2697e-04, -1.3468e-03, -1.6520e-03],\\n\",\n      \"           [-4.2077e-03, -7.1252e-03, -2.7488e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.8551e-03, -9.0391e-04, -1.1682e-03],\\n\",\n      \"           [-2.5869e-03, -5.8046e-05, -1.7057e-03],\\n\",\n      \"           [-5.7363e-03, -4.7161e-03, -2.6736e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.3758e-03, -7.7604e-04, -2.4657e-03],\\n\",\n      \"           [-5.3118e-03, -1.4180e-03, -1.0809e-03],\\n\",\n      \"           [-4.6170e-03, -3.5602e-03, -2.5841e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.9149e-04, -2.6458e-03, -3.7914e-03],\\n\",\n      \"           [-2.5188e-03, -3.0428e-03, -2.7629e-03],\\n\",\n      \"           [-9.3404e-03, -5.0421e-03, -1.7837e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.0460e-03, -4.2842e-03, -3.0937e-03],\\n\",\n      \"           [ 9.1042e-04,  1.6958e-03,  5.0723e-03],\\n\",\n      \"           [ 8.4864e-03,  8.7933e-03,  9.5737e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.0668e-03, -4.9385e-03, -3.1603e-03],\\n\",\n      \"           [ 3.5545e-03,  2.3084e-03,  3.7772e-03],\\n\",\n      \"           [ 7.4204e-03,  9.6025e-03,  1.1773e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.5064e-03, -4.0856e-03, -3.0707e-03],\\n\",\n      \"           [ 4.9769e-03,  2.6598e-03,  3.7723e-03],\\n\",\n      \"           [ 1.0120e-02,  8.5663e-03,  8.8780e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0958e-02,  7.5211e-03,  1.1028e-02],\\n\",\n      \"           [ 6.7236e-03,  2.4551e-03,  4.8067e-03],\\n\",\n      \"           [-9.8742e-05,  5.4723e-03,  2.9684e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8230e-02,  2.4709e-02,  1.4035e-02],\\n\",\n      \"           [ 2.0111e-02,  1.9456e-02,  1.3967e-02],\\n\",\n      \"           [ 9.4405e-03,  6.4039e-03,  3.6692e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3533e-02,  2.1951e-02,  3.3215e-02],\\n\",\n      \"           [ 1.9448e-02,  2.6062e-02,  2.1652e-02],\\n\",\n      \"           [ 1.7907e-02,  2.1007e-02,  9.0616e-03]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 40.00 %\\n\",\n      \"Val loss: 0.724308\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[8,    60] loss: 0.66183\\n\",\n      \"[8,   120] loss: 0.66732\\n\",\n      \"Time elapsed: 0h:17m:4s\\n\",\n      \"train accuracy_score: 60.40 %\\n\",\n      \"tensor([[[[[-4.5898e-01, -4.2469e-01, -3.2149e-01],\\n\",\n      \"           [-4.4406e-01, -3.6909e-01, -3.1528e-01],\\n\",\n      \"           [-3.8025e-01, -3.3015e-01, -3.1509e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.5478e-01, -4.0312e-01, -3.0893e-01],\\n\",\n      \"           [-4.2364e-01, -3.6417e-01, -3.0113e-01],\\n\",\n      \"           [-3.7562e-01, -3.0870e-01, -2.9705e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.0481e-01, -3.7183e-01, -2.7710e-01],\\n\",\n      \"           [-3.8815e-01, -3.0704e-01, -2.8374e-01],\\n\",\n      \"           [-3.3654e-01, -2.9238e-01, -2.6804e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.3490e-02, -1.0044e-02, -3.5974e-03],\\n\",\n      \"           [-2.3430e-02,  7.8267e-04,  9.6504e-03],\\n\",\n      \"           [-6.1395e-03,  4.3829e-03,  1.2245e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.6838e-02, -1.3803e-02, -9.6912e-03],\\n\",\n      \"           [-3.8878e-02, -8.8267e-03, -1.4168e-03],\\n\",\n      \"           [-1.2691e-02, -1.0685e-02,  1.3742e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.5750e-02, -3.4016e-02, -2.6131e-02],\\n\",\n      \"           [-4.1142e-02, -2.2888e-02, -1.1803e-02],\\n\",\n      \"           [-2.7328e-02, -2.1088e-02, -7.6550e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.8773e-01, -1.4189e-01, -1.2126e-01],\\n\",\n      \"           [-1.4652e-01, -1.4809e-01, -1.1637e-01],\\n\",\n      \"           [-1.4947e-01, -1.2877e-01, -1.0165e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.6145e-01, -1.2871e-01, -1.0315e-01],\\n\",\n      \"           [-1.6115e-01, -1.1695e-01, -4.5031e-02],\\n\",\n      \"           [-1.0913e-01, -1.0768e-01, -6.0225e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.5080e-01, -1.0817e-01, -9.0939e-02],\\n\",\n      \"           [-1.4747e-01, -6.2298e-02, -5.2189e-02],\\n\",\n      \"           [-5.2230e-02, -9.7190e-02, -2.4786e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.7772e-02,  5.0630e-02,  6.5939e-02],\\n\",\n      \"           [ 5.0754e-02,  4.4040e-02,  7.0442e-02],\\n\",\n      \"           [ 3.3227e-02,  6.7522e-02,  9.8284e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.2020e-02,  7.0844e-02,  1.0529e-01],\\n\",\n      \"           [ 4.7236e-02,  4.6661e-02,  9.2664e-02],\\n\",\n      \"           [ 6.0320e-02,  8.4693e-02,  1.0898e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.5298e-02,  7.3717e-02,  1.2132e-01],\\n\",\n      \"           [ 5.0843e-02,  7.3618e-02,  1.2729e-01],\\n\",\n      \"           [ 7.1659e-02,  1.0593e-01,  1.3796e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.9680e-03,  1.9645e-03, -4.1326e-03],\\n\",\n      \"           [ 2.0983e-03, -5.0985e-03, -8.1449e-03],\\n\",\n      \"           [ 1.7343e-03, -7.2124e-03, -5.5968e-03]],\\n\",\n      \"\\n\",\n      \"          [[-9.2642e-04,  6.6456e-04,  3.2873e-03],\\n\",\n      \"           [ 3.1789e-03, -2.3850e-03, -7.4328e-03],\\n\",\n      \"           [ 4.5182e-04, -8.7428e-03, -7.4152e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.4919e-03,  1.3672e-03, -1.9760e-03],\\n\",\n      \"           [-1.8813e-03,  5.6221e-04, -2.4382e-03],\\n\",\n      \"           [ 1.4591e-03, -7.9029e-06, -4.2541e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.4946e-03,  7.3686e-03,  4.6124e-03],\\n\",\n      \"           [ 9.7435e-04,  3.3053e-03,  2.9960e-03],\\n\",\n      \"           [ 1.4643e-03,  3.8261e-03,  3.2038e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 9.8679e-04,  3.4035e-04, -3.9576e-04],\\n\",\n      \"           [-1.1231e-03,  2.6570e-04,  2.5835e-03],\\n\",\n      \"           [-1.5229e-03, -1.1580e-04,  1.8533e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 6.0522e-04, -8.5069e-04, -7.0292e-04],\\n\",\n      \"           [ 4.8200e-04,  8.4580e-05,  3.3697e-03],\\n\",\n      \"           [-1.3872e-03,  1.0249e-03,  4.4651e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.7368e-02,  8.3797e-03,  2.2472e-03],\\n\",\n      \"           [ 1.2925e-02,  6.8547e-03,  5.1516e-03],\\n\",\n      \"           [ 8.3789e-03,  3.0329e-03,  2.4579e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4345e-02,  6.7674e-03,  4.9249e-03],\\n\",\n      \"           [ 1.5151e-02,  6.4699e-03,  2.4677e-03],\\n\",\n      \"           [ 1.2989e-02,  6.4146e-03,  5.4751e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5363e-02,  8.4224e-03,  6.8285e-03],\\n\",\n      \"           [ 1.5138e-02,  8.4172e-03,  7.1134e-03],\\n\",\n      \"           [ 1.5151e-02,  6.1320e-03,  6.2922e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.7813e-02,  3.6631e-02,  3.2437e-02],\\n\",\n      \"           [ 1.9229e-02,  2.3982e-02,  2.6299e-02],\\n\",\n      \"           [ 2.4715e-02,  2.0014e-02,  1.4683e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3229e-02,  1.6671e-02,  2.5981e-02],\\n\",\n      \"           [ 2.0505e-02,  2.3007e-02,  1.8954e-02],\\n\",\n      \"           [ 2.2161e-02,  2.4712e-02,  1.7076e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0992e-02,  1.5951e-02,  3.0524e-02],\\n\",\n      \"           [ 2.0285e-02,  2.2802e-02,  2.0175e-02],\\n\",\n      \"           [ 9.6183e-03,  1.4258e-02,  1.4563e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 50.00 %\\n\",\n      \"Val loss: 0.678893\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[9,    60] loss: 0.63989\\n\",\n      \"[9,   120] loss: 0.63346\\n\",\n      \"Time elapsed: 0h:18m:58s\\n\",\n      \"train accuracy_score: 65.85 %\\n\",\n      \"tensor([[[[[ 9.6072e-01,  8.5998e-01,  6.5087e-01],\\n\",\n      \"           [ 9.0124e-01,  7.3450e-01,  5.8231e-01],\\n\",\n      \"           [ 8.0144e-01,  6.7743e-01,  6.2851e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 9.4570e-01,  7.6967e-01,  5.9825e-01],\\n\",\n      \"           [ 8.9598e-01,  7.4679e-01,  5.7857e-01],\\n\",\n      \"           [ 7.8328e-01,  6.5269e-01,  6.4854e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 8.6242e-01,  7.1708e-01,  5.9589e-01],\\n\",\n      \"           [ 8.2516e-01,  6.7041e-01,  5.7541e-01],\\n\",\n      \"           [ 7.4865e-01,  6.6972e-01,  6.1814e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.7516e-02,  1.8216e-02,  4.8767e-02],\\n\",\n      \"           [ 3.4524e-02,  2.6412e-02,  3.8715e-02],\\n\",\n      \"           [ 2.5615e-02,  7.2642e-03,  1.3324e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0355e-02,  1.3444e-02,  4.8929e-02],\\n\",\n      \"           [ 3.5855e-02,  2.3040e-02,  2.2056e-02],\\n\",\n      \"           [ 3.1484e-02,  2.2853e-02,  5.5569e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.0048e-02,  5.4270e-02,  5.2247e-02],\\n\",\n      \"           [ 3.3213e-02,  2.7729e-02,  2.7979e-02],\\n\",\n      \"           [ 5.5683e-02,  3.3457e-02,  1.9722e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.2532e-01,  3.3266e-01,  2.9789e-01],\\n\",\n      \"           [ 2.9540e-01,  2.7409e-01,  2.8342e-01],\\n\",\n      \"           [ 2.0966e-01,  2.0089e-01,  2.2571e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3815e-01,  1.7713e-01,  2.1065e-01],\\n\",\n      \"           [ 2.2177e-01,  1.9294e-01,  1.8780e-01],\\n\",\n      \"           [ 8.9293e-02,  1.4808e-01,  1.4869e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.7469e-01,  2.0647e-01,  1.3318e-01],\\n\",\n      \"           [ 1.3191e-01,  6.9942e-02,  9.7313e-02],\\n\",\n      \"           [ 3.5340e-02,  8.0869e-02,  3.5707e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.5590e-02, -3.8222e-02, -3.9200e-02],\\n\",\n      \"           [-3.4687e-02, -3.2028e-02, -8.0455e-02],\\n\",\n      \"           [ 4.0305e-02,  1.7466e-02, -8.5002e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.6147e-02, -2.9968e-02, -5.9009e-02],\\n\",\n      \"           [-2.2777e-02, -1.8028e-02, -8.2280e-02],\\n\",\n      \"           [ 1.2378e-02,  1.2894e-03, -8.5808e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.8753e-03,  2.2840e-02, -4.8033e-02],\\n\",\n      \"           [-2.3822e-02, -1.0615e-02, -1.0274e-01],\\n\",\n      \"           [-4.5274e-02, -1.0334e-01, -1.9027e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.3478e-02, -2.8901e-02, -4.1180e-02],\\n\",\n      \"           [-2.4320e-02, -1.0743e-02, -2.1591e-02],\\n\",\n      \"           [-2.0808e-02, -1.3695e-02, -1.2379e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.6793e-02, -1.7585e-02, -2.9254e-02],\\n\",\n      \"           [-6.4273e-03,  6.6563e-03, -9.6553e-03],\\n\",\n      \"           [ 8.9373e-04,  6.1470e-03,  1.2802e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1284e-02,  4.1110e-04, -1.2353e-02],\\n\",\n      \"           [ 7.8666e-03,  8.6950e-03, -4.6366e-03],\\n\",\n      \"           [ 9.4271e-03,  2.1035e-02,  2.0500e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.5065e-03, -1.7991e-03, -9.2816e-04],\\n\",\n      \"           [ 4.1538e-03,  1.5952e-03, -1.9339e-03],\\n\",\n      \"           [ 9.2065e-03,  7.4688e-03,  2.3409e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.2327e-04,  3.0054e-04,  1.7569e-03],\\n\",\n      \"           [ 6.1596e-03,  2.6802e-03,  1.0353e-03],\\n\",\n      \"           [ 7.0358e-03,  8.0431e-03,  3.3334e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1980e-03,  8.9647e-03,  8.0231e-03],\\n\",\n      \"           [ 4.2210e-03,  6.7448e-03,  4.4073e-03],\\n\",\n      \"           [ 1.0379e-02,  6.2448e-03,  3.0668e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.5322e-02, -1.6574e-02, -3.9767e-03],\\n\",\n      \"           [-2.9913e-02, -1.3931e-02, -1.0562e-02],\\n\",\n      \"           [-2.6706e-02, -2.2926e-02, -1.2878e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.6668e-02, -8.1812e-03, -6.4226e-03],\\n\",\n      \"           [-3.3693e-02, -1.6491e-02, -1.2768e-02],\\n\",\n      \"           [-2.9617e-02, -2.8805e-02, -1.9922e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.1768e-02, -2.2748e-02, -1.8036e-02],\\n\",\n      \"           [-3.0668e-02, -1.4754e-02, -1.7006e-02],\\n\",\n      \"           [-4.2746e-02, -2.5994e-02, -2.6951e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.4259e-02, -4.4474e-02, -3.4155e-02],\\n\",\n      \"           [-3.2913e-02, -2.7161e-02, -2.2455e-02],\\n\",\n      \"           [-2.6490e-02, -1.4328e-02, -1.2452e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.9309e-02, -3.8036e-02, -2.6327e-02],\\n\",\n      \"           [-3.0336e-02, -2.4978e-02, -2.6070e-02],\\n\",\n      \"           [-3.6433e-02, -2.4115e-02, -2.3496e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.7468e-02, -3.4354e-02, -4.0376e-02],\\n\",\n      \"           [-5.5802e-02, -4.3226e-02, -3.9898e-02],\\n\",\n      \"           [-4.1012e-02, -4.8887e-02, -3.7097e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 71.00 %\\n\",\n      \"Val loss: 0.625676\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[10,    60] loss: 0.64145\\n\",\n      \"[10,   120] loss: 0.62138\\n\",\n      \"Time elapsed: 0h:20m:52s\\n\",\n      \"train accuracy_score: 65.28 %\\n\",\n      \"tensor([[[[[-8.9704e-01, -8.1745e-01, -6.3475e-01],\\n\",\n      \"           [-8.3666e-01, -7.1423e-01, -5.6061e-01],\\n\",\n      \"           [-6.9394e-01, -5.7724e-01, -5.5661e-01]],\\n\",\n      \"\\n\",\n      \"          [[-8.8651e-01, -7.3602e-01, -5.6133e-01],\\n\",\n      \"           [-8.4233e-01, -6.9473e-01, -5.6859e-01],\\n\",\n      \"           [-7.2110e-01, -5.8770e-01, -5.6550e-01]],\\n\",\n      \"\\n\",\n      \"          [[-8.4636e-01, -7.3722e-01, -5.8646e-01],\\n\",\n      \"           [-7.7969e-01, -6.5874e-01, -5.2938e-01],\\n\",\n      \"           [-7.1054e-01, -6.0589e-01, -5.2108e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1798e-01, -8.9165e-02, -1.0406e-01],\\n\",\n      \"           [-1.1732e-01, -1.0585e-01, -1.1673e-01],\\n\",\n      \"           [-1.0713e-01, -1.0541e-01, -1.1534e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.2262e-01, -9.0750e-02, -1.0038e-01],\\n\",\n      \"           [-1.1722e-01, -1.0120e-01, -1.0504e-01],\\n\",\n      \"           [-1.1299e-01, -1.1596e-01, -9.4385e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.3168e-01, -1.2902e-01, -1.1214e-01],\\n\",\n      \"           [-1.2954e-01, -1.2416e-01, -1.2626e-01],\\n\",\n      \"           [-1.1756e-01, -1.1843e-01, -9.6226e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.4663e-01, -2.5570e-01, -2.5001e-01],\\n\",\n      \"           [-9.8918e-02, -1.9043e-01, -1.9963e-01],\\n\",\n      \"           [-6.1312e-02, -8.4086e-02, -1.2033e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.0977e-01, -1.2446e-01, -1.1352e-01],\\n\",\n      \"           [-1.1014e-01, -1.1276e-01, -9.7170e-02],\\n\",\n      \"           [-5.4823e-02, -7.1662e-02, -1.0082e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.3137e-01, -1.1023e-01, -4.9416e-02],\\n\",\n      \"           [-4.0432e-02,  1.0184e-02, -1.2256e-02],\\n\",\n      \"           [-1.8733e-02,  2.4573e-03, -1.0337e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.0613e-02, -1.6696e-02, -3.2924e-02],\\n\",\n      \"           [ 1.7013e-03, -2.7786e-02,  1.8321e-02],\\n\",\n      \"           [-1.7331e-02,  3.5988e-02,  1.1263e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.6951e-02, -4.8198e-03,  1.0696e-02],\\n\",\n      \"           [ 3.7416e-02,  1.6217e-02,  8.8926e-02],\\n\",\n      \"           [ 6.9738e-02,  7.1457e-02,  1.6443e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0871e-02,  3.9242e-02,  6.6523e-02],\\n\",\n      \"           [ 6.1721e-02,  9.2622e-02,  1.5680e-01],\\n\",\n      \"           [ 1.3062e-01,  1.5323e-01,  2.6676e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.2969e-02,  2.9668e-02,  3.4336e-02],\\n\",\n      \"           [ 1.0904e-02,  1.0301e-02,  4.2700e-03],\\n\",\n      \"           [ 5.5276e-03, -2.2213e-03, -1.4488e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0134e-02,  1.3978e-02,  2.8298e-02],\\n\",\n      \"           [ 3.6813e-03, -9.7993e-04, -1.7426e-04],\\n\",\n      \"           [-7.0228e-03, -1.8035e-02, -1.2870e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8152e-02,  8.4274e-03,  1.6121e-02],\\n\",\n      \"           [-3.4256e-03, -1.1175e-02, -6.4475e-03],\\n\",\n      \"           [-5.4142e-03, -2.1726e-02, -1.4843e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.2039e-02,  1.3049e-02,  1.2559e-02],\\n\",\n      \"           [ 1.1732e-02,  7.7978e-03,  9.5520e-03],\\n\",\n      \"           [ 1.4998e-03, -1.3905e-03,  5.1411e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.4889e-03,  2.6169e-03,  4.0814e-03],\\n\",\n      \"           [-1.5538e-03, -3.0373e-03,  3.0477e-03],\\n\",\n      \"           [-1.0254e-02, -3.4766e-03,  2.7429e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.4750e-04, -5.8278e-03, -5.1672e-03],\\n\",\n      \"           [-3.7722e-03, -4.9358e-03,  3.9044e-03],\\n\",\n      \"           [-7.5644e-03, -5.9565e-03,  1.5574e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.6855e-02,  5.7737e-02,  4.4539e-02],\\n\",\n      \"           [ 7.0836e-02,  5.5869e-02,  4.4106e-02],\\n\",\n      \"           [ 6.4101e-02,  5.0359e-02,  4.0861e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.3840e-02,  4.9090e-02,  3.6841e-02],\\n\",\n      \"           [ 7.6509e-02,  6.4896e-02,  4.9475e-02],\\n\",\n      \"           [ 6.5963e-02,  5.9694e-02,  3.8785e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 8.0374e-02,  4.7443e-02,  3.6801e-02],\\n\",\n      \"           [ 6.9689e-02,  4.8815e-02,  4.0063e-02],\\n\",\n      \"           [ 6.8504e-02,  5.3738e-02,  3.7138e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1479e-02,  1.2915e-02,  1.3277e-02],\\n\",\n      \"           [-7.5547e-03,  1.6237e-03,  2.9183e-03],\\n\",\n      \"           [-1.3334e-02, -1.2299e-02, -2.1298e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 9.6958e-03,  1.0670e-02,  1.5811e-02],\\n\",\n      \"           [ 1.5283e-02,  2.2222e-02,  1.0028e-02],\\n\",\n      \"           [ 1.4589e-02,  2.6038e-03,  2.5286e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6537e-02,  1.0828e-02,  1.4142e-02],\\n\",\n      \"           [ 2.0605e-02,  2.0978e-02,  2.3955e-02],\\n\",\n      \"           [ 2.1089e-02,  2.2983e-02,  1.7160e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 76.00 %\\n\",\n      \"Val loss: 0.597721\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[11,    60] loss: 0.57867\\n\",\n      \"[11,   120] loss: 0.57859\\n\",\n      \"Time elapsed: 0h:22m:47s\\n\",\n      \"train accuracy_score: 70.30 %\\n\",\n      \"tensor([[[[[ 6.9263e-01,  6.0267e-01,  4.6875e-01],\\n\",\n      \"           [ 6.4188e-01,  5.1146e-01,  4.4383e-01],\\n\",\n      \"           [ 5.2677e-01,  4.8005e-01,  5.0282e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.4538e-01,  5.2281e-01,  4.0880e-01],\\n\",\n      \"           [ 5.9355e-01,  4.9986e-01,  3.9570e-01],\\n\",\n      \"           [ 5.3710e-01,  4.6272e-01,  4.7202e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.9809e-01,  4.9981e-01,  3.7846e-01],\\n\",\n      \"           [ 5.4911e-01,  4.4124e-01,  3.5119e-01],\\n\",\n      \"           [ 5.1905e-01,  4.5562e-01,  4.2079e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.1605e-02,  5.0925e-02,  5.9940e-02],\\n\",\n      \"           [ 5.7883e-02,  4.1241e-02,  4.5256e-02],\\n\",\n      \"           [ 4.4341e-02,  3.8105e-02,  4.0947e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.4900e-02,  4.1439e-02,  5.5217e-02],\\n\",\n      \"           [ 5.3768e-02,  2.5282e-02,  3.2397e-02],\\n\",\n      \"           [ 4.1651e-02,  4.6395e-02,  2.6987e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.8311e-02,  5.2285e-02,  5.8246e-02],\\n\",\n      \"           [ 5.0499e-02,  2.7721e-02,  2.0262e-02],\\n\",\n      \"           [ 2.7114e-02,  3.3976e-02,  2.2202e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.4771e-01, -2.4407e-01, -2.5204e-01],\\n\",\n      \"           [-2.4240e-01, -2.0833e-01, -1.9368e-01],\\n\",\n      \"           [-2.7196e-01, -2.2225e-01, -2.0739e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.3615e-01, -3.1835e-01, -2.9077e-01],\\n\",\n      \"           [-2.2564e-01, -2.6390e-01, -2.0416e-01],\\n\",\n      \"           [-2.7749e-01, -2.3345e-01, -2.0595e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.1627e-01, -3.0080e-01, -2.8785e-01],\\n\",\n      \"           [-2.7175e-01, -3.3763e-01, -2.4459e-01],\\n\",\n      \"           [-3.0196e-01, -2.3224e-01, -2.5251e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.0101e-02, -1.2820e-02, -2.3982e-02],\\n\",\n      \"           [-4.2536e-02, -1.9761e-02, -4.3161e-02],\\n\",\n      \"           [-4.1829e-02, -6.1368e-02, -9.2328e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.7526e-02, -1.4639e-02, -2.2283e-02],\\n\",\n      \"           [-2.9119e-02, -6.5528e-03, -3.9121e-02],\\n\",\n      \"           [-3.1001e-02, -4.3000e-02, -7.5801e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.4432e-02, -3.6498e-02, -4.7376e-02],\\n\",\n      \"           [-1.8240e-02, -4.4414e-02, -4.4920e-02],\\n\",\n      \"           [-1.6706e-02, -6.7997e-02, -9.7814e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.1090e-03,  7.5454e-05, -9.4527e-03],\\n\",\n      \"           [ 1.3852e-02,  1.7524e-02,  8.7743e-03],\\n\",\n      \"           [ 2.2674e-02,  2.3470e-02, -1.1102e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.0335e-02, -1.8672e-03, -8.9306e-03],\\n\",\n      \"           [ 3.0347e-03,  1.3846e-02,  1.0305e-03],\\n\",\n      \"           [ 2.7542e-02,  2.6139e-02,  1.7035e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.1526e-03, -1.2697e-03,  1.9561e-03],\\n\",\n      \"           [ 1.4622e-02,  2.0485e-02,  1.1373e-02],\\n\",\n      \"           [ 3.2220e-02,  3.7428e-02,  2.1105e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.6128e-04, -2.0580e-03, -7.1418e-03],\\n\",\n      \"           [-3.6784e-05, -2.6693e-03, -2.0937e-03],\\n\",\n      \"           [ 1.0708e-02,  2.7888e-03, -3.0532e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.4979e-03, -1.2618e-03,  4.3216e-03],\\n\",\n      \"           [ 1.0562e-02,  4.3226e-03,  1.5785e-03],\\n\",\n      \"           [ 1.2307e-02,  4.9730e-03,  1.5916e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.9210e-03, -2.3996e-03, -2.9232e-03],\\n\",\n      \"           [ 3.0716e-03,  3.1998e-04, -7.5830e-03],\\n\",\n      \"           [ 1.2881e-02,  7.2016e-03,  3.7062e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.0378e-01, -7.4837e-02, -5.3814e-02],\\n\",\n      \"           [-9.1167e-02, -7.7176e-02, -6.3892e-02],\\n\",\n      \"           [-8.0162e-02, -6.9188e-02, -7.2534e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.4577e-02, -5.1378e-02, -4.4148e-02],\\n\",\n      \"           [-8.3395e-02, -6.6591e-02, -4.7212e-02],\\n\",\n      \"           [-9.2049e-02, -7.2437e-02, -5.8467e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.8824e-02, -5.2184e-02, -4.2074e-02],\\n\",\n      \"           [-6.4563e-02, -4.6576e-02, -4.4479e-02],\\n\",\n      \"           [-8.2943e-02, -6.4259e-02, -6.1249e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.4421e-02, -3.7109e-02, -2.6553e-02],\\n\",\n      \"           [-2.2074e-02, -1.5857e-02, -1.5402e-02],\\n\",\n      \"           [-9.9878e-03, -1.6102e-02, -2.2541e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.7185e-02, -3.9454e-02, -2.6368e-02],\\n\",\n      \"           [-1.6540e-02, -2.2623e-02, -2.1585e-02],\\n\",\n      \"           [-1.5690e-02, -1.7749e-02, -2.5540e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.9100e-02, -3.4751e-02, -3.8595e-02],\\n\",\n      \"           [-3.0844e-02, -2.5286e-02, -2.3975e-02],\\n\",\n      \"           [-1.6349e-02, -2.1485e-02, -6.3528e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 70.00 %\\n\",\n      \"Val loss: 0.564606\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[12,    60] loss: 0.54758\\n\",\n      \"[12,   120] loss: 0.55403\\n\",\n      \"Time elapsed: 0h:24m:42s\\n\",\n      \"train accuracy_score: 69.15 %\\n\",\n      \"tensor([[[[[ 4.8240e-01,  4.7268e-01,  3.7116e-01],\\n\",\n      \"           [ 4.2465e-01,  3.8476e-01,  3.3875e-01],\\n\",\n      \"           [ 3.9823e-01,  3.5680e-01,  3.3657e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.6539e-01,  4.1633e-01,  3.6350e-01],\\n\",\n      \"           [ 4.3387e-01,  4.0908e-01,  3.4051e-01],\\n\",\n      \"           [ 4.0834e-01,  3.3541e-01,  3.4680e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.5584e-01,  4.1215e-01,  4.0027e-01],\\n\",\n      \"           [ 4.1138e-01,  3.9714e-01,  3.5300e-01],\\n\",\n      \"           [ 3.7452e-01,  3.5195e-01,  3.6267e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.8423e-02,  5.6596e-02,  6.8953e-02],\\n\",\n      \"           [ 4.5224e-02,  5.0598e-02,  6.8393e-02],\\n\",\n      \"           [ 5.2353e-02,  4.9384e-02,  6.1459e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.4084e-02,  4.2770e-02,  6.8115e-02],\\n\",\n      \"           [ 5.5650e-02,  5.9724e-02,  5.5486e-02],\\n\",\n      \"           [ 5.2164e-02,  5.4219e-02,  5.8931e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.0071e-02,  4.2430e-02,  5.0463e-02],\\n\",\n      \"           [ 4.7289e-02,  2.9923e-02,  4.2965e-02],\\n\",\n      \"           [ 5.2206e-02,  3.1100e-02,  4.7908e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.1291e-01,  9.6971e-02,  6.8432e-02],\\n\",\n      \"           [ 7.6776e-02,  5.8548e-02,  4.8763e-02],\\n\",\n      \"           [ 1.4402e-02,  2.2157e-02,  6.9572e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4046e-01,  4.9336e-02,  8.6267e-03],\\n\",\n      \"           [ 7.2155e-02,  3.5046e-02, -1.4659e-02],\\n\",\n      \"           [ 3.3718e-02,  2.3344e-02, -1.7928e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2584e-01,  6.1548e-02,  1.2515e-02],\\n\",\n      \"           [ 7.1470e-02,  2.7678e-02, -3.5564e-02],\\n\",\n      \"           [-1.0318e-02,  1.4371e-02, -5.2061e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.5477e-03, -7.3227e-03,  1.2457e-02],\\n\",\n      \"           [ 1.6771e-02, -1.8950e-03, -7.2358e-03],\\n\",\n      \"           [-6.0753e-03, -1.8588e-02, -9.3073e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 4.6358e-02,  3.0464e-02,  4.3020e-02],\\n\",\n      \"           [ 6.1428e-03, -1.1144e-03, -1.0049e-03],\\n\",\n      \"           [-7.8369e-04, -1.2449e-02, -2.1260e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 7.7042e-02,  5.6454e-02,  1.0325e-02],\\n\",\n      \"           [ 3.0951e-02,  9.2986e-03, -1.0864e-03],\\n\",\n      \"           [-1.1008e-02, -3.8813e-02, -3.2122e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.5361e-02, -4.0197e-02, -3.9925e-02],\\n\",\n      \"           [-7.0867e-02, -4.5928e-02, -3.8967e-02],\\n\",\n      \"           [-6.4011e-02, -5.1607e-02, -4.3913e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.4944e-02, -5.1738e-02, -4.4436e-02],\\n\",\n      \"           [-4.2742e-02, -4.3420e-02, -4.1096e-02],\\n\",\n      \"           [-6.4674e-02, -4.9901e-02, -3.7268e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.3692e-02, -3.0653e-02, -2.5820e-02],\\n\",\n      \"           [-2.3019e-02, -1.6387e-02, -2.2577e-02],\\n\",\n      \"           [-4.3415e-02, -2.6315e-02, -1.7669e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.6959e-03, -8.7571e-03, -7.9262e-03],\\n\",\n      \"           [ 1.5978e-03, -3.5386e-03, -1.9459e-03],\\n\",\n      \"           [ 8.0903e-03,  7.7440e-03,  4.1283e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.1141e-03, -6.0917e-03, -8.0317e-03],\\n\",\n      \"           [ 1.5442e-03, -7.9826e-04,  4.8065e-04],\\n\",\n      \"           [ 1.3549e-02,  7.3333e-03, -2.7572e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 7.1710e-03,  4.8507e-03, -7.3822e-04],\\n\",\n      \"           [ 6.0637e-03, -6.7786e-04, -2.7541e-03],\\n\",\n      \"           [ 1.1802e-02,  8.0815e-03,  4.3164e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.9711e-02, -5.6420e-02, -6.0453e-02],\\n\",\n      \"           [-6.7677e-02, -5.8953e-02, -5.6906e-02],\\n\",\n      \"           [-6.0627e-02, -4.3340e-02, -6.2320e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.3175e-02, -5.5812e-02, -5.7023e-02],\\n\",\n      \"           [-6.9769e-02, -6.9520e-02, -6.5963e-02],\\n\",\n      \"           [-5.7365e-02, -6.2850e-02, -6.7569e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.2511e-02, -7.2834e-02, -5.6906e-02],\\n\",\n      \"           [-7.8387e-02, -5.9351e-02, -7.6544e-02],\\n\",\n      \"           [-7.4276e-02, -5.9895e-02, -8.4964e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.3084e-03, -6.2764e-03, -2.1915e-03],\\n\",\n      \"           [ 1.6384e-02, -6.8407e-03, -1.0916e-02],\\n\",\n      \"           [ 1.1574e-02,  2.3485e-03, -7.7448e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.0985e-03,  3.9854e-03, -5.4697e-03],\\n\",\n      \"           [ 1.3014e-02, -9.3322e-04, -2.8100e-03],\\n\",\n      \"           [ 6.8805e-04,  4.3090e-03, -8.1486e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.1551e-03, -8.6073e-03, -5.2257e-03],\\n\",\n      \"           [-1.6521e-03, -6.5564e-04, -6.4160e-03],\\n\",\n      \"           [-1.0853e-03, -1.1119e-02, -5.1076e-03]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 44.00 %\\n\",\n      \"Val loss: 0.772320\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[13,    60] loss: 0.54486\\n\",\n      \"[13,   120] loss: 0.49595\\n\",\n      \"Time elapsed: 0h:26m:37s\\n\",\n      \"train accuracy_score: 73.03 %\\n\",\n      \"tensor([[[[[ 3.0738e-01,  2.7370e-01,  1.8320e-01],\\n\",\n      \"           [ 3.1387e-01,  2.5360e-01,  1.7203e-01],\\n\",\n      \"           [ 2.7495e-01,  1.9254e-01,  1.5704e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.0147e-01,  2.3702e-01,  1.3575e-01],\\n\",\n      \"           [ 3.1745e-01,  2.4867e-01,  1.6786e-01],\\n\",\n      \"           [ 2.8105e-01,  2.1717e-01,  1.7186e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.9285e-01,  2.5971e-01,  1.6503e-01],\\n\",\n      \"           [ 2.7353e-01,  2.2338e-01,  1.8058e-01],\\n\",\n      \"           [ 2.6483e-01,  2.4018e-01,  1.8798e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.5603e-02,  3.7148e-02,  2.9885e-02],\\n\",\n      \"           [ 4.5547e-02,  2.5394e-02,  7.8484e-03],\\n\",\n      \"           [ 3.0887e-02,  9.3820e-03, -1.5289e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.5575e-02,  4.8031e-02,  3.0149e-02],\\n\",\n      \"           [ 4.8885e-02,  4.2398e-02,  1.6725e-03],\\n\",\n      \"           [ 4.5190e-02,  2.7855e-02,  5.2903e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 8.2260e-02,  5.7788e-02,  3.5589e-02],\\n\",\n      \"           [ 7.0666e-02,  4.0419e-02,  1.8626e-02],\\n\",\n      \"           [ 5.3873e-02,  3.3867e-02,  4.3048e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.0629e-01,  1.5881e-01,  1.9346e-01],\\n\",\n      \"           [ 1.4646e-01,  1.5687e-01,  1.5034e-01],\\n\",\n      \"           [ 1.3718e-01,  1.5076e-01,  1.6239e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6705e-01,  1.5890e-01,  1.8564e-01],\\n\",\n      \"           [ 1.7035e-01,  1.5880e-01,  1.3343e-01],\\n\",\n      \"           [ 1.6418e-01,  1.4848e-01,  2.0850e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.8079e-01,  1.4307e-01,  1.2957e-01],\\n\",\n      \"           [ 1.5087e-01,  1.3947e-01,  1.5683e-01],\\n\",\n      \"           [ 1.0841e-01,  1.3284e-01,  1.5972e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.7646e-02, -1.9338e-02, -9.2989e-03],\\n\",\n      \"           [-3.5761e-02, -3.3723e-02, -3.0025e-02],\\n\",\n      \"           [-2.9067e-02, -3.1116e-02, -5.6869e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.1618e-02, -3.2002e-02, -4.7203e-02],\\n\",\n      \"           [-4.6189e-02, -4.7032e-02, -6.8753e-02],\\n\",\n      \"           [-3.2875e-02, -7.8672e-02, -8.1458e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0156e-02, -1.7704e-02, -4.4678e-02],\\n\",\n      \"           [-4.5676e-02, -6.5919e-02, -8.3117e-02],\\n\",\n      \"           [-5.2463e-02, -8.7577e-02, -9.4870e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.2197e-02,  3.4425e-02,  4.4053e-02],\\n\",\n      \"           [ 6.8857e-02,  6.9129e-02,  7.0471e-02],\\n\",\n      \"           [ 7.2806e-02,  1.0108e-01,  7.8148e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3571e-02,  3.2587e-02,  3.1240e-02],\\n\",\n      \"           [ 4.1925e-02,  5.4902e-02,  5.4933e-02],\\n\",\n      \"           [ 6.3649e-02,  7.2601e-02,  7.3965e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.9605e-03,  1.1110e-02,  1.6603e-02],\\n\",\n      \"           [ 2.6543e-02,  3.2169e-02,  2.6260e-02],\\n\",\n      \"           [ 5.5878e-02,  4.2874e-02,  5.4490e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.8125e-02, -1.3847e-02, -1.0278e-02],\\n\",\n      \"           [-1.1077e-02, -4.2399e-03, -6.2828e-03],\\n\",\n      \"           [-8.4661e-03, -4.4841e-03, -8.3472e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.1799e-03, -1.1660e-03, -7.3760e-03],\\n\",\n      \"           [-5.4723e-03, -3.3018e-03, -7.3129e-03],\\n\",\n      \"           [-4.7018e-04, -2.8643e-03, -5.6714e-03]],\\n\",\n      \"\\n\",\n      \"          [[-6.8554e-05, -3.9869e-04, -6.3325e-03],\\n\",\n      \"           [-2.6823e-03, -8.3151e-03, -8.1904e-03],\\n\",\n      \"           [-2.8434e-03, -1.8594e-03, -8.4053e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.6098e-02, -3.2236e-02, -2.3277e-02],\\n\",\n      \"           [-2.1179e-02, -2.1016e-03, -1.0977e-02],\\n\",\n      \"           [ 2.2687e-02,  2.8065e-02,  1.9543e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.9902e-02, -4.5780e-02, -5.1050e-02],\\n\",\n      \"           [-2.9177e-02, -2.0495e-02, -2.4348e-02],\\n\",\n      \"           [ 4.8426e-03,  1.8972e-02,  6.0856e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.2790e-02, -4.8521e-02, -4.7999e-02],\\n\",\n      \"           [-4.1030e-02, -3.0017e-02, -2.3527e-02],\\n\",\n      \"           [-2.0932e-02, -7.7808e-03, -1.0881e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.6061e-02, -3.2987e-02, -2.6947e-02],\\n\",\n      \"           [-1.4440e-02, -8.2695e-03, -1.5338e-02],\\n\",\n      \"           [ 2.4488e-04, -3.1098e-03, -1.2666e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.3974e-02, -1.3151e-02, -9.5007e-03],\\n\",\n      \"           [-8.4569e-03, -4.0179e-03, -4.0623e-03],\\n\",\n      \"           [ 8.0938e-03, -1.7636e-03,  7.2368e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.7874e-02, -1.8921e-02, -1.1625e-02],\\n\",\n      \"           [-1.2437e-02, -7.6695e-03, -9.7658e-03],\\n\",\n      \"           [-1.4567e-02, -7.7401e-03,  7.5221e-04]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 81.00 %\\n\",\n      \"Val loss: 0.522522\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[14,    60] loss: 0.54655\\n\",\n      \"[14,   120] loss: 0.50858\\n\",\n      \"Time elapsed: 0h:28m:32s\\n\",\n      \"train accuracy_score: 73.46 %\\n\",\n      \"tensor([[[[[-4.8854e+00, -4.4698e+00, -3.6956e+00],\\n\",\n      \"           [-4.6850e+00, -3.9288e+00, -2.8731e+00],\\n\",\n      \"           [-3.9549e+00, -3.1694e+00, -2.7680e+00]],\\n\",\n      \"\\n\",\n      \"          [[-4.3731e+00, -3.7825e+00, -3.0333e+00],\\n\",\n      \"           [-4.0756e+00, -3.6484e+00, -2.7861e+00],\\n\",\n      \"           [-3.5020e+00, -2.8336e+00, -2.6746e+00]],\\n\",\n      \"\\n\",\n      \"          [[-3.9445e+00, -3.1695e+00, -2.5232e+00],\\n\",\n      \"           [-3.2638e+00, -2.9819e+00, -2.7097e+00],\\n\",\n      \"           [-3.0728e+00, -2.8149e+00, -2.6670e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.7477e-02,  1.7131e-01,  2.2009e-02],\\n\",\n      \"           [-5.6472e-02,  2.1247e-01,  1.3766e-01],\\n\",\n      \"           [ 2.5463e-02,  1.8293e-01,  3.0805e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.9494e-01, -5.5164e-02, -9.8692e-02],\\n\",\n      \"           [-3.5755e-01, -7.2935e-02,  1.6924e-01],\\n\",\n      \"           [-3.7316e-01, -1.0168e-01,  2.3775e-01]],\\n\",\n      \"\\n\",\n      \"          [[-7.0999e-01, -5.2453e-01, -3.2905e-01],\\n\",\n      \"           [-6.8660e-01, -3.5428e-01, -1.2557e-01],\\n\",\n      \"           [-6.3734e-01, -2.7142e-01, -9.8052e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.0891e+00,  3.8192e+00,  4.2256e+00],\\n\",\n      \"           [ 4.3731e+00,  3.8464e+00,  3.8658e+00],\\n\",\n      \"           [ 4.3948e+00,  3.9180e+00,  4.1542e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 4.7120e+00,  4.6717e+00,  4.5065e+00],\\n\",\n      \"           [ 4.3248e+00,  4.0464e+00,  4.0101e+00],\\n\",\n      \"           [ 4.2973e+00,  3.6943e+00,  3.7393e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 4.4409e+00,  4.3770e+00,  4.5037e+00],\\n\",\n      \"           [ 4.1683e+00,  4.1643e+00,  3.8226e+00],\\n\",\n      \"           [ 3.8035e+00,  3.6121e+00,  3.7995e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.6936e-01,  1.7107e-01, -4.4038e-03],\\n\",\n      \"           [ 1.4105e-01,  1.4447e-01,  3.4636e-01],\\n\",\n      \"           [ 1.0984e-01,  1.8751e-01,  3.8689e-01]],\\n\",\n      \"\\n\",\n      \"          [[-8.5379e-02, -1.7544e-01, -1.5521e-01],\\n\",\n      \"           [ 2.0893e-01, -4.6260e-02,  1.4301e-01],\\n\",\n      \"           [ 9.0145e-02,  2.6651e-01,  1.4580e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.9431e-01, -4.0050e-01, -1.2873e-01],\\n\",\n      \"           [-1.4855e-01, -5.9521e-02,  2.4150e-02],\\n\",\n      \"           [ 8.5564e-02,  2.8699e-01,  4.4712e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.4219e-01,  2.6351e-01,  1.4987e-01],\\n\",\n      \"           [ 3.9548e-01, -5.3501e-02, -2.7722e-02],\\n\",\n      \"           [ 3.9166e-01,  4.3019e-02, -1.8590e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.5384e-01,  3.3983e-01,  3.1785e-01],\\n\",\n      \"           [ 4.9549e-01,  6.4349e-02,  2.6479e-02],\\n\",\n      \"           [ 3.9951e-01,  6.8649e-02, -7.5584e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.7004e-01,  4.5844e-01,  4.4183e-01],\\n\",\n      \"           [ 5.2734e-01,  2.2388e-01,  8.9814e-02],\\n\",\n      \"           [ 7.9115e-01,  4.1445e-01,  7.7422e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.2563e-01, -1.6601e-01, -4.8536e-02],\\n\",\n      \"           [-2.3031e-01, -1.7182e-01, -1.1109e-01],\\n\",\n      \"           [-2.3771e-01, -1.7573e-01, -9.5566e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.1497e-01, -1.5409e-01, -7.8573e-02],\\n\",\n      \"           [-2.8284e-01, -1.6416e-01, -9.8611e-02],\\n\",\n      \"           [-2.7929e-01, -1.9124e-01, -8.5520e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.0022e-01, -1.5813e-01, -1.7674e-01],\\n\",\n      \"           [-1.8761e-01, -1.7070e-01, -1.4564e-01],\\n\",\n      \"           [-2.9920e-01, -2.3171e-01, -1.3276e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.2015e-01,  5.0939e-01,  3.7061e-01],\\n\",\n      \"           [ 6.4895e-01,  6.3129e-01,  5.8512e-01],\\n\",\n      \"           [ 4.8260e-01,  5.2825e-01,  5.6941e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.8595e-01,  5.5280e-01,  4.1288e-01],\\n\",\n      \"           [ 6.6477e-01,  6.3362e-01,  6.5041e-01],\\n\",\n      \"           [ 5.8301e-01,  5.8537e-01,  7.9446e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 8.2632e-01,  7.2634e-01,  6.2659e-01],\\n\",\n      \"           [ 7.4839e-01,  6.6937e-01,  7.3953e-01],\\n\",\n      \"           [ 8.5024e-01,  8.1283e-01,  9.2058e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.5842e-01, -2.5433e-01, -3.5490e-01],\\n\",\n      \"           [-3.3390e-01, -1.9054e-01, -1.9127e-01],\\n\",\n      \"           [-3.4203e-01, -2.4194e-01, -1.9971e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.6723e-01, -2.7459e-01, -2.6645e-01],\\n\",\n      \"           [-2.2906e-01, -2.3695e-01, -3.0230e-02],\\n\",\n      \"           [-2.9134e-01, -2.1999e-01, -1.1873e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.0138e-01, -2.2831e-01, -2.9864e-01],\\n\",\n      \"           [-1.2119e-01, -9.1164e-02,  1.0086e-02],\\n\",\n      \"           [-1.2530e-01, -1.0796e-01, -1.0008e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 72.00 %\\n\",\n      \"Val loss: 0.505576\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[15,    60] loss: 0.50264\\n\",\n      \"[15,   120] loss: 0.49194\\n\",\n      \"Time elapsed: 0h:30m:25s\\n\",\n      \"train accuracy_score: 75.47 %\\n\",\n      \"tensor([[[[[-2.9899e+00, -2.6325e+00, -2.0794e+00],\\n\",\n      \"           [-2.8122e+00, -2.2992e+00, -1.8131e+00],\\n\",\n      \"           [-2.3139e+00, -1.8953e+00, -1.6773e+00]],\\n\",\n      \"\\n\",\n      \"          [[-2.7931e+00, -2.2532e+00, -1.7469e+00],\\n\",\n      \"           [-2.7274e+00, -2.1007e+00, -1.7281e+00],\\n\",\n      \"           [-2.2456e+00, -1.8119e+00, -1.6395e+00]],\\n\",\n      \"\\n\",\n      \"          [[-2.6490e+00, -2.1533e+00, -1.6657e+00],\\n\",\n      \"           [-2.3834e+00, -1.9091e+00, -1.6862e+00],\\n\",\n      \"           [-2.1942e+00, -1.8030e+00, -1.6807e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.2838e-02,  8.9471e-02,  5.9379e-02],\\n\",\n      \"           [ 9.1463e-03,  7.3924e-02, -1.9126e-04],\\n\",\n      \"           [ 4.0272e-02, -7.4613e-03, -8.3605e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.6911e-03,  4.8977e-02,  6.5550e-03],\\n\",\n      \"           [-3.4833e-02,  3.7806e-02, -6.0670e-03],\\n\",\n      \"           [-6.2679e-02, -4.7050e-02, -8.6645e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.8694e-02, -3.5244e-02, -8.3340e-02],\\n\",\n      \"           [-9.4952e-02, -4.2858e-02, -3.4805e-02],\\n\",\n      \"           [-1.2395e-01, -1.2789e-01, -1.0679e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.5085e-01, -8.5151e-01, -8.9927e-01],\\n\",\n      \"           [-6.5363e-01, -6.8860e-01, -7.9884e-01],\\n\",\n      \"           [-5.1077e-01, -4.6557e-01, -5.0180e-01]],\\n\",\n      \"\\n\",\n      \"          [[-6.0677e-01, -5.0201e-01, -6.0048e-01],\\n\",\n      \"           [-5.1481e-01, -4.6568e-01, -5.6267e-01],\\n\",\n      \"           [-3.6940e-01, -3.8220e-01, -4.8176e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.9541e-01, -3.8817e-01, -5.6053e-01],\\n\",\n      \"           [-3.1686e-01, -2.1313e-01, -3.4954e-01],\\n\",\n      \"           [-3.3681e-01, -2.8980e-01, -2.8370e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.4739e-01,  2.0846e-02, -5.8256e-02],\\n\",\n      \"           [ 9.5582e-02,  2.6823e-02,  3.7721e-02],\\n\",\n      \"           [-7.1391e-02, -6.0797e-03,  4.7846e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6191e-02, -6.5243e-02, -1.3853e-01],\\n\",\n      \"           [ 7.1678e-02, -1.4031e-02,  2.1908e-02],\\n\",\n      \"           [-6.4522e-02, -2.7353e-02,  8.5559e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.3100e-01, -1.7156e-01, -1.2263e-01],\\n\",\n      \"           [-7.0286e-02, -5.5089e-02,  2.8935e-02],\\n\",\n      \"           [-3.4516e-02,  1.4508e-02,  1.2848e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.0964e-02, -5.7057e-02, -1.8162e-02],\\n\",\n      \"           [-1.0300e-01, -1.4140e-01, -7.4148e-02],\\n\",\n      \"           [-1.3299e-01, -1.6094e-01, -7.9484e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.8973e-03, -4.8504e-02, -4.2802e-02],\\n\",\n      \"           [-1.0683e-01, -1.2708e-01, -7.1419e-02],\\n\",\n      \"           [-1.0806e-01, -1.1996e-01, -6.1608e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1508e-02, -1.7977e-02, -1.9068e-02],\\n\",\n      \"           [-5.4508e-02, -6.7493e-02, -6.4541e-02],\\n\",\n      \"           [-1.7330e-02, -1.0591e-01, -3.7209e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.6788e-03, -4.1435e-04,  1.4386e-02],\\n\",\n      \"           [ 1.3766e-02, -2.0048e-03,  8.0782e-03],\\n\",\n      \"           [-2.2981e-02, -1.0292e-02, -3.9726e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 4.7340e-03, -1.4617e-03, -3.3886e-03],\\n\",\n      \"           [-1.6255e-02, -1.0322e-02, -1.3086e-02],\\n\",\n      \"           [-2.7787e-02, -1.0279e-02, -6.1719e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 6.2044e-03, -3.6280e-03,  2.9260e-03],\\n\",\n      \"           [-2.5709e-03, -3.4349e-03,  6.4510e-03],\\n\",\n      \"           [-1.9437e-02,  8.0149e-04,  1.3190e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.8524e-01,  1.2940e-01,  1.0642e-01],\\n\",\n      \"           [ 1.8106e-01,  1.6003e-01,  1.5582e-01],\\n\",\n      \"           [ 1.3508e-01,  1.1767e-01,  1.0755e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2050e-01,  7.0501e-02,  9.9856e-02],\\n\",\n      \"           [ 1.9571e-01,  1.8574e-01,  1.9565e-01],\\n\",\n      \"           [ 2.0491e-01,  2.2302e-01,  2.3224e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6770e-01,  1.3378e-01,  1.6491e-01],\\n\",\n      \"           [ 1.7916e-01,  1.8160e-01,  2.0441e-01],\\n\",\n      \"           [ 2.3221e-01,  2.5542e-01,  2.7701e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.2566e-02,  4.7495e-02,  4.3736e-02],\\n\",\n      \"           [ 4.1810e-03,  3.7800e-02,  1.1857e-02],\\n\",\n      \"           [-1.7842e-02,  1.8139e-02, -3.2792e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0678e-02,  3.4842e-02,  3.8823e-02],\\n\",\n      \"           [ 3.4845e-02,  3.7321e-02,  2.7632e-02],\\n\",\n      \"           [ 4.5110e-03,  6.5004e-02, -4.1654e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6808e-02,  1.0088e-02, -2.6813e-02],\\n\",\n      \"           [ 3.4763e-02,  6.6378e-02,  9.0226e-02],\\n\",\n      \"           [ 3.8239e-02,  5.2137e-02,  3.6642e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 80.00 %\\n\",\n      \"Val loss: 0.472496\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[16,    60] loss: 0.52277\\n\",\n      \"[16,   120] loss: 0.49130\\n\",\n      \"Time elapsed: 0h:32m:20s\\n\",\n      \"train accuracy_score: 77.76 %\\n\",\n      \"tensor([[[[[-1.1430e+00, -1.0444e+00, -8.2483e-01],\\n\",\n      \"           [-1.0609e+00, -8.5684e-01, -6.8475e-01],\\n\",\n      \"           [-8.5012e-01, -6.5500e-01, -5.2322e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.1042e+00, -8.5369e-01, -6.3807e-01],\\n\",\n      \"           [-1.1173e+00, -8.1758e-01, -6.2474e-01],\\n\",\n      \"           [-8.4452e-01, -6.4336e-01, -5.6275e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.4708e-01, -7.9140e-01, -6.3290e-01],\\n\",\n      \"           [-8.7106e-01, -6.7493e-01, -5.8924e-01],\\n\",\n      \"           [-7.7927e-01, -6.5731e-01, -6.4977e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.8636e-01, -1.1970e-01, -1.2331e-01],\\n\",\n      \"           [-1.3985e-01, -1.1729e-01, -9.2828e-02],\\n\",\n      \"           [-1.1515e-01, -9.6943e-02, -1.1011e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.4338e-01, -1.2365e-01, -1.1283e-01],\\n\",\n      \"           [-1.3544e-01, -9.5852e-02, -8.0698e-02],\\n\",\n      \"           [-1.1909e-01, -1.0666e-01, -1.0499e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.3463e-01, -1.2235e-01, -1.0208e-01],\\n\",\n      \"           [-1.5448e-01, -1.3375e-01, -9.6960e-02],\\n\",\n      \"           [-1.6149e-01, -1.4838e-01, -1.1311e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.2202e-01, -6.8835e-01, -7.0884e-01],\\n\",\n      \"           [-5.1746e-01, -5.7525e-01, -6.2437e-01],\\n\",\n      \"           [-3.3095e-01, -3.9202e-01, -4.2289e-01]],\\n\",\n      \"\\n\",\n      \"          [[-6.2849e-01, -6.1796e-01, -6.4517e-01],\\n\",\n      \"           [-5.3473e-01, -5.9514e-01, -5.8162e-01],\\n\",\n      \"           [-3.9985e-01, -4.7254e-01, -5.1712e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.7822e-01, -5.9771e-01, -6.4206e-01],\\n\",\n      \"           [-4.7206e-01, -4.6487e-01, -5.6965e-01],\\n\",\n      \"           [-4.0309e-01, -4.3654e-01, -4.0765e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.9426e-02, -8.8708e-02, -1.2756e-01],\\n\",\n      \"           [-8.4425e-02, -8.9820e-02, -5.7837e-02],\\n\",\n      \"           [-9.9330e-02, -3.7664e-02, -6.2834e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.0364e-02, -9.5981e-02, -1.0242e-01],\\n\",\n      \"           [-4.0171e-02, -6.6088e-02, -3.3350e-02],\\n\",\n      \"           [-1.6459e-02, -2.5865e-02, -1.6333e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.9494e-02, -5.6586e-02, -6.2732e-02],\\n\",\n      \"           [-9.6748e-03, -2.8186e-02, -1.8754e-02],\\n\",\n      \"           [ 4.8037e-02,  4.2304e-02,  5.0511e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.4054e-01,  7.6935e-02,  4.4867e-02],\\n\",\n      \"           [ 3.2629e-02, -2.4670e-02, -1.7625e-02],\\n\",\n      \"           [ 5.4765e-03, -3.3395e-02, -1.6729e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1875e-01,  5.4792e-02,  3.5802e-03],\\n\",\n      \"           [ 7.9466e-03, -6.5577e-02, -7.8453e-02],\\n\",\n      \"           [-4.1876e-02, -8.0442e-02, -8.7892e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0309e-01,  4.7700e-02, -8.3951e-03],\\n\",\n      \"           [ 1.8311e-02, -7.5415e-03, -8.6108e-02],\\n\",\n      \"           [-5.5742e-02, -1.0146e-01, -1.3066e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.1197e-02,  1.6720e-02,  2.3178e-02],\\n\",\n      \"           [-1.4540e-02,  1.9749e-03,  3.0090e-03],\\n\",\n      \"           [-1.8036e-02, -6.8611e-03,  1.7644e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 7.1863e-03,  4.5005e-03, -1.4454e-03],\\n\",\n      \"           [-1.2556e-02, -1.2354e-03,  3.1144e-03],\\n\",\n      \"           [-1.4327e-02, -3.1055e-03,  3.7364e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.8492e-03, -4.1467e-03, -1.1128e-02],\\n\",\n      \"           [ 1.7874e-03,  1.2185e-02,  7.8577e-03],\\n\",\n      \"           [-5.1951e-03,  9.7256e-03,  1.2421e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0251e-01,  6.9641e-02,  2.1466e-02],\\n\",\n      \"           [ 5.0136e-02,  1.5272e-02, -8.8608e-03],\\n\",\n      \"           [-3.8132e-02, -5.4299e-02, -1.4337e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 8.9251e-02,  4.6851e-02,  4.1144e-02],\\n\",\n      \"           [ 3.9687e-02,  2.9163e-02,  2.1833e-02],\\n\",\n      \"           [ 5.1614e-03, -7.3399e-03,  9.7596e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 8.8224e-02,  4.0169e-02,  7.5085e-02],\\n\",\n      \"           [ 6.8506e-02,  4.5292e-02,  5.1749e-02],\\n\",\n      \"           [ 7.6763e-02,  4.4264e-02,  6.6734e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.5493e-01,  1.5368e-01,  1.3594e-01],\\n\",\n      \"           [ 8.8953e-02,  1.0821e-01,  8.9419e-02],\\n\",\n      \"           [ 5.1185e-02,  7.2693e-02,  6.9366e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0154e-01,  1.0793e-01,  1.0138e-01],\\n\",\n      \"           [ 8.4063e-02,  1.0775e-01,  8.4415e-02],\\n\",\n      \"           [ 8.2268e-02,  9.5811e-02,  7.7924e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 9.8260e-02,  8.2523e-02,  8.6805e-02],\\n\",\n      \"           [ 1.1795e-01,  9.5870e-02,  9.2416e-02],\\n\",\n      \"           [ 1.0754e-01,  1.0557e-01,  1.0113e-01]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 82.00 %\\n\",\n      \"Val loss: 0.505288\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[17,    60] loss: 0.45093\\n\",\n      \"[17,   120] loss: 0.47070\\n\",\n      \"Time elapsed: 0h:34m:13s\\n\",\n      \"train accuracy_score: 80.77 %\\n\",\n      \"tensor([[[[[-2.1456e+00, -2.1060e+00, -1.4207e+00],\\n\",\n      \"           [-1.9465e+00, -1.5929e+00, -1.1370e+00],\\n\",\n      \"           [-1.3205e+00, -1.1867e+00, -1.0098e+00]],\\n\",\n      \"\\n\",\n      \"          [[-2.1528e+00, -1.7765e+00, -9.0948e-01],\\n\",\n      \"           [-2.0767e+00, -1.3449e+00, -9.3097e-01],\\n\",\n      \"           [-1.4356e+00, -8.7331e-01, -8.8189e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.6775e+00, -1.3073e+00, -7.4068e-01],\\n\",\n      \"           [-1.7149e+00, -8.7051e-01, -6.1816e-01],\\n\",\n      \"           [-1.3068e+00, -8.6125e-01, -4.8228e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.0682e-01,  4.2896e-01,  3.2943e-01],\\n\",\n      \"           [ 2.9476e-01,  3.3267e-01,  3.7262e-01],\\n\",\n      \"           [ 4.0376e-01,  4.0028e-01,  3.0148e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5546e-01,  3.5935e-01,  3.3504e-01],\\n\",\n      \"           [ 1.8372e-01,  2.9920e-01,  3.3543e-01],\\n\",\n      \"           [ 2.6190e-01,  3.0687e-01,  2.6731e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4622e-01,  1.8499e-01,  1.8947e-01],\\n\",\n      \"           [ 1.6468e-01,  1.2339e-01,  2.1338e-01],\\n\",\n      \"           [ 2.3563e-01,  1.3551e-01,  2.4581e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.1632e+00,  2.1623e+00,  2.0672e+00],\\n\",\n      \"           [ 2.4218e+00,  2.3493e+00,  2.2063e+00],\\n\",\n      \"           [ 2.9741e+00,  2.7148e+00,  2.7701e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1841e+00,  2.6930e+00,  2.3129e+00],\\n\",\n      \"           [ 2.1852e+00,  2.1306e+00,  2.3977e+00],\\n\",\n      \"           [ 2.3585e+00,  2.1419e+00,  2.5713e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5481e+00,  2.1211e+00,  2.1483e+00],\\n\",\n      \"           [ 2.2471e+00,  2.2099e+00,  2.4773e+00],\\n\",\n      \"           [ 1.8521e+00,  1.8186e+00,  2.5695e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.4853e-01, -2.4860e-01, -3.1479e-01],\\n\",\n      \"           [-5.7601e-02, -1.9789e-01, -6.2408e-03],\\n\",\n      \"           [-6.6937e-02,  4.5867e-02,  1.7089e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.8561e-01, -2.7383e-01, -5.7599e-01],\\n\",\n      \"           [ 8.3900e-03, -1.8542e-01, -2.7243e-01],\\n\",\n      \"           [ 1.1710e-02, -1.6457e-01, -5.9975e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.1655e-01, -2.5517e-01, -4.7090e-01],\\n\",\n      \"           [-1.3308e-01, -3.2385e-01, -3.0632e-01],\\n\",\n      \"           [-3.3894e-02, -2.2192e-01, -5.4157e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.9672e-02, -2.4103e-01, -3.0951e-01],\\n\",\n      \"           [-5.1105e-01, -4.9615e-01, -5.1261e-01],\\n\",\n      \"           [-6.3730e-01, -8.3279e-01, -4.9118e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.7812e-02, -1.8950e-01, -2.2349e-01],\\n\",\n      \"           [-3.3423e-01, -4.4050e-01, -3.6187e-01],\\n\",\n      \"           [-6.0409e-01, -6.8700e-01, -5.0344e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.3373e-02, -2.2174e-02, -1.1154e-01],\\n\",\n      \"           [-3.3241e-01, -3.5030e-01, -2.6612e-01],\\n\",\n      \"           [-3.5433e-01, -5.2431e-01, -3.7530e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.0482e-01, -5.0507e-02, -8.5810e-05],\\n\",\n      \"           [-9.7220e-02, -4.9495e-02,  2.2142e-02],\\n\",\n      \"           [-1.0440e-01, -7.6414e-02, -3.1430e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.3624e-01, -8.2911e-02, -3.9794e-02],\\n\",\n      \"           [-1.6201e-01, -1.0999e-01, -2.0707e-02],\\n\",\n      \"           [-1.5044e-01, -1.1393e-01, -2.6183e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.2046e-01, -8.9737e-02, -3.8774e-02],\\n\",\n      \"           [-1.3179e-01, -9.6503e-02, -2.3087e-02],\\n\",\n      \"           [-2.0688e-01, -1.3951e-01, -3.0225e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.5485e-01,  5.2822e-01,  6.8502e-01],\\n\",\n      \"           [ 6.9647e-01,  7.4278e-01,  8.0919e-01],\\n\",\n      \"           [ 3.3931e-01,  4.1792e-01,  5.7143e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.6106e-01,  5.6527e-01,  8.2956e-01],\\n\",\n      \"           [ 8.7291e-01,  9.3644e-01,  9.2796e-01],\\n\",\n      \"           [ 6.3059e-01,  7.6751e-01,  7.4718e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 8.6303e-01,  8.9505e-01,  9.0112e-01],\\n\",\n      \"           [ 7.0566e-01,  8.7226e-01,  9.3787e-01],\\n\",\n      \"           [ 8.0774e-01,  8.7167e-01,  9.7258e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.9226e-01,  4.1368e-01,  3.1877e-01],\\n\",\n      \"           [ 2.0918e-01,  2.7906e-01,  2.2452e-01],\\n\",\n      \"           [ 6.0309e-02, -2.2680e-02, -5.1819e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.8678e-01,  3.3076e-01,  2.7491e-01],\\n\",\n      \"           [ 2.2778e-01,  2.1182e-01,  1.6731e-01],\\n\",\n      \"           [ 7.7323e-02,  3.5938e-02,  5.9736e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7557e-01,  1.7954e-01,  1.8473e-01],\\n\",\n      \"           [ 7.4423e-02,  1.7532e-01,  1.8172e-01],\\n\",\n      \"           [ 1.4746e-01,  1.7163e-01,  9.8573e-02]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 75.00 %\\n\",\n      \"Val loss: 0.476376\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[18,    60] loss: 0.47726\\n\",\n      \"[18,   120] loss: 0.47126\\n\",\n      \"Time elapsed: 0h:36m:7s\\n\",\n      \"train accuracy_score: 80.49 %\\n\",\n      \"tensor([[[[[-7.2110e-01, -7.3779e-01, -4.6736e-01],\\n\",\n      \"           [-7.4351e-01, -5.3389e-01, -2.0935e-01],\\n\",\n      \"           [-4.2789e-01, -1.8935e-01, -8.8705e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.9009e-01, -3.6533e-01, -8.7500e-02],\\n\",\n      \"           [-6.2761e-01, -3.6244e-01, -9.8395e-02],\\n\",\n      \"           [-3.9276e-01, -8.5571e-02,  3.0964e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.0935e-01, -1.1871e-01,  1.1515e-01],\\n\",\n      \"           [-2.6495e-01, -1.0218e-01,  7.2450e-02],\\n\",\n      \"           [-2.6864e-01, -2.5092e-02,  2.1645e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.5414e-02,  1.7273e-01,  1.5361e-01],\\n\",\n      \"           [ 8.4383e-02,  1.7128e-01,  1.4333e-01],\\n\",\n      \"           [ 7.8364e-02,  1.0036e-01,  9.6563e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.6367e-03,  8.6404e-02,  8.7335e-02],\\n\",\n      \"           [ 8.7994e-03,  7.7976e-02,  9.3560e-02],\\n\",\n      \"           [ 1.5540e-02,  5.1371e-02,  4.3537e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.1131e-02, -6.6089e-03, -1.9776e-04],\\n\",\n      \"           [-1.0183e-01, -1.6194e-03,  1.2516e-02],\\n\",\n      \"           [-6.2513e-02, -5.5679e-02, -1.4159e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0673e-01,  2.3489e-02, -1.1054e-01],\\n\",\n      \"           [ 1.4469e-01, -3.7562e-02, -2.5507e-02],\\n\",\n      \"           [ 1.5505e-01, -2.7942e-02, -2.8963e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5652e-01,  2.1002e-01,  3.2935e-02],\\n\",\n      \"           [ 2.1919e-01, -6.3653e-03,  3.1688e-02],\\n\",\n      \"           [ 1.2573e-01, -4.1751e-02, -9.6869e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5195e-01,  2.5985e-01,  9.9516e-02],\\n\",\n      \"           [ 2.4808e-01,  1.5884e-01,  1.2429e-01],\\n\",\n      \"           [ 3.1371e-02,  7.4272e-02,  1.0420e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.7635e-02, -6.9694e-02, -7.0886e-02],\\n\",\n      \"           [-4.2170e-02, -2.9928e-02,  2.3549e-02],\\n\",\n      \"           [-9.1982e-02, -6.7848e-02, -7.3800e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.1281e-02, -1.0526e-01, -8.5609e-02],\\n\",\n      \"           [-5.4193e-03, -3.8570e-02, -1.3663e-03],\\n\",\n      \"           [-3.7121e-02,  2.9196e-03, -3.0640e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.6136e-01, -1.3788e-01, -1.0356e-01],\\n\",\n      \"           [-8.7445e-02, -1.9600e-02,  3.0287e-02],\\n\",\n      \"           [-2.3427e-02,  4.9756e-02,  6.5100e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.9966e-02, -1.9761e-02, -9.8867e-02],\\n\",\n      \"           [ 5.8615e-03, -1.3700e-01, -1.5940e-01],\\n\",\n      \"           [-4.9367e-02, -1.1850e-01, -1.8492e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4004e-01,  4.6050e-03, -3.8345e-02],\\n\",\n      \"           [ 1.7790e-02, -1.0206e-01, -9.0072e-02],\\n\",\n      \"           [-3.8196e-02, -9.8873e-02, -1.6995e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0007e-01,  7.9364e-02,  3.5290e-02],\\n\",\n      \"           [ 7.1915e-02,  1.0147e-02, -8.1580e-03],\\n\",\n      \"           [ 7.0961e-02, -3.8305e-02, -6.6075e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.9250e-02,  3.9265e-02,  4.4202e-02],\\n\",\n      \"           [ 1.8668e-02,  3.4046e-02,  3.4768e-02],\\n\",\n      \"           [ 9.9680e-03,  1.2677e-02,  3.9115e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.1503e-02,  2.7751e-02,  2.5132e-02],\\n\",\n      \"           [ 2.1167e-02,  1.1551e-02,  3.1436e-02],\\n\",\n      \"           [-9.1294e-03,  7.3522e-03,  5.6721e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.8004e-03,  6.8392e-03,  2.4327e-02],\\n\",\n      \"           [-1.7245e-02,  4.7566e-03,  2.4342e-02],\\n\",\n      \"           [-2.6790e-02, -9.4659e-03,  2.8180e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.6559e-01,  4.8273e-01,  4.3682e-01],\\n\",\n      \"           [ 4.7459e-01,  4.8757e-01,  4.6630e-01],\\n\",\n      \"           [ 3.8399e-01,  3.5354e-01,  3.2618e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.1378e-01,  5.0216e-01,  4.9735e-01],\\n\",\n      \"           [ 5.5409e-01,  5.5989e-01,  5.0681e-01],\\n\",\n      \"           [ 4.4920e-01,  4.4924e-01,  4.2967e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.3336e-01,  5.9962e-01,  5.4708e-01],\\n\",\n      \"           [ 5.5476e-01,  5.4642e-01,  5.2937e-01],\\n\",\n      \"           [ 5.3921e-01,  4.9895e-01,  5.5112e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.3621e-02,  6.2117e-02,  6.0796e-02],\\n\",\n      \"           [-1.0532e-02,  3.0328e-02,  4.3945e-02],\\n\",\n      \"           [-5.7168e-03,  1.6307e-02,  2.1095e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0730e-01,  9.0232e-02,  9.1448e-02],\\n\",\n      \"           [ 7.7599e-02,  9.7203e-02,  7.3314e-02],\\n\",\n      \"           [ 1.0848e-01,  1.0354e-01,  7.0615e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3742e-01,  1.2769e-01,  1.1805e-01],\\n\",\n      \"           [ 1.6657e-01,  1.3915e-01,  1.0833e-01],\\n\",\n      \"           [ 1.5718e-01,  1.5835e-01,  1.0474e-01]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 79.00 %\\n\",\n      \"Val loss: 0.418232\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[19,    60] loss: 0.38189\\n\",\n      \"[19,   120] loss: 0.42734\\n\",\n      \"Time elapsed: 0h:38m:1s\\n\",\n      \"train accuracy_score: 84.36 %\\n\",\n      \"tensor([[[[[-1.0072e+00, -1.0095e+00, -8.3252e-01],\\n\",\n      \"           [-9.2483e-01, -8.4564e-01, -5.8767e-01],\\n\",\n      \"           [-6.7449e-01, -4.7997e-01, -3.2103e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.0037e+00, -8.3470e-01, -5.8868e-01],\\n\",\n      \"           [-9.3395e-01, -8.6536e-01, -5.4509e-01],\\n\",\n      \"           [-7.3055e-01, -4.5305e-01, -2.8715e-01]],\\n\",\n      \"\\n\",\n      \"          [[-8.4682e-01, -7.1264e-01, -4.9251e-01],\\n\",\n      \"           [-7.7436e-01, -6.2716e-01, -3.9529e-01],\\n\",\n      \"           [-7.0979e-01, -4.4373e-01, -2.2512e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.4905e-03,  5.1593e-02,  4.0040e-02],\\n\",\n      \"           [ 9.0771e-03,  4.6246e-02,  6.3699e-02],\\n\",\n      \"           [ 9.6522e-03,  5.6267e-02,  5.4731e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.3844e-03, -8.4422e-03, -2.7546e-04],\\n\",\n      \"           [-5.7705e-02, -1.4121e-02,  1.6294e-02],\\n\",\n      \"           [-3.6658e-02,  1.1515e-03,  2.8476e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.5358e-02, -4.7524e-02, -3.4154e-02],\\n\",\n      \"           [-8.3264e-02, -3.1195e-02, -2.7578e-02],\\n\",\n      \"           [-9.9294e-02, -6.7674e-02, -2.8690e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.9579e-01, -8.6834e-01, -8.7645e-01],\\n\",\n      \"           [-7.7125e-01, -9.0818e-01, -9.0290e-01],\\n\",\n      \"           [-4.8835e-01, -6.9692e-01, -6.8463e-01]],\\n\",\n      \"\\n\",\n      \"          [[-7.9995e-01, -7.1321e-01, -7.3067e-01],\\n\",\n      \"           [-6.2554e-01, -7.4310e-01, -7.1417e-01],\\n\",\n      \"           [-5.1089e-01, -6.0107e-01, -7.1369e-01]],\\n\",\n      \"\\n\",\n      \"          [[-6.7853e-01, -6.7102e-01, -7.1874e-01],\\n\",\n      \"           [-5.1294e-01, -5.2238e-01, -6.8183e-01],\\n\",\n      \"           [-5.3714e-01, -5.3841e-01, -5.6057e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.4515e-01, -1.9816e-01, -2.6559e-01],\\n\",\n      \"           [-9.4366e-02, -1.2324e-01, -9.8332e-02],\\n\",\n      \"           [-1.2600e-01, -5.4962e-02, -1.3308e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.7598e-02, -1.9097e-01, -2.1584e-01],\\n\",\n      \"           [-6.2733e-02, -1.3380e-01, -8.9681e-02],\\n\",\n      \"           [-9.5844e-02, -9.8947e-02, -1.4856e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.9564e-02, -1.4942e-01, -1.8125e-01],\\n\",\n      \"           [-4.3383e-02, -7.2023e-02, -6.6830e-02],\\n\",\n      \"           [-9.3602e-02, -1.0491e-01, -5.8536e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.6137e-01,  7.7420e-02,  6.0783e-02],\\n\",\n      \"           [ 9.2869e-02,  3.5991e-02,  4.8739e-04],\\n\",\n      \"           [ 7.0642e-02,  5.9027e-02, -1.5427e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3184e-01,  9.3677e-02,  8.3009e-02],\\n\",\n      \"           [ 8.3148e-02,  5.7292e-02,  1.7576e-02],\\n\",\n      \"           [ 4.4135e-02,  3.6009e-02, -1.9535e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3030e-01,  1.3199e-01,  7.6941e-02],\\n\",\n      \"           [ 7.7298e-02,  8.2438e-02,  3.7663e-02],\\n\",\n      \"           [ 1.3170e-01,  6.6843e-02,  1.0178e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.8751e-03,  1.5448e-02,  1.9028e-02],\\n\",\n      \"           [ 1.6600e-02,  2.0050e-02,  7.7081e-03],\\n\",\n      \"           [ 1.1303e-02, -3.6473e-03, -4.7832e-04]],\\n\",\n      \"\\n\",\n      \"          [[-7.2781e-04,  1.2123e-02,  1.1521e-02],\\n\",\n      \"           [ 7.9931e-03,  4.2361e-03,  3.4032e-03],\\n\",\n      \"           [-1.5339e-02, -4.5810e-03, -6.5409e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.1385e-02, -6.9220e-03, -8.4122e-03],\\n\",\n      \"           [-1.3727e-02, -1.0838e-02, -4.6697e-03],\\n\",\n      \"           [-3.3558e-02, -2.3648e-02, -4.7813e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.1449e-01,  4.4961e-01,  3.4098e-01],\\n\",\n      \"           [ 4.5992e-01,  4.3254e-01,  3.3092e-01],\\n\",\n      \"           [ 3.0479e-01,  2.9747e-01,  2.6454e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.3426e-01,  4.5101e-01,  3.8263e-01],\\n\",\n      \"           [ 5.2625e-01,  5.1106e-01,  4.2591e-01],\\n\",\n      \"           [ 3.8989e-01,  3.7268e-01,  3.6524e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 5.0076e-01,  5.0962e-01,  5.0615e-01],\\n\",\n      \"           [ 5.0436e-01,  5.0042e-01,  4.8990e-01],\\n\",\n      \"           [ 4.4382e-01,  4.3379e-01,  4.4815e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.0868e-02, -3.4490e-02, -8.9901e-03],\\n\",\n      \"           [-1.1479e-02,  3.5354e-02, -9.9042e-04],\\n\",\n      \"           [-1.7755e-02, -6.5120e-03, -5.3711e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.8871e-02, -1.1728e-02,  4.7701e-04],\\n\",\n      \"           [ 3.2322e-02, -3.9040e-03,  2.7331e-02],\\n\",\n      \"           [ 4.4188e-02,  5.0435e-03, -1.4708e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.6837e-03, -9.4910e-03,  3.2242e-02],\\n\",\n      \"           [ 4.4656e-02,  1.6742e-02,  7.2724e-02],\\n\",\n      \"           [ 6.5176e-02,  5.7108e-02,  2.4601e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 43.00 %\\n\",\n      \"Val loss: 1.289261\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[20,    60] loss: 0.35876\\n\",\n      \"[20,   120] loss: 0.38245\\n\",\n      \"Time elapsed: 0h:39m:56s\\n\",\n      \"train accuracy_score: 84.07 %\\n\",\n      \"tensor([[[[[ 1.0023e-01,  1.0424e-01,  8.6888e-02],\\n\",\n      \"           [ 8.7432e-02,  8.5016e-02,  7.3141e-02],\\n\",\n      \"           [ 6.2499e-02,  6.1045e-02,  4.9685e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0451e-01,  9.9013e-02,  7.7228e-02],\\n\",\n      \"           [ 9.1260e-02,  8.7780e-02,  6.8603e-02],\\n\",\n      \"           [ 7.3667e-02,  6.0945e-02,  4.9986e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 9.4003e-02,  8.3520e-02,  7.0869e-02],\\n\",\n      \"           [ 7.3742e-02,  6.7732e-02,  5.5409e-02],\\n\",\n      \"           [ 6.9324e-02,  5.2010e-02,  3.5952e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.5151e-02,  8.8261e-03,  9.6431e-03],\\n\",\n      \"           [ 1.1999e-02,  6.7420e-03,  6.2557e-03],\\n\",\n      \"           [ 8.9699e-03,  9.0577e-03,  8.3607e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4285e-02,  8.6728e-03,  1.0520e-02],\\n\",\n      \"           [ 1.0264e-02,  2.9589e-03,  6.9223e-03],\\n\",\n      \"           [ 1.1198e-02,  8.7566e-03,  7.0435e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2798e-02,  1.0559e-02,  1.0518e-02],\\n\",\n      \"           [ 1.1659e-02,  6.5596e-03,  6.4762e-03],\\n\",\n      \"           [ 1.0952e-02,  1.0749e-02,  5.4844e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.5225e-01,  1.6068e-01,  1.7161e-01],\\n\",\n      \"           [ 1.3451e-01,  1.5709e-01,  1.6430e-01],\\n\",\n      \"           [ 1.2305e-01,  1.4446e-01,  1.5413e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4884e-01,  1.5395e-01,  1.6366e-01],\\n\",\n      \"           [ 1.3225e-01,  1.4269e-01,  1.5364e-01],\\n\",\n      \"           [ 1.2478e-01,  1.4010e-01,  1.5290e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.4492e-01,  1.5258e-01,  1.6949e-01],\\n\",\n      \"           [ 1.2575e-01,  1.3997e-01,  1.5653e-01],\\n\",\n      \"           [ 1.2379e-01,  1.3259e-01,  1.4169e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.0608e-03, -5.6412e-03, -4.3125e-03],\\n\",\n      \"           [-1.2186e-03, -2.9105e-03, -6.9894e-03],\\n\",\n      \"           [-2.3409e-03, -6.5547e-03, -7.1985e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.2211e-02, -5.9675e-03, -6.9846e-03],\\n\",\n      \"           [-3.2853e-03, -3.7859e-04, -3.8277e-03],\\n\",\n      \"           [-3.6752e-04, -4.1925e-03, -6.0661e-03]],\\n\",\n      \"\\n\",\n      \"          [[-6.6020e-03, -1.0597e-02, -8.7132e-03],\\n\",\n      \"           [-4.3164e-03, -7.8393e-03, -7.6849e-03],\\n\",\n      \"           [-3.7120e-03, -4.0834e-03, -1.1058e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3901e-02, -4.0737e-03,  4.7604e-03],\\n\",\n      \"           [-5.9559e-03,  3.7304e-03,  6.1444e-03],\\n\",\n      \"           [ 1.8033e-03,  4.0115e-03,  2.2145e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.4749e-02, -4.8445e-03,  7.9392e-04],\\n\",\n      \"           [-4.0788e-03,  5.6579e-03,  1.0217e-02],\\n\",\n      \"           [ 2.9261e-03,  7.0281e-03,  7.0557e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.1301e-02, -3.7347e-03,  1.1137e-03],\\n\",\n      \"           [ 1.3934e-03,  5.8822e-03,  8.1421e-03],\\n\",\n      \"           [ 3.3093e-03,  1.1124e-02,  1.0662e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.6597e-06, -6.9678e-04, -4.7045e-04],\\n\",\n      \"           [ 9.4864e-04,  6.9536e-04,  1.2025e-03],\\n\",\n      \"           [ 2.0087e-03,  1.7751e-03,  4.2623e-04]],\\n\",\n      \"\\n\",\n      \"          [[-5.2790e-04, -4.0510e-05,  1.6956e-03],\\n\",\n      \"           [ 9.0894e-04,  1.3487e-03,  5.2786e-04],\\n\",\n      \"           [ 2.2378e-03,  1.8107e-03, -1.7294e-05]],\\n\",\n      \"\\n\",\n      \"          [[ 2.1518e-04,  5.5941e-04,  7.2888e-04],\\n\",\n      \"           [ 9.0593e-04,  1.2067e-03, -2.9258e-04],\\n\",\n      \"           [ 2.8944e-03,  1.7040e-03, -4.3103e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.0078e-02, -1.8665e-02, -1.5803e-02],\\n\",\n      \"           [-2.2261e-02, -2.1076e-02, -1.8005e-02],\\n\",\n      \"           [-2.3403e-02, -2.0301e-02, -1.7253e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.3347e-02, -1.9972e-02, -1.3718e-02],\\n\",\n      \"           [-2.5459e-02, -2.3679e-02, -2.0705e-02],\\n\",\n      \"           [-3.0190e-02, -2.2553e-02, -1.9297e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.9763e-02, -1.7698e-02, -1.9065e-02],\\n\",\n      \"           [-2.4357e-02, -2.1215e-02, -2.1539e-02],\\n\",\n      \"           [-3.2598e-02, -2.4478e-02, -1.9709e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.4034e-03, -8.3892e-03, -1.0792e-02],\\n\",\n      \"           [-5.6945e-03, -6.6552e-03, -6.5395e-03],\\n\",\n      \"           [-2.1576e-03, -2.6043e-03, -2.4533e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.3950e-03, -3.6663e-03, -4.4020e-03],\\n\",\n      \"           [-4.4507e-03, -4.7150e-03, -5.2147e-03],\\n\",\n      \"           [-3.6226e-03, -4.4008e-03, -3.1432e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.9026e-03, -9.4049e-04, -2.0677e-04],\\n\",\n      \"           [-1.9438e-03, -2.7766e-03, -4.6925e-03],\\n\",\n      \"           [-3.8422e-03, -4.3900e-03, -2.2681e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 81.00 %\\n\",\n      \"Val loss: 0.574352\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[21,    60] loss: 0.42106\\n\",\n      \"[21,   120] loss: 0.39145\\n\",\n      \"Time elapsed: 0h:41m:50s\\n\",\n      \"train accuracy_score: 83.93 %\\n\",\n      \"tensor([[[[[ 4.4392e-02,  7.1210e-03,  3.6800e-02],\\n\",\n      \"           [ 3.9359e-02,  3.0715e-02,  7.0022e-02],\\n\",\n      \"           [ 6.8295e-02,  1.3966e-01,  1.3513e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 6.3208e-02,  8.7396e-02,  1.1430e-01],\\n\",\n      \"           [ 4.8967e-02,  8.0175e-02,  1.0930e-01],\\n\",\n      \"           [ 8.4282e-02,  1.6554e-01,  1.8082e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3146e-01,  1.5084e-01,  1.9123e-01],\\n\",\n      \"           [ 1.4329e-01,  1.3963e-01,  1.3277e-01],\\n\",\n      \"           [ 1.0222e-01,  1.7477e-01,  2.2434e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.7043e-02,  2.9333e-02,  1.3349e-02],\\n\",\n      \"           [ 2.8565e-02,  2.5047e-02,  1.7048e-02],\\n\",\n      \"           [ 2.3204e-02, -9.5095e-04,  9.2577e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.5398e-02,  2.0924e-02,  6.8696e-03],\\n\",\n      \"           [ 2.1757e-02,  2.0333e-02,  1.6033e-02],\\n\",\n      \"           [ 1.0831e-03, -9.8392e-03, -3.0388e-05]],\\n\",\n      \"\\n\",\n      \"          [[-1.2891e-02, -9.0969e-03, -1.2643e-02],\\n\",\n      \"           [ 4.3966e-03,  3.5484e-04,  1.9909e-04],\\n\",\n      \"           [-1.0483e-02, -1.5235e-02,  4.1056e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.9531e-02,  1.9188e-02,  1.5801e-03],\\n\",\n      \"           [ 5.7068e-02,  1.2606e-03, -1.5164e-02],\\n\",\n      \"           [ 8.9460e-02,  8.3834e-02,  3.2988e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.4842e-02,  4.9713e-02,  4.3511e-02],\\n\",\n      \"           [ 8.7041e-02,  4.7667e-02,  2.4919e-02],\\n\",\n      \"           [ 6.1261e-02,  5.7133e-02,  6.2404e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 9.0262e-02,  7.0361e-02,  5.8101e-02],\\n\",\n      \"           [ 7.2063e-02,  2.6402e-02,  3.5501e-02],\\n\",\n      \"           [ 2.5828e-02,  5.0522e-02,  6.4281e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.0570e-02, -6.0203e-02, -5.5493e-02],\\n\",\n      \"           [-3.1672e-02, -2.2309e-02, -7.8742e-03],\\n\",\n      \"           [-2.3358e-02, -2.3533e-02, -1.9506e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.1237e-02, -1.9835e-02, -2.6057e-02],\\n\",\n      \"           [-3.2560e-03, -9.7744e-03,  3.6971e-03],\\n\",\n      \"           [ 1.2463e-02,  1.2219e-03, -1.4648e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.0493e-02, -2.3207e-02, -2.0949e-02],\\n\",\n      \"           [ 7.7476e-04, -1.5510e-03,  3.8225e-03],\\n\",\n      \"           [ 1.0649e-02, -7.7734e-04,  6.5593e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.1005e-01,  9.5321e-02,  9.2729e-02],\\n\",\n      \"           [ 9.0378e-02,  7.5387e-02,  7.8020e-02],\\n\",\n      \"           [ 7.1289e-02,  5.6930e-02,  3.9898e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0885e-01,  8.5049e-02,  8.4303e-02],\\n\",\n      \"           [ 9.0570e-02,  6.1696e-02,  7.9263e-02],\\n\",\n      \"           [ 4.9690e-02,  5.1443e-02,  5.9723e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 9.3417e-02,  8.2601e-02,  7.5869e-02],\\n\",\n      \"           [ 4.5898e-02,  2.9396e-02,  4.4265e-02],\\n\",\n      \"           [ 7.2715e-02,  5.5000e-02,  5.5684e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.3006e-03, -7.8654e-04,  7.4565e-04],\\n\",\n      \"           [-1.2513e-02, -5.8069e-03,  1.5549e-03],\\n\",\n      \"           [-1.4349e-02, -1.1967e-02, -1.5616e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.4066e-02, -6.1714e-03, -1.5871e-03],\\n\",\n      \"           [-2.0553e-02, -9.3402e-03,  9.2136e-04],\\n\",\n      \"           [-2.0080e-02, -1.4423e-02, -1.4313e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.3976e-02, -9.1774e-03, -7.1462e-03],\\n\",\n      \"           [-1.9031e-02, -1.2671e-02, -3.1568e-03],\\n\",\n      \"           [-2.6937e-02, -1.7433e-02, -1.0689e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0284e-01,  1.3171e-01,  1.4625e-01],\\n\",\n      \"           [ 1.3482e-01,  1.7432e-01,  1.5921e-01],\\n\",\n      \"           [ 8.0681e-02,  9.4310e-02,  1.0928e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0489e-01,  1.1485e-01,  1.3999e-01],\\n\",\n      \"           [ 1.3530e-01,  1.5195e-01,  1.4883e-01],\\n\",\n      \"           [ 8.9973e-02,  1.1281e-01,  1.2749e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2624e-01,  1.2601e-01,  1.5094e-01],\\n\",\n      \"           [ 1.0623e-01,  1.2257e-01,  1.5368e-01],\\n\",\n      \"           [ 8.8776e-02,  1.1115e-01,  1.3517e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.7890e-03, -1.8335e-02, -2.8217e-02],\\n\",\n      \"           [-1.3106e-02, -2.7225e-03, -2.0664e-02],\\n\",\n      \"           [-3.1939e-02, -2.0562e-02, -2.5410e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7859e-03, -7.7637e-03, -9.5265e-03],\\n\",\n      \"           [-9.7288e-03,  2.1267e-04, -1.9436e-03],\\n\",\n      \"           [-2.0593e-02, -1.5328e-02, -1.9729e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.4021e-03,  6.6516e-04, -2.2879e-02],\\n\",\n      \"           [-8.6594e-03,  3.6735e-03,  2.7821e-03],\\n\",\n      \"           [-2.3881e-02, -2.4638e-02, -2.3589e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 88.00 %\\n\",\n      \"Val loss: 0.362015\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[22,    60] loss: 0.40505\\n\",\n      \"[22,   120] loss: 0.32271\\n\",\n      \"Time elapsed: 0h:43m:44s\\n\",\n      \"train accuracy_score: 83.50 %\\n\",\n      \"tensor([[[[[ 0.2167,  0.3100,  0.2048],\\n\",\n      \"           [ 0.2314,  0.2232,  0.1359],\\n\",\n      \"           [ 0.1766,  0.1050, -0.0596]],\\n\",\n      \"\\n\",\n      \"          [[ 0.2171,  0.1774,  0.1783],\\n\",\n      \"           [ 0.2084,  0.1949,  0.1298],\\n\",\n      \"           [ 0.1411,  0.0537, -0.0498]],\\n\",\n      \"\\n\",\n      \"          [[ 0.1740,  0.1701,  0.0897],\\n\",\n      \"           [ 0.1066,  0.1118,  0.0510],\\n\",\n      \"           [ 0.0912,  0.0516,  0.0023]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-0.0005, -0.0214, -0.0185],\\n\",\n      \"           [-0.0245, -0.0352, -0.0277],\\n\",\n      \"           [-0.0121, -0.0196,  0.0025]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0076,  0.0007, -0.0114],\\n\",\n      \"           [-0.0020, -0.0074, -0.0100],\\n\",\n      \"           [ 0.0154, -0.0105,  0.0037]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0158,  0.0192,  0.0012],\\n\",\n      \"           [ 0.0110, -0.0049, -0.0128],\\n\",\n      \"           [ 0.0047, -0.0089, -0.0161]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-0.1353, -0.1229, -0.1265],\\n\",\n      \"           [-0.1548, -0.1474, -0.1543],\\n\",\n      \"           [-0.2063, -0.1712, -0.2191]],\\n\",\n      \"\\n\",\n      \"          [[-0.0960, -0.1541, -0.0931],\\n\",\n      \"           [-0.1813, -0.1476, -0.1585],\\n\",\n      \"           [-0.1589, -0.1477, -0.1581]],\\n\",\n      \"\\n\",\n      \"          [[-0.0335, -0.0450, -0.1306],\\n\",\n      \"           [-0.1190, -0.1143, -0.0932],\\n\",\n      \"           [-0.1271, -0.1280, -0.1587]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 0.0506,  0.0580,  0.0926],\\n\",\n      \"           [ 0.0032,  0.0560,  0.0895],\\n\",\n      \"           [ 0.0121,  0.0405,  0.0751]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0371,  0.0552,  0.0898],\\n\",\n      \"           [ 0.0039,  0.0271,  0.0684],\\n\",\n      \"           [ 0.0041,  0.0412,  0.0983]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0442,  0.0354,  0.0432],\\n\",\n      \"           [ 0.0215,  0.0357,  0.0574],\\n\",\n      \"           [ 0.0074,  0.0225,  0.0341]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-0.0456, -0.0253,  0.0025],\\n\",\n      \"           [-0.0172,  0.0245,  0.0379],\\n\",\n      \"           [-0.0083, -0.0028, -0.0128]],\\n\",\n      \"\\n\",\n      \"          [[-0.0476, -0.0091, -0.0145],\\n\",\n      \"           [-0.0182,  0.0282,  0.0173],\\n\",\n      \"           [ 0.0168,  0.0309,  0.0208]],\\n\",\n      \"\\n\",\n      \"          [[-0.0547, -0.0206, -0.0080],\\n\",\n      \"           [-0.0120,  0.0138,  0.0408],\\n\",\n      \"           [ 0.0103,  0.0190,  0.0418]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 0.0111,  0.0141,  0.0068],\\n\",\n      \"           [ 0.0211,  0.0125,  0.0083],\\n\",\n      \"           [ 0.0121,  0.0209,  0.0123]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0140,  0.0111,  0.0121],\\n\",\n      \"           [ 0.0229,  0.0142,  0.0100],\\n\",\n      \"           [ 0.0239,  0.0251,  0.0138]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0159,  0.0171,  0.0181],\\n\",\n      \"           [ 0.0172,  0.0238,  0.0210],\\n\",\n      \"           [ 0.0321,  0.0333,  0.0189]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-0.1818, -0.1581, -0.1218],\\n\",\n      \"           [-0.1510, -0.1203, -0.0966],\\n\",\n      \"           [-0.0746, -0.0384, -0.0499]],\\n\",\n      \"\\n\",\n      \"          [[-0.1883, -0.1634, -0.1489],\\n\",\n      \"           [-0.1729, -0.1306, -0.1055],\\n\",\n      \"           [-0.1184, -0.0836, -0.0608]],\\n\",\n      \"\\n\",\n      \"          [[-0.2119, -0.1755, -0.1553],\\n\",\n      \"           [-0.1775, -0.1528, -0.1449],\\n\",\n      \"           [-0.1665, -0.1352, -0.1249]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-0.0628, -0.0422, -0.0384],\\n\",\n      \"           [-0.0400, -0.0337, -0.0218],\\n\",\n      \"           [-0.0346, -0.0190, -0.0164]],\\n\",\n      \"\\n\",\n      \"          [[-0.0329, -0.0339, -0.0282],\\n\",\n      \"           [-0.0469, -0.0218, -0.0238],\\n\",\n      \"           [-0.0481, -0.0180, -0.0178]],\\n\",\n      \"\\n\",\n      \"          [[-0.0708, -0.0487, -0.0227],\\n\",\n      \"           [-0.0861, -0.0676, -0.0422],\\n\",\n      \"           [-0.0603, -0.0483, -0.0241]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 85.00 %\\n\",\n      \"Val loss: 0.407304\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[23,    60] loss: 0.34153\\n\",\n      \"[23,   120] loss: 0.31930\\n\",\n      \"Time elapsed: 0h:45m:39s\\n\",\n      \"train accuracy_score: 87.66 %\\n\",\n      \"tensor([[[[[-2.1298e+00, -1.9740e+00, -1.1220e+00],\\n\",\n      \"           [-1.7419e+00, -1.4372e+00, -6.0552e-01],\\n\",\n      \"           [-6.7975e-01, -6.8362e-01,  7.3811e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.8900e+00, -1.3223e+00,  7.4113e-02],\\n\",\n      \"           [-1.2883e+00, -6.0134e-01,  2.9734e-02],\\n\",\n      \"           [-1.4350e-01,  1.5497e-01,  6.2385e-01]],\\n\",\n      \"\\n\",\n      \"          [[-8.4468e-01, -2.6487e-01,  9.0856e-01],\\n\",\n      \"           [-5.9540e-01,  4.0525e-01,  1.3025e+00],\\n\",\n      \"           [-1.4154e-01,  5.6008e-01,  1.5994e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.7369e-01,  6.0033e-01,  3.4429e-01],\\n\",\n      \"           [ 5.0616e-01,  4.9680e-01,  2.5198e-01],\\n\",\n      \"           [ 6.1611e-01,  3.6137e-01,  5.2632e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.3256e-01,  6.5518e-01,  4.4822e-01],\\n\",\n      \"           [ 6.8338e-01,  6.2194e-01,  3.2736e-01],\\n\",\n      \"           [ 6.3370e-01,  4.3947e-01,  8.9598e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.5647e-01,  5.8460e-01,  4.9870e-01],\\n\",\n      \"           [ 7.1833e-01,  6.6412e-01,  4.0111e-01],\\n\",\n      \"           [ 6.9245e-01,  4.2040e-01,  2.0597e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.8244e-01, -1.2436e+00, -1.2213e+00],\\n\",\n      \"           [-2.4776e-01, -6.6042e-01, -8.0426e-01],\\n\",\n      \"           [ 1.4649e-01, -1.8842e-01,  2.5039e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.2203e+00, -1.0793e+00, -9.9680e-01],\\n\",\n      \"           [-8.6664e-01, -5.8801e-01, -4.1021e-01],\\n\",\n      \"           [-1.1714e-01, -5.1918e-01, -1.5768e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.6411e+00, -1.4151e+00, -1.0096e+00],\\n\",\n      \"           [-9.9120e-01, -8.0163e-01, -3.8735e-01],\\n\",\n      \"           [-5.3465e-01, -5.7685e-01, -8.3760e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.5141e-01, -6.2460e-01, -3.8827e-01],\\n\",\n      \"           [-5.1127e-01, -4.1096e-01, -2.5960e-01],\\n\",\n      \"           [-3.6317e-01, -3.0073e-01, -2.3998e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.7220e-01, -3.2858e-01, -3.5700e-01],\\n\",\n      \"           [-3.6547e-02, -1.9413e-01, -1.8001e-01],\\n\",\n      \"           [-2.0094e-01, -8.1005e-02, -1.4364e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.1956e-02, -1.1917e-01, -2.0840e-01],\\n\",\n      \"           [ 1.1422e-01, -7.4854e-02, -1.4671e-01],\\n\",\n      \"           [-1.9396e-02, -1.3173e-01, -1.8030e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.5698e-01, -4.7713e-01, -4.7812e-01],\\n\",\n      \"           [-3.7683e-01, -6.3582e-01, -5.6109e-01],\\n\",\n      \"           [-5.8078e-01, -8.0650e-01, -4.3843e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.8372e-01, -4.9467e-01, -2.5471e-01],\\n\",\n      \"           [-5.9029e-01, -6.0468e-01, -4.0220e-01],\\n\",\n      \"           [-6.4654e-01, -5.7096e-01, -4.6110e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.4220e-01, -3.1751e-01, -1.6210e-01],\\n\",\n      \"           [-6.2751e-01, -4.8213e-01, -2.2512e-01],\\n\",\n      \"           [-3.5671e-01, -4.5590e-01, -1.9557e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.8023e-02, -1.8575e-04,  3.2110e-02],\\n\",\n      \"           [-2.4279e-02,  7.4522e-03,  2.8363e-03],\\n\",\n      \"           [-5.7881e-02, -8.9282e-02, -3.5268e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6764e-02,  7.0701e-03, -1.1409e-02],\\n\",\n      \"           [-3.6136e-02, -3.2677e-02,  1.7769e-02],\\n\",\n      \"           [-1.3304e-01, -1.1612e-01, -3.1468e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.0452e-02, -5.9704e-02, -4.7545e-02],\\n\",\n      \"           [-6.1752e-02, -6.8763e-02, -3.7354e-02],\\n\",\n      \"           [-1.4559e-01, -6.5641e-02, -1.0768e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.3488e-01, -5.7070e-01, -5.7665e-01],\\n\",\n      \"           [-7.3884e-01, -5.9249e-01, -6.3655e-01],\\n\",\n      \"           [-1.2432e+00, -1.0259e+00, -9.0407e-01]],\\n\",\n      \"\\n\",\n      \"          [[-2.8073e-01, -2.4563e-01, -2.9852e-01],\\n\",\n      \"           [-4.6437e-01, -3.6777e-01, -2.6846e-01],\\n\",\n      \"           [-7.2887e-01, -6.9295e-01, -6.2722e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.7153e-03, -5.2988e-02,  5.8673e-02],\\n\",\n      \"           [-1.8406e-01, -1.3331e-01, -8.0915e-02],\\n\",\n      \"           [-3.9339e-01, -2.3328e-01, -1.7501e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.0959e-01,  5.3984e-01,  5.5666e-01],\\n\",\n      \"           [ 2.2329e-01,  2.9715e-01,  3.2865e-01],\\n\",\n      \"           [ 8.5110e-02,  1.6385e-01,  2.1084e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.2106e-01,  4.8541e-01,  5.0463e-01],\\n\",\n      \"           [ 2.7598e-01,  3.5098e-01,  5.0913e-01],\\n\",\n      \"           [ 3.0810e-01,  2.9020e-01,  4.0587e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.9076e-01,  3.3726e-01,  5.6721e-01],\\n\",\n      \"           [ 4.3739e-01,  4.4305e-01,  5.4853e-01],\\n\",\n      \"           [ 4.0129e-01,  5.5079e-01,  5.8819e-01]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 83.00 %\\n\",\n      \"Val loss: 0.352413\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[24,    60] loss: 0.36062\\n\",\n      \"[24,   120] loss: 0.33386\\n\",\n      \"Time elapsed: 0h:47m:32s\\n\",\n      \"train accuracy_score: 85.08 %\\n\",\n      \"tensor([[[[[ 5.4134e-01,  5.0482e-01,  3.7826e-01],\\n\",\n      \"           [ 5.0902e-01,  4.2290e-01,  3.3322e-01],\\n\",\n      \"           [ 4.1926e-01,  3.3626e-01,  3.1699e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.8721e-01,  4.2799e-01,  2.5790e-01],\\n\",\n      \"           [ 4.7228e-01,  3.7723e-01,  2.9177e-01],\\n\",\n      \"           [ 3.4948e-01,  2.7522e-01,  2.5573e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 4.1407e-01,  3.6803e-01,  2.1721e-01],\\n\",\n      \"           [ 3.5147e-01,  3.0266e-01,  1.9759e-01],\\n\",\n      \"           [ 2.8524e-01,  1.9183e-01,  1.4127e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 7.0694e-04, -1.0458e-02,  7.9052e-03],\\n\",\n      \"           [ 7.0829e-04,  4.9042e-03,  2.3238e-02],\\n\",\n      \"           [ 2.0034e-02,  3.9576e-02,  7.0683e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.9209e-02, -2.1742e-03,  2.3360e-02],\\n\",\n      \"           [ 1.1572e-02,  1.1504e-02,  2.5349e-02],\\n\",\n      \"           [ 3.6661e-02,  5.4009e-02,  7.4787e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 8.8523e-03,  2.4652e-02,  3.7298e-02],\\n\",\n      \"           [ 2.8051e-02,  2.5597e-02,  3.2901e-02],\\n\",\n      \"           [ 6.7466e-02,  7.9729e-02,  7.5472e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.4111e-02, -9.9544e-02, -3.9477e-02],\\n\",\n      \"           [-1.6070e-01, -7.0682e-02, -8.0209e-02],\\n\",\n      \"           [-1.8102e-01, -1.2696e-01, -1.0085e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.2810e-01, -1.0892e-01, -4.2162e-02],\\n\",\n      \"           [-1.0336e-01, -6.8721e-02, -7.3651e-02],\\n\",\n      \"           [-9.5136e-02, -5.9908e-02, -4.0838e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.1329e-02, -9.1087e-02, -4.2860e-02],\\n\",\n      \"           [-3.2126e-02, -6.3025e-02, -6.1612e-02],\\n\",\n      \"           [-8.8956e-03, -6.1151e-02, -2.9033e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.6725e-02,  3.6074e-02,  4.7133e-02],\\n\",\n      \"           [ 2.7304e-02,  3.8903e-02,  2.8959e-02],\\n\",\n      \"           [ 5.0008e-02,  5.1079e-02,  3.6948e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.2075e-06,  7.1563e-03,  3.9603e-02],\\n\",\n      \"           [ 1.2509e-03,  1.1463e-02,  3.2096e-02],\\n\",\n      \"           [ 4.0443e-02,  3.0031e-02,  2.8809e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.0112e-02, -3.2515e-03,  2.6601e-02],\\n\",\n      \"           [-2.2945e-02,  1.9689e-02,  2.1784e-02],\\n\",\n      \"           [ 1.2904e-02,  3.7479e-02,  1.5848e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1805e-03,  3.2030e-02,  6.3073e-02],\\n\",\n      \"           [-1.6838e-02,  3.3464e-02,  3.9306e-02],\\n\",\n      \"           [ 2.1535e-03,  1.2962e-02,  1.9438e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.6471e-03,  4.6638e-02,  2.5798e-02],\\n\",\n      \"           [-3.7373e-02,  2.5824e-02,  1.6642e-02],\\n\",\n      \"           [-5.6247e-02, -2.8262e-02, -9.0454e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.4951e-02, -9.4095e-03, -3.3655e-03],\\n\",\n      \"           [-4.9548e-02,  7.6142e-03,  1.0295e-02],\\n\",\n      \"           [-8.1736e-02, -5.2100e-02, -4.5833e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.7072e-03, -6.8649e-03, -1.4026e-03],\\n\",\n      \"           [-7.6591e-03, -7.1610e-03, -4.3299e-03],\\n\",\n      \"           [-9.3045e-03, -7.3353e-03, -7.3566e-03]],\\n\",\n      \"\\n\",\n      \"          [[-9.0744e-03, -5.8218e-03,  3.3897e-03],\\n\",\n      \"           [-9.4499e-03, -1.3426e-03, -2.1867e-03],\\n\",\n      \"           [-2.6705e-03, -3.0873e-03, -2.6069e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.8940e-03,  4.4460e-03,  3.7419e-03],\\n\",\n      \"           [-2.1629e-03, -1.3949e-03, -3.2066e-03],\\n\",\n      \"           [-3.9773e-03, -3.7585e-03, -9.0321e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.3056e-02, -4.6911e-02, -2.7549e-02],\\n\",\n      \"           [-6.4486e-02, -4.7255e-02, -3.5415e-02],\\n\",\n      \"           [ 1.1951e-02, -1.9576e-03,  1.2097e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.9752e-02, -4.1885e-02, -4.2727e-02],\\n\",\n      \"           [-5.4660e-02, -6.9157e-02, -4.8410e-02],\\n\",\n      \"           [-1.2410e-02, -2.7577e-02, -2.3688e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.4445e-02, -6.2409e-02, -6.4582e-02],\\n\",\n      \"           [-6.8274e-02, -9.7245e-02, -8.6948e-02],\\n\",\n      \"           [-6.8186e-02, -7.4967e-02, -7.5178e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.2895e-03,  1.2214e-02,  1.3729e-02],\\n\",\n      \"           [ 1.2912e-02, -6.5718e-04,  1.5353e-03],\\n\",\n      \"           [ 1.0194e-02,  1.9351e-02,  3.7187e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3165e-02,  1.6535e-02,  8.6823e-03],\\n\",\n      \"           [ 5.2239e-03, -4.9276e-03, -3.8443e-03],\\n\",\n      \"           [-1.2999e-02, -4.5324e-03, -1.1386e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.3277e-02, -1.5232e-02,  8.7403e-03],\\n\",\n      \"           [-3.8047e-02, -2.5793e-02, -2.8807e-02],\\n\",\n      \"           [-3.3254e-02, -2.2963e-02, -2.1828e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 82.00 %\\n\",\n      \"Val loss: 0.357670\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[25,    60] loss: 0.30187\\n\",\n      \"[25,   120] loss: 0.28476\\n\",\n      \"Time elapsed: 0h:49m:26s\\n\",\n      \"train accuracy_score: 88.52 %\\n\",\n      \"tensor([[[[[-0.1494, -0.1883, -0.1263],\\n\",\n      \"           [-0.1142, -0.1308, -0.0949],\\n\",\n      \"           [-0.0821, -0.0611, -0.0799]],\\n\",\n      \"\\n\",\n      \"          [[-0.1353, -0.1350, -0.0865],\\n\",\n      \"           [-0.1287, -0.1034, -0.0883],\\n\",\n      \"           [-0.0245,  0.0164, -0.0189]],\\n\",\n      \"\\n\",\n      \"          [[-0.0722, -0.0628, -0.0493],\\n\",\n      \"           [-0.0656, -0.0176, -0.0140],\\n\",\n      \"           [ 0.0377,  0.0560,  0.0780]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-0.0498, -0.0467, -0.0469],\\n\",\n      \"           [-0.0530, -0.0459, -0.0508],\\n\",\n      \"           [-0.0071, -0.0272, -0.0254]],\\n\",\n      \"\\n\",\n      \"          [[-0.0383, -0.0289, -0.0345],\\n\",\n      \"           [-0.0301, -0.0232, -0.0238],\\n\",\n      \"           [-0.0305, -0.0285, -0.0084]],\\n\",\n      \"\\n\",\n      \"          [[-0.0534, -0.0364, -0.0335],\\n\",\n      \"           [-0.0343, -0.0246, -0.0067],\\n\",\n      \"           [-0.0142, -0.0134, -0.0115]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 0.0978,  0.1555,  0.1268],\\n\",\n      \"           [ 0.2226,  0.2292,  0.2463],\\n\",\n      \"           [ 0.2799,  0.3045,  0.2726]],\\n\",\n      \"\\n\",\n      \"          [[ 0.1601,  0.2309,  0.2080],\\n\",\n      \"           [ 0.2511,  0.2835,  0.2765],\\n\",\n      \"           [ 0.2302,  0.2029,  0.2193]],\\n\",\n      \"\\n\",\n      \"          [[ 0.1072,  0.1759,  0.2102],\\n\",\n      \"           [ 0.1588,  0.2091,  0.2639],\\n\",\n      \"           [ 0.1929,  0.2129,  0.2758]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-0.0010, -0.0204, -0.0445],\\n\",\n      \"           [ 0.0035,  0.0044,  0.0153],\\n\",\n      \"           [ 0.0109,  0.0038,  0.0465]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0359,  0.0095, -0.0161],\\n\",\n      \"           [ 0.0206,  0.0097, -0.0106],\\n\",\n      \"           [-0.0253, -0.0088,  0.0049]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0409,  0.0079, -0.0120],\\n\",\n      \"           [ 0.0363,  0.0203,  0.0154],\\n\",\n      \"           [-0.0144,  0.0013,  0.0074]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 0.0535,  0.0076, -0.0560],\\n\",\n      \"           [ 0.0329, -0.0470, -0.0768],\\n\",\n      \"           [-0.0229, -0.0663, -0.0900]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0982,  0.0434, -0.0041],\\n\",\n      \"           [ 0.0611, -0.0151, -0.0294],\\n\",\n      \"           [ 0.0038, -0.0060, -0.0501]],\\n\",\n      \"\\n\",\n      \"          [[ 0.1018,  0.0465,  0.0098],\\n\",\n      \"           [ 0.0579,  0.0181, -0.0254],\\n\",\n      \"           [ 0.0394,  0.0240, -0.0184]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-0.0176, -0.0184, -0.0116],\\n\",\n      \"           [-0.0209, -0.0132, -0.0100],\\n\",\n      \"           [-0.0211, -0.0086, -0.0172]],\\n\",\n      \"\\n\",\n      \"          [[-0.0076, -0.0139, -0.0080],\\n\",\n      \"           [-0.0141, -0.0110, -0.0042],\\n\",\n      \"           [-0.0176, -0.0175, -0.0107]],\\n\",\n      \"\\n\",\n      \"          [[-0.0114, -0.0079, -0.0008],\\n\",\n      \"           [-0.0014, -0.0022, -0.0035],\\n\",\n      \"           [-0.0229, -0.0095, -0.0150]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 0.1995,  0.2098,  0.1578],\\n\",\n      \"           [ 0.2187,  0.2005,  0.1595],\\n\",\n      \"           [ 0.1456,  0.1179,  0.0975]],\\n\",\n      \"\\n\",\n      \"          [[ 0.2061,  0.1951,  0.1686],\\n\",\n      \"           [ 0.2137,  0.1982,  0.1742],\\n\",\n      \"           [ 0.1853,  0.1609,  0.1208]],\\n\",\n      \"\\n\",\n      \"          [[ 0.2248,  0.1989,  0.1617],\\n\",\n      \"           [ 0.2163,  0.1882,  0.1558],\\n\",\n      \"           [ 0.1989,  0.1779,  0.1294]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 0.0616,  0.0443,  0.0282],\\n\",\n      \"           [ 0.0304,  0.0511,  0.0334],\\n\",\n      \"           [ 0.0260,  0.0085, -0.0093]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0828,  0.0681,  0.0330],\\n\",\n      \"           [ 0.0528,  0.0183, -0.0055],\\n\",\n      \"           [ 0.0530,  0.0196, -0.0074]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0939,  0.0456,  0.0279],\\n\",\n      \"           [ 0.0797,  0.0524, -0.0008],\\n\",\n      \"           [ 0.0594,  0.0428,  0.0060]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 87.00 %\\n\",\n      \"Val loss: 0.351057\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[26,    60] loss: 0.27525\\n\",\n      \"[26,   120] loss: 0.28456\\n\",\n      \"Time elapsed: 0h:51m:20s\\n\",\n      \"train accuracy_score: 90.96 %\\n\",\n      \"tensor([[[[[ 0.0321, -0.0093,  0.0161],\\n\",\n      \"           [ 0.0724,  0.0409,  0.0148],\\n\",\n      \"           [ 0.1037,  0.1110,  0.0759]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0640,  0.0233,  0.1103],\\n\",\n      \"           [ 0.1194,  0.1006,  0.0495],\\n\",\n      \"           [ 0.1919,  0.2060,  0.1433]],\\n\",\n      \"\\n\",\n      \"          [[ 0.1760,  0.1609,  0.1614],\\n\",\n      \"           [ 0.1844,  0.2302,  0.2143],\\n\",\n      \"           [ 0.2444,  0.2878,  0.2746]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-0.0741, -0.0557, -0.0548],\\n\",\n      \"           [-0.0755, -0.0411, -0.0396],\\n\",\n      \"           [-0.0783, -0.0648, -0.0517]],\\n\",\n      \"\\n\",\n      \"          [[-0.0856, -0.0563, -0.0703],\\n\",\n      \"           [-0.0773, -0.0621, -0.0551],\\n\",\n      \"           [-0.0888, -0.0780, -0.0606]],\\n\",\n      \"\\n\",\n      \"          [[-0.1290, -0.1039, -0.0836],\\n\",\n      \"           [-0.1043, -0.0981, -0.0664],\\n\",\n      \"           [-0.1008, -0.0874, -0.0763]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-0.2735, -0.3386, -0.3694],\\n\",\n      \"           [-0.1873, -0.3165, -0.3791],\\n\",\n      \"           [-0.1365, -0.2777, -0.3397]],\\n\",\n      \"\\n\",\n      \"          [[-0.2389, -0.2943, -0.3252],\\n\",\n      \"           [-0.2270, -0.3029, -0.2895],\\n\",\n      \"           [-0.2221, -0.3013, -0.3512]],\\n\",\n      \"\\n\",\n      \"          [[-0.2588, -0.2843, -0.2953],\\n\",\n      \"           [-0.2664, -0.3196, -0.2990],\\n\",\n      \"           [-0.2463, -0.2456, -0.2814]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-0.0123, -0.0144,  0.0214],\\n\",\n      \"           [-0.0297, -0.0162,  0.0274],\\n\",\n      \"           [-0.0367, -0.0234,  0.0108]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0060,  0.0037,  0.0202],\\n\",\n      \"           [-0.0055,  0.0074,  0.0388],\\n\",\n      \"           [-0.0288,  0.0069,  0.0075]],\\n\",\n      \"\\n\",\n      \"          [[-0.0082, -0.0070,  0.0116],\\n\",\n      \"           [ 0.0065,  0.0187,  0.0418],\\n\",\n      \"           [-0.0023,  0.0096,  0.0274]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 0.0323,  0.0135, -0.0189],\\n\",\n      \"           [ 0.0358, -0.0099, -0.0426],\\n\",\n      \"           [ 0.0067, -0.0302, -0.0516]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0500,  0.0150, -0.0264],\\n\",\n      \"           [ 0.0173, -0.0272, -0.0342],\\n\",\n      \"           [ 0.0122, -0.0195, -0.0260]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0816,  0.0368, -0.0083],\\n\",\n      \"           [ 0.0193,  0.0128, -0.0120],\\n\",\n      \"           [-0.0082, -0.0085, -0.0160]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 0.0413,  0.0381,  0.0247],\\n\",\n      \"           [ 0.0290,  0.0277,  0.0161],\\n\",\n      \"           [ 0.0149,  0.0162,  0.0111]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0192,  0.0223,  0.0207],\\n\",\n      \"           [ 0.0221,  0.0146,  0.0167],\\n\",\n      \"           [ 0.0038,  0.0056,  0.0044]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0119,  0.0091,  0.0067],\\n\",\n      \"           [ 0.0067,  0.0160,  0.0155],\\n\",\n      \"           [-0.0048, -0.0028,  0.0059]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 0.0998,  0.0991,  0.0685],\\n\",\n      \"           [ 0.0913,  0.0823,  0.0407],\\n\",\n      \"           [ 0.0262,  0.0190, -0.0235]],\\n\",\n      \"\\n\",\n      \"          [[ 0.1077,  0.0864,  0.0789],\\n\",\n      \"           [ 0.1058,  0.0950,  0.0669],\\n\",\n      \"           [ 0.0684,  0.0729,  0.0343]],\\n\",\n      \"\\n\",\n      \"          [[ 0.1178,  0.0973,  0.0768],\\n\",\n      \"           [ 0.1046,  0.0904,  0.0817],\\n\",\n      \"           [ 0.1101,  0.0989,  0.0686]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 0.0965,  0.0988,  0.0839],\\n\",\n      \"           [ 0.0463,  0.0701,  0.0783],\\n\",\n      \"           [ 0.0290,  0.0490,  0.0346]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0562,  0.0607,  0.0752],\\n\",\n      \"           [ 0.0358,  0.0342,  0.0547],\\n\",\n      \"           [ 0.0604,  0.0499,  0.0595]],\\n\",\n      \"\\n\",\n      \"          [[ 0.0745,  0.0680,  0.0541],\\n\",\n      \"           [ 0.0744,  0.0853,  0.0579],\\n\",\n      \"           [ 0.0522,  0.0530,  0.0641]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 85.00 %\\n\",\n      \"Val loss: 0.371111\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[27,    60] loss: 0.24989\\n\",\n      \"[27,   120] loss: 0.25773\\n\",\n      \"Time elapsed: 0h:53m:14s\\n\",\n      \"train accuracy_score: 89.53 %\\n\",\n      \"tensor([[[[[ 9.7426e-02,  1.1110e-01,  8.6083e-02],\\n\",\n      \"           [ 9.3223e-02,  9.5379e-02,  7.0410e-02],\\n\",\n      \"           [ 5.7032e-02,  4.2044e-02,  3.3614e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 9.5090e-02,  8.4803e-02,  4.6551e-02],\\n\",\n      \"           [ 8.6925e-02,  8.0127e-02,  5.4886e-02],\\n\",\n      \"           [ 4.4938e-02,  1.4910e-02,  1.4264e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.7314e-02,  4.8791e-02,  4.4760e-02],\\n\",\n      \"           [ 4.0383e-02,  3.8472e-02,  1.0535e-02],\\n\",\n      \"           [ 2.8514e-02,  2.9159e-03, -9.4910e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-8.4806e-03, -2.0475e-02, -1.4978e-02],\\n\",\n      \"           [-1.2322e-02, -1.8370e-02, -1.4460e-02],\\n\",\n      \"           [-8.0366e-03, -1.3299e-02, -9.2775e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.1520e-03, -1.5311e-02, -1.1175e-02],\\n\",\n      \"           [-1.0658e-02, -1.7693e-02, -6.7522e-03],\\n\",\n      \"           [-3.0698e-03, -1.0045e-02,  1.0959e-04]],\\n\",\n      \"\\n\",\n      \"          [[-5.2104e-03, -1.1638e-02, -7.7065e-03],\\n\",\n      \"           [-4.0530e-03, -8.5462e-03, -5.7944e-03],\\n\",\n      \"           [-2.4014e-03,  3.8175e-03,  1.1926e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.0622e-01,  1.1011e-01,  1.2528e-01],\\n\",\n      \"           [ 9.5418e-02,  1.2562e-01,  1.2991e-01],\\n\",\n      \"           [ 9.7616e-02,  1.0655e-01,  9.9016e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0871e-01,  9.9405e-02,  1.2078e-01],\\n\",\n      \"           [ 9.7406e-02,  1.1320e-01,  1.2169e-01],\\n\",\n      \"           [ 9.1251e-02,  1.0063e-01,  1.1417e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 7.0153e-02,  7.2943e-02,  1.1581e-01],\\n\",\n      \"           [ 7.5201e-02,  9.3718e-02,  1.0471e-01],\\n\",\n      \"           [ 1.0381e-01,  9.8796e-02,  1.1496e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-9.4979e-04,  3.2729e-03,  4.1191e-03],\\n\",\n      \"           [-7.2965e-04,  6.4300e-04,  1.1177e-02],\\n\",\n      \"           [ 1.2579e-02,  1.5807e-02,  1.8545e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.6886e-03,  5.6834e-03,  1.2628e-02],\\n\",\n      \"           [-1.0713e-03,  8.3715e-03,  1.3608e-02],\\n\",\n      \"           [ 1.7618e-02,  2.1977e-02,  2.7792e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.6605e-03,  1.2358e-02,  9.5098e-03],\\n\",\n      \"           [ 1.0552e-02,  9.0928e-03,  9.6385e-03],\\n\",\n      \"           [ 2.1001e-02,  2.0173e-02,  1.5544e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.6021e-02, -1.2042e-02, -7.0265e-03],\\n\",\n      \"           [-2.6410e-02, -1.6590e-02, -5.8438e-03],\\n\",\n      \"           [-1.9800e-02, -1.5253e-02, -1.2967e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.5723e-02, -9.2320e-03, -8.0790e-03],\\n\",\n      \"           [-2.2729e-02, -4.7693e-03, -6.3125e-03],\\n\",\n      \"           [-2.2845e-02, -1.6576e-02, -1.8237e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.6867e-02, -1.8731e-03, -7.7210e-03],\\n\",\n      \"           [-1.5579e-02,  3.8250e-04, -2.6040e-03],\\n\",\n      \"           [-2.2452e-02, -1.8284e-02, -1.9996e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.4620e-04, -3.0408e-03, -2.9774e-03],\\n\",\n      \"           [-5.2725e-04,  5.3628e-04, -5.7155e-04],\\n\",\n      \"           [ 3.4568e-03,  4.1366e-03,  6.4318e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7326e-03, -7.2943e-04, -1.5145e-04],\\n\",\n      \"           [ 7.9003e-04,  3.9947e-04, -2.3975e-03],\\n\",\n      \"           [ 3.7860e-03,  1.6123e-03, -3.0363e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.9626e-03,  2.3982e-03, -6.2506e-04],\\n\",\n      \"           [ 2.7965e-03,  2.8949e-04, -3.8096e-04],\\n\",\n      \"           [ 5.9089e-03,  2.2865e-03, -7.8733e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.1175e-02, -2.1975e-02, -1.1328e-02],\\n\",\n      \"           [-2.7725e-02, -2.8566e-02, -2.4519e-02],\\n\",\n      \"           [-2.1349e-02, -1.8548e-02, -1.6571e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.6579e-02, -1.5057e-02, -1.1154e-02],\\n\",\n      \"           [-2.1285e-02, -2.4191e-02, -2.1793e-02],\\n\",\n      \"           [-2.6247e-02, -2.4867e-02, -2.1449e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.0446e-03, -1.1057e-02, -1.8000e-02],\\n\",\n      \"           [-2.0354e-02, -2.7004e-02, -2.7485e-02],\\n\",\n      \"           [-3.0022e-02, -3.1662e-02, -3.5538e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.9152e-03, -1.7221e-02, -1.3404e-02],\\n\",\n      \"           [-1.1533e-02, -2.0636e-02, -1.0594e-02],\\n\",\n      \"           [-9.1681e-03, -1.4168e-02, -8.0932e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.5310e-03, -3.3497e-03, -6.1978e-03],\\n\",\n      \"           [-1.2206e-02, -1.3909e-02, -6.3626e-03],\\n\",\n      \"           [-1.5491e-02, -9.2783e-03, -5.3252e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.1109e-02, -1.0721e-02, -5.1470e-03],\\n\",\n      \"           [-2.2228e-02, -2.3563e-02, -1.1476e-02],\\n\",\n      \"           [-2.1518e-02, -1.3991e-02,  8.2223e-04]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 84.00 %\\n\",\n      \"Val loss: 0.382940\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[28,    60] loss: 0.28748\\n\",\n      \"[28,   120] loss: 0.24467\\n\",\n      \"Time elapsed: 0h:55m:8s\\n\",\n      \"train accuracy_score: 90.39 %\\n\",\n      \"tensor([[[[[-1.3036e-01, -1.3480e-01, -9.9874e-02],\\n\",\n      \"           [-1.2327e-01, -9.8763e-02, -6.5604e-02],\\n\",\n      \"           [-8.9027e-02, -4.8855e-02, -2.0533e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.2836e-01, -1.1581e-01, -4.8519e-02],\\n\",\n      \"           [-1.2998e-01, -9.2191e-02, -4.6672e-02],\\n\",\n      \"           [-9.6917e-02, -4.6946e-02, -1.9079e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.7284e-02, -7.3876e-02, -2.8279e-02],\\n\",\n      \"           [-9.1200e-02, -4.4197e-02, -1.7152e-02],\\n\",\n      \"           [-8.2117e-02, -3.7599e-02,  1.3910e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.8254e-02, -8.0653e-03, -1.0100e-02],\\n\",\n      \"           [-1.3748e-02, -1.1921e-02, -1.4935e-02],\\n\",\n      \"           [-1.8633e-02, -2.8195e-02, -3.3375e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.8118e-03, -8.0437e-03, -1.6517e-02],\\n\",\n      \"           [-1.3592e-02, -1.2127e-02, -1.5130e-02],\\n\",\n      \"           [-2.7205e-02, -2.6065e-02, -3.6134e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.1973e-02, -1.4844e-02, -2.2075e-02],\\n\",\n      \"           [-1.9702e-02, -1.9038e-02, -2.3329e-02],\\n\",\n      \"           [-3.1348e-02, -3.6808e-02, -3.8607e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-5.4836e-02, -6.4869e-02, -7.5873e-02],\\n\",\n      \"           [-4.4651e-02, -6.9353e-02, -9.3726e-02],\\n\",\n      \"           [-3.7278e-02, -5.7868e-02, -7.5448e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.1782e-02, -4.2425e-02, -6.4473e-02],\\n\",\n      \"           [-4.8457e-02, -6.5137e-02, -5.3770e-02],\\n\",\n      \"           [-4.2629e-02, -6.5959e-02, -7.0876e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.1858e-02, -2.3451e-02, -5.2187e-02],\\n\",\n      \"           [-4.8713e-02, -4.1852e-02, -4.5388e-02],\\n\",\n      \"           [-4.9310e-02, -4.3595e-02, -5.3626e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1671e-02, -2.4664e-02, -2.7624e-02],\\n\",\n      \"           [ 2.9124e-04, -6.8356e-03, -1.6083e-02],\\n\",\n      \"           [ 4.3241e-03, -9.0460e-03, -1.2928e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.5935e-03, -1.1901e-02, -2.8580e-02],\\n\",\n      \"           [ 6.3228e-03, -2.2953e-03, -1.6610e-02],\\n\",\n      \"           [ 7.3235e-03, -6.8035e-04, -1.2001e-02]],\\n\",\n      \"\\n\",\n      \"          [[-7.1582e-03, -1.0207e-02, -1.9285e-02],\\n\",\n      \"           [ 1.6282e-03, -1.0329e-02, -1.2103e-03],\\n\",\n      \"           [ 1.1362e-02, -1.0444e-04,  3.8533e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.9418e-02, -2.3522e-02, -2.1246e-02],\\n\",\n      \"           [-1.4470e-02, -2.4798e-02, -1.4181e-02],\\n\",\n      \"           [-1.6255e-03, -1.4750e-02, -1.3053e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.8531e-02, -1.7079e-02, -1.4907e-02],\\n\",\n      \"           [-1.2695e-02, -2.8047e-02, -9.8122e-03],\\n\",\n      \"           [-1.1342e-02, -9.0306e-03,  8.0277e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.7161e-02, -2.6674e-02, -1.9463e-02],\\n\",\n      \"           [-2.2393e-02, -2.3790e-02, -1.9637e-02],\\n\",\n      \"           [-1.7918e-02, -1.1080e-02,  1.1428e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.5327e-04,  1.3864e-03,  1.7573e-03],\\n\",\n      \"           [-1.3977e-03, -7.1145e-05,  6.6618e-04],\\n\",\n      \"           [-3.3132e-03, -3.8829e-03, -2.4817e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 5.2292e-04,  3.5464e-04, -6.8778e-04],\\n\",\n      \"           [-2.3724e-03, -9.7237e-04,  1.7729e-03],\\n\",\n      \"           [-2.7856e-03, -2.4034e-03, -1.2105e-03]],\\n\",\n      \"\\n\",\n      \"          [[-8.6061e-04,  1.9529e-03,  9.3813e-04],\\n\",\n      \"           [ 1.3030e-03,  2.2352e-03,  2.2188e-03],\\n\",\n      \"           [-2.0673e-03,  1.7092e-03,  1.7728e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 9.2549e-02,  8.7634e-02,  8.2313e-02],\\n\",\n      \"           [ 8.5261e-02,  8.5081e-02,  8.4323e-02],\\n\",\n      \"           [ 5.1829e-02,  5.3312e-02,  5.8397e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 8.3906e-02,  7.8454e-02,  7.3553e-02],\\n\",\n      \"           [ 8.5067e-02,  9.0382e-02,  8.3741e-02],\\n\",\n      \"           [ 6.6941e-02,  7.4214e-02,  5.9552e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.3025e-02,  6.7555e-02,  5.8478e-02],\\n\",\n      \"           [ 7.8685e-02,  7.6283e-02,  7.0507e-02],\\n\",\n      \"           [ 7.4193e-02,  7.3177e-02,  8.1221e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.8279e-03, -2.3606e-03, -3.4691e-03],\\n\",\n      \"           [-2.8419e-03,  9.3539e-03,  6.3461e-03],\\n\",\n      \"           [-4.2617e-03,  1.9429e-03,  3.9656e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.4205e-02, -1.3595e-02, -1.4973e-02],\\n\",\n      \"           [-5.7737e-03, -5.7278e-03, -5.1579e-03],\\n\",\n      \"           [ 5.1618e-03,  7.4504e-03,  9.0735e-03]],\\n\",\n      \"\\n\",\n      \"          [[-7.7320e-03, -2.3782e-02, -2.4045e-02],\\n\",\n      \"           [-1.8326e-03, -8.1022e-03, -6.2123e-03],\\n\",\n      \"           [ 9.6256e-03,  7.0138e-03,  9.8630e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 89.00 %\\n\",\n      \"Val loss: 0.335193\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[29,    60] loss: 0.24845\\n\",\n      \"[29,   120] loss: 0.17380\\n\",\n      \"Time elapsed: 0h:57m:4s\\n\",\n      \"train accuracy_score: 92.11 %\\n\",\n      \"tensor([[[[[-4.3483e-02, -4.2736e-02, -3.6612e-02],\\n\",\n      \"           [-4.0556e-02, -4.1165e-02, -3.9541e-02],\\n\",\n      \"           [-3.5899e-02, -3.2705e-02, -3.8281e-02]],\\n\",\n      \"\\n\",\n      \"          [[-4.2125e-02, -4.3931e-02, -3.3200e-02],\\n\",\n      \"           [-4.1418e-02, -4.0062e-02, -3.9474e-02],\\n\",\n      \"           [-2.9461e-02, -2.6303e-02, -3.7739e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.8921e-02, -4.1220e-02, -3.3244e-02],\\n\",\n      \"           [-3.5303e-02, -3.7016e-02, -3.6936e-02],\\n\",\n      \"           [-2.8165e-02, -2.7607e-02, -3.6018e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.2434e-03,  5.9845e-04, -6.1193e-04],\\n\",\n      \"           [-2.2352e-03,  1.4545e-03, -3.7980e-04],\\n\",\n      \"           [ 7.6548e-04, -6.2018e-04, -2.6636e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.4968e-03, -2.9223e-05, -1.1653e-03],\\n\",\n      \"           [-4.3527e-03, -1.1225e-03, -1.6071e-03],\\n\",\n      \"           [-3.4980e-03, -2.2387e-03, -3.2303e-03]],\\n\",\n      \"\\n\",\n      \"          [[-5.4166e-03, -3.3455e-03, -1.9084e-03],\\n\",\n      \"           [-7.5913e-03, -4.5906e-03, -3.8173e-03],\\n\",\n      \"           [-7.2257e-03, -3.6020e-03, -5.4813e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.3797e-02,  5.4533e-02,  4.9578e-02],\\n\",\n      \"           [ 5.3249e-02,  4.7555e-02,  5.0141e-02],\\n\",\n      \"           [ 4.6147e-02,  4.0818e-02,  4.4871e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.8173e-02,  5.7972e-02,  5.6042e-02],\\n\",\n      \"           [ 4.8617e-02,  4.6707e-02,  5.3079e-02],\\n\",\n      \"           [ 4.3359e-02,  4.1392e-02,  4.4023e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 5.3405e-02,  5.4220e-02,  5.8494e-02],\\n\",\n      \"           [ 4.6145e-02,  4.6383e-02,  5.7012e-02],\\n\",\n      \"           [ 3.8125e-02,  4.2996e-02,  5.2365e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.8610e-03,  1.1755e-03,  4.6192e-03],\\n\",\n      \"           [-1.8955e-03,  2.5188e-03,  6.2865e-03],\\n\",\n      \"           [-4.3935e-03, -4.2220e-04,  7.5482e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.2772e-03,  2.5136e-03,  2.5347e-03],\\n\",\n      \"           [ 1.5715e-04,  1.9245e-03,  7.0518e-03],\\n\",\n      \"           [-2.2481e-03,  2.5461e-03,  6.5538e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.5144e-03,  3.2473e-03,  4.0866e-03],\\n\",\n      \"           [-3.2263e-04,  3.6835e-03,  5.5864e-03],\\n\",\n      \"           [-1.2155e-03,  2.5017e-03,  5.0995e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.9747e-02,  1.3140e-02,  1.0930e-02],\\n\",\n      \"           [ 2.1434e-02,  1.5522e-02,  1.4316e-02],\\n\",\n      \"           [ 2.1948e-02,  1.9195e-02,  1.5345e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7617e-02,  1.3688e-02,  1.0439e-02],\\n\",\n      \"           [ 1.8836e-02,  1.3705e-02,  1.3459e-02],\\n\",\n      \"           [ 2.2308e-02,  1.5435e-02,  1.3080e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6285e-02,  1.0557e-02,  1.0468e-02],\\n\",\n      \"           [ 1.8328e-02,  1.2038e-02,  1.0528e-02],\\n\",\n      \"           [ 2.5778e-02,  1.5849e-02,  1.0944e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3300e-03, -1.0397e-03, -7.5317e-04],\\n\",\n      \"           [-2.1316e-03, -1.3206e-03, -4.7743e-04],\\n\",\n      \"           [-2.2449e-03, -1.2959e-03, -8.3437e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.1687e-03, -4.6893e-04, -4.3308e-04],\\n\",\n      \"           [-1.8795e-03, -8.6529e-04, -3.9208e-04],\\n\",\n      \"           [-2.2388e-03, -9.4234e-04, -1.4262e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.4033e-03, -1.1957e-04, -7.4149e-05],\\n\",\n      \"           [-1.5293e-03, -6.1452e-04,  1.5290e-04],\\n\",\n      \"           [-2.4475e-03, -1.5459e-03, -2.6053e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.5043e-02,  2.1209e-02,  1.6197e-02],\\n\",\n      \"           [ 2.7317e-02,  2.7766e-02,  2.3390e-02],\\n\",\n      \"           [ 2.3729e-02,  2.2765e-02,  1.9881e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6653e-02,  2.3810e-02,  2.0263e-02],\\n\",\n      \"           [ 3.0714e-02,  2.9650e-02,  2.4297e-02],\\n\",\n      \"           [ 3.0427e-02,  2.8624e-02,  2.2045e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1267e-02,  2.7683e-02,  2.1735e-02],\\n\",\n      \"           [ 3.2403e-02,  2.8801e-02,  2.3905e-02],\\n\",\n      \"           [ 3.3769e-02,  3.0528e-02,  2.4588e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.5836e-03, -7.2292e-03, -7.6487e-03],\\n\",\n      \"           [-5.7911e-03, -4.1511e-03, -3.7637e-03],\\n\",\n      \"           [-4.4569e-03, -3.5726e-03, -4.5559e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.2471e-03, -5.7966e-03, -5.9473e-03],\\n\",\n      \"           [-2.8626e-03, -3.2353e-03, -3.9521e-03],\\n\",\n      \"           [-6.1291e-04, -2.6014e-03, -3.7117e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.0854e-03, -4.4043e-03, -9.0502e-03],\\n\",\n      \"           [-2.0207e-04, -1.2040e-03, -3.7320e-03],\\n\",\n      \"           [ 8.4043e-04, -3.0492e-03, -3.0980e-03]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 83.00 %\\n\",\n      \"Val loss: 0.359166\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[30,    60] loss: 0.18356\\n\",\n      \"[30,   120] loss: 0.25192\\n\",\n      \"Time elapsed: 0h:58m:59s\\n\",\n      \"train accuracy_score: 92.40 %\\n\",\n      \"tensor([[[[[ 1.2137e+00,  1.3695e+00,  9.8608e-01],\\n\",\n      \"           [ 1.2824e+00,  1.1424e+00,  7.2769e-01],\\n\",\n      \"           [ 6.9137e-01,  5.8260e-01,  2.9813e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0760e+00,  1.0996e+00,  8.1255e-01],\\n\",\n      \"           [ 1.1695e+00,  9.4210e-01,  6.2095e-01],\\n\",\n      \"           [ 6.1621e-01,  4.4130e-01,  2.6751e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 9.6960e-01,  8.3692e-01,  5.9566e-01],\\n\",\n      \"           [ 7.9188e-01,  5.8273e-01,  3.2259e-01],\\n\",\n      \"           [ 6.0255e-01,  2.7119e-01, -2.9106e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.7858e-02, -1.1610e-01, -6.7124e-02],\\n\",\n      \"           [-5.6033e-02, -1.1675e-01, -7.6735e-02],\\n\",\n      \"           [-9.1127e-02, -6.8698e-02, -1.1614e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.9392e-02, -7.3773e-02, -4.3027e-02],\\n\",\n      \"           [-4.3333e-02, -1.0223e-01, -8.3448e-02],\\n\",\n      \"           [-7.8499e-02, -6.9178e-02, -2.1879e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.3760e-02, -2.9731e-02, -3.4039e-02],\\n\",\n      \"           [-4.7072e-02, -8.8627e-02, -7.6814e-02],\\n\",\n      \"           [-8.9609e-02, -4.9671e-02, -7.3326e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.2563e-01,  3.9688e-01,  5.8039e-01],\\n\",\n      \"           [ 2.6301e-01,  4.6992e-01,  5.2980e-01],\\n\",\n      \"           [ 1.5217e-01,  3.5170e-01,  3.7661e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.6962e-01,  4.2015e-01,  5.6046e-01],\\n\",\n      \"           [ 3.3322e-01,  4.9144e-01,  4.8802e-01],\\n\",\n      \"           [ 3.3700e-01,  4.6314e-01,  4.9563e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4227e-01,  4.6276e-01,  6.1608e-01],\\n\",\n      \"           [ 3.7310e-01,  6.4211e-01,  7.0806e-01],\\n\",\n      \"           [ 4.9294e-01,  5.4858e-01,  6.4758e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.2940e-04,  7.4177e-02,  4.4529e-02],\\n\",\n      \"           [ 1.9120e-02,  5.5008e-02,  4.1987e-02],\\n\",\n      \"           [ 9.7944e-02,  1.2868e-01,  1.2636e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6140e-02,  8.9902e-02,  7.4488e-02],\\n\",\n      \"           [ 8.4115e-02,  1.3007e-01,  6.7395e-02],\\n\",\n      \"           [ 1.1517e-01,  1.6779e-01,  1.8209e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.6062e-02,  7.4436e-02,  1.0811e-01],\\n\",\n      \"           [ 4.8388e-02,  1.1934e-01,  9.4780e-02],\\n\",\n      \"           [ 1.0264e-01,  1.0247e-01,  1.1664e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.6357e-01, -1.3440e-01, -1.8890e-02],\\n\",\n      \"           [-2.0940e-01, -1.3111e-01, -4.9818e-02],\\n\",\n      \"           [-1.6859e-01, -1.4220e-01, -1.6700e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.6797e-01, -1.3099e-01, -2.2720e-02],\\n\",\n      \"           [-1.7937e-01, -1.1184e-01, -4.5483e-02],\\n\",\n      \"           [-2.2261e-01, -2.1758e-01, -1.9496e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.9634e-01, -7.8898e-02,  1.1944e-02],\\n\",\n      \"           [-1.3798e-01, -6.9362e-02, -1.3244e-02],\\n\",\n      \"           [-2.4634e-01, -1.8710e-01, -1.8317e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.6592e-02,  3.6751e-02,  7.6460e-03],\\n\",\n      \"           [ 3.2413e-02,  3.9198e-02,  4.4870e-03],\\n\",\n      \"           [ 2.8481e-02,  2.9298e-02, -7.5710e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 3.7855e-02,  2.6300e-02,  4.8806e-03],\\n\",\n      \"           [ 3.0589e-02,  6.2098e-03, -3.2858e-03],\\n\",\n      \"           [ 4.5928e-02,  2.1227e-02,  1.6557e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.2745e-02,  3.1069e-02,  6.6872e-03],\\n\",\n      \"           [ 3.3315e-02,  1.3427e-02, -1.3430e-02],\\n\",\n      \"           [ 2.2849e-02,  4.3009e-03,  6.5766e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 8.3741e-02,  2.3207e-02,  1.7185e-02],\\n\",\n      \"           [ 3.3935e-02, -4.1163e-02,  1.9654e-02],\\n\",\n      \"           [ 7.1905e-02,  5.7453e-02,  7.5998e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.6647e-02, -1.8489e-02,  3.0183e-02],\\n\",\n      \"           [-3.2338e-02, -8.2222e-02, -3.5492e-02],\\n\",\n      \"           [ 1.6179e-02,  1.3152e-02,  7.2704e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.8116e-02, -8.2817e-02, -7.0567e-02],\\n\",\n      \"           [-8.3416e-02, -1.5472e-01, -1.0151e-01],\\n\",\n      \"           [-7.3565e-02, -8.7363e-02, -5.7327e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.3216e-01, -2.4244e-01, -2.7752e-01],\\n\",\n      \"           [-1.5937e-01, -2.4066e-01, -1.9435e-01],\\n\",\n      \"           [-1.2282e-01, -1.4958e-01, -1.4583e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.5202e-01, -2.7564e-01, -2.9551e-01],\\n\",\n      \"           [-1.5436e-01, -2.8230e-01, -2.4994e-01],\\n\",\n      \"           [-1.9300e-01, -1.8912e-01, -1.8717e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.5863e-01, -2.4074e-01, -3.0295e-01],\\n\",\n      \"           [-1.9562e-01, -2.7099e-01, -2.9034e-01],\\n\",\n      \"           [-1.4983e-01, -2.2387e-01, -1.9495e-01]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 87.00 %\\n\",\n      \"Val loss: 0.410536\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[31,    60] loss: 0.21621\\n\",\n      \"[31,   120] loss: 0.23314\\n\",\n      \"Time elapsed: 1h:0m:53s\\n\",\n      \"train accuracy_score: 91.54 %\\n\",\n      \"tensor([[[[[-8.4318e-02, -7.7813e-02, -1.4037e-01],\\n\",\n      \"           [-7.4605e-02, -8.1066e-02, -1.5829e-01],\\n\",\n      \"           [-1.1982e-01, -1.5720e-01, -1.9129e-01]],\\n\",\n      \"\\n\",\n      \"          [[-9.3910e-02, -1.2947e-01, -2.1176e-01],\\n\",\n      \"           [-7.5225e-02, -1.2488e-01, -1.9178e-01],\\n\",\n      \"           [-1.2324e-01, -1.7847e-01, -2.4083e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.8713e-01, -2.4280e-01, -2.6206e-01],\\n\",\n      \"           [-1.6463e-01, -2.3184e-01, -2.6003e-01],\\n\",\n      \"           [-1.5079e-01, -2.4675e-01, -3.0302e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.9408e-02, -3.9804e-02, -3.1284e-02],\\n\",\n      \"           [-3.0428e-02, -2.9541e-02, -2.7867e-02],\\n\",\n      \"           [-1.2552e-02, -1.5973e-02, -1.4124e-05]],\\n\",\n      \"\\n\",\n      \"          [[-1.9692e-02, -2.8253e-02, -7.4607e-03],\\n\",\n      \"           [-1.5223e-02, -7.0262e-03, -7.8226e-03],\\n\",\n      \"           [ 7.5552e-03, -3.0331e-04,  4.4525e-03]],\\n\",\n      \"\\n\",\n      \"          [[-9.1511e-03, -7.4256e-03,  1.5763e-03],\\n\",\n      \"           [ 1.1498e-02,  4.1944e-03,  1.8118e-03],\\n\",\n      \"           [ 1.7777e-02,  2.0889e-02,  7.3879e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.7209e-02,  1.9471e-02,  6.8519e-02],\\n\",\n      \"           [-3.4251e-02,  2.6364e-02,  4.0322e-02],\\n\",\n      \"           [-5.7858e-02,  1.6757e-02,  4.3293e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.3243e-02,  3.9049e-02,  6.6344e-02],\\n\",\n      \"           [ 2.7443e-02,  5.4292e-02,  5.1865e-02],\\n\",\n      \"           [ 2.8609e-02,  5.3760e-02,  8.2946e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 6.7504e-02,  6.4983e-02,  8.7532e-02],\\n\",\n      \"           [ 4.3116e-02,  8.7421e-02,  9.6792e-02],\\n\",\n      \"           [ 7.7170e-02,  1.0926e-01,  1.0107e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.6792e-02,  3.6653e-02,  4.1785e-02],\\n\",\n      \"           [ 7.6400e-03,  1.8265e-02,  3.4727e-02],\\n\",\n      \"           [ 2.3116e-02,  3.7479e-02,  5.0080e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.0238e-02, -4.4228e-03,  1.0368e-02],\\n\",\n      \"           [-1.4132e-02,  8.4068e-03,  1.6479e-02],\\n\",\n      \"           [ 9.1857e-03,  1.4412e-02,  2.4006e-02]],\\n\",\n      \"\\n\",\n      \"          [[-1.7433e-02, -1.1273e-02,  4.8245e-03],\\n\",\n      \"           [-2.4450e-02, -5.0795e-03, -5.9489e-03],\\n\",\n      \"           [-8.2358e-03,  2.0020e-02,  5.2508e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1685e-02, -1.1933e-02,  4.3428e-03],\\n\",\n      \"           [-1.9647e-03,  1.2049e-02,  3.0037e-02],\\n\",\n      \"           [ 1.5053e-02,  3.1896e-02,  2.2563e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.4268e-03, -1.1551e-02,  2.9116e-03],\\n\",\n      \"           [ 8.8413e-03,  1.7589e-02,  1.7531e-02],\\n\",\n      \"           [-1.6080e-03,  3.4832e-03,  8.6092e-03]],\\n\",\n      \"\\n\",\n      \"          [[-3.9077e-03, -1.0459e-03,  7.1144e-03],\\n\",\n      \"           [-3.4151e-03, -1.8308e-03,  1.7663e-02],\\n\",\n      \"           [-2.3720e-02, -1.1714e-02, -2.3063e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3982e-02, -1.7025e-02, -1.3165e-02],\\n\",\n      \"           [-1.0004e-02, -1.3400e-02, -1.1481e-02],\\n\",\n      \"           [-1.1006e-02, -1.0730e-02, -1.0976e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.1457e-03, -5.6805e-03, -5.4098e-03],\\n\",\n      \"           [-4.0055e-03, -5.0240e-03, -1.1932e-02],\\n\",\n      \"           [ 1.7210e-03, -2.6042e-03, -4.2602e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 5.5650e-03, -2.7406e-03, -8.8542e-03],\\n\",\n      \"           [ 6.9122e-03,  2.2749e-05, -1.1311e-02],\\n\",\n      \"           [ 1.0779e-02,  6.7377e-03, -2.5110e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.4669e-02,  1.9001e-03,  5.7227e-03],\\n\",\n      \"           [ 8.5028e-03, -1.0901e-02, -1.1040e-02],\\n\",\n      \"           [ 4.9154e-02,  4.0029e-02,  2.6450e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.1985e-02, -1.7294e-02, -9.4296e-03],\\n\",\n      \"           [-1.7629e-02, -3.2969e-02, -2.4702e-02],\\n\",\n      \"           [ 2.0023e-02, -2.0256e-03,  8.9268e-04]],\\n\",\n      \"\\n\",\n      \"          [[-3.0119e-02, -3.1993e-02, -3.2213e-02],\\n\",\n      \"           [-2.9929e-02, -3.6725e-02, -3.5549e-02],\\n\",\n      \"           [-1.7053e-02, -3.2588e-02, -4.9021e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.3607e-02, -4.8569e-02, -4.6241e-02],\\n\",\n      \"           [-3.1647e-02, -4.4543e-02, -3.4941e-02],\\n\",\n      \"           [-4.4319e-02, -2.6876e-02, -3.2304e-02]],\\n\",\n      \"\\n\",\n      \"          [[-3.4428e-02, -3.6749e-02, -4.3485e-02],\\n\",\n      \"           [-4.8892e-02, -5.8171e-02, -4.7304e-02],\\n\",\n      \"           [-6.7805e-02, -5.3094e-02, -3.7408e-02]],\\n\",\n      \"\\n\",\n      \"          [[-6.3799e-02, -5.6619e-02, -7.1430e-02],\\n\",\n      \"           [-8.4194e-02, -9.7343e-02, -7.9623e-02],\\n\",\n      \"           [-8.4888e-02, -8.7136e-02, -6.8729e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 79.00 %\\n\",\n      \"Val loss: 0.689972\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[32,    60] loss: 0.21867\\n\",\n      \"[32,   120] loss: 0.15132\\n\",\n      \"Time elapsed: 1h:2m:49s\\n\",\n      \"train accuracy_score: 92.11 %\\n\",\n      \"tensor([[[[[ 3.0720e-04,  3.0328e-04,  4.6796e-04],\\n\",\n      \"           [ 3.7803e-04,  2.1847e-04,  7.4275e-04],\\n\",\n      \"           [ 4.4269e-04,  4.9516e-04,  4.0301e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 9.1905e-05,  2.4053e-04,  5.6547e-04],\\n\",\n      \"           [-3.2345e-04,  3.5576e-05,  1.5421e-04],\\n\",\n      \"           [-1.5306e-04,  7.3140e-04,  3.4650e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0006e-04,  4.0294e-04,  6.8291e-04],\\n\",\n      \"           [-4.7375e-04,  3.0673e-04,  6.2232e-04],\\n\",\n      \"           [ 2.8245e-06,  4.4325e-04,  7.0701e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.7319e-04, -2.7132e-04, -1.9968e-04],\\n\",\n      \"           [-3.1746e-04, -1.9406e-04, -2.2832e-04],\\n\",\n      \"           [-3.6805e-05, -4.3681e-05, -6.1667e-05]],\\n\",\n      \"\\n\",\n      \"          [[-2.8425e-04, -2.2575e-04, -1.9220e-04],\\n\",\n      \"           [-2.6623e-04, -1.5055e-04, -7.1671e-05],\\n\",\n      \"           [-2.6161e-04, -8.0580e-05,  1.4705e-05]],\\n\",\n      \"\\n\",\n      \"          [[-3.7154e-04, -3.3070e-04, -2.6667e-04],\\n\",\n      \"           [-1.9421e-04,  1.0717e-05,  1.0051e-04],\\n\",\n      \"           [-9.9321e-05,  1.9079e-04,  1.1871e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.9442e-03,  3.3542e-03,  2.5310e-03],\\n\",\n      \"           [ 3.9485e-03,  3.4876e-03,  3.0629e-03],\\n\",\n      \"           [ 3.7440e-03,  3.3192e-03,  3.1680e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1813e-03,  3.7009e-03,  3.1544e-03],\\n\",\n      \"           [ 3.6474e-03,  3.3969e-03,  3.2687e-03],\\n\",\n      \"           [ 3.3489e-03,  2.4756e-03,  2.4651e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6050e-03,  2.6487e-03,  2.7637e-03],\\n\",\n      \"           [ 2.4368e-03,  2.3596e-03,  2.6259e-03],\\n\",\n      \"           [ 2.4330e-03,  2.1688e-03,  2.6065e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.6781e-05, -1.4902e-04, -1.7310e-04],\\n\",\n      \"           [ 1.1498e-05, -3.1283e-05,  2.0850e-04],\\n\",\n      \"           [ 9.2011e-05,  1.7124e-04,  4.6285e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7515e-04, -4.7543e-05, -2.3372e-04],\\n\",\n      \"           [ 6.2529e-05,  9.0243e-05,  1.2897e-04],\\n\",\n      \"           [-1.5697e-04,  1.2618e-05,  2.9335e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 2.6279e-04, -2.0087e-05, -8.6125e-05],\\n\",\n      \"           [ 3.1508e-04,  1.1165e-04,  1.7057e-04],\\n\",\n      \"           [ 1.8408e-05, -1.0656e-04,  9.3554e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.0086e-04,  3.9900e-04,  2.4824e-04],\\n\",\n      \"           [ 2.9654e-04, -8.7065e-05, -3.4353e-04],\\n\",\n      \"           [-2.1893e-04, -4.3426e-04, -5.7127e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 7.4044e-04,  7.1526e-04,  3.5132e-04],\\n\",\n      \"           [ 4.2123e-04,  3.1121e-04,  3.1578e-05],\\n\",\n      \"           [ 4.4703e-05,  6.8094e-05, -1.5618e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 7.2661e-04,  5.5513e-04,  2.6173e-04],\\n\",\n      \"           [ 4.1026e-04,  8.0003e-05, -5.9635e-05],\\n\",\n      \"           [ 1.9085e-04,  1.3862e-04,  5.8712e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.7954e-05,  3.9670e-05,  5.1539e-05],\\n\",\n      \"           [-1.2608e-04, -3.1680e-05,  2.3724e-05],\\n\",\n      \"           [-1.6569e-04, -1.0553e-04, -1.4742e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.0276e-04,  1.2338e-06,  5.1221e-05],\\n\",\n      \"           [-1.1693e-04, -8.3000e-05,  1.8274e-05],\\n\",\n      \"           [-1.7574e-04, -1.5677e-04, -1.2710e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.1838e-04, -7.0035e-06, -1.5494e-05],\\n\",\n      \"           [-4.0787e-05, -5.2470e-05, -9.3803e-05],\\n\",\n      \"           [-2.6003e-04, -9.4163e-05, -1.9608e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.4578e-03,  1.5279e-03,  1.3997e-03],\\n\",\n      \"           [ 1.3949e-03,  1.4177e-03,  1.3072e-03],\\n\",\n      \"           [ 8.8918e-05,  2.5151e-04,  3.2234e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.5033e-03,  1.4915e-03,  1.4101e-03],\\n\",\n      \"           [ 1.2977e-03,  1.3445e-03,  1.2183e-03],\\n\",\n      \"           [ 5.7827e-04,  6.0107e-04,  3.3769e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.7910e-03,  1.7633e-03,  1.4466e-03],\\n\",\n      \"           [ 1.5148e-03,  1.3648e-03,  1.2933e-03],\\n\",\n      \"           [ 8.5648e-04,  8.9848e-04,  6.9905e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.3673e-03,  1.2316e-03,  1.0434e-03],\\n\",\n      \"           [ 1.0132e-03,  1.1053e-03,  8.7771e-04],\\n\",\n      \"           [ 7.9220e-04,  5.7658e-04,  3.3965e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0979e-03,  1.2147e-03,  9.4906e-04],\\n\",\n      \"           [ 8.5900e-04,  8.6533e-04,  6.5108e-04],\\n\",\n      \"           [ 1.0504e-03,  7.1337e-04,  3.3479e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.2132e-03,  1.1043e-03,  1.0399e-03],\\n\",\n      \"           [ 1.0319e-03,  1.0003e-03,  6.1738e-04],\\n\",\n      \"           [ 6.8663e-04,  6.4086e-04,  4.7069e-04]]]]], device='cuda:5')\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"val accuracy_score: 89.00 %\\n\",\n      \"Val loss: 0.402837\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[33,    60] loss: 0.22929\\n\",\n      \"[33,   120] loss: 0.18157\\n\",\n      \"Time elapsed: 1h:4m:43s\\n\",\n      \"train accuracy_score: 92.68 %\\n\",\n      \"tensor([[[[[ 4.6147e+00,  4.6395e+00,  3.8119e+00],\\n\",\n      \"           [ 4.0872e+00,  4.2533e+00,  2.8412e+00],\\n\",\n      \"           [ 3.7952e+00,  2.5664e+00,  1.2236e+00]],\\n\",\n      \"\\n\",\n      \"          [[ 4.1538e+00,  3.8646e+00,  1.8137e+00],\\n\",\n      \"           [ 3.5633e+00,  2.7525e+00,  1.9577e+00],\\n\",\n      \"           [ 1.9591e+00,  5.8960e-01, -7.8518e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3584e+00,  2.0110e+00,  8.7042e-01],\\n\",\n      \"           [ 1.6665e+00,  4.1389e-01,  1.8535e-01],\\n\",\n      \"           [ 4.0546e-01, -4.4087e-01, -1.1964e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.6030e-01, -5.5630e-01, -6.1810e-01],\\n\",\n      \"           [-6.2549e-01, -9.0918e-01, -8.3546e-01],\\n\",\n      \"           [-9.1686e-01, -7.5957e-01, -6.7797e-01]],\\n\",\n      \"\\n\",\n      \"          [[-5.4248e-01, -6.8052e-01, -6.2742e-01],\\n\",\n      \"           [-5.8949e-01, -9.8186e-01, -8.8586e-01],\\n\",\n      \"           [-8.5578e-01, -9.0364e-01, -7.3236e-01]],\\n\",\n      \"\\n\",\n      \"          [[-7.0137e-01, -7.3310e-01, -5.6840e-01],\\n\",\n      \"           [-7.8582e-01, -9.0492e-01, -6.8321e-01],\\n\",\n      \"           [-6.5578e-01, -7.0634e-01, -6.9620e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.2379e+00, -1.0337e+00, -6.5915e-01],\\n\",\n      \"           [-1.7219e+00, -7.0216e-01, -1.0123e+00],\\n\",\n      \"           [-2.0115e+00, -1.1699e+00, -1.1936e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.7561e+00, -1.9822e+00, -1.3664e+00],\\n\",\n      \"           [-2.1493e+00, -1.5735e+00, -1.4878e+00],\\n\",\n      \"           [-1.3884e+00, -1.1756e+00, -1.2809e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.4367e+00, -1.9120e+00, -1.3442e+00],\\n\",\n      \"           [-1.9338e+00, -1.3696e+00, -1.3601e+00],\\n\",\n      \"           [-1.0951e+00, -9.2106e-01, -1.4718e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.9424e-01,  4.9338e-01,  4.6905e-01],\\n\",\n      \"           [ 8.6109e-01,  8.6751e-01,  7.5470e-01],\\n\",\n      \"           [ 1.1447e+00,  1.1741e+00,  9.3609e-01]],\\n\",\n      \"\\n\",\n      \"          [[-1.1702e-01,  3.3612e-01,  5.8420e-01],\\n\",\n      \"           [ 2.7265e-01,  6.4841e-01,  5.3538e-01],\\n\",\n      \"           [ 6.8930e-01,  9.1003e-01,  9.5919e-01]],\\n\",\n      \"\\n\",\n      \"          [[-3.7259e-01, -1.0056e-01,  3.3467e-01],\\n\",\n      \"           [-1.3604e-01,  4.2057e-01,  5.7619e-01],\\n\",\n      \"           [ 4.1801e-01,  5.0477e-01,  5.0106e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.4571e+00, -1.4652e+00, -1.4338e+00],\\n\",\n      \"           [-1.3600e+00, -1.0817e+00, -1.0631e+00],\\n\",\n      \"           [-1.5446e+00, -1.4989e+00, -1.2724e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.0068e+00, -1.1711e+00, -1.1638e+00],\\n\",\n      \"           [-1.1618e+00, -1.1077e+00, -1.2452e+00],\\n\",\n      \"           [-1.1534e+00, -1.1243e+00, -1.1903e+00]],\\n\",\n      \"\\n\",\n      \"          [[-1.1789e+00, -1.1180e+00, -6.8852e-01],\\n\",\n      \"           [-8.5049e-01, -9.2946e-01, -8.9953e-01],\\n\",\n      \"           [-1.6563e+00, -1.6079e+00, -1.3454e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.3019e-01, -2.8769e-01, -1.2718e-01],\\n\",\n      \"           [-2.4020e-01, -1.2210e-01, -1.6630e-01],\\n\",\n      \"           [ 1.2444e-02, -7.1373e-02, -9.5141e-02]],\\n\",\n      \"\\n\",\n      \"          [[-2.4224e-01, -2.0500e-01, -8.0145e-02],\\n\",\n      \"           [-1.5416e-01, -9.3665e-02, -9.9056e-02],\\n\",\n      \"           [ 9.1724e-02,  7.0834e-02,  2.4611e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.7732e-02, -1.1605e-03,  2.8111e-02],\\n\",\n      \"           [-4.7198e-02,  4.6785e-02, -1.3173e-02],\\n\",\n      \"           [ 1.1891e-01,  1.1757e-01,  1.3850e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-6.4522e-01, -4.2549e-01, -9.7567e-02],\\n\",\n      \"           [-9.0016e-01, -7.7813e-01, -2.7918e-01],\\n\",\n      \"           [-6.2491e-01, -3.0567e-01, -9.9135e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.7064e-01, -5.3835e-01, -5.6350e-01],\\n\",\n      \"           [-9.8897e-01, -9.4235e-01, -5.6389e-01],\\n\",\n      \"           [-8.7324e-01, -7.4502e-01, -3.4902e-01]],\\n\",\n      \"\\n\",\n      \"          [[-7.8582e-01, -5.8497e-01, -7.1946e-01],\\n\",\n      \"           [-1.0697e+00, -1.0664e+00, -1.0349e+00],\\n\",\n      \"           [-1.0124e+00, -1.0976e+00, -1.0123e+00]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.1763e+00, -8.2465e-01, -7.4756e-01],\\n\",\n      \"           [-7.9470e-01, -8.1229e-01, -7.4083e-01],\\n\",\n      \"           [-6.6086e-01, -7.3095e-01, -4.5649e-01]],\\n\",\n      \"\\n\",\n      \"          [[-7.3529e-01, -7.0890e-01, -6.2575e-01],\\n\",\n      \"           [-5.8845e-01, -8.2932e-01, -6.4336e-01],\\n\",\n      \"           [-8.5581e-01, -8.8479e-01, -5.2827e-01]],\\n\",\n      \"\\n\",\n      \"          [[-8.5788e-01, -6.3981e-01, -7.3627e-01],\\n\",\n      \"           [-1.0917e+00, -1.1455e+00, -9.7496e-01],\\n\",\n      \"           [-7.8958e-01, -9.5993e-01, -8.0321e-01]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 85.00 %\\n\",\n      \"Val loss: 0.352228\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[34,    60] loss: 0.16463\\n\",\n      \"[34,   120] loss: 0.16644\\n\",\n      \"Time elapsed: 1h:6m:36s\\n\",\n      \"train accuracy_score: 93.69 %\\n\",\n      \"tensor([[[[[-2.9494e-03, -3.3619e-03, -3.0150e-03],\\n\",\n      \"           [-2.2278e-03, -2.9060e-03, -3.0545e-03],\\n\",\n      \"           [-1.6526e-03, -1.4672e-03, -2.3771e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.4399e-03, -2.0735e-03, -1.4690e-03],\\n\",\n      \"           [-2.2086e-03, -2.1934e-03, -1.9694e-03],\\n\",\n      \"           [-1.3954e-03, -9.1407e-04, -2.1140e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.7076e-03, -1.3175e-03, -6.7314e-04],\\n\",\n      \"           [-9.9498e-04, -9.4881e-04, -7.4473e-04],\\n\",\n      \"           [-3.7375e-04, -3.8985e-04, -1.4454e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-2.0345e-04,  3.9053e-05, -1.0459e-04],\\n\",\n      \"           [-1.0607e-04, -2.4530e-05, -3.9056e-05],\\n\",\n      \"           [-8.0994e-05, -1.3673e-04, -7.0773e-05]],\\n\",\n      \"\\n\",\n      \"          [[-2.3893e-04, -2.4832e-04, -1.2714e-04],\\n\",\n      \"           [-1.8200e-04, -2.1960e-04, -1.7538e-04],\\n\",\n      \"           [-2.5148e-04, -3.2006e-04, -6.2519e-05]],\\n\",\n      \"\\n\",\n      \"          [[-4.2781e-04, -4.1227e-04, -3.2023e-04],\\n\",\n      \"           [-4.2009e-04, -4.0676e-04, -1.9366e-04],\\n\",\n      \"           [-5.7161e-04, -3.4247e-04,  1.8035e-05]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.9904e-03, -2.1602e-03, -2.3445e-03],\\n\",\n      \"           [-1.7529e-03, -2.0205e-03, -1.8706e-03],\\n\",\n      \"           [-9.2774e-04, -1.0069e-03, -1.3580e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.0079e-03, -2.2846e-03, -2.0165e-03],\\n\",\n      \"           [-1.8932e-03, -1.5314e-03, -1.2647e-03],\\n\",\n      \"           [-1.5293e-03, -1.1319e-03, -1.6934e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.4775e-03, -1.7279e-03, -1.8629e-03],\\n\",\n      \"           [-2.0576e-03, -1.1868e-03, -9.5397e-04],\\n\",\n      \"           [-1.7807e-03, -6.8210e-04, -3.9851e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.6098e-04, -1.3232e-04, -1.2704e-04],\\n\",\n      \"           [-6.9677e-04, -8.4291e-05,  1.2514e-04],\\n\",\n      \"           [-9.6420e-04, -2.9299e-04,  1.1426e-04]],\\n\",\n      \"\\n\",\n      \"          [[-3.4901e-04, -6.9871e-05,  1.8959e-04],\\n\",\n      \"           [-2.9110e-04, -4.2210e-05,  1.6419e-04],\\n\",\n      \"           [-4.0401e-04, -3.4059e-05,  5.8395e-05]],\\n\",\n      \"\\n\",\n      \"          [[-3.5866e-05,  2.5751e-04,  4.9496e-04],\\n\",\n      \"           [ 1.0107e-04,  4.4177e-04,  4.8013e-04],\\n\",\n      \"           [ 3.6430e-05,  3.4849e-04,  5.2207e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.5366e-04, -2.5690e-06, -3.4294e-04],\\n\",\n      \"           [ 5.1754e-04, -2.4587e-04, -4.2814e-05],\\n\",\n      \"           [ 2.1784e-04, -6.0458e-05,  3.2424e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6838e-04, -1.6285e-04, -2.8941e-04],\\n\",\n      \"           [ 3.4443e-04, -4.1767e-04,  8.4272e-05],\\n\",\n      \"           [ 5.0667e-04,  7.4759e-05,  5.1268e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3319e-04, -4.7508e-04, -3.1988e-04],\\n\",\n      \"           [ 3.5411e-04, -1.9754e-04, -2.7199e-05],\\n\",\n      \"           [ 4.9856e-04,  4.6658e-04,  4.3455e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.5460e-04, -5.4856e-05, -8.2758e-05],\\n\",\n      \"           [-2.5734e-04, -1.7901e-04, -9.1959e-05],\\n\",\n      \"           [-3.9866e-04, -2.8153e-04, -1.2950e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.0701e-04, -1.0138e-04, -8.9472e-05],\\n\",\n      \"           [-2.7824e-04, -1.9389e-04, -9.4192e-05],\\n\",\n      \"           [-4.4718e-04, -3.2954e-04, -2.1154e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.0941e-04, -1.7323e-04, -1.7167e-04],\\n\",\n      \"           [-2.1668e-04, -2.4616e-04, -2.0066e-04],\\n\",\n      \"           [-4.4189e-04, -2.4440e-04, -3.2751e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 2.4154e-03,  1.5827e-03,  1.2249e-03],\\n\",\n      \"           [ 2.1543e-03,  1.9659e-03,  1.6240e-03],\\n\",\n      \"           [ 1.4516e-03,  1.4870e-03,  1.1507e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 2.0188e-03,  1.3358e-03,  1.0995e-03],\\n\",\n      \"           [ 2.2718e-03,  1.9421e-03,  1.5387e-03],\\n\",\n      \"           [ 1.7758e-03,  1.8100e-03,  1.1466e-03]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6446e-03,  1.2473e-03,  1.0921e-03],\\n\",\n      \"           [ 2.0796e-03,  1.8291e-03,  1.3713e-03],\\n\",\n      \"           [ 2.0298e-03,  1.9533e-03,  1.5190e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 6.8910e-04,  6.1972e-04,  4.3708e-04],\\n\",\n      \"           [ 5.9768e-04,  4.8603e-04,  3.2996e-04],\\n\",\n      \"           [ 1.8149e-04,  2.4438e-04,  1.5588e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 3.9494e-04,  3.0392e-04,  1.3080e-04],\\n\",\n      \"           [ 5.5605e-04,  3.7485e-04,  1.7649e-04],\\n\",\n      \"           [ 5.9177e-04,  2.7861e-04, -4.4019e-05]],\\n\",\n      \"\\n\",\n      \"          [[ 5.6727e-04,  3.3492e-04,  8.6589e-05],\\n\",\n      \"           [ 7.7872e-04,  6.2750e-04,  3.2908e-04],\\n\",\n      \"           [ 7.3042e-04,  3.6247e-04,  8.8263e-05]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 88.00 %\\n\",\n      \"Val loss: 0.464162\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[35,    60] loss: 0.14988\\n\",\n      \"[35,   120] loss: 0.12855\\n\",\n      \"Time elapsed: 1h:8m:28s\\n\",\n      \"train accuracy_score: 94.12 %\\n\",\n      \"tensor([[[[[-4.2037e-03,  6.3048e-02, -9.3873e-03],\\n\",\n      \"           [ 4.2753e-02,  4.6279e-02, -8.8677e-03],\\n\",\n      \"           [-8.4163e-03, -4.5846e-02, -1.1843e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3298e-02,  5.1003e-02, -7.8079e-02],\\n\",\n      \"           [ 7.0128e-02,  1.9432e-02, -3.7246e-02],\\n\",\n      \"           [-7.1422e-02, -1.3250e-01, -1.1485e-01]],\\n\",\n      \"\\n\",\n      \"          [[-4.3402e-02, -1.0941e-01, -1.3243e-01],\\n\",\n      \"           [-8.0552e-02, -1.0098e-01, -1.8479e-01],\\n\",\n      \"           [-1.3643e-01, -1.8309e-01, -1.7557e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.4297e-02,  3.7568e-02,  3.7827e-02],\\n\",\n      \"           [ 5.1936e-02,  3.3492e-02,  2.9333e-02],\\n\",\n      \"           [ 3.9516e-02,  3.1626e-02,  3.3615e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 7.7918e-02,  4.5073e-02,  4.5090e-02],\\n\",\n      \"           [ 6.4698e-02,  2.3842e-02,  1.8614e-02],\\n\",\n      \"           [ 6.6917e-02,  4.0654e-02,  2.3980e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 9.0078e-02,  6.5197e-02,  4.6328e-02],\\n\",\n      \"           [ 9.1625e-02,  4.4514e-02,  2.2796e-02],\\n\",\n      \"           [ 7.0004e-02,  5.4201e-02,  3.9143e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 4.0640e-01,  3.6463e-01,  4.3429e-01],\\n\",\n      \"           [ 2.8186e-01,  3.6825e-01,  4.2144e-01],\\n\",\n      \"           [ 2.4967e-01,  3.1114e-01,  3.5369e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.7074e-01,  3.1098e-01,  4.0160e-01],\\n\",\n      \"           [ 2.8256e-01,  3.5391e-01,  4.0401e-01],\\n\",\n      \"           [ 3.0154e-01,  3.3286e-01,  3.5999e-01]],\\n\",\n      \"\\n\",\n      \"          [[ 3.2063e-01,  3.6586e-01,  3.8713e-01],\\n\",\n      \"           [ 2.7877e-01,  3.2620e-01,  3.8796e-01],\\n\",\n      \"           [ 2.8095e-01,  2.9838e-01,  3.2612e-01]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 3.1892e-02,  9.5829e-03,  2.4200e-02],\\n\",\n      \"           [ 5.2668e-03,  4.1871e-03,  1.6589e-04],\\n\",\n      \"           [-1.3095e-02,  9.6712e-03,  1.3690e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.3444e-02,  1.0351e-02,  2.9812e-02],\\n\",\n      \"           [-3.7471e-03,  9.2611e-03,  7.5442e-03],\\n\",\n      \"           [ 9.9114e-03,  2.6868e-02,  2.6706e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.9495e-02,  3.5144e-02,  6.2895e-02],\\n\",\n      \"           [ 1.8209e-02,  2.6505e-02,  1.2543e-02],\\n\",\n      \"           [ 3.9149e-02,  3.8150e-02,  1.0926e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 5.2330e-02,  7.3921e-02,  1.0581e-01],\\n\",\n      \"           [ 7.0144e-02,  9.0692e-02,  8.2861e-02],\\n\",\n      \"           [ 7.0167e-02,  6.3244e-02,  2.9251e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 3.1770e-02,  4.9440e-02,  6.9714e-02],\\n\",\n      \"           [ 5.5615e-02,  6.7317e-02,  8.6259e-02],\\n\",\n      \"           [ 4.4593e-02,  3.6379e-02,  4.3428e-02]],\\n\",\n      \"\\n\",\n      \"          [[ 2.4041e-02,  5.3532e-02,  4.8283e-02],\\n\",\n      \"           [ 3.0965e-02,  7.0033e-02,  7.6155e-02],\\n\",\n      \"           [ 3.9549e-02,  4.4520e-02,  5.6493e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.6364e-02, -1.8669e-02, -1.7625e-02],\\n\",\n      \"           [-1.4824e-02, -1.2659e-02, -1.3640e-02],\\n\",\n      \"           [-1.6604e-02, -9.1690e-03, -3.1110e-03]],\\n\",\n      \"\\n\",\n      \"          [[-2.3936e-02, -1.2385e-02, -5.3690e-03],\\n\",\n      \"           [-1.6189e-02, -8.6889e-03, -1.0691e-02],\\n\",\n      \"           [-8.2106e-03, -3.1924e-03,  2.5117e-04]],\\n\",\n      \"\\n\",\n      \"          [[-1.1638e-02, -1.0900e-02, -1.3641e-02],\\n\",\n      \"           [-1.8867e-03, -3.0617e-03, -1.1180e-02],\\n\",\n      \"           [-5.8630e-03, -7.2249e-03,  5.1277e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-3.9224e-02, -2.5689e-02, -3.9411e-02],\\n\",\n      \"           [-3.2983e-02, -4.5861e-02, -3.8978e-02],\\n\",\n      \"           [ 8.5933e-03, -7.6508e-03, -1.9297e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.7799e-02, -5.2543e-02, -3.7723e-02],\\n\",\n      \"           [-5.5935e-02, -5.6424e-02, -5.8986e-02],\\n\",\n      \"           [-1.1353e-02, -2.0649e-02, -4.2686e-02]],\\n\",\n      \"\\n\",\n      \"          [[-8.5412e-02, -8.1490e-02, -7.6121e-02],\\n\",\n      \"           [-8.0699e-02, -6.4508e-02, -6.9930e-02],\\n\",\n      \"           [-4.5770e-02, -6.1609e-02, -7.4516e-02]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-7.4446e-02, -7.1454e-02, -8.9715e-02],\\n\",\n      \"           [-6.9442e-02, -8.2178e-02, -5.6249e-02],\\n\",\n      \"           [-3.5236e-02, -3.4156e-02, -3.9877e-02]],\\n\",\n      \"\\n\",\n      \"          [[-5.9330e-02, -7.2814e-02, -6.3542e-02],\\n\",\n      \"           [-5.8197e-02, -7.5400e-02, -6.9406e-02],\\n\",\n      \"           [-6.3745e-02, -7.5658e-02, -5.3079e-02]],\\n\",\n      \"\\n\",\n      \"          [[-9.5536e-02, -8.9915e-02, -8.7786e-02],\\n\",\n      \"           [-9.4263e-02, -1.1061e-01, -8.7611e-02],\\n\",\n      \"           [-7.3843e-02, -9.3407e-02, -6.7810e-02]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 79.00 %\\n\",\n      \"Val loss: 0.663196\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"[36,    60] loss: 0.21639\\n\",\n      \"[36,   120] loss: 0.12686\\n\",\n      \"Time elapsed: 1h:10m:19s\\n\",\n      \"train accuracy_score: 93.69 %\\n\",\n      \"tensor([[[[[-1.5797e-03, -1.9297e-03, -1.4631e-03],\\n\",\n      \"           [-1.8494e-03, -1.4727e-03, -5.5170e-04],\\n\",\n      \"           [-1.6130e-03, -3.3019e-04,  9.5076e-05]],\\n\",\n      \"\\n\",\n      \"          [[-1.5152e-03, -1.3524e-03, -6.4870e-04],\\n\",\n      \"           [-1.8370e-03, -1.2814e-03, -2.1230e-05],\\n\",\n      \"           [-1.1306e-03,  2.5275e-04,  5.9429e-04]],\\n\",\n      \"\\n\",\n      \"          [[-7.7202e-04, -7.6207e-04, -6.8168e-04],\\n\",\n      \"           [-1.2594e-03, -6.4295e-04,  4.6977e-04],\\n\",\n      \"           [-7.2916e-04,  4.7280e-04,  1.4567e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.6399e-03, -1.4265e-03, -1.2862e-03],\\n\",\n      \"           [-1.5346e-03, -1.2341e-03, -1.2881e-03],\\n\",\n      \"           [-1.5647e-03, -1.6603e-03, -1.3178e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.4808e-03, -1.3622e-03, -1.3017e-03],\\n\",\n      \"           [-1.4039e-03, -1.3669e-03, -1.2290e-03],\\n\",\n      \"           [-1.6212e-03, -1.3290e-03, -1.4433e-03]],\\n\",\n      \"\\n\",\n      \"          [[-1.5213e-03, -1.4944e-03, -1.3757e-03],\\n\",\n      \"           [-1.3197e-03, -1.3362e-03, -1.2805e-03],\\n\",\n      \"           [-1.4122e-03, -1.3577e-03, -1.2419e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-4.3756e-03, -4.7496e-03, -6.1113e-03],\\n\",\n      \"           [-3.5064e-03, -5.2565e-03, -5.9790e-03],\\n\",\n      \"           [-3.3128e-03, -4.3057e-03, -5.9018e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.7013e-03, -5.2934e-03, -6.3776e-03],\\n\",\n      \"           [-4.0587e-03, -5.7644e-03, -6.4944e-03],\\n\",\n      \"           [-4.0023e-03, -5.3002e-03, -6.3743e-03]],\\n\",\n      \"\\n\",\n      \"          [[-4.7779e-03, -6.1269e-03, -6.4947e-03],\\n\",\n      \"           [-5.0484e-03, -6.3598e-03, -6.6939e-03],\\n\",\n      \"           [-5.2507e-03, -5.4088e-03, -6.0038e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.4306e-04, -1.1668e-04, -7.9907e-05],\\n\",\n      \"           [ 1.1649e-04,  8.7533e-05, -6.2140e-05],\\n\",\n      \"           [-2.6485e-04,  1.3656e-05,  2.1074e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 3.5876e-04,  8.8065e-05,  2.4453e-05],\\n\",\n      \"           [ 2.3026e-04,  2.3015e-04,  1.3141e-04],\\n\",\n      \"           [ 1.5126e-04,  9.4763e-05,  7.1873e-05]],\\n\",\n      \"\\n\",\n      \"          [[ 1.9582e-04,  2.1693e-04,  2.2806e-04],\\n\",\n      \"           [ 1.8148e-04,  1.2978e-04,  3.4513e-04],\\n\",\n      \"           [ 2.6071e-04,  1.0388e-04,  1.0387e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.1324e-03,  8.0282e-04,  3.3757e-04],\\n\",\n      \"           [ 5.1975e-04,  1.4202e-04,  2.9399e-04],\\n\",\n      \"           [ 1.8083e-04,  4.9057e-04,  4.4425e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0700e-03,  3.7957e-04,  2.5249e-04],\\n\",\n      \"           [ 5.2307e-04,  2.9216e-04,  3.6025e-04],\\n\",\n      \"           [ 3.5851e-04,  5.3418e-04,  4.3939e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.0541e-03,  5.0884e-04,  3.4596e-04],\\n\",\n      \"           [ 5.1919e-04,  3.4220e-04,  1.7626e-04],\\n\",\n      \"           [ 6.1274e-04,  6.7106e-04,  1.9527e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[-1.3513e-04,  2.8174e-05, -4.3081e-05],\\n\",\n      \"           [-1.5757e-04, -8.5840e-05, -6.5287e-05],\\n\",\n      \"           [-2.9523e-04, -2.5680e-04, -3.0371e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3168e-04,  1.2957e-04, -4.9098e-06],\\n\",\n      \"           [-1.4786e-05, -2.1884e-05, -4.4606e-05],\\n\",\n      \"           [-1.9307e-04, -1.7355e-04, -3.0678e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.3495e-04,  7.0493e-05, -1.5262e-05],\\n\",\n      \"           [ 1.4835e-05, -5.2464e-05,  3.8604e-05],\\n\",\n      \"           [-1.3126e-04, -1.0785e-04, -2.0504e-04]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.4982e-03,  1.5571e-03,  1.3780e-03],\\n\",\n      \"           [ 1.7628e-03,  1.6201e-03,  1.3347e-03],\\n\",\n      \"           [ 1.2149e-03,  8.4627e-04,  2.6207e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6354e-03,  1.5795e-03,  1.6212e-03],\\n\",\n      \"           [ 2.0240e-03,  2.0610e-03,  1.5995e-03],\\n\",\n      \"           [ 1.6909e-03,  1.3388e-03,  5.7641e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 1.6776e-03,  1.7625e-03,  1.4612e-03],\\n\",\n      \"           [ 2.0707e-03,  2.0097e-03,  1.5306e-03],\\n\",\n      \"           [ 2.1070e-03,  1.7404e-03,  1.0202e-03]]]],\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        [[[[ 1.1313e-04, -5.9121e-05, -7.2362e-05],\\n\",\n      \"           [ 2.2536e-04,  2.8055e-04, -1.5337e-04],\\n\",\n      \"           [ 2.7882e-04, -9.6126e-05, -2.6940e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 3.3092e-04, -5.3272e-05, -1.1091e-04],\\n\",\n      \"           [ 2.1469e-04,  1.5248e-04, -1.0195e-05],\\n\",\n      \"           [ 3.5246e-04, -2.9896e-05, -4.4930e-04]],\\n\",\n      \"\\n\",\n      \"          [[ 6.5803e-04,  4.7102e-04,  9.6271e-05],\\n\",\n      \"           [ 5.4556e-04,  5.1451e-04,  4.6069e-05],\\n\",\n      \"           [ 4.1124e-04,  2.7722e-04, -5.2034e-04]]]]], device='cuda:5')\\n\",\n      \"val accuracy_score: 86.00 %\\n\",\n      \"Val loss: 0.422817\\n\",\n      \"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1\\n\",\n      \"Early stopping in epoch 37\\n\",\n      \"Total time elapsed: 1h:10m:27s\\n\",\n      \"Writing model to disk...\\n\",\n      \"Best result during training: 0.89. Saving model..\\n\",\n      \"Finished fold.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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2VpF001cGaP8KWF6H4gHPPbQ+trbDxP/DVg3Ai0/r+foBNH9FSys+Azzpte97k903ACKdG1s0I25Vceuml/O53v2vrsjRp0iRWrFhBSUkJ27dvJzg4mNTUVLMWvxqNL1Fa08BNr24hJjyYN342naToUOdfZNdbcKoUELDzdTjvEedfwxZKDkB9pfr9i3vhhlXKZtKP0XVJnYiKimLevHncfPPNLFmyBFAdlZKTkwkODubbb7/l2LFjHo5S4/M01qpRrYeQUvLHD/dSXd/MKzdNdU0zitYWNVoeOBlGXwS734bmRudfxxbyMtTj9Nsh+3s48Klj52mo9tw92IkWdzMsWbKE3bt3c/XVVwNw7bXXsm3bNqZMmcKbb77JqFGjPByhxud5/TJYvsBjAr96dyFr9p3gt/NPY2S/aNdcZP9qqMiGWb+BiTfAqTI49IVrrmWN3M0QmQwLHoKk0bD2j9Bk57fvqiL4z2Q18vcBtLeMGS677DJMrZATExPZtGmT2X11jbvGbsqzIW+z+v2zu2DRM3aforVVcvub20lLjOKehSPtmtwvrqrnL6v2MXFwHLfOHWr3tW1CSlj/BPQZBqMuVNuiB6jUTPolrrlmd+RlwODpEBgMCx+F1y+FjGdhzu9sO76lCd69CWpOQNZadX9entbRI3eNxt1krlKPE66FnW/AjtfsPsVHuwpYs+8Ez39/hKe/OWzzcVJK/vDhXuqbWvjnlae7bnFR9joo2gWzfgUBgepnwjVw+CuoKrR+vDOpPg4VOTBohno+7CwYeSGs+6cajdvCVw+oD4hhZ0NlnvpG4uVocddo3E3mRzBgElzyHxg6Dz69C4p223x4fVMLj685yPiUWC6fOJD/+/IQ72zNs34g8OHOAr7aX8zd541kWFIUZK6GkoOO3Ud3bHhCpUHGX92+beK1IFth15v2ny9/m/rAcIRcQ7598Iz2bec9DK1N8PVfrR+/7yPY9DRMuw0W/l1tczQWN+J14u6pzlCeorfdb6+nIgcKd8KYS9Vo9orlEJkI79wAdRU2nWL5+myKKuv54wWj+fvi8cwZkch9H+7lmwPdl+cer6zngdX7mDIknqWz0lRJ4DvXw0tnQ9aXTrg5A0W74cg3MON2CDZpaN1nKKTOUd9WWq0bj7VRXwVvXw3v/dS+44zkbYagcOg3vmMsM38Bu99SHxyWKM2CVXdAylRY8DAkjoCofpD9g/1xuBmvEvewsDDKysp6jeBJKSkrKyMszHkd3TVeTuZq9ZhuWOEcmQhXroDKfPjwdqviVVLdwLPfHmZBel+mD00gODCA566bTHr/GH7+5g525pr/gJBSct8He2hsaeVxYzpm41MQHAHxafDWVbDtFefc44anICQaptzc9bWJ16sPuGMbbD/fun9AbQnUFsPxPfbHk5uhKnaCOtXwz7kTovrC5/eY/7s31sL/rlfHXblCPQoBaXPVyN3LdcqrJlRTUlLIz8+npKTE06G4jbCwMFJSdJOBXkPmR9B/AsSntm8bNA3O+xt8/nuVzuhmku+Jrw7R0NzKvee3V2xFhQbxyk1TWfz8Rm5esZX3bj9DpVxMeHd7Pt8eLOH+i9OVRcDJPNj7Lky9Bc7+I7x3M3zyWyW85zwAAQ6O+ypyYN8HalQcHtf19fRL4LO71cRq2hzr5ys9DBnPw8gL4ODn6hvGgAm2x9NYq75JzP5N19dCo+HcB+Cj22HvO3C6SQpJSvX3KDkA138AsSb/R9Pmqv1LDkKyA5Vzra2O/33twKvEPTg4mLS0NE+HodG4hpO5ahn+uQ90fW3arSp98M1DapQ59Mwuu2SdqGbl1jyumz6YoZ3EOyk6lP8uncYVz23kxle28MHtZ5Aco74RFp6s46GPM5me1ocbZ6aqAzKeUwI28xdK5K5+2/Dh8qQS6MtegGAHat83PQMiEGb83PzrweEw7gq1uOn8f5j/ADBl7R8hKAwueqK9UuXMu22Pp2A7yJb2ydTOjL8atrwEX94Poy6CUMPfddty2PM/OOtPahLVFOOHUvY6+8VdSnhupppcdrENglelZTQav6ZzSsYUIeDipyBhhBpFm6koefTzA0QEB/Lrc08ze/rUxEheXTqV8tpGbnx1K9X1TUgpuef9PbRIyeOLTycgQMCpcti+AsYthjiDJ2BgEFz4f7DgERXnfy+G2lL77q+2FHa8DuOvgphuGl5PvB6a6+HH97s/X9ZXqi7+zLshui+MWAD5W6G2zPaYcjcDAgZNNf96QID6kKk5Duv/pbblb4cv7lPXm3Nn12PiUyFusFoMZS8FO9S3gcgk+4+1Ey3uGo27yPxITer1sVBbHhoFV72uFja9e5OqrTaw4XAp3xwo5hdnD6dPpGX/l/EpcTx33WSyTlSz7I3t/HdjDj9klXLfBaMZnBChdtq2HJpqu44chYAz7oCfvKZM+14+R00o2sqWl6C5TpU/dseAidB3rErNWKKlCdbcp/5W0w0WViPmAxKOfG17THkZkDwawuMt7zNoqhrBb3xajfTfvVFNml72guX0SdpcyFmvVuHaQ+aHEBAMI8+37zgH0OKu0biDynw16hxzaff7JY2ERf9RKZov/wKoBUuPfLqfgXHh3HRGqtVLnXlaEv9YPJ4Nh8t44ONMZg1P4NpphuY4TXUqhz18PvQdY/4E6ZfATZ9CQw28fK5tlSGNtbDlBZUbTxrZ/b5CqNF74U44/qP5fba+DKWH1FxEkMHzpv9EiEhUqRlbaG2BvC0wqHNvITOcez8EBMErC1X65yf/hYg+lvdPOxPqT9rnXCsl7Fulyl+7+7BxElrcNRp30JaSsSLuAGOvUKPVjGfhm4f5YEc+mUVV/H7hSKs+60Yun5TCny4czaA+4fz9ivEqHQPtRl7mJhhNSZkCt3wNUcnw34vgtUvh0BrL1Tw731ClnLbmkcf/RNnvmhu915bCt4/CsHPgtIXt2wMC1Oj98Fe2jZiL90NDVcf6dkvEDIC5d0JLo0rTDJzU/f6phrx7jh0lkYU7oTLX+ge8k9DirtG4g8yPoO84SBhm2/4LHoZJN8C6x4n+7HYmD4zg4vHd5LHN8LM5Q1l391mkxBvSMaZGXkNmWT9BfCr87Cs4+88qT/zWT+DpyWrkX2/SrqGlSaU0Bs2wTUhBjYpHXagmLZsbOr72zcPQWKNsAjov8R8xX32IFGy3fo08M4uXumP27+Dnm2HyTdb3jekPiafZt5gp8yP17WDkBbYf0wO8qlpGo/EF/rX2IGszTzAwLpyU+HBS4iMYGN/+e3xEcEevl6pClWY5+0+2XyQwGC5+io3l0ZyX8wwzA+sJqD+9+1SBGTrEYTTymv9X231RwmJh7l1qRL5/tRL2L+5RAjzxWlXlU7BDjUjP/7tdsTHxetj3oXJoHHu52la0R032Tl9mPr0z7GxVjZO1VpWQdkfuZpU7jxtiWzxC2Ff9kjZXNSJpaVLvV3dIqVa6pp1p93voKFrcNRo72JN/kv98e5iRfaMprKxnS3Y51Q0d2wWHBwcyIC6M+IgQYsKDuejUKi4HlpefTsu6I0SHBRMTFsyAuDBG9YshPMR8qqW4poGfHZ3Lb/vFcUvZ47B8Plz7ruUJ2e4wZ+RlD4HBKl009go1at78Amxdrh5DoyFxZMcUii0MPQtiB6nUzNjLVYxf3Kfy0fPuMX9MeLzKoWettf5hmWswC3OVwVfqHDU3ULjT+gdN0W44eUx9ULoJLe4ajY20tkr+vGofCZGhvLNsZlsD6Mq6Jgoq6sivOEV+RR35FXUUVdZRWddEcXU9wyq+4hCD+duWZlpaO3YjChCQlhjJ6P4xpA+IYXT/GMb0jyEpOpR/f5lFY3Mr5/7kF1A7B1YuUROcV7+tRMsejEZeFz+pbA96wsDJcPmLMP8htap1z/+U0Nq7MCcgQJmnff93wxqAHXBsPVz4r+4nHEfMh68fVIZg0f3M71NVqL5NzLRQb+8MjHn37O+ti3vmR+obx0gHPlgdRIu7RmMj72zLY3feSf71k9PbhB0gNjyY2PBg0gfEdD2oqgj+dQDO+gOH557PqcYWquqbqKxr4ljZKfYXVZFZWMWuvJN8sqfdoTAhMoSKU43cMDNVrShNnAk/+xreXKxq0C9/AcZcZnvw5oy8ekp0XzjrPvXjKBOuUeK+dTn8+IEqkbSW8x6xQIn74a9g4nXm9zGahdlSKeMokQlqHiV7HcztZmGVlMoJNG2uOsZNaHHXaGzg5KlG/v7FAaamxnPZxIG2H7j/Y0BC+iKEEESGBhEZGkT/2HBG9YvhvDHtI8/KuiYOFFWRWVTF/qIqymsb+fU5Jt0rE4bBT79SI/h3b4KKYyoXbi3tULRHGXmdc39HIy9vIH6IWo274Qn1/NJPrH+z6DtGecNnrbUs7nmblW9Ov3HOjbczaXNVaqap3vLf9vheKD/q9sbcWtw1Ghv459qDVNY18eAlY+1qjEHmR6rzj7Xab9Q3gOlDE5g+tJvRXWQC3LBa+aF8db9aSHPGHWqizlJcG560bOTlDUy8Ho5+p1bu2uI3I4RKzez70PJkZu4mlT6yNtHZU9LmQsYzag2DpdiNKZlRF7k2lk7oUkiNxgo/FlTy5uZcbpiZaj71YonqE3Bso/PrmoPDlFXwuQ+qybzXFsGzM2Hbq9B4quO+FTlKBKfcZN3HxVOMvgTm3afqy21lxAJVw25Mv5jSUKMWRw2e6bwYLTHkDCXclkoijVUyqbOVA6gb0eKu0XSDmkT9kYTIEH4737yni0X2r8aYknE6AQFqIdJv98Glz6kR6ie/gX+NVitbTxqad2x6BkSAZSMvbyAoBObda3ly1BxDz1TL+M2tVi3YpszC7J10doSwGGWnYEncT+yD8iNuW7hkihZ3jX9xMtexhg4WeH9HPjtzT3LPwlHEhtv5FT9zlSoRTB7ttHi6EBymJiVvWwdLv1Cit/E/8OR4+N91thl5+SKh0WrUbK7JSG4GIFSDDXeQNkd9oDSY6aec+ZH6cB11sXtiMUGLu8Y/MNZxPzFOtURzApV1TTz2+QEmDY7jikl2eu7XFKuGFK4YtZtDCBgyU5l+/XoPnPFL5QnT0qh+90dGLICS/eoD3ZTcDDXpGhbrnjjS5kJrc9cUkTElM2QWRLneBbIzWtw1vk9Lk0pJfHW/+qq+9x2nnPbfXx6i4lQjf100tt2bxVb2f6z6hXrg6zhxg9Qq1N/thzu2OtZQwhcYsUA9mo7eW1tU2zxbLQecwaAZ6t9dZwvg4v1QluWZfwNocdf4OvVVqkXc9hXKG+Tc+1XpWdmRHp02s7CK1zblcO30IYwd6MAIMHOV8mZPTu9RHD0iJMJ2LxtfJHGEshYwFfcT+6Cx2nJzDlcQEqEWMXXOu2d+BAiPpGRAi7vGXVQVtnf/cRaV+cqi9eh3qtHFufe3uy5mrnL4tFJK7l/9I3ERIdy1wHoJYxdqS5VbYPoi1y191xhKIheoEXNTvdpmTI24YzLVlLS5qr+raZPzzFUqJRPd172xGNDirnEPP34AX9xrn/91dxTtVkvxT+Yqv5XJN6rtcYNg4BTDqKkrq3YVcMMrW/j9e7t56ussPtiRz5bscgpO1tHSqj54PtxZwNacCu5ZOJLYCAfqpE/8qFIyQ+c5dm8a2xmxAJpOtTfczstQC5xiB7k3jrS56j0/tlE9Lz6gnDQ9lJIBGxcxCSEWAk8CgcDLUsrHOr0eC7wBDDac859SyledHKvGl6k/qR6z10H/8T0716E18O5S5T/y0zVdm06MuRTW/gnKs6FPe0/e7cfKufOd3SRHh7K/qIqS6o5Ws0EBgv5xYVTUNjFhUBxXTnZQIOor1aOb3P96NamzVY/VrC9h+DnKCXLwDPd/Yxo4BYLC1b/vURcavjkKGO2ZlAzYIO5CiEDgGWA+kA9sFUKsllJmmuz2CyBTSnmxECIJOCiEeFNK2eiSqDW+h1HwstepFZWOsuUl1ci571i45h3lq92Z0Zcocc9c1daUorSmgV+8uZMBceF8/MvZxIYHU9/UQsHJOoPpV7vxV2lNA3++KN3+SVQjxnt1V7VGbyYkQhl4Za1Rzb6r8mGwlTZ/riAoRH2oGPPumR+pRVT21O47OyQb9pkGHJZSHgUQQqwEFgGQveMyAAAgAElEQVSm4i6BaKHWZUcB5UBz5xNpejFGwTu2wTb/a3PsfAM+u0tZy16xvL1TfWfih8CASeo/2Ozf0NIq+c3KXZSfauSD289oq1cPCw5kWFIUw5IsnMdRjPcaasdqVo3jjFgAn9+tukyBa83CuiNtrjI0y9kAxZn2rbh1Abbk3AcCeSbP8w3bTHkaGA0UAnuBX0spu6wkEULcKoTYJoTYVlJS4mDIGp/EKHiNNVC4y7Fz7F4JSaPg6rcsC7uRMZeqpfkVx3jyq0OsP1zKQ4vGOFb5Yi/1VWrhSoiTPzQ05hkxXz1uehqCI9W3Ok+QdqZ6/NzgRe/BlAzYJu7mvpt2Lnk4D9gFDAAmAE8LIboMW6SUL0opp0gppyQlub+oX+NB6ivbywJz7GhNZnp87ibVNd4WP3LD4qHD37/JU98c5srJKVw1dbD913WE+kq1gtJef3ONY/RJUy3vGqpg0FQI9JAfYv/T1be1E3tVKaaHVwXb8q8vHzCdWUpBjdBNWQp8IBWHgWzAT1dOaByivlJ1EOo71r6+k0aOfqdWARoXrlgjPpXG5PHU7XqfUf2i+esiO0ZzJzKhpQdZxYYqnW93N8Z/F+6sb+9MYFB7b1oPVskYsUXctwIjhBBpQogQ4Gpgdad9coFzAIQQfYGRwFFnBqrxceorISxO5SVzM7o2RbZG1loIjYUUKx1vDDQ0t/B2zSTGcZiXLulrsZVdFw6tgedmwqEv7IvPlPpKLe7uxminO3SeJ6NQKaLAUDWp72GsiruUshm4A1gD7AfekVLuE0IsE0IsM+z2EHCGEGIv8DVwj5Sy1FVBa3wQo+ClzoHmeuV/bStSGkrdzrb5K/fDn+xnecXpAAw6bsZcyhzNjaqHJ0B1Uff7dkd9lfog0riPITPht5nq0ZNMvgl+vRti7Wjo4iJs+p8ipfwM+KzTtudNfi8EbPy+rOl1tDSridSwWIP/dYBKzaTOtu3443ug5oTNKZlVuwp4PeMYt8yZAXnjVdWMLeWXW15Q9qzQXpfvCPWVEOem/L6mHS8QVAICzZfnegA946NxPQ1V6jEsVjWM6D/Bvry70bN7+LnWdz1Rzb3v72Vqajy/XzhK5T7ztyqrgu6oKYbv/wHD56v2bHU9FPcwXQap8Sxa3DWuxzgKNuah0+Yq577GWtuOP7RW1a1HJXe7W3ltI8ve2E5kaBBPXzOJ4MAAE6+ZztNEnfjmIbWMfeGjKk5j6aYjNOicu8bzaHHXuJ7OKzbT5kJrk/kWaZ2pLVMjbyspmbKaBq55KYP8ijqevmYifWMMzYoThqkO9Ra8ZgBVd7/jdZi+TDkNhsU5Lu6trYacux65azyLFneN6+ks7oON/tc2pGaOfAPIbsW9tKaBa17aTHZpLctvnMqMzg2m0xdB3mblTNkZKZWhWUQCzL27PU5Hc+6NNSpePXLXeBgt7p7k27/Bmz/xdBSup7O4h0RCyhTbxD1rLUQkqj6VZiipbmDJixkcK6/llZumMnuEmSbEY7pJzez7QC2OOufP7Q2kw3swcte+MhovQYu7pyg+AOv+qYTF3zEneGlzoWhX9xOXrS1w+Cs1kWpmtWdxdT1LXsogr+IUr9w0lVnDLXSXTxwByWO6erw3noK1f4F+42Hi9e3bw2Idn1Btu1edltF4Fi3unsCYCpAtqpKktcXTEbkWS+Ju6n9tjoIdUFfe7h1iQnFVPUtezKCgoo5Xb5rGGcMsCLuR9EXqg7T6ePu2DU8qF8Hz/97R0qAnOXfTyiCNxoNocfcEBz+Ho98qPwxoFwRPUZFjPh/tLOoruxpppUxVPtw5P1g+LmutOm7Y2R02F1fVc/VLGRRV1rNi6VRmDkuwcAITxlwKyPbUzMk82PAEjLlc1d6bYqyWae3ifWcd7Qip8RK0uLub5gZY8wdIHKn8p6FnZXc9paUJXr0QVl7jumvUVyqxM02tBIV29L82R9ZaZTdg0vTiRFU9V7+YwfHKelYsncb0zpOnlkgaCUmj21MzX/5FPc7/a9d9w2IBqXpx2ku9HrlrvAMt7u4m4zmoyFb11BGGVIInxX3veyo1UbjTeS3wOmPJayV1jmpJV2vGqaL6hMrJm6RkjlcqYT9RVc9rN09jWpqdnY7SFyk/+X0fqonUWb9Rbfk6Y5xYdSTv3paCirP/WI3GiWhxdyfVJ2Dd4zDyAtUSzCh4nhJ3KVXeuc8wCAxRtd6uwJK4G/2vzaVmDn+lHg0lkAeOV3HZsxsoqW7gtZ9OY0qqAy3sjKmZD26FmBSY9Wvz+/XkfdETqhovQYu7O/n6ryots+Bh9dzT4p61Fkr2q/ruURfBnv+1d5F3JpbEfcBECIk2n5rJWgtR/aDfOH7IKmHxc5uQEt65bSaThzjYmzR5tEqHtTTCgr+qFm3mMI66Hal1b6hU9gWOdJrSaJyIFnd3UbAddr0BM3+uVk2C58V9w5NqBDtuMUy6XonZwU+dfx1L4h4YpCYzO4t7SxMc+RZGzOedbfksfXUrKfHhfPiLM0gf0MMR8Zw7YdINaiLVEj0duevJVI0XoMXdHUgJn98Lkckw56727Z4U97ytKv888+dqlJk2D2IHuyY1Y/RyN0faXCg73LFaJ28LNFSyqnYsv39/D2cMT+TdZTPpHxve81hOvwou+Q+Ibppf9yjnrht1aLwDLe7uYO97kL8Fzr2/Yy42NAYQnhH3DU8owZ10o3oeEAATr1Udj07mOvda3TWvSJurHrPb8+7Nh9bQQiB/3JPA1VMHsfzGKUSHuTHN0dORu863a7wALe6uprFWld31nwCndyo3DAhQAu9ucS/NggOfwtSfdWw0PcEQn7GLvDMw9XI3R9+xEB7flpo5eaqRgi2r2Nwykp8vnMijl49T7o7uJCRa1dc7LO565K7xPFrcXc36f0N1IZz/D/MNk3tqL+sIG59SdebTl3XcHjdYtSnb+aZjC3jMYW3FZkCAatqR/T25pbXc9swqhjTnEHv6hfx83nBEd+kTV9H2oevIhKpOy2i8Ay3urqTiGGx4Csb9BAZPN7+Pu8W9+jjsXgkTroWopK6vT7oeKnMh+zvnXK+zl7uB8tpGNhwu5eUfjvJ++TCozOPGf73D2FNbABgzd7Fzru8ojpqH6QlVjZdgW0NKjWN8+WflWXLuA5b3cbe4ZzwHrc2W286NvFDl4ne83mXZv0MY7q1GRPLi2oP8WFhFZmEVx6vaSy6nR6dxBXDPqGLmyhyoGKxWlHoSR83D9ISqxkvQ4u4qCneppe7z/tB9b8ewWDh5zD0x1VfCtlfUSs0+Q83vExwG46+C7a/CqfIOS/8dvibwXEYpz+UEclrfaGYOSyC9fwyj+8cwun80CZEh8H9/Y2Hoj3B4vcr9eyIdY4ojH7pN9dDSoCdUNV6BFndXseFJ9fV8xrLu93PnyH37CpUTtrQy08ik61Wz6L3vwvTbenZNw719ndPAXeeN5OfzhpvfL3UO/Pg+1hpzuI2wOCg9ZN8x2std40XonLsrKD+q2rpNWWr9P7q7xL25ATY9q5b8W2h80Ua/caq6Z8frqka/BzTVVgAQF5/Iz2Zb+LYAhpJICYGhSug9jSPvS9vksfaV0XgeLe6uYNMzEBAE02+3vm9YrHs83fe8AzXHrY/ajUy8Dk7sVeZdPWDL/mwAfnnhZEKCuvnnZqx3T5tj2RbAnYTH2Z9z13a/Gi9Ci7uzqSmBnW+ovHVMf+v7G0f2rvR0b21VaaJ+42yfJB13pfJb3/mGw5ctOFnHnsO5tBLArNGp3e8cnwpTb2m3QfY0YbHQXKe+8diKTstovAgt7s5my4tKEGwdIbvDguDQ51CWpSxubZ2oDI+D0ZfAnnehqc6hyz7yaSbR1Hb1cjeHEHDhP51ToeMM2szD7HhftCOkxovQ4u5MGmqUuI+6UPXttAVXi7uUsP4JtUAp/VL7jp10vXI53P+x3Zddn1XKZ3uPM6VfAAHhPjiS7ZG4++D9avwOLe7OZOfratHOrN/Yfoyrxf34XuVrM/MO5cJoD0NmQ9wQ2PGaXYc1Nrfyl9U/MiQhghExrb4pdo6Yh+n+qRovwiZxF0IsFEIcFEIcFkLca+b1u4UQuww/PwohWoQQPSyQ9jFamtRE6uAzYNBU249ztbiXZalHRypQAgJg4vWqmUZ5ts2HvbIhm6MltTxw8RgCG310UY8j70t9JYhA5eeu0XgYq+IuhAgEngHOB9KBJUKIdNN9pJSPSyknSCknAPcB30spy10RsNfy4/tQmQez7Ri1g+vF3WilGzPAseMnXIMUAez++BmKq6w38iiqrOOpr7M4d3RfzhqV7LtGWo407DCuTvX0AiyNBttG7tOAw1LKo1LKRmAlsKib/ZcAbzsjOJ/B2K4uaTQMn299f1PcIe7BkY4LbOxAjsZMJ+no+8z9x9c8sHofxysti/wjn+6npVVy/8WGz//uvNy9mbb3xR5x13a/Gu/BFnEfCOSZPM83bOuCECICWAi8b+H1W4UQ24QQ20pKSuyN1XvJ+hKKM1WFjLWqkM642tO9Ml+N2h0cTVbXN/HyyckMEOXcNqqBNzKOMffxb/nLqh8pquxYRbPxSCmf7Cni9nnDGNTHkJrw2ZG7g2kZX7xXjV9iixKZUwVLyxYvBjZYSslIKV+UUk6RUk5JSjLjSOirbHgSYgbC2CvsP9bVnu5Vhd1721jh7S25ZDWq6ZPfzojl27vmccWkgby1OZcz//Edf/poLwUn62hqaeX+VfsY1CecZWca2gha83L3ZoLDVJ2/vROqegGTxkuwpXwiHxhk8jwFKLSw79X0tpRM/jY4th7O+xsEhTh2DldaEFQVwrCzHDq0sbmV5euzmTU4FU4AtSUMGh7Bo5eP5xdnDefZ747wv615/G9rHqenxJFVXMPLN0whLDhQncDXq0fsfV/qKy0bsmk0bsaWkftWYIQQIk0IEYIS8NWddxJCxAJnAqucG6KXs+EJJQKTbnD8HK4S95ZmZTng4GTqql0FnKhq4PI5Bi+amuK211LiI/jbZeP47u6zuGrqIPbkV3Lu6L6cm963/QQWvNx9hrA4ByZUfXB+QeOXWB25SymbhRB3AGuAQOAVKeU+IcQyw+vPG3a9DFgrpax1WbTeRmkW7P8E5twJodGOn8dV4l5zAmSrQ+Le2ip5cd1RRvWLZlZ6qjL0qu06TzIwLpyHLx3H3QtGERbSaazg64t6HBm56wlVjZdg06oWKeVnwGedtj3f6fkKYIWzAvMJNv4HAkN6bovrKk/3qgL1GGN/zv3bg8VkFdfwxFUTEAEBEJVsVtyNxEaYaWDt6+IeHtfh20q3tLZAY7Xv3qvG79ArVB2l+jjsfhsmXquErye4auTeA3F/4fujDIwL58LxBvOzyCTbhc6Ir4u7Pe+LcX5BT6hqvAQt7o7y9UNqVepMC+3q7MFl4u7YAqbtxyrYklPOT2enERxo+CcSmQS1vU3c7ci5+/q9avwOLe6OsPMN2PUGzP4tJAzr+flc5eleVQhB4RAeb9dhL647Qmx4MFdNNSmSikqC2lL7ru/rgmf80LWlYUm9j1cGafwOLe72UrQHPr1TNZc464/OOaerPN0dWMB0pKSGtZknuGHmECJDTaZkIg0599ZW269fXwkiAEKi7AjaiwiPUxPSDdXW99V2vxovQ4u7PdSdhHeuVyPhK16x32XREq6yIHBgAdPLPxwlODCAG89I7fhCVDK0Ntu/HN8WL3dvxZ73xddr+jV+h4/+r/MAra3w0e1qNHzlCpWmcBauFHc7JlOLq+t5f3sBV05OITEqtOOLkYb7tWdS1deX49vjL6Nb7Gm8DC3utrLxSTj4Gcx/CAbPcO65XSHurS1QXWTXZOqKDTk0tbZyyxwzqyyN4m7PpKrPi7sdDTt8fX5B43docbeF7B/g67+qTkYzbGh6bS+uEPeaYpAtNot7TUMzr2cc4/yx/UhNjOy6g7Hcs5ta9y74vLjb8b7U61JIjXehxd0aVUXw3lLoMwwWPe0ar25XiLuhxl1GD+DHgkoq65q63X3lllyq65u5ba6F6p9Ig7jX9CJxt6cbU32lmjh21jyMRtND9L/E7mhpUsLeWAs3ftwzi4HucKG4Z5SFs2TFegIEpA+IYXpaAjOGJjAttU/bqlKjQdiMoX04fZAFb5TweNVlyO60jA97rdg1oerjH2Qav0OLe3d89QDkboLLX4bk0a67jis83Q0LmF7d08iA2DCunDKIzdllvJ5xjOXrsxECRveLYfrQPgQFCIoq6/nb5eMsny8gwP5VqvWV7aNfXyQ0FvW+2Dhy1ykZjRehxd0S+z6CTU/D1Ftg/JWuvZYrPN2rCmgNDGXtsSbuWTic2+epdEt9Uwu7806ScbSczdllvLU5l4bmVkb1i2beaVYqgCKTbM+5tzT5rpe7EXveF19PQWn8Di3u5qivhNW/goGT4bxH3HPN8Fj7GkNYo6qQisBEQoICO6w0DQsOZPrQBKYPTQBG0NDcwo8FlQyIC0dYm0+IskPc/WXFpq3vS31Vzz2GNBonoidUzbHtFZVDvejfEBRqfX9n4GR/meaKfI40xLLo9AH0ibTcRCQ0KJDJQ/rQPzbc+kkjk22fUPV1L3cjtr4veuSu8TK0uHemuQEynoOhZ0H/09133bA4p4p7XVku+a19uq407QlRBvMwm7xW/KTu21bzsIYq379XjV+hxb0zu1eqJhezfu3e6zpx5N7a0kJ4fTFED2DsQCcKTmQyNNfb6bXi44Jny/sipZ5Q1XgdWtxNaW2FjU+pEfvQee69thPFfdOeAwTRwvARpznlfG20rVK1ITXjN+IeZz3n3nRK+e74+r1q/Aot7qYc/BTKDqtRuysWK3WHE8X9q807ABh9mpPLN6N6obiH25Aua5s81iN3jfegxd2IlLD+CYhPhdGL3H/9sFjVpq2luUenyS6tpTD3CADB8SnOiKydtlWqNtS6+4u4h8VCU60q7bSEv9yrxq/Q4m7k2EYo2KY6K3liCbmTPN1f25TDwMBy9cSB9nrd0uYvY6O4+7KXuxFbzMO03a/GC9HibmTDkxCRCBOv88z1nWBBUNvQzHvb8jkjqVE17o5IcFJwBozns6Uc0lga6O70lrOx5X1ps/vV4q7xHrS4A5zIhKw1MP02CLah3tsVOEHcP9hZQHVDMxPjTkF0f+c3yQgMhvA+to/c/WEka4t5mE7LaLwQLe6gKmSCI2DqzzwXQw/FXUrJaxtzGDcwlj7Nxc5PyRiJSrZ9QtUfxM6Whh26xZ7GC9HifjIP9r4Lk26EiD6ei6OH4r7xSBlZxTXceEYqwoH2ejYTmWRfWsbXacu565G7xrfQ4p7xnKqUmfkLz8bRQ3H/78Yc+kSGcNG4vnZ3YLKLqOTelZax5X1pqFJzHEFh7olJo7GB3i3up8ph+woYtxjiBlnd3aX0QNzzK07x1f4TXD11EGGNJ6Gl0XVpmd42crc15x4a4/uTxxq/oneL+7blqobZ3VYD5giJxlFP99czjgFw3YwhbU06XDZyj0xS9fhNdd3v5+uNOowEhalRubVqGX/4INP4Fb1X3JvqION5GD4f+o7xdDSqsiXMfk/3+qYW/rc1jwXp/RgQF+56cbell2pLk/rQ9AfBE8Kweri7kXuVnkzVeB02ibsQYqEQ4qAQ4rAQ4l4L+8wTQuwSQuwTQnzv3DBdwK634FQpzP6NpyNpxwELgtW7Cjl5qqnd/dHQgYkYJ69ONWJLL1V/8XI3Ys2xU4/cNV6I1aWYQohA4BlgPpAPbBVCrJZSZprsEwc8CyyUUuYKIby7a0FrC2z8j2rGMWSWp6Npx05x35ZTzkOfZjK6fwwzhhoqfaoKICCo3eTL2bT5y3QzqeovXu5Gwqw07Gioguh+7otHo7EBW0bu04DDUsqjUspGYCXQ2XzlGuADKWUugJTSjkabHuDgZ1CRDbN+412TYHZ4un93sJjrlm8mMSqUl26Y3N5FqaoQogc4fwGTEeOHRnf+Mv5WGmjNPEyP3DVeiC0KMBDIM3meb9hmymlAvBDiOyHEdiHEDeZOJIS4VQixTQixraTExo4+riB7nfI8GXWh52Iwh40j9493F3LLa9sYmhjFu8tmkhIf0f5iZYHr8u3QnpbpLufub+JuNeeuxV3jfdgi7uaGtp1b8QQBk4ELgfOAPwshupiJSylflFJOkVJOSUpyUdrAFgq2w4CJEBDouRjMYYO4v7U5l1+t3MnEQfGsvG0GiVGd2gBWFbhuARNAcJgq++tV4t7NyL2lSfm5+8u9avwGW8Q9HzAtAk8BCs3s84WUslZKWQqsA9zYo84Omhvg+F4YOMnTkXTFirg/+91h/vDhXuadlsR/b55GTFhwxx2kVGkZV47cwVDr3ovSMsb3xVx7QX+bPNb4DbaI+1ZghBAiTQgRAlwNrO60zypgjhAiSAgRAUwH9js3VCdx4ke1yGfgZE9H0hULnu5SSh79fD//+OIgiyYM4MUbphAeYuZbx6lyaGlw3QImI9b8ZfxN3MPjVKelxtqurzUYHSF1KaTGu7BaLSOlbBZC3AGsAQKBV6SU+4QQywyvPy+l3C+E+ALYA7QCL0spf3Rl4A5ToLoUMcBLR+6gqi8MPjctrZI/friXlVvzuH7GEB68ZAwBARYmgV1d424kMhFKDll+3V+83I2Yrh4O7XRP/vZBpvEbbOpKIaX8DPis07bnOz1/HHjceaG5iILtalIw1kV14D3BVEQi+pBTWsujn+9nzb4T/PLs4fxu/mntVTHmaBN3F4/cI5MhZ4Pl1/3Fy92IqXlY5/kMnZbReCkeaDnkYQq2q5SMNwqPQSC+2H6Qlw8Xse1YBQEC/nThaH42Z6j1490l7lHJUFeuJhMDg7u+7m/VI935/mi7X42X0rvEvb4SSrNg3E88HUkHWlol6w+XsntDMb8C/vvtHioSpvH7hSO5bOJA+sfa2ECkqhBEYLtFgKsw1rrXlkJM/66v+6u4m1vIpNMyGi+ld4l74S5Aek2lzImqel7ZkM1HOws4UdXA1LAmfgX87fxBpM4+s/sUjDmqCg0dmFxc4mnaS7U3iHt4N31Ujf1T9YSqxsvoXcZhBdvV44CJno0DKKluYPHzG3n5h2zGDYzluWsn8cYdCwBIi2y2X9hBpWVcPZkKJqtULVTM+Ju4d9ewo74SEFrcNV5H7xq5F2yHPsM823EJqGloZumKLZRWN/LesplMHByvXjCODB3to1pZAP3HOyfI7mhLy/QScTcKt9mcexWERrvO7kGjcZDe9S+yYIfHUzKNza3c/sZ29hdV8+y1k9qFHXrk6d6+gMnFk6nQMS1jDn/xcjcSGKTeG0s5d3/6INP4Db1H3KsKobrQo4uXWlslv39vNz9klfLo5eM4a1SniU8HPd0BqKuA5jr3pGVCoiAo3PwqVX/ycjfFknmYFneNl9J7xN24eMmD4v73Lw7w0a5C7lpwGj+ZYqGtnwOe7oCJj7sbxF0IlZoxl5bx17pvS+ZhDVU6367xSnqRuG9XPuf9xnnk8svXZ/PCuqNcP2MIvzhruOUdHRZ3N9W4G4my4C/jb17uRiyZh9Wf9L971fgFvUfcC3eodnrBNtaMO5GPdxfy0CeZLBzTjwcuGdN9JYwdnu4dcLe4RyarOvfO+Gvdt6WGHbrFnsZL6R3i3toKBTs9kpLZeLiUO9/ZzbTUPjxx9QQCLfnCGOlJWkYEQFRfxwK1l6gk8xOq/izuOueu8SF6h7iXH1HufW4W98zCKm59fTupiRG8dMMUwoJtWFzUE3GP6qcqO9yBceTe2tpxu7+Ku7kJVSlVzt3f7lXjF/SOOnfj4iU3iHtjcyubjpbxZeZxPt5dRFRoECuWTiM2wowHizl6knN3x2SqkcgkkC3KYyYysX27v4q7qR2z8QO0sQZkq55Q1XglvUfcgyMhsUtzKKdQVd/EtweK+TLzBN8fLKG6oZnw4EDmnpbI3eeNYkCcHXl+cyJiC5UFkDza/uAdJcqkl2qvEHdD3b6JHbPf3qvGL+g94u7ktnpV9U2s2lnA2swTZBwto6lFkhgVwoXj+zM/vS+zhifalobpjBlPd6sYFzCNmG//9RzFUi9Vf/NyN9JmHlZhIu7Gsk89ctd4H/4v7sa2etOXOe2UO3MruOOtnRScrGNoYiQ3z05jQXpfJgyKtz5hao1Onu42UV+pFg65My0T1Y24+5OXuxFz5mF65K7xYvxf3J3YVq+1VbJ8fTZ//+IAfWPCeG/ZTKakOtmnpjvvcEu4cwGTkUiTtIwp/lo90va+mJRDanHXeDH+L+5OWplaUdvIne/u5psDxZw3pi//uOJ02ydJ7aFH4u6mGndQOeiAoK7lkH4r7mZG7m12v354vxqfp3eIew/b6m3NKedXb++krKaRBy8Zww0zhzhmyWsLDol7vnp0p7gHBKjRe2fbX78VdzMNO/TIXePF9AJxd7ytXmur5Lnvj/CvLw+REh/O+7efwbgUF/9HdnjkLiC6n0tCsog5f5n6Skjsxl7BV+k2564nVDXeh3+Le30llB6CcVe2bZJSsiW7nA2HSwkPCSImPIiYsGBiwoOJCQsyPAbT3NrK79/bww9ZpVw0vj+PXj6O6DAXpGE645C4F6iVqeb6mbqSqOTek5YJjlBpqM4596AwCAr1XFwajQX8W9zb2upNpL6phY93F/Lqhhwyi6psOjw0KIC/XTaOJdMGuS4N0xlHPN2rCt07mWokMhmKD3Tc5m9e7kaE6LrArL5SL2DSeC3+Le6GlanPHoxh+dvfUFbbyGl9o3j08nFcOmEgEklVXTNV9U1U1TVRXd/+e01DC+eMTua0vtHujdkRT/fKAkgc4bqYLBGZqEbuUirx81cvdyNhcR1z7tp6QOPF+K247847CZu/IVb24/H1JZwzKpmls9I4Y1hCh1F4REgQ/WLDPBipGey1IKgqhGFnuS4eS0QlqzJTo8j5q92f+2YAAA1aSURBVJe7EXMjd3+9V43P43fi3tDcws/f2MHXB4rJCN1HccIUvr1mHqmJkZ4OzXbsEff6KmVX4Km0DKiKGdNmFv4qeJ3Nw7Tdr8aL8StXSCkl972/l68PFHP/vD70E+WMn3a2bwk72Ofp7okadyNGfxnjpKq/lwZ27sakR+4aL8avxP3pbw7zwc4C7px/GktTy9VGD7bVcxh7Ru5tTTo8MXLvtErV78W988hdT6hqvBebxF0IsVAIcVAIcVgIca+Z1+cJISqFELsMP39xfqjd8/HuQv7vy0NcPnEgd5w93ONt9XqEQ+LugZF7Z/Mwvxd3QzcmKdVzPaGq8WKs5tyFEIHAM8B8IB/YKoRYLaXM7LTrD1LKi1wQo1V25FZw57u7mZoaz6NXjFMTpgXbPdZWr8fYJe6GtEx0f9fFY4mIBED0HnEPj4PWJmiqUw6jzfX+e68an8eWkfs04LCU8qiUshFYCSxybVi2k1d+iltf20a/mDBeuH4KoUGB7W31BkzydHiOYerpbo2qAjWCDgpxfVydCQxSAt9r0jIm5mH+Xhmk8XlsEfeBQJ7J83zDts7MFELsFkJ8LoQYY+5EQohbhRDbhBDbSkpKzO1iF1X1Tfz0v1tpbG7llZum0ifSIHAeaqvnNEw93a1RdhTiBrk2nu6ISu44cheB/uflbsTUPMzfP8g0Po8t4m5uaabs9HwHMERKeTrwH+AjcyeSUr4opZwipZySlJRkX6SdaG5p5Y63dnK0pJbnrpvM8GQTQXFjWz2XYKsFQUuTuteUqa6PyRKRiR1H7v7o5W7E1DzM+N7oCVWNl2KLuOcDpkPDFKDQdAcpZZWUssbw+2dAsBAiERchpeTBjzNZd6iEhy4dy6zhnS5lbKuXNNJVIbgWW8X9+B5oroNB010fkyUikzuWQvrzSNZ05N6gR+4a78YWcd8KjBBCpAkhQoCrgdWmOwgh+gnDsk8hxDTDecucHayRFRtzeD3jGLfOHcqSaYO77uCCtnpuxVZxz81Qj4NnuDae7ohKhtpS9bu/i3ubM+RJnZbReD1Wq2WklM1CiDuANUAg8IqUcp8QYpnh9eeBxcDtQohmoA64WkrZOXXjFL49WMxDn2QyP70v9ywc1XWHhhoo2g1n/NIVl3cP5hpDmCM3A+IGe6bG3UhkEjTWQOMp/xd30w/dIINlhV6hqvFSbLIfMKRaPuu07XmT358GnnZuaOYZlhjFheMH8PcrxpnvV5qbAa3NkDbXHeG4BltG7lJC3mZIO9M9MVmirZdqscHL3QMGZu7CNOduLLH15w8zjU/jc94ygxMi+M+SiZZ3yP4eAoJhkAdTFT3FFnGvyIGaEzDYg/l2MFmlWuL/I/fAYDWXU1+p6t1FgP9WBml8Hp8Td6vk/ACDpkFIhKcjcZyQKCUc3Ym7Md/u6Q+xSBN/GX8Xd2g3D2ttUpUy/loZpPF5/MpbhroKlW/35ZQMKE/3UCue7nkZqjFz8mj3xWUOY1qmqtDg5e6HjTpMMZqH1VfqfLvGq/GvkfuxjSBbIXWOpyPpOdYsCHI3w6Cpnq8IMo7cy46oR38fuRvNw1pb/P9eNT6Nf43cs9dBUDikTPF0JD2nO3Gvq4CS/Z5PyYDqHxoWC2VZ6rm/C57RPMxf2wlq/Ab/E/fBM/yjYXF34p63RT16ejLVSGQSlPYScTfm3Buq9OpUjVfjP+JeUwLFmb6fbzfSnbjnZig7Y2+xV4hMhpO56nd/F/cOOXc/v1eNT+M/OfecH9Sjp+u+nUV33ZjyNkO/8RDiJR2mopJosxvyd8ELi1Wj9tYWPaGq8Wr8Z+SevU59Te5/uqcjcQ6WRu7NjcpewZOWA50xNu2AXiDuhjx7U63/36vGp/EvcR9yhvIY9wcseboX7VZNIjxpFtaZqN4k7rHmf9dovAz/EPfKfOXh7i/5drDs6Z7nBWZhnYk0uHKKQO9JFbmKcJMKGT2hqvFi/EPcs435dj8U986pmdwMiE+F6H5uD8kixrSMP3u5G9Ejd42P4Cfivg7C+0Cy2QZQvok5cTeahXlDfbspUSbi7u+Y1rbrCVWNF+P74i6lqpRJm6OW7fsL5sS9/Khqaect9e1GjKtUe4W465G7xjfwfTWsyIbKPP9KyYB5cW9rzjHT/fF0R28Sd9Oce2+4X43P4vvinr1OPab2AnHPy1DbE72sfWBoFARH9A6xC4lSE8egjNs0Gi/FP8Q9qp//NYloE/eT7dtyN6sSSG9MP6VMVQur/B0h2t8bnXPXeDG+XRQupRL3oWf5X5VGZ0/3U+VQehDG/8SzcVnixtXW9/EXwmLVWoPAYE9HotFYxLfFveSAmmD0t3w7dPV0z9usHr2pvr23YhR3jcaL8W1x98f6dlNMLQhyM1T7wAGTPBuTRk2qNtV5OgqNplt8XNy/h7ghED/E05G4BlNxz9usfHN8uX2gvzD1lo5zIRqNF+K74t7aAjnrYfRFno7EdRjFvbkBCnbAtFs8HZEG/PvfnMZv8MKyCxs5vleNnvzF4tccRnEv3AUtDd5lFqbRaLwa3xX3tvp2P+iXagmjp7s3moVpNBqvxrfFPfE0iOnv6Uhch3HknrsZ+gztaK2r0Wg03eCb4t7SBLmb/LdKxkhYLDTWQO5G7zML02g0Xo1vinvhTiV6vUHcAeoqvM8sTKPReDW+Ke7Z36vHIbM9G4erMfVq8TazMI1G49XYJO5CiIVCiINCiMNCiHu72W+qEKJFCLHYeSGaIXsd9B0HkQkuvYzHMYp7eDwk+Jl3jkajcSlWxV0IEQg8A5wPpANLhBDpFvb7O7DG2UF2oKleTTD6e0oG2sXdW83CNBqN12KLYkwDDkspj0opG4GVwCIz+/0SeB8odmJ8Xcnfomq+e5u4azQajR3YIu4DgTyT5/mGbW0IIQYClwHPd3ciIcStQohtQohtJSUl9saqCAiGEQtgSC/IQSeNgjN+BROu9XQkGo3Gx7DFfsCcl67s9PwJ4B4pZYvoxnpXSvki8CLAlClTOp/DNobMhCHvOnSozxEYBAse8nQUGo3GB7FF3POBQSbPU4DCTvtMAVYahD0RuEAI0Syl/MgpUWo0Go3GLmwR963ACCFEGlAAXA1cY7qDlDLN+LsQYgXwiRZ2jUaj8RxWxV1K2SyEuANVBRMIvCKl3CeEWGZ4vds8u0aj0Wjcj02Wv1LKz4DPOm0zK+pSypt6HpZGo9FoeoIuntZoNBo/RIu7RqPR+CFa3DUajcYP0eKu0Wg0foiQ0rG1RD2+sBAlwDEHD08ESp0YjivxlVh1nM7HV2LVcToXV8c5REqZZG0nj4l7TxBCbJNSTvF0HLbgK7HqOJ2Pr8Sq43Qu3hKnTstoNBqNH6LFXaPRaPwQXxX3Fz0dgB34Sqw6TufjK7HqOJ2LV8Tpkzl3jUaj0XSPr47cNRqNRtMNWtw1Go3GD/E5cbe1WbenEULkCCH2CiF2CSG2eToeU4QQrwghioUQP5ps6yOE+FIIkWV4jPdkjIaYzMX5gBCiwPB33SWEuMCTMRpiGiSE+FYIsV8IsU8I8WvDdq/6m3YTp1f9TYUQYUKILUKI3YY4HzRs96q/p5VYPf439amcu6EJ9yFg/v+3dz8hNkZxGMe/TxOloUhoNhJbCcmGNAuJlT9FWY0VC4qdsiGlJGRnIYsh1NT4M0s2wkYaCWEpC9PMQmK2PBbnDDfmvXPL4px7+31qmnfeaerpqffMe897Z36kISIvgIO23xUNNgtJH4FNtqv7owtJ24Bp4IbttfncBeCL7fP5l+YS2ycrzHkGmLZ9sWS2VpIGgAHbLyUtAsaBPcAhKuq0Tc4DVNSp0tSfftvTkuYBz4DjwD4q6nOOrDsp3Gm33bl3Oqw7tGH7CfDlr9O7geF8PEy66ItqyFkd2xO2X+bj78B70pzhqjptk7MqTqbzl/Pyh6msT2ibtbhuW9znHNZdEQMPJY1LOlw6TAdW2J6AtAgAywvnaeeYpNd526b4S/NWklYBG4DnVNzpXzmhsk4l9Ul6BUwBj2xX22dDVijcabct7p0M667FFtsbgV3A0bzFEP7fVWANsB6YAC6VjfOHpIXAKHDC9rfSeZrMkrO6Tm3/sL2eNLN5s6S1pTM1achavNNuW9w7GdZdBduf8+cp4B5pS6lmk3lPdmZvdqpwnlnZnswX00/gGpX0mvdbR4Fbtu/m09V1OlvOWjsFsP0VeEzaw66uz1atWWvotNsW99/DuiXNJw3rHiuc6R+S+vMDKyT1AzuAt+1/qrgxYCgfDwEPCmZpNHNxZ3upoNf8UO068N725ZZvVdVpU87aOpW0TNLifLwA2A58oLI+oTlrDZ121btlAPJbiq7wZ1j3ucKR/iFpNeluHdKc2ts15ZR0Bxgk/WvSSeA0cB8YAVYCn4D9tos+zGzIOUh6qWvgI3BkZh+2FElbgafAG+BnPn2KtJ9dTadtch6kok4lrSM9MO0j3YCO2D4raSkV9Qlts96kcKddt7iHEEKYW7dty4QQQuhALO4hhNCDYnEPIYQeFIt7CCH0oFjcQwihB8XiHkIIPSgW9xBC6EG/ANUXvQa02/5RAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"################################\\n\",\n      \"################################\\n\",\n      \"All accuracies: [0.87, 0.85, 0.81, 0.86]\\n\",\n      \"0.8475\\n\",\n      \"0.022776083947860723\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"fold_metric, models = run(net=net, data=adni_data_train,\\n\",\n    \"                  k_folds=-1,\\n\",\n    \"                  callbacks=callbacks,\\n\",\n    \"                  shape=-1,\\n\",\n    \"                  masked=False,\\n\",\n    \"                  metrics=metrics,\\n\",\n    \"                  num_epochs=num_epochs,\\n\",\n    \"                  retain_metric=retain_metric,\\n\",\n    \"                  b=b,\\n\",\n    \"                 )\\n\",\n    \"\\n\",\n    \"print(np.mean(fold_metric))\\n\",\n    \"print(np.std(fold_metric))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"# Start inference\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# load models\\n\",\n    \"models = []\\n\",\n    \"for i in range(4):\\n\",\n    \"    net = ClassificationModel3D()\\n\",\n    \"    net.load_state_dict(torch.load(model_path))\\n\",\n    \"    models.append(net)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_loader = DataLoader(\\n\",\n    \"            adni_data_test, batch_size=1, num_workers=1, shuffle=False\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fold 0\\n\",\n      \"0.8796563573883162\\n\",\n      \"Fold 1\\n\",\n      \"0.862680412371134\\n\",\n      \"Fold 2\\n\",\n      \"0.881786941580756\\n\",\n      \"Fold 3\\n\",\n      \"0.8566323024054983\\n\",\n      \"######## Final results ########\\n\",\n      \"          0\\n\",\n      \"0  0.879656\\n\",\n      \"1  0.862680\\n\",\n      \"2  0.881787\\n\",\n      \"3  0.856632\\n\",\n      \"Balanced accuracy mean 87.02 %\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"metrics = []\\n\",\n    \"lr = 1e-5\\n\",\n    \"wd = 1e-3\\n\",\n    \"criterion = nn.BCEWithLogitsLoss().cuda(gpu)\\n\",\n    \"optimizer = optim.Adam(net.parameters(), lr=lr, weight_decay=wd)\\n\",\n    \"    \\n\",\n    \"for fold, model in enumerate(models):\\n\",\n    \"    print(\\\"Fold {}\\\".format(fold))\\n\",\n    \"\\n\",\n    \"    all_preds = []\\n\",\n    \"    all_labels = []\\n\",\n    \"    \\n\",\n    \"    net = model.cuda(gpu)\\n\",\n    \"    net.eval()\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        for sample in test_loader:\\n\",\n    \"            img = sample[\\\"image\\\"]\\n\",\n    \"            label = sample[\\\"label\\\"]\\n\",\n    \"\\n\",\n    \"            img = img.to(torch.device(\\\"cuda:\\\" + str(gpu)))\\n\",\n    \"            output = net.forward(img)\\n\",\n    \"            pred = torch.argmax(F.softmax(output, dim=1))\\n\",\n    \"            all_preds.append(pred.cpu().numpy().item())\\n\",\n    \"            all_labels.append(label.numpy().item())\\n\",\n    \"    \\n\",\n    \"    balanced_acc = balanced_accuracy(all_labels, all_preds)\\n\",\n    \"    print(balanced_acc)\\n\",\n    \"    '''trainer = Trainer(\\n\",\n    \"                net,\\n\",\n    \"                criterion,\\n\",\n    \"                optimizer,\\n\",\n    \"                scheduler=None,\\n\",\n    \"                metrics=metrics,\\n\",\n    \"                callbacks=None,\\n\",\n    \"                device=gpu,\\n\",\n    \"                prediction_type=\\\"binary\\\"\\n\",\n    \"            )\\n\",\n    \"    computed_metrics = trainer.evaluate_model(test_loader, metrics=[balanced_accuracy])'''\\n\",\n    \"    net.train()\\n\",\n    \"    metrics.append(balanced_acc)\\n\",\n    \"print(\\\"######## Final results ########\\\")\\n\",\n    \"metrics_df = pd.DataFrame(metrics)\\n\",\n    \"print(metrics_df)\\n\",\n    \"print(\\\"Balanced accuracy mean {:.2f} %\\\".format(np.mean(metrics_df[0])*100))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python (mort1)\",\n   \"language\": \"python\",\n   \"name\": \"mort1\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.5\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "Beta Correlation.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pickle\\n\",\n    \"import os\\n\",\n    \"import h5py\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"beta_path_suffix = [\\\"\\\", # this is beta 0.5\\n\",\n    \"                    \\\"beta0\\\",\\n\",\n    \"                    \\\"beta1\\\",\\n\",\n    \"                    \\\"beta0_25\\\",\\n\",\n    \"                    \\\"beta0_75\\\",\\n\",\n    \"                   ][1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data_path = \\\"/analysis/ritter/projects/Methods/LRP/data/rieke-copy1/2Node_trial0/\\\" + beta_path_suffix\\n\",\n    \"\\n\",\n    \"hippo_file = h5py.File('/analysis/ritter/data/ADNI_HDF5/Splits_Eitel/holdout_AD_CN_2Yr15T_plus_UniqueScreening_ID_Hippocampus.h5', 'r')\\n\",\n    \"rs_per_area_LRP = dict()\\n\",\n    \"rs_per_area_GB = dict()\\n\",\n    \"cases = ['AD', 'HC', 'TP', 'TN', 'FP', 'FN']\\n\",\n    \"for case in cases:\\n\",\n    \"    with open(os.path.join(data_path, \\\"LRP_area_evdcs_{case}.pkl\\\".format(case=case)), 'rb') as file:\\n\",\n    \"            rs_per_area_LRP.update({case: pickle.load(file)})\\n\",\n    \"\\n\",\n    \"    with open(os.path.join(data_path, \\\"GB_area_evdcs_{case}.pkl\\\".format(case=case)), 'rb') as file:\\n\",\n    \"            rs_per_area_GB.update({case: pickle.load(file)})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LRP:  -0.5603004848271849 .\\n\",\n      \"GB:  0.09615624758406163 .\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# idcs are the same for both LRP and GB\\n\",\n    \"idcs = np.array([p_idx for p_idx, _ in rs_per_area_LRP['TP'][\\\"Hippocampus\\\"]])\\n\",\n    \"\\n\",\n    \"# True values\\n\",\n    \"AD_Hvals = np.array(list(hippo_file['Hippocampus']))[idcs]\\n\",\n    \"# Relevance values (LRP)\\n\",\n    \"AD_Hrels = np.array([r for _, r in rs_per_area_LRP['TP'][\\\"Hippocampus\\\"]])\\n\",\n    \"# Sensitivity values (GB)\\n\",\n    \"AD_Hsens = np.array([r for _, r in rs_per_area_GB['TP'][\\\"Hippocampus\\\"]])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"mask = ~np.isnan(AD_Hvals)\\n\",\n    \"\\n\",\n    \"actual_LRP_corrcoef = np.corrcoef(AD_Hvals[mask], AD_Hrels[mask])[1, 0]\\n\",\n    \"actual_GB_corrcoef = np.corrcoef(AD_Hvals[mask], AD_Hsens[mask])[1, 0]\\n\",\n    \"\\n\",\n    \"print(\\\"LRP: \\\", actual_LRP_corrcoef, \\\".\\\")\\n\",\n    \"print(\\\"GB: \\\", actual_GB_corrcoef, \\\".\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Permutation test\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from copy import copy\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LRP bigger than observed:  1000 .\\n\",\n      \"GB smaller than observed:  795 .\\n\",\n      \"LRP random order mean:  -0.010661391220588571 .\\n\",\n      \"LRP random order std:  0.13238323601255558 .\\n\",\n      \"GB random order mean:  0.011859570091072004 .\\n\",\n      \"GB random order std:  0.13538990822633692 .\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"idcs = {case: np.array([p_idx for p_idx, _ in rs_per_area_LRP[case][\\\"Hippocampus\\\"]]) for case in cases}\\n\",\n    \"\\n\",\n    \"AD_Hvals_case = {case: np.array(list(hippo_file['Hippocampus']))[idcs[case]] for case in cases}\\n\",\n    \"\\n\",\n    \"AD_Hrels_case = {case: np.array([r for _, r in rs_per_area_LRP[case][\\\"Hippocampus\\\"]]) for case in cases}\\n\",\n    \"AD_Hsens_case = {case: np.array([r for _, r in rs_per_area_GB[case][\\\"Hippocampus\\\"]]) for case in cases}\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"LRP_coeff_rand = []\\n\",\n    \"GB_coeff_rand = []\\n\",\n    \"for i in range(1000):\\n\",\n    \"    idcss = copy(idcs['TP'])\\n\",\n    \"    np.random.shuffle(idcss)\\n\",\n    \"    AD_Hvals = np.array(list(hippo_file['Hippocampus']))[idcss]\\n\",\n    \"    mask = ~np.isnan(AD_Hvals)\\n\",\n    \"\\n\",\n    \"    LRP_corrcoef = np.corrcoef(AD_Hvals[mask], AD_Hrels[mask])[1, 0]\\n\",\n    \"    GB_corrcoef = np.corrcoef(AD_Hvals[mask], AD_Hsens[mask])[1, 0]\\n\",\n    \"    \\n\",\n    \"    LRP_coeff_rand.append(LRP_corrcoef)\\n\",\n    \"    GB_coeff_rand.append(GB_corrcoef)\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print(\\\"LRP bigger than observed: \\\", np.sum(actual_LRP_corrcoef < np.array(LRP_coeff_rand)), \\\".\\\")\\n\",\n    \"print(\\\"GB smaller than observed: \\\", np.sum(actual_GB_corrcoef > np.array(LRP_coeff_rand)), \\\".\\\")\\n\",\n    \"\\n\",\n    \"print(\\\"LRP random order mean: \\\", np.mean(LRP_coeff_rand), \\\".\\\")\\n\",\n    \"print(\\\"LRP random order std: \\\", np.std(LRP_coeff_rand), \\\".\\\")\\n\",\n    \"print(\\\"GB random order mean: \\\", np.mean(GB_coeff_rand), \\\".\\\")\\n\",\n    \"print(\\\"GB random order std: \\\", np.std(GB_coeff_rand), \\\".\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x360 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sort_idcs = np.array(sorted([(i, val) for i, val in enumerate(AD_Hvals_case['TP']) if not np.isnan(val)], key=lambda x: x[1]), dtype=int)[:, 0]\\n\",\n    \"\\n\",\n    \"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\\n\",\n    \"\\n\",\n    \"for case in ['TP', 'FP']:\\n\",\n    \"    ax1.scatter(AD_Hvals_case[case], AD_Hrels_case[case], label='True positives' if case == 'TP' else 'False positives')\\n\",\n    \"for case in ['TP', 'FP']:\\n\",\n    \"    ax2.scatter(AD_Hvals_case[case], AD_Hsens_case[case], label='True positives' if case == 'TP' else 'False positives')\\n\",\n    \"ax1.legend()\\n\",\n    \"ax2.legend()\\n\",\n    \"ax1.set_ylabel('LRP relevance in hippocampus')\\n\",\n    \"ax1.set_xlabel('Hippocampal volume')\\n\",\n    \"ax2.set_xlabel('Hippocampal volume')\\n\",\n    \"ax2.set_ylabel('GB susceptibility in hippocampus')\\n\",\n    \"z = np.polyfit(AD_Hvals_case['TP'][sort_idcs], AD_Hrels_case['TP'][sort_idcs], 1)\\n\",\n    \"linear = lambda x: z[1] + z[0]*x\\n\",\n    \"mini, maxi = AD_Hvals_case['TP'][sort_idcs][0], AD_Hvals_case['TP'][sort_idcs][-1]\\n\",\n    \"ax1.plot(np.linspace(mini, maxi, 2), linear(np.array([mini, maxi])))\\n\",\n    \"z = np.polyfit(AD_Hvals_case['TP'][sort_idcs], AD_Hsens_case['TP'][sort_idcs], 1)\\n\",\n    \"linear = lambda x: z[1] + z[0]*x\\n\",\n    \"mini, maxi = AD_Hvals_case['TP'][sort_idcs][0], AD_Hvals_case['TP'][sort_idcs][-1]\\n\",\n    \"ax2.plot(np.linspace(mini, maxi, 2), linear(np.array([mini, maxi])))\\n\",\n    \"\\n\",\n    \"ax1.text(4000, -0.010, 'Corr. coeff. : {:.3f}'.format(actual_LRP_corrcoef))\\n\",\n    \"ax2.text(6500, 6.5, 'Corr. coeff. : {:.3f}'.format(actual_GB_corrcoef))\\n\",\n    \"\\n\",\n    \"ax1.set_title('LRP')\\n\",\n    \"ax2.set_title('GB')\\n\",\n    \"fig.tight_layout()\\n\",\n    \"# fig.savefig(\\\"./correlation.pdf\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0, 0.5, 'corrcoef')\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Correlation values for different betas\\n\",\n    \"corrs = [-0.5603004848271849, -0.5618695011449486, -0.5253121992485645, -0.45670034315239927, -0.36082410679630594]\\n\",\n    \"\\n\",\n    \"plt.plot([0, .25, .5, .75, 1], corrs)\\n\",\n    \"\\n\",\n    \"plt.xlabel('beta')\\n\",\n    \"plt.ylabel('corrcoef')\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Innvestigate\",\n   \"language\": \"python\",\n   \"name\": \"innvestigate\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.1\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "Compare intersection of top 10 regions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import matplotlib as mpl\\n\",\n    \"\\n\",\n    \"from settings import settings\\n\",\n    \"for k in settings.keys():\\n\",\n    \"    print(\\\"Adding \\\" + k + \\\" to namespace\\\")\\n\",\n    \"    globals()[k] = settings[k]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_path_beta0 = \\\"path/to/beta0/\\\"\\n\",\n    \"data_path_beta0_25 = \\\"path/to/beta0_25/\\\"\\n\",\n    \"data_path_beta0_5 = \\\"path/to/beta0_5/\\\"\\n\",\n    \"data_path_beta0_75 = \\\"path/to/beta0_75/\\\"\\n\",\n    \"data_path_beta1 = \\\"path/to/beta1/\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sorted_lbls = dict()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# load sortings\\n\",\n    \"sorted_lbls_beta0 = dict()\\n\",\n    \"sorted_lbls_beta0[\\\"gain_tp\\\"] = np.load(os.path.join(data_path_beta0, \\\"sorted_lbls_LRP_gain_tp.npy\\\"))\\n\",\n    \"sorted_lbls_beta0[\\\"area_norm\\\"] = np.load(os.path.join(data_path_beta0, \\\"sorted_lbls_area_norm.npy\\\"))\\n\",\n    \"sorted_lbls_beta0[\\\"absolute\\\"] = np.load(os.path.join(data_path_beta0, \\\"sorted_lbls_absolute.npy\\\"))\\n\",\n    \"sorted_lbls[\\\"sorted_lbls_beta0\\\"] = sorted_lbls_beta0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# load sortings\\n\",\n    \"sorted_lbls_beta0_25 = dict()\\n\",\n    \"sorted_lbls_beta0_25[\\\"gain_tp\\\"] = np.load(os.path.join(data_path_beta0_25, \\\"sorted_lbls_LRP_gain_tp.npy\\\"))\\n\",\n    \"sorted_lbls_beta0_25[\\\"area_norm\\\"] = np.load(os.path.join(data_path_beta0_25, \\\"sorted_lbls_area_norm.npy\\\"))\\n\",\n    \"sorted_lbls_beta0_25[\\\"absolute\\\"] = np.load(os.path.join(data_path_beta0_25, \\\"sorted_lbls_absolute.npy\\\"))\\n\",\n    \"sorted_lbls[\\\"sorted_lbls_beta0_25\\\"] = sorted_lbls_beta0_25\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# load sortings\\n\",\n    \"sorted_lbls_beta0_5 = dict()\\n\",\n    \"sorted_lbls_beta0_5[\\\"gain_tp\\\"] = np.load(os.path.join(data_path_beta0_5, \\\"sorted_lbls_LRP_gain_tp.npy\\\"))\\n\",\n    \"sorted_lbls_beta0_5[\\\"area_norm\\\"] = np.load(os.path.join(data_path_beta0_5, \\\"sorted_lbls_area_norm.npy\\\"))\\n\",\n    \"sorted_lbls_beta0_5[\\\"absolute\\\"] = np.load(os.path.join(data_path_beta0_5, \\\"sorted_lbls_absolute.npy\\\"))\\n\",\n    \"sorted_lbls[\\\"sorted_lbls_beta0_5\\\"] = sorted_lbls_beta0_5\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# load sortings\\n\",\n    \"sorted_lbls_beta0_75 = dict()\\n\",\n    \"sorted_lbls_beta0_75[\\\"gain_tp\\\"] = np.load(os.path.join(data_path_beta0_75, \\\"sorted_lbls_LRP_gain_tp.npy\\\"))\\n\",\n    \"sorted_lbls_beta0_75[\\\"area_norm\\\"] = np.load(os.path.join(data_path_beta0_75, \\\"sorted_lbls_area_norm.npy\\\"))\\n\",\n    \"sorted_lbls_beta0_75[\\\"absolute\\\"] = np.load(os.path.join(data_path_beta0_75, \\\"sorted_lbls_absolute.npy\\\"))\\n\",\n    \"sorted_lbls[\\\"sorted_lbls_beta0_75\\\"] = sorted_lbls_beta0_75\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# load sortings\\n\",\n    \"sorted_lbls_beta1 = dict()\\n\",\n    \"sorted_lbls_beta1[\\\"gain_tp\\\"] = np.load(os.path.join(data_path_beta1, \\\"sorted_lbls_LRP_gain_tp.npy\\\"))\\n\",\n    \"sorted_lbls_beta1[\\\"area_norm\\\"] = np.load(os.path.join(data_path_beta1, \\\"sorted_lbls_area_norm.npy\\\"))\\n\",\n    \"sorted_lbls_beta1[\\\"absolute\\\"] = np.load(os.path.join(data_path_beta1, \\\"sorted_lbls_absolute.npy\\\"))\\n\",\n    \"sorted_lbls[\\\"sorted_lbls_beta1\\\"] = sorted_lbls_beta1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"metrics = [\\\"absolute\\\", \\\"area_norm\\\", \\\"gain_tp\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"top = 10\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"intersection_matrix = np.zeros(shape=(3, 5, 5))\\n\",\n    \"\\n\",\n    \"for m_idx, metric in enumerate(metrics):\\n\",\n    \"    for i_idx, i in enumerate(sorted_lbls.keys()):\\n\",\n    \"        for j_idx, j in enumerate(sorted_lbls.keys()):\\n\",\n    \"            intersect = set(sorted_lbls[i][metric][-top:]).intersection(sorted_lbls[j][metric][-top:])\\n\",\n    \"            intersection_matrix[m_idx, i_idx, j_idx] = (len(intersect)/top) * 100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"ticks = [0., 0.25, 0.5, 0.75, 1.]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"vmin = intersection_matrix.min()\\n\",\n    \"vmax = intersection_matrix.max()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x288 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12, 4), sharey=True)\\n\",\n    \"fmt = '.2f'\\n\",\n    \"thresh = intersection_matrix.max() * 0.65\\n\",\n    \"\\n\",\n    \"# Absolute values\\n\",\n    \"mm = 0\\n\",\n    \"im = ax1.imshow(intersection_matrix[mm], interpolation='nearest', cmap='Reds', vmin=vmin, vmax=vmax)\\n\",\n    \"ax1.set(xticks=np.arange(intersection_matrix[mm].shape[1]),\\n\",\n    \"        yticks=np.arange(intersection_matrix[mm].shape[0]),\\n\",\n    \"        xticklabels=ticks, yticklabels=ticks,\\n\",\n    \"        title=\\\"Relevance sum\\\",\\n\",\n    \"        ylabel='Beta',\\n\",\n    \"        xlabel='Beta')\\n\",\n    \"# Rotate the tick labels and set their alignment.\\n\",\n    \"plt.setp(ax1.get_xticklabels(), rotation=45, ha=\\\"right\\\",\\n\",\n    \"         rotation_mode=\\\"anchor\\\")\\n\",\n    \"\\n\",\n    \"# Loop over data dimensions and create text annotations.\\n\",\n    \"for i in range(intersection_matrix[mm].shape[0]):\\n\",\n    \"    for j in range(intersection_matrix[mm].shape[1]):\\n\",\n    \"        ax1.text(j, i, format(intersection_matrix[mm][i, j], fmt),\\n\",\n    \"                ha=\\\"center\\\", va=\\\"center\\\",\\n\",\n    \"                color=\\\"white\\\" if intersection_matrix[mm][i, j] > thresh else \\\"black\\\")\\n\",\n    \"\\n\",\n    \"# Normalized values\\n\",\n    \"mm = 1\\n\",\n    \"im = ax2.imshow(intersection_matrix[mm], interpolation='nearest', cmap='Reds', vmin=vmin, vmax=vmax)\\n\",\n    \"ax2.set(xticks=np.arange(intersection_matrix[mm].shape[1]),\\n\",\n    \"        yticks=np.arange(intersection_matrix[mm].shape[0]),\\n\",\n    \"        xticklabels=ticks, yticklabels=ticks,\\n\",\n    \"        title=\\\"Relevance density\\\",\\n\",\n    \"        xlabel='Beta')\\n\",\n    \"# Rotate the tick labels and set their alignment.\\n\",\n    \"plt.setp(ax2.get_xticklabels(), rotation=45, ha=\\\"right\\\",\\n\",\n    \"         rotation_mode=\\\"anchor\\\")\\n\",\n    \"\\n\",\n    \"# Loop over data dimensions and create text annotations.\\n\",\n    \"for i in range(intersection_matrix[mm].shape[0]):\\n\",\n    \"    for j in range(intersection_matrix[mm].shape[1]):\\n\",\n    \"        ax2.text(j, i, format(intersection_matrix[mm][i, j], fmt),\\n\",\n    \"                ha=\\\"center\\\", va=\\\"center\\\",\\n\",\n    \"                color=\\\"white\\\" if intersection_matrix[mm][i, j] > thresh else \\\"black\\\")\\n\",\n    \"\\n\",\n    \"# Normalized values\\n\",\n    \"mm = 2\\n\",\n    \"im = ax3.imshow(intersection_matrix[mm], interpolation='nearest', cmap='Reds', vmin=vmin, vmax=vmax)\\n\",\n    \"ax3.set(xticks=np.arange(intersection_matrix[mm].shape[1]),\\n\",\n    \"        yticks=np.arange(intersection_matrix[mm].shape[0]),\\n\",\n    \"        xticklabels=ticks, yticklabels=ticks,\\n\",\n    \"        title=\\\"Gain of relevance\\\",\\n\",\n    \"        xlabel='Beta')\\n\",\n    \"# Rotate the tick labels and set their alignment.\\n\",\n    \"plt.setp(ax3.get_xticklabels(), rotation=45, ha=\\\"right\\\",\\n\",\n    \"         rotation_mode=\\\"anchor\\\")\\n\",\n    \"\\n\",\n    \"# Loop over data dimensions and create text annotations.\\n\",\n    \"for i in range(intersection_matrix[mm].shape[0]):\\n\",\n    \"    for j in range(intersection_matrix[mm].shape[1]):\\n\",\n    \"        ax3.text(j, i, format(intersection_matrix[mm][i, j], fmt),\\n\",\n    \"                ha=\\\"center\\\", va=\\\"center\\\",\\n\",\n    \"                color=\\\"white\\\" if intersection_matrix[mm][i, j] > thresh else \\\"black\\\")\\n\",\n    \"        \\n\",\n    \"fig.tight_layout()\\n\",\n    \"fig.subplots_adjust(right=1.05)\\n\",\n    \"cax,kw = mpl.colorbar.make_axes([ax1, ax2, ax3], pad=0.02)\\n\",\n    \"plt.colorbar(im, cax=cax, **kw)\\n\",\n    \"\\n\",\n    \"plt.show()\\n\",\n    \"fig.savefig(os.path.join(data_path, \\\"intersection_beta_top10_regions.pdf\\\"), bbox_inches='tight')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python (mort1)\",\n   \"language\": \"python\",\n   \"name\": \"mort1\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.5\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "Data Split ADNI.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/fabiane/anaconda2/envs/mort1/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n      \"  from ._conv import register_converters as _register_converters\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import time, datetime\\n\",\n    \"\\n\",\n    \"import nibabel as nib\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from scipy.ndimage.interpolation import zoom\\n\",\n    \"\\n\",\n    \"from nitorch.data import load_nifti\\n\",\n    \"from settings import settings\\n\",\n    \"from tabulate import tabulate\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Binary brain mask used to cut out the skull.\\n\",\n    \"mask = load_nifti(settings[\\\"binary_brain_mask\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# set random state seed\\n\",\n    \"r = 43\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_table = \\\"PATH/TO/YOUR/MODIFIED/ADNI_MERGE\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"df = pd.read_csv(data_table)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Clean the data table\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Sometimes pre-processing fails and we removed\\n\",\n    \"# failed pre-processing in 067_S_0077/Screening \\n\",\n    \"\\n\",\n    \"failed_idx = list(df.loc[(df[\\\"PTID\\\"]==\\\"067_S_0077\\\") & (df[\\\"Visit\\\"] == \\\"Screening\\\")].index)\\n\",\n    \"df = df.drop(index=failed_idx)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# remove all MCI subjects\\n\",\n    \"df = df[df['DX'] != 'MCI']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def print_df_stats(df, df_train, df_val, df_test):\\n\",\n    \"    \\\"\\\"\\\"Print some statistics about the patients and images in a dataset.\\\"\\\"\\\"\\n\",\n    \"    headers = ['Images', '-> AD', '-> CN', 'Patients', '-> AD', '-> CN']\\n\",\n    \"\\n\",\n    \"    def get_stats(df):\\n\",\n    \"        df_ad = df[df['DX'] == 'Dementia']\\n\",\n    \"        df_cn = df[df['DX'] == 'CN']\\n\",\n    \"        return [len(df), len(df_ad), len(df_cn), len(df['PTID'].unique()), len(df_ad['PTID'].unique()), len(df_cn['PTID'].unique())]\\n\",\n    \"\\n\",\n    \"    stats = []\\n\",\n    \"    stats.append(['All'] + get_stats(df))\\n\",\n    \"    stats.append(['Train'] + get_stats(df_train))\\n\",\n    \"    stats.append(['Val'] + get_stats(df_val))\\n\",\n    \"    stats.append(['Test'] + get_stats(df_test))\\n\",\n    \"\\n\",\n    \"    print(tabulate(stats, headers=headers))\\n\",\n    \"    print()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"         Images    -> AD    -> CN    Patients    -> AD    -> CN\\n\",\n      \"-----  --------  -------  -------  ----------  -------  -------\\n\",\n      \"All         969      475      494         344      193      151\\n\",\n      \"Train       697      360      337         248      145      103\\n\",\n      \"Val         100       40       60          36       18       18\\n\",\n      \"Test        172       75       97          60       30       30\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Patient-wise train-test-split.\\n\",\n    \"# Select a number of subjects for each class, put all their images in the test set \\n\",\n    \"# and all other images in the train set. This is the split that is used in the paper to produce the heatmaps.\\n\",\n    \"test_subjects_per_class = 30\\n\",\n    \"val_subjects_per_class = 18\\n\",\n    \"\\n\",\n    \"subjects_AD = df[df['DX'] == 'Dementia']['PTID'].unique()\\n\",\n    \"subjects_CN = df[df['DX'] == 'CN']['PTID'].unique()\\n\",\n    \"subjects_CN = [p for p in subjects_CN if p not in subjects_AD]  # subjects that have both a CN and an AD scan should belong to the AD group\\n\",\n    \"\\n\",\n    \"subjects_AD_train, subjects_AD_test = train_test_split(subjects_AD, test_size=test_subjects_per_class, random_state=r)\\n\",\n    \"subjects_AD_train, subjects_AD_val = train_test_split(subjects_AD_train, test_size=val_subjects_per_class, random_state=r)\\n\",\n    \"subjects_CN_train, subjects_CN_test = train_test_split(subjects_CN, test_size=test_subjects_per_class, random_state=r)\\n\",\n    \"subjects_CN_train, subjects_CN_val = train_test_split(subjects_CN_train, test_size=val_subjects_per_class, random_state=r)\\n\",\n    \"\\n\",\n    \"subjects_train = np.concatenate([subjects_AD_train, subjects_CN_train])\\n\",\n    \"subjects_val = np.concatenate([subjects_AD_val, subjects_CN_val])\\n\",\n    \"subjects_test = np.concatenate([subjects_AD_test, subjects_CN_test])\\n\",\n    \"\\n\",\n    \"# Compile train and val dfs based on subjects.\\n\",\n    \"df_train = df[df.apply(lambda row: row['PTID'] in subjects_train, axis=1)]\\n\",\n    \"df_val = df[df.apply(lambda row: row['PTID'] in subjects_val, axis=1)]\\n\",\n    \"df_test = df[df.apply(lambda row: row['PTID'] in subjects_test, axis=1)]\\n\",\n    \"\\n\",\n    \"print_df_stats(df, df_train, df_val, df_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# load images in matrix\\n\",\n    \"def create_dataset(dataset, z_factor, mask=None):\\n\",\n    \"    data_matrix = [] \\n\",\n    \"    labels = [] \\n\",\n    \"    for idx, row in dataset.iterrows():\\n\",\n    \"        path = row[\\\"filepath\\\"]\\n\",\n    \"        struct_arr = np.NAN\\n\",\n    \"        scan = nib.load(path)\\n\",\n    \"        struct_arr = scan.get_data().astype(np.float32)\\n\",\n    \"        if mask is not None:\\n\",\n    \"            struct_arr *= mask\\n\",\n    \"        if z_factor is not None:\\n\",\n    \"            struct_arr = zoom(struct_arr, z_factor)\\n\",\n    \"        data_matrix.append(struct_arr)\\n\",\n    \"        labels.append((row[\\\"DX\\\"] == \\\"Dementia\\\") *1)      \\n\",\n    \"    return np.array(data_matrix), np.array(labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"z_factor = None\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Starting at Fri May 24 14:48:48 2019\\n\",\n      \"Train dataset..\\n\",\n      \"Time elapsed: 0:15:50.849694\\n\",\n      \"Validation dataset..\\n\",\n      \"Time elapsed: 0:17:59.846478\\n\",\n      \"Holdout dataset..\\n\",\n      \"Runtime: 0:21:22.336500\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Starting at \\\" + time.ctime())\\n\",\n    \"start = time.time()\\n\",\n    \"\\n\",\n    \"print(\\\"Train dataset..\\\")\\n\",\n    \"train_dataset, train_labels = create_dataset(df_train, z_factor=z_factor, mask=mask)\\n\",\n    \"print(\\\"Time elapsed: \\\" + str(datetime.timedelta(seconds=(time.time()-start))))\\n\",\n    \"print(\\\"Validation dataset..\\\")\\n\",\n    \"val_dataset, val_labels = create_dataset(df_val, z_factor=z_factor, mask=mask)\\n\",\n    \"print(\\\"Time elapsed: \\\" + str(datetime.timedelta(seconds=(time.time()-start))))\\n\",\n    \"print(\\\"Holdout dataset..\\\")\\n\",\n    \"holdout_dataset, holdout_labels = create_dataset(df_test, z_factor=z_factor, mask=mask)\\n\",\n    \"\\n\",\n    \"end = time.time()\\n\",\n    \"print(\\\"Runtime: \\\" + str(datetime.timedelta(seconds=(end-start))))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(697, 193, 229, 193)\\n\",\n      \"(100, 193, 229, 193)\\n\",\n      \"(172, 193, 229, 193)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(train_dataset.shape)\\n\",\n    \"print(val_dataset.shape)\\n\",\n    \"print(holdout_dataset.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.imshow(mask[:,:,50], cmap='gray')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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WfM+fhGG+DKoI6Qxu/blxvjttvPhzPq88rCz6yPp7EL0srcIwrgax9lolw3x7GyCvX74d+6Ic2bV7/+tfvzKOUtVMFppRSSimrowpMeUTx5Cc/eetvLGJPSHbCCSdIWpSX0047TdK2HweWL6/4kNxzzz2bNqgaJGGjL0/Ytleq+1GkEq9utacvBaoK45EWRQlfBl494Vumr+fVo5BIcpaWvCswjBE1gzm7b1DOh3G4MsWY6QsFxOeOSsB1QYVwHw/WB+UEFWOkinA8c3Z1JSPJUrXxftOXZ+R3k8UpUW28D/flkcY+VFmuwJUT+vAkedLYr4njuCZ+3dN3hvvar+kzn/lMSdK73vWunTGWslaqwJRSSilldfQHTCmllFJWR7eQyuedr/u6r9u8Z6sEmR5HXZKrSbvJynj1EF62gdj+YMvDty9wgM3tC7aU/H1WQnYn09yO8T7YUmF7geN9C4lxsF3A+Xw+bGWwHYTjsW+jcG7ajJLcZfVo8K0N2jBGxjMK7+Uz1ncE23C33nrrTlve0z/n8/XluuBEPKr+zHYJr2zvjLZsMiR6tIVEm3TClZY153qP1pR+s36Un4e5c508bB7og22lUU0l5pUh9oS2S8v9y7Nzyy237PRVytqoAlNKKaWU1VEFpjzsvPCFL5S0qAif+tSnNt9hQWNxokpQZVhaVJlMVuYOpFi+ezmtSos1TF84z955552bNqgjqBF85yHOWMOc25UTSCdgr1iNpZwqhisekHN1KxtnUB+btK3EMA5UA169L9aMMY4cWblOKA0oXa4M0AZlgPN6KDEwd+Y1SkCHEsM6+7w4DhUjHX99PBw3cgZm7VI5Gzkej5QbyIR4rIsfw33HK2P3cGzap7O13z+sfaqOHqqf6s7pp5++eX/DDTfsOY9SHslUgSmllFLK6qgCUx42XvziF0taVAOUF7fIMxQVK3mkrmCNYu16yGym00/FwcFKR4EZ+SlggdO3Kx+eHC/JkgaM2ceBcoIiRZ+uFqEMMB/aeKj13XffLWkJp85jnAyVHikw9M/au0WP8sI6Mx9fC8J5aYOvkqs0nJswefp2VSSVKdQEb8P7VFVc8ciilKMEhIwnw5W9L87DaypW/ln6wPh4chxcA+8rlSDWzkOkM4FijkvaVXv8Hq8CU9ZKFZhSSimlrI7DUmCmabpZ0p9J+pykz87z/LRpmh4r6W2STpF0s6Tnz/P8J4c3zLJWXvGKV2zeo1qgNPC3qxFY/ViamSbdv8uoElcaMs07fbi1mpEmvHrkSioeFAv0NljMWNvub5CKT/YlLUoJSgxqhPvSMG78I/BHcfXqjjvukLREmlBE0VWAvdbXFZhMp09bj5xKtYA53HXXXZs211xzzdZcKQfB+KRdNYPxuEqTkUojxS0VF87j15vvWFf+HqkZeyk63ibvPx8z79OHxhWYvP9Gfjuj5Ih5nrynuDco1Omk/4+0JIL8+Z//+Z32pTySORIKzEXzPJ8/z/PTDv39zyW9Z57nMyW959DfpZRSSilHjKOxhfTtkv7toff/VtLzjkIfpZRSSjnAHK4T7yzpndM0zZLeOM/zJZKOnef5Lkma5/muaZq++nAHWdbDq171KkmLw6ZvcZA8y7cipO2tAORztlHYPvD6MGwBZCIxl/AzlBkJ3rcL2FrJbSp30GWsSO6nnnqqpO1tENrffvvtkrbDwrPicNbqkXZDm7P+kvfPvAjvdqdZtqDYSmLbwtcuQ5Dpc1SNOkO+2frz9oyH840qMt92222SpI9//ONbbaVlS45rwf3i215siTBn7gnfauE9fY6cXRkzn43CsbMSd4a/+/EZsu3nya3DdHqWFgdstt9y684/43yjCtocz7nZOvT6YcyDNqN6X6WsjcP9AfP18zzfeehHyrumabrmgR44TdNLJL3kMPsvpZRSygHksH7AzPN856HXe6ZperukCyXdPU3TEw+pL0+UdM8ex14i6RJJOqTglBXzmte8RtKiTGDpffSjH920QZlAKUDxcDUCq5FQYJLWHXvssZs2JLVL69jDjrMKNBa1W5soJ1jCKAyuENEHygt9u2LxyU9+cmstXP1hTPSRVrt/Rtgx64IlLS2qQzqionT5HEm2Rx+uFmVF6HSilRZFLEOsva8M9eZ6k+5fWq7hzTffLEm68sorJW2rBxkCzBr459wTOKX62sNeFbi9bTr6pqOuf7eXg7e/zxB5b7NXkjtXZjLceTSvhMrefo/l/Y8K5mNARUM9ynIKpayRh+wDM03Tl07T9OW8l/TNkj4m6bclvehQsxdJ+o+HO8hSSimlFOdwFJhjJb390K/8L5D06/M8v2Oapj+S9BvTNP0jSbdK+vuHP8zySOTnfu7nNu9POOEESYu/wj333Ce8efIz9urTP8DVCAornnjiiZIWq9v38zOlPPv5ft5MY5/FGKWlUCOhvygfnjgNFYRikvhhuG8Fig2J8LCSpcUKRhXBEh6FWnNOxjiy4rOwoiseHEdfFE90HxjWN8sw+Hiw7pnHKBEe18Dn6sdKi38LqhXrfN11123asOYcN1ofVCOUuzPOOGPrWGk3hDh9PqRlzdLXxO8bzsNn7o8Co+Oy7SiZnH/u4+G6j9QwzpljdwWFObOGmbxxhN83o+tbyhp4yD9g5nm+UdJTBp9/WtIzDmdQpZRSSin70VIC5SFzzDHHbN5jJWNNom54lAxqSCbTGqXux6ocWfpZKA8r19Pqo7gwHqx3Xr19RtB44chMCsf5XIHBGkZ1ch8a/GwYTyZBc1yJyrGyRljOGYkl7ZZLQMXw5HKoMfgWoZJQhsDPiRLAurjCwDVgXKNSAulHQhu/TvhFcd1p4xFPrCfjYQ6urqSKgWLnY86Ec348ZAkK/t5vnUfKCaTfjasieyXU83sD5YV15j5yhYpnI9fQI41SuRtFwZWyNlpKoJRSSimroz9gSimllLI6uoVUHjS/9mu/JmnbERCpnO0BnHeRvKVFqkaOR/LGQVZaQm8zkZ07GmbiOZxX3WGYcFy2cJDT3akSCZ8+2RJzR1T6ZWtkFLLNvLKmkffLtgXHjbaQMtmdbxPkeWiDU660OBGzdmzP+LoQ0sw4WJ+RIyfbJ6PtD64z14BtDHeszYRv/O3bgThFn3nmmVtz8NB0EiAyDxL1sc0oLdtcXANe3SE7k9yNalflfTKqhg55L4zqHNGGvv15yDD13GqTdu8J+vBt1wyJHiW7Y4yMw+fTRHZlrVSBKaWUUsrqqAJTHjA//dM/LWlxohxV6MWa49UdCQEHUpxlCZn29ySuw0J3axKFgz6ysrK0KAue1l/attoJ/WY+WLdemTnVkKx2PJqjp9N3S1lanIF9PljZWN6EEru1Th98N1JOUJAy+Z+HjmdpA5QqD7VmblmJ2eeSTrNZmsDPkynzvS8qIfOKQuBtUFGuvfZaSdLVV1+9M69zzjlH0q7y4Y666fQ9Ujzyu5FSxpz5LitOe7+8ZlsnFZxRYr39QrZZ53Qq9vvH30vbKiH3VClrowpMKaWUUlZHFZhyv3zf932fpMXnZaRCpLWNVeeWIv4a+GigRnjYMmG9vIKH1aJGYNWOCvfRBsWF8438JrCAmYP7BGQJAPr0UGlUHlQD90vh3Pj5jEKAUUHoi3BsEtH53FhX/BxG/huoVszV/X44HgscBYdjfMys6yj0O0O+wftC7cF/iONdyWGMvDIe95OhhAEh7Zdddpmk7aKbhO1zTXl1VSQLIo6UJT7L5Ih+H/MZ8/H7DlJ543zeNlWrTDPg56F/VCd/9rhvaMN3/sxwHG083H3k31PKGqgCU0oppZTVUQWmDPmO7/iOzXsiQ1Bgcn9fWizLTNzmljpWNtb+qDBdRklwXt/Dz6gLrH63OAGrHX+QUWr4jFxBAZGWqJ203l35YIwoBV6EkTXDkubVfVewhlFyOJ7oKGlRGJhj+sRIi5WNypO+Nd4/8+DV/XYyimUUwcW143wj9Yp5McZRAjnWAVWPvn19OB415eyzz95aCz8+/VrcP4nP8KkZqWEcn8nd/J7LiLBMbCcta8d8WB8fT37HPH08qX5xjUf+LawP12CksqT/zmiupayFKjCllFJKWR39AVNKKaWU1dEtpDLEnToJz0V2ziRf0iJXs/2CVO5OlMD2AFsuhP1KyxYPDqCZBE1a5HC2q3CCdZmfMdMH2wUeess5cZa9/fbbt87n7bMekM+LbSIchdlykxaHXuaMw6WPg3Nm/SdPFAjpDOrOxGy/0X9u3TisK9fQx5wJ/XKrw8eR20u+tZHJ7mgz2tZhq5F19XuCc1KviXFRqdzJUGTfwkyn7VHtIcgkfqM1TGdg32LLukvpcO7n5lr4fQeckzashW8PAdt5nNcTGGZdKq9H9VM/9VM75yplDVSBKaWUUsrqqAJTtnj+858vabvSNJZrJspyS5rU7ygCKA5uAWNdk0COV1ca0hodhd5iTWJhojTgqCstakiqB+6ge9NNN0laUtPT9yj0G4UiHZD9O8LB3RJn/qlUeJtMRMbaeTI31ojzpIOsj5/vGKOrRdmGa+nn4RqiQoxKAHA9aDOy7Hmf19BVGu4brh1zd7UnlaiRsytrxfUerXOqgaPkclyvDHH282RJjJFCldXQ/fjsn2vKPe/qCmoTbTivr3OqTZzX1znLBYwc3ktZG1VgSimllLI6qsCULVJxcLJ43V133bX5DmUji8a5msE5UUdQedw6xTeE83C8W5NYqliVWOiE10qLRU4YNCrLDTfcsGlDaDKgrngYNL4YqEWM1cN8GSufuTIAmRjNfXrSz4dXT7p36qmnSlrWFb8dXzvOc+edd0qSzjjjjK05SIvlznGoGe5Lc+ONN0pawo1RXlxxy2KJzNnPwz3BuBi7F1ikf8bFd65CoJxwfQgvHyXx4zjuIw8R5v5jPqN0AIwxVQz3k0FpST+XUcJAxsV97Ofh3IwVny5XCZlj+k6N/HYyhNxVtVSbPAlgKWulCkwppZRSVkcVmCJJ+of/8B9KWtQH93fACvTIJGnbSsYKTb8A9+PAckblwTp1SzoLCJLy3i17FAYs6jPPPFPStgWM4oICw+ttt922aYNPBoUEUxmSFv8f5sE8XYFJxcX9ftJaHyXmow3qAyqA+2zQH1b1yJ8EnyA+4xr6NeAz/HU4ZpTgj+sySjWf6gPXiUguaVHKsk9X99JHKNPqex/MHYXIlYr0CUoFRFruE5Q6FC73gUn/mvRPknbXKpMu+nu+Yz6uiqCC5KuvIb4qzI/5jJLdZUI6v26sM68jn5xS1kYVmFJKKaWsjv6AKaWUUsrq6BZSkbRUiCac1bcm2L5giwN53JNypTTNFodXmnbnWGm3qrS0W6uFLQ6SmPk4qPDM394GGZ7PkO5JbCctWwqcZxR2nI6Ro0R2WZ3b5XnWiu2YrMfj5xolRMtx0BfXxNcOR0/6ZFvHnZWzXhPn8/mwbcZxo3Bj1pN50dYraPMd9wCJ57xaN/3mfHwN8l74xCc+IWnbIZvrfcstt2z17XBvP+UpT9l6PfHEEzdt2L5jPKMw6gwd5znw7apcs9FWKO/ZCqMWlm9zck2vuuqqnfnABRdcsDXmrHvkY/Stz1LWThWYUkoppayOKjAHnJe97GWSFisZS80TZRGGi/WGNekWOU67KDFY2e74izNpJvByFYHPSHCGE6Nb7YyHz1AD3PkR6xaHUcKQPSkaSgOvWKzupJkVh0fVkjPJmFviHM85UV5GljCqAevrTpioKoSu04bxSIuSRJ/My9c3E9jRxpUyQnVRGlAIcJ728wDj8fsmnXdxjh6pT4yDNu54nCHDXAvva69wY4fvUqlyp2vC5bleWZrASaXMFQ++SydgV4ZwcmZduec//OEP7/SVPO1pT9v5jHPnPSftKoH7KTqlrIUqMKWUUkpZHVVgDjgoFOz9p4UuLdYwVuQouRdKAKGpnBfLWtr1HeDVrXkUlwzd9mRsWPS0wWfAw2qxqjmeEGlXchh/FtpzfxDUAs7HuriFn4Ur3VpP/5ZcCz8eFQOL3OcDzAe1xa8TigfXKUO3pUV9SJ8gV0V4n0UUfTz4azAv+madpcWv6uSTT976zteC+4Pry/x8XlwfjkMl8XuLMaLgfPzjH5e0hNP7fFgP5un4YQQvAAAgAElEQVRj5jPuZ56LkbqS6+vqCp9leQhXbbje3PMj1Si56KKLto71eUEWiZR2FaVSHg1UgSmllFLK6qgCcwD5gR/4gc1796GQFsvRfVdQVTI5lxdYxFKlLa9+/kz4hRVJdIm3Sd8MrHlpUSzwc+E8bt2iGjHGLOS3H+4TkWn0R1EyGZnkVm4WwCRZmUdwYUGjaqB0uJ8M8+cVpcHXl7FmYjSPzkKByQRrDn349ZXGkT1c97POOkuSdN555+18x/pwT/i1oA198p3fE6xhJrlzHyzOTVJCro+PGf+hjDRynyWUqVFiP6B/7uP06ZKW9eU8o9Ia6bNCG6Kj/JwomqNEiLTJZIu+Ptwb9FHKo4EqMKWUUkpZHf0BU0oppZTV0S2kA4g7kPKeLQ62DbweELIz0jRtRqHAWcHY+8rKwxnO6n2lU6e3YeuIMFQkdN/y8HBpadlOcQk/Kw+zdeRjzkRmtPU2rN3IaTarI7MG7rCZWxEkmXOHY7bS2DJiriMHXbYvRsnu2HrglfH4lgTnTAdW38ZjSwOH2nPOOUfSdg0t4DxZV8rnw3qOagbl9cnkgtJumDpJCn2rji0b1oVtJ99W2csx1++bdOLNJIc+1nTm3W/rceQMzNpzbTmP3+usIedjXL7O3Euj61PKWqkCU0oppZTVUQXmAPH93//9kratt7RqsdBcwcgQ0HQW9O+waunDk43hbItiglLgagaWIhY+1igOrtLilIrFSl9e5TgTzrljbs59v/TqmZY9lQJpUVlGzsR7OXG6oy99MJ4s6+D9sh7M3R1HM7ydcfj6MlcUqVGl6Uzwx9ip4uxjzdIPfk9kVe1MWiftVtVOp2efazqRexvmyCvK3YUXXrhpw73NvcT6cs86KGSj6s/MgzVgzK6G5XUCQqalRUnMNiMFMEsBjCpfMy7W3Z9hvvvP//k/78y1lLVSBaaUUkopq6MKzAECC9SVCnwGsPCwKt0vAIsww07dUkSVyfO5QuDvpXFa/tyjJ5zWE5Lh+4JaQ+ishxRzHIoHfXib7H+U7h2Yz8iHBQWG49z3gPnQB9+51c8YUyVyHw9Uh0x2NlrfDI32ApaoDlj/lF/wIoMoFJyPdfZyA4wnfXy875FaJY39UvYrL5FtuQajsHnUCMbjfin4xeBjxL3q93qWhcjipCNG9w3rkj5HN95446YNa5/3oYeZZ+HUVLy8TfoYeXLC3/qt39pz/KWslSowpZRSSlkd/QFTSimllNXRLaQDBNsHvsWR20PI2S7PI5GnM6ZvcSBfc3yGj0qLnJ9ZYF3qRs5nm2BUJ4YtMLZGRltRmRWXNqMQ3ty28Doz6dwMvnVDHzhNupNqZjoehTZn2CtjdwdbtiRyrO7I6lszPg7fIuGcrB3zuv766zdtqC3FNWTM7oDKNWDMjM/HkCHRowy6mRV3FG6cYeo4ho9Cv7nHss6V93XSSSdt9ZXr5uejja/hXll6fZuJ+5W6Vtdee62kZW2l3eeAa+H36L333itpue6+dpCV0tMJu5RHK1VgSimllLI6qsAcIHDGdKUCSzeTjbly4M660mIRu7Mh7zkffXhf6YhKH67A4MhKPSCsbQ8JZR6MNa1uHzPfYR27oyyWa47LLXvGSNuRJcw4CN31OafzL/Nxiz7r0+AMOqp3k+NwZQgn27xeruRkkjsSBvq1RIHC+ueaenI57gHmPgolzsSAzMvvLc7D66hqcyboY71cBcNpey+10PvPkOv94Pr5GqZik47D0qK0UBUbh11Xb7h2mdhxpAhlqL+vT9b5Yi3f+ta37jOzUtZPFZhSSimlrI4qMAeAH/uxH5O0WHFu8WOxYi2P9vexCDOFe/p3SLtJ6hwsXvrCenffARKs4TuAb4UndcNy5Ty8ep+MDeuUPtwvAIs3k7q5ZZ7nxtp2JWe/Cr+ZAp4kdd5HqjSj9P6sPf1S6sH7zjDs/ZQcrjPj8xBg1ooxoix4G8554oknbrV1BYZryBhTHfG+uDf52+8JFAXGzHg8SSIKDOMahatnSPtIgUHFS1XD26bil9dPWkLRCf/nu5tvvnnT5vzzz986N+fxdaaPDLEeVeLmnsgyGqU8WqkCU0oppZTVUQXmAJCFCEdRKZni3tWMTHWeyocfhzU5SmOfqghWsluu+F2kv4KrPZlWHWt0lBCPvkbWdiodtPXzpHWcUTN+XEY+SYs1nAnR3IJO/xj68jmzVrxyfdwPhDVLRYDEf9KiVHBuoom8eCfnwReGY/79v//3mzbf8A3fsNWWJHG+LhnJg5rmKhjRUChTvnYJa0gbzudzzsipUdmCTErn9/deZRhcmczkclwDj15jzVGLRqUsMsliloLw/jPSzufFNeSe/7Vf+7Wdvkp5NFIFppRSSimroz9gSimllLI6uoV0AMBJNeV1abfGCq+jKsdZvXnk7JpbNS69I4OzDcPfvsWRTrKEWHuodTq0jpxDkdg5ju0Zr/nDvGg7SiDHOtBXhlVLy5rttQZ+HrZ1fNvCnWylZZtotP2RzrLuyJpbSLR1p07WgzUcJT2jPXMdzef3f//3t44jhNy3oviMvlgDryzOeJgz94Zv8+Ckmts6vmWTa8C2l2/d5D3FdfNrwb3Nfcta+vXeq34T258+xwzZPu+88zZt2PLJ2lseRs01oC+23Ah/93O/5S1vUSkHiSowpZRSSlkdD1mBmabpbElvs49Ok/R/SvpKSS+WhJn1o/M8/78PeYTlsEGBGVmsWHbpZOgKDMelE647XGaiLVcGIMNYsTRHFmc6FXviLqzzDF92pSAdhVF5PB0+lniGFruSgxNmOvF6uncYzQd1hfVgvV2ZynIFvPp8OC4dbEcKDIpFqkbZr7dxUIk4X4YUO7/3e78naazKHX/88ZIWhWPkpErCQo7HmddVI+bD2nP8CSecsGnDGpIwjnVxdY+xpcrjCkiW1uD+8zaZHJHv7rrrrj3nNVpnVCLuv1Fb+mAtTznlFEnb998o8V0pB4GH/ANmnudrJZ0vSdM0PUbSHZLeLuliSW+Y5/l1R2SEpZRSSinBkfKBeYakG+Z5vmW/MMjy+YFwU6zCs846a/Md1iRWH5as+2V4inRpN4xU2k2IhyXr1nuqKWnpS7sF6Tiv31cZyozPyqj8wUh5gUxcRx+uEGSRy1FJggzHdh+aLLo4KjzJfDgn6+rXIAv/8ffId4Uxss5+nXif53E/EBQ7VxTuj9/5nd+RJD33uc/dfEb/qCr4x/h4uC45LicLNXJev7fwDQF8UD75yU9uPkPxQM1gnfz+3kvNcEUyFUjW6dZbb90Zc97HoyKg3AvpByRJj3/84yUtCQO5f3zM/+bf/JvhmEt5tHOkfGBeIOlS+/v7p2n66DRNb56m6atGB0zT9JJpmi6bpumyIzSGUkoppRwQDluBmabpiyR9m6RXHvroFyS9WtJ86PX1kr4nj5vn+RJJlxw6x5zflyMHViVKDJaotJuoKyNGpN1kd2m953vHVZGM2hilYMe6zUKNfv70J8BnxCNX6AtfiFEByjwfVrOrAIwD65j18SRqKCVpUfuYsv+RWsQcObdb2embwXhGpR+YD/Nw3yeisLgH+M4jg/AjQR3Zr1RC8p/+03/a+ex7v/d7JS3Kx+ie4DqhEvp1ShWOObtqxNrhL0MfFP70Npwno7X8nFn0kogqaVlf1vKaa66RJN19992bNhnFlH07XEsii/w+zgKsXJNf+qVf2jlPKQeNI6HAPFvSf53n+W5Jmuf57nmePzfP819JepOkC49AH6WUUkopG47ED5gXyraPpml6on33HZI+dgT6KKWUUkrZcFhbSNM0fYmkZ0r6Pvv4Z6ZpOl/3bSHdHN+VzwPI2Dg0UiFXWqTxTGjnWxM4hyJ1j6o/7yWR+/ZD1o7x5GCQNZWQzn27gG0PxsUcfCsAx2COyxpN0m6SMI7xMRNqzfEZDi3tOt/6vNL5d1SbibVjK4q1Z2vM1yXP532x9cQrx3ioNU7N3Av87W1ya4WkdQ8Vtju++7u/W9L2tg7bSvTF9tVHP/rRnTZUJGebyB1uuR58l/WppL23kEZh7+lw7ts63CdsHV177bWStrdduYfYHmV+7nTNtXzCE54gaXFEHjnHU9n70kvd1fDB83f+zt+RtP2csma/+7u/e1jnLuXh5rB+wMzz/OeSHheffddhjaiUUkop5X5oKYEDQDpzYs1Ji2VIm0x9Li3KS4bnugqRoa6jitVYwLxiaXqbTOXOeV2BwXJFocDa9XDsVHl4dQdS5oMDK9a3p8NHgcFBkz5GlbgzmZ/3wRhHYep7OUC7tY6SQL+sj58Hax91hb/diZdrjxMv15mKxv7ZKPHh4fArv/IrO5/98A//sCTptNNOk7Q4srpShmJIuDJj9fISe4WQ+3m43/L+dZUmq4dnmQdpcd694YYbtv52rr/++p3P9uIZz3iGpEV98vB1xvbOd77zAZ9vxEUXXSRpudd87VB+XvCCF2y1cQWQ9eA5TBVL2lVUuVdd7cnK5Jzvfe9730ObWDnQtJRAKaWUUlZHFZgDQFpLntyL77DIsPrdIs/kbSPFIC20/VKo0waVxNUe9zWQlqRq7pODIoSFiFXn1iCWKyoEbVzVyLBp5uwqDWGraYn7mDONvisxvKd/fE3cWs214jyeLp73GXbs40JVQWXBovcwdebM+hx33HGSFj8MaVkj1vP888+XtH0NuHaoNKyTq3L0y7hGiQtf97rthN0/8iM/ImnbD4nrkcnp/D5EuTn99NMljf1kUs3L9ADScv9l0U6/LzMEnbYoMg+W1772tQ/puL34hm/4hp3PuLfwF/Pnm+vNtUt/OGl3rUb/B2RiSO5ZV3voA38mroX7RXGf8Hy//e1v32e25SBTBaaUUkopq6MKzAHA1QJp27LCGuYV68stNKx1VJZMay/tqggjf5D0S0A98OgNLDysWtqQjl5arEjGlYUfpUWFYD5YfA790hfnHSkNtGHsPj8sxfRPcVgr98mALGBJG0+extzwt8gClNJupBSp7X19USrwezj55JMlSWefffamDT5AWM6oD369OSfJ9rCaXe1BoUAxYTzug0UfWOIjNeKlL32ppCUaibVwHx2uRypTnnAQUnlztSd9aEaJ54g+wlfloSovR5rnPOc5W3+7kpgJ8dy/Jf3eRgkQeY8imv8X+Hv64HXkY8Rno/s4ldkXvehFkraVO65rI6cONlVgSimllLI6+gOmlFJKKaujW0gHgP3q8CAFey0caZHrpe0QZmlcIRp5F2marRuvu4Tsi8zP9oM7tPIZkjLyvjvfInGzTUBfo2RsHJdOvT53nA1HSfPSyXlU+ZpzZ60nJ0OtR4nwctvN27C+rOF+DqhZWdwdqWnDnNmacydetpCySvZobL7VKG3PnTasJ9fWHY8/8YlPSJKuvPLKrfF49fCf//mf3+lfWsJ+fT6Meb+q4Vkfy7eQst4X19a3Lx5pTqWsA9uBjNmfB54x5jeqCcb9x1z9OuU2K9fUt+jY8klndL9HOU+mBRiNZ6/Egz635z//+Vvj8P9vmMcVV1yxc+7y6KAKTCmllFJWRxWYAwCObt/5nd8padvSxyrGckFpGKWfR13BitrPMsf6H6VpxxLmbw9RZWwcj4JCWOyoDecZJRRjjLRxS5pz84p64CpNWvKpRkmLhTly3uWcHM/5RgnAcu28GnVWqB4lE2T+jH/kRJnVrB//+MdLGjtJp6Lk8+O7TF7m65OlFZg7jpzSovacdNJJkqSPfOQjkqQ/+IM/2LQh/f0HP/jBrb48HUCqTaNEirxnPNzPriT+7M/+rNbGt3zLt0haHG251/1a4Hyboc7Scl24zqN7nGckS4/4edIpGnVuVM4h1ZVR6gHOzTPr9xrn5L4dqYU8K89+9rO3vnN1795775W0OGaXdVEFppRSSimrowrMAQKrxRUPVwKkxTLydOZ8hl8MlmuGZztYSx5mmSG3I58T2mOlo7y4ZcW5sbrTt8HnivU28l0htDQT4bk1mIm7Rnv1jJ9z+/F7JU1zNYP+saBHPj2oK6kIuaKEUkZbLGIPxz7llFMkSWeddZYk6YQTTtgau7Sr7oyKVDKfVGd8Xpwn19f9ZFhP7i2sdldpSKP/Td/0TZKkd7/73ZK21+eyyy7TQYW1ymduFPbOs+LPPc9BXlO/J1DsUMxQZPz+43j6YFxemiPHxpj9mUGVyYKs/v9E3lPcC/5/EuP3RHrZhn6zHInPi349/L88MqgCU0oppZTVUQXmAOKWFdYWVjoqifsXYB1jUWM1u2WfRRhHybDYb6Yw4ihJGHvaWHyZQE7atRRHycpoTxvG55Y9Y02fCAelw61ZP0ba9Rlw1SkjMbAY3QLOiK39iihiGWZiO0m67bbbJC17/Bk5Ii3WML4v7lsE+FBkeQgfM+vB/TLyicjSCqO1ZD7cUyQcfO5zn7tznne84x2SpGc+85mStn2VDjKsL0rDKOLOFYUko7LAnweeR9QaFAu/t4Bng+tN0kRp1z9npFpm29EcuN941vDx8YgyPmNd6MvXhTmjON9yyy1b53WYB88XbcvnjyowpZRSSlkd/QFTSimllNXRLaQDRG4BSYs0zGdsSdxzzz2bNrxHEkaSHVWsRnZGIma7SJKuvfZaSYtknSGQfh5eR1Wt2YJg64gkfL49xHvOjXw8CsdOydul9GzDlpRvabHFkc7Fvg5I3qPwcrZhmBfj8KRcbCvlGNk28nVgPEjeLuEz/9yac+dbxoxkP6oInuvK2F16z3D3DLl2OA553reiqIZ90003SVqcekfhvgcRwqgvvfRSSYuzqd8/3G9ZP0xa1p42XFvf6uP+4/6njT9zmfKAe9SfJ/rnM6qh+xZSVgLnXvB7K5NFct/4lmpum3E+33JmHjizUysMh3gf8x133CFp+f8LR3hJuu6661QefqrAlFJKKWV1VIE5QKTDpbQ4dWaSOZQZaTc8F4vEwyOxZHhFDbjxxhs3bajaiyMgFt4oHT6WXYYxS7up6UchvJw7Qyndsk/FZFQiIVOw892oFMCo0jTHpzXoFidz5bqgePh1wuLkM66TO1szDizDU089VdKSJE5aLEyub4a+eh/cEyOHRtYhVZX91pc18/XN0HWOcXWFsX7rt36rpCU1PGUIyn1wDXleXUXlnsqEik6m+R+lDMDZFcXD72OuL88a9zXPu7Q8RxyHGjJy6Oe6Z1V0aTc5Imqwt8kSBKyP/z+BapQO76zXaGzcdx7GXz4/VIEppZRSyuqoAnOAoAjdxRdfvPkM6wiLiP1rwmylxdLAKkZd8TBCjsMCwlJj31haLCBCZdOvQ1osH9QVL+IIHMd4Rnv2kPvpHqKMWsBxWYzOjxslYYP0axlZeHyGquFWXYaS0oerYMwxC+7hQyAtFjPXLosySss1wIJm7m4lw15FPL3//YoDpuIyKu6XqeVH5Sm4/7jHnvrUp0pafGLKfeCHcfPNN0sa+7GNEjJyv1DQc1QqhM+4htzXXu6CZx7fG8YxSnjJvY2a4c8D9weqCvem+9xlKgf6GCVJ5HXkM8XzxHxQe7y4Kf8n8X8Zz5MrMCSIZM7l4aEKTCmllFJWRxWYA4hHrgDW+khFQFVBaUCBcc979ruxzFE63ELDysKCyT1qaVEI0hdilDhuVKwQUlnCf8etJqyt3E/35FxZAJCxj4oxjooecnwWevTjmQ9tMjLM+8jz+brs1cZJRYnr5UnD0r+F6+5WckYvsS6jcgO0GUUjZXI7+vD076wH5zn99NMljf04DjIUwiQKydXGjBJ0fy2uQSolfp1oTxtUG1fu+D+AZx7/Fh8H9xsKIK9Z0sTHPIp44p7IMgrua8ZceZ752xP0cZ/x7I4KjrI+zJXvRmtYBebhpQpMKaWUUlZHf8CUUkopZXV0C+kAQjVfSfrGb/xGSbv1UDx0Fgc+pGGc9Tz0ljpHOL8hNfuWDdIt0jKJ7DwUMytUp7OotGytcD7G7A6/OAqnA7I7+uZxI2dD1oG2rIVvp9BmVKU7t02Yh0vdjBEZGsncnShzywe8TV475urOi3tVH/frlHWocstO2t2+oW+/Tpngb+REyVxzG21Uu4frxPhGtasOMiT4O/nkkyVt3xs4wGa9JGkJrSeZG4kP96uqPnK2zirSbB35FhJblTjG8n+IbzXvVeuM/3d8/Hkf+n3D+Jk7TuDu1M7W+RlnnCFpSTngbYD7jTa+zTlKoVCOPlVgSimllLI6qsAccN7//vdLki666CJJi7U8CknGWuI7D1nEsRenPCyYkaMbFhmWoqe6x2rHGY4+3NrhPU51WEZuNWHpYdlhmbk6gkWWVr87L6ZiwfHu9Er7UdmDDLHGcnTrNp1cWWdXuFgzQlTp350NUZ34LEPI/X2mefd1warleKxln3OGfnNPjCzRrMDtKg3H5zhGoezp0O1V1cvCqFI0SgPPjofo53OI0uUK117O1iMHep6jdMj391xL2vi9lcoN5UhcpWEcHE9bLyXAfcwzk8qtrwMKZJZD8Lmznqg1/J/ncy4PL1VgSimllLI6qsAUSdJ73/teSdJ5550nabuYWVq6Ix8NLCisbCwb/FykxepCIcCi8aR57Hdn0Tj3ORmpD9J4/xtQBkb+NplO35UcrDbmlQXmpN2w5VFadPpgXd0fhPnw2WjPn0Rkv/qrv7p13uc85zmb9894xjMkLUoMCpWPJ9Uz1szHQ3uuIdao+zJk21Ehy1TxssBmtpeWazLyR+Izxsw9W7bhurkCk8qCKwxZ9JO27l+SKmGGz0vL9eYz7jFXErmG/J8yCp9GMUFx4flwPy187lB8eT743PvPEireJ31df/31khY/IP9/KxUt2rjPnRfOLA8fVWBKKaWUsjqqwJQtPvaxj+18du6550paLBf2kN2XAWsHCxorEDVAWiwxLLts623oC6vdVYQsAcCr77Vn0bhR0rOM/uF4T8GOpYjFOtrzZzy8jooVsj5YjG5N0h8JyH75l395p4+9+J3f+Z3Ne/bmUdGY+0gVSR8GVzyyyCZ+E67SZJI8zpPFHf34UVmILFI5ioID5uO+EGWXyy+/XJL07Gc/e/MZ9x34teD+4BqiOLjyyvVA/Rr5lHEcSl2WJ/E+UFU43pVRymPspxIS7YjywSvqpbT4bvEZz9woApB7Hn8b96VBIc5El/4MvPzlL1d5+KkCU0oppZTV0R8wpZRSSlkd3UIq98tVV1219ff5558vaXurBPkYafrEE0+UtNStkRZZNh1+/TxsJ7HtMKqMmw65I4fPbMNWlodrcs5MiOYOeVnniPO5fMw8RrV+aEdfzNW3dV796lfvjP+hwPoitY/Gk862o7Bl5spajWoqQdaz8vNk6G2uobOXk6ifJ7fhyv64E286sPo9zhYm2zts0fl2ad4TOJj783TaaadJWp41ruFoe5J7dOTwznH0yfaV3zdZH4vtHXe+zXQEbBe5U3E6CrNmvoWZFdPp87u+67t2xl4eXqrAlFJKKWV1VIEpD5orrrhCknThhRduPstQQyysW265ZdMGy4zU3pnYTtpOWCftn/gNtYfzuMWIZYe6gqXlyfcyAd1+SgNW2yh8FOstyw1Iuw7LzG+UqvxwIcT67/29vydpmesouRxjZV5uCbNmzCOdO6VdK522IzUsE5u503aWG/DrAzhy44zpDpZlby699NLN+6//+q+XtJsWQFquXZbdcBUiq8uTDsDVPVSds88+W9LyfI5CjLMkwag6e5bf8P8DMuFmOp5Lu/cx96/PKxPrsT7uZJ99fc/3fM/OfMrnhyowpZRSSlkdVWDKQ+YP//APN+/ZD6YwXCazknaLqmFR71fkb5QuHqud4+jDrcH0NRklzMp05hzvYZa5/56qhJ871QTvn/Pw96hUw5HixhtvlLQoXu6XkmpTvnqbVHBGBfxYQ5QmV3LSxyitZe8j/RNIwufj4bo0jPrBk8qWP3OoK7TBJ8bXmXs6w6ld3aP8B3DdXDnJlAfcd35P8Dzxf8CoSCr3GfcYffhzOXoes68M22ctCMGWpDe84Q0qj0yqwJRSSilldVSBKUcErDYiRLCaiEaSloRQGVXiZDRLJr7y92nhjc7HeUZ+MhmdgDU2ioDJMgFueWZUjUc5ZNQFlt7R9ONAgcEXwUs1MI6cz36WK2MeRZ1xvZjnyEqmDddtlH6e77Dsva8sSuk+NGVvnv70p2/e448yUipYc64FPkeoqdLufT961jJZZBZllJbrnKqIj4d7k+s8ut75XPP/jY+BvvJZ82eXefBcpL+WJH3bt32bJOm3f/u3d8ZRPr9UgSmllFLK6ugPmFJKKaWsjm4hlSNChj6yleTOcDjxnnnmmZLGNZWQdzOscVRReRReCSl5j6ols92R2yDeJhOrjRxZCY3eLxSYc+MY6fWWjjQk82LbwOX53A5iPqOtn1x7nxdrluviWwPpqDxaO/plW4nXUcgs18Ll/bI3vlVHpenRtQS2X4455pitV2k3oWSGQUvLNeO68+x/8pOf3LTJGkgc71s/uRU1qtfFPJgj3/mzy5iz6rzfl4yRcRFY4KHWbCt93dd9naTl3r/ssstUPr9UgSmllFLK6qgCUx4y7iSYqcZRUNzaIakdCsEoqRbHZ9ixp/vP6s+cZ2ThZwp/t+KysnSqSD6OrJrr1m2mzPf5oLjwSgp2Kk8fDU444QRJizLk64JiMgpPhww9H4V+pyqSYbF+fFYdd+uW7xgj18CvU1bFHlUELwtPetKTJG07r7LmqHL+POG0i0KK864nlcz7ZaTgpLP1yJEfuM48F664ZbXnUSmBVHvoc6TSZFVrT5PAejBXHN49aICkfXzGmEkOKEn/5b/8l505lqNPFZhSSimlrI77VWCmaXqzpOdIumee5/MOffZYSW+TdIqkmyU9f57nPzn03Ssl/SNJn5P0g/M8/95RGXk5qrzpTW/avMe6ySRPriJcc801kqRPfepTW4axEe8AACAASURBVOdxq+nWW2+VtFg5KDFYOA6WUYZU+jmxttgzH4WGZgHBkRWXfjduxaG0pJIzKm0wSmfOOTOk8957792Z8+Fy8cUXS1rWl7G6cpKqF/4krLe/z5DZUVFIoI+Rv02Gbo+K++X6upWMxUubhlHvD34crgSmn5criKwnzyOKzH6lNfiOttKSfJC+RkpZlt0YFXPMciKj1AW8zzIZft9w36a/l6tPqE6ZrNEVJv7fY+x33HGHpCVdgbT4x3zoQx/amU85ejwQBeZXJD0rPvvnkt4zz/OZkt5z6G9N03SOpBdIOvfQMf/PNE27xVFKKaWUUg6D+1Vg5nn+wDRNp8TH3y7p7x56/28lvU/SKw59/u/mef4LSTdN03S9pAsl/cGRGW452vzsz/6spO1U41jMRCWQIM0tNNQVCj2iyHgUEvAZRd6uvPLKzXdYS/hxjKxu9tpTORkVUcwoIofvsEZzz93bZEI7t/ywOLOkgI8bVeRv/a2/JUk66aSTNm3YS0edufvuuyVJb3nLW3bGvB9Y0LzmOvn7jMhwaz2tWtr4umTiOhQZP0+WLRiVT0gfhvRtkJb7hWvgZQbKLk94whMkjaP7UGdchUhVhuP8WnKdOY7r5f5VKBX0lcdIi7qSZSXc5wRVMH3cvE2WuRhFGI3u2xwzzydjHKlGJLrk+eR4Ii2lxbftggsukCRdfvnlO+cpR56H6gNz7DzPd0nSodevPvT58ZJus3a3H/psh2maXjJN02XTNDUWrZRSSikPiiMdhbS7oSnNg880z/Mlki6RpGmahm1KKaWUUkY81B8wd0/T9MR5nu+apumJksjKdbskj5k7QdLRixctR4w3v/nNkra3P4CtiL2SWUmL/HzKKadIWpxUXfZlC4q2t912n1jHVpK0bP2ks6s7Cx533HGSFge8USK7rH2Ulau9TTrqunSe4dfI6z6v3EJyx0SkduZFiKo7G7KurCdOzSeffPKmDcnAfvEXf3Frni9+8Ys375/2tKdJWpxwkeJHtZ0YV1bj9eP4DAl9JM9nNeBRKHtuU43q3qQTsF/TTJrmCdbKAuHKOD37tmtep5GTalb79vuGa8A15J71LWKOZzuGZ863WjLZ4yh5JPdkbkuOnMh55Z4dVZ3fbxuZ55JEf9xb3jZrRfH/hdeK4hnh/7Ty8PBQt5B+W9KLDr1/kaT/aJ+/YJqmL56m6VRJZ0r6w8MbYimllFLKNg8kjPpS3eewe8w0TbdL+nFJ/7ek35im6R9JulXS35ekeZ6vmqbpNyR9XNJnJb10nuddz73yiOGtb32rpEVh8HBayMRxOAJ+7GMf27RBMcGiwQpDJZEWiwUHXSwaTzWOpYdlhfXjqfc5dzoUjiwszpfqkR+HxZdKk58zx+XnSadXtwLTuXDkJMiaYR2jNnkCMKzkJz/5yZKWdfH1xYpMq3SUvC+r+Y7CWdPp1lO8pxPlKJV7hu6OKlZzv+U1HF2ndKi+8MILN23+8A9rJ7G+o1B/D5tOeP5QQrmneE6l5Z7kPqQvrzSNcoOj7sghOyuT87c7+gKqRpYf8HNnOgDGIO2mRRilOWCuOD6jIKPyel9ZkoCEe97e+y9HnwcShfTCPb56xh7t/4Wkf3E4gyqllFJK2Y+WEjiA/Pqv//rmfe5pw2i/mVDB6667TtK2H0j6cWBZu6KDooBSgIpw2mmnbdqwX54p+91Hg345flSgLn1W+G6Uoj79OVxpSP+NUQItjmMN9kumBW4Rp19AKkPSEhqN1UfbkT9Jlj8YXcsshOnjyfWkj9GYWQfO42NOdYbzjPwLMjX9qOhm9l3V5T7OOOMMSbvhwr7O3NOop+67wvPEvUWCyuuvv37TBvWU+5Dr5efh2eV68Tz4PYFCgXLDfePPXPpejeaTCecYh6d/yLnzf5ErUxzHPXXsscdKGqceAObzFV/xFZvPmM9IwS5Hj5YSKKWUUsrqqAJzAGG/2MFywBpzHwQ+u/322yUtVo8nFMO6Tr+LUapxLHn2jb0v+sDaIgGc76NnJE1GWEj7FyKEjF7ifG7J5nkYl1uVGc3kliLnxDIcJbvjO6KPRlZkkn5JPg78Cbim3iZVnpG6wvxTpXElh/lnVJb3lX4x9D1SBhg753GrlzGOygwU6dxzz5W0lPHAHw21RVrUkFxvaVnPvM6ugNx8882SFhUWRtFMgC+MX2/GRJ+Mw6933qMc7+dJXzLudfdLyQKsjMejszgOfx9XVXI8qEdZDsHH/0Ce3XLkqAJTSimllNXRHzCllFJKWR3dQjpAXHrppZK2Q5KzguwoERR1jnDSQ4L17aGsR4SznycdQ95F/kXOdtmW79jyYTvEx5XVp5GK93OMzVpG0m416kyg5WRFW99+ysR+fnwm0hu1yWReSPe+VZJhpxzvTpSch223M888U9LiND0aP+NzCX8Ubpptcgspt/Wc3HYbbUVxTZmfzz2drT0xWpG+6Zu+SdLidHvttddKWp5baXHMxRnXnWZ5Rgkp5l7zBJOZCHKUPDKfS55rfy5zK3W0Hcj9wf8lmQzSz7lfGD9jzLQLXu2a/5OYO/ca23A+Z5Jq8gz79hlr9Zu/+Zs78ylHjyowpZRSSlkdVWAOEOlAJy3WUoYZuwWCA19a727t4AyHKjOydrCoMu28OxRiDaaDrjvH5fF85466tOG7tPS9TVZEdue8tDRHFZ6xWNNhV9pVtNIh1ftPx1xXKrJyNngSwBtuuEHSYoFjMT7lKU/ZtKEKNteL+bhjN/dHVt72Oec4Rk7SsJ8zMMcxjlTgvD1z9fDespSQIBkb1/gjH/nIps3VV18tabkf/TlAhUWp43q5ApMlIrjHvQ3Xif8DcNL30OJMfcB1djU3UyCMVGHIoAO/t/iMc3PPuqM5x+OcnA7E0lIeACd71s6fgTe+8Y17jrEcParAlFJKKWV1VIE5ALAviwLjqkImacI6ccseNQYLjXBCPw97yZkMbpRoLf0nRtZ2prwfhediBdLWraYMvRwVe8OCSsssE4JJu+HYo6KXWG+ZlG3U3v9mPTJk2y1XxoiiRVsPCSWM9aqrrtr6231Z0q+Fvz2RIf1jnY4sTtqwVukDJe2qKVwDV6g4d66vzx2foMsvv1zSdgmLg8p73vOezXvW8ElPepKkxf/Mn4f04/DnHrWUa8c95un9ud9QXLh/R4om/xdwb/rzTR+oPhzvqftTpRkVc2T83FMjHzeOy6R3Hl6OP9Xdd98taXlmXCHlOxSYD3zgAyqPDKrAlFJKKWV1VIE5AGA5jCx7LA2sZSxqLDU/LkvYu/U/svqSVHvyfNKuQoD1NlJFYBSBwPss9ubnz5T7WIOuEIxKEOTfo/4Bq5R+RwnoUDaymKQXhmMemWTOo7ye+tSnbo2ZxGY+n5wr19CVsoxCSjXMz5mJ+fye4DiUnJyntJvkbnQNPvShD0mS3vnOd0pa/BUovCctfloHhWc8YylF9/a3v13Sophw3Tz6DIUji5tKixrCK8+jKxVcO/pAufBkd/xfwn3LMX6PcjwqDf8XjXzlIFW+/fBnMMtUcB96FBuKEq/8X8malkc2VWBKKaWUsjr6A6aUUkopq6NbSAcAZF6k2dEWErIxEqqHRyLh8oo07KGPJK1iS2FUL2RUnTbJisXIxqPqz7QdJdViHJnszh1sM1w0nYK9r2SUjA153J1d05GQc7szMe85jjYuq7MFxroyDz8PCewIqyWcelQhl7mOag9xnVgztpRcws9kY+mY7WTYuzt1Zvg18/yjP/qjzWfvfve7JUnvf//7t9pShVlaHFgh19vfM3aSl/n1YguMz37pl35pZz6PNHguuf+4Tn4teD5JZOdbmFRg5n7jefDnm3uIJJijyu1Z2X5UJT4dhDl+VCU+K577/xvMmftl5EBPm3RA/vEf/3GVRwdVYEoppZSyOqrAHACwyPar/IolQ9Imd+BLJ85RVddMbpdpvKXFqqXPTKHvfWS12ZFykonw/JhMbodV6M7AzJnPsuK0tFiIfJbr5WDl+nyw/lLFGFWz3mt+Prd0Jh6lTsdBksRmo9DSTIfuilIm0kvH4dE46NvVsHRcZqzunMx9Rqj+lVdeKUn63d/93U2bD37wgxrB/KRFReAajBL0ZSXmkRN5psF//etfL2m7ynGWNHjFK14xHN/DxfOf//ytv9/xjndI2p47c2Sd0lFWWpTaVNek3XIdrJM78eL4z/8L+Vz5udM5359LnhFeKZfh9w3Ho6q8/OUv35lPefRTBaaUUkopq6MKzAEAP4eR9Q8oL7fccoukbeuNvXAsK1QVt8KzSOF+RQ9zv9rVlf18KWAvPxvfj8fqI5SSvvF7kHZ9X9IvxOfIGmThRh8PfY0UpUz85vPLtUrVxseBLwJrNioLQfgsFrSHxO9VtNPviVSiUNxcEcrjM/ndqA1j5V6TlhT31113naSlHAKh0yMuvvhiSduKWyY7oy+/TqkE8Z2fh+Ox9rkGhG5LS7IzrsE/+Af/YGe+b3vb2/Yc/9HmWc96liTpDW94w+YznmfuH38uKfSYCRjdB4v15JX/A0ZJMdP3xOEZIzkcf7tSy5rzisrzspe9bK8plwNKFZhSSimlrI4qMAcAygJksjppsTg/8YlPSFosI7e2UV5QIVA6XKXBWsoSAiPLPv8e7ZGnSjQqFpj75yM/jmzrn2P1pSrhlmj6vGQRQ2nXP8bHnmrKyO8iSwhkBJaPn3OPfHroPws/jixp7gn+dmUqrzNjdX8HfFcYI+vj0WuoGLySWI97TVoUlyuuuEJ78c/+2T+TtPihcI+6KsI60Iaxu0KQUTGM2f1beJ/qk6s0+L7ccccdW+PxBGlPf/rTJX1+087/03/6T3c+e93rXidp+77h3szEb37/8axwb/GdPw9EwQH3tfuuZIJJ7qmf+qmfehAzK+U+qsCUUkopZXX0B0wppZRSVke3kA4AJJ9CBscJUVq2B2666SZJixOmy8HI6rxyHncgTWdZpGV3VkWqzlDeUTVqpGa2Snz7IhNm5efSsnVAH2x5uLMgobKMNavg+ntkcNqMqi6PnKOR2nMLy+ec/TIud0pmHbk+nG/kJE0bzuvXgO+4J0YVyhkH52Ouvr7p8My2g99bbNEQBvu+971Pe/G85z1PknTWWWdJ2r7/6AMHc7ZufBuE8OBTTz1V0rIN5tuTua3INfBtENY8nYL9WGoGUeOHtmyRScu6nnfeeVt9XHbZZXusgPTN3/zNO/NiW2q/LbYHww//8A/vfIZzLPcLz5pfS+b6cz/3c5KW7Sn/P4A5/sIv/MIRGWsp90cVmFJKKaWsjiowBwAcK7HER4nNaINC4Y6fKC8oAyNnYCwxzpfp4719OuSOnHj5DAdid6zNENmRU3GGfqeDoo+VuWaSLT93Jt4ajRmlwtUMPuP4kaMvY8s+fO2wbgmDHTkcp8I1Kt2Q42DsOPVKi2LCK21+//d/X0cCwnylRTk56aSTJC3zwzFWWtRBlAEUEK9GzfE4I7N2rhqxzqhHqCR+32RyxJGqxhhJy08fozFniD2KjLTcmygdrPMorPv000/fGs/111+/M66Hyr/+1//6QR/jIdqlfL6oAlNKKaWU1VEF5gCQiewcLHL2+NMvRFpUGSxGrEBXYLKEwMgHBqs/k7mNQq2xitMfw9swr1HIbKb6HxUSxKLneBLAuRrBebK0gcM48JNhDaXFWueVcbjvAOPOZGHud8Fxo4KTwFrlmnnJhxNPPFHSorhh/aO2SNLNN98sSbrqqqskba/H4fCSl7xE0nbIdvqR0JeP5/jjj5cknXDCCZKkxz3ucZK27630EQJXyrgnWR+urftX8R0KXoa4S8ua0RYFDzVJWkK8acvxPq/0McKHZlRslWeHY1B/vK9SDhpVYEoppZSyOqrAHADSz8XVFd4T9YFl51Yg+/BYuRlxNGLkO8BxWMmpTkiLhYpVPEqYlZE8tMFq9rExdlQO9xlhHJ/+9KclLWvgkUqMI316RinUGTuFCb0P1g61x1UazpXr6yoN42D8WWBTWpQW+qBv981gbqlG4FciLWuGj8goJfwD4dnPfrYk6eSTT5YknXnmmVvnl3avN2N3XxGuSyYM5LpJu8U7UcM8WgyFjes1Up+yDAOMSiTQhmvpkVNZQiKfIWl5HlFuuKZ+vfA/49pmBJ+0XDtPpFfKQaAKTCmllFJWR3/AlFJKKWV1dAvpAMDWCk6C7siK/Ix0/oQnPEHStoSONJ3Osp5oLUN26cMlc2Rv5P2ssCwt0ntuLXhYtzulOl5zhTacj60Tl955z/YAWwo+ngxX9q0jYHuAPt3hk62NDOH1PlgH5si6eiKxrI/ENRmFxLPtgLOr98U8clvI58WWCM62bHW88IUv3LS59NJLcxkkSc997nM378844wxJixPuaIuEOXIfnnbaaVtzcLKulM+BbTscc9n+8m1F1mO/atTcJ5m8z9c56wFx/XBI9r58O9HHLkm33367pOW+wUnZnYrzWcmtUR8Ha3fjjTeqlINAFZhSSimlrI4qMAcA1BVSsbuD7XHHHSdpSeGOte5KRaYWx2J0Z1dUA47HYswK1H4+vhspOViVWLDufIta4P1nGyxm2vDq1jZtUHuw2t0xlvlw7lF6fmDMbh1n0r2RgkN/rBkOth5GzbowRix5X18UNtqMVIysWJ2WvY+R64wy5E7Fybd/+7dLki644ILNZ6gfONty/7iahoMvigUKoI+d9eReYJ6uUDFnruUoFB3SkXoUro4qNwqjTgVo5ETOc8Uzh5Iycm7/2Mc+tjUOQt19XsyVPt2pPSuVP+lJT5IkXXPNNTt9lfJoogpMKaWUUlZHFZgDAP4Kf/tv/21J20oFvgeEcmIFeiK4tEaxQN2axKrlNX1hpEW1wGLEj2NUtgDrnzauCGHNYi2n6uN9Zaj3yNcjw5a9KB/rg3VNH+5vg3U+8s3I8N5RwrUsv5DJBX0erA9r5udBpcmCjT6eHAfXyxUGVIxUDUZqBqHS+F+4CsE1Q0HJkH1pUVz4jPX265SqE+dzXxHWmbGigPi9hfrGufHxGalpWRrD14drwXF8575G+P1wHzOH/eBe93XmPPSJ34w/V4wxFTzWQJLuvPPO++2/lLVRBaaUUkopq6MKzAGCJHV33HHH5jNUCCz7kcWZRQqx2t1S5H36vrilmCnu2df3SA3OzXhQglyBYYwoDczBfWlo/0D8dlANsOJdfcpyBxzjkVBZKNLHmj4PtHU/mTw3a+jHMueMHsqEaw59eF+pEo2SEqLc3HrrrVvHu5rxgz/4g1ufoWa4n4wrWdLi5+L+LSguXEPW2+fJuqK4sC6u0nBdieThb1dXuF+43ziP+5Ok78yo3AX9pj+T94Xf2amnnippUVdc/aQYJcoQc3a1hu/wFUJV8fIOt912m6SxTxlUgSmPRqrAlFJKKWV19AdMKaWUUlZHt5AOEEj3XkMmnW/ZPvGtCbYHUqIeVUROyX20VULCr5GjLzI/WwBI8SNZHadJpHfvK7dasiaRv+c8HO81ZdhSwLE1HSb9PVsRoznnlo1vWzAOZH7+ZstF2k3Mlw6yTibv875yXGyD+PoyDraAcLT1asscl6HIvm3EPNhmYgvTnXhzXtwLvtWCM3GGu/t2IONgzUaOx2y1XHHFFZKWLRwckP243O70ezSrSI8cs5kzyfxYF9/K4bpkhXHfzmPb7KabbpIknX766ZKkc845Z9OGNaQta+H3Bmv/7ne/W6U8WqgCU0oppZTVUQXmAIFyMkqClWGo7qCIhYdFh+U5UmCwVDneLeC0arGg6VtarEjGmFautG3peh9ucaYDa4aC+zgyqZtXZsb6Z4z33nvv1lykRcHB6vZ1yfUYqSE4GmeSuZETL2uWY3cyLNjVjHSypu+77rprZ84oZTjGnnTSSTvjAdbZx8xYOQ9qmjsDM1aOZ738GqMY+jxyDA+klAUlEXCARVkc3RM5Hl9n1idLSPgzw/2C2kR17VHiwXScd/UzyxaQGM/bkB6BuTNPHw/PVRWY8miiCkwppZRSVkcVmAME1iV75NJi6WJNZji0tGvdYtm5dZshxG5lJ1iVWOhuAWMp0ichzj5mLGjaZrI4nytkEjQff/qpuHXLOqBU4CPkagB+LfQxCtVO3xWfM9cgw199rPh/0BbVx63sLEnAurh/S6o7+Pt4aD3H4/OSIfbSbrgz83KFifmgQjDm0X3DuJgz85UW1QtlAVzpYj7MmdeRSsN8aOMJ8fKeZDw+ZtaZufLq6grn5DjWcpQAkbD1LDvhx/PKPUb5AWlZw6/5mq+RtPi7+DNDm9e+9rWSpB/5kR9RKWunCkwppZRSVsf9KjDTNL1Z0nMk3TPP83mHPnutpOdK+ktJN0i6eJ7nz0zTdIqkqyVde+jwD83z/I+PwrjLQ+AXf/EXJUnf+Z3fufkMqxbrD6ttv0R2WIiueOxVJNBVhVQ8OJ+rNak0jPx2OC7H7hY6VjavHO8+GpkYjTG7Ze/p4X1+7qPBe/p3CxorP5UtH8fIzydhjiRhQ4XydWGsvLJ2XhyQ9eV4Xj15H74h+K4wLtLYe78ZeeXXG+UllSVfU9aKa8HfrnBxn7mvlLR9ndJ3BXxerB2KB+qT+zzh78M9NvL34jvmwXf+PGRRUpQdIrp8zoyZa+yKWZYr4P7xhIEf/ehHJS33IcnzfA1zrKU8GnggCsyvSHpWfPYuSefN8/xkSddJeqV9d8M8z+cf+tcfL6WUUko54tzvD5h5nj8g6Y/js3fO88zm8ocknXAUxlZKKaWUMuRIOPF+j6S32d+nTtP0EUl/KunH5nn+/45AH+UI4k6mbCkgp7Nt4HI9MjbhohzjbdKRlq0Ar3uDfM22A5K3b6dkci+cHt0hkWRlbE0QZuuyelYITidaaZHYszKzO8ZmLSO2BtzJmb6ygrG0u03F8b7dwHu+S2dcaTd8mu0QdwplC4stCRyg/Xqz5lxvrqWvL/V26Is2N9xww6YNa0YyuKzW7ePI5HA4REvLmvlWj7R9nTgn919W75aWa8Ar47vuuus2bd75zndK2k345g7DZ599tqTlPsykddJuIsfsW1rWnPkxZ39m0tnanYnhmmuu2fr73HPPlbR9/7B2H/zgByVJN954o6Ql6Z20PIeEsv/0T//05rtXvtIF9FLWw2H9gJmm6f+Q9FlJbz300V2STprn+dPTNF0g6bemaTp3nuedWvLTNL1E0ksOp/9SSimlHEwe8g+YaZpepPuce58xHzI95nn+C0l/cej95dM03SDpLEmX5fHzPF8i6ZJD59rNxlWOGm95y1s275/3vOdJWqw2QjDd4sxU525lAxYh1iBt3ErGWZLzjJLLYRVznlE4bFq+WJdu2TOedE52spowfbgzcDpRZjI+aTdlvzvj5viZlysO6SjKuDJZnLQoSlnB2ufKuqIs+Hyw8nFkZZ3cuRQFhqRphOySzt77Qgk688wzJS1Vk6VF4SIMGrXIFaF0uuXvkQLD/cP6ehvOzXfM713vetemDdeCdP6st1dDp02WJPBrmp+Nqn5neDl/u8pC8kCuNwpTOis7V1111c5nqGCsK4pkqlrSUjbhrLPO2rOPUtbCQwqjnqbpWZJeIenb5nn+c/v88dM0PebQ+9MknSnpxiMx0FJKKaUUeCBh1JdK+ruSjpmm6XZJP677oo6+WNK7DlkjhEs/XdL/NU3TZyV9TtI/nuf5j4cnLo8IsOAJp8Vy9bTxWPSEd2K5uoWXPisjBQYLk+NH5+E933FeV1cy7Jg5uAXMOLCuR5Y0qgrW8agAJQoFr6gsriIQBs6cR/4tOQ9XTmiTidpGSffwNckEeT4mlA/8XLxMwCc+8Ymt8YzS+2f5A/p0VY5zUyCR83noLtZ+XmdXTjLkm+NHRSpTmfBryfhpc/XVV0va9hHyMHDvy5WuLCKaSqC3H5VxgCzZMCptwLpwT/B8eJj5k5/8ZElLqDSgkkm7SRtZO587n1177X0ZLtzvp5S1cr8/YOZ5fuHg41/eo+1vSvrNwx1UKaWUUsp+tJTAAQfLlX1zrFSSYUmL1UgbrNP0IZF29/rdOqQvLHn+dgvYI4mkXX8ZaTdtfSaic1A1GLtbtxmlQ18eeYSVTv/7FalMtca/Q2HAkh8V7GPNULpGESuZbM9VgCzwyFhd4WJ9SW53zjnnSFqsfz8Pn9HWryXf4d9y991374wZ8KvKhHY+n4z68euU99RI3UNZ+vCHPyxpUYZSnXCytIC0tz+Tr2FGOjG+UakF+uB8PDs+NtQiro2Ph8/wc4FRNF1GsTmpBOLfJC2FJr08QSlroKUESimllLI6+gOmlFJKKaujW0gHnEsuuUSS9N3f/d2SFidenHqlJbkd2zGj6skZ+ss2iNfqQYbPLZuRA2lWhnYJn3EQGk0IsG/LcDzOiiMH0qzJNHKMRcLPJHc+Hs6ZodY+pgyRdnLrizXw7SHmw1bYaLuAfll7HJh9a4MtDEKSmZc7lzIf+qCSsm8zcRxbN4Qms5Uk7Trksi3ptX4gkxx62DvzYlxcE0L/Jen973+/pMW5mGP8/iNJHedhPDgbZ7/Scr38urENk0687njM+7zH/XqxhowVB1vmIC1rjkMua+fXwrcsvQ+/D/O+8+dglECvlDVQBaaUUkopq6MKTJG0VA7GKdOd/HAqxOLE0ddTlbtFKC1WoYc/ZwhxJiiTFqsRK5TzuGXMZyhBnNetbRx007F2lIId65ZjfDyAGpFqgLSrNIyqSrN2tPFxYNGnw6+3ydBd/vawZeaDeoZS5ePBofb444+XtFjr7nxLH6ny+PqiLOR8UPCk5bqgLDAvT3ZHX9x3KBWoftJyPRgHyosndaNfxsWcXd1izQhBJkTZHWQ9PF4aOyVnRfHRNc0kjSPnZNYzE0R6yDRr6OqitK1i5Xfg153+R4oS46cSd4abl/JIpQpMKaWUUlZHFZgiSXrta18rSXrFDgM+wQAAHshJREFUK14haTvRFRZ9qjTeBsuSV9r4/jrWJFYuVqBbkFilnIfj3b+ANvhC0Bfp3/095+bVw7FRL5hHlh/w41APRuUCIMOXHc7Jce4LkeUKMgRX2i34l6n8peU6pWrg6hjKC34tnM+PGRWTlLZ9KjgnKtxjH/vYrTFIiyqSJQVQHKRFccnw5ZEfEZ9lEU5p8WNhXKOQbVQ8FKAnPelJkhblwceT18T9XXifqsaokGUW+PTzMA/mjC+Xq1jcm1wnjvfrlWkEsgCpzydLLvh7vy6lrIEqMKWUUkpZHf0BU0oppZTV0S2kssVrXvMaSdKrXvWqzWef+tSnJO3K8x7uyWcpgxNeKy3yN9s4yOsueSPzs6WAVO51gmhPW0KLveYPEj4OyLy6MzDyfDrYeog0c+Q7ZHqv3owEn9WJpWWbAKff0fYMcx3VdALWivGwrqPtBuaIwy7bRdKyRZKOp95nZo/Nv6XlGmZ1bm/DOHA4pS93PGbbguuTda783KwhbS+44IJNG8bP1iNr6iHkrAdOvDgKj7IiZ+0i3/JLh3C+820rzsN1G9VCYlsot1a9zhH3PeehjW+FZhoA+vbxMEZe/R4f3W+lrIEqMKWUUkpZHVVgyhCsVWlJToY1SFirO+jivJlhuu7UiZKDxYkl7tZtOt3iHOptOB6LHEvfnW/TMRf1yJPmYQ3zHZaoW7coHjlmV5boY+REyTj2c7REUUApYV4eKst5UFyuv/56SdsKA2HgzJF1cYWBa5g1mbwN68KcWVd3QKUN90IqH/6e9XTlBVAJuM6cz5Uy1ozzEf581llnbdpwnVjLkWMq9yTnYw18DbkHeGXOrpihMqVy4fco65GqnJ+H9nzHOHCWl6Qzzjhjqw3PkK9lqkTcz76GWYvLHfBxBOc7nJw9lUIpj0SqwJRSSilldVSBKUP+yT/5J5v3//Jf/ktJi/WeYa3SosbQhjBm97+45pprJC2WPValhz+T6j4VC7cm2fMnJBTr0q1SlArUI/rACvc+mE9WRpYWS5q+MozZv8t079KiQDFm1BW3+vHFwLrm+JtvvnnTBoWBsXG8ry+Wc4555NPD+qB88CotSgfrwfq6MkUbFA/G5QoD6kcm+nNVhLGivHA+v7e4X3gdJZfLytCcx/tK1Yk+RqHNqcC47wrXcr9Ednlv0cZVGsbGuFhfb8P1TSXI7x+Oz+vlSfN4fpiHP3OMEVWxPjFlLVSBKaWUUsrqqAJT7pcf/dEflSS98pWvlLRY76gl0mIhoiJgOZIsTFpUC/bWaevRQ/hvcO6Rb0X6s7Cf7wnk6Cujd1wdwQLGd4TzugJDH4wDpcItYPpAIfCIKZKkpSU+sqA5flR4kjZY56PoGOaT/g5ezoG1wu8Bq9+jmTgnVrr7DQFrhdKBiuRKWZaXQCFwVSQVBZLduWqEkuR+G9K2KpJFMzmv+/agirAu6e/ix2Vkml8L+uV+82sAqZjQ1sfMeFhDxuVKIsdzH/G3n4fU/+n343PnGo58sLi3mesdd9yxM59SHolUgSmllFLK6ugPmFJKKaWsjm4hlQcM0jTyvm/HIIPzGdtEJ5100qbNOeecI2nZErjtttskbYdap9SOPO+SNxI7cjgSuDuQIpkjy7OF5LWD+I7P2KZiS8nPw7YMr6P6Tbx6AjHGxDzYsvEtCcbGFgnbKb7VkttDo1BZ+sqEcX6dWNcMPfdtJq4P2zlcE99KYksjt7R8XlyXDNX2LRfmyhqw/cYYpN2q45m4zefj/WdfuT3kzruQ993o/qNNOpr7/TdKaphteJ/3qm+fsR7MGWdnf6643rRlzH7/ZLK9UVg419QTVJbySKYKTCmllFJWRxWY8oD5yZ/8SUlLmQFXM7C2US+yiq60qDSnnHLKVhtXCAjlzHBjD/OlL6zukYMkx2MlY426RU6/tEkHVz93OhczF2lRbujDnVfT6mc9fF1QGFAd+Nudkulvv3Ewf+ZFHx7+TAkEHHRHSepQxm666SZJi4pAmLa0W4KA9Xb1IJWkVKOkRXXI6uWupKC8MI90rJZ2Hbvp05UTD3N2RpXFsxq1Kxauvnkf7pgNWYV6VIUcuJ/9WjCv/VQR2nPdMrGij5E+fe1Yc9bMVb1SHslUgSmllFLK6qgCUx40r371qyVJL3/5yzefYf1h4WPNubqCokAyuVEILxY8SdywLr3AHeUKMlW+7/ljcdInbdxvgrFlEUcfMwpFFn70ZGqpjrjVn6G6hI4T+vr/t3d/sZZW5R3Hf0+k7UVbExtbo/JvSgYEqk5bYxr/kKFpWts0GWzSFi8aYwkyYbCFYOKfG00akl5o8YLKBCOOTShqSmW8MJXGYInRoNAOOsCIwzgO0xkhRYNN09iIqxdn/877nGevM3M4M7D3Yr6fhOw9e6+93/W+7z5hrWc9a618DEchHHHIvWRHtnzOfsxTvs11c5l87dyjr/kf+d/+TufQ+F7k6bWOiNVF73JUoeYq9SIVLuMNOR2B60XBHDXylO3eQm2+B71NM31dfL1rnkt9LvUXqatbYPSOVSM3ve+p+Tq9RRv9m66Rrvw9NXer/lal+SnfvY05ezlgwDIjAgMAAIZDBAab9rGPfWz1+Q033CBpmlHknnDu6TmyUXNh3KOWpCNHjkiSDh06JGmazZR7q7UX6ahC7gG7x+kyjsDk+vh5jUbkYzkq44iAZ3rkqFHd0iDnrvg9f86Rl7wZpI9R8xRyL9vf7WvnDf9yvk1d7K43Y8rcs3c0JJ+zoyvnnHOOpPntB6TpPvmYjqrlmUp10818XWoZR2D8G+ltteBz7W1/4KiBH3t5UfWcXfdcps466s0m8vnUz+ffUZ3p1Is++XmNFuYoUP2N+9j5WL6G/m24TM6z8uylurhgPoYjPzmvClhmRGAAAMBwaMAAAIDhMISE0+Lmm2+WNCX2Olk1J8166q/D8R4ayCHrOszg78n74Hh4yaFy77uUhy8cDq9DAjn07no4KdNDNjmE7yEJJ4w6kTQPBXiYyMNM+T0PHXnIyEMleb8kD0X4Ojjcn5Mp67CJ69NL4q0LyOXhjzqd3MNXef8dJ3M6Ibu3uJyTtr3btxN98/32vfQxXdfeEJuvi+V9ti644AJJ05Cjk7h7Q0g+fu9+e6jH59dbyM6f8/Xp7Ubt++X7XB9zGX++DjtlPpavS29BPF9D/8bzkN96w0xOCpemRF2X6S1u6Otyxx13zNURWEZEYAAAwHCIwOC0cmLvzp07JU09dGl+l2X3dvM0X0dT6mJceSGvHL2QpmmxXgpdmnri9Vg50uDPrbfAmTQfRXBv1Z+V5qMIeVl+93QdsXBPuNf7d+/aCbo5AlN3hq7Ta/M5OvLSOy9fT9c/J3rWejgS5B5+jgzUqec+nxxdqREgHyuX8Xk4CdiRsxxd8W/C18Dv5cido06+7z5mXsTP18P3MJ9PVRNz89R419/HqFsL9M7Z9cgL/bm8Ix91WwZpfpsKn3O+7/Ue1GNL0+++Jg5L89cFGAW/WAAAMBwiMHhe7N69e+61HTt2SJryABypyNEVR1Hc03RvNy+i5lwT92o9NTmP63s6t/M4HE3obe7XW2TM/J57t66rIyr5+H7M51MjFTUfJNfJdazRlly+TtXOEaWa7+Pedo54+No7WtTLyfB7NW+jF13xMRwtet3rXrdaxtOwax5I7unX6caORuTpvnV6r69LXvDN9cnXI1+LfAzXuea51PLrqWV8XjkqVxdF9GOOzvm4Plff47wgY83P8vXJvx9fB98nl81RLPN356n1Ll8X1gOWHREYAAAwHCIweMHs3btXkvSWt7xF0hSxyD3F48ePS5Je+9rXSppmo+ResnvinmXhiEce83dv0j1YR2ucTyFN0Qw/Opclz3jy59zjdc/aGx1K0sGDByVNPeCc5+B61xlO7klLUw/adXNOUG82Ss1zyNel9vprpCFzJMDfk5flr7OFehth+j1fuy1btkiSLr744tUyeYHCfA45wlD1ZuLUGTiua/4el/c51+X+83fXa9fLI6rRmjzDyOX9Wt1uQpruge+JH3MZ19G/O0dicnTE51pzpnIUqG4BUDeQzOV9DXO00b9bb9IKjIIIDAAAGA4NGAAAMByGkPCC++pXvyppGkrysJE0vxjcZZddJmntlNk63bO3Q7RD5p7G7aTbnPx43nnnSZqGsDyskneK9nCBE2s9bJXLOGG47rWTeSjDZXICqofJ/Ohk3t7+O37NwxZe6C8/93XwUEQeHvLx6xBJ5mEhD8v4872yvi+uu6+TNF3XundVXuzOQxk+r7ovUK5H3d8ql6nJwHXoLtfDj/n6VnWYKP+2/NyPHrrMCbp+zffJQ5G5jIeTfJ88BNTbr6jWubd/k69LnVLeO698XW688ca5csAIiMAAAIDhEIHBwjgSk23btk2SdP/990uaerBO6pWmHqqTFx0FyEvd+3PuqboH7GiJNEV7HA1xomVefM89VSekugedd2b2d9dp2fm1GnnJicteuM7n0VuIrk7Hdt0fffTR1TKOwHjquJOCc9TJ18PRCx8jRxjq0vK9nrzLuK5ODs0JqHVasL8vR4SsRk5ygq6f1+nh+X77vRpVyVEjRyp8T3pTpmuydC/qVHcLr4ni0vwWDX4vT7X2cy/M6Onr+bdRE45rNEqaj7g4Gpbvqeva2zoCGBURGAAAMBwiMFgq+/btW/NvR1tyz/Xcc8+VNPU03bvsLQBWl43P3+MohvNL3APOkQ9/Pi9cl78/l+lt/OcohPMbXGdHSaQpAuNz7U0ldi/98ccflyQdOHBA0tooxIUXXihJ2rp165pj9zYidPTBGyPmyIl9//vfl7Q238ccSXKdXdc8ddf3wxGCXkSnRjMc8ciRlBpNqwvj5c+7HjXSVOuWy+QoS62Hoza5TP1N+Vr2luKv+TK97QYc8fNvKi/IWLdE8LXM20zU3B5f3xx5q1siXHXVVXN1BUZDBAYAAAyHCAyW2n333Tf32uWXXy5pflO/3maDdWGz3At3r9Y9Xv+7l39RFzTrRWl6y7S7N+wcGm9amBfUcxSjbtCYF8RzNMSRF89gueiii1bLOPLi6Ehvw0j30usidTky5UiUo06eUZNzVxy58UwuR5Ryr9/XwdegbloozW9XUDcdzGpUpLdAX90OIkdF6iJ1Nacmf74uPJjvqe9PnRGU67NeVC5fQx/X18X3O8/K83VxtNARvKyelz+Tz8vfvWvXrrnPA6MiAgMAAIZDAwYAAAyHISQM59577+2+vn379tXndfikNzThIQAPPXn4IyeQnmixM/PnnViZp8G6HnUn5bwXUt1F2nX1onnStN+SE2o9dHPppZeulvECeB5CqMMY+Rg+Lw9N5Gnh3vnbwzseHsrn5SExn5ffy+dVE2p76nCZp6nnfa3qLsse/sqLAfq4OWl3vWPXXbHzb6Luzl1/Iz1+Lycn+3ndaypPtfbn/JrP+dixY6tlPLzpYSXXLy925+/xo38/11577bp1Bl4MiMAAAIDhnDQCExG3S/pjSU+11n5j9tqHJV0tyeuYf7C19sXZex+QdJWkZyX9VWvtS89DvYE5X/nKV07p845q5GRM9/oduXAvN5fxc+8i7WnR0tSD9vd42nOOMDiS4O9xNMKJu9IUFXGEwVGR3Ot3Qm5deC5PN/Zid468uH65jBOMHS1y/XLEw9EeRxZ62xbUqEgvadbRAkdeXK8cfcrTiqUpUpajEN7CoC7Hn6ctOxJVo3G9ZOe6u3VNDpbmozs5SuPrURfEy78bl3Ed/dvI5+vnvj5HjhyRtDbR/K677pqrG3Am2EgEZo+kt3Vev7m1tm32nxsvl0i6UtKls898PCLWj70CAABswkkjMK21+yLi/A1+3w5Jn2mt/UTS9yLioKQ3Svr6pmsIvEByxONkPJVbmnr/jsDkKbd+7p70/v37Ja3Nc3j9618vaYp4OM/lscceWy3jz+ccE2lt5MTP63L8OVJQow2O1uTNMh2BcZ5Lb1q2oyp1Gfs8jbpuMmg5z8T5H446/ehHP5I0RRqkKfrk73P98saRPse6xUGOCG1kurH5Ovv78nW2mjuVr7O/08f0sXqbVPr6nn322ZLWRmAcLXJ05qGHHlrzCJzJTiUH5rqI+FZE3B4RL5u99mpJecnSo7PX5kTEuyPigYh44BTqAAAAzkCbnYV0q6S/kdRmjx+V9JeS5ndHWykz/2Jrt0m6TZIiolsGWFZ5JtTu3bslTb10L/svTT1xLxTnZeNzFMLRD+dvOBKUN5V0eUchnBORF7urORW9Je7r1gaONOS8Hb/n73MkJ+dv1IXw/O8cYXDeiM+9tyy/3/P51eiINM3A8Ywen1cv0lXzgHLEyu/5GD523hbCx3DEo84Qy+dYl+7P18flXdbfl++Xj+V74kXqcv6PfwPeQgLAZFMRmNbak621Z1trP5P0Ca0ME0krEZdzUtGzJR2rnwcAADgVm2rARMQr0z/fLmn/7PkXJF0ZEb8QEVskbZX0jVOrIgAAwFobmUZ9p6Ttkl4eEUclfUjS9ojYppXhocOSrpGk1trDEfE5SY9I+qmkXa21+fmHwIvIzp07JUkf+chHJK0dJvBwSZ1y6yEgSTp8+LCkaejGSbw5cdTf4wRPDy046VWakoA9ROLHPNTiYYo65JOnXtfpz73hoboLteuaF2rzEJLf8xBOHh6qi9T1FozzkJyHYTzclZOK6x5PHsLJ19DfXRfPy0N+ddp8LxHaQz4+lq/viRKYXTYvjFincbtM3u/ISd8A5m1kFtI7Oi9/8gTlb5J006lUCgAA4ETYSgA4Td773vdKkq677rrV1xwBcC/dU39z5MTPnVTqRe5yxMMREkcseoueOaLg3r+jBnnBN0dp/H29nZTrlOi6LH7+nM/PibA54uHzcb18PrmM3/N31+skTREJT6euCbLSFClxnf3vvACdIx51p/IcDXN9aqJwL/pUFwzMib69ncnruddkYtc13y8A62MrAQAAMBwiMMBpdsstt8y99q53vUvStDhc7mW7d+/oih9zZMDcy3dvPeeTuHdfNyvMkYK6RL7L5twTRyjq1OYcTahTmh0R8qM05cO4rOuTIzkuXxeZ6y0cVxeryzknPkdfyxNt4uh6OWqUc5a81YLr4XygHKFyed9DP+bp2DX3xfIWAH7P9XGdb7qJEXhgI4jAAACA4RCBAV4An/rUp57zZ17zmtesPs+RFmnqyfciJ44e5KhBLVMXmctRER/Ly/D7vbzdgKMZNa8lR40ckXCEwcfMkRx/zsfwY17MzQvZOTfIde5to1C3BciRGJdxBMWP/r783NfAZfJ1fuaZZyRN0RlvbOn8ImmKytTF83KEyhtX+rpcf/31ArBxRGAAAMBwaMAAAIDhMIQELKkDBw7MveZF3Dx9+gc/+MHqex7O8dBNb5flmtxad7CWpiGfughfHv5w0m1NCs4JsX5epwvnoR8PGfk9D6scOnRotYzPtZbNw14+Rl2ILg/91CGjumO0NL9fU07Mrd/jYSZfn/w9rltdmC8v9Pee97xn7rsBbBwRGAAAMBwiMMBAPM3Xj3kqsSMDjtJ4MTgnmebyNXm2t9uyE1F7U5vrQnaWIw514Th/T45COIrhpF0vVnfs2LQHrCMmrrujLDlJ2e+5XnU7haxOL8/X0Im4TtCtydP5uP6cr1Nvmrmvj7ebAHD6EIEBAADDIQIDDKy32d8ll1wiaVo076Uvfenqe+stsJbzSerGio6OvOpVr1ot4yhPjl7kz0rzi+458pHLOMLhDRWfeOIJSdLTTz89VzdHPhzpyBGYGiWq07wzT2125MTTs3vH8LFzLozL+Pr0cmCuvvrqueMCOL2IwAAAgOEQgQFeZB555JE1/37Tm960+rwuw99bBr+WdXSktyWBF7dz2TzzyZEKRyZyhMIcjamL3jkHRZrfisDfmyMwfl4jJrlMzZ2pG2326uhj5kiOz/2aa66ZOx8ALxwiMAAAYDg0YAAAwHAYQgJe5L72ta+t+96OHTskrR0i8X5GHsapC79J87tHe3gmDzPV3az9mMt4kbr1FqKTpiEbP24kibfuv5TrUxfEy2XqlG/XIw+x3XDDDQKweERgAADAcIjAAGewvXv3rvveFVdcIWlaEC/vRu3ojCMVdcn8XMZRDUdw8uJwjoL4sTcluSbfOoKTF6mry/v783nRPD/35/yZ3sJ673vf++bOB8ByIQIDAACGQwQGQNfdd9+97nt79uyRNEUznENTN4uUpvwRR2I8LVuaNm/0RpFeZC5Pba5RHv/b+TPSfE6Ovzfn7Vx77bXrng+A8RCBAQAAw4neZmcveCUiFl8JAKfs1ltvXX1eZ/A4GuKtCSTpsccekzRFTOry/JJ0zz33PI81BrBkHmytvWEjBYnAAACA4dCAAQAAw2EICQAALAuGkAAAwIsXDRgAADAcGjAAAGA4NGAAAMBwaMAAAIDh0IABAADDoQEDAACGQwMGAAAMhwYMAAAYDg0YAAAwHBowAABgODRgAADAcGjAAACA4dCAAQAAw6EBAwAAhkMDBgAADIcGDAAAGA4NGAAAMBwaMAAAYDg0YAAAwHBowAAAgOGctAETEbdHxFMRsT+99tmI2Df773BE7Ju9fn5E/G96b/fzWXkAAHBmOmsDZfZIukXSP/iF1tqf+3lEfFTSM6n84621baerggAAANVJGzCttfsi4vzeexERkv5M0u+e3moBAACs71RzYN4q6cnW2nfTa1si4j8i4t8i4q3rfTAi3h0RD0TEA6dYBwAAcIbZyBDSibxD0p3p38clndtaezoiflvS3RFxaWvtx/WDrbXbJN0mSRHRTrEeAADgDLLpCExEnCXpTyR91q+11n7SWnt69vxBSY9LuvBUKwkAAJCdyhDS70k60Fo76hci4lcj4iWz578uaaukQ6dWRQAAgLU2Mo36Tklfl3RRRByNiKtmb12ptcNHknSZpG9FxEOS/knSztbaD09nhQEAAKK1xaefkAMDAAAkPdhae8NGCrISLwAAGA4NGAAAMBwaMAAAYDg0YAAAwHBowAAAgOHQgAEAAMOhAQMAAIZDAwYAAAyHBgwAABgODRgAADAcGjAAAGA4NGAAAMBwaMAAAIDh0IABAADDoQEDAACGQwMGAAAMhwYMAAAYDg0YAAAwHBowAABgODRgAADAcGjAAACA4dCAAQAAw6EBAwAAhkMDBgAADIcGDAAAGA4NGAAAMBwaMAAAYDg0YAAAwHBowAAAgOHQgAEAAMOhAQMAAIZDAwYAAAyHBgwAABgODRgAADCcsxZdgZn/kvQ/s0eM6+XiHo6Oezg27t/4zvR7eN5GC0Zr7fmsyIZFxAOttTcsuh7YPO7h+LiHY+P+jY97uHEMIQEAgOHQgAEAAMNZpgbMbYuuAE4Z93B83MOxcf/Gxz3coKXJgQEAANioZYrAAAAAbAgNGAAAMJylaMBExNsi4jsRcTAi3r/o+mBjIuJwRHw7IvZFxAOz134lIv41Ir47e3zZouuJFRFxe0Q8FRH702vr3q+I+MDsb/I7EfEHi6k1snXu4Ycj4j9nf4f7IuKP0nvcwyUSEedExL0R8WhEPBwRfz17nb/DTVh4AyYiXiLp7yX9oaRLJL0jIi5ZbK3wHFzeWtuW1i14v6Qvt9a2Svry7N9YDnskva281r1fs7/BKyVdOvvMx2d/q1isPZq/h5J08+zvcFtr7YsS93BJ/VTSja21iyX9jqRds/vE3+EmLLwBI+mNkg621g611v5P0mck7VhwnbB5OyR9evb805KuWGBdkLTW7pP0w/Lyevdrh6TPtNZ+0lr7nqSDWvlbxQKtcw/Xwz1cMq214621f589/29Jj0p6tfg73JRlaMC8WtIT6d9HZ69h+TVJ90TEgxHx7tlrr2itHZdW/lgl/drCaoeNWO9+8Xc5lusi4luzISYPP3APl1hEnC/pNyXdL/4ON2UZGjDReY253WN4c2vtt7Qy/LcrIi5bdIVw2vB3OY5bJV0gaZuk45I+Onude7ikIuKXJN0l6frW2o9PVLTzGvdwZhkaMEclnZP+fbakYwuqC56D1tqx2eNTkj6vldDmkxHxSkmaPT61uBpiA9a7X/xdDqK19mRr7dnW2s8kfULTEAP3cAlFxM9ppfFyR2vtn2cv83e4CcvQgPmmpK0RsSUifl4rCUtfWHCdcBIR8YsR8ct+Lun3Je3Xyr1756zYOyXtXUwNsUHr3a8vSLoyIn4hIrZI2irpGwuoH07C/+ObebtW/g4l7uHSiYiQ9ElJj7bW/i69xd/hJpy16Aq01n4aEddJ+pKkl0i6vbX28IKrhZN7haTPr/w96ixJ/9ha+5eI+Kakz0XEVZKOSPrTBdYRSUTcKWm7pJdHxFFJH5L0t+rcr9bawxHxOUmPaGXmxK7W2rMLqThWrXMPt0fENq0MLRyWdI3EPVxSb5b0F5K+HRH7Zq99UPwdbgpbCQAAgOEswxASAADAc0IDBgAADIcGDAAAGA4NGAAAMBwaMAAAYDg0YAAAwHBowAAAgOH8P+gwz52cBZ7lAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(12, 8))\\n\",\n    \"plt.imshow(train_dataset[-1][:,:,50], cmap='gray')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(12, 8))\\n\",\n    \"plt.imshow(holdout_dataset[6][:,:,48], cmap='gray')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import h5py\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"h5 = h5py.File(settings[\\\"train_h5\\\"], 'w')\\n\",\n    \"h5.create_dataset('X', data=train_dataset, compression='gzip', compression_opts=9)\\n\",\n    \"h5.create_dataset('y', data=train_labels, compression='gzip', compression_opts=9)\\n\",\n    \"h5.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"h5 = h5py.File(settings[\\\"val_h5\\\"], 'w')\\n\",\n    \"h5.create_dataset('X', data=val_dataset, compression='gzip', compression_opts=9)\\n\",\n    \"h5.create_dataset('y', data=val_labels, compression='gzip', compression_opts=9)\\n\",\n    \"h5.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"h5 = h5py.File(settings[\\\"holdout_h5\\\"], 'w')\\n\",\n    \"h5.create_dataset('X', data=holdout_dataset, compression='gzip', compression_opts=9)\\n\",\n    \"h5.create_dataset('y', data=holdout_labels, compression='gzip', compression_opts=9)\\n\",\n    \"h5.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"quit()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python (mort1)\",\n   \"language\": \"python\",\n   \"name\": \"mort1\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.5\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "Evaluate GB and LRP.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### Import packages\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext autoreload\\n\",\n    \"%autoreload 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import sys \\n\",\n    \"import os\\n\",\n    \"sys.path.insert(0, os.getcwd())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/fabiane/anaconda2/envs/mort1/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n      \"  from ._conv import register_converters as _register_converters\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Adding model_path to namespace\\n\",\n      \"Adding data_path to namespace\\n\",\n      \"Adding ADNI_DIR to namespace\\n\",\n      \"Adding train_h5 to namespace\\n\",\n      \"Adding val_h5 to namespace\\n\",\n      \"Adding holdout_h5 to namespace\\n\",\n      \"Adding binary_brain_mask to namespace\\n\",\n      \"Adding nmm_mask_path to namespace\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch \\n\",\n    \"import pickle\\n\",\n    \"import nibabel\\n\",\n    \"\\n\",\n    \"from jrieke.utils import load_nifti, save_nifti\\n\",\n    \"from innvestigator import InnvestigateModel\\n\",\n    \"from settings import settings\\n\",\n    \"from utils import load_data, scale_mask\\n\",\n    \"from jrieke import interpretation\\n\",\n    \"from nmm_mask_areas import all_areas\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pickle\\n\",\n    \"import jrieke.models as models\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for k in settings.keys():\\n\",\n    \"    print(\\\"Adding \\\" + k + \\\" to namespace\\\")\\n\",\n    \"    globals()[k] = settings[k]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Load model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"WARNING: With the chosen beta value, only negative contributions will be taken into account.\\n\",\n      \"Hence, if in any layer only positive contributions exist, the overall relevance will not be conserved.\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"device = 6\\n\",\n    \"net = models.ClassificationModel3D_2Node()\\n\",\n    \"net.cuda(device)\\n\",\n    \"net.load_state_dict(torch.load(model_path,\\n\",\n    \"                              map_location='cpu'))\\n\",\n    \"net.eval()\\n\",\n    \"net = torch.nn.Sequential(net, torch.nn.Softmax(dim=1))\\n\",\n    \"inn_model = InnvestigateModel(net, lrp_exponent=1,\\n\",\n    \"                                  method=\\\"b-rule\\\",\\n\",\n    \"                                  beta=0, epsilon=1e-6).cuda(device)\\n\",\n    \"inn_model.eval();\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Load ADNI Data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import h5py\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def min_max_normalization(subset):\\n\",\n    \"    for i in range(len(subset)):\\n\",\n    \"        subset[i] -= np.min(subset[i])\\n\",\n    \"        subset[i] /= np.max(subset[i])\\n\",\n    \"    return subset\\n\",\n    \"    \\n\",\n    \"def load_data(skip_train=True, skip_val=True, skip_test=False, dtype=np.float32):\\n\",\n    \"    \\\"\\\"\\\" Load hdf5 files and extract columns. \\\"\\\"\\\"\\n\",\n    \"    X_train, y_train, X_val, y_val, X_holdout, y_holdout = np.nan, np.nan, np.nan, np.nan, np.nan, np.nan\\n\",\n    \"    # train\\n\",\n    \"    if not skip_train:\\n\",\n    \"        train_h5_ = h5py.File(train_h5, 'r')\\n\",\n    \"        X_train, y_train = train_h5_['X'], train_h5_['y']\\n\",\n    \"        X_train = np.expand_dims(np.array(X_train, dtype=dtype), 1)\\n\",\n    \"        X_train = min_max_normalization(X_train)\\n\",\n    \"        y_train = np.array(y_train)\\n\",\n    \"        print(\\\"Total training set length: {}\\\".format(len(y_train)))\\n\",\n    \"        print(\\\"Number of healthy controls: {}\\\".format(len(np.array(y_train)[np.array(y_train)==0.])))\\n\",\n    \"        print(\\\"Number of AD patients: {}\\\".format(len(np.array(y_train)[np.array(y_train)==1.])))\\n\",\n    \"    if not skip_val:\\n\",\n    \"        # val\\n\",\n    \"        val_h5_ = h5py.File(val_h5, 'r')\\n\",\n    \"        X_val, y_val = val_h5_['X'], val_h5_['y']\\n\",\n    \"        X_val = np.expand_dims(np.array(X_val, dtype=dtype), 1)\\n\",\n    \"        X_val = min_max_normalization(X_val)\\n\",\n    \"        y_val = np.array(y_val)\\n\",\n    \"        print(\\\"Total validation set length: {}\\\".format(len(y_val)))\\n\",\n    \"    if not skip_test:\\n\",\n    \"        # test\\n\",\n    \"        holdout_h5_ = h5py.File(holdout_h5, 'r')\\n\",\n    \"        X_holdout, y_holdout = holdout_h5_['X'], holdout_h5_['y']\\n\",\n    \"        X_holdout = np.expand_dims(np.array(X_holdout, dtype=dtype), 1)\\n\",\n    \"        X_holdout = min_max_normalization(X_holdout)\\n\",\n    \"        y_holdout = np.array(y_holdout)\\n\",\n    \"        print(\\\"Total test set length: {}\\\".format(len(y_holdout)))\\n\",\n    \"   \\n\",\n    \"    return X_train, y_train, X_val, y_val, X_holdout, y_holdout\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Total test set length: 172\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X_train, y_train, X_val, y_val, X_holdout, y_holdout = load_data()\\n\",\n    \"\\n\",\n    \"image_shape = X_holdout.shape[1:-1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Load mask\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"mri_shape = X_holdout.shape[2:]\\n\",\n    \"\\n\",\n    \"if False:\\n\",\n    \"    neuromorph_map = nibabel.load(nmm_mask_path).get_data()\\n\",\n    \"    nmm_mask = scale_mask(neuromorph_map, mri_shape)\\n\",\n    \"    save_nifti(nmm_mask_path_scaled, nmm_mask)\\n\",\n    \"else:\\n\",\n    \"    nmm_mask = load_nifti(nmm_mask_path_scaled)\\n\",\n    \"\\n\",\n    \"# all_areas holds the area name and a tuple with the minimum \\n\",\n    \"# idx in the NMM mask and the maximum idx in the NMM mask belonging to that area\\n\",\n    \"area_masks = {k: None for k in all_areas.keys()}\\n\",\n    \"for name, (min_idx, max_idx) in all_areas.items():\\n\",\n    \"    area_mask = np.zeros(mri_shape)\\n\",\n    \"    area_mask[np.logical_and(nmm_mask>=min_idx, nmm_mask<=max_idx)] = 1\\n\",\n    \"    area_masks[name] = area_mask\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluate Guided Backprop and LRP on dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def run_guided_backprop(net, image_tensor):\\n\",\n    \"    return interpretation.guided_backprop(net, image_tensor, cuda=True, verbose=False, apply_softmax=False)\\n\",\n    \"\\n\",\n    \"def run_LRP(net, image_tensor):\\n\",\n    \"    return inn_model.innvestigate(in_tensor=image_tensor, rel_for_class=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"cases = [\\\"AD\\\", \\\"HC\\\", \\\"TP\\\", \\\"TN\\\", \\\"FP\\\", \\\"FN\\\"]\\n\",\n    \"mean_maps_GB = {case: np.zeros(mri_shape) for case in cases}\\n\",\n    \"mean_maps_LRP = {case: np.zeros(mri_shape) for case in cases}\\n\",\n    \"rs_per_area_LRP = {case: {k: [] for k in all_areas.keys()} for case in cases}\\n\",\n    \"rs_per_area_GB = {case: {k: [] for k in all_areas.keys()} for case in cases}\\n\",\n    \"counts = {case: 0 for case in cases}\\n\",\n    \"area_sizes = {k: 0 for k in all_areas.keys()}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#####  Run evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Completed 100.00%  \\r\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_samples = len(X_holdout)\\n\",\n    \"\\n\",\n    \"ad_score_list = []\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for i, (image, label) in enumerate(zip(X_holdout, y_holdout)):\\n\",\n    \"    image_tensor = torch.Tensor(image[None]).cuda(device)   \\n\",\n    \"    GB_map = run_guided_backprop(inn_model, image_tensor).squeeze()\\n\",\n    \"    AD_score, LRP_map = run_LRP(inn_model, image_tensor)\\n\",\n    \"    AD_score = AD_score[0][1].detach().cpu().numpy()\\n\",\n    \"    LRP_map = LRP_map.detach().numpy().squeeze()\\n\",\n    \"    ad_score_list.append(AD_score)\\n\",\n    \"    \\n\",\n    \"    true_case = \\\"AD\\\" if label else \\\"HC\\\"\\n\",\n    \"    if AD_score.round() and label:\\n\",\n    \"        case = \\\"TP\\\"\\n\",\n    \"    elif AD_score.round() and not label:\\n\",\n    \"        case = \\\"FP\\\"\\n\",\n    \"    elif not AD_score.round() and label:\\n\",\n    \"        case = \\\"FN\\\"\\n\",\n    \"    elif not AD_score.round() and not label:\\n\",\n    \"        case = \\\"TN\\\"\\n\",\n    \"    \\n\",\n    \"    mean_maps_GB[case] += GB_map\\n\",\n    \"    mean_maps_LRP[case] += LRP_map\\n\",\n    \"    counts[case] += 1\\n\",\n    \"    mean_maps_GB[true_case] += GB_map\\n\",\n    \"    mean_maps_LRP[true_case] += LRP_map\\n\",\n    \"    counts[true_case] += 1\\n\",\n    \"    \\n\",\n    \"    for name, (min_idx, max_idx) in all_areas.items():\\n\",\n    \"        area_mask = area_masks[name]\\n\",\n    \"        summed_LRP = (LRP_map * area_mask).sum()\\n\",\n    \"        summed_GB = (GB_map * area_mask).sum()\\n\",\n    \"        \\n\",\n    \"        # Keep index in test set for identification\\n\",\n    \"        rs_per_area_LRP[case][name].append((i, summed_LRP))\\n\",\n    \"        rs_per_area_LRP[true_case][name].append((i, summed_LRP))\\n\",\n    \"        rs_per_area_GB[case][name].append((i, summed_GB))\\n\",\n    \"        rs_per_area_GB[true_case][name].append((i, summed_GB))\\n\",\n    \"        \\n\",\n    \"        if i < 1:\\n\",\n    \"            area_size = area_mask.sum()\\n\",\n    \"            area_sizes.update({name:area_size})\\n\",\n    \"    \\n\",\n    \"    print(\\\"Completed {0:3.2f}%  \\\\r\\\".format(100*(i+1)/num_samples), end=\\\"\\\")\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'AD': 75, 'FN': 8, 'FP': 13, 'HC': 97, 'TN': 84, 'TP': 67}\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"counts\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for case in cases:\\n\",\n    \"    mean_maps_LRP[case] /= counts[case]\\n\",\n    \"    mean_maps_GB[case] /= counts[case]\\n\",\n    \"    save_nifti(os.path.join(data_path, \\\"LRP_{case}.nii\\\".format(case=case)),\\n\",\n    \"               mean_maps_LRP[case])\\n\",\n    \"    save_nifti(os.path.join(data_path, \\\"GB_{case}.nii\\\".format(case=case)),\\n\",\n    \"               mean_maps_GB[case])\\n\",\n    \"    with open(os.path.join(data_path, \\\"LRP_area_evdcs_{case}.pkl\\\".format(case=case)), 'wb') as file:\\n\",\n    \"        pickle.dump(rs_per_area_LRP[case], file)\\n\",\n    \"    with open(os.path.join(data_path, \\\"GB_area_evdcs_{case}.pkl\\\".format(case=case)), 'wb') as file:\\n\",\n    \"        pickle.dump(rs_per_area_GB[case], file)\\n\",\n    \"\\n\",\n    \"with open(os.path.join(data_path, \\\"area_sizes.pkl\\\"), 'wb') as file:\\n\",\n    \"    pickle.dump(area_sizes, file)\\n\",\n    \"\\n\",\n    \"np.savetxt(os.path.join(data_path, \\\"ad_scores.txt\\\"), ad_score_list)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Plotting individual heatmaps\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib as mpl\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import matplotlib.colors as mcolors\\n\",\n    \"from mpl_toolkits.axes_grid1 import make_axes_locatable\\n\",\n    \"from copy import deepcopy\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def get_heatmaps(idx):\\n\",\n    \"    image_tensor, label = X_holdout[idx], y_holdout[idx]\\n\",\n    \"    image_tensor_LRP = torch.Tensor(image_tensor[None]).cuda(device)\\n\",\n    \"    print(\\\"Running GB\\\")\\n\",\n    \"    rel_GB = run_guided_backprop(inn_model, image_tensor_LRP)\\n\",\n    \"    print(\\\"Running LRP\\\")\\n\",\n    \"    AD_score, rel_LRP = run_LRP(inn_model, image_tensor_LRP)\\n\",\n    \"    return rel_LRP.detach().numpy().squeeze(), rel_GB.squeeze()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Running GB\\n\",\n      \"Running LRP\\n\",\n      \"0 tensor([[0.0034, 0.9966]], device='cuda:6')\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"patient_idx = np.random.randint(0, len(X_holdout)-1)\\n\",\n    \"LRP_map, GB_map = get_heatmaps(patient_idx)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"def plot_idv_brain(heat_map, brain_img, ref_scale, fig=None, ax=None, \\n\",\n    \"                  x_idx=slice(0, mri_shape[0]), y_idx=slice(0, mri_shape[1]), z_idx=slice(0, mri_shape[2]),\\n\",\n    \"                  vmin=90, vmax=99.9, set_nan=True, cmap=None):\\n\",\n    \"\\n\",\n    \"    if fig is None or ax is None:\\n\",\n    \"        fig, ax = plt.subplots(1, figsize=(12, 12))\\n\",\n    \"    \\n\",\n    \"    img = deepcopy(heat_map)\\n\",\n    \"    if set_nan:\\n\",\n    \"        img[nmm_mask==0]=np.nan\\n\",\n    \"    if cmap is None:\\n\",\n    \"        cmap = mcolors.LinearSegmentedColormap.from_list(name='alphared',\\n\",\n    \"                                                  colors=[(1, 0, 0, 0),\\n\",\n    \"                                                         \\\"darkred\\\", \\\"red\\\", \\\"darkorange\\\", \\\"orange\\\", \\\"yellow\\\"],\\n\",\n    \"                                                  N=5000)\\n\",\n    \"        \\n\",\n    \"    if brain_img is not None:\\n\",\n    \"        ax.imshow(brain_img[x_idx, y_idx, z_idx].T, cmap=\\\"Greys\\\")\\n\",\n    \"\\n\",\n    \"    vmin_val, vmax_val = np.percentile(ref_scale, vmin), np.percentile(ref_scale, vmax)\\n\",\n    \"    im = ax.imshow(img[x_idx, y_idx, z_idx].T, cmap=cmap, \\n\",\n    \"               vmin=vmin_val, vmax=vmax_val, interpolation=\\\"gaussian\\\")\\n\",\n    \"       \\n\",\n    \"    ax.axis('off')\\n\",\n    \"    plt.gca().invert_yaxis()\\n\",\n    \"    \\n\",\n    \"    fig.tight_layout()\\n\",\n    \"    fig.subplots_adjust(right=0.8)\\n\",\n    \"\\n\",\n    \"    cbar_ax = fig.add_axes([0.8, 0.15, 0.025, 0.7])\\n\",\n    \"    cbar = fig.colorbar(im, shrink=0.5, ticks=[vmin, vmax], cax=cbar_ax)\\n\",\n    \"    cbar.set_ticks([vmin_val, vmax_val])\\n\",\n    \"    cbar.ax.set_yticklabels(['{0:.1f}%'.format(vmin), '{0:.1f}%'.format(vmax)], fontsize=14)\\n\",\n    \"    cbar.set_label('Percentile of  average AD patient values\\\\n', rotation=270, fontsize=14)\\n\",\n    \"    return fig, ax\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x864 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plot_idv_brain(LRP_map, X_holdout[patient_idx].squeeze(),\\n\",\n    \"                            ref_scale=mean_maps_LRP[\\\"AD\\\"],\\n\",\n    \"                            z_idx=90)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 864x864 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plot_idv_brain(GB_map, X_holdout[patient_idx].squeeze(),\\n\",\n    \"                            ref_scale=mean_maps_GB[\\\"AD\\\"],\\n\",\n    \"                            z_idx=90)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python (mort1)\",\n   \"language\": \"python\",\n   \"name\": \"mort1\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.5\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "MNIST example.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Notebook to exemplify usage of InnvestigateModel\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.optim as optim\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets, transforms\\n\",\n    \"\\n\",\n    \"from mnist_test import Net, train, test\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Network parameters\\n\",\n    \"class Params(object):\\n\",\n    \"    batch_size = 64\\n\",\n    \"    test_batch_size = 20\\n\",\n    \"    epochs = 5\\n\",\n    \"    lr = 0.01\\n\",\n    \"    momentum = 0.5\\n\",\n    \"    no_cuda = True\\n\",\n    \"    seed = 1\\n\",\n    \"    log_interval = 10\\n\",\n    \"    \\n\",\n    \"    def __init__(self):\\n\",\n    \"        pass\\n\",\n    \"\\n\",\n    \"args = Params()\\n\",\n    \"torch.manual_seed(args.seed)\\n\",\n    \"device = torch.device(\\\"cpu\\\")\\n\",\n    \"kwargs = {}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"###  Load data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_loader = DataLoader(\\n\",\n    \"    datasets.MNIST('../data', train=True, download=True,\\n\",\n    \"                   transform=transforms.Compose([\\n\",\n    \"                       transforms.ToTensor(),\\n\",\n    \"                       transforms.Normalize((0.1307,), (0.3081,))\\n\",\n    \"                   ])),\\n\",\n    \"    batch_size=args.batch_size, shuffle=True, **kwargs)\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    datasets.MNIST('../data', train=False, transform=transforms.Compose([\\n\",\n    \"                       transforms.ToTensor(),\\n\",\n    \"                       transforms.Normalize((0.1307,), (0.3081,))\\n\",\n    \"                   ])),\\n\",\n    \"    batch_size=args.test_batch_size, shuffle=True, **kwargs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Train model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train Epoch: 1 [0/60000 (0%)]\\tLoss: 2.332586\\n\",\n      \"Train Epoch: 1 [640/60000 (1%)]\\tLoss: 2.323807\\n\",\n      \"Train Epoch: 1 [1280/60000 (2%)]\\tLoss: 2.262323\\n\",\n      \"Train Epoch: 1 [1920/60000 (3%)]\\tLoss: 2.307054\\n\",\n      \"Train Epoch: 1 [2560/60000 (4%)]\\tLoss: 2.248528\\n\",\n      \"Train Epoch: 1 [3200/60000 (5%)]\\tLoss: 2.214086\\n\",\n      \"Train Epoch: 1 [3840/60000 (6%)]\\tLoss: 2.219406\\n\",\n      \"Train Epoch: 1 [4480/60000 (7%)]\\tLoss: 2.205292\\n\",\n      \"Train Epoch: 1 [5120/60000 (9%)]\\tLoss: 2.088662\\n\",\n      \"Train Epoch: 1 [5760/60000 (10%)]\\tLoss: 1.981406\\n\",\n      \"Train Epoch: 1 [6400/60000 (11%)]\\tLoss: 1.929761\\n\",\n      \"Train Epoch: 1 [7040/60000 (12%)]\\tLoss: 1.844703\\n\",\n      \"Train Epoch: 1 [7680/60000 (13%)]\\tLoss: 1.841034\\n\",\n      \"Train Epoch: 1 [8320/60000 (14%)]\\tLoss: 1.777270\\n\",\n      \"Train Epoch: 1 [8960/60000 (15%)]\\tLoss: 1.767565\\n\",\n      \"Train Epoch: 1 [9600/60000 (16%)]\\tLoss: 1.510257\\n\",\n      \"Train Epoch: 1 [10240/60000 (17%)]\\tLoss: 1.443801\\n\",\n      \"Train Epoch: 1 [10880/60000 (18%)]\\tLoss: 1.443562\\n\",\n      \"Train Epoch: 1 [11520/60000 (19%)]\\tLoss: 1.388440\\n\",\n      \"Train Epoch: 1 [12160/60000 (20%)]\\tLoss: 1.218236\\n\",\n      \"Train Epoch: 1 [12800/60000 (21%)]\\tLoss: 1.336145\\n\",\n      \"Train Epoch: 1 [13440/60000 (22%)]\\tLoss: 1.201790\\n\",\n      \"Train Epoch: 1 [14080/60000 (23%)]\\tLoss: 1.168254\\n\",\n      \"Train Epoch: 1 [14720/60000 (25%)]\\tLoss: 1.000875\\n\",\n      \"Train Epoch: 1 [15360/60000 (26%)]\\tLoss: 1.047581\\n\",\n      \"Train Epoch: 1 [16000/60000 (27%)]\\tLoss: 0.967720\\n\",\n      \"Train Epoch: 1 [16640/60000 (28%)]\\tLoss: 0.925321\\n\",\n      \"Train Epoch: 1 [17280/60000 (29%)]\\tLoss: 0.835187\\n\",\n      \"Train Epoch: 1 [17920/60000 (30%)]\\tLoss: 0.828951\\n\",\n      \"Train Epoch: 1 [18560/60000 (31%)]\\tLoss: 0.916817\\n\",\n      \"Train Epoch: 1 [19200/60000 (32%)]\\tLoss: 0.839610\\n\",\n      \"Train Epoch: 1 [19840/60000 (33%)]\\tLoss: 1.041386\\n\",\n      \"Train Epoch: 1 [20480/60000 (34%)]\\tLoss: 0.930578\\n\",\n      \"Train Epoch: 1 [21120/60000 (35%)]\\tLoss: 0.651056\\n\",\n      \"Train Epoch: 1 [21760/60000 (36%)]\\tLoss: 0.773282\\n\",\n      \"Train Epoch: 1 [22400/60000 (37%)]\\tLoss: 0.595067\\n\",\n      \"Train Epoch: 1 [23040/60000 (38%)]\\tLoss: 0.634835\\n\",\n      \"Train Epoch: 1 [23680/60000 (39%)]\\tLoss: 0.900788\\n\",\n      \"Train Epoch: 1 [24320/60000 (41%)]\\tLoss: 0.677627\\n\",\n      \"Train Epoch: 1 [24960/60000 (42%)]\\tLoss: 0.893621\\n\",\n      \"Train Epoch: 1 [25600/60000 (43%)]\\tLoss: 0.681112\\n\",\n      \"Train Epoch: 1 [26240/60000 (44%)]\\tLoss: 0.800910\\n\",\n      \"Train Epoch: 1 [26880/60000 (45%)]\\tLoss: 0.666306\\n\",\n      \"Train Epoch: 1 [27520/60000 (46%)]\\tLoss: 0.550727\\n\",\n      \"Train Epoch: 1 [28160/60000 (47%)]\\tLoss: 0.722241\\n\",\n      \"Train Epoch: 1 [28800/60000 (48%)]\\tLoss: 0.665945\\n\",\n      \"Train Epoch: 1 [29440/60000 (49%)]\\tLoss: 0.696771\\n\",\n      \"Train Epoch: 1 [30080/60000 (50%)]\\tLoss: 0.885756\\n\",\n      \"Train Epoch: 1 [30720/60000 (51%)]\\tLoss: 0.600921\\n\",\n      \"Train Epoch: 1 [31360/60000 (52%)]\\tLoss: 0.862743\\n\",\n      \"Train Epoch: 1 [32000/60000 (53%)]\\tLoss: 0.448395\\n\",\n      \"Train Epoch: 1 [32640/60000 (54%)]\\tLoss: 0.613134\\n\",\n      \"Train Epoch: 1 [33280/60000 (55%)]\\tLoss: 0.595018\\n\",\n      \"Train Epoch: 1 [33920/60000 (57%)]\\tLoss: 0.727929\\n\",\n      \"Train Epoch: 1 [34560/60000 (58%)]\\tLoss: 1.013557\\n\",\n      \"Train Epoch: 1 [35200/60000 (59%)]\\tLoss: 0.514824\\n\",\n      \"Train Epoch: 1 [35840/60000 (60%)]\\tLoss: 0.916126\\n\",\n      \"Train Epoch: 1 [36480/60000 (61%)]\\tLoss: 0.594341\\n\",\n      \"Train Epoch: 1 [37120/60000 (62%)]\\tLoss: 0.534796\\n\",\n      \"Train Epoch: 1 [37760/60000 (63%)]\\tLoss: 0.788180\\n\",\n      \"Train Epoch: 1 [38400/60000 (64%)]\\tLoss: 0.711917\\n\",\n      \"Train Epoch: 1 [39040/60000 (65%)]\\tLoss: 0.518607\\n\",\n      \"Train 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    \"Train Epoch: 3 [51200/60000 (85%)]\\tLoss: 0.371585\\n\",\n      \"Train Epoch: 3 [51840/60000 (86%)]\\tLoss: 0.411934\\n\",\n      \"Train Epoch: 3 [52480/60000 (87%)]\\tLoss: 0.293109\\n\",\n      \"Train Epoch: 3 [53120/60000 (88%)]\\tLoss: 0.236648\\n\",\n      \"Train Epoch: 3 [53760/60000 (90%)]\\tLoss: 0.242662\\n\",\n      \"Train Epoch: 3 [54400/60000 (91%)]\\tLoss: 0.219401\\n\",\n      \"Train Epoch: 3 [55040/60000 (92%)]\\tLoss: 0.256547\\n\",\n      \"Train Epoch: 3 [55680/60000 (93%)]\\tLoss: 0.214393\\n\",\n      \"Train Epoch: 3 [56320/60000 (94%)]\\tLoss: 0.241843\\n\",\n      \"Train Epoch: 3 [56960/60000 (95%)]\\tLoss: 0.174864\\n\",\n      \"Train Epoch: 3 [57600/60000 (96%)]\\tLoss: 0.224447\\n\",\n      \"Train Epoch: 3 [58240/60000 (97%)]\\tLoss: 0.158270\\n\",\n      \"Train Epoch: 3 [58880/60000 (98%)]\\tLoss: 0.187875\\n\",\n      \"Train Epoch: 3 [59520/60000 (99%)]\\tLoss: 0.268266\\n\",\n      \"\\n\",\n      \"Test set: Average loss: 0.1046, Accuracy: 9679/10000 (97%)\\n\",\n      \"\\n\",\n      \"Train Epoch: 4 [0/60000 (0%)]\\tLoss: 0.279634\\n\",\n      \"Train Epoch: 4 [640/60000 (1%)]\\tLoss: 0.283117\\n\",\n      \"Train Epoch: 4 [1280/60000 (2%)]\\tLoss: 0.308924\\n\",\n      \"Train Epoch: 4 [1920/60000 (3%)]\\tLoss: 0.251605\\n\",\n      \"Train Epoch: 4 [2560/60000 (4%)]\\tLoss: 0.423164\\n\",\n      \"Train Epoch: 4 [3200/60000 (5%)]\\tLoss: 0.312933\\n\",\n      \"Train Epoch: 4 [3840/60000 (6%)]\\tLoss: 0.219388\\n\",\n      \"Train Epoch: 4 [4480/60000 (7%)]\\tLoss: 0.277688\\n\",\n      \"Train Epoch: 4 [5120/60000 (9%)]\\tLoss: 0.312480\\n\",\n      \"Train Epoch: 4 [5760/60000 (10%)]\\tLoss: 0.264209\\n\",\n      \"Train Epoch: 4 [6400/60000 (11%)]\\tLoss: 0.349272\\n\",\n      \"Train Epoch: 4 [7040/60000 (12%)]\\tLoss: 0.465780\\n\",\n      \"Train Epoch: 4 [7680/60000 (13%)]\\tLoss: 0.419743\\n\",\n      \"Train Epoch: 4 [8320/60000 (14%)]\\tLoss: 0.535963\\n\",\n      \"Train Epoch: 4 [8960/60000 (15%)]\\tLoss: 0.211782\\n\",\n      \"Train Epoch: 4 [9600/60000 (16%)]\\tLoss: 0.216041\\n\",\n      \"Train Epoch: 4 [10240/60000 (17%)]\\tLoss: 0.149442\\n\",\n      \"Train Epoch: 4 [10880/60000 (18%)]\\tLoss: 0.287836\\n\",\n      \"Train Epoch: 4 [11520/60000 (19%)]\\tLoss: 0.409700\\n\",\n      \"Train Epoch: 4 [12160/60000 (20%)]\\tLoss: 0.304777\\n\",\n      \"Train Epoch: 4 [12800/60000 (21%)]\\tLoss: 0.716861\\n\",\n      \"Train Epoch: 4 [13440/60000 (22%)]\\tLoss: 0.131154\\n\",\n      \"Train Epoch: 4 [14080/60000 (23%)]\\tLoss: 0.230954\\n\",\n      \"Train Epoch: 4 [14720/60000 (25%)]\\tLoss: 0.324571\\n\",\n      \"Train Epoch: 4 [15360/60000 (26%)]\\tLoss: 0.150562\\n\",\n      \"Train Epoch: 4 [16000/60000 (27%)]\\tLoss: 0.197104\\n\",\n      \"Train Epoch: 4 [16640/60000 (28%)]\\tLoss: 0.246563\\n\",\n      \"Train Epoch: 4 [17280/60000 (29%)]\\tLoss: 0.414175\\n\",\n      \"Train Epoch: 4 [17920/60000 (30%)]\\tLoss: 0.189419\\n\",\n      \"Train Epoch: 4 [18560/60000 (31%)]\\tLoss: 0.200866\\n\",\n      \"Train Epoch: 4 [19200/60000 (32%)]\\tLoss: 0.401251\\n\",\n      \"Train Epoch: 4 [19840/60000 (33%)]\\tLoss: 0.341399\\n\",\n      \"Train Epoch: 4 [20480/60000 (34%)]\\tLoss: 0.131123\\n\",\n      \"Train Epoch: 4 [21120/60000 (35%)]\\tLoss: 0.173365\\n\",\n      \"Train Epoch: 4 [21760/60000 (36%)]\\tLoss: 0.195578\\n\",\n      \"Train Epoch: 4 [22400/60000 (37%)]\\tLoss: 0.139107\\n\",\n      \"Train Epoch: 4 [23040/60000 (38%)]\\tLoss: 0.204380\\n\",\n      \"Train Epoch: 4 [23680/60000 (39%)]\\tLoss: 0.287038\\n\",\n      \"Train Epoch: 4 [24320/60000 (41%)]\\tLoss: 0.249120\\n\",\n      \"Train Epoch: 4 [24960/60000 (42%)]\\tLoss: 0.162842\\n\",\n      \"Train Epoch: 4 [25600/60000 (43%)]\\tLoss: 0.116750\\n\",\n      \"Train Epoch: 4 [26240/60000 (44%)]\\tLoss: 0.344774\\n\",\n      \"Train Epoch: 4 [26880/60000 (45%)]\\tLoss: 0.271378\\n\",\n      \"Train Epoch: 4 [27520/60000 (46%)]\\tLoss: 0.259847\\n\",\n      \"Train Epoch: 4 [28160/60000 (47%)]\\tLoss: 0.307237\\n\",\n      \"Train Epoch: 4 [28800/60000 (48%)]\\tLoss: 0.171560\\n\",\n      \"Train Epoch: 4 [29440/60000 (49%)]\\tLoss: 0.262232\\n\",\n      \"Train Epoch: 4 [30080/60000 (50%)]\\tLoss: 0.412715\\n\",\n      \"Train Epoch: 4 [30720/60000 (51%)]\\tLoss: 0.147482\\n\",\n      \"Train Epoch: 4 [31360/60000 (52%)]\\tLoss: 0.284561\\n\",\n      \"Train Epoch: 4 [32000/60000 (53%)]\\tLoss: 0.148972\\n\",\n      \"Train Epoch: 4 [32640/60000 (54%)]\\tLoss: 0.449416\\n\",\n      \"Train Epoch: 4 [33280/60000 (55%)]\\tLoss: 0.285807\\n\",\n      \"Train Epoch: 4 [33920/60000 (57%)]\\tLoss: 0.532931\\n\",\n      \"Train Epoch: 4 [34560/60000 (58%)]\\tLoss: 0.331563\\n\",\n      \"Train Epoch: 4 [35200/60000 (59%)]\\tLoss: 0.316975\\n\",\n      \"Train Epoch: 4 [35840/60000 (60%)]\\tLoss: 0.259934\\n\",\n      \"Train Epoch: 4 [36480/60000 (61%)]\\tLoss: 0.149166\\n\",\n      \"Train Epoch: 4 [37120/60000 (62%)]\\tLoss: 0.387015\\n\",\n      \"Train Epoch: 4 [37760/60000 (63%)]\\tLoss: 0.320126\\n\",\n      \"Train Epoch: 4 [38400/60000 (64%)]\\tLoss: 0.379405\\n\",\n      \"Train Epoch: 4 [39040/60000 (65%)]\\tLoss: 0.229334\\n\",\n      \"Train Epoch: 4 [39680/60000 (66%)]\\tLoss: 0.376493\\n\",\n      \"Train Epoch: 4 [40320/60000 (67%)]\\tLoss: 0.133036\\n\",\n      \"Train Epoch: 4 [40960/60000 (68%)]\\tLoss: 0.313329\\n\",\n      \"Train Epoch: 4 [41600/60000 (69%)]\\tLoss: 0.571482\\n\",\n      \"Train Epoch: 4 [42240/60000 (70%)]\\tLoss: 0.276484\\n\",\n      \"Train Epoch: 4 [42880/60000 (71%)]\\tLoss: 0.239233\\n\",\n      \"Train Epoch: 4 [43520/60000 (72%)]\\tLoss: 0.204877\\n\",\n      \"Train Epoch: 4 [44160/60000 (74%)]\\tLoss: 0.339874\\n\",\n      \"Train Epoch: 4 [44800/60000 (75%)]\\tLoss: 0.268665\\n\",\n      \"Train Epoch: 4 [45440/60000 (76%)]\\tLoss: 0.367013\\n\",\n      \"Train Epoch: 4 [46080/60000 (77%)]\\tLoss: 0.190838\\n\",\n      \"Train Epoch: 4 [46720/60000 (78%)]\\tLoss: 0.258449\\n\",\n      \"Train Epoch: 4 [47360/60000 (79%)]\\tLoss: 0.382290\\n\",\n      \"Train Epoch: 4 [48000/60000 (80%)]\\tLoss: 0.075350\\n\",\n      \"Train Epoch: 4 [48640/60000 (81%)]\\tLoss: 0.333272\\n\",\n      \"Train Epoch: 4 [49280/60000 (82%)]\\tLoss: 0.186367\\n\",\n      \"Train Epoch: 4 [49920/60000 (83%)]\\tLoss: 0.172071\\n\",\n      \"Train Epoch: 4 [50560/60000 (84%)]\\tLoss: 0.331753\\n\",\n      \"Train Epoch: 4 [51200/60000 (85%)]\\tLoss: 0.212581\\n\",\n      \"Train Epoch: 4 [51840/60000 (86%)]\\tLoss: 0.311099\\n\",\n      \"Train Epoch: 4 [52480/60000 (87%)]\\tLoss: 0.238993\\n\",\n      \"Train Epoch: 4 [53120/60000 (88%)]\\tLoss: 0.213367\\n\",\n      \"Train Epoch: 4 [53760/60000 (90%)]\\tLoss: 0.249195\\n\",\n      \"Train Epoch: 4 [54400/60000 (91%)]\\tLoss: 0.150308\\n\",\n      \"Train Epoch: 4 [55040/60000 (92%)]\\tLoss: 0.257593\\n\",\n      \"Train Epoch: 4 [55680/60000 (93%)]\\tLoss: 0.301869\\n\",\n      \"Train Epoch: 4 [56320/60000 (94%)]\\tLoss: 0.292284\\n\",\n      \"Train Epoch: 4 [56960/60000 (95%)]\\tLoss: 0.163008\\n\",\n      \"Train Epoch: 4 [57600/60000 (96%)]\\tLoss: 0.173252\\n\",\n      \"Train Epoch: 4 [58240/60000 (97%)]\\tLoss: 0.258717\\n\",\n      \"Train Epoch: 4 [58880/60000 (98%)]\\tLoss: 0.159236\\n\",\n      \"Train Epoch: 4 [59520/60000 (99%)]\\tLoss: 0.250371\\n\",\n      \"\\n\",\n      \"Test set: Average loss: 0.0905, Accuracy: 9709/10000 (97%)\\n\",\n      \"\\n\",\n      \"Train Epoch: 5 [0/60000 (0%)]\\tLoss: 0.107032\\n\",\n      \"Train Epoch: 5 [640/60000 (1%)]\\tLoss: 0.113897\\n\",\n      \"Train Epoch: 5 [1280/60000 (2%)]\\tLoss: 0.160795\\n\",\n      \"Train Epoch: 5 [1920/60000 (3%)]\\tLoss: 0.287165\\n\",\n      \"Train Epoch: 5 [2560/60000 (4%)]\\tLoss: 0.260616\\n\",\n      \"Train Epoch: 5 [3200/60000 (5%)]\\tLoss: 0.137496\\n\",\n      \"Train Epoch: 5 [3840/60000 (6%)]\\tLoss: 0.174347\\n\",\n      \"Train Epoch: 5 [4480/60000 (7%)]\\tLoss: 0.161436\\n\",\n      \"Train Epoch: 5 [5120/60000 (9%)]\\tLoss: 0.366263\\n\",\n      \"Train Epoch: 5 [5760/60000 (10%)]\\tLoss: 0.312606\\n\",\n      \"Train Epoch: 5 [6400/60000 (11%)]\\tLoss: 0.253356\\n\",\n      \"Train Epoch: 5 [7040/60000 (12%)]\\tLoss: 0.259438\\n\",\n      \"Train Epoch: 5 [7680/60000 (13%)]\\tLoss: 0.217265\\n\",\n      \"Train Epoch: 5 [8320/60000 (14%)]\\tLoss: 0.153942\\n\",\n      \"Train Epoch: 5 [8960/60000 (15%)]\\tLoss: 0.241278\\n\",\n      \"Train Epoch: 5 [9600/60000 (16%)]\\tLoss: 0.219466\\n\",\n      \"Train Epoch: 5 [10240/60000 (17%)]\\tLoss: 0.342719\\n\",\n      \"Train Epoch: 5 [10880/60000 (18%)]\\tLoss: 0.093201\\n\",\n      \"Train Epoch: 5 [11520/60000 (19%)]\\tLoss: 0.281473\\n\",\n      \"Train Epoch: 5 [12160/60000 (20%)]\\tLoss: 0.168444\\n\",\n      \"Train Epoch: 5 [12800/60000 (21%)]\\tLoss: 0.234364\\n\",\n      \"Train Epoch: 5 [13440/60000 (22%)]\\tLoss: 0.540312\\n\",\n      \"Train Epoch: 5 [14080/60000 (23%)]\\tLoss: 0.325057\\n\",\n      \"Train Epoch: 5 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Epoch: 5 [24320/60000 (41%)]\\tLoss: 0.158186\\n\",\n      \"Train Epoch: 5 [24960/60000 (42%)]\\tLoss: 0.203720\\n\",\n      \"Train Epoch: 5 [25600/60000 (43%)]\\tLoss: 0.292472\\n\",\n      \"Train Epoch: 5 [26240/60000 (44%)]\\tLoss: 0.158577\\n\",\n      \"Train Epoch: 5 [26880/60000 (45%)]\\tLoss: 0.458952\\n\",\n      \"Train Epoch: 5 [27520/60000 (46%)]\\tLoss: 0.092585\\n\",\n      \"Train Epoch: 5 [28160/60000 (47%)]\\tLoss: 0.187624\\n\",\n      \"Train Epoch: 5 [28800/60000 (48%)]\\tLoss: 0.242264\\n\",\n      \"Train Epoch: 5 [29440/60000 (49%)]\\tLoss: 0.157994\\n\",\n      \"Train Epoch: 5 [30080/60000 (50%)]\\tLoss: 0.176017\\n\",\n      \"Train Epoch: 5 [30720/60000 (51%)]\\tLoss: 0.242022\\n\",\n      \"Train Epoch: 5 [31360/60000 (52%)]\\tLoss: 0.098211\\n\",\n      \"Train Epoch: 5 [32000/60000 (53%)]\\tLoss: 0.204082\\n\",\n      \"Train Epoch: 5 [32640/60000 (54%)]\\tLoss: 0.240008\\n\",\n      \"Train Epoch: 5 [33280/60000 (55%)]\\tLoss: 0.167538\\n\",\n      \"Train Epoch: 5 [33920/60000 (57%)]\\tLoss: 0.189121\\n\",\n      \"Train Epoch: 5 [34560/60000 (58%)]\\tLoss: 0.369302\\n\",\n      \"Train Epoch: 5 [35200/60000 (59%)]\\tLoss: 0.236134\\n\",\n      \"Train Epoch: 5 [35840/60000 (60%)]\\tLoss: 0.163128\\n\",\n      \"Train Epoch: 5 [36480/60000 (61%)]\\tLoss: 0.166319\\n\",\n      \"Train Epoch: 5 [37120/60000 (62%)]\\tLoss: 0.204159\\n\",\n      \"Train Epoch: 5 [37760/60000 (63%)]\\tLoss: 0.093605\\n\",\n      \"Train Epoch: 5 [38400/60000 (64%)]\\tLoss: 0.601231\\n\",\n      \"Train Epoch: 5 [39040/60000 (65%)]\\tLoss: 0.219544\\n\",\n      \"Train Epoch: 5 [39680/60000 (66%)]\\tLoss: 0.156221\\n\",\n      \"Train Epoch: 5 [40320/60000 (67%)]\\tLoss: 0.218163\\n\",\n      \"Train Epoch: 5 [40960/60000 (68%)]\\tLoss: 0.267369\\n\",\n      \"Train Epoch: 5 [41600/60000 (69%)]\\tLoss: 0.260841\\n\",\n      \"Train Epoch: 5 [42240/60000 (70%)]\\tLoss: 0.339091\\n\",\n      \"Train Epoch: 5 [42880/60000 (71%)]\\tLoss: 0.181196\\n\",\n      \"Train Epoch: 5 [43520/60000 (72%)]\\tLoss: 0.208809\\n\",\n      \"Train Epoch: 5 [44160/60000 (74%)]\\tLoss: 0.183667\\n\",\n      \"Train Epoch: 5 [44800/60000 (75%)]\\tLoss: 0.239543\\n\",\n      \"Train Epoch: 5 [45440/60000 (76%)]\\tLoss: 0.339730\\n\",\n      \"Train Epoch: 5 [46080/60000 (77%)]\\tLoss: 0.331925\\n\",\n      \"Train Epoch: 5 [46720/60000 (78%)]\\tLoss: 0.101478\\n\",\n      \"Train Epoch: 5 [47360/60000 (79%)]\\tLoss: 0.148440\\n\",\n      \"Train Epoch: 5 [48000/60000 (80%)]\\tLoss: 0.044304\\n\",\n      \"Train Epoch: 5 [48640/60000 (81%)]\\tLoss: 0.374366\\n\",\n      \"Train Epoch: 5 [49280/60000 (82%)]\\tLoss: 0.272847\\n\",\n      \"Train Epoch: 5 [49920/60000 (83%)]\\tLoss: 0.190093\\n\",\n      \"Train Epoch: 5 [50560/60000 (84%)]\\tLoss: 0.199625\\n\",\n      \"Train Epoch: 5 [51200/60000 (85%)]\\tLoss: 0.252107\\n\",\n      \"Train Epoch: 5 [51840/60000 (86%)]\\tLoss: 0.172530\\n\",\n      \"Train Epoch: 5 [52480/60000 (87%)]\\tLoss: 0.223062\\n\",\n      \"Train Epoch: 5 [53120/60000 (88%)]\\tLoss: 0.296443\\n\",\n      \"Train Epoch: 5 [53760/60000 (90%)]\\tLoss: 0.264518\\n\",\n      \"Train Epoch: 5 [54400/60000 (91%)]\\tLoss: 0.045535\\n\",\n      \"Train Epoch: 5 [55040/60000 (92%)]\\tLoss: 0.210670\\n\",\n      \"Train Epoch: 5 [55680/60000 (93%)]\\tLoss: 0.118962\\n\",\n      \"Train Epoch: 5 [56320/60000 (94%)]\\tLoss: 0.330683\\n\",\n      \"Train Epoch: 5 [56960/60000 (95%)]\\tLoss: 0.231080\\n\",\n      \"Train Epoch: 5 [57600/60000 (96%)]\\tLoss: 0.149765\\n\",\n      \"Train Epoch: 5 [58240/60000 (97%)]\\tLoss: 0.274601\\n\",\n      \"Train Epoch: 5 [58880/60000 (98%)]\\tLoss: 0.163322\\n\",\n      \"Train Epoch: 5 [59520/60000 (99%)]\\tLoss: 0.357964\\n\",\n      \"\\n\",\n      \"Test set: Average loss: 0.0765, Accuracy: 9756/10000 (98%)\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model = Net().to(device)\\n\",\n    \"dropper = nn.Dropout2d()\\n\",\n    \"dropper.train(True)\\n\",\n    \"\\n\",\n    \"optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)\\n\",\n    \"\\n\",\n    \"for epoch in range(1, args.epochs + 1):\\n\",\n    \"    train(args, model, device, train_loader, optimizer, epoch)\\n\",\n    \"    test(args, model, device, test_loader)\\n\",\n    \"torch.save(model.state_dict(), \\\"./mymodel\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Innvestigate\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import matplotlib.colors as mcolors\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"from innvestigator import InnvestigateModel\\n\",\n    \"from utils import Flatten\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = Net()\\n\",\n    \"model.load_state_dict(torch.load(\\\"./mymodel\\\"))\\n\",\n    \"# Convert to innvestigate model\\n\",\n    \"inn_model = InnvestigateModel(model, lrp_exponent=2,\\n\",\n    \"                              method=\\\"e-rule\\\",\\n\",\n    \"                              beta=.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"WARNING: LogSoftmax layer was turned into probabilities.\\n\",\n      \" \\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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E4xuZcw+LcQ6Ax8F8DMlgHfAW4neDFfSfBonpAIqQ8lvVpP2fcAh0nq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\\nKC7AHcdxMooLcMdxnIzy/yrM7F3iT8vDAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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/u+BjhE0vqVBXkE+6OoMYK9pK2BcUC3gzt34nhgODCT1Aw0mXRNAeAy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\\nNPcAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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C2pS7zvSfANrwDeJgzsDTezjwEkbQ8MAXIG+W2EM4COwEzCD8N4gs8f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\\nAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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t/nko5Gy1akiYilkhaSBqcuZ2A/0uf5cmnZLyQtI32PT4geRtxxALdm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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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RExvcVZGjC3gVtpRUTDtcuI+FQr82JlUKmBdw4HcDMbJhzAzRoSEfsN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\\nGtYknLkAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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iU8kNPK+zpJawmGyETrIGSau1CcanK0pO0kDQDOByZHd8vzQD9Jn5bU\\nTFC06cDCC4FRHYz+KIy83k/SDnHUyOaEwL4/M7MlqWO6FM08jmC5H7hI0mBJvRSivCfnvwX4pqSR\\nkjYC1vOJdpNJwKnAx4FbU/m3AP8taSOF6O3fqPB5c4YBq8tcWGxmY1PLVcXr7BqStie4Vb6ayj4q\\nulb2jssxxY5N4wrcqSY3EDr/FgD9gG8CmNlbwNeBXwGvEizytKsjUVJvSHq6nbp/AxwU/d3E+icR\\nopk/SYhmflZSWNIHCP7KDoP8tsOxQB/Ca/ISwmvxB+K+q4H7CNb908Dt6QMlXSHpig7qX1owDvy/\\nUvtuJDx8HjCztAV3HsFt8hLhAdPGnyrpHknf7UQbc44R+qHLWUryKm3fuEbGvKJlFD7o2hB4I26P\\nBO4AjjWzF1qkC6OhMLNlhP/y7h0J4kGNnaog6SHgt2b2qyqe43+ARWZ2SRllLwJeMLNfVEsep7EZ\\nO3YXmz79wbLKShvNMLOxxfepN+Et8pMERf0UcKSZzUyV+Q9gRzM7WdIE4PNmdpikIYQO8/PM7PaC\\nOoeY2eL4VnojMNXMSj743QfuZBYzK9u6NLPTqimLkwUSC7ybtQSf9imEt64m4FozmynpfGC6mU0B\\nrgFukDQbeJPWEUunEIaini3p7Jh3AOEt9L6ovJuAqYQ3u5K4Anccp4dQGQUOYGZ3E/ph0nlnp9Kr\\ngS8WOW4iMLGdaj/cWTlcgTtVwcz2qbcMjtOWyinwRsEVuOM4PQQjjAbND67AnU6zf68vZrbn+0/r\\nblWp/QOlzLZthVnJtjlugTuO42QUV+CO4zgZxhW44zhOBnEL3HEcJ6OsI34mnxtcgTuO00NwC9xx\\nnApRbGL0tal9a4vsd7qLK3DHcbpAZyJZuPKuBm6BO47jZBRX4I5TVXpvNhKAf54W1n2WhhmP//3T\\nTzNtQYhXsPyZTQAYfeEs1i59qw5Sdkyhtb2WVqs6iTychDO6htYYaX+I6zerJ1oPxhW44zhORkkC\\nOuQHV+BOw/Dmlz7Kj74XAp98sn+wVx9bvQ6Aaxd9nHfeawZgxvEXA/CJnY5lyCVjAOj9vzNqLW6n\\n6EdrxIr+1ickjn4XgCd+BwPjvrkfD+tTHoHrYl5zXL9XfTFzjlvgjlMxmoYGV8jsnwd3ydS9fsIR\\ns44F4Ec/2BCAPv96DYA1CxYyIoZ+3P/IbwEw48Ir2PKwkwDY5n9rJ3dHpEeQnBDXlz1DiGoJDNS7\\nbco3p9LXPRLWi+6HFw8I6Wlx3zq8c7N7uAJ3HMfJKEbeHoGuwJ3a0it07/X6tw/ygSteAeA3I0KU\\ns93u/U+2+8FCANbM+UtYF6li6datoVw3u6e+E/C1N5Z7RSJijGB56sYhACi0WtzrCtYAWyWJ/Y1H\\nCW1LqupF3tRPrXEL3HEcJ8O4AneczhMt7wXf2AOAZ77zC154bzkAn/r+twHY5qrHS95eC7+5JwAz\\nTgoxjLe89+ts+9BzQO0t02KW94C4XmBLgCEAjFawopcWOaZXKj0krp+wkHunWt8sks7LznwI5BTD\\n50JxnM7Tq4lVnw3h/h759kUA7PTj0xg5JXRQDn3x8Q6rUO/erNknjPm+5q2tAdj2W8+z9u23qyFx\\nu5RSogtisJc9tRHPxLxCd0kx1gFfSzaWh5JH0VbBg7tPuo+7UBzHcTKKK3DH6TSzf7ob93wuWN6H\\nHv0NAN7/0J87dSu9/L3d+cdHQmfn2LOCvbrJ2x1b7tVmP+D2h0N6cN+w7kUY9w3FrebEJZJY52OA\\n71j4XUbr5y11JLjlXUlcgTuO42QQt8Adp2zW7rMrAI9+/kI+f+bpAAx+6IlO1aHe4S96wdHXM+Gl\\nfQHY9Ka/AaX9ytUmsYpvtysYoZOBVou6mfW/muxVJJ3I//RCYGKwvN8o2OdUEu/EdJyyWfm+8Mn4\\nA6u2YPCkzilu++hOAHz2mgfCeuBKJl71QQA2WlF/18nAltTZLSohUeBNtHZ2JvvWFslb8a2YGGZs\\ndlYYddInVT5fqqYRcAvccRwnw7gCd5yyWDmscCBcW2yvnQFoWh7G32nBG6xduCjk/SWM71783iAA\\nXnhvOZvcGgbnNYJ7YeOW1FCaCTIncr1Haeu5ZSjirq15yyoom9MeboE7juNkFFfgjlM2w2asAGB0\\n8yIOnBks6XsXbt+y/4atLwfgI7efBsCHLnsHogW+9HPBOj9n0ysA2PHiMxi+8s+1EbwM5rakZrLA\\nvh6St/4yrB8FfhbmITxcYVLYu1LHtsw+eIwBsK/U4vtO5ilshLeM/OEK3HEcJ6MY8E69hagorsCd\\nqqE/B5/1d775NVZ9bQkA+w0Pvu2bnh3L0d8LluvWj4cRKmtpHTb48TNC3qK1wYrf/MaXG8p2Sqzo\\nIRLzY7p/mNKFBZfC6Euva1OuiVareulH29Y1jfU/ufePd6qBW+CO02n63fUk/aIPYUYcBb01Txct\\nq+1ChJ0L3ncTANs//hUARs6bWWUpu87IJPGT1rykozLtCmlRyn8OrpPz4oRVg2g73NCpFq7AHcdx\\nMoorcMepKk2Xto0yv+Gtg+okSfkUWs1FrW7gkoJyl7ZzvFMt8qfASw/UdRzHyRVrylxKI2mcpOck\\nzZZ0ZpH9fSXdHPdPkzQq5u8vaYakZ+N639QxH475syVdKqnDcFNugTsNQ9NGG3HC8McAmLcmBHsY\\n8vTrQONYqcXkKJzbJP3ZfFMq76QpceOn4b5Mf4LvwwZrQWXmQpHUBFwO7A/MA56SNMXMZqWKnQgs\\nMbMxkiYAFwCHE4LsHWxmr0naAbgPGBGP+SVwEqFf+25gHHBPKVlcgTsNw5rttuDQDR4EYLenvwTA\\nxs8/X0+R2mVtO+lSeRwcxoYfp7YjVDzafK2omAtld2C2mb0IIOkmYDyQVuDjgXNjejJwmSSZ2V9S\\nZWYC/SX1JXzcO9jMnoh1/gY4hA4UuLtQHMfpISQKvCwXylBJ01PLV1IVjSD9LVewwkfQlpYyZrYG\\neAvYpKDMocDTZvZOLD+vgzrXwy1wp2H41zF9WtIDf7FhHSWpPB8D2DFY3pNjnse4rAdlW+CLzWxs\\ntaSQtD3BrXJAd+pxBe44Tg+hYi6UV4HNUtsjY16xMvMk9QY2JE73LmkkcAdwrJm9kCo/MnV8sTrX\\nwxW40zD87JO/bYlUP+DlEKw4L77h8cALf2+bl5e2ZYeKBXR4Ctha0pYEJTsBOLKgzBTgOOBx4AvA\\nA2ZmkoYAfwTONLPHksJmNl/S25I+QujEPBb4eUeCuAJ3GoqJ8w8EYO3M5+osSWVZSrjLnXpSGQvc\\nzNZIOoUwgqQJuNbMZko6H5huZlOAa4AbJM0G3qT18p9CCIN6tqSzY94BZrYI+DpwPdCf0HlZsgMT\\nXIE7jtOjqMx7j5ndTRjql847O5VeDXyxyHETgYnt1Dkd2KEzcrgCdxqGP7yxK+M3CaOsfsmYOktT\\nWe7AgzbUn/x9iekK3HGcHoIrcMepGvM+sjx3lnfCrI6LOFXHFbjjOE5GMSo0CqVhcAXuOE4PwS1w\\nx+FP627tcJa0rLLCLLdtc1yBO47jZBfL1+dTrsAdx+k55GzeXlfgjuP0DIzczV/gCtxxnJ6BAe/V\\nW4jK4grccZyegVvgjuM4GcZ94I7jOBnELXDHcZwM4wrccRwng3gnpuM4TkZxF4rjOE6G8U5Mx3Gc\\nDOIWuOM4ToZxC9xxHCeDuAXuOI6TUXI4CqVXvQWoFpJMUj7jc5Hv9uW5bZDv9jV02xILvJwlIzSU\\nApc0R9IqSctTy2U1OO8oSQ9KWinpn5L2q9J56tW+70t6VtIaSedW6Rw1b5ukYZJulPSapLckPSZp\\njyqdq17X7kFJr0t6W9IzksZX4Rx1aVvq/J+Iin9i1U+WMwXeiC6Ug81sao3PeSPwOHBQXCZL2trM\\nXq/CuerRvtnAGcDJVT5Prdu2AfAU8F/AIuBE4I+SRpnZ8iqcrx7X7lRglpmtiQ+nqZK2MbP5FT5P\\nPdqGpGbgZ8C0qp/MyF0nZkNZ4O0hqa+kpZJ2SOVtGq2GYXH725LmR2vsS52oextgV+AcM1tlZrcB\\nzwKHVrodJWSoWvsAzOzXZnYPsKzCondINdtmZi+a2U/NbL6ZrTWzq4A+wAcr35Li1ODa/c3Mkjhg\\nBjQDm1WsASWodtsipwH3A/+skNilyZkFngkFbmbvALcDR6SyDwMeNrNFksYBpwP7A1sDnXGBbA+8\\naGZp5fZMzK8JVW5fXall2yTtTFDgs7suceeoRfsk3SVpNcFKfQiY3l25y6HabZO0BfAl4PzKSNwB\\nSSdmOUtGaEQF/vv41E+Wk2L+JGBCqtyRMQ/Cn+o6M/u7ma0Azu3E+TYA3irIewsY1HnRy6LW7asl\\ndWubpMHADcB5ZlZ4PStFXdpnZp8h/B8PAu43s2o4AurRtkuBs6rk7lqfHHZiNqIP/JB2fHEPAgOi\\nH3AhsDNwR9w3HJiRKvtyJ863HBhckDeY6rkbat2+WlKXtknqD9wJPGFmP+zs8Z2gbtfOzN4D7pF0\\nqqTZZjalK/WUoKZtk3QwMMjMbu6ivF0jZz7wRlTgRTGztZJuIbzOLQTuSrk95tPWL7h5J6qeCWwl\\naVCqvp1otTJqQhXbV3eq2TZJfYHfA/OAr1ZA3E5T42vXGxjdzTrKpopt+yQwVtKCuL0hsFbSjmZW\\n8ZE2QC4/5GlEF0opJgGHA0fRVsHeAhwvaTtJA4Bzyq3QzJ4H/gqcI6mfpM8B/wbcVjmxy6bi7YPQ\\n0y+pH+F6947tbKqU0GVS8bbFEQyTgVXAcVVyLZRLNdr3IUkHSuofr+HRwMeBhyspeBlU4395FrAN\\nwaLfGZgCXA2cUBGJ2yNnLhTMrGEWYA7hZlyeWu4oKDMbeBPoU5B/JrAAeI3QMWLAmLjvu8A9Jc47\\nitA5tAp4DtgvZ+27PpZPL8dnvW3AJ2LZlQXn3TsP1w7YltBxuQxYShgy+bk8tK2d/+jESrctvXx4\\nDGZ/LG8BpldTlkotij+e4zhOrhk7Rjb9ovLK6hBmmNnY6krUfTLjA3ccx+k2WXKPlIErcMdxegbe\\niek4jpNh1pW5dICkcZKekzRb0plF9veVdHPcP03SqJi/icL8NuvNNyPpoVjnX+MyrCM53AJ3HKdn\\nUCELPI7gupzwBeo84ClJU8xsVqrYicASMxsjaQJwAWEkz2rCCJwd4lLIUWZW9pe2dVPgA6XM9p6u\\nMFOp/XluG+S7fXluG+S/fSWpnAtld2C2mb0IIOkmYDyQVuDjaf0ydTJwmSRZ+GL1UVVoyl13oTiO\\n0zOo3FwoI4C5qe15Ma9oGQuTkb0FbFKGlNdF98lZkjp8YLkCdxyn51C+D3yopOmp5Ss1kO4oM9sR\\n2Dsux3R0gPvAHcfpGXTOhbK4xDjwV2k7hcDImFeszDxJvQlTBbxRUjyzV+N6maRJBFfNb0od4xa4\\n4zg9h8p8Sv8UsLWkLSX1IczWWDi52BTguJj+AvCAlfhqUlJvSUNjuhn4DPD3jgRxC9xxnJ5BhSLy\\nWIiOdApwH9AEXGtmMyWdT/gEfwpwDXCDpGQKgpYpeSXNIcx42kfSIcABhJkc74vKuwmYSpgbpiSu\\nwB3H6RlUMCq9md0N3F2Qd3YqvRr4YjvHjmqn2g93Vg5X4I7j9Bxy9iWmK3DHcXoGOfyU3hW44zhV\\npZlW13Pd9adH5HEcx8kgboE7jtNdklBIOdMlNMd1v7hOjN13U2UGxvVq6tB+V+CO43SXnOkQICjt\\npF2looGvKFK+ZlRwFEqj4ArccZyeg/vAHccpl666S5q6cEwtSdwlfeJ6RXsFSxy/unLilIe7UBzH\\ncTKMK3DHcUrRROskQ111uTaynumXSnfW8k6OXVkhWTpFhT6lbyRcgTtOhUi7SxpZAXeVRPlWwvVR\\nt98nZxfGFbjjOD0DH4XiOE4heR3XnZB0WHbH8k7Gf3fW5VJRvBPTcRwnw7gP3HGchGZy91behn50\\n3+ddiToqglvgjuNUmrw+BJpT64ZQ4OAK3HEcJ5N4J6bjOGma6LpOaEqtG1WvdEeuhnM3uwvFcRwn\\nwzTcU6V7uAJ3nG7SXCQvsa5L+X4rMTyv2vTqIL+UhZ60r9TshDXFLXDHcZyM4p/SO46TZjWt1naa\\ncizTYsc1Gul25GKqgMwKXhxX4I7TTTqrEwbF9VuVFqTKlNvOhnUN+SgUx3GcjOI+cMdxukphZ2fO\\ndEkLDe1mztmP7grccZyegXdiOo7TVZL5tN+pqxTVIxOzMja0cJ3HFbjj1IhEgb9ZVyl6MG6BO47j\\nZBQD3q23EJXFFbjj1IgkDmR4JbYjAAAO00lEQVTO3uKzhVvgjuM4GcSHETqO0xWayJ3uyB6uwB3H\\n6SoN92VihcmEbnQXiuM4TgbxT+kdx+kKmbBO804OXSjtTffrOI6TP9aWuXSApHGSnpM0W9KZRfb3\\nlXRz3D9N0qiYv4mkByUtl3RZwTEflvRsPOZSSepIDlfgjuP0DJIPecpZSiCpCbgcOBDYDjhC0nYF\\nxU4ElpjZGOBi4IKYvxo4Czi9SNW/BE4Cto7LuI6a5ArccZyeQ2Us8N2B2Wb2opm9C9wEjC8oMx74\\ndUxPBj4pSWa2wswepaBPW9IHgMFm9oSZGfAb4JCOBHEF7jhOzyDxgZenwIdKmp5avpKqaQQwN7U9\\nL+ZRrIyZrSFM/75JCelGxHpK1bkedevEXGHWoX8nq+S5bZDv9uW5bZD/9pWkc6NQFpvZ2OoJUxnc\\nAnccp+dQAR848CqwWWp7ZMwrWkZSb2BD4I0O6hzZQZ3r4QrccZyeQedcKKV4Ctha0paS+gATgCkF\\nZaYAx8X0F4AHom+7uGhm84G3JX0kjj45FvhDR4L4OHDHcXoOFRgHbmZrJJ0C3EeYJeFaM5sp6Xxg\\nuplNAa4BbpA0mzCD8ITkeElzgMFAH0mHAAeY2Szg68D1QH/gnriURCUeCo7jOLlhbC/Z9L7lldVq\\nZmTBB+4WuOM4PQOfD9xxHCfD+GRWjuM42SRnU6G4Anccp2eQw7msXIE7jtNzyJkHxRW44zg9A7fA\\nHcdxMkoO4zm4Anccp2fgFrjjOE6GcR+44zhOBsmjBZ7byawkmaQx9ZajWuS5fXluG+S7fY3etgpF\\nVGsYGkqBS5ojaVWMF5csl3V8ZMXPe3+NzlOT9sVznyrpJUkrJP1D0jYVrr/mbZO0ecH5lkcFcloV\\nzlWv/+bOkv5P0luS5kk6qwrnqFfb9pT0pKRlkv4m6WPVPF+FIqo1FI3oQjnYzKbm+Lw1b5+kLxNi\\n9H0a+AewFbCkCqeqadvM7BVgg2Rb0pbAbOC2Kp2yHv/NScAdwD7AKOBRSc/EGe8qSU3bJmlj4E7g\\nZOB24AjgTklbmVk1/pu5HIXSUBZ4e8QIz0sl7ZDK2zRaDcPi9rclzZf0mqQv1U/azlPN9knqBZwD\\n/KeZzbLAC2b2ZuVbUvT8tbx2xwKPmNmcbopdNjVo3yjgd2a21sxeAB4Ftq9YA0pQ5bbtCSwws1tj\\n234LvA58vrKtaIu7UOqAmb1D61M64TDgYTNbJGkcIcrz/oRozvt14TS/k/S6pPsl7dRtoTtBlds3\\nMi47SJob3SjnRcVedWp07ZBaJsH/dUdlK0kN2ncJcKykZkkfBD4K1MRSrkHbCsO7CdihWMFKULl4\\nDg2EmTXMAswBlgNLU8tJcd9+wAupso8Bx8b0tcCPUvu2IVyvMWWedy/CJOoDgP8GFgBD8tA+gqVj\\nwB+BIQSL7vnkvFluW8H5947n3yBn/809CW6hNfG48/LQNkKA36WEh0MzIXrNOuDKalw/M2N7sJll\\nLoTADFWRo5JLI/rAD7HivrgHgQGS9gAWAjsTfIMAw4EZqbIvd+aEZvZYavOHko4jKIQ7O1NPmdS6\\nfavi+sdmthRYKulK4CDg6k5J3jE1v3YpjgNuM7PlXTy+HGravugnvhc4heALfz8wWdJCM/tFF+Qv\\nRU3bZmZvSBoPXAhcTohuM5W2kdkrSh6HETaiAi+Kma2VdAvhib0QuMvMlsXd82kbZHTz7p6O9V/v\\nqkoV2/ccYRr7dOilmoZhqva1k9Qf+CLwue7K2hWq2L6tgLVm9pu4PU/STYSHb6UVeFGqee3M7GFg\\nN2gJ/PsicFG3hW7vfHgnZr2ZBBwOHBXTCbcAx0vaTtIAQqddWcShaHtJ6iOpn6RvA0MJr4q1puLt\\nM7OVwM3AGZIGSRoJfAW4q3Jil0XF25bic4RRNQ92W8quU432PU9w7x8pqZek98dz/K1SQpdJVa6d\\npF2ib38wwRKfa2b3VUroQtwHXuWF4ItbRfDHJcsdBWWSIKF9CvLPJPiuXwO+RMoXB3wXuKedc25P\\nuCFWAG8A/wuMzUv74v7BwE3AMmAucDYxHmrW2xbL3Ad8P2//zbh/X0IU9LdiHVcDA3LSthtju94i\\nGBnDqnkNPwT2RJkLGfGBe1Bjx3F6BB+S7Noyy+6FBzV2HMdpKDLlHikDV+CO4/QIkk/p84QrcMdx\\negRGGI6VJ1yBO47TY3AL3HEcJ4P4hzwVZKCU2eEvK8xKfuST57ZBvtuX57ZB/tvXEW6BO47jZBC3\\nwB3HcTKMK3DHcZwMkse5UFyBO47TI3AXiuM4TobxTkzHcZwyaCqStza1r9bWsFvgjuM47VBMYbdH\\nPRRpHj+lz9p84I7jOF0i+ZS+nKUjJI2T9Jyk2ZLOLLK/r6Sb4/5pkkal9v13zH9O0qdS+XMkPSvp\\nr5Kml9Mmt8CdhiQJ87I0rvckhBaCMPk0wOqaSuSkKbS204EQdovr5DpdQ5j4G+APcf1m9UQrSSUs\\ncElNhDBw+xNCwD0laYqZzUoVOxFYYmZjJE0ALgAOl7QdMIEQh2A4MFXSNmaW/Hz/bmaLy5XFLXDH\\ncXoEFYzIszsw28xeNLN3CcFSxheUGQ/8OqYnA5+UpJh/k5m9Y2YvEQJl7N7VNuXSAk+sg+a4TsZ+\\nri1SJm+dGlmmH3B9TB98aFj/321hfRfwr7jvm3F9IyG8kFN/+tEajbi/9QmJo4Mz4onfwcC4b+7H\\nw/qUR+C6mFd4n1aTTtzvQwvcGFeZ2VUxPYK2f715wB4Fx7eUMbM1kt4CNon5TxQcOyKmDbhfYbqD\\nK1Pna5fcKfB+tP4RSr1i17M33GlL8s9/YBeY9JeQHnhb2zLpV/Yk6u0VwFkx/Xpc+7WsPul75oS4\\nvuwZ4MCQHqi2XuTmVPq6R8J60f3w4gEhPS3uW0d1r18nOzEX1yEiz8fM7FVJw4A/SfqnmT1S6gB3\\noTiO02OokAvlVVq7aQBGxryiZST1BjYkxNxt91gzS9aLgDsow7WSeQu80F3S2Y6tZtxqqxc/ieuv\\nRzvnoOnwcMwr1kmW8P64Phw4qVrCOUD7Y7lXJKZf7G47dWP4VcxK7sV1BWuArZLE/sajhMkFk6p6\\nUX0LvEJumqeArSVtSVC+E4AjC8pMAY4DHge+ADxgZiZpCjBJ0k8JnZhbA09KGgj0MrNlMX0AcH5H\\ngmRegTuO45RDpT7kiT7tU4D7CM+4a81spqTzCdHspxAG39wgaTZh0M2EeOxMSbcAs4A1wH+Y2VpJ\\n7wPuCP2c9AYmmdm9HclSt6j0lZiXuF8q3VnLOzn2PTp/UX1O6a63L7HOlm4A7BjSwx4P63cp7aNM\\nrtmincL66GfCe2Zn8GtXXvuKWd4D4nqBLQGGADA6KByW0mrdFvPLDonrORb23ql1HBXzyu2P6u58\\n4CMk+2qZZc/xqPTVI7mRKzEO2N0ntWVJXG+2HN6MirucL/jWAjslGyvC6o4ix/r17B6lrsWCd8J6\\nT23EMzGv0F1SjHXA15KN5aHkUayv6Kt97fxTesdxnIziCrzOdLWjMk0lrXenPEYBT8b0BnHdROnJ\\njkiVg9Bx+adPh/SIP7bdV+w4pzLsB9wee5YH9w3rXrTeR8V+98SVktyvY4Dv2DcAGK2ft9SRUMtr\\nl7e5UDKlwB3HcbqKB3SoI4OAZd2sI/2Rj1M7Zm4Jm7wU0onV3Ivi1lB7Pu0XdoFXo+W9rGCfU3mS\\n3/Z2u4IROhlotaibWf8+6lUknVzfpxcCE4Pl/UbBvlriLpQ60p0LnvzxBlC/SXR6IokynvlS8REK\\nhTd6Oi8p/1iy4+kT2EXh4+vmVFl/IFeHgS2ps1vcjcnvnnZ/JfvWFslb8a2YGGZsdlYYQNInVb4e\\nbkxX4I7jOBkkj/OBZ0aBlzNHr9NYJB1dW7H+kLFy571Y0JLqt57F5vNAVI+NW1JDaWYR0Kr83qO0\\n9dziBtu1Na+77s9K4Ra44zhOBvFOzDqS7txKLIFerO93K0ZiCbr/u7bE723oPxaWxqnun10Z1lsA\\ng/cL6T2nhvUzqWMTf+u4yWG9s365ns88b6/DjUTrXKkzWWBfD8lbfxnWjwI/C/MQHh77Je5KHdsy\\n++Ax4aPPfaUW33fyJu2dmJUhMwrccRynu+TtoZ8ZBb6a9YeYraP0K1FTwdqpLcnvPnA6nB7TH4zr\\nK4HrprYtl75OLaNPBofVv/AgHLUksaKHSMyP6f7fDusFl8LoS9uOCGqiVTku/Wjbuqax/if39Qpq\\nnLf/TmYUOHT+x09cJ43SgdJTaQIuLrNsco23t2EAfEehA83H8NePkUniJ615ycM0bdG23J9/Dq6T\\n8+JEV4NoO9ywntT7/JUmUwrccRynq/gwwozgM9Rli/T1+V5LKrw/XRm38nbjZYnC+6eo1Q1cUlDu\\n0naOrxdG/oYj51KBO47jFOIWeEZIhps1ypO/p5KeqL+9fYV89/iY2PcVoNXv7R3RtaHYdSkcvpn+\\nbD7dsXzSlLjx0+D7Tn+C3yiKM286IZcK3DsvG4uObpo2o0vizP/7xlD1yegF78CsLWvbSZfK4+Aw\\nNvy4gjlrqh1tvlzcAnccx8kwjfAgqSS5VOB5e8rmneSm2gN4N1re02Je+lU9bzdfnvgYwI7B8o4f\\nzzac28s/pXccx8ko/iFPBmjGZ6nLKqez/g2Wtxsur4wHXvh727xGvHaNKFN3yJ0CB1hZbwGcLrEU\\n+HS9hXC6xFJgQr2F6ADvxHQcx8kwboE3OI0yZMnpPBcCQ+othNMl7qDxh+26Be44jpNR/FP6DODW\\nd3Z5rt4COF1mVr0FKBO3wB3HcTKIDyN0HMfJKK7AK8gKM9Xr3NUmz22DfLcvz22D/LevI9yF4jiO\\nk0HcAnccx8koPheK4zhOhnEL3HEcJ4P4hzyO4zgZxi1wx3GcDOKdmI7jOBnFOzEdx3EyjPvAHcdx\\nMsg6uG8FDC2z+OKqClMhZGb1lsFxHMfpAh59zHEcJ6O4Anccx8korsAdx3Eyiitwx3GcjOIK3HEc\\nJ6O4Anccx8korsAdx3Eyiitwx3GcjOIK3HEcJ6O4Anccx8korsAdx3Eyiitwx3GcjOIK3HEcJ6P8\\nf19TdavGMUy+AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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iqse5g04voYYEae3wFA0suBiUDNgX77cAIwAniI1H1zDanPH+CinN/v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\\nVlIO4GZmJeUAbmZWUv8fhfF4WAgQ1hAAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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b2Y59vAN4BfEcwru5Hy4C5pV2B5HE64LhwJdAXmxPwnE2z+ANcCUwja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\\nZBQX4I7jOBnFBbjjOE5GcQHuOI6TUf4fkgOYKoJvJ6wAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Txe51QNDuDWTBeSbv7dD4wjj4QeESuA/wC+BSwilcjLVR1FkHpY0h/7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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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KXAhGhueQvoKukbkjoRRn+kHQsvAPpJauj5zPe8vrekHRS8om9KMCc8\\nZWZLUtc0y5t59An5KHBVHMnSQcFLe1L+XcAPJPWVtDF5I1lKwDjgXIJ39rQT4LuAn0naWFJfggNg\\np14MWFXkxiIzG5LabiqcZ/OQtBPBrPLdVPSJ0bSyX9xOKnRtGhfgTjm5ndD5Nx/oSvSEHoXq2cAt\\nwByCRp4elZIIqQ8lvVJP3mOBwyR1i8f9gb8T7NxvEDTm45PEkr4ALDOzF5vZlhFAZ8Jn8seEz+It\\n47mbCcMQXwVeAe5JXyjpBkk3NJL/4rxx4D9OnRtPePk8YWZpDe4SgtnkXcILZj17qqSHJf1PE9pY\\n4RihH7qYrUHmsP4XV98YVzCNwoSujYAP43Ff4F5ghJm9s652YTQU0eQ3jmCqaRB3auyUBUlPAX82\\ns1vKWMYvgQ/M7LdFpL0bGG1mD5WrPk77ZsiQ3W3y5CeLSitt/LKZDSl8Th0JX5FfJwjql4ATzGxK\\nKs33gV3M7CxJw4FvmdmxknoROswvMbN78vLsZWaL4lfpeOBxM2vwxe82cCezmFnR2qWZHV3OujhZ\\nINHAW5hLsGmPJHx11QC3mtkUSZcCk81sIjAauF3SdOAjciOWRhKGol4k6aIYdzDhK/SRKLxrgMcJ\\nX3YN4gLccZwqoTQCHCB+yT2UF3dRan8V8O0C111OakmHPPZsaj1cgDtlwcz2b+s6OM76lE6Atxdc\\ngDuOUyUYoW+7cnAB7jSZgzp8O7M934+t/asaOt9dymzblps12DbHNXDHcZyM4gLccRwnw7gAdxzH\\nySCugTuO42SUtcRp8hWDC3DHcaoE18Adxykj6cXS8xcqqgFq8+LWFohzGqKyBLgvZuVUFLZPfQ58\\n2p6aIraEQn/MVQSBvZawLu9qXHg3jZItZtVucA3ccZwqwU0ojlMytPtOALz3zY0A6Pmuscm9bwCw\\ndunSvMSiZqOeAKz84iAAZh4pzt73H+slO77n9Yz/ZH0t/O73d2ejw6aXvP7F0D2Gq8hpy51imHZz\\nn5xLtPBvAbeujgczYjjI4PAwV+exB0PUpYT1a51icAHuOI6TURKHDpWDC3CnVem4RfB5PO2qrfjd\\n3sE378Hdlq87/+CFQRu/ad99QsSaoDG9eXU/3j4wLC2+lifWpV+yNvwhR75/BAD/t6SuV7Elk3qz\\nEa2ngfcgeq4g+NWC4G0isWsfHMNE2z4cGGcPxKNvxPDB3P71cYZ8f7Eyat7JLzatdNWuAlwDd5xm\\n0aFH8D987rNB+H612wq+ODl4jDpndhDaG2/zMQ/seisAE+79DIAVa4LB4bl+17LvaycD0P3XIX2n\\nj1ai1cH4UDv1rXrL3oZ/lbQt9RFfOfwI+F3cT7wMDwTu3znsLwpWIrol7p0XzqNWwblPzwL5do1h\\nusNybYE4pzFcgDuO42QUo9JeeS7AnVbhw6OD+vm1bk8BcMnCPeg97E0AeqfSDT88uII85JdPAzD6\\n1aDXjvjfc+j5z/9bL8+1tD015DolJ8bwGuD5vHRzge5R8066XcdG75a3act1eaS17Xwtu1Cnp9MU\\nXAN3HMfJMC7AHafJ7HfOpPWOXzx7D8SrddJ1fSA4jX/2mdDZuQPBaXftJ5+UuYbN58NEfT4zBL+4\\nPtdBmXRcriUXdzDr04mcdr02FVeTl649fHFkG18LxXGaxRZdlqx3PG/f7mzVQN9iexbYaWqBx+J4\\n7YOuuw+A/tcfyay8dJ2oawpJz7ZM9pOh324iKQduQnEcx8koLsAdp1mMve0QAH74wzDcb/K5v2PP\\nDucC0PeqyQDY6s/apnItoAY4O+6/zTAAXrcJ7KhjgNB5mU/+Oie+IFVr4gLccRwng7gG7jjNos81\\noXNyh13OAODtr9/Cq+dcB8CgnU4L5y5ZTO30d9umgi1gQQxvVpgxeYZ9hTcfD3H/fWAIr6Nup6TT\\n2ngnpuM0C4tT4geeEkaeDDl7JEefGWZlvnlAmCK/+mu17PTg9wH4/LWhE7N2SvucLJ4I41py48B/\\nEsPf6hmmxMgrLUztv/L29zl9RIgbn5dHB+p2YjrlwDVwx3GcDOMC3HGaz9rQXdf7un/x3DO7AbDH\\n0AMAGHX6n3nr8BsAeO3gkO6CEd8FoEPeLMy2Jt3pmGjNiUY9C+geIzfX+wDcD9ximwBwy30fAdD9\\nqLr5pjV7p9S4Bu44jpNRXIA7TslY+1pYC2Wr18Lx6AcP5eejOgPwxj63AfCrsTcC8L1LzmXjMfkr\\njLQPCjloSDTpj2L4ReAyhaMfmwGwfGro9NxiMKyI6dJDDF0LLzUuwB3HcTKKAZ+2dSVKigtwp+x0\\n6NqVtasaH75VO2Ua/U7eAIB9jgmjUZ654loAnvvFdey4a4gb+NM48WdN+9Cm8m3gHai7kmANMCru\\nXxWHG865LBzPtwc4VYcDcE8qDx+ZUmpcA3ecJrPgrm3Z/JvFDQdcuyIYE3qNDeaSL3cOvm3O++kd\\nvHns9QDstvAcAPr+snUcNTREf3IuKwuZPAqN/U686Wx9YQhn9TicW6NZZWoU7m9T1zTjJpWW4gLc\\ncRwno1SeAM9flsFxSs4vB99HzU47ULPTDk2+dtNbnmfTW57nuv89lgW1K1lQu5KVO65i5Y7tY0bd\\ndwrE1RbYCrEibr1+mIsbG7fEocNa1jenOC0hEeDFbNnAnwvHcaqI0ghwSUMlTZM0XdIFBc53kXRn\\nPD9JUr8Yf5CklyW9HsMDUtfsGeOnS7pWiva0BnATilN2hm7wKe/8NdirJw7etFl5dJ8wiUOOD5N6\\n7v9KsIX/mC+VpoItYC5wfNwf31DCRrD4Xx0UbeFIJGszuiOHUlGatVAk1QDXAwcBs4GXJE00s6mp\\nZKcBH5vZAEnDgSuB44BFwBFmNlfSzsAjQJ94zR+BM4BJwEPAUODhhuriAtwpO5+/4Wxe++7vAVj2\\nevD6eMM/v0afJ4LQ6vF2/c4bpp0R/LSff8ADfKvHTQC8sGrzcla3SdwCXB33e8TwYajj0KG2wH7y\\nr51yFHBPFNw3NKp0Oc2mZDbwvYDpZjYDQNIdwDAgLcCHkRt4NAG4TpLMLD2leArQTVIXYBOgp5m9\\nEPMcCxxJIwLcTSiO41QJTbKBbyZpcmo7M5VRH9Z/R88m9z6uk8bM1gBLgPzPz6OBV8zs05h+diN5\\n1sE1cKfsbPeHaew46HQA3vraaADOGzaN6P+gaF74tBsAF//2FAB60/bDCFcDl8b9H8RwGDAn7ieO\\n5JaSGxa4bwx/ls5ot6B594tuQt0DfbkoWgNfZGZDylULSTsRzCr5LlKbhAtwx3GqhJKZUOYAW6eO\\n+5J7Z+enmS2pI7AR8CGApL7AvcAIM3snlb5vI3nWwQW4U3ZqF33IwNOWAbDL+SPXxW/51fDF+PXe\\n9U/yGf3qPgB0mdaNba/+NwC9V7S95p1mYQwvTcV1jWH/GPYjGDkhtz7K7jFcfm8uD1+NsJyUzKHD\\nS8BASdsRhOxw4IS8NBOBk4HngWOAJ8zMJPUCHgQuMLPnksRmNk/SJ5K+SOjEHAH8vrGKuAB3WgX7\\nNKxBsfVlKeEbp5I/Tbd6rxtArs+nvY/GWF1g/9W8sDFccJeT0mjgZrZG0kjCCJIa4FYzmyLpUmCy\\nmU0ERgO3S5pOeGcPj5ePBAYAF0m6KMYdbGYfENyrjgG6ETovG+zABJAlw5Ycp0gO6vDtzD40j639\\na4PDPLpLmW3bcjMfwtIAQ4Z0scmTtywqrfTey+W0gZcK18Adx6kSKm8qvQtwx3GqBBfgjuM4GcUF\\nuOM4TkYxSjQKpd3gAtxxnCrBNXDHaXQkR5bxkRyVjAtwx3Gc7GKVNdLeBbjjONVDe58N1kRcgDuO\\nUx0YFTfV1QW44zjVgbH+egcVgAtwx3GqA9fAHcdxMozbwB3HcTKIa+CO4zgZxgW44zhOBvFOTMdx\\nnIziJhTHcZwM452YjuM4GcQ1cMdxnAzjGrjjOE4GcQ3ccRwno1TgKJQObV2BciHJJA1o63qUi0pu\\nXyW3DSq7fe26bYkGXsyWEdqVAJc0U9JKSctS23WtUG4/SU9KWiHpTUkHlqmctmrfZZJel7RG0qgy\\nldHqbZPUW9J4SXMlLZH0nKS9y1RWW927JyUtlPSJpFclDStDGW3StlT5X42C//KyF1ZhArw9mlCO\\nMLPHW7nM8cDzwGFxmyBpoJktLENZbdG+6cD5wFllLqe127Yh8BLwY+AD4DTgQUn9zGxZGcpri3t3\\nLjDVzNbEl9PjkgaZ2bwSl9MWbUNSJ+B3wKSyF2ZUXCdmu9LA60NSF0mLJe2cits8ag294/FPJc2L\\n2tipTch7ELAHcLGZrTSzu4HXgaNL3Y4G6lC29gGY2W1m9jCwtMRVb5Ryts3MZpjZ1WY2z8xqzewm\\noDOwQ+lbUphWuHevmVniB8yATsDWJWtAA5S7bZGfAI8Cb5ao2g1TYRp4JgS4mX0K3AMcn4o+Fnja\\nzD6QNBQ4DzgIGAg0xQSyEzDDzNLC7dUY3yqUuX1tSmu2TdJuBAE+vfk1bhqt0T5JD0haRdBSnwIm\\nt7TexVDutknaFjgVuLQ0NW6EpBOzmC0jtEcBfl986yfbGTF+HDA8le6EGAfhofqTmb1hZsuBUU0o\\nb0NgSV7cEqBH06teFK3dvtakzdomqSdwO3CJmeXfz1LRJu0zs8MJz+NhwKNmVg5DQFu07VrgwjKZ\\nu+pSgZ2Y7dEGfmQ9trgngQ2iHXABsBtwbzy3FfByKu17TShvGdAzL64n5TM3tHb7WpM2aZukbsD9\\nwAtmdkVTr28CbXbvzGw18LCkcyVNN7OJzcmnAVq1bZKOAHqY2Z3NrG/zqDAbeHsU4AUxs1pJdxE+\\n5xYAD6TMHvNY3y64TROyngL0l9Qjld+u5LSMVqGM7Wtzytk2SV2A+4DZwHdLUN0m08r3riOwfQvz\\nKJoytu3rwBBJ8+PxRkCtpF3MrOQjbYCKnMjTHk0oDTEOOA44kfUF7F3AKZIGS9oAuLjYDM3sLeDf\\nwMWSuko6Cvh/wN2lq3bRlLx9EHr6JXUl3O+OsZ01pap0kZS8bXEEwwRgJXBymUwLxVKO9u0o6VBJ\\n3eI9/C/gK8DTpax4EZTjubwQGETQ6HcDJgI3A98pSY3ro8JMKJhZu9mAmYQ/47LUdm9emunAR0Dn\\nvPgLgPnAXELHiAED4rn/AR5uoNx+hM6hlcA04MAKa9+YmD69nZL1tgFfjWlX5JW7XyXcO+DzhI7L\\npcBiwpDJoyqhbfU8o5eXum3pbc8BmD1Y3AZMLmddSrUp/niO4zgVzZABsslXFZdWR/KymQ0pb41a\\nTmZs4I7jOC0mS+aRInAB7jhOdeCdmI7jOBlmbZFbI0gaKmmapOmSLihwvoukO+P5SZL6xfhNFda3\\nqbPejKSnYp7/jlvvxurhGrjjONVBiTTwOILresIM1NnAS5ImmtnUVLLTgI/NbICk4cCVhJE8qwgj\\ncHaOWz4nmlnRM23bTIB3lzLbe7rcTA2dr+S2QWW3r5LbBpXfvgYpnQllL2C6mc0AkHQHMAxIC/Bh\\n5GamTgCukyQLM1afVYmW3HUTiuM41UHp1kLpA8xKHc+OcQXTWFiMbAmwaRG1/FM0n1woqdEXlgtw\\nx3Gqh+Jt4JtJmpzazmyF2p1oZrsA+8XtpMYucBu44zjVQdNMKIsaGAc+h/WXEOgb4wqlmS2pI2Gp\\ngA8brJ7ZnBgulTSOYKoZ29A1roE7jlM9lGYq/UvAQEnbSepMWK0xf3GxicDJcf8Y4AlrYNakpI6S\\nNov7nYDDgTcaq4hr4I7jVAcl8shjwTvSSOARoAa41cymSLqUMAV/IjAauF1SsgTBuiV5Jc0krHja\\nWdKRwMGElRwficK7BnicsDZMg7gAdxynOiihV3ozewh4KC/uotT+KuDb9Vzbr55s92xqPVyAO45T\\nPVTYTEwX4I7jVAcVOJXeBbjjONWDe+RxHMfJIK6BO47jZBQX4I7jOBmlhKNQ2gsuwB3HqR7cBu44\\njpNB3ITiOI6TYVyAO47jZJASTaVvT7gAdxynenAN3HEcJ4P4KBTHcZyM4p2YjuM4GcZt4I7jOBnE\\nNXDHcZwM4wLccRwng3gnpuM4TkZxE4rjOE6G8U5Mx3GcDOIauOM4TkbxqfSO4zgZxjVwx3GcDOKj\\nUBzHcTKK28Adx3EyjAtwx3GcDOKdmI7jOBnGNXDHcZwM4hq44zhORjHgs7auRGlxAe44TvXgGrjj\\nOE4G8WGEjuM4GcUFuOM4ToZxE4rjOE4G8an0juM4GaUCTSgd2roCjuM4rUZtkVsjSBoqaZqk6ZIu\\nKHC+i6Q74/lJkvrF+E0lPSkT4piKAAAQKElEQVRpmaTr8q7ZU9Lr8ZprJamxergAdxynOkgm8hSz\\nNYCkGuB64FBgMHC8pMF5yU4DPjazAcA1wJUxfhVwIXBegaz/CJwBDIzb0Maa5ALccZzqoTQa+F7A\\ndDObYWafAXcAw/LSDANui/sTgK9LkpktN7NnCYJ8HZK2BHqa2QtmZsBY4MjGKuIC3HGc6iCxgRcn\\nwDeTNDm1nZnKqQ8wK3U8O8ZRKI2ZrQGWAJs2ULs+MZ+G8qxDm3ViLjdr1L6TVSq5bVDZ7avktkHl\\nt69BmjYKZZGZDSlfZUqDa+CO41QPJbCBA3OArVPHfWNcwTSSOgIbAR82kmffRvKsgwtwx3Gqg6aZ\\nUBriJWCgpO0kdQaGAxPz0kwETo77xwBPRNt24aqZzQM+kfTFOPpkBPC3xiri48Adx6keSjAO3MzW\\nSBoJPALUALea2RRJlwKTzWwiMBq4XdJ04COCkAdA0kygJ9BZ0pHAwWY2FTgbGAN0Ax6OW4OogZeC\\n4zhOxTCkg2xyl+LSahUvZ8EG7hq44zjVga8H7jiOk2F8MSvHcZxsUmFLobgAdxynOqjAtaxcgDuO\\nUz1UmAXFBbjjONWBa+CO4zgZpQL9ObgAdxynOnAN3HEcJ8O4DdxxHCeDVKIGXrGLWUkySQPauh7l\\nopLbV8ltg8puX3tvW4k8qrUb2pUAlzRT0sroLy7Zrmv8ypKX+2grldMq7YtlnyvpXUnLJf1H0qAS\\n59/qbZO0TV55y6IA+UkZymqrZ3M3Sf+UtETSbEkXlqGMtmrbPpJelLRU0muS9i1neSXyqNauaI8m\\nlCPM7PEKLrfV2yfpdIKPvm8A/wH6Ax+XoahWbZuZvQ9smBxL2g6YDtxdpiLb4tkcB9wL7A/0A56V\\n9Gpc8a6UtGrbJG0C3A+cBdwDHA/cL6m/mZXj2azIUSjtSgOvj+jhebGknVNxm0etoXc8/qmkeZLm\\nSjq17WrbdMrZPkkdgIuBH5nZVAu8Y2Yflb4lBctvzXs3AnjGzGa2sNpF0wrt6wf8xcxqzewd4Flg\\np5I1oAHK3LZ9gPlm9tfYtj8DC4FvlbYV6+MmlDbAzD4l95ZOOBZ42sw+kDSU4OX5III35wObUcxf\\nJC2U9KikXVtc6SZQ5vb1jdvOkmZFM8olUbCXnVa6d0jrFsG/rbG0paQV2vdbYISkTpJ2AL4EtIqm\\n3Apty3fvJmDnQglLQen8ObQjzKzdbMBMYBmwOLWdEc8dCLyTSvscMCLu3wr8KnVuEOF+DSiy3C8T\\nFlHfAPgZMB/oVQntI2g6BjwI9CJodG8l5Wa5bXnl7xfL37DCns19CGahNfG6SyqhbQQHv4sJL4dO\\nBO81a4Eby3H/zIydwKYUuREcM5SlHqXc2qMN/EgrbIt7EthA0t7AAmA3gm0QYCvg5VTa95pSoJk9\\nlzq8QtLJBIFwf1PyKZLWbt/KGP7azBYDiyXdCBwG3NykmjdOq9+7FCcDd5vZsmZeXwyt2r5oJ/47\\nMJJgC98CmCBpgZn9oRn1b4hWbZuZfShpGPAb4HqCd5vHWd8ze0mpxGGE7VGAF8TMaiXdRXhjLwAe\\nMLOl8fQ81ncyuk1Li6Pu511ZKWP7phGWsU+7XmpVN0zlvneSugHfBo5qaV2bQxnb1x+oNbOx8Xi2\\npDsIL99SC/CClPPemdnTwBdgnePfGcBVLa50feXhnZhtzTjgOODEuJ9wF3CKpMGSNiB02hVFHIr2\\nZUmdJXWV9FNgM8KnYmtT8vaZ2QrgTuB8ST0k9QXOBB4oXbWLouRtS3EUYVTNky2uZfMpR/veIpj3\\nT5DUQdIWsYzXSlXpIinLvZO0e7Tt9yRo4rPM7JFSVToft4GXeSPY4lYS7HHJdm9emsRJaOe8+AsI\\ntuu5wKmkbHHA/wAP11PmToQ/xHLgQ+AfwJBKaV883xO4A1gKzAIuIvpDzXrbYppHgMsq7dmM5w8g\\neEFfEvO4GdigQto2PrZrCUHJ6F3Oe7gj2AtFbmTEBu5OjR3HqQp2lOzWItN+GXdq7DiO067IlHmk\\nCFyAO45TFSRT6SsJF+CO41QFRhiOVUm4AHccp2pwDdxxHCeD+ESeEtJdyuzwl+VmDU7yqeS2QWW3\\nr5LbBpXfvsZwDdxxHCeDuAbuOI6TYVyAO47jZJBKXAvFBbjjOFWBm1Acx3EyjHdiVgg1qf38JRlr\\nqPumXlsgznHKSU3jSfyZbAKugWeEYh78hELr6a5K5VFpN9zJBuln2J/B0lCJU+mzth644zhOs0im\\n0hezNYakoZKmSZou6YIC57tIujOenySpX+rcz2L8NEmHpOJnSnpd0r8lTS6mTRWjgXeP4SpyGkun\\nGKbfusm5RMP5FnBr0jU9I4aDDA4PcwYeezBEXQq8UsoKN4GkHU3tQa8Busb99G+yIi/OKR/J77+q\\nidcNAF6J6tX78QHe5l64MvocSv7dLxAW6XaKoxQauKQaghu4gwgu4F6SNNHMpqaSnQZ8bGYDJA0H\\nrgSOkzQYGE7wQ7AV8LikQWaW/B2/ZmaLiq2La+CO41QFJfTIsxcw3cxmmNlnBGcpw/LSDANui/sT\\ngK9LUoy/w8w+NbN3CY4y9mpumzKtgfcAfhD3+8SwP7m30sExTLTtw4FxlngS+0YMH8ztXx9n6vYX\\nK6PmvTymmla6ahdFV4IrFgiuu5Mw2Z+bl74G2CS1D+FTcG1qHzLoMiqDdAJOyIv7jOAOCer6sutH\\nzkdaTfLQLia4DwYm3xTC6UflvAYn93NJy6tbVTTh2d8sz4xxk5nFO0EfgmerhNnA3nnXr0tjZmsk\\nLQE2jfEv5F2biC8DHlVY7uDGVHn1kkkBvk8MfwT8Lu4nD/ZA4P6dw/6iN0LYbbN4cuE8arUlEHyM\\n5ZNvboCcAGwtoTcwhgcS/IRB8CQLsAHhzw7QOYbJC2YTcu68kzindRkcw+8Af477yXPZCxgR99Ne\\ngAF+sxH8PEriax5NnXgxBN6hXhqa2Im5qA088uxrZnMk9QYek/SmmT3T0AVuQnEcp2ookQllDuu/\\nh/vGuIJpJHUENiL43K33WjNLwg+AeynCtJIZDbyGXGfexBheAzyfl24u0D1q3v+IcWNjl8Bt2nJd\\nHmltO1/LLtTpWW4SLSsxpP2LXJ9qwnJgYdzfKoaJSWUhTluR3LufxfAeID6C656fxYSOcAhfV5DT\\nzrun7CCFhg+65l0aSjiV/iVgoKTtCMJ3OHWtZhOBkwki6hjgCTMzSROBcZKuJvyNBwIvSuoOdDCz\\npXH/YHKPTL1kRoA7juO0hFJN5Ik27ZEEK2cNcKuZTZF0KcGb/URgNHC7pOmEgULD47VTJN0FTAXW\\nAN83s1pJnwPuDf2cdATGmdnfG6tLm3mlb+q6xDXAJ4n6fGbM4/qcxpLYghqycXUocL5TgXTpt3Sh\\nG16ONaVHxvBLMTwxda41baDVvqZ0c9o2JoZJ38wg6k4ma+q9KzQZrbE8qv3eNUYfyb5bZNqL3St9\\naakFHouS9aDr7gOg//VHrtcVDEEg55tC0ob+ZD8R0u3l8/TtGF4ZbSO95ubMIwmFpvg7bU8yVOHb\\nfwzh5t+rOza7oZmVhe6r3+fS41PpHcdxMooL8DakBjg77r8du/petwnsqGOAuuOioe4Qm/a8INXT\\nMVwZG/IuoXsacsMCm7LGi9N6JLM1roydkTN/AFtcG/aTsd/pe9dS84rTfCptLZTMCHDHcZyW4A4d\\n2phkQsvNoaeWM+wrvPl4iPvvODbrOrKpqSZrZSQT8c4HkhVy/hTDGdTt0PRJHm1PsrbMCfGGjeub\\nG8J6Vgzbah0dJ0clmlDa/SiUtIDKX5xqa2BKEvnZNiG8/X1Oj1PexuflkTaptORN3BqezdNT6X8V\\nw0nAb+J+/tT+Ui0/Wu0jGZrTtvyXaC/gv+L+lV1C+MSncF6MK7QsQylexNV+7xrjc5INLzLttT4K\\nxXEcp/1QieuBt3sNvBCFtM3NY3g/sIvFZZ3uC4O5uh9V97r865tCa2jgaZJZo8eTGy8+P4bfqJu8\\nRVS7FleuezcM+G3cfzWGQ0tZEH7vGqO3ZMcUmfaProE7juO0H7wTs40p5KAh0aqTiRNfBC5TOPpx\\n/LpYPjW8uLcYnOtwStvD23vHRtLB+SdyE34eOTSELzwcwv2ovIezEkju3Z3AzLj/RNTrXoszgPYk\\nd++8U7p8VGInZqYEuOM4TkuoNBt4pgR4vpaSXtskPaxuVNy/Kg43nHNZOJ5vD3CqDgfCinFJHvnT\\n69szz8awV9S8x8TjxRvCbsvCfqKl+9T79sWkGPaKmveN8XhxX9gjLuY+Pca5U+PS4xp4G9Kf3PKq\\nhW5CoQ7KZAbj1heGcFaPw7k1mlWmRuH+NnVNM619k7cg1ymZ0JjwTV42p8Tw9mXw7zgWvk8cG78U\\n/yQvN5vT9KV8k3uXzCwePBte+UnY3/6qEC6k7kxiv4ctp9J+w8wIcMdxnJZQicMIM+OR5zsF4or1\\npLEibr1+mIsbG7fEocNa1jentCZX1RNfQ/2zSpNzSd2HQ/iU6BSc8+U76HPKw5nNuCa5d6vj9mUI\\nqvzmcCxhc0qPEXyJFrNlBdfAHcepCipRA8+MAJ9LmMgCuSnyzcGi7XtQMoFJWvfGbaubOwn4fdw/\\nJ4a11NW+G7KL1wAjY8fmPRuGsPuybK4LkyVmAN+L+39sQT5Xx3VUrhgVwutHtV2fTCVTab9lZgT4\\nLcDVcb9HDB+GOg4dagvs94nhlKOAe6LgvqFFk7pKymhyTgHGxPDH1HUKUKhtvWJ4NXBcdAbx0NzC\\n1zil515yL99krZNbqXvv0uTfu9HA0Lhi2fuFbIVOSXAN3HEcJ8NUmkKTqbVQkvVOfhDDBQSX0ACJ\\nY++l5IYF7hvDxFt456NYNxaxX1yQIq0pFXtzy7GexsAY3hfDCcCHcT8ZP7yK3BC0/jEcHMNdyY0h\\nviKVvqlU+3oazWlb/PDhohg+T25o4cwYriD3fG0dw8Q7/a7kHD98P6lnUyuB37vG6CnZF4pM+4Sv\\nheI4jtN+qMSJPJnSwBPSnuST1d4SjbQfENciXNeBF+e1sJycZtSSCS7lXNEuse+vJkzwAdg2hp3I\\naeCJxpZMblpF8zTufKpdi2tJ25JnsZacfbtX3rn0fjJjdjXN07jzqfZ71xg9JNutyLTPugZePlYX\\n2H81L2yM9vomXpran5kXOu2b9At0YV7otD3eiek4jpNh2qvi1lxcgDuOUxW4Bu44jpNRkqn0lYQL\\ncMdxqgbXwB3HcTJIJQ4jdAHuOE5V4AK8hLR0TGd7ppLbBpXdvkpuG1R++xrDTSiO4zgZxDVwx3Gc\\njGJkw+9tU3AB7jhO1eAauOM4TgbxiTyO4zgZxjVwx3GcDOKdmI7jOBnFOzEdx3EyjNvAHcdxMsha\\neGQ5bFZk8kVlrUyJaDOPPI7jOE7L6NDWFXAcx3Gahwtwx3GcjOIC3HEcJ6O4AHccx8koLsAdx3Ey\\nigtwx3GcjOIC3HEcJ6O4AHccx8koLsAdx3Eyigtwx3GcjOIC3HEcJ6O4AHccx8koLsAdx3Eyyv8H\\nmA70BtX0+igAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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aTa/R+An9Wkfa6kc3s4/pKa88C/XNp2BenH59aIKLcBnEJqNvk76Qfm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\\nRf1/JdweuPJHIEkAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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kTgx5I2k9Qf+G6J+60xDPigyC0buAJ3yskthMm/RcBGwPcAzGw5cCZw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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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pCUXP9+4FuS+knaDLiwpddoxj3AeYT7Bg+k9t8PXCRpM0n9gG+W+LoZ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\\nc85ljAd255zLmP8PszSWY/5V7uoAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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AB4g9CxN9LMlkJZvJkfD3QHJhFeDPcSbP4ANwJjCdr9i8Cf0idK+m2e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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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dOlpJN/c4HRwMcAIuJZ4CPA94HZpBp5uamjCFJPSbqvh31fAuyf27vJ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\\nlAO4mVlFOYCbmVXU/wcoHeJaCbpUhQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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cxel/QewTpMu2HS9SYylq3eLDJ37lwmTJhQUl5Jm0lKZ77OzK6L292p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\\n3XEcJ2O4Ynccx8kY/w/XsTaf1OUoVAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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ZCaL2laM2X/ETgg2ruJ5d8OLAGeBZ4i2N0BkLQRMBwo6Oi3GY4GugIv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\\nCwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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nxnTWml9R+OiLENztIacxu4lVZE1Fy7jIhPNjIvVgaVGnjzcAA3sxbh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\\nTzwmvAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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r4PYGbvA98Ffg/MIWjkaVNHIqTelfRSK2XfDBwS7d3E8m8DlgIvAM8R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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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2lrSYOAs8p83AZjwJoiH9ngAdxV0s2Ezr8FQC/g6wBmtgz4GnAtMJ9Q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\\nAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for data, target in test_loader:\\n\",\n    \"\\n\",\n    \"    data, target = data.to(device), target.to(device)\\n\",\n    \"    batch_size = int(data.size()[0])\\n\",\n    \"\\n\",\n    \"    evidence_for_class = []\\n\",\n    \"    # Overlay with noise \\n\",\n    \"    # data[0] += 0.25 * data[0].max() * torch.Tensor(np.random.randn(28*28).reshape(1, 28, 28))\\n\",\n    \"    model_prediction, true_relevance = inn_model.innvestigate(in_tensor=data)\\n\",\n    \"\\n\",\n    \"    for i in range(10):\\n\",\n    \"        # Unfortunately, we had some issue with freeing pytorch memory, therefore \\n\",\n    \"        # we need to reevaluate the model separately for every class.\\n\",\n    \"        model_prediction, input_relevance_values = inn_model.innvestigate(in_tensor=data, rel_for_class=i)\\n\",\n    \"        evidence_for_class.append(input_relevance_values)\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    evidence_for_class = np.array([elt.numpy() for elt in evidence_for_class])\\n\",\n    \"    for idx, example in enumerate(data):\\n\",\n    \"\\n\",\n    \"        prediction = np.argmax(model_prediction.detach(), axis=1)\\n\",\n    \"        \\n\",\n    \"\\n\",\n    \"        fig, axes = plt.subplots(3, 5)\\n\",\n    \"        fig.suptitle(\\\"Prediction of model: \\\" + str(prediction[idx]) + \\\"({0:.2f})\\\".format(\\n\",\n    \"            100*float(model_prediction[idx][model_prediction[idx].argmax()].exp()/model_prediction[idx].exp().sum())))\\n\",\n    \"        \\n\",\n    \"        vmin = np.percentile(evidence_for_class[:, idx], 50)\\n\",\n    \"        vmax = np.percentile(evidence_for_class[:, idx], 99.9)\\n\",\n    \"        \\n\",\n    \"        axes[0, 2].imshow(example[0])\\n\",\n    \"        axes[0, 2].set_title(\\\"Input (\\\" + str(int(target[idx]))+ \\\")\\\")\\n\",\n    \"        axes[0, 3].imshow(evidence_for_class[prediction[idx]][idx][0], vmin=vmin,\\n\",\n    \"                          vmax=vmax, cmap=\\\"hot\\\")\\n\",\n    \"        axes[0, 3].set_title(\\\"Pred. Evd.\\\")\\n\",\n    \"        for ax in axes[0]:\\n\",\n    \"            ax.set_axis_off()\\n\",\n    \"        for j, ax in enumerate(axes[1:].flatten()):\\n\",\n    \"            im = ax.imshow(evidence_for_class[j][idx][0], cmap=\\\"hot\\\", vmin=vmin,\\n\",\n    \"                          vmax=vmax)\\n\",\n    \"            ax.set_axis_off()\\n\",\n    \"            ax.set_title(\\\"Evd. \\\" + str(j))\\n\",\n    \"        fig.colorbar(im, ax=axes.ravel().tolist())\\n\",\n    \"        plt.show()\\n\",\n    \"    break\\n\",\n    \"        # fig.savefig(\\\"./mnist_example{0}.png\\\".format(idx))\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "Plotting brain maps.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Adding model_path to namespace\\n\",\n      \"Adding data_path to namespace\\n\",\n      \"Adding ADNI_DIR to namespace\\n\",\n      \"Adding 1.5T_table to namespace\\n\",\n      \"Adding 1.5T_image_dir to namespace\\n\",\n      \"Adding 3T_table to namespace\\n\",\n      \"Adding 3T_image_dir to namespace\\n\",\n      \"Adding binary_brain_mask to namespace\\n\",\n      \"Adding nmm_mask_path to namespace\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from jrieke import interpretation\\n\",\n    \"from jrieke.utils import load_nifti\\n\",\n    \"import jrieke.models as models\\n\",\n    \"import torch\\n\",\n    \"from innvestigator import InnvestigateModel\\n\",\n    \"from copy import deepcopy\\n\",\n    \"from jrieke import interpretation\\n\",\n    \"import jrieke.utils as utils\\n\",\n    \"import matplotlib.colors as mcolors\\n\",\n    \"\\n\",\n    \"from copy import deepcopy\\n\",\n    \"import pickle\\n\",\n    \"import os\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import matplotlib.colors as mcolors\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"from settings import settings\\n\",\n    \"from utils import load_data\\n\",\n    \"from nmm_mask_areas import short_name_map\\n\",\n    \"for k in settings.keys():\\n\",\n    \"    print(\\\"Adding \\\" + k + \\\" to namespace\\\")\\n\",\n    \"    globals()[k] = settings[k]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data_path = \\\"/analysis/ritter/projects/Methods/LRP/data/rieke-copy1/2Node_trial0/beta0\\\"\\n\",\n    \"mask_path = \\\"/analysis/ritter/projects/Methods/LRP/data/\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"model_path = \\\"/analysis/ritter/projects/Methods/LRP/models_pytorch/rieke_copy1_OHE/repeat_0_cv_fold_0_BEST_ITERATION.h5\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Load results from evaluation notebook\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cases = [\\\"AD\\\", \\\"HC\\\", \\\"TP\\\", \\\"TN\\\", \\\"FP\\\", \\\"FN\\\"]\\n\",\n    \"\\n\",\n    \"mean_maps_LRP = dict()\\n\",\n    \"mean_maps_GB = dict()\\n\",\n    \"rs_per_area_LRP = dict()\\n\",\n    \"rs_per_area_GB = dict()\\n\",\n    \"for case in cases:\\n\",\n    \"    mean_maps_LRP[case] = load_nifti(os.path.join(data_path, \\\"LRP_{case}.nii\\\".format(case=case)))\\n\",\n    \"    mean_maps_GB[case] = load_nifti(os.path.join(data_path, \\\"GB_{case}.nii\\\".format(case=case)))\\n\",\n    \"    with open(os.path.join(data_path, \\\"LRP_area_evdcs_{case}.pkl\\\".format(case=case)), 'rb') as file:\\n\",\n    \"        rs_per_area_LRP[case] = pickle.load(file)\\n\",\n    \"    with open(os.path.join(data_path, \\\"GB_area_evdcs_{case}.pkl\\\".format(case=case)), 'rb') as file:\\n\",\n    \"        rs_per_area_GB[case] = pickle.load(file)\\n\",\n    \"\\n\",\n    \"nmm_mask = load_nifti(os.path.join(mask_path, \\\"rescaled_nmm_mask.nii\\\"))\\n\",\n    \"with open(os.path.join(data_path, 'area_sizes.pkl'), 'rb') as file:\\n\",\n    \"    area_sizes = pickle.load(file)\\n\",\n    \"    \\n\",\n    \"ad_score_list = np.loadtxt(os.path.join(data_path, \\\"ad_scores.txt\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"mask_GB = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# mask out brains for GB\\n\",\n    \"if mask_GB:\\n\",\n    \"    for case in cases:\\n\",\n    \"        mean_maps_GB[case] = mean_maps_GB[case] * nmm_mask\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Load test dataset for individual brains\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import h5py\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def min_max_normalization(subset):\\n\",\n    \"    for i in range(len(subset)):\\n\",\n    \"        subset[i] -= np.min(subset[i])\\n\",\n    \"        subset[i] /= np.max(subset[i])\\n\",\n    \"    return subset\\n\",\n    \"    \\n\",\n    \"def load_data(skip_train=True, skip_val=True, skip_test=False, dtype=np.float32):\\n\",\n    \"    \\\"\\\"\\\" Load hdf5 files and extract columns. \\\"\\\"\\\"\\n\",\n    \"    X_train, y_train, X_val, y_val, X_holdout, y_holdout = np.nan, np.nan, np.nan, np.nan, np.nan, np.nan\\n\",\n    \"    # train\\n\",\n    \"    if not skip_train:\\n\",\n    \"        train_h5 = h5py.File(\\\"/analysis/ritter/data/ADNI_HDF5/Splits_Eitel/train_AD_CN_2Yr15T_plus_UniqueScreening_quickprep_(193, 229, 193)_reducedValSize.h5\\\", 'r')\\n\",\n    \"        X_train, y_train = train_h5['X'], train_h5['y']\\n\",\n    \"        X_train = np.expand_dims(np.array(X_train, dtype=dtype), 1)\\n\",\n    \"        X_train = min_max_normalization(X_train)\\n\",\n    \"        y_train = np.array(y_train)\\n\",\n    \"        print(\\\"Total training set length: {}\\\".format(len(y_train)))\\n\",\n    \"        print(\\\"Number of healthy controls: {}\\\".format(len(np.array(y_train)[np.array(y_train)==0.])))\\n\",\n    \"        print(\\\"Number of AD patients: {}\\\".format(len(np.array(y_train)[np.array(y_train)==1.])))\\n\",\n    \"    if not skip_val:\\n\",\n    \"        # val\\n\",\n    \"        val_h5 = h5py.File(\\\"/analysis/ritter/data/ADNI_HDF5/Splits_Eitel/val_AD_CN_2Yr15T_plus_UniqueScreening_quickprep_(193, 229, 193)_reducedValSize.h5\\\", 'r')\\n\",\n    \"        X_val, y_val = val_h5['X'], val_h5['y']\\n\",\n    \"        X_val = np.expand_dims(np.array(X_val, dtype=dtype), 1)\\n\",\n    \"        X_val = min_max_normalization(X_val)\\n\",\n    \"        y_val = np.array(y_val)\\n\",\n    \"        print(\\\"Total validation set length: {}\\\".format(len(y_val)))\\n\",\n    \"    if not skip_test:\\n\",\n    \"        # test\\n\",\n    \"        holdout_h5 = h5py.File(\\\"/analysis/ritter/data/ADNI_HDF5/Splits_Eitel/holdout_AD_CN_2Yr15T_plus_UniqueScreening_quickprep_(193, 229, 193)_reducedValSize.h5\\\", 'r')\\n\",\n    \"        X_holdout, y_holdout = holdout_h5['X'], holdout_h5['y']\\n\",\n    \"        X_holdout = np.expand_dims(np.array(X_holdout, dtype=dtype), 1)\\n\",\n    \"        X_holdout = min_max_normalization(X_holdout)\\n\",\n    \"        y_holdout = np.array(y_holdout)\\n\",\n    \"        print(\\\"Total test set length: {}\\\".format(len(y_holdout)))\\n\",\n    \"   \\n\",\n    \"    return X_train, y_train, X_val, y_val, X_holdout, y_holdout\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Total test set length: 172\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X_train, y_train, X_val, y_val, X_holdout, y_holdout = load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Plotting brain maps\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"shape = mean_maps_LRP[\\\"AD\\\"].shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Getting contours for areas\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from nmm_mask_areas import all_areas\\n\",\n    \"\\n\",\n    \"colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)\\n\",\n    \"\\n\",\n    \"# Sort colors by hue, saturation, value and name.\\n\",\n    \"by_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name)\\n\",\n    \"                for name, color in colors.items())\\n\",\n    \"sorted_colors = [name for hsv, name in by_hsv]\\n\",\n    \"contour_configs = {name: (mini, maxi, cname) for (name, (mini, maxi)), cname in zip(all_areas.items(), sorted_colors[::2])}\\n\",\n    \"contours = {k:None for k in contour_configs.keys()}\\n\",\n    \"for key, (mini, maxi, col) in contour_configs.items():\\n\",\n    \"    tmp_map = np.zeros_like(nmm_mask)\\n\",\n    \"    tmp_map[np.logical_and(nmm_mask>=mini, nmm_mask<=maxi) ] = 40\\n\",\n    \"    contours[key] = tmp_map\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Plotting functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_contours(areas, x_idx, y_idx, z_idx, c, fig=None, ax=None):\\n\",\n    \"    \\n\",\n    \"    if not areas:\\n\",\n    \"        return\\n\",\n    \"    \\n\",\n    \"    if fig is None or ax is None:\\n\",\n    \"        fig, ax = plt.subplots(1, figsize=(12, 12))\\n\",\n    \"\\n\",\n    \"    for c_ , name in zip(c, areas):\\n\",\n    \"\\n\",\n    \"        cmap_area = mcolors.LinearSegmentedColormap.from_list(\\n\",\n    \"            name=\\\"colored\\\", colors=[(0,0,0,0), c_], N=100)\\n\",\n    \"        cmap_area_border_b = mcolors.LinearSegmentedColormap.from_list(\\n\",\n    \"            name=\\\"black\\\", colors=[(0,0,0,0), \\\"black\\\"], N=100)\\n\",\n    \"        cmap_area_border_w = mcolors.LinearSegmentedColormap.from_list(\\n\",\n    \"            name=\\\"white\\\", colors=[(0,0,0,0), \\\"white\\\"], N=100)\\n\",\n    \"        slice_area = contours[name][x_idx, y_idx, z_idx].T\\n\",\n    \"        \\n\",\n    \"        cs_ad = ax.contour(slice_area, [0, 10], cmap=cmap_area_border_b, linewidths=2.5, alpha=1, bordercolors=\\\"black\\\")\\n\",\n    \"\\n\",\n    \"        cs_ad = ax.contour(slice_area, [0, 10], cmap=cmap_area, linewidths=2, alpha=1, bordercolors=\\\"black\\\",\\n\",\n    \"                           \\n\",\n    \"                          label= \\\" \\\".join(areas))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_idv_brain(heat_map, brain_img, ref_scale, fig=None, ax=None, contour_areas=[],\\n\",\n    \"                  x_idx=slice(0, shape[0]), y_idx=slice(0, shape[1]), z_idx=slice(0, shape[2]),\\n\",\n    \"                  vmin=90, vmax=99.5, set_nan=True, cmap=None, c=None):\\n\",\n    \"\\n\",\n    \"    if fig is None or ax is None:\\n\",\n    \"        fig, ax = plt.subplots(1, figsize=(12, 12))\\n\",\n    \"    \\n\",\n    \"    img = deepcopy(heat_map)\\n\",\n    \"    if set_nan:\\n\",\n    \"        img[nmm_mask==0]=np.nan\\n\",\n    \"    if cmap is None:\\n\",\n    \"        cmap = mcolors.LinearSegmentedColormap.from_list(name='alphared',\\n\",\n    \"                                                  colors=[(1, 0, 0, 0),\\n\",\n    \"                                                         \\\"darkred\\\", \\\"red\\\", \\\"darkorange\\\", \\\"orange\\\", \\\"yellow\\\"],\\n\",\n    \"                                                  N=5000)\\n\",\n    \"        \\n\",\n    \"    if brain_img is not None:\\n\",\n    \"        ax.imshow(brain[x_idx, y_idx, z_idx].T, cmap=\\\"gray\\\",\\n\",\n    \"                 vmin=grey_vmin, vmax=grey_vmax, alpha=.45)\\n\",\n    \"\\n\",\n    \"    vmin, vmax = np.percentile(ref_scale, vmin), np.percentile(ref_scale, vmax)\\n\",\n    \"    im = ax.imshow(img[x_idx, y_idx, z_idx].T, cmap=cmap, \\n\",\n    \"               vmin=vmin, vmax=vmax, interpolation=\\\"gaussian\\\")\\n\",\n    \"    \\n\",\n    \"   \\n\",\n    \"    ax.axis('off')    \\n\",\n    \"\\n\",\n    \"    plot_contours(contour_areas, x_idx, y_idx, z_idx, fig=fig, ax=ax, c=c)\\n\",\n    \"    plt.gca().invert_yaxis()\\n\",\n    \"    return fig, ax, im\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Individual maps\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### Load model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"WARNING: With the chosen beta value, only negative contributions will be taken into account.\\n\",\n      \"Hence, if in any layer only positive contributions exist, the overall relevance will not be conserved.\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"device = 2\\n\",\n    \"net = models.ClassificationModel3D_2Node()\\n\",\n    \"net.cpu()\\n\",\n    \"net.load_state_dict(torch.load(model_path,\\n\",\n    \"                              map_location='cpu'))\\n\",\n    \"net.eval()\\n\",\n    \"net = torch.nn.Sequential(net, torch.nn.Softmax(dim=1))\\n\",\n    \"inn_model = InnvestigateModel(net, lrp_exponent=1,\\n\",\n    \"                                  method=\\\"b-rule\\\",\\n\",\n    \"                                  beta=0, epsilon=1e-6).cpu()\\n\",\n    \"inn_model.eval();\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### TODO: Fix warning, getting warning for wrong beta.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Obtaining individual heatmaps\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def run_guided_backprop(net, image_tensor):\\n\",\n    \"    return interpretation.guided_backprop(net, image_tensor, cuda=False, verbose=False, apply_softmax=False)\\n\",\n    \"\\n\",\n    \"def run_LRP(net, image_tensor):\\n\",\n    \"    return inn_model.innvestigate(in_tensor=image_tensor, rel_for_class=1)\\n\",\n    \"\\n\",\n    \"def get_heatmaps(idx):\\n\",\n    \"    image_tensor, label = X_holdout[idx], y_holdout[idx]\\n\",\n    \"    image_tensor = torch.Tensor(np.expand_dims(image_tensor, 0))\\n\",\n    \"    image_tensor_LRP = image_tensor.cpu()\\n\",\n    \"    print(\\\"Running GB\\\")\\n\",\n    \"    rel_GB = run_guided_backprop(inn_model, image_tensor_LRP)\\n\",\n    \"    print(\\\"Running LRP\\\")\\n\",\n    \"    AD_score, rel_LRP = run_LRP(inn_model, image_tensor_LRP)\\n\",\n    \"    print(label, AD_score)\\n\",\n    \"    return rel_LRP.squeeze().detach().cpu().numpy().squeeze(), rel_GB.squeeze()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Running GB\\n\",\n      \"Running LRP\\n\",\n      \"1 tensor([[0.0001, 0.9999]])\\n\",\n      \"Running GB\\n\",\n      \"Running LRP\\n\",\n      \"1 tensor([[0.0179, 0.9821]])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"patientA, patientB = 2, 169\\n\",\n    \"brain_A, brain_B = X_holdout[patientA][0], X_holdout[patientB][0]\\n\",\n    \"grey_vmin, grey_vmax = np.min([brain_A, brain_B]), np.max([brain_A, brain_B])\\n\",\n    \"\\n\",\n    \"LRP_map_patient_A, GB_map_patient_A = get_heatmaps(patientA)\\n\",\n    \"LRP_map_patient_B, GB_map_patient_B = get_heatmaps(patientB)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### Define brain slices\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"first_slice= 57\\n\",\n    \"first_areas = [\\\"Hippocampus\\\", \\\"Temporal pole\\\",\\n\",\n    \"               \\\"Amygdala\\\", \\\"MTG\\\", \\\"Parahippocampal gyrus\\\"]\\n\",\n    \"second_slice= 66\\n\",\n    \"second_areas = first_areas\\n\",\n    \"third_slice= 88\\n\",\n    \"third_areas = [\\\"MTG\\\", \\\"STG\\\", \\\"Frontal pole\\\", \\\"TrIFG\\\"]\\n\",\n    \"\\n\",\n    \"# Colors for areas slice 1 & 2\\n\",\n    \"cs = [\\\"linen\\\", \\\"blue\\\", \\\"cyan\\\", \\\"lime\\\", \\\"hotpink\\\"]\\n\",\n    \"# Colors for slice 3\\n\",\n    \"cs_3 = [\\\"grey\\\", \\\"black\\\", \\\"white\\\", \\\"lime\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Plotting\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x1152 with 7 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"brain = deepcopy(brain_A)\\n\",\n    \"brain[nmm_mask==0] = np.nan\\n\",\n    \"vmin, vmax = 90, 99.5\\n\",\n    \"fig, (ax1, ax2, ax3) = plt.subplots(3, 2, figsize=(12, 16), sharey=True)\\n\",\n    \"\\n\",\n    \"fig, ax, im = plot_idv_brain(LRP_map_patient_A, brain, mean_maps_LRP[\\\"AD\\\"], z_idx=first_slice, \\n\",\n    \"                              contour_areas=first_areas,\\n\",\n    \"                            vmin=vmin, vmax=vmax, fig=fig, ax=ax1[1],\\n\",\n    \"                             c=cs);\\n\",\n    \"ax.set_title(\\\"Patient B\\\", fontsize=22)\\n\",\n    \"ax.set_ylabel(\\\"Slice number \\\" + str(first_slice))\\n\",\n    \"\\n\",\n    \"legend_pics = [plt.Rectangle((0,0),.5,.5,fc = pc, edgecolor=\\\"black\\\", linewidth=1) \\n\",\n    \"    for pc in cs]\\n\",\n    \"\\n\",\n    \"ax.legend(legend_pics, first_areas, bbox_to_anchor=[.175, 1, 0, 0], fontsize=10)\\n\",\n    \"\\n\",\n    \"fig, ax, im = plot_idv_brain(LRP_map_patient_A, brain, mean_maps_LRP[\\\"AD\\\"], z_idx=second_slice, \\n\",\n    \"                              contour_areas=second_areas,\\n\",\n    \"                            vmin=vmin, vmax=vmax, fig=fig, ax=ax2[1],\\n\",\n    \"                             c=cs);\\n\",\n    \"ax.set_ylabel(\\\"Slice number \\\" + str(second_slice))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"ax.legend(legend_pics, second_areas, bbox_to_anchor=[.175, 1, 0, 0], fontsize=10)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"fig, ax, im = plot_idv_brain(LRP_map_patient_A, brain, mean_maps_LRP[\\\"AD\\\"], z_idx=third_slice, \\n\",\n    \"                              contour_areas=third_areas,\\n\",\n    \"                            vmin=vmin, vmax=vmax, fig=fig, ax=ax3[1],\\n\",\n    \"                             c=cs_3);\\n\",\n    \"ax.set_ylabel(\\\"Slice number \\\" + str(third_slice))\\n\",\n    \"\\n\",\n    \"legend_pics = [plt.Rectangle((0,0),.5,.5,fc = pc, edgecolor=\\\"black\\\", linewidth=1) \\n\",\n    \"    for pc in cs_3]\\n\",\n    \"\\n\",\n    \"ax.legend(legend_pics, [\\\"{s:<30}\\\".format(s=s) for s in third_areas], bbox_to_anchor=[.175, 1, 0, 0], fontsize=10)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"brain = deepcopy(brain_B)\\n\",\n    \"brain[nmm_mask==0] = np.nan\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"fig, ax, im = plot_idv_brain(LRP_map_patient_B, brain, mean_maps_LRP[\\\"AD\\\"], z_idx=first_slice, \\n\",\n    \"                              contour_areas=first_areas,\\n\",\n    \"                            vmin=vmin, vmax=vmax, fig=fig, ax=ax1[0],\\n\",\n    \"                             c=cs);\\n\",\n    \"ax.set_title(\\\"Patient A\\\", fontsize=22)\\n\",\n    \"\\n\",\n    \"ax.set_ylabel(\\\"Slice number \\\" + str(first_slice))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"fig, ax, im = plot_idv_brain(LRP_map_patient_B, brain, mean_maps_LRP[\\\"AD\\\"], z_idx=second_slice, \\n\",\n    \"                              contour_areas=second_areas,\\n\",\n    \"                            vmin=vmin, vmax=vmax, fig=fig, ax=ax2[0],\\n\",\n    \"                             c=cs);\\n\",\n    \"\\n\",\n    \"ax.set_ylabel(\\\"Slice number \\\" + str(second_slice))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"fig, ax, im = plot_idv_brain(LRP_map_patient_B, brain, mean_maps_LRP[\\\"AD\\\"], z_idx=third_slice, \\n\",\n    \"                              contour_areas=third_areas,\\n\",\n    \"                            vmin=vmin, vmax=vmax, fig=fig, ax=ax3[0],\\n\",\n    \"                             c=cs_3);\\n\",\n    \"\\n\",\n    \"ax.set_ylabel(\\\"Slice number \\\" + str(third_slice))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"fig.tight_layout()\\n\",\n    \"fig.subplots_adjust(right=0.8)\\n\",\n    \"\\n\",\n    \"cbar_ax = fig.add_axes([0.85, 0.15, 0.025, 0.7])\\n\",\n    \"cbar = fig.colorbar(im, shrink=0.5, ticks=[vmin, vmax], cax=cbar_ax)\\n\",\n    \"vmin_val, vmax_val = np.percentile(mean_maps_LRP[\\\"AD\\\"], vmin), np.percentile(mean_maps_LRP[\\\"AD\\\"], vmax)\\n\",\n    \"cbar.set_ticks([vmin_val, vmax_val])\\n\",\n    \"cbar.ax.set_yticklabels(['{0:.1f}%'.format(vmin), '{0:.1f}%'.format(vmax)], fontsize=16)\\n\",\n    \"cbar.set_label('Percentile of  average AD patient values', rotation=270, fontsize=20)\\n\",\n    \"fig.savefig(os.path.join(data_path, \\\"patient_brains.pdf\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x864 with 8 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, axes = plt.subplots(4, 2, figsize=(6, 12), sharey=True, sharex=True)\\n\",\n    \"vmin, vmax = 50, 99.5\\n\",\n    \"\\n\",\n    \"for ax, idx in zip(axes[:, 0], [60, 70, 80, 90]):\\n\",\n    \"    ax.text(-25, 140, \\\"Slice \\\" + str(idx), rotation=\\\"vertical\\\", fontsize=20)\\n\",\n    \"\\n\",\n    \"    fig, ax, im = plot_idv_brain(mean_maps_LRP[\\\"AD\\\"], None, mean_maps_LRP[\\\"AD\\\"], z_idx=idx, contour_areas=[],\\n\",\n    \"                                vmin=vmin, vmax=vmax, fig=fig, ax=ax, set_nan=False, cmap=\\\"hot\\\");\\n\",\n    \"ax.text(80, -20, \\\"AD\\\", fontsize=20)\\n\",\n    \"\\n\",\n    \"for ax, idx in zip(axes[:, 1], [60, 70, 80, 90]):\\n\",\n    \"\\n\",\n    \"    fig, ax, im = plot_idv_brain(mean_maps_LRP[\\\"HC\\\"], None, mean_maps_LRP[\\\"AD\\\"], z_idx=idx, contour_areas=[],\\n\",\n    \"                                vmin=vmin, vmax=vmax, fig=fig, ax=ax, set_nan=False, cmap=\\\"hot\\\");\\n\",\n    \"\\n\",\n    \"ax.text(80, -20, \\\"HC\\\", fontsize=20)\\n\",\n    \"    \\n\",\n    \"fig.tight_layout()\\n\",\n    \"fig.subplots_adjust(right=0.8, top=0.95, hspace=0.05, wspace=0.05)\\n\",\n    \"fig.suptitle(\\\"LRP (beta = 0)\\\", fontsize=22, x=.41)\\n\",\n    \"\\n\",\n    \"cbar_ax = fig.add_axes([0.85, 0.15, 0.025, 0.7])\\n\",\n    \"cbar = fig.colorbar(im, shrink=0.5, ticks=[vmin, vmax], cax=cbar_ax)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"vmin_val, vmax_val = np.percentile(mean_maps_LRP[\\\"AD\\\"], vmin), np.percentile(mean_maps_LRP[\\\"AD\\\"], vmax)\\n\",\n    \"cbar.set_ticks([vmin_val, vmax_val])\\n\",\n    \"cbar.ax.set_yticklabels(['{0:.1f}%'.format(vmin), '{0:.1f}%'.format(vmax)],\\n\",\n    \"                       fontsize=16)\\n\",\n    \"cbar.set_label('Percentile of  average AD patient values', rotation=270, fontsize=20)\\n\",\n    \"fig.savefig(os.path.join(data_path, \\\"LRP_AD_HC_brain.pdf\\\"), bbox_inches='tight')\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x864 with 9 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, axes = plt.subplots(4, 2, figsize=(6, 12), sharey=True, sharex=True)\\n\",\n    \"vmin, vmax = 50, 99.5\\n\",\n    \"\\n\",\n    \"for ax, idx in zip(axes[:, 0], [60, 70, 80, 90]):\\n\",\n    \"    ax.text(-25, 140, \\\"Slice \\\" + str(idx), rotation=\\\"vertical\\\", fontsize=20)\\n\",\n    \"\\n\",\n    \"    fig, ax, im = plot_idv_brain(mean_maps_GB[\\\"AD\\\"], None, mean_maps_GB[\\\"AD\\\"], z_idx=idx, contour_areas=[],\\n\",\n    \"                                vmin=vmin, vmax=vmax, fig=fig, ax=ax, set_nan=False, cmap=\\\"hot\\\");\\n\",\n    \"ax.text(80, -20, \\\"AD\\\", fontsize=20)\\n\",\n    \"\\n\",\n    \"for ax, idx in zip(axes[:, 1], [60, 70, 80, 90]):\\n\",\n    \"\\n\",\n    \"    fig, ax, im = plot_idv_brain(mean_maps_GB[\\\"HC\\\"], None, mean_maps_GB[\\\"AD\\\"], z_idx=idx, contour_areas=[],\\n\",\n    \"                                vmin=vmin, vmax=vmax, fig=fig, ax=ax, set_nan=False, cmap=\\\"hot\\\");\\n\",\n    \"\\n\",\n    \"ax.text(80, -20, \\\"HC\\\", fontsize=20)\\n\",\n    \"    \\n\",\n    \"fig.tight_layout()\\n\",\n    \"fig.subplots_adjust(right=0.8, top=0.95, hspace=0.05, wspace=0.05)\\n\",\n    \"fig.suptitle(\\\"GB\\\", fontsize=22, x=.41)\\n\",\n    \"\\n\",\n    \"cbar_ax = fig.add_axes([0.85, 0.15, 0.025, 0.7])\\n\",\n    \"cbar = fig.colorbar(im, shrink=0.5, ticks=[vmin, vmax], cax=cbar_ax)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"vmin_val, vmax_val = np.percentile(mean_maps_GB[\\\"AD\\\"], vmin), np.percentile(mean_maps_GB[\\\"AD\\\"], vmax)\\n\",\n    \"cbar.set_ticks([vmin_val, vmax_val])\\n\",\n    \"cbar.ax.set_yticklabels(['{0:.1f}%'.format(vmin), '{0:.1f}%'.format(vmax)],\\n\",\n    \"                       fontsize=16)\\n\",\n    \"cbar.set_label('Percentile of  average AD patient values', rotation=270, fontsize=20)\\n\",\n    \"fig.savefig(os.path.join(data_path, \\\"GB_AD_HC_brain.pdf\\\"), bbox_inches='tight')\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib as mpl\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x864 with 17 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, axes = plt.subplots(4, 4, figsize=(12, 12), sharey=True, sharex=True)\\n\",\n    \"vmin, vmax = 50, 99.5\\n\",\n    \"\\n\",\n    \"for ax, idx in zip(axes[:, 0], [60, 70, 80, 90]):\\n\",\n    \"    ax.text(-25, 140, \\\"Slice \\\" + str(idx), rotation=\\\"vertical\\\", fontsize=18)\\n\",\n    \"    \\n\",\n    \"    fig, ax, im = plot_idv_brain(mean_maps_LRP[\\\"TP\\\"], None, mean_maps_LRP[\\\"AD\\\"], z_idx=idx, contour_areas=[],\\n\",\n    \"                                 vmin=vmin, vmax=vmax, fig=fig, ax=ax, set_nan=False, cmap=\\\"hot\\\");\\n\",\n    \"\\n\",\n    \"ax.text(22, -20, \\\"True positives\\\", fontsize=18)\\n\",\n    \"\\n\",\n    \"for ax, idx in zip(axes[:, 1], [60, 70, 80, 90]):\\n\",\n    \"    fig, ax, im = plot_idv_brain(mean_maps_LRP[\\\"FP\\\"], None, mean_maps_LRP[\\\"AD\\\"], z_idx=idx, contour_areas=[],\\n\",\n    \"                                 vmin=vmin, vmax=vmax, fig=fig, ax=ax, set_nan=False, cmap=\\\"hot\\\");\\n\",\n    \"\\n\",\n    \"ax.text(18, -20, \\\"False positives\\\", fontsize=18)\\n\",\n    \"\\n\",\n    \"for ax, idx in zip(axes[:, 2], [60, 70, 80, 90]):\\n\",\n    \"    fig, ax, im = plot_idv_brain(mean_maps_LRP[\\\"TN\\\"], None, mean_maps_LRP[\\\"AD\\\"], z_idx=idx, contour_areas=[],\\n\",\n    \"                                 vmin=vmin, vmax=vmax, fig=fig, ax=ax, set_nan=False, cmap=\\\"hot\\\");\\n\",\n    \"ax.text(18, -20, \\\"True negatives\\\", fontsize=18)\\n\",\n    \"\\n\",\n    \"for ax, idx in zip(axes[:, 3], [60, 70, 80, 90]):\\n\",\n    \"    fig, ax, im = plot_idv_brain(mean_maps_LRP[\\\"FN\\\"], None, mean_maps_LRP[\\\"AD\\\"], z_idx=idx, contour_areas=[],\\n\",\n    \"                                 vmin=vmin, vmax=vmax, fig=fig, ax=ax, set_nan=False, cmap=\\\"hot\\\");\\n\",\n    \"ax.text(16, -20, \\\"False negatives\\\", fontsize=18)\\n\",\n    \"\\n\",\n    \"fig.suptitle(\\\"LRP\\\", fontsize=24, x=.42)\\n\",\n    \"fig.tight_layout()\\n\",\n    \"\\n\",\n    \"fig.subplots_adjust(top=0.95, right=0.8, hspace=0.05, wspace=0.05)\\n\",\n    \"cbar_ax = fig.add_axes([0.85, 0.15, 0.02, 0.7])\\n\",\n    \"cbar = fig.colorbar(im, shrink=0.5, ticks=[vmin, vmax], cax=cbar_ax)\\n\",\n    \"\\n\",\n    \"vmin_val, vmax_val = np.percentile(mean_maps_LRP[\\\"AD\\\"], vmin), np.percentile(mean_maps_LRP[\\\"AD\\\"], vmax)\\n\",\n    \"cbar.set_ticks([vmin_val, vmax_val])\\n\",\n    \"cbar.ax.set_yticklabels(['{0:.1f}%'.format(vmin), '{0:.1f}%'.format(vmax)],\\n\",\n    \"                        fontsize=16)\\n\",\n    \"cbar.set_label('Percentile of  average AD patient values', rotation=270, fontsize=20)\\n\",\n    \"fig.savefig(os.path.join(data_path, \\\"LRP_TP_FP_TN_FN_brain.pdf\\\"))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x864 with 17 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, axes = plt.subplots(4, 4, figsize=(12, 12), sharey=True, sharex=True)\\n\",\n    \"vmin, vmax = 50, 99.5\\n\",\n    \"\\n\",\n    \"for ax, idx in zip(axes[:, 0], [60, 70, 80, 90]):\\n\",\n    \"    ax.text(-25, 140, \\\"Slice \\\" + str(idx), rotation=\\\"vertical\\\", fontsize=18)\\n\",\n    \"    \\n\",\n    \"    fig, ax, im = plot_idv_brain(mean_maps_GB[\\\"TP\\\"], None, mean_maps_GB[\\\"AD\\\"], z_idx=idx, contour_areas=[],\\n\",\n    \"                                 vmin=vmin, vmax=vmax, fig=fig, ax=ax, set_nan=False, cmap=\\\"hot\\\");\\n\",\n    \"\\n\",\n    \"ax.text(22, -20, \\\"True positives\\\", fontsize=18)\\n\",\n    \"\\n\",\n    \"for ax, idx in zip(axes[:, 1], [60, 70, 80, 90]):\\n\",\n    \"    fig, ax, im = plot_idv_brain(mean_maps_GB[\\\"FP\\\"], None, mean_maps_GB[\\\"AD\\\"], z_idx=idx, contour_areas=[],\\n\",\n    \"                                 vmin=vmin, vmax=vmax, fig=fig, ax=ax, set_nan=False, cmap=\\\"hot\\\");\\n\",\n    \"\\n\",\n    \"ax.text(18, -20, \\\"False positives\\\", fontsize=18)\\n\",\n    \"\\n\",\n    \"for ax, idx in zip(axes[:, 2], [60, 70, 80, 90]):\\n\",\n    \"    fig, ax, im = plot_idv_brain(mean_maps_GB[\\\"TN\\\"], None, mean_maps_GB[\\\"AD\\\"], z_idx=idx, contour_areas=[],\\n\",\n    \"                                 vmin=vmin, vmax=vmax, fig=fig, ax=ax, set_nan=False, cmap=\\\"hot\\\");\\n\",\n    \"ax.text(18, -20, \\\"True negatives\\\", fontsize=18)\\n\",\n    \"\\n\",\n    \"for ax, idx in zip(axes[:, 3], [60, 70, 80, 90]):\\n\",\n    \"    fig, ax, im = plot_idv_brain(mean_maps_GB[\\\"FN\\\"], None, mean_maps_GB[\\\"AD\\\"], z_idx=idx, contour_areas=[],\\n\",\n    \"                                 vmin=vmin, vmax=vmax, fig=fig, ax=ax, set_nan=False, cmap=\\\"hot\\\");\\n\",\n    \"ax.text(16, -20, \\\"False negatives\\\", fontsize=18)\\n\",\n    \"\\n\",\n    \"fig.suptitle(\\\"GB\\\", fontsize=24, x=.42)\\n\",\n    \"fig.tight_layout()\\n\",\n    \"fig.subplots_adjust(top=0.95)\\n\",\n    \"\\n\",\n    \"fig.subplots_adjust(top=0.95, right=0.8, hspace=0.05, wspace=0.05)\\n\",\n    \"cbar_ax = fig.add_axes([0.85, 0.15, 0.02, 0.7])\\n\",\n    \"cbar = fig.colorbar(im, shrink=0.5, ticks=[vmin, vmax], cax=cbar_ax)\\n\",\n    \"\\n\",\n    \"vmin_val, vmax_val = np.percentile(mean_maps_GB[\\\"AD\\\"], vmin), np.percentile(mean_maps_GB[\\\"AD\\\"], vmax)\\n\",\n    \"cbar.set_ticks([vmin_val, vmax_val])\\n\",\n    \"cbar.ax.set_yticklabels(['{0:.1f}%'.format(vmin), '{0:.1f}%'.format(vmax)],\\n\",\n    \"                        fontsize=16)\\n\",\n    \"cbar.set_label('Percentile of  average AD patient values', rotation=270, fontsize=20)\\n\",\n    \"fig.savefig(os.path.join(data_path, \\\"GB_TP_FP_TN_FN_brain.pdf\\\"))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Innvestigate\",\n   \"language\": \"python\",\n   \"name\": \"innvestigate\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.1\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "Plotting result graphs.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Adding model_path to namespace\\n\",\n      \"Adding data_path to namespace\\n\",\n      \"Adding ADNI_DIR to namespace\\n\",\n      \"Adding 1.5T_table to namespace\\n\",\n      \"Adding 1.5T_image_dir to namespace\\n\",\n      \"Adding 3T_table to namespace\\n\",\n      \"Adding 3T_image_dir to namespace\\n\",\n      \"Adding binary_brain_mask to namespace\\n\",\n      \"Adding nmm_mask_path to namespace\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from jrieke import interpretation\\n\",\n    \"from jrieke.utils import load_nifti\\n\",\n    \"\\n\",\n    \"import pickle\\n\",\n    \"import os\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import matplotlib.colors as mcolors\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"from settings import settings\\n\",\n    \"from nmm_mask_areas import short_name_map\\n\",\n    \"for k in settings.keys():\\n\",\n    \"    print(\\\"Adding \\\" + k + \\\" to namespace\\\")\\n\",\n    \"    globals()[k] = settings[k]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data_path = \\\"/analysis/ritter/projects/Methods/LRP/data/rieke-copy1/2Node_trial0/beta0/\\\"\\n\",\n    \"mask_path = \\\"/analysis/ritter/projects/Methods/LRP/data/\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Load results from evaluation notebook\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cases = [\\\"AD\\\", \\\"HC\\\", \\\"TP\\\", \\\"TN\\\", \\\"FP\\\", \\\"FN\\\"]\\n\",\n    \"\\n\",\n    \"mean_maps_LRP = dict()\\n\",\n    \"mean_maps_GB = dict()\\n\",\n    \"rs_per_area_LRP = dict()\\n\",\n    \"rs_per_area_GB = dict()\\n\",\n    \"for case in cases:\\n\",\n    \"    mean_maps_LRP[case] = load_nifti(os.path.join(data_path, \\\"LRP_{case}.nii\\\".format(case=case)))\\n\",\n    \"    mean_maps_GB[case] = load_nifti(os.path.join(data_path, \\\"GB_{case}.nii\\\".format(case=case)))\\n\",\n    \"    with open(os.path.join(data_path, \\\"LRP_area_evdcs_{case}.pkl\\\".format(case=case)), 'rb') as file:\\n\",\n    \"        rs_per_area_LRP[case] = pickle.load(file)\\n\",\n    \"    with open(os.path.join(data_path, \\\"GB_area_evdcs_{case}.pkl\\\".format(case=case)), 'rb') as file:\\n\",\n    \"        rs_per_area_GB[case] = pickle.load(file)\\n\",\n    \"    \\n\",\n    \"with open(os.path.join(data_path, 'area_sizes.pkl'), 'rb') as file:\\n\",\n    \"    area_sizes = pickle.load(file)\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### Helper funtion to compute mean\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def comp_mean(rs_per_area, case, area_norm=False):\\n\",\n    \"    \\n\",\n    \"    mean = dict()\\n\",\n    \"    for area, result_list in rs_per_area[case].items():\\n\",\n    \"        mean[area] = np.mean([area_sum for pat_idx, area_sum in result_list])\\n\",\n    \"        if area_norm:\\n\",\n    \"            mean[area] /= area_sizes[area]\\n\",\n    \"    \\n\",\n    \"    return mean\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### Compute mean scores per area  and sort labels according to importance\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Mean for AD and HC in LRP case\\n\",\n    \"mean_ad_LRP = comp_mean(rs_per_area=rs_per_area_LRP, case=\\\"AD\\\")\\n\",\n    \"mean_hc_LRP = comp_mean(rs_per_area=rs_per_area_LRP, case=\\\"HC\\\")\\n\",\n    \"# Mean for AD and HC in GB case\\n\",\n    \"mean_ad_GB = comp_mean(rs_per_area=rs_per_area_GB, case=\\\"AD\\\")\\n\",\n    \"mean_hc_GB = comp_mean(rs_per_area=rs_per_area_GB, case=\\\"HC\\\")\\n\",\n    \"# Mean for TP -- LRP case\\n\",\n    \"mean_tp_LRP = comp_mean(rs_per_area=rs_per_area_LRP, case=\\\"TP\\\")\\n\",\n    \"# Mean for TP -- GB case\\n\",\n    \"mean_tp_GB = comp_mean(rs_per_area=rs_per_area_GB, case=\\\"TP\\\")\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"sorted_lbls_LRP = {\\n\",\n    \"    \\n\",\n    \"    \\\"absolute\\\": np.array(sorted(\\n\",\n    \"        [(k, v) for k, v in mean_ad_LRP.items()], key=lambda x: float(x[1])))[:, 0],\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\\"area_norm\\\": np.array(sorted(\\n\",\n    \"        [(k, v/area_sizes[k]) for k, v in mean_ad_LRP.items()], key=lambda x: float(x[1])))[:, 0],\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\\"gain_ad\\\": np.array(sorted(\\n\",\n    \"        [(k, v/mean_hc_LRP[k]) for k, v in mean_ad_LRP.items()], key=lambda x: float(x[1])))[:, 0],\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\\"gain_tp\\\": np.array(sorted(\\n\",\n    \"       [(k, v/mean_hc_LRP[k]) for k, v in mean_tp_LRP.items()], key=lambda x: float(x[1])))[:, 0]\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"sorted_lbls_GB = {\\n\",\n    \"    \\n\",\n    \"    \\\"absolute\\\": np.array(sorted(\\n\",\n    \"        [(k, v) for k, v in mean_ad_GB.items()], key=lambda x: float(x[1])))[:, 0],\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\\"area_norm\\\": np.array(sorted(\\n\",\n    \"        [(k, v/area_sizes[k]) for k, v in mean_ad_GB.items()], key=lambda x: float(x[1])))[:, 0],\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\\"gain_ad\\\": np.array(sorted(\\n\",\n    \"        [(k, v/mean_hc_GB[k]) for k, v in mean_ad_GB.items()], key=lambda x: float(x[1])))[:, 0],\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\\"gain_tp\\\": np.array(sorted(\\n\",\n    \"       [(k, v/mean_hc_GB[k]) for k, v in mean_tp_GB.items()], key=lambda x: float(x[1])))[:, 0]\\n\",\n    \"}\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# save sortings for comparison\\n\",\n    \"np.save(os.path.join(data_path, \\\"sorted_lbls_LRP_gain_tp.npy\\\"), sorted_lbls_LRP[\\\"gain_tp\\\"])\\n\",\n    \"np.save(os.path.join(data_path, \\\"sorted_lbls_area_norm.npy\\\"), sorted_lbls_LRP[\\\"area_norm\\\"])\\n\",\n    \"np.save(os.path.join(data_path, \\\"sorted_lbls_absolute.npy\\\"), sorted_lbls_LRP[\\\"absolute\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Plotting function\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_ad_hc(mean_ad, mean_hc, plt_opts, sorting=\\\"absolute\\\", fig=None, ax=None,\\n\",\n    \"              idv_plots=None, plot_idvs=False, sort_dict=None,\\n\",\n    \"              rs_per_a=None, patA=23, patB=64, the_labels=None, shift=False):\\n\",\n    \"    \\n\",\n    \"    if fig is None or ax is None:\\n\",\n    \"        fig, ax = plt.subplots(figsize=(12,6))\\n\",\n    \"\\n\",\n    \"    if the_labels is None:\\n\",\n    \"        the_labels = np.array(sort_dict[sorting], dtype=str)\\n\",\n    \"    ad_vals = np.array([mean_ad[l] for l in the_labels], dtype=float)\\n\",\n    \"    \\n\",\n    \"    if mean_hc is not None:\\n\",\n    \"        hc_vals = np.array([mean_hc[l] for l in the_labels], dtype=float)\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    x_vals = np.arange(len(the_labels))\\n\",\n    \"    if shift:\\n\",\n    \"        # Necessary for violin plots\\n\",\n    \"        x_vals = x_vals+1\\n\",\n    \"    \\n\",\n    \"    if mean_hc is not None:\\n\",\n    \"        \\n\",\n    \"        ax.scatter(np.array(x_vals), hc_vals, color=plt_opts[\\\"color_hc\\\"],\\n\",\n    \"                   label=plt_opts[\\\"hc_label\\\"], **plt_opts[\\\"scatter_style\\\"],\\n\",\n    \"                   zorder=20)\\n\",\n    \"    \\n\",\n    \"    ax.scatter(np.array(x_vals), ad_vals, color=plt_opts[\\\"color_ad\\\"], \\n\",\n    \"               label=plt_opts[\\\"ad_label\\\"], **plt_opts[\\\"scatter_style\\\"],\\n\",\n    \"              zorder=20)\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    if plot_idvs:\\n\",\n    \"        pat_A_idx = np.where(np.array(rs_per_area_LRP[\\\"TP\\\"][\\\"CSF\\\"])[:, 0] == patA)[0][0]\\n\",\n    \"        pat_B_idx = np.where(np.array(rs_per_area_LRP[\\\"TP\\\"][\\\"CSF\\\"])[:, 0] == patB)[0][0]\\n\",\n    \"        ax.plot(x_vals, np.array([np.array(rs_per_a[\\\"TP\\\"][l])[pat_A_idx, 1]/(area_sizes[l]) for l in the_labels], dtype=float), \\n\",\n    \"                **plt_opts[\\\"idvs_style\\\"], linestyle='dotted', label=\\\"Patient A\\\")\\n\",\n    \"        ax.plot(x_vals, np.array([np.array(rs_per_a[\\\"TP\\\"][l])[pat_B_idx, 1]/(area_sizes[l]) for l in the_labels], dtype=float), \\n\",\n    \"                **plt_opts[\\\"idvs_style\\\"], linestyle='dashed', label=\\\"Patient B\\\")\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    ###################\\n\",\n    \"    ##################\\n\",\n    \"    # Formatting\\n\",\n    \"    ax.set_xticks(x_vals)\\n\",\n    \"    ax.tick_params(axis='x', which='major', pad=10, length=5)\\n\",\n    \"\\n\",\n    \"    ax.set_xticklabels([\\\"{0:>25s}\\\".format(short_name_map[e]) for e in the_labels], rotation=\\\"vertical\\\",\\n\",\n    \"                      fontsize=12)\\n\",\n    \"    \\n\",\n    \"    if \\\"title\\\" in plt_opts:\\n\",\n    \"        ax.set_title(plt_opts[\\\"title\\\"], fontsize=16)\\n\",\n    \"    \\n\",\n    \"    ax.set_ylabel(plt_opts[\\\"y_label\\\"], fontsize=16)\\n\",\n    \"    \\n\",\n    \"    if \\\"ylim\\\" in plt_opts:\\n\",\n    \"        ax.set_ylim(plt_opts[\\\"ylim\\\"])\\n\",\n    \"\\n\",\n    \"    ax.ticklabel_format(style='sci',scilimits=(-3,3),axis='y')\\n\",\n    \"\\n\",\n    \"    return fig, ax \\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Absolute scores \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### LRP\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"first_n = 25\\n\",\n    \"the_labels = sorted_lbls_LRP[\\\"absolute\\\"][-first_n:]\\n\",\n    \"sizes = np.array([area_sizes[k] for k in the_labels])\\n\",\n    \"sizes /= np.max(sizes)\\n\",\n    \"\\n\",\n    \"plt_opts = {\\n\",\n    \"    \\\"scatter_style\\\": {\\n\",\n    \"        \\\"edgecolors\\\":'grey', \\n\",\n    \"        \\\"lw\\\":1.5, \\n\",\n    \"        \\\"s\\\": sizes*1000,\\n\",\n    \"        \\\"alpha\\\": .7\\n\",\n    \"    },\\n\",\n    \"    \\\"color_ad\\\": \\\"darkorange\\\",\\n\",\n    \"    \\\"color_hc\\\": \\\"forestgreen\\\",\\n\",\n    \"    \\\"y_label\\\": \\\"Total sum of relevance\\\",\\n\",\n    \"    \\\"ad_label\\\": \\\"AD\\\",\\n\",\n    \"    \\\"hc_label\\\": \\\"HC\\\",\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots(figsize=(12,6))\\n\",\n    \"fig, ax = plot_ad_hc(mean_ad_LRP, mean_hc_LRP, plt_opts, sorting=\\\"absolute\\\",\\n\",\n    \"          the_labels=the_labels, fig=fig, ax=ax)\\n\",\n    \"\\n\",\n    \"ax.set_xlim(-0.5, len(the_labels))\\n\",\n    \"ax.set_ylim(0, 1.1 * np.max(mean_ad_LRP[the_labels[-1]]))\\n\",\n    \"leg = fig.legend(bbox_to_anchor=[.2, .85])\\n\",\n    \"for lh in leg.legendHandles: \\n\",\n    \"    lh._sizes= [60]\\n\",\n    \"    \\n\",\n    \"fig.suptitle(\\\"Relevance sum – LRP\\\", y=1.017, fontweight=\\\"bold\\\", fontsize=18)\\n\",\n    \"\\n\",\n    \"fig.tight_layout()\\n\",\n    \"# fig.savefig(os.path.join(data_path, \\\"absolute_LRP.pdf\\\"), bbox_inches='tight')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### GB\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"first_n = 25\\n\",\n    \"the_labels = sorted_lbls_GB[\\\"absolute\\\"][-first_n:]\\n\",\n    \"sizes = np.array([area_sizes[k] for k in the_labels])\\n\",\n    \"sizes /= np.max(sizes)\\n\",\n    \"\\n\",\n    \"plt_opts = {\\n\",\n    \"    \\\"scatter_style\\\": {\\n\",\n    \"        \\\"edgecolors\\\":'grey', \\n\",\n    \"        \\\"lw\\\":1.5, \\n\",\n    \"        \\\"s\\\": sizes*1000,\\n\",\n    \"        \\\"alpha\\\": .7\\n\",\n    \"    },\\n\",\n    \"    \\\"color_ad\\\": \\\"darkorange\\\",\\n\",\n    \"    \\\"color_hc\\\": \\\"forestgreen\\\",\\n\",\n    \"    \\\"y_label\\\": \\\"Susceptibility\\\",\\n\",\n    \"    \\\"ad_label\\\": \\\"AD\\\",\\n\",\n    \"    \\\"hc_label\\\": \\\"HC\\\",\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots(figsize=(12,6))\\n\",\n    \"fig, ax = plot_ad_hc(mean_ad_GB, mean_hc_GB, plt_opts, sorting=\\\"absolute\\\",\\n\",\n    \"          the_labels=the_labels, fig=fig, ax=ax)\\n\",\n    \"\\n\",\n    \"ax.set_xlim(-0.5, len(the_labels))\\n\",\n    \"ax.set_ylim(0, 1.5 * np.max(mean_ad_GB[the_labels[-1]]))\\n\",\n    \"leg = fig.legend(bbox_to_anchor=[.2, .85])\\n\",\n    \"for lh in leg.legendHandles: \\n\",\n    \"    lh._sizes= [60]\\n\",\n    \"    \\n\",\n    \"fig.suptitle(\\\"Susceptibility sum – GB\\\", y=1.017, fontweight=\\\"bold\\\", fontsize=18)\\n\",\n    \"\\n\",\n    \"fig.tight_layout()\\n\",\n    \"# fig.savefig(os.path.join(data_path, \\\"absolute_GB.pdf\\\"), bbox_inches='tight')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Area normalized scores\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### Compute corresponding means\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# How many patients per group – used for loop in cell below\\n\",\n    \"num_ADs, num_HCs = len(rs_per_area_LRP[\\\"AD\\\"][\\\"CSF\\\"]), len(rs_per_area_LRP[\\\"HC\\\"][\\\"CSF\\\"])\\n\",\n    \"\\n\",\n    \"# Means for ADs\\n\",\n    \"mean_ad_LRP_norm = comp_mean(rs_per_area_LRP, \\\"AD\\\", area_norm=True)\\n\",\n    \"mean_ad_GB_norm = comp_mean(rs_per_area_GB, \\\"AD\\\", area_norm=True)\\n\",\n    \"\\n\",\n    \"# Means for HCs\\n\",\n    \"mean_hc_LRP_norm = comp_mean(rs_per_area_LRP, \\\"HC\\\", area_norm=True)\\n\",\n    \"mean_hc_GB_norm = comp_mean(rs_per_area_GB, \\\"HC\\\", area_norm=True)\\n\",\n    \"\\n\",\n    \"# First 25 most important labels under this metric\\n\",\n    \"labels_norm_LRP = np.array(sorted_lbls_LRP[\\\"area_norm\\\"], dtype=str)[-first_n:]\\n\",\n    \"labels_norm_GB = np.array(sorted_lbls_GB[\\\"area_norm\\\"], dtype=str)[-first_n:]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"###### Get individual patients' scores for violin plots\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Alzheimer's disease patients\\n\",\n    \"evdc_vals_ad_LRP = np.array([[rs_per_area_LRP[\\\"AD\\\"][l][patient][1]/(area_sizes[l]) \\n\",\n    \"                         for l in labels_norm_LRP] for patient in range(num_ADs)], dtype=float)\\n\",\n    \"evdc_vals_ad_GB = np.array([[rs_per_area_GB[\\\"AD\\\"][l][patient][1]/(area_sizes[l]) \\n\",\n    \"                         for l in labels_norm_GB] for patient in range(num_ADs)], dtype=float)\\n\",\n    \"\\n\",\n    \"# Healthy controls\\n\",\n    \"evdc_vals_hc_LRP = np.array([[rs_per_area_LRP[\\\"HC\\\"][l][patient][1]/(area_sizes[l]) \\n\",\n    \"                         for l in labels_norm_LRP] for patient in range(num_HCs)], dtype=float)\\n\",\n    \"evdc_vals_hc_GB = np.array([[rs_per_area_GB[\\\"HC\\\"][l][patient][1]/(area_sizes[l]) \\n\",\n    \"                         for l in labels_norm_GB] for patient in range(num_HCs)], dtype=float)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_opts = {\\n\",\n    \"    \\n\",\n    \"    \\\"scatter_style\\\": {\\n\",\n    \"        \\\"edgecolors\\\": (.95, .95, .95), \\n\",\n    \"        \\\"lw\\\":1.5, \\n\",\n    \"        \\\"s\\\": 60, \\n\",\n    \"        \\\"alpha\\\":1},\\n\",\n    \"        \\n\",\n    \"    \\\"idvs_style\\\": {\\n\",\n    \"        \\\"alpha\\\": .75, \\n\",\n    \"        \\\"lw\\\":2, \\n\",\n    \"        \\\"color\\\": \\\"navy\\\",\\n\",\n    \"        \\\"zorder\\\": 10},\\n\",\n    \"    \\n\",\n    \"    \\\"color_ad\\\": \\\"darkorange\\\",\\n\",\n    \"    \\\"color_idvs\\\": \\\"navy\\\",\\n\",\n    \"    \\\"color_hc\\\": \\\"forestgreen\\\",\\n\",\n    \"    \\\"y_label\\\": \\\"Size-normalised sum of relevance\\\",\\n\",\n    \"    \\\"ad_label\\\": \\\"AD\\\",\\n\",\n    \"    \\\"hc_label\\\": \\\"HC\\\",\\n\",\n    \"    \\\"ylim\\\": (-1e-7, 3.2 * 1e-6)\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"fig, ax = plot_ad_hc(mean_ad_LRP_norm, mean_hc_LRP_norm, plt_opts, sorting=\\\"area_norm\\\",\\n\",\n    \"                     plot_idvs=True, rs_per_a=rs_per_area_LRP,\\n\",\n    \"                     patB=2, \\n\",\n    \"                     shift=True,\\n\",\n    \"                     patA=169,\\n\",\n    \"                    the_labels=labels_norm_LRP)\\n\",\n    \"\\n\",\n    \"parts = ax.violinplot(evdc_vals_ad_LRP, widths=.75, showextrema=False, points=100)\\n\",\n    \"for pc in parts['bodies']:\\n\",\n    \"    pc.set_facecolor(plt_opts[\\\"color_ad\\\"])\\n\",\n    \"    pc.set_edgecolor('black')\\n\",\n    \"    pc.set_alpha(.2)\\n\",\n    \"\\n\",\n    \"parts = ax.violinplot(evdc_vals_hc_LRP, widths=.75, showextrema=False, points=100)\\n\",\n    \"for pc in parts['bodies']:\\n\",\n    \"    pc.set_facecolor(plt_opts[\\\"color_hc\\\"])\\n\",\n    \"    pc.set_edgecolor('black')\\n\",\n    \"    pc.set_alpha(.2)\\n\",\n    \"\\n\",\n    \"ax.set_xlim(0.25, first_n+.75)\\n\",\n    \"leg = fig.legend(bbox_to_anchor=[.2, .85])\\n\",\n    \"for lh in leg.legendHandles: \\n\",\n    \"    lh.set_alpha(1)\\n\",\n    \"\\n\",\n    \"fig.suptitle(\\\"Relevance density – LRP\\\", y=1.0, fontweight=\\\"bold\\\", fontsize=18)\\n\",\n    \"\\n\",\n    \"fig.tight_layout()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# fig.savefig(os.path.join(data_path, \\\"size_norm_LRP.pdf\\\"))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_opts = {\\n\",\n    \"    \\n\",\n    \"    \\\"scatter_style\\\": {\\n\",\n    \"        \\\"edgecolors\\\": (.95, .95, .95), \\n\",\n    \"        \\\"lw\\\":1.5, \\n\",\n    \"        \\\"s\\\": 60, \\n\",\n    \"        \\\"alpha\\\":1},\\n\",\n    \"        \\n\",\n    \"    \\\"hipps_style\\\": {\\n\",\n    \"        \\\"alpha\\\": .75, \\n\",\n    \"        \\\"lw\\\":2, \\n\",\n    \"        \\\"color\\\": \\\"navy\\\",\\n\",\n    \"        \\\"zorder\\\": 10},\\n\",\n    \"    \\n\",\n    \"    \\\"color_ad\\\": \\\"darkorange\\\",\\n\",\n    \"    \\\"color_hipps\\\": \\\"navy\\\",\\n\",\n    \"    \\\"color_hc\\\": \\\"forestgreen\\\",\\n\",\n    \"    \\\"y_label\\\": \\\"Size-normalised sum of susceptibility\\\",\\n\",\n    \"    \\\"ad_label\\\": \\\"AD\\\",\\n\",\n    \"    \\\"hc_label\\\": \\\"HC\\\",\\n\",\n    \"    \\\"ylim\\\": (-1e-5, 1.2 * 1e-3)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"fig, ax = plot_ad_hc(mean_ad_GB_norm, mean_hc_GB_norm, plt_opts, sorting=\\\"area_norm\\\",\\n\",\n    \"                     rs_per_a=rs_per_area_GB,\\n\",\n    \"                     shift=True,\\n\",\n    \"                    the_labels=labels_norm_GB)\\n\",\n    \"\\n\",\n    \"parts = ax.violinplot(evdc_vals_ad_GB, widths=.75, showextrema=False, points=100)\\n\",\n    \"for pc in parts['bodies']:\\n\",\n    \"    pc.set_facecolor(plt_opts[\\\"color_ad\\\"])\\n\",\n    \"    pc.set_edgecolor('black')\\n\",\n    \"    pc.set_alpha(.2)\\n\",\n    \"\\n\",\n    \"parts = ax.violinplot(evdc_vals_hc_GB, widths=.75, showextrema=False, points=100)\\n\",\n    \"for pc in parts['bodies']:\\n\",\n    \"    pc.set_facecolor(plt_opts[\\\"color_hc\\\"])\\n\",\n    \"    pc.set_edgecolor('black')\\n\",\n    \"    pc.set_alpha(.2)\\n\",\n    \"\\n\",\n    \"ax.set_xlim(0.25, first_n+.75)\\n\",\n    \"leg = fig.legend(bbox_to_anchor=[.2, .85])\\n\",\n    \"for lh in leg.legendHandles: \\n\",\n    \"    lh.set_alpha(1)\\n\",\n    \"\\n\",\n    \"fig.suptitle(\\\"Susceptibility density – GB\\\", y=1.0, fontweight=\\\"bold\\\", fontsize=18)\\n\",\n    \"\\n\",\n    \"fig.tight_layout()\\n\",\n    \"\\n\",\n    \"fig.savefig(os.path.join(data_path, \\\"size_norm_GB.pdf\\\"), bbox_inches='tight')\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Gain scores\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# How many patients per group – used for loop in cell below\\n\",\n    \"num_TPs, num_TNs = len(rs_per_area_LRP[\\\"TP\\\"][\\\"CSF\\\"]), len(rs_per_area_LRP[\\\"TN\\\"][\\\"CSF\\\"])\\n\",\n    \"\\n\",\n    \"# Calculate gains for TP (true positives) and TN true negatives – LRP\\n\",\n    \"mean_gain_TP_LRP = {l: v/mean_hc_LRP[l] for l, v in comp_mean(rs_per_area=rs_per_area_LRP, case=\\\"TP\\\").items()}\\n\",\n    \"mean_gain_TN_LRP = {l: v/mean_hc_LRP[l] for l, v in comp_mean(rs_per_area=rs_per_area_LRP, case=\\\"TN\\\").items()}\\n\",\n    \"\\n\",\n    \"# Calculate gains for TP (true positives) and TN true negatives – GB\\n\",\n    \"mean_gain_TP_GB = {l: v/mean_hc_GB[l] for l, v in comp_mean(rs_per_area=rs_per_area_GB, case=\\\"TP\\\").items()}\\n\",\n    \"mean_gain_TN_GB = {l: v/mean_hc_GB[l] for l, v in comp_mean(rs_per_area=rs_per_area_GB, case=\\\"TN\\\").items()}\\n\",\n    \"\\n\",\n    \"# Get first_n most important areas under this metric\\n\",\n    \"labels_gain_LRP = np.array(sorted_lbls_LRP[\\\"gain_tp\\\"], dtype=str)[-first_n:]\\n\",\n    \"labels_gain_GB = np.array(sorted_lbls_GB[\\\"gain_tp\\\"], dtype=str)[-first_n:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"###### Get individual patients' scores for violin plots\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# True positives LRP and GB\\n\",\n    \"gain_vals_TP_LRP = np.array([[rs_per_area_LRP[\\\"TP\\\"][l][patient][1]/(mean_hc_LRP[l]) \\n\",\n    \"                         for l in labels_gain_LRP] for patient in range(num_TPs)], dtype=float)\\n\",\n    \"gain_vals_TP_GB = np.array([[rs_per_area_GB[\\\"TP\\\"][l][patient][1]/(mean_hc_GB[l]) \\n\",\n    \"                         for l in labels_gain_GB] for patient in range(num_TPs)], dtype=float)\\n\",\n    \"\\n\",\n    \"# True negatives LRP and GB\\n\",\n    \"gain_vals_TN_LRP = np.array([[rs_per_area_LRP[\\\"TN\\\"][l][patient][1]/(mean_hc_LRP[l]) \\n\",\n    \"                         for l in labels_gain_LRP] for patient in range(num_TNs)], dtype=float)\\n\",\n    \"gain_vals_TN_GB = np.array([[rs_per_area_GB[\\\"TN\\\"][l][patient][1]/(mean_hc_GB[l]) \\n\",\n    \"                         for l in labels_gain_GB] for patient in range(num_TNs)], dtype=float)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Plot LRP\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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dcgdNOOy31Y+7+5jfRPHIEJ+cQRYM4rosfLS7iha96VebHEtGjabddFwKLiF4D4PkALmei87FAINjgSJIEBiBgrNQFz3qCMbYqakFEEIf8jU3fdVFLRBVkxtDv98c8gvP4/T5m6nXUFAWebVc2LhBeKSfGIEsS/ApfMwBY3S5O1DQA4fdMI4Jt22g0GuWNaVnQxhzPNCKYhsF9XM/zELjuxOhGXdOw1G5zH38t6HW7qGWM5mhEsCyr0LiebReOIslEcF0302OCIIDEKepNRAiCgMtzjWPNz+xE9FwAfwzghYyx3lrPRyAQCMpGkiQwIgTAcZEeCEQCa+A+iu4XbFxc216RlqdIEjzPq2x8z3WhSBJkWYaXcVFXlCAIIEsSJCL4vl/p2L1uF/VIYAF8FteTMNpt1MZE42uqCqMEgWPbNrQUx9GapsHqdLiOnfn4xRiXY16v20Ut8fmmQVNV9LrdQuPallU440IFMh8DAt8HOJ0rJUkKn69kqrZp/zSAuwGcR0T7ieh1AP4OwDSA24joXiLaVeWcBAKBoGqSkZvjJYIVBAEwZGFRxZVEwdrhOg7UhMCSgEojWK5tQ5FlqIoCt2SBMYjv+5CIIMsy+hWKSsYYzE5nxQK8CoFlttsrRN0gdV1HrwSBNSlyFqPXajALCoxVDInMj4PXBbU8AquuaegVFJhOrwetYARLlWXYppnpMYHvc3vvCACr4LxTaYogY+wVQ+7+xyrnIBAIBGuNJElh+tBxEr0CwkXf4BJIgohgbXQ8x4GSSEtTiCoVWI5lQVEUKIoCLzK7qArf90FRGnCVKYKO40BibEXkUCNCr1dukpDRauG0MbV19VoNRqvFfdxer5dKYKmKAuZ58DyPW93rsMj8OHhF7c1uF3pG05RaQYHleR4kDmntqqrCzrgvBoyJFEGBQCAQjIeI4AOVO5qtJaNOaEJgbWw8113hFiYhe3pQofEdB6qiQJVlOBXXYPm+D5mo8hqsXq8HfWAxqmtaKel5MYyxMII1xtVO1zRYpsl9cdszTdRSLr5rksRVaAY5jl9Fj3mMMTimOTYdcxh6rVYoRdB1XagcMi40RYGTMZrKM+JERKjirCMElkAgEFSMJEkIMqaW8GR+fh6HDh2qND2PDbkCSRApghudOEUvRka1AsuxrDBFUFVhr1GKYNURLMuyVgmOeq0GY2mp1DFVjE95liQJKmPcUxWNViu1XbkKcB2fZcxE4GHs4zgO5ByRJFVR0Hec3BFk13W5pL2pqpo5RTDr+zwOIuJWCzcOIbAEAoFgDWCMgdao/uqOm2/G3V/6EpZKXHANMtTkQrgIbmj6/f5yilxM1SYXjmWhpqrQ1sBF0Pd9SABkSao0LdIwjBXOjQDQqNXQLfH7bprmqqjZMHRJgplxcT0JY0zvrUFqnFMlWRBkFjpFj3mWZaGW89wRu0nmwXVdKBxEjqoocDJ+BsNcaNc7QmAJBAJBxSzngK/RCaPvOGgqSqXRo1FjiQjWxqXf70MZWAiqslyp2YTT60FRFMiyDN91K3XziyNYRARirLKxO4uL0AfSx/RaDZZhlPZ9M00znZMfEQzOVu1Gu416yr5quqLA5OgkmLUGS+JwUSmOFuahJkm5I3hu5MhZFE1Vc6Xr8j5bigiWQCAQbDDiHPC1uCK3fFKpOHoURIvNJBJjQmBtYIalFCmKkrnAPS9BEIQuhlENmCbLcCo0uogjWEDY+6cqgdVdXESzXl9xnyRJ0IDSjC667TbqKdzlGqqKLkejC9/3M9Uj6ZwjeVnd7XikRdu2DTXnuUMtILAcx4HC4Zyh5LjYwftcVcWZVwgsgUAgWAPWKjEuvuJatYPf0IVIBXnwgrXD8zwM2rioFbr5ua4LTZKW9zuVaM0ElhT9XQXdpSU0hkR09BKiRzGdhYWhYw7S0HW05+e5jdvr9VCLooRpaOg6V7OPIGOKIA8XQbPbhZ7TKl1D/ho0x7JWtFwogipJmb+LIkVQIBAIBBNZq1NFMqVlzSNYkV29YGMyLKVIVZTMBe55GbzSrwCVCqx+v78iglVFHZbruuhb1tAeSTUi7vVPMe35eUwNRM2G0ajX0V5c5DZur9fLVI+kc7aKD3w/m007h6i92W5Dz9gDK6amablTJHuGAY2Tvb0qSXCzNP4u4VwlUgQFAoFAwI3kSaVSgTXMBUpEsDY0nuetKorPY9GcF9u2V9QFrUUES47GrypF0DAM1EcIjkatxlXcxPi+D7PTSVUHVa/V4HS73MRmr9dLVfsVoyoK0O9z2w/8IICUIarDoweT2W6ndk0cpK5pMHNG8FzL4iawFCCbwDoGEQJLIBAIKiZrYXQZY/NqeJkW3/dXuSZKklSp6YCgWhzHWVWDpapqpQIrOb4mSehVFD0Dwh5gcdRWBiqJYBmGAX2EwGpyTs+LMU0T9ZRpekQEXZK4pSoanU6q2q8kOsdeWEG/n6kBrsxBYPUMI7fA0ms19HIKLNs0l+sZi6ISra3AquDcJwSWQCAQVExsOcuzeWIW4j4gVTI0RXAN3wNB+di9HrSBq/uqoqDveZWkhloD6WM1VeXqIDcJz3GWUySlilIEu+32aIFVr5cisLrdbiqL9pg6x1owY2kptUV7DE+rdr/fz1yDVeSiUhAEsHM0GY7RazWYOZsN26bJLYIlo9p03VVUUM+Veq8goiuI6AtE9DMi2j3w83CZkxQIBIKNBGMM8hrXH1VdMDzM5IKIKm3AKqgWyzCgDlmQVZWq111aWlGLpNdq6FUpsFwXSnTFv6oIVnt+HlONxtD/1TQNgeNwf+877TbqGdLk6rKMDqc6KKPdziywNI61aH6/DzmDwJIKRrBs24aG/MdvRZaBfj9X9Mixba41WFVFsteKVHsFET0PwM0AGgDOB/BzAHsBnAEgAHBHWRMUCASCjUYQBGGqyHGUHiciWMcfo654VyWwzE5nxeK7wdngYBKuZYULWoSLrSoaLLfm51dZtCcpw0mwdeQImin7UAHAVKOB1pEjXMbuLi2l7oEVU9c0LlbxQRBkboBbtB+aZVmZas6GUZOkzM2Gfd+H77rL+3NRNFWFlUXkHmMOgkD6CNafAvgIgOdFf/8JY+xSAE9AeGHmq/ynJhAIBBsTxljhK5m85lHZWEPsjEnUYG1obMMYmsqkopr0IGPADECv1dDrdivb7z3XhRwtSBVJKj2C5fs+zHZ7rF16XZLQzZkiNorOmKjZMKbqdS4Cy3EcoN/PXBdU13UYHMw+fN9f1YZgEkXt+i3Lglaw2a+K7P3QXNddle5bBE1V4WScA/deWCWLtrSf0vkAvoQwWsUQGoCAMfYAgJ0IBZhAIBAIUuBH0RweTSePFfwhESwCjqso3vFGzzSHFuOrOa6gZyUIAtiGsSKCJUkSFMZy9wHKimvby4t/mQhuya85jdlEQ1HQ4dloNwjQbbVS9cCKqes6zHa78MWVXq+XqfZrefxajUuKou/7mdIDgeLGPlldE4ehEmX+DjiOs8oRtNAcMjYc5y6G1lENVgCgz0L5eATAjsT/DgI4m/fEBAKBYKMSNyCtyrp5FFXWYQW+v+qkJkmSqMHaoPi+Dy8hMJJoORZ4WRnVgFYvsRfUIJ7jLDdmVRQFTskCK43ZRJNT9CjGMAzUGMtm9EDEJZKWtQdWTL1Wg20YhS9uJRtJp0VirFAk0+x0oBesg6pJUrb0PEQ97TieL2qqmrkfHs/4VRVR7LT7xi8APCa6fQ+AtxLRqUR0EoCrAezhPzWBQCDYmMT9caQ1FlhVMiyCJR1ndWjHE7Ztj1z81lQVPc5paoMYhjFUbNQkqTKB5TrOssmFKsvwShZYacwmmo0G115Y3W53ZN+tcdSJCgsswzBQy7HoJyIuToLJRtJpKXpRrdfp5LZoj9FrNRgZrdpd113VcqEIWds1lHExcL2kCH4SwOOj2+9DWHu1H8AsgGcB+DP+UxMIBIKNSd/zIBFBQjXOYuuBYIidsSRJwuRig9Lr9aCN+J9eq8Es2WzCNM2hi++6oqCbsw9QFhhjcG172RRAqaDBcnt+fmKqnq5pcLpdboYbWR0EY3g4CXaXlqBro/ay8dQ49MLq9/uZa7AUWS6UKmq227lfc4yuaZnbFbiuC5WjIFFkGZ7rZooiHms1WKkEKWPsI4nbPyCiXwLw6wDqAG5njP2spPkJBALBhiNeeMn9/vEjsIJgaATreHn9xxvjivHrtRr2lSxy2ouLaAxZiDZ0HZ2FhVLHBqLoRiJ1TqtAYHUWFvDYMQ6CwNH0PMMwcMIJJxQes3X4cCYHwRgeToLdpSVsyzE2AGjIbvQwSL/fh5xxkS5HwiIvZqcD/ZRTcj8eyNds2LHtzK91HEQEVZLgui70FJ8hSRLAM9thHdVgrYAxtp8x9g+MsQ8LcSUQCATZcB0HsixDIarEunk9wHx/qItgIATWhsQ0jJEpgrqmodfplFoH0R5hVz5VUrPdQVzXhZp4/YqiwC1RYDHG0FlcHGvRHsPTqr2V0UEwhsfnYLTbmS3aY3hEMj3Pyyw6ikSw+v0+fNfN7Jo4iK5psEwz0/fP7vWgFRx3EIWx1Oc/IuJ2vKjKRTRLo2EiohcS0YeI6AYiOjO6/xIiOq28KQoEAsHGIi7+l48jgTWyBmuDpwj6vo+5ubmhP2U76a0l49K3ZFmGxFipVu2jBFZd12F2OqXXPg6aAmiqWqrJRa/Xg5rSbKIuy1zSJIMggJHRQXB5DrqOXqeTO4Idu0TmTZer12owCrop5qnBUmUZXs793rKsXKYeg0iSBDnj98/p9QoLu0EUovQNjzlGnBhjqMLeKdW7RUQnIGw0/N8AdABMA7gOwKMAfhfAIoC3lDRHgUAg2FA4loUTjjOB1ff9MM0jwfHgIrh//37811e/iuZAYbrlODjzV34Fv/zkJ6/RzMrFaLVw6piFdw2RzXbOCMQ4HMdBYNuoDVl8ExEaUYrcpk2buI8dMyiwFFmG77qhwQ3HfkIxWcwmmpyieIZhQAcyOQjGENGyo2Oez2GUS2Ra6rUaDnOIYGV11pMLRLB49MCKqUkSLMtK/f1zbRtTvAVWlCKYBp4RLCDfPpuVtO/WBwGcAeBXAXwfQPIduR3AOznPSyAQCErlFz/7GeZnZ3HSqafi3Mc/fvIDOOJYFrRaDQqQ/greMU7Q7x+XLoKdpSWc0mziMaetTPSYb7WwcPjwGs2qfIx2G/UxNT565Oa3ZcsW7mN3u100xiyg9MjBrnSBNXBfXHNST5HGl5VRph7DaOg6ZjnUoRmGkctBMCa2as8tsIqMreswZmdzPx6IamkzzkFVlNxukrZto5hBe2Ie0fOlxbEs/imCGS4wSkTcbNoZY5W0KEm7Z7wIwHsZY3djtRX9XoTiSyAQCI4Z9j/8MJyHH8b+3bsrH9uO0i1UWc7UbJEHyauAVeWiA2EfrGEuglXa1H/vrrvw5X/5F9z82c9ivoI6HCCqURlWC9RooFXRHKqm3+/DNc2x6Vu6JMEoyaq90+lAH7PwrUsS2hyb7Q7DcZyhAqustMhRph7DaOg6uq1W4e9/p9OBXiAaV5ckdDO62cUUbbirKgqY5xW6wOXY9rINf1oURcm9DxQVlUmyNht2LSvza52EzFimFEFuNVjR85VN2k9qCsCBEf/TgUrSGQUCgYAbQRBgptmsPIISBAHcqAZLU1VYnIrNM1OhuAIAv9+HvMYpgvP79uHsWg21drtwD560LB05guYQEwBd0+Dbdql1SGuFaZoTrbvruo4Ox35MSVoLC6tSMpNMN5tYKjl6aPd6y02GY1Si0j7v7sJCasMHWZahMla42XNnYQGNAj2ZGrqeO1XR7HZz9cBKohXsheVa1qrPeOKYBcxOet0utIJNhmNqsoxehnOPO6JpeBEUSUrtqDiYXl6E9RbB+gWA54z43yUAfsJnOgKBQFANfr8PRZYrd7GL+4kQEbQc3ey5UcEJJoYxFtq0DwqsilMEHcvCVKMBXVUrSc10XReeYaA+YhHaIEIn5xX89YxhGJi07C7TLn1pdhbTY5ztphoNLBW0CJ9Er9tdVQOmlCiwOq1WKgfBmDhFs9CYCwuZxhykWa/n3ge6S0u5HQRj4jqkvDi9HtSMgkeSJCAIcpl7WN1u4R5YMTWlIFEAAAAgAElEQVRNS93sm0Vufwrn2kFVUVJncBAR14uCPAXbKNKO8BEAbyWi9wLYEd23mYh+B8AfRP8XCASCY4bA96EoCvyKXexs214uVNY1rfII1nKaBWOVpQgGQTA0zaHKCFYQLWpURYEiSXAqSM3sdDrja4GiGpSNRndCih4QLq67S0vc90HGGNoTrMPrtRo8wyhVZNumuSraoAKlCCzf92FnXHxrHARWd2kpl4NgTF3XYeRMVTTb7ZEXLtJStBeW6zi56pLytufodbuo8YpgZWg2HJt58I76KIqS2lFRkqSNWYPFGPsHAP8bwPsBPBTdfRuAjwL4G8bYJ8uZnkAgEJSDHwSQ18DFzrbt5dqBLFcReRFb1FZxgokJgmBov5gq3/+k6YCmaZXUvnU6nbEmAM1arfRIChC+/8mfsoV1Z3Fx4sJbkWVIvs/dqt40TShBMPFqe73k6OGwaENNUUr5vvd6PeiSlOk7rUtSIat227bBPK9Q2pgiy5CCINc+YHAQWLqiwCjyHlhW5ggWkNGePIHZ6Qx1xsxDXdNgpdwX87glpkHN4KjIuw9WFee/1N8Mxti7iOjvATwbwMkAFgDcxhirvkJcIBAIChL0+1AajcprsGzbXj7wqooC33XR7/e5FxCPYvnkUmEPKt/3R0aw4gV/2Sc813WXI4eqoqBbQWrm0vz8+FqgRgP75+ZKnYPjOLj5s59FP5EK1dy6Fc/7zd8sbcz2/Dx2pEgdq0cNb3m66rXbbTRS7EsNSUK73caJJ57IbewkPcNA7ZRTVtxXixoscx8rsizPQtE+UL1er5CDYExs1Z5lH/B9H65lFa5Hqut6IYHlmCa0rVszPy52k8xCEATwHIdfDZamwUpZh5in31caslrW8zS5qCJFcOIZnYg0AH8N4FOMse8DuL70WQkEAkHJBFE/mqoFVs8wVjhBaZGb0/T0dCXjL0ewUJ2LoO/7QyNYQJhGEQRBKb2BkiRd3WqqWkmK4NLsLE4fk6o23WigvX//0Po0XrTbbdRdFxedddbyfXc/8ggsyyrFLpwxhu7iIprbt0/cthYJrJNOOonb+O2lJTRTXKxo1mpYmJvD2WefzW3smH6/j77jrIru1DQN8yVFsLI66tV1HUcKiAvTNMEjlpLHaCJuuFv0ooyuaZjLKXj7/T4o5/c2T3uOOPOB14UoWZYB34fneROjcKVFsBQF/QwmFzwjWOvCRZAx5gJ4IwD+R+J1iGVZ+OH3v4//uuee46YBqEBwPBL4fmhyUbHAMtpt6ImoRk2WC7t5ZSHZxb7KGqxRJxuJqBKrdsdxlhcJqqrCKllgBUGA9vz8WLMFWZahBQGMEuvwlhYX0RxYBDaI0C7YZHUUlmVBir5bk2ioKlqcjS4W5+bG1l/FzDSbaJXkJGjb9lA7bb1Wy+TclpaeYaCW8QJFvVZLXYMzDNM0uViG1yQJZsb3xLKswg6CQPh5mDm/B67rQs35+hVkr8VzHAcqZ1GQ1tWyzAhW2veBONZgAdVYn6d9z/4LwC8VHYyIPkZEh4novsR9W4joNiJ6MPo9ujNhBbTbbey75x48+sMfVrroEQgE1eJHdRpV9mECAKPVWlE7oAGVHmtiwwmpQpOLUSmCQMUCK7qtKQqcXq/U128YBrQUkbmGJJVaCzR/8CBmBgTHtKJgoaTaL8MwJlq0xzTqdXQ49wJbmJ3FdLM5cbupRgPdhYVS9r04wjJILXIN5Z2aa7RaKy7apEFTVfRtO5ebHRCaTPBwtKvXauhmTFW0LIuL2NBUFX3HyfUeDOtzlhZVkuBkrDsrQ2BpkpSq/q3f74/MQCiCIsvw00awOI7PGFvV9L4M0gqsqwG8g4ieT8Ve5Y0Anjtw37sAfJ0xdg6Ar0d/rxlBEKCp69AUpbL6BIFAUC1BEACMhc5EFQoNICrOThgA1CQJZoVGF8mTS5U1WKNOaFL0/7KxTBO1KGVLkiTIyJ6mk4V2uz3WQTCmoShYKqnhMGMMRw4cwOaB9NOZqSkc3r+/lDG73e5Ei/aYqUYDbY69sGzbhm9ZqRb+kiShBpTi4jhKABARtGiePDE7nVxiRyPKPRdjaSmzqBuGnsHNLoZnw928Vu2O4+SOYKmqCitjDWgyAs+LTBGsEgSJnKEPFsAx44KxdWXT/lkAWwH8GwCbiPYR0d7Ez6NpnoQxdgeAwaPpiwB8PLr9cQC/kXJOpZCsTxACSyDYmCRT1qpa4CMaxzHNFVa7ZTZcHcayuKzY5GLUCVquaB6DTTprKa/e5mVpfh7NFAXp080mFmdnS5lDt9uFZNurnMdOmJ7G4sGDpez37cXFscYeSXRNg2ea3IRuu91elQ45jtjogjfmQJ1lkqLNbYeO1+3mEjtF+kD1DKNQk+EYvVZDL+Nn0Ot0uNmVp43iDOK6bm7BU1NVOBkFlmVZmevsJiEjXaqi7/uQS7gIKUkSwFiq4xDXGiygkhqstBHOrwNc0x+TbGOMHQIAxtghIjp51IZE9AYAbwCAHTt2jNqsEGtRnyAQCKolCILlBX+VQsM0TegDhcoNXcdCq1XJ+MDRFMEqLyL5vj+6BgvInaaUBdswsDUhNBSEkYRNmzaVMt7i7CxOSpGqNtNs4sG5uVKcFA8fPoyZIYYPsiyjAWBxcZGrwQQQOghuy9AbqSHLMAwDW7ZsKTx2a2kpVdQwZkpVsXjkCM4888zCYycxxqTPFW1uOwhjLLxok8fNrkAEq9fpoDYzk+uxSXRNg2WamfZ/s9PBCZzsytXIZCgrRVL2VEXJHMGyTRMqZyMgTZJgp3jtnueVllInRynik1KpeR8bq0gRTCWwGGNXlTyPVDDGPoqw9xYuvvjiUtRP7C5CFfQKEQgEa0PS1a5KgTUstaWh6+iWFMEYRtyTSiKqrAfVJIFVRQTR7HaxPbHw1ySplKavQHgeWTp8GGcP2HQPQ1NVSJ6HXq+HZgpBloXZRx/FCSOec5OiYPbgwVIE1lkZrM9rksRNYC3OzmI6gzPidLOJgyV894xWC9tGRHfymDqMw3VdSFG6c1byCizXdUGcnD8lSYLEGBzHgZ5SmFvdLk7hJLDy1sBahpE7RTCuxcuCbZrQOUXtYlRVTdUP0Pc8yCWl1MlE6Pf70CZ8nrz7YFVB+UmIk5kjolMBIPpdjq1PSpJNOIXAEgg2JknThSpTBE3TXOV+pSoKmOeVttgfJAgCEFHY5Lcip9RxKYKEimqwDGNFqlwZqVoxvV4PUoYmrGWkqvm+j8N792Lr5s1D/79182Yc3M23jaXjOAgcJ1Mz1LokocMpgrs4N4eZDCJ1OnIS5G46MVBnmUTXNK4pwY7j5E4d02QZVg6x5zgOeC71axkvdvQGvsuFxlbVXM2fLdOcKApGoWla5vfd6fVyNTUeR9pUxb7npXIFzUPcpmMSvGum1kUfrCRE9MsAzgOw6sjBGPunnHP4dwCvAfBX0e9/y/k8XGCMARV55AsEgrUhGcGKUxSqoL24iMaQk3JdkkLxxaGmYRKxuJQkqZLUvHhMacQFqyref8/zwAYWCTVVzW3RPIlWq5UpVa0py2gtLuK0007jNocjR46gETllDmOm2YS1Zw9M0+QWOTNNM3Pz2Wa9jg4Hq3bXdWF3OqhnSJVTZBkaYzAMAzMc0t2AcF+3u13oI6J4DV3HIY4pwUVS1TRNSxXBGMS27eWm3TzIYlvu+z482+bacNfII7AMA1M556DIMpjvZ2owb5smN1EZkzZVse95pTWCl6IIVhp4hTyqCp6k+mSJaDOArwB4WnxX9Ds5y4kCi4g+DeBSACcS0X4A70MorD5DRK8DsBfAS1PNvEyIgIqdxQSCtSAIguWrR7Isl3YQXW8kU9aqqgECgO7CArYOubJdI4JpmlzSpCYRG3xIkoR+RRGscTa/Vdi0D7PN1ms1LJVkj95aXMRUyoUTEEZSFmZngQsv5DaH/Y8+ii1jBDsRYZMs49ChQ3jc4x7HZUzDMDL3J2rW6zjAQWDFBhdZj2FxTzBeAmtYneWK8XQdXY69v4qYLaiKgm7GVLWiYw6dR0o3O6BYxG4YmqrmiuLZplnIRTFOUU4tsCwLaor+blmoqSqcFOmRfc+DtsYRLN5UsdZJewb4S4Qugv8DwJ0AXgygDeC1AJ4O4OVpnoQx9ooR/7o85TxKJxZVx8cyU3C8c+fXv44je/aAMYZfvuQSnHveeWs9pUpI2s5W1YcJADqtFnYMWcjpkoRuuw2ccUbpc4jT9eQKI1h91x2Zwy8xVvo8er3eqkVZvVbDvpIiWItzc9icYTE03Wxi99wct/GDIMD+Bx7ARRNqoU6amcG+Bx/kJrC67Tb0jJGNeq0Gc24uFP4FoiKtVgv1HIumpqpiaX4eZ3D67pmmCX3MYrSmaehbFjzP45LyVaQfk6YoqRbYPMccRpYIFu/oWU3T0MtxocUyDGhTU7nHjevf0kaPXdtOnXKcFiXl5x/0+6XVYKVN0ecuiNZRH6wrEIqs70R/72eMfZMx9moAtwP4ozImJxAIyqXXbuNJp56KHfV6rhPtsUq/30e8BKoqgtXv9+F0u0PdxRq6jnZJvZCGzUNiDLIsV2Zy4Y0RWHKGFJG8DLM41jUNZrtdSqbCYspmt8m5+JbFzTZ+fn4emuNM7I20ZWYGSwcOcHO16ywsoJnBZAIII6kqY4Xr4RZmZzPVX8XMNJuYP3So0NhJ0vQB0yNjDx64rgulQD+mNC5ygzi2nXvMYSiSBDflvs+7H1Qey3Tf99F33UKCJ0vULj5mF7kAMXQOigLPcSYeA8vqgwVkNJnaoCYXpwLYzRjzAdgAkl0LPw/gSt4TEwgE5cN8H0QESZIqi+KsB5InjCoW+EB0ZXtEClOzXkd3aan0OQBHI1iSJKFfYqPdJK7jjHQckyuYh9HpQB9YDMmyDDlyL+OJbdsIbDtz49cmR6OLvY88gq0p0pckScIJsowDBw5wGbe7tIRGBov2GD2qQSzCYk6BNd1sohXZ5POgu7Q0sT8Uj9cb4/R6ue27VUWBl0PUO5YFjWM0JYvQ4y2wYhdDL0O6tOM4haNoWaJ2nudxFbQxRJQqgyDo90szhUjbLoR3BKuKFMG079gsgNiK6FGEaYExfHIL1hmi/kpwPODHvUcqtCpfD/T7/eXGiXLGk2teDMMYmcLUiJoNV3HcicWlXGUNluOMNFtQZBluyQ6K3cVF1IcsenkudGPa7XYmg4sYXk6Cvu9j/y9+gW0prdJP3rQJe+6/v/C4jDEYrdZI97xxxDWIefE8D1arlUvcqYoCxfe5RZRaR45MjOLVJQkdTmLasazUdTyDKLKMvudlPva7tp17zGFoipI6imTbNveGu1nEDsDHRTFtDyogElglCQJFkiae/8a5wBalyn6MVZP2LPCfOCqqPgHgfUT0f4joIwA+CODWMia3Fiyr2uOk2F9wfMMiy26Kbh8vJNNqZKJKBFa301lltBAjyzJU5OvHkpW4HqpSgeV5oyNYsgyvbIE1IrJSdGE/jLwCa6pexyKHOqy5uTnonpc6grZlZgbG3FxhgeE4DmiMa+E46ooCo4DgaLVauQwuYpqShBYnZ780aZINXUf7yBEu4/Vdt5CFtpIjgu/aNtemt7Isw0sZxS4SsRuFmtEm3nGc3D2wYrQM9vCu66Ici4nw8590/gt8v7QaLNrAF3fTvmPvB3BLdPuDAD6CMC3wFQht1v+Q/9QEAkHpJPq+HU+4lrUssFRZhlNSP6QkncXFsVfYa0TcrqKPIxY7VQosb0IEq8wUQcbYSIGlS1Iui+ZxLB0+jKmMdUgAMN1oYImDwHr0oYdwUgaDDSLCCbKMfY8+WmjcXq8HPedxpK7rMAoInFarldkePklsdFEUy7IAz5toIT7VaKDFqebSHfPdSkOaBfaqMW2bS5PhGFVRUtdg2abJzaI9RkEoYtJi23bhiFJN01I3G+73+6U1rU2VIhiVEpQBMZZKYB2LWWWpPjPG2MOMsTuj2x5j7GrG2OmMsS2MsVcyxvh5jq4HYifB42zRKTi+ORYPYHmxE00b0zopFaWzsDBWYOklRFOG4TlOGMGK0oOqYNxV9rIjWJZlQY3GGaQMc5HW3FwugdWIREaRWkjP83DwoYdwcka7/1O3bCmcJjjMqTEt9VqtkMBamJ3FTAEL65lmE/MHD+Z+fIxhGKmEHo/POmbcxYs05OlDNy7lNw9K1ggWb4FFlFlgFZ1BlgbH49pcFCVNDbLv+9wNNmLWKkWwivVO+a2Mj0WEsBIcZ7C4Fus4IVmkralq6qunReguLY1NHaorCjoVGF34nle5wHJcd+QV77JrsLrd7kjr8Ga9jg5HgRUEAbqtVmYnPSAstq8ToVsgonbw4EFsYiyzu9nM1BS8VguLi4u5xy4isPRaDWaBnmSLhw5hU4FmyTNTU2gdPlx40dXpdFJF8eLPmkfEelz6bRryuKi6Y77PeciUIsjZYAMIHf2yCKxet1u46a+maama/ALluvjJmPz5syAorXURIYPYmfQeEAGKMnG7qtY6fPfSYwTP87B///6hlrj9fh+bn/AEMIS57PMVWSdvdHRdx+mnn879ypOgIETcuqMfSySvgqqKkjpVI/d4jgPmeWMXvo16Ha0CC9y0rEgRrKoPluOMfO2qLOdyMkuLYRgYFTds1uvoHjlSuAfT4Fh5nysWWJs3b5688RAeuf9+nJyzYe5JmoZ9e/bkbnZttNvQcx7fVUVB4Lq5ekO5rgur3Ua9QJNuRZah+j663W6hhsNLR45gKmXz2ToROp0ONm3alHs8IIpuFBFYeSJYnsc9guWnFDi2bUMt+J4NG9/OkCZuGwamCq5lNEWB0+uluriZNGXijZTiPBAEwZq7CI4VYYoCdds2kK6HYlCSwGwb3twcMOq1VRDBOi4F1v79+zE9PY3HPOYxq3Zsx3HgdLtgABqbNglBwAHGGBYWFrB//3489rGPXevpCBIk9/+yDqDrEbvXgzoddpvQVBU25zqcQUzTnJg61NR17KtKYEWGAHH+e1npH0D4/Y/HHEbZKYKt+Xk0Ryx6JUmCxhhM08T09PTQbbLQ7XYL1QLVZTl3w2nLsrC4fz/OPfPMXGNv27oVP7n/fjzxSU/KdYXX6nYxk1JcDKMmSWE6Z8ZzbrvdLmRwEdOIjC4KCay5OZyRMlWxoShoLS4WbnDsj+kxl4a0jV6TFDXWGCTLxR6314NaQEwPQ82YJm4ZBrYUXBtKkgQZ4QWC2oTvjTfm+FkUKQgmR7BQXtSHiFJHsIbOQFGg7dgBt9XC4te/DvvAAejbt2PLM54R3r9371CRJVIES8K2bWzduvW4SolaS4gIW7du5dZEU8CR6OB2vEWxbNNELTpBaqpaeg2WaZqYlFCi12rodbul56P7/f7yybqKHmBxk8xRx1tFlktN0Vyam8PUmEVvQ5LQKZCelqTb6YxMR0xDs15HK6e73IEDB3CCLOcWyw1dh2JZubM2eiOaaKdFlaRc54ilpaVcro2DTBU0ugiCAJ2FBUynFFi8TE38guljWQUWK+GiDBEBjE2ch+/7oaMdbxdBRYGb4Rxgm2bhFEEA0FI2G+4XFNHjkFP0wGS+v+YpgqNcjtVt2+C2Wnj4mmtw6KabsPSd7+DQTTfh4WuugdtqQd22bfWYRJW4Jh+XAgsQBhZVI97v9c/x8hl5ngcKguWTtCLLCPr9UoWGaRjQJywKJEmCyljpVu1+omZDqsAid1KTTFmWwYKglHkwxtCenx8vsGQZbU61b+0JRiaTaNbraC/k84za+8ADOKlA9AUAttZq2LdnT67Hmp1OoUWnCuQSWIuzs6lFzTimm00sHDqU+/GGYUBLHFcmMdVsYunw4dzjxfgFBQdlTBH0fb+UhaOc4lhUVsPdrHWgVuICXaFxUwqsoMQ+VBIR/EkRrHVQox1ELWVWQATSdSzedRfsAZMa++BBLN51F0jXV9VkERGC9RDBIiKNiH5IRM8pfTbrGNu2MTc3J6IwAsExzrAeJmrKE11eOiMa3Q6iSxJ6JVvG9z1v+USVJz0oK2maZCqSlKnIPC2GYUCd0JtpqtHgstAFJjtFTqKh6zDb7czpK7Zto3XwILYUFFgnb9mCfQ88kHn8IAjgOU4h+2yNKNfFhcXZ2UIGFzFFjS46nQ4aGYSOrmkIHKfQmiLgYD6Q1iY7OWYZi33C5GOR53ml1LWoipK6DjS+QMcjgqciXYPjvueVlsYtpWjXwYaJm1GkNJo4unm6i3xD5xBdnLMPHBj6GPvAgfD7PPC9JKCSGqyJnxhjzAXwWADVVEOvM2ZnZ7Fz505cdtlluPLKK3HZZZdh586dmJ2dzf2cCwsLuOiii3DRRRfhlFNOwfbt25f/JiJcdNFFuPDCC/HSl750xWLrjW98I7797W/jqquuwuc+97kVzzk1NbV8+4EHHsDznvc8PO5xj8PjH/94vOxlL8PckFSEBx98EM9//vNx9tln4ylPeQouu+wy3HHHHWPnfs899+Atb3lL7tcuWL8cLzbttm2vcjvTcqYnpcVotVIJLK0CgeUnmkZWJbAmLTsVZHcyS0NcnzOO6WYTCwWO50lG9dtKiyRJ0IDM+8Ds7Cw2SVLhRVhD1yFbFpYyRvQcxylsW60pCqyMrnqe54UGFwXe8xhFlqH4fm5nv6X5+UwCCwAaRGgXaLDMI5okIZtNtl9SNCVNBKvf7xfuPzUMRVHgpDz+O44DldMcFKQTWH5BI5NxpOmHmGptoChQt2+HdtZZUM84I/y9fXsotiaQugZr8H33fZAkQd++fej2+vbt4WMGz3HrLEXwNgDHXQRrbm4Ov3PVVfjazTfjqZqGl27ejKdqGr528834nauuyi2ytm7dinvvvRf33nsv3vSmN+Ftb3vb8t/NZhP33nsv7rvvPmiahl27di0/7rvf/S6e9rSnjX1u27Zx5ZVX4s1vfjMeeugh3H///Xjzm9+MIwN5/fF2b3jDG/Dwww/jBz/4Aa677jrs3r177PNffPHF+PCHP5zrdQvWH/FVoaqu6KwHhp0gNaJSBZbZ7UJPIbBqROiV7Gjo9/vLC/EqepCk6eGStQ9NWpYWFtCYcIKv12ro93qFP3/HcYB+P7NF+iBajn5oBx95BFs5RHEAYLOqYjZjTyjbtqEVFHeaqsLOKG7a7TYaHAwuYpqSlFvwLM7OYjrjZ9CUZbQKpKcOTZvKSNY04SAISkkRTBPBKsuuPEuzc54CS1OUVFbtvuelf915IkgpLrKN3c8iownPsjD7la/g0euvx+xXvgLPsqDt2DFWZKVdewTD6sAYA7NtbHnGM6CfdtqKf+mnnYYtz3gGmG2ven4igr+OXASvA/DPRKQA+CKAQ8DKunjG2PiV+THIRz/6UbQXF/GHJ56I7YnUh6c3GrhuYQG7du3Czp07Sxv/mc98Jn784x8DAO6//36ce+65E69ifOpTn8LTn/50vOAFL1i+77LLLlu13Sc/+Uk8/elPxwtf+MLl+y688EJceOGFAIDvfe97eOtb3wrLslCv13HDDTfgvPPOwze/+U186EMfwpe//GXs3LkTe/fuxe7du7F371689a1vFdGtYwwWpXtkzcM/lrEsC8rAwVVBvvqPNARBANswoJ944sRt9VoN3ZJ7YSXz+auowXJdd+JVZxlhNII38wcP4uREdH8UU5GD3CmnnJJ7rF6vl6oH0iRqRJkiWEEQYO7RR3HxqacWHhsAts7M4MDu3bggOhekwXXdwqlbmqpiKWPkrtPpcDG4iKnLMlqLizj99NMzPY4xhsW5OTx2SEH9OKbrdSzOzgLnn5/pcclxCwuOPCmCxUYcSpqLPWmi4XnI1Oh4SIp5XjRVTSWwGGOTo9N5rMqRzqZ9UoQpaTSRrIVauvtunH311VC3bYM3Io0vzfMDo9MUvbk5aDt24Oyrrw5rsZIugps3hy6CA1RlcpH2mPit6PfbAbxtxDblxC/XAEK42Lrtttvw1Hp9hbgCgO2ahovrdXzt1lvxrne9CzqH9IRB+v0+vvrVr+K5z30uAKy4DQDvfOc78YEPfGDV4+677z485SlPmfj8P/3pT/HkJz955P/PP/983HHHHVAUBbfffjve85734Kabblq13c9//nN84xvfQLfbxXnnnYc3v/nNwtr+GCKOZlTZE2mt6XW7q/r11BQFPQ5NP4dh2zY0pDMR0TUNc5wc7UbhJ+oHsqYH5cHzvIk9XBQi7gKLMYbW3Bwel0J4NGUZS4uLhQVWjcPCqyZJMDO0DVhaWkLN9wtHzmI2TU3hZ3v2wHVdaClNKxzHKWw+UNO0zP3olo4cGWnBn4eZnMYTvV4P5LqZa9Cmm03sLZCeyiNFUJakiSYHSXhEzYbOI2WKYCniLmpZ0e/3oUz4HjmOw60OrKaqWEoTwZrk4pfTqhzIZpM+4glCo4mvf32k0cQpV14ZRtQKjDNSZPb7cPfuhbptG0658splQw5m2yNft7TOBNbvlDqLdUi324Xnedg+IuS/XVVxl2mi3W5zFViWZeGiiy4CEEawXve61wEAbr31Vtxwww3L233wgx/Eb/3Wby3/PZXiKu04XvziF+PBBx/Eueeei89//vNot9t4zWtegwcffBA0ZvFz5ZVXolaroVar4eSTT8bc3Fzmq3+CtSO27JZlOdNJ9ljGbLdRH1g46poGs0AtxDgsy0qdPqVrGqwSe3IxxoABR6iyBZZj2xMX33E/GJ50u13IE5o7x8w0m5g/eBC44ILc4/V6vcJ1SABQ13UYGfbFhYUFTHGM4kiShClJwmIGwem67qqocFay9iICQgv+0zk4CMY0Gw3szmGT32q1MJWjRqah6/AOHYLjOBN7IQ2DMVbc5CJlilhMWS6CaSJp/X6/tCv5sV15KoE1SWAShcYKvj9WVGiqmuqiAguCsSl/RSJIEhFYkeyVLEYTQ9YYlFJ4+f3+aGHf74evL+X7LklSpn0+L6kEFmPs458FD/UAACAASURBVGVPZL0QK/np6WmoqooDI4TFAc+DpqqFu7APUq/Xce+99664r9frodVq4bSBHNNhPOEJT8C3vvWtVNslDS0+85nP4Dt33433vPe9MA0D73nPe3DZZZfhC1/4Avbs2YNLL7106PMkTwqyLB+TURDTNNFqtQCExa7bMqZ5HKv4vg9EVyNlScpkU3ss0+t2ccLAYqamaVgoSWDFEaw06LUaehx644wiCAJQ4sRTRQ2W3etNjGqX4SLYarUwlTKqM9Ns4uFDhwrZERtDhHsedE3DUoZ98cj+/Vxc9JJMKwoW5+dTCyzHsqAWLMJXFQWe46T+DBhj6CwsYGpEgXse6rUaPMPIFL0DgMUcBhcxzSg9Nc95h4ejXxqb7iQ8RN3QeUjSxGOR7/sT6zlzjx9FsCYJXcswRlu0Z0zTU1UVdoqMhbGfc8EIUuEIVl6jiYwEvj/5uMDY2HTIGIqer2wyXYggIomILiSiS4iI7xF9naHrOp797GfjHsvCgYET/wHXxT2WhedccUUp6YGDfOMb3xhaRzWMV77ylbjrrrvwla98Zfm+W265BT/5yU9Wbfftb38b//7v/w4A8FwXRqsVWrbaNlpLS9gefWFuvPFGPi9knfLzn/4U937pS/j5176GO7/85WNSJObB87zlRVG8uDkeGGY4Udf1TGlZWbBtO7XzlSRJkBgrxfABWH2iloqeXFPg9nrQJggdTVEyp4dNYn5uDtMpF8k1TQM5TmZziSRWt8ul+aheq2XaFxdmZzFTMINhkJmMzoqOZRVOUSSiTLV4lmVB8v2xFvx5aMhyZifBxUOHcn8GDUnC0uJirscGQVDYnEhK0Wh2cMwyUgTT2MV7rltaPygl5fvg9HrD9/UcRg+aosBNUXc41iY9p1V5TFqb9NGTy2c0kRWe7pWSJMFfRy6CIKLfBzAL4McA/gPAedH9XySiDeNskFxsvP71r8emLVtw3cICPtdq4W7TxOdaLVy3sIBNW7bgTW96UyVzGqy/Gke9XseXv/xlXHfddTjnnHNwwQUX4MYbb8TJJ588dLtdu3bhrLPOwqWXXYYPXXst3vPOd0JVFLzlD/8Q7373u/Grv/qrG978wFhcxJknnYQn7NgBHdktko9VkoXpqqrCLrnB7XrA9304Q5pE1lQVTq9XSjTH7vWgZVgEluloOBgdqCSClWLxreRID5vE/IEDmRa9U5KU2Z48idnpQOcgsGqqCsc0Uwlfx3HQ7/VStQDIwlSzGZovpGTkojMjWWrxTNNEvQTr6hpRJoEVG1xsyimwZhqNTO91Eh4RrKwpgqWZXKSpwXLd5RYTKZ4wk5uehHStIpwREflkmt6hm27C0ne+g0M33YSHr7kGbqsFdUiEUpZlMN8vZjJRMILEQ7J4c3PQNm/G2VdfjVNf8hKc8LSn4dSXvARnX301tM2bwwjeCBhjqT6jINFepChxWmLZ575UR0Qi+l0AfwvgYwC+BuAziX/fCeAlADaMdzchtEjctm0bbrjxRuzatQtfu/VW3GWa0FQVz3ne8/CmN72pUEF0zKAL4bAD+1133YVrr712+e9hEaXk484//3zccsstE8c+//zzcfPNNwMAOq0WVMagyDICxnDxxRfjgQceWN72z//8zwEAl1566XK64ODc77vvvoljrkeMVgtnTE8DCBe3vV4PMwUbdh4LJO1mNUVZXtStdcf2MrEsC7XINTEJES27txWtZxykZxiZIhtlNj0eXJBVIbDcXm9i8b+qKOhyvLDR7/fRmZ/H9Jlnpn7MlKpi4fBhnHHGGbnGtEwTtRNOyPXYJJIkQUb4/ZyUIdHtdlEv4fuqaxp8206dKufYNjbxEFgIL/w0U6Q8GoaROvU2C7okwcgQQTRNE1LKWr9hzExN4dGcAouLyYUsw8tqclFwzGGkMdzp9/uQJonqvG56SNcT0LGs1SmCBdL01KhFxaTar5EkIkhLd9+9YnyeEaSx5DCayErQ70PiaKAW729lNXAG0ptcvB3ANYyxPyaiwb375wDeyXdaa0e8Y8R1Cqeccgp27tyJd73rXWi329i0aVMlaYFJfvjDH5Y+Bks6i0UOKxt9sQ2EB9SeYaAeWWjXMFzkbkRs214WWMnUtDzF1scK45zealGTX94CyzZNnJDh5FlWTyhgSA1WBTbttmVBqdfHbhNHEHnRarXQJMp08tw0PY19+/cDKVxYBwmCAE6vB20gUyAvKtIJLF7OhcPQJQmmaaYSWJ5tc4lgyRkiWEangzon58QkdV3P1Cqh1WoVMhmp12romyZs2868tuAhdiQi+BkcPMsSWGku9vRdd7xhTgE3PSllqxLbsqAOXkgpYPQQH+8bBcxa8liVZyFVnVZGo4nB55+E7/uQOGQIxFTRoiTt0emxAG4d8T8TwGY+01l7kjtR8rau65ULqyoJggCUuDJEWJ1OtBExTRM6jn7BG7qOTsl9iNYLlmWtaJhYkyTYtr2hBZZpmtBG7NMaykkPtaPId1oUoNQIVvLVp6l7KAJjDI5lTazBUlUVdmQ0w4PFxUU0My56Z5pNtPfuDQvpM6aexem2vI6XWvRdnGSiZBpGaQJLI4JlWTghRVTOtm0oHKL+mVIE220uKZmD6JqGpQz74tL8/MRm1pOIjS6yZsXwSBGU10sNVooFbxC53o6ikJse0kXznV4P6kknrbyzQJoelwtqJUeQMn3eKY0mljcPB5i4Xd/3J0cvM5BWUBcaI+V28wAeM+J/5wEY3UHsGCMuJowFxvEAY2x1bUYF6n49YBgG9MQBu6nraM/Pr+GMqsMYWKDEC6qNTLfVGnnVu64o6JbgJGhbVqbecIokwSmx6XFygVK2wIobg06KJGmKwjWCNX/gQGZnPUmS0ACWHUWzkEy35YGSMk3Uyijes6AiffPtPq8IFtKbXJjtNhdTkUH0Wg1mhiyGhQIGFzFNWUYrx4U9HimCkiRlj2CVsDZKcyzyfX/0sSRO07vrrpFpeqTrIxfzaRsdS8P6MRUweuDWAzCKILm7d8Pbuzf8feBAKrEzca1bgRnSJHzP41aDBVTTAzLtbL8E4M+I6KzEfYyITkTYePiL3Ge2VkQ7UVWdntcDw4pWjxeB2Wm3VxRKTzUax4/AWlpaURyvAYVc1I4FuktLaI5IV2voOro53bzG4aWI4CTRFAV2SUYrvu+vWBxlvXqdFdd1UwmPOEWVV7Ph+ZyL3kbU/ykrvAVWnCI4cdwhhi28SCv0WfS58XDzyyKwbNMsRWBlMRlhjGFpbg4zBW3yp+v1XEYX/XG9gVIiyzL6GQVWKSYXmLzgHdsLqaibHibXYHmeN9IRNq/RQ1x3OA7KIiziCFLK9RvD5AtgZWYyjWwgPIDveVwdJOUKIlhpz/p/AuBZAO4D8F2En8mHAZwP4DCA/1XK7NaAgDHIRGA4PgQGMDwVsIri9/VA68gRNBOpn5qqgjlOrnz4Y43u4iJOj8w9AKCuqhs+PbKzuIhTRnyuDV3HgYUFruP5vh82c86w+FQUBW5JEax+v7+ij4wsSZkWV1lJ1ZQzIo7aZIn2DaPX6yGwLNRz9BXa1GhgYXYW55xzTqbH8RZYmiynsq13bRszJTjpAYAqy6n2w3GLzqwospy6XYTd66G2ZQuXcZMkxf6k+jPTNCH1+4Wjd9PNJvbm6H/n+z6kgudpWZLQz5CilqkGK0M9TpqaGL/fH73I5uCmN2l813VH9+HKmaYnp4hWS0QISlqPpikFKdwrawKTxmeM5UrdHkdaU5MipG00vEBEFwN4K4ArADwcPfbvAFzLGJvcKe0YYTlFcCBcbdv2mplclM2wA2bZ4dMjR46krjOZnp7m3tA5ZunwYZwzUFzakGV0Op0N9zknGTT3AIBGvZ7r6v2xQhAEMFot1Ec4yzV0Hebhw1ydhfIsPlVFgVFSquZgLxEl5SI6L1mER+yeWNRkJDa4yMOmZhP3j7gCPg7HcVJfrUyDqqqwUgisPqfI0TCklOKbq8BSlFTtInzfD22bS3rtcdrWJIHVbrcz1/oNo6Hr8A4dguM4mWpgPQ5pU7IsZ4ocp6r7yuHklyaCFIxLESzoppcma8fzvPHHsxxGD6osw52wz5MklSZwGGMTI2QkSSgz3DBJYPFIhR0krS1/EVKfExhjXQB/Hv1sWFgQgBRluQZpdnYWu3btwq23fg2e50JVNVxxxXMK2bQvLCzg8ssvBwDMzs5ClmWcFBVN/uhHP8Lb3/52XHPNNQCAD33oQzAMY9kS/dChQ3jNa16Dj370o3j+85+/whp9586dmJqawjve8Y7lx15//fVQFAWyLOPqq6/Gq1/96lXzufbaa/GP118PTdMgSRKedckl2PknfwIaI7Be//rX4+1vfzsuuOCCzK/fNE3c+W//hs0pTo6MMZi6jue//OXc7TT7/T7MVgvNxzxmxf11hIu0wd5hG4lBcw8gPMk/soEFlmEY0DE6HUKSJGiMwTRNTCcie0XwPC/zwlstMYLled6KK7BlRsuAbMKDlz19a3ERzZwL70a9Dmd2NvNC17Gs5abdPNBUFWaKNNGxC86CSJKUagHS7/e5LX4UWYabYh/o9/vj3eQKknbx1V5aQpOTk2FTktDpdJbXAmnwHKewwM4SNQRSpCXmdPKTJQnBhPd8krgr4qaXJoIW15ROJIPRQ5oegCRJpdmsBykEVpk1WEGKCBrPJsMxVZhcZDoyENEMgAsBbAewH8BPN1T0KopaEREkIszNzuIP/ujtWFzsgLEXADgfnvdzfOUrX8Hdd38PH//4x3KJrK1bt+Lee+8FsFoU6bqOz3/+83j3u9+NExPRhZhbbrkFV1xxxcQxdu3ahdtuuw3f+973MDMzg3a7jS9+cXWp3K5du3D77bfjP269FdtOPBGu6+Jv/u7v4Ng25DG2oddff33al7uK/fv2Yass47wdO1Jt/8NHHsH8/Dx3wdPpdNAY0hNpql7H0uHDwLnnch1vPWGa5gpzDyC0C3YOHeIeil8vdLtd1CecSOqShG63y1dgZVwIpk3NyoPrupATiwhVlkuLlgGRwUfK18/LPXH+4EFsKVAT05AktNvtTMebnmFwMXmISVuHxwbcX3mSNi0oz0WEUaiyDCPFvt/v99MtdHMiE6USWEuHD2Pm/7L35lGSXPWZ6Hdjz7WyqnqXurUhBAghgbUZYwwHG+MjeNgHmffemAe84YhjwMYG2XrwPGDsMXgeNswxeAUz4zODxp4xjJ9tMAJbRniREEhCEgIkpN67uvbKPTL2+/7IjKK6OjPuLyLuVQs/f0d1Wt0VmTcyMpbf91u+r4S89k44jKHb7V4QghWFIVk1OMlq00NxJT+NMWHFVHi+l1HTI4hsyKgY7oZpGBgI7nu6riurIFG+d10hwaKsH8extCp5iqejRZB8pjDG3g/gNMbGwv8dwD8DOM0Y+3cydoQx9i7G2LcYY48xxv6UMfa092dxzsEwLhUzxvCf/uRPJuTqPwF4L4CfAvBecP4pbG318Id/+IfS98EwDLz1rW89x1h4J+666y78xE/8hPB9PvShD+H3f//3tw1z5+bm8KY3vem87T74wQ/iP370o1hojZX2LcvCHe9+N+ZaLSRxjLe97W24/vrrcfXVV+NXf/VXt1/3spe9DA888AAAoF6v41d+5Vdw7bXX4uabb8aqoJf85OOPY1+Lruy/aNs4dfw4eXsqut3u1IC7Wathc3lZ+nrPJPT7/fMIFmMMFU1T6gPm+z4efvBBPPT1r8/8EZ0/RbFb0GQaqrqOnkQlwd0zTxToCgmW57rnCG5Ypkma9SmKPCp3pqaR2sNE2FpdRaMEwUols/MgGI2kCi6Ypkk7Fhd4NgKYnOOSgk7DMITtUoA6JbsUVI+crbU11CURrLrjoL2+nus1wWhUmmAxxqBxTm6XirO+7xJKfpquCwPeTBXBFAXV9DRNoxEsyeedoetCMRmVqs5JkkAXJIeYrqszKp6S5N4NmVXyFM8YmXbG2K8B+ADGxOrHAFwD4EcB/A8Av8YY+0CZnWCMXQTgnQCu55w/H2Mxof+tzHsWwU5/B9/38eV7vgLObwGwu5JxFTi/BV/84t+SZWzz4B3veAfuvPNOdHcFenEc44knnthuyzt69Ciuu+667Z+U8PX7ffT7fVxxxRWZ6/T7fQwGAxw5fPi8m5bGGJI4xgc/+EE88MADePTRR/GVr3wFjz766HnvMxwOcfPNN+ORRx7BS1/6Unzyk5+cueZgMIC7vo5WjgrB/j17cOa735V+g9lcXUVjShtQrVLBqNORpmj2TER3cxPVKZ/dYUwpwVpZWcHS17+O+Mknp/70v/lNPD6p7spGZ20NdYHhbc1x0JZI8Io8kE3DyNWykwfDXu8cImBZFmnWpyi8wYCscmcR546y4Lou4PulyE69UsmdYPGGQ+kVLJ9wLFTOZiST2RkRyG1TBBi6ThJcoCqPFQVV8CDo989RYi2DRrWKztpartcEnldaFAYYK0ZSn3dRFM0+L0oo+ZErWKS9RG41PTZ5/yyEIqPjAjANA5FI5MIwlIpciPyllIpsQJzIKZKoFCFPUqHwGsTtbgPwEc75Wznnf885/9bkz9sA/EcAb5WwLwaACmPMAFAFcFawvXSkFSwA6PX7iKIQY5uvabgKYeifR4JkoNls4o1vfCM+9rGPnfPv999/P2666abtv19xxRV4+OGHt39+9md/9nufg3Ayptulyjxf+ru/w/UveQmuvOYafPVrXwM4x5/92Z/hRS96EV74whfiW9/6Fr797W+f9z6WZeHVr341AOAHfuAHcOLEiZlrnl1awvxkxo0Kx7JgBQE2JSu8bZ49i7kpA/WMMVQLZLG/n9Db2EB1ioiHzRj6PXVdvytnzmB/s4nD+/dP/bni4ouxsbSkJLPUXltDQ5BtbtRqubPIWYgE7TTToGkaeJIoyVi6uwiWbZrwXVdZJs/t98lkxzZNeCXJfbfbRbVkANSs1XKTbIqZch7oug4kiTAA0A0DsarMNufCzDYgmWAZBqlNVLXCL+WKHQwGwpbjPKhVKuhtbub6bL7nSZn9yyOPHwXB7ApWCSU/XdOQCO5DPElIprRFwCZJ5SwEniddVMYwDGEFS9N1tRUswWdSKrLBmDBZomIGi9oGXAbUu8McgC/O+N1dk98XBud8CcBvAzgFYBlAl3P+pd3bMcbeyhh7gDH2wLrEICjFTjW9ZqMBwzDB+eMztn4CpmkrU7f7xV/8RXzqU586x5foC1/4Al71qlcJX9tsNlGr1XDs2DHSdidPnABjDK/80R/FA//0T7j6uc9FEAQ4deoUPvrRj+Luu+/Go48+iltuuWVqxc40zW3CpOt65kl7+qmnsKfAMVuwLJw9fTr362YhiiL0t7ZmeiLVNA3tf8GS5bP8oKqOg55CoYvVU6ewkPH9G7qOCufSj30YhnC7XVQEypBVx4Hb7Uq78RadFdFlmU/uQr/bPYdYM8ZgY1L5UYCR65JbBG3Lgtvvl1qv2+mgWjIASs+BPMffz2kmTYGpaUJ/HMM01RGsOCZ9pt3CKWVg6rowm/90gBJM9vt9YctxHui6DiNJcl2LeU3MZ4FyrqVIsloESxjuUgyPE6jzZKLMHPqjEQyJiRSAVrXVFVawKIkUTdfVESzQKlia5PV1TVPWKZKCSrDuB3DDjN/dMPl9YTDG5gG8FsBlAA4BqDHG3rB7O875Jzjn13POr88zCErFTpbsOA5e8bIfgab9DYAndm35BBj7PH78x39MmZT3wsICXv/61+NTn/rU9r/dfffd2+qDIrz3ve/FO97xDvQm1Yher4dPfOIT5213xx134JfuuGO7WsM53yZRg34f1WoVc3NzWF1dxRe+8IVSn4lzju7yMuYLCAjsabWwdPRoqfV3ot1uo5aROZmr1bBRQK75+wG+7yMJgqmBb9Vx0JNcKUwxHA6RDAZTK2c70dB1bEo2e+73+6jpOsnvo8rY9nVTFkFWtjcDOuRLyEZRBL/fh7OrouRomhKDac55rhZB27IwKkn02qurqJe8J6cV7D6R7CVJMm4dkp3ZZkwY9JqWpSyzHSUJDEL7WyBRQTFN0Ik+k6Ywow7Qsuq9TkcqwQKQewY22DVTWRQUs9sUojmoooa7mqYhFt3ziN05RUBpEYxUXOe6jjAIMs9nwzCE+1YUCefQRDNYKlsECbYoMuc8U1AVS0utQdzunQD+gjEWAfhzAKsA9gN4PYB/C+C1jLHtT885z3sm/CiA45zzdQBgjP1PAC8G8Omc71MKu9Vx3vKmN+HrDz2Cdvstk1msq5CSq4WF5nZLngzsvLjS/3/3u9+N3/3d3wUw9o1yHGdbtEKEt73tbRgMBrjhhhtgmiZM08Ttt99+3na33XYbultb+KFXvAK2baNeq+HFN92E617wAjjVKl7wghfg6quvxuWXX44f+qEfKvUZ4zhGQ9MK9c7Xq1UEq6sYDoeolRhgT7G1uZnpXzLXaODE2ae9S/VpwXA4hDPjIVWrVNBfWVGy7tbWFhqEh1OrXsfamTO46jnPkbZ2t9ud+Zl3ozKRS16QYGIael6hnn0V7QupiuLuAKUyUc0rajsxC0EQQMdsWfzdSOeOqC3O09BeX8c+CfeHPOdA6gMlO/CjECzDthEqanOJ4ljoAwUAge9LVR01Jud+1tqapkGdSyNtxqu3uYmG5ASrPZmB3U8wyQ7DEJCk+JpHwTMKguxjU9Rwl+C7pro1VOiDJflcB7DtuxrH8czqmOpWYFFVTjMMcFXtdIRkRhRF8sVFFM46b69B3C5VNvgPk5+dYAC+uePvPMf7pjgF4GbGWBXACMArADyQ8z1KI97lrXHwwAH8we99DP/1zjvxxS9+DmH4WZimjR//8R8r5YO1Ex/4wAcQhiHak4z9qePHt//f0nWsnD2LZquFT3/603jlK1+5/bpLL730HA+s9L1SMMZwxx134I477shcP4ljvOvnfg7/17vedd7vojjGH/ze76ExpaXrnnvu2f7/nRm3W2+9FbfeeuvMtZol2hkamobNzU0pBGvj7Fm0MgxNHcsCRiNphO6ZBNd1Yc8IBk3DAA9DBEFACq7yYH1lBQ3Ce87V63hqMgwtK2htb2ygTvw8NdNEe2MDl+7yRyuCMAgKPZANBQRr1nxSvVLB1soKcNWsedNi8DwPdg5yqWkadIwDvSKdAXEcw+12UZ2fz/3a3agaBrpbWwDhHAiCgGymnAcG58I2RadSgaso8IkZI7WfBaMRqhLbpigmv7quKyVYCcSJgd7WFvZLJlhVy0KP2B4dBAHZAkGEPAqemSqCKQoY7pIrWKS9VAMVlWpgQi6jaDbBUtkKzLnwGaXrurr1cWFELgxdh/sMIVi/Dqgzcuac388Y+wyAhwBEAL4B4Px+NoXgkwzCzlKppmlYXFjABz7wAbznPe9Bt9vF3Nyc9LbAMAxhMXbeMDgHMPR9JEmCN7zhvI5JKYjCcOZNmmo0SUUcx5gnVuCmoek4WF9exhGif9YscM6xvrSEi6f4jO1EXdOwtbX1L45gDQaDzMDXZgyu60onWBtLS7iE0B5qmSb0IEC/3ydXbEVor6zgEFFOuVGrYVmSkmDoeXAKPJA10IfOqdhcXUV9SsvXXK2GJQW2BJ7n5SYe1sRsuMg9tt/vw5FUSapVq9gkKrqlFSzZ0AkVLLtaRVeRQEkMkO4BYRBInUuhzB+qDPgAIBZk9TnnGHa7qBw+LHXdim2jTRRX8n1fGrG3TBMjYmtiHMf0dq0chru6piEWnO8X2pIgDAIYCsZCdGR7MhkqZ7AAIcHSFMq0c4iTGYHnSW8R1DXtmdEiyDn/gNK9GK/xqwB+VbihIsRxDA3nqgdpjG2reTmOo2zeKgqCqT3sDN/T6lchScs5RxxFcGZkKTXGgMnnL7s+5xw8jkt5hrTqdRw7cwbYoaRYBP1+HxpByrlhWVhfWcFhyQ/RC41+u50pLWwzhuFwiFYOrzIRoihCb2MDjUsuIW1fNwx0Oh0pBItzju7GBq6aoWx13trVKjrLy1IqaGEQoF4g+FTRIrh2+jSeNaVqW61UEK6swHVdVCV5+gDFAkCLMXieV0g8qN/voyIp4KxXqzhKnAMMgkAJwaK0CFqWhVBR4BNxTmsRlKRkl8KAOLlgGAZUOtjEkzVmwfd9GBAHpnlRcRycJhKsIgmMWXAsCx0iwYrCEJqCWEgjtAiqhojARWEIXUHCVXS/V2o0DPF5rOu6UpENIcHyfeniIqZhkCwhykCdkcT3GWbJQKo2I+Ocjy/aGSe4Dii76aRmjVlBpCy36ziOx4aGJYharVJBv9MpvT/UWaD5ZhNrp06VWuuZiEGnAyeDYFmMYSTB8HUnOp0Oqjm+/4ZlYUNSFcl1XehxTG7tMA0DehRJUdYrKusrW+RiNBph1OnMTHA0GJNug+C6LqycAaAxIVhF0CcYSVPhWBZizyMN/suUKd8JU9eFpruWZSFWFPiESUJrEZQsXU2pYDHGYJgmIlXVuyTJDOhGo9HMNusycCwLLlFgx/M8CL8dxgDDEEqb25ZFrmBFUaQk4atpmtKqJAXCVrWCokUiaMi+3+u6riyhQK1gqRLToVSwQt+X3pqp6/ozZgbrXzxm9XhqGPccy5bgPWddzPbd0HUdYRgi2x61GCjeAmlmpeznj+O4dO+0pmlwMM5Ul6murC4toUnIwNWrVbgnTsDzPCXVy+8+/jge/sd/PO/fn3fjjXj+tddKXy+F2+2iktGqV7Es9CV7gLXb7UxRkd1o1us4JUlkJBV3yIPKREWubHto4PswC5w7smXa19fX0cxon2tVKjh76pTUaq3b75Ml2lNYjMEtqGjY3diYabtQBKnptkjoQhXBMgxDOBdjWRZUOblEAGyKiqDvw5DUygvQq7em4yCMIumBF+ccCWNCgkWuHuWYQ9J1HSxJSDOwo+EQ1qzPbhgw9+8Hc5yxOa+mgXvef+cE2QAAIABJREFUWMFvyrG1TBMuMcGSKdNeAjpBpl2VgiBAaz+MwlDNDJYgka+yghWDSLAuYItg6PvSFTufDhXBf61gTRDNGEbXczicF4GI5KQ3HRW9xxQTVFllexkECwAqk6CnDFZPnsz0YkrBGNsW1pANzjm++/DDeNGBA3jp5Zdv/9x48cV46pFHlFVNkyTBaDg8T6p7JxzbxlCygfbG2bNo5mg/a1Sr6G5sSKni9Hq93ATLkSTVXljkQrJHx9KJE1jIIIt75uexfOyY1PvMaIokvAi2aRb2wuptbsolWETJ7CAIpA9gA+NKaiio5tm2jVBBZplzjhgQJtY45wh9H6bMGSzOSZVD27aVGIWGUQTTtjODed/3xdUjw4B50UWwLr8c5uHD4z8vumhcUcqARaziDneZhu9c1zpyBOFohJXPfx4n//iPsfL5zyMcjWAdOTJ1fcs0EXgeqUoRK6pg6RSRC8bUiQEIkCSJMpl4UQXLMAxloi4UFUHdMJRWsETPSCUGz09DBetfCRbGF04y46ahT0zgVA1XikrOOyU8n+61gcnnl0CwkiiScmOydb2UZ0+/3wd33cwZpJ1omiZWFci1r66uQhsMzmvZciwL1TDEkiIPLs/zYAp6niu2jaHkCtbG8jLmMlQbd0PTNFQxVr4ri+7mJqrE7ztF1bbRl2B2HBacTzEMA37BVrndiOMYK8eOYU9G1dexLJhBIDWZ4PZ6uQmWY9vkFqmd4Jyj3+kIPdZy7YumoU84/2T6QO0EJcNqWRZUpP+CMIRdrZINQGUGnYamISQQLKtSUSJRH0URbAFR90ajbAW/AiQnhUmYvQPGCYxpBMvcvx9Bp4OjH/kIlj/7WbS/+lUsf/azOPqRjyDodGBOkYBnjJGJHUlFsAA0TUMSx5mxlsoKluj94zhWkkgBxKMYuq4rawWmtAiqJFiUGSwVLYKapimLrbfXUPbO30eIoug8gYsUmiKCU6/Xx9m/yfzVf7nzTvzCL/3S9u//65/+Ka67+WZce9NNuPklL8Fv/dZvbf/uvvvuw2233YZ77rkHr371q8953ze/+c34zGc+A2DcuvKe97wHV155JZ7//Ofjxhtv3DYLTgUudE3Dj95yCx586CEAwP9y663bpsPp50eSlP78SZLIIVimiWGJysLGxgaaOS7Uhbk5rJw4UXi9WfjuN7+JgzPa9A61Wnj8G9+QviaQLdGeomLb6Hc60pIKo9EIEcFgeDdqkqqHvY2N/GtXKuiWNDtOkgRJQZ8aU9cRSCJYa2trqMSxsF1v0bJw5uRJKWsCwHBGAJiFPDMoO+G6LizQPbcoqFUqJNNt3/OkD2ADtAyrZVmIIV9dLQhDOIRqYLTL2kQGTMOAR5h/tCctgrIRRBEsQUJmNBxmVveKkJwUVE8qdzA4P4HBGJjjYOvee+HtSgx6Z89i6957wRxn6kyWCQgJVpIkUu0zdkNjLDuQZ0xZspsDYBnnMqXjpygYCCqCSlYWK2YCz4wWQRX3WNUdav9KsDBRhsn4glUoegET0pEk5120d/3t3+Ljv//7+Ju/+As8cv/9uPfLX0Z9R/b/rrvuwqte9Srh+7/vfe/D8vIyHnvsMTz22GP467/+a/Qn7TezBC7+6jOfOW++SSQhSkEyEbkoC8eyMCrYQgQAKydPopWnVa1Wg9/tShE8SNFut7F14gT2z5jt2NNqwV9bwxpRJjoPXNeFRaha6pyTjSdF2NraQr3Ad9+q1bAhoZJXpLJRdZzSFawgCAoHn4ZhCAUOqDh59Cj2EILlfQsLOPn441IylXEcI/S83DNYtmXBLdACPBwOc3luUUA9ByIF2VVgYoQpCHgZY7AcB4HkICEgVHEANRL1VANQu1qlEyyi2AMwDqQtwWfPrEyXIDkA3R5lagVL18GTBN6M+6Y38RfElH23NU0obpQkiZJ5wxQigqVBnVS7iDgmSaIsYL6QFayY4INlmKayChZlBkyZ/xjkikntBpkSMsZeCOB9AF4KoAXgRs75Q4yxDwH4B875XYr2UTne/va3Q2fsnArWK17+ctz6Uz8Fz/PwC7/8y+CMwdxxM3vNa16D17zmNeh0OueZ+X7iEzQLr1kZkQ9/9KP4f37jN3Do4EEAQK1Ww//xb/7N9u/vvvtuvPvd78Y3Mqocruvik5/8JI4fP749qLx//368/vWvz1z7ymuuwX333IPBYIDX3Horfujmm3Hv/ffj0EUX4XOf+xwqlQqOHj2Kd7zjHVhfX0e1WsUnP/lJPOc5z5m5L5xzaRWsPH4du5EkCVZPncIL9+7N9bqmpmFjY6O0/1aKbz/yCC6qVjOzNoebTXzroYewj0Ck82DQ68EhBKKOpmE4HEoR91hbXkazgKdWq9HA8dOnS2VMoyhC5Hm5Kym2ZSFw3UzzRxHCMBTPacyAaRjwJRCsMAyx/NRTuH5yL8lC1XFgjkZYX1/H/owMOwV5TYZTmIaBJAhyH/fhcAi5rm1jyez+mTPC86/onJ0Ihq6TWsWcSatc3nM8C2EYwiEIvKgQ+DB0HUNC9dauVNATBUc5xR4AIIxjIbkMPA+1WednHpIzZR8MQPi9h2EIPk3gI47BNA3ODEsK56KLxufylGDeAK2CpbJNL1VtnlUdZJqmzo+J87Hf0wyobBEUiVyotCWgVLB0hT5cnNIiqIhgGYqKJylIT0DG2EsA3AfgOQD+267XJQB+Vv6uPT2IJz2/WZcNm2RVpA6Aj0a44YYb8JKXvxzXv+Ql+LUPfWj7d9/69rfxouuu2/576scVxzE2NjZgmua2T8w//uM/4rrrrtv++au/+isAwFNPPYUjR47M9BGKCc7YTx09ip+97TY8dN99mGs28dnPfhYA8Na3vhUf//jH8eCDD+K3f/u38fa3vz3zfUTHNw/MEpn9TqcDIwhyByKtSgXLp08XWnM32u021p98Ehft25e53f7FRQxOn5ZexeoS2+VsCWIiKaiiIuftg2VBD4JSYhNl5JQpGd0slBE/MA0DvoSq6dLSEupxTBYh2Fut4viTT5ZedzQaFSY8VoHjPuj1Chk6Z8HQdWiESq6K+YB0fYpPi1WtSq9ghVFEJ1iyK1jE9ljLcRBlZdULzkGFYSgkWGHW/HIJkgNMOmYE3+doNJqewOAc3POw8OIXwzl06Nx1Dx3CwotfDO55U0mKbRjC6nEypeNGJrTJGrPANE2pH1RWhTP1SlUBjbFMgQ/DMJRI2HPOwRm7oD5YogpaFEVCO6Gi0CH23CsDaorwPwD4IoCfnOzTz+343UMA3ih5v542hGGIj3/0o6jMCLodx8EffuxjcH0flbm586RTW60WuWK1E5VKBf9wzz2oaBo0TcN/ufNOPJhRkUrbFL/0pS/hla985fa///AP/zA+97nPbf/9zW9+M2n9KAyzh3QBXHbJJbjuBS9AwjlecM01OH78OAaDAe6991789E//9PZ2ogBEOsEq2Lq2trqaa/4qxWKrhYePHQN/8YtLX+SPPfggLq7VhBkbxhiOzM3h0a99Da+45RZpN5fu5ib2Elokq4aBXqcDEI2BZ8HzPLjtNuqXXlro9U1dx9raWiHjWWAS6BcMfi1Ng+d5aGRI2mehjAGtZZpSJGSPf+c7OJDj2B1YXMTXv/tdBDfeSDKZnYXRaCRsRZ2FlGDlOe6DdluqwEUKhzG4rptZyY2CAIbE6lEKTdPGc3wCo3enUkFYom16GoIoQp3YIiibYFHUE4GxwmFW7nnnHNTOVr32fffhittvh7l/P8IpVaYwjtEQnEuZQg87SE77vvvOWVtEcgCacq/neTOf3+HqKqwjR3DF7beP2xSXluBcdBEWXvxiWK0WghnejrZlCRU8VZIMAMCk22UWNJUzWIJAX5YS8jSIvnNVFazU700UX2iapmQGjHMunMGaZaEkAyL1RhnvT8GLAPwBH5/Zu8/uDQD5eq6eQQh9nzQnQVU2ygMex1NPrOc997l46OGHz/m31BfnC1/4Amn+6lnPehZOnTq1PXN1zro7BC6ykA76aoxtDwMmSYJWq4WHH354++c73/lO5vvIvCEaul74ezh7/DgWCwTqjmVB9/1zxD+KYH19HVvHj+MQsUVx/+IivOVlLC8vl1o3RRzH6BMD0Xqlgs76euk119bWMv2XRJiv17FcQmTE8zy6X80uUFW1ZqEMwUqrF2X63vv9PrpnzmSqB+6GaRiY4xynS1ZsyxAsyrD9bgx7vUzz7KKgmG5HYahEthqgzb/mmkUiIkoSkgdWGIbQJQe8hmGQ5j/NrLmQEnNQURwLRS7iKMoMxsPVVVitFq64/XYcfN3rMH/zzTj4utfhittvh9VqjVsUZ0CnEqxZ95YoQnDqFMxKBQduuQWX3HYbDtxyC8xKZUyuZpwrtmkKBWZkJkunQTiDZRjKCBaQrSKosnqnTxQUZ4FNfNlki61FcQyDcJ3ruq6EYCVJIqyepV6xKqBKXyEF9angAZiV9j4IQK5pztOEJEnG2UdC+4wxqZzIvLhnsfI73v1uvPf978fK5Cbs+z7+6I//GKHn4dFHH8V1O9oHZ6FareItb3kL3vnOd273cy8vL+PTn/70TIGLLLBJ5qjZbOKyyy7Dn//5nwMY33AfeeQR8evJK4n3g3Ge+6IIwxDt5WXMF6xGNHUdaxkPRQq++fWv45JmM1cwdunCAh796lelDJj2+31UGCOtX6/VsFXy8wLA2VOnMJ9DVGQ3FubmsHHmTOGbIMmvZgaoal6zEARB4bUBwNS0UuufOHoUeywrN7k9OD+Po489VnhdYOLRU3B2zda03KIywwKS8BSYgJhgKZKtBibeQIKgylKgpkfxwAJyCLnkEJkwdZ1UwTIMA/Gs9ysh9pBomjAm4EmS/UwrSHIAkJTyhC24UYRwaQnBsWMIT50a/7m0lLmuY1nC+eZE9LlLggk+O2NMWauaSC5c5WdnhLZQw7YRSiZYcRzDIFznqoyO4ySBLlj//w8VrH8C8IuMsZ13o/R4vwXA30vdq6cJ6YAu5avTGANLEqlfxqyT5ide+Uq87bbb8KrXvhbX3nQTbvqRH0Ecx3j4G9/AddddRw6YfuM3fgN79+7F8573PDz/+c/HT/7kT2Lv3r1Cc+NpYPheb/Sdd96JT33qU7j22mtx9dVX4y//8i8zX8s5Jz1YqUgHYfNgY2MDDSK5mIbFRgPLx48Xei0ArKysYHjmDPYvLuZ63Z5WC3xrq3RFARh7SlWJn9+xLCSjUakZpCRJhP5LIhi6jmqSYKOgZLrnuoU9ikxdLyU0MRoMSvkjmYwVJlhJkuDYY4/h0J49uV+7MDcHb20N7RIqim6JipJtmrmMrpMkQVBAsZC0L4YhbpsSVDPKQKQuBozFHkjzGTlIDplgjUbZBKuA2a6u60jiWPi5TdOcraxWYg4qmbRNZYEnifg4FiA5wCQeEZAIt9+HRUlgcD5ej0BKbALBUlk9AgAmahHUdaWGtyIfLE3R59cIJsumZWVWuYqAUq0FFFawOKcRLAVrA+orWNQU4/sA/DOARwB8BuNz8U2MsY8C+AEAN6jZPbUIcg4nG5o2zkqXfJBzznH65MlzbuJv/JmfwRt/5me2//6mN7wBb3rDG8553a//5m/iFa94xfbfX/ayl+FlL3vZOdv8yZ/8yfb/W5aFD3/4w/jwhz98zjau657DrP/u85/f/v8nv/lNAMCexUU8/NWvbv/7u37+55FM9veyyy7DXXfRRSNltxWkyi+UFpYUK0tLmCvxvc03m3hiUknJqyrHOcc3v/Y1XLqwUKhV7tLFRXzra1/D4cOHS7UibayuopHjmNU1De12GxXCLMY0tNttmAVERXZj3rKwfOYMDhw4kPu1nuuSBR52g+rHMwuj4bDUHFMZgrW8vAzH9wvPJe1zHBx/6inM31Ds1j7sdrGv4Gd3LAvtHMImvu/DLNGGmgXLsjASGJurMl4FaATLMAxEWYFfASW9mHPScy7TA2wiMhF0Oti6++5zZ4GOHMms5BgTqfIs4popXV1iDiqZtGNlgjG6ml1KcvJAcC57gwGqkiu2uq4DcTxWP53x3XPOlYpciCpYmq4rI3mJIAGrUkFRY0xInkzLkl7BCqMIJqGrRxnBimPhtaa0Q4BQOSwD0l5zzh/BWJ59FcCvYJxkSYUufoRz/oSa3VOH7fbAHAQrbV0oe4HHcTzV/0qE9/7yL+OnXvvaUmsDQDwxN84DXdcLM33ZN0RK0LEby8ePF1KyS6HrOmpAIePb1dVV+Kur2Ds/X2jt+WYTWrdbuoq1fvo0Wjv81ESoGwY2SrQJLi8toSWhqrBnfh5LTz1V6LXBaFSYYFmmWUrJz+33S1VVyrQoHn/8cezL8V3vxsE9e3Dq298ufM27BUyGUzi2nctsOCVYKmCZJryMrL5q49VUwTYLpmnODn4KKulFBOlmYOwBNuv6KmO2q3MuVPcSBX1F56BiwlxIliFtWYgqKcAkeaOgYpsK+8yC6hmsdI1ZUKlmJxJbUEqwBDNYwHgmPlLQImgS7tOqjnucJMIWRZXCKrqmkTz3ioK835zzhzjnrwDQAHAxgCbn/OWc89nSd89gpMO5eS4YTdOktAkWVV4ydB3RRGiiDKIoyl0J0RgDj+PCa8u8LeUt67quC7/bRYMgO5yFOcPAWgHBiccfeQQXz5DLp+LwwgIe/8Y3CpPVbTW/HPNQ880mVmcoTlFwtmR7YIpapYJkMCgk1+6XIFhGyRZBbzicqU5KQVGRjdFohLXjx2caWVNgWxaqUVRIYCWKIkS+XzgAdGwbwxyqeGXERESwBH5knPN8D/8cbXqAWLYamFRyZvyuKMlJABLB8j1vepKypNmuMRF1yoIw6Cs4B5VAbHyqKZwF4gLVSGB8b7EVECxR1Vx5i6Dg97phqGsRFKgIprPrKqARZi1N21YicmESuhxUVbCiOBZea0VGWqgoUziggOqDZTLGagDAOfc452c55+7kdzXGmPwrXTFmPhgE0CdtgmVQ1DeFMQatgMDDTiRJAhRUwylSOQImUpycSzMI1HISrM3NTTQkXKDzzSZWTp7M9Zput4vOqVO5Z692Y3FuDuHGRuFZpPQY5EkoNGs19NbWCp3vo9EIg/V1NEuS2hRzuo6VlZXcrwtLzOZYpkkatp+F0WBQqkXQNk3h/M80nD51CvO6XlrZbn+jgRNP5G9OGI1GsEpcb4aug4ch2Z8kDENyr3vufREo2pGVxQrMIgHimZR0H6duUYLkxBCTDCDjWVZCZAKYKOkJ7vEk6egCc1AkX6AsBcOSSDgXZvU91y11b5kFismxamRWsFQSLGRXsFRW7xhjSATnu2Hb8tVCo4g+g6WqgiU4j6MoUjb7phO9BouC+gT+YwCfnPG7P5r8fN/AsixsbW4WGkxOjW6LZnLiOC7UopeirFx8mXJrEXEJYNxn2+v3YUjKvug5Seba8jLmJHjkNGs19NbXcxnTnTh6FPtsW0prwf5KpbAJ7MrSElo5j4GmaWjoeiFSt76+jjldl9ZSsVBQZKRoIgUYB65FZdrDMAQPw1IGtLZlYVSAYJ14/HEckFA53Ds/j/UTJ3IfA8/zCku0p8hj8pwrw5mzgmToOiIBwRK/SbE2PWBirCp41swMfsoo6RHa5IBxAmNqpaus2S6hRVAjHJtt5BB7SAidLbppKjF+BcTKamkMocTcusTcpxQIvh9NYYtgAkKLoJKVxfL0AGA7jvwKVpLAJBKsmfOOJZAQWgRVy+PHzwCj4ZcD+OUZv/srAL8lZ3eeHnDOsb60hEGnU0jdLogiWCsrhUhSGIaIM/rWReCcI4hjVGq1QsFrmfXjOAY3jFziEsBYTCQZDLBPUqYg9eSiYu3UKVxZskUPGN98a5qGra0t7M+YH0jBOceJ73wH15SsXqXYv7iIB554Ai+68cbcQhtLR4/i+QVm0FoTgYlDhw7let3y6dNoFRTHmIb5ZhPfPXMGMaGlYCfKtAhak2RKEZSt4gBjgrWWsy1yMBjAXVtD67LLSq0NjM/3lq7j7NmzuPzyy8mvE352xsZBfRzPDKhSs+Em4bolZTgLCD0AYk8iytB/UcNb4Fz11lmYWckpo6QHWgUrmDXHXNJsl1zBUhH0CeS6gfE8TCwQPymKOEkyBUaCIJhpMlwWOspZU5SGoMtCJbGltAiqCvQZReTCtjGQPYOVJBdURZAq066S2D4TVAT3AVib8bt1AOJo8xmEM089hcXBAHsKMtfTKysYXX45bvjBH8z92q986UuY73axp8R8xIMnTuCm174WewpIMN/75S+jtr6OPQWC/oHr4qkgwKte97pcr/vqP/wDnOVl6AX2dxoo2c0Uvu9j1OmgdumlUtau6zq2NjdJBGtrawvGaIRqAfW7abBME7Ukwfr6Og4ePEh+XbfbBQYDVPfty73mnlYLjx09ihfdcEMuQr966hSeL4HUpjB0HRXO0W63yed9GIbQCMHSLGiaBkxmLvMSWhlVHMeyMMwpqnJ2aQnzhiGtcri30cDpp57KRbBc14U97bPnIDkmweA3hbB1p4SaXVolmSVkIVw7bdO7++6ZbXoHbrllpiodg3juRZ+lrFaQ5HDOhe1SKbLa3cPVVVhHjuCK228ftynuPO6t1vi4z4CWJMJ7PGMMXEXAS7DzMBQouqWIBSqGYRiqE3URJJVUiTykEJ3rhmEoO+6iCpbK+TON4H1mWhYiyeQyBm3WUlUFiyKykahUaSWYepcBNWpYA3ANgC9P+d01APJLq10geJ6HrTNn8OxLLin8HnsXFvDwk0/iB266KVfwFoYhNk6fxrMOHy68NgAsmCbOnj5diGBtrq7iQEGz3VqlgsHKSqaM6zT4rouGxIHcPP5EnU4HNU2T9mCYq9WwcfYs8LznCbddW1kpJQ0/DS3LwsrSUi6CtbqygrmCVZxqpQIsL6PX62GOWAFzXRfJcFiI0GWhPqkeUs97GZlea2L2m5dgyahgWaaJYDTKVbVbOnYMeynEllBFAoCFSeUwzzU/7HbPH8DPSXJsxjAiKjiKVPzKVJBEawgJVp42vYKZ1KxZpCIkhxOTEnEcg2Vtm4pM7N+PA7fcsn0MuecJzXYpXQqqgn2Kip/lOMoy35QKlipfINM04We0BDPGlBjO7lgg89gblqWsgiWq2iYKxRZIFSzTnG2sXRAJaH53uq5vG2DLvO7iOBbOSMdRpOy4a5oGroiwA3SC9TkA72OM3cM5fzT9R8bYNRjLtv+Fip1TgZWVFbQ0rdQAuGNZsMMQGxsb2JcjiFxdXUUTtNaLLOydn8cTTz6JF7zwhble53kewn4f1b17C63LGENN09DpdLA3x3v4rkszRSTCMk24AkPEFO2tLdQk9qo3azUcJSqrrZw6hb0lpLKnYb7ZxPFTp4Drrye/5szRo9hXopo0ZxhYWV4mE6xOpyP1mKdoVqvYXF4Gnv1s0vae55XO9KZS6bWcYh3DwWB6FScHGGPbs0h1wnkURRE2z57FVUeOzN4oZ6ucruuoYyySQvUhc3s9zO9qO8lLcmzLwiCH2fDM7G/JCtLkzWcGFdsCPrNQchaJUsHK9A4qQHISzqERrl+SP81EZIJK6FMYE0uULDDGlAR9FILlVKtoqyJYQKaARRRFhVSIKTB0PXPmUjXBElVODdNUtn4ssCbgCmXaGWNj8+oMGIYhvSWWaseQrh8nidTZP4rIhUp5fIoNRhlQI4D3A+gAeJAxdi9j7H8wxv4ZwEMAugD+naodlI0zR49iUULQu2BZOJNTUW7p5EnMS5hLqVeriLrd3LLV7XYb9ZIXR21iPpsHI9ctbTa7E7ZpwiP2v7dXV1GXIHCxvbZlAUEgbF/inKO9soJWwWrhLDRqNfS3tsjZ0yAI0D57FgslCNZis4mlY8fI23c7HVQVlPQbtRraa7M6lc+H53koe9YVlUofdDpwcs4qToOtaXCJlZytrS3UkRGgFBRbaBoG1nP4obm9Hpyd13sBNTvHtuESCZamabODrpJqdpxzQFABz3z472jTc3bNMVJmkSAicCAEvTmV9KiEJYoieiUlh8gEMCb2lDZwCgHNC8r72Y6DUFFgFiGbYIVhqKyCpet6pi9QLmGRAhCRW13XpbfJpRC1CMZxrDbQF7UImuZMO4aioNoxAIBuWdJ9uBLOhS2C8SRJpQIagdiWen/KRpzzDQA3APhNjO9p103+/CCAGya/f8YjiiKsnTyJRUkKW0tHj5JvNkmSYPno0cJms7vRmlQV8mBjbQ2NkpWkuVoNG4J2mp1IkgSB5xUWGpgG27LoBGttrbT/1W5UGBOSW9d1ocexdKUnxhiqhPVTrK+vo0mYKcjCQrOJ9tmzZPne7sYGqhJJbYqq42DY7ZIzTqPRqHQFywTIBGcnBp0OKlkEi6hmZwHkWSRRtbaoJ1KzXh+3xRIx7PfPJZcFSE7FsshVal3Xkcw6jiUrSHGS5G4P3Y2ihrcArQ0ureQIkYPkkAmWwkoKxQBUZcCbBdu2oWo0PuA8U0hKpfGqoeuZKsUkafwSEAmMGIYx+1qXsHbmta5Sph0gVbBkz0FFgnbUnTAtS9jGmBexQFgEmFQOpa76PVBaM8sgj9Fwh3P+fs75D3LOn805fzHn/AOcc3ofxwXGxsYGapxLCXqrlQp4v08OdNvtNswgkFbJWWw0sHziRK7XrJ85g7mS1btWo4G1M2fIxNLzPJiQ+yC0iP5AcRxj2OtJD/YdxjAQBH/9fh8VRVkXR9MwJBLM5dOnc8uz74amaWgwhvX1ddL2g05HCcFijOUiHL12u5TRLwBUbRv9Tif36wbdLirTjkFOPyRb08imu1urq2jMqpCX8ERq1mroECuHU+XpC5Ac27LgEu+tma0zJStIURwLW1iEKGh4mwcywy7OOYmwqQ70Kf40me2RCmHbtpIKFuccEeeZFSzVxz1Ltlo1wRK1CM4UdJGASBDsy25FPQdZLcoTmKYpnWCiaZ8tAAAgAElEQVTFjJEJlmGa8itYAkEXQO1xV93yqsqf8RmJlaUltCS2qs0ZBtbW1kizKetra2hKrOLMN5t44vRpssJZHMdor6zgqpICG7ZlQfN9DAYDNAjtb57nlZ5F2Y10+D8RON4Ph0M4kJ/lrNo2ultbwBVXzNzGdd3SIgezYOchWCdO4OoC8uy70bJtrJ49i4tmBMk74Q4GsCVVancjnUmizET1Nzexp2RLbsVxsLW1les1cRzDHw5h757PLKBmV3EcMsHrZX3eEmILpmGAhyE8z4MjIM5Tr/cCana6roMlCYIgEBqqmqaJKCPwKKNmF0WR8DOTUHAW6UKBWsFSOXxOIliKkliUClag4PvzwxB2tZq5fi7ft5xgjGWqqqkynE0hMrg2DEN6mxzwvTmfC6UiSEkUGIYhvWoa5ajQG5aFqKBtySwIq4ZQa/Ccvr8qkCN+xtibAPzvAI4A2P3E4Zzz2dHmMwSrJ07gcgnBZor5eh2rp07hyiuvFG67cuIE9kicx9F1HVWMK2MUwYl2u40KygtsAGOp8s3NTRLBkqGmths7h/+zAu2ZctElUbFtbArks73RqLRM9yxQK3iDwQBxCVGTnViYm8N3T5wAbrhBuK3vurAkrDkNBmP0VsWtLVxSkujVKhUcyymV7rounClzO0XU7Cq2jU0iwXP7fTizhChKtso5k+tNRDZGo9FU5cYiJCe9xkUEy7Ks7MCjhJpdGvBKQ9qmR95cTjufCsRxLPYfKwiR/1gKBrnVO4CWMXccRw3BCgJUBV0mqlXVslT6dF1XpuIHiA2uVfoxiQx3lVawAOHnMiV7gHHOwRkjx4SmZUn3fhMRatVQfeckESzG2PsA/BqAxwA8DOACOtEVg+/7GLbbqEvyQwLG7XJHT58WXnhJkmBzeRnPuvhiaWsDQMMwsLmxQSJYmxsbqEsK+Ju2jbWlJVxKOJae58FQ8CAyJ+8tJFgKbohV28YpwQD+aDAQyo8WhW2apArW5uamtKppvVqFt7oqrGLEk8x8mZmvLGgASeDD931Ergsny6+MUE2o2DbCwYBUSUkx9bwrqGZXdRwMCK2ZcRwjCoLZs44ljV9NxkgGpGlL8HkoQHLMicCIqEPAcRwEosCjYAUpCENUJAvV5MIFIk8UqKyk6JpGmo1gCkQXEkIgbZomdMtCGEVS54v9IEBVYEORKBRbEA3967oOTI65in0Q+TKpUNIDxq3Aola5C9GKuhPprKmsYx9NPKio72Va1vj5LhEi5UbVUP2NUj/ZWwD8Duf8XSp3RiXa7bZUPyRgXEkwogj9fh/NDJW2fr8PW9Ls105sy1Y/97nCbVdPn8a8pEBhodnEt0+fJm077PXgKCAaFhMbkQ77fdgKLl7HtuGurmbe6ELfh6MoM6PrOgKCst3G6ioaEpTsUtQZw9bWFg7tmmXZiSRJ8s0H5Ax42WQNEXq9HqqzrvWcUuVVTUOv1yP7bw2Hw/MJVsEWPcs0EXue0IeKIjhQyvgVID1cMyvWOUlOmkQRwbZtxBO5XSGxz1lBGnkeWhK7HvKCIhl+oZAkiVKCRW0RlC2zzAVeTClqzSY835dKsEaeh7pAhEu1bLVQbGEyiyPzcwPY9vvL9MEyDCUqghTDW5WgitmYEyU/Gcc+jKJcn9m0bfkzWBD7cKlsS+WcKzMxBugEaxHAXyvbi6cBnXZbiTdPVdPQ7XYzCVa321UmW32CIJ/MOcfm0hIuy2FOm4VqpYJwZQWj0QgVwYzLsNtFVWKQn8KatA9lQVUVSdM0aJwjCIKZak9JDnPY3OszRgp2N8+exaWiWaUcBKduGGhvbmYSrPFbEh7+OUnO9nuL3xnA2ItrqshIgTmo6sT7jUqwBt0unN0PwBIteunMXSsj8OKci4PdEq1yVGI77PWEQj5JHGN9YwP7FhYyzxVL00hmw4wx1ObmMPJ91CTYYOyEnySZLVvKjVfxzCVYqdGwCjBNQ0wgwpphKDn+lAp8tdmE125LVan1whCLF7JiSoAxCbRlEyyKoIyqGawwimBQ4pQLfC2aEqumURTBynG/NC0L7gVQEYTCeyznXNkcJ0BXEfwKgGuV7QUAxliLMfYZxtjjjLHvMMZ+UOb7t9fWpPohpagaBjqCGYl+t4uKgmA7bV8S+YX0ej0YklsZGppGGv53+32pHlgpbMPAUKAy5g2Hytr0rByzQEIQJbvzIEkS9DY3UZ81P5JTzQ4YE/otAaGn+HkU9WMC6NnlzeVlNKd89iJS5Y1KBZsrK8I1U/TbbVR3P7hKqNk5jAml4smBfk5PpO/tPq0tZdTvwxZcc73hEF958BhGgpZD2zSF13iKxvw83AJ+ZSL4SZLZhpzpwSUBquc+yiBRKJ+sEeWTqa2EVJCqoBPU5ubgEdpm8yBkDFXBzJ/K84GiIGkraBUDxgTLEsRoKqTKgUkFi0KwLnCboFWpSDv2eRVSTceRXj2MOBdWsDRNU3bcVRMsasT9iwD+J2NsE8DfADgvsuaclz3yvwPgLs75rYwxC4DEyWKBwlYJ1CoV9ARD8P12O9sTpwQcXYfruplzCjIMhnejbprYXFsTqsq5wyHsEia3s2BbllDowR+NhMFeUYjEFjRdF98IC1ZxEkLWZzAYwMGMTGyBKg4wnsM6sZFteafrOrigT7yI2EMKymcHgI2zZ/G83ZWHgnNQrXod38nh/TZot7F/yvVetEXP1jShLYBhGPkGoHO2ylHkdIFxQmUv4aGdcHGQ6FgWOkQvrLnFRQzPnpXmM5hixHmmmI/qChYv6WGnEqSqaUFoROIku30oThLoxERkfW4Oy7KSbBP4SSLsCtF0XXpbZApOmJ81HYfkUZYXYRTBEsRJqnywogvcIkid7zIsC6EkJb8ojmHnKDqYpimdYFFmsHTDgKfofI+TBJrC751KsL47+fM/z/g9z/Fe54Ex1gTwUgBvBgDOeQBA6p1r0OmgIllkAhgPoS+325nbDLtdtBQRLBPjuYcsgrWxsiJ1FgcA5up1nF1aAl74wpnbcM7hDQawFxelrg2MCdYGgWDp1HaLnLNAOmOZlUOrUkGYFSAUJDnA2GtIlOkbDAYzBT6KEhzHsuD3+5nWAIyx7B79giQnRQxxz7bneQimqScWnIOqOA78lRX4vp9pAAqMz/l+p4PKNDuEgi16jmVhIBBV0XUdbELqVbSmirx5UoyGQ1iSSI5tWRhRCdbCAtYl+EntRBCGYJaVKeqiKRBZ2AmucN5m5prEz6OygkWZBQIAXbKyWpwk0IlJuWq1Kl1J0OdcWMHSFM6kJJyDCe4fdqWCqID5ughRFMESKCiySaInimOpM+1RHAvb5VQnOijXku04iIj3RBHCKIKZk2DJJLdJkkDTdeFxNUxTWUIhThIYipLwAJ0U/TrUCm5cDmAdwH9mjF0L4EEAv8A5P0cqjTH2VgBvBYAjR46Q3zwIArAkkS4yAYwFD4aCCpY/GsGSKfW7A4amCVvVtlZWxLM4OdGo1dCZGA7PFHoIQ+gg3phyEhzLMOALlPQyVdVSFKwi6UAmwao1m1jP+F7KVHH8IEBNMHg/U0GxBMFhjMHRNLiumzlz6NRqCMJw+rEv4ccEAGGSCElOp9OZXrEtOAfFGENN09But3Fglgx6uv+eBz2rylZAza5i29gSJHGAyUxIEEifQwLGmXWRRHuSJPBd93z/rymg1H0s04RLlMhvNBoYSX4I910XcwKFVk3TlPoCUStYskkehdQp9QUCbebPtCypwX4esYNqtQpf4jGI4hgwDOH9zVA0dwaMj7moomA6DgKChH5ehHEMm3DvMhxnPDMlmWDVBcddpak11etJptBEHMe5PP5yd0kIEEYRqUVRV6QcCUxELhSqGJLemXP+AWV78L39eBGAn+ec388Y+x0A7wHwvl378QkAnwCA66+/nnzEfd+fLh0sAYauIw6CzKx+4PswFLTJAYDOeaZs9fYsDoWQ5gj6DF2HyTmGwyHqM7JOQRDAED2oCxIcy7LgC4KvMAiyFWJKVJEYslXV6vU6Ts+6GZWs4rhRhH0Cpal+pzNdvbEkwbEm80BZBKtar8Nz3emBfkk/poBz4UOhvbk5XdCmhFR5XdfR3toSEqzUA0uIHC16VdvGKYLZcL3VgtvpSCdYcRwjYkzYuuT7PkzCjBxjDIyJg3jLNBF4HmkuptlsYoR8MzQiDIZDLAisKDRNU+LLkyKBOEElu8LFQTPwVTkfRg1mTdtGTPAEpCJPq5hsgjXyPNQJipW6riuZQwLG17ooo+9UqxhKrhYD43ihQkhE27YtfQaMMv8FqDcbFsFyHPiSjn0Yx7k6m0zTRCzxeo+Jx1zV3B2Qfw4tLy6cAP25OAPgDOf8/snfP4MxwZKCMAxhKCzvmrqeSbDiMKRLQRZoVcu62QyHQ1iivuqCJMdhDP1+fybBCsMw+wQrQXAMXUcYBDMf8pxzYT95mSqShuwM69zcHAazvpeSJMdNEqE30Kjfn56RK0lwUm+iLNTn5zHa2gKm7WMJkhPHMWLGhBne9bNnsTCjYlt0DqpZq40tEZ73vMy1XdeF7Nt1agsgIg+tvXsxUDCHNByN0FxcFAYAKcESId2CQsRS/y0RudN1HY35eQxHI2mqboMwxEGBciSbEEqZxG4nRMar6T7IDPyoxEm1LxCJYE2qGbIQRhEsYlu5bdtgpilN1c31PDQI1V/DMJSR+jCOhd02TrWKjgKRizCO0SIQLNO2pc+AUcQWVIohgNCaCYwJ1sy4IieiOBbOvO2EaZpSiU5AmLkD1M3dAZOEiqLxHSAHwZoIT/wEgKsA7KadnHP+74vuBOd8hTF2mjF2Fef8CQCvAPDtou+3G7kMEXMSHADQCFUk4QOrIMlJ338WhsNhdka9BMmxJ+8/C1EUZRLLMgQHGAtNRFE09cYoDBJKVpEYsgOAer0OVq1i5PvnC5yUIDlhFMHXtEzJbmAsUb8w7YFR0nDW0jQhwWrMz2Ml4wFYlOS4noemQNobGLfEXjIrMC44B9Wo1XCMoCToui5syQ9iTdNgcA7P8zLnM+YXF7GmILPcGw6xcPnlwu3ydApojHZvpRIsAFg4eBDd48elESw3SYTXGTCeEYhVESzGhARLdhWNE0jd0wEKyXMqFbgSz/koikhZ9RT1Vgsjz4MpmB2iYOT7qBOSI6ZpKpEqB2if37ZtyG8QBMLJe4tgVyoIBeMBeUGZ7VXZDpwQBWMsiS2CiablMvk1TROR5JZYi3CvVmUuDUzaJC90BYsxdgjAPwG4FJMOgsmvdn7qwgRrgp8HcOeEyB0D8H+WfL9tkMxPSxAcbZLBLIwSJEeETONPlCM5FcvKlFHO9EkpSXCA7/lBzbwxZl2UJatI47fPvugPXnYZNk6cwOHd0t8lSM5Wt4t9R44IgznfdWcODJcxnDV1HZ7g4dZsNnEi69gUJDmD0QhzAtVKz/OQ+H62NUCBOSjHshCPRvA8L7NFcdjtKrlh2xPft0yCNT+PgYJh4L7v4zJBayQwIVgEksEYAxhtqNucvC8Few4cwInHHydtK0IYRQh1PVNBMIVhWYiTREkbOsUnRrZUPFWpU7VcOIlg1WroSjzngzCEk4Og1+fm4K6uoimDYEUR9hNGCSzLkhro7kQYRbAFn9+2bSXrR4T2b0B+1RIgEixdVzuDRbh3mqaJRFIiJyZU7XavLfN7D4k+XKZpQn7acIxnSgXrtzAWoXgpgFMAbpr8/d8C+F8BvLLsjnDOHwZwfdn3mfHe2RuUJDiUVpcslCE5ogeRNxrBmnVBliQ5tmXBzSBYmcRWAsHJatMTts2UbJXjEH+vF196KR79zncwRU+uMMnZGAxwxY03Zq4LAL7nwZyVfS9hOGuaJnyBTGyz2YQrCnoKkJy+6+KAoIWm3++jRn0A5ZQqrzI2lr8XEKx5FcbahNbMWq0GVqtNr5qWQD+OsbCwINzO8zwyyWCgBejGpIJFQavVwlBSsN0dDLBw4ABtH01TiS8QQJMxZpMqlywFyThJoBEy25qmqZMLB60ly7ZthDKDvjjGXA6y1FxcRC8jIZUHAZDpuZbCtm0Eio57EIaYJxAsmcc8RZgkJKVS23Ewkk2wiC2CKsUWNMK1K3MeKS/BMgwD0WT0QkZyhVotViEPv70PnJOqpkVBJVg/DOCXAKRReMI5PwHg/YwxHcDHALxW/u7JgcirpGyrmqgFMJVVnfq4KFvJEQyVe647uz+8JMkxDANBRtCXqYxTkuDsXGMaGGPjG+Ks1p2SrXIc4uHzffv2YaTrCMLwfMPjAiQnjmN0OcfBgwcz102SBKFIQbEAwQEA0zAwEBCsarUK5jjwgkBczclBctwkwbyghWY4HM5OKJSEresYDofYkzGX4/Z6OKiggpXaMYiw/8gRbJ48iYunGCYXwcj3wR2HVMnxhkOYkisfZg6CNTc3B39S1S5LNPrDIfZceSVpW8u2pWfUgclzRdNInyWVK5dBsKI4hkGYhRlXItVUsahBnG3bCGVWsIhVlBS1RgNrksi1F8dCiXZAHcEBgCBJ4AiqCo7jKCF4IUBqBbarVfQkJzQo5E5l22zCOSmpIXMOijJ3thOapo1VO2dZsOREGEWoU2buFLbExkT7kaKgRiKLAM5OzISHAHZGOX8P4GWS90sq9KzSbkpw7r13JsFhjpP5IBFJ6WZ6peQhOVOQCIQcQt+ffWMoSXJSoYlMzDpuOwiOc+jQuesSCM74LQRKhwKPlHB1FVarhStuvx0HX/c6zN98Mw6+7nW44vbbYbVa4/bQGaDMRhiGgYuvugors8x5JyQnOHYM4alT4z+XlmYSjrV2G/suvVSYcYmiSKzemCIlOMSbtqHrmaQ6xZ5Dh9CVqO7FOceQc+FMjJvh/1UWNmMYCjxIRsNhdnti0bUNQ2isDQAHDx9GR5IRJQBsdjo4cNllpGDXc13Sw4qqIggAFqElNYWmaWjt24eehPmMfhRhQSBwkcJyHGlzETsRRhG5fcWaBD4yEBOH35VWsIgZfcdxpM4DhXkJVq0mxQsrSRIEAIlgGYYBQxGpp5Ac27YRKxBWCYjVBBUtkhFoM1gyZcp3Ik2miCCzXY7SFrkbtuNk2tPkQZgkpPuMZVlSkyg7ETH2jCBYZwCkT5ujOLcl8EYA4ojrAsIwjNkMuCTBAcaZgKw2Di3LP0BCq1oWweJJMnt4siTJ0QRmkKKh0DIEBwAgILaGaCA0rSJVKjhwyy245LbbcOCWW2BWKsJWOWp5/dJnPQtrIkJCJDmrgwEue85zhGuGYUgnWDlhGgYCQkVh78UXoyNxELk3HKKxZ4/wmA/7fSUEBxjLhmcZ38ZxjND3pWT3zlvbsjAiHM99+/ahN2njkIH2aISLLrmEtK03HJI/O/X0NE2T9LlT7L3oInQkEPshoVqawqpUlAS7VOloYDyXkiW0lAdUYqdpGrii+wy1TbFSqcCXWcFKElIVJUW1WoUnYX3P91Gt18lCKbW5OYwIia68oPjdMcZg12rwJXphBWEIy3FIn9+yLKkVDc45qZqj8nxPOCcZ3l7IChYwrh6GshI5AIncmKYJriiZEymuYFEjgS8D+BEA/y+APwLwe4yx6zBOePz45N+escgczpPQqibKBOhZJ0fZVjVRJUXQHllG8IBznhkpCW9IJWaBgDHJybohO5UKwjDMnkcp2CoXEfvF9+zZA9TrGLguqRw+C14QwDNNoQ8TMFFvVPQg0ClVS4w/95MSs/qdXg/7BBLpABB6HuqKjAMt08QwozoUBAFJprzo2j1B9QwYZ/Sb+/ah3ethgeCpk4UkSdDnHPsI0tHAWFjFzvnAFsEyDAxzGMku7ttXWknR9TzotRo50LYVEawgDEnGq+k+BJIqxlEUkT67ruvKAk6eJCQjWcuyAMMYtzVKaOHycxKsSqWCUIJMv+v7aBDmHFPU5+YwWluTIq6xE36SkObAqrUaPN+XJujjBwFqRK9Qy7Kkih6kcuWi70/l+U4xeAbkCk2ICgPTYFUqCAmejKT1QSNYAFCZEHqZs8UAyFXToqAe3X8HYAEAOOd/wBgzMBa3qAL4MIBfV7N7cpBZUi5JcMIogmGa2ZUU08xk32VIjqhFUNP1bOZfguQkgjYOkrxmQYKTrp9FLp1aDQH1ZpBT8IAqKcsYw2VXX43lBx/ElSUI1srGBo4897mkPvAoisil6bwwdB0RgWC1Wi14mibNI6YbBHi+YPYMAKIwVCKXDYwTBlFG1pZkrF0QlmEIxUVSHH72s7Fy332lCdZWt4uFiy4iPwQ9z4NJXJMaI1BEVXZChtBFbzDAXsK5lsKp1TCQmM1PEUYRHIJMPDDJLLfbctaNYywQAnd9MlusAnGSkA1A65NqTll5/iAMoTtO7rmUarM5rkCVMPge+T7qR46Qt28sLKAvSVwjRZ7PX5ubg7e8DBA9w0TwggBVYkuudDU7YiJDZUsstWKbCk2URTQxlM77rLQrFQSzRh7y7kOO6pFTq8EPAqkEK61cXnCCxTnfALCx4+8fB/BxVTslG6ZpIskYfi5DcCiyrpphKCU5WUE3SXGnaBVH4GOQ2Zq5GzkJTpIk4IxlZmBqkyyfCgSEVooURy69FN+97z48q4T6zobv4wcJXkTAuFVNWQVrQjJEQ+iapmHv4cNodzrYlyMzOw2ccww4x+LionhbiudcyX2ZBZUVLNM04RMrOQcPHcLjEioq670eDl93HXn7wHVhEr7rPO2LpmHAz1GZqdVq4LYNPwgKt4r2XBcHdrVMZ8FxHCWiA34YwiFWKCq1GkaExAcF1KyurutSjUd3Iopj6FSC1WrBXV8vTbBGvo96gaREY34ebqdTmmAdymEQ3pibkyaukWI4GqFJvFfX5uYwPHlS2tqe76NGPPayZ7ACopqdruvKDJ7jCeERQdO0McEsWbENowhmjlnDFJV6HUNJyaQ85KZSr0sjdinSDgGV8QLVB+udAP7bhGh9X6IyYcBTb4IlCI4XBKgKHoK6YSAWPfwKkhxRi2AuxZ2cJCcIQ9gZVRnZmaadCAk3xfrcHFYlBR074QcBzEqFXF5vNBqo7d+PTr+PeWIbxE4MRyOgXidJZQMTY+3cq9DAGNuWxxdV0w5ccgmWz5wpTbB6wyHqe/aQbsaaYWTOBZYB5xx6xnceRRHd0DwnDF1HRJy5aDabsObn0RsMCrcQcc7RSRLcRCQacRyD51TvoxAtyzRJoio7sXjwIHqdDvYWJFgUtcqdsG1biU+LHwTYQ6wQVBsNdCS1KUagVecNw5Dqv7UTcZLAJH5/jYUFDE6fLr2m63loXnxx7tfVWy2MRPPCAlAFLlI0Gg34kp+tw9EIc8TrvdZoYEtiW6wXhljI0SIoU/QgjCJUCORO13UkClsEqe3VqQ9YGYIVRVGhyk2lVkNb0vceEMcsgEnFNEPJuwi8IEBVUgV2Fqhx2EcAnGWMfY4x9tOMMXU1NUWozs1lZ/hyKrql8HwfVcHFKVKzOwc5Vd1ELYKVSgXyKcYYor5pleovYRQJy/r1el36QwgYP4gaOQIwADjy7GdjrWDv8trWFg4/+9nkTEscxyQT16JIDZ5F2Lt3L/oSvv92r4cDRKEFU6Ka2m5EcZzZthTHMVQJ+eqalku96XCJ8w0YH/P63r3koC8IAhjEdhPOOblF0NB1hGGYqzVn8cAB9AsKrHDOMUwSzOWoZDiOo+Q+l0fRTqaaHlXoQVa70jREORQUm62WUGhiq9vFuqCF0vU8cgXnnPXn5+GWTOT5nJNmn1I0Gg24sitYvo/5vXtJ21arValxRcA5+V5jGAbYxPdNBvwgyEwU71xXaUsskWDZEgRtilawLMtCJMMDK46hmSY5SV1tNOBLnnP1fJ8891cUVIJ1GMB7AVwE4L8DWGWMfZIx9sPK9kwy6q0WPIqfSk6C4wWBsK3AtCx1vbuCQcVqtYpAUcDpJQlqGRkA2TfCnRBVz4AxwRopWHvoeWgRB/9THDx0CN2CN4h2GOLQ4Wl2xdMh8mUrC6GJ8wStVguRaSIo2VLQDUPsI87EVOr10uvNQhAEqGQEQXEcKzvujDEwzDbW3o2LDh9Gu8RxWO92ceSqq8jbh2FIHugFxuqn1FZBg7Fc5LK1sIBhwet+5HlwGo1cylKO4yhJYoWckwUXqtWqFLlwgC70oNKfJo+CYr1ehyf4vh/49nH8w4NPZm7jxTEaBQIuGWSD6oGVwjRN2I0GXIlKgqMkIfndAZPErcw2PdA8sFI41ao0FcMwiuBQCZaUFc9HnCR0SwbHQVCSbERxLPQ7mwbbtoVJlY12G1+696HMbYIgIB3zFLLPNyBfW2pRkAgW53yFc/4RzvkLAVwL4BMYqwd+hTF2gjH271XupAw05ucxIhpW5oGfJKgLbkr6pGdWBUQEq16vY6Qo6+LHsTDr5lSr8BW06QVhmBnsAuPPHmiadII38H3MEwdyUzSbTaBez/1ADKMIvq6T5o92QmUFC0QZcMYY9h0+jHavV3ipJEkwSBLy5681m/AUESwvDDMzXkmSgCm61gBst2ZS0Gq1wOp1uAU9sdpRhEMzlFWnIQxD8twf5xwY/0eCwViujO3c3BzcggmtvutigaDUuROO4yAC/buhIshBsCqVipRqfRCG0G2bJHSgugWcqqBIesZx8XfjTd4rL2q1Wimp+CLiGgCwsH9/4UrtNLickyu31WpVSGrzwEuSXATTrlalKXeGcUwK9lXOHIriuJ2wJFSwgjCEVYBgUeZNXc/DVi/7evAJ2gU7oYRgCZ7nMpB7VINz/k3O+R0ALgHwGgA6gP9b9o7JRqPRkOqXkYJS2rcrFWkeJbsRC4QeHMcBs20lWX2X8zFxyECl0ZDql5HCJ/TPapqGuT17pBiP7sQoZwsRMCYbBy65BFvdbq7XtXs97D18OLfaj7owH3QTI5T3w+oNh2ju3UsOPmq1GuSnUcbwJ++fBaXEFvSqD2Ns3CZYQN+RdYoAACAASURBVFmuNxjAWVggZ7OB/N5rCadvm7eCVa1WERtGoQBsMBrlrk4zxlCp1eBJTiTlkQxPSV7ZZNLI91EnKhemFSyZhrMpoiQhV7Bs24Y2ETYpCs45RklSiGCV9cJyPa+QuMbCgQPoSzIVd0cjWPU6uXJrmiZ05/9j791jJcnu87DvVHW9uqr63fd9h7M7O8slKT5EUaQY0SIpSqYkykpsSYFsOJAFBfojNmIjNhzIAuzESawkQoD8FUSKkQCREyBR5ECyYDiyEolSrMgWl5RI7nKXS+5yuTszOzsz995+1fuckz+q696evv2ox6nTuxQ/YIDd+6pHV53ze3y/7zOFxBUJpUCjUWgmSCRTIa9ceJ2UWDr/+3kgIrks0iF+7NiGkS/R2bK8hzm0CxbRbDaFj3sUnXssg1Kz8ISQjyPtYv0TpLTBZ0WeVB2wbbuWTk6eRdloNiu3dFeBc566YW9ZHLp7e+KTjDBEw7a3LorNmihbEaWwcrygw5OTXP5BecEYgw8UTrAAYP/4GOOCHazz6RR7BeiBwNx/rMZOCt8y97eIwWCASYWg72IywX4R+eIaZhMyFKHQvBVwdHqKsxJB54OLC5zcvl3od5IkgZLzmct+Ku8zqgKFEixCCNr9PqYF/LMyeJSinTPBWITT6QhlSMRJAkXXcxcWCCGpml1FypgXBLn9mAgh0AyjHpNl5BPayNAeDDZ61IFsrgv5QYBmq1VIpCVDo9GAZlmlEzy/oAdWhm4FKuwyRrMZ+gWsCYC5PL6AZ94vkWBmst0ikNdypU5K7DYv1UWI8N1Lcnbtrh17ThHc1q3f7L6aJlhWgb1UVAHpsXMoMPdXFrkTLELIM4SQ/4IQ8g2kxsM/COC/A/AuzvmHazo/YXAcBwFjQgPPIIqgmubWBKcuGd9MSW9bsDs4OhKaZADAaDLBIAeVptlq1UIRzDsA3t/bw0RggjeaTtHZ2yu1Efd6PUwLVjpnBehxGerkigPFDArb7TZCRSlNkZ3EMfoFOgq2bYMJmPtaRhhF4Lq+cUFWFKU2lSkAYPNj5EW/30fUaBS+FxdxjMMC9ECgmLl1JnKRO8EqSBEEgM7e3uaAew3KJtFOtwtf5DxMEOTuJGVo9XpCEqwiQg91mSznKRwuomqCNfV9dHIKPKyC0+mUvvd+EBQWTQKuPN9ExDRjz8OggDUBACEJPTBP6gtev2Xb4iiCjOVTqFUUKDWMHACpWFneBEs3TSQV2VhxgeMtghAC03G2spK2bQURpYUU/AghqZKgwFgyLEhLLYNcuzUh5HMAngPw1wH83wA+yTm/yTn/Bc75i3WeoCg0Gg1Yrit0E5x6Hjo5ZnEsyxKm8LSIvCoo/eEQE8Gb4MjzMMwhaWs5Ti0zMbkTrH4/v0x9DlxMJtgr0FFZhOM4YLqeO+DNumWdgoFWnbMRRQ0KFUVB7/AQo5IJ/rSgZDYhBP2jo9LHW4fRdIrB8fFW76+6Ooecc3AUS7AURcHBk0/iYQE1wSCKEBtGbkuADEXNrXkBMqUy//tF0O73MSu41jPGEBFSSM0tg9PpCF3nvBIJ1tYkIwcCStEqoqAosJOwCIpiHazuYIDZhvNQwDdW1aeeVynBqjLjHeSY414FwzBgtttCkpwZY4XfebfXExJPeUEAp2CCZQoU74oL+DFppom4hgQrQf4OlggfMFbgeMto5njnFWXz+eVVKl2EiALS5fFLzj0WRd498T6AvwLggHP+s5zzz9Z4TrWhu7+PSQnayDrMPA/d/f2tPyd6IDRD3k0465yIDP4mlObqrFiWVY8JZ84X1LIsmJ1OKbrQKoySBHsFh+AzFA3+x7MZ2sNh4W6ZaZqIalKtDKMoFzVzEYPDw1IdVD8MoVhW4SrT/ukpzguY0+ZBHqpmo9Gobwh6LuNbVKXw6B3vwHmBZ//h+TkOn3yy8HHiOIZacE4wLxQUp4Y4joOo4DX4c1WpovOO2fFCgWt8GcnwVqcDv+J773NeaA7Jsu1aZmyjnF2FDK7rbrx2so0iyBhaJaihl8fvdvOpFK9AVKGSPjg+riQiBKTvlofihTzHdREIWO8CSuEWpAiK8p7jnCPmPHe31LSsQnTlvEgKdJREUBWTAte8DLvV2vqsb1t5YyC3BUUGt9stLdq0jJnvw63wvudFXhXBz3DO/zfOubj2zw7Q3d/HVNAHBACzJEEnR5Jh2zYiQoSrTHlBkKvqpus6Wnt7wqr6cZIgbjRyLcgi/VkWERXwiDm4eROPKngCZaCUYgYUpuwtYnB0lHsebjydol+QtgFceZ/V0U3xS3hHdAcDeCU6qJPZDIOCcwEAMNzbE9q1BIAJYxhuedfqpGYmlJbyLRkOhxgVKK6MggBHJTq0SQEfrKIo08Gybbuw8EBZH6TL45X6zdUIGCscdLoVA17GWOEEq+m6iOpQiS3QVQDmCdamayfYGPVVna90XLe00EVQYRZk7+gI44pzUOPZDJ0Shbyq6okZQsYKd40NwxDiPRfFMQzLyl1UyUx+RSMpQIkV0cHKK+yxCnnGPrbdziIKqRncTge+oHtfdu6xKIrMYNmEkP+QEPJ/EEJ+lxBye/71nyKEPFPfKYpDp9sVNhQKAF5ONTlFUdAqOXS9CbMCdI6DmzcrV7oynI1G2LtxI9eiZFmWcPXGOEmg6nruOaDDkxNcCAgCzicT9A8Pcx93FYoMJk8Lzh9lUBQFdqsl1CMlw8zz0C4oUd9utzEr8QxMS0hmA3P/LV0XRl0KogiJrm8tKIiqqq5CFEWlfEssy4Ld6+WSc+acY8QYBgU/XwCIowhKibnEPFAVpXCClSm7FSkyeL5fmJaXwbZtoSyFMkGn4zjwGCtdyMsYEUUC7ToMQKM4hmaahc7DMAyoprl2RkMBoJDVz0KZxHIZZVXOGGNICCkcbGbo9XqYVnzuLiYT7Oc0cl+EbduVO6ZAmmCWSrAqHzmf3csizBpmDhlj4IqSX6ZdRIJVtYO15R6se9eAdJ8JCxTIMziOI6RjCgBeGKJVoVCeF3lnsE4BfBHALwG4DeD7AGTlnk8C+Du1nJ1gZEOhIkApRUhI7qpX/+gII8FKfjNKc7f19w8PMRLU2j6bTnGQc0E2TbMWg7giEp+DwQBTVPepeTQe4+jJJyv9jSLJRpHPdxldwR4pGWZJUtgDLOvgFqV5eQXnQTKUmT3ahLy0OV3XhT/rGaI4LqX6BOQvrkw9D81ut1SwR5MESk0CHwohoAXXLlVVYRSkrwVJUkouG0hpOw3LEjaEHWC7JcAyVFWtJLZQZg6pDgp43tniZXSGw7VFzE2PZpnEchllpdozWmpZg3LXdcEMo9JzN0kSDEoU8kzTBEraIWSglIKpauE1R5RwWBjHhSjvpuMIV4SOkgRGgXdd1/XK1x4zVnr+KI8n1aYZrDCOYTSbpTqmVewQFhFQKkUROG8H679BagNzG8B34fFm+2eRJlxveViWBdW2hUiLZmpyueWq9/cxESjj64chSLOZexPu9/vwKqi5LWJEKfZyLsiqqkIT5JeRIYwiNAsEQpqmoX90hLOKHbwxpdjLMXO3CZZlQckRiCWUIlKU0lXVwdERRoI7pgAwLZH0ZX5kRem5HmNbfdbW4bjg7NEmnPs+jnMUFAzDAIV4w1kg7aI1S96L4cFBruLK+XhcWsCFJUmhGSwCnjuoVBUFtERQ47TbhYbwo4qyva1+X8iMQJwkQKNRuMILpJYcZeeMp0GAbsFAO6Mji0QQRaUSrO7eHmbrrn3DDFZVgQsgDfhpmSJSCTGTRRBCsH96iouSexvnPPc89SpUFR7Irr9ogqlpGrgARb8wimAWSbCaTeEKtUXZCVU7WJRSEFUtzcRpNptb3/lNH2cQhrBLJDeWZYGpqpAYNqjYsc6LvDviDwL4B5zzb+K6h+kdpF5YbwsMDw8xEjAAP5pOMSwgZdzv9yv5AS3jfDwu5A+kqir2btwobHS7jJnvQ2u1CmX/eYYii8CPItgFK81HTz6JRxUSLC8IwJvNUv5Xy8gj+jCZ8+LLDNwDc/8pwYF+EEXgplmq8tMeDApRZCmlpRXdAGB/fx+jHH4dec5jDOQqKBBC0HRdoc96hjCOCz/zGfLSiCZRhL0SM2/AvIOV81klhBTxqgYpQREE0nWnCE20yFznKrQHg8LKhasw8/1Sst3APMkoeQ5lPMAsyxJuAOoHQalnvd3rrZ31JBtmsGY5Z5k3gRACt0T30PP90nN/GfZOT3FRMqkez2Zwh8PSdDG3YoI18/3SVC3LcSrTwMOCybxpWUJmvxZRlJ2gKAoaul460YiSBHqFQlKz2dw69kE2UAT9MCysGpn+TZI+bxWLWFVMxYsib/SmA1iXlbSBWnQMasHw5ERIZX+SJBgU6GY4jgPVcYSpoFx4Hg4LVpsPb97Eo4pCFxldqgjsVkusf0GJCuf+wUEl4YNHFxc4KqGutgrdvb2tycbU89AtqVYIXM0hibzvZRXmgJS+UyTwywQHyiaYuq6jf3KCRxULCmfjMQYnJ7kDEKfTgVdDghVQWnpDME0TRqu1de0pKom/iEIJFjJVt3zPkUJIqQ5Ws+C6U2bwehGtblfIZ18l6Gx3OqWNtv0SHeM6DECDJCml8OU4zsaZIGWNTLtfkoq8DLfXK8yOKXuti6hi5l60ULuMdr9fLcEKArSrJFgVu0kRY7AKJBt1zNmGcVyoiwak/nNlxWXiOC41z5tB1/Wt1NBNK7sfBKWf+Va/X1moroqpeFHkjV6+CODH13zvhwE8K+Z06ke/369c2eecY1rCN+Lo1i0hcyHZMPo2VbNl7O3tYVSRP3wRRTjM4X+1CLsgVWcbyhjEtVotcNsuvRlchCEOt8h050W7290aBE2jCL0KVVVCCE6efhpvPnpU+m8s41FOqtwqtFqtQgPRU99Hq4TYwiJOnnoKDyvSQh9OJjh56qncPy+iwrYKIYrP5CxicHKCiw3FlSCKwA2j9DE4Y7kTprSDld8JS1EU8BJrdrNAAMY5R4Ji3kvLcBxHSDfHj6LCQjIZWq3WZjW9NYiTBKzRKLyuZgagImj3GcKSVM2tUu1rop2qCoKXxy9hvFv2WhfRarVAS4r6jOMYwwqFPLfVqiTuElAKpyT12XKcygXEvH6aGeqwQIniuDBlzrCs0rNgUZLArLCXACn9ehNTY927BqTPfBmKIJAWaqsWsaqaihdB3gTrlwD8LCHkf8DVvNW7CSH/KYCfnX//bYF2u41YVSvxaLNh8KJ0ksOTE5wL2IjGsxma/X7hhdl1XWitVmkzSkoppoQUVhlz2u2tqjNFEAGlAoGjJ54oJdfOGMO4REK7DnmCoIDzypv+6c2beCQo8IniGDNFwX7JGbSiEtKzIMhl4r0Jh4eHuKC0tFw95xwXlOKwAG2u3evBE8zR55zDr9DBAoD+/j7GG977yWyGfkl6IJD6dOVNmAgh8y5WzoQMKJVgmaaJJOcxssHrKh3qbR2UvKgygG1ZFlgJ4YHJ3HOvzPW3SnRuNqFMAQ1IK+uNZnNt0L3qyhhjCAAhdCGn3S4sIx2UUItcBiEEe6enhVWCs0JxFdsRx3FKJfQZQpS/93arVdkioGiCVUcHK6K0sICRadulxUUyafoq2GasvWkVqeL75rou/Ird8ioMgaLI64P1TwH8BwB+EsDvzL/8PwP4WwD+Buf8X9RzeuKhKAr2btyoJFl+Nhrh4ObNwr83HA4xI6TykN7Diwsc37pV6ncPn3yytCdUJlNeVH0mz1BkEZSt+h2enuKiRAfrYjJB5+CgNE99GbZtI8RmMQQRHOHhcIjYsoR0VN54+BCnzzxTaTA2LjAELoK2Y9s27OGwtP/bxWQCZzgs9Ky1Wi1hUrIZ/DCE7rqVXOc7W0xoJ7MZ+hUq2UU7WIpSIMEiBKzEmlnEKyeKokJyzauQPeNV5/6qDGATQtAeDgsriE49D92yxZNut3TRbhmc81IKihk6/X6hc/HDEE6nU5qKvAjbtgt1MDnnlTvTGcrMYU1mMziDQaWubabsVraIVaVw1HRdBBuKWePpFM8+/9LGcys6d2mapnjbGRQ33TVtu3STIC4oTb8KTolubYagYoJVdX/1KUW7JBW+KHKvKpzz/x6pmMWnAfxVpNTAE875r9R0brVh/8YNnFWYRbqIIuyXMIBtNBrYv3kTD8/PSx8bAM7jGEcFaXoZDo6PcVGy2ng2HuPwiScK/54oQ0Ig7aIlKL4gAXOuOueFN4OzyQRHJa57HRRFQdN111aAojiGYhiVNr7sOE+85z248/Bhpb8DAG8GAZ64fbv07xNCUhGAnMGPKNrOyVNP4WHJOayHo1EheiCQJlie4A146nnolZBRXsS28/IoRafCsH2hBAvFVAQJIaUCOMMwcssZR3FcmTajKEopoYNFcM7hl/AFWkRvf7+w5+IsjgvbL2RodbviDECDAE3HKT0f0RoM1isJrsDM90vPAC2j6D7nz+1GRCR3w+EQ04LrzlnF+SsgjWmMDXvZJgRRBM22SxeOsvm/dZh4Hl78xmhtwYNzjhjFaMF1qCIX7aIBgFUhwYoorU4RbLVKvfOMMcQVfN/yFKe3QVRskQeF3mzO+Yxz/juc8/+Vc/5/cc6ry/HtAHt7e6XnsBhjmHJeyowTAE5u3cKjCv5EXhCAWlbpYfR+v49piSQDACaUYlgi0Ctj+rkO3nxAsgyVxTAMuMMhxgXv/zhJMKwoz74Mt9dbO5fmBUFpP55lPHHrFh4mSaUF6Xw8RqPXq0QlAZBbqp0xVklBcBEHR0e4KLkRjSjFfkHanK7rMASK2QBpoFDGcHkRuq5D3xAIeQI2ndzvJCEgymYaiQgUMSMNBSRYQDqEXaWbUzXBAOZdnILPfMB5aUsEx3GE+dPMggDtClTsdq8Hf821r3o8pyWM09ehaAfTCwK4FRUEM7RaLSSaVijoHkdRpfmrDJ2S9gQz30e7wvWbprmdGbNhkclowUUTXNGqyGEJcR3DshBt6Oq/9sZ9vH7/zZXfK5pUrsK2bu26rcAPQzRbrdJFBUVRNnbMKaX4l3/0hbUqzTIVBIENCRYh5PuK/JNytoLQarUA2y61KJxPJugeHZWuuuzv72PEWOmA98HZGU6ffrr0rIBhGHAGg8JJRpwkiBqNUsa3WZVLhKKdFwSV+LP7BaXqE0rhE1I6oV0Hp9NZG+wGc9qKkOM4DoZPPIE3Kohd3D0/x+33v7+ygmJnOMyVYM3m0sUiKrvdbhdJCSPOIIqQ6Hqpz713cCDUVHxGKboCArHOmuq+yIQ2DwghUEj+DlZZaJoGCuQq7ERxXMi8fB2qJlheEFQWdykqKMM5h1dh7st13UpzOIuomvA4joMi/cOAMbiCilmZ4EfeDqbn+8ISLEIIhicnuUcfOOeYcl65aAagEDNhETPPq/SsW5a1UXAiW1/WrTNl/ZhEqiIzxkoxcrbNgv3BF17HHz/3xsrvUc7FJFglYthMHbgKNhVqE0rx4Jyu/Xz8MIRRkW5fBJsimN8D8Ls5/mU/97bC8a1bpSScH41GlehipmmiU8H09iyKcFyxrb9/40ZhY8LRZIL+0VHpoLfd6xWibqzDzPcrbcDDgwNMCrS2LyYTDI6PhUt62q3WWv54UMLnaxNuf8d34G5J77cgijBRVdyo+MwB+eeTJp6HdkVKXAZCSKm5wyqS9P2jI4wFmjxPGStV2FhGe03wLzKhzYNtgY/I4xiWlauqH1NaefAbANx2u9KMgAjKmuu68AowBqrO+BmGAdU0hQSdVecjbNteq2q36nETITKxiCKy5V4coyWwcLd3eorznIWdTKiraqANzO0JStDFvDgW0sFa95xvm/UMowjNMgnWFgW9IgiiCKZtF14Lt4ptcL523jnmvPI8edl5UxG+b0UtXxYx8310JSkIApsTrE8C+P4c/7Kfe1uhrKLfiFLsV2yrn96+XUo+OopjBKpaWc1usL+PSUEKycVshmHJuS8gf/diG3zG0KoQbGaGz3mDj4vpFHuC5NkXYVnW2vmQMEmEVNMz7O3todHvlzKZfu3+fTz5vvcJqfjknU+aCl4ED09PcVHw2RsFQWlZ/l6vh5kgypTn+zBct5IBboZ1gVAVY9tFFKEAb5LxFQmz2cyVYFWVaM/gOE4l2WpPgC+SpmkwC8zFiJjxy+PtlwdeCS+uRRQN/KoIiqzCOuPdVcFuBDHqhRn6/X4uQ3EgpX3vCSiaAZspokEUrWdqVFTKVRQFhmWttWIgwMZOeRCGpQqZTrsNX1AHq+w5GIaBpGQhp8zM1zLK2jP4Ata3Vru9Vklwm0Lt1POkSbQDGxIszvlni/yTdsaCMBwOMeG8UAbuhyGYaVauJh8eHuKiRMXnwfk5Dm/dqlxp7na7hQdip5SiX6Fz1O71SlW5llF1QFHXddi9Xu5gYJokla57HUzTXJtglRXxWAdCCJ754Adxp6C4SkIpHiQJnnrnO4Wch2VZ4DnmBHzO0RbYwRsMBoVNpqvI8ne7XfgQY756PplUKmwsYp1PkxcEledQiKLkTrCKilxwXp5OaNp2Li+sWABtBph3UKrIVgsK+DvDYW7aloigo7u3V1i5cBmZZHqV9Z0Qkpp956hwx0kComlCPvcMrU7nWvD38Pwcv/F7z15TD/YpFdo963Q6CBUll0rxRND8FbBZqv0PPv88Pvu551Z+T0Ry23Tdjf5fCln/LgZxDLtEMm9ZFmJB3Xe/5DiAYRilhcMixoQ88+sYEanP4erfEaGaucnvjmyZ75WpIAgUFLkghAwIIT9KCPlpQkhv/jWTEDH1SEKISgj5AiHkt0T8vU3QNA39glS9RxcXpWlDi3AcB2avt3YQbx3OPA/HJeThl9FsNkEKUDo455hRWimxLDoXsAoiNmAAGG4xXc3AOceMc+HzV8Bmw8KIMaEJFgCcnp7CM4xCc4d333wTR888U9kIMwMhBJ29PUy2JLdVq9jLaDab0Fut3Nc+830YrVZppSNVVdHZ3y8857gK4yDAsIRi6SqsC/4DzkubfWYokmABxQQuOOdQSlJ0jWYzl19MAgixYTAMA6Sg2MAiRFHW2oNB7iKST2klVgAwFzqoWECb+T7cTqcyHbu1pou0DC8IhHRuF7GKokgZQxA9/n5kamqi1lYg7ej0Dw9xkYMOPmEMPUHzX6ZpQtH1lc+8H0bww+vPRZwkYKpaeZ/bJjixKVQrI48OpPuJCENxIO3wleno6LoOig1qemuuO04SaIYhhA7u9nqFZ+8CAUUF27YRbbB8UQhf+8H7AvxFiyDXXSYpfgnA6wB+E8D/CODm/Nu/AeAXBJ3P3wTwFUF/aysOn3gCjwokWBe+jyNBbfWT27fxoMBcCGMMY85LG70uY3B0lDvB84IAVrtdKQBptVrwUU1ec+p5aPX7lReH/v4+pjmSy6nnwel2axmINE1zrcJZUW+OPFBVFU9/53fimw8e5Pp5zjnu+T6efve7hZ5Hb38f0w2JRxhFIIZROrlZh+Hpae5iypkA+sze6WmuQGcbJpQKGUQHrmSNlzemUMCmpyoKCoUcBTIsjjSBKwPLtnOZkYrqYAH5A/xlUEqRVJAwXkS7281tyCki6Gi325XtCcazGboCuirraHrLqCPBWiWykdGWlo9tt9vC5x73Tk+3+v75YQil2RTaPVsr7rJmUcjmPqsWq7fNQ226vTFQ6l1rNpuVaMCLCCiFXaKLRwiB0Wwi2lDUICu6d+F85ksEWp1OoftAKRVSVFAUBa01yZ0y756teq5kKwgC+TtYPw/gbwD4hwA+gse3x38G4Eernggh5ATAZwD846p/Ky/2Dw5yy7VzzjHmvPL8U4bD42OcF6hyno1G6J+cCDO77R0cbO0kZBjPZpVlolVVRasANa+u8wBSCtcsx8Iwmk7RPz6ufLxV0DQNXFFWc/Nr6GABqWT7Gee5KvoPzs/hHh8Lq3Jm6A4GmG04/sTz0BMsiQ8A+8fHGOfki0+CAMOC8uzLGO7vY1zRKyWMIjDDEFZxy3jzy53rEKi86RFVFWLDsAqMsfIJluNslDPOIDLByhvgLyML+EWIf2yi0SyCcy6EquU4DqIt9LSXX7+7cf2fBAEGFd87IL9Hjy9QJj2DaZpAo3FtjSVLYgsi/bcW0RsMts5hjSYTYV3xDOuU3QhJ6cDLmPl+JTn+DHarhWDDZ72JIhhyXmrd03UdqmEI8cKKUJ4yt6l4lM4hXf96KMBkOENmMp0XVSx2ltHe21vzvBEQwlfW7/wggOW6aDQalY+fF3l3rX8fwD/knP8jAJ9f+t7XANwScC7/LYC/C2DtJ0YI+TlCyOcIIZ97kLMSvwntdhuJrueiyo2mU7jDobBNuNfrFZKPfjAeFzY93YROziQDEOPDAwC9w8NKtKlJGKIvIPh2XRfhhhaz6OOtQ3OF3GsUx9AsS7hqIZBu/qfvehfu5Xh37oxGeOf73y/8HLYJXUxmM3RruOd5k2ognTesSgvt9XqYVrBjAOaD6CcnQtX2ludTysoEL0NV1UrXugmMc6glN8U8XlhRHAujzQBpNb9sglVVYSuD4zjwcygJ+kEAy3EqBx2KoqC7v7+RFfFHX7qLBxvmQEXNXtq2vd0fCSkd06mBLuR2uzgfj+EFAbwgQBBFiJMI/vz/vSDAaDoV5r+1iG63i9mWz33keRgILhx2BgN4awpYq5avWRAISTAty9r4fivK6vuQrXtl47lNNitF4DNWurjVdN31Ah/K6g5WFMewBD3zjuMUmjetarGziM5gsFZJcJ3IxdT3pQpcAPkTrGMAf7TmexGASikxIeRHAbzJOX92089xzn+Fc/4hzvmHRHSSCCE4uHkzl7ra+XiMAwHzT4vHPn7qqY0bziJGlOJA0FAqkF/RDUg3PhEy0f39fUxKymsC6WyOiPNQFAXtwWBrB89nTKjYwjLsVuvaIh2EYanB27y49c534p7v49W7d9f+e/n1XQ4ZbQAAIABJREFU1xHbNg4FVJOXkXnmrAvGPUrREVxVzo4bq+rWAfA4SUA1rXLXSNd1tPb2KhUULjxPuIKl0+k8RqkJwhBN162cxKmNRn0JFqWlE4BNYjIZwihCUyBdynXdUgPoM98XFoBompbLe3AmwHcrw+DoqPTzzhjDTFCClZfCFVYIbjfh6Ikn8DpjeGE6xQvTKb7uebgfBviq511+beq6GAiyoliEYRgwW621JvYA4NUwV7wu2FZSRZtrqGJsvQjLshCVMLwNoghWCXn0DGW71IuIkwRoNEoXtyzHWSvwsa5zF0YRLEEUOdM0wVd0aznnWPWRVLXYWcS6mf5Nn6dsBUEAyLtr3QHwHVjtd/V+AK9UPI/vBfBjhJAfAWACaBFC/gnn/K9W/LtbcXB6ipdfegnbGuajJMGTAhMcADi6cQNffv55bAuhJrMZzF5PKHe02WxeKrrpW2aMZoJEBzqdDmYlaUSUUgSECEt4eoeHmHz1q+isCaQ55/AEbQLr4HS78B8+fOxrXhDAqbFr1uv18J5PfAL+luTyyf39WnyRMqrozPfhrghq60pqM4GN8XSK3oa/P57N0NnfF9I12r9xA+df+tLaZ2wbJpQKoyRnsFst3F+oevpRJCShVxoN0CJJRYFlgHGORsk5SNM0EW85rzCKYAl85mzbLmW8GzCGE4Hn0e71MBuPYW2o0s98Hx1BzIj+3h7e3ECb2vRGTeYGwyLoO81m89IfafE9Xv5IQs5rMdd+93vfi3e/972X///w4UOw5v+Hz/zUXxB+rFXoHx3h4vXX0VwxX8Q5x0xQoXIR66TaOfhq/zFBapnNZnMzTW3NQ+eHIewqol29Hs6+/vXSvw9UnwG0HAdnG943ZRVFMEkwFBhHZvOm7Rx/M2CssphSBtd113bP1m3dPqU4Ffzcb0Pe1ezXAPx9QsjncdXJ4oSQpwH8bQC/UuUkOOc/j3TOC4SQTwD4OzKSKyANOL+4ZQPOqmui51H29vYwRZo8bKKEPby4wNHCgi0Ci4pu/Q2behBFUE1TyExQu92+pOYVpcCNZzN0hkNhQX9vOMQrX/7y2u/PfB92p1MrX9ftdHBvqYPlCaJObMJTt2/X+ve3obu/j8lrr11LsDKVyLqGUPuHh5g899zGBGsym6FfwUh8EcODA7zxhS+U+t0wikB1XXiC32w2H6v4BmGIpoBOZUPTkBRIsIqkH5QxqBUSrE0VbiBd45oC73PR2YQMogP+9mCA2f37GGwI4nxKcSiom9HpdDbaf2zSGh5NJhg884yQ81AUBU3HgR+GaK7ZtxhjSAipZdZ19TlJOQyAVEjo7ksvrfze1PPgdrvC97XMf2zV3r48g8UYQ4Tqc59AyhRgJWKKIAxhV1j33FYLdysKXXhBALfCLJzVbK5VIk6V9K5/XbQFjNvvY/baa7kSLFEWFMAVDbjI5y5bQRDITxH8TwC8AOD3AWRv7q8B+NL8//9L4WcmCa7rghnGY63WhNLH5nMmnofWYCBcTU7TNAxOTraqm53HMQ4FeeEsYpuiGwBMZzN0BVEZMp7+NpWjVRhNp8L8gIC0xbyJPzzxPGHXvQ6rqjABY3BrpCW+FbDOdHrqeWj1erV0zoDtAhtAavTaEZTgFjW1XsT5eIy901Oh81fAdYPrIIrgCHjeGpqWu4PFAXBOct8XxnnpgFDXdaDR2EgNDeO4lNnnOmiaBt22c8/XZvAFSbRnWGcsvYhAoKqWbdsglrV2NmWT4MAkijAQ2LlfNSOz+Lj5c4NX0e/XOshMsDqdztoOqiilxmUQQtKEfmldX3V/Mzl+Eev8OuGebai67lX1uwOqF1MzVdiVIKs7WCLFfIC5v+kKquSqWyNSwY8QslJJcB09UZSYT1HkesI55z6ATwD4awD+EMDvAPhjAD8H4Ac552JsrdNj/R7nvLIqYV4QQtBfEl/4Z599Fn/w+ecv/380maAvWHUnw9GTT+LRBjnnKI4RqqowqeZFdPp9eNuEHjxPqOjA8ORkbYL1pZe+gVfvvrHye9M4Rl9gwuO6LmYbgt+p79citrAIx3EwTRJ8/fXXL/+Nw1D6IiAb7XZ7ZQAw9X10akxq8yirifTg0nUd7nBYai6ljvkrIN2UF+eDIsZgCagka4aRy+AUuNoE8yZYlJBKFXfbdTdKOZdVE9sEt9st5DmXmd6KrC47jrN1FsynVOh6s3d8jFEJe4KJAGGZRTjd7sY5JD8IShm8loXMBKvdbq8V9JkGQS0qrcBcqn3pnvM1CoKiZg0BwGm31wtOrFliQsZKyaNfHnNOiYyTBAml1/7FSYI//NPncT4er/x+QikCSuFW2Gs2zZemudUKkQvBFjBuq5Vr3jSKYyiGIUwFGwDaw+FKoYu0gPf4tYsS8ymKrUcjhGgAfgTAFznnvwrgV2s/K8kYHB3h/M4dDOcLfBAqmDauHpppHONWTYHfwcEBnt8QmDwajXDwxBO1VPVbrdbWBMtLEpwIpEb2h0O8seaYX3nlAfb7Jt5xdL3CNmVM6AasaRos14UfBCu56nULXABpFey9n/wkkoUq80BRhPPj32pYl+jMggD7NQ6hZsddns3IUEeVa//GDZw/91wuCsUipozVUlRZNriOBW24mqaB5kyYOOdgkjpYwDzYPjuDsyaJiigV7rvWHgwwe+mljXTURcx8X7gnUyYosw5Z0COyoj04PsadV17Bqh7Jug5WEEUgliX0vXM6HTzYMJ/ih6HUBEtSowxAWtjRXRdeEFyjSNa5r7X7fTz82te2/pwXBELFBuxOB/4KZdw00F79OzHnld55TdPQOTnB595YXRCmjOF3n7uDM4Wiv+a9Vi2r0meRh/68DNEWMI7jrFxjlr+S+Z6JxKrnLS3eXTcGECnmUwRbdy3OeUwI+d8B/BCqi1m8JdHudPD6QtDP8fgD4gusai/DdV2ojpPO/Kx44c89D7cEqhcuYtH8d10C5wsWeuj1epgU5C57QQDFtoVXmTt7e5g+erQ2waqbr0sIwe0dz0PtApZlgc0V/RoL/Om673mj0UiT6jWzGXX4ZAz29/H8n/xJod+JkwSxqtYSCKmqioZhIE4SaI2GsASr0WiA5Y0iOQdjJJVfz/HjDKhkW+B0OvDv3l37/TrEDlrdLu4UUBnzggAtwR3LTOUre88eXlzgD//k6/iB73kPmqYpvJMApLTYr66paK97PC7GYwwFy4bbto3Xl87jsT09inD0LZpgAUAv29uW3m2vxjkUt9XCneVgexVVjFKcCowpnHYb99dQBBlffeMDAQqSn/rhH177PUopxvyf49Of/iCOa/LSNAwDCa6LuWRY/lKcJFB1Xej+tm7edPkxmPk+XMHUVLfVWvmOM3a9gyVSzKcI8rZFXgZQ70DKDnGtor6QYXHOax+OO7p1C48uLq59nXOOUQ1KYhlUVYXb6ax2YMdcuQ8Qeu2macJwnEL0mdF0KtwYEcBKzjiQXneiKLVI+H4baWLpruBPBxKS2tZgcPnscc7h+f7lYjwTXFkF5nNYBQUPLiYT9I+Pa5tFM237cuY0RnkvmEU0Gg2wvJQ/xlZugmt/nvNK869Ouw1/TTeDMYaYEOEdLMdxUMQlxwvDWnyR3G738j3zgwBvPFIvP/uZ7ws32u10OggIyU0XBVI6rMj5WuC6mAsA8IXXUJTIQl7ITrA6w+E1U+cwiqAYRm3CHiul2vn1JEc0S2DVZw2kNDnGVjMVIkD4O//YsQmBoqC2NTw7htFsrvXCWkYYRWgKHj9oNBor502XPw4/ioSLd62aYWeMgbHr7AifUrQEMwTyIO+n/18D+AVCiFwReUmwbRsRIZc+Lul8QPpi+mEIs2bu5sHxMS5WVGCmnger2611I+gcHKz1g5rOfQtELxKD01OcF+DpjzxPeIUTAFqdDvwVgUBW2ZU1AP1nEa1+/7EEizGGiJDag572wnFH0yl+6w++fDkTWAeNwTRNmK3W2iLGKlxMp9irQdQmg+U4COMYnHNhQ8+6rq8fuF4C4xx03sHKAwpUSrA2Gc/WJXaQZ97vsfOgtJbiQmuhiMQ5B2O4vO91BD2KoqB/eIiLAuv7jFLhdNhV8t1sQVatjrm7txLa3S7uhSE+941vXP77k9dfR68Gb8MMtm1fM7dmINe6WCKFVYD1CRaQdjOWEUQRTMepPfkhZLMvkwg0HWftfOnyLQmiSJjJ8CJaC0Wcq2MvJdWCP3Ng9fPGOV85g1XH8fMgb9bw/QB6AF4hhPwRgHt4/LXhnPOfFn1ysqAoCppz6pBtWY9xd70gEB50LWMwGGBC6TWq3tlohIN3v7vWY/f29nD3q19d+b3xbIberVvCj7l3dIRvvPgiVqVMKw3qGBMukQ+s9+6YBQFaNbX1v40Uyz4imeFtnZsekErjP5rPvFHG4IfqZWHFTxIc1EAbGpyc4Pwb31hJAV6FGaV4pka+uGXbiEcjJJRC03Uh91wrINPOGAOlBCxnlyOu2sFyHEwAPPvqq9e+l1CK4dNPl/7b67BJtnoV6goA2r0eHs4lu9Og46q6G9TEzNg7PcX5s89ikONdSihFUMPcqWEYYIpyef/5Eu+/LpPhtwqOj4/x5//yX772dZHzdstoNBowluXxlzpYQRRBs22hisybvLDoig5WEIawJdD/ZQibNF0X4Ztv5vrZIAxh11C4aw0G8F566TG7n+WuZR00bFVV07h9YY6eZUWkpedBpIJhEeRNsD6GlE3yAMCt+b9FVNOrfAvA6XTgTyZpgsUJ+LzaFYQhnBs3aj22ruto7e1hPJtBIQRvnl/gqdMTjOIY31FjxQlIKR1fv+zcccx8H03ThKIomIYhbtYg7tHtdvHcqsHIFU9RMjcYrkP4wXGclVVmLwhqERj4Nq7guO5jPiJeEAinK608ruNcVjuzin722IWCZbIzDA8P8Y0XX8z1s4wxTGswAl2EadsI4xhRHMMQFGRqmoakAEWQMuSWdU8Yq5xg/fmf/Mlrm26GOihTmYywFwQrDbUXUaf/m9tq4ethiHsPH+LN83NcTGd48+wMcZJgFse1PO/94RDfXJE8r3o6LiYT9A8PhRdWCCGX6nJOswnwq/0lU2wUqWi2DRUVvQuDECLd8weYy3ZPJpcJFmWPd5HqEHMxDAN0zkBafI4Y50jo9a/7YQinZgsWQI5yZLPVQvj669e+nj5uS0lOHKNXUwfr7lIXjdJFg2+OoKa9tTUYwLu4uEyw+Hy+d/F1y973OosL65BXpv2JLf+erPtE64bdbl+2WvnCYhxEEZoSFqq901NcTCZ4cHGBf/Un98E5r01JbBHtdhve/GLHsxl+43efxxuPHgGoT3Go1WohUhTESx4tfAWdYDSdontwUEtnQ9d1qKaJMIrw4PwcX3jhZQBAQCmcHWxOf5bQbDYfo23JUvV6bCiX83ngdVXRryPI7Xa7mOVMJqY1ee4twmg2ESUJojiGKSjB0nUdeaduGGOgTMmVYDHGAEWpTNF2HAetVmvlv7oC7fYav7dleHPJ8DrWuH6/j733vQ/R8TGi/X3QdhvRwQGi42OcfuADtTzvvV4PsyXqDnCdNgSk63sddgTAXD1yvqczjsuiqWyJdkB+grUruL3eY/PV6TzM1fe9IBA+a3jphbUc5DMGysi1dcYPQ+FJ3irISLDsVgvhFq+7DBHnQiw5rp2DbV+bN1285WEcQ7ftWsZsrnlhcQ6Ox2ew6kjq80KiO8NbG03XvRwWTD+g9Osx6h2GzDDY38fkcliRYOp5MNvt2rNuXdehOQ68IAClFFO/ATr3hxLpCbQIRVHQPzq67peysAlmGNc8j5JVmR+cXeDLXzsHkAbadVRbvo0rNJtNhAuLoB9FcCUEPZZlIZ5XO7NZIM55KmxCSC3djFarhXBOF9uG0WxWm+deBsMwMA0CPBqNhCVYjUYDFNepGatAGUOcXA98ViFKEhhv03exMxis9GlZxtTz0KmJEmqaJr77ox/FRz72MXzgQx/CjZtP4IMf+Qg+8rGP4bs+/OFakjpN0+AOBpgs+b+t+rgnlKJfk4iT0+lcemFxfiVy4YehEHPtIthVghUEAZ599ou4e/eelOO1e73HBGUYJ4/RxbwgqEVswG614C0lWIxSJCvWmYhzNCWsKYpS/wzWWql2fv2ZixmrJZa1bfuaF9YiNdOrMcFpdbvwFxLMVSqCXhAIV0vNi28nWHOYloWYMTDGwHla8QKARLDz9Tp0u12MAXzlzh289ugMz925I1xZaR36+/uYeh5Y5iHAObwggN3t1ibusXdygtHyBrxiUZgkCQY1tvOdTgd+GD42d1cHX/jbeByZC30WkMsqZCxWO9MkK022giiqRewASAsKnTkFeBumYYh+zQbXuq7jyy/fw2//4VehC7rnmaJVlKOaSueBT55kLIoimBKeizrQardzCV3MgqBWg+1lyBDvGZ6cXBO6WFZ045xjRmkt87VAqh55MZvh4cUFLiYzXEw9PLy4wNloJL2DVVBIVBhGoxF++7dfxYsvvibleMvdDM7JY597OP8Z0VjsVmaga2Y9I0nzdzI0sjaZDS9/NUY9dOhsBm4xqUnoUlJd0zvebrfxEMDvv/wyfv/ll/H/vvIKXnr4CP/61Vcvv/a1yaQ2c+1tkGtr/BaGruugnM+Nyq4CP1kJVrPZxKf+0l/CCy+8gHPtFXzvT/xAbd5by+ju7+Psm99Ey7Yvs//xbIZujfNf/eEQry4tfJw9XvnIaJIiDYaX4Xa7OPva1y7N6XbBz/+zCEIILNvGF174Ot48m2LvuCMlwQLSpNq7uEg5+vMKp1czbah/eIjJV76C7pZ32pNgcK1pGkJKEEYEusAN17QsxHEMc8u7wxhDQvN19KIkgSlpHRQN13W3GrkDcwlhyR2VujE8OMCLX/ziY1+7VjybzeDWSIft9/u499RTOGcMY8uGxxOct1potNs4kCxilJPFJRyUUhCiIo7lZHjLvkiU8scC/brmXFd5YSWUIVlBEZSlIElIfiuKsrAs61r3CLjOBALSe19HgpVJtYfztZ9nKrHz2Tc/irBfUwzX6/XwEz/zM5f/Px6PMSGfxY/+2Ptxo2bthDz4doI1h67rSMhcOpgDlM4r6xUVrIqg0+mg1erAth30er1K5ppFkBktu5yD8XQ4dOr72Ksx6+92u5jNqYhZRTXlS18tDDPfr50maTsO7nEOxtLunef70qubAHKrjX0rwXJdvPilb+LRhQJ7L67Nn2UZTqcD//59AEBM0w04qTnB6g2H+NpSwLkMxhh8oPbCiqZpiGOGmHJoAt8ts9lEOJ1i2/RiQikSmm8GK04SYUIcsmHbNpIFJbt1qIuKvUt0u91rhvJ0aQZrNJ1iUIOCY4ZOp4OPfepTAIBHM2AyCfDnfvAHazveOqSqmbgmtiDr2IqiXcYzdSObrc2ulYOAsStRobro97ZtI1qmqVEOvmQHwTlHyLm0Yl7dWEcRXFbNpJSCK0ptsZTTbsPzfZi6nu6nSXrfFaQdQ5mMIM7rT2zz4tsUwTkymWHOOUDUxyiCshIsAJcdNJkPSKvVgj/v3mUzKXUJXGTQNA1Ov39phsjmA6lJcnXdF5NJ7TRJy7IQUYqEpS+mF4awJQc7r7zyCn75l/85vvKVfEpz3ypoui68IAZlQEiptATL7XQQxDHiJEEcEyRJgiCOa50Ba7Va8LYkFNkwbt2JtqZpiCgHZWKNME3HQZTD9DKhDAlFrg5WGEWwdiCvKwKEELT6/Y1CF5RSxIoiOQCpf2+xbRvEsi4NSFcZgI7DEANJ1B1K03+7AGMMSZLvea/j2IAKSuXEE4qiwLLtS8EJxq4ogkEUwXTdWtY3y7Kued1R9jgb6fIcavbAysB5/XRcVVWhW9bWdbfudTQbtQDS9Z2yK4ZCCLmm3m+R3ArAhg4WIeTzAP49zvlzhJC/D+Afc87vyjs1ubgc0uYchKhgLH04ZCdYScKkJ1hZ1SlOEoCnlYe6PFIWMTg5wcVLL8G1bURJAsoIwuhqExqHIZ6sWaY+E1sgMU0TLN+XPgA9mwU4O2tiMtk+EP+thNTDIgKlFhRNk1bdtSwLEedQYgYODVGcICb1bgKu6yLAVWU3oRRvPHwIQ9cxnNMnJp6HjoS5S8MwEKsaxrya/PkyLMfBLEeCFcUcPKeKYBjHGL5NEywgpV+/+Kd/CmMNbZIyhtbenqSAj0sNPgZHRxg9fAiz37+0RKDs6gRmNdO/F8HY7uagkiRBHKcJlsxYAkg/c0VRc807ioLb7+Orr7wCXddx99EZGAeef+01xHEMtybFyFVeWAllqejBophSEEjb32Xd8qbrIghD6EvP1uKr7kdRrYXjbNQCABKarjPZ+h5I9JzL4ua3SgdrE0XwvQCyne0fAPgXAL5lE6zMx4UxBoUoYPyKtiWzrU8pSzciiYuxoiipmt5ZuhhGUYK4odT+UgwPDvDil76Evu+nIhtUxWjqX8q8TpOktgHoDFmLXaFp984LQ+xJlmj3/RiG0YTnbQ9Ov5VgOQ5mYQJN0YRS1bYed55g8ZDB1JuYBRSwlFppI6qqwp778tiWhYfn5/jN37uPjhvhr/zIhwCkYgf7EsQONE3D+777e3B+LtYGouk4OMuRYMWUQVE1JDloSwkhO/EvEYXv+MAH8I4tZu0ygw+ZxbvB0RFeev55THwfSZLgwfkFXrmbgChz5U5Nk2b+ucsOVirqkiZassEYAyGqNIogALz/wx/G+JlnAACDuwQAwa1PfjyNM2oK8jMvrEU6LuPk2vMeRBGcg4NazmEZlErqFrdaCB4+xKY7G0YRrBq7xdmoBZA2CbJudUIp0Gi8rdfwKtiUYN0F8O8QQu4jdSw7IISsnRrjnH9T9MnJRKPRAL00wFIBThDFMRqSxQ7imK10oq4brX4fj+7cAefA1AvhvuOo9vb2YDDA870eXvA8jCcTvJko4HGC56ZTqIoC98aN2rtoqqpCM02M71+AUgI/SaTzs7MEKww9qccFUpW2Bw8eQFVV7O/vS1EYy9BsNvEoTMBYjO+SSMvMupaxn8A0XHjeGVRTrz3Q7QwGmNy/D9uyEEYRmtYxEnoHcZJAazTgS5zFCYIYjYaLOEdClBeGYSDJ8fwkCUND1ZAkORIsSSJDdcE0zceor2dnZ2CModfrSZ/HuTTWlpRg3XjHO6B8+tMA0nXGeRXovPcQ+++6DQC4ZdvS1ptdJlhxHCOKdpNgpSBSK/rtdhvtdhucc3S7fQAEJycntVKfL9Vhowh2tn+vUCX2wxA9CTPWjDHEsZx3zXJdhHev9z4WDx2EITo1du6azSai+QuW0PTYlFIEYQhbYsF6FyM2m7ApwfplAP85gL+LtNv4f275W2/rCX1VVQFCkFCatlZJaoSrS97coygBY/IX41a/j2/6PhjXMfH8S9pSnWg2m/ihv/gXAQD37t3DRfw6gAv8wL/9MalJjtVs4s7sPkB0BEkiPaBLE6wWZrMLqccFgNdeew2//usvodmM8dM//YnaE9pF7O/v4wMf/RiAY7z/g/V2KhdhGAYSAFMvRtN0MfHuw+3ptStHtodDnL/6KgAgiGIoShucG4jiGFqjUZvR8TKSJAGlBKpqCU2wNkkGL4JSDlVppNXNLQhr8m7ZBcIwxK/92r9CGAI/9VMfwZ5EaXbgih0hq3hnWRZu306TKd/3MRi8jJOT08uvyUQYJqA0ffbrsh5ZhzhOdpZgpSJSuxm1vxKwUqR1cvzJ5CrBWoGQMdiS1lhZn3nTdfFgaR1fvt0RY7BqnPNcnIFLEprO3nGOMAxhSzb5fYvkVgA2JFic839ECPmXAN4N4H8C8IsAXpZ1YruAZhiIkgQECgCCOEmkd7B8P77cCGTCcV0ElEJVDEy8qXRjtjAMARgADIRhKDfBcl2MZyE0tQc/Od9BghXNO1jyN+DJJEC3+zQovY8gCKQmWEmSQFFMAKbU5z2TiH9t8giWYWM0CbDvHtReTXdbLdybJxVTj0JTdcTUQBBFsAwDEeTQxaIoAqCDc33+3onBOsngZSTzBCum2z/ziHNp4id1I0kScN6EYbR3IniQsSN2JbbAGKTJhS+Cc44oolAUYycJlu/H82vfHQV8FxX99JgpRVDG8Z1uF2+88sqlwMabZ48wnoa49+DB5demUSQltoiiCEGQrbX1otlsXlMSXJZpr9tn0rIshJnN0fxrjLG0gyVxpv3t1MEC5/yPAfwxIeSvAfhVzvkLUs5qR9A0DXGSgEMBgZp2sCQbzgZBDEJ0KS/mIprNJmZRAq1hYhZRqW1dAAjDCJzrO7l2o9nE1IvhNm3MovvSE6woSmAYTQSB/A14NPKhaV1QasHfoHZWB9KAowFCNESR3OTSarVwPvFxtGfibOyhKYGa5zjOpQnnxIuhNQxEiYkojlNj73ZbCm0sDEMQYoAxA0EgLsFaJxm8DMqywfvNPxsnCVRdFxoQv/rqa3j55TegaQTf/d3vlfqup50jBZwr0ingwBU7YhcJFqV0nmTIP3aa2KpQFD31aZOcsKdFU2VnCZZM2vcismBXhh8UAJzevAm+8F6RgAH3RyA3b4LMuyinqoqOBIpgGIYIQ8DzxK2v62CaJvwkeczY2/d9JIly+TW/ZpVeRVFgNJsI4xgJTe1+OIAgjrEvkf7/tkqwMnDOP1n3ibwVoFsW4skkbalzgjCOhRpx5sFsFqHRcHeSYI0DiouZD+hyO0gA4HkRGg0blBpCq+p5oDQaSBIFRDHhh4l0k2Hfj+E4FsZj+R2s0ciHYVjwfROeJz/BIkSDojQQBHIVFLt7e3gQEzA/wKOEoDMY1H7MRRNOL6BoNDSA64iiAJ6ioC3hHIB08+fcQKNhYDabCvu72TxjGEVrVfMAgDGeCgltGbwParBMeOGF1/HVrzpIknt45zvHGA6HQv/+JmR0Lc7JjhIsClU1dkJVS2mpkF5IAa7WGaCxkyTH82KoanNsNS0tAAAgAElEQVTHHaxdHDOjCMpJ8IbD4WPvM1MsvPHgVXzgQx/CQNLamiEIAkQRMB7Xv6+5rgvn5k3cXYibzlUVmqLg7rw41bl5s3YrCHuuZsjn8vicMUQSFQQB+XOm25C7NEgIeS9SNcGPA+gCOAPwewD+M875l2o5O8nQDAPh2RnAVXCoiOIYpuRuxnQaQNP2hVaW88CyLLzjfR9E3Oyh2+0KVRfLA8+L5p0UXfpGpGgaKDegKBpiqkqv+EVRAlXVwPl2U1LRmEwCGIYJXbcwHsudAUsryw00GhqCQG7gdfuZZ/D+D49xfPxx3LlDcftd76r9mLquQzHSmSs/SGDoGggxEEZTcARwJXHVgyAA5wY0zcB4/Ejo37ZbrVwJFiEE27bAIIpgCa42T6ch9vbeg/PzRHpSf0WZkjOTsowwTKCq5k6SnPRdV6S/59mx0wRL20ly6fsRdN1GEMgtmgJvBdnq60p+0o68m8YdgHSN1bQ2Li7qL1qapomPLxlof+NNDsNo4Pt/9IdrP34Gp9fDC889hxfu3cfr5wxf+CZgtdtSi/UZFXkXBaxVyJVgEUK+G8BnAfgAfhPAGwAOAPwFAJ8hhHwf5/zZ2s5SEox5qxUwQKAgiiK0JXaw4jgGpSp03cZ4PNn+CwJBCIFtd3Hjxi0wNtkJT11RGgA0RJHcBIsxliZYpIGQyb1uzjmShENVVXCuIkkSqQnWbBbBcdJgezqVm9SngU8DqtqQPn8WRREISZOAlKIYSTF7deZS7WFM0TTTaw8iCqYA+5K46kEQghATum4K915z2m34b76J1oZBcg4y7+RsOc8whFNDguW6BjiX3ym/SrB2E3AGQQzDsHZCRY7jGI2GDd+Xn2RkVGTOd9XBimCafcxm8q8dADjfHUUQSGOL3cyAST/kJUYjD7bdx2h0byfH34Xv2wc//GEE73sf+O/8G7DOCB/9sffh4OBAqol6lmDJtCXYhLyE/18E8GUANznnP8M5/3nO+c8AeGL+9V+s6wRlQres+TCkCpAGwjiGIbG9mc7AmDAMS0preRmzWQjLchEEVHoFIAgSNBpaGnBKrnJyzkFhgBAVEZMrhpkmGekxCWlInY+I4xiMKVAUBY2GLj0AyDpYiqJKr6qnlew0mea8Ia2y7XQ6mPk+4iSdQ1IUFWHMEHEujUoxmYRoNNKkejYTm2TY7fblQPk6ZGPQWxOsOBZKEeScw/djNBo6VFX8tec5vsyh/2VkdhC+Lz/JCMMQqmrv5NhX77q893wRnhfBstydJFhXtNTdilzsArvs3p2deXCcHiaTcCfdlEvHIYnQNA2u68I0LZhmE7Ztw3EcqYygXYrprELeBOt7APwi5/yxtsr8//8rAB8VfWK7gNlsIogicN4AuIqQUqnzOJ7nAbCg65aU1vIyJpMAmmYCMKTTZ6IouQw4ZQfbqXyxDgIVCZW7GTDGwHn2GirSEywgNbNWVU168JNea/aZyx1+z46dQpV2391uF14QAJyAEAKFKKCUI5TIVZ9OQ2haTQlWq4UgZxC7bd+NOBda/bwSVSFoNHTpxt67TrCCIIau7ybBCoIIuu7C8yLp104pBecqALkFLCBd36OIwTCaO0ywVFD6ra8iuIwkYTujJ45GPkzTBufyYylgNx2sDJSmz9wuEstdiumsQt4Ea9sT+taYKKsIw7Lw5niMe+dj3Duf4MzzpKpM+b4PztMEazSSm2BFUQRKVaiqCkLMncwnEJJSh2RvBkkC0IaO+54PruhSFwbG2GUHC5CrLrbYxVHVBuJYbmKbXmuaYMleENNNN13+CJF335uOg4BSgGTHJmAMCDmXxlVPBWX0+WfOhV77KsngZShIu1h5EiyR92T5eZdNlbsK9HZT0U8pgs2dzEFNpyEMownGVOk0vatiitwCFpDuq5xr0DRDiqLcMpKEQlEaO6FMLSZYuwm22aXhrWyMRj503QIgX503E3rYVYKVxTS7SaoTAOpOLG9WIe/Ayb8G8PcIIb+z2MUihNgA/mMAf1THycnGyckJbv+578OZ3gTg4T2fbEpVmZrNAiiKBU3TEYZMqmfHlQ8VdlJ1SSWbdzOfoKoGPvDRfwvd7hEePGCglEqRywayJCO9bpmBfnbsLLlTFBVJIndFzrp3aVIt99iLz5jMx82yLMRzv5DsPBKaQDdbEt/1lI6bIp1LEVVIajabCLfcUJLqNmNbohEI7upRSnf6vO+yop95Qdm2hTCU38EajwM0Ggbi2EQYhlKZIbuUx89mPRsNDZPJLmbfGBoNbScV/ey+76qDlbIiNOkJVmoyzNFoaDspVqcdW2VnXZwkYTvrYGXemm+3BOvvIVUMfJUQ8lsA7iEVufgMAAvAJ+o4OdkwTRO93h6GwyYo9bG3Z0HTtO2/KAjjcQBNy+RE0xfTkeA6DlxPsGTLxCsKmSdZHIoit8IbxxSGYcGyLKiqDkqptM+dPxZoyt2IFo+ddlJkU3cYCElnwGQfO+WFZ8fk0njilmUhAUBwlWBRJnbWaBvimEJR6qFHNpvNrWbDytwXZ5OOIKUUlBChDILUI2V3z/suE6ws6FJVbSfBR0ZL9X1DuqF59rkrym46WGmCpSMI4gXpcjkIwwSa5kpnJwBX81+ymRkZgiCGqlrSO6aZkTsAMCbf1zNTzeQ8ka5KDGQqsdpOPvMwjKFpuxHyWYVcZXrO+b9BOof1/wD4NID/CMAPzf//e+aGxN8SSKVsU8Uh2Wp22SYEIJVvlqhylXqFZFVF+YuCqirzjgaHqsrpHmXIZKMzyA5+ro4tN7FcTDLSzVDq4ecJVjqLJLuDlXYos2MyaR1Ly7IQA1BVgDEKxik4Z2hKDDiz+55C7AC8pmlQdB3xpjksAjDOQDYUUvwwhN1uCw1G02ct/e9dPO+7pExl9MhdzLgCqYCSphk7Kd4BV2us7LU9ve/aXElPlS6yEQQxNE0HY0R6cpkl9Zn9iGykVOim9DjuyhYAADTpCV4cx1AUbWeqmYwxKEpjJwnWLoV8ViE3J4Vz/kUAP1HjubwlkCZYGlQ1RhjKbe1msxEp5PpBZYpuAKAo8pNLTVMxm1EwRqFpcisuivJ4RVm2D9ausJhkcM6kJ7bpLd9NRT+t6mWbvrwqn67r0DsdvHL+Ndzz72AyeQjanOC9vZ6U4wPLz7f4++50OvCCAO013ff0fWNQNyS1fhgK9wVTFAWcp58550z6OpN1UnYhm53RI3cx7wikgY9t6+Bc20mClUH22h7H8eW+mqkYymTF+H4891hMA32Z3Yz0mWtgF7NvQKbe2JKuFpollkBGRZZ77Zl4laIQxHEMU6LVEJBRBHeZYLUQBA+kH3sV5Jr+vA0Qhqmanao2pCubpYazmXS03GpX2j1KNx9FUaTPJxhGA4xRUJrAMOQGPo2GAs6z65U3f5XhKrmQm2QsBpy7SGwXfVJkI513ovPjU6m+b5/58R/HjHYBfBhnZ6/jU5/6Xjz99NPSjt9oLAY8THjQ5Xa78O/dW59gYf7Zb/jYPd+H++STQs+r0WiA83RNpTSBru/ied9NQSGbt9zF2s4YQxxnz9luzH4BvqMuPUUWZsm24QDSDlajoUFRUlaKzGA7U+0EFOmfOWMMvp/AslyMRnekHhvAThkxGUWRc7KTYkaS7LqDZb+9KIJ1gxBySgj5XULIVwghzxFC/uauziWbT9hFpS99MLOPRO6Q4K7pM4bRAKUxOI+h6/IqfFfHTjcAzuUJiwCraHrybvxiwMkYha7LrbfsirYDZNeeLcLyP/Nm0wClMQiJpQ78A1kxIwt4xBtbt3q9VIp+DYhCtqoI+kkCV7DJcNo5SD/zJInQbMq971dKqfLnv7ICWvrOyaUoLovpyN5X0z01TbBkz/deCT0Au+jkZKyYrIMlEykrZjdUtVRYwoBhWMLN1LchpYOm7xdj8pkhqXKljl2MegApBX1XypVpgmUhDOlO4oplvCUSLAAJgL/NOX8X0lmvv04IefcuTiR9OFJlM9mVvhS7qXykm9Du6DOWpSFJYhAil0KRHZvSeC5uoUjtYGWbfwp5s0BA1sVJA+0kiXeQYAHptctNLIHHr112Ug1cPe9AJD3BMs302GngK757Z7vuViVBABtFLmJAuC+YoigwDBVJEu8swcqSnF0kWIRc2RLsSkwHkD9vOT8L7GKdWb522UiFHjQQInfkALiSqN9FoB8EAQjZjeVNanWzyI6QG0tdCZbJnePPkM5gyVdpBbKOrQ5CdjN/toy3RILFOb/HOf/8/L8nAL4C4HgX55LyR5W54pDcB2RXymbA4wEnY/KDbdPULiv6soNd09TBeRp0WZbc5G5XYgvA1RxS6ptBYRhy77uqKvOgU35leZEiWEcXZxuaTf3yeZddUHAcA3EcXiYZotcZ27YRVqzUB5QKNRnO0GzqSJIIhEQwjN10sNIO0q4UDIFdycTP/0v6u57tq4TIT7BSXDEUZGNRtEt2wBmGKVWNEB1BIDfB8jwPnFtzM/VYasdW0zQwlt5rzuWv77NZBFU15nY78hMsSjkURZW+xgFXBYVdiIuswlsiwVoEIeQmgO9E6r21/L2fI4R8jhDyuQcP6hti2xV1KVPSSyE32NZ1HYSkiyAh8qvqhpHR1eR3sNLjRaA0hmnuIsG6moeR+ZkTQqDr6uXsm+yZlEzsYBc0ClVVL+mRnMudwQKyDlYEQP4G7LppghXHIWxbvJG6bdsINqydnHEQELA1Yg+ccwScC+9gpeeWXvsu1rhddrBS7CbBShkhu6NMLRYuZSdY6Zwrmx+bS13fKaVgjMyP2ZA+B5WpIqdJjuwEywdgzT9vU6rZbxpLpcG9oshf3yeT4PK+T6e762DtYo0Lwxiq2gAh306wroEQ4gD4dQB/i3M+Xv4+5/xXOOcf4px/SKYBsCw0GgoYk69sBgCGYYDz3SVYV/NA8ula2WxGkshPsNJA/+ozl33tjUY667cb9caseydfml9RFKhqKnagqkR64GWa6dwf5/ILCrZtgvM0wXIc8QmWaZpIyHpZaIZ54LlmAw6iCKbr1rL+7ZKamWI3xbs0uZN6yEuoqgpF4fPiYQJN24W21q6Kpld0Mc7ldsrT968ev7s8mExCNBqpB5jsQH889tFoWAAAQiypCZaiKNB1BUkSg/NQqJdfHmSJra6bmEzk3vdsnSFEPgOMUgpKybyoIb+gsAq5VzpCyJMA/l0ANwAsS9FwzvnPVjkRkhoH/DqA/4Vz/k+r/C1RkB106XoDQbDLBCsdBuU8kC7tmW5EDLKvG0iTO0L+f/bePLjR9L7z+7y4DxK8SfBssu+7e6Z7Do16ZiTNSBqNJMse2TORJStWsuXKbrZSlc1mK6ukUpvsWpWKHW8ltdmKLW9GG0u2R7OSbc3d53Q3+2Q3m+wmm/d9XyAJEjfwPvnjJXgDBEC8L0bq91vFKhIA8X2f932O3/2LrlQw1Pbwjwv6ShK69qFqSphe7pQcSRIrFi/tQ3eUMOAoJpP2diYlLj9KLua7olgsqKZgSZJEfmEhgVCIvG28UPEQkkQHsD8YxJXlAhdxxPMtcxG6Ew/Ty5UHay0yQ3tFw2o1E4tFc5Jjqyh20kpPJm3HHT9bFGhrPFxfYCMXY/f7w5jNViRJ0lzBWlwMYrGUAyCEbaXohXZYH4qstSFneTmEyWRFkgyaF/iIN5c2GAya73Hx8vQAkpSraqUbkdJqlyTpW8C7KKt1Gti8WnZ1JyVl5/8PQIcQ4s92813ZQK6qj5jNax4sScp++eRkMJlMmExixcql/aaw5s2QNVds416kXFTSAzCbTSvPXXthO5dYq7akvXIHa/3PcpGXoTznMLloCxAPB45Gwzid6qxzV1kZ7V1dmLaZz50TU0wtmDGNT2J0bRW2w5EI9WfOqHJdZrMxZ2stl1Wt1nuwcjHf4zm2ENZcwVL6Eikl6iMRbYWueHSEcv+1Vy7XN7HPhYJlMlkwGAz4/dqGCHq9QcxmxUicCwXL6bSytBRCiHBOPFgFBVaMRqPmiu36XE+tFSylwXO8zdFno8hFqtLkvwE+Bb4rhFAj+enzwB8AjyRJall57YdCiA9V4EqKXDaZjYdrKdA2HwfWEu+FyFX4jMiJwBv3pCjNdrV//mazkWg0jNFo0HzssVi8uliuqnvlTvCMz7Vc8CvPWVEutV7nipAXJRaLqFbU5czzzxM4dWrb9+YtNyiarWHPnhnOfenstp9RI/8K4mHYubnva8jNGZPL3jw2m3lFyNbecxgXvAwGE6GQtoK2xWJBiPBqf8cnpYk9KAUHHA4zkmTA59NW2PX710LzjEYry8vae7Dm57Wf77IsEwrFMJstxGJG5udzEyKYi0I+kUgEJRAOPitFLlJVsPailFFXpbKEEKKRXJ06m5BLK6MSJhWvOKS9J8dsNq3kJ+TKkyLlTODN5XNXGr/mJlQtHM5dY+31fYFycf93ClX7TcVaYRWlX4kasFgsCY00BoOFwsISTCYvBQUFqvAngpIfkLv+a58F5GLYNpuZpaUouVCwAoEIkmTBbLbg92srdK3l94ax23NhtIxjfRVJbRAMRsjPNyNJEuGwtp5Dny+8Gv5sMlnw+7ek9KsKm81EKBTAYtFWqVbKsivzzGg0Eo1KRKPahabm8kxXQgLNK9fx2cjBSlWi6wRK1LyQzxI+CwdwbsKWJGQ5lpOk/7V4ce1jxeNxw7nKjTAY4vddWwVLlmViMWUjNpnMmnc/V/LODDnpmaGMXWA0mohGRY7KViuew1x0vId4HzLtEQpFsVjsBALaWxiVRvLKPpOr+77LiPrMGNfN71wIPxaLcaWZu/bGu6WlIBaLDZPJonlOitlsRohITvJ7cwkhBNGovFLgxIAsa7fPKdUTWfVQ5+Jss1pNRKMhzZ+50m9sTZGXpFz0woorWNqyRiIRhFDut9Fozsn5shmpSnT/AvjhSqGL32jE+2DlotGwYklfi5nWWgCQZZGT5ESIWx+MgFFzy4OSd5YbQV9Bbjw4661duaj0FInEQ3eMmls44+EEirVN+3jteF4IGHOwzmXAiBDaKxmyLBMMRrHZnAQC2uZlgKLcKV477S2cuQwPy3XDW5MpXkxH+9B3r1dRsCwWu+YKVrzf3pOmYCmNrdcr0tpVMYxGo6uCNoDRaCIU0natm0xKQ/NcNBmWpPU5X9o2eV6/z2gt06wvcqEo1bn3YKW64v8VigerQ5KkHsCz6X0hhHg5mxeWK0SjMSwWIwaDkUhE25CpcHi9dU/7sqrhcBSr1UxcydGy4lEkomyKuehfEN+Qc7ERQ+76RgQCASRJKWVrsdiZn9e2473PF8Zkcq1YlrUVtjda+pRDSMu8w2AwgsFgWU3G1bJqZ9zSp1j5tBU4w+EwkqSUbg4EIprnXCqVzSyEQtoKHrBe4NBewVnzmCr8WivW65+x1orm/Lwfq9WByWRlejqg6ZyTJAmDAWQ5pnkI+EYBV1sj3voKhsq1aDfn1hc7AFZy77Q91+M9HrW2qYTDYYTInQdrTcHKTQ5WXMEyGs2aF1bZDqmu+BjQBdwEZlb+Xv/zG5PEEO98bjKZNV+Ua12oQeskPSHEisBrQWurB6xVHBJCe+74wsyVWzkSiWE2WzWvcKV0u1eKCSjzHU3v/dKS0q/DZDITDsuaGhSCwSCSFFdqtK8y5fMpgn4uQjiUsdqwWGx4vdqOO25hVQRci+ZjV5pw2nJSWWx9o+Fc9IgRQjnuJUl74108MkRr5U6WZbzeEBaLHaPRiCxbNO2JBOtDcXMRdq8YbA0GbSNy4qXx45Ak7UL/N/b/IifG8riioXVgymYFS2t5Kj7uXERChcPR1SIXuZDft0NKLgohxBdUvo7PBOLhK0Zj3LqrraAfCISx2RT3rtYLIxQKIcvmlX5UDvx+v2qVvLaDUla1mFgsqLnQFfdmmExm/H7tO58HAhFcLiuxmEFTz+Hysm9VwQKQJCc+n08zT87iYgCLxb7yl9IMMi8vTxPuQCCAEHFuZb5rifjYw2HtBf3lZT9gX/FaajvuYDCIEBsVW628d0IIFhcDlJXZATs+n7aCdiwWQ5Li5cK1VbCUcMj4vqJ9eGTccClJ2nIvLy8DjnUeqzyWl5c1O9viea5K+LnWSm087B7NKyjGjQlr0DYHa72ClYvIlGhUxmQya/7Mg8EwBkPuQgTXFGvtjUh+fxij0QX8+nmwnggo4SuKddVstuLzhTWzumz0IIEQVoJB7YR95SByAiDLzpW/tUM8Tt5otGkeJ+/3K1Z1k0l55loiHA4jy8aVUvHaCtuzs8vYbOsVmjx8Pp9m/AsLSuiOAm2VnKUlH5LkXOVWlA7tMDfnWxm7k6Ul7e45rD13my2PuTltuZVnHH/mdk2feTAYJBazYDQasdmcmo99LRRZe+vq+pBYIbQPw/b5QjmJUFhaWgLyV/8WIn/lNW2ghKuZc1JoIR6OC2heQVHJwVovXmqX77k5RDAXFXKDwQhms03zda40GV4fImjR1FEQz71TKhhqq2AFAhFMpvU5WJ/hIheSJL0kSVLeut+T/mh3yepBCR1QrKmKi9OsmTclFAoRi5lWE4C1Dt/xer0IoWj/JpOLmZlFzbhhTdi2Wh14PNoKu2vKnZFYzKipAKAImBu9OFphenqrgrW4qI3wEQ6HCQaVgx9ACG2V+tlZH1aromDZbE5mZrTjjkajeL1hrFYHNlse09PaCXwAU1NL2O35mM0WgkGDpnPO6/VjMMQVLCc+n3ZrfXl5mZUjLSf3PW5AM5ks+Hzah0YajbkLiY1X8tN6j1tYWEKSXKt/W60upqe1K9mt3GcrZrP2hsNQKIQQijfDbLZqyh/31sYhSdoV81FyTNdaASgKVlTTHDSfL4zN5iQUimkaErveSA/x567dXqMYkYw5KVwVCKyl2BiNn3EFC6Wx8NF1v19J8BN/79ceG62roKWFVckJsa/+bbHYWVjQ7iCamFjAai0EIC+vkLEx7RSsSCTC8nIUi8WG1epkbk5b79nCwlqomiRpKwAoHqO4kpOnmZIhhGBuzofdvqZg2e35TE9rw7/eYwpgMuWxsKCdR2FmZml17DZbHrOz2s05xZiRhyRJ2O0upqa0E/ii0SgeT2BVsZYkF4uL2q31mZk1pd5iydN0rXu9XmRZ8WbEvXdaCj/xfcZisbG4qG144vy8f104rrYe20gkgt+v5JlqzT0xsYjdvqZg2e0uJia0W28+n+IpV4wZQlPPod8fIG68s1jsms65jSGpANpVat1a6EFClrWtFBs3KGgdlbK8HFpZZwrMZqum1YHj1XlzoeDE8/ghnoOlrXK7HZIpWF8EHq/7/UsJfuLv/dpjaWmj0KdlyJQi1G9WsLQ7iEZH58nLKwLA4ShgampZs0TopaUlJCku+DjxeAKaJmFvDlXTMkxuft67OnaTKZ+ZGW0Of7/fvxIutXYI2mx5zMxoY9VXFKy10B27XTuPgizLeDz+VUE/LmxrZeFcWFgAFGOG3Z6HxxPS7PBXlLv8daWyC5mf107Bmpry4nAoAq/dns/EhLbcVms8Rt+ILDs09Zp6PEpYqFIuPKTp4b8+HNhmy2NqSjvv3WbPoZbcExOLOBxrzawdDhezs9op1ouLSwih7HOSlI/Xq51y5/H4sFgUeUZRsIKajXtzPyawaLbH+f3rCxgpMBi0VXQWFhSDRjyfXSt4vYEVT7ECi0Vbz6mi3FpXvPTaF0pbr1zmoljbZiRUsIQQV4UQy+t+T/qj3SWrB8W6uib0SVIeCwvaHAabvWdWq3YWp2AwiMcTwWZTNmODwYAQLubn5zXhXx+eqAh+eZrFyUejUZaWIus2pTyWl7VTsMbGFnA4FGHb6SxgbGxBE15FsNxYUMJmczI/H9DkEF5YWBO6FG7tvEg+nw9Ztq8qGYqwbdNMsR4fn8dqVYwZSvJ9oWZrbWFhASHWBE67vUAzJScQCOD1ylitiiHJ4ShgZsanmTFlcnKjsA0Fmnnv1iv1yrzTLiRW8VoGVz22DoeLyUntFNvFxcXVOed0FmjGHQgE8PlYnW+gnC+xmFOz5z4+voDdroxdlrWbbwDT00urSrVypts12+OU8MQ1QV+WrZpFhsTL8q+HENpFIwUCASIRJe9Oy3x2WZZZXg5jNq9XsLT1XMbTbJRWGLJmBW2EEFu8d1or1dtBL3KxDpOTXuz29VZ1F5OT2licvN4ARuPaQWA0mojFTJrkgHk8HiSpeFNvkGJmZze3O1MHExPzWCyFq38LoZ3AqShyeatjt1rzNfPiCCEYG1vA6VTG7nAUMD2tjcC5MTRRQfwQ1uIgmp5e2hCeaLXaWV6OaWLl3Jz4DkqonFZK/dCQh/z84nXcxczMaLPWpqYWV0OBAZzOQsbHtVHq5+bmkKSS1b+VPNcCPB71xx6LxZiZ8W/Y3w2GAmZntRF4l5aWiMUcq30OZdm14slUHwpPweoeZ7M5WViIapZfPDm5sLq/W60OzbjXe4o3okiTey+EYGTEsxoZYrMVMTqqzToXQjA7u7xhvoNLMw+a1+tDktaUHKPRiderjYIzN+dbNRavQbtoJMWgoNx3s7mA6WntDApCWDc08lZaoEiaeXI8Hh8mk/LctfTeKTUMzBvGLoS26R7bIWUFS5Kk45Ik/VtJkj6UJOnypp9Lal6kFohGo8zNBTYk/TudBYyPa7M4trO6aDVBp6Y8GAwlG15zOEoYGppTnRtgeNhDXt6awGm1FjM2ps1BtN66Cop1Vysv0tLSEuGwbbXQg6LgFGiiXC4s+DAaNx9Ca6Xa1cbWwx+0ykFbH7YThxD5LCyoL3z4/X6WltggADidxZqttfHxjV4cq9XO0pLQxNI3NDSFxVK+4TWDoZzR0SnVueN5b+sPYC33d4/HgxBFq3+bzcVMTmpjRJqZmQPWK/QSklTM3Jz2+7ui5BVpolRPT3swGIq2vG61FmlyviwtLREIWFajI/LzSxgcnL7cDKUAACAASURBVNMkFNnn8xEOW1arqoFiRJqf10bBWu89A+1CQ4UQK8a7jfu7xZKvWa7r3NzC6rxzOgsZHdVmnW8XlaJAu9zueAElUM5UrRR6Jc1k89jzVtJ+coeUGu5IkvQccBUYBA4AD4EioA4YBXpVur6EmJub4yc/+cmG106fPs3p06fx+/38/Oc/3/I/Z8+e5fjx4ywuLvJ3f/d3G95bWlpibq6U6moDXu8sTU3vAzA/304wOIjVauWll15i7969TE5O8vHHH2/5/ldeeYXa2lpGRka4dGmrzvnaa6/hdrvp7+/n2rVrG95raurg9Ol/DMDoaBddXbeYnx/E52uiuFg5nH7nd36HgoIC2trauHfv3pbvf/PNN3E4HLS0tNDS0rLl/e9+97uYzWaamppob29fff3evU6i0Vq+/vX/GoCOjpuMjHTg87UzNtaBwWDAZDLxve99D4CrV68yMDCw4bvtdjtvvfUWABcvXmR0dHTD+y6XizfeeAOAjz/+mMnJSUCxPNy40cOePX6effabAHR33+HmzVv09Dxctbq63W5ee+01AH75y19uWbg1NTW8+uqrALzzzjtbLBcNDQ28/PLLAPz0pz9ddV339g4xPq6Uyj5y5AXs9nx+8Yv38Xr7N/SjOnbsGM888wyRSISf/exnW+5tJnNvenqatrYQxcVDHDr0OWpqDrG8bODP//zHVFdXbvj/bM+9trY+vN6y1XyYZ575Bi5XKRMT0/zH/3iV8vKNgnA2597Dhw+5dq2NwsLh1ef7yit/CORz5cqVLc8223PvypVbzM0V0dV1G1AEn337zjA5OUZv73tbBM9szr0f//jHNDfPU1Q0tu779xEILBGLxfirv/qrLfcuW3PP4/Hwq1/9AwUF/RsUjcJCO4uLiywvL/P+++9v+f9szL3S0lKuX3/I6Ghsw7oKh4PIcgmnTx+ju7ubW7dubfn/bMy9xcVFBgZG6Oz8yerrsVgMn6+d119/AUmSuHnzJt3d3Rv+N1tzb2Rkjp6eDrq6HgAQCgW5f7+fqalhvvlNZd977z115p7PZyIv7yBXrvwUWVb2vcXFaSYnQ3zlK6/wwgsvAGw5T2H3c+/48ePMzUUoLIxx6ZLy/QsL00xNhamvr+Fzn/schw4dYnZ2Nutzr7m5i0OH/gCAycl+2tuVMzcSCXHnTi+9vY/4xje+QWlpKV1dXVmfe9PT0zgcrwDQ09PE8HA7Hs9jfL5+7HYlWuUP//APAbI+9+bn5+nsjFBdrfDfv/8x4+PddHdPc+LEfgBKSkpUm3s3b7ZRXf0ViosrV/g/xOdrY3y8c/UzBw8ezPrcCwQC3L49SFHRNPv3n2XPnuP4fIs0NX3AvXsDdHe3rv6/WnOvtbUHn8+N3V5AQUE5Dx/24/H0bulvme25NzExSWenTFGRco9ffvm7mExmBgaG+cu/vLnlTM/23JNlmevXH5Gffxyns4iGhlNMT8/T1ta2Ku/Fke25Nzg4xOPHYYqKuldkCbDZXExMTHDw4JZbpxlS9WD9CPglcAyli9h/KYSoB15F6ej2b1S5Og2haLoF27yjfgytLMsrZT03Nz+0EgioG0oRz0Ha7D1T+hhYVfegLS56AdeG18xmK+GwWTNPynpLmyRJCKGNF2dubhmLZaPVJS+vWJMQxaWl4KaEUAVGo0P1EtKKt8S2KSQVjEaXJmEkSlET+4bXnE5tcpE8niWMxs2hmUaiUfVzHpVDyrZBuVKQr3qhi5GRUcLhoi1ChtKOws7UlLperKmpBczmjVZtpS2DSZP9va9vdpPn0Ibfr77nMBqNMjLi3RAhAIrwoUX4+/z8PJJUtmGtK9VK1eWORqMsLoZWw6/Xw2y2Egig+r1XKpWWbXhNqdqp/n1XQvQ27jMWi4P5eb/qHrRgMEgwKGGxrJ0vRqOZcNioesjW8vIyQmyNzLBYbPh86oegx2KxlSqta/nsUKxJ+Pn6oibrYbW6NKnQ6/P5iMXsGAxKGHR+fgn9/bOq8wJ4PMuYzRvHnpenRIZoWZ5/M6RUyCVJmgH+c+BjIAo8J4RoWnnvHwN/KIR4Ts0L3YyzZ8+K7bT6TPH++40sLByioGDjhjgzM8zBgzOcO3cma1yb4fV6+eu/vk919Rc3vO7xTFBbO8KXvvSsatxjY2O899441dXPbPNeBy+/bODIkUOq8X/00Q1mZvZTVFSx4fXx8S6eey7K6dPHVONeWlripz+9Q03Nqxten5zs5/TpJZ555pRq3LIs8/bbn1BU9MpqiGD89cnJT/jBD17BYrEk+YbMEY1G+cu/PE9V1de2KDmLizOUlPTwta+9oAo3wMDAAB9/vERNzckNr3u9c7hcj/nmN19UjTsYDPL221epqfnqlvdGRz/mBz/4AjabbZv/zA5+9rMLWCwvbMkRGBvr5Nw5wfHjR1Tj7u/v55NPlrfcd7X3GSEEv/jFFUKhU7hcJVven5sbp6Kin6997Zwq/AD/6T9dIRp9GqdzoxFtdLSZr3+9lLq6OtW4Z2dneffdDmpqNs7r0dFWvvzlPPbv36ca9+joKO+9N0FNzdb9fXT0Et/97jO4XK5t/jM7uHLlLkND1ZSUVG/ivsj3vvcc+fmbw4Szg2TjVt5/xKuv2jlwYL8q/NFolLffvkBZ2Zc3VGpdWJimpKSH11//vCq8cfzd310lEDixIdcTYHT0Cr//+09TULCdMTk76Ovr5/z5JWpqNp6fo6MP+fKXnarO90TzTeFv4pvfrKSmpkY1/u3m3czMMAcOTPPii2dV443FYrz99nlKSl7dEBYKSpTA8vKnfP/7X91y3mcTDx600dRkpbLyAKDs++PjF/ne9z5HXt52oYvZQSwW4yc/Ob9FlgJlvn/nO09RWLhdLmZmkCTpvhAipYeZqgfLDPiEEDLgAdbHL3UBx9O7xM8WgsEgo6PL5OdvPfwLCyvo6ZlRtbKaEo++NVY8L6+IkRGPqhr4wMAkFkvFtu+5XBV0dk5u+1424PP5GBz0bVFqAYqLa3j0aEzV+z46Oo4kVW55vajITUfHpKrcMzMzhMMFWzYEpcpVqaoWfcWTkb/tZutwKJ4cNefcyMgcNlvxlteVggtLqlYemp2dZX0+ykaUqJqX4vV6WVoybpOADS5XOT0906pxw8bqheuh9j4zPDzC1JRtW+UKoLi4koGB6JYwkmwhHA4zOxtcDYddD4ulhPFxdXORBgfHMRq37jP5+VV0dIyryt3TM47NtpUbQJKqGB4e2/a9bCASidDbO0dBQfmW9ySpktFR9caebNwALlelqvd+enqaaLR4g3Kl8JYyMrKkapGPcDjM1JR/W+8dlDIzo65Xobt7AqfTveX1/Hx173k0Gk043wCsVjd9fROq8QN0dY1it1dteK2w0E1n54yq3rOpqSkikaItyhXEvXfOlbNPHciyTFvbOEVFa2NX5IsqhoZGE/9jFjA5OUkoVLRFlgIwGKoYGFCXPxlSVbD6gLhJ4CHwX0iSZJAkyQD8AFBPCtcAo6NjCOHeJnQmHk5QoKrA298/hc2mKBkGg8BsljEYBBaLDb/fplrFo2g0SlfXDIWF7i3coAhek5Nh1dzbXV39GAx12953m83J0pKLsTF1BAAhBK2tIxQWbrVmWa0OlpfzVH3mfX1jmM1V277ncFTR3a2e4LM+6X3zMzebLYTDNtWSU+PhUnGlej2/Ui69mJmZGVW4QSm0YLVub1CwWMoZHFTvmY+MjAPJ1lpItXA1IQSDg3O4XKVb3lP2GYsq6zwSiXD9eiclJYk90ZIk4XIdo7HxsSoVNJX5VLqtQcHlKqWvb1Y15TIWi/Ho0TglJVv3GZerlNHRoGr7aygUorvbQ1HRVmEXoKiomocPR1Ub+9jYOJFI2bZCX0GBetzBYDDpuEEJXxodDam2z/X0jGG1bt3flcqZFYyNqavcCVG67bnqdJbT26veHpfMaKr2fB8fHycSKd12voFiOO3unlOtop7P56OnZ3FVnorDbLYQiZQzMqKeoN/RMYzdXpvwfYulls7OIdX4R0fHWFpybTEeFhfvobl5WNXKyK2tAzide7Z9r6SkjpaWUU2bTK9HqgrWe8AXVn7/EfA1wAvMA78P/FnWr0wjCCF48GCIwkLlAW0WfAAcjjoePRpI9BW7QigUord3nvLycurrQxw+7GffvgCHD/uprw/hdNbQ3z+iCvfw8AihUCkOh2lbbotFIEm19PQMZp07GAzS3DxGaWk9sP19d7n2cfdujyqH8Pj4OB6PfTVkaDO/w9FAS0tf1nlBsTA+fjxNcXHVttyFhW56exdVy3/r7p6kpGT7+WY2y0A54+Pq2EwmJycJBApwOMzb8rtc1XR1qTPfI5EInZ2JDQpFRW46O6dV8aDJskxLywgVFTUJ15rBUEN//3DWuUEpWe31mldzz7auN3We+YMH7SwvV20IzdturRcUlDE9XcDjx93bfc2u0Ns7jsWy/TO32Zx4vWbV8t+GhoYJBEpWK8mt55ckCaNxD52d/apw9/UNIsuVq16UrXuci4UFuyqeQ+VcHcDl2v5czcsrZG7OzPR09r22vb2DyHL1Bu/RZn5JkjCZ6unoyP69D4VCdHXNrRZ42Iz8/BoePVJnnQN0d4+telE2j7ugoIzBQa9q+WebjaZazveWlkHy8tZCfTeP3WQyE41WMDiozr3v7OxDkvastmJYj6Kivdy926dKVMzi4iLd3UsUFSnzbbv9tbS0hvb2OVVyy2VZ5u7dHgoKDmx5z27PY2mpmL4+deTnmZkZBgcjq8aUzWO3Wu0EgxX09qrDvxNSqiIohPhX636/KEnS88DvAnbgYyHEeXUuT32Mj48zPW1h374CqqtDOBxRZBkMBvD7TYyNmSkqqqS3t4OzZz2rFf2yhZ6eAczmGg4ciODxwHvvmRgcNFBfL/PKKzGef97N3bt3OH48hNW6tShBpojFYty920dFxRn27Qtuy71vX4xotJ7m5k85evRAVnNTWls7iUb34HRaEt73goIyhoftDAwMsndvQ9a4ZVnm9u0uXK4TmM0y1dWRLfwmUwX9/d1MTk7idie2hGaC3t4BIpFKHA5TwrELUUdnZx9PP30iq9yLi4vMzMR48UUnHk9s22fu99fQ2trEoUMHtrWC7gYPHvRRXHwg4Zx79tliGht7OHt2Oetx24pBoSzpfZ+cLGVoaJh9+/ZmlXtkZJRQqIBnnzUlvO/h8B7u3bvO4cP7s55/NzAwislUnXC+h8PVPHr0gEOHDmQtTn9ycpKmpjmqq5UKiom4x8bMRCIG3O7jNDZepbq6Imv7bDAYpLNzjtra09TWbv/MTaZauruHeP757O7tsViMO3d6KSo6m3DskrSH5uYrHDt2AIdjc5GjzBGNRmlqGqSk5PNJ73te3n7u3evmm99MHE6XCSYnJxkfN7BvX0nCteZwKNxf//r2HuVMEA6HaWoaoqxMyXdLNvaysnpaWy9z/LgPp3Nr2G6m6O8fIhbbqNgajYJYTEKWpRVPToy5uTlKSrYPm80Ufr+f7u4F6urOJJzvQlTT3z/E0aPZza0OBALcvz9GWdkXNZ/v09PTjI7K1NSUJ33mxcX7uHv3Nvv2NWyrCGWK5eVl7t+foKxMyaPf/MyVMOx8+vsH2b8/u2fLvXsdmM37sVpJuNbAhMGwl5aWTj7/+ezWE+jrG2Bqykld3fZzubT0MDdu3KCuriarMqQsy9y40U5e3hEsFkF1dTjBOj/ErVvXqa+vXa3eqRVSUrA2QwjxAHiQ5WvRHLFYjBs3OqmoOJVUyejrs2GzHeLOnce89trnsyaAxEuKnjlzDo8HfvhDG8PDawLtpUsyP/pRkIaGg7S1dXHmzMkk35Yeurv78HgKefFFBx5PLCH3nj0SY2P1NDc/5oUXns4K9+zsLPfuzdDQ8IUd73tp6XGuXr1JVVVl1hZnV1cvU1N57N1bkpB///4Yy8vHuHbtIW+8Ubql+lmmCIVC3LkzSHX1uR0U2wbu3bvKoUN7s3r4P3zYzZEjh5POtwMHbFy/7mRkZJQ9e7KX/D82NsbgoODFFwvxeOSE/IcPH+X+/ce8/HL2ii5Eo1Fu3+7D7X426X33+w9w+/Zd9uypy9ozj0QiNDZ2cerUszuscxNjY9U8etSZ1bUeDod58GCMysqXE4795Ekr16/bmJ6epqJi9wJvIBDg/PmHFBc/g9FowmyWd1zrYMXpPMWFC8288cbLmM3bh/qkg8ePe7DZ6hMasPbtixGL1dLaeoXjx7Or1Hd19eLxFLFvX0HCsR86BB7PAe7da+ell7YvyJAJOjt78fsrqKhwJL3vQpTR19fN2NgY1dVbCwNkAkXw6aC8/MQO3BX09/dmlfvRo05CoWpKSx0pzTlJ2sf9+4+zdu8jkQh37w5QWnouqaBvs+3nwYNuXn31c1nhjaO9vQe7vYEDB8IJxx0KNdDUdIMDB/ZmZY3F0dLSgSzX43CYd5jv+2lufsy5c9kp+iCE4NatDpzOQ1gsYodnnkd/fwnd3X0cOZKd+t1CCG7ffoTBcCCp8a6k5CjXr9+kpqYqa7LMxMQEnZ1BGhrqdpzr5eV7aWm5wsGDM5SVbQ3hzAQ+n4+rV3spL18rULRZubTb85if30NT0yNefDF7e1xXVy9jY3b27SvfceweTwN37jzkC1/QtBZf+grWSt7VegiRyzqIu0BHRw8zMwW89FJ+UiWjujpCOFxDX98gQ0PD1NdvH++ZDpRNoRXYh8sl8c47xg3cAMPDBi5dMvLWW0VcuNDFnj2zlJZuzaFIF4uLi1y/PoDb/SIOR5T33jMl4Y5SWbmfBw+u0dAwQWXl7iydkUiEixdbcLlOUVsrJxU4q6sjRCL5LC420Nj4gFdeeX7Xyu3CwgLXrg1QXv4S1dWRpPyHD7tobCylubmNZ589vSveOO7dayMcrqOhwbSjYjsxsY87dx7xpS89nxVupe/WEl/5Sv4O8y1KaelRrl27w1tvubPiTQmFQly+3E5p6TM4nTHefz/ZnMvn/PkAe/eOUlubnYpPbW1dLC6WcvKkLel9P3DARmNjKY8fd3Py5NGscN+/38byspvCQtOO972q6hB37lylri57h2B7ezfhcDX19YakYz9+/BC3bz/it36rfFfrTAjB9evNBIMNFBUpRTV2WmvV1REGB60UFVUwNjbD7dstuz6MFxcXuXt3guefT27AqqsTTEzs5+7dtqyttcXFRRobB3G7X95x7KdPV3Pt2jB7945RU7N7RcPr9XLz5hDl5Ttz19REWVg4waef3uP3fq8sK2u9vb2LqSkXL73kSjrf4txXrjTxe79XuusIjfn5ee7encTtVrwIqcy5UGgvDx9+ysGD2YlUaG/vxu93U1FhTyr0yXI1XV19HD+evQgJr9fLvXuTvPDCi0nHvXevmcZGN+3t3Vmr0js9PU1z8zzV1ad2vO+nTlXT2DjM/v3ZGXtvbz+joxbq6iqprg7t+MyXl49y48Y16uqqs2K87O3tp7Mzxr59e3YQ9J0sLu7NmiwTDoe5fLmNoqIz1NZGU9pfXa6TXLnykDfeeHnXBkRZlrl6tRk4iM3mTGpQcLsP0tp6nbq64awYbefn57l+fRC3e2c5Tlnn+2lra6SubpC9e+t3zZ8qEsb/SJLkliTpA0mSvr/uNSMQ2fSzIElS9nz8GmF+fp7GxmGqqo7hcES5dCmx4ONwRDEaoazsNJcvd2YljrWrq5fHj2NUV+9FlmFwcPtHMTRkQJYliotPcf78g13HTisKzn0sluM4HLYUuMFiMVBc/BSffPJwV2MXQnDz5gMWFiopLi5L6b4bDAK3+wCdnYKOjt3lZ4RCIT755B52+ynsdltK/NXVR7l7d4GBgd0niI6MjNLS4qWy8kBK3JWVe3n8OEJv7+5j1kOhEBcvtlJaejKlZ+5y5eP313D79tbGrekiLnAHAnsoKChIib+i4jQXLrRnpejD3Nwct2+PU1V1NMX7foQbN0ZXqnvuDgMDQ9y7t0hNzZGUxm21migoeIpPPnmQlZ4xCwsL3LkzTmXlwR3HXlJiY3zcTk/P7nIP29u76O424nYrJbANBpHyWgeorDxKa2uA/v7BjK8hGo1y+XIzDscx8vPFjtxud0PW1tr6PdZms+44dqczRnn501y40Lbr+R6Lxfj00weYTEdS4nY4orhchSwtVXH37sNdcYMSndDYOEp19fGUuX2+Wm7datlVrm0sFuPy5Qc4HCcwmcwpzzmTSaK4+DSXLj3adWW/+fl5bt0ao6LiyAah7+23rVy5Yubtt6388Ic2PB6orY1SWHiCTz9ty0oCvizLXLvWgt1+hLy8ned7ZeUhbt0ay0ruYSgU4sKFVgoLT2E2G3a873l5MiUlp7l48eGu5ZmlpaUVD8rJlJ+53W4DDnL9+oNd53fPzc1x+XIfbvfT1NREkz7z6uoIbvd+Ojuhra1z5y/fAbdutbC8XENBQVHK+2thYTlzc2U0Ne1+rT940MbAgI2KioZVb7HfH+Odd0z8yZ9YeOcdE35/jH37glitUF5+hvPnO3ZdtC0cDnP+/H1stpMp73Emk0RFxRkuXuxSrWjcdkiWYPFPgKeBdze9LgF/CfyvwL8GxoH/SpWrUwnxB+RwnMRut6Yk+BiNAocjH1k+yKef3t9VsuLExASXLw9SWXkGWTZgMEB9/fbft2ePjMEATmcpy8v1XL7clHFFFiEEN240MzNTTklJNbGYlBJ3LKbEEMdih7h4sSnjA6Gjo5tHjyJUVh7BaBQp33dJkqiqOsOnnw5nnJAdi8W4cqWJhYUaiorcKfNbLEYqKp7h/PmuXVW383q9nD/fTmnpGSwWQ0rcJhO43We4fLl3V8K+0mH9PktLtTgcJSk/c7f7MA8fhuns7MmYG5SNuKvLgNt9MOU5Z7W6gKN88sndXVV9CoVCnD/fjNN5CpvNnNJ9t9ksOJ2n+OST+7sSumZmZvjkky4qKp4BjCnfd5erlFBoP+fP39mV8BWJRLh0qRmb7XjKY6+uPs6nn/ZlfAh5PB4aG0eoqnpq1UKbzloHpdJaRcUZLl/uyqjimBKy08LkZDHl5ZVprbVLl3p3VapflmUaG++v7rGpjt3lykeII3zyyd1dPfPm5jZGRvIoK6tL675XVh6hpcW/KwUzEAjw0UfNuFxPYbNZUuZ2uw/R1hbdlQHtwYM2pqaKVgtLpDP2/PxilpZquH27NWN+RaluxuE4gdVqTknoKywsYWHBzZ07mfPG8fBhB0NDNtzumjT2uJNcvNi8q/kmhKCxsZnl5VpcrtKU73thYSGBQD3XrzdnrOQoZ3ozBsMRbDZnWs+8vLye3l4T7e1dGXGDEh73wQf3cTqfwm63p2yor6o6w7Vr4wwPZ17Mqbe3n0ePQrjdh9LeXysrj3H/vpehocyLfQwMDHHr1hzV1U8B7GhQqK6OYLfnYbWe4uOP72WsWMfn28JCVVpynNEosNmcWK0n+eSTe6pVktyMZArWa8CPhRCbzagC+HMhxP+yUvzi3wGvq3R9WUdcyYg/oHSUDICKigYGBx3cv/8oI36Px8MHHzykuPhZLBYbsizh95t45ZUYdXUbr6GuTnEv+/0mZFmisvIAg4N53LyZ2abU0tJOW5ugqkoJC0iHG6C8vJ7x8RJu3Eiff2JigitXRqiqOrvS6ym9+242WykqeoaPPmpNW+hSwjEf0N/voLJSSexNh99mc5KXd4YPPmjOqKyv4jlrwmg8jsPhSovbanVgt5/mww/vZVxV8P79R3R1maisPJTWMzcYDFRWnuXSpcGMywr39PRx86aHqqqnkSQpLf6yslpmZ6u4fPluRkYFJYThHktLdRQWlqd134uKKvB6a7l69V5GxpTFxUXef78Zl+ssNpszg7W2l4mJ0oz540rG9HQZJSVVKY/daHRgsymHYLrKpSLwtOBwnMBstq57Pb21Dkp1P4PhMNevp+/ZePiwgwcPQlRVnUh7reXlPc0HH9zPuJR0c/MjHj+WVvfYdPjLyuqYnXVz5crdjJ55f/8gd+4oYVrpchsMBtzus1y82JtRv5xoNMqFC3cJh/fjcpWmzV1VdZYrV0YYHU2/PcXExAR3785RVbVWECjdOVdZeYhHj0IZVTuLny9zcxUUF1emqdge3bViOzIyyo0bk1RVPZXmHudmbs7N9ev3M1Zy2to66eggo3O1ouIA3d1GWlsfZ8Td3NzG6Gg+ZWV1aXMrRtunuHZtJKNKlqFQiA8/vE00epiCgrK0nrnZbKGs7Dk+/LAjI+75+XkuX+7F7T6TkSxlNBopKzvDhQsdGckza4bDZzEaTWlFKBQVufF692TsKGhr6+TxY8UgBOmv8+LiShYWqrl2LfM5nw6SKViHgJvbvL45cLR75bO/Fnj8uJv2dnn1AaUr+ABUV5/mzh1P2haA5eVl3n//HlbrUxvKFivVbeBHPwrygx+E+NKXIvzgByF+9KMgxcWsVIGJc5+itTVGc3N6Ct7AwBA3bsxQXX1mQ+xvOtwK/3Ha2kRam+Ly8jIfffSQkpJnVgWvTO57Xl4hQhzl/PmmtEppP3rUSUtLmOrq06tjT5dfaZJ6nI8+upuW4CnLMleuNDE/X7PaXT5d7sLCcoLB/Vy4cDftEuKdnT3cubNIdfXTq2NP55lbLDZKS5/jww8fpR1OMjo6xvnzA1RWPrehN0k6/JWVh+jvd2ZkVLh//xE9PRbcbiWZOd37Xll5iJ4eS9prLRAI8MEHdzEYTpKfv1aZLt21VlV1jK4uM3fupB+m2dbWSWtrJCNjinII1nLlSlNagn5PTz+Tk64t/YcyWesAZWV7GBgwpGXp7e3t5/r1KWpqnl3pOZTuOi8lFjvKhx/eSdvK2tnZw61bCxv22PTn3BF6emzcvp1e+JLH4+HChW4qKp5ZrV6XLrfV6iA//2k++qg5LWNO3Gg5NlZEefnejLjNZiulpc/yf77tIwAAIABJREFU0Udtae0zwWCQCxceUVT09Iay7OnyK17Tp7lypTtt5bq9vYuHDyNUVio5m+krts9w4UJvRhES8/PzfPRRO6Wlz2IymdMed1XVUR4/Voyv6WJsbJxr18aoqspsvitKztPcuDGVtmI9PDzCnTseqqrWigFlMucKC8/w8cctaYVjR6NRLl68w/x8bUbKHSjly12us7z//gMWFxdT5o57Sm22k1itjozGDeBw5GMwHOXKlea0FB2fz8dHHz0gP//Mas+r9D1oBxgYcKZ9rk1OTu5qvsVRWXmYzk5pV97LVCEl2sQlSQoBrwghGje9XgHMCiFiK3+/CFwQQmSv/mIKOHv2rLh3715a/zMzM8O777bgdr+0wcK6vtrQpUtGhoYM7NmjPJziYujrsxGJbJw8gcAyXu8N3nzzc7hcrh25I5EIv/rVdRYX968uyvXYqYTxesRiUUZHb/Daa7UplfxcXFzknXduU1R0bksjuHS5AaLRCOPj13jjjaM7Fr2QZZkPPrjOzEw9ZWUbi4Nkct8BRkdbeeopmeeff2rHsY+OjvEP/9BJZeWLWzp9Z8I/MdFFff0sX/7yCyklqd6920JTU5Ta2o2KbSbco6MPOX48xLlzZ1Pinpyc5Be/eITb/eJqH571/Ok884WFaQyGVr797RdTqoC0sLDAu+/eweX6HA7H1vWRDr8sy4yM3OYLXyjm+PHDO3KDYs3/8MMhamo+v0HwSve+x2JRRkYa+frX61NKjpVlmY8+amRiopqKin27GrfCH2N09CZf/WoVBw5s/b7tMDw8wnvv9VBZeW7DnE9n7EIIRkaaefppic99bufqodFolJ/97BIOR+I9JpO1vrw8jyTd5623Xtlxzk9OTvLLXz6ivPzcar+vTLknJnqorBzn9dfPpVTOeae1lg6/LMsMD9/k1VfLU6p0FgqF+MUvrhGLnaKwsHxX3ADT0/1UVo7x2mufT6lNQ1tbJ59+6qG29vkNn8+Ee35+ErP5EW+88VJKRS+uX2+io8O16kHZ7dhnZoaorBxJuVrwxMQEv/xl+8r5sna99fUh/P6tBT7q6pTEe4fDyODg2ue93jlisft8+9ufT7nwQiAQ4Je/bCQWO0lR0VoafLrjjkYjjI018vWv76WhIbUCXj6fj5//vBG7/fkNxuJM+P1+L8vLt3jzzRfIz89PifuddxpxOreeLZmu9YaGGV599XM7PnMlRO0ebW0WampObXgv3WcOa/P9d34ntXO1qamVe/ckamo2VpnNdH8dHr7PuXN2Tp3auaBTLBbj/fevMzfXsEGWMxgEhw/7eecdE2+/vXXN/uAHId56K0pnp2NV0YnFYoyM3OBrX6tJSYYNhUK8885VzOZnyMsr2vXYI5EwU1NXefPNp9NulSBJ0n0hREolMJMpWCPA/yiE+P92IPs+8MdCiMRtpFVAugpWNBrl3XevEIud3rbLeLqCD8DMzDBlZQN84xsv7bgwlUVpo7r6eNLPbS5xmQihkB+Pp5E333yWwsLChJ8TQvDBB9eZnm6grCz5I0qVGxTBJxZr4q23vpi01GtPTx/nz89SV7d9ecxM7nssFmN8/FPefPNU0qqKoVCIv/mbT7HZth4CmfIrgucdvvzlUg4e3J+QGxTl7u//vpuampe2FdLS5VYErxu8/noN+/Yl7wsWCAR4551rWK3PkZeXeH6k88wnJnqor5/eUbmMxWL8/d9fZXn5SMJmm+nyRyJhJiev8uabT+1YSdPr9fK3f3srawaFYNDHwkIjb721szGlra2Tq1eXqKtLXgEvnfseCgXweK7xne/sLIAsLCzw85/fobDw89jtW0uOp2fIia0YcnY+BEdGRnj//UlqahKPO5O1DjA62si3v32Q8vLyhJ+JC3w22/bzPRPukZEHPP00OxpygsEg77xzFYvl2S2Hf6b84XCQ6elrvPXW2R17gilni5Pq6iNZ4QYYHr7LF79YsGOvpLm5OX7+82YqKjYaLXfDPT7eyaFDi+xUUnl2dpZ33mmlpuaLCRXBzPb3G3zrW/XU1CSvYJpNJQNgamoAt3uE118/t6NiK8syn3xyk9FR92oxmd2MOxj0MT/fyJtvPpdUngDlHilGpFrKy+u3/Uy6/DMzw5SXD/L1r7+4oyx18eItBgbKcbu3Nzhl8syHh2/wjW/U7lghuq9vgI8+GqO29oUtzyhTJWdioot9++Z55ZXkFUwXFhb4m79porLyixuMhpmOG5RzdWbmCt/97ud3bFHx4EEbt26Fqa3danDLRLlUZNjrKZ1rt28/oKXFltU9bn5+kvz8Dn77t7+QVkXHdBSsZHUaG4E/AJIqWMD3gRspXlvO0NnZy8JCGTU125c+jkQMDA5aMRgsKQs+ZWV1DA+PMTw8krT05PT0NK2tS9TU7GwJluWdeUEJ57BYjtHY+JBvfOOlpNzDw1Bbu7P+myo3sNI4r5TBweGElnVZlrlzp4/S0sS9PjK570ajEbv9EK2tvbzySmJh+/HjHoLBGkpKtleuMuGXJIny8hM0Nl6noWFPQuUyGo1y5Uo7paXPJbSAp8uthJM8zdWrjdTUVCW18N6/30443EBJSfLDMp1n7nbvp6dnmsOHR6mrSzyflFCxAurqdi7pnyq/2WwhL+8EjY1tfOtbLyfcEJWKha2YTEe3Va4g/fuuJFAfobGxlddffzHh58LhMLdvD+J2f2HH8aRz361WOwbDAVpaunjxxcT7eiwW49KlZiyWE9sqV5De2I1GI273WS5fvk55eWlS5XJkZBarNXkx2UzWunIdFUxOziZVsG7ffkQsdiChMSET7qqqkzQ3X2Xv3umk3I8edREI1FFcvL1ylQm/xWLDbj/OzZttSfd3j8fDw4dKCHC2uAHc7pPcvPkp+/bVJ9xnlLX2CKfzxLbKVebcB3n8+BpHjkwl7cn28GEfdvvBpMpIJvt7QcEh7t9/vKOC1djYQix2YFvjXSRioK/PRnV1hLfe2ij09fVtL/RVVDQwNDRLR0cPx44lV2y7unrp7zdTV7e9kS+TPc5iOcHly8389m9/Iek9HR4eYWDASF1dfcLPpMtfVlbH4OAYg4NDNDQk/t6ZmRm6u4PU1CQ2+GTyzEtLT3Djxl3q6moTjj0cDnPtWjcVFdsrwJk8c1Dme0fHNY4eTd4Gp7W1B7P5wLbKVSbjBuVcNRj20dbWk9SQtLS0xN274wnPtrExM/v2xfjRj4IJlMutcpLV6sBoPMSdO21J+8H5fD5aWqZxu19J+JlMxl5U5GZkpJ/x8fGs9eHbjGRmkv8L+JIkSX8qSdKWJypJkkmSpD8DvgD8n6pcXZYgyzL37w9RVrZzuIUsS0QihpSFn8LCg9y/nzxB9eHDfpzO5AdBJigpqWZ4WE5aYW5sbBqzOTu9hDYjP7+G/v7ESZoLCwssLdlwOHZ2+6d734uKKunt9SSNH25vH6ekpD6l70uH32ZzEg4XJ42ZHxkZZWmpJKHnbDfcoVBl0rLxgUCA9vY5ystTCylLFZIkUVh4iObm5PO9tXWY0tIDWeUGZUOcmCBpjsbMzAxDQ2JHby2kd99LS2sZHJSTJiXPzMwQjZZsCRHLBkpKaunqmk6al9PXN8DkZD4lJVU7fl+qY7daHRgMh2hqSp5zOT/vx2pNLbQp3bVusTiZn0+cE+T1eunuXqasrD6r3EajEYfjEC0ticvWy7LMw4fjKa+1dPiLiysZGQknTUTv6RnGbG5I6WxJh9tisRGJVCbNjfF4PIyPS1ty7nbLbTAYcDgO0N4+mPAzsViMvj7Pjh7yTPhdrlLGx0NJ83Kmpqbo7Y1SXp44kiAu9HV2Oujrs9PZ6WBw0JrUW+t2n+DWrYGkeb6RSISbN/upqNi5GXk64y4pqWJiwrljbnlTUy9FRamFaqfHf5i7d5O3iOjuHsFq3ZuSxyEdbqezgMVFZ9L9fXx8nECgLKHhDjJ75pIkkZd3gMePE9/3aDRKV9cspaU7y3Lp7q8lJXU8fjyZNN+2q2sASWrYkmYRR1y5dDiUfo7//J+HeeutKA6HMaHnDqC0tI7u7uWkeY/j4xMIUZVQsVyPdMdut9fR25tZAa9UkPCpCyFuAf8C+G+BUUmS/kqSpD9e+fkrYBT4b4B/ufLZzywWFxcJBGyrSYHZRH5+MVNTwaQb4vDwPAUFiS2gu4EklSVVsHy+cELr4m5hNlvx+RKXuwwGg0hS9u85KMKPEJaE912WZZaWwkk3w91ACGfSA3h0dA67XZ32cHl5boaHEz9zRQEpSSl3JF3k55cwOelLWGwjGo0yPx/aNu8qG5CkoqSb8eDgBGZz9qOVJUnCbK5laGgi4WcikQhCqLPWTCYz0ShJDQoPHgxRUrKzESldlJXV0d3tTdoDz2CQUArMqgGx8v3bQymrXp51AxZAYaGboaHEay0YDBKJmBMKHruBJEkYDAVJ7/vY2CJ5eclDCDOF1VrM1FTiBPz5+XkkafeN77eDy1XG8HBiQ4rf7ycWs6YkdKUL5b7nJ+1J1tU1gt2efUFfUWwrmJhIvM9MTEwQDpdtyDPMFlyuBjo6EivVfr+f2dnYhuI92UJeXhEej0h638fGFsjPTy9nJlUYDKV4PInbU3g8S5jNib3U65GuoJ+XV8jkZGJDitfrRZbzVJnvZrOFcNiadJ/p75+loCC5ISUT5dJgMCBJFUmN1XNzy1itOxuqM4HDUcD09O57bSZC0hNJCPF/AK8CLcC3gX+58vPtlde+IoT4k2xciCRJr0mS1CVJUq8kSf9DNr4zDqXPgzqCj7IZW3esq69FScjtUFaWTzCoTmM1n2+BiorE3imbzYYQu2/KvB1isRiSFE4YvmIwGMjPtxAMqsMvST7s9sQHXDQqI0nZF/iAldKsia1NylzbXZf4RFAECinhfDYajRiNSnEIdRBOmvM3Px9IGB63W9hseczPJ1aqlThyddaa3++loMCCybT9ARsOh5mfj6TkMU0XyiFYnLQ3VkVFPj5f6tWw0kEwuEhZ2c5ecHWQfN+2WCxIUjjj3oQ7QZb9SUOBzWYDQmTekzEZhJAxmdTZw1JgT/quYmBT554riCU1UE1OehPm2+0WZnMRs7OJhe2lJR8GgzoGLKezgJmZxAKnYjTNvmIXh8HgSGqsjkZlVQwpAJKU/Fy12czEYrtrRp0IkUgIuz2xkUY5b9Vbi5JkTCqj+v2pG+rTVS7BSiiUWH62WIyqyROyHMVszr4hOo4d1WEhxBXgiiRJRqAERXJbrSKYDax89/8NfBnFM9YkSdKvhBCZNUjYBEUQVkfQVtyqwaQVYOrrixkYmKCoKLVwhnQgxBTFxacTvl9XV4PReJ25ucKsC/yhUB+HDiUOUygqKqKwMMzU1GDWvWjLy/McO1aa9BA8frya69fvqRKy5XQuUF6eOO+hqqqQ9vZJVTxoi4sTnDiROLeqsLAQIdpZXMy8MXIihMMBSkutCZUcSZI4cKCUR486si7sCyEwmeYoLT2V8DN5eRbGxlIvuZsOIpEgeXmJ53FxcTFud4yhoQcbytJnA+HwPF/9auIEbEXxFQgh0krYTR3Jv7empoJbtzpYXMy+4CfL41RWJi6eoVSB6mVmJr22GakgEFji8OHEFnOTycSRI2U8ftyb9f09EFiitDRKUVFiQX7PnhIaG8dU2eNCoQmqqhKHJBUXFyNEK+FwdkORATyeCfbvT+wlsdvt5OcLgkFf1vfYaDSCwbCUNOfQZDISCqkj9MViyYU+i8WMLGfWqHUnKIJ+4r3Lbrcjy37V9hlZ9iWVpSorXQwOelSJRorFPBQVJQ6vrq6uRJbvsrCQfe/d4uIwX/pSYg+RUllySZX7rhir/Tgcie9pYaEDv38Zszn7Y5ckH05nYk94RUUxkcgIsHO1wXTh9U5z+LA6EQCQgoIVx4pClX5XtNTwLNArhOgHkCTpb4FvAVlRsPLz86msNDI2dpFsW/aFkDl0qDipVf306QNMTNzD6+3IKjfAsWMlSatMORwOvvKVowwOJg45yBRlZbVJK7pJksQXv3iSrq7MO5YngtEocerU9hVl4jhx4jBVVXOqeA+dTmdCbwJAfX0dBw7cx+9vzTp3Q4OR/fsTJ6Q6nU5eeKGOiYnerHMDnDyZvKTr2bNHycvrR5bT65uVCtzu41gsiS199fUVPHz4iPHxzBt3JoIsB6mvT2xQkCSJL3/5mYyaR+4EozE/aaEas9lMZaWdsbEbqlh4zeZFiotPJHy/rKyMM2fG8XqT51BkgmPHKikoSKysu1wuXnllLwsL2Z9vAIcPJ5/vZ84cZXn5AcHgZFZ5LRY4d+5kUoHqwIEGBgbu4PNNZZUbYP9+B1VViQXO4uJijh3LY2Tketa58/MlTp8+k/B9SZJ46qk6rly5g9mcXeUyEgnzzDOVSff3vXtLaWy8j9eb/cgYWfbhdic2mrrdFdjt1xkbS5yHmymEEJw7l7g6rt1uZ88eO0ND57Mu6AshqKmxJS1Tf+RIHZ2drYyPp98QeicUFPhxuxMbTQsKCnjppTpVzpaGBjMHDyY2VNhsNvbsyWdg4E7WwwQjkTDHjpUkne/Hj9dw/vwtvN7sny15eRIVFccSvl9ZWUlZWQdjY1eyPufM5gAHDyYuIrRbJCzTriUkSfpd4DUhxD9a+fsPgOeEEP900+f+CPgjgLq6ujNDQ6lvMNFoNK0GsenAbrer5rbWoUNHelheXlZFqVaSkdUJP8wGgsFg0jj63cBisaTUo0aHDq0gyzILCwuqrPXCwsKk0RGyLKfVIDYdGI3GHdtBpNMAPF2kUiJeSbvIPsxm8478Sj5S9sdvs9lS6kWVKwSDwaT59rtBSUnJjn3nEuVd7xYGg2HHZx6JRNJu/J4KzGZz2s88W2XatcR2aumWXVMI8RfAX4DSBysdApPJlFRD16FDx28GPstKkJr4rAsIOnRkEwaDYcceYWpyJwvd1II/l9ypNIFWCzspn7+psNlsST3KaiOX8rPZbE4aJfZZxWfF7TIKrI9/qQHUq52oQ4cOHTp06NChQ4cOHSrgs6JgNQEHJElqkCTJAvxnwK9yfE06dOjQoUOHDh06dOjQkRY+EzFzQoioJEn/FPgEMAL/rxCiPceXpUOHDh06dOjQoUOHDh1p4TOhYAEIIT4EPsz1dejQoUOHDh06dOjQoUNHpvishAjq0KFDhw4dOnTo0KFDx689dAVLhw4dOnTo0KFDhw4dOrIEXcHSoUOHDh06dOjQoUOHjizhM9FoOBNIkjQDZL+VuYJSYFal7/6s8z+p3Lnmf1K5c83/pHLnmv9J5c41/5PKnWv+J5U71/w695PHrzb3HiFEWSof/LVVsNSEJEn3Uu3U/JvG/6Ry55r/SeXONf+Typ1r/ieVO9f8Typ3rvmfVO5c8+vcucGTPPb10EMEdejQoUOHDh06dOjQoSNL0BUsHTp06NChQ4cOHTp06MgSdAVLhw4dOnTo0KFDhw4dOrIEXcHaHn/xBPM/qdy55n9SuXPN/6Ry55r/SeXONf+Typ1r/ieVO9f8OveTx5/rsa9CL3KhQ4cOHTp06NChQ4cOHVmC7sHSoUOHDh06dOjQoUOHjixBV7B06NChQ4cOHTp06NChI0vQFSwdOnTo0KFDhw4dOnToyBJ0BUuHDh06dOjQoUOHDh06sgRdwdKhQ4cOHTp06NChQ4eOLEFXsHTo0KFDhw4dOnTo0KEjS9AVLB06dOjQoUOHDh06dOjIEnQFS4cOHTp06NChQ4cOHTqyBF3B0qFDhw4dOnTo0KFDh44sQVewdOjIASRJ0teeDh06dOjQoUPHbyAkIUSuryHnkCTJIoQIb/N6kRBiXgN+B7AfyFv/uhDiptrcOrSHJElGYBkoFEKEcn09WkOSpL3At4FKIcQ/kyTpAGASQnTk+NJ0qABJko4Cc0KIKUmS8oD/HogBfyqE8Of26nTo0PGbBEmSXGyVpcZzdDmaYsVwWyGEmMj1tejQFSwAJEl6BHxXCPFw3WtfA34shKhRmfv7wL8DwkBg3VtCCFGnJve6a7AAh4BSQFp3AZdV5m0A/hg4zdYNUdWxS5JkAr4DPLUN9x+pyb3C3wp87bO08UuS9JwQ4o7KHN8C/gPwPvCGEMIlSdJzwL8WQnxFJU4LUCuE6Fv5+7uAcd1H3hVCBLb95+xfy2ngRbautf9ZC/5trueBEOIplTlagLeEEF2SJP0/KHtNEJgVQvyBmtzrruErbL/PqH7fc7i/SsA/QtnnSoUQJyVJeglwCyF+rib3Dtf1e0KId1XmOAX8WzY+cwnlXLWoyZ3kmjxCiOJccK/w/4MQ4ls54tbibHkV+AtgD+vWGcozN27/X1m/hpzs75IkFQL/HvhdICKEcEqS9FvAs0KI/0ll7u8ALUKIDkmSDgE/BqLAPxFCdKrJvcN1/ZkQ4p/lih/AlEvyzxD+PfCpJEn/28rvfwp8A+VwUhv/O/BtIcQFDbi2QJKkc8C7gBVwAV4gHxgB9qpM/9dAH/DfAVpbsn8KnID/n73zDrOrqrv/Z4Ueemih946IIAgoyitKR0ABAUFBxYIKosALr1QpIiJBRREQBELvvSkgvYOgdJAWWiCE0Pv6/fHdN/fMnXsn4cfsfcaZu55nnsycuZN1Zu45++xvW4vLgBcKcwOcAlws6XfAGGBipiP3xqsP/J1473PiYGAd23emYAvgbmIjlAs7A/OlfyEewnelz+ciHoaHZ+QHQNL3iE3flcB6xLW3NnBBbu4+MKoAx0IpuBKwKbAskUx6vAA3ko4EtgCuoec6kz27WPP6+kvgy8ARwJ/TsTHEe15bgAUcQPxNcuI04BxgJ3omLuvEpjXz314jd4lny3HEtXU6NbznNa/vfwbGE8Hl/enYzcBvgawBFnAgsHr6/DDgNqJD50/AFzNz94VZa+QGuhWsiZC0GHAhcYFeBWxn++UCvE8Bi9p+LzdXB/7bgVNtj5I03vaskvYB3rR9WGbuV4k2uQ9z8nTgfoWoarxWmjvxd9pc2nbujVdbSBqW+72oZnEbn6dq4nO258jEeRdRQXkkfT3e9qzp88WB022vlIO75TweBba3fX3lXlsP2NL2tzLyTgF8CziljpZUSS8QLdDLAH+0/en0nr9se6YC/OOAFWw/nZurDXed6+vTwKdsv1ThFvF3r33zkROSXgZmc+ENTrrX9gUOquleGwasCdzQbuyhLhR6trwAzGP7g5w8ffDXsr4n7heJ3/29lmfsBNszZ+Z+NXWiTAs8B4wE3iM6FLJWbNN6Nj8wpo595KTQHbRn4qK0NZHNvgxYCVi5EP3ewOGSZi/E14olgN+1HDsE2KUA93VEi14duB+orV3D9sIdPrIGV5KmkPQPSdO0OacSC9TdkrZsOfZV4I6MnPM3gquEyxufpONFWnGBOW1fnz7/MG06LgM2ykmaNhyH1zjvdypwNXAicEI6tiKFKljAOOCVQlytqHN9bcx6QrNaN0PlWDakdeaxdutMIZxIPNOLIt1rPyI2mMWR1vAL6giuBsCzZRSwe9p014Fa1veECUQnxkRIWoAIeHLjxVSgWA+4PT1npqVnm2ZOPFCQ6yOh2yIYuBF4B1jR9pOS1gf+Kuk82z/KzP0w0cqxY2VdaPSKl+gbnkC0rrwCPNcYSKdlViETngCukHQu8Hz1GwVmI7YB/iLpSlpaBG2flJkbAElTAasSmaczJE2f+N/IxWn7gzT7VldyZWfgSknbA8MlXUAE2etk5JxG0iy2XwGwvVXjG6l3vdQmcIykhWw/Qdz3G0t6iZi/zI2LJG1k+6ICXD1ge5c0A/We7WvS4Q8pE2RAtMmcIulX9L7X/5OZu8719TIiebcLTMz2HgBkvwbSOvMBsdGqI7A/BLhZ0v/R+z3P3bZ0IvADokWqDlwnaVXbt5QkHQDPlnOAK4A907o6EYW6Qupc3/8CnCPpF8AwSasR7fh/7vvH+gUHAHcSwkVfT8fWAu7JTWzbinn2RYm/+YBCN8AKnAf8ptFOYPtSScsDRxfgHg2cBJxBPb3i5wLrE1nm44g5hffI3yMPMD3xsJ+KKPM2UKKtYztiGHVWWsRFiPcjKyR9gmhJfYeYDzoD+ALRyvX1Pn60P7A/cJSkfek9/5U102j732kQdlPgBmIW5VuN4CcT7iCqZMe3+d7XaM5j5cahwNJEYuGXwNnA1MScSG5MC5wt6Wbib159z7+Zm9z2lS1f56xYtuKo9O+GLcdNT7GTHKhzfd2FWMsmEGvs68R8SPb3O+EI4ExJB9N7nckd2J5NVEjPo/xzdRXgJ5J2p/e99vkC/E8Cl6XkVSt/7sRlbc8W4j2/nri36thL1bm+/5oQDvojca8fT+xfW6vn/Q7bJ0g6M33emHG9FWjtVMmFvwOXSzqe3td7kWR5J3RnsGqGpPHAiNK94p0gaQ0iu3rFQOxp7S9ImgCs6pqkwSXdABxte3SlX3t64GHb82bmbryv1WuuZNW0KCR9mXjo7kdseJ8H5iaCrn2BLVoDgELnNTUwte0SLVv7dvqe7f0z8F3PZCRKCm04BwxKra+pWrUw8BTRCr0g8LTt5/v8wf49h06/X/Z1RtJrxAxWHa1yHedtbJ9YgP+vnen97czctT1b6pzpboeS63udUB+eniXei/Ss6UBf7/OlG2ABqWd4H0LOdjbbM6eWliVsH5mZ+3BC4rLWSLsuJJGBrYB5gWeA01rmZXLxPkwMgGdrx5sE/8TAup3wQ2buBTt9z/aTGfj+xuRttrPItKdz2JxoFasGr88Au7qgZLWk2YiKxty2D5U0DzDM9phS51ACfW0yqyix4WxA0vzAvKVbp+qCpDeAGQfKhrMkJF0K/J/tf9bAPUVdQgt1o/SzpYV7NHCi7b/n5GnhnKx2yBz3oKTJanV1fjuID+nwfB+MCduPgm6LYOAIYB7gG0TfOsB9xNBk1gCLaCf4ceqdbe0VzxJ9D5Q+O63eAAAgAElEQVTssqSNSHLlRFvDksAdkra1fWFObuK9PUUhzT+2+o0C7SsQbQQrURF3kLQK8GgB7pld8XwrgLMLcrWFw3fnrNSeODthfFvUo0PSF4g5gTuAzxItJYsDu5J5EFqhpHgSoWg3dlKv/7goGThNCmnY+zTCCsDADJI2A9a13e9WHANlfSXsD5YAavGiUfjwXFJTsPE4Met5Hr2fq7nb5J6TdBpwku07M3P1QpppPon425cW2yj9bKliGuDCdP+1vue52mLfp+97XeRrRT5uMl5j8ttBLNzy9dzAHhSY9QSQdChxr/27BN9HQbeCBUh6DljM9hst1YRXbM+Smbt4O8FAyS4rDJ53qgy+I2lN4Ejby2Xmrq19JfFvSCyQfyZ8wA4iBqN3yN2uppB0fZaY/zvFg9z1XdKurkhiSxpZslWqwns3UTG7qtIWOi3wpO25MnN/jUggrUOod44GznMBg+XUrlab4a2ky4jZjEOIwHpWSTMD99rumHH/GHwDZX09kBDzOYHeswnt5hH7m/8eInF5BjDamY1mW7g7tclhe/vM3CsR99pWhLjJaOBk20/l5K3w/zzxL0j4nY22fVMh7tqeLaXboBPnZK0fuat3Aw1pfb3d9hIFuI4iDJafIa67UwfKnqYbYAGSngSWtz1BTW+eOYBbbC9a9/kNVqQ2uTlsv185NiXhn5A1sB0IkLQisfFckNgAHVsi45n+xhsQm6/1gJuIjOe5lSHVnPxbAdvSbAsdbfu0jHyvuuK3VKINs8N5VP23GuvMMOBF27MVOocRhOnuNsByxEzayTnbSCQdQMXw1vYskhYBznIZ/7FxxDrzYekEWp2QdE2Hb9n5lfQa5/BJ4lrbCniDZrDxRAn+OpHu7bWJ338jQkxnNHBGidZ0Scsm7q0JYZWTiKDnsYyctT5buhgYSO3Y97qQ355CkXkDIrGwHiGgdRJwfp3XXTfAAiQdRhhh7kLITS5LbAYetf2LzNwdh04LZRlr408bgMtt/7pybHdgfdtr5uSuG5I2T21rrcc3s12spS5lmjYnlI4WJlS3jrZ9Yya+3YAfEvfXk4QH1c7AMbYPzcT5mu0ZK1+PL7Xwt5zHjcAvbV9RCbDWJmZF1ix4HsMJgY/dieD+RUI2fccc8wuq2fBW0v3AJrYfrvzdlyEMppfPzF3r+j5QkN7vtYg5yOUIa5SjiZnbHPMpHduiCrWAN85jISLY2I64x54mbCl2tj260DmsQYw6LEcoSd4O/Nx2VhntGp4tHZMGueeQKufwFUINeHZoejNlbFFs8M5EiDi1487q85hm36qBxHDg80Qi4Sc5uTuczzJES/gniOv9VGD/Oqpa3QCLiWovhxLVhOHAm8CxwB7ObM7ZJss4ktD0v9H2/+Tkrptf0lJEn+70xINnfiLL+RVnVvfra06iwGxEr6pK5Xix6oqkGQiZ8m0J49dzCNWx7xD9+/3uASfpMWDtahZV0qLA32239nL3F+dAqWCtSswbXkJUkU4iMtsb2749M/cwooq0LSFXfjOVNsHUQvhH2yMzcD8LLGL77UqAMyNwv+35J/Xz/cD/bWIm4FeEbPH3gf8DDrF9SmbuWtf3ynmInpuuYsIX6f7eJn18SFz3TwE7As/Z/moGzsbgfdWAtGHDklvNblbi/t6WkO3u0aYnaWXgypzJBcWsaaN69S5xr48mkik7Aj/Jtd4m/jqeLa3G5XMQMuljXMAHK7Uo/gA4nVhjjib+/mfYzirVLulkwu5lFHAy8d7vBpxje1Rm7tbWzDcI4baSYiON620bYrb9fMKP7iliBGMN258sdT4Tz6sbYPVEag18yTX+YdKGYGnbuw12/tRSsBoxGPkscKsLDOa2mZMYSSz+J9v+ZUbexkJ/L5FhqW4AFiGGNefJxZ/OYQPiwbcekUlulNLfTt8fATxlu9/NUCWNBRaszv6kisqTtufob770/79LPPQa+DoxFzIRuTOMlXOZh3gINNpCT3YBBUFJzwMvEe/1ybafbfOaa3Js+iX9hdjk7QI8B8xGbASmtr1jf/N1OIdNgO/R/Lv/2fb5JbjbnEuR9VXSvETl4vNAj1bI3EFG4v8Rsc4sRgQZJ7mi4Jju+7E51pk25zKSsGS43vapmbneIPzOTgIuaJeklXSC7e0y8d8BLESscSe5zeybpMdzBFh1PlvanMsUwF7Aa7YPL8D3JLCBw+vxldQKvQqwl+2vZOYeS6wp4yrc8wIX2V4xJ3fdkHQ6ocx7M8121Or+YhgwodrFUuzcugFW54y2pLG256zhfIYRQV7xLPtA4K8LkhYD/mp7jYwc7TKrDTxPlLKzGlwrxEUaG+22ZXNJ37X9lwzcpxKKSg0TzgUIx3nZ3qq/+RJnx+HnBpxpCLpyDlMAVwHr5K6Kd+D/tMua+1a5ZyKut3UJE8y3SYa3tl+r45zqRKn1VdJFRDfGr4BriUBrP+BS28fm5E78FxNZ5AvcwY9K0tou5EGnsGN52BmETVp45rL9wqRfmY1/M+DCTn/zzNy1PVs6cE1JVLD6vTLfhmuC7ZnT52MJS4j3qsczcr9EiAa9L2kM0Q76KvBKu06ZDPzfJuYs5yES5acDx5coVEjag6gQP9PHa2ay/Wruc+nF2w2wes9opGNTAc878/C5evsoDCcy3LsXKmsX5U/zGJMjYZy1b7gdJE1HvOdZF8PEda3tL+TmGWhI7TNHEzNADQnbc4Hv2x5f57nlRspwLuUCyn1tuDvdy+8QbVolDCHnpAbD28S9BjH70iNzbvvgzLy1re8KcY8FHOq4jaz2COAm20vl5B6IkLQ8cFWuSnmFp9Ms0DvEZj+3H1Rbb6aSbaEDBZLWA47L3RWSuO4CtrV9n6SriTa18cABthfKzH0VcLBDofY0ohX3dWAl25/OzH0osDG956ovsr17Tu6BjiHtg1WZw5lW0nUt356PUMDJjXY+Cs8AOxTgroN/m0z/70eCeg+fNwb/S5mQth0wlnSE7Z/mJJbUqQXyHWAMITySJQObgqgtFHOPI4mANmumtY/gonpeJQbf9weOShW1MfSUzc69+Xm0wid63vMfSrqQELno9/c9tV2/ZXts2vR/U9L7hKJZicDuD8RMzPVANbgtkV2sc33/IPEDvJLeh1fpabadDZJO6vCtxjpzvjMJLaj3jO1wQrwqW/t3BccRmXyAcURLLITf4khJ9wJb2n4kE39bb6Z0zz1LJLT2tf16fxPX+Wxpk7wdDkxLzJyVwF403+s9CY/PGQrx70CzK2Ynomo9C1Ci9X07YMVqq7ukSwjVzOwBVpq9a7eWN665cwkRreJ+fEO6gpXmcAQcRQwnNmDCqO7q3PNA6u2j8Ibtl3JyDiT+uqDew+dvAP8ERtkeV4C/k8jFuAJV09OBTYHbaIqLrEIIjsxHzIZ9zfblmfgXJHwrGu0EZ+fM6k6iLRPKeZ81gonqoqsS/JK+QyhM7U+zNXNvIol0LfBr4D3bm2XgvhX4ge27FcbeGxGy0dfY3qW/+drwvwws127urAB3betrahE83vZ5ko4mTK3fAobnmLVrw38kMY9zIc11ZiOifWgW4CvEddEpEPs43K0ztm8A92QMaqrcewEzA/s4BGSmI+67CUSW/7fAora/nIn/R8AmhO9b417fnRDXeYiYRbvPeUy2a3u2KIzcq3iDaAkt3hpWEqn9fF/goJrazx8jAqwJlWOzAHe6gM2RpP8lEvdH0rzedyRmECcQs79n2d4j97n0OrehHGDBxIvzdGCbOi7OgYaU7f8gdxtDhW8FYA16S4vuU4K/NCqVsyOBH7d8exFgc9tLZj6HMwl55PMqxzYGtrb99bQ52cX2Chm4NyPmMv5Os53gS8B2LihPXwfabLYnokDb0BjCTP3tyrHhxAZkvtS6+Yjt2TNwjwdG2HY6j9WJ9pX7bM/d33xt+O8BvlgicTIZ51JsfU2bnGG2X06b/F2JjPoRneZj+pn/SmKm9MbKsdUIq4IvS1o3ncugaldUmO3O7Z7+jlMBz9qeQ9L0RKtgFhXBSW14FeIHd+aYS6rz2VIHJqc7AvJ3SKji9ZeTpwP3T2gG9GOIoHo34ALg0sbrcv0NJP2bmG1+pnJsPqJaupykpYErahk7GeoBFpS/ONu0L7SFy8iFnwb8wfZNkrYH/kT07+5k+7jM3N8j1MSuJFSHLiOMGS+wvXUGvtoXw0rlbA2iZWkiLVE1/Z0rSluZzmECseH9oHJsCmC87ZnS56+0ziX2E/ejxLzVVZVjXwT+UmLmcKhCIZX+RdsPVo4tRVSR5k4bwBedwXhXMYA9L7AE4T21rAoqO0n6NCHLfhpxj02E7dbW8P7mrm19rRtpnZmtTaDxku2ZJYlQeOsXRbk+2tN6IHfyTtITwFa2b64cW5WQ614wBbvPZgywXgSWrwbRCvXSe23Pntb3l51h1rj0s0W9PZjawplUYlu6I3p1JlT4c3coHE74tv4pJ08H7snZN2fr0kgJvAVcEUxSeLA94abn4qslnjWtGNIzWBWcSLQIlro4iyjoTCbWAhrtFD8jqgmvEAOauTcAuwPr2r5eYUC6qWIodctMfI05lD4XQ0LlLgsarTmSDrS9Vy6eSeAxwuz3yMqxH6TjENXENzJxz0q0pFVxPdFSM+ihmowoidakqyX9lWijmA/YPh0H2ICQuc2BywiZ7tloyuUvQ8wilcBKRALn8/Sewcqd1Sy6vg6UICPhn8BBkvZ1eKBNS6gYNuauFgZe7ke+7J5qk4l9gCvTXGPjXtsIaJiurgXkrNafBPxN0u8q/DsT+xyIJOZDmbhLP1se7cf/6yPD9kRBkZRA+RJxjT9JCPrsQ6jH5sYqwE8kNdR5q8Fd1kR99W9QEy4BzpN0IFFBm49IqF2Svr8q8X4UR7eCBUi6AfgM8cAvenHWDfX0TLjN9rzpeNsZoX7mnshRrSKqgBFsX4uh7RNycrc5l6ImoJJWJAY/pyCu+XmJgfiv2r5L0ueBJZ1BylnSfkRi55e231WIXewNvO/MUul1QzUaUSb+dYHNidm354Azc8xCtOGdhggy3iPkdN+XtCYhK3x6nz/cP/zjgK+7oPFlhbvo+poC6EnC9vb9zd3mXBYCTgU+TQRSI4A7gG/YfjxVFkfavjj3uZSGpGUI49PGvXa27fsLcQ8jPN963OvAsbY/SIGunEHNtM5nS91I7c+Lu7fH48O258vM3TpzOBG2T+z0vcGAVBE+gLje5yau97MIIZc3UvV2GtutRtT5z60bYNV7caq3ml2V+/ic3In/H8AVRIAxzPb30mbg1gKLwv3A+rafkHQzcChhhnpWjv7wFu7aFsPENQ/wR+ozAZ2KyOw0HsA3u4zB8yOECeZ7wItERnNqoMfiZ3uJfuL7pDMplX1UqEYjyoGE9ED8wIV8eiQ9Rcyf1eEL9A9qWl8HCiTNT1pnbD9ViLNju3HueZihjhqfLZ3k8bF9dQH+Z4G1bD9QObY0IZaWfda0LkhagBDZaGeD0S/P8f9WdFsEqT3C37bl65HAooQLevYAC/gOEf2/RwwmAqxGSIzmxqHA0sAThHzu2cRmO3s2HxhGbPQfqBxbkIztgS04mjABXYsWE9AS5OmBd/0kX9j/aBX2yI3rgUaV9BHbixfmr2IW2/9On78raSrbt6m3+tWggqTDiGrZbZI2IO5zS/q67YsKnMI+wBGpfW5s9RsF5m7rXF+RtDhhADovUVE4zQWU9Kqw/TTRGVIS1XbwiaeS/i21xg9J1PhsaW25nYPYT4whBKRyYxQ927DnJyTMRxXgbnTlbEvzXh9te7Iq2h8TZwEPEutscY/HgYxuBSuhxouz3bl8G1ja9m6TfPEgQmoXm9oZ/DnacO1GzES0LoZH2D60AH/XBLQAUvViR+B+4F5CJriXXHuJrLZqNKKsE5KeI2Sp31RIth9KyOeOsv2JAvy1yePXCUkbEYHcxTQVOzckrsEL6zy30pA0ksiyX2/71LrPp4v8SIIaexFCKocX4qyrDfsXhOfVb2mOPOwCnGz7oMzcE4BZCySr/uvQDbCo9+LscD7DCKWlrHNIFb61gRXoXd4tIpUuac423CU2vLUshol7LDC/7XeS6tTKhAnoS3Wo3QxWSNoU+A2pRYs2wRXlfLDWB163fV1qDTyVZERp+9zc/HVB0gSHatxswIO250jHs895Jp7a5PETfy3rq6R/EWqF11SOrQkcaXu5nNwDEWkW8GHbHa+HLgYXJE1JSOJnHTmoGwqz3TWr61la967Lfb1LOhk4rrrOdBHoBljUfnG2KrAMJ0zTdncB2WqFGeQWwDVEy9pE5B6ETgHOcURbZI9WjsGcWQZQzSagQxGSXusGr+Uh6XZCrXAxYsB9a0mzEz5Yc9V7dnlR8/o6nhAOqsqkT0kkcfpdjn+gQ9LyhIjRHHWfSxdloFAlPs72PAW4Oil4vkO0KV5u+4UOr/m43GOBhWy/WTk2A/Af23Pm4KzwjCAM6x+jtw1GR42BoYDuDFZgemLgvopxwHQFuN+nt4/DM4QKUAlsBayQ+uRL44/EfMKJORSNWiHpF42KZB+LYanK3bZERQXgp1RMQHOQtQnk22KQl/lng4l/i7mAFwbz76uaPWIq2BH4HTGH1HjgrkP43xWB6pPHr3N9/Sfwc+DXlWM/S8ezoC9xiSpydyiot9fkcGBZYtY3K+oY+u/redbC3+/PtoHybJHUQwGaeM+nJdafElgC2BS4jebYwSrARYRM/58kfS1Tl8zlwCmS9gCeIjo2DiIEdnLjr4RS5AMUmsGSNFnrtu2Tcp9LX+gGWIE6L86FW75+w/ZLBXgbGEf4stSBWYGjXa6MWlXtqtUzxfYrlc/fIgLNnGgXyFfR8AIbzJXDaST9hfBZmxJ4T9LpRBvVhHpPLQtq9YhpwPbtwOotx06hnNBDVR5/cyry+AXo61xffwhcJGlnYsO3APA6kFOxsp24RCtKrDOtXpNvAPcUEvioY+i/zufZQHm2bNPy9RtES+irmXkbGAZsafu8xgFJGwNb2141qVUfQuw3+xs/JrzH7iGEPd4j1rcSgmFfBOZxxei3AHaYjNeY8ISrDd0WQUDSTMTFuQUwFXFxnklsvLI+HJNk75u2x1eOzQpMZ/vZnNyJ6/uEyeiv6F3ezZ1l/A3wgAvI0Q80pFadrWif5ez36mVfcygt3P0+kyLpmMnkzlq1lXQCMCOwJ81Zy4OI+6+jVUMXHx+SvkwEtnPa3kjhgTRTIfnk2uTx61xfE/+UNCWznyXk4bNLZg9lDLWh/zqfLS3nMQ3wYfX6TpLxw2y/k5M7cU0ARtj+oHJsCmC87ZnS56/kbFNP1cTZiTbgItefpBsJb7snSvD9N6FbwQJShuObkraj8MVJKIl9m1ATa2A+IgP3mQL8R6V/N2w5niXj1NK6IWDnVDl8vgd5BoPngdK+knAyoWh3GS0brxwoMczfB8bVyF3FusAilT71h5N66GM1nlMxJJXOJendJpc1yJH0E2BnYk3bLB1+C/g9LZWtTKhTHr/o+toGbvkYKpv+cwmVyusrx9YAdra9Weef7BdcRLSj1jr0L2lGet/r/f5sq/nZUsXfgN2BWyrHViKqRmsW4H+MqBofWTn2A5rPl9mJqloWJEuGLUjJFElnFqrYXg1cmeTpW5NIQy55XkW3gsXEfs5/2r63cuyTwPK2R2fmbquk1VDeysldB9SHqXMVzuBNluSaJ9m+UkhR7hVCRbBkWb3KX9dMSm1Iao1faBGzWYgQs1kgE2dH88sqCgQ5nyNal6YhfMFeJap5T+cW05H0GGHA+YSk8bZnTdncsbZny8md+IeqPP7yxO86DTHXOx/wNrCpC5hvp+rZjrRfZ/o9gdbCPY6ollarCVMSc5dZr7m6h/4lLUO0336S5vPOib/Es62WZ0sSdRlRHTlIFZ1xtmfNyZ24VgTOJRInzxCWPx8AX7V9l6TPEyI/x2bg3ho4BriEpiXDBsD3ndmWQFKnRIJtT9bz72Pyz0PMrjeuueoJ1Dry0K1gBQ4gZHSreBq4EMgaYAFjJS1me+K8hKTFGDhZ/35FjsDpI3BP1jBuIdwPjACKB1g1z6Q02iYWofcD+KbM1H8B/ibpcHraMUxWC+P/J1rNL9vB5DfCHAUcantUCnJGSNqHFmW7TJiRptFsY/MzFfBuAW4IL5zGpnoPmvL4PyrEXxeOJ4SEDrdtSSKu9+OJzH5ujCLmM44hWnF/QWT4Ty/A/TYhXlWdv5mBaP/PjeJD/y34E1E9+x/gcWAhokU19/pa97NlAiFeVO2GmYuMVaMqUhC1OGEkPjdh/XJzo2XR9nXAdZnoDwTWTxzAxIrtaGK9ywbXr3r8Z+JZsgFwFbHm7EsEm7WiW8FiYuZj9ja9sy/nriJJ+j/g68TD5z/AokTAd6btgzNxXm573fR5q9rSROTOMib+bxOzSI0ZgdMJ+fJBfWGmdsWjCSW11ixn1sHMmmdSPkNUUmYjMutvp39fdGYp3bTB3J544Deut9MYGtfbxLmQShVpauBx2/Nm5j4buNv2QZJeTsHd7oS63tY5uevAQFlfJb1KvOdtZ0JycieuZ4DVbD9VWWeWIoSNsrZnpnalaYkM/qtpzvpPwPu2t8vM/Rrlh/6r/OOJ6t17lb/79MC/bbeKavU3d53Plt8SM8070dxLHQ78y/bPcnIn/p2AU11WpKzB/SJxzbXOnz3rgrYE6RlbTZpmb0lO1eoFbb9eueZmA26wvXRu/r7QrWAF7ge+RghbNLApkYHKjUOIrNphhBLQU0TWO6fzeHUD36q2VAySDgU2Jsq7jbL2rsScyO4Z+DpudqooEVgC2wFrEEqK1SxnCeWbOmdSfkfMpRwCvExU8Q4AxuYmTkHU8eljqGEC0Rr4CvBcaiMaR4vASib8hFCz2wGYUdJDRGVhowLcNIK6NsfHOo9HzIBYX4FLCcXA8yrHNqJcZnc4zcrlW5KG235Q0qcKcP+MmHN9WVJjnbmMsMfIjXuJBFItARaRtGqIdb2kkI0fT7OKmxN1Plt+AfyWkElvJO+OJ0SNSuBLwMGS/kGsAReUENdIODxx7237bUnTAfuTdx8JTBRqOxL4PNDqr1eiRe8Dmt0QEyTNQTzv5uv8I2XQrWAxcT7hUmJI8jHCEHMtouR6Y0beKYBvAacUvBEHDBTmeCvaHlM5Nj9wV46sS53zX23OZQKwqu0SQXwrd20zKaooLVUqKdMAj9quVTo/N1IWfT/azydkmQGrcB8B3Gb7VEk/JxIY7xHml9/NyZ34BaxMtGU+nc6llMpVL4PplN19vsQMWF2QdBYRYN1J05dnJeACYvMJ5JuNkXQT8NO0wb6ISFi+SiiOZcssp2ttYSJZOTvxez9t+/k+f7D/+A8gulJqGfqXdCZwqe0TJB1CBNXvAE/Z3iQzdy3PljRrtSZwI7HZboiVFd3gpsrJloRk/FLAOcBJ1da9TLxPAyOJBO14InErok1xInI8Z9K9/SbRhnotEWjtR1yD/T5v1ob/EuAY2xcobFgWTOczS+5K+STPrRtgBdLG/hukxZgIerKbQzZKmrl5BiIUw+8ruuJBJGkW4E7bi9Z3Zvkh6WHgU7aL9Ie3cK8PvG77utSydwpRydjR9rmZuZ8Glk1tOw8SFcxxwGO523HrhqSTiazaKCK7vg2wG3CO7VGFz+VzxGzUFbkCnbTRO4EI4oqr11Uq1qsBN7d8ez7gPttFqmh1IM3DTBK298/EvzLwQWU25SjimtvVFXW/TNxvADPWdN3VOvTfci7DiJboGYmNftbnTcuzZRWa844lni29Eil1QiEyMxpYjthTHgv8zvbrGbgmK5CwfW0G7nHAArbfqLTojQBusr1Uf/O14R9BSPG/JGk4kTyckZg9fSY3f5/n1g2w6oWk0cS81UV1n0tpKOSbNyHaxcYQwe1uRIb10sbrnEk2XdJchNN6azWhRJbxh8A6xO/eoz0u1+87ECDpT8TCe7KkPQmDxHeB63Nl0gcKUsV2advjKg+ieYGLbK+YkXcK4GFgmZKVckmjiGyuiI3WCa4otRbg/1biPooYvG/ARGXhanc9oQYlJN0AfNf2g4V5J1bPbL9fkjvxT0EM+q9TuiumUkW6wXYpAZsq/yVEpeyWSb4473msRSTPNgbuAE4kqqk7A3PZXqPG0+t3pOfa/LbfUSj1rkxUql/KHfCm6/0YIoAfcF1g3QCrZlTaOG4mshwT35AhsOGcnOyinUFqU9ImRBXhEWBZ4D4i03SDC6ji9PG7Z/l92/DPTMy6tZocZzd+bTmPLxHZpotyb0gkbW77rDbHN7N9dk7uxPMSMNL2+5LGENfbq4T5ZFbRgVQxXblaLS6BtOlal+aG4xFiPuEU29n939I5LFV6o91FQGFT0EPEyPZVBXgPJK65E+j9XM2aQKuzepb4nwSWsl1cwbDOKlJK3m1FJGhb3/N9CvAfRiSUJhBr3MnVCkpqSx5vu9/nXhUWBFsRIh+tz/Tv9TdfC/dFhFDUeZKOBhYn5sqHF9pLPUcEeMUTGpNCN8CqGX21ceRq3egCJP0b2N/2WZVZoO2J9rVd6z6/nFAYav8ReJ2eMt12Rk+klG36F9EaWTzbpM6ec21FEDLwXwUcbPsqSacRpq+vAyvZ/nRm7h2JAOdgolpc3XwUqZimGbTNiI3v6sDfbbca8Obg7eg9VKJaPVQh6WeELP5faYoYbU/YBfw2M3dtbXp1Vc8q/N8m5mD2pfe9njXoq7OKpFCObAvb2xfgPxI40fbtfbwmS7JH0unAJwghlx6Bte29+5uvhXsWokXvZYW4xs+JpOkRtp/r+6f7hX9PQlBnP1cUUwcCugFWF20haSvbpxXimh+Yt+SiXN1sVwKsYcTgew5lsQEDhXzyd21fVgP3o8Tc3auTfHH/cTaCxnuJh1DVaHoRYjYhq0R85Txk+zGF0tGviAfR/rbvz3O5hoAAACAASURBVMxda8W0ch5zEwHWj4kh5Oxzd2022yMJCecbS2RY26Hk+loX0jqzjpuqckhaFvhbifutLtRZPUv8jXu9urkTBe71uqtIQxWSXiGqOHUpV9YGSY8TM7XvEa3f1Wsut79kn+jKtA8ASPoyUVqe0/ZGkj4NzFS6XasFvyA8grJBIR97GmHybGAGSZsB6zq/stlYSXOlNqUnJK0GvEQZWdG6MSXhv1UHDgNOSZuQ1uzqs5k4H008IlRCq3ieUDzKjmqlyPaLQHb1vgpfbSbbkqYFvkoopq4JXA/sDWRvy4T2Rpgpy1+nR0r29XWA4NGWr//DZFhl9AcUim7rE225v5E0D5FpHzOJH/24+Cxh8NsqPNCwiciNrF5Xk8B0hHIg9JTJLvWeL01Uyeey/WNJSwLTlJj9TPuZfWnfprdEZvr7CSuC4gGWpHOBUa4I1yhMjne2vVmBUyj2HP2oGLIVLA0QT6Qk9LAz4Zeyp+2ZU5bvWNur5+SuG5IuIzZbhwDjUhVpZuBe2wtm5v5fQhr8HEnfJAYlPwR+m7ukXjdS686MRCtH0TmBOispkq513bKt0tpEQqH1AVwku5s2AfMCY5xZJVXSmsA3CY/B5whFrZNy804OUrX6pRKtoXUibXTabfiymNi3cO9ABNT70RQx2puQcp4YaORYgxSqaucQIgOftT1jOrarB7FyZBXpGp8LeKGuebCSkLQ5YSZ9DrC17ZlSsvoQ218qwH8r8CBwFr3b9LLOHabuiKOJxGmrNUBWX02FiuCc7mloPuhtMCYHQznAGhCeSAqp8rVsP1FpVZsCGFvi4pT0e9s7tTl+hO2fZuYeB8xh+8PqHIxqkK5PG8/pXYMvVWmo6ZnxLiGRPhHO78c0Tafv5ZzLUk1Kei3ncCSwBXANPWffss8IpNa80wnJ8nGE6egtwJa5KoeSxifOk2y3yqQXQ9poVjGcaOHaPXcLSc3r6x+I6+16WszMXUBAqSWZ0qggt36dJbEi6W4imLqq8lydFnjS9lz9zdeGf1bCf2pe4BlCxGd8bt7EPRNh/Lol0a3wHnEf7uQCIjcKSf6taP7up9l+pADvA8BWtv9Zec+nAp51Bl/NNvwTgFnrCGYl/ZKQJ/83ve/13EWCZwh13Fcrx2YBHrQ9Mid34pqSMJPeluY1N5oIrGtViR2yAdZAgULicm6H8erLtkekB8HjtucuwN9p8H9c7gBP0v3AJrYfrvzuyxBKU8tn4hSwA6HgdpftE3LwTOa5TAWsCsxj+wxJ0wM4v1dJxyqOM/hkdDiHOWlWUl4sxPkwsIrtV0rwteEfB6xQRwVH0vmEVPCeDr+S6QnBi4VtfyUT5zR1BbMt5/EhvbsVngF2sH1FZu4619eXgeUytt5Oin+yuhBsP5mBe7ztWdPnjWfLMODFAn/31YBLiGpGQ9xjaWCDEokGSScQHQp7Jv4FgYOAN21PVmL5Y3BvRPgqXkzzd9+QMB++MDP3OGB2266851MSAVb2uWqFz+FxtjsJrOTkngCsWkeCWNLxRGvo9x3+ljMRlcT3bW9XgP+3RFvuL2le73sBN9v+eW7+vtCdwUpQfZ5I1xFKSwdVju1EZLmzQU1lrSnVW2VrEWIeKTcOAy6W9Kt0HlsB/0e0DObk3JrI6v5K0sK2J8uQsz8h6RPAhcA7RK/6GUTP/reAr+fkLhVEtUMKrE4G1iIkymdSqOtt6/yy3UcAZ0iqS0lvHFBLcAd8jkjkvAcRxEvanQg0smAgBFcJrTMpb9jOur4NkPX1aWJ9qQU5AqePgPslrdMSQH+JUDHNjSMIX57TGwckfR34PeERlBvrAovYblTJH1Yo5LbOn+bAwcDG1SAjtQofSTzvcuJOoopRbYnbErgtM28DOwE3pa6k1ja9jkqm/YQXiARaHfg58Uwfn4LcEYSa4baF+LcglIkba+p9km4H/pnOrTZ0K1iAavRESq07FxGB3bzEEPCrwEa2n8/I21gA1yCCjQYaJpy/cwFVv/S3/x6RdXgKONr2+X3/1Mfie5poyXw4VcsusL14Lr4+zuMG4ncdXWlnmB542Pa8Gfh+Yfug9PkvO70u9yyQpLOJQGN32xPSzN2viR7ur2bmrtt77PvABoR6YOsDOGuAJ+kRYDPb91SOLQ+ca3uxnNx1Is28NarV/yjIW/v6muZP/o8Q02i93q7LyZ34ZyY2ne1mwNbOzL0qUUW5hNiAnUS07G3sPmS0+4l7PDBbtVUstSi/1KiqZeZ/AvhCNcCVtBBwXYEW8PFE2//7lWNTEr971rZ/SUsRM0iPE50h/wCWANYu1KJ4AbAY9Uil/xBYh0hOj23hLmXDMZKYs3w65961De+zRKX+5cqx2YB/uWa10m6ABahmT6TUtrYyEWQ8DdxWqo9X0oG29yrBNRDQ2rJTolWnw3mMB0ZU2xnS8SyeTJKOsv3D9HltfiGSXiQk+d+tHJuWaBWcPSd33agzwFMIDhwMHEezjWJ7YG/bx+TkrgsKIZt9iITZMsD/2v5j4XOobX1NAf0RwBv0nsvIutFO/FcSqqzn0XvDeVwB/nmBb9B8rp7s/AqCSLqN8AA6tXJsS2ImLKvfXeLaixCXOZzmvb4LMNr2gZm5rwEut/3ryrHdgfVtr5mTO3ENJ1oSG+/5xbZfz82buF8j2v3rUPKrO3nYOnN4cTXgycz9e2BFQsHxKeK93we4x/ZPSpxDx3PrBlg9N92qwRMpZbdWpel2f4sLG6al1q3WLGPurPrviXmrmyrHVge2cKYB8LQILk+zDfQuIsNabQvNnvFRDGHvYPuOSr/4KsCRtlfJzV8XFD5YG9u+r3JsGWIIfNH6zmzwQ9IXifbYxjpzquu1gsgKSf8h1MRukfRZ4BjbyxY+hzmAt2y/ntb5bwIfEJv93Kav44Cv2/57Tp4++F8lKjm1DZqn5OXsRAWllFT46kT17GEiwFkIWBzYsPqsy8gvInlSvddPA47P/TdIVaSLgOmJAGcBwkz9K6Xmg1JgPQ8xe5WtBboN743AN2w/UYpzIGAAzBxOQwRXWwNzE4q1pxFFk7dz8/d5bt0Aa+Km77O2X0gb3x2JHvlbclc3UpvO+cA0ROQ/H/A2sGm1nScj/zqEZG6roEaJrHq7asY0RIk5S2Cr5sC7OrykVMZnQ6Ka8GeiT/gg4AdE0JXVo0pN491WvAM8l3PjJ+lHhAfQMTSzqzsAv7J9ZC7exD0lcW9/gd6zllmVlrooj4FQrVZIN//A9t2Sfk1k198DrrG9S2bup4DFqutrSUi6FNjDBTyI2nDPAvwB2ByYivibn0V48/R7Zl3SrK6oBKaM/gY0A5xLS2X060ZaZ6sJ41tLBNkKJeBTEvd4YFbgViLoyT4PKOkAYn76r/RuyS3hf4ak+Yk9VfbxjgrnrYQPVuvM4a62S8wcDlh0AywmtpK080Q6PHd7h6Q7iGj78NQuJqKc/w3bK+XkTvyPAb8BTrT91qRe38/cY4EFqlmGVOJ/arC3iwFIWpEwyWu0Mxxr+84CvFVVNdFTYe1DYhh5R2cSnZC0Pi3ZVduX5OBq4f0D8EXi/j6ICPR+SFRR98vEebntddPnHb33cgd4kqYmlJW2ovl3Px04qO4sXy6kCsrMxDUu4EVCnr4aWOeuIlVbgccAqxMZ/fucWSVW0naEcNMv6T2Xkb0FPXVFXEpscls3nB3nQPuJ+zyiUrg3zUTO/sDUtjfJwFftgvm7C/guTeJ8vk3vez17BStx19KRk9oT7wF+4RDxmQE4gBBAWLMQfzvY9hczcy9A7CNXSHwzSNoMWNd2ViNe1TxzmPg+T8v17hrFvBroBlhtoIKeSGkTMKt7mrRNAYx3G3nfDPwvEzdH8QtB0jnEQOruDi+sYcSQ5uK2Ny19PiUhaXPbZ7U5vpntszNzf4eo4uxPs41jb+AmwgT018B7LuPCXgwKv47VbD+l5LWWWlqOdiYDYklbN2Yx1If3nvP77R0HLEkElo0N555EYim3wlUtUG959moyIZsHU8s5vETMJSxBPPSXTevcBNszZuZubHh6/Q0KVemPBb5CDT5ckl4hVDPfqhwbTrSN9bvYgqTnCZXCB4jqSSOw74FCge2hwMbE/F3jXt+JaMPePTN3bR057VpSU2JpXO57rW5Iuoy4zw4hft9ZFSIz99qeLLuEj8Fd98zhT4nk4Yk0WxS/RSQPj8jN3+e5dQMskHSB7Y3bHD/X+ZXNTgfOsH1e5dgmRO/8Vjm5E9dvgAdKlbBbuOcjetXnpnljPEcoKGYfRq4Tre1LleNZRC5aOMYQrUOtlcOHbc+X2lseyVFFVHh/7Ubv7OpvcrcytVQTngMWtf1mp/diMCHN4yzqigeYpBFEgJX1eqsLmgwfptytQ5JGAzMRlbMrbB8gaTngbNtLZebu+PsXapl6DVjC9nO5udpw3wpsV02SpmTKibY/k4Hvh8BvicCi7UsoF9iOBVasPkNT69hdzmy4W2dHjkJUZX/bN1aOrQ7s58yqlR3OZwTxnPuWM89Vp/V9jpSoropmvZIjodDCXffM4RhCROXeyrFPEGIr/a7I/FHQ9cEKdJJiX7MA9xTA6ZLuJKoJ8wMrARdImujnkDHjtyqwk6Q9gB7SmrnblmyPSW1ynyEyXUUVFOtAZf5pmKSF6ZnlXITI9uXGMGIRfLBybAHiWoRoYcq1NvyBUPzZh2Z29X8T//czcTbwAKHWeRtwB7BfynqWHIRem2jjaBWUySqPT9zbw+npwzUdkdAYlCgRREwGvktkU98DRqdjswP75SYeAL//f4jfuw5cBVyZAtzGc3UbYLQqvmT9lVi0fVSq2I0k1tWiYioteC19tB57tQD3EkQ1wxARpaTfUeB6J3y+LpV0Cc33fH3gVFWsSXKutWn+bAPinl+feLb8ORdfBS8QEvEPV85lGQp4Y9m+SdKiNGcOL6LszKGAh1qOPULnOftiGNIBVuWmm1q9vYEWITaAufHv9NHA/cAVHV6bA39JH7UgBVM3p7YZACQNG8RB1qM0RTZajR+fp8yD6AjgaoVc+9NEcLt9Og6xUOZS/9kMWNL2uPT1XWk26SHyB1g7E3MZAD8DjgJmJHzYskPSkYQnzzXAm5N4eX9jNHB5mkMbQ2w+fgScpFAXBMCDWFWwDjjMlo9J69tckl5wIT+uFFx0mvnL2qKXMBq4MF1zrTNYua+z1Yi1drX0AbHerp4+IP42/da54fB+GiPpUzUHt0cA50o6hOa9vhswqpLgy6WWeynRFnpe5dhGhMpcbkwLnJs+n5MQbTqPSCTNn45nadmStBIRVG1F05rgbaIlfWxfP9tPOAy4WNKvCHPzrQgPvEMKcGN7fJpBa6g3lhR02ZdYY/ejeb3vnT5qxZBuEVTTD+gbhPpMAw0zyONsP1r8xAYAJC1j+/7MHCsCfyRk06dtHKZQK0WdkHRtrrmfyeRfl1DYmoeoYpxp+/ICvA8Aa7oioKEwKLzG9tK5+etEauNYwfbTNXA/Phkvs+1OCpNd/H9A0kxE1XZLmmp2pwM72Z6QmXvflkMjiQTHKc5kg9HC3+maG3TXmQaIkXvin5zkZJZnrKSziACrV0cOle6MQgF+ESh8VBchgstTCQ+od1Mb+icLBViN0ZLvEV0hTxGzxecX4K1bvbHTrGnjWG17yiFdwXIyVZV0k+1j6zoPSf8DbEvTpO3kOjLJCvfrrYDtgE/QuZ+8v3AiUU7+NuUz+rWiruBKIaByPPC9EgFVGxxPtHEcQTPbtBNwXOrlBqLtIAd5qta0qg1dlYOrDcbRs0WvGGwvXAdvF/yeaAf9BM2W2IPS8Y6iJ/0B2/u3HktiJ62BVy7+Wq85lTU/na/y+fwdX1UAtodN+lXZUGtHjqTFifW98Z6fZvuRzLTDic6It4h9TPG2WEmfScHU+S3HV7F9W2b6E4mAel33VG88kTJjNosX4Pj/wpCuYFWRFFeWpPdsRNZAR9J3gYOJNr2G0MN3gL1LBH2pZ3hDmj3DUwKHAr/PPZyc5l9m9hC5CDVwJLufI+Tx63gQTM41ZdvzZOD+GbAH4VPSuNe2Bw61/dv+5kuc1Uz9l4n2y1/Ru2Uqu7l1F+WhUJdbxPablWMzAI/ZnquG85kSeHkIiLrUan7aRXlI2oiopFxM8z3fENjW9oWZuT9P7KE2Iyp1ZxIzf0vYfjEnd+KvUzRryKo3TgrdAAtQ+IX8kRjur1ZSsrcySHoY2NwVCVOF1Ok5trNF5pI+TbNnGOBsorx9JrB8ibK2pBOBU22XnDmrDRo4kt27A7MA+9YRZNUFhUz7Orb/XTm2LPC3HAFd+v8nZWwNZeTCPwmMoqfARqN1Yuqc3EMZkp4AvlBtlZG0EHCd7QUyc7d67wwnWhUXs71qTu7EPxMxU9rO2Dv3716b+WkSFxhn+4UUTO9GVDgOqwbaGfkXIKqUn6J3wniJAvwLEW3/rdyntnt9P/L+i2i9vaZybE3gSNvL5eSu8E0HfA34JuG5+BDwR9t/ysQ3jLivXiHUSqvPmUWBG23PmYO7cg61qjemdeZHtL/e18/N3xe6ARYTN17ftX1ZDdzjgJEt0f80xKDgbBl5PyRalnYm5m/eT8eL9Q1LOoNo4biB3gqGg6ZHe6BB0tPEPMYHhPnqxEUg98anTqT7fFH3lKefjpAqr1XONTck3Q+cA5xBT08ibLeKrXTRT5C0F7HZOpxmi+AuwGjbB2bmbp2BegP4J9EdMTkzeR+X/2SidW4UcDKR0d+NSB6Oysxdm/mppH8SNisPSfoz0RnzduLeNid34r+VqNydRe97PWs7tKQ9CYXY++jtfZa7M2M8IVX+fuXYlMTfPatUeYfzmZe497e1vUwmjlavvyo+JLyg9svBXTmHo4CtiYpxD/VG4KXG63LNH0q6nJjhP5/e1/vROTgnF90AC5D0AjCPC7iNt+G+gBhI/F+HJ8/0RAvRwrY3ysi7H/HAm5uYgzoFuCydywqFAqyOswDt5gcGG1STZLekjvNfzux+nipGv6F9JSVr25KkHYie8P3oqTZ0LRU1MWdQsEybu4eBZRzKckWhGg3FhzIkiWhD3Zrm3N9pwPGD/b1Q+DEtbXucmsbe8xKGtytm5q7N/LTyu4pIHC5LbPwez11NSPwTgFlzrGOTwf0S8HlnFsjqwH0N4X3068qx3QmPpDVLn08JKLzuRDzDqgGsgRddMdrOeA5/nfSrsDMZ2qfrfQ5n9tH8/0E3wGLibMaMwAGlFyVJcxOqUqsDLwMjgJuArWw/W4D/80SWZXNiOHMGok+91OD/kIT6kOx2El8ZjEhtHJcRrait2ab7MnNX7+3Wtr3sakOpHXgVV8x+S0HSKOAO26dM8sVd9BskjXBZyeJ25zAnvZM42Wf+0mZ7pO33FWagyxFeTK8USKbUZn6aEraLAcsQ7WGfLjn7liqHx1Vb5UpB0kPAp0q0QrbhXopIFk9Ps5LyBvAVVwynuxhcSBWsXaut/wMF3QCLHi1T7xJtcxNRqmVK0nw0PQTGTOr1GfinA75KBFtrEa7vWd3HE++XibmAOW1vlGbDZvIg9+NRjZLdiX8FYA16z0bkrp6NB0bUkb1P2b5JwpmkZSXtCGxMiNqMoWdrZtYNr6S5CG+zt+gtsNE6q9PFx4SkVQlPnrmI93pj2/8sfA7rAscRXQpVZJ/5S/xXAQfbvkrSaUTL0uvASrmrSIl/Vprmp89SyPw0JTM+RyRtj7R9pKRVgGNtf7IAfyNJ+xi97/UsVYQK93qE7c0RQI8uGNvZTW9TILsqzff81qE0Z1wXFOqNW9D8u5/p/OqNDe45iWTKzfS+3g8ucQ6d0A2wqLdlaiAitXFsUy21Z+L5CTED9hdgT9szpxayY22v3vdP/3cjVTNWsv1aDdzfI+YirgTWIypKawMX2N46M/cfCc+rs3PyDESosz9N9g2vQrXyXcIAs7VyeFxO7qEISTcANxJSxdsTWf0vFT6Hx4h23BNLtAq14V+E2GM8JmkOovV9RmIgvngLWUmk9u/3GlWkkonDNHawGLGut97rWc1XJW0MHEsk7lqoB7e35VCFpK2BY4gZrIZ64wbA951Z2CTx/5lQb7yB3nN/Wfczk0I3wOqiNqQNwFq2n5A03vasaVZlrDMKfNQFDRDJbkmPAtvbvr7yd18P2NJ2Vm+etNG6hVA9av29a1X8GcyQ9BoxgzXg+tQHI9LM2xy2P0iiRU/aHlnDOXTn7oYY0r0+T03Ju2cIBcPT6R3cFZ9x7yI/JP0H2M72dZVjaxBCPgsV4H8VWKrESM1HxZA2Gm5A0lTAXoTZb6PEOZpQYOluSPJhRqJXGprtUlMRmfbBiEfpPfuzYctrDOTO9M1p+/r0+YeShtm+TFKJ+ZwziPvrAloewEMFChnleYExBVtErydmQoq2qQ1hTNnYUNp+R+ELUxrHEdWz4yf1whyQ9HvCyPumyrHVgS1s/7SOcyoB1ehxmHAvMBtQPMAi9pR/7QZTQwozEu15VdxCzMKVwJNAceGoyUE3wAocCqwC/ICmlO7ehK/ALjWe12DHdYTx60GVYzsRwg+DDraH1X0OCWMkLWT7CWIIfOM0kF4isF2FyKoPyAUxJyqCNqsRs56zSbqFqBzmzr49Dlwp6Tx6Vw6zzt0NUUwr6aTK19O3fF3CimJVYCdJe9DbBqPERn8rYNeWY3cScsqDNsAiWt6rGAl8h5CqL4GriXv9r/S+13MH24cBe0g6uFs5LQfV6312OHCwpL1tv53m+fdPx0vgBOACSe3m/q5r+xOF0G0RBJLC0Sdtj6scmx24xxn8cVpaxTqihNJTnUgbzouIfu15gf8QKlMb2X6+r5/9b4ek5W3fWxP3dsALqWq1HmEyPTVh0nhUZu4rgZ+VVvypWyY9ncP5hA3CnrbfUFgyHExYMnwlM3dHKd3BrFpZF9SHBUUDzmxFoRrNzBP/WGAB9/SdGw48Zbt1RmdQQ9JiRGVnjQJcnRKUzi1oU1owTGG0O0m4Bsn6klC93meN99zAeGBWokvnuZbzyCIYl/jbwaVE6jqhG2AxsW94+TYB1r2258nA1zCHUx8vGxJDoZJEVDUWINoFbxvsiyGApKeIEvr1hIfFtcDdNanrTQ1Mbfv1AlxHEGpDZ1FY8ScJi6xse0JOnj74XwLmdm9T8WeG2oazi8EPSecQldPdbX+YNsOHAIvb3jQD39N0Nl2diDo2XSmr/7ztmUtzl0RpwTD1bbQLma03BgpUr/dZx/e8iqEoGNdtEQycBVwkaX8iw7wgMZN1Zg6yAdQqVjtSQHFr+hgysL1AqmR+HvgC8GOiZewG261zWVkgaSYq7QSSZirQqjYXEUzOmT4aKBFYHgGcKam4THrCeGIO6p7KsSUJwY/skLQ0obY0l+0fS1oSmKauSmoX+ZHk+Vehtx1DibmsnQn55OckNdTFngM2ysS3Tab/9yNBUqsU+nDCAuWWgucwG7A+4UP2G0nzAMOc2QKmhk30woX5BiouIvYRxccrBkLglOT5VwbmtX12SmhQh3pqj/PqVrAmZvD3ArYmRC6eIWYlDhyKsyI5IekB20unzztmHOsu7ZaCpCWANYnFcV3gMWf2H5P0JUJWteEC38CgzvTVKZOe+HcgWgKPoznruT2wt+1jMnNvDvwJOAfY2vZMSTr6EBeWD++iDCRtQsz9PAIsC9xHmP3eYPt/Cp3DMCLAm58h0qHQpkXvDUJcZlS1SyYj/xeI+/wO4LO2Z0zHdrWdK7htcE8D7EPM383msF5ZG1jC9pE5uYcyVNj7TNIvbB+UPv9lp9eVmO9VWPtckL4caXsGSRsC37C9VW7+vjDkA6w0m/Et4NRqr3hB/imBHYkNdmuWscQgclFI+pztG9LnQ9Z/TNLpwGeJYP4fhODH9SWkdVM2+QBqktJNlbuvEYvhz9N8wlS2H8jNXTckfZFmIudZYt0p4Y3zALCV7X9WpPmnIozN58jN30V5SPo34Tl1VuU93x5Y1nar+ESuc5gSWJ2YsX0GuMn2+4W4azFTrxuS7iaCqasq7/u0hFXAXJm5/0S814cAl9meReGreaXtZXNyJ/6v0H4vlVtQplaosPeZpKNs/zB9Xut8r6TrgONtn1C53mcAHrQ9X27+Ps9tqAdYAJJesT1LTdx/AL5IVBQOAn4B/JCQt92vjnPqIj8UXlRTAlcQAda1BdrzGtwvED4pxaV0FUaUxxMtDV9NlZTPAAfYXrv0+ZSEpM1tn9Xm+GbObLwsaRwwu21Letn2iLT5fdb2nJP6+S7++yDpVdszpc8bG49hxCxQ9vdc0lLEfT4t0ZI7P/A2IWKUNZmiGszUJf3e9k6Vr1exfVsOrkmcx3jbs6bPG/f6MOBFZ/aXlPQcsJhDxOdl2yPS8ex7rCQs8wMicfh94GgimXVG9X0ZjFCN3md1Q9J4YET12ZaOT/y8LnRngQIXScpaOu8DXwXWs/074P307yZAkRaOuiBpLkmHSrpZ0kPp319LKmrGWRdsL0bIKF8NfA64TNLDklolfnNgFLB7EhgpjYOBtW1vBzQCvLuBFXITS5pS0k6SzpF0raTrGh+5uROO63A8a3tgwp2Ez18VWwLFN4BdFMPYNIMF8ISk1YBFye+z18CfiGt7AdurpWzyn9Px3NgdWDeJabyV/t0MeK/vH/tY2K7l68szcvWF+yWt03LsS8C/CnC/S8tsv8JcPntrJPBt4Mu2dwHeTf9uBCxUgLtuNLzPikHSsEl9FDqVJwl5+uq5fZpol6wVXZGLwLTA2ZJuJvrEq8PvuUvLw2ma7b4labjtByV9qq8f+m9GCqLuBF4kemefJdoKNgK2lbSS7ef6+C8GBWw/L+khol1sPiKoXq8A9TlE5WxPhbJd9Zwmy0LgY2Bu23c26NK/H9K3omZ/YRQdqsU5SdW0ZRgmaWF6/q6LEFn93NiJ8Mb5DuHJdAWwBJHV72Jw4lgieXMOph9cpAAAHMJJREFUce1fQ9xrvy3EvwKx4a22yRxB3He5UYeZeusaVkcCC+DnwMWSLgGmk3Q08WzduAD3WcCJknaBiVYsR5B5jU2YxU37j3clTWX7tr5GEQYR6vA+e5/O4lRK3yuRzNkHuCS1p04taTfgR8SzvVZ0WwTp27PE+b1KbgJ+mhaCi4AHCC+obzTEIAYbUlvkSODr1YHnVFE5nWhl+HFd51cCki4kNj+vEap61xFtgo8U4L6HGLquwzPjKuBY26dX2le2ALa3nTW4VNgxrGb7qUbLSmpjOtp2toew+rZleB7YL7fIRTqP4cCGhLjG08DFLiDN38XAgMKMdPpSs45pBmyn6oyhpP8Bjsw9jyPpfmB920+kxOmhwEvAWbazdElUWzLT17W1KClUA7ehea+f7MwKgol3auJv/V0iefwmEejv4cyCYZLuAra1fZ+kqwlD6/FE+/lCObnrhmrwPpO0YPVLQh13+RbyJ3NwtzmXlYEdaF7vx9TRntuKIRtgSfqN7d3S518sMWje4TxWBj6wfZekxYGjgBmJIdXr+/7p/05IehDYtN2DXiElfb7tJcufWTkozH6vtf14DdyvEtm+OjwzliPmIv5FDCNfQZT31ykwl1Ht1X4OWNT2m60bo4z81+YM5CbB3WM+pHL8CNs/reOcuhjcUAgOnEpItTdUMzcAtrF9QV8/2w/c21HYTF3S+4SSWwOrATdXX+MCwlWSdrV9WJvjP7N9eG7+Ct8cwEsutMmUtD7wuu3rJK1CXHszAD+yfU6JcxjKqCuhIGlT2+e1Ob6J7fNLn0+PcxjCAdYEJ9O/UhusDucx0vbzk3t8MEBhijeinciCYvB+vO0Zy5/Z0ICk0cCJtv9eE/+MwKY0s03n287uBTUUq8UNdFrjJI3LPfjeRTlogJntKmwotqCpmnmm7YczcwpYGHjKSbFQBczUJX1rUq+xfWIu/sp5dLrXs2+AO3FIGtsV08mHvuadSiVSawywarveJ4WhPIN1j6SzgfuBadRBy9/5JV0fBtoFd/cDtV4cOdEuuErH35c0NKP+cpgGuFDS9fTu184ycyjpX7Y/kTheA07KwTMJ7ExTWONnNKvF3ytBnuavDiJmU2aofi/XhldN09Mp1dsAdRGibaqLwYMBYbbbgO2HJR1EyGYXqWakCvW/iHu7cexdQoAhJ2/24KkvKCwgAKZIrZits54lFOamanNeU1FgFmeIB3d9zUMNSm/L1PIMMds8P72v99o9bIdygLUZsbFqmK3O3+Y1JTb6veYyJM1EDCMPVkwnqdMGW0QA0EU+3Jc+SmKhwny9YPv2yuePEMpaJXEqoWz0c2I2oQQayoFT01NF0ERwPcmsexf/PXDyD5Q0C7HJeMQ1STenc/gDsDmx8X5P0lnAzrZfzkx/NyHi8mBmnoGEhkrptIQVRgMmZj1/kos4JesMTKveqqzz0bN1MhdqC+4GABZu+XpuYA/CJiEL2hQlpms9lrlA8QTNPXrrrNdLQEdthVIYsi2CVUj6qwsYorVwNlo5Gq0TVcwGnGb7uyXPqRT6EhVpILe4SBdlUVcbbiWr2ydKzGDWPPt2oO29SvN2UR6SNgDOBKYjqhab2O40BJ/zPM4jKsZ705zB2p9o1dskM/eBRDXvBHorA+dSVRsQkHRSrk6EPji/RSRHjyK8qBpoJHKutp1FIr8S3PWaeSOCu/ts12XDUxskzQzcbnuJTP9/R4PhBNtu7ZroT/4piGvuWqA62+g6nrHt0A2waoJCOlTApfSU5jYxnPtQLSfWRTb01SddRYnFIc0kLElvx/ssgUabAfBeyDEALmlyRETs/PL0SLoY2NdNmfouuuh3JDW149PHDoRa6+o1nMcrhC3DW5Vjwwlz69yms8VV1boASUvZLlo1rDO4G8hIbXP3OplOd1Ee3QCrZih8r0q1C3VRIypy3R1fQmwAsrY0SPocIdE+DTH/9yoxr/B0rkBD0lv0fPj1Qt0zDLkh6UjC3PdcomVnIgrMenYxRKBkQZA+n5IIaIrPoEi6Fdiuqg6qsEU40fZnSp9PF4MbdQR3AwVJuKq6txhOVHXOsJ2tNbSLvjGUZ7AGCt6X9D3aD74XLfN3kR2tfdJ1YRRwqO1RksY7vKj2Ie9c0HuDPYCaDExP9MRPRfuZzy666A9MrJQn0aC6nvNXEeano4k2vfmJtr3RVcGVHC17ku62/ak2x++w/en+5utiQOCh/9fencdJVpVnHP89QEBBGRZZhDCggBBAVFTcBReUgIKJEpYoASWaYIJxiSsKggpxAzTiEkkEFEEFIYLEBaMsgcTEiEFMJqCyzAwCMzCyOELgyR/n9sydnupplLr3FFXP9/PxY9et6jkv09NV95zznveV9KfAgcCjbO8s6bnApra/VDm2rl0z7fFdwKdqVQqOIjtYlUk6k9Kc7WtMu8HNOaToQlMmf33b9zcTrPWblMGf2d68ozHvqF16f1UlrPsqWx3RtRHqx/RAzn11krI36P2mKd++qHbp5uiGpGOBPYATKZOL9SQ9ltJc+sl1o4tJlB2s+l4MPKaPPkAxWppGnLux8jmorncul1BSA28HFkraAVjEtB3UIftAh3/2AzW9hPWjKaXbz+xyUEn72f5y6/F27TOWkv7K9oldxhAT5TXTHp8y8FUds/28vsdsVaddc0Cl2q3ov3pq9OcQ4Em2b5U01Uz6Z5RqmmNP0osYnAmV9PNKMsFahZ4qn11PypJPnKaS4p9Rbu73Az4NHASc1cPw5wB7UcqGnwL8M3Av5VxWJ2wf19Wf/RvE8L3p1yR9F/gn4KQOhz6FFf9uL2fFHnfHUFZde1ezyXp0Y5RScZtS7XuzvFru123f1uGQ187wtYHL6PA9btS1exFWGPtA21/seJjVgalG0lOZCo9oXRtbzfneP6J8lrczoSY2RU3Sebb3rRpDUgRnJunZti/teIw3U26wT2Llpq+dl46OOiRdB+xt+6qpQ+mSdgWOtL1Pz7E8m1Lk4hujUt60L5LWB35ue06HY6yQrjSVljnT833q4z0uJlPTIuEc4H8oZdrnAtsDL7d9Ucdjv9j2N7oc46FG0kG2z6g09lW2d+p4jM9Smkm/EVhIaXdzAqUtwOFdjl2bpEXAE23fUDuWUSHpSNvvqxpDJlh1raKMdC+lo6MOSUumbuol3Qxsbvve9vWOxl0dmAfsYLt6p/M+DWiMuDZlJ+9Htg/ocNwVdokkLW6fA+lqF0nSWbb3b74+1PZsfUsihkbS1cDR7QIDkvYDjrW9fQ/jbwc8gZVTpsa6D9akkrQucBqwJ6WQ0FLgm8DBrtRsuy+S5gFPHvf/zoeaiZ1gtasYrUrejKMLTa+aV9n+saTvAOcCt1FuPrbqeOx5wFNtL+lynFEzoDHiXcAPgdO7nGw2DYbnUM7ZCbiFsro69fi2jiZYt1OKmTipgNG35t/fhrbva11bA7i1hz5Y7wTeA1zJtJSpceyDNUoN1QEkbU6TFmp7fh9jtsbehLJbeoPtm2Z7/TiQ9DpKKu5xrJwJ9dMqQfWkaf2w2PbNktYB3kRpcH5CuwdfldgmeILVrnAk4FmU3jRT5WQ3AS7r46Bu86HzTGBz4Ebgctv/1/W4UY+kvYA7bV/cpAaeQVlpPdz2OR2PfTiwL6XwxI208rTH/c24hgH9z9R63FnvM0lfAzai7FgewAzFPNIOIrog6WPANbY/1rr2l8C2to/oeOybgRfa/lGX44yKAZkwm1PeYxaxfDHnxq6zYiTNBb5AqVy5mHLW9Argj21f18F4cnMTK2m1mV437qnvzWfMIJ331axN0g+BA2z/t6STgR0pqaILbP9J1dgmdYLVJunjwLXtSl6S3gBs3cMHwfaUEu0PZ/nkbinw0naDxhgfzQfB7sCltu+pMP5EvhlLejtwke3vt67tCuxu+4MdjrvlbK/p6ObjYcArgC0pq/kDKzmmHUR0QdKlwNMoK+rzKTf9GwP/yoqLOkMvGd+ccd22xvtrbc3u3YbAu23fLWltSiGdRV0XG2oWrq8E3mX7LkmPAI6lVPfbvYPxlu3MD1jIgg4XsGI0tM6wi7JJshPwK+CnrtBgfYXYMsEqh84pjenaqQyrU1IZ1p/5O4cy9neAC4EPt1Zi3kIpgNB7mdvoxyj0hZo0khYC29i+q3XtEcA825vVi6x7ko62fXTtOGJySHpAq8ddVD2UdDAlK+VoVk6ZGvfdjFuAzWzf27r2O5QV/Y06HvuXlLTQ9thrUiZ3Q/+8k7TFVGGHVS1kdbGAFaNB0i+ArYEdgE/afnJz/95J6v1vImXai5uAfYCvtq69FLi5h7GfCOzhFWe6JwLv6mHsqOdiSU+3fUWtAFp58vNtL6gVR4/WpJSjb7sHeFiFWHpl+2hJc4DtWPnQf6qVxtB1MXH6DXyu+f/DWtemUnPHfTfjLmBXSln6KU9lxbNoXbliwNhPYVqj62FpTa5WB04FXjxpxZtg2TGTwxncV7PzpuKVnQV8m9Lb81PNtV2An9cKaEomWMURwFck/TUlTW8uZTa8Xw9jL6D8UrRvcp7TXI/xdR1woaTzKP/m2ikznTYGHJQnL6mzPPkR8h+UD6F2z6k/A35QJ5z+NLsJJ1N6wkzvk5JqpTEUkl5l+/Tm6xkLSfVQPOoxHf/5o+zdwD81ZzCnjh28BHh9D2NfC3xd0gWtsfcCzmhXcR32Z5zt+yQ9BpjxHNaYOwF4PvAZ4P2UBfo/Z4Zzt2PmDcDvA/fa/lbr+psrxbNMUgQbkh5F+SFtRumhcIHtRT2Muw+lwMH5lJvuLSnVYF5p+7yux486BlS0m2LbD6jC5YMYu9c8+VEhaUfgW5Tf72uBbSjFbPawfXXN2LomaT5wmO0La8cS40vS123v1Xz9zzO8rLdKfs15101sL+xjvFEhaQfg5Sy/n/lKH+9xq/hca+vkM66Z0D8XOIqVizeNe1rofOAZtq9vnUnaHvi07d1qxzepMsFqkbQFpR9Rr2lbkrYF9md5t/sv2Z7XZwwxOfrOkx8lzWTyJZSV1RuA823fWTeq7jV56pu1z5lGjCtJ61F2bF9BWdlep1nM3NX2kXWjiy60ijetVLF13ItcNHUENmhaciykFGi7exLaczRn746lHLeZnv5eNTsjKYIsS5n6IuUHZOARkl4B7Gn7sFV+84Mbd3XgIkrecNWO09G/ZmL9R6w4sf7fHobuNU9+FGjFBsuTkDYx3d8AR0o6dtxXc2O0SNqYlW98um4H8SlKX8Etgamdm8uBjwBjN8GS9Bnbr22+Pp2Vq+kB3bdkkDTjDW0PP/NJTgv9CeWc3b8B/w4c3Syk9tqDrJIzKIul76Kfc4YPWCZYxaeBCyhnn6bSAr9FeTPuTPKGJ5ekgyj50hdQUkMfD7xd0utsn9Hx8FXy5Gtqftfuo7RDmLhD0MAbgU2Bt0paIfXZ9tw6IcU4k7QncArw6GlP9VFo4gU0lfQkGcD2Lc1kbxy1+2BdUy2KMrZpFVlg+WSv05/5mJ8fns0bKM11oTTa/STwSOC11SLqz+OB545idkZSBIHmhmMj2/dLWmx7g+b67e6+4/zE5g1PMkk/BQ6xfXHr2nOA021v1fHY1fLka5rkBsuSZszDt/29PmOJySDpWuBDwKm2f9Xz2NcAz7G9cOozvclU+abt7fuMZZJJ2pRyb3NJDwuHU2faB1XSSzP1MSXp65Tz5P9ZO5bpMsECJF0NvMz2vNab8Q7AmbZ37njsic0bnmQ1e5VMqkltsBxRg6TFlLOevd9kNE3F96GkDX2VUsDqA8B5tk9c1fc+VEmatRx3e0GvL5LWovQanLXh+oMc5yhKVdgzgddRMpMOAs6yfUSXY9cg6QEVihn3NhySTgIOBL5Cabm0jO1jBn5TTzLBYtku0tuB44CTKL+c7wSOt/2FjsdOc7wJJOkdwAbAu20vlfRw4L2U5njHdTx2zTz56NGqSmVP6aFkdkwgSR8CflLj35ckUdKmXks5h3U95Yb7pBoTvj40C0g3U3r7acBLXCMdWNLOwEU9NDm+Dtjb9lWtSnq7Akfa3qfLsWuQ9LPZX4VrF3roWnPmcBDX3rnMBKsh6WUsfzO+AfiU7XM7HG9T2zfN/soYF5La/a5EORNjymHs9ZtrC7v+EGw+iAfmyU/CTk6taqE1rKJU9pTeSmbH+JN0CSu+xz2N0vBz+sryuDc/7Z2kcyl/3+cAp9n+1woxtH/+AGsDOwLH9LBwuMT2nObrmynv8fe2r0f0KROsSqaXz5R0ju0/rBlTdGtV52Da+j4T03eefC3Tq4Xa7qVaaMSkaBpaz8r2qR2N/yxgH9tvG/Dc8cC547ywImkDSrrUwcAc4HTKZOuGnsaf/vO/C7iyj+q4kn4AvMr2jyV9BziXsnh5bNfnmmtpdmr/FNgJ+IHtz9WNqJ4mC2j62bvr60U04RMsSbNuH9o+raOx72j3HGoX14joW1958jVJuhC4BDie0vNrfUlzgB+N8393xKRoKqOebPuCAc/tCbze9kv7j6x/kh4H/CXlXNJutv+lckidkrQXcKfti5vUwDMo7QEOt31O3ei6IekjlHNml1CqYH/G9lF1o+pX01D5dODJLM/MGYmMnEmfYN1PKSt6EzPnLHeSyjBgBysTrAnSTGjeQ1lt3ND2HEkvAh5n+28rxNNLnnxNNauFRkwiSZtQeu5NX1nu5FyWpPnA3EElmyWtAVxve7Muxh4Vza7Gi4A/AZ4PXAi8o8sjCc3k9ZdTkzhJWwOnUXZWLgcOtb2wq/EnVXPs4AVNgbYdKEVctq0dV5+a3cqrgGOA/wW2oRS0udT2TOezejHpfbA+Run0fgflzeBc2331yFlD0vNY/qEz/fHYV3+ZcCcAmwN/TPkABPhxc73TCdaq8uS7HHcE/ILy5jtv6kLzoVQ1jSBiHDXnmj9PuenZkfL+thNwKdBV4Yt1gTWBQWXhf4fSG2gsSXo8JTVwf0pz5dOA1/RUIv9YoF2p7++BJZTdlVcDH6Z81g1dM4k/gSZNDniz7UWr/q6xMcf2PADbVzcpopPmiZQ0/3skyfYiSW8C/ouys1XNRO9gAUhaHdiT8sb0XOB8St+OSzse9+fM0G29MfbVXyaZpIXANrbvqtB7rVqefE01q4VGTBpJVwHvtf1lSbc1KbmHAjvafktHY34feJ/t8wY8ty+lotxTuxi7tiYj53+ALwALBr2mw53DxcDGtv9PpZnzAmBL2/MlPYqSht3JzqGks4H1gLOBlwPza1eP64ukO4CdWb4w/wPgSay4UD/WlYGbe6mtbd/d9N7bnXL2bkE7S6xKbJM+wWqTtC5wJKUT9h62Z6vAFfFbaUrK7mx7Sav32kbAFba3rh3fuOq7WmjEpGqnwbcmWKsBN9neuKMxDwI+ChxOyUi5vxnzZcAngDfZ/mIXY9cm6bvMvmjbScXQaROsfYEPT6WqNSmLS7q62W0qBj7O9u3NZO4/bW/RxVijZoaKwG2ufQ6pa5K+DHzN9mmSPkjZMPk1pSJz1fL8k54iCEBz0P0ASs7yRpTt7h9WDSrG3ZeBUyW9EUDSo4ETKU0SO5E8eWgmU5lQRXTvZkmb2P4F8HNJzwBuBTq74bN9RlMV9VRgLUm3Us5/LQWOGtfJFYDt3SsO/33gCEmfBQ5jedo7wGMpP/euPMz27QC2b5W0TodjjRTbq9WOoTbb+zWTeCgZKldTUoH/oV5UxUTvYEl6CWVS9SzgH4HTbV9WN6qYBJLWBD5I+TBaG7gb+Dvgbbbv6WjM7wNH2L68efw9SnrgJyh58kttd5InPyqaNMEDgc0oaSxnAn/vSX4jjOiApLcB19g+u6nY+xngfuAjtt/d8djrAs8ANgQWAZfb/mWXY06y5izr14CtKIXDnmd7QfPc0cBWtg/paOxfUXYsp26yP06pnrhMV6mRUVdzxOcblAbTfdVPeMAmfYI1lbN8PoMPxWL7Pb0GFWOvSVnZHbjM9q+b1MBbu77Jr5knPwqa9IF9KTuF11HSBI+gpBe8tWZsEeNG0mq27289ngusY/snFcOKDknacHqBCUnrAffYvrujMb9LpdTIqK85brGd7aW1Y5lu0idYn2P2X8xX9xROTJDpfdB6GrNanvwoaHL1d7F9Y+vaFpQGjWNbnj6ib83K8p3AeqO4shwR40HSIZQeYO+xPb9yOCuY6DNYXW1ZRzwAF0t6uu0rehyzZp78KLij+d/0a0kdihgi2/dJmkdJ0RtY0S4iYgim0j8PWX4UqzQbrl3gY6InWBEVXQdcKOk8SjW7ZTupHaalvpGSJ/8hSp7861rPvQq4uKNxR8WJwDmSjgduBLYA/ho4QdKylgjjXtY2oidfAM6XdBLl9639HpcejxExDCPbWHmiUwQjapE0Y4Ub24d2PHbvefKjoDlzOZvqq14R40DSz2Z4Kj0eI+JBkXSB7b1rx7EqmWBFRERERMRDQrvP3qhKimBEJZJ+D3gFsIntv5C0HbCW7R9VDm1sSdqW0hfnFtvX1I4nIiIixk92sCIqkLQfcDJwNnCQ7XUlPQU43vYL60Y3fiT9IfBRYG7r8vXAW2x/pU5UEeOr6UV1NLAbZVFj2Ql023Nn+LaIiFlJuofSx3JGtg/uKZyBsoMVUccxwB62fyhp/+balcATKsY0liTtTenq/n7gS8BC4NHA/sBnJS21fX7FECPG0cnA71Le6z4PvJJSVObsmkFFxFgwcG3tIFYlO1gRFUhaBDzKtiUttr2BpDWABbY3rh3fOJF0BXCi7ZVWuyQdCPyV7af1H1nE+Gr6zv2e7UWSbre9nqTNKY29d6kdX0Q8dD0UzmCtVjuAiAn1H5TS6G0HAP9WIZZxtyPw1RmeOwfYocdYIibFasCS5us7m0qlC4Ft6oUUEWNCs7+krqQIRtRxBPBNSa8B1pH0DeBxwIvqhjWWfg2sC9wy4Ln1gHv6DSdiIlxJOX91EXAJ8AngTmBezaAiYix8vnYAs0mKYEQlktYGXgJsSWk2fL7tO+tGNX4kfR5YavuwAc+dAjzc9kH9RxYxvprm3bJ9raSNgOOARwLvtX113egiIrqVCVZEjyQ9HNja9lUDntsJuMb20v4jG1/NuY9LgTsoB+ynily8nLKz9WzbN9aLMGL8SNrA9uLacURE1JAzWBH9eivwmhmeO5RSZSuGyPZ8YBfgPGBPyt/xns3jXTK5ihgeSU+XtAC4RdJ1kp5UO6aIiL5lByuiR5KuppRnnz/guc2Bb9lO0YWIeEiSdClwGXAqZdHoSentFxGTJhOsiB5JWmJ7zm/7fETEKJO0GNjI9n2S1gKus71p7bgiIvqUFMGIft0laYtBT0iaC9zdczwREcO0hu37AGz/GlizcjwREb3LDlZEjyR9FljL9vQeWEg6Fbh3ULW7iIiHAkn3AO2m3vsDZ7VfY/vgXoOKiOhZJlgRPZK0KXA58EtKk9upinZ/QKlo90zbN9WLMCLityfpqNleY/u9fcQSEVFLJlgRPZO0PvBm4AXAhsAi4NvAR23fVjO2iIiIiHhwMsGKiIiIiIgYkhS5iIiIiIiIGJJMsCIiIiIiIoYkE6yIiIiIiIghyQQrIiIiIiJiSDLBioiIiIiIGJJMsCIiIiIiIoYkE6yIiIiIiIghyQQrIiIiIiJiSDLBioiIiIiIGJJMsCIiIiIiIoYkE6yIiIiIiIghyQQrIiIiIiJiSDLBioiIiIiIGJJMsCIiIiIiIoYkE6yIiIiIiIghyQQrIiIiIiJiSDLBioiIiIiIGJJMsCIiIiIiIoYkE6yIiIiIiIghyQQrIiIiIiJiSDLBioiIiIiIGJJMsCIiIiIiIoYkE6yIiIiIiIgh+X8QdKq15TdsdAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_opts = {\\n\",\n    \"    \\\"scatter_style\\\": {\\n\",\n    \"        \\\"edgecolors\\\": (.95, .95, .95), \\n\",\n    \"        \\\"lw\\\":1.5, \\n\",\n    \"        \\\"s\\\": 60, \\\"alpha\\\":.8},\\n\",\n    \"   \\n\",\n    \"    \\\"color_ad\\\": \\\"firebrick\\\",\\n\",\n    \"    \\\"color_hc\\\": \\\"green\\\",\\n\",\n    \"    \\\"y_label\\\": \\\"Gain of relevance per area\\\",\\n\",\n    \"    \\\"ad_label\\\": \\\"TP/HC Gain\\\",\\n\",\n    \"    \\\"hc_label\\\": \\\"FN/HC Gain\\\",\\n\",\n    \"    \\\"shift\\\": 0,\\n\",\n    \"    \\\"ylim\\\": (-0.5, 12)\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"fig, ax = plot_ad_hc(mean_gain_TP_LRP, None, plt_opts, sorting=\\\"gain\\\",\\n\",\n    \"                    idv_plots=None, shift=True,\\n\",\n    \"                    the_labels=labels_gain_LRP)\\n\",\n    \"ax.set_xlim(-1, 1+ len(labels_gain_LRP))\\n\",\n    \"parts = ax.violinplot(gain_vals_TP_LRP, widths=.75, showextrema=False, points=100)\\n\",\n    \"for pc in parts['bodies']:\\n\",\n    \"    pc.set_facecolor('firebrick')\\n\",\n    \"    pc.set_edgecolor('black')\\n\",\n    \"    pc.set_alpha(.3)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"plt_opts.update({\\n\",\n    \"    \\\"ad_label\\\": \\\"TN/HC Gain\\\",\\n\",\n    \"    \\\"color_ad\\\": \\\"blue\\\",\\n\",\n    \"})\\n\",\n    \"fig, ax = plot_ad_hc(mean_gain_TN_LRP, None, plt_opts, sorting=\\\"gain\\\",\\n\",\n    \"                    idv_plots=None, shift=True,\\n\",\n    \"                    the_labels=labels_gain_LRP, fig=fig, ax=ax)\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"parts = ax.violinplot(gain_vals_TN_LRP, widths=.75, showextrema=False, points=100)\\n\",\n    \"for pc in parts['bodies']:\\n\",\n    \"    pc.set_facecolor('blue')\\n\",\n    \"    pc.set_edgecolor('black')\\n\",\n    \"    pc.set_alpha(.3)\\n\",\n    \"ax.hlines(1, 0, len(labels_gain_LRP), linestyle=\\\"dashed\\\", alpha =.5, label=\\\"HC/HC line\\\")\\n\",\n    \"\\n\",\n    \"leg = fig.legend(bbox_to_anchor=[.185, .85])\\n\",\n    \"\\n\",\n    \"for lh in leg.legendHandles: \\n\",\n    \"    lh.set_alpha(.8)\\n\",\n    \"    if \\\"_edgecolors\\\" in lh.__dict__:\\n\",\n    \"        lh._edgecolors = [(.2, .2, .2)]\\n\",\n    \"\\n\",\n    \"ax.set_xlim(0.25, len(labels_gain_LRP)+.75)\\n\",\n    \"\\n\",\n    \"fig.tight_layout()\\n\",\n    \"fig.suptitle(\\\"Gain of relevance – LRP\\\", y=1.017, fontweight=\\\"bold\\\", fontsize=18)\\n\",\n    \"fig.savefig(os.path.join(data_path, \\\"gain_LRP.pdf\\\"), bbox_inches='tight')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Plot GB\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_opts = {\\n\",\n    \"    \\\"scatter_style\\\": {\\n\",\n    \"        \\\"edgecolors\\\": (.95, .95, .95), \\n\",\n    \"        \\\"lw\\\":1.5, \\n\",\n    \"        \\\"s\\\": 60, \\\"alpha\\\":.8},\\n\",\n    \"    \\\"color_ad\\\": \\\"firebrick\\\",\\n\",\n    \"    \\\"color_hc\\\": \\\"green\\\",\\n\",\n    \"    \\\"y_label\\\": \\\"Gain of susceptibility per area\\\",\\n\",\n    \"    \\\"ad_label\\\": \\\"TP/HC Gain\\\",\\n\",\n    \"    \\\"hc_label\\\": \\\"FN/HC Gain\\\",\\n\",\n    \"    \\\"shift\\\": 0,\\n\",\n    \"    \\\"ylim\\\": (-0.5, 12)\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"fig, ax = plot_ad_hc(mean_gain_TP_GB, None, plt_opts, sorting=\\\"gain\\\",\\n\",\n    \"                    idv_plots=None, shift=True,\\n\",\n    \"                    the_labels=labels_gain_GB)\\n\",\n    \"ax.set_xlim(-1, 1+ len(labels_gain_GB))\\n\",\n    \"parts = ax.violinplot(gain_vals_TP_GB, widths=.75, showextrema=False, points=100)\\n\",\n    \"for pc in parts['bodies']:\\n\",\n    \"    pc.set_facecolor('firebrick')\\n\",\n    \"    pc.set_edgecolor('black')\\n\",\n    \"    pc.set_alpha(.3)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"plt_opts.update({\\n\",\n    \"    \\\"ad_label\\\": \\\"TN/HC Gain\\\",\\n\",\n    \"    \\\"color_ad\\\": \\\"blue\\\",\\n\",\n    \"})\\n\",\n    \"fig, ax = plot_ad_hc(mean_gain_TN_GB, None, plt_opts, sorting=\\\"gain\\\",\\n\",\n    \"                    idv_plots=None, shift=True,\\n\",\n    \"                    the_labels=labels_gain_GB, fig=fig, ax=ax)\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"parts = ax.violinplot(gain_vals_TN_GB, widths=.75, showextrema=False, points=100)\\n\",\n    \"for pc in parts['bodies']:\\n\",\n    \"    pc.set_facecolor('blue')\\n\",\n    \"    pc.set_edgecolor('black')\\n\",\n    \"    pc.set_alpha(.3)\\n\",\n    \"ax.hlines(1, 0, len(labels_gain_GB), linestyle=\\\"dashed\\\", alpha =.5, label=\\\"HC/HC line\\\")\\n\",\n    \"\\n\",\n    \"leg = fig.legend(bbox_to_anchor=[.185, .85])\\n\",\n    \"\\n\",\n    \"for lh in leg.legendHandles: \\n\",\n    \"    lh.set_alpha(.8)\\n\",\n    \"    if \\\"_edgecolors\\\" in lh.__dict__:\\n\",\n    \"        lh._edgecolors = [(.2, .2, .2)]\\n\",\n    \"\\n\",\n    \"ax.set_xlim(0.25, len(labels_gain_GB)+.75)\\n\",\n    \"\\n\",\n    \"fig.tight_layout()\\n\",\n    \"fig.suptitle(\\\"Gain of susceptibility – GB\\\", y=1.017, fontweight=\\\"bold\\\", fontsize=18)\\n\",\n    \"fig.savefig(os.path.join(data_path, \\\"gain_GB.pdf\\\"), bbox_inches='tight')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Choosing individual patients\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Get all AD scores\\n\",\n    \"ad_score_list = np.loadtxt(os.path.join(data_path, \\\"ad_scores.txt\\\"))\\n\",\n    \"# Get indices of True positives\\n\",\n    \"allowed_idcs = [i for i, _ in rs_per_area_LRP['TP']['Hippocampus']]\\n\",\n    \"# Get indices of the TP indices that have a score higher than .9\\n\",\n    \"# Store true indices and scores in one array\\n\",\n    \"tmp = np.array([(idx, score) for idx, score in enumerate(ad_score_list) if idx in allowed_idcs and score>.9])\\n\",\n    \"idcs = np.array(tmp[:, 0], dtype=int)\\n\",\n    \"scores = tmp[:, 1]\\n\",\n    \"M = np.zeros((len(idcs), len(idcs)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"idx_mapper = dict()\\n\",\n    \"for n, idx in enumerate(idcs):\\n\",\n    \"    _idx = np.argwhere(np.array(rs_per_area_LRP['TP']['CWM'])==idx)[0][0]\\n\",\n    \"    # print(idx, _idx, rs_per_area_LRP['TP']['CWM'][_idx])\\n\",\n    \"    # break\\n\",\n    \"    idx_mapper.update({n: np.array([np.array(rs_per_area_LRP['TP'][l])[:, 1][_idx] for l in labels_norm_LRP])})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"mini = np.inf\\n\",\n    \"for i in range(len(M)):\\n\",\n    \"    for j in range(len(M)):\\n\",\n    \"        \\n\",\n    \"        first = idx_mapper[i]\\n\",\n    \"        second = idx_mapper[j]\\n\",\n    \"        # Calculate cosine similarity\\n\",\n    \"        M[i, j] = np.dot(first/np.linalg.norm(first),second/np.linalg.norm(second))\\n\",\n    \"        if mini > M[i, j]:\\n\",\n    \"            mini = M[i, j]\\n\",\n    \"            max_pair = (i, j)\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Most distant high AD patient indices: [  2 169]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Most distant high AD patient indices:\\\", idcs[list(max_pair)])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Innvestigate\",\n   \"language\": \"python\",\n   \"name\": \"innvestigate\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.1\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "README.md",
    "content": "# Layer-wise relevance propagation for explaining deep neural network decisions in MRI-based Alzheimer’s disease classification\n\n**Moritz Böhle, Fabian Eitel, Martin Weygandt, and Kerstin Ritter**\n\n**Preprint:** https://arxiv.org/abs/1903.07317\n\n**Abstract:** Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer's disease (AD) detection based on structural magnetic resonance imaging (MRI) data. However, the network decisions are often perceived as being highly non-transparent making it difficult to apply these algorithms in clinical routine. In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data. Similarly to other visualization methods, LRP produces a heatmap in the input space indicating the importance / relevance of each voxel contributing to the final classification outcome. In contrast to susceptibility maps produced by guided backpropagation (\"Which change in voxels would change the outcome most?\"), the LRP method is able to directly highlight positive contributions to the network classification in the input space. In particular, we show that (1) the LRP method is very specific for individuals (\"Why does this person have AD?\") with high inter-patient variability, (2) there is very little relevance for AD in healthy controls and (3) areas that exhibit a lot of relevance correlate well with what is known from literature. To quantify the latter, we compute size-corrected metrics of the summed relevance per brain area, e.g. relevance density or relevance gain. Although these metrics produce very individual 'fingerprints' of relevance patterns for AD patients, a lot of importance is put on areas in the temporal lobe including the hippocampus. After discussing several limitations such as sensitivity towards the underlying model and computation parameters, we conclude that LRP might have a high potential to assist clinicians in explaining neural network decisions for diagnosing AD (and potentially other diseases) based on structural MRI data.\n\n## Requirements\n\nIn order to run the code, standard pytorch packages and Python 3 are needed.\nMoreover, add a settings.py file to the repo, containing the data paths and so forth as follows:\n\nPlease use the example settings.py with more information.\n\n```python\nsettings = {\n    \"model_path\": INSERT,\n    \"data_path\": INSERT,\n    \"ADNI_DIR\": INSERT,\n    \"train_h5\": INSERT,\n    \"val_h5\": INSERT,\n    \"holdout_h5\": INSERT,\n    \"binary_brain_mask\": \"binary_brain_mask.nii.gz\",\n    \"nmm_mask_path\": \"~/spm12/tpm/labels_Neuromorphometrics.nii\",\n    \"nmm_mask_path_scaled\": \"nmm_mask_rescaled.nii\"\n}\n```\n\nWith the \"Evaluate GB and LRP\" notebook, the heatmap results and the summed scores per area can be calculated.\nThe notebooks \"Plotting result graphs\" and \"Plotting brain maps\" can be used to calculate and plot the results according to the defined metrics and show the heatmaps of individual patient's brains and average heatmaps according to LRP and GB.\n\n## Quickstart\n\nYou can use the visualization methods in this repository on your own model (PyTorch; for a Keras implementation, see heatmapping.org) like this:\n\n```python\nmodel = Net()\nmodel.load_state_dict(torch.load(\"./mymodel\"))\n# Convert to innvestigate model\ninn_model = InnvestigateModel(model, lrp_exponent=2,\n                                method=\"e-rule\",\n                                beta=.5)\nmodel_prediction, heatmap = inn_model.innvestigate(in_tensor=data)\n```\n\n`heatmap` contains the relevance heatmap. The methods should work for 2D and 3D images alike, see the MNIST example notebook or the LRP and GB evaluation notebook for an example with MRI images.\n\n### Docker\n\nTo run [MNIST example.ipynb](./MNIST%20example.ipynb) in a Docker container (using only CPU) follow the steps below:\n\n```sh\ncd docker/\ndocker-compose up --detach\n```\n\nVisit [localhost:7700](http://localhost:7700) in your browser to open Jupyter.\n\n## Code Structure\n\nThe repository consists of the general LRP wrapper ([innvestigator.py](innvestigator.py) and [inverter_util.py](inverter_util.py)), a simple example for applying the wrapper in the case of MNIST data, and the evaluation notebook for obtaining the heatmap results discussed in the article.\n\n## Heatmaps\n\nThe methods for obtaining the heatmaps are shown in the notebook **Evaluate GB and LRP**\n\n## Data\n\nThe MRI scans used for training are from the [Alzheimer Disease Neuroimaging Initiative (ADNI)](http://adni.loni.usc.edu/). The data is free but you need to apply for access on http://adni.loni.usc.edu/. Once you have an account, go [here](http://adni.loni.usc.edu/data-samples/access-data/) and log in. Settings.py gives information about the required data format.\n\n## Citation\n\nIf you use our code, please cite us. The code is published under the BSD License 2.0.\n"
  },
  {
    "path": "docker/Dockerfile",
    "content": "FROM jupyter/scipy-notebook:76402a27fd13\n\nWORKDIR /home/jovyan/work\n\nUSER root\n\n# Style profile\nRUN echo \"export CLICOLOR=1\" >> /root/.bashrc \\\n  && echo \"export PS1='\\u@\\h:\\[\\e[0;34m\\]\\W\\[\\e[0m\\]\\$ '\" >> /root/.bashrc \\\n  && echo \"export LS_OPTIONS='--color=auto'\" >> /root/.bashrc \\\n  && echo \"export LSCOLORS=excxhxDxfxhxhxhxhxFxFx\" >> /root/.bashrc \\\n  && echo 'alias ls=\"ls $LS_OPTIONS\"' >> /root/.bashrc \\\n  && echo 'alias ll=\"ls $LS_OPTIONS -l\"' >> /root/.bashrc \\\n  && echo 'alias l=\"ls $LS_OPTIONS -lA\"' >> /root/.bashrc\n\nUSER jovyan\n\nCOPY requirements.txt /requirements.txt\nRUN pip install -r /requirements.txt\n"
  },
  {
    "path": "docker/docker-compose.yml",
    "content": "version: '3.8'\nservices:\n  lrp:\n    build:\n      context: ..\n      dockerfile: docker/Dockerfile\n    tty: true\n    command: bash -c \"jupyter lab --port=8888 --no-browser --ip=0.0.0.0 --allow-root --NotebookApp.token='' --NotebookApp.password=''\"\n    volumes:\n      - ..:/home/jovyan/work\n    ports:\n      - '7700:8888'\n    restart: unless-stopped\n    container_name: lrp\n    environment:\n      - JUPYTER_ENABLE_LAB=yes\n"
  },
  {
    "path": "innvestigator.py",
    "content": "import torch\nimport numpy as np\n\nfrom inverter_util import RelevancePropagator\nfrom utils import pprint, Flatten\n\n\nclass InnvestigateModel(torch.nn.Module):\n    \"\"\"\n    ATTENTION:\n        Currently, innvestigating a network only works if all\n        layers that have to be inverted are specified explicitly\n        and registered as a module. If., for example,\n        only the functional max_poolnd is used, the inversion will not work.\n    \"\"\"\n\n    def __init__(self, the_model, lrp_exponent=1, beta=.5, epsilon=1e-6,\n                 method=\"e-rule\"):\n        \"\"\"\n        Model wrapper for pytorch models to 'innvestigate' them\n        with layer-wise relevance propagation (LRP) as introduced by Bach et. al\n        (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130140).\n        Given a class level probability produced by the model under consideration,\n        the LRP algorithm attributes this probability to the nodes in each layer.\n        This allows for visualizing the relevance of input pixels on the resulting\n        class probability.\n\n        Args:\n            the_model: Pytorch model, e.g. a pytorch.nn.Sequential consisting of\n                        different layers. Not all layers are supported yet.\n            lrp_exponent: Exponent for rescaling the importance values per node\n                            in a layer when using the e-rule method.\n            beta: Beta value allows for placing more (large beta) emphasis on\n                    nodes that positively contribute to the activation of a given node\n                    in the subsequent layer. Low beta value allows for placing more emphasis\n                    on inhibitory neurons in a layer. Only relevant for method 'b-rule'.\n            epsilon: Stabilizing term to avoid numerical instabilities if the norm (denominator\n                    for distributing the relevance) is close to zero.\n            method: Different rules for the LRP algorithm, b-rule allows for placing\n                    more or less focus on positive / negative contributions, whereas\n                    the e-rule treats them equally. For more information,\n                    see the paper linked above.\n        \"\"\"\n        super(InnvestigateModel, self).__init__()\n        self.model = the_model\n        self.device = torch.device(\"cpu\", 0)\n        self.prediction = None\n        self.r_values_per_layer = None\n        self.only_max_score = None\n        # Initialize the 'Relevance Propagator' with the chosen rule.\n        # This will be used to back-propagate the relevance values\n        # through the layers in the innvestigate method.\n        self.inverter = RelevancePropagator(lrp_exponent=lrp_exponent,\n                                            beta=beta, method=method, epsilon=epsilon,\n                                            device=self.device)\n\n        # Parsing the individual model layers\n        self.register_hooks(self.model)\n        if method == \"b-rule\" and float(beta) in (-1., 0):\n            which = \"positive\" if beta == -1 else \"negative\"\n            which_opp = \"negative\" if beta == -1 else \"positive\"\n            print(\"WARNING: With the chosen beta value, \"\n                  \"only \" + which + \" contributions \"\n                  \"will be taken into account.\\nHence, \"\n                  \"if in any layer only \" + which_opp +\n                  \" contributions exist, the \"\n                  \"overall relevance will not be conserved.\\n\")\n\n    def cuda(self, device=None):\n        self.device = torch.device(\"cuda\", device)\n        self.inverter.device = self.device\n        return super(InnvestigateModel, self).cuda(device)\n\n    def cpu(self):\n        self.device = torch.device(\"cpu\", 0)\n        self.inverter.device = self.device\n        return super(InnvestigateModel, self).cpu()\n\n    def register_hooks(self, parent_module):\n        \"\"\"\n        Recursively unrolls a model and registers the required\n        hooks to save all the necessary values for LRP in the forward pass.\n\n        Args:\n            parent_module: Model to unroll and register hooks for.\n\n        Returns:\n            None\n\n        \"\"\"\n        for mod in parent_module.children():\n            if list(mod.children()):\n                self.register_hooks(mod)\n                continue\n            mod.register_forward_hook(\n                self.inverter.get_layer_fwd_hook(mod))\n            if isinstance(mod, torch.nn.ReLU):\n                mod.register_backward_hook(\n                    self.relu_hook_function\n                )\n\n    @staticmethod\n    def relu_hook_function(module, grad_in, grad_out):\n        \"\"\"\n        If there is a negative gradient, change it to zero.\n        \"\"\"\n        return (torch.clamp(grad_in[0], min=0.0),)\n\n    def __call__(self, in_tensor):\n        \"\"\"\n        The innvestigate wrapper returns the same prediction as the\n        original model, but wraps the model call method in the evaluate\n        method to save the last prediction.\n\n        Args:\n            in_tensor: Model input to pass through the pytorch model.\n\n        Returns:\n            Model output.\n        \"\"\"\n        return self.evaluate(in_tensor)\n\n    def evaluate(self, in_tensor):\n        \"\"\"\n        Evaluates the model on a new input. The registered forward hooks will\n        save all the data that is necessary to compute the relevance per neuron per layer.\n\n        Args:\n            in_tensor: New input for which to predict an output.\n\n        Returns:\n            Model prediction\n        \"\"\"\n        # Reset module list. In case the structure changes dynamically,\n        # the module list is tracked for every forward pass.\n        self.inverter.reset_module_list()\n        self.prediction = self.model(in_tensor)\n        return self.prediction\n\n    def get_r_values_per_layer(self):\n        if self.r_values_per_layer is None:\n            pprint(\"No relevances have been calculated yet, returning None in\"\n                   \" get_r_values_per_layer.\")\n        return self.r_values_per_layer\n\n    def innvestigate(self, in_tensor=None, rel_for_class=None):\n        \"\"\"\n        Method for 'innvestigating' the model with the LRP rule chosen at\n        the initialization of the InnvestigateModel.\n        Args:\n            in_tensor: Input for which to evaluate the LRP algorithm.\n                        If input is None, the last evaluation is used.\n                        If no evaluation has been performed since initialization,\n                        an error is raised.\n            rel_for_class (int): Index of the class for which the relevance\n                        distribution is to be analyzed. If None, the 'winning' class\n                        is used for indexing.\n\n        Returns:\n            Model output and relevances of nodes in the input layer.\n            In order to get relevance distributions in other layers, use\n            the get_r_values_per_layer method.\n        \"\"\"\n        if self.r_values_per_layer is not None:\n            for elt in self.r_values_per_layer:\n                del elt\n            self.r_values_per_layer = None\n\n        with torch.no_grad():\n            # Check if innvestigation can be performed.\n            if in_tensor is None and self.prediction is None:\n                raise RuntimeError(\"Model needs to be evaluated at least \"\n                                   \"once before an innvestigation can be \"\n                                   \"performed. Please evaluate model first \"\n                                   \"or call innvestigate with a new input to \"\n                                   \"evaluate.\")\n\n            # Evaluate the model anew if a new input is supplied.\n            if in_tensor is not None:\n                self.evaluate(in_tensor)\n\n            # If no class index is specified, analyze for class\n            # with highest prediction.\n            if rel_for_class is None:\n                # Default behaviour is innvestigating the output\n                # on an arg-max-basis, if no class is specified.\n                org_shape = self.prediction.size()\n                # Make sure shape is just a 1D vector per batch example.\n                self.prediction = self.prediction.view(org_shape[0], -1)\n                max_v, _ = torch.max(self.prediction, dim=1, keepdim=True)\n                only_max_score = torch.zeros_like(self.prediction).to(self.device)\n                only_max_score[max_v == self.prediction] = self.prediction[max_v == self.prediction]\n                relevance_tensor = only_max_score.view(org_shape)\n                self.prediction.view(org_shape)\n\n            else:\n                org_shape = self.prediction.size()\n                self.prediction = self.prediction.view(org_shape[0], -1)\n                only_max_score = torch.zeros_like(self.prediction).to(self.device)\n                only_max_score[:, rel_for_class] += self.prediction[:, rel_for_class]\n                relevance_tensor = only_max_score.view(org_shape)\n                self.prediction.view(org_shape)\n\n            # We have to iterate through the model backwards.\n            # The module list is computed for every forward pass\n            # by the model inverter.\n            rev_model = self.inverter.module_list[::-1]\n            relevance = relevance_tensor.detach()\n            del relevance_tensor\n            # List to save relevance distributions per layer\n            r_values_per_layer = [relevance]\n            for layer in rev_model:\n                # Compute layer specific backwards-propagation of relevance values\n                relevance = self.inverter.compute_propagated_relevance(layer, relevance)\n                r_values_per_layer.append(relevance.cpu())\n\n            self.r_values_per_layer = r_values_per_layer\n\n            del relevance\n            if self.device.type == \"cuda\":\n                torch.cuda.empty_cache()\n            return self.prediction, r_values_per_layer[-1]\n\n    def forward(self, in_tensor):\n        return self.model.forward(in_tensor)\n\n    def extra_repr(self):\n        r\"\"\"Set the extra representation of the module\n\n        To print customized extra information, you should re-implement\n        this method in your own modules. Both single-line and multi-line\n        strings are acceptable.\n        \"\"\"\n        return self.model.extra_repr()\n"
  },
  {
    "path": "inverter_util.py",
    "content": "import torch\nimport numpy as np\nimport torch.nn.functional as F\nfrom utils import pprint, Flatten\n\n\ndef module_tracker(fwd_hook_func):\n    \"\"\"\n    Wrapper for tracking the layers throughout the forward pass.\n\n    Args:\n        fwd_hook_func: Forward hook function to be wrapped.\n\n    Returns:\n        Wrapped method.\n\n    \"\"\"\n    def hook_wrapper(relevance_propagator_instance, layer, *args):\n        relevance_propagator_instance.module_list.append(layer)\n        return fwd_hook_func(relevance_propagator_instance, layer, *args)\n\n    return hook_wrapper\n\n\nclass RelevancePropagator:\n    \"\"\"\n    Class for computing the relevance propagation and supplying\n    the necessary forward hooks for all layers.\n    \"\"\"\n\n    # All layers that do not require any specific forward hooks.\n    # This is due to the fact that they are all one-to-one\n    # mappings and hence no normalization is needed (each node only\n    # influences exactly one other node -> relevance conservation\n    # ensures that relevance is just inherited in a one-to-one manner, too).\n    allowed_pass_layers = (torch.nn.BatchNorm1d, torch.nn.BatchNorm2d,\n                           torch.nn.BatchNorm3d,\n                           torch.nn.ReLU, torch.nn.ELU, Flatten,\n                           torch.nn.Dropout, torch.nn.Dropout2d,\n                           torch.nn.Dropout3d,\n                           torch.nn.Softmax,\n                           torch.nn.LogSoftmax,\n                           torch.nn.Sigmoid)\n    # Implemented rules for relevance propagation.\n    available_methods = [\"e-rule\", \"b-rule\"]\n\n    def __init__(self, lrp_exponent, beta, method, epsilon, device):\n\n        self.device = device\n        self.layer = None\n        self.p = lrp_exponent\n        self.beta = beta\n        self.eps = epsilon\n        self.warned_log_softmax = False\n        self.module_list = []\n        if method not in self.available_methods:\n            raise NotImplementedError(\"Only methods available are: \" +\n                                      str(self.available_methods))\n        self.method = method\n\n    def reset_module_list(self):\n        \"\"\"\n        The module list is reset for every evaluation, in change the order or number\n        of layers changes dynamically.\n\n        Returns:\n            None\n\n        \"\"\"\n        self.module_list = []\n        # Try to free memory\n        if self.device.type == \"cuda\":\n            torch.cuda.empty_cache()\n\n    def compute_propagated_relevance(self, layer, relevance):\n        \"\"\"\n        This method computes the backward pass for the incoming relevance\n        for the specified layer.\n\n        Args:\n            layer: Layer to be reverted.\n            relevance: Incoming relevance from higher up in the network.\n\n        Returns:\n            The\n\n        \"\"\"\n\n        if isinstance(layer,\n                      (torch.nn.MaxPool1d, torch.nn.MaxPool2d, torch.nn.MaxPool3d)):\n            return self.max_pool_nd_inverse(layer, relevance).detach()\n\n        elif isinstance(layer,\n                      (torch.nn.Conv1d, torch.nn.Conv2d, torch.nn.Conv3d)):\n            return self.conv_nd_inverse(layer, relevance).detach()\n\n        elif isinstance(layer, torch.nn.LogSoftmax):\n            # Only layer that does not conserve relevance. Mainly used\n            # to make probability out of the log values. Should probably\n            # be changed to pure passing and the user should make sure\n            # the layer outputs are sensible (0 would be 100% class probability,\n            # but no relevance could be passed on).\n            if relevance.sum() < 0:\n                relevance[relevance == 0] = -1e6\n                relevance = relevance.exp()\n                if not self.warned_log_softmax:\n                    pprint(\"WARNING: LogSoftmax layer was \"\n                           \"turned into probabilities.\")\n                    self.warned_log_softmax = True\n            return relevance\n        elif isinstance(layer, self.allowed_pass_layers):\n            # The above layers are one-to-one mappings of input to\n            # output nodes. All the relevance in the output will come\n            # entirely from the input node. Given the conservation\n            # of relevance, the input is as relevant as the output.\n            return relevance\n\n        elif isinstance(layer, torch.nn.Linear):\n            return self.linear_inverse(layer, relevance).detach()\n        else:\n            raise NotImplementedError(\"The network contains layers that\"\n                                      \" are currently not supported {0:s}\".format(str(layer)))\n\n    def get_layer_fwd_hook(self, layer):\n        \"\"\"\n        Each layer might need to save very specific data during the forward\n        pass in order to allow for relevance propagation in the backward\n        pass. For example, for max_pooling, we need to store the\n        indices of the max values. In convolutional layers, we need to calculate\n        the normalizations, to ensure the overall amount of relevance is conserved.\n\n        Args:\n            layer: Layer instance for which forward hook is needed.\n\n        Returns:\n            Layer-specific forward hook.\n\n        \"\"\"\n\n        if isinstance(layer,\n                      (torch.nn.MaxPool1d, torch.nn.MaxPool2d, torch.nn.MaxPool3d)):\n            return self.max_pool_nd_fwd_hook\n\n        if isinstance(layer,\n                      (torch.nn.Conv1d, torch.nn.Conv2d, torch.nn.Conv3d)):\n            return self.conv_nd_fwd_hook\n\n        if isinstance(layer, self.allowed_pass_layers):\n            return self.silent_pass  # No hook needed.\n\n        if isinstance(layer, torch.nn.Linear):\n            return self.linear_fwd_hook\n\n        else:\n            raise NotImplementedError(\"The network contains layers that\"\n                                      \" are currently not supported {0:s}\".format(str(layer)))\n\n    @staticmethod\n    def get_conv_method(conv_module):\n        \"\"\"\n        Get dimension-specific convolution.\n        The forward pass and inversion are made in a\n        'dimensionality-agnostic' manner and are the same for\n        all nd instances of the layer, except for the functional\n        that needs to be used.\n\n        Args:\n            conv_module: instance of convolutional layer.\n\n        Returns:\n            The correct functional used in the convolutional layer.\n\n        \"\"\"\n\n        conv_func_mapper = {\n            torch.nn.Conv1d: F.conv1d,\n            torch.nn.Conv2d: F.conv2d,\n            torch.nn.Conv3d: F.conv3d\n        }\n        return conv_func_mapper[type(conv_module)]\n\n    @staticmethod\n    def get_inv_conv_method(conv_module):\n        \"\"\"\n        Get dimension-specific convolution inversion layer.\n        The forward pass and inversion are made in a\n        'dimensionality-agnostic' manner and are the same for\n        all nd instances of the layer, except for the functional\n        that needs to be used.\n\n        Args:\n            conv_module: instance of convolutional layer.\n\n        Returns:\n            The correct functional used for inverting the convolutional layer.\n\n        \"\"\"\n\n        conv_func_mapper = {\n            torch.nn.Conv1d: F.conv_transpose1d,\n            torch.nn.Conv2d: F.conv_transpose2d,\n            torch.nn.Conv3d: F.conv_transpose3d\n        }\n        return conv_func_mapper[type(conv_module)]\n\n    @module_tracker\n    def silent_pass(self, m, in_tensor: torch.Tensor,\n                    out_tensor: torch.Tensor):\n        # Placeholder forward hook for layers that do not need\n        # to store any specific data. Still useful for module tracking.\n        pass\n\n    @staticmethod\n    def get_inv_max_pool_method(max_pool_instance):\n        \"\"\"\n        Get dimension-specific max_pooling layer.\n        The forward pass and inversion are made in a\n        'dimensionality-agnostic' manner and are the same for\n        all nd instances of the layer, except for the functional\n        that needs to be used.\n\n        Args:\n            max_pool_instance: instance of max_pool layer.\n\n        Returns:\n            The correct functional used in the max_pooling layer.\n\n        \"\"\"\n\n        conv_func_mapper = {\n            torch.nn.MaxPool1d: F.max_unpool1d,\n            torch.nn.MaxPool2d: F.max_unpool2d,\n            torch.nn.MaxPool3d: F.max_unpool3d\n        }\n        return conv_func_mapper[type(max_pool_instance)]\n\n    def linear_inverse(self, m, relevance_in):\n\n        if self.method == \"e-rule\":\n            m.in_tensor = m.in_tensor.pow(self.p)\n            w = m.weight.pow(self.p)\n            norm = F.linear(m.in_tensor, w, bias=None)\n\n            norm = norm + torch.sign(norm) * self.eps\n            relevance_in[norm == 0] = 0\n            norm[norm == 0] = 1\n            relevance_out = F.linear(relevance_in / norm,\n                                     w.t(), bias=None)\n            relevance_out *= m.in_tensor\n            del m.in_tensor, norm, w, relevance_in\n            return relevance_out\n\n        if self.method == \"b-rule\":\n            out_c, in_c = m.weight.size()\n            w = m.weight.repeat((4, 1))\n            # First and third channel repetition only contain the positive weights\n            w[:out_c][w[:out_c] < 0] = 0\n            w[2 * out_c:3 * out_c][w[2 * out_c:3 * out_c] < 0] = 0\n            # Second and fourth channel repetition with only the negative weights\n            w[1 * out_c:2 * out_c][w[1 * out_c:2 * out_c] > 0] = 0\n            w[-out_c:][w[-out_c:] > 0] = 0\n\n            # Repeat across channel dimension (pytorch always has channels first)\n            m.in_tensor = m.in_tensor.repeat((1, 4))\n            m.in_tensor[:, :in_c][m.in_tensor[:, :in_c] < 0] = 0\n            m.in_tensor[:, -in_c:][m.in_tensor[:, -in_c:] < 0] = 0\n            m.in_tensor[:, 1 * in_c:3 * in_c][m.in_tensor[:, 1 * in_c:3 * in_c] > 0] = 0\n\n            # Normalize such that the sum of the individual importance values\n            # of the input neurons divided by the norm\n            # yields 1 for an output neuron j if divided by norm (v_ij in paper).\n            # Norm layer just sums the importance values of the inputs\n            # contributing to output j for each j. This will then serve as the normalization\n            # such that the contributions of the neurons sum to 1 in order to\n            # properly split up the relevance of j amongst its roots.\n\n            norm_shape = m.out_shape\n            norm_shape[1] *= 4\n            norm = torch.zeros(norm_shape).to(self.device)\n\n            for i in range(4):\n                norm[:, out_c * i:(i + 1) * out_c] = F.linear(\n                    m.in_tensor[:, in_c * i:(i + 1) * in_c], w[out_c * i:(i + 1) * out_c], bias=None)\n\n            # Double number of output channels for positive and negative norm per\n            # channel.\n            norm_shape[1] = norm_shape[1] // 2\n            new_norm = torch.zeros(norm_shape).to(self.device)\n            new_norm[:, :out_c] = norm[:, :out_c] + norm[:, out_c:2 * out_c]\n            new_norm[:, out_c:] = norm[:, 2 * out_c:3 * out_c] + norm[:, 3 * out_c:]\n            norm = new_norm\n\n            # Some 'rare' neurons only receive either\n            # only positive or only negative inputs.\n            # Conservation of relevance does not hold, if we also\n            # rescale those neurons by (1+beta) or -beta.\n            # Therefore, catch those first and scale norm by\n            # the according value, such that it cancels in the fraction.\n\n            # First, however, avoid NaNs.\n            mask = norm == 0\n            # Set the norm to anything non-zero, e.g. 1.\n            # The actual inputs are zero at this point anyways, that\n            # is why norm is zero in the first place.\n            norm[mask] = 1\n            # The norm in the b-rule has shape (N, 2*out_c, *spatial_dims).\n            # The first out_c block corresponds to the positive norms,\n            # the second out_c block corresponds to the negative norms.\n            # We find the rare neurons by choosing those nodes per channel\n            # in which either the positive norm ([:, :out_c]) is zero, or\n            # the negative norm ([:, :out_c]) is zero.\n            rare_neurons = (mask[:, :out_c] + mask[:, out_c:])\n\n            # Also, catch new possibilities for norm == zero to avoid NaN..\n            # The actual value of norm again does not really matter, since\n            # the pre-factor will be zero in this case.\n\n            norm[:, :out_c][rare_neurons] *= 1 if self.beta == -1 else 1 + self.beta\n            norm[:, out_c:][rare_neurons] *= 1 if self.beta == 0 else -self.beta\n            # Add stabilizer term to norm to avoid numerical instabilities.\n            norm += self.eps * torch.sign(norm)\n            input_relevance = relevance_in.squeeze(dim=-1).repeat(1, 4)\n            input_relevance[:, :2*out_c] *= (1+self.beta)/norm[:, :out_c].repeat(1, 2)\n            input_relevance[:, 2*out_c:] *= -self.beta/norm[:, out_c:].repeat(1, 2)\n            inv_w = w.t()\n            relevance_out = torch.zeros_like(m.in_tensor)\n            for i in range(4):\n                relevance_out[:, i*in_c:(i+1)*in_c] = F.linear(\n                    input_relevance[:, i*out_c:(i+1)*out_c],\n                    weight=inv_w[:, i*out_c:(i+1)*out_c], bias=None)\n\n            relevance_out *= m.in_tensor\n\n            relevance_out = sum([relevance_out[:, i*in_c:(i+1)*in_c] for i in range(4)])\n\n            del input_relevance, norm, rare_neurons, \\\n                mask, new_norm, m.in_tensor, w, inv_w\n\n            return relevance_out\n\n    @module_tracker\n    def linear_fwd_hook(self, m, in_tensor: torch.Tensor,\n                        out_tensor: torch.Tensor):\n\n        setattr(m, \"in_tensor\", in_tensor[0])\n        setattr(m, \"out_shape\", list(out_tensor.size()))\n        return\n\n    def max_pool_nd_inverse(self, layer_instance, relevance_in):\n\n        # In case the output had been reshaped for a linear layer,\n        # make sure the relevance is put into the same shape as before.\n        relevance_in = relevance_in.view(layer_instance.out_shape)\n\n        invert_pool = self.get_inv_max_pool_method(layer_instance)\n        inverted = invert_pool(relevance_in, layer_instance.indices,\n                               layer_instance.kernel_size, layer_instance.stride,\n                               layer_instance.padding, output_size=layer_instance.in_shape)\n        del layer_instance.indices\n\n        return inverted\n\n    @module_tracker\n    def max_pool_nd_fwd_hook(self, m, in_tensor: torch.Tensor,\n                             out_tensor: torch.Tensor):\n        # Ignore unused for pylint\n        _ = self\n\n        # Save the return indices value to make sure\n        tmp_return_indices = bool(m.return_indices)\n        m.return_indices = True\n        _, indices = m.forward(in_tensor[0])\n        m.return_indices = tmp_return_indices\n        setattr(m, \"indices\", indices)\n        setattr(m, 'out_shape', out_tensor.size())\n        setattr(m, 'in_shape', in_tensor[0].size())\n\n    def conv_nd_inverse(self, m, relevance_in):\n\n        # In case the output had been reshaped for a linear layer,\n        # make sure the relevance is put into the same shape as before.\n        relevance_in = relevance_in.view(m.out_shape)\n\n        # Get required values from layer\n        inv_conv_nd = self.get_inv_conv_method(m)\n        conv_nd = self.get_conv_method(m)\n\n        if self.method == \"e-rule\":\n            with torch.no_grad():\n                m.in_tensor = m.in_tensor.pow(self.p).detach()\n                w = m.weight.pow(self.p).detach()\n                norm = conv_nd(m.in_tensor, weight=w, bias=None,\n                               stride=m.stride, padding=m.padding,\n                               groups=m.groups)\n\n                norm = norm + torch.sign(norm) * self.eps\n                relevance_in[norm == 0] = 0\n                norm[norm == 0] = 1\n                relevance_out = inv_conv_nd(relevance_in/norm,\n                                            weight=w, bias=None,\n                                            padding=m.padding, stride=m.stride,\n                                            groups=m.groups)\n                relevance_out *= m.in_tensor\n                del m.in_tensor, norm, w\n                return relevance_out\n\n        if self.method == \"b-rule\":\n            with torch.no_grad():\n                w = m.weight\n\n                out_c, in_c = m.out_channels, m.in_channels\n                repeats = np.array(np.ones_like(w.size()).flatten(), dtype=int)\n                repeats[0] *= 4\n                w = w.repeat(tuple(repeats))\n                # First and third channel repetition only contain the positive weights\n                w[:out_c][w[:out_c] < 0] = 0\n                w[2 * out_c:3 * out_c][w[2 * out_c:3 * out_c] < 0] = 0\n                # Second and fourth channel repetition with only the negative weights\n                w[1 * out_c:2 * out_c][w[1 * out_c:2 * out_c] > 0] = 0\n                w[-out_c:][w[-out_c:] > 0] = 0\n                repeats = np.array(np.ones_like(m.in_tensor.size()).flatten(), dtype=int)\n                repeats[1] *= 4\n                # Repeat across channel dimension (pytorch always has channels first)\n                m.in_tensor = m.in_tensor.repeat(tuple(repeats))\n                m.in_tensor[:, :in_c][m.in_tensor[:, :in_c] < 0] = 0\n                m.in_tensor[:, -in_c:][m.in_tensor[:, -in_c:] < 0] = 0\n                m.in_tensor[:, 1 * in_c:3 * in_c][m.in_tensor[:, 1 * in_c:3 * in_c] > 0] = 0\n                groups = 4\n\n                # Normalize such that the sum of the individual importance values\n                # of the input neurons divided by the norm\n                # yields 1 for an output neuron j if divided by norm (v_ij in paper).\n                # Norm layer just sums the importance values of the inputs\n                # contributing to output j for each j. This will then serve as the normalization\n                # such that the contributions of the neurons sum to 1 in order to\n                # properly split up the relevance of j amongst its roots.\n                norm = conv_nd(m.in_tensor, weight=w, bias=None, stride=m.stride,\n                               padding=m.padding, dilation=m.dilation, groups=groups * m.groups)\n                # Double number of output channels for positive and negative norm per\n                # channel. Using list with out_tensor.size() allows for ND generalization\n                new_shape = m.out_shape\n                new_shape[1] *= 2\n                new_norm = torch.zeros(new_shape).to(self.device)\n                new_norm[:, :out_c] = norm[:, :out_c] + norm[:, out_c:2 * out_c]\n                new_norm[:, out_c:] = norm[:, 2 * out_c:3 * out_c] + norm[:, 3 * out_c:]\n                norm = new_norm\n                # Some 'rare' neurons only receive either\n                # only positive or only negative inputs.\n                # Conservation of relevance does not hold, if we also\n                # rescale those neurons by (1+beta) or -beta.\n                # Therefore, catch those first and scale norm by\n                # the according value, such that it cancels in the fraction.\n\n                # First, however, avoid NaNs.\n                mask = norm == 0\n                # Set the norm to anything non-zero, e.g. 1.\n                # The actual inputs are zero at this point anyways, that\n                # is why norm is zero in the first place.\n                norm[mask] = 1\n                # The norm in the b-rule has shape (N, 2*out_c, *spatial_dims).\n                # The first out_c block corresponds to the positive norms,\n                # the second out_c block corresponds to the negative norms.\n                # We find the rare neurons by choosing those nodes per channel\n                # in which either the positive norm ([:, :out_c]) is zero, or\n                # the negative norm ([:, :out_c]) is zero.\n                rare_neurons = (mask[:, :out_c] + mask[:, out_c:])\n\n                # Also, catch new possibilities for norm == zero to avoid NaN..\n                # The actual value of norm again does not really matter, since\n                # the pre-factor will be zero in this case.\n\n                norm[:, :out_c][rare_neurons] *= 1 if self.beta == -1 else 1 + self.beta\n                norm[:, out_c:][rare_neurons] *= 1 if self.beta == 0 else -self.beta\n                # Add stabilizer term to norm to avoid numerical instabilities.\n                norm += self.eps * torch.sign(norm)\n                spatial_dims = [1] * len(relevance_in.size()[2:])\n\n                input_relevance = relevance_in.repeat(1, 4, *spatial_dims)\n                input_relevance[:, :2*out_c] *= (1+self.beta)/norm[:, :out_c].repeat(1, 2, *spatial_dims)\n                input_relevance[:, 2*out_c:] *= -self.beta/norm[:, out_c:].repeat(1, 2, *spatial_dims)\n                # Each of the positive / negative entries needs its own\n                # convolution. TODO: Can this be done in groups, too?\n\n                relevance_out = torch.zeros_like(m.in_tensor)\n                # Weird code to make up for loss of size due to stride\n                tmp_result = result = None\n                for i in range(4):\n                    tmp_result = inv_conv_nd(\n                        input_relevance[:, i*out_c:(i+1)*out_c],\n                        weight=w[i*out_c:(i+1)*out_c],\n                        bias=None, padding=m.padding, stride=m.stride,\n                        groups=m.groups)\n                    result = torch.zeros_like(relevance_out[:, i*in_c:(i+1)*in_c])\n                    tmp_size = tmp_result.size()\n                    slice_list = [slice(0, l) for l in tmp_size]\n                    result[slice_list] += tmp_result\n                    relevance_out[:, i*in_c:(i+1)*in_c] = result\n                relevance_out *= m.in_tensor\n\n                sum_weights = torch.zeros([in_c, in_c * 4, *spatial_dims]).to(self.device)\n                for i in range(m.in_channels):\n                    sum_weights[i, i::in_c] = 1\n                relevance_out = conv_nd(relevance_out, weight=sum_weights, bias=None)\n\n                del sum_weights, m.in_tensor, result, mask, rare_neurons, norm, \\\n                    new_norm, input_relevance, tmp_result, w\n\n                return relevance_out\n\n    @module_tracker\n    def conv_nd_fwd_hook(self, m, in_tensor: torch.Tensor,\n                         out_tensor: torch.Tensor):\n\n        setattr(m, \"in_tensor\", in_tensor[0])\n        setattr(m, 'out_shape', list(out_tensor.size()))\n        return\n"
  },
  {
    "path": "jrieke/datasets.py",
    "content": "import numpy as np\nimport pandas as pd\nimport os\nfrom jrieke import utils\nfrom tqdm import tqdm_notebook\nimport multiprocessing\nfrom settings import settings\n\n\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\n\nfrom tabulate import tabulate\n\n# Binary brain mask used to cut out the skull.\nmask = utils.load_nifti(settings[\"binary_brain_mask\"])\n\n# ------------------------- ADNI data tables -----------------------------------\n\n# Ritter/Haynes lab file system at BCCN Berlin.\nADNI_DIR = settings[\"ADNI_DIR\"]\n\n# Filepaths for 1.5 Tesla scans.\ntable_15T = None#os.path.join(ADNI_DIR, settings[\"1.5T_table\"])\nimage_dir_15T = None #os.path.join(ADNI_DIR, settings[\"1.5T_image_dir\"])\ncorrupt_images_15T = ['067_S_0077/Screening']\n\n\n# TODO: Maybe rename to load_table or load_adni_table\ndef load_data_table(table, image_dir, corrupt_images=None):\n    \"\"\"Read data table, find corresponding images, filter out corrupt,\n    missing and MCI images, and return the samples as a pandas dataframe.\"\"\"\n\n    # Read table into dataframe.\n    print('Loading dataframe for', table)\n    df = pd.read_csv(table)\n    print('Found', len(df), 'images in table')\n\n    # Add column with filepaths to images.\n    df['filepath'] = df.apply(lambda row: get_image_filepath(row, image_dir), axis=1)\n\n    # Filter out corrupt images (i.e. images where the preprocessing failed).\n    len_before = len(df)\n    if corrupt_images is not None:\n        df = df[df.apply(lambda row: '{}/{}'.format(row['PTID'], row['Visit']) not in corrupt_images, axis=1)]\n    print('Filtered out', len_before - len(df), 'of', len_before, 'images because of failed preprocessing')\n\n    # Filter out images where files are missing.\n    len_before = len(df)\n    # print(df[~np.array(map(os.path.exists, df['filepath']))]['filepath'].values)\n    df = df.loc[map(os.path.exists, df['filepath'])]\n    print('Filtered out', len_before - len(df), 'of', len_before, 'images because of missing files')\n\n    # Filter out images with MCI.\n    len_before = len(df)\n    df = df[df['DX'] != 'MCI']\n    print('Filtered out', len_before - len(df), 'of', len_before, 'images that were MCI')\n\n    print('Final dataframe contains', len(df), 'images from', len(df['PTID'].unique()), 'patients')\n    print()\n\n    return df\n\n\ndef load_data_table_3T():\n    \"\"\"Load the data table for all 3 Tesla images.\"\"\"\n    return load_data_table(table_3T, image_dir_3T, corrupt_images_3T)\n\n\ndef load_data_table_15T():\n    \"\"\"Load the data table for all 1.5 Tesla images.\"\"\"\n    return load_data_table(table_15T, image_dir_15T, corrupt_images_15T)\n\n\ndef load_data_table_both():\n    \"\"\"Load the data tables for all 1.5 Tesla and 3 Tesla images and combine them.\"\"\"\n    df_15T = load_data_table(table_15T, image_dir_15T, corrupt_images_15T)\n    df_3T = load_data_table(table_3T, image_dir_3T, corrupt_images_3T)\n    df = pd.concat([df_15T, df_3T])\n    return df\n\n\ndef get_image_filepath(df_row, root_dir=''):\n    \"\"\"Return the filepath of the image that is described in the row of the data table.\"\"\"\n    # Current format for the image filepath is:\n    # <PTID>/<Visit (spaces removed)>/<PTID>_<Scan.Date (/ replaced by -)>_\n    # <Visit (spaces removed)>_<Image.ID>_<DX>_Warped.nii.gz\n    filedir = os.path.join(df_row['PTID'], df_row['Visit'].replace(' ', ''))\n    filename = '{}_{}_{}_{}_{}_Warped.nii.gz'.format(df_row['PTID'], df_row['Scan.Date'].replace('/', '-'),\n                                                     df_row['Visit'].replace(' ', ''), df_row['Image.ID'], df_row['DX'])\n    return os.path.join(root_dir, filedir, filename)\n\n\n# ------------------------ PyTorch datasets and loaders ----------------------\n\nclass ADNIDataset(Dataset):\n    \"\"\"\n    PyTorch dataset that consists of MRI images and labels.\n\n    Args:\n        filenames (iterable of strings): The filenames fo the MRI images.\n        labels (iterable): The labels for the images.\n        mask (array): If not None (default), images are masked by multiplying with this array.\n        transform: Any transformations to apply to the images.\n    \"\"\"\n\n    def __init__(self, filenames, labels, mask=None, transform=None):\n        self.filenames = filenames\n        self.labels = torch.LongTensor(labels)\n        self.mask = mask\n        self.transform = transform\n\n        # Required by torchsample.\n        self.num_inputs = 1\n        self.num_targets = 1\n\n        # Default values. Should be set via fit_normalization.\n        self.mean = 0\n        self.std = 1\n\n    def __len__(self):\n        return len(self.filenames)\n\n    def __getitem__(self, idx):\n        \"\"\"Return the image as a numpy array and the label.\"\"\"\n        label = self.labels[idx]\n\n        struct_arr = utils.load_nifti(self.filenames[idx], mask=self.mask)\n        # TDOO: Try normalizing each image to mean 0 and std 1 here.\n        # struct_arr = (struct_arr - struct_arr.mean()) / (struct_arr.std() + 1e-10)\n        struct_arr = (struct_arr - self.mean) / (self.std + 1e-10)  # prevent 0 division by adding small factor\n        struct_arr = struct_arr[None]  # add (empty) channel dimension\n        struct_arr = torch.FloatTensor(struct_arr)\n\n        if self.transform is not None:\n            struct_arr = self.transform(struct_arr)\n\n        return struct_arr, label\n\n    def image_shape(self):\n        \"\"\"The shape of the MRI images.\"\"\"\n        return utils.load_nifti(self.filenames[0], mask=mask).shape\n\n    def fit_normalization(self, num_sample=None, show_progress=False):\n        \"\"\"\n        Calculate the voxel-wise mean and std across the dataset for normalization.\n\n        Args:\n            num_sample (int or None): If None (default), calculate the values across the complete dataset,\n                                      otherwise sample a number of images.\n            show_progress (bool): Show a progress bar during the calculation.\"\n        \"\"\"\n\n        if num_sample is None:\n            num_sample = len(self)\n\n        image_shape = self.image_shape()\n        all_struct_arr = np.zeros((num_sample, image_shape[0], image_shape[1], image_shape[2]))\n\n        sampled_filenames = np.random.choice(self.filenames, num_sample, replace=False)\n        if show_progress:\n            sampled_filenames = tqdm_notebook(sampled_filenames)\n\n        for i, filename in enumerate(sampled_filenames):\n            struct_arr = utils.load_nifti(filename, mask=mask)\n            all_struct_arr[i] = struct_arr\n\n        self.mean = all_struct_arr.mean(0)\n        self.std = all_struct_arr.std(0)\n\n    def get_raw_image(self, idx):\n        \"\"\"Return the raw image at index idx (i.e. not normalized, no color channel, no transform.\"\"\"\n        return utils.load_nifti(self.filenames[idx], mask=self.mask)\n\n\ndef print_df_stats(df, df_train, df_val):\n    \"\"\"Print some statistics about the patients and images in a dataset.\"\"\"\n    headers = ['Images', '-> AD', '-> CN', 'Patients', '-> AD', '-> CN']\n\n    def get_stats(df):\n        df_ad = df[df['DX'] == 'Dementia']\n        df_cn = df[df['DX'] == 'CN']\n        return [len(df), len(df_ad), len(df_cn), len(df['PTID'].unique()), len(df_ad['PTID'].unique()),\n                len(df_cn['PTID'].unique())]\n\n    stats = []\n    stats.append(['All'] + get_stats(df))\n    stats.append(['Train'] + get_stats(df_train))\n    stats.append(['Val'] + get_stats(df_val))\n\n    print(tabulate(stats, headers=headers))\n    print()\n\n\n# TODO: Rename *_val to *_test.\ndef build_datasets(df, patients_train, patients_val, print_stats=True, normalize=True):\n    \"\"\"\n    Build PyTorch datasets based on a data table and a patient-wise train-test split.\n\n    Args:\n        df (pandas dataframe): The data table from ADNI.\n        patients_train (iterable of strings): The patients to include in the train set.\n        patients_val (iterable of strings): The patients to include in the val set.\n        print_stats (boolean): Whether to print some statistics about the datasets.\n        normalize (boolean): Whether to caluclate mean and std across the dataset for later normalization.\n\n    Returns:\n        The train and val dataset.\n    \"\"\"\n    # Compile train and val dfs based on patients.\n    df_train = df[df.apply(lambda row: row['PTID'] in patients_train, axis=1)]\n    df_val = df[df.apply(lambda row: row['PTID'] in patients_val, axis=1)]\n\n    if print_stats:\n        print_df_stats(df, df_train, df_val)\n\n    # Extract filenames and labels from dfs.\n    train_filenames = np.array(df_train['filepath'])\n    val_filenames = np.array(df_val['filepath'])\n    train_labels = np.array(df_train['DX'] == 'Dementia', dtype=int)  # [:, None]\n    val_labels = np.array(df_val['DX'] == 'Dementia', dtype=int)  # [:, None]\n\n    train_dataset = ADNIDataset(train_filenames, train_labels, mask=mask)\n    val_dataset = ADNIDataset(val_filenames, val_labels, mask=mask)\n\n    # TODO: Maybe normalize each scan first, so that they are on a common scale.\n    # TODO: Save these values to file together with the model.\n    # TODO: Sample over more images.\n    if normalize:\n        print('Calculating mean and std for normalization:')\n        train_dataset.fit_normalization(200, show_progress=True)\n        val_dataset.mean, val_dataset.std = train_dataset.mean, train_dataset.std\n    else:\n        print('Dataset is not normalized, this could dramatically decrease performance')\n\n    return train_dataset, val_dataset\n\n\ndef build_loaders(train_dataset, val_dataset):\n    \"\"\"Build PyTorch data loaders from the datasets.\"\"\"\n\n    # In contrast to Korolev et al. 2017, we do not enforce one sample per class in each batch.\n    # TODO: Maybe change batch size to 3 or 4. Check how this affects memory and accuracy.\n    train_loader = DataLoader(train_dataset, batch_size=5, shuffle=True, num_workers=multiprocessing.cpu_count(),\n                              pin_memory=torch.cuda.is_available())\n    val_loader = DataLoader(val_dataset, batch_size=5, shuffle=False, num_workers=multiprocessing.cpu_count(),\n                            pin_memory=torch.cuda.is_available())\n\n    return train_loader, val_loader\n"
  },
  {
    "path": "jrieke/interpretation.py",
    "content": "import numpy as np\n\nfrom jrieke.utils import resize_image\nfrom tqdm import tqdm_notebook\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\n\n# ---------------------------- Interpretation methods --------------------------------\ndef sensitivity_analysis(model, image_tensor, target_class=None, postprocess='abs', apply_softmax=True, cuda=False,\n                         verbose=False):\n    \"\"\"\n    Perform sensitivity analysis (via backpropagation; Simonyan et al. 2014) to\n    determine the relevance of each image pixel\n    for the classification decision. Return a relevance heatmap over the input image.\n\n    Args:\n        model (torch.nn.Module): The pytorch model. Should be set to eval mode.\n        image_tensor (torch.Tensor or numpy.ndarray): The image to run through the `model` (channels first!).\n        target_class (int): The target output class for which to produce the heatmap.\n                      If `None` (default), use the most likely class from the `model`s output.\n        postprocess (None or 'abs' or 'square'): The method to postprocess the heatmap with. `'abs'` is used\n                                                 in Simonyan et al. 2014, `'square'` is used in Montavon et al. 2018.\n        apply_softmax (boolean): Whether to apply the softmax function to the output. Useful for models that are trained\n                                 with `torch.nn.CrossEntropyLoss` and do not apply softmax themselves.\n        appl (None or 'binary' or 'categorical'): Whether the output format of the `model` is binary\n                                                         (i.e. one output neuron with sigmoid activation) or categorical\n                                                         (i.e. multiple output neurons with softmax activation).\n                                                         If `None` (default), infer from the shape of the output.\n        cuda (boolean): Whether to run the computation on a cuda device.\n        verbose (boolean): Whether to display additional output during the computation.\n\n    Returns:\n        A numpy array of the same shape as image_tensor, indicating the relevance of each image pixel.\n    \"\"\"\n    if postprocess not in [None, 'abs', 'square']:\n        raise ValueError(\"postprocess must be None, 'abs' or 'square'\")\n\n    # Forward pass.\n    \n    X = Variable(image_tensor, requires_grad=True)  # add dimension to simulate batch\n    output = model(X)\n    if apply_softmax:\n        output = F.softmax(output)\n\n    # Backward pass.\n    model.zero_grad()\n    output_class = output.max(1)[1].data[0]\n    if verbose: print('Image was classified as', output_class, 'with probability', output.max(1)[0].data[0])\n    one_hot_output = torch.zeros(output.size())\n    if target_class is None:\n        one_hot_output[0, output_class] = 1\n    else:\n        one_hot_output[0, target_class] = 1\n    if cuda:\n        one_hot_output = one_hot_output.cuda(image_tensor.get_device())\n    output.backward(gradient=one_hot_output)\n\n    relevance_map = X.grad.data[0].cpu().numpy()\n\n    # Postprocess the relevance map.\n    if postprocess == 'abs':  # as in Simonyan et al. (2014)\n        return np.abs(relevance_map)\n    elif postprocess == 'square':  # as in Montavon et al. (2018)\n        return relevance_map ** 2\n    elif postprocess is None:\n        return relevance_map\n\n\ndef guided_backprop(model, image_tensor, target_class=None, postprocess='abs', apply_softmax=True, cuda=False,\n                    verbose=False):\n    \"\"\"\n    Perform guided backpropagation (Springenberg et al. 2015) to determine the relevance of each image pixel\n    for the classification decision. Return a relevance heatmap over the input image.\n\n    Note: The `model` MUST implement any ReLU layers via `torch.nn.ReLU` (i.e. it needs to have an instance\n    of torch.nn.ReLU as an attribute). Models that use `torch.nn.functional.relu` instead will not work properly!\n\n    Args:\n        model (torch.nn.Module): The pytorch model. Should be set to eval mode.\n        image_tensor (torch.Tensor or numpy.ndarray): The image to run through the `model` (channels first!).\n        target (int): The target output class for which to produce the heatmap.\n                      If `None` (default), use the most likely class from the `model`s output.\n        postprocess (None or 'abs' or 'square'): The method to postprocess the heatmap with. `'abs'` is used\n                                                 in Simonyan et al. 2013, `'square'` is used in Montavon et al. 2018.\n        apply_softmax (boolean): Whether to apply the softmax function to the output. Useful for models that are trained\n                                 with `torch.nn.CrossEntropyLoss` and do not apply softmax themselves.\n        cuda (boolean): Whether to run the computation on a cuda device.\n        verbose (boolean): Whether to display additional output during the computation.\n\n    Returns:\n        A numpy array of the same shape as image_tensor, indicating the relevance of each image pixel.\n    \"\"\"\n    layer_to_hook = nn.ReLU\n\n    def relu_hook_function(module, grad_in, grad_out):\n        \"\"\"\n        If there is a negative gradient, change it to zero.\n        \"\"\"\n        return torch.clamp(grad_in[0], min=0.0),\n\n    hook_handles = []\n\n    try:\n        # Loop through layers, hook up ReLUs with relu_hook_function, store handles to hooks.\n        for module in model.children():\n            # TODO: Maybe hook on ELU layers as well (or on others?).\n            if isinstance(module, layer_to_hook):\n                # TODO: Add a warning if no activation layers have been hooked, so that the user does not forget\n                #       to invoke the activation via nn.ReLU instead of F.relu.\n                if verbose: print('Registered hook for layer:', module)\n                hook_handle = module.register_backward_hook(relu_hook_function)\n                hook_handles.append(hook_handle)\n\n        # Calculate backprop with modified ReLUs.\n        relevance_map = sensitivity_analysis(model, image_tensor, target_class=target_class, postprocess=postprocess,\n                                             apply_softmax=apply_softmax, cuda=cuda, verbose=verbose)\n\n    finally:\n        # Remove hooks from model.\n        # The finally clause re-raises any possible exceptions.\n        if verbose:\n            print('Removing {} hook(s)'.format(len(hook_handles)))\n        for hook_handle in hook_handles:\n            hook_handle.remove()\n            del hook_handle\n\n    return relevance_map\n\n\ndef occlusion(model, image_tensor, target_class=None, size=50, stride=25, occlusion_value=0, apply_softmax=True,\n              three_d=None, resize=False, cuda=False, verbose=False):\n    \"\"\"\n    Perform occlusion (Zeiler & Fergus 2014) to determine the relevance of each image pixel\n    for the classification decision. Return a relevance heatmap over the input image.\n\n    Note: The current implementation can only handle 2D and 3D images.\n    It usually infers the correct image dimensions, otherwise they can be set via the `three_d` parameter.\n\n    Args:\n        model (torch.nn.Module): The pytorch model. Should be set to eval mode.\n        image_tensor (torch.Tensor or numpy.ndarray): The image to run through the `model` (channels first!).\n        target_class (int): The target output class for which to produce the heatmap.\n                      If `None` (default), use the most likely class from the `model`s output.\n        size (int): The size of the occlusion patch.\n        stride (int): The stride with which to move the occlusion patch across the image.\n        occlusion_value (int): The value of the occlusion patch.\n        apply_softmax (boolean): Whether to apply the softmax function to the output. Useful for models that are trained\n                                 with `torch.nn.CrossEntropyLoss` and do not apply softmax themselves.\n        three_d (boolean): Whether the image is 3 dimensional (e.g. MRI scans).\n                           If `None` (default), infer from the shape of `image_tensor`.\n        resize (boolean): The output from the occlusion method is usually smaller than the original `image_tensor`.\n                          If `True` (default), the output will be resized to fit the original shape\n                        (without interpolation).\n        cuda (boolean): Whether to run the computation on a cuda device.\n        verbose (boolean): Whether to display additional output during the computation.\n\n    Returns:\n        A numpy array of the same shape as image_tensor, indicating the relevance of each image pixel.\n    \"\"\"\n\n    # TODO: Try to make this better, i.e. generalize the method to any kind of input.\n    if three_d is None:\n        three_d = (len(image_tensor.shape) == 4)  # guess if input image is 3D\n\n    image_tensor = torch.Tensor(image_tensor)  # convert numpy or list to tensor\n    if cuda:\n        image_tensor = image_tensor.cuda()\n    output = model(Variable(image_tensor[None], requires_grad=False)).cpu()\n    if apply_softmax:\n        output = F.softmax(output)\n\n    output_class = output.max(1)[1].data.numpy()[0]\n    if verbose: print('Image was classified as', output_class, 'with probability', output.max(1)[0].data[0])\n    if target_class is None:\n        target_class = output_class\n    unoccluded_prob = output.data[0, target_class]\n\n    width = image_tensor.shape[1]\n    height = image_tensor.shape[2]\n\n    xs = range(0, width, stride)\n    ys = range(0, height, stride)\n\n    # TODO: Maybe use torch tensor here.\n    if three_d:\n        depth = image_tensor.shape[3]\n        zs = range(0, depth, stride)\n        relevance_map = np.zeros((len(xs), len(ys), len(zs)))\n    else:\n        relevance_map = np.zeros((len(xs), len(ys)))\n\n    if verbose:\n        xs = tqdm_notebook(xs, desc='x')\n        ys = tqdm_notebook(ys, desc='y', leave=False)\n        if three_d:\n            zs = tqdm_notebook(zs, desc='z', leave=False)\n\n    image_tensor_occluded = image_tensor.clone()  # TODO: Check how long this takes.\n\n    if cuda:\n        image_tensor_occluded = image_tensor_occluded.cuda()\n\n    for i_x, x in enumerate(xs):\n        x_from = max(x - int(size / 2), 0)\n        x_to = min(x + int(size / 2), width)\n\n        for i_y, y in enumerate(ys):\n            y_from = max(y - int(size / 2), 0)\n            y_to = min(y + int(size / 2), height)\n\n            if three_d:\n                for i_z, z in enumerate(zs):\n                    z_from = max(z - int(size / 2), 0)\n                    z_to = min(z + int(size / 2), depth)\n\n                    # if verbose:\n                    #     print('Occluding from x={} to x={} and y={} to y={} and z={} to z={}'.format(\n                    #         x_from, x_to, y_from, y_to, z_from, z_to))\n\n                    image_tensor_occluded.copy_(image_tensor)\n                    image_tensor_occluded[:, x_from:x_to, y_from:y_to, z_from:z_to] = occlusion_value\n\n                    # TODO: Maybe run this batched.\n                    output = model(Variable(image_tensor_occluded[None], requires_grad=False))\n                    if apply_softmax:\n                        output = F.softmax(output)\n\n                    occluded_prob = output.data[0, target_class]\n                    relevance_map[i_x, i_y, i_z] = unoccluded_prob - occluded_prob\n\n            else:\n                # if verbose:\n                #     print('Occluding from x={} to x={} and y={} to y={}'.format(\n                #         x_from, x_to, y_from, y_to, z_from, z_to))\n                image_tensor_occluded.copy_(image_tensor)\n                image_tensor_occluded[:, x_from:x_to, y_from:y_to] = occlusion_value\n\n                # TODO: Maybe run this batched.\n                output = model(Variable(image_tensor_occluded[None], requires_grad=False))\n                if apply_softmax:\n                    output = F.softmax(output)\n\n                occluded_prob = output.data[0, target_class]\n                relevance_map[i_x, i_y] = unoccluded_prob - occluded_prob\n\n    relevance_map = np.maximum(relevance_map, 0)\n\n    if resize:\n        relevance_map = None  # utils.resize_image(relevance_map, image_tensor.shape[1:])\n\n    return relevance_map\n\n\ndef area_occlusion(model, image_tensor, area_masks, target_class=None, occlusion_value=0, apply_softmax=True,\n                   cuda=False, verbose=False):\n    \"\"\"\n    Perform brain area occlusion to determine the relevance of each image pixel\n    for the classification decision. Return a relevance heatmap over the input image.\n\n    Args:\n        model (torch.nn.Module): The pytorch model. Should be set to eval mode.\n        image_tensor (torch.Tensor or numpy.ndarray): The image to run through the `model` (channels first!).\n        target_class (int): The target output class for which to produce the heatmap.\n                      If `None` (default), use the most likely class from the `model`s output.\n        occlusion_value (int): The value of the occlusion patch.\n        apply_softmax (boolean): Whether to apply the softmax function to the output. Useful for models that are trained\n                                 with `torch.nn.CrossEntropyLoss` and do not apply softmax themselves.\n        cuda (boolean): Whether to run the computation on a cuda device.\n        verbose (boolean): Whether to display additional output during the computation.\n\n    Returns:\n        A numpy array of the same shape as image_tensor, indicating the relevance of each image pixel.\n    \"\"\"\n\n    image_tensor = torch.Tensor(image_tensor)  # convert numpy or list to tensor\n    if cuda:\n        image_tensor = image_tensor.cuda()\n    output = model(Variable(image_tensor[None], requires_grad=False))\n    if apply_softmax:\n        output = F.softmax(output)\n\n    output_class = output.max(1)[1].data.cpu().numpy()[0]\n    if verbose: print('Image was classified as', output_class, 'with probability', output.max(1)[0].data[0])\n    if target_class is None:\n        target_class = output_class\n    unoccluded_prob = output.data[0, target_class]\n\n    relevance_map = torch.zeros(image_tensor.shape[1:])\n    if cuda:\n        relevance_map = relevance_map.cuda()\n\n    for area_mask in tqdm_notebook(area_masks) if verbose else area_masks:\n        # TODO: Maybe have area_mask as tensor in the first place.\n        area_mask = torch.FloatTensor(area_mask)\n        if cuda:\n            area_mask = area_mask.cuda()\n        image_tensor_occluded = image_tensor * (1 - area_mask).view(image_tensor.shape)\n\n        output = model(Variable(image_tensor_occluded[None], requires_grad=False))\n        if apply_softmax:\n            output = F.softmax(output)\n\n        occluded_prob = output.data[0, target_class]\n        relevance_map[area_mask.view(image_tensor.shape) == 1] = (unoccluded_prob - occluded_prob)\n\n    relevance_map = relevance_map.cpu().numpy()\n    relevance_map = np.maximum(relevance_map, 0)\n    return relevance_map\n\n\ndef all_children(model):\n    \"\"\"Return a list of all child modules of the model, and their children, and their children's children, ...\"\"\"\n    children = list(model.children())\n    for child in model.children():\n        children.extend(all_children(child))\n    return children\n\n\ndef grad_cam(model, image_tensor, target_class=None, last_conv_layer=None, resize=True, interpolation=1,\n             apply_softmax=True, cuda=False, verbose=False):\n    \"\"\"\n    Perform Grad-CAM (Selvaraju et al. 2017) to determine the relevance of each image pixel\n    for the classification decision. Return a relevance heatmap over the input image.\n\n    NOTE: THIS IMPLEMENTATION IS BUGGY AND IS ONLY PROVIDED FOR COMPLETENESS.\n\n    Args:\n        model (torch.nn.Module): The pytorch model. Should be set to eval mode.\n        image_tensor (torch.Tensor or numpy.ndarray): The image to run through the `model` (channels first!).\n        target_class (int): The target output class for which to produce the heatmap.\n                      If `None` (default), use the most likely class from the `model`s output.\n        last_conv_layer (torch.nn.Module): The last conv layer of the `model`. If `None` (default),\n                                           infer from the structure of the model itself.\n        resize (boolean): The output from the occlusion method is usually smaller than the original `image_tensor`.\n                          If `True` (default), the output will be resized to fit the original shape.\n        interpolation (int): The interpolation to use for the resizing (0-5).\n        apply_softmax (boolean): Whether to apply the softmax function to the output. Useful for models that are trained\n                                 with `torch.nn.CrossEntropyLoss` and do not apply softmax themselves.\n        cuda (boolean): Whether to run the computation on a cuda device.\n        verbose (boolean): Whether to display additional output during the computation.\n\n    Returns:\n        A numpy array of the same shape as image_tensor, indicating the relevance of each image pixel.\n    \"\"\"\n\n    image_tensor = torch.Tensor(image_tensor)\n    if cuda:\n        image_tensor = image_tensor.cuda()\n    X = Variable(image_tensor[None], requires_grad=False)  # add dimension to simulate batch\n\n    # Step 1: Find the last conv layer if it is not set manually.\n    if last_conv_layer is None:\n        for module in all_children(model)[::-1]:  # traverse model backwards\n            if type(module) == nn.Conv1d or type(module) == nn.Conv2d or type(module) == nn.Conv3d:\n                last_conv_layer = module\n                if verbose: print('Found last conv layer:', last_conv_layer)\n                break\n        if last_conv_layer is None:\n            raise ValueError('Could not find last conv layer automatically, please set it manually')\n\n    # Step 2: Register hooks on the last conv layer to store the (forward) feature maps and the gradient.\n    store = {}  # hacky way to store values in this function from hooks\n\n    def feature_maps_hook(module, input, output):\n        store['feature_maps'] = output.data.cpu().numpy()[0]\n        print('Stored feature maps')\n\n    def gradient_hook(module, grad_in, grad_out):\n        store['gradient'] = grad_out[0].data.cpu().numpy()[0]\n        print('Stored gradient')\n\n    hook_handle_forward = last_conv_layer.register_forward_hook(feature_maps_hook)\n    hook_handle_backward = last_conv_layer.register_backward_hook(gradient_hook)\n\n    try:\n        # Step 3: Run model forward (will store feature maps of the last conv layer).\n        output = model(X)\n        if apply_softmax:\n            output = F.softmax(output)\n\n        # Step 4: Run model backwards (will store gradient of the last conv layer).\n        model.zero_grad()\n        output_class = output.max(1)[1].cpu().data.numpy()[0]\n        if verbose: print('Image was classified as', output_class, 'with probability',\n                          output.max(1)[0].cpu().data.numpy()[0])\n        one_hot_output = torch.zeros(output.shape)\n        if cuda:\n            one_hot_output = one_hot_output.cuda()\n        if target_class is None:\n            one_hot_output[0, output_class] = 1\n        else:\n            one_hot_output[0, target_class] = 1\n        print(one_hot_output)\n        output.backward(gradient=one_hot_output)\n        # print(one_hot_output)\n\n        # TODO: Maybe implement this in native pytorch and return a tensor.\n        # Step 5: Calculate CAM as linear combination of each feature map and its averaged gradient.\n        coefficients = store['gradient'].mean(axis=tuple(range(1, store['gradient'].ndim)), keepdims=True)\n        print(np.min(coefficients), np.mean(coefficients), np.max(coefficients))\n        print(coefficients.shape)\n        print(store['feature_maps'].shape)\n        # print(coefficients)\n        # store['feature_maps'] = np.array([resize_image(fm, mask.shape)*val_dataset.std+val_dataset.mean for fm in store['feature_maps']])\n        cam = (coefficients * store['feature_maps']).sum(axis=0)\n        print(cam.shape)\n        print(np.min(cam), np.mean(cam), np.max(cam))\n\n        vmin = np.min(coefficients * store['feature_maps'])\n        vmax = np.max(coefficients * store['feature_maps'])\n        # for fm, g, c in zip(store['feature_maps'], store['gradient'], coefficients.flatten()):\n        #    if c in sorted(coefficients, reverse=True)[:5]:\n        # print(c)\n        # utils.plot_slices(fm*c, num_slices=3, vmin=vmin, vmax=vmax)\n        # utils.plot_slices(mask, overlay=resize_image(fm, mask.shape)*val_dataset.std+val_dataset.mean, num_slices=3)\n\n        cam = np.maximum(cam, 0)\n        print(np.min(cam), np.mean(cam), np.max(cam))\n\n        # set_trace()\n\n        if resize:\n            cam = resize_image(cam, image_tensor.shape[1:], interpolation=interpolation)\n\n        return cam\n\n    finally:\n        hook_handle_forward.remove()\n        hook_handle_backward.remove()\n\n\n# ----------------------------------- Averages over datasets ---------------\n\ndef average_over_dataset(interpretation_method, model, dataset, num_samples=None, seed=None, show_progress=False,\n                         **kwargs):\n    \"\"\"Apply an interpretation method to each sample of a dataset, and average separately over AD and NC samples.\"\"\"\n\n    if seed is not None:\n        np.random.seed(seed)\n\n    # Run once to figure out shape of the relevance map. Cannot be inferred\n    # from image shape alone, because some interpretation methods contain\n    # a channel dimension and some not.\n    struct_arr, label = dataset[0]\n    relevance_map = interpretation_method(model, struct_arr, **kwargs)\n\n    avg_relevance_map_AD = np.zeros_like(relevance_map)\n    avg_relevance_map_NC = np.zeros_like(relevance_map)\n\n    count_AD = 0\n    count_NC = 0\n\n    if num_samples is None:\n        idx = range(len(dataset))\n    else:\n        idx = np.random.choice(len(dataset), num_samples, replace=False)\n    if show_progress:\n        idx = tqdm_notebook(idx)\n\n    for i in idx:\n        struct_arr, label = dataset[i]\n\n        relevance_map = interpretation_method(model, struct_arr, **kwargs)\n\n        if label == 1:\n            # print(label, 'adding to AD', relevance_map.shape)\n            avg_relevance_map_AD += relevance_map  # [0]\n            count_AD += 1\n        else:\n            # print(label, 'adding to NC', relevance_map.shape)\n            avg_relevance_map_NC += relevance_map  # [0]\n            count_NC += 1\n\n    avg_relevance_map_AD /= count_AD\n    avg_relevance_map_NC /= count_NC\n\n    avg_relevance_map_all = (avg_relevance_map_AD * count_AD + avg_relevance_map_NC * count_NC) / (count_AD + count_NC)\n\n    return avg_relevance_map_AD, avg_relevance_map_NC, avg_relevance_map_all\n\n\n# ------------------------------ Distance between heatmaps ----------------------------\n\ndef heatmap_distance(a, b):\n    \"\"\"Calculate the Euclidean distance between two n-dimensional arrays.\"\"\"\n\n    def preprocess(arr):\n        \"\"\"Preprocess an array for use in Euclidean distance.\"\"\"\n        # arr = arr * mask\n        arr = arr.flatten()\n        arr = arr / arr.sum()  # normalize to sum 1\n        # arr = arr.clip(1e-20, None)  # avoid 0 division in KL divergence\n        return arr\n\n    a, b = preprocess(a), preprocess(b)\n\n    # Euclidean distance.\n    return np.sqrt(np.sum((a - b) ** 2))\n\n    # Wasserstein Distance.\n    # return sp.stats.wasserstein_distance(a, b)\n\n    # Symmetric KL Divergence (ill-defined for arrays with 0 values!).\n    # return sp.stats.entropy(a, b) + sp.stats.entropy(b, a)\n"
  },
  {
    "path": "jrieke/models.py",
    "content": "from torch import nn\n\nclass ClassificationModel3D_2Node(nn.Module):\n    \"\"\"The model we use in the paper.\"\"\"\n\n    def __init__(self, dropout=0.4, dropout2=0.4):\n        nn.Module.__init__(self)\n        self.Conv_1 = nn.Conv3d(1, 8, 3)\n        self.Conv_1_bn = nn.BatchNorm3d(8)\n        self.Conv_1_mp = nn.MaxPool3d(2)\n        self.Conv_2 = nn.Conv3d(8, 16, 3)\n        self.Conv_2_bn = nn.BatchNorm3d(16)\n        self.Conv_2_mp = nn.MaxPool3d(3)\n        self.Conv_3 = nn.Conv3d(16, 32, 3)\n        self.Conv_3_bn = nn.BatchNorm3d(32)\n        self.Conv_3_mp = nn.MaxPool3d(2)\n        self.Conv_4 = nn.Conv3d(32, 64, 3)\n        self.Conv_4_bn = nn.BatchNorm3d(64)\n        self.Conv_4_mp = nn.MaxPool3d(3)\n        self.dense_1 = nn.Linear(5120, 128)\n        self.dense_2 = nn.Linear(128, 2)\n        self.relu = nn.ReLU()\n        self.dropout = nn.Dropout(dropout)\n        self.dropout2 = nn.Dropout(dropout2)\n\n    def forward(self, x):\n        x = self.relu(self.Conv_1_bn(self.Conv_1(x)))\n        x = self.Conv_1_mp(x)\n        x = self.relu(self.Conv_2_bn(self.Conv_2(x)))\n        x = self.Conv_2_mp(x)\n        x = self.relu(self.Conv_3_bn(self.Conv_3(x)))\n        x = self.Conv_3_mp(x)\n        x = self.relu(self.Conv_4_bn(self.Conv_4(x)))\n        x = self.Conv_4_mp(x)\n        x = x.view(x.size(0), -1)\n        x = self.dropout(x)\n        x = self.relu(self.dense_1(x))\n        x = self.dropout2(x)\n        x = self.dense_2(x)\n\n        # Note that no sigmoid is applied here, because the network is used in combination with BCEWithLogitsLoss,\n        # which applies sigmoid and BCELoss at the same time to make it numerically stable.\n\n        return x"
  },
  {
    "path": "jrieke/utils.py",
    "content": "import numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport matplotlib.animation\nimport json\n\nimport nibabel as nib\nfrom scipy.ndimage.interpolation import zoom\n\n\ndef save_history(filename, trainer):\n    \"\"\"Save the history from a torchsample trainer to file.\"\"\"\n    with open(filename, 'w+') as f:\n        json.dump(trainer.history.epoch_metrics, f)\n\n\ndef load_history(filename):\n    \"\"\"Load the history from a torchsample trainer from file.\"\"\"\n    with open(filename) as f:\n        return json.load(f)\n\n\ndef plot_learning_curve(history):\n    \"\"\"\n    Plot loss and accuracy over epochs, as recorded in a History object\n    from training with keras or torchsample.\n    \"\"\"\n\n    # noinspection PyTypeChecker\n    fig, axes = plt.subplots(2, sharex=True, figsize=(10, 7))\n\n    epochs = range(1, len(history['loss']) + 1)\n\n    plt.sca(axes[0])\n    plt.grid()\n    plt.plot(epochs, history['loss'], 'b-', label='Train')\n    try:\n        plt.plot(epochs, history['val_loss'], 'b--', label='Val')\n    except KeyError:\n        pass\n    plt.ylabel('Loss')\n    plt.ylim(0, 1.5)\n    plt.legend()\n\n    plt.sca(axes[1])\n    plt.grid()\n    plt.plot(epochs, history['acc_metric'], 'r-', label='Train')\n    try:\n        plt.plot(epochs, history['val_acc_metric'], 'r--', label='Val')\n    except KeyError:\n        pass\n    plt.xlabel('Epoch')\n    plt.ylabel('Accuracy / %')\n    plt.legend()\n\n\ndef load_nifti(file_path, mask=None, z_factor=None, remove_nan=True):\n    \"\"\"Load a 3D array from a NIFTI file.\"\"\"\n    img = nib.load(file_path)\n    struct_arr = np.array(img.get_data())\n\n    if remove_nan:\n        struct_arr = np.nan_to_num(struct_arr)\n    if mask is not None:\n        struct_arr *= mask\n    if z_factor is not None:\n        struct_arr = np.around(zoom(struct_arr, z_factor), 0)\n\n    return struct_arr\n\n\ndef save_nifti(file_path, struct_arr):\n    \"\"\"Save a 3D array to a NIFTI file.\"\"\"\n    img = nib.Nifti1Image(struct_arr, np.eye(4))\n    nib.save(img, file_path)\n\n\n# Transparent colormap (alpha to red), that is used for plotting an overlay.\n# See https://stackoverflow.com/questions/37327308/add-alpha-to-an-existing-matplotlib-colormap\nalpha_to_red_cmap = np.zeros((256, 4))\nalpha_to_red_cmap[:, 0] = 0.8\nalpha_to_red_cmap[:, -1] = np.linspace(0, 1, 256)  # cmap.N-20)  # alpha values\nalpha_to_red_cmap = mpl.colors.ListedColormap(alpha_to_red_cmap)\n\nred_to_alpha_cmap = np.zeros((256, 4))\nred_to_alpha_cmap[:, 0] = 0.8\nred_to_alpha_cmap[:, -1] = np.linspace(1, 0, 256)  # cmap.N-20)  # alpha values\nred_to_alpha_cmap = mpl.colors.ListedColormap(red_to_alpha_cmap)\n\n\ndef plot_slices(struct_arr, num_slices=7, cmap='gray', vmin=None, vmax=None, overlay=None,\n                overlay_cmap=alpha_to_red_cmap, overlay_vmin=None, overlay_vmax=None):\n    \"\"\"\n    Plot equally spaced slices of a 3D image (and an overlay) along every axis\n\n    Args:\n        struct_arr (3D array or tensor): The 3D array to plot (usually from a nifti file).\n        num_slices (int): The number of slices to plot for each dimension.\n        cmap: The colormap for the image (default: `'gray'`).\n        vmin (float): Same as in matplotlib.imshow. If `None`, take the global minimum of `struct_arr`.\n        vmax (float): Same as in matplotlib.imshow. If `None`, take the global maximum of `struct_arr`.\n        overlay (3D array or tensor): The 3D array to plot as an overlay on top of the image. Same size as `struct_arr`.\n        overlay_cmap: The colomap for the overlay (default: `alpha_to_red_cmap`).\n        overlay_vmin (float): Same as in matplotlib.imshow. If `None`, take the global minimum of `overlay`.\n        overlay_vmax (float): Same as in matplotlib.imshow. If `None`, take the global maximum of `overlay`.\n    \"\"\"\n    if vmin is None:\n        vmin = struct_arr.min()\n    if vmax is None:\n        vmax = struct_arr.max()\n    if overlay_vmin is None and overlay is not None:\n        overlay_vmin = overlay.min()\n    if overlay_vmax is None and overlay is not None:\n        overlay_vmax = overlay.max()\n    print(vmin, vmax, overlay_vmin, overlay_vmax)\n\n    fig, axes = plt.subplots(3, num_slices, figsize=(15, 6))\n    intervals = np.asarray(struct_arr.shape) / num_slices\n\n    for axis, axis_label in zip([0, 1, 2], ['x', 'y', 'z']):\n        for i, ax in enumerate(axes[axis]):\n            i_slice = int(np.round(intervals[axis] / 2 + i * intervals[axis]))\n            # print(axis_label, 'plotting slice', i_slice)\n\n            plt.sca(ax)\n            plt.axis('off')\n            plt.imshow(sp.ndimage.rotate(np.take(struct_arr, i_slice, axis=axis), 90), vmin=vmin, vmax=vmax,\n                       cmap=cmap, interpolation=None)\n            plt.text(0.03, 0.97, '{}={}'.format(axis_label, i_slice), color='white',\n                     horizontalalignment='left', verticalalignment='top', transform=ax.transAxes)\n\n            if overlay is not None:\n                plt.imshow(sp.ndimage.rotate(np.take(overlay, i_slice, axis=axis), 90), cmap=overlay_cmap,\n                           vmin=overlay_vmin, vmax=overlay_vmax, interpolation=None)\n\n\ndef animate_slices(struct_arr, overlay=None, axis=0, reverse_direction=False, interval=40, vmin=None, vmax=None,\n                   overlay_vmin=None, overlay_vmax=None):\n    \"\"\"\n    Create a matplotlib animation that moves through a 3D image along a specified axis.\n    \"\"\"\n\n    if vmin is None:\n        vmin = struct_arr.min()\n    if vmax is None:\n        vmax = struct_arr.max()\n    if overlay_vmin is None and overlay is not None:\n        overlay_vmin = overlay.min()\n    if overlay_vmax is None and overlay is not None:\n        overlay_vmax = overlay.max()\n\n    fig, ax = plt.subplots()\n    axis_label = ['x', 'y', 'z'][axis]\n\n    # TODO: If I select slice 50 here at the beginning, the plots look different.\n    im = ax.imshow(np.take(struct_arr, 0, axis=axis), vmin=vmin, vmax=vmax, cmap='gray', interpolation=None,\n                   animated=True)\n    if overlay is not None:\n        im_overlay = ax.imshow(np.take(overlay, 0, axis=axis), vmin=overlay_vmin, vmax=overlay_vmax,\n                               cmap=alpha_to_red_cmap, interpolation=None, animated=True)\n    text = ax.text(0.03, 0.97, '{}={}'.format(axis_label, 0), color='white',\n                   horizontalalignment='left', verticalalignment='top', transform=ax.transAxes)\n    ax.axis('off')\n\n    def update(i):\n        im.set_array(np.take(struct_arr, i, axis=axis))\n        if overlay is not None:\n            im_overlay.set_array(np.take(overlay, i, axis=axis))\n        text.set_text('{}={}'.format(axis_label, i))\n        return im, text\n\n    num_frames = struct_arr.shape[axis]\n    if reverse_direction:\n        frames = np.arange(num_frames - 1, 0, -1)\n    else:\n        frames = np.arange(0, num_frames)\n\n    # noinspection PyTypeChecker\n    return mpl.animation.FuncAnimation(fig, update, frames=frames, interval=interval, blit=True)\n\n\ndef resize_image(img, size, interpolation=0):\n    \"\"\"Resize img to size. Interpolation between 0 (no interpolation) and 5 (maximum interpolation).\"\"\"\n    zoom_factors = np.asarray(size) / np.asarray(img.shape)\n    return sp.ndimage.zoom(img, zoom_factors, order=interpolation)\n"
  },
  {
    "path": "license.txt",
    "content": "Copyright 2019 Moritz Böhle\n\nRedistribution and use in source and binary forms, with or without \nmodification, are permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright notice, this\nlist of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright notice, \nthis list of conditions and the following disclaimer in the documentation \nand/or other materials provided with the distribution.\n\n3. Neither the name of the copyright holder nor the names of its contributors \nmay be used to endorse or promote products derived from this software without \nspecific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND \nANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED \nWARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE \nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR \nANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\nLOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON\nANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\nSOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.```\n\n"
  },
  {
    "path": "mnist_test.py",
    "content": "from __future__ import print_function\n\nimport torch\nimport torch.nn as nn\n\nimport torch.nn.functional as F\n\n\nclass Net(nn.Module):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n        self.max_pool1 = nn.MaxPool2d(2, stride=(2, 2))\n        self.max_pool2 = nn.MaxPool2d(2, stride=(2, 2))\n        self.conv2_drop = nn.Dropout2d()\n        self.softmax = nn.LogSoftmax(dim=1)\n        self.relu = nn.ReLU()\n        self.fc1 = nn.Linear(320, 50)\n        self.fc2 = nn.Linear(50, 10)\n\n    def forward(self, x):\n        x = self.relu(self.max_pool1(self.conv1(x)))\n        x = self.relu(self.max_pool2(self.conv2_drop(self.conv2(x))))\n        x = x.view(-1, 320)\n        x = self.relu(self.fc1(x))\n        x = self.conv2_drop(x)\n        x = self.fc2(x)\n        return self.softmax(x)\n\n\ndef train(args, model, device, train_loader, optimizer, epoch):\n    model.train()\n    for batch_idx, (data, target) in enumerate(train_loader):\n        data, target = data.to(device), target.to(device)\n        optimizer.zero_grad()\n        output = model(data)\n        loss = F.nll_loss(output, target)\n        loss.backward()\n        optimizer.step()\n        if batch_idx % args.log_interval == 0:\n            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n                epoch, batch_idx * len(data), len(train_loader.dataset),\n                100. * batch_idx / len(train_loader), loss.item()))\n\n\ndef test(args, model, device, test_loader):\n    del args\n    model.eval()\n    test_loss = 0\n    correct = 0\n    with torch.no_grad():\n        for data, target in test_loader:\n            data, target = data.to(device), target.to(device)\n            output = model(data)\n            test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss\n            pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability\n            correct += pred.eq(target.view_as(pred)).sum().item()\n\n    test_loss /= len(test_loader.dataset)\n    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n        test_loss, correct, len(test_loader.dataset),\n        100. * correct / len(test_loader.dataset)))\n"
  },
  {
    "path": "nitorch/callbacks.py",
    "content": "import os\nimport copy\nfrom copy import deepcopy\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_pdf import PdfPages\nfrom mpl_toolkits.mplot3d import Axes3D\n# pytorch\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n# nitorch\nfrom nitorch.data import show_brain\n\nclass Callback:\n    \"\"\"\n    Abstract class for callbacks.\n    \"\"\"\n\n    def __init__(self):\n        pass\n\n    def __call__(self):\n        pass\n\n    def reset(self):\n        pass\n\n    def final(self, **kwargs):\n        self.reset()\n\nclass ModelCheckpoint(Callback):\n    \"\"\"\n    # TODO\n\n    Arguments:\n        path:\n        num_iters: number of iterations after which to store the model.\n            If set to -1, it will only store the last iteration's model.\n        prepend: string to prepend the filename with.\n        ignore_before: ignore early iterations.\n        store_best: boolen whether to save the best model during\n            training.\n        store_best_metric: name of the metric to use for best model\n            selection.\n        mode: \"max\" or \"min\".\n    \"\"\"\n\n    def __init__(\n        self,\n        path,\n        retain_metric=\"loss\",\n        prepend=\"\",\n        num_iters=-1,\n        ignore_before=0,\n        store_best=False,\n        mode=\"max\"\n        ):\n        super().__init__()\n        if os.path.isdir(path):\n            self.path = path\n        else:\n            os.makedirs(path)\n            self.path = path\n        # end the prepended text with an underscore if it does not\n        if not prepend.endswith(\"_\") and prepend != \"\":\n            prepend += \"_\"\n        self.prepend = prepend\n        self.num_iters = num_iters\n        self.ignore_before = ignore_before\n        self.best_model = None\n        self.best_res = -1\n        self.store_best = store_best\n        self.retain_metric = retain_metric\n        self.mode = mode\n\n        print(self.path)\n\n    def __call__(self, trainer, epoch, val_metrics):\n        # do not store intermediate iterations\n        print(self.path)\n        if epoch >= self.ignore_before and epoch != 0:\n            if not self.num_iters == -1:\n            \n                # counting epochs starts from 1; i.e. +1\n                epoch += 1\n                # store model recurrently if set\n                if epoch % self.num_iters == 0:\n                    name = self.prepend + \"training_epoch_{}.h5\".format(epoch)\n                    full_path = os.path.join(self.path, name)\n                    self.save_model(trainer, full_path)\n\n            # store current model if improvement detected\n            if self.store_best:\n                current_res = 0\n                try:\n                    # check if value can be used directly or not\n                    if isinstance(self.retain_metric, str):\n                        current_res = val_metrics[self.retain_metric][-1]\n                    else:\n                        current_res = val_metrics[self.retain_metric.__name__][-1]\n                except KeyError:\n                    print(\"Couldn't find {} in validation metrics. Using \\\n                        loss instead.\".format(self.retain_metric))\n                    current_res = val_metrics[\"loss\"][-1]\n                    \n                if self.has_improved(current_res):\n                    self.best_res = current_res\n                    self.best_model = deepcopy(trainer.model.state_dict())\n\n    def reset(self):\n        \"\"\"\n        Reset module after training.\n        Useful for cross validation.\n        \"\"\"\n        self.best_model = None\n        self.best_res = -1\n\n    def final(self, **kwargs):\n        epoch = kwargs[\"epoch\"] + 1\n        if epoch >= self.ignore_before:\n            name = self.prepend + \"training_epoch_{}_FINAL.h5\".format(epoch)\n            full_path = os.path.join(self.path, name)\n            self.save_model(kwargs[\"trainer\"], full_path)\n        else:\n            print(\"Minimum iterations to store model not reached.\")\n\n        if self.best_model is not None:\n            best_model = deepcopy(self.best_model)\n            best_res = self.best_res\n            print(\"Best result during training: {:.2f}. Saving model..\".format(best_res))\n            name = self.prepend + \"BEST_ITERATION.h5\"\n            torch.save(best_model, os.path.join(self.path, name))\n        self.reset()\n\n    def save_model(self, trainer, full_path):\n        print(\"Writing model to disk...\")\n        model = trainer.model.cpu()\n        torch.save(model.state_dict(), full_path)\n        if trainer.device is not None:\n            trainer.model.cuda(trainer.device)\n\n    def has_improved(self, res):\n        if self.mode == \"max\":\n            return res >= self.best_res\n        elif self.mode == \"min\":\n            # check if still standard value\n            if self.best_res == -1:\n                return True\n            else:\n                return res <= self.best_res\n        else:\n            raise NotImplementedError(\"Only modes 'min' and 'max' available\")\n\n\nclass EarlyStopping(Callback):\n    \"\"\" \n    Stop training when a monitored quantity has stopped improving.\n\n    Arguments\n        patience: number of iterations without improvement after which\n            to stop\n        retain_metric: the metric which you want to monitor\n        mode: {min or max}; defines if you want to maximise or minimise\n            your metric\n        ignore_before: does not start the first window until this epoch.\n            Can be useful when training spikes a lot in early epochs.\n    \"\"\"\n\n\n    def __init__(self, patience, retain_metric, mode, ignore_before=0):\n        self.patience = patience\n        self.retain_metric = retain_metric\n        self.mode = mode\n        self.ignore_before = ignore_before\n        self.best_res = -1\n        # set to first iteration which is interesting\n        self.best_epoch = self.ignore_before\n\n    def __call__(self, trainer, epoch, val_metrics):\n        if epoch >= self.ignore_before:\n            if epoch - self.best_epoch < self.patience:\n                if isinstance(self.retain_metric, str):\n                    current_res = val_metrics[self.retain_metric][-1]\n                else:\n                    current_res = val_metrics[self.retain_metric.__name__][-1]\n                if self.has_improved(current_res):\n                    self.best_res = current_res\n                    self.best_epoch = epoch\n            else:\n                # end training run\n                trainer.stop_training = True\n\n    def has_improved(self, res):\n        if self.mode == \"max\":\n            return res > self.best_res\n        elif self.mode == \"min\":\n            # check if still standard value\n            if self.best_res == -1:\n                return True\n            else:\n                return res < self.best_res\n        else:\n            raise NotImplementedError(\"Only modes 'min' and 'max' available\")\n\n    def reset(self):\n        \"\"\" Resets after training. Useful for cross validation.\"\"\"\n        self.best_res = -1\n        self.best_epoch = self.ignore_before\n\n    def final(self, **kwargs):\n        self.reset()\n\n        \n# Functions which can be used in custom-callbacks for visualizing 3D-features during training \n# (using the argument 'training_time_callback' in nitorch's Trainer class )\ndef visualize_feature_maps(features, return_fig=False):\n    \n    if(features.is_cuda):\n        features = features.cpu().detach().numpy()\n\n    num_features = len(features)\n    plt.close('all')\n    figsize=((num_features//8 + 5)*3 ,(num_features//8)*10 )\n    fig = plt.figure(figsize=figsize)\n\n    for i, f in enumerate(features, 1):            \n        # normalize to range [0, 1] first as the values can be very small            \n        if((f.max() - f.min()) != 0):\n            f = (f - f.min()) / (f.max() - f.min())\n\n            idxs = np.nonzero(f)\n            vals = np.ravel(f[idxs])                \n            if(len(vals)):\n                # calculate the index where the mean value would lie\n                mean_idx = np.average(idxs, axis = 1, weights=vals)\n                # calculate the angel ratios for each non-zero val            \n                angles = (mean_idx.reshape(-1,1) - idxs)\n                angles = angles/ (np.max(abs(angles), axis=1).reshape(-1,1))    \n            else: # if all values in f are zero, set dummy angle\n                angles = [1, 1, 1]\n\n#             print(\"values = \",vals)\n            ax = fig.add_subplot(num_features//3+1, 3, i,\n                                  projection='3d')\n            ax.set_title(\"Feature-{} in the bottleneck\".format(i))\n            ax.quiver(*idxs\n                      , angles[0]*vals, angles[1]*vals, angles[2]*vals\n                     )    \n            plt.grid()\n\n        else:\n            ax = fig.add_subplot(num_features//3+1, 3, i)\n            ax.text(0.5, 0.5, \"All values zero!\", transform=ax.transAxes)\n            plt.axis('off')\n            \n    plt.tight_layout()\n    if return_fig:\n        return fig\n\n\nclass CAE_VisualizeTraining(Callback):\n    ''' \n    training_time_callback that prints the model dimensions,\n    visualizes CAE encoder outputs, original image and reconstructed image\n    during training.\n    \n    NOTE : The forward() function of the CAE model using this callback\n    must return a (decoder_output, encoder_output) tuple.\n    '''\n    def __init__(self, model, max_train_iters, show_epochs_list=[], plotFeatures=True, plot_pdf_path=\"\", cmap=\"nipy_spectral\"):\n        self.model = model\n        self.max_train_iters = max_train_iters\n        if plot_pdf_path is not None:\n            assert isinstance(plot_pdf_path, str), \"pp is not a path!\"\n        self.plot_pdf_path = plot_pdf_path\n        assert isinstance(plotFeatures, bool), \"plotFeatures not boolean object!\"\n        self.plotFeatures = plotFeatures\n        assert isinstance(show_epochs_list, list), \"show_epochs_list is not a list!\"\n        self.show_epochs_list = show_epochs_list\n        self.cmap = cmap\n        self.ave_grads = []\n        self.layers = []\n        # inform the model to also return the encoder output along with the decoder output\n        try:\n            if(isinstance(model, nn.DataParallel)): \n                model.module.set_return_encoder_out(True)\n            else:\n                model.set_return_encoder_out(True)\n        except AttributeError:\n            raise \"The CAE model must implement a setter function 'set_return_encoder_out'\\\n for a flag 'encoder_out' which when set to true, the forward() function using this callback \\\nmust return a (decoder_output, encoder_output) tuple instead of just (encoder_output). See the CAE class in models.py for the framework.\"\n\n    def __call__(self, inputs, labels, train_iter, epoch):\n        debug = False\n        visualize_training = False\n        tmp_show_epoches_list = []\n\n        # if show_epochs_list is empty, all epoches should be plotted. Therefore, add current epoch to the list\n        if not self.show_epochs_list:\n            tmp_show_epoches_list.append(epoch)\n        else:\n            tmp_show_epoches_list = self.show_epochs_list\n\n        # check if epoch should be visualized\n        if epoch in tmp_show_epoches_list:\n            # print the model's parameter dimensions etc in the first iter\n            if (train_iter == 0 and epoch == 0):\n                debug = True\n            # visualize training on the last iteration in that epoch\n            elif(train_iter==1 and epoch==0) or (train_iter == self.max_train_iters):\n                visualize_training = True\n\n        # for nitorch models which have a 'debug' and 'visualize_training' switch in the\n        # forward() method\n\n        if(isinstance(self.model, nn.DataParallel)):\n            self.model.module.set_debug(debug)\n        else:\n            self.model.set_debug(debug)\n\n        outputs, encoder_out = self.model(inputs)\n        \n        if(visualize_training):\n            # check if result should be plotted in PDF\n            if self.plot_pdf_path != \"\":\n                pp = PdfPages(os.path.join(self.plot_pdf_path, \"training_epoch_\" + str(epoch) + \"_visualization.pdf\"))\n            else:\n                pp = None\n            \n            # show only the first image in the batch\n            if pp is None:\n                # input image\n                show_brain(inputs[0].squeeze().cpu().detach().numpy(),  draw_cross=False, cmap=self.cmap)\n                plt.suptitle(\"Input image\")\n                plt.show()\n                if(not torch.all(torch.eq(inputs[0],labels[0]))):\n                    show_brain(labels[0].squeeze().cpu().detach().numpy(),  draw_cross = False, cmap=self.cmap)\n                    plt.suptitle(\"Expected reconstruction\")\n                    plt.show()  \n                # reconstructed image\n                show_brain(outputs[0].squeeze().cpu().detach().numpy(),  draw_cross = False, cmap=self.cmap)\n                plt.suptitle(\"Reconstructed Image\")\n                plt.show()\n                # statistics\n                print(\"\\nStatistics of expected reconstruction:\\n(min, max)=({:.4f}, {:.4f})\\nmean={:.4f}\\nstd={:.4f}\".format(\n                    labels[0].min(), labels[0].max(), labels[0].mean(), labels[0].std()))\n                print(\"\\nStatistics of Reconstructed image:\\n(min, max)=({:.4f}, {:.4f})\\nmean={:.4f}\\nstd={:.4f}\".format(\n                    outputs[0].min(), outputs[0].max(), outputs[0].mean(), outputs[0].std()))   \n                # feature maps\n                visualize_feature_maps(encoder_out[0])\n                plt.suptitle(\"Encoder output\")\n                plt.show()\n            else:\n                # input image\n                fig = show_brain(inputs[0].squeeze().cpu().detach().numpy(),  draw_cross=False, return_fig=True,\n                                 cmap=self.cmap)\n                plt.suptitle(\"Input image\")\n                pp.savefig(fig)\n                plt.close(fig)\n                if(not torch.all(torch.eq(inputs[0],labels[0]))):\n                    fig = show_brain(labels[0].squeeze().cpu().detach().numpy(),  draw_cross = False, cmap=self.cmap)\n                    plt.suptitle(\"Expected reconstruction\")\n                    pp.savefig(fig)\n                    plt.close(fig)\n                # reconstructed image\n                fig = show_brain(outputs[0].squeeze().cpu().detach().numpy(), draw_cross=False, return_fig=True, cmap=self.cmap)\n                plt.suptitle(\"Reconstructed Image\")\n                pp.savefig(fig)\n                plt.close(fig)\n                # feature maps\n                if self.plotFeatures:\n                    fig = visualize_feature_maps(encoder_out[0], return_fig=True)\n                    plt.suptitle(\"Encoder output\")\n                    pp.savefig(fig)\n                    plt.close(fig)\n\n            # close the PDF\n            if pp is not None:\n                pp.close()\n \n        if(isinstance(self.model, nn.DataParallel)):\n            self.model.module.set_debug(False)\n        else:\n            self.model.set_debug(False)\n\n        return outputs\n"
  },
  {
    "path": "nitorch/data.py",
    "content": "import numpy as np\nimport nibabel as nib\nfrom scipy.ndimage.interpolation import zoom\nimport matplotlib.pyplot as plt\nimport nibabel\nimport os\n\ndef load_nifti(file_path, dtype=np.float32, incl_header=False, z_factor=None, mask=None):\n    \"\"\"\n    Loads a volumetric image in nifti format (extensions .nii, .nii.gz etc.)\n    as a 3D numpy.ndarray.\n    \n    Args:\n        file_path: absolute path to the nifti file\n        \n        dtype(optional): datatype of the loaded numpy.ndarray\n        \n        incl_header(bool, optional): If True, the nifTI object of the \n        image is also returned.\n        \n        z_factor(float or sequence, optional): The zoom factor along the\n        axes. If a float, zoom is the same for each axis. If a sequence,\n        zoom should contain one value for each axis.\n        \n        mask(ndarray, optional): A mask with the same shape as the\n        original image. If provided then the mask is element-wise\n        multiplied with the image ndarray\n    \n    Returns:\n        3D numpy.ndarray with axis order (saggital x coronal x axial)\n    \"\"\"\n    \n    img = nib.load(file_path)\n    struct_arr = img.get_data().astype(dtype)\n    \n    # replace infinite values with 0\n    if np.inf in struct_arr:\n        struct_arr[struct_arr == np.inf] = 0.\n    \n    # replace NaN values with 0    \n    if np.isnan(struct_arr).any() == True:\n        struct_arr[np.isnan(struct_arr)] = 0.\n        \n    if mask is not None:\n        struct_arr *= mask\n        \n    if z_factor is not None:\n        struct_arr = zoom(struct_arr, z_factor)\n    \n    if incl_header:\n        return struct_arr, img\n    else:\n        return struct_arr\n\n\ndef show_brain(img, cut_coords=None, \n               figsize=(10,5), cmap=\"nipy_spectral\",\n               draw_cross = True,\n               return_fig = False\n               ):\n    \"\"\"Displays 2D cross-sections of a 3D image along all 3 axis\n    Arg:\n        img: can be (1) 3-dimensional numpy.ndarray\n                    (2) nibabel.Nifti1Image object\n                    (3) path to the image file stored in nifTI format\n\n        cut_coords (optional): The voxel coordinates\n        of the axes where the cross-section cuts will be performed. \n        Should be a 3-tuple: (x, y, z). Default is the center = img_shape/2 \n        \n        figsize (optional): matplotlib figsize. Default is (10,5)\n        cmap (optional): matplotlib colormap to be used\n        \n        draw_cross (optional): Draws horizontal and vertical lines which\n        show where the cross-sections have been performed. D\n        \n\n        example:\n            >>> show_brain(img, figsize=(7, 3), draw_cross=False)\n            >>> plt.show()\n        \"\"\"\n    \n    if(isinstance(img, str) and os.path.isfile(img)):\n        img_arr = load_nifti(img)\n    elif(isinstance(img, nibabel.Nifti1Image)):\n        img_arr = img.get_data()\n        \n    elif(isinstance(img, np.ndarray)):\n        assert img.ndim == 3, \"The numpy.ndarray must be 3-dimensional with shape (H x W x Z)\"\n        img_arr = img\n    else:\n        raise TypeError(\"Invalid type provided for 'img'- {}. \\\nEither provide a 3-dimensional numpy.ndarray of a MRI image or path to \\\nthe image file stored as a nifTI format.\".format(type(img)))\n        \n    # print(img_arr.shape)\n    # img_arr = np.moveaxis(img_arr, 0, 1)\n    # print(img_arr.shape)\n\n    x_len, y_len, z_len = img_arr.shape\n    # if cut_coordinates is not specified set it to the center of the image\n    if(cut_coords == None):\n        cut_coords = (x_len//2, y_len//2, z_len//2)\n\n    f, ax = plt.subplots(nrows=1, ncols=3, figsize=figsize)\n\n    ax[0].set_title(\"Saggital cross-section at x={}\".format(cut_coords[0]))\n    ax[0].imshow(\n         np.rot90(img_arr[cut_coords[0],:,:]), cmap=cmap, aspect=\"equal\")\n    #draw cross\n    if(draw_cross):\n        ax[0].axvline(x=cut_coords[1], color='k', linewidth=1)\n        ax[0].axhline(y=cut_coords[2], color='k', linewidth=1)\n\n    ax[1].set_title(\"Coronal cross-section at y={}\".format(cut_coords[1]))\n    ax[1].imshow(\n        np.rot90(img_arr[:,cut_coords[1],:]), cmap=cmap, aspect=\"equal\")\n    ax[1].text(0.05, 0.95,'L', \n        horizontalalignment='left', verticalalignment='top',\n        transform=ax[1].transAxes\n        , bbox=dict(facecolor='white')\n        )\n    ax[1].text(0.95, 0.95,'R', \n        horizontalalignment='right', verticalalignment='top'\n        , transform=ax[1].transAxes\n        , bbox=dict(facecolor='white')\n        )\n    #draw cross\n    if(draw_cross):\n        ax[1].axvline(x=cut_coords[0], color='k', linewidth=1)\n        ax[1].axhline(y=cut_coords[2], color='k', linewidth=1)\n\n    ax[2].set_title(\"Axial cross-section at z={}\".format(cut_coords[2]))\n    ax[2].imshow(\n        np.rot90(img_arr[:,:,cut_coords[2]]), cmap=cmap, aspect=\"equal\"\n        )\n    ax[2].text(0.05, 0.95,'L'\n        , horizontalalignment='left', verticalalignment='top'\n        , transform=ax[2].transAxes\n        , bbox=dict(facecolor='white')\n        )\n    ax[2].text(0.95, 0.95,'R', \n        horizontalalignment='right', verticalalignment='top'\n        , transform=ax[2].transAxes\n        , bbox=dict(facecolor='white')\n        )\n    #draw cross\n    if(draw_cross):\n        ax[2].axvline(x=cut_coords[0], color='k', linewidth=1)\n        ax[2].axhline(y=cut_coords[1], color='k', linewidth=1)\n\n    plt.tight_layout()\n    if return_fig:\n        return f\n"
  },
  {
    "path": "nitorch/inference.py",
    "content": "import numpy\nimport torch\nfrom torch import nn\n\ndef predict(\n    outputs,\n    labels,\n    all_preds,\n    all_labels,\n    prediction_type,\n    criterion,\n    **kwargs\n    ):\n    \"\"\" Predict according to loss and prediction type.\"\"\"\n    if prediction_type == \"binary\":\n        if isinstance(criterion, nn.BCEWithLogitsLoss):\n            all_preds, all_labels = bce_with_logits_inference(\n                outputs,\n                labels,\n                all_preds,\n                all_labels,\n                **kwargs\n            )\n        elif isinstance(criterion, nn.BCELoss):\n            all_preds, all_labels = bce_inference(\n                outputs,\n                labels,\n                all_preds,\n                all_labels,\n                **kwargs\n            )\n    elif prediction_type == \"classification\":\n        all_preds, all_labels = crossentropy_inference(\n                outputs,\n                labels,\n                all_preds,\n                all_labels\n        )\n    elif prediction_type == \"regression\":\n        # TODO: test different loss functions\n        all_preds, all_labels = regression_inference(\n                outputs,\n                labels,\n                all_preds,\n                all_labels\n        )\n    elif prediction_type == \"reconstruction\":\n        # TODO: test different loss functions\n        all_preds, all_labels = regression_inference(\n                outputs,\n                labels,\n                all_preds,\n                all_labels\n        )\n    elif prediction_type == \"variational\":\n        # TODO: test different loss functions\n        all_preds, all_labels = variational_inference(\n                outputs,\n                labels,\n                all_preds,\n                all_labels\n        )\n    else:\n        raise NotImplementedError\n\n    return all_preds, all_labels\n\n\ndef bce_with_logits_inference(\n    outputs,\n    labels,\n    all_preds,\n    all_labels,\n    **kwargs\n    ):\n    sigmoid = torch.sigmoid(outputs)\n    if kwargs[\"class_threshold\"]:\n        class_threshold = kwargs[\"class_threshold\"]\n    else:\n        class_threshold = 0.5\n    print\n    predicted = sigmoid.data >= class_threshold\n    for pred, label in zip(predicted, labels):\n        all_preds.append(pred.cpu().item())\n        all_labels.append(int(label.cpu().item()))\n    return all_preds, all_labels\n\ndef bce_inference(\n    outputs,\n    labels,\n    all_preds,\n    all_labels,\n    **kwargs\n    ):\n    if kwargs[\"class_threshold\"]:\n        class_threshold = kwargs[\"class_threshold\"]\n    else:\n        class_threshold = 0.5\n    predicted = outputs.data >= class_threshold\n    for pred, label in zip(predicted, labels):\n        all_preds.append(pred.cpu().item())\n        all_labels.append(label.cpu().item())\n    return all_preds, all_labels\n\ndef crossentropy_inference(\n    outputs,\n    labels,\n    all_preds,\n    all_labels,\n    **kwargs\n    ):\n    _, predicted = torch.max(outputs.data, 1)\n    for pred, label in zip(predicted, labels):\n        all_preds.append(pred.cpu().item())\n        all_labels.append(label.cpu().item())\n    return all_preds, all_labels\n\ndef regression_inference(\n    outputs,\n    labels,\n    all_preds,\n    all_labels\n    ):\n    # Multi-head case\n    # network returns a tuple of outputs\n    if isinstance(outputs, (list,tuple)):\n        predicted = [output.data for output in outputs]\n        for head in range(len(predicted)):\n            for j in range(len(predicted[head])):\n                try:\n                    all_preds[head].append(predicted[head][j].cpu().numpy()[0])\n                    all_labels[head].append(labels[head][j].cpu().numpy()[0])\n                except IndexError:\n                    # create inner lists if needed\n                    all_preds.append([predicted[head][j].cpu().numpy()[0]])\n                    all_labels.append([labels[head][j].cpu().numpy()[0]])\n        return all_preds, all_labels\n    # Single-head case\n    else:\n        predicted = outputs[0].data\n        # TODO: replace for loop with something faster\n        for j in range(len(predicted)):\n            try:\n                all_preds.append(predicted[j].cpu().numpy().item())\n                all_labels.append(labels[j].cpu().numpy().item())\n            except:\n                all_preds.append(predicted[j].cpu().numpy()[0])\n                all_labels.append(labels[j].cpu().numpy()[0])\n        return all_preds, all_labels\n\ndef variational_inference(\n    outputs,\n    labels,\n    all_preds,\n    all_labels\n    ):\n    \"\"\" Inference for variational autoencoders. \"\"\"\n    # VAE outputs reconstruction, mu and std\n    # select reconstruction only\n    outputs = outputs[0]\n    predicted = outputs.data \n    # TODO: replace for loop with something faster\n    for pred, label in zip(predicted, labels):\n        all_preds.append(pred.cpu().item())\n        all_labels.append(label.cpu().item())\n    return all_preds, all_labels\n"
  },
  {
    "path": "nitorch/initialization.py",
    "content": "# Initialize weights\nfrom torch.nn import init, Conv3d, BatchNorm3d, Linear\n\n\ndef xavier(x):\n    return init.xavier_normal_(x)\n\ndef xavier_uniform(x):\n    return init.xavier_uniform_(x)\n\ndef he(x):\n    return init.kaiming_normal_(x)\n\ndef he_uniform(x):\n    return init.kaiming_uniform_(x)\n\n\ndef weights_init(m, func=he_uniform):\n    if isinstance(m, Conv3d):\n        func(m.weight.data)\n        if m.bias is not None:\n            init.constant_(m.bias, 0)\n    elif isinstance(m, BatchNorm3d):\n        m.reset_parameters()\n    elif isinstance(m, Linear):\n        m.reset_parameters()\n"
  },
  {
    "path": "nitorch/license.txt",
    "content": "NiTorch\nCopyright 2019 Fabian Eitel\n\nRedistribution and use in source and binary forms, with or without \nmodification, are permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright notice, this\nlist of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright notice, \nthis list of conditions and the following disclaimer in the documentation \nand/or other materials provided with the distribution.\n\n3. Neither the name of the copyright holder nor the names of its contributors \nmay be used to endorse or promote products derived from this software without \nspecific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND \nANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED \nWARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE \nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR \nANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\nLOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON\nANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\nSOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.```\n"
  },
  {
    "path": "nitorch/loss.py",
    "content": "import torch\nimport torch.nn.functional as F\n\nclass BCE_KL_loss(torch.nn.Module):\n    \"\"\" \n    Reconstruction loss for variational auto-encoders.\n    Binary-cross entropy reconstruction + KL divergence losses summed\n    over all elements and batch. \n    Mostly taken from pytorch examples: \n        https://github.com/pytorch/examples/blob/master/vae/main.py\n\n    Arguments:\n        outputs: List of the form [reconstruction, mean, logvariance].\n        x: ground-truth.\n    \"\"\"\n    def __init__(self):\n        super(BCE_KL_loss, self).__init__()\n\n    def forward(self, outputs, target):\n        recon_x, mu, logvar = outputs\n        BCE = F.binary_cross_entropy(recon_x, target, size_average=False)\n\n        # see Appendix B from VAE paper:\n        # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014\n        # https://arxiv.org/abs/1312.6114\n        # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)\n        KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n\n        return BCE + KLD\n\nclass MSE_KL_loss(torch.nn.Module):\n    \"\"\" \n    Reconstruction loss for variational auto-encoders.\n    Mean squared error reconstruction + KL divergence losses summed\n    over all elements and batch. \n    Mostly taken from pytorch examples: \n        https://github.com/pytorch/examples/blob/master/vae/main.py\n\n    Arguments:\n        outputs: List of the form [reconstruction, mean, logvariance].\n        x: ground-truth.\n    \"\"\"\n    def __init__(self):\n        super(MSE_KL_loss, self).__init__()\n\n    def forward(self, outputs, target):\n        recon_x, mu, logvar = outputs\n        MSE = F.mse_loss(recon_x, target, size_average=False)\n\n        # see Appendix B from VAE paper:\n        # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014\n        # https://arxiv.org/abs/1312.6114\n        # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)\n        KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n\n        return MSE + KLD\n\nclass Multihead_loss(torch.nn.Module):\n    \"\"\"\n    Compute the loss on multiple outputs.\n\n    Arguments:\n        outputs: List of network outputs.\n        target: List of targets where len(outputs) = len(target).\n        loss_function: List of loss functions with either\n            len(loss_function) = len(targets) or len(loss_function) = 1.\n        weights: List of weights for each loss. Default = [1]\n    \"\"\"\n    def __init__(self, loss_function, weights=[1]):\n        super(Multihead_loss, self).__init__()\n\n        self.loss_function = loss_function\n        self.weights = weights\n\n    def forward(self, outputs, target):\n        assert(len(outputs) == len(target))\n        assert(len(self.loss_function) == len(target) \\\n            or len(self.loss_function) == 1)\n\n        # expand loss_function list if univariate\n        if len(self.loss_function) == 1:\n            self.loss_function = [self.loss_function[0] for i in range(len(target))]\n        # expand weights list if univariate\n        if len(self.weights) == 1:\n            self.weights = [self.weights[0] for i in range(len(target))]\n\n        # compute loss for each head\n        total_loss = 0.\n        for out, gt, loss_func, weight in zip(outputs, target, self.loss_function, self.weights):\n            loss = loss_func(out, gt)\n            total_loss += loss * weight\n        return total_loss\n"
  },
  {
    "path": "nitorch/metrics.py",
    "content": "from sklearn.metrics import recall_score, roc_curve, auc\n\ndef specificity(y_true, y_pred):\n    return recall_score(y_true, y_pred, pos_label=0)\n\n\ndef sensitivity(y_true, y_pred):\n    return recall_score(y_true, y_pred, pos_label=1)\n\n\ndef balanced_accuracy(y_true, y_pred):\n    spec = specificity(y_true, y_pred)\n    sens = sensitivity(y_true, y_pred)\n    return (spec + sens) / 2\n\ndef auc_score(y_true, y_pred):\n    fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n    return auc(fpr, tpr)"
  },
  {
    "path": "nitorch/models.py",
    "content": "import numpy as np\n# pytorch\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport matplotlib.pyplot as plt\n\nfrom nitorch.data import show_brain\n\n\nclass _CAE_3D(nn.Module):\n    '''\n    Parent Convolutional Autoencoder class for 3D images. \n    All other Convolutional Autoencoder classes must inherit from this class.\n    '''\n    def __init__(self, conv_channels):        \n        super().__init__()        \n        # check if there are multiple convolution layers within a layer of the network or not\n        self.is_nested_conv = ([isinstance(each_c, (list, tuple)) for each_c in conv_channels])\n        if(any(self.is_nested_conv) and not all(self.is_nested_conv)):\n             raise TypeError(\" `conv_channels` can't be a mixture of both lists and ints.\")\n        self.is_nested_conv = any(self.is_nested_conv)\n        \n        self.layers = len(conv_channels)\n        self.conv_channels = self._format_channels(conv_channels, self.is_nested_conv)\n        self.valid_activations = {'ELU': nn.ELU, 'HARDSHRINK': nn.Hardshrink, 'HARDTANH': nn.Hardtanh,\n 'LEAKYRELU':nn.LeakyReLU, 'LOGSIGMOID': nn.LogSigmoid, 'PRELU':nn.PReLU, 'RELU':nn.ReLU, 'RELU6': nn.ReLU6, \n 'RRELU': nn.RReLU, 'SELU': nn.SELU, 'SIGMOID': nn.Sigmoid, 'SOFTPLUS': nn.Softplus, \n 'SOFTSHRINK': nn.Softshrink, 'TANH': nn.Tanh, 'TANHSHRINK': nn.Tanhshrink, 'THRESHOLD': nn.Threshold}\n\n    def _format_channels(self, conv_channels, is_nested_conv = False):\n        channels = []\n        if(is_nested_conv):\n            for i in range(len(conv_channels)):\n                    inner_channels = []\n                    for j in range(len(conv_channels[i])):\n                        if (i == 0) and (j == 0):\n                            inner_channels.append([1, conv_channels[i][j]])\n                        elif (j == 0) :\n                            inner_channels.append([conv_channels[i-1][-1], conv_channels[i][j]])\n                        else:\n                            inner_channels.append([conv_channels[i][j-1], conv_channels[i][j]])\n                    channels.append(inner_channels)\n        else:\n            for i in range(len(conv_channels)):\n                if (i == 0):\n                    channels.append([1, conv_channels[i]])\n                else:\n                    channels.append([conv_channels[i-1], conv_channels[i]])\n\n        return channels\n\n\n    def assign_parameter(self, parameter, param_name, enable_nested = True):\n        ''' Wrapper for parameters of the Autoencoder. \n        Checks if the len and type of the parameter is acceptable.\n        If the parameter is just an single value,\n        makes its length equal to the number of layers defined in conv_channels\n        '''\n        if(isinstance(parameter, (int, str))):\n            if(self.is_nested_conv and enable_nested):\n                return_parameter = [len(inner_list)*[parameter] for inner_list in self.conv_channels]\n            else:\n                return_parameter = (self.layers * [parameter])\n        # Perform sanity checks if a list is already provided \n        elif(isinstance(parameter, (list, tuple))):\n            if(len(parameter) != self.layers): \n                raise ValueError(\"The parameter '{}' can either be a single int \\\nor must be a list of the same length as 'conv_channels'.\".format(\n        param_name))\n        \n            if(self.is_nested_conv and enable_nested):\n                if(any(\n                    [len(c) != len(p) for c, p in zip(self.conv_channels, parameter)]\n                    )):\n                    raise ValueError(\"The lengths of the inner lists of the parameter {} \\\nhave to be same as the 'conv_channels'\".format(param_name)) \n            # if all length checks pass just return the parameter\n            return_parameter = parameter\n            \n        else: \n            raise TypeError(\"Parameter {} is neither an int/ valid str nor a list/tuple but is of type {}\".format(\n                param_name, parameter))\n\n        return return_parameter\n\n\n    def add_conv_with_activation(self, inp_channels, out_channels, kernel_size, padding, stride, activation_fn):\n        node = nn.Sequential(\n            nn.Conv3d(inp_channels, out_channels, kernel_size, padding = padding, stride = stride),\n            self.valid_activations[activation_fn](inplace=True))        \n        return node\n        \n\n    def add_deconv_with_activation(self, inp_channels, out_channels, kernel_size, padding, stride, out_padding, activation_fn):\n        node = nn.Sequential(\n            nn.ConvTranspose3d(inp_channels, out_channels, kernel_size\n                , padding = padding, stride = stride, output_padding=out_padding),\n            self.valid_activations[activation_fn](inplace=True))        \n        return node\n        \n        \n    def add_pool(self, pool_type, kernel_size, padding, stride):\n        if(pool_type == \"max\"):\n            node = nn.MaxPool3d(kernel_size, \n                        padding = padding, \n                        stride = stride, \n                        return_indices = True)\n        elif(pool_type == \"avg\"):\n            node = nn.AvgPool3d(kernel_size, \n                        padding = padding, \n                        stride = stride)\n        else:\n            raise TypeError(\"Invalid value provided for `pool_type`.\\\nAllowed values are `max`, `avg`.\")\n            \n        return node\n        \n        \n    def add_unpool(self, pool_type, kernel_size, padding, stride):\n        if(pool_type == \"max\"):\n            node = nn.MaxUnpool3d(kernel_size, \n                        padding = padding, \n                        stride = stride)\n        elif(pool_type == \"avg\"):\n            node = nn.MaxPool3d(kernel_size, \n                        padding = padding, \n                        stride = stride)\n        else:\n            raise TypeError(\"Invalid value provided for `pool_type`.\\\nAllowed values are `max`, `avg`.\")\n            \n        return node\n    \n        \n    def nested_reverse(self, mylist):\n        result = []\n        for e in mylist:\n            if isinstance(e, (list, tuple)):\n                result.append(self.nested_reverse(e))\n            else:\n                result.append(e)\n        result.reverse()\n        return result\n\n    \nclass CAE_3D(_CAE_3D):\n    '''\n    3D Convolutional Autoencoder model with only convolution layers. Strided convolution\n    can be used for undersampling. \n    '''\n    def __init__(self\n        , conv_channels\n        , activation_fn = \"RELU\"\n        , conv_kernel = 3\n        , conv_padding = 1\n        , conv_stride = 1\n        , deconv_out_padding = None\n        , second_fc_decoder = []\n        ):\n        '''\n        Args:\n            conv_channels : A list that defines the number of channels of each convolution layer.\n            The length of the list defines the number of layers in the encoder. \n            The decoder is automatically constructed as an exact reversal of the encoder architecture.\n            \n            activation_fn (optional):  The non-linear activation function that will be appied after every layer\n            of convolution / deconvolution. \n            Supported values {'ELU', 'HARDSHRINK', 'HARDTANH', 'LEAKYRELU', 'LOGSIGMOID', 'PRELU', 'RELU', \n            'RELU6', 'RRELU', 'SELU', 'SIGMOID', 'SOFTPLUS', 'SOFTSHRINK', 'TANH', 'TANHSHRINK', 'THRESHOLD'}\n            By default nn.ReLu() is applied.\n            Can either be a a single int (in which case the same activation is applied to all layers) or \n            a list of same length and shape as `conv_channels`.\n            \n            conv_kernel (optional): The size of the 3D convolutional kernels to be used. \n            Can either be a list of same length as `conv_channels` or a single int. In the\n             former case each value in the list represents the kernel size of that particular\n            layer and in the latter case all the layers are built with the same kernel size as \n            specified.\n\n            conv_padding (optional): The amount of zero-paddings to be done along each dimension.\n            Format same as conv_kernel.\n\n            conv_stride (optional): The stride of the 3D convolutions.\n            Format same as conv_kernel.\n\n            deconv_out_padding (optional): The additional zero-paddings to be done to the output \n            of ConvTranspose / Deconvolutions in the decoder network.\n            By default does (stride-1) number of padding.\n            Format same as conv_kernel.\n            \n            second_fc_decoder (optional): By default this is disabled. \n            If a non-empty list of ints is provided then a secondary fully-connected decoder \n            network is constructed as per the list.\n            Each value represents the number of cells in each layer. Just like `conv_channels`\n            the length of the list defines the number of layers.\n            If enabled, the forward() method returns a list of 2 outputs, one from the Autoencoder's\n            decoder and the other from this fully-connected decoder network.            \n        '''\n        super().__init__(conv_channels)\n\n        assert not(self.is_nested_conv), \"The conv_channels must be a list of ints (i.e. number of channels).\\\nIt cannot be a list of lists.\"\n\n        self.conv_kernel = self.assign_parameter(conv_kernel, \"conv_kernel\")\n        self.conv_padding = self.assign_parameter(conv_padding, \"conv_kernel\")\n        self.conv_stride = self.assign_parameter(conv_stride, \"conv_stride\")\n        if(deconv_out_padding == None):\n            deconv_out_padding = [s-1 for s in self.conv_stride]\n        self.deconv_out_padding = self.assign_parameter(deconv_out_padding, \"deconv_out_padding\")\n        \n        self.activation_fn = self.assign_parameter(activation_fn, \"activation_function\")\n\n        for activation in self.activation_fn:\n            assert activation.upper() in self.valid_activations.keys(), \"activation functions can only be one of the following str :\\n {}\".format(\n                    self.valid_activations.keys())\n            \n        # set the switches used in forward() as false  by default\n        self.debug = False\n        self.return_encoder_out = False\n        \n        if(second_fc_decoder):\n            self.second_fc_decoder = self._format_channels(second_fc_decoder)[1:]\n        else:\n            self.second_fc_decoder = []\n\n        self.convs = nn.ModuleList()\n        self.deconvs = nn.ModuleList()\n        \n        for i in range(self.layers):\n            # build the encoder\n            self.convs.append(\n                self.add_conv_with_activation(\n                    self.conv_channels[i][0], self.conv_channels[i][1], \n                    self.conv_kernel[i]\n                    , self.conv_padding[i]\n                    , self.conv_stride[i]\n                    , self.activation_fn[i]\n                    )\n                )\n            # build the decoder\n            self.deconvs.append(\n                self.add_deconv_with_activation(\n                    self.conv_channels[-i-1][1], self.conv_channels[-i-1][0], \n                    self.conv_kernel[-i-1]\n                    , self.conv_padding[-i-1]\n                    , self.conv_stride[-i-1]\n                    , self.deconv_out_padding[-i-1]\n                    , self.activation_fn[-i-1]\n                    )\n                )\n        if(self.second_fc_decoder):\n        # build the second fc decoder\n            self.fcs = nn.ModuleList()\n            for layer in self.second_fc_decoder:\n                self.fcs.append(\n                    nn.Linear(layer[0], layer[1])\n                )\n                \n                \n    def set_debug(self, bool_val):\n        self.debug = bool_val\n    \n    def set_return_encoder_out(self, bool_val):\n        self.return_encoder_out = bool_val        \n\n    def forward(self, x):\n\n            if(self.debug): print(\"\\nImage dims =\"+str(x.size()))\n\n            #encoder\n            for i, conv in enumerate(self.convs):\n                x = conv(x)\n                if(self.debug): print(\"conv{} output dim = {}\".format(i+1, x.size()))\n            \n            encoder_out = x\n            \n            if(self.debug): print(\"\\nEncoder output dims =\"+str(encoder_out.size())+\"\\n\")\n            \n            #decoder\n            for i, deconv in enumerate(self.deconvs):\n                x = deconv(x)\n                if(self.debug): print(\"deconv{} output dim = {}\".format(i+1, x.size()))\n                        \n            if(self.debug): print(\"\\nDecoder output dims =\"+str(x.size())+\"\\n\")\n            \n            if(self.return_encoder_out):\n                \n                return [x, encoder_out]\n            else:\n                \n                return x\n\n            \n            \nclass CAE_3D_with_pooling(_CAE_3D):\n    '''\n    3D Convolutional Autoencoder model with alternating Pooling layers. \n    '''\n    def __init__(self\n        , conv_channels\n        , activation_fn = nn.ReLU\n        , conv_kernel = 3, conv_padding = 1, conv_stride = 1\n        , pool_type = \"max\"\n        , pool_kernel = 2, pool_padding = 0, pool_stride = 2\n        , deconv_out_padding = None\n        ):\n        '''\n        Args:\n            conv_channels : A nested list whose length defines the number of layers. Each layer\n            can intern have multiple convolutions followed by a layer of Pooling. The lengths of the \n            inner list defines the number of convolutions per such layer and the value defines the number of\n            channels for each of these convolutions.\n            The decoder is constructed to be simply an exact reversal of the encoder architecture.\n            \n            activation_fn (optional):  The non-linear activation function that will be appied after every layer\n            of convolution / deconvolution. By default nn.ReLu() is applied.\n            Supported values {'ELU', 'HARDSHRINK', 'HARDTANH', 'LEAKYRELU', 'LOGSIGMOID', 'PRELU', 'RELU', \n            'RELU6', 'RRELU', 'SELU', 'SIGMOID', 'SOFTPLUS', 'SOFTSHRINK', 'TANH', 'TANHSHRINK', 'THRESHOLD'}\n            Can either be a a single int (in which case the same activation is applied to all layers) or \n            a list of same length and shape as `conv_channels`.\n            \n            conv_kernel (optional): The size of the 3D convolutional kernels to be used. \n            Can either be a list of lists of same lengths as `conv_channels` or a single int. In the\n             former case each value in the list represents the kernel size of that particular\n            layer and in the latter case all the layers are built with the same kernel size as \n            specified.\n\n            conv_padding (optional): The amount of zero-paddings to be done along each dimension.\n            Format same as conv_kernel.\n\n            conv_stride (optional): The stride of the 3D convolutions.\n            Format same as conv_kernel.\n\n            deconv_out_padding (optional): The additional zero-paddings to be done to the output \n            of ConvTranspose / Deconvolutions in the decoder network.\n            By default does (stride-1) number of padding.\n            Format same as conv_kernel.\n            \n            pool_type (optional): The type of pooling to be used. Options are (1)\"max\"  (2)\"avg\" \n            \n            pool_kernel, pool_padding, pool_stride (optional): Can either be a single int or a list\n            of respective pooling parameter values.\n            The length of these list must be same as length of conv_channels i.e. the number of layers. \n            \n            second_fc_decoder (optional): By default this is disabled. \n            If a non-empty list of ints is provided then a secondary decoder of a fully-connected network  \n            is constructed as per the list.\n        '''\n        \n        super().__init__(conv_channels)\n        \n        assert (self.is_nested_conv), \"The conv_channels must be a list of list of ints Ex. [[16],[32 64],[64],...] (i.e. number of channels).\\\nIt cannot be a list.\"\n        \n        self.conv_kernel = self.assign_parameter(conv_kernel, \"conv_kernel\")\n        self.conv_padding = self.assign_parameter(conv_padding, \"conv_padding\")\n        self.conv_stride = self.assign_parameter(conv_stride, \"conv_stride\")\n        self.pool_kernel = self.assign_parameter(pool_kernel, \"pool_kernel\", enable_nested=False)\n        self.pool_padding = self.assign_parameter(pool_padding, \"pool_padding\", enable_nested=False)\n        self.pool_stride = self.assign_parameter(pool_stride, \"pool_stride\", enable_nested=False)        \n        \n        self.activation_fn = self.assign_parameter(activation_fn, \"activation_function\")\n\n        for activations in self.activation_fn:\n            for activation in activations:\n                assert activation.upper() in self.valid_activations.keys(), \"activation functions can only be one of the following str :\\n {}\".format(\n                    self.valid_activations.keys())\n        \n        self.deconv_channels = self.nested_reverse(self.conv_channels)\n        self.deconv_kernel = self.nested_reverse(self.conv_kernel)\n        self.deconv_padding = self.nested_reverse(self.conv_padding)\n        self.deconv_stride = self.nested_reverse(self.conv_stride)\n        \n        # set the switches used by forward() as false by default\n        self.debug = False\n        self.return_encoder_out = False\n        \n        if(deconv_out_padding is not None):\n            self.deconv_out_padding = self.nested_reverse(\n                self.assign_parameter(deconv_out_padding, \"deconv_out_padding\")\n            )                \n        else:\n            self.deconv_out_padding = [[s-1 for s in layer] for layer in self.deconv_stride]\n            \n        self.convs = nn.ModuleList()\n        self.pools = nn.ModuleList()\n        self.deconvs = nn.ModuleList()\n        self.unpools = nn.ModuleList()\n        \n\n        for i in range(self.layers):\n            \n            self.convs.append(\n                nn.ModuleList(\n                    [self.add_conv_with_activation(\n                        inner_conv_channels[0], inner_conv_channels[1]\n                        , self.conv_kernel[i][j]\n                        , self.conv_padding[i][j]\n                        , self.conv_stride[i][j]\n                        , self.activation_fn[i][j]) \\\n                    for j, inner_conv_channels in enumerate(self.conv_channels[i])]\n                    )\n                )\n\n            self.deconvs.append(\n                nn.ModuleList(\n                    [self.add_deconv_with_activation(\n                        inner_deconv_channels[0], inner_deconv_channels[1] \n                        , self.deconv_kernel[i][j]\n                        , self.deconv_padding[i][j]\n                        , self.deconv_stride[i][j]\n                        , self.deconv_out_padding[i][j]\n                        , self.activation_fn[i][j]) \\\n                    for j, inner_deconv_channels in enumerate(self.deconv_channels[i])]\n                    )\n                )\n\n            self.pools.append(\n                self.add_pool(\n                    pool_type,\n                    self.pool_kernel[i], \n                    stride = self.pool_stride[i], \n                    padding = self.pool_padding[i]\n                )\n            )\n            self.unpools.append(\n                self.add_unpool(\n                    pool_type,\n                    self.pool_kernel[-i-1], \n                    stride = self.pool_stride[-i-1], \n                    padding = self.pool_padding[-i-1]\n                )\n            )\n        \n    def set_debug(self, bool_val):\n        self.debug = bool_val\n    \n    def set_return_encoder_out(self, bool_val):\n        self.return_encoder_out = bool_val\n        \n    def forward(self, x):\n            '''return_encoder_out : If enabled returns a list with 2 values, \n            first one is the Autoencoder's output and the other the intermediary output of the encoder.\n            '''\n            pool_idxs = []\n            pool_sizes = [x.size()] #https://github.com/pytorch/pytorch/issues/580\n            \n            if(self.debug):\n                print(\"\\nImage dims =\"+str(x.size()))\n                \n            #encoder\n            for i,(convs, pool) in enumerate(zip(self.convs, self.pools)):\n                for j, conv in enumerate(convs):\n                    x = conv(x)\n                    if(self.debug):print(\"conv{}{} output dim = {}\".format(i+1, j+1, x.size()))\n                        \n                x, idx = pool(x)\n                pool_sizes.append(x.size()) \n                pool_idxs.append(idx)\n                if(self.debug):print(\"pool{} output dim = {}\".format(i+1, x.size()))\n            \n            encoder_out = x\n            \n            if(self.debug):\n                print(\"\\nEncoder output dims =\"+str(encoder_out.size())+\"\\n\")\n                \n            #decoder\n            pool_sizes.pop() # pop out the last size as it is not necessary\n\n            for i,(deconvs, unpool) in enumerate(zip(self.deconvs, self.unpools)):\n\n                x = unpool(x, pool_idxs.pop(), output_size=pool_sizes.pop())\n                if(self.debug):print(\"unpool{} output dim = {}\".format(i+1, x.size()))\n                \n                for j, deconv in enumerate(deconvs):\n                    x = deconv(x)\n                    if(self.debug):print(\"deconv{}{} output dim = {}\".format(i+1, j+1, x.size()))\n                        \n            if(self.debug):\n                print(\"\\nDecoder output dims =\"+str(x.size())+\"\\n\")\n                \n            if(self.return_encoder_out):\n                return [x, encoder_out]\n            else:\n                return x\n            \n            \n            \n            \nclass MLP(nn.Module):\n    '''\n    Constructs fully-connected deep neural networks \n    '''\n    def __init__(self\n        , layers = []\n        , output_activation = nn.LogSoftmax\n        ):\n        '''\n        Args:\n            layer_neurons : Each value represents the number of neurons in each layer. The length of the list\n            defines the number of layers. '''\n        super().__init__()   \n        self.layers = self._format_channels(layers)        \n#         self.output_activation = output_activation\n        self.debug = False\n        \n        # build the fully-connected layers\n        self.fcs = nn.ModuleList()\n\n        for layer in self.layers:\n            if(layer) is not self.layers[-1]:\n                self.fcs.append(self.add_linear_with_Relu(layer))\n            elif(output_activation is not None):\n                self.fcs.append(\n                    nn.Sequential(\n                        nn.Linear(layer[0], layer[1]),\n                        output_activation()))\n            else:\n                self.fcs.append(\n                    nn.Linear(layer[0], layer[1]))\n                \n    def set_debug(self, bool_val):\n        self.debug = bool_val\n        \n    def _format_channels(self, layers):   \n        layer_inout = []\n        for i in range(len(layers)-1):\n            layer_inout.append([layers[i], layers[i+1]])\n        return layer_inout\n\n    def add_linear_with_Relu(self, layer):\n        node = nn.Sequential(\n            nn.Linear(layer[0], layer[1]),\n            nn.ReLU(True))        \n        return node\n        \n    def forward(self, x):\n        for i,fc in enumerate(self.fcs):\n            x = fc(x)\n            if(self.debug):print(\"FC {} output dims ={}\".format(i, x.size()))\n                \n        return x"
  },
  {
    "path": "nitorch/trainer.py",
    "content": "import time\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom sklearn.metrics import confusion_matrix\nimport itertools\nimport matplotlib.pyplot as plt\nfrom nitorch.inference import predict\nfrom nitorch.utils import *\n\n\nclass Trainer:\n    def __init__(\n            self,\n            model,\n            criterion,\n            optimizer,\n            scheduler=None,\n            metrics=[],\n            callbacks=[],\n            training_time_callback=None,\n            device=torch.device('cuda'),\n            prediction_type=\"binary\",\n            **kwargs\n    ):\n        \"\"\" Main class for training.\n        # Arguments\n            model: neural network to train.\n            criterion: loss function.\n            optimizer: optimization function.\n            scheduler: schedules the optimizer.\n            metrics: list of metrics to report. Default is None.\n            callbacks: list of callbacks to execute at the end of training epochs. Default is None.\n            training_time_callback: a user-defined callback that executes the model.forward()\n                and returns the output to the trainer.\n                This can be used to perform debug during train time, Visualize features,\n                call model.forward() with custom arguments, run multiple decoder networks etc.\n                Default is None.\n            class_threshold: classification threshold for binary\n                classification. Default is 0.5.\n            prediction_type: accepts one of [\"binary\", \"classification\",\n                \"regression\", \"reconstruction\", \"variational\", \"other\"].\n                This is used to determine output type.\n            device: The device to use for training. Must be integer or\n                    a torch.device object. By default, GPU with current\n                    node is used.\n        \"\"\"\n        if not isinstance(model, nn.Module):\n            raise ValueError(\"Expects model type to be torch.nn.Module\")\n        self.model = model\n        self.criterion = criterion\n        self.optimizer = optimizer\n        self.scheduler = scheduler\n        self.metrics = metrics\n        self.callbacks = callbacks\n        self.training_time_callback = training_time_callback\n        if isinstance(device, int):\n            self.device = torch.device(\"cuda:\" + str(device))\n        elif isinstance(device, torch.device):\n            self.device = device\n        else:\n            raise ValueError(\"Device needs to be of type torch.device or \\\n                integer.\")\n        if \"class_threshold\" in kwargs.keys():\n            self.class_threshold = kwargs[\"class_threshold\"]\n        else:\n            self.class_threshold = None\n        self.stop_training = False\n        self.start_time = None\n        self.prediction_type = prediction_type\n\n    def train_model(\n            self,\n            train_loader,\n            val_loader,\n            inputs_key=\"image\",\n            labels_key=\"label\",\n            num_epochs=25,\n            show_train_steps=25,\n            show_validation_epochs=1\n    ):\n        \"\"\" Main function to train a network for one epoch.\n        Args:\n            train_loader: a pytorch Dataset iterator for training data\n            val_loader: a pytorch Dataset iterator for validation data\n            inputs_key, labels_key: The data returned by `train_loader` and `val_loader`can\n                            either be a dict of format data_loader[X_key] = inputs and\n                            data_loader[y_key] = labels or a list with data_loader[0] = inputs\n                            and data_loader[1] = labels. The default keys are \"image\" and \"label\".\n        \"\"\"\n        assert (show_validation_epochs < num_epochs) or (num_epochs == 1), \"\\\n'show_validation_epochs' value should be less than 'num_epochs'\"\n        assert (show_train_steps>0) and (show_train_steps<=len(train_loader)),\"\\\n'show_train_steps' value out of range. Must be > 0 and < len(train_loader)\"\n\n        val_metrics = dict()\n        train_metrics = dict()\n\n        self.start_time = time.time()\n        self.best_metric = 0.0\n        self.best_model = None\n\n        for epoch in range(num_epochs):\n            if self.stop_training:\n                # TODO: check position of this\n                print(\"Early stopping in epoch {}\".format(epoch))\n                return self.finish_training(train_metrics, val_metrics, epoch)\n            else:\n                # running_loss accumulates loss every 'show_train_steps' cycles until it must be printed.\n                running_loss = np.array([])\n                epoch_loss = 0.0\n                if self.scheduler:\n                    self.scheduler.step(epoch)\n\n                # Reset all metrics related variables at the start of each epoch\n                all_preds = []\n                all_labels = []\n                self.multi_batch_metrics = dict()\n                # train mode\n                self.model.train()\n\n                for i, data in enumerate(train_loader):\n                    try:\n                        inputs, labels = data[inputs_key], data[labels_key]\n                    except TypeError:\n                        # if data does not come in dictionary, assume\n                        # that data is ordered like [input, label]\n                        try:\n                            inputs, labels = data[0], data[1]\n                        except TypeError:\n                            raise TypeError\n                    # in case of multi-input or output create a list\n                    if isinstance(inputs, list):\n                        inputs = [inp.to(self.device) for inp in inputs]\n                    else:\n                        inputs = inputs.to(self.device)\n                    if isinstance(labels, list):\n                        labels = [label.to(self.device) for label in labels]\n                    else:\n                        labels = labels.to(self.device)\n\n                    # zero the parameter gradients\n                    self.optimizer.zero_grad()\n                    # forward + backward + optimize\n\n                    if self.training_time_callback is not None:\n                        outputs = self.training_time_callback(\n                            inputs, \n                            labels,\n                            i,\n                            epoch\n                        )\n                    else:\n                        outputs = self.model(inputs)\n\n                    if self.prediction_type == \"classification\":\n                        labels = labels.squeeze(1)\n                    loss = self.criterion(outputs, labels)\n                    loss.backward()\n\n                    # enable the below commented code if you want to visualize the \n                    # gradient flow through the model during training\n                    # plot_grad_flow(self.model.named_parameters())\n                    self.optimizer.step()\n\n                    # store results\n                    all_preds, all_labels = predict(\n                        outputs,\n                        labels,\n                        all_preds,\n                        all_labels,\n                        self.prediction_type,\n                        self.criterion,\n                        class_threshold=self.class_threshold\n                    )\n                    # update loss\n                    running_loss= np.append(running_loss, loss.item())\n                    epoch_loss += loss.item()\n                    # print loss every X mini-batches\n                    if (i % show_train_steps == 0) and (i != 0):\n                        print(\n                            \"[%d, %5d] loss: %.5f\"\n                            % (epoch , i , \n                               running_loss.mean())\n                        )\n                        running_loss = np.array([]) #reset\n\n                    # compute training metrics for X/2 mini-batches\n                    # useful for large outputs (e.g. reconstructions)\n                    if self.prediction_type == \"reconstruction\":\n                        if i % int(show_train_steps/2) == 0:\n                            self.estimate_metrics(\n                                all_labels,\n                                all_preds,\n                            )\n                            # TODO: test if del helps\n                            all_labels = []\n                            all_preds = []\n\n                # report training metrics\n                # weighted averages of metrics are computed over batches\n                train_metrics = self._on_epoch_end(\n                        train_metrics,\n                        all_labels,\n                        all_preds,\n                        phase=\"train\"\n                    )\n                epoch_loss /= len(train_loader)\n\n                # add loss to metrics data\n                if \"loss\" in train_metrics:\n                    train_metrics[\"loss\"].append(epoch_loss)\n                else:\n                    train_metrics[\"loss\"] = [epoch_loss]\n\n                #<end-of-training-cycle-loop>\n            #<end-of-epoch-loop>\n\n            # validate every x iterations\n            if epoch % show_validation_epochs == 0:\n                self.model.eval()\n                validation_loss = 0.0\n                all_preds = []\n                all_labels = []\n                self.multi_batch_metrics = dict()\n\n                with torch.no_grad():\n                    for i, data in enumerate(val_loader):\n                        try:\n                            inputs, labels = data[inputs_key], data[labels_key]\n                        except TypeError:\n                            # if data does not come in dictionary, assume\n                            # that data is ordered like [input, label]\n                            try:\n                                inputs, labels = data[0], data[1]\n                            except TypeError:\n                                raise TypeError(\"Data not in correct \\\n                                 sequence format.\")\n                        # in case of multi-input or output create a list\n                        if isinstance(inputs, list):\n                            inputs = [inp.to(self.device) for inp in inputs]\n                        else:\n                            inputs = inputs.to(self.device)\n                        if isinstance(labels, list):\n                            labels = [label.to(self.device) for label in labels]\n                        else:\n                            labels = labels.to(self.device)\n\n                        # forward pass only\n                        if self.training_time_callback is not None:\n                            outputs = self.training_time_callback(\n                                inputs, \n                                labels,\n                                1,  # dummy value\n                                1  # dummy value\n                            )\n                        else:\n                            outputs = self.model(inputs)\n\n                        if self.prediction_type == \"classification\":\n                            labels = labels.squeeze(1)\n                        loss = self.criterion(outputs, labels)\n                        # compute validation accuracy\n                        all_preds, all_labels = predict(\n                            outputs,\n                            labels,\n                            all_preds,\n                            all_labels,\n                            self.prediction_type,\n                            self.criterion,\n                            class_threshold=self.class_threshold\n                        )\n\n                        validation_loss += loss.item()\n\n                        # compute training metrics for X/2 mini-batches\n                        # useful for large outputs (e.g. reconstructions)\n                        if self.prediction_type == \"reconstruction\":\n                            if i % int(show_train_steps/2) == 0:\n                                self.estimate_metrics(\n                                    all_labels,\n                                    all_preds,\n                                )\n                                # TODO: test if del helps\n                                all_labels = []\n                                all_preds = []\n\n                    # report validation metrics\n                    # weighted averages of metrics are computed over batches\n                    val_metrics = self._on_epoch_end(\n                        val_metrics,\n                        all_labels,\n                        all_preds,\n                        phase=\"val\"\n                    )\n\n                    validation_loss /= len(val_loader)\n                    print(\"Val loss: {0:.6f}\".format(validation_loss))\n                    # add loss to metrics data\n                    if \"loss\" in val_metrics:\n                        val_metrics[\"loss\"].append(validation_loss)\n                    else:\n                        val_metrics[\"loss\"] = [validation_loss]\n            if self.callbacks is not None:\n                for callback in self.callbacks:\n                    callback(self, epoch, val_metrics)\n        # End training\n        return self.finish_training(train_metrics, val_metrics, epoch)\n\n    def finish_training(self, train_metrics, val_metrics, epoch):\n        \"\"\"\n        End the training cyle, return a model and finish callbacks.\n        \"\"\"\n        time_elapsed = int(time.time() - self.start_time)\n        print(\"Total time elapsed: {}h:{}m:{}s\".format(\n            time_elapsed // 3600, (time_elapsed // 60) % 60, time_elapsed % 60))\n        # execute final methods of callbacks\n        if self.callbacks is not None:\n            for callback in self.callbacks:\n                # find all methods of the callback\n                method_list = [\n                    func\n                    for func in dir(callback)\n                    if (callable(getattr(callback, func))\n                        and not func.startswith(\"__\"))\n                ]\n                if \"final\" in method_list:\n                    callback.final(trainer=self, epoch=epoch)\n        # in case of no model selection, pick the last loss\n        if self.best_metric == 0.0:\n            self.best_metric = val_metrics[\"loss\"][-1]\n            self.best_model = self.model\n\n        return (self.model,\n                {\n                    \"train_metrics\": train_metrics,\n                    \"val_metrics\": val_metrics,\n                    \"best_model\": self.best_model,\n                    \"best_metric\": self.best_metric}\n                )\n\n    def visualize_training(self, report, metrics=None, save_fig_path=\"\"):\n        # Plot loss first\n        plt.figure()\n        plt.plot(report[\"train_metrics\"][\"loss\"])\n        plt.plot(report[\"val_metrics\"][\"loss\"])\n        plt.title(\"Loss during training\")\n        plt.legend([\"Train\", \"Val\"])\n        if (save_fig_path):\n            plt.savefig(save_fig_path)\n        plt.show()\n        if metrics is None:\n            metrics = self.metrics\n        for metric in metrics:\n            plt.figure()\n            plt.plot(report[\"train_metrics\"][metric.__name__])\n            plt.plot(report[\"val_metrics\"][metric.__name__])\n            plt.legend([\"Train\", \"Val\"])\n            plt.title(metric.__name__)        \n            if(save_fig_path):\n                plt.savefig(save_fig_path+\"_\"+metric.__name__)\n            plt.show()\n\n    def evaluate_model(\n            self,\n            val_loader,\n            additional_gpu=None,\n            metrics=None,\n            inputs_key=\"image\",\n            labels_key=\"label\"\n    ):\n        # predict on the validation set\n        \"\"\"\n        Predict on the validation set.\n        # Arguments\n            val_loader : data loader of the validation set\n            additional_gpu : GPU number if evaluation should be done on\n                separate GPU\n            metrics: list of\n        \"\"\"\n        all_preds = []\n        all_labels = []\n\n        self.model.eval()\n\n        if additional_gpu is not None:\n            device = additional_gpu\n        else:\n            device = self.device\n\n        with torch.no_grad():\n            for i, data in enumerate(val_loader):\n                inputs, labels = data[inputs_key], data[labels_key]\n                inputs = inputs.to(device)\n                labels = labels.to(device)\n                # forward + backward + optimize\n                outputs = self.model(inputs)\n                # run inference\n                all_preds, all_labels = predict(\n                    outputs,\n                    labels,\n                    all_preds,\n                    all_labels,\n                    self.prediction_type,\n                    self.criterion,\n                    class_threshold=self.class_threshold\n                )\n\n        # compute confusion matrix\n        cm = confusion_matrix(all_labels, all_preds)\n        plt.imshow(cm, interpolation=\"nearest\", cmap=plt.cm.Blues)\n\n        # Visualize the confusion matrix\n        classes = [\"control\", \"patient\"]\n        tick_marks = np.arange(len(classes))\n        plt.xticks(tick_marks, classes, rotation=45)\n        plt.yticks(tick_marks, classes)\n\n        fmt = \"d\"\n        thresh = cm.max() / 2.\n        for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n            plt.text(\n                j,\n                i,\n                format(cm[i, j], fmt),\n                horizontalalignment=\"center\",\n                color=\"white\" if cm[i, j] > thresh else \"black\",\n            )\n        plt.title(\"Confusion Matrix\")\n        plt.ylabel(\"True label\")\n        plt.xlabel(\"Predicted label\")\n        plt.show()\n\n        # print metrics\n        if metrics is not None:\n            for metric in metrics:\n                if isinstance(all_preds[0], list):\n                    print(\"{}: {}\".format(metric.__name__, np.mean([metric(labels, preds) for preds,labels in zip(all_preds, all_labels)])))\n                else:\n                    print(\"{}: {}\".format(metric.__name__, metric(all_labels, all_preds)))\n\n\n        self.model.train()\n\n    def report_metrics(\n        self,\n        metrics_dict,\n        phase\n        ):\n\n        # report execution time only in training phase\n        if (phase == \"train\"):\n            time_elapsed = int(time.time() - self.start_time)\n            print(\"Time elapsed: {}h:{}m:{}s\".format(\n                time_elapsed // 3600, (time_elapsed // 60) % 60, time_elapsed % 60))\n\n        \"\"\" Store and report a list of metric functions. \"\"\"\n        for metric in self.metrics:\n            # report everything but loss\n            if metric.__name__ is not \"loss\":\n                # weighted average over previous batches\n                # weigh by the number of samples per batch and divide by\n                # the total number of samples\n                batch_results = np.zeros(shape=(\n                    len(self.multi_batch_metrics[\"len_\" + metric.__name__])))\n                n_samples = 0\n                for b_idx, batch_len in enumerate(\n                    self.multi_batch_metrics[\"len_\" + metric.__name__]\n                    ):\n                    batch_results[b_idx] = self.multi_batch_metrics[\n                        metric.__name__][b_idx] * batch_len\n                    n_samples += batch_len\n\n                result = np.sum(batch_results) / n_samples\n\n                if metric.__name__ in metrics_dict:\n                    metrics_dict[metric.__name__].append(result)\n                else:\n                    metrics_dict[metric.__name__] = [result]\n                # print result\n                if isinstance(result, float):\n                    print(\"{} {}: {:.2f} %\".format(\n                        phase, metric.__name__, result * 100))\n                else:\n                    print(\"{} {}: {} \".format(\n                        phase, metric.__name__, str(result)))\n        return metrics_dict\n\n    def estimate_metrics(\n        self,\n        all_labels,\n        all_preds\n        ):\n        \"\"\" Estimate a list of metric functions. \"\"\"\n        n_predictions = len(all_preds)\n\n        for metric in self.metrics:\n            # report everything but loss\n            if metric.__name__ is not \"loss\":\n                if isinstance(all_preds[0], list):\n                    result = np.mean([metric(labels, preds) for preds,labels in zip(all_preds, all_labels)])\n                else:\n                    result = metric(all_labels, all_preds)\n \n                if metric.__name__ in self.multi_batch_metrics:\n                    self.multi_batch_metrics[metric.__name__].append(result)\n                    self.multi_batch_metrics[\"len_\" + metric.__name__].append(\n                        n_predictions)\n                else:\n                    self.multi_batch_metrics[metric.__name__] = [result]\n                    self.multi_batch_metrics[\"len_\" + metric.__name__] = [n_predictions]\n\n    def _on_epoch_end(\n        self,\n        metrics_dict,\n        all_labels,\n        all_preds,\n        phase\n        ):\n        # check for unreported metrics\n        if len(all_preds) > 0:\n            self.estimate_metrics(\n                    all_labels,\n                    all_preds,\n                )\n            # TODO: test if del helps\n            all_labels = []\n            all_preds = []\n\n        metrics_dict = self.report_metrics(\n            metrics_dict,\n            phase\n        )\n\n        return metrics_dict\n        \n"
  },
  {
    "path": "nitorch/transforms.py",
    "content": "import numpy as np\nimport numbers\nimport torch\nfrom scipy.ndimage.interpolation import rotate\nfrom scipy.ndimage.interpolation import zoom\n\n\ndef normalize_float(x, min=-1):\n    \"\"\" \n    Function that performs min-max normalization on a `numpy.ndarray` \n    matrix. \n    \"\"\"\n    if min == -1:\n        norm = (2 * (x - np.min(x)) / (np.max(x) - np.min(x))) - 1\n    elif min == 0:\n        if np.max(x) == 0 and np.min(x) == 0:\n            norm = x\n        else:\n            norm = (x - np.min(x)) / (np.max(x) - np.min(x))\n    return norm\n\n\ndef normalize_float_torch(x, min=-1):\n    '''\n    Function that performs min-max normalization on a Pytorch tensor \n    matrix. Can also deal with Pytorch dictionaries where the data\n    matrix key is 'image'.\n    '''\n    import torch\n    if min == -1:\n        norm = (2 * (x - torch.min(x)) / (torch.max(x) - torch.min(x))) - 1\n    elif min == 0:\n        if torch.max(x) == 0 and torch.min(x) == 0:\n            norm = x\n        else:    \n            norm = (x - torch.min(x)) / (torch.max(x) - torch.min(x))\n    return norm\n\n\ndef normalization_factors(data, train_idx, shape, mode=\"slice\"):\n    \"\"\" \n    Shape should be of length 3. \n    mode : either \"slice\" or \"voxel\" - defines the granularity of the \n    normalization. Voxelwise normalization does not work well with only\n    linear registered data.\n    \"\"\"\n    print(\"Computing the normalization factors of the training data..\")\n    if mode == \"slice\":\n        axis = (0, 1, 2, 3)\n    elif mode == \"voxel\":\n        axis = 0\n    else:\n        raise NotImplementedError(\"Normalization mode unknown.\")\n    samples = np.zeros(\n        [len(train_idx), 1, shape[0], shape[1], shape[2]], dtype=np.float32\n    )\n    for c, value in enumerate(train_idx):\n        samples[c] = data[value][\"image\"].numpy()\n    mean = np.mean(samples, axis=axis)\n    std = np.std(samples, axis=axis)\n    return np.squeeze(mean), np.squeeze(std)\n\n\nclass CenterCrop(object):\n    \"\"\"Crops the given 3D ndarray Image at the center.\n    Args:\n        size (sequence or int): Desired output size of the crop. If size is an\n            int instead of sequence like (h, w, d), a cube crop (size, size, size) is\n            made.\n    \"\"\"\n    def __init__(self, size):\n        if isinstance(size, numbers.Number):\n            self.size = (int(size), int(size), int(size))\n        else:\n            self.size = np.asarray(size)\n        assert len(self.size) == 3, \"The `size` must be a tuple of length 3 but is \\\nlength {}\".format(len(self.size))\n        \n    def __call__(self, img):\n        \"\"\"\n        Args:\n            3D ndarray Image : Image to be cropped.\n        Returns:\n            3D ndarray Image: Cropped image.\n        \"\"\"     \n        # if the 4th dimension of the image is the batch then ignore that dim \n        if len(img.shape) == 4:\n            img_size = img.shape[1:]\n        elif len(img.shape) == 3:\n            img_size = img.shape\n        else:\n            raise ValueError(\"The size of the image can be either 3 dimension or 4\\\ndimension with one dimension as the batch size\")\n            \n        # crop only if the size of the image is bigger than the size to be cropped to.\n        if all(img_size >= self.size):\n            slice_start = (img_size - self.size)//2\n            slice_end = self.size + slice_start\n            cropped = img[slice_start[0]:slice_end[0],\n                          slice_start[1]:slice_end[1],\n                          slice_start[2]:slice_end[2]\n                         ]\n            if len(img.shape) == 4:\n                cropped = np.expand_dims(cropped, 0)\n        else:\n            cropped = img\n        \n        return cropped\n\n    def __repr__(self):\n        return self.__class__.__name__ + '(size={0})'.format(self.size)\n    \n\nclass Normalize(object):\n    \"\"\"\n    Normalize tensor with first and second moments.\n    By default will only normalize on non-zero voxels. Set \n    masked = False if this is undesired.\n    \"\"\"\n\n    def __init__(self, mean, std=1, masked=True, eps=1e-10):\n        self.mean = mean\n        self.std = std\n        self.masked = masked\n        # set epsilon only if using std scaling\n        self.eps = eps if np.all(std) != 1 else 0\n\n    def __call__(self, image):\n        if self.masked:\n            image = self.zero_masked_transform(image)\n        else:\n            image = self.apply_transform(image)\n        return image\n\n    def denormalize(self, image):\n        image = image * (self.std + self.eps) + self.mean\n        return image\n\n    def apply_transform(self, image):\n        return (image - self.mean) / (self.std + self.eps)\n\n    def zero_masked_transform(self, image):\n        \"\"\" Only apply transform where input is not zero. \"\"\"\n        img_mask = image == 0\n        # do transform\n        image = self.apply_transform(image)\n        image[img_mask] = 0.\n        return image\n\n\nclass IntensityRescale:\n    \"\"\"\n    Rescale image itensities between 0 and 1 for a single image.\n\n    Arguments:\n        masked: applies normalization only on non-zero voxels. Default\n            is True.\n        on_gpu: speed up computation by using GPU. Requires torch.Tensor\n             instead of np.array. Default is False.\n    \"\"\"\n\n    def __init__(self, masked=True, on_gpu=False):\n        self.masked = masked\n        self.on_gpu = on_gpu\n\n    def __call__(self, image):\n        if self.masked:\n            image = self.zero_masked_transform(image)\n        else:\n            image = self.apply_transform(image)\n\n        return image\n\n    def apply_transform(self, image):\n        if self.on_gpu:\n            return normalize_float_torch(image, min=0)\n        else:\n            return normalize_float(image, min=0)\n\n    def zero_masked_transform(self, image):\n        \"\"\" Only apply transform where input is not zero. \"\"\"\n        img_mask = image == 0\n        # do transform\n        image = self.apply_transform(image)\n        image[img_mask] = 0.\n        return image\n\n\n########################################################################\n# Data augmentations\n########################################################################\n\nclass ToTensor(object):\n    \"\"\"\n    Convert ndarrays to Tensors.\n    Expands channel axis\n    # numpy image: H x W x Z\n    # torch image: C x H x W x Z\n    \"\"\"\n\n    def __call__(self, image):\n        image = torch.from_numpy(image).unsqueeze(0)\n        image = image.float()\n        return image\n\n\nclass Flip:\n    \"\"\"\n    Flip the input along a given axis.\n\n    Arguments:\n        axis: axis to flip over. Default is 0\n        prob: probability to flip the image. Executes always when set to\n             1. Default is 0.5\n    \"\"\"\n    def __init__(self, axis=0, prob=0.5):\n        self.axis = axis\n        self.prob = prob\n\n    def __call__(self, image):\n        rand = np.random.uniform()\n        if rand <= self.prob:\n            augmented = np.flip(image, axis=self.axis).copy()\n        else:\n            augmented = image\n        return augmented\n\n\nclass SagittalFlip(Flip):\n    \"\"\"\n    Flip image along the sagittal axis (x-axis). \n    Expects input shape (X, Y, Z).\n    \"\"\"\n    def __init__(self, prob=0.5):\n        super().__init__(axis=0, prob=prob)\n    \n    def __call__(self, image):\n        assert(len(image.shape) == 3)\n        return super().__call__(image)\n\nclass CoronalFlip(Flip):\n    \"\"\"\n    Flip image along the coronal axis (y-axis). \n    Expects input shape (X, Y, Z).\n    \"\"\"\n    def __init__(self, prob=0.5):\n        super().__init__(axis=1, prob=prob)\n\n    def __call__(self, image):\n        assert(len(image.shape) == 3)\n        return super().__call__(image)\n\n\nclass AxialFlip(Flip):\n    \"\"\"\n    Flip image along the axial axis (z-axis). \n    Expects input shape (X, Y, Z).\n    \"\"\"\n    def __init__(self, prob=0.5):\n        super().__init__(axis=2, prob=prob)\n\n    def __call__(self, image):\n        assert(len(image.shape) == 3)\n        return super().__call__(image)\n\n\nclass Rotate:\n    \"\"\" \n    Rotate the input along a given axis.\n\n    Arguments:\n        axis: axis to rotate. Default is 0\n        deg: min and max rotation angles in degrees. Randomly rotates \n            within that range. Can be scalar, list or tuple. In case of \n            scalar it rotates between -abs(deg) and abs(deg). Default is\n            (-3, 3).\n    \"\"\"\n    def __init__(self, axis=0, deg=(-3, 3)):\n        if axis == 0:\n            self.axes = (1, 0)\n        elif axis == 1:\n            self.axes = (2, 1)\n        elif axis == 2:\n            self.axes = (0, 2)\n\n        if isinstance(deg, tuple) or isinstance(deg, list):\n            assert(len(deg) == 2)\n            self.min_rot = np.min(deg)\n            self.max_rot = np.max(deg)\n        else:\n            self.min_rot = -int(abs(deg))\n            self.max_rot = int(abs(deg))\n\n    def __call__(self, image):\n        rand = np.random.randint(self.min_rot, self.max_rot + 1)\n        augmented = rotate(\n            image,\n            angle=rand,\n            axes=self.axes,\n            reshape=False\n            ).copy()\n        return augmented\n\n\nclass SagittalRotate(Rotate):\n    \"\"\"\n    Rotate image's sagittal axis (x-axis). \n    Expects input shape (X, Y, Z).\n    \"\"\"\n    def __init__(self, deg=(-3, 3)):\n        super().__init__(axis=0, deg=deg)\n\n\nclass CoronalRotate(Rotate):\n    \"\"\"\n    Rotate image's coronal axis (y-axis). \n    Expects input shape (X, Y, Z).\n    \"\"\"\n    def __init__(self, deg=(-3, 3)):\n        super().__init__(axis=1, deg=deg)\n\n\nclass AxialRotate(Rotate):\n    \"\"\"\n    Rotate image's axial axis (z-axis). \n    Expects input shape (X, Y, Z).\n    \"\"\"\n    def __init__(self, deg=(-3, 3)):\n        super().__init__(axis=2, deg=deg)\n\n\nclass Translate:\n    \"\"\"\n    Translate the input along a given axis.\n\n    Arguments:\n        axis: axis to rotate. Default is 0\n        dist: min and max translation distance in pixels. Randomly \n            translates within that range. Can be scalar, list or tuple. \n            In case of scalar it translates between -abs(dist) and \n            abs(dist). Default is (-3, 3).\n    \"\"\"\n    def __init__(self, axis=0, dist=(-3, 3)):\n        self.axis = axis\n\n        if isinstance(dist, tuple) or isinstance(dist, list):\n            assert(len(dist) == 2)\n            self.min_trans = np.min(dist)\n            self.max_trans = np.max(dist)\n        else:\n            self.min_trans = -int(abs(dist))\n            self.max_trans = int(abs(dist))\n\n    def __call__(self, image):\n        rand = np.random.randint(self.min_trans, self.max_trans + 1)\n        augmented = np.zeros_like(image)\n        if self.axis == 0:\n            if rand < 0:\n                augmented[-rand:, :] = image[:rand, :]\n            elif rand > 0:\n                augmented[:-rand, :] = image[rand:, :]\n            else:\n                augmented = image\n        elif self.axis == 1:\n            if rand < 0:\n                augmented[:,-rand:, :] = image[:,:rand, :]\n            elif rand > 0:\n                augmented[:,:-rand, :] = image[:,rand:, :]\n            else:\n                augmented = image\n        elif self.axis == 2:\n            if rand < 0:\n                augmented[:,:,-rand:] = image[:,:,:rand]\n            elif rand > 0:\n                augmented[:,:,:-rand] = image[:,:,rand:]\n            else:\n                augmented = image\n        return augmented\n\n\nclass SagittalTranslate(Translate):\n    \"\"\"\n    Translate image along the sagittal axis (x-axis).\n    Expects input shape (X, Y, Z).\n    \"\"\"\n    def __init__(self, dist=(-3, 3)):\n        super().__init__(axis=0, dist=dist)\n\n\nclass CoronalTranslate(Translate):\n    \"\"\"\n    Translate image along the coronal axis (y-axis).\n    Expects input shape (X, Y, Z).\n    \"\"\"\n    def __init__(self, dist=(-3, 3)):\n        super().__init__(axis=1, dist=dist)\n\n\nclass AxialTranslate(Translate):\n    \"\"\"\n    Translate image along the axial axis (z-axis).\n    Expects input shape (X, Y, Z).\n    \"\"\"\n    def __init__(self, dist=(-3, 3)):\n        super().__init__(axis=2, dist=dist)\n        "
  },
  {
    "path": "nitorch/utils.py",
    "content": "import torch\nimport matplotlib.pyplot as plt\nfrom matplotlib.lines import Line2D\nimport numpy as np\n\ndef dataset_length(data_loader):\n    \"\"\"\n    Return the full length of the dataset from the DataLoader alone.\n    Calling len(data_loader) only shows the number of mini-batches.\n    Requires data to be located at \n    \"\"\"\n    sample = next(iter(data_loader))\n\n    if isinstance(sample, dict):\n        try:\n            if isinstance(sample[\"label\"], torch.Tensor):\n                batch_size = sample[\"label\"].shape[0]\n            else:\n                # in case of sequence of inputs use first input\n                batch_size = sample[\"label\"][0].shape[0]\n        except:\n            KeyError(\"Expects key to be 'label'.\")\n    else:\n        if isinstance(sample[1], torch.Tensor):\n            batch_size = sample[1].shape[0]\n        else:\n            # in case of sequence of inputs use first input\n            batch_size = sample[1][0].shape[0]\n    return len(data_loader) * batch_size\n\n\ndef count_parameters(model):\n    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n\n\ndef plot_grad_flow(named_parameters):\n    '''Plots the gradients flowing through different layers in the net during training.\n    Can be used for checking for possible gradient vanishing / exploding problems.\n    \n    Usage: Plug this function in Trainer class after loss.backwards() as \n    \"plot_grad_flow(self.model.named_parameters())\" to visualize the gradient flow'''\n    ave_grads = []\n    max_grads= []\n    layers = []\n    for n, p in named_parameters:\n        if(p.requires_grad) and (\"bias\" not in n):\n            layers.append(n)\n            ave_grads.append(p.grad.abs().mean())\n            max_grads.append(p.grad.abs().max())\n    plt.bar(np.arange(len(max_grads)), max_grads, alpha=0.1, lw=1, color=\"c\")\n    plt.bar(np.arange(len(max_grads)), ave_grads, alpha=0.1, lw=1, color=\"b\")\n    plt.hlines(0, 0, len(ave_grads)+1, lw=2, color=\"k\" )\n    plt.xticks(range(0,len(ave_grads), 1), layers, rotation=\"vertical\")\n    plt.xlim(left=0, right=len(ave_grads))\n    plt.ylim(bottom = -0.001, top=0.02) # zoom in on the lower gradient regions\n    plt.xlabel(\"Layers\")\n    plt.ylabel(\"average gradient\")\n    plt.title(\"Gradient flow\")\n    plt.grid(True)\n    plt.legend([Line2D([0], [0], color=\"c\", lw=4),\n                Line2D([0], [0], color=\"b\", lw=4),\n                Line2D([0], [0], color=\"k\", lw=4)], ['max-gradient', 'mean-gradient', 'zero-gradient'])\n    \n    \n    \ndef is_bad_grad(grad_output):\n    grad_output = grad_output.data\n    return grad_output.ne(grad_output).any() or grad_output.gt(1e6).any()"
  },
  {
    "path": "nmm_mask_areas.py",
    "content": "\n\nall_areas= {\n    \"Accumbens\": (23, 30),\n    \"Amygdala\": (31, 32),\n    \"Brain Stem\": (35, 35),\n    \"Caudate\": (36, 37),\n    \"Cerebellum\": (38, 41),\n    \"Hippocampus\": (47, 48),\n    \"Parahippocampal gyrus\": (170, 171),\n    \"Pallidum\": (55, 56),\n    \"Putamen\": (57, 58),\n    \"Thalamus\": (59, 60),\n    \"CWM\": (44, 45),\n    \"ACG\": (100, 101),\n    \"Ant. Insula\": (102, 103),\n    \"Post. Insula\": (172, 173),\n    \"AOG\": (104, 105),\n    \"AG\": (106, 107),\n    \"Cuneus\": (114, 115),\n    \"Central operculum\": (112, 113),\n    \"Frontal operculum\": (118, 119),\n    \"Frontal pole\": (120, 121),\n    \"Fusiform gyrus\": (122, 123),\n    \"Temporal pole\": (202, 203),\n    \"TrIFG\": (204, 205),\n    \"TTG\": (206, 207),\n    \"Entorh. cortex\": (116, 117),\n    \"Parietal operculum\": (174, 175),\n    \"SPL\": (198, 199),\n    \"CSF\": (46, 46),\n    \"3rd Ventricle\": (4, 4),\n    \"4th Ventricle\": (11, 11),\n    \"Lateral Ventricles\": (49, 52),\n    \"Diencephalon\": (61, 62),\n    \"Vessels\": (63, 64),\n    \"Optic Chiasm\": (69, 69),\n    \"Vermal Lobules\": (71, 73),\n    \"Basal Forebrain\": (75, 76),\n    \"Calc\": (108, 109),\n    \"GRe\": (124, 125),\n    \"IOG\": (128, 129),\n    \"ITG\": (132, 133),\n    \"LiG\": (134, 135),\n    \"LOrG\": (136, 137),\n    \"MCgG\": (138, 139),\n    \"MFC\": (140, 141),\n    \"MFG\": (142, 143),\n    \"MOG\": (144, 145),\n    \"MOrG\": (146, 147),\n    \"MPoG\": (148, 149),\n    \"MPrG\": (150, 151),\n    \"MSFG\": (152, 153),\n    \"MTG\": (154, 155),\n    \"OCP\": (156, 157),\n    \"OFuG\": (160, 161),\n    \"OpIFG\": (162, 163),\n    \"OrIFG\": (164, 165),\n    \"PCgG\": (166, 167),\n    \"PCu\": (168, 169),\n    \"PoG\": (176, 177),\n    \"POrG\": (178, 179),\n    \"PP\": (180, 181),\n    \"PrG\": (182, 183),\n    \"PT\": (184, 185),\n    \"SCA\": (186, 187),\n    \"SFG\": (190, 191),\n    \"SMC\": (192, 193),\n    \"SMG\": (194, 195),\n    \"SOG\": (196, 197),\n    \"STG\": (200, 201),\n}\nshort_name_map = {\n     'Accumbens': 'Accumbens',\n     'Amygdala': 'Amygdala',\n     'Brain Stem': 'Brain Stem',\n     'Caudate': 'Caudate',\n     'Cerebellum': 'Cerebellum',\n     'Hippocampus': 'Hippocampus',\n     'Parahippocampal gyrus': 'Parahippocampal gyr.',\n     'Pallidum': 'Pallidum',\n     'Putamen': 'Putamen',\n     'Thalamus': 'Thalamus',\n     'Diencephalon': 'Diencephalon',\n     'CWM': 'Cerebral white matter',\n     'ACG': 'Ant. cingulate gyr.',\n     'Ant. Insula': 'Ant. insula',\n     'Post. Insula': 'Post. insula',\n     'AOG': 'Ant. orbital gyr.',\n     'AG': 'Angular gyr.',\n     'Cuneus': 'Cuneus',\n     'Central operculum': 'Central operculum',\n     'Frontal operculum': 'Frontal operculum',\n     'Frontal pole': 'Frontal pole',\n     'Fusiform gyrus': 'Fusiform gyr.',\n     'Temporal pole': 'Temporal pole',\n     'TrIFG': 'Triangular part of IFG',\n     'TTG': 'Trans. temporal gyr.',\n     'Entorh. cortex': 'Entorhinal area',\n     'Parietal operculum': 'Parietal operculum',\n     'SPL': 'Sup. parietal lobule',\n     'CSF': 'CSF',\n     '3rd Ventricle': '3rd Ventricle',\n     '4th Ventricle': '4th Ventricle',\n     'Lateral Ventricles': 'Inf. Lat. Ventricles',\n     'Vessels': 'Vessels',\n     'Optic Chiasm': 'Optic Chiasm',\n     'Vermal Lobules': 'Cereb. Verm. Lob.',\n     'Basal Forebrain': 'Basal Forebrain',\n     'Calc': 'Calcarine cortex',\n     'GRe': 'Gyrus rectus',\n     'IOG': 'Inf. occipital gyr.',\n     'ITG': 'Inf. temporal gyr.',\n     'LiG': 'Lingual gyr.',\n     'LOrG': 'Lat. orbital gyr.',\n     'MCgG': 'Mid. cingulate gyr.',\n     'MFC': 'Med. frontal cortex',\n     'MFG': 'Mid. frontal gyr.',\n     'MOG': 'Mid. occipital gyr.',\n     'MOrG': 'Med. orbital gyr.',\n     'MPoG': 'Post. gyr. med. seg.',\n     'MPrG': 'Pre. gyr. med. seg.',\n     'MSFG': 'Sup. frontal gyr. med. seg.',\n     'MTG': 'Mid. temporal gyr.',\n     'OCP': 'Occipital pole',\n     'OFuG': 'Occipital fusiform gyr.',\n     'OpIFG': 'Opercular part of IFG',\n     'OrIFG': 'Orbital part of IFG',\n     'PCgG': 'Post. cingulate gyr.',\n     'PCu': 'Precuneus',\n     'PoG': 'Postcentral gyr.',\n     'POrG': 'Post. orbital gyr.',\n     'PP': 'Planum polare',\n     'PrG': 'Precentral gyr.',\n     'PT': 'Planum temporale',\n     'SCA': 'Subcallosal area',\n     'SFG': 'Sup. frontal gyr.',\n     'SMC': 'Supp. motor cortex',\n     'SMG': 'Supramarginal gyr.',\n     'SOG': 'Sup. occipital gyr.',\n     'STG': 'Sup. temporal gyr.'\n}\n"
  },
  {
    "path": "requirements.txt",
    "content": "torch==1.4.0\ntorchvision==0.5.0\nnibabel==3.1.1\ntabulate==0.8.7\n"
  },
  {
    "path": "settings.py",
    "content": "\"\"\"\nSettings for re-running the experiments from the paper \"Layer-wise\nrelevance propagation for explaining deep neural network decisions\nin MRI-based Alzheimer’s disease classification\".\n\nPlease note that you need to download the ADNI data from \nhttp://adni.loni.usc.edu/ and preprocess it using \nhttps://github.com/ANTsX/ANTs/blob/master/Scripts/antsRegistrationSyNQuick.sh\n\nPlease prepare the data, such that you will get three HDF5 files,\nconsisting of a training, a validation and a holdout (test) set.\nEach HDF5 file is required to have 2 datasets, namely X and y,\ncontaining the data matrix and label vector accordingly. We have\nincluded the \"Data Split ADNI.ipynb\" file as a guideline for data splitting.\nPlease note that it is highly dependent on the format of your data storage\nand needs to be individualized as such. \n\nFurthermore you will need SPM12 https://www.fil.ion.ucl.ac.uk/spm/software/spm12/\nin order to access the Neuromorphometrics atlas.\n\nArguments:\n    model_path: Path to the trained pytorch model parameters\n    data_path: Path where the outputs will be stored and retrieved\n    ADNI_DIR: Path to the root of your downloaded ADNI data\n    train_h5: Path to the training set HDF5 file\n    val_h5: Path to the validation set HDF5 file\n    holdout_h5: Path to the holdout set HDF5 file\n    binary_brain_mask: Path to the mask used for masking the images,\n        included in the repository.\n    nmm_mask_path: Path to the Neuromorphometrics mask. Needs to be\n        acquired from SPM12. Typically located under \n        ~/spm12/tpm/labels_Neuromorphometrics.nii\n    nmm_mask_path_scaled: Path to the rescaled Neuromorphometrics mask.\n\n\n\"\"\"\n\nsettings = {\n    \"model_path\": INSERT, \n    \"data_path\": INSERT,\n    \"ADNI_DIR\": INSERT,\n    \"train_h5\": INSERT,\n    \"val_h5\": INSERT,\n    \"holdout_h5\": INSERT,\n    \"binary_brain_mask\": \"binary_brain_mask.nii.gz\",\n    \"nmm_mask_path\": \"~/spm12/tpm/labels_Neuromorphometrics.nii\",\n    \"nmm_mask_path_scaled\": \"nmm_mask_rescaled.nii\"\n}\n"
  },
  {
    "path": "utils.py",
    "content": "import torch\nfrom scipy.ndimage import zoom\nfrom sklearn.model_selection import train_test_split\nimport jrieke.datasets as datasets\nimport numpy as np\n\n\ndef pprint(*args):\n    out = [str(argument) + \"\\n\" for argument in args]\n    print(*out, \"\\n\")\n\n\nclass Flatten(torch.nn.Module):\n    def __init__(self):\n        super(Flatten, self).__init__()\n\n    def forward(self, in_tensor):\n        return in_tensor.view((in_tensor.size()[0], -1))\n\n\ndef load_data():\n\n    df = datasets.load_data_table_15T()\n\n    # Patient-wise train-test-split.\n    # Select a number of patients for each class, put all their images in the test set\n    # and all other images in the train set. This is the split that is used in the paper to produce the heatmaps.\n    test_patients_per_class = 30\n\n    patients_AD = df[df['DX'] == 'Dementia']['PTID'].unique()\n    patients_CN = df[df['DX'] == 'CN']['PTID'].unique()\n\n    patients_AD_train, patients_AD_test = train_test_split(patients_AD, test_size=test_patients_per_class,\n                                                           random_state=0)\n    patients_CN_train, patients_CN_test = train_test_split(patients_CN, test_size=test_patients_per_class,\n                                                           random_state=0)\n\n    patients_train = np.concatenate([patients_AD_train, patients_CN_train])\n    patients_test = np.concatenate([patients_AD_test, patients_CN_test])\n\n    return datasets.build_datasets(df, patients_train, patients_test, normalize=True)\n\n\ndef scale_mask(mask, shape):\n\n    if shape == mask.shape:\n        print(\"No rescaling necessary.\")\n        return mask\n\n    nmm_map = np.zeros(shape)\n    print(\"Rescaling mask\")\n    for lbl_idx in np.unique(mask):\n        nmm_map_lbl = mask.copy()\n        nmm_map_lbl[lbl_idx != nmm_map_lbl] = 0\n        nmm_map_lbl[lbl_idx == nmm_map_lbl] = 1\n        zoomed_lbl = zoom(nmm_map_lbl, 1.5, order=3)\n        zoomed_lbl[zoomed_lbl != 1] = 0\n        remain_diff = np.array(nmm_map.shape) - np.array(zoomed_lbl.shape)\n        pad_left = np.array(np.ceil(remain_diff / 2), dtype=int)\n        pad_right = np.array(np.floor(remain_diff / 2), dtype=int)\n        nmm_map[pad_left[0]:-pad_right[0], pad_left[1]:-pad_right[1], pad_left[2]:-pad_right[2]] += zoomed_lbl * lbl_idx\n\n    return nmm_map\n\n"
  }
]