Repository: MorvanZhou/PyTorch-Tutorial Branch: master Commit: dbe154a9e566 Files: 56 Total size: 183.3 MB Directory structure: gitextract_kp7bk2c5/ ├── .gitignore ├── LICENCE ├── README.md ├── tutorial-contents/ │ ├── 201_torch_numpy.py │ ├── 202_variable.py │ ├── 203_activation.py │ ├── 301_regression.py │ ├── 302_classification.py │ ├── 303_build_nn_quickly.py │ ├── 304_save_reload.py │ ├── 305_batch_train.py │ ├── 306_optimizer.py │ ├── 401_CNN.py │ ├── 402_RNN_classifier.py │ ├── 403_RNN_regressor.py │ ├── 404_autoencoder.py │ ├── 405_DQN_Reinforcement_learning.py │ ├── 406_GAN.py │ ├── 406_conditional_GAN.py │ ├── 501_why_torch_dynamic_graph.py │ ├── 502_GPU.py │ ├── 503_dropout.py │ ├── 504_batch_normalization.py │ └── mnist/ │ ├── processed/ │ │ ├── test.pt │ │ └── training.pt │ └── raw/ │ ├── t10k-images-idx3-ubyte │ ├── t10k-labels-idx1-ubyte │ ├── train-images-idx3-ubyte │ └── train-labels-idx1-ubyte └── tutorial-contents-notebooks/ ├── .ipynb_checkpoints/ │ ├── 401_CNN-checkpoint.ipynb │ └── 406_GAN-checkpoint.ipynb ├── 201_torch_numpy.ipynb ├── 202_variable.ipynb ├── 203_activation.ipynb ├── 301_regression.ipynb ├── 302_classification.ipynb ├── 303_build_nn_quickly.ipynb ├── 304_save_reload.ipynb ├── 305_batch_train.ipynb ├── 306_optimizer.ipynb ├── 401_CNN.ipynb ├── 402_RNN.ipynb ├── 403_RNN_regressor.ipynb ├── 404_autoencoder.ipynb ├── 405_DQN_Reinforcement_learning.ipynb ├── 406_GAN.ipynb ├── 501_why_torch_dynamic_graph.ipynb ├── 502_GPU.ipynb ├── 503_dropout.ipynb ├── 504_batch_normalization.ipynb └── mnist/ ├── processed/ │ ├── test.pt │ └── training.pt └── raw/ ├── t10k-images-idx3-ubyte ├── t10k-labels-idx1-ubyte ├── train-images-idx3-ubyte └── train-labels-idx1-ubyte ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ .idea tutorial-contents/*pkl ================================================ FILE: LICENCE ================================================ MIT License Copyright (c) 2017 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================


### If you'd like to use **Tensorflow**, no worries, I made a new **Tensorflow Tutorial** just like PyTorch. Here is the link: [https://github.com/MorvanZhou/Tensorflow-Tutorial](https://github.com/MorvanZhou/Tensorflow-Tutorial) # pyTorch Tutorials In these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. Thanks for [liufuyang's](https://github.com/liufuyang) [**notebook files**](tutorial-contents-notebooks) which is a great contribution to this tutorial. * pyTorch basic * [torch and numpy](tutorial-contents/201_torch_numpy.py) * [Variable](tutorial-contents/202_variable.py) * [Activation](tutorial-contents/203_activation.py) * Build your first network * [Regression](tutorial-contents/301_regression.py) * [Classification](tutorial-contents/302_classification.py) * [An easy way](tutorial-contents/303_build_nn_quickly.py) * [Save and reload](tutorial-contents/304_save_reload.py) * [Train on batch](tutorial-contents/305_batch_train.py) * [Optimizers](tutorial-contents/306_optimizer.py) * Advanced neural network * [CNN](tutorial-contents/401_CNN.py) * [RNN-Classification](tutorial-contents/402_RNN_classifier.py) * [RNN-Regression](tutorial-contents/403_RNN_regressor.py) * [AutoEncoder](tutorial-contents/404_autoencoder.py) * [DQN Reinforcement Learning](tutorial-contents/405_DQN_Reinforcement_learning.py) * [A3C Reinforcement Learning](https://github.com/MorvanZhou/pytorch-A3C) * [GAN (Generative Adversarial Nets)](tutorial-contents/406_GAN.py) / [Conditional GAN](tutorial-contents/406_conditional_GAN.py) * Others (WIP) * [Why torch dynamic](tutorial-contents/501_why_torch_dynamic_graph.py) * [Train on GPU](tutorial-contents/502_GPU.py) * [Dropout](tutorial-contents/503_dropout.py) * [Batch Normalization](tutorial-contents/504_batch_normalization.py) **For Chinese speakers: All methods mentioned below have their video and text tutorial in Chinese. Visit [莫烦 Python](https://mofanpy.com/tutorials/) for more. You can watch my [Youtube channel](https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg) as well.** ### [Regression](tutorial-contents/301_regression.py) ### [Classification](tutorial-contents/302_classification.py) ### [CNN](tutorial-contents/401_CNN.py) ### [RNN](tutorial-contents/403_RNN_regressor.py) ### [Autoencoder](tutorial-contents/404_autoencoder.py) ### [GAN (Generative Adversarial Nets)](tutorial-contents/406_GAN.py) ### [Dropout](tutorial-contents/503_dropout.py) ### [Batch Normalization](tutorial-contents/504_batch_normalization.py) # Donation *If this does help you, please consider donating to support me for better tutorials. Any contribution is greatly appreciated!*
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================================================ FILE: tutorial-contents/201_torch_numpy.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.1.11 numpy """ import torch import numpy as np # details about math operation in torch can be found in: http://pytorch.org/docs/torch.html#math-operations # convert numpy to tensor or vise versa np_data = np.arange(6).reshape((2, 3)) torch_data = torch.from_numpy(np_data) tensor2array = torch_data.numpy() print( '\nnumpy array:', np_data, # [[0 1 2], [3 4 5]] '\ntorch tensor:', torch_data, # 0 1 2 \n 3 4 5 [torch.LongTensor of size 2x3] '\ntensor to array:', tensor2array, # [[0 1 2], [3 4 5]] ) # abs data = [-1, -2, 1, 2] tensor = torch.FloatTensor(data) # 32-bit floating point print( '\nabs', '\nnumpy: ', np.abs(data), # [1 2 1 2] '\ntorch: ', torch.abs(tensor) # [1 2 1 2] ) # sin print( '\nsin', '\nnumpy: ', np.sin(data), # [-0.84147098 -0.90929743 0.84147098 0.90929743] '\ntorch: ', torch.sin(tensor) # [-0.8415 -0.9093 0.8415 0.9093] ) # mean print( '\nmean', '\nnumpy: ', np.mean(data), # 0.0 '\ntorch: ', torch.mean(tensor) # 0.0 ) # matrix multiplication data = [[1,2], [3,4]] tensor = torch.FloatTensor(data) # 32-bit floating point # correct method print( '\nmatrix multiplication (matmul)', '\nnumpy: ', np.matmul(data, data), # [[7, 10], [15, 22]] '\ntorch: ', torch.mm(tensor, tensor) # [[7, 10], [15, 22]] ) # incorrect method data = np.array(data) print( '\nmatrix multiplication (dot)', '\nnumpy: ', data.dot(data), # [[7, 10], [15, 22]] '\ntorch: ', tensor.dot(tensor) # this will convert tensor to [1,2,3,4], you'll get 30.0 ) ================================================ FILE: tutorial-contents/202_variable.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.1.11 """ import torch from torch.autograd import Variable # Variable in torch is to build a computational graph, # but this graph is dynamic compared with a static graph in Tensorflow or Theano. # So torch does not have placeholder, torch can just pass variable to the computational graph. tensor = torch.FloatTensor([[1,2],[3,4]]) # build a tensor variable = Variable(tensor, requires_grad=True) # build a variable, usually for compute gradients print(tensor) # [torch.FloatTensor of size 2x2] print(variable) # [torch.FloatTensor of size 2x2] # till now the tensor and variable seem the same. # However, the variable is a part of the graph, it's a part of the auto-gradient. t_out = torch.mean(tensor*tensor) # x^2 v_out = torch.mean(variable*variable) # x^2 print(t_out) print(v_out) # 7.5 v_out.backward() # backpropagation from v_out # v_out = 1/4 * sum(variable*variable) # the gradients w.r.t the variable, d(v_out)/d(variable) = 1/4*2*variable = variable/2 print(variable.grad) ''' 0.5000 1.0000 1.5000 2.0000 ''' print(variable) # this is data in variable format """ Variable containing: 1 2 3 4 [torch.FloatTensor of size 2x2] """ print(variable.data) # this is data in tensor format """ 1 2 3 4 [torch.FloatTensor of size 2x2] """ print(variable.data.numpy()) # numpy format """ [[ 1. 2.] [ 3. 4.]] """ ================================================ FILE: tutorial-contents/203_activation.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.4 matplotlib """ import torch import torch.nn.functional as F from torch.autograd import Variable import matplotlib.pyplot as plt # fake data x = torch.linspace(-5, 5, 200) # x data (tensor), shape=(100, 1) x = Variable(x) x_np = x.data.numpy() # numpy array for plotting # following are popular activation functions y_relu = torch.relu(x).data.numpy() y_sigmoid = torch.sigmoid(x).data.numpy() y_tanh = torch.tanh(x).data.numpy() y_softplus = F.softplus(x).data.numpy() # there's no softplus in torch # y_softmax = torch.softmax(x, dim=0).data.numpy() softmax is a special kind of activation function, it is about probability # plt to visualize these activation function plt.figure(1, figsize=(8, 6)) plt.subplot(221) plt.plot(x_np, y_relu, c='red', label='relu') plt.ylim((-1, 5)) plt.legend(loc='best') plt.subplot(222) plt.plot(x_np, y_sigmoid, c='red', label='sigmoid') plt.ylim((-0.2, 1.2)) plt.legend(loc='best') plt.subplot(223) plt.plot(x_np, y_tanh, c='red', label='tanh') plt.ylim((-1.2, 1.2)) plt.legend(loc='best') plt.subplot(224) plt.plot(x_np, y_softplus, c='red', label='softplus') plt.ylim((-0.2, 6)) plt.legend(loc='best') plt.show() ================================================ FILE: tutorial-contents/301_regression.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.4 matplotlib """ import torch import torch.nn.functional as F import matplotlib.pyplot as plt # torch.manual_seed(1) # reproducible x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1) # x data (tensor), shape=(100, 1) y = x.pow(2) + 0.2*torch.rand(x.size()) # noisy y data (tensor), shape=(100, 1) # torch can only train on Variable, so convert them to Variable # The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors # x, y = Variable(x), Variable(y) # plt.scatter(x.data.numpy(), y.data.numpy()) # plt.show() class Net(torch.nn.Module): def __init__(self, n_feature, n_hidden, n_output): super(Net, self).__init__() self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer self.predict = torch.nn.Linear(n_hidden, n_output) # output layer def forward(self, x): x = F.relu(self.hidden(x)) # activation function for hidden layer x = self.predict(x) # linear output return x net = Net(n_feature=1, n_hidden=10, n_output=1) # define the network print(net) # net architecture optimizer = torch.optim.SGD(net.parameters(), lr=0.2) loss_func = torch.nn.MSELoss() # this is for regression mean squared loss plt.ion() # something about plotting for t in range(200): prediction = net(x) # input x and predict based on x loss = loss_func(prediction, y) # must be (1. nn output, 2. target) optimizer.zero_grad() # clear gradients for next train loss.backward() # backpropagation, compute gradients optimizer.step() # apply gradients if t % 5 == 0: # plot and show learning process plt.cla() plt.scatter(x.data.numpy(), y.data.numpy()) plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5) plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color': 'red'}) plt.pause(0.1) plt.ioff() plt.show() ================================================ FILE: tutorial-contents/302_classification.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.4 matplotlib """ import torch import torch.nn.functional as F import matplotlib.pyplot as plt # torch.manual_seed(1) # reproducible # make fake data n_data = torch.ones(100, 2) x0 = torch.normal(2*n_data, 1) # class0 x data (tensor), shape=(100, 2) y0 = torch.zeros(100) # class0 y data (tensor), shape=(100, 1) x1 = torch.normal(-2*n_data, 1) # class1 x data (tensor), shape=(100, 2) y1 = torch.ones(100) # class1 y data (tensor), shape=(100, 1) x = torch.cat((x0, x1), 0).type(torch.FloatTensor) # shape (200, 2) FloatTensor = 32-bit floating y = torch.cat((y0, y1), ).type(torch.LongTensor) # shape (200,) LongTensor = 64-bit integer # The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors # x, y = Variable(x), Variable(y) # plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=y.data.numpy(), s=100, lw=0, cmap='RdYlGn') # plt.show() class Net(torch.nn.Module): def __init__(self, n_feature, n_hidden, n_output): super(Net, self).__init__() self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer self.out = torch.nn.Linear(n_hidden, n_output) # output layer def forward(self, x): x = F.relu(self.hidden(x)) # activation function for hidden layer x = self.out(x) return x net = Net(n_feature=2, n_hidden=10, n_output=2) # define the network print(net) # net architecture optimizer = torch.optim.SGD(net.parameters(), lr=0.02) loss_func = torch.nn.CrossEntropyLoss() # the target label is NOT an one-hotted plt.ion() # something about plotting for t in range(100): out = net(x) # input x and predict based on x loss = loss_func(out, y) # must be (1. nn output, 2. target), the target label is NOT one-hotted optimizer.zero_grad() # clear gradients for next train loss.backward() # backpropagation, compute gradients optimizer.step() # apply gradients if t % 2 == 0: # plot and show learning process plt.cla() prediction = torch.max(out, 1)[1] pred_y = prediction.data.numpy() target_y = y.data.numpy() plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=pred_y, s=100, lw=0, cmap='RdYlGn') accuracy = float((pred_y == target_y).astype(int).sum()) / float(target_y.size) plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 20, 'color': 'red'}) plt.pause(0.1) plt.ioff() plt.show() ================================================ FILE: tutorial-contents/303_build_nn_quickly.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.1.11 """ import torch import torch.nn.functional as F # replace following class code with an easy sequential network class Net(torch.nn.Module): def __init__(self, n_feature, n_hidden, n_output): super(Net, self).__init__() self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer self.predict = torch.nn.Linear(n_hidden, n_output) # output layer def forward(self, x): x = F.relu(self.hidden(x)) # activation function for hidden layer x = self.predict(x) # linear output return x net1 = Net(1, 10, 1) # easy and fast way to build your network net2 = torch.nn.Sequential( torch.nn.Linear(1, 10), torch.nn.ReLU(), torch.nn.Linear(10, 1) ) print(net1) # net1 architecture """ Net ( (hidden): Linear (1 -> 10) (predict): Linear (10 -> 1) ) """ print(net2) # net2 architecture """ Sequential ( (0): Linear (1 -> 10) (1): ReLU () (2): Linear (10 -> 1) ) """ ================================================ FILE: tutorial-contents/304_save_reload.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.4 matplotlib """ import torch import matplotlib.pyplot as plt # torch.manual_seed(1) # reproducible # fake data x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1) # x data (tensor), shape=(100, 1) y = x.pow(2) + 0.2*torch.rand(x.size()) # noisy y data (tensor), shape=(100, 1) # The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors # x, y = Variable(x, requires_grad=False), Variable(y, requires_grad=False) def save(): # save net1 net1 = torch.nn.Sequential( torch.nn.Linear(1, 10), torch.nn.ReLU(), torch.nn.Linear(10, 1) ) optimizer = torch.optim.SGD(net1.parameters(), lr=0.5) loss_func = torch.nn.MSELoss() for t in range(100): prediction = net1(x) loss = loss_func(prediction, y) optimizer.zero_grad() loss.backward() optimizer.step() # plot result plt.figure(1, figsize=(10, 3)) plt.subplot(131) plt.title('Net1') plt.scatter(x.data.numpy(), y.data.numpy()) plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5) # 2 ways to save the net torch.save(net1, 'net.pkl') # save entire net torch.save(net1.state_dict(), 'net_params.pkl') # save only the parameters def restore_net(): # restore entire net1 to net2 net2 = torch.load('net.pkl') prediction = net2(x) # plot result plt.subplot(132) plt.title('Net2') plt.scatter(x.data.numpy(), y.data.numpy()) plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5) def restore_params(): # restore only the parameters in net1 to net3 net3 = torch.nn.Sequential( torch.nn.Linear(1, 10), torch.nn.ReLU(), torch.nn.Linear(10, 1) ) # copy net1's parameters into net3 net3.load_state_dict(torch.load('net_params.pkl')) prediction = net3(x) # plot result plt.subplot(133) plt.title('Net3') plt.scatter(x.data.numpy(), y.data.numpy()) plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5) plt.show() # save net1 save() # restore entire net (may slow) restore_net() # restore only the net parameters restore_params() ================================================ FILE: tutorial-contents/305_batch_train.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.1.11 """ import torch import torch.utils.data as Data torch.manual_seed(1) # reproducible BATCH_SIZE = 5 # BATCH_SIZE = 8 x = torch.linspace(1, 10, 10) # this is x data (torch tensor) y = torch.linspace(10, 1, 10) # this is y data (torch tensor) torch_dataset = Data.TensorDataset(x, y) loader = Data.DataLoader( dataset=torch_dataset, # torch TensorDataset format batch_size=BATCH_SIZE, # mini batch size shuffle=True, # random shuffle for training num_workers=2, # subprocesses for loading data ) def show_batch(): for epoch in range(3): # train entire dataset 3 times for step, (batch_x, batch_y) in enumerate(loader): # for each training step # train your data... print('Epoch: ', epoch, '| Step: ', step, '| batch x: ', batch_x.numpy(), '| batch y: ', batch_y.numpy()) if __name__ == '__main__': show_batch() ================================================ FILE: tutorial-contents/306_optimizer.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.4 matplotlib """ import torch import torch.utils.data as Data import torch.nn.functional as F import matplotlib.pyplot as plt # torch.manual_seed(1) # reproducible LR = 0.01 BATCH_SIZE = 32 EPOCH = 12 # fake dataset x = torch.unsqueeze(torch.linspace(-1, 1, 1000), dim=1) y = x.pow(2) + 0.1*torch.normal(torch.zeros(*x.size())) # plot dataset plt.scatter(x.numpy(), y.numpy()) plt.show() # put dateset into torch dataset torch_dataset = Data.TensorDataset(x, y) loader = Data.DataLoader(dataset=torch_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,) # default network class Net(torch.nn.Module): def __init__(self): super(Net, self).__init__() self.hidden = torch.nn.Linear(1, 20) # hidden layer self.predict = torch.nn.Linear(20, 1) # output layer def forward(self, x): x = F.relu(self.hidden(x)) # activation function for hidden layer x = self.predict(x) # linear output return x if __name__ == '__main__': # different nets net_SGD = Net() net_Momentum = Net() net_RMSprop = Net() net_Adam = Net() nets = [net_SGD, net_Momentum, net_RMSprop, net_Adam] # different optimizers opt_SGD = torch.optim.SGD(net_SGD.parameters(), lr=LR) opt_Momentum = torch.optim.SGD(net_Momentum.parameters(), lr=LR, momentum=0.8) opt_RMSprop = torch.optim.RMSprop(net_RMSprop.parameters(), lr=LR, alpha=0.9) opt_Adam = torch.optim.Adam(net_Adam.parameters(), lr=LR, betas=(0.9, 0.99)) optimizers = [opt_SGD, opt_Momentum, opt_RMSprop, opt_Adam] loss_func = torch.nn.MSELoss() losses_his = [[], [], [], []] # record loss # training for epoch in range(EPOCH): print('Epoch: ', epoch) for step, (b_x, b_y) in enumerate(loader): # for each training step for net, opt, l_his in zip(nets, optimizers, losses_his): output = net(b_x) # get output for every net loss = loss_func(output, b_y) # compute loss for every net opt.zero_grad() # clear gradients for next train loss.backward() # backpropagation, compute gradients opt.step() # apply gradients l_his.append(loss.data.numpy()) # loss recoder labels = ['SGD', 'Momentum', 'RMSprop', 'Adam'] for i, l_his in enumerate(losses_his): plt.plot(l_his, label=labels[i]) plt.legend(loc='best') plt.xlabel('Steps') plt.ylabel('Loss') plt.ylim((0, 0.2)) plt.show() ================================================ FILE: tutorial-contents/401_CNN.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.4 torchvision matplotlib """ # library # standard library import os # third-party library import torch import torch.nn as nn import torch.utils.data as Data import torchvision import matplotlib.pyplot as plt # torch.manual_seed(1) # reproducible # Hyper Parameters EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch BATCH_SIZE = 50 LR = 0.001 # learning rate DOWNLOAD_MNIST = False # Mnist digits dataset if not(os.path.exists('./mnist/')) or not os.listdir('./mnist/'): # not mnist dir or mnist is empyt dir DOWNLOAD_MNIST = True train_data = torchvision.datasets.MNIST( root='./mnist/', train=True, # this is training data transform=torchvision.transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0] download=DOWNLOAD_MNIST, ) # plot one example print(train_data.train_data.size()) # (60000, 28, 28) print(train_data.train_labels.size()) # (60000) plt.imshow(train_data.train_data[0].numpy(), cmap='gray') plt.title('%i' % train_data.train_labels[0]) plt.show() # Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28) train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True) # pick 2000 samples to speed up testing test_data = torchvision.datasets.MNIST(root='./mnist/', train=False) test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[:2000]/255. # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1) test_y = test_data.test_labels[:2000] class CNN(nn.Module): def __init__(self): super(CNN, self).__init__() self.conv1 = nn.Sequential( # input shape (1, 28, 28) nn.Conv2d( in_channels=1, # input height out_channels=16, # n_filters kernel_size=5, # filter size stride=1, # filter movement/step padding=2, # if want same width and length of this image after Conv2d, padding=(kernel_size-1)/2 if stride=1 ), # output shape (16, 28, 28) nn.ReLU(), # activation nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14) ) self.conv2 = nn.Sequential( # input shape (16, 14, 14) nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14) nn.ReLU(), # activation nn.MaxPool2d(2), # output shape (32, 7, 7) ) self.out = nn.Linear(32 * 7 * 7, 10) # fully connected layer, output 10 classes def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = x.view(x.size(0), -1) # flatten the output of conv2 to (batch_size, 32 * 7 * 7) output = self.out(x) return output, x # return x for visualization cnn = CNN() print(cnn) # net architecture optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) # optimize all cnn parameters loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted # following function (plot_with_labels) is for visualization, can be ignored if not interested from matplotlib import cm try: from sklearn.manifold import TSNE; HAS_SK = True except: HAS_SK = False; print('Please install sklearn for layer visualization') def plot_with_labels(lowDWeights, labels): plt.cla() X, Y = lowDWeights[:, 0], lowDWeights[:, 1] for x, y, s in zip(X, Y, labels): c = cm.rainbow(int(255 * s / 9)); plt.text(x, y, s, backgroundcolor=c, fontsize=9) plt.xlim(X.min(), X.max()); plt.ylim(Y.min(), Y.max()); plt.title('Visualize last layer'); plt.show(); plt.pause(0.01) plt.ion() # training and testing for epoch in range(EPOCH): for step, (b_x, b_y) in enumerate(train_loader): # gives batch data, normalize x when iterate train_loader output = cnn(b_x)[0] # cnn output loss = loss_func(output, b_y) # cross entropy loss optimizer.zero_grad() # clear gradients for this training step loss.backward() # backpropagation, compute gradients optimizer.step() # apply gradients if step % 50 == 0: test_output, last_layer = cnn(test_x) pred_y = torch.max(test_output, 1)[1].data.numpy() accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0)) print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy) if HAS_SK: # Visualization of trained flatten layer (T-SNE) tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000) plot_only = 500 low_dim_embs = tsne.fit_transform(last_layer.data.numpy()[:plot_only, :]) labels = test_y.numpy()[:plot_only] plot_with_labels(low_dim_embs, labels) plt.ioff() # print 10 predictions from test data test_output, _ = cnn(test_x[:10]) pred_y = torch.max(test_output, 1)[1].data.numpy() print(pred_y, 'prediction number') print(test_y[:10].numpy(), 'real number') ================================================ FILE: tutorial-contents/402_RNN_classifier.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.4 matplotlib torchvision """ import torch from torch import nn import torchvision.datasets as dsets import torchvision.transforms as transforms import matplotlib.pyplot as plt # torch.manual_seed(1) # reproducible # Hyper Parameters EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch BATCH_SIZE = 64 TIME_STEP = 28 # rnn time step / image height INPUT_SIZE = 28 # rnn input size / image width LR = 0.01 # learning rate DOWNLOAD_MNIST = True # set to True if haven't download the data # Mnist digital dataset train_data = dsets.MNIST( root='./mnist/', train=True, # this is training data transform=transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0] download=DOWNLOAD_MNIST, # download it if you don't have it ) # plot one example print(train_data.train_data.size()) # (60000, 28, 28) print(train_data.train_labels.size()) # (60000) plt.imshow(train_data.train_data[0].numpy(), cmap='gray') plt.title('%i' % train_data.train_labels[0]) plt.show() # Data Loader for easy mini-batch return in training train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True) # convert test data into Variable, pick 2000 samples to speed up testing test_data = dsets.MNIST(root='./mnist/', train=False, transform=transforms.ToTensor()) test_x = test_data.test_data.type(torch.FloatTensor)[:2000]/255. # shape (2000, 28, 28) value in range(0,1) test_y = test_data.test_labels.numpy()[:2000] # covert to numpy array class RNN(nn.Module): def __init__(self): super(RNN, self).__init__() self.rnn = nn.LSTM( # if use nn.RNN(), it hardly learns input_size=INPUT_SIZE, hidden_size=64, # rnn hidden unit num_layers=1, # number of rnn layer batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size) ) self.out = nn.Linear(64, 10) def forward(self, x): # x shape (batch, time_step, input_size) # r_out shape (batch, time_step, output_size) # h_n shape (n_layers, batch, hidden_size) # h_c shape (n_layers, batch, hidden_size) r_out, (h_n, h_c) = self.rnn(x, None) # None represents zero initial hidden state # choose r_out at the last time step out = self.out(r_out[:, -1, :]) return out rnn = RNN() print(rnn) optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted # training and testing for epoch in range(EPOCH): for step, (b_x, b_y) in enumerate(train_loader): # gives batch data b_x = b_x.view(-1, 28, 28) # reshape x to (batch, time_step, input_size) output = rnn(b_x) # rnn output loss = loss_func(output, b_y) # cross entropy loss optimizer.zero_grad() # clear gradients for this training step loss.backward() # backpropagation, compute gradients optimizer.step() # apply gradients if step % 50 == 0: test_output = rnn(test_x) # (samples, time_step, input_size) pred_y = torch.max(test_output, 1)[1].data.numpy() accuracy = float((pred_y == test_y).astype(int).sum()) / float(test_y.size) print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy) # print 10 predictions from test data test_output = rnn(test_x[:10].view(-1, 28, 28)) pred_y = torch.max(test_output, 1)[1].data.numpy() print(pred_y, 'prediction number') print(test_y[:10], 'real number') ================================================ FILE: tutorial-contents/403_RNN_regressor.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.4 matplotlib numpy """ import torch from torch import nn import numpy as np import matplotlib.pyplot as plt # torch.manual_seed(1) # reproducible # Hyper Parameters TIME_STEP = 10 # rnn time step INPUT_SIZE = 1 # rnn input size LR = 0.02 # learning rate # show data steps = np.linspace(0, np.pi*2, 100, dtype=np.float32) # float32 for converting torch FloatTensor x_np = np.sin(steps) y_np = np.cos(steps) plt.plot(steps, y_np, 'r-', label='target (cos)') plt.plot(steps, x_np, 'b-', label='input (sin)') plt.legend(loc='best') plt.show() class RNN(nn.Module): def __init__(self): super(RNN, self).__init__() self.rnn = nn.RNN( input_size=INPUT_SIZE, hidden_size=32, # rnn hidden unit num_layers=1, # number of rnn layer batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size) ) self.out = nn.Linear(32, 1) def forward(self, x, h_state): # x (batch, time_step, input_size) # h_state (n_layers, batch, hidden_size) # r_out (batch, time_step, hidden_size) r_out, h_state = self.rnn(x, h_state) outs = [] # save all predictions for time_step in range(r_out.size(1)): # calculate output for each time step outs.append(self.out(r_out[:, time_step, :])) return torch.stack(outs, dim=1), h_state # instead, for simplicity, you can replace above codes by follows # r_out = r_out.view(-1, 32) # outs = self.out(r_out) # outs = outs.view(-1, TIME_STEP, 1) # return outs, h_state # or even simpler, since nn.Linear can accept inputs of any dimension # and returns outputs with same dimension except for the last # outs = self.out(r_out) # return outs rnn = RNN() print(rnn) optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters loss_func = nn.MSELoss() h_state = None # for initial hidden state plt.figure(1, figsize=(12, 5)) plt.ion() # continuously plot for step in range(100): start, end = step * np.pi, (step+1)*np.pi # time range # use sin predicts cos steps = np.linspace(start, end, TIME_STEP, dtype=np.float32, endpoint=False) # float32 for converting torch FloatTensor x_np = np.sin(steps) y_np = np.cos(steps) x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis]) # shape (batch, time_step, input_size) y = torch.from_numpy(y_np[np.newaxis, :, np.newaxis]) prediction, h_state = rnn(x, h_state) # rnn output # !! next step is important !! h_state = h_state.data # repack the hidden state, break the connection from last iteration loss = loss_func(prediction, y) # calculate loss optimizer.zero_grad() # clear gradients for this training step loss.backward() # backpropagation, compute gradients optimizer.step() # apply gradients # plotting plt.plot(steps, y_np.flatten(), 'r-') plt.plot(steps, prediction.data.numpy().flatten(), 'b-') plt.draw(); plt.pause(0.05) plt.ioff() plt.show() ================================================ FILE: tutorial-contents/404_autoencoder.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.4 matplotlib numpy """ import torch import torch.nn as nn import torch.utils.data as Data import torchvision import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm import numpy as np # torch.manual_seed(1) # reproducible # Hyper Parameters EPOCH = 10 BATCH_SIZE = 64 LR = 0.005 # learning rate DOWNLOAD_MNIST = False N_TEST_IMG = 5 # Mnist digits dataset train_data = torchvision.datasets.MNIST( root='./mnist/', train=True, # this is training data transform=torchvision.transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0] download=DOWNLOAD_MNIST, # download it if you don't have it ) # plot one example print(train_data.train_data.size()) # (60000, 28, 28) print(train_data.train_labels.size()) # (60000) plt.imshow(train_data.train_data[2].numpy(), cmap='gray') plt.title('%i' % train_data.train_labels[2]) plt.show() # Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28) train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True) class AutoEncoder(nn.Module): def __init__(self): super(AutoEncoder, self).__init__() self.encoder = nn.Sequential( nn.Linear(28*28, 128), nn.Tanh(), nn.Linear(128, 64), nn.Tanh(), nn.Linear(64, 12), nn.Tanh(), nn.Linear(12, 3), # compress to 3 features which can be visualized in plt ) self.decoder = nn.Sequential( nn.Linear(3, 12), nn.Tanh(), nn.Linear(12, 64), nn.Tanh(), nn.Linear(64, 128), nn.Tanh(), nn.Linear(128, 28*28), nn.Sigmoid(), # compress to a range (0, 1) ) def forward(self, x): encoded = self.encoder(x) decoded = self.decoder(encoded) return encoded, decoded autoencoder = AutoEncoder() optimizer = torch.optim.Adam(autoencoder.parameters(), lr=LR) loss_func = nn.MSELoss() # initialize figure f, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2)) plt.ion() # continuously plot # original data (first row) for viewing view_data = train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255. for i in range(N_TEST_IMG): a[0][i].imshow(np.reshape(view_data.data.numpy()[i], (28, 28)), cmap='gray'); a[0][i].set_xticks(()); a[0][i].set_yticks(()) for epoch in range(EPOCH): for step, (x, b_label) in enumerate(train_loader): b_x = x.view(-1, 28*28) # batch x, shape (batch, 28*28) b_y = x.view(-1, 28*28) # batch y, shape (batch, 28*28) encoded, decoded = autoencoder(b_x) loss = loss_func(decoded, b_y) # mean square error optimizer.zero_grad() # clear gradients for this training step loss.backward() # backpropagation, compute gradients optimizer.step() # apply gradients if step % 100 == 0: print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy()) # plotting decoded image (second row) _, decoded_data = autoencoder(view_data) for i in range(N_TEST_IMG): a[1][i].clear() a[1][i].imshow(np.reshape(decoded_data.data.numpy()[i], (28, 28)), cmap='gray') a[1][i].set_xticks(()); a[1][i].set_yticks(()) plt.draw(); plt.pause(0.05) plt.ioff() plt.show() # visualize in 3D plot view_data = train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255. encoded_data, _ = autoencoder(view_data) fig = plt.figure(2); ax = Axes3D(fig) X, Y, Z = encoded_data.data[:, 0].numpy(), encoded_data.data[:, 1].numpy(), encoded_data.data[:, 2].numpy() values = train_data.train_labels[:200].numpy() for x, y, z, s in zip(X, Y, Z, values): c = cm.rainbow(int(255*s/9)); ax.text(x, y, z, s, backgroundcolor=c) ax.set_xlim(X.min(), X.max()); ax.set_ylim(Y.min(), Y.max()); ax.set_zlim(Z.min(), Z.max()) plt.show() ================================================ FILE: tutorial-contents/405_DQN_Reinforcement_learning.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou More about Reinforcement learning: https://mofanpy.com/tutorials/machine-learning/reinforcement-learning/ Dependencies: torch: 0.4 gym: 0.8.1 numpy """ import torch import torch.nn as nn import torch.nn.functional as F import numpy as np import gym # Hyper Parameters BATCH_SIZE = 32 LR = 0.01 # learning rate EPSILON = 0.9 # greedy policy GAMMA = 0.9 # reward discount TARGET_REPLACE_ITER = 100 # target update frequency MEMORY_CAPACITY = 2000 env = gym.make('CartPole-v0') env = env.unwrapped N_ACTIONS = env.action_space.n N_STATES = env.observation_space.shape[0] ENV_A_SHAPE = 0 if isinstance(env.action_space.sample(), int) else env.action_space.sample().shape # to confirm the shape class Net(nn.Module): def __init__(self, ): super(Net, self).__init__() self.fc1 = nn.Linear(N_STATES, 50) self.fc1.weight.data.normal_(0, 0.1) # initialization self.out = nn.Linear(50, N_ACTIONS) self.out.weight.data.normal_(0, 0.1) # initialization def forward(self, x): x = self.fc1(x) x = F.relu(x) actions_value = self.out(x) return actions_value class DQN(object): def __init__(self): self.eval_net, self.target_net = Net(), Net() self.learn_step_counter = 0 # for target updating self.memory_counter = 0 # for storing memory self.memory = np.zeros((MEMORY_CAPACITY, N_STATES * 2 + 2)) # initialize memory self.optimizer = torch.optim.Adam(self.eval_net.parameters(), lr=LR) self.loss_func = nn.MSELoss() def choose_action(self, x): x = torch.unsqueeze(torch.FloatTensor(x), 0) # input only one sample if np.random.uniform() < EPSILON: # greedy actions_value = self.eval_net.forward(x) action = torch.max(actions_value, 1)[1].data.numpy() action = action[0] if ENV_A_SHAPE == 0 else action.reshape(ENV_A_SHAPE) # return the argmax index else: # random action = np.random.randint(0, N_ACTIONS) action = action if ENV_A_SHAPE == 0 else action.reshape(ENV_A_SHAPE) return action def store_transition(self, s, a, r, s_): transition = np.hstack((s, [a, r], s_)) # replace the old memory with new memory index = self.memory_counter % MEMORY_CAPACITY self.memory[index, :] = transition self.memory_counter += 1 def learn(self): # target parameter update if self.learn_step_counter % TARGET_REPLACE_ITER == 0: self.target_net.load_state_dict(self.eval_net.state_dict()) self.learn_step_counter += 1 # sample batch transitions sample_index = np.random.choice(MEMORY_CAPACITY, BATCH_SIZE) b_memory = self.memory[sample_index, :] b_s = torch.FloatTensor(b_memory[:, :N_STATES]) b_a = torch.LongTensor(b_memory[:, N_STATES:N_STATES+1].astype(int)) b_r = torch.FloatTensor(b_memory[:, N_STATES+1:N_STATES+2]) b_s_ = torch.FloatTensor(b_memory[:, -N_STATES:]) # q_eval w.r.t the action in experience q_eval = self.eval_net(b_s).gather(1, b_a) # shape (batch, 1) q_next = self.target_net(b_s_).detach() # detach from graph, don't backpropagate q_target = b_r + GAMMA * q_next.max(1)[0].view(BATCH_SIZE, 1) # shape (batch, 1) loss = self.loss_func(q_eval, q_target) self.optimizer.zero_grad() loss.backward() self.optimizer.step() dqn = DQN() print('\nCollecting experience...') for i_episode in range(400): s = env.reset() ep_r = 0 while True: env.render() a = dqn.choose_action(s) # take action s_, r, done, info = env.step(a) # modify the reward x, x_dot, theta, theta_dot = s_ r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8 r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5 r = r1 + r2 dqn.store_transition(s, a, r, s_) ep_r += r if dqn.memory_counter > MEMORY_CAPACITY: dqn.learn() if done: print('Ep: ', i_episode, '| Ep_r: ', round(ep_r, 2)) if done: break s = s_ ================================================ FILE: tutorial-contents/406_GAN.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.4 numpy matplotlib """ import torch import torch.nn as nn import numpy as np import matplotlib.pyplot as plt # torch.manual_seed(1) # reproducible # np.random.seed(1) # Hyper Parameters BATCH_SIZE = 64 LR_G = 0.0001 # learning rate for generator LR_D = 0.0001 # learning rate for discriminator N_IDEAS = 5 # think of this as number of ideas for generating an art work (Generator) ART_COMPONENTS = 15 # it could be total point G can draw in the canvas PAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)]) # show our beautiful painting range # plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound') # plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound') # plt.legend(loc='upper right') # plt.show() def artist_works(): # painting from the famous artist (real target) a = np.random.uniform(1, 2, size=BATCH_SIZE)[:, np.newaxis] paintings = a * np.power(PAINT_POINTS, 2) + (a-1) paintings = torch.from_numpy(paintings).float() return paintings G = nn.Sequential( # Generator nn.Linear(N_IDEAS, 128), # random ideas (could from normal distribution) nn.ReLU(), nn.Linear(128, ART_COMPONENTS), # making a painting from these random ideas ) D = nn.Sequential( # Discriminator nn.Linear(ART_COMPONENTS, 128), # receive art work either from the famous artist or a newbie like G nn.ReLU(), nn.Linear(128, 1), nn.Sigmoid(), # tell the probability that the art work is made by artist ) opt_D = torch.optim.Adam(D.parameters(), lr=LR_D) opt_G = torch.optim.Adam(G.parameters(), lr=LR_G) plt.ion() # something about continuous plotting for step in range(10000): artist_paintings = artist_works() # real painting from artist G_ideas = torch.randn(BATCH_SIZE, N_IDEAS, requires_grad=True) # random ideas\n G_paintings = G(G_ideas) # fake painting from G (random ideas) prob_artist1 = D(G_paintings) # D try to reduce this prob G_loss = torch.mean(torch.log(1. - prob_artist1)) opt_G.zero_grad() G_loss.backward() opt_G.step() prob_artist0 = D(artist_paintings) # D try to increase this prob prob_artist1 = D(G_paintings.detach()) # D try to reduce this prob D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1)) opt_D.zero_grad() D_loss.backward(retain_graph=True) # reusing computational graph opt_D.step() if step % 50 == 0: # plotting plt.cla() plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',) plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound') plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound') plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 13}) plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 13}) plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=10);plt.draw();plt.pause(0.01) plt.ioff() plt.show() ================================================ FILE: tutorial-contents/406_conditional_GAN.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.4 numpy matplotlib """ import torch import torch.nn as nn import numpy as np import matplotlib.pyplot as plt # torch.manual_seed(1) # reproducible # np.random.seed(1) # Hyper Parameters BATCH_SIZE = 64 LR_G = 0.0001 # learning rate for generator LR_D = 0.0001 # learning rate for discriminator N_IDEAS = 5 # think of this as number of ideas for generating an art work (Generator) ART_COMPONENTS = 15 # it could be total point G can draw in the canvas PAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)]) # show our beautiful painting range # plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound') # plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound') # plt.legend(loc='upper right') # plt.show() def artist_works_with_labels(): # painting from the famous artist (real target) a = np.random.uniform(1, 2, size=BATCH_SIZE)[:, np.newaxis] paintings = a * np.power(PAINT_POINTS, 2) + (a-1) labels = (a-1) > 0.5 # upper paintings (1), lower paintings (0), two classes paintings = torch.from_numpy(paintings).float() labels = torch.from_numpy(labels.astype(np.float32)) return paintings, labels G = nn.Sequential( # Generator nn.Linear(N_IDEAS+1, 128), # random ideas (could from normal distribution) + class label nn.ReLU(), nn.Linear(128, ART_COMPONENTS), # making a painting from these random ideas ) D = nn.Sequential( # Discriminator nn.Linear(ART_COMPONENTS+1, 128), # receive art work either from the famous artist or a newbie like G with label nn.ReLU(), nn.Linear(128, 1), nn.Sigmoid(), # tell the probability that the art work is made by artist ) opt_D = torch.optim.Adam(D.parameters(), lr=LR_D) opt_G = torch.optim.Adam(G.parameters(), lr=LR_G) plt.ion() # something about continuous plotting for step in range(10000): artist_paintings, labels = artist_works_with_labels() # real painting, label from artist G_ideas = torch.randn(BATCH_SIZE, N_IDEAS) # random ideas G_inputs = torch.cat((G_ideas, labels), 1) # ideas with labels G_paintings = G(G_inputs) # fake painting w.r.t label from G D_inputs0 = torch.cat((artist_paintings, labels), 1) # all have their labels D_inputs1 = torch.cat((G_paintings, labels), 1) prob_artist0 = D(D_inputs0) # D try to increase this prob prob_artist1 = D(D_inputs1) # D try to reduce this prob D_score0 = torch.log(prob_artist0) # maximise this for D D_score1 = torch.log(1. - prob_artist1) # maximise this for D D_loss = - torch.mean(D_score0 + D_score1) # minimise the negative of both two above for D G_loss = torch.mean(D_score1) # minimise D score w.r.t G opt_D.zero_grad() D_loss.backward(retain_graph=True) # reusing computational graph opt_D.step() opt_G.zero_grad() G_loss.backward() opt_G.step() if step % 200 == 0: # plotting plt.cla() plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',) bound = [0, 0.5] if labels.data[0, 0] == 0 else [0.5, 1] plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + bound[1], c='#74BCFF', lw=3, label='upper bound') plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + bound[0], c='#FF9359', lw=3, label='lower bound') plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 13}) plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 13}) plt.text(-.5, 1.7, 'Class = %i' % int(labels.data[0, 0]), fontdict={'size': 13}) plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=10);plt.draw();plt.pause(0.1) plt.ioff() plt.show() # plot a generated painting for upper class z = torch.randn(1, N_IDEAS) label = torch.FloatTensor([[1.]]) # for upper class G_inputs = torch.cat((z, label), 1) G_paintings = G(G_inputs) plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='G painting for upper class',) plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + bound[1], c='#74BCFF', lw=3, label='upper bound (class 1)') plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + bound[0], c='#FF9359', lw=3, label='lower bound (class 1)') plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=10);plt.show() ================================================ FILE: tutorial-contents/501_why_torch_dynamic_graph.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.4 matplotlib numpy """ import torch from torch import nn import numpy as np import matplotlib.pyplot as plt # torch.manual_seed(1) # reproducible # Hyper Parameters INPUT_SIZE = 1 # rnn input size / image width LR = 0.02 # learning rate class RNN(nn.Module): def __init__(self): super(RNN, self).__init__() self.rnn = nn.RNN( input_size=1, hidden_size=32, # rnn hidden unit num_layers=1, # number of rnn layer batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size) ) self.out = nn.Linear(32, 1) def forward(self, x, h_state): # x (batch, time_step, input_size) # h_state (n_layers, batch, hidden_size) # r_out (batch, time_step, output_size) r_out, h_state = self.rnn(x, h_state) outs = [] # this is where you can find torch is dynamic for time_step in range(r_out.size(1)): # calculate output for each time step outs.append(self.out(r_out[:, time_step, :])) return torch.stack(outs, dim=1), h_state rnn = RNN() print(rnn) optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters loss_func = nn.MSELoss() # the target label is not one-hotted h_state = None # for initial hidden state plt.figure(1, figsize=(12, 5)) plt.ion() # continuously plot ######################## Below is different ######################### ################ static time steps ########## # for step in range(60): # start, end = step * np.pi, (step+1)*np.pi # time steps # # use sin predicts cos # steps = np.linspace(start, end, 10, dtype=np.float32) ################ dynamic time steps ######### step = 0 for i in range(60): dynamic_steps = np.random.randint(1, 4) # has random time steps start, end = step * np.pi, (step + dynamic_steps) * np.pi # different time steps length step += dynamic_steps # use sin predicts cos steps = np.linspace(start, end, 10 * dynamic_steps, dtype=np.float32) ####################### Above is different ########################### print(len(steps)) # print how many time step feed to RNN x_np = np.sin(steps) # float32 for converting torch FloatTensor y_np = np.cos(steps) x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis]) # shape (batch, time_step, input_size) y = torch.from_numpy(y_np[np.newaxis, :, np.newaxis]) prediction, h_state = rnn(x, h_state) # rnn output # !! next step is important !! h_state = h_state.data # repack the hidden state, break the connection from last iteration loss = loss_func(prediction, y) # cross entropy loss optimizer.zero_grad() # clear gradients for this training step loss.backward() # backpropagation, compute gradients optimizer.step() # apply gradients # plotting plt.plot(steps, y_np.flatten(), 'r-') plt.plot(steps, prediction.data.numpy().flatten(), 'b-') plt.draw() plt.pause(0.05) plt.ioff() plt.show() ================================================ FILE: tutorial-contents/502_GPU.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.4 torchvision """ import torch import torch.nn as nn import torch.utils.data as Data import torchvision # torch.manual_seed(1) EPOCH = 1 BATCH_SIZE = 50 LR = 0.001 DOWNLOAD_MNIST = False train_data = torchvision.datasets.MNIST(root='./mnist/', train=True, transform=torchvision.transforms.ToTensor(), download=DOWNLOAD_MNIST,) train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True) test_data = torchvision.datasets.MNIST(root='./mnist/', train=False) # !!!!!!!! Change in here !!!!!!!!! # test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[:2000].cuda()/255. # Tensor on GPU test_y = test_data.test_labels[:2000].cuda() class CNN(nn.Module): def __init__(self): super(CNN, self).__init__() self.conv1 = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2,), nn.ReLU(), nn.MaxPool2d(kernel_size=2),) self.conv2 = nn.Sequential(nn.Conv2d(16, 32, 5, 1, 2), nn.ReLU(), nn.MaxPool2d(2),) self.out = nn.Linear(32 * 7 * 7, 10) def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = x.view(x.size(0), -1) output = self.out(x) return output cnn = CNN() # !!!!!!!! Change in here !!!!!!!!! # cnn.cuda() # Moves all model parameters and buffers to the GPU. optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) loss_func = nn.CrossEntropyLoss() for epoch in range(EPOCH): for step, (x, y) in enumerate(train_loader): # !!!!!!!! Change in here !!!!!!!!! # b_x = x.cuda() # Tensor on GPU b_y = y.cuda() # Tensor on GPU output = cnn(b_x) loss = loss_func(output, b_y) optimizer.zero_grad() loss.backward() optimizer.step() if step % 50 == 0: test_output = cnn(test_x) # !!!!!!!! Change in here !!!!!!!!! # pred_y = torch.max(test_output, 1)[1].cuda().data # move the computation in GPU accuracy = torch.sum(pred_y == test_y).type(torch.FloatTensor) / test_y.size(0) print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.cpu().numpy(), '| test accuracy: %.2f' % accuracy) test_output = cnn(test_x[:10]) # !!!!!!!! Change in here !!!!!!!!! # pred_y = torch.max(test_output, 1)[1].cuda().data # move the computation in GPU print(pred_y, 'prediction number') print(test_y[:10], 'real number') ================================================ FILE: tutorial-contents/503_dropout.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.4 matplotlib """ import torch import matplotlib.pyplot as plt # torch.manual_seed(1) # reproducible N_SAMPLES = 20 N_HIDDEN = 300 # training data x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1) y = x + 0.3*torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1)) # test data test_x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1) test_y = test_x + 0.3*torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1)) # show data plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', s=50, alpha=0.5, label='train') plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.5, label='test') plt.legend(loc='upper left') plt.ylim((-2.5, 2.5)) plt.show() net_overfitting = torch.nn.Sequential( torch.nn.Linear(1, N_HIDDEN), torch.nn.ReLU(), torch.nn.Linear(N_HIDDEN, N_HIDDEN), torch.nn.ReLU(), torch.nn.Linear(N_HIDDEN, 1), ) net_dropped = torch.nn.Sequential( torch.nn.Linear(1, N_HIDDEN), torch.nn.Dropout(0.5), # drop 50% of the neuron torch.nn.ReLU(), torch.nn.Linear(N_HIDDEN, N_HIDDEN), torch.nn.Dropout(0.5), # drop 50% of the neuron torch.nn.ReLU(), torch.nn.Linear(N_HIDDEN, 1), ) print(net_overfitting) # net architecture print(net_dropped) optimizer_ofit = torch.optim.Adam(net_overfitting.parameters(), lr=0.01) optimizer_drop = torch.optim.Adam(net_dropped.parameters(), lr=0.01) loss_func = torch.nn.MSELoss() plt.ion() # something about plotting for t in range(500): pred_ofit = net_overfitting(x) pred_drop = net_dropped(x) loss_ofit = loss_func(pred_ofit, y) loss_drop = loss_func(pred_drop, y) optimizer_ofit.zero_grad() optimizer_drop.zero_grad() loss_ofit.backward() loss_drop.backward() optimizer_ofit.step() optimizer_drop.step() if t % 10 == 0: # change to eval mode in order to fix drop out effect net_overfitting.eval() net_dropped.eval() # parameters for dropout differ from train mode # plotting plt.cla() test_pred_ofit = net_overfitting(test_x) test_pred_drop = net_dropped(test_x) plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', s=50, alpha=0.3, label='train') plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.3, label='test') plt.plot(test_x.data.numpy(), test_pred_ofit.data.numpy(), 'r-', lw=3, label='overfitting') plt.plot(test_x.data.numpy(), test_pred_drop.data.numpy(), 'b--', lw=3, label='dropout(50%)') plt.text(0, -1.2, 'overfitting loss=%.4f' % loss_func(test_pred_ofit, test_y).data.numpy(), fontdict={'size': 20, 'color': 'red'}) plt.text(0, -1.5, 'dropout loss=%.4f' % loss_func(test_pred_drop, test_y).data.numpy(), fontdict={'size': 20, 'color': 'blue'}) plt.legend(loc='upper left'); plt.ylim((-2.5, 2.5));plt.pause(0.1) # change back to train mode net_overfitting.train() net_dropped.train() plt.ioff() plt.show() ================================================ FILE: tutorial-contents/504_batch_normalization.py ================================================ """ View more, visit my tutorial page: https://mofanpy.com/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.4 matplotlib numpy """ import torch from torch import nn from torch.nn import init import torch.utils.data as Data import matplotlib.pyplot as plt import numpy as np # torch.manual_seed(1) # reproducible # np.random.seed(1) # Hyper parameters N_SAMPLES = 2000 BATCH_SIZE = 64 EPOCH = 12 LR = 0.03 N_HIDDEN = 8 ACTIVATION = torch.tanh B_INIT = -0.2 # use a bad bias constant initializer # training data x = np.linspace(-7, 10, N_SAMPLES)[:, np.newaxis] noise = np.random.normal(0, 2, x.shape) y = np.square(x) - 5 + noise # test data test_x = np.linspace(-7, 10, 200)[:, np.newaxis] noise = np.random.normal(0, 2, test_x.shape) test_y = np.square(test_x) - 5 + noise train_x, train_y = torch.from_numpy(x).float(), torch.from_numpy(y).float() test_x = torch.from_numpy(test_x).float() test_y = torch.from_numpy(test_y).float() train_dataset = Data.TensorDataset(train_x, train_y) train_loader = Data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,) # show data plt.scatter(train_x.numpy(), train_y.numpy(), c='#FF9359', s=50, alpha=0.2, label='train') plt.legend(loc='upper left') class Net(nn.Module): def __init__(self, batch_normalization=False): super(Net, self).__init__() self.do_bn = batch_normalization self.fcs = [] self.bns = [] self.bn_input = nn.BatchNorm1d(1, momentum=0.5) # for input data for i in range(N_HIDDEN): # build hidden layers and BN layers input_size = 1 if i == 0 else 10 fc = nn.Linear(input_size, 10) setattr(self, 'fc%i' % i, fc) # IMPORTANT set layer to the Module self._set_init(fc) # parameters initialization self.fcs.append(fc) if self.do_bn: bn = nn.BatchNorm1d(10, momentum=0.5) setattr(self, 'bn%i' % i, bn) # IMPORTANT set layer to the Module self.bns.append(bn) self.predict = nn.Linear(10, 1) # output layer self._set_init(self.predict) # parameters initialization def _set_init(self, layer): init.normal_(layer.weight, mean=0., std=.1) init.constant_(layer.bias, B_INIT) def forward(self, x): pre_activation = [x] if self.do_bn: x = self.bn_input(x) # input batch normalization layer_input = [x] for i in range(N_HIDDEN): x = self.fcs[i](x) pre_activation.append(x) if self.do_bn: x = self.bns[i](x) # batch normalization x = ACTIVATION(x) layer_input.append(x) out = self.predict(x) return out, layer_input, pre_activation nets = [Net(batch_normalization=False), Net(batch_normalization=True)] # print(*nets) # print net architecture opts = [torch.optim.Adam(net.parameters(), lr=LR) for net in nets] loss_func = torch.nn.MSELoss() def plot_histogram(l_in, l_in_bn, pre_ac, pre_ac_bn): for i, (ax_pa, ax_pa_bn, ax, ax_bn) in enumerate(zip(axs[0, :], axs[1, :], axs[2, :], axs[3, :])): [a.clear() for a in [ax_pa, ax_pa_bn, ax, ax_bn]] if i == 0: p_range = (-7, 10);the_range = (-7, 10) else: p_range = (-4, 4);the_range = (-1, 1) ax_pa.set_title('L' + str(i)) ax_pa.hist(pre_ac[i].data.numpy().ravel(), bins=10, range=p_range, color='#FF9359', alpha=0.5);ax_pa_bn.hist(pre_ac_bn[i].data.numpy().ravel(), bins=10, range=p_range, color='#74BCFF', alpha=0.5) ax.hist(l_in[i].data.numpy().ravel(), bins=10, range=the_range, color='#FF9359');ax_bn.hist(l_in_bn[i].data.numpy().ravel(), bins=10, range=the_range, color='#74BCFF') for a in [ax_pa, ax, ax_pa_bn, ax_bn]: a.set_yticks(());a.set_xticks(()) ax_pa_bn.set_xticks(p_range);ax_bn.set_xticks(the_range) axs[0, 0].set_ylabel('PreAct');axs[1, 0].set_ylabel('BN PreAct');axs[2, 0].set_ylabel('Act');axs[3, 0].set_ylabel('BN Act') plt.pause(0.01) if __name__ == "__main__": f, axs = plt.subplots(4, N_HIDDEN + 1, figsize=(10, 5)) plt.ion() # something about plotting plt.show() # training losses = [[], []] # recode loss for two networks for epoch in range(EPOCH): print('Epoch: ', epoch) layer_inputs, pre_acts = [], [] for net, l in zip(nets, losses): net.eval() # set eval mode to fix moving_mean and moving_var pred, layer_input, pre_act = net(test_x) l.append(loss_func(pred, test_y).data.item()) layer_inputs.append(layer_input) pre_acts.append(pre_act) net.train() # free moving_mean and moving_var plot_histogram(*layer_inputs, *pre_acts) # plot histogram for step, (b_x, b_y) in enumerate(train_loader): for net, opt in zip(nets, opts): # train for each network pred, _, _ = net(b_x) loss = loss_func(pred, b_y) opt.zero_grad() loss.backward() opt.step() # it will also learns the parameters in Batch Normalization plt.ioff() # plot training loss plt.figure(2) plt.plot(losses[0], c='#FF9359', lw=3, label='Original') plt.plot(losses[1], c='#74BCFF', lw=3, label='Batch Normalization') plt.xlabel('step');plt.ylabel('test loss');plt.ylim((0, 2000));plt.legend(loc='best') # evaluation # set net to eval mode to freeze the parameters in batch normalization layers [net.eval() for net in nets] # set eval mode to fix moving_mean and moving_var preds = [net(test_x)[0] for net in nets] plt.figure(3) plt.plot(test_x.data.numpy(), preds[0].data.numpy(), c='#FF9359', lw=4, label='Original') plt.plot(test_x.data.numpy(), preds[1].data.numpy(), c='#74BCFF', lw=4, label='Batch Normalization') plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='r', s=50, alpha=0.2, label='train') plt.legend(loc='best') plt.show() ================================================ FILE: tutorial-contents/mnist/processed/training.pt ================================================ [File too large to display: 45.3 MB] ================================================ FILE: tutorial-contents/mnist/raw/train-images-idx3-ubyte ================================================ [File too large to display: 44.9 MB] ================================================ FILE: tutorial-contents-notebooks/.ipynb_checkpoints/401_CNN-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 401 CNN\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* torchvision\n", "* matplotlib" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", "from torch.autograd import Variable\n", "import torch.utils.data as Data\n", "import torchvision\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "torch.manual_seed(1) # reproducible" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Hyper Parameters\n", "EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch\n", "BATCH_SIZE = 50\n", "LR = 0.001 # learning rate\n", "DOWNLOAD_MNIST = True # set to False if you have downloaded" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", "Processing...\n", "Done!\n" ] } ], "source": [ "# Mnist digits dataset\n", "train_data = torchvision.datasets.MNIST(\n", " root='./mnist/',\n", " train=True, # this is training data\n", " transform=torchvision.transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to\n", " # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]\n", " download=DOWNLOAD_MNIST, # download it if you don't have it\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([60000, 28, 28])\n", "torch.Size([60000])\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot one example\n", "print(train_data.train_data.size()) # (60000, 28, 28)\n", "print(train_data.train_labels.size()) # (60000)\n", "plt.imshow(train_data.train_data[0].numpy(), cmap='gray')\n", "plt.title('%i' % train_data.train_labels[0])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)\n", "train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# convert test data into Variable, pick 2000 samples to speed up testing\n", "test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)\n", "test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1)).type(torch.FloatTensor)[:2000]/255. # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)\n", "test_y = test_data.test_labels[:2000]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "class CNN(nn.Module):\n", " def __init__(self):\n", " super(CNN, self).__init__()\n", " self.conv1 = nn.Sequential( # input shape (1, 28, 28)\n", " nn.Conv2d(\n", " in_channels=1, # input height\n", " out_channels=16, # n_filters\n", " kernel_size=5, # filter size\n", " stride=1, # filter movement/step\n", " padding=2, # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1\n", " ), # output shape (16, 28, 28)\n", " nn.ReLU(), # activation\n", " nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14)\n", " )\n", " self.conv2 = nn.Sequential( # input shape (1, 28, 28)\n", " nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)\n", " nn.ReLU(), # activation\n", " nn.MaxPool2d(2), # output shape (32, 7, 7)\n", " )\n", " self.out = nn.Linear(32 * 7 * 7, 10) # fully connected layer, output 10 classes\n", "\n", " def forward(self, x):\n", " x = self.conv1(x)\n", " x = self.conv2(x)\n", " x = x.view(x.size(0), -1) # flatten the output of conv2 to (batch_size, 32 * 7 * 7)\n", " output = self.out(x)\n", " return output, x # return x for visualization" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CNN(\n", " (conv1): Sequential(\n", " (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", " (1): ReLU()\n", " (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", " )\n", " (conv2): Sequential(\n", " (0): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", " (1): ReLU()\n", " (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", " )\n", " (out): Linear(in_features=1568, out_features=10, bias=True)\n", ")\n" ] } ], "source": [ "cnn = CNN()\n", "print(cnn) # net architecture" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) # optimize all cnn parameters\n", "loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/yoshitaka-i/.conda/envs/tensor/lib/python3.5/site-packages/ipykernel_launcher.py:29: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 2.3101 | test accuracy: 0.20\n" ] }, { "data": { "image/png": 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g5MiRyit5DAavqS6NcEBlKQf+YwW7om3cP2pwYHOns79bVc10g25eQm1/6zo9VmQq2wCwta6Jj76o47gD+2qPaXCHJ4pfREqIKf3ZSqkn4oc3isje8dX+3sAmL+bSwa1df/n7X/D/fv0C0ZY2Dj18IFdfP9pD6Qz5yoKaetY3RFk1dji1La187YXVnLF3Bd00mo3U9u+dtXlJJry0v2dCx9k8peV1T+d8bNFazj9mCLqLRYN7XCt+if1vPQh8qJRKTuV7BriI2I7xIuBpt3Pp4kbptzRHmXnnC9x1z7foUWFvPbV89c2djhmnb+GggOqSCJEiobIkQnObolVzjzp93TNpz+lU7nRif/fLJ1Dz+lJPQzNnL1zDA9892rPxDNnxYsV/HDAJeF9ElsSP/YSYwp8nIlOAtcD5HszlOyWlxTzw8GTPxjNO38LhtAEVPLpmB8cv+ISmNsWVB/ahe3FwOZB27O9LgDWfP82X45VC79Kco6pmKzOHjHMjpi1Wb6qnKdrKlweZpi1B4kVUz+tAuj3aqWmOGwx5R5EIDx2bu3JqduoCrQQiNspDJ9ApKf2Xc6+nentdh2NOzT/79atg8S2nO7rX4BxTssFg8JiqGgelHjSYD7wE/BWwDJFL4lBfJIiRqvTB2WeuajC74VzRZUo2JDts/zznO7kWx1DAzBwyjiXADUDnNKlgONincTeXlbJXY3On49rmodkXeCyRwQldQvG7cdhmw0QAGaxIDr8sJIb2rmLXF1tyLYbBJV1C8S/5zzq6dy/l2qv+RsPuZv40+zsZr9dV5n4+UAz5S3LP3Sma9wTpVK1duZYnjpjEmAW/tR2dc8Gap8BN/+CJc2P/9iyDe77pfByDK7qE4t+8sY6VH9Xw+N8vY9euztvUZOwo89QHyg9+dDJHHb2vl6Ib8hA7PXfd3OOUJTNmpS3LnAk7SVvZB2vUu67MRPv4QUEp/nSJWz17lXPEkUOoqCyjorIs4xh2lHnqA+XSSbN4Zv4VJhGlUOjZx35NfhuNWHLB5rc/oHxAbyRD0pnXCVppmfBoMPMYOlFQij9dzPxhIwZx98yXiEZbaWqMZlzJ21HmqQ+UXtXd2bZ1F336Vnj2mQw55A+a1XDy6EG/5LZZfO2Bn/D2j1NbHvpPsgkMgKRG65kwTdi9p0uEc1ZVlTNh8jFcPOEhLp38cMZrk5V5/wFV7crcisNGDGLNp1uJRlvZVd/Etq276FXd3Y+PYCgAaleu5c/lJ1Lz+tKczK9bKtkv+Zyas0wTdu8pqBV/Jr5xzhF845zsjqzU3UEmZV5VVc7cJ6cCUFwR4dkXp3kqs6GwcGpb9wrdUsnPnvwD13P5XULa4I4uo/h1Sd4dRKNtXHXtaUQ0inDlghPWrGFrq72G2X0iEV7b1zigg0bHtm7F58SqcXqB3VLJTgmqhLTBOUbxW6C7O8g1dpW+03sM7nFqW/erQES2Uslu8LqFo8F7wrmUNRgKCF3buhVLgRoHc3oaemmT8cAtSe8/I3vfgFz7P7oaZsVvKFwGDICNDtr59e8PNTbUbf/+GefRta2n8tGiayjeq4KtgG5QaV2rcP+w/KuNmGv/R1ejyyr+luYoJaXh+PhObfWGLDhR+k7uS31IpIQpOrWtt+5lPyy4MuK+iV3Qjlmn/g+Dc8Kh+Xxk4WureOappdzxm//ucHzJf9Zx9DFDfZ8/EumR9RqvbPWtdXWsvfhipLSUtoYG+l1zDRXHHWd7bB2cPKzAOJf9tK17RdCO2VzmFnRVCv4Rm4i1T2Xzxjq2bXNWFnb7tl0opbeyytZ964Q1axzJYEVRjx4MnTuXoXPmMPiuu9h0552ejZ2KUyexcS6Hm9KarYE6Zt34PwzOKXjFnwjPTKVnr3LuuOVf7e8njX+QrVvqLcdY+Noqrpv+t/b30y6ba5nUpfswSMZLRShFRUhxbBPXVl9P2UEHeTa2ofB4sOT4Tj9XDhnHK8R6+y5lT29fp9wEfBU4Kc35dv/HWdP54sV3eOe631O/xok722CHUJp63DZLT8UqNNNOopbutWEo19BSU8O6adNo/vRTBt5xR05lCSv5nFzkVRnwz//5huVx3d6+yfV80vUMTo3n/4XFNUHlFhg6klPFPyW6sAbon3h/xKK7mBJdyPQA+tTaSdTSvTYM5RpKBgxg2Lx5NK9bx5oJE6g85ZRci9SJQ1avtnW9l36BfE4u8rIM+NalH6c9Z6e3byZS4/mzkQ/+j0Ih1yv+/tkv8Q87iVo61+Y6w7etqYmibjGFEKmooKiHtWN5dvQJJhafG6RorvDSHJYuuSgfuinoVo5NXoEvA2ZaVNscccNF8PP7Leex09s3E4cS21E1Ax+6GMfgPblW/AAMGnw0kY2l7e8fRy8SpVvfes5+63/9EitnOI3OaVq5ko0zZkBRESoapf9Pf2p5XQPZa6E7jdoJO6nKKGHD9tK4UEWJ7cJiOj1r7VSOTVbeVbYkifX2tUNt/9703Lit0/FUs1H+1DAtfEKh+JOVvh2athRm+eNIZSXDHn/c9n3lhx3G0Llzs143c+2xzMTa3JIwqxSi0gd9G7YbHJUQHgykBgesvrnDWztlwJOVt24XMDv8ufzE9g5e09c9k9bOn2w2mumDHAZnFHxUj8Eeharwk7kceBWYDhyGcxt20ISpDLhulu1o4BTg5mwXGgIlFCt+Lzlkv5ssjy9fbb56QeBXEtkJa9Z45uB1ZcP+/sn2u3L17KPf1CUDYaoc22NQP63rknceV9VsdVSTv4oS2/cYMlNwij/s+GE7v2qfRVrX7Wot4b71R2W/0AWJJDIpLqZ57VrWTZvmieL38ndm14bdAbtK3+k9aQhL5diPH3kOHnnO1j1pm8k7yH8xuCPUiv8/PMS73IcgjOFuBnKk47EikR6e5gbozplKLk0pPSItfHreeY5X4zqreSkqgqLYKtQkkTnkicugsRaO9fd3l84payh8Qqv4G9jOW/yWS1lEHet5gklMwXkTaKvSCV4miqUzMYUNN6tx3dW8SSJzSWOt9qUHvvsxJS0pi4nZF3R4++C8NDVwfpMm+Wti9gABT7HqWWy3QqrBFqFV/Ot4i335GsWUUs0wmqknShPFHkZcB70DCAM6JR0eiM7hqn2s70+YizLdnw9JZFmZEGA+b2KFn8Ihiz7KeFtLSaSz0g+YTg3UNamq2Zre9APOK6satAit4m9gG2VUt78voycNbKOSvXMoVf7jdjXeI9LCp+PHp71fN4ksgS/OYCcOWBdcsXg9i7c10Kpg+vC+fHtoL3sD2FjhJ+OV0v9gXS2XP7QYgKaWNt60ca/jBuoO7zN4Q2gVfzm9aWRH+/tGaiknd12F/MQr5Xd55XcYNmp/AI6deDxfu+SkTtd4sRrPdL9uElkCX5zBCaVfVgyNUXdjZWHZjkaW1zaxaPT+1LW0MuJfq+wr/hxz8OCevPLTWPOWeYvWwt2d6/jkc30jQ2dCq/gHcwwv8VNaaaGODZRS4amZx4rFg4Yxcv2nvs5hhVfKr9eg3vz4xcyKNoHOatzJ/bpJZAl8dQaf82V48kP7yr9M/89iYHkxpUVCS5uirqWN3qW5zQpIXb2vrKlj6x/1y3P8ZeFnjE85ls/1jQzWeKL4ReRPwFhgk1Lq0Pix3sBjwFBibTfHK6W0K7yWU83RXM6fORFBOIO7vBA1Iy2RYpqLIpS22dtC6zRbyYRXym9nzQ7uPOVX9OhTwfg7L6Tv0M45qZ9dcIH2ajwdn02Y4Or+dJQdfDADb7/d0zE558vejpdCdWmEAypLOfAfK9gVbeP+UYN9nS8byat3gNrd+qUjttY18dEXdZ2O53N9I4M1Xq34HwJ+BzycdOx64EWl1O0icn38/XV2Bj2SSziSSzwSUY93B+9n+55LJ99Oh4/uIGHHi0iY21bdRWXfSpbNf49ZU+/n6vk/6XSNzmr8zlNjrbLTmYuGzpnjSL58Qtduv6CmnvUNUVaNHU5tSytfe2E1Z+xdQTeXiVVuV+4JenbXT356bNFazj9mCDz9QYfjTusb1a5cyxNHTGov7ZDKe2Q2GV0VfdtZ+QtDVjxR/Eqp10RkaMrhcezpvzALeAWbit8JN2eoBNWjP1wTRISYA8eiF7b3yr6VABw6+nDmTHvI1r1TB71Lj0h8dZjFXJScMDZz7bG25vEEh87brU1Rznjls6wOWDt2ewVUl0SIFAmVJRGa2xStHuQjpdrdX1ruf5TL7IVreOC7R3dS/E7rG2VroP5DMpuM7Ba6M+jjZ753f6XUBoD4v3o53i5o3JzZ5LIrpBFibU17mts5tb031jfS1toGwLr31lLRx14Bu3alnw84jNjp062Yl04Zxk/fz/xFOLRXGS+fGtv5VZZE+OTs4WmvPW1ABW0ojl/wCV9d8AlXHtiH7sXe/ln9ZeFnXHj8UE/HTGX1pnqaoq18eZB1C0S79Y0SDdQzlXbwu62jIT25du5uxGVN/se/ZC9xKrEj6NEfxlg3IeqATqSMW+xGwlix4YP1PHL5g5RVliEiTLrHj5qMnele1MzuNmfVVW3jQWx9NkVulyIRHjpWp82IMxJ29+MO7OvbHAD79atg8S2npz1vt76RTgN1P0piG/TwU/FvFJG9lVIbRGRvYFPqBQ8WHzcA4ObYjjlQdFf/diJlnGI3EsaKYaO+xM8X3+qRRPp8b/C/HZt7/CroVkgk7O5WNffBO19ANuzUN9JtoO5HSWyDHn4q/meAi4h1b7sIeNrqopuFUOdl60TKFDJ+7nj8KuhWSLTb3dOQC19ANtobqL/5PtuXraZ2xRpOnvPLTr1086kkdqHhVTjno8QcuX1FZB2xfI/bgXkiMgVYC5yf5vactl/Mhk6kTC7pXtTs6/g6O56r9lnkaNXvdQy/6wzakJHN7p7KXxZ+xrVj/Q1f1UG3gfqsoAUztONVVM+305w6Nc3xvMFNpIwfjP/SfgxqyRxO+cd1R9q2u89ceyzdi5r53uB/dzju947Hq4JufmTQ5vpBks3unkxQvgC7ZGqgblb7uSPXzl3f8KKkc2N9I6XlpRRFihxFyuQKp85Wq/t0dzypPQHaFBSlmKWtdgU6Yax96rbz2h0XZ5Q9NRLnrdFfynh9Njx9kPT0vy5NNl+AwZBMKBW/W6XtVUln3UiZpta2zgk7aSJQ+lz3Z7ZWVluey0a3zbE1UmtNGZEB2Rume4HTHU+q0rdCt6BbNqVvRd9uel/tZTsaObRXWafj2qUY5rxnW7YOPHGZu/vjZPMF2GFL37703bLFk7EM4SR0it8LpZ1a0jnd/elCQYv6N7D3uie1I2WsEnaqx/yf9cXvQb+mrax46ZvtSiNd0lm6B2DNkD1RG9nMPm7we8fjRRhrxvGtHsgpDCy3/hMIrBSDw8qcyej6AnQjgE58661Ox5bvZ5HRHsTuIlsIr0dtLbsaoVP8XtThP4AzOIAzHMvQtrHc1vV2E3Y2dcu+9fe6EY0T/M4N8CKMNRM6GbTVaVbyfpVi8ANdX4CrCKA56dx4/lDbX7MSb4DltwuJ0Cl+U4c/hteNaJzEzOcqN0AHHcerzgM5nU189N6VjN47Zubq3a2Y5WcdaD1A6oo0T1agXkUAVTltoF5jrbBr+/dm+rpn3IplyELoFH9XqsOfCa8fgH7GzP/fn17xJaM5HaGuge/hCtSv5CwvI4AydtHSYEpLsLtYQ4zQKf5c1OEPI14/ADPFzHco0OYAL5T+q7d/h771O7JfSMcInu7FRTmvge8XfiVn+R0BZJq2hJ/QKf4g6vB7EeqZoHxHveXxuhljkKJiyqfcTaTfUNvj6j4ALy2e0OnYTFZbjpkuZt5tgbYtn212Hd+vq/RTiYjw00N8r/+Xc7xMzvIyAigV07QlPwid4gd/6/DrOk3Xl0yIlXE+xdl6pfLG52hZOp/GJ26lx2X32b7fjwegX03QdTOa/Sro9pOlNaF1vHqBl6YZu9nACfp87wktM5N205ZvHwbAg5O/b0sOgzeEUvHbYT2LGcRI7evtOE13bSTmrLNpt91YGjfJREqRiPWvePZvr6AhGgvFHJRmwT2IMs7mivi7lbTWrO0QymkHu03Q7bB1rV7Md2pW8E8Oms6tH/3G9fxe1cBPdhi/c/r+Wa9G0tOFAAAgAElEQVQJKps3m2nmkGNndHi/fFH6bFk72cDJnD9KrwKp06YthmDJe8VvR+mDfafpzfd23KimNnOpfsQ6Dls17aLx8V/R/bvWRWwbetmPiXeTtOVXzHxrS6t2fL9feQEfe1BmOdVhrHNNUE5lu6aZ6tZ72R5xnhhm+eCYoje/06YthmDJe8VvF7dOU51yzirawq7fXUK3s68iMsjD5uEucBIzr1OZ8zen36od35+rngE6pGbqVpZ0dhjnorG6U9NMLrk8/rOMWKXGwnS95zddTvF7ETXUIdP24Y7nVFsbu++dSslRZ1F61Fj3AucQncqcP37pZ9rjhTkvIDVTd9O5B2e9JojG6k5NM7nEbtMWQ/B0OcXvd9RQy+K/07J0Pm07N9H8xmNEBh9M98l3ejpHUHSlXgSpmbo614Q5mzeX2GnakkDHd6JTgsOgRxgUv+v2i3bxM2qodNQ4Skc5S2qx2/Skh8+/tbD3IvCS1KbpOtd45VR2S8Imv6WkghOPuiHH0thH13cSht91oZBzxX+Tijn84524Qt2UxW90TCs3BfjlD1svAvAvqua0ARU8umYHxy/4hKY2ZRnVk3qNH43V3dC3JZZTUjdjDDXTBzOgwhvrum4op1N0fSdh+l3nOzlX/AluUgwYOXKkWrx4Mb8ekN2J6mUSVjaCmssr00qfSIStrdbmCl3C2IvAz6ganabpfjdW94rKG59j/6XzaX7z8fYcEjdRPrqhnE7Jhe+kqxMaxZ+MTuTMV/gOX+E7vssSZJVMr0wrr+27LwCHrLbO4NUhjBE4uYiqyVsy5JDY5cLjh3oyTjqM7yR4Qqn4w4TXVTIzESbTipsInDtPvQVI76ewKjMRC/zLTEGuDMt6elKTP5lsOSQJdIvApc0Y7lkGtQ5yS8o6qh1t38nXD4QXVtqfz9AJo/izkC3hq/sOYXcve4b3fmWdMzC1TSsTDg996d9sfgqn6K4Mj35+Vf40Wz/33s7HLGrf26nUqZtDolsELm0xt3u+mXF8S/6xvNMhbd9JWUnnYwG0tSxEjOLPQjm9+SrT299fzKsdzk+dVmV7zJsUPBDteMyWaSXPm088EO3cNexSjft0V4YvnTIsvf3/0ffb68TkE3YqdTrJIfGyCFwCy4eVRZa1K99JiBdAYaZLKf4/MILv8pYtM81gjqEb9pW7XeyaVtzY7/MV3ZVhVvt/QwuUW6wevSCAFWg2Jd38xmO2cki8LAKXjF9lpdsxq33H5L3itxNx46SZSTnOGqNn4mZJX5gtwYQr76Z77a6055f/tPM2e0tFL068/iGX0oUX3ZXhiH99nNn+/9RHehNa7QzcNld3STYl3dosVP7kWVtj+l2fH5IeVh9vcjdQjn//hUJeK367ETf51M0rk9JPR7qa9paNspOYHV1CA84LwHnB7p49HH1mKz4668CCjQzJpqQ/+vdPbBfH8bM+P6Q8rNwqfoMn5LXi14246erdvLIxsbijk9DKBu8V6bKT59x9Zfs1lxZP6NzL1gZeZdWGsUSA10o6iCJwQewoDPbIa8WvW2LZj7o8QSaQ5RPNu5sp7Z6+2co9dQ/5LsNXF3ySNatWpz1g2EoE+KGkgygC1+FhVRqBZgfJhSZnw1PyWvHrlli+lDc8n9ttAllrTZmr+vp+Uk6ZY9NPJqUfFOmaqCTQbQ+Y6cExO/qEo99ROWWddli65GOlzk4Pq9GZQ0zvWfAx67c3MGO86dTrJ3mt+PO5MXvWTlrf7pzQ5GWdGqeKy4orel3CvkcOo0efCi7/61Wux3sgOocJHtr8U9FuD5gBp7+7XPtSgsbuw8pvf4MhRl4r/iAas4cFr+vUeKmAkktNeEUHm/+V93uau9AV2gPaSfgKC/nYdCZfyWvFD/6WWHaLl36AMNepSS414QseJ6x1hfaAvsfQ+0A+mrLylbxX/GFG1w+g84AIa52a1FITgw/fp9M1dvsMBIFf7QHD+Fn9yMo15DddXvErFIK9MDOvI3p0chHCWsEwtdSEVStGnT4DgdCwJ2vOr/aAnnxWDwu3+ZWVa8hvfFf8InIGcBexRdUDSqnsZRh9ooHtPMxpXMqb1LGBvzHBdollP8o0W+UipBJE9ycnq1WdUhM5beH46PuWh520B4TsOQ6efNZE4TaXxdrAxNAbrPFV8YtIhNiC6jRiPrR3ROQZpdQHme7r0V+vJr9dvHAG+1Gm2SoXIZUguj/5tTIvlBaOu3v2yHqN35/Vru3eRMkYrPB7xT8KWKWUWg0gInOBcUBGxX9NTedjOl25dHDrDNZNGrODTi5CEN2f/FqZJzt/fXMA+8ADD19v+54geypks917FSVz0i0v5k1kkEEPvxX/IGI5MgnWAcckXyAiU4GpAPvs09kxmOCamlhxs1yjmzSWQMcfsJbXQ5GLUCgr81wRZLtKHdu9TpSMjunolZ+eGo7IoDIT5ukVfit+K1XdwTKtlLoPuA9g5MiRIUuS74ydpDFdf0BYchHC1AEsrCT8IFYmsSDbVXplu9c1HY0/dh++OTJNJNmERy39EZ4x4VH/xu6i+K341xFLjkwwGPjC6WB+2f7tYMdPoOsPCEMuQhibq3tCWTE0RrNfl8Lu/tary0x+EDftKu3ih+0+m+moNJNPyYcWkgb/8FvxvwMcICLDgPXABcRyZxxhZfvPhF+mIV1F7Yc/wC59InoR6l6uVp1EB915yq+4YOZkhhyxr+N5LTknjSLLUtd9TpronYQfxCpsNSj8yHB1HfZ57r3uV/0T52Y/3r8/1NhUBIZO+Kr4lVJREbkCeJ5YOOeflFKdm276RK53CHb9AcnUN/ShotxmxmrPPllr76fDy9Wqk+igIBXpIbc8BWk6mPWJRHht3/QPn4QfJJf4keHq1HQ0vPUONj2SWOnfy/bIZZ7K1YmN4c9Azgd8j+NXSv0T+Kefc/R+pBZL50CWrnPdd4ijnrm6uCki979PdawXeVPovR97yGncfha2VGSub7S1NXPJ4Fwr/WROOPJ6tpZW8uq7t9G3pd7VWE5NR5vIvuvIx7pBhU5BZO461Ym7e/mrTb0sIrd89c1Zr4lEenDQvtc4nsMrwhQddMgtT9m/Z/Vq4FjLc92Lmvne4H+7lMobtpbGHkInHnWD7XuXL7qx/bXfxdHysW5QoROu9kIFyJFcwqW8wRQWMoiRjsbo1ldvNdfa6k8ZY7skRwdtXbslx9J4y+62Uv7n5F/mWgxPCbI42l8WfsaFxw8NZC5DegpixZ/vWJlxdFb4YSTI6KATrvszWyurs1/oMde+/HPH95ZT5qEkuWF46x1pTTzVrfdaHu9HLW/s/lFWB7JOZzSDe7q84texnYchccwuH635dcYdwH+lOd5cFOHdwc4cxOB9LHsmRZkLpQ8wc23MDJTO7HNpsePAtY44CJFsratj7cUXI6WltDU00O+aa6g47jhv5ImjY9e3uiebA1m3M5rBPQWr+KOfLaXhkWuhqAgpKqZ8yt1E+g3NtViW+LG6d2r2KW1rTau4dJqwu4kOSp33hDVr2Nrayq0O7PRBsLvN5zaT594LT1yWVvkn2+k7cM1wtpRU8F97TWTdtGmeK36nZHMge9EZzaBHwSr+ol4DqLjmcaS8kpal82l84lZ6XHZfrsXKazYMPoe2jeXt7we1ZH8QuCFbhI3XBLFato3DpKi+LfW01ddTdlDmHrdBks2B3BU6o4WFAlb8/fe8iZQiEf8+6thFv2bB2Mto2mLfnq3ruA0DyUrfDzrvKKwjazLhRnlHKisZ9vjj7e+j27fbnj9MrLnoIgbecYe3g054FB5x9jDK5kDuCp3RwkLBKv4EqmkXjY//iu7fdd5uI1siWNleuzj7rf91PL4uy9//gv/36xeItrRx6OEDufr60TkZI8wU9ejB0LlzkeJimteudWXqKK525kM4JE1yGGRPEPOSYU8+yZoJE6g85RRf5/HSrOpXZzRDRwpa8atoC7t+dwndzr6KyCDnW95spSKWp/87z4quIm5pjjLzzhe4655v0aPCmdWzpTnK4CHV3D9rcmaZ0vgczvsk5cBa985gr5GiIiiKRSnnwtSxq7Uk4/kgzVeRigqKemTvIWALEXh4R4dDXppV/eqM1oHvn2y/j3PPPvCHwnE1F6ziV21t7L53KiVHnUXpUWNzLY4ldpT5kv+so3v3Uq696m807G7mBz86maOO7rxyzOQoXvKfdRx9zFC3YnegtM2+IqvfWkdFH2cZsMmmnKFzrH0MLTU1rJs2jeZPP/Xe1JFCIsInSHQzYddOnUr/n/rf8tJLs6rTzmi2sKv0nd4TYgpW8bcs/jstS+fTtnMTzW88RmTwwXSfnKWGQ8DoKnOAzRvrWPlRDY///TJ27Wrm0kmzeGb+FbZqq2zeWOeV6B34R0lz1p4DDx4xtD3E84KZk9Mq/uQCb8V//ken88mmnHSUDBjAsHnzaF63LhBTR9DoZsIOmzcvSLE8MasagqFgFX/pqHGUjhqXUxmymXHsKPOevco54sghVFSWUVFZRq/q7mzbuos+ffUdyj17+eOc1ek5oBvimVzgbebazueTTTlWtDU1UdQttnvyxdQRMrKVUg4K12bVHQ3eC2VIS8Eq/lyjY8axo8wPGzGIu2e+RDTaSlNjlG1bd9GrurstmQ4bMcjRZ8mGlz2Ikwu88b+PWV6TMOVYrWibVq5k44wZUFSEikYDMXWko625maJS/2L9XZdStksa5axrVk3O6t0euSx9GWaD7xjF7xM6Zhw7yryqqpy5T04FoLgiwrMvTrMtU1WVvRW/ruPZy54DyQXeFqS5JmHKsaL8sMMYOjccCsVPpQ/uunDdOO89Zoy3Lohw08S5tsom5INZNRtXLF7P4m0NtCqYPrwv3x6auYprvmMUv09YmXH+vuDKDtdUVZUzYfIxXDzhIaLRNq669jQikXDUzbPjeHbac8CK5AJvCyxMPYY9uOnCVfP4faQtpjH+ChLGo+R6slU1W5k5pLP51JFZdcKjMP1l+/X1+/fPfo1Nlu1oZHltE4tG709dSysj/rXKKH6DM6zMOFZ845wj+MY5RwQsXXbsOJ69ahafWuCNXsFHzLih5u1q2lqyP7irF3ZMgOpXJqw4315fCL9LKVuxc0AfT8bpVxbfoYSkk9bA8mJKi4SWNkVdSxv7n7qaMa0WD5hv2WvhWd0T5twXThUbTqlsIjirye9n7TUrM04+Ycfx7FXPgdQCb8UPjXfzEbLidYkGHaVvxaZG+99eN6WUS4qCLYWRYPvkNKvoHLdTrC6NcEBlKQf+YwW7om0cNc6bXcX2ELcgLgjFv21ScKseXazMOPmEHcezTg/i/1r7seXx5ASw1AJvVlE9XuI2y/fT887r8NCAb/gnrAPSFXGbMv6KgCXJQhZzz1XRt9lJi+1hqyhhZvGorNctqKlnfUOUVWOHU9vSykTbM+UfBaH4w4rfZhw/yy94EUWkg5MEsASqrS0W3ukQt1m+qQ+Nsh+HS/EXCk6Uvp37FFBdEiFSJFSWRJy39LNgTIp5KCzmn9xLYHCEFyUcMhFmx3MyKhrtoHz3e8peCWc3Wb6JJLKwVcF0Su3KtTxxxCTGLPgtA45Pv2Dpt2Mjm3rZM4f025HbdosTpkbbTS/PpRQAOm1ABY+u2cHxCz6hqU3R9+v+yREW849R/HlKsvP13DnT6e6gBEJJa5SR6z9Nez6sjucEXtTl0cnyrV+40NIElPrQsMqLzqe+EEtmzGLACSOyXrdi2nBQFstiB2GlQZFJ4RaJ8NCxQ9rfj7HYhL7z6pns3PEfhh5wJV86OHc9pL3CKP48Jdn5+r7DujctPpaq9oLuRc1Zm524WbHrZvluuvNOS8Wf+tDocdv5na5xXcDMQRcuJ2x++wPKB/RGfNjVFUI7xcOOvo8tG1+kqWG967FSzT+pBGEOCvdfviEtyc7XsNNUspmLJzxk2w+R3NYwXTE0N3V5dLN8s+0kMj00XBcwOzelh+2cb9u7P0FylqyFc3fJbbP42gM/4e0f/87Z+Gnwu52irnlKlwmt1mXiyroPdj22LondSbJ5KhtnjG85asy3oolt2MbnHivO2L/GKP48Jdn5mmvO+8S6ImhLc5TvXzqHu+75FhMrvuP5vG7r8uhm+VaOtn5YfXbBBR0eGjszjKFbwGz4X3dmCe9M38x8ReS6jGOn4/N/vkHfow6irI/30XF+t1PUNU/psp2Ayl9o4MIfkNUBYxS/B0QiPRz3uHVKsvP18kW3Bzq3LrpJYDrNyWfSuelBUHV5an7xC8udROpDY+dC6/vtFDBzEtMPWRqgJ9fYsch83br0Y2pe/Q/Pv/k+25etpnbFGk6e80sq9nXf9NDPdop+mqcKHaP4PeCgfa/Jeo0fDdUTztc305xPLnF87MTj+dolJ3kuQya8KCWdiaDq8iR2ElMHvUuPSIYQwX06H9rZUMr1X58TTF8InaJnNTUQ7fiEGnHDRYy44SIAXrtkBgdeMjaz0rdRNsHPdopOzVNjWv+d/aICxyj+gMjFriC5xLGXnH3a3VoK3ItS0mEgsZPIqPTTUFXeHFgBs+p4Z6x+OzbGIm9S0VDYJ/zJOumrHatoniwk2imef/bnDCwfgOXjL4PDc+Dszsf8NE9Zseyd77Fj6yLa2pqo3fYuRx7/t6z3vPPqmRx94j8tj+c6Qsgo/oDItiv4aM2vPX8wJJc4Hn/nhfQd6s1aS1eB6yaBTYmmsZF0wFm1Ty9wU8YBoNf97iNB7LCpV39HCtovEu0Uu5V7YeCJoWueyhZBo8uhR//R9j1WSh+8jRBKR5KjFyycvUbxh4SD9r3Gc3NQconjWVPv5+r53qwudLN48yUJLOzkUy6AFYk4mTEejmnbPBUigowQitM/NeLHKP4CJrnE8ZxpD3k2rh0FHvYkMLcE4UdxkgtQ/Uito6qfYeSLicdkPL8/z9D2B/jiDwEJlN/0B7PiL1hSSxxX9PHOrv7104Nv9VdS1EpLWyT7hQHjlx8lGae5AJsaFcP/urMglL9fhMHenguM4i9QUkscT7onbduNdp55cmnOVufZEnGOGrzJ8r4Hi63t74es7hz+6Qd++VGscNLM3Co8tIoSx9UunTDh7M/Znsa+n1C8p47b4GhstwRhbw8jrhS/iJwP/AL4MjBKKbU46dwNwBSgFZimlHrezVxdAS8jf1JLHOuQS5OM14k4fSIRtrb6n9zmhR+lvTFJBlw3M09Cp1Sxl6RT+rBH8eYKt/Z2JzsGnQghXae00x2L2xX/MuBcoIPLW0QOBi4gFrY7EHhBRA5USuU+zTTE6OQDWPFmdI7HkgSLH4k4r+1r3S0MvN0N6PhRtrvsF6HbzDzn9O9vu5ViJsWbD2YYJzsGJxFCXs4P4OovTSn1oVJqhcWpccBcpVSTUupTYBUQ7DLDkBfsKiplyW2zOPzaCzNeV/P60oAk0qexvpG21jYAz/0oySSamTe/8Rh1t57F7od/7Ms8rqmpiYWRpv445LCj72P44eHMSk+QgwgdT+b3y8Y/CFiU9H5d/FgnRGQqMBVgn30sUh8NoefA2/9BSX2T7ftqe1Zx/phxgSbi6JAo+HbAa69lvM6JH8UJjpqZFwC5VqqFTFbFLyIvYF1a40al1NPpbrM4ZvnoV0rdB9wHMHLkyPBknRi0caL0AXrW7vS1TowdnBR8c+JHMYQLJxm5hUBWxa+UctKPZh2xYnwJBgNfOBjHoEE5ZTTQaPu+klZvshrdEJZEnKAKvhk6klC8g4ddlJP5vbS35xN+mXqeAeaIyG+IOXcPIFaW2+ADE4vPdXZjMbCfFxJ4s0rKWifGR4Iq+GboiB+KN0incK53DE7ndxvOeQ5wN7GCe8+KyBKl1OlKqeUiMg/4gFiZjh+YiB6DG9657veOTEDW9e37ZL2vqKSNAaO225rL4C1OlVqQsfm6D651n86iqWG95w8ipw9OV4pfKfUk8GSaczOAGW7GNxgSHH3HDxyZgJzWt29ryU1NoX5l4khm1bATKQ9Xhq6dDlJWOFVqYXQK58qUlQ6TuWvwjSsWr2fxtgZaFUwf3pdvD+3leCwv2uq5ZVdrie3SzA2tJbb+ylLLK1Q/oqc5Myn91DHO/OaHlJXb24BXUWI78cuN0jf4i1H8Bc4Ja9bYzmDtE4lYJ0B9/2So3ao1xrIdjSyvbWLR6P2pa2llxL9WuVL8YeC+9UexfD+bThGXf2FOdwCZsKv0AXbS0uEBEvYCcGFbYYcNo/gLHCdlC9Leo6n0AQaWF1NaJLS0Kepa2uhdGr4Ca3bpE3H3GbL30+2MTjmHXOD1wyjX+GWDDytG8Rt8obo0wgGVpRz4jxXsirZx/6jw2V2zYXt1nwYnCj9BoSnYrkrYyk8YxW/whQU19axviLJq7HBqW1r52gurOWPvCrql1ON5cPL321/X9qxi+t13BC2q7xjlbQhRFdCNYBS/wScUUF0SIVIkVJZEaG5TtGbRfz1rdwYim1e4Wcnbwa2dv/62s7W6dmUrjR1WwraatqKs++BQ+B0SLRiN4u9itNbVsfbii5HSUtoaGuh3zTWuesou29HIob3KOh0/bUAFj67ZwfELPqGpTXHlgX3oXuxtiKTT+vC66EbU+M2K86tcPWS6nXmlVtcur0tjB4Xb1XSuk7BygVH8XYyiHj0YOncuUlxM89q1rJs2zZXiH1hu/RUqEuGhY4dYnstEusYqXpKPPWyzRdBkfEhpdO3yozS2F+is5t3G7XuRPZyLXcfa29ZmvaaorojBt3b+/YTrf9ngO1JUhBTHlEBbfT1lB7lr6lGdh9E6iR62lTc+174azgVt9duomzGG+tvOpnXTZ77Mkeja1e3MaRmv0ymNrcuEqVHtRiLZyIfSzBBeOdsq2yyPG8XfBWmpqeHT8eNZc9FFVI4e7WqsBTX1HkkVHEW9+iPlsQYqdnrYei5HRW9fHz66Xbs+/+cbnpbG9jJxK4xZuFY4kfOdV8/kxaf35pMPgl94GFNPF6RkwACGzZvXXne+8pRTHI8VVLxK70dqbc+lEwFfcuhJlBx6kgOJ0tNWv42iit7a15ccMZriA47xVAY7XbuCLI2dD47YoMhlpI9R/F0MJ3XnM3HaAH+6TqXi5AHj9UNJ1zdgR+knkO7eNqJJdO1q27mJ5jceIzL4YLpPvtPy2iBLY/uh7PLVOet2N+MgUKO9L6ZR/F0Mr+vOF0k4M0szodrakCL7Vs6Eb0DKK2lZOl8rUiZXOO3a5XdpbD9MN121pr5uoMZzjxV3+iM1ir+LEaa680opJAcPDidKH2K+gXZy6BvoSvi1mk8et6LqENfjJo8XlAlLioog/l22G6hhvrmGQHh+Qx2PranlT8cOpqVNcfCzK1k65gC6d16MhJ5EpEz37/4+16IEys++uYBfPXVaoHP6tZr3etxc7TpaampYN20azZ9+ysA79LPejeI3BILXCV3Rz5ZSPDT47FLdSJlCpKq8OdciFBRe7GacBmoYxW/wneGnPMWmbn3gzD3Hbor/dCKeiJSt7G9Rr+D78tqJlPGSTMlZQZdH3j45qbT25DQXjU/fsyBfHbF+4HaX4CZQwyh+g+9s6pa91WGne7KUJ+hgb3eI3QxeO5EyQZH4Pdmp51NCKy3YT7x7k315s+V1y3NVNVuZOSS7M7mrOmL9wE2ghlH8BU6fSMRRIxZLevaxVZM/7NiN0nEaKRMEdlb9D0TXpT33JhYNeDTYOcD+w93gDjeBGkbxFziWnbSc8oeXO76fcLjlZcNPeYoVL33Tu3k1sbuCN1E6hcFz4yalPXfcL16g6isbPJ2vpaWWkhJv8y6CxnzTDc5JswNwYtqxi1UsvtM4e7+idPKxGFw+8dxjcfU1x/r86k31NEVbqe62g+1N3rX9zHelD0bxG9yQugNI8EgtG0t70795m+VpXYWYyalpFYvvZAXvZ5ROPiV8FSL79atg8S2nA1d2Ojfm6UeCF8hDWivst1RNxih+gy8cdOrTac8lK0Q/0F3B+x2lU0impCOja+iWRfwHWmYD8N/Maz/WuKMbz/4gnH6RfKC6J8y5r5hDVq/2dNz8/SYa8hYvInLSYWcFH1SUTlgSvmZHn6CBxozX/BdrOh1rpohSh5qirFeTsxsLlIQizzW5l8Bg8BA7K/ggonTClPCVTemnoxTrmu65pNqlmd1ru78V7T4ID3ASnRdno9VBo/gNnmC3NaCKtiDF3rdODFOcfa4SvvKVbj0bGHvP39vfl1PGxOJzfZlrzhkxu3++2PqzReeJyLtKqZG64xnFb/AEW0o/rhB7XPFnR3Nlah7e6/7ga5unI4wJX7ngv2fPy36RBdo7lLKe0Oig+0uZf9E5bnckfmMUvyFwEgoxFd1oH93m4bkmzAlfXnF55XcYNmp/AI6deDxfu+Sk4IU4917Ht1b/K+qoY1hYbPVOyV/JDXlLOoWoHf4YYITM7od/HMgqPZcx/26Ud69Bvfnxi+56OuSSfFbebuian9oQSnTDH4OMkAnKNJPLmH83yntnzQ7uPOVX9OhTwfg7L6Tv0L08ls7gB0bxG0JHtvDHMETIeE0uY/7dKO/bVt1FZd9Kls1/j1lT7+fq+fpNSEJhJuqiGMVvCBU64Y9uImRUawv1t44NbRmFXMT8u1HelX1jSXiHjj6cOdMesjVvvpuJ8hnnnTAAEblTRD4SkfdE5EkR6ZV07gYRWSUiK0TkdPeiGgod3fDHulvPYvfDP057vmXZK+y+/3LrOeq2UXHN41Te+Fy7kzgs5CrmP1l5b127Rfu+xvpG2lpjMf7r3ltLRZ8KW/Mmdhr3nD+TLZ9ttnWvwR1uV/wLgBuUUlERuQO4AbhORA4GLgAOAQYCL4jIgUopdwUmDAWNbvhj5U+eTTtGthVzWMsoOI3571fmrnVlY30jpeWlFEWKbCvvDR+s55HLH6SssgwRYdI9U2zN7WanYXCHq2+9Uio5Jm8RcPfeNrsAAAczSURBVF789ThgrlKqCfhURFYBo4A33cxnyH9UWxu775lC8aEn0+2kji2cvAh/1F0xh6WMQgLdh972Sd4GiLtR3sNGfYmfL3a+Y3JjJjK4w8vlziXAY/HXg4g9CBKsix/rhIhMBaYC7LPPPh6KYwgjyQouVfF7gc6KOUxlFBLkKubfrfJ2ipudhsE9WRW/iLwAWDU4vVEp9XT8mhuBKDA7cZvF9ZapnUqp+4D7AEaOHKmf/mnIS/xWcM1vPJZxxWzXpJKIr6/82fN+iNtlcWsmMrgjq+JXSn0903kRuQgYC5yqlEoo7nXAkKTLBgNfOBXSYNAlk/0f7JdRSMTXp2KarLgjVzsNQwxXph4ROQO4DjhRKbU76dQzwBwR+Q0x5+4BwNtu5jIYvMDujiNdCWnTZMWQz7gK5wR+B1QCC0RkiYjcC6CUWg7MAz4A/gX8wET0GLygX5l47uB0QlGv/nsayYQoOshg0MFtVM/+Gc7NAGa4Gd9gAO8jWeySqYS03eig7ZN62i5hncBt6GY5ZY5r8hsKC7NMMRgykKmEtNPooBXnV3kpojZ2a9s/EE3TxdyQ97g19RgMgPvVaFjnS1dCuqs2WWmsD27HUE5ZYHN1NcyK3+AJK86vovoRB4XNXcyng1uZ0jmDu2qTlQ0frGfYqC95Nt6lxRM8G8ugj1H8hoKmX5k4sqdnoys0WbHCS6VvVvS5wyh+Q0ET9E7EkBmzwg8HoVL877777hYRWRPwtH0B/ZKE4SJUslf9/pMjiir7eP6dEpF33dzf6+EdR3kli1vcfhYP0P7O3N8y2/Pfm8vPH6rvu038lj1zN/YUQqX4lVKBt+8RkcV2utOHiXyTfdDdO9XuXvbMLt13CNtdfsbqR2pDUwok1/9fdr4zD0TneP172+jm8+fb9z2ZsMkeKsVvKGymTnMYxnil66k3AtYpuAZfuLR4QrBhXgZbGMVvKHi2T+ppVWTQFh7tGjZ6MIbB4Bqj+OOVQfOUfJa9S7B9Us+wrXztfGec7pT8esDl8/c9VLLLnoKaBoO/3CzWpbmzcZOyLPMdKE5X/CFU/AaDydw1BIqTlWBYzCP5LLvB0AGz4jcYDIYuRpdd8YvIr0TkvXg56fkiMjB+XETktyKyKn7+yFzLmoqI3CkiH8Xle1JEeiWduyEu+woROT2XclohIueLyHIRaRORkSnnQi07xHpQxOVbJSLX51qeTIjIn0Rkk4gsSzrWW0QWiMjH8X+rcyljOkRkiIi8LCIfxr8vP4wfD738IlImIm+LyNK47DfHjw8Tkbfisj8mIqU5E1Ip1SV/gKqk19OAe+OvzwSeI9Y+8ljgrVzLaiH7aKA4/voO4I7464OBpUA3YBjwCRDJtbwpsn8ZGA68AoxMOp4Pskficu0HlMblPTjXcmWQ9wTgSGBZ0rH/Aa6Pv74+8d0J2w+wN3Bk/HUlsDL+HQm9/HHdURF/XQK8Fdcl84AL4sfvBb6fKxm77IpfKbUz6W0P9vQEHgc8rGIsAnqJyN6BC5gBpdR8pVQ0/nYRsdaWEJN9rlKqSSn1KbAKGJULGdOhlPpQKbXC4lToZScmzyql1GqlVDMwl5jcoUQp9RqwLeXwOGBW/PUs4JuBCqWJUmqDUurf8dd1wIfAIPJA/rjuqI+/LYn/KOAUINHHM6eyd1nFDyAiM0Tkc2Ai8PP44UHA50mXrYsfCyuXENuhQP7Jnkw+yJ4PMmajv1JqA8SUK9Avx/JkRUSGAl8htnLOC/lFJCIiS4BNwAJiO8UdSQu2nH53Clrxi8gLIrLM4mccgFLqRqXUEGA2cEXiNouhAveAZ5M9fs2NQJSY/JBHslvdZnEsbJEH+SBjQSEiFcDfgB+l7NJDjVKqVSk1gthufBQxE2eny4KVag8FncCllPq65qVzgGeBm4g9iYcknRsMfOGxaFnJJruIXASMBU5VcaMheSJ7GkIhexbyQcZsbBSRvZVSG+ImzE25FigdIlJCTOnPVko9ET+cN/IDKKV2iMgrxGz8vUSkOL7qz+l3p6BX/JkQkQOS3n4D+Cj++hlgcjy651igNrG1DAsicgZwHfANpdTupFPPABeISDcRGQYcALydCxkdkA+yvwMcEI/OKAUuICZ3PvEMcFH89UXA0zmUJS0iIsCDwIdKqd8knQq9/CKyVyLSTkTKga8T81G8DJwXvyy3sufaA56rH2IriWXAe8DfgUFqj0f+98Rscu+TFHkSlh9ijs/PgSXxn3uTzt0Yl30FMCbXslrIfg6xlXMTsQSn5/NF9riMZxKLMPkEuDHX8mSR9VFgA9AS/51PAfoALwIfx//tnWs508h+PDFTyHtJ3/Mz80F+4HDgP3HZlwE/jx/fj9hiZhXwV6BbrmQ0CVwGg8HQxeiyph6DwWDoqhjFbzAYDF0Mo/gNBoOhi2EUv8FgMHQxjOI3GAyGLoZR/AaDwdDFMIrfYDAYuhj/H1jKeg7wiLTUAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.6699 | test accuracy: 0.88\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0701 | test accuracy: 0.94\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.1329 | test accuracy: 0.94\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0395 | test accuracy: 0.96\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0199 | test accuracy: 0.96\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.1077 | test accuracy: 0.97\n" ] }, { "data": { "image/png": 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ZdPzBnb73orMD3iYLk+wNW9cg+5HJ03UkF78xnNPvpU6iB10ildZIEoUCwDk/vh+86PlXEn76Cuu4iNx2L7OSDRoUMkhORh+86+yUasEYlfuoIIZz+r3USdi2kvoU8/X7lXVwowRajPyxlVOWj1dVTV0XkVvtHivZoIEb4xKkzk6pHcE91YcYf2ZJRNz92FTSsiQIPf+os3xMNkF3q7ufQ9dFdB6vMoFZDGSU1njsit8wW3V2XnuPNfMWs/bDpRw542d6MgpF8Oo+yhGbNE1b+VuW9OhRx4OPTaSpaRP/ce70stDz/ze+x7/xPSP38qqq6cdFVAxr+A3jVWenFH5kmr2macZmsrAkhl69duCBR88veFwpVbF+fy+qmqYar3TEGv4AOeze64zdy4/7yEuaZpxy+mMX27B4xovf3yRxL8TqiJfGKyVYCdbH74u1C5cZvVexnrk6Ms0T0rMcr/WSpllosijFJOBg4AjgXY3zdYg626ncSKW6RfZsJ7//OeOm8Otbng3l+Uky+rC9i+jPnMFx/Lfne01SyCTFALArfl/MvXFqyUwcN/cqZtzGzLwd8OY+2hsYR6aYYjdgBLBjiWtGknHvbCGjupebLIo9NahdQqn/x/kTnk3vLE3HTJli+jmmcfL7u2niEiVBVt4Ww0TjFbIr/RzW8PvAlNF3E7j16j66MPszj0wlXarE+V4mizhU/tr0zmAJSpahkM5/XAir8tYrP0XeUkrppfRgDX8sMNVf954uhxY8diyZgqy+wP9o3s/tZOFll2BJDkHKMvz02idjXfBVKK3Sp789Mqzhj5iw+ut68aC6nSy87BIsySFflmHzpi1cdOmRxlbpXgu+dPEb1A0qrTIqrOEPiVfOvdHRTRNU3r8JvEwWbncJAOelZ2H1uONPkLIMJgq+iuE3qBtUWmVUWMMfEgdN/qHj90Hl/UeFF5eSW6Nv0zujwassg05cIO4FXwGkVRqjWwMdQrelsYbfADqGqKZn6S2sn7z/Ho3+1BVNEUZSnk3vjAYv3bh04wLf+OZepodrFK+Vt27QyRqaVGCV9GOXmy5r+A0QpSEqFtAtR/xKV1i846Ubl5e4wF++dgUtX2QWSo+4GF+3BviRObHYThhKqyxImFlD1vD7JCmGqFzkF0xlQFm8oduNK4eXuEDO6LvFrR8/rLz8d5nBahZyJD8FMkqa/86DnQLDYWYNxdtaJYC5N09l3yu/G/UwipJfWHU/4BxtiD9hZUBZzJEfF2gY0GNrXCBqcnn53+MlTuaPPM0lgT1LNzDslDUUFHbF74MwDZEfl04cCqtMEOcMKIszXuICYRBmXr5uYDjMrCFr+DXoQRfHitCwDJHfwG0SC6s+f3MBO47ee7vvyi0DqhLwEhf49486S0g0f96Nnx/Uz5hrJsy8fN3AcE6vP4ysIWv4Nchpv3QUQfNriD5/cwEf//lFmj9vYs9zT9yaEWQ6YJvEwqq5N0/lmMduLXjcpPKpJVjcxgWcqN1xI28ww1jwM+y8fJ3AcNBZQ/lYw1+KAQO2NQ9pLfwXzYshCjNQ6aWwqiPGAsQdAnv3FDhtgtf7W8oSk66ZIPLy22glRRfP1wedNZSPNfylCKhjVNiBSi+FVfnESZ/fUpnkXDNXfPtIJtW1Awd2Pmlcoau3FwnXdb8Um1w2s4apHB1b4bZiWMMfEV7iA7kV96senue3sKpcAsSW+HL++GlFq3tzrpnudWaKFXVW2MX8/kkWbrOGPyK8xAduyP4ExcFk3DgvORxLYoDYso3p6UfZTLPzwcF7OH7dpS3NqBUfF7ynaYnm//6f4tW9ueBnmBTz+ydZuM0a/hgQl0Blzo3jRBQB4h6Nq1k3oK+7a3z4WN1Q1JAWoY5azqw+OYARFcfLWFtThc1DEBLNpap7c66Znxl5mh7FVu9JFm6zht8FXgyRn+uiYFCRYyYCxG6YPGgsE4oE1AHuqT4k4FE448WQ+rkubgQh0XzrbScXre7d5pqJR9152MJt3RrM3csafhdMHjTW87WlDJgJJuHPFZRz4xRKvPMbIA6CXIptklou3p2eAUS3+jdBEBLNblU/8/nBnBXM+XIzbQouH96PM4b08jwOXfwItwWtK1QKa/jLhFzWjR9ybpx7CxwPpx22N9bRWrDZfFwnhc00b50EIFkTgVeJ5mJ4re6d19TM/LUtvH7s7qxvbWP/vy/qZPi71X7BxuZ+nsdWCJ0AcSFFzSixht8gUQqh5WfdeOVlMm6cciM3KcR1AsgRthvowvrvMXT07gAcdOahfP3cI7SvdSPFoBsEdlPdm8/OddXUVAmt7Yr1re30qenshPzRd47ihgfedbi6MrGG3xCl8tx7NK1lXS//OftzAScB6I5ZNweQybzRzbrJd+P8ycf48l1aPRpXF3WPhT1RVloj9lIB6F4D+/Dj56/3dG9dKQY3QeD7H/ZWste7JsUe9TXs+dcP2Zhu5w+jd/F0n0rCGn5DlMpzn3zJ1Z2uaVeKc99YzqL1W2hpV5w1pBeXDC++HV2Is+H3m3UThBunVEA76PTUJJLv+ilFKddQqR3EusYmfnnUz+nWtzun/vK79BviLk9LR4pBNwj8yG6TXD07n5mNG1ixOc2iE4eztrWNrz+3mON26k7XmEulR4k1/IbwkudeJcKUg4rl0Tg/pxBhZ92ETa7T2TmbX456KLHAr2vo5kW/ob5fPfOefZepE//AFc9ea2hk2wiyT28OBfTukiJVJdR3SbGlXdEWQ796nDBi+EXkXuBEYJVSamT2uz7AQ8AQYAlwqlJqjYnnxZGw8tz3LnIs6qybtQ3B5jAnveWiH596ENT3qwdg5LH7MuOSKYE8w3QQ+NrT9gG2nzSOGdCdB5Y2cejMj2hpV1y8Z192qI7Jaj8mw+iIqRX/FOAOYFred1cDzyulbhGRq7OfrzL0vMC5LP1mxifsMg3zq42rmTBobCQr7qCzbsJISS2ETqezuDdhv3P9lKiHsJXmDc3U1NVQlapi+bvL6N7X2RCPGDaJ+Yu9O+RM6/EfPPMjZn9z9+2+87JzdsP/sj/n84Zjjn7UaZleMWL4lVKviMiQDl+PBY7I/j6VjBJAYgy/10DgugF9uQGfK+7aamhO+7lDaHg1tm4DuzpKpknfEYTJZwtWcP+F91BbX4uIcNadwWihetHjL0ZHox8GhaQY4pimqUuQPv4GpdRnAEqpz0Skv9NJIjIRmAgwePDgAIcTHn6yYgD4zl6Z/z7wnt87dWJtQx96rnTf0q2QG8eLsXWr9KmjZJqU3sduCNI1NHT0bvxkzk3G7lcMXT3+sHrguiVJUgy6RB7cVUrdBdwFMGrUqATPoSXY3Ap14ejIFOPy5U8ChZvAuMGrsXWr9NlRydSJcmzC7ifdMiyeevI9br/teR75ywV0r6/1fJ9cD9wgJY4LrdBv6BBn3swapnEM5/FaKFIMURCk4V8pIjtlV/s7AasCfFb8efyDqEewHSYMpdd76GRAXfbJE1vTQfOVTAtRrFtXUtFJt7w7PSPSit/84K0f4iRx7EeKISkEafifBMaTySwcDzwR4LNCw3QAMcwippy7xkQTmE+eepW3X3ybfgNOyHxx/9Pa1+pkQHkVtfv0+dmxbsLuxn2jm24ZpfBbfvC2utp7OkPgEsc93f19CrMbVhSYSud8gEwgt5+ILCdjz24BHhaRCcAy4BQTz4oakwHEMLpaOWXimGgSv/qdf9Fv42bP4wqq5mDnow+MdRN2N+6bMNIt/ZIfvPVaeQsGJI579oX/tT3hdDGV1XNGgUNHm7h/XDAdQIyqq5XfJvG5e/CTP3geQ1A1B3HpbVAI3WpZ3XTLHB0rfuvw53pxcw8TzdS1JY5nWL0dE0Qe3E0SpgOIcehqVcpQ3rbLtzxlAZXCa81BXHL166j15GLRdd/4Tbf06/45r7pg81rjNH/erSL86nHCGn5N3PjF30XPVx9FVyu3BGH0/RCXXP38YKqbbly67psw0y3DYMSwbVo8HbNocpS7Xz1OWMOviRu/+OHTmhzuAGvO7twcwpSvO3f9Hz1en8PECj+ogHVcc/WdMmqcxNbcum8s4dCtATaudH9NkrGGXxMTfnEn/Pq6TfvK/Rr9IAPWSc/VD6ta1uKOJEou+MUafg+YDCBq+bqVggEDYGXnZUncumLtD+QSOwdhzuibSEENqxF7IZLkvslvD+nXsZZKdfM/IItRrOFPCo2N4EHKNohG7zlXzqtG77qNyz7pXPIx6PiDGXT8wUDxifeeLodmflHlWwQeJn6DxPm+fUt8iJez1OLMjDMyPx6YPGjsNmNogHxXTlCYnqgqmQvrv8fGLzdEPYzt8OofT7pfPU7YFX8AbLj5JOom/JZU/yFRD2UrPehipPWgid6+lvDoNbAP3fr4CyK/NngPALq0pRm14mPt6wq5eCrRpx43rOEPgK7HX0zzozfR7YK7oh7KVkw1Gc+vPagxckdLkKxrdM4w80JrqnrrJOBEmLn/Fn9YV08BtrR5+1+zpa0KUjVIqvOcurKnozJ1cXrWOv/uhgZze+T82oNCTAIOJqPhUal1liYqZ01w8yJbCGXpjF3xF+DtFZ2N5eb33mPljTdCVRUqnWbHH/6Q7occ0um85kcuZofzOydXfuW3C1lzlsuslHzf/p3fdnftuAfcna9JrvbAiaD1h+JSuVsKrwVepskVjFks+VjD74K6ffZhyIMPljyv60mXkRr4lcDGsWD5Wi6cMgeAltZ2FjauZ/XvNWR5H70Amtf6fn6uduAFh2NB6w/FpXLXDfmTgFNhV1DkF4yZ4pdH/yIW/YIt/rCGPwBqDjgx0PvvvUtPXro+o3/38OvLeGG+ZtmhAaMPxWsHvOgPHYFehW9cK3fjSq5gTLd2QEcyOu6NYSx62H9BAbD+phPYNO3HoTzrj7OW8N1Dh4TyLB3yYwC/QU9/6H7ghxr3nnvzVPa98rv+BhgxYfr+3RaM5SSjf/z89QVX9HeeMpkvlnxuZoCWyLAr/gCov/ZvoTxn9foWPvh0PYfs2S+U5+niVn9IxyVkonLXOP9xJKxd7eqSM8FROz5MF1AhdCSjDzv/6KKqopZkYA1/gnno9WWc8rVBiIeK3iBxqx/0DqVdQiaaxxjHpdHf7rpxHRxb0672Px6f6EhGx6kpjNegeZStKuOCNfwF6JtKsbqtzfV1bVvCM8IXHlM4p1qXjoHi13zf0b1+kI5LKCiRvEqkeUMztd07u5x0JKPjpCrqNVMqylaVccEa/gK8suuuBY/1vt9MkDRwZpwBtT3h5N8VPKVjoJjfBinG4MzluJOkLimSZ7BuIQmcVz3OlavoswUrGDp6t+2+05WMfuDSqVZVtAywhr/ccZHJ88dZSzg1wKEU4gYMtl+sUHE2Nx3BOhp90JeM/vEL/+lrnJZ4YA2/B/rXCqua3RmYD6qupEHWgZsYXm14gcxcoFj1rEXWhrsV/lOoT4sn5519i/a5m3p2Y8ZvLwa2ZQkV81nr7AaSJBlt8Y81/B748JQe7i+asc79NYby7nXYGig+dfug44RTf1DwGpOqnxZ9dli70eriFEGnHqHSsYbfAsD0WUu5+/wDXV2ztqFP7HryWiy5egRLYazhrwRKaPkvXrWBlnQb3z/pZlbXdNB2WVb4uoNfnU2XqjYO2GUVYKZfL3hvHqPVYctD7j3gmHtviSc69QiVjjX8EeFZbycAhvXvzpxffJMRHY2+Bq3t23JxLl/+5Nbf76nuIF7notZg8qCxnb80FbT1k3sPnieO1S1pjntpCZcP78cZQ3p5G4NH3AR+/TwjLtzRdG/Jc/JbS1ZiTr81/BHhWW/HEi0eJ46+Xat54aih7P/3RaEb/jOrTzZSGVyORrJSc/qt4Y8Bpx40mFMPGhz1MIwRdVPzuLK+tZ0+NbrVCvHBBpLLD2v4Lb7p5NZJID+Ys4I5X26mTRGYO2b/v/+LP4zexfh9LRa3WMNfRpiOG7StX8+yc85Bampo37yZ/j/6kWPjmaQzr6mZ+WtbeP3Y3Vnf2lbYHdNRX8clq07emy+a07S0tdO1gLR0GBOQxWINfxlhOm5Q1a0bQx58EKmuZsuyZSy/5BLvhr+hAVbGM46xc101NVVCa7sKzh3z2PvQnGYp5HzOAAAgAElEQVSrjuoZ+3Q6RXsCqhRKBdI1hO10cvqnpx/dLnZx2NKlnnS6+qZSRaVe4oQ1/BGyeNUGhvUPRvDqj7OWcOWJezkfLKTfs3jxdh+lqgqqMivT9g0bqP2Kj65ijY2uMnt84TLzpk/Xap45ciifN6eDc8c0p0ueEsoEFCFuDep8rxlYeejk9HcM8Hox+rnrRixenIgJwBr+CClk9P26bErq9Devdc7tP+jGTl+1Njay/JJL2PLxx+x8663aY4gUjwZjx9pqPjhhT77+3GKO26l7QXdMUPSuSbFHfQ17/vVDNqbbjU5AXlM6TaZpejWoftDN6c9P7wR/rT2jeE+3BG74ReQ4Msq7KeBupZS+KEmF4tdlY1Knv8uAAQx9+GG2LF/O0nHjqD/qKN/3jDP1XVJsaVe0+SwbOPCZRa599DMbN7Bic5pFJw5nbWub0Qmo3NIwddHpMZBPpaR3BrqkEZEUGeHFMWS68p0hInsH+cxyw0trxemzlvLdQ/xvNdtbWrb+nurenapu3fzd0KtccogyywfP/IiL9+zLDtX+/mm8cNRQrn/P3YStgN5dUqSqxNgEVI78YM4KDnp2EZtWldayyu8xsHrZF0EPLTEEveIfDSxSSi0GEJEHgbHAgoCfGx8evcCz2JqX1oo5+YW9BvpX9mxZuJCVN94IVVWodJqG633qnzQ2+h5T0Mz+5u5G7uPFR3/MgO48sLSJQ2d+REu7MjIBdWLAAG9B9oaGWPz5bRcA/+Ed7P/3RXx00nDudgj06vYYAPj98q+yqb0myKHHiqAN/0Dgk7zPy4Gv5Z8gIhOBiQCDB5dPEdNWfChs9q3vysJfn+Dqmpz8ggnq9tmHIQ8+aOReQORGx1SqpM59vASJq0SYctAgT2PSxmtmVQAZWV7Shd0EwHV7DAAVZfQheMPv5GTebvOqlLoLuAtg1KhRdmMbIfNfL9zZ6osu3Tn8gGv8PSBCo2MqVVL3Po3f2Yuq/BiLQ/pm4igUM6qqAg8BTS/pwm4C4LbHQGGCNvzLgfwlzC7ApwE/0xIA/Vo3FD5YQv2zVPvHMDCVKql7n6ogU1d7ulcuDZT2dk+XeUkXLhQAt7gjaMM/G9hDRIYCK4DTASv8UWmE2FCmEKZSJYNMudRm7erOVcQJlY12my5sA+BmCNTwK6XSIvID4Bky6Zz3KqXmB/lMS0x59AKYfrr765o2w0VPFD9Ho2DLVKpkkCmXvvBZ7DSJTO/jsHGbLhxKADxLOUuWBJ7Hr5R6Cngq6OckmThp87ul75b1eid6XfX3qit9jobRM7VSLMcV51zgzQie297SQlXXroB+unAoAfDcs0xKlsQMW7kbA0xr7CxetYFTb5+lnd2jO/EUC/7GHVMrRb/30coseuC97T8HHBheCBxQ5PgkYCZQA9wOOErVaQjY5bb6X3TvxeFXTzGaLlzXtIHNvcz6+vNjEDWDBzPs8ceN3j9KrOGPGUU1djRxm9JpauL5f5Nmxna3Ymql6Oc+2plFIWcAjSRj0J3I7QZeJZOXfTbgN5LQb0MTYDZd+MxL7nDM5bc4Yw1/jPBSsGUaPxPPZWOGB9JJ7IuHz+PwDgJy+fS96j5eufUc4881TVxF2PamcMZF/m5gEPAx0AJ0DWFcluCwhj9GmNTY8UKpieeIXzxf1BVkYrfiRNFUUmB1fW/jzwyCWGQEFeDCAt/ndgNbgPfJ5GevAQaENC5LMFjDHyOmz1rK3ecfGNnzS008L11/dFFXUNS7lbgT24wg4FjgWYfvc7uBY4DdgBGAk75l7zH/V/T+/VtW8+EL3/Y5Sm/oaPJXGtbwxwSTGjte0Zl4iq3qo9ytmGZeUzMje5mTJIYQMoLyA6x5ef35OviFcqmdjH6OC7M/84BbyORlu2VV121FZ4dddZ/WNSN+sS2Y2nf9Gs/uPB1N/lKUW2qnNfwxwaTGTiFy2Tu5QG4+OhNPKVeQF0XQIFJZlVK+J6Cd66r5siVNn67m/onoZgQ9sKTJf+etvBRXv/rwxwJpoC8ZqV0/HHbVfZ5cc6vre3sO3upq8hfDTWpn31Q8YjfFsIa/gsjP3umIzsRTyhXkZbeim1G0ZNw47ZXWzMYNHLtT/XbfzWtq5uK3PuXFo4dtzaj56KThBe/RuybFxW99yh2jBjoe9yL4ppsRdP17K2PVcrHYbsAtUcRj3GryO6ErLzF/2DBfYw0La/gTQhyKvKbPWsrLBw2Ev3oovq5JwbHFtViKuZGGzJihXUTj5D1xm1GT88c7EXRv3EJjK9ak3VKYfE3+kceWrjcoRCK70RXAGv6EYLrIywst6Taq094EudhS3N2gk8qqK+R1zIDOhTxuM2py/ngngk7LvH5Ef8fv41whvP7n34SqKqSqmroJvyXVf0jUQzJOOXWjs8uHoKk1H6z10pXLBEHGIEq5kT4+9VSWjh9P/bHHlryXkzJmfkbNByfsybXvNNLSVngSO2ZAd9od9w7bTyL7//1fBQ21VwqNzSkecOAzi3hgSZP2vb/oF0zWVfcfPUL9dU/T9fiLaX60/KSQjXejixi74g+ajnLEPjpyQTyKvIKgVEaR35WW24yaYv74oNMy3WT7vHDUUFeupsPfeGO7z80LFvDltGnsfMstzN9tN7dD3YrUZWMqqRok5c6s6GbMbGzrQrdUq+cx+sF4N7qIsYY/bArp0pfStM/it8hr8aoN9KjrQr/6+NRe6qay+llpmVR1DDIt84uWtKuxeXU1Ofmr20RIKfcvsrJnZsfTMGo1qUP2ge/vA3QWzhtxiLPWjW7GzF0rMjXElw1+3fUYS5F/z8nLDup03Hg3uoixhj9h+C3yGtY/mKYVftoa6mQULRk3ztdKy6SqY5DSwP26VnPJcP3dnJcWj+Dsr+43dY3r++RQ6VZSHtcSbjNm7k53NvzNG5qp7e5cd2ELuDpjDX+C0FkZF8vVD4qgs1wgk9UTF8KUBi7FByfs6drV5EUOuRiqvZ1Nv5sIh//K8z10MmYOW7qUV3Z1rhX5bMEKho52dlWZKODS5e504b+nddRyZnU8BAyt4U8QOivjYrn6QRFX8bFKwIurybS/unXOX2h9x1+2v07GTLFCtEJGH/wXcE0c+JaR2MJmmn3fwxTW8Ft8EwvxsRnvbv9ZQx++HDh45keuXU2m/dU1o8dSM3osTn59HUzvQDrit4ArqoBykFjDb/FNnMXHPJE/icR8Apn9zd0Du3d6yTtsvv9KX/n5HTN2hj32WKdzgs6YyS/gmnHJlJLn71C1hU3tNUbHEDes4S9TNrWk2cGgzkwxYtmOsGdfb31oe/YtfU6FUNVrAN1/9AhSV0/rO8/S/OhNdLvgLnf36JCx40SQGTPNG5qpqauhKlXF8neX0b1v6eSG7+/yNgDXfuVybvrgtkDGFTWJMfzjJqZZ4yH9vXdPmHFXYl7TGMfc/BJ3n39gKGqfQTfA/qJL8X+sjqJY/+u3T5SlqlfDtg8e8vOhc8ZO2Hy2YAX3X3gPtfW1iAhn3TlB+9pcbODHL/xngCOMhsRYRC9GP3fdmNOcNVfKdVIIW+I56CyXfqf8oaCccOB43Dms3dJGT4cgt2nFzzBQLRtpfuTn7HC+N23O/Iyd4bNn+x5PHbXagdKho3fjJ3O8VRLnYgOFSHKaaLL+BhrG62QSCLU9fVX05hOGxHPFoLNz6BAHaFeKH779qeNk2LsmxbrWNnoU0AGKGyrdysY7zqXrSZeRGlhaJ8mJ/Iwdv0xPPxpadkwxow/hpomapqINf6zwWdHrhTgofpYjpeQe/nfRlzxyyOCtgfC3j9u9UyD8my9+zF8PH8LnzWnG/t9S90HcvFhF31TKkyZ/Lj+/ywEnUHPAiQXP2/mQ1bQ1NQGdazc6Zuz4JSyjnx8bKIQJnf+osIa/gomD4meloRsI10qP7ZjCWoBCRU8d6T1r+x1nLj+/fd0qtrz6EKld9maHs3/peO2yiRMZ+vDDnb7vmLGz6x//SFVN/DNm8mMDV774E8dzTOj8R4U1/BaguBb+dk3WizQvsZRGNxAeh/TYQRd9ndRlHScX53iHk9EHbxk7bU1NLDnjDHZ7+mlX15lEJzbgNk00TlS84R9zWrpsg7y6lFL83K7Jek2qpLa+I16reQOQtY4S3UB4HNJjUzXR5OQumzgx9uqXXtJE40TlWrs8YhXk7YjBoG8hdBQ/t+4Ihvv0Y457wN/1FUI7KrD02DiynSzC65dnvzWvwmkKP2miccAa/riTC/r61PEvRinFz3LtARBn4iICFxZJk0XwkyYaB6zhTwoBGX2dnH+/PQAs8eCwpUtLZPfYquVKwRr+hOM3JVMn599rD4C+33/UpofGiFIpnTsfUrpQTbdbliXeJNrwz375eNY1/ZMhe1zMbnsnJ5UKgAEDYKXL9Mnpp3f6KoyUTK9VwKeMLu2uKL0K7UzfVEo7RTFKklSopYtutywn8nP645C5U8n4MvwicgrwU2AvYLRSak7esWuACUAbcIlS6hk/z3JinwPv4ouVz9OyeYXve3WUdQg808et0degWEqmH7xWAes0hPdSWKR7TZ/71xZol16c/rXCh6f08HDlNuY1NXPxW5/y4tHDtjan+cgpFXbGu7FXAM1Ht1tWwWuzpHr12s7oO7U7zKHWrqHt0rNKyiIU68LlleXvLmOXfQcbuVcdZsfmB7+WbR5wMvD7/C9FZG/gdGAEsDPwnIjsqZQq+S/WjRhb7Q7B6b7HOtPHgTgGYKMei9dkxFXN/tMYXTWn8aIHFKGKqE63LCekSxdPz5OevbWkEYp14fLKA5dO5fJnriXlc+d2XvU4QyMygy/Dr5R6H3AK+o0FHlRKtQAfi8giYDTwWql7Js3gxoW4BWDXb26lvs7bP/RywFVzmgiVRHM++6GPPKJ9jU63LNPcecrkkrIIbo2+jshaKWXOjvn80/7jbq6d9TNX44iCoHwZA9k+CXd59rtOiMhEYCLA4MGD2dHshJ04JgEzgRrgdkDXCVAqALtS9aBB1vkfoCbLv9wUmjpoHAmrOY0b0bLL8jwWG9u6cNeKA6jq3t1VZW1H7Z2hjz/uarxeOez8o43LIpgQWUtqPn9Jwy8izwEDHA5dp5R6otBlDt857p+VUncBdwGMGjUq6vYdkTIXeBN4FfgEOBvQWQvqpGR+pf2/tv6+JnWBv4Fq4NXol0vWSFjNabyKluXy5kUEqvXXfx21dwpJNZjGjyxCId+/CZG1pObzl/wTV0p9w8N9lwP5KR27AJ96uA+Qyd458PCnOn0/b/b3aVr9Ou3tLaz98i2+euifvT4iFiwEDsj+Pgj4GGgBupa4rpxkmP1kjZTCRCtBXYJuTmOS1sZGugxwWtt1xnS3LN2J3o8sQiHff5JF1vwSlKvnSWCGiNxGJri7B5nFrCf2OdC53dvIA3/v+H1SGUnGvbMFeJ/M7LkG5+1WueIna6QUJloJaj8r4OY0JtE1+kGgO9E/cOlUz26UQr7/JIus+cVvOud3gN8COwJ/E5G5SqlvKqXmi8jDwAIgDVykk9FTiCCzd+LE3sA44BhgNzIpUYEofOf0cgLU+veD16yRUphoJWgpjltXne5Eb7r9YdJF1vziN6vnMeCxAsduBG70c/9K5MLszzzgFqC8yn/0CDprxG8rwSTgpi3gktNPN+a+8eKqC2qiL0ZSg7KmsEuemHEsmS1SX6B8zVJhOmaNVHXrZvT+uq0Ee99fOq/YRKFXULjJWDHps/fiqosiPTTMoGycCrdyVKThj7PUw7PFDjZthl51ru+5UnkzTotXbWBYf4NbYA1t/Y5ZIyZ12XVbCepiotArKKJsC+hmBR/0RB8FcSvWcqIiDb9JqYdQuSibPTv9dHq3FejRaxC3Rn94262soohx3wh0WEnv3MELYDprJB83rQRd4aXyNnddQJjIWPGqreNmBR/kRG8pTEUaft1gse3O5Y6iRj8Ahv9pnatVd83osdSMHmt+IBFW3hbCRMaKF6PsdgUf5ERvKYy1aCWwEhIlyO+opeEXN0mcXS1RYipjxYtRDmoFX0zELZ8dqrbw/V3eNvJML8TRn++ENfwJpT9rPa2w+9fGQ8sn7oRZ7GWaKDNWol7Bb2qvcfSxu5G2KEUSfPilsIZfg46SzTl8uYEaGrxJM/fMrCg+TF2V+VyhPWx1sm78EGaxl2mSKiMQJGdWb98Q6O70jIhGEg8q0vCbknrw5QZqbNz2e0wLqUpSIktHZ9XctkVI1bhz2bRtCX7Xolvs5XYCinMKqKVyiJXh77vr30J5TrlJPUSCxk5DZ9W8cnYf1pzVeQIJekWvi+lir1XNquC7hT0pfDqrL1Vd2tlxr2Wk6utDe64lemJl+Kuqk9fsORA3UFjU9vTWxF0jHx+8SSS4zdTxSv5kU8gQ6xZ7mSKKYHV7axVV3bqhlAq1l0OuqneYC1lnk8qtddR68vknJXhbiphbJn2ef2Injh77WdTD2EoisoFODr4WANytmv0aP1NBWdPFXnEmvyVi84IFfDltGjvfckugz/QiwGdSubWjz7/SiKdOrAdMG/3ZLx/P80/sxEcLQgiSaa6gjV0XImGvmnPupfrrnqbr8RfT/Ki3P79csdeWVx9i/U0nsGnajw2P1AxeV6DrNtds97m1sZGPTz2VpePHU3/ssSaGVpB0U5On50hVFZLtHWBaubXSKJsVv2lCre4NaeUdNlGsmk0pcHop9koveYfqIft5ep5X8leufuIipvVy2pqaSPXq5Xisulcvhj72mKfnRCHoVo7E3vBHpatTKVLQQRKYRIIGOu4l0/GEql7x6pyg6/YKQi9n2cSJnbpzmXhOFIJu5UjsDb/flXd+6mb3HiMS36UrSZiQSPDis9d1L5kOpm6328gSZSGYbi1CENW2Ti0Z/T6nHAXdoiL2ht/vytumbiYbt4VUcQvKRlkIpuv2Cqva1u9zrKCbOWJv+C2VjVuffZTuJSfi0PWrXBrPRC0HUU5Yw1+AcmvknnR0jZeueymIAjHV3r5dauR2xyIyvmFnVVmSQaSGf8xp6Uags2M0BlgXUXxIivFqnfMXx0knqvHHze1liQ9Rr/hLGn278k4O/WvFdcC0lFpokoyXo9GPcPxxc3tZ4kPUhr8kduWdHILQmYnaePnNyoly/IE1nrEkntgbftPY9M5kEbXx8puVE/X4dZg/bBgAIxYvjngk/umbSkU9hERQcYbf7iAs7RvWoDat1Vq5xyErp9zpm0qxuq3N03Wv7LprACMqf+zfYktF0N60Eum6A1JXT9tHs9ny2iOuVu7lkhJZDD8G2Mt1OazxDp+yMfxRSTtYkoGflXtSsor84scAH7Z0qedJwxI+ZWP4QxVVSyiXpd9kHa2ur+tBFyZXjw5gROHjduWepKyiILKqdLGr9mRRNoY/bqJqvWOomOzF6Pu5zgtejFf7hi/Z+JszS2bdeFm5R51V5Abb0tGiS9kY/rgR++5bMaWY8XKqts357uuve7po1o3XlXsSsnIsFrdY61QEGzfYxoT0LK3zmjeneOrxvULrH6vru++4cu9+2QNIXfxWyHHpx2spbxJh+IMywKXua+MG7qmtywT4wu4fW8p3n/SVu1OTdjsZWLySCMOvY4C9SDscePhTRY97jRvE0b9fjLULl/HofmcxZubtDDjUewepvz02nJbmLls/6wqh+TVgcc+6Ue3tbLpzAtUjj6TrEWcbu28Uzdkt5YEvwy8ivwROArYAHwHnKKWasseuASYAbcAlSqlnvD5HxwBHWZj19EOJmD8LMvfGqQw4bH/f98k3+m7wY8CSkHWT72YyafgtFq/4bbY+ExiplNoXWAhcAyAiewOnAyOA44A7RcQm7MaQz99cQN2APnQb2F/7mrULl3Ff3eE0/uOdAEe2PcP/tM7x+yQ0Ra8ZPZZef1hB/bV/i3ooFgvgc8WvlHo27+PrwL9nfx8LPKiUagE+FpFFwGjgNT/Ps5hn7s1T+frd1/Lmj+/Qv8bQDsENhXYFJn33UbVJjLI9o6UyMemjOBd4KPv7QDITQY7l2e8SRblLQn/y1Kv0O+Ar1PbVD0rkdgiS8rtZjB9RtUmsHrIf9f/p2RNqsbimpOEXkeeAAQ6HrlNKPZE95zogDUzPXeZwvtOSbSUx1uQvd0G31e/8i8aX/8kzr73HmnmLWfvhUo6c8TO67+r0x53B7Q4hSatZK8hmqRRK/s1WSn2j2HERGQ+cCBytlMoZ9+XAoLzTdgE+7Xjt0w9VDwAYc1q6aHSv3A1wVOx/zXj2v2Y8AK+ceyN7nntiUaPvZYcQZbPxfNpWLdGecCpBkM1S2fjN6jkOuAo4XCm1Ke/Qk8AMEbkN2BnYA3jTz7PizJjT0lt/790zmVW7h917XclzvOwQ4rKK1p1w4p4aarGYwO+/wjuArsBMEQF4XSl1gVJqvog8DCwg4wK6SCnlXbfVI1FU3q4x38M7NrjdIeQT5SpatW7RmnCCSA1df9MJALHW+LFUHn6zenYvcuxG4EY/9/eLrbwNDp0dQo4oV9GqZSMbbv6W1oQThCBbsRTOJMU/LOVF8nwSLoibYmcl4nUVPfxP63xXprqdcMKWdcjP5oky/mGpPMra8EdFzuefVH+/Sbyuon0b/RAqejuu2Ltf8xfvN7NZRJYQsX/TAiRu/v4edAlVWx+iE0cLQ0e/Y8aSH7qMPIIuI48wMzCLpQTW8AdMfsZPIcLaGbjpoqUrwxxXwphwOmYsWSxJIS7llyuDuOm82d9nyYe3sWLJNMfjs18+nuef2ImPFtxU8B465/glbjsDyOwOLHrkMpYslqQQixV/rpBr1KhRas6cOQXP01k951Oq8Esn66dSM4NyuwMTQVZTxDELJj+A7EQcx2yxxMLwR4VO1k+lZwa50cnX1d/3SlyqgHPoBJDjNmaLBeLj6omMnQadWvFtFU3Rv9ZJosncdVW9GpC6+syHGGTBdJSEdkJ3zBtuPom2VUsCGqnFsj0VveIHSFXXRT2EsiGsNoBx0dJxE0AuNeaux19sdwOW0Kj4Fb8lWSRRS0drzB52MF53WBZLRa/4583+fskAcLlr8ieJJLRZ7IjumN3sYNaclbCmzpbYUdGGX0fuWVcS+vkndvIlBlcsY8lWAGcIoyjLifSSd6ge4q0Jve6Yk7SDsSSfRFmT3j3jmfMOMHzfWwJL+YzrO4dNVFXAVb30FEid0B1zUnYwlvIgUYa/1KrXbZ6/xaLDdhW6AbH+phOsdLMlNGxw12LRRLVsZP1Pj6ZtxQfG711/7d+s0beERlkZ/t425mUJiLhkE9lMHosJEuXqKUW+Kyiubp8ouoJZ/BF1NpHN4rGYpqwMv0mWfzyVls0rtIyzm5TPStX+cUv/WjGqEaTSraiWjVR161X0HKnuLE6nm5ljeswWS1BYw++A29x93ZRPsNo/unitAnbSC8qt2KtHHknXI852vC53Trcf3NfpmG5mTrExB61jZLG4wRp+B9wYckv86bhid+qDmzsnKLzuBqxP3xIE1vBbyh6dFXvQNQJh6RhZLDqUVVaPxWKxWEpjV/whY7V/LBZL1FjDHzI2flCeWF+8JUmUreEPU9fHTeqnpTywufWWJFO2ht+tmqXXgi/ruokXNpfeYilN2Rr+sLCum3jhJnvGayN569axJB1r+C0Vi02xtFQqsTL8b7311hcisjSKZx93ausBUTxXhy0tX6RFdnon5Mf2A74I+ZlBYN8jXtj3CIZd3ZwsSll/KMCY09KNQPDC6w48/VB17HwHIjJHKTUq6nH4xb5HvLDvEQ9iteKPkqcfqnZsszTmtLSdGS0WS1lhK3ctFoulwrCG31KIu6IegCHse8QL+x4xwPr4SxCCq2dlITeTxWKxBIH18ZdmJe6DvtaYWyyW2GJX/BaLxVJhWB+/IUTklyLygYi8KyKPiUivvGPXiMgiEflQRL4Z5ThLISKniMh8EWkXkVEdjiXmPQBE5LjsWBeJyNVRj0cXEblXRFaJyLy87/qIyEwR+Vf2v72jHKMOIjJIRF4Ukfezf6d+mP0+Ue8iIrUi8qaIvJN9jxuy3w8VkTey7/GQiNREPVZdrOE3x0xgpFJqX2AhcA2AiOwNnA6MAI4D7hSRVGSjLM084GTglfwvk/Ye2bH9DzAG2Bs4I/sOSWAKmf/H+VwNPK+U2gN4Pvs57qSBK5RSewEHARdl/wyS9i4twFFKqf2A/YHjROQg4FZgcvY91gATIhyjK6zhN4RS6lmlVE7p7XUg11x3LPCgUqpFKfUxsAgYHcUYdVBKva+U+tDhUKLeg8zYFimlFiultgAPknmH2KOUegX4ssPXY4Gp2d+nAt8OdVAeUEp9ppR6O/v7euB9YCAJexeVYUP2Y5fsjwKOAh7Jfh/798jHGv5gOBd4Ovv7QOCTvGPLs98ljaS9R9LGW4oGpdRnkDGoQP+Ix+MKERkC/BvwBgl8FxFJichcYBWZ3f1HQFPeYi9Rf79sVo8LROQ5wClb5zql1BPZc64js8WdnrvM4fxII+o67+F0mcN3cc4MSNp4yxYR6Q78GbhUKbVOJHYKJSVRSrUB+2djd48BezmdFu6ovGMNvwuUUt8odlxExgMnAkerbelSy4FBeaftAnwazAj1KPUeBYjde5QgaeMtxUoR2Ukp9ZmI7ERm5Rl7RKQLGaM/XSn1aPbrRL4LgFKqSUReIhOz6CUi1dlVf6L+fllXjyFE5DjgKuBbSqlNeYeeBE4Xka4iMhTYA3gzijH6JGnvMRvYI5t5UUMmMP1kxGPyw5PA+Ozv44FCO7PYIJml/T3A+0qp2/IOJepdRGTHXJaeiNQB3yATr3gR+PfsabF/j+1QStkfAz9kgp2fAHOzP7/LO3YdGZ/gh8CYqMda4j2+Q2a13EKmeO2ZJL5HdrzHk8mw+oiMGyvyMWmO+wHgM6A1+2cxAehLJgPmX9n/9ol6nBrvcSgZ98e7ef8ujk/auyUIST4AAABdSURBVAD7Av/Mvsc84CfZ74eRWfwsAv4EdI16rLo/toDLYrFYKgzr6rFYLJYKwxp+i8ViqTCs4bdYLJYKwxp+i8ViqTCs4bdYLJYKwxp+i8ViqTCs4bdYLJYK4/8HX43oRTXs6HYAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0391 | test accuracy: 0.97\n" ] }, { "data": { "image/png": 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R498PmJuM85cAC5RST4jIu8B8EbkReAO424W5NElml44BYEq8a5WJGa0dp3o8dpgBLCbhEI1w5LhJ7Ox1m+ZVq9g8cyaUlKDicQZcd13eMV4lmsPAyrc38atfPEO8tZ0jjhzIFT+1FvpyOl7jDm5U9bwF/IfB52tIxPs1PmNGa8dLPZ4ZQOaDfvoqOBteOG6nVI4cybD58y2N8SrRHDStLXFm3/oMt//uXKqqrd/G7I7XNwv30ZINBYYZrR0v9HhSpBx8Jumr4EInPV9xIO4mmoPkzTfq6d69nKsu+ytNn7VwyY+/xNHHmBemszPe6c1GY4x2/GFi4fdh705rY865tNNbI62dry65K+85+RQ7wZw+TzYH74a6phucmLTjSI/ncZqvCCNbNzey6v0GHv7b99mzp4XvTJzL44suRUQ6nZdaof9x7oW2xqdj9maRmUzWJZ650Y4/TFh1+gakq3G27NxNec/qnOeYUey0QsrBZ5K+CnYqtOaE+/En2eo0XxFGevaq5KjPD6G6poLqmgp69e7Op9v2UNu3899YaoVud3w6dm4WoEs886FF2goYI6efidt6PCkHb0Sq3DJI0pOtXrKIhK7QX4D+Hs/lFyNHDWL92m3E423s2d3Mp9v20Kt39y7npVbodsenk36zGFDXo+NmoXGGXvFrTHHZR4+xq66202fZQj8XZ7lGahWcS2jNC94lsTX8OXInW2tjMVu6+rVbtzo1MRL06FHJhAuP5aIJ9xKPt3PZVacQM6gKS63Q7Y5PZ+SoQfx69nPE4200742bullo8qMdf8h5t34nF9+7HIDm1nZWNTSy7Q/f8N2OTKefi3EYV+gEVbVjNtlqq4NWnpBDoXHW14/irK/nfkL8xws/djQ+HTs3C01+tOMPOYcN7skL150MwIKlG3hupb+qjidiHLPPhRsOfgGJHa83Jt8PAz7Afi18ISZbiwWrNwtNfrTjjxB/XrKOq840kkHyjlQy9ABfZ3W/Fr4Qk60ajV20448I2xqbeX9TI2MP7tvp8x5Ne9hVmV9GwA49GrZ1JEP9dvxu18J7FmYaMMCetv6AAe7botGYRDv+iPDQ0g2cfeyQLmVss//2p8SLCQ92GZNNzmHtX59n79YdHDz5TMOKnivKju9IhqaUMoMgEuGZhoagLdAASqm8JZ6afWjHHxEeWLKeOd89xvF1zEg1GK22g0CHZ8KLVRkFr2UX8u0H0HRGO/4IsGbLbprjbXxukDOJBStSDZmrbSO29+lB7093ObIpF35XARk9IaXTg7IOcbxiIBarMtwIZVVGwWvZhe2f6hJPq2jHHwEO6F/N8htPdXwdK1INmavt6QbXO+oLIzueHrKFjcCc1EMUyCWDXYikJA8y5RCsau441fgx4vFHVvCXB5cTj7cz5XvH8+VT/S16iDra8RcRVqQa8q22vRR6C4KdA/oEbUJksCqjYFd2IRe6xNMZ2vEXKSfcc62j8XaF3sLGlNaXgjYhcljV3LGj0aPxFr0FLgws/D7MOz9oKywxavokTl98B6c+eRsDTz6GY2ZdEjmnr7GHVc0dOxo9Gm/RK/4w4IIqZ5A4fXoICqPwzs5VG1h41EROX3yHq+J1hYRVGQWz5+/c0cScO/9PN1rxAe34C4GKwoize4nZkE7Pg4dyUZN3GqKH/GUXW/YqS2P6VwgfnN3DI4vsYTXGbub8nr0qtdP3Ce34o0z6pi2jJi4ZTVo0wWPV6dsdU+zEYt7sZi8UtOMvFAzCRXbkHJp2K0pHHAslJUhJKZVTfk2s/zC+wTudzrtt8Fn03PypI5OdciL+dNPShIvDD5gRtAmRRzv+EOKWFHOHnEMWerfd2eWz9h2bqb7yYaSyhtYVi9i78Caqvn9Xl/OCdvrgXzctjabQcFzVIyJDROR5EXlPRFaKyI+Sn/cRkcUi8u/kf3s7N7c4SEkxv3DdyVx2+iGcPWaIb3OX9BqAVNYk3sTKkVhibbC3KXxKOX5109L4xy9vyb2DRIdw3MGNFX8cuEIp9S8RqQFeF5HFwLeBZ5VSt4jIT4GfAle7MF9hsbBrp6J0zjluKOccN9QnY/ahmvew9+Gf0/27CZWcvz/aeWfk3b5b1JWUgJwTueagq3ji61bQdP9VXUJrYSKbdIMRra1tlJXZXyRkS+7q8I67OHb8SqmPgY+TrxtF5D1gEDCeRBgWYC7wAtrxd8VJKadHtf8q3sqe30ym21cvIzboUE/mMMuJZI/j345zueY3Z86l7oRRDq7gjJJedaZCa0GSkm5IJ1PGARKaPD/4zjzm3Hdhp8+XvbqO+/+0lLa29rySDa8vW+9YzkGTH1dj/CIyDPgP4FVgQPKmgFLqYxEx7DktIlNJtERl6FD/V7aazqj2dj67cyplR59B+dFnejbPDGAxUE7uBG2uOP7lZJdrTpVv9mjYxuwh4w2vvfW1d6ms64ME2MqvpFeaLn9aaC2KpDR5MrEi2aCdvj+49lcmItXAX4EfK6V2mdXhUErdBdwFMHr06MKuWzMquQwZrcv/RuuKRbTv2kLLyw8RG3wY3S+81fb1ZgCZa8M3gdeAl4GPyJ2gTY/jZ+o63kB+ueZcvYLNSFT7RWZoLYqkHHwmWrIhfLji+EWkjITTf0AptTD58WYR2S+52t8P2OLGXJEm5E4foHzMeMrHGK+Q7dA1IACjgKeSr4eQ6K9r5Nghdxz/Lw7sCpPIXJhCa05IOfhMRo4axK9nP0c83kbz3riWbAgBjh2/JJb2dwPvKaVuSzv0ODCJxNP4JOAxp3NFAhdW9W6Vc0aFfkADxglaN+L4RoRFZM6v0JofpBx8JlYlHjTe48aKfywwEXhbRN5MfnYNCYe/QESmABuAs12YK/y4sKpPlXMCLFi6gedW2ujpGjGyOfZccXwnWJGoTtGDMpetcD+0FiQpB2+EllEOF25U9bwEZAvon+z0+sXOKSPrAinn9Jtsjt1MHD8fW0pz3zbyiczdXTrWoQXZcTu0FjTauUeD6JYQFAHbGpuprXG/VV2UcBLHT3HR4jts1/lrwoFR+WgsVmVYaqrJj3b8IeahpRu4+JQRnl2/PzvZgvXkZv8Ka52TzJZuukWm3LKWV3bG++t/YXoDl1OUUqY7c/llUyGiHX+IeWDJek8d/wexqzsrfHqAldJNq0xpfYnFX7/aVN9fjX38dLC6zNMfdGo9pKzZspvmeJv3E807P69shBNWAUcnX7utrROmkkyNc7Z/qss8/UKv+EPKAf2rWX7jqYbHXC/39HB/wREkwjstwHs419ZJx4+STC8qeTT7ePyRFfzlweXE4+1M+d7xfPnUz+UfpHGMdvw+44bTjlK552HABOAU4EDcqcm/7KPH2FVXyyjoKMn0Ai+qefpXiK0OXIWKLvMMBu34fcZtp/3nJeu46swAVkkDBsBmc7ZfnPx5B2s1+TsH9OHy+sft2RdSwtZC0W1SQm23/+5cqqqLuyItzGjHHyBOnfa2xmbe39TI2IP7umiVSRoyNFnq6rLeCMaR0O6uxXxNvtkeuZpwkRJqu+qyv+ZV4tQEh3b8AeGG035o6QbOPnaI6fK3oMjdWiM4vNDifyC+kCb2Wh5XSQXfKo2+LIcVJU5NcGjHHxD5nPaJNz4L0BEWMuKBJeuZ891jPLHPMibDPmHCCy1+O07fyTgvsVO/r5U4o4F2/AGRz2nncviwr9zzc4MKq5Qxlbi1g5UVfBi0+DOZE58HhGf1b6d+XytxRgPt+APADaedq9wzhaUKonnnQ0VP+EbXBux+Ytfpg7UVfJi0+DMJ4+rfLG4qca58exO/+sUzxFvbOeLIgVnbMmqsox1/AJhx2m5guYIoAv0CcmF2Ba83fnmLGyWarS1xZt/6jK4O8gjt+IuEwMo+feTIqy4wtYIPixZ/LlJhHwhP6MdPdHWQt2jH7zYVPUO3cg607NNHzK7g7WjxB0mUQz920dVB3qIdv9ukx8jnnR+cHWlEpewzH/mSt0+fcbnlFXw+Lf5CwVaZ6dARlLXFGb1xrTdG5SCs1UG/qIM9NgrYqgbAlV3bEQdGeEoaNJ7xwJL1XDA2+o/J+ZK3pz55GwNPPoZjZl0S6hV8ENh9amiNBbM2HDlqEOvXbiMeb2PP7ubQVAfZcfpOxnmFXvF7iY2wj9sCbIVS9mm2/LJQV/DpMf9sFFIuQPfp9Rbt+L0kFfaxEPIxW4mT2uCV7+bgVwWR14S5/DIsFFouQAu4eYd2/CEmVyVOVNQ53UCXX9rDrnyEGcJSY39DlrRV2GLqYUM7/pBithKnGMo0o1B+GUbMOP2La77N8DEHAXDct47ni5NPzDsmCjX2fsbU3+BeXucuBOF0fs1APu/f5DZxxfGLyD3AmcAWpdQRyc/6AA8Bw4B1wDlKqe1uzFcMmKnE8aRM004lUoW3K/Egyi+LpQFLr0F9+Mmz11kao2vs99HEdl7lDr7DUhrZyEImMoXwK8u6teK/F/gNcF/aZz8FnlVK3SIiP02+v9ql+QoeMwJsoSnT9HHfgpPkbQ/KmF06xkVrnGNlxW1ndZ6PXQ07uPWkn1NVW805t15A32H52+QUWo293RJNgHpeZX++SCnl9GY4LewmTjOlhPNJKIUrjl8p9aKIDMv4eDxwYvL1XOAFtOM3hdlKnFw3B9fbM0aAu8uONz6grHW88hMrK247q/N83Lz6dmr61vDOoreYO/WPXLHomrxjwlJjv3drlSvXcRIWGsFpjOC0jvc/4E0XLPIeL2P8A5RSHwMopT4Wkf5GJ4nIVGAqwNChQz00JzqYqcTJd3OIUnvGYsbKitvMuQ/EF1oq6azpWwPAEeOOZN60e02NCUqB8+EDZ3S83hdXf8R0XN3Jyr7QCDy5q5S6C7gLYPTo0eFdmjnBAxkHK2WaviSALbRi7DIuvZtXfIl7NmXjB1+CndusjelZC79/Pu9plVRYqqSxsuI2c66Vuffu3kt5ZTklsRLq39pAda25FbvXNfa/vGVRziohu3F17fT34aXj3ywi+yVX+/sBWzycK9x8487A5BusJoBPvPHZnKGh2u8tjFbIaMCArp9ZdfoWxqSvts1surKy4razOs/Fx+9u5P6L76aipgIRYeLvppge62WNfb7S0KjG1cOEl47/cWASif7ak4DHPJxLkwWrCeAXrjs5Z2jo7DFDjAfa7cCVMa4HZeyi1fJlelAWulh+vtW/lRW33dV5LoaPOZD/Xn6T4+vkwk69/+vL1uesEmriUyro3fG+gp408Sk17OeKzcWAW+WcD5JI5PYVkXpgBgmHv0BEpgAbgLPdmCuyBKTaaac9Y67Q0AXHD8sy0XnmLr6jCS7JvgYIW9WNE/Kt/q2suJ2szoPCbr3/9dc8nrNKqJI+7GVHx/u97KSSPo7tLSbcqurJFsfI3T+wmDDqbOVx+MeOTk++0JDjPQO9Kp2NLyCsrLitnGsmxOQHduv906uEDr7lCcp2NwNw+Pl/zTjzSCCxyoR9i5vdTbX88tH8+Rjwd/NV5i7jIHcXB57c1XiHHZ2efKGhqNZq5+PS5RtZ/mkTbQouP6Qv5w/rFbRJkcduvX96lVDK6VuhurJrPuYXBvv9gt58lZlsdlJ1dD3q6BsEo1jn5hmKLr+9dvyaTtgJDUWdd3bsZeXOZpaOO4jG1jZG/WO1seOfcOS+1yarfIoZu/X+XihxGjnUsCWJPao6Mqhu0I4fgBPWr2dbW5ulMbWxGC/uX1jb1ONt7QUh4WyVgZWllJcIre2KxtZ2+pTH8g+yUxkUQcra4lmP7drVxNRJ9/Pnv0yhtLTrd2a33v/Lp7pTepxNwC1FsSSJ054EOlb/2vGDZadvd0zYKY2VFISEs1V6l8cYUVPOwU98wJ54O38cMzhokwLlO6UTOl6v3HBD1vPS6/nvX9A12exVvb9bYTmnSeIIirN1rP6149cUPYsbdrOxKc7qMw9hZ2sbX3xmDaftV003F8MNVjd2RYV89fxu1/ubDsuZYDDH8hzX0UYrjXxMOdWmwzxB5wecoh2/xhcOP27mvjcfJl7Xbt3Ki8cdF5BF+1BA77IYsRKhpixGS7uizeUtAVY3dhmx9rUPO5V0njf7QoYc5X64Md2+/+f61Z1hKyyXhUp6cwwX8yf+E0E4jdtNj3UN80lxAAAgAElEQVQrP5AvHOUVkXb8E6bG2W6xNL53T5h3V6R/bcukNmTdOSVcSdtt/fIrQXbgoczCKXXVPLh+B8cv/pDmdsUPD66le6l3bf7srv792HAVdtwOy32eyXyeyZbHRT0/EGkPaNXpmx3T1tjIhosuQsrLaW9qov+VV1I9dqz1yRySqbD5yg2n2LqO11o9viiB2pVZMHHDKBHh3uOy7EjORXqVTyY5bjqp1X9Y6u2jhB9hOTNEfRNZpB2/XU4/N6NS4ebOb0uqqhg2fz5SWkrLhg3UT5sWiOPPVNg0IuV0U+dl4kmzljx2hkoJNKjqmyKp+nGDw6/btzGrXSkmv1rP6sYWmtsVy049qNO5dsJyTWynMm117gZO8gNhQLetN0BKSpDSxD2xffduKg49NGCLEqt2I9KdrhFWtXrO+uWLvPTBVsNjqQbv+ezMKuug0WQQa+wc8ko9fb10yoFdnD4kwnLtKI5f/CFfWPyhqbBcPa+6ajN0zg/8lfM5jV+5PodZ3uBe5vAF7mYsm/iXqTFFueI3Q2tDA/XTptGydi0DZ80K1JbUqt0OVjZk5Xs6yHWDyTd+95IlgTw1BcqEI3OGfMJe6XPryTey8E/nOr5OLFbFoftfaXwwV7jMADthuSY+tXS+WezmB9IxUxI6hy8gCFPoKllut7pIO/4slNXVMXzBAlrq61k/YQI1J50UmC2pVbtVrGr1OG3lmGv8lltvLT7HDzlDPqlYv52t+iUDmtiv/hEnluXlJ89exyvJ12VtcUZvXGt67OEHzMh/kk+ENfZu1ml/mxdoZKPhNexWF0XS8dup5rFCe3MzJd0SX1ysupqSKndavNnFroxCh1bPovehJf+Gs4sBusfgiZWJD8pjMG5fmGtbYzO1Ndn/oHLZGYZwWYqw6fLY2arfvtlfsbvWmHlXEYsF++8lk8EcG7QJhph12qnjRtitLoqk4/fS6QM0r1rF5pkzoaQEFY8z4Dp3+5xawY7CZhdMOH0z4x5auoGLTxlheGo+O2vGZdFhN2qU4iFubgAyzQ++VBC6Pq8M7fz/Pn2Hb5hxO7FrlWzJZTdKQu1WF0XS8XtN5ciRDJs/P2gzAPP9dw/o732j6weWrM/q+PPZ2XD99cbhsgZ/dWnd3ADUhUfeg71ZtG3uzBI+GzAACEibNwz0rC34Cqh6Xu3UkD2FGyWhdquLtOMvAPxw+gDNcfv6RF6Hy8yGb9zeAJQ+77JsTj8XdjuXmUApFX4Z7XxPQjY27u1uqnVgkPtkSy67URJaSW++R2IPTS+G5k3s3iA0zFDUacevMY0TATcvw2VWwjdubgDKnJeH33X6q3TgVAAsJe9gdqfvxTXfZviYRPnkcd86ni9OPtGixR5hMUTmlwRCnBbTVTSH8V+Gn1t12i4xAEK84reawF32z6+wa8cbDBvxQw487BrvDCsQ/E5welnRYyV846YuT+a8NfYu0wU3BMCsyjv0GtSHnzwbXC4rSjSzi270MF1FE8aNXaF1/FYTuCOPuYtPNj9Lc5Nx2ZNmH4EkOLMwJd61NjmTHpQxO8dxK+EbN3V5MufdYnDODGAxUA7cQapZYG7MVnukkqtuSD/satjBrSf9nKraas659QL6DrOgo1REvMU8trGKL3E9ED2NnhShdfxWqehuP1Zb0lhCe027pTG1MReTgj7jaYLTgE/KOucgvnrsUCo/Sf3pHWDqGjfwFlUVn3Dl17smiK2Eb2zr8hiQOS8L3+t0/E3gNeBl4CPgQsBM4CIIAbCbV99OTd8a3ln0FnOn/pErFkXzqblqgGedrIDoa/SkKBjH74TBN3W9aURZxbOLuNsxAzsddzvB2Uly2QT7nL419uw13lHsh6yymXkzWQUcnXw9BFgLNEPeB/8gnEtN30Sg6ohxRzJv2r2ezuUl2ZqXuxX7D7tGj9nckOeeTUROA24HYsAcpdQtdq+ViuOfPP5j1+zLhtd7BTpx8aOw0+LW/QED4LYvGR7qIu72SWe5h7AoHLqF37LKKU7dr4ZT90s4zLISgfNHdjp+TvInVV2zLst1ZhgEgLY0wW8e9ce57N29l/LKckpiJdS/tYHqWn+qxKKIEw3/TNzu4GUlN+Sp4xeRGPBb4BSgHlgmIo8rpWyVP6Ti+AWHVacPpssA/7xkHecc0rm8zdIKecKDeeeotdGz2E3cDN94gZ2Syv6VmHYuTjV/Pn53Y6cGLxN/17WNohHZcguVVHRqPBMG3AwBuaHRY9ZJKxSCub8fK/INXq/4xwCrlVJrAERkPjAesOX4c8Xx31n2PXZsW0p7ezM7P32dzx//16znFgsd4m4Zjt/tFbKVpvOJZK65uH6x8x1eNnWekZO1kvB1u8FLGIXnsoWA0vGzG5ZZJ72bBtO5HSu5Ia8d/yASea0U9dBZOENEpgJTAYYOHWp7oiOO+YPtsYFR0RP2ehdTyibuFtYVcgSbV2s0tjDrpK3kdqzkhrwOhBrdQzsFFZRSdymlRiulRvez0oqvEPjGnZ5e/oEl67lgrPs9Wb0g9ej7bV7gG/yZp5gWtElstbMTV6MxgVknbSW3M5hj2cBLtNHKDjbkzA157fjrSRQ07LMNNjm54ODhkxwZFBVmAF8ATgTesjHeFXE3H8n26OsnnzbHOfX5tbS2KzZ91spX/rnO1LhLl2/kuEWrOebp1Ty4bkf+ATbHaLylyke9QCtO2ixWmsN4HepZBowQkeHARuA8IBqSfn5R0dUx263/TseMuFuYCEPz6lSZ646WNgZ2LzPsAJWJnc1wYdpAZ4eUvMPFf/kxVX2MK4By5RjCmPyFrnkAL2P+blYHpWM28eyp41dKxUXkUuBpEuWc9yilVno5Z+T4xp1A5/yE3frvKGM6PumhmmOqzLVfhfl/FnY2w7mxgc7P1WkmTuUdwpj8NcJuJdBnfEI3eiZLcavojrFonBvVQXbxvI5fKfV34O9ez2OWKGj6HEFie38L8B6JeNl2oM7uBctjtjT5N5f34dD7uyaf+1cIH5zdw641hpjeGJNNtMtiCz8jUmWuVrCzGc7smBnnp/1O8+wE/LJjRpTtoxXrGXJU1xxRSt7hJ8/9zFWbwka+SqBsTwTv8zj/Yk7HSj6b47eLmQ5b+Yjm1lQHREHT5zAS8bBTgAOBwwFHae9xxt2verfZSy5v2ev+tlivHn2tkCpztYKdzXBh2EBnZtVu5PRhn7yDxhivV/JuhECLzvE70fTxk4uTP+8At5CIkxU6QT76gr0yVztyEUFJTKTjRJRNO/1gcUO+I3DHf/q58QaSGtH9DlzK6efqEjqAcUAcqCWx9dltNit3QzWFTkrGeum4zglfO5vhvJCYeCC+0FLs3K4oW7q8g8Z/PmOb09DRZgiB4yfp9MPI6efGAxNrW2RznN3wTbGglOKHr2/iyU2NrD3LXAP49CqcTOw8JXixgc5qwtSuKFu6vMNVz/+3pTk19vgX97iSM5ih9u2rCoPjDzXbd5LzKSTKKp5GxNetoOn+q6CkBCkppXLKr4n1Hxa0Wa6RHl83S3oVTllJyFsZmsCJKJtb8g5z4vNCW9bpBzMMQnvZksVehEALx2MlyVe147amj68qnj5Q0quO6isfRipraF2xiL0Lb6Lq+3c5vu4JHUJu+2FfmMM56fF1s6RX4Zh9SnCVnu5WhdgVZXObqJR1ZsNuuWeQpbgpCs7x56vaiaSmj1kmPAgG5ZdWKOmV9lcZK0di1v9ETsij1tnUN25Lk9+NfzDp8fWXTjnQ1Bg7Twmu0LPWcs9ZM7gtypZOaHv3eoAZ4bcQ0ekWFUrH76TWPipVO17Rv0JcKbdUzXvY+/DP6f5d66nlfBLNf3t1g+lrrTzAXSVPp5U7vuLRRjUv0b17w8kM1XkbUCgdfxRq7V1lwADT+vod9Kww/NjqxqreBk8IKt7Knt9MpttXLyM2KIDQRshIf0p49IT96dstlP9sQoHu3RtKujiXUP4FF92qvSHjmXHe+cHYAaj2dj67cyplR59B+dFnBmZHmAirjHUYKZTevUHgYrOYzZkr/EwCdfzJGn5NiGhd/jdaVyyifdcWWl5+iNjgw+h+4a1Bm5UfDzV8/ODBdTtCK9SWWQV03w/mcM2S/zE812qZ6APxhUVb2ZOJk5yBiLyulBpt9vygV/yu57fTq3aGHvQDysp75x8UNuw0aDFQ+bRD+ZjxlI8Z78q10mlrbGTDRRch5eW0NzXR/8orqR471r0Jfv+8K3o9QXHd25tD6/hLy0s7NmwNPnJoVqdvp0w06pU9USVox+86Vqt26tfOpblpY7gE2zxu0BIEJVVVDJs/HyktpWXDBuqnTXPX8UccOwqdflFabs5NhKVMVJOfUDp+3T+38JCSEihJrBrbd++m4tCQJY3nvRXoE8N1h/cPbG638LJMVOMuoXT8ftXa6xuMv7Q2NFA/bRota9cycNasoM0JFdesaPBdoVNTvITS8ftFQW/mCiFldXUMX7CAlvp61k+YQM1JJwVtkmU+bY5z/ssf8fSXhrt2TaVUIAqdmuKlqB2/xt6Gr/4V1jcytTc3U9It0TwiVl1NSVWV5Wt4iskwT0q+wQ2+vfQjVje2uKbQqdGYRTv+IsftTlrZaF61is0zZ0JJCSoeZ8B10dzdmZJvcAOnewOsSjHb4TsX3sKc+37q6Rwa/9GOX+OIQ/6yq8sTw0CDYp3KkSMZNn++t8b4UMtvpz2jV3jt9Ct37Pb0+prg0I5f4wgv2jCmYyQpkZUvPAqY6AnsoHons4nKxGG9mHZI3y7nXbp8I09uauzoqTt+cDgb33znwluCNkETAI4cv4icDVwPfA4Yo5RannZsOjAFaAOmKaWetnLtKDRFL1aMVvnpbFrSVUa4pKydujHbvTSrAy9vRmblG4LuqavR5MLpiv8d4BtAp/IYETkMOI9En/CBwDMicrBSKrdsYxpREWrr7c6G2Uhhx7G2t4bf6aXaK7YpuPyQvo520gbdU9cJmd+DpvBw5PiVUu8BiHSp8hgPzFdKNQNrRWQ1MAZ4xey1oyDU9tRDOlLmNqmnhZKydvqN2uHbvOntFRtb2xj1j9WOHH87ytWeumZxqodv9D1M92guTXB45bkGAUvT3tcnP9NostLWsm8B0d5awuZlfbqc41VryPT2io2t7Y4lFFxR87TRecusHv6EwRfTfXPX/MkRwPMAD75NDfAhQNm3Oo5/NqAn8+p/Z2mufMyJz8t6rJjbM3pJXscvIs+AocTntUqpx7INM/gsFA+7OncQHEaxf6t41Royvb1iKiHrNYff+GjWY7WxGC/uv3/He6OuZpcZ9LA0o4f/nTRHbpX0m4Uf2vtaxM0b8jp+pdSXbVy3Hkhf8gwGNtm4juu4lTsoxth+Nvxs0O5Ga0gj0tsrphKyZw2qMQpj+kKmk8/X1SyFn3r4Wns/ungV6nkcmCcit5FI7o4AXrNyAa90dJzkDnRM3xivVuG5cNIa0vB6dE3INrUpupdmOP55b3V+HzIpaKt6+FGZS+MuTss5vw78GugHPCkibyqlTlVKrRSRBcC7QBy4JEtFz2ayaPJrHZ3o4NUqPBtetIbMrM8Pg4TC4WvWWDrfjh6+XfycS+M+Tqt6HgEeyXJsJjAz1/inHiqtAzj93Hgo4v8aZ7i9Cjecw6PWkLbbK9rcLfxJtftNV5zq4c8AFgPlwB1ArmcZN7X3dXWQ/wQeu9DtFwsDs6twp/mA0LWG/P3zeU/JXLm3NTYSq6lx3RQnevhvkojFvgx8BFxIsrrHg7kycas6SGOewB0/HrRfzIXW4HcfK6twp/kAr1pD+okXTt8Kh3/4ISsPPLDTZ6uAo5OvhwBrgWagmw/25KsOylXuCbrk0w5hcPy+onMH7mNlFe53PkBjjiNIhHdagPdIlOVtx7iO222cVgfpkk/rFNy/Ol2n7z92VuF+5AP8wqjGPmocBkwATgEOJKG14n5VvjG6Osh/Cs7xR0XjxyyXxV9jF62WxvSgjNmlYyzP1ef+nb7ssvOiKscSduWbs+ykjbrTT3Fx8ucd4BbAD/FpXR0UDJFy/GZW81HQ+LGCVadvdwz4s7Xaq6ocS5hIyEIWFVJDmeh9NwQvVEizJYP3xEupKrXWFGZPW1nWY+NI1F7XAn49h7lZHaQxT6Qcf6Gt5ouR0FXl5MAvFdK2xkY2XHQRUl7OsHldE5nZ2lTe/nQ3Ns/8RUdXs34/+hHVYw264HSZ65uGxxaZtLfxk8aOmPzTv3jC0Y5dM9VButzTfSLl+Cu6D2bw8El5zzNzjlW0RIM7+FGVY6cncJCUVFUxbP58pNT4n6OUGN9M7HQ1S83FIYdYtjOF3zF5Xe7pPpFy/G6SXtZZ3ePwrGWdWqYhevjVRzgX6av49qYm+l95ZdbVuJSUQBbn7jZuzNXe1p43Jm/U2ctu714/xOCKjaL1alEv69y5agMLj5rI6YvvoO74ozybx+yGq+0Te1prk1jgpK/iWzZsoH7atJxhmNaGBuqnTWP4ggWe29ba4GzP5I3HXudrTF6LwblP0Tr+qPPmzLnUnTAq6/Ep8SVZj+1tivH3Rz9nap4gBNhS+Kn66bY96Svr9t27qTg0d/VSWV1dVqff3txMSTf3tlKV1Tmrzndrx65ZdLmn+4S/H56mC1tfe5fKuj5UDepva3xFpfnyw5JeA5DKZEWJzxuuUjedmmufottXfsjehf46HKf2tDY0sPacc1g/aRI148ZlPa+9uTnndZpXrbJlb665Pulrr6XiZwPsJ7sqd+y2PEa1K9rb2gF0uaeLRH7FnyrxPHn8x0Gb4htv3jyXL865htd+8hvf5rSz4crpij1su3yt2pNaxbfU17N+wgRqTjrJ8LxsydsUlSNHWrY1G6knh+OffpoNU6dmrQRae845tKxdy52bf+3a3N+alv/vNTMPsG75Gl3u6QGRd/ypEs9i4aO/v0zfow+lota/MiO7G66shIlUvBUpNa4xD9suXzP2pIdnYtXVWUsyAaQse229V8R69cqZT9h3bGnWc6xip5m9m2Jwmn1E3vHn2rDlVL4hjCWc21b8m4Z/vsHTr7zN9nfWsPOD9Xxp3v9QvX/nuK1byV+zG66MErtmV8ipOaou/VPXYzZuOum29K8QV6t8zNrTvGoVm2fO7KixH3BdcZcjut3MXuOMyDt+yF63X4gbvkZNn8So6Ynf98XJMzl48pldnD7kT/6axY0NV/lWyKk5uoxzYZevnU1Y2bBij50a+zCyp62Mqpi1neBGsXy3m9lrnFEQjj8bTuUbtu+ECVPjzLsrnF/TCfdcm/VYZV0fJOY8d+90w5WZFXK2Odza5WulzDTXE4Kfu46t7ANwc2wmd208Ov9JGay87mtdPguimb0mO2HwaFnbL4aB7REtTT/yqgt8Tf4a4XTFHoT2fq4nBD/tsboPwK2xXmHUzP60/arp5sLiRGOdwB1/qv0iwOjRo1W/A91LJhUzfiZ/sxElXR4n/Oxri+lR2WJ53J62sqwraqv7ANwa6xVGzezbDO6xlVRY1tevpMIdI4uIwB1/FDj93K4KiL17EtoQEMDTZ1yeNfn75CP2dVqsUAjdssxgx+kDeWPnqd28LWvXMnDWLEvXdjLWC8w2s9edtPwhvJ7LBcy0WXz2sf1sVf2EPQR06pO3ZU3+Nu/1v3xQYx2z+wDcHusFtpvZazzBkeMXkVuBr5Lo2PYhcJFSakfy2HRgCtAGTFNKPe3QVsuY0eM55MhbQl3104My2/r6uZK/btL+2U5KugcfWspH2CQgcmFlH4CbY7PhZsJYEzxOV/yLgelKqbiIzAKmA1eLyGHAeSQ6uA0EnhGRg5VShdGqyEfyddLKpcnjB63vvEDrKwvo/t3fBWqHGazqDvWvEEfloE505DP3AQx/7DHTY9N1fRSY2kOQ6dgPeOSRztcMYcJYYx9Hjl8plV58vRRIdXgYD8xXSjUDa0VkNTAGeMXJfJpwkarPr/6Z7w9ztrAquWB249ecLE2wnOjIu7UPoLRXL1MOOtOxZxLGhLHGPm7WUk0Gnkq+HgR8lHasPvlZF0RkqogsF5HlW7duddEcjZek1+c70dBp3/0pu2/+Km1b1hkeb37hvpzjW157jB3fHUTjTWfQeNMZfHbfT/LOmbphdfvKNDsmmyalI/+7s2fzybpw/21LSUlHI5j23cZiamZF5zThJ++/WBF5BjDScb1WKfVY8pxrSbTrfCA1zOB8w2dmpdRdwF2QKOc0YbMmYJRSSGkZ1Vc8ZPsa7Ts2I926U1Ldp0Pp0ijs0vLyQzlLQa1WDpnZUJba8OVU7iFqOvLplUCHLFvW5bjZhPHhNz4KQG3jdl6cdZF5A7I0s9e4T17Hr5T6cq7jIjIJOBM4WSmVctz1QHoKfzCwya6RXrLug9tyVv1ouiLivLWh2bBLzTVPOp4rhdUNZU7lHqKmI5/u2DOxkzDeVtO74yaQycoDDnBmrMYRTqt6TgOuBv5TKfVZ2qHHgXkichuJ5O4I4DUnc3nF8aetsD02vb4/qLp+J1U/RqjmPey++Sy6f/e3lpQ4ncznl/KmnQ1ldlf/e3fvpbyyPG+LQie4WWmT6dgz0aJzhYVTT/UboBuwOLkKXKqU+r5SaqWILADeJRECuqTQK3qCquvPV/VjxEKMjbUrv2wXv+dzsqHM6ur/43c3eq4j72alTcqxD7nzTmK9uqpmForonCaB06qeg3IcmwnMdHJ9jX841dWxWiPvhvJmmPFDR97NShvt2IuL0O3c7d0z/LtiCxGnujpWa+SLRcfHLJuW5E5sDhy7zfDzsEkzaKJB6Bx/rji5kWaOxh2c6upYrZEvFh0fr7EqzZDKCwx/+GGfLNSEkdA5fk20cTtZGyWZBb+xU2mTygtoipuCc/z1a+caduRqbd1JWVn49WSijBfJWqshpCjS1mKvPNZOpU16XkBTvBSc48+GFadfv3YuzU0bbfXpLVa8StZaDSHlonXFIlpeedj1G4cdDXlI6PEDbF7Wx968NhOyrQ0NSEUFpQbVO5rioKAcf0qGOVsPXo13eJ2sdSWE5PDGkQ2rGvKHr1njug1WKKtLbMRP5QVGvPii4XlakbNwKSjHb0aGWWNddTIzzl49/W9dzvEyWZsvhGQ2D+DXRrEwYyUvoBU5C5dIOX4/Sj3NNG+JOrl2oBo1Js+Mszth9y/PpfzEiabDQWZCSGbzAH5tFAszVvICWpGzcImU408v9fSqtFM/NXQlM87uBKs5ADMhJLN5gELcKGYVq3kBL/YJ1MZirlxHY59IOX5NsKTi7GVHnGj7GvnUNjOxEkLKlwdovOmMUGwUq43F2NZmTcGkba8iVuFcHM8qTls4ajG2cKIdv8YU6XF2J7iptpmOmVJSr+a2yov779/pfe8l+eOXm1/v2/E6V9ezbDt87eBFC0dNOIis49fSDv4Rdl2dsNvnJmarm9Kdtl20ImfhElnHbyTtoCUdvCEzzl592YNIpf0GJW5TLLo/VjbINa9aReXIkY7mcyrcpmP54SWyjt8I/RTgDWHX1Qm7fW5g9anGqdO3g47nR4eCcvzZBN78eBLordUgCpr+ASRW0/HqqSblrIPeVKbxl4Jy/F7w1EP6KypWtk8Mz9087E81OqwTLYpCrcnualyv4oNHNe+h8fqTadv4ftCmeEbQTxNOWXnAAV0qlTThpiiWs0H0wtU4x+/WjOn46Ywzd1Ib7Z72Gjt7C1LjNNFDe0RNJ6zq+DhFxVuR0rKun7tcomm1WXoxkO609Yq9uNCOX9MJqzo+Tkg596pL/9TlmNlkZpji8GFAV9ZozKAdvyYwUs7diLAnMzWaKFMUyV2NO9iNe7fv3ELjTWd0+bx8zHh6/XGjU7MKDrvfc9STxBr/EKX8i+fmQ0S2AuvTPuoLfBKQOVEiFN9Tr/t2HO33nDsu7PW6hdND8T2FHP0dmSNs39P+Sql+Zk8OVagn03ARWa6UGh2UPVEhLN9T7/t3+r6KsPJ7h+V7CjP6OzJH1L8nHerRaDSaIkM7fk2U2Ry0ARpNFAlVqMeArv3zNEYUzfe0fWJPJxnMovmeHKC/I3NE+nsKVXJXE21637+zARiQ90QHOHT8Go2G8K/4NRFi+8SedWbOc3CD0KEdjcYF9Ipfo9FoioxQJ3dF5EoRUSLSN/leROQOEVktIm+JyOeDtjFIRORWEXk/+V08IiK90o5NT35PH4jIqUHaGTQiclrye1gtIj8N2p6wICJDROR5EXlPRFaKyI+Sn/cRkcUi8u/kf3sHbWvQiEhMRN4QkSeS74eLyKvJ7+ghESkP2kYrhNbxi8gQ4BRgQ9rHpwMjkj9Tgd8HYFqYWAwcoZQ6ElgFTAcQkcOA84DDgdOA34lIUcooJn/v35L42zkMOD/5/WggDlyhlPoccBxwSfK7+SnwrFJqBPBs8n2x8yPgvbT3s4DZye9oOzAlEKtsElrHD8wGrgLSY1HjgftUgqVALxHZLxDrQoBSapFSKtVebCkwOPl6PDBfKdWslFoLrAbGBGFjCBgDrFZKrVFKtQDzSXw/RY9S6mOl1L+SrxtJOLZBJL6fucnT5gJfC8bCcCAig4EzgDnJ9wKcBDycPCVy31EoHb+InAVsVEqtyDg0CPgo7X198jMNTAaeSr7W39M+9HdhAhEZBvwH8CowQCn1MSRuDkD/4CwLBb8isQhtT76vBXakLboi9zcVWFWPiDwDGFWBXAtcA4wzGmbwWUFnp3N9T0qpx5LnXEvisf2B1DCD8wv6e8qB/i7yICLVwF+BHyuldiUWtBoAETkT2KKUel1ETkx9bHBqpP6mAnP8SqkvG30uIiOB4cCK5B/gYOBfIjKGxJ11SNrpg4FNHpsaKNm+pxQiMgk4EzhZ7SvRKrrvKQf6u8iBiJSRcPoPKKUWJj/eLCL7KaU+ToZStwRnYeCMBc4Ska8AFUAPEk8AvUSkNLnqj9zfVOhCPUqpt5VS/ZVSwzzd/CsAAAEjSURBVJRSw0j8w/28UqoBeBy4MFndcxywM/VIWoyIyGnA1cBZSqnP0g49DpwnIt1EZDiJZPhrQdgYApYBI5JVGOUkkt6PB2xTKEjGqu8G3lNK3ZZ26HFgUvL1JOAxv20LC0qp6UqpwUlfdB7wnFLqW8DzwDeTp0XuO4raBq6/A18hkaz8DLgoWHMC5zdAN2Bx8uloqVLq+0qplSKyAHiXRAjoEqWU9YaqBYBSKi4ilwJPAzHgHqXUyoDNCgtjgYnA2yLyZvKza4BbgAUiMoVEVd3ZAdkXZq4G5ovIjcAbJG6gkUFv4NJoNJoiI3ShHo1Go9F4i3b8Go1GU2Rox6/RaDRFhnb8Go1GU2Rox6/RaDRFhnb8Go1GU2Rox6/RaDRFxv8HWeOMvP2MODwAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0789 | test accuracy: 0.97\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.1067 | test accuracy: 0.97\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0277 | test accuracy: 0.97\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0251 | test accuracy: 0.98\n" ] }, { "data": { "image/png": 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5qOKIbsfG2GzD62Cq31k5jYP6+dBqdFBDrbhFDb8XbG0EoFfLZjZVu99A3S1BZ8X4/fwfrpzp8xMUpbhRw+8hU/7425zn3a4IlMRsv/HjFTw74nzGzv5l1j2NrVwTFE5dNYriFWr4lcgwoT2hMbpw/G15i93CvGdxVIz+ZXXfZtjorwJw2LeO4MiLjgm0P4p3qOGPCM3LGwoe9A0LjYP67eDeOXr6f+W8PsiCuCCxYqgfjD8OWMvI6TO4H9e+fLPX3VRCgBp+n3h/ZSOXTZsPQGt7Jx83NHHBaRc6igF0tLWXrNEHe7pDkCiIO/LBG5l77X0+9Sic3N80zfK1VlYdmxo2cvdxt1HTv5Zz7j6PAUM1MbdYUMPvE/sN6c2rNyekimbMWcEri9dkjAFY8fvHKiuynxw0CNascdzPYiOIgrhi9dnfseRe6gbUsWjWu0yf+ABXz7ox6C4pHqGGvwD87o1lXHfqvv403pDcj1ey6eQVhlTOvpviLy8IoiDOC6MfRn963YA6APYfcyD7j9ENMIsJNfw+s66plQ8/b+LwvQYE3RVf2Z/gjT7YK4jrRY6VVIEJwp+e8vcXPfX1zlbFgwZtn1gVGWr4feapOSs4+9BdkIBn5MWE1VTNXAVxD5Uf7kfXHBNVf3r64BFaCQenrtAidqGq4feZx95YzoOXHBLY87tW0RbDjvdhTtV0ilt/+t8ffjVw91ALW22tIkI7UJQApZXvVmCWrm2mNd7BvoOzBxp7UeHY5WDlvlnAK8DThMPop3b7OgZ4N8s1uWScU6maNYPD8DbWuazu2znPp/vT16340nb7QRt9JxRjQDwq6IzfR3YfWMv820/Mec2UmQ9vk3ywRVVvOOs3DnsWDFY3ackl4xzVVM0+g7PrC21t3kpldSVlsTJWvruC2v61ttv/ctkXkXEPpaMrhGDQGX/QODH6bu4LEKubtGQzX0HvXeCGTQ0bs55b/f4qbj/0Zn527E944gfTOf/+CRmvu/v42/n7w69mPDd94gOO+pVvJRImbK8Q6usDz3YLKzrjt8Kzl0bS0IYNq5u0ZJNxzpSqecpr/0NZzJ7wcxDZPHcsuTfruWGj9+DH83+at41cWT9O3EOQeyXSlTCmnOakiIOzblHDbwU1+p7gdpOW9FRNu/Siginlox3d6wUpH74b7j97StaMHyfuIci9EumK3ZRTPwYKO5ITSnbU8BcLEangDWqTlk20F+hJ3Un34bvhqEuOz5rxk809lI9cK5Gu2E059bM2QQPD7lAff7HQ0ADGWPsJkDHAccCtwD2B9qRwpHz4bsmV8bPLiN0ctWlnJXLHknu59pX/2jYA5SM1UNx/9hS+XPaFo/4p/qAzfr8Z90T3Y4+fW/h+eMgkYDZQScJnb6eYP9smLVd99gc21fd327VQYtWHnw+nGT+56OzotLwSSU85ffzKaXmv91vr58H44+rycYjO+B3y/spGjrn9ZY65/WX+fdJs+n/n2fw3PXtp5I1+ekrmo8D3PWrXC6Pf+PEKflt9NA2vv+NBj8JF8/rmnBk/TrG6EtnavJXOjk7A+gDktjbBCk5cPlZqSYodnfE7JJP6Zl6KIEicLSWzR2A92k4xVvSmqO1Xy7Wv5N6HwAlWVyJVtVXbfh9y4K7c+MZPcl5vOg3GGMe1CX5lEFmtJSl21PB7wDmH7co5h+2a+WRqhl8VvdzzTFhNySw0pbr5SliRMuG2UTdRVVeFiNheqdgJDD8Wf9ayuyfME5dCooa/UBRotn/U8uWs6+jIec1iF+27Tcn0i6hW9BYzbuIadjKI7Lh7wjpxKTRq+IuMfEbfCwqdkplPjTPoit5qqjS90GPsBoYzST9cnOG6sE5cCo0nhl9EHgZOBdYaY/ZPHusHPAUMBZYB5xhjNnjxvGIi0xaN6/7X3yyFLwcMYMCXzoNtXRU/nWAni6f3XrtyYcvfsp4PYvOVdLzMKvFKIz9yVbZdsJtBZIegaknChFcz/mnAfcAjaceuB142xtwpItcnP//Io+cVDelBYoDGLf4XGh391lvdjv3t0EMtDwbZUjLt4GXqpp3NV8KOV6sHvzd28XNg8UK0LheWJy6DBnn63DDhieE3xrwmIkO7HD6DRMYUwHTgVaJo+J+9NO8lXs7ae/cMZleoo996i8W7777jwQgKXOXafCUK2F09ZFshWPWRX1w+ztEqw8+BZfX7q3j0soccB4bzYWniEnCho9/46eMfZIxZDWCMWS0iGQXURWQiMBFg112zZMYEiYWgrKPUTi9IzxjKIdHc0dTEigsvRCor6WxpYeA111B7eLh2oFK8xe/iKT93DHNS8Hb38bcD0XRrBUHgwV1jzFRgKsCoUaMiP8z6urF6NvIMTmU1NQx98kmkvJy2FStYeeWVaviLHD995OD/wGKXQu9XHHX8NPxrRGSn5Gx/J2Ctj88KBWHdWF3KyqAskd/e2dxM1T77BNyj3FjdU9dzvnssNK6zf1/v/vA/4SkD8ttHDs4Hlp8d+xMu//0PqennfZ+2bNjMlsYtnqw+il0Owk/DPxMYTyJwPh74g4/P8gaXuvv5NlYPIoMnRXtDAyuvvJK2Tz9l57vusnZTQIqfgVXgOjH6bu7zCb995G4Gluv++mNP+5JOz7419Oxb41l7xZyi61U65xMkArkDRGQlCTmMO4EZIjIBWAGc7cWzfMVlkdU3/n1X1jZm/2MJLBYAVNTXM2zGDNpWrmT5uHHUHXdc/psaGrb/XqBAr18VuEFsvhIUdn3kdjOJ/B5YvGLLoN70XGP/3/SWQcVRZZ8Lr7J6simPHZ/leFHSv7YH/WutFX8XMhbQ2dpKWY9Ev2K1tZTVOJgVFWj276QC96HyEMQrxmXQKA2ZC6gr1ST0dzK5M3Jl+nilNuo3j6+8P+guhJbAg7uliC+xgFSGz2GTu51q/fhj1kyeDGVlmHicQTdnCYTlUg6959juxza2wOXeefCCrsD1nJC5gLqSy3+t1cjFjRr+AMgXC0jhVUyg+oADGPrkk476mpM+1Z42F3QFbjaumL+K+etb6DDww70HcO7QPtZvzrQSSBHiFUFqUPCqkjiqZHr/Ygj6quEPgMfeWM6DlxyS97ogYwJB4KQCt1fDOhhi80E2s3fuGzUYgKb2Dkb+3xJ7hj8XIV8RhAknlcJ+VRcXw0pIDb9FvJp9L13bTGu8g30H23NnBFIfECC5KnAfqjhixwN2qywdGtym9k76VYZL2cWpSybl3w+Kcd/7FT0bN/PgI9dbut5JpbDfshVRRg2/RezOvt9f2ch+Q7ob990H1jL/9hNtPTuo+oB/nzS7oCmnYWfk//2LB0bbXV7kIZcrKJ0sbqGouhx6Nm7OeHxr89YdNn1J4aRS2M/q4qiju1Y44HdvLOO8I4bmvCaT0XdK/7oefPyLU/LGBAD+tuAOz5571di9OXv0LrkvCsEG7oXiw1P24sZ3GmhNbkFYUErELbT6/VUZj9vd6N3pPaWCzvhtEtbq3BQD2psd3de/ranbMVvupYCKvWwpKDqtzE1SVxGjrdPQYXGccxUUzkS21UGIg8R2GTZ6j4zHnVQKO60ujrqktRXU8NvEakZOkCyek1uhMlO8YnEGd46tAa6hoXBqnk5XGDmMvhUj/R+zP+F7e/WnZ7m1hXIqKOw7jeu6DwpFNBg4qRR2U11sJTaQL9sp7Jk/avhtYjUjJ8x03QMgG7YHOBuz/l4N6xxp8vtRgbto41YWN7YyZ8xXc2buzDvxq54/O4Xnq4OQuoaat1Zy63MncO3YUwC4mz/lvcdJpbCb6mIvYgNhz/xRw28Dpxk5UeW8w3ezd4MNiYcpu5yR/WSBYwY7V5dTWSa0d5rAMnesDDzFQG1Vm+17nFQKu6kuDpvyqB+o4beBk4wcLym0yJvtAS5d5O6xb1q/z+MKYLv0rYyxZ10le73wEZvjnd5n7ljAl4EnosVjQeO3pHUYUMMfIUJZ0JVL5sEqHlcA22V2QzOrWuIsOXVvGts7OPKlpZy0Uy09PBaKy0XBB56QuII2tVTSq9r+KsAvrMYGoh4AVsMfAo65/WXA3iy+1Aq6/MQAfStixMrEduaOFaz47+0OPJ7HBALitudP6Hbs7nPz+/39wmpsIOrFYWr4Q4DdWbyblNIg9wQIKyfU1/LE8o0cMfsTWjtNzswduwbXauD4uSMT8ZR+PcpZfMpenrQZBHZnwvFl79Dy6HVQVoaUlVM94Vf+dzIHVmMDUS8OU8OfTlVv15r8brA6i3eTUto1o6dxS7vtNrxm+GGT4ZM0VdGlS7f92j8W47XdbAaZbVImwrTD8hSq4czg+hE4ttrmYbOWuF4R2JWEsDsTLutTT+01zyDVdbS/M4udjoXNHRXUxIL/u8xF1APAavjTSd+w3OVuXGBvdm1nFu9lSmnvnuHeoGRdR0fQXdiGEyPuR+DYapterAiy5aJny2O3MhPe+fD0+EI50EZH23pWLqqkvE8dU1cdbLufV+06Z9vvU1Yclvf6xTd/3bJOUCaiHgBWw5+N9EEghc1App1grNVZfNhSSktJz8eJEc8UOD5jSC9X/RAR7hs1mPStaowx3f52gkhPdToTjlUatj5zG3xnhs899IbOjk5f9zT2GzX8HrB0bTO7D8z95edz41idxQedUtqVq8buHY7sogLgJPvn6IE1jNkpMTu04r93SqYJg50B6qjly22trq7aNfNxNzPhvWdHw+gD3H7ozaHfejIXavg9IJ/Rz+fG8WsW70Ugd11TK/9+y0t89POTMxqXXAOa1ecvGzeOzpYWBl5zDbWHh2AbxSw4yf4pZEpoV+wMUF641NzIJPhNR1MTKy68EKmspLOlBYZD9cZmWvo462MUtp7MhRr+ApDPjePXLN6LvP98fc81oFl9/tDHH6dtxQpWXnllqA2/neyfMOBXemo2gtyEPeXXT/f1pxOrq2PYM89s+zwcYFPyJ0n5+i+pG3ssrZ2G84f24cq9B7iKA4QZNfwFIAz6Pk7z/vP13Wp2Ub7ndzY3U7XPPrb7V0isZv8A8MR7if+ee4B/HcpDoQcoq6mQzW+84fkAv/TMMxl4zTVsHux8g5l4vwG8fkJmddBiQw2/z4QhGOs0799K363o+eR7/qfnnEPbp5+y81132eqfkhurRmzvpzexdqtNwbwsPn4rrL37bs8N/5B772XllVcy9fDnPW23WFHD7zNhCMY6zfu30ncrA1q+5w+bMYO2lStZPm4cdccdZ6uPUcRKEVghK3PXbi2sKJ4fKzuvVozDb+8ycKzY/mvPsja+M+Sfrp8RBtTw2yHgAq+czPoQ2jIH6C4D6BmDFxZ3P1kZgzH+ulisuLpitbWU1dT42g969w9co8ZqEVhYK3O9oG7MGM/bXD5+vO8rxi2dlb62X0jU8NshPbffC3EyL8li9H27zyJW3EXLxo3DxOMMutln7ROrapRW98HNwbtAplasFoEFLRPtJw233JJ1Zdc1+8Zqptew557LuWJ02q5Tgt7MPh9q+J0S5tl/iLDiLhr6eO7djKJGR0s73wcyDTNWi8CClonOh1NVzc3tsZwru7KaGoY++SRSXm4r06tyyBD2fO01z9vtyvcOupPdn+8eR1i8++622woSNfxO6VrZG7YVgOIvqaydJDNIzPJvB2LAp0Ar0KPLbVaLwIKWic4knrbzKb2IVSbiAQ99cZDltlree481kydDWVnelZ2UlUFZ4j29zPTq2u5ujz7qqB2n94UNNfxeoSsA3/lw+c/p6Nhs655YrIZ9drsGsF6dmiESkpf9gV8CbcAHwEpgA1Df5TqrRWB+ykQDiXhHDrqKp2199qfEzrzD0aOqDziAoU8+afn69oYGVl55peeZXunt7j1vnqM2YnV1nvUnSHyfQojISSLykYgsEZHirIaAxApg3BOJASBEXDF/FYfNWsIhf1nCE8s22r7//tn/4qYZ7/rQM/vYNfpd7/FT8G0/YBxwAnAviQKhTEK9J9TX0onhiNmf5Ny83co1mVi0cSvHvpxQN21q72CPP36U+cI88Y6yPoOQ6qSRi1UiscLNESvq6xk2YwbDnnuOhltu8aXdUsfXb1NEYsCvSfx7WAnME5GZxpj3/XxuoHgg7pYNuxIMXui2h6H4LCrzefHTAAAd1UlEQVRclvxZBNxJwuXTFatFYJYLxbrgtQy0ad3M1mduo+clv3bVjlU6W1sp65FwkHmZ6dW13VLH72F8NLDEGLMUQESeBM4Aitfw+4hdCQbLRmDcExkHp0IUn31ZUTz/CMcAcaA/idlOEHgpA23i7Wy+7yJ6nHYVscH7AP6nwqaMM0CsTx/2ePFFT9pt/fjjHeIMw2ZERxDOD/w2/IOBz9I+rwQO9fmZ4cMH/78VCQa3RsDL4rOjDrqedZX2/aP9Y9FJZZwVdAfwbv9g09nJlt9MpOLgU6g8+NSM1xQ6RdIKHU1NGf3wduMMxY7fhj9TqeYOYSoRmQhMBNh1Vxd14GHGY/ePVQmGMGwinuK1f965/UNV78z/Txyw+L3P+e+fv0S8vZP9D9yZq6+3XhyUzXB9WduHAc324yFhwKv9g9vn/5H2d2bRuWktbW8+RWzIfnD4jiE6r1IkvcSNayiMA5lf+G34VwLpzsohwOfpFxhjpgJTAUaNGlXY2vGIYlWCwe9NxIGEmygg2tviTLn7Je69/xvU1HZNnMxPNsN19PXTdrguY472E/a3vSwEXiiIDjpkPbHDj4Crcgf1/Uq9dIOUOZ/UhHEg8wu/Df88YE8RGQasAr5JIvmhdKmvhzUW5ZEf+2bmwxYDrn7LCB910PWsS9sf1ype7aO78O2V9OxZyXVX/Z6WLW1c/oNjOfgQ6+0WxHC1tEN14ba3tBo8Hr7D97ZjamcqV98KfqVeBkEYBzK/8NXwG2PiInIF8BcSSQ4PG2OcpEkXD1aNfhbsBFxtyQg7wInPHnZMq0woQ1ozNK93mXx9saaJjz9s4Jk/XsrmzW1cfP50Zs66wpYYne+G6/kPd/ycRaZ5fWucc9/8jBeOHsoXW+Oc8fflzDvxq973x2NSKZJei+wF5XYppoEsF74n5xpj/gz82e/nlAq+qX0GVIDmRhmyd59qRhy0C7V1VdTWVdGnb0/Wr9tM/wHWM4X8Mlx28WNTdr/xK/USnLtd0vvkBCd/D1FKQEihlbtKAifBVgduHi85YORgfjXlFeLxDlq3xlm/bjN9+va0fL8rwzVokP3VW1X2f25hCsRbpWuKZDYpBiezd6dul659+sr3v295pWD17yFqujyZUMOv0PdR6zP9gVXCR2f38rE31unVq5pxFxzKheOmEY93ctV1JxCzYSitGq6MNDQk/ttFydOpy6YggfgslFV00tluf4CxmiLpdPbuxO3iJm3T1d9DxFDDHxImAbOBShKaL+6Fgf0hn2vGC99sJoGw2MChGa89/cwRnH7mCFvtp/Ajt9upy6aQ+/l+Wbtj9Xb96A07fG8c7q1aqtPZe6HdcKWU66+GPwQsBOYCb5KodruAzJK+OamMOdLWX1PZz/Y9ufAiJS6TQFjNpVM97adfOHXZeBaIfzy/rtLRGVx06d+bH9idvfsZP1BCZvj77/Ynxn4jbuuevr3h8amheg3bfAwcnPx9F9IkfTe2QJ9qa40kd9FaY3qxT+fPdji14fzuGUB23Dt28CIlrqzPoO0fCiwQ5pYgXTb5FDdzkf69+YHd2XspuV2CINB/UWO/EW8Atv0rLyu3/4e7oZEdBosoDgRZJX0v/8O2a/o+Ep1KUiuzO6sDT8X+x1Cx/zEe9s5ffHfZWJjROyX1vXmtY+Nk9l5KbpcgCNpCDsp/iT02RFASP13Sdw+yS/r6jR3fei7CkiIZBH7XTvhJ6nvzGp29h4+gDb8vRHEFYEXS12+88K1bmd01zO3rqH/r2nrSv3KLrXtise3P7x+L2dbkj2KOthOs5r+nX9excSPLzj03r4JmULN3P4rAiuXvIfwW0SVRWQHkk/QduHENa/vYXyANrLJexeqFb93K7M5J6iDAGfMm7vA5U+wiF17IRASKCx9+PtK/t1x7IEdp9m410aAY8vLtEnrDP+9vJ7Np49sM3fN77LHfjUF3xzfySfp+dOXeWbV7AE/F0uxuvtEwt2+aMT+Gyu8ds+3cJmDTG551LbTsfdzzrO1h3zAPbF3HR6983drFeXbNcoPVWXmUfO+lpL1jl9Ab/kOOVrWHQtJ98438OJ3BFxNOjL6b+9yy42CdmZ0P93/jFb8pFe0du4Te8HtBrhTRqMQACoGVzTeKATvCcOkEWrXssZunVAbrUk40yEXJW7yoxABy4tEG75k23+h5wd2etJ0Lp9lEuVJCcxlpp8Jw6fdtGzzG/t1RWzvgY4pmKaNFYNkpecMfKXze9KRy9BlUjj7D12dkwo9K3bVbDXs/vcm3GbobVVGlMEQpEF1oQm/4CxHcHfuNeOFcPk5UHVP3hYxc6ZF2ZvF+Vequ3Wp8q1COMv1jsR23wUvS9TvjhWm22544eAE1sXbb923uqGDqqoPzX5iBbH+HUQpEF5rQG/4DDpnKl2teprVlla/P6eryGTcx7sgNlHcASak6FgGp9Mi+b3T/H+VkFm83m6hYSA1OhYohvLbbbvQl/3cW39hEeR97m+04Mfq57rO6W9vwgCXCo0boDX9Vz2A2pHDq+y+KmIEH2J3FO8km8hKvqpbdELT7qOt3tuwHP6PnJfd3u86vbJ9SzKcPitAbfiXaWJnFhyGbyG2cIQwDh1eU6sqrlFDD7wPjJsZDnyI6sEocpzRaxeos3kk2kdeGNn22WzFiDBUjxti8P7pS0ukEvfJSCkO4rZNHWA0Q25WEzkYU3D1++5LtzOKdZBPZNbRNk8dSd1NuTRk3RFlKOoWfK6/L6r7NsNGJncgO+9YRHHnRMZ62r9gj9H+di+Z9h43r5tDZ2Urj+gUcdMTvbbdRqABxKdHv0UZyrRf8rgmwa2j9NPrpuHWTFDrQm46f31mfwf249mX/0imdCPCl7itFQm/49z/kf123EVSAuJjJ5yQqVE1AmPzRXrpJggj0+vmdbWrYyN3H3UZN/1rOufs8Bgz1Vng88gJ8BSZow78GHzT5ldLAjqE1rZuRHv5VboYhQF0o0rNvrKZR3rHkXuoG1LFo1rtMn/gAV88qXsHFKBCoYMeLT5XXB/n8TMz728m8/Ied+OT9n+a8Lt95xV/sGtrN913U7Vh82Ts03XYiTZPH0nzHaXSsXZa3nb6PNmYsCku5SdrefIqmn57ClkeutfQeUceqq6RuQKIeYP8xB7JuxZd+dkmxQNAz/tCh8YBoYNcfXXv1U92OlQ8dQe3NLyJS5joTJyi5i6Dp6mJ5MD6n2zVbm7dSWV1JWayMle+uoLZ/baG6p2ShJAy/nQBxVc8hDBk2Pm+bxbw3QBTwytCKJBe9Ec3EiQKr31/Fo5c9RFVdFSLC+fdPCLpLJU9J/KV7ESBWipcwBYiLkWGj9+DH89U1GiZKQ5RbKTjti15lywOXZTxnd8tEvynFgiU7hXhe3KeEi5KY8SuFxekM2g/Zg6bbTszbnpUAcde+1d7wR1f9CprANpRRQoErwy8iZwO3APsCo40x89PO3QBMADqAK40xf3HzLCUauMll90P2wEp7PY65wHbfOrc0UtYzXCsXxXt+Xg+bHaio1wyCa0IsxOvW1bMIOAt4Lf2giOwHfBMYDpwE3C8ikS2Rs5riWeq4zWUv6zMIqU7KAHsUbPWqvfS+VYwYo0a/RHBi9N3cVyhc/csyxnwAINLN73cG8KQxphX4VESWAKOBf7h5XlBoiqc1vCr59zrYqsHbwlFNFS1sdXSfUjj88vEPBtITelcmjwVCejpnba/htvV+VPLBGl6kWHqtDqlqk4XlW+VnBd0FxQJ5Db+IvARkqrC9yRjzh2y3ZTiWTXzEd9kGTeeMBl7LHpSSjIKi2CGv4TfGfM1BuyuBXdI+D4GM23zuINswatQoM3/+/EyXeSaZrIQXr9UhrbbXfMdpkd44RSk8bzONBUxFEMbyK3bmoKC7ZAu/XD0zgcdF5B5gZ2BPYK5PzyoIVqp5U3QPeShWsOoqar7jNEvplFbb63Hy9/JmEBXTDluW+e6x0Ohgm8Xe/eF//up9fwhHlk0LG3iLX3Ixc2hiFc9yPhN43ZvGC4TbdM4zgV8BXwH+JCILjTEnGmMWi8gM4H0gDlxujLEvlh1RjNlxhZJ3A3bFFj1O/p63DVrI+CmWHbZs4cTou7nPAm6ybG7NMiGzOyhU05dL+ScAfRmW1ejfKuFN63Sb1fMc8FyWc5OByW7aLxaisCOXXYT8mvzZ7nNNrNKLVrZhJeOn0DtsaYVs4cg1KHjRdhjRaajiiPUByi7Ehh7oaXt2Mn4KkRoaxO5bSmmhhl/xlb2f3uT5blJltf08bc9qxo+fqaFh0y9Sihs1/A6wunl72LkqPpdNtNu+b2tLjD8/v68PPcqPaWtBKqs9a6+zeQObf3le3gwiP1NDo+TWuWL+Kuavb6HDwA/3HsC5Q/vYb8Rm0Pjnz73C5q0Dcl4T9SybQqOG3wHFUsnrxOgDVFV3cNa5i/Je5+UA0Tb3D2x54DJiw0ZSd+OfPGkToKy2r6X2nKSaFtssftHGrSxubGXOmK/S1N7ByP9b4szw2wz+5jP6QWbZRHXAibThT1XkHnHSOwV9rlbyWqOq2rtErlypmYVItSzVHbbSif30LO7rVc3i5OeZd7Ht924svXX7fbEa9tntGt/6tZK32I0jKaeSvgyjjWbitFJOD9+eCdYHnFtjQKf99v3MCIq04deKXAVKNNUyADp7OXOxdXRs9rgnO9LCeqrou+1zFb1pYT117OTrcy0POA6MPvibERRpw69EDz9m54VOtVTsszhtBcDt/y/ntbGmrexzl3V3XjX92MrGbZ+30khtRpUZbwlqwPEC/ReiZKTx4xU8O+J8xs7+JfVHjPCsXT9n56rCmZ8Pl//c8gw89qNT6KgrvGpmR10VTyzbaDl+MIRDeYWb6aCdJlZTSS3iTcVITjINONV4m3HmF2r4HWBn8/aosnDydOqPGpn3OrsDhF+zc1XhtIYdt4tVo7/4vc/575+/RLy9k/0P3Jmrrx9jq0973fkCFc2tOxwbnmb0J52buW6juaU/v3j+r1TTl0O4jN9yNIJwEvfaen4mrARtMw04duIKQQaG1fA7oNhjC1/MfZ/q+n5ILP8+PVYHiK7km50PrBLL+f92Uy1N62aa7zidnpf8WgcJl7S3xZly90vce/83qKl1FkztavStUlu9PTvoIC7iIC5y1E5XrAZt3Qw4Qev9RGaz9b7FlRkXahbeMZ0Drzsv73WpAaJm8EBb7VuZnX90di82nN/bUkpkKtWy7c2naPrpKWx55FpXz7b6Ds2/+AZtC15w3EYxsPDtlfTsWcl1V/2ei741jQXzlgfdpZy8zTQe5D94iMP5PKm305WUFk8qaJvLIB/ERVzMm0zgDQYzynI/sgWGC0VkZvz5RM5UttkbPvvzmww4eB+q+uc3uAvvmM6RD97I3Gvvs9y+H4VQVlMtvXq26vxv54s1TXz8YQPP/PFSNm9u4+LzpzNz1hWZduXrxi/unGXbLeSGoGfZO/Yl2MBwZAy/UhjWvfMvGv72Nn/5x3tsWLSUxo+Wc+zjP6F2tx2zJOwMEOl4rbkfxLODfIew0btPNSMO2oXauipq66ro07cn69dtpv+A2rz3FtLoQ3D5/pmeEXRgWA2/sgMjbxjPyBsSew+8dtFk9rro1G5GH6wPEF0JshDKq2cXezFXKlj7wPQL8l57wMjB/GrKK8TjHbRujbN+3Wb69O1p6TkL5i3n4EN2y3reE3mINIKaZWd6RnpgWCjjuyzMeG8u1VA3BV5Fb/jDoqszbmI8cpr8Rz18U9ZzVgcIJVqkB2ut0KtXNeMuOJQLx00jHu/kqutOIGYhKQDglhtnMnPWFRnPeSYPkUZQs+xMz6imL98h826DVnFT4BUtS+SAsOjqbGjMHIcohk1acg0QSrRID9b++oFxlu45/cwRnH5m7lReY0w3v3/KLZSJnavLqSwT2jsNTe2d9KuMWXuBHDhNv3SbdumnKym1IrgFc/Ctkn1f80lmx4q2aFscC1jV1QlqZVCMm7Qo0SU9WOslmfz+udxCfStj7FlXyV4vfMTmeCcPjHavj+Uk/TJMAWEXDEobFNZMMtQXjeHv29udEQ3LykCxjom3s/ne86g85vySz67xivRgrZdkMvC53EKzG5pZ1RJnyal709jewZEvLeWknWrpYdGNlA27+f5BBYR9ZBBEKI8/H27dJaq4GR6s6NOHPaUyShr76RwwcjDLP11HPO7tFtmZDPzXTswu2W2AvhUxYmVCXUWMtk5Dh7f7+VgiW0DYb6zUG7ihaGb8in16UeFYk98Ofmwl6EdKpensZMv9E6i54reu2omyDn96sPbRGRMC68cJ9bU8sXwjR8z+hNZOw/f26k/P8sLPU4MICBfCvVT0hr8UdHWcMqV8tOVrnezW1YsK34ygHymVqcGk1LESrPWbMhGmHbZL3usmZVkFeLV5uls9HifYdS85CT4XveEvdl2dQmFnkIgqxZ6fr9jHDwG4fNipN3C6Oih6w2+V9JVBlPfRVYIlqr79YqNmkHcbmXgpAGcFO+4lp8FnNfxJdGWguMGPOIbinEwVrV65f/zGjnvJaTWyGn6HeJn3n0tgrhgKvIqNKAdvY7Ea37dCVDJj1Rdvx73kNPisFsUhhcr71wIve9r8Sm66bnpuZ0euTGza1MLE8Y/yu6cnUF7urLq2vbaHM03+3v0dPS8I7PrirbqXnAafi8rwuy3isoPm/RcOP1woez+9ydFgUmw+/PSBYId9cS3iRfrnx9dnrsMYvvskR+2FEb8KwdI1f/qwq+W0z6Iy/F1dIuMmxnXGrGRE/fHeYSX90+32jFEnaP39rrgy/CJyN3Aa0AZ8AlxojNmYPHcDMAHoAK40xvzFZV9tk803HsYB4ZP3f6rZREpR4sX2jF7gZaaPXYLW3++K2xn/bOAGY0xcRO4CbgB+JCL7Ad8EhgM7Ay+JyF7GGG/rwB3SdUDwc/cuq0HgXOc0+KtEmXTFz5YtbVz+g2Nz6vD7hV3tei+zgIIoBMuFK4thjEkvc5wD/Gfy9zOAJ40xrcCnIrIEGA38w83zwoTVimC/g8BhW7koSlfcbM9YLARRCJYLL6eKFwFPJX8fTGIgSLEyeaxosJr3r0FgpdRxsz1jMeFXIZgTyYa8qkci8pKILMrwc0baNTcBceCx1KEMTWVMoRCRiSIyX0Tmf/HFF3k77Ad9o5uWrSi+svi9z7lk/CNcOG4av7jTmY5RuuLn5uZWW9szKrlJpYl+m1c5i9/xIldaui/vjN8Y87Vc50VkPHAqcLwxJmXcVwLpCktDgM+ztD8VmAowatSoQJK1033kfvr7rRCWrSIVxaugrJvtGYPESTC40AFkB2mia8B9Vs9JwI+Ao40xW9JOzQQeF5F7SAR39wTmunlWqaAbwihhwWpQ9pLxj+TdmN2J4mfXuoJYrKZbAZqfON3IvJDSEHbSRCeZ7Z4Ytz7++4AewOxkoGaOMeZSY8xiEZkBvE/CBXR5WDJ6Co1dWWiNCShhwWpQ9r9/bW1jdreo1ER3ApFsMMZ8Nce5ycBkN+2Hhfb2RioqnAUCVPxNiSpWg7J2Nmb3E6fyE4VeSWRjC+t4gtNtbeiukg0+4tToK0qUOWDkYH415RXi8Q5at8azBmXvuuesgvUpk6xEynA7XREEvZJoYQPTOZ6LmcNZ/M7Wjls20kR3iDyo4VcUJSNWg7Jeb8xul6ANt1vc6vhYSROdZKhP/6yGP2ToVpFKmLASlI3HOxwrcyrB6Pio4Q8ZGhNQokY2Zc4/z3yPk08/IIAe2ceqMqkf8YAgdHzU8CuK4opscsyFNPq5DLeXyqB+uJWC0PFRw9+FQmr6K4qSHWNMt9TRX9w5y5bhDkIZ1G4RVxA6Pmr4u5BJ6TKMMs6KElWszsAzpY7ana0HoQyaXvhltZjLSoDWRVVwt7vU8Fvg8anlgUs5KIrfFGI/Xjsz8Ewibu3tHVRUWA8k21EGDftmMbkqiUVkgTFmlNW21PArigJ03483hZMtGbORPgN3UvRVURHL6ALKhtUitNSAdPqHC2lbVwdvwTMPZG7zmQzHagY5l3gIgvArJSk5UWVRJYykVD27kpqBuyn6sqPjb1UZNDUgta2rc9SnoHb2corO+C0SVND3xaf0K1KCxa4LKN2d05X0GXghsFqElhqQjipIr4JHrYpFcm1vqP5/pZjJlbeeyQ2Uy52TLgNRqKIvK0VoqQGJzwrSJdvUDPK2PXX1eIC6WxRlO7ncOekz8Ex4sfGLE1IuIa/xwmBPMt7HD3TG7wFWNjt3khKqA4oSRfK5c7LNwIPIuU+RGpDasixunGxvCDsa7J/XO9vYxQ/U8BcIK4ODohQDTt05dnLu/Ui9PP3METyTwfCntje8mDk0scqWemY6Ycr6UWukKIqnpLtzssk5ZMJqzn2hVwZu1TPDiPr4FUXxnNPPHGHL6MOOLqJB9b225dx3JX1lcNG3prFg3nKvup2RbOqZUUYNv6IoocBqzn168PiOX5zFLTfOxBiTsc3mpq2sadjEaSf8Kus1+QhCPdNv1NWjKEoosJpzb7UaF7B0TT6CUM/0GzX8iqKEBis591a3hAQsXZOPINQz/UYNv6IokcLqygDIe83w3Sft8DmTDg9YU8+MEmr4FUVxjFNFz02NLfTqXe34uVZWBpB9k5hSRw2/oiiOsSvn8OeZ7/HLe17mmT9e6me3lDxoVo+iKAWj0CJtSmbU8CuK4guxWE23Y+kpm52dztIrFfeEytWzYMGCL0XE32oM5wwAvgy6Ez6g7xUtIvVeiz655eD0z10DsxO+cwRfO3HfoLqHiCxI//xfg1oPjq2ptN1Ox6A2RHosyHCqUN+Xrf0kxWlRQ6khIvPtbG0WFfS9okXU3mvx0lvDbGDWDN99Un36gQnxNxoAJ9Joax4qP7y+68Gwfl+hmvEriqJ4wfDdJ1nfpiuNTMa7GFEfv6IoSomhht86U4PugE/oe0WLYn2vYiWU35cafosYY0L5BbpF3ytaFOt7ZSLprnGyjXlotj4P6/elPn5FUfxkDQ6DpQBdg6+KN2hWj6IoSomhrp48iMjdIvKhiLwrIs+JSJ+0czeIyBIR+UhETgyyn3YRkbNFZLGIdIrIqC7nIvteACJyUrLvS0Tk+qD74xQReVhE1orIorRj/URktoj8K/nfvrnaCBsisouI/FVEPkj+/X0/eTzq71UlInNF5J3ke92aPD5MRN5KvtdTImK/SMAH1PDnZzawvzHmQOBj4AYAEdkP+CYwHDgJuF9ErG8wGjyLgLOA19IPRv29kn39NTAW2A84N/lOUWQaie8gneuBl40xewIvJz9HiThwtTFmX+Aw4PLk9xP192oFjjPGjABGAieJyGHAXcCU5HttAEKhGqeGPw/GmFnGmHjy4xxgSPL3M4AnjTGtxphPgSXA6CD66ARjzAfGmI8ynIr0e5Ho6xJjzFJjTBvwJIl3ihzGmNeg2x5/ZwDTk79PB75e0E65xBiz2hjzz+TvTcAHwGCi/17GGNOc/FiR/DHAcWxXew7Ne6nht8dFwIvJ3wcDn6WdW5k8FnWi/l5R738+BhljVkPCiAIDA+6PY0RkKPBvwFsUwXuJSExEFgJrSXgKPgE2pk0cQ/O3qFk9gIi8BGTKHrjJGPOH5DU3kVimPpa6LcP1oYqUW3mvTLdlOBaq98pD1PtfEohILfB74AfGmE0ijgptQ4UxpgMYmYwDPgdkEiEKxd+iGn7AGPO1XOdFZDxwKnC82Z4GtRLYJe2yIcDn/vTQGfneKwuhf688RL3/+VgjIjsZY1aLyE4kZpeRQkQqSBj9x4wxzyYPR/69UhhjNorIqyRiGH1EpDw56w/N36K6evIgIicBPwJON8ZsSTs1E/imiPQQkWHAnsDcIProMVF/r3nAnslsikoSgeqZAffJS2YC45O/jweyrdxCiSSm9g8BHxhj7kk7FfX3+koq409EqoGvkYhf/BX4z+Rl4XkvY4z+5PghEdz8DFiY/PlN2rmbSPjxPgLGBt1Xm+91JonZcSuJYpm/FMN7Jft/MokMrE9IuLUC75PD93gCWA20J7+rCUB/Elkv/0r+t1/Q/bT5TkeQcHe8m/Zv6uQieK8DgbeT77UI+HHy+O4kJk5LgKeBHkH31RijBVyKoiilhrp6FEVRSgw1/IqiKCWGGn5FUZQSQw2/oihKiaGGX1EUpcRQw68oilJiqOFXFEUpMf4/XVWmXA3CFnwAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# following function (plot_with_labels) is for visualization, can be ignored if not interested\n", "from matplotlib import cm\n", "try: from sklearn.manifold import TSNE; HAS_SK = True\n", "except: HAS_SK = False; print('Please install sklearn for layer visualization')\n", "def plot_with_labels(lowDWeights, labels):\n", " plt.cla()\n", " X, Y = lowDWeights[:, 0], lowDWeights[:, 1]\n", " for x, y, s in zip(X, Y, labels):\n", " c = cm.rainbow(int(255 * s / 9)); plt.text(x, y, s, backgroundcolor=c, fontsize=9)\n", " plt.xlim(X.min(), X.max()); plt.ylim(Y.min(), Y.max()); plt.title('Visualize last layer'); plt.show(); plt.pause(0.01)\n", "\n", "plt.ion()\n", "# training and testing\n", "for epoch in range(EPOCH):\n", " for step, (x, y) in enumerate(train_loader): # gives batch data, normalize x when iterate train_loader\n", " b_x = Variable(x) # batch x\n", " b_y = Variable(y) # batch y\n", "\n", " output = cnn(b_x)[0] # cnn output\n", " loss = loss_func(output, b_y) # cross entropy loss\n", " optimizer.zero_grad() # clear gradients for this training step\n", " loss.backward() # backpropagation, compute gradients\n", " optimizer.step() # apply gradients\n", "\n", " if step % 100 == 0:\n", " test_output, last_layer = cnn(test_x)\n", " pred_y = torch.max(test_output, 1)[1].data.squeeze()\n", " accuracy = (pred_y == test_y).sum().item() / float(test_y.size(0))\n", " print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)\n", " if HAS_SK:\n", " # Visualization of trained flatten layer (T-SNE)\n", " tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n", " plot_only = 500\n", " low_dim_embs = tsne.fit_transform(last_layer.data.numpy()[:plot_only, :])\n", " labels = test_y.numpy()[:plot_only]\n", " plot_with_labels(low_dim_embs, labels)\n", "plt.ioff()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[7 2 1 0 4 1 4 9 5 9] prediction number\n", "[7 2 1 0 4 1 4 9 5 9] real number\n" ] } ], "source": [ "# print 10 predictions from test data\n", "test_output, _ = cnn(test_x[:10])\n", "pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()\n", "print(pred_y, 'prediction number')\n", "print(test_y[:10].numpy(), 'real number')" ] } ], "metadata": { "kernelspec": { "display_name": "tensor", "language": "python", "name": "tensor" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/.ipynb_checkpoints/406_GAN-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 406 GAN\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* numpy\n", "* matplotlib\n", "\n", "Note: Below cells only work for pytorch version lower than 1.5, if you are using pytorch 1.5 or higher, then you will get in place error when you run For loop for Step(10000). Fixed version has been added below." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", "from torch.autograd import Variable\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "torch.manual_seed(1) # reproducible\n", "np.random.seed(1)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Hyper Parameters\n", "BATCH_SIZE = 64\n", "LR_G = 0.0001 # learning rate for generator\n", "LR_D = 0.0001 # learning rate for discriminator\n", "N_IDEAS = 5 # think of this as number of ideas for generating an art work (Generator)\n", "ART_COMPONENTS = 15 # it could be total point G can draw in the canvas\n", "PAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# show our beautiful painting range\n", "plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')\n", "plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')\n", "plt.legend(loc='upper right')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def artist_works(): # painting from the famous artist (real target)\n", " a = np.random.uniform(1, 2, size=BATCH_SIZE)[:, np.newaxis]\n", " paintings = a * np.power(PAINT_POINTS, 2) + (a-1)\n", " paintings = torch.from_numpy(paintings).float()\n", " return Variable(paintings)\n", "\n", "G = nn.Sequential( # Generator\n", " nn.Linear(N_IDEAS, 128), # random ideas (could from normal distribution)\n", " nn.ReLU(),\n", " nn.Linear(128, ART_COMPONENTS), # making a painting from these random ideas\n", ")\n", "\n", "D = nn.Sequential( # Discriminator\n", " nn.Linear(ART_COMPONENTS, 128), # receive art work either from the famous artist or a newbie like G\n", " nn.ReLU(),\n", " nn.Linear(128, 1),\n", " nn.Sigmoid(), # tell the probability that the art work is made by artist\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "opt_D = torch.optim.Adam(D.parameters(), lr=LR_D)\n", "opt_G = torch.optim.Adam(G.parameters(), lr=LR_G)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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ampp6lZmfnx8eHh7odDoMBoO632QykZubS3R0NDExMQAEBQVRU1NDdna2S2Vm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\nLWb+0WIGymX/i8dUVVgXGf3g5D+prPxhxqbGdXxZ9T7/q5nv8wQPyjF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wWVlZTJgwwaP+mjVrEELw66+/AnDw4EGEECxcuJCbb76ZiIgIxo5V8s29++67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MfHx8cHNzQ6fTYTAY1P0mk4m8vDwiIyOJiooCICAggNraWnJyclwqM5PJRG5u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\nRFS1VvB9zWKWVn/O+vrVXmXPnFiwMjz+aI5PnMyxSScHf588KVUd1mV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+uLm5odPpMBgM6n6TyURubi6RkZFERUUBEBAQQE1NDdnZ2S6VmclkIicnh/Dw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gq6knHf1ri7jVMC5z46ubFSV2oFAJddW34aU15zR+fn6Eh4eTk5NTZ50uXbrw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\nkglOc5M80CwdSrS39puImX96j5iB/w/UgIFwqv0fKGFvdjVs563Sf/B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RN6N/RMyEjlG+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\nEEIeETNBEAQh5Pl/WXhCtKyEW6cAAAAASUVORK5CYII=\n", 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XS+inQBSQ7HXOGGCzlHIfgBDiUeBtlGVWQ52fnxFCTPQ6Lx6YDbwAXA8cFEK0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\nhQ52bSe3dgMrqhazvHIRG2vWeGTsaE5aVD8uTL2GGT1OlHyKgcJhh29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GcnJyiIyMJCoqCgB/f3+qq6vJyspyqsyMRiPZ2dmEh4fbrZMLDg5WTbnz5s0j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NDZ1Oh8FgUPebTCZycnKIiooiOjoagICAAKqrq8nKynKpzEwmE9nZ2URERDis\nkwsJCVGXLMyePZuBAwfywQcf8OGHH56WUi60/l+eE0I8bVVUNl6SUv4HlPk1IAcYAbwlpUwVQuxG\nUV4brXW8gFEoyhEhhD+KwntaSjnP2uYGIYQv8IQQ4k0ppW0+IgQYJKXcZetcCPE8UA6MlFJWWPed\nRlGktjoCRdl+KKX8h91+I/C6EOI5KWU+gJSySAhxHOhNPcpMMzPWQXt/R+/GtReIubGhkYaUkpdf\nfplLLrkEHx8fPDw8uOOOOzAajeqanh9++IHg4GAHRWbP1q1bqaioqNfTrCkMHz681r7U1FTGjBlD\nREQE7u7ueHh4cODAAdLS0gDlh/vXX39l4sSJLtdMXXfddeh0OtV8ZDabKSwsdJhT8vX15eKLL25w\nq89RorEEBAQQHR1NQEAAAQEBxMfHExQURFZWVqNHiPVRXV3d4KiwKURERBAeHo6fnx/BwcF07doV\nDw+PRs8N2lNeXo5er3dQLJ6enhgMhlqj56aO7G3mRXvvRe8zvA0VFRVYLJY6zciVlZUuvUDLysqw\nWCwulZ3ZbGbnzp0OSyusfIbyG36V0/7vbB+sCuEUEON03o12JsqhgB9gCxFzFaAHVgohdLYN+AGI\ncGorw16RWbkS2GBTZFa+dqrTFegArKijD28g0al+HsoIzyWaMnNBcnxNVmqbudHSahYbNJ3Kykry\n8/PVt/y6ePnll5k+fTpjxozhq6++4rffflNHRTaTVH5+vsObsjO2+YP66jQFZ3lLSkq4/vrrOXHi\nBIsWLeKnn35i+/btXHbZZaqMhYWFSCnrlUEIgV6vJz8/HyklBQUFSCkJDq4x27u5uakjtfo22+jF\nNtJwdrKxlZuqTIKCgjCZTOqcpbu7O2azuZZyM5vNuLm51et8IaWsdfxs2nPG3d2dgIAAhwXRjaWq\nqqrOe6PT6WqNZpt6D+3Ni24CgrxR72dTX0Jsysr5PFvZlXOV7Rpc9ZeXl0d1dXVdz6bNjOI8l1Tk\nVK5CURA2PgNCgWut5VuArVJK2ypz2xvbXqDabrOZJe3tVHWZciIBhxBPUspKwP7Nw9bHWqc+jtTR\nB4DR6RpqoZkZXWAzN756gZgbN27ciMlk4qqrnF/yali5ciXjxo3jmWeeUfft2+cYbzokJKTet2/b\n22dWVpbDKMceb2/vWk4lrtb0OI+stm7dysmTJ9mwYYPDuir7ifSgoCDc3NwaHCUYDAaMRiMlJSXk\n5+cTGBisvvfiAAAgAElEQVTo8GPZVDOjl5cXQggqKysd5o5sSrYuk1ZTsM1tGY1Gh3muysrKBr0C\ndTpdrR/bs2mvLhoTOaQuPD096/RUNZlMtZRXU/owSyXppg2b9+Lp06fx8PBo8v/DpoycR7k2Jeds\nXrZhq1tdXV2nQgsNDcXDw6MuD1Kbdmt4AtMOKWW6ECIFuEUI8TMwEmXOyoatvRHUrazsv/R1veJn\nA2H2O4QQ3oDBbpetj3uA3+to44hTOZAGrlMbmdVDjD9ccwGYG4uKipgxYwadO3eud5FqRUVFrQfc\nPrUIKOa5goICVq9eXWcbV111FT4+PvVGZ4iJiWH//v0O+7777jsXtWvLCI6KYcuWLRw9elQt6/V6\n+vTpw4cffliviU6n0+Hv709mZialpaW1lG9TzYw2b0Fn54mCggIMBkOTRxWFhYXodDp1wbHBYMDd\n3d2hfbPZTFFRUYPmNy8vL4xGo8O+s2nPGYvFQlFRkTrf2BT0ej1lZWUO8lVVVVFaWuowZ9VUKu0G\ndbbF0adPn6awsNDBsaYuhBC1XP9tc2nOL16FhYV4e3u7HHnp9Xrc3Nxcej26u7vTs2dPh2DPVm4G\nLCgOEk1lOTDGuvkA9o1vBSqAaCllSh1bSQNtbweShRD2Dh/O8w4HgAwgzkUf6s2wel52ANLq61Qb\nmTXAoHjYmwvZVu/GFalwXyv2bjSZTGzbtg1QTHI7duzgzTffpLy8nPXr17t8ewRITk5m8eLF9OnT\nh06dOrFs2TKH3FO2OoMHD+b2229n9uzZXHHFFWRlZbF582befvttAgMDefLJJ5k1axZVVVUMGzYM\no9HImjVrmDNnDu3atWPMmDG8//77PPTQQwwfPpyNGzeqC70bom/fvhgMBu6++24ee+wxTp48ydy5\nc9WJdBv//ve/GTRoEEOHDuWee+5Br9ezdetWevXqxYgRI9R6oaGhHD58GE9PT/z9/R3acHd3d+nM\n4IqoqCgOHDjA8ePHCQoKori4mOLiYrp06aLWMRqN7Nmzh7i4OFWBHjp0CL1ej6+vL1JKEhMTmTlz\nJuPGjVNHI25ubkRGRpKVlaUuNrY59NgWB7vCYDDUcrdvbHt5eXls2bKFUaNGqaOQQ4cOERISgpeX\nl+oYUV1dfUbm5ZCQELKzszl48CDR0dGqN6NOp6tT6QwcOJDx48fz97//3WWbFgmm6mqqK0oRAoR7\nNcdOFZOfn4+/vz+RkfVOz+Dj46P+73Q6HV5eXuh0OiIiIsjKykIIga+vL0VFRRQXFzssRHdGp9MR\nFRVFRkYGUkoCAgLUSC4ZGRm0a9eOefPmMXjwYNtcs78QYjqKw8a7Ts4fjWUFigPG88Bma6ovQHW4\nmAu8IoSIBTajDHy6AtdIKcc00PbLwBTgGyHESyhmx3+hOIVYrH1YhBCPAB9ZHU7WoZhDOwKjgXFS\nStvQIQFlVPdLfZ1qyqwBnM2NR4rglxPwtw4Nn3s+UlxczFVXXYUQAn9/fzp37sz48eN54IEHGnyA\nZ8+eTW5urposcezYsSxevFhd3wXKG+sXX3zBk08+ycsvv0xubi7R0dHcfvvtap2ZM2cSHBzMK6+8\nwttvv01QUBADBgxQTW/Dhw/n2Wef5Y033uC9995j1KhRvPLKK4waNarB64uIiGDlypVMnz6dUaNG\n0aVLF9566y0WLlzoUG/AgAFs2LCBJ598kvHjx+Pp6UmPHj3UsFA2AgMDEUIQEhJyxmYye/z8/OjU\nqROZmZnk5ubi5eVFx44dGxzpeHt7k5+frzp82Ob8nOdRbP/DrKwsTCYTer1edb6oj6CgILKzsx28\nN8+0PZunX1ZWFtXV1bi5uaHX60lISGiy8re117VrV06cOKGOsG338UycVipNim2ssqSAypIChBCc\n1unw8fEhLi6O4ODgBv/XUVFRGI1GDh8+jNlsVl88bF6Mubm5qjdkfHy8w1yrq/Z0Oh05OTnk5uai\n0+mwWCzqM3H99dezfPlyW9SWzsA04EUUr8MmI6U8IYTYghKucF4dxxcKITKBh4BHUNaQpWHnkVhP\n2xlCiOHAK8B/gVTgLmADYB+U/jOrl+Pj1uNm4DCwGkWx2Rhi3V+XOVJFC2fVSNanw/dHlc8ebvBQ\nHwhrusVEoxWRmppKdHQ0Bw8eJDEx8ZyEVTpT4uLieO+995oUu7Ah9u7dS0hISIMvNXXNVR09epT4\n+Phm94o8E+obmVmksobU5vTho4MQn/Mze3RbCmclhOgP/ARcK6WsOz2563O3AmuklE/XV0+bM2sk\ng+Ih0mqer7bAyn1t27vxQiczM5PKykpOnjxJQEDAeaXInDEajUybNo3o6Giio6OZNm2aOr+UlJTE\n559/DsAvv/yCEII1a9YA8P3333P55Zer7fzwww9cffXVBAUFMXjwYI4dO6YeE0Lw+uuv06VLFweT\nqDP/+c9/iI6OJioqymFNYn0yLl26lP79+zu0I4RQTdiTJk1iypQpDB8+HD8/P/r06UN6erpa1+bs\nExAQwNSpU+udBy128l4M9D4/FVlrRwixQAhxqzUSyL0oc3S7gcalQahppw9wEfBaQ3U1M2Mj0bnB\nLRfbmRuLW7e5UaN+3nnnHfr27UtsbCwdOnSA125v+KTmYuonTar+zDPPsG3bNnbt2oUQglGjRvH0\n00/z1FNPkZSUxKZNm7jxxhv58ccf6dixI5s3b2b48OH8+OOPJCUlAfDVV1/xyiuvsHTpUnr27MlL\nL73Ebbfd5pAB+ssvv+TXX3+tFcHEno0bN3Lw4EEOHz7Mtddey+WXX86gQYPqlbExLF++nHXr1nHF\nFVcwceJEZs2axfLly8nLy2Ps2LEsWbKEUaNG8dprr/HWW29x55131mqj0mlxtBZ78ZzihTIfFwGU\noKx9e1hKWX/AzNoEAxOllM7LDWqh/SubQIw/XBtXU16XDrlt0LtRQ8kwEBsby8UXX3zWLvPnmmXL\nljF79mzCw8MJCwtjzpw5fPTRR4AyMrPlBNu8eTMzZ85Uy/bK7K233mLmzJkMGDAAvV7P448/zq5d\nuxxGZ7a5zvqU2Zw5c9Dr9XTv3p3Jkyfz6aefNihjYxgzZgy9e/dGp9Nxxx13sGuXsk537dq1dOvW\njXHjxuHh4cG0adPqNJNaJBTahXL0cZHaRaN5kFJOk1K2l1J6SilDpJS32TuZNKGddVJK5wXXdaIp\nsyZyXRxE2ZkbV2jmRo0WJjMzk9jYmjUksbGxZGZmAspSiLS0NHJycti1axcTJkzgxIkT5OXl8dtv\nvzFgwABASf/y4IMPEhgYSGBgIMHBwUgpycjIUNu1D7XkCvs69nLUJ2NjsFdQvr6+auQPW3oaG0KI\nOuXUzIttH83M2ERs3o2LtytK7Ggx/HwCBmjmxrZNE01/fyXR0dEcO3aMbt26AXD8+HHVq87X15ee\nPXvyyiuvkJiYiKenJ1dffTWLFi2iU6dOqut/+/btmTVrFnfccYfLfhrjzXnixAl1sbq9HPXJqNfr\nHSKDNCVRbFRUlEMWAyllrawGmnnxwkD7l54B7fyUEZoNzdyo0ZLcdtttPP300+Tm5pKXl8f8+fMZ\nP368ejwpKYnXXntNNSkOHDjQoQxw33338dxzz6lpU4qLi+tapNsgTz31FOXl5ezdu5clS5Zwyy23\nNCjjZZddxt69e9m1axeVlZXMnTu30f0NHz6cvXv38t///heTycTixYsdlKFmXrxw0JTZGXJtXI25\n0WSBzzRzo0YL8cQTT9CrVy8uvfRSunfvzhVXXKGuBQRFmZWUlKgmRecyKHNSM2bM4NZbb8Xf35/E\nxETWrVvXZFmSkpLo3Lkz1113HdOnT+f6669vUMauXbsye/ZsBg0aRJcuXWp5NtZHaGgoK1eu5F//\n+hchISEcPHiQfv36qceLK2vHXtTMi20TbZ3ZWZBRUmNuBBjRBZI0c2ObwdU6H43WQaXJ0WIS7A36\nZs2DfG5pS+vM/gq0kdlZ4GxuXJ8Op8paTBwNDQ0rmnnxwkNzADlLrotTYjdmlirmjBWp8I+e52fs\nxrlz5zJvnhK5RghBQEAAnTt35vrrr29UOKsLncrKSnJycigtLaWiogI/Pz8SEhIaPK+iooITJ05Q\nUVGByWTCw8MDf39/oqOj1SDBoATjzc7OJj8/n6qqKjw9PQkODiYqKqrBdCtSSvbt20dERITLbAS2\nPjIyMigrK6OsrAwpJb16NfySb7FYOHLkCOXl5VRVVeHu7o6vry/t2rWrFaKqoKCA7OxsKisrcXd3\nx9/fn3bt2jlca10UFRVx8uRJjEYjHh4eXHrppQ3K5Yr6zIu2uId5eXlqDjIPDw/8/PwICwurN3hx\nWVkZxcXFqvOKxrnBmq36AHCplPJwY87RRmZnibvVu9GmvI4Vw0/H6z+nJQkICGDr1q1s2bKF5cuX\nM3bsWD766CO6d+/Ojh07Wlq885rKykqKi4vx9vZuUkQQs9mMl5cXMTExdO3alejoaE6fPs2hQ4cc\nolVkZGSQnZ1NWFgYXbp0ISwsjOzsbE6ebDiObGFhIWazucEYgBaLhby8PNzc3M4o4nxkZCRdunQh\nNjYWi8VCWlqaQzT7oqIiDh8+jMFgoHPnzsTExFBSUlLrWp2RUnLkyBF8fHzo2rUrnTt3brJsNipN\nUGqXBzPQW3lObf0cPnyYY8eO4evrS3x8PF27dlVjLe7fv79eOcvKypq0pEDjzJBSZqDEgZzdUF0b\n2sisGYj2g0Fx8J01A8/6w3BxKIQ3PabqOUen09G3b1+1PHjwYO6//34GDBjArbfeyv79++uNnN8S\nVFRU1LtQ968iICBAHS2kp6fXSgzpCoPB4KA4/Pz88PT0JC0tTc2iDMqIJiwsTB0h+/v7U11dTX5+\nvhKFpB5OnTpFSEhIgyM4nU7H5ZdfjhCCU6dOUVLSUDYPBTc3Nzp16uSwz9/fn127dlFYWKjKnJ+f\nj6+vr4O87u7uHDp0iMrKSpf/x+rqasxmMyEhIQ653pqKRUJBhQWkACEU86Ldr9ypU6coLCyka9eu\nDlkQbKOy3NzcOlrVaAghhI9TZunmYAnwvRDiEfuUMK7QRmbNxLVxyhwa1JgbW4t3Y2BgIAsXLuTQ\noUNs2LDBZb2ioiL+/ve/Ex0djbe3Nx06dODuu+92qLN7925GjhxJYGAgBoOB3r17O7R55MgRRo8e\njb+/P35+fowcObJWGhkhBIsWLWLatGmEhYXRvXt39dhXX31Fr1698Pb2JjIykscee8xlOvrmwP4t\nvTmi5tuwvTDYty+lrPUi0ZgXi8rKSkpLSwkKCmpU3811HbZs02d7DXl5eezevRtQUsekpKSoox+z\n2czx48f5448/2LFjB/v27auVqubAgQOkp6eTm5vLnj17yDywE4upuk7vxZycHIKCgmql87ERFhbm\n8v7k5eVx/LhidklJSSElJcUhOevp06dJTU1lx44davQUV9ml7SkvL+fgwYP8/vvv7Ny5k9TUVIdr\ndH5mgM5CCIehqxBCCiEeFEI8K4TIFUKcEkK8LoTwsh6Pt9YZ7nSeuxAiWwjxtN2+RCHEGiFEiXVb\nKYSItDs+0NrWYCHE10KIUqyxE4UQQUKI5UKIMiFEphBihhDiBSHEUad+O1jrFQghyoUQ3wohnG32\nv6Ak5Ly1wZuINjJrNtzd4OaLFe9Gs1TMjZuPw8DYhs89Hxg4cCA6nY5t27YxZMiQOus8/PDDbNmy\nhZdeeonIyEhOnDjB5s2b1eP79++nX79+JCQk8NZbbxESEkJKSoq6iNVoNHLdddfh4eHBu+++i06n\nY86cOSQlJbFnzx4HE9nzzz/PgAED+Oijj9QkiCtWrOC2227j3nvv5dlnnyU9PZ2ZM2disVjUoLZS\nykb9gDQmsrst7UpzpX+xpW6pqqoiIyMDvV7vMN8UGhpKbm4u/v7++Pj4UF5eTm5uboO5yEpKSnBz\nc/tLRq82xWUymdT1XPb/t9DQUNLT08nLyyMoKIjq6moyMjLw8/NzKV9AQACdOnUiPT2dmJgYDAaD\nOr927NgxioqKaNeuHd7e3uTm5nLo0CG6du3qMIIrLS2lotKIb0g7hJs7wt2dIDvzIigJPauqqs4o\np5pNzoiICHJyctSF4TZFXVFRwcGDB/H396dTp07q/9hoNNK1a1eXbVZUVLB//368vb2JjY1Fp9NR\nWlpKfn4+3t7edT4z48aN8wJ+FEJ0l1LaZ3p9BPgBGA9cCjwHHAMWSimPCCF+Q0noucbunCSU+InL\nAaxK8hcgxdqODiVv2jdCiN7S0Qb7Psro6WWUFDEAS4H+wIMoGacfQsmDpj6UQohg4GcgH7gPJc/Z\nv4D/CSG62kZ4UkophNgGDAJed3kTrWjKrBmJ9oPr4uE763Tlt4fhkvPU3OiMt7c3oaGhavLFuvjt\nt9+YMmWKuhAWcFicO2/ePAICAvjpp5/UH67k5GT1+JIlSzh+/DhpaWlqssI+ffrQsWNH3n77bWbO\nnKnWjYqK4rPPalInSSl59NFHmTBhAm+88Ya638vLiylTpjBz5kxCQkL48ccfueaaaxq83iNHjhAX\nF1dvnZiYGE6ePFmn6Sk3Nxez2Vwr23B95OTkUFmpPPOenp6Eh4fXyqhdWlrKzz//rJZtJknn0Yg9\nNocR57YaoqSkhIKCAlJTUxt9TnFxMUVFSsxXNzc3wsPDOXzYcX5eSsmOHTtUxefl5UV4eHi9/ZhM\nJnUuz/bdqa6uJjMzk5CQEDXbtZSSoqIiduzYoeZyy87OpqqqCr/QdnBK+f56uEGZk7+J0WhU+8jL\ny3OQ1576Xlxs98w5ykhubi5VVVX4+PiQlaWEIDSbzRw+fJjy8nKX8T1zc3MxGo20a9fO4dnz9vYm\nJiaG999/v9Yzg5JX7GLgXhSFZeOolHKS9fO3Qoh+wFjAlsxvOTBHCOElpbRNdN4C7JVS/mktz0FR\nQkOllFXW+7Eb2A8Mw1ERrpRSPml33xJRMkrfLKVcad33PXACKLU77yFAD1xuU8ZCiF+Aoyh5zewV\n1x+Ao/nHFba3xb9669mzp2yLmMxSvvSrlNP/p2yLf5PSbGlpqRTmzJkjQ0JCXB6PiIiQ9913n8vj\nd9xxh2zfvr18/fXX5YEDB2odDw8Plw8//LDL8ydPniyvvPLKWvsHDhwohw0bppYBOWvWLIc6+/fv\nl4Bcu3atrK6uVrcjR45IQG7atElKKeXp06fl9u3bG9yMRqNLOU0mk0MfFkvtf+CNN94ok5KSXLZR\nF2lpaXLbtm3yo48+kgkJCfKKK66QFRUV6vEFCxbIoKAg+eqrr8off/xRLl68WAYEBMgnn3yy3nZH\njhwphwwZ4rDPbDY7XIPZbK513quvviqVn4DGk5WVJbdv3y6//vprOWTIEBkSEiL37t2rHv/hhx+k\nwWCQjz32mNy4caNcvny5vOiii+TAgQOlyWRy2a7t//jNN9+o+z744AMJyLKyMoe6c+fOlb6+vmo5\nKSlJXnRFP/WZm71JyuLK2n1s27ZNAvLbb7912D9lyhSJkq+zlgzOuLpn8fHx8tFHH3XYZzKZpE6n\nkwsXLnTZ3pk8Myijpo0oOb5sylgCT0i731jgWeCkXbkdSqbnUdayDsgFnrSrkwX823rMfksH5ljr\nDLT2N8ipv0nW/d5O+5ejKFpbeat1n3MfPwBLnM6dClRjXRNd36bNmTUzNu9Gd+vL3fHTirnxfKey\nspL8/PxamYvtee211xg9ejTz588nISGBLl26sHz5cvV4fn5+vSacrKysOtuPiIhQ37zt99lje5Me\nNmwYHh4e6hYfHw+gvikbDAYuv/zyBrf63MRtZh3bZosyf7Z06dKFPn36MH78eL799lt+//13Pvnk\nE/X6nnjiCRYsWMDUqVMZMGAADzzwAAsWLOC5557j1KlTLtutrKys9eY/f/58h2uYP39+s1xDZGQk\nvXr1YuTIkXzzzTeEhITw73//Wz3+yCOPcMMNN7BgwQIGDhzILbfcwpdffsmmTZv46quvmtRXVlYW\nBoMBX1/HLLgRERGUl5erXpQVJjD71nxfRieAfx0DIZs7vbN36GOPPcb27dv5+utGBWd3Kavzd9bd\n3d1hVFkXZ/rMADko6VHscU6TUgWobrdS8RD8GWU0BnAdEIrVxGglFJiBokDst46AcwRnZzNOJFAi\npax02u9s2gi1yuDcxzV19GGkRtnVS4MVhBDtgQ9R7KoSeEdK+YpTHYGSInsYiv1zkpRyZ0Ntt1Wi\nDEoyz2/tzI1dgxUz5PnKxo0bMZlMXHXVVS7rBAYGsnjxYhYvXszu3btZuHAhd9xxB5deeimXXHIJ\nISEhqomlLqKiotTYf/bk5OTUcil3NvXYjr/zzjv06NGjVhs2pdYcZsa3337bwcuvMWvJmkpsbCzB\nwcGqie7w4cNUV1c7JMsE6NGjByaTiWPHjrmcOwsODq4VnPeee+5hxIgRavlcrIvS6XR0797dwcy4\nf/9+brvtNod6CQkJ+Pj4OCTUbAxRUVGUlpZSXl7uoNBycnLw9fXFy8uLsmolUIGHn/J9SQyDy128\nj7Vv3564uDi+++477rrrLnV/hw4d6NChA0ePHm2SfM6yOr9wmM1m8vPz610ucabPDMrvsWst6ZrP\ngH8LIXxQFMrvUsqDdscLgC+A9+o4N8+p7Ozilg34CSG8nRRamFO9AuBrlLk4Z5zdawOBUillg15e\njRmZmYBHpJSXAH2BKUKIS5zqDAW6WLd7gDcb0W6b5ppYR+/GpbuhrKr+c1qKoqIiZsyYQefOnRk0\naFCjzrn00kt5/vnnsVgs6lzNddddx4oVK9R5IWf69OnDjh07OHLkiLovIyODLVu2NBiPLyEhgXbt\n2nH06FF69epVawsJCQGgZ8+ebN++vcGtvh/3hIQEh7bPxlXcFQcOHCA/P19Vwrb0KDt3Or4D2tb+\n1Te/l5CQ4HBPQVFe9tdwLpRZZWUlO3fuVK8BlOtwvobU1FQqKioanKN05sorr0QIwapVq9R9UkpW\nrVpF//79MVvg4z01i6N9PWBsQv2xF6dNm8aqVavYtGlTk2SxYRvRO3/H+/TpwxdffOHgfGQLflzf\nd/tMnhnAA7gaZZTVVFYCPsAY67bc6fj3QDdgh5QyxWk72kDbtviEN9h2WJVmslM9Wx976+jjgFPd\nOJQ5wgZpcGQmlYRqWdbPJUKIVBTb6z67aqOAD6323G1CiEAhRJQ8g2RsbQV3N7itG7y6HYxmJbTO\nR3vg7h6OHlZ/NSaTiW3btgHKZPaOHTt48803KS8vZ/369fW6Uffv358xY8aQmJiIEIJ3330XvV5P\n7969ASUx45VXXsmAAQN45JFHCAkJ4ffffyckJIS77rqLSZMmsWDBAoYOHcr8+fNxd3dn3rx5hIaG\ncu+999Yrt5ubGy+++CJ33nknp0+fZujQoXh6enL48GG+/PJLVq1aha+vL35+fo2KaHEmlJeXs3bt\nWkBRwqdPn1Z/aIcNG6aOHjp37kxSUhLvv/8+ANOnT0en09GnTx8CAwNJTU1l4cKFdOrUiVtvVbyO\nIyIiGD16NDNmzKCyspJLL72UXbt2MXfuXG666SbCwpxfbmvo168f8+fPJzc3t956NtatW0dZWZma\n4NJ2DVdeeaWqVP/v//6PH3/8UV028emnn7Ju3TqGDBlCdHQ0WVlZvPHGG2RlZfHwww+rbd933308\n9NBDREdHM3ToUHJycpg/fz5xcXEMGzas8TcbuPjii7ntttuYOnUqJSUldOrUiXfffZf9+/fz5ptv\n8s1BOFRYU/+mi8GvgTyqDzzwAJs3b2bo0KHce++9JCcn4+fnx6lTp9T7UN9icpsX4yuvvMK1116L\nv78/CQkJPPHEE/To0YPRo0dz//33c/LkSWbMmMHgwYPrtXacyTODMmjIA95u3J2sQUp5SgixCXgB\nZdSzwqnKXOA3YI0Q4j/WftqhKKSlUspN9bT9pxDiG+BNIYQfykjtYRRrnb2n1CIUT8kfhBCvAhko\nI80k4Gcp5ad2dXuheFc26uIavaFoyeOAv9P+1UB/u/L3QK86zr8HRXundOjQweWkZ1ti7ykpH/1f\njUPI56ktJ8ucOXPUSW4hhAwICJA9e/aUjz/+uMzKymrw/OnTp8vExERpMBhkQECAHDhwoNy8ebND\nnT/++EMOHTpUGgwGaTAYZO/eveX//vc/9Xh6erocNWqUNBgMUq/Xy+HDh8u0tDSHNgD56quv1inD\n2rVrZf/+/aWvr6/08/OTl112mZw1a5asrq4+gzvSNGxOCnVtR44cUevFxsbKiRMnquVPP/1UXn31\n1TIoKEj6+PjIhIQE+fDDD8vc3FyH9ouLi+UjjzwiO3bsKL29vWWnTp3ko48+Kk+fPl2vXEajUQYH\nB8sPP/ywUdcRGxtb5zUsWbJErTNx4kQZGxurlnfu3CmHDRsmIyIipKenp4yNjZU333yz/PPPPx3a\ntlgs8o033pDdu3eXvr6+Mjo6Wt58880yPT29XpnqcgCRUsqysjI5depUGR4eLj09PWXPnj3l+vXr\n5a8ZNc9UzKVJsv+QGxt17VIqzjHvv/++7Nevn/Tz85MeHh4yNjZWjh8/Xm7ZsqXecy0Wi3z00Udl\nVFSUFEI4OAH973//k71795ZeXl4yLCxM3n///bKkpKRBeZr6zKDMjXWRjr+tEpjqtG8ukCdr/w7/\n3Vp/q/Mx6/GLgFUo5sAK4BCK4oyRjg4giXWcG4xiyixDmVObDbwL7HKqF43i1p+DMi92FPgY6GZX\nJwzFMphUl5zOW6Oj5gshDMCPwDNSyv86HVsN/FtK+bO1/D0wQ0rpMix+W4ia31i+P6oEIbZx40XQ\nt12LiaPRBnnwwQc5dOgQa9asabhyK+dIEby9U1nPCdA9DMZ3Pz/joZ4LWlPUfCGEDvgT+FVKObGJ\n594LTAe6ykYoqkatMxNCeACfA8ucFZmVDBy9UGKs+zSAa2MhqwT+sM4Pf3EAwn2hY+MCNmhoNMij\nj5LCMUkAACAASURBVD5K165dSUtLq3eRbmunqBI+3F2jyKIMjrFRNVoWIcRNKKOuPYA/yhqxLsCE\nJrYjUBZeP9MYRQaNcACxNvo+kCqlXOSi2tfABKHQFyiWF/B8mTNCwM2X1DiEWCR8uAcKmzuSmcYF\nS0xMDP/5z3/q9Yxr7VSZFUcqWxBhvQdMuhS8tNAP5xNlwGQUnfApiqlwpJTytya2EwksAz5q7AkN\nmhmFEP2Bn1A0rW0S73GgA4CU8i2rwnsNGIIy2Te5PhMjXFhmRhuFlbD4t5qHMdoAU3qB5/kV11dD\n47xDSvhkL+yyrmxyE3BPD+h0AVo3WpOZ8a+kMd6MPwP1DuKtw8ApzSVUWyXIGyZcWmPvzyyFz/bB\n+EQtlbuGRn1sPFajyABGdb0wFZmGa7QIIH8x8YEwxm4N7u5T8MPRFhNHQ+O8Z1+eowNV33ZwdUzL\nyaNxfqIpsxagj9PDuP6wkq1aQ0PDkZwy+OTPmlAT8YHKqExDwxlNmbUQN3RxNJN8uheyS13X19C4\n0CivhqV/KEEHwGqm7w467VdLow60r0UL4e4GdyYqDygoD+zS3coDrKFxoWO2wLI/Ic/q8evhpngu\nGlzHh9a4wNGUWQui94TJl9V4M+ZXwMd/Kg+yhsaFzNp0SLMLo3vrJed3oG6NlkdTZi1MlEF5UG0c\nLIDVh1pOHg2NliYlyzFt0qA4uNR1ZiINDUBTZucF3cMhuSbwOD+fgO2ZLSePhkZLcbwYPrdLmN0t\nDJI7uq6voWFDU2bnCYPilRhzNj7fD0eLW04eDY2/mmIjfLC7JqVLhF6xWmihqjQag6bMzhPchBJj\nLsqafcIslQe7qO40RxoabYpqs/J9P23N+eerU+aTvbVQVRqNRFNm5xFeOsVjy9dDKZdWKQ94tbn+\n8zQ0WjNSwqr9cOK0UnYTcGd3CPFpWbk0WheaMjvPCPZR1tLYTCsnS2BlqvLAa2i0RTYfh53ZNeWR\nXaBzcMvJo9E60ZTZeUinIMcoB7/nwKZjLSePhsa5Yn8+rLHz3u0dDf20UFUaZ4CmzM5TrmoHfaJr\nyuvSITWv5eTR0GhuTpUpC6NtRofYACVuqRZ0W+NM0JTZeYoQMDpBiUUHygP/yZ9KrDoNjdZOhUmJ\neFNpUsoBXjBRC1WlcRZoX53zGJ2bMn8WaA15VWlWYtVpIa80WjMWqbyY5ZYrZZ01VJWfV8vKpdG6\n0ZTZeY7BU3nQPaz/qbwKxTRj0RxCNFop69KVuTIbN18MMf4tJ49G20BTZq2Adn7KGjQbaQWOk+Ya\nGq2FndmOzkzXxEKPyJaTR6PtoCmzVsJlEXBdXE1583Elhp2GRmvhxGllmYmNi0NgSKeWk0ejbaEp\ns1bE9R3hktCa8qpU2J3jur6GxvnCydPw/q6aUFXhvnBbohaqSqP50JRZK8JNwG3dlJh1oIS8+vhP\nbYSmcX5ztBje/h3KrI5LPjqYdJnyV0Ojufj/9s47PKoq/eOfM5NOSCUJkEDoHURAAXtHsaArCuta\nUBTLWtbyE1w7qyvquth2dV3srr2ioq6grhURUKRICTUkIQnpbZLJzPn9ce5kZpJJgyT3TuZ8nmce\n5p577+TlZnK/97znLVrMgoyoMLhivHqyBRWy/8Ym+H6vqWZpNAHJKoZ//+wNwY8Og8vHQ0qMuXaZ\nycbqn3FJXaOuowlKMat0VZhtgqnER8HVE71FiQHe26KrhGisxW/74dl1UGfct3uEw1UToH+8uXaZ\nybqqVSzYfQX3Zt9AhavcbHO6FUEnZt+WL+eyrNPZUL3GbFNMJTbCuDH4hDR/nAWf7dB1HDXmsy5f\nJUV71sjiI+GaiaHdLTq/LpdFOQtw42ZN1fc8tW+R2SZ1K4JKzD4vXcqinPlUuSv5S/bNZNfuNNsk\nU4kJhysOhUEJ3rHlO1Wnai1oGrNYneefC5kUpYQstYe5dpmJw13DfXtvptxVCkCivRdzU2802aru\nRVCJ2biYScTbVTntSnc5d2dfT0l9UStndW+iwmDueBie7B37eg+8u0UnVmu6nu/3qjVcz1cvNUYJ\nWVIIt3ORUvJE3n3sqN0CQBhh/DnjYZLDU1o5U9MegkrM0iL6ck+/x4gUqr5TvjOHhdk34nDXmGyZ\nuUTYVZWQMT5/Gytz1E3F5TbPLk1o8eVutXbroW+sWtuNjzLPJivwfvF/+Kr8k4btq3rfyqiYQ0y0\nqHsSVGIGMDR6FPPTH8BmmL7VsYGHc24P+eigMBtcOAYm+FRTWLtPhe7Xa0HTdCJSwmfbYZlPVZr+\ncXDlBLW2G8qsq1rFcwWPNWxPSziH0xJnmmhR96VVMRNCPCeEKBBCbGhm/3FCiDIhxC/G666ON9Of\nyT2P5cq0/2vYXln5FUvy/97ZP9by2G2q7NWUdO/YhkK1EF8X2lqv6SSkhA+3wfJd3rHBCWot19Mx\nPVQpcHoCPtQf34josVydNt9kq7ovbZmZvQCc2sox30gpxxuvhQdvVuuckTSLc5IuatheWvIa7xf/\npyt+tKWxCfjdcDimv3dsS5GqvuDJ9dFoOgK3hHc2wzfZ3rERyWoNNyrEE6Id7hruy77FL+Djz+l/\nI9wW4lPVTqRVMZNSfg0Ud4Et7eay1Bs4sudJDdtL8v/Od+UrTLTIGggBZwyBkwd6x3aUwjM/6/Yx\nmo7B5YbXN8GPud6xsSlwyTgIt5tnlxXwBHxsr90MeAI+HtIBH51MR62ZTRVCrBNCfCKEGN3cQUKI\neUKI1UKI1YWFhQf9Q23Cxs19FzIyWi2mSiR/y72DzTW/HvRnBztCqFqOpw/xjmWXw9NroaLWPLs0\nwU+9G17eAD/v845N6A1/GKObawJ8UPKqX8DHlb1vZVTMeBMtCg064qu3FsiUUh4CPAG839yBUspn\npJSTpJSTUlI65ikl0hbFnRl/p2+E8qvVyVruzf4TeXXZrZwZGhyXqVrRe8irhKfWQqnDPJs0wUud\nC55fBxt9nkWnpKu1WrsWMtZV/cSz+Y82bE9LOIfTEs410aLQ4aC/flLKcillpfF+GRAuhOjVymkd\nSnxYIvf2e5w4u8oeLneVclf2dZTVl3SlGZbliAx1s/EUKC+shn+ugaLQzmjQtBNHvVp73eqz6HBs\nf7VGq6vfewI+5jcEfAyPGsPVafMRQl+cruCgxUwI0VsYvy0hxOHGZ3Z5JnPfiP7clbGYCKF6r+fW\n7eG+vTdT59Y+NYBJfeDCsWA3/q5KHErQ8qvMtUsTHFQ71ZrrjlLv2MkDlRtb36uh1u3gvr2NAj4y\ndMBHV9KW0PzXgB+A4UKIvUKIuUKIq4QQVxmHzAQ2CCHWAY8Ds6U0p5jSyJhDuKXvfQhjDrKp5hce\nyb0Lt9SJVgDjUtUCvWddo7wWnloDOaFdt1nTChW1yjWd7VMX94whak1WC5lPwIdDBXzYjYCPXuGp\nJlsWWgiTdIdJkybJ1atXd8pnv1f0CksKvHln5yZdzGVpf+qUnxWMZBXD8z65Z9FGSazMEK5mrglM\nqUPNyAqr1bZArcFOzTDVLEvxQfGrPJP/t4bta3rfxumJ53XazxNCrJFSTuq0HxCkdMsl27OT/sAZ\nibMatt8pfomPS94y0SJrMSQJ5h3qzQWqqVc3rO16iVHjw35jbdVXyGaN0kLmy7qqn1iSv7hh+5T4\ns5meoCt8mEG3FDMhBPPSbmFy7LENY0/ve5BVFV+baJW1yIxXLWR6GFUa6lyw5BfVg0qjya9SrsUS\nI+rVLtSa68Q+5tplJQIFfFzTe4EO+DCJbilmAHZh59b0vzIsSqW9uXGzKGcB22o2mWyZdUjvqQrB\nxqmYGerd8OKv8FOubiETyuwoUWup5UbsVJhNFbIep5eAGmgc8JFgT9YBHybTbcUMIMoWzV39HiUt\nXBUrrJUO7s3+EwXO3FbODB3SeqgWHYlGZXOXhDd/g5fWQ2WdubZpuhanS9VZfHotVBmVYiLtcPl4\nGNGlyTbWRkrJk/vu1wEfFqNbixlAYlgy9/Z7nFibaslc4trP3Xuup9KlQ/g8JEcbzRNjvGMbCuFv\nK2F9gXl2abqO7HJ4dJXqheeZlEeHqYLBgxNNNc1yLC15jS/KPm7Ynpd2C6NjDjXRIg2EgJgB9Isc\nyJ39HiFMqAWiPXU7uG/vzTjdeurhISEKrj/Mv+J+lVPN0F7bqGs6dlfq3fDZDnhyNRRUe8eHJcFN\nk3WEa2N+rVrtF/BxcvyMTo1c1LSdkBAzgDExE7mxz70N2+urV/NY3kLMSk2wIpFhcO4I5VaKj/SO\nr90Hj/wIm0O7qXe3Y1+lErHlO71dySPsqqLH5ePVA47GS4Ezjwdybm0I+BimAz4sRciIGcBx8ady\nScq1Ddtfli/jlcKnTLTImgxPhpsn+zf6LK9VpYze2axbyQQ7bqm6Qj+6yj9hfmCCmo1NzdDJ0I1R\nAR83+wR8JHF7xsNE2CJbOVPTVYRc16Hzki9lnzOHz0rfA+D1oiWkRfTllISzTbbMWkSHw+9Hw5gU\nJWCegICVObC1SOUbDdJrKUFHYTW8sQl2l3nHwmxw6mA4up+usRgIKSX/2PdXv4CP2zIeold4msmW\naXwJOTETQvDH3rex35nPmqrvAXgi7356haUxIXaqydZZj7Gp6on9nc0qKASg2KEi3o7qB6cN1v2r\nggG3hB/2wsdZ4PSp7pbRE2aPVlGtmsB8WPI6K8o+atiel3YzY2ImmGiRJhAh5Wb0YBdhLEh/kMGR\nIwBw4+KvObeyw7HVZMusSWwEXDwWLhitItxARbx9kw2LV8GeshZP15hMSQ38+2d4f6tXyGxGv7tr\nJ2kha4lfq1bz73xvabyT48/i9MTzTbRI0xwhKWYAMfYe3N3vMVLC1MJQjbuKe7Kvp9C5r5UzQxMh\n4NDeai1teLJ3vLAa/rEGPt2uIuM01kFKlQD/yI+Q5VOqrHcPFbl68kDdg6wlCpx5fhU+hkWN5pre\nt+mAD4sS0l/l5PAU7un3ODG2WACK6guYv/tycuv2mGyZdYmPgrmHwMwRKqEWlAtrxS54/CfI1el7\nlqC8VhWTfvM3qDUKSgvg+Ey44XBV/UXTPNWuKu7fewtlLvUUoAI+/qYDPixMSIsZwICoIdye8TBh\nxvJhvjOXW3ddzi7HNpMtsy5CwOR0Ffk2KME7nlepBO2LXeDSszTT+CUfHlnpX2ezVzRcMwmmD/G2\nANIEZrtjCzfs+gNZjt8AT8DHgzrgw+LorzUwvsdk7uy3mEihEmtKXPuZv/sKttRsMNkya5MUDVdO\ngLOGem+QLgmfbFfV1gt0488upaoOXlkP/9kA1T7pE0dmwI2TYYBOgG4RKSXLSt7m5l2X+Hln5qXd\nwpiYiSZapmkL3bKf2YGyoXot92TfQI1b3YWjbTHcmbGYQ3ocZrJl1qegCl7f5N/AMdymZgJHZOiQ\n785mUyG8tdm/nmZCFMwaqVr+aFqm2lXJE/vu4+vy/zaMRdtiuLb37RwXf5qJljVF9zMLjBazRmyr\n2cRd2dc2JEeGiwgWpD/IlJ7HtnKmxuWGr/bA5zvUDM3D4AQ4f5SayWk6lpp6+HAr/JTnP354Xzhz\nqLdnnaZ5tjs2s2jvfHKd2Q1jAyOHcVv6g6RHZppoWWC0mAVGi1kA9tTu4I49V1NUrxKrbNi5ue9C\nyz2hWZXcCjVLy6v0jkXY4bA+qvZj71jzbOsulNXCqhyVxF7uMxvrGQEzR8IoXeW+VaSUfFzyFv8u\neIR66S0+elrCuVyRdjORNmvW89JiFhgtZs2wry6HO/ZcTZ5zLwACwdW9F+iiom2k3q1q/n2xy1uF\n3cOgBCVqY1N1MEJ7cEvIKoYfcmDTfm89RQ/j0+Ds4d6Gq5rmqXJV8FjeX/iuYnnDWLQthut638mx\n8dNMtKx1tJgFRotZCxQ7C7kj+4/srs1qGLsk5TrO73WpiVYFF3vK4K3fYF+AYJAe4codNiVduyBb\nosoJq3PVLGx/TdP9sRFw9jA4RAfbtYltNZtYlLOAfcaDKhhuxYyHSI/ob6JlbUOLWWC0mLVChauM\nu/Zcx1aHN7JxZvIc5qRcp5Mn24hbwvYSVU5pY4AZhQCGJcPUdBiRrBN5QSU87y5Ts7BfCwInpA9K\nUNdsjJ7htgkpJR+VvMGSgsV+bsXpCedxRdpNQZNDpsUsMFrM2kC1q4q/7L2RX6u99k5PmMnVvRdg\nE/ou0h7KamFVLvyYo943Jj5S5bAd3te/DU2o4KhXLXdW5vivOXqICoNJvdVsNk2vPbYZ5VZcyHcV\nKxrGom09uKHPnRwdd4qJlrUfLWaB0WLWRurctTyQM59VlV83jB0Xdxo39r2noemnpu243Ko/2soc\n2FLUdF3NJmB0L5iSAUMSu39of26FmoX9vM9bscOXjJ6qNcv4NBVMo2k7gdyKgyNHsCBjEX2DwK3Y\nGC1mgdFi1g7qpZPFuffwVfknDWOHxx7DbekPBo2LwooU1aiZ2qpcb6sZX3pFq5nIpL7dK7jB6YJ1\nBcr9uqe86f5wm6qHOSUd+sV1vX3BjpSSD0ve4Nn8v1OPN4v89MTzuDw1eNyKjdFiFhgtZu3ELd08\ntW8Ry0rfbhgbFzOJOzMWE2PX5ccPhno3bChQM5QdpU33h9lgXKqaoWTGBW8DycJqNSNdnetfqcND\nWg+1Fjaht+orp2k/la4KHsu7l+8rvmgYU27Fuzg67mQTLTt4tJgFRovZASCl5MXCJ3mr6PmGsWFR\nY1jY/wl62nXNoI4gv1KJ2pp9gTtb94lVN/xxacExW6tzKbfqD3v9K9h7sAuVqjA1XfWPC1ahtgJb\nazayKGcB+c6chrHBUSNYkB6cbsXGaDELjBazg+DN/c/zYuETDduZkUO4r98/SApPMdGq7kWdSxXO\n/WEv7G2mIn90GCRH+7xi1L9J0SqIpCvW26RUpaSKHFBUrVynnldxDVTUBT4vKUq5EQ/rq0LsNQeO\nlJKlJa/xXP6jfm7FMxJncXnqjYTbuscF1mIWmFbFTAjxHHAGUCClHBNgvwAeA6YD1cAcKeXa1n5w\ndxAzgI9L3uKpfYuQRghDn/AM7u//NGkRfU22rPuRXa7ccz/v8++W3BJ2oUQtOcArKbp9XbJdbihx\n+IuU7/tAgRuBEMDIXspdOiyp+we3dAUVrnIey72XHyq/bBiLscVyQ5+7OCruJBMt63i0mAWmLWJ2\nDFAJvNSMmE0HrkOJ2WTgMSnl5NZ+8AGLWWUJ/PoZTJ4JdmsUnvuybBl/z727oYlfclgq9/X/J/0j\nB5lsWfekxqncj2vyIL+q7cIWiPjIpmKXEGXMsmr8X6WOpjlybcUu1GePS1WpBwnWrJQUlCi34nzy\nnbkNY0OiRrIgfRF9IvqZaFkzrPsU0oZA7yEHdLoWs8C0qgZSyq+FEANaOGQGSugksFIIkSCE6COl\nzGvhnAOjrho+egj274ai3TDtBogw/65wfPx0om09WJQzH6esa2jyubDfkwyNHmW2ed2O6HA4qp96\nSalceA2iU+3v6gsUHelLWa167QwQcNJeouxeF6dn5ucrkHoG1vGsqviGv+b8H07p9eOemTibual/\nsp5bUbrh2//Auk8gqifMvAcS+phtVbehTWtmhph91MzM7CNgkZTyW2N7BTBfStlk2iWEmAfMA+jf\nv//E3bt3t8/aX5bBt694t1MGwpm3Qow1gi7WVa1iYfaNOKSqORRji+Xufo8yJmaCyZaFLo76pi5B\nj+iV1rZ/phUXGdhlmRwNMeE6cKMr+ab8cx7OuR2XsT7WwxbLDX3u5si4E022LAD1dbD8Kcj60Ts2\n7Eg45Y/t/ig9MwtMl4qZLwfkZpQSfnwLVr/vHYtLgTPnQ6I11qg216zn7j3XUelWiUMRIpLbM/7G\npNgjTbZM05jGa2CeV5lDBWMc7BqbpvNYXvohj+XdixvlY04LT+e+/v+wZrSioxKW/R1yN3vHBh2m\nhCys/bNHLWaB6Qgx+xfwlZTyNWN7C3Bca27GgwoA2bAC/vecEjeAqFg4/RboM+zAPq+D2eXYxh17\n/kiJS/WttxPGH/v8mWkJZ5tsmUYT/HxU/CZP5S9q2M6IGMD9/Z+iV7gFKy2XF8KHD0GJN02AcdPg\nqIvAdmCl8LSYBaYjCgsuBS4WiilAWaesl/ky5kSYfjOEGRn8jkp4/37Y/lOn/ti2MiBqKA8NWEJq\nuPKHu6jn8byFLMn/Oy7ZxpA3jUbThHeKXvQTsoGRQ3kwc4k1haxwF7x9t7+QHXEBHH3xAQuZpnla\nvaJCiNeAH4DhQoi9Qoi5QoirhBBXGYcsA3YAWcC/gWs6zVpfBk6Ac26HaKPOj8sJnzwKv/635fO6\niL4R/Xk48zkGRnpni+8Vv8LC7BupdgWoIKvRaJpFSskrhU/xXMFjDWPDosbwQOYzJIQlmWhZM+xZ\nD+/+BaqNyCJbGJxyLUw4Qy+sdhLBnzRdlg9LF6l/PUw4E6bOAgtUtK9xV/NIzp1++S+ZkYO5M2Mx\nfSIyTLRMowkOpJQ8W/Ao7xW/3DA2JmYCd2c8Zs0Scpu/gS+eAbfhhYmIgek3QUbHRDZrN2NgzL/b\nHyzxaTDzXpW34WHth/D5P9VszWSibTH8OeNhzk/2NvTcXbudm3ZdzIbqVnPLNZqQxi3d/HPfA35C\nNqHHVO7t94T1hExKFZy2/CmvkMUmwbl3d5iQaZon+MUMlKvx7NthgE8I/NbvYemDUBugxXEXYxM2\nLkm9jpv7LmxoF1PuKuX23VfxeekHJlun0VgTl6xncd7dfkW9p/Y8nrsyFhNls1hrcrdLBaWtfNM7\nltxPPWgnWzBxuxvSPcQMIDwSpt+ogkM85GyCdxZCZZF5dvlwQvwZLOr/DAl25eOvp55H8+7l2fzF\nOjBEo/HBKZ08mHMbX5R93DB2XNxp3Jb+oPWSoZ0OWLZYRVl7yBgNv7sbYpPNsyvE6D5iBmCzw7GX\nwdTZ3rHibBVRVJRtnl0+jIw5hMUDX2Jg5NCGsXeLX+a+vTfpwBCNBqh1O7gv+ya/rtDTEs7hpr4L\nsQtrlLBroKZcRVLv8lkyGHakyn2NjDHPrhCke4kZqEihiWfBSVcrcQOoLIZ37oW9G821zSA1vC8P\nD3ieybHHNoytqvyGW3ZfRn5dbgtnajTdm2pXFXdnX8/qqu8axmYkXcB1ve/ALiyWsV66Tz0o52/3\njk04C06+2jJ1Y0OJ7idmHkYcrUpdhRu+9bpqFfW49Xtz7TKItsVwR8YjzEye0zC2uzaLP+26kI3V\nP5tnmEZjEhWucu7Mvob11d4o59nJl3NF6s0Iq4Wz52fBO/f4RFELOGYOHDHbElHUoUj3vur9xsK5\nd0GPRLXtdsF/n1TRjialJPhiEzYuTb2em/r4B4b8ec9VLC/90GTrNJquo6y+hD/vvpLNNesbxuak\nXM9FqddYT8h2roH37lMuRgB7OEz/E4w7xVy7QpzuLWYAvTJVRFFSunfs+9fg6xfBfRC9QzqQExPO\n4IH+/yLerkS3XjpZnHc3z+U/qgNDNN2e/U7VZWJH7ZaGsavSbuW8XnPMM6o5NqxQdRbrjSr9UbEq\nknrQYebapQkBMQPo2UtFFvUd4R1b/1/49DHvl9JkRsWMZ/GAlxkQ6c2Xe6f4Je7bezPVLvPTCzSa\nziC/Lpf5uy8nu24nADZs/KnP3ZyZNLuVM7sYKVXY/VfPer06cSlw7r2WqQkb6oSGmIF6gppxGwyZ\n4h3b8RO8/1eoqTDPLh/SIvrycObzHB57TMPYqsqv+b/dl1Lg1IEhmu7F3tpd3Lp7LvucewFVkPv/\n0u/n5IQZJlvWCFc9LH/av1tH6iCYuRASdT8yqxA6YgbKtz3tWhg/3Tu2b6tayC0vMM0sX2LsPbgj\n4xHOTb6kYWxXbRY37ryYTdXrTLRMo+k4djq2Mn/35eyvVwEUYSKc2zMe5pi4aSZb1oi6avjoYdjy\njXcsczycfYdl+ihqFKElZqAijY66ULVgwFhYLs1TIbYFO0w1zYNd2Lks9Qb+1Ocewoxm4KWuYm7b\nM48VpR+ZbJ1Gc3BsrdnIgt3zKHUVAxAporin3+NM7nlsK2d2MZUlqlhwtjcohVHHw+k3W6LDvcaf\n0BMzD+NPg1OvV7M1gOoyeO8vsH2VuXb5cHLCWfw181/E2RMAFRjy97y7eL7gcdzSGsErGk172FC9\nlj/vuaqheW2MLZa/9P8Hh/aYbLJljSjYAe/cDft3e8cOnwnHX+7NX9VYitAVM4Ahk9U6WqRRsNRZ\nq9rIfP2iJYoUA4yOOZTFA14m0ycw5O2iF7h/7y3UuKtNtEyjaTtSSr4p/5y79lxLjVsFNPW0x/PX\n/k8zOuZQk63zQUr49TN4+x6oUM11ETY4YR4c/jvdvsXCBH8LmI6gOAc+ekh1hfWQMlDN3OKt0fSv\n2lXJQ7l/5qfKbxvG+oRncFHKHzk67mRsOlFTY1G21mzk2YLFfl0iEuzJ3N//KQZEDWnhzC6mtgpW\nPKMCwzyER6v7QOYh5tnVCN0CJjBazDwE+iJHRKsnsiHWcIG4pIsXCh7nXZ92GKC67V6c8kcOiz3a\negmmmpAlvy6XFwuf5H/ln/qNp4T15v7Mp0mP6G+SZQHI3w6fPd7ogXYATLseEnqbZlYgtJgFRouZ\nLx4Xw3f/8fYjAhh7Mhz5BwizRrXu5aUf8kz+36hy+6cUjIw+hEtSrmVsj4kmWabRqLJUb+5/lqUl\nr1Mvve56O2FMT5zJ73tdQXxYookW+iAl/PopfPdqo7/5U+CoP3jX1C2EFrPAaDELRBA8pVW4ynm3\n6CU+KH6VWunw2zehxxQuTrmWodG6IaCm63BKJ8tK3uK1/f+mwlXmt++InicwJ+U60iMzTbIuAI5K\n1RF6h899yGLemEBoMQuMFrPmaM5/fsIVMHRK8+d1MSX1Rby5/zmWlb7t9xQM6gZyUco19I8cZJJ1\nmlBASsl3FSt4oeBx8owEaA/Do8YwN+1GawV5gCoU/OkTUGHddfLm0GIWGC1mLSGlKnv17X/Ack2z\nJwAAGLBJREFUXe8dH3OSylWziNsRoMCZy6uFz7Ci7CPceMP2bdg4Pn46F/S6kt4R6S18gkbTfn6r\nXseSgsVsrvnVbzwtPJ1LU6/jqJ4nW2sdV0pY9yl838itOG4aHHmBJd2KjdFiFhgtZm2hYAd8+rh/\nlZBemeopLsFa5Wyya3fySuHTfFvxud94GGGcmvg7ZiXPJSk8xSTrNN2F3Lo9vFDwJN9VLPcbj7XF\nMbvX5ZyReL71OkI7KmHFv1TVew8RMXDiPBh8uHl2tRMtZoHRYtZWaqvhy39D1o/esfBoOOFyGDrV\nPLuaIavmN14u/Kdfk0NQ1RbOTJrNzORL6GnX5Xg07aO8vpTX9/+bj0veoh6vtyJMhHNm4ixm9Zpr\nze/Vviy1Du7JHQNVX/HU6yEu1Ty7DgAtZoHRYtYepIQNy+Gblxu5HU9U5bEs5Hb0sKF6LS8VPMnG\nml/8xmNssZybfDEzki4g2qbbu2taps5dy4clr/PG/mepclf67Tsm7hQuTrmWPhEZJlnXAlLCL8vg\nh9f93YqHnAZH/D4oO0JrMQuMFrMDoWCnespr6DKLcjtOu96SVbSllKyp+p4XC5706xkFEG9PZFav\nuZyWcC4RtkiTLNRYFbd083X5f3mx8AkKnHl++0ZHj+eytBsZET3WJOtaIZBbMTIGTrwyqPuPaTEL\njBazA6WuGr5YAlkrvWPhUap227AjzLOrBdzSzXcVK3il8Cn21u3y25cS1pvfp8zjpPgzsIvge1rV\ndDzrq9bwbMFitjk2+Y33jejPpSnXM7Xn8dYK7vBl3zb47Al/t2LaYPXAGRfca8ZazAKjxexgkBI2\nrlBuR99ajqNPgKMvtqTbEcAl61lR9hGvFj5DYf0+v33pEZnM7jWXY+KmESasH9ml6Xh2OLbySuFT\n/Fj5P7/xOHsCF/Sax2mJ51r3uyHd8PMyWPlGt3ErNkaLWWC0mHUEhbtU12pft2Nyf7W4nNjXNLNa\nw+mu45PSd3hj/7MN7Tg8pIb34XdJF3FywgyibNEmWajpSjbXrOeN/c+yqvJrv/FwEcHZSX/gvOQ5\n9LD3NMm6NlBTASuehl0/e8ciY+DEq2BQ97n3azELTJvETAhxKvAYYAeWSCkXNdo/B3gYyDGGnpRS\nLmnpM7uVmIFyO365BLb5uh0j4bi5MPwo8+xqAzXuapYWv8Y7RS82WdyPsycwI+kCTk88n572OJMs\n1HQWUko2VK/l9aIl/FL1Y5P9J8SfzkUp15Aabr21YD/ytiq3YmWRdyxtCEy7Lujdio3RYhaYVsVM\nCGEHtgInA3uBn4DfSyk3+RwzB5gkpby2rT+424kZGG7HL+Cbl/zdjqOOgyMvVE+JFqbCVc7HJW/y\nQfGrlLtK/fZF22I4LeFczk66kGSdpxb0SClZW/UDb+xf0iTSVSA4oucJzOp1OYOjhptkYRtx1cPP\nH8OPbykXo4fxp8PUWd3CrdgYLWaBaYuYTQXukVJOM7ZvA5BSPuBzzBy0mHnZv1slWZf6RH/FJKiq\nIUOnWr4nksNdw+elH/BO0UtN1tTCRDgnxp/BucmXWKvquaZNuKWbHyv/x+v7l5Dl+M1vnw0bx8ad\nyvm9LguOEmh5W+DL56A42zsW2QNOugoGdt9i21rMAtMWMZsJnCqlvNzYvgiY7Ctchpg9ABSiZnE3\nSimzA3xcA91azADqauDLZ2Hb9/7jGaPh2EstvZbmoV46+br8M97a/wJ76nb47RMIjux5Euclz2FI\n9EiTLNS0FZd08W35ct4oepbdtVl++8II48SEM5mZfAl9g+EBpaYcvn8NfvMPUCFtiFqn7tnLHLu6\nCC1mgekoMUsGKqWUtUKIK4FZUsoTAnzWPGAeQP/+/Sfu3r278SHdCylVxZBvXoJqH7edLQwmnAGT\nzrZsxKMvbulmVeU3vFX0HJtr1jfZP6HHFGYmX8q4mEnWDdUOUeqlky/LlvFm0fPk1u3x2xchIpmW\ncA7nJl9MSrg1ukG0iHTDpv8pIav1WdsNi1RdoA85rVu6FRujxSwwHeJmbHS8HSiWUrZY06bbz8x8\nqauGH99WvdJ8r3dcKhw7BzLHm2Zae5BSsrFmLW/tf6FJmSxQFdLP63Upk2OP1Z2vTabOXcvnZUt5\nu+iFJsnOUSKa05PO55ykC0kMSzbJwnayfzd89ZzKH/Nl0CSVBtPNZ2O+aDELTFvELAzlOjwRFa34\nE3CBlHKjzzF9pJR5xvtzgPlSyhb7pISUmHko3KX+IPP93TwMPhyOvghig+TGAmx3bOHtohf4tvxz\nvyr9AP0iBjIzeQ7HxZ9q3XykborDXcMnJe/wbvFLFNfv99vXw9aTs5Jmc1bi74kLSzDJwnZSV+Pz\nIOjzPeuZAsdcAgMnmGebSWgxC0xbQ/OnA4+iQvOfk1LeL4RYCKyWUi4VQjwAnAXUA8XA1VLKzS19\nZkiKGag/yI1fqlpxtVXe8fBIOHymakURRK6S3Lo9vFv0Mp+XLW3STy0lrDfnJF/ItIRzdK5aJ1Pl\nquCjkjd5v/g/TSJR4+wJnJN0IacnnmftPDFfpITtq1RBgiqfHEibHQ41XPThoVl+TYtZYHTStFlU\nlynf/2b/BFWS+8Fxl0Efi4dEN6LYWcgHJa/xcclb1Lir/PbF2ROYnngek3ocwZDoUYTr2VqHUVZf\nwtKS1/mw+LUmOYLJYSn8LvliTk34XXA9TJTlw/9egD3r/MfTR6ngqaTQ7sunxSwwWszMJuc3+N9z\nUJzjPz7yODhiNkQHV6JypauCZSVv8UHxq02qioBqQTMiehxjYiYwJmYCw6PHEGmLMsHS4KNeOtnl\nyGKLYwNbajawtWYDe+t2IfH/G04L78vM5DmcFH9mcBWPdjlhzYew5gP/PM3oOJXWMuxIy6e1dAVa\nzAKjxcwKuOph3Sew6l2or/WOR8bCkb+HkcdCkAVU1LodfF66lHeLXyLfmdvscWGEMSx6TIO4jYw+\nhBh7jy601JpIKcl35irRMsRru2MzdbK22XPSIzI5P/my4Fyr3LMe/vc8lPnmNQoYexJMOV/lj2kA\nLWbNocXMSpQXqjB+35YVAL2HKddjryDIAWqES9bzfcUXrK78jvXVa8l35rR4vA07g6OGMyZmImNi\nJjA6Zrw1mz12MBWucrbVbGRLzQa2ONaztWYjZa6SVs+zYWdY9ChmJF3AkT1Pwi7sXWBtB1JZAt+9\n7F8GDiBloPrOpw02xy4Lo8UsMFrMrMjONfD1i/7tK4QNDjkVDj8XIoJo/aMRhc59bKz+mQ3Va1lf\nvaZJK5pADIgcYszcJjI65lCSwoI7DNspnex0bGVLzXq2OpSA5dS1LecyLbwvw6JGMzx6LMOjxzAo\nanhwrYd5cLtg/X9h5dvgrPGOR0TDlFkw5iSwBZc3oqvQYhYYLWZWxVkLq99Tded8W1n0SFJh/IMP\n7xbrB6X1xX7itqt2W5M1oMakR2Qa4nYo6REDiBARRIhIwm3GvyKcCBFJmAjv1CRuKSVOWYfDXYND\nVuNwO9R741Ura3y2HRTXF7KlZj3ba7c0ifwMRA9bLMOixzA8agzDokczLHpM8OSFtcS+LLVOXLjL\nf3zYEaqGaY8gSRswCS1mgdFiZnWKc9RaQo5/g0T6HwLHXAwJFq9m3k4qXOX8Vv2LIW5ryXL8hhtX\n6yc2gxI3JXIRtoiG9+EinAhb432RRIgIwkQ4dbKWWo84GaJU6yNMDqm2G+fYHSh2whgYNZTh0WMY\nHjWWYdGjSY/I7F7J5zUVsPJNVYzb94EloY9yKWaMNs20YEKLWWC0mAUDUsLW7+DbV1RdOg9CqMLF\nE84KyvW0tlDjrmZz9a8NM7ctjg1tmtVYnd7hGQyPVjOu4VFjGBw1IrgiD9tDZTH8skw1snX6BLDY\nw+Gwc+DQ09V7TZvQYhYYLWbBhKNSPdluWAGNXXEDJ8LEGdB7iCmmdRV17lq2OjawofpnNlX/QoWr\njDpZS52sw+muo07W4pRO6mRtl4hemAgnSkQTZVOvSFuU33aULZpIEU2ULYoe9p4MjhrBsKjRxIcl\ndrptplOWD2s/hN++Bne9/77M8aqCR3yaObYFMVrMAqPFLBjJ365ELbtp0V8yRitRyxjdLdbUDga3\ndOOUdYbQKcFTYlenxt3ebd/3TllHhIgg0hbdSKiM94Y4RdmisYvgqdbSZezfA2uXwrYf/GuRgioK\ncPhMVVMxxL+fB4oWs8BoMQtm8rfDmqWw46em+9IGw8Sz1IytO627aKzLvm2w+gPYtbbpvrQhqgTV\ngEO1iB0kWswCo8WsO1C0Vz0Jb/3evxgrqNI/E2eotTVbkOUgaayPlLB3gxKxxkFKAP3Gqu9f+kgt\nYh2EFrPAaDHrTpQXwNqPVNNCV6P1orgUmHAmjDgmKHqoaSyOdKt8yNUfQMGOpvsHHaY8AzrpucPR\nYhYYLWbdkaoS+OUT2LAcnA7/fTEJMH46jDkxqJOvNSbhdqm1sDUfNK0nKmwqV2ziWZCUYY59IYAW\ns8BoMevOOCrh1//Cuk/9O/OCqnU3bpp6RQdJWxCNedTXqQ4Paz9UZdd8sYfDqONUiH1cqinmhRJa\nzAKjxSwUqHPApi9UNZGqRvX+wiNh9ElqthYbAuHimvZRV6Nm+L98AtX+fdIIj4KxJ8Mhp+mqHV2I\nFrPAaDELJVxO2PyNerouy/ffZwuDkceodTWd+6OpqVDdnX/9zL+JLEBUrKoTOvYU9V7TpWgxC4wW\ns1DE7YKsH9XifXG2/z4hIGOMin4cNEnfrEKJ+jrVEHPbSti51r8dEUCPROVKHH2CmpVpTEGLWWC0\nmIUy0g27flailp/VdL/NDv3HKWEbOAEiYrreRk3n4qpXyffbflDRiXU1TY+JT1Ml00YcpctOWQAt\nZoHR5QtCGWFTSdUDJqiO12s+8K8q4nYpsdv1s7qJZY6HoVNU4qt+Mg9e3C7Yu1HNwHb81NSN6KFX\npnI7D5mscxQ1lkeLmcZwLY5Sr4r96iaXtdI/f8jlVDe+HT9BWCQMPBSGTIXMQ3TeWjDgdkPuZsj6\nAbJWgaMi8HHxaTBkipqNJ/fTic6aoEG7GTXNU5bvFbb9zTSPDI+GQRPVDbD/OLDr5yPLIN2qxNS2\nlWqNtHE0ooeevQwBm6I6PGsBszTazRgYLWaatlGSo26K21aq94GIjFGVH4ZOVYWOtWuq65FSzag9\nDyGVRYGP65Go3IdDp6q6iVrAggYtZoHRYqZpH1JCUba6UW77oWmIv4eonqob9tAp0Hck2HSx405D\nSjVz9ghYeUHg46LjlIANmQJ9h+sC1EGKFrPAaDHTHDhSQuEur7BV7A98XEyCckX2HgqpgyChrxa3\ng0FKda0LdqjOCTvXQGle4GMjY2GwMVtOH6lny90ALWaB0WKm6RikVDfWbT+o9Zmq4uaPDY+ClAFK\n2Dyv+DTt6mqOyhIoNISrYKcSseYCOEClUAya5HX36nXMboUWs8Dob7mmYxBCdbnuPQSO+gPkbfUK\nW025/7FOh4qsy93sHYuMUcEHqYMgdTCkDlSBCaEmcDXlSqwKdkC+8W9zgRu+hEepXMChU41AHJ0P\npgkt9MxM07m43ZD7G+RtUbOK/O1tuzmDWndLHQRpxuwtZVD3qh/pqITCnd7rUrizeVdtYyJilOCn\nDlYPEP3H6RSJEEHPzAKjZ2aazsVmU66ujNHeMY/bzHf2Echt5qhQ5ZX2rPOOxSSoHlmpxiwuvrfq\nABDZw5rrcNKtCj3XVkJFkXfWVbCj+eCZxoRH+sxatVtWowlEm8RMCHEq8BhgB5ZIKRc12h8JvARM\nBIqAWVLKXR1rqqbbEJsIsRNV9RHwD2hoeO2Euuqm51aXqoCHnWua7ouIUaIWZYhbVKwhdLHeMb/3\nxrHh0S0Lg5SqbmFtJTiqVMUMv/fGy1HZ6N8qqKtS57cVe7jPeqIx80roY02h1mgsRKtiJoSwA/8A\nTgb2Aj8JIZZKKX17pM8FSqSUQ4QQs4EHgVmdYbCmGyKE6oQdl6JCx0HNaMryvQEPBTuUG85Z2/zn\n1FWrV0Vh88cE/Pk2f/GLiFZFdh0+IuWuP/D/X3PY7JDc3+tGTR0Eiek6YEOjOQD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Z+fj44Obmhk6nw2AwqPtNJhP5+flERkYSFRUFQEBAAHV1deTm5rpUZiaTiby8\nPMLDwx3WyYWEhKhLFubOncvQoUN5//33+eCDD05JKRdZv5d/CSGesioqGy9JKf8DyvwakA+MBd6U\nUqYJIfaiKK/N1jpewHgU5YgQwh9F4T0lpVxgbXOTEMIXeFwI8YaU0jYfEQIMk1LusXUuhHgOqALG\nSSmrrftOoShSWx2Bomw/kFL+1W6/EXhNCPEvKWURgJSyVAhxDOhPE8pMMzM2Qmd/R+/G9ReIubG5\nkYaUksWLF9OzZ098fHzw8PDg9ttvx2g0qmt6vvvuO4KDgx0UmT3bt2+nurq6SU+z1jBmzJgG+9LS\n0pgwYQLh4eG4u7vj4eFBeno6GRkZgPLH/fPPPzN16lSXa6auvfZadDqdaj4ym82UlJQ4zCn5+vpy\n8cUXN7s15SjRUgICAoiKiiIgIICAgADi4+MJCgoiNze3xSPEpqirq2t2VNgawsPDCQsLw8/Pj+Dg\nYBISEvDw8Gjx3KA9VVVV6PV6B8Xi6emJwWBoMHpu7cjeZl609170Ps3bUF1djcViadSMXFNT49IL\ntLKyEovF4lLZmc1mdu/e7bC0wsonKP/hVzrt/8b2waoQTgIxTufdaGeiHAX4AbYQMVcCemC1EEJn\n24DvgHCntrLtFZmVK4BNNkVm5UunOglAF2BVI314A0lO9QtRRngu0ZSZC4bH12eltpkbLe1msUHr\nqampoaioSH3Lb4zFixcza9YsJkyYwBdffMEvv/yijopsJqmioiKHN2VnbPMHTdVpDc7ylpeXc911\n13H8+HFefPFFfvjhB3bu3Mmll16qylhSUoKUskkZhBDo9XqKioqQUlJcXIyUkuDgerO9m5ubOlJr\narONXmwjDWcnG1u5tcokKCgIk8mkzlm6u7tjNpsbKDez2Yybm1uTzhdSygbHz6Q9Z9zd3QkICHBY\nEN1SamtrG703Op2uwWi2tffQ3rzoJiDIG/V+tvYlxKasnM+zlV05V9muwVV/hYWF1NXVNfZs2swo\nznNJpU7lWhQFYeMTIBS4xlq+GdgupbStMre9saUCdXabzSxpb6dqzJQTATiEeJJS1gD2bx62PtY7\n9XG4kT4AjE7X0ADNzOgCm7nxlQvE3Lh582ZMJhNXXun8klfP6tWrmTRpEk8//bS6b/9+x3jTISEh\nTb59294+c3NzHUY59nh7ezdwKnG1psd5ZLV9+3ZOnDjBpk2bHNZV2U+kBwUF4ebm1uwowWAwYDQa\nKS8vp6jV7mGHAAAgAElEQVSoiMDAQIc/y9aaGb28vBBCUFNT4zB3ZFOyjZm0WoNtbstoNDrMc9XU\n1DTrFajT6Rr82Z5Je43RksghjeHp6dmop6rJZGqgvFrTh1kqSTdt2LwXT506hYeHR6u/D5sych7l\n2pScs3nZhq1uXV1dowotNDQUDw+PxjxIbdqt+QlMO6SUWUKIFOBmIcSPwDiUOSsbtvbG0riysv/R\nN/aKnwd0st8hhPAGDHa7bH3cDfzaSBuHncqBNHOd2sisCWL84eoLwNxYWlrKY489Rvfu3ZtcpFpd\nXd3gAbdPLQKKea64uJi1a9c22saVV16Jj49Pk9EZYmJiOHDggMO+b775xkXthjKCo2LYtm0bR44c\nUct6vZ4BAwbwwQcfNGmi0+l0+Pv7k5OTQ0VFRQPl21ozo81b0Nl5ori4GIPB0OpRRUlJCTqdTl1w\nbDAYcHd3d2jfbDZTWlrarPnNy8sLo9HosO9M2nPGYrFQWlqqzje2Br1eT2VlpYN8tbW1VFRUOMxZ\ntZYau0GdbXH0qVOnKCkpcXCsaQwhRAPXf9tcmvOLV0lJCd7e3i5HXnq9Hjc3N5dej+7u7vTt29ch\n2LOVmwALioNEa1kJTLBuPoB949uBaiBKSpnSyFbeTNs7geFCCHuHD+d5h3QgG4hz0Yd6M6yel12A\njKY61UZmzTAsHlILIM/q3bgqDe5tx96NJpOJHTt2AIpJbteuXbzxxhtUVVWxceNGl2+PAMOHD2fJ\nkiUMGDCAbt26sWLFCofcU7Y6I0aM4LbbbmPu3Llcfvnl5ObmsnXrVt566y0CAwN54oknmDNnDrW1\ntYwePRqj0ci6deuYN28e0dHRTJgwgffee48HH3yQMWPGsHnzZnWhd3MMHDgQg8HAXXfdxaOPPsqJ\nEyeYP3++OpFu49///jfDhg1j1KhR3H333ej1erZv306/fv0YO3asWi80NJRDhw7h6emJv7+/Qxvu\n7u4unRlcERkZSXp6OseOHSMoKIiysjLKysro0aOHWsdoNLJv3z7i4uJUBZqZmYler8fX1xcpJUlJ\nScyePZtJkyapoxE3NzciIiLIzc1VFxvbHHpsi4NdYTAYGrjbt7S9wsJCtm3bxvjx49VRSGZmJiEh\nIXh5eamOEXV1dadlXg4JCSEvL4+DBw8SFRWlejPqdLpGlc7QoUOZPHkyf/nLX1y2aZFgqqujrroC\nIUC413H0ZBlFRUX4+/sTEdHk9Aw+Pj7qd6fT6fDy8kKn0xEeHk5ubi5CCHx9fSktLaWsrMxhIboz\nOp2OyMhIsrOzkVISEBCgRnLJzs4mOjqaBQsWMGLECNtcs78QYhaKw8Y7Ts4fLWUVigPGc8BWa6ov\nQHW4mA+8LISIBbaiDHwSgKullBOaaXsxMAP4SgjxEorZ8R8oTiEWax8WIcTDwHKrw8kGFHNoV+AG\nYJKU0jZ0SEQZ1f3UVKeaMmsGZ3Pj4VL46Tj8qUvz556PlJWVceWVVyKEwN/fn+7duzN58mT+9re/\nNfsAz507l4KCAjVZ4sSJE1myZIm6vguUN9bPPvuMJ554gsWLF1NQUEBUVBS33XabWmf27NkEBwfz\n8ssv89ZbbxEUFMSQIUNU09uYMWN45plneP3113n33XcZP348L7/8MuPHj2/2+sLDw1m9ejWzZs1i\n/Pjx9OjRgzfffJNFixY51BsyZAibNm3iiSeeYPLkyXh6etKnTx81LJSNwMBAhBCEhISctpnMHj8/\nP7p160ZOTg4FBQV4eXnRtWvXZkc63t7eFBUVqQ4ftjk/53kU23eYm5uLyWRCr9erzhdNERQURF5e\nnoP35um2Z/P0y83Npa6uDjc3N/R6PYmJia1W/rb2EhISOH78uDrCtt3H03FaqTEptrGa8mJqyosR\nQnBKp8PHx4e4uDiCg4Ob/a4jIyMxGo0cOnQIs9msvnjYvBgLCgpUb8j4+HiHuVZX7el0OvLz8yko\nKECn02GxWNRn4rrrrmPlypW2qC3dgZnACyheh61GSnlcCLENJVzhgkaOLxJC5AAPAg+jrCHLwM4j\nsYm2s4UQY4CXgf8CacCdwCbAPij9J1Yvx39aj5uBQ8BaFMVmY6R1f2PmSBUtnFUL2ZgF3x5RPnu4\nwYMDoFPrLSYa7Yi0tDSioqI4ePAgSUlJ5ySs0ukSFxfHu+++26rYhc2RmppKSEhIsy81jc1VHTly\nhPj4+LPuFXk6NDUys0hlDanN6cNHByE+52f26I4UzkoIMRj4AbhGStl4enLX524H1kkpn2qqnjZn\n1kKGxUOE1TxfZ4HV+zu2d+OFTk5ODjU1NZw4cYKAgIDzSpE5YzQamTlzJlFRUURFRTFz5kx1fik5\nOZlPP/0UgJ9++gkhBOvWrQPg22+/5bLLLlPb+e6777jqqqsICgpixIgRHD16VD0mhOC1116jR48e\nDiZRZ/7zn/8QFRVFZGSkw5rEpmRctmwZgwcPdmhHCKGasKdNm8aMGTMYM2YMfn5+DBgwgKysLLWu\nzdknICCA+++/v8l50DIn78VA7/NTkbV3hBDPCiFusUYCuQdljm4v0LI0CPXtDAAuAl5trq5mZmwh\nOje4+WI7c2NZ+zY3ajTN22+/zcCBA4mNjaVLly7w6m3Nn3S2uP+jVlV/+umn2bFjB3v27EEIwfjx\n43nqqad48sknSU5OZsuWLdx44418//33dO3ala1btzJmzBi+//57kpOTAfjiiy94+eWXWbZsGX37\n9uWll17i1ltvdcgA/fnnn/Pzzz83iGBiz+bNmzl48CCHDh3immuu4bLLLmPYsGFNytgSVq5cyYYN\nG7j88suZOnUqc+bMYeXKlRQWFjJx4kSWLl3K+PHjefXVV3nzzTe54447GrRR47Q4Wou9eE7xQpmP\nCwfKUda+PSSlbDpgZkOCgalSSuflBg3QvspWEOMP18TVlzdkQUEH9G7UUDIMxMbGcvHFF5+xy/y5\nZsWKFcydO5ewsDA6derEvHnzWL58OaCMzGw5wbZu3crs2bPVsr0ye/PNN5k9ezZDhgxBr9fzz3/+\nkz179jiMzmxznU0ps3nz5qHX6+nduzfTp0/n448/blbGljBhwgT69++PTqfj9ttvZ88eZZ3u+vXr\n6dWrF5MmTcLDw4OZM2c2aia1SCixC+Xo4yK1i8bZQUo5U0rZWUrpKaUMkVLeau9k0op2NkgpnRdc\nN4qmzFrJtXEQaWduXKWZGzXamJycHGJj69eQxMbGkpOTAyhLITIyMsjPz2fPnj1MmTKF48ePU1hY\nyC+//MKQIUMAJf3LAw88QGBgIIGBgQQHByOlJDs7W23XPtSSK+zr2MvRlIwtwV5B+fr6qpE/bOlp\nbAghGpVTMy92fDQzYyuxeTcu2akosSNl8ONxGKKZGzs2rTT9/ZFERUVx9OhRevXqBcCxY8dUrzpf\nX1/69u3Lyy+/TFJSEp6enlx11VW8+OKLdOvWTXX979y5M3PmzOH222932U9LvDmPHz+uLla3l6Mp\nGfV6vUNkkNYkio2MjHTIYiClbJDVQDMvXhhoX+lpEO2njNBsaOZGjbbk1ltv5amnnqKgoIDCwkIW\nLlzI5MmT1ePJycm8+uqrqklx6NChDmWAe++9l3/9619q2pSysrLGFuk2y5NPPklVVRWpqaksXbqU\nm2++uVkZL730UlJTU9mzZw81NTXMnz+/xf2NGTOG1NRU/vvf/2IymViyZImDMtTMixcOmjI7Ta6J\nqzc3mizwiWZu1GgjHn/8cfr168cll1xC7969ufzyy9W1gKAos/LyctWk6FwGZU7qscce45ZbbsHf\n35+kpCQ2bNjQalmSk5Pp3r071157LbNmzeK6665rVsaEhATmzp3LsGHD6NGjRwPPxqYIDQ1l9erV\n/OMf/yAkJISDBw8yaNAg9XhZTcPYi5p5sWOirTM7A7LL682NAGN7QLJmbuwwuFrno9E+qDE5WkyC\nvUF/VvMgn1s60jqzPwJtZHYGOJsbN2bByco2E0dDQ8OKZl688NAcQM6Qa+OU2I05FYo5Y1Ua/LXv\n+Rm7cf78+SxYoESuEUIQEBBA9+7due6661oUzupCp6amhvz8fCoqKqiursbPz4/ExMRmz6uurub4\n8eNUV1djMpnw8PDA39+fqKgoNUgwKM4LeXl5aigkHx8foqOjWxTUV0rJ/v37CQ8Pd5mNAJSAv9nZ\n2VRWVlJZWYmUkn79mn/Jt1gsHD58mKqqKmpra3F3d8fX15fo6OgGIaqKi4vJy8ujpqYGd3d3/P39\niY6OdrjWxigtLeXEiRMYjUY8PDy45JJLmpXLFU2ZF21xDwsLC9UcZB4eHvj5+dGpU6cmgxdXVlZS\nVlamOq9onBus2arTgUuklIdaco42MjtD3K3ejTbldbQMfjjW9DltSUBAANu3b2fbtm2sXLmSiRMn\nsnz5cnr37s2uXbvaWrzzmpqaGsrKyvD29m5VRBCz2YyXlxcxMTEkJCQQFRXFqVOnyMzMdIhWkZeX\nR05ODp06daJ79+54e3uTmZlJZWXzw/2SkhLMZnOzMQAtFguFhYW4ubmdVsT5iIgIevToQWxsLBaL\nhYyMDIdo9qWlpRw6dAiDwUD37t2JiYmhvLy8wbU6I6Xk8OHD+Pj4kJCQQPfu3Vstm40aE1TY5cEM\n9FaeU1s/hw4d4ujRo/j6+hIfH09CQoIaa/HAgQNNyllZWdmqJQUap4eUMhslDuTc5ura0EZmZ4Eo\nPxgWB99YM/BsPAQXh0JY62OqnnN0Oh0DBw5UyyNGjOC+++5jyJAh3HLLLRw4cKDJyPltQXV1dZML\ndf8oAgIC1NFCVlZWg8SQrjAYDA6Kw8/PD09PTzIyMtQsyhaLhdzcXCIiItTI8gEBAezfv5+cnJwm\nQ0gBnDx5kpCQkGYTZup0Oi677DKEEJw8eZLy8uayeSi4ubnRrVs3h33+/v7s2bOHkpISdVRfVFSE\nr6+vEjXFiru7O5mZmdTU1Lj8Huvq6jCbzYSEhDjkemstFgnF1RaQAoRQzIt2/3InT56kpKSEhIQE\nhywItlFZQUFBI61qNIcQwscps/TZYCnwrRDiYfuUMK7QRmZniWvilDk0qDc3thfvxsDAQBYtWkRm\nZiabNm1yWa+0tJS//OUvREVF4e3tTZcuXbjrrrsc6uzdu5dx48YRGBiIwWCgf//+Dm0ePnyYG264\nAX9/f/z8/Bg3blyDNDJCCF588UVmzpxJp06d6N27t3rsiy++oF+/fnh7exMREcGjjz7qMh392cD+\nLf1sRM23YXthsLVvNBqxWCwN0sz4+/tz6tSpBrmz7KmpqaGiooKgoKAW9X22rsOWbdr+HkkpG7wM\nNfdyVFhYyN69ewEldUxKSoo6+jGbzRw7dozffvuNXbt2sX///gapatLT08nKyqKgoIB9+/aRk74b\ni6muUe/F/Px8goKCGtxnG506dXJ5fwoLCzl2TDG7pKSkkJKS4pCc9dSpU6SlpbFr1y41eoqr7NL2\nVFVVcfDgQX799Vd2795NWlqawzU6PzNAdyGEw9BVCCGFEA8IIZ4RQhQIIU4KIV4TQnhZj8db64xx\nOs9dCJEnhHjKbl+SEGKdEKLcuq0WQkTYHR9qbWuEEOJLIUQF1tiJQoggIcRKIUSlECJHCPGYEOJ5\nIcQRp367WOsVCyGqhBBfCyGcbfY/oSTkvKXZm4g2MjtruLvBTRcr3o1mqZgbtx6DobHNn3s+MHTo\nUHQ6HTt27GDkyJGN1nnooYfYtm0bL730EhERERw/fpytW7eqxw8cOMCgQYNITEzkzTffJCQkhJSU\nFHURq9Fo5Nprr8XDw4N33nkHnU7HvHnzSE5OZt++fQ4msueee44hQ4awfPly9Y981apV3Hrrrdxz\nzz0888wzZGVlMXv2bCwWixrUVkrZoj+QlkR2t6VdOVvpX2ypW2pra8nOzkav16vzTTaF4NyPEAIp\nJUaj0eWopry8HDc3tz9k9GqT02Qyqeu57L+30NBQsrKyKCwsJCgoiLq6OrKzs/Hz83MpX0BAAN26\ndSMrK4uYmBgMBoM6v3b06FFKS0uJjo7G29ubgoICMjMzSUhIcBjBVVRUUF1jxDckGuHmjnB3J8jO\nvAhKQs/a2trTyqlmkzM8PJz8/Hx1YbhNUVdXV3Pw4EH8/f3p1q2b+h0bjUYSEhJctlldXc2BAwfw\n9vYmNjYWnU5HRUUFRUVFeHt7N/rMTJo0yQv4XgjRW0ppn+n1YeA7YDJwCfAv4CiwSEp5WAjxC0pC\nz3V25ySjxE9cCWBVkj8BKdZ2dCh5074SQvSXjjbY91BGT4tRUsQALAMGAw+gZJx+ECUPmvpQCiGC\ngR+BIuBelDxn/wD+J4RIsI3wpJRSCLEDGAa85vImWtGU2Vkkyg+ujYdvrNOVXx+CnuepudEZb29v\nQkND1eSLjfHLL78wY8YMdSEs4LA4d8GCBQQEBPDDDz+of1zDhw9Xjy9dupRjx46RkZGhJiscMGAA\nXbt25a233mL27Nlq3cjISD75pD51kpSSRx55hClTpvD666+r+728vJgxYwazZ88mJCSE77//nquv\nvrrZ6z18+DBxcXFN1omJieHEiRONmp4KCgowm81Njpicyc/Pp6ZGeeY9PT0JCwtTM2rb5rJSU1Md\nRg0nT56kurqa9PR0lzEii4qKqK2tbZCduznKy8spLi4mLS2txeeUlZVRWqrEfHVzcyMsLIxDhxzn\n56WU7Nq1S1V8Xl5ehIWFNdmPyWRS5/Jsv526ujpycnIICQlRs11LKSktLWXXrl1qLre8vDxqa2vx\nC42Gk8rv18MNKp38TYxGo9pHYWGhg7z2NPXiYrtnzlFGCgoKqK2txcfHh9xcJQSh2Wzm0KFDVFVV\nufzuCgoKMBqNREdHOzx73t7exMTE8N577zV4ZlDyil0M3IOisGwckVJOs37+WggxCJgI2JL5rQTm\nCSG8pJS2ic6bgVQp5e/W8jwUJTRKSllrvR97gQPAaBwV4Wop5RN29y0JJaP0TVLK1dZ93wLHgQq7\n8x4E9MBlNmUshPgJOIKS18xecf0GOJp/XGF7W/yjt759+8qOiMks5Us/Sznrf8q25BcpzZa2lkph\n3rx5MiQkxOXx8PBwee+997o8fvvtt8vOnTvL1157Taanpzc4HhYWJh966CGX50+fPl1eccUVDfYP\nHTpUjh49Wi0Dcs6cOQ51Dhw4IAG5fv16WVdXp26HDx+WgNyyZYuUUspTp07JnTt3NrsZjUaXcppM\nJoc+LJaGX+CNN94ok5OTXbbRGBkZGXLHjh1y+fLlMjExUV5++eWyurpaPX7bbbfJ8PBw+d1338mi\noiK5ZMkSqdPpJCC3b9/ust1x48bJkSNHOuwzm80O12A2mxuc98orr0jlL6Dl5Obmyp07d8ovv/xS\njhw5UoaEhMjU1FT1+HfffScNBoN89NFH5ebNm+XKlSvlRRddJIcOHSpNJpPLdm3f41dffaXue//9\n9yUgKysrHerOnz9f+vr6quXk5GR50eWD1Gdu7hYpy2oa9rFjxw4JyK+//tph/4wZMyRKvs4GMjjj\n6p7Fx8fLRx55xGGfyWSSOp1OLlq0yGV7p/PMoIyaNqPk+LIpYwk8Lu3+Y4FngBN25WiUTM/jrWUd\nUAA8YVcnF/i39Zj9lgXMs9YZau1vmFN/06z7vZ32r0RRtLbydus+5z6+A5Y6nXs/UId1TXRTmzZn\ndpaxeTe6W1/ujp1SzI3nOzU1NRQVFTXIXGzPq6++yg033MDChQtJTEykR48erFy5Uj1eVFTUpAkn\nNze30fbDw8PVN2/7ffbY3qRHjx6Nh4eHusXHxwOob8oGg4HLLrus2a0pN3GbWce22aLMnyk9evRg\nwIABTJ48ma+//ppff/2Vjz6qj/m4ePFievbsyTXXXENISAjPPfecGiWjqWUTNTU1Dd78Fy5c6HAN\nCxcuPCvXEBERQb9+/Rg3bhxfffUVISEh/Pvf/1aPP/zww1x//fU8++yzDB06lJtvvpnPP/+cLVu2\n8MUXX7Sqr9zcXAwGA76+jllww8PDqaqqUr0oq01g9q3/vdyQCP6NDIRs7vQnTpxw2P/oo4+yc+dO\nvvyyRcHZXcrq/Jt1d3d3GFU2xuk+M0A+SnoUe5zTpNQCqtutVDwEf0QZjQFcC4RiNTFaCQUeQ1Eg\n9ltXwDmCs7MZJwIol1LWOO13Nm2EWmVw7uPqRvowUq/smqTZCkKIzsAHKHZVCbwtpXzZqY5ASZE9\nGsX+OU1Kubu5tjsqkQYlmefXdubGhGDFDHm+snnzZkwmE1deeaXLOoGBgSxZsoQlS5awd+9eFi1a\nxO23384ll1xCz549CQkJUU0sjREZGanG/rMnPz+/gUu5s6nHdvztt9+mT58+DdqwKbWzYWZ86623\nHLz8WrKWrLXExsYSHBzsYKLr1KkT3333HSdOnKCsrIzExEQWL15MREREkybR4ODgBsF57777bsaO\nHauWz8W6KJ1OR+/evR2u4cCBA9x6660O9RITE/Hx8XFIqNkSIiMjqaiooKqqykGh5efn4+vri5eX\nF5V1SqACDz/l95LUCS5z8T7WuXNn4uLi+Oabb7jzzjvV/V26dKFLly4cOXKkVfI5y3ry5EmHfWaz\nmaKioiaXS5zuM4Pyf+xaS7rmE+DfQggfFIXyq5TyoN3xYuAz4N1Gzi10Kju7uOUBfkIIbyeF1smp\nXjHwJcpcnDPO7rWBQIWUslkvr5aMzEzAw1LKnsBAYIYQoqdTnVFAD+t2N/BGC9rt0Fwd6+jduGwv\nVNY2fU5bUVpaymOPPUb37t0ZNmxYi8655JJLeO6557BYLOpczbXXXsuqVavUeSFnBgwYwK5duzh8\n+LC6Lzs7m23btjUbjy8xMZHo6GiOHDlCv379GmwhISEA9O3bl507dza7NfXnnpiY6ND2mbiKuyI9\nPZ2ioiJVCdsTExNDr169MJlM/Oc//3H443Ulr/09BUV52V/DuVBmNTU17N692+EaYmNj2b3b8T02\nLS2N6urqZuconbniiisQQrBmzRp1n5SSNWvWMHjwYMwW+HBf/eJoXw+YmNh07MWZM2eyZs0atmzZ\n0ipZbNhG9M6/8QEDBvDZZ585OB/Zgh839ds+nWcG8ACuQhlltZbVgA8wwbqtdDr+LdAL2CWlTHHa\njjTTti0+4fW2HValOdypnq2P1Eb6SHeqG4cyR9gszY7MpJJQLdf6uVwIkYZie91vV2088IHVnrtD\nCBEohIiUp5GMraPg7ga39oJXdoLRrITWWb4P7urj6GH1R2MymdixYwegTGbv2rWLN954g6qqKjZu\n3NikG/XgwYOZMGECSUlJCCF455130Ov19O/fH1ASM15xxRUMGTKEhx9+mJCQEH799VdCQkK48847\nmTZtGs8++yyjRo1i4cKFuLu7s2DBAkJDQ7nnnnualNvNzY0XXniBO+64g1OnTjFq1Cg8PT05dOgQ\nn3/+OWvWrMHX1xc/P78WRbQ4Haqqqli/fj2gKOFTp06pf7SjR49WRw/du3cnOTmZ9957D4BZs2ah\n0+kYMGAAgYGBpKWlsWjRIrp168Ytt9R7HS9fvpy6ujq6du3KsWPHeOmll3B3d3dwjGmMQYMGsXDh\nQgoKCujUyfkluCEbNmygsrJSTXBpu4YrrrhCzTn2f//3f3z//ffqsomPP/6YDRs2MHLkSKKiosjN\nzeX1118nNzeXhx56SG373nvv5cEHHyQqKopRo0aRn5/PwoULiYuLY/To0c3fZDsuvvhibr31Vu6/\n/37Ky8vp1q0b77zzDgcOHOCNN97gq4OQWVJf/88Xg18zeVT/9re/sXXrVkaNGsU999zD8OHD8fPz\n4+TJk+p9aGoxuc2L8eWXX+aaa67B39+fxMREHn/8cfr06cMNN9zAfffdx4kTJ3jssccYMWJEk9aO\n03lmUAYNhcBbLbuT9UgpTwohtgDPo4x6VjlVmQ/8AqwTQvzH2k80ikJaJqXc0kTbvwshvgLeEEL4\noYzUHkKx1tl7Sr2I4in5nRDiFSAbZaSZDPwopfzYrm4/FO/KFl1cizcULXkM8HfavxYYbFf+FujX\nyPl3o2jvlC5duric9OxIpJ6U8pH/1TuEfJrWdrLMmzdPneQWQsiAgADZt29f+c9//lPm5uY2e/6s\nWbNkUlKSNBgMMiAgQA4dOlRu3brVoc5vv/0mR40aJQ0GgzQYDLJ///7yf//7n3o8KytLjh8/XhoM\nBqnX6+WYMWNkRkaGQxuAfOWVVxqVYf369XLw4MHS19dX+vn5yUsvvVTOmTNH1tXVncYdaR02J4XG\ntsOHD6v1YmNj5dSpU9Xyxx9/LK+66ioZFBQkfXx8ZGJionzooYdkQUGBQ/vLli2TCQkJ0svLS4aF\nhcm7775bFhYWNiuX0WiUwcHB8oMPPmjRdcTGxjZ6DUuXLlXrTJ06VcbGxqrl3bt3y9GjR8vw8HDp\n6ekpY2Nj5U033SR///13h7YtFot8/fXXZe/evaWvr6+MioqSN910k8zKympSpsYcQKSUsrKyUt5/\n//0yLCxMenp6yr59+8qNGzfKn7Prn6mYS5Ll4JE3tujapVScY9577z05aNAg6efnJz08PGRsbKyc\nPHmy3LZtW5PnWiwW+cgjj8jIyEgphHBwAvrf//4n+/fvL728vGSnTp3kfffdJ8vLy5uVp7XPDMrc\nWA/p+N8qgfud9s0HCmXD/+G/WOtvdz5mPX4RsAbFHFgNZKIozhjp6ACS1Mi5wSimzEqUObW5wDvA\nHqd6UShu/fko82JHgA+BXnZ1OqFYBpMbk9N5a3HUfCGEAfgeeFpK+V+nY2uBf0spf7SWvwUek1K6\nDIvfEaLmt5RvjyhBiG3ceBEMjG4zcTQ6IA888ACZmZmsW7eu+crtnMOl8NZuZT0nQO9OMLn3+RkP\n9VzQnqLmCyF0wO/Az1LKqa089x5gFpAgW6CoWrTOTAjhAXwKrHBWZFaycfRCibHu0wCuiYXccvjN\nOnmcbkAAACAASURBVD/8WTqE+ULXlgVs0NBolkceeYSEhAQyMjKaXKTb3imtgQ/21iuySINjbFSN\ntkUI8WeUUdc+wB9ljVgPYEor2xEoC6+fbokigxY4gFgbfQ9Ik1K+6KLal8AUoTAQKJMX8HyZM0LA\nTT3rHUIsEj7YByVnO5KZxgVLTEwM//nPf5r0jGvv1JoVRypbEGG9B0y7BLy00A/nE5XAdBSd8DGK\nqXCclPKXVrYTAawAlrf0hGbNjEKIwcAPKJrWNon3T6ALgJTyTavCexUYiTLZN70pEyNcWGZGGyU1\nsOSX+ocxygAz+oHn+RXXV0PjvENK+CgV9lhXNrkJuLsPdLsArRvtycz4R9ISb8YfgSYH8dZh4Iyz\nJVRHJcgbplxSb+/PqYBP9sPkJC2Vu4ZGU2w+Wq/IAMYnXJiKTMM1WgSQP5j4QJhgtwZ370n47kib\niaOhcd6zv9DRgWpgNFwV03byaJyfaMqsDRjg9DBuPKRkq9bQ0HAkvxI++r0+1ER8oDIq09BwRlNm\nbcT1PRzNJB+nQl6F6/oaGhcaVXWw7Dcl6ABYzfS9Qaf9a2k0gvazaCPc3eCOJOUBBeWBXbZXeYA1\nNC50zBZY8TsUWj1+PdwUz0WD6/jQGhc4mjJrQ/SeMP3Sem/Gomr48HflQdbQuJBZnwUZdmF0b+l5\nfgfq1mh7NGXWxkQalAfVxsFiWJvZdvJoaLQ1KbmOaZOGxcElrjMTaWgAmjI7L+gdBsPtgqf/eBx2\n5rSdPBoabcWxMvjULmF2r04wvKvr+hoaNjRldp4wLF6JMWfj0wNwpKzt5NHQ+KMpM8L7e+tTuoTr\nFauFFqpKoyVoyuw8wU0oMeYirdknzFJ5sEsbT3OkodGhqDMrv/dT1px/vjplPtlbC1Wl0UI0ZXYe\n4aVTPLZ8PZRyRa3ygNeZmz5PQ6M9IyWsOQDHTyllNwF39IYQn7aVS6N9oSmz84xgH2Utjc20cqIc\nVqcpD7yGRkdk6zHYnVdfHtcDuge3nTwa7RNNmZ2HdAtyjHLwaz5sOdp28mhonCsOFME6O+/d/lEw\nSAtVpXEaaMrsPOXKaBgQVV/ekAVphW0nj4bG2eZkpbIw2mZ0iA1Q4pZqQbc1TgdNmZ2nCAE3JCqx\n6EB54D/6XYlVp6HR3qk2KRFvakxKOcALpmqhqjTOAO2ncx6jc1PmzwKtIa9qzEqsOi3klUZ7xiKV\nF7OCKqWss4aq8vNqW7k02jeaMjvPMXgqD7qH9ZsqrFZMMxbNIUSjnbIhS5krs3HTxRDj33byaHQM\nNGXWDoj2U9ag2cgodpw019BoL+zOc3RmujoW+kS0nTwaHQdNmbUTLg2Ha+Pqy1uPKTHsNDTaC8dP\nKctMbFwcAiO7tZ08Gh0LTZm1I67rCj1D68tr0mBvvuv6GhrnCydOwXt76kNVhfnCrUlaqCqNs4em\nzNoRbgJu7aXErAMl5NWHv2sjNI3zmyNl/H975x3eVnn98c/rve0sZ9jZkyRkk0EYgTCSQBklLWlL\nCxQI0FL4USgQaCFAWYWySlllFii7QGhJwl4lAZJAQnacYccj3ntben9/vNeWZMuxZEuWZJ/P8+ix\ndO7V1fG1fL/3Pe95z+GJ76DaSlyKjYALppqfguArRMxCjJgIuGSaubMFk7L/6nb4KjugbgmCWzJK\n4B/fOVLwYyPg4mkwIC6wfgk9DxGzECQ5Bi6f6ShKDPDWLqkSIgQXO4rg6c3QYNUWjY+Ey2bAsOTA\n+iX0TETMQpSEKOvC4JTS/N8MWLtP6jgKgWdzvlkU3TxHlhwNv5kp3aIF/yFiFsLERcIl02FUisP2\n4X7TqVoETQgUG/Jc10L2jTFClhofWL+Eno2IWYgTEwEXTYPx/Ry2z7Pg37tkYbXQ/XyVbeZwm796\nqXFGyPpKOxfBz4iY9QCiwk2VkMlOnarX55iLis0eOL+E3sUnmWbutpkhCWZuNzkmcD4JvQcRsx5C\nRBicNxlmOFVT2HTIpO43iaAJfkRrWLsX3nOqSjMsCS6dYeZ2BaE76FDMlFLPKKUKlFJb29m+QClV\nrpT63nrc7Hs3BU8IDzNlr+amOWxbC81EfIN0qxb8gNbw7h748IDDNjrFzOU2d0wXhO7Ak5HZc8Ci\nDvb5Qms9zXrc1nW3hM4SpuDH4+G4YQ7brmJTfaF5rY8g+AK7hjd3whcHHbYJ/cwcbowsiBa6mQ7F\nTGv9OVDSDb4IPkIpOH0MnDzSYdtXBk9+J+1jBN9gs8Mr2+HrXIftyAFw/hSIDA+cX0LvxVdzZvOU\nUpuVUquVUpPa20kptVwptUEptaGwsNBHHy24QylTy/G0MQ7bwQp4fBNU1gfOLyH0abLDC1vhu0MO\n24xB8IvJ0lxTCBy++OptAoZrracCfwPebm9HrfWTWutZWutZAwYMaG83wYcsGG5a0TeTVwWPbYKy\nusD5JIQuDTZ4djNsc7oXnZtm5mrDRciEANLlr5/WukJrXWU9fw+IVEr17+BtQjdydLq52DQXKC+s\ngUc3QnFtQN0SQoy6JjP3uttp0uH4YWaOVqrfC4Gmy2KmlBqklFLW89nWMYsP/y6hu5k1GM47EsKt\ni05pnRG0/OrA+iWEBjWNZs51X5nDdvJIE8ZWImRCEOBJav7LwDpgvFIqWyl1kVLqMqXUZdYuS4Gt\nSqnNwMPAMq2lmFIwMiXVTNA3z2tU1MNjGyGnMrB+CcFNZb0JTR+scNhOH2PmZEXIhGBBBUp3Zs2a\npTds2BCQz+7tZJTAs05rz2KtkljDpZq50IqyOjMiK6wxrxVmDnZeekDd6tUopTZqrWcF2o9gQ6Zs\neyFj+sLy6Y61QLVN5oK1tzSwfgnBRZE1t+osZOdOFCETghMRs17K8GTTQibeqtLQYIOnvjc9qAQh\nv9qEFkutrNdwZeZcZw4OrF+C0B4iZr2YtERTCDYp2rxussPzW+DbXGkh05vZV2rmUius9YgRYaaQ\n9ZTUwPolCIdDxKyXMzDetOjoY1U2t2l4bQf88weoagisb0L30mgzdRYf3wTVVqWY6HC4eBpMkMU2\nQpAjYibQL9ZqnhjnsG0thPvWww8FgfNL6D4OVsCD35heeM2D8tgIUzB4dJ+AuiYIHiFiJgCQEgNX\nHuVacb+60YzQXt4mNR17Kk12WLsPHtkABTUO+7i+8Ps5kuEqhA5S21poIToCzplgmny+vgPKrTmT\nTYcgoxR+coSpii70DA5VmWLBzusMo8LNGrK5abKGTAgtRMyENozvB9fMgbd3GyEDkwzw9PfmInfa\nGGnxEcrYNXyWZRpq2pwSfUammNT7frGB800QOotckgS3xEbCzyaZUdqbOx0JAetzYHexueiNkrmU\nkKOwBl7dDpnlDltEGCwaDccOlRqLQugSkmKmtUZJDKRbODLV3LG/udMkhQCU1JmMt2OGwuLR0r8q\nFLBrWJcN/82ARrvDnp4IyyaZrFZBCGVCLgFke81mrjlwPsWN0g+tu0iIgl8dCT+fZDLcwGS8fXEQ\nHvgGssoP+3YhwJTWwj++M2HjZiELs/rdXTFLhKw7sekmHsq9jS3VUsrP14SUmGXW7+XWg1exq24r\n12ZeQE59ZqBd6jUoBdMHmbm08U5JIIU18PeNsGavyYwTggetzQL4v35tEniaGRRvMldPHik9yLoT\nm7Zxf+4tvF/+NisPXsnm6m8C7VKPIqS+ygfr91NrN/nDBY15XJt5IXtqtwfYq95FcgxcNBWWTjAL\nasGEsD46AA9/C7lSgT8oqKg3xaRf2wH1VkFpBZwwHK6abaq/CN2HTdt4MG8ln1asBqBe1/FN1RcB\n9qpnEVJidkzSSdw89AGilSlXUWErY0XWcr6r/jrAnvUulII5aWYd0qgUhz2vygjaxwfAJqO0gPF9\nPvx1vWudzf6x8JtZsGSMowWQ0D3YtZ2H827j4/L/ttiWpCzlotSrA+hVzyPkvtazEuZz5/DHSQw3\nqzlr7TWszPodX1S8H2DPeh99Y+HSGXDGWMcF0qZh9V5Tbb1AGn92K9UN8OIP8NJWqGly2Oenw9Vz\nYIQsgO52jJDdzofl77bYTk05m8sH3UCYCrnLb1ATkmdzQuwU/jL8afpHDASgiSbuyVnBf0peC7Bn\nvY8wBccOg6tnw9Akhz3LKo/05UEThhT8y/ZCuO9r2OxUfiwlBi6dDmeNN4uhhe7Fru08cugOPih/\np8V2SvJZXDHoJhEyPxDSzTkLGw/xx6zfkN1woMX28/7L+Xn/SyV1PwDY7PBpFnywz3Ux7ugU+OlE\nM5ITfEttE7y7G77Nc7XPHgI/GiuL2wOFXdt59NBdrC57s8V2UvKPuGrwLV0WMmnO6Z6QFjOAiqYy\nVh68kl11W1tsS1J+wmWDriNcye1oIMitNGWS8qoctqhwOGqwqSAyKCFwvvUUyuvhmxyziL3CqbtB\nYhQsPQImSpX7gKG15tFDd/Ne2esttoXJp3PV4Ft8ck0SMXNPyIsZQJ29ljuyr2VT9boW2/zEk/jD\nkD8TGRblk88QvKPJDh/uN8kgrb9ho1KMqB2ZKskI3mDXkFEC63Jge1Hb8O20gSak2NxwVeh+tNY8\nnv8X/lP6aovthKQlXD3kVp/dXIuYuadHiBlAo27kwVxH6ivA1Lij+GP6/cSFy6rQQJFVbooWH3KT\nDBIfacJhc9MkBHk4qhthQ64ZhRXVtt2eEAVnjYOpA7vfN8GB1pon8+9jVenLLbYFSYv5/ZDbfBol\nEjFzT48RMzBx6qfy/8o7Tl+m0TETuG3oI6RE9PXpZwmeY9ewt9SUU9rmZkShgHH9YF6aqcovC3nN\ngufMcjMK21LgfkH6qBRzzibLCDfgaK15quB+3i55qcV2XNKpXDvkdsKVbycuRczc06PEDMyX6vXi\nZ3m+8JEW25DIodw+7FEGRaUd5p1Cd1BeD9/kwtc5jhYzziRHmzVss4eY572NuibTqWB9juucYzMx\nETBrkBnNDpS5x6BAa83TBQ/yVskLLbZjEk/murQ7fC5kIGLWHj1OzJpZW/oWjxy6AzvmlrZvRH9u\nG/oII2PG+e0zBc+x2WFnsblo7ypuO68WpmBSf5ibDmP69Pxq7rmVZhT23SFHxQ5n0hNhXrqZF5M0\n++BBa82zhQ/zZvHzLbb5iQu5Lu1OIpR/Ji9FzNzTY8UMYF3lJ9yTs4JGbdK94sMSuHnog0yOm+HX\nzxW8o7jWjNS+yXW0mnGmf6wZicwa0rOSGxptZl3YumyzLq81kWGmHubcNNc1fEJwoLXm+cJHeL34\n2RbbvMQTuCHtbr8JGYiYtUePFjOAH6o3clv21dTYTcwmSkVzfdrdzE083u+fLXhHkx22FpgRyr6y\nttsjwmBKqhmhDE8K3U7IhTVmRLoh17VSRzMD481c2IxBpq+cEHxorXmh8FFeLX66xTY3YQE3pN9D\npB+FDETM2qPHixnA3rpd3Jx1BWW2YgDCCOfKwX/k5JQzu+XzBe/JrzKitvGQmUdqzeAEc8GfMjA0\nRmsNNhNWXZftWsG+mXBllirMSzP940JVqHsLLxY+xstF/2h5PTvhOG5Mv9fvQgYiZu3RK8QMIK/h\nIH/K+i15jdkttgtTr+ScvudLtZAgpsFmCueuy4bsdiryx0ZAv1inR5z52TfWJJF0x3yb1lDVAMV1\nUFxjQqfNj5JaqGxw/76+MSaMeNQQk2IvBD//KnyCl4qeaHl9VMIx3JR2X7etaRUxc0+HYqaUegY4\nHSjQWk92s10BDwFLgBrgAq31po4+uLvFDKCkqYhbsn7HvvpdLbaz+/6SX6deJbXSQoCDFSY8990h\n127JhyNcGVHr5+bRN9a7Ltk2O5TWuYqU83N3iRvuUMAR/U24dFzfnp/c0pN4pegpXih8tOX1rPj5\n3JR+H1Fh3Zd6K2LmHk/E7DigCvhnO2K2BPgdRszmAA9pred09MGdFrOqUtiyFuYshXDv016rbZXc\nnn0NP9Q4PvvE5NO4avDNfp20FXxHbaMJP27Mg/xqz4XNHcnRbcUuJcYaZdW6PsrqOl80OVyZY09J\nNUsPUmI677MQGF4resZlyc+M+Hn8Kf1+74Vs8xoYOAYGjemUHyJm7vEozKiUGgH8px0xewL4VGv9\nsvV6F7BAa53Xel9nOiVmDTXw79uhKBOGT4VTr4Io768KDfZ6/pJ7I+sqP3H4E38MK9LvISZMSlGE\nElqbEF6L6NS4hvrcZUf6i5hwR4izeeTnLJAyAgtdXi96jucKH255PT1+Ln9Kv5/oMC+uP9oOX74E\nm1dDTCIsXQkpg732RcTMPb4Qs/8Ad2utv7RefwRcr7Vuo1RKqeXAcoBhw4bNzMzM9M7b79+DL190\nvB4wEn50HcR536jJpm38/dCdrC17q8U2IXYKK4c+1NIrTQh96prahgSbRa+s3vuRVlK0+5Blv1iI\ni5TEjZ7Im8X/5JmCB1teT42bzc1DH/DuxrepAT58DDKcGgmPmw+n/NZrf0TM3NOtDSK01k8CT4IZ\nmXl9gKmLoa4KNrxtXhfuhzduhh9dD32GeHWocBXO7wb9kT7h/Xil+CkAdtZu4cbMy7hj2GMkRaR0\ncAQhFIiJgLRE82hN6zmw5kd5nUnG6OocmxD6vFX8oouQTYmb5b2Q1VXBe/dD7k6HbdRRcOIlPvRU\n8IWY5QBDnV6nWzbfoxTM/Skk9IPPnjExpopCeHMlnHYtDPauuodSil+m/obkiL48mX8vGs2++l3c\nmGUELTmij19+DSE4CA+D/nHmIQiteafkXzxVcH/L6yPjZnLL0Ie8E7KKQnj3L1DqdEmccioc80sI\nk6QzX+KLs7kK+JUyzAXKO5ov6zKTF8KSayDCmnitq4K374C933bqcGf0XcZVg29GYWJE++t3c2PW\npZQ3uVkQJAhCj0ZrzUuFj/Nk/n0ttkmx070XssID8MYtrkJ29M/h2F+JkPmBDs+oUuplYB0wXimV\nrZS6SCl1mVLqMmuX94B9QAbwD+A3fvPWmZEz4OybINaq82NrhNUPwpb3O3W4k1PO5P8Gr2wRtAP1\nGazIWk5pU7GvPBYEIchptDfw19w/8a+iJ1tsE2OnceuwvxEb5sUQPusHk6xWY5WyCYuAU66AGafL\nxKqfCP1F0+X5sOpu87OZGT+CeedCJ9aOfVz+Xx7IvaWlQPHQqJHcOfwJ+kZI615B6MlUNJXx5+xr\n2Fb7XYttRvxcVqTd611PxJ1fwMdPgt1aeBgVB0t+D+kTfeKnJIC4J/THuskDYemtZt1GM5vehQ8e\nNaM1Lzkx+TSuGXI7YdapOdiwnxWZyylpLPSVx4IgBBm5DVlck3mBi5AtSvkxtwx9yHMh09okp334\nmEPIEvrCObf4TMiE9gl9MQMTajzrJhjhVA1/91ew6h6od9PiuAMWJC/mD2l3EIZJXctuOMANWcsp\naizwlceCIAQJ22u+55oDF5DbkNVi+3XqVVwx6CbPCynYbSYpbf1rDlu/oeZGu9/Q9t8n+IyeIWYA\nkdGw5GqTHNJMznZ48zao8n7e67ikU7ku7c4WQctpyGRF5iUUNeZ38E5BEEKFT8vXsCLrUipsZm4r\nSkWzIu0vnNPPi5qtjXXw3gOw9SOHLX0S/PgWk3ktdAs9R8wAwsLh+F/DvGUOW8lBk1FUfNDrwx2b\ndDI3pN1NuLWCIbfxIDdkXkJh4yFfeSwIQgDQWvNK0VPcm3sjTdpMR6SE9+Wu4U9yTNJJnh+otsJk\nUh9wKkc7br5Z+xotaz66k54lZmAyhWaeASddbsQNoKoE3rwVsrd5fbj5SQtZkX4PEZag5TVmc33m\nJRQ05vrSa0EQuolG3ciDeStdCgYPjRrJX0c8z4TYIz0/UNkhc6Ocv9dhm3EGnHx5p+rGCl2j54lZ\nMxOONaWuIq11IQ01Jutx91deH2pe4gncmH5vi6DlN+ZwQ+Zy8htE0AQhlKi0VXBz1m/5sPzdFtvU\nuKO4b8RzDIpK8/xA+RmmWENLFrWC4y6Ao5d1Kota6Do9+6wPPRLOuRnirUoedhu8/4jJdvRyScKc\nxOO5Kf2vLRPC+Y25XJ95MXkN2R28UxCEYCCvIZtrD1zAFqeOGScnn8mtwx4hIdxNvbP22L8R3vqz\nCTEChEfCkv+DKaf42GPBG3q2mAH0H24yivo63XV99TJ8/jzYvesdMjvxWP6Ufj+RyjThK2w6xIrM\n5eQ1eD8fJwhC97GzdgvXHDif7IYDLbbzB1zBVYNv9q479NaPTJ3FJqvbakyCyaQedZRvHRa8pueL\nGUBif5NZNGSCw/bD+7DmIceX0kNmJcw3PYyUKaVV2HSI6zMvIccprVcQhODhi4oPWJF5KeU2U54u\nUkVxfdpd/LT/rz3PWNTapN1/+rQjqpM0AM651euasIJ/6B1iBuYO6swVMGauw7bvW3j7Tqit9OpQ\nMxOO5uahD7YIWnFTASsyLyG7/oAPHRYEoStorXm96DnuzrmeBl0PQFJ4CncOe4Ljkk71/EC2Jvjw\ncUe3DoDUUbD0NujjfT8ywT/0HjEDE9s+9QqYtsRhO7TbTORWeLcgenr8HFYOfYhoZZrzFTcVckPm\ncg7W7/ehw4IgdIYm3cjfDv3ZpaFmetQI7h/xPBPjpnp+oIYa+M+9sOsLh234NDjrj53qoyj4j94l\nZmAyjY45z7RgsIoKU5ZnUmwL9nl1qKnxs1k59OEWQSu1FXFD5nKy6r07jiAIvqPaVsktB690abx7\nZNxM7hvxLIOjvKjGUVVqigUf/MFhm3gCnHZNpzrcC/6l94lZM9MWw6IrzWgNoKYc3rod9n7j1WGm\nxM/itmF/I0aZJQBltmJWZC7nQF2Grz0WBKED8htyufbAhXxf7ejovDD5dG4f9qh3HeQL9sGbt0BR\npsM2eymccLFj/aoQVPReMQMYM8fMo0VbhUQb600bmc+f96pI8eS4mdw27JGWFhFlthJWZC3nQN0e\nf3gtCIIbdtVu5fcHzierwREZOa//5Vw9+FbPMxa1hi1r4Y2VUFlkbCoMTlwOs38s7VuCmN4tZmAy\nHM9ZaTKTmmn+Mpd7XodxUtx0bhv6d2LDjDBW2MpYkXUp++p2+9ZfQRDa8L+Kj1iRuZwym6nDGqEi\nuXbIn/nZgEs8z1isr3bczNqbjC0yFk7/A0xc4B/HBZ8hYgZmDdq5d7quFSncD6/eCBlft/++VkyM\nm8qfh/2duLAEwAjajVmXsrdup689FgQBsOkmXit6hrtyrqNe1wGQGJ7MHcMe44TkJR2824n8veb/\nfZ9Tt/oBI+DcO2C4FwkjQsAI/eacvqQ5xPC/lxz9iACOPBnm/wIiojw6zK7arfwp6zdU26sASAhL\n4rq0O5mZcLQ/vBaEXsm2mu947NDd7K93hPOHRA5l5bC/kRY1zLODaA1b1sD//tXqf/4UOOYXjjn1\nIEKac7pHxMwd+Xth7cNQ4dSQc8AIOPVKSBnk0SH21G7npqzLqbabNWwKxc/6X8Ky/pcQrmQCWRA6\nS2lTMc8WPMRH5f9xsU+KncZN6X8lOaKPZweqqzIdofc5XYeiYs382Jg5PvTYt4iYuUfErD3qq+Gj\nJ13DDpGxcOIlMHZu++9zIqN2B7dmX0VJU1GLbVr8HK4bcqfn/3CCIAAmpPjf0td5ofAxaqyoB0C0\nimFZ/4s5u98vPU/0yM+ANX+DSucb1pEmwzl5oI899y0iZu4RMTscWpuyV1++5JgQBph8klmr5kHY\nsbSpmHtzbmRzjUMU+0WkckPaPd4t3hSEXoy7kCLA/MSFXDzw96RGeliJQ2vYvAa+ahVWnHIqzP95\nUIYVWyNi5h4RM08o2AdrHnatEtJ/uLmLS+n4n8imbfyr8AleKX6qxRZOBBemXslZfX/hebaVIPQy\n2gsppkUN57KB1zEjYZ7nB6urgo+eMFXvm4mKg4XLYfRsH3nsf0TM3CNi5in1NfDJP1yzGyNj4cSL\nYaxn/1Abqv7Hfbl/pNJW3mI7OvFE/m/wLcR704JCEHo4JqT4Bi8UPuo+pNj3PCLDPEvIAuBQhpkH\nr3SE/EkdZW5Ik1J96Ln/ETFzj4iZN2gNWz+EL15oFXZcaMpjeRB2LGjM467s69ldt7XFNiRyKCvS\n72VUjFTfFgSfhRTB/M9+/x6se8U1rDh1MRz9s5DsCC1i5h4Rs85QsN/c5Tkvqu4/3GQ7elBFu1E3\n8nT+A7xb+kqLLUpFc/mg6zkl5Sx/eCwIQY8JKT7MR05doKGTIUVwH1aMjoOFl4Z0/zERM/eImHWW\nhhr4+CnIWO+wRcaY2m3jPFtP9nnFWh7Ou51ae02L7eTkM7hs0PXEhMX62mNBCEqaQ4ovFj7asjYT\nuhBSBDi0B9b+zTWsOHC0ueF0rvYTgoiYuUfErCtoDds+MmFH51qOk06EY3/lUdjxYP1+7sq5jsz6\nvS22kdFjWZF+r+cLPwUhRNle8z2PHrqb/fWuZd86FVIE0Hb47j1Y/2qPCSu2RsTMPSJmvqDwgOla\n7Rx27DfMTC73GdLh2+vstfz90J18XP7fFltsWDxXD17J/KSFfnBYEAKLz0OKYJrsfvQ4HPjOYYuO\ng4WXwaiec+0XMXOPR2KmlFoEPASEA09pre9utf0C4F4gxzI9orV+isPQo8QMTNjxk6dgj3PYMRoW\nXATjj+nw7Vpr1pa9xeP5f6FRN7TYz+z7cy5MvcrzxaCCEMT4JaQIkLfbhBWrih22gWPg1N+FfFix\nNSJm7ulQzJRS4cBu4GQgG/gW+JnWervTPhcAs7TWV3j6wT1OzMAKO34MX/zTNew4cQHMP8/cJXZA\nRu0O7sy5jvzGnBbbhNgp3JB2NwMiPSulJQjByNaajTx+6F7fhRQBbE3w3X/h69dNiLGZaafBvHN7\nRFixNSJm7vFEzOYBK7XWp1qvVwBore9y2ucCRMwcFGWaRdZleQ5bXIqpGjJ2Xoc9kSptFTyQewtf\nV33WYksKT+EPQ+7oXPhFEAJIVv0+ni14mG+qPnexdymkCJC3Cz55BkoOOmzR8XDSZTByZhc80lbJ\nMwAAFhFJREFUDm5EzNzjiZgtBRZprS+2Xv8SmOMsXJaY3QUUYkZxV2utD7o5XAs9WswAGmrhk6dh\nz1eu9vRJcPyFHc6laa35d8k/ea7gEeyYiWwpViyEEsWNhbxU9DgflL2DHceoqcshxdoK+Opl2PGZ\nq33gGDNPndi/i54HNyJm7vGVmPUDqrTW9UqpS4FztdYnujnWcmA5wLBhw2ZmZma23qVnobWpGPLF\nP6GmzGEPi4AZp8OsszrMeNxas4l7cm5wKVY8PX4ufxhyhxQrFoKSGlsVbxQ/z9slL7X0GANzM3Zi\n8mmcN+DyzoUUtR22f2aErN4x30ZEtOkCPXVxjwwrtkbEzD0+CTO22j8cKNFaJx/uuD1+ZOZMQw18\n/YbpleZ8vpNS4fgLYPi0w769tKmYv+SsYEuN43z1i0jl/wbfwvT4uVLbUQgKGnUjq0vf5OWiJ6mw\nlblsmxE/jwtTr+p8lZuiTPj0GbN+zJlRs8wymB4+GnNGxMw9nohZBCZ0uBCTrfgt8HOt9TanfQZr\nrfOs52cD12utD9snpVeJWTOFB8w/ZH6Gq330bDj2l5DQr9232rSNlwof59Xip13s6VEjWJTyY05M\nPk1GakJA0FrzZeUHPF/wCHmN2S7bRkdP4MKBVzE9vpP9wRpqnW4EnRI8EgfAcefDyBld8Dw0ETFz\nj6ep+UuABzGp+c9ore9QSt0GbNBar1JK3QWcATQBJcDlWuudhztmrxQzMP+Q2z4xteLqqx32yGiY\nvdS0ojhMqOTbqi+5L+ePVNkrXOwRKpJjEk9iUcqPmRw3Q0ZrQrfwQ/VGnil4yKXWKEBq5GB+NeC3\nHJ+0iDAV5v2BtYa935iCBNUlDntYOEy3QvSR0V30PjQRMXOPLJoOFDXlJva/0zXDi35DYcGvYfD4\ndt9a2HiI14qe4ZOK1dTaq9tsl9Ga4G8y6/fyXMHDfFP1hYs9ISyJZf0v5vQ+P+1ccgeY4gOfPQdZ\nm13taRNN8lTftM4dt4cgYuYeEbNAk7MDPnsGSnJc7UcsgKOXQWxSu2+ttdfwecX7rCl9k91129ps\nl9Ga4GuKGgt4qfBxPixf5ZKhGKmiOKPvz/hJvwtJDG//O3tYbI2w8V3Y+I7rOs3YJLOsZdz8Dpe1\n9AZEzNwjYhYM2Jpg82r45t/QVO+wRyfA/J/BEcdDB6GavXU7WVP6bxm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z8/PDw8MDnU6HwWBQ99fU1JCbm0t0dDQxMTEABAUFYTKZyM7OdqvMampqyMnJ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\na9jnMX5CzBSuTLpZF5RuC1LChv/BD/8Eu+GBEBY46So4ZrKppjnQYuY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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for step in range(10000):\n", " artist_paintings = artist_works() # real painting from artist\n", " G_ideas = Variable(torch.randn(BATCH_SIZE, N_IDEAS)) # random ideas\n", " G_paintings = G(G_ideas) # fake painting from G (random ideas)\n", "\n", " prob_artist0 = D(artist_paintings) # D try to increase this prob\n", " prob_artist1 = D(G_paintings) # D try to reduce this prob\n", "\n", " D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1))\n", " G_loss = torch.mean(torch.log(1. - prob_artist1))\n", "\n", " opt_D.zero_grad()\n", " D_loss.backward(retain_variables=True) # retain_variables for reusing computational graph\n", " opt_D.step()\n", "\n", " opt_G.zero_grad()\n", " G_loss.backward()\n", " opt_G.step()\n", "\n", " if step % 1000 == 0: # plotting\n", " plt.cla()\n", " plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',)\n", " plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')\n", " plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')\n", " plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 15})\n", " plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 15})\n", " plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=12);plt.draw();plt.pause(0.01)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# below is for pytorch version => 1.5\n", "# this fix is done by albanD - https://github.com/pytorch/pytorch/issues/39141\n", "\n", "for step in range(10000):\n", " \n", " artist_paintings = artist_works() # real painting from artist\n", " G_ideas = torch.randn(BATCH_SIZE, N_IDEAS, requires_grad=True) # random ideas\n", " G_paintings = G(G_ideas) # fake painting from G (random ideas)\n", "\n", " prob_artist1 = D(G_paintings) # D try to reduce this prob\n", " \n", " G_loss = torch.mean(torch.log(1. - prob_artist1)) \n", " opt_G.zero_grad()\n", " G_loss.backward()\n", " opt_G.step()\n", " \n", " prob_artist0 = D(artist_paintings) # D try to increase this prob\n", " prob_artist1 = D(G_paintings.detach()) # D try to reduce this prob\n", " D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1)) \n", "\n", " opt_D.zero_grad()\n", " D_loss.backward(retain_graph=True) # reusing computational graph\n", " opt_D.step()\n", "\n", " if step % 1000 == 0: # plotting\n", " plt.cla()\n", " plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',)\n", " plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')\n", " plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')\n", " plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 15})\n", " plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 15})\n", " plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=12);plt.draw();plt.pause(0.01)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/201_torch_numpy.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 201 Torch and Numpy\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* numpy\n", "\n", "Details about math operation in torch can be found in: http://pytorch.org/docs/torch.html#math-operations\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\nnumpy array: [[0 1 2]\n [3 4 5]] \ntorch tensor: tensor([[ 0, 1, 2],\n [ 3, 4, 5]], dtype=torch.int32) \ntensor to array: [[0 1 2]\n [3 4 5]]\n" ] } ], "source": [ "# convert numpy to tensor or vise versa\n", "np_data = np.arange(6).reshape((2, 3))\n", "torch_data = torch.from_numpy(np_data)\n", "tensor2array = torch_data.numpy()\n", "print(\n", " '\\nnumpy array:', np_data, # [[0 1 2], [3 4 5]]\n", " '\\ntorch tensor:', torch_data, # 0 1 2 \\n 3 4 5 [torch.LongTensor of size 2x3]\n", " '\\ntensor to array:', tensor2array, # [[0 1 2], [3 4 5]]\n", ")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\nabs \nnumpy: [1 2 1 2] \ntorch: tensor([ 1., 2., 1., 2.])\n" ] } ], "source": [ "# abs\n", "data = [-1, -2, 1, 2]\n", "tensor = torch.FloatTensor(data) # 32-bit floating point\n", "print(\n", " '\\nabs',\n", " '\\nnumpy: ', np.abs(data), # [1 2 1 2]\n", " '\\ntorch: ', torch.abs(tensor) # [1 2 1 2]\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([ 1., 2., 1., 2.])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor.abs()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\nsin \nnumpy: [-0.84147098 -0.90929743 0.84147098 0.90929743] \ntorch: tensor([-0.8415, -0.9093, 0.8415, 0.9093])\n" ] } ], "source": [ "# sin\n", "print(\n", " '\\nsin',\n", " '\\nnumpy: ', np.sin(data), # [-0.84147098 -0.90929743 0.84147098 0.90929743]\n", " '\\ntorch: ', torch.sin(tensor) # [-0.8415 -0.9093 0.8415 0.9093]\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([ 0.2689, 0.1192, 0.7311, 0.8808])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor.sigmoid()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([ 0.3679, 0.1353, 2.7183, 7.3891])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor.exp()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\nmean \nnumpy: 0.0 \ntorch: tensor(0.)\n" ] } ], "source": [ "# mean\n", "print(\n", " '\\nmean',\n", " '\\nnumpy: ', np.mean(data), # 0.0\n", " '\\ntorch: ', torch.mean(tensor) # 0.0\n", ")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\nmatrix multiplication (matmul) \nnumpy: [[ 7 10]\n [15 22]] \ntorch: tensor([[ 7., 10.],\n [15., 22.]])\n" ] } ], "source": [ "# matrix multiplication\n", "data = [[1,2], [3,4]]\n", "tensor = torch.FloatTensor(data) # 32-bit floating point\n", "# correct method\n", "print(\n", " '\\nmatrix multiplication (matmul)',\n", " '\\nnumpy: ', np.matmul(data, data), # [[7, 10], [15, 22]]\n", " '\\ntorch: ', torch.mm(tensor, tensor) # [[7, 10], [15, 22]]\n", ")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "ename": "RuntimeError", "evalue": "dot: Expected 1-D argument self, but got 2-D", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m'\\nmatrix multiplication (dot)'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m'\\nnumpy: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# [[7, 10], [15, 22]]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;34m'\\ntorch: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 30.0. Beware that torch.dot does not broadcast, only works for 1-dimensional tensor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m )\n", "\u001b[0;31mRuntimeError\u001b[0m: dot: Expected 1-D argument self, but got 2-D" ], "output_type": "error" } ], "source": [ "# incorrect method\n", "data = np.array(data)\n", "tensor = torch.Tensor(data)\n", "print(\n", " '\\nmatrix multiplication (dot)',\n", " '\\nnumpy: ', data.dot(data), # [[7, 10], [15, 22]]\n", " '\\ntorch: ', torch.dot(tensor.dot(tensor)) # NOT WORKING! Beware that torch.dot does not broadcast, only works for 1-dimensional tensor\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that:\n", "\n", "torch.dot(tensor1, tensor2) → float\n", "\n", "Computes the dot product (inner product) of two tensors. Both tensors are treated as 1-D vectors." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([[ 7., 10.],\n [ 15., 22.]])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor.mm(tensor)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([[ 1., 4.],\n [ 9., 16.]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor * tensor" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor(7.)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "torch.dot(torch.Tensor([2, 3]), torch.Tensor([2, 1]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/202_variable.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 202 Variable\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "\n", "Variable in torch is to build a computational graph,\n", "but this graph is dynamic compared with a static graph in Tensorflow or Theano.\n", "So torch does not have placeholder, torch can just pass variable to the computational graph.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "from torch.autograd import Variable" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " 1 2\n", " 3 4\n", "[torch.FloatTensor of size 2x2]\n", "\n", "Variable containing:\n", " 1 2\n", " 3 4\n", "[torch.FloatTensor of size 2x2]\n", "\n" ] } ], "source": [ "tensor = torch.FloatTensor([[1,2],[3,4]]) # build a tensor\n", "variable = Variable(tensor, requires_grad=True) # build a variable, usually for compute gradients\n", "\n", "print(tensor) # [torch.FloatTensor of size 2x2]\n", "print(variable) # [torch.FloatTensor of size 2x2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Till now the tensor and variable seem the same.\n", "\n", "However, the variable is a part of the graph, it's a part of the auto-gradient.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7.5\n", "Variable containing:\n", " 7.5000\n", "[torch.FloatTensor of size 1]\n", "\n" ] } ], "source": [ "t_out = torch.mean(tensor*tensor) # x^2\n", "v_out = torch.mean(variable*variable) # x^2\n", "print(t_out)\n", "print(v_out)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "v_out.backward() # backpropagation from v_out" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ v_{out} = {{1} \\over {4}} sum(variable^2) $$\n", "\n", "the gradients w.r.t the variable, \n", "\n", "$$ {d(v_{out}) \\over d(variable)} = {{1} \\over {4}} 2 variable = {variable \\over 2}$$\n", "\n", "let's check the result pytorch calculated for us below:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Variable containing:\n", " 0.5000 1.0000\n", " 1.5000 2.0000\n", "[torch.FloatTensor of size 2x2]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "variable.grad" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Variable containing:\n", " 1 2\n", " 3 4\n", "[torch.FloatTensor of size 2x2]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "variable # this is data in variable format" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", " 1 2\n", " 3 4\n", "[torch.FloatTensor of size 2x2]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "variable.data # this is data in tensor format" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 2.],\n", " [ 3., 4.]], dtype=float32)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "variable.data.numpy() # numpy format" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we did `.backward()` on `v_out` but `variable` has been assigned new values on it's `grad`.\n", "\n", "As this line \n", "```\n", "v_out = torch.mean(variable*variable)\n", "``` \n", "will make a new variable `v_out` and connect it with `variable` in computation graph." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "torch.autograd.variable.Variable" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(v_out)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "torch.FloatTensor" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(v_out.data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/203_activation.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 203 Activation\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* matplotlib\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "import torch.nn.functional as F\n", "from torch.autograd import Variable\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Firstly generate some fake data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "x = torch.linspace(-5, 5, 200) # x data (tensor), shape=(200, 1)\n", "x = Variable(x)\n", "x_np = x.data.numpy() # numpy array for plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Following are popular activation functions" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\morvanzhou\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\torch\\nn\\functional.py:1006: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\nC:\\Users\\morvanzhou\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\torch\\nn\\functional.py:995: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n" ] } ], "source": [ "y_relu = F.relu(x).data.numpy()\n", "y_sigmoid = torch.sigmoid(x).data.numpy()\n", "y_tanh = F.tanh(x).data.numpy()\n", "y_softplus = F.softplus(x).data.numpy()\n", "\n", "# y_softmax = F.softmax(x)\n", "# softmax is a special kind of activation function, it is about probability\n", "# and will make the sum as 1." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot to visualize these activation function" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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ASt5SPFlZfo1qdrYf505NDTsikeA552uQP/+8L7Bi5pP23XdrCElKlZK3FM/d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\nWgTPPw+DBsEff8Ahh8A//+l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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(1, figsize=(8, 6))\n", "plt.subplot(221)\n", "plt.plot(x_np, y_relu, c='red', label='relu')\n", "plt.ylim((-1, 5))\n", "plt.legend(loc='best')\n", "\n", "plt.subplot(222)\n", "plt.plot(x_np, y_sigmoid, c='red', label='sigmoid')\n", "plt.ylim((-0.2, 1.2))\n", "plt.legend(loc='best')\n", "\n", "plt.subplot(223)\n", "plt.plot(x_np, y_tanh, c='red', label='tanh')\n", "plt.ylim((-1.2, 1.2))\n", "plt.legend(loc='best')\n", "\n", "plt.subplot(224)\n", "plt.plot(x_np, y_softplus, c='red', label='softplus')\n", "plt.ylim((-0.2, 6))\n", "plt.legend(loc='best')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/301_regression.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 301 Regression\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* matplotlib" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "import torch.nn.functional as F\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "torch.manual_seed(1) # reproducible" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1) # x data (tensor), shape=(100, 1)\n", "y = x.pow(2) + 0.2*torch.rand(x.size()) # noisy y data (tensor), shape=(100, 1)\n", "\n", "plt.scatter(x.data.numpy(), y.data.numpy())\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Net(torch.nn.Module):\n", " def __init__(self, n_feature, n_hidden, n_output):\n", " super(Net, self).__init__()\n", " self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer\n", " self.predict = torch.nn.Linear(n_hidden, n_output) # output layer\n", "\n", " def forward(self, x):\n", " x = F.relu(self.hidden(x)) # activation function for hidden layer\n", " x = self.predict(x) # linear output\n", " return x" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Net(\n (hidden): Linear(in_features=1, out_features=10, bias=True)\n (predict): Linear(in_features=10, out_features=1, bias=True)\n)\n" ] } ], "source": [ "net = Net(n_feature=1, n_hidden=10, n_output=1) # define the network\n", "print(net) # net architecture" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "optimizer = torch.optim.SGD(net.parameters(), lr=0.2)\n", "loss_func = torch.nn.MSELoss() # this is for regression mean squared loss" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.ion() # something about plotting" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for t in range(100):\n", " prediction = net(x) # input x and predict based on x\n", "\n", " loss = loss_func(prediction, y) # must be (1. nn output, 2. target)\n", "\n", " optimizer.zero_grad() # clear gradients for next train\n", " loss.backward() # backpropagation, compute gradients\n", " optimizer.step() # apply gradients\n", "\n", " if t % 10 == 0:\n", " # plot and show learning process\n", " plt.cla()\n", " plt.scatter(x.data.numpy(), y.data.numpy())\n", " plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)\n", " plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color': 'red'})\n", " plt.show()\n", " plt.pause(0.1)\n", "\n", "plt.ioff()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/302_classification.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 302 Classification\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* matplotlib" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", "from torch.autograd import Variable\n", "import torch.nn.functional as F\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "torch.manual_seed(1) # reproducible" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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kQsCU5TB0dYZVh5Yyz3cZPxBh/xxQeA9T93owa1pf5TnGnb0pHdTLKPiQgter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\nOqpwrTy7MA9bfU/jz37TsdfvErbfP8uqhglbPRq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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# make fake data\n", "n_data = torch.ones(100, 2)\n", "x0 = torch.normal(2*n_data, 1) # class0 x data (tensor), shape=(100, 2)\n", "y0 = torch.zeros(100) # class0 y data (tensor), shape=(100, 1)\n", "x1 = torch.normal(-2*n_data, 1) # class1 x data (tensor), shape=(100, 2)\n", "y1 = torch.ones(100) # class1 y data (tensor), shape=(100, 1)\n", "x = torch.cat((x0, x1), 0).type(torch.FloatTensor) # shape (200, 2) FloatTensor = 32-bit floating\n", "y = torch.cat((y0, y1), ).type(torch.LongTensor) # shape (200,) LongTensor = 64-bit integer\n", "\n", "# torch can only train on Variable, so convert them to Variable\n", "x, y = Variable(x), Variable(y)\n", "\n", "plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=y.data.numpy(), s=100, lw=0, cmap='RdYlGn')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Net(torch.nn.Module):\n", " def __init__(self, n_feature, n_hidden, n_output):\n", " super(Net, self).__init__()\n", " self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer\n", " self.out = torch.nn.Linear(n_hidden, n_output) # output layer\n", "\n", " def forward(self, x):\n", " x = F.relu(self.hidden(x)) # activation function for hidden layer\n", " x = self.out(x)\n", " return x" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Net (\n", " (hidden): Linear (2 -> 10)\n", " (out): Linear (10 -> 2)\n", ")\n" ] } ], "source": [ "net = Net(n_feature=2, n_hidden=10, n_output=2) # define the network\n", "print(net) # net architecture\n", "\n", "# Loss and Optimizer\n", "# Softmax is internally computed.\n", "# Set parameters to be updated.\n", "optimizer = torch.optim.SGD(net.parameters(), lr=0.02)\n", "loss_func = torch.nn.CrossEntropyLoss() # the target label is NOT an one-hotted" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.ion() # something about plotting" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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K8rNo1Qh+Jzch7thlqQBV2dv1/8BGxjsvWYQqVO2ojTQWpCiKMgJwAsD/aJqW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\nIIRYLKV82MF6ORy9TyoHIUQc0FRKqRNgGiCE6Ap8CLSXUiY5Wh9nQQj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Ulp9F68bwPbUZccevSAWoyt5u+Ac2Mt55ySJUoWpHbaSxIEVRlBGAkwC+pWla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\nRG/aWOmXVbllJHfdbznvzKd2KAZvLwxenlR94F7ar5yuBaq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ksvxM2jZD17PbEH/qulSAqixs8z+wkPHOSxZhFap21EdqC1IURekBOAPgW5qm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\nhgWsPbGHqMcx+Jcoz6AmL1K/bDWaVqiFb4EiXL1nvoFi3/p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H05bLZPmZtW+O7me2Iv7kNZkAVVn4xn9gJeedlzyiKlTtqI80FqQoijIAcBrA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\nmsQI0RxYAjwF/gfcBZoC44EqQFL+WRYAbYDLwFLgIVAK6Ax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f2YD7k5YoZPNZtGiIjic24+3RSwrBqbSwDf/ARsk7LmUk5ajGUdNoLUBRFGUE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\n6inE5kzwLYzZ/T1yi/57FXfo8VUsOb0NJ6f/gvZ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GPbMvMB0IRkrjI6Ti0G0hr6CcGbPS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\neBxIeeC8lPKilDIMWAQ0s3NMDkdKeV1KeTjqz49Qb8B57BuV4xFC+AK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y/CxaNET705vw7sRVmQBVVvj6/bCR885LHpEaVTuqI60FKYqijACcAvA/mqZl\n8jgpipoCYAoAuLiwK39PEF+i7NfRuD9sttytK2ixGEFTlsLY0w027VvIvd59/ECE/71P6RjmXnVh\n0aSe2nOMP31dNkCVUfg+Fa9WbkeLzcv+u+bMTcZ+k67chbhYIPcxFtstSNRl4GwP3yenELXjGGL2\nn0VhUhr07axQ49sB8JgyFLoWJe+TMkPeIHrPSRS8S4KupRncRvUp3evLeUBX2Hdvi7hjl5H5/DW4\nujpw7NMJNu1Karp+StpQpjCJxS7FFAXTeh7q/7LViFaCFEVRfJQEqMM0TctdTk7T9A4AO4CSKuja\nGJcgqqKIfw4q3VuJlkjwZt1ehUHKrGFt1Ph2AN7uPyP3PMXlovHKHzWaY/Suk4xt3h48h6brFpYG\nHDb1+miJBOKiYrlBqjy3INGztYJFs/rg8Pmo99MU1PtpikwbiViMoElLELNP+iMqevdJ2HRoifZn\nt0DH1Bg8A324jx8k9/oPLDajLErNAMXjKc26tO3krXLCSnWlcQ4kVbKybjeA1zRNr9N8SgTxZUs4\nf5uxTeJFP6UVI1rtWoHa//tWZnGtoasj2p3ZBAff9nKvk4jFeH/FH1E7jiH+9HW5+yBlvniDjGev\nGOcoys1HcXomxAIBkm8GsAoyHD1dhWuhXAZ3B1dfj7EPdXjOGMlYFT5k0TqZAPVJql8QHoxQHvgp\nilK4kLgsDo+LJn/OU3xeh4+a48tnD6yvkTbupL4BMAZAKEVRzz8eW0TT9GUl1xDEV0tcyLzlOC0W\nQyIUKczu4vB4aPb3IjT4+Tsknr8NYW4+jNxd4ODbTuH6mth/L+L5/DUoSEguPaZjYYZ6Cyaj3rxJ\nKExJR8Couaw3KqS4XMTsPYWIfw4preRQlqSoGJnPX8t9FKljboo6c8bj1YqtrPpiy3VEb9RfNE12\nLkIh4k/fQPypaxBk5iDFT/nC56Qrd5EZ8gbmjevIPU9xOLDp0FLpY1IAsO3cGnVmj4OevTWe/e93\nFKVI/+0kAiEejp6HwvepqDdvEsNvR2gju+8+AOavFwTxlStKy0D07pOs/t9g7OnGKv1Y18KM1c6z\nsf9eRMCouTLrfAQZWXg+fw2EWblIOH9bpY0UDVwd8OJn1Zc8vjt2WeH7ska/zQIkNF6v3cNqu3l5\nODp8GNZ0hmldd3hMHQb7bm1l7nDyYhPg5ztJ5U0Q3524ojBIAUDtWWOVBymKQu0fxgAAdMyMZQJU\nWc/nr4FJyo5tAAAgAElEQVRly0ZyFxcT/yFLnglCC2KPXMBZZx+ELFwLwYcsxvae00dobWyJWIzn\n89coXYga9udOlXf6zZeTJcdGUVoG3vy9D7e7TcDNjmPwbO5q5EbFASh5ZNb499noH++PZhsWw9Lb\nS+XKCxKBELlvYpAXFYf8mHjQYrH0eZEIfj0mq7VLr7wq52U59e2M+ksUlI+iKDRdtwDWbUr2pQrf\ncIBxvIiNB1WeY3VDto8nCA2l3n+CWx3GynxYKmLTvgU6Xt+jtYWciZf94d9LNlGgsnD0dCEp+uyR\nJ0Wh6doFqDN7nEz7wpR0xB25iMKkVMQdv4qCuESVxrPv0R4+57aUvpN6d+oa7g/+Qa25N9+0VGYb\nD3lS/AIRsekw0h48A0WVJELUmjlGanv4ozoNIBEKlfbDNzHCkGx2OyID1XP7eFK7jyA09HrNblYB\nStfKHO6ThqDB0hlqBaiCxBTE7D2FvOh48E2N4Dq8F6y8vVAQz1xPriLJBCgAoGk8+3EljDxc4NSn\nk9QpfVur0uBl06GVygE36cpdvP5rD+ovnApA/W3auQb6rPd8su3QSuF6MACgaZrVVira3m7la0Qe\n9xGEBsRFxXh/yZ+xnWENJ/RPuAuvlXPAUyPD7cWyjTjn2hEvft6AmH2nEb7hAK63HobbXcez3l6j\nKni9ZrfUz5nPXyPlziPkvS15tOjY0wdN1y9SWGNPkcit/0Ly8YuCiOV27p9rsmae1vZ8oigKVt6N\nGdtZeXtpZbyvGbmTIggNiAoKWd1F0Uoy+ZiEbzyAl79tlnsu+WYAJGIx+GYmyvdCUme793KQdu8J\nij9kIun6A7xcvgU5r6NLz9l28obX6rmoM+tbOPT0QeTWf5HxOBSZz18zBp6C+CQUxCfByM0JpnXd\nkchiGcAnXEN9uI3qg5py1kZpwnPGKMbq7LW+Z360WN2ROymC0ICOmQn0bK0Y2xnXlr+jKxOJUIiw\nVTuUtkm9Ewi3Eb2UtnEb3RdmjWqrNLZJOVVEiNjyLwJGzpEKUACQcvsRbvqMQXpgCEw83dBs3UJ0\nvXcExp5urPr9lOHnMWWYSndi4vxCRO84jkt1eyInMpb1dUzcRvRGzQmKA5/njFFw6tdFa+N9rUiQ\nIggNUBwO3CcOZmznMWWoWv2n+j9GYVIa8zz4PDRYOgMcHekFrRSHg5rjBqLVrhXodHMf7Lu3ZTVu\nvYVT0fn2fli00O4GfbpW5ni5YovC8+KCQjz5/jepY7ad5FcuL8vIwxUGLg4l/17TGQ2Wqr4NRn5c\nIvx6TGZMdlBFq12/w3v/alg0b1B6zNLbC22OrEWLTUu1Ns7XjGT3EYSGBJnZuP7NCJk7g0/se7SH\nz4Vt4HC5Kvf97uRV3B8yi7FdjbH90Xr/ahSlfsDbQ+dR8O49dK3M4TayD4xqSlczz34djaSr91Cc\nkYWMp6+Q6v+4tOSRSV131J07Ae4TSgIvTdNIufUQ705ehSAzB++v3NWoSKyOuQkEmcq3aAcA36en\nYdG0ZKv23Oh3uFS3p9Lg0XT9ItSZ9a3UsaidxxG2eifyPu64S3G5oHhcxvVZ3xz7G65DezLOUVXy\n9p5SVXXM7iNBiiC0oCg9A8FzViPu2OXSD0G+mQk8pgxFo+WzwNVR74Mp49krXG3GvJi3wbLv0eiX\nmWqNIczJQ15MPLj6ujCpXVNhu6gdxxA0tWK+/dt0aAWKQ0HHzAQuw3pAVFiMwAmLADnZcI79u6D9\nqX/krreiaRqZwWEQ5RWgOCML9wYo3+8KKKlg8c0R5RXTK0t1DFIkcYIgtEDPygKt969Gk7U/ISsk\nHBSPC8sWDcEz0NeoX4um9WHetD4yldTao7hcuCt598GEb2IEc6+6jO3YbMaoLal+/+32E3/6OnRt\nLOUGKADICg5DYVIaDORscUJRVOkdWdKNB6zGFsupd1hWflwiPgS9ADgc2LRvAb1yLJxLkCBFEFql\nZ2UBu86y26Zroum6BbjTbQIkAvmPu+rOnwRdawtE7zmJpKv3IBGJYdmiIdwnDoaejSVj/xKxGAln\nbyJ61wmpNVjuEwaVbKv+EdvNGMtDsZLagflx7/F8wV9oc3CN0j5M67qD4nIZszHFhUXIjYorrVIu\nzM1D7KHzSAsIRtqDZ8iPSwQkJU+gODp8uI7ojeb/LAHfWH5hXUIz5HEfQZQTiUiExAt3kPUyAjx9\nPTj26wwTlplqn0vxC8Sz2SuR+fx16TE9G0vU/WkybNo3h3/vaShKSZe6hqOrg5Y7lqPm2P6fd1dK\nXFQM/37fIfn6fZlz+o626HRjL0zrugMAbnb+FqkMxVUrC0dXBwMS70LX0lxpO/9+09mlp1MUHPt0\nhFOfTng6+w/GFHhLby90uXOg3NesVcfHfSRIEYQK0gKeITfqHfgmRrDv9o3Cx3mJl/wQNOVnFL4v\nswEeRcGpfxe03rdK4XYWTD48CS252zEzhm3HVhBk5uBy/V4oVlAvkOJw0OnWPoXVEZ7MXI6ITYcU\njqdjbgL7nj7IfBaG3IhYVmvC9GwtlRZWLS9dA47CunUTpW3yYuJx/ZsRKEpmzphUVcudK+AxSfGO\ny9pAglQFIUGK+NKk+AXiycwVUkVa+abGqP2/b9Fw2fdSVbhT7z7G7S7jFWaj2bRvgc53DqhcWFWe\n0OWbEbp0o9I29j3ao+PlnTLHBdm5OOPQjtVmhmxQfB4ar/gfao4biNP2bRW+QyrLoZcPQJc8cky+\nJns3p4oewWdZvVvLiYrDlUZ9Gd89qcrI3QW9Xl3SWk1GeapjkCLrpAiCQerdx7jTfaJMFXFhdi5e\n/rpJZl3Pi6UblaZLp959jPdX7mplbvEnrzG2Sb52H8I82bTx1LuPtRagzJvUg+/jU6g3fzL0bCxh\n4CSbxCBPs/WL0eHSDjRdu0Cj8Q3dHFkvVs4Ji9J6gAKAvOh3uNZyMOu9twh2SJAiCAbB8/5UmLQA\nAJFbjiAnPAZASeZXqr/yzfUA4O2Bs1qZG9PWEkBJEVNRvmwwooWKtzdni+Jx4XNpO3o8OyO1D1O9\n+ZMZrzX3qluanGBW3xPW3zRVex51fhzP+s40NyJW7XGYZL0Ix/1h/yu3/qsjEqQIQomslxEl6cYM\nonefBAAUsnwXU5ScztyIBRMW5ZZ0Lc2ga2kmc9y8SV2NHznSIjEg541BzXEDYOBsr/Taegukq503\n37wMfCUFXnkK3uPVmjkGtWeOYZ7sR+q+D2Qr1S8IGUqWDBCqIUGKIJTIj2W3t9Gndvp2zHX8AEDP\n3lrtOZXlMWUYY5uaEwaBw5NdbWJUwxn2vu00n4ScOnk8QwN0vL4bhjWcZJtzOGiyZj5ch0lXdTBv\nXAdd7x+BY5+OUsHTrHEdtD2+HgMS/NF881LYdWkDq9ZN4D5xMLo/PonmG5eoNF3Hfp1L954qL4kX\n7pRr/9UJWSdFEEroWJgyNyrTztDFAbadvJVvMY6SOw1tcOrXGY59OylMqzb2dEPd+ZMUXt9iyzLc\naDsSBQnJao3P1deFdRv5GXWmddzR5vAaBM9ZjYynr0BLaOg72qDJ6rlwHSa/IK5Zg1rwOb8NhUmp\nyI97D76ZMUzruJeer/XdKNT6TrPK4fq2Vqg5cRCith1lbEtxOaDFqu/5pOzxMKEacidFEEpYeXvJ\nvRv4nNvIPqX/3mj5LJlCr2XZdvKGfXct3MGg5K6k3cmNqLdgitTCW44OH26j+qDr/SPQs1JcEcHQ\n1RHdHh2H54xRaj0GqzGmP3TMTOSeC//nIG60GYH0h88hEQhBi0QoiHuPB8N/xMNxyhMl9O1tYOXt\nJRWgtKnZhsVwHa68crxtx1bodHO/Wu/KzBqrVnGeUIykoBMEg+i9p0rqxilg06Elutw5KHUs6fp9\nBE7+GQXv3pceozgcOA/xRatdK8A3MtT6PEWFRfgQ9AK0SAyzRrVVLtcjLhagOC0DgZOXIOnqPcb2\nxrVrwPfJKbm/S3rQC1xvpXzNkNea+ag3dyK7uQkEiD91HfEnr0GYkwfjWm7wmDJMKllDHRnBYYjZ\ndxoFCSkQFxZB38EGxjWd4dDTRyqdPSs0HI/GLWT1rknPzhr9390pl0eK1TEFnQQpgmDh9do9CFn8\nt0wFbbtubdH22N9y7yZoiQTvr9xF9ssIcA304dS3EwxdHStqymoTFxXjyczliNl3BrRITgYgRcFl\nWA+03rda4ZqgOz0mMQY6vpkJhmQ+ZpxPXmwC7nSfKDcrr9b3o9Fs4xKpdWrlIScyFhdr+zJuHEnx\nefA5vxUOvu3LZR4kSFUQEqSIL1FRegbe7j+L3Kg48E2M4Dq0ByyaNWC+sIoSCwSIP3kNqf4lgcK6\nXTO4DOlRGngKU9KRePEOMp+FoSAhGXwzY5jV90TN8YMY79KOGTRmtRapd8Q1paWiJGIxLjfso3Ab\nFABosmY+6rK8I1NX+MYDeDrrd8Z2HtOGo+XWX8ttHtUxSJHECYJgSc/KAnXnTKjsaWhF2sNg3B80\nU2pDxagdxxA890+0PbkBNm2bQ9/WCh4ThwBqfP7LvQOTIz82UWmQSjx/W2mAAoA36/ai9qyx5Zqx\nxzYRQt9OO1mbxH9I4gRBVDN5MfHw6zFZ7o6/RSnp8Os5BblRcRqNocui+joAxirt8aevM/ZRmJSG\n9EchrMZTl3mTeizbMZdlIlRDghRRZRR/yETCuZuIP3ND7ZRogln4xgMQZucqPC/Kzceb9fs1GoPN\n+i19BxvGxAd5lTLkttNSeSdFbDt5My6cNnBxgEOvDuU6j+qIBCmi0gnz8hE4eQnOOvngbv8ZuDfw\ne5x17Yi7A79HYVIqcweESuKOXmZu8+8ljcZosGQ69BgWNrOp12da34OxDcXhsKq8oQmKouC9bxV4\nRgZyz3P19dD6wGpwuNxynUd1RIIUUanExQL4+U5C9K4TEBcV/3dCIkHCmRu42mooitIrb7O9r5Eg\nM5uxjTArR6MxKA4HvV5ehEld2XVOHD4fzTctZVynBAAek4cylm6y694WRm7Ma9k0ZeXthW4Pj8Fl\naI/S918Ujwfngd3Q9cG/sPVpWe5zqI5I4gRRqWIPn0fag2cKzxfGJyF47p9ovW9VBc7q62ZUwwk5\n4W+VtmGzgJmJrqU5eoddRnpgCOKOXYYoLx9mDWqhxph+UguPlc7DxQGNls9CyOK/FYxhhqbrNKug\nrgqzBrXQ9th6CHPyUJSWAV1LM4WLmQntIEGKqFRRO44ztok9dB7ee1eW+1qYL1WKfxAiNx9G2oNn\noDgc2HRshdozR8OyRSO57d0nDUHwvD+V9ukxWXub91m1agyrVo3Vvr7+omnQd7RF2KodyHlTUm2e\n4nLh2LcTvFbNgUmt8n3UJw/fxKjcC9USJcg6KaJSnTBtBmFOHmO7Dld3wUFLpYS+Ji+WbsDL5Vvk\nnrPp0BL23dvBbVQfGJapSC7MzcONtiOR9SJc7nWm9T3RLeBolfwQzgoNhzA3H0Y1natlund1XCdF\n3kkRlYpi+aL50zdo4j8JF24rDFBAyZYRIQvX4nyNznj8/W+QfNz6nW9shM6398NlWE9QZaqjUzwe\nnAd3R+c7B6pkgAIAs4a1Yd2mabUMUNWVVh73URTlC2ADAC6AXTRNkxcIBCumDT2Rdpf5rppnoF8B\ns/myhG84wKodLRYjcvNhcPg8NPu7pAahrqU52h79GwXvU5AeEAzQNKzaNIWBI7sddQmiomh8J0VR\nFBfAZgA9ANQDMIKiKHYr34hqr8GiaYxtKB4XDj19KmA2Xw5aImHcDuRzkZuPoDBFerNFAwdbuAz2\nhcuQHiRAEVWSNh73tQQQRdN0DE3TAgBHAfTTQr9ENWDfvR0smiuvf+c6rCf5AP0MLZEwFjv9nEQo\nxLtjzGukCKIq0UaQcgQQX+bnhI/HpFAUNYWiqCcURT1JS5Mtx0JUXx0u7YCZgsoDNh1aouX23yp4\nRlUfh8djDO7yFH/IUms8QVYOClPSS4IjQVSgCktBp2l6B4AdQEl2X0WNS1R9ejaW6B50AvGnruPt\nwXMoTsuAgZMdao4fCMfeHRkXc1ZXtWaMwqPxC1W6xqBMlh8b8aev4/XaPSXvrQAYONnBfcpQ1J0z\ngbwnJCqExinoFEW1BvALTdPdP/68EABoml6p6BqSgk5UZdFpCUjPy4KDqTWcLaruY0aaphEwei7i\njlxk1Z5nZIABifdYZ+69/H0rXixZL/ecVesm6HRzLwlUFaw6pqBr407qMQBPiqJqAEgEMBzASC30\nS1QhErEYieduIWLLEWSHRYFnoAeXoT1Rd8546FqaV/b0tOJ6WCCWXdyJR29fAiip19alTgv83nca\nWrhVvVwgiqLQ5tBfsO3ojYhNh5AV8kZp+4a/zGQdoDJD3igMUACQ/jAYr/7YhsYrZqs0Z4JQlVYW\n81IU1RPAepSkoO+haVrp7mDkTurLIi4WwL/vNCRffyBzjqOrA59L22HfuU0lzEx7jj+9iRG7l0JC\ny75z0efr4trMDWjn6VUJM2NPVFCImH1n8PK3zSgqk8Wna2WOBktnoPbMMaz7Cpq6FFE7jilto2dj\nif4J/uW6jxMhrTreSZGKEwSjJ7NWIGLjQYXnKR4X/eL8YOBgU4Gz0p5CQREcF/ZFZoHioqp17dwQ\ntuxoBc5KfRKhEO+v3EXh+1To2VrBoaePwm3eFbnSdAAyg8MY2/WJugFjdxd1p0qoqDoGKVK7j1BK\nmJuHaIb6erRIjMfTlsLn/LYKmpV2HX96S2mAAoDXybHwj3gGn1pNK2hW6uPw+XDq21mjPigeu0og\nHJbtCEJdJEgRSqU9eCa9hYYCSdcfgJZItJqJl5z9AbsenMPF0AcQiIXwcqqF6e0Hav390KskdiWX\nXiXFfBFBShscfNsh43Go0jYmdd1h6Cqz2oQgtIoEKUIpiVDEql2eRABhdi7rLRiY3Al/in7b5iG3\nqKD0WHB8BPY+vIgF3cdiZf/vtDIOABjo6Gm13dfAY+pwvP5rD8SFRQrb1J41tgJnRFRXZAEKoZRF\nE3Z3Leeb6iEwNUorYyZnf5AJUGWtunYABx5pr3LCAK8OjG10eHz0avCN1sas6gwcbdH2xAZw9eUH\nZs/pI+A5dXgFz4qojkiQIpQycLKDSZ2aStsU8YBb9XQxYt8vEInZ3Xkps/PBOYUB6pPZJ9ZjyM5F\nWHR2K2LSEjUar7GTJ7rWVb6r6jjvXrA2/jpS7dly7NUBvV5dRN15E2FS1x1G7i5wHtgNnW7uQ4st\nv1T29IhqgmT3EYwygsNwqeUgcEWy6dliCtjS2RCPPEqyx05M/gODm3bSaLxWqycgKJY5s+wTiqLw\nU7cxGj0CzMjPRs9NPyIw9pXMud4Nv8HJySuhy1ctQ44gtI1k9xFfjMKUdERuPoy3h86jOD2ztIyQ\n1ZhekOjrwMrIFDyudv7ntWhSD9n/TEbQ5n1oFyGAoYCGhAJCnHm46KWHNw7/rZN5GBOqcZAqFglV\nak/TNFZdOwA7E0vM6jRMrTEtDE3xYN4OXAp9gENB15CWlwlnc1uMb90bHWs3U6tPgiA0R4LUFyj7\ndTRud/4WhUn/Fer1z4/D/Htb8TpmFwDA1sQCE9v0wU/dxsJE31DjMQ0b1cLBtgY40lofxkU0ivkU\nCnVkt3PXxhbvTZxrISQhUuXr1tw4hBk+g6SCc1DsKzyIfgEOxUGn2s3Q0NFD4fVcDhd9G7dH38bt\n1Zo3QRDaR4LUF4amadwbNFMqQF1toIuDbQ2k2qXkZOCPq/tx6WUA/GZvgZmBsUbjdqzdFByKAzFX\ngixDxYGoS50WGo0DANPbD8S+h5dUvi4xKw0BMaFo79kE4clxGL3vFzyJey3VpkOtpjg47hc4mX+Z\nC48JorohiRNfmOSbAch5HV36c4oJB4faKC7yGZIQiSXnt2s8rpulA/o2aqe0TW1bV3Sv5y1z/Hl8\nBBac2YzpR1ZjzfVDSM3JUNpPS7f6mNd1lFrzzCnKx/usNHRcP0MmQAGAX8QzdPz7O2QV5KrVP0EQ\nFYsEqS9Myp1AqZ9v1dMFzVH+iO1A4GXkMWTLsbFz9EI0UvC4zN7UCqenrpJ63JdblI8+W+agyR9j\nsfr6QWy7dwbzz2yC8+J++OPKPqVj/TlwJvaN/VnheIp4Wjtj3a1/kZSdrrBNVFoCdt4/p1K/BEFU\nDhKkvjCFYgGirbl4a8WFiAPEWDOXpcktKkBE6jvGdgKREI9iXuJe5HNk5suWCbIyMkPAvJ34Z9gc\neDnVgoWhCWrZuODX3pPxfNEB1LOvIdV+6M7FuBgqW5RWIBJi8flt2Op/Sul8vm3dCyFLDiHu97O4\nM3szKCgPxu09m6C2nSurR4X7Hqn+OJEgiIpH3kl9IfKKCrDk/HbsLbiKnEEmAACTAgn0hOyWEPA4\nioOZWCLGist7seXuKaTmZgIA9Pi6GN68C0Y074q4jGTo8XXRvW4r2JhY4PsOQ/B9hyFKxwt8+xJX\nwx4pbfP71X2Y3LYfYxaii4UdXCzssMj3W/x+dZ/cNoa6+lg3aBYEIiE+5Gcr7Q8oeX9FEETVR4JU\nFXTlZQC23j2N5wmR0OHx0a1uKzyIDsGLROmKDjkGHCgvi1rC2dwW9R3kL8ilaRojdi/FiWe3pI4X\nCYux7+ElqbsSHR4fo1v6YtOwOdBnKBH07+MbjPNKzEqDf2QwOrNMtljRbxrsTC2x+vpBJGSmlh5v\n79kE6wbNQjPXki3ozfSNkVWo/J2TrbEFqzEJgqhcJEhVITRNY8rhldj14LzU8a1pCRr1O7PDEHAV\n3EldDL0vE6AUEYiE2BNwAfGZKbj6/XpwlBSTzWCoKv5JpooJDN93GILp7QciIDoUucUFcLdyRG07\nV6k2Y1r54h+/E0r7GevdQ6VxCYKoHOSdVBWy9e4pmQDFFke2GAQAYEyrHpjTRfFGyTvun1V5rBuv\ng3D5VYDSNm6W9qz6uvbqkdIkB3m4HC7aeXqhZ4M2MgEKAH7sMgKWhooL3Tqb22JquwFIy83EuZC7\nOPvcX+U5EARRMUhZpCqCpmnU+WUYqwQHecz0jTGz4xCcDfFHgaAYDRxqYlq7AfCt31rpdR5LByNa\njTu1/o19cGbaaoXn36a/h8fSwXJ3uv2clZEZrs1cj6YudVSehyIhCZEYtmsJwlPipI7XtnFBO4/G\nCHj7EhEp7yCSiAEAfC4PA706YNPwubAyMtPaPAhCm6pjWSQSpKqIuA9JcFsyQO3rnc1t8e4P1dOq\nvX4fo1Z1h2YudfBk4T6lbeac3IB1t/5l1Z+jmTVilp+GDk97W5HTNI2bb4IQEB2KfEERroY9RGhi\ntNJr6tnXQMC8nTDVN1J5PKFYhJPPbmP3g/OIzUiGuYExRjTviglt+mi8mJoggOoZpMjjviri0zd6\ndQ3w8lHruoEstqmQR9njtE/+GvQDfu87jdUHdGJWGk4+u63WXBShKApd67bCgu5jceUVc4ACgLCk\nt9jE8D5LnvziQnTdMBMj9yzFrfAniE5LwJO415hzaiMarRiNSDXvkAmiuiNBqopwtbCDnYmlWtfq\n83XxfYfBAIAXCZH46cwmTDr4O367tBvvMpKVXjulbX+YG5ioPOboVr6MbSiKwsAmHdCmRkNWfd54\nE6TyPNg4/uwWXr5nDlCfqLPQ94fj6+AfGSz3XHxmCvpunQeJhPnRJ0EQ0kiQqiJ4XB6mtO2v8nVG\nugY4PXUVnMxsMGj7AjT+fQz+vH4IuwMuYNnFnaj58yDMPbURih7r2pla4tJ3a1V6zFbPrgaGNu2s\ntE2xUICFZ7eg3q8jGJMsPhGX04f44aBrKrWPy0iGWIU727TcTMYx3iTHMa4bIwhCFglSVchC37Fo\n79mEdfsfOgxF7Ioz8K3fGuMOLMfp534ybcQSMdbePIIVV/Yq7Od18lsIVNgeI7MwF+tu/Sv3gzy/\nuBALzmyG9TxfrLp2ADTYv/Ns5Vb/vzHycxCTlqiVck7peVkqtTfSNVCYsi/PnYinKBYJGNtdYRms\nCYL4D1knVYXo8XVxbeZ6jD+wHEef3FTa1s3SHn8P+R84HA7eJMfi+FPla53W3jyCOV1GwuCzRbhF\nwmLMPrlBpXkmZadj0bmteJ4QgaMTV5TW6ysQFKHrxh/wMCZUpf4AgMvhYOf9szjy+BoKhcUISYiC\nhJZAj6+LIU07YWnPCfCwcVa5X6AkqeTpuzes2w9rpvwu8XNClrsRi8SavXckiOqI3ElVMXp8Xez/\ndhlqWDoobTe3y6jSxbRHnzBXd8guzMPll7Lf5NffPoacony15nr86S2cDfEv/XnD7WNqBSig5FFf\nSGIUAmJCERwfUZq6XiQsxsHAK/D+cxJevY9Rq+/xrXuxbmugo4cflawrk6e5S1127VzZtSMI4j8k\nSFVBOjw+rv2wHjWtHOWen9tlFGZ8TJQA2FdtyPysCgRN09h+74z6EwXw68XdWutLmQ/52Zh06A+1\nru3dsC0612bO2rUwNMH56WtkCuUyqW3nik4M/ZvpG2NEi24q9UsQBHncV2V52rggbOm/OPHsFk4G\n30F+cSHq2rlharsBMnX4mO66FLXLLMhB7Ickjeb5OvktgJI7tTiGTEJNPXr7Es/jI+DlXKv0WG5R\nPg4GXsGN148hkojgXaMBJn/TDzYm/9Xm43A4OP/dX5hxdA0OB12TejxnYWCCNu6N0L9xe4xo0U3m\ncWhwfDi23T2DsKS3MNTVxwAvH4xu6QtDXek9vHaMWoB2a6fJrVyhw+Pj4PhlMn0TBMGMLOb9KCEz\nFfseXsTbD0kwNzDGyBbdtFoBoTyl52XBaWFfpS/va1o5IvLXE1L19nKL8mEyW7X3L/LQWx+hUFAE\nw/91VJhFqC27xyzGhDZ9AAD3o56j37b5yPhsWxFdng52j1mEUS3/S5N/EB2CLf6nEPg2DEWiYtSz\nq4Fp7QdgYJOOCseafWI91t8+KnPc0cwa12ZukPmyEJ+RgpXX9uNQ0FXkFhWUbEffqC0WdB+LlmWS\nQviDRJcAACAASURBVAhCXdVxMS8JUgAWn9uK1dcPyWSr9WrwDY5OXA4jPQMFV1YdK6/ux6JzW+We\n41AcnJ66Cv0at5c5137tNNyLeq7R2I5m1vC0cUZ6bhZeJqn33ogtLocLKyNTNHOuA7+IpygQFits\n5zd7M9p6eOGnM5vw5/VDMm30+Lo4OnG53L/LJr8TmHlsrcJ5OJnbIOKX43KrwRcLBfiQnw0TPcMv\n4r8d4stRHYNUtX8n9deNw/jj6n656dSXXj7AiD0/V8KsVLfQ91tsGjZXZkFwbVtXnJv+p9wPYgBK\ni8+ylZiVBr+IZ+UeoICSlPqUnAxcfhWgMEB9avfXzSM4HHRVboACSpIyhu/+GW/T30sdl0gkWHvz\niNJ5JGSmKszA1OXrwMHMmgQogtCCan0nVSwUwHlRP6TlZSpt92zRfjRxrl1Bs1Luwot7+MfvBO5F\nhYAC4OPZBD90HIoeDdoAKEmHvh3+BB/ysuFsbou2Ho2ltnSX5/cre7Hk/PYKmH3F4nK4aOTgjuCE\nCKXt5nUdhT8Hziz9OTg+HE3/+Jax/76N2uHc9DUaz5Mg2KqOd1LVOnHiVvgTxgAFAP8+vl4lgtTc\nUxtlvuFfDXuEq2GPsNh3HFb0mwY+l4fu9bxZ93k6+A5uhz8Fj8sDLZFAn68LQz19eFg7obGjJ3IK\n83Ho8VVt/yoVQiwRMwYooORvWDZIFQoU36GVVajkTo4gCO3QKEhRFLUGQB8AAgDRAMbTNK3a8v5K\n9HlKtuJ2qm3MVx7OPvdX+gjq96v70M7TS6UANfPYX9jkd1LqWJ6gEHmCQkxtOwC/9pkMABjfpjf+\n8TsB/8hg5BUXsF68WtlsjS2QkpvB2E4gkv59atm6QIfHZ6zC0dDBXaP5EQTBTNN3UjcANKBpuhGA\nCAALNZ9SxWGbuq1ovVJFYtppFgD+ucO+evfxpzdlAlRZv13ejVtvHgMAOtVpjjPTViNj7XW1q6ZX\nhint+sHJ3IaxXXNX6SxOKyMzDPJSnPUHlBTPndpO/a1VCIJgR6MgRdP0dZqmP30NfQTASfMpVZw2\n7o0YF27yOFyMU6FiQXlhk4F3N0p+FW552AQ0eYGxVY0vI5W6oaM75nQZxapo73ftB8kc+3Pg93A2\nt1V4zbKeE1HL1kWjORIEwUyb2X0TAFzRYn8VYv2Q/4GnpJjoIt9xsDe1qsAZycemQvinHBimZBiJ\nRIIHMS8Y+7sbKRsYx3n30uqiVGczxYFAHfp8XUxs0wf+s7fCVN8I87qOQjsPL4Xtf+o2Bm3cG8kc\ndzK3QcC8nRjbqif0+Lqlx+vZ18C+sT9jWe9JWp03QRDyMWb3URR1E4CdnFOLaZo+97HNYgDNAQyk\nFXRIUdQUAFMAwMXFpVlcXJy8ZhXuXUYyVl7dj9PP/ZCa+18Sha2JBRZ0G4v/dR5eibMrcezJDQzf\nzZwKX9PKATwOD5Fp8TDU0cegJh3wY+cRaOTkCQDIyM/G3ocX8TDmJU4F32Hsj8/l4eG8XWj22eOw\nn05vwp835Kd1q8LC0ASjW/pi453jGvfVq8E3mNd1FBo5esDcUHp/rCJhMf66cRjb759FQmYqgJI6\nerM7DcfIlt0Z+87Mz8HbD+9hqKOP2nauGs+VINRVHbP7NE5BpyhqHICpADrTNM1qX4WqkIJeKCjC\n1COrcTjoWmkxUwAw0TPE9x0G45fek8HnVlzyo0AkRIGgCCZ6hqBBIzQxGgKxELVsXNBz849qF27V\n5eng5JQ/UCwUYOz+31AgKFLpej2+Li5+9xc612lRemzAtp+kCstqgsfhooVrXTx8+1LtPvR4Okhb\nc5VxXdKnNVY6PD6sjMzUHo8gKgsJUqpeTFG+ANYB8KFpOo3tdVUhSPXdMhcXQu/LPcehOLjw3V/o\n+XHtUXl6HBuGP28cwtnn/hBJxDDS1QeH4pRWJtfn62qc6qzP14VIIlY7K8/B1Bpxv58B72PQbrV6\nAoJiwzSaU1m6PB38M3QOjj69geD4cLWyKc9PX4M+jdppbU4EURVVxyCl6TupTQCMAdygKOo5RVHb\ntDCnchcQ/UJhgAIACS3B4nPl/6ucD7mLtmun4uSz2xB9rHiRV1wotXWGNtbiFAqLNUobf5+dhnMh\nd0t/Vnebe0WKRQKk5mXg1v82IWb5abX6eC+nsCtBEF8+TbP7PGiadqZp2uvjP9O0NbHydCCQOb/j\neUIEQhIiy20OOYX5GLV3mUo74lamZ/Hhpf8+tlUPrfcf8PFxprGeAUz1jVS+XtuBkyCIqqFaVpyQ\nt52CPCk5zAtBy8oqyMX+R5cREPMCHIqDTrWbYVRLX7nZcAceXUZecaFK/VcmHS4fEokEZ0P8sf3e\nWehweRAouDujKErlaugUSko3cTlcjG3Vg9W6sE+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F/XELZLL8LJrVR7sTG/D26CWZAFVWxNo9sJHzzUsekRpVO6ojrQUpiqKMABwH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\nsA34EGiNlOb3kqhzBgA/AvuAGkj5LHzcEyVAxYHSEfdQx34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UvYLDcH/ILHhf3AaulnSVCeeRvfEh6IXCtdj39gHfgP0mXravPct/f4zZfQLPF65Dcblr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\nAQuBpnbWyeGQUl6XUh6M+vdD1Adwbvtq5XgIIXyBhsAse+viKKRpAyW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UvYLDcH/ILHhf3AaulnSVCeeRvfEh6IXCtdj39gHfgP0mXravPct/f4zZfQLPF65Dcblr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\nAQuBpnbWyeGQUl6XUh6M+vdD1Adwbvtq5XgIIXyBhsAse+viKKRpAyW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UvYLDcH/ILHhf3AaulnSVCeeRvfEh6IXCtdj39gHfgP0mXravPct/f4zZfQLPF65Dcblr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\nAQuBpnbWyeGQUl6XUh6M+vdD1Adwbvtq5XgIIXyBhsAse+viKKRpAyW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E44mLpbL5zFo2QoczWxB/8ppUcCovfOMBWMn4xiWLUIVqHDWNxgIURVEGAE4D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\n91mq8/Fnilu6Vzfd67fGkF2LcPaF4uQIexNLNLC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E44mLpbL5zFo2QoczWxB/8ppUcCovfOMBWMn4xiWLUIVqHDWNxgIURVEGAE4D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\n3BfYBpQEykTfQ107BBQDBiLlxBjjFQ1zjgIlrGWVaV/Ia0i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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for t in range(100):\n", " out = net(x) # input x and predict based on x\n", " loss = loss_func(out, y) # must be (1. nn output, 2. target), the target label is NOT one-hotted\n", "\n", " optimizer.zero_grad() # clear gradients for next train\n", " loss.backward() # backpropagation, compute gradients\n", " optimizer.step() # apply gradients\n", " \n", " if t % 10 == 0 or t in [3, 6]:\n", " # plot and show learning process\n", " plt.cla()\n", " _, prediction = torch.max(F.softmax(out), 1)\n", " pred_y = prediction.data.numpy().squeeze()\n", " target_y = y.data.numpy()\n", " plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=pred_y, s=100, lw=0, cmap='RdYlGn')\n", " accuracy = sum(pred_y == target_y)/200.\n", " plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 20, 'color': 'red'})\n", " plt.show()\n", " plt.pause(0.1)\n", "\n", "plt.ioff()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/303_build_nn_quickly.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 303 Build NN Quickly\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "import torch.nn.functional as F" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# replace following class code with an easy sequential network\n", "class Net(torch.nn.Module):\n", " def __init__(self, n_feature, n_hidden, n_output):\n", " super(Net, self).__init__()\n", " self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer\n", " self.predict = torch.nn.Linear(n_hidden, n_output) # output layer\n", "\n", " def forward(self, x):\n", " x = F.relu(self.hidden(x)) # activation function for hidden layer\n", " x = self.predict(x) # linear output\n", " return x" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "net1 = Net(1, 10, 1)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# easy and fast way to build your network\n", "net2 = torch.nn.Sequential(\n", " torch.nn.Linear(1, 10),\n", " torch.nn.ReLU(),\n", " torch.nn.Linear(10, 1)\n", ")\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Net (\n", " (hidden): Linear (1 -> 10)\n", " (predict): Linear (10 -> 1)\n", ")\n", "Sequential (\n", " (0): Linear (1 -> 10)\n", " (1): ReLU ()\n", " (2): Linear (10 -> 1)\n", ")\n" ] } ], "source": [ "print(net1) # net1 architecture\n", "print(net2) # net2 architecture" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/304_save_reload.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 304 Save and Reload\n", "\n", "\"\"\"\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* matplotlib" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", "from torch.autograd import Variable\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "torch.manual_seed(1) # reproducible" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate some fake data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1) # x data (tensor), shape=(100, 1)\n", "y = x.pow(2) + 0.2*torch.rand(x.size()) # noisy y data (tensor), shape=(100, 1)\n", "x, y = Variable(x, requires_grad=False), Variable(y, requires_grad=False)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def save():\n", " # save net1\n", " net1 = torch.nn.Sequential(\n", " torch.nn.Linear(1, 10),\n", " torch.nn.ReLU(),\n", " torch.nn.Linear(10, 1)\n", " )\n", " optimizer = torch.optim.SGD(net1.parameters(), lr=0.5)\n", " loss_func = torch.nn.MSELoss()\n", "\n", " for t in range(100):\n", " prediction = net1(x)\n", " loss = loss_func(prediction, y)\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", "\n", " # plot result\n", " plt.figure(1, figsize=(10, 3))\n", " plt.subplot(131)\n", " plt.title('Net1')\n", " plt.scatter(x.data.numpy(), y.data.numpy())\n", " plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)\n", "\n", " # 2 ways to save the net\n", " torch.save(net1, 'net.pkl') # save entire net\n", " torch.save(net1.state_dict(), 'net_params.pkl') # save only the parameters" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def restore_net():\n", " # restore entire net1 to net2\n", " net2 = torch.load('net.pkl')\n", " prediction = net2(x)\n", "\n", " # plot result\n", " plt.subplot(132)\n", " plt.title('Net2')\n", " plt.scatter(x.data.numpy(), y.data.numpy())\n", " plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def restore_params():\n", " # restore only the parameters in net1 to net3\n", " net3 = torch.nn.Sequential(\n", " torch.nn.Linear(1, 10),\n", " torch.nn.ReLU(),\n", " torch.nn.Linear(10, 1)\n", " )\n", "\n", " # copy net1's parameters into net3\n", " net3.load_state_dict(torch.load('net_params.pkl'))\n", " prediction = net3(x)\n", "\n", " # plot result\n", " plt.subplot(133)\n", " plt.title('Net3')\n", " plt.scatter(x.data.numpy(), y.data.numpy())\n", " plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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24g7JcOLL0HDOFQpWK+iZXJclvGoxhm9fpJSfDnuAsE3JLuGER3WsomLjuX7L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\npXxcC3VnpKc7oTtWuSC8QQibItpxZGJ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# save net1\n", "save()\n", "# restore entire net (may slow)\n", "restore_net()\n", "# restore only the net parameters\n", "restore_params()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/305_batch_train.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 305 Batch Train\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", "import torch.utils.data as Data\n", "\n", "torch.manual_seed(1) # reproducible" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "BATCH_SIZE = 5\n", "# BATCH_SIZE = 8" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = torch.linspace(1, 10, 10) # this is x data (torch tensor)\n", "y = torch.linspace(10, 1, 10) # this is y data (torch tensor)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y)\n", "loader = Data.DataLoader(\n", " dataset=torch_dataset, # torch TensorDataset format\n", " batch_size=BATCH_SIZE, # mini batch size\n", " shuffle=True, # random shuffle for training\n", " num_workers=2, # subprocesses for loading data\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | Step: 0 | batch x: [ 6. 7. 2. 3. 1.] | batch y: [ 5. 4. 9. 8. 10.]\n", "Epoch: 0 | Step: 1 | batch x: [ 9. 10. 4. 8. 5.] | batch y: [ 2. 1. 7. 3. 6.]\n", "Epoch: 1 | Step: 0 | batch x: [ 3. 4. 2. 9. 10.] | batch y: [ 8. 7. 9. 2. 1.]\n", "Epoch: 1 | Step: 1 | batch x: [ 1. 7. 8. 5. 6.] | batch y: [ 10. 4. 3. 6. 5.]\n", "Epoch: 2 | Step: 0 | batch x: [ 3. 9. 2. 6. 7.] | batch y: [ 8. 2. 9. 5. 4.]\n", "Epoch: 2 | Step: 1 | batch x: [ 10. 4. 8. 1. 5.] | batch y: [ 1. 7. 3. 10. 6.]\n" ] } ], "source": [ "for epoch in range(3): # train entire dataset 3 times\n", " for step, (batch_x, batch_y) in enumerate(loader): # for each training step\n", " # train your data...\n", " print('Epoch: ', epoch, '| Step: ', step, '| batch x: ',\n", " batch_x.numpy(), '| batch y: ', batch_y.numpy())\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Suppose a different batch size that cannot be fully divided by the number of data entreis:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | Step: 0 | batch x: [ 3. 10. 9. 4. 7. 8. 2. 1.] | batch y: [ 8. 1. 2. 7. 4. 3. 9. 10.]\n", "Epoch: 0 | Step: 1 | batch x: [ 5. 6.] | batch y: [ 6. 5.]\n", "Epoch: 1 | Step: 0 | batch x: [ 4. 8. 3. 2. 1. 10. 5. 6.] | batch y: [ 7. 3. 8. 9. 10. 1. 6. 5.]\n", "Epoch: 1 | Step: 1 | batch x: [ 7. 9.] | batch y: [ 4. 2.]\n", "Epoch: 2 | Step: 0 | batch x: [ 6. 2. 4. 10. 9. 3. 8. 5.] | batch y: [ 5. 9. 7. 1. 2. 8. 3. 6.]\n", "Epoch: 2 | Step: 1 | batch x: [ 7. 1.] | batch y: [ 4. 10.]\n" ] } ], "source": [ "BATCH_SIZE = 8\n", "loader = Data.DataLoader(\n", " dataset=torch_dataset, # torch TensorDataset format\n", " batch_size=BATCH_SIZE, # mini batch size\n", " shuffle=True, # random shuffle for training\n", " num_workers=2, # subprocesses for loading data\n", ")\n", "for epoch in range(3): # train entire dataset 3 times\n", " for step, (batch_x, batch_y) in enumerate(loader): # for each training step\n", " # train your data...\n", " print('Epoch: ', epoch, '| Step: ', step, '| batch x: ',\n", " batch_x.numpy(), '| batch y: ', batch_y.numpy())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/306_optimizer.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 206 Optimizers\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* matplotlib" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "import torch.utils.data as Data\n", "import torch.nn.functional as F\n", "from torch.autograd import Variable\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "torch.manual_seed(1) # reproducible" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "LR = 0.01\n", "BATCH_SIZE = 32\n", "EPOCH = 12" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate some fake data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# fake dataset\n", "x = torch.unsqueeze(torch.linspace(-1, 1, 1000), dim=1)\n", "y = x.pow(2) + 0.1*torch.normal(torch.zeros(*x.size()))\n", "\n", "# plot dataset\n", "plt.scatter(x.numpy(), y.numpy())\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Put dataset into torch dataset" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y)\n", "loader = Data.DataLoader(\n", " dataset=torch_dataset, \n", " batch_size=BATCH_SIZE, \n", " shuffle=True, num_workers=2,)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Default network" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Net(torch.nn.Module):\n", " def __init__(self):\n", " super(Net, self).__init__()\n", " self.hidden = torch.nn.Linear(1, 20) # hidden layer\n", " self.predict = torch.nn.Linear(20, 1) # output layer\n", "\n", " def forward(self, x):\n", " x = F.relu(self.hidden(x)) # activation function for hidden layer\n", " x = self.predict(x) # linear output\n", " return x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Different nets" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "net_SGD = Net()\n", "net_Momentum = Net()\n", "net_RMSprop = Net()\n", "net_Adam = Net()\n", "nets = [net_SGD, net_Momentum, net_RMSprop, net_Adam]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Different optimizers" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "opt_SGD = torch.optim.SGD(net_SGD.parameters(), lr=LR)\n", "opt_Momentum = torch.optim.SGD(net_Momentum.parameters(), lr=LR, momentum=0.8)\n", "opt_RMSprop = torch.optim.RMSprop(net_RMSprop.parameters(), lr=LR, alpha=0.9)\n", "opt_Adam = torch.optim.Adam(net_Adam.parameters(), lr=LR, betas=(0.9, 0.99))\n", "optimizers = [opt_SGD, opt_Momentum, opt_RMSprop, opt_Adam]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "loss_func = torch.nn.MSELoss()\n", "losses_his = [[], [], [], []] # record loss" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0\n", "Epoch: 1\n", "Epoch: 2\n", "Epoch: 3\n", "Epoch: 4\n", "Epoch: 5\n", "Epoch: 6\n", "Epoch: 7\n", "Epoch: 8\n", "Epoch: 9\n", "Epoch: 10\n", "Epoch: 11\n" ] }, { "data": { "image/png": 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lsRwHYLvwvcZa1iEQ0XgACQCbhMWPWy6yp4koGbDfXUS0lIiWdrZCaiKmIKMb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\n7bte0PvRA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FxhBoPBYHArpsdiMBgMBrdieiwGg8FgcCumYTEYDAaDWzENi8Fg\nMBjcimlYDAaDweBWTMNiMBgMBrfy/yB4amTjla2eAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# training\n", "for epoch in range(EPOCH):\n", " print('Epoch: ', epoch)\n", " for step, (batch_x, batch_y) in enumerate(loader): # for each training step\n", " b_x = Variable(batch_x)\n", " b_y = Variable(batch_y)\n", "\n", " for net, opt, l_his in zip(nets, optimizers, losses_his):\n", " output = net(b_x) # get output for every net\n", " loss = loss_func(output, b_y) # compute loss for every net\n", " opt.zero_grad() # clear gradients for next train\n", " loss.backward() # backpropagation, compute gradients\n", " opt.step() # apply gradients\n", " l_his.append(loss.item()) # loss recoder\n", "\n", "labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']\n", "for i, l_his in enumerate(losses_his):\n", " plt.plot(l_his, label=labels[i])\n", "plt.legend(loc='best')\n", "plt.xlabel('Steps')\n", "plt.ylabel('Loss')\n", "plt.ylim((0, 0.2))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/401_CNN.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 401 CNN\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* torchvision\n", "* matplotlib" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", "from torch.autograd import Variable\n", "import torch.utils.data as Data\n", "import torchvision\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "torch.manual_seed(1) # reproducible" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Hyper Parameters\n", "EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch\n", "BATCH_SIZE = 50\n", "LR = 0.001 # learning rate\n", "DOWNLOAD_MNIST = True # set to False if you have downloaded" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", "Processing...\n", "Done!\n" ] } ], "source": [ "# Mnist digits dataset\n", "train_data = torchvision.datasets.MNIST(\n", " root='./mnist/',\n", " train=True, # this is training data\n", " transform=torchvision.transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to\n", " # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]\n", " download=DOWNLOAD_MNIST, # download it if you don't have it\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([60000, 28, 28])\n", "torch.Size([60000])\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot one example\n", "print(train_data.train_data.size()) # (60000, 28, 28)\n", "print(train_data.train_labels.size()) # (60000)\n", "plt.imshow(train_data.train_data[0].numpy(), cmap='gray')\n", "plt.title('%i' % train_data.train_labels[0])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)\n", "train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# convert test data into Variable, pick 2000 samples to speed up testing\n", "test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)\n", "test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1)).type(torch.FloatTensor)[:2000]/255. # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)\n", "test_y = test_data.test_labels[:2000]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "class CNN(nn.Module):\n", " def __init__(self):\n", " super(CNN, self).__init__()\n", " self.conv1 = nn.Sequential( # input shape (1, 28, 28)\n", " nn.Conv2d(\n", " in_channels=1, # input height\n", " out_channels=16, # n_filters\n", " kernel_size=5, # filter size\n", " stride=1, # filter movement/step\n", " padding=2, # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1\n", " ), # output shape (16, 28, 28)\n", " nn.ReLU(), # activation\n", " nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14)\n", " )\n", " self.conv2 = nn.Sequential( # input shape (1, 28, 28)\n", " nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)\n", " nn.ReLU(), # activation\n", " nn.MaxPool2d(2), # output shape (32, 7, 7)\n", " )\n", " self.out = nn.Linear(32 * 7 * 7, 10) # fully connected layer, output 10 classes\n", "\n", " def forward(self, x):\n", " x = self.conv1(x)\n", " x = self.conv2(x)\n", " x = x.view(x.size(0), -1) # flatten the output of conv2 to (batch_size, 32 * 7 * 7)\n", " output = self.out(x)\n", " return output, x # return x for visualization" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CNN(\n", " (conv1): Sequential(\n", " (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", " (1): ReLU()\n", " (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", " )\n", " (conv2): Sequential(\n", " (0): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", " (1): ReLU()\n", " (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", " )\n", " (out): Linear(in_features=1568, out_features=10, bias=True)\n", ")\n" ] } ], "source": [ "cnn = CNN()\n", "print(cnn) # net architecture" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) # optimize all cnn parameters\n", "loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/yoshitaka-i/.conda/envs/tensor/lib/python3.5/site-packages/ipykernel_launcher.py:29: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 2.3101 | test accuracy: 0.20\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.6699 | test accuracy: 0.88\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0701 | test accuracy: 0.94\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.1329 | test accuracy: 0.94\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0395 | test accuracy: 0.96\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0199 | test accuracy: 0.96\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.1077 | test accuracy: 0.97\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0391 | test accuracy: 0.97\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0789 | test accuracy: 0.97\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.1067 | test accuracy: 0.97\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0277 | test accuracy: 0.97\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0251 | test accuracy: 0.98\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# following function (plot_with_labels) is for visualization, can be ignored if not interested\n", "from matplotlib import cm\n", "try: from sklearn.manifold import TSNE; HAS_SK = True\n", "except: HAS_SK = False; print('Please install sklearn for layer visualization')\n", "def plot_with_labels(lowDWeights, labels):\n", " plt.cla()\n", " X, Y = lowDWeights[:, 0], lowDWeights[:, 1]\n", " for x, y, s in zip(X, Y, labels):\n", " c = cm.rainbow(int(255 * s / 9)); plt.text(x, y, s, backgroundcolor=c, fontsize=9)\n", " plt.xlim(X.min(), X.max()); plt.ylim(Y.min(), Y.max()); plt.title('Visualize last layer'); plt.show(); plt.pause(0.01)\n", "\n", "plt.ion()\n", "# training and testing\n", "for epoch in range(EPOCH):\n", " for step, (x, y) in enumerate(train_loader): # gives batch data, normalize x when iterate train_loader\n", " b_x = Variable(x) # batch x\n", " b_y = Variable(y) # batch y\n", "\n", " output = cnn(b_x)[0] # cnn output\n", " loss = loss_func(output, b_y) # cross entropy loss\n", " optimizer.zero_grad() # clear gradients for this training step\n", " loss.backward() # backpropagation, compute gradients\n", " optimizer.step() # apply gradients\n", "\n", " if step % 100 == 0:\n", " test_output, last_layer = cnn(test_x)\n", " pred_y = torch.max(test_output, 1)[1].data.squeeze()\n", " accuracy = (pred_y == test_y).sum().item() / float(test_y.size(0))\n", " print('Epoch: ', epoch, '| train loss: %.4f' % loss.data, '| test accuracy: %.2f' % accuracy)\n", " if HAS_SK:\n", " # Visualization of trained flatten layer (T-SNE)\n", " tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n", " plot_only = 500\n", " low_dim_embs = tsne.fit_transform(last_layer.data.numpy()[:plot_only, :])\n", " labels = test_y.numpy()[:plot_only]\n", " plot_with_labels(low_dim_embs, labels)\n", "plt.ioff()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[7 2 1 0 4 1 4 9 5 9] prediction number\n", "[7 2 1 0 4 1 4 9 5 9] real number\n" ] } ], "source": [ "# print 10 predictions from test data\n", "test_output, _ = cnn(test_x[:10])\n", "pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()\n", "print(pred_y, 'prediction number')\n", "print(test_y[:10].numpy(), 'real number')" ] } ], "metadata": { "kernelspec": { "display_name": "tensor", "language": "python", "name": "tensor" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/402_RNN.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 402 RNN\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* matplotlib\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "from torch import nn\n", "from torch.autograd import Variable\n", "import torchvision.datasets as dsets\n", "import torchvision.transforms as transforms\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "torch.manual_seed(1) # reproducible" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Hyper Parameters\n", "EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch\n", "BATCH_SIZE = 64\n", "TIME_STEP = 28 # rnn time step / image height\n", "INPUT_SIZE = 28 # rnn input size / image width\n", "LR = 0.01 # learning rate\n", "DOWNLOAD_MNIST = True # set to True if haven't download the data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Mnist digital dataset\n", "train_data = dsets.MNIST(\n", " root='./mnist/',\n", " train=True, # this is training data\n", " transform=transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to\n", " # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]\n", " download=DOWNLOAD_MNIST, # download it if you don't have it\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([60000, 28, 28])\n", "torch.Size([60000])\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot one example\n", "print(train_data.train_data.size()) # (60000, 28, 28)\n", "print(train_data.train_labels.size()) # (60000)\n", "plt.imshow(train_data.train_data[0].numpy(), cmap='gray')\n", "plt.title('%i' % train_data.train_labels[0])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Data Loader for easy mini-batch return in training\n", "train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# convert test data into Variable, pick 2000 samples to speed up testing\n", "test_data = dsets.MNIST(root='./mnist/', train=False, transform=transforms.ToTensor())\n", "test_x = Variable(test_data.test_data, volatile=True).type(torch.FloatTensor)[:2000]/255. # shape (2000, 28, 28) value in range(0,1)\n", "test_y = test_data.test_labels.numpy().squeeze()[:2000] # covert to numpy array" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class RNN(nn.Module):\n", " def __init__(self):\n", " super(RNN, self).__init__()\n", "\n", " self.rnn = nn.LSTM( # if use nn.RNN(), it hardly learns\n", " input_size=INPUT_SIZE,\n", " hidden_size=64, # rnn hidden unit\n", " num_layers=1, # number of rnn layer\n", " batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)\n", " )\n", "\n", " self.out = nn.Linear(64, 10)\n", "\n", " def forward(self, x):\n", " # x shape (batch, time_step, input_size)\n", " # r_out shape (batch, time_step, output_size)\n", " # h_n shape (n_layers, batch, hidden_size)\n", " # h_c shape (n_layers, batch, hidden_size)\n", " r_out, (h_n, h_c) = self.rnn(x, None) # None represents zero initial hidden state\n", "\n", " # choose r_out at the last time step\n", " out = self.out(r_out[:, -1, :])\n", " return out" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RNN (\n", " (rnn): LSTM(28, 64, batch_first=True)\n", " (out): Linear (64 -> 10)\n", ")\n" ] } ], "source": [ "rnn = RNN()\n", "print(rnn)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters\n", "loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 2.3127 | test accuracy: 0.14\n", "Epoch: 0 | train loss: 1.1182 | test accuracy: 0.56\n", "Epoch: 0 | train loss: 0.9374 | test accuracy: 0.68\n", "Epoch: 0 | train loss: 0.6279 | test accuracy: 0.75\n", "Epoch: 0 | train loss: 0.8004 | test accuracy: 0.75\n", "Epoch: 0 | train loss: 0.3407 | test accuracy: 0.88\n", "Epoch: 0 | train loss: 0.3342 | test accuracy: 0.89\n", "Epoch: 0 | train loss: 0.3200 | test accuracy: 0.91\n", "Epoch: 0 | train loss: 0.3430 | test accuracy: 0.92\n", "Epoch: 0 | train loss: 0.1234 | test accuracy: 0.93\n", "Epoch: 0 | train loss: 0.2714 | test accuracy: 0.92\n", "Epoch: 0 | train loss: 0.1043 | test accuracy: 0.93\n", "Epoch: 0 | train loss: 0.2893 | test accuracy: 0.93\n", "Epoch: 0 | train loss: 0.0635 | test accuracy: 0.94\n", "Epoch: 0 | train loss: 0.2412 | test accuracy: 0.94\n", "Epoch: 0 | train loss: 0.1203 | test accuracy: 0.92\n", "Epoch: 0 | train loss: 0.0807 | test accuracy: 0.94\n", "Epoch: 0 | train loss: 0.1307 | test accuracy: 0.94\n", "Epoch: 0 | train loss: 0.1166 | test accuracy: 0.95\n" ] } ], "source": [ "# training and testing\n", "for epoch in range(EPOCH):\n", " for step, (x, y) in enumerate(train_loader): # gives batch data\n", " b_x = Variable(x.view(-1, 28, 28)) # reshape x to (batch, time_step, input_size)\n", " b_y = Variable(y) # batch y\n", "\n", " output = rnn(b_x) # rnn output\n", " loss = loss_func(output, b_y) # cross entropy loss\n", " optimizer.zero_grad() # clear gradients for this training step\n", " loss.backward() # backpropagation, compute gradients\n", " optimizer.step() # apply gradients\n", "\n", " if step % 50 == 0:\n", " test_output = rnn(test_x) # (samples, time_step, input_size)\n", " pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()\n", " accuracy = sum(pred_y == test_y) / float(test_y.size)\n", " print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[7 2 1 0 4 1 4 9 5 9] prediction number\n", "[7 2 1 0 4 1 4 9 5 9] real number\n" ] } ], "source": [ "# print 10 predictions from test data\n", "test_output = rnn(test_x[:10].view(-1, 28, 28))\n", "pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()\n", "print(pred_y, 'prediction number')\n", "print(test_y[:10], 'real number')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/403_RNN_regressor.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 403 RNN Regressor\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* matplotlib\n", "* numpy" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "from torch import nn\n", "from torch.autograd import Variable\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "torch.manual_seed(1) # reproducible" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Hyper Parameters\n", "TIME_STEP = 10 # rnn time step\n", "INPUT_SIZE = 1 # rnn input size\n", "LR = 0.02 # learning rate" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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gqKTCu+8CuXKp1+AiLVtK2rO2nEoDa9YAK1dKwkNIiGk1lueff4C6dYH9+71/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\nEBoqvvmOHV7Ox/QPgoPlNB0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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# show data\n", "steps = np.linspace(0, np.pi*2, 100, dtype=np.float32)\n", "x_np = np.sin(steps) # float32 for converting torch FloatTensor\n", "y_np = np.cos(steps)\n", "plt.plot(steps, y_np, 'r-', label='target (cos)')\n", "plt.plot(steps, x_np, 'b-', label='input (sin)')\n", "plt.legend(loc='best')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class RNN(nn.Module):\n", " def __init__(self):\n", " super(RNN, self).__init__()\n", "\n", " self.rnn = nn.RNN(\n", " input_size=INPUT_SIZE,\n", " hidden_size=32, # rnn hidden unit\n", " num_layers=1, # number of rnn layer\n", " batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)\n", " )\n", " self.out = nn.Linear(32, 1)\n", "\n", " def forward(self, x, h_state):\n", " # x (batch, time_step, input_size)\n", " # h_state (n_layers, batch, hidden_size)\n", " # r_out (batch, time_step, hidden_size)\n", " r_out, h_state = self.rnn(x, h_state)\n", "\n", " outs = [] # save all predictions\n", " for time_step in range(r_out.size(1)): # calculate output for each time step\n", " outs.append(self.out(r_out[:, time_step, :]))\n", " return torch.stack(outs, dim=1), h_state" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RNN (\n", " (rnn): RNN(1, 32, batch_first=True)\n", " (out): Linear (32 -> 1)\n", ")\n" ] } ], "source": [ "rnn = RNN()\n", "print(rnn)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters\n", "loss_func = nn.MSELoss()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "h_state = None # for initial hidden state" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(1, figsize=(12, 5))\n", "plt.ion() # continuously plot" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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EcmL3HQ0DEMEgLg2oGED9mNSr856JfWibxcOsqG92doBXTNiBOwdRDwPIXYzs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\ncykIZc7aR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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for step in range(60):\n", " start, end = step * np.pi, (step+1)*np.pi # time range\n", " # use sin predicts cos\n", " steps = np.linspace(start, end, TIME_STEP, dtype=np.float32)\n", " x_np = np.sin(steps) # float32 for converting torch FloatTensor\n", " y_np = np.cos(steps)\n", "\n", " x = Variable(torch.from_numpy(x_np[np.newaxis, :, np.newaxis])) # shape (batch, time_step, input_size)\n", " y = Variable(torch.from_numpy(y_np[np.newaxis, :, np.newaxis]))\n", "\n", " prediction, h_state = rnn(x, h_state) # rnn output\n", " # !! next step is important !!\n", " h_state = Variable(h_state.data) # repack the hidden state, break the connection from last iteration\n", "\n", " loss = loss_func(prediction, y) # cross entropy loss\n", " optimizer.zero_grad() # clear gradients for this training step\n", " loss.backward() # backpropagation, compute gradients\n", " optimizer.step() # apply gradients\n", "\n", " # plotting\n", " plt.plot(steps, y_np.flatten(), 'r-')\n", " plt.plot(steps, prediction.data.numpy().flatten(), 'b-')\n", " plt.draw(); plt.pause(0.05)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/404_autoencoder.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 404 Autoencoder\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "Dependencies:\n", "\n", "* torch: 0.1.11\n", "* matplotlib\n", "* numpy" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", "from torch.autograd import Variable\n", "import torch.utils.data as Data\n", "import torchvision\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "from matplotlib import cm\n", "import numpy as np\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "torch.manual_seed(1) # reproducible" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Hyper Parameters\n", "EPOCH = 10\n", "BATCH_SIZE = 64\n", "LR = 0.005 # learning rate\n", "DOWNLOAD_MNIST = False\n", "N_TEST_IMG = 5" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Mnist digits dataset\n", "train_data = torchvision.datasets.MNIST(\n", " root='./mnist/',\n", " train=True, # this is training data\n", " transform=torchvision.transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to\n", " # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]\n", " download=DOWNLOAD_MNIST, # download it if you don't have it\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([60000, 28, 28])\n", "torch.Size([60000])\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot one example\n", "print(train_data.train_data.size()) # (60000, 28, 28)\n", "print(train_data.train_labels.size()) # (60000)\n", "plt.imshow(train_data.train_data[2].numpy(), cmap='gray')\n", "plt.title('%i' % train_data.train_labels[2])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)\n", "train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class AutoEncoder(nn.Module):\n", " def __init__(self):\n", " super(AutoEncoder, self).__init__()\n", "\n", " self.encoder = nn.Sequential(\n", " nn.Linear(28*28, 128),\n", " nn.Tanh(),\n", " nn.Linear(128, 64),\n", " nn.Tanh(),\n", " nn.Linear(64, 12),\n", " nn.Tanh(),\n", " nn.Linear(12, 3), # compress to 3 features which can be visualized in plt\n", " )\n", " self.decoder = nn.Sequential(\n", " nn.Linear(3, 12),\n", " nn.Tanh(),\n", " nn.Linear(12, 64),\n", " nn.Tanh(),\n", " nn.Linear(64, 128),\n", " nn.Tanh(),\n", " nn.Linear(128, 28*28),\n", " nn.Sigmoid(), # compress to a range (0, 1)\n", " )\n", "\n", " def forward(self, x):\n", " encoded = self.encoder(x)\n", " decoded = self.decoder(encoded)\n", " return encoded, decoded" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AutoEncoder (\n", " (encoder): Sequential (\n", " (0): Linear (784 -> 128)\n", " (1): Tanh ()\n", " (2): Linear (128 -> 64)\n", " (3): Tanh ()\n", " (4): Linear (64 -> 12)\n", " (5): Tanh ()\n", " (6): Linear (12 -> 3)\n", " )\n", " (decoder): Sequential (\n", " (0): Linear (3 -> 12)\n", " (1): Tanh ()\n", " (2): Linear (12 -> 64)\n", " (3): Tanh ()\n", " (4): Linear (64 -> 128)\n", " (5): Tanh ()\n", " (6): Linear (128 -> 784)\n", " (7): Sigmoid ()\n", " )\n", ")\n", "Epoch: 0 | train loss: 0.2323\n" ] }, { "data": { "image/png": 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Pj87JybFqo6urK0aNGoXKykpEREQAgBTg5/dHjRqFEydOAIA8h3379gEwnmJcVlYGQDuY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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0559\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 5 | train loss: 0.0374\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 5 | train loss: 0.0349\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 9 | train loss: 0.0367\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 9 | train loss: 0.0351\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "autoencoder = AutoEncoder()\n", "print(autoencoder)\n", "\n", "optimizer = torch.optim.Adam(autoencoder.parameters(), lr=LR)\n", "loss_func = nn.MSELoss()\n", "\n", "# original data (first row) for viewing\n", "view_data = Variable(train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.)\n", "\n", "for epoch in range(EPOCH):\n", " for step, (x, y) in enumerate(train_loader):\n", " b_x = Variable(x.view(-1, 28*28)) # batch x, shape (batch, 28*28)\n", " b_y = Variable(x.view(-1, 28*28)) # batch y, shape (batch, 28*28)\n", " b_label = Variable(y) # batch label\n", "\n", " encoded, decoded = autoencoder(b_x)\n", "\n", " loss = loss_func(decoded, b_y) # mean square error\n", " optimizer.zero_grad() # clear gradients for this training step\n", " loss.backward() # backpropagation, compute gradients\n", " optimizer.step() # apply gradients\n", "\n", " if step % 500 == 0 and epoch in [0, 5, EPOCH-1]:\n", " print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0])\n", "\n", " # plotting decoded image (second row)\n", " _, decoded_data = autoencoder(view_data)\n", " \n", " # initialize figure\n", " f, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2))\n", " \n", " for i in range(N_TEST_IMG):\n", " a[0][i].imshow(np.reshape(view_data.data.numpy()[i], (28, 28)), cmap='gray'); a[0][i].set_xticks(()); a[0][i].set_yticks(())\n", " \n", " for i in range(N_TEST_IMG):\n", " a[1][i].clear()\n", " a[1][i].imshow(np.reshape(decoded_data.data.numpy()[i], (28, 28)), cmap='gray')\n", " a[1][i].set_xticks(()); a[1][i].set_yticks(())\n", " plt.show(); plt.pause(0.05)\n", " " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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WGJqGSUXEUywtq7eIcejdvK8mqlyZU1i5GBl5cpFvvvVFPb5DwiHPN910E266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\nrUZ1dbUkXWlYlsV///tf/O53v8Pq1auxdOlS2Wx0Ozo6sGzZMlitVth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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# visualize in 3D plot\n", "view_data = Variable(train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.)\n", "encoded_data, _ = autoencoder(view_data)\n", "fig = plt.figure(2); ax = Axes3D(fig)\n", "X, Y, Z = encoded_data.data[:, 0].numpy(), encoded_data.data[:, 1].numpy(), encoded_data.data[:, 2].numpy()\n", "values = train_data.train_labels[:200].numpy()\n", "for x, y, z, s in zip(X, Y, Z, values):\n", " c = cm.rainbow(int(255*s/9)); ax.text(x, y, z, s, backgroundcolor=c)\n", "ax.set_xlim(X.min(), X.max()); ax.set_ylim(Y.min(), Y.max()); ax.set_zlim(Z.min(), Z.max())\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/405_DQN_Reinforcement_learning.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 405 DQN Reinforcement Learning\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "More about Reinforcement learning: https://mofanpy.com/tutorials/machine-learning/reinforcement-learning/\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* gym: 0.8.1\n", "* numpy" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", "from torch.autograd import Variable\n", "import torch.nn.functional as F\n", "import numpy as np\n", "import gym" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2017-06-20 22:23:40,418] Making new env: CartPole-v0\n" ] } ], "source": [ "# Hyper Parameters\n", "BATCH_SIZE = 32\n", "LR = 0.01 # learning rate\n", "EPSILON = 0.9 # greedy policy\n", "GAMMA = 0.9 # reward discount\n", "TARGET_REPLACE_ITER = 100 # target update frequency\n", "MEMORY_CAPACITY = 2000\n", "env = gym.make('CartPole-v0')\n", "env = env.unwrapped\n", "N_ACTIONS = env.action_space.n\n", "N_STATES = env.observation_space.shape[0]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Net(nn.Module):\n", " def __init__(self, ):\n", " super(Net, self).__init__()\n", " self.fc1 = nn.Linear(N_STATES, 10)\n", " self.fc1.weight.data.normal_(0, 0.1) # initialization\n", " self.out = nn.Linear(10, N_ACTIONS)\n", " self.out.weight.data.normal_(0, 0.1) # initialization\n", "\n", " def forward(self, x):\n", " x = self.fc1(x)\n", " x = F.relu(x)\n", " actions_value = self.out(x)\n", " return actions_value" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class DQN(object):\n", " def __init__(self):\n", " self.eval_net, self.target_net = Net(), Net()\n", "\n", " self.learn_step_counter = 0 # for target updating\n", " self.memory_counter = 0 # for storing memory\n", " self.memory = np.zeros((MEMORY_CAPACITY, N_STATES * 2 + 2)) # initialize memory\n", " self.optimizer = torch.optim.Adam(self.eval_net.parameters(), lr=LR)\n", " self.loss_func = nn.MSELoss()\n", "\n", " def choose_action(self, x):\n", " x = Variable(torch.unsqueeze(torch.FloatTensor(x), 0))\n", " # input only one sample\n", " if np.random.uniform() < EPSILON: # greedy\n", " actions_value = self.eval_net.forward(x)\n", " action = torch.max(actions_value, 1)[1].data.numpy()[0, 0] # return the argmax\n", " else: # random\n", " action = np.random.randint(0, N_ACTIONS)\n", " return action\n", "\n", " def store_transition(self, s, a, r, s_):\n", " transition = np.hstack((s, [a, r], s_))\n", " # replace the old memory with new memory\n", " index = self.memory_counter % MEMORY_CAPACITY\n", " self.memory[index, :] = transition\n", " self.memory_counter += 1\n", "\n", " def learn(self):\n", " # target parameter update\n", " if self.learn_step_counter % TARGET_REPLACE_ITER == 0:\n", " self.target_net.load_state_dict(self.eval_net.state_dict())\n", " self.learn_step_counter += 1\n", "\n", " # sample batch transitions\n", " sample_index = np.random.choice(MEMORY_CAPACITY, BATCH_SIZE)\n", " b_memory = self.memory[sample_index, :]\n", " b_s = Variable(torch.FloatTensor(b_memory[:, :N_STATES]))\n", " b_a = Variable(torch.LongTensor(b_memory[:, N_STATES:N_STATES+1].astype(int)))\n", " b_r = Variable(torch.FloatTensor(b_memory[:, N_STATES+1:N_STATES+2]))\n", " b_s_ = Variable(torch.FloatTensor(b_memory[:, -N_STATES:]))\n", "\n", " # q_eval w.r.t the action in experience\n", " q_eval = self.eval_net(b_s).gather(1, b_a) # shape (batch, 1)\n", " q_next = self.target_net(b_s_).detach() # detach from graph, don't backpropagate\n", " q_target = b_r + GAMMA * q_next.max(1)[0] # shape (batch, 1)\n", " loss = self.loss_func(q_eval, q_target)\n", "\n", " self.optimizer.zero_grad()\n", " loss.backward()\n", " self.optimizer.step()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dqn = DQN()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Collecting experience...\n", "Ep: 201 | Ep_r: 1.59\n", "Ep: 202 | Ep_r: 4.18\n", "Ep: 203 | Ep_r: 2.73\n", "Ep: 204 | Ep_r: 1.97\n", "Ep: 205 | Ep_r: 1.18\n", "Ep: 206 | Ep_r: 0.86\n", "Ep: 207 | Ep_r: 2.88\n", "Ep: 208 | Ep_r: 1.63\n", "Ep: 209 | Ep_r: 3.91\n", "Ep: 210 | Ep_r: 3.6\n", "Ep: 211 | Ep_r: 0.98\n", "Ep: 212 | Ep_r: 3.85\n", "Ep: 213 | Ep_r: 1.81\n", "Ep: 214 | Ep_r: 2.32\n", "Ep: 215 | Ep_r: 3.75\n", "Ep: 216 | Ep_r: 3.53\n", "Ep: 217 | Ep_r: 4.75\n", "Ep: 218 | Ep_r: 2.4\n", "Ep: 219 | Ep_r: 0.64\n", "Ep: 220 | Ep_r: 1.15\n", "Ep: 221 | Ep_r: 2.3\n", "Ep: 222 | Ep_r: 7.37\n", "Ep: 223 | Ep_r: 1.25\n", "Ep: 224 | Ep_r: 5.02\n", "Ep: 225 | Ep_r: 10.29\n", "Ep: 226 | Ep_r: 17.54\n", "Ep: 227 | Ep_r: 36.2\n", "Ep: 228 | Ep_r: 6.61\n", "Ep: 229 | Ep_r: 10.04\n", "Ep: 230 | Ep_r: 55.19\n", "Ep: 231 | Ep_r: 10.03\n", "Ep: 232 | Ep_r: 13.25\n", "Ep: 233 | Ep_r: 8.75\n", "Ep: 234 | Ep_r: 3.83\n", "Ep: 235 | Ep_r: -0.92\n", "Ep: 236 | Ep_r: 5.12\n", "Ep: 237 | Ep_r: 3.56\n", "Ep: 238 | Ep_r: 5.69\n", "Ep: 239 | Ep_r: 8.43\n", "Ep: 240 | Ep_r: 29.27\n", "Ep: 241 | Ep_r: 17.95\n", "Ep: 242 | Ep_r: 44.77\n", "Ep: 243 | Ep_r: 98.0\n", "Ep: 244 | Ep_r: 38.78\n", "Ep: 245 | Ep_r: 45.02\n", "Ep: 246 | Ep_r: 27.73\n", "Ep: 247 | Ep_r: 36.96\n", "Ep: 248 | Ep_r: 48.98\n", "Ep: 249 | Ep_r: 111.36\n", "Ep: 250 | Ep_r: 95.61\n", "Ep: 251 | Ep_r: 149.77\n", "Ep: 252 | Ep_r: 29.96\n", "Ep: 253 | Ep_r: 2.79\n", "Ep: 254 | Ep_r: 20.1\n", "Ep: 255 | Ep_r: 24.25\n", "Ep: 256 | Ep_r: 3074.75\n", "Ep: 257 | Ep_r: 1258.49\n", "Ep: 258 | Ep_r: 127.39\n", "Ep: 259 | Ep_r: 283.46\n", "Ep: 260 | Ep_r: 166.96\n", "Ep: 261 | Ep_r: 101.71\n", "Ep: 262 | Ep_r: 63.45\n", "Ep: 263 | Ep_r: 288.94\n", "Ep: 264 | Ep_r: 130.49\n", "Ep: 265 | Ep_r: 207.05\n", "Ep: 266 | Ep_r: 183.71\n", "Ep: 267 | Ep_r: 142.75\n", "Ep: 268 | Ep_r: 126.53\n", "Ep: 269 | Ep_r: 310.79\n", "Ep: 270 | Ep_r: 863.2\n", "Ep: 271 | Ep_r: 365.12\n", "Ep: 272 | Ep_r: 659.52\n", "Ep: 273 | Ep_r: 103.98\n", "Ep: 274 | Ep_r: 554.83\n", "Ep: 275 | Ep_r: 246.01\n", "Ep: 276 | Ep_r: 332.23\n", "Ep: 277 | Ep_r: 323.35\n", "Ep: 278 | Ep_r: 278.71\n", "Ep: 279 | Ep_r: 613.6\n", "Ep: 280 | Ep_r: 152.21\n", "Ep: 281 | Ep_r: 402.02\n", "Ep: 282 | Ep_r: 351.4\n", "Ep: 283 | Ep_r: 115.87\n", "Ep: 284 | Ep_r: 163.26\n", "Ep: 285 | Ep_r: 631.0\n", "Ep: 286 | Ep_r: 263.47\n", "Ep: 287 | Ep_r: 511.21\n", "Ep: 288 | Ep_r: 337.18\n", "Ep: 289 | Ep_r: 819.76\n", "Ep: 290 | Ep_r: 190.83\n", "Ep: 291 | Ep_r: 442.98\n", "Ep: 292 | Ep_r: 537.24\n", "Ep: 293 | Ep_r: 1101.12\n", "Ep: 294 | Ep_r: 178.42\n", "Ep: 295 | Ep_r: 225.61\n", "Ep: 296 | Ep_r: 252.62\n", "Ep: 297 | Ep_r: 617.5\n", "Ep: 298 | Ep_r: 617.8\n", "Ep: 299 | Ep_r: 244.01\n", "Ep: 300 | Ep_r: 687.91\n", "Ep: 301 | Ep_r: 618.51\n", "Ep: 302 | Ep_r: 1405.07\n", "Ep: 303 | Ep_r: 456.95\n", "Ep: 304 | Ep_r: 340.33\n", "Ep: 305 | Ep_r: 502.91\n", "Ep: 306 | Ep_r: 441.21\n", "Ep: 307 | Ep_r: 255.81\n", "Ep: 308 | Ep_r: 403.03\n", "Ep: 309 | Ep_r: 229.1\n", "Ep: 310 | Ep_r: 308.49\n", "Ep: 311 | Ep_r: 165.37\n", "Ep: 312 | Ep_r: 153.76\n", "Ep: 313 | Ep_r: 442.05\n", "Ep: 314 | Ep_r: 229.23\n", "Ep: 315 | Ep_r: 128.52\n", "Ep: 316 | Ep_r: 358.18\n", "Ep: 317 | Ep_r: 319.03\n", "Ep: 318 | Ep_r: 381.76\n", "Ep: 319 | Ep_r: 199.19\n", "Ep: 320 | Ep_r: 418.63\n", "Ep: 321 | Ep_r: 223.95\n", "Ep: 322 | Ep_r: 222.37\n", "Ep: 323 | Ep_r: 405.4\n", "Ep: 324 | Ep_r: 311.32\n", "Ep: 325 | Ep_r: 184.85\n", "Ep: 326 | Ep_r: 1026.71\n", "Ep: 327 | Ep_r: 252.41\n", "Ep: 328 | Ep_r: 224.93\n", "Ep: 329 | Ep_r: 620.02\n", "Ep: 330 | Ep_r: 174.54\n", "Ep: 331 | Ep_r: 782.45\n", "Ep: 332 | Ep_r: 263.79\n", "Ep: 333 | Ep_r: 178.63\n", "Ep: 334 | Ep_r: 242.84\n", "Ep: 335 | Ep_r: 635.43\n", "Ep: 336 | Ep_r: 668.89\n", "Ep: 337 | Ep_r: 265.42\n", "Ep: 338 | Ep_r: 207.81\n", "Ep: 339 | Ep_r: 293.09\n", "Ep: 340 | Ep_r: 530.23\n", "Ep: 341 | Ep_r: 479.26\n", "Ep: 342 | Ep_r: 559.77\n", "Ep: 343 | Ep_r: 241.39\n", "Ep: 344 | Ep_r: 158.83\n", "Ep: 345 | Ep_r: 1510.69\n", "Ep: 346 | Ep_r: 425.17\n", "Ep: 347 | Ep_r: 266.94\n", "Ep: 348 | Ep_r: 166.08\n", "Ep: 349 | Ep_r: 630.52\n", "Ep: 350 | Ep_r: 250.95\n", "Ep: 351 | Ep_r: 625.88\n", "Ep: 352 | Ep_r: 417.7\n", "Ep: 353 | Ep_r: 867.81\n", "Ep: 354 | Ep_r: 150.62\n", "Ep: 355 | Ep_r: 230.89\n", "Ep: 356 | Ep_r: 1017.52\n", "Ep: 357 | Ep_r: 190.28\n", "Ep: 358 | Ep_r: 396.91\n", "Ep: 359 | Ep_r: 305.53\n", "Ep: 360 | Ep_r: 131.61\n", "Ep: 361 | Ep_r: 387.54\n", "Ep: 362 | Ep_r: 298.82\n", "Ep: 363 | Ep_r: 207.56\n", "Ep: 364 | Ep_r: 248.56\n", "Ep: 365 | Ep_r: 589.12\n", "Ep: 366 | Ep_r: 179.52\n", "Ep: 367 | Ep_r: 130.19\n", "Ep: 368 | Ep_r: 1220.84\n", "Ep: 369 | Ep_r: 126.35\n", "Ep: 370 | Ep_r: 133.31\n", "Ep: 371 | Ep_r: 485.81\n", "Ep: 372 | Ep_r: 823.4\n", "Ep: 373 | Ep_r: 253.26\n", "Ep: 374 | Ep_r: 466.06\n", "Ep: 375 | Ep_r: 203.27\n", "Ep: 376 | Ep_r: 386.5\n", "Ep: 377 | Ep_r: 491.02\n", "Ep: 378 | Ep_r: 239.45\n", "Ep: 379 | Ep_r: 276.93\n", "Ep: 380 | Ep_r: 331.98\n", "Ep: 381 | Ep_r: 764.79\n", "Ep: 382 | Ep_r: 198.29\n", "Ep: 383 | Ep_r: 717.18\n", "Ep: 384 | Ep_r: 562.15\n", "Ep: 385 | Ep_r: 29.44\n", "Ep: 386 | Ep_r: 344.95\n", "Ep: 387 | Ep_r: 671.87\n", "Ep: 388 | Ep_r: 299.81\n", "Ep: 389 | Ep_r: 899.76\n", "Ep: 390 | Ep_r: 319.04\n", "Ep: 391 | Ep_r: 252.11\n", "Ep: 392 | Ep_r: 865.62\n", "Ep: 393 | Ep_r: 255.64\n", "Ep: 394 | Ep_r: 81.74\n", "Ep: 395 | Ep_r: 213.13\n", "Ep: 396 | Ep_r: 422.33\n", "Ep: 397 | Ep_r: 167.47\n", "Ep: 398 | Ep_r: 507.34\n", "Ep: 399 | Ep_r: 614.0\n" ] } ], "source": [ "\n", "print('\\nCollecting experience...')\n", "for i_episode in range(400):\n", " s = env.reset()\n", " ep_r = 0\n", " while True:\n", " env.render()\n", " a = dqn.choose_action(s)\n", "\n", " # take action\n", " s_, r, done, info = env.step(a)\n", "\n", " # modify the reward\n", " x, x_dot, theta, theta_dot = s_\n", " r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8\n", " r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5\n", " r = r1 + r2\n", "\n", " dqn.store_transition(s, a, r, s_)\n", "\n", " ep_r += r\n", " if dqn.memory_counter > MEMORY_CAPACITY:\n", " dqn.learn()\n", " if done:\n", " print('Ep: ', i_episode,\n", " '| Ep_r: ', round(ep_r, 2))\n", "\n", " if done:\n", " break\n", " s = s_" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/406_GAN.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 406 GAN\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* numpy\n", "* matplotlib\n", "\n", "Note: Below cells only work for pytorch version lower than 1.5, if you are using pytorch 1.5 or higher, then you will get in place error when you run For loop for Step(10000). Fixed version has been added below." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", "from torch.autograd import Variable\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "torch.manual_seed(1) # reproducible\n", "np.random.seed(1)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Hyper Parameters\n", "BATCH_SIZE = 64\n", "LR_G = 0.0001 # learning rate for generator\n", "LR_D = 0.0001 # learning rate for discriminator\n", "N_IDEAS = 5 # think of this as number of ideas for generating an art work (Generator)\n", "ART_COMPONENTS = 15 # it could be total point G can draw in the canvas\n", "PAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# show our beautiful painting range\n", "plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')\n", "plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')\n", "plt.legend(loc='upper right')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def artist_works(): # painting from the famous artist (real target)\n", " a = np.random.uniform(1, 2, size=BATCH_SIZE)[:, np.newaxis]\n", " paintings = a * np.power(PAINT_POINTS, 2) + (a-1)\n", " paintings = torch.from_numpy(paintings).float()\n", " return Variable(paintings)\n", "\n", "G = nn.Sequential( # Generator\n", " nn.Linear(N_IDEAS, 128), # random ideas (could from normal distribution)\n", " nn.ReLU(),\n", " nn.Linear(128, ART_COMPONENTS), # making a painting from these random ideas\n", ")\n", "\n", "D = nn.Sequential( # Discriminator\n", " nn.Linear(ART_COMPONENTS, 128), # receive art work either from the famous artist or a newbie like G\n", " nn.ReLU(),\n", " nn.Linear(128, 1),\n", " nn.Sigmoid(), # tell the probability that the art work is made by artist\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "opt_D = torch.optim.Adam(D.parameters(), lr=LR_D)\n", "opt_G = torch.optim.Adam(G.parameters(), lr=LR_G)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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ampp6lZmfnx8eHh7odDoMBoO632QykZubS3R0NDExMQAEBQVRU1NDdna2S2Vm\nMpnIyckhMjLSYZ1cWFiYumRh6tSppKSk8PHHH/PJJ5+clFLOsn4v/xZCvGBVVDZel1J+BMr8GpAL\nDALmSylThRC7UJTXOmsdH2AIinJECBGIovBekFLOsLa5VgjhDzwrhJgnpbTNR4QBvaWUO22dCyFe\nASqAwVLKSuu+kyiK1FZHoCjbT6SU/7TbbwTeFkL8W0pZACClLBZCHAW60IAy08yM9dA80NG7cdVl\nYm50N9KQUvLGG29w1VVX4efnh5eXF/fccw9Go1Fd0/PTTz8RGhrqoMjs2bRpE5WVlQ16mjWFgQMH\n1tmXmprK0KFDiYyMxNPTEy8vL/bv38+BAwcA5Y97y5YtjB492uWaqZtvvhmdTqeaj8xmM0VFRQ5z\nSv7+/lx55ZVut4YcJRpLUFAQMTExBAUFERQURMuWLQkJCSE7O7vRI8SGqKmpcTsqbAqRkZFEREQQ\nEBBAaGgo7dq1w8vLq9Fzg/ZUVFSg1+sdFIu3tzcGg6HO6LmpI3ubedHee9H3FG9DZWUlFoulXjNy\nVVWVSy/Q8vJyLBaLS2VnNpvZsWOHw9IKK1+g/Id3d9r/g+2DVSGcAOKczhtmZ6LsDwQAthAx3QE9\nsEwIobNtwE9ApFNbmfaKzMr1wFqbIrPyrVOddkALYGk9ffgCSU7181FGeC7RlJkL+rSszUptMzda\nLprFBk2nqqqKgoIC9S2/Pt544w0mTZrE0KFD+eabb/j999/VUZHNJFVQUODwpuyMbf6goTpNwVne\n0tJSbrnlFo4dO8bs2bPZuHEjW7du5dprr1VlLCoqQkrZoAxCCPR6PQUFBUgpKSwsREpJaGit2d7D\nw0MdqTW02UYvtpGGs5ONrdxUZRISEoLJZFLnLD09PTGbzXWUm9lsxsPDo0HnCyllneOn054znp6e\nBAUFOSyIbizV1dX13hudTldnNNvUe2hvXvQQEOKLej+b+hJiU1bO59nKrpyrbNfgqr/8/Hxqamrq\nezZtZhTnuaRip3I1ioKw8QUQDtxkLY8ANkkpbavMbW9se4Aau81mlrS3U9VnyokCHEI8SSmrAPs3\nD1sfq5z6yKinDwCj0zXUQTMzusBmbnzzMjE3rlu3DpPJRPfuzi95tSxbtozhw4fz4osvqvv27nWM\nNx0WFtbg27ft7TM7O9thlGOPr69vHacSV2t6nEdWmzZt4vjx46xdu9ZhXZX9RHpISAgeHh5uRwkG\ngwGj0Uh8ED0pAAAgAElEQVRpaSkFBQUEBwc7/Fk21czo4+ODEIKqqiqHuSObkq3PpNUUbHNbRqPR\nYZ6rqqrKrVegTqer82d7Ou3VR2Mih9SHt7d3vZ6qJpOpjvJqSh9mqSTdtGHzXjx58iReXl5N/j5s\nysh5lGtTcs7mZRu2ujU1NfUqtPDwcLy8vOrzILVpN/cTmHZIKdOFENuAEUKIX4DBKHNWNmztDaJ+\nZWX/o6/vFT8HaGa/QwjhCxjsdtn6eAD4o542MpzKwbi5Tm1k1gBxgXDjZWBuLC4u5umnn6ZNmzYN\nLlKtrKys84DbpxYBxTxXWFjIihUr6m2je/fu+Pn5NRidIS4ujn379jns++GHH1zUrisjOCqG3377\njcOHD6tlvV5P165d+eSTTxo00el0OgIDA8nKyqKsrKyO8m2qmdHmLejsPFFYWIjBYGjyqKKoqAid\nTqcuODYYDHh6ejq0bzabKS4udmt+8/HxwWg0Ouw7nfacsVgsFBcXq/ONTUGv11NeXu4gX3V1NWVl\nZQ5zVk2lym5QZ1scffLkSYqKihwca+pDCFHH9d82l+b84lVUVISvr6/LkZder8fDw8Ol16Onpyed\nOnVyCPZs5Q7AguIg0VSWAEOtmx9g3/gmoBKIkVJuq2crddP2VqCPEMLe4cN53mE/kAkkuOhDvRlW\nz8sWwIGGOtVGZm7o3RL25EGO1btxaSqMu4i9G00mE5s3bwYUk9z27duZN28eFRUVrFmzxuXbI0Cf\nPn2YO3cuXbt2pXXr1ixatMgh95StTt++fbn77ruZOnUq1113HdnZ2WzYsIF3332X4OBgnnvuOaZM\nmUJ1dTUDBgzAaDSycuVKpk2bRmxsLEOHDuXDDz/kscceY+DAgaxbt05d6O2Obt26YTAYuP/++3nq\nqac4fvw406dPVyfSbfznP/+hd+/e9O/fnwceeAC9Xs+mTZvo3LkzgwYNUuuFh4dz6NAhvL29CQwM\ndGjD09PTpTODK6Kjo9m/fz9Hjx4lJCSEkpISSkpKaNu2rVrHaDSye/duEhISVAWalpaGXq/H398f\nKSVJSUlMnjyZ4cOHq6MRDw8PoqKiyM7OVhcb2xx6bIuDXWEwGOq42ze2vfz8fH777TeGDBmijkLS\n0tIICwvDx8dHdYyoqak5JfNyWFgYOTk5HDx4kJiYGNWbUafT1at0UlJSGDlyJP/4xz9ctmmRYKqp\noaayDCFAeNZw5EQJBQUFBAYGEhXV4PQMfn5+6nen0+nw8fFBp9MRGRlJdnY2Qgj8/f0pLi6mpKTE\nYSG6MzqdjujoaDIzM5FSEhQUpEZyyczMJDY2lhkzZtC3b1/bXHOgEGISisPG+07OH41lKYoDxivA\nBmuqL0B1uJgOzBFCxAMbUAY+7YAbpZRD3bT9BjAe+E4I8TqK2fFfKE4hFmsfFiHEE8CnVoeT1Sjm\n0FbAbcBwKaVt6JCIMqr7taFONWXmBmdzY0Yx/HoM/tbC/bkXIiUlJXTv3h0hBIGBgbRp04aRI0fy\n8MMPu32Ap06dSl5enpos8fbbb2fu3Lnq+i5Q3li/+uornnvuOd544w3y8vKIiYnh7rvvVutMnjyZ\n0NBQ5syZw7vvvktISAi9evVSTW8DBw7kpZde4p133uGDDz5gyJAhzJkzhyFDhri9vsjISJYtW8ak\nSZMYMmQIbdu2Zf78+cyaNcuhXq9evVi7di3PPfccI0eOxNvbm44dO6phoWwEBwcjhCAsLOyUzWT2\nBAQE0Lp1a7KyssjLy8PHx4dWrVq5Hen4+vpSUFCgOnzY5vyc51Fs32F2djYmkwm9Xq86XzRESEgI\nOTk5Dt6bp9qezdMvOzubmpoaPDw80Ov1JCYmNln529pr164dx44dU0fYtvt4Kk4rVSbFNlZVWkhV\naSFCCE7qdPj5+ZGQkEBoaKjb7zo6Ohqj0cihQ4cwm83qi4fNizEvL0/1hmzZsqXDXKur9nQ6Hbm5\nueTl5aHT6bBYLOozccstt7BkyRJb1JY2wETgNRSvwyYjpTwmhPgNJVzhjHqOzxJCZAGPAU+grCE7\ngJ1HYgNtZwohBgJzgP8CqcB9wFrAPij9F1Yvx2esx83AIWAFimKz0c+6vz5zpIoWzqqRrEmHHw8r\nn7084LGu0KzpFhONi4jU1FRiYmI4ePAgSUlJZyWs0qmSkJDABx980KTYhe7Ys2cPYWFhbl9q6pur\nOnz4MC1btjzjXpGnQkMjM4tU1pDanD78dBDmd2Fmj76UwlkJIXoCG4GbpJT1pyd3fe4mYKWU8oWG\n6mlzZo2kd0uIsprnayywbO+l7d14uZOVlUVVVRXHjx8nKCjoglJkzhiNRiZOnEhMTAwxMTFMnDhR\nnV9KTk7myy+/BODXX39FCMHKlSsB+PHHH+nQoYPazk8//cQNN9xASEgIffv25ciRI+oxIQRvv/02\nbdu2dTCJOvPRRx8RExNDdHS0w5rEhmRcuHAhPXv2dGhHCKGasMeMGcP48eMZOHAgAQEBdO3alfT0\ndLWuzdknKCiICRMmNDgPWuLkvRjse2EqsosdIcTLQog7rZFAHkSZo9sFNC4NQm07XYErgLfc1dXM\njI1E5wEjrrQzN5Zc3OZGjYZ577336NatG/Hx8bRo0QLeutv9SWeKCZ83qfqLL77I5s2b2blzJ0II\nhgwZwgsvvMDzzz9PcnIy69evZ9iwYfz888+0atWKDRs2MHDgQH7++WeSk5MB+Oabb5gzZw4LFy6k\nU6dOvP7669x1110OGaC//vprtmzZUieCiT3r1q3j4MGDHDp0iJtuuokOHTrQu3fvBmVsDEuWLGH1\n6tVcd911jB49milTprBkyRLy8/O5/fbbWbBgAUOGDOGtt95i/vz53HvvvXXaqHJaHK3FXjyr+KDM\nx0UCpShr3x6XUjYcMLMuocBoKaXzcoM6aF9lE4gLhJsSasur0yHvEvRu1FAyDMTHx3PllVeetsv8\n2WbRokVMnTqViIgImjVrxrRp0/j0008BZWRmywm2YcMGJk+erJbtldn8+fOZPHkyvXr1Qq/X88wz\nz7Bz506H0ZltrrMhZTZt2jT0ej3t27dn7NixLF682K2MjWHo0KF06dIFnU7HPffcw86dyjrdVatW\ncfXVVzN8+HC8vLyYOHFivWZSi4Qiu1COfi5Su2icGaSUE6WUzaWU3lLKMCnlXfZOJk1oZ7WU0nnB\ndb1oyqyJ3JwA0XbmxqWauVHjPJOVlUV8fO0akvj4eLKysgBlKcSBAwfIzc1l586djBo1imPHjpGf\nn8/vv/9Or169ACX9y6OPPkpwcDDBwcGEhoYipSQzM1Nt1z7Ukivs69jL0ZCMjcFeQfn7+6uRP2zp\naWwIIeqVUzMvXvpoZsYmYvNunLtVUWKHS+CXY9BLMzde2jTR9HcuiYmJ4ciRI1x99dUAHD16VPWq\n8/f3p1OnTsyZM4ekpCS8vb254YYbmD17Nq1bt1Zd/5s3b86UKVO45557XPbTGG/OY8eOqYvV7eVo\nSEa9Xu8QGaQpiWKjo6MdshhIKetkNdDMi5cH2ld6CsQGKCM0G5q5UeN8ctddd/HCCy+Ql5dHfn4+\nM2fOZOTIkerx5ORk3nrrLdWkmJKS4lAGGDduHP/+97/VtCklJSX1LdJ1y/PPP09FRQV79uxhwYIF\njBgxwq2M1157LXv27GHnzp1UVVUxffr0Rvc3cOBA9uzZw3//+19MJhNz5851UIaaefHyQVNmp8hN\nCbXmRpMFvtDMjRrniWeffZbOnTtzzTXX0L59e6677jp1LSAoyqy0tFQ1KTqXQZmTevrpp7nzzjsJ\nDAwkKSmJ1atXN1mW5ORk2rRpw80338ykSZO45ZZb3MrYrl07pk6dSu/evWnbtm0dz8aGCA8PZ9my\nZfzrX/8iLCyMgwcP0qNHD/V4SVXd2IuaefHSRFtndhpkltaaGwEGtYVkzdx4yeBqnY/GxUGVydFi\nEuoL+jOaB/nscimtMzsXaCOz08DZ3LgmHU6UnzdxNDQ0rGjmxcsPzQHkNLk5QYndmFWmmDOWpsI/\nO12YsRunT5/OjBlK5BohBEFBQbRp04ZbbrmlUeGsLneqqqrIzc2lrKyMyspKAgICSExMdHteZWUl\nx44do7KyEpPJhJeXF4GBgcTExKhBggFcWSqEEHTq1KnBPqSU7N27l8jISJfZCEAJ+JuZmUl5eTnl\n5eVIKenc2f1LvsViISMjg4qKCqqrq/H09MTf35/Y2Ng6IaoKCwvJycmhqqoKT09PAgMDiY2NdbjW\n+iguLub48eMYjUa8vLy45ppr3MrliobMi7a4h/n5+WoOMi8vLwICAmjWrFmDwYvLy8spKSlRnVc0\nzg7WbNX7gWuklIcac46mzE4TT6t34xyrufFICWw8Csnx7s89HwQFBalBe0tKStixYwfz5s3jvffe\nY82aNW7/NC9nqqqqKCkpQa/XNykhptlsxsfHh7CwMLy9vTEajWRlZVFRUcGVV16pegnap6yxkZaW\n1qjI8EVFRZjNZrcxAC0WC/n5+ej1egwGA6Wl7gKgOxIVFaVmzbZlj77qqqvUtXjFxcUcOnSIiIgI\n4uLiqKmpITMzk7S0NIdrdUZKSUZGBoGBgcTHxzcY8NodVSYos8uDGeyrPKe2fg4dOkRxcTHNmjUj\nKioKT09PNZ/fvn376NSpk0s5y8vLycrK0pTZWcYa3/ELYCowpjHnaMrsDBATAL0T4AdrBp41h+DK\ncIhoekzVs45Op6Nbt25quW/fvjz00EP06tWLO++8k3379p3WH8nZoLKyssGFuueKoKAgdbSQnp5e\nJzGkKwwGg4NCCggIwNvbmwMHDqhZlG317CkvL8dkMrlVUAAnTpwgLCzMbcJMnU5Hhw4dEEJw4sSJ\nRiszDw8PWrdu7bAvMDCQnTt3UlRUpI7qCwoK8Pf3V6KmWPH09CQtLY2qqiqX32NNTQ1ms5mwsDCH\nXG9NxSKhsNICUoAQinnR7l/uxIkTFBUV0a5dO4csCLZRWV5eXj2tarhDCOHnlFn6TLAA+FEI8YR9\nShhXaHNmZ4ibEpQ5NKg1N14s3o3BwcHMmjWLtLQ01q5d67JecXEx//jHP4iJicHX15cWLVpw//33\nO9TZtWsXgwcPJjg4GIPBQJcuXRzazMjI4LbbbiMwMJCAgAAGDx5cJ42MEILZs2czceJEmjVrRvv2\n7dVj33zzDZ07d8bX15eoqCieeuopl+nozwT2I7AzETXfhu2FoaERXmFhIR4eHm4j6ldVVVFWVkZI\nSEij+j5T12HLNm1/DVLKOi9D7l6O8vPz2bVrF6CMRLdt26YuqDabzRw9epQ///yT7du3s3fv3jqp\navbv3096ejp5eXns3r2brP07sJhq6vVezM3NJSQkpE46HxvNmjVzeX/y8/M5elRJxrxt2za2bdvm\nkJz15MmTpKamsn37djV6iqvs0vZUVFRw8OBB/vjjD3bs2EFqaqrDNTo/M0AbIUQb+zaEEFII8agQ\n4iUhRJ4Q4oQQ4m0hhI/1eEtrnYFO53kKIXKEEC/Y7UsSQqwUQpRat2VCiCi74ynWtvoKIb4VQpRh\njZ0ohAgRQiwRQpQLIbKEEE8LIV4VQhx26reFtV6hEKJCCPG9EMLZZv8rSkLOO93eRLSR2RnD0wPu\nuFLxbjRbzY0bjkLKBWpudCYlJQWdTsfmzZvp169fvXUef/xxfvvtN15//XWioqI4duwYGzZsUI/v\n27ePHj16kJiYyPz58wkLC2Pbtm3qIlaj0cjNN9+Ml5cX77//PjqdjmnTppGcnMzu3bsdRiCvvPIK\nvXr14tNPP1WTIC5dupS77rqLBx98kJdeeon09HQmT56MxWJRg9pKKRv1B9KYyO62tCtnKv2LLXVL\ndXU1mZmZ6PV6lylRpJQUFhYSHBzsVhmUlpbi4eFxTkavNsVlMpnU9Vz231t4eDjp6enk5+cTEhKi\nmhkDAgJcyhcUFETr1q1JT08nLi4Og8Ggzq8dOXKE4uJiYmNj8fX1JS8vj7S0NNq1a+cwgisrK6Oy\nyoh/WCzCwxPh6UmInXkRlISe1dXVp5RTzSZnZGQkubm5qknY9t1UVlZy8OBBAgMDad26tfodG41G\n2rVr57LNyspK9u3bh6+vL/Hx8eh0OsrKyigoKMDX17feZ2b48OE+wM9CiPZSSvtMr08APwEjgWuA\nfwNHgFlSygwhxO8oCT1X2p2TjBI/cQmAVUn+CmyztqNDyZv2nRCii3R8+/oQZfT0BkqKGICFQE/g\nUZSM04+h5EFTH0ohRCjwC1AAjEPJc/Yv4H9CiHa2EZ6UUgohNgO9gbdd3kQrF6Uyy685QZjO9RvU\n+SImAG5uCT9Ypyu/PwRXXaDmRmd8fX0JDw9Xky/Wx++//8748ePVhbCAw+LcGTNmEBQUxMaNG9U/\nrj59+qjHFyxYwNGjRzlw4ICarLBr1660atWKd999l8mTJ6t1o6Oj+eKL2tRJUkqefPJJRo0axTvv\nvKPu9/HxYfz48UyePJmwsDB+/vlnbrzxRrfXm5GRQUJCQoN14uLiOH78eL2mp7y8PMxmc51sww2R\nm5tLVZXyzHt7exMREVEno7YNm7OJyWQiNTW1wXYLCgqorq522ZYrSktLKSwsdNu+PSUlJRQXKzFf\nPTw8iIiI4NAhx/l5KSXbt29XFZ+Pjw8REREN9mMymcjPz3dQyjU1NWRlZREWFqZmu5ZSUlxczPbt\n29Vcbjk5OVRXVxMQHgsnlN+vlweUO/mbGI1GtY/8/HwHee1p6H/Fds+co4zk5eVRXV2Nn58f2dlK\nCEKz2cyhQ4eoqKhwGd8zLy8Po9FIbGysw7Pn6+tLXFwcH374YZ1nBiWv2JXAgygKy8ZhKeUY6+fv\nhRA9gNsBWzK/JcA0IYSPlNKWtnsEsEdK+Ze1PA1FCfWXUlZb78cuYB8wAEdFuExK+ZzdfUtCySh9\nh5RymXXfj8AxoMzuvMcAPdDBpoyFEL8Ch1Hymtkrrj8BR/OPK2xvi+d669Spk2wqZotZfluwWA5N\n7S5/LP6uyeefC0xmKV/fIuWk/ynb3N+lNFvOt1QK06ZNk2FhYS6PR0ZGynHjxrk8fs8998jmzZvL\nt99+W+7fv7/O8YiICPn444+7PH/s2LHy+uuvr7M/JSVFDhgwQC0DcsqUKQ519u3bJwG5atUqWVNT\no24ZGRkSkOvXr5dSSnny5Em5detWt5vRaHQpp8lkcujDYqn7BQ4bNkwmJye7bKM+Dhw4IDdv3iw/\n/fRTmZiYKK+77jpZWVlZb91x48bJkJCQBuW0MXjwYNmvXz+HfWaz2eEazGZznfPefPNNqfwFNJ7s\n7Gy5detW+e2338p+/frJsLAwuWfPHvX4Tz/9JA0Gg3zqqafkunXr5JIlS+QVV1whU1JSpMlkctmu\n7Xv87rva5/rjjz+WgCwvL3eoO336dOnv76+Wk5OT5RXX9VCfuanrpSypqtvH5s2bJSC///57h/3j\nx4+XKPk668jgjKt71rJlS/nkk0867DOZTFKn08lZs2a5bO9UnhmUUdM6lBxfNmUsgWel3X8s8BJw\n3K4ci5LpeYi1rAPygOfs6mQD/7Ees9/SgWnWOinW/no79TfGut/Xaf8SFEVrK2+y7nPu4ydggdO5\nE4AarGuiG9ouqjmz74qWMD93FkZZxfycWeTVND6G27nC5t3oaX25O3pSMTde6Ni8uZwzF9vz1ltv\ncdtttzFz5kwSExNp27YtS5YsUY8XFBQ0aMLJzs6ut/3IyEj1zdt+nz22N+kBAwbg5eWlbi1btgRQ\n35QNBgMdOnRwuzXkJm4z69g2W5T506Vt27Z07dqVkSNH8v333/PHH3/w+ed1Yz6aTCa+/PJLhg0b\n5tadHZTvzvnNf+bMmQ7XMHPmzDNyDVFRUXTu3JnBgwfz3XffERYWxn/+8x/1+BNPPMGtt97Kyy+/\nTEpKCiNGjODrr79m/fr1fPPNN03qKzs7G4PBgL+/YxbcyMhIKioq1HxolSYw+9f+Xm5LhMB6BkI2\nD8Tjx4877H/qqafYunUr337bqODsLmV1/s16eno6jCrr41SfGSAXJT2KPc5pUqoBNRGflDITxbxn\nM63cDIRjNTFaCQeeRlEg9lsrwDmCs7MZJwoolVJWOe13Nm2EW2Vw7uPGevowUqvsGsRtBSFEc+AT\nFLuqBN6TUs5xqiNQUmQPQLF/jpFS7nDXdlO5Jfg2vi1cQk7NccotZbyRPYPnm7+Nh7iwdHK0QUnm\n+b2dubFdqGKGvFBZt24dJpOJ7t27u6wTHBzM3LlzmTt3Lrt27WLWrFncc889XHPNNVx11VWEhYWp\nJpb6iI6OVmP/2ZObm1vHY8/Z1GM7/t5779GxY8c6bdiU2pkwM7777rsOXn6NWUvWVOLj4wkNDa1j\nogMlaWZeXh533XVXo9oKDQ2tE5z3gQceYNCgQWr5bLiS63Q62rdv73AN+/btqyN3YmIifn5+Dgk1\nG0N0dDRlZWVUVFQ4KLTc3Fz8/f3x8fGhvEYJVOAVoPxekppBBxfvY82bNychIYEffviB++67T93f\nokULWrRoweHDh5skn7OsJ06ccNhnNpspKChw+G1LKflv4afcHDSIYF3oKT8zKP/HrrWka74A/iOE\n8ENRKH9IKQ/aHS8EvgI+qOfcfKeys/dSDhAghPB1UmjNnOoVAt+izMU54+xeGwyUSSndenk1RguY\ngCeklFcB3YDxQoirnOr0B9patweAeY1ot8n4efjzeMwMBMoPd2f5FlYVNT0Y6rngxnhH78aFu6C8\nuuFzzhfFxcU8/fTTtGnTht69ezfqnGuuuYZXXnkFi8WiztXcfPPNLF26VJ0XcqZr165s376djIwM\ndV9mZia//fab23h8iYmJxMbGcvjwYTp37lxnCwsLA6BTp05s3brV7dbQn3tiYqJD26fjKu6K/fv3\nU1BQoCphexYvXkx0dDQpKSmNaisxMdHhnoKivOyv4Wwos6qqKnbs2OFwDfHx8ezY4fgem5qaSmVl\npds5Smeuv/56hBAsX75c3SelZPny5fTs2ROzBT7bXbs42t8Lbk9sOPbixIkTWb58OevXr2+SLDZs\nI2Xn33jXrl356quvHJyPbMGP7X/b3xYt5qMTbzAxYyRplamn9MwAXsANKKOsprIM8AOGWrclTsd/\nBK4Gtksptzlth920bVv1f6tth1Vp9nGqZ+tjTz197Heqm4AyR+gWtyMzqSRUy7Z+LhVCpKLYXvfa\nVRsCfGK1524WQgQLIaLlKSRjc8fV/h25PfReviz8BICPTsyho6E7sd4XVlBETw+462p4cysYzUpo\nnU93w/0dHT2szjUmk4nNmzcDymT29u3bmTdvHhUVFaxZs6ZBz7mePXsydOhQkpKSEELw/vvvo9fr\n6dKlC6AkZrz++uvp1asXTzzxBGFhYfzxxx+EhYVx3333MWbMGF5++WX69+/PzJkz8fT0ZMaMGYSH\nh/Pggw82KLeHhwevvfYa9957LydPnqR///54e3tz6NAhvv76a5YvX46/vz8BAQGNimhxKlRUVLBq\n1SpAUcInT55U/2gHDBigjh7atGlDcnIyH374IQCTJk1Cp9PRtWtXgoODSU1NZdasWbRu3Zo773T0\nOjYajXz99deMGTPG7ZoxGz169GDmzJnk5eXRrJnzS3BdVq9eTXl5uZrg0nYN119/vZpz7P/+7//4\n+eef1WUTixcvZvXq1fTr14+YmBiys7N55513yM7O5vHHH1fbHjduHI899hgxMTH079+f3NxcZs6c\nSUJCAgMGDGjU9di48sorueuuu5gwYQKlpaW0bt2a999/n3379jFv3jy+OwhpRbX1/34lBLjJo/rw\nww+zYcMG+vfvz4MPPkifPn0ICAjgxIkT6n1oaJG6zYtxzpw53HTTTQQGBpKYmMizzz5Lx44due22\n23jooYc4fvw4Tz/9NH379lWtHTvLt/BB7usA5JlyWFP831N6ZlAGDfnAu026oYCU8oQQYj3wKsqo\nZ6lTlenA78BKIcRH1n5iURTSQinl+gba/ksI8R0wTwgRgDJSexzFWmfvKTUbxVPyJyHEm0Amykgz\nGfhFSrnYrm5nFO/KRl1cozcULXkUCHTavwLoaVf+Eehcz/kPoGjvbS1atHA56ekOo7lKPpT+dzlg\nb0c5YG9H+XjGaGmyuJ5cPp/sOSHlk/+rdQj5MvX8yTJt2jR1klsIIYOCgmSnTp3kM888I7Ozs92e\nP2nSJJmUlCQNBoMMCgqSKSkpcsOGDQ51/vzzT9m/f39pMBikwWCQXbp0kf/73//U4+np6XLIkCHS\nYDBIvV4vBw4cKA8cOODQBiDffPPNemVYtWqV7Nmzp/T395cBAQHy2muvlVOmTJE1NTWncEeahs1J\nob4tIyNDrRcfHy9Hjx6tlhcvXixvuOEGGRISIv38/GRiYqJ8/PHHZV5eXp0+vvrqKwnITZs2NVou\no9EoQ0ND5SeffNKo+vHx8fVew4IFC9Q6o0ePlvHx8Wp5x44dcsCAATIyMlJ6e3vL+Ph4eccdd8i/\n/vrLoW2LxSLfeecd2b59e+nv7y9jYmLkHXfcIdPT0xuUqT4HECmlLC8vlxMmTJARERHS29tbdurU\nSa5Zs0Zuyax9puKuSZY9+w1r1LVLqTjHfPjhh7JHjx4yICBAenl5yfj4eDly5Ej522+/NXiuxWKR\nTz75pIyOjpZCCAcnoP/973+yS5cu0sfHRzZr1kw+9NBDsrS0VEopZbbxuByxP0X9z3rs0L2y2qw4\n9zT1mUGZG2srHf9bJTDBad90IF/W/R/+h7X+Judj1uNXAMtRzIGVQBqK4oyTjg4gSfWcG4piyixH\nmVObCrwP7HSqF4Pi1p+LMi92GPgMuNquTjMUy2ByfXI6b42Omi+EMAA/Ay9KKf/rdGwF8B8p5S/W\n8o/A01JKl2HxTzdqfnrVPh7LGIUZJQrD6GYPc0f42FNu72zy42ElCLGNYVdAt9jzJo7GJcijjz5K\nWt7wA2cAACAASURBVFoaK1eudF/5IiejGN7doaznBGjfDEa2vzDjoQJUWSqZdHgMGUZlaipUF84b\nCYsI83I/iq6PiylqvhBCB/wFbJFSjm7iuQ8Ck4B2shGKqlF2DCGEF/AlsMhZkVnJxNELJc6676zR\n2vcK7m72gFpelDePQ1WNMq2ec26Kh2sjastf7YdDRa7ra2g0lSeffJJ169Zx4MCF+QycKYqr4JNd\ntYos2qB4D1+oikxKyetZ01VFphNeTIl79ZQV2YWOEOLv1kgkNwkhbgO+QTGLul307NSOQFl4/WJj\nFBk0QplZG/0QSJVSznZR7VtglFDoBpTIszBf5szfw8bQzjcJABMmZmc9R43lwvOyEALuuKrWIcQi\n4ZPdUHSmI5lpXLbExcXx0UcfNegZd7FTbVYcqWxBhPVeMOYa8LmAQz8sLfiIX0prw7mNj5rMFX6n\nng3gIqAcGIuiExajmAoHSyl/b2I7UcAi4NPGnuDWzCiE6AlsBHZTO4n3DNACQEo536rw3gL6oUz2\njW3IxAhnLjnnMWMGj2TcTbV1QfsdYWMZHfHwabd7Niiqgrm/1z6MMQYY3xm8L6y4vhoaFxxSwud7\nYKd1ZZOHgAc6QuvGhaM8L/xeupGZxycirR7sg0JG8FDU06fd7sVkZjyXuB2ZSSl/kVIKKeU1UsoO\n1m2VlHK+lHK+tY6UUo6XUraWUrZ3p8jOJM19WjI24hG1vLzgY1Ir/jxX3TeJEF8YdU3tguqsMvhi\nr/KgamhouGbdkVpFBjCk3YWtyI4ZM3gla4qqyNr7d+b+yMfdnKVxOlxYq41PkUEhI7jGX3lRsWBh\ndvY0qiwXpg2vZTAMtVuDu+sE/HT4vImjoXHBszff0YGqWyzcEHf+5HFHubmUF44/QYVFCUcY4RXN\n5NiX0Qkt1fXZ5JJQZh7Cg8dipuPnoUT0zao+yoITc9ycdf7o6vQwrjmkZKvW0NBwJLccPv+rNtRE\ny2BlVHahYpZmXsmawvHqwwD4CF+ejZtNkO4CHkZeIlwSygwgwiuGByMnqeUVRUv5o3zLeZSoYW5t\n62gmWbwHcspc19fQuNyoqIGFfypBB8Bqpm8Pugv4X+uzvHlsLasNzDExZhqtfc98ODSNulzAP4um\n0zvoVroaktXyG1nTKTM3LS38ucLTA+5NUh5QUB7YhbuUB1hD43LHbIFFf0G+dbbAy0PxXDS4j7t8\n3th4ci1LCz5Sy8PDxtArsO95lOjy4pJSZkIIHo5+lkDPYADyTbm8l/vKeZbKNXpvGHttrTdjQSV8\n9pfyIGtoXM6sSocDdmF077zqwg7UfajqwP+3d97hUVX5/3+dSSaN9N4IvVcBFcRVFGxY9ycq7trW\ngrrqqqir7tp7R113117Xr33tILuCLooUAUGQIp0kpEFCeplyfn+cOy1MCpDkTjmv57nPzD23zCc3\nM/d9zzmfwpzdd7vXJ/SazEUZ15hoUfgRUmIGkBKZxrXZf3WvL6j+gh9qFppoUfvkxKsfqovNlfDF\nFvPs0WjMZkWJb9mkaX1hdNuViUyn2l7FA0WzaTYSxedGFXBL3kNECB1z05OEnJgBTE6cypTEU9zr\nz5U+yD77wVRL6BlGZcIJXsnTvy+EH3ebZ49GYxa7quEjr4LZIzLghP5t7282Dmnn0eLbKLOpH2ys\npRd35j9FfEQAdyNDlJAUM4Crsm8lLVLlkKp2VPFcyYP7lUcPJKb1UznmXHy0EXZUm2ePRtPTVDfD\nGz97Srpk9VKjFoGaqgrglbI5rGn40b1+c+4DFEQHsPqGMCErZgkRidyQ4xnDXlL3DQurAzcJq0Wo\nHHM5RvUJh1Q/7H3+yxxpNCGFzaG+7zVGNrq4SDWfHBPAqar+u+9TPq3yVCu5IP1qJiYc284Rmu4k\nZMUMYFz8JKYnn+Nef77sMSpspe0cYS7RkcpjK86IraxrUT9wm6P94zSaYEZK+HAjFNaodYuAC0dB\nWqy5drXHxsa1PFf6kHv9qITjOS/9MhMt0oS0mAFclnUDOVYVodzgrGPO7ntwysB1F0yNVbE0rqGV\nolr4YINOeaUJXRbtglVez5inD4KBqebZ0xGVtgoeKroZu1RxNH2iBzI79z4sIuRvpwFNyF/9GEss\ns3PvQ6DUYU3Dcr6sal1cNbAYkOKb5eCnMvh2p3n2aDTdxca98KWX9+4RuTA5gFNV2ZwtPFh8M3vt\nKmVPvCWRO/OfJNYSZ7JlmpAXM4DhcWM5O81TF+618mcpbg5sdZiUB0fmetbnbYUNe8yzR6Ppasrr\nVWC0a9ChT5LKWyoC1OFDSsk/Sh9hY+NaACxYuC3/UXKiendwpKYnCAsxA7gg/Sr6Rg8EoFk28VTJ\nXTik3WSr2kYIOGuIykUH6gf/f+tUrjqNJthptKuMN03GTzApGi4O8FRVX1a9z3+qP3GvX5p5A4f1\nOtJEizTeBPBXp2uxWqKYnXs/kSj3qI2Na/lo75smW9U+kRY1f5ZspLxqcqhcdTrllSaYcUr1YFbR\noNYjjVRVCdHm2tUea+tX8mLZk+714xKnc1bq7020SNOasBEzgAExQzg/Y5Z7/e2K59nWFNhl5uOj\n1A/davyn9jSqoRmndgjRBCnztqq5MhfnDoP8RPPs6Yhy224eKr4FB6obOShmONfl3IEI1PHQMCWs\nxAzgnLRLGBIzEgA7dp7cfQc2Z4vJVrVPXoKKQXPxa6XvpLlGEyysKvV1ZjquDxyWbZ49HdHkbOSB\nwpupcewDIDkilb/mP0G0JcZkyzStCTsxixCRzM69j2ihvow7mrfw9p4XTLaqY8ZkwdS+nvVFu1QO\nO40mWCisUWEmLoalwckDzLOnI6SUPFNyH1ubVX6tSCL5S/7jZFgDWH3DmLATM4D86L5ckvkn9/pH\ne99gfcMaEy3qHCf2h+HpnvUPN8DPZW3vr9EECkU18MpqT6qqzDg4f2Rgp6r6qPINFtXMd69flf1n\nRsQdZqJFmvYISzEDOC3lXMbEHQ6AEydzdt9Fk7PRZKvaxyLg/BEqZx2olFf/Wqd7aJrAZkc1vPAT\n1BuOS7GRcMkY9Rqo/Fj3Pa+X/829fkry2ZySMsNEizQdEbZiZhEWbsi9hziLSoa421bIq+VPm2xV\nx8REwhVj1ZMtKJf999bDD0WmmqXR+GVLJbz0k8cFPzYSLh8LGQEcY7yjaTOPFt+ONCLgRsSO5crs\nP5tslaYjwlbMADKtOVyZdYt7/cuqD/i+5msTLeocSTFw9XhPUmKAjzfpLCGawGLDHnhlDbQYuUV7\nWeGqcVCQZK5d7VFp38M9hdfT6FQBnZnWHG7PfxyrsJpsmaYjwlrMAKYmncbE+Cnu9UeKb+WjvW8G\ndLkYUC77V42DAi+X5i+3wPxtOo+jxnzWlKmgaNccWVI0/HF8YFeLbnY28UDhbCrsKlFkrKUXd+c/\nQ0pkmsmWaTpD2IuZEILrcu4g20hGLJG8Wv40z5bcj00GdnRynBWuOAz6J3vavt6uKlVrQdOYxYoS\n31jI1BglZJm9zLWrPZzSydMl97CpaR1gpKrKe4S+MQNNtkzTWcJezACSI1N5qu8bDI8d6277T/Un\n3Lnrj9TY95loWcfERMJlY2GI18Pjol3w7006sFrT8/xQpOZwXV+9zDglZKkBXM4F4P/2vMCimv+4\n16/IupkJ8ZNNtEhzoGgxM0iKTOGhgueZmnSau21tw0pm77iIwubtJlrWMVERKkvISK9K1UuL1U3F\nEbjVbjQhxjc71dyti9x4NbebFODxxd9Uz+WdPS+5109LOZczUmeaaJHmYNBi5oXVEsWNOfdyccZ1\n7rYSWxE37biYn+qXmWhZx0Ra4IKRMM4rnnNVqXLdt2tB03QjUsL8rTDXKytNQSJcOU7N7QYy6xtW\n83TJve71cb0mMSvrZhMt0hwsHYqZEOJVIUS5EGJdG9unCCGqhRCrjeWurjez5xBCcG76H/hL3uPu\nLCH1zjru2nUtc6s+NNm69omwqLRXE/M8besq1ER8i65WrekGpITPN8PXOzxtA5LVXG5cgDsAlrYU\n80DRTe4imwVR/bkt7xEiRAAHwGnapDM9s9eBkzvY5zsp5Vhjue/QzTKfyYlTeazPK6RFqrE7Jw7+\nXvoQL5Q+HtClYywC/t8QOKbA07Zpr8q+0BS4ZmuCEKeEjzbCd4WetqFpag43JsD1oN5Ry72F11Pt\nqAIgKSKFu3s/Q6+IAHa31LRLh2ImpVwEVPaALQHHwNhhPNX3LQbGDHO3fVb1DvcW3kC9o9ZEy9pH\nCDhtIJzQz9O2bR+8+JMuH6PpGhxOeHc9LNvtaRuVARePBmuEeXZ1Boe080jxrexq2QZApLByR/5T\nZEfldXCkJpDpqjmzSUKINUKIeUKIEW3tJISYJYRYIYRYUVFR0UUf3b2kWzN5tM/LTE6Y6m5bWf8D\nt+y8lNKWYhMtax8hVC7HU708iwtr4PlVUNtsnl2a4MfuhLfWwU+lnrZx2fD7kYFdXBNU8uAXyh5n\nVf1Sd9sNOXczPG6MiVZpuoKu+OqtAvpIKccAfwM+aWtHKeWLUsoJUsoJGRkZbe0WcMRYYrkt71HO\nS7vM3bazeSs37riQ9Q2rTbSsY6b0UaXoXZTUwT9Xwb4m82zSBC8tDnhtDfzi9Sw6MU/N1UYEuJAB\nfF71Hl9WfeBePz/9Co5Lmm6iRZqu4pC/flLKGillnfF+LmAVQqR3cFjQYREWLsq8hpty7yfSSG1T\n49jH7buuZGH1FyZb1z5H5aubjStBeUUD/GMl7A3svMqaAKPJruZef/WadDi2QM3RBnL2exc/1n3P\nS2VPuNePSTyJ36dfZaJFmq7kkMVMCJEtjJKrQogjjHPubf+o4OX4pFN5uOAFkiJSALBLG0/uvos3\nyv+GUwauD/yEHLhgFEQYN52qJiVoZfXm2qUJDhpsas51m1cOgRP6qWHsYCi47Eoe7ET9RofGjuKG\nnLt1tegQojOu+e8AS4AhQogiIcRlQoirhBCuR5oZwDohxBrgWWCmDPTEhofI8LixzOn7Fn2iPZUF\n39/7Gg8X/zmgy8iMzlQT9K55jZpm+OdKKA5cXxZNAFDbrIamC2s8bacNVHOywaAFVfa93Ft0gzt5\ncEZkNnfkP6WrRYcYwizdmTBhglyxYoUpn91VNDjqeLT4dlbUL3a3DYgZyl35T5NuzTTRsvbZUgmv\necWexRopsfoEcDZzjTnsa1I9sooGtS5Qc7CT8k01q9M0O5v4y64r2di4FlDJg5/o8yp9YwaZbNnB\nI4RYKaWcYLYdgUYQTNkGLnER8dzVew5npv7O3ba1aSOzd1zI5sb1JlrWPgNTYdZhnligRru6YW2t\nMtcuTWCxx5hb9Ray84YHj5BJKXm65F63kFmwcGvew0EtZJq20WJ2iESISGZl3cy12X/Bggqw2Wuv\n4Nadl7O4ZoHJ1rVNnyRVQqaXkaWhxQEvr1Y1qDSasno1tFhleL1GCDXnOj7HXLsOBJU8eL57/Yqs\nmzg8/mgTLdJ0J1rMuohTUmZwf8Fz9LKoDALNsomHim/hvT2vBGxttLwElQg2MVqt253wxs/w425d\nQiac2Val5lJrjHjESItKZD06cEfO9+Pb6nn8354X3eunppzD6Sk6eXAoo8WsCxnb60ie6vsGudbe\n7rY3K/7OUyV3ufO/BRpZvVSJjhRjLtwh4f0N8OZaqGsx1zZNz2JzqDyLz6+CeuPrGh0Bl4+FoUEU\nbLO+YQ1zSu5xr4/rNZErs27RnoshjhazLiY/ui9P9n2DUXGe+dmF1V/yQukT7RxlLmmxRvHEOE/b\nugp4YimsLTfPLk3PUVgDTy9XtfBcnfLYSJUweECKqaYdECp58OxWyYMf1cmDwwAtZt1AYmQy9xf8\nnROTznK3zd33AV9UvmeiVe2THAN/Otw34369TfXQ3vlF53QMVexOmL8NnlsB5Q2e9sGpMPvI4PJw\nbZ08ODEiWScPDiP040o3YRVW/pRzJ02y0T0J/ULZE+RGFTAufpLJ1vknOhLOHqqKfH6wAaqNOZNV\npbClCs4ZprKia0KD0jqVLNg7zjAqQsWQTcwLjhgyFyp58G0+yYPv1MmDwwrdM+tGhBDckHM3g2NU\n7mUnDh4pvjXgK1cPSYObjvQt9FnTrFIZfbRRl5IJdpxSVYV+ermvkPVLVr2xSfnBJWQAL5Y9war6\nJe51lTx4rIkWaXoaLWbdTLQlhjvznyItUrmC1TvruLfwemrs+zo40lxirXD+CLholMd9H2BpMcxZ\npjzeNMGHKy/n3C3K2QeUt+Jpg1SoRlqsufYdDJ9VvssXVe+712fq5MFhiRazHiDVmsHdvZ92V64u\nsRXxUPGfsQWoh6M3ozLh5olq6NFFZZPyePvsV+UBpwl8nBIWF6oHkZ3Vnvb8BLjhCJUwOBiSBbfm\nk8q3eaHsMff6MYkncoFOHhyWaDHrIQbEDOWm3Pvd62sbVvDP0kcCNgbNm/go1UP73Qjl4QbK4+27\nQpizHHZVt3u4xmSqGuGln+CTX8Fm5MK2GPXurp2gwjOCDad08krZHF4qe9LdNjhmJDfk3KNd8MMU\nLWY9yOTEqVyUcY17ff6+j/ms6h0TLeo8QsBh2WoubYiXE0hFA/x9JXy1VXnGaQIHKVUA/JPLlAOP\ni+xeynP1hH7BUYOsNTZp48ndd/LvyrfcbcNix3Bv72d18uAwRnsz9jDnpl3KruZtfFszD4CXy54i\nL6oPE+Inm2xZ50iKgcvGwPLdKsC22aGGsBbsgPV7YOZwyNWe0KZT0wwfbvRNTyZQxVpP7B/4FaHb\nosFRx4PFt7C6fpm7bWL8FP6c95AWsjBHZ803gRZnM7fvmuVOgBpniefJvq9TEN3fZMsOjMpGeG+9\nb42rCGP46tiC4HzqDwVWl8HHG6HBy+s0PRbOGwF9gyhurDWV9j3cves6tjVvcredknw2V2ffGlZB\n0Tprvn+0mJlElX0vN26/kAp7KQDZ1nye6vsGSZFBlG4Bj2PB3FbDjAWJKsN6ZhDOxwQr9S3w8SZY\n0ypry+R8mD5QxZAFK0XNO7ir8FrKbLvdbRdm/JHz0i4LuzkyLWb+0c/OJpESmcZdvZ8mRihf6FJb\nEQ8V3xIUHo7eWAT8pgBuPAJ6J3radxnpkb4vVIKn6V7WV8ATy3yFLDkGrjwMzhoS3EK2sfFnbtl5\nqVvILETwp5y7mJl+edgJmaZttJiZSP+YwdyS9xAC9YNc17CKv5c8FBQejq3J7AXXjIeTB6ihRlCe\nc5/+Ci+uUkOSmq6n0Q7vr1fFVr0TQx+Rq5x1BqaaZ1tXsLx2EX/ZeRU1DjWWHS1U3OZJyWd1cKQm\n3NDDjAHAB3te5/WKZ93rl2fO5rdpF5ho0aGxu1alSSqp87RFRcDhOSpNUna8ebaFCtXNsLxYBbHX\neIlYQhTMGAbDgyjLfVvMr/qY50ofxIkav06MSOae3s8yJHakyZaZix5m9E/4zJoGMDPSLqawZRsL\nqr8A4JXyOeRFFXBEwjEmW3Zw5CYo1++vt8PCHSomrcUBi4vU0j9ZidqozOD1qjMDp4QtlbCkWHmO\nth6+HZulhhS9M7YEI1JK3tnzIm/vecHdlmXN4/7ez5EX3cdEyzSBjO6ZBQg2Zwt/2XUV6xtXAxBr\nieOJPq/TN2agyZYdGruqVdLi0vr9t/WyquGwiXmQGoRplHqKehus2K16YXv8DNfGR8FZg2FMVs/b\n1tU4pJ2/lz7M/H0fu9sGRA/lnoJnSY0Mge5mF6B7Zv7RYhZA7LNXcuOOCym3lQCQZc3lqb5vkhwZ\n3BMfTglbq2BJEfzip0chgMFpMClPZeXXLv0q4HlnteqF/VzuPyC9f7K6ZiNDpIfb5Gzk0eLbWV63\nyN12WK+J/CXvceIitFusCy1m/tFiFmDsaNrMzTv/QKNTFZcaHjuWhwqex2qJMtmyrqG6WQVcLyv2\nlJjxJikajsxTPbak6J63z2ya7KrkztJi3zlHFzGRMCFb9WazQmjusdpexX1FN7hjLwGOS5zO9bl3\nYxVBPm7axWgx848WswBkee0i7iu6EWnU/J2WdHrI5ZxzOGHjXnXT3rTXU93YhUXAiHSYmA8DU4Iz\nCe6BsLtW9cJ+KlVZVVqTn6BKs4zNCm43e3+UtezmrsJrKWrZ4W47O+1iLsm4DosIgS5nF6PFzD9a\nzAKUf+99i1fK57jXL828nrPTLjbRou5jb6PqqS3freaHWpMeq3oiE3KD37nBG5tDxYUtKVJxea2x\nWlQ+zIl5vjF8ocTWpk3cves6qhwq75ZAMCvrZs5IPd9kywIXLWb+0WIWoEgpeabkPv5b/SmgfuR3\n5D/FxIRjTbas+7A7YV256qFs81PuLdICozNVD6VPYvAVkHRR0aB6pCt2+6accpHVS82FjctWdeVC\nldX1y3ig6GYanco7KFJYuTn3AX6TeILJlgU2Wsz8o8UsgLFJG3fsupp1DasAiBGxPNH3NfrFDDbZ\nsu6nrE6J2spS/5Wtc+LVDX90VnD01localh1SZFvBnsXEUKFKkzKUxWfg1WoO8u31V8xZ/dd2FH/\n3F6WeO7If4rRvfQ9uiO0mPlHi1mAU22v4sYdF1FmKwYgIzKbOf3eIiUyrYMjQ4MWh0qcu6QIimr9\n7xMbqSoku5c49Zoaq5xIemK+TUqVgWNvE+xtUEOnrqWyEWpb/B+XGqOGEQ/PVS724UDrIfS0yEzu\n6/03+sYMMtGq4EGLmX86FDMhxKvAaUC5lHK/0HuhvBKeAaYDDcAlUspVHX2wFrPOs7N5KzftuMQ9\nHDM0djQPF7xAlCW83P0Ka9Tw3E+lniKTHREhlKil+VlSY8F6AM4UDidUNfmKlPd7f44b/hDAsHQ1\nXDo4NfSdW1w4pZNXyufwSeXb7raCqP7cV/AcGdZsEy0LLrSY+aczYnYMUAe82YaYTQeuQ4nZkcAz\nUsojO/rggxazuir4eT4cOQMiwieByYq6xdxbeL07tc9xidO5Kff+kPJw7CyNNjX8uLIEyuo7L2z+\nSIreX+ySY4xeVqPvsq/p4JMmRwh17tGZKvQgOcxKb1XZ9/Jsyf0+MWQjYsdyZ+85JEQEcV2ag2HN\nV5A1ELIPLiGCFjP/dKgGUspFQoi+7exyJkroJLBUCJEshMiRUpZ0kY0eWhrgi8dgz07YuxNOuh6i\nwuOuMCF+Mpdl3eguE/9NzVzyo/syM/1yky3reWKtcHRvtUiphvDcotPgO9TnzzvSm+pmtWz343By\noMREeIY4XT0/b4EMlx5YaxbXLOC50gfdyYIBjko4nptzHwivgprSCd+/DWvmQUwCzLgHknPMtipk\n6IquTR5Q6LVeZLTtJ2ZCiFnALICCgoID/6T13yohA9i5Bj6+H07/M8SFx5PdmSm/Y1fzNneqn7cq\n/kGFrZSrsv4cMkHVB4oQkBitln7J+29vsu8/JOgSvX3NB97TSoz2P2SZFgtx1tB33DgQah01PF/6\nqLuquoszU3/HZZk3EiFCLGCuPewt8PU/YYtRIbupFpb/G068xly7QogeHaeTUr4IvAhqmPGATzDm\nFGiqgxWfqPWK7fDhXXD6rZCS25WmBiRCCK7Ovo2SlkJ+blBDtF/t+zc7mjdze97jpFszTbYw8IiJ\nhLwEtbSm9RyYa6luUs4YhzrHFs6srPuBZ0ruZa+9wt2WHpnF9Tl3MS5+komWmUBTHcx9CnZv9LT1\nPxyOv8I8m0KQTnkzGsOMX7QxZ/YC8K2U8h1jfRMwpaNhxkNyAFm3AP73qhpjAoiJh1NvhpzQd1kH\nlcPubyUP+DzxJkek8Zf8xxgRd5iJlmnCnUZnA6+UzWHevo982qcmncasrFuIj/DzVBHK1FTA549B\nVbGnbfRJcPSFYDm47CZ6zsw/XZEr5jPgIqGYCFR3y3yZNyOnwvSbINLw5muqg08ehK0/duvHBgox\nllhuzn2AK7JuwoLqKuxz7OX2nVfyReV7QVncUxP8rGtYxbXbZvoIWVJECnfkP8ns3PvCT8gqdsCH\nd/sK2VG/g99cdNBCpmmbzngzvgNMAdKBMuBuwAogpXzecM1/DjgZ5Zr/Byllh12uLnHNL9sCXzwB\nja5cQAKOuRhGn3ho5w0ifq5fwSPFt1Lt8ETiTk06nWuybw+vyXWNabQ4m3mz4h98Uvkvdz5RgEkJ\nx3Ft9l+DvurDQbFrLcx7GmxGzR5LJEy7CgYfdcin1j0z/wR/0HR1GXz2iHp1Me50mHQehEmS0gpb\nKQ8W3czmpvXutgExQ7kj/wkyraE/l6gxj82N63ly950Utmx3t/WyxHNV9q0clzg9LENH2PgdLHwR\nnEbgYVQcTJ8N+cO75PRazPwT/Hf7pCyYca+K23Cx6nP47z/A0YFfdoiQYc3msT6vcELSGe62rU0b\nuX77BaypX26iZZpQxS5tvF3xPLN3XOwjZON6TeIf/T/g+KRTw0/IpFTOaV//0yNk8alw9t1dJmSa\ntgn+npkLWzPM/xvs8Eo+kjccpt8I0eFR2E9Kydx9H/Ji6ePunHcWLPwh83p+m3pB+N1cNN3CruZt\nPLn7TrY0bXC3xYhYLsu6kVOSzw7P75nTAYteV85pLtJ6q9Ch+K5NPad7Zv4JHTED/1+o1N5wRtd/\noQKZ9Q2reajoz+6yGgDHJJ7E9Tl3EWOJNdEyTTDjkA4+qXybtyr+gU16kk2OiB3Ljbn3khPV20Tr\nTMTWBPOf832Qzh8Bp9wI0XFd/nFazPwTWmIGqqu/6nNY8q6nLT5VxaKlhc+Pba+tgoeKb2Fj48/u\ntr7RA7kj/8nwveloDpqSliLm7L6bXxp/crdFCisXZVzDWam/D68AaG8aa+CLx6Fsq6dt8GSYemW3\npdvTYuaf4J8za40QMP4MmHY1WIwfWF0lfHQvFP1irm09SJo1g0f6vMT05Bnuth3NW7h++wWsqFts\nomWaYEJKybyqD7l223k+QjYgZijP9vs/zk67KHyFbF+pcr33FrJxZ8AJV4dV3thAIfR6Zt4U9aNW\nhQAAGs9JREFUroW53u6xEUrkusA9NpiYv+8T/lH6MHapHGIEggsz/si5aZeG5/yGplPssZXzTMm9\nrKpf4m6zEMHM9Ms4L/0yIkUQFJLrLkwMC9I9M/+EtpiByuX4+WNQ71UR8ajz4bDTwiqR3qbGdTxU\ndAt77J4QhkkJxzE75z7iIsLDQUbTOaSUfFszj3+WPkq901NEriCqP7Nz72NQbJh75m1fqZzN7Ma8\nYYQVTrpWpajqAbSY+Sf0xQygdg98/ihUekXijzox7CLx99kreaT4VtY2rHS39Y7qxx35T5If3dc8\nwzQBw87mrbxY9gSr65e52wSCs1Iv4KKMP4ZdDb39CIBUelrM/BMeYgZtJ/s88RqIDJ+M83Zp49Wy\np/m06h13W5wlnpty72diwrEmWqYxk1pHNf+qeJ65VR/ixFNlNMuax+zcexgZN95E6wIAKWHZB54k\n5wCJGXD6bZDSs2VctJj5J3zEDFQQ9X//CVuWetqyB8OpN0FseOWNW1j9JX8reYAW2exum5l+Bb9P\nvxJLmGRO0YBD2plX9RH/2vM8tY5qd7sFC9NTzuGSzOuItXS9e3lQ4bDDwpdg03eetsz+cNotppSf\n0mLmn/ASM1AF8hb/H6ye62lLzoEzboXE8CqhsrVpIw8U3US5zZMX+vD4o7kx516SIlNMtEzTE6yu\nX8aLZU+ws3mrT/uYuMOZlXUzfWMGmWRZANHSAPOeUc5kLvqMhZP+ZFphYC1m/gk/MXOxeh58/y9w\nJUaNS1JPWpn9zbPJBKrtVTy2+y8+cyRWEcUxiSdxesp5erI/BClpKeSVsqdZUveNT3u2NZ/Ls25k\nYvwU7eUKUFflqWzvYvhxMOVST9iPCWgx80/4ihmoqq/eORyt0cp1f8AR5trVwziknTcq/s5He9/Y\nb9vgmJGcnnouv0k4MWyrWYcKDY563tv7Cp9Uvu0O0wCViuq89Ms5K/V32sHDRfk2lfW+1pNFhyNm\nwOG/Nd0LWouZf8JbzEA5hHz5JDTXe9pGnwSTf6dcbsOI5bWLeGfPS/zatH9weVJECicl/5bpKTPI\nsGabYJ3mYHFKJwuqv+CN8ud8UpyBKhd0cca1pFkzTLIuwJAS1v4Hvn8bnCq/KcICx10Ow6eYapoL\nLWb+0WIGymX/i8dUVVgXGf3g5D+prPxhxqbGdXxZ9T7/q5nv8wQPyjF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wWVlZTJgwwaP+mjVrEELw66+/AnDw4EGEECxcuJCbb76ZiIgIxo5V8s29++67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MfHx8cHNzQ6fTYTAY1P0mk4m8vDwiIyOJiooCICAggNraWnJyclwqM5PJRG5u\nLuHh4Q7r5EJCQtQlC7Nnz2bo0KF89NFHfPzxx6ellAus38sLQohnrYrKxutSyv+AMr8G5AGjgHel\nlKlCiL0oymu9tY4XMAZFOSKE8EdReM9KKedZ+1wnhPAF/imEeEdKaZuPCAGGSSn32E4uhHgZqARG\nSymrrPtOoyhSWx2Bomw/llL+zW6/EXhLCPGClLIQQEpZIoQ4AfSjEWWmmRkboKO/o3fj6ovE3NjU\nSENKyRtvvEH37t3x8fHBw8ODu+++G6PRqK7p+emnnwgODnZQZPZs3bqVqqqqRj3NWsLIkSPr7UtN\nTeXWW28lPDwcd3d3PDw8OHjwIOnp6YDyx719+3YmTZrkcs3U9ddfj06nU81HZrOZ4uJihzklX19f\nLr300ia3xhwlmktAQABRUVEEBAQQEBBAfHw8QUFB5OTkNHuE2Bi1tbVNjgpbQnh4OGFhYfj5+REc\nHExCQgIeHh7Nnhu0p7KyEr1e76BYPD09MRgM9UbPLR3Z28yL9t6L3md4G6qqqrBYLA2akaurq116\ngVZUVGCxWFwqO7PZzO7dux2WVlj5AuU//Gqn/T/YPlgVwikgxqndbXYmypsAP8AWIuZqQA8sF0Lo\nbBvwExDu1FeWvSKzchWwzqbIrHzrVCcB6AQsa+Ac3kAPp/oFKCM8l2jKzAVJ8XVZqW3mRssFs9ig\n5VRXV1NYWKi+5TfEG2+8wYwZM7j11lv55ptv2LFjhzoqspmkCgsLHd6UnbHNHzRWpyU4y1tWVsYN\nN9xAZmYmr732Gps3b2bnzp1cccUVqozFxcVIKRuVQQiBXq+nsLAQKSVFRUVIKQkOrjPbu7m5qSO1\nxjbb6MU20nB2srGVW6pMgoKCMJlM6pylu7s7ZrO5nnIzm824ubk16nwhpax3/Gz6c8bd3Z2AgACH\nBdHNpaampsF7o9Pp6o1mW3oP7c2LbgKCvFHvZ0tfQmzKyrmdrezKucp2Da7OV1BQQG1tbUPPps2M\n4jyXVOJUrkFREDa+AEKB66zl8cBWKaVtlbntjW0/UGu32cyS9naqhkw5EYBDiCcpZTVg/+ZhO8dq\np3McbeAcAEana6iHZmZ0gc3c+K+LxNy4fv16TCYTV1/t/JJXx/Llyxk3bhzPPfecuu/AAcd40yEh\nIY2+fdvePnNychxGOfZ4e3vXcypxtabHeWS1detWTp48ybp16xzWVdlPpAcFBeHm5tbkKMFgMGA0\nGikrK6OXREeHAAAgAElEQVSwsJDAwECHP8uWmhm9vLwQQlBdXe0wd2RTsg2ZtFqCbW7LaDQ6zHNV\nV1c36RWo0+nq/dmeTX8N0ZzIIQ3h6enZoKeqyWSqp7xacg6zVJJu2rB5L54+fRoPD48Wfx82ZeQ8\nyrUpOWfzsg1b3dra2gYVWmhoKB4eHg15kNq0W9MTmHZIKTOEECnAeCHEz8BolDkrG7b+RtGwsrL/\n0Tf0ip8LdLDfIYTwBgx2u2znuB/4tYE+jjqVA2niOrWRWSPE+MO1F4G5saSkhCeffJKuXbs2uki1\nqqqq3gNun1oEFPNcUVERK1eubLCPq6++Gh8fn0ajM8TExJCWluaw74cffnBRu76M4KgYtmzZwrFj\nx9SyXq+nf//+fPzxx42a6HQ6Hf7+/mRnZ1NeXl5P+bbUzGjzFnR2nigqKsJgMLR4VFFcXIxOp1MX\nHBsMBtzd3R36N5vNlJSUNGl+8/Lywmg0Ouw7m/6csVgslJSUqPONLUGv11NRUeEgX01NDeXl5Q5z\nVi2l2m5QZ1scffr0aYqLix0caxpCCFHP9d82l+b84lVcXIy3t7fLkZder8fNzc2l16O7uzt9+vRx\nCPZs5XbAguIg0VKSgVutmw9g3/lWoAqIklKmNLCVNdH3TiBJCGHv8OE873AQyALiXJxDvRlWz8tO\nQHpjJ9VGZk0wLB7250Ou1btxWSo8eAF7N5pMJrZt2wYoJrldu3bxzjvvUFlZyZo1a1y+PQIkJSWx\ncOFC+vfvT5cuXVi6dKlD7ilbneHDh3PXXXcxe/ZsrrzySnJycti0aROLFy8mMDCQp59+mlmzZlFT\nU8OIESMwGo2sWrWKOXPmEB0dza233soHH3zAI488wsiRI1m/fr260LspBgwYgMFg4L777uOJJ57g\n5MmTzJ07V51It/Hiiy8ybNgwbrrpJu6//370ej1bt26lb9++jBo1Sq0XGhrKkSNH8PT0xN/f36EP\nd3d3l84MroiMjOTgwYOcOHGCoKAgSktLKS0tpVu3bmodo9HIvn37iIuLUxXo4cOH0ev1+Pr6IqWk\nR48ezJw5k3HjxqmjETc3NyIiIsjJyVEXG9scemyLg11hMBjquds3t7+CggK2bNnCmDFj1FHI4cOH\nCQkJwcvLS3WMqK2tPSPzckhICLm5uRw6dIioqCjVm1Gn0zWodIYOHcqECRP461//6rJPiwRTbS21\nVeUIAcK9luOnSiksLMTf35+IiEanZ/Dx8VG/O51Oh5eXFzqdjvDwcHJychBC4OvrS0lJCaWlpQ4L\n0Z3R6XRERkaSlZWFlJKAgAA1kktWVhbR0dHMmzeP4cOH2+aa/YUQM1AcNv7t5PzRXJahOGC8DGyy\npvoCVIeLucCbQohYYBPKwCcBuFZKeWsTfb8BTAW+E0K8jmJ2/AeKU4jFeg6LEOIx4BOrw8n3KObQ\nzsAtwDgppW3okIgyqvulsZNqyqwJnM2NR0vgl0z4U6em256PlJaWcvXVVyOEwN/fn65duzJhwgT+\n7//+r8kHePbs2eTn56vJEseOHcvChQvV9V2gvLF+9dVXPP3007zxxhvk5+cTFRXFXXfdpdaZOXMm\nwcHBvPnmmyxevJigoCAGDx6smt5GjhzJ888/z9tvv83777/PmDFjePPNNxkzZkyT1xceHs7y5cuZ\nMWMGY8aMoVu3brz77rssWLDAod7gwYNZt24dTz/9NBMmTMDT05PevXurYaFsBAYGIoQgJCTkjM1k\n9vj5+dGlSxeys7PJz8/Hy8uLzp07NznS8fb2prCwUHX4sM35Oc+j2L7DnJwcTCYTer1edb5ojKCg\nIHJzcx28N8+0P5unX05ODrW1tbi5uaHX60lMTGyx8rf1l5CQQGZmpjrCtt3HM3FaqTYptrHqsiKq\ny4oQQnBap8PHx4e4uDiCg4Ob/K4jIyMxGo0cOXIEs9msvnjYvBjz8/NVb8j4+HiHuVZX/el0OvLy\n8sjPz0en02GxWNRn4oYbbiA5OdkWtaUrMB14FcXrsMVIKTOFEFtQwhXOa+D4AiFENvAI8BjKGrJ0\n7DwSG+k7SwgxEngT+C+QCtwLrAPsg9J/YfVyfMp63AwcAVaiKDYbN1r3N2SOVNHCWTWTNRnw4zHl\ns4cbPNIfOrTcYqJxAZGamkpUVBSHDh2iR48e5ySs0pkSFxfH+++/36LYhU2xf/9+QkJCmnypaWiu\n6tixY8THx7e6V+SZ0NjIzCKVNaQ2pw8fHYT4nJ/Zo9tTOCshxCBgM3CdlLLh9OSu224FVkkpn22s\nnjZn1kyGxUOE1Txfa4HlB9q3d+PFTnZ2NtXV1Zw8eZKAgIDzSpE5YzQamT59OlFRUURFRTF9+nR1\nfmnIkCF8+eWXAPzyyy8IIVi1ahUAP/74I7169VL7+emnn7jmmmsICgpi+PDhHD9+XD0mhOCtt96i\nW7duDiZRZ/7zn/8QFRVFZGSkw5rExmT88MMPGTRokEM/QgjVhD158mSmTp3KyJEj8fPzo3///mRk\nZKh1bc4+AQEBTJs2rdF50FIn78VA7/NTkV3oCCFeEkLcYY0E8gDKHN1eoHlpEOr66Q9cAixqqq5m\nZmwmOjcYf6mdubH0wjY3ajTOe++9x4ABA4iNjaVTp06w6K6mG7UW0z5rUfXnnnuObdu2sWfPHoQQ\njBkzhmeffZZnnnmGIUOGsGHDBm677TY2btxI586d2bRpEyNHjmTjxo0MGTIEgG+++YY333yTDz/8\nkD59+vD6669z5513OmSA/vrrr9m+fXu9CCb2rF+/nkOHDnHkyBGuu+46evXqxbBhwxqVsTkkJyfz\n/fffc+WVVzJp0iRmzZpFcnIyBQUFjB07liVLljBmzBgWLVrEu+++yz333FOvj2qnxdFa7MVzihfK\nfFw4UIay9u1RKWXjATPrEwxMklI6Lzeoh/ZVtoAYf7gurq78fQbkt0PvRg0lw0BsbCyXXnrpWbvM\nn2uWLl3K7NmzCQsLo0OHDsyZM4dPPvkEUEZmtpxgmzZtYubMmWrZXpm9++67zJw5k8GDB6PX63nq\nqafYs2ePw+jMNtfZmDKbM2cOer2enj17MmXKFD7//PMmZWwOt956K/369UOn03H33XezZ4+yTnf1\n6tVcdtlljBs3Dg8PD6ZPn96gmdQiodgulKOPi9QuGq2DlHK6lLKjlNJTShkipbzT3smkBf18L6V0\nXnDdIJoyayHXx0GknblxmWZu1GhjsrOziY2tW0MSGxtLdnY2oCyFSE9PJy8vjz179jBx4kQyMzMp\nKChgx44dDB48GFDSvzz88MMEBgYSGBhIcHAwUkqysrLUfu1DLbnCvo69HI3J2BzsFZSvr68a+cOW\nnsaGEKJBOTXzYvtHMzO2EJt348KdihI7Vgo/Z8JgzdzYvmmh6e+PJCoqiuPHj3PZZZcBcOLECdWr\nztfXlz59+vDmm2/So0cPPD09ueaaa3jttdfo0qWL6vrfsWNHZs2axd133+3yPM3x5szMzFQXq9vL\n0ZiMer3eITJISxLFRkZGOmQxkFLWy2qgmRcvDrSv9AyI9lNGaDY0c6NGW3LnnXfy7LPPkp+fT0FB\nAfPnz2fChAnq8SFDhrBo0SLVpDh06FCHMsCDDz7ICy+8oKZNKS0tbWiRbpM888wzVFZWsn//fpYs\nWcL48eOblPGKK65g//797Nmzh+rqaubOndvs840cOZL9+/fz3//+F5PJxMKFCx2UoWZevHjQlNkZ\ncl1cnbnRZIEvNHOjRhvxz3/+k759+3L55ZfTs2dPrrzySnUtICjKrKysTDUpOpdBmZN68sknueOO\nO/D396dHjx58//33LZZlyJAhdO3aleuvv54ZM2Zwww03NCljQkICs2fPZtiwYXTr1q2eZ2NjhIaG\nsnz5cv7xj38QEhLCoUOHGDhwoHq8tLp+7EXNvNg+0daZnQVZZXXmRoBR3WCIZm5sN7ha56NxYVBt\ncrSYBHuDvlXzIJ9b2tM6sz8CbWR2FjibG9dkwKmKNhNHQ0PDimZevPjQHEDOkuvjlNiN2eWKOWNZ\nKvytz/kZu3Hu3LnMm6dErhFCEBAQQNeuXbnhhhuaFc7qYqe6upq8vDzKy8upqqrCz8+PxMTEJttV\nVVWRmZlJVVUVJpMJDw8P/P39iYqKUoME2zCZTGRlZVFSUoLJZMLLy4uIiAiXGQZsSCk5cOAA4eHh\njda1WCxkZWVRUVFBRUUFUkr69m36Jd9isXD06FEqKyupqanB3d0dX19foqOj64WoKioqIjc3l+rq\natzd3fH39yc6OrretTpTUlLCyZMnMRqNeHh4cPnllzcplysaMy/a4h4WFBSoOcg8PDzw8/OjQ4cO\njQYvrqiooLS0VHVe0Tg3WLNVHwQul1IeaU4bbWR2lrhbvRttyut4KWw+0XibtiQgIICtW7eyZcsW\nkpOTGTt2LJ988gk9e/Zk165dbS3eeU11dTWlpaV4e3u3KCKI2WzGy8uLmJgYEhISiIqK4vTp0xw+\nfNghWoXZbCYtLY3Kyko6duxIt27dCAsLa1byzeLiYsxmc5MxAC0WCwUFBbi5uZ1RxPmIiAi6detG\nbGwsFouF9PR0h2j2JSUlHDlyBIPBQNeuXYmJiaGsrKzetTojpeTo0aP4+PiQkJBA165dWyybjWoT\nlNvlwQz0Vp5T23mOHDnC8ePH8fX1JT4+noSEBDXWYlpaWqNyVlRUtGhJgcaZIaXMQokDObupuja0\nkVkrEOUHw+LgB2sGnjVH4NJQCGt5TNVzjk6nY8CAAWp5+PDhPPTQQwwePJg77riDtLS0RiPntwVV\nVVWNLtT9owgICFBHCxkZGfUSQ7rCYDA4KA4/Pz88PT1JT09XsygDahDhxMRENfGlc6R+V5w6dYqQ\nkJAmE2bqdDp69eqFEIJTp05RVtZUNg8FNzc3unTp4rDP39+fPXv2UFxcrI7qCwsL8fX1VaKmWHF3\nd+fw4cNUV1e7/B5ra2sxm82EhIQ45HprKRYJRVUWkAKEUMyLdv9yp06dori4mISEBId7axuV5efn\nN9CrRlMIIXycMku3BkuAH4UQj9mnhHGFNjJrJa6LU+bQoM7ceKF4NwYGBrJgwQIOHz7MunXrXNYr\nKSnhr3/9K1FRUXh7e9OpUyfuu+8+hzp79+5l9OjRBAYGYjAY6Nevn0OfR48e5ZZbbsHf3x8/Pz9G\njx5dL42MEILXXnuN6dOn06FDB3r27Kke++abb+jbty/e3t5ERETwxBNPuExH3xrYv6W3RtR8G7YX\nBvv+CwoKCA0NbVEGZ1BGjOXl5QQFBTWrfmtdhy3btP01SCnrvQw19XJUUFDA3r17ASV1TEpKijr6\nMZvNnDhxgt9++41du3Zx4MCBeqlqDh48SEZGBvn5+ezbt4/sg7uxmGob9F7My8sjKCjI5UtChw4d\nXN6fgoICTpxQzC4pKSmkpKQ4JGc9ffo0qamp7Nq1S42e4iq7tD2VlZUcOnSIX3/9ld27d5Oamupw\njc7PDNBVCOEwdBVCSCHEw0KI54UQ+UKIU0KIt4QQXtbj8dY6I53auQshcoUQz9rt6yGEWCWEKLNu\ny4UQEXbHh1r7Gi6E+FYIUY41dqIQIkgIkSyEqBBCZAshnhRCvCKEOOZ03k7WekVCiEohxFohhLPN\n/heUhJx3NHkT0UZmrYa7G9x+qeLdaJaKuXHTCRga23Tb84GhQ4ei0+nYtm0bN954Y4N1Hn30UbZs\n2cLrr79OREQEmZmZbNq0ST2elpbGwIEDSUxM5N133yUkJISUlBR1EavRaOT666/Hw8ODf//73+h0\nOubMmcOQIUPYt2+fg4ns5ZdfZvDgwXzyySdqEsRly5Zx55138sADD/D888+TkZHBzJkzsVgsalBb\nKWWz/kCaE9ndlnaltdK/2FK31NTUkJWVhV6vV0dlRqMRk8mEu7s7hw4d4vTp07i7uxMSEkJ0dHSj\nCq6srAw3N7c/ZPRqU1wmk0ldz2X/vYWGhpKRkUFBQQFBQUHU1taSlZWFn5+fS/kCAgLo0qULGRkZ\nxMTEYDAY1Pm148ePU1JSQnR0NN7e3uTn53P48GESEhIcRnDl5eVUVRvxDYlGuLkj3N0JsjMvgpLQ\ns6am5oxyqtnkDA8PJy8vT10YblPUVVVVHDp0CH9/f7p06aJ+x0ajkYSEBJd9VlVVkZaWhre3N7Gx\nseh0OsrLyyksLMTb27vBZ2bcuHFewEYhRE8ppX2m18eAn4AJwOXAC8BxYIGU8qgQYgdKQs9Vdm2G\noMRPTAawKslfgBRrPzqUvGnfCSH6SUcb7Acoo6c3UFLEAHwIDAIeRsk4/QhKHjT1oRRCBAM/A4XA\ngyh5zv4B/E8IkWAb4UkppRBiGzAMeMvlTbSiKbNWJMoPro+HH6zTlWuPQPfz1NzojLe3N6GhoWry\nxYbYsWMHU6dOVRfCAg6Lc+fNm0dAQACbN29W/7iSkpLU40uWLOHEiROkp6eryQr79+9P586dWbx4\nMTNnzlTrRkZG8sUXdamTpJQ8/vjjTJw4kbffflvd7+XlxdSpU5k5cyYhISFs3LiRa6+9tsnrPXr0\nKHFxcY3WiYmJ4eTJkw2anvLz8zGbzfWyDTdGXl4e1dXKM+/p6UlYWJiaUdtoNFJQUEBhYaGq5IxG\nIwcOHCAzM7PRUVdhYSE1NTX1snM3RVlZGUVFRaSmpja7TWlpKSUlSsxXNzc3wsLCOHLEcX5eSsmu\nXbtUxefl5UVYWFij5zGZTOpcnu23U1tbS3Z2NiEhIWq2ayklJSUl7Nq1S83llpubS01NDX6h0XBK\n+f16uEGFk7+J7R67ublRUFDgIK89jb242O6Zc5SR/Px8ampq8PHxISdHCUFoNps5cuQIlZWVLuN7\n5ufnYzQaiY6Odnj2vL29iYmJ4YMPPqj3zKDkFbsUeABFYdk4JqWcbP28VggxEBgL2JL5JQNzhBBe\nUkrbROd4YL+U8ndreQ6KErpJSlljvR97gTRgBI6KcLmU8mm7+9YDJaP07VLK5dZ9PwKZQLldu0cA\nPdDLpoyFEL8Ax1Dymtkrrt8AR/OPK2xvi3/01qdPH9keMZmlfH27lDP+p2wLd0hptrS1VApz5syR\nISEhLo+Hh4fLBx980OXxu+++W3bs2FG+9dZb8uDBg/WOh4WFyUcffdRl+ylTpsirrrqq3v6hQ4fK\nESNGqGVAzpo1y6FOWlqaBOTq1atlbW2tuh09elQCcsOGDVJKKU+fPi137tzZ5GY0Gl3KaTKZHM5h\nsdT/Am+77TY5ZMgQl300RHp6uty2bZv85JNPZGJiorzyyitlVVWVlFLKX375RQKyf//+Dm3mzZsn\nvby8ZEVFhct+R48eLW+88UaHfWaz2eEazGZzvXb/+te/pPIX0HxycnLkzp075bfffitvvPFGGRIS\nIvfv368e/+mnn6TBYJBPPPGEXL9+vUxOTpaXXHKJHDp0qDSZTC77tX2P3333nbrvo48+kkC9a587\nd6709fVVy0OGDJGXXDlQfeZmb5CytLr+ObZt2yYBuXbtWof9U6dOlSj5OuvJ4IyrexYfHy8ff/xx\nh30mk0nqdDq5YMECl/2dyTODMmpaj5Ljy6aMJfBPafcfCzwPnLQrR6Nkeh5jLeuAfOBpuzo5wIvW\nY/ZbBjDHWmeo9XzDnM432brf22l/MoqitZW3Wvc5n+MnYIlT22lALdY10Y1t2pxZK2PzbnS3vtyd\nOK2YG893qqurKSwsrJe52J5FixZxyy23MH/+fBITE+nWrRvJycnq8cLCwkZNODk5OQ32Hx4err55\n2++zx/YmPWLECDw8PNQtPj4eQH1TNhgM9OrVq8mtMTdxm1nHttmizJ8t3bp1o3///kyYMIG1a9fy\n66+/8tlnSsxH28jLeVR53XXXYTQaHfJ3OVNdXV3vzX/+/PkO1zB//vxWuYaIiAj69u3L6NGj+e67\n7wgJCeHFF19Ujz/22GPcfPPNvPTSSwwdOpTx48fz9ddfs2HDBr755psWnSsnJweDwYCvr2MW3PDw\ncCorK1UvyioTmH3rfi+3JIJ/AwMhmzv9yZMnHfY/8cQT7Ny5k2+/bVZwdpeyOv9mbWZi59+2PWf6\nzAB5KOlR7HFOk1IDqG63UvEQ/BllNAZwPRCK1cRoJRR4EkWB2G+dAecIzs5mnAigTEpZ7bTf2bQR\napXB+RzXNnAOI3XKrlGarCCE6Ah8jGJXlcB7Uso3neoIlBTZI1Dsn5OllLub6ru9EmlQknmutTM3\nJgQrZsjzlfXr12Mymbj66qtd1gkMDGThwoUsXLiQvXv3smDBAu6++24uv/xyunfvTkhIiGpiaYjI\nyEg19p89eXl59VzKnU09tuPvvfcevXv3rteHTam1hplx8eLFDl5+zVlL1lJiY2MJDg5WTXRdunTB\n09OznsnLVm5sziw4OLhecN7777+fUaNGqeVzsS5Kp9PRs2dPBzNjWload955p0O9xMREfHx8GlXI\nDREZGUl5eTmVlZUOCi0vLw9fX1+8vLyoqFUCFXj4Kb+XHh2gl4v3sY4dOxIXF8cPP/zAvffeq+7v\n1KkTnTp14tixYy2Sz1nWU6dOOewzm80UFhY2ulziTJ8ZlP9j11rSNV8ALwohfFAUyq9SykN2x4uA\nr4D3G2hb4FR2dnHLBfyEEN5OCq2DU70i4FuUuThnnN1rA4FyKWWTXl7NGZmZgMeklN2BAcBUIUR3\npzo3Ad2s2/3AO83ot11zbayjd+OHe6GipvE2bUVJSQlPPvkkXbt2ZdiwYc1qc/nll/Pyyy9jsVjU\nuZrrr7+eZcuWqfNCzvTv359du3Zx9OhRdV9WVhZbtmxpMh5fYmIi0dHRHDt2jL59+9bbQkJCAOjT\npw87d+5scmvszz0xMdGh77NxFXfFwYMHKSwsVJWwp6cnSUlJrF/vmFH+xx9/xNfXt9F1V4mJiQ73\nFBTlZX8N50KZVVdXs3v3bvUaQFHSu3c7vsempqZSVVXV5BylM1dddRVCCFasWKHuk1KyYsUKBg0a\nhNkCn+6rWxzt6wFjExuPvTh9+nRWrFjBhg0bWiSLDduI3vk33r9/f7766isH5yNb8OPGfttn8swA\nHsA1KKOslrIc8AFutW7JTsd/BC4DdkkpU5y2Y030bYtPeLNth1VpJjnVs51jfwPnOOhUNw5ljrBp\nmrJDOm/AN0CS077FwJ125YNAZGP9tNc5M3tyy6Wctb5u/uydFGVOra2YM2eODAgIkFu3bpVbt26V\nP/zwg3zhhRdkp06dZGhoqExJSWm0/cCBA+Urr7wi16xZI9euXSvHjRsn9Xq9zMzMlFIq81p+fn7y\nqquuksnJyXLdunVywYIF8oMPPpBSSlldXS3j4+NlYmKi/OKLL+SKFStkz549ZVRUlCwsLFTPA8h/\n/etf9c6fnJwsPTw85LRp0+SqVavkunXr5OLFi+VNN93U6JxSa1FRUSGXL18uly9fLgcMGCC7d++u\nlu3P36VLF3nvvfeq5ccee0w++eST8r///a/86aef5FtvvSVjY2Nlly5dZHl5uVpv+/bt0sPDQ06e\nPFmuXbtWvvzyy9LLy0s+++yzjcq1du1aCchTp0416zpWr14tly9fLv/yl79IQL2GY8eOqXXuvfde\n2aVLF7X82WefyXvuuUcuXbpUrl+/Xn722Wdy0KBB0tvbW+7evVut98Ybb0ghhHz00UflunXr5Kef\nfioTEhJkXFycw7U609CcmZRS3nXXXdLPz08uWrRIfv/993Ls2LFSp9PJzZs3y6/SlOcq5vIhstuf\nbpP7mnH5ZrNZjh07Vnp7e8uHH35Yrly5Um7cuFEuX75cjh8/XgJy/fr1Lttv3LhRAvLFF1+UO3bs\nkGlpaVJKKX///Xfp4eEhR40aJVetWiUXL14sAwMD5fDhwxuV50yeGRTrVxYQLB3nzKZJx//luUCB\nrP8f/j8g29omzulYAoq5cjUwDmV+7G4UL8Wh0nHOrEcDfX+L4qX4F2CkVXFlAkfs6oQCJ1Dmzu5C\n8ai8HcXx406n/rYDC53P09DWUkUWZxXC32n/SmCQXflHoG8D7e9H0d4pnTp1avRLbi/sPyXl4/+r\nU2hfpradLHPmzFEnuYUQMiAgQPbp00c+9dRTMicnp8n2M2bMkD169JAGg0EGBATIoUOHyk2bNjnU\n+e233+RNN90kDQaDNBgMsl+/fvJ///ufejwjI0OOGTNGGgwGqdfr5ciRI2V6erpDH66UmZTKH/Gg\nQYOkr6+v9PPzk1dccYWcNWuWrK2tPYM70jJsf7gNbUePHlXrxcbGykmTJqnlzz//XF5zzTUyKChI\n+vj4yMTERPnoo4/K/Pz8eudYs2aN7N27t/T09JQxMTFy/vz5DTpv2GM0GmVwcLD8+OOPm3UdsbGx\nDV7DkiVL1DqTJk2SsbGxann37t1yxIgRMjw8XHp6esrY2Fh5++23y99//92hb4vFIt9++23Zs2dP\n6evrK6OiouTtt98uMzIyGpXJlTKrqKiQ06ZNk2FhYdLT01P26dNHrlmzRm7PqnumYi4fIgfdeFuz\nrl1KRaF98MEHcuDAgdLPz096eHjI2NhYOWHCBLlly5ZG21osFvn444/LyMhIKYRwcAL63//+J/v1\n6ye9vLxkhw4d5EMPPSTLysqalKelz4xV2XSTjv+tLVFmf7XW3+p8zHr8EmAFijmwCjhsHbDEyKaV\nWTCKKbMCZU5tNvBvYI9TvSgUt/48lHmxY8CnwGV2dTqgWAaHNCSn89bsqPlCCAOwEXhOSvlfp2Mr\ngRellD9byz8CT0opXYbFbw9R85vLj8eUIMQ2brsEBkS3mTga7ZCHH36Yw4cPs2rVqqYrX+AcLYHF\nu5X1nAA9O8CEnudnPNRzwYUUNV8IoQN+B7ZLKSe1sO0DwAwgQTZDUTVrnZkQwgP4EljqrMisZOHo\nhRJj3acBXBcLOWXwm3V++KuDEOYLnZsXsEFDo0kef/xxEhISSE9Pb3SR7oVOSTV8vLdOkUUaHGOj\napic1toAACAASURBVLQtQog/o4y69gH+KGvEugETW9iPQFl4/VxzFBk0wwHE2ukHQKqU8jUX1b4F\nJgqFAUCplNK1i85FhhBwe/c6hxCLhI/3QXFrRzLTuGiJiYnhP//5T6OecRc6NWbFkcoWRFjvAZMv\nBy8t9MP5RAUwBUUnfI5iKhwtpdzRwn4igKXAJ81t0KSZUQgxCNiMomlt4Q6eAjoBSCnftSq8RcCN\nKJOTUxozMcLFZWa0UVwNC3fUPYxRBpjaFzzPr7i+GhrnHVLCZ/thj3Vlk5uA+3tDl4vQunEhmRn/\nSJp8p7HOgzU6iLcOA6e2llDtlSBvmHh5nb0/uxy+OAATemip3DU0GmP98TpFBjAm4eJUZBqu0SKA\n/MHEB8Ktdmtw956Cn461mTgaGuc9BwocHagGRMM1MW0nj8b5iabM2oD+Tg/jmiNKtmoNDQ1H8irg\ns9/rQk3EByqjMg0NZzRl1kbc3M3RTPL5fsgtd11fQ+Nio7IWPvwNjNagGkHeMLEn6LR/LY0G0H4W\nbYS7G9zTQ3lAQXlgP9yrPMAaGhc7Zgss/R0KrB6/Hm6K56LBdXxojYscTZm1IXpPmHJFnTdjYRV8\n+rvyIGtoXMyszoB0uzC6d3Q/vwN1a7Q9mjJrYyINyoNq41ARrDzcdvJoaLQ1KTmOaZOGxcHlrjMT\naWgAmjI7L+gZBkl1gcf5ORN2ZredPBoabcWJUvjSLmH2ZR0gqbPr+hoaNjRldp4wLF6JMWfjyzQ4\nVtp28mho/NGUGuGjvXUpXcL1itVCC1Wl0RwuOGUmpWRn+c9YZPuaWHITSoy5SINSNkvlwS5pOM2R\nhka7otas/N5PW3P++eqU+WRvLVSVRjO5oJSZ0VLNmznzmJv5d5YV/qetxWl1vHSKx5avh1Iur1Ee\n8Fpz4+00NC5kpIQVaZB5Wim7CbinJ4T4tK1c54qC2lNNV9JoMReUMltdvIJ1pd8C8Gn+O+wu39rG\nErU+wT7KWhqbaeVkGSxPVR54DY32yKYTsDu3rjy6G3QNbjt5ziUnjEd48MhY/p33KmZpamtx2hUX\nlDK7OfgOevr2AUAieTl7Fqdq25+nRJcgxygHv+bBhuNtJ4+GxrkirRBW2Xnv9ouCge00VFWFuYxn\nTj5KlaWSr4uWsjDnmbYWqV1xQSkzd6HjiegXCNaFAnDaXMILJ5+k1lLTxpK1PldHQ/+ouvL3GZBa\n0HbyaGi0NqcqlIXRNqNDbIASt7Q9Bt22SAuvZP+T7BplzYGX8OaW4LvbWKr2xQWlzACCdaHMjF6A\nuzXgf3r1ft7Le6WNpWp9hIBbEpVYdKA88J/9rsSq09C40KkyKRFvqq2WtgAvmNSOQ1V9XvAeO8o3\nq+WHI+cQ760FmWxNLsifTnffXvwlfLpaXl2ygh9LVrahROcGnZsyfxZoDXlVbVZi1WkhrzQuZCxS\neTHLr1TKOmuoKj+vtpXrXLGtbOP/t3fe4VFV6R//nJn0XkgjoSNKryIoKqKIoqIoK+raddEtunZF\n/anrrmvDte669oKuBVYUFSviYgGRIlKU3pJAeu+ZOb8/ztTkBgLM5E45n+eZJ3PPvTPz5mZyv/e8\n5y38p/R51/a5aZdwYvIUEy0KTYJSzACmpV7ICUmnuraf2fcA2xs3m2iRf0iIUv/okY6/VGmDcs3Y\ndUCIJkj5ZJtaK3Ny/kDISzLPHn+yp2kHcwrvdm0PjxvL5ZnXmWhR6BK0YiaE4Pqce+gRpUpnNMsm\n/p5/C7W2GpMt8z25iSoHzcnmcu9Fc40mWFi9zzuY6aReMDLbPHv8Sb2tlr/l30yDXa0NZEbmcHvu\ng1iFTp7zB0ErZgCxljjuyptDrCUOgL0t+TxW+H8hl1ANMDwLTu7t3l66W9Ww02iChT3VKs3EycB0\nOK2fefb4E7u084/Ce8lv3glAlIjm7rzHSI7Q7bH9RVCLGUCP6D7ckHOfa3tF7VLmlb1inkF+5NS+\nMKibe3v+L/BzUcfHazSBQn41vPSTu1RVZhxcOCR0S1W9W/Yyy2qXuLavy7mbfjFHmWhR6BP0YgYw\nIekUpqdd7Np+o+RZ1tT9YKJF/sEi4MLBqmYdqJJXb6zXMzRNYLOzCp5bA3WOwKXYCLh8uPoZiqyo\n+YY3Sp51bZ+ddhGTks8w0aLwICTEDODyzOsYHDsSADt2HimYTUnLvgO8KviIiYDfjVB3tqBC9t/Z\nCN/nm2qWRmPI1nJ4YY07BD82Aq4eARlx5trlLwqadzOn8C6kI3tuaNwYrsz8s8lWhQchI2YRIpI7\nch8i1eqZUH1bSCZUJ8fA70e7ixIDLNikq4RoAotfSuGltdDsqC0aHwnXjoKeyeba5S/qbXX8bc9N\n1NlrAciIyOaO3IeIEJEmWxYehIyYAaRFZnBH3kNYUK2bNzWu54Xix0y2yj8kRDkuDB4hzR9vhc+2\n6zqOGvNZW6SSop1rZMnR8IfRodstWkrJE3vvY3fzdgAiRRR35c0hJSJEi0wGICElZgBD4kZ5Tes/\nrpjHV1Ufm2iR/4iLhN+NhL4p7rEvd6hO1VrQNGaxcq93LmRajBKyzHhz7fIn88pe5buaxa7tP2bf\nyRGxg/bzCo2vCTkxAzgn7bdMSJzs2n5m7wPsCMGEalBraFeNgCPT3WNLd8N7m3Ritabr+T5freE6\nv3qZcUrI0kK0nQvAytrveL3kGdf2makzmZwyzUSLwpOQFDMhBH/OuYe8qN4ANMlG/p5/a0gmVANE\nWVWVkCEenaqXF6iLii30Uu40AcqSXWrt1kn3BLW2mxxjnk3+Zm/zHh4puNMV8DE4diS/y7rJZKvC\nk5AUM4A4azx35c0hRqhbwsKWPTxeeC8yRP1vERa4eAiM8qimsHqfCt1v1YKm8SNSwmfbYJFHVZqe\nSXDNKLW2G6o02hv4W/7N1NnVTXJ6RCaz8x7RAR8mcUAxE0K8LIQoFkKs72D/RCFElRDiJ8fjHt+b\neWj0jO7LDd3vdW0vr/2a+WWvmWiRf7FaVNmrcbnusfUlaiG+WXer1vgBKeHDLfDlTvdYvxS1lhsX\nwtd0KSVP7v0LO5uUgkeISO7Me5TUiPQDvFLjLzozM3sVOO0Ax3wjpRzheNx/+Gb5juOTTuXstItc\n26+XPMPauhUmWuRfLALOPRJO6Oke21Smqi806sa2Gh9il/DfX+GbPe6xo9LVGm5MiCZEO3mvfC5L\nqz93bf8hezZHxQ410SLNAcVMSrkUKO8CW/zGlZl/ZnDsCEAlVD9cMJvSltCtAyUEnNkfJvdxj22v\nhOfX6PYxGt9gs8PbG+EHj0bvQzPgsmEQaTXPrq5gTd0PvFr8lGt7asoMpqScY6JFGvDdmtl4IcRa\nIcQnQojBHR0khJglhFgphFhZUlLio48+MCqh+mFSrMoFUGWr4MGC22iRoXtlF0LVcjyjv3tsTzX8\nezXUNJlnlyb4abXD3PWwxqPAzqhs+O2Q0G2u6WRfcwEPF9yBHbUQPTB2OLOybzXZKg34RsxWA72k\nlMOBp4H3OzpQSvm8lHKMlHJMRkZGR4f5hbTIDGZ7JFT/2rCOF4v+0aU2mMHEXqoVvZO9tfDsaqhs\nNM8mTfDSbINX1sIGj3vRcblqrdYa4kLWaG/ggfxbqLFVAarr/Z25jxCpAz4CgsP++kkpq6WUtY7n\ni4BIIUS3A7zMFIbEjeaKzOtd2x9VvMOSqkUmWtQ1HJunLjbOAuUl9fCvVVDWYKpZmiCjsVWtvW72\nWHQ4sadaow3V6vdOpJQ8vfdvbG9SuQcRRDA791HSIrv2plzTMYctZkKIbCGEcDwf63jPsv2/yjym\np13McYknu7af3vs3djaGfqfLMTlw8VCwOi46FY1K0IrqzLVLExzUt6g11+2V7rHJfZQbW4S4kAEs\nrHiLr6s/cW1fm30bg+KGm2iRpi2dCc1/C1gGHCmEyBdCXCWEuFYIca3jkBnAeiHEWuAp4AIZwMlc\nQghuyLnXO6G64FbqbbXmGtYFDMtUC/TOdY3qJnh2FRSEZi65xkfUNCnX9J5q99iZ/dWabDgI2c91\nK3mx6HHX9pSU6ZyWcp6JFmmMEGbpzpgxY+TKlStN+WyAXU3buHHHJTRJtXh0bOIk7sx9FBEG/51b\ny+EVj9yzWEdJrF4hWs1cc+hUNqoZWUm92haoNdjxeaaa1WUUt+zlhh0XU2WrAODImCE83OtFIi3m\nZYMLIVZJKceYZkCAEuJLth3TK7off85xJ1R/X/MV75W/bqJFXUf/NJg10p0L1NCqLljbKsy1SxNY\nlDrWVj2FbOag8BGyJnsjD+Tf4hKyFGs6d+bNMVXINB0TtmIGcGLyFKalXujafrX4adbW/WiiRV1H\nr2TVQibeEYjVbIMXf1I9qDSaojrlWqxwRL1ahVpzHZ1jrl1dhZSSf+57kK2NvwBgJYLZeQ/TLTLT\nZMs0HRHWYgZwZdYNDIxVC7l27DxYcBt7m8OjbXNuoioEmxSttlvt8NrP8GOhbiETzmyvUGup1Y58\nxAiLKmQ9LEyu4zZp499Fj7C46kPX2KysmxkSN8pEqzQHIuzFLNKRUO3sUF1jq+Kv+TeGRUAIQFa8\natGR6qhsbpPw7i/w+jqoDb0m3Zr90GJTdRb/vRrqHPUEoq1w9Qg4KiCTbXxPo72Bv+ffykcV77jG\nTkk+izNSzzfRKk1nCHsxA+gWmcndPeYQKZQvfFfTNh4tvBubDI/qvOmxjuaJce6x9SUwZzmsKzbP\nLk3XsacanliheuE5J+WxEapgcL9UU03rMipby5m96xqW137tGjs+cTJ/yr4rLALDgh0tZg6Oih3G\n9Tl3u7ZX1C5lbsm/TLSoa0mJgeuP9q64X9eiZmhvbdA1HUOVVjt8th2eWQnF9e7xAWlw0zHhE+Fa\n0LSLm3dezuZGd3OQ89Iu5bbcB3XAR5AQ4rWtD45JyWeys3Er/3VENc4re4Ve0f04KXmqyZZ1DdER\ncN5RqsnnvF+gyrFmsnofbK2A3wxUVdE1ocG+WlUs2DPPMMqqcsjG5YZHDhnAxvqfuD//RleZKgsW\nrsm6lTPTZppsmeZg0DOzNlyWeR1HJ0xwbT+59342NRi2cgtZjkyHm4/xbvRZ3aRKGf33V91KJtix\nS9UV+okV3kLWJ0XNxsbnhY+QfVv9JXfuvtYlZNEihrvyHtNCFoSEbdL0/qi31XLzzsvZ3bwdUAVF\nH+/9RliG5a4rVgJW5+FmTItR+UZ9w2QtJZQoqYd3NsKuKvdYhAVO6wfH9wj9GotOpJS8X/4mLxU/\njnSsEiZbU7m3x5McGTvEZOv2j06aNkaLWQfsbd7DjTsvdd2xHREziId7vUi0JcZky7qe2mYlaOs9\nKqULYEIPOL1f6PevCgXsEpblw8dbocXuHs9LhAsGq6jWcMEmbbxQ9BgfVrztGsuN6sVfejxNTlTg\nZ4RrMTNGuxk7ICeqB7NzH3a1jNnSuJEn9v6FAC476TcSouDSoXDRYBXhBiri7Zs98PgK2F2135dr\nTKaiAV5YA+9vdguZxdHv7k9jwkvInKH3nkI2KHYEc3q9EhRCpukYLWb7YXj8WK7JcjfeW1r9Ge+W\nvWyiReYhBIzMVmtpR3oEgZTUwz9XwafbVGScJnCQUiXAP/aDCuBxkh2vIlcn9wn9HmSeGIXeT0ic\nzAM9nyUpIsU8wzQ+QUczHoAz085nV9NWFlXOB+D1kn/SM7of4xMnmmuYSSTHwFXDYUWhSrBtsikX\n1uKdsLEULhgE3RPNtlJT3QTzf/UuTyZQzVpP7Rv6HaHbUtC0i3v2XMe+Fnd1n3PTLuGKzD9jEWF2\nMkIUvWbWCVplC3fv/iPr6pW9MSKWOb1foU/MAJMtM5fyBhVM4NnjyupwX53YM7zu+gOJn4pgwa9Q\n7xF12i0WZg6G3mGSN+aJUej9rKxbOCvtApMtOzT0mpkxWsw6SXVrJTfuvNR1Z5cZmcMTvd8gOSK8\nQ/rsEr7bA4vauBl7JqmIx8wwWo8xm7pmWLAJ1rap2nJcHkztr3LIwo1vq79kTuHdtEhVmy1axHBr\n7t+D2rOixcwYfe/cSZIiUrinx+PEWtTVubhlLw/k30KLDO/SGBYBx/eEG8dCjyT3+G5HeaRv9yjB\n0/iXjSUw5wdvIUuJgWtGwjlHhp+QSSlZUPYGDxXc7hKyZGsqf+/1XFALmaZjtJgdBL2i+3Fb9wcQ\nqGScDQ1reHbfQ2EZ4diWzHj442iVr2R15Cq12OGDzfD8auWS1PiehlZ4d6NqtupZGHpsdxWs0z/N\nPNvMwiZtPFf0KC8W/8OVQ9Y9qieP9X6Vo2KHmmydxl9oN+MhMK/0VV4tecq1fU3WbUwLUv+7Pyis\nUWWS9no0HoiywtE5qkxSdoJ5toUKVU2wogCWF0C1h4glRsGMgTAoTKrct6XR3sCcgrtZVrvENTYw\ndjj35D0eMhGL2s1ojI5mPARmpF/GrqatLKleBMALRY/RI7oPI+OPMdmywKB7ogr9/nIHfLVT5aQ1\n2+C7fPXom6JEbWhm+EXVHQ52CVvLYVmBihxt674dkaVcis6Gq+FGZWs59++5gU0exYKPSzyFm7vf\nH5bFDsINPTM7RJrtTdy+63euKtvxlkQe7zOX3KieJlsWWOyuUkWL99W13xcfqdxh43IhLbbrbQsW\n6lpgZaGahZUauGsTouCcATA8q+ttCxSMQu+np13ClSEYeq9nZsZoMTsMyltKuGHnxZS1qjpPeVG9\neaz3ayRYdaKVJ3YJ2ypUOaUNBjMKAQxIh/G5qiq/DulXCc+7qtQs7Odi44T0vinqnA0J8xnuxvq1\n/DX/RqptKkdEIJiVdWvIuv61mBmjxeww2dKwkdt2XUWzVP1SRscfy709nsQqwix8rJNUNamE6x8K\n3C1mPEmOhmNy1YwtObrr7TObxlbVcmd5gfeao5OYCBiTrWazWXrtka+rPuGJvX8JqdD7A6HFzBgt\nZj5gafVnPFww27U9Pe1irs66yUSLAh+bHX4tUxftTWXu7sZOLAIGd4NxedA/NfSruRfWqFnYmn2q\nqkpb8hJVa5YRWeEXZm+EXdp5o+RZ3il7yTWWbE3lnh5PhHzEohYzY3QAiA84IWkKOxu3uv6xFpS/\nQa/o/kxOmWayZYGL1QKDM9SjrEHN1FYUulvN2CWsK1GPbrFqJjKme2gFN7TYVF7YsnyVl9eWSIuq\nhzku1zuHL9xptDfwWOH/8X3NV66xvKje3NfjSXKiephomcZM9MzMR9ilnb/n3+oKCY4QkTzY83kG\nxQ032bLgodUO64vVDMWzRJaTCAsMy1QzlF5JwdtAsqRezUhXFnqXnHKSFa/WwkZlQ2wIibcvKG0p\n4v78G9nW+KtrbFT8eO7IfYj4MFmr1jMzY7SY+ZAGez237LycnU1bAUixpvF4n7lkRuaYbFnwUVSr\nRG3VPuPO1jkJ6oI/LCs4ZmvNNuVWXZbvXcHeiVWoVIXxuarjc7AKtT/Z3LCBv+bfSHmru3ry2akX\nclXWjVhF+DiZtJgZo8XMxxQ1F3LDzotdkVV9ogcwp/crxFh07Pmh0GxThXOX5UN+jfExsRGQHuvx\niFM/02JVEElXrLdJqSpwlDVCWb1ynTof5Q1Q02z8urQY5UY8ursKsdcY80315/yj8F5XoJWVCH6f\nfRunp84w2bKuR4uZMQcUMyHEy8CZQLGUsl0/cSGEAJ4EpgL1wOVSytUH+uBQFTOA9fWruHPX77Gh\nphTHJk7i9twHiRBBMIUIYPZUK/fcmn3e3ZL3h1UoUUs3eKTFHlyXbJsdKhq9RcrzuVHghhECGNhN\nuUsHpIV+cMvhIKXkrdLnebP0OddYgiWJO/MeYXj8WBMtMw8tZsZ0RsxOAGqB1zsQs6nAdSgxOwZ4\nUkp5wFIYhyxmtRXw82dwzAywBq5r4bOKBTy176+u7T7RA7gh5176xw400arQoKFFuR9X7YWius4L\nmxHJ0e3FLiXGMctq8H5UNh560WSrUO89LFOlHqToghQHpMneyBN772Np9eeusdyoXtzb48ngLk6w\n9lPI6g/Z/Q/p5VrMjDmgGkgplwoheu/nkLNRQieB5UKIFCFEjpRyr49sdNNcDx89AqW7oGwXTPkz\nRAXmVWFK6nR2NW3lg4q3ANjRtJkbd17KeemXclG3WURZwjCJykfERsKEHuohpXLhuUSn3tvVV3eA\npgZVTeqxwyDg5GCJsbpdnM6Zn6dA6hlY5ylrKeFv+TexuXGDa2xE/DHckfswidYgDe2Udvj2TVj7\nCcQkwoz7IEWvp/sKX0xtcoE9Htv5jrF2YiaEmAXMAujZ8xDurDZ+rYQMYNdaWPBXOOs2iAvMjoNX\nZ91Mt8hs5pb8i2bZhB0b88peYVnNEv6ccw+D4kaYbWLQIwQkRatHH4M6so2t7V2CTtGrbDr4mVZS\ntLHLMj0W4iJ14IYv2NrwC/fn30hZq7ufzRmpv2FW1i3B66pvbYYvn4WtP6jtxhpY8R6c+kdz7Qoh\nOhUA4piZfdSBm/Ej4CEp5beO7cXA7VLK/foQD8nNKCX8MA9Wvu8eS8qAs26H1O4H915dSGHzbp7a\n+1fW1a9yjQkEZ6VewGWZf9LBISbRdg3M+ahqVMEYh7vGpjl4vqtezGOF/0eTbATAgpVrsm7hzLSZ\nJlt2GDTWwqJ/QKE7nYC+Ryshizj4qB/tZjTGF2L2HPC1lPItx/YmYOKB3IyHFQCyfjH872UlbgAx\nCXDGLZAz4NDerwuwSzufVr7Hy8VP0GCvd41nReZyfc7djNAV9zVhjJSSd8peYm7Jv1xj8ZYEZuc+\nwsiEcSZadphUl8CHj0BFgXts2BSYcAlYDq2gphYzY3xRnnQhcKlQjAOq/LJe5smQk2HqzRDhWHdq\nrIX3H4BtP/r1Yw8Hi7AwNXUG/+o7j9Hxx7rGi1oKuGv373lq71+ps3UQe67RhDDN9ibmFN7tJWTd\nI3vwWO/XglvISnbC/Hu9hezYi+D4Sw9ZyDQd05loxreAiUA3oAi4F4gEkFL+2xGa/wxwGio0/4oD\nuRjBR6H5RVvhoznQ4KwFJOCEy2DYqYf3vn5GSslXVR/zfNEcau3uOkbpERn8MftOjkk80UTrNJqu\no7y1lAfyb+bXhnWusWFxY7gz71ESrYG5Ft4pdq+DT56AFkfPHksEnHItDDh2/6/rBHpmZkzwJ01X\nFcHCh9RPJ6POgvEzIcD7GJW3lvLvfQ/zXc1ir/GJSaczK+sWkiNSTbJMo/E/2xs3c/+eGyhp3eca\nm5IynT9k3xG8gR4Av34DXz0PdkfiYVQcTL0J8gb55O21mBkT/GIGamb20Rw1U3My4Fg4+RqwBv4/\nxbfVX/LsvoeotJW7xpKtqVybfRvHJ56K0CFymhBjWc3XzCm4i0apZi4WLFyddRPTUi8M3u+7lLDq\nA1j+rnssIU0FqKX7rgCyFjNjQkPMAFqa4LOnYadH8ZHcQTD1RoiO993n+Inq1kpeKH6Mr6o+9hof\nlzCRP2TPJj0ywyTLNBrfIaVkftlrvFbyNNLR+CfOksDtuQ8yJuE4k607DOw2WPqqCk5zkt5DpQ4l\npPv0o7SYGRM6YgbGX6i0HjDN918of/Fj7bc8s/cBSlvdbtN4SwJXZ93E5OSzg/euVRP2tNibeXrf\nAyyu+tA1lh2Zx709nqBndF8TLTtMWhrhs2e8b6TzBsPpN0J0nM8/TouZMaElZqCm+qs/hGVvu8f8\nMNX3J/W2Wl4pfopFlfO9xkfGj+O67LvJigrcnDqNxojilkIeyr+DTY3rXWODY0dyV96c4F4bbqiG\njx6Fom3usQHHOZY4/FNuT4uZMaEnZk4MF2FvVHdMQcLPdSt5au/97G3Jd43FiFguz7yOM1LPxxLg\nAS4aDcCKmqU8VniPV+Tu5OSz+WPOnUQGc6BH5T748OE2wWfTYPz5fg0+02JmTOiKGcCedbDIMzzW\nCqf83ifhsV1Fo72BN0qe5f3yN11rDACDY0dwfc495EX3Ns84jWY/tMoWXi/+J/8tf901ZiWCKzKv\n55y03wa3y9zEtCAtZsaEtpiBquX44SNQ59ER8dgLYeSZQVVI79eGn3mi8C/sad7hGosggjPTZnJB\nt98Fb/FVTUhS2lLEwwWz2djwk2usW0QWd+Q+xMBg776+Y5UKNmt1NKmzRsKUP6kSVV2AFjNjQl/M\nAGpKlTug3CMTf+ipQZeJ32Jv5u3SF5lX9qqrVxpAkjWF33a7htNTzwurjruawGRV7ffMKbzb1aAW\nYEz8cdzU/f7gXh+DgCilp8XMmPAQM/B5sU8z2da4ief2PcwGj7tegB5Rfbg666bgDnHWBC02aeM/\nJc/xTtlLLpe4BQuXZPyRGemXBfcab4dFzu+A1K5t46LFzJjwETMAWwt88SxsXe4eyx4AZ9wMsYld\na8thIqXku5rFvFz8BEUthV77Rscfy9VZNwV3uLMmqChvKeGRwrtYV+/+n06L6MbtuQ8yJG60iZb5\nAFsrfPUCbPrGPZbZF8681ZT2U1rMjAkvMQPVIO+7/8BPi9xjKTkw7XZIyux6ew6TZnsTH5S/xTtl\nL9Fgr3ONW7AyNfU8Lup2TfC7djQBzdq6FTxScBeVtjLX2Ij4Y7i1+wOkRKSZaJkPaK6HT55UwWRO\neo2AKdeb1hhYi5kx4SdmTn76BL59A5wRgnHJ6k4rMzhnMxWtZcwt+RdfVH6AHbtrPN6SwIXdZnFm\n2szgDoPWBBw2aeOd0pf4T+lzLreiQHBRt2uY2e0qrCLIm7/VVrg72zsZdBJMvFJFRpuEFjNjwlfM\nQHV9/eJfyv0IEBmtQvf7jTXXrsNgR+NmXij6B2vrV3iNd4/swZVZNzIu4cTgDonWBASVreU8WngX\nP9X94BpLsaZxa+4DodGbr3i7qnpfU+oeGzsDjp5uehS0FjNjwlvMQAWEfPwYNLlddAybAsddFBRF\nio2QUvJD7VJeKn6cwubdXvuGxY3hd1m30DcmcBuZagKb9fWreLhgNuWt7gv90Lgx3Nb9AdKCSYSS\nLAAAGepJREFUvYaolLDuc/j2TbA7IoaFBU66GgZNNNU0J1rMjNFiBipk/6NHVFdYJxl94LTrITnL\nPLsOkxbZwscV7/Kfkueps7sbfwoEp6acwyUZfyA1IjhqVmrMxy7tzC97lbkl/3K5sgWCmelXcVHG\nrOBPC2mqg8XPw3aPJr+Rseo60CtwcuO0mBmjxcyJ0Rc5KhYmzYL+we02qW6t5M3S51hUMR87Ntd4\nrCWe89Ov5Jy0i4iyRJtooSbQqWqt4B+F97Cy7jvXWJI1hVu6/43RCcFTUadDirbBZ0+1uaHtrQI9\nUrJNM8sILWbGaDHzREr4+TP47k13TUeAoZPhuN8GXT5aW3Y3beeloidYWfet13hWZHeuyPwzExJP\n0etpmnZsrF/LwwV3eHVyGBw7gttyH6RbZPB6LgDH//ynKsLZ63/+VJjw24BcatBiZowWMyOC6C7t\nUFhV+z0vFv2D3c3bvcYHx47gd1m3cESsbzriaoIbKSXvlc/lteJnvCrOnJd+GZdm/CG4u0GDKqTw\n1fOw3eM6FATeGC1mxmgx64iO/OeTfgdHjDPPLh9hk618WrmAN0qe9So7BDA+8STOTbuUQcFeQ09z\nyNTYqnm88F5+qP2fayzBksTN3e9nbOIJJlrmI4q2wqdPQ03wrZNrMTNGi9n+MIpsAhhyCky4OOjd\njgC1threLn2RD8vfotXj7hvgqNihnJt2KeMSJwZ/zpCmU5S2FPFtzZd8UP4filv2usaPjBnCHXkP\nkRkZ5L30pIS1n8L3bdyKQRTBrMXMGC1mnaF4O3z6FFQXu8e69VJ3cSldW5fNXxQ27+bl4idZVrOk\n3b7syDymp/2WU1KmEWOJNcE6jT8paynhu5rFfFP9uVeVeydnp13EFZl/Dv6k+8ZaWPycqnrvJCoO\nTp4VVLmlWsyM0WLWWZrqYckLKtHaSWQsTLoajhhvnl0+ZnfTdhaUvcFX1R/TKlu89iVak5maMoMz\n02aSFtHNJAs1vqCitYzvqhfzTc3nbKhf49Urz0m8JYEbcu7j2KRJJljoY/ZtVevgnknQmX3VDWmQ\nlbHTYmaMFrODQUpY/yV8M7eN2/FkmHBJSLgdnZS3lvJR+TssqpxPja3Ka1+EiOSkpKmcm36JLmYc\nRFS1VvB9zWKWVn/O+vrVXmXPnFiwMjz+aI5PnMyxSScHf588KVUd1mV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+uLm5odPpMBgM6n6TyURubi6RkZFERUUBEBAQQE1NDdnZ2S6VmclkIicnh/Dw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gq6knHf1ri7jVMC5z46ubFSV2oFAJddW34aU15zR+fn6Eh4eTk5NTZ50uXbrw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\nkglOc5M80CwdSrS39puImX96j5iB/w/UgIFwqv0fKGFvdjVs563Sf/B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RN6N/RMyEjlG+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\nEEIeETNBEAQh5Pl/WXhCtKyEW6cAAAAASUVORK5CYII=\n", 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XS+inQBSQ7HXOGGCzlHIfgBDiUeBtlGVWQ52fnxFCTPQ6Lx6YDbwAXA8cFEK0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\nhQ52bSe3dgMrqhazvHIRG2vWeGTsaE5aVD8uTL2GGT1OlHyKgcJhh29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GcnJyiIyMJCoqCgB/f3+qq6vJyspyqsyMRiPZ2dmEh4fbrZMLDg5WTbnz5s0j\nMTGRDz/8kI8++uislHKx5f/ynBDiaYuisvKylPI/oMyvATnAKOBtKWWKEGIfivLabKnjAYxBUY4I\nIfxQFN7TUsonLW1uEkJ4A48LId6SUlqDcgYDQ6SUe62dCyGeB8qB0VLKCsu+syiK1FpHoCjbj6SU\n/7DZbwDeEEI8J6XMB5BSFgkhTgJ9qUeZaWbGOmjvZ+/duP4iMTc2NNKQUvLKK69w6aWX4uXlhZub\nGxMnTsRgMKhren744QeCgoLsFJkt27dvp6Kiol5Ps6YwcuTIWvtSUlIYN24c4eHhuLq64ubmxuHD\nh0lNTQWUH+5ff/2VKVOmODXLXXfddeh0OtV8ZDKZKCwsJCQkRK3j7e3NJZdc0uBWn6NEY/H39ycq\nKgp/f3/8/f3p2LEjgYGBZGVlNXqEWB/V1dUNjgqbQnh4OGFhYfj6+hIUFES3bt1wc3MjKyuryW2V\nl5ej1+vtFIu7uzs+Pj61Rs9NHdlbzYu23oue53gbKioqMJvNdZqRKysrnXqBlpWVYTabnSo7k8nE\nnj177JZWWPgc5Td8gMP+76wfLArhDBDtcN6NNibK4YAvYA0RMwDQA6uEEDrrBvwAhDu0lWGryCxc\nBWyyKjIL3zjU6QZ0AFbW0YcnEO9QPw9lhOcUTZk5IaljTVZqq7nR3GoWGzSdyspK8vPz633Lf+WV\nV5g1axbjxo3j66+/ZufOneqoyGqSys/Pt3tTdsQ6f1BfnabgKG9JSQnXX389p06d4qWXXuKnn35i\n165dXH755aqMhYWFSCnrlUEIgV6vJz8/HyklBQUFSCkJCqox27u4uKgjtfo26+jFOtJwjDRvLTdV\nmQQGBmI0Gp3GR2wKUspaoyxXV1dMJlMtZWkymXBxcWmSk4mrqyv+/v52C6IbS1VVVZ33RqfT1RrN\nNvUe2poXXQQEeqLez6a+hFiVleN51rKzDAPWa3DWX15eHtXV1XU9m1YziuNcUpFDuQpFQVj5HAgB\nrrWUbwG2Symtq8ytb2wHgGqbzWqWtLVT1WXKiQDsQjxJKSsB2zcPax/rHfo4VkcfAAaHa6iFZmZ0\ngtXc+NpFYm7cvHkzRqORAQMcX/JqWLVqFRMmTOCZZ55R9x08aB9vOjg4uN63b+vbZ1ZWlt0oxxZP\nT89aP9DO1vQ4jqy2b9/O6dOn2bRpk926KtuJ9MDAQFxcXBocJfj4+GAwGCgpKSE/P5+AgAC7H8um\nmhk9PDzQZ31BAAAgAElEQVQQQlBZWWk3d2RVsnWZtP4qdDpdrR9b61yZwWCwmzerrKw8J3f5c3VO\ncXd3p6Ki9sJPo9FYS3k1pQ+TVJJuWgnwUJ77s2fP4ubm1uT/h1UZOY5yrUrOmXemtW51dXWdCi0k\nJAQ3NzfOnDnjeMiq3RqewLRBSpkuhEgGbhFC/AyMRpmzsmJtbxR1KyvbL31dr/jZgN1krhDCE7B1\nbbX2cTfwWx1tHHMoB9DAdWojs3qI9oNrLgJzY1FREbNnz6ZLly71LlKtqKio9YDbphYBxTxXUFDA\n2rVr62xjwIABeHl51RudITo6mkOHDtnt++6775zUri0j2CuGbdu2cfz4cbWs1+vp168fH330Ub0m\nOp1Oh5+fH5mZmZSWltZSvk01M1q9BR2dJwoKCvDx8WnyqKKwsBCdTteoaPMN4eHhgcFgsNvn4+OD\nq6urnbwmk4mioqKmm/PMZoqKitT5xqag1+spKyuzk6+qqorS0tImu/7bUmkzqLMujj579iyFhYV1\nOtbYIoTAbLZPgmidS3N88SosLMTT09PpyEuv1+Pi4uLU69HV1ZXevXvbBXu2cDNgRnGQaCorgHGW\nzQuwbXw7UAFESSmT69hKGmh7F5AkhLB1+HCcdzgMZACxTvpQb4bF87IDkFpfp9rIrAGGdIQDuZBt\n8W5cmQL3tmLvRqPRyI4dOwDFJLd7927eeustysvL2bhxY71re5KSkliyZAn9+vWjc+fOLF++3C73\nlLXO0KFDuf3225k3bx5XXnklWVlZbN26lXfeeYeAgACeeOIJ5s6dS1VVFSNGjMBgMLBu3Trmz59P\nu3btGDduHB988AEPPvggI0eOZPPmzepC74bo378/Pj4+3HXXXTz66KOcPn2aBQsW1FoT9e9//5sh\nQ4YwfPhw7r77bvR6Pdu3b6dPnz6MGjVKrRcSEsLRo0dxd3fHz8/Prg1XV1enzgzOiIyM5PDhw5w8\neZLAwECKi4spLi6ma9euah2DwcD+/fuJjY1VFWhaWhp6vR5vb2+klMTHxzNnzhwmTJhgNxqx/uhb\nRwMlJSWqI0N9svr4+NRyt3dxcSEiIoKsrCx18bLVQci67gwUM9i2bdsYM2aM2m9aWhrBwcF4eHio\njhHV1dXnZF4ODg4mOzubI0eOEBUVpXoz6nQ6p96ckyZN4u9//7vTNs0SjNXVVFeUIgQI12pOnCkm\nPz8fPz8/IiLqnZ7By8tL/d/pdDo8PDzQ6XSEh4eTlZWFEAJvb2+KioooLi62W4juiE6nIzIykoyM\nDKSU+Pv7I6UkPz+fjIwM2rVrx5NPPsnQoUOtc81+QohZKA4b7zk4fzSWlSgOGM8DWy2pvgDV4WIB\n8KoQIgbYijLw6QZcI6Uc10DbrwDTgTVCiJdRzI6PoTiFmC19mIUQDwMfWxxONqCYQzsBY4EJUkrr\n0CEOZVT3S32dasqsARzNjceK4JdT8LcODZ97IVJcXMyAAQMQQuDn50eXLl2YNGkS999/f4MP8Lx5\n88jNzVWTJY4fP54lS5ao67tAeWP98ssveeKJJ3jllVfIzc0lKiqK22+/Xa0zZ84cgoKCePXVV3nn\nnXcIDAxk8ODBqult5MiRPPvss7z55pu8//77jBkzhldffZUxY8Y0eH3h4eGsWrWKWbNmMWbMGLp2\n7crbb7/N4sWL7eoNHjyYTZs28cQTTzBp0iTc3d3p1auXGhbKSkBAAEIIgoODz9lMZouvry+dO3cm\nMzOT3NxcPDw86NSpU4MjHU9PT/Lz81WHD+ucn+M8ypkzZ+ze8K2R8oODg+t1Vw8MDCQ7O9vOexNQ\nvxNZWVkYjUb0er3qzOEMq6dfVlYW1dXVuLi4oNfriYuLa7Lyt7bXrVs3Tp06pY6wrffxXJxWKo2K\nbayypIDKkgKEEJzV6fDy8iI2NpagoKAG/9eRkZEYDAaOHj2KyWRSXzysXoy5ubnqS0THjh3t5lqd\ntafT6cjJySE3NxedTofZbFafieuvv54VK1ZYo7Z0AWYCL6J4HTYZKeUpIcQ2lHCFT9ZxfLEQIhN4\nEHgYZQ1ZKjYeifW0nSGEGAm8CvwXSAHuBDYBtkHpP7d4Of7LctwEHAXWoig2K8Ms++syR6po4awa\nycZ0+P648tnNBR7sB6FNt5hotCJSUlKIioriyJEjxMfHn5ewSudKbGws77//fpNiFzbEgQMHCA4O\nbvClpq65quPHj9OxY8dm94o8F+obmZmlsobU6vThpYNgrwsze3RbCmclhBgE/ARcK6WsOz2583O3\nA+uklE/XV0+bM2skQzpChMU8X22GVQfbtnfjxU5mZiaVlZWcPn0af3//C0qROWIwGJg5cyZRUVFE\nRUUxc+ZMdX4pISGBL774AoBffvkFIQTr1q0D4Pvvv+eKK65Q2/nhhx+4+uqrCQwMZOjQoZw4cUI9\nJoTgjTfeoGvXrnYmUUf+85//EBUVRWRkpN2axPpkXLZsGYMGDbJrRwihmrCnTp3K9OnTGTlyJL6+\nvvTr14/09HS1rtXZx9/fnxkzZtQ7D1rs4L0Y4HlhKrLWjhBikRDiVkskkHtQ5uj2AY1Lg1DTTj+g\nO/B6Q3U1M2Mj0bnALZfYmBuLW7e5UaN+3n33Xfr3709MTIwSR/H12xs+qbmY8WmTqj/zzDPs2LGD\nvXv3IoRgzJgxPP300zz11FMkJCSwZcsWbrzxRn788Uc6derE1q1bGTlyJD/++CMJCQkAfP3117z6\n6qssW7aM3r178/LLL3PbbbfZZYD+6quv+PXXX+uNf7l582aOHDnC0aNHufbaa7niiisYMmRIvTI2\nhhUrVrBhwwauvPJKpkyZwty5c1mxYgV5eXmMHz+epUuXMmbMGF5//XXefvtt7rjjjlptVDosjrZ6\nL2qcFzxQ5uPCgRKUtW8PSSnN9Z5VmyBgipTScblBLbR/ZROI9oNrY2vKG9Ihtw16N2ooGQZiYmK4\n5JJLWtRlvjEsX76cefPmERYWRmhoKPPnz+fjjz8GlJGZNSfY1q1bmTNnjlq2VWZvv/02c+bMYfDg\nwej1ev71r3+xd+9eu9GZda6zPmU2f/589Ho9PXv2ZNq0aXz22WcNytgYxo0bR9++fdHpdEycOJG9\ne5V1uuvXr6dHjx5MmDABNzc3Zs6cWaeZ1Cyh0CaUo5eT1C4azYOUcqaUsr2U0l1KGSylvM3WyaQJ\n7WyQUjouuK4TTZk1ketiIdLG3LhSMzdqtDCZmZnExNSsIYmJiVEdPwYMGEBqaio5OTns3buXyZMn\nc+rUKfLy8ti5cyeDBw8GlPQvDzzwAAEBAQQEBBAUFISUkoyMDLVd21BLzrCtYytHfTI2BlsF5e3t\nrUb+sKansSKEqFNOzbzY9tHMjE3E6t24ZJeixI4Xw8+nYLBmbmzbNNH091cSFRXFiRMn6NGjBwAn\nT55Uveq8vb3p3bs3r776KvHx8bi7u3P11Vfz0ksv0blzZ9X1v3379sydO5eJEyc67acx3pynTp1S\nF6vbylGfjHq93i4ySFMSxUZGRtplMZBS1spqoJkXLw60f+k50M5XGaFZ0cyNGi3JbbfdxtNPP01u\nbi55eXksXLiQSZMmqccTEhJ4/fXXVZNiYmKiXRng3nvv5bnnnlMj7RcXF9e1SLdBnnrqKcrLyzlw\n4ABLly7llltuaVDGyy+/nAMHDrB3714qKytZsGBBo/sbOXIkBw4c4L///S9Go5ElS5bYKUPNvHjx\noCmzc+Ta2Bpzo9EMn2vmRo0W4vHHH6dPnz5cdtll9OzZkyuvvFJdCwiKMispKVFNio5lUOakZs+e\nza233oqfnx/x8fFs2LChybIkJCTQpUsXrrvuOmbNmsX111/foIzdunVj3rx5DBkyhK5du9bybKyP\nkJAQVq1axWOPPUZwcDBHjhxh4MCB6vHiytqxFzXzYttEW2f2J8goqTE3AozqCgmaubHN4Gydj0br\noNJobzEJ8gT9n4/89ZfRltaZ/RVoI7M/gaO5cWM6nClrMXE0NDQsaObFiw/NAeRPcl2sErsxs1Qx\nZ6xMgX/0vjBjNy5YsIAnn1Qi1wgh8Pf3p0uXLlx//fWNCmd1sVNZWUlOTg6lpaVUVFTg6+tLXFxc\ng+dVVFRw6tQpKioqMBqNuLm54efnR1RUlF2QYLPZTHZ2Nvn5+VRVVeHu7k5QUBCRkZENpluRUnLw\n4EHCw8OdZiOw9pGRkUFZWRllZWVIKenTp+GXfLPZzLFjxygvL6eqqgpXV1e8vb1p165drRBVBQUF\nZGdnU1lZiaurK35+frRr167BgMhFRUWcPn0ag8GAm5sbl112WYNyOaM+86I17mFeXp6ag8zNzQ1f\nX19CQ0PrDV5cVlZGcXGx6ryicX6wZKs+DFwmpTzamHO0kdmfxNXi3WhVXieK4aeT9Z/Tkvj7+7N9\n+3a2bdvGihUrGD9+PB9//DE9e/Zk9+7dLS3eBU1lZSXFxcV4eno2KSKIyWTCw8OD6OhounXrRlRU\nFGfPniUtLc0uWkVGRgbZ2dmEhobStWtXQkNDyc7O5vTphuPIFhYWYjKZGowBaDabycvLw8XF5Zwi\nzkdERNC1a1diYmIwm82kpqbaRbMvKiri6NGj+Pj40KVLF6KjoykpKal1rY5IKTl27BheXl5069aN\nLl26NFk2K5VGKLXJgxngqTyn1n6OHj3KiRMn8Pb2pmPHjnTr1k2NtXjo0KF65SwrK2vSkgKNc0NK\nmYESB3JeQ3WtaCOzZiDKF4bEwneWDDwbj8IlIRDW9Jiq5x2dTkf//v3V8tChQ7nvvvsYPHgwt956\nK4cOHao3cn5LUFFRUe9C3b8Kf39/dbSQnp5eKzGkM3x8fOwUh6+vL+7u7qSmpqpZlEEZ0YSGhqoj\nZD8/P6qrq8nPz1eikNTDmTNnCA4ObnAEp9PpuOKKKxBCcObMGUpKGsrmoeDi4kLnzp3t9vn5+bF3\n714KCwtVmfPz8/H29raT19XVlbS0NCorK53+H6urqzGZTAQHB9vlemsqZgkFFWaQAoRQzIs2v3Jn\nzpyhsLCQbt262WVBsI7KcnNz62hVoyGEEF4OmaWbg6XA90KIh21TwjhDG5k1E9fGKnNoUGNubC3e\njQEBASxevJi0tDQ2bdrktF5RURF///vfiYqKwtPTkw4dOnDXXXfZ1dm3bx+jR48mICAAHx8f+vbt\na9fmsWPHGDt2LH5+fvj6+jJ69OhaaWSEELz00kvMnDmT0NBQevbsqR77+uuv6dOnD56enkRERPDo\no486TUffHNi+pTdH1Hwr1hcG2/allLVeJBrzYlFZWUlpaSmBgYGN6ru5rsOabfrPXkNeXh779u0D\nlNQxycnJ6ujHZDJx8uRJfv/9d3bv3s3Bgwdrpao5fPgw6enp5Obmsn//fjIP78FsrK7TezEnJ4fA\nwMBa6XyshIaGOr0/eXl5nDypmF2Sk5NJTk62S8569uxZUlJS2L17txo9xVl2aVvKy8s5cuQIv/32\nG3v27CElJcXuGh2fGaCLEMJu6CqEkEKIB4QQzwohcoUQZ4QQbwghPCzHO1rqjHQ4z1UIkS2EeNpm\nX7wQYp0QosSyrRJCRNgcT7S0NVQI8Y0QohRL7EQhRKAQYoUQokwIkSmEmC2EeEEIcdyh3w6WegVC\niHIhxLdCCEeb/S8oCTlvbfAmoo3Mmg1XF7j5EsW70SQVc+PWk5AY0/C5FwKJiYnodDp27NjBsGHD\n6qzz0EMPsW3bNl5++WUiIiI4deoUW7duVY8fOnSIgQMHEhcXx9tvv01wcDDJycnqIlaDwcB1112H\nm5sb7733Hjqdjvnz55OQkMD+/fvtTGTPP/88gwcP5uOPP1aTIK5cuZLbbruNe+65h2effZb09HTm\nzJmD2WxWg9pKKRv1A9KYyO7WtCvNlf7FmrqlqqqKjIwM9Hq93XxTSEgIubm5+Pn54eXlRXl5Obm5\nuXa5w+qipKQEFxeXv2T0alVcRqNRXc9l+38LCQkhPT2dvLw8AgMDqa6uJiMjA19fX6fy+fv707lz\nZ9LT04mOjsbHx0edXztx4gRFRUW0a9cOT09PcnNzSUtLo1u3bnYjuNLSUioqDXgHt0O4uCJcXQm0\nMS+CktCzqqrqnHKqWeUMDw8nJydHXRhuVdQVFRUcOXIEPz8/OnfurP6PDQYD3bp1c9pmRUUFhw4d\nwtPTk5iYGHQ6HaWlpeTn5+Pp6VnnMzNhwgQP4EchRE8ppW2m14eBH4BJwGXAc8AJYLGU8pgQYidK\nQs91NuckoMRPXAFgUZK/AMmWdnQoedPWCCH6Snsb7Acoo6dXUFLEACwDBgEPoGScfhAlD5r6UAoh\ngoCfgXzgXpQ8Z48B/xNCdLOO8KSUUgixAxgCvOH0JlrQlFkzEuUL13WE7yzTld8ehUsvUHOjI56e\nnoSEhKjJF+ti586dTJ8+XV0IC9gtzn3yySfx9/fnp59+Un+4kpKS1ONLly7l5MmTpKamqskK+/Xr\nR6dOnXjnnXeYM2eOWjcyMpLPP69JnSSl5JFHHmHy5Mm8+eab6n4PDw+mT5/OnDlzCA4O5scff+Sa\na65p8HqPHTtGbGxsvXWio6M5ffp0naan3NxcTCZTrWzD9ZGTk0NlpfLMu7u7ExYWViujdmlpKT//\n/LNatpokHUcjtlgdRhzbaoiSkhIKCgpISUlp9DnFxcUUFSkxX11cXAgLC+PoUfv5eSklu3fvVhWf\nh4cHYWFh9fZjNBrVuTzrd6e6uprMzEyCg4PVbNdSSoqKiti9e7eayy07O5uqqip8Q9rBGeX76+YC\nZQ7+JgaDQe0jLy/PTl5b6ntxsd4zxygjubm5VFVV4eXlRVaWEoLQZDJx9OhRysvLncb3zM3NxWAw\n0K5dO7tnz9PTk+joaD744INazwxKXrFLgHtQFJaV41LKqZbP3wohBgLjAWsyvxXAfCGEh5TSOtF5\nC3BASvmHpTwfRQkNl1JWWe7HPuAQMAJ7RbhKSvmEzX2LR8kofbOUcpVl3/fAKaDU5rwHAT1whVUZ\nCyF+AY6j5DWzVVy/A/bmH2dY3xb/6q13796yLWI0Sfnyr1LO+p+yLdkppcnc0lIpzJ8/XwYHBzs9\nHh4eLu+9916nxydOnCjbt28v33jjDXn48OFax8PCwuRDDz3k9Pxp06bJq666qtb+xMREOWLECLUM\nyLlz59rVOXTokATk+vXrZXV1tbodO3ZMAnLLli1SSinPnj0rd+3a1eBmMBicymk0Gu36MJtr/wNv\nvPFGmZCQ4LSNukhNTZU7duyQH3/8sYyLi5NXXnmlrKioUI8vWrRIBgYGytdee03++OOPcsmSJdLf\n318+8cQT9bY7evRoOWzYMLt9JpPJ7hpMJlOt81577TWp/AQ0nqysLLlr1y75zTffyGHDhsng4GB5\n4MAB9fgPP/wgfXx85KOPPio3b94sV6xYIbt37y4TExOl0Wh02q71/7hmzRp134cffigBWVZWZld3\nwYIF0tvbWy0nJCTI7lcOVJ+5eVukLK6s3ceOHTskIL/99lu7/dOnT5co+TpryeCIs3vWsWNH+cgj\nj9jtMxqNUqfTycWLFztt71yeGZRR02aUHF9WZSyBx6XNbyzwLHDaptwOJdPzGEtZB+QCT9jUyQL+\nbTlmu6UD8y11Ei39DXHob6plv6fD/hUoitZa3m7Z59jHD8BSh3NnANVY1kTXt2lzZs2M1bvR1fJy\nd/KsYm680KmsrCQ/P79W5mJbXn/9dcaOHcvChQuJi4uja9eurFixQj2en59frwknKyurzvbDw8PV\nN2/bfbZY36RHjBiBm5ubulmzJ1vflH18fLjiiisa3OpzE7eadaybNcr8n6Vr167069ePSZMm8e23\n3/Lbb7/x6aefqtf3+OOPs2jRImbMmMHgwYO5//77WbRoEc899xxnzpxx2m5lZWWtN/+FCxfaXcPC\nhQub5RoiIiLo06cPo0ePZs2aNQQHB/Pvf/9bPf7www9zww03sGjRIhITE7nlllv46quv2LJlC19/\n/XWT+srKysLHxwdvb/ssuOHh4ZSXl6telBVGMHnXfF/GxoFfHQMhqzu9o3foo48+yq5du/jmm0YF\nZ3cqq+N31tXV1W5UWRfn+swAOSjpUWxxTJNSBahut1LxEPwZZTQGcB0QgsXEaCEEmI2iQGy3ToBj\nBGdHM04EUCKlrHTY72jaCLHI4NjHNXX0YaBG2dVLgxWEEO2Bj1DsqhJ4V0r5qkMdgZIiewSK/XOq\nlHJPQ223VSJ9lGSe39qYG7sFKWbIC5XNmzdjNBoZMGCA0zoBAQEsWbKEJUuWsG/fPhYvXszEiRO5\n7LLLuPTSSwkODlZNLHURGRmpxv6zJScnp5ZLuaOpx3r83XffpVevXrXasCq15jAzvvPOO3Zefo1Z\nS9ZUYmJiCAoKUk10R48epbq62i5ZJkCvXr0wGo2cOHHC6dxZUFBQreC8d999N6NGjVLL52NdlE6n\no2fPnnZmxkOHDnHbbbfZ1YuLi8PLy8suoWZjiIyMpLS0lPLycjuFlpOTg7e3Nx4eHpRVK4EK3HyV\n70t8KFzh5H2sffv2xMbG8t1333HnnXeq+zt06ECHDh04fvx4k+RzlNXxhcNkMpGfn1/vcolzfWZQ\nfo+da0nnfA78WwjhhaJQfpNSHrE5XgB8Cbxfx7l5DmVHF7dswFcI4emg0EId6hUA36DMxTni6F4b\nAJRKKRv08mrMyMwIPCylvBToD0wXQlzqUGc40NWy3Q281Yh22zTXxNh7Ny7bB2VV9Z/TUhQVFTF7\n9my6dOnCkCFDGnXOZZddxvPPP4/ZbFbnaq677jpWrlypzgs50q9fP3bv3s2xY8fUfRkZGWzbtq3B\neHxxcXG0a9eO48eP06dPn1pbcHAwAL1792bXrl0NbvX9uMfFxdm1/WdcxZ1x+PBh8vPzVSVsTY+y\nZ4/9O6B17V9983txcXF29xQU5WV7DedDmVVWVrJnzx71GkC5DsdrSElJoaKiosE5SkeuuuoqhBCs\nXr1a3SelZPXq1QwaNAiTGT7ZX7M42tsNxsfVH3tx5syZrF69mi1btjRJFivWEb3jd7xfv358+eWX\nds5H1uDH9X23z+WZAdyAq1FGWU1lFeAFjLNsKxyOfw/0AHZLKZMdtuMNtG2NT3iDdYdFaSY51LP2\ncaCOPg471I1FmSNskAZHZlJJqJZl+VwihEhBsb0etKk2BvjIYs/dIYQIEEJEynNIxtZWcHWB23rA\na7vAYFJC63y8H+7qZe9h9VdjNBrZsWMHoExm7969m7feeovy8nI2btxYrxv1oEGDGDduHPHx8Qgh\neO+999Dr9fTt2xdQEjNeddVVDB48mIcffpjg4GB+++03goODufPOO5k6dSqLFi1i+PDhLFy4EFdX\nV5588klCQkK455576pXbxcWFF198kTvuuIOzZ88yfPhw3N3dOXr0KF999RWrV6/G29sbX1/fRkW0\nOBfKy8tZv349oCjhs2fPqj+0I0aMUEcPXbp0ISEhgQ8++ACAWbNmodPp6NevHwEBAaSkpLB48WI6\nd+7MrbcqXsfh4eGMHTuW2bNnU1lZyWWXXcbevXtZsGABN910E6Ghji+3NQwcOJCFCxeSm5tbbz0r\nGzZsoKysTE1wab2Gq666SlWq//d//8ePP/6oLpv47LPP2LBhA8OGDSMqKoqsrCzefPNNsrKyeOih\nh9S27733Xh588EGioqIYPnw4OTk5LFy4kNjYWEaMGNH4mw1ccskl3HbbbcyYMYOSkhI6d+7Me++9\nx6FDh3jrrbdYcwTSCmvq33QJ+DaQR/X+++9n69atDB8+nHvuuYekpCR8fX05c+aMeh/qW0xu9WJ8\n9dVXufbaa/Hz8yMuLo7HH3+cXr16MXbsWO677z5Onz7N7NmzGTp0aL3WjnN5ZlAGDXnAO427kzVI\nKc8IIbYAL6CMelY6VFkA7ATWCSH+Y+mnHYpCWial3FJP238IIdYAbwkhfFFGag+hWOtsPaVeQvGU\n/EEI8RqQgTLSTAB+llJ+ZlO3D4p3ZaMurtEbipY8Cfg57F8LDLIpfw/0qeP8u1G0d3KHDh2cTnq2\nJQ6ckfKR/9U4hHyR0nKyzJ8/X53kFkJIf39/2bt3b/mvf/1LZmVlNXj+rFmzZHx8vPTx8ZH+/v4y\nMTFRbt261a7O77//LocPHy59fHykj4+P7Nu3r/zf//6nHk9PT5djxoyRPj4+Uq/Xy5EjR8rU1FS7\nNgD52muv1SnD+vXr5aBBg6S3t7f09fWVl19+uZw7d66srq4+hzvSNKxOCnVtx44dU+vFxMTIKVOm\nqOXPPvtMXn311TIwMFB6eXnJuLg4+dBDD8nc3Fy79ouLi+XDDz8sO3XqJD09PWXnzp3lI488Is+e\nPVuvXAaDQQYFBcmPPvqoUdcRExNT5zUsXbpUrTNlyhQZExOjlvfs2SNHjBghw8PDpbu7u4yJiZE3\n33yz/OOPP+zaNpvN8s0335Q9e/aU3t7eMioqSt58880yPT29XpnqcgCRUsqysjI5Y8YMGRYWJt3d\n3WXv3r3lxo0b5a8ZNc9U9GUJctCwGxt17VIqzjEffPCBHDhwoPT19ZVubm4yJiZGTpo0SW7btq3e\nc81ms3zkkUdkZGSkFELYOQH973//k3379pUeHh4yNDRU3nfffbKkpKRBeZr6zKDMjXWV9r+tEpjh\nsG8BkCdr/w7/3VJ/u+Mxy/HuwGoUc2AFkIaiOKOlvQNIfB3nBqGYMstQ5tTmAe8Bex3qRaG49eeg\nzIsdBz4BetjUCUWxDCbUJafj1uio+UIIH+BH4Bkp5X8djq0F/i2l/NlS/h6YLaV0Gha/LUTNbyzf\nH1eCEFu5sTv0b9di4mi0QR544AHS0tJYt25dw5VbOceK4J09ynpOgJ6hMKnnhRkP9XzQmqLmCyF0\nwB/Ar1LKKU089x5gFtBNNkJRNWqdmRDCDfgCWO6oyCxkYO+FEm3ZpwFcGwNZJfC7ZX74y8MQ5g2d\nGkQwRxkAACAASURBVBewQUOjQR555BG6detGampqvYt0WztFlfDRvhpFFuljHxtVo2URQtyEMura\nD/ihrBHrCkxuYjsCZeH1M41RZNAIBxBLox8AKVLKl5xU+waYLBT6A8XyIp4vc0QIuPnSGocQs4SP\n9kNhc0cy07hoiY6O5j//+U+9nnGtnSqT4khlDSKsd4Opl4GHFvrhQqIMmIaiEz5DMRWOllLubGI7\nEcBy4OPGntCgmVEIMQj4CUXTWifx/gV0AJBSvm1ReK8Dw1Am+6bVZ2KEi8vMaKWwEpbsrHkYo3xg\neh9wv7Di+mpoXHBICZ8egL2WlU0uAu7uBZ0vQutGazIz/pU0xpvxZ6DeQbxlGDi9uYRqqwR6wuTL\nauz9maXw+UGYFK+lctfQqI/NJ2oUGcCYbhenItNwjhYB5C+mYwCMs1mDu+8M/HC8xcTR0LjgOZhn\n70DVvx1cHd1y8mhcmGjKrAXo5/AwbjyqZKvW0NCwJ6cMPv2jJtRExwBlVKah4YimzFqIG7ram0k+\nOwDZpc7ra2hcbJRXw7LflaADYDHT9wSd9qulUQfa16KFcHWBO+KVBxSUB3bZPuUB1tC42DGZYfkf\nkGfx+HVzUTwXfZzHh9a4yNGUWQuid4dpl9d4M+ZXwCd/KA+yhsbFzPp0SLUJo3vrpRd2oG6NlkdT\nZi1MpI/yoFo5UgBr01pOHg2NliY5yz5t0pBYuMx5ZiINDUBTZhcEPcMgqSbwOD+fgl2ZLSePhkZL\ncbIYvrBJmN0jFJI6Oa+voWFFU2YXCEM6KjHmrHxxCI4Xt5w8Ghp/NcUG+HBfTUqXcL1itdBCVWk0\nBk2ZXSC4CCXGXKQl+4RJKg92Ud1pjjQ02hTVJuX7ftaS889bp8wne2qhqjQaiabMLiA8dIrHlreb\nUi6tUh7walP952lotGakhNWH4NRZpewi4I6eEOzVsnJptC40ZXaBEeSlrKWxmlZOl8CqFOWB19Bo\ni2w9CXuya8qju0KXoJaTR6N1oimzC5DOgfZRDn7LgS0nWk4eDY3zxaF8WGfjvds3CgZqoao0zgFN\nmV2gDGgH/aJqyhvSISWv5eTR0GhuzpQpC6OtRocYfyVuqRZ0W+Nc0JTZBYoQMDZOiUUHygP/6R9K\nrDoNjdZOhVGJeFNpVMr+HjBFC1Wl8SfQvjoXMDoXZf4swBLyqtKkxKrTQl5ptGbMUnkxyy1XyjpL\nqCpfj5aVS6N1oymzCxwfd+VBd7P8p/IqFNOMWXMI0WilbEhX5sqs3HwJRPu1nDwabQNNmbUC2vkq\na9CspBbYT5praLQW9mTbOzNdEwO9IlpOHo22g6bMWgmXh8N1sTXlrSeVGHYaGq2FU2eVZSZWLgmG\nYZ1bTh6NtoWmzFoR13eCS0NqyqtTYF+O8/oaGhcKp8/CB3trQlWFecNt8VqoKo3mQ1NmrQgXAbf1\nUGLWgRLy6pM/tBGaxoXN8WJ45zcoszgueelg6uXKXw2N5kJTZq0Mz/9v77zDo6rSP/45M+kJSSCN\nkAYhgIjSpdgXrIht7buusspiWcuu7s+6q1ixroq69rrrWhYbuior2JUOoogCAQIJhPQ2SSaZzJzf\nH+dmSphACHMzmcn5PM88mXvuvXPe3Ezu955z3hIBfxirnmxBuey/uQG+KwmqWRqNXwqr4bm1Hhf8\n2AiYPRbS4oJrlyb80GIWgiTFwBUTPEmJAd7dqLOEaHoXP1fCC+ug1cgtGh8Jl4+H3KTg2qUJT7SY\nhSgJUcaNwcul+b+FsGirzuOoCT7rylRQdPsaWVI0XDlBV4vWmIcWsxAmLhL+MA7ykz1ti7epStVa\n0DTBYlWpbyzkgBglZOnxwbVLE95oMQtxYiLg0rEwIsXT9tUOeGejDqzW9Dzflag13PavXnqcErIB\nupyLxmS0mIUBUVaVJeQQr0rVy3aqm4rTFTy7NH2Lz7ertdt2BiWotd2kmODZpOk7aDELEyIscOEh\nMN4rm8Ka3cp1v00LmsZEpIRFW+Ajr6w0uYlw2Xi1tqvR9AT7FDMhxItCiHIhxPpO9h8rhKgTQnxv\nvG4LvJmarmC1qLRXU7I8besr1EJ8q65WrTEBKeGDzbC4yNM2NFmt5bZXTNdoeoKujMxeBk7axzFf\nSynHGq87D9wsTXexCPj1CDg619O2sUplX2iP9dFoAoFLwtu/wNfFnraDUtQabowOiNb0MPsUMynl\nV0B1D9iiCRBCwMwCOH6Ip21rLTy7VpeP0QQGpwve2ADLd3naDk2Di0dDpDV4dmn6LoFaM5sqhFgn\nhPhYCDGqs4OEEHOEEKuEEKsqKioC1LXGH0KoXI6nFHjaiuvh6TXQ0BI8uzShT5sL/rke1u72tI0f\nCL89RBfX1ASPQHz11gB5UsoxwOPAe50dKKV8Vko5UUo5MS0trbPDNAHk2DxVir6dUhs8tQZq7cGz\nSRO6tDrhpXXwk9ez6JQstVZr1UKmCSIH/PWTUtZLKW3G+4+ASCFE6j5O0/Qgh2erm017gvKKJvjH\naqhqDqpZmhDD3qbWXjd5LTock6vWaHX2+65T7ihF6qwGAeeAxUwIMVAIIYz3k4zPrNr7WZqeZmIm\nXHgoWI2bTo1dCVpZY3Dt0oQGTQ615rq11tN2/BA1jS20kHWZHS1buWrr+fxj9zycUntkBZKuuOa/\nDiwFRgghSoQQlwohLhdCXG4ccjawXgixDpgPnC/1Y0evZHS6WqBvX9eob4GnVsPOhuDapendNLSo\nqeniek/bzAK1JquFrOtUOyq4bcdVNLoa+Kh2AU+U3htsk8IKESzdmThxoly1atV+n1fTVsVrFU8z\nO+M6Yiw6R053KKyGl7xiz2KNlFh5Opu5pgO1djUiq2hS2wK1Bjs1O6hmhRzNriZuLJrNlpZfAIgR\nsdyf9zwFsSP3+7OEEKullBMDbWOoE1JLtlWOCm7a/gc+rn2be0qup9Wl3fK6Q8EAmDPOEwvU3KZu\nWFtqgmuXpndRaaytegvZeQdrIdtfnLKN+0pudAuZBSs3Zz/QLSHTdE5IidkK21eUtBYBsKZxGfN2\n3oBD6sCp7pCXpErIxBtZGlqd8Pz3qgaVRlPWqKYWawyvV6tQa64TMoNrV6ghpeTJ3fNY1fitu+2q\ngbcwMeGIIFoVnoSUmJ3c/ywuTL3Cvb3C9jUP7rxFL6R2k6x+KhFsYrTabnPBKz/Ayl26hExfZmuN\nWkutNyY+IiwqkfXo9ODaFYq8VfUii2rfdW+fnzKbE/ufGUSLwpeQEjOA81Nnc27K793b3zYs4e+7\nbscpdfLB7pARr0p09DcymzslvPUzvPoj2FqDa5umZ3E4VZ7Fp9dAozHhEW2F2WPhIB1ss998Vvdf\nXq140r09LekULky7Yi9naA6EkBMzIQQXpV3FGQN+6277ov5jHi+9G5fU6eG7Q0qsUTwxztO2vgIe\nWgY/lgfPLk3PUVwPj65QtfDaB+WxESph8ND+QTUtJFnXuILHdt3h3h4TdxjXZN6G0O6fphFyYgZK\n0GanX8eM5HPcbZ/Wvc9Tu+/XwYjdJDkGrjnMN+N+o0ON0F7/Sed0DFfaXLBoKzyxCsqbPO3DB8B1\nk7WHa3coshdyd8lfaEMtf+RFF3Br9kNECl1GwExCNre1EIIrBt6IQ7bwad1CAD6q/Q9Rlihmp1+n\nn4C6QXQEnHWQKvL5n5+hzlgzWbMbCmvgnJEqK7omPNhtU8mCveMMo6wqhmxKlo4h6w6VjnJuL76a\nJpcNgJSINO7ImU+8tV+QLQt/QnJk1o5FWLg6828ck+ipUPNe9Ws+89Sa/WdEClw/2bfQZ32LSmX0\n9i+6lEyo45KqKvSjK3yFbEiyGo1NzdZC1h2anI3MLb6GyrYyAGIt8czNeZy0yIH7OFMTCEJ2ZNaO\nVVi5ftCdOGQr3zV8BigPomhLDOenzg6ydaFLbCRcMEqN0t7+xeMQsGwnbKpS8Ub5ei0l5Khogjc3\nwPY6T1uEBU4aCkfl6ByL3aVNOpi38//Y1rIJACsR3JL1APkxw4NsWd8hpEdm7VhFBDdkzeOwhCPd\nbf+s+AdvV70aRKvCg0PT4S9TlKi1U21XHm8LNykPOE3vxyXh22J4ZLmvkGX3gz9NUgmDtZB1Dykl\nT5Tey5rGZe62qzNvZXzC1CBa1fcICzEDiBSR3JL1IOPip7jbXix/lA+q3wiiVeFBQhRcdCj8ZpTy\ncAPl8fZ1MTyyAnbU7fV0TZCpaYbn1sJ7m8BhOPxajHp3V01U4Rma7vN65XN8Wve+e/s3qXM4Pvn0\nIFrUNwkbMQOIskTz1+yHOSRuvLvt6bIH+KTmnSBaFR4IAeMGqrW0EV5OIBVN8ORq+GSL8ozT9B6k\nVAHwDy9XDjztDIxXnqvHD9E1yA6UxbUf8Frl0+7t45JO5TeplwXRor5L2H2VYyyx3J79GAfFjna3\nPbH7Hj6r+zCIVoUPSTFw6Rg4+yAVUAtqCmtJEcxfCbt0Bv5eQX2LSib91s/QYkwFC+BXeXDtJJX9\nRXNgrLUtY37pXe7tcfFTuDrzr9qTOkiEnZgBxFnjuSPncQpiVCJPieSRXXP5uv5/QbYsPBACJmcp\nz7f8ZE97qU0J2mdF4NSjtKDxfRk8vMw3z2ZqLFw5EWYUeEoAabrPNvsm7tn5fziNWLIh0cO4JesB\nInQsWdAI2691grUfd+U8yeDoAgBcuHhw519Z2vBFcA0LIwbEwmXj4bRhnhukU8LHW1S29XJd+LNH\naWyFf/0Ir62HJq/wiSOy4c+TYbAOgA4IlY4ybi++hmaX+oKnRmQwN+dx4qwJQbasbxO2YgaQGJHM\nPblPkx01GAAnbdy380ZW2b7d+4maLmMRcFQu/HkS5CR62ncY6ZG+KVbTkBpz2VABDy2HdV7px5Jj\n4LJxcMYIFQytOXAanQ3cXnw1VW3qQsdZErgjZz6pkToLc7AJazEDSI4YwL25z5AZqYowtUkH95T8\nhXWNK4JsWXiRHg9/nKDilazGkoHDBe9vgmfXQHVzcO0LV5rb4K0Nan3MOzH0pEHKWadgQPBsCzcc\n0sG9O2+gqKUQULFkt2Y/yOCYYUG2TAMhWGm6u5Q7Srlx+2zKHaUARIsY7sp9klFx43rMhr7CrgaV\nJqnU5mmLssJhmSpN0kA9G3PA1LXAip0qiL3eS8T6RcHZI+FgneU+oEgpeaT0dpZ4OZJdl3kn05Nn\n9rgtutK0f/qMmAGUtpZw4/bZ7imCWEs89+Q+xYjYQ3rUjr5AmwsWb1POIB2/YfnJStQOTdfOCPuD\nS0JhNSzdCRsq95y+HZuhphTjtQ9CwPlXxVO8Xvmce/t3aVcGLcOQFjP/9CkxAyhpKeLG7X+g1lkF\nQLylH/PynmFozEE9bktfYEedSlq8248zSHykmg6bkqWcSTT+aXTAql1qFFbpZ7o2IQrOGA5jMnre\ntr7Aotr3mF96p3v7xOQzuXpg8FzwtZj5p8+JGagSDTfvmEO9sxaARGsy83KfZXBMQVDsCXdcErbU\nwNIS+MnPiEIAw1NgapbKyq8DeVXA8/Y6NQr7odx/QHp+srpmh+gRrmmstn3H3OJrcaGC9SbEH85t\nOY8E1QVfi5l/+qSYAWyx/8LN2y+j0aWifJOtKTyQ9zxZ0XlBs6kvUNcCK3bB8p2eEjPeJEWrGLZJ\ng9T7voa9TZXcWbbTd82xnZgImDhQjWYz9NqjqWyxb+TG7ZfS7FKF3vKjR3B/3vPEWYOb/0uLmX/6\nrJgBbGxez607rnDHi6REpHNf3rMMisoNql19AacLfqlSN+2NVXuuq1kEjEqFKdlQ0D/8k+DualCj\nsLW7PRk7vMnup0qzjM3QbvY9QUlLETfvmEN1m4o8T4sYyMODXyElMm0fZ5qPFjP/9GkxA/ipaS1/\n2/FHWqQdgCRrf+bmzGd47KggW9Z3qGpWI7UVuzylZrxJjVUjkYmDwsu5weFUcWFLS1RcXkciLSof\n5pQs3xg+jbmsbVzOvJL/o9EosBlvSeDBwS+RFz00yJYptJj5p8+LGcC6xhXMLb6WVqnmvaJFDDdn\nP+BTUkZjPm0uWF+uRihba/fcH2GB0elqhJKXGLoFJCua1Ih01S7fTB3tZMSrtbDxA1VdOU3P8VHN\nAp7afb97jSxaxDA3Zz6j43uPdmgx848WM4MNTeu4s+RPNDhVPRMLVq7OvJUTks8IsmV9kzKbErXV\nu/1Xts5MUDf80RmhMVprdapp1aUlvhns27EKFaowNUtVfA5VoQ5VnNLJC2V/5/2a191tKRHp3J7z\naK/zdNZi5h8tZl4Ut2zjtuKr3IHVAL9NvZwLUv+gM2EHiVanSpy7tARKOsnIHxsBKbFerzj1c0Cs\nciLpifU2KVUGjio7VDWpqdP2V3UzNLT6P29AjJpGPGyQcrHX9DxNThv377yFVY3fuNsKYkZyW/aj\nvWKNrCNazPyzTzETQrwIzATKpZR7RBcLdZd/DJgBNAGzpJRr9tVxbxQzgGpHBbcXX8PWlo3uthOT\nz+SPA2/GKiKCaJmmuF5Nz63d7SkyuS+sQolaip/XgFiI3A9nCqcLauy+IuX93p/jhj8EMDJVTZcO\nHxD+zi29mbLWXdxR8ie2GymqAI7odxzXDbqDGEvvDH7UYuafrojZ0YANeLUTMZsBXI0Ss8nAY1LK\nyfvquNtiZquBHxbB5LPBao64NDlt3LvzBtZ6lUGflHAUN2bd12u/4H2JZoeaflxdCmWNXRc2fyRF\n7yl2yTHGKKvZ91Vr737SZKtQnz06XYUeJMd032ZNYNjQtI67S66jzumZ9z0v5VIuTLsCizAxcG/d\nJ5BRAAO7F9eqxcw/XZpmFEIMBj7sRMyeAb6QUr5ubG8EjpVSlnY81ptuiVlrE7xzF1Ruh7wxcOK1\nEGXOXcEhHcwvvZPP6v7rbhsecwhzcx4jKaK/KX1q9h8p1RSeW3SafKf6/HlHmkWM1TPF2T7y8xZI\nPQLrPXxe9xGPlt5Bm1RfkAgRybWZf2Nakom5FqULvnkN1n0MMf3g7LmQnLnfH6PFzD+BGNpkAcVe\n2yVG2x5iJoSYA8wByM3tRizXhi+UkAFsXwfv3gWn3gBxgS/UFCkiuS7zTlIj0nmr6iUANtnX85ei\nWdyZ+wSZUTkB71Oz/wgBidHqNSR5z/32tj2nBNtFr7Zl/0daidH+pyxTYiEuUjtu9HZc0sVrlc/w\nhleexURrMn/L/jsHx401r+O2Vlj8FBQuV9v2BljxDpzwR/P67GP06CKQlPJZ4FlQI7P9/oAxJ4Pd\nBqveU9sV22DBbXDqjdB/UCBNBUAIwcXpV5MSkcHTZfcjkexyFHN90SwdixYixERAVj/16kjHNbD2\nV51dOWMc6BqbpndhdzXzyK65fNPwqbstNyqf23MeY2BUlokd2+Cjv8OuXzxt+YfBtD+Y12cfJBBi\nthPwHqZkG22BRwiYci4kpMCXL6o5pvoKeHsunPIXyBxuSrczB5zLgIhUHtx1K62yhTpnDTdt/4OO\nRQtxrBZIjVMvTXhT7ajgrpLr2GT/yd02If5wbsyaR7zVz5NOoKivgA8egBqvW+LoE+HI34FFJ9QM\nJIG4mguBi4RiClC3r/WyA+aQ6TDjeogwkvfZbfDePbBlpWldHp44jXtyn6afVU1ptkg7dxb/mf/V\nvmdanxqN5sDZYt/In4su8hGy0/pfwO05j5orZBVFsOB2XyE7/Ddw1EVayExgn1dUCPE6sBQYIYQo\nEUJcKoS4XAhxuXHIR8BWoBB4DrjSNGu9GTIezrwVYo08P04HfPwo/PA/07o8OG4MD+a9SHqkWrR1\n4eSx0jv5d8WzBCteT6PRdM7Shi+4oegSKtvKAJUM4YqMm7hs4P+ZG2qz40flrNZkpLKxRMAJV8H4\nmXph1SRCP2i6rgwW3qd+tjP+VJh6HpjkXqtj0TSa3o2UkneqX+Wl8vlII411vCWBm7MeYFzCFHM7\n/+Vr+OxZcBmBh1FxMOM6yD44IB+vvRn9E/pj3aQMOPsOFbfRzpoP4NN/qNGaCQyITOP+vOcYF+/5\np1hU+y53l1yP3eWneqJGo+kxHNLBY6V38mL5Y24hGxiZzUODXzZXyKRUzmmLn/IIWcIAOOv2gAmZ\npnNCX8xATTWecSsMHu9p2/QdLLwfWvyUOA4AcdYEbs95jGlJp7jbVti+5ubtl1HX5if5nkajMZ36\ntlr+tuNKPq173902KnYcfx/8CrnR+eZ17HIqp7Rlb3naUnLUg3aKDuPpCcJDzAAio2HGn5VzSDs7\nN8Dbd4KtypwujVi0c1N+725rj0UrbS3ey5kajSbQFLds47qii/ixabW7bXrSqdyT+5S5iQ4cdvjo\nEVi/xNOWPQp+fbvyvNb0COEjZgAWKxxzCUw939NWXaw8iqrMEZf2WLQrMm5CoBZ222PRNjX/tI+z\nNRpNIFjbuJzriy6m1FHibpuVdg1/zpxLpMXEDM7N9cqTusgrHe3wI1Tsa7SO+ehJwkvMQHkKTTgN\njrtCiRuArRrevgNKzBOXmQPO5ZasB4kSKlygPRZtle1b0/rUaDTwcc0CbttxlbuYZrSI4dashzgn\ndZa51S5qd6sH5bItnrbxp8HxV5iWN1bTOeEnZu0cdJRKdRVpJAZubVJej5u+M61Lf7FodxT/Scei\naTQm4JIuXiqfzxO773UX00yJSOOBvBc4PHGauZ2XFapkDW4vagFHz4LDzzfNi1qzd8L7quccCmfd\nBvHGfLnLCf97Qnk7mhSSoGPRNBrzcbhaeWjXX1lQ9bK7rSBmJH8f/E8KYkea2/m21fDu3WqKEcAa\nCTP+BKNPMLdfzV4JbzEDSM1THkUDvHKvffc6fPUKuA6gdsheyIkewsN5L5MfPcLd9lrl0zy++26c\n0k/ZZI1G02UanPX8rfiPfFn/ibttUsLR3J/3PKmR6eZ2vn6JyrPYZlRbjUlQntT5h5nbr2afhL+Y\nAfRLVZ5Fg7zKn//4P/jkMc+XMsB0Fot2V8l1OhZNo+km5Y5d/F/R7308Fk/pfw5/zX7Y3FqDUiq3\n+y9e8MzqJKbBWXeYlhNWs3/0DTED9QR1+s1Q4BU0uXUlvHcvNDeY0mV7LNp0rxpJK23fcPP2OdS2\nVZvSp0YTrhQ2/8x122ZR3LrN3XZJ+rVckXETVmFiOQNnGyx+2lOtAyA9H86+E/rvfz0yjTn0HTED\nNbd94lUwdoanbfcmtZBbX25Kl5Eikj9n3sG5KZe42zbZf+IvRbPY1brDlD41mnBjle1bbtw+mxpn\nJaCKad4waB5npVxsrsdiaxN8+CBs/NrTljcWzvirKXUUNd2nb4kZKE+jIy9UJRiMuDBqS5WLbflW\nc7oUgovTr+LKgTdjMS55qaOEvxT9no3N603pU6MJFz6peYc7iv+EXarp+XhLP+7J/QfHJJ1obse2\nGpUsuPhHT9vBv4JTrjetwr2m+/Q9MWtn7Mlw0jVqtAbQVKcqV29ZYVqXp/Q/h1uyH/KJRbt5+xxW\nNHxlWp8aTagipeTV8id5fPfdbtf79MhMHhr8EofETTC38/Kt8Pbtnsr2AJPOhl/N9sSvanoVfVfM\nAAomq3W06Hi17WhRZWS+esW0JMVT+x3LvblPk2hNBlQs2l0l1/FJzTum9KfRhCIO6eDhXX/jzaoX\n3G1DYw7iYbNzLEoJPyyCBXOhQU1pIiwwbQ5M+rUu39KL6dtiBsrD8ay5yjOpnfYvs3dZmQAyMm4M\nD+W9REakChdw4eLx3Xfzr4qndCyaps9jczZw244/8nn9R+62ifFHcn/e8wyISDWv45ZGz8Osywih\niYyFmf8HBx9rXr+agKDFDFQM2nn3+saKVGyDN2+BwuWmdJkVncdDg1+iIMYT4Pl65XM8VnoHbdKc\nUaFG09upcOzmhu2X8EOTp9bhScm/5racvxNrMTHXYdkW9f++1atafdpgOO8eyBtjXr+agBH6xTkD\nSfsUw7eveeoRARx6PBzxW4gIfMLSZlcT80puYHWjJ83WxPgjuCn7fnP/eTWaXsZW+ybmFl9NVVuF\nu+3itKs4J+X35nksSgk/fALf/rvD//wJcORvPWvqvQhdnNM/Wsz8UbYFFs2Hes8/FWmD4cRrIHlg\nwLtrkw6eKL2HT+sWutsKYkYyN2c+/SN0CQlN+LPGtpR7d95As0vVH4wggj8Nmsuvkmbs48wDwG5T\nFaG3et2HomLV+ljBZPP6PUC0mPlHi1lntDTCkmd9px0iY2HaH2BY4KvVSin5V+XTvFH5nLstIzKL\nu3KeICs6L+D9aTS9hU9r3+fx0ntwotap4i0J3Jr9MGPiTUwRVVYInzwODd4PrEOUh3NShnn9BgAt\nZv7RYrY3pFRpr755zbMgDHDIcSpWzYRpx49r3uYfu+fhQuWNTLQmc3vOoxwUOzrgfWk0wURKyb8r\nn+Hflc+629IiBjI3Zz6DYwrM6hTWfQLfdZhWHH0iHPGbXjmt2BEtZv7RYtYVyrfCJ/N9s4Sk5qmn\nuOTAp7NZ0fAV9+28iRZpB1R9phuy5jGl3zEB70ujCQZt0sHjpXezuO4Dd1t+9Ajm5swnJTJtL2ce\nAHYbLHlGZb1vJyoOps+BoZPM6dMEtJj5R4tZV2lpgs+f8/VujIyFabNh2NSAd7exeT1zi6+h3lkL\ngAULVwy8iRn9zw54XxpNT9LktHHvzhtY27jM3TY+fio3Zz1AnDXenE53F6p18PbYMVD5FU+6BhJN\nzrQfYLSY+UeL2f4gJaxfDF//s8O043SVHivA0467Wndw246rfErBn5dyKb9Lu9LcfHQajUlUOsqZ\nW3w121o2u9uOTzqdqzJvIUKYMMUnJXz/ESx9w3dacczJcPgFIVkRWouZf7SYdYfybeopzzuoOjVP\neTsGOIt2bVs1dxRfyyb7T+626Umnck3mX83559doTGJz8wbuLrmeyjbP/82FqVdwfupscx7O/E0r\nRsfB9MtCuv6YFjP/aDHrLq1N8NnzUOiZKiEyRuVuG354QLuyu5qZV3Ijqxq/cbeNj5/CzVkPPNXa\nqwAAGEtJREFUmjcto9EEiLLWXbxa8SRf1H/sbrMSwTWZf+O45FPN6XT3Zlj0uO+0YsZQ9cCZaNKa\nXA+hxcw/WswOBCnhpyVq2tE7l+OoaXDURQGddnTKNp7cPY9Fte+62zIjs7kgdQ7HJp2EVYTedIkm\nvKlrq+HNqhf4b81/fLLaxFriuTX7IcbFmxDLJV2w9iNY9mbYTCt2RIuZf7SYBYKKIlW12nvaMSVX\nLS73HxSwbqSUvF75LK9VPuPTnhmZzXmpl/KrpBl66lETdOyuZt6v/jcLql6hyWXz2Tcl4VguSb/W\nnNjJ5gZY8jQUrfW0RcfB9MshP3zu/VrM/NMlMRNCnAQ8BliB56WU93XYPwt4ENhpND0hpXx+b58Z\nVmIGatrx8+dhs/e0YzQceymMODKgXS2u/YBnyx6kscONIiMyi/NSLmFa8kwitahpehinbOPT2oW8\nVvk01W2VPvtGxo7hkvRrOThurDmdl25S04q2Kk9bRgGceHXITyt2RIuZf/YpZkIIK7AJOB4oAVYC\nF0gpN3gdMwuYKKW8qqsdh52YgTHt+Bl8/arvtOPBx8IRF6qnxABhczbwQfUbvFf9GjZXvc++tIiB\nnJt6CccnnUakJfCB3RqNN1JKltm+4OXyxylpLfLZlx01mFnp1zAl4RhznDycbbD2v7D8P2qKsZ2x\np8DU88JiWrEjWsz80xUxmwrMlVKeaGzfDCClnOd1zCy0mHmo3K6CrGtLPW1xySpryLCpAa2J1OS0\n8UHNm7xb/S8anHU++1IjMjgnZRYnJJ9BlCU6YH1qNO1saPqeF8sf4+fmdT7tKRFp/Db1co5LPtW8\n9dzSjfD5i1Bd7GmLjofjLochJhfvDCJazPzTFTE7GzhJSjnb2P4dMNlbuAwxmwdUoEZxf5ZSFvv5\nODdhLWYArc3w+Quw+Tvf9uxRcMzvA7qWBtDkbOS/Nf/hnepX3YHW7aREpHF2yixOTD6TaIsu9645\ncHa0bOXl8sdZbvvSpz3OksA5KbM4bcAFxFhizem8uR6+ex1+9u2bjAK1Tt3PxJpnvQAtZv4JlJil\nADYpZYsQ4jLgPCnlND+fNQeYA5Cbmzth+/btHQ8JL6RUGUO+fhWavATGEgHjZ8LEMwIeaG13NStR\nq3qVWme1z77+1lTOSrmIk/ufZd6NRhPWVDrKeK3iGRbXLXTnDwWIEJHM7H8u56ZcQlJEf3M6ly7Y\n8KUSshav9eKIaFUFeszJYTmt2BEtZv4JyDRjh+OtQLWUMmlvnxv2IzNvWptg+QJVK837eiemwzGz\nIC/wi+J2VzOf1L7DgspXqHH6LsYnWwfw65SLOKX/OVrUNF3C5mxgQdXLvF/9b1pli7tdIDg28WR+\nl3YlGVGBnW3woXI7fPGiih/zJn+iCoMJ89GYN1rM/NMVMYtATR1OR3krrgR+I6X8yeuYTCllqfH+\nTOBGKeVe66T0KTFrp6JI/UOWFfq2D50ER/0OEgJfu6zFZWdR7XssqHrJp+ghqIz8vx6gRE0HX2v8\n4XC18mHNW7xZ9cIea7Lj46cyK/0ahsaMMM+A1mavB0EvB49+aXD0xTBkvHl991K0mPmnq675M4BH\nUa75L0op7xFC3AmsklIuFELMA04D2oBq4Aop5S97+8w+KWag/iF/+lzlimtp9LRHRsOks1UpChOm\nSlpdLfyv9n3+U/WSTzohgH7WJM4ccCGn9j+POGtCwPvWhB5O6eTL+o/5Z8VTlDtKffYNjTmIS9Kv\nZawZQc/tSAlbVqiEBI1e0+UWK4wzpugj+6ZTkxYz/+ig6WDRVKfm/n/5yrc9JQeOvQQyzXnadbha\nWVy3kDcrX6SibbfPvgRLImcM+C3Tk2eSHhn40jaa3o/N2cDX9Yv4b81/fJIBAwyMzOaitD9yVOLx\nWITFPCPqyuDLl2GHr4ckWQcr56kBWeb1HQJoMfOPFrNgs/Nn+PJFqN7p2z7yWDj8fIhNNKVbh3Tw\nWe2HvFn1AmWOXXvsHxxdwMSEIzks4QhGxo7R6bLCGKdsY03jMpbUfsAy25c4ZKvP/kRrMhekzuHk\n/meZG4zvdMDqD2D1+75xmrGJKqxl+BEBDWsJVbSY+UeLWW/A2QbrPoYV70CbZ3Gd6AQ44gIYeQyY\n9CTcJh18XvcRb1a+4FNqxpt4Sz/Gx0/hsISjmJBwOMkRA0yxRdOzFNk3s7juQ76o+3gPJyFQRWHP\nTPkdZw34nfnTzzt+hC9fgjrv2QIBhx4HU85V8WMaQItZZ2gx603UVyg3fu+SFQADh6upx9Rc07p2\nyja+rP+Ez+s+4oem1T6JYb0RCIbFjOKwhCM4LOEohsYcZO6Ukyag1LXV8EX9xyyp/ZAtLf6XtYdG\nH8T05Jkcm3iyeW727dhq4Nt/+qaBA0gbor7zGUPN7T8E0WLmHy1mvZFtq+GrV3zLVwgLjDkJJp0F\nUea60ze7mljXuJJVtm9YaftmD4cRb5KtKUxMOJyJCUcyPn4K8dZ+ptqm2X8c0sHKhq9ZUvchK23f\n4KRtj2OSrSn8KmkGxyXNZHDMMPONcjnhx//BsgXgaPa0R8XClPPgkOPAoh+S/KHFzD9azHorjhZY\n9a7KO+ddyiJ+gHLjHzqpR9YPpJRsbylkhe0bVtm+4efmH3Dh9HuslQgOjhvDRGPUlhuVrytiBwkp\nJZvtG1hS9wFf1i/aw60eIFJEMSXhGKYnn8r4+Ck9ty66u1CtE1cU+bYPP1zlMI1P7hk7QhQtZv7R\nYtbbqd6p1hJ2bvBtzx0DR18EyT3rddjgrGdt41JW2r5hle3bPVJneZMemcnEeOVEMjr+MB2g3QNU\nOsr5vO4jPqv7kB2tW/0ec1DsaI5LmsmRiSfQz2qOg5Ffmhtg2VsqGTde953kTDWlmD2q52wJYbSY\n+UeLWSggJWz6Fr75l8pL144QKnHx+NNMXU/rDKd0stm+wT0dWWj/udNjrUSQEz2Y/JgR5EePID9m\nOEOih5MYoZ/CDxS7q5llDV+wpO4Dvm9c4ZNmqp20iIFMSzqF6Ukzzakltjds1fD9R6qQrcPLwcka\nCYedCeNOUe81XUKLmX+0mIUSdpt6sl2/BJ8nW1BZwiecDgMLgmIaQHVbJatt37LS9i1rG5ftUZjR\nH6kRGeTHDDcETolcRmSWdirZB3ZXM2sbl7G84Uu+bfjM77WOEbEckTid6UkzOTRuYs9f07oyWPMB\n/PwVuDqs0+WNVRk8kjJ61qYwQIuZf7SYhSJlW5SoFf+4577sUUrUskcFNSanTTrY0LTOPWrrbMrL\nH7GWePKjhzHES+Tyoof2+TI2lY4yVti+ZnnDV6xrWrFHPFg7o+MmMj3pVI5InE6sJXA19LpM5Q5Y\nsxA2L/XNRQoqKcCks1VORb2e2i20mPlHi1koU7YFVi+ErSv33JcxFCacpkZsvWCU0+S0sa1lM1vt\nG9lq38S2lk0UtRR2ekPuiAUrOdGDGRI93BjBjSA/erj5ruNBREpJof1nVti+YnnDV5260gMMispl\netJMpiXNID3SxIS/e2P3Zlj1PhSt2XNfRoFKQTV4nBaxA0SLmX+0mIUDVSXqSXjTd77JWEGl/plw\nulpbs1iDY18nOGUbJa3bDYHbyLaWzWyx/7JXp5KOJFsHkBmVw6CoXAZF5TAoKofMyByyonJDMs9k\ni8vOusaVrLB9xQrbV3skh/YmL7qAyQlHMbnfsYyIOSQ4nqNSQsl6JWIdnZQAcg5V37+skVrEAoQW\nM/9oMQsn6sthzYeqaKGzQ9BzYhqMPxUOOjrgNdQCiZSS6rZKtrYYAmffxNaWTexq3YHsuE64D5Ks\n/b1ELpfMyBy34PWmeLjqtkpW2r5hecOXfN+4nBZp93tcBBEcEj+ByQnHMCnhKAZGBTFHoXSpeMhV\n70O5nynk/MPUzIAOeg44Wsz8o8UsHGmsge8/hvWLwdHhxhiXDGNnwCHTTQ++DiTNriaK7JvZ2rJJ\nCZx9I0UthZ3e+PdFojXZI3SRuWRGqdFcZlQOCSYLnZSSopbNLDemDzfZ13d6bD9rEhPjj2Byv2OY\nED81+KNNl1Otha1+f898osKiYsUmnAYDsoNjXx9Ai5l/tJiFM3Yb/PA/WPeJb2VeULnuRp+oXrG9\nZ5SyP7iki8q2Mna17qC0tZidrcWUtharbUdJl9fjOpJoTSbekkCUJZooYbzc76OIssQQLaKJtESp\nnyKaaH/HWozjRTRRlhhq26rc618dKxZ4kx01mMkJRzOp39GMjB3dO5I8t7WqCg9rPlBp17yxRsLB\nxyoX+8T0oJjXl9Bi5h8tZn2BVjts+ExlE2ms8d0XGQ2jjlOjtYTwcaYwS+jMwIKVUXFj1fRhv6PJ\niur5mMFOaW1WI/zvP4amDmuZkTFw6PEw5mSdtaMH0WLmHy1mfQmnA375Wj1d13XIt2iJgJFHq3W1\nMI/96Q1CF29JYELC4UxOOIYJCYfTz5pkep/7RXODqu78wyLfIrIAMQkqT+ihJ6j3mh5Fi5l/tJj1\nRVxOKFyuFu+ri333CQHZhyjvx/yJfe5m5ZIuatqqsMtmWl12WmUrra4WWqV6tbhacLh/ttIi7cb+\nVlqlnVZXqzrW65z29wILh8aNZ3K/YxgVN44IM2uDdYe2VlUQc/My2LbGtxwRQHx/NZU4apoalWmC\nghYz/2gx68tIFxStVaJWVrjnfosVckcrYRsyHqKCEICrMRdnmwq+37xUeSe2Nu95TFKGSpl20JE6\n7VQvQIuZf3rByrImaAiLCqoePF5VvF79vm9WEZdTiV3RWnUTyxsLw6aowFf9ZB66uJxQ8pMagW1d\nuec0YjupeWrauWByr4tR1Gg6osVMY0wtHqxeDZXqJle4zDd+yOlQN76tKyEiGoaMg4KpkDemV8et\naQxcLtj1CxQuhcIVYG/wf1xSBhRMUaPxlBwd6KwJGfQ0o6Zz6so8wla53f8xkbGQP0HdAHNHg1U/\nH/UapEulmNq8TK2RdvRGbKdfqiFgU1SFZy1gvRo9zegfLWaarlGzU90UNy9T7/0RHacyPwybqhId\n66mpnkdKNaJufwixVfk/Lr6/mj4cNlXlTdQCFjJoMfOPFjPN/iElVBWrG+XmpXu6+LcT009Vwx42\nBQaNBEvwkx2HLVKqkXO7gNWX+z8uNlEJWMEUGDSiVySg1uw/Wsz8o8VM032khIoij7A1VPo/Li5Z\nTUUOHAbp+ZA8SIvbgSClutblW1XlhG2robbU/7HRCTDUGC1njdSj5TBAi5l/tJhpAoOU6sa6eala\nn2ms7vzYyBhIG6yErf2VlKGnujrDVgMVhnCVb1Mi1pkDB6gQivyJnulevY4ZVmgx84/+lmsCgxCq\nyvXAAjjyt1C6ySNszfW+xzrsyrNul1d9rug45XyQng/pQyF9iHJM6GsC11yvxKp8K5QZPztz3PAm\nMkbFAg6bajji6HgwTd9Cj8w05uJywa6foXSjGlWUbenazRnUult6PmQYo7e0/LDKH4ndBhXbPNel\nYlvnU7UdiYpTgp8+VD1A5I7WIRJ9BD0y848emWnMxWJRU13Zozxt7dNm3qMPf9Nm9gaVXmnHOk9b\nXLKqkZVujOKSBqoKANHxvXMdTrpUoucWGzRUeUZd5Vs7d57pSGS016hVT8tqNP7okpgJIU4CHgOs\nwPNSyvs67I8GXgUmAFXAeVLKosCaqgkbEvpDwgSVfQR8HRrcr23Q2rTnuU21yuFh2+o990XFKVGL\nMcQtJsEQugRPm89749jI2L0Lg5Qqb2GLDeyNKmOGz3vjZbd1+NkIrY3q/K5ijfRaTzRGXsmZvVOo\nNZpexD7FTAhhBZ4EjgdKgJVCiIVSSu8a6ZcCNVLKAiHE+cD9wHlmGKwJQ4RQlbAT05TrOKgRTV2Z\nx+GhfKuahnO0dP45rU3q1VDR+TF++7f4il9UrEqya/cSKVdb93+/zrBYISXXM42ang/9s7TDhkbT\nDbryXzMJKJRSbgUQQrwBnA54i9npwFzj/QLgCSGEkMFakNOEPsKiRiTJmap6Maj1t9pdnpFb+TZo\nqjFGRn5GcV1FutSU5t48BA+EyBgllDH9VL7DDGP9LzVHO2poNAGiK2KWBXjXCSk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NDZ1Oh8FgUPebTCZycnKIiooiOjoagICAAKqrq8nKynKpzEwmE9nZ2URERDis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Z+fj44Obmhk6nw2AwqPtNJhP5+flERkYSFRUFQEBAAHV1deTm5rpUZiaTiby8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\n9AZEzNwjYhYM2Jpg82r45t/QVO+wRyfA/J/BEcdDB6GavXU7WVP6bxm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z8/PDw8MDnU6HwWBQ99fU1JCbm0t0dDQxMTEABAUFYTKZyM7OdqvMampqyMnJ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\na9jnMX5CzBSuTLpZF5RuC1LChv/BD/8Eu+GBEBY46So4ZrKppjnQYuY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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for step in range(10000):\n", " artist_paintings = artist_works() # real painting from artist\n", " G_ideas = Variable(torch.randn(BATCH_SIZE, N_IDEAS)) # random ideas\n", " G_paintings = G(G_ideas) # fake painting from G (random ideas)\n", "\n", " prob_artist0 = D(artist_paintings) # D try to increase this prob\n", " prob_artist1 = D(G_paintings) # D try to reduce this prob\n", "\n", " D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1))\n", " G_loss = torch.mean(torch.log(1. - prob_artist1))\n", "\n", " opt_D.zero_grad()\n", " D_loss.backward(retain_variables=True) # retain_variables for reusing computational graph\n", " opt_D.step()\n", "\n", " opt_G.zero_grad()\n", " G_loss.backward()\n", " opt_G.step()\n", "\n", " if step % 1000 == 0: # plotting\n", " plt.cla()\n", " plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',)\n", " plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')\n", " plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')\n", " plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 15})\n", " plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 15})\n", " plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=12);plt.draw();plt.pause(0.01)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# below is for pytorch version => 1.5\n", "# this fix is done by albanD - https://github.com/pytorch/pytorch/issues/39141\n", "\n", "for step in range(10000):\n", " \n", " artist_paintings = artist_works() # real painting from artist\n", " G_ideas = torch.randn(BATCH_SIZE, N_IDEAS, requires_grad=True) # random ideas\n", " G_paintings = G(G_ideas) # fake painting from G (random ideas)\n", "\n", " prob_artist1 = D(G_paintings) # D try to reduce this prob\n", " \n", " G_loss = torch.mean(torch.log(1. - prob_artist1)) \n", " opt_G.zero_grad()\n", " G_loss.backward()\n", " opt_G.step()\n", " \n", " prob_artist0 = D(artist_paintings) # D try to increase this prob\n", " prob_artist1 = D(G_paintings.detach()) # D try to reduce this prob\n", " D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1)) \n", "\n", " opt_D.zero_grad()\n", " D_loss.backward(retain_graph=True) # reusing computational graph\n", " opt_D.step()\n", "\n", " if step % 1000 == 0: # plotting\n", " plt.cla()\n", " plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',)\n", " plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')\n", " plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')\n", " plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 15})\n", " plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 15})\n", " plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=12);plt.draw();plt.pause(0.01)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/501_why_torch_dynamic_graph.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 501 Why Torch Dynamic Graph\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* matplotlib\n", "* numpy" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "from torch import nn\n", "from torch.autograd import Variable\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "torch.manual_seed(1) # reproducible\n", "\n", "# Hyper Parameters\n", "INPUT_SIZE = 1 # rnn input size / image width\n", "LR = 0.02 # learning rate" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class RNN(nn.Module):\n", " def __init__(self):\n", " super(RNN, self).__init__()\n", "\n", " self.rnn = nn.RNN(\n", " input_size=1,\n", " hidden_size=32, # rnn hidden unit\n", " num_layers=1, # number of rnn layer\n", " batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)\n", " )\n", " self.out = nn.Linear(32, 1)\n", "\n", " def forward(self, x, h_state):\n", " # x (batch, time_step, input_size)\n", " # h_state (n_layers, batch, hidden_size)\n", " # r_out (batch, time_step, output_size)\n", " r_out, h_state = self.rnn(x, h_state)\n", "\n", " outs = [] # this is where you can find torch is dynamic\n", " for time_step in range(r_out.size(1)): # calculate output for each time step\n", " outs.append(self.out(r_out[:, time_step, :]))\n", " return torch.stack(outs, dim=1), h_state" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RNN (\n", " (rnn): RNN(1, 32, batch_first=True)\n", " (out): Linear (32 -> 1)\n", ")\n" ] } ], "source": [ "\n", "rnn = RNN()\n", "print(rnn)\n", "\n", "optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters\n", "loss_func = nn.MSELoss() # the target label is not one-hotted" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20\n", "30\n", "20\n", "20\n", "30\n", "30\n", "10\n", "10\n", "30\n", "20\n", "20\n", "20\n", "30\n", "10\n", "10\n", "30\n", "10\n", "10\n", "30\n", "20\n", "20\n", "10\n", "30\n", "10\n", "30\n", "10\n", "20\n", "20\n", "30\n", "30\n", "20\n", "20\n", "30\n", "30\n", "10\n", "20\n", "20\n", "10\n", "30\n", "10\n", "20\n", "10\n", "20\n", "10\n", "20\n", "30\n", "30\n", "10\n", "10\n", "20\n", "20\n", "20\n", "20\n", "10\n", "10\n", "20\n", "10\n", "10\n", "20\n", "30\n" ] }, { "data": { "image/png": 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SBEvM8DcWf4DdJW1tWOceLEdNnFfHrG00vroNx1WAxvWZtEpEK2dJhRo2jrCD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\njg/izq99PtlWul0e3hxp1aDfvvOt+Dhej196if5+3nYb8Cd/om3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EQoNYKOgT2NhYWPkG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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "h_state = None # for initial hidden state\n", "\n", "plt.figure(1, figsize=(12, 5))\n", "plt.ion() # continuously plot\n", "\n", "######################## Below is different #########################\n", "\n", "################ static time steps ##########\n", "# for step in range(60):\n", "# start, end = step * np.pi, (step+1)*np.pi # time steps\n", "# # use sin predicts cos\n", "# steps = np.linspace(start, end, 10, dtype=np.float32)\n", "\n", "################ dynamic time steps #########\n", "step = 0\n", "for i in range(60):\n", " dynamic_steps = np.random.randint(1, 4) # has random time steps\n", " start, end = step * np.pi, (step + dynamic_steps) * np.pi # different time steps length\n", " step += dynamic_steps\n", "\n", " # use sin predicts cos\n", " steps = np.linspace(start, end, 10 * dynamic_steps, dtype=np.float32)\n", "\n", "####################### Above is different ###########################\n", "\n", " print(len(steps)) # print how many time step feed to RNN\n", "\n", " x_np = np.sin(steps) # float32 for converting torch FloatTensor\n", " y_np = np.cos(steps)\n", "\n", " x = Variable(torch.from_numpy(x_np[np.newaxis, :, np.newaxis])) # shape (batch, time_step, input_size)\n", " y = Variable(torch.from_numpy(y_np[np.newaxis, :, np.newaxis]))\n", "\n", " prediction, h_state = rnn(x, h_state) # rnn output\n", " # !! next step is important !!\n", " h_state = Variable(h_state.data) # repack the hidden state, break the connection from last iteration\n", "\n", " loss = loss_func(prediction, y) # cross entropy loss\n", " optimizer.zero_grad() # clear gradients for this training step\n", " loss.backward() # backpropagation, compute gradients\n", " optimizer.step() # apply gradients\n", "\n", " # plotting\n", " plt.plot(steps, y_np.flatten(), 'r-')\n", " plt.plot(steps, prediction.data.numpy().flatten(), 'b-')\n", " plt.draw()\n", " plt.pause(0.05)\n", "\n", "plt.ioff()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/502_GPU.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 502 GPU\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* torchvision" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", "from torch.autograd import Variable\n", "import torch.utils.data as Data\n", "import torchvision\n", "\n", "torch.manual_seed(1)\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "EPOCH = 1\n", "BATCH_SIZE = 50\n", "LR = 0.001\n", "DOWNLOAD_MNIST = False" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "train_data = torchvision.datasets.MNIST(\n", " root='./mnist/', \n", " train=True, \n", " transform=torchvision.transforms.ToTensor(), \n", " download=DOWNLOAD_MNIST,)\n", "\n", "train_loader = Data.DataLoader(\n", " dataset=train_data, \n", " batch_size=BATCH_SIZE, \n", " shuffle=True)\n", "\n", "test_data = torchvision.datasets.MNIST(\n", " root='./mnist/', train=False)\n", "\n", "# !!!!!!!! Change in here !!!!!!!!! #\n", "test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1)).type(torch.FloatTensor)[:2000].cuda()/255. # Tensor on GPU\n", "test_y = test_data.test_labels[:2000].cuda()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class CNN(nn.Module):\n", " def __init__(self):\n", " super(CNN, self).__init__()\n", " self.conv1 = nn.Sequential(\n", " nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2,), \n", " nn.ReLU(), \n", " nn.MaxPool2d(kernel_size=2),)\n", " self.conv2 = nn.Sequential(\n", " nn.Conv2d(16, 32, 5, 1, 2), \n", " nn.ReLU(), \n", " nn.MaxPool2d(2),)\n", " self.out = nn.Linear(32 * 7 * 7, 10)\n", "\n", " def forward(self, x):\n", " x = self.conv1(x)\n", " x = self.conv2(x)\n", " x = x.view(x.size(0), -1)\n", " output = self.out(x)\n", " return output" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CNN (\n", " (conv1): Sequential (\n", " (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", " (1): ReLU ()\n", " (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", " )\n", " (conv2): Sequential (\n", " (0): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", " (1): ReLU ()\n", " (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", " )\n", " (out): Linear (1568 -> 10)\n", ")" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cnn = CNN()\n", "\n", "# !!!!!!!! Change in here !!!!!!!!! #\n", "cnn.cuda() # Moves all model parameters and buffers to the GPU." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)\n", "loss_func = nn.CrossEntropyLoss()\n", "\n", "losses_his = []" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 2.3145 | test accuracy: 0.10\n", "Epoch: 0 | train loss: 0.5546 | test accuracy: 0.83\n", "Epoch: 0 | train loss: 0.5856 | test accuracy: 0.89\n", "Epoch: 0 | train loss: 0.1879 | test accuracy: 0.92\n", "Epoch: 0 | train loss: 0.0601 | test accuracy: 0.94\n", "Epoch: 0 | train loss: 0.1772 | test accuracy: 0.95\n", "Epoch: 0 | train loss: 0.0993 | test accuracy: 0.94\n", "Epoch: 0 | train loss: 0.2210 | test accuracy: 0.95\n", "Epoch: 0 | train loss: 0.0379 | test accuracy: 0.96\n", "Epoch: 0 | train loss: 0.0535 | test accuracy: 0.96\n", "Epoch: 0 | train loss: 0.0268 | test accuracy: 0.96\n", "Epoch: 0 | train loss: 0.0910 | test accuracy: 0.96\n", "Epoch: 0 | train loss: 0.0982 | test accuracy: 0.97\n", "Epoch: 0 | train loss: 0.3154 | test accuracy: 0.97\n", "Epoch: 0 | train loss: 0.0418 | test accuracy: 0.97\n", "Epoch: 0 | train loss: 0.1072 | test accuracy: 0.96\n", "Epoch: 0 | train loss: 0.0652 | test accuracy: 0.97\n", "Epoch: 0 | train loss: 0.1042 | test accuracy: 0.97\n", "Epoch: 0 | train loss: 0.1346 | test accuracy: 0.97\n", "Epoch: 0 | train loss: 0.0431 | test accuracy: 0.98\n", "Epoch: 0 | train loss: 0.0276 | test accuracy: 0.97\n", "Epoch: 0 | train loss: 0.0153 | test accuracy: 0.98\n", "Epoch: 0 | train loss: 0.0438 | test accuracy: 0.98\n", "Epoch: 0 | train loss: 0.0082 | test accuracy: 0.97\n" ] } ], "source": [ "for epoch in range(EPOCH):\n", " for step, (x, y) in enumerate(train_loader):\n", "\n", " # !!!!!!!! Change in here !!!!!!!!! #\n", " b_x = Variable(x).cuda() # Tensor on GPU\n", " b_y = Variable(y).cuda() # Tensor on GPU\n", "\n", " output = cnn(b_x)\n", " loss = loss_func(output, b_y)\n", " losses_his.append(loss.data[0])\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", "\n", " if step % 50 == 0:\n", " test_output = cnn(test_x)\n", "\n", " # !!!!!!!! Change in here !!!!!!!!! #\n", " pred_y = torch.max(test_output, 1)[1].cuda().data.squeeze() # move the computation in GPU\n", "\n", " accuracy = torch.sum(pred_y == test_y).type(torch.FloatTensor) / test_y.size(0)\n", " print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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Y1XuO4rJXluD1H+3NgFc4UlKB7EnfYUNBccSybq0+qO2MFJ+qxHPfbMZd7+eY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\njHQAMQdFWIGVQgIx/uKzcMZ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(losses_his, label='loss')\n", "plt.legend(loc='best')\n", "plt.xlabel('Steps')\n", "plt.ylabel('Loss')\n", "plt.ylim((0, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Test" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " 7\n", " 2\n", " 1\n", " 0\n", " 4\n", " 1\n", " 4\n", " 9\n", " 5\n", " 9\n", "[torch.cuda.LongTensor of size 10 (GPU 0)]\n", " prediction number\n", "\n", " 7\n", " 2\n", " 1\n", " 0\n", " 4\n", " 1\n", " 4\n", " 9\n", " 5\n", " 9\n", "[torch.cuda.LongTensor of size 10 (GPU 0)]\n", " real number\n" ] } ], "source": [ "# !!!!!!!! Change in here !!!!!!!!! #\n", "test_output = cnn(test_x[:10])\n", "pred_y = torch.max(test_output, 1)[1].cuda().data.squeeze() # move the computation in GPU\n", "\n", "print(pred_y, 'prediction number')\n", "print(test_y[:10], 'real number')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/503_dropout.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 503 Dropout\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* matplotlib\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", "from torch.autograd import Variable\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "torch.manual_seed(1) # reproducible" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "N_SAMPLES = 20\n", "N_HIDDEN = 300" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# training data\n", "x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)\n", "y = x + 0.3*torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))\n", "x, y = Variable(x), Variable(y)\n", "\n", "# test data\n", "test_x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)\n", "test_y = test_x + 0.3*torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))\n", "test_x, test_y = Variable(test_x, volatile=True), Variable(test_y, volatile=True)\n", "\n", "# show data\n", "plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', s=50, alpha=0.5, label='train')\n", "plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.5, label='test')\n", "plt.legend(loc='upper left')\n", "plt.ylim((-2.5, 2.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "net_overfitting = torch.nn.Sequential(\n", " torch.nn.Linear(1, N_HIDDEN),\n", " torch.nn.ReLU(),\n", " torch.nn.Linear(N_HIDDEN, N_HIDDEN),\n", " torch.nn.ReLU(),\n", " torch.nn.Linear(N_HIDDEN, 1),\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "net_dropped = torch.nn.Sequential(\n", " torch.nn.Linear(1, N_HIDDEN),\n", " torch.nn.Dropout(0.5), # drop 50% of the neuron\n", " torch.nn.ReLU(),\n", " torch.nn.Linear(N_HIDDEN, N_HIDDEN),\n", " torch.nn.Dropout(0.5), # drop 50% of the neuron\n", " torch.nn.ReLU(),\n", " torch.nn.Linear(N_HIDDEN, 1),\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sequential (\n", " (0): Linear (1 -> 300)\n", " (1): ReLU ()\n", " (2): Linear (300 -> 300)\n", " (3): ReLU ()\n", " (4): Linear (300 -> 1)\n", ")\n", "Sequential (\n", " (0): Linear (1 -> 300)\n", " (1): Dropout (p = 0.5)\n", " (2): ReLU ()\n", " (3): Linear (300 -> 300)\n", " (4): Dropout (p = 0.5)\n", " (5): ReLU ()\n", " (6): Linear (300 -> 1)\n", ")\n" ] } ], "source": [ "print(net_overfitting) # net architecture\n", "print(net_dropped)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "optimizer_ofit = torch.optim.Adam(net_overfitting.parameters(), lr=0.01)\n", "optimizer_drop = torch.optim.Adam(net_dropped.parameters(), lr=0.01)\n", "loss_func = torch.nn.MSELoss()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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ntR9+cGsQkmVCgkWLJJeMyQ8/iMNzVsybJ5omSByuw4ft87blhMuXxTP87FnZ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\nfGtTtsJyzrWZ9HGTpY+3FExXcFHBZWU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rpfYrpU4mKfXrWqUef1wpf3+VDOoywWov9dQqOqpdhCnVooVS8+Ypdfu2uqyU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\nCnRzliJH3jzly8vDRlSUCF2zJ4GbmQx0BsYbBm2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RCSVPCFYT8ib7eOdLRGVUn9bTvxyDwVAsKArq+H5AvXw+hyFjvFccmF+OwWDIALtSdd00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\n14azZ9WZywcfOOyoL13S99p9+/Svd911+sJy6JB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s9nG33w779mVvGTApSZYiL16U62XL4L6uolKagEjrithPBxISxKzF7NMyKkoe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\nXCjCzewv+ORJa15mfPfa0qePmFb27m0dx5gxYmK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kbEFBpgbbTkCHjrIfEwpxPeHChczZaV0Owvje8HAz196EpkDQgZM13uPUKahS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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for t in range(500):\n", " pred_ofit = net_overfitting(x)\n", " pred_drop = net_dropped(x)\n", " loss_ofit = loss_func(pred_ofit, y)\n", " loss_drop = loss_func(pred_drop, y)\n", "\n", " optimizer_ofit.zero_grad()\n", " optimizer_drop.zero_grad()\n", " loss_ofit.backward()\n", " loss_drop.backward()\n", " optimizer_ofit.step()\n", " optimizer_drop.step()\n", "\n", " if t % 100 == 0:\n", " # change to eval mode in order to fix drop out effect\n", " net_overfitting.eval()\n", " net_dropped.eval() # parameters for dropout differ from train mode\n", "\n", " # plotting\n", " plt.cla()\n", " test_pred_ofit = net_overfitting(test_x)\n", " test_pred_drop = net_dropped(test_x)\n", " plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', s=50, alpha=0.3, label='train')\n", " plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.3, label='test')\n", " plt.plot(test_x.data.numpy(), test_pred_ofit.data.numpy(), 'r-', lw=3, label='overfitting')\n", " plt.plot(test_x.data.numpy(), test_pred_drop.data.numpy(), 'b--', lw=3, label='dropout(50%)')\n", " plt.text(0, -1.2, 'overfitting loss=%.4f' % loss_func(test_pred_ofit, test_y).data[0], fontdict={'size': 20, 'color': 'red'})\n", " plt.text(0, -1.5, 'dropout loss=%.4f' % loss_func(test_pred_drop, test_y).data[0], fontdict={'size': 20, 'color': 'blue'})\n", " plt.legend(loc='upper left'); plt.ylim((-2.5, 2.5));plt.pause(0.1)\n", "\n", " # change back to train mode\n", " net_overfitting.train()\n", " net_dropped.train()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/504_batch_normalization.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 504 Batch Normalization\n", "\n", "View more, visit my tutorial page: https://mofanpy.com/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* matplotlib\n", "* numpy" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "from torch.autograd import Variable\n", "from torch import nn\n", "from torch.nn import init\n", "import torch.utils.data as Data\n", "import torch.nn.functional as F\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline\n", "\n", "torch.manual_seed(1) # reproducible\n", "np.random.seed(1)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Hyper parameters\n", "N_SAMPLES = 2000\n", "BATCH_SIZE = 64\n", "EPOCH = 12\n", "LR = 0.03\n", "N_HIDDEN = 8\n", "ACTIVATION = F.tanh\n", "B_INIT = -0.2 # use a bad bias constant initializer" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ypRmYn4LKUv3vEU1A71GJfd8I3P9eOHqPifwO09pCH4vBXY8ATq6Xrv6tLQOD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\ngWvFLfd1TXfD8ft0raS74cj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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# training data\n", "x = np.linspace(-7, 10, N_SAMPLES)[:, np.newaxis]\n", "noise = np.random.normal(0, 2, x.shape)\n", "y = np.square(x) - 5 + noise\n", "\n", "# test data\n", "test_x = np.linspace(-7, 10, 200)[:, np.newaxis]\n", "noise = np.random.normal(0, 2, test_x.shape)\n", "test_y = np.square(test_x) - 5 + noise\n", "\n", "train_x, train_y = torch.from_numpy(x).float(), torch.from_numpy(y).float()\n", "test_x = Variable(torch.from_numpy(test_x).float(), volatile=True) # not for computing gradients\n", "test_y = Variable(torch.from_numpy(test_y).float(), volatile=True)\n", "\n", "train_dataset = Data.TensorDataset(train_x, train_y)\n", "train_loader = Data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,)\n", "\n", "# show data\n", "plt.scatter(train_x.numpy(), train_y.numpy(), c='#FF9359', s=50, alpha=0.2, label='train')\n", "plt.legend(loc='upper left')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Net(nn.Module):\n", " def __init__(self, batch_normalization=False):\n", " super(Net, self).__init__()\n", " self.do_bn = batch_normalization\n", " self.fcs = []\n", " self.bns = []\n", " self.bn_input = nn.BatchNorm1d(1, momentum=0.5) # for input data\n", "\n", " for i in range(N_HIDDEN): # build hidden layers and BN layers\n", " input_size = 1 if i == 0 else 10\n", " fc = nn.Linear(input_size, 10)\n", " setattr(self, 'fc%i' % i, fc) # IMPORTANT set layer to the Module\n", " self._set_init(fc) # parameters initialization\n", " self.fcs.append(fc)\n", " if self.do_bn:\n", " bn = nn.BatchNorm1d(10, momentum=0.5)\n", " setattr(self, 'bn%i' % i, bn) # IMPORTANT set layer to the Module\n", " self.bns.append(bn)\n", "\n", " self.predict = nn.Linear(10, 1) # output layer\n", " self._set_init(self.predict) # parameters initialization\n", "\n", " def _set_init(self, layer):\n", " init.normal(layer.weight, mean=0., std=.1)\n", " init.constant(layer.bias, B_INIT)\n", "\n", " def forward(self, x):\n", " pre_activation = [x]\n", " if self.do_bn: x = self.bn_input(x) # input batch normalization\n", " layer_input = [x]\n", " for i in range(N_HIDDEN):\n", " x = self.fcs[i](x)\n", " pre_activation.append(x)\n", " if self.do_bn: x = self.bns[i](x) # batch normalization\n", " x = ACTIVATION(x)\n", " layer_input.append(x)\n", " out = self.predict(x)\n", " return out, layer_input, pre_activation" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Net (\n", " (bn_input): BatchNorm1d(1, eps=1e-05, momentum=0.5, affine=True)\n", " (fc0): Linear (1 -> 10)\n", " (fc1): Linear (10 -> 10)\n", " (fc2): Linear (10 -> 10)\n", " (fc3): Linear (10 -> 10)\n", " (fc4): Linear (10 -> 10)\n", " (fc5): Linear (10 -> 10)\n", " (fc6): Linear (10 -> 10)\n", " (fc7): Linear (10 -> 10)\n", " (predict): Linear (10 -> 1)\n", ") Net (\n", " (bn_input): BatchNorm1d(1, eps=1e-05, momentum=0.5, affine=True)\n", " (fc0): Linear (1 -> 10)\n", " (bn0): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\n", " (fc1): Linear (10 -> 10)\n", " (bn1): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\n", " (fc2): Linear (10 -> 10)\n", " (bn2): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\n", " (fc3): Linear (10 -> 10)\n", " (bn3): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\n", " (fc4): Linear (10 -> 10)\n", " (bn4): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\n", " (fc5): Linear (10 -> 10)\n", " (bn5): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\n", " (fc6): Linear (10 -> 10)\n", " (bn6): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\n", " (fc7): Linear (10 -> 10)\n", " (bn7): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\n", " (predict): Linear (10 -> 1)\n", ")\n" ] } ], "source": [ "nets = [Net(batch_normalization=False), Net(batch_normalization=True)]\n", "\n", "print(*nets) # print net architecture" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "opts = [torch.optim.Adam(net.parameters(), lr=LR) for net in nets]\n", "\n", "loss_func = torch.nn.MSELoss()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0\n", "Epoch: 1\n", "Epoch: 2\n", "Epoch: 3\n", "Epoch: 4\n", "Epoch: 5\n", "Epoch: 6\n", "Epoch: 7\n", "Epoch: 8\n", "Epoch: 9\n", "Epoch: 10\n", "Epoch: 11\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, axs = plt.subplots(4, N_HIDDEN+1, figsize=(10, 5))\n", "plt.ion() # something about plotting\n", "def plot_histogram(l_in, l_in_bn, pre_ac, pre_ac_bn):\n", " for i, (ax_pa, ax_pa_bn, ax, ax_bn) in enumerate(zip(axs[0, :], axs[1, :], axs[2, :], axs[3, :])):\n", " [a.clear() for a in [ax_pa, ax_pa_bn, ax, ax_bn]]\n", " if i == 0: p_range = (-7, 10);the_range = (-7, 10)\n", " else:p_range = (-4, 4);the_range = (-1, 1)\n", " ax_pa.set_title('L' + str(i))\n", " ax_pa.hist(pre_ac[i].data.numpy().ravel(), bins=10, range=p_range, color='#FF9359', alpha=0.5);ax_pa_bn.hist(pre_ac_bn[i].data.numpy().ravel(), bins=10, range=p_range, color='#74BCFF', alpha=0.5)\n", " ax.hist(l_in[i].data.numpy().ravel(), bins=10, range=the_range, color='#FF9359');ax_bn.hist(l_in_bn[i].data.numpy().ravel(), bins=10, range=the_range, color='#74BCFF')\n", " for a in [ax_pa, ax, ax_pa_bn, ax_bn]: a.set_yticks(());a.set_xticks(())\n", " ax_pa_bn.set_xticks(p_range);ax_bn.set_xticks(the_range)\n", " axs[0, 0].set_ylabel('PreAct');axs[1, 0].set_ylabel('BN PreAct');axs[2, 0].set_ylabel('Act');axs[3, 0].set_ylabel('BN Act')\n", " plt.pause(0.01)\n", " \n", "# training\n", "losses = [[], []] # recode loss for two networks\n", "for epoch in range(EPOCH):\n", " print('Epoch: ', epoch)\n", " layer_inputs, pre_acts = [], []\n", " for net, l in zip(nets, losses):\n", " net.eval() # set eval mode to fix moving_mean and moving_var\n", " pred, layer_input, pre_act = net(test_x)\n", " l.append(loss_func(pred, test_y).data)\n", " layer_inputs.append(layer_input)\n", " pre_acts.append(pre_act)\n", " net.train() # free moving_mean and moving_var\n", " plot_histogram(*layer_inputs, *pre_acts) # plot histogram\n", "\n", " for step, (b_x, b_y) in enumerate(train_loader):\n", " b_x, b_y = Variable(b_x), Variable(b_y)\n", " for net, opt in zip(nets, opts): # train for each network\n", " pred, _, _ = net(b_x)\n", " loss = loss_func(pred, b_y)\n", " opt.zero_grad()\n", " loss.backward()\n", " opt.step() # it will also learns the parameters in Batch Normalization\n", " \n", "plt.ioff()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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P92PXxM6t6977ItU1rX36/ypd/y+2kFpkU9V2m+3fvATiSjT2rofB8H9KOoKW\nNFRX6tG7PfzLIHhkZvArtXwzPDYbzh8CWcn8P5aTB4PHwaCx8MVsWLcUdnwFO7aF21fBtjPc3/7V\n3s1dyarcHmxbm/0sNiKZKSc3/R+R9k+QfTakM5zaH54PR93OXgsvfA6n9d+Hf6RZVs3zIo2p2hmX\nYL6q2d8Zf1zH+zu2wc6vmvG/7EQkVZQ8mrlv9gzmwfrb8uD4vfLgIcIxBzd83X7JyQu2wuLGz92T\n7wpqHbHEUl1NUHfysFbuYXLxmiRT6zX+/YbOj9uvdf6+2NcLnYabhOp5r8HEn+qmm338bk2e/5v6\nA5u4KS/hj0vgxETuVdgh0Q/cZ0oeLcD4Q4ME8tma4PjFhcFMvKUHRRtXnSwr6DTPax1MnSIiGSnq\nJ8wlAVkWLCLVt31N2RNzYNHG6GISkQObkkcLkZMFFxwBncPnPao9eCL9S60TJSIRUPJoQVrnwg9K\noSh83mN7Fdw3HTZtjzYuETnwKHm0MMWt4KJSyA+f96jYAfd9VrOolIhIU1DyaIG6t4XvxT3vsWor\nPDyz5oFCEZF0U/JooQ7rCP98eM3xgg3w1Fw9YiEiTUPJowUr6won9q05/uRLeGVxdPGIyIFDyaOF\nG9cbRu6xkNT75VFFIyIHCiWPFs4MzjgMBnSsKXtuPsxZF11MIpL5lDwyQH0LSS3fFGlYIpLBlDwy\nRH4OXDQUOhQEx5W74P7PYJ0WDxSRNFDyyCBt8+EHw4KHCQG+qgweIty6M9q4RCTzKHlkmJLWcOER\nwXQmAOu+hgc+g50NLtorIpIcJY8MFFtIKjZz8/LN8Ngs2KVnQEQkRSJLHma21Mxmmtl0M5salnUw\nsylmtiB8LQ7LzcxuNbOFZjbDzIZHFXdLEVtIKmb2Ovjf+XqIUERSI+qax1h3L41bM3cS8Lq79wNe\nD48BTgb6hdtE4M4mj7QF+mZPOKZXzfH7K+Ct5dHFIyKZo7ktBnUaMCbcfwh4C/h5WP6wuzvwgZm1\nN7Ou7r4qkihbkD0Xkpq8ED5eCQe3q9m6FCa5LrqIHPCiTB4OvGpmDvyPu98NdIlLCF8CXcL97sAX\ncdeWh2W1koeZTSSomdCrVy+kZiGpLTthcUVQtnZbsE0Nf3r52dCrHfQqqkkosRFbIiJ1iTJ5fNPd\nV5hZZ2CKmc2Lf9PdPUwsCQsT0N0AZWVlat0PxRaSenpu0PexZ8f5jupgYsUFG2rKSloHSSSWUA5q\no9qJiNQfXY5PAAAPUklEQVSILHm4+4rwdY2ZPQeMAFbHmqPMrCsQNrawAugZd3mPsEwS1DoXzj8C\nKquhfAss21SzbanjOZC6aic942omvdpBoWonIgesSJKHmRUCWe6+Jdw/Afg18AJwAXBD+Pp8eMkL\nwOVm9gQwEtik/o59k5sNfdoHGwSjryq2xyWTzbBiS921k4Ubgy2mpHWQRA5W7UTkgBNVzaML8JyZ\nxWJ4zN1fNrOPgSfN7PvAMuDs8PzJwHhgIbANuLDpQ85MZsHqhMWtoPSgoGzP2snyTbC5gdrJNNVO\nRA445hk68L+srMynTp0adRgZYXftZHNNQlm5BaoT+NXp0x5O7w/d2qY/ThHZP2Y2Le7RiQY1t6G6\n0gzVqp2E499itZPlcc1dm3fsfe2SCrjlYxjTC47rEzSbiUjLp+Qh+6TOvpMdNc1cyzYFyWWXB9sb\ny2DGGjjrcDikONrYRWT/KXlISphBcUGwxWona76Cp+cFtQ8IJmm86xMY0Q2+daieJRFpyaKenkQy\nWOdCuGQ4nHkYFMQ1V320Em78IKiJZGiXm0jGU/KQtMoy+EYP+PdRMLikpnzLTnhkJjw0EzZtjy4+\nEdk3Sh7SJNoVBE+5nz8EivJqymevDWoh75dryniRlkTJQ5rUkM5BLWRkt5qy7dXw7Hy4a1rQTyIi\nzZ+ShzS5VrnBqKtLhkOnVjXlSzbBTR/ClCVQtSu6+ESkcUoeEplDiuEnI+HY3jXTmlQ7vLoYbv4o\nGO4rIs2TkodEKjcbTj4ErjgqmNokZvVX8Kep8Px82F4VXXwiUjclD2kWurWFy8vg1H6QG/5WOvBu\nOfzhA5i7LtLwRGQPSh7SbGQZHN0r6FDv36GmvGIH3P8ZPDoLttYxQaOIND0lD2l2OrSCH5TCeYNq\nz8o7fTX8/v1gjRE9XCgSLSUPaZbMYPhB8LNRwWvMtir4yxy4Zzqs/zq6+EQOdEoe0qwV5gU1kB+U\nBvNmxSzYEPSFvLUMqjWsV6TJKXlIi3BYR/jpSDi6J8QWK6zcBS8uhNumBqsfikjTUfKQFiM/B07t\nDz86Crq2qSlfsQVu/ThIJKu2wlc71Scikm6akl1anJ5FwXMhf1te8zT6Lg+asN5aFpyTbVCUD23z\ngtd2+Xsft82H1jlB/4qIJKfJk4eZ9QQeJljH3IG73f0WM/slcDGwNjz1anefHF5zFfB9oBr4sbu/\n0tRxS/OSnRU8mT6kMzw9FxZX1H6/2mHj9mBr8D5hkinKDyZs3L0fJppY0mmlJCNSSxQ1jyrgp+7+\niZm1BaaZ2ZTwvT+6+43xJ5vZQOBcYBDQDXjNzPq7e3WTRi3NUklr+OFwmLYKPl0dTO++eUcw2WIi\nEk0yOVk1tZZYkunYCg7vBJ1a7//3EGlpmjx5uPsqYFW4v8XM5gLdG7jkNOAJd98BLDGzhcAI4P20\nBystQpbBUd2CLWZndZBENu+AzTvr3t+SRJKp2lV3knlhQdD/MrgEjugMXQpVQ5EDQ6R9HmbWGxgG\nfAiMBi43s/OBqQS1k40EieWDuMvKaTjZiJCXHdQIGqsV7KgKEsqWMKFsituPTzY7Gkgyq7YG25Ql\nwSzBQzoHW4+2SiSSuSJLHmbWBngGuNLdN5vZncD1BP0g1wN/AC5K8p4TgYkAvXr1Sm3AkpHyc6Ak\nJ2j+akgsyWyOSyyLK+DzDbWnj1/3Nby5LNja58PgzjCkBHq3r5k5WCQTRJI8zCyXIHE86u7PArj7\n6rj37wH+LzxcAfSMu7xHWLYXd78buBugrKxMgzUlZepKMsccHMz4O289zFoDc9cHzWUxFTvg3S+C\nrU1e0LQ1uAQOLQ46/EVasihGWxlwHzDX3W+KK+8a9ocAnAHMCvdfAB4zs5sIOsz7AR81Ycgi9SrI\ngdIuwVZZHdREZq6FOWvh67ip5LfuhA9WBFurHBjYKaiVHNYhmJZepKWJouYxGvgeMNPMpodlVwPn\nmVkpQbPVUuCHAO4+28yeBOYQjNT6V420kuYoNxsGlQRb9S5YtDFIJLPWwNbKmvO+roJpXwZbXjYM\n6Bj0kQzoGCQjkZbAPEMfxS0rK/OpU6dGHYYIuxyWVsCstUEyqahnWHBOVjAV/eAwAbXOrfu8TLPx\n6yChdilUc17UzGyau5clcq7+nSOSZlkGfYuD7ZR+UL4lqI3MXAtrt9WcV7UL5qwLtqx5wTK9Q8JE\nUpQfXfypVlkNiypg/jqYv6HmZ5CfDX3aB9/7kGLo3laDDJoz1TxEIuIeLLc7cy3MXBMM962LAQe3\nCx5IPLhdMD1LXgvqJ3EPRqHNC5PFoo21R6jVpyAb+oSJ5NDi4HkaJZP0Us1DpAUwg4PaBNvxfWDd\ntrBpaw0s31xzngNLNwUbBH9Au7aBXkXBEOCD20GHgub1TMmOqiBJzFsP89fDhgae4M/Ngla5wfDn\neNurg+WHY0sQt8qBvnE1k4OUTCKlmodIM7Rpe00fyeKNQQJpSJvcIInEth5NXDuJ1aLmrw8SxpKK\nYOqX+nRuHUyzf1jHICHkZAWLey3aGDRpLdq4dzLZU+vc4NpDw2Sip/v3XzI1DyUPkWZu687gX99L\nN8GyTbDmq8aTSZZBtza1E0pximsn26uCRbnmh7WLigb+2OdnB3/kYwmjQ6uG7x1r6lq0MdgWbmx8\n/frC3JpaySHFQYJSMkmOkgdKHpK5vq6C5WEiWbYpaOLaXtX4dW3y4pJJUdB3kswzJu5Bv0ysKWrp\npmAkWX0OahM8xzKgY9C8lrMfI6ncYc22mmSyaCN8VdnwNW3zaieTTq2UTBqj5IGShxw4dnlQG1kW\nl1DWbGv8uljtpHdc7aT9HrWTbZVB7WLeevh8fTBFS30KsqFfBxjQKRhy3L6g/nP3V6yZLFYrWVwR\nxNqQovwwkbSHbm2DZNomVw9pxlPyQMlDDmzbKoMaSSyZfLEpsRmEi/KgV7tgQsklFUENp6G/EN3b\nhk1RHYLkE9VzGrscvtxaUytZXFH7Cf+GFOQESaRtXphQ8mr22+YH78XK8jN8iJGSB0oeIvH2tXay\np9Y50D/st+jfofk+f7IrbGJbGCaTJRsTn36/IblZcYmlrmQT91rQAhcQ01BdEaklK25Y8MhwQYNt\nlbWTyReb95563ghGbh3WMei76FnUMobHZllQK+reFo7pFSSTFVvCRFIRPOW/tTLohG+o32ZPlbuC\nYccNDT2Oybaa5FKQHYx+i9/yGznesywnq3klIyUPkQNU69zgwcPDOwXHsaafZZuCP47d2gR9GG3y\noo0zFbIsSHw9i2DMwTXluzxo3tq6A7bsDJLJlp01iaVW2c7EHm6MqXbYtCPYUsGoP8nsmWy6toEj\nu6bmc+uj5CEiQNiB3jbYDhRZFgzxLcyFLo2c6x7UzPZMKLHXPfcbWkBsXzjBPRO57+ASJQ8RkWbB\nLOjHKEhg8TAI1naJJZKd1TXbjup9O27oocs9NcUDokoeIiJpkJcdPAzZ2AORiareVXdyqausc2Fq\nPrMhSh4iIi1Adha0CucBaw40e76IiCRNyUNERJKm5CEiIklT8hARkaS1mORhZieZ2XwzW2hmk6KO\nR0TkQNYikoeZZQN/Ak4GBgL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Hj4pxBvKb7q6y2DhpGn/2RubxL8yGOepdHZCxYaGDnMVLS0XMExPlTJ6cHLLI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\nfOJKC2swvr1wKb8r+Swd7sgrqTkNe7l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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot training loss\n", "plt.figure(2)\n", "plt.plot(losses[0], c='#FF9359', lw=3, label='Original')\n", "plt.plot(losses[1], c='#74BCFF', lw=3, label='Batch Normalization')\n", "plt.xlabel('step');plt.ylabel('test loss');plt.ylim((0, 2000));plt.legend(loc='best')\n", "\n", "# evaluation\n", "# set net to eval mode to freeze the parameters in batch normalization layers\n", "[net.eval() for net in nets] # set eval mode to fix moving_mean and moving_var\n", "preds = [net(test_x)[0] for net in nets]\n", "plt.figure(3)\n", "plt.plot(test_x.data.numpy(), preds[0].data.numpy(), c='#FF9359', lw=4, label='Original')\n", "plt.plot(test_x.data.numpy(), preds[1].data.numpy(), c='#74BCFF', lw=4, label='Batch Normalization')\n", "plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='r', s=50, alpha=0.2, label='train')\n", "plt.legend(loc='best')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorial-contents-notebooks/mnist/processed/training.pt ================================================ [File too large to display: 45.3 MB] ================================================ FILE: tutorial-contents-notebooks/mnist/raw/train-images-idx3-ubyte ================================================ [File too large to display: 44.9 MB]