[
  {
    "path": ".gitignore",
    "content": ".idea\n*/.DS_Store\n"
  },
  {
    "path": "Caffe/00-classification.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Classification: Instant Recognition with Caffe\\n\",\n    \"\\n\",\n    \"In this example we'll classify an image with the bundled CaffeNet model (which is based on the network architecture of Krizhevsky et al. for ImageNet).\\n\",\n    \"\\n\",\n    \"We'll compare CPU and GPU modes and then dig into the model to inspect features and the output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1. Setup\\n\",\n    \"\\n\",\n    \"* First, set up Python, `numpy`, and `matplotlib`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# set up Python environment: numpy for numerical routines, and matplotlib for plotting\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"# display plots in this notebook\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"# set display defaults\\n\",\n    \"plt.rcParams['figure.figsize'] = (10, 10)        # large images\\n\",\n    \"plt.rcParams['image.interpolation'] = 'nearest'  # don't interpolate: show square pixels\\n\",\n    \"plt.rcParams['image.cmap'] = 'gray'  # use grayscale output rather than a (potentially misleading) color heatmap\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Load `caffe`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# The caffe module needs to be on the Python path;\\n\",\n    \"#  we'll add it here explicitly.\\n\",\n    \"import sys\\n\",\n    \"caffe_root = '../'  # this file should be run from {caffe_root}/examples (otherwise change this line)\\n\",\n    \"sys.path.insert(0, caffe_root + 'python')\\n\",\n    \"\\n\",\n    \"import caffe\\n\",\n    \"# If you get \\\"No module named _caffe\\\", either you have not built pycaffe or you have the wrong path.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* If needed, download the reference model (\\\"CaffeNet\\\", a variant of AlexNet).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"CaffeNet found.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"if os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\\n\",\n    \"    print 'CaffeNet found.'\\n\",\n    \"else:\\n\",\n    \"    print 'Downloading pre-trained CaffeNet model...'\\n\",\n    \"    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2. Load net and set up input preprocessing\\n\",\n    \"\\n\",\n    \"* Set Caffe to CPU mode and load the net from disk.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"caffe.set_mode_cpu()\\n\",\n    \"\\n\",\n    \"model_def = caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt'\\n\",\n    \"model_weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\\n\",\n    \"\\n\",\n    \"net = caffe.Net(model_def,      # defines the structure of the model\\n\",\n    \"                model_weights,  # contains the trained weights\\n\",\n    \"                caffe.TEST)     # use test mode (e.g., don't perform dropout)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Set up input preprocessing. (We'll use Caffe's `caffe.io.Transformer` to do this, but this step is independent of other parts of Caffe, so any custom preprocessing code may be used).\\n\",\n    \"\\n\",\n    \"    Our default CaffeNet is configured to take images in BGR format. Values are expected to start in the range [0, 255] and then have the mean ImageNet pixel value subtracted from them. In addition, the channel dimension is expected as the first (_outermost_) dimension.\\n\",\n    \"    \\n\",\n    \"    As matplotlib will load images with values in the range [0, 1] in RGB format with the channel as the _innermost_ dimension, we are arranging for the needed transformations here.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"mean-subtracted values: [('B', 104.0069879317889), ('G', 116.66876761696767), ('R', 122.6789143406786)]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# load the mean ImageNet image (as distributed with Caffe) for subtraction\\n\",\n    \"mu = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')\\n\",\n    \"mu = mu.mean(1).mean(1)  # average over pixels to obtain the mean (BGR) pixel values\\n\",\n    \"print 'mean-subtracted values:', zip('BGR', mu)\\n\",\n    \"\\n\",\n    \"# create transformer for the input called 'data'\\n\",\n    \"transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\\n\",\n    \"\\n\",\n    \"transformer.set_transpose('data', (2,0,1))  # move image channels to outermost dimension\\n\",\n    \"transformer.set_mean('data', mu)            # subtract the dataset-mean value in each channel\\n\",\n    \"transformer.set_raw_scale('data', 255)      # rescale from [0, 1] to [0, 255]\\n\",\n    \"transformer.set_channel_swap('data', (2,1,0))  # swap channels from RGB to BGR\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3. CPU classification\\n\",\n    \"\\n\",\n    \"* Now we're ready to perform classification. Even though we'll only classify one image, we'll set a batch size of 50 to demonstrate batching.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# set the size of the input (we can skip this if we're happy\\n\",\n    \"#  with the default; we can also change it later, e.g., for different batch sizes)\\n\",\n    \"net.blobs['data'].reshape(50,        # batch size\\n\",\n    \"                          3,         # 3-channel (BGR) images\\n\",\n    \"                          227, 227)  # image size is 227x227\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Load an image (that comes with Caffe) and perform the preprocessing we've set up.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x7f09693a8c90>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Ou7KPqu8PRu4/b0Dm2V3/nXfwuA7akim0IZFKmUUhYvtVhBa3KZGDiN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\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GXwqiHaE8LjdmdNb8RsDpaQlB2sGJDmeHn5sb+EIinq+W7p3/L86SOp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\\n3gAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f09b97eab50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"image = caffe.io.load_image(caffe_root + 'examples/images/cat.jpg')\\n\",\n    \"transformed_image = transformer.preprocess('data', image)\\n\",\n    \"plt.imshow(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Adorable! Let's classify it!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"predicted class is: 281\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# copy the image data into the memory allocated for the net\\n\",\n    \"net.blobs['data'].data[...] = transformed_image\\n\",\n    \"\\n\",\n    \"### perform classification\\n\",\n    \"output = net.forward()\\n\",\n    \"\\n\",\n    \"output_prob = output['prob'][0]  # the output probability vector for the first image in the batch\\n\",\n    \"\\n\",\n    \"print 'predicted class is:', output_prob.argmax()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* The net gives us a vector of probabilities; the most probable class was the 281st one. But is that correct? Let's check the ImageNet labels...\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"output label: n02123045 tabby, tabby cat\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# load ImageNet labels\\n\",\n    \"labels_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\\n\",\n    \"if not os.path.exists(labels_file):\\n\",\n    \"    !../data/ilsvrc12/get_ilsvrc_aux.sh\\n\",\n    \"    \\n\",\n    \"labels = np.loadtxt(labels_file, str, delimiter='\\\\t')\\n\",\n    \"\\n\",\n    \"print 'output label:', labels[output_prob.argmax()]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* \\\"Tabby cat\\\" is correct! But let's also look at other top (but less confident predictions).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"probabilities and labels:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0.31243637, 'n02123045 tabby, tabby cat'),\\n\",\n       \" (0.2379719, 'n02123159 tiger cat'),\\n\",\n       \" (0.12387239, 'n02124075 Egyptian cat'),\\n\",\n       \" (0.10075711, 'n02119022 red fox, Vulpes vulpes'),\\n\",\n       \" (0.070957087, 'n02127052 lynx, catamount')]\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# sort top five predictions from softmax output\\n\",\n    \"top_inds = output_prob.argsort()[::-1][:5]  # reverse sort and take five largest items\\n\",\n    \"\\n\",\n    \"print 'probabilities and labels:'\\n\",\n    \"zip(output_prob[top_inds], labels[top_inds])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* We see that less confident predictions are sensible.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4. Switching to GPU mode\\n\",\n    \"\\n\",\n    \"* Let's see how long classification took, and compare it to GPU mode.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1 loop, best of 3: 1.42 s per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%timeit net.forward()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* That's a while, even for a batch of 50 images. Let's switch to GPU mode.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"10 loops, best of 3: 70.2 ms per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"caffe.set_device(0)  # if we have multiple GPUs, pick the first one\\n\",\n    \"caffe.set_mode_gpu()\\n\",\n    \"net.forward()  # run once before timing to set up memory\\n\",\n    \"%timeit net.forward()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* That should be much faster!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 5. Examining intermediate output\\n\",\n    \"\\n\",\n    \"* A net is not just a black box; let's take a look at some of the parameters and intermediate activations.\\n\",\n    \"\\n\",\n    \"First we'll see how to read out the structure of the net in terms of activation and parameter shapes.\\n\",\n    \"\\n\",\n    \"* For each layer, let's look at the activation shapes, which typically have the form `(batch_size, channel_dim, height, width)`.\\n\",\n    \"\\n\",\n    \"    The activations are exposed as an `OrderedDict`, `net.blobs`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"data\\t(50, 3, 227, 227)\\n\",\n      \"conv1\\t(50, 96, 55, 55)\\n\",\n      \"pool1\\t(50, 96, 27, 27)\\n\",\n      \"norm1\\t(50, 96, 27, 27)\\n\",\n      \"conv2\\t(50, 256, 27, 27)\\n\",\n      \"pool2\\t(50, 256, 13, 13)\\n\",\n      \"norm2\\t(50, 256, 13, 13)\\n\",\n      \"conv3\\t(50, 384, 13, 13)\\n\",\n      \"conv4\\t(50, 384, 13, 13)\\n\",\n      \"conv5\\t(50, 256, 13, 13)\\n\",\n      \"pool5\\t(50, 256, 6, 6)\\n\",\n      \"fc6\\t(50, 4096)\\n\",\n      \"fc7\\t(50, 4096)\\n\",\n      \"fc8\\t(50, 1000)\\n\",\n      \"prob\\t(50, 1000)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# for each layer, show the output shape\\n\",\n    \"for layer_name, blob in net.blobs.iteritems():\\n\",\n    \"    print layer_name + '\\\\t' + str(blob.data.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Now look at the parameter shapes. The parameters are exposed as another `OrderedDict`, `net.params`. We need to index the resulting values with either `[0]` for weights or `[1]` for biases.\\n\",\n    \"\\n\",\n    \"    The param shapes typically have the form `(output_channels, input_channels, filter_height, filter_width)` (for the weights) and the 1-dimensional shape `(output_channels,)` (for the biases).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"conv1\\t(96, 3, 11, 11) (96,)\\n\",\n      \"conv2\\t(256, 48, 5, 5) (256,)\\n\",\n      \"conv3\\t(384, 256, 3, 3) (384,)\\n\",\n      \"conv4\\t(384, 192, 3, 3) (384,)\\n\",\n      \"conv5\\t(256, 192, 3, 3) (256,)\\n\",\n      \"fc6\\t(4096, 9216) (4096,)\\n\",\n      \"fc7\\t(4096, 4096) (4096,)\\n\",\n      \"fc8\\t(1000, 4096) (1000,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for layer_name, param in net.params.iteritems():\\n\",\n    \"    print layer_name + '\\\\t' + str(param[0].data.shape), str(param[1].data.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Since we're dealing with four-dimensional data here, we'll define a helper function for visualizing sets of rectangular heatmaps.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def vis_square(data):\\n\",\n    \"    \\\"\\\"\\\"Take an array of shape (n, height, width) or (n, height, width, 3)\\n\",\n    \"       and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # normalize data for display\\n\",\n    \"    data = (data - data.min()) / (data.max() - data.min())\\n\",\n    \"    \\n\",\n    \"    # force the number of filters to be square\\n\",\n    \"    n = int(np.ceil(np.sqrt(data.shape[0])))\\n\",\n    \"    padding = (((0, n ** 2 - data.shape[0]),\\n\",\n    \"               (0, 1), (0, 1))                 # add some space between filters\\n\",\n    \"               + ((0, 0),) * (data.ndim - 3))  # don't pad the last dimension (if there is one)\\n\",\n    \"    data = np.pad(data, padding, mode='constant', constant_values=1)  # pad with ones (white)\\n\",\n    \"    \\n\",\n    \"    # tile the filters into an image\\n\",\n    \"    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\\n\",\n    \"    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\\n\",\n    \"    \\n\",\n    \"    plt.imshow(data); plt.axis('off')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* First we'll look at the first layer filters, `conv1`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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F/5PC9I0jkRdYPbFjkUesxeZGVSPqNy+xWR+zHNUPQerNp7bf30KWiz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\\nAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f09692b9ed0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# the parameters are a list of [weights, biases]\\n\",\n    \"filters = net.params['conv1'][0].data\\n\",\n    \"vis_square(filters.transpose(0, 2, 3, 1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* The first layer output, `conv1` (rectified responses of the filters above, first 36 only)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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K77bK1W05NeXIwxRtxpq1AoaLuhXSYmJrwgmBY6nY7Z5jht4pTd6XT0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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f09692c7350>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"feat = net.blobs['conv1'].data[0, :36]\\n\",\n    \"vis_square(feat)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* The fifth layer after pooling, `pool5`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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5Oby+zsbBKLpmFHnxlRrEouX/rSl7LWvfjii0lsZ2en1lzqJpfYoORy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\\nHzreW7zFsFB/AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f09691fce50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"feat = net.blobs['pool5'].data[0]\\n\",\n    \"vis_square(feat)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* The first fully connected layer, `fc6` (rectified)\\n\",\n    \"\\n\",\n    \"    We show the output values and the histogram of the positive values\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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tMBMwx1AB/q3iq72WadCXPLVPc6q5sbbsi2/LDtw3ZDHVQeYNUBAZA/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\\nFLIAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f096914f6d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"feat = net.blobs['fc6'].data[0]\\n\",\n    \"plt.subplot(2, 1, 1)\\n\",\n    \"plt.plot(feat.flat)\\n\",\n    \"plt.subplot(2, 1, 2)\\n\",\n    \"_ = plt.hist(feat.flat[feat.flat > 0], bins=100)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* The final probability output, `prob`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x7f09587dfb50>]\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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UQM2r4+3b/O9pmStko6VVLN9tckLYuIN5u17eaLAdA5o6PSoUPJMhU8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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f0969270290>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"feat = net.blobs['prob'].data[0]\\n\",\n    \"plt.figure(figsize=(15, 3))\\n\",\n    \"plt.plot(feat.flat)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note the cluster of strong predictions; the labels are sorted semantically. The top peaks correspond to the top predicted labels, as shown above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 6. Try your own image\\n\",\n    \"\\n\",\n    \"Now we'll grab an image from the web and classify it using the steps above.\\n\",\n    \"\\n\",\n    \"* Try setting `my_image_url` to any JPEG image URL.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# download an image\\n\",\n    \"my_image_url = \\\"...\\\"  # paste your URL here\\n\",\n    \"# for example:\\n\",\n    \"# my_image_url = \\\"https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_Sarawak%2C_Borneo%2C_Malaysia.JPG\\\"\\n\",\n    \"!wget -O image.jpg $my_image_url\\n\",\n    \"\\n\",\n    \"# transform it and copy it into the net\\n\",\n    \"image = caffe.io.load_image('image.jpg')\\n\",\n    \"net.blobs['data'].data[...] = transformer.preprocess('data', image)\\n\",\n    \"\\n\",\n    \"# perform classification\\n\",\n    \"net.forward()\\n\",\n    \"\\n\",\n    \"# obtain the output probabilities\\n\",\n    \"output_prob = net.blobs['prob'].data[0]\\n\",\n    \"\\n\",\n    \"# sort top five predictions from softmax output\\n\",\n    \"top_inds = output_prob.argsort()[::-1][:5]\\n\",\n    \"\\n\",\n    \"plt.imshow(image)\\n\",\n    \"\\n\",\n    \"print 'probabilities and labels:'\\n\",\n    \"zip(output_prob[top_inds], labels[top_inds])\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"description\": \"Instant recognition with a pre-trained model and a tour of the net interface for visualizing features and parameters layer-by-layer.\",\n  \"example_name\": \"Image Classification and Filter Visualization\",\n  \"include_in_docs\": true,\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  },\n  \"priority\": 1\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Caffe/01-learning-lenet.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Solving in Python with LeNet\\n\",\n    \"\\n\",\n    \"In this example, we'll explore learning with Caffe in Python, using the fully-exposed `Solver` interface.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1. Setup\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Set up the Python environment: we'll use the `pylab` import for numpy and plot inline.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from pylab import *\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Import `caffe`, adding it to `sys.path` if needed. Make sure you've built pycaffe.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"caffe_root = '../'  # this file should be run from {caffe_root}/examples (otherwise change this line)\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"sys.path.insert(0, caffe_root + 'python')\\n\",\n    \"import caffe\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* We'll be using the provided LeNet example data and networks (make sure you've downloaded the data and created the databases, as below).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading...\\n\",\n      \"Creating lmdb...\\n\",\n      \"Done.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# run scripts from caffe root\\n\",\n    \"import os\\n\",\n    \"os.chdir(caffe_root)\\n\",\n    \"# Download data\\n\",\n    \"!data/mnist/get_mnist.sh\\n\",\n    \"# Prepare data\\n\",\n    \"!examples/mnist/create_mnist.sh\\n\",\n    \"# back to examples\\n\",\n    \"os.chdir('examples')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2. Creating the net \\n\",\n    \"\\n\",\n    \"Now let's make a variant of LeNet, the classic 1989 convnet architecture.\\n\",\n    \"\\n\",\n    \"We'll need two external files to help out:\\n\",\n    \"* the net `prototxt`, defining the architecture and pointing to the train/test data\\n\",\n    \"* the solver `prototxt`, defining the learning parameters\\n\",\n    \"\\n\",\n    \"We start by creating the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\\n\",\n    \"\\n\",\n    \"This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from caffe import layers as L, params as P\\n\",\n    \"\\n\",\n    \"def lenet(lmdb, batch_size):\\n\",\n    \"    # our version of LeNet: a series of linear and simple nonlinear transformations\\n\",\n    \"    n = caffe.NetSpec()\\n\",\n    \"    \\n\",\n    \"    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\\n\",\n    \"                             transform_param=dict(scale=1./255), ntop=2)\\n\",\n    \"    \\n\",\n    \"    n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\\n\",\n    \"    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\\n\",\n    \"    n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\\n\",\n    \"    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\\n\",\n    \"    n.fc1 =   L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\\n\",\n    \"    n.relu1 = L.ReLU(n.fc1, in_place=True)\\n\",\n    \"    n.score = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\\n\",\n    \"    n.loss =  L.SoftmaxWithLoss(n.score, n.label)\\n\",\n    \"    \\n\",\n    \"    return n.to_proto()\\n\",\n    \"    \\n\",\n    \"with open('mnist/lenet_auto_train.prototxt', 'w') as f:\\n\",\n    \"    f.write(str(lenet('mnist/mnist_train_lmdb', 64)))\\n\",\n    \"    \\n\",\n    \"with open('mnist/lenet_auto_test.prototxt', 'w') as f:\\n\",\n    \"    f.write(str(lenet('mnist/mnist_test_lmdb', 100)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The net has been written to disk in a more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"layer {\\r\\n\",\n      \"  name: \\\"data\\\"\\r\\n\",\n      \"  type: \\\"Data\\\"\\r\\n\",\n      \"  top: \\\"data\\\"\\r\\n\",\n      \"  top: \\\"label\\\"\\r\\n\",\n      \"  transform_param {\\r\\n\",\n      \"    scale: 0.00392156862745\\r\\n\",\n      \"  }\\r\\n\",\n      \"  data_param {\\r\\n\",\n      \"    source: \\\"mnist/mnist_train_lmdb\\\"\\r\\n\",\n      \"    batch_size: 64\\r\\n\",\n      \"    backend: LMDB\\r\\n\",\n      \"  }\\r\\n\",\n      \"}\\r\\n\",\n      \"layer {\\r\\n\",\n      \"  name: \\\"conv1\\\"\\r\\n\",\n      \"  type: \\\"Convolution\\\"\\r\\n\",\n      \"  bottom: \\\"data\\\"\\r\\n\",\n      \"  top: \\\"conv1\\\"\\r\\n\",\n      \"  convolution_param {\\r\\n\",\n      \"    num_output: 20\\r\\n\",\n      \"    kernel_size: 5\\r\\n\",\n      \"    weight_filler {\\r\\n\",\n      \"      type: \\\"xavier\\\"\\r\\n\",\n      \"    }\\r\\n\",\n      \"  }\\r\\n\",\n      \"}\\r\\n\",\n      \"layer {\\r\\n\",\n      \"  name: \\\"pool1\\\"\\r\\n\",\n      \"  type: \\\"Pooling\\\"\\r\\n\",\n      \"  bottom: \\\"conv1\\\"\\r\\n\",\n      \"  top: \\\"pool1\\\"\\r\\n\",\n      \"  pooling_param {\\r\\n\",\n      \"    pool: MAX\\r\\n\",\n      \"    kernel_size: 2\\r\\n\",\n      \"    stride: 2\\r\\n\",\n      \"  }\\r\\n\",\n      \"}\\r\\n\",\n      \"layer {\\r\\n\",\n      \"  name: \\\"conv2\\\"\\r\\n\",\n      \"  type: \\\"Convolution\\\"\\r\\n\",\n      \"  bottom: \\\"pool1\\\"\\r\\n\",\n      \"  top: \\\"conv2\\\"\\r\\n\",\n      \"  convolution_param {\\r\\n\",\n      \"    num_output: 50\\r\\n\",\n      \"    kernel_size: 5\\r\\n\",\n      \"    weight_filler {\\r\\n\",\n      \"      type: \\\"xavier\\\"\\r\\n\",\n      \"    }\\r\\n\",\n      \"  }\\r\\n\",\n      \"}\\r\\n\",\n      \"layer {\\r\\n\",\n      \"  name: \\\"pool2\\\"\\r\\n\",\n      \"  type: \\\"Pooling\\\"\\r\\n\",\n      \"  bottom: \\\"conv2\\\"\\r\\n\",\n      \"  top: \\\"pool2\\\"\\r\\n\",\n      \"  pooling_param {\\r\\n\",\n      \"    pool: MAX\\r\\n\",\n      \"    kernel_size: 2\\r\\n\",\n      \"    stride: 2\\r\\n\",\n      \"  }\\r\\n\",\n      \"}\\r\\n\",\n      \"layer {\\r\\n\",\n      \"  name: \\\"fc1\\\"\\r\\n\",\n      \"  type: \\\"InnerProduct\\\"\\r\\n\",\n      \"  bottom: \\\"pool2\\\"\\r\\n\",\n      \"  top: \\\"fc1\\\"\\r\\n\",\n      \"  inner_product_param {\\r\\n\",\n      \"    num_output: 500\\r\\n\",\n      \"    weight_filler {\\r\\n\",\n      \"      type: \\\"xavier\\\"\\r\\n\",\n      \"    }\\r\\n\",\n      \"  }\\r\\n\",\n      \"}\\r\\n\",\n      \"layer {\\r\\n\",\n      \"  name: \\\"relu1\\\"\\r\\n\",\n      \"  type: \\\"ReLU\\\"\\r\\n\",\n      \"  bottom: \\\"fc1\\\"\\r\\n\",\n      \"  top: \\\"fc1\\\"\\r\\n\",\n      \"}\\r\\n\",\n      \"layer {\\r\\n\",\n      \"  name: \\\"score\\\"\\r\\n\",\n      \"  type: \\\"InnerProduct\\\"\\r\\n\",\n      \"  bottom: \\\"fc1\\\"\\r\\n\",\n      \"  top: \\\"score\\\"\\r\\n\",\n      \"  inner_product_param {\\r\\n\",\n      \"    num_output: 10\\r\\n\",\n      \"    weight_filler {\\r\\n\",\n      \"      type: \\\"xavier\\\"\\r\\n\",\n      \"    }\\r\\n\",\n      \"  }\\r\\n\",\n      \"}\\r\\n\",\n      \"layer {\\r\\n\",\n      \"  name: \\\"loss\\\"\\r\\n\",\n      \"  type: \\\"SoftmaxWithLoss\\\"\\r\\n\",\n      \"  bottom: \\\"score\\\"\\r\\n\",\n      \"  bottom: \\\"label\\\"\\r\\n\",\n      \"  top: \\\"loss\\\"\\r\\n\",\n      \"}\\r\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!cat mnist/lenet_auto_train.prototxt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's see the learning parameters, which are also written as a `prototxt` file (already provided on disk). We're using SGD with momentum, weight decay, and a specific learning rate schedule.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"# The train/test net protocol buffer definition\\r\\n\",\n      \"train_net: \\\"mnist/lenet_auto_train.prototxt\\\"\\r\\n\",\n      \"test_net: \\\"mnist/lenet_auto_test.prototxt\\\"\\r\\n\",\n      \"# test_iter specifies how many forward passes the test should carry out.\\r\\n\",\n      \"# In the case of MNIST, we have test batch size 100 and 100 test iterations,\\r\\n\",\n      \"# covering the full 10,000 testing images.\\r\\n\",\n      \"test_iter: 100\\r\\n\",\n      \"# Carry out testing every 500 training iterations.\\r\\n\",\n      \"test_interval: 500\\r\\n\",\n      \"# The base learning rate, momentum and the weight decay of the network.\\r\\n\",\n      \"base_lr: 0.01\\r\\n\",\n      \"momentum: 0.9\\r\\n\",\n      \"weight_decay: 0.0005\\r\\n\",\n      \"# The learning rate policy\\r\\n\",\n      \"lr_policy: \\\"inv\\\"\\r\\n\",\n      \"gamma: 0.0001\\r\\n\",\n      \"power: 0.75\\r\\n\",\n      \"# Display every 100 iterations\\r\\n\",\n      \"display: 100\\r\\n\",\n      \"# The maximum number of iterations\\r\\n\",\n      \"max_iter: 10000\\r\\n\",\n      \"# snapshot intermediate results\\r\\n\",\n      \"snapshot: 5000\\r\\n\",\n      \"snapshot_prefix: \\\"mnist/lenet\\\"\\r\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!cat mnist/lenet_auto_solver.prototxt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3. Loading and checking the solver\\n\",\n    \"\\n\",\n    \"* Let's pick a device and load the solver. We'll use SGD (with momentum), but other methods (such as Adagrad and Nesterov's accelerated gradient) are also available.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"caffe.set_device(0)\\n\",\n    \"caffe.set_mode_gpu()\\n\",\n    \"\\n\",\n    \"### load the solver and create train and test nets\\n\",\n    \"solver = None  # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\\n\",\n    \"solver = caffe.SGDSolver('mnist/lenet_auto_solver.prototxt')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[('data', (64, 1, 28, 28)),\\n\",\n       \" ('label', (64,)),\\n\",\n       \" ('conv1', (64, 20, 24, 24)),\\n\",\n       \" ('pool1', (64, 20, 12, 12)),\\n\",\n       \" ('conv2', (64, 50, 8, 8)),\\n\",\n       \" ('pool2', (64, 50, 4, 4)),\\n\",\n       \" ('fc1', (64, 500)),\\n\",\n       \" ('score', (64, 10)),\\n\",\n       \" ('loss', ())]\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# each output is (batch size, feature dim, spatial dim)\\n\",\n    \"[(k, v.data.shape) for k, v in solver.net.blobs.items()]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[('conv1', (20, 1, 5, 5)),\\n\",\n       \" ('conv2', (50, 20, 5, 5)),\\n\",\n       \" ('fc1', (500, 800)),\\n\",\n       \" ('score', (10, 500))]\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# just print the weight sizes (we'll omit the biases)\\n\",\n    \"[(k, v[0].data.shape) for k, v in solver.net.params.items()]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'loss': array(2.365971088409424, dtype=float32)}\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"solver.net.forward()  # train net\\n\",\n    \"solver.test_nets[0].forward()  # test net (there can be more than one)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"train labels: [ 5.  0.  4.  1.  9.  2.  1.  3.]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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3W8QtjD6XTidrvx+/243W7m5+cZGRnh+vXrzM3Nick9wemylwlcobjk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\\nSUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f51a65ee690>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# we use a little trick to tile the first eight images\\n\",\n    \"imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')\\n\",\n    \"print 'train labels:', solver.net.blobs['label'].data[:8]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"test labels: [ 7.  2.  1.  0.  4.  1.  4.  9.]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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PV6SkpK+Mu//EsaGxvJyMgQd3xC6Gl4eJju7m4+//xzFhYWCAaDJBIJ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\\nRK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f51a4030a50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')\\n\",\n    \"print 'test labels:', solver.test_nets[0].blobs['label'].data[:8]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4. Stepping the solver\\n\",\n    \"\\n\",\n    \"Both train and test nets seem to be loading data, and to have correct labels.\\n\",\n    \"\\n\",\n    \"* Let's take one step of (minibatch) SGD and see what happens.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"solver.step(1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Do we have gradients propagating through our filters? Let's see the updates to the first layer, shown here as a $4 \\\\times 5$ grid of $5 \\\\times 5$ filters.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(-0.5, 24.5, 19.5, -0.5)\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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tEZgyKmT6AJyc6qIrSPBhnLc58NVZEfQs2BHMIsXn6w+8MpEGYYoVXL\\niApi6pwjFTwwn93j7J28IyBzl56sTpaurot9FallO3DApo6nS85qMkS9O7qfyl2WnLeCHYvQYu8o\\nrBP2HIhdg8dqwzo7cE2XnQi119fXd28lM8xiQqiA5gCmAtxEnAmpc3y2V+dUXgegI5vbFhalVfcy\\nmKlz7FmXAtpk2YkAmy49bwG1u0Rok+cqFSAw4lDQYobdSQczBTdV/mTJ6URnWRBq+V6mw7z8XGu9\\nMdZssMwBsD6EGFsmueKAg13L9qpcVg/edwRgzImZ/qr2ZL2pfY7OXJhhm9yNQayaANVk6MDsrwRa\\nSGXwMbCd5AHL9TqGrtqUy9tdclbR2c6SUy01s9Gq+1hb8rWTmZc5B7apA1k15t19FRhYHeoaBbWj\\n8HKd2NlXdh3pSWT28PAgx9iBmQMxF2Sdvh17YHL3JSczfhdm2THZ4FdpdtzJZMmJZbN2HllyRvuZ\\nk2F9FeTWWnQ5UTks1s0gtwM11xHy/Wx8Owiw89W9O2DrdNe1Ge2ku5fBS4EO2zmJzHa3CfTVmDty\\ntyXnTmMRYBi1sHon9TDYKLg6wK0g1gEnHzNhDhcww3urutbSz9CywVdAU1CrZAdoVdkVxFidHdS6\\n/u46adferMuq3Wv9s+zsgJXBhhBjUENbcN92Tvr+UVD79AiNzTrObM6cW+V3dU/EhVnkqT6p5d4E\\naqz/CLNYVlRloM7cN5wVvFj+NEqbGDTChu3Vueq+CmK32FS7VF4FN9ZGtQTNS8587WTJ+RkR2ZcF\\nmmqIAtk0aguHuZWwtmCeug+Pq0iPRX0d1FT9bPAzzBCECnJhuN1HtQgr1gYWNWMa+8D6w/p5VBzQ\\nTTcEhMpjIK3SKi/GF/9axoEZggzbvAMsx1aq/lVj9aWA9vPnT5rvRg476ZBOCa+vr39C9c4o8zJO\\nlaWOMVrKaQQebs6va+R/UqL+YYmj206yYXX14rErCJYujQ5UQeTnz5/r8fFxXS6XNyB+fn5e1+uV\\nLsk6x490tzyLY+XcyuEnNhF9u1wuf+qJvuLEiGXnbzlzfrQf/aR6JOH4ExsfNY5/FdA6Z6ucj51j\\nUkUFlZKVUisjxH0GGIKsA5rKr2BS/QemTq8qMmD5Cl6qDSFsnDBP6dKFGnOMtda6XC7r8fFxPT4+\\nvvlk5eXlZV2v1z/lPT8/S7Ax550s2bq+YbqbzLI9vLy8/OkjTj5xjbI3BbRc9o6/dMtOdf7LAu0/\\n/uM/5DnXgSM/71lePocKUMc7AxH3sWOWjn2GmgKaSsex85to3TVK90pXOT+314nOMEKrxk+1oZtA\\n3C3ac7lc3vyhfgAs0vm7LndfLcnUcoz1jU2ILtCen5/fBBAKSOp8jFVezh55DqbGB/OrKPvLAa2K\\n0GLvOF6+p0pnUbNeTsdA7g5aNfPkj31jkAJiDGjuvovUIt91BqXDyojc6Ez9tJPqF6u3Op44EwN4\\ndqDn5+c/5xm4qrwqast5Hcx2gZZtLCTbSCxDUfJEiXYZk4Bqn9N+5/oOaNgvR+665KwiBxZBdMcM\\nXiqPDVgHKnXMDJYZSVybgZb7UYG7isKOQE1NCkovbnSWI7Rusura0LWtOlbpGAt2vgMZ5jvpKRAm\\nPsLuCZihbaJddTqsxmJyjOnOj1hk6ciXANoOzJRUMwXuHYdgRp2hlJ+TOVEfGlHVPzS+DCpnuYcG\\nrnSMbaykis5Yu9wJTNXljnEFB/d5l/uMqAJe9YC8ayeCp9NX6DufD93jy4nKrhjA2IRbSbYlR5yJ\\n46+N0JTzTcWZ+dwZPKerpUXALLeBQZFtE1Fw6mBSgSU/EGa6ZJv7UmAaJe5MYJUj5v31ev3zWUrO\\nj7x424kvBRTEJtGFen5WTaS5/06UlvPz80L2hrWbONU5d8/KY+W7E8NfB7TO8NfyqO/O1h3k2LXs\\nzZVyRgUzlcf6wPJxOeks99xIbSJdfTttyFFGrgfrVbqp0qG7p6entdY/39zFuF6v1/X09PRnr+DV\\nwc2BXrTHAVr02QVbBlm88cyTMApOZkz3FUS7tlX76Oe3Appj6EwqEExnwWpmz2n8W8eIygJsR9qi\\n6szpKsJyI6TqONfXbZOXAgxqDMyVM6g8pnM1DnlijDGLdIDs9+/f6/fv34fe7k03Zg+RZsBgadR9\\nfJ6SI1K0t6q8zt6mwYjKd5fquFzu5C5AqxTDHG0tbyaOfWU0qpzqOJ5JBMgeHv7/RxFRFDQVSKt9\\nGHWGmguIatmJ6Vzf5XIhI/a2f9OXAgxsVQQXfXVmeUdCfzgBRfQSQPv169f69etXCbSsqx1gOeci\\n3wHOWv98zhNR2fV6/bN8xgitgg/Tf7UawLQDuGxzDFwKahP5dKC5jpZn1RAFgEhPDaeTDLTY4sty\\ndm0FL9V+N+0YmYrQqqioAgTqLL4+d6OzKp8dr7X3RrTrQ8Dser2+gXjk/f79e/369Wv97//+7xhg\\nauwdaFVldkDPQMvLTIzQ1NLNCSi6FYBrb5i31l8ItGg4y58AbS0voolopoIXGkulLDynYJU3fDlQ\\n7bsyu/btSNbTWm/14EK/A2EFnZzvRAhVOpfTSQfukA5eU6DtQC3yo82Rxv7kfAYrpbuJ/zkQw0nO\\ngVq0B1+q5fSuD3w60JwOZ5JnJ+z2IZ0S2Hl1jzJkfKDZfTzJ8ibXsHZWfc7Qqu7pAHZr6eCC0GP3\\nOXCqyt69fy3vkYbKn+TlNuMExPpVbWwpOH184UTkLjAzzHL7WXoqn/6PhqcR2hRmWPcUbioyY/BS\\naSzHAViX7sCE7WWzN5aRv6dTTrkjLKpA2YnQ4vizZCeyqnTpHqOwPjO9sfzsU+rRw250ppacHczY\\neP/4wf/Bz1S+VYSWQ1e8l8kEECpCYx9mYjkdyNjxpH1deq33MMN6GMg+K2rLUkVo7n1Z1GTXlefY\\ngLOx+3MeS0/6l887Wwe1Cj4Ix+r5WXUc7c0Qy33A/Il8iQhNKSTKcSM0NNYqiqvAg3kMYmrPyuvq\\nmogLsOo+dq5yxhAcUzbGTnSW71czNqtjZ+becYpucsvpI4DL5XfRGdp4Pq90WEVf7r6LxFyQ5bzc\\nD9ZmTE/kLhHa5OGhG6HFnx3lZVTUp2DmQM2J0HKkNil/ItgPx+kcqSD2EREaOh47n6/LeZie1Lkj\\nO/ByPvvIZWIetluBLOdVURRLOxCrgDYBHYPbWksuMb8s0LoIrXt+lpecsa+ghgqKrYrMME8ZnTJY\\ntuzEOiq4dfpSOpxEaQ6UusjsVmBTfXQitF0oTdsTsmsT1bmu3NizqCzn57Y7EZsCTAe6Wzw/Yxu2\\ntYPbRL58hIbgqiI0R4khncHmdAc1B2isruhf5OFgo0GrPqj2Y/ns/qxXhJq6b0eU4+F5B2qOobNo\\nx5FuwnNgNonSVB0/frz9f7POZNBBbBK1udGXCzHcFMC+LNBUYyqAdS8FolzMi/wKYDnPhVm+nhkt\\nvgzoIhzMq4w1t5vN2i50Oiiy/jOwHREGM3Y8gZpb55E+OJNbBbLKLhz7yBO4018GBwab7rmaev61\\nu+RkvukeT+WuEVoHtbi+i9BwRuvgtpYXmeW0Y8zdZxssjX2JNjPgVdGDSrPJAOve+SKbieN8CKkO\\nZgxqE9m5H3XBoITActKsfGYvWCdu+U1hBTIFpS7tAmwHZthW9qgon5/K3SO0CmprcafHCO3h4Z+X\\nAhkKnUxhhlEZ26aGiwBTenRm9NxmBTKlB7Wp9hwVBpojULtVNJnLYzroIjP2axFswlB2gvUoYROD\\ngpjyMwdyLswY1FTbcj5ew56nTeTTgaZmZdbhEHRQlC7aYuc6gOVjtqxEkMXfzuW6unQ3+KyfyqEy\\n7JnzVMZRgawDxW4ExXRRtXMS+bFJw2lD1dYO+szmunJV2x27mEREFeim53aux/bmYzyXJ+FuImby\\n6UDD8+4szByNzZKTn3/BctgxewGQf8kg/0jgEaA5RpNnrvx3cHmfwYaQUOBQeunGjh0ro1U6UBBm\\nNlCBXt3bOYRrK1Oosbbn9nQTPlvO7S7xHBAdLRNf5LlAy/pSYJvIp//ndOwU7nOaGUdlVBOosbJU\\n+Wp5mX8nDX8RNcpi6TiezHShEwdqWX95XwGOwQz3ClzqWIkaE3R6VQ5zBKXnqg2YvgXUJm2t+nf0\\nOVW2p+6B/065VduiD+pYTeYMbu54hnzpCM2FGlsSdmBj5XRQy3UxmO0CLQwi8tTbXhaNMaiF7hTA\\nWFpBrRPmoO6sqoBQ3c/shd3rzPDdmN8CaNGGrG/Vz9y3I+C5VQSnQKbORdsRYjnNxqeD20Q+PUJb\\nawaz3DlmgN2r8gm0unPquVk+7iCW02EIr6+vbwwl+p+XmZGXn5epCC33gwEM24QO2Tkny1Ngq+pE\\noFb1oG5yO/E48pT+WX9vAbEKbpWts3QGyCRa+4hvxjqQMdtlPs7g1oGL2U8ld3kpwPYqj0kFnC49\\nMVQGMbX07CI0PA6I4Z7pJ84fidCyThFunZOyMazAps7nupmuc78VgJm9KLBVUGP9nmz4iY6rN3ff\\nwav6tuwWEHNBx/JVnxi0u60DHsqXiNBYHhpnCIukcMm5s9R0DLgCWfz6aW4ntpv1IwYtQy33P6K0\\nyFPPy3JeLpuBDEHhRiBsHCt4sbHMkGVAQxhVNjIBmSM7dsHarvQQaebkmKc+oUCgHAXZ7rM51hY2\\nRh3glO3syl1eCuQ9y2OddKG0E425szF7IYBgy+3F9rM+BcgyzHJ6rSUjNBWxZR1miDGQZbA4EFOi\\nZmolue6sH4RY7kuuJ1/HQJavcQFenVM2lsvM+0pHTF+Ypx7kuwD6qIitg2oeL+XjqKOuzonc5aXA\\nFGYoVQR1a8Dhm9MuSsttxDYzPSDIEGZ4fQe1fK0yCHR05pyqvROpIBppzM+6Z3aCxwjGKs3k6GSH\\nZTEdsL648Og+jJ3C6JbfmLGXV1nXnZ8fAZeSu0ZoCmZVB12Q3QJmDGrqpYD6Di23m+ki16NAFtd2\\nMMOXArnuyngwQuna7G5KlH7Q8au6c78QkkwHXTumtqCgz/pXOXEFCrWfwuejv1tjz35VmumCje8u\\n4L58hJaPHQPMwLkFzFi5eblZvRTIUgGt02PU70It7nONI8MMweaAYVe68VSSIaYitE632Gfsuxud\\nTfTjgojBYgq3W4LMBZuCGR5XIGP6mshdXgowUYbZwQW3W0LMBRzWr/qHEgMWEHL67cAsl+1s2L4d\\n+HblM13gZIXlK7A5oJpK5Yy3qItFJhUcHKg547oDrHwfK0OVq3RX2cCuPpXcBWgIrZBwzHizx6Ii\\ntszL34BVoMnH0Y5q9lVtr+DW9TlLhhcrvzuOuqMsLHsXaEom0ML7quMsFdhiy3bDQMz2WKYCR5Sf\\nn2sigHKe64RMZxW0qnRVzs64sH0Fqm78Oul8rhrvTu4aoalG5oiDvU1kD+MZtPIxpitosLYppePn\\nIkycGb8bWDzGTzSw7snMjW2sAOTAcaqDMFqlD6WXfMz2mGZtCiDElstQYMvXuVJFN1UE5ixHWdks\\nnduCaXaPOp6KM0lVwcRE13cDmmpkNhwGMgYzBrQMG3YutwGh0bVbRWlTI1eD6BpAFRFWAKtm4A5s\\nHcAmEcJUOpCxPDYmHVxeX1/fgQwjs3z/TpTWPRvrPpx1lp3YV2wLpjswVhNdFma7eOw+IprK3Zac\\noQw0xjCQDLQKZAxobI/piTOwCInBDJ9hKWHnugit2hjYJiBjBpvHobuuA5yjk0ofVaSGaZWHzsFA\\nxmCGy9AMNbcfrD4Hbrtgy/Wx/mKeSneTVtVfZ5yYT1Zj7MinAy1DS+XHnj0Lq4CGyqmOc70szdqN\\n6SpCyzDIkvveRWgd4AJk+cNbrMsFWXaQfD8ri92P12E7HMl9q6CPeXmPeQpkrA+45MQ/TVPXVv2p\\n9K2eoTFITV8EsP6qPBeEHci7gCCPySTQmMiXeoaGsGMQU2BzQli23HTalfMZjBAwjgPjNQpiqt6X\\nl5d3IIsy0fkmIItyc3ls34GsM37W/wpieL4DGdMd1q/AwmDG4FYJXtPpH2F2q88yKpmAzRUGM5Wn\\nIrMjULvrW8613j+DyOkqQmOgqxRVAWOn/Qxm+UF93mNfM3g6eKJD48AHgPLehVnkBcDwuVGWrky8\\nLh+zvatj1JUDMMxjomDSRWZupMLgW0HsFm803U3pQulmCjcGLjxmkdktoHb379BYg3OEhlFZteyM\\ne5WC1GwvhCxfAAAgAElEQVSPos5VIGPgUcaTDVyV20VpoZ8MsSizgxk7DgknVnpSMFMg2xEGMoR5\\n1kNOs+gM71POm/vOnp3tPkNzQcPgdgtgoahrXfA59XQ+yGCWr4n0VL7MW052XH2Hhr9ywb4ty8cs\\nvRZ/aM3OVY6SB6mCGaunGjQGybXeLgtzRBZlT2CG/Wdt6pwH09jP6eye9ZPblHWQr8/7Kl31KS/1\\nEGoIper5GWs/q68C2hGYTQFXXduVgf6xs1Urqx2565Kzm2Gdj2pzXtyPZbE9GzwGMryPzTY4MK4D\\nI9i6LUv+CBnzHJhhOh93UMttV3lVf5UomFVQ69KqL7ntCDOVRrBNpALYZMnZATKfw7rZOaedzn2V\\nTGDG7pnIXd5yuvvuA1ncqvJYGh3GdUQFtUr5u8bA2p5FPTPrgJb7rEDmRmq5fw7cOj2rsdmFGquj\\nAgZbWqr+KgjHNaoNDDiVftn97DyWW51ztq5MJhW03Gfdu/JhQFONcoCj7t2ZdSqjcvfTwa+Mkenj\\n6AAjmDpjV5Eo09G0P1VZbJ/rYWkU1AWDrts/1j4lKoLANnRAzfdmJ8/AZtEgG+MoCyf2ym5uYc9K\\nVyoCY9+E3sLumXwY0Kqv2NfylgpKHHiwmZIZe7fP9ailQL6GtXUKA2dg0cAnchRMla6ceqtJQ0kX\\n9WA9+RrXIXNdLoywfV1abRlq+SVPHmcWNa61JCiYXpS+HLAxXWRddzDDD9BVm49A7dMjNHXeMVK1\\nnzg1gqrbV9GPGvQKbpXkmTkfM11hWaqd+Rmb0w42MTAdT/rX6dyZoLCfnXRtccqYRhBTm8Y68ENp\\nBrLc/ww0jM6wfqVjNnF316h+sP5gBKmAVh1P5MsAjeWra1wHyLNHhgSWwfaYN/nbuXw/A4TqN4IM\\n03hdPsYZnX0oq3SCbe2g4hh4dZ2CWSdO1K7O7UwwrlQg6yI0ls8iNAYyBJoqm/W3st8qr+q7ijzV\\nkjjfk8tS7XfkbkBzr8niDggOYjWomGb7yav03DZsK6sXBQ0YB5fBLKezkecPZbMuXMjiNV0fnTK6\\n8iqnwb7iNV2/jgCsi9Cc6Gy6xOom37hm5xlaTjs2vaufqn25jypvKncF2s71O4pHqEUeplW04A48\\nK8eNPBBikZ/31T2Yzn89gEBkzq/0xvpwBBSsTKU/JqgjdW01cU1ArMBQ5au8DOTY8qQVzp/HTPU1\\np51naGoymtq26lsFMQY11BWz9b8aaEeEGakCGDozc6KclyFROXjlKLuOqoDGwMTy8vdTcQ27h4nT\\nR9WnCiCqzG5SwT7GNV1kVk1SnTC9d5FZla8gls+ttd4tOXNfWfudZ2h43xRqeI/qswIbQg3bdxRk\\nIZ/+lrMzJOd85Qj5umpAc9rJm8xcTrmVw0Y651fRlQIcm/UxOmBt75y/m7WZTCKAIzDDOvM1k/Yy\\n/eMxA15VVgYZlpvHCcuqbBmhwUCZr1eAqgAXeaxfrE/qDWd8EF9N1Ep3rtzlO7QKAhWpp46V81VZ\\nypEms1c1+BMnYu2s8rLDZqfBOtmbTkeUkXf3TMufQp7BW0GY7SeAU2BjQGP3sL4oEKh7K6dmP1ev\\ngBtSwWs60WA7VYTGlpxdWTty9yUnM9bqukgzA3XqqcpU105fCqjypnAL6ZaczLmzw7CZm4mj+5xX\\n9V+Vs+MoKB3McrlOhIb5FVRYtOa0N7cHy8v7vNx0ZfIMbTpRx7VYFvaNgawC20fJ3YGWBSMNdU3s\\nXUeq6qvS2SEmP7DHymH1VMIMkjlGdb9aeuAyBIUZ8xF9M4BNy1NLMKbzajKp4DbpVzc5sHYzEFeR\\nmVPmWrNnaGvVunACBWWbbnTWAW3Xn9f6QKBdr1ea7ypzrfevpKtnA5iH4oTyTJGvr69v/vg94Pb4\\n+Lh+/vz5p12qb13ezixZzZi5jdWWr7lcLutyuazHx8d1uVz+9C/yI2/6SxDqD67VjxhOpNJnTr++\\nvv5pf97nPucN9ZfTz8/P63K5rOv1ui6Xy3jJuTsxVpLHKspe6x/IXa/Xdb1e1+/fv9fv37/fjDna\\nBOZ1QQX2ufqPbPhDrE6ZO/JhQIvfJ2PiOr562NnNPNWSYPogMgYqQys7Q7QvjDX3pwPUZGP9VKLA\\noX6zHh0dnX7yO114TVd33k9ETYwI/tfX13f9QZjlNJsM8JddLpeL/KfS1fPObuzzOdZXJgpoEQw8\\nPz+vp6en9fT0tH79+rUul0s74akxqdqRg48MMsxDoE2g6chdIrQOaDgobE2OZcb1ShjknH0eqKgr\\nHARn9DCWzsl3tolMy2YzNEtXfWH5GOF0gMPx7PqorsdjjEIU2AJo6PABMIw4ppOlsneV5/YtAy3q\\nzRFTRGiPj4/2mOCLJDYmOa9bYuLWlVflVXK3CE1BLJ9DkOXlHXueFOnYd6DK6eo4jDfPtJfL5U+d\\n2ehV9DH5vasKas6zM+Ysas8M2Z29VX+wr8qJptHAxLizvjqIIdB+/Pjno+QY18vl8i76qADG0hWw\\nFMS6812EFkvOiNAmY6j0rsZBfUzL0l2ZU5CF3C1CYwBjgHPf4KzlRWisrG7DZ2gIs+fn5z/LT+W4\\nzJHReCrQYT/c46wfJS5wHDgzmHW6cIDmGLha7mF09vj4+Oc5GMItYBZAe3l5+QMz/J4q19nBjInq\\nUzcR5XQADfWY2/n09PQGeJMJ1B0XDDpUGp+fVeXvQO3TI7QOaHlTkdPuMzQGK/Z6WZ3L7Q0DeX19\\nXY+Pj28iOCfS2YnSqmWzSjuO5sBJgUzBTYGri/jYeGI6SwWPGJO8bMZnhAG3AFqGVgBNLacqHbNz\\nXfux7xXYEGjVM7SI0JTfsfomY5F91d2qMo9A7a4RmjNDVMvGXCbmZVFKVeFwNaOsxT9UzQaGm3qj\\n1PUfHb3SAYJf9ZvlT8HKgKZgN4F71qNKu3YR+4iwFMRin6PsyjbwvNMGN52PO9gwoKGt5GdoAR0F\\nyaPpqLOzwzh2x3kqd43QOmdZy1tSTYUZbLX+72CDUYmKCHDvlpsHFw2D5TkRZ047kUB2oApoOzCL\\nLdej2oN2oGCdr8kwy+mAWzh+RNqOziZAU2NW7VUEhXldhBYwy2Vn6cAxAZsTsUa6AuoRqH16hFYt\\nteIcEjwL5rPrGARVhMaejSDcMKpigGLLGXQePHZhxoDWwUwBG/NQV51+O4CxPPebpwqs2JYqCkUd\\nYb0Istjyd4XKXlTEy8ZCtdM5rib8fB7tLZeVn/XlN/VdkJCPu+hpCh0VnXWRoxvIfHqElh+cozPn\\naCgv6xy4qU7jzKCgVn0zE/CJetAx8rdM7I2a2hyQZV0oh1X9wn6o40oY8NjzsS46cz4JmUSKCjC4\\nPTw82OORn4U6es55Ttrdcp+7TS3d85tEhFsH3ZzXgQbzJseTCcyVu0Vo8dA1BgZhhmEpU0q+J9J5\\nn0VFMczB8avmKC/KDkf8+fPn+vnz55+/Gvj58+c7B2HpOO6Axp6huVv1pTamUT/VXgFNAa6LZNmS\\nswKZAppKh81VEMv6uFXkxc5VL6ByXu67M+nhuag7+oR+5sAWfQ3Tk/3uOazbkbv8G7us9LXeRlcI\\nIyfcRIPBfTcbMoNl4s6S6qG3imgc43X6z46dc6zPeXLIovqiQIf96ZbVTjq3JQMgtx37gukOnJU+\\ncNLMoMjHeC63l9XFJnFnqyT8oJoYVd5RYb7L9Kfa7fg+yt7vyhyUriPsWAHJgVj1LZtqy45hVQ7K\\njE/V3emn6lv3dq4DPeqA9YM9LmBgw/yJIyKEWDvYOLF7Md2dV9d3dXYAVvewc1Wec46NpxprJyLD\\nfuM5dX8nKvJl7XLkLv85HQUjATUTqntYSO+CLd+Poupmzqmct+tznsnxPOZXcMe+O58d5AfFrA0s\\nv+pvBzBWzsTBMcrINsLy8tgpuKj+YoSFdeX68tio43wPqwPPTUDG6sAyWRvcyLyqK+dV9l61EfOY\\njlz5EkBbq4ZaCB7jwCnnVtEIm8VQKudkTqwcGvvK+h/1VddmY6y+nas+Q0GdoG5VpML0US2rq0jN\\n0bmqM7epghlzNOV87JhBLdeFaXXM6nFgyHTS6U4BHvtU2b2yBUePrKxOlC7+qgitInQ+DlEdzHnO\\nQ1cV2nZw68CGTt7NohO9sHaqCI19isJghg/AUc8sanLhriA2fTZYXcvgVUEtj0EHbAX5atJhdsv6\\nN43QHD0xUdGqmiBRKttl7VETzaS9+b5Kh5V8mQgtxI1U2H0VyFhUoiBWRQOVg06fF3V9VXCvoFbB\\nS0VyuX9VZKOgVS01u4iVgVMJu7eCGd6rymR5zMFwPDqIdfBizutEJZ1tdbqoIFbpbWeCrkQFNbn8\\naZR2V6B10Uh3Lx530YvaOnGiFBV9OEbggq2D2CQyqyI01ndnU5Bz3mxO6gtdYBnowBVAVV8VXCr4\\nOGBz7q/a302OKKws19a7fKa36t6unezeKchC7h6hOaFlN8NgxOVADCMeJlNHY87Lyoo6nTzV3w7g\\n3fM0BXQVsdwSZqqeTiqAxTlMq7459bMoQY1P5ZAILJbGPu3oh7Url8ukq0fBq7p/Z4x3AYZyd6CF\\nKKNh4FF5uxuWGW1A2OY9g9juJwpMD8xBsL0qUmNQY6CPa/JHreicqs8IsQ7uDPRKN50Oc14ViSiA\\nVU7IRMFK2Wl1/SQ9kUlAgO129LEDtnvIlwFalioC6/ZHIMbEjcyqreunO+urPjJIdYBTzxRZlKBA\\n1kVo7iccqOtuDDCPRWWTaKeCHbORHFmpMWLlVREapnftq7NpBnw1iTF9sLwjk0WWW0RpXxJoWRBW\\nVV4Fru4cExWpxF5FIyqi6PrYRQIVwCuIOR/YhqEz43ZA1uV1DsmcpNIjOn7WVT5mZbrjwtqFUkVm\\n7PxHRWhYV3cuAx/z8x7zUab5Xbvcc0ru8m/s3Fkol3MEbCpfGb0K0dVgY38RDAwarO544xhLwJyP\\nEEJYVVGb0gMeu0ZYRUsqryrbcR52DUIsO2lVvwOyrj1Mqog7zrOoaCdC69qpbDin1SSt+uCOS5ee\\ntHkKtQ8DWv4CHcWZrVlHnb3rvG5kxvKqejEvnlGptNId6uX19ZX+tBFCDvvl9DfrfOJMyrkq471l\\neXEOwTbtzw50K6nsikXdLN3Vh7Yxaa8Dst1JZnIcUq1QJnpf64sATeVlQYiwdBWxYRrv65xdCYuM\\nAlpdGxi4lF4wKuveXHZ9yn1zjaYbo0lZzn3TtuUoeAdarEx1HPXkce8E4ZXLVOec9k91PgWaW+cU\\naBXIVB2dfKn/KcCOGYCqtIIIy9uFWL7/VtEZOiHqogKa84mKCzjUgQOwXZBFGaysKXhYdFNBrapL\\nOXPl5PhLGiFZ5wxe7Ji1XZ2rRJ3vQHYLmHV5rM9HQBby6RGaclqWRmEO2UGt2qsyw0FUO5hU0ZkC\\nW5zP0HKB5gKsmhCYOE7kAscFyREYVpHOFGIdvLr2IqSwXXhdZWOsPR18O6ng5fqgm9e1jcFMAW4i\\ndwNapFne1MArqLE8do1qq2pTBxEGLkzjMoNFGvn8NErDtjJ9sf5i3g5wpo52C6hhvfijkV00gmU4\\n+2znDIIMWs5Si0E5l30EIrtAm8LUGdNKFzs2cRegxd41HFe6CMxJ53aiUll7OphVaWX0bB/3VJFa\\n9xxtMuNVEYKzdeVWjjoF3E59biRSOb2yDzY5KejmcwwYFcic9qs+sb5VoJ/CzLnmI5addwWas6/K\\nYFI58M7yi9XHIj0EiLPszGVkB1CDqv42012CVn1W4O4M3ZEOUurcrlG7IM7XsrFmfcfr2K/m4r5b\\nTqnoROndSVfHU6A5MHPg1d1zi2Xn3YCW08qYWLrKy8IU4Sgnl4vLP1aWG53FtficDWdwZTwYhXV7\\nbKOrCzU+DiS68jC/i0Cq+6vzOG7d35MqR2fHVRvzHqM01Lfbrx3odOmun1UZznEWJ+K65bLzbp9t\\n5P3RdBx3wJo8O4r8ylmrKEhBjJ3PbaiWv7is7D7ZcJec1XgokFX6wjKqa5z7XKOu6qucuKpDwTvG\\nJD86yABjUOsk24KCD7Z1J+1AUulld2yYVCDbLfuuQMO0OufkOdGXCvOPrtujjEl0lh0B26bEfWbG\\n4OqALaSbyVUe3t/lu1FG1U62ZGd1TKIRd2PlYFpFaK5+VFura7u8Sfud9impQF4tL4/44V2WnGiE\\n3Tk2A+JybaoEvMed0eLeapm5Fn+2gnnTNlcA2/kWrVqSKlGG3k1MCo7TiCGXyWCGAHEAxtITQTtG\\nfU70q4T1T0E82oR7N6107uR1QUN17gjIQj79bzmnIHJmOXWuKtuZ0aqBRZi9vPz//7jMz8cinV8A\\nYN5kEF1wTcEX+mPQy7rpQF/BqsvvohHMY3ZUgaOzj+k4xF7pdPfD5w7SzqSu9DeZOCa6meod5RYg\\nC7nLH6d34sCrmo2xDQ401Z4ZQy4/jDfOK4AhzBjMHcPZich2ozcHbKgnlacg5gKN1Y3Ovyus3SgY\\neXQAm/xpGsvHPndQU5Ovq2dnEnfkVuOwK3f9tY0szCgZtJTgNWj4bMZRecwJsf0MZmutUYSmBlDl\\n3xJe+DZUga3StwO3XahVe0wz2LBz2E4nL8rIkVDOd3Xd6ZtBLtu/0gfqhE3icR2uCiZAU3o8AjBW\\nz3SSR7nrZxtT6WaqXD6DpipTORzmZckwy33didCcQXWdZwd0uY0KaEw33TlXrztAY46b813pIhIF\\nsziuADZZcmJ5na3nfbY3Z2wmumZ+tatrFDVBdWNSyZdbcuIMu3svzm442Mz5nI0Zb5YjEVo1wEeB\\n5m4hOa2imQngOpBNgcbauSusfAVKBAoDWZenIIb5LtTWemt3ClwTHU8mit1JBI+d8e7kbi8F2GyS\\nZTJDsTIn9Tib6gtCTQEMYYaGl/cs7TpDtam/98SPfdXYVfqo4DTZOn3cEmoVwLB8FaEwaLHILKfz\\nvWpMp3bvjBt7EVXBpKsP9bEjCr6Y58qXi9COiAOw6l7HyUIQBmGw1RtNzHMdNzvTrbdcbu4Xph19\\nsWtwPwGaKgPbxvKy4LXdxNFBk00wDFxs2RllVZNTHE+hlsvB/k4nDBYYKGHnJj6I7aj8rpO7P0Or\\nALQzoOycEuaMlaPlNmNf47owKIzGWF5lZFOgdeerrfpBykpXlT4VxCodu46G44pjPxUFOCVK3+qv\\nOCZLzsib2H7Yn+r7w8PbP//CvlaAz+3Jfce00pELJMfvHPkybzmz7Bimgpqqz4FYGEK+hvUvl7/z\\nDK3as9n3FhDrnKrTdQcl1Fl3jwu0nGYgQ9upnJz1iV2vykCIMZipa+J+NZE4MMN24/gpW1b6ZHpx\\ndMAmP7xP6du1CVe+/JLTnanytZGOdnTwjD2m1bIw18vqmzxDU/VjXpTnzO5HgXZ07BigqrQDNDUG\\nOY3t7vrhOEtVpgMxBrV8L5aD/VF2nvfO2Cn94jGe27EFBjE2XlU7vxzQlKiOqU46MHPhtZYGRz7n\\nKFQZOgObyuscHfvTzew7YMvXVv3bnUUdOE1hFsfM2XKf2DkEUiXsWga0CmJ5wzZU41DZObYx/80w\\n6ihPzGysqmNWp9IZg9hEVNu+DNDUfzRay5ux4zjvnTxUgutwFdzWqmfqfBwvCKo/Uq+MgkUdOBOz\\ndNW2qu9R/mTWzvXHFmXhNvmxS4xCqsmq+l04BZTcpu65lrNXdeY93leNYT52JvGsEzUpB8x+/Pix\\nLpeLZReVqIkkt8ERNVnnclw7zvIlgYbpfF+Vru53ylLlZANaSxv4WstyYCcyYPVWdbvRRzUDqwmh\\nEwU1F2bsw1DsO+rDgVneVxvri5NWdTv1dcc4Zgxi7DoWleX/ZK/Grxtf1h7M60RBzI0CHfl0oHXw\\n6JYe1fGRcqp72QAqQ8+/qKHeHjoDxgy4Apsj2I9q8sjn3XbmNk1ghv1iURkCjkHEWfo5wHGhU0WA\\nR8pFiOe0iljzODKQMaCxenJ+LtfJ25VblbPWnYHmggjvPVpuFemx0F0pnBkr+0jV+XC1moHV9V0a\\n9YMzIjNSFqXl86yt6IgMbAh5tuTM7eygxoBVQU3BJsp14MPGGoHZvdHEMtVxN26O3SLULpfLmzrU\\nRO0CrPINZzL8CPmrgFYJK6sqRxlE1Q5nZsffRVtrScBFmQxgzJDzeSbOTMfKVQBj+lB1YvSEzxLj\\n2gy2nM7ldZFJ1IGA6iK0buyi/Ml266hPQQ31o8Ypj1d+fpYjtFxnBTXVji6vEmXvt5IvBzQ8F+I6\\nq7PP13dgw+vZ4KOBr/UeYtUsnMtSkckRYdEUpquNCRomtpEtLZ2XAkoP2G4FrGqZ6TzrijpdkGF0\\n5kRpDtBU9OPCDCO0y+VCgaZsq2pHFZlVbazqUOem8mFAq96ouOCZRikMRg4sFdjQybB+ZuAVyBB2\\nTBTUOplEtBj17EpndC7MqpcCLL1WH6EpsHVvNzuAVUCrrndhlnW6s0phS01cclb9U+W6UJvArPLp\\nDppK7vaW09lj2ulwVUYVfUUegxuDGe4RWkycPwCP8tCBu5nZHXwGMyciq0TV7cIsR2gYGSi4dwDD\\n8xVcKtioh/1d3VN45ryJVOPI4KZsNT8CYON6q6iMCdo42sBEvjzQ1pp3sINZPqccOeehs0Wb2CxX\\nRWjMiKOsqs/sGPXRGZRabqprnaWnamOkXZjtfLZRRWiT8x1oVHkVNKdLWsxj49EJW3IGxHIabTVD\\nrANKd+yKmqhYvVNQfnmgVUaer8H7nDRrF9uqQWOGH2W5QMN+dn1j7T5ynRuhqXNKP9lZu2dq+R5l\\n7Ln9qHPnedoEMh28piBzYYbAQP0646Ois8vl8mZpiVDDSSPn5TpuAbXoVzeBTeVLAY3lqVmrK1sd\\n30KU8aPTRv0O0KJc1eY84CradKSK+CZlqckln0Nw4RtOdKbYo1Ezo2egmr4AYGCJPaZ3YJbLzGV1\\nefmcMx5sNcE+rI10jopjuR+63QHJEait9d62j5T1YUBTg9BFIY5xsfS0DSwcz1vO7yKADkTZ6J32\\nqfPZ4BSIEABu2XH9y8v///eqh4eH9fz8/O4cOk4uC/dZj0znmIdlqDwFjdz/7Ljs4fjj4+ObB/rP\\nz8/r8fGxhZcLNHYu2trZeO4rggmjr4eHh/Xz58/1+Pi4Hh8f/+RFmc/Pz+t6vb6bCCZ7taH9Z1tx\\npQtApgHJXSI01WGlHAYyB2hK1Myl0s7g5rJZ21n0xu5hbcV2u8vKiWBbGcwmQEOn6/TMNiwLozhm\\nL1k3YYOXy+XN9vLy8i4vw2zX4btjBrK8z2kXZg8PD+vx8fFNX/KE9vLysq7X61qLvxnuwOxu2A+0\\nLVd2bDfLp0doWdhAqtmrUiIz7E6Yo6m9Y4iqz7ltuLzq7sVzleM75VRlZ4PG9ucoNbcD24TnphHa\\nZGPRDfY/ro3/l8qAliM1F15VBKNgEW3N+yrtwkytLtb6x96iHfjZxnQ53p1Du3GPnbQrdwPa6+v7\\nP2aOfAdmSrm5/KpeFUGw9E4fox5sCz6zcMrMsGDtwajQjfhQFMxii6WoA7RphFY5LTtGvUbdeXxz\\nHy6Xy3p+fn6zD5jFvgOYG82o/KxXJpGvdNhFbfneKC9H1tfrVfpWl4fn2Tnsm/JJtq/yJvLpS86u\\nwxMDUQNQlR8ycaK43o1MqvrZfSgqLyIlBBv2S0kH4LXezuoPD/882FeRYZU3jdDYg+s4DjAxPWNU\\nxvSW3/LFPsMMn58xu3MiMnVO6VuN0wRmWB6OZc5zYeKcw2uq66pyXX925K4RWk5XRuIaUi63Axxz\\n0Grp04X9qt+5Hdn4OifE4zx745uqfC6nO8HILgSXlnjsgI1BbQK2gFj0SUEtR2W5T7lvEZUh2DLU\\nprbmTrJ5yVnpH/MnQHOe7TG4TtIdoKb7bgJAv3blLkDLhprz2KBMH2BGWdU+t9HZ2DOKcK7seF2E\\nlvMciLHjDDOE5S408/1d+yo94fkpwNhx9DP0jXWotubzDGJsm4Bqei1KF2UyHarJ9fn5+c+LnOfn\\n5zdti7zYOqlslh0zH875Fczc55MT+fQlZ36GhMJmlKqzFdGr9Fr6wTbLjz/sjT1GZJ3SK6C6xzmd\\nnTz3z4nKWLlMP9j2fP1km0Zo1d8b5v6zPnUTEkKNlV9BKut6F2hVJIl2xyDGVgbX6/XPm8xoV/Ql\\nPtvI1zA7mEgFPQU1TGcfRn/H9ETu8h1a3rpnENVbJ8xbSz+4ZA7L4MXS7O1YiFpuKshNoFZFWLl+\\n1JuK0qoymb5YnooSJjCrIrTX11cKN5Ts0Cy6QcfH52cMSqz/LtyOAI3lOTCL9O/fv9+MeURi0b/r\\n9bqenp7W09NTaQeOvSnpggkGNHebyN2AxkR1VpEb046xxTk1U7J9fsaS+4HPslSf8h7TqKsqnaMp\\n5TDYPyXsvLO8Z8BSIHNhlqETQItJQ+kqL/Wxz9gOhFnU49oNy1fXVuPDJk0FtgpiqPc8fvGsMNoX\\nS82np6f1+/fvd/bE0sw+HMBN9BXL5NjyMZ6byF3ecjKooeNUUFPH09lyrX52Qojk/uGzrLg29yn3\\nLacROl0adZbbhXVMBPvX6b8CmjqevAR4fX19E5nlaJg5WdZlFSEizJRNVE5YjWU3geIYsvZmWCuI\\nsSVnhkT8VcBab5ecT09P69evX239yt6U/rO4k0EGbQaY2k/k0/9zen54iZ1i+VVUtvMgtwMai4pi\\n1nPqXMt7exTH1cynZtAKcmrfncsbA0RuewUPBTPnGL/ez88uWT5uKj8mnoAauwahegRo7BzTW5U3\\nidBUxKZskIGI2R1e29krExf2zHd25MOApt6oMHCprXtudhRolZPH/uHhoQXoWv7nIt2AuVBiDsHO\\nVWXg/dlBWDSWr+8iB4RVBbIKaApqFZSculikiJNDBlGOBBFSzMYw3x23bozVZIY2xO6pbM4BFZs8\\nUVdTce3clU8HmorGcLter+ULAYTaUaApCFSRGXugzPaYRnHAytrZGXzVtwpomI+AU0tIB2Zq6yIy\\nPEPF/3AAAA/CSURBVOfC6uHhoY3McjkKShXI8FhFd9Nx7EBWjX9na45UAMN0zuvgps5XgHfl05ec\\nCl4O0Dq4reU/13Bh8fr69m8ZGcxuGULfytB3YZjhFVFZPmbQqvJYNKXypstNVh9rE74QUHDLOmGw\\nwnMIserYsbmdsVY21J2f5KtrUF+Y79o+1jsBNMpdlpw5Sssww3QFMpW3Vg+1tXpw5GP2Krkqe2cg\\nq7QDI/faqozqfH4W5YBEAYx909fBqwNatX94qCM0vEdFXU4abS8fo82pMe+AVo0XAkBdj2OsbNLJ\\nU1BT0kVnVZsd+TLP0AJmee98RsCipbX6h5EuGDLQHFCyZaYLuinI1HXT6xFaVdoBWOQhvKr9dMmZ\\n29KluxcBVYR2JJ3tkI1vlVeNU1UGO3Ztrru/AnIcY6Q2XaG4bVFy1yUng1j+splBpIKcC7W1eogh\\n0KIuBbeoL6SCWDXI7kzrQiofqzTmVWDLoGAwc56NORDrwIZtYLqJJXNuC4Nbvp5FaLtQyzYY5eFY\\nq7SzVXZTXcds7ojk/lYQcyb0qo+OfMkIrQJaBzIHamv5zyry87PJC4EqnaUauF2IOQBj90Qe9l2B\\nzYXZJPJyl5wTp2cwY/3IfWdwYmkFL5aX9Yvj3I27O8aOjTmgYPbQ7R2orcV9Ae//a4CGbznxuVne\\npgBzYRaG6DrE5BORkGmo7QL21sc5H/Nin5+fTWCm4Ja3+NlodV5FdBOHj/GrIrPYdiKxCm7MuTtH\\nrWwAr0FYVjCooLYDD4RZB7Is2G42UXwpoE3fcrJtAjAXapGXjdkFWgW1XC4O6hRuTHYh1R1j+Rle\\nbJ/L6UDG4NXlOdFaHjPsC9OF8+wM71ORmAs1hNtkjHG8Krh1AKugMAUG6hXzKqg5umA6nMqXWXIy\\nyN0qGmN5YcgsWkOHUW84qw9sMV3lZXEM+cixAloAK86x/SQqq8Cl0pPPNqp2opNNIssKWrlMBiy1\\nd8Z9agcMVh30sm5UnW4+ls+iUCdqUxDbgdlad/rTJ7bh5xy3XmJWUMsgizbGcfeJhpIdmDGpINSV\\nq2Y5ZuBdtJHrqWDJNvXMigHQWabGOOX2V8eTLe45EpW5zqxk0t4MbKX3bkJDOCldqrxKOj/IaTZx\\nTOUuz9DUd107X/3vQi4PZgZZdpbdsBcHohpUd8bs2lH10SmfneuckrXPdUSEXM53tiPC2qIiNKYL\\nF1ZsUmC669rXAVg9t0T9TiLbibiT9w6gpvJlIjTnE41dgLHr2DOiuH+t9cZ4JxsaQwc3B5rV+Wn0\\nyBzraFRROYYTNUxeNITTOnpl+Q54q6gs6wzTOzpjx7tAq54RsomgitCmkGP6VmPgyo5u7w60IwA7\\nErmtVT8I3x2M6no22N0MXhlUvq8CWr5X1XXE6Lp9FaWp5agCWY6koj+oE6Yn1WZsiwJZB7OcxnHB\\nMWYAcXXGgIa6qcBW1auOmVTRp+sDjkyjxbsCLSDW/Y1kt+1Ecmu9fRCOTqJgEceOMIBN7kdRYELH\\nQ2hnp+vKd6O0blbvQKaOq+dscazGJeuGpbsoKC85me4U5DqJe6pJCvPU8y83Qst6xSUnE2YbU5hM\\nQbbrB5XcPUKr/kbzKMCqvyIICSNGsKl7EHpKXJhVM50T7uP90Ze8hFb3h1MoiHVgq6KLnK6iM/Ws\\nR0Vq+KzLGSvWZwXX3O8uQsNr2bio+iudreUBLfTgLtkngJpcW8FK2c9HwGytOwItA8d9KaDOT+9j\\n4oBsZ5bpwOZETeo6BBGWzxxTOZlyWNUeduzCTEGlAhhbcuaJKPcZdVEJ1tuBTB0zYWPMdIT6WqsG\\nWj4X106foU2AomyU6dvRR2dfR2B3lw9rqwht8nJg91qUbJzOrM9m406qyAFBU0FDlYuGkB2T1ZHL\\nPjpbZueqjicw66K0GM+13v4f0dxXNYl1gD0KMpR8L4sGWZ4CGosoGfBV5IuQ7SbpSaSG9zsAu7Xc\\n9bONAM30sw3nZUJ1TZaHh7f/SBedIO8rKGWpBjKfq6ImJggKVm4+zpsDSRWpIfRUG5Sz5rTjdApi\\n2WnXevuZTXw72DmPAgm+bJiATU3elX7YhnrqYOZGaFm/lY7UeFfCorTuekdPWLYrd3+GhlBDAFVg\\n6sCmjkMCZjGAOc2gFmmVr0TNWNWAMUNX5ao6VYSS68jpiUFj+6rznZNOn6PlOvHPsdBpWT8qqKHO\\nnAgNn8M6Ous2B2hRb/WWE3WZJ9IsCKYJSLqJW13TyQSsIXcH2u6ycZpWQIu9ApmK0EI6he/ADEXB\\nLOczA8Vj1odu1nbbt7NlB3WWmhihoS5QT1WfsB0BxjjXgY1JlFO14Qi82HHU60Rm7BkhSnWukiPg\\nuqV8GNCqsLbbWBlOeiIs8sE8jJA6AFXwUnsnkmL1IMzY7FrpdafeXWF6VFESy6u2uB6F6dhtI5ts\\nMthUXXFeHaOt7fZ5917VJhdik6jtXvJhQOtkZxBC8kyI4T7OQjlKy4OGz2XYLzrg3w/mGS8Li+yq\\nfaSVc1QO6MBMAauaRKpPXG4JvKqsKsp09TvZunay67oJuDvOY7WWfl7kHrsTVmejIRlwU7mVnRwB\\n56cDrYt0HKCt9T68x+cfCLHstBF+s1916ICG4X+I43CY1zlY53gI6coYpyC7Jcw6UFXXKCjsQqxz\\nfNX+bhxuAbIdyLF2uDBTsrPkxHZMREXD7Fwnd4vQsiiAVUBbiz+zyNcruGWgMXAxyLFnEiEZDlkq\\noOX78JgZKsKKgYvN2gxgTIed4R8FWweE7lo1YVRpB2Sqnu5cVW7VZgUy5bgd5BxRbWX1hEygNrEN\\ntgRnde+UvdadgaYghs9GqkFUkRlz7ihrrVVGZ+q3uBh4QyYGwwbUcep8ji0zGWQRZqivLkLr2sJ0\\nUIG5K5Ndg3l5XFW93YZtdvrTXeOW2UGN2TueX+u9/eyOo8p3wMLGqBMGMlXXXxOhqWjMWW525TJj\\nj3RIFZ05z9ByG5XjZVGOqtrK7qkiMsfY8zF7Btk5xEeLA7Pch5yewA3LwHqq9nXArvImIMN78LzT\\nZtXvHTm6DK3ux3NfMkLrnpNV6YAFG9Suzuzka713jDg/AVoFs5DOaDrjd9K5/QpmLOqr0mq56Up3\\nnwP27p6cx/rowIydZ+2Z6oJdWwFvGp11x6otlU5c34z787lbT3DVEnQana31BZacmEaoTcpiMxxz\\noPwMrYrI1EsB9QxtArTuvh2woHHkPIRYPDtjS3bWNlXnR0o1KVUQccDG9qoNXXl4rcrrJqBdqHX1\\nVnbW+VgFMdfOnbIV2P6aJWcWBrO8V/eomYcNOEr32/XdSwGUAEUl2I4qksQ8FpWpPd7Dyo99/tgY\\nHaDS30Sq8lzYKPhUsHE+RXGg5vTNhdtHQY21RaXX4tDAAEPBZldXTplH67rLZxtqj5u6nxmLs0TN\\n5ydAy3msjcqoqzbk9BHnYlBj+mAgi0h1px1HIedIB/tbbKwv0/vZfZjHJpouEnGgxtKoM2wX1o+S\\nfXIClx2bUKuKKO+vitDUzIB50/Kc65z/LMRgxj7bWIvPyJ04EUPeq2hMlc3S+Q+641hdXzmM0y/l\\nZKq8ql62dzb8UQKVZsdH+4plO1BzIFY956qAzfqmbIlNkpUOXFEriIltV3IXoCmAdREaU26O2Bwl\\nRNn4N4LO8zOEWWVYEz3swqzbs3a5z0R2ZttcBxvDiZF2gGXXTze872h5rKxsmwpGu87LdND1wV3y\\nHq17IreC2Vpf6BlaBTS2zMQ8BklWT+zVLxRk2HV/7FvV4fZb3dM5XNaBawjTtlVGqsbs6KZEgUj9\\nwEE+x34Iwf2fFpMfT3ABh7aaN7YS6PzCGVc2OeI4szF3JttKcNLOeawP2M6pfMm/5YwlEf7GVYZY\\nNQiRr44daLG3mhjduQ5Z5VdRnnIKppMdmEX9nS47OQIvB2TsWAFJ/YqLc44By4Gegi2zVUdvFcyY\\n/nJ5lVQRmrIBBi4GNRdsuc7qut2I79N/baMSNrBK0RluyqnVYEfZ3X/LQfBNDUz1sRMVbbCZDct0\\n4VaV5QKOlbMDNXWvAgTLuyXcMF3l5baotBPNoK25+lSwDGETgpoQXaixc6y/VV/xGO9x/ITJ3Zec\\nODjK2FmUhuk4n+9TaTdCyyDrlgEVUFk+62uWzkDwPgUzZxbv4NWVyfo0iTg6qYCmlpJsydktTxm0\\n1J7BqotemOO6E2U1ETi6y8cd1PB+TFd9UhLtZBDeBRjK3d9y5s6xwWPwYvdiuZif0xlWzrO07JTq\\n0w0FtSpPtU/12dHnRNgsXxk21lM5HNOl67woyqGq6KmDWvVsrSor52F7WBtVP5ges94q/bKxc8QZ\\n53yu69+0rw7I3KBAyV2foeWILPJir5abHeTynuVFfZN/zuo+qJ0OVOfE3VLFGWzVJkxPHAPLv9XG\\n+lQt31iUxkA03RgMFdywnepYncsgU/pQNrwr2Xe6SawDWU5Xdspgin3K12OeK58ONNZp1cl8TqVx\\nIJSi0BimS0619MxlYttZX1jfcz6bHfHbMdSFKlsJ06+7V2UcBVnVB7Z862A2ic6qt6BVtMfaWEkV\\nbaNNZn24Nq1Ah5PBdKwdcHdQw3q7vuS8idx9ybnW+2c/8XeGFcBwALDMTlGTTzaysaiZNNe1k87C\\njCJDDfXCdIiiYHpEWP+VjiqnU46YhUVo1ScVLrjY/ep6lqf04uqu01Fl250ogDqRGbtHHTv3K1+t\\nAoMvBbTJEoY1nC05K6jlctQMEOlbLTM7sHUgq2ZVpkOcBZmRKH12bXONW4ERN2d57gIPpQKRSk/e\\ndLqQU7pBHUaf1OoE9YYRGtO9MzEyvcX1zHc6G6igxo5zuSzt2sVEvsRbzu68mlEq6lfp7oXA5FON\\nHah1My0zgJyf90w/E2EG3IHN1cXOZMB0gVvOV6CqzqnIbhKdIdBQZ9gntiRTToyPF1SZ7jms14GZ\\ngvMkr2rnjk85cvcfeKyMoJo11CB0QMOZ0PkGrZtFVN8qgHVwq4zGWXo77cD2TMGG1zpQY9fmPOxv\\nthMGtGrp6T4jm0RleJ/SKY6HWv65Tjx1bNRj3jOYYV1O9OVGcqoOdxKcCJ8KTjnllC8l7sTyb5cT\\naKec8hfIkQjt3yQn0E75VNl1zH+7Q58Rmicn0E75VNl1zH+7Q//bge7KCbRTTvkL5N8OdFceTkWd\\ncsop30XOCO2UU075NnIC7ZRTTvk2cgLtlFNO+TZyAu2UU075NnIC7ZRTTvk2cgLtlFNO+TZyAu2U\\nU075NnIC7ZRTTvk2cgLtlFNO+TZyAu2UU075NnIC7ZRTTvk2cgLtlFNO+TZyAu2UU075NnIC7ZRT\\nTvk2cgLtlFNO+TZyAu2UU075NnIC7ZRTTvk2cgLtlFNO+TZyAu2UU075NnIC7ZRTTvk28n9AwRVK\\nLtEpzAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f519c0eab90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"imshow(solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5)\\n\",\n    \"       .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray'); axis('off')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 5. Writing a custom training loop\\n\",\n    \"\\n\",\n    \"Something is happening. Let's run the net for a while, keeping track of a few things as it goes.\\n\",\n    \"Note that this process will be the same as if training through the `caffe` binary. In particular:\\n\",\n    \"* logging will continue to happen as normal\\n\",\n    \"* snapshots will be taken at the interval specified in the solver prototxt (here, every 5000 iterations)\\n\",\n    \"* testing will happen at the interval specified (here, every 500 iterations)\\n\",\n    \"\\n\",\n    \"Since we have control of the loop in Python, we're free to compute additional things as we go, as we show below. We can do many other things as well, for example:\\n\",\n    \"* write a custom stopping criterion\\n\",\n    \"* change the solving process by updating the net in the loop\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration 0 testing...\\n\",\n      \"Iteration 25 testing...\\n\",\n      \"Iteration 50 testing...\\n\",\n      \"Iteration 75 testing...\\n\",\n      \"Iteration 100 testing...\\n\",\n      \"Iteration 125 testing...\\n\",\n      \"Iteration 150 testing...\\n\",\n      \"Iteration 175 testing...\\n\",\n      \"CPU times: user 12.6 s, sys: 2.4 s, total: 15 s\\n\",\n      \"Wall time: 14.4 s\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%time\\n\",\n    \"niter = 200\\n\",\n    \"test_interval = 25\\n\",\n    \"# losses will also be stored in the log\\n\",\n    \"train_loss = zeros(niter)\\n\",\n    \"test_acc = zeros(int(np.ceil(niter / test_interval)))\\n\",\n    \"output = zeros((niter, 8, 10))\\n\",\n    \"\\n\",\n    \"# the main solver loop\\n\",\n    \"for it in range(niter):\\n\",\n    \"    solver.step(1)  # SGD by Caffe\\n\",\n    \"    \\n\",\n    \"    # store the train loss\\n\",\n    \"    train_loss[it] = solver.net.blobs['loss'].data\\n\",\n    \"    \\n\",\n    \"    # store the output on the first test batch\\n\",\n    \"    # (start the forward pass at conv1 to avoid loading new data)\\n\",\n    \"    solver.test_nets[0].forward(start='conv1')\\n\",\n    \"    output[it] = solver.test_nets[0].blobs['score'].data[:8]\\n\",\n    \"    \\n\",\n    \"    # run a full test every so often\\n\",\n    \"    # (Caffe can also do this for us and write to a log, but we show here\\n\",\n    \"    #  how to do it directly in Python, where more complicated things are easier.)\\n\",\n    \"    if it % test_interval == 0:\\n\",\n    \"        print 'Iteration', it, 'testing...'\\n\",\n    \"        correct = 0\\n\",\n    \"        for test_it in range(100):\\n\",\n    \"            solver.test_nets[0].forward()\\n\",\n    \"            correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)\\n\",\n    \"                           == solver.test_nets[0].blobs['label'].data)\\n\",\n    \"        test_acc[it // test_interval] = correct / 1e4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Let's plot the train loss and test accuracy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x7f5199b33610>\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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XlRRN4XkfdE5JoM690uIvNFZIaIDKvpcU466QAZB9q9G+6+24TmxRfh\\n3/+Gxx+Hww8vdMscx3FqRT4toD3Adap6BHAC8DURqdRbisiZQD9V7Q/8L3B3TQ9y8skwaVIumttA\\n2bvXShoMHAjPPgtPPw1//7tNIHUcxyli8iZAqrpaVacHr7cBs4H0kfGxwJ+Ddd4A2opIp5oc56yz\\nLFnzmjU5aHRDYt8+ePhhK3Xw4IPwl7/A+PFw3HGFbpnjOE5OqJcxoKD40TAgvYxcXH3ybjXZd6tW\\ncO651j+XBPv3W46hoUPhzjstP9uLL1q+NsdxnBIi76l4RKQV8ARwbYaqeumpwmPrQ4yL5N0pKyuj\\nrKzso+VLLoGrroL/+z+QBpt4vBpU4V//gu99Dxo1gl/9CsaMKeIv5DiOk5281gMSkYOAfwLjVfW2\\nmM9/D5Sr6mPB8hzgFFVdk7ZelXpAUfbvt7H5v/7VStcUFaowcaIJz/bt8KMfwTnnuPA4jlNn4uoB\\nicgY4DasIup9qnpL2ucdgL8AH8OMlF+p6p/y0b58RsEJcD8wK058Ap4FvhSsfwKwKV18ktCoEVx0\\nkQ2ZFBUvv2xZqa++Gq67DmbMMH+ii4/jOHlARBoDdwBjsNLbF6YHhwFXAdNU9WigDLhVRPLiLcvn\\nGNBI4IvAqSIyLXh8SkSuiNQffw5YJCILgHuAK2t7sAsvtKjkffty0vb88vrr8IlPwJe/DP/zP5Y2\\n5/zzTUkdx3Hyx3BggaouVtU9wGPAOWnrrALaBK/bABtUdW8+GpO3MSBVnUwCgVPVq3JxvEGDoFMn\\nMypOPTUXe8wD06aZq+3dd+35y1/24m+O49QncYFfx6etcy/wgoisBFoDn8tXY4q2HlAcF1wAjz3W\\nAAXo/fetFMJrr8G3v21Rbl7m2nGcHFNeXk55eXm2VZIM+n8bmK6qZSLSF5ggIkep6tZctDFKXoMQ\\nckV1QQghS5ZYRenVq6FJQ5DWefPg5pstyOCb34SvfhVatCh0qxzHOUBID0IIxtrHqeqYYPkmYH80\\nEEFEngN+oqqvBsuTgBtVdWqu21dSgw49e1oJnIJPSv3gA7j0Upu7M3iwJQv9xjdcfBzHKTRTgf4i\\n0ktEmgLnY8FgUeYApwMEiQEGAovy0ZiSEiCAzp1h5coCHXz5crNyjj0WunWD+fOtRk/r1gVqkOM4\\nToogmOAq4HlgFvC4qs6OBocBPwWOFZEZwETgm6pakY/2NARHVU7p3NmqUdc7P/2pFYC7/HJzvR16\\naAEa4TiOkx1VHQ+MT3vvnsjr9cDZ9dGWkhOgLl0KYAHt3Ak/+5mVv+5Wo0xCjuM4Bywl6YKrdwto\\n8mTLTu3i4ziOk5iSE6AuXQogQBMmwOmn1/NBHcdxipuSE6CCBCFMnOgC5DiOU0NKUoDq1QJavx4W\\nLoTj0ycTO47jONkoOQGq9yCESZOsLKun1HEcx6kRJSdAnTqZUbI3L6nzYpg40RKLOo7jODWi5ASo\\nSRNo3x7Wrq2Hg6l6AILjOE4tKTkBgnqMhFu4EPbsgcPTy2k4juM41VGSAlRvgQih9eMF5BzHcWpM\\nSQpQvQUiePi14zhOrSlJAaoXC2jfPnjxRRcgx3GcWlKyApR3C+jtt83U6tw5zwdyHMcpTUpSgLp1\\ng6eftujo11/P00Hc/eY4jlMnSlKAPvUpq3rdrh28+mqeDjJhgs//cRzHqQMlKUAHHQQnnQTDhsG6\\ndXk4wPbt8NZblgHBcRzHqRUlKUAhHTpYVoScM3kyfPzjXunUcZyiQ0TGiMgcEZkvIjfGfH69iEwL\\nHjNFZK+ItM1HW0pagA47LE8WkGc/cBynCBGRxsAdwBhgMHChiFSaSa+qv1LVYao6DLgJKFfVTflo\\nT0kLUN4sIA9AcBynOBkOLFDVxaq6B3gMOCfL+p8HHs1XY0pagPJiAa1ZA4sXw/DhOd6x4zhO3ukK\\nLIssLw/eq4KItAA+CTyZr8Y0ydeOGwJ5sYBeeAFOOcWynjqO4zQgysvLKS8vz7aK1mB3ZwOT8+V+\\nAxDVmrSnMIiI1qad+/dDs2awY0cOy/VcdpmF1111VY526DiOkx9EBFWVyPIJwDhVHRMs3wTsV9Vb\\nYrZ9GnhcVR/LV/tK2gXXqJHNBaqoyNEOvfyC4zjFzVSgv4j0EpGmwPnAs+kricghwMnA3/PZmJIW\\nIMjxOND8+SZCAwfmaIeO4zj1h6ruBa4CngdmYRbObBG5QkSuiKx6LvC8qu7MZ3tKfiAjp+NAXn7B\\ncZwiR1XHA+PT3rsnbfnPwJ+r25eINFbVfbVti1tANcHDrx3HcaLMF5Ffisjg2mxc8gKUMwto714o\\nL3cBchzHSXE0MB+4T0TeCFx5bZJuXPIClDMLaOpU6N4dOnXKwc4cx3GKH1Xdoqp/UNURwI3A94HV\\nIvJnEelX3fYlL0A5s4AmTvTs147jOBFEpImInCMizwC3AbcCfYB/AM9Vt33JC1DOLCAPv3Ycx0ln\\nHpbK5xeqerSq/lpVV6vqE1ikXVbyGgUnIn8EzgLWquqQmM/LsDjzRcFbT6rqj3PZhpxYQNu2WQVU\\nL7/gOI4TZaiqbov7QFWvrm7jfFtAD2BZV7PxUph5NdfiAykLaM8eWLGiljt55RU49lho2TKnbXMc\\nxyly7oyWahCR9oHhkYi8CpCqvgJsrGa1vE6qCS2gG26AT36yljtx95vjOE4cR0VzxalqBfDxpBsX\\negxIgREiMkNEnqttLHk2OnSAlSvhiSfMAlq1qhY78QAEx3GcOERE2kcW2gONk25caAF6B+iuqkcB\\nvwOeyfUBDj4YOnaE++6D0aNh0qQa7mD1ali2DI45JtdNcxzHKXZuBaaIyI9E5MfAFOCXSTeuNghB\\nRFoBO1V1n4gMBAYC44NiRnVCVbdGXo8XkbtEpH1gxlVi3LhxH70uKyujrKws8XE++MCE6IMPzJj5\\n4hdr0MhJk6CszMsvOI7jpKGqD4rI28BozKP1X6o6K+n21ZZjEJF3gFFAO+BV4C1gt6p+IdEBRHoB\\n/8gQBdcJi5BTERkO/FVVe8WsV6tyDOnMnw+nnmoGTeJ0bpdcAscdB1deWefjO47j1Cfp5RjyeJxO\\nQHOCekOqujTJdklccKKqO4DPAHep6n8DRyZs1KPAa8BAEVkmIpemZV09D5gpItOxSUwXJNlvbenX\\nDxo3hrlzE27g5Rccx3EyIiJjRWQ+NpWmHFhMWqLTbCTyK4nIicAXgMuCtxKNHanqhdV8fidwZ5J9\\n5QIRs4BefhkGDUqwwdy5plj9++e9bY7jOEXIj4ETgQmqOkxETgUuSrpxEiH5OnAT8LSqvi8ifYEX\\na9XUBsCAATYWlAgvv+A4jpONPaq6HmgUlGZ4ETg26cbVCpCqvqSqY1X1FhFpBKxT1Wvq0OCC0r07\\nLE3kncTLLziOU3KIyBgRmSMi80XkxgzrlInINBF5T0TKs+xuo4i0Bl4BHhaR24HYzAhxVCtAIvKo\\niLQRkZbAe8BsEflm0gM0NHr0SChAe/bASy/BaaflvU2O4zj1gYg0Bu7AMtQMBi4UkcPT1mmLDY2c\\nrapHYmP1mTgH2AFcB/wbWACcnbQ9SVxwg1V1C1aidTzQixr4+BoaiQXorbegVy+bROQ4jlMaDAcW\\nqOriYCrNY5iIRPk8lpdzOUDgYquCiDQB/qmq+1R1j6r+SVVvV9UNSRuTRICaiMhBmAD9I2h03WOi\\nC0TXrpYNYe/ealb07AeO45QeXYFlkeXlwXtR+gPtReRFEZkqIrEGh6ruBfZHc8HVlCRRcPdgoXXv\\nAi8H83o21/aAhaZpU0tQumqVjQdlZMIE+O53661djuM49UAS4+EgLJ/baUALLNPB66o6P2bd7dhU\\nmv9grjgATRonUK0AqertwO3hsogswWa9Fi3du9tk1IwCtHUrTJsGJ51Ur+1yHMepC+Xl5ZSXl2db\\nZQUQ7fm6Y1ZQlGXAelXdCewUkZeBo7DS2+k8FTyiJPaQJcmE0Bb4ARAWwykHfqiq9WYF5SoTQsjn\\nPgef+QxckGna6z//Cb/+NbzwQs6O6TiOU9+kZ0IIxm3mYtbNSuBN4EJVnR1ZZxAWqPBJoBnwBnB+\\nTVLsJCWJC+6PwEzgv7HSCRdhdX4+k+vG1BfVBiJ4+LXjOCWIqu4VkauwaqWNgftVdXaYnUZV71HV\\nOSLyb2zYZT9wbybxEZG4WZWqqn2StCeJBTQjyFad9b18kmsL6PbbYd48uOOODCsceSQ88IDlgHMc\\nxylS8p0LTkQ6RBabYyHbh6rq95JsnyQKbqeIfDQYIiKjSA02FSVZLaCVK+3x8cQ1lRzHcQ5IVHV9\\n5LFcVW8Dzkq6fRIX3FeAB0XkkGB5I3BxLdraYAiDEGKZNMkKBzVOXFPJcRzngEREjiEVdNAIS8OT\\nuPNMEgU3HRgqIm2C5S21aGeDokcPWLIEbroJHnvMni+9NCj549mvHcdxknIrKQHai03Z+VzSjTMK\\nkIh8I7KokfcFG2T6dY2a2YDo0AF27YLXXoN774VvfMPyjV7+P2oBCN//fqGb6DiO0+BR1bK6bJ/N\\nAmpNEWc8yIaIGTrHHWcTU88+G9asAWbPhmbNoG/fQjfRcRynwSMiPwV+oaqbguV2wDdUNdEs/owC\\npKrjctLCBsrIkanXrVrBpk14+QXHcZyacaaqfjtcUNWNInIWkEiAEhWWK3VatYJt2/D8b47jODWj\\nkYg0DxdE5GCgadKNE1VELXVatoRdW/dYqdQHHih0cxzHcYqFh4FJIvJHLFHBJcCDSTd2AcIsoM5L\\n37Cxnw4dqt/AcRzHIShU+i6W2gcsTdvzSbevVoAC8+qzWB2gcH1V1R/WsK0NllatYOCyifBZd785\\njuMkRUR6A+WqOj5YPlhEeqnq4iTbJxkD+jswFtiDlVrdhqXgLhlatYKj1vr8H8dxnBryBLAvsrw/\\neC8RSVxwXVX1kzVtVTHRhi302f4ujBpV6KY4juMUE41VdXe4oKofBgVME5HEAnpNRIbWqmlFwmHv\\nlzO92fFw8MGFborjOE4xsV5EPirpHbyOLeEdRxIL6CTgkiDt9ofBe6qqJSNKh7w1kfLGpzOy+lUd\\nx3GcFF8BHhaRsLbAcqxkTyKSlGPoFfd+0kGmXJDrcgzp7Bs0mLKlD/HKjmPydgzHcZz6Jt/lGCLH\\naY0ZJttqsl22XHBtgsSjRZ98NCvLl9No/Vpe33U0+/dDI5+a6ziOkxgR+TQwGGguQRaZpFHS2brb\\nR4Pnd4C3Yx6lwaRJyOjRHNS8MTt3FroxjuM4+UVExojIHBGZLyI3xnxeJiKbRWRa8MiYVkdE7sGy\\nX1+DTUT9HNAzaVuy5YI7K3julXRnRUmQ/61VuaXj2boVHnwQvvnNQjfMcRwnt4hIY+AO4HRgBfCW\\niDyrqrPTVn1JVccm2OUIVR0iIu+q6s0icivw76TtSeRwEpF2IjJcRE4OH0kP0KDRoPzC6ad/lA9u\\nxgz4858L3TDHcZy8MBxYoKqLVXUP8BhwTsx6SceNQr/RDhHpitUE+ljSxiTJhHA5Zl51B6YBJwBT\\ngNFJD9Jgee89SwTXpw+tWsH27bBhA1RUFLphjuM4eaErEK0HvRw4Pm0dBUaIyAzMSrpeVWdl2N8/\\nghIMvyQ1NHNv0sYkCcO+FjgOmKKqp4rIIOBnSQ/QoAmsH0hlxF6/3gRI1asyOI5TciQJJ34H6K6q\\nO0TkU8AzwIDYnan+KHj5pIj8C2ge1gZKQhIB2qWqO0UEEWmuqnNEZGDSAzRoJk6ESy4BKgvQ7t2w\\nY4cZR47jOMVCeXk55eXl2VZZgXmzQrpjVtBHqOrWyOvxInKXiLRX1ay+IVXdBeyqSXuTCNDywMR6\\nBpggIhuxut/Fze7d8MorFnFAZQECs4JcgBzHKSbKysooKyv7aPnmm29OX2Uq0D+Y37kSOB+4MLqC\\niHQC1qqqishwbL5oXgYmqhUgVT03eDlORMqBNtQgyqHB8vrrMGAAHHookBKgDRvs44oK6N49y/aO\\n4zhFhqruFZGrgOeBxsD9qjpbRK4IPr8HOA/4qojsBXYAF+SrPVkFSESaAO+p6qCgceX5aki9M2FC\\npeqnUQuoUSMPRHAcpzQJSieMT3vvnsjrO4E7k+xLRCap6mnVvZeJrGHYqroXmCsiiScWFQ2RAAQw\\nd1soQL16uQA5juNkIqj7cyhwmIi0jzx6YZF2iUgyBtQeeF9E3iRVB0iTTFIKyrSehfkTh2RY53bg\\nU5ip92UUT3DSAAAgAElEQVRVnZao5XVh82YLwR6ZSj8atYCGDnUBchzHycIVWIR0FypnxtmKTXRN\\nRBIB+i5VJyUlzQz6APA7MtQIF5EzgX6q2l9EjgfuxuYZ5ZcXX4QTT4TmzT96q1UrWL7cBKh/fxcg\\nx3GcTKjqbcBtInK1qv6utvtJkgnhLFUtjz6AMxM28hVgY5ZVxgJ/DtZ9A2gbRGDkl4kTK43/gAnQ\\nmjXQpAl07eoC5DiOk4A1QSZsROR7IvKUiHw86cZJBOgTMe8lEqAExM3K7ZajfWdmQtXy261aweLF\\n0KEDtG/vAuQ4jpOA76nqVhEZBZwG/BH4fdKNMwqQiHxVRGYCA0VkZuSxGHi3rq2OHiptOX+FfwCW\\nLjV1OeqoSm+3agVLlrgAOY7j1IB9wfOngXtV9Z9A4pLc2caAHsFC9X4O3EhKKLaq6oZaNDSO9Fm5\\n3YL3qjBu3LiPXqdPtqoRkybBaadVKfzTqhWsXAlHHukC5DiOk5AVIvIHzFP2cxFpTsIk15C9HMNm\\nYDN5nIQEPAtcBTwmIicAm1R1TdyKUQGqEzHuNzABApuX6gLkOI6TiM8BnwR+qaqbRKQzcEPSjZNE\\nwdUaEXkUOAXoICLLgB8QmGeqeo+qPiciZ4rIAizE+5J8tof9+80C+lnVXKqhALkLznEcJxmqul1E\\n1gGjgPlYOYYFSbfPqwCp6oUJ1rkqn22oxMyZ0KYN9Kw6rzbM++YC5DiOkwwRGQccAwzEpt00BR4C\\nRmbZ7CMS++pKgrTsB1GiFlCLFrB3L+zaBUcfDatW1WMbHcdxiof/wgrabQdQ1RVA66QbH3gC9Im4\\nqPLKY0AiZgW98opVSF22LHYTx3GcA50PVXV/uCAiNaohcOAI0Icfwquvwqmnxn7crBk0bmwWEJgA\\nPfqovQ5LNDiO4ziV+JuI3IMlEfhfYBJwX9KN8zoG1KCYMgUOPxzatYv9WMSsoKgAPfWUre4C5DiO\\nUxVV/aWInIHlgBuATUydkHT7A0eAMoRfRznxxFQNoPbtLTnpl77kAuQ4jhOHiNyiqjcC/4l5r1oO\\nHBdclgCEkPHjTXjAnk84Afr2dQFyHMfJwBkx7yVO1XZgCNDGjTBrFowYkXiTHj1g7FhzybkAOY5T\\nKojIGBGZIyLzRSSjpSIix4nIXhH5TMxnOUnVdmC44F580Wr/NGuWeJMw8cJTT7kAOY5TGohIY6xe\\nz+lY2rO3RORZVZ0ds94twL+pmq8TcpSq7cAQoCzh15mQ4HS6BeQ4TgkxHFigqosBROQxbB7P7LT1\\nrgaeAI6L20muUrUdGC64BAEImTjsMFi3LsftcRzHKQxxJXAqldAWka6YKN0dvJW3CgWlbwEtXmwl\\nuIfEVgSvFreAHMcpFsrLyykvL8+2ShIxuQ34lqqqiAjxLricIKr5Lb+TC0REa93O+++3BKSPPFKr\\nzffutcrdH35oE1Udx3GKBRFBVSWyfAIwTlXHBMs3AftV9ZbIOotIiU4HYAdwuao+m+v2lb4FNGEC\\nnBEXKZiMJk0sf+mmTZamx3Ecp4iZCvQXkV7ASuB8oFLSaFXtE74WkQeAf+RDfKDUx4DC8gu1HP8J\\ncTec4zilgKruxWqwPQ/MAh5X1dkicoWIXFHf7SltC2jGDJtR2qNHnXYTCtDAgTlql+M4ToFQ1fFY\\nCHX0vXsyrJvXGm2lbQElyH6QBLeAHMdxck9pC9CECTWe/xOHC5DjOE7uKV0B2rXLMmCXldV5Vx06\\n+Fwgx3GcXFO6AvTaa3DkkdC2bZ135RaQ4zhO7ildAapD9oN0QgF69lkbVnIcx3HqTulGwU2cCLfe\\nmpNddegAL70E//qX5Yh7++1U3SDHcRyndpSmAG3YAHPnWkGfHNCxIyxfDs8/D6+/bkXqJk70zAiO\\n4zh1oTRdcC++CKNGQdOmOdnd8cfDe+/B6NFw442Wluehh3Kya8dxnAOW0hSgWpRfyIZIahJq48bw\\nq1/B978PO3fm7BBZ+fBDy6fqOI5TSpSmAOUwACGOESPg2GPhjjvydohKPPIIfP3r9XMsx3Gc+qL0\\nBGjRIti+3UKw88h3vgP33pvXQ3zExo2wZEn9HMtxHKe+KD0BCtPvSN5KWABwxBGwdCns25fXwwCw\\nbRusWJH/4ziO49QnpStAeaZ5c8tzunJl3g/F9u0mQEVQuslxHCcxpSVA+/fDCy/UiwAB9O4NH3yQ\\nfZ3du2060u9/b97B2rBtm4mQByI4jlNKlJYATZsGhx0G3brVy+F697aK39mYOxduuQUefhhuvz3z\\neps3Z05bt22bPR/obrgD/fs7TqlRWgKU4/Dr6ujVq3oLaPVqGDIELr4Ytm7Nvt7kyfFutu3b7flA\\n7oCXL7f5WI7jlA6lJUB5Dr9OJ4kLbs0a+NjHoHXr7AK0aZMFNGzZUvWzbdssG8OBLEDr1rkL0nFy\\ngYiMEZE5IjJfRG6M+fwcEZkhItNE5G0RGZ2vtpSOAO3cCW+8AaecUm+HTCpAnTolEyCAioqqn23f\\nDgMGHNgCVFEBO3Z4IEZ9s3ixVbV3SgMRaQzcAYwBBgMXisjhaatNVNWjVHUY8GXgD/lqT+kI0Kuv\\nwtChcMgh9XbIUIBU4VOfsjxx6axeXXcB2rbNMjEsX56bdhcjFRUWY/Lhh4VuyYHF88/Db39b6FY4\\nOWQ4sEBVF6vqHuAx4JzoCqq6PbLYCshbMZrSEaB6dr+BxTqsXg1Tp1rw3dVXWycZpSYuOLA8qumE\\nAnSgW0BgVpBTf1RU2DXslAxdgWWR5eXBe5UQkXNFZDYwHrgmX40pnWzYEyfW+63aQQdBly7wi1/A\\ndddZyYaHHrKAg5CkFtDGjfacyQU3cCA8+mhu219MhOdnxw6bf+XUDxUVdg07xUF5eTnl5eXZVknk\\nxFbVZ4BnROQk4CFgYN1bV5W8CpCIjAFuAxoD96nqLWmflwF/B8IZMk+q6o9rfKD162HBgoKESfXu\\nDU8+CdOnw3/9F3z2s3DBBdCsmX3uFlDNWb/eRPxf/0q9Fwrz9u3x2zj5IRQg1bwnF3FyQFlZGWWR\\n+Rw333xz+iorgGg1s+6YFRSLqr4iIk1E5FBVjemd6kbeXHAJB7sAXlLVYcGj5uID5v866SQzSeqZ\\n3r3h8MMt1Pr44+35L39JfR4NQoiLcAvZtMnu7NMtIFXrdHv3NivgQBgDWbUKXn658nvugisMFRU2\\nmbohRiDu2VPoFhQlU4H+ItJLRJoC5wPPRlcQkb4idrshIh8HyIf4QH7HgKod7Aqo+31VPc//iVJW\\nZpmqw7vDb33LXHL79tljwwabG9u8uS3v3h2/n02boG/fqhbQrl1W1qhpU7OkVq3K69dpEGzZYlZf\\nVGzdAioM4XlviG64Y4+tPgrVqYyq7gWuAp4HZgGPq+psEblCRK4IVvssMFNEpgG/BS7IV3vyKUBJ\\nBrsUGBHEnD8nIoNrfBTVggQghHzpS3D55anlk082S+aZZ8yV1K4dNGliApXNDbdpE/TpU9UC2rYN\\nWra01127HhiRcOE5iopxRYWdR7eA6peKCgssDQUoPcimkKxcCQsXFroVxYeqjlfVgaraT1V/Frx3\\nj6reE7z+haoeGXilTlLVt/LVlnyOASUZ7HoH6K6qO0TkU8AzwIC4FceNG/fR60p+zkWL7FZ5cM21\\nKx+IwGWXwVNPQb9+5n4LCQXo0EOrbrdxIxxzjI0lRdm2DVq1stejRsETT9hzKRMVoC5d7PXGjSbA\\nbgHVLxUV9tdas8bEv08fG4tsCOXot26FZcuqX89puORTgKod7FLVrZHX40XkLhFpr6pVYsGiAlSJ\\n0PppQCOko0bBT36SGv8Jqc4C6tvXhrOibN+esoD+7/+sDMRNN1Xeb0hYObVjx9x8j0KRyQLq1cst\\noLqwc6e5gmvyV6mosDluq1fDvHl2TVdUmFu5kOzebdf7geARKGXy6YJLMtjVKTLYNRyQOPHJSgHH\\nfzIxYICNY0ybZuM2IW3aVC9A6WNAUQuoc2f4/Ofh17+O38ejj8LXvlb39hea8Bytj0x/q6gwC8gF\\nqPaMHRs/WToTYcn53r1NgGbPtuW1a2t3/FxGcYbXiAtQcZM3AUo42HUeNtg1HQvXrtlg1759ZjKc\\ndloOW153GjWyst1PP53MAlLNPgYUChDADTfAPffEp6SZN6/uf8ibbjKXy//9H7z7bt32VVvSLaAP\\nP7SIp44di9sFF36PQrFyZc2CWCoqbDzzYx8zAZozx95ft67mx1a1qQThfK66EkaUuguuuMlrJoQE\\ng113BoNdR6vqCFWtwf0Z8M47ZhaEAwUNiBEjLDVdEgHatcvcIp07mxBFB3qjLjiAnj3tOe6PvGBB\\n3aPk3nsPLrrIRO/MMy27Q6555RWbtJuJrVttjCG0gDZutI6wZcvitoBuusluHgrFxo01E4ANG+y8\\nd+pkrrc5cywQpDYW0Pr1di3HzXOrDeE1UtMbrr/+tbC/gVOZ4k7F0wDdbyEjR9pz1AWXSYA2boS2\\nbe3P3apV5TkX6RYQWAqgOHfGwoUmQHVJ2Ll6tRmUP/wh/Oc/9kjKnj3JSpQ/9ZQFU2Ri61bo0SPV\\nWYV34i1aFLcFNHduYcPoaypA6RbQ7NkW+lwbAQqv17hMH7VhyxYL8qmpBfTGG/Dvf+emDYXi6KNz\\ndx4LTXELUAHDr6vj2GNNUJJYQJs2Wbg22B8+epe4fXtVAYoLx1Y1C0g1cyezbZuNEaW7gX7965Rw\\nhJkbwKytpUuTC9p11yXLhrRmTfZS5lu32rhDaAFVVNj5KXYLaMmSVMaL+mbnTrO0a3L8qACtXAnz\\n51uATW1ccLkWoK1bLShl165UwcYkrF4N77+fmzYUgv37zTVeXSHMYqF4BWjHDnjzzXotv1ATWrSw\\nOUKHR3I/ZBOgtm3t9aGHVv6TRucBhUQtoOnT7W5wwwZzSfTpk/ku+zvfgbvuSvnywYTsG9+w/ama\\nOIRRdC1bmvglSUapamNe8+dXv24SAerVK94CKlYBUrVOI1djIDUlPG5tLKCOHVPXRe/etbOAwhum\\nXFpAbdpk9gZkYvVq8xTs2pWbdtQ3mzfbtVQqY1/FK0CvvALDhlmv3kC5/35zJYUkEaD0dDxxLrio\\nBXT11fCnP5n107evjSPFCdCrr8Lf/gZnnGHReSFTptjzsmXWObVoYaG6Ib162Z17yK5dNpbRu7fd\\nGY8da+2dPt1EZenS6s5K9QK0ZUtVCygcAwpdcOvWFVdtoA0brO3FKEBNm9rz4Ydb+HVDcMFt3Wr/\\np27datYZr15tnom5c3PTjvomPH+lEv1XvAI0cWKDdb9lIqkFlO6Cy2YBzZ1rnsiFC80n3rlzfNqU\\nX/4SfvQjGD06swCFmbuj9OpV2dwvL4d//MMyPbz5pn2nW26Bf/7T9p1UgFatyjyrPt0CCoMQohbQ\\nZz9bOVlpyLZtFgDS0MRpyRILNCm0ANXGBQd2szFokFlBtXXBdeqUWwFq0wa6d69ZZ7x6tV0fs2ZV\\n/SzJ+GWhCf8TLkCFpgEHIGQiWxBCdAwoqQVUUWH7e/llGyDOZgG9/z6ceKIZjVEBeu01G69atqzy\\n+E9Iz56VBWjNGtvHUUeZdfeLX8C991oC1iuvrF6A9uyxTrBly8rzfKKEY0BRF1y7dpWDEFavNqsu\\nncWLTVQz7RssCCJq1eWLffvse6xYYe0aMKB+BWjPHrOOISXitbGAwK6Lww83AYqzgNatg8mTM+9r\\nxQpL1JvLMOzQAkraGX/4oV1bJ50UPw40erTV9spF2/KVIijsG9wFV0jWrrUshMcdV+iW1IjaWEDZ\\nouDmzoUjjzTL55FHUhZQugDt2mUXbL9+Jh7Tp5uFsHMnzJxpZSRCCyhdgNJdcOnZHbp2hUsvtW3H\\njrVOd/NmW/7Od6p+13Xr7Dt2757ZDbd1q33HrVth797KLrjQAtqwIWW9RQn/mAsWxO8b4De/ibee\\ncs306SY8kyfb87BhmTvgfJTamD/fbgrCwJQ+feKPv307nHtu1fejAvTNb8LZZ2d2wU2YAD/Okss+\\nFKB8WEBJO+O1a01Ajzwy3gKaOdMye9WVv/wFrr227vuJo6LC/n9uARWSF16w4IMClF+oC0kFKOri\\niHPBhRbQ3Lk2ue8TnzA9zmQBzZ9vd+JNm1oH0qqVdYhvv22TTgcOzOyCi7OA0tf57nfhscfs5+jR\\nw/b18svw059WFYJw+y5dsgvQIYeY1VNRUTUIIRS5qVOrRvSFnVG2YIjly1Oz+vPJpEnW5tdes3N4\\n9NH2W8e5B4891n6PXLJihd1kbN5s57BPn3gX3Jw58Pe/Vw3wiArQJz9pv1n79vb7pJ/3TZuqituO\\nHanzvHx5bgWoNhZQeIM1eHBVC6iiwtqfizD5efMqB/rkkooKGDrULaDC0oDDr7ORqSZQVIAGD7Y7\\nsZA4C+jQQ61jmT49JUCQ2QKaNatyNF7ohpsyxfzh4V1knAsu3QKKE6m2bS1fGJgALV1qbWvVCu67\\nr/K61QlQWP+odWvo0MFcaelBCBs3mkD17Fk1W8OyZVYMMJMFtH+/dcxxd8C55oUX4CtfMQFassR+\\nq7iM3uvWZXYp1oXQqlq50s5ZWFMqXQDDAfn03yMqQCGNGtn1l+7ijJtj9Mgj8LnP2ffdudOuz2wC\\n9MQTdq6SEFpAtRGgAQPs94iW+wivl1yUnZg/324I81G7a8MGE6AwarXYKT4BKnD5hboQtYCiIhGd\\nB3TssZbgYe9eW44TIBHrwF94wTq1kSPhv//b/lxxNYNmz66cLPzooy0c+1e/MrdKKEBxLrjQAgov\\n9jiRitK9uwnQjBlmGf3pT5XvlqsToO3bLQqvceOUNThnju03tIA2bLDPRoyo6oZbtszGujIJ0Lp1\\nJkL5toB277bO9PrrTexmzTIxb9u2akcdimG+BahzZxOQMMdbSChA6W7AOAGCeDdcnAC99JJl1njj\\nDbPao1MMNm6sGgr97LPZM2RECS2gTp2SR+WF13fTpmYNRm9eFi60c5PJAspUxyvkpZdSrvN58+wY\\n2dzAtaWiwkQ32xhqMVF8ArRggfUggwYVuiU1JhSgmTOtQ33kEbtLWrIkZQG1bWsXWNgpxbngwNaZ\\nOdMEqHlzSzESpvOJE6CoBXTSSSYSzzxjOt6pk4ngkiVVrZs2bcyiCP9ccS64KKELbsYMOP98u9t8\\nNpKCNhSwrl1Td3Hz5qU+D8NrwTqsxx+35yOOSAUhrF9v1tGJJ8YL0OjRmV1wK1bYGMCWLfmdFPr6\\n6/bbdO5srqeFC03M27Wr2lG//z6cemryu/+khIKyYkUq0CXu+HPmmPs0qQDFRcKFAhTeqKhap3zs\\nsWYFd+1aOcDmuuvg7rsr72PDhuQuutACCq3kJHWKojdYX/wi/O53qc8WLDDLIk6ANmww93Y2vvc9\\nu1b37LFrsKwsP264iorUGGopuOGKT4AaYPmFpIQC9MIL1uFcf7110B07WjnvkOHDLcQZ4i0gsD80\\nQP/+ld9v29bu1qIpa9JdcGecYZ3+iSfacqNGZpG88068dRMNxa5OgLp3t/1s22Yd7pVXVs6OkG4B\\nTZpkHfVFF9kfPSpAHTrAH/+YKvgXBiFUZwGdeqoJUJyLYvlya+Phh9fNCqpu9v0LL5gQgrWzTRv7\\nbTIJ0Nix9rslCWPPxq5dKTfvihV2fYQWUChA6cI7d65dC1EB2rXL2hN37cVFwm3caGNz4XlZssS2\\nv+YaePJJu17DMT1VO2b6mNeGDclzxYUWUNOm9pxEuKICdOWV8NxzqaCDBQvsxizOBbdkiV032SZB\\nr1xpNxAffGA3h0OH5k+A2revmesxHREZIyJzRGS+iNwY8/kXgiKh74rIqyIytK7tzkTxCVARhl+H\\ntG5tf9AXX7TIsfJy+POfbQ5Nmzap9dIFKJMF1KOHWQVRQiso/CPt3Wt/ruoMxu7d7U8dJy49e9qf\\nMIxI69Ah83569LDvN3SoteW882zbN96wz9MF6K9/hR/8wDqrn/60qgXUuLGVoACz9D780Dq/Qw81\\n8V6zJuXWVLU/5dFH23JcZ7Z8uZ27ugjQ0qV2jrPNh5k7184BmAD16mXnI5MAHXGErVcbKyicv6Jq\\n5/vGoEtZscICRVeuTIWyp7sA9+83sT711Mou0UWLUm1OJ5MLLvr80ksWJ/TJT9pv1rWrWdLNmtnN\\n0YIFVYsvZrOAzjuv8vmOXidJ3XBRAWrbFq64wubHgbVn1Kh4Cyjs6DO551TtXE+ZYjd2AwbY/y0f\\nAhQmiK3pBNwQEWkM3AGMAQYDF4rI4WmrLQJOVtWhwI+AP9St1ZkpLgHau9d6twZWfiEpTZrYH3DS\\nJPtzDhhgpno6UQGKywUH9oceODD+OFE33KJF9qdLF6p0ugelA+OK2fXta3+sMIS6SZYyhj16WIcT\\nikCTJuZuufVWWw6DGLp0sY786afhkkvgwgvtGNGOpXt3+MIXLOAAzFI7+GD743XoYMuDBqXclRs2\\n2Plt1cru/ON88KEADR5c+0CEX/7Sbgyy5RRbuTKVpP3ss1PzceIskFCARo6s2TjQli2WlaJ1a7jq\\nKgv/nTw51bGHAhS64Nq3ryqAy5fb+R00qLIFNG9e5usrkwsuOtH25ZetPH3HjuaG69bN3m/f3qyE\\n7dvt2oyOR2USoJ07be5WdP5amIonbE9NBQjg61+3GloVFXatjBhh7U+P8IuOpcWxaZNZYhUVdv77\\n97fzmY9sC6EFVNMJuBGGAwtUdbGq7gEeA86JrqCqU1Q1TIn8BtCtLm3ORnEJ0Ntv25WcbRS8gdO6\\ntXVM2SpIHHWUdQDbt2ceA/r0p82FF0e3bimX2XvvJatW3r27depxke1Dh9p4U3Xut/DY4XcIuewy\\nc0ktWpTaRzgrvm9fs7D69rVOIHStgHWq6eMELVqYAIVlzaMhtcuWpYS0X7/4caC6WkCrV8PDD5vL\\nLJsArViR+o2bNbPIQ6gqAGvX2n1V587mBstUMO6tt+ChhyrfhX//+yaiU6fa9XLppTYO8f771omu\\nXw8f/3h2F9ycOSY04ZhcyNy5doMUR1yHv2mTnft0Cwjs5iOcZ9S+vX2X/v1t/+E53LfP9hEnQHPn\\nmpURvWGI3qiktydMzJtOugAddpj9j+64w24owkCJ9O9WnQW0YoVdU8cfbzcBAwbYOZ0zJ/eRalEX\\n3NKlduzZs2s0f6krELWdlgfvZeIy4LnatbZ68lmSO/cUsfstpHXreKsnSrNmZkFcdJEJQpzF0bdv\\n5oHRESPsDvSLX7SOYNSo6tvVvXtmcRk61DIeJBGg5s1tndACAvvOl18Ot92W2keYKfz8822dPn1M\\nNDdvTnUsIuaCi9Kypf3xTjjBlo84ItUxRQWoOguoe/faWUB33GEuwf79M2+vWtkCihJ1ge3Zk7J+\\nRKzTiptBf999Nql35EjL/Td+vHV2Tzxhf4lBg2xi7YwZZm20aWMuz8MOM4s0WxDC3Lm2fboAzZtX\\neVwySrduNq4TZeNGE7uKCrOAly5N3ficfHJqvfbtzbrv189+5+nTrc1hAEOcAM2ebdZuKFa7d5tg\\nhTkL0wVoxgwYM8YEI3QhqsZHeX7lKzaFoG/fyu7rrpEuecUKs6ozWUDhzcaJJ8Lzz5sAtW9v1vqq\\nVZlvNletshu7M86I/zyd/ftTEbMDBsCXv2y/f7t2ZlXOnw+LFpVTXl6ebTeJJVFETgUuBUYm3aam\\nFJcFVKTh11FatzZ/e3U8+aR5Gq+7rubHGD3aPJWQvGBsv36VE6dGOfxw6xjjouTimDDBOqMoV19t\\nd4cbN6bGkL72tdT4TsuW9qedMyd7ftkWLaxzC/dxxBHxFtCoURbAkG7lhALUp491HDWdq/HOOzau\\nEefC+9a3bH+bN5vAxn2PUADefNNcX9/+tn0HsLvv3bsr14N67z0LZ3/lFXND/fjHlsnhjTdMzMKx\\nvYMOso4cLOru+eetEw3dsY0aWYedPgYUTmYO1wujybJZQOkTOfftM0u9Z0/b98qV1tE3iuld2rVL\\nCdDRR6fchRs22O8SBilEmT3bRCw836H1E4pLmK07ZNUqW44GdGzbZuunu7NHjrR29+tny3FRpCtW\\nwDHHZLaAVq60cz1ihC2HgUHVjQM995xFzyVl82Zrf5MmJnYffpgqFBhOPSgrK2PcuHEfPWJYAXSP\\nLHfHrKBKBIEH9wJjVTVvCaSKR4C2bzdfQ/R2qgi59VZz31RH587WQf/sZzU/xpFH2p3S229bh5su\\nBnGccUbmInHNmtkdYnl5MgEaMqTq4HWXLnDOOSYyoUX3ne9U3l+/fubnr06Aoi64TAJ0xhkW1DB6\\ndMqqCIMUuna1NnTpUtWPXl00VRhFFz0uWAd3yy3Wca9YUfkOOkooQK+9Zu6fE06Az3zGPhOpOvF3\\nxozUeCHAxRfbGOJvfmMD83EceaQVXQsH/tu1S4VTp7vgZs+2jvLgg61zi85lyTQG1KOHdYbhfjZt\\nMqsrzDUXdshxtG9vd/1xAtS1q4lWerTZ7NmWfPb99+03DEOwQ9ItoDAAJxxHBXNNxnkMROwmILRC\\nwuJ7UZYvt7G0bC64Ll3MYuzfP3UN9u2b3TX2wQcmqknddOlh8U2bpl736ZPYDTcV6C8ivUSkKXA+\\n8Gx0BRHpATwFfFFV8zCbKUXxCNDLL9ttSNyIfBExenT1AQF1pVEj67S+/33T62xBAyEi2dt11FFm\\n2dRl+O2GG7J7UPv2tQ4p2rmk07KlhQiHAtSrl/0xt2ypLEBgLswLLrAxG7DOMQxSgFTBvZBt22z7\\nMIT9hz+0CMUo4TE6dzZrJRyMD8eb5s3L7H6DlAC9/ba5iX7zG7OoQtKzj6fP4Wrd2r7X3/5mk4/j\\nGDLE7tVCEejSJTXROeqCU7XzHY7XhW64cJJopt+6UaPKVlC6ey+bALdvb2Ne/frZcd9916yuDRvM\\nqk1Pxhueg5NOsg531arK44RQNQpuzRpbNxSghx6ySMvHH49v0+c/b644yGwBhdGEcYSC26aN/f6h\\n2xq6hs8AABB7SURBVDiMHs3E4sV2zUWj2R580Mby4sg0Lwssy0UoQEuWZI7QVNW9wFXA88As4HFV\\nnS0iV4jIFcFq3wfaAXeLyDQReTN+b3WneASoCMsvFJLRo83Ez1XA4NChNqidxALKxODB5obLRL9+\\n9uevzgKClAsujISbMsXuUYYMqbz+2WenEo+G7reQMG1QyIwZdvcdCsC//20dV3iHum2buT3atzfB\\nHjw45eILJ9POm1c5ACGd0AJ5++14yzRdgGbNqhpEcs01JqyZgkvCcxCKQDgHByq74JYtM9dd586p\\n9VasSIUSZ5tqF7UAw5LyoQBlE+CwA+3Xz9Zv08Z+g3BuV3pBxr17zYIdMCB1zCQW0OjRJkBbtlhi\\n0AkTks1dT7eAtmxJzXvPZgHFCW51AvTBB3ZDFboWZ8yw4pBPPBE/5yg8R3FELaCf/MSmN2RCVcer\\n6kBV7aeqPwveu0dV7wle/4+qHqqqw4LH8Mx7qxvFJUBFHoBQn4TjTLkUIKibAFVH6IfPJkBhRGD0\\nTnDwYBuQHTs2FW0WMmqUucXWrq0qQOkdRBjmG/6RFy60TueFF2w53D7smKNWwNy51nmFFlAmC6Bt\\nW9vPkiWpsZ8o1VlAYJ3No49mFohBg0yYq7OA3nmn8vnq0iWVZT3T+E/IEUfY+BSkBsaTWkDNmqU+\\n79fPzvP69da5pltAixZZuw4+OCVA6RZQugCtWWPuzXfesXHA009PFgkKVS2g8Lt06VK9Cy6d6PUV\\njtdE+eAD69Lef9/G0c4/3yZtH3NMagw3SjYLqE8f2x/YdZz+P2ioFI8ALV2aGmV1qmXQILj99uR/\\nvOqoDwEKffTVWUBt2lT2fw8ZYm61X/yi6vpNm5oIjx8PDzxQuYJHugU0bZoJ3KJFdpe9dau54cLJ\\niukuvmgE3rx51uklsYBC8YkLee/dO9WR7N5tr6sTg3QOPti2CcU2XYDCsZtp0ypbYV27mnhmG/8J\\nOfLIzC646saA+vZNBSiE4ffh3X26AEUFOAz8SGIBhZF9N99s4fxJSbeAQgE69FBzzabn0YPM3zcq\\nQI88YlGpIbt22Xf+xCfsO73+ul0Pn/88nHmmeS927DB3ayh81QnQokUWWTlrVur/2tApHgEqK0s2\\nmOEAdnd89dW5y1jUpYt1BNEOONckFaB0N8SVV9oEwLj5UgBnnWUDzXPm2HNInACddZbdkS9aZH/q\\niy6yzmHduqoCNGRIKp1MVICydcChEGQKDIlaQAsWWBubNYtfNxt/+1sqXmf06NQge9u2qQ4+/U65\\nZ0+bd3XffTWzgOLGgDIJ8LBh8D//k1oOLaBMAjRrVsp1NmSIuVo3bap8jRxyiAlDmNw0DPUfPtx+\\nr5NOyv5donTubKIfphSKWr1xAQp792Z2TXfrZuKxd6+516ZMSU1yXbLE2jZkiAn5M89YXS5ICdC4\\nceY6vPzyVIh6JgHq2NEE6803bb/FMlRePALk4z8FRcQ6g0w+6FzQtq2N7VTngktvQ8uW2S2zM8+0\\nzumRR1JzR6DyHeru3SZQ555r4rNwoQlis2YmFmFEYdSFN2qUdR7r1pnbatQo62xmzszcAbdoYXe6\\nxxwT/3lUgOLcb0k58siUhVVWZhklwDrYxo2tM0wXoIsvtvDtBx+ML1AXpWtXcyutW1ezIIQ+fSpP\\nLejbt6oARSvh3nVXKmp05EgTm9/+tvI1IlLZCgrn+3z1q5ZwtCY3YX362I3EqFF2wxH9Lp07Vw1E\\nWLPGrtm4e+Ow/tbKlXZNhCVUwESud++UFf300ykBGjzYxp0eeMCCSVautHM2YUJmARKxtj/5ZPG4\\n36CYBMjHfw4IvvCF7JmHW7TInosujo99zDqK9ACF0AIKZ9n36mUdd1SAwMTi7berWkDNm9t90QMP\\nWGcf5qdbuDCzAIlYJ5JJgMIosU2bqiaRzQUHHWTRkVddZWMpvXunPmvSxEKjzzjD3HjZEEmNydQk\\nCCGdbC64q66yEPXQkmvUCO6/385veqRkGAkXlt1u397mxoTZGJIiAn/4g7nCTjnFLIrwpqNzZxOk\\nsWNTgS3ZrD1IlTN5910LiAnLli9ebNdbWOdq797U5O3Qe3HXXXbsxx4z8Ro9OhWyH0fv3jZXzAUo\\nH6SnfXZKkttuy3z3DPEWUBLi7lBbtkzVVQmtgXAMZsGCygL0zjtVBQisM/rtb1Muq/A5jCyLY/z4\\nzJ1EdC5Qeh2nXHHxxdZJH310/GTRpBxzjLknoxbQ+vVmYWWzYqPEWUAVFTZP6o03qs6DGzjQXITp\\nk7lDCygsu12X7yViJci/+lUrJRIN5vjFL8yd9vWvm9UczivLRM+eJmL791vYfChAoQUE9hufe25l\\nS+3661Nh9gMGwD332HhWz56Zj9Wnj103LkD5oAjLLzi5Z+DA3Mai9Ohhf9pQgFq1srvryZNTApTJ\\nBQfm3lu9OjVoP2CAuV2iQRLpDBuW/XLu3dtSKE2eHB8pV1eaNLGxnuhYTG047TSLEAyj4Jo2NQs1\\nW4ecTtu2ZknOn185DHvSJLvbj5ub9sUvZhagJOmiknLDDea2Da2ozp3NhfbsszZ2de219jjnnMz7\\n6NnT5pINGWJjUZMnm8UdFaAbb6xZoEQm+vSx52ISIB/Vd4qK0E+eK3r0sLGfv/0N/vMfe69vXxsj\\nCQWoXz+7y1+1qqoFdNhhloIlagHVpAOOo1cvuwP+0Y+SZbGoDbkIzz/lFBODYcNSwRXt2yd3v4X0\\n7WtWQtQCevllm++UlI4d7ffp0CG3uYovvDD1+tRT7QblqKOsmnBZmVkm2dxiPXtaFOVXv2rXWvPm\\nZl2HLjhInguuOvr0SSUVLhZcgJwDmp49Ldpo1KjUGFGfPub+Cd0djRpZJzt1aqpybZTbbzchglQR\\nurpw7bXwv/9r41ENmbZtbYxqypTKYd41FeB+/WyQ/uCDTYDWrDFrM9tkynSGD7exuI4d8zdVYORI\\ne4BZpmvXVu+Y6dHD5viE19Z559lw9rp1lcffcsHJJ8Odd+Z2n/nGBcg5oOnRw4IOnnkm9V6fPvZ+\\n1I12zDHWMcZ1OFGXR8eOlqWgLlRX/rkhcdppVl4hFOZ27WpnAYXjemFC2qFDU6KWhDPOsLIfRx9d\\nf9VakowKhDcx4bycW281q2rChNwLZZs2FuhQTBTPGJDj5IFRo8zXH7U2+vZNZWUIOe64lMvESRG6\\n8upiAaULENQ85/Ahh5gV9Oij+Z0sXVN69rTIw+hY3rHHWiFBH9Z2C8g5wDn++Kp1bz77WQvhjXLe\\neUVbiDevjBxp4h1aQBdfXPPQ8REjUrWbDj7Y5l7VJun9pz9t82UaUr3KVq3suyWNCjzQEM11yb48\\nICJaDO10HKfunHVWajynJixcaJbrCy8kq7l1ICAiqGqDtbVcgBzHKRnGjIF7781vyqhiwgUoB7gA\\nOY7j1JyGLkB5DUIQkTEiMkdE5ovIjRnWuT34fIaIFNEUKsdxnOKjun5ZRAaJyBQR2SUi38hnW/Im\\nQCLSGLgDGAMMBi4UkcPT1jkT6Keq/YH/Be7OV3ucFOXl5YVuQsng5zK3+PnML0n6ZWADcDXwq3y3\\nJ58W0HBggaouVtU9wGNAetKKscCfAVT1DaCtiDSgIMrSxP/kucPPZW7x85l3qu2XVXWdqk4F9uS7\\nMfkUoK5ApNo5y4P3qlsnLduW4ziOkyOS9Mv1Rj4FKGnUQPoAmUcbOI7j5IcG1b/mcyLqCiAaDNkd\\nU9ts63QL3quC+LThnHLzzTcXugklg5/L3OLnM68k6ZfrjXwK0FSgv4j0AlYC5wMXpq3zLHAV8JiI\\nnABsUtU16TtqyGGEjuM4RUSSfjkk7/1u3gRIVfeKyFXA80Bj4H5VnS0iVwSf36Oqz4nImSKyANgO\\nXJKv9jiO4xzoJOmXReRjwFtAG2C/iFwLDFbVbbluT1FMRHUcx3FKjwadDTvJRFYnOyKyWETeFZFp\\nIvJm8F57EZkgIvNE5D8iElPlxgEQkT+KyBoRmRl5L+P5E5Gbgut1jojkqNRYaZDhXI4TkeXB9TlN\\nRD4V+czPZRZEpLuIvCgi74vIeyJyTfB+0VyfDVaAEk6YcqpHgTJVHaaqw4P3vgVMUNUBwKRg2Ynn\\nAewajBJ7/kRkMOZTHxxsc5eINNj/WAGIO5cK/Dq4Poep6njwc5mQPcB1qnoEcALwtaCPLJrrsyH/\\noEkmsjrJSB9M/GgCcPB8bv02p3hQ1VeAjWlvZzp/5wCPquoeVV0MLMCuY4eM5xLiB7v9XFaDqq5W\\n1enB623AbGxOT9Fcnw1ZgBrUhKkiRoGJIjJVRC4P3usUiTZcA3j2iZqR6fx1oXJIq1+zybg6yAV5\\nf8Rd5OeyBgRRbcOANyii67MhC5BHR+SGkao6DPgUZqKfFP0wSDPu57qWJDh/fm6zczfQGzgaWAXc\\nmmVdP5cxiEgr4EngWlXdGv2soV+fDVmAGtSEqWJFVVcFz+uApzGTe00QaomIdAbWFq6FRUmm85d4\\nYrVjqOpaDQDuI+US8nOZABE5CBOfh1T1meDtork+G7IAfTRhSkSaYoNnzxa4TUWFiLQQkdbB65bA\\nGcBM7DxeHKx2MfBM/B6cDGQ6f88CF4hIUxHpDfQH3ixA+4qGoIMM+S/s+gQ/l9Uilh7mfmCWqt4W\\n+ahors98ZkKoE5kmTBW4WcVGJ+DpII1RE+BhVf2PiEwF/ioilwGLgc8VrokNGxF5FDgF6CAiy4Dv\\nAz8n5vyp/n97dxBiVRmGcfz/hJAKtQhc5yLFEGpaGIYVA4E7Ny1qk0EbCQ1clGRt2gruXLZxkbTQ\\noNypLaxMionScphoFW0KZqMgQqHyujjfwcv11mClRz3/32a459xzvpnDDM983z3nfWspyVFgCbgG\\n7LaT4k0zruUHwHySObqloF+B/oFIr+XKtgGvAT8lOde2vcd99Pvpg6iSpEHcy0twkqQHmAEkSRqE\\nASRJGoQBJEkahAEkSRqEASRJGoQBpFFJcrZ9fTzJ33WC/Lfnfn/WWJJm8zkgjVKSeeDtqtpxG8es\\nqqpr/7D/clU98n98f9IYOAPSqCTp2wofAF5oTdD2JnkoycEkC60y8672/vkkZ5IcBxbbts9adfHF\\nvsJ4kgPAmna+jybHSudgkgvpmgO+MnHuL5IcS/JzkiN392pIw7pnS/FId0g/5X8XeKefAbXAuVRV\\nzyZ5GPg6yan23meAzVX1W3v9RlVdTLIGWEjySVXtT7KnVR6fHutl4GngKWAd8F2Sr9q+OboGYX8A\\nZ5NsqyqX7jQKzoA0VtNN0LYDr7eaWt8CjwFPtH0LE+EDsDfJeeAbuurCG1YY63ng41b0eRn4EthC\\nF1ALVfV7q8l1Hlj/H34m6b7iDEi66a2q+nxyQ/us6MrU65eArVX1Z5LTwOoVzlvcGnj97OiviW3X\\n8W9SI+IMSGN1GZi8YeAksDvJKoAkG5OsnXHco8DFFj6bgK0T+672x085A7zaPmdaB7xIVwZ/Vitq\\naTT8b0tj0888fgSut6W0w8AhuuWvH1qflWW6/jTTHSVPAG8mWQJ+oVuG631IVxr/+6ra2R9XVZ8m\\nea6NWcC+qlpO8iS3dqT0tlSNhrdhS5IG4RKcJGkQBpAkaRAGkCRpEAaQJGkQBpAkaRAGkCRpEAaQ\\nJGkQBpAkaRA3ABGGQ9Z+SfjXAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f519bf16690>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"_, ax1 = subplots()\\n\",\n    \"ax2 = ax1.twinx()\\n\",\n    \"ax1.plot(arange(niter), train_loss)\\n\",\n    \"ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\\n\",\n    \"ax1.set_xlabel('iteration')\\n\",\n    \"ax1.set_ylabel('train loss')\\n\",\n    \"ax2.set_ylabel('test accuracy')\\n\",\n    \"ax2.set_title('Test Accuracy: {:.2f}'.format(test_acc[-1]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The loss seems to have dropped quickly and coverged (except for stochasticity), while the accuracy rose correspondingly. Hooray!\\n\",\n    \"\\n\",\n    \"* Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f5199aaab50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f5199a4ba10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f51999dd210>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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WqR02Lcnt/v96lUKoY077///ht5M/eJ20oaaWAgfqZqtUqhULiTRPTb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\\nYII=\\n\",\n 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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f519c0b0c10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f5199603550>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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oikU2oi2WyWbDZr5rzPklAA5hgspzG59vf32d/fN6O3Qp6TkxMzmlut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\\njYuZ4ZLGxcz4f041SDwzkyB1AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f51994fb990>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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aprmKldJ0lz584tjs0cI5njOlN/MFuvNFNLc/v27cWxmbp4terjZvOF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\\nhkioAAAAGiKhAgAAaOj/AYSDQCwV4p2TAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f519948af90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for i in range(8):\\n\",\n    \"    figure(figsize=(2, 2))\\n\",\n    \"    imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\\n\",\n    \"    figure(figsize=(10, 2))\\n\",\n    \"    imshow(output[:50, i].T, interpolation='nearest', cmap='gray')\\n\",\n    \"    xlabel('iteration')\\n\",\n    \"    ylabel('label')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We started with little idea about any of these digits, and ended up with correct classifications for each. If you've been following along, you'll see the last digit is the most difficult, a slanted \\\"9\\\" that's (understandably) most confused with \\\"4\\\".\\n\",\n    \"\\n\",\n    \"* Note that these are the \\\"raw\\\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAI0AAACPCAYAAADHlliuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nQaRarSYPJFOFw/UC5301+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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f51991d5790>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f5198fc5750>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f5198dc0d90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f519c085f10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f519998c390>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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oikU2oi2WyWbDZr5rzPklAA5hgspzG59vf32d/fN6O3Qp6TkxMzmlut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\\njYuZ4ZLGxcz4f041SDwzkyB1AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f519c0f7b50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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9axICJ66497JS1oZGeQxvZSSWdLelSMYUuwPc32GtXG6IGIeEaMXSv5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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f5199b2b090>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for i in range(8):\\n\",\n    \"    figure(figsize=(2, 2))\\n\",\n    \"    imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\\n\",\n    \"    figure(figsize=(10, 2))\\n\",\n    \"    imshow(exp(output[:50, i].T) / exp(output[:50, i].T).sum(0), interpolation='nearest', cmap='gray')\\n\",\n    \"    xlabel('iteration')\\n\",\n    \"    ylabel('label')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 6. Experiment with architecture and optimization\\n\",\n    \"\\n\",\n    \"Now that we've defined, trained, and tested LeNet there are many possible next steps:\\n\",\n    \"\\n\",\n    \"- Define new architectures for comparison\\n\",\n    \"- Tune optimization by setting `base_lr` and the like or simply training longer\\n\",\n    \"- Switching the solver type from `SGD` to an adaptive method like `AdaDelta` or `Adam`\\n\",\n    \"\\n\",\n    \"Feel free to explore these directions by editing the all-in-one example that follows.\\n\",\n    \"Look for \\\"`EDIT HERE`\\\" comments for suggested choice points.\\n\",\n    \"\\n\",\n    \"By default this defines a simple linear classifier as a baseline.\\n\",\n    \"\\n\",\n    \"In case your coffee hasn't kicked in and you'd like inspiration, try out\\n\",\n    \"\\n\",\n    \"1. Switch the nonlinearity from `ReLU` to `ELU` or a saturing nonlinearity like `Sigmoid`\\n\",\n    \"2. Stack more fully connected and nonlinear layers\\n\",\n    \"3. Search over learning rate 10x at a time (trying `0.1` and `0.001`)\\n\",\n    \"4. Switch the solver type to `Adam` (this adaptive solver type should be less sensitive to hyperparameters, but no guarantees...)\\n\",\n    \"5. Solve for longer by setting `niter` higher (to 500 or 1,000 for instance) to better show training differences\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration 0 testing...\\n\",\n      \"Iteration 25 testing...\\n\",\n      \"Iteration 50 testing...\\n\",\n      \"Iteration 75 testing...\\n\",\n      \"Iteration 100 testing...\\n\",\n      \"Iteration 125 testing...\\n\",\n      \"Iteration 150 testing...\\n\",\n      \"Iteration 175 testing...\\n\",\n      \"Iteration 200 testing...\\n\",\n      \"Iteration 225 testing...\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x7f5199af9f50>\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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w9YKQjHcZzqMw5YoqrLAERkMnAWMD+yz2pgdPC+PbChGOIDUHK131u3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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f5198f46a10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"train_net_path = 'mnist/custom_auto_train.prototxt'\\n\",\n    \"test_net_path = 'mnist/custom_auto_test.prototxt'\\n\",\n    \"solver_config_path = 'mnist/custom_auto_solver.prototxt'\\n\",\n    \"\\n\",\n    \"### define net\\n\",\n    \"def custom_net(lmdb, batch_size):\\n\",\n    \"    # define your own net!\\n\",\n    \"    n = caffe.NetSpec()\\n\",\n    \"    \\n\",\n    \"    # keep this data layer for all networks\\n\",\n    \"    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\\n\",\n    \"                             transform_param=dict(scale=1./255), ntop=2)\\n\",\n    \"    \\n\",\n    \"    # EDIT HERE to try different networks\\n\",\n    \"    # this single layer defines a simple linear classifier\\n\",\n    \"    # (in particular this defines a multiway logistic regression)\\n\",\n    \"    n.score =   L.InnerProduct(n.data, num_output=10, weight_filler=dict(type='xavier'))\\n\",\n    \"    \\n\",\n    \"    # EDIT HERE this is the LeNet variant we have already tried\\n\",\n    \"    # n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\\n\",\n    \"    # n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\\n\",\n    \"    # n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\\n\",\n    \"    # n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\\n\",\n    \"    # n.fc1 =   L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\\n\",\n    \"    # EDIT HERE consider L.ELU or L.Sigmoid for the nonlinearity\\n\",\n    \"    # n.relu1 = L.ReLU(n.fc1, in_place=True)\\n\",\n    \"    # n.score =   L.InnerProduct(n.fc1, num_output=10, weight_filler=dict(type='xavier'))\\n\",\n    \"    \\n\",\n    \"    # keep this loss layer for all networks\\n\",\n    \"    n.loss =  L.SoftmaxWithLoss(n.score, n.label)\\n\",\n    \"    \\n\",\n    \"    return n.to_proto()\\n\",\n    \"\\n\",\n    \"with open(train_net_path, 'w') as f:\\n\",\n    \"    f.write(str(custom_net('mnist/mnist_train_lmdb', 64)))    \\n\",\n    \"with open(test_net_path, 'w') as f:\\n\",\n    \"    f.write(str(custom_net('mnist/mnist_test_lmdb', 100)))\\n\",\n    \"\\n\",\n    \"### define solver\\n\",\n    \"from caffe.proto import caffe_pb2\\n\",\n    \"s = caffe_pb2.SolverParameter()\\n\",\n    \"\\n\",\n    \"# Set a seed for reproducible experiments:\\n\",\n    \"# this controls for randomization in training.\\n\",\n    \"s.random_seed = 0xCAFFE\\n\",\n    \"\\n\",\n    \"# Specify locations of the train and (maybe) test networks.\\n\",\n    \"s.train_net = train_net_path\\n\",\n    \"s.test_net.append(test_net_path)\\n\",\n    \"s.test_interval = 500  # Test after every 500 training iterations.\\n\",\n    \"s.test_iter.append(100) # Test on 100 batches each time we test.\\n\",\n    \"\\n\",\n    \"s.max_iter = 10000     # no. of times to update the net (training iterations)\\n\",\n    \" \\n\",\n    \"# EDIT HERE to try different solvers\\n\",\n    \"# solver types include \\\"SGD\\\", \\\"Adam\\\", and \\\"Nesterov\\\" among others.\\n\",\n    \"s.type = \\\"SGD\\\"\\n\",\n    \"\\n\",\n    \"# Set the initial learning rate for SGD.\\n\",\n    \"s.base_lr = 0.01  # EDIT HERE to try different learning rates\\n\",\n    \"# Set momentum to accelerate learning by\\n\",\n    \"# taking weighted average of current and previous updates.\\n\",\n    \"s.momentum = 0.9\\n\",\n    \"# Set weight decay to regularize and prevent overfitting\\n\",\n    \"s.weight_decay = 5e-4\\n\",\n    \"\\n\",\n    \"# Set `lr_policy` to define how the learning rate changes during training.\\n\",\n    \"# This is the same policy as our default LeNet.\\n\",\n    \"s.lr_policy = 'inv'\\n\",\n    \"s.gamma = 0.0001\\n\",\n    \"s.power = 0.75\\n\",\n    \"# EDIT HERE to try the fixed rate (and compare with adaptive solvers)\\n\",\n    \"# `fixed` is the simplest policy that keeps the learning rate constant.\\n\",\n    \"# s.lr_policy = 'fixed'\\n\",\n    \"\\n\",\n    \"# Display the current training loss and accuracy every 1000 iterations.\\n\",\n    \"s.display = 1000\\n\",\n    \"\\n\",\n    \"# Snapshots are files used to store networks we've trained.\\n\",\n    \"# We'll snapshot every 5K iterations -- twice during training.\\n\",\n    \"s.snapshot = 5000\\n\",\n    \"s.snapshot_prefix = 'mnist/custom_net'\\n\",\n    \"\\n\",\n    \"# Train on the GPU\\n\",\n    \"s.solver_mode = caffe_pb2.SolverParameter.GPU\\n\",\n    \"\\n\",\n    \"# Write the solver to a temporary file and return its filename.\\n\",\n    \"with open(solver_config_path, 'w') as f:\\n\",\n    \"    f.write(str(s))\\n\",\n    \"\\n\",\n    \"### load the solver and create train and test nets\\n\",\n    \"solver = None  # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\\n\",\n    \"solver = caffe.get_solver(solver_config_path)\\n\",\n    \"\\n\",\n    \"### solve\\n\",\n    \"niter = 250  # EDIT HERE increase to train for longer\\n\",\n    \"test_interval = niter / 10\\n\",\n    \"# losses will also be stored in the log\\n\",\n    \"train_loss = zeros(niter)\\n\",\n    \"test_acc = zeros(int(np.ceil(niter / test_interval)))\\n\",\n    \"\\n\",\n    \"# the main solver loop\\n\",\n    \"for it in range(niter):\\n\",\n    \"    solver.step(1)  # SGD by Caffe\\n\",\n    \"    \\n\",\n    \"    # store the train loss\\n\",\n    \"    train_loss[it] = solver.net.blobs['loss'].data\\n\",\n    \"    \\n\",\n    \"    # run a full test every so often\\n\",\n    \"    # (Caffe can also do this for us and write to a log, but we show here\\n\",\n    \"    #  how to do it directly in Python, where more complicated things are easier.)\\n\",\n    \"    if it % test_interval == 0:\\n\",\n    \"        print 'Iteration', it, 'testing...'\\n\",\n    \"        correct = 0\\n\",\n    \"        for test_it in range(100):\\n\",\n    \"            solver.test_nets[0].forward()\\n\",\n    \"            correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)\\n\",\n    \"                           == solver.test_nets[0].blobs['label'].data)\\n\",\n    \"        test_acc[it // test_interval] = correct / 1e4\\n\",\n    \"\\n\",\n    \"_, ax1 = subplots()\\n\",\n    \"ax2 = ax1.twinx()\\n\",\n    \"ax1.plot(arange(niter), train_loss)\\n\",\n    \"ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\\n\",\n    \"ax1.set_xlabel('iteration')\\n\",\n    \"ax1.set_ylabel('train loss')\\n\",\n    \"ax2.set_ylabel('test accuracy')\\n\",\n    \"ax2.set_title('Custom Test Accuracy: {:.2f}'.format(test_acc[-1]))\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"description\": \"Define, train, and test the classic LeNet with the Python interface.\",\n  \"example_name\": \"Learning LeNet\",\n  \"include_in_docs\": true,\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  },\n  \"priority\": 2\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Caffe/02-fine-tuning.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Fine-tuning a Pretrained Network for Style Recognition\\n\",\n    \"\\n\",\n    \"In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\\n\",\n    \"\\n\",\n    \"The advantage of this approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \\\"semantics\\\" of the general visual appearance. Think of it as a very powerful generic visual feature that you can treat as a black box. On top of that, only a relatively small amount of data is needed for good performance on the target task.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, we will need to prepare the data. This involves the following parts:\\n\",\n    \"(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\\n\",\n    \"(2) Download a subset of the overall Flickr style dataset for this demo.\\n\",\n    \"(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"caffe_root = '../'  # this file should be run from {caffe_root}/examples (otherwise change this line)\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"sys.path.insert(0, caffe_root + 'python')\\n\",\n    \"import caffe\\n\",\n    \"\\n\",\n    \"caffe.set_device(0)\\n\",\n    \"caffe.set_mode_gpu()\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"from pylab import *\\n\",\n    \"%matplotlib inline\\n\",\n    \"import tempfile\\n\",\n    \"\\n\",\n    \"# Helper function for deprocessing preprocessed images, e.g., for display.\\n\",\n    \"def deprocess_net_image(image):\\n\",\n    \"    image = image.copy()              # don't modify destructively\\n\",\n    \"    image = image[::-1]               # BGR -> RGB\\n\",\n    \"    image = image.transpose(1, 2, 0)  # CHW -> HWC\\n\",\n    \"    image += [123, 117, 104]          # (approximately) undo mean subtraction\\n\",\n    \"\\n\",\n    \"    # clamp values in [0, 255]\\n\",\n    \"    image[image < 0], image[image > 255] = 0, 255\\n\",\n    \"\\n\",\n    \"    # round and cast from float32 to uint8\\n\",\n    \"    image = np.round(image)\\n\",\n    \"    image = np.require(image, dtype=np.uint8)\\n\",\n    \"\\n\",\n    \"    return image\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1. Setup and dataset download\\n\",\n    \"\\n\",\n    \"Download data required for this exercise.\\n\",\n    \"\\n\",\n    \"- `get_ilsvrc_aux.sh` to download the ImageNet data mean, labels, etc.\\n\",\n    \"- `download_model_binary.py` to download the pretrained reference model\\n\",\n    \"- `finetune_flickr_style/assemble_data.py` downloadsd the style training and testing data\\n\",\n    \"\\n\",\n    \"We'll download just a small subset of the full dataset for this exercise: just 2000 of the 80K images, from 5 of the 20 style categories.  (To download the full dataset, set `full_dataset = True` in the cell below.)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading...\\n\",\n      \"--2016-02-24 00:28:36--  http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz\\n\",\n      \"Resolving dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)... 169.229.222.251\\n\",\n      \"Connecting to dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)|169.229.222.251|:80... connected.\\n\",\n      \"HTTP request sent, awaiting response... 200 OK\\n\",\n      \"Length: 17858008 (17M) [application/octet-stream]\\n\",\n      \"Saving to: ‘caffe_ilsvrc12.tar.gz’\\n\",\n      \"\\n\",\n      \"100%[======================================>] 17,858,008   112MB/s   in 0.2s   \\n\",\n      \"\\n\",\n      \"2016-02-24 00:28:36 (112 MB/s) - ‘caffe_ilsvrc12.tar.gz’ saved [17858008/17858008]\\n\",\n      \"\\n\",\n      \"Unzipping...\\n\",\n      \"Done.\\n\",\n      \"Model already exists.\\n\",\n      \"Downloading 2000 images with 7 workers...\\n\",\n      \"Writing train/val for 1996 successfully downloaded images.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Download just a small subset of the data for this exercise.\\n\",\n    \"# (2000 of 80K images, 5 of 20 labels.)\\n\",\n    \"# To download the entire dataset, set `full_dataset = True`.\\n\",\n    \"full_dataset = False\\n\",\n    \"if full_dataset:\\n\",\n    \"    NUM_STYLE_IMAGES = NUM_STYLE_LABELS = -1\\n\",\n    \"else:\\n\",\n    \"    NUM_STYLE_IMAGES = 2000\\n\",\n    \"    NUM_STYLE_LABELS = 5\\n\",\n    \"\\n\",\n    \"# This downloads the ilsvrc auxiliary data (mean file, etc),\\n\",\n    \"# and a subset of 2000 images for the style recognition task.\\n\",\n    \"import os\\n\",\n    \"os.chdir(caffe_root)  # run scripts from caffe root\\n\",\n    \"!data/ilsvrc12/get_ilsvrc_aux.sh\\n\",\n    \"!scripts/download_model_binary.py models/bvlc_reference_caffenet\\n\",\n    \"!python examples/finetune_flickr_style/assemble_data.py \\\\\\n\",\n    \"    --workers=-1  --seed=1701 \\\\\\n\",\n    \"    --images=$NUM_STYLE_IMAGES  --label=$NUM_STYLE_LABELS\\n\",\n    \"# back to examples\\n\",\n    \"os.chdir('examples')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Define `weights`, the path to the ImageNet pretrained weights we just downloaded, and make sure it exists.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\\n\",\n    \"assert os.path.exists(weights)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Load the 1000 ImageNet labels from `ilsvrc12/synset_words.txt`, and the 5 style labels from `finetune_flickr_style/style_names.txt`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Loaded ImageNet labels:\\n\",\n      \"n01440764 tench, Tinca tinca\\n\",\n      \"n01443537 goldfish, Carassius auratus\\n\",\n      \"n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias\\n\",\n      \"n01491361 tiger shark, Galeocerdo cuvieri\\n\",\n      \"n01494475 hammerhead, hammerhead shark\\n\",\n      \"n01496331 electric ray, crampfish, numbfish, torpedo\\n\",\n      \"n01498041 stingray\\n\",\n      \"n01514668 cock\\n\",\n      \"n01514859 hen\\n\",\n      \"n01518878 ostrich, Struthio camelus\\n\",\n      \"...\\n\",\n      \"\\n\",\n      \"Loaded style labels:\\n\",\n      \"Detailed, Pastel, Melancholy, Noir, HDR\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Load ImageNet labels to imagenet_labels\\n\",\n    \"imagenet_label_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\\n\",\n    \"imagenet_labels = list(np.loadtxt(imagenet_label_file, str, delimiter='\\\\t'))\\n\",\n    \"assert len(imagenet_labels) == 1000\\n\",\n    \"print 'Loaded ImageNet labels:\\\\n', '\\\\n'.join(imagenet_labels[:10] + ['...'])\\n\",\n    \"\\n\",\n    \"# Load style labels to style_labels\\n\",\n    \"style_label_file = caffe_root + 'examples/finetune_flickr_style/style_names.txt'\\n\",\n    \"style_labels = list(np.loadtxt(style_label_file, str, delimiter='\\\\n'))\\n\",\n    \"if NUM_STYLE_LABELS > 0:\\n\",\n    \"    style_labels = style_labels[:NUM_STYLE_LABELS]\\n\",\n    \"print '\\\\nLoaded style labels:\\\\n', ', '.join(style_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.  Defining and running the nets\\n\",\n    \"\\n\",\n    \"We'll start by defining `caffenet`, a function which initializes the *CaffeNet* architecture (a minor variant on *AlexNet*), taking arguments specifying the data and number of output classes.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from caffe import layers as L\\n\",\n    \"from caffe import params as P\\n\",\n    \"\\n\",\n    \"weight_param = dict(lr_mult=1, decay_mult=1)\\n\",\n    \"bias_param   = dict(lr_mult=2, decay_mult=0)\\n\",\n    \"learned_param = [weight_param, bias_param]\\n\",\n    \"\\n\",\n    \"frozen_param = [dict(lr_mult=0)] * 2\\n\",\n    \"\\n\",\n    \"def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1,\\n\",\n    \"              param=learned_param,\\n\",\n    \"              weight_filler=dict(type='gaussian', std=0.01),\\n\",\n    \"              bias_filler=dict(type='constant', value=0.1)):\\n\",\n    \"    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\\n\",\n    \"                         num_output=nout, pad=pad, group=group,\\n\",\n    \"                         param=param, weight_filler=weight_filler,\\n\",\n    \"                         bias_filler=bias_filler)\\n\",\n    \"    return conv, L.ReLU(conv, in_place=True)\\n\",\n    \"\\n\",\n    \"def fc_relu(bottom, nout, param=learned_param,\\n\",\n    \"            weight_filler=dict(type='gaussian', std=0.005),\\n\",\n    \"            bias_filler=dict(type='constant', value=0.1)):\\n\",\n    \"    fc = L.InnerProduct(bottom, num_output=nout, param=param,\\n\",\n    \"                        weight_filler=weight_filler,\\n\",\n    \"                        bias_filler=bias_filler)\\n\",\n    \"    return fc, L.ReLU(fc, in_place=True)\\n\",\n    \"\\n\",\n    \"def max_pool(bottom, ks, stride=1):\\n\",\n    \"    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\\n\",\n    \"\\n\",\n    \"def caffenet(data, label=None, train=True, num_classes=1000,\\n\",\n    \"             classifier_name='fc8', learn_all=False):\\n\",\n    \"    \\\"\\\"\\\"Returns a NetSpec specifying CaffeNet, following the original proto text\\n\",\n    \"       specification (./models/bvlc_reference_caffenet/train_val.prototxt).\\\"\\\"\\\"\\n\",\n    \"    n = caffe.NetSpec()\\n\",\n    \"    n.data = data\\n\",\n    \"    param = learned_param if learn_all else frozen_param\\n\",\n    \"    n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4, param=param)\\n\",\n    \"    n.pool1 = max_pool(n.relu1, 3, stride=2)\\n\",\n    \"    n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\\n\",\n    \"    n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2, param=param)\\n\",\n    \"    n.pool2 = max_pool(n.relu2, 3, stride=2)\\n\",\n    \"    n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\\n\",\n    \"    n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1, param=param)\\n\",\n    \"    n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2, param=param)\\n\",\n    \"    n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2, param=param)\\n\",\n    \"    n.pool5 = max_pool(n.relu5, 3, stride=2)\\n\",\n    \"    n.fc6, n.relu6 = fc_relu(n.pool5, 4096, param=param)\\n\",\n    \"    if train:\\n\",\n    \"        n.drop6 = fc7input = L.Dropout(n.relu6, in_place=True)\\n\",\n    \"    else:\\n\",\n    \"        fc7input = n.relu6\\n\",\n    \"    n.fc7, n.relu7 = fc_relu(fc7input, 4096, param=param)\\n\",\n    \"    if train:\\n\",\n    \"        n.drop7 = fc8input = L.Dropout(n.relu7, in_place=True)\\n\",\n    \"    else:\\n\",\n    \"        fc8input = n.relu7\\n\",\n    \"    # always learn fc8 (param=learned_param)\\n\",\n    \"    fc8 = L.InnerProduct(fc8input, num_output=num_classes, param=learned_param)\\n\",\n    \"    # give fc8 the name specified by argument `classifier_name`\\n\",\n    \"    n.__setattr__(classifier_name, fc8)\\n\",\n    \"    if not train:\\n\",\n    \"        n.probs = L.Softmax(fc8)\\n\",\n    \"    if label is not None:\\n\",\n    \"        n.label = label\\n\",\n    \"        n.loss = L.SoftmaxWithLoss(fc8, n.label)\\n\",\n    \"        n.acc = L.Accuracy(fc8, n.label)\\n\",\n    \"    # write the net to a temporary file and return its filename\\n\",\n    \"    with tempfile.NamedTemporaryFile(delete=False) as f:\\n\",\n    \"        f.write(str(n.to_proto()))\\n\",\n    \"        return f.name\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now, let's create a *CaffeNet* that takes unlabeled \\\"dummy data\\\" as input, allowing us to set its input images externally and see what ImageNet classes it predicts.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"dummy_data = L.DummyData(shape=dict(dim=[1, 3, 227, 227]))\\n\",\n    \"imagenet_net_filename = caffenet(data=dummy_data, train=False)\\n\",\n    \"imagenet_net = caffe.Net(imagenet_net_filename, weights, caffe.TEST)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Define a function `style_net` which calls `caffenet` on data from the Flickr style dataset.\\n\",\n    \"\\n\",\n    \"The new network will also have the *CaffeNet* architecture, with differences in the input and output:\\n\",\n    \"\\n\",\n    \"- the input is the Flickr style data we downloaded, provided by an `ImageData` layer\\n\",\n    \"- the output is a distribution over 20 classes rather than the original 1000 ImageNet classes\\n\",\n    \"- the classification layer is renamed from `fc8` to `fc8_flickr` to tell Caffe not to load the original classifier (`fc8`) weights from the ImageNet-pretrained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def style_net(train=True, learn_all=False, subset=None):\\n\",\n    \"    if subset is None:\\n\",\n    \"        subset = 'train' if train else 'test'\\n\",\n    \"    source = caffe_root + 'data/flickr_style/%s.txt' % subset\\n\",\n    \"    transform_param = dict(mirror=train, crop_size=227,\\n\",\n    \"        mean_file=caffe_root + 'data/ilsvrc12/imagenet_mean.binaryproto')\\n\",\n    \"    style_data, style_label = L.ImageData(\\n\",\n    \"        transform_param=transform_param, source=source,\\n\",\n    \"        batch_size=50, new_height=256, new_width=256, ntop=2)\\n\",\n    \"    return caffenet(data=style_data, label=style_label, train=train,\\n\",\n    \"                    num_classes=NUM_STYLE_LABELS,\\n\",\n    \"                    classifier_name='fc8_flickr',\\n\",\n    \"                    learn_all=learn_all)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Use the `style_net` function defined above to initialize `untrained_style_net`, a *CaffeNet* with input images from the style dataset and weights from the pretrained ImageNet model.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Call `forward` on `untrained_style_net` to get a batch of style training data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"untrained_style_net = caffe.Net(style_net(train=False, subset='train'),\\n\",\n    \"                                weights, caffe.TEST)\\n\",\n    \"untrained_style_net.forward()\\n\",\n    \"style_data_batch = untrained_style_net.blobs['data'].data.copy()\\n\",\n    \"style_label_batch = np.array(untrained_style_net.blobs['label'].data, dtype=np.int32)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Pick one of the style net training images from the batch of 50 (we'll arbitrarily choose #8 here).  Display it, then run it through `imagenet_net`, the ImageNet-pretrained network to view its top 5 predicted classes from the 1000 ImageNet classes.\\n\",\n    \"\\n\",\n    \"Below we chose an image where the network's predictions happen to be reasonable, as the image is of a beach, and \\\"sandbar\\\" and \\\"seashore\\\" both happen to be ImageNet-1000 categories.  For other images, the predictions won't be this good, sometimes due to the network actually failing to recognize the object(s) present in the image, but perhaps even more often due to the fact that not all images contain an object from the (somewhat arbitrarily chosen) 1000 ImageNet categories. Modify the `batch_index` variable by changing its default setting of 8 to another value from 0-49 (since the batch size is 50) to see predictions for other images in the batch.  (To go beyond this batch of 50 images, first rerun the *above* cell to load a fresh batch of data into `style_net`.)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def disp_preds(net, image, labels, k=5, name='ImageNet'):\\n\",\n    \"    input_blob = net.blobs['data']\\n\",\n    \"    net.blobs['data'].data[0, ...] = image\\n\",\n    \"    probs = net.forward(start='conv1')['probs'][0]\\n\",\n    \"    top_k = (-probs).argsort()[:k]\\n\",\n    \"    print 'top %d predicted %s labels =' % (k, name)\\n\",\n    \"    print '\\\\n'.join('\\\\t(%d) %5.2f%% %s' % (i+1, 100*probs[p], labels[p])\\n\",\n    \"                    for i, p in enumerate(top_k))\\n\",\n    \"\\n\",\n    \"def disp_imagenet_preds(net, image):\\n\",\n    \"    disp_preds(net, image, imagenet_labels, name='ImageNet')\\n\",\n    \"\\n\",\n    \"def disp_style_preds(net, image):\\n\",\n    \"    disp_preds(net, image, style_labels, name='style')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"actual label = Melancholy\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Kqox2MXUTyRijqcloVtW3m8yHjEqlA0CGPR8GSIxHiahS++pLFTgKoF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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f75dc02e4d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"batch_index = 8\\n\",\n    \"image = style_data_batch[batch_index]\\n\",\n    \"plt.imshow(deprocess_net_image(image))\\n\",\n    \"print 'actual label =', style_labels[style_label_batch[batch_index]]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"top 5 predicted ImageNet labels =\\n\",\n      \"\\t(1) 69.89% n09421951 sandbar, sand bar\\n\",\n      \"\\t(2) 21.76% n09428293 seashore, coast, seacoast, sea-coast\\n\",\n      \"\\t(3)  3.22% n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty\\n\",\n      \"\\t(4)  1.89% n04592741 wing\\n\",\n      \"\\t(5)  1.23% n09332890 lakeside, lakeshore\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"disp_imagenet_preds(imagenet_net, image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can also look at `untrained_style_net`'s predictions, but we won't see anything interesting as its classifier hasn't been trained yet.\\n\",\n    \"\\n\",\n    \"In fact, since we zero-initialized the classifier (see `caffenet` definition -- no `weight_filler` is passed to the final `InnerProduct` layer), the softmax inputs should be all zero and we should therefore see a predicted probability of 1/N for each label (for N labels).  Since we set N = 5, we get a predicted probability of 20% for each class.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"top 5 predicted style labels =\\n\",\n      \"\\t(1) 20.00% Detailed\\n\",\n      \"\\t(2) 20.00% Pastel\\n\",\n      \"\\t(3) 20.00% Melancholy\\n\",\n      \"\\t(4) 20.00% Noir\\n\",\n      \"\\t(5) 20.00% HDR\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"disp_style_preds(untrained_style_net, image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can also verify that the activations in layer `fc7` immediately before the classification layer are the same as (or very close to) those in the ImageNet-pretrained model, since both models are using the same pretrained weights in the `conv1` through `fc7` layers.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"diff = untrained_style_net.blobs['fc7'].data[0] - imagenet_net.blobs['fc7'].data[0]\\n\",\n    \"error = (diff ** 2).sum()\\n\",\n    \"assert error < 1e-8\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Delete `untrained_style_net` to save memory.  (Hang on to `imagenet_net` as we'll use it again later.)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"del untrained_style_net\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3. Training the style classifier\\n\",\n    \"\\n\",\n    \"Now, we'll define a function `solver` to create our Caffe solvers, which are used to train the network (learn its weights).  In this function we'll set values for various parameters used for learning, display, and \\\"snapshotting\\\" -- see the inline comments for explanations of what they mean.  You may want to play with some of the learning parameters to see if you can improve on the results here!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from caffe.proto import caffe_pb2\\n\",\n    \"\\n\",\n    \"def solver(train_net_path, test_net_path=None, base_lr=0.001):\\n\",\n    \"    s = caffe_pb2.SolverParameter()\\n\",\n    \"\\n\",\n    \"    # Specify locations of the train and (maybe) test networks.\\n\",\n    \"    s.train_net = train_net_path\\n\",\n    \"    if test_net_path is not None:\\n\",\n    \"        s.test_net.append(test_net_path)\\n\",\n    \"        s.test_interval = 1000  # Test after every 1000 training iterations.\\n\",\n    \"        s.test_iter.append(100) # Test on 100 batches each time we test.\\n\",\n    \"\\n\",\n    \"    # The number of iterations over which to average the gradient.\\n\",\n    \"    # Effectively boosts the training batch size by the given factor, without\\n\",\n    \"    # affecting memory utilization.\\n\",\n    \"    s.iter_size = 1\\n\",\n    \"    \\n\",\n    \"    s.max_iter = 100000     # # of times to update the net (training iterations)\\n\",\n    \"    \\n\",\n    \"    # Solve using the stochastic gradient descent (SGD) algorithm.\\n\",\n    \"    # Other choices include 'Adam' and 'RMSProp'.\\n\",\n    \"    s.type = 'SGD'\\n\",\n    \"\\n\",\n    \"    # Set the initial learning rate for SGD.\\n\",\n    \"    s.base_lr = base_lr\\n\",\n    \"\\n\",\n    \"    # Set `lr_policy` to define how the learning rate changes during training.\\n\",\n    \"    # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\\n\",\n    \"    # every `stepsize` iterations.\\n\",\n    \"    s.lr_policy = 'step'\\n\",\n    \"    s.gamma = 0.1\\n\",\n    \"    s.stepsize = 20000\\n\",\n    \"\\n\",\n    \"    # Set other SGD hyperparameters. Setting a non-zero `momentum` takes a\\n\",\n    \"    # weighted average of the current gradient and previous gradients to make\\n\",\n    \"    # learning more stable. L2 weight decay regularizes learning, to help prevent\\n\",\n    \"    # the model from overfitting.\\n\",\n    \"    s.momentum = 0.9\\n\",\n    \"    s.weight_decay = 5e-4\\n\",\n    \"\\n\",\n    \"    # Display the current training loss and accuracy every 1000 iterations.\\n\",\n    \"    s.display = 1000\\n\",\n    \"\\n\",\n    \"    # Snapshots are files used to store networks we've trained.  Here, we'll\\n\",\n    \"    # snapshot every 10K iterations -- ten times during training.\\n\",\n    \"    s.snapshot = 10000\\n\",\n    \"    s.snapshot_prefix = caffe_root + 'models/finetune_flickr_style/finetune_flickr_style'\\n\",\n    \"    \\n\",\n    \"    # Train on the GPU.  Using the CPU to train large networks is very slow.\\n\",\n    \"    s.solver_mode = caffe_pb2.SolverParameter.GPU\\n\",\n    \"    \\n\",\n    \"    # Write the solver to a temporary file and return its filename.\\n\",\n    \"    with tempfile.NamedTemporaryFile(delete=False) as f:\\n\",\n    \"        f.write(str(s))\\n\",\n    \"        return f.name\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we'll invoke the solver to train the style net's classification layer.\\n\",\n    \"\\n\",\n    \"For the record, if you want to train the network using only the command line tool, this is the command:\\n\",\n    \"\\n\",\n    \"<code>\\n\",\n    \"build/tools/caffe train \\\\\\n\",\n    \"    -solver models/finetune_flickr_style/solver.prototxt \\\\\\n\",\n    \"    -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\\\\n\",\n    \"    -gpu 0\\n\",\n    \"</code>\\n\",\n    \"\\n\",\n    \"However, we will train using Python in this example.\\n\",\n    \"\\n\",\n    \"We'll first define `run_solvers`, a function that takes a list of solvers and steps each one in a round robin manner, recording the accuracy and loss values each iteration.  At the end, the learned weights are saved to a file.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def run_solvers(niter, solvers, disp_interval=10):\\n\",\n    \"    \\\"\\\"\\\"Run solvers for niter iterations,\\n\",\n    \"       returning the loss and accuracy recorded each iteration.\\n\",\n    \"       `solvers` is a list of (name, solver) tuples.\\\"\\\"\\\"\\n\",\n    \"    blobs = ('loss', 'acc')\\n\",\n    \"    loss, acc = ({name: np.zeros(niter) for name, _ in solvers}\\n\",\n    \"                 for _ in blobs)\\n\",\n    \"    for it in range(niter):\\n\",\n    \"        for name, s in solvers:\\n\",\n    \"            s.step(1)  # run a single SGD step in Caffe\\n\",\n    \"            loss[name][it], acc[name][it] = (s.net.blobs[b].data.copy()\\n\",\n    \"                                             for b in blobs)\\n\",\n    \"        if it % disp_interval == 0 or it + 1 == niter:\\n\",\n    \"            loss_disp = '; '.join('%s: loss=%.3f, acc=%2d%%' %\\n\",\n    \"                                  (n, loss[n][it], np.round(100*acc[n][it]))\\n\",\n    \"                                  for n, _ in solvers)\\n\",\n    \"            print '%3d) %s' % (it, loss_disp)     \\n\",\n    \"    # Save the learned weights from both nets.\\n\",\n    \"    weight_dir = tempfile.mkdtemp()\\n\",\n    \"    weights = {}\\n\",\n    \"    for name, s in solvers:\\n\",\n    \"        filename = 'weights.%s.caffemodel' % name\\n\",\n    \"        weights[name] = os.path.join(weight_dir, filename)\\n\",\n    \"        s.net.save(weights[name])\\n\",\n    \"    return loss, acc, weights\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's create and run solvers to train nets for the style recognition task.  We'll create two solvers -- one (`style_solver`) will have its train net initialized to the ImageNet-pretrained weights (this is done by the call to the `copy_from` method), and the other (`scratch_style_solver`) will start from a *randomly* initialized net.\\n\",\n    \"\\n\",\n    \"During training, we should see that the ImageNet pretrained net is learning faster and attaining better accuracies than the scratch net.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Running solvers for 200 iterations...\\n\",\n      \"  0) pretrained: loss=1.609, acc=28%; scratch: loss=1.609, acc=28%\\n\",\n      \" 10) pretrained: loss=1.293, acc=52%; scratch: loss=1.626, acc=14%\\n\",\n      \" 20) pretrained: loss=1.110, acc=56%; scratch: loss=1.646, acc=10%\\n\",\n      \" 30) pretrained: loss=1.084, acc=60%; scratch: loss=1.616, acc=20%\\n\",\n      \" 40) pretrained: loss=0.898, acc=64%; scratch: loss=1.588, acc=26%\\n\",\n      \" 50) pretrained: loss=1.024, acc=54%; scratch: loss=1.607, acc=32%\\n\",\n      \" 60) pretrained: loss=0.925, acc=66%; scratch: loss=1.616, acc=20%\\n\",\n      \" 70) pretrained: loss=0.861, acc=74%; scratch: loss=1.598, acc=24%\\n\",\n      \" 80) pretrained: loss=0.967, acc=60%; scratch: loss=1.588, acc=30%\\n\",\n      \" 90) pretrained: loss=1.274, acc=52%; scratch: loss=1.608, acc=20%\\n\",\n      \"100) pretrained: loss=1.113, acc=62%; scratch: loss=1.588, acc=30%\\n\",\n      \"110) pretrained: loss=0.922, acc=62%; scratch: loss=1.578, acc=36%\\n\",\n      \"120) pretrained: loss=0.918, acc=62%; scratch: loss=1.599, acc=20%\\n\",\n      \"130) pretrained: loss=0.959, acc=58%; scratch: loss=1.594, acc=22%\\n\",\n      \"140) pretrained: loss=1.228, acc=50%; scratch: loss=1.608, acc=14%\\n\",\n      \"150) pretrained: loss=0.727, acc=76%; scratch: loss=1.623, acc=16%\\n\",\n      \"160) pretrained: loss=1.074, acc=66%; scratch: loss=1.607, acc=20%\\n\",\n      \"170) pretrained: loss=0.887, acc=60%; scratch: loss=1.614, acc=20%\\n\",\n      \"180) pretrained: loss=0.961, acc=62%; scratch: loss=1.614, acc=18%\\n\",\n      \"190) pretrained: loss=0.737, acc=76%; scratch: loss=1.613, acc=18%\\n\",\n      \"199) pretrained: loss=0.836, acc=70%; scratch: loss=1.614, acc=16%\\n\",\n      \"Done.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"niter = 200  # number of iterations to train\\n\",\n    \"\\n\",\n    \"# Reset style_solver as before.\\n\",\n    \"style_solver_filename = solver(style_net(train=True))\\n\",\n    \"style_solver = caffe.get_solver(style_solver_filename)\\n\",\n    \"style_solver.net.copy_from(weights)\\n\",\n    \"\\n\",\n    \"# For reference, we also create a solver that isn't initialized from\\n\",\n    \"# the pretrained ImageNet weights.\\n\",\n    \"scratch_style_solver_filename = solver(style_net(train=True))\\n\",\n    \"scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\\n\",\n    \"\\n\",\n    \"print 'Running solvers for %d iterations...' % niter\\n\",\n    \"solvers = [('pretrained', style_solver),\\n\",\n    \"           ('scratch', scratch_style_solver)]\\n\",\n    \"loss, acc, weights = run_solvers(niter, solvers)\\n\",\n    \"print 'Done.'\\n\",\n    \"\\n\",\n    \"train_loss, scratch_train_loss = loss['pretrained'], loss['scratch']\\n\",\n    \"train_acc, scratch_train_acc = acc['pretrained'], acc['scratch']\\n\",\n    \"style_weights, scratch_style_weights = weights['pretrained'], weights['scratch']\\n\",\n    \"\\n\",\n    \"# Delete solvers to save memory.\\n\",\n    \"del style_solver, scratch_style_solver, solvers\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's look at the training loss and accuracy produced by the two training procedures.  Notice how quickly the ImageNet pretrained model's loss value (blue) drops, and that the randomly initialized model's loss value (green) barely (if at all) improves from training only the classifier layer.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x7f75d49e1090>\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Nn+ZfTIskfo4WUPU/LkZKqor2j9vF7/8XWauGYivbrpVSqtLaVp66dR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\\ncoiIiIb/AxSD6Sq0YLMCAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f75d496e890>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot(np.vstack([train_loss, scratch_train_loss]).T)\\n\",\n    \"xlabel('Iteration #')\\n\",\n    \"ylabel('Loss')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x7f75d49e1a90>\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Rde6F451NeTyyibpSDyO95h/b3XXqM4AkDKIZGgff/qr2gbZysxrNxK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\\nFwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f75d47cad90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot(np.vstack([train_acc, scratch_train_acc]).T)\\n\",\n    \"xlabel('Iteration #')\\n\",\n    \"ylabel('Accuracy')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's take a look at the testing accuracy after running 200 iterations of training. Note that we're classifying among 5 classes, giving chance accuracy of 20%. We expect both results to be better than chance accuracy (20%), and we further expect the result from training using the ImageNet pretraining initialization to be much better than the one from training from scratch. Let's see.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def eval_style_net(weights, test_iters=10):\\n\",\n    \"    test_net = caffe.Net(style_net(train=False), weights, caffe.TEST)\\n\",\n    \"    accuracy = 0\\n\",\n    \"    for it in xrange(test_iters):\\n\",\n    \"        accuracy += test_net.forward()['acc']\\n\",\n    \"    accuracy /= test_iters\\n\",\n    \"    return test_net, accuracy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy, trained from ImageNet initialization: 50.0%\\n\",\n      \"Accuracy, trained from   random initialization: 23.6%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_net, accuracy = eval_style_net(style_weights)\\n\",\n    \"print 'Accuracy, trained from ImageNet initialization: %3.1f%%' % (100*accuracy, )\\n\",\n    \"scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights)\\n\",\n    \"print 'Accuracy, trained from   random initialization: %3.1f%%' % (100*scratch_accuracy, )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4. End-to-end finetuning for style\\n\",\n    \"\\n\",\n    \"Finally, we'll train both nets again, starting from the weights we just learned.  The only difference this time is that we'll be learning the weights \\\"end-to-end\\\" by turning on learning in *all* layers of the network, starting from the RGB `conv1` filters directly applied to the input image.  We pass the argument `learn_all=True` to the `style_net` function defined earlier in this notebook, which tells the function to apply a positive (non-zero) `lr_mult` value for all parameters.  Under the default, `learn_all=False`, all parameters in the pretrained layers (`conv1` through `fc7`) are frozen (`lr_mult = 0`), and we learn only the classifier layer `fc8_flickr`.\\n\",\n    \"\\n\",\n    \"Note that both networks start at roughly the accuracy achieved at the end of the previous training session, and improve significantly with end-to-end training.  To be more scientific, we'd also want to follow the same additional training procedure *without* the end-to-end training, to ensure that our results aren't better simply because we trained for twice as long.  Feel free to try this yourself!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Running solvers for 200 iterations...\\n\",\n      \"  0) pretrained, end-to-end: loss=0.781, acc=64%; scratch, end-to-end: loss=1.585, acc=28%\\n\",\n      \" 10) pretrained, end-to-end: loss=1.178, acc=62%; scratch, end-to-end: loss=1.638, acc=14%\\n\",\n      \" 20) pretrained, end-to-end: loss=1.084, acc=60%; scratch, end-to-end: loss=1.637, acc= 8%\\n\",\n      \" 30) pretrained, end-to-end: loss=0.902, acc=76%; scratch, end-to-end: loss=1.600, acc=20%\\n\",\n      \" 40) pretrained, end-to-end: loss=0.865, acc=64%; scratch, end-to-end: loss=1.574, acc=26%\\n\",\n      \" 50) pretrained, end-to-end: loss=0.888, acc=60%; scratch, end-to-end: loss=1.604, acc=26%\\n\",\n      \" 60) pretrained, end-to-end: loss=0.538, acc=78%; scratch, end-to-end: loss=1.555, acc=34%\\n\",\n      \" 70) pretrained, end-to-end: loss=0.717, acc=72%; scratch, end-to-end: loss=1.563, acc=30%\\n\",\n      \" 80) pretrained, end-to-end: loss=0.695, acc=74%; scratch, end-to-end: loss=1.502, acc=42%\\n\",\n      \" 90) pretrained, end-to-end: loss=0.708, acc=68%; scratch, end-to-end: loss=1.523, acc=26%\\n\",\n      \"100) pretrained, end-to-end: loss=0.432, acc=78%; scratch, end-to-end: loss=1.500, acc=38%\\n\",\n      \"110) pretrained, end-to-end: loss=0.611, acc=78%; scratch, end-to-end: loss=1.618, acc=18%\\n\",\n      \"120) pretrained, end-to-end: loss=0.610, acc=76%; scratch, end-to-end: loss=1.473, acc=30%\\n\",\n      \"130) pretrained, end-to-end: loss=0.471, acc=78%; scratch, end-to-end: loss=1.488, acc=26%\\n\",\n      \"140) pretrained, end-to-end: loss=0.500, acc=76%; scratch, end-to-end: loss=1.514, acc=38%\\n\",\n      \"150) pretrained, end-to-end: loss=0.476, acc=80%; scratch, end-to-end: loss=1.452, acc=46%\\n\",\n      \"160) pretrained, end-to-end: loss=0.368, acc=82%; scratch, end-to-end: loss=1.419, acc=34%\\n\",\n      \"170) pretrained, end-to-end: loss=0.556, acc=76%; scratch, end-to-end: loss=1.583, acc=36%\\n\",\n      \"180) pretrained, end-to-end: loss=0.574, acc=72%; scratch, end-to-end: loss=1.556, acc=22%\\n\",\n      \"190) pretrained, end-to-end: loss=0.360, acc=88%; scratch, end-to-end: loss=1.429, acc=44%\\n\",\n      \"199) pretrained, end-to-end: loss=0.458, acc=78%; scratch, end-to-end: loss=1.370, acc=44%\\n\",\n      \"Done.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"end_to_end_net = style_net(train=True, learn_all=True)\\n\",\n    \"\\n\",\n    \"# Set base_lr to 1e-3, the same as last time when learning only the classifier.\\n\",\n    \"# You may want to play around with different values of this or other\\n\",\n    \"# optimization parameters when fine-tuning.  For example, if learning diverges\\n\",\n    \"# (e.g., the loss gets very large or goes to infinity/NaN), you should try\\n\",\n    \"# decreasing base_lr (e.g., to 1e-4, then 1e-5, etc., until you find a value\\n\",\n    \"# for which learning does not diverge).\\n\",\n    \"base_lr = 0.001\\n\",\n    \"\\n\",\n    \"style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\\n\",\n    \"style_solver = caffe.get_solver(style_solver_filename)\\n\",\n    \"style_solver.net.copy_from(style_weights)\\n\",\n    \"\\n\",\n    \"scratch_style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\\n\",\n    \"scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\\n\",\n    \"scratch_style_solver.net.copy_from(scratch_style_weights)\\n\",\n    \"\\n\",\n    \"print 'Running solvers for %d iterations...' % niter\\n\",\n    \"solvers = [('pretrained, end-to-end', style_solver),\\n\",\n    \"           ('scratch, end-to-end', scratch_style_solver)]\\n\",\n    \"_, _, finetuned_weights = run_solvers(niter, solvers)\\n\",\n    \"print 'Done.'\\n\",\n    \"\\n\",\n    \"style_weights_ft = finetuned_weights['pretrained, end-to-end']\\n\",\n    \"scratch_style_weights_ft = finetuned_weights['scratch, end-to-end']\\n\",\n    \"\\n\",\n    \"# Delete solvers to save memory.\\n\",\n    \"del style_solver, scratch_style_solver, solvers\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's now test the end-to-end finetuned models.  Since all layers have been optimized for the style recognition task at hand, we expect both nets to get better results than the ones above, which were achieved by nets with only their classifier layers trained for the style task (on top of either ImageNet pretrained or randomly initialized weights).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy, finetuned from ImageNet initialization: 53.6%\\n\",\n      \"Accuracy, finetuned from   random initialization: 39.2%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_net, accuracy = eval_style_net(style_weights_ft)\\n\",\n    \"print 'Accuracy, finetuned from ImageNet initialization: %3.1f%%' % (100*accuracy, )\\n\",\n    \"scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights_ft)\\n\",\n    \"print 'Accuracy, finetuned from   random initialization: %3.1f%%' % (100*scratch_accuracy, )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We'll first look back at the image we started with and check our end-to-end trained model's predictions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"top 5 predicted style labels =\\n\",\n      \"\\t(1) 55.67% Melancholy\\n\",\n      \"\\t(2) 27.21% HDR\\n\",\n      \"\\t(3) 16.46% Pastel\\n\",\n      \"\\t(4)  0.63% Detailed\\n\",\n      \"\\t(5)  0.03% Noir\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Kqox2MXUTyRijqcloVtW3m8yHjEqlA0CGPR8GSIxHiahS++pLFTgKoF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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f75d0683e10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.imshow(deprocess_net_image(image))\\n\",\n    \"disp_style_preds(test_net, image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Whew, that looks a lot better than before!  But note that this image was from the training set, so the net got to see its label at training time.\\n\",\n    \"\\n\",\n    \"Finally, we'll pick an image from the test set (an image the model hasn't seen) and look at our end-to-end finetuned style model's predictions for it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"actual label = Pastel\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Knal9mqqcfO8Z+XeHTlKapVYd40tlmrz+3+ZoW7wuyzVqe8lMU1mEm8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\\nQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f75d05ad810>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"batch_index = 1\\n\",\n    \"image = test_net.blobs['data'].data[batch_index]\\n\",\n    \"plt.imshow(deprocess_net_image(image))\\n\",\n    \"print 'actual label =', style_labels[int(test_net.blobs['label'].data[batch_index])]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"top 5 predicted style labels =\\n\",\n      \"\\t(1) 99.76% Pastel\\n\",\n      \"\\t(2)  0.13% HDR\\n\",\n      \"\\t(3)  0.11% Detailed\\n\",\n      \"\\t(4)  0.00% Melancholy\\n\",\n      \"\\t(5)  0.00% Noir\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"disp_style_preds(test_net, image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can also look at the predictions of the network trained from scratch.  We see that in this case, the scratch network also predicts the correct label for the image (*Pastel*), but is much less confident in its prediction than the pretrained net.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"top 5 predicted style labels =\\n\",\n      \"\\t(1) 49.81% Pastel\\n\",\n      \"\\t(2) 19.76% Detailed\\n\",\n      \"\\t(3) 17.06% Melancholy\\n\",\n      \"\\t(4) 11.66% HDR\\n\",\n      \"\\t(5)  1.72% Noir\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"disp_style_preds(scratch_test_net, image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Of course, we can again look at the ImageNet model's predictions for the above image:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"top 5 predicted ImageNet labels =\\n\",\n      \"\\t(1) 34.90% n07579787 plate\\n\",\n      \"\\t(2) 21.63% n04263257 soup bowl\\n\",\n      \"\\t(3) 17.75% n07875152 potpie\\n\",\n      \"\\t(4)  5.72% n07711569 mashed potato\\n\",\n      \"\\t(5)  5.27% n07584110 consomme\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"disp_imagenet_preds(imagenet_net, image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\\n\",\n    \"\\n\",\n    \"http://demo.vislab.berkeleyvision.org/\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"description\": \"Fine-tune the ImageNet-trained CaffeNet on new data.\",\n  \"example_name\": \"Fine-tuning for Style Recognition\",\n  \"include_in_docs\": true,\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  },\n  \"priority\": 3\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Caffe/CMakeLists.txt",
    "content": "file(GLOB_RECURSE examples_srcs \"${PROJECT_SOURCE_DIR}/examples/*.cpp\")\n\nforeach(source_file ${examples_srcs})\n  # get file name\n  get_filename_component(name ${source_file} NAME_WE)\n    \n  # get folder name\n  get_filename_component(path ${source_file} PATH)\n  get_filename_component(folder ${path} NAME_WE)\n    \n  add_executable(${name} ${source_file})\n  target_link_libraries(${name} ${Caffe_LINK})\n  caffe_default_properties(${name})\n\n  # set back RUNTIME_OUTPUT_DIRECTORY\n  set_target_properties(${name} PROPERTIES\n    RUNTIME_OUTPUT_DIRECTORY \"${PROJECT_BINARY_DIR}/examples/${folder}\")\n\n  caffe_set_solution_folder(${name} examples)\n\n  # install\n  install(TARGETS ${name} DESTINATION bin)\n\n  if(UNIX OR APPLE)\n    # Funny command to make tutorials work\n    # TODO: remove in future as soon as naming is standartaized everywhere\n    set(__outname ${PROJECT_BINARY_DIR}/examples/${folder}/${name}${Caffe_POSTFIX})\n    add_custom_command(TARGET ${name} POST_BUILD\n                       COMMAND ln -sf \"${__outname}\" \"${__outname}.bin\")\n  endif()\nendforeach()\n"
  },
  {
    "path": "Caffe/brewing-logreg.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Brewing Logistic Regression then Going Deeper\\n\",\n    \"\\n\",\n    \"While Caffe is made for deep networks it can likewise represent \\\"shallow\\\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"import os\\n\",\n    \"os.chdir('..')\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"sys.path.insert(0, './python')\\n\",\n    \"import caffe\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"import os\\n\",\n    \"import h5py\\n\",\n    \"import shutil\\n\",\n    \"import tempfile\\n\",\n    \"\\n\",\n    \"import sklearn\\n\",\n    \"import sklearn.datasets\\n\",\n    \"import sklearn.linear_model\\n\",\n    \"\\n\",\n    \"import pandas as pd\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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4M332zDbK5Ao9Fy+vQh5s1r5cEHH5gxh0RRFLq7u/nFT36CLpmmvnry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\\nVFRUMsr/B18y5d10ojlGAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f8611575a50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"X, y = sklearn.datasets.make_classification(\\n\",\n    \"    n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \\n\",\n    \"    n_clusters_per_class=2, hypercube=False, random_state=0\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# Split into train and test\\n\",\n    \"X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\\n\",\n    \"\\n\",\n    \"# Visualize sample of the data\\n\",\n    \"ind = np.random.permutation(X.shape[0])[:1000]\\n\",\n    \"df = pd.DataFrame(X[ind])\\n\",\n    \"_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy: 0.781\\n\",\n      \"Accuracy: 0.781\\n\",\n      \"Accuracy: 0.781\\n\",\n      \"Accuracy: 0.781\\n\",\n      \"1 loop, best of 3: 372 ms per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%timeit\\n\",\n    \"# Train and test the scikit-learn SGD logistic regression.\\n\",\n    \"clf = sklearn.linear_model.SGDClassifier(\\n\",\n    \"    loss='log', n_iter=1000, penalty='l2', alpha=5e-4, class_weight='auto')\\n\",\n    \"\\n\",\n    \"clf.fit(X, y)\\n\",\n    \"yt_pred = clf.predict(Xt)\\n\",\n    \"print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Save the dataset to HDF5 for loading in Caffe.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Write out the data to HDF5 files in a temp directory.\\n\",\n    \"# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\\n\",\n    \"dirname = os.path.abspath('./examples/hdf5_classification/data')\\n\",\n    \"if not os.path.exists(dirname):\\n\",\n    \"    os.makedirs(dirname)\\n\",\n    \"\\n\",\n    \"train_filename = os.path.join(dirname, 'train.h5')\\n\",\n    \"test_filename = os.path.join(dirname, 'test.h5')\\n\",\n    \"\\n\",\n    \"# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\\n\",\n    \"# To show this off, we'll list the same data file twice.\\n\",\n    \"with h5py.File(train_filename, 'w') as f:\\n\",\n    \"    f['data'] = X\\n\",\n    \"    f['label'] = y.astype(np.float32)\\n\",\n    \"with open(os.path.join(dirname, 'train.txt'), 'w') as f:\\n\",\n    \"    f.write(train_filename + '\\\\n')\\n\",\n    \"    f.write(train_filename + '\\\\n')\\n\",\n    \"    \\n\",\n    \"# HDF5 is pretty efficient, but can be further compressed.\\n\",\n    \"comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\\n\",\n    \"with h5py.File(test_filename, 'w') as f:\\n\",\n    \"    f.create_dataset('data', data=Xt, **comp_kwargs)\\n\",\n    \"    f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\\n\",\n    \"with open(os.path.join(dirname, 'test.txt'), 'w') as f:\\n\",\n    \"    f.write(test_filename + '\\\\n')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's define logistic regression in Caffe through Python net specification. This is a quick and natural way to define nets that sidesteps manually editing the protobuf model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from caffe import layers as L\\n\",\n    \"from caffe import params as P\\n\",\n    \"\\n\",\n    \"def logreg(hdf5, batch_size):\\n\",\n    \"    # logistic regression: data, matrix multiplication, and 2-class softmax loss\\n\",\n    \"    n = caffe.NetSpec()\\n\",\n    \"    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\\n\",\n    \"    n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))\\n\",\n    \"    n.accuracy = L.Accuracy(n.ip1, n.label)\\n\",\n    \"    n.loss = L.SoftmaxWithLoss(n.ip1, n.label)\\n\",\n    \"    return n.to_proto()\\n\",\n    \"\\n\",\n    \"train_net_path = 'examples/hdf5_classification/logreg_auto_train.prototxt'\\n\",\n    \"with open(train_net_path, 'w') as f:\\n\",\n    \"    f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))\\n\",\n    \"\\n\",\n    \"test_net_path = 'examples/hdf5_classification/logreg_auto_test.prototxt'\\n\",\n    \"with open(test_net_path, 'w') as f:\\n\",\n    \"    f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now, we'll define our \\\"solver\\\" which trains the network by specifying the locations of the train and test nets we defined above, as well as setting values for various parameters used for learning, display, and \\\"snapshotting\\\".\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from caffe.proto import caffe_pb2\\n\",\n    \"\\n\",\n    \"def solver(train_net_path, test_net_path):\\n\",\n    \"    s = caffe_pb2.SolverParameter()\\n\",\n    \"\\n\",\n    \"    # Specify locations of the train and test networks.\\n\",\n    \"    s.train_net = train_net_path\\n\",\n    \"    s.test_net.append(test_net_path)\\n\",\n    \"\\n\",\n    \"    s.test_interval = 1000  # Test after every 1000 training iterations.\\n\",\n    \"    s.test_iter.append(250) # Test 250 \\\"batches\\\" each time we test.\\n\",\n    \"\\n\",\n    \"    s.max_iter = 10000      # # of times to update the net (training iterations)\\n\",\n    \"\\n\",\n    \"    # Set the initial learning rate for stochastic gradient descent (SGD).\\n\",\n    \"    s.base_lr = 0.01        \\n\",\n    \"\\n\",\n    \"    # Set `lr_policy` to define how the learning rate changes during training.\\n\",\n    \"    # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\\n\",\n    \"    # every `stepsize` iterations.\\n\",\n    \"    s.lr_policy = 'step'\\n\",\n    \"    s.gamma = 0.1\\n\",\n    \"    s.stepsize = 5000\\n\",\n    \"\\n\",\n    \"    # Set other optimization parameters. Setting a non-zero `momentum` takes a\\n\",\n    \"    # weighted average of the current gradient and previous gradients to make\\n\",\n    \"    # learning more stable. L2 weight decay regularizes learning, to help prevent\\n\",\n    \"    # the model from overfitting.\\n\",\n    \"    s.momentum = 0.9\\n\",\n    \"    s.weight_decay = 5e-4\\n\",\n    \"\\n\",\n    \"    # Display the current training loss and accuracy every 1000 iterations.\\n\",\n    \"    s.display = 1000\\n\",\n    \"\\n\",\n    \"    # Snapshots are files used to store networks we've trained.  Here, we'll\\n\",\n    \"    # snapshot every 10K iterations -- just once at the end of training.\\n\",\n    \"    # For larger networks that take longer to train, you may want to set\\n\",\n    \"    # snapshot < max_iter to save the network and training state to disk during\\n\",\n    \"    # optimization, preventing disaster in case of machine crashes, etc.\\n\",\n    \"    s.snapshot = 10000\\n\",\n    \"    s.snapshot_prefix = 'examples/hdf5_classification/data/train'\\n\",\n    \"\\n\",\n    \"    # We'll train on the CPU for fair benchmarking against scikit-learn.\\n\",\n    \"    # Changing to GPU should result in much faster training!\\n\",\n    \"    s.solver_mode = caffe_pb2.SolverParameter.CPU\\n\",\n    \"    \\n\",\n    \"    return s\\n\",\n    \"\\n\",\n    \"solver_path = 'examples/hdf5_classification/logreg_solver.prototxt'\\n\",\n    \"with open(solver_path, 'w') as f:\\n\",\n    \"    f.write(str(solver(train_net_path, test_net_path)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Time to learn and evaluate our Caffeinated logistic regression in Python.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy: 0.770\\n\",\n      \"Accuracy: 0.770\\n\",\n      \"Accuracy: 0.770\\n\",\n      \"Accuracy: 0.770\\n\",\n      \"1 loop, best of 3: 195 ms per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%timeit\\n\",\n    \"caffe.set_mode_cpu()\\n\",\n    \"solver = caffe.get_solver(solver_path)\\n\",\n    \"solver.solve()\\n\",\n    \"\\n\",\n    \"accuracy = 0\\n\",\n    \"batch_size = solver.test_nets[0].blobs['data'].num\\n\",\n    \"test_iters = int(len(Xt) / batch_size)\\n\",\n    \"for i in range(test_iters):\\n\",\n    \"    solver.test_nets[0].forward()\\n\",\n    \"    accuracy += solver.test_nets[0].blobs['accuracy'].data\\n\",\n    \"accuracy /= test_iters\\n\",\n    \"\\n\",\n    \"print(\\\"Accuracy: {:.3f}\\\".format(accuracy))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Do the same through the command line interface for detailed output on the model and solving.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"I0224 00:32:03.232779   655 caffe.cpp:178] Use CPU.\\n\",\n      \"I0224 00:32:03.391911   655 solver.cpp:48] Initializing solver from parameters: \\n\",\n      \"train_net: \\\"examples/hdf5_classification/logreg_auto_train.prototxt\\\"\\n\",\n      \"test_net: \\\"examples/hdf5_classification/logreg_auto_test.prototxt\\\"\\n\",\n      \"test_iter: 250\\n\",\n      \"test_interval: 1000\\n\",\n      \"base_lr: 0.01\\n\",\n      \"display: 1000\\n\",\n      \"max_iter: 10000\\n\",\n      \"lr_policy: \\\"step\\\"\\n\",\n      \"gamma: 0.1\\n\",\n      \"momentum: 0.9\\n\",\n      \"weight_decay: 0.0005\\n\",\n      \"stepsize: 5000\\n\",\n      \"snapshot: 10000\\n\",\n      \"snapshot_prefix: \\\"examples/hdf5_classification/data/train\\\"\\n\",\n      \"solver_mode: CPU\\n\",\n      \"I0224 00:32:03.392065   655 solver.cpp:81] Creating training net from train_net file: examples/hdf5_classification/logreg_auto_train.prototxt\\n\",\n      \"I0224 00:32:03.392215   655 net.cpp:49] Initializing net from parameters: \\n\",\n      \"state {\\n\",\n      \"  phase: TRAIN\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"data\\\"\\n\",\n      \"  type: \\\"HDF5Data\\\"\\n\",\n      \"  top: \\\"data\\\"\\n\",\n      \"  top: \\\"label\\\"\\n\",\n      \"  hdf5_data_param {\\n\",\n      \"    source: \\\"examples/hdf5_classification/data/train.txt\\\"\\n\",\n      \"    batch_size: 10\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"ip1\\\"\\n\",\n      \"  type: \\\"InnerProduct\\\"\\n\",\n      \"  bottom: \\\"data\\\"\\n\",\n      \"  top: \\\"ip1\\\"\\n\",\n      \"  inner_product_param {\\n\",\n      \"    num_output: 2\\n\",\n      \"    weight_filler {\\n\",\n      \"      type: \\\"xavier\\\"\\n\",\n      \"    }\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"accuracy\\\"\\n\",\n      \"  type: \\\"Accuracy\\\"\\n\",\n      \"  bottom: \\\"ip1\\\"\\n\",\n      \"  bottom: \\\"label\\\"\\n\",\n      \"  top: \\\"accuracy\\\"\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"loss\\\"\\n\",\n      \"  type: \\\"SoftmaxWithLoss\\\"\\n\",\n      \"  bottom: \\\"ip1\\\"\\n\",\n      \"  bottom: \\\"label\\\"\\n\",\n      \"  top: \\\"loss\\\"\\n\",\n      \"}\\n\",\n      \"I0224 00:32:03.392365   655 layer_factory.hpp:77] Creating layer data\\n\",\n      \"I0224 00:32:03.392382   655 net.cpp:106] Creating Layer data\\n\",\n      \"I0224 00:32:03.392395   655 net.cpp:411] data -> data\\n\",\n      \"I0224 00:32:03.392423   655 net.cpp:411] data -> label\\n\",\n      \"I0224 00:32:03.392442   655 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\\n\",\n      \"I0224 00:32:03.392473   655 hdf5_data_layer.cpp:93] Number of HDF5 files: 2\\n\",\n      \"I0224 00:32:03.393473   655 hdf5.cpp:32] Datatype class: H5T_FLOAT\\n\",\n      \"I0224 00:32:03.393862   655 net.cpp:150] Setting up data\\n\",\n      \"I0224 00:32:03.393884   655 net.cpp:157] Top shape: 10 4 (40)\\n\",\n      \"I0224 00:32:03.393894   655 net.cpp:157] Top shape: 10 (10)\\n\",\n      \"I0224 00:32:03.393901   655 net.cpp:165] Memory required for data: 200\\n\",\n      \"I0224 00:32:03.393911   655 layer_factory.hpp:77] Creating layer label_data_1_split\\n\",\n      \"I0224 00:32:03.393924   655 net.cpp:106] Creating Layer label_data_1_split\\n\",\n      \"I0224 00:32:03.393934   655 net.cpp:454] label_data_1_split <- label\\n\",\n      \"I0224 00:32:03.393945   655 net.cpp:411] label_data_1_split -> label_data_1_split_0\\n\",\n      \"I0224 00:32:03.393956   655 net.cpp:411] label_data_1_split -> label_data_1_split_1\\n\",\n      \"I0224 00:32:03.393970   655 net.cpp:150] Setting up label_data_1_split\\n\",\n      \"I0224 00:32:03.393978   655 net.cpp:157] Top shape: 10 (10)\\n\",\n      \"I0224 00:32:03.393986   655 net.cpp:157] Top shape: 10 (10)\\n\",\n      \"I0224 00:32:03.393995   655 net.cpp:165] Memory required for data: 280\\n\",\n      \"I0224 00:32:03.394001   655 layer_factory.hpp:77] Creating layer ip1\\n\",\n      \"I0224 00:32:03.394012   655 net.cpp:106] Creating Layer ip1\\n\",\n      \"I0224 00:32:03.394021   655 net.cpp:454] ip1 <- data\\n\",\n      \"I0224 00:32:03.394029   655 net.cpp:411] ip1 -> ip1\\n\",\n      \"I0224 00:32:03.394311   655 net.cpp:150] Setting up ip1\\n\",\n      \"I0224 00:32:03.394323   655 net.cpp:157] Top shape: 10 2 (20)\\n\",\n      \"I0224 00:32:03.394331   655 net.cpp:165] Memory required for data: 360\\n\",\n      \"I0224 00:32:03.394348   655 layer_factory.hpp:77] Creating layer ip1_ip1_0_split\\n\",\n      \"I0224 00:32:03.394358   655 net.cpp:106] Creating Layer ip1_ip1_0_split\\n\",\n      \"I0224 00:32:03.394366   655 net.cpp:454] ip1_ip1_0_split <- ip1\\n\",\n      \"I0224 00:32:03.394374   655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_0\\n\",\n      \"I0224 00:32:03.394386   655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_1\\n\",\n      \"I0224 00:32:03.394395   655 net.cpp:150] Setting up ip1_ip1_0_split\\n\",\n      \"I0224 00:32:03.394404   655 net.cpp:157] Top shape: 10 2 (20)\\n\",\n      \"I0224 00:32:03.394424   655 net.cpp:157] Top shape: 10 2 (20)\\n\",\n      \"I0224 00:32:03.394443   655 net.cpp:165] Memory required for data: 520\\n\",\n      \"I0224 00:32:03.394450   655 layer_factory.hpp:77] Creating layer accuracy\\n\",\n      \"I0224 00:32:03.394462   655 net.cpp:106] Creating Layer accuracy\\n\",\n      \"I0224 00:32:03.394479   655 net.cpp:454] accuracy <- ip1_ip1_0_split_0\\n\",\n      \"I0224 00:32:03.394489   655 net.cpp:454] accuracy <- label_data_1_split_0\\n\",\n      \"I0224 00:32:03.394497   655 net.cpp:411] accuracy -> accuracy\\n\",\n      \"I0224 00:32:03.394510   655 net.cpp:150] Setting up accuracy\\n\",\n      \"I0224 00:32:03.394536   655 net.cpp:157] Top shape: (1)\\n\",\n      \"I0224 00:32:03.394543   655 net.cpp:165] Memory required for data: 524\\n\",\n      \"I0224 00:32:03.394551   655 layer_factory.hpp:77] Creating layer loss\\n\",\n      \"I0224 00:32:03.394562   655 net.cpp:106] Creating Layer loss\\n\",\n      \"I0224 00:32:03.394569   655 net.cpp:454] loss <- ip1_ip1_0_split_1\\n\",\n      \"I0224 00:32:03.394577   655 net.cpp:454] loss <- label_data_1_split_1\\n\",\n      \"I0224 00:32:03.394587   655 net.cpp:411] loss -> loss\\n\",\n      \"I0224 00:32:03.394603   655 layer_factory.hpp:77] Creating layer loss\\n\",\n      \"I0224 00:32:03.394624   655 net.cpp:150] Setting up loss\\n\",\n      \"I0224 00:32:03.394634   655 net.cpp:157] Top shape: (1)\\n\",\n      \"I0224 00:32:03.394641   655 net.cpp:160]     with loss weight 1\\n\",\n      \"I0224 00:32:03.394659   655 net.cpp:165] Memory required for data: 528\\n\",\n      \"I0224 00:32:03.394665   655 net.cpp:226] loss needs backward computation.\\n\",\n      \"I0224 00:32:03.394673   655 net.cpp:228] accuracy does not need backward computation.\\n\",\n      \"I0224 00:32:03.394682   655 net.cpp:226] ip1_ip1_0_split needs backward computation.\\n\",\n      \"I0224 00:32:03.394690   655 net.cpp:226] ip1 needs backward computation.\\n\",\n      \"I0224 00:32:03.394697   655 net.cpp:228] label_data_1_split does not need backward computation.\\n\",\n      \"I0224 00:32:03.394706   655 net.cpp:228] data does not need backward computation.\\n\",\n      \"I0224 00:32:03.394712   655 net.cpp:270] This network produces output accuracy\\n\",\n      \"I0224 00:32:03.394721   655 net.cpp:270] This network produces output loss\\n\",\n      \"I0224 00:32:03.394731   655 net.cpp:283] Network initialization done.\\n\",\n      \"I0224 00:32:03.394804   655 solver.cpp:181] Creating test net (#0) specified by test_net file: examples/hdf5_classification/logreg_auto_test.prototxt\\n\",\n      \"I0224 00:32:03.394836   655 net.cpp:49] Initializing net from parameters: \\n\",\n      \"state {\\n\",\n      \"  phase: TEST\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"data\\\"\\n\",\n      \"  type: \\\"HDF5Data\\\"\\n\",\n      \"  top: \\\"data\\\"\\n\",\n      \"  top: \\\"label\\\"\\n\",\n      \"  hdf5_data_param {\\n\",\n      \"    source: \\\"examples/hdf5_classification/data/test.txt\\\"\\n\",\n      \"    batch_size: 10\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"ip1\\\"\\n\",\n      \"  type: \\\"InnerProduct\\\"\\n\",\n      \"  bottom: \\\"data\\\"\\n\",\n      \"  top: \\\"ip1\\\"\\n\",\n      \"  inner_product_param {\\n\",\n      \"    num_output: 2\\n\",\n      \"    weight_filler {\\n\",\n      \"      type: \\\"xavier\\\"\\n\",\n      \"    }\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"accuracy\\\"\\n\",\n      \"  type: \\\"Accuracy\\\"\\n\",\n      \"  bottom: \\\"ip1\\\"\\n\",\n      \"  bottom: \\\"label\\\"\\n\",\n      \"  top: \\\"accuracy\\\"\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"loss\\\"\\n\",\n      \"  type: \\\"SoftmaxWithLoss\\\"\\n\",\n      \"  bottom: \\\"ip1\\\"\\n\",\n      \"  bottom: \\\"label\\\"\\n\",\n      \"  top: \\\"loss\\\"\\n\",\n      \"}\\n\",\n      \"I0224 00:32:03.394953   655 layer_factory.hpp:77] Creating layer data\\n\",\n      \"I0224 00:32:03.394964   655 net.cpp:106] Creating Layer data\\n\",\n      \"I0224 00:32:03.394973   655 net.cpp:411] data -> data\\n\",\n      \"I0224 00:32:03.394984   655 net.cpp:411] data -> label\\n\",\n      \"I0224 00:32:03.394994   655 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\\n\",\n      \"I0224 00:32:03.395009   655 hdf5_data_layer.cpp:93] Number of HDF5 files: 1\\n\",\n      \"I0224 00:32:03.395937   655 net.cpp:150] Setting up data\\n\",\n      \"I0224 00:32:03.395953   655 net.cpp:157] Top shape: 10 4 (40)\\n\",\n      \"I0224 00:32:03.395963   655 net.cpp:157] Top shape: 10 (10)\\n\",\n      \"I0224 00:32:03.395970   655 net.cpp:165] Memory required for data: 200\\n\",\n      \"I0224 00:32:03.395978   655 layer_factory.hpp:77] Creating layer label_data_1_split\\n\",\n      \"I0224 00:32:03.395989   655 net.cpp:106] Creating Layer label_data_1_split\\n\",\n      \"I0224 00:32:03.395997   655 net.cpp:454] label_data_1_split <- label\\n\",\n      \"I0224 00:32:03.396005   655 net.cpp:411] label_data_1_split -> label_data_1_split_0\\n\",\n      \"I0224 00:32:03.396016   655 net.cpp:411] label_data_1_split -> label_data_1_split_1\\n\",\n      \"I0224 00:32:03.396028   655 net.cpp:150] Setting up label_data_1_split\\n\",\n      \"I0224 00:32:03.396036   655 net.cpp:157] Top shape: 10 (10)\\n\",\n      \"I0224 00:32:03.396044   655 net.cpp:157] Top shape: 10 (10)\\n\",\n      \"I0224 00:32:03.396051   655 net.cpp:165] Memory required for data: 280\\n\",\n      \"I0224 00:32:03.396059   655 layer_factory.hpp:77] Creating layer ip1\\n\",\n      \"I0224 00:32:03.396069   655 net.cpp:106] Creating Layer ip1\\n\",\n      \"I0224 00:32:03.396075   655 net.cpp:454] ip1 <- data\\n\",\n      \"I0224 00:32:03.396085   655 net.cpp:411] ip1 -> ip1\\n\",\n      \"I0224 00:32:03.396100   655 net.cpp:150] Setting up ip1\\n\",\n      \"I0224 00:32:03.396109   655 net.cpp:157] Top shape: 10 2 (20)\\n\",\n      \"I0224 00:32:03.396116   655 net.cpp:165] Memory required for data: 360\\n\",\n      \"I0224 00:32:03.396138   655 layer_factory.hpp:77] Creating layer ip1_ip1_0_split\\n\",\n      \"I0224 00:32:03.396148   655 net.cpp:106] Creating Layer ip1_ip1_0_split\\n\",\n      \"I0224 00:32:03.396157   655 net.cpp:454] ip1_ip1_0_split <- ip1\\n\",\n      \"I0224 00:32:03.396164   655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_0\\n\",\n      \"I0224 00:32:03.396174   655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_1\\n\",\n      \"I0224 00:32:03.396185   655 net.cpp:150] Setting up ip1_ip1_0_split\\n\",\n      \"I0224 00:32:03.396194   655 net.cpp:157] Top shape: 10 2 (20)\\n\",\n      \"I0224 00:32:03.396203   655 net.cpp:157] Top shape: 10 2 (20)\\n\",\n      \"I0224 00:32:03.396209   655 net.cpp:165] Memory required for data: 520\\n\",\n      \"I0224 00:32:03.396216   655 layer_factory.hpp:77] Creating layer accuracy\\n\",\n      \"I0224 00:32:03.396225   655 net.cpp:106] Creating Layer accuracy\\n\",\n      \"I0224 00:32:03.396234   655 net.cpp:454] accuracy <- ip1_ip1_0_split_0\\n\",\n      \"I0224 00:32:03.396241   655 net.cpp:454] accuracy <- label_data_1_split_0\\n\",\n      \"I0224 00:32:03.396250   655 net.cpp:411] accuracy -> accuracy\\n\",\n      \"I0224 00:32:03.396260   655 net.cpp:150] Setting up accuracy\\n\",\n      \"I0224 00:32:03.396270   655 net.cpp:157] Top shape: (1)\\n\",\n      \"I0224 00:32:03.396276   655 net.cpp:165] Memory required for data: 524\\n\",\n      \"I0224 00:32:03.396283   655 layer_factory.hpp:77] Creating layer loss\\n\",\n      \"I0224 00:32:03.396291   655 net.cpp:106] Creating Layer loss\\n\",\n      \"I0224 00:32:03.396299   655 net.cpp:454] loss <- ip1_ip1_0_split_1\\n\",\n      \"I0224 00:32:03.396307   655 net.cpp:454] loss <- label_data_1_split_1\\n\",\n      \"I0224 00:32:03.396317   655 net.cpp:411] loss -> loss\\n\",\n      \"I0224 00:32:03.396327   655 layer_factory.hpp:77] Creating layer loss\\n\",\n      \"I0224 00:32:03.396339   655 net.cpp:150] Setting up loss\\n\",\n      \"I0224 00:32:03.396349   655 net.cpp:157] Top shape: (1)\\n\",\n      \"I0224 00:32:03.396356   655 net.cpp:160]     with loss weight 1\\n\",\n      \"I0224 00:32:03.396365   655 net.cpp:165] Memory required for data: 528\\n\",\n      \"I0224 00:32:03.396373   655 net.cpp:226] loss needs backward computation.\\n\",\n      \"I0224 00:32:03.396381   655 net.cpp:228] accuracy does not need backward computation.\\n\",\n      \"I0224 00:32:03.396389   655 net.cpp:226] ip1_ip1_0_split needs backward computation.\\n\",\n      \"I0224 00:32:03.396396   655 net.cpp:226] ip1 needs backward computation.\\n\",\n      \"I0224 00:32:03.396404   655 net.cpp:228] label_data_1_split does not need backward computation.\\n\",\n      \"I0224 00:32:03.396412   655 net.cpp:228] data does not need backward computation.\\n\",\n      \"I0224 00:32:03.396420   655 net.cpp:270] This network produces output accuracy\\n\",\n      \"I0224 00:32:03.396427   655 net.cpp:270] This network produces output loss\\n\",\n      \"I0224 00:32:03.396437   655 net.cpp:283] Network initialization done.\\n\",\n      \"I0224 00:32:03.396455   655 solver.cpp:60] Solver scaffolding done.\\n\",\n      \"I0224 00:32:03.396473   655 caffe.cpp:219] Starting Optimization\\n\",\n      \"I0224 00:32:03.396482   655 solver.cpp:280] Solving \\n\",\n      \"I0224 00:32:03.396489   655 solver.cpp:281] Learning Rate Policy: step\\n\",\n      \"I0224 00:32:03.396499   655 solver.cpp:338] Iteration 0, Testing net (#0)\\n\",\n      \"I0224 00:32:03.932615   655 solver.cpp:406]     Test net output #0: accuracy = 0.4268\\n\",\n      \"I0224 00:32:03.932656   655 solver.cpp:406]     Test net output #1: loss = 1.33093 (* 1 = 1.33093 loss)\\n\",\n      \"I0224 00:32:03.932723   655 solver.cpp:229] Iteration 0, loss = 1.06081\\n\",\n      \"I0224 00:32:03.932737   655 solver.cpp:245]     Train net output #0: accuracy = 0.4\\n\",\n      \"I0224 00:32:03.932749   655 solver.cpp:245]     Train net output #1: loss = 1.06081 (* 1 = 1.06081 loss)\\n\",\n      \"I0224 00:32:03.932765   655 sgd_solver.cpp:106] Iteration 0, lr = 0.01\\n\",\n      \"I0224 00:32:03.945551   655 solver.cpp:338] Iteration 1000, Testing net (#0)\\n\",\n      \"I0224 00:32:03.948048   655 solver.cpp:406]     Test net output #0: accuracy = 0.694\\n\",\n      \"I0224 00:32:03.948065   655 solver.cpp:406]     Test net output #1: loss = 0.60406 (* 1 = 0.60406 loss)\\n\",\n      \"I0224 00:32:03.948091   655 solver.cpp:229] Iteration 1000, loss = 0.505853\\n\",\n      \"I0224 00:32:03.948102   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\\n\",\n      \"I0224 00:32:03.948113   655 solver.cpp:245]     Train net output #1: loss = 0.505853 (* 1 = 0.505853 loss)\\n\",\n      \"I0224 00:32:03.948122   655 sgd_solver.cpp:106] Iteration 1000, lr = 0.01\\n\",\n      \"I0224 00:32:03.960741   655 solver.cpp:338] Iteration 2000, Testing net (#0)\\n\",\n      \"I0224 00:32:03.963214   655 solver.cpp:406]     Test net output #0: accuracy = 0.7372\\n\",\n      \"I0224 00:32:03.963249   655 solver.cpp:406]     Test net output #1: loss = 0.595267 (* 1 = 0.595267 loss)\\n\",\n      \"I0224 00:32:03.963276   655 solver.cpp:229] Iteration 2000, loss = 0.549211\\n\",\n      \"I0224 00:32:03.963289   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\\n\",\n      \"I0224 00:32:03.963299   655 solver.cpp:245]     Train net output #1: loss = 0.549211 (* 1 = 0.549211 loss)\\n\",\n      \"I0224 00:32:03.963309   655 sgd_solver.cpp:106] Iteration 2000, lr = 0.01\\n\",\n      \"I0224 00:32:03.975945   655 solver.cpp:338] Iteration 3000, Testing net (#0)\\n\",\n      \"I0224 00:32:03.978435   655 solver.cpp:406]     Test net output #0: accuracy = 0.7732\\n\",\n      \"I0224 00:32:03.978451   655 solver.cpp:406]     Test net output #1: loss = 0.594998 (* 1 = 0.594998 loss)\\n\",\n      \"I0224 00:32:03.978884   655 solver.cpp:229] Iteration 3000, loss = 0.66133\\n\",\n      \"I0224 00:32:03.978911   655 solver.cpp:245]     Train net output #0: accuracy = 0.8\\n\",\n      \"I0224 00:32:03.978932   655 solver.cpp:245]     Train net output #1: loss = 0.66133 (* 1 = 0.66133 loss)\\n\",\n      \"I0224 00:32:03.978950   655 sgd_solver.cpp:106] Iteration 3000, lr = 0.01\\n\",\n      \"I0224 00:32:03.992017   655 solver.cpp:338] Iteration 4000, Testing net (#0)\\n\",\n      \"I0224 00:32:03.994509   655 solver.cpp:406]     Test net output #0: accuracy = 0.694\\n\",\n      \"I0224 00:32:03.994525   655 solver.cpp:406]     Test net output #1: loss = 0.60406 (* 1 = 0.60406 loss)\\n\",\n      \"I0224 00:32:03.994551   655 solver.cpp:229] Iteration 4000, loss = 0.505853\\n\",\n      \"I0224 00:32:03.994562   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\\n\",\n      \"I0224 00:32:03.994573   655 solver.cpp:245]     Train net output #1: loss = 0.505853 (* 1 = 0.505853 loss)\\n\",\n      \"I0224 00:32:03.994583   655 sgd_solver.cpp:106] Iteration 4000, lr = 0.01\\n\",\n      \"I0224 00:32:04.007200   655 solver.cpp:338] Iteration 5000, Testing net (#0)\\n\",\n      \"I0224 00:32:04.009686   655 solver.cpp:406]     Test net output #0: accuracy = 0.7372\\n\",\n      \"I0224 00:32:04.009702   655 solver.cpp:406]     Test net output #1: loss = 0.595267 (* 1 = 0.595267 loss)\\n\",\n      \"I0224 00:32:04.009727   655 solver.cpp:229] Iteration 5000, loss = 0.549211\\n\",\n      \"I0224 00:32:04.009738   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\\n\",\n      \"I0224 00:32:04.009749   655 solver.cpp:245]     Train net output #1: loss = 0.549211 (* 1 = 0.549211 loss)\\n\",\n      \"I0224 00:32:04.009758   655 sgd_solver.cpp:106] Iteration 5000, lr = 0.001\\n\",\n      \"I0224 00:32:04.022734   655 solver.cpp:338] Iteration 6000, Testing net (#0)\\n\",\n      \"I0224 00:32:04.025177   655 solver.cpp:406]     Test net output #0: accuracy = 0.7824\\n\",\n      \"I0224 00:32:04.025193   655 solver.cpp:406]     Test net output #1: loss = 0.593367 (* 1 = 0.593367 loss)\\n\",\n      \"I0224 00:32:04.025545   655 solver.cpp:229] Iteration 6000, loss = 0.654873\\n\",\n      \"I0224 00:32:04.025562   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\\n\",\n      \"I0224 00:32:04.025573   655 solver.cpp:245]     Train net output #1: loss = 0.654873 (* 1 = 0.654873 loss)\\n\",\n      \"I0224 00:32:04.025583   655 sgd_solver.cpp:106] Iteration 6000, lr = 0.001\\n\",\n      \"I0224 00:32:04.038586   655 solver.cpp:338] Iteration 7000, Testing net (#0)\\n\",\n      \"I0224 00:32:04.041016   655 solver.cpp:406]     Test net output #0: accuracy = 0.7704\\n\",\n      \"I0224 00:32:04.041033   655 solver.cpp:406]     Test net output #1: loss = 0.593842 (* 1 = 0.593842 loss)\\n\",\n      \"I0224 00:32:04.041059   655 solver.cpp:229] Iteration 7000, loss = 0.46611\\n\",\n      \"I0224 00:32:04.041071   655 solver.cpp:245]     Train net output #0: accuracy = 0.6\\n\",\n      \"I0224 00:32:04.041082   655 solver.cpp:245]     Train net output #1: loss = 0.46611 (* 1 = 0.46611 loss)\\n\",\n      \"I0224 00:32:04.041091   655 sgd_solver.cpp:106] Iteration 7000, lr = 0.001\\n\",\n      \"I0224 00:32:04.053722   655 solver.cpp:338] Iteration 8000, Testing net (#0)\\n\",\n      \"I0224 00:32:04.056171   655 solver.cpp:406]     Test net output #0: accuracy = 0.7788\\n\",\n      \"I0224 00:32:04.056187   655 solver.cpp:406]     Test net output #1: loss = 0.592847 (* 1 = 0.592847 loss)\\n\",\n      \"I0224 00:32:04.056213   655 solver.cpp:229] Iteration 8000, loss = 0.615126\\n\",\n      \"I0224 00:32:04.056224   655 solver.cpp:245]     Train net output #0: accuracy = 0.8\\n\",\n      \"I0224 00:32:04.056236   655 solver.cpp:245]     Train net output #1: loss = 0.615126 (* 1 = 0.615126 loss)\\n\",\n      \"I0224 00:32:04.056244   655 sgd_solver.cpp:106] Iteration 8000, lr = 0.001\\n\",\n      \"I0224 00:32:04.068853   655 solver.cpp:338] Iteration 9000, Testing net (#0)\\n\",\n      \"I0224 00:32:04.071291   655 solver.cpp:406]     Test net output #0: accuracy = 0.7808\\n\",\n      \"I0224 00:32:04.071307   655 solver.cpp:406]     Test net output #1: loss = 0.593293 (* 1 = 0.593293 loss)\\n\",\n      \"I0224 00:32:04.071650   655 solver.cpp:229] Iteration 9000, loss = 0.654997\\n\",\n      \"I0224 00:32:04.071666   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\\n\",\n      \"I0224 00:32:04.071677   655 solver.cpp:245]     Train net output #1: loss = 0.654998 (* 1 = 0.654998 loss)\\n\",\n      \"I0224 00:32:04.071687   655 sgd_solver.cpp:106] Iteration 9000, lr = 0.001\\n\",\n      \"I0224 00:32:04.084717   655 solver.cpp:456] Snapshotting to binary proto file examples/hdf5_classification/data/train_iter_10000.caffemodel\\n\",\n      \"I0224 00:32:04.084885   655 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/hdf5_classification/data/train_iter_10000.solverstate\\n\",\n      \"I0224 00:32:04.084960   655 solver.cpp:318] Iteration 10000, loss = 0.466505\\n\",\n      \"I0224 00:32:04.084977   655 solver.cpp:338] Iteration 10000, Testing net (#0)\\n\",\n      \"I0224 00:32:04.087514   655 solver.cpp:406]     Test net output #0: accuracy = 0.77\\n\",\n      \"I0224 00:32:04.087532   655 solver.cpp:406]     Test net output #1: loss = 0.593815 (* 1 = 0.593815 loss)\\n\",\n      \"I0224 00:32:04.087541   655 solver.cpp:323] Optimization Done.\\n\",\n      \"I0224 00:32:04.087548   655 caffe.cpp:222] Optimization Done.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!./build/tools/caffe train -solver examples/hdf5_classification/logreg_solver.prototxt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you look at output or the `logreg_auto_train.prototxt`, you'll see that the model is simple logistic regression.\\n\",\n    \"We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\\n\",\n    \"That network is given in `nonlinear_auto_train.prototxt`, and that's the only change made in `nonlinear_logreg_solver.prototxt` which we will now use.\\n\",\n    \"\\n\",\n    \"The final accuracy of the new network should be higher than logistic regression!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from caffe import layers as L\\n\",\n    \"from caffe import params as P\\n\",\n    \"\\n\",\n    \"def nonlinear_net(hdf5, batch_size):\\n\",\n    \"    # one small nonlinearity, one leap for model kind\\n\",\n    \"    n = caffe.NetSpec()\\n\",\n    \"    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\\n\",\n    \"    # define a hidden layer of dimension 40\\n\",\n    \"    n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))\\n\",\n    \"    # transform the output through the ReLU (rectified linear) non-linearity\\n\",\n    \"    n.relu1 = L.ReLU(n.ip1, in_place=True)\\n\",\n    \"    # score the (now non-linear) features\\n\",\n    \"    n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))\\n\",\n    \"    # same accuracy and loss as before\\n\",\n    \"    n.accuracy = L.Accuracy(n.ip2, n.label)\\n\",\n    \"    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\\n\",\n    \"    return n.to_proto()\\n\",\n    \"\\n\",\n    \"train_net_path = 'examples/hdf5_classification/nonlinear_auto_train.prototxt'\\n\",\n    \"with open(train_net_path, 'w') as f:\\n\",\n    \"    f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))\\n\",\n    \"\\n\",\n    \"test_net_path = 'examples/hdf5_classification/nonlinear_auto_test.prototxt'\\n\",\n    \"with open(test_net_path, 'w') as f:\\n\",\n    \"    f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))\\n\",\n    \"\\n\",\n    \"solver_path = 'examples/hdf5_classification/nonlinear_logreg_solver.prototxt'\\n\",\n    \"with open(solver_path, 'w') as f:\\n\",\n    \"    f.write(str(solver(train_net_path, test_net_path)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy: 0.838\\n\",\n      \"Accuracy: 0.837\\n\",\n      \"Accuracy: 0.838\\n\",\n      \"Accuracy: 0.834\\n\",\n      \"1 loop, best of 3: 277 ms per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%timeit\\n\",\n    \"caffe.set_mode_cpu()\\n\",\n    \"solver = caffe.get_solver(solver_path)\\n\",\n    \"solver.solve()\\n\",\n    \"\\n\",\n    \"accuracy = 0\\n\",\n    \"batch_size = solver.test_nets[0].blobs['data'].num\\n\",\n    \"test_iters = int(len(Xt) / batch_size)\\n\",\n    \"for i in range(test_iters):\\n\",\n    \"    solver.test_nets[0].forward()\\n\",\n    \"    accuracy += solver.test_nets[0].blobs['accuracy'].data\\n\",\n    \"accuracy /= test_iters\\n\",\n    \"\\n\",\n    \"print(\\\"Accuracy: {:.3f}\\\".format(accuracy))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Do the same through the command line interface for detailed output on the model and solving.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"I0224 00:32:05.654265   658 caffe.cpp:178] Use CPU.\\n\",\n      \"I0224 00:32:05.810444   658 solver.cpp:48] Initializing solver from parameters: \\n\",\n      \"train_net: \\\"examples/hdf5_classification/nonlinear_auto_train.prototxt\\\"\\n\",\n      \"test_net: \\\"examples/hdf5_classification/nonlinear_auto_test.prototxt\\\"\\n\",\n      \"test_iter: 250\\n\",\n      \"test_interval: 1000\\n\",\n      \"base_lr: 0.01\\n\",\n      \"display: 1000\\n\",\n      \"max_iter: 10000\\n\",\n      \"lr_policy: \\\"step\\\"\\n\",\n      \"gamma: 0.1\\n\",\n      \"momentum: 0.9\\n\",\n      \"weight_decay: 0.0005\\n\",\n      \"stepsize: 5000\\n\",\n      \"snapshot: 10000\\n\",\n      \"snapshot_prefix: \\\"examples/hdf5_classification/data/train\\\"\\n\",\n      \"solver_mode: CPU\\n\",\n      \"I0224 00:32:05.810634   658 solver.cpp:81] Creating training net from train_net file: examples/hdf5_classification/nonlinear_auto_train.prototxt\\n\",\n      \"I0224 00:32:05.810835   658 net.cpp:49] Initializing net from parameters: \\n\",\n      \"state {\\n\",\n      \"  phase: TRAIN\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"data\\\"\\n\",\n      \"  type: \\\"HDF5Data\\\"\\n\",\n      \"  top: \\\"data\\\"\\n\",\n      \"  top: \\\"label\\\"\\n\",\n      \"  hdf5_data_param {\\n\",\n      \"    source: \\\"examples/hdf5_classification/data/train.txt\\\"\\n\",\n      \"    batch_size: 10\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"ip1\\\"\\n\",\n      \"  type: \\\"InnerProduct\\\"\\n\",\n      \"  bottom: \\\"data\\\"\\n\",\n      \"  top: \\\"ip1\\\"\\n\",\n      \"  inner_product_param {\\n\",\n      \"    num_output: 40\\n\",\n      \"    weight_filler {\\n\",\n      \"      type: \\\"xavier\\\"\\n\",\n      \"    }\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"relu1\\\"\\n\",\n      \"  type: \\\"ReLU\\\"\\n\",\n      \"  bottom: \\\"ip1\\\"\\n\",\n      \"  top: \\\"ip1\\\"\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"ip2\\\"\\n\",\n      \"  type: \\\"InnerProduct\\\"\\n\",\n      \"  bottom: \\\"ip1\\\"\\n\",\n      \"  top: \\\"ip2\\\"\\n\",\n      \"  inner_product_param {\\n\",\n      \"    num_output: 2\\n\",\n      \"    weight_filler {\\n\",\n      \"      type: \\\"xavier\\\"\\n\",\n      \"    }\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"accuracy\\\"\\n\",\n      \"  type: \\\"Accuracy\\\"\\n\",\n      \"  bottom: \\\"ip2\\\"\\n\",\n      \"  bottom: \\\"label\\\"\\n\",\n      \"  top: \\\"accuracy\\\"\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"loss\\\"\\n\",\n      \"  type: \\\"SoftmaxWithLoss\\\"\\n\",\n      \"  bottom: \\\"ip2\\\"\\n\",\n      \"  bottom: \\\"label\\\"\\n\",\n      \"  top: \\\"loss\\\"\\n\",\n      \"}\\n\",\n      \"I0224 00:32:05.811061   658 layer_factory.hpp:77] Creating layer data\\n\",\n      \"I0224 00:32:05.811079   658 net.cpp:106] Creating Layer data\\n\",\n      \"I0224 00:32:05.811092   658 net.cpp:411] data -> data\\n\",\n      \"I0224 00:32:05.811121   658 net.cpp:411] data -> label\\n\",\n      \"I0224 00:32:05.811143   658 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\\n\",\n      \"I0224 00:32:05.811189   658 hdf5_data_layer.cpp:93] Number of HDF5 files: 2\\n\",\n      \"I0224 00:32:05.812254   658 hdf5.cpp:32] Datatype class: H5T_FLOAT\\n\",\n      \"I0224 00:32:05.812677   658 net.cpp:150] Setting up data\\n\",\n      \"I0224 00:32:05.812705   658 net.cpp:157] Top shape: 10 4 (40)\\n\",\n      \"I0224 00:32:05.812721   658 net.cpp:157] Top shape: 10 (10)\\n\",\n      \"I0224 00:32:05.812729   658 net.cpp:165] Memory required for data: 200\\n\",\n      \"I0224 00:32:05.812739   658 layer_factory.hpp:77] Creating layer label_data_1_split\\n\",\n      \"I0224 00:32:05.812752   658 net.cpp:106] Creating Layer label_data_1_split\\n\",\n      \"I0224 00:32:05.812762   658 net.cpp:454] label_data_1_split <- label\\n\",\n      \"I0224 00:32:05.812774   658 net.cpp:411] label_data_1_split -> label_data_1_split_0\\n\",\n      \"I0224 00:32:05.812785   658 net.cpp:411] label_data_1_split -> label_data_1_split_1\\n\",\n      \"I0224 00:32:05.812798   658 net.cpp:150] Setting up label_data_1_split\\n\",\n      \"I0224 00:32:05.812808   658 net.cpp:157] Top shape: 10 (10)\\n\",\n      \"I0224 00:32:05.812816   658 net.cpp:157] Top shape: 10 (10)\\n\",\n      \"I0224 00:32:05.812824   658 net.cpp:165] Memory required for data: 280\\n\",\n      \"I0224 00:32:05.812831   658 layer_factory.hpp:77] Creating layer ip1\\n\",\n      \"I0224 00:32:05.812841   658 net.cpp:106] Creating Layer ip1\\n\",\n      \"I0224 00:32:05.812849   658 net.cpp:454] ip1 <- data\\n\",\n      \"I0224 00:32:05.812860   658 net.cpp:411] ip1 -> ip1\\n\",\n      \"I0224 00:32:05.813179   658 net.cpp:150] Setting up ip1\\n\",\n      \"I0224 00:32:05.813196   658 net.cpp:157] Top shape: 10 40 (400)\\n\",\n      \"I0224 00:32:05.813210   658 net.cpp:165] Memory required for data: 1880\\n\",\n      \"I0224 00:32:05.813230   658 layer_factory.hpp:77] Creating layer relu1\\n\",\n      \"I0224 00:32:05.813241   658 net.cpp:106] Creating Layer relu1\\n\",\n      \"I0224 00:32:05.813251   658 net.cpp:454] relu1 <- ip1\\n\",\n      \"I0224 00:32:05.813258   658 net.cpp:397] relu1 -> ip1 (in-place)\\n\",\n      \"I0224 00:32:05.813271   658 net.cpp:150] Setting up relu1\\n\",\n      \"I0224 00:32:05.813279   658 net.cpp:157] Top shape: 10 40 (400)\\n\",\n      \"I0224 00:32:05.813287   658 net.cpp:165] Memory required for data: 3480\\n\",\n      \"I0224 00:32:05.813294   658 layer_factory.hpp:77] Creating layer ip2\\n\",\n      \"I0224 00:32:05.813304   658 net.cpp:106] Creating Layer ip2\\n\",\n      \"I0224 00:32:05.813313   658 net.cpp:454] ip2 <- ip1\\n\",\n      \"I0224 00:32:05.813321   658 net.cpp:411] ip2 -> ip2\\n\",\n      \"I0224 00:32:05.813336   658 net.cpp:150] Setting up ip2\\n\",\n      \"I0224 00:32:05.813345   658 net.cpp:157] Top shape: 10 2 (20)\\n\",\n      \"I0224 00:32:05.813379   658 net.cpp:165] Memory required for data: 3560\\n\",\n      \"I0224 00:32:05.813401   658 layer_factory.hpp:77] Creating layer ip2_ip2_0_split\\n\",\n      \"I0224 00:32:05.813417   658 net.cpp:106] Creating Layer ip2_ip2_0_split\\n\",\n      \"I0224 00:32:05.813426   658 net.cpp:454] ip2_ip2_0_split <- ip2\\n\",\n      \"I0224 00:32:05.813434   658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_0\\n\",\n      \"I0224 00:32:05.813446   658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_1\\n\",\n      \"I0224 00:32:05.813457   658 net.cpp:150] Setting up ip2_ip2_0_split\\n\",\n      \"I0224 00:32:05.813465   658 net.cpp:157] Top shape: 10 2 (20)\\n\",\n      \"I0224 00:32:05.813473   658 net.cpp:157] Top shape: 10 2 (20)\\n\",\n      \"I0224 00:32:05.813480   658 net.cpp:165] Memory required for data: 3720\\n\",\n      \"I0224 00:32:05.813488   658 layer_factory.hpp:77] Creating layer accuracy\\n\",\n      \"I0224 00:32:05.813499   658 net.cpp:106] Creating Layer accuracy\\n\",\n      \"I0224 00:32:05.813508   658 net.cpp:454] accuracy <- ip2_ip2_0_split_0\\n\",\n      \"I0224 00:32:05.813515   658 net.cpp:454] accuracy <- label_data_1_split_0\\n\",\n      \"I0224 00:32:05.813524   658 net.cpp:411] accuracy -> accuracy\\n\",\n      \"I0224 00:32:05.813539   658 net.cpp:150] Setting up accuracy\\n\",\n      \"I0224 00:32:05.813547   658 net.cpp:157] Top shape: (1)\\n\",\n      \"I0224 00:32:05.813555   658 net.cpp:165] Memory required for data: 3724\\n\",\n      \"I0224 00:32:05.813565   658 layer_factory.hpp:77] Creating layer loss\\n\",\n      \"I0224 00:32:05.813585   658 net.cpp:106] Creating Layer loss\\n\",\n      \"I0224 00:32:05.813599   658 net.cpp:454] loss <- ip2_ip2_0_split_1\\n\",\n      \"I0224 00:32:05.813616   658 net.cpp:454] loss <- label_data_1_split_1\\n\",\n      \"I0224 00:32:05.813627   658 net.cpp:411] loss -> loss\\n\",\n      \"I0224 00:32:05.813642   658 layer_factory.hpp:77] Creating layer loss\\n\",\n      \"I0224 00:32:05.813663   658 net.cpp:150] Setting up loss\\n\",\n      \"I0224 00:32:05.813671   658 net.cpp:157] Top shape: (1)\\n\",\n      \"I0224 00:32:05.813679   658 net.cpp:160]     with loss weight 1\\n\",\n      \"I0224 00:32:05.813695   658 net.cpp:165] Memory required for data: 3728\\n\",\n      \"I0224 00:32:05.813704   658 net.cpp:226] loss needs backward computation.\\n\",\n      \"I0224 00:32:05.813712   658 net.cpp:228] accuracy does not need backward computation.\\n\",\n      \"I0224 00:32:05.813720   658 net.cpp:226] ip2_ip2_0_split needs backward computation.\\n\",\n      \"I0224 00:32:05.813729   658 net.cpp:226] ip2 needs backward computation.\\n\",\n      \"I0224 00:32:05.813735   658 net.cpp:226] relu1 needs backward computation.\\n\",\n      \"I0224 00:32:05.813743   658 net.cpp:226] ip1 needs backward computation.\\n\",\n      \"I0224 00:32:05.813751   658 net.cpp:228] label_data_1_split does not need backward computation.\\n\",\n      \"I0224 00:32:05.813760   658 net.cpp:228] data does not need backward computation.\\n\",\n      \"I0224 00:32:05.813772   658 net.cpp:270] This network produces output accuracy\\n\",\n      \"I0224 00:32:05.813787   658 net.cpp:270] This network produces output loss\\n\",\n      \"I0224 00:32:05.813809   658 net.cpp:283] Network initialization done.\\n\",\n      \"I0224 00:32:05.813905   658 solver.cpp:181] Creating test net (#0) specified by test_net file: examples/hdf5_classification/nonlinear_auto_test.prototxt\\n\",\n      \"I0224 00:32:05.813944   658 net.cpp:49] Initializing net from parameters: \\n\",\n      \"state {\\n\",\n      \"  phase: TEST\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"data\\\"\\n\",\n      \"  type: \\\"HDF5Data\\\"\\n\",\n      \"  top: \\\"data\\\"\\n\",\n      \"  top: \\\"label\\\"\\n\",\n      \"  hdf5_data_param {\\n\",\n      \"    source: \\\"examples/hdf5_classification/data/test.txt\\\"\\n\",\n      \"    batch_size: 10\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"ip1\\\"\\n\",\n      \"  type: \\\"InnerProduct\\\"\\n\",\n      \"  bottom: \\\"data\\\"\\n\",\n      \"  top: \\\"ip1\\\"\\n\",\n      \"  inner_product_param {\\n\",\n      \"    num_output: 40\\n\",\n      \"    weight_filler {\\n\",\n      \"      type: \\\"xavier\\\"\\n\",\n      \"    }\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"relu1\\\"\\n\",\n      \"  type: \\\"ReLU\\\"\\n\",\n      \"  bottom: \\\"ip1\\\"\\n\",\n      \"  top: \\\"ip1\\\"\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"ip2\\\"\\n\",\n      \"  type: \\\"InnerProduct\\\"\\n\",\n      \"  bottom: \\\"ip1\\\"\\n\",\n      \"  top: \\\"ip2\\\"\\n\",\n      \"  inner_product_param {\\n\",\n      \"    num_output: 2\\n\",\n      \"    weight_filler {\\n\",\n      \"      type: \\\"xavier\\\"\\n\",\n      \"    }\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"accuracy\\\"\\n\",\n      \"  type: \\\"Accuracy\\\"\\n\",\n      \"  bottom: \\\"ip2\\\"\\n\",\n      \"  bottom: \\\"label\\\"\\n\",\n      \"  top: \\\"accuracy\\\"\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"loss\\\"\\n\",\n      \"  type: \\\"SoftmaxWithLoss\\\"\\n\",\n      \"  bottom: \\\"ip2\\\"\\n\",\n      \"  bottom: \\\"label\\\"\\n\",\n      \"  top: \\\"loss\\\"\\n\",\n      \"}\\n\",\n      \"I0224 00:32:05.814131   658 layer_factory.hpp:77] Creating layer data\\n\",\n      \"I0224 00:32:05.814142   658 net.cpp:106] Creating Layer data\\n\",\n      \"I0224 00:32:05.814152   658 net.cpp:411] data -> data\\n\",\n      \"I0224 00:32:05.814162   658 net.cpp:411] data -> label\\n\",\n      \"I0224 00:32:05.814180   658 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\\n\",\n      \"I0224 00:32:05.814220   658 hdf5_data_layer.cpp:93] Number of HDF5 files: 1\\n\",\n      \"I0224 00:32:05.815207   658 net.cpp:150] Setting up data\\n\",\n      \"I0224 00:32:05.815227   658 net.cpp:157] Top shape: 10 4 (40)\\n\",\n      \"I0224 00:32:05.815243   658 net.cpp:157] Top shape: 10 (10)\\n\",\n      \"I0224 00:32:05.815253   658 net.cpp:165] Memory required for data: 200\\n\",\n      \"I0224 00:32:05.815260   658 layer_factory.hpp:77] Creating layer label_data_1_split\\n\",\n      \"I0224 00:32:05.815270   658 net.cpp:106] Creating Layer label_data_1_split\\n\",\n      \"I0224 00:32:05.815279   658 net.cpp:454] label_data_1_split <- label\\n\",\n      \"I0224 00:32:05.815287   658 net.cpp:411] label_data_1_split -> label_data_1_split_0\\n\",\n      \"I0224 00:32:05.815299   658 net.cpp:411] label_data_1_split -> label_data_1_split_1\\n\",\n      \"I0224 00:32:05.815310   658 net.cpp:150] Setting up label_data_1_split\\n\",\n      \"I0224 00:32:05.815318   658 net.cpp:157] Top shape: 10 (10)\\n\",\n      \"I0224 00:32:05.815326   658 net.cpp:157] Top shape: 10 (10)\\n\",\n      \"I0224 00:32:05.815335   658 net.cpp:165] Memory required for data: 280\\n\",\n      \"I0224 00:32:05.815341   658 layer_factory.hpp:77] Creating layer ip1\\n\",\n      \"I0224 00:32:05.815351   658 net.cpp:106] Creating Layer ip1\\n\",\n      \"I0224 00:32:05.815358   658 net.cpp:454] ip1 <- data\\n\",\n      \"I0224 00:32:05.815367   658 net.cpp:411] ip1 -> ip1\\n\",\n      \"I0224 00:32:05.815383   658 net.cpp:150] Setting up ip1\\n\",\n      \"I0224 00:32:05.815398   658 net.cpp:157] Top shape: 10 40 (400)\\n\",\n      \"I0224 00:32:05.815413   658 net.cpp:165] Memory required for data: 1880\\n\",\n      \"I0224 00:32:05.815435   658 layer_factory.hpp:77] Creating layer relu1\\n\",\n      \"I0224 00:32:05.815450   658 net.cpp:106] Creating Layer relu1\\n\",\n      \"I0224 00:32:05.815459   658 net.cpp:454] relu1 <- ip1\\n\",\n      \"I0224 00:32:05.815469   658 net.cpp:397] relu1 -> ip1 (in-place)\\n\",\n      \"I0224 00:32:05.815479   658 net.cpp:150] Setting up relu1\\n\",\n      \"I0224 00:32:05.815486   658 net.cpp:157] Top shape: 10 40 (400)\\n\",\n      \"I0224 00:32:05.815495   658 net.cpp:165] Memory required for data: 3480\\n\",\n      \"I0224 00:32:05.815501   658 layer_factory.hpp:77] Creating layer ip2\\n\",\n      \"I0224 00:32:05.815510   658 net.cpp:106] Creating Layer ip2\\n\",\n      \"I0224 00:32:05.815518   658 net.cpp:454] ip2 <- ip1\\n\",\n      \"I0224 00:32:05.815527   658 net.cpp:411] ip2 -> ip2\\n\",\n      \"I0224 00:32:05.815542   658 net.cpp:150] Setting up ip2\\n\",\n      \"I0224 00:32:05.815551   658 net.cpp:157] Top shape: 10 2 (20)\\n\",\n      \"I0224 00:32:05.815559   658 net.cpp:165] Memory required for data: 3560\\n\",\n      \"I0224 00:32:05.815570   658 layer_factory.hpp:77] Creating layer ip2_ip2_0_split\\n\",\n      \"I0224 00:32:05.815579   658 net.cpp:106] Creating Layer ip2_ip2_0_split\\n\",\n      \"I0224 00:32:05.815587   658 net.cpp:454] ip2_ip2_0_split <- ip2\\n\",\n      \"I0224 00:32:05.815600   658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_0\\n\",\n      \"I0224 00:32:05.815619   658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_1\\n\",\n      \"I0224 00:32:05.815640   658 net.cpp:150] Setting up ip2_ip2_0_split\\n\",\n      \"I0224 00:32:05.815654   658 net.cpp:157] Top shape: 10 2 (20)\\n\",\n      \"I0224 00:32:05.815662   658 net.cpp:157] Top shape: 10 2 (20)\\n\",\n      \"I0224 00:32:05.815670   658 net.cpp:165] Memory required for data: 3720\\n\",\n      \"I0224 00:32:05.815677   658 layer_factory.hpp:77] Creating layer accuracy\\n\",\n      \"I0224 00:32:05.815685   658 net.cpp:106] Creating Layer accuracy\\n\",\n      \"I0224 00:32:05.815693   658 net.cpp:454] accuracy <- ip2_ip2_0_split_0\\n\",\n      \"I0224 00:32:05.815702   658 net.cpp:454] accuracy <- label_data_1_split_0\\n\",\n      \"I0224 00:32:05.815711   658 net.cpp:411] accuracy -> accuracy\\n\",\n      \"I0224 00:32:05.815722   658 net.cpp:150] Setting up accuracy\\n\",\n      \"I0224 00:32:05.815732   658 net.cpp:157] Top shape: (1)\\n\",\n      \"I0224 00:32:05.815738   658 net.cpp:165] Memory required for data: 3724\\n\",\n      \"I0224 00:32:05.815747   658 layer_factory.hpp:77] Creating layer loss\\n\",\n      \"I0224 00:32:05.815754   658 net.cpp:106] Creating Layer loss\\n\",\n      \"I0224 00:32:05.815762   658 net.cpp:454] loss <- ip2_ip2_0_split_1\\n\",\n      \"I0224 00:32:05.815770   658 net.cpp:454] loss <- label_data_1_split_1\\n\",\n      \"I0224 00:32:05.815779   658 net.cpp:411] loss -> loss\\n\",\n      \"I0224 00:32:05.815790   658 layer_factory.hpp:77] Creating layer loss\\n\",\n      \"I0224 00:32:05.815811   658 net.cpp:150] Setting up loss\\n\",\n      \"I0224 00:32:05.815829   658 net.cpp:157] Top shape: (1)\\n\",\n      \"I0224 00:32:05.815843   658 net.cpp:160]     with loss weight 1\\n\",\n      \"I0224 00:32:05.815867   658 net.cpp:165] Memory required for data: 3728\\n\",\n      \"I0224 00:32:05.815876   658 net.cpp:226] loss needs backward computation.\\n\",\n      \"I0224 00:32:05.815884   658 net.cpp:228] accuracy does not need backward computation.\\n\",\n      \"I0224 00:32:05.815892   658 net.cpp:226] ip2_ip2_0_split needs backward computation.\\n\",\n      \"I0224 00:32:05.815901   658 net.cpp:226] ip2 needs backward computation.\\n\",\n      \"I0224 00:32:05.815908   658 net.cpp:226] relu1 needs backward computation.\\n\",\n      \"I0224 00:32:05.815915   658 net.cpp:226] ip1 needs backward computation.\\n\",\n      \"I0224 00:32:05.815923   658 net.cpp:228] label_data_1_split does not need backward computation.\\n\",\n      \"I0224 00:32:05.815932   658 net.cpp:228] data does not need backward computation.\\n\",\n      \"I0224 00:32:05.815938   658 net.cpp:270] This network produces output accuracy\\n\",\n      \"I0224 00:32:05.815946   658 net.cpp:270] This network produces output loss\\n\",\n      \"I0224 00:32:05.815958   658 net.cpp:283] Network initialization done.\\n\",\n      \"I0224 00:32:05.815978   658 solver.cpp:60] Solver scaffolding done.\\n\",\n      \"I0224 00:32:05.816000   658 caffe.cpp:219] Starting Optimization\\n\",\n      \"I0224 00:32:05.816016   658 solver.cpp:280] Solving \\n\",\n      \"I0224 00:32:05.816030   658 solver.cpp:281] Learning Rate Policy: step\\n\",\n      \"I0224 00:32:05.816048   658 solver.cpp:338] Iteration 0, Testing net (#0)\\n\",\n      \"I0224 00:32:05.831967   658 solver.cpp:406]     Test net output #0: accuracy = 0.4464\\n\",\n      \"I0224 00:32:05.832033   658 solver.cpp:406]     Test net output #1: loss = 0.909841 (* 1 = 0.909841 loss)\\n\",\n      \"I0224 00:32:05.832186   658 solver.cpp:229] Iteration 0, loss = 0.798509\\n\",\n      \"I0224 00:32:05.832218   658 solver.cpp:245]     Train net output #0: accuracy = 0.6\\n\",\n      \"I0224 00:32:05.832247   658 solver.cpp:245]     Train net output #1: loss = 0.798509 (* 1 = 0.798509 loss)\\n\",\n      \"I0224 00:32:05.832281   658 sgd_solver.cpp:106] Iteration 0, lr = 0.01\\n\",\n      \"I0224 00:32:05.859506   658 solver.cpp:338] Iteration 1000, Testing net (#0)\\n\",\n      \"I0224 00:32:05.862799   658 solver.cpp:406]     Test net output #0: accuracy = 0.8156\\n\",\n      \"I0224 00:32:05.862818   658 solver.cpp:406]     Test net output #1: loss = 0.44259 (* 1 = 0.44259 loss)\\n\",\n      \"I0224 00:32:05.862853   658 solver.cpp:229] Iteration 1000, loss = 0.537015\\n\",\n      \"I0224 00:32:05.862864   658 solver.cpp:245]     Train net output #0: accuracy = 0.7\\n\",\n      \"I0224 00:32:05.862875   658 solver.cpp:245]     Train net output #1: loss = 0.537015 (* 1 = 0.537015 loss)\\n\",\n      \"I0224 00:32:05.862885   658 sgd_solver.cpp:106] Iteration 1000, lr = 0.01\\n\",\n      \"I0224 00:32:05.883155   658 solver.cpp:338] Iteration 2000, Testing net (#0)\\n\",\n      \"I0224 00:32:05.886435   658 solver.cpp:406]     Test net output #0: accuracy = 0.8116\\n\",\n      \"I0224 00:32:05.886451   658 solver.cpp:406]     Test net output #1: loss = 0.434079 (* 1 = 0.434079 loss)\\n\",\n      \"I0224 00:32:05.886484   658 solver.cpp:229] Iteration 2000, loss = 0.43109\\n\",\n      \"I0224 00:32:05.886497   658 solver.cpp:245]     Train net output #0: accuracy = 0.9\\n\",\n      \"I0224 00:32:05.886508   658 solver.cpp:245]     Train net output #1: loss = 0.43109 (* 1 = 0.43109 loss)\\n\",\n      \"I0224 00:32:05.886518   658 sgd_solver.cpp:106] Iteration 2000, lr = 0.01\\n\",\n      \"I0224 00:32:05.907243   658 solver.cpp:338] Iteration 3000, Testing net (#0)\\n\",\n      \"I0224 00:32:05.910521   658 solver.cpp:406]     Test net output #0: accuracy = 0.8168\\n\",\n      \"I0224 00:32:05.910537   658 solver.cpp:406]     Test net output #1: loss = 0.425661 (* 1 = 0.425661 loss)\\n\",\n      \"I0224 00:32:05.910905   658 solver.cpp:229] Iteration 3000, loss = 0.430245\\n\",\n      \"I0224 00:32:05.910922   658 solver.cpp:245]     Train net output #0: accuracy = 0.7\\n\",\n      \"I0224 00:32:05.910933   658 solver.cpp:245]     Train net output #1: loss = 0.430245 (* 1 = 0.430245 loss)\\n\",\n      \"I0224 00:32:05.910943   658 sgd_solver.cpp:106] Iteration 3000, lr = 0.01\\n\",\n      \"I0224 00:32:05.931205   658 solver.cpp:338] Iteration 4000, Testing net (#0)\\n\",\n      \"I0224 00:32:05.934479   658 solver.cpp:406]     Test net output #0: accuracy = 0.8324\\n\",\n      \"I0224 00:32:05.934496   658 solver.cpp:406]     Test net output #1: loss = 0.404891 (* 1 = 0.404891 loss)\\n\",\n      \"I0224 00:32:05.934530   658 solver.cpp:229] Iteration 4000, loss = 0.628955\\n\",\n      \"I0224 00:32:05.934542   658 solver.cpp:245]     Train net output #0: accuracy = 0.7\\n\",\n      \"I0224 00:32:05.934553   658 solver.cpp:245]     Train net output #1: loss = 0.628955 (* 1 = 0.628955 loss)\\n\",\n      \"I0224 00:32:05.934583   658 sgd_solver.cpp:106] Iteration 4000, lr = 0.01\\n\",\n      \"I0224 00:32:05.955108   658 solver.cpp:338] Iteration 5000, Testing net (#0)\\n\",\n      \"I0224 00:32:05.958377   658 solver.cpp:406]     Test net output #0: accuracy = 0.8364\\n\",\n      \"I0224 00:32:05.958395   658 solver.cpp:406]     Test net output #1: loss = 0.404235 (* 1 = 0.404235 loss)\\n\",\n      \"I0224 00:32:05.958432   658 solver.cpp:229] Iteration 5000, loss = 0.394939\\n\",\n      \"I0224 00:32:05.958444   658 solver.cpp:245]     Train net output #0: accuracy = 0.9\\n\",\n      \"I0224 00:32:05.958456   658 solver.cpp:245]     Train net output #1: loss = 0.39494 (* 1 = 0.39494 loss)\\n\",\n      \"I0224 00:32:05.958466   658 sgd_solver.cpp:106] Iteration 5000, lr = 0.001\\n\",\n      \"I0224 00:32:05.978703   658 solver.cpp:338] Iteration 6000, Testing net (#0)\\n\",\n      \"I0224 00:32:05.981973   658 solver.cpp:406]     Test net output #0: accuracy = 0.838\\n\",\n      \"I0224 00:32:05.981991   658 solver.cpp:406]     Test net output #1: loss = 0.385743 (* 1 = 0.385743 loss)\\n\",\n      \"I0224 00:32:05.982347   658 solver.cpp:229] Iteration 6000, loss = 0.411537\\n\",\n      \"I0224 00:32:05.982362   658 solver.cpp:245]     Train net output #0: accuracy = 0.8\\n\",\n      \"I0224 00:32:05.982373   658 solver.cpp:245]     Train net output #1: loss = 0.411537 (* 1 = 0.411537 loss)\\n\",\n      \"I0224 00:32:05.982383   658 sgd_solver.cpp:106] Iteration 6000, lr = 0.001\\n\",\n      \"I0224 00:32:06.003015   658 solver.cpp:338] Iteration 7000, Testing net (#0)\\n\",\n      \"I0224 00:32:06.006283   658 solver.cpp:406]     Test net output #0: accuracy = 0.8388\\n\",\n      \"I0224 00:32:06.006301   658 solver.cpp:406]     Test net output #1: loss = 0.384648 (* 1 = 0.384648 loss)\\n\",\n      \"I0224 00:32:06.006335   658 solver.cpp:229] Iteration 7000, loss = 0.521072\\n\",\n      \"I0224 00:32:06.006347   658 solver.cpp:245]     Train net output #0: accuracy = 0.7\\n\",\n      \"I0224 00:32:06.006358   658 solver.cpp:245]     Train net output #1: loss = 0.521073 (* 1 = 0.521073 loss)\\n\",\n      \"I0224 00:32:06.006368   658 sgd_solver.cpp:106] Iteration 7000, lr = 0.001\\n\",\n      \"I0224 00:32:06.026715   658 solver.cpp:338] Iteration 8000, Testing net (#0)\\n\",\n      \"I0224 00:32:06.029965   658 solver.cpp:406]     Test net output #0: accuracy = 0.8404\\n\",\n      \"I0224 00:32:06.029983   658 solver.cpp:406]     Test net output #1: loss = 0.380889 (* 1 = 0.380889 loss)\\n\",\n      \"I0224 00:32:06.030015   658 solver.cpp:229] Iteration 8000, loss = 0.329477\\n\",\n      \"I0224 00:32:06.030028   658 solver.cpp:245]     Train net output #0: accuracy = 0.9\\n\",\n      \"I0224 00:32:06.030040   658 solver.cpp:245]     Train net output #1: loss = 0.329477 (* 1 = 0.329477 loss)\\n\",\n      \"I0224 00:32:06.030048   658 sgd_solver.cpp:106] Iteration 8000, lr = 0.001\\n\",\n      \"I0224 00:32:06.050626   658 solver.cpp:338] Iteration 9000, Testing net (#0)\\n\",\n      \"I0224 00:32:06.053889   658 solver.cpp:406]     Test net output #0: accuracy = 0.8376\\n\",\n      \"I0224 00:32:06.053906   658 solver.cpp:406]     Test net output #1: loss = 0.382756 (* 1 = 0.382756 loss)\\n\",\n      \"I0224 00:32:06.054271   658 solver.cpp:229] Iteration 9000, loss = 0.412227\\n\",\n      \"I0224 00:32:06.054291   658 solver.cpp:245]     Train net output #0: accuracy = 0.8\\n\",\n      \"I0224 00:32:06.054314   658 solver.cpp:245]     Train net output #1: loss = 0.412228 (* 1 = 0.412228 loss)\\n\",\n      \"I0224 00:32:06.054337   658 sgd_solver.cpp:106] Iteration 9000, lr = 0.001\\n\",\n      \"I0224 00:32:06.074646   658 solver.cpp:456] Snapshotting to binary proto file examples/hdf5_classification/data/train_iter_10000.caffemodel\\n\",\n      \"I0224 00:32:06.074808   658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/hdf5_classification/data/train_iter_10000.solverstate\\n\",\n      \"I0224 00:32:06.074889   658 solver.cpp:318] Iteration 10000, loss = 0.532798\\n\",\n      \"I0224 00:32:06.074906   658 solver.cpp:338] Iteration 10000, Testing net (#0)\\n\",\n      \"I0224 00:32:06.078208   658 solver.cpp:406]     Test net output #0: accuracy = 0.8388\\n\",\n      \"I0224 00:32:06.078225   658 solver.cpp:406]     Test net output #1: loss = 0.382042 (* 1 = 0.382042 loss)\\n\",\n      \"I0224 00:32:06.078234   658 solver.cpp:323] Optimization Done.\\n\",\n      \"I0224 00:32:06.078241   658 caffe.cpp:222] Optimization Done.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_logreg_solver.prototxt\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\\n\",\n    \"shutil.rmtree(dirname)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"description\": \"Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.\",\n  \"example_name\": \"Off-the-shelf SGD for classification\",\n  \"include_in_docs\": true,\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  },\n  \"priority\": 4\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Caffe/cifar10/cifar10_full.prototxt",
    "content": "name: \"CIFAR10_full_deploy\"\n# N.B. input image must be in CIFAR-10 format\n# as described at http://www.cs.toronto.edu/~kriz/cifar.html\nlayer {\n  name: \"data\"\n  type: \"Input\"\n  top: \"data\"\n  input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }\n}\nlayer {\n  name: \"conv1\"\n  type: \"Convolution\"\n  bottom: \"data\"\n  top: \"conv1\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 32\n    pad: 2\n    kernel_size: 5\n    stride: 1\n  }\n}\nlayer {\n  name: \"pool1\"\n  type: \"Pooling\"\n  bottom: \"conv1\"\n  top: \"pool1\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"relu1\"\n  type: \"ReLU\"\n  bottom: \"pool1\"\n  top: \"pool1\"\n}\nlayer {\n  name: \"norm1\"\n  type: \"LRN\"\n  bottom: \"pool1\"\n  top: \"norm1\"\n  lrn_param {\n    local_size: 3\n    alpha: 5e-05\n    beta: 0.75\n    norm_region: WITHIN_CHANNEL\n  }\n}\nlayer {\n  name: \"conv2\"\n  type: \"Convolution\"\n  bottom: \"norm1\"\n  top: \"conv2\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 32\n    pad: 2\n    kernel_size: 5\n    stride: 1\n  }\n}\nlayer {\n  name: \"relu2\"\n  type: \"ReLU\"\n  bottom: \"conv2\"\n  top: \"conv2\"\n}\nlayer {\n  name: \"pool2\"\n  type: \"Pooling\"\n  bottom: \"conv2\"\n  top: \"pool2\"\n  pooling_param {\n    pool: AVE\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"norm2\"\n  type: \"LRN\"\n  bottom: \"pool2\"\n  top: \"norm2\"\n  lrn_param {\n    local_size: 3\n    alpha: 5e-05\n    beta: 0.75\n    norm_region: WITHIN_CHANNEL\n  }\n}\nlayer {\n  name: \"conv3\"\n  type: \"Convolution\"\n  bottom: \"norm2\"\n  top: \"conv3\"\n  convolution_param {\n    num_output: 64\n    pad: 2\n    kernel_size: 5\n    stride: 1\n  }\n}\nlayer {\n  name: \"relu3\"\n  type: \"ReLU\"\n  bottom: \"conv3\"\n  top: \"conv3\"\n}\nlayer {\n  name: \"pool3\"\n  type: \"Pooling\"\n  bottom: \"conv3\"\n  top: \"pool3\"\n  pooling_param {\n    pool: AVE\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"ip1\"\n  type: \"InnerProduct\"\n  bottom: \"pool3\"\n  top: \"ip1\"\n  param {\n    lr_mult: 1\n    decay_mult: 250\n  }\n  param {\n    lr_mult: 2\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 10\n  }\n}\nlayer {\n  name: \"prob\"\n  type: \"Softmax\"\n  bottom: \"ip1\"\n  top: \"prob\"\n}\n"
  },
  {
    "path": "Caffe/cifar10/cifar10_full_sigmoid_solver.prototxt",
    "content": "# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10\n# then another factor of 10 after 10 more epochs (5000 iters)\n\n# The train/test net protocol buffer definition\nnet: \"examples/cifar10/cifar10_full_sigmoid_train_test.prototxt\"\n# test_iter specifies how many forward passes the test should carry out.\n# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,\n# covering the full 10,000 testing images.\ntest_iter: 10\n# Carry out testing every 1000 training iterations.\ntest_interval: 1000\n# The base learning rate, momentum and the weight decay of the network.\nbase_lr: 0.001\nmomentum: 0.9\n#weight_decay: 0.004\n# The learning rate policy\nlr_policy: \"step\"\ngamma: 1\nstepsize: 5000\n# Display every 100 iterations\ndisplay: 100\n# The maximum number of iterations\nmax_iter: 60000\n# snapshot intermediate results\nsnapshot: 10000\nsnapshot_prefix: \"examples/cifar10_full_sigmoid\"\n# solver mode: CPU or GPU\nsolver_mode: GPU\n"
  },
  {
    "path": "Caffe/cifar10/cifar10_full_sigmoid_solver_bn.prototxt",
    "content": "# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10\n# then another factor of 10 after 10 more epochs (5000 iters)\n\n# The train/test net protocol buffer definition\nnet: \"examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt\"\n# test_iter specifies how many forward passes the test should carry out.\n# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,\n# covering the full 10,000 testing images.\ntest_iter: 10\n# Carry out testing every 1000 training iterations.\ntest_interval: 1000\n# The base learning rate, momentum and the weight decay of the network.\nbase_lr: 0.001\nmomentum: 0.9\n#weight_decay: 0.004\n# The learning rate policy\nlr_policy: \"step\"\ngamma: 1\nstepsize: 5000\n# Display every 100 iterations\ndisplay: 100\n# The maximum number of iterations\nmax_iter: 60000\n# snapshot intermediate results\nsnapshot: 10000\nsnapshot_prefix: \"examples/cifar10_full_sigmoid_bn\"\n# solver mode: CPU or GPU\nsolver_mode: GPU\n"
  },
  {
    "path": "Caffe/cifar10/cifar10_full_sigmoid_train_test.prototxt",
    "content": "name: \"CIFAR10_full\"\nlayer {\n  name: \"cifar\"\n  type: \"Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TRAIN\n  }\n  transform_param {\n    mean_file: \"examples/cifar10/mean.binaryproto\"\n  }\n  data_param {\n    source: \"examples/cifar10/cifar10_train_lmdb\"\n    batch_size: 111\n    backend: LMDB\n  }\n}\nlayer {\n  name: \"cifar\"\n  type: \"Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TEST\n  }\n  transform_param {\n    mean_file: \"examples/cifar10/mean.binaryproto\"\n  }\n  data_param {\n    source: \"examples/cifar10/cifar10_test_lmdb\"\n    batch_size: 1000\n    backend: LMDB\n  }\n}\nlayer {\n  name: \"conv1\"\n  type: \"Convolution\"\n  bottom: \"data\"\n  top: \"conv1\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 32\n    pad: 2\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.0001\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"pool1\"\n  type: \"Pooling\"\n  bottom: \"conv1\"\n  top: \"pool1\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 3\n    stride: 2\n  }\n}\n\n\n\nlayer {\n  name: \"Sigmoid1\"\n  type: \"Sigmoid\"\n  bottom: \"pool1\"\n  top: \"Sigmoid1\"\n}\n\nlayer {\n  name: \"conv2\"\n  type: \"Convolution\"\n  bottom: \"Sigmoid1\"\n  top: \"conv2\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 32\n    pad: 2\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\n\n\nlayer {\n  name: \"Sigmoid2\"\n  type: \"Sigmoid\"\n  bottom: \"conv2\"\n  top: \"Sigmoid2\"\n}\nlayer {\n  name: \"pool2\"\n  type: \"Pooling\"\n  bottom: \"Sigmoid2\"\n  top: \"pool2\"\n  pooling_param {\n    pool: AVE\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"conv3\"\n  type: \"Convolution\"\n  bottom: \"pool2\"\n  top: \"conv3\"\n  convolution_param {\n    num_output: 64\n    pad: 2\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 1\n  }\n\n}\n\nlayer {\n  name: \"Sigmoid3\"\n  type: \"Sigmoid\"\n  bottom: \"conv3\"\n  top: \"Sigmoid3\"\n}\n\nlayer {\n  name: \"pool3\"\n  type: \"Pooling\"\n  bottom: \"Sigmoid3\"\n  top: \"pool3\"\n  pooling_param {\n    pool: AVE\n    kernel_size: 3\n    stride: 2\n  }\n}\n\nlayer {\n  name: \"ip1\"\n  type: \"InnerProduct\"\n  bottom: \"pool3\"\n  top: \"ip1\"\n  param {\n    lr_mult: 1\n    decay_mult: 0\n  }\n  param {\n    lr_mult: 2\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 10\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"accuracy\"\n  type: \"Accuracy\"\n  bottom: \"ip1\"\n  bottom: \"label\"\n  top: \"accuracy\"\n  include {\n    phase: TEST\n  }\n}\nlayer {\n  name: \"loss\"\n  type: \"SoftmaxWithLoss\"\n  bottom: \"ip1\"\n  bottom: \"label\"\n  top: \"loss\"\n}\n"
  },
  {
    "path": "Caffe/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt",
    "content": "name: \"CIFAR10_full\"\nlayer {\n  name: \"cifar\"\n  type: \"Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TRAIN\n  }\n  transform_param {\n    mean_file: \"examples/cifar10/mean.binaryproto\"\n  }\n  data_param {\n    source: \"examples/cifar10/cifar10_train_lmdb\"\n    batch_size: 100\n    backend: LMDB\n  }\n}\nlayer {\n  name: \"cifar\"\n  type: \"Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TEST\n  }\n  transform_param {\n    mean_file: \"examples/cifar10/mean.binaryproto\"\n  }\n  data_param {\n    source: \"examples/cifar10/cifar10_test_lmdb\"\n    batch_size: 1000\n    backend: LMDB\n  }\n}\nlayer {\n  name: \"conv1\"\n  type: \"Convolution\"\n  bottom: \"data\"\n  top: \"conv1\"\n  param {\n    lr_mult: 1\n  }\n  convolution_param {\n    num_output: 32\n    pad: 2\n    kernel_size: 5\n    stride: 1\n    bias_term: false\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.0001\n    }\n  }\n}\nlayer {\n  name: \"pool1\"\n  type: \"Pooling\"\n  bottom: \"conv1\"\n  top: \"pool1\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 3\n    stride: 2\n  }\n}\n\nlayer {\n  name: \"bn1\"\n  type: \"BatchNorm\"\n  bottom: \"pool1\"\n  top: \"bn1\"\n  param {\n    lr_mult: 0\n  }\n  param {\n    lr_mult: 0\n  }\n  param {\n    lr_mult: 0\n  }\n}\n\nlayer {\n  name: \"Sigmoid1\"\n  type: \"Sigmoid\"\n  bottom: \"bn1\"\n  top: \"Sigmoid1\"\n}\n\nlayer {\n  name: \"conv2\"\n  type: \"Convolution\"\n  bottom: \"Sigmoid1\"\n  top: \"conv2\"\n  param {\n    lr_mult: 1\n  }\n  convolution_param {\n    num_output: 32\n    pad: 2\n    kernel_size: 5\n    stride: 1\n    bias_term: false\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n  }\n}\n\nlayer {\n  name: \"bn2\"\n  type: \"BatchNorm\"\n  bottom: \"conv2\"\n  top: \"bn2\"\n  param {\n    lr_mult: 0\n  }\n  param {\n    lr_mult: 0\n  }\n  param {\n    lr_mult: 0\n  }\n}\n\nlayer {\n  name: \"Sigmoid2\"\n  type: \"Sigmoid\"\n  bottom: \"bn2\"\n  top: \"Sigmoid2\"\n}\nlayer {\n  name: \"pool2\"\n  type: \"Pooling\"\n  bottom: \"Sigmoid2\"\n  top: \"pool2\"\n  pooling_param {\n    pool: AVE\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"conv3\"\n  type: \"Convolution\"\n  bottom: \"pool2\"\n  top: \"conv3\"\n  param {\n    lr_mult: 1\n  }\n  convolution_param {\n    num_output: 64\n    pad: 2\n    kernel_size: 5\n    stride: 1\n    bias_term: false\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n  }\n}\n\nlayer {\n  name: \"bn3\"\n  type: \"BatchNorm\"\n  bottom: \"conv3\"\n  top: \"bn3\"\n  param {\n    lr_mult: 0\n  }\n  param {\n    lr_mult: 0\n  }\n  param {\n    lr_mult: 0\n  }\n}\n\nlayer {\n  name: \"Sigmoid3\"\n  type: \"Sigmoid\"\n  bottom: \"bn3\"\n  top: \"Sigmoid3\"\n}\nlayer {\n  name: \"pool3\"\n  type: \"Pooling\"\n  bottom: \"Sigmoid3\"\n  top: \"pool3\"\n  pooling_param {\n    pool: AVE\n    kernel_size: 3\n    stride: 2\n  }\n}\n\nlayer {\n  name: \"ip1\"\n  type: \"InnerProduct\"\n  bottom: \"pool3\"\n  top: \"ip1\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 1\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 10\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"accuracy\"\n  type: \"Accuracy\"\n  bottom: \"ip1\"\n  bottom: \"label\"\n  top: \"accuracy\"\n  include {\n    phase: TEST\n  }\n}\nlayer {\n  name: \"loss\"\n  type: \"SoftmaxWithLoss\"\n  bottom: \"ip1\"\n  bottom: \"label\"\n  top: \"loss\"\n}\n"
  },
  {
    "path": "Caffe/cifar10/cifar10_full_solver.prototxt",
    "content": "# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10\n# then another factor of 10 after 10 more epochs (5000 iters)\n\n# The train/test net protocol buffer definition\nnet: \"examples/cifar10/cifar10_full_train_test.prototxt\"\n# test_iter specifies how many forward passes the test should carry out.\n# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,\n# covering the full 10,000 testing images.\ntest_iter: 100\n# Carry out testing every 1000 training iterations.\ntest_interval: 1000\n# The base learning rate, momentum and the weight decay of the network.\nbase_lr: 0.001\nmomentum: 0.9\nweight_decay: 0.004\n# The learning rate policy\nlr_policy: \"fixed\"\n# Display every 200 iterations\ndisplay: 200\n# The maximum number of iterations\nmax_iter: 60000\n# snapshot intermediate results\nsnapshot: 10000\nsnapshot_format: HDF5\nsnapshot_prefix: \"examples/cifar10/cifar10_full\"\n# solver mode: CPU or GPU\nsolver_mode: GPU\n"
  },
  {
    "path": "Caffe/cifar10/cifar10_full_solver_lr1.prototxt",
    "content": "# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10\n# then another factor of 10 after 10 more epochs (5000 iters)\n\n# The train/test net protocol buffer definition\nnet: \"examples/cifar10/cifar10_full_train_test.prototxt\"\n# test_iter specifies how many forward passes the test should carry out.\n# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,\n# covering the full 10,000 testing images.\ntest_iter: 100\n# Carry out testing every 1000 training iterations.\ntest_interval: 1000\n# The base learning rate, momentum and the weight decay of the network.\nbase_lr: 0.0001\nmomentum: 0.9\nweight_decay: 0.004\n# The learning rate policy\nlr_policy: \"fixed\"\n# Display every 200 iterations\ndisplay: 200\n# The maximum number of iterations\nmax_iter: 65000\n# snapshot intermediate results\nsnapshot: 5000\nsnapshot_format: HDF5\nsnapshot_prefix: \"examples/cifar10/cifar10_full\"\n# solver mode: CPU or GPU\nsolver_mode: GPU\n"
  },
  {
    "path": "Caffe/cifar10/cifar10_full_solver_lr2.prototxt",
    "content": "# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10\n# then another factor of 10 after 10 more epochs (5000 iters)\n\n# The train/test net protocol buffer definition\nnet: \"examples/cifar10/cifar10_full_train_test.prototxt\"\n# test_iter specifies how many forward passes the test should carry out.\n# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,\n# covering the full 10,000 testing images.\ntest_iter: 100\n# Carry out testing every 1000 training iterations.\ntest_interval: 1000\n# The base learning rate, momentum and the weight decay of the network.\nbase_lr: 0.00001\nmomentum: 0.9\nweight_decay: 0.004\n# The learning rate policy\nlr_policy: \"fixed\"\n# Display every 200 iterations\ndisplay: 200\n# The maximum number of iterations\nmax_iter: 70000\n# snapshot intermediate results\nsnapshot: 5000\nsnapshot_format: HDF5\nsnapshot_prefix: \"examples/cifar10/cifar10_full\"\n# solver mode: CPU or GPU\nsolver_mode: GPU\n"
  },
  {
    "path": "Caffe/cifar10/cifar10_full_train_test.prototxt",
    "content": "name: \"CIFAR10_full\"\nlayer {\n  name: \"cifar\"\n  type: \"Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TRAIN\n  }\n  transform_param {\n    mean_file: \"examples/cifar10/mean.binaryproto\"\n  }\n  data_param {\n    source: \"examples/cifar10/cifar10_train_lmdb\"\n    batch_size: 100\n    backend: LMDB\n  }\n}\nlayer {\n  name: \"cifar\"\n  type: \"Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TEST\n  }\n  transform_param {\n    mean_file: \"examples/cifar10/mean.binaryproto\"\n  }\n  data_param {\n    source: \"examples/cifar10/cifar10_test_lmdb\"\n    batch_size: 100\n    backend: LMDB\n  }\n}\nlayer {\n  name: \"conv1\"\n  type: \"Convolution\"\n  bottom: \"data\"\n  top: \"conv1\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 32\n    pad: 2\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.0001\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"pool1\"\n  type: \"Pooling\"\n  bottom: \"conv1\"\n  top: \"pool1\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"relu1\"\n  type: \"ReLU\"\n  bottom: \"pool1\"\n  top: \"pool1\"\n}\nlayer {\n  name: \"norm1\"\n  type: \"LRN\"\n  bottom: \"pool1\"\n  top: \"norm1\"\n  lrn_param {\n    local_size: 3\n    alpha: 5e-05\n    beta: 0.75\n    norm_region: WITHIN_CHANNEL\n  }\n}\nlayer {\n  name: \"conv2\"\n  type: \"Convolution\"\n  bottom: \"norm1\"\n  top: \"conv2\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 32\n    pad: 2\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"relu2\"\n  type: \"ReLU\"\n  bottom: \"conv2\"\n  top: \"conv2\"\n}\nlayer {\n  name: \"pool2\"\n  type: \"Pooling\"\n  bottom: \"conv2\"\n  top: \"pool2\"\n  pooling_param {\n    pool: AVE\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"norm2\"\n  type: \"LRN\"\n  bottom: \"pool2\"\n  top: \"norm2\"\n  lrn_param {\n    local_size: 3\n    alpha: 5e-05\n    beta: 0.75\n    norm_region: WITHIN_CHANNEL\n  }\n}\nlayer {\n  name: \"conv3\"\n  type: \"Convolution\"\n  bottom: \"norm2\"\n  top: \"conv3\"\n  convolution_param {\n    num_output: 64\n    pad: 2\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"relu3\"\n  type: \"ReLU\"\n  bottom: \"conv3\"\n  top: \"conv3\"\n}\nlayer {\n  name: \"pool3\"\n  type: \"Pooling\"\n  bottom: \"conv3\"\n  top: \"pool3\"\n  pooling_param {\n    pool: AVE\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"ip1\"\n  type: \"InnerProduct\"\n  bottom: \"pool3\"\n  top: \"ip1\"\n  param {\n    lr_mult: 1\n    decay_mult: 250\n  }\n  param {\n    lr_mult: 2\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 10\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"accuracy\"\n  type: \"Accuracy\"\n  bottom: \"ip1\"\n  bottom: \"label\"\n  top: \"accuracy\"\n  include {\n    phase: TEST\n  }\n}\nlayer {\n  name: \"loss\"\n  type: \"SoftmaxWithLoss\"\n  bottom: \"ip1\"\n  bottom: \"label\"\n  top: \"loss\"\n}\n"
  },
  {
    "path": "Caffe/cifar10/cifar10_quick.prototxt",
    "content": "name: \"CIFAR10_quick_test\"\nlayer {\n  name: \"data\"\n  type: \"Input\"\n  top: \"data\"\n  input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }\n}\nlayer {\n  name: \"conv1\"\n  type: \"Convolution\"\n  bottom: \"data\"\n  top: \"conv1\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 32\n    pad: 2\n    kernel_size: 5\n    stride: 1\n  }\n}\nlayer {\n  name: \"pool1\"\n  type: \"Pooling\"\n  bottom: \"conv1\"\n  top: \"pool1\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"relu1\"\n  type: \"ReLU\"\n  bottom: \"pool1\"\n  top: \"pool1\"\n}\nlayer {\n  name: \"conv2\"\n  type: \"Convolution\"\n  bottom: \"pool1\"\n  top: \"conv2\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 32\n    pad: 2\n    kernel_size: 5\n    stride: 1\n  }\n}\nlayer {\n  name: \"relu2\"\n  type: \"ReLU\"\n  bottom: \"conv2\"\n  top: \"conv2\"\n}\nlayer {\n  name: \"pool2\"\n  type: \"Pooling\"\n  bottom: \"conv2\"\n  top: \"pool2\"\n  pooling_param {\n    pool: AVE\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"conv3\"\n  type: \"Convolution\"\n  bottom: \"pool2\"\n  top: \"conv3\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 64\n    pad: 2\n    kernel_size: 5\n    stride: 1\n  }\n}\nlayer {\n  name: \"relu3\"\n  type: \"ReLU\"\n  bottom: \"conv3\"\n  top: \"conv3\"\n}\nlayer {\n  name: \"pool3\"\n  type: \"Pooling\"\n  bottom: \"conv3\"\n  top: \"pool3\"\n  pooling_param {\n    pool: AVE\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"ip1\"\n  type: \"InnerProduct\"\n  bottom: \"pool3\"\n  top: \"ip1\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 64\n  }\n}\nlayer {\n  name: \"ip2\"\n  type: \"InnerProduct\"\n  bottom: \"ip1\"\n  top: \"ip2\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 10\n  }\n}\nlayer {\n  name: \"prob\"\n  type: \"Softmax\"\n  bottom: \"ip2\"\n  top: \"prob\"\n}\n"
  },
  {
    "path": "Caffe/cifar10/cifar10_quick_solver.prototxt",
    "content": "# reduce the learning rate after 8 epochs (4000 iters) by a factor of 10\n\n# The train/test net protocol buffer definition\nnet: \"examples/cifar10/cifar10_quick_train_test.prototxt\"\n# test_iter specifies how many forward passes the test should carry out.\n# In the case of MNIST, we have test batch size 100 and 100 test iterations,\n# covering the full 10,000 testing images.\ntest_iter: 100\n# Carry out testing every 500 training iterations.\ntest_interval: 500\n# The base learning rate, momentum and the weight decay of the network.\nbase_lr: 0.001\nmomentum: 0.9\nweight_decay: 0.004\n# The learning rate policy\nlr_policy: \"fixed\"\n# Display every 100 iterations\ndisplay: 100\n# The maximum number of iterations\nmax_iter: 4000\n# snapshot intermediate results\nsnapshot: 4000\nsnapshot_format: HDF5\nsnapshot_prefix: \"examples/cifar10/cifar10_quick\"\n# solver mode: CPU or GPU\nsolver_mode: GPU\n"
  },
  {
    "path": "Caffe/cifar10/cifar10_quick_solver_lr1.prototxt",
    "content": "# reduce the learning rate after 8 epochs (4000 iters) by a factor of 10\n\n# The train/test net protocol buffer definition\nnet: \"examples/cifar10/cifar10_quick_train_test.prototxt\"\n# test_iter specifies how many forward passes the test should carry out.\n# In the case of MNIST, we have test batch size 100 and 100 test iterations,\n# covering the full 10,000 testing images.\ntest_iter: 100\n# Carry out testing every 500 training iterations.\ntest_interval: 500\n# The base learning rate, momentum and the weight decay of the network.\nbase_lr: 0.0001\nmomentum: 0.9\nweight_decay: 0.004\n# The learning rate policy\nlr_policy: \"fixed\"\n# Display every 100 iterations\ndisplay: 100\n# The maximum number of iterations\nmax_iter: 5000\n# snapshot intermediate results\nsnapshot: 5000\nsnapshot_format: HDF5\nsnapshot_prefix: \"examples/cifar10/cifar10_quick\"\n# solver mode: CPU or GPU\nsolver_mode: GPU\n"
  },
  {
    "path": "Caffe/cifar10/cifar10_quick_train_test.prototxt",
    "content": "name: \"CIFAR10_quick\"\nlayer {\n  name: \"cifar\"\n  type: \"Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TRAIN\n  }\n  transform_param {\n    mean_file: \"examples/cifar10/mean.binaryproto\"\n  }\n  data_param {\n    source: \"examples/cifar10/cifar10_train_lmdb\"\n    batch_size: 100\n    backend: LMDB\n  }\n}\nlayer {\n  name: \"cifar\"\n  type: \"Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TEST\n  }\n  transform_param {\n    mean_file: \"examples/cifar10/mean.binaryproto\"\n  }\n  data_param {\n    source: \"examples/cifar10/cifar10_test_lmdb\"\n    batch_size: 100\n    backend: LMDB\n  }\n}\nlayer {\n  name: \"conv1\"\n  type: \"Convolution\"\n  bottom: \"data\"\n  top: \"conv1\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 32\n    pad: 2\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.0001\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"pool1\"\n  type: \"Pooling\"\n  bottom: \"conv1\"\n  top: \"pool1\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"relu1\"\n  type: \"ReLU\"\n  bottom: \"pool1\"\n  top: \"pool1\"\n}\nlayer {\n  name: \"conv2\"\n  type: \"Convolution\"\n  bottom: \"pool1\"\n  top: \"conv2\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 32\n    pad: 2\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"relu2\"\n  type: \"ReLU\"\n  bottom: \"conv2\"\n  top: \"conv2\"\n}\nlayer {\n  name: \"pool2\"\n  type: \"Pooling\"\n  bottom: \"conv2\"\n  top: \"pool2\"\n  pooling_param {\n    pool: AVE\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"conv3\"\n  type: \"Convolution\"\n  bottom: \"pool2\"\n  top: \"conv3\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 64\n    pad: 2\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"relu3\"\n  type: \"ReLU\"\n  bottom: \"conv3\"\n  top: \"conv3\"\n}\nlayer {\n  name: \"pool3\"\n  type: \"Pooling\"\n  bottom: \"conv3\"\n  top: \"pool3\"\n  pooling_param {\n    pool: AVE\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"ip1\"\n  type: \"InnerProduct\"\n  bottom: \"pool3\"\n  top: \"ip1\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 64\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.1\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"ip2\"\n  type: \"InnerProduct\"\n  bottom: \"ip1\"\n  top: \"ip2\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 10\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.1\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"accuracy\"\n  type: \"Accuracy\"\n  bottom: \"ip2\"\n  bottom: \"label\"\n  top: \"accuracy\"\n  include {\n    phase: TEST\n  }\n}\nlayer {\n  name: \"loss\"\n  type: \"SoftmaxWithLoss\"\n  bottom: \"ip2\"\n  bottom: \"label\"\n  top: \"loss\"\n}\n"
  },
  {
    "path": "Caffe/cifar10/convert_cifar_data.cpp",
    "content": "//\n// This script converts the CIFAR dataset to the leveldb format used\n// by caffe to perform classification.\n// Usage:\n//    convert_cifar_data input_folder output_db_file\n// The CIFAR dataset could be downloaded at\n//    http://www.cs.toronto.edu/~kriz/cifar.html\n\n#include <fstream>  // NOLINT(readability/streams)\n#include <string>\n\n#include \"boost/scoped_ptr.hpp\"\n#include \"glog/logging.h\"\n#include \"google/protobuf/text_format.h\"\n#include \"stdint.h\"\n\n#include \"caffe/proto/caffe.pb.h\"\n#include \"caffe/util/db.hpp\"\n#include \"caffe/util/format.hpp\"\n\nusing caffe::Datum;\nusing boost::scoped_ptr;\nusing std::string;\nnamespace db = caffe::db;\n\nconst int kCIFARSize = 32;\nconst int kCIFARImageNBytes = 3072;\nconst int kCIFARBatchSize = 10000;\nconst int kCIFARTrainBatches = 5;\n\nvoid read_image(std::ifstream* file, int* label, char* buffer) {\n  char label_char;\n  file->read(&label_char, 1);\n  *label = label_char;\n  file->read(buffer, kCIFARImageNBytes);\n  return;\n}\n\nvoid convert_dataset(const string& input_folder, const string& output_folder,\n    const string& db_type) {\n  scoped_ptr<db::DB> train_db(db::GetDB(db_type));\n  train_db->Open(output_folder + \"/cifar10_train_\" + db_type, db::NEW);\n  scoped_ptr<db::Transaction> txn(train_db->NewTransaction());\n  // Data buffer\n  int label;\n  char str_buffer[kCIFARImageNBytes];\n  Datum datum;\n  datum.set_channels(3);\n  datum.set_height(kCIFARSize);\n  datum.set_width(kCIFARSize);\n\n  LOG(INFO) << \"Writing Training data\";\n  for (int fileid = 0; fileid < kCIFARTrainBatches; ++fileid) {\n    // Open files\n    LOG(INFO) << \"Training Batch \" << fileid + 1;\n    string batchFileName = input_folder + \"/data_batch_\"\n      + caffe::format_int(fileid+1) + \".bin\";\n    std::ifstream data_file(batchFileName.c_str(),\n        std::ios::in | std::ios::binary);\n    CHECK(data_file) << \"Unable to open train file #\" << fileid + 1;\n    for (int itemid = 0; itemid < kCIFARBatchSize; ++itemid) {\n      read_image(&data_file, &label, str_buffer);\n      datum.set_label(label);\n      datum.set_data(str_buffer, kCIFARImageNBytes);\n      string out;\n      CHECK(datum.SerializeToString(&out));\n      txn->Put(caffe::format_int(fileid * kCIFARBatchSize + itemid, 5), out);\n    }\n  }\n  txn->Commit();\n  train_db->Close();\n\n  LOG(INFO) << \"Writing Testing data\";\n  scoped_ptr<db::DB> test_db(db::GetDB(db_type));\n  test_db->Open(output_folder + \"/cifar10_test_\" + db_type, db::NEW);\n  txn.reset(test_db->NewTransaction());\n  // Open files\n  std::ifstream data_file((input_folder + \"/test_batch.bin\").c_str(),\n      std::ios::in | std::ios::binary);\n  CHECK(data_file) << \"Unable to open test file.\";\n  for (int itemid = 0; itemid < kCIFARBatchSize; ++itemid) {\n    read_image(&data_file, &label, str_buffer);\n    datum.set_label(label);\n    datum.set_data(str_buffer, kCIFARImageNBytes);\n    string out;\n    CHECK(datum.SerializeToString(&out));\n    txn->Put(caffe::format_int(itemid, 5), out);\n  }\n  txn->Commit();\n  test_db->Close();\n}\n\nint main(int argc, char** argv) {\n  FLAGS_alsologtostderr = 1;\n\n  if (argc != 4) {\n    printf(\"This script converts the CIFAR dataset to the leveldb format used\\n\"\n           \"by caffe to perform classification.\\n\"\n           \"Usage:\\n\"\n           \"    convert_cifar_data input_folder output_folder db_type\\n\"\n           \"Where the input folder should contain the binary batch files.\\n\"\n           \"The CIFAR dataset could be downloaded at\\n\"\n           \"    http://www.cs.toronto.edu/~kriz/cifar.html\\n\"\n           \"You should gunzip them after downloading.\\n\");\n  } else {\n    google::InitGoogleLogging(argv[0]);\n    convert_dataset(string(argv[1]), string(argv[2]), string(argv[3]));\n  }\n  return 0;\n}\n"
  },
  {
    "path": "Caffe/cifar10/create_cifar10.sh",
    "content": "#!/usr/bin/env sh\n# This script converts the cifar data into leveldb format.\nset -e\n\nEXAMPLE=examples/cifar10\nDATA=data/cifar10\nDBTYPE=lmdb\n\necho \"Creating $DBTYPE...\"\n\nrm -rf $EXAMPLE/cifar10_train_$DBTYPE $EXAMPLE/cifar10_test_$DBTYPE\n\n./build/examples/cifar10/convert_cifar_data.bin $DATA $EXAMPLE $DBTYPE\n\necho \"Computing image mean...\"\n\n./build/tools/compute_image_mean -backend=$DBTYPE \\\n  $EXAMPLE/cifar10_train_$DBTYPE $EXAMPLE/mean.binaryproto\n\necho \"Done.\"\n"
  },
  {
    "path": "Caffe/cifar10/readme.md",
    "content": "---\ntitle: CIFAR-10 tutorial\ncategory: example\ndescription: Train and test Caffe on CIFAR-10 data.\ninclude_in_docs: true\npriority: 5\n---\n\nAlex's CIFAR-10 tutorial, Caffe style\n=====================================\n\nAlex Krizhevsky's [cuda-convnet](https://code.google.com/p/cuda-convnet/) details the model definitions, parameters, and training procedure for good performance on CIFAR-10. This example reproduces his results in Caffe.\n\nWe will assume that you have Caffe successfully compiled. If not, please refer to the [Installation page](/installation.html). In this tutorial, we will assume that your caffe installation is located at `CAFFE_ROOT`.\n\nWe thank @chyojn for the pull request that defined the model schemas and solver configurations.\n\n*This example is a work-in-progress. It would be nice to further explain details of the network and training choices and benchmark the full training.*\n\nPrepare the Dataset\n-------------------\n\nYou will first need to download and convert the data format from the [CIFAR-10 website](http://www.cs.toronto.edu/~kriz/cifar.html). To do this, simply run the following commands:\n\n    cd $CAFFE_ROOT\n    ./data/cifar10/get_cifar10.sh\n    ./examples/cifar10/create_cifar10.sh\n\nIf it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be the dataset, `./cifar10-leveldb`, and the data set image mean `./mean.binaryproto`.\n\nThe Model\n---------\n\nThe CIFAR-10 model is a CNN that composes layers of convolution, pooling, rectified linear unit (ReLU) nonlinearities, and local contrast normalization with a linear classifier on top of it all. We have defined the model in the `CAFFE_ROOT/examples/cifar10` directory's `cifar10_quick_train_test.prototxt`.\n\nTraining and Testing the \"Quick\" Model\n--------------------------------------\n\nTraining the model is simple after you have written the network definition protobuf and solver protobuf files (refer to [MNIST Tutorial](../examples/mnist.html)). Simply run `train_quick.sh`, or the following command directly:\n\n    cd $CAFFE_ROOT\n    ./examples/cifar10/train_quick.sh\n\n`train_quick.sh` is a simple script, so have a look inside. The main tool for training is `caffe` with the `train` action, and the solver protobuf text file as its argument.\n\nWhen you run the code, you will see a lot of messages flying by like this:\n\n    I0317 21:52:48.945710 2008298256 net.cpp:74] Creating Layer conv1\n    I0317 21:52:48.945716 2008298256 net.cpp:84] conv1 <- data\n    I0317 21:52:48.945725 2008298256 net.cpp:110] conv1 -> conv1\n    I0317 21:52:49.298691 2008298256 net.cpp:125] Top shape: 100 32 32 32 (3276800)\n    I0317 21:52:49.298719 2008298256 net.cpp:151] conv1 needs backward computation.\n\nThese messages tell you the details about each layer, its connections and its output shape, which may be helpful in debugging. After the initialization, the training will start:\n\n    I0317 21:52:49.309370 2008298256 net.cpp:166] Network initialization done.\n    I0317 21:52:49.309376 2008298256 net.cpp:167] Memory required for Data 23790808\n    I0317 21:52:49.309422 2008298256 solver.cpp:36] Solver scaffolding done.\n    I0317 21:52:49.309447 2008298256 solver.cpp:47] Solving CIFAR10_quick_train\n\nBased on the solver setting, we will print the training loss function every 100 iterations, and test the network every 500 iterations. You will see messages like this:\n\n    I0317 21:53:12.179772 2008298256 solver.cpp:208] Iteration 100, lr = 0.001\n    I0317 21:53:12.185698 2008298256 solver.cpp:65] Iteration 100, loss = 1.73643\n    ...\n    I0317 21:54:41.150030 2008298256 solver.cpp:87] Iteration 500, Testing net\n    I0317 21:54:47.129461 2008298256 solver.cpp:114] Test score #0: 0.5504\n    I0317 21:54:47.129500 2008298256 solver.cpp:114] Test score #1: 1.27805\n\nFor each training iteration, `lr` is the learning rate of that iteration, and `loss` is the training function. For the output of the testing phase, **score 0 is the accuracy**, and **score 1 is the testing loss function**.\n\nAnd after making yourself a cup of coffee, you are done!\n\n    I0317 22:12:19.666914 2008298256 solver.cpp:87] Iteration 5000, Testing net\n    I0317 22:12:25.580330 2008298256 solver.cpp:114] Test score #0: 0.7533\n    I0317 22:12:25.580379 2008298256 solver.cpp:114] Test score #1: 0.739837\n    I0317 22:12:25.587262 2008298256 solver.cpp:130] Snapshotting to cifar10_quick_iter_5000\n    I0317 22:12:25.590215 2008298256 solver.cpp:137] Snapshotting solver state to cifar10_quick_iter_5000.solverstate\n    I0317 22:12:25.592813 2008298256 solver.cpp:81] Optimization Done.\n\nOur model achieved ~75% test accuracy. The model parameters are stored in binary protobuf format in\n\n    cifar10_quick_iter_5000\n\nwhich is ready-to-deploy in CPU or GPU mode! Refer to the `CAFFE_ROOT/examples/cifar10/cifar10_quick.prototxt` for the deployment model definition that can be called on new data.\n\nWhy train on a GPU?\n-------------------\n\nCIFAR-10, while still small, has enough data to make GPU training attractive.\n\nTo compare CPU vs. GPU training speed, simply change one line in all the `cifar*solver.prototxt`:\n\n    # solver mode: CPU or GPU\n    solver_mode: CPU\n\nand you will be using CPU for training.\n"
  },
  {
    "path": "Caffe/cifar10/train_full.sh",
    "content": "#!/usr/bin/env sh\nset -e\n\nTOOLS=./build/tools\n\n$TOOLS/caffe train \\\n    --solver=examples/cifar10/cifar10_full_solver.prototxt $@\n\n# reduce learning rate by factor of 10\n$TOOLS/caffe train \\\n    --solver=examples/cifar10/cifar10_full_solver_lr1.prototxt \\\n    --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate.h5 $@\n\n# reduce learning rate by factor of 10\n$TOOLS/caffe train \\\n    --solver=examples/cifar10/cifar10_full_solver_lr2.prototxt \\\n    --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate.h5 $@\n"
  },
  {
    "path": "Caffe/cifar10/train_full_sigmoid.sh",
    "content": "#!/usr/bin/env sh\nset -e\n\nTOOLS=./build/tools\n\n$TOOLS/caffe train \\\n    --solver=examples/cifar10/cifar10_full_sigmoid_solver.prototxt $@\n\n"
  },
  {
    "path": "Caffe/cifar10/train_full_sigmoid_bn.sh",
    "content": "#!/usr/bin/env sh\nset -e\n\nTOOLS=./build/tools\n\n$TOOLS/caffe train \\\n    --solver=examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt $@\n\n"
  },
  {
    "path": "Caffe/cifar10/train_quick.sh",
    "content": "#!/usr/bin/env sh\nset -e\n\nTOOLS=./build/tools\n\n$TOOLS/caffe train \\\n  --solver=examples/cifar10/cifar10_quick_solver.prototxt $@\n\n# reduce learning rate by factor of 10 after 8 epochs\n$TOOLS/caffe train \\\n  --solver=examples/cifar10/cifar10_quick_solver_lr1.prototxt \\\n  --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate.h5 $@\n"
  },
  {
    "path": "Caffe/cpp_classification/classification.cpp",
    "content": "#include <caffe/caffe.hpp>\n#ifdef USE_OPENCV\n#include <opencv2/core/core.hpp>\n#include <opencv2/highgui/highgui.hpp>\n#include <opencv2/imgproc/imgproc.hpp>\n#endif  // USE_OPENCV\n#include <algorithm>\n#include <iosfwd>\n#include <memory>\n#include <string>\n#include <utility>\n#include <vector>\n\n#ifdef USE_OPENCV\nusing namespace caffe;  // NOLINT(build/namespaces)\nusing std::string;\n\n/* Pair (label, confidence) representing a prediction. */\ntypedef std::pair<string, float> Prediction;\n\nclass Classifier {\n public:\n  Classifier(const string& model_file,\n             const string& trained_file,\n             const string& mean_file,\n             const string& label_file);\n\n  std::vector<Prediction> Classify(const cv::Mat& img, int N = 5);\n\n private:\n  void SetMean(const string& mean_file);\n\n  std::vector<float> Predict(const cv::Mat& img);\n\n  void WrapInputLayer(std::vector<cv::Mat>* input_channels);\n\n  void Preprocess(const cv::Mat& img,\n                  std::vector<cv::Mat>* input_channels);\n\n private:\n  shared_ptr<Net<float> > net_;\n  cv::Size input_geometry_;\n  int num_channels_;\n  cv::Mat mean_;\n  std::vector<string> labels_;\n};\n\nClassifier::Classifier(const string& model_file,\n                       const string& trained_file,\n                       const string& mean_file,\n                       const string& label_file) {\n#ifdef CPU_ONLY\n  Caffe::set_mode(Caffe::CPU);\n#else\n  Caffe::set_mode(Caffe::GPU);\n#endif\n\n  /* Load the network. */\n  net_.reset(new Net<float>(model_file, TEST));\n  net_->CopyTrainedLayersFrom(trained_file);\n\n  CHECK_EQ(net_->num_inputs(), 1) << \"Network should have exactly one input.\";\n  CHECK_EQ(net_->num_outputs(), 1) << \"Network should have exactly one output.\";\n\n  Blob<float>* input_layer = net_->input_blobs()[0];\n  num_channels_ = input_layer->channels();\n  CHECK(num_channels_ == 3 || num_channels_ == 1)\n    << \"Input layer should have 1 or 3 channels.\";\n  input_geometry_ = cv::Size(input_layer->width(), input_layer->height());\n\n  /* Load the binaryproto mean file. */\n  SetMean(mean_file);\n\n  /* Load labels. */\n  std::ifstream labels(label_file.c_str());\n  CHECK(labels) << \"Unable to open labels file \" << label_file;\n  string line;\n  while (std::getline(labels, line))\n    labels_.push_back(string(line));\n\n  Blob<float>* output_layer = net_->output_blobs()[0];\n  CHECK_EQ(labels_.size(), output_layer->channels())\n    << \"Number of labels is different from the output layer dimension.\";\n}\n\nstatic bool PairCompare(const std::pair<float, int>& lhs,\n                        const std::pair<float, int>& rhs) {\n  return lhs.first > rhs.first;\n}\n\n/* Return the indices of the top N values of vector v. */\nstatic std::vector<int> Argmax(const std::vector<float>& v, int N) {\n  std::vector<std::pair<float, int> > pairs;\n  for (size_t i = 0; i < v.size(); ++i)\n    pairs.push_back(std::make_pair(v[i], i));\n  std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare);\n\n  std::vector<int> result;\n  for (int i = 0; i < N; ++i)\n    result.push_back(pairs[i].second);\n  return result;\n}\n\n/* Return the top N predictions. */\nstd::vector<Prediction> Classifier::Classify(const cv::Mat& img, int N) {\n  std::vector<float> output = Predict(img);\n\n  N = std::min<int>(labels_.size(), N);\n  std::vector<int> maxN = Argmax(output, N);\n  std::vector<Prediction> predictions;\n  for (int i = 0; i < N; ++i) {\n    int idx = maxN[i];\n    predictions.push_back(std::make_pair(labels_[idx], output[idx]));\n  }\n\n  return predictions;\n}\n\n/* Load the mean file in binaryproto format. */\nvoid Classifier::SetMean(const string& mean_file) {\n  BlobProto blob_proto;\n  ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);\n\n  /* Convert from BlobProto to Blob<float> */\n  Blob<float> mean_blob;\n  mean_blob.FromProto(blob_proto);\n  CHECK_EQ(mean_blob.channels(), num_channels_)\n    << \"Number of channels of mean file doesn't match input layer.\";\n\n  /* The format of the mean file is planar 32-bit float BGR or grayscale. */\n  std::vector<cv::Mat> channels;\n  float* data = mean_blob.mutable_cpu_data();\n  for (int i = 0; i < num_channels_; ++i) {\n    /* Extract an individual channel. */\n    cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data);\n    channels.push_back(channel);\n    data += mean_blob.height() * mean_blob.width();\n  }\n\n  /* Merge the separate channels into a single image. */\n  cv::Mat mean;\n  cv::merge(channels, mean);\n\n  /* Compute the global mean pixel value and create a mean image\n   * filled with this value. */\n  cv::Scalar channel_mean = cv::mean(mean);\n  mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);\n}\n\nstd::vector<float> Classifier::Predict(const cv::Mat& img) {\n  Blob<float>* input_layer = net_->input_blobs()[0];\n  input_layer->Reshape(1, num_channels_,\n                       input_geometry_.height, input_geometry_.width);\n  /* Forward dimension change to all layers. */\n  net_->Reshape();\n\n  std::vector<cv::Mat> input_channels;\n  WrapInputLayer(&input_channels);\n\n  Preprocess(img, &input_channels);\n\n  net_->Forward();\n\n  /* Copy the output layer to a std::vector */\n  Blob<float>* output_layer = net_->output_blobs()[0];\n  const float* begin = output_layer->cpu_data();\n  const float* end = begin + output_layer->channels();\n  return std::vector<float>(begin, end);\n}\n\n/* Wrap the input layer of the network in separate cv::Mat objects\n * (one per channel). This way we save one memcpy operation and we\n * don't need to rely on cudaMemcpy2D. The last preprocessing\n * operation will write the separate channels directly to the input\n * layer. */\nvoid Classifier::WrapInputLayer(std::vector<cv::Mat>* input_channels) {\n  Blob<float>* input_layer = net_->input_blobs()[0];\n\n  int width = input_layer->width();\n  int height = input_layer->height();\n  float* input_data = input_layer->mutable_cpu_data();\n  for (int i = 0; i < input_layer->channels(); ++i) {\n    cv::Mat channel(height, width, CV_32FC1, input_data);\n    input_channels->push_back(channel);\n    input_data += width * height;\n  }\n}\n\nvoid Classifier::Preprocess(const cv::Mat& img,\n                            std::vector<cv::Mat>* input_channels) {\n  /* Convert the input image to the input image format of the network. */\n  cv::Mat sample;\n  if (img.channels() == 3 && num_channels_ == 1)\n    cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY);\n  else if (img.channels() == 4 && num_channels_ == 1)\n    cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY);\n  else if (img.channels() == 4 && num_channels_ == 3)\n    cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR);\n  else if (img.channels() == 1 && num_channels_ == 3)\n    cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR);\n  else\n    sample = img;\n\n  cv::Mat sample_resized;\n  if (sample.size() != input_geometry_)\n    cv::resize(sample, sample_resized, input_geometry_);\n  else\n    sample_resized = sample;\n\n  cv::Mat sample_float;\n  if (num_channels_ == 3)\n    sample_resized.convertTo(sample_float, CV_32FC3);\n  else\n    sample_resized.convertTo(sample_float, CV_32FC1);\n\n  cv::Mat sample_normalized;\n  cv::subtract(sample_float, mean_, sample_normalized);\n\n  /* This operation will write the separate BGR planes directly to the\n   * input layer of the network because it is wrapped by the cv::Mat\n   * objects in input_channels. */\n  cv::split(sample_normalized, *input_channels);\n\n  CHECK(reinterpret_cast<float*>(input_channels->at(0).data)\n        == net_->input_blobs()[0]->cpu_data())\n    << \"Input channels are not wrapping the input layer of the network.\";\n}\n\nint main(int argc, char** argv) {\n  if (argc != 6) {\n    std::cerr << \"Usage: \" << argv[0]\n              << \" deploy.prototxt network.caffemodel\"\n              << \" mean.binaryproto labels.txt img.jpg\" << std::endl;\n    return 1;\n  }\n\n  ::google::InitGoogleLogging(argv[0]);\n\n  string model_file   = argv[1];\n  string trained_file = argv[2];\n  string mean_file    = argv[3];\n  string label_file   = argv[4];\n  Classifier classifier(model_file, trained_file, mean_file, label_file);\n\n  string file = argv[5];\n\n  std::cout << \"---------- Prediction for \"\n            << file << \" ----------\" << std::endl;\n\n  cv::Mat img = cv::imread(file, -1);\n  CHECK(!img.empty()) << \"Unable to decode image \" << file;\n  std::vector<Prediction> predictions = classifier.Classify(img);\n\n  /* Print the top N predictions. */\n  for (size_t i = 0; i < predictions.size(); ++i) {\n    Prediction p = predictions[i];\n    std::cout << std::fixed << std::setprecision(4) << p.second << \" - \\\"\"\n              << p.first << \"\\\"\" << std::endl;\n  }\n}\n#else\nint main(int argc, char** argv) {\n  LOG(FATAL) << \"This example requires OpenCV; compile with USE_OPENCV.\";\n}\n#endif  // USE_OPENCV\n"
  },
  {
    "path": "Caffe/cpp_classification/readme.md",
    "content": "---\ntitle: CaffeNet C++ Classification example\ndescription: A simple example performing image classification using the low-level C++ API.\ncategory: example\ninclude_in_docs: true\npriority: 10\n---\n\n# Classifying ImageNet: using the C++ API\n\nCaffe, at its core, is written in C++. It is possible to use the C++\nAPI of Caffe to implement an image classification application similar\nto the Python code presented in one of the Notebook examples. To look\nat a more general-purpose example of the Caffe C++ API, you should\nstudy the source code of the command line tool `caffe` in `tools/caffe.cpp`.\n\n## Presentation\n\nA simple C++ code is proposed in\n`examples/cpp_classification/classification.cpp`. For the sake of\nsimplicity, this example does not support oversampling of a single\nsample nor batching of multiple independent samples. This example is\nnot trying to reach the maximum possible classification throughput on\na system, but special care was given to avoid unnecessary\npessimization while keeping the code readable.\n\n## Compiling\n\nThe C++ example is built automatically when compiling Caffe. To\ncompile Caffe you should follow the documented instructions. The\nclassification example will be built as `examples/classification.bin`\nin your build directory.\n\n## Usage\n\nTo use the pre-trained CaffeNet model with the classification example,\nyou need to download it from the \"Model Zoo\" using the following\nscript:\n```\n./scripts/download_model_binary.py models/bvlc_reference_caffenet\n```\nThe ImageNet labels file (also called the *synset file*) is also\nrequired in order to map a prediction to the name of the class:\n```\n./data/ilsvrc12/get_ilsvrc_aux.sh\n```\nUsing the files that were downloaded, we can classify the provided cat\nimage (`examples/images/cat.jpg`) using this command:\n```\n./build/examples/cpp_classification/classification.bin \\\n  models/bvlc_reference_caffenet/deploy.prototxt \\\n  models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n  data/ilsvrc12/imagenet_mean.binaryproto \\\n  data/ilsvrc12/synset_words.txt \\\n  examples/images/cat.jpg\n```\nThe output should look like this:\n```\n---------- Prediction for examples/images/cat.jpg ----------\n0.3134 - \"n02123045 tabby, tabby cat\"\n0.2380 - \"n02123159 tiger cat\"\n0.1235 - \"n02124075 Egyptian cat\"\n0.1003 - \"n02119022 red fox, Vulpes vulpes\"\n0.0715 - \"n02127052 lynx, catamount\"\n```\n\n## Improving Performance\n\nTo further improve performance, you will need to leverage the GPU\nmore, here are some guidelines:\n\n* Move the data on the GPU early and perform all preprocessing\noperations there.\n* If you have many images to classify simultaneously, you should use\nbatching (independent images are classified in a single forward pass).\n* Use multiple classification threads to ensure the GPU is always fully\nutilized and not waiting for an I/O blocked CPU thread.\n"
  },
  {
    "path": "Caffe/detection.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[R-CNN](https://github.com/rbgirshick/rcnn) is a state-of-the-art detector that classifies region proposals by a finetuned Caffe model. For the full details of the R-CNN system and model, refer to its project site and the paper:\\n\",\n    \"\\n\",\n    \"> *Rich feature hierarchies for accurate object detection and semantic segmentation*. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. CVPR 2014. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\\n\",\n    \"\\n\",\n    \"In this example, we do detection by a pure Caffe edition of the R-CNN model for ImageNet. The R-CNN detector outputs class scores for the 200 detection classes of ILSVRC13. Keep in mind that these are raw one vs. all SVM scores, so they are not probabilistically calibrated or exactly comparable across classes. Note that this off-the-shelf model is simply for convenience, and is not the full R-CNN model.\\n\",\n    \"\\n\",\n    \"Let's run detection on an image of a bicyclist riding a fish bike in the desert (from the ImageNet challenge—no joke).\\n\",\n    \"\\n\",\n    \"First, we'll need region proposals and the Caffe R-CNN ImageNet model:\\n\",\n    \"\\n\",\n    \"- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\\n\",\n    \"\\n\",\n    \"-Run `./scripts/download_model_binary.py models/bvlc_reference_rcnn_ilsvrc13` to get the Caffe R-CNN ImageNet model.\\n\",\n    \"\\n\",\n    \"With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"WARNING: Logging before InitGoogleLogging() is written to STDERR\\n\",\n      \"I0218 20:43:25.383932 2099749632 net.cpp:42] Initializing net from parameters: \\n\",\n      \"name: \\\"R-CNN-ilsvrc13\\\"\\n\",\n      \"input: \\\"data\\\"\\n\",\n      \"input_dim: 10\\n\",\n      \"input_dim: 3\\n\",\n      \"input_dim: 227\\n\",\n      \"input_dim: 227\\n\",\n      \"state {\\n\",\n      \"  phase: TEST\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"conv1\\\"\\n\",\n      \"  type: \\\"Convolution\\\"\\n\",\n      \"  bottom: \\\"data\\\"\\n\",\n      \"  top: \\\"conv1\\\"\\n\",\n      \"  convolution_param {\\n\",\n      \"    num_output: 96\\n\",\n      \"    kernel_size: 11\\n\",\n      \"    stride: 4\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"relu1\\\"\\n\",\n      \"  type: \\\"ReLU\\\"\\n\",\n      \"  bottom: \\\"conv1\\\"\\n\",\n      \"  top: \\\"conv1\\\"\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"pool1\\\"\\n\",\n      \"  type: \\\"Pooling\\\"\\n\",\n      \"  bottom: \\\"conv1\\\"\\n\",\n      \"  top: \\\"pool1\\\"\\n\",\n      \"  pooling_param {\\n\",\n      \"    pool: MAX\\n\",\n      \"    kernel_size: 3\\n\",\n      \"    stride: 2\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"norm1\\\"\\n\",\n      \"  type: \\\"LRN\\\"\\n\",\n      \"  bottom: \\\"pool1\\\"\\n\",\n      \"  top: \\\"norm1\\\"\\n\",\n      \"  lrn_param {\\n\",\n      \"    local_size: 5\\n\",\n      \"    alpha: 0.0001\\n\",\n      \"    beta: 0.75\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"conv2\\\"\\n\",\n      \"  type: \\\"Convolution\\\"\\n\",\n      \"  bottom: \\\"norm1\\\"\\n\",\n      \"  top: \\\"conv2\\\"\\n\",\n      \"  convolution_param {\\n\",\n      \"    num_output: 256\\n\",\n      \"    pad: 2\\n\",\n      \"    kernel_size: 5\\n\",\n      \"    group: 2\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"relu2\\\"\\n\",\n      \"  type: \\\"ReLU\\\"\\n\",\n      \"  bottom: \\\"conv2\\\"\\n\",\n      \"  top: \\\"conv2\\\"\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"pool2\\\"\\n\",\n      \"  type: \\\"Pooling\\\"\\n\",\n      \"  bottom: \\\"conv2\\\"\\n\",\n      \"  top: \\\"pool2\\\"\\n\",\n      \"  pooling_param {\\n\",\n      \"    pool: MAX\\n\",\n      \"    kernel_size: 3\\n\",\n      \"    stride: 2\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"norm2\\\"\\n\",\n      \"  type: \\\"LRN\\\"\\n\",\n      \"  bottom: \\\"pool2\\\"\\n\",\n      \"  top: \\\"norm2\\\"\\n\",\n      \"  lrn_param {\\n\",\n      \"    local_size: 5\\n\",\n      \"    alpha: 0.0001\\n\",\n      \"    beta: 0.75\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"conv3\\\"\\n\",\n      \"  type: \\\"Convolution\\\"\\n\",\n      \"  bottom: \\\"norm2\\\"\\n\",\n      \"  top: \\\"conv3\\\"\\n\",\n      \"  convolution_param {\\n\",\n      \"    num_output: 384\\n\",\n      \"    pad: 1\\n\",\n      \"    kernel_size: 3\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"relu3\\\"\\n\",\n      \"  type: \\\"ReLU\\\"\\n\",\n      \"  bottom: \\\"conv3\\\"\\n\",\n      \"  top: \\\"conv3\\\"\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"conv4\\\"\\n\",\n      \"  type: \\\"Convolution\\\"\\n\",\n      \"  bottom: \\\"conv3\\\"\\n\",\n      \"  top: \\\"conv4\\\"\\n\",\n      \"  convolution_param {\\n\",\n      \"    num_output: 384\\n\",\n      \"    pad: 1\\n\",\n      \"    kernel_size: 3\\n\",\n      \"    group: 2\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"relu4\\\"\\n\",\n      \"  type: \\\"ReLU\\\"\\n\",\n      \"  bottom: \\\"conv4\\\"\\n\",\n      \"  top: \\\"conv4\\\"\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"conv5\\\"\\n\",\n      \"  type: \\\"Convolution\\\"\\n\",\n      \"  bottom: \\\"conv4\\\"\\n\",\n      \"  top: \\\"conv5\\\"\\n\",\n      \"  convolution_param {\\n\",\n      \"    num_output: 256\\n\",\n      \"    pad: 1\\n\",\n      \"    kernel_size: 3\\n\",\n      \"    group: 2\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"relu5\\\"\\n\",\n      \"  type: \\\"ReLU\\\"\\n\",\n      \"  bottom: \\\"conv5\\\"\\n\",\n      \"  top: \\\"conv5\\\"\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"pool5\\\"\\n\",\n      \"  type: \\\"Pooling\\\"\\n\",\n      \"  bottom: \\\"conv5\\\"\\n\",\n      \"  top: \\\"pool5\\\"\\n\",\n      \"  pooling_param {\\n\",\n      \"    pool: MAX\\n\",\n      \"    kernel_size: 3\\n\",\n      \"    stride: 2\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"fc6\\\"\\n\",\n      \"  type: \\\"InnerProduct\\\"\\n\",\n      \"  bottom: \\\"pool5\\\"\\n\",\n      \"  top: \\\"fc6\\\"\\n\",\n      \"  inner_product_param {\\n\",\n      \"    num_output: 4096\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"relu6\\\"\\n\",\n      \"  type: \\\"ReLU\\\"\\n\",\n      \"  bottom: \\\"fc6\\\"\\n\",\n      \"  top: \\\"fc6\\\"\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"drop6\\\"\\n\",\n      \"  type: \\\"Dropout\\\"\\n\",\n      \"  bottom: \\\"fc6\\\"\\n\",\n      \"  top: \\\"fc6\\\"\\n\",\n      \"  dropout_param {\\n\",\n      \"    dropout_ratio: 0.5\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"fc7\\\"\\n\",\n      \"  type: \\\"InnerProduct\\\"\\n\",\n      \"  bottom: \\\"fc6\\\"\\n\",\n      \"  top: \\\"fc7\\\"\\n\",\n      \"  inner_product_param {\\n\",\n      \"    num_output: 4096\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"relu7\\\"\\n\",\n      \"  type: \\\"ReLU\\\"\\n\",\n      \"  bottom: \\\"fc7\\\"\\n\",\n      \"  top: \\\"fc7\\\"\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"drop7\\\"\\n\",\n      \"  type: \\\"Dropout\\\"\\n\",\n      \"  bottom: \\\"fc7\\\"\\n\",\n      \"  top: \\\"fc7\\\"\\n\",\n      \"  dropout_param {\\n\",\n      \"    dropout_ratio: 0.5\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"layer {\\n\",\n      \"  name: \\\"fc-rcnn\\\"\\n\",\n      \"  type: \\\"InnerProduct\\\"\\n\",\n      \"  bottom: \\\"fc7\\\"\\n\",\n      \"  top: \\\"fc-rcnn\\\"\\n\",\n      \"  inner_product_param {\\n\",\n      \"    num_output: 200\\n\",\n      \"  }\\n\",\n      \"}\\n\",\n      \"I0218 20:43:25.385720 2099749632 net.cpp:336] Input 0 -> data\\n\",\n      \"I0218 20:43:25.385769 2099749632 layer_factory.hpp:74] Creating layer conv1\\n\",\n      \"I0218 20:43:25.385783 2099749632 net.cpp:76] Creating Layer conv1\\n\",\n      \"I0218 20:43:25.385790 2099749632 net.cpp:372] conv1 <- data\\n\",\n      \"I0218 20:43:25.385802 2099749632 net.cpp:334] conv1 -> conv1\\n\",\n      \"I0218 20:43:25.385815 2099749632 net.cpp:105] Setting up conv1\\n\",\n      \"I0218 20:43:25.386574 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\\n\",\n      \"I0218 20:43:25.386610 2099749632 layer_factory.hpp:74] Creating layer relu1\\n\",\n      \"I0218 20:43:25.386625 2099749632 net.cpp:76] Creating Layer relu1\\n\",\n      \"I0218 20:43:25.386631 2099749632 net.cpp:372] relu1 <- conv1\\n\",\n      \"I0218 20:43:25.386641 2099749632 net.cpp:323] relu1 -> conv1 (in-place)\\n\",\n      \"I0218 20:43:25.386649 2099749632 net.cpp:105] Setting up relu1\\n\",\n      \"I0218 20:43:25.386656 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\\n\",\n      \"I0218 20:43:25.386663 2099749632 layer_factory.hpp:74] Creating layer pool1\\n\",\n      \"I0218 20:43:25.386675 2099749632 net.cpp:76] Creating Layer pool1\\n\",\n      \"I0218 20:43:25.386682 2099749632 net.cpp:372] pool1 <- conv1\\n\",\n      \"I0218 20:43:25.386690 2099749632 net.cpp:334] pool1 -> pool1\\n\",\n      \"I0218 20:43:25.386699 2099749632 net.cpp:105] Setting up pool1\\n\",\n      \"I0218 20:43:25.386716 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\\n\",\n      \"I0218 20:43:25.386725 2099749632 layer_factory.hpp:74] Creating layer norm1\\n\",\n      \"I0218 20:43:25.386736 2099749632 net.cpp:76] Creating Layer norm1\\n\",\n      \"I0218 20:43:25.386744 2099749632 net.cpp:372] norm1 <- pool1\\n\",\n      \"I0218 20:43:25.386803 2099749632 net.cpp:334] norm1 -> norm1\\n\",\n      \"I0218 20:43:25.386819 2099749632 net.cpp:105] Setting up norm1\\n\",\n      \"I0218 20:43:25.386832 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\\n\",\n      \"I0218 20:43:25.386842 2099749632 layer_factory.hpp:74] Creating layer conv2\\n\",\n      \"I0218 20:43:25.386852 2099749632 net.cpp:76] Creating Layer conv2\\n\",\n      \"I0218 20:43:25.386865 2099749632 net.cpp:372] conv2 <- norm1\\n\",\n      \"I0218 20:43:25.386878 2099749632 net.cpp:334] conv2 -> conv2\\n\",\n      \"I0218 20:43:25.386899 2099749632 net.cpp:105] Setting up conv2\\n\",\n      \"I0218 20:43:25.387024 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\\n\",\n      \"I0218 20:43:25.387042 2099749632 layer_factory.hpp:74] Creating layer relu2\\n\",\n      \"I0218 20:43:25.387050 2099749632 net.cpp:76] Creating Layer relu2\\n\",\n      \"I0218 20:43:25.387058 2099749632 net.cpp:372] relu2 <- conv2\\n\",\n      \"I0218 20:43:25.387066 2099749632 net.cpp:323] relu2 -> conv2 (in-place)\\n\",\n      \"I0218 20:43:25.387075 2099749632 net.cpp:105] Setting up relu2\\n\",\n      \"I0218 20:43:25.387081 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\\n\",\n      \"I0218 20:43:25.387089 2099749632 layer_factory.hpp:74] Creating layer pool2\\n\",\n      \"I0218 20:43:25.387097 2099749632 net.cpp:76] Creating Layer pool2\\n\",\n      \"I0218 20:43:25.387104 2099749632 net.cpp:372] pool2 <- conv2\\n\",\n      \"I0218 20:43:25.387112 2099749632 net.cpp:334] pool2 -> pool2\\n\",\n      \"I0218 20:43:25.387121 2099749632 net.cpp:105] Setting up pool2\\n\",\n      \"I0218 20:43:25.387130 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\\n\",\n      \"I0218 20:43:25.387137 2099749632 layer_factory.hpp:74] Creating layer norm2\\n\",\n      \"I0218 20:43:25.387145 2099749632 net.cpp:76] Creating Layer norm2\\n\",\n      \"I0218 20:43:25.387152 2099749632 net.cpp:372] norm2 <- pool2\\n\",\n      \"I0218 20:43:25.387161 2099749632 net.cpp:334] norm2 -> norm2\\n\",\n      \"I0218 20:43:25.387168 2099749632 net.cpp:105] Setting up norm2\\n\",\n      \"I0218 20:43:25.387176 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\\n\",\n      \"I0218 20:43:25.387228 2099749632 layer_factory.hpp:74] Creating layer conv3\\n\",\n      \"I0218 20:43:25.387249 2099749632 net.cpp:76] Creating Layer conv3\\n\",\n      \"I0218 20:43:25.387258 2099749632 net.cpp:372] conv3 <- norm2\\n\",\n      \"I0218 20:43:25.387266 2099749632 net.cpp:334] conv3 -> conv3\\n\",\n      \"I0218 20:43:25.387276 2099749632 net.cpp:105] Setting up conv3\\n\",\n      \"I0218 20:43:25.389375 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\\n\",\n      \"I0218 20:43:25.389408 2099749632 layer_factory.hpp:74] Creating layer relu3\\n\",\n      \"I0218 20:43:25.389421 2099749632 net.cpp:76] Creating Layer relu3\\n\",\n      \"I0218 20:43:25.389430 2099749632 net.cpp:372] relu3 <- conv3\\n\",\n      \"I0218 20:43:25.389438 2099749632 net.cpp:323] relu3 -> conv3 (in-place)\\n\",\n      \"I0218 20:43:25.389447 2099749632 net.cpp:105] Setting up relu3\\n\",\n      \"I0218 20:43:25.389456 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\\n\",\n      \"I0218 20:43:25.389462 2099749632 layer_factory.hpp:74] Creating layer conv4\\n\",\n      \"I0218 20:43:25.389472 2099749632 net.cpp:76] Creating Layer conv4\\n\",\n      \"I0218 20:43:25.389478 2099749632 net.cpp:372] conv4 <- conv3\\n\",\n      \"I0218 20:43:25.389487 2099749632 net.cpp:334] conv4 -> conv4\\n\",\n      \"I0218 20:43:25.389497 2099749632 net.cpp:105] Setting up conv4\\n\",\n      \"I0218 20:43:25.391810 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\\n\",\n      \"I0218 20:43:25.391856 2099749632 layer_factory.hpp:74] Creating layer relu4\\n\",\n      \"I0218 20:43:25.391871 2099749632 net.cpp:76] Creating Layer relu4\\n\",\n      \"I0218 20:43:25.391880 2099749632 net.cpp:372] relu4 <- conv4\\n\",\n      \"I0218 20:43:25.391888 2099749632 net.cpp:323] relu4 -> conv4 (in-place)\\n\",\n      \"I0218 20:43:25.391898 2099749632 net.cpp:105] Setting up relu4\\n\",\n      \"I0218 20:43:25.391906 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\\n\",\n      \"I0218 20:43:25.391913 2099749632 layer_factory.hpp:74] Creating layer conv5\\n\",\n      \"I0218 20:43:25.391923 2099749632 net.cpp:76] Creating Layer conv5\\n\",\n      \"I0218 20:43:25.391929 2099749632 net.cpp:372] conv5 <- conv4\\n\",\n      \"I0218 20:43:25.391937 2099749632 net.cpp:334] conv5 -> conv5\\n\",\n      \"I0218 20:43:25.391947 2099749632 net.cpp:105] Setting up conv5\\n\",\n      \"I0218 20:43:25.393072 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\\n\",\n      \"I0218 20:43:25.393108 2099749632 layer_factory.hpp:74] Creating layer relu5\\n\",\n      \"I0218 20:43:25.393122 2099749632 net.cpp:76] Creating Layer relu5\\n\",\n      \"I0218 20:43:25.393129 2099749632 net.cpp:372] relu5 <- conv5\\n\",\n      \"I0218 20:43:25.393138 2099749632 net.cpp:323] relu5 -> conv5 (in-place)\\n\",\n      \"I0218 20:43:25.393148 2099749632 net.cpp:105] Setting up relu5\\n\",\n      \"I0218 20:43:25.393157 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\\n\",\n      \"I0218 20:43:25.393167 2099749632 layer_factory.hpp:74] Creating layer pool5\\n\",\n      \"I0218 20:43:25.393175 2099749632 net.cpp:76] Creating Layer pool5\\n\",\n      \"I0218 20:43:25.393182 2099749632 net.cpp:372] pool5 <- conv5\\n\",\n      \"I0218 20:43:25.393190 2099749632 net.cpp:334] pool5 -> pool5\\n\",\n      \"I0218 20:43:25.393199 2099749632 net.cpp:105] Setting up pool5\\n\",\n      \"I0218 20:43:25.393209 2099749632 net.cpp:112] Top shape: 10 256 6 6 (92160)\\n\",\n      \"I0218 20:43:25.393218 2099749632 layer_factory.hpp:74] Creating layer fc6\\n\",\n      \"I0218 20:43:25.393226 2099749632 net.cpp:76] Creating Layer fc6\\n\",\n      \"I0218 20:43:25.393232 2099749632 net.cpp:372] fc6 <- pool5\\n\",\n      \"I0218 20:43:25.393240 2099749632 net.cpp:334] fc6 -> fc6\\n\",\n      \"I0218 20:43:25.393249 2099749632 net.cpp:105] Setting up fc6\\n\",\n      \"I0218 20:43:25.516396 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\\n\",\n      \"I0218 20:43:25.516445 2099749632 layer_factory.hpp:74] Creating layer relu6\\n\",\n      \"I0218 20:43:25.516463 2099749632 net.cpp:76] Creating Layer relu6\\n\",\n      \"I0218 20:43:25.516470 2099749632 net.cpp:372] relu6 <- fc6\\n\",\n      \"I0218 20:43:25.516480 2099749632 net.cpp:323] relu6 -> fc6 (in-place)\\n\",\n      \"I0218 20:43:25.516490 2099749632 net.cpp:105] Setting up relu6\\n\",\n      \"I0218 20:43:25.516497 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\\n\",\n      \"I0218 20:43:25.516505 2099749632 layer_factory.hpp:74] Creating layer drop6\\n\",\n      \"I0218 20:43:25.516515 2099749632 net.cpp:76] Creating Layer drop6\\n\",\n      \"I0218 20:43:25.516521 2099749632 net.cpp:372] drop6 <- fc6\\n\",\n      \"I0218 20:43:25.516530 2099749632 net.cpp:323] drop6 -> fc6 (in-place)\\n\",\n      \"I0218 20:43:25.516538 2099749632 net.cpp:105] Setting up drop6\\n\",\n      \"I0218 20:43:25.516557 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\\n\",\n      \"I0218 20:43:25.516566 2099749632 layer_factory.hpp:74] Creating layer fc7\\n\",\n      \"I0218 20:43:25.516576 2099749632 net.cpp:76] Creating Layer fc7\\n\",\n      \"I0218 20:43:25.516582 2099749632 net.cpp:372] fc7 <- fc6\\n\",\n      \"I0218 20:43:25.516589 2099749632 net.cpp:334] fc7 -> fc7\\n\",\n      \"I0218 20:43:25.516599 2099749632 net.cpp:105] Setting up fc7\\n\",\n      \"I0218 20:43:25.604786 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\\n\",\n      \"I0218 20:43:25.604838 2099749632 layer_factory.hpp:74] Creating layer relu7\\n\",\n      \"I0218 20:43:25.604852 2099749632 net.cpp:76] Creating Layer relu7\\n\",\n      \"I0218 20:43:25.604859 2099749632 net.cpp:372] relu7 <- fc7\\n\",\n      \"I0218 20:43:25.604868 2099749632 net.cpp:323] relu7 -> fc7 (in-place)\\n\",\n      \"I0218 20:43:25.604878 2099749632 net.cpp:105] Setting up relu7\\n\",\n      \"I0218 20:43:25.604885 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\\n\",\n      \"I0218 20:43:25.604893 2099749632 layer_factory.hpp:74] Creating layer drop7\\n\",\n      \"I0218 20:43:25.604902 2099749632 net.cpp:76] Creating Layer drop7\\n\",\n      \"I0218 20:43:25.604908 2099749632 net.cpp:372] drop7 <- fc7\\n\",\n      \"I0218 20:43:25.604917 2099749632 net.cpp:323] drop7 -> fc7 (in-place)\\n\",\n      \"I0218 20:43:25.604924 2099749632 net.cpp:105] Setting up drop7\\n\",\n      \"I0218 20:43:25.604933 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\\n\",\n      \"I0218 20:43:25.604939 2099749632 layer_factory.hpp:74] Creating layer fc-rcnn\\n\",\n      \"I0218 20:43:25.604948 2099749632 net.cpp:76] Creating Layer fc-rcnn\\n\",\n      \"I0218 20:43:25.604954 2099749632 net.cpp:372] fc-rcnn <- fc7\\n\",\n      \"I0218 20:43:25.604962 2099749632 net.cpp:334] fc-rcnn -> fc-rcnn\\n\",\n      \"I0218 20:43:25.604971 2099749632 net.cpp:105] Setting up fc-rcnn\\n\",\n      \"I0218 20:43:25.606878 2099749632 net.cpp:112] Top shape: 10 200 1 1 (2000)\\n\",\n      \"I0218 20:43:25.606904 2099749632 net.cpp:165] fc-rcnn does not need backward computation.\\n\",\n      \"I0218 20:43:25.606909 2099749632 net.cpp:165] drop7 does not need backward computation.\\n\",\n      \"I0218 20:43:25.606916 2099749632 net.cpp:165] relu7 does not need backward computation.\\n\",\n      \"I0218 20:43:25.606922 2099749632 net.cpp:165] fc7 does not need backward computation.\\n\",\n      \"I0218 20:43:25.606928 2099749632 net.cpp:165] drop6 does not need backward computation.\\n\",\n      \"I0218 20:43:25.606935 2099749632 net.cpp:165] relu6 does not need backward computation.\\n\",\n      \"I0218 20:43:25.606940 2099749632 net.cpp:165] fc6 does not need backward computation.\\n\",\n      \"I0218 20:43:25.606946 2099749632 net.cpp:165] pool5 does not need backward computation.\\n\",\n      \"I0218 20:43:25.606952 2099749632 net.cpp:165] relu5 does not need backward computation.\\n\",\n      \"I0218 20:43:25.606958 2099749632 net.cpp:165] conv5 does not need backward computation.\\n\",\n      \"I0218 20:43:25.606964 2099749632 net.cpp:165] relu4 does not need backward computation.\\n\",\n      \"I0218 20:43:25.606971 2099749632 net.cpp:165] conv4 does not need backward computation.\\n\",\n      \"I0218 20:43:25.606976 2099749632 net.cpp:165] relu3 does not need backward computation.\\n\",\n      \"I0218 20:43:25.606982 2099749632 net.cpp:165] conv3 does not need backward computation.\\n\",\n      \"I0218 20:43:25.606988 2099749632 net.cpp:165] norm2 does not need backward computation.\\n\",\n      \"I0218 20:43:25.606995 2099749632 net.cpp:165] pool2 does not need backward computation.\\n\",\n      \"I0218 20:43:25.607002 2099749632 net.cpp:165] relu2 does not need backward computation.\\n\",\n      \"I0218 20:43:25.607007 2099749632 net.cpp:165] conv2 does not need backward computation.\\n\",\n      \"I0218 20:43:25.607013 2099749632 net.cpp:165] norm1 does not need backward computation.\\n\",\n      \"I0218 20:43:25.607199 2099749632 net.cpp:165] pool1 does not need backward computation.\\n\",\n      \"I0218 20:43:25.607213 2099749632 net.cpp:165] relu1 does not need backward computation.\\n\",\n      \"I0218 20:43:25.607219 2099749632 net.cpp:165] conv1 does not need backward computation.\\n\",\n      \"I0218 20:43:25.607225 2099749632 net.cpp:201] This network produces output fc-rcnn\\n\",\n      \"I0218 20:43:25.607239 2099749632 net.cpp:446] Collecting Learning Rate and Weight Decay.\\n\",\n      \"I0218 20:43:25.607255 2099749632 net.cpp:213] Network initialization done.\\n\",\n      \"I0218 20:43:25.607262 2099749632 net.cpp:214] Memory required for data: 62425920\\n\",\n      \"E0218 20:43:26.388214 2099749632 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: ../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel\\n\",\n      \"I0218 20:43:27.089423 2099749632 upgrade_proto.cpp:626] Successfully upgraded file specified using deprecated V1LayerParameter\\n\",\n      \"GPU mode\\n\",\n      \"Loading input...\\n\",\n      \"selective_search_rcnn({'/Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg'}, '/var/folders/bk/dtkn5qjd11bd17b2j36zplyw0000gp/T/tmpakaRLL.mat')\\n\",\n      \"Processed 1570 windows in 102.895 s.\\n\",\n      \"/Users/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2453: PerformanceWarning: \\n\",\n      \"your performance may suffer as PyTables will pickle object types that it cannot\\n\",\n      \"map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\\n\",\n      \"\\n\",\n      \"  warnings.warn(ws, PerformanceWarning)\\n\",\n      \"Saved to _temp/det_output.h5 in 0.298 s.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!mkdir -p _temp\\n\",\n    \"!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\\n\",\n    \"!../python/detect.py --crop_mode=selective_search --pretrained_model=../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=../models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This run was in GPU mode. For CPU mode detection, call `detect.py` without the `--gpu` argument.\\n\",\n    \"\\n\",\n    \"Running this outputs a DataFrame with the filenames, selected windows, and their detection scores to an HDF5 file.\\n\",\n    \"(We only ran on one image, so the filenames will all be the same.)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(1570, 5)\\n\",\n      \"prediction    [-2.62247, -2.84579, -2.85122, -3.20838, -1.94...\\n\",\n      \"ymin                                                     79.846\\n\",\n      \"xmin                                                       9.62\\n\",\n      \"ymax                                                     246.31\\n\",\n      \"xmax                                                    339.624\\n\",\n      \"Name: /Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg, dtype: object\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"df = pd.read_hdf('_temp/det_output.h5', 'df')\\n\",\n    \"print(df.shape)\\n\",\n    \"print(df.iloc[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"1570 regions were proposed with the R-CNN configuration of selective search. The number of proposals will vary from image to image based on its contents and size -- selective search isn't scale invariant.\\n\",\n    \"\\n\",\n    \"In general, `detect.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\\n\",\n    \"Simply list an image per line in the `images_file`, and it will process all of them.\\n\",\n    \"\\n\",\n    \"Although this guide gives an example of R-CNN ImageNet detection, `detect.py` is clever enough to adapt to different Caffe models’ input dimensions, batch size, and output categories. You can switch the model definition and pretrained model as desired. Refer to `python detect.py --help` for the parameters to describe your data set. There's no need for hardcoding.\\n\",\n    \"\\n\",\n    \"Anyway, let's now load the ILSVRC13 detection class names and make a DataFrame of the predictions. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"name\\n\",\n      \"accordion      -2.622471\\n\",\n      \"airplane       -2.845788\\n\",\n      \"ant            -2.851219\\n\",\n      \"antelope       -3.208377\\n\",\n      \"apple          -1.949950\\n\",\n      \"armadillo      -2.472935\\n\",\n      \"artichoke      -2.201684\\n\",\n      \"axe            -2.327404\\n\",\n      \"baby bed       -2.737925\\n\",\n      \"backpack       -2.176763\\n\",\n      \"bagel          -2.681061\\n\",\n      \"balance beam   -2.722538\\n\",\n      \"banana         -2.390628\\n\",\n      \"band aid       -1.598909\\n\",\n      \"banjo          -2.298197\\n\",\n      \"...\\n\",\n      \"trombone        -2.582361\\n\",\n      \"trumpet         -2.352853\\n\",\n      \"turtle          -2.360859\\n\",\n      \"tv or monitor   -2.761043\\n\",\n      \"unicycle        -2.218467\\n\",\n      \"vacuum          -1.907717\\n\",\n      \"violin          -2.757079\\n\",\n      \"volleyball      -2.723689\\n\",\n      \"waffle iron     -2.418540\\n\",\n      \"washer          -2.408994\\n\",\n      \"water bottle    -2.174899\\n\",\n      \"watercraft      -2.837425\\n\",\n      \"whale           -3.120338\\n\",\n      \"wine bottle     -2.772960\\n\",\n      \"zebra           -2.742913\\n\",\n      \"Name: 0, Length: 200, dtype: float32\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with open('../data/ilsvrc12/det_synset_words.txt') as f:\\n\",\n    \"    labels_df = pd.DataFrame([\\n\",\n    \"        {\\n\",\n    \"            'synset_id': l.strip().split(' ')[0],\\n\",\n    \"            'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]\\n\",\n    \"        }\\n\",\n    \"        for l in f.readlines()\\n\",\n    \"    ])\\n\",\n    \"labels_df.sort('synset_id')\\n\",\n    \"predictions_df = pd.DataFrame(np.vstack(df.prediction.values), columns=labels_df['name'])\\n\",\n    \"print(predictions_df.iloc[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's look at the activations.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x114f15f90>\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x114254b50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": [\n       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\"WCOzHjbzwxqZ9bCZH9bIrIfN/LBGZj1s5oc1Muv/AuHZAPr9VeA9AAAAAElFTkSuQmCC\\n\"\n      ],\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x114254ad0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.gray()\\n\",\n    \"plt.matshow(predictions_df.values)\\n\",\n    \"plt.xlabel('Classes')\\n\",\n    \"plt.ylabel('Windows')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's take max across all windows and plot the top classes.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"name\\n\",\n      \"person          1.835771\\n\",\n      \"bicycle         0.866110\\n\",\n      \"unicycle        0.057080\\n\",\n      \"motorcycle     -0.006122\\n\",\n      \"banjo          -0.028209\\n\",\n      \"turtle         -0.189831\\n\",\n      \"electric fan   -0.206788\\n\",\n      \"cart           -0.214235\\n\",\n      \"lizard         -0.393519\\n\",\n      \"helmet         -0.477942\\n\",\n      \"dtype: float32\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"max_s = predictions_df.max(0)\\n\",\n    \"max_s.sort(ascending=False)\\n\",\n    \"print(max_s[:10])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The top detections are in fact a person and bicycle.\\n\",\n    \"Picking good localizations is a work in progress; we pick the top-scoring person and bicycle detections.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Top detection:\\n\",\n      \"name\\n\",\n      \"person             1.835771\\n\",\n      \"swimming trunks   -1.150371\\n\",\n      \"rubber eraser     -1.231106\\n\",\n      \"turtle            -1.266037\\n\",\n      \"plastic bag       -1.303265\\n\",\n      \"dtype: float32\\n\",\n      \"\\n\",\n      \"Second-best detection:\\n\",\n      \"name\\n\",\n      \"bicycle     0.866110\\n\",\n      \"unicycle   -0.359139\\n\",\n      \"scorpion   -0.811621\\n\",\n      \"lobster    -0.982891\\n\",\n      \"lamp       -1.096808\\n\",\n      \"dtype: float32\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.patches.Rectangle at 0x118576a90>\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": [\n       \"iVBORw0KGgoAAAANSUhEUgAAAXAAAAEACAYAAACqOy3+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\n\",\n       \"AAALEgAACxIB0t1+/AAAIABJREFUeJzsvdmPZVl23vfb0znnTjFHZGZlZmVWVdaQVd1FqmVSomjD\\n\",\n       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\"3srjArwa+FQck4eAn43P37Jjcs34fBcDCuWWH5N1K/3a1ra2tb1Abd2Juba1rW1tL1BbO/C1rW1t\\n\",\n       \"a3uB2tqBr21ta1vbC9TWDnxta1vb2l6gtnbga1vb2tb2ArW1A1/b2ta2theorR342ta2trW9QG3t\\n\",\n       \"wNe2trWt7QVq/w/Uvjt8hhUJzgAAAABJRU5ErkJggg==\\n\"\n      ],\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x116bcb210>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Find, print, and display the top detections: person and bicycle.\\n\",\n    \"i = predictions_df['person'].argmax()\\n\",\n    \"j = predictions_df['bicycle'].argmax()\\n\",\n    \"\\n\",\n    \"# Show top predictions for top detection.\\n\",\n    \"f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])\\n\",\n    \"print('Top detection:')\\n\",\n    \"print(f.order(ascending=False)[:5])\\n\",\n    \"print('')\\n\",\n    \"\\n\",\n    \"# Show top predictions for second-best detection.\\n\",\n    \"f = pd.Series(df['prediction'].iloc[j], index=labels_df['name'])\\n\",\n    \"print('Second-best detection:')\\n\",\n    \"print(f.order(ascending=False)[:5])\\n\",\n    \"\\n\",\n    \"# Show top detection in red, second-best top detection in blue.\\n\",\n    \"im = plt.imread('images/fish-bike.jpg')\\n\",\n    \"plt.imshow(im)\\n\",\n    \"currentAxis = plt.gca()\\n\",\n    \"\\n\",\n    \"det = df.iloc[i]\\n\",\n    \"coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\\n\",\n    \"currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\\n\",\n    \"\\n\",\n    \"det = df.iloc[j]\\n\",\n    \"coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\\n\",\n    \"currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"That's cool. Let's take all 'bicycle' detections and NMS them to get rid of overlapping windows.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def nms_detections(dets, overlap=0.3):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Non-maximum suppression: Greedily select high-scoring detections and\\n\",\n    \"    skip detections that are significantly covered by a previously\\n\",\n    \"    selected detection.\\n\",\n    \"\\n\",\n    \"    This version is translated from Matlab code by Tomasz Malisiewicz,\\n\",\n    \"    who sped up Pedro Felzenszwalb's code.\\n\",\n    \"\\n\",\n    \"    Parameters\\n\",\n    \"    ----------\\n\",\n    \"    dets: ndarray\\n\",\n    \"        each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']\\n\",\n    \"    overlap: float\\n\",\n    \"        minimum overlap ratio (0.3 default)\\n\",\n    \"\\n\",\n    \"    Output\\n\",\n    \"    ------\\n\",\n    \"    dets: ndarray\\n\",\n    \"        remaining after suppression.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    x1 = dets[:, 0]\\n\",\n    \"    y1 = dets[:, 1]\\n\",\n    \"    x2 = dets[:, 2]\\n\",\n    \"    y2 = dets[:, 3]\\n\",\n    \"    ind = np.argsort(dets[:, 4])\\n\",\n    \"\\n\",\n    \"    w = x2 - x1\\n\",\n    \"    h = y2 - y1\\n\",\n    \"    area = (w * h).astype(float)\\n\",\n    \"\\n\",\n    \"    pick = []\\n\",\n    \"    while len(ind) > 0:\\n\",\n    \"        i = ind[-1]\\n\",\n    \"        pick.append(i)\\n\",\n    \"        ind = ind[:-1]\\n\",\n    \"\\n\",\n    \"        xx1 = np.maximum(x1[i], x1[ind])\\n\",\n    \"        yy1 = np.maximum(y1[i], y1[ind])\\n\",\n    \"        xx2 = np.minimum(x2[i], x2[ind])\\n\",\n    \"        yy2 = np.minimum(y2[i], y2[ind])\\n\",\n    \"\\n\",\n    \"        w = np.maximum(0., xx2 - xx1)\\n\",\n    \"        h = np.maximum(0., yy2 - yy1)\\n\",\n    \"\\n\",\n    \"        wh = w * h\\n\",\n    \"        o = wh / (area[i] + area[ind] - wh)\\n\",\n    \"\\n\",\n    \"        ind = ind[np.nonzero(o <= overlap)[0]]\\n\",\n    \"\\n\",\n    \"    return dets[pick, :]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"scores = predictions_df['bicycle']\\n\",\n    \"windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values\\n\",\n    \"dets = np.hstack((windows, scores[:, np.newaxis]))\\n\",\n    \"nms_dets = nms_detections(dets)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Show top 3 NMS'd detections for 'bicycle' in the image and note the gap between the top scoring box (red) and the remaining boxes.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"scores: [ 0.86610985 -0.70051557 -1.34796357]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": [\n       \"iVBORw0KGgoAAAANSUhEUgAAAXAAAAEACAYAAACqOy3+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\n\",\n       \"AAALEgAACxIB0t1+/AAAIABJREFUeJzsvUmMZll23/e7wxu+KeaInKuys+aq7ibdraZE0oIgU4Qt\\n\",\n       \"mrAsGISgrTfaWAa88tYbwzagnQEbhGUv5I1XNiBKIE3SNCi2SDfZbLC72TVmVWVV5RQZ8ze+9+7k\\n\",\n       \"xb3vfV9ERTYJg8Vim3G6oyLjG95w371n+J9z/leEELiSK7mSK7mSnzyRX/YFXMmVXMmVXMn/N7lS\\n\",\n       \"4FdyJVdyJT+hcqXAr+RKruRKfkLlSoFfyZVcyZX8hMqVAr+SK7mSK/kJlSsFfiVXciVX8hMqX4gC\\n\",\n       \"F0L8B0KId4UQHwgh/ssv4hxXciVXciV/3UX8RdeBCyEU8B7w94BHwB8B/ziE8M5f6Imu5Equ5Er+\\n\",\n       \"mssX4YH/DHA/hPAghGCA/w34B1/Aea7kSq7kSv5ayxehwG8Bn638/TC9diVXciVXciV/gfJFKPCr\\n\",\n       \"3vwruZIruZK/BNFfwDEfAXdW/r5D9MI7EUJcKfkruZIruZI/p4QQxGWvfxEK/LvAK0KIu8Bj4B8B\\n\",\n       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\"PhxCuCuE8P3AzwKfCSH8ZQ7wmvS2D53kn0LQBk8Av7DfXdxb+Lk/CjwH1Egf4K8Am8BvAb8H/CZw\\n\",\n       \"ePT8D8c1+ibw3v0+/u/SmrwDqWk+hDipB4H3HeR1Ad4APBDX5BHg5+L9B3ZNrlufH2VAoRz4NVmN\\n\",\n       \"0q9sZStb2W1qq0nMla1sZSu7TW3lwFe2spWt7Da1lQNf2cpWtrLb1FYOfGUrW9nKblNbOfCVrWxl\\n\",\n       \"K7tNbeXAV7ayla3sNrWVA1/Zyla2stvUVg58ZStb2cpuU/t/6S2bnP6vZqYAAAAASUVORK5CYII=\\n\"\n      ],\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x114f15d10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.imshow(im)\\n\",\n    \"currentAxis = plt.gca()\\n\",\n    \"colors = ['r', 'b', 'y']\\n\",\n    \"for c, det in zip(colors, nms_dets[:3]):\\n\",\n    \"    currentAxis.add_patch(\\n\",\n    \"        plt.Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\\n\",\n    \"        fill=False, edgecolor=c, linewidth=5)\\n\",\n    \"    )\\n\",\n    \"print 'scores:', nms_dets[:3, 4]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This was an easy instance for bicycle as it was in the class's training set. However, the person result is a true detection since this was not in the set for that class.\\n\",\n    \"\\n\",\n    \"You should try out detection on an image of your own next!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"(Remove the temp directory to clean up, and we're done.)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!rm -rf _temp\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"description\": \"Run a pretrained model as a detector in Python.\",\n  \"example_name\": \"R-CNN detection\",\n  \"include_in_docs\": true,\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.9\"\n  },\n  \"priority\": 6\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Caffe/feature_extraction/imagenet_val.prototxt",
    "content": "name: \"CaffeNet\"\nlayer {\n  name: \"data\"\n  type: \"ImageData\"\n  top: \"data\"\n  top: \"label\"\n  transform_param {\n    mirror: false\n    crop_size: 227\n    mean_file: \"data/ilsvrc12/imagenet_mean.binaryproto\"\n  }\n  image_data_param {\n    source: \"examples/_temp/file_list.txt\"\n    batch_size: 50\n    new_height: 256\n    new_width: 256\n  }\n}\nlayer {\n  name: \"conv1\"\n  type: \"Convolution\"\n  bottom: \"data\"\n  top: \"conv1\"\n  convolution_param {\n    num_output: 96\n    kernel_size: 11\n    stride: 4\n  }\n}\nlayer {\n  name: \"relu1\"\n  type: \"ReLU\"\n  bottom: \"conv1\"\n  top: \"conv1\"\n}\nlayer {\n  name: \"pool1\"\n  type: \"Pooling\"\n  bottom: \"conv1\"\n  top: \"pool1\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"norm1\"\n  type: \"LRN\"\n  bottom: \"pool1\"\n  top: \"norm1\"\n  lrn_param {\n    local_size: 5\n    alpha: 0.0001\n    beta: 0.75\n  }\n}\nlayer {\n  name: \"conv2\"\n  type: \"Convolution\"\n  bottom: \"norm1\"\n  top: \"conv2\"\n  convolution_param {\n    num_output: 256\n    pad: 2\n    kernel_size: 5\n    group: 2\n  }\n}\nlayer {\n  name: \"relu2\"\n  type: \"ReLU\"\n  bottom: \"conv2\"\n  top: \"conv2\"\n}\nlayer {\n  name: \"pool2\"\n  type: \"Pooling\"\n  bottom: \"conv2\"\n  top: \"pool2\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"norm2\"\n  type: \"LRN\"\n  bottom: \"pool2\"\n  top: \"norm2\"\n  lrn_param {\n    local_size: 5\n    alpha: 0.0001\n    beta: 0.75\n  }\n}\nlayer {\n  name: \"conv3\"\n  type: \"Convolution\"\n  bottom: \"norm2\"\n  top: \"conv3\"\n  convolution_param {\n    num_output: 384\n    pad: 1\n    kernel_size: 3\n  }\n}\nlayer {\n  name: \"relu3\"\n  type: \"ReLU\"\n  bottom: \"conv3\"\n  top: \"conv3\"\n}\nlayer {\n  name: \"conv4\"\n  type: \"Convolution\"\n  bottom: \"conv3\"\n  top: \"conv4\"\n  convolution_param {\n    num_output: 384\n    pad: 1\n    kernel_size: 3\n    group: 2\n  }\n}\nlayer {\n  name: \"relu4\"\n  type: \"ReLU\"\n  bottom: \"conv4\"\n  top: \"conv4\"\n}\nlayer {\n  name: \"conv5\"\n  type: \"Convolution\"\n  bottom: \"conv4\"\n  top: \"conv5\"\n  convolution_param {\n    num_output: 256\n    pad: 1\n    kernel_size: 3\n    group: 2\n  }\n}\nlayer {\n  name: \"relu5\"\n  type: \"ReLU\"\n  bottom: \"conv5\"\n  top: \"conv5\"\n}\nlayer {\n  name: \"pool5\"\n  type: \"Pooling\"\n  bottom: \"conv5\"\n  top: \"pool5\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"fc6\"\n  type: \"InnerProduct\"\n  bottom: \"pool5\"\n  top: \"fc6\"\n  inner_product_param {\n    num_output: 4096\n  }\n}\nlayer {\n  name: \"relu6\"\n  type: \"ReLU\"\n  bottom: \"fc6\"\n  top: \"fc6\"\n}\nlayer {\n  name: \"drop6\"\n  type: \"Dropout\"\n  bottom: \"fc6\"\n  top: \"fc6\"\n  dropout_param {\n    dropout_ratio: 0.5\n  }\n}\nlayer {\n  name: \"fc7\"\n  type: \"InnerProduct\"\n  bottom: \"fc6\"\n  top: \"fc7\"\n  inner_product_param {\n    num_output: 4096\n  }\n}\nlayer {\n  name: \"relu7\"\n  type: \"ReLU\"\n  bottom: \"fc7\"\n  top: \"fc7\"\n}\nlayer {\n  name: \"drop7\"\n  type: \"Dropout\"\n  bottom: \"fc7\"\n  top: \"fc7\"\n  dropout_param {\n    dropout_ratio: 0.5\n  }\n}\nlayer {\n  name: \"fc8\"\n  type: \"InnerProduct\"\n  bottom: \"fc7\"\n  top: \"fc8\"\n  inner_product_param {\n    num_output: 1000\n  }\n}\nlayer {\n  name: \"prob\"\n  type: \"Softmax\"\n  bottom: \"fc8\"\n  top: \"prob\"\n}\nlayer {\n  name: \"accuracy\"\n  type: \"Accuracy\"\n  bottom: \"prob\"\n  bottom: \"label\"\n  top: \"accuracy\"\n}\nlayer {\n  name: \"loss\"\n  type: \"SoftmaxWithLoss\"\n  bottom: \"fc8\"\n  bottom: \"label\"\n  top: \"loss\"\n}\n"
  },
  {
    "path": "Caffe/feature_extraction/readme.md",
    "content": "---\ntitle: Feature extraction with Caffe C++ code.\ndescription: Extract CaffeNet / AlexNet features using the Caffe utility.\ncategory: example\ninclude_in_docs: true\npriority: 10\n---\n\nExtracting Features\n===================\n\nIn this tutorial, we will extract features using a pre-trained model with the included C++ utility.\nNote that we recommend using the Python interface for this task, as for example in the [filter visualization example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb).\n\nFollow instructions for [installing Caffe](../../installation.html) and run `scripts/download_model_binary.py models/bvlc_reference_caffenet` from caffe root directory.\nIf you need detailed information about the tools below, please consult their source code, in which additional documentation is usually provided.\n\nSelect data to run on\n---------------------\n\nWe'll make a temporary folder to store things into.\n\n    mkdir examples/_temp\n\nGenerate a list of the files to process.\nWe're going to use the images that ship with caffe.\n\n    find `pwd`/examples/images -type f -exec echo {} \\; > examples/_temp/temp.txt\n\nThe `ImageDataLayer` we'll use expects labels after each filenames, so let's add a 0 to the end of each line\n\n    sed \"s/$/ 0/\" examples/_temp/temp.txt > examples/_temp/file_list.txt\n\nDefine the Feature Extraction Network Architecture\n--------------------------------------------------\n\nIn practice, subtracting the mean image from a dataset significantly improves classification accuracies.\nDownload the mean image of the ILSVRC dataset.\n\n    ./data/ilsvrc12/get_ilsvrc_aux.sh\n\nWe will use `data/ilsvrc212/imagenet_mean.binaryproto` in the network definition prototxt.\n\nLet's copy and modify the network definition.\nWe'll be using the `ImageDataLayer`, which will load and resize images for us.\n\n    cp examples/feature_extraction/imagenet_val.prototxt examples/_temp\n\nExtract Features\n----------------\n\nNow everything necessary is in place.\n\n    ./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 leveldb\n\nThe name of feature blob that you extract is `fc7`, which represents the highest level feature of the reference model.\nWe can use any other layer, as well, such as `conv5` or `pool3`.\n\nThe last parameter above is the number of data mini-batches.\n\nThe features are stored to LevelDB `examples/_temp/features`, ready for access by some other code.\n\nIf you meet with the error \"Check failed: status.ok() Failed to open leveldb examples/_temp/features\", it is because the directory examples/_temp/features has been created the last time you run the command. Remove it and run again.\n\n    rm -rf examples/_temp/features/\n\nIf you'd like to use the Python wrapper for extracting features, check out the [filter visualization notebook](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb).\n\nClean Up\n--------\n\nLet's remove the temporary directory now.\n\n    rm -r examples/_temp\n"
  },
  {
    "path": "Caffe/finetune_flickr_style/assemble_data.py",
    "content": "#!/usr/bin/env python\n\"\"\"\nForm a subset of the Flickr Style data, download images to dirname, and write\nCaffe ImagesDataLayer training file.\n\"\"\"\nimport os\nimport urllib\nimport hashlib\nimport argparse\nimport numpy as np\nimport pandas as pd\nfrom skimage import io\nimport multiprocessing\n\n# Flickr returns a special image if the request is unavailable.\nMISSING_IMAGE_SHA1 = '6a92790b1c2a301c6e7ddef645dca1f53ea97ac2'\n\nexample_dirname = os.path.abspath(os.path.dirname(__file__))\ncaffe_dirname = os.path.abspath(os.path.join(example_dirname, '../..'))\ntraining_dirname = os.path.join(caffe_dirname, 'data/flickr_style')\n\n\ndef download_image(args_tuple):\n    \"For use with multiprocessing map. Returns filename on fail.\"\n    try:\n        url, filename = args_tuple\n        if not os.path.exists(filename):\n            urllib.urlretrieve(url, filename)\n        with open(filename) as f:\n            assert hashlib.sha1(f.read()).hexdigest() != MISSING_IMAGE_SHA1\n        test_read_image = io.imread(filename)\n        return True\n    except KeyboardInterrupt:\n        raise Exception()  # multiprocessing doesn't catch keyboard exceptions\n    except:\n        return False\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(\n        description='Download a subset of Flickr Style to a directory')\n    parser.add_argument(\n        '-s', '--seed', type=int, default=0,\n        help=\"random seed\")\n    parser.add_argument(\n        '-i', '--images', type=int, default=-1,\n        help=\"number of images to use (-1 for all [default])\",\n    )\n    parser.add_argument(\n        '-w', '--workers', type=int, default=-1,\n        help=\"num workers used to download images. -x uses (all - x) cores [-1 default].\"\n    )\n    parser.add_argument(\n        '-l', '--labels', type=int, default=0,\n        help=\"if set to a positive value, only sample images from the first number of labels.\"\n    )\n\n    args = parser.parse_args()\n    np.random.seed(args.seed)\n\n    # Read data, shuffle order, and subsample.\n    csv_filename = os.path.join(example_dirname, 'flickr_style.csv.gz')\n    df = pd.read_csv(csv_filename, index_col=0, compression='gzip')\n    df = df.iloc[np.random.permutation(df.shape[0])]\n    if args.labels > 0:\n        df = df.loc[df['label'] < args.labels]\n    if args.images > 0 and args.images < df.shape[0]:\n        df = df.iloc[:args.images]\n\n    # Make directory for images and get local filenames.\n    if training_dirname is None:\n        training_dirname = os.path.join(caffe_dirname, 'data/flickr_style')\n    images_dirname = os.path.join(training_dirname, 'images')\n    if not os.path.exists(images_dirname):\n        os.makedirs(images_dirname)\n    df['image_filename'] = [\n        os.path.join(images_dirname, _.split('/')[-1]) for _ in df['image_url']\n    ]\n\n    # Download images.\n    num_workers = args.workers\n    if num_workers <= 0:\n        num_workers = multiprocessing.cpu_count() + num_workers\n    print('Downloading {} images with {} workers...'.format(\n        df.shape[0], num_workers))\n    pool = multiprocessing.Pool(processes=num_workers)\n    map_args = zip(df['image_url'], df['image_filename'])\n    results = pool.map(download_image, map_args)\n\n    # Only keep rows with valid images, and write out training file lists.\n    df = df[results]\n    for split in ['train', 'test']:\n        split_df = df[df['_split'] == split]\n        filename = os.path.join(training_dirname, '{}.txt'.format(split))\n        split_df[['image_filename', 'label']].to_csv(\n            filename, sep=' ', header=None, index=None)\n    print('Writing train/val for {} successfully downloaded images.'.format(\n        df.shape[0]))\n"
  },
  {
    "path": "Caffe/finetune_flickr_style/readme.md",
    "content": "---\ntitle: Fine-tuning for style recognition\ndescription: Fine-tune the ImageNet-trained CaffeNet on the \"Flickr Style\" dataset.\ncategory: example\ninclude_in_docs: true\npriority: 5\n---\n\n# Fine-tuning CaffeNet for Style Recognition on \"Flickr Style\" Data\n\nFine-tuning takes an already learned model, adapts the architecture, and resumes training from the already learned model weights.\nLet's fine-tune the BVLC-distributed CaffeNet model on a different dataset, [Flickr Style](http://sergeykarayev.com/files/1311.3715v3.pdf), to predict image style instead of object category.\n\n## Explanation\n\nThe Flickr-sourced images of the Style dataset are visually very similar to the ImageNet dataset, on which the `bvlc_reference_caffenet` was trained.\nSince that model works well for object category classification, we'd like to use this architecture for our style classifier.\nWe also only have 80,000 images to train on, so we'd like to start with the parameters learned on the 1,000,000 ImageNet images, and fine-tune as needed.\nIf we provide the `weights` argument to the `caffe train` command, the pretrained weights will be loaded into our model, matching layers by name.\n\nBecause we are predicting 20 classes instead of a 1,000, we do need to change the last layer in the model.\nTherefore, we change the name of the last layer from `fc8` to `fc8_flickr` in our prototxt.\nSince there is no layer named that in the `bvlc_reference_caffenet`, that layer will begin training with random weights.\n\nWe will also decrease the overall learning rate `base_lr` in the solver prototxt, but boost the `lr_mult` on the newly introduced layer.\nThe idea is to have the rest of the model change very slowly with new data, but let the new layer learn fast.\nAdditionally, we set `stepsize` in the solver to a lower value than if we were training from scratch, since we're virtually far along in training and therefore want the learning rate to go down faster.\nNote that we could also entirely prevent fine-tuning of all layers other than `fc8_flickr` by setting their `lr_mult` to 0.\n\n## Procedure\n\nAll steps are to be done from the caffe root directory.\n\nThe dataset is distributed as a list of URLs with corresponding labels.\nUsing a script, we will download a small subset of the data and split it into train and val sets.\n\n    caffe % ./examples/finetune_flickr_style/assemble_data.py -h\n    usage: assemble_data.py [-h] [-s SEED] [-i IMAGES] [-w WORKERS]\n\n    Download a subset of Flickr Style to a directory\n\n    optional arguments:\n      -h, --help            show this help message and exit\n      -s SEED, --seed SEED  random seed\n      -i IMAGES, --images IMAGES\n                            number of images to use (-1 for all)\n      -w WORKERS, --workers WORKERS\n                            num workers used to download images. -x uses (all - x)\n                            cores.\n\n    caffe % python examples/finetune_flickr_style/assemble_data.py --workers=-1 --images=2000 --seed 831486\n    Downloading 2000 images with 7 workers...\n    Writing train/val for 1939 successfully downloaded images.\n\nThis script downloads images and writes train/val file lists into `data/flickr_style`.\nThe prototxts in this example assume this, and also assume the presence of the ImageNet mean file (run `get_ilsvrc_aux.sh` from `data/ilsvrc12` to obtain this if you haven't yet).\n\nWe'll also need the ImageNet-trained model, which you can obtain by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet`.\n\nNow we can train! The key to fine-tuning is the `-weights` argument in the\ncommand below, which tells Caffe that we want to load weights from a pre-trained\nCaffe model.\n\n(You can fine-tune in CPU mode by leaving out the `-gpu` flag.)\n\n    caffe % ./build/tools/caffe train -solver models/finetune_flickr_style/solver.prototxt -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel -gpu 0\n\n    [...]\n\n    I0828 22:10:04.025378  9718 solver.cpp:46] Solver scaffolding done.\n    I0828 22:10:04.025388  9718 caffe.cpp:95] Use GPU with device ID 0\n    I0828 22:10:04.192004  9718 caffe.cpp:107] Finetuning from models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel\n\n    [...]\n\n    I0828 22:17:48.338963 11510 solver.cpp:165] Solving FlickrStyleCaffeNet\n    I0828 22:17:48.339010 11510 solver.cpp:251] Iteration 0, Testing net (#0)\n    I0828 22:18:14.313817 11510 solver.cpp:302]     Test net output #0: accuracy = 0.0308\n    I0828 22:18:14.476822 11510 solver.cpp:195] Iteration 0, loss = 3.78589\n    I0828 22:18:14.476878 11510 solver.cpp:397] Iteration 0, lr = 0.001\n    I0828 22:18:19.700408 11510 solver.cpp:195] Iteration 20, loss = 3.25728\n    I0828 22:18:19.700461 11510 solver.cpp:397] Iteration 20, lr = 0.001\n    I0828 22:18:24.924685 11510 solver.cpp:195] Iteration 40, loss = 2.18531\n    I0828 22:18:24.924741 11510 solver.cpp:397] Iteration 40, lr = 0.001\n    I0828 22:18:30.114858 11510 solver.cpp:195] Iteration 60, loss = 2.4915\n    I0828 22:18:30.114910 11510 solver.cpp:397] Iteration 60, lr = 0.001\n    I0828 22:18:35.328071 11510 solver.cpp:195] Iteration 80, loss = 2.04539\n    I0828 22:18:35.328127 11510 solver.cpp:397] Iteration 80, lr = 0.001\n    I0828 22:18:40.588317 11510 solver.cpp:195] Iteration 100, loss = 2.1924\n    I0828 22:18:40.588373 11510 solver.cpp:397] Iteration 100, lr = 0.001\n    I0828 22:18:46.171576 11510 solver.cpp:195] Iteration 120, loss = 2.25107\n    I0828 22:18:46.171669 11510 solver.cpp:397] Iteration 120, lr = 0.001\n    I0828 22:18:51.757809 11510 solver.cpp:195] Iteration 140, loss = 1.355\n    I0828 22:18:51.757863 11510 solver.cpp:397] Iteration 140, lr = 0.001\n    I0828 22:18:57.345080 11510 solver.cpp:195] Iteration 160, loss = 1.40815\n    I0828 22:18:57.345135 11510 solver.cpp:397] Iteration 160, lr = 0.001\n    I0828 22:19:02.928794 11510 solver.cpp:195] Iteration 180, loss = 1.6558\n    I0828 22:19:02.928850 11510 solver.cpp:397] Iteration 180, lr = 0.001\n    I0828 22:19:08.514497 11510 solver.cpp:195] Iteration 200, loss = 0.88126\n    I0828 22:19:08.514552 11510 solver.cpp:397] Iteration 200, lr = 0.001\n\n    [...]\n\n    I0828 22:22:40.789010 11510 solver.cpp:195] Iteration 960, loss = 0.112586\n    I0828 22:22:40.789175 11510 solver.cpp:397] Iteration 960, lr = 0.001\n    I0828 22:22:46.376626 11510 solver.cpp:195] Iteration 980, loss = 0.0959077\n    I0828 22:22:46.376682 11510 solver.cpp:397] Iteration 980, lr = 0.001\n    I0828 22:22:51.687258 11510 solver.cpp:251] Iteration 1000, Testing net (#0)\n    I0828 22:23:17.438894 11510 solver.cpp:302]     Test net output #0: accuracy = 0.2356\n\nNote how rapidly the loss went down. Although the 23.5% accuracy is only modest, it was achieved in only 1000, and evidence that the model is starting to learn quickly and well.\nOnce the model is fully fine-tuned on the whole training set over 100,000 iterations the final validation accuracy is 39.16%.\nThis takes ~7 hours in Caffe on a K40 GPU.\n\nFor comparison, here is how the loss goes down when we do not start with a pre-trained model:\n\n    I0828 22:24:18.624004 12919 solver.cpp:165] Solving FlickrStyleCaffeNet\n    I0828 22:24:18.624099 12919 solver.cpp:251] Iteration 0, Testing net (#0)\n    I0828 22:24:44.520992 12919 solver.cpp:302]     Test net output #0: accuracy = 0.0366\n    I0828 22:24:44.676905 12919 solver.cpp:195] Iteration 0, loss = 3.47942\n    I0828 22:24:44.677120 12919 solver.cpp:397] Iteration 0, lr = 0.001\n    I0828 22:24:50.152454 12919 solver.cpp:195] Iteration 20, loss = 2.99694\n    I0828 22:24:50.152509 12919 solver.cpp:397] Iteration 20, lr = 0.001\n    I0828 22:24:55.736256 12919 solver.cpp:195] Iteration 40, loss = 3.0498\n    I0828 22:24:55.736311 12919 solver.cpp:397] Iteration 40, lr = 0.001\n    I0828 22:25:01.316514 12919 solver.cpp:195] Iteration 60, loss = 2.99549\n    I0828 22:25:01.316567 12919 solver.cpp:397] Iteration 60, lr = 0.001\n    I0828 22:25:06.899554 12919 solver.cpp:195] Iteration 80, loss = 3.00573\n    I0828 22:25:06.899610 12919 solver.cpp:397] Iteration 80, lr = 0.001\n    I0828 22:25:12.484624 12919 solver.cpp:195] Iteration 100, loss = 2.99094\n    I0828 22:25:12.484678 12919 solver.cpp:397] Iteration 100, lr = 0.001\n    I0828 22:25:18.069056 12919 solver.cpp:195] Iteration 120, loss = 3.01616\n    I0828 22:25:18.069149 12919 solver.cpp:397] Iteration 120, lr = 0.001\n    I0828 22:25:23.650928 12919 solver.cpp:195] Iteration 140, loss = 2.98786\n    I0828 22:25:23.650984 12919 solver.cpp:397] Iteration 140, lr = 0.001\n    I0828 22:25:29.235535 12919 solver.cpp:195] Iteration 160, loss = 3.00724\n    I0828 22:25:29.235589 12919 solver.cpp:397] Iteration 160, lr = 0.001\n    I0828 22:25:34.816898 12919 solver.cpp:195] Iteration 180, loss = 3.00099\n    I0828 22:25:34.816953 12919 solver.cpp:397] Iteration 180, lr = 0.001\n    I0828 22:25:40.396656 12919 solver.cpp:195] Iteration 200, loss = 2.99848\n    I0828 22:25:40.396711 12919 solver.cpp:397] Iteration 200, lr = 0.001\n\n    [...]\n\n    I0828 22:29:12.539094 12919 solver.cpp:195] Iteration 960, loss = 2.99203\n    I0828 22:29:12.539258 12919 solver.cpp:397] Iteration 960, lr = 0.001\n    I0828 22:29:18.123092 12919 solver.cpp:195] Iteration 980, loss = 2.99345\n    I0828 22:29:18.123147 12919 solver.cpp:397] Iteration 980, lr = 0.001\n    I0828 22:29:23.432059 12919 solver.cpp:251] Iteration 1000, Testing net (#0)\n    I0828 22:29:49.409044 12919 solver.cpp:302]     Test net output #0: accuracy = 0.0572\n\nThis model is only beginning to learn.\n\nFine-tuning can be feasible when training from scratch would not be for lack of time or data.\nEven in CPU mode each pass through the training set takes ~100 s. GPU fine-tuning is of course faster still and can learn a useful model in minutes or hours instead of days or weeks.\nFurthermore, note that the model has only trained on < 2,000 instances. Transfer learning a new task like style recognition from the ImageNet pretraining can require much less data than training from scratch.\n\nNow try fine-tuning to your own tasks and data!\n\n## Trained model\n\nWe provide a model trained on all 80K images, with final accuracy of 39%.\nSimply do `./scripts/download_model_binary.py models/finetune_flickr_style` to obtain it.\n\n## License\n\nThe Flickr Style dataset as distributed here contains only URLs to images.\nSome of the images may have copyright.\nTraining a category-recognition model for research/non-commercial use may constitute fair use of this data, but the result should not be used for commercial purposes.\n"
  },
  {
    "path": "Caffe/finetune_flickr_style/style_names.txt",
    "content": "Detailed\nPastel\nMelancholy\nNoir\nHDR\nVintage\nLong Exposure\nHorror\nSunny\nBright\nHazy\nBokeh\nSerene\nTexture\nEthereal\nMacro\nDepth of Field\nGeometric Composition\nMinimal\nRomantic\n"
  },
  {
    "path": "Caffe/finetune_pascal_detection/pascal_finetune_solver.prototxt",
    "content": "net: \"examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt\"\ntest_iter: 100\ntest_interval: 1000\nbase_lr: 0.001\nlr_policy: \"step\"\ngamma: 0.1\nstepsize: 20000\ndisplay: 20\nmax_iter: 100000\nmomentum: 0.9\nweight_decay: 0.0005\nsnapshot: 10000\nsnapshot_prefix: \"examples/finetune_pascal_detection/pascal_det_finetune\"\n"
  },
  {
    "path": "Caffe/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt",
    "content": "name: \"CaffeNet\"\nlayer {\n  name: \"data\"\n  type: \"WindowData\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TRAIN\n  }\n  transform_param {\n    mirror: true\n    crop_size: 227\n    mean_file: \"data/ilsvrc12/imagenet_mean.binaryproto\"\n  }\n  window_data_param {\n    source: \"examples/finetune_pascal_detection/window_file_2007_trainval.txt\"\n    batch_size: 128\n    fg_threshold: 0.5\n    bg_threshold: 0.5\n    fg_fraction: 0.25\n    context_pad: 16\n    crop_mode: \"warp\"\n  }\n}\nlayer {\n  name: \"data\"\n  type: \"WindowData\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TEST\n  }\n  transform_param {\n    mirror: true\n    crop_size: 227\n    mean_file: \"data/ilsvrc12/imagenet_mean.binaryproto\"\n  }\n  window_data_param {\n    source: \"examples/finetune_pascal_detection/window_file_2007_test.txt\"\n    batch_size: 128\n    fg_threshold: 0.5\n    bg_threshold: 0.5\n    fg_fraction: 0.25\n    context_pad: 16\n    crop_mode: \"warp\"\n  }\n}\nlayer {\n  name: \"conv1\"\n  type: \"Convolution\"\n  bottom: \"data\"\n  top: \"conv1\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 2\n    decay_mult: 0\n  }\n  convolution_param {\n    num_output: 96\n    kernel_size: 11\n    stride: 4\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 0\n    }\n  }\n}\nlayer {\n  name: \"relu1\"\n  type: \"ReLU\"\n  bottom: \"conv1\"\n  top: \"conv1\"\n}\nlayer {\n  name: \"pool1\"\n  type: \"Pooling\"\n  bottom: \"conv1\"\n  top: \"pool1\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"norm1\"\n  type: \"LRN\"\n  bottom: \"pool1\"\n  top: \"norm1\"\n  lrn_param {\n    local_size: 5\n    alpha: 0.0001\n    beta: 0.75\n  }\n}\nlayer {\n  name: \"conv2\"\n  type: \"Convolution\"\n  bottom: \"norm1\"\n  top: \"conv2\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 2\n    decay_mult: 0\n  }\n  convolution_param {\n    num_output: 256\n    pad: 2\n    kernel_size: 5\n    group: 2\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 1\n    }\n  }\n}\nlayer {\n  name: \"relu2\"\n  type: \"ReLU\"\n  bottom: \"conv2\"\n  top: \"conv2\"\n}\nlayer {\n  name: \"pool2\"\n  type: \"Pooling\"\n  bottom: \"conv2\"\n  top: \"pool2\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"norm2\"\n  type: \"LRN\"\n  bottom: \"pool2\"\n  top: \"norm2\"\n  lrn_param {\n    local_size: 5\n    alpha: 0.0001\n    beta: 0.75\n  }\n}\nlayer {\n  name: \"conv3\"\n  type: \"Convolution\"\n  bottom: \"norm2\"\n  top: \"conv3\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 2\n    decay_mult: 0\n  }\n  convolution_param {\n    num_output: 384\n    pad: 1\n    kernel_size: 3\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 0\n    }\n  }\n}\nlayer {\n  name: \"relu3\"\n  type: \"ReLU\"\n  bottom: \"conv3\"\n  top: \"conv3\"\n}\nlayer {\n  name: \"conv4\"\n  type: \"Convolution\"\n  bottom: \"conv3\"\n  top: \"conv4\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 2\n    decay_mult: 0\n  }\n  convolution_param {\n    num_output: 384\n    pad: 1\n    kernel_size: 3\n    group: 2\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 1\n    }\n  }\n}\nlayer {\n  name: \"relu4\"\n  type: \"ReLU\"\n  bottom: \"conv4\"\n  top: \"conv4\"\n}\nlayer {\n  name: \"conv5\"\n  type: \"Convolution\"\n  bottom: \"conv4\"\n  top: \"conv5\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 2\n    decay_mult: 0\n  }\n  convolution_param {\n    num_output: 256\n    pad: 1\n    kernel_size: 3\n    group: 2\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 1\n    }\n  }\n}\nlayer {\n  name: \"relu5\"\n  type: \"ReLU\"\n  bottom: \"conv5\"\n  top: \"conv5\"\n}\nlayer {\n  name: \"pool5\"\n  type: \"Pooling\"\n  bottom: \"conv5\"\n  top: \"pool5\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"fc6\"\n  type: \"InnerProduct\"\n  bottom: \"pool5\"\n  top: \"fc6\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 2\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 4096\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.005\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 1\n    }\n  }\n}\nlayer {\n  name: \"relu6\"\n  type: \"ReLU\"\n  bottom: \"fc6\"\n  top: \"fc6\"\n}\nlayer {\n  name: \"drop6\"\n  type: \"Dropout\"\n  bottom: \"fc6\"\n  top: \"fc6\"\n  dropout_param {\n    dropout_ratio: 0.5\n  }\n}\nlayer {\n  name: \"fc7\"\n  type: \"InnerProduct\"\n  bottom: \"fc6\"\n  top: \"fc7\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 2\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 4096\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.005\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 1\n    }\n  }\n}\nlayer {\n  name: \"relu7\"\n  type: \"ReLU\"\n  bottom: \"fc7\"\n  top: \"fc7\"\n}\nlayer {\n  name: \"drop7\"\n  type: \"Dropout\"\n  bottom: \"fc7\"\n  top: \"fc7\"\n  dropout_param {\n    dropout_ratio: 0.5\n  }\n}\nlayer {\n  name: \"fc8_pascal\"\n  type: \"InnerProduct\"\n  bottom: \"fc7\"\n  top: \"fc8_pascal\"\n  param {\n    lr_mult: 10\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 20\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 21\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 0\n    }\n  }\n}\nlayer {\n  name: \"loss\"\n  type: \"SoftmaxWithLoss\"\n  bottom: \"fc8_pascal\"\n  bottom: \"label\"\n}\nlayer {\n  name: \"accuracy\"\n  type: \"Accuracy\"\n  bottom: \"fc8_pascal\"\n  bottom: \"label\"\n  top: \"accuracy\"\n  include {\n    phase: TEST\n  }\n}\n"
  },
  {
    "path": "Caffe/hdf5_classification/nonlinear_auto_test.prototxt",
    "content": "layer {\n  name: \"data\"\n  type: \"HDF5Data\"\n  top: \"data\"\n  top: \"label\"\n  hdf5_data_param {\n    source: \"examples/hdf5_classification/data/test.txt\"\n    batch_size: 10\n  }\n}\nlayer {\n  name: \"ip1\"\n  type: \"InnerProduct\"\n  bottom: \"data\"\n  top: \"ip1\"\n  inner_product_param {\n    num_output: 40\n    weight_filler {\n      type: \"xavier\"\n    }\n  }\n}\nlayer {\n  name: \"relu1\"\n  type: \"ReLU\"\n  bottom: \"ip1\"\n  top: \"ip1\"\n}\nlayer {\n  name: \"ip2\"\n  type: \"InnerProduct\"\n  bottom: \"ip1\"\n  top: \"ip2\"\n  inner_product_param {\n    num_output: 2\n    weight_filler {\n      type: \"xavier\"\n    }\n  }\n}\nlayer {\n  name: \"accuracy\"\n  type: \"Accuracy\"\n  bottom: \"ip2\"\n  bottom: \"label\"\n  top: \"accuracy\"\n}\nlayer {\n  name: \"loss\"\n  type: \"SoftmaxWithLoss\"\n  bottom: \"ip2\"\n  bottom: \"label\"\n  top: \"loss\"\n}\n"
  },
  {
    "path": "Caffe/hdf5_classification/nonlinear_auto_train.prototxt",
    "content": "layer {\n  name: \"data\"\n  type: \"HDF5Data\"\n  top: \"data\"\n  top: \"label\"\n  hdf5_data_param {\n    source: \"examples/hdf5_classification/data/train.txt\"\n    batch_size: 10\n  }\n}\nlayer {\n  name: \"ip1\"\n  type: \"InnerProduct\"\n  bottom: \"data\"\n  top: \"ip1\"\n  inner_product_param {\n    num_output: 40\n    weight_filler {\n      type: \"xavier\"\n    }\n  }\n}\nlayer {\n  name: \"relu1\"\n  type: \"ReLU\"\n  bottom: \"ip1\"\n  top: \"ip1\"\n}\nlayer {\n  name: \"ip2\"\n  type: \"InnerProduct\"\n  bottom: \"ip1\"\n  top: \"ip2\"\n  inner_product_param {\n    num_output: 2\n    weight_filler {\n      type: \"xavier\"\n    }\n  }\n}\nlayer {\n  name: \"accuracy\"\n  type: \"Accuracy\"\n  bottom: \"ip2\"\n  bottom: \"label\"\n  top: \"accuracy\"\n}\nlayer {\n  name: \"loss\"\n  type: \"SoftmaxWithLoss\"\n  bottom: \"ip2\"\n  bottom: \"label\"\n  top: \"loss\"\n}\n"
  },
  {
    "path": "Caffe/hdf5_classification/nonlinear_train_val.prototxt",
    "content": "name: \"LogisticRegressionNet\"\nlayer {\n  name: \"data\"\n  type: \"HDF5Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TRAIN\n  }\n  hdf5_data_param {\n    source: \"examples/hdf5_classification/data/train.txt\"\n    batch_size: 10\n  }\n}\nlayer {\n  name: \"data\"\n  type: \"HDF5Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TEST\n  }\n  hdf5_data_param {\n    source: \"examples/hdf5_classification/data/test.txt\"\n    batch_size: 10\n  }\n}\nlayer {\n  name: \"fc1\"\n  type: \"InnerProduct\"\n  bottom: \"data\"\n  top: \"fc1\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 2\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 40\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 0\n    }\n  }\n}\nlayer {\n  name: \"relu1\"\n  type: \"ReLU\"\n  bottom: \"fc1\"\n  top: \"fc1\"\n}\nlayer {\n  name: \"fc2\"\n  type: \"InnerProduct\"\n  bottom: \"fc1\"\n  top: \"fc2\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 2\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 2\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 0\n    }\n  }\n}\nlayer {\n  name: \"loss\"\n  type: \"SoftmaxWithLoss\"\n  bottom: \"fc2\"\n  bottom: \"label\"\n  top: \"loss\"\n}\nlayer {\n  name: \"accuracy\"\n  type: \"Accuracy\"\n  bottom: \"fc2\"\n  bottom: \"label\"\n  top: \"accuracy\"\n  include {\n    phase: TEST\n  }\n}\n"
  },
  {
    "path": "Caffe/hdf5_classification/train_val.prototxt",
    "content": "name: \"LogisticRegressionNet\"\nlayer {\n  name: \"data\"\n  type: \"HDF5Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TRAIN\n  }\n  hdf5_data_param {\n    source: \"examples/hdf5_classification/data/train.txt\"\n    batch_size: 10\n  }\n}\nlayer {\n  name: \"data\"\n  type: \"HDF5Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TEST\n  }\n  hdf5_data_param {\n    source: \"examples/hdf5_classification/data/test.txt\"\n    batch_size: 10\n  }\n}\nlayer {\n  name: \"fc1\"\n  type: \"InnerProduct\"\n  bottom: \"data\"\n  top: \"fc1\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 2\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 2\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 0\n    }\n  }\n}\nlayer {\n  name: \"loss\"\n  type: \"SoftmaxWithLoss\"\n  bottom: \"fc1\"\n  bottom: \"label\"\n  top: \"loss\"\n}\nlayer {\n  name: \"accuracy\"\n  type: \"Accuracy\"\n  bottom: \"fc1\"\n  bottom: \"label\"\n  top: \"accuracy\"\n  include {\n    phase: TEST\n  }\n}\n"
  },
  {
    "path": "Caffe/imagenet/create_imagenet.sh",
    "content": "#!/usr/bin/env sh\n# Create the imagenet lmdb inputs\n# N.B. set the path to the imagenet train + val data dirs\nset -e\n\nEXAMPLE=examples/imagenet\nDATA=data/ilsvrc12\nTOOLS=build/tools\n\nTRAIN_DATA_ROOT=/path/to/imagenet/train/\nVAL_DATA_ROOT=/path/to/imagenet/val/\n\n# Set RESIZE=true to resize the images to 256x256. Leave as false if images have\n# already been resized using another tool.\nRESIZE=false\nif $RESIZE; then\n  RESIZE_HEIGHT=256\n  RESIZE_WIDTH=256\nelse\n  RESIZE_HEIGHT=0\n  RESIZE_WIDTH=0\nfi\n\nif [ ! -d \"$TRAIN_DATA_ROOT\" ]; then\n  echo \"Error: TRAIN_DATA_ROOT is not a path to a directory: $TRAIN_DATA_ROOT\"\n  echo \"Set the TRAIN_DATA_ROOT variable in create_imagenet.sh to the path\" \\\n       \"where the ImageNet training data is stored.\"\n  exit 1\nfi\n\nif [ ! -d \"$VAL_DATA_ROOT\" ]; then\n  echo \"Error: VAL_DATA_ROOT is not a path to a directory: $VAL_DATA_ROOT\"\n  echo \"Set the VAL_DATA_ROOT variable in create_imagenet.sh to the path\" \\\n       \"where the ImageNet validation data is stored.\"\n  exit 1\nfi\n\necho \"Creating train lmdb...\"\n\nGLOG_logtostderr=1 $TOOLS/convert_imageset \\\n    --resize_height=$RESIZE_HEIGHT \\\n    --resize_width=$RESIZE_WIDTH \\\n    --shuffle \\\n    $TRAIN_DATA_ROOT \\\n    $DATA/train.txt \\\n    $EXAMPLE/ilsvrc12_train_lmdb\n\necho \"Creating val lmdb...\"\n\nGLOG_logtostderr=1 $TOOLS/convert_imageset \\\n    --resize_height=$RESIZE_HEIGHT \\\n    --resize_width=$RESIZE_WIDTH \\\n    --shuffle \\\n    $VAL_DATA_ROOT \\\n    $DATA/val.txt \\\n    $EXAMPLE/ilsvrc12_val_lmdb\n\necho \"Done.\"\n"
  },
  {
    "path": "Caffe/imagenet/make_imagenet_mean.sh",
    "content": "#!/usr/bin/env sh\n# Compute the mean image from the imagenet training lmdb\n# N.B. this is available in data/ilsvrc12\n\nEXAMPLE=examples/imagenet\nDATA=data/ilsvrc12\nTOOLS=build/tools\n\n$TOOLS/compute_image_mean $EXAMPLE/ilsvrc12_train_lmdb \\\n  $DATA/imagenet_mean.binaryproto\n\necho \"Done.\"\n"
  },
  {
    "path": "Caffe/imagenet/readme.md",
    "content": "---\ntitle: ImageNet tutorial\ndescription: Train and test \"CaffeNet\" on ImageNet data.\ncategory: example\ninclude_in_docs: true\npriority: 1\n---\n\nBrewing ImageNet\n================\n\nThis guide is meant to get you ready to train your own model on your own data.\nIf you just want an ImageNet-trained network, then note that since training takes a lot of energy and we hate global warming, we provide the CaffeNet model trained as described below in the [model zoo](/model_zoo.html).\n\nData Preparation\n----------------\n\n*The guide specifies all paths and assumes all commands are executed from the root caffe directory.*\n\n*By \"ImageNet\" we here mean the ILSVRC12 challenge, but you can easily train on the whole of ImageNet as well, just with more disk space, and a little longer training time.*\n\nWe assume that you already have downloaded the ImageNet training data and validation data, and they are stored on your disk like:\n\n    /path/to/imagenet/train/n01440764/n01440764_10026.JPEG\n    /path/to/imagenet/val/ILSVRC2012_val_00000001.JPEG\n\nYou will first need to prepare some auxiliary data for training. This data can be downloaded by:\n\n    ./data/ilsvrc12/get_ilsvrc_aux.sh\n\nThe training and validation input are described in `train.txt` and `val.txt` as text listing all the files and their labels. Note that we use a different indexing for labels than the ILSVRC devkit: we sort the synset names in their ASCII order, and then label them from 0 to 999. See `synset_words.txt` for the synset/name mapping.\n\nYou may want to resize the images to 256x256 in advance. By default, we do not explicitly do this because in a cluster environment, one may benefit from resizing images in a parallel fashion, using mapreduce. For example, Yangqing used his lightweight [mincepie](https://github.com/Yangqing/mincepie) package. If you prefer things to be simpler, you can also use shell commands, something like:\n\n    for name in /path/to/imagenet/val/*.JPEG; do\n        convert -resize 256x256\\! $name $name\n    done\n\nTake a look at `examples/imagenet/create_imagenet.sh`. Set the paths to the train and val dirs as needed, and set \"RESIZE=true\" to resize all images to 256x256 if you haven't resized the images in advance.\nNow simply create the leveldbs with `examples/imagenet/create_imagenet.sh`. Note that `examples/imagenet/ilsvrc12_train_leveldb` and `examples/imagenet/ilsvrc12_val_leveldb` should not exist before this execution. It will be created by the script. `GLOG_logtostderr=1` simply dumps more information for you to inspect, and you can safely ignore it.\n\nCompute Image Mean\n------------------\n\nThe model requires us to subtract the image mean from each image, so we have to compute the mean. `tools/compute_image_mean.cpp` implements that - it is also a good example to familiarize yourself on how to manipulate the multiple components, such as protocol buffers, leveldbs, and logging, if you are not familiar with them. Anyway, the mean computation can be carried out as:\n\n    ./examples/imagenet/make_imagenet_mean.sh\n\nwhich will make `data/ilsvrc12/imagenet_mean.binaryproto`.\n\nModel Definition\n----------------\n\nWe are going to describe a reference implementation for the approach first proposed by Krizhevsky, Sutskever, and Hinton in their [NIPS 2012 paper](http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf).\n\nThe network definition (`models/bvlc_reference_caffenet/train_val.prototxt`) follows the one in Krizhevsky et al.\nNote that if you deviated from file paths suggested in this guide, you'll need to adjust the relevant paths in the `.prototxt` files.\n\nIf you look carefully at `models/bvlc_reference_caffenet/train_val.prototxt`, you will notice several `include` sections specifying either `phase: TRAIN` or `phase: TEST`. These sections allow us to define two closely related networks in one file: the network used for training and the network used for testing. These two networks are almost identical, sharing all layers except for those marked with `include { phase: TRAIN }` or `include { phase: TEST }`. In this case, only the input layers and one output layer are different.\n\n**Input layer differences:** The training network's `data` input layer draws its data from `examples/imagenet/ilsvrc12_train_leveldb` and randomly mirrors the input image. The testing network's `data` layer takes data from `examples/imagenet/ilsvrc12_val_leveldb` and does not perform random mirroring.\n\n**Output layer differences:** Both networks output the `softmax_loss` layer, which in training is used to compute the loss function and to initialize the backpropagation, while in validation this loss is simply reported. The testing network also has a second output layer, `accuracy`, which is used to report the accuracy on the test set. In the process of training, the test network will occasionally be instantiated and tested on the test set, producing lines like `Test score #0: xxx` and `Test score #1: xxx`. In this case score 0 is the accuracy (which will start around 1/1000 = 0.001 for an untrained network) and score 1 is the loss (which will start around 7 for an untrained network).\n\nWe will also lay out a protocol buffer for running the solver. Let's make a few plans:\n\n* We will run in batches of 256, and run a total of 450,000 iterations (about 90 epochs).\n* For every 1,000 iterations, we test the learned net on the validation data.\n* We set the initial learning rate to 0.01, and decrease it every 100,000 iterations (about 20 epochs).\n* Information will be displayed every 20 iterations.\n* The network will be trained with momentum 0.9 and a weight decay of 0.0005.\n* For every 10,000 iterations, we will take a snapshot of the current status.\n\nSound good? This is implemented in `models/bvlc_reference_caffenet/solver.prototxt`.\n\nTraining ImageNet\n-----------------\n\nReady? Let's train.\n\n    ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt\n\nSit back and enjoy!\n\nOn a K40 machine, every 20 iterations take about 26.5 seconds to run (while a on a K20 this takes 36 seconds), so effectively about 5.2 ms per image for the full forward-backward pass. About 2 ms of this is on forward, and the rest is backward. If you are interested in dissecting the computation time, you can run\n\n    ./build/tools/caffe time --model=models/bvlc_reference_caffenet/train_val.prototxt\n\nResume Training?\n----------------\n\nWe all experience times when the power goes out, or we feel like rewarding ourself a little by playing Battlefield (does anyone still remember Quake?). Since we are snapshotting intermediate results during training, we will be able to resume from snapshots. This can be done as easy as:\n\n    ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt --snapshot=models/bvlc_reference_caffenet/caffenet_train_iter_10000.solverstate\n\nwhere in the script `caffenet_train_iter_10000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).\n\nParting Words\n-------------\n\nHope you liked this recipe!\nMany researchers have gone further since the ILSVRC 2012 challenge, changing the network architecture and/or fine-tuning the various parameters in the network to address new data and tasks.\n**Caffe lets you explore different network choices more easily by simply writing different prototxt files** - isn't that exciting?\n\nAnd since now you have a trained network, check out how to use it with the Python interface for [classifying ImageNet](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb).\n"
  },
  {
    "path": "Caffe/imagenet/resume_training.sh",
    "content": "#!/usr/bin/env sh\nset -e\n\n./build/tools/caffe train \\\n    --solver=models/bvlc_reference_caffenet/solver.prototxt \\\n    --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate.h5 \\\n    $@\n"
  },
  {
    "path": "Caffe/imagenet/train_caffenet.sh",
    "content": "#!/usr/bin/env sh\nset -e\n\n./build/tools/caffe train \\\n    --solver=models/bvlc_reference_caffenet/solver.prototxt $@\n"
  },
  {
    "path": "Caffe/mnist/convert_mnist_data.cpp",
    "content": "// This script converts the MNIST dataset to a lmdb (default) or\n// leveldb (--backend=leveldb) format used by caffe to load data.\n// Usage:\n//    convert_mnist_data [FLAGS] input_image_file input_label_file\n//                        output_db_file\n// The MNIST dataset could be downloaded at\n//    http://yann.lecun.com/exdb/mnist/\n\n#include <gflags/gflags.h>\n#include <glog/logging.h>\n#include <google/protobuf/text_format.h>\n\n#if defined(USE_LEVELDB) && defined(USE_LMDB)\n#include <leveldb/db.h>\n#include <leveldb/write_batch.h>\n#include <lmdb.h>\n#endif\n\n#include <stdint.h>\n#include <sys/stat.h>\n\n#include <fstream>  // NOLINT(readability/streams)\n#include <string>\n\n#include \"boost/scoped_ptr.hpp\"\n#include \"caffe/proto/caffe.pb.h\"\n#include \"caffe/util/db.hpp\"\n#include \"caffe/util/format.hpp\"\n\n#if defined(USE_LEVELDB) && defined(USE_LMDB)\n\nusing namespace caffe;  // NOLINT(build/namespaces)\nusing boost::scoped_ptr;\nusing std::string;\n\nDEFINE_string(backend, \"lmdb\", \"The backend for storing the result\");\n\nuint32_t swap_endian(uint32_t val) {\n    val = ((val << 8) & 0xFF00FF00) | ((val >> 8) & 0xFF00FF);\n    return (val << 16) | (val >> 16);\n}\n\nvoid convert_dataset(const char* image_filename, const char* label_filename,\n        const char* db_path, const string& db_backend) {\n  // Open files\n  std::ifstream image_file(image_filename, std::ios::in | std::ios::binary);\n  std::ifstream label_file(label_filename, std::ios::in | std::ios::binary);\n  CHECK(image_file) << \"Unable to open file \" << image_filename;\n  CHECK(label_file) << \"Unable to open file \" << label_filename;\n  // Read the magic and the meta data\n  uint32_t magic;\n  uint32_t num_items;\n  uint32_t num_labels;\n  uint32_t rows;\n  uint32_t cols;\n\n  image_file.read(reinterpret_cast<char*>(&magic), 4);\n  magic = swap_endian(magic);\n  CHECK_EQ(magic, 2051) << \"Incorrect image file magic.\";\n  label_file.read(reinterpret_cast<char*>(&magic), 4);\n  magic = swap_endian(magic);\n  CHECK_EQ(magic, 2049) << \"Incorrect label file magic.\";\n  image_file.read(reinterpret_cast<char*>(&num_items), 4);\n  num_items = swap_endian(num_items);\n  label_file.read(reinterpret_cast<char*>(&num_labels), 4);\n  num_labels = swap_endian(num_labels);\n  CHECK_EQ(num_items, num_labels);\n  image_file.read(reinterpret_cast<char*>(&rows), 4);\n  rows = swap_endian(rows);\n  image_file.read(reinterpret_cast<char*>(&cols), 4);\n  cols = swap_endian(cols);\n\n\n  scoped_ptr<db::DB> db(db::GetDB(db_backend));\n  db->Open(db_path, db::NEW);\n  scoped_ptr<db::Transaction> txn(db->NewTransaction());\n\n  // Storing to db\n  char label;\n  char* pixels = new char[rows * cols];\n  int count = 0;\n  string value;\n\n  Datum datum;\n  datum.set_channels(1);\n  datum.set_height(rows);\n  datum.set_width(cols);\n  LOG(INFO) << \"A total of \" << num_items << \" items.\";\n  LOG(INFO) << \"Rows: \" << rows << \" Cols: \" << cols;\n  for (int item_id = 0; item_id < num_items; ++item_id) {\n    image_file.read(pixels, rows * cols);\n    label_file.read(&label, 1);\n    datum.set_data(pixels, rows*cols);\n    datum.set_label(label);\n    string key_str = caffe::format_int(item_id, 8);\n    datum.SerializeToString(&value);\n\n    txn->Put(key_str, value);\n\n    if (++count % 1000 == 0) {\n      txn->Commit();\n    }\n  }\n  // write the last batch\n  if (count % 1000 != 0) {\n      txn->Commit();\n  }\n  LOG(INFO) << \"Processed \" << count << \" files.\";\n  delete[] pixels;\n  db->Close();\n}\n\nint main(int argc, char** argv) {\n#ifndef GFLAGS_GFLAGS_H_\n  namespace gflags = google;\n#endif\n\n  FLAGS_alsologtostderr = 1;\n\n  gflags::SetUsageMessage(\"This script converts the MNIST dataset to\\n\"\n        \"the lmdb/leveldb format used by Caffe to load data.\\n\"\n        \"Usage:\\n\"\n        \"    convert_mnist_data [FLAGS] input_image_file input_label_file \"\n        \"output_db_file\\n\"\n        \"The MNIST dataset could be downloaded at\\n\"\n        \"    http://yann.lecun.com/exdb/mnist/\\n\"\n        \"You should gunzip them after downloading,\"\n        \"or directly use data/mnist/get_mnist.sh\\n\");\n  gflags::ParseCommandLineFlags(&argc, &argv, true);\n\n  const string& db_backend = FLAGS_backend;\n\n  if (argc != 4) {\n    gflags::ShowUsageWithFlagsRestrict(argv[0],\n        \"examples/mnist/convert_mnist_data\");\n  } else {\n    google::InitGoogleLogging(argv[0]);\n    convert_dataset(argv[1], argv[2], argv[3], db_backend);\n  }\n  return 0;\n}\n#else\nint main(int argc, char** argv) {\n  LOG(FATAL) << \"This example requires LevelDB and LMDB; \" <<\n  \"compile with USE_LEVELDB and USE_LMDB.\";\n}\n#endif  // USE_LEVELDB and USE_LMDB\n"
  },
  {
    "path": "Caffe/mnist/create_mnist.sh",
    "content": "#!/usr/bin/env sh\n# This script converts the mnist data into lmdb/leveldb format,\n# depending on the value assigned to $BACKEND.\nset -e\n\nEXAMPLE=examples/mnist\nDATA=data/mnist\nBUILD=build/examples/mnist\n\nBACKEND=\"lmdb\"\n\necho \"Creating ${BACKEND}...\"\n\nrm -rf $EXAMPLE/mnist_train_${BACKEND}\nrm -rf $EXAMPLE/mnist_test_${BACKEND}\n\n$BUILD/convert_mnist_data.bin $DATA/train-images-idx3-ubyte \\\n  $DATA/train-labels-idx1-ubyte $EXAMPLE/mnist_train_${BACKEND} --backend=${BACKEND}\n$BUILD/convert_mnist_data.bin $DATA/t10k-images-idx3-ubyte \\\n  $DATA/t10k-labels-idx1-ubyte $EXAMPLE/mnist_test_${BACKEND} --backend=${BACKEND}\n\necho \"Done.\"\n"
  },
  {
    "path": "Caffe/mnist/lenet.prototxt",
    "content": "name: \"LeNet\"\nlayer {\n  name: \"data\"\n  type: \"Input\"\n  top: \"data\"\n  input_param { shape: { dim: 64 dim: 1 dim: 28 dim: 28 } }\n}\nlayer {\n  name: \"conv1\"\n  type: \"Convolution\"\n  bottom: \"data\"\n  top: \"conv1\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 20\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"pool1\"\n  type: \"Pooling\"\n  bottom: \"conv1\"\n  top: \"pool1\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 2\n    stride: 2\n  }\n}\nlayer {\n  name: \"conv2\"\n  type: \"Convolution\"\n  bottom: \"pool1\"\n  top: \"conv2\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 50\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"pool2\"\n  type: \"Pooling\"\n  bottom: \"conv2\"\n  top: \"pool2\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 2\n    stride: 2\n  }\n}\nlayer {\n  name: \"ip1\"\n  type: \"InnerProduct\"\n  bottom: \"pool2\"\n  top: \"ip1\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 500\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"relu1\"\n  type: \"ReLU\"\n  bottom: \"ip1\"\n  top: \"ip1\"\n}\nlayer {\n  name: \"ip2\"\n  type: \"InnerProduct\"\n  bottom: \"ip1\"\n  top: \"ip2\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 10\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"prob\"\n  type: \"Softmax\"\n  bottom: \"ip2\"\n  top: \"prob\"\n}\n"
  },
  {
    "path": "Caffe/mnist/lenet_adadelta_solver.prototxt",
    "content": "# The train/test net protocol buffer definition\nnet: \"examples/mnist/lenet_train_test.prototxt\"\n# test_iter specifies how many forward passes the test should carry out.\n# In the case of MNIST, we have test batch size 100 and 100 test iterations,\n# covering the full 10,000 testing images.\ntest_iter: 100\n# Carry out testing every 500 training iterations.\ntest_interval: 500\n# The base learning rate, momentum and the weight decay of the network.\nbase_lr: 1.0\nlr_policy: \"fixed\"\nmomentum: 0.95\nweight_decay: 0.0005\n# Display every 100 iterations\ndisplay: 100\n# The maximum number of iterations\nmax_iter: 10000\n# snapshot intermediate results\nsnapshot: 5000\nsnapshot_prefix: \"examples/mnist/lenet_adadelta\"\n# solver mode: CPU or GPU\nsolver_mode: GPU\ntype: \"AdaDelta\"\ndelta: 1e-6\n"
  },
  {
    "path": "Caffe/mnist/lenet_auto_solver.prototxt",
    "content": "# The train/test net protocol buffer definition\ntrain_net: \"mnist/lenet_auto_train.prototxt\"\ntest_net: \"mnist/lenet_auto_test.prototxt\"\n# test_iter specifies how many forward passes the test should carry out.\n# In the case of MNIST, we have test batch size 100 and 100 test iterations,\n# covering the full 10,000 testing images.\ntest_iter: 100\n# Carry out testing every 500 training iterations.\ntest_interval: 500\n# The base learning rate, momentum and the weight decay of the network.\nbase_lr: 0.01\nmomentum: 0.9\nweight_decay: 0.0005\n# The learning rate policy\nlr_policy: \"inv\"\ngamma: 0.0001\npower: 0.75\n# Display every 100 iterations\ndisplay: 100\n# The maximum number of iterations\nmax_iter: 10000\n# snapshot intermediate results\nsnapshot: 5000\nsnapshot_prefix: \"mnist/lenet\"\n"
  },
  {
    "path": "Caffe/mnist/lenet_consolidated_solver.prototxt",
    "content": "# lenet_consolidated_solver.prototxt consolidates the lenet_solver, lenet_train,\n# and lenet_test prototxts into a single file.  It also adds an additional test\n# net which runs on the training set, e.g., for the purpose of comparing\n# train/test accuracy (accuracy is computed only on the test set in the included\n# LeNet example).  This is mainly included as an example of using these features\n# (specify NetParameters directly in the solver, specify multiple test nets)\n# if desired.\n#\n# Carry out testing every 500 training iterations.\ntest_interval: 500\n# The base learning rate, momentum and the weight decay of the network.\nbase_lr: 0.01\nmomentum: 0.9\nweight_decay: 0.0005\n# The learning rate policy\nlr_policy: \"inv\"\ngamma: 0.0001\npower: 0.75\n# Display every 100 iterations\ndisplay: 100\n# The maximum number of iterations\nmax_iter: 10000\n# snapshot intermediate results\nsnapshot: 5000\nsnapshot_prefix: \"examples/mnist/lenet\"\n# Set a random_seed for repeatable results.\n# (For results that vary due to random initialization, comment out the below\n# line, or set to a negative integer -- e.g. \"random_seed: -1\")\nrandom_seed: 1701\n# solver mode: CPU or GPU\nsolver_mode: GPU\n\n# We test on both the test and train set using \"stages\".  The TEST DATA layers\n# each have a stage, either 'test-on-train-set' or 'test-on-test-set'.\n# test_iter specifies how many forward passes the test should carry out.\n# In the case of MNIST, we have test batch size 100 and 100 test iterations,\n# covering the full 10,000 testing images.\ntest_iter: 100\ntest_state: { stage: \"test-on-test-set\" }\n# The train set has 60K images, so we run 600 test iters (600 * 100 = 60K).\ntest_iter: 600\ntest_state: { stage: \"test-on-train-set\" }\n\n# The net protocol buffer definition\nnet_param {\n  name: \"LeNet\"\n  layers {\n    name: \"mnist\"\n    type: DATA\n    top: \"data\"\n    top: \"label\"\n    data_param {\n      source: \"examples/mnist/mnist_train_lmdb\"\n      backend: LMDB\n      batch_size: 64\n    }\n    transform_param {\n      scale: 0.00390625\n    }\n    include: { phase: TRAIN }\n  }\n  layers {\n    name: \"mnist\"\n    type: DATA\n    top: \"data\"\n    top: \"label\"\n    data_param {\n      source: \"examples/mnist/mnist_test_lmdb\"\n      backend: LMDB\n      batch_size: 100\n    }\n    transform_param {\n      scale: 0.00390625\n    }\n    include: {\n      phase: TEST\n      stage: \"test-on-test-set\"\n    }\n  }\n  layers {\n    name: \"mnist\"\n    type: DATA\n    top: \"data\"\n    top: \"label\"\n    data_param {\n      source: \"examples/mnist/mnist_train_lmdb\"\n      backend: LMDB\n      batch_size: 100\n    }\n    transform_param {\n      scale: 0.00390625\n    }\n    include: {\n      phase: TEST\n      stage: \"test-on-train-set\"\n    }\n  }\n  layers {\n    name: \"conv1\"\n    type: CONVOLUTION\n    bottom: \"data\"\n    top: \"conv1\"\n    blobs_lr: 1\n    blobs_lr: 2\n    convolution_param {\n      num_output: 20\n      kernel_size: 5\n      stride: 1\n      weight_filler {\n        type: \"xavier\"\n      }\n      bias_filler {\n        type: \"constant\"\n      }\n    }\n  }\n  layers {\n    name: \"pool1\"\n    type: POOLING\n    bottom: \"conv1\"\n    top: \"pool1\"\n    pooling_param {\n      pool: MAX\n      kernel_size: 2\n      stride: 2\n    }\n  }\n  layers {\n    name: \"conv2\"\n    type: CONVOLUTION\n    bottom: \"pool1\"\n    top: \"conv2\"\n    blobs_lr: 1\n    blobs_lr: 2\n    convolution_param {\n      num_output: 50\n      kernel_size: 5\n      stride: 1\n      weight_filler {\n        type: \"xavier\"\n      }\n      bias_filler {\n        type: \"constant\"\n      }\n    }\n  }\n  layers {\n    name: \"pool2\"\n    type: POOLING\n    bottom: \"conv2\"\n    top: \"pool2\"\n    pooling_param {\n      pool: MAX\n      kernel_size: 2\n      stride: 2\n    }\n  }\n  layers {\n    name: \"ip1\"\n    type: INNER_PRODUCT\n    bottom: \"pool2\"\n    top: \"ip1\"\n    blobs_lr: 1\n    blobs_lr: 2\n    inner_product_param {\n      num_output: 500\n      weight_filler {\n        type: \"xavier\"\n      }\n      bias_filler {\n        type: \"constant\"\n      }\n    }\n  }\n  layers {\n    name: \"relu1\"\n    type: RELU\n    bottom: \"ip1\"\n    top: \"ip1\"\n  }\n  layers {\n    name: \"ip2\"\n    type: INNER_PRODUCT\n    bottom: \"ip1\"\n    top: \"ip2\"\n    blobs_lr: 1\n    blobs_lr: 2\n    inner_product_param {\n      num_output: 10\n      weight_filler {\n        type: \"xavier\"\n      }\n      bias_filler {\n        type: \"constant\"\n      }\n    }\n  }\n  layers {\n    name: \"accuracy\"\n    type: ACCURACY\n    bottom: \"ip2\"\n    bottom: \"label\"\n    top: \"accuracy\"\n  }\n  layers {\n    name: \"loss\"\n    type: SOFTMAX_LOSS\n    bottom: \"ip2\"\n    bottom: \"label\"\n    top: \"loss\"\n  }\n}\n\n# Expected results for first and last 500 iterations:\n# (with portions of log omitted for brevity)\n#\n# Iteration 0, Testing net (#0)\n# Test score #0: 0.067\n# Test score #1: 2.30256\n# Iteration 0, Testing net (#1)\n# Test score #0: 0.0670334\n# Test score #1: 2.30258\n# Iteration 100, lr = 0.00992565\n# Iteration 100, loss = 0.280585\n# Iteration 200, lr = 0.00985258\n# Iteration 200, loss = 0.345601\n# Iteration 300, lr = 0.00978075\n# Iteration 300, loss = 0.172217\n# Iteration 400, lr = 0.00971013\n# Iteration 400, loss = 0.261836\n# Iteration 500, lr = 0.00964069\n# Iteration 500, loss = 0.157803\n# Iteration 500, Testing net (#0)\n# Test score #0: 0.968\n# Test score #1: 0.0993772\n# Iteration 500, Testing net (#1)\n# Test score #0: 0.965883\n# Test score #1: 0.109374\n#\n# [...]\n#\n# Iteration 9500, Testing net (#0)\n# Test score #0: 0.9899\n# Test score #1: 0.0308299\n# Iteration 9500, Testing net (#1)\n# Test score #0: 0.996816\n# Test score #1: 0.0118238\n# Iteration 9600, lr = 0.00603682\n# Iteration 9600, loss = 0.0126215\n# Iteration 9700, lr = 0.00601382\n# Iteration 9700, loss = 0.00579304\n# Iteration 9800, lr = 0.00599102\n# Iteration 9800, loss = 0.00500633\n# Iteration 9900, lr = 0.00596843\n# Iteration 9900, loss = 0.00796607\n# Iteration 10000, lr = 0.00594604\n# Iteration 10000, loss = 0.00271736\n# Iteration 10000, Testing net (#0)\n# Test score #0: 0.9914\n# Test score #1: 0.0276671\n# Iteration 10000, Testing net (#1)\n# Test score #0: 0.997782\n# Test score #1: 0.00908085\n"
  },
  {
    "path": "Caffe/mnist/lenet_multistep_solver.prototxt",
    "content": "# The train/test net protocol buffer definition\nnet: \"examples/mnist/lenet_train_test.prototxt\"\n# test_iter specifies how many forward passes the test should carry out.\n# In the case of MNIST, we have test batch size 100 and 100 test iterations,\n# covering the full 10,000 testing images.\ntest_iter: 100\n# Carry out testing every 500 training iterations.\ntest_interval: 500\n# The base learning rate, momentum and the weight decay of the network.\nbase_lr: 0.01\nmomentum: 0.9\nweight_decay: 0.0005\n# The learning rate policy\nlr_policy: \"multistep\"\ngamma: 0.9\nstepvalue: 5000\nstepvalue: 7000\nstepvalue: 8000\nstepvalue: 9000\nstepvalue: 9500\n# Display every 100 iterations\ndisplay: 100\n# The maximum number of iterations\nmax_iter: 10000\n# snapshot intermediate results\nsnapshot: 5000\nsnapshot_prefix: \"examples/mnist/lenet_multistep\"\n# solver mode: CPU or GPU\nsolver_mode: GPU\n"
  },
  {
    "path": "Caffe/mnist/lenet_solver.prototxt",
    "content": "# The train/test net protocol buffer definition\nnet: \"examples/mnist/lenet_train_test.prototxt\"\n# test_iter specifies how many forward passes the test should carry out.\n# In the case of MNIST, we have test batch size 100 and 100 test iterations,\n# covering the full 10,000 testing images.\ntest_iter: 100\n# Carry out testing every 500 training iterations.\ntest_interval: 500\n# The base learning rate, momentum and the weight decay of the network.\nbase_lr: 0.01\nmomentum: 0.9\nweight_decay: 0.0005\n# The learning rate policy\nlr_policy: \"inv\"\ngamma: 0.0001\npower: 0.75\n# Display every 100 iterations\ndisplay: 100\n# The maximum number of iterations\nmax_iter: 10000\n# snapshot intermediate results\nsnapshot: 5000\nsnapshot_prefix: \"examples/mnist/lenet\"\n# solver mode: CPU or GPU\nsolver_mode: GPU\n"
  },
  {
    "path": "Caffe/mnist/lenet_solver_adam.prototxt",
    "content": "# The train/test net protocol buffer definition\n# this follows \"ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION\"\nnet: \"examples/mnist/lenet_train_test.prototxt\"\n# test_iter specifies how many forward passes the test should carry out.\n# In the case of MNIST, we have test batch size 100 and 100 test iterations,\n# covering the full 10,000 testing images.\ntest_iter: 100\n# Carry out testing every 500 training iterations.\ntest_interval: 500\n# All parameters are from the cited paper above\nbase_lr: 0.001\nmomentum: 0.9\nmomentum2: 0.999\n# since Adam dynamically changes the learning rate, we set the base learning\n# rate to a fixed value\nlr_policy: \"fixed\"\n# Display every 100 iterations\ndisplay: 100\n# The maximum number of iterations\nmax_iter: 10000\n# snapshot intermediate results\nsnapshot: 5000\nsnapshot_prefix: \"examples/mnist/lenet\"\n# solver mode: CPU or GPU\ntype: \"Adam\"\nsolver_mode: GPU\n"
  },
  {
    "path": "Caffe/mnist/lenet_solver_rmsprop.prototxt",
    "content": "# The train/test net protocol buffer definition\nnet: \"examples/mnist/lenet_train_test.prototxt\"\n# test_iter specifies how many forward passes the test should carry out.\n# In the case of MNIST, we have test batch size 100 and 100 test iterations,\n# covering the full 10,000 testing images.\ntest_iter: 100\n# Carry out testing every 500 training iterations.\ntest_interval: 500\n# The base learning rate, momentum and the weight decay of the network.\nbase_lr: 0.01\nmomentum: 0.0\nweight_decay: 0.0005\n# The learning rate policy\nlr_policy: \"inv\"\ngamma: 0.0001\npower: 0.75\n# Display every 100 iterations\ndisplay: 100\n# The maximum number of iterations\nmax_iter: 10000\n# snapshot intermediate results\nsnapshot: 5000\nsnapshot_prefix: \"examples/mnist/lenet_rmsprop\"\n# solver mode: CPU or GPU\nsolver_mode: GPU\ntype: \"RMSProp\"\nrms_decay: 0.98\n"
  },
  {
    "path": "Caffe/mnist/lenet_train_test.prototxt",
    "content": "name: \"LeNet\"\nlayer {\n  name: \"mnist\"\n  type: \"Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TRAIN\n  }\n  transform_param {\n    scale: 0.00390625\n  }\n  data_param {\n    source: \"examples/mnist/mnist_train_lmdb\"\n    batch_size: 64\n    backend: LMDB\n  }\n}\nlayer {\n  name: \"mnist\"\n  type: \"Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TEST\n  }\n  transform_param {\n    scale: 0.00390625\n  }\n  data_param {\n    source: \"examples/mnist/mnist_test_lmdb\"\n    batch_size: 100\n    backend: LMDB\n  }\n}\nlayer {\n  name: \"conv1\"\n  type: \"Convolution\"\n  bottom: \"data\"\n  top: \"conv1\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 20\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"pool1\"\n  type: \"Pooling\"\n  bottom: \"conv1\"\n  top: \"pool1\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 2\n    stride: 2\n  }\n}\nlayer {\n  name: \"conv2\"\n  type: \"Convolution\"\n  bottom: \"pool1\"\n  top: \"conv2\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 50\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"pool2\"\n  type: \"Pooling\"\n  bottom: \"conv2\"\n  top: \"pool2\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 2\n    stride: 2\n  }\n}\nlayer {\n  name: \"ip1\"\n  type: \"InnerProduct\"\n  bottom: \"pool2\"\n  top: \"ip1\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 500\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"relu1\"\n  type: \"ReLU\"\n  bottom: \"ip1\"\n  top: \"ip1\"\n}\nlayer {\n  name: \"ip2\"\n  type: \"InnerProduct\"\n  bottom: \"ip1\"\n  top: \"ip2\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 10\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"accuracy\"\n  type: \"Accuracy\"\n  bottom: \"ip2\"\n  bottom: \"label\"\n  top: \"accuracy\"\n  include {\n    phase: TEST\n  }\n}\nlayer {\n  name: \"loss\"\n  type: \"SoftmaxWithLoss\"\n  bottom: \"ip2\"\n  bottom: \"label\"\n  top: \"loss\"\n}\n"
  },
  {
    "path": "Caffe/mnist/mnist_autoencoder.prototxt",
    "content": "name: \"MNISTAutoencoder\"\nlayer {\n  name: \"data\"\n  type: \"Data\"\n  top: \"data\"\n  include {\n    phase: TRAIN\n  }\n  transform_param {\n    scale: 0.0039215684\n  }\n  data_param {\n    source: \"examples/mnist/mnist_train_lmdb\"\n    batch_size: 100\n    backend: LMDB\n  }\n}\nlayer {\n  name: \"data\"\n  type: \"Data\"\n  top: \"data\"\n  include {\n    phase: TEST\n    stage: \"test-on-train\"\n  }\n  transform_param {\n    scale: 0.0039215684\n  }\n  data_param {\n    source: \"examples/mnist/mnist_train_lmdb\"\n    batch_size: 100\n    backend: LMDB\n  }\n}\nlayer {\n  name: \"data\"\n  type: \"Data\"\n  top: \"data\"\n  include {\n    phase: TEST\n    stage: \"test-on-test\"\n  }\n  transform_param {\n    scale: 0.0039215684\n  }\n  data_param {\n    source: \"examples/mnist/mnist_test_lmdb\"\n    batch_size: 100\n    backend: LMDB\n  }\n}\nlayer {\n  name: \"flatdata\"\n  type: \"Flatten\"\n  bottom: \"data\"\n  top: \"flatdata\"\n}\nlayer {\n  name: \"encode1\"\n  type: \"InnerProduct\"\n  bottom: \"data\"\n  top: \"encode1\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 1\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 1000\n    weight_filler {\n      type: \"gaussian\"\n      std: 1\n      sparse: 15\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 0\n    }\n  }\n}\nlayer {\n  name: \"encode1neuron\"\n  type: \"Sigmoid\"\n  bottom: \"encode1\"\n  top: \"encode1neuron\"\n}\nlayer {\n  name: \"encode2\"\n  type: \"InnerProduct\"\n  bottom: \"encode1neuron\"\n  top: \"encode2\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 1\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 500\n    weight_filler {\n      type: \"gaussian\"\n      std: 1\n      sparse: 15\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 0\n    }\n  }\n}\nlayer {\n  name: \"encode2neuron\"\n  type: \"Sigmoid\"\n  bottom: \"encode2\"\n  top: \"encode2neuron\"\n}\nlayer {\n  name: \"encode3\"\n  type: \"InnerProduct\"\n  bottom: \"encode2neuron\"\n  top: \"encode3\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 1\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 250\n    weight_filler {\n      type: \"gaussian\"\n      std: 1\n      sparse: 15\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 0\n    }\n  }\n}\nlayer {\n  name: \"encode3neuron\"\n  type: \"Sigmoid\"\n  bottom: \"encode3\"\n  top: \"encode3neuron\"\n}\nlayer {\n  name: \"encode4\"\n  type: \"InnerProduct\"\n  bottom: \"encode3neuron\"\n  top: \"encode4\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 1\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 30\n    weight_filler {\n      type: \"gaussian\"\n      std: 1\n      sparse: 15\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 0\n    }\n  }\n}\nlayer {\n  name: \"decode4\"\n  type: \"InnerProduct\"\n  bottom: \"encode4\"\n  top: \"decode4\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 1\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 250\n    weight_filler {\n      type: \"gaussian\"\n      std: 1\n      sparse: 15\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 0\n    }\n  }\n}\nlayer {\n  name: \"decode4neuron\"\n  type: \"Sigmoid\"\n  bottom: \"decode4\"\n  top: \"decode4neuron\"\n}\nlayer {\n  name: \"decode3\"\n  type: \"InnerProduct\"\n  bottom: \"decode4neuron\"\n  top: \"decode3\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 1\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 500\n    weight_filler {\n      type: \"gaussian\"\n      std: 1\n      sparse: 15\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 0\n    }\n  }\n}\nlayer {\n  name: \"decode3neuron\"\n  type: \"Sigmoid\"\n  bottom: \"decode3\"\n  top: \"decode3neuron\"\n}\nlayer {\n  name: \"decode2\"\n  type: \"InnerProduct\"\n  bottom: \"decode3neuron\"\n  top: \"decode2\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 1\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 1000\n    weight_filler {\n      type: \"gaussian\"\n      std: 1\n      sparse: 15\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 0\n    }\n  }\n}\nlayer {\n  name: \"decode2neuron\"\n  type: \"Sigmoid\"\n  bottom: \"decode2\"\n  top: \"decode2neuron\"\n}\nlayer {\n  name: \"decode1\"\n  type: \"InnerProduct\"\n  bottom: \"decode2neuron\"\n  top: \"decode1\"\n  param {\n    lr_mult: 1\n    decay_mult: 1\n  }\n  param {\n    lr_mult: 1\n    decay_mult: 0\n  }\n  inner_product_param {\n    num_output: 784\n    weight_filler {\n      type: \"gaussian\"\n      std: 1\n      sparse: 15\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 0\n    }\n  }\n}\nlayer {\n  name: \"loss\"\n  type: \"SigmoidCrossEntropyLoss\"\n  bottom: \"decode1\"\n  bottom: \"flatdata\"\n  top: \"cross_entropy_loss\"\n  loss_weight: 1\n}\nlayer {\n  name: \"decode1neuron\"\n  type: \"Sigmoid\"\n  bottom: \"decode1\"\n  top: \"decode1neuron\"\n}\nlayer {\n  name: \"loss\"\n  type: \"EuclideanLoss\"\n  bottom: \"decode1neuron\"\n  bottom: \"flatdata\"\n  top: \"l2_error\"\n  loss_weight: 0\n}\n"
  },
  {
    "path": "Caffe/mnist/mnist_autoencoder_solver.prototxt",
    "content": "net: \"examples/mnist/mnist_autoencoder.prototxt\"\ntest_state: { stage: 'test-on-train' }\ntest_iter: 500\ntest_state: { stage: 'test-on-test' }\ntest_iter: 100\ntest_interval: 500\ntest_compute_loss: true\nbase_lr: 0.01\nlr_policy: \"step\"\ngamma: 0.1\nstepsize: 10000\ndisplay: 100\nmax_iter: 65000\nweight_decay: 0.0005\nsnapshot: 10000\nsnapshot_prefix: \"examples/mnist/mnist_autoencoder\"\nmomentum: 0.9\n# solver mode: CPU or GPU\nsolver_mode: GPU\n"
  },
  {
    "path": "Caffe/mnist/mnist_autoencoder_solver_adadelta.prototxt",
    "content": "net: \"examples/mnist/mnist_autoencoder.prototxt\"\ntest_state: { stage: 'test-on-train' }\ntest_iter: 500\ntest_state: { stage: 'test-on-test' }\ntest_iter: 100\ntest_interval: 500\ntest_compute_loss: true\nbase_lr: 1.0\nlr_policy: \"fixed\"\nmomentum: 0.95\ndelta: 1e-8\ndisplay: 100\nmax_iter: 65000\nweight_decay: 0.0005\nsnapshot: 10000\nsnapshot_prefix: \"examples/mnist/mnist_autoencoder_adadelta_train\"\n# solver mode: CPU or GPU\nsolver_mode: GPU\ntype: \"AdaDelta\"\n"
  },
  {
    "path": "Caffe/mnist/mnist_autoencoder_solver_adagrad.prototxt",
    "content": "net: \"examples/mnist/mnist_autoencoder.prototxt\"\ntest_state: { stage: 'test-on-train' }\ntest_iter: 500\ntest_state: { stage: 'test-on-test' }\ntest_iter: 100\ntest_interval: 500\ntest_compute_loss: true\nbase_lr: 0.01\nlr_policy: \"fixed\"\ndisplay: 100\nmax_iter: 65000\nweight_decay: 0.0005\nsnapshot: 10000\nsnapshot_prefix: \"examples/mnist/mnist_autoencoder_adagrad_train\"\n# solver mode: CPU or GPU\nsolver_mode: GPU\ntype: \"AdaGrad\"\n"
  },
  {
    "path": "Caffe/mnist/mnist_autoencoder_solver_nesterov.prototxt",
    "content": "net: \"examples/mnist/mnist_autoencoder.prototxt\"\ntest_state: { stage: 'test-on-train' }\ntest_iter: 500\ntest_state: { stage: 'test-on-test' }\ntest_iter: 100\ntest_interval: 500\ntest_compute_loss: true\nbase_lr: 0.01\nlr_policy: \"step\"\ngamma: 0.1\nstepsize: 10000\ndisplay: 100\nmax_iter: 65000\nweight_decay: 0.0005\nsnapshot: 10000\nsnapshot_prefix: \"examples/mnist/mnist_autoencoder_nesterov_train\"\nmomentum: 0.95\n# solver mode: CPU or GPU\nsolver_mode: GPU\ntype: \"Nesterov\"\n"
  },
  {
    "path": "Caffe/mnist/readme.md",
    "content": "---\ntitle: LeNet MNIST Tutorial\ndescription: Train and test \"LeNet\" on the MNIST handwritten digit data.\ncategory: example\ninclude_in_docs: true\npriority: 1\n---\n\n# Training LeNet on MNIST with Caffe\n\nWe will assume that you have Caffe successfully compiled. If not, please refer to the [Installation page](/installation.html). In this tutorial, we will assume that your Caffe installation is located at `CAFFE_ROOT`.\n\n## Prepare Datasets\n\nYou will first need to download and convert the data format from the MNIST website. To do this, simply run the following commands:\n\n    cd $CAFFE_ROOT\n    ./data/mnist/get_mnist.sh\n    ./examples/mnist/create_mnist.sh\n\nIf it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be two datasets, `mnist_train_lmdb`, and `mnist_test_lmdb`.\n\n## LeNet: the MNIST Classification Model\n\nBefore we actually run the training program, let's explain what will happen. We will use the [LeNet](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf) network, which is known to work well on digit classification tasks. We will use a slightly different version from the original LeNet implementation, replacing the sigmoid activations with Rectified Linear Unit (ReLU) activations for the neurons.\n\nThe design of LeNet contains the essence of CNNs that are still used in larger models such as the ones in ImageNet. In general, it consists of a convolutional layer followed by a pooling layer, another convolution layer followed by a pooling layer, and then two fully connected layers similar to the conventional multilayer perceptrons. We have defined the layers in `$CAFFE_ROOT/examples/mnist/lenet_train_test.prototxt`.\n\n## Define the MNIST Network\n\nThis section explains the `lenet_train_test.prototxt` model definition that specifies the LeNet model for MNIST handwritten digit classification. We assume that you are familiar with [Google Protobuf](https://developers.google.com/protocol-buffers/docs/overview), and assume that you have read the protobuf definitions used by Caffe, which can be found at `$CAFFE_ROOT/src/caffe/proto/caffe.proto`.\n\nSpecifically, we will write a `caffe::NetParameter` (or in python, `caffe.proto.caffe_pb2.NetParameter`) protobuf. We will start by giving the network a name:\n\n    name: \"LeNet\"\n\n### Writing the Data Layer\n\nCurrently, we will read the MNIST data from the lmdb we created earlier in the demo. This is defined by a data layer:\n\n    layer {\n      name: \"mnist\"\n      type: \"Data\"\n      transform_param {\n        scale: 0.00390625\n      }\n      data_param {\n        source: \"mnist_train_lmdb\"\n        backend: LMDB\n        batch_size: 64\n      }\n      top: \"data\"\n      top: \"label\"\n    }\n\nSpecifically, this layer has name `mnist`, type `data`, and it reads the data from the given lmdb source. We will use a batch size of 64, and scale the incoming pixels so that they are in the range \\[0,1\\). Why 0.00390625? It is 1 divided by 256. And finally, this layer produces two blobs, one is the `data` blob, and one is the `label` blob.\n\n### Writing the Convolution Layer\n\nLet's define the first convolution layer:\n\n    layer {\n      name: \"conv1\"\n      type: \"Convolution\"\n      param { lr_mult: 1 }\n      param { lr_mult: 2 }\n      convolution_param {\n        num_output: 20\n        kernel_size: 5\n        stride: 1\n        weight_filler {\n          type: \"xavier\"\n        }\n        bias_filler {\n          type: \"constant\"\n        }\n      }\n      bottom: \"data\"\n      top: \"conv1\"\n    }\n\nThis layer takes the `data` blob (it is provided by the data layer), and produces the `conv1` layer. It produces outputs of 20 channels, with the convolutional kernel size 5 and carried out with stride 1.\n\nThe fillers allow us to randomly initialize the value of the weights and bias. For the weight filler, we will use the `xavier` algorithm that automatically determines the scale of initialization based on the number of input and output neurons. For the bias filler, we will simply initialize it as constant, with the default filling value 0.\n\n`lr_mult`s are the learning rate adjustments for the layer's learnable parameters. In this case, we will set the weight learning rate to be the same as the learning rate given by the solver during runtime, and the bias learning rate to be twice as large as that - this usually leads to better convergence rates.\n\n### Writing the Pooling Layer\n\nPhew. Pooling layers are actually much easier to define:\n\n    layer {\n      name: \"pool1\"\n      type: \"Pooling\"\n      pooling_param {\n        kernel_size: 2\n        stride: 2\n        pool: MAX\n      }\n      bottom: \"conv1\"\n      top: \"pool1\"\n    }\n\nThis says we will perform max pooling with a pool kernel size 2 and a stride of 2 (so no overlapping between neighboring pooling regions).\n\nSimilarly, you can write up the second convolution and pooling layers. Check `$CAFFE_ROOT/examples/mnist/lenet_train_test.prototxt` for details.\n\n### Writing the Fully Connected Layer\n\nWriting a fully connected layer is also simple:\n\n    layer {\n      name: \"ip1\"\n      type: \"InnerProduct\"\n      param { lr_mult: 1 }\n      param { lr_mult: 2 }\n      inner_product_param {\n        num_output: 500\n        weight_filler {\n          type: \"xavier\"\n        }\n        bias_filler {\n          type: \"constant\"\n        }\n      }\n      bottom: \"pool2\"\n      top: \"ip1\"\n    }\n\nThis defines a fully connected layer (known in Caffe as an `InnerProduct` layer) with 500 outputs. All other lines look familiar, right?\n\n### Writing the ReLU Layer\n\nA ReLU Layer is also simple:\n\n    layer {\n      name: \"relu1\"\n      type: \"ReLU\"\n      bottom: \"ip1\"\n      top: \"ip1\"\n    }\n\nSince ReLU is an element-wise operation, we can do *in-place* operations to save some memory. This is achieved by simply giving the same name to the bottom and top blobs. Of course, do NOT use duplicated blob names for other layer types!\n\nAfter the ReLU layer, we will write another innerproduct layer:\n\n    layer {\n      name: \"ip2\"\n      type: \"InnerProduct\"\n      param { lr_mult: 1 }\n      param { lr_mult: 2 }\n      inner_product_param {\n        num_output: 10\n        weight_filler {\n          type: \"xavier\"\n        }\n        bias_filler {\n          type: \"constant\"\n        }\n      }\n      bottom: \"ip1\"\n      top: \"ip2\"\n    }\n\n### Writing the Loss Layer\n\nFinally, we will write the loss!\n\n    layer {\n      name: \"loss\"\n      type: \"SoftmaxWithLoss\"\n      bottom: \"ip2\"\n      bottom: \"label\"\n    }\n\nThe `softmax_loss` layer implements both the softmax and the multinomial logistic loss (that saves time and improves numerical stability). It takes two blobs, the first one being the prediction and the second one being the `label` provided by the data layer (remember it?). It does not produce any outputs - all it does is to compute the loss function value, report it when backpropagation starts, and initiates the gradient with respect to `ip2`. This is where all magic starts.\n\n\n### Additional Notes: Writing Layer Rules\n\nLayer definitions can include rules for whether and when they are included in the network definition, like the one below:\n\n    layer {\n      // ...layer definition...\n      include: { phase: TRAIN }\n    }\n\nThis is a rule, which controls layer inclusion in the network, based on current network's state.\nYou can refer to `$CAFFE_ROOT/src/caffe/proto/caffe.proto` for more information about layer rules and model schema.\n\nIn the above example, this layer will be included only in `TRAIN` phase.\nIf we change `TRAIN` with `TEST`, then this layer will be used only in test phase.\nBy default, that is without layer rules, a layer is always included in the network.\nThus, `lenet_train_test.prototxt` has two `DATA` layers defined (with different `batch_size`), one for the training phase and one for the testing phase.\nAlso, there is an `Accuracy` layer which is included only in `TEST` phase for reporting the model accuracy every 100 iteration, as defined in `lenet_solver.prototxt`.\n\n## Define the MNIST Solver\n\nCheck out the comments explaining each line in the prototxt `$CAFFE_ROOT/examples/mnist/lenet_solver.prototxt`:\n\n    # The train/test net protocol buffer definition\n    net: \"examples/mnist/lenet_train_test.prototxt\"\n    # test_iter specifies how many forward passes the test should carry out.\n    # In the case of MNIST, we have test batch size 100 and 100 test iterations,\n    # covering the full 10,000 testing images.\n    test_iter: 100\n    # Carry out testing every 500 training iterations.\n    test_interval: 500\n    # The base learning rate, momentum and the weight decay of the network.\n    base_lr: 0.01\n    momentum: 0.9\n    weight_decay: 0.0005\n    # The learning rate policy\n    lr_policy: \"inv\"\n    gamma: 0.0001\n    power: 0.75\n    # Display every 100 iterations\n    display: 100\n    # The maximum number of iterations\n    max_iter: 10000\n    # snapshot intermediate results\n    snapshot: 5000\n    snapshot_prefix: \"examples/mnist/lenet\"\n    # solver mode: CPU or GPU\n    solver_mode: GPU\n\n\n## Training and Testing the Model\n\nTraining the model is simple after you have written the network definition protobuf and solver protobuf files. Simply run `train_lenet.sh`, or the following command directly:\n\n    cd $CAFFE_ROOT\n    ./examples/mnist/train_lenet.sh\n\n`train_lenet.sh` is a simple script, but here is a quick explanation: the main tool for training is `caffe` with action `train` and the solver protobuf text file as its argument.\n\nWhen you run the code, you will see a lot of messages flying by like this:\n\n    I1203 net.cpp:66] Creating Layer conv1\n    I1203 net.cpp:76] conv1 <- data\n    I1203 net.cpp:101] conv1 -> conv1\n    I1203 net.cpp:116] Top shape: 20 24 24\n    I1203 net.cpp:127] conv1 needs backward computation.\n\nThese messages tell you the details about each layer, its connections and its output shape, which may be helpful in debugging. After the initialization, the training will start:\n\n    I1203 net.cpp:142] Network initialization done.\n    I1203 solver.cpp:36] Solver scaffolding done.\n    I1203 solver.cpp:44] Solving LeNet\n\nBased on the solver setting, we will print the training loss function every 100 iterations, and test the network every 500 iterations. You will see messages like this:\n\n    I1203 solver.cpp:204] Iteration 100, lr = 0.00992565\n    I1203 solver.cpp:66] Iteration 100, loss = 0.26044\n    ...\n    I1203 solver.cpp:84] Testing net\n    I1203 solver.cpp:111] Test score #0: 0.9785\n    I1203 solver.cpp:111] Test score #1: 0.0606671\n\nFor each training iteration, `lr` is the learning rate of that iteration, and `loss` is the training function. For the output of the testing phase, score 0 is the accuracy, and score 1 is the testing loss function.\n\nAnd after a few minutes, you are done!\n\n    I1203 solver.cpp:84] Testing net\n    I1203 solver.cpp:111] Test score #0: 0.9897\n    I1203 solver.cpp:111] Test score #1: 0.0324599\n    I1203 solver.cpp:126] Snapshotting to lenet_iter_10000\n    I1203 solver.cpp:133] Snapshotting solver state to lenet_iter_10000.solverstate\n    I1203 solver.cpp:78] Optimization Done.\n\nThe final model, stored as a binary protobuf file, is stored at\n\n    lenet_iter_10000\n\nwhich you can deploy as a trained model in your application, if you are training on a real-world application dataset.\n\n### Um... How about GPU training?\n\nYou just did! All the training was carried out on the GPU. In fact, if you would like to do training on CPU, you can simply change one line in `lenet_solver.prototxt`:\n\n    # solver mode: CPU or GPU\n    solver_mode: CPU\n\nand you will be using CPU for training. Isn't that easy?\n\nMNIST is a small dataset, so training with GPU does not really introduce too much benefit due to communication overheads. On larger datasets with more complex models, such as ImageNet, the computation speed difference will be more significant.\n\n### How to reduce the learning rate at fixed steps?\nLook at lenet_multistep_solver.prototxt\n"
  },
  {
    "path": "Caffe/mnist/train_lenet.sh",
    "content": "#!/usr/bin/env sh\nset -e\n\n./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt $@\n"
  },
  {
    "path": "Caffe/mnist/train_lenet_adam.sh",
    "content": "#!/usr/bin/env sh\nset -e\n\n./build/tools/caffe train --solver=examples/mnist/lenet_solver_adam.prototxt $@\n"
  },
  {
    "path": "Caffe/mnist/train_lenet_consolidated.sh",
    "content": "#!/usr/bin/env sh\nset -e\n\n./build/tools/caffe train \\\n  --solver=examples/mnist/lenet_consolidated_solver.prototxt $@\n"
  },
  {
    "path": "Caffe/mnist/train_lenet_docker.sh",
    "content": "#!/usr/bin/env sh\nset -e\n# The following example allows for the MNIST example (using LeNet) to be\n# trained using the caffe docker image instead of building from source.\n#\n# The GPU-enabled version of Caffe can be used, assuming that nvidia-docker\n# is installed, and the GPU-enabled Caffe image has been built.\n# Setting the GPU environment variable to 1 will enable the use of nvidia-docker.\n# e.g.\n#   GPU=1 ./examples/mnist/train_lenet_docker.sh [ADDITIONAL_CAFFE_ARGS]\n#\n# With any arguments following the script being passed directly to caffe\n# when training the network.\n#\n# The steps that are performed by the script are as follows:\n# 1. The MNIST data set is downloaded\n#    (see data/mnist/get_mnist.sh)\n# 2. An LMDB database is created from the downloaded data\n#    (see examples/mnist/create_mnist.sh.\n# 3. A caffe network based on the LeNet solver is trained.\n#    (see examples/mnist/lenet_solver.prototxt)\n#\n# For each of these, a step is executed to ensure that certain prerequisites\n# are available, after which a command that actually performs the work is\n# executed.\n#\n# In order to provide additional flexibility, the following shell (environment)\n# variables can be used to controll the execution of each of the phases:\n#\n# DOWNLOAD_DATA: Enable (1) or disable (0) the downloading of the MNIST dataset\n# CREATE_LMDB: Enable (1) or disable (0) the creation of the LMDB database\n# TRAIN: Enable (1) or disable (0) the training of the LeNet networkd.\n#\n# As an example, assuming that the data set has been downloaded, and an LMDB\n# database created, the following command can be used to train the LeNet\n# network with GPU computing enabled.\n#\n# DOWNLOAD_DATA=0 CREATE_LMDB=0 GPU=1 ./examples/mnist/train_lenet_docker.sh\n#\n\n\nif [ x\"$(uname -s)\" != x\"Linux\" ]\nthen\necho \"\"\necho \"This script is designed to run on Linux.\"\necho \"There may be problems with the way Docker mounts host volumes on other\"\necho \"systems which will cause the docker commands to fail.\"\necho \"\"\nread -p \"Press [ENTER] to continue...\" key\necho \"\"\nfi\n\n\n# Check if GPU mode has been enabled and set the docker executable accordingly\nif [ ${GPU:-0} -eq 1 ]\nthen\nDOCKER_CMD=nvidia-docker\nIMAGE=caffe:gpu\nelse\nDOCKER_CMD=docker\nIMAGE=caffe:cpu\nfi\necho \"Using $DOCKER_CMD to launch $IMAGE\"\n\n# On non-Linux systems, the Docker host is typically a virtual machine.\n# This means that the user and group id's may be different.\n# On OS X, for example, the user and group are 1000 and 50, respectively.\nif [ x\"$(uname -s)\" != x\"Linux\" ]\nthen\nCUID=1000\nCGID=50\nelse\nCUID=$(id -u)\nCGID=$(id -g)\nfi\n\n# Define some helper variables to make the running of the actual docker\n# commands less verbose.\n# Note:\n#   -u $CUID:$CGID             runs the docker image as the current user to ensure\n#                              that the file permissions are compatible with the\n#                              host system. The variables CUID and CGID have been\n#                              set above depending on the host operating system.\n#   --volume $(pwd):/workspace mounts the current directory as the docker volume\n#                              /workspace\n#   --workdir /workspace       Ensures that the docker container starts in the right\n#                              working directory\nDOCKER_OPTIONS=\"--rm -ti -u $CUID:$CGID --volume=$(pwd):/workspace --workdir=/workspace\"\nDOCKER_RUN=\"$DOCKER_CMD run $DOCKER_OPTIONS $IMAGE\"\n\n# Download the data\nif [ ${DOWNLOAD_DATA:-1} -eq 1 ]\nthen\n$DOCKER_RUN bash -c \"mkdir -p ./data/mnist;\n                     cp -ru \\$CAFFE_ROOT/data/mnist/get_mnist.sh ./data/mnist/\"\n$DOCKER_RUN ./data/mnist/get_mnist.sh\nfi\n\n# Create the LMDB database\nif [ ${CREATE_LMDB:-1} -eq 1 ]\nthen\n$DOCKER_RUN bash -c \"mkdir -p ./examples/mnist;\n                     cp -ru \\$CAFFE_ROOT/examples/mnist/create_mnist.sh ./examples/mnist/;\n                     sed -i s#BUILD=build#BUILD=\\$CAFFE_ROOT/build## ./examples/mnist/create_mnist.sh\"\n$DOCKER_RUN ./examples/mnist/create_mnist.sh\nfi\n\n# Train the network\nif [ ${TRAIN:-1} -eq 1 ]\nthen\n$DOCKER_RUN bash -c \"cp \\$CAFFE_ROOT/examples/mnist/lenet_solver.prototxt ./examples/mnist/;\n                     cp \\$CAFFE_ROOT/examples/mnist/lenet_train_test.prototxt ./examples/mnist/\"\n    # Ensure that the solver_mode is compatible with the desired GPU mode.\n    if [ ${GPU:-0} -eq 0 ]\n    then\n    $DOCKER_RUN sed -i 's#solver_mode: GPU#solver_mode: CPU##' ./examples/mnist/lenet_solver.prototxt\n    fi\n$DOCKER_RUN caffe train --solver=examples/mnist/lenet_solver.prototxt $*\nfi\n"
  },
  {
    "path": "Caffe/mnist/train_lenet_rmsprop.sh",
    "content": "#!/usr/bin/env sh\nset -e\n\n./build/tools/caffe train \\\n    --solver=examples/mnist/lenet_solver_rmsprop.prototxt $@\n"
  },
  {
    "path": "Caffe/mnist/train_mnist_autoencoder.sh",
    "content": "#!/usr/bin/env sh\nset -e\n\n./build/tools/caffe train \\\n  --solver=examples/mnist/mnist_autoencoder_solver.prototxt $@\n"
  },
  {
    "path": "Caffe/mnist/train_mnist_autoencoder_adadelta.sh",
    "content": "#!/bin/bash\nset -e\n\n./build/tools/caffe train \\\n  --solver=examples/mnist/mnist_autoencoder_solver_adadelta.prototxt $@\n"
  },
  {
    "path": "Caffe/mnist/train_mnist_autoencoder_adagrad.sh",
    "content": "#!/bin/bash\nset -e\n\n./build/tools/caffe train \\\n  --solver=examples/mnist/mnist_autoencoder_solver_adagrad.prototxt $@\n"
  },
  {
    "path": "Caffe/mnist/train_mnist_autoencoder_nesterov.sh",
    "content": "#!/bin/bash\nset -e\n\n./build/tools/caffe train \\\n  --solver=examples/mnist/mnist_autoencoder_solver_nesterov.prototxt $@\n"
  },
  {
    "path": "Caffe/net_surgery/bvlc_caffenet_full_conv.prototxt",
    "content": "# Fully convolutional network version of CaffeNet.\nname: \"CaffeNetConv\"\nlayer {\n  name: \"data\"\n  type: \"Input\"\n  top: \"data\"\n  input_param {\n    # initial shape for a fully convolutional network:\n    # the shape can be set for each input by reshape.\n    shape: { dim: 1 dim: 3 dim: 451 dim: 451 }\n  }\n}\nlayer {\n  name: \"conv1\"\n  type: \"Convolution\"\n  bottom: \"data\"\n  top: \"conv1\"\n  convolution_param {\n    num_output: 96\n    kernel_size: 11\n    stride: 4\n  }\n}\nlayer {\n  name: \"relu1\"\n  type: \"ReLU\"\n  bottom: \"conv1\"\n  top: \"conv1\"\n}\nlayer {\n  name: \"pool1\"\n  type: \"Pooling\"\n  bottom: \"conv1\"\n  top: \"pool1\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"norm1\"\n  type: \"LRN\"\n  bottom: \"pool1\"\n  top: \"norm1\"\n  lrn_param {\n    local_size: 5\n    alpha: 0.0001\n    beta: 0.75\n  }\n}\nlayer {\n  name: \"conv2\"\n  type: \"Convolution\"\n  bottom: \"norm1\"\n  top: \"conv2\"\n  convolution_param {\n    num_output: 256\n    pad: 2\n    kernel_size: 5\n    group: 2\n  }\n}\nlayer {\n  name: \"relu2\"\n  type: \"ReLU\"\n  bottom: \"conv2\"\n  top: \"conv2\"\n}\nlayer {\n  name: \"pool2\"\n  type: \"Pooling\"\n  bottom: \"conv2\"\n  top: \"pool2\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"norm2\"\n  type: \"LRN\"\n  bottom: \"pool2\"\n  top: \"norm2\"\n  lrn_param {\n    local_size: 5\n    alpha: 0.0001\n    beta: 0.75\n  }\n}\nlayer {\n  name: \"conv3\"\n  type: \"Convolution\"\n  bottom: \"norm2\"\n  top: \"conv3\"\n  convolution_param {\n    num_output: 384\n    pad: 1\n    kernel_size: 3\n  }\n}\nlayer {\n  name: \"relu3\"\n  type: \"ReLU\"\n  bottom: \"conv3\"\n  top: \"conv3\"\n}\nlayer {\n  name: \"conv4\"\n  type: \"Convolution\"\n  bottom: \"conv3\"\n  top: \"conv4\"\n  convolution_param {\n    num_output: 384\n    pad: 1\n    kernel_size: 3\n    group: 2\n  }\n}\nlayer {\n  name: \"relu4\"\n  type: \"ReLU\"\n  bottom: \"conv4\"\n  top: \"conv4\"\n}\nlayer {\n  name: \"conv5\"\n  type: \"Convolution\"\n  bottom: \"conv4\"\n  top: \"conv5\"\n  convolution_param {\n    num_output: 256\n    pad: 1\n    kernel_size: 3\n    group: 2\n  }\n}\nlayer {\n  name: \"relu5\"\n  type: \"ReLU\"\n  bottom: \"conv5\"\n  top: \"conv5\"\n}\nlayer {\n  name: \"pool5\"\n  type: \"Pooling\"\n  bottom: \"conv5\"\n  top: \"pool5\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 3\n    stride: 2\n  }\n}\nlayer {\n  name: \"fc6-conv\"\n  type: \"Convolution\"\n  bottom: \"pool5\"\n  top: \"fc6-conv\"\n  convolution_param {\n    num_output: 4096\n    kernel_size: 6\n  }\n}\nlayer {\n  name: \"relu6\"\n  type: \"ReLU\"\n  bottom: \"fc6-conv\"\n  top: \"fc6-conv\"\n}\nlayer {\n  name: \"drop6\"\n  type: \"Dropout\"\n  bottom: \"fc6-conv\"\n  top: \"fc6-conv\"\n  dropout_param {\n    dropout_ratio: 0.5\n  }\n}\nlayer {\n  name: \"fc7-conv\"\n  type: \"Convolution\"\n  bottom: \"fc6-conv\"\n  top: \"fc7-conv\"\n  convolution_param {\n    num_output: 4096\n    kernel_size: 1\n  }\n}\nlayer {\n  name: \"relu7\"\n  type: \"ReLU\"\n  bottom: \"fc7-conv\"\n  top: \"fc7-conv\"\n}\nlayer {\n  name: \"drop7\"\n  type: \"Dropout\"\n  bottom: \"fc7-conv\"\n  top: \"fc7-conv\"\n  dropout_param {\n    dropout_ratio: 0.5\n  }\n}\nlayer {\n  name: \"fc8-conv\"\n  type: \"Convolution\"\n  bottom: \"fc7-conv\"\n  top: \"fc8-conv\"\n  convolution_param {\n    num_output: 1000\n    kernel_size: 1\n  }\n}\nlayer {\n  name: \"prob\"\n  type: \"Softmax\"\n  bottom: \"fc8-conv\"\n  top: \"prob\"\n}\n"
  },
  {
    "path": "Caffe/net_surgery/conv.prototxt",
    "content": "# Simple single-layer network to showcase editing model parameters.\nname: \"convolution\"\nlayer {\n  name: \"data\"\n  type: \"Input\"\n  top: \"data\"\n  input_param { shape: { dim: 1 dim: 1 dim: 100 dim: 100 } }\n}\nlayer {\n  name: \"conv\"\n  type: \"Convolution\"\n  bottom: \"data\"\n  top: \"conv\"\n  convolution_param {\n    num_output: 3\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"gaussian\"\n      std: 0.01\n    }\n    bias_filler {\n      type: \"constant\"\n      value: 0\n    }\n  }\n}\n"
  },
  {
    "path": "Caffe/net_surgery.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Net Surgery\\n\",\n    \"\\n\",\n    \"Caffe networks can be transformed to your particular needs by editing the model parameters. The data, diffs, and parameters of a net are all exposed in pycaffe.\\n\",\n    \"\\n\",\n    \"Roll up your sleeves for net surgery with pycaffe!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"# Make sure that caffe is on the python path:\\n\",\n    \"caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\\n\",\n    \"import sys\\n\",\n    \"sys.path.insert(0, caffe_root + 'python')\\n\",\n    \"\\n\",\n    \"import caffe\\n\",\n    \"\\n\",\n    \"# configure plotting\\n\",\n    \"plt.rcParams['figure.figsize'] = (10, 10)\\n\",\n    \"plt.rcParams['image.interpolation'] = 'nearest'\\n\",\n    \"plt.rcParams['image.cmap'] = 'gray'\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Designer Filters\\n\",\n    \"\\n\",\n    \"To show how to load, manipulate, and save parameters we'll design our own filters into a simple network that's only a single convolution layer. This net has two blobs, `data` for the input and `conv` for the convolution output and one parameter `conv` for the convolution filter weights and biases.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"blobs ['data', 'conv']\\n\",\n      \"params ['conv']\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": [\n       \"iVBORw0KGgoAAAANSUhEUgAAAlIAAAHNCAYAAADVB5V4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\n\",\n       \"AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWuMZdl13/c/tx733np393T3PPkYDUccPsQZiaRkCYpE\\n\",\n       \"CYklOwYhfwjCIAEiJDLswAkQf3AQIEoC64OcIEDiIHESBAiCCAkkJ4GtJHCM+KHQjmGZtmxKJBVC\\n\",\n       \"wxkOZyac4Uz3dHe97q1bt+7Jh+r/rt/517499ER008xZQKGq7j1nn73XXns9/mvtfZq2bdVTTz31\\n\",\n       \"1FNPPfXU0z86DR52B3rqqaeeeuqpp57+SaXekeqpp5566qmnnnp6j9Q7Uj311FNPPfXUU0/vkXpH\\n\",\n       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\"An2cTMbHuQNp4jWeBPIEXv4sqVarsV57v4cvIjYPCAgICAgICPj/gCA2DwgICAgICAh4JoIjFRAQ\\n\",\n       \"EBAQEBDwTARHKiAgICAgICDgmQiOVEBAQEBAQEDAMxEcqYCAgICAgICAZyI4UgEBAQEBAQEBz0Rw\\n\",\n       \"pAICAgICAgICnongSAUEBAQEBAQEPBPBkQoICAgICAgIeCaCIxUQEBAQEBAQ8EwERyogICAgICAg\\n\",\n       \"4JkIjlRAQEBAQEBAwDMRHKmAgICAgICAgGciOFIBAQEBAQEBAc9EcKQCAgICAgICAp6J4EgFBAQE\\n\",\n       \"BAQEBDwTwZEKCAgICAgICHgmgiMVEBAQEBAQEPBMBEcqICAgICAgIOCZCI5UQEBAQEBAQMAz8T90\\n\",\n       \"n59+FodZjgAAAABJRU5ErkJggg==\\n\"\n      ],\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x112797f50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Load the net, list its data and params, and filter an example image.\\n\",\n    \"caffe.set_mode_cpu()\\n\",\n    \"net = caffe.Net('net_surgery/conv.prototxt', caffe.TEST)\\n\",\n    \"print(\\\"blobs {}\\\\nparams {}\\\".format(net.blobs.keys(), net.params.keys()))\\n\",\n    \"\\n\",\n    \"# load image and prepare as a single input batch for Caffe\\n\",\n    \"im = np.array(caffe.io.load_image('images/cat_gray.jpg', color=False)).squeeze()\\n\",\n    \"plt.title(\\\"original image\\\")\\n\",\n    \"plt.imshow(im)\\n\",\n    \"plt.axis('off')\\n\",\n    \"\\n\",\n    \"im_input = im[np.newaxis, np.newaxis, :, :]\\n\",\n    \"net.blobs['data'].reshape(*im_input.shape)\\n\",\n    \"net.blobs['data'].data[...] = im_input\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The convolution weights are initialized from Gaussian noise while the biases are initialized to zero. These random filters give output somewhat like edge detections.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": [\n       \"iVBORw0KGgoAAAANSUhEUgAAAicAAACbCAYAAAC5xzv6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\n\",\n       \"AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVuMbVl2pvWvfb/FjkueW568VN5dXSUbl4sHbBBYbYRK\\n\",\n       \"jRqEJW7qfkD90MItN4gGgQC3QHYJiwdejJFfcNvgRtBuaBAPyA9gt5FBcrnc1bbLVemqPFmZlZdz\\n\",\n       \"TuaJc+KybxH7sniI8839rxFrx4lMU7mjKveQQhGx97rMNeeYY/zjH2POleV5ro1sZCMb2chGNrKR\\n\",\n       \"qyKVdTdgIxvZyEY2spGNbMRlA042spGNbGQjG9nIlZINONnIRjaykY1sZCNXSjbgZCMb2chGNrKR\\n\",\n       \"jVwp2YCTjWxkIxvZyEY2cqVkA042spGNbGQjG9nIlZJPDTjJsuyHsiz7x1mWHWVZ9jezLPuVLMt+\\n\",\n       \"7vF3P5ll2TvrbuNGNvJxZKPbG/lBlY1uf3rlUwNOJP2Hkv6vPM/7eZ7/13me/0ye518uOzDLsrey\\n\",\n       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\"31Tbg5N927d927d927d9+6baHpzs277t277t277t2zfV/i+IAQDEy/wsagAAAABJRU5ErkJggg==\\n\"\n      ],\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x113e04090>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# helper show filter outputs\\n\",\n    \"def show_filters(net):\\n\",\n    \"    net.forward()\\n\",\n    \"    plt.figure()\\n\",\n    \"    filt_min, filt_max = net.blobs['conv'].data.min(), net.blobs['conv'].data.max()\\n\",\n    \"    for i in range(3):\\n\",\n    \"        plt.subplot(1,4,i+2)\\n\",\n    \"        plt.title(\\\"filter #{} output\\\".format(i))\\n\",\n    \"        plt.imshow(net.blobs['conv'].data[0, i], vmin=filt_min, vmax=filt_max)\\n\",\n    \"        plt.tight_layout()\\n\",\n    \"        plt.axis('off')\\n\",\n    \"\\n\",\n    \"# filter the image with initial \\n\",\n    \"show_filters(net)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Raising the bias of a filter will correspondingly raise its output:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"pre-surgery output mean -0.02\\n\",\n      \"post-surgery output mean 0.98\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# pick first filter output\\n\",\n    \"conv0 = net.blobs['conv'].data[0, 0]\\n\",\n    \"print(\\\"pre-surgery output mean {:.2f}\\\".format(conv0.mean()))\\n\",\n    \"# set first filter bias to 1\\n\",\n    \"net.params['conv'][1].data[0] = 1.\\n\",\n    \"net.forward()\\n\",\n    \"print(\\\"post-surgery output mean {:.2f}\\\".format(conv0.mean()))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Altering the filter weights is more exciting since we can assign any kernel like Gaussian blur, the Sobel operator for edges, and so on. The following surgery turns the 0th filter into a Gaussian blur and the 1st and 2nd filters into the horizontal and vertical gradient parts of the Sobel operator.\\n\",\n    \"\\n\",\n    \"See how the 0th output is blurred, the 1st picks up horizontal edges, and the 2nd picks up vertical edges.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": [\n       \"iVBORw0KGgoAAAANSUhEUgAAAicAAACbCAYAAAC5xzv6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\n\",\n       \"AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWuMbNl13/c/9eh6V7/uvT1zHzNDzgw5HNIWNInpMCEi\\n\",\n       \"2wkCwYElBFASBTLg2DCM2LATSAkSJ5GlWDJi5EMAA0ngL/EjkQPFcuIQgREEcCIbAkJD9JhDgdJ4\\n\",\n       \"yOFjHnfuq2/fflV1VXc9Tj7U/e3+1+pTfe+MqOkmWQtodHfVOfvsvfbaa/3XY++T5XmuJS1pSUta\\n\",\n       \"0pKWtKTLQqWL7sCSlrSkJS1pSUtaktMSnCxpSUta0pKWtKRLRUtwsqQlLWlJS1rSki4VLcHJkpa0\\n\",\n       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\"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x112bc2190>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ksize = net.params['conv'][0].data.shape[2:]\\n\",\n    \"# make Gaussian blur\\n\",\n    \"sigma = 1.\\n\",\n    \"y, x = np.mgrid[-ksize[0]//2 + 1:ksize[0]//2 + 1, -ksize[1]//2 + 1:ksize[1]//2 + 1]\\n\",\n    \"g = np.exp(-((x**2 + y**2)/(2.0*sigma**2)))\\n\",\n    \"gaussian = (g / g.sum()).astype(np.float32)\\n\",\n    \"net.params['conv'][0].data[0] = gaussian\\n\",\n    \"# make Sobel operator for edge detection\\n\",\n    \"net.params['conv'][0].data[1:] = 0.\\n\",\n    \"sobel = np.array((-1, -2, -1, 0, 0, 0, 1, 2, 1), dtype=np.float32).reshape((3,3))\\n\",\n    \"net.params['conv'][0].data[1, 0, 1:-1, 1:-1] = sobel  # horizontal\\n\",\n    \"net.params['conv'][0].data[2, 0, 1:-1, 1:-1] = sobel.T  # vertical\\n\",\n    \"show_filters(net)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"With net surgery, parameters can be transplanted across nets, regularized by custom per-parameter operations, and transformed according to your schemes.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Casting a Classifier into a Fully Convolutional Network\\n\",\n    \"\\n\",\n    \"Let's take the standard Caffe Reference ImageNet model \\\"CaffeNet\\\" and transform it into a fully convolutional net for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\\\times$ 8 classification map on a 451 $\\\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional network (convnet) structure by amortizing the computation of overlapping receptive fields.\\n\",\n    \"\\n\",\n    \"To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 $\\\\times$ 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1,2c1\\r\\n\",\n      \"< # Fully convolutional network version of CaffeNet.\\r\\n\",\n      \"< name: \\\"CaffeNetConv\\\"\\r\\n\",\n      \"---\\r\\n\",\n      \"> name: \\\"CaffeNet\\\"\\r\\n\",\n      \"7,11c6\\r\\n\",\n      \"<   input_param {\\r\\n\",\n      \"<     # initial shape for a fully convolutional network:\\r\\n\",\n      \"<     # the shape can be set for each input by reshape.\\r\\n\",\n      \"<     shape: { dim: 1 dim: 3 dim: 451 dim: 451 }\\r\\n\",\n      \"<   }\\r\\n\",\n      \"---\\r\\n\",\n      \">   input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }\\r\\n\",\n      \"157,158c152,153\\r\\n\",\n      \"<   name: \\\"fc6-conv\\\"\\r\\n\",\n      \"<   type: \\\"Convolution\\\"\\r\\n\",\n      \"---\\r\\n\",\n      \">   name: \\\"fc6\\\"\\r\\n\",\n      \">   type: \\\"InnerProduct\\\"\\r\\n\",\n      \"160,161c155,156\\r\\n\",\n      \"<   top: \\\"fc6-conv\\\"\\r\\n\",\n      \"<   convolution_param {\\r\\n\",\n      \"---\\r\\n\",\n      \">   top: \\\"fc6\\\"\\r\\n\",\n      \">   inner_product_param {\\r\\n\",\n      \"163d157\\r\\n\",\n      \"<     kernel_size: 6\\r\\n\",\n      \"169,170c163,164\\r\\n\",\n      \"<   bottom: \\\"fc6-conv\\\"\\r\\n\",\n      \"<   top: \\\"fc6-conv\\\"\\r\\n\",\n      \"---\\r\\n\",\n      \">   bottom: \\\"fc6\\\"\\r\\n\",\n      \">   top: \\\"fc6\\\"\\r\\n\",\n      \"175,176c169,170\\r\\n\",\n      \"<   bottom: \\\"fc6-conv\\\"\\r\\n\",\n      \"<   top: \\\"fc6-conv\\\"\\r\\n\",\n      \"---\\r\\n\",\n      \">   bottom: \\\"fc6\\\"\\r\\n\",\n      \">   top: \\\"fc6\\\"\\r\\n\",\n      \"182,186c176,180\\r\\n\",\n      \"<   name: \\\"fc7-conv\\\"\\r\\n\",\n      \"<   type: \\\"Convolution\\\"\\r\\n\",\n      \"<   bottom: \\\"fc6-conv\\\"\\r\\n\",\n      \"<   top: \\\"fc7-conv\\\"\\r\\n\",\n      \"<   convolution_param {\\r\\n\",\n      \"---\\r\\n\",\n      \">   name: \\\"fc7\\\"\\r\\n\",\n      \">   type: \\\"InnerProduct\\\"\\r\\n\",\n      \">   bottom: \\\"fc6\\\"\\r\\n\",\n      \">   top: \\\"fc7\\\"\\r\\n\",\n      \">   inner_product_param {\\r\\n\",\n      \"188d181\\r\\n\",\n      \"<     kernel_size: 1\\r\\n\",\n      \"194,195c187,188\\r\\n\",\n      \"<   bottom: \\\"fc7-conv\\\"\\r\\n\",\n      \"<   top: \\\"fc7-conv\\\"\\r\\n\",\n      \"---\\r\\n\",\n      \">   bottom: \\\"fc7\\\"\\r\\n\",\n      \">   top: \\\"fc7\\\"\\r\\n\",\n      \"200,201c193,194\\r\\n\",\n      \"<   bottom: \\\"fc7-conv\\\"\\r\\n\",\n      \"<   top: \\\"fc7-conv\\\"\\r\\n\",\n      \"---\\r\\n\",\n      \">   bottom: \\\"fc7\\\"\\r\\n\",\n      \">   top: \\\"fc7\\\"\\r\\n\",\n      \"207,211c200,204\\r\\n\",\n      \"<   name: \\\"fc8-conv\\\"\\r\\n\",\n      \"<   type: \\\"Convolution\\\"\\r\\n\",\n      \"<   bottom: \\\"fc7-conv\\\"\\r\\n\",\n      \"<   top: \\\"fc8-conv\\\"\\r\\n\",\n      \"<   convolution_param {\\r\\n\",\n      \"---\\r\\n\",\n      \">   name: \\\"fc8\\\"\\r\\n\",\n      \">   type: \\\"InnerProduct\\\"\\r\\n\",\n      \">   bottom: \\\"fc7\\\"\\r\\n\",\n      \">   top: \\\"fc8\\\"\\r\\n\",\n      \">   inner_product_param {\\r\\n\",\n      \"213d205\\r\\n\",\n      \"<     kernel_size: 1\\r\\n\",\n      \"219c211\\r\\n\",\n      \"<   bottom: \\\"fc8-conv\\\"\\r\\n\",\n      \"---\\r\\n\",\n      \">   bottom: \\\"fc8\\\"\\r\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!diff net_surgery/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The only differences needed in the architecture are to change the fully connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"fc6 weights are (4096, 9216) dimensional and biases are (4096,) dimensional\\n\",\n      \"fc7 weights are (4096, 4096) dimensional and biases are (4096,) dimensional\\n\",\n      \"fc8 weights are (1000, 4096) dimensional and biases are (1000,) dimensional\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Load the original network and extract the fully connected layers' parameters.\\n\",\n    \"net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \\n\",\n    \"                '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \\n\",\n    \"                caffe.TEST)\\n\",\n    \"params = ['fc6', 'fc7', 'fc8']\\n\",\n    \"# fc_params = {name: (weights, biases)}\\n\",\n    \"fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\\n\",\n    \"\\n\",\n    \"for fc in params:\\n\",\n    \"    print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Consider the shapes of the inner product parameters. The weight dimensions are the output and input sizes while the bias dimension is the output size.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (4096,) dimensional\\n\",\n      \"fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (4096,) dimensional\\n\",\n      \"fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1000,) dimensional\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Load the fully convolutional network to transplant the parameters.\\n\",\n    \"net_full_conv = caffe.Net('net_surgery/bvlc_caffenet_full_conv.prototxt', \\n\",\n    \"                          '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\\n\",\n    \"                          caffe.TEST)\\n\",\n    \"params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\\n\",\n    \"# conv_params = {name: (weights, biases)}\\n\",\n    \"conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\\n\",\n    \"\\n\",\n    \"for conv in params_full_conv:\\n\",\n    \"    print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The convolution weights are arranged in output $\\\\times$ input $\\\\times$ height $\\\\times$ width dimensions. To map the inner product weights to convolution filters, we could roll the flat inner product vectors into channel $\\\\times$ height $\\\\times$ width filter matrices, but actually these are identical in memory (as row major arrays) so we can assign them directly.\\n\",\n    \"\\n\",\n    \"The biases are identical to those of the inner product.\\n\",\n    \"\\n\",\n    \"Let's transplant!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for pr, pr_conv in zip(params, params_full_conv):\\n\",\n    \"    conv_params[pr_conv][0].flat = fc_params[pr][0].flat  # flat unrolls the arrays\\n\",\n    \"    conv_params[pr_conv][1][...] = fc_params[pr][1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, save the new model weights.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"net_full_conv.save('net_surgery/bvlc_caffenet_full_conv.caffemodel')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To conclude, let's make a classification map from the example cat image and visualize the confidence of \\\"tiger cat\\\" as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\\\times$ 451 input.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[282 282 281 281 281 281 277 282]\\n\",\n      \" [281 283 283 281 281 281 281 282]\\n\",\n      \" [283 283 283 283 283 283 287 282]\\n\",\n      \" [283 283 283 281 283 283 283 259]\\n\",\n      \" [283 283 283 283 283 283 283 259]\\n\",\n      \" [283 283 283 283 283 283 259 259]\\n\",\n      \" [283 283 283 283 259 259 259 277]\\n\",\n      \" [335 335 283 259 263 263 263 277]]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x12379a690>\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n     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\"fwl8Grh5f/NUr8sBMm3/f1fe5qO7tfF2P3t1D5Ju5S+7RQJfFUI8K4TYS9bLo8CWEOLvhRDnhRB/\\n\",\n       \"I4Sw9kHfnqpXHXQwf8+sg5TZfc076dlXrUKIMvCvwB9KKcc3P3dQWqSUqZTyEWAZ+EkhxM8cpA4h\\n\",\n       \"xC8Cm1LK57lND+mgr8sB857WPvHRL5L56K5zCd/Cz356l3re1V/ukieklB8kKy7y+0KIp3ZpRwM+\\n\",\n       \"BPy1lPJDgAP86V6EiR9Wr/qX3do46GB+HVi56fcVfrTnda/pCiHaAEKIBWDzNrqWJ+f2BSGEThbI\\n\",\n       \"Py+l/NI0tQBMbgn/C3j0gHV8GPiEEOISWXGHjwghPn/AGqbJtP3/ttzko/90k4/uiZv87LFdmriV\\n\",\n       \"v/zjHvRsTH5uAf/O7ofsrgHXpJTfm/z+RbLgvhfesXrVnXDQwfxZ4AEhxJHJN9GvAF8+wPa/DHxq\\n\",\n       \"cvwp4Es3nf9VIYQhhDgKPADsabb7BkIIAfwd8KqU8q+mpUUI0bqxSkQIUQR+Dnj+IHVIKT8rpVyR\\n\",\n       \"Uh4lu6X8mpTyNw5Sw5SZtv/fknfw0d3Yup2f3TW38Zff3KUuSwhRmRyXgI8Cu1oJJKXsAFeFECcn\\n\",\n       \"p34WeGU3tm5i79Wr9mNm9y5nbD9ONlu+CnzmHrbzBWAdCMnGKX8baAJfBV4HvgLUb/r7z040XQR+\\n\",\n       \"fh91PEk23neBzKmfBz520FqA9wPnJzpeBD49OX/gn8nE9k/xw9UsU9Ewjcd++f9N/h3c8O/99tH9\\n\",\n       \"9LP99Jddvv7oRNMF4OW9xh7gYeB7wAvAv7GH1SxACdgGKnvRlG/nz8nJybkPyHeA5uTk5NwH5ME8\\n\",\n       \"Jycn5z4gD+Y5OTk59wF5MM/Jycm5D8iDeU5OTs59QB7Mc3Jycu4D8mCek5OTcx+QB/OcnJyc+4D/\\n\",\n       \"A43ph1xlbAoPAAAAAElFTkSuQmCC\\n\"\n      ],\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x12666be50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"# load input and configure preprocessing\\n\",\n    \"im = caffe.io.load_image('images/cat.jpg')\\n\",\n    \"transformer = caffe.io.Transformer({'data': net_full_conv.blobs['data'].data.shape})\\n\",\n    \"transformer.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))\\n\",\n    \"transformer.set_transpose('data', (2,0,1))\\n\",\n    \"transformer.set_channel_swap('data', (2,1,0))\\n\",\n    \"transformer.set_raw_scale('data', 255.0)\\n\",\n    \"# make classification map by forward and print prediction indices at each location\\n\",\n    \"out = net_full_conv.forward_all(data=np.asarray([transformer.preprocess('data', im)]))\\n\",\n    \"print out['prob'][0].argmax(axis=0)\\n\",\n    \"# show net input and confidence map (probability of the top prediction at each location)\\n\",\n    \"plt.subplot(1, 2, 1)\\n\",\n    \"plt.imshow(transformer.deprocess('data', net_full_conv.blobs['data'].data[0]))\\n\",\n    \"plt.subplot(1, 2, 2)\\n\",\n    \"plt.imshow(out['prob'][0,281])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\\n\",\n    \"\\n\",\n    \"In this way the fully connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\\n\",\n    \"\\n\",\n    \"Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"*A thank you to Rowland Depp for first suggesting this trick.*\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"description\": \"How to do net surgery and manually change model parameters for custom use.\",\n  \"example_name\": \"Editing model parameters\",\n  \"include_in_docs\": true,\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.9\"\n  },\n  \"priority\": 5\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Caffe/pascal-multilabel-with-datalayer.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Multilabel classification on PASCAL using python data-layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In this tutorial we will do multilabel classification on PASCAL VOC 2012.\\n\",\n    \"\\n\",\n    \"Multilabel classification is a generalization of multiclass classification, where each instance (image) can belong to many classes. For example, an image may both belong to a \\\"beach\\\" category and a \\\"vacation pictures\\\" category. In multiclass classification, on the other hand, each image belongs to a single class.\\n\",\n    \"\\n\",\n    \"Caffe supports multilabel classification through the SigmoidCrossEntropyLoss layer, and we will load data using a Python data layer. Data could also be provided through HDF5 or LMDB data layers, but the python data layer provides endless flexibility, so that's what we will use.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1. Preliminaries\\n\",\n    \"\\n\",\n    \"* First, make sure you compile caffe using\\n\",\n    \"WITH_PYTHON_LAYER := 1\\n\",\n    \"\\n\",\n    \"* Second, download PASCAL VOC 2012. It's available here: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html\\n\",\n    \"\\n\",\n    \"* Third, import modules:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import sys \\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import os.path as osp\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"from copy import copy\\n\",\n    \"\\n\",\n    \"% matplotlib inline\\n\",\n    \"plt.rcParams['figure.figsize'] = (6, 6)\\n\",\n    \"\\n\",\n    \"caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\\n\",\n    \"sys.path.append(caffe_root + 'python')\\n\",\n    \"import caffe # If you get \\\"No module named _caffe\\\", either you have not built pycaffe or you have the wrong path.\\n\",\n    \"\\n\",\n    \"from caffe import layers as L, params as P # Shortcuts to define the net prototxt.\\n\",\n    \"\\n\",\n    \"sys.path.append(\\\"pycaffe/layers\\\") # the datalayers we will use are in this directory.\\n\",\n    \"sys.path.append(\\\"pycaffe\\\") # the tools file is in this folder\\n\",\n    \"\\n\",\n    \"import tools #this contains some tools that we need\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Fourth, set data directories and initialize caffe\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# set data root directory, e.g:\\n\",\n    \"pascal_root = osp.join(caffe_root, 'data/pascal/VOC2012')\\n\",\n    \"\\n\",\n    \"# these are the PASCAL classes, we'll need them later.\\n\",\n    \"classes = np.asarray(['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'])\\n\",\n    \"\\n\",\n    \"# make sure we have the caffenet weight downloaded.\\n\",\n    \"if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\\n\",\n    \"    print(\\\"Downloading pre-trained CaffeNet model...\\\")\\n\",\n    \"    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet\\n\",\n    \"\\n\",\n    \"# initialize caffe for gpu mode\\n\",\n    \"caffe.set_mode_gpu()\\n\",\n    \"caffe.set_device(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2. Define network prototxts\\n\",\n    \"\\n\",\n    \"* Let's start by defining the nets using caffe.NetSpec. Note how we used the SigmoidCrossEntropyLoss layer. This is the right loss for multilabel classification. Also note how the data layer is defined.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# helper function for common structures\\n\",\n    \"def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):\\n\",\n    \"    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\\n\",\n    \"                                num_output=nout, pad=pad, group=group)\\n\",\n    \"    return conv, L.ReLU(conv, in_place=True)\\n\",\n    \"\\n\",\n    \"# another helper function\\n\",\n    \"def fc_relu(bottom, nout):\\n\",\n    \"    fc = L.InnerProduct(bottom, num_output=nout)\\n\",\n    \"    return fc, L.ReLU(fc, in_place=True)\\n\",\n    \"\\n\",\n    \"# yet another helper function\\n\",\n    \"def max_pool(bottom, ks, stride=1):\\n\",\n    \"    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\\n\",\n    \"\\n\",\n    \"# main netspec wrapper\\n\",\n    \"def caffenet_multilabel(data_layer_params, datalayer):\\n\",\n    \"    # setup the python data layer \\n\",\n    \"    n = caffe.NetSpec()\\n\",\n    \"    n.data, n.label = L.Python(module = 'pascal_multilabel_datalayers', layer = datalayer, \\n\",\n    \"                               ntop = 2, param_str=str(data_layer_params))\\n\",\n    \"\\n\",\n    \"    # the net itself\\n\",\n    \"    n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4)\\n\",\n    \"    n.pool1 = max_pool(n.relu1, 3, stride=2)\\n\",\n    \"    n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\\n\",\n    \"    n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2)\\n\",\n    \"    n.pool2 = max_pool(n.relu2, 3, stride=2)\\n\",\n    \"    n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\\n\",\n    \"    n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1)\\n\",\n    \"    n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2)\\n\",\n    \"    n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2)\\n\",\n    \"    n.pool5 = max_pool(n.relu5, 3, stride=2)\\n\",\n    \"    n.fc6, n.relu6 = fc_relu(n.pool5, 4096)\\n\",\n    \"    n.drop6 = L.Dropout(n.relu6, in_place=True)\\n\",\n    \"    n.fc7, n.relu7 = fc_relu(n.drop6, 4096)\\n\",\n    \"    n.drop7 = L.Dropout(n.relu7, in_place=True)\\n\",\n    \"    n.score = L.InnerProduct(n.drop7, num_output=20)\\n\",\n    \"    n.loss = L.SigmoidCrossEntropyLoss(n.score, n.label)\\n\",\n    \"    \\n\",\n    \"    return str(n.to_proto())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3. Write nets and solver files\\n\",\n    \"\\n\",\n    \"* Now we can crete net and solver prototxts. For the solver, we use the CaffeSolver class from the \\\"tools\\\" module\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"workdir = './pascal_multilabel_with_datalayer'\\n\",\n    \"if not os.path.isdir(workdir):\\n\",\n    \"    os.makedirs(workdir)\\n\",\n    \"\\n\",\n    \"solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \\\"trainnet.prototxt\\\"), testnet_prototxt_path = osp.join(workdir, \\\"valnet.prototxt\\\"))\\n\",\n    \"solverprototxt.sp['display'] = \\\"1\\\"\\n\",\n    \"solverprototxt.sp['base_lr'] = \\\"0.0001\\\"\\n\",\n    \"solverprototxt.write(osp.join(workdir, 'solver.prototxt'))\\n\",\n    \"\\n\",\n    \"# write train net.\\n\",\n    \"with open(osp.join(workdir, 'trainnet.prototxt'), 'w') as f:\\n\",\n    \"    # provide parameters to the data layer as a python dictionary. Easy as pie!\\n\",\n    \"    data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', pascal_root = pascal_root)\\n\",\n    \"    f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))\\n\",\n    \"\\n\",\n    \"# write validation net.\\n\",\n    \"with open(osp.join(workdir, 'valnet.prototxt'), 'w') as f:\\n\",\n    \"    data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', pascal_root = pascal_root)\\n\",\n    \"    f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* This net uses a python datalayer: 'PascalMultilabelDataLayerSync', which is defined in './pycaffe/layers/pascal_multilabel_datalayers.py'. \\n\",\n    \"\\n\",\n    \"* Take a look at the code. It's quite straight-forward, and gives you full control over data and labels.\\n\",\n    \"\\n\",\n    \"* Now we can load the caffe solver as usual.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"BatchLoader initialized with 5717 images\\n\",\n      \"PascalMultilabelDataLayerSync initialized for split: train, with bs: 128, im_shape: [227, 227].\\n\",\n      \"BatchLoader initialized with 5823 images\\n\",\n      \"PascalMultilabelDataLayerSync initialized for split: val, with bs: 128, im_shape: [227, 227].\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"solver = caffe.SGDSolver(osp.join(workdir, 'solver.prototxt'))\\n\",\n    \"solver.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\\n\",\n    \"solver.test_nets[0].share_with(solver.net)\\n\",\n    \"solver.step(1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Let's check the data we have loaded.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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gNnvEr+4w8RvLnjW8sH35MskJbW7ofh8rnwDDGr3EKOujMsrdbajAWk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\\npfLk1EJeqVQqT04t5JVKpfLk/AsNeRHC6zkzzAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fac306356d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"transformer = tools.SimpleTransformer() # This is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\\n\",\n    \"image_index = 0 # First image in the batch.\\n\",\n    \"plt.figure()\\n\",\n    \"plt.imshow(transformer.deprocess(copy(solver.net.blobs['data'].data[image_index, ...])))\\n\",\n    \"gtlist = solver.net.blobs['label'].data[image_index, ...].astype(np.int)\\n\",\n    \"plt.title('GT: {}'.format(classes[np.where(gtlist)]))\\n\",\n    \"plt.axis('off');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* NOTE: we are readin the image from the data layer, so the resolution is lower than the original PASCAL image.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4. Train a net.\\n\",\n    \"\\n\",\n    \"* Let's train the net. First, though, we need some way to measure the accuracy. Hamming distance is commonly used in multilabel problems. We also need a simple test loop. Let's write that down. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def hamming_distance(gt, est):\\n\",\n    \"    return sum([1 for (g, e) in zip(gt, est) if g == e]) / float(len(gt))\\n\",\n    \"\\n\",\n    \"def check_accuracy(net, num_batches, batch_size = 128):\\n\",\n    \"    acc = 0.0\\n\",\n    \"    for t in range(num_batches):\\n\",\n    \"        net.forward()\\n\",\n    \"        gts = net.blobs['label'].data\\n\",\n    \"        ests = net.blobs['score'].data > 0\\n\",\n    \"        for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\\n\",\n    \"            acc += hamming_distance(gt, est)\\n\",\n    \"    return acc / (num_batches * batch_size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Alright, now let's train for a while\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"itt:100 accuracy:0.9526\\n\",\n      \"itt:200 accuracy:0.9563\\n\",\n      \"itt:300 accuracy:0.9582\\n\",\n      \"itt:400 accuracy:0.9586\\n\",\n      \"itt:500 accuracy:0.9597\\n\",\n      \"itt:600 accuracy:0.9591\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for itt in range(6):\\n\",\n    \"    solver.step(100)\\n\",\n    \"    print 'itt:{:3d}'.format((itt + 1) * 100), 'accuracy:{0:.4f}'.format(check_accuracy(solver.test_nets[0], 50))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Great, the accuracy is increasing, and it seems to converge rather quickly. It may seem strange that it starts off so high but it is because the ground truth is sparse. There are 20 classes in PASCAL, and usually only one or two is present. So predicting all zeros yields rather high accuracy. Let's check to make sure.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Baseline accuracy:0.9238\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def check_baseline_accuracy(net, num_batches, batch_size = 128):\\n\",\n    \"    acc = 0.0\\n\",\n    \"    for t in range(num_batches):\\n\",\n    \"        net.forward()\\n\",\n    \"        gts = net.blobs['label'].data\\n\",\n    \"        ests = np.zeros((batch_size, len(gts)))\\n\",\n    \"        for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\\n\",\n    \"            acc += hamming_distance(gt, est)\\n\",\n    \"    return acc / (num_batches * batch_size)\\n\",\n    \"\\n\",\n    \"print 'Baseline accuracy:{0:.4f}'.format(check_baseline_accuracy(solver.test_nets[0], 5823/128))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### 6. Look at some prediction results\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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jvtshYvK/lKadKPd59HfZuXYLnQcC9bdq/USOkDx91qUtJlEQelOv4s\\nJHDPobU6fbFLLXsBmq7V4sfJYuLpkshFi3VJRv512b99g51ZcVlg91XobevinkNZUcACqLpPeQD0\\nxXTd5/leHgiFPvvSzDK9sF9YJQ7oESlrkYWwqfylY2YBWc1oanWoh4WDhooWGh1DM5jngmp/H+BQ\\nWy5ZGaXsNd1kZfD6Obg0NNY1TQHBBfMv08bhioA8VPCiMUP8ZBTqGrH8rh0kYo8WQ9DPqir9Hbu8\\nkbUCeyu1gZB/GhbAXmh1w8R/7MpdvOTyy9oSUvqB52Vwm8ihtS0PxmoZVONJBXBXtVaRnzMuqdTC\\nqcajNuCWE8jo6GiVntuH5gfJQkIcr5YakJhSKW4yaWmnPWliNeW/zbosS2GdeVt7VwtQOZ7nOiHk\\nOay1XJ+q5bNOroWe1dloIanjcdcVDSQ7oVBLFJrZSTrG1zHn6dfWbi1A0yqzbHX8OlDwLZOFXXhB\\nuBh7HJnufUeLrztIoD9btWhD177lCsyN1fyO5tVq6AJBtvkbgnurTJja4kxf2aMpwyQdyP4GQnUs\\n9eafOgiCdtkrT0UpNvCuKz3HXb+U30QW/Sp+HnmT1TqYX6mNnP5P/dLkYel812UFk3FAoE2j8Rpi\\n4VMFmBXbVf99BUarLdwv12Bts80Lk17Lk/6VPBjWPCCW/SSlZLH2Ni1CqBqCr6P+0wNpzn3Q4v5B\\nz4diVy401PcraNOBhF/qei4AVZvvgU26CddPXmq/Vu4KXQsaLJUXVtrX2bsXbXWSuylSS756FrWZ\\nWfTG2kOyoGk5thcftBuzrS5thftu7niFf94x9LK2rqS+I6izpa/U5c8t9sPKl7Eyhfp5rq0dtZ2l\\nT/v6emu7vfNEe71nj5bqpMvdusCP7/aLDTk9eMMZjWjoJG0eyLIU+5RW7O3eG+pyHHJPpDf1Xrbi\\nuyIbef2Hfi3iJmWW+up42nVtPw/bu64gcWB+OfP6MvqdellkSI6Z0jXhwF4ni6lQXsl/i4dE0dta\\nM3Wx7BU/NhZA4SvxDDrs7CoM18eBo3mZoYFvqduDXm8CkcWbB6XUh31+PezfMlHqOHCTUv20ogPx\\nbjR1GuwhfR4/+rGTO0rXMkvXj337euBwBC8fHIBIK8X5QJQJcDiwHUnLelf6Q7AVQ19xTS3vks4V\\nuunBqzSmCYI2JpsZydNb2rlCS10NNYHh9w7UV+zt8G5+1Acirgv9CmKp4EilqY7xzpbenncdI7ry\\nvNXlFQxjv/dc0sO2u/aUfJqFX5/ziwjI26T2Qy9rzAufpwIUzWtF1l1ypF8CHWpw0Mu2VnP5LLna\\nZrJxMt1h5nI3PO+TUztIWj7nPekVkv5NSV25nuaDSVZxfSHoWo21pNo6NcC19vlldD+gqmVSltq7\\nW924pXHvK6INLCrNbgLVVYjjkyNeqinCMcjVtWYDr+/6ceSW89JX3gDGvxs8DYvmVFD0HHBj1NFi\\n5Lnxd4jmudyA31/o3nFVVG020G2EiVzChAoQ/lenRXtJnnFoWVIFtYJhHd1WfqW5mvAWwr+CrTOX\\n6NLmXea2n++LfskCobNWd5N5IUmdwCsrybaYbi3VCqZtTGkmwwNvo9tRXGnUxWAJi+95djobfJC+\\nf7WS3zrMai7j12sYGSd0qVy0dOUa+RLUD5a0vrH0wHWYd2HPkgJCTUtZDv+1guXgg9MUDn46kBbt\\n47Kzcg827a3nQQdlThi4xtTPTSnrAR4NGeCVaxq5LTPPj3s2khg0EVJk2Gu2RQvDEEDMpptSIqZE\\nVAUJEALIgCDMIbATYVZFo3ChA6+HkXMZmCWwXDIu8aa0OeG++4kcONA+JE9GcnsqLtC2nX2vWLv7\\nyb34uuwiVxd0B4Yc7jX/8vKb7y/6rnFtWM45yUBS7MRe0Pv2LVcvjcCMOs47RbravDZOnfzdvk1u\\nVLVVL2VSaVttk/aaaJH9FWBaaoqKN2po/Vdd+fYxdTkPDBFSSlhMxTomDvuyqTG+kNSEp+8s3/ZO\\n+PgVAd0H8SaCOvcOkUW9IKvvlQofd6Dejy3tnhxYCVy6Uq+Vkg42aGrqOJ3BYeE5sFJW3bXuDOql\\nyGUn02tYtAnX0eM6dm3HvthZu2bIYiAUtUKpk6u6jnl1sWtaHuh1sLgJWX1L+zpLus3MzwsXfMXp\\nnu0Eo0bY7ZB5B3OEQTgNWyQIc4rMcc+cZlJKSBgRmexvGNmLcAHMKDPCvSj8k/3Iw+mUNG5JmmjD\\nz6On/96AvCNU29GNCoSuMUpRbg6PeKyZghoNmnndcil0/V3ml7SuaXkKYHbjZwHQFdSdZl050ejv\\nAc/KLrhcTlHKos0tv2uZ9DxoKxU9aM8BVw5xsvNI6WSFb0P9koWIa7dnUX1A3QvsKqssdPXVehHG\\nFJlQRmdiaYqutVoyHU1stfGvVcLYKjcBSUMdszOLvqu16MEvvWl0waQ1MF9r68FPb+c9qeOhE0lL\\nPFqkK/Mjtz9eczvcdfen3zrt9pLGSCftPdiK/3OYRwRNXqsrn6jPKi6UpZefXdXLQlhbdpPz9/4c\\njqay2XYo2FtFVbssg8AjXv5fAUkZJIQX0p4v38z8i7eE4dYWDTDfH3h48YDz+SExCM9unmYMA7t5\\nxy6eQZoJooxMDCExTDBOQopKjDOMwl4ir8x73rq3I117mu31p9inmVQAWYQkbSgmlKSQCCTEJpwE\\nA/UM9Cq2qNROzWrAb6tKrWVLeQ9BQ2GJGzvuqyZjT8r5Gxg33hYaKvhjl3Kq1MVCJ2yWM0tpLnAl\\nz4HZSxTUtEPjR3leQNF5PNSGeGDX2s2aB2rdcBVPWxlI7ryogDpPH78Y6vQSB2CqBRzz82zj8d5G\\nkk1kykKYLV0YcYUWwJViNxJGVa4x82zac5uIDoFBYVBlSGq0iyJJMo+F5FcmkmkVIYiwQ9kBexXu\\n68h9Ru7rRCJlWkIV9t5cVTahc1d2/Sx1DPb9evhtRaGp/Ovf6A/ylf6S+m/vSL0cUy1drdcKnrBD\\nT4F2ArSgKE6SX9KiRfneDtctq1zSg3cOiKuahnSZWrm9C1w/qDuaCiB3dCwPhOjib5dr8W6utwzL\\nuiOvnKBcV+Uk2aRICQYVZoV9gItRkZSYIsicGHYKOjCMI0MYkTAg2GRCFB0GwmbinAvGdI/xtU/x\\n7ucu+OB1Ie7PSTEaxaHZIotLZAJUAhoCYbDykypJEykmGANIyJMxN0t60C0DOdWNLKmgUIBEQqj8\\nSUmrdqPJJpKikEy4lMmq0Z5raAJRVZnVJn0QQSQ0QeAETRVAtS9MUBUAVC2eRgIhELP5RoEUQKL2\\n+6olbx6zmkBDLjO3MWkiJUUkVFDT8p+2YirvxISFBuz/Igg101LeqcBhtKcOlqSCWO2PshGdK1RV\\n0iWbqmvfLJ8yEDhR5en9A94zKs9uA2kIEBNhHxlizALO5pEUIRCEpAlNiaSJMA6EYSDIwL35IRca\\n0WHg/j7wE3qdfzw8xXkYidL7oTRRjVv5UrFmsWB/zP6HP3PCAZ6191ee12elUj+m9ICfy3R1phUn\\niMCDdn5cn7us9XDB4xvV2tyD62PpcF8rA8VlkXwMfYmxNU+vTntp6p+ug7W4v46KWvaaq6IcfpJm\\n+xSFDcoJMBIIe6MxzTANE9vTU+K1gXAuyD4x7BMSBRkGwniCDKGSp6qEEAghMEwbJM5onAkP3uTZ\\n2zf4UHiGxDmqMyWokPfdJQgaQp58A0MYmcJIUogaibojMCFhsBVPBnFvGisTWSWXlRwXlQokMg71\\nvRhTfm6/JxSGATk5sdXDPCNnF6S4R1NW2xV0GEibiShASsgcCTEZqIq0ASYGfgXIrd5gAkHMsVlT\\nIqUIIaBhZL/Z2G+AaiKkPTJHt9LLMJnRXpPCYAJQRUgp2V7GPBPGkbqyQbOgKn0m9bsEMQFHFjpl\\nuJWVSBGKipnIpGiTzehQBJOtrtw8yKtZ1QQpEWN+cxiK6M3KZttnKH1capiGgS1wY3eP58eJp8ct\\nSSClGZXIQKxjPWThZYAeSBqNJxoZdCQwIoy8NT9kp3uEiYcXiT3Kj25usAtj9T1rw2th6++ncpOr\\nUuZiGXSLOViVu6VDZSmnVzD9sftl3lZooZFa7lq6Io28aApNgi3NBt2yRlpDKBJ5mbwgXUjMx8Up\\nKFpZmUS1QzxQFyFTXxKaTVz9D+6lvjtFtWmprtsK7K0KJy/c3Og6LMHoDmVCYprFNigngzBtJnSX\\n0BiRvbI9vc50coubt08ZX76LXjwCVYYQCNMG2W6N53NEo9nSCYEwDcg4kARi3rQdQ2AaB+bNaGCj\\nptlqnCFGtLw7mKavISAhICKMEmw3f7blsAQTJGZ5KeDTYmAggowDOgyQFE2mjRENRBRBpkAYAoIw\\nzwk0IcmgLoURbt0gvP/dxre7D9BPfhp9NMOsiCbTKrcT8YXbcP0U2e3h9bcY7j9Edns0Fa1f6lAo\\nKWgB+YAw5KeJxGyAOWyZb56gpycEIDw8Y5h3hP0FiljMDgzgi2BCIehg5YWAkohpJqY9k2whDG5/\\nQiE1WEo0PobBDuiYCVHzYZjM0xAowlfTnMswnqcy1kLW6lMizXPTqSUY4MYIMZH2EZFAmEZQ05RV\\nbWxWZXcYzNFDDOCn7cQwDJzpBWEYYQyMF3vmlLL5xFZEItLisRRFT4VAQCQwyEBgAAls40CIEZLA\\nReRUZoZhj4w2tqX2VZlWHrkXKlVViB0O9LLMa5tuQ/Qx0RS1ALjb0F8im3/u2ruWrs79sNiVPYMK\\n4a71NRBO5exyWeS4uArkvay7VDsv/ar0zKy1SVsC555sJC2ERece4cC8jICu9DWnw0KQp7u92sn7\\nKnSKu5X9F3RmG4TtGAjjaBuYqE3spISLxPBwhjlBghBBhhGZRmQzELYT6XxHfBht8iuIBHQQZk3s\\ndjbhQ0yEmBjHkTSoaWdkMB8GUkqEcSRME+Nmk/2wAgHT5AIwnJ4wbDdIENP29nt0VhgHZLJ3ZRoN\\n6IYA42AmjTCg04Rev5YFqxJGIZzt4NEF49kFerqF61t0M9n7N64zfuDLkDSjn36V+JnX0Hk2oFJB\\nU0Seusnw4Q8SXnwWvf+A9PEfy8MsILdvINPGBul+B/fuI2cXSFL02ilcv4ZcOzGBNCc42xHu3yNt\\nJvSpWwzvexfh2aeQcSC+8gb82E+ir91BT7bojWvIZjJXzLsPkLMLNCb0xg043SLTCDEic0TmGYaR\\nsJ8JZxekkw26GY1nClzMjGcXSJqRYYBpQ9pMyBjMvDInwtkFcrFjGMRWG1FhzmYGxWxxEtDB+l0E\\nSEoaTZCKAiGQUkBn21LMMpMhmL0ZhrpKKDO105GCMAJBE0GUoEqIisyaN0wFGRqQexfXdjparP/K\\nCiJFpjAQzxOP3rrDPiqcbNkGIQRt87KHjUaXSieglyt8VPMKqs3rBupeS1/Vy+sfvxYv2n5h1qVu\\nl+u6+9W5HxabsAHU4uRUaZ14Ji9cwPriFijsu0YOOurgXcpyU5wIXtBaaVvvmKXnTXMh600N/Zuy\\nfHBpWsiELqtIPwACygBsQ2AzmpabkZiQbbQyJ+RsxkwBZv8N0wbG0cB+O9lE288VxAmmZc4xsYvR\\nlrfFBJLt6ZmbpBRJc0R3ZhoI40AYTVuyfhd0NnNGGEb7LYhpz9HARIIYgG9MwAQZcum5LTevo889\\niz59C00RYiRcP0UenSN37sOde/DcU+gzt9FB0EcXsN0QXngWmffowzN0M0GMENVAK85w+wa8+DzD\\ne19A77zF/NMvw2YD16/B809D1LyqgPTqG/DmXQPz974Lbl6HaYT9Hhkm6/7XXkf2e/TaNeTF5xje\\n8wIyWp/oq6+jj87R2zfQ2zfgxjU4OUVefR196z56sSe87z3IzetonNGzPQyB4doWOd8j9x7C3QeE\\ndz2Lbicz44wjYR+RB+dw547RfusmcnpCMuXbNOa7D5E378K9+7CbM88DzLaK0azN6xhsD4MMpONo\\nXk9KFoCBVEwsxbSSVyaI7TGU0S4KMUWry7uHSi4LkLzA8GtvySsCUbEVmLr8NHt+CUM7SCAkZX9+\\nzpwEHWdbWFLMMks8aG6Sl9rA24SjbTKXFffilYrS4l+zmhbYdlhPK6BZLxqda+mKgNz5tmaEKsqq\\n1O9eLfcvLzXs9eflxQMG1/cc7Kh2+S5zhzyIZXFZKioIvhPX3+sPEB4eufdlNhm11OqpjBBsGTsq\\nbMbAZqIHyZpxAAAgAElEQVQuiTUlRLK2FBViQqYRjRt0jsh2Y7bNmDf+TiYknhhgDMHAJyVinJk1\\nMU4Tw8kG3W6I+71p29NAGgfm83PmOaKaOJWADAMxJfMVCBkYhryJFxPEGTTYhD3ZANnUMk1mUlC1\\n8vcRPb8gKcjzzxPe/27SzVP0/kO4uCC8711IjOide/DSq4T3vhuef4b04AH605+B/d5MRdH4MkxT\\nWxMNg23Y3riOnmxtJTMMyGYyMH3+WeSpm8yvvka4dsr04vOk1++QPvky6Y27DB/9atiOpLv3mF9+\\nlfDuFxheeA5eegU++RI8PDNtdhhMwyUSNhPcvMH8/G10u0GfukV4/3vQ26foa28S7z9i80u/gnD7\\nJvvPvEa6c5/h9g02H/4A6eXXSZ9+nfTmXcZf8gvNU+jVNxievolMG+R8R/x/fhS5cY3w/ndZ+RcX\\nprG+50X0zn3Sj7+E/vAnSBd7W9FMI7KPsI/INGR7vxPYhLyhKU4zDQbQc0JCQuopxjb2QzbfCIru\\nY+enLYL1yWBnFyRI3YsQsolPnYko7c38NA62EkpqAiaZyjQEsf2NPGZ1F/P412qK6W2xi7laQX0R\\n6aTLJt17/aEow7WyMVyETXe2wnv++E/aikjiiutW44fpSk92NkwyKr2/cRsJSl1qdNq2K+1Agq41\\ntnczktWyWgmrYuFSEM+y3JWp7l97d2Eyqpr8opjLaJKWRS+h0Iu/IAUrbSNKYjJNZxxhjJhv1wD7\\nGSWhYjZnGczeKOc7K1GEVCZh9pSYk5pGPo3IyRaubwgxf95MZpfemEmEiz3hxjUDxbML26jMNmZR\\ncwKLYmYYCXawqGzMCWKmhJg3Iqc8uaYBUSWdPSS99GnTOGOEaTIBlfKiKhg4SIAwz6S8ugATZJoS\\nUYLtCYRAEEUfzAY0krW7OaJnF8yfeR0ePkJuXTegm7ZoGOHkBL1xjXSxZzjdmjnibAcPL2AfzQy0\\nnWz18eCctNsTNCFJCA/OzCSx3SDPPE146iacnpiv/90H6NkObt6Ek2zXPT8nPHyU9zCGPCgSut8x\\nv/a6ge7ZOfL0zdwXG+T2DbNV37tPGgIpJZgm2yC9toVb15AhEDQL9zTbyB2D22zOYBnyuFOFYmuH\\n7Jlj80OmwWgdQtbaswaS3Wg0a9p1CKeIZlea6k1YBEd+JygGxKLMRNjvzCZPIuT9FoLzFJGAEBiH\\nidOTayT2MNjmem4QLUiVn6c9dhSUciqfs5r2rqwN38v8LxijPYAvVfEqS3LdISuWiIkbr+9qp+B3\\n6cq8VtYkS9XGPaB1TOg/Lu1HyzL7JUkvTPt6D1C2Me6AZg/RTiD1Ruw6dvudai+1GxmluYeeKZ5c\\nOaTfDcw2IkyjCALjKIRJTNvJk6NpOmKa4fkOndsmmKpCirDDQLAIWk2mOQdhP8/s5tkANxRZbKYQ\\npoEgI2P2dOFky3CytYk5J2QMpnkptpxXDMA3m4xLMU9GG8HdGYIg2SVSswkmog8foftsBx4GeHSG\\nXuzgrXvIw3O4/xA9mZD93jQ9BT07s3y7PWwm9MY12E6mjStma5ZEevQI7j+AR+cGSmB1jCMSE3p2\\njj54aOA5z+ijM+OBCGGzMXvyg4fo3fswz2g2H+k8w8UM9x4aQJ1uCbdvwrUT680Hj5DznQnOcTLx\\nPM/IxQ652MNuRudo/RRndLdDH56Zhpr7UpJp1ez3tucQMDNSCOYddH4BZ/n/lHJATQfQwUClqMxF\\nAwcqwHaukuW3YgIJThkrq+/qh16AS5utXfrxXYG81pnQWUlqJjRRkBgt82B2dJuvpf7AOE1sT07Z\\nJTPfWcVuqq5MtypzChZ1E86paUJ1gT3Qv1xbpGVcaGP5eSnR62YOS6TKPCf8VtKV+ZGvR5dbobOa\\nDLR+X4O7tlFAFaxymGNRmXvhwAZzUMCK8GndXAPteO+SS9pZ00JILUOadnV0trZ2erWTNlL1BEZV\\nhikQTgS2o03qpGiKBshF4ueJTox5TkbSPhFC2avIoImARNOKLnbsLnakFInznv3ZGXq2I2Q3PU43\\nDMAwjoSTEQSz3W5t85Ihe7eUnbEQkO1krd4VlzmzyUsRcCJwukGy7ZN9JMgIYSRd28IwIkMgvfEm\\n8tY95PW78OiMJIruLwibibQZTWC98Sby6AzOHiGnW/Rdz8CN67DboeOAbkZUZ3j9dXj1NXj0iDDc\\ngmlCblxDwmCa5GuvE1/6DPLaWwwXM7z0MvrULeRkg7zwDEoivfQy+hM/bZvKz9xATib0/By9/5Bw\\n94H13bUtcn1D3F2gD8+QR7tshx8Z5oicnaNDgF2EOaL7PfHRI7jYoRc75PzChkAYQBSNkXTvvnnl\\nfOa1PDhu20aqmH82b91DX30L/eRnGM/3bS64YadZ02WQNuay2aRzrVVqf9rM0RYyRPJGZcmcvWUk\\nmzNFbd+GEKo+bDLbxntUSCGPwXkmne8Im9E20MuBIrX83YGkIRBkYhRl2s3mIZWFTG+Y73Uwj7eK\\n8aDMvaJhNzhv87s5qjizUiVHasOaH36rv309VBt9Vq88LtMVHghagtzjyMz5D8qpr7WB0vfRQTnr\\nwkIP3HqKNt9pIkUbl9aVQFsudoRc1o4maWtdRVgVSe2zrzbHbOnqGl9ipwTMajIBU4BxDDBNMEWY\\nEyElzKQYSTNoTIgKwzjZBMeWqrrfw2xufOwTMgoSzGq4m/e8eXaPT7z2Eq9f3OWfvvIp87XO3iUE\\ns9MHEYZQ/NHNhjlkP29VZYhqecYRGUdTAlNiBCYJjDISxpBdEwNhM9mSXYQQ1cBtHNEpHwQJAzoN\\n5umxn9H9DD9lNnwdQjYvqfnKp4jsZvT8Av1J8wgJMcHF3kDl4x9H9hE5uzDvjtdeR17eIj9+Sths\\nzZYczDwSLvYmXB68BScbEwZzQmIk7PbIwzPT5E+3yBtvgARkt0fu3DO3ybfeIt69QxrMXCIK8ui8\\nmmZ4eNfmyt2H8OAMXnkVfeM1whyRB4/g7gNSnE3QIMjLn0FiRB6dm7AIAX10hr7yRuVfBOTRBfLo\\ngpRMQNs5jbwyE6mfSUrCDuUUMLcVXNaaxU5eJrXVlPVXVcApK70aE1+0epkMkPsxGI+Kolank42R\\nVmcW8tNo+atvYHkvCx23jh2HiVFGCub04XUXzr+Z/91qX8qeQFHwtJbtZ2i9Xq5M86USVwLXrdpH\\nChU+nEQpyJtt11XyqzOtNM458FtoBav5lz8sQO5xGnB75cDs0vFIFiyT9azrVT2+/iJKOrxfil7I\\nA7KYO3ylniapJkhxNAYRNiJsQmAUO4gj40iYEmmOraxiLsmF6D5avdNgoC5qoFa0KhFIyjzveXD2\\nkE/deY2X7r7Oj72yNQ2r5FWtE7poVpKFXdWKkhKCMEhgGMy33CybylaE68PIjbAhZG0wZLMKIdix\\necW8JFKqk1dESENgH4Q9Fhem9GEKNoHMLu/CFmmqh3FCxgTbEJPqTx2CEybZlbI8G2KyY+SIbcRl\\nDRaFELN7ZsI8P6bBTECKmTBiqrTN40Acs/tgCLCfISbDjnEAxIB7Z66SerIxGmIyU8towCbZHBZi\\ntFOReTwU4LQ+AeaIxOz+KcFMPDGDdOkw8mlQbLwklIgdCFLN2mqZBHMk7WdEAkMQwiDVbdWKq6he\\n46UAjEkJpxumayfcDltuTRNT6YeCm06LDYPxMOQ9leWxywLpqkDClAcJhLy6K3mKq67N47wJ6+ce\\n2k036jt+jnZVU8woZW4vDxaVeXe5surq8r+46i6Dtys7EFSWKYWRpQvQohUs33kyQD+53lbGQWmd\\ny6HvrnUJ2PWPrD1de7eVuy58tCkYS+1dG/1LH9flrnoANgG2YWCUYP7aw4BOI2m/r1qPiNSohwp2\\nJHoI5omikkFpsAMXGWA0ReI8c3ZxzpsPH/Dw4two1US2pDY6fYvVdB9ZjOTyTg2yJMr1ceL5zQnv\\n3lzDRSTJAJI1uxCIyQSTBZ6yY+uzwMMUeZAiD1xkDS3RoAp9fiVnnKfEIDFNsZkDlAz+WTANmBtb\\nkMAowiTCBAzZH0Lyikkk2AGV1LTdICEDogHhrJjQkXxqsnBNCsi0oVn8PkQaiJYj64XXQUpeo22Q\\nwACMKBsVNghT0UxzewPFbJLHhOneBDXPiWQyjQtNXGhin3slOd6A0RmkCP4ivK0NVUgWIZy1owFb\\nkd2+cZOPvu/DfOj0lOtcdyAubR9GhLCZbOWXTTEtQF7WTeoKwL6YsMqCuKwWnJZmvCyW7v7Upndm\\n8Vpym7/9/NbFo85kUwK3Cb3QWIF1Wfl2aK7p0xXHWskQpI2Fl6aFJFtbnXRg3xWnVWsD6uEd/55T\\nxi8Xe77Eg+O1BZIOKvd2mkP6yiDMeczN2kZAKRdPUwbhFoBnMSoFBhFOQ2C7DYxTQGbT/jRm98Zq\\n6xTz344jJR6IHa3GAlGVOqfBvC+GgM7CLiXOk8J0HRlvEaaskVM0ulw+xQ5qJGo9zK511Jfltpl7\\n7J0Xn3qGj77nffyy93+Q1DyEsx02T7WTibSPpIs9MWXdW2AeBj7z1h0+8crL/MOXfoJH8y5PcNc7\\nVTkscU7sQeseO+VZw0b4//PsFITNMPILX3g3773xFM9sT/KKIu8TStbqI8TdjhQjSRNzgH20zeJ9\\nTPzEgzu8fHafBzrnqm2a1/gt4vWadlDE9Trm4VAA3vIFDCRRYQjCzWnDVz77Lp7enLCRkE9c2klW\\nSdmUEgJDsENcZaUTBgtBsNvtefnOK3z60X3OitjIvCvCAy3j0VaTPlhrSIWBJiDKYR8VYbvZ8mVR\\nSe8p/ZxblVcuCnZYbRgpG5ktZpFUkBfyJivF40PqidQWo6WspqXjX6G7CzB7oHH5Rw3aa6GZjqqA\\n1X9KLd20X5Tqk8+l9d3HpS8qrxVLXqTVf1ru0sldzjUN3j9esE0Oj+1/1hq/LG15a2Qsd7R7u7YH\\nd6NXuoIEzafjDmWLl9C+KMlAfiLCNA12am/e2zIzZ6w+qcFOSUrMx9yD+XsbmDu+jEN1K2MK7IEL\\nRjZPv59w+wWmW89RfP5MY87gq5o9D6joWQJXSQHxclKvfFbluRvXeN+7nuMXvO9ddtIzqdmSixdD\\nELNFz8mAfM4+7MNAGgI3X7/Dg+kW18dbxBgx73UnPMhlasoHSDJsqhOQXth3dEJZJUzjyAc/8F6+\\n4umnefHkWr48QLMJOANdTKRH56T9TNJEHAL7uGcf98xR2b32Cq/dfZM785zrzaQVOrNQLJS3o9rU\\n1YaQQZtyIjH7cmNlnQThmevX+fkf+DDvvn6DjQhpnut6RWaFMZvfhgGdLX4JeWURY+L87IKXY2I3\\nw/3cxuqtkrXMYjIpQNfMV+TwMi5vbtccI08lAQmMw8hQNkb9nFWp3jClPBvQLVRCWU1IVmYkH3Qz\\nk2DqzGn1hOjBJCq2cDpsLtR4Qd//LXkWCqF7o0EyLC0OJT5RzSkOKTp3lst1zCvzI5cVtjTU8yB7\\nqOVWJtWZ1YNx1bilMHcN6NdlYjNtHKZONjrSsqLhOsh3oK6/b0Q0EKvaQtswqbR0NC3a4nbJi1kg\\niLIZlDEfq07znmEw75Gi0QjZ3rgZ84anmi/1OJrr3H5vNuIg5jY3ZJo2IzsJXIQtJy++l/FDH+H0\\nvR8ixewzrBZ4STxDtbW+auw0MNDCJ7XNzqf1ITe2OzZbg1g7IWqHQEQwj4RhQoOiYSDtbMXAZgIC\\n128N3JTbPP3iR5hkskNHih0iyWFkSRafhSx4yHFIDPBTFUBaNNfilqeKaCSocnsIfPj9t/nK26e8\\nuNkYWGXbt2aw0H1EH12g+xynZRxt41BnJAXeuvUin37zPvfnVM0B5X8l0+U3CVuoRVpY2jIVCtNT\\npVVVuTkI7751nY/8ol/MB27dZJvU+Cn5QE9UO/Q0jWbnzzF2NJvd9ruZ8/tn/Hga+anNM1zU+MOa\\nQwSnzBccjw71zIqXRDPpxRm9eMi1TeDZ67c4nSbGEKqapsEJ4KJwpdx/IjUqZBlm5dQmYgfaZAjV\\nzzyLuQaXue2AHcnPTC1hiy/TM+vs6wT7AohravQd4NfCKlDMO06Xz3TmcvDIeJiuBshb1EgO5ViP\\npKbYCJ12mwd2D/Su/M7G3dfgyy1i133sQNwfiFiro6u5ZtWFIDjQL/pvWYMwOhw9BslW5oKmLq6x\\nR/GcBoQTsc4VQIeheiEIIJsx/50Yoh2q0Kjm0TCVeB15ciIW+ArQqKR9ZJ4jUWHYnNihn6wJtXs1\\n19rsW6+1bOinQQCmODPKDPNFDhlgdEmyyHzVRznlQGCAZJfERCKpEDenjNdfZBqmPOBSZWTRb+sq\\noX5fo7aAaukje2dUuIlycnLGKLOtakpDSuTIhK1wTjekAHqxR9Pe6lQhJSU8916uPX+Np6cTkgrF\\ntNLGnOIJ64Zi01ccb0El1XGkCLc0cnuITDdPEGKOi0JdlURVQrSQA3Hn+kU1Hy8MsNlw8sGPcPvL\\nBuZxU/llCvniejJ1nx1r215OtHEVZ3ZvvMIz91/iaX3ARkY7mIQpA6nwfh8NnIt5hAzWc7RzBWIr\\nkRKdtGysSzYXlY1ps7r0ZpbSZ72SLB0M9Zp5+cH6yqF5NWu1nij09mPL7yGVypueVp5Kj0Vg+1as\\np6sPY1s+1iNTnqsNqHtsXMDiAWK4B9WEcgkZSzou+Vb6ffW3rrw1wdLe6iai3yuoMsy1N2uSLlur\\nwxXU2XLFTktu08ywF9tFq9qomk/wmCPzhUDYbkyrnSMyhLpxmBGhhW0VARJxjuz3swUiGrfIMNXf\\n8yv0Iq4NzC75pa1SJ1lAGZMwZL/hchJUZIBkYFOAXJPmoF/J8VNIIqRhQranhHFDYW4ZZ02H6FdL\\nHZNrHyzJzoetFLYpMjATJLa+zXGy67uiZrYIBgJ6savYnEiwvcZw8hzb0+vE7CJSBIsWwdFpdY7+\\nFa23rQzz2k6ETdyz0XNCOLNQCLGZkmqkw5RIs8IczcMjhLxiyetnCQw3n2IabnKyOanKhMF+qtT1\\nbF3yN4Mf0XzA48w4bLkVdtw+SwzZ2G43OjmznBhfJWT+ulZXs4RQaSIq9a5TMbNMECnn4urYLuO1\\n0/hzWV7JWKBwp0CZzJLKc0o7Fy13r3fldALOvbuCIksrS5eu/GKJMhTyL7SLERqIgyysFoeuQSJS\\nsa1q22Uwu/cupan8u6LxePtGq2MhrjvOrz6krSL8zw5kcD21qMdH3az3MNYxnN/O69mgic1+h+wS\\nusmXNZSQzjmeRd1EKqf9MpBqMo2t0FAmA9ltbp4jF7uZ3azIaLFZqrztJkDjQdM2fN87myfqJhgM\\nKRLmaCEZR0N5GQQNFiIgBCHO0Y7ZF7OQKARzgUwCMZ9M7K1z0rG4C2i01l0Z7PwmWZvwipBs5eKW\\n6caE0H1UUULYAJB2OWZ7Dp1g3h9S+aSLMVLYL4i3wDmiXXtotNVrzwIWmiGphTGY88GwULosM6TE\\nLJkjOmZBGbMQUczs5OOhrIzxzhtoqZXXvAHFhN0QYNhuubE54ca8zQK6eA5ZGaqQhhzIbQjVZGXM\\nNGGX0LxH4Lya1O6htSrNtXV046zMutLv5Vg8ro8b1c30UuHe5UlZoh7Mdl3M98onutlRhJF4jFpZ\\nERyW1NIV2cjLh14SVem0aHyzWRXJ52zLDiUqgEsezJrBzde7KPsA2g9mdM/Gxuw8q0Lp6JJrUX4H\\nyMsBkv8pWqL3dyoDQ2umWn7VyN2grFNSbKpc04iMA/ut0ThEIcwKu2RLUlVEczTBEpNETLtKpi5a\\nmYNU/+eEsteZXZrZq8I45ciKh6kuXTMCGrj4DNKa5nhuK4rFxlTudsnIrGBH1BW7XCGp2ciLi2RI\\npOglHyhhfZ9hAYYdiJY5lYG0btI50JAYQaN5+YRg46365tskjfsd5di6bCYL6hUjaRYiQpLg4mp4\\ngdCPpyBtHBTCu30yqOaHNiYCMJuwy7HKRSzMgYGc2lH/wiqFcvF2CDY2yrgy0eU2GDE3R6+b19ja\\nAv7C4UJPpTWbPU7HkRvTxPVxgjCQhsE2KS2aOjKMeX/GUF2S8VVE2glh10fdqsXkRj4kZ1fGSe7j\\nMre0kuX6vcOMctCnjRMTMm08h8wbO7Ha+sLfFYvvu25uN+40CdPm9kFacc2GKzWt9CDeS7SVT6Xl\\n3cRbAVz3qGnmBz+t6RNtsJU8S1BfahmLcbnsHLdiO6BBfG/K8ldXn4jL4obbYlKXfhfsVOdpUGSO\\npBSQmxt0D3oWkTO1YFfYpGdoFz3UOxkHuxTADhLZxALQlJjnmX2MzIBMEyGYx3USoRtiC3AuS9lV\\nZvjval43Q75VqAZBKtK8DPTsvSJhsucl1KraRMyW84OVky767/BzQ8bDDXHxRZluWdz3EkjIGnwy\\nd8piT7cveQU05tOIMeZ7O413RVWs7pCy5NdixObxb8NjOQ76SWDPiqki+8No8XVfDGstwy7zPAjk\\nyyEKnUHsggs/LkvnNSjt/ulYXBQfATZD4GQc2AzmnW99nHOKbXzKEFCNrd9dNxVB30JWZAqK9MnF\\nBbXI6B1PpeamJ7zxzv9UaBf3bmNdodn1l0r/SFo5tXy1NvjeVTe3l7C9MouAK7x8WZZATlViumeW\\n2gaijxtehJMueKiu7LW5WnUq7btn2ckHJazw8NB9sTn7HzLd+wG7BuTvpg3p8pU8UL3QaPwqA6pM\\n4IAyiXA6QrjYozth2J4akFwkO1GZ45qkGM1zRQQZBuZ5NgvFJCh2VD1MY/bxThnII3NMzALTtDHf\\nXhb9doDomuk85EdukOOnCaJBAppttXVZ7T0FxJ6ZaWfIwbgw33IxTVfzLJblpFiLqwNu9Zanprbf\\nuiTmlTGIhUMosUWk2Jtto8HoVTtinvJ1aN7n2twMs57vx/WCJ+s6mG/Bgka3CSfeVAFGV41xk09o\\ntrCAFt6gaoTmvleQyzTfsiLM47fbtO4FUGda8KaLKnBgHAamYWAMA3U1HgSVUPujmGxUc/1F2JU9\\nn/Kel3G5n9AcKkLErHR0Pd7vV8phGwq9y722Lv5dxR8vFaWyqMyPshlazDiln+2MgxPHld9aWEfd\\noL0kfRHYyMuzbuxSmOG16jIdS74Q/LJnHbwXtXQa2ZN8yD1Atb3FpRDy+d3AYNkeT1kDOw/KkifP\\natmrPBOnSYMdtrDj+dfHgY3uCecz6a0dYQZ2mqPv2dI+ztEuGJjMlS8wZr/tfIdktolqaku9Oe7Z\\np0hCkHzCTnNj64TuZTCX6xE9gBeNbhBlkBw9MQ+MYgZv+UMVQISBEoaXZB41cwaEw3q8y9QTINLP\\n8PrIOrh4QQwku1JMFWSqMdYNzGOO+xKJoswowzwTktEREVKQasOtK8BLxlfWPtpKrWub1q8F6ArA\\nCcVclQVMjG0IqlqM9yLuZruyjXxHaQVroUU29FAY6rqDxXGaDrbb9JH2o+SQAeY4SIzZrz0MyKi2\\nMVtunAII+ao3yPbxcsUftu+DzYmQN8PNF942h4JI9uKS+q92tBgPV1S6A1t3L2gXW5XliLL69cli\\n/op/qw1sb4l3JDUXSzpNt0tX6rWyBOD6Q2384S8d48QPEu3ysHjasX0VYJda2sGYe+y0Xy7hH3fA\\nSNw/ndZBK6K3fYojUftynGBDhEBiEuU0CONgd2xqjp1S/Gg1RdJ+b0Gl9BRBTWOMMd+Wk/2rkTxB\\nygEemGO0uxQJ5rFSNjuX3HRrxWWfHPLO960yYjccaZGK4vhfViHlgmORuomidmsdESFKaB43biJ2\\ny+D1nrmcTtceEWjGAPcwa7Gq7XZogbwXq+Zlg2lhCYgSSDl+zGXpYOwdDNfFmBBqYCbN8mwIQg4r\\nX32lNeYTnClfLF2E+LzPlxoIkiTf/JM3nqXVsVhPHnLNE+4nkxYim0Awn3Vzy5R6OXUWzur5W+hW\\nKGEhQm/WE6cVarGpYyY7w3iHPLKcUYd9358YdxPTldMiQGREcnDQrcIdx5a1tpJ7jPMmh8sw6Oo0\\nctev0JYP/VP3Tn2hj1LgG9+/+RggXQXZx88kKUS6Z48t85LiSjuCA37Jg0skH2L0gL2Q5G3Gq3tf\\n6gQLKKMk8yOfRttcm0Zkv7ciNE/YHCGwXpuFwjxXrwarqrgfZi0wJfbzTFQlhQHGqQax8kfgC9m6\\nmMRdjuWGjtrvgcCgZntGhHIKFC+wVdsSv2jsgEpAEaIIM0Ot0WyQReS7sg5OzTrp0yVlqaUZrzM4\\n5CvRRMpVdn7VpjAIQ1Q7N5OktithJqDolAsvbmpzPUWFsXVjXP2LzgTQwEakuN5JlX31+HvUBpgl\\nJv2+hDzIl1dkbxY2bkzmjUxFHYBlrfGyaeKxXNqwK+admE/vipJBPfdXPphWSDGDfT7nMA75WL6r\\nMLTCTaCGrJErwQ+6hvfNbOfY3+RNEzZVOOViDMTVac2+vc2EUkSd1P8aU3oR6HDBPWrQsw7lV3Zn\\nJzQAK2D0pNvu68tl3HeNPYTwbgIsSVgwvs93+FTr17Y3XgHKz6eFuUZxWkx92wumoi3l7zFPJqTD\\n7Go9Ce2ZJ9HYKATNIWwHGLYWpU/vnSMXSoh2mjOhMEQIQiqa+may0LUpb3ymbJtOiXR+QfGU2c97\\nZgWGDWHcGHhVIdybuQ42Cx3YLA85FZ7YppSZzcplAeYulzIQZr/xqHayUzDXRMkn/caRNJjXSgH6\\nfppkseBtYJU215FPSBZF0QDSNL1s4y228ewLjWTPGmYLbVti2aREUrsrtZ0mbKOkUtGT2fGrixmU\\nSa8bZ9LMBybnlRZGley6aX9jtuOHcuGHArNdZN0OvhkvJTSCqimlTkatfD2026f60W92FhA2BUZJ\\n0Q55laicKlSTjvEt7+uMo+1357ZW80s2w1UBgZpJBhglNaUJmqsz4uaw1CBd4mit3eNAvAKwLFvr\\nsEjad7+XUMWGtl96E1T9sf7ayjtMV2ojb0TJ4vlyoErXgMVrGV8PW3gZiD85z6Xw3y/LFq/IQQfW\\nH7nw7pUAACAASURBVHxX5N9b9wzZp7ZYCZIKJQJoU0C0yJFVGkt5I8IkMEmOhBch7PJFuZo102CX\\nIacxgwruTsUMiJLd6cgC1saVcjHv2SMwbgjjmONbFyLCKuf6WBI0kFGtPCiTxGy5imgy00osszLk\\nCap1dVs09BqHIy8uZuwcFFIUhTbhOueGTuQv+XqZUmGVDwITmiMXlPGaoLA6H6+vYzNrw9lIRc5K\\nlKzcZrA92PFZI7GycUFbNx/sn6oRZi22hpZVzSaTZJc6007mmpO5rW7MFVFr8ClffufX/rjJlvtB\\niwoujTLbazD3yxJTpyBxdWWkhc6VIEiOUa/RqXfedz8/S9nUVdZgQ5kTlazLT0p2PvH9Px2vVwvw\\n/ZAHqluvNJBWOulQnTdW6VlW3KcrC2PbP8BpdT5fFZPtJW/v1CW4fS7E5L+u/mV0xEbLSp7aaXKQ\\nF/rj+v0JTKnAbXH1DcyjmLXDXNPUQL1sOvUVHqwbBGHMID4Kdfkc9uS4FJhmlCdDBeG8gdYds5dg\\nN88LlPV4As7n2YB82iLjWO2Th2YxrX8NSw/XR4dAZGqUaMo3wljMDFTMPcQQwdxFlgyQ0hS7AmG/\\n+Lnc4NRw/bJJ6fuxI4464cS08TEDQ8gA5ZfedeWgdjtS0QyL257mzc8Ej7WPH1TveOfH6aHXVBbR\\nkiMjFnNavoC4CRTNm7GBFCxEbw2BIJKv5dMq1Ot+gJJXHZfxsaF8GR9SjtCXfsy+/pLBXPIlyuVA\\nTylFk51tEBHCmC8XQaCcVoZ8nqOQYfWK2sEjkvXRSN3S7fq4klvaUM1SONt330Z/IG+JBQddt4Cx\\nA61bTdB44VF08W7/44vtiL6UW6y9aO8AD/e9imX3Y/nYDyJh8c5KquFanVa4fEUuqaPzeFk4l4j/\\n1w2EZS6BGhO5jBm7SUcYBwhBGbJiss93OxTg9wBUB7vDzwRMSdmijGBL5XHIbswmHVIyu2Jx45Lq\\noWB80SG0/YqMemHI5+YSXOz2RB0I04kBeQV+pcYJcRxrfFx+X+mjDHCSzAceTSZMkp1ElSHYDUTj\\nYL7Mc7RgXXPKE9YYFxHmrE16+bIS06xP1bYK1JvWC91FQ7detJVDalEHy/uZZxJzwC9V5OzCjrwP\\n+URkdvczIM+be3lMPn7Xk0vAu3zOVlg3pMvheVGFfcoeM2YimYOQBuvzFMRugZLAOI2EcUAGIYzB\\nbMCB+r/nXzNPFz70c3kpvjsjhNSFIio5vnm211fOqxJjsj2IHJmzmMukCBmaYKs3GIWQL8XILqCa\\nmFBb7WWXTC2Ajxf2ZZ4X84trS4+rrh3i/6yn7LKqkK+la+2r9XWCsPG011bXofwKbeSuS8MlHHAN\\n8P9C3xxZDB6vvCybvzxh+TjvkkNyfIct3yuD4hDED3zmC15kJE8oUQVN5s2QFen6uWya9FqZ+OKA\\nDOQC2yCEKQ/8MCAh2xzSjMyz5c/AWBE6teiF9RSgFFptWRtj5Gy3IyKEzdbeDxm4gBagf0HjGi+d\\nRtmtYNQiILKfifNsN+uEAMOInkj1ErHLJQQJI1LulMyAq5p9c6sG2bTDOkFXBbinsB9hTaXKQC52\\nwKRcNtF3seQwCKA1NgIVwOvvQ76urIZjlZ4e9++hHtPztmhwB0OzmOzEhK0dzc+3CE0DKSjFKV6H\\nbNLb2N2qQfJJ2RwJsUyzzu4rjr6Fm95aqgtrccpC+Z8MbGr7NhYXfbYj+OPWInMOg9VW4tLjwDXT\\nWMRuDTkwKyIDw9hW8M3dtxSSx1XHvjaa/bRrXdB6SN1vJQSyLvL1zoWuz6oAqQVYDnUMqyxf5+/V\\nxiOX+oV1SdPPsjqBcfwX/62X+MsO8X6Yl1ZJGZzqTKttkC1XAZRyBNbG8Jp2X0OcYnpfVPLN5zay\\nFBtoqSzrahGZ/rqYMQJDIUZhI7AZsAtqywXK2VYoZXms2PdpNLDPy1qjIR8IidqOvOdDInOMnO/2\\nzAgybbPdtPH/cTbHNX54Fkr+rdzbKfNM0h2a7Ho3GRSmIXs1JKstHxQqyxvr5YRqgfvcKQcnxnDA\\nv0LIshHdOBXsKjjblB2y6aKX3wbUUo7EjyMVYUpRIT8PQz1WvxQdSxIMLB4jHHG0Oy2xyQmtD0LR\\nDgcoXuSKEjUxhsns0OX4fbang9bxJN7mIG4+amXxoh1968oZCKPRbapmjdr4lbJ2Xswqg+3l5Gea\\nJ4myHFeZmpTs3tOoEJLdTX3AMGq/+hOyZY71PHeNWyK8639vStEn9VeVbI0Gv7pyHLu0HLiyMLZe\\n13AiMX8tA7I7oemv6YL2Zn1NumWWz1Pm0MHpKPexSUvqaFyOjaDNnuKD7JideOUd5AC4yru5UmIG\\nlc7lsNIkl/KoARc18FoQ2ARlE8Q8JbJfsAyCBVwZLOzsYLfAyH5v12YNQy5wBp1hnilxTRiC3dqu\\nypxmLuY9s0zIZlsnnyj1GH/f/Co+C8kLD87WOqNQs/ukgaTGREw5VMCQCNsRjWYGqOFzM54YaIcW\\nzU8GOltKd5qo/Altukj/Y/e1A3cboEE0b3a2DdtqL4MSrsSqDdkckBJ67jR0pJoVhOKl5E79lV53\\nivzSA3b18FUZECId70UzqBfhl0BmZRxG9g8fEs/PQcRuricgU35Ti0afBaYU7XGhvzo+eaFSci1X\\nFQX0yiXd9VKJ7PVj0b60xRkHKKuDpIRkHjeU1V0R0EOwVSZZOZFg0RZr5cajRpNkv3k3y3Ibe/FT\\nBpvzKKsdpXkM569S+kLyz/6Ua3tPLmFYxaGFqe0yQL+6oFn97HG/LQB9HXcPCjzYbPT88UzKXOwE\\nIU1QrNZVCnHqRt2IFXte+/WJS8v139c12X6iPKFgACaF7SCEkw3NZFLv2TINEDV74JBdDNHs7pU1\\noJgvNK7LAtOOUkyc7yNRtoTNtm6AEQoYrtHqwPqSplTeqRJItlErXuPKm3KPLuz+0RM7+RPyZcMF\\nsDSlfLvNZD7lHljzB/UVshw3dONGV3KV/gvABmXQZKATpJ1yVHOPNETM2m49IZltuZj9vAXKagv7\\ng1Wlp1ecIlAoW/K1CoZWlrTcNX88v2B/dk5QQaIyjiMyjoz5cmmUHIegbHQuqCu2iaqESLf4WWrl\\nXjhWEM8eUgdjQzAADmp7MQq6j/kkbOZ1slqamayBbClfymZ5pT8/d3xpQTGtD9yIdfR0InEVvg6b\\n4F0XG+BUeFp0nQntiuKr6HhZunrTimbpjj4WBJerm/651C+1E7qj+2WnXg8ERDdh6yR2HF7xtmjE\\nyKXgtN6GzyLzJakGOyrAtKBvI8rJKMi1rR3w2ZNd+PKAVs02z+ynO8/UyZjyxmdMdpGDYoIAso08\\nsZtn4hAI06bGQdEcKZHyZ9lH7p8uIlx5nAWjACOJcRDGcUDSYDbOlA+snO9siZ8MJGUzoZup3lrP\\nbPeSJsUmPOLGRFsZ2LdlXyz1Rjrp0/ypjY2DKBtNdnhJbHOteHjojEVnzEf3lZQ9hpqUKKdQc+GP\\nmbwd0ZWWYiZcrMwdODjhU35wUkwlj4PdHiUwhGAXjWw2DNNIyHaI4jVU7Nlt38ERVPm54OrqF60f\\ni2m8mlmqWcV+lJDdEofR9hvm2W6rcocpavTD3M7Kj7I6CXmHVoqrI62uSpa6d91vjvSy7irCfrkn\\nLa7i5fjuvucHa1CwMnXcw8djx9X6kXcDItsJvRAseXBaiNABYtUAFlHX6yAu/SKlo3uG1MntJeGi\\nfA9Evg2PO8C0bOvj0tpu+GWukB5QaskFcFTZCmzHwHCyQfYCwdwPzS2gxadgnkkxonuLtyKnW2wp\\nakA+nIz55qC5lp3mmV1M6BQI48ZuJ8fmSTWF1W44gGt6ch0CZW0nqDCqsJ0mNpwSNgPp7Azdz6QE\\nYTY3Prs5xny07fYg86wo9n07+k7BcaNNqYApB2p5g3gvxIspoRKqVS1oGnmx048DTCNpjsz7Gb3Y\\nmWfLEMz1023EZbXdTi96gKga3GJz23V403KLeCqfy1hsrDfAt1+DkkPAYlanYWC6fmp3dO6sj2UY\\n7GxAPq1bomIW7b4gb9sEL6y7ZIyro6k+k9oXRf8trrj1TlaUYRxJ2XRiMjCvziIWM7+0O8dn0SEw\\nFIRNmDKSNF9rODAQGKXVJdLuNSrjsV6W4bRGyfsEXpEsC5HWTG3l1H5qmZbcEehWLl05ZeVQVm4Z\\nl1q/rvP6iyBoll+q9U2XS99ZAcgCwuW7Piavf1b9UJ1LUJHQteBDwNYnlLtuKml5DmBO1rvIm4la\\n5ib/ysC0oaNMKTLNifTogpBi3hgyY62ixBTh4RlEi1dOBm6Z8xVgWYssdn+NOTYIZgrYp2Txs6d2\\nM1ChyH3ksDXrA1DqqDaNaRRl3JjWFXdKnLLNdj9DCAwFjEMOv5tPeeTbzex/MeAJecapZJOGHyPd\\nur+fVXXO+gmrLa9gNvyx9E/IB2gkkAZFx0CaxFYNwUDTXDznKkztftBQ2138yaUypdGi/osflUsT\\nS+mpxftGYxlHRVQA2415gYS9ZRxsz0FyaGNKsLTid35grmq0ejaVT20zkyxMcw8lCwGQ0kxMZ+x0\\nx3n27jnTyHmyC7NFCqiqnc4UMSUkm1YoAbRQwhCa8MtAbeMjuCiW2ikRnrdaCP3/mXvvJ9uR7M7v\\nczITwDXlnmszPdOc5dItSYlSxAYlhf7/CIkhsxuiluSO62n7+vky18BkHv2QBolbVd3U/vKImX51\\nLy6QSKT5Hn/OQ95M1XUPctI1lmSUP8mwmedLUhvznk94UenQ5bTd3LWTEa6Pj5c0qzqWi6CafKof\\nqsG6Z/A8aed+AyfPWwyQLChhYZsWm/pkVufOPt7uw5cswf/042PonxurwP5UNyoJFprgscOA3zmy\\nvyzGEDQwDSPj4Yg99lHMbGKYMyHmXVHv58i6uPLQEDN7EzSWeQuBYCKQFy8YZlH20RcvLzlvluWG\\nkhK0YQioeiY/EZwQPPghEhhjItdoG4dpbMmnHosqU/yR602y5KzvDT310C842kfuEKKnioXkOht3\\nWeYCZ5ejxMVni2YiiDmhjqfmdPNmPQGB9HUJJFKkg0UPC9CfsPGZyJrcduqXtdBZmJInUKXHz4m/\\nchwDkoHcVNN8f4/NwyfVt4D6Hj/sGYcDOu5R9ZgwYQ93/Hh8Rxf2OBFuRuHtbuRMG846S9eZlEbC\\nYtXSepi8MnoYxOC9R1Rw1tFaR2saWnFYiWBvTATyEo+RBxp9OJKyxoGT93tcxlxMwCPrRk+kpdNb\\nM7V76Px8z6nWJx//BjjyyumnlllOEFrmm++3V665/1uRRvJE1lx1GhUjVXhyvRRP1TCPBWPc68z8\\n8UGThVReLvmd9MFXO2k4BzLke/OSixKEEbBhRPoDwz6kKuIOrGOaBg77PYfrWzZtx6pbY7qOqR8J\\n4xRxJoN4EukEjVkTNXLmfhwZfUyYJa5JYms1X1J9ngdt8XsBhVMORGJ2wCYEZH8kHPeEqY+6fmti\\ndOQw4pzDrhpk1aSApKiXzhs1JHDULPXk1V/5uOvJHM8Jk5b9L8RV6rnJQrMW4lmMxeOInQIyTAy3\\nQwTOzqCa1FPlfeMMjsQEXzmfyMkiqQdx7t/pIqnUKPFDpa4hSSFGyZWsADSEWOTYpFqiooR+jFJa\\n4yD7W2tAjC1vGx8fQT7LPzHgZt5gJXla+pw5cL//gd27r7l98zXT4R2iI1aEG698EMvvGsvZusHf\\nvcTejHz6vOEv/+SSz5+v6e96dOdo+pZLs+LmMPH+2PO+HzhMAbBs7IbnqwtebC74bHPB1tqoSlFF\\n1ES1HaSEuTOY/9xRGyyXrObpvelspTaq+f3i5SYkP/OHW8kL7hSnyjp9BCM+KkdecxfxU9XL+wTx\\nQVDMl1aE7/Sm5d/F8+tn3+vYw9eXix8B8keO+/7TFXT8/HpacFun6pZM/hxK5wNtzvGhgvpAmAZC\\nmDAI3WqFFRMNhI1FWgfjFI2JKfcKk6KNifrypGIJwTNOnlFBrYsc+YLuPgDiqeNZBT3n8liCeCZO\\nBqUFrAZs1ofeHWGasEEw2KiVT2HlBZqtLAiQItGPu6idqk3BTEyo/3KyvqqJyeNdO/NFNVB8F4WY\\nU9wISEyt2p5tySqBnOBDQuTkNSlIY/ravGlPMq0olYtbPrvs3ywEVe9Qq7pI+mCS6ilLWkXVkgDG\\nRre/oCQpy5R3yv7aZVxP133dN1n8goYJq29Y8w3PPnnHXXPLD8Fg3DOaztF1DY1ztM2KVdfy5GLi\\n+tUNh/e3nH3as/33lrPPhItgYRRkDDT+yLgfmfYDsh/4w7d3/OGPe77+amRjG666DZ9sL/hy+4Qv\\nN5d8ubnkiWlBXLSfFBHjdERnVnkh4SfQrZf1Q0T/5CbqQ5nVjpnEArMq7cGjZhrz38ev/nheK3L6\\n+X4nTznOBZF7pK1ZNDqlBQ8JgvW9p+Hl5fTJl0wxH+7LQwTlQV36A7f+3JE3TtE0VYvHEEP7W4HO\\nGlzTYlWiATB4jFcaY3HrDdIPiE8cZddGQJmmqHtMlXiwNqkpotjt/RTLvCmosTFFbslAtCBLD1j0\\nT1+kBoNZlWVI1YHI2Cf4IXKKRlNypUA0zhXNhSAuoZrGZFSRezSJCZakMsh51e/3c0mg64+S/1/O\\nZkNpDNGHLEqJjc8kSSliJBqTfQxeyQCa71fJQL4MaFpwXz85iEtGpO5+/VeE0tfsvVIiKrMKKPcN\\nU0Lg54aSTv9Ed7zYYzJ/y31SDQy7N2zsH7g8/z2//MRytzWoOWNyHefnK84vOoZJCcFhraO7GmhD\\nYNQR3w4Mqwm9slxdNtGO4KOXlR4n3HFie/R8E/a8+fYt//fLDxiBVdNw1q74cv2Ev7z8lL/75Av+\\n+/NP8G0T14HcfwdYosOp/azs+gqnMjKV+3ObjzGSJzs+MwX3Lr+nBeBfdfwbUK0sz9eeIEt98n14\\nvtfKiY5qibVJzMqDdw9s5FFgnvHytD9J13ii916oc0Tu9/O/9ajfL4v8VSCC00DbOlarNavtGWac\\nIrctBrxGQ+Gqwe96GAe0bbCbDmkcfn9MRYFnDlODJwTF+IAfJ4ZpxKsg1mGdi8anRFhMrSw6feFH\\n9IKFKJJC3RNXG0tzWYwJGGPnSjUiMHn8/ohdtVF94gSZMjBFrlNTvc8aIJedmscxemTGDX4yjct/\\nTzZ/fPVQ8mQj6ZmEklY3csIaVVcupjMI5AQ6hkD0aIkgOg/SfUN+zY7UUoIuO5ffKRNHjS8oSIlA\\nLepLAVL+HU19j7lVXBmTWkaofbUlEaTknl1UK6UyEoHge25e/iOri9/z7Fd7zp/9Cc3lGUdnePkB\\nNlcNnz03/P4P17x9e6A/jrzZWsJhz3h3w4cf3qG0rNbnPL18hm0E6zzaKusm0Kzju2++CkgzcRwG\\ncMJeB14fb/nq5Wv+y+p7/un9S1Z/8fd8/mQDblVWwpLZmA3gQsqamAczQA72yV5APMb0saATCyZ9\\nmba5mrp7dz906COf5+PjBQRBRb5OKDqnm0oyZi7ArOih7pE1zc3dZ6hn4foesjzkcfLY5q4B61GR\\np8a0PHs/cdScTf3ER68n68wiEDgUZ6KPs/FTTE+ash2a9SqG3HcOGSf0OBCGEbNZxVD9tiFcH2Og\\nzaqLJeBQ1FowwhQ8/TgyETlyY92sWsnsSs20la4/OAnzZ9VyqxCTGxkNSY+YakbGWHhs46LhVpiL\\nHYwWtVGXqwqjD0wGgjW1BuFkQvI8zyHnpxLbUkWRX2UGzlh1h+Q9k7a1ajQca0gAHdUUODcT+JL0\\nyRBCTpY19yl+rDxr0m96b6+c9K1aOkUHnOdHtOyJzCDFvWQwhATKgto8FqDZOS9oKv2W7wdJ7Uki\\nFLmPQhyPaX9L//Yr3v3hP+M33/BsDVfbnpubhj/+88CP7zybv7xidX7JxeEDu5sj++uJ61fZKDlh\\nGFlvzri8OMNaAfUEnQgaC1AM48Sx3/Pdtzd8/90d/TAiHlQUP3pkEF6PN0zB8/tP/pTV5jnOnVc1\\nO6XMyalacN6GMjMQeezqaXmAo66PfC1lny5nrdYY6Ml9QKUTzwRkqWOoj49Y6m3e9XPHT4BT5vP1\\n93Jj1YCU3VrRysItLM4uAFmXJxddy9zcPNiZ57wP5qc9e3CCqx9qtVF8txOA+5mjbP7MTWrUkTvR\\n6E87TdFAmD0PmhbJuSpWLd579NBDCLGAsjVoKkMmVf3DWBPTMAXlOE4xq6CJovBCzVyc4atJrMGc\\nBQldcOkZvGyARkLO8JE2mykZ96SJfTEhg0g0MkquUp82eSBxmGWa6o1Qr6PIWRl5eLxPZala5WFQ\\nHKGAoqKVp0oaAxOlF2OjjUF9mEvjqRDULozsS6GwWlsyA08t7i8W2kwN6xaYMV0rcK8ASm0cvywZ\\nLHzeoxSruVpPVvdULOWiCwhWAjq9g9t/xh6+wbp3MKwYbt9w/VJ5+bsj7248784P3Hwy4O9uCbue\\n4Xri2Mdo3aY1WBe4vvF8/7KnP4445zHW4xpQHRmHI7vdLT98d8vrH3f4aSSXywy9ol6ZmBjDyA/7\\na74YD3TM718TT4XZjlPOxjEqQF/etSKy6XiM8TuZifLpIXzIMtl85QKE0tQ/jgsfSbWyROxTFxs4\\nZV4zp35CD2U5QIX7qyYpc/Nx7+R7Z4v+3EKYwbW+v56Zqk8Peb8s3vHemUrPv7juERB5jMt/sP24\\nwazGPCW5NmTQQAgeM8Ziu4aABjsHPoQQudqs15VY7T0Ejx3HqLYxBozFa6CfPEFMLK9l51K2pUen\\nXdaZE8yIsjTy1lweOAMriSl9s/pdJCptvAYm7zE+EaZVG0E+gXp+TNDkfmjMcuPdG8+KHOcOMM/l\\nvQCtvNWS90u0SaToWNUcx5JUXUkAtyapWYCjwugJJhXz8LG2aG0Im9fjcqvf0+WX8ZsXgpYpqMR+\\nTcQ47wGRlABfCEaLB4cIYCNBNM5GVVC+Pm3V7NIZ250jK2Nel5xnR2lMj+VHNPwTv3y248VnHb/6\\n1SV4y931xN2NoR8tr97AP/9uRHvD+6PldvD0h4A1HjcGcMp//sf3/O6rG842sD0Tzi8MT55a1usR\\nI0fG/Z7XP96xuzkiwWOtRTW6G4b0P4tyHQ7cak87GwsWaxEoScRO/b+p1s4po7kA/eqOPAOKVGv8\\ngf38k7/N5+u4k8dUOh8pje0DIEne0EtQrwdPZH6dGtRrDitzIOV7xTbMaojT5yrUNScXPz9GOOpE\\nWT8NunWbDwG3VoD2EAlYclnxZSJDpKV/RiI3a6PmNVrobRq/0SNdun5ItTuNxdhYcxOlFFzGT2jf\\no20bvVhsrIk4BuUwTQRrwbkI5pmzUVLQTR6vJZeWe5p1qzOAQlEjGMF5aAlV3YiopgCwxqKjj7nI\\nmya5jMax06AEowQNqTK9LYmqZF4mj06MPDDxiymtDI+ZPFiUhsyU6BzhKHXofTxnrCnBK0VylOx6\\nmAicLiD4PuFZgPyCV7l/TdJZF2mnPlIqASMUIhQg6vqNxKAgLxhPCSLSkjkzq4JquaHKr6LKefuB\\n7dO3tO2R5q8+5cXnZ3zxJ1exUlW448PbD3z3amTce777+hZRZRgC3utc9i2A8Yb+Thl2I9fii4aq\\n6wzGTUDP6I988xLW5y1/8yVMe8/1tfLjGwiT0BhL2xn+39ff06ye87fbXzEGjbns52Gq5ioTrwXH\\ndm8u6rGfWcPclsy3USnsChGk7IX5EVVHagnrdG4ld/r+8fG9VqoXnH+bX2wW2+vLpdxyyt1XrSzb\\nT18WoeH5fpZN3O/SCeGom11s+AdEpsWEPzwJi4msF9eDr5H7qwsthlHoRGkkVkuPxWvnfB9Z64H3\\nkZu2NurBkxoFTYmyRKIBLAQQl4p/CpMGeu8LkGPMnEde8lycdLtKEVfUBguiStJAxFVqUZowRdVK\\nBvIkjUkCQuNiruyswig50VP7ExBK6sF50z22B+5z3tV8pXmpiVI2LlvAaaXiIeXzyQWEA6iEUp9C\\nk2ERyc+c/d1P5JRqltPc1v0+XR/59APrV6r/lieqRaUUo2VRB8EMZhnIilqmelbal5mAWyYuVm/4\\n/OmOp786Z312zvnVGWcXa/r9yJNngU8/O3AclbvDxGHXM3llSkE+fopzakzMhqgh5iTPRlkBjDFM\\nfmQKA5Mo+7uG1cry/BPDcNszTSPh1RhnywjGWr69fs/59jXPf3HLFAJGJGZNTGNQxktOh3ZWwcwS\\nT438M2GWahIWeWfmwSpDNmPPKWmuuJzqKPF5J/NcHx+v1Fua/HtA/dBmq1iL5YLPqpQEkw9KHVrf\\nWLZMfGS4tygXzy335w09e9LOv6VO37tf5/fLXZWq1eyfKlI+I/P1p1btmlvLwmyRYDRO5FqgawTj\\nJG6AYUC8R8SWG1WzDlzQFBlJVkCYmOpWXARvTSAeXRIDUwggqVhFLrdV51hdcBUzii6ZngTSZVPE\\nMTCSKu6ECZK7W+1HLTblj0k51MO+j6DjYn+zsD+pEIjc42ngWSE4nBxaXyMn52p96py6IXoJaQqW\\nSrrUXHpMs241Zj300xQjZ9UTQipEQYyULH7mGeTLSFVzX6unHpEuFlkHdR7ruD20jE+8OENNHGOz\\nYCtJtT0DopETNzkUPoH97PmlZF27wdOYnifbH/nl5wNffPonGNfhFY7DyM1xYJSJ7aVwtRfsDez2\\nnn707HrlcFSGMdp0YvR9nFMjEdgbG//DBPrDxNArAYdTR9cIXi3S9IjdMQzHVI3J4YPj9njk9d0N\\nL3cfYlrnohGYp/qBpBnpHSuYPQVtzZ46SpEsy0pK9pSKw86EIsDJldVzHzYI/ozP+cesEJQJ/D3W\\nHOA0DH+p96tBX4GiFjkhBpr+WYou971Wakq8HL8TnjwbTiuiUESonNWvohtGoElecBpgUi1FlRec\\nTen73BGpgC6ezv2eJQIp10UOsbNCIzGvc2w8RT2msGolprXVEItFhHFEQhcNXKbiFvzsdmY0lohF\\n9AAAIABJREFUPjFABHIT9eMmJVYqxW2zeqH0N42tFmXQgpBV22MONlHFhimVcAuEEKK+P3g0CJIy\\nHYYpFUmTlKEv5LS7gk/gaVKSvCyILJ67EA1YHgUrpbpY0rrJ6gpJHHmIpfLSu2v9NCGWoBs9WlIJ\\nx/ZisQQhWEcuWJ2ZlYVBeNGv3P8HuRWkfh8zMzVCIpIp30wE9tKV+E9KLCVI9MMXSS45+aLZuycy\\nD7M/vqYBa8zEWXPH+WpECLz7oPz46i1ff3PDN19fM02e/jhy2A8cD4Hh6DkcPXe30cg5TrHYc9bj\\nK4EJSbECAWuVxgmr1iCtoRHwo+JH2N8qx35ERDgeVzy5uuIw9lFLLoHt2rFqLSb59Nf7p4xDGT5Z\\nSG61XeveUjkF2zxPFRjnkorVwjvBGLn36XSG/zWq249afLkIlIlF1+qFH9hfy895w/2E/uoh53wp\\n27oKrEGoc4qXQdf6vmqDU4GozAxVPJ/Lf8XqMa2NT/KiECSnqF5w1zPhqfSPJ5xXLQ2X56sCHvU9\\n07TndrjhG9kxSUxNyuRpVNi4lq07p20cRgNMECZPGEfaECLwq8bEYblauo3VgSQFqMTCEgFsLLcl\\nKeqPei4jRavmT5a/18CpM0nKso5RaIrrYZ7bEMElVYVhCmg/kqND5xwVcQMFwDihbQzBwKSxWn2a\\n4hPW5tQfOPe63szZyJkIN9GYbInunpK5cjLHlVUTEvuac9jkPNqR8sTyfikgqLi1Sd2DmQOsd/6D\\ne7riQvKemIdZyhpd3GzMXOXbkuZaoufSqQwvkShGqQmCmZ8ZGRwwOiDjO27ef6C/fs/ubuC3v3vP\\nH373nm++uU7gr4UIqIdpDByPgXEUgppZXWcN1tlUqtCkXEExaZtHER+loBA0EusAw0HxXvHe0HUd\\nKoEpTIhVWiesneFMLIfElCwklLKpavZi+Vs97PXWlGrc52mQ+cLq5pOvi3NzX6TywFvspIJ5Dx0f\\nPSAoe0ucou9Dknr8WQpLXN+22I6PzYRo2fjLWAqtqGbVmlTtpVEs+szEmSBEI36IlNcIOImFlBsr\\nMWxbFSRyjSYB+ZQMTfFpWl59XgO1VJIpe4I+k6IydUI4EMI7jrs3/PH9S279jk0YCcHQiuHctTzr\\ntnwRPuHp5ox1a5nGHj+M6OBZDy0r62gHn3yJDUGkpDPFT1E9oIExeDAdhd1NGevyNMSIypPVtsDN\\nOcSknnNN2fUM0Col4ZGojZw3GkG6H6L+fpiwzhG9R5KuOXuPGINrLF1rmIJk9WqMUC3kO41pPcnM\\nRLqsAs3vVv0rGoFcFZc0C3kAJOfJVmaduKfKvidVmoJUbDgzMWl95h4Vr5G5N5xKqvMv84BLpeYr\\nT83TInldmRigFPxcUckKEiQSyVT8OAf9ACVQK3v4zKMXx9UPR3b7H/nNy5fcvP2O77694Xe/+cCb\\n13cc9seZSSXpwEWwArkKawobiME4ztJ1De2qpV21NF0HavAejr1n8jGC1xhh3UHbCjYY7q5Hht4j\\nAq1paSxgBpzAxjietyt+FMOYx+n+Ul0Sel2ukOw9kpmbB4/MYKR5mdnDGXOWnnBVDxI3/1A8y6Ij\\nDxwflSOHeTuV8wklsxEze2dkrWN2e6gYpupDxcnlr3lF1teeJACf8XJBZx/ot5SJKE2jlORAeY8K\\nBBUGL5igSW1aUW2Jy3fOhzwTGyOl4XIENOorBSyezvRcrj7wdPuGq+0rhuMNL7/9wH/63Wt+NJGb\\nAYv1BjMJMgTOv/uOZ5dP+cVnn3N7+MDt9Xvurt9zdrbhTy4u+Q9XT3mxPUdUOB6VZmfYjB1rBTd5\\nBh/og8aCE8UXOnU7cXBl+ZUNUi3IBCbzCFcUWCT5ZSsdSmMtVh1BU6rdtLj1OJTGpImeMyTpgRAI\\nAkcRejWMwcQKPJkjrWKoizRWLZ24iZf8uRGppnUJXiWFSnmpxN3rfG8QUm6PTLDiC2dPkalM9TyY\\nWvWxdK78MB+LpGNySp5qcKg/zoxDsdFkbxSb8qr4SNAxUhKpacrdUwN5ISwIBM/u7Qfe/8tvuX39\\nR24/vOLm9sjNzZHQT1hJAU2JOMXI3fhf7l4QUp55RcfAcZo4HnqMNdjG0rQtTdfgWocYB1j8pOyC\\nchDFYQjG4NqUlVKVyQvjAFYazun4dLPhnTWze7EshidN48nez3t+Hvg5YyWZ6NbSfMUYnEpG1fnc\\n1hIME5F8wJ05z9tjWP5xk2Y99mXxbkvu+XHPj3st1qSUGR3lgXb03q3LZmagPZE45xM1IacEws1B\\npgU5sqGoBoble9Tdro2enR3Ytrc8P/+R5+dveXb+gcvtHdd3ytA7Lj+9YFqtka6NzhKhIQzKtO+5\\nmRyct6yeC8MovG+U744D+7c/8t2ho3ef8pdPn2Ma4cOxZ3zbsjId59LQTfD18TU3esS7C6zNiyoT\\n13kM5uE8AfH8u96fQUkSkVNoEaw1GLWRa9WQihiDTtEIamwy0tZ6XI069RFhwDBqlBhmL4FMvKsN\\nyP1pP1kpNb0q85l1zkaiaF+AOsxgmfUZWWtVIUHSBGlUUZysm5mXSDclRiZ35FTQPP2ahrjcHt+h\\nlim0YnCkkq7ibxp8tIPU6oHMtaOzG3bVZ+NH9LCjf/OO6x9uuL3ZM44eMymtCCbFIYTUqZkjj3sg\\n5HfM46ekwK4pAqCBqRmZ2gjkxrXJ88rFtAc52lezSpMoBXmLhoYvzp7y5dkTrtoVrgp2q5JKzMD8\\nc1bFn9BXLzhpfQB0K8JbQPwh42bmzKtbZ4L/8PGRgDx7AuiDi3im9acDXXt5VPhct1E9I/6pwlzv\\niaun9z1CJOor00Nrp7FTAS1DXHZLi31dtBI3Q2mr3rRxF0YjVQ7NjuL8RXfL55ff8qe/+CeenN+x\\nXYNxZxzlCZvnW/7s7yy62WJXDUZHvHSxZGffc+wNjYOzC9isLJt3LXdrxzf/6SW3uzdwuIX2PY0E\\n3tzsePuqx/iG83bNKrRcjz236wmzvcK6HtFjGgsHmvop84zNo1ONrmZxs0xNAoXZpa+TVJneRkul\\neh/HSEFNiPV4hchBmtiIJqOjBs+kMGrUjUeGWpOLH1X/Tud1PjGrU2riM18vCFZjFK2NiTgKTJZ3\\ny0tPJKpbUtSppL6UdZLzuc9cCrNReF5X91QnJ/3X6rukdTcT1ySRFAZmvqkECOVGQtQ950GWJEEI\\npMCrrNKbpQdRxYxHmnHPWgc6DUzG0LWG1gq99/Q+0KfsiyH3w8QkaHORZAp4zUMu0QF1Ckw+ptrt\\niakaXNfRrNY0qxYRwQcllppVsNFIHwIYLH/z/Bf89bPP2DoXU9tm5kOz9Djv6Xl/z8OyWCdpDJcu\\ngfdBWE9w5jEIlpM1eXq+Ig38FJR/RB35qWdK7U0S/5H5R2br00mgQ7UyH/aAqcTOxXjXMHNP2H6g\\nz1SzejpJUghr1EPeJyi6uG0Wcpf9LQ55pV0rSmNHrrobPn/yBz5/8hUXmw84MzKOjmmcuD0qNyN8\\nmDx3r3eMk0f0gNeY+IrpQL8fmQ4ToQ9sLy0TE+9vd4hRJhW+f9sz/T9veXpuuDwz/OnfnnF52bBZ\\nG8bjwN3NxIvbwF7fMLh/ZLC3DPyKUZ8zcl4BxzwfJ6M/v5nU7z5fboiV6REhiBAsKaRdk3teJHLl\\nBg2oJ4aEAiImejqIKeA4E8zMDZ08thJja8Npnc1OUDBhvj5EoHA+9SFVqxGRWFPURzUPqVBHqcSU\\nQ/gzsTeWkDyG5hVwQgDvifonw5zvqRi73EIBTSo1kCUF/hALSvjYr0CMOfDBI5gScCWeOB4keJPK\\nqBsCdjzi33zF9ONv8TevsGGidQ0qFj8csZrUWili2OuUpDKHSQFrMZo4St4hkcOQcDakdRLS/m4k\\n1nW100A4eIYxFowWY/HJVdIHw3gckUlZmY5fP3nBLy6fEmqVhVaeOMiML2ndzLVx55Vab/+aoazn\\nIwP7Q0jyWD2Dn2QudKlQeQj04d9CQFD+Xn2umYSfur4GzMde8LTt2NZ9ruZnQfynL0mLOx8/RRR+\\n4rws+yMSWLs7Ltdv+fzqe55ffsv59hVWBrz3eB/1fcMBbt5c8/W/7NjdBUbvUTOgNtoaRDzTbmC8\\nGRluAm5jkJWgXQAdUFFuDyP9twPvOuHqzHF9O/Dik4bnnziunsDFubIalOOw527/NbvDB47+R47h\\nM9DPGc3nqFkj9ZJaED3lnjNsGbTI6VkUm9j0QDKlRoY23ZoMmykvyIJEJHDxEo21S+L/APo9MH91\\nl2ujtiaVgjPQCJxZZYUlyIrBaKrsbiLo+5T2YJqQKeZikZxAi2jwVg2oSAxcyhV3FpR+uZqL6awC\\n6Xl7545X95Z3mSU7g5Tw9CL6RxSdVT8a5uCg3JpEL4+wAK3o2SLHHXx4yfjyt0zvv8P4kcYI3jk8\\nBuuaWElJQpRiNL5nDvgRsQuGJuruk+2oSBJpDRSinCTYVARFiETVGMUTi26PxiDDxFnT8ouLDU/P\\nNqy7ll2Vo17zoplf6mQ91JL/bOQ81YHXqhGBpb68OrS6/hSrTrHspCMny+FhpPtI2Q9PFyrzhk4d\\nL+qQdC6L4QtRphqUOmhoYQl+iKup72Fe2Mvz+hOjqwv1Vs08LYxQ1ebUBxuTe59zwigkYKXnfPWK\\nzy5+zy+ff0XX3WFkZPIB1EcDYTMw3d7w4Y8H/vAPL/FDQBywEdzG4TqHcw49eHQXsHtl2E3IRuie\\nNqwbD06QQel3A/u3nh96pfcTT543/Om/3/Af/37Ls2cO04LVEcMNMnyPGX6LnZ5j9E/x7f+EtL/A\\nuAtqt8TlmJ3yHhHBlJQChJi5EeJmnlIgi0kbfFZBSEnylOMCskw1GUlAXhuN7ou6i1moQFDS8wRK\\nyLiI0Imwccq2gQsjnG9bRn/B3koxUFuicduOE7IfMGYf125rI8B7TxBBCYQAPj3JIBVInK67gvLM\\napx4vib6emLAr0aYNBylDiuqSIrelRz8Y6NePNe+rD1WlEhUsyyBKtYHONwSXn/D9PYbwu49nbW0\\n1uHVoypYa3EIXgJtiMZS1ZjywWbuWdMri0/ceHo3qfT6tTcPibMVMGJK3IRFCNPIGAITUa1z0Tm+\\nfLrFucCIT+/IvXYLk8Ep83gP3RfXSHWuZhl+Kq/K6fWn1xRHiqojUtl2HrGDfhwgfzDbXHHslAcH\\npRaVq4YWOZlKC3nGgcWuKIbTmq9ZMo6LqRKqdiWJmDOVzpdGCh/PmxxPnVibmdrO6pTyfhEtMg2r\\nCBpY0/Ns80c+OfsdT8++pm2OGJ1iZBoTRixiHD5Yfvj2PX/87XvG3YG2EZ6cOT77vOPy+Yazy47V\\nuqE1qShDD0FCFOcdHP1E308cd56bt5abdz3X7wa+e+/54duR63c9jR341a8d2zO4fj9y967ncDMw\\njQE/vWHQt+y6wPkv/1fOnl8s52L5ZflR84YyWHz0zdYQC0uoYAIxM99MrePacTnSJ/5Wu96PRI68\\ngH6+uXLJq3kiY2b5L8OkAZzAtlUuWrjqlIu143xl2LSKDZ42CIM2DAkArAibVYcTYQqB6TDE7JL9\\nhENgf0RuDzS7I6JKGDJHntQ45r7vfb0UZ44vXmOY11whksnNsdYSZCJnMIVJioYTLW6GpERoxqbq\\nS6oF3EkEgGSPEFWMCjIeI6d++Rluf007eobhK1bORYfRACPKGCZEA401ONuAEayx5AJyEzAYGL1G\\nV828J6v8wyUNQmaMJHLzzjZ0TUfnXPR0GoR+mpiCsnEW40fevXnNP3dfoaz49IunhclYGjvjmBfB\\nhOrZiTOfSf18Lv+eOXFN8/SgCuVEF17aLExfBJLMtGoNBmkmH0uYBR9VtXJK7R76WOuS74N/DcD1\\nFUIlVZ/cl4ekpowP0IIIvHWDeZvV+ZfzYGdAWhANWdADhQW43OtV3qVJ9BbtkekbGr6jNR8wSKzQ\\nM0VuZxwDw6Ac+8Dr1xPDAVZtwy+/2PLnf37BX//1OWcXLatNQ9taGpfSPfmog1RiHotJoR8C/X5i\\nfzdye93z7s2e33xzy29+e8urV0fevRk4v4hFFI6HiXH0DKNnfzPy4f2Rm73naP+ZX7Z/xvry32E7\\nV8ZjXpT1rFLGIJ815BJvMZozg2ne1MZEUqhCSQUrRI8GEILGzCw+VXqvN0l82jJmsv49V88xEjnu\\ny87wZGO4WAnnLZy1sO0sXSO0FoKPucRDcLHQhiQ3vq5FTcwg6TctYVzH6E6Sf/a+Z9r1mHc3jLcj\\nFodx0Z9aq56WHublVK/l6vOCX6kXWrlN5nWX3f+Cpj5piWIzLubdMdYgQWOMQQgllUMUgKQ80fhY\\nZo92jTxpWYUR60fYvcP7gA89E0JD9kiJxb9DcvnprEtZOj3DJBxH6EcYTajeN4GrBvzks00UMQZn\\nHI1zNK5h3bZ0TYMzFlCswDRObKxjJQYTPEYDSmAIvkpoNnO/9/bpydpZDHEFzLL8YQH+j6lQ6uvv\\n3f/APC5+exzHP56xU047noewkkBm1ztZ3AswG41yE/NGzeug/FY1LSwnIzVWQHTu0D1oJjMEJy8z\\n/1rew5y8V6b697becggSiIMSfM/h7iXj+j0aBoYedseJu0Ng9B03Hyaurz27Hbx9a3Bmw/m24de/\\nfs7f/Q/P+Y9//xzXRN1h8YRJHnvZj9iHgDEtGsCPnqDQHwY+fLjjl795i3OG3d3E2MPuTunWRJG5\\ndbgVTNfKmw8j3788MukPXPziHS9+3eO6Zpag8jxJTQTzO0shukZiWljnYwBQ9OozqaKOYJo5Fk9t\\nBpgIoDkLog+xMHSu9F6NLIu4AWZOyorQGOis0hl4vhE+v7B8+bRhs7K0LrsaJk5JA95ACDG/R1CZ\\nq7VbmwhPwLYNOBcFh9SfEJRhmJCrDf7dgc2d4YwYoDIpJeq3LEk5WX+SmYBZBMln5lWoZVzzKo4R\\nlSl/jdeku0++5EYwrYtAbmKOHslpbANRcpMUwJ/UVdZPeOOQVYe1hsYa7Lhnevs1fjwy+Yl+glYc\\nRiyti5kpp8RTrp2lNdGjZPJCZ+AgEqOfhUS0DQEYvafXIQlUBmMs1rlU67Nh1Xa0ziEiBG0Q9Yze\\ns7KWTdNwuW759PyCi9Uq2o7QChfz+quxY0ny/zWFj8vKKuqgxH5W4r7UVypL/Ku8gO6hw6lO/pHj\\no/qRQ+5cBtE6qEIeHbgCetT7Vepxgmoxz0zyHFu88Fop96Y7qptqaJ/bqX9Zfjy549H+lz6KUKqQ\\n580bIAzK7W1Pf+kZNfDymxt+fBN4/c7x/kbYX3uGXUxspCOE0dK0ytu3B/7lv77Dtp7zC0fTCiEl\\nCjo7a3jxYo011UKzkcP3GqLvrnhca1lv12zPNnRuz8210A+B77+faJqRxiVgazvExk0KElPjllqZ\\nzCiUFrgpw6yLxQtzyLtTXVSUV2XOAyMxKtFYW2ikhAAhZsoDCMaR86urzCvBVKJ0VBEInRUuGvj8\\n3PB8K1yuhItNw7ozNE5xxd85B3WlpE4m6metRN100AiKk58KwRYxYALBp1DxLDZbQ7ja4DYtfzbA\\n5VF4c/S828O7Xth5IeicFC0kkSa+vlQc66wiimORIN3MTMhizWWUCCG6dKaC1VGqmTk+MbEgd/b0\\nkSTJCSS3ywhuwaUUDiFWZzJNh1ttaNcbvJ/wOmGDMklMTzFpYNSAR6NqhyhYWSM0q4azdfQRHyZP\\nCHB+tubQD9zeHblJkVPGGIzYqHtvHG3b0TUN1sTgL5FIDAcf2AJnmw2/evGcp2dXbNyKXrP9Q4tR\\n957Qw7w2YcbRErVb7eH6mFUl843mxPtp0WjNkNZgXV2ycLIp5x/GlY8bEHSPM86c6/yt1lyVvwtR\\nI3PCQuXeMONxyc18/1m5D5rarHuxGPf6b7nolHY+dMcD7/zQFeX1Zr2ZsZZ21fHuw8jtzQfe/Hjk\\n9Vvh3fuJ3c6jg2DU0LWO7VbYbi2uEdwK3t8e+S//4mlXFmPBJ/F+s7U8e9ZFsTbln+66NqpeWqFr\\nDC6tiIuLjmdPOy4vG+52ym4HPniMAyOxWsuqc4weVusWz5Z2tca17cm7yYLwyWIe59fPhRqM+liy\\nS0Os/hPl+rL4F9KcKohJOtxo5PTWomKLFLJMtZr5V8PGwbO18OWV4cWZ4Wpt2LZC28a6oUFTwE+e\\nmyLbZxtPYjyUqPMOyXVOY6SvyXneVQk+lPtEBE3cb2c9L1rlbCO82Ajf38KPe+VtL1VfTxiG+qvM\\nbq9Lzm95i0m2BSMUT5WYhz4xDwnIcxWgmAUz9luSQTS7YRqil46k+qoFoJqO9vwZxveJaNzCOMV7\\ni7HO0VjhvLGct5Z1I6h6fIiZNb0oPunCrTFM/YgNwtq1Uc6VVNfVRq68bRw2GTDVBxCDs471as2q\\naznv1jxfX7Ft1rQm6u6zP/08XjWUx8/3sfIE6os+5qfumZ+xUDPMP1SfterLQ/Obo2oe1638m1Ct\\n5E9LS+3JSxfSVt9b8eTCDOLpurn9h4F1YYjIclDFQeZnzNi9VLQ8Pqz1vdVOy4tHs8g2vwWJAhvi\\nhnNNFA3f/NDz47cfuH3vuL2zHA+K+onGNaxWLWICbmVZXUK3MkyT8v6u5+XbXQyS8DBNgaBC08B6\\nI1iNOmfnDOtNw9mZ4+K84fKi4/zMsdnaWJF8Zbh6YukHpT+CH2AcDIejZxgmViuPAbquQZsrus0Z\\nrm1RQhmrgnnzkBRiNYN78ljREGuNpvFR1VT2MiJxDrcvxiZJIJ2SLfkM5KZSi0mt2omqlM4KL1bK\\nLy/hz184NiuhdRKNkinzHpqIiEr0x05FkyMHmzh+heStHZ8TNJZFy6sjATkaz3uJUauS1phXz7oz\\nXGwEc27ZNB4nymES+qClclwZpPLhPsd4eszWpWjktCZ7gmhyOawD9pNKJmgERNWkrgpYT0yuldey\\nkBKqxTkxEgmB6dY0V5/Riqcl4MKIHI6YaaQPcb6ME1zX8unlGc/PVlx0limM9OPEfhi4OR5AokSy\\nuzvgB494ZW1d9Msn5tqXlFTLpeImuY4nIjRNy6pp2K46zrsNT7szNq6jM5aAJCPrqe/PMpCrLNWi\\nApnXal1Hczkx9ydi4WlVMZxxa0g1+pVSuALBfNlsBl8aUuvjoybNKmOky/Pzj0l0rF78lCPJx+kl\\nWZKs2eiZYJxQvtN2a4ieFfWzGoglptfn5x/vdXfJs9congFe0gkJqB/Y799z+/7IzeuGu5uGMURx\\n1jSWYIVjim4cjp63fWCaRg53E8fdyHAcYyrYKeC9R6wkl+UQ9aQhpTa14KyhaRyrlWW1cWzPGi6v\\nOqwR2pXls18I1nR0rdCuHT++uuWbb655/erA4W5EbOCTXz+h3WwxzhAmH19Kak5lHrDFeKfhbYLS\\nhIAJvqQrNIaYojYI4iIBlBCBKOfJNhJ15AEikKeMgoUYp/ZFY1/OG+XLS+HzC+HZVmidxjEJxMpC\\nIUVohpQjT5IOPOd0T3MXMToUEPTqGcYRIbrd1fxV3IzpSjXlM2LxKlgMXeP49FIQ8fTe88NeuB0N\\nBlNSJAuGObT1/hHXtS5+r2GoEJhaJZPWroa4VsIU1UfonI8l74gY+GQjZy7RuIwxWLdCtlcInrYJ\\nGDNyIQfOVjEX+M3hyPWhx6jlou14sXJ8ul1xsVkxhJH9NND1Dkzg+nbP3V3P0McapzYVhM46+ky4\\n49zHyE/vPeM4kguMeAKbdsWz7TmfXF5wtdrgbMsocb2YChzvbdJ67B6hkvdo672jwrfqnpm5kMW5\\nMk95XZ1y5rkNfbxPH7WwRKGL9/o2v+j8dQ6FZvnrAsRrsKjhtX7EaVRW/F1Ovtedvf/L4nQe4AeJ\\npTzwad5EFY4XXXmYJob9gfff3XHzVjn2HbsD9METZKT4nRjF3ChoIEwT0zgxHiam3uOnmHskAmFI\\nHFTa7Mmlb47Xi+HSxoFzlqa1rDYNXWdoG4MYy3rtODtvuXiyRpxwdtlyOMY6i8MkIDYWA0heI9kn\\nRcunepyTioIkLgOdUVoBKwZJHgWSEvSRaouGcUxVX0x0lavmNxADV3KVesnPqpbLWSN8emb4d88M\\nF2tYNykvx3xJ/FeJOtFEiAQtATF5muMwpgRYIRC853jYY62jW60wYtNvHu99sSOEJG1M3sfK70aY\\nJsPkJqyxnK3hy6vIkU8+0AdJ3Gj9ttU6eowlJ66nJI+QZyQLCpK8KkTj+lGVuaBElhq8Ft26EjMT\\nanILzIE9ChgruLbFcsY2nGPHM/ywYbNuuAwbej+w73tEDKum5UnXcdG0bJxD+ol+9Eg/0IyBJsSg\\nKy+KM+Bt9uo3BcitNcWLqR9HDv3A8XhEnMVZS2ctL9bnfL694qpZs7ENIibm8SGD5smuzJvwoT1c\\nMcpLA/SJEbPMkpR/IXPxiQhVV0tiEue9MXPtD871ozP9EQtL1EEzuYOhHswCbPmYN1JmtWtjT/5U\\nBqUAa/WARybpX3Hqsdc47d7PHNVF+f2rf7KBw/vA4TDy5qXncC3se8vt/kDvR7yOMZe4xnDqEAJh\\nmCIn5X2pvSlELlU0FYeoB7peu5k5TIvqmH2RgRx4aJxltW7ZXnRcPdvQrixBlG4dA44mdVjnkqEz\\nq77mh0jqR3ztedSMpAINIrSitAaccyBJXWFANEYe6pgkCzWRMEFJAZv7G0SK2G8KhMWXFIHLFXx2\\nbvj0wmFtzhuynEetAoxKtj9mbleT+k7TuUwsNSh+8qBSqs5riH0OforeKOlB3gfGcYpcJIFRlD3C\\nqlthjOOim7hqlf3R4tUw1jaBahWdgnjeGzVzgM7EaE6aL0WizJ4s+BDdElWhTWM4hNRlX/ZcqIlk\\nGgtrFOeEhpb1Zo3p14y7FQbHRQPNOhpMCQKTIONEk3zWwzDiDz3hMNCqcNY0GGI+lxBCJNA5dbVI\\nKmIV15kPgWEYOBwPHPoe4x2brmPVrvh0e8Fnmws2pqVL9Vsb5qRfp3A8G8bjmMUrtHBoQx94AAAg\\nAElEQVSdeW/mX+aApTgv5W7JdgMhG/XvEY3cet4b+dfHiEj+7SeA6eMEBNVFCKuEzsvgnplU1RlI\\nFrRqvnUG/3R+OWRLUIEHOJnq62mK0Pp8ufwnOKGfPerXX+j748Ix1uLcCjFP2N295u3r99x+iIVq\\nc+pPDRHIJ+9jngofuUaT2oy6ZIqRq4yCPNiNsiBLX5JnBx4keIbxyLjref/jHWpjUWbTONQL27M1\\n50+f067XiMwVc6Re2HkKinXfFFvklKQEA7GOqE1coigGl6QKxa26+E55Y6W+ion5VbwQixJYgxNJ\\n6YSjTNCI8mxjeHGW9KyagLxUU4peEcGHNC/MY1HmSWZpTmfdshLn7Pz8vCzOaRijJwVgkjoi1+cc\\nvGcKgaZZodOeaew5DBN9PzAF5W6/R6aWC7vBq+UuWAYVpKhYHl5QNZ3W6oQhRsxKCHnRUdhSERgm\\nwnFEhwlpG4yz4FL1pZQX3ho/G5U1pSPIY0hM6yvWIpstcjxDXcvt7Y4uGNbnF1xenuEah58Ch9s7\\njnd77vZ79sORnoDZtDy7uGAaR+7udhi5A+0xOjH6QInmdAZMVJ+MXhl9wKuAsTjjOGvXfH7+lGeb\\nc7bdOvqbG0dIKpXKbh4Zgczs3R/OeyOdh62C/ByDVZjOuKbvM2z1WpqxShb7bnHfKVcuiz/3jo+W\\n/fAhpcc9XTOnSzTrj6QMZp6LxXgtPj9m6nzgcpFHQfyh7/UzTh/7rz1OnoAQMMYSmhVNe4HQYMeJ\\nKydYsSCOPoDxHmFEvKZgi5QPo1ogwqy6oMZo0ZMhyhROEkBpGTfJsmTydggEJvUx8KaZsMbg7C3X\\nb/7Am+8/xXYt24tnWOuiPrUyQMc+zdLC3CUpQUCZydakdoq7xyBWY9WYdI+xdvYekKQjV5gwyfw4\\nJ1ezAmsLZ52yaRXRbLhMunCTAXoeo7p0n5b/FCW6FGZjbNAwMw5VTHvuuwCqgWmaokGOyJGHoCAT\\nmlUow8i47zn0Ize7PWJacBOXXcswrhiDo7CFVGvxFINO16JEkLUQ0wTk3CpJwpBJCaNHfIgSFQI+\\nlqLLjoeaVV1E/261JqpdCEhKFYEIo+04yBbbXuDXF/RHxYeedj+wPVNWm5bNtuH87AzfjwyHI3f7\\nHTf7Ow5DjzGCHydCEEKQxNQkI7QmFDYGT65YFfO6qwExhnXbctWted5tMAHGMUZ5ZtfEmO4hRXlk\\nXDhlbKo9XqPOYowrnr6+WBLrvDCgPnAsYaQsnoozn1lXPdmrj6HMR3Q/TOYgYSZrpZN68m3+UjvZ\\nl5YymOdr846Uk8kp50++V+f+f3PahSI/8vMjHH39BpLGwuBpxx0OZTQTd23DWduiXcP52tG1HR7H\\n693AfhjpB0ujMMnIFDxBzKKwa4msPiVIzNxFBv6oNqDkda4vTj4aiMRNZQIMIUSDlBGOcsOHH/6F\\npm1QP/DiV/+BzeUz2vUWZ5qF4PTQ8Kpk90MtnFKpniT5nmjgygYtMSmjYLouoEwKE9nLBPKqsAKb\\nRlk3SmOT6qmoTELipqqAI1WCj+JCaUljbvSggRB8xkFAlyrAxO5ln3kfFD8GhmFknKZoSJQIJ1OY\\nCH5inJRxnLjbHdkdjuyOA8aMuE7pbINRi+AqlcnJ88pynxmhev/MEfYa67H6kPRuaU36yDyYlG8l\\nhMQdGoMawRvBd2BbR9s6vI05ZYx6TAg0CohlFEvvBdNdwuYpY79i7G+R4y3ruwnXTLRtx3rTYddC\\nOPM0dytoLOH6mt1ux/7Qsz9GT5ZM+KzJBEXwSJznBNBIXN9iDa1r2LiWc9vgx4l9f+SwWnFmA060\\nEO/M6BQOWGf72wJzFvJPYR3rrbGE6wLmCcEe8DCpl//Mymr1PUnTOoP4zLw+jk0fKWlW+iuGU7ol\\nC5COf2NeiaVIE1+sWrSZgzt9CFDuNJndOxlCObm8aqv23jzl7WuZQk7Oca9P9/M1ZA5VFax61v6O\\nT27+wJkMBJ2AHXbj2D9/youN49nlBSqOf/zjD7y62XMngUlahknpPYx+LiEnRC4spy/NuaWzz0PC\\nw7JQAjGqL3Lfp2MZjaG5jkPGEe995EyngbB7xfUf/neGt1/x4Yff84u/+p/55Mu/ot04aifM3F4m\\nXnn8nMaAIGNMTFkaJHmO5EELhGNMCSudK1JD3sgJo6rIzpmrcUbZtkprdU5KpplI1NxmcmXzU/wv\\n6cijr3L0khBjy/eibkkcbsb22IZPBtIQ0ymM0bMi+AmX0q6GaWKYPH0/MowTx35gGEesieqDvj/y\\n4e57+vMV0nWRG5Z5/c9cd7X2kgomw5IJORlZwnwf0CFF2OSFoFH3H/BgYqUd3zjUGnxrORjL7szS\\nPdnw9LxFU0VxQ8Bqi0weH+CIoOMKH46Mt8/Qywum4yXX/R3hw2t2h/dc3dzRJskqeM/heOD2bsfN\\n7Y673YH94cju2NP3Q/RaQaMqRaMz0xBgpKrDKkSCI0KvyuADJiQPruHI7XHPWbNiZdsFXhhkrtAl\\nUmwYC9tatUMWjGPaMzUfKlVby8SzNd84E4NFizrfW76nGIYSSZCZikcYzY+ba0Xml3oo5WjNZRdj\\nGRUk1gBcPuvMXd97aD6X/ZDTdqjaqcG5/L5ookqYE1+kXFukpOUj5ztleS5rkkSE9njD5v3v6f/r\\nP2DNyOpswy/lyOqyY7d+wvOzLc8vzmmc42zT8Zuvf+APP7zmx75PdiphDIExpOrzJMOQiYllY66M\\nnBM2Rma2JiVfgiiqSuQggxB9ost4xnY6Y3DGxKyEEtU5gwpit2wuv+Bs09F1DWb/lttv/wWHZfXv\\n/xbTtJURdB6DmmZbjdGdZf7ShtLsSRFisibFlgLRkjaTIniByUAwptJwZElH6YxiUtbBgIlJqnIS\\ntFg6npidVaNXjGmiqsHEgCMphCFFeTJ7spQVmfVCIpHwTNE7ZZgmhmnC+5ACWgyNc6zXHcPgOTqX\\n1oLinKEffXTDO3qOvsGuPdJF+Lkn5p+qWKo1lsfXoEXeCAKjESYBugZtW3COsFqh2zXhfMO0XeO7\\nFm0cU2sZreUglm235rOmLV40qU4y+BDXDxLdXXct4+cb+t2B492e4W5HeLvl+sOPfHj1Fu13MA0E\\nPzKNI/0wJGI20Cfilm0/XmN6Wk9MUeyRShqa39T7wE5HXpsDXx/3eOOwTYMberbDkSCOsckMQJqr\\n4so572GpNnEp4bgY77KbK8Iw441IXpP3MXfGmBNO/aELK7zLAFXz7afHRw7RT92U+917CIRNHiAo\\n3M8CDYCfVY1I/THPihR3rIfaKQSi9PchKi1lM0KtnZinT+aPpY3sedGNd2yvv2X4/ndIK7TmBett\\ng9k6dpstzy6e8PRsy1nb8MmTM7YWGqIR8McbGHwPqnOS/sxlA1iZK6OQGTHBmagFDZpBKVO0aEC0\\nUAhCZwwra+mcY/ABxePF4toLVk++4NM//R+5OF/TuolpuIub7rhPREWT/jU+vWBONUZOA1ZnDjxJ\\nwuRgGk2h4GIlVtYxkvS0iRAlFUDI1ixmcdmgOBMz92lI3i2hykOTNmVI5eKC97HYQghMfmKaJqbg\\n8VP8HF0KA96Hsrni/NvoUZESZ5lUD3OcAsM4EUKga6L6om0sm/WGqQsx6ZM1rNqGTT+wOwz0h4E9\\nnuw6JKUc2wwED6nM6k9la4igBqa2wTvDAaVvHGGTwLtt8dsN/mJLuNziNytC16LOEazBW4MXYaVK\\nqzl6NaZLcM6W7yqR4w/jhml4Qn97y2F34HB35PDqjN0PW3bfWfq3rzh+uON4/ZZpGJh8jO4M6iNw\\nKwnEI+c9pVB3NaC2dh2e7SAalMM08YYjdn/LJAbfNqhfczb1YBuCaaMqrcx6WmjlzEwhiuuq1jTj\\nhKOWE6bu3jyU7f7fcCzxJbf7WFsfyf1wueSW1CsOaeQ9tLpFFu8Vr5GCmFJm5l/z/Hv4X4k3cx8S\\nps3Z++SkEV2+h0BM4qSVaFWJwvMiSQAkma4LLgS26nnx5IJnLy45++JTfnj7GhkHLNCayEGvWse6\\n23DxV3/Grz55zpf/9Sv+r999y29evuO9TDRGGINP+sUIagETrfZI8iGf3cmmEBi85+gDQzIeIcrK\\nCJ21bK3DGaExQmMsq6ZlP3r6MOHU8MkXf8Xnf/2/8Ku//Xu6zQYRRcOEqsHYBrvqSv4VwyxSZuk1\\nj44LHhtiMIp6UtCSlrQsgmC6FtomuhiSuJ/UiEqs3YjYSrhLwJdrZxLwChaXhibmxbbOAYIPE8f+\\nyN1ux+3dLR8+XPPh/XvevXvLYb9jf7djf3fHMA6M48g0TUDy6lNoXEPTNjRd9Px4+uw5n3/2BS8+\\n/RxjXASnARqzxaxXOCM462JAVmsJ4Yxx9BwOI+erFe9uD7y+C9y2LT1V3o7TDS7VGdWFi6CGyCyM\\nbcPNes10dcbu6Tnj+TZy3uuWqbFxTJ3FOFuiQMu+yN4ppXanzXqp4s8PiVAagzgDrcPZgN10tE+U\\nqy+eo3/zZ/jbG26++Zbv/o9/4Lv/83/j4Hv6YWTwE0FDrO2ZpCxFUrWnHBsglWQejdaxGElkTAYN\\n3A49w+0NB4G7dcMgW85NLFOiY49Xj5EAYpMTQFojUite5mfMsF0fVaxlToNcxIR8T8a1tNcrtcLP\\nuV8U19B7+dIfv/PjBQQxA9psLMpiz6xOSQxFTHYj81CpJs+Hn+HAS+ay9MT8nKzf1Oq6jNdFO1YT\\nRRF+5lGFwtcgXr1ZIQySVQcVsBtncKsVl08vEfF8eP2aD6/esPMjftWhFzFThBNHZyzr7RmbbsPV\\n9oLnz57x5Vff8k9//J7vb+94fxwYfHp+2pS5IjwSkt48cuFTUMaQjUeppJZGMXZUoQ8Ba1KghWto\\nXIv4AbGwXV/wyZd/wS/+4r9jc/UkAmJetQV1UhKtBYhLBcQx93ijiktuhll/rpr9vKvx0oB4H8+F\\nFDpuDJpKfWmuCl+eAc4oKzvR2vgeNoFV8CO3d7e8fvOWV69e8/3Ll7x7946b6xv2dzuO+z3Hw57j\\n8YCfohrAj2PyOkn+43kWNbov2hSsJEbYbLZcXT3hsy9+yadf/JJPPvuMp5dPSp4Sk7ImGmNi8FPQ\\nFGHbst22XF6NPN2P/Di0vB8Du8kwJi4VKs60VjumdZorGq3WQvf0knFzzs3VBn++YdisCG2DuljE\\n2iR3IZPWiyb1XLUdi2osSnMZvCVt0hSc5QM+5ToXFLGWbmXpjMUKME1MmxXWNNy9esX7r7/icDwS\\nhiF6HPkY2KV5vnP5vLSXKHsyrWlMTORlwFlFpiQhGYu0LeurLZ/8yXPa8yvuevjwYc/1zXeMTJiz\\np0i7QsWlVLs1PpwM6s9yiLVO9eFr5SfaKd5X6f6cmXJGD/nZ9j+ejjz/rcZAT7luqVyGRAsgBIrR\\nPVUVkUdkjnl6lgz7A59OnotWzPRjlDB3N1PQ+ny98OpHSKbWs1CPKKFZ4bdPaKcr2L+n3+1xYnBI\\nFPUzV0rMqtaahs1Zx7Mnz7g82/LiYstl5/jnH97w9fsb3u+GqMsUgzM2Zv8LgXGa8H5MeuFaX2ji\\nGGtyMpPo3jepxox1yehnjUOMxxhYdx3nF5dsL59inSvcST1u9wXEiiNnfnxRrSQgnx2PKuKrRN29\\npKyPIQVVGCFYi/embP58GFFaE/OMtynxdz/0HPc73r9/xw8/fM9Xf/yKP/7xa77++ls+fHjP/u6O\\n8Tig01QMrpJS2ZrcoxMivZDoZJ7rtu344bvv+PWfv2Ucjvx/zL3nkyTJeeb5cxEqdZboqlbTGAyA\\nAQiKI5fcWzu7+//P7NZ2yV2SAzHgiJ7uLp2VKpSr++ARKap7QH4bhllZZUaGdI94Xv28+meS0SiH\\niFOxpiIEVBCxfZ93XVaMR0lPri2jdkXrtrhWgsxBZniR0Gs2+2d7/0xlqWRcKGaThMksgVlGNR0Q\\nshQv1XEOeM9e2e3bc7wE9kRfCLoahih8+qKn7gdCXwjV0eVKBFLpLj1VIQkxz9+l5JMpg9NzirNn\\nyOv3UHexiK4Bc+g03adxlX60I5OlRKiYoaQDJB6kdLEKOS84Pzvl9atL3ry5ZFQU1JuG1rfY8gpr\\nKpQtSWbnqGwEKsUEgessAUS8hhCePrud9v6R7v7Ue8CBD3j/tB8+80duXI5B4iDp7j+8/MQa+TGk\\nip3K2v8Le5wRe61A9iPcPW17iX0ED/FjYPd512psV2d7cCGH13c8yscf9yr8p2MUR9sfXNORRD6Q\\ntd31temY1eQF1t8zVJAPPa/nU97e3fJhsdhpu9GXKwleEpyARDKbz/kyT7k4n/Hqm7f86zfv+N33\\n11QdI1yeZLEFl7NsqoplvaU2pgvmdPwdQtK1C0ARfepKCrToswUCqQskKuazJzgSs8Wvbmkfb0mK\\nIfQseruhPZyD3g0Sx+TI2AldZ6CuQrJf73v2wd3DLTqlvKMoCPFIQYnoy5VqpwyI7mXUAnItGOUp\\niYKmrblf3PP7333Fv/zzv/C//uUr7m7vKDcbhI3gqUQcA+f7fPF46ki61PebDMSYqdxZviF0QeYD\\nPnRrDHfX15i2Ybtc4ayjKFJO5rMu26LjabGGdbllvdmwXG1YbzZsy4qqqTvBppBekwxfQH6BSU67\\neeMILHqNbjbWvH4+4PTZiGyQIHT0dQchdlZODy+7/OfAzoespe7AtJtCCX1muRQCa0w3zwIpY+G7\\nRZIoFYWfc1G4d++sqyrauqGtm0j3m6bo8RSZJpEAK3QpkbtMpd6VEtW1mMURdhTCQki8CJGrRgq0\\n9ihhkTphMhryF1/+gr/+8nPeXM5xOEyRk+cZ1e/e0tzc0jx8z+TzLxmfvyYtzlgYSelk9Mf3kydA\\nHmQjyZ1PXOzGbRfyPHzUu+8HDpbux04AiAO05mDn3Vex3+4/uPykGvkuoNCv6+8v7G+iH0RHVBNE\\n2A/WPslKHByVPch/8qx7belo2YlfsdeonsrjT4zr4T0cyoadtSXEbv1eQOwleq/tWJVRZ3Pa0Rnz\\nQjPRgXRSUOuASxQkKd57qqoiNJZWN+hEQylpXUtVl6zXKxLheX0+YTrMaVoLSNI8ZzjICd6zWm/5\\nt/dXvH9Y8lhXBKB1Mjav9cepi1oIEilJlCBTCYnS8TcBWgRCXeHLLaGuuvE5zqo4UMx3c7QPFHX3\\n32mGOvgdF3nU7ujajvUPdTxO7zfsG3D0riOHoO3NcSIApdIxVA2JLXn//p7bq/e8e/cDb9+94/37\\n99xc33B/v8C0NXhH1hXESBkFQfS97oG6VzZUJ+SUitAWQgxuBiFi/0glO9dVlzbnPdv1hg8/vMV7\\nT7l65PqH75lOxxhj2JYly9WG+8cli+Wa1XrLtqqpTYt1loHWDJKUPM0pTi4ZvvwLRj/7e0Q6AKnj\\nOByUsE8GipN5yuw0Q2bghYvl8ISuuMcTpNx7dEUf1O4Ee9grTXConIj9q6JUVKjYv5Cx4jbysQQ6\\nmly/J27rFS7TtJRVw6YyNC6CcbQI/V6ZCx1pGSCVZFhkDPKMPE1oW0PVWkrr6CQLyitOplNevHrB\\n3//D3/I3f/UbXp7NKbBUTdVxmCe8fnUGIvD+dsnm3bfk0nMyksxmM7ZGsSgNG6tpiQHenjWzL4w7\\nAmexf5OfIFCnu0TF5VNw/B9JytgnY/z5TeEnzCM/hN8jvfwoErwH8/j/AMUPQPfIRfUfOftT5Z1D\\nkP7YGSB2v/eT93T69tsdKOz7LJuDWT6sSerHISDwUtPqAWV+gkeQJg0qkQyHA6bOU9mAt4a6abGu\\n3rH+td6yrUq2VUnTVKR5zsXpjNcvUpaLNduqxQnJeJiCCKTCc/uQMkwUrVFYws7acTtzufOxihhg\\nTZUk1wmp0vScI0r0CqFEKL2/oSdzsROIB8JR7O56XwiU4FH4TrkM+xfhEG26xbOvApVdOzLfaYRB\\nCJQMKN8gq3uq6o7V5obfP77j22+iC+X91RXltsS0hhBiA4lU7dM1lYxFRD50f90l9NpY7+7bxR7o\\n3A0i8mhrKWLgznucDxgfaOqG2hiqqqEqN9x8eMdoNKBualbrDQ/LNQ/LNY/rLduyib0nXWxIPEpT\\nxnnGbDBgevKBZ06Qnf2MZPYCqSJtcD86UgrGA8VkrMiKyIkTrO1c2SqW0WvFvjQmCuDebXSoiRxy\\n5uySUQ8s4L3idVhvvf/r+WaCs/RFN0IKvLXUdcumbmmt37k0ejdZn61EiKmgWZown4w4m4+ZDgdU\\nVc26rHksW7Y+UBlP1XouXrzgL/7y1/w//9ff8/z5BUWaYLebyJ9uWpT3PLs8wVhHVbXUTYlb30M1\\n5nSWM80yCuG52TrWTlOjY4bTkzd8l4IsDh7N0AdiD5wsB/vulMeD/Y78Kwf7RAWnm4X/ILD9NFwr\\n/Su9C8Ad/37IE75b13tTjlwoYfejeFodunsen47WAZLuj97VRTx1Th2j/f5yxfEv/bUdrNuB/+Fp\\nwsExdjZr99AjMEKzUFNmtmRcrxBNzD1OkxwrYpFKawxV3WKspWoaHjdb7haPlHVNlii++OJnzE/n\\njEYjHpcbru8X3CxK8iICVWM87x6WlE1LITRlsJGuVnauGvaVgFp0WrkQFCpyVqxNgwseJTQqyUnH\\nZ6ST0y6heB/HOCab2s9rCPJoiAUC3QO5CHug6KlUfT/icQ/fuVOQ8oDeOAJ5EPEvlZakeWD1/f/H\\nv/3+f/PNN3/i/d0Nm01F01ps8B3rXzcpncYliUCYKEWiFcZ6auepO26UPm88lZIERSYFmRZo2Wnm\\nHQNkENAYQ208lbE0zhEEaKcwvsS/u2J9vyDNFK0xlHXDtmnZtpbaOOyuR2UEwjrEOdFKEe7vyKbv\\nmT9eoUfPEJmKY9QF2ZWAURFIk5g+qYJAW4+yLo5VnhKUxHbWRegskN73vweYPSzHeew1j/3aiLXd\\nnB+kJe7+XOcqE9EdFaTfWWHOBRobaEOsyO1P1Lsw+hcuSRTT0ZDnz0549fyUs9kU7x1l1bJ43LKy\\ngXVjWW5q/uH//j/5q7/5S37+s1eRYhdBMpnHtt7bDa6tKEZDLi5BipS7xRoTHKuba05Pp8wmmlmi\\nyPyWq0pzbwtaobs+sFHp2L/rB1lEvZLSAfqnll0sUOwJuo5A46Md/tyPHy8/Hfth/3I+tUk+3vTo\\n0y4x8AlAhl4qHoLnE03uY2vmcMN/x9zpELsf3iMxIPZX9nTwj7R28fG63bFELFR5SOb4AHc2RbZb\\nrFQE4Rn5K3So4rl0Zxk4jw0tXniSLGF2Nmd8MiUdDiDRXL5+QTGd8GpTsV6vuF8sWWyW1K2lsbEt\\nWcyCiLwZiVJda7O+rVfMNU+6rJUgwFiL95BkE+av/4rJm1+RTmc75sPD+zoe09CNXz/Wey6TvdA4\\nGMUDrUWEbqPOtSYEEdE7qRhC6IAchqJiff+Ou+//N3/4p/+X9+/fc794ZFvXtDamZQZiMK5/n6Q4\\nqD0NscrPOI8NEYQynZD25wmBLocK4wXax7TMTOnotwWMd7TW0TqP8yA7mFRdTrhxntoYECEGNcXu\\nbSAmi3ZaW+jcIcFjrGNTNwTvmGyX2OU7shc/J5UTai92Y6qEI13XaLuEB41OU5JiiCoK2uCgXEVL\\n4tlJzFzpSLH+3GMfOtDvA5F7BNoHQnt1ZGetBuiuHsI+Rz+4uC7NUoajISsdGz7sGl30YyFj44jh\\nIOfZyZiL0znnJydMpxO0VFgH83PD99cPhE3NYDbnzeevuXx+QZIk3XMa02mTYkAhJapO8EA6nDI6\\nPeeF8QQh0UnKcDxECjBtxVBumHiLMZpHNSfIAV6mHHjPn+iLgp3Pt3el7GDgUOnsheFeAexB6aMK\\n98OTPD3UJ5afvGcn8BEu/9g2+w2fqrl8RM7Uf36iU8MnJuOpIt4/UMCO3vXgbMcg3v8/BJ7dNRzf\\n1ZOrPrjWThsSklIUGC14dBnClwSlSEUL7SNFWKNDVM8EInZcSQXjUYZQCbPZFBEE1bqikQ1aSObD\\nEQOdkgZPU7Ukuowdx7vgJvTBr4CSgazTziOQx0pOraKJWbuY46uSAcOTl5x8+V8YvXiDynMOxdOn\\n6YL6yTkgB+qGSBLQRN/zzmSn+7wTAAf79CZsIPqvfIDg8aaiullw/cNXvP3DP/KnP/6exXpL3bod\\nCAdi0Cxm5uznq+dGV6LjZkeQSqLbgQh0nr4VmUAoSaIl8yLjZDJiNhohhGS53XC3XNJYg3EqFl2J\\n7thKoLXugmbxwVNItFAoIXcUq10iy+6eY5qop7UWJWC9fuTh3dfMTl+glUZlZzjRdZYSntBUVJsl\\nZVsxGA4YXjynyJ4TtCYS2zeI2RihFUh1MLK9MDu2WvuAXq8kHlIu9CXpn8rk6B9072PxVF9Q5YIn\\nG+TMz054GIwwZYUNLTth37VsGxYZp/Mxr59f8PLygvPzU4rhEC01jXW06y1CbcgLwfzilNlsRKLB\\ntA1ta7BdeqiUGp1mSKlojEHoQDYcopSmd5M4YzC2JdiGlJphqChbx0NT44szRDHrmotEOH9KLhLY\\nC7Te8u4tGw7HTxyM6A40urens0g/VhKh5wH4z9UhiH2Vo+ilfb+E4y13n0T4xFPCE0Tst2WPpuFg\\nsA4G7uOUk+O0/49Y5D61T786hKPLOGRP/FTl19H3w6BSt8okA0wy7ASUx7Ur1vWQ4B/IbWz+GwCt\\nBONhynw6QusMRMb65pFFdYNAkKcZSkqctwgRGCcpz6ZjtlWLDVBbi+yrGbuOQVpBriQJMmZoiNhW\\nq7Se1jq0Sijmz5l+9ltmX/4NyXQe082keHJ3O9vpeP0umh3nRobIM6OCjwx9MvKNx7Q3TRDmyBvW\\nR/x98DEDw0twHuEb7PaKb//4P/nq69/z7t07to2hcRbXJav25dl7nhuxO64UoosJRO066SyRREqU\\nUFgRsCF09ASCItFMhwU/e3bKF29e8/r1K4L3fP3dt/zT7/8YC0+kobExYCplzFPLbOQAACAASURB\\nVHeWKo5rIiQpMSjakyP1AqV3H+7qJDow9CH62x8fF/zpq38iVZIX1jL5+X8jyAwlon++lI7H9R2r\\n998xmky5SAWXz05JJjNoDa6qUKbjrlc9kB+zXkY5c2hB7fPyHRy4LCW7fqad9n3ozowEZH7HwV43\\nDa2xFKMBF6+ec/fHM2xZ04QS79NOoRAkieZkNub183N+9cXPeXZxzng6JckyrHOUD0veXT/wuC7J\\nRkNev36O9C3rxQ1KaZomusOECGT5iCwboLOC5WaDMYYk0SRaEpylbWqapqVPT5J4UuFImhXVu7e4\\n+RvyixSRjuhz5o8Ta8UBJu8tzY/V9/3z9kkc+vTHeFTx6fX98pP37DzUtP6sWv5kUA5N8MPvH7k2\\nDvnOf8x1Iv78b/3yVBbutfFjKSKe/O+3/YTM2R13V5B05D4LBCGxOmczeomr1zTbBZltEd4QnMOH\\nmJUynkwYDKeYWUu7rGgXW4QH11jaMma1NHWFKSsoG4SJnWtcZwBHtOj+VMxskMSKOk+sAI2NHRTF\\n9BmTV1+g86IrQ9/nxvfByk8OJUSt42CFEKAJMWMlhCdk++EALwQi0fG/c/vCIRlpVcv1kpvr7/n+\\nm3/j8eFhx5zX2S4gBErSadyiqxqNwB4rLBVKKqZ5zvPZlFen0R87Ho5Is2zXCd56h1KSPNOMBinz\\n0YjT01Nm8xmmbdHSgbckWcbN44Z1ZWIQuRvbQCzd10qRJoqAow1N9MFH2kGEiARWAfYd7qETYIG2\\nNWw3W25u7iku1wxCZGZUeHIa7MM71PaBqRK0t1eYTOGGGUEqaBoIniC7CowQOiVpzwu4e6cOMrmO\\ntM8uVTFeUn/dfdzhQKsP0f3nbGzbJgJoEStpfZaTDEecXTwnbRrq5AHnYxZSliZMpiNeX57y5uU5\\nr15eMhyN0FmGF4Kbm3u+fXvF1c2CfFRwejYh1QrbtJTE1oXbssG4gFAS/1iSpDn5YMB2vYwcL1rS\\ndBaf9x5rHE3dsi0rHhZrHh83LB9Lyo2hkBnDyYD5+A3bkLO1khBUfD5FPzpyN2aHFn3/hu/cU934\\nHuLB/gX5cU11hw0/glM/HZAffufHvhxLvcP9dz8f/r7D8iPdevfTj8mJj5gTn5yxH/RPGQ49WO8K\\nlUM4AvYf08SPgoFHQu143wB4mdLkz/D5Pa2+I9tuyIIlEQKdZqR5QT4YMBgN8ElKoxSlc9Srirqp\\n2W63bMoy+onbFmwkoYoNgUP3GIrOPy52bHm9VuyTMUmeMZwlCJEwefVLBpevkWlKVxZ4dHM9YIud\\nrckn5jsGJiWBpCsGEoRdZeJuw54PVojY7EAKRIgVn/2kBqBuW1brFevHJXVVd/7RyPFSpJIiz0kT\\nhZIS7wLbKo5FCB6tFInS5Erx8nTGr1+95MtXrzidzxmPR2R5gZMe4yytaUF40lSR5ympTsiKAVme\\noURgNCw4m015ua0YZBm1cQgkwQZMa6malsZFThGpJcYZBG3M7ugxkD5wG3bvSh8AjOCqEKpAFmeI\\nfNalIAokhoSK3DckSQLDCYv1GqqSsFnT3N0jhEInXZroIfjurJ291bOrhw4B3drODRy79wTdZXQc\\naB99wPNwCaFjguyYMqWUpFkWowbjCc9eveY8AVZj2tYhpCQrUubzKRdnU56dThmPx6gkobGW+9WW\\n79/f8OF2gfGB+bAgSxOWixWbZYnumjKvtxVIwWA8xDtIkoqmKSnXq8jiWWQ4FwPLpo1ZLOtNxWq1\\n4WGxptzWtCYmGkyywFnheDETrCzcl4F1E1kYe3Vh/9z/yFsvjtcGIQ6arOxz+ne/f4QCxx+fLj+d\\nj/zPBRaPluM0qGgd7/S1PcKK/SF7DTd+eapN7Af9WIB8+nrEwd+f08iPgOpQiz8sAjg85oHw2GfL\\n9OlseynsO3CzyRQ7fEFdLtg+/sBcBdI8Jx9kpMUAoWKAJ0iPH4A/0azKigezZNlsaYOjkY5GgVew\\nK4/txk/KSOSUK0kqxV5zTVLC8DnDZ59zcv4CnaTkl69Izy+gL8mHo2ftUKM+HttwMF1iJwATH4Fc\\ndkIwdGkzvS+a0AG8FEQSKdVZuAEvIvOe8QLrolYthIxUssGhlWY6GPDy8oz5qCBRkrI0fHd9w93K\\nEkLMUsm1YpgofvHyOX/z61/wqzevSZOMJE1QSYKTgcbUbMstjamjoAPqtok0BnWNbQyr1Za6aRkN\\nC0ajgiJLmQ5GBBuotg13ixU3jysWZRmBILg4HiHsGh7sS1BAhJjSqDrtUQpBlg8Zn7/mxa//G6ef\\n/SUiSZEhoKjQoeRkOiVhQtt4qu2WdDhBJRnbq1v0bIo6n8dmC72b60BN6d1XfVg/CJDGka+qXWMK\\nrKOdDrBFipNdRko/p94dgXn0bEYWSB8CQmnSQpJoRSYE2Zs3nF5MObErtqsIvkmWMpwMyLQi6Xp2\\nNq3hbrnmn//0He+uH2iM4+RkxnBYYBrLu+/f4oNASUWWp6w2G4pRzsvPLimyFIejbktWi3vyPGeQ\\nndHULavVlsVizf39mtVyS1k2XbNyST4YcHZxwsXLZ7x4ecqry5yqFVwtLF/fwdZLrJC7WodAB9B8\\n+v3v3+woN/durMMAaTi0WA8RYyfkP738ZBr5XkL1j8CPA/tHnYOONL/j/3sTZr/tIXTvI89dPmj3\\n29My2R0Z18HKI0GxO0a/pr/Wp9e93+eJ4rrbfl/2zU47Ojz6LuCXDgnFKVbmLNstJhhOkozUQ9ZR\\nfiqhyNICPU0RQZMNRgyWSx7XWx7XWzbrLcELNIqhkjvTUBIYJDqm1glBrjKUyAlqxDqfkT7/nNkv\\n/yJqh1mBVMn+Ady/xsdzshvG/RzvH9z9vSkfffWRCGn/QuwoRzunozeGnpxaAEGJHdOgKmYML7/g\\ncrXC/PA14eEK1wTGgwGXZ3N+/tkLRplE+EhKdb96ZLGKLiMhIUs0ZydjxqMcKTyPj4/oNEWqaELX\\ndUXd1hhvGM2nrOuG2+t7ru8eMM7HgLALLMuKxbaicZ6T+ZTnzzJkqpDSEaxkNi0wOJyEdV1TtTHF\\n0AMmBExwuzTK+JoItJKkSpGohOnZc85ff8nzX/4dp69+TjYcdQJQ4uqK9faacWIwVclyuUHXDUFu\\ncP4dqnTIX/2C8OoS7x3K+y5WHBA+VnT2547PhUQGT7LcMvyf35JfL5Fli9WC1d99TvX5M/xkEOfJ\\nxYYbfREMApRSpEkalS8XqOuabVmxvL7C1AYRBMoblAukQTGaDsmzSDympATncKalbgJvb+757sMt\\nb6/uqFuLUIrNtqSuW/BgWhvjD1rjvSfRCWmS4I0hKEFdV5TbDVIpqsrw3XdXLBarWHy1rWgag0Ay\\nHBZM5xPmZyfMTk4YTabIPEdriQ+GNBjmquZ1ZrkzGUuXU4k8cuAfI8ETjNljwEGi58H7/QQ8PrWE\\nH9/kJ0w/PPj6RKs72vTIzoan5CVHWck9mB92dnlyjt15w8H2/WGfnOroRAdH24PQ8cGeCqPDvY4n\\nYK9xBwLy4LxHvv/9O4GWDpmK2Po9z2jaLcuqxorIh2K9Z+AceZKQSI0QiiQvKCYerwIukRgFpXek\\n3mMEiC5wGjW9QCoVWkbAOJ9eEKxm1UpEPiaZnFCcPiN4tzMnRTiei08NnXj6Wez0cRACRSAJbt/J\\nqK8u7JtcsMNxQmN3K4TWICRByFjlqTTpaMbF61/EdEwl0I+PDAcZs8mI+XRELj2ubQjGkMg+QBWi\\nP10IijxDSkHTNDw8RlqEEALWGOqqBC3JxgOETlCpQOgU6wXrdUld1ay2NevWUroAUiOSjPHE0FiD\\nqFvqbUnTGJw1ndAKtNZSGkPVpyuGQEf2h5SxI1MiBUWWMxqf8Pzz33Lx+V9y9voXFKNJx9Xi0Fqg\\nfYtYL2i2S0pneUSQG0t+fUf27gYbBMnZjGA/j8VPXZD6wHh98nz2pr/E5QkuUYREYXONT3W0nGBH\\nM8zOVcBOECklSdMETIr3AdsaRNVgbhe0dYPLM/QoZTKccqItg1SilYzVoN5RNy3v7x75+t0VP1w/\\nsN7WUUBkEm8cNkiKYsCzyzkqkSilYp2KFHhvqLZb8Jamrlk8PKKTHOegLFvW6xJjLVJKprMJ4/GE\\n6XTG7HTGdD5lOB6hVUppDK1tqDYrtDdo1zD0LcuNxZaCrSvQo1NUMepcjXEMj5SaDl+OFLojvNuD\\nwKFSGZ78/GNQ/xNp5GF/c8c//OiFHup++4DYPhf4yCsuPgUo4Qgou5X0e4uDdfvu7Pvf99uJ3fbH\\n+eC7K6LPzNiVVAgOM426gMgezMXBh723PfJo9CllqTSopETlFck44aGSPJYN99sNq7LkpBpzMh4w\\nynMynSCRmL69mJTkRcbIWWpjaLzD4RF1BBTVsfCJromtSgdcXnxGWRoebpeIvEAmmj7RbN9kQdDz\\n1/R5If1wfSp4HA2dAyEoIhVA5mMuNV1TZyE8uO5svbDwIQJ5FxCVKqZQBilxWJxrEFJw9vINeZYw\\nLQakP/wRKQODPEMLSfAO27a02y3O2njFAhoXYqk4gqppWK7XVCLegzOWpqox3jE6nTGeXKCyAeNB\\nSj6YMprMuHp/xdu373h/v2JZGwwKnWq2VcvDcsM4V7j1hs39I9uqpfRQeYFxnm3T8ljVrJoYFJUy\\nBn/7NMhMKJSEYjDg/PkbXv/FPzB/8XO0SsBbvKnRSYrWikJbCrPB/OkbmtEI+9lLHpcN4cN7/Ps7\\n3GzC4PPXjFYrsvmUVEik0CBjrWzMkOmLdgSyM4/cbMjqH75guywRxmGLBDvK8InGO4+3Nvq/xaEw\\ngBC6gKqCJM9QOjJ3ytGUh/d33F/dshgMGIw+w51eIP0S4WpwsSWeC4FV1fCvf/qOH24fWZctWmmy\\nNCFPMnKdMjqZcfn6Bb/+za+ROp7cu0C52fLh/Vv+9Kf3VHVOVTbc3y2xZom1kbNfSMlwVHByNuez\\nn33G5eUls/kJKtEx1dQa2tpg24qmrlg2G1IJwTnaTcn2/RXLmyW3JmHy+d8yvPw5Okt3/EnHb8CB\\nprjLOT8sZNwrb731H7p36kk26CeXn7D58v7C9slX+5+P03vCwQ3uB2jXJebwsJ01vg/YHPwW1fV4\\nRNHD7pOzh0ORwV6KdtfwNCy6O8LuXsIO3Pb30v3fTcixeN3TDu/HQAhIFYwyz0DVuPqGzfIHto/v\\nGVnD+WzEJNN89+GWh8cVi+Wam0HGME3ItEYRm8865/A+9oysm4ayqmlbE1Osktif0flA1VpaK6iM\\nofY1v1/890grqnIuLz5nImJX+xhs7MRNb8YcCMi9wNzNMj+6iJhDngZLz/wROuKs0PdkPNjWtS0C\\ngUx0JHcKAXygti21FNg0QwpLcXrBSZKCbDHNhlQLRNdr0zoXO7c7i5CSaVqwqR3LxvDH+wXT4RAN\\nTFJN2sUAfBqZIFd1yfrrbxDhLaa1NNuKZltSlxVlWROqhtDEsvNaljhT0TYbqvWCoZRo66mNpfWe\\n2nrua8PNtuKxMRjvO/+0RAlNmuUUWcE4H9LUa3KdkHWEUTHYqTuemfjU5Ykhy4A8Y9VUlKslptzS\\nisCjENQXZyR5jhKaWevJiiEqTeNj5/e59WE/E9Hf3b0JUincpCAYS+sspm1wbYW3DhkEHk/rPVmS\\nIqTEB0+7LbFti3eWjhAebx3t6YTx3/2WyV//mkvrKYY5ephRmhS2C7JqhcJz8/DIH759yzfvr2iM\\nJ8ty5vM5rz57xfMXl5ydzshHQ9IsRUmomorWRqbQ1eKW26srfnh7RfASZyPAZ1nOcDRkOptwcXnB\\n2bMzZvMZg2FOkiYICdbW1E1NVW7ZbkrqpomNL7KU+82G2+s7/vCH77i6uuaxajHFKT8/+Zzhs+iq\\n2r0HP+bQfmLZP30n9lizB/M/+x7xEwP5/ttTEP/EHh+tfJKnfGi2dLb4U9f6p67iqf9dPNlgP5Cf\\nPsDedfP0nvb/j4TJ3lF2AP7H55UCEgWzoWGUtBSyBi1xG0fTlpi2YTbMmQ4ynPO8u13wsNqw2ZQk\\nKpriWkDYBZ4iRaoxse2YtQ7rPNZ6jA+01tNYR2Vg3XjWraNdPCKUZjCcU2wfmK3vqNcLkrwrpBDy\\naFR6AOfAIjm2sMSR/7/fWRFIvYtUqj4Q8JHO9OnoBeiJo3fiW8SOQJVtacjxaQZ2hS7GFDpjun5L\\nszQE12BtFF4hEFuJeY+QktFggA0NW2N4v9jwdXZPsJaX44JBlpJojdASnSfoNEEphaksvqxw6y2q\\nbilsiGyBxQCN5CE0lAGctVR1w1IGQqLJhaCxlq2xPDSGD2XNY9vSdFzwfaOJNMkY5UNGgzHj4ZhK\\nuqh1SxnB1btdUJLgCa5FA8UkI3nzGe3tAv+4jK6hIkdmOUmWk2QZ6YtLRKJpraHdrGPmkjME6yLB\\nFbExCUCeZVRViWlbEhGrfpuq5OHmFidiI2qcRwmJcZbatAyLIUIKTNtSr9e0ZXxeZZYCkf62RZCm\\nKZlOMa1lOB5Szaf40QSbjhggkNWGdw8rvr+6xSE4vzjj8vKSyxfPuXxxydn5KZPxEKk1TV1xf3vL\\nw2OkqvABlje33Fxfs3zcIEjIiwHT+ZTTs1NOz044OZ3HY0zG5EUe6S/ahqasqeuaqqqoq5JyW1JV\\nNXXdULWG65s73r2/5od3t5ig0KMTRhdvyMYztNI7+pHdI/4Et44t+b2lf9j38xAV9m/Anwfzn9C1\\n8il59ARUD++3x75Pchk89VGHp8PJ3uWx96ofYu9uIDvuiEPfeu+j/TjouvOH7Fd9ys9P51LYAdk+\\navHRBMrYlmyUes6nNQkblLcMR6fUy3s2XmKbBjnMmE6GjMdDkkTjrOft9T3GtIjg0B0BVN9kOYTI\\nwme8j8BtPE3raJyndR7jArWFso1A471HJQFlG1aLGx6vv2M4HjO9eI0cTCBJu7noqtEO3VE9Q+Eh\\nuB+NWT8GnUbuLLKjCuy5OYSI3WeCdQfzKdg1Pe6m0iuoKkubSEgyMAFUgpAarRUWh7EtdS3J00jN\\nGohl+FJK8jyncF2bsFXFH7ilaWrc6YRpnkbGvSLnpEiZz8bMTmaE0tI+VjSDLVQG07RUbUNlHeNt\\nRV6WPDiLkaCSyOHuQ6DxlsoZFpXhqqx5X24xYe/KU0KR6pQ8HzAshoyKAUWeI1yGVBqpFc62eNNA\\nlsZ59Q5nW5SH4dmQyZu/IhnNqBaP2KZF5zkqzxBpAmmKHg4olWN79Q5rLLZtMVWFrUpc2wCCsmkI\\nMnY4eri5plwvyVW0UDaPj/zp91/FR7Vz/wgCpjXUZcV4PAYBVbXFbjbU2w1VVaIGBQiJc4EqQNAp\\nQmdYH3j++iW/+PJXfPnlb7Hn55jRAN8G3q1q7ldbLi4v+PLXv+QXv/yC569fRuEK4D3GGartmofb\\nK25urlmtN7Q2sL5/ZLFY4l1gOCp4dnHOZ29e8epnrzk7P2U0GiJCbA5i2oqqLNmWG7ab2J7OmhZn\\nLKZpKNdrbu8f+f6HK/70wxU3izWqGHH++ksuf/E3vPjNf2UwnpGkKcIfIMRTZW2nre9qZbsslZ6u\\nO+ys8nCAZfvl0zXT8FNmrfSfD9Va9gGCviIzfj8w1MVBkcgODz92h3zyXE9S+w5+YO+IgkOE72ks\\n93h9uOeBtvjknD2U9dqj2B0gdKQ5cYc+yCc6l0yqYVRYJkUJ8g7nLcFLymrB4uoHlu9+4DSYXd51\\npjWvL88osozJMOf7D9fcL5asa7s7PsQqwUipGrDOd/9jpaILYAMR0L3vKGJj2luqBYSGzd23XMsa\\na2qmL79gcHKBcMR7ORCCu/Hcz+5uDA6LfYSM1LPSB7LguwbRRF8r7HzjIdA1u2DHztcXyTgfBVBw\\nIKRDKIuSGgsxg+Jg/tvWkWkZuaulQguBc5bFZhuFlogB303T8v5xTXCWi0HGyaBgOnIUg5y2NThr\\nSfMEZrHhc7lYUwMNglZBojJORilDKdjWFVVVI10kw2ralutNze224aFpqazBhd4ED0ipUDohTXPy\\nfEDWZUvQ5WIb52J2Seg64QhFsBWuvmNYPGcymzI6mZOPxrimxdYV6/t73v/wlre/+5b7uzuatsHZ\\nGPxz1hCsxRuLM/FzQOC8QynFaDSiXm8wdR3HTGusMayWS5SKaZ50Yx06F95KdUVkwcfjdS3cfFVh\\nvacxltoHKuupbUwr3dxesbr+wOOHa37127/iszdvSIxnMJ/z5a9/wS9/80tevHjBbDZHEqi2a9qm\\nxpkWYw1lucU7Q5rERhbl+pFtWYJSPHt+wWdv3vDi1UsuLs8ZjUboRNHUW9q6oq7in2kbTNNg2hbf\\nNghrUdZA21B4w1QFZmnCfHqCmL3m4pd/x8mLz5mcvSAfT5EqibDV+1aOFNWAeIobvc5zBB3HvvGd\\nBvaJIz5dfjIa26P/B4ruUxfKJ4NmPSB+lCL4iZOEJ9+fbt8P6KdcIwcX+lGnn93hn/i7Dv4dnjrA\\nwSRFH7OQkeRIyYDWnjT1DDJFlpQkeomUDQQRgzf1gtXDLdViwfB8RiIFeIdQmtEg7zqxgJKBRAqu\\nbh8jOZaLpPyOA1pWH0HdHwB8bPnmcCH6apXWTGdjnr+45NmLC7wPrBcfCDJHZAXpYEyq83hTfWuq\\n49zLzoKKo7Qfyr01Jgld1kpHi9sLVB9iwVK3/U5eiiOZHotNAIREBA++RRLwpsWaBkSC0hopoXUW\\n57quoTJWeQbv2FYlqVZoCYM0IRCorWVRNkjvMdbTGIdK5I7jZj6fkRUZ4ywjHw0xjaFtTex9aiyl\\naVmWJeHBUJeOpnVsmoZFXXO9iZkttY3dgFznAlR9hF4IlE5I0owkzZBaxqpab8ltS88K6YzB+SWp\\ntsynmul0QD4sINEkeY5uGvyD5ermij/+/nd8/bvf8XDzAVPXEVzb2CVKdrEG0VlDrivqEVKwSBKC\\ndUjn0SK2sCOAcxYv1S5Do89YIUDTPfChD4gLETv52Njj1NoW6wJta2mMRSqNUZJtovkgJCkBv1ky\\nL1JSDecvLnjx8pLxaADBsF1vaOqStmlihaa1bNZbHh9W3N8vuLl74Or2nm1ZUwyHvHh2wfnzc87O\\n52R5gjU1TdnSNDVtU9PWFW1dIZxDhkAWYsAZFQOiVoLUApdp5sOcZ8mMwegVL379XxjMzkiynFjU\\nIDuFZq/chQMUEL0CF/qnf0cM/BGmROz5lLv3x/3EPyGQHyKt2IH5U0CEj8F9v8EePH5UVu3A8985\\n3pPg6PFxwyeoAA6vov8k6PvtxUndWwp9o2DZuRO09Cjl0NqQJpYidwyHMMoTrFtTt0sECXiJtZay\\nWrLdPmLrmskwJ091vN4uh7jIU55fzEkTSZ4ovLHcLUs2dcs+ZLW3SkQkVyG40BXUeIx3sdRbSsbT\\nMa/fvOBXf/FLnr/6jKurO/7t67fcXX+LGowpRjPU/BKZpp2AOjBdDkdJ7LWRWMm2l4oCUHhS/I4w\\nKgS61MNwVCl4SNTUl5Z3HvWuQ00A20Kw+NZi6wqBQHXNjYMxsRUcMWc75u97msaQiIRUKabDnNaa\\nmFtNoDQefINpWqwz1HVNU7d4Lzg7P2M6mzI/SSFIrI2kVtuqZLFcUr2v8d7Sti3LxnK7LbnZViya\\nlp7I+bDwJxbV7KtRtY6NENCK1kfgDsZ0c+6xbY2zJYNZxsWzUwajnABUdYVKM+xmw8OH9/zz//jv\\nfPWP/4v3335PU65QwZNI2XWAir1DlegbZMSxtz4yP5q6IZUqkqjJgHQx11wJEQnIvN+51GIII+y5\\n27tenkIqpJSR70b0DRpjYVOiNFmWMh0WzLIMNise/vR7xMMV7vyEl5cXTJ49J000bV1impqqqmk7\\nq4LOpfO4WHP1/p63P1zx4eaOm+UjxlrOLwWvkgSdKJw3rJY1rqlp65qmraNQdBZhDZkQ5ErHIiQp\\n8Q6MgJYQ+Y5cwmyU02SvGJ39mvnlZ0gp8aHvq/VEwTvsgLP73lP+cgDUe1/4R9QWP0J18anlJ+Ij\\nfwrjvab146AcFbsn5e+H2R+BT2rvnzreU2X9qcb4qShp78I5PPdR1WaveYqnJfo+atzKkyY1WrtI\\nTJUHksQhtUGrQKo1qc7QgHeAC1R1g6s8pmyo6w1SOYbjlOFoQDEokFrHKw8e33VAGKQpF6czZIBv\\n391wfb9k1ZFkORHdKKLLPsELHJEP2nrwIfZZHI6G/O3f/zW//bvf8Pmv3uCCpvGW4c09V4s1H97/\\nDuNqXn3xfzCeX5AORqiOGW5nNu6tx52ZeJSa1qG2Cp7UW1RvLrjOteMjn3V3wbs2cr1C3rMZxgrO\\nQPAm8n6YCmEcbNds796Rq4Y01ciqxdkoqFpjEYCWEitiDvsg1ZznAx7XG5wxFFowKRIyFUm5ltua\\nujEsVhtu7xaczaecTGcMsxwpBM55mr6V3nrDzWLJ/XrLoqxZGsuiaqiM6bQtv3M1ySB25GQiAM4R\\nTI0IJrqQ9AihsgiiIloRwVmUEgwKTaYD2/WSDz8YVJpGtsx8wObulrdf/TO/+x//yLtvvqFcr0lE\\nINOKQkbhF/3bxACkj4HvxkXKXNMVKUlhSZWk6KpfU6W6VncxzuA6K6S2ltZHUFMyVm4WaUKuFZnW\\ntN5jnSdgoiUZIifKaJDz4vyM1y+eR8oJGXvHru6umRYJ9cmY1cMdUnTB48ayXm1iIc+6oiwbNpst\\nj4sVm7JBSsV0MCB4x0hJmuWCr3/3FZPxkOFwQJaorimKJwmeXCmKoiDbxZMC3llCiM9KIMYBlFJk\\naUKiZYw/iUBXdNyRiLELge3qLI5cueIApw6xRXxk8T8BnQOs+s/mI2dvguzNiD+vWYtDZDhc/6Oq\\n+KfPe/jleNfD4z91ej/5unOnP9mGjrNEebQOpAloaUkTT5YFBDXO1ThnGQ5y8lyjlCbgaaqGxWpL\\nkWR4DMEJhIGEFKEFG+fIp1NynTF5dkmRqC7Q1ZNfWfAxJ7zIEk5mI9rWHC3slQAAIABJREFURO6U\\nhxWbJhad+F4YhoAXXSPr0D18QjCeTvjszSu+/MvfMD8/5X7xyLv3tywfNuhcMBxLlKqw5gN37yXb\\n9RnFcE6ejVFpjk5y0nyA0hlCqP0cfWxoRTANgcRasJZgHMH2RT8diPfXK/bTIIhCyeJpnGOzfcQ6\\nRyo8tBtEWRLWK+TqgWKSQ55Qdf0jrY1AroQg1YqqNUgRGCSSi2mB9oa2jg0jzidF9LlWDW1tuhRN\\nGwF9uWFc3FPoNHYHCgETArUxbJuWZVWzaQwbE4PHlbVdiqHYveyC2JRCdQ+xFJAqyShPSJIUZNIR\\nFGpUknYMl3EElM5QGprNIzc33xLmKWnadXmSmturG775wx9YfXiH3a5RzpKnmpFWDJMErRO0VCjR\\n88x7auuorKEWltp62s5CdQEsIjZt1nFf7wPOxfuqrKdxARtbvxKCQHqBdQEXZTJdW4ZIZ+uixaVU\\nzzmeMRsPoDXUVUVjWrJckejoClsuHmkaE0F7W7PZVpTbirpqu65WkA2GjKZTkjQhzTXNZkVbl7TV\\nmodmi63HJPKc1CekaUKeaDKZRNpmFSkQeoXId+4rKfqWfpHOOdGKRHiS0FJIh1QiKg8h9tGNz3S8\\nnv69cqG3u7ont9PUj7Dn31O8exnwZ9oF/YRcK/2Hw0DAU4f28fdeKT6MqR26Yg7l1XGo4UkBT3+g\\nXf/Hj0H+aHR32+837gNwUdMMOxM1EYI0NRS5YzCALDHkeSDPBXXjWa9rVus11k4wJsc5jTGG6w+3\\n3N/cc3F+xmCUkiaxeCIrhtjM87i5YfLsJSdJzvTijNTU+HKLbRuMaTGmwQQfg3lKUmQpl6czMh1d\\nC9eLDWFb43smOtFxmBCrGqWS5EnCs8szvvjVzzl/ecFqs+Krf/lXvvrqD4wnE169esF8npEVKTpx\\nbB7/hCk/0AxmDAbn6GxEUowZTc7JiikqjZkKomvEcOx0CZGcK3i0dWBi0M17tzczDysFd26I6NmP\\ngbOWsm1YL64RwTNTIJol1fKRbL2CpkYOUoxQbLMUFwJ1a2iNQUtJrjUrEQnCci2YDTWYjFbHebyc\\nj5BSci+gEpK6sRjrWHdtyu4e1yTIrjoy9ni0CIwP1N7SWEfT0f9aFzVcJcSuO5FCRppcKTDBkWjJ\\nqEg5mYxIihHIFGcaEiVRqiApRjF/XCt0WuCDpVmsKL/7V05fDdEZ+LahrA2rq3vW378ntRXjRBGU\\nYJhoJnnOpCgYpDlFmpElaQxiWk/VGjZ1xbpp2bSGrXG0XfehRAkmg4JhXpDqlNo0rMuSTWNxxMBn\\nqvtWeRItJD4IjAPp4jsTeclt1MyF6Phj+tfNUZdbHh+XWG95NXtJmuUEF7i7XfC43PK43LJclzgT\\nA9mJ0hTDAaPxmPFkxGw+YzafMJ2PeLx6z7vvvuHrP/6BTW2xeUqmJAMpmWjNMM/JEo3sgrKRWZK+\\nBVXnQpIEGauqtVIopTpKgYpcRoKv4EPkN+rSfLv+J5FDPkiMD9gg8DuAEUeYdATihxr4YfLFodb5\\nI6D/k5Xo7z92IC36i++rBQ/dE4dXv0fdj7JQDgCeI1Dfrz8+R6d7H2nnB9ZBf/ywP15P1BU6f58k\\nIHFM85TJUDMeaKSuUdqidEw901qSJLHQo21arL3lq6++Y7UuaRrLZrPl4fqeZlvzX//hb/niFz9j\\nPnnGcDRDkdO2DdPJGdk852R8xuDsFL28IiyvEabGtC2mbqnrCiETlErIEsMgLxiNxpyfn/Pu6o4f\\nru74/vaBkoB3ASEDUgkUniJVvP75a569fkY2T/jq9//K3e0DP7x9z+LhkazICNpjMZydTXnx8jmp\\nGzEYjEnSjLryLBdbtuUVdv1IzjO0nBNkDmpIIO1GsNNcRMfv4QPC+eizJxBkx28diP8R0Z3gPSG0\\n0dxtPdvtmsdmzabdMmpqTvIEnWu2ZkuDo1KaByVZNjWtEowGBZVtoYo584oO2LKURIC1nm1VMcxT\\nZlmKJvBsPkFrTaYSqk4bXG0bWmcJIbpFZPeSORFfVhdCpAYOXSZQCLQd818st1e7KlgZoEg1Wgrq\\nFs5HBc/nIybDITbNYmqirRikGVmWM5rMyQZDdJIgpcc9fEBvbzgdBN48nzMdZ7RNTdU2zEeKWe75\\nfiDZbBuc9SjnOZ3MuDw953x+wulszmQ8IUtzBALTGlarFVe3d1w/PHJXVVTGIgSMBwmvX79gPp2C\\nCTw8PvDu5pav311zv22oTfRZJzK6R5RUXfPi6GKwzuB6bvidmyx2Q9ps1rx/LyhXW6x1ZEWOlBl3\\nt2sWt1uqssVYhxeQpSnD2ZDJdMLJ2Snz0zmz+YzxZIJSEmtbqu2aYjxmenrGs8cF5uqGJHgmieJk\\nOGCQpmgVm6dEBbknoo1uPCUVAomXgYBF2IhSWtD5yx2V61oT9p2qOrdqjzpeiE5Jimiy45nfYdQT\\nBfTQn77DsAM8Cwexh08s/3myVghHF//n7A2x3+kTx/mRfY7O8dSV8/Rc4UhIfBRBFvFFTLVjkAlG\\nRcLJqGA41KSZp/EG6ysCEi8kDklwEo9DJp5ilDDc5CwXK27fX3N/f8/D3T1NXTMcJrjQ4IPnxbMM\\nXMt2vWXTNKjRAJGnVKkmLXJSOyJvNWliMUmL0hqlE7KswDvf3XTk4j47PePi2T3nH274cLfkbrXl\\nsWrZNA2jQUIxn/DFb39GPi3Y1hVvv7/i5uqe5WJJ8AKEpHWOsm5oaoOWipeXzxiOB6AE203FaCQw\\nTQClSDOB0g0+GESQOC+ojcL4aKZD6Bove6Tzu6dXSNH5eSL4BTxCeAQOoQwhGII1aGkYSAfCgfQU\\n0kcucOlxOJxrqZoGgmOYKEQ6ARHiS9pZU0pKijTBexdNamsphjnDJCURgslwgFQS2zoGacaoKBgN\\nahbLDXVjIqdP9272WYTRd+/p+1bGpgoh+peTGPiruzZyiVZdv09I85STQcp8kDAuNDZPsUFhUchi\\nTFqMKGYTBqMRWZogXQ3bK1R7zyCDLFORcEoFpPLYcUZ7MmL1uI75+UFwNp3y4vwZl2fPOBmOmQxG\\nUcNOMqSQeO+pp2NmwwFnszF35QYnBGmSMBnmXDy/YDIegRdcXw0YpArvHfOyxXmihq8ViuhCedhU\\nrJuGypnY69UHEBKpiGyPIsYH/n/m3rQ5jizN0nvu6kus2AiQzL2yuqtnpnskmclMpk8y05/Q3+5p\\nWc9MV1VWkskFJJbYfbubPlwPLMzs/poKM5BAMOBEONzPfe95z3vO0Dm2HGgPHUVRUhQ10Qv6PmCU\\npJpMOalrJrOayXzKZDphOp0ym8+YTCejTFNx2O9Zr+64/vCe9XrN3c1nPt/cs2saJnWJIY1N3tHZ\\n89n9nKdahczlWRR5Cjj3vNK4p8zsd0owJIGKApFG90PB2MzP2JL3jjyJxR439I8Q84RuzOzAsyL8\\nV+j2COi/9fjdBoIePn/4g+fdsF+94Dd6w0+R+6Hx+OyA/8FP8Wvwfn7Y45kW46rJw2ZAihyLNq0C\\np3PFi6VlPikxVuJEQ990DOFACPmCkXHceidHkAPTeUlhXiGcZHfb0OgdZaHxIfHuwzuKSlNPSyaT\\nmqEL3N3es9pvEFqziHPuukgtBmaFoSJhlEUpgxASpQuqEACJNll+p5Tk8srz1es93351x1/fvOOX\\nT3d8ut+xaVrErGb27SWXP75g3/dsbj9z/fmG+7s1QzdQVzXa2HyhJ03oBaGPTKYl1dQQRCAJyWK+\\nwOiCpLMaJnlP8AHpC/yg2R00WydJMe9jJBGZxqnOY5NZyZFnTTnQOfn8EQcQOVBDpkBdagpbMy0E\\n/T5ijUZIhVKSnsAuOobgqYKgTolkFH0yeYeEJuBAQGk03XD0ZM/N4nldo7VmMpkggM46VKlgKlgu\\nPBrY7Bq6wY8c8tj4HpU0D43Y0WogJVhMSk6mJf0wsOs9fYS6tPiQHQOnRjOvNFMrKXUk6UhSGqUM\\nyCKD+WxKOZ2hjSLt1+jhDh23GCsYXI/z+oHr1UpSjwNNh/1ATIKXr1/yzatXvJifUCAxSSK8J45U\\nB2R/lUldgFxQLUuKqqAsCgqpqaYTbFmitKHZVyymFRcnc2azQFlUXMxPmRgFIdI0HX+7vuV6s+O2\\nObCL+0w/CIUaFVxKjvRFSHiXUNqyWCy5OLugqiZMpxOmsxnL0xNOz05ZnC6YzmfZHVFJpADvA01z\\n4LDfc/3xA+/fv+Pt27fc3t6x2Wxp2oZZVXB5lkjRE4MnRoWSDyj60LdIAlJS+f4PIEUY4X1ElBgR\\nSIQ05Ba5HMNQHgEipadKnif/B/ka+RWGja98qMITPISvjJ8/f/1vV6u/E5D/1nPiV58/arRHkB3f\\n3fNA3+M3PX3Dxxlv8QWd8h/9VP8e6B8JgcdfgZEwncDZaWI5D1jbEZWkj4kh7nCxxcWO3oVRcKGQ\\nKFxsSQSUFJTVhO9++JGLs69ZrT5ze/+Ou9U13ifOz884OZuAGljv73j/+T332y3zk5JBzri7eUtt\\nDae6QETJDIVVFmtBKQsiN8i0LpA6X5gxJqp6xmx5wsXlFf9pf2B7aNm0DdtSsl1a7potn24+8f7j\\nNZ0fkEZihCVKsKXh6vKcF5cvmS8rZsuKKBM+eoSKaKMgBRwtYVSeSCkwVlFVktQncHtiEBANvZgg\\n8ajkUTGnxyBBKAFaIo1C6gIRHEOzo902DLstpbXMpjPy0IlCyES/3SGTxChLIRVJSUxp+ceqJqWI\\ns5qgFathACJKQvD5Bq2MxnmPGCvPxaTibD5FKcPJ8hxiwreBYfBIrZjNZhTacnuz4vPNPX1IWR4Y\\nQaSQuWAEPoncqyWrM15fnPD1izl//vkDkJgIlTXUMvcqKpObbiJ63H6F7z3alpSFIkSFICJMlS2E\\nBaRuzcR6kg907cD9/RqjBMt5TYo5iWdal7y6XBJDZLPp0EqPqVEDh86BC7laD4Hgs1Ry37bs2g5s\\nwYtvv0Lpiqbp+Ondz/m60gptCz5+vuFmtWHT9EymBYtpxcncUilFHDzCS87nNV3wbLs+JwWNma9P\\noxClhMJqThZzLl5c8vVXr3n1+iWn52fM5hOqusIWJUqrzEmnhBs6Dn1P17fcfv7M+3cf+Omnv/Hz\\nuw9cf75jtdky9B6jJItpyVeXl8xncwbnGIJDhzzhq8UxqxVIIdcSYytmXNaQQo1Ne4EPAVWYTG+N\\nWafj0s2xCf1oevecEn7wYHo2yn30uHkCP+J4zCdwhhgXmX+fdfjdMjufP/F05frtNedZof2rx3Na\\nJj174a/plF8d+3jw9PTEi2c/z4NqRiSMDUxmA0UdkOY48p3VFill06fgA/3QZfdCqdHK4p0DAkIr\\nlIV6PmM5ecFkWjJZapaHCik008l0TEWRCB1RFqqpQRaJQfY4HG1MrH0ieUBUnAiLtiU6gZBZRqi1\\nySZLQEgRqS3SWJQtqOczTgfHGsfP7YZf1p948/YXPny4oekapBbYSfaSXkynfP31Bd9/95Lz8yuS\\nhJQC/dAjTMTIfLHneLJx0SQihcSogsIWJKHQukWmBuElpCUag04RScThQAp0oZGFQuq82fUp0fYt\\n280aEwJRK1wYslwvBpSLoxxO4Xxku2/ohgGlJKf1hOgdjRzzSb2HGHJCDDxUzZXR1KXGGsVEK+Za\\nYYxiWpYYW1IZw2a9xceIKizTyYT5bM7JcsmnmxX3u4bYDzjIFThjM1kIlFYsq4JZXVIazaQwFEYT\\nhaL1fqQXJKUWlFpilCB5x9CvCcYykVOiTwQMbr+Fw47SSGzsqEtFSgbveu5u79EC6sLkY2qF0op6\\nUjKdlvR9GL1DDtRJ4puOMHi8C7hhYL/fs9ntWTcNpqw4vbhAGosuyjx0tliyurvj0+d77vYH7vYN\\nh8FndaiW7HZ7PoT3FFIRXaBrHZtuoG1bhjDQB0dI8aGSFkBpDRfnZ/zdjz/y4x9+5PLlFbPZjLqq\\nKKoSBIQY8ri/dwx9R9M0dIcDu92W+9WKt7984Jf317z7+Jn17oDzAWsMr19d8erqgm9eX/Ly4oxp\\nIel9hwt5ECvFPIH64FAa01gZi/y8ONpBZJdNEiMfLhE6e6pkAM8Sxd8eGHwc+nmCySPMpIfXHDHq\\n0cn1uczweEf9B6KV30u18mT5EU+fe15pC34bfsUTtUn++vFw+R+O1fN/XGU/fu9jtf7gMCny6Zfj\\nL0FIgVKgVKSsBsr6gNARnxRZ+Sczz5giMYxA3vVoKRAajDCkkBUXEUEyAa0VZTnBp46gFqjaU0+m\\nVEWFUYZh6KnqkpOzBbM4oZwWWfdssn68DZ4heaqomSjL1OYAXiGzvauSMie2x3xxRJH1u9IkrJLo\\nsuCgPE234pcPH/nw7iPr7Z4kBEVpqSeKqir45vUVP/79N3z19SV1taDrBpquzYDqE0JZBAIfcwKo\\nVWbcRgqUKNC6yIoANeDcNcN+wIcTlL1Aup7Bt0QRQGtMKVBFTp6PQ6A57Dlst/T7A9V0gpQC5wak\\nVIgQEC5glAIB3eBYrXf0YcCWFmU0SoFNgaF34DwixnGK9AjkkdJoamtyao2ESiQKEaliYlKUnM2n\\nlMZw6HqilNR1jXqhePXKMf35PeWnWz6ttqSmzVYvIaJlQkuFkILTeY2WgsEF5nVBQuIiDPsBJbLk\\nsDSK0mqMklmq2HUkP5BqzXDoaA8ON5T0WKZ1wTytsTNQpaVvFXf3GzSJ03mFMVlqKZRCW0NdF7TN\\nQNsc2BlLESF2LoO487Rtx/1mw/1my6breVFWyLIg+MjQZ+17PZlyd3PP/XrHX95/ZB8gCInWmrLQ\\n4B3b2zsMIg/TuEgXI1vnaYcel3y2szUq53ZKwWRS8+rVFf/wn//EP/7TPzKbTen6nv2+YbVZcTgc\\naJsG5wdc39E1Dc1uR7Pfs1qv+Xh9w9uPN9ysdjQuUNc1y8WCq/NT/vDDN3z39SteXV0y+IF+v2Xf\\n98xjyMKUJz4fYpQbHieHHzhvme+lB7hPj0Avya8/ynmP/HiOcDti0ZFuO2LNI8n9QLM89V96gnuP\\nSHicD31qoPXrx+9TkT9sL76E6MdmwVMQ/5VZ1dNjHQFYPIHtJ6nkR7OsfJrS0+L/yTGeTBCOY9iP\\n5E5ena0WzCdQVh2mbBGqxSeIDmK/Jw0CJXOjcQgdg+vphx4vsu2r1QXeBxKZbslDBx5ERCkFSeCG\\nQCiztEpKhVSK2XKGLS2khCrGwFctEEllkLaKwxDZpcDc5lH9x588a3fjMadBQJIhT3SS03Gu91s+\\nrTd0+57JbELUkqbzqKC5ujjnD3/4ij/+6XvmiznWlNTFjMlMMIsdu/YOHx2xDUgpcTH7ajilUSmS\\nVEnUo8+1CqB61nfX3FzfIETJdPlH+hY+bq45P5tTVgatEyIGUooMQ8/tuzek5sBiNmGyWGSPjxiQ\\nIeRYPKWwKrvvDV3LsG/QPlB4QVN32EIjhYKuA+dy/uSx2ZUEJEHXewqddxVOS5yIFG7A3txTBYm5\\nOoP5grryDCFQT2fYukQZw9VXr/l0fcPffnrLv/30lk+rNeuYUCHlqk0k5qUmuJ5D4zmbT0gIdu3A\\nagOkhFZQjIMzUgrafiCJHMogU2C/3rHa3tDGd8j/+c9MZjMuzue8+qfXVJWhNBLXD6zv13yuFbPF\\nlLIsMcpgtKcsC+pJj+s8+2aLCB4rDISsO0cpqumEs9KyABYnJ1ir+fz+Pd2+pT20tF3P7rBntdvj\\nnSf6PIcQiNyuBvqq4GxW41zIHu4+MgAHN9AODaVRFEJnFZA2FIVlfjLjxeUptlSsVrf89NNfuLu7\\n5+5uxf1qTdM0tF1H17UQ8/SvEoKu69nsGj7drmh7h5Cay9MFP3z/LV9/dcWL8xPaoeft+/f8t3/+\\nV67v75nVhn/49orz+QI5yf77UkpkSsg4lsMPen6R5aLpmFv7aJ8tRZarPtSOY8P2EYQfjyPESPJ+\\n2cQUT0D7SCMnxgzbx9DwI1am8WBf5qE+ffwuQJ5DZR+/fvY5PH/jD9KeLw9yrMzH1fNJk0B88XEE\\n9qeUi/jiRI5L8bNXCQFGwLRWzCeaaSVBtwTpGXCkKAgpMYRhXEgC0stxwZBIpbK7nHDEYlRhjPRM\\nEmkcJsip7EqNo8wx4GPAjFSOVAJt86XiXCAMAV2OY/Gj9Gm92aN9z3m1QBbZOIhxKo103KrFcSso\\nkVLjSbTJ8fb2hve3N3RDT5IwnU85u6h4cXLBy6sLrl6eMZnXCJWyAkXl1HQ39AztMPKciRh7Qsq+\\nLkkFhNQgFRFF5/bs13f8+d/+hZ//8hcOmwOLxYINmqoTVO09YtXQHgxSyXzuEMgYkUNDJQMTArJr\\niCLzpNJaxBjDFpvM8+oUWZiCoKEuKxaqwJPogstZmz4QvUdrjVIeJTOn6bxn3w18vNvxYjHlpC4p\\npKQ/7Ak+Mh0GRJEHf5SQVDOBVQZdFJjplElVslzOuLw85/rzPTd3a+43Gzb7PV3fUmhIIWCE4HRa\\n4WIihsBSZz94LQVa5inJPoU8eSoVKQT6Q4tpB2Ztjw2Rkxgp0gD+QHdZoBclQ9sTnccrQes8psux\\nZWocgirLgtl8Sm8cMoJL2aMkeUEKeULTlAWTyYLZYs5sPsdoTbtr2Ntd1rELEMETQ2AQYAaf1Tci\\nUVjDbDbl/OKEZrVlF5rMjfuBLnq0kVwtF5SFxWjFMISsrQ6JX959YLs7YG3Bdrtlvdmx3e7ZHxp6\\n53CjR0sOB8+7TOcD3eA4NB1KaUprsGXBZr9j+Hng7fsPNF1Le2ho9g27tuXqYsm3l8uH3k0e9pEo\\n8m77McAhjc6YAhFzSSfFY4atJGV/mlxhjv7j6RGlxSOhcsSwpwKMJ1zEs7+P7qjPaWEeqJg0vub/\\nVxX5cfUTX7xhxm3Jg8PhsRJ/WAHF82M86+oezZiegPkTjvv5CUgcF87jYiCSHLXh8eG/kUJQFYnl\\nFJYzQWk1A5IuJpKLD1V+FBGpIcSBto9oKzCqwJpA03tCdqjKlaEEJXM1jcj/h1YaYyzGGELMEVdH\\nakKM5Fki0bc9ro/MVQE2W7gqIdh1DekQeVmfo5SmKnNTi8S4YBwvrvG8y8xnNyFyu92xPjR5HFtr\\nFicLXl1d8cM333JyOkcVikNzIPqY/a0JtF3LZrulax3WWLSRDC5PuimlcmCwsChZEomsN595//av\\n/Ou//DOrj2t0Ukzrgt3hmmqA0+Tor2/ou56tdyCzZepMa74vLfPCUCaPaw/jFKrEIUArRMyTmil4\\nTBKc1VOSlBRlydKW7IVj53MTrwt5cMMWFjNkRU0i4oOgHTyHuy1fnS84m9bUdU3jeqadQzU9tq5I\\nxpC0wRYlVqicNTmBqio5nbzk9XLJ/dWGz5/v+PTpmpubG1brFQd3YOgCOiYWRtPEwKAE50Zl21Ol\\nGMiZpB7wIWFEpuda5ymdpwKSlHxrNFbAtj0wfPjM6lDSE0kugNX0LuJ8QodEkqMaRymszZ4wcrRB\\n6PoON0SiF4BiUhiqSc3FizPm8znGGNzJQLtc0GwPtJsdu82O+f5A3basmp5107JrGmaTivl0yqSu\\naHYHBqCNgTYEhJYspzWXF0smpYUYuF03bFrPvum4/+ntQ69icANdN+Q4vJgdOmNK2eRMjDYGjIZv\\nKRclxmqszQv659tb2ran7brs2Q5okbXsbpgwuGzjcNzlSzny9aMdc7a1zUWeGIH7y79T8AQ3kFLg\\ngZJ9NlH4uJfnCGFPYOfhL/FYdR+/K37x/UfcSg8H+bKafXz8bl4r8IT6OH7x+NcTymR84suVD56D\\n+APgPV/tHkyixu9/KLzFlzuB4xRhFhxJoNCR2SKhy2ac2LMkGRHCkMjVs5RQ2oK2azk0B7rdwOXF\\nSyo7wxuDF6CSQGKxCsToq6KFxUpLYSoUisrUNKaidQ3RgyTnZ0YJQSYiQ/ZG3nTIqKhOCsqpwWjN\\nTsK6a/l4/YmyKCmr8sjE5VX+qApBPNhsuhQ5xIAuaibVkhQUtha8+uqSP/zwHcvFgkikaVucC8yq\\nBYv6lBBhdxhYbToUCq0sIlmIiaIsKEsDyVPZOdqUeFp+/um/8+f/9v/y7q8fmNRT6sUEqRNBB4JU\\nxIXhzZ/f8/O7a/7WdnghKKzlfDbh/3j1iv/14ozZwmAQSJ8YnKNb3RKMRitN27SkFNBSspzOCQKk\\nkhTWooXJ/iEx4kKmgGZVwTAMeO9ycLPWpJDYtwMf7racVBVXkylOCfqUde9q36LdHuMDRevQsymi\\ntDCpoLRIa5goRakkZ8s5r/DcK/ikBT+vArveZUrDBboYsClxaS0ToUgSPgfH4AIOkavBFIkucOg9\\n85iYKkUFnGrF0hZIo3jzac3tbaKpDLY2xEGyvd8xmy/R5QxrNM1uS3vIfiQxwbQsqcqCYd/gho6u\\njyAMSUnkVufw4pA4WS6YTKZMJ3PiRWRoc+jCoW3Zdg2f79e8/3TLm3cfMFrRti2/vOty9GDb0QZH\\nURmWiymX5wsuTuf4oefufkXXD+wOPet2wCc/au7JPZYYx0lZhVbjvTkOGWmR/VCOVWtKj+fKdQ0q\\nQm00la6JMY5DZNlHBmBweV4gZ9RmADgmreWJzGyqRnzcsT96hGbQ6JuGvVqhhw5bVA/Wyke6JB3r\\npeP99wRkjoAsn6DUU3iTDzJGnv37QyH6Bfw9ffxuI/pP3t4z6eEDTfJQiYtn25WHt/dku/KrNyie\\nnqRHnuYxai0/8WxNEEeHw7wrKA1Ma6hKRzus2Xcts7qkKC3IgJARHzwqSUpTEQN4FYl4cpavpLAF\\nUtnRZE1n6ZYUGKmxssSqOYWeo+gobUNlZjgPWlQYkYdRoESIAhdaTk8KTmtJYaYE00EccgU+neKb\\nxPqwp+laZsMEjlFopNGpLlumJpHwJA4+seoShVlydVYQTwO6cixPpxSmhqBICLQoKasls2qGUQV3\\n60/cre/YHQ6czk6JKSfuaF2gtUVrgxIVUhe0bcf19Rv+9m8/8fEoKnf2AAAgAElEQVSXawbnOZsW\\nLM7nLJdTdGkpXYT7hsoKVAzsupZkCxbLOd/88TumL684aMnbvuXUJaokM58t9dhrcKQUGJzDS8l8\\nXuFSwguB1xZrJJMUMEIhk8dIyWQxoRsG+sERPNmTm7ybaHrPoR+ywZjM7nd9cMxshUqC1A3YpkMn\\nQWp60vZAkGIc/MoDQDlgeI867CkOLbMkqLRFxEhqB4yCUklMYXmh82Rp7CN3MeLIKhoBhN7R9I5p\\nEFRacVUWvJzPqZSmHXrmPrHpBjZNi9trylnFaRJ8/rSid+SeR4A+SVwUWWYYE0JpZos5wxDY7be0\\nw8C+69k2DZvdnt1mx+ZkwXw2wxqDSOCH3Bht+p5de2C739J2DUCWAu4HDv1A5wNCCcrKMqsNi6ll\\nUlpiyIqiz3cbNk1gfWi5bzqUetqRgqMtQx7MyRWvHE260vHjyfb6weM+jVW2IFOeAuKIi1aCESlX\\n0yGD+dEC4sjlPucG8lcPjpwxjdLJxND1tGwpmy2FVlhbPLg+xpETeMSr59XosVh8JFCOC9KIg8+B\\n8eFneu5m/tvkyu+nWhlP/HHFgbGTe+RDnq5qvwLdZ0dCPFnJ/r0V69lJ+qL6/7KM1zIxqSTTCQjZ\\ns+/uaLoNqAmYJUZalMymQCDQ0lJqTbSKvpTZZGh0qMsZgIm+95TSIIVGojNYyxqrKohgVInVBUY6\\ntChQoszRUSIAGhJMZgvK5YRCLNgPt7RxhRSSsp4xzODQ7TgMHW3XjBODx8smjXab2fZ1HzzrfmA/\\nQGnnaD1HW4WZDNhaoVQJyaCFQGtJXc4RCXa7HR9vPnCzuse7yHwyJbmIipLZdIbS40UsJU3bcH9z\\ny1//x5+5fndN2zkWF0vOXp1xcXXOdDZFGkO56wibhkVpOSkN1U6ip1O+/vYr/uv//k+cnJwgmwN3\\n19fIzQHhIzOhMFoTUyS4ASESPuYbbW40IeZE+gGBLcsc1jGpMN6hBczqkklV0rQ9TghSl7fJ07JA\\nqzxC70kElRufrXPMJ3M0iiA7bPDofiC5iJdjryLEDBQ+e31LP6D6FuMdMyExRYGMsPaRoBXSGqJP\\nzEzBXAq8DAx9Tx8iWht8jDjhGVxASc3MWq6mU5aTGpUE3nuWyrCKkegj+y4QLcyC4u52x7ZxFJMK\\nZXS28O2zXr+0iSgk08WCvvfsmo5d29J1Hdv9nvVmx3q14eZmwmxSY7VGjsk+IWZTsH3Xsjoc2DYd\\nrR/Ytz3bLk9xWmuZVxXLec1iYpjPasqqoOscq23D/faAKCZIo7N6yydgNBMbFSJ5GDnTGUoKzOi4\\nKGX2O4E81yGPRlZaoR5CLvII/QNgkwNDltM62yOEkPsDYzZsvufTI+akxwWEkTaJcTSmiymHd8cD\\n3f6eeV1hSvsQzJKSeAyiOZK8T8DqyAb8mu59kkr2BKePr3kQgDz/pmeP30m1EhBCcWwkfOm7e6Q4\\nnn790CT4okv6jEZ5+IcnK+wXx3m+zo3PPhb2KBITK1nMBWUdud3v2LVrOrehconC1ShpMVhQIGSe\\n8NKyoC4rhJzQdVt8t6OoJUl2uOjYtC3CzFCmQMU0cmxHfwZPTP6Bv8vEWBqd6bL6QeVlHoFmUp6i\\nlUIPhja1CJNgknAOtjim7ZZ5URLFKJ1K2Qs6A5Tg427DzRCIFNhCg4/46JmXM6q6QBcFhS5HS1EB\\nWK4/v+Wnd/+dt58/EGOkKiru9p+xRlFXJUtbIbTCp56h3fHpl8+8/+k9b/78hmHwnF694Ie//5bL\\n1xfMZhOEj4QhIbaOftuzkIKLQnNlDfWLM/7ww9f88U9/oKwqgnO056cc/vIT8vMKfWhR2uSdjkvo\\ncbPqgMFamral7TtsYXHVAllWnLQXFH1D6nuUyv7tk7qkbTsQAqMVl/OKi3nFclIRoyBKjZeOJuYk\\nHz1efzoGTAogDeV0mpuuiVwFxkAMjtA5bLNFNFuSaykjmCSQOMqq4GAEdz4glWSmDD8UmsN2Q9v3\\nRCXZxUgY05qm1nAxqbmazvGjm8FiMkUaxWmqeKE0EwVmUlNNZ3nBfXvLer9FKEVVWGZVwemkoDSW\\nOJPosuL08hJhCkL6yP39LjsLtj2r3T6DpMwAinh0yfQx0ceYaaAYCUR2bccQHIURvDjJ4/0niwl1\\nXVPPZ9h6wvb9J9reI5Lg+29fcdEFqo+3XN/eM3j3ANqZjwYl1KMdbmGoizF2ryzQUmGtoa5KppOK\\ncgxf9j7r4r0POSw5CUbSBKUEldXEBP3g8DbksAxk7pMdK70neCTIC0McfxcxjmETrqfZ3uIXS+R0\\nihDyoSEZxFNI+WL38IQ2eYp4T7MPnjEr6WmVPqpWfqX6yI/fBcjvPn+gnsypJ3MYo6Hgt6vpfF6e\\nbnyegPTT9yWeL1b5pPx7S1h6pFme/GlkorCC0xOFsT3tsGa9/cy+3ZDoQUBMAZkkdXGBCz0+eoKT\\nKCRKQmk1fR/wcWDwAmQ2mmrbHcoEoiiJyWKVwcWW3h/o3JYu7BhSQ58a8BEzCJIcCCkyxB4XO2Ly\\nKAx92JFkwpqSEByUBVJEpBHsm47bfo8hYW2Bkoqj48OQPCvX8efbd6yGyHR6QVFMMUUkpY5t39Bi\\nqHxNefKKwlYQBbv9nkO7p/cdSWTjoyEM9L5FKIOPis63dL6haxrWn1dcv71jfbOjKGd888N3vHx9\\nzovX52gtSD7iugEtS0Jq2XeJWcimUpXSnMwmzGcTrDW5CisV6lwxCEEsP3D/5j3VoaMQCm0tloRx\\nOaHHdT0hBYKVHFRET0rU2Yy56KhWd4jPPUPvMErlEXnn0Foyanyy/jt6xP4wNh4FqOxDIqWmnE0x\\nNlNIqAKMyZVkytxnIBGiIElBWRRMqDnsBrT36ATnVUlXF6ytYOcGGHX2J0pyFR0HBRskKiWKaoJ5\\nMWd5ck51eko8OUVMJihbYIWkFomvypL5ZE4YVUkxRG5vbvh884n79T3GlpRlSV1ZJoXkbFqynFfM\\nZjMEidnijMXJObef7ri5ueP2bsWh63DOM/i8+I/2+A8UgktHi1YIZFoJOc42DInVwdGLntNiQnAJ\\ns2tYb3YYa/nmh++4ev0KoRSXL7OZ24frWz7fren6HgHZ+bOwLGYTlrMJs2lNXVgKO4ZhHxUkModT\\npCRxLhG8xPuEc5GY5GiRkKv9qPLvp++z+6UPnihlNqGSGQOOWHCMGsw8e3hi2hbxztH7HXJzQ392\\nyTA/A21GkyweF4RnliGPGPOlhPDfweVRqHDcUY+YNTIZv/X4XYD8/vaalCLWWlIyKDVqfY+Pp+z+\\n+MRTP/LHLcfzZgE8XQyOqpinr376yucVugAKLZnXisVc0YeOfbNi19zRuSa7Fyo9Kl0EVk4RaEgt\\nvR9AehKRmAYSAzF5gpcoJUFEmuGA7kAoj1QVkzjgQk/vD/R+zxAODLHDpR5CxDgQOnt5uOhGIDco\\nWg7DJnOXKmuutRZEofFSszl09Pseues4OzlhUlfj+5Ts3cDPmxs+7tY4aZlpgRcdPnV0YUt/GChD\\nidE5Ck1JhXeB9XrFarNi3zb44EeHvwAi0bYtbhioypqhb1nf3XP95prDxmFkzetvv+aHP3zD1dU5\\n9aTMYbzDgRAgusSh6fm8PzD1AaRkpjXzSUlV2vzbiSn3FqoS/fKSNqXcgP3lA7XzVFKTxsGN5AYO\\n+z2uNsSJpbGCqtTU8ykz+5LqzS+IuzXb3QFlDWVhaFqFVQLvE90wEL1CO0fdZZ4cFFAQfUDYAjst\\nUMYgpcngpdTY6zrGpeXKTQkw1lKKgN5D8hGJ4qKs6AuLNInbyqCKGqkrZj5x5ivuUmDnPFFIVFEw\\nP71g8c33VFcvSacnmJMTbFmiEZRKUdY1V9MFIoYMMl3L8vaGs/s7Dvs9tqwe/Ha0TswMzK1gUhfo\\nTChz+arnxYt7bq5veP/+I7e396w3W/ZtTx/Cs2zXkBJR5NIgCkFIAiENAkVIif0gaVNADS2D6Tj0\\noIms9j1VXTG/eIGdTJlNKl6cn3J6smA+m1KVn7i9X+FGr/i6sMzqisV0mikeY7JEd7RfJiW8y/2I\\nlLIFxZH2TlGCyA1NocYpZyMpjCKExDCMskYpUVI/2umKo1f8WBWOC8ERxGMM9H1P03aEO83h/DWT\\n5SVmYjLoike+/zlX8pxFyBf208beIzZ9SbnkOzcdJ0J+xV4cH78LkB+2K4pCU5YGaUrKcoK1JTKN\\n7YB0rLzHxkd63H6kh+fHk/+YwfZYuT9bBL6o04/nUTxtOOTPZ7XhfGkpi0BzaGmHNX1oiCKgjKW0\\nJdoYItANHZEenzp8avApW3W23R4Xhzw4kDRWavSYoj44j/P+SVZmTnh30eFC/kgxEkXAhQHtFUhB\\nSPkmlVISpacdGpLQCOUZ4o4QE857hv7A3WHD8HnN6t7xj3//95SFzRmEUnDXHPjn929Jesbl2RWv\\nX73ip+v/yafNRw7DDm0sr8pXLOZzClsQY2LfHPh084FfPvzCh/sPDNFRVQWlzR/v371ju95Qq4LD\\ndsenj9e8f/eJi9NXfP/Dt/zTf/1fqIsFVpZor5CqxpQHLDs+vPsLb6//xs93H6hjZGkMFzbTAIXV\\nuXqJfrTByDs3c3pK+BHutxt2n1ZMDgNW6+z97SKr1RozOcUu5+T8gogVIM4umJxcIMw1n27vODs/\\nxY6JL1YKWh/Ydj29FRS24GWt2SUPIRK8YxCOaBO2KnO0nNQwNlxJeSYgjQIyjQBriSKhRZaShhhR\\nSnM2mdJbgU89SyEpT6aoeor6tKNWlloanBvoXIKypDq/Yvp3/5n59z8wPV1iS5N3WRGicxkmpCIi\\n0bXFzqfY2ZTLb75DigzgITicG0BAIaHWiakJGJEgBdzQcXJ2wdVXX/P19/e8/ekNP//0hrfvPxK6\\nHkcgBXKaFI9NxJhEjtpT+SZ1PhEpiQ5CN9D2dxRWY41GF4YgClSb8OsDAcnpfMr5i3NOTk7443df\\n8/bdR+7u1my3B4Z+IPSBzWrPYdsyMtcIoXJc4tH3XI2xeFphrMVagzVmlCVaqqKgrEqMNUiZSM0G\\n5x29G7BSjcEealTKkFOTFKO6JcNHinmn43yk6TrWmw41DExOPlGfvGReL8YFhkyzPIky/PJxpI3T\\n0Q7qSYGajjj3UHhnm18tEoUcm7XR/yam/i5Avl29IYQ1bXOL0hUnp69YnlxS2CrfJF9ozI+uco8V\\n+QMDxWNF/YQkeXoGHz5PR9pp7HQf176EEoJZWXA6nzCfGYa0ftCpptE4R8vsLS4E2RkkdARa+rDn\\n0O948GhQEkXWB4PiZHLF1L5koi7woUWoAcYV/mjmJRG5MtAGpUKe6pQaJUx+lyJnTyqpcyV4rDxS\\nNq0K44CJiIoQYds23H34zIuzc+azKbPFhJXv+NQdWO17fvzhj5ydXtD6lkO/RynB5fkls9mM5ewE\\nKSSr1T3r+x0frz/y8/u/cru7pXEtUSTmesa8mlGXlsqWdKqj7wfubles7rZM6jnffvcV3377FRqN\\noaQ0M8qywLsD29Cy2d9wu/nE7WHNVkQO311y0gTO3txQL6ZU1jwOQsDDSLPSGjOdUX73DYck2b69\\nZljtCd2AiIHaWjh0+EOHWC4IRuNDIOwaLk4vePX6Oz7e/wt92yNJFEpQKkk7XifFEJhFwUU9QYWO\\nrnNEH0AohLYIU4zX1aOSIuvVJEImhFIPDXgtwUaDEAKjDUVZURQVSiZmPrBICVzLrgctBnahpxs6\\nnHPsDwN4w+WwpzKJui6wVY1g3PKHhJD6QVGRh38ghoQUGmEk2lhsoYne4wYH4jhaHnEqoFTA4tBK\\nUZiCqigpy4qqnnL64pLLt+9588sHPn6+Y3VoaMe4nyCOSgqBTIJAQMUcfSa1ySPuCQIFHovAEIMm\\nHASHYc/9tmO97XlxOnB1MWNSGBbLGT+WBa9etbRNS9t2mVaTGonMAc+kLAAYpzIfQFwptM4DeEop\\npBIPO2cpc1MUIIZI1w8oD31hMjWnJFrlginryseBsxFfRMzDZt55hsHRtC37fYNF4JzPirCxGoen\\nBeJvgdHTXt/ji572/x5olJTQAlQKpKHl9uYD2/vP7Pdr+H/+719h6u8C5OvbXzjsPrO+m6BNzdAe\\nSMGxPLlEm7ylDmEAQEqNNROkeiTDv5xvEv/BV/nxOLJ/JNMfrXQTWkkWU8t0qjFFomn60UMhX4Ra\\nJIw0424pEMhUxxAO7NsN99s7YhRYa5lOp/R9RwoJQ+TFfMqinFKqKbvDLZ1bE+KBwff0oUVGwxD6\\nzNn5SHCeQEJGw2S0oRXS4enGfEqPIdujRnK6et5WCqTINq1DCHzc3HN9d8f52SnFsubN9o73uy1a\\nTzg7vWQyrbnZvEdpwbJecH52QT2psdIQfaDb7bm/v+PTzTX32zuaoSGIPCxVFxNOJqdYmahtSWcr\\nuqYnuERdzji/uOLy6hWz+QKZFFrpnJupJb7v2e7vef/pDTerzxxChz6ZYP70LcUAtdTMzhZUZfFQ\\nvTz9lUohMNZSXV7StT3tdsdqvcU3e8qYWBYWicJjkLMT0mxJLKfQOy5OLvjmquNvf/4JP3gG4SgL\\nw8RonFbZCyUI5kKzqCf4NldDQeQ0GKTOE6s8sR0dm93INA6ViYeqSkkwSuVJ0lJl32xjUQqmwXOC\\nZr1v2HQdTR/ZuI4m5rxIiaAqLK/PZpxPNVObcpABEGL2+05Cj7QC6NFAJrpIjJlSSOLIrEpSUoSQ\\nNdpJawQaQXZ9LLRCSY9WGm0KqsmU+ekpp6dnLJYnnLz7yKe7FetDy74faJ3HxzErdQxgEMYiiwlS\\nlzif6IYuS1KVzUoTmQOQfRgIQdC0is0uspxDWSiKwlBOppycZB66G3qEyJO0JEEcw5aNyRa2SmYr\\nWjk25J9gKUcr4UyH5KIphEQKCRcCfQh0XU+hc/UeLHkhVjnpiJgeaBZSJPjAMAw03UDXDzg/oELE\\n+wHve1LMtOBzWclvk9+/7tp9QaaMsmElcnM9up5uv2G3XrHZrmmazW8e93cB8ttP7xFSIpXGFiXd\\nYcXQbUjpv2Crmpgcze4OElTlgrOLHzDCPKysR/H9Yw0+noyRUnmmbHl21kYZD+MU16g7LTQsFgpd\\ndPSxZQh7QkiIVKBlBVKhpcFHz9HJxCVB0x24W63429s3KCSzyYR0/oLb9S3eBabFjJcnB+qypiwV\\nLlg8Budg328IyjOIJqe6tAcOzZ7Dfk9wgkJ7lsULSjNB4Wj8hjY0OOVYlBUhisxXRw8IpFIYYyiK\\nPCyyCwOfVvdcrO+pvznlXz684eYw8PXVjyxOTgii59BvODs/ZT6fMZ8uGIYe33t87xCpoCpqFvMT\\ndv2a0EYIHSlF5tWc89kLmvYWqwyFNRxWe84XF5x+d87Fq9ekIGibyOyspJwrpHHsDzs+3vzEz7/8\\nd/781/9B07VIa3j5wyuu/ulH5lEwaIEt5xRViXowD37E8yRyLF1RVswuL3IizGbLerujHYZMJc2X\\n9GcvERdfoS5eoeZzpHOc6Bu+XW3504tL/nLziaZ3LBcTFnWHbgd8FziXhqUqKG3JrO0xMhIrMEJy\\nlJOJdBSFiXwDK5WTjWIA5Uh+QIS8LTYqq3qSDqhCI6zBSMk0RC5SxW6z4n5oOISIE5JG5dT6F4sp\\nX3/3Df/X//m/cXK6RCtH61soa5LM11CMmQJAgDC5Qo4+y19lzJV71/T4ITK0A03XUNSG6axCYAlR\\n0COZqVw3Zx8RgdASPcsh3C9eX/F36x2fP37m/YdPfPp8x91mx6Hrs+UBAl2XLM5fcPnV9xhb0g6O\\n1XYDST1MTSYpcCFP4RKzAkPLBMoQRU4TwueoQmMLlDlOA43hHD7bVBRWo1WmQgBSyNx1cAHSkUPO\\n7+WI7MdxfG0MWylxztN1HZW1uKIkq4glQiqkVCQRs3adzJHnSnxg13TEGLEm7wCG4UCzX7HwLVrl\\n738cs/8Srh8LkqObbUaSR5V4SongPEZErBaICPv9nvvVmlQuOf3mkktrfxNTfxcgd64fp7IkInl2\\n62tSbDkcbqmqGVobQvB0zQ6tCnarO5ZnL5nOTinKCWo0F3rWFP1C5vO8G/zY9xUiPqhdtAzUE8F8\\nJsDsaHzL4A50Pjv7gWdqp0Q8xkD2b80J5NYYBpNDcsuqpm0a1tst/WguFLxnp9Z8dfk1WktCEDjn\\nEElQ6IIhdHjX421WTmQnukjXDQSXkKUhFweZm5Oixjzo0h0hZH4+6MzB5gAGDVoibEFQktvdlp/v\\nPuP3JzQ+Mpsu+fbb7/OOwrcs5zPq+YSqmqBFiUsREUElKMycWFq62nGYb1Glpo8DKUZenn/DxfJr\\n/rJaM7RgRMWL11cUtqIoShQaO7WY0tDrHZu+J+w86+sVf3nzr7y/fsPBDVTzGRcXL/j2m+9ZLBeE\\n3YGm0qi6zgEGYxamFBIl1CgGyIu4TNnaoCgr9LymnFbYKKmMwe8PiA/X2MkUO19i5yeIsxNUUbJM\\ngh92LXdC065u6IeUG2l1Qewcr5dLLudLjDDM6wWVcQTvSVEjU+YpH8o/KUBrMAa0gpAbwAT/0I7R\\nyrCYzem6JldbwSOSxgjFspzycuyR/O2wpUmRQ5K4KPnT3/3AP/ynf+Dy/JSynCCMQquBg1ekqEee\\ndQRxLfBuvB1SIh4NoFLmYqWQaGspRUJocPGYAJ8tmvaASRFDopR5ulGm8aOQiBOJsYbF+QnfHloO\\nbUfbO5wPBASqqrHTBcXsHG1Nlvg5T/QB1zv6wWXTNCEBDaOHiZICWwm0Hs2oSKTgcENDs18RQkfE\\nZw25kQ/n3cd4TAvPwDkGJnvnGHpH2/SEmO1qwxgA7UPKAL7fclJq5uUy7yhE3vVLlXe/8kFKmPNF\\nB+fYHlpuNnvum54WhTAGYwr2Nzd80n+mOr3i5ExTlVPiQxbC88czFuEBxB/L0BgS3jna/QZVWqpq\\nxrbtEOWExYsx+1bpTD3/xuN3AXKt0khdJBCeod+xWTXsd/eU5ZSymGKLkmZ/T4yRoW3ZbTOYL5cv\\nmC3OKar6Yfz++TYmPTlpj93gR0omHYso6jIynymms0QQWwa/pw9NlrHFnExjlCQJi1ESkkZhKFVF\\nqWcMPlAUNZPplP3hQNsc6LuWru8JztOlA7v9PbPpNPuOjAkjSmi8D0g3ENwwRo3loQM3uPzjyogL\\nLWLIW2klyzFt/RhLFnLat8pmSykJtFbYsqCazqgWS7wqWMWI2O+op6ecLV5y+eIlu+6eJBSni1Ns\\nPUVJk10cB0nyCo2m1DMoKoY60LgtdII+9pS25mL5kkl5QtdG9puWlCKTr2bUk2kOAFARVCDKSBsc\\noVe4xrHd3nNo96AEpy9ecHZ6xsurl3z99TcIEWndBhcS2hYY8/8x9569lV1Zmuaz3XHX0UQwvKRU\\nKtVZNVXdQPUMMMD8j0H/6AHGNKarK1NSyoZjBMnrjtl2PqxzyVBmZX/VHICBMIxL8p591l77Xa+p\\nThks9x8yL0j3Q3DxqdG42rFadCyyEybGMFDnwurDR5pHj7BXT6BbQlWxRPMqFn7yif1fNMNxS6Mr\\nqspTajOLhVqsdtja4kzFFD0+arI1RKHzyHekHjxz7ikTudwH+KJks1l3K8Gvo6ekRMnSbdXKcqZq\\n9tnyo4+MPjDZhvXFBV9/+QVff/k568USY2uKMRidickTU8FnK7bIRozVok/3eSoFGdDFIENKEOzW\\nGDEISzngumo+4WpGFL4UXIFiCk5ljEroLP45Va0wzrFYr0gp4WMSA7ckeDnVgmRboq6xtRV2F5ro\\nA9PkmcIcPmIMVlfkPEvrtQjgY0zkKPYJOXqOh1vev3tPTBPG5pmGOlsbl5MM/kRskPc6qzlWscAU\\nIzEGmY3kh0IeYyIrsfcVqEzYTlrr2Sdd7mcmwhwUPY4T20PPzf7IwUcCFtu0bM42EDyl3xH6HXlz\\nKZtq+bTm/I+v02dqFDEGxv5I9CO6NlTGiHlX1dBVzf2r/T0DxN+kkK+Wdl7rckzOpczG8ZE4ZVJT\\nMGtJ8JjiwNtf/sy7dz+zPnvMy5df8dmX/wnnhAZ2kqF/WswfQlDlDHPqzsXSNmNtoWkzFxeFxcJh\\nrebgD0zpiE+BWDKJSCqemAPW1hhdQa6pdceyWtPV54SYOTZHVsslr9++YQwe17ZoK57M2cM4jfgw\\n0jbCiU7JEcKEHwOUTKzDaZo3S4gjVV1RNZp9/566WlGZJZVezIPOTMoCS2kUvgRKkqe1MhWrpeHy\\n0WOevviCrmoxy4bdMfGH5//I88evWLRr0IFUHMYpoCGEhPc905AgieWu1S1drUgLOIQtxzDiMzy7\\nfMXZ8hE5a477iQ/vb4hh4vmz5yw3HYtNS8wDR78nDgFXOZZtJyETVc2zZy94ap6xXq/ZrFYsuyVN\\n1bLffiBue9R+orrQVNpIriMPIhE+wT4f2EuFuhQ2i461QVwQKTQxcHl3h9tuUcNARmOajvax5Wq5\\n4MtcOKD5b//vnzBKPM1tpci6kACjZ9aMhqQtqbHkrkZ1NcU6VFbYWHA5o8cJVTIqRggeYhSoxYg1\\n7sq20BWmNJJCRpUk8WZjwKRCXTQ2F4axwLrlP3z5Fb/77DMeX15iTIWyBmUEE16WLNBjyKSkxLlP\\nK8bJU6JACLrS5JjoxyAsCp0pJIiFYZDszM60qMaAVRQNPsOUFV5lljrRzoX2pFakPNi4OqMx2sm7\\nbzWTaslFDN/sjDbpIoPgpq6puhZrHU4rDOLdX3QiqcA4ZlJI5JCgUmjryEXz/c9vSHhW65bHF2c4\\nfQKzTurv+UQykyC0NtRzNJ42mjhj6tqYWTUqsEkaRZnrNBKFaKwMadVM8yvzZpEzMUb648B2f2Tb\\nj3jE0bPpOl58/hwbRrRxNMqj52CZv+aNn65P+eMzkHKatKAVhGlkv72lazWVlfBnreWEoJS6Fwvq\\nX7/s/fWbSfTvMa45+DYlBRlCHtEZRgXaSDKe0YkQJw7bW81r84kAACAASURBVN7yLUZLKsr501dY\\nbedprwwztFZY6+6P4BKvpGfJbaFyiW6ZWa2RQFarScWLsCaLmlIpg7IK6kLK4qtSVCAli84Ntb6g\\nc48IjWJsA/t+z4tHz1jYhn440jYLcIriNcZWJBK+jGjbYoslpppFu0JXmaISEVDW0C0XEgZgDG3T\\noKzBOUvjamxpsFoc7KYYMA60jXhdESqP1oquXRJyxNBh/nmBxuLqhmbZ8urqdywXKxIT1jmYTwEx\\n9UzTxDAe8eGALoakLUUFitZk5TkMd9wdbvA54qxBkUhxwDpNCIH9Ycsxbnlzk6iPFctFh6kcVd1i\\nrMPSUbuW9vEV67QjlIFcMpNHDN3rmjIaOlpWzYrGWBF8zAVcA2oWo9wX8pykI02FGsXKGM5qDdlg\\n64Z6vaJ+/Bj15Bl5cwb7W8LtHenmI2p7x+Ptji+dJb54yrhzxJtMyTvu+iPX21sety3q7Bx/vmRf\\nFaJxUFfYpmYqGhWhnhLr2wPNMGLnFPkZuJZlbi04izIamxXBZw6HI41tcLbGuBpXErWtaL1l07R0\\nz57zD3/8mrOzM5Q2KGU5pQ2VorC60NjEwkRKMUxBM2wV0yD0QldbTDSkIFL1unEooyVirUAwMkwt\\npRB9lPdvadBFk7NlCnLfvYZaiQujLgowoOcZgZYNNitDwtEHmEqiaCOfVzQlz8/4zL8uSTpmPeex\\n5jmph8RM30z4kCgxsrvb8vrNNdt+T91WPLna8fLpBRfrBeZUaE8noNnQqiAQkjaadtHhvafMiURl\\ndldMyROTR+dEmmcep81Bwr7VrNz0eC8uirvjEeMMZ5sVw24kBE8ej/ih59GjDevVhmzBzgQE/TcZ\\nm3+LGTz86TTby+ToCeNAvVpSV0ZCbJSYT3NSfP8Prt+kkMcgHtxKaVJMhFiIaeZwktEZ0gzqi7Vs\\nIsUEReGnlpuPP4MuRBJ11Yp380yWr1xN0y6xtkLPvgzqNMixhbYLdAvoWktlDbkEfDwSsyfnDEj0\\nltJGFFtNnqlvGYoci0t2GF3TVEsWzZquXnJ1/ozWLrjb35FKJPlMMoK/xRIhD9TKkosiRXB1i7ES\\n/YYW0YvRHU21EEzY6Pk9svdiB60rjK5IahJ/CZeAcB9CUVcLbIlUZx3n7RP2x5GYClXV0HYdusoc\\nhx0hj/OijuKAN/b0Y4/3vQx1bUUsEwVFLCPHccfhuCOWjFGZlCe8LyhT8F7yIn/48WdWZws26zXO\\nPWfVtvL9qAada8g1CodmIIeefhowxmGaCtU6jHLUpmHZraiNwVCQvuQT86LTyYU5Tm/Omiwh4Yyl\\nq1tQClPX2G4hgpXdjvjjD4ShJ1x/IN98RO33WBQXBV7lyBbFvhh6D/s88L7c8cjUNOs1eVEzrgzZ\\nWLAyF8lZic9KSVR+QB+PqBDvQwTUPMAq2oMxmMqhyVRRMYZMmWmKtqopTtPGwNoteHl5zuazz3jx\\n7Cld10lDcRrw31NVBfroTGIKgWFS9F6Rc0JZUClRkvi45JPopBRIRUR3Ss06hjRTZCHHjEbCq1MI\\nTGiyUkQydTE4mAuU4d7bX2lSMUzR0k8DhykQS8HZx1hlyVmyUXVRmJkeG8lghdVTciJHSdPKKQkj\\nK2fCOLLb7vlwu+Vmt8U4w+Q9XetYdY2cMDgV8lMRn09qukgN0QIzljLL7wtQ5oi32XNIzYNdMxMe\\nyEUglTQX8mkS75sYaaqK85Xl5hjJcWQYeo77A/rqnG7RzKZpDwFt/z7h8Nd/I8NP+d6EipxQRJrK\\nzMHilspqCYT51ev8+yX9Nynk0xCwTopPDJlxTIRUqOYABYue8WCh16XoSTFjbMNifc447Xnz+k/c\\n3b2natYY60AlnDN03ZrV+or12RV1081hAIIPLtpMu5poGodFjs5T7On9HSH72RVN47RGFYXVhkXT\\nEkKQhakgxMDgR5YpoC2SvtKuBHJpz7k4f8bN7Uf2+x1TkS4ppUBJAa0tIUoizdK5OVlEAh1AVGaN\\n65BDXkEbR/CBwzDQmAqj27kTR/I4tZGHgSxJInmmrTUdFxdXfP/6Zz7c3tIPntXa4VTibnqHD5Kc\\nTs5zIR8Zh5HoPcXVpNLg8xGSJqSeceoZhiOpZAqiRh2mRFaJ0Xuu394y+H/l1e+e89UfKrRxVK6h\\ntQtsXpJGxXEI9OOefnxLP95wDEceXT5htVzTrRdMYaBqKlbLBU5p9Dx0nV13Zc41u9ZJXRIq2egn\\n+n4kugV2s6FpKkqWwVZ//YHp7VsmPzHtdsRhIId4qmtopXjsDJ3RNMeBd31iKIH3U2aRFI9ePaGx\\nZ5QZFsNYshGf9DwFpmHAhwnrJ3TIcyPwIFbJPpFTwlmHqSpqY9GmxdgaXTl002FKy0ppLs8PXH75\\nBWe//5zFosOcJoCnQpSzdI6piPmVybhwJPeJ3htcU2HQ+BDR92nvWuYvMYmRV21IOeNzIOCpdIsq\\nhtgnbMV911+0IpqCT5oxQ4Vi4SKmiNe8VopUDD4p+lDo+5Hbuzs+7vesFitqV5NyRlkhNNgE4xRE\\noKU0rlKipcgy4ItRtBDFKKZJ8kMP/cDkPSZpbj7esbs6YzhfY+rqfuhdspidnSC3nONpkjInZc36\\nCoSXbSgkrTGUmTvvqIzBIMVUxmKZ4D3BTxIpaA2dseCgrQbIPcNxYHuzZ3915Oxsg1kuUCeE4T6d\\n7NRt/9rV8CR4ZP6eipJTnFGFpjIsGkfX1nRdTTOOxCkTCkJvnYe7/9712/iRzzzblAoxFhmaxMI0\\nm8GrHJnCRAyRVIJMs2MijD3bm7eg5Q3bbW8wpkFRyGWSIaZ1WNux2lzgqhptLM+ffMbvfv8Z56+e\\nEJFoJ50MEY9PA1Me55snR01rnOzyKt+rvRrT0NUXWNMQ0sTt4TXFDIxpj668mNZr5rSUBcbANA3U\\ndSU8YqspKWNtxXopkuPaKWyT8NmLoX1xVLRish89/eCJ0c83+o6AwqqMMtJpFZXQNpHiSEyC0Rrj\\nJEz3OPB//+t/Zewnvv7ya0I6EEbP6HtilIdIZXF2OyUXKSM2BNZZwR2thPgejwNxCjSdWPjm7Mkx\\nUTkr5kWrjs+/eMkf/+EPfP7FK9q2Y5wGxuMA/oY0QQ5iPKRcoDuraWzFZrOkXhpCObL78AuL2wNP\\ndD2nm5c5B5G5eKv7AAKho4nB/ziOvL65Y1I9RHh1toEkToIhitNd9J44TiQfSSGQUyTGRCzi0+6s\\nZZkTuXYch4kyeD6ULe7bH8EY6uZ3mOUpA9SgcsQME4vdSDMllA/0kyfHBxzTaPPgz2EzNiXhUyuh\\nvqqUUTlxPOzY3t7iU+Bs2bHerGe9gtgpnyxOKZJcc4JuSkpUKlGVEcaR41RTTpgqlmbR0C4advs7\\nVJCAbGsN0zhyPO5pnGW10NSVWDCnlFBGo610seXeIKoQUaBqKlNwRmiVhyHRh0LAsD47p1ttuPKR\\nzWIlNMiSSUXPWK9Gkcg5E32GxH1xTSESvBdMW2mG7Y5+e8e6rhgGwxgSxhVCyjKstJ924XKCBmYG\\nz8mPJJNTui/2Eus3Q17znyujqQwYlUU9nBFhXYyitk5ij9FUFl9kGGyMkU45B8Z+z+uf3uKDZvVq\\nwVmbabsyK+9Pc7vTvXuofSd8/ITtn9a21prKQo6BkhPGqHvs/mHG97cgzen6TQq5MSf61JwlWdSM\\nmxWUVsSU2B16Of6pQl2Jsiv6ieP+BmXlgUg5QpGuI4YJpU4cck27WOJcRdt0vHh8QddBuzBMvqJS\\nLZVZ4MvhvjCUotHKzsf2TCzC0VaqUBmH1i1O12hlyNlzGA5MecZ7VURaSKRYO4WrxGWtlIKhonEN\\nIUeUdrhmiaGi0pZKF0g7SbkJ4qNsjaMzNbpMTLknlJ5QjvgsZkpG1WSVKSXi00BIo/BtscQYOOxH\\n3r655fXbn1lUCxZdTUoTPh2ZhpGYpYM3mHkRGZyrKHOSjKsczlYiy06R3XZPDJG2XqENFB5wVpSi\\nbmpevHrK0+dXrM8WTHFkHI4EH9CpwpYKp2uqpsK2Nbq2qBoWyw5jFcfDLYfr97hjoDp/9kAtZaaS\\nzuu+zNzoh0IeiZNnHEZ6lRnHieg9BlHmWWPucx6tsZSum/F1Ga7HEMghEnJmkRMLZ9mqA/0wcRw9\\n2/e36MUKtT7DrmrKokY1FWqcsHc97vqAOvSk0RNiEGoqzHwy7o/+Oc1sCaWwbi62WRKB7m5v+XB7\\nw6gVrutou/ZeWCJH9SzPx/xeyM+fyDFIFF4YSYc79tGCa2nrBUUJXEdJ9HvhS3erGoyiIP4ku+0R\\njXgcaWtQRQo0Wt07/pUiHjSxiCd40Uri/FKi9xFfwDaGytXoGQI02tw3BznJwFNpgVFPCslsRPUq\\nHO2REIMU9NEzHnYQRy5WHdtjz3EKjFNgdxjYHXpaox/Yag94230BFYhV7nGZC0z5hBOeQkI5gzMK\\nC5KROeP4pCKngxTldKWgqgwlKZgkj7aUjNGZykI/jtwePZ1boYw79eD8qnL/TQP90KE/gCUFZzXL\\nrqKtLI3T1E7P7qMCGatPBs7/3vXbFHJXCYyRZeeX44cMtqwRbuvt7oDWisoZtHLz/CgyjHuUVmij\\ncE4RM3if8GOYMxjlEYjR0zUNS2d5+eIRV08uMNZQ5yWd3dC6DWVM6OxQyVIZ6ZoVEOJI8T0hSYSW\\nsZqkZbJubKKowpiPfNxfM8aermtxzqBsYQqerBI+Hdkeb+iqJeuuonVnlNADDqcbShZRhskFxpGw\\nGxmOE057zs7OWJ+v2LTn7IZb7obAkHeoKN14zUboymWiHw+kIuwQYzX9vueXt7/wf/xf/w9n7oIX\\nl89ZrTpxNhx6hl4UokZbsCJpds5gXAUqU9tq9qhYMBwG+uHI3e2WYhJt285FVni74xQIIWKt4dGT\\nC9pVhc8Dd7s7hkHi4Tq3oO4alssVq8WaYkeymSg60jQNeQps371jeH/DpjjslZ1x4U97FzX7XUjm\\nZooSEJBCBJ+ocqZzEr5QVME6R9U0Yi+rBLHOgHNWmE6mJqdA9BN+HAi7A+M4csyRlau5vtvxZn9k\\nO3q4vsGaHzC1wW463Nmadj/g9gP6MDD2g3R9WmPnjl3P4hY5+ovHiphWCZ9bWznxpGniw8cbXm/v\\nUC+eo9oGW1ezSlB+esF0JeTj5MiXTgUxRtI04nc79hO45TmLbkVKhfEwMO6PRA+LJx3L8xUhF2wv\\nCtv9YcBq8UFpXCuFXyvESTCToxh/pSgzomAdJWlUgugjUzIYp2kXlSha5xOej/6eIqji7Iyo1MzP\\nzoTJk60ma0UpkXE6EmMmhcz+4wfiuKerFVfnK663e653e4Yxcv1hy6qtWdUO507DcH1vdaFOIsHT\\nIPxTv/F5MFpyIs0nZ3sSdp3gupQpSSiHOSUoYrplrRG73pwYQ2AMnlwyZ+uGaBtoF2yuXlK1S/4m\\nupLTGJZfiRRli5Z4OqWlBtZO40zLxbpls6hpnZlDwsUYTzj4f5/S+JsU8r73p9MiKPEKVrMU98TD\\ndM5ymkinPOufZgaDUdJxlShdmlEyvCzzNDslRY5wvnnMP/+nf+HpZ89oV43EtzVLKtVRULNoAEqp\\n0MXhbDN7qoxo3WC0QCc5gE+JKQ8EHUh4+rLjz9/+G9c313R1y/MXz7m8vGTRdfg0kY6Zu+OOpRs4\\nWyU0FUp56fgZEF4b4BWdL3x8/Z7vvv2OrC3njy65ePKE3Czpx5FhvMPbWy4uF9jzCmfXKKWxuqKt\\nO4qKaCvF73A48vH9Rz6+fs/65YaiMtd31+z7D+z7G47jFlfXYlFLoWBBQ5b5OAqFUyPOjNzcXfP9\\nj3/hbr+lXQi9zAcP6cD2cOT9+3cc9sfZrN/T9wdSqrDGcrY5o65qls2aVXtJV5/R1h1JDyR6Qhkg\\nJPbvP/LLn77hfPC0iw4QnFYYKsj3WPJM0RZnwZLk1HQKcxhTYps9N33P0mp8yeSYpICnk0eHYXV2\\nRrvZwLKhdCuSu2ByFW6/ZT0OrFJh4T3q/TX7f/uGEiJmf6SubgS79xF1e6DKYJJAA4uulRBfY9GV\\ng6qiuFowaj+AH6XrnWmCJ/+PPMMK70LijXF88eyKarXAnMIVZt8NqUVispvnE0mKkRgCKcpHTh5r\\na5G51xXj5GmWS9arFSprnHPESTpYg6TatEvLar2kWbbzkDMTwxyePYdoK20wxsma1TCOnjwXOlfV\\nVK6ihHkgOzNPconC9AJSyaiY0chr5BTx00CJCqUKcfK8e3NNmkZMiZRpZFlrOrfmuNvhhOpNKYrb\\nux1/KVJQn1ys2SxbKmsf8GYjWPNpjpJzvmd7pAw5JYKfSN6DlQLJTDPMKQkrKqW5GxeV6Em/kGMm\\n+IAPkTTPpMbkefXl51z97j/StpLmdcoO+BWUov62h1YK2XDkW0dpwxQyu/2BpnZs1pGmUvdUQ3WC\\n2YB7/+2/un6TQr5enIsAJsuCOfkGAzNNKBNzIpfThFvNqSEz4T6fWMRALsIBtZoY08zuMJyvz/n8\\ny8/56p++YnXeUTmD0xWVbVFFEeNAZBK5t+swWsQfCg0lYTRUTkNVUNlSqRanWlKKhBQYw0GEStsd\\nH8aPDMPE9m7Ho8sLiilstzsOh56xEROkQkSpRNYBXzykgC4dteroXE2lHWkMjMOeaXfg5sMN1cUj\\nsgIfBw75A3WjeXQpBeHEVKlURUK4vikmhn6gPxxJ/cR6ueb88pKqqqlCQ13VhCyRcyLGSOKdXcS3\\nRSlNUpEpeqzvudvd8u7DO8ZppOqsnHRCpB9u+fnn9/z0/U/stzuMU3jvpZOBGaox1HVF09bUTUvT\\ntDR1SySLMdRxYn99w/sffuLN9z+yaS5wa/twdCxzN1NO3HHZtE8fOSVKCuQQGEPE+4jhjo0qtEqG\\naVkZiBFdCrquyJMneU8JHtW2qKbFNUJBtVOHDgKlBaMZt3s+Xn+U47iPtGgWWrj72mpUpSla44zk\\nXGojir9cObJ1aFNB6FBhRJ0GlRSYaYopJo4h8DFl9nXF5ukVzaKbhS+a08jsfjObN7SU4xygIMyK\\naRwZhwG7XNA2DW3b4pqabtGxXi3JUdLqSy4oramqmsVygWssTVdhnJ6ZI8zPDvf548aq2SOliH1D\\nkGe0qgyuFiWk5FzO0MUsVjuRZShZ/OsLZCIxeyY/kUikGBgPRz6++YXGFC6WDVWjqSvL5PnV6VoB\\n0xi4KXugMA4Dj8/XPDrfUFnh13+C00rRm38gNc+LY4hMk0eniMpZILCSZ9th2QBSkuH0qRYxs1lS\\nkijBMQRSkZZn10+Ydsnq4rF4088F+69hk9Na/vQ6RcSf/odSmpQL/eC56yeexIi1s2iKE8z499MV\\n4Dcq5H/48mt8mITrOUthc0koLYViGCYOx3HOvpwBf13mHwzBwZR0KXMjKrJkiVinchVffvUFX//z\\n73n+9RWuku6pti1GW0IamfKOyAHjoHPt7LAmKSNSeAtGV3TthlqtqPUKpxyjP+D7gTwF1u2K9WLN\\njze/8N23f+GXn37h4vycxWpBzInxMBFWEymNpHwEPZLKREiB7DWYMxbWUS8WnD9+wtXTO25/+pm3\\n76/Z/vyGp1/01OuWaCJ3+xuevTynqitJT7EWbUAnMxtnCewwDANh8rTK8uzqCZ999jnr1YrFouEw\\n1OzGijAvXrJIg3PJZJ2wVuTJMUUm33Pod+wOe9KMnQJ4H3j39gPf/Olbvv/mB4Z+4OzRinGY0Bja\\npmUMgUwmpIgPE8lJejkacooM/YGPb97y+pvvePf9T9y+uearl+t7poH4Fp/kEsynt5lylkUvINCK\\nJ/mJaZK5gB8jL2uLbhuscSTrcNpQKaibGpQiTBNZa0zdYKqKrshcBusoUYQiF8uW5uVzfEgcjz2D\\nsti2Y3G2xtVuVo9oUNKzyZRdg7WSNakU2taUtoGyQMVJjo8poXpPiZEQInfTyJZC7DoeP7mk61oZ\\nzOmT997DVUomF+kYYwyEEBjHkcPhyG5/oFs/kQ27qlm0NXVj0ZUwe1ACRWI0TddiKmFhGKvn1xTo\\nSs0VaGY6YqwUkjhE9rs7wFK3Le2qvRerkEURmTmdgBQUPUvJBVrKJYhhVpQA8Sl5xuOBw81HDte/\\nsL7ccLU+o+1avA/4cbyX3ZcszpKlFPwUuL6+Y78/sNv3aGM4W7a0zgrOPUMoesbD1dwhlywbZwyR\\nioQqGVOydMXzYBTU3Bx8MpxU0jzEnJhSZIjh3u1w3weOQRGUw51W6txofuqAeHovfwWb/7X6c/73\\nGGE3BvqYpFlTp3lA/pvX+OvrNynk/+V//y+knInzDpiyYJ4lS8Crnzz9JKyVEAIhTKRZ9ZhiJETx\\n7k5JpLQ+eI7DQH/sKaXQrVr+p3/5R559foWPI7WtiTnQ+54Qbhn8jjFuZ14pUAyVrkkx40MgREn2\\nNgpq46gaN/O7HYmESy02dKzac56eGaxa8v76Dfv9ll9+ecuqW6GtJaTAfrfncNgyxTNiHmWISmII\\nAZUty2rNVCoWFxe8+ur3bD+8Zz9ObHc9n0fFRV1hVg3LR47z8/XMVhEo5D5s456OBbuPdzKky4ZF\\ns2bRbFDJUpslpUlgJU0+xkiKQYQaSOfE7DbnlMFmR21aFs0KZysqW1GS4i/f/MAP37zmuz/9zHAY\\nCSkxDJ7rtx959eo5i+WShZHvTyuotaNbtEILJHDz4R1vfvyW1998y4f377m72dL3CYVYBZ/6OzlO\\n3uel3+PDOSVyiqKenAJ5kMLYGcOjRcv5ZonLWYQb1lAtW1zb4qxDzUfzsN+Kp3YYMc7SuGqmPCZs\\nKfIePL7g85z5+OGWfrsjJoF1MPa+cBetUPc2asz2xMx4AChjydrJ54UJFQtUDrQmTJ4Phx5VOTbn\\nGxarNa6q5s0BEd7MCsZcpCPP8zMTYhBPn92Bt+8/8O2Pb7iyK0IxHPcHioK6rVksFrSN2F2Y2mFt\\nQFeGuhKZvLg0zjj8XDhTlshBrSGEwn7f8/H6Pa9//pZmecbjp8+5fHwuroPMLBcUMSSG8UBOM3at\\n574zC8yZggwaY4r0+4Hbt285Xr/m8brj+dUF5+slMSV8TrN/epitJ2QTOxls5JyZpsh2N/Dm/S1O\\nK9yyhXzyHBLwUpUim6rmPks1xUTWD4wROezMO9fc+Co1b9LayAaCPFd5zkooCnRds372O1YXT6mb\\njjLnhZ7K81+V6Ye/+1V1/xRPn08OWkkuaCrE2WxM2D0Pn/l3kJXfppB/9dUf5oV5ekDLTNWa/RuS\\n3PB0oo+F2QRnDlWNUTqT06ILwTMMwoVOOaEqzfnFmmE/8v7de7pqgTWt2LzaDHUEJ/SimArRJ1Kf\\n8KOfTwkz00EbWttQLh32rMNYWRzGyU3WWvjSm8WKEiO6GK5vPtAfPTH0DH3Ps+WVbD5hmnfq+chM\\npKhI1hGvMqo21JuG0lnMpqapFCwUbuNYXy05X1a0m1asPbWRI70pBIRpo60FY2mrNcv2DHfRsWjP\\nMKolRdC0WB2w6igCJJfAZFRxYjiEF6VdTlDAmY6uPmO1vKSrlySf+fD2lt3Nlnevb9jdDeJkpxQp\\nKm4+7DkcxAytrZtZ4GRwyuJcLXLn/ZYPv7zh+ue33L7fMh4jqjQs2o62WVA5d7/I5aFCuLbzMCrP\\nm3lOkRwDafLkwaNiYulqnqyXnJ9vMDHS9wPTLOJKORKVxc0QiFWa3HQUZVAhSLo9zKZmQh2sqprH\\nlxdU1rKdPWyY7Vhn/1q5j0qJB0tVEdtOWBo5Y2ehZ0my5SpboebNRI0T/nDktu9pFy3rx49om2b2\\nFJdCouYqJD73Ao2cmp8QIt577nY7bndHDj7xsqklb9UptLU4V6GUJYRISD2MCuWS2OjqBlWMSNSt\\nDJfVvQozzLatkeMY8VOComnqdqbSinLTMKf1zPBPnr+v2YVKnkOFoMxRCzNs3oiPux27D9eE7S0X\\nL37PZrXAGSMb9Pw5cp/n/zOLdu7nBXPKzzCMeB/F0vceVpH1o5HhK1pRYrofkN/j0vw6Om0mhcyu\\nhCfK1ENOaUqSkIRxdKtzXn71j2wun2CMmzeFTzr58jDgzHler1mox3JS+bS9/gRDV4oQE8MU6cdA\\nzHNmQWHOUHhwSvzr67cxzXLyZWUjFIzoHls6HetOQ58ZWzwdtB9+kPkBp8gCTPIxxcBx7Pnhl+/4\\n4afv+OX1j1BO6TyJ9XnH+vGS7qIVUUkIDIeBu7c3jIeRFCPWClbXVA2dW2N9S02DrRt0V0hlwscJ\\nnzy5JKyGR+ePqVxLSIXjvmd/t+XD63f88dXvIWb8OOHqSgIjAGsNxkFxmaBlIBdtwl40XOorNqqg\\nNzXqqqZ9tmF9vkGm8pbaNDRVJ4V8Oohizxms7bh69Ip4tPjB03YbUtSkBNbWqFKRokJhcLrG1IZK\\nt6SSmdJAiB7vB1L0uHrBojtntbigqxdsdzdsP+64vd4xDZGM5FzWdUvdLAle48dCCgqjKxb1ksY1\\nwqdOln5/5PqXN3x8fc3htqdQ0TY1y7qiNUvOzzbUlbt/GH/NIS+zEjDNw7ZIChN5nKSQh8K6djxZ\\nLjk/W2OA5tiz2+4IfhTbAqVw6zVuvaZanIGtIAXY31HCjDn3PRSFsY66zTRtjbs4Y+UcTin0nM95\\nDz/MFMNY1wzrNeOjJxRrscFjb27QuyNMnlhb6Jaoppb5y+0tA4XdOLJ++pgXT66oKitrXuv7TpzT\\nM5FPsyNpbEKMTD5ws92xHz1useHp8xc8ffEUWznqdoXRjhQKd9st/fHI5D1ZRZxzONdglaNtFzSL\\nDtdUWCtfN8fAcZzY7g8chz1NtaJdrvhi/UdUJfbBOSHeL0pL9FsWhSb385ZITAKPyczJonK6hzEO\\ndzcMuztc9GxWC5q6noNRHu496RSxJlBGPkEmc8EsswYg53yvi9Cz2vM0jziljIkdxVzs55OCLqeT\\nH/NaewC2T016KeK5HnMhpILPGVXVrC6v+N3X/8TZKFPr1QAAIABJREFUxeN7e5AH7vj8OrOfeZ6f\\nKe9H2m6Nc83sac/9+lYnL6EyC9lGz74P+DTDRZ9c5d+v47+RIEjPRzpksClYmnQFU99TUqZdr2ch\\nAQ8m78ibe8IT5O8LBY0xQuVprME6w1eff8XV+WO+evkHPt5e8+1fvuXf/vwnvvvTtyQiqjZzVqhM\\nkPGRtq1pFxVFQbtcsO7WPH3+kvX5gqgP7I/vONxsOQx3HI470A6ra5p2TciJkBOXlxcM+x7fj0x7\\nz+31npv3O1bnS5ZrhWsdpjK0pkOjGfOBWrU0TcdFe8V//t/+Vwa/Z4oDWWesE263VWIIVOmOpb6g\\nsSuKjlhzQJckO7ZSbB6tMVahsTRLQ8hHKttiXSabgo6glZmFPw6nHZaCylClirqy5ORZNA3mo1gO\\nD/1RsjBDEEBHi+9GVTV89vkrvvr69zx/+ZSv/8PvefnkJZVr6eoOraH3N/ipZxoPRO9puyVPXtQY\\nV7GsOxb1mmV1xtXxHU0a7yf0p478NNYW8Yc8jCkm0uTxw8jYj4whoZCkH50VrqnRtqJyNT54ilZU\\nyxV2scQsFrCopRj0keRHhv2e6diT+4FUIGpNNIamaajNXMSbGuWE3qjm9atSls3gmIXh8egZbM5R\\nVjNk6bxVSuQXL9CXl+imJux25N2e28OR/TjxYtlxcXmGUnP2qJkH/HM9OAUA5xTJIQjlMIhn/Ifb\\nPVE7Pv/95zx7+RkXF2ek4FHKMo6e3WHP9Yd3eB9ROCpnSTmRp4HDtGdX7ai7muW6oa4anBUrge//\\n8i3f//QTUwoY1/Hk6jn/+T/+E20tISEKM3eJGWXkvTfWUtUtMQbpQkNkmvrZL90AnnGcGKeJ490t\\nuiTOzzesVkvqykkSz5z644wW6p20+rM9wOn55x6LNkpRUiaGeM8Zn2M0ZrO1uaDPg09rLM4q7MlI\\n6zR/U9zj8afhZ85ZbG6LImWFz+Kl7jZnrC8es1xtcPbErJvX633s5FyvcqGkgB8P7Ha3xOhpuxVV\\ns8Tpk4cOfLLK54Frnn2fZguBE8OFkxf+316/DY989o+QY4NYTZaSKElx/PCRMI7UXYey9h4rvB98\\nPTQq8qtSs5+COKtptHCjrZNghOWGs7Nz2mbJstvwww/f8/rdG65vrxn6gZwipiSaoinLlnbTsVrU\\nPHI1j0PiMgy0wx2q9Kh4JI87sh+wtqCtwlpN5Sz4TFMMZ2dL7tYd3aqj6RrevHmLceBzz6svXnJx\\ndUZjarSCQiLmgTHuMc7Q1WseLZ6RyyNiGhhDT0gSJ2WUw6qKSi+onETiJeI8FBQ3wJQ97dJRuTOc\\nqsXgSk3EUohxwucDGX8faiOeG9Ln5CLQTyEQkud2d82b9z/x+u3PDONELhptahYrx3IpR+lxd+D5\\n8yv++A+/4/lnT7h69Ihlu6IyK5yxpDyQgmeapNs3zrJab1Ba03SNJLRki/WJSitskbSXk+Oh1hLw\\ne2J9nGTYKU4kHwjDRBg9jRUb2+VqgTYGbaVzNq7G5iRReVUFTszU4jiQxhF/ONDvbhn3PakfUN5T\\nUCStScagS8E68XEpVlOCIRt93y3f0wTrmrJcgtYzhxlyU6MuL9AhkRctxRrJaRUKBTYnNsuO87MV\\nq1Unm8PJ1U89bF4ng6l7/vysVPXTRMiK5eaC33/9NZuNFJbkJ8bDgX4YmI5HdPLYuSCVUBinHu89\\nw2yyZZymahyPnlxxfiE5popMDmJSF9UWpRTv715wdbZh1VZYrYkpEnMCnclJPYiBXIY4i2+iZMlC\\nFH+e0RNCIE4Di7biyYsnLBcddeUoSaiClbXUztFU4paocoJ52C7FkrnBk6LrJ8+gT6f7jJ5Dl08+\\nPUoVDDK7UErYMHoutPLvD3Xpk+1CnqmZLRVTZgoJHyLr5ZqLq6fUrhHb3ZzvcYJPm82HQX0ip0CY\\neo5F8P+ugG4XWCsmeGqGTchZbHdn9t19oXsgrnzyVX59/TbQin34slpriHEWOCQO19dM+yOPP/8C\\nZe3D4Of0+erhCMS9vSkPbxwaiiFrcUJzlaNbrnjy5AX/8Md/5rvvvuW//+m/8a9/+ld++fkndrcf\\nScOIVZpGaVbG8GrRcaUM5zHS3X3AFU/pGrCJHD1GK2LXEpWZE2sCKnm0SbS1ZXW+YHNY0x963r59\\ny7HfkRil4+8cdW3EOEkpii5MfoezjqZaYtUSY5dku8TqHUMYCDGilMWoGqNaWUB4fD7OToKJTCIm\\nj6scjW1xuUEpRUgT+35PUj1ZjWQ9yftVEioq0FGglTjO/ssDx/2Bw+3IN999x8+vf6SgaBcrUcrW\\nLV1boUh8+PkdT56c8+T5OY+fLlnUFquhsTWpBEIc8aHH+5GUI6511I3DzgrSnDzj7o74fs+l02hn\\n505JPQg+tGgL1LySc47EOJG9J4wTYfKsqprz1ZLVanmvTFRaQ1WLinhW9iWjiCXhjyPjdsthv+N2\\nOFDGgIoJp8AojXGOqhYGiJ29OHJKxGmcDZ4yaI22whsPdU1cLijBU+5uKVrsmc3lhazXw458M5IA\\nNQ3Y4cjKKF5ePeLR2Zq2qSTv85O1XOahXcmFkiLlfjYQJZx5HFG6YnN+yWefv6KqqzlKLXI87PHD\\ngIqBdd0QbcGHImykfs/usGWcRJZOFoW0Lom60oS6oaktF+sl4+GGox857G74y4/fU8Ir1Llm2WjG\\nMJByQBvh/UsAiKTrFCV2AkUJc0WU14ng0+yRHlmeL3jy/Iq2a6itJaeMUgqXInXlWLYttdGYnISP\\nXgT6OWVmkGftwtCTU5hrg5CHjFJYJX5NWkOlmambYrwnk4fM34wj55PQ/T2Ym4cQpZDHUlienXF5\\n9QyrzcPJ8dOXgHlwPA9eTyeqHBkHmUMpY7HzyesEIasZLrrvyE8cznszrpme93ewld9Iom9+9Xtj\\nDF7BbtgyxUBCchmtk5zMk6m8+tXoV65T9uanYwCBm8zDjZiNebRWfP2HP/DyxUv+53/5X/j22z/z\\nzX/9P7n+7k+8WFa82Kx4dr7icr2ga2vcSYVHJg09oSSqEGhSZNgWRmvpreVgDfsY6P3E2Asepm3B\\nNXOyymbF5188Y7npsEZsRiud0EajZ4WXznM3oSu0chQc2ojwJKoDqkSsarHGkMrI4Pcc/S192N6L\\nTUIJNNphdIWmJkbPx9stf/7hezYbw2pd0a3aewaIONdrfPIchwPb21s+vL/m3dt33F5vORw86+WK\\n54sz6s5Rt5pu0XD56Jy2rfj5+59wznJz+57NZc2iEgl/VAMh94z5lpgP+DjgYxA4Yjb693l2pNSB\\nto44Xc3UO2EaoIWGZ7QofYki2igpkXwgTSP9MLAfJhbGUuWMGgbG4OXIqsWoQi1WmMUK03aUTUdx\\nBX17gwotJkFdFEVPqDrjlKaqG+rFkur8jMY6bEqo/kiaRsZ+YNzumKZhtmm14mz48SP67VvcYolu\\nKqE/PrlC1Q15mkg/fU8axQc8FbA3N9iSef70EYvVAqcN0kNKQSyfVHShHeZZDCQBwmEKHPuecRyp\\np4k4TZTKobSlqjtW55bYebKPUAQSGGc21pRGXFBgarpmTWNqxu0Hdj//zPvv/sx+8LTLjm7Z8er5\\nc277Hp8VaX/L+wS79x9xKHwQDUbXdVRNTV3XuKpmCsNclOWZNc7ilEWhGPvINE7klCQdqBIOvrEO\\nMweGuGipa/EjWtYVNQjDa/ZkVwVUSiQPw6FHxchoDcrIO2g02JnfL+HMELQYysWU6EwtBZ0HQGMu\\nJPJ3nzJYkHUYcsKnQrvecPH4GReXjwXi+MRe9r4qFUVOnhAnYvDEMOK9ZwojztVCh5wm7sJb2nbF\\nev1kDlmX+y12IeJUaZQMlMVGYP6E/z8Jgv5a7WStJQZPfzyQSsHUtSR2zJ4basZ/1ScduLzQ/S8n\\n1Pz+9+WT3VbUdHo2valYLVdcnJ+xbGtW/sDbtOeqq7hctpwvWpZtjZ0ZDjNQK+6HGXTRVGi6kBhS\\npp4mVEn040gcJoZxYvIJJlg3Kx7/4ZLPvnjGV//4B84352xMw3ICMwsT5LitMMdMHDL9ciJoxZD8\\nHMosCUJWN+ybLA5vpSeGAzkM6FwwKs8FMKNq6VxCTLx++543799we/ORxXJN0ZaQIrFImkyOmWm6\\n47Dfc7e9Y3+348O7a96/ecdhe0QpS9OucCRcke6IUAiDxeqGbuHIOXLc3/HjN5nXfEDnVvDeMgID\\nWg3iMWItTdtinUMbRSqJWBIqe3T2GO1kQ1InBSRyF0sWAcdMBzVzcVcpMU6B7TRhK5kIGgopRRQS\\nD6eMnPZyDBK/NgzQB/Sup4qgrMN1K3LdzuvEYhRoYyjTyLQ/MAwDcb9jOoocfxxHSo6irHUVVV1j\\nfcROExwO2Ep8y9PxQLaOEgP67hbjA7pIt1imiWw0bim0QDG6mjNAT+yHT37+nMVwLOcyH/MDh2Mv\\nkyUrqfExB0rMTGNgGj1+8kQfZvEcwpxxjna5pJhCKprWLam1o9WJd28O9LsDuSjImbquefz0KauQ\\nmCaxb27qhtpVWCVYfikCmRwPI/utJBIdj3tKKdSNWCfXtYQl+9Gz3+447LZEfzKCmzFgLUCHyXJv\\nnbUsuoZFW9O42SN9ZsFYo2eRlue4PxBGyc/UWmGVxMcZo6mMZHtaOwc3JCnky2pDykWaw9N6m1Xl\\np5PQiSlCEZfNMSaitlx9/pLHT1+y6FanbpGTL87DdYLFEiFNYqnrHNZYou8J457j/h3K1YT1FXWz\\nwprVfbDFKdJJmVNq0QPh4+/BKvAbFfJSHgKSS5E3tZTCYbujFEXddZ/AJvID/DXGfyrup+QNNQ8/\\n56/wK5qOtRat9cydFtphpSuePLqkPHvM8uaSzhlJE3eGomRc8jDXlgeQmaRfFUNOmUXKtDHgfCDs\\nR/ww4UNmDJkVNY/Pz/jsy2e8/Oo5L373nCUN3THSHo7o0ZP9SEiRVAqpOpDaO/zqmh2B29BzCCOp\\nGJTusO0ThnXF0BVyOWJzoCmwokaXgNGRxhqq2mGUwYfAz2/e8f76mnpWWVpniFkGZcFHwpjYb2+5\\nu7lle3OH7z37my3jXU/xUdR/RqHCyBSO9FtPUYmb9+7/Y+69eiy5sizN7yiTV7kKTZVMZonuwjQK\\nGDQwmN8w/3peGoMBCiOqsyuZ1CFdXGHyyHk4dt2ZVTnPzAs4g2QI9/Brtu3svdf6Vj6FVQZjFGNK\\nvP/xPYdPM+PRI5OgrQTbVnG5Nmw2LZvdGnO5o1qtkCjmYLNZaJhhcoi2WeRsPLWUKUvNCBEVI1qI\\nPEMtSialCVIwpESbIklmIJVXWd+shEIYnYORYyDMI6I/gLUwzhSmpNCGtigJKbtjPZI4DfjTCXs3\\nEU49tuuZhwE72UzfA0yRF8VRZqWUt44gBNZm3o8C0tv3BFLWdMslDX75u43B40pDYRTl+bDA+X7I\\nvygXlacifv7wITLOlkM/EoQGrYkpMbsJbz3TkDXms7VLbGB+iOrCoIymLbbUq5YYJUoYVAJRZ6a4\\nA6rVClMVrLYbrp695EYYrIsMw0zTrqjKbKrzfsbakWk+0R17ulPHcX/g8LAnxkhd17Rtkw1sSuGd\\nY+h7uv0DbpogxGVYIB7PY+cDl9YqK5rqirYq6eZ5Ga/EfI0ISN4zOsesxEIKPH8sI1WpMnRKZzjf\\n7HJ84/NtnR2sSmVTk1TLrPs8TInLbiI/OG2ITCmRqoY33/wjV89fUejiEVks4HEx/fjPlMc+s+0x\\nygABSeTU3zP0D1g/sdq8RKqSoT1SGEOMjvPWKpEeI+jkci8skn/+8qHx9PpNCnmMcSm6+WkTl/HH\\n6fYBLTRV2+anWgx/cZE/nVbE+Q96+jmxPCDOn+RxgSwW67HEmKy5DiERo8AvJxzvAzalHDZLQj7G\\nGUhU9vLyGPqbd/QIqSlFbq9LXdCYmpfO0ztP5wSqabl4dsPqZk2xrdHTTNMNNIOjnD0x2HxxxvxU\\nD+OEP3W4j5/Q80QcTrw93vNhnjiYCvfZPyBffoG8uiEWilSUSFXSiIRMmjomblB8yZobapRyCLPm\\n6kLyuzevEHUH0oEUHG4PPNwf6A8zGk+ZBC82G6yZuCwLXlxfMNhAu9qx215hh55fvv+Od9/9RDcO\\nmV5X5oVUVZdoo4k+8fCpY9pPbKXhszeX/G6140oUVLOluD9gjj1oRRRgFoJhNpXUUEZSkW+KmDKR\\nkuAWGFMu5EUSYArkao148Zztxz3rTwdKFzF1TbHeYOpquUlzsn30eSnqDweCy7F6RldoPMk7bEq4\\nacJOc565O5cRtzFAzLyNarVhdWkwZYkuSwqTVQ8hJmY7M8wz3Tgwe4e1Dm8t0eZxng8ZERBTwpOw\\nAm4RyJsr/vmz59RSsFoOLYKnyz38yj6eFlCW8w7nLN048enQ06k1VTfx/pePFI2hrGuqdk2z2i33\\nymJiSvmBIqQkO6IjpBxzprVC6cTq5ppxHJG6QC9jj+ox3CKPkIxRpJiYxoCzBYUrqauKwqypm4HN\\n1Yb2dkW3PzKdOm5/eYuzMzGGLMGXAhF8fqiSFq07nGWC565bKUlRZBbMdrdhb+fM+MmzpizRBILL\\n/BPhwah8yDo7Y+1ygI2CPOP2gUjW4Uul0KpA6QKlTWbhEBYZtEQsck8bIv0YCMWKdnvFm6/+wGq9\\nwfuJEGNGZkv9yMw/F9l5OLB/eMfDw3ticszzSN8d6A97pmkgEKirS/zcc9i/g2gznmMaMqAr5b3A\\n+eEkBI8H1r+pZef5aXa+2ELMZpHhcOT65gWr3WZxmZ1P2uFXIxaZ51O/ru//bvP8/9+AZJMPi/FI\\nKg2mwClNHAeCFYTCEItICJGiiGglnz6VEMR8QMQtTJgksqypkoIkc0u/awuaVcu21DBb0oMn7RN1\\nUBRRoM4t2aLG8dZmCt84EV3AkLgAvjQNaxR3QvPOOj4ej/SqoL664mvd8Lpu0CI9hkVHEt/P8G2a\\n6K3juG65ahq+XO2IQ0c6nIjzhLi7ZzNM7LxAR4dKedEnZUEoDZPy3PqRsqjYrdcoA8+fX/AVLzgG\\nx+1p5O40YW1CRocxkcoYtqsaXdVcFjWfXa54Xlc0QqFcRLiZlKZMUViMYNLlTMlQWuLmilCd2RiZ\\nYnd2OZ5vcK3V0/zy6oov//A7yqpGHTq2V9ew3iALnefozme2yrI8UmWJLMt84lM6jzhSQoaQZ6ra\\nEMoyS8/SgqlaFq5a5k4t73P0kmWZDwGmLDB1RelzKLF1lnmecKeesR8YrKOfJ4YQ6GLkEAO3UrFa\\nr/lnU+Rr8Fy9l2viPGR5KsZhYag77DzRdz2fHk7MpUHfd7z/+ROqNDnrtchmLLkcdv4yFu1p5CiS\\nRKiMsJUmc3qCj0g5Z2WHBCkPi1BCZjOT9Av46qwbz9JhH3IkYsJjTMFqvaEyBWNRMJxO9N2ReRwI\\ndiZ6i/QWrRVlWT5+nY9LbrkY3oymrSt2mzX3s82GuuAWRUc+iEkB3kZm75ljXlQ/KlKWuufJqpMQ\\nA1qJjGzQmVKppFri9AQinFUxS/BbApcSU5KY7Y7t9WvKqsROJwY7gy5omw1a6uV8+TRbl4tpTyKZ\\npplxODH2J/ruhPMOVRRMY8dx/4F+2jP29yQPc2+J5SofZNKy8CQ9/fi3NloZ+iMpelJ0ED3zPHH/\\n8Y7heEQ+f0FRmsyKDmJZdManGbnMp3ABjzNV4LE1y61O4rFpW4r+mW8gpVzmygJMiV5tEZtLutOJ\\neconqrl01N5ThYLCLHNTmcNQrc+QexfAxYAQgqo0zC7iXAIv2baGjRZU04gdcoyVlAJdNQhtCEJk\\nizhL3F3MMq15ngmTRWjDqqpYb1e8SZF9hG+TwJ46jsLgN5e8kIb/qcib/SFEDt5zHxw/Ost3buZP\\nfqKpDd8kwYMdqe+PqIdb4vFAPYzUCLZVTZgcyXsUibZpQMJApHKBMIzUXUcbJz7b1JjVG+Za8f2n\\njj+/O3LfjeA9lRJs65L1ZcGqKFmXNaUUaJELZQo5qi8sLIu4bPXjnJkag56Znw24ZpVlciKRFKBE\\nHpHIfJqUCczScmpj+LyqePb8GdPdAxs0zpTIGLJ6ZJ6Js8tOS20wbY0ySxp7CogoICSM98SqenIG\\niidQUZ51shh/lozQmN/PSL7eyqKkKCtaAT7lBClrJ2xZMlYlXVmixpHkM6+D2eYA47KgLiu00o/X\\n79OMAc6ulLMbMviM3Z2nkX4Y2XcDxlzifaI7DXBSj+MYsUgkc3FZ+v4FZvX4SjwxY9TipE1y0Vjn\\nX5BSxJ9HOjERkyUlnw1BCyhLyoSQegG5ZaNbDrIuqFYrEgnnLH134HTY48aBm21DWZVUVfUoZGC5\\nn+WyAylMVq5s2pamH4mxQMQsvZxdHnORNFMMhMU4Uyj15FBNT/hiAK0ETaFpq5LKmFxolcrz8V9/\\nT8jh2yFlm7xThmJ3RXF1zTyemIYO52aq1QV1UcGShoQ8VxwB5HqllYEoFrJkfhjlr1Mvf84IWjAU\\nt0gKUjRUulzGOuFRfppplOf9yd/QaOXnb/8FNx1w054UZ6bO0u093T5x2L9ndVdSzVfookAZg9Im\\nt3hLHl+I+YbLrHL5FwVdiLPqIV9oIvIkGF1ukJyFWeCEYPfqc+Zg+eP9A4dPH9CngaYytHVJU1dU\\npaHQ2QihdOI0zMwuYIxBak0ScBwd/WQhJpqiwOgMf7LOY0NAakmpC6zLhojzKUcZgzEF1apEmpIk\\nDPv5lv54wj0cqFct2iiqlPjdfEfUFWoc+LdVy/+pJMfo+aqqqKWmkoqvTMVKSkie78eZcp5QQ0ff\\n7bna33IxDBQhMIwj3nts3+GdIwHKmEzOM5pGFVxOBfvDAw/v3nPnHZWGTaNZX694udqw+/0N0zAg\\nncfEiFnavxAiznl8ypZyGc9Zp/GRd5Et1Sl3IdbjI1w9HCjrlqqGqPWTZFZKhFCLjljkLFUVc1em\\nNFIbiqZCTI7TMCP3HYUPlGVJsVpzlq4KKUlKkaQgRpHT7m1eOHufjTbJhyfpI8vNHEPm0i9dxNl4\\n9Sh2ICfLuOCZrMVZS/B5Vl6vWjaXF3xeFkQhsCFwOnW8HXr8ZsWqNEhx9gTGMyssX7PxjCWIROdw\\n84wd+rxwtY6kNF/87jNev/6Cy9Umn0zn7PjMO6W0MPzjkqwEIJdiuYw0WZ4Xy3151lnHEBf2UX6I\\n+JBwLhC8IIQc5OLc2Qk840PIIC9vyfPghUgqMnWb4Lm7u+OwvyOFma++eMH2YktRZK44gicpn8x0\\nycIYNnXNuiiZTj1FU7LebCiN5NSd6Lqe2br8MDAFaMnFqqE2GrHosSN5cX6OgGuN5nqzpi7KhY+S\\nn54x/goXAngiIWXlW6wqTNUSYuL9D/8dn6BqN1yXNVoIvB2xbkLpvFwFwf7uFz59+onTdMJNI9Z2\\nzG4gLUE5KY5Zbh0VOI0MCSXzz/nZZlVWWEiLixkoe2Xi05v1716/SSH/f77vKNIMPnDsRuKhg8GT\\n5Ia7hxPz9z8SwlvyLkdmmJAqEapEqxKpFWWZi61WeVkBuYCLRc6oTLYFR2uJqMxu8FnGpFW2Qzs7\\nMM8jow18nBL7wwT9iXWhaauCtsnZeW1V0JQldVmRx7QKoxWzD8w+5lO6jxiVlRJJiHyS8R5QGGko\\nq3rhn+dCFmLMTOmQ07u9c4SUxz39bPl4f2D+dEDVJWWhqGZLaWo+myNOlVyZhpv1Bc+k4lP0fGdH\\n9sFRTiPqtOd/uf/Iduy5nnpu5h7TdYTJYn3e+AcpoMhBB0IIhMomD3wuK4VRlAL6eabrR2YpcHPB\\n7CLCzIiy5GJTg5L4OZJsIPlI9DHD+1MutiIu9voQ8IsEzvuA95FhGOjGmX72lJcfiKrg5iZRliWl\\nKSAZkhJEnbIUi6eH+WMuo1RIY4jaEqXCpYi0Du1z9qsInmgdzlpsAp9ynFeBwCxjlnm2TP3A3A8U\\nSmKUwugse5xCoJ/njFImyyOBx4J/NvKkpZ8vCoU0uX0vypy2JJXi1A10+yMf9nsO3mIqkxkvKWuu\\n5XnB+av75Kwz1sZQFQWurmkbz+XFjs9fB775/ee8+vwL2qrFjT4HNCzwqxhSDoVgWZj6gHcZV4xI\\nZ64X52ImlUJpidZxgVyRlTLkB3M/DMyTy4vdkEdI1uaDSQg+87rtjLUzzs3Mdsbb+VEDfxwGJu+o\\nleLq+pLNZs15aHneDQiRHwBKKbTWbNYtV9uWRkB/6jnEwPV1doOu2gYfMmNIGUNRaJqqyOlFh57v\\n3n3MWbeFoTCSpjRs25q2qSmLpTOTZ3PXE/MphKfv3ewc+2GiDz9h79/zcPeOol1zXUjqImK7W7ru\\nxGBHmnZDWdWkGLm/+5n7ux/pphPeOlKSmHKDEiXBjTjXI6JD+BwYEuyIKRrKMvNmUvQQA0pA8pZp\\nOKJMgVKGvEr/j6/fpJB//zGwq0pKBD98ssiHE4111FvB4TRxsjPdvmOyHT4tKfGmRpsGo1tMXdC0\\nJdt1RdMUmFJnA4bO+vOyqjKAKTrcODCPkvE4MZxOjClRVAV1qZmnEyFEulPPz3cHDocJMTo2RWLl\\nBG2QtFGxjpp1iqxSoKpyqopQWTI5uYB1Idt/jaGsSqRU+BSYvKNcJERS5gIfhUAQ8okvxCV8Np8K\\n3WwZreO+G/nh7sgdGn+xpdy0PHeJi+BZp4GX6T03zz7j9WvBK11ysCMf7cR/G458sX/g7+4+8T9/\\nfMvGDtTRoaUgWo9zAedCbpOlQiidqYAkfPT044Sc84IF0mObG0NktonZJbp+RsQ9plCoz66YI/Sj\\nIw6BUiq0EMvDKSynWJa0+0y0m53D2swPPw0Dx35kPzhU+54gDWjJum1p6xpiRTARnQwmpUezh8rH\\nPYSUaJORqUEoglSkQhFGix0tcnKIyeKHgWEcGUPCxkRyfnkwlxipmGJi8IFhmimVpNSSMuWuYHYZ\\nzhTT0hGos+osv6csxVyqnJijpaAQglJJjMj/MBImAAAgAElEQVT7EDdZHu7u+f7dB344HuklXFQl\\n3uYZfogRmc6r/zOJnEcFR0WeJeclboGXBard8vXnz7l4eYEpGtyUdwpS6QwJC/Ex3SctEKp5zjC6\\nSESrXy2ZhERrgykkykRSUESfJaxCCULwdH3H2M/Mk81UUmuZrWWc5tyV+KwR74eecdG4j12HnWe8\\nn2nljqoyrI3g2bMbVm3zNMpaFp7i7MqUEmU0q3XL9eWWZ6uWb99/4G7s0Qa2qxVNXaK0pl23tE1F\\nU2RZ69jPxClw7EduHw40VcGmKSlUS2lk7rBN7vAfu3SWbjLm3VgKEHxinC2fHu55CB+xKTJ0J1YX\\nVzSrinG443B7x93tLVNwrDc7yrLGBc/d7U8c9u8Yxo7gIsq0NNsNpVHY4BnmGXRAIJdMBiAl6mL9\\nxIVJkVJpkpvoD5+gMBhTY3T1V2vqb8NaiRYjKzQl/RBJJ5s3wcVIEVpqYShk5E/3t/x8e0s/WJqy\\npClKjNZ4CboSrDeK11/uuLhpqNeadmUoi5aq3VCXGVA064n51tH9sOfnP3/k/3r7jlRJNruG06GH\\nqEhR8v7DHVoq1u2GuF4T25bUNMSmYtSSIBK995RjonKBSoFUmqISxDTSVobdesX17gLvJ8Z5xEZQ\\nwjPbCY55GRZTTl4pq5KiylZ7HwJD13M8dvzx+5/49sM9b+fE9PIZ97//PfbVC/5we8fYD5Rz4JOT\\nxG5k3fd8dnnFN2WLSPBuHHjT9Tz7+JHil1+IGmxhSGVNUa8xK4UOiXmemJ1jdj4vbp1jHAas82iZ\\nL3ZTqIwSTdmoMVnL7GOOLCMhu8jbhyO3s6ebIk2Cz68vebZbkcKCH/aRALiFv22dY5xnxtkyzZZ5\\nmhlGSzcF/vjTJ7qkccrw6nLkersmrDfURUlR5JQooyImb+FIakndERKpFm6GLBBa4pVhUgrrHTI4\\nopsZU8IZjZcKVpJRSNySbCPbCqqCYr0ihoAlzzxVcGgr2ShwIebdhtGAIJIX39Y6onWkYUJ6hwwe\\nHSOVlNRljs1zSXJ3+8DtsWMUii4G1JS7ADtOqMYijMkLN5GRqQBaSLQyFMpQFSVt07DbBG6eXfM7\\nG1CVJtkTLnoSJagqR9IlgQoR7/KZWy47hpjEY8RiodRSvBbsK+KMWF98ADC7GV1ohIKLtGEesztx\\nGiaCXXjofU9ZVZAS8zByf7zHBY/ShuSWpKboUUpguz2pf+D5zXV29saEUE8n4nMykVRZStq0DTdX\\nl/zhy8+4Px64e/uWH4eeqiyp64p2lSWOdVVS6XyYG2fH3cOB2/s998cTh5PkYDK64LrRaCEyBXMR\\nJpwdlRmtcIZrZSrqOFo+3d5yjAGhFSl6xsMdH74P2Cnn3wbrKMs1c/dAjBmmN/QHxnlg9gEZJclO\\n7IefKLQiRIedHKJUkHLykDCJukwUOn/fz7iCptQIP3L4+BN9tGhTUpUN8L/9h5r6mxTy+cMfkfI1\\nq/WOZ2bGX5TIqAh+pNWR63VDLxPN6UR16Bl8t8yLAoUwfPnlC559vmL3IrG9KanXGlVICiMpS09Z\\njhiZNbRGB0Y9E+2J4XDPeH/PxWcrPv9ig0stx73gcK94WW1Zr3dc7HaUdUVdN8uMvMgSxBRJ3mVr\\ncAqMJPTiGAv1xBgdMgnMOEO0WOeZQ6LS4lEpkURO0pn6juP9Qz7lqbx0GeeZT3f3/Hx34ENveRCa\\nwzhzMgXp+XPeX16QXKCwnl8my8/bmvd+4NQ98J/qFa90wX9tN7ytWn40JY3SvFhMFRhDjIl+mpm7\\nTGb0PmQJYVMjlcpdTBmXOekyQZV5KVcUltBPnPoxj6aWGWxnLQOQtKSoSgbvuD0OtEbTDTP94ma0\\nwWMXDMNsl4WyzSaPaDRF02JXDXc+8P2PH5nu7jnu1lzcXHCx3bJetdR1TWkMpdYYpZEkVMoEvnwq\\nzvsSLWSOLlOapBSh0Ni25jgMnGKkj4ExhcdWPkZHURlSgpHcViugNBKVCqIVzCrhAbRCl2Yxl+XW\\nPLqQHZTOUymZjT+TpRawMoZGa/CwKhRfXm55geBuHBCVJk4D03EPRZZKhhRRMSy5n/Kx/T/b3yUm\\ns19iSR0jKQo8ARcGPC7zcqImijNpRIHUWZkiJQiVZ+Upz8IRAZ0iIeZQFTirR/Ti73Bk5AUQc0ci\\nU0I5B8FSKyi2G5TODzdfVxS1wTqbyaILFtp7R9PU6E2LiddsLlqKolzGUiwL2TOy96wlzzPs3XbL\\nN19/xbvbO+4OBz4ej9g55w8c+x5jVMYoLHpw6wOncaIfp8VEFRkW+WZTaqoiL2PlWf22IBDiGRkL\\nJCFwMTJ6R29nBu+QWoGIeAKh88zvpqyGkiZ3Qb7D2S6jC3wOylEq5vSglDuhfGVFlM5mqpQSyBzI\\n4bxlHHuMdIvsNOGDYJpGjof3PEwP2ZQo/nrJ/m2cneMD0u2o1Zo3G0Vab4g+MtzdcoPnUoKra1ar\\nDZfrLcr2TLNDq8jlheLv//6KL/7xgtWLmaQCUcQ8+3Rztsb6gYIGo9aItM6z1CUUQiuTbecvn2F2\\nhttbwcd3CiEu2O6u2O42CCUpinyaMkqTxBJIYHPKiPceHz2WBDEQSsc4HpnchB0DKiW8h9EC0ZMT\\nU843lEIA8zgxTjPOR6KAfpr5dDjx4TjwMDuOMnF/v8cdOxRw++IZXhlUiLwf88z6l2TZnx4QwO/L\\nihtT8v/WLferDfV6k8c6EcLs8LPFjRNzN9J1AxJYKUWwAVFKdKGBJdbKO1LMbG6ts9Y4JhitRyxM\\njBATwxyQjaZt86hr7PJ88qqtOfQTx34ghMAcAi7GhUFBVgOIHFasy4Jm3SKbFWWU+IeO2wfH8WFP\\neTzw4uaKm8sLLrcb1nVDU1WUZYlKCa0iWv4K/7oUc6E00hTEuiS0NWxG6E74KXdKh3nICpMUmYLD\\nGJnfA++JIbf3hYaYJE4o5qQRWmNKQ1WZTOgzmW9OSATr8LPL3cJkkeNMqyRrY2iVQtmA2ja0CbZC\\nUp5O2JADU4buQDCSqDQ6eFSZXcV6ecBL/VSkskkkq3jUoqaJKRJSJKQZHy0+SgIaJTRKGpAGooKk\\nciFHLYu+lJ3AZDVA4GmXJh75HoLoM7QqLrsGnEN7iwpTNt5UZSZiCgGiYKUbZqeZrWMiMCKZpKKs\\nS+piTaMlukoZQvZI/2Mp4nIZWaVHldhqLXnz2Su++fA5tw8P3PUdk3XM3jPM9hFQhRBI5OLE9LgY\\nlnDmjEVQWrFt60WxsnzSmMdOZ+55SrmICynwKTF4h40BG3zmyItIIOu85zABClNUmLkg+B5nO2Y3\\nAeLx7yFkWPY5EYHPyh4hiNHlTkgnEBIfHMM0sq5yB5wLOczWMgwHjqf3zNNI9JG/9vpNCvnV5/9A\\n0ZYgI29evaHpZqpDh2m2FP3M/OEDD4WmdpbruuTV6xf8j5/fEo3j+e9LVq9HXOX56cORbhiYrMX6\\nQH+YsL0DD8+Lr9lt/5H24nP8PEPh0e17zKVmcNe8/f6S9ZstSTVsX5SUuqSpskoFIkIpIoJpCbJw\\n3hN9Ng9lHkJchPoCnzRRb0C07FPAzz127rF9oOjv2SnPs3XFzeUlm/WKy5s1u6tn9MeO2493vPv4\\nkQ8Pe97tT+zHicM4cwwJF4+kP31LuL7g7p//iX2VTxGhLgBBEIL/expwAv7VluiU+LNWHLc77Pic\\n99/+md2Hj8hpYFdX7NqG3WbF66sLKmPQCU6nE+M8YafANFuGYWIeLW1dsmoqSqPx4ZxUnheA2V6f\\nw20bLWmMZPIzp8HhhoC3jt5O9NOUtdU+EIVEFxWqbsBkLb8SilYKLoTgc1mx0xopND+eDvzw6YFf\\nPnzg+cUtXz675vevnvH85oqL7Y5VEggVs1LAJEqtKZNALyAyueioU0poU2Dqmna95tmUkbzH04GH\\nceRhnrmPEUciKDCNRkhFjInZRbphIriA0pJ2VWY+eZEjzpRSCK0JITLGwL6b+PhxzzgMiBS5uFjR\\nioZCFPnkKwWl1mybiqLdUrlApyR+nrCnA1ZqjHWYqkIXBq3VgonQKJP/PY8EzkU9K3nOZ28tBMWi\\nP84BwpYQIEaFjwKXcqK9VCWYiqCKLK8UGY0qkyCKlN2q0i+4WElyDhE9RmXedylBrQ0yOmQKCIZH\\nRVySCdRMJBG0hNowOMPRSeJqmwc4wTMAOiWKBDotkhWZgCz1BUAotABVFAit+cM3X9ONE9++f48L\\nPT7kZHmfQl4WL1ruECM25ieSJI+UkIKqLNit2ixRTOS9QSLHBnqIcYltWDoCnyKj8zlCUiaSjPnX\\npTxP1yogJFg/cb9/jxQBkicmTwy5e9FKYP0MAlSZgX/5+hR4mz0oUpGXLjLkz3MmX5JVrzFBCIl5\\nynmnj3mi/+71mxTyi0bQbGpUWTH98g757hZz+0C7PxGjZSw19uaCVVNRbzdQveLLzQuGdOLW90z/\\n/Q75bxPDdELISJSJkARuBrykEAVXa8NMZByPfPj0ibtPe/axpr26ZLW9QbTPCWKN0CWlUUgENoId\\nc2uT8MRlXhlDWFK5n2hmIuWtciRhF7B9lnkKkqxIpULIioP1fDzc8q/vfmFl3rOpS3ZtTVsawuw4\\nHTv2XU83O+xivECFrLBJkfjzW8L/8S9Q16TPX8N2s2jmZb4QSfwyT3QuW/ZVErxoNlw9l0wny4MV\\nbPZ36HWDqAo6BIfTgJtmbDcy9T1aS9pVToOvioamXFEXEmNE1rSS8CmD9WPKS9uz3luaCtNeIozB\\nHT9wP91xPw4M1jK6vHRrqop2taG+vOHm5Uucd3z7b3/E9T1FyiqVcfIMQnCKgXFlYLNjR+A4eP7t\\n7oG9tbzpel5enXh2cUHbtBRVTVHUlEWi0rmgay3RgoVfwtKqKyQRd+iJH28RhyPFNNNYS/SOsdJM\\njUI0GtloUllgyszIcC6f0pUxOeTaB4zKig43jHy623N3t+dw7PIJclWz27asVjVaa2IEZx2n2RH6\\niQ+TxwAliVYILp1m6wL15KmanrKpcydYFo/gOG3ygs4Yg9YLaErp5XR+Vm2dZZMSqXKBTgogXych\\npZyrKWZS9FlnnbEepKQJMacChHPhAoqgwM2I4NAqm7SE8iAtyQ54N+OtIy7BHC5Yoo8kYUiqZpos\\nD3PgkxXQHDFVSVUW1GWDVBrtJVoEKpU111LpLI8UT3mVKSXKooQ3kWmeePvxE//ypz/z8+0dLi2R\\nDikuuu9l5p/OM/d8v9bGUJeGXVstaGeWXE+fpYeErPkUT4Y06x29m7LKR4KUCXS234cU0eTdjFzG\\nrjH6RZ21kA/JaICUcl7q2Wh0phlmpWIeLUUH0UXc7BExZ4pmQ1p2l6costIt/I3JD8NaIbYlSijG\\n0x3m01vC2zv83YHBWR5Kw8Nska+vkHWDVRuu3ryiTgM/ffoTn24/MJ4OHI8PXF421KsaWdUY1VDo\\nCq1KSDvmKXHqHvju518YrKVcv2C3WtNuLtCbHUlVj8aGmCLWn40r+ZsdE7il1c6L5Yj3OehYpkxw\\nSykrIc4RUzHmhaBSFVpXuNbx0E3c9feE/g4VLJWEdZGTgoJPeARRZnh9H2EO4Pwih/p4SxxmZLNs\\n+X//FaIs0GpRRyDAeYbk8THxEsUzCnbVjtPla4Q3VOWKoi1JMnGaJ47Die7o6PcDOgYuWkOr6xwO\\noLMVm+SY/cQ4jhxGy2l2DN4TUUs7nL8Xl9qwbjdQ1aTiwIMPnIaRwTqsy1zll1XLypQkZfKDynrs\\ncSAej8wxMjQNsi6ZC82tkZSbDdWmplGCd+/3HIaZ7uFA7x3HeeIwjVxtt6zaNU21oiormqqkKQvK\\nZCiiwpzhWoJ8ipkt9nbP+Msnpm7I72PwxOhhVGhfstItslYkqfFSUgqJ1YrZLrp4l/PbgnTMs2V/\\n7Pnhp/fsDydCTLx8cc1mt+LyakdZFKSUF70hQXSBKXjcPC12dLKZS8AAlMOIORwpTElVV/mjqimr\\nirLMcK6yLJ8K+rmoL1r6PLL7NbdEPlneOWfYxGzES25JnGdxJYql2EMQkG382T1J8vnQEnIGKzLv\\nFRyO6EdCf6I79YzTzBQjSjfIUiEKwf1x4sOx58PoMe3Aartms9uiVMUcJEOQFMmhyywnlqrI9448\\nsyDzl2iMQYtEcK/5r//0n+jGkUPfc9vn4IqcZxBxMRfzFLMpMC6z5pVSlEZTqNxtuRCROi/Ko4wk\\nFbNhcDmhxxCY5pluGvF+KaqZapVzWWHxJ5xzc8WT6c2f34C0pN7lh2zwLF1H/sgywtx+nLG3MYQl\\nezQsKGb1+L7mw2V6lFr/+9dvUsi/fbnjSyNZjQNV4VhraAXMbuJeKd4Vmp/mGXd3IFlIFXzW/iOm\\n2aBjj4+KsYv8/N0Dpduwki9pV6/Y7S6piwq9GDRGmziGnodpZrW94ovf/4G4IF+VEAjhCS4uc9Gn\\nZJDkQ55HIojOYoosKRzGmXGcsS6L9c9tmEAt2ZKZVW1ULoZGQ9lsuHwuKaqWTx/e8en9W27fv8OO\\nPVKIzO8w1WPO4TDkm8Jal+OuUiQNI/zv/y232+s14vUr1lJyieQKgTy7/5znmQ/cOEFtA1u1Rl5X\\nqN0LfIrZdThPxGJANgP1rueq1ty0BRdtwXDqsPPE4CzdsedwPHI4njieerpxZnQ5JCCeZ+nW8qWI\\nvG4lkxF8awwnUXLnRsYp7xKkkLxA4KeJd3/6Iz//jz+C9RTDRJU8TiS+O3n+y/olz55dUN6s8FUF\\nRiFk5PJmS9eNjMPEvXcMD3s+DAMv+p7rzYqLVcuqbNi2G/xqTRUqKmOotMYohVIC4T3h1DE9nJiO\\nAzZFOgV3RvBWgRKRrYbXukDJkilK9qMlPHLA80w2Lnrr3s3c3e/55e0dH+8OlGXB8+eXvH55yWrV\\nLJCmpzmvMYm6zq5H7yLDMOVRnZG4puSkJLefOuaHE7hA29Ss1yvWq1Ve9DY1TVPnhW+V03zKwqD1\\n0wn97FIUQmaLvpSPp9uzcxLOFnLyoeBc+BdQ0yPUVfilI40ko8itp3g8/QoUXhakNGNnx/tPRx4s\\n2PaCq+svaC+eIYuGo/2OQ/+eIezZSI3SJUZn70dCMnnB3lkqU7MrS7Qyj9AxfnWqFgKSUlxs1vzn\\nf/iG97e3fLh74O2xy+/LMub0acnX9DF/7YBMIo8RpWIcHQ/dSNI1rTEUVZMRsXYipHE59YKbPd0w\\ns+8m+sHlsVta1FEsDlQE0ecDoJQQfFYJWRvRWuZc34WTEkPEzyxwrfNYNuVOUStSiEgT0WY5DIbs\\n7JRaPnZhiDyjl1rw116/SSF/9/0PrMqKHQKTEuPlCmLETh3fucAfgbfjjJaSVhRcFQM//PhnvFDY\\nbs92U/Hi5jlyHKiVwvUj08OBIWlSGymKiqreYQFnZ65ffUZd1SgCtYBKiqWdA6HTsozIo6pzelck\\n4iMMIoLKF7U2EWwg+QAxEZNY9LpuibhaUAJMKAWFlphlUYgymGbL5gZ01XB/94m+6+hmh/I2ByCH\\nkCV/PjzCjkiAC7A/UP/wjtXzn5HNlquV4EZqLkJCuYAMGSq1DZEmgAoRkkRIA0oSUgBhULqmqlYU\\na0fyjkJJZi14kAkrVtnhGDxq84J6HAhdB12PHAZU39MdDwynE9M8IoThp4ee8Oe3RF2w7yfKumG1\\nyLec7/Eh8mF/ZOx7Su9QIVHEhFmcdMoo2rpm/eKS9rMb/K4BpfP33zvWUWCUYlVXyOWkZpYotoP3\\nzH1P0Q+s+55td2TTNGzbFeu6oSwrjJaoGJGVRl+tKHEgEvdhYmQmGsV2VXGzu+T58zeUmyuSKBh7\\ny3B8YBxOzHbKi1HvOY0j33265f7jA8dTz9XlhsvLLc+uL9htVmiVaYQixkfDkFYKUeab0ntPVZUI\\noCgUdV2iRS4Mw+x4+Ljnx31PU57YNDW7dct202Y+d1PT1C1VXT9+lGVNUVRIFf6CVyIXw1J2MS/O\\nZ7G48iEXhQhycX+mc5yYlAiZsmYxLVrzJJbZ7CJnFIJSG+RqhdYC1Wx4HgShbCibNTbOHPZHDJbL\\nyxXbqzVV3VBWLWVZIbXi/vYj3f0tF42glc+42VSElLKKS8q/wG5nt7bAGEW7qvnyzSu+fv+Rf/nx\\nl0xADMtCNorHGXZafp/Riq9fXvF3nz3nxc0lu7alKjQEi+2y5d+NI9ZZXAgLQykx+MToFodnTESf\\nT/pFKTBakhKLIzRz/dMZ77/I0yX5wS1SIiz4DiHTrzqdhFQRnQ3KKCNQCrQAsfCnlEiZqW4yjteH\\nLE38a6/fpJD7H36mLyoejKGeZ0JTMDzf4eeRD/uBX/qZd7OlQLJTE2U7cbv/gclFVkrRVM9oqxWv\\nb65I85jNJ+OAbzbYMhGMAlXmiLNk2bQNq0KxEZa1UawLRaNzIX+84JeOJUaB9zns1YXE5BQ+kU9x\\nMlGHxDEGBiJzzHP1sCyYQgQpNSFYsBEnycTFlG3eqqho1hJd1kRpSOqesD8Q/JwXqs4t9nVxtrqB\\nECipqKuG50lzPURU59ni2QhofUD7iA5QCYE5740g34PpzJlRj6oOY0rO+ZeJxJgiY0yksoYis78F\\nCbO2NJsJNU2YcaDoOtTDPeLhHtWd0AJ6Bd/vLSlZhgBSGZq6ZRozUN9ay2kcEV6zqgqMdxQxUiws\\nBVNV7J5dop9fIK+3eQmeEtHHbKJC5jHFuTAm8cjPcVLkLiYEphAYnWOwE6PNbXFd1tRlQa01jZGI\\nqzVFrZFa0KSRrRwp9cz1bsvN5XOurr+grC+QQZMOI53R9Eox9EdMYfApchxHxvsDp6pn3iRevLzh\\n8mLLuq2z+mW5uYXwS7aFRmiJSsvyVWfJ5HmEIJfxnKwrYlXiyoJhFghVkoKke+jwqiDpiE8OG2ca\\nWRAqRRIlSdYkWf+K4X4uzmmRaC67giXzVC3/HyIqJWR8SssRIjujHznd5D0IKT1qm8/IAyUloiiQ\\nOkfqrUPEIxHK0o+Oeeq51JF1aaAwJCToxXmtNd57+mNHJQzTODGPI1prkjGgFzb9r8YrcumGisLw\\n8vkNX71+xbPNhsE7BueylX3R3y8DC0ptuFg1/OH1DX9484wXVxe0ZY3UmojE2TxeI2RXcggRv4yY\\nImIZt4gFVRBzV2I0OTzi7ASF4Bb+06J/z7koec7PWZfuE498tPMuLfF0wpfZNayX9zEszmglRHai\\nn/MZ4t/QaOX5pwMiPnCHoKoKqssLit2K2HyJ/eUW+cstcX5gmC3+ZJmKmePhnuA8c3uBLAauW81O\\nVlyus7j/JEuqzSVpfcUsS5wLxLkn9A8YN7Lbrvj68oaLTUFTaAolWd6nR/gcv3qanxclwQd8zPPz\\ncfacRGIvAve95RgkncxqAi0lPilEUTEPyyIoRuZ+yoU9OlCagGKwinJ1xVYWSF1y2t9n0mPI7W5i\\ngTqJgJCJuml48+Uf+OYP/8Rnb36PCSXpGElpJgcqZKBUiIHA8uZLuSxi8onijAeJyS9X+hletYDs\\nkQsgSiBSttSTJOiKoi3Q9Zr24hk3L79YzBA9yXuOpyPHwykHL/QnnB8RQlGWFVU147zPy9S64sXl\\nBdP9gTROGKlBScrLDdXXL+gv14SyQAiJ8wu+VhVUVZEvmhSJ3i9SsUT+7UshjBCS4JRkTpef7jDc\\n0qqCi/Waq82G6+0GsWngYoWpKr5Yeb6sZoTsKaorTP0M3T5HzAlOI2LuqWJijok4TbSlYbvdUb18\\nxXXV8Or5J/7cHahXq9zWO4dzcQkwyIuqSki0Idv9Y8YnK6Xz6W357xAzddDZgJeC5uqCizdXXF9c\\n42bLt9/+GXX5imK3QyAIhSatLyhevEY3K4SpiarI2N7lfQ3BLwjgkA8o3pHcBN7mj+BIyaFSysVc\\nJJTIXan+VXrX02ECiOlJdbFo3FOKBGcZDnuGIbNPyqbFFCUvWkMKMMwT+9M93ejw9Q61e01RN1xd\\nv6QtGioGBJLucKKsDbGsSEXMI6OznDQ+USi1llxcbPj81Uv+/s0b9tPEYRx/VfLPhVywqkt+9+KS\\nv3tzw5fPrrhYrWnqGm1KolJ4FwhrR5izIqSfJ07jRNdNlFrSGINMIlMrRC7Wzi0O2gXMF0PCzjFL\\nMXXm/suFXBhDPpCEkE/sIoFU+eQdU9bKV2U+woskEUnkIAy1UCsXTLcQGpJCCr1o9v/j6zcp5F8f\\nZwbr6JznFviq3LC7bDnMHS8vL0hJ8vF4YrZQpMh1SFxryZw8vfvE/sMeX5WEVY0tC6r1Gr19SapW\\nOG/pjh853H2kIfLZbsXXL655ebnmclVSnVUN8KhhfdJPpQVhmr+RWZYFCjBCgFaopqLSmk3jOc2O\\n4zizH3r23jIGSaKhKkqsLphstkPHmPBRoJVEaahKjZ0DShnqdoWQhqLqH3GXIVmEiJiq4vL6Oc9e\\nf8GrL75id/2MJBTRBaSIOWxaJLTIkP7EcmoVEREdJvNWsdZnK/vCgpnmzHURQiIe5UyLjpYESWKD\\nyPuwkLXyeaYaid5hfcBFQcIgi4bVVrO5uKbtjhz299zd3WXXa1FR6Jkvnl3yzbNLvr5a86/ecztb\\nBIn2ckX18ormxSWUhgAYJMHnZV1pTN5bhLCQMPONnUgorZY5arY4x3TujPKpc06Jzvbc9ife39/z\\n1csX3FzfsLm8pHz+DF0HhMxmFS1apGoQUeGGE2LsKZLPvJmiwEhNf/+Atpb62XNuNhummHAp8XEc\\nscvXEGJazE4ekPjZ4+asUz//vI9PGH0pJWmRt3rr83VH4P7hA23bcHP9gv/19ec01dOMPC4L5EPv\\nOU4nytLTNC2FNnk2rsRjslVMgSQU0jTIepOzLYOH6JHCEaYRNw7EqUd4m3HG+twp5K6BZbkWQ+Ac\\n3iIXtHMMATtNdF3Pqe84DSPu/Uecy7NmVdZEbZiQvLs9IlaRS7nji8trtpc7ttstWnhMmhmSJZCD\\nnJP3AAhFHkcsLBuh5JI0VXJ9teO/fOrUyZQAACAASURBVPM7fry/48PxwOgsv2pE0VKyqyu+vrlg\\nVxoKsVBdpMwdksxs9agEaIGRJa3WqKJivUrsXeTTMHLrRga/gMZC7g0k+aECiSgCcpUw2lCagros\\niTE7omMSIBVKRIiessqn6hQkUeQFqkjZYaqSQoRE9JmSOHtHUgZlirwLEYo5gif81Zr6mxTy1wfH\\n3Tzj7IxWko0ouChX3N8eCC7jG0mJVfC8iI5vVMC2ikNQfPQTfonzSmbDqGucKNEhcfj0kWE40t1/\\nwgTLy8sdX22f8dWzLRfrhkKeuQ4LsjTJ/JgkLSOI9LTJj+ecxEV6uPAwCqnQpeD/Y+69eizLsju/\\n3zbHXR8mbVVl2e5iD9mkCImaESToSQLmTdAHmBd9V+lRIwkEMTTd7C6XJjIj4prjttXD2vdmUdOC\\nnoRiFAJVGZEVcc05a6/1X3/TVYZFpVjbzFp71krRz4EpnwgmM1UNY11zGAV3d06266DK0ksWYcbW\\n1K2Y56Scid6TlMRd3Tx9zovPvuT5p5+zvbmhbsT03ySxnbUKLImuMjRGYzXURgkXN2WsaHyYdcJa\\nwe8ymT6K3D6j8JQFEeCzJmYlnymLz3TpKnTOaBIqBQk5iFk2+kmhTYWtahYridR6PBzQPmBtRVfV\\nfPXiOb/94gWfrC2v7+750Pe4BFdPdnRPd+hFQ8wKfCQlhfexqGGTbPGTOPhdGBnF40QSx/N5wJDw\\nhpQusVkhRfwwMs0zT3ZbblBU7YJqtaNugeSJ8wDRoFxCuZG435OHQeAlq2m6muVywf7tgT4m1u2S\\nqutYGcMKxTsfiCpjKglQBnF7VEomhRQSEZmhQ0yMLgrLggJPgEi1Qyj4tiLEkWF4JN1c8/LTb7C2\\nxVY1pqoZTz1+csSQMEr42DFFQjLkWAqDzqUpkTFMKznEs5ZwZOHwR1KuSMkQkyLnCeUn9OyojKI2\\ncqjqAr+lWBwnlUJHgYP8PDP0PfePRx5PJw7DyHgaGIaJwUV0vYRuRahb3j+MdHmmngMxiuLRtJWo\\no8PM5EfhsCPXVyqLwZpc3m9536211HXN1W7Nn331iv/jD3/gD+/uGPcOLsvbjDWGbdfwcrsQWK3c\\n2GfOiOa8NxAxkzIKi6LTBozl5e0135xOfHd44DjOqATTOBGR17hSkqmLScLGShqdDWouj9MYbGVp\\njSW4wCH20jAGhUoaVRd7AmWwymIw6JjJTjJYvZc4QaMralVRY3FKQln+1McvUsiv91EMg3Lg+dWO\\nr9Y7usWavp/4h59e8/2He4Zh4tc+8Vur+KvW8/eLilmveeZaTL2k6bYs1td4VXGaRu5e/56fvv8j\\n+w/vYZ759//13/Bvv/mCv/r2K5SVxbtGSWCBcK5IlIBeXQp3WWDkJGNNPrsUFi/tmD7amSoFi6pi\\nUTfcbLe44Dmejnz4cM/BHZjMkrB+gtG5GAopQgjkHIESS+YDKVAsLTPGVnTdAtW0rNc7/vxv/jue\\nPH9G10ngQWOgNRmNRyHy6ioGrquaq7Zlu6hZVprWIIb3JUIvR1PwPBE2jSYyzJ7eeQYUpwx9ALIR\\nnnjSmEthlGniXBA6YzE2gvNM80TynhwiLvTY2qKrhsV6QwgBPykWdcOXn77kN19/zoqe57dr7oae\\nh6RZvrxhcbXGOXGJTCkS/SjKRaUIzl3gEyhTUnkfY8p4nwg+ylSiRQTkQy42qlk6mibRVBXLtqGr\\nDZZMmBxGd+Aj/sNBRC85CwSyvxcHv8rSrTqaVcWWK44Pj8yj4/ThgFoHxuGIG04YLZ7kpm3LUtuj\\nbU1lDdbI7kUrYTWkKKlEwcciH4+0JYLvvKRsjOGmqhiGe96+1exunvDJyy+pbMPhcc/D/QNKa7bX\\n11xfb2mbGpUhJeEbJ59wMXD2Ug+uLM4Re9hc+n5llYQeL65R3ZY0TcT+iD+8o4mZThs6LCYLf/ys\\nk0AJJz3GyDDOPDwe+endA/eHnsF7stJMVOwjjMeA60dindG6oqkaCVsYHJWesI1QeMUQbEmqKnzy\\nzGkiRLHEVUjE3xnWMdpQWct6teCzz57zzcsX/NMPr/lp//jRqRrxklnUlm1rhHSTM0nlYoOQCl9c\\numuF7FpSeZ5JZXbLjq+fP+OP9w8cDj1unHnTzyQFJmtskkPeNhqvIqejYzyJwVq1qlluOm6Wa24X\\nS4JzfNcH7o8T3gcao1lsLaayJGsxqcZkjQmQJkecHMnJAteiqbEscgVVQ6r/FWHkp/nEu+T50cBK\\nRZaHR9bvKuLhiNofWY4zV+sl/1VW/Ga94vbpDb/3PQbF1fU1x8nxODnePH7Pw+Oe0+nAOEi24pPF\\ngt9+9QV/85uv+fzFLbaSTD9FoaQUZzGgLIAUoClVvGygc/FhloxEVyK7UuGQKpSkkRSKF9rQVKCX\\nK2pt2Ywjj9PM+4fvqUYwUyA6J7Mi5yiziHOiNIzBkaOnUpnNds3VdsfTp8/55HZDV2eIPeM0cjrt\\nOQ4nquR5drPlxe0VT25W7FZLVm1DYw2V5hIPdaZwYQuemTQhGhaVZdMFQoq4IOGyg48cnedhdNyP\\nkSmZMuYK1ndeifUBYtRkXdO0mTnDMDsOx9MlF7RqOirbkCr584fTwO9ev+NaOza7Nd9UNW98ousa\\nlMpUlSHGwoxQ+SIGSekcvqyKda18HRRVJf7wodIEL+9VjkXOV/jjGkWylqQUh37gtD9gtSUcTyzW\\na2qj0GMvghatsbqlspXEw/UjdC26rjCNpt1sOKUDPw4DyWYeU2LfLUSAopQIYQquqaAc2oYsRj2y\\nONNGfn6WTteaBq0FL/c+lnT3XOxdDdo0GF2L46DKuMlxdX2NrSqMMeQE0yTwzTx45tHhJomYy8hr\\ndpbdn6PURB8h3h+zmxingWE4MU893g1EP9FVlkVTs6wboXAqMCUGEQBdDMNmT38a2fdwmgzDLCre\\nYRZ/fp8dts4sVg1X22tavcP3hnc/9Tw2TtSx5aA25rwABmsSjYnsWsX10nC1VLRGIFGlDNZUNHXD\\nYtXx7Vev+PHDPb9/+4aT87gYISs2i5pNY8R3PosXj+ySlFgbKC5GXSVoTjDVQvm1GnbrJd9+/gVO\\nt4xzJP/H/4ibDhglzUOnLSkb3j4ONCiWbUPdKtrVAlvX5DmQtUdh2axuyYzoELjSkOpMqgxR15zG\\nkZgTuq0R17IsO7UCdW0XS768fUYwN6iu/pM19Rcp5H10HA3sm4q0ang99dy/jRxOB6axJ/uJrq3Z\\nXW1Z314zrVrCKclCoFnxePcD797dcdgfOOwfmMaBFALbtuPV7TX/7W9/w7evXnB7taKyGmuUdNfF\\n5UydL8hitp8v5jnFSD9lMcuPER+kkPsLX1W6AmVUCY8t/hDKYOqmeH8oYnScDke6AE0UMyS50JNg\\npCkQ/USlE7YYZygiTS0Wu42Fef8WfxBTH6MSTXR0BNaLipfrmpfbjtvdkmUnvGKj1MfnRv7YRWVF\\nymc5t3TbtYacNMEEvMmsdGKpoI4R42cep4SfI2lKKNuAqUEZXEjie12gGWVsCQAp2I221O1CljJK\\noXXFhyHx+/cD9+nI1fWK28WK/jSIZ7yScVmdcZOcpUiVZTPnzX5WGGNKoVboyqLLYjqmQhEjXcJ3\\ntVJyMCvNnDJvjyfqqibmTLOY0G6EpqZRmlDgsxAiylRoZYnuxHg8YZoWdEWoLUNtOAZPTIFewdzU\\n0gCEVGwbcrHWFV/rmBPRn1ksqgyCGqU+HjghyVQxO2kWcpbdR0bjnBy+3kcobI/FaoExBj97+uOA\\nnyPTFHDjzDw6/OQvEN5Hv+0CKoinhAjbYmCcRvrhxLE/cBqPDOOJYZTGQpGoy8FTGSuQnbz0koWa\\nxIPfuSBRayFIkEoIxAgZMWLr6g3rxRWb5TVttSI5zSk5TO8/smM0GFNweSuNlzWZvjMMk2V2iU1n\\nWNWathKRTFVVtG3HF5+84M8/PPB3v/8Dv7u7477viVmxairWjcVS0r2ULfma5/eh0ATVxxCR0tJd\\n1LwWeLa7wmxu8dkwfniHP3UQPVOAOWf208xwmtktWrZdRaPharejqmv600Ei7dY3vPj6M4H9ppHq\\n9Mjj8ECfAo6KuZdcg5QE1lG6mJMpiiGYJSVDt1iyvLr+kzX1l4l6M4rYWPRmQX5+w0/JMb994Ngf\\neUiy+JhPR+6+/oLtZ885hpG+XpC85nAc+P0//RM/fPdHfIjFylTegOfbLb/9/BX/7rd/xnLTUbf1\\nhcsZgoyznLFWFJS0bCKXpVoIQaLckjAQJIcxFIWeLNmyFQvb8lOKNlROdaUMuvh8r+qGyfUsdaQx\\n8NOHOw7HQWTHVrPsaq7XLTob/JyYJkd0Aw/3jn7/DhVFsttZw5fPb/j6s5d88+pzPv3sOW27EFmz\\n0lgtkVtndgGpZF4WR7ezyi1GYeGkeF4gSrJLjBGVEguj0J2hRmEmyVpMhxmzfYZebMna4JNE0s2z\\nI6dEV1nW6y1t2+BjyaVIAaWVhEhoywfXEo4Vr497/vpqS9c0+PtH2q4WT/TKog0YnUhaFROhhI+p\\niFyMFKVSyJXOaGNK8AHCIjAf4QlTusiQHAEYQ+CxHxlTZlbwddfhpgGTAuv1jpzk0J6dk+g1XZFy\\n5v7NHVQVzW7HY3A81oreNiSt8UnMpmxB6nxKZLQkuhuBicbRM04O52OhIipISgRVwDw7YggEL5BX\\nTpEcA1NwOJ8x1Y5xiqyXUHc1i0WHtkryO93M3bsD/dHhnXCPz7oDpWXyU6mIfAor62w2qJWhqSoq\\n27LoNmw2NxyOe97f33E6/IGfXr/lw/0dLjqMttS2pq3rErMni1QfAiGKCjqqwt9Ocgh33Yqr7S03\\n15/w7PYZ11c3VLaRDlyJm2JKSVTxunTIUQpuCCVdSCvmMXI4Bj4cDE83hue7imc7acwMliY1PHty\\nw7efv+Lf/upbjvPMfhwgZxaVZdXULNoWYyqMtlhdFdpfoRwX58yoxO1Q+AyaYfBMk2eOiabqWDY1\\ndbek/vYVebzB+cibEf7uj3/k3d0H+nnm6aplU2ly8Nzutmw2G6YHmOua66++4S/++/+Zq/Waef/I\\nj//pb/n9P/xfvL+/5xhhPwTcMBKzJptadk5KUZ9zP33id6/vuXnxnC9e3v7JmvqLFPL//VnLSWV6\\n6+HxNTklWmN4+dkzrr94zpQiyUWqbz/j/XrJP//9a+ZQsT95Xr+5Y/+4L2+GOAnW2rBua/7LX3/F\\nX3zzOYtlR21rrJJxCq0wlSYrg4rnVHKJ9oqlG0sl9y/mn2HipbCHkqweZSOKVkZ8F5QqlD7pclIx\\n8I/ekwqVsJwdKCC6GUNkteywOhJcz7v9HfM0FAN+L/45WmONprGGv/zyFX/zm1/xZ998zpPrK9aL\\nBVVti3opiKtiUZrpM60wi8rzjOUrrbHlMaBEhi0USwl8mKaJcZpEDJHFeKg2wrc/Ks/bn37PbDrs\\n6ort1S3NesHUtszOE7wjzE66/ZyEx24tilTSxsGoiZurHS9e/hqfJt6/fsub1++kuBhNipGuE/64\\nmxxN18hzMpqmaVG6MCS8FzVsZXAh42bPPDmqqqJqKrQCNwcm7yFnKnVmWgrW/34YiR/umWJkU1tu\\nFgtMQg6FGBmHA5WtCfPE/v6RP7x/j2ssKxWYU8Yp8CBdqAvMrizYyrLMoGiaiqauCNGT0ohzCXXe\\ntZzj3LQwb7wPeBcYx5nH/QEN1JVltWww2RPDyPu3P7LqxKMkZw0Bxn7m4f2e/jgTvcANMZWffeYu\\no8jl/Sz7+6JoLpCgAXRGGahVxWa9papqlss1T25e8O7uNd/99AceDg+cZkn3ETqgKTsL4fSrok9Y\\ntB3r5Zrd9ord5ort5prlYk3XLtC6AsoeqrCjzpOj0h8zUiUImYv81ChwPjP5xDBFHk6R+1PkdlOx\\nbRW1VjSN5fmTK/6bv/wN3z9+4L7veX04MniPi4m2rkjThO970mJFBsI4khCjMTcL66afJpngQuDN\\nuz0fHo6cZkfz5Irb44Hrqx1LmzCrhhAzqU7crVvysxtu/803XK9bbI68f//A9dWam90OvaoIRtMt\\nKtqH74h+i9ENTz//NWE+oRQMb+/IysjEVbxbjBZChLEWtMXUCz759bf8+ttv+cu/+Os/WVN/kUL+\\ndmEv3gWpeAsYY7naLFjc3qBXS0KKLDYbxnnmqDLRTwTn0D6wWayxVgQ/GsWua/jiZs2ff/2KT59d\\nY4uUlos4UvBJZRUQydlL0S6BB6EEPqAkFDnGVHIISxF3XjDyLPi4tQVXy6L8ooTyxhAkAmt2+Nnh\\nnWccPS5AZWsqDdlAV8PYD/SnI6fTiXEai4dLwegRIdFys+GzJ9f85a+/5PNXn9C2LQqF806okWWp\\nJ9Y6xQoiRtLs5LUqEmAj0TplvM9FlVr+HWK5mI9iC4p4pIeQ0CliU8AfHznM94QP9/jxRLe5purW\\ndG2Ds4boLSZFxtOBeezlMY4jOkU2tuFJpXneVHyy2fD2/Q8c9ifGYeR4OEn+qlY8ud3SVPZygCoj\\nSeptV2GM8LDnqbxGSKBDCAmlDMaakhQk3VZyklgjlqRaTKaqjMuZ/TTBHg7WMEwzPmWhi04z797c\\noYwFlUnJ8XoaGb2ivjMgnB1ZFSYuAdK6SOKNEcjt7H+SMxgbMFXA5ESYRPAlij5ZPk+Tw4fI5DzD\\nMOOdwxqND0vqWuPcxMP9W549f06IG8iKaXIc9z0PH/akUCA9IywSmTJ/1lgkLkHRGYVKiqTBcE7j\\nodAJLXVdcGRTU5karSt8iGSl2B/3hTUksIYxutwHNW3d0XVLVss1m/WG7fqK9WrLoltitJVDq3j4\\nCxR55qOX353PdrYyIV+YJBGSygQFhMw8Z8byeRwT1yvDts00WrNeL/nm85f8xZevePv4yPvTgNaa\\n2hrWdUN2nvFwordiguZCwOdEow3TOLM/nuSgMhqVMm/ffuD1+z0H59lZg9OKYTyy6iybRUtdtawW\\nFS9vd1yvFnz56Qu6zjK4kWxgubAsG+iaJdkYjInEd7/n+NCi109prl6xaGpqoyUZzAuDj0pfEoJC\\nkoPOVBWL7ZavXmz46utf8fL5yz9ZU3+RQm5zxbKuWbTVRYTTVpZ1VrzYXLF79Sl63fLw/Y+8fvee\\n1XJNPz6yNponn7zi6ALHGOnJWAyf7Zb8zRfXfP3yltWiJgVPVJmsLEoJ/nqOWTvbfIqAQzo67wNZ\\nK7QVpkciF8xUJLzBe2EdAFqbiwl9TBKOmrIUxOAFdhiHkeHUczqceDj0jLqhWm1Y1DU2B4yK3N9/\\noD/1smxJ8nOVMaggh0Ntaz67ueZXn77ky09fUDWN3KRRLAK0FvGAVUkUZCDfd444TsTJoUscljUG\\nVYohyGJOZcF2c4x45+nHgYeHoxwiTYOLmWmcCSFgc2Y6PPJu/z0/fP9Hnn7yipeff8XuyQu6xZqM\\nIswj+/u3vH/zI3H2nA4PNErzzfqar+qOl0mxngJ3QwCXqIyhP42chok5CfRwtV1RGYsPgUpr2qah\\nrSX70lqDd55pckzTzDjNGGNp21Ziy4qmu+0alNbMsyKliLo8f3uRN/cpMc2Bg3PcDQMLU3F8PPCP\\n//h7nFJ06wVPXlzjq4o5Zcaf7olBQqq1sdRNRde1dF2H0VUp4lryOpVgrDEW6KWqqJCi7ebi0YOS\\nwINxKk1gBGU49iPTNPH+/sDz51dUVcf+cI/zc7luYX+/5/HhyNCP1KYTu2VpxT8K2WI+7+0v9LqS\\nASTvVRb1Z754BeVLelAqDpd1s+TF81cYbVnW75kmEXppbbBGk1WmaTqudrc8uXrCarmSJayu0dqi\\nyETxuuC86FBlkWuNQDTamEIELJdvPlMJ5H8RwV5GFTrskBLTnHl/CCw7xe3W8NlOs6trNrslf/nV\\n57y7f+TvvvuJVdOybjs2dctpnhkOB+7nmcPhxBg8QUFrLNPoOBxHUmNYLDrqqhZ9SEgcMFhq+v3I\\nj497lq3h1fNbXtxWmKbjs2fPqLXm5dUWXRv2fuDISEUg+x5dtaJWDQP9m/ccZ4/ZfcJO1UyHD4zD\\nkcFPzMOReRyIdUWcHc55htkRU6JqG3ZPb3n+8obb3VXJAf4TNfX/j0L9//XxP11/i61ksXF+66w2\\nLHNF/VPE7t+iWo0+9SxGw7O0ZaprIFBj8StFDxxihtpwvev49HpFYwQ/jDGUCyCJLaS2IhpJiTDN\\n+Fk+Zyd0oJRAKxn/ZN8myyDvz2k2ToJelcZoS7RBQmi1uOGllIlOOvFxGDnsDzw8PnL34ZF9Mpy0\\nZ+pHjvt7tNZ0ywVttyZlSSTP8RzKHMg6oVOmspa2a6nquuCKJcQ4CG4u1qz5oygmlhs3lzCIpcW2\\nDbapxZjnLHpCnl8Mgo/PsxTFYZg5jRMpQx0TLiQOw8z+OHAY5mIalZimIz989zs+3L/n6dOXPP/0\\nFbubp0KxmyemoySHx9mjtCUEzdv3D5yOPbXVnNJAthXPPnlK23YSaGEUm82Kpq7lps0ZXYIcUMJm\\niV4gjZTB2IqqylirqWtR1HkfmFzpbrzgzposWa5a7IFtLYvGmCI5aSYfeLt/ZBon+lPPwQWubnZM\\nteUff7rjMAZ8kg56s2pZLjoWraVtG2wlOLjSRhSwKdNVkq2ZClU15FBizyzL5UK6bSc2DM4HrDWM\\n48w4TRz7Hsrid5xG3r0XWK6xlYTxkgkxcDwe6U89ZCVLWqI4BZbYOymAmrNJZQEwLoZaYqMqNrwZ\\ndfH0yUhws48JHyWWb5odVdWw21zhmq7smAQa2u6u2G62bJZrjJFmKQYxVEN5MYtKEv7MeamoRWwU\\nreyQrJIGRkYDc7EF+GjyVa5Z9REXTDmTI/SjWLuOQ2ZpA50KVOsbvvnic/7dmzek6MghF5m7xlY1\\nuu2I/UQMUQ7lusYkhXGJujEl8StR7a5Bd7jJccqKu/d79kPP8+dPWH9xw9PbT2lCJOUDMTgGN7Ay\\nC2ptqbuOPimCarH1WqyMgaldkNVMmGbu//H/5P7xnsdh4HAaUVqzXi1ZLqUpyUkQgqxyid4TkiQ5\\nE9O/okL+rd2UJaUSnZK4+IjQ4OjJQ4BW0ZBZU3FjDflqAUrGwpjARTG1Sa2h3lrWiwqjueDaKieI\\nsSTHnE11ImH2BfZw4m2SEtIjFR/jUlBDkEI+O8mXjCGWZY26UKYoQokQAm6aGPuR07Hn8Ljn7mHP\\n64cDQ7cSrvY4oYaeVbfk1lhWt0/w6UaYNNGLM6GX6SDERFNbdpsNla2IMYkwIwRSCJdNu0RVnRWZ\\nstw6j+7GWExdi8qPsyS8PDcf8MHh/Cz4+DgyDTOxiFeSc2JH0I+c+pHJzXITA9EHTsMjh8MJP81U\\n1tDWlmaxxE8902lP8EFuHmXog8cfTzycBP+LNqAWClM3NJ1mtVpxdb2mbhpSFtxbR5E8gyzVYkwY\\nJfJ7kJu/OSf1WEkISiljdMRlX9hFWehsJWFHFX8TVCZ5oZWeJs/bB4G35klCJOoYaZLBZYWyllop\\n6loK8Xq9YLVciNGVNYX5IPCBKfFkGkUkEZKwL6pai9UsIHhBKrarsh+oKktVlJJGy+ItZjieBozW\\nbFdL3r//wGKxoa0t/alnGkasrUnaUtkkHh7nQi7bkgsuzplaV+LiKGv5y260/EXp5uXvgS7sm4q2\\nXci9ET3ZS5jHarVhtyme8NZKmHcKYpWQ5p/ZXBQzrsJEqqqKumkwWV67ixgHhMnzc7HLz12zyn9K\\nL5LICnyCEBTjGKh0oNaRzlrW21v+7b/5NT++eUNnM/PgCDlhitFJQsnjzUXwJ4OpcPtDBG2J7RUh\\nWCZ/II8TDk17/ZTPf/Nb1p+8JK6W4EdcHJiHkehOZCO7h9ZUhPUOu3rCev0cXfYXZg7MD+8YDw+c\\njkfuTz2P48TkItpWqBIzmAq0mFK8bKej89x/+ICKgbTxf7Km/jLQyixYoLYap3KBQSg3oEZXmpws\\nVSvxaDFBvVtiFjWJjD858hB4OiXCUhM6TTwbvBdf4NJmy4WifLloE9ELh1a6GUhKeCeJs5ozXcKC\\nvRff6TO7wBS5sjCYhP3hncdNM8MwcDqceHw4cNofeXcY+P40oncKHxPj44EnVc3LyvJJVbN88gzT\\ntSSjyX6WSCcn3fEc5bHfbISXHuZZ2ApBundTVUAx3fm5SKkwzLTWaCscXTKkECVZ3TncPF8wuXme\\nGMeBaRhxs8MUfDLGXLr0iXGcZJzLGY0W1kXMRDyn/sjx8Eh/2GB1xg0HxuGIxtA1C6w2HNwoomYl\\nBvvZzcQ5EXtDUpr1ds1us6VpK5wP7OOpYLBi/+m9Q4zDBHIxRuxEm9pijaT15CTWoY2SBHlyIhtZ\\nPNqSFpRkwSEdvdFMMdBPMx8OJ9w8Er0Uqv3hxFZvuL7ZseyWNLXFWCUS7LahWzTUlS2Hw5m2ds5l\\nPXOThRXR1Ea+hoEQcQjS4EtqEkqJE2KGvp849dLRKmOZ3cxxGHnYn/jn774jY/jk2VPGYWCeHLlW\\ngv3buuxBzKWQlzauTGH5AqtckuNz8dm5hDAIDKRUxuqKyla0TYu1mslakZwfIz7MVJXl6mpHbSti\\n8BzdCEowc5UVsxf7X3JCa+mgU0pYW4NaUDdNyak9W+9yUVdTune5yc76o0KfzIWZQ/wII5GYgydG\\nkcRXBq6bBX/1mz9jWRni4z37w4AvC9bYtkL1nAMhehpjmOZIPzkm72XHWrecbmsmlRgjTP2B9dNn\\nfPXnf8G//x//B0Y3cNi/JXWGk1Pcu8R7N5Ay7EzLUhvM7acsP/sNz5/9ihyl+YnTyLvf/S2jh2nK\\nPEzv2Y+ehMLWNdM4cjgdua0XIhzLkkBEzrhx4sP9A9NwIqd/RRL9/y38iPGSO6jUxw22DhmTxVhG\\n1RW2qajqispUmGONsQaVILmA9okqQlffUOklWltyDIX77bFWFlRZq0vnkc9GKlmJAY2RmKtzxNMl\\nK684Ec7OS0EPQWh7OsPsCDEy95DPVQAAIABJREFUDjBNM1M/MvQDYz8wnAb6fuDu2PO+n3iYAysX\\n+Xyz489f/YrPdjtuliuWbUdV14LJG01UDd4GfBOJS/HwSAqq2rINmvDYg+ZSrJOxJJ2LZWe6jMda\\nn8OH5fmkGMlZHrsLM9758ulw88Q09IzDgPdeVIWVJWSYgoRU+BDxQWiK3kdG55lSpFks2V1d8cUX\\nX/HJJ5/QtQ0/fvdHjo+P4q2sFdE7XMpYram1dMVV1iQiy7Zm9/KKxbojRce7t2+5fXpD09ZsNwuc\\nCyKs0YquK3sUMjmXA4yMd56goqg5nS9FOpeD1wsenjM0NVVVim5hj6jCfLFGEbxnnOR1ySHy9MUt\\nNzdbdpu1ONmVlPfZR9BBQqbHWWif1lBXVXGm4+Jn73xgGCT8V4amCApsZfGH0wXjD8FTGyONRxYK\\npPOhwH0JHyLHfub+4cDV5sCT7YYUpFvzIWBqysErz8laMWQS9mHpvIsoLJfKmCm+JRSBTxb8POVC\\n4zWZphWHwBgDbbNg0S5Ydkv2+/e4eWL/cAcoKlvTLTs0Vq4R57B1jVKZEBzORaq6ZrFcYo0kHn20\\n2C2DOKXxPEMIhU6cz5xJpFtXpMKSEWwfpMinKEyzHDw+waA1+3bBk+cvOVnF7378CaMiV2LGw+9+\\nuuNhmMBoPnEwuMT3jwN3YyAkha4TaXhP0AodFKejZ/ukorENb396yzQ84Ib31LvMu3zgh6onMjEM\\nni9Y8sX1Detmia4aTjpdmGQ5ah7GwLvi839/GphCxDYNzjuGkNgPjm0s06QqYrhCiQTYbLd89urV\\nn6ypv0gh/4fqgE5iSKV/RsTXBjjzS70V2aq31KYinXLB0mWzbJKizpov84obvaDKZwphIEVLKtLf\\n88VL/jitaaXJuoyclAURBX4oiSPnaKUQU+H5RhRRcNqUcGWpOZXPYRjph4l+mHicPb0PEDMtnhdY\\n/ovVDU8WaxZ1g8VgA+icUFHCiEMyxKxQpozFZ/xwiKS7AzMRaoOxFbWxMhXkwl8vS8ysRF6ssviM\\nnEU1stgVS1nvBBef5olxGpndTExRDL0qi0qJOaifvQaRcfYMsxcoC8t6e82Ll59yc3NL9I539+95\\n/d13jMcjC2O5XSx4slizaVq0glYbGi2pPYGI2lZUL3ZEo9CVIkWHnyc5WLW+5BJaLSZQ5W1EK83s\\nAsM4MU1zWaAphmG8mDzNhZIYSzyf80FS6fW5MChUyfY0Rn6X94KZrtZLVquFfK4XciAV2mPOgtEf\\njwOVNRgty1RrzqwRyk5FhDHxbIYVErOL5WcVUy+tqKwp1seyMG/aGj1MpanQLBcdm/WSp7fX3F5v\\nWHY10TlSdKRCO73gIkghPFPt1aXDFZz1PL2lkm9JBqVk6tNZE1XhcoM0xbqI3oRciNWaqtLi6Okd\\n/WkvpmZNgzWZUKDHrBRt7ogxMI69hEerBTk2VE1H2zQ0bUtVN1RGJiql9c8KeHHs/H8UchHLnQv6\\n+UYWeuWZrpe1EBmmkHiYYFVtUFvQvmZdJTY1NJVm5cGfRuYQ6LPhfg7cTYE3vWeOgIno+b2EdeSE\\nHwNx3+O+f837ydNkT4ujOVmussPHjE8GcwrMeiLVEX1/JDR3vNFGGsmYSC7w3fvXvHv7luF4zxwj\\ntm4wlWKaBsZxZJhnbK1ZLRu2ywaVBWJcbdcsdi3Pnj1js978yZr6ixTyd60riw0ZnxVcusgYUknu\\ndnICZyBI15VTpq4NRmsqZWio2GjPRiVsLEnf2RSjKyM8WT6q20rZlmBebcpFLq50OaWLh0RMwhxJ\\nWYqZ95F5luCI4DzzNHE69kzTyDzNBD9zGh2naeY4zMTCDmi0YqUU66xZBnCHgWgmefwFU8OoIqgo\\nWGLBW42WlJE4JsIRjq5H75a02xWtFYochY+cy5PLWRwApUMXXvG5U/JuLoV8Zp6EHTHOMy54IFNX\\nUjRT8fsW0ZC8F/3o6GcvsXe2YbO54vrqhuQd3//he97+8APzqadRmieLJX92+5Rvnz7l+WoNOVJr\\nI1054FXksFTcPak4uJmkMm1TEYJnHAQumCZ3EfboopY0RsQRQ5joT4OYTBUvmf1hkO64toQCh3kf\\nLkstOQSMZGFWghM3TXVZyPkQaeqKZ8+vWa8WkivZNWgkxXyaRE4eQmD/OLHbrlCVHBq2OJNpJQKf\\naXI4F6hqcdebJ8cwzufRE6Wg6xqaukIbhZsdOWXRPgwz1RwwJnG9W/L86TWvPnnBi2fPWDZL/DhA\\ndBcipCp0WK2Ls6bRxZpB/FQkGgxALJuJ56slX+h/F2k6cq/lYmEcY5AJFpn0qrrBVg2gmMZe/qzB\\nnTKHhw9kYLHZ4qJjnkaOpwPL9QajYbaW5XJD07QslxK+ce7MBQ46J+/oy6GdOGPi55MnI1ayUszP\\n8npZAalCl3SElHgYHWNV0y5e8OTrVzxbJK7qwNI4Fi8G9seex/2R+4cDMR1gjEgUaakB0yDMngy1\\n0uiHPf4ffsfwwxs2yzVPlkvWHxqWZD6jIumavR/JNjJwovI/Mp0m3vWPeKsJWZhMP/z0Bx7f/Egc\\nB+r1hm5ZQ0rs33riOBDDTNMZdtuOm3WHjgmjDZvrHbvdkt169a+LtaIoLAwofuDqX9hvogXTlM5A\\nTmy7qJE3NRQbyZa2WqO7ikjCTQ6VwFQyVKachE1Q8L8CuAkWf7l2S/VDKInenbtWh3Mz0zwzDCOn\\n00Dfj8yzo+97xnHEu8DsPL1zsrTwAuucDfjP2N7rGHB33/P9aS/jbzGEqpQqLJjCdVYigba6BA+U\\nZBmjFLrSxF3Hi1+94tOvP2fVLshKi4+HymXSKDBRlFs4FTFMDPK8/DwVps7MOA2M84yPgjde6HlK\\nyQ4hBEKQAud8ZPIOYqRVmppAfHzPmzDhhoE0Dqx85MvVlq+vbvj65gmf396wWXQ0tVxepgi3cop4\\nnVBN5MEmqpQkYzHLxR6CJ8+O2UeUghCDFFStSGlG5cw8zWgi60WNrazkqnrpwMlRlLxal/TxJNeV\\nuKLhvSMlQ1VXDOPM4+HEw/7E6TTCaoFGOuXaaowSf/dMpqosdS0yaWfB6Fi674j3SpacujQIxUog\\nBpHb17WmqpdFrxBoNi3BJ5yPrHXLqDWjmpmmmboybNcLurbiyc2W3WZBjp4//tPfY5xilStutjds\\nujVD1dEtljRNh62sQGkFPpTutTwmXZaOUQ54rTRK21LEucAuGSVJSkhDFIotrLz2gXmeUFpSjiCR\\n3MA4HRmdI6RI1y5oVeZ4f4dzM1orhv17NInNZoe9HDbmow0usqxXZ9YNssPI5+3m5RgWuEHOQl1q\\nSMaajCERdcKrhNJW3DpTIObI6BVjyAyjJ24NT54tWXQdL26vSTEzB89pmtj3Ax8eB/anmX6Qe985\\nR5g9KmSe1C3PmgUbW9NqS6W0XG+UCLgMVW5lLfc+kPcPVK97bv/pDUkLTz4B2w8TY+5wRtPUG3Rl\\n8d6jdEMyDY1JVKcZ93hiPA4SwB4j7njkP/3ubzE607QNr/7D//Kf1dRfpJBPcyiFXN4oazUoU8aq\\nsmHXuaRmFPy6dMhn0QM6Y4t0T1zzvEhus5EcPZKEJiQZLy84ec6X7jcn4YLHwlBxs7t0qv0wcjwN\\nHI49x8OJ02lgGEdOfc84SmDCFCKjj/TeiTQ9Z3QSr4xz/z/mzNt0EiFKwQXNBR88S4XPCehcDKP+\\nxdeNJt9VfKNBLxbc7K7KLG0uhVxlgVQSRZ1Zxu4UxcMjeI+bZcHpnMjyZXzVxevCEApzRyCBeNk3\\nZO9RQZgjJiWm/SP+dCLME2tl2DUtz+uWK2OpY2Q8nfDzWLouXZ5HOWCt4thpZqPxOREVqKiorBSN\\nrBEKFhTutGzzYwxIJF9mtaipqhqlFS4mmtrgXElxUeZiP2wqXbBwczmzswKtIrMPTGWR7cruYJ5F\\nWxBKdN80O5yXYICmsQVrr8rjEN742VVSK6F9ai2huuMUZeGnoFtUBG/w3hBzYsqeFBOmrulPI30/\\nMhRxUNNYnj7ZcnO1pqkqXO/w93vaGZ4vdjxZRWYyo5IDxlZFXIcUcYHkfsbFjvEjc0SXkUCd2wy5\\nTKWzzx8X5arAM1kVxbLcI03bsd1ek/zA3B+Y+z1uFIViUhCOlvH0SIyRumk5zQPKVizHgW2QoAtR\\nRhfYJAusdF6WXtgu56kBPuKhSg4nuTIiSgWUikAEEgFfrByQ1PlyqGmVeRgdral5cVNz3UInwzop\\nZ7arjtvtimcbyZmd5kCM0swFJ8ZnC12xVBYdIM6B6AIh5sugkDJopFmR69WDC9h+vNSBjMLGRNA1\\nsTLYKIdZSIq4uuaGmkNw7OoNt0eP/e4tM78TYdDdHad//idGL9g+/+E/r6m/jGnW4GWjXoJL69pA\\nVgLwl8Xd2WQoxYxS4nuSknxdk6hUROcgaRzF8IqymMpEMppzJJPYKX0MjEhnm9okeZtntdc0TYzD\\nxGkYOfQD+1PP/iDCntOppx9GhnFimGemEBiDJHfLtr9s7ikLnNIlZyCkxIC7PH/FR3yeTDFJKh0K\\nZUop3ytmbUQUc1Ox3mz4s6+/Oo8yclOeu/JcmDdZkr9zkuy/lGNRkEnRkkxQeSRnJZ02Iu2OOeOT\\n8NpDoUHpGDFB7HcTMM0CjRkFtmlpEbz/w/HAw/GAVhR4SCwSKlWwW6uxtiLtOmZ2uNZIyHLO1DXU\\nxQzLWDGTuiwKUyIljzFCBayrCoXGR6Fknt0tU+FGCyxQmCy1KESt0cXgMkl+KQU6ujCVPP04Mo4j\\n4yj0wlM/iy2uNiy6quDhhhQj1la0VS0wkIK20cJmMRU5wzzri3tjU2nayuK9YZgdXscLA2qeHY/7\\nE/0o3OtFV/PkZs16vSTHzHycWQfFK1Xx14stCsPbEPjBevEoM6o0LOp8cV0Ku0BswkGXnYAlY37W\\n9V7KebkWJGfSWkMI51zKdGFKdO2Crm6ojeHh/WtUiDBN1ESa6FH9EROF/bQgMYSAGweGwyNuc4Wf\\n1/jWU1XVx2E4g3Db5T69UGkLvn9mYgkbp2DkeCRKL1xgwJxDmb6NTKtZY5Sm0oZHn/kwJr47Qq0S\\n1opqOUd5f2yCdVWxUJpQGbSqyKmVfrHpQFlSAjd6xuNE7B1qiiKoSx+9m+SxFxQgy1JWnzvIJPe5\\nNVZ8mryEvDcafrW75ZvtEyKJaDIMGfPPPzE/DESrseNI8+YD0zQw539FrJUz3q0U1FVFiqJumzmv\\nbvIF9sjp44kslCpdyqBmaVtqKzeY1vpfuL3JBRhK4s85lfBnGPLFn9sz+6IYHEb608ixFPHDUT7H\\nkj9JiNLZeoEeMsIWyNpgs3iNZF08KJT6GRU2gwaTy2Cozvt6eWL6wii4/A/y7XJRC91RMR563ry7\\n49SfWG86dNUUuEguplw68XTm7yZRbqaz4Kh4YEcfJWkmJHFMPI+7UUIcQjz7YchzOS+lP/6DKAEz\\n3KWBBzfxu9Oec4LMRxl28YBBFWaCfO3q6TVfX3UsF0sZwecRaxNWN8V0qjBQnBeYBTBWeNDBJ3IM\\nNI3YeaaYmefIaRBsXGmFcxJmrbQqDKRYsko1xsr+oaktbSMF/lxUYhbF5/E04aMkKymlaVrxgQlB\\nFr85I/FoTm4q29iSdK7KoZAYRk/fSxRf29a0jbBb6sagVC3mTP100VOc+p6r3ZqbqxXPn+5ompoU\\nEq3S3ISGL0LL59sn3FvLnbVoXRcaXuEc52IHq9WlA5eJQO6lrHSRfReoUdpdztmR5QoqC1M5sLwX\\nwuTZy8e5Ga0Ui+WGuul4en2LenzH0zixSIGUM6Pz5aCEu0VDnzJp3jM+vEEZQzaSx1lXzeVaEfg7\\nFSqwfFymZmSS0BdrunMnnlBoSUFCJgmyJicwJBKalA0+WhbNkpwCr989sjkEsJFOJ3ldJPmYnOHu\\nzQd++uFHBu+wBpZtzXa3Zble0S4WMgUuLW1j0C7je4+fPFmy4EozlcvjkJtXXw4r+Z7KwtLhYiue\\nIRWYq3DOswcVAio8Qs50MfBKLYh18y+EfT//+EUK+ZlSppRgktp8FNiccTCltYzK53V6gbmlW4dG\\nWzbtkq5q5ITPjUAWuqjbUiapJAXnXHz4SBGLRcgze880z4zjRN8PnPqe42nk1A/0w4SbXUmWT3jv\\nxTe6SL1V2V6lgsnqpGU5pz525ufiBhSbWTh7ap9rueVj0T935qVhvpR2hQQtHI9H9ocDm6s1urHS\\n/atcRmEuJmBkOfGJHxN2ZBo5L5EowiYpQLkcbKFQ9FIJ4DBKUV3Gw/N78PHQySmJFSuhNIXlYCpP\\nT+Vzp/jx+YaD5fphz+Kqo17UEiOmIFGgFMDNjtOhL9Q6SRIPdYU1lqrKVE198ctxzl8gkm5RY6wG\\nn5hLw5CzFKcmW6osDJZU3sOzb4hw1oVt4pyXsGwt6eXp7EWfFd4HjLES4edl+Y4SplMqzYlYIcjr\\nOTuHORi6tqauLblAC1rBMIzEGEX8tV1yc7VivWyJzuMQ1tJi1RBrRx8CBz/zUFcM1mDK/ZDKNKqN\\nvvQH+dLqlseVz94rH03cKIvOmBQxCPc9ZURv4F3ZO3gRl1lNVVtSUTfXdS07gBCxMfLpas2tlSJ0\\n/n0pR07eM4XARGbfdYzANI0YbYhdomlahCp9tgbIF2HT5Tmcl58lIBk8Wke0LjYEhVkWgyrXsewE\\nzq9FQhfbBMf7ceC21jQqolOQFZoxoCu5Tx9mulGjUoXWUCWDUpHoZnwPuYmgxdPcnAOYs/oYvnY+\\nHM+NqIJzIDSldp1BI3Xu1MrfvTRsZbLXCNvFpIQJmRYx0Pp/qeO/TCFfr2T7LQUtXxJgYhnfdaE/\\nxeKjrbQY6OQyRhmjabRl1S5oqobKNtIFlp+lypgD6sI5PdP0zi6FIQgX9yxFHoeJfhg4DSP9NDGM\\nM+5MqyojsnhGS8q93IzSaSctyzpNpo7y284F+4KFlz9fZMiIbNlqxbqqqfQ5dEI6w5AvQIsc6EqW\\nwTZm+sOJYRypl624Dp4LueICGamUSjp4hFRw0jP6KL+80DBFtOGjLDbloDr/TkGrKn023CqF/Nw5\\n5Vzw1oJply9r1CUI+OPlKnNUypngHI8fHnj2yTV2u5Bu1cqkFYof/DjMHA998aDRKK8JAeqawtYR\\n2MV7Rwxe5PIK+VkKxpQZhlkOXaWo66o0A1JIQzFGk4M/E0NkHJ0UjCR00+VyUSiK8eIprjIFFhGm\\nkUAbmXEWlpUs51Vhi4jD4eEw4nykbS211VSmIsXI6dTjvaNpDE/bHTe7FYumEopjU9N2Latly71y\\nDJMjTIGp/pShtaALhJakEColS/aU1McpthwmoQQAn31dQnlNUhLGVoqycE4548Msdro5gUqixDSW\\nurIEFS9c7n5/j/vwmuX4SLv+lKtuUUI1SnhzaSxyjvic+b7q+FHXvAuesT/JfW4kuFxS6mVq1MUq\\nN1MaDbQU7BDJJTRam4wxJUO2RA+m9BFCDSlhlUHpSMyBlGK5z2ceQ8MmRJbjQDa5CAYNKinaBC/a\\nK7knKAeiV2QP/jSTbOCSG2v0Rb+h4DLxQzGkK/Xngi6c6Tjly+pyI8qfk/pYyA1nwRNyaCjK1/K/\\nmFp+/vGLFPJzQE8qnh+6qPWEF1qw8ZQvRePMbCGXFBgouXiVqMMqg9Y1mlzM7zUI8CEXLGdRhHhM\\nhIIBx7MpVjHG8iHgUyCk8HG0y+fOKjI6Mc3Xpd00ZZHn4fLm1YihEKr8vTNE8TNoQSP83KWxPO2W\\n/M2nX3KzXAsUUBaTwXtClmITUyRpRQCaVcdyiqRBDprGWJnfyrWSigxcpVxglVBuglT48qX4ao3K\\nsuz1xXN9nGbmYlugS+7gmQqZy2t6wfYRDPC8OMvIBSdd+BkOKhNWGd0F78/gA4f7A6dDz2LdUVnZ\\nZ5xHaTfNBC9udG1XFxbK2YNDQZab23vh9Wst3u5KK9pGRCfeRx4eezJQVQK95SRsDaN14XqXkN+c\\n8bNn/3givLiiqgytqWgaK3aiJYtTa0vXSWJ9TPIktdK4KdD3E5CxxmK0ZXayR+ha8cCmQHralIWn\\n88xFPRyihIuI/YMFI0KjpjJonRkt/PN05H99/QNfLhZc1f83c2/SZEmSXOt9Nrj7HWLKoaq6qyd0\\nAyBAEQIrCjfkgiLkH+OPo3DNDcnH9yAYiEYPVVmZGcO97m6DcqFq5h7VjbdN3JKozIy44dcHMx2O\\nHj0aCFE45YQLowY6VUfI5VzIqRl4R+uLMqRFax9ZNWBa1uVo3y8sa7LsQ+G16Wj87xhZl0WHb9fK\\nH377D7jnD7y7P/GSr3x3FWpKqqDoPFryteu2zG6cBobo1QAXlUAOjp4NiggpJ0pdTKBOeeYIWrSt\\nBcj4oI5LaqZaDaia/HQV0ajZFbxPEGaGcCDEO47upAyX8qRwiGhDmRNU7hpt4W/yv9XskN4gnV0r\\nJEq/Nt0HTdWxoyj7BqwWmTs2uNE3Q+0M+tX+D2XxaPm2Zb06KxdCNfEz1zGZV68vYsjvTw/kkkh5\\npfRmAGe4kTUI+dC7GatFGFU8xMbuMBW1RmnyQlNXa3pqDYrKLW0Tw5GrCQNllacttSBO0+8hRpMi\\nzcZrdQbDWLEVjVIF9ZKDbZaA02kk3c5tkIrr37cJPWbM305n/ubhG/7uJ7/k/d29yfpWalbjW4pq\\noFcEiUYviw7nRiQV0jozDJPi8naxYgMl2tfmuGpP+bWDtRorpWihd1Ud71wrzntTxdN7HdAF+eO0\\nrkIvltrSZnSeWz9wiqoh0tN6gQwUKkRPLI71aebp8cLxPHEImp1o16KOK2s49zgOWji1NnTv9DrX\\nlHmZV67zqnBbUMzbu0iaBiuOKR1wHALHw8D5NBGcRv/DEFpoRCmFl5eZWpUNcpgmfHDEQTteY6sj\\nGKsoeKE41fbRLFE1pEWcZnEGHYYQlAZpDjHnyjwvfH56YU3aLJRSoo6By+VC9I7jYbQ1rM6tRM/F\\nV/756SP3lxem5Z4Dkcv8TBUYYjSxMFPqbOqHJrerRe6Fdbmano/q7OSS1PGL6RDVSq4gBEIcmaYz\\nN+WWko4k77jMF+bLM+vLZ37/3b9Sro+k5cgHawTD1k5wOsi452oeyukOefMN/t1PmcYjSCWvCyWt\\nXS4A3AYNOo/zeQvgajPk+qV896R/igU7jdUWIkgGt4APTKNGyWldOBwn7lLhbl6ITmtEwXsqClO2\\nPqRmO3xb306NdePuux510zBUgFd7xKyWWrR2mA6Sq7xDFmG1Kl4wWKx6lRtuv12ksloEvw+k9q8v\\nYsjf3b/VCuxy0cjVaHa4VoGWbkw0hd60lUtVRcNpUB2NEExzWwGpHaRhXY8i1AzeugUFsYHKGpWU\\nqhGqj8ovPtRKLkJaCmtIlq5qyo9sBrmiEgMAo93INsigIeSCFfncRjcMOAKe6D1fn27523c/5Rdv\\nvuLu5qyZh1gEWxXjFucgeFzUbZEpfJYXPubCvC6MTtkhOp9xA9qdYcMVyLYYqiWAipdqZLYmpeIt\\nlnIL9Gk7Lbr2ssFBQIdW2v9brcDhOPrIz6YbfnFzy8N0IElTAlTpgUyleCgxsiRhvSz4KeBSUG13\\nUTiiGFQSgk5NnwZNw4s5p1oLyRzRvCQtyFmDUIxB54fGYHrnyow6HQdORzWSayl9ZqTSW4U1ZcPT\\nlbaYS4bqGayBRdUDrfsQpfm9vCSKCMMYOBwipUBazBC5YLBGCyA06lXKoQ7dWFNhXlZyDXj3rJFn\\nOVNzNRropFG1E5a0cLm+GM585uXlkZILh+mGuQ0nSasV+rdBJ+vywvXyxPX6zLrOrHlhTWkz/iZt\\nUUQoeIobidOJ27v35LKSpxO+Fl4uTzw9fuD54x/49PgDyzLz4emRgNV/eoGbPjAZNHsOxxPvrwvf\\nhpGH+3c40Cy4qJMHxberRdm+6Rqhz1qpi/ql/85W5FWacarZnk9AXKC6CC7gfOR00Ole83rl4B8Y\\nrzPp8yfGWohOobJo6ouaabseXHZJgaYXZIGikwab6v/6gAy2clDbLw1ObVh4p+K0NAkV9XPeDIDO\\np8M5p7WYWjqdkrrzFLvXFzHkb89H5gDrGBnjwDTp1PFqOaAYQ6AVPvZ0qmxMjLvpyHGy4bA+6NR4\\nU71rrfnGnwKnWJhiii1ttEHKwREZwIfOe86lMsfFFAa3G9gifbHF2rIuD0S7/y0Kxx763rMre0PT\\n8fs48ovbB/7q/U+ZWjW6NGejhsVVs+oO3AqYENYkEZ+FkhJrjIzeEQhKw7RAwblgoXTAFd+V1MRJ\\nj2B040J1Cge5OKiTIfcGDdd6utF72KCiBqm0zdswvEOI/PJ8x//47a/5yzfvSR2iUkfSxpwlB//n\\n6cpvj5V1iLw8Xzv0kNaVEDy35yNvTPNEO24NbgueVDIxeG5vTuiQhsTlmonDyvk0EqM6y8vlQkor\\n9zeq5y5VWGvh8nLlel2JTQc+hD46r+TK9XLh5boSolLPpuNEHMyRhwZHVDPCopTJUjgdDtzdnljX\\nmWVxXK/Cslaeni+UWjlMI6WKdaIqe2RZM/O6giiN9TBEi0IVSnz6fOHlaabmwtPTJz6dz3gPKa8K\\nLy2JvGbWdSYtL+R1pqwX6vqC1AWpGUflNnrC6HHuQK4Ta5pY1oWXeeb5uujA5LTwtDwzP37ku08f\\nWb658PZ8j1sSl6cPXJ4/sl4fGbLK5xbRkAWp9NZ5HBltPlI2kFDTquoupZK//pZxOiEu4qczISod\\nMS8zaX0hrTo3dE0m8FYS1KIt69TefNW455pNFA3AxFoGxzPDdMPhcMt688A0nRjHA0sW/vOnD3z/\\nL/+J+XqBWszp+B5QqmFXaYIGLUan6ymaqqc29XmiMYWi8xvl1jn7suzEh97o581JDF7pkZrM6jGC\\n7cMaHHlwel5Yo6A0ds9/IEN+fzpxnkblKHcsNmzpiTXqtGkwTcVP8XIHtXIKA2OMBqu0KElx3Iba\\nKuZqRQL7EnMW3rWOPE8Gx8tHAAAgAElEQVQIEKwjs+RCdL6zgxqNi8YXdZvnLb7BC2as2Qy52321\\nBeLNtEfn+OXtW355/563N7ea1OWq+ulGoWz4IkjnqkrWKFWdhi42sHKoQ52enrQukOrAInbtqNMC\\nkAteo/wqhKiLsHrfO15T0YYX77bGEtfyzX5xbvu7nUXLVC4lE53nbjj0Aqg6AisHOShO+NcDfBpX\\nstOuzlwqoUZSysQ4cj4fGMdALlXb3HGEIRAGzzIr/x9QHBpYV4PAShtgECybkw6RrasqDOai93Ya\\nIuMQkDpyPJwYzIiWZA0yzlFEtWx0iLVQXNUuTiz1LY0pFKij9Ait1bumaaBKweXMOAbiNHCcRoYY\\nefz8zKdcmFftTKZq4XWIURvcCjw/vvDp4xOUwvXyQk4rh+kAOEpOPM8L6fLC6AsPx8DN2xtOhwem\\nwRFMuEm5/dYSL6rIua4rl/nK56cnfv/9D/z+u4/87vuPPM2rQjCCNrdIpqaZmmbIC1JyD0raWvVI\\nQ0ZpHPVW83AI5Mry/MgPtZIuT5xu7jmcHzjevaMiLMsL1+cfoMxEJxyGwM0YGU+RYdLh4odx4DAN\\njMPAMOjelyKWWSaeL1c+Pz7z/ccnfvfpMx8/fSC5gfu7t7y5f8/b+/e4deDz82d+//LM0+WZWpLi\\n+bt6T7s2BybmZdfYDD7Wle1ag5/reuttr/emPnbj9doxmuE3Y79vAFQ6M4hXOxH2TgYth/2vf8am\\nfhFDfj5M4A4AvaAI1pRiAWBjMHRjjt3QoLHfwceNP+68YVi1QwCNbymNdieNMLd5XbyNBhOnmGzJ\\nLXDFMJ2tUsQ+2kbxKqet8MiebtjeslsUmBG3ItDBR/7i4T0/f3jHcZoMErHCk9sMplbU7YCm3obT\\nrGNwqsWt/HldUd57461qIUctkac4FdvypajWdymEKhpFG0bkSrFiMETb/A0m2nBw+n1ojUp6jVpN\\nd+gQ4u+WC5/XmTVn4mCDk83JGJxOcMI0DsSQ++AEbcNWxbchBqaDFvLyqhi+954pOrw45uuqhTmE\\naVJpB+dyZ2kE76y9XGywcuY6q7OoIsyLdlcOURt5HJ7bmyPTELUDUoRDGIijGo0YNfVu9FWC3vcQ\\nICehZqH6wJoKzqmU63Vema+LzhSNqrZ5nDQAqSrmw6jat6rgaIX1eVmJeMiV9TIzv8ykNeEqOrRj\\nXVWnvk2kul6Y6gsP9wd+8dMHvvnJV7x7+8Dt7Zlh0EHUztazdjJX1qTiaS+XCz98/szDb3/P6HXQ\\nxQ/XGbcmHQlXM6SVul4JUojek1ugY+vCAwMqO9EK6iqTYcGUMc4krVwfPzI/f+Z8/4b7d6pauaSF\\n58tH8vKJ2+PA7e0N7+5vePvmnjf3d9ze33C+OXE+qR78NE2M46jNZTbhap4XPn165A9//I5/+Zd/\\n4+Xyn/n99z/wh8uVp+sjOc2MUkk+8Pz4kSUnhVZL6XTZDnt3hLIFgN0s9f0tGE5Oy0GcBVObFWiq\\nrs1BgJEeMKPcHYbbIvOdjQnNYRh7pzHy/rc/Y1O/iCH3ppfcIu1mzJvRaIyHtjAaXou01nPzUq0B\\nyDUeOla4qf2zGk6uhqIVq6w46pWCRakb3gUbFCDtATdIAXMYJv1pDzlKM9gbz79dS4tX9FHA5ANv\\nxgO/fPOedze3iBe8cY8EDB7ROMaZwJOzNLtJ17kAwQfGOOCHqPQ802xpTQdOtFlC703A+6gLv4oZ\\nMwde8EXwxcaPBcihEr3rLf8aiLYiy8YcEbBIFYs8FMnJtfD9euXDcuE5LTxMN7hoKne0YpI6pBog\\nu8qaM0NUIbQYPOfjgePxQPCBWvRab84Hk4v15Fq4zImXy4I44ZvTrTJLqvD0oqJV3jlyrSyp8Pyy\\n8G+/+8S7h8J0GLjOV55fZi6XmbQqjBSMITIMnsNhIEZHKY7xcODu/qwc8qz3zlk3r/eOgw3CXpNq\\naV8uVx4fX/De8cOHTzx+fuR8PnE4TpyOEw6vBeYlcZ0XqohpDEHK2lRTc8GliiyZuiSkVkK1mswy\\nsz4/8fL5M2lZmecLuVz5m//mZ/zNr3/GL3/xU27ONxymA4NpsLRhKSXr/FlXE8UpNjwMkdPhwLuH\\ne66XKz98/MzvH5+I14XJQfr8A09Pn5E1MQ6RMUSyC32/DDiig0MI3ISh7+WKKYmKTR5C+62L6BpZ\\nL898co7leiHVBD7zy5++5a9//Ut+86tf8NX7d9zf3HA8TBp9R5VaiDYX1UfNMEtr0lsSx2HkOAzc\\njBMfPj7yw+ML379ckGVhff7MxTmerjPr9QWXEpPz4GO/FvFWmpQt01X7sdELpXeh6j7wbLWyhug2\\nS9J7LexbLcaiH1M6z1xsJoNDeenZstcoTuW+oTNg/tzrixjyXFQ03YnvxkekWpT7+ib0KJh2I0yy\\nVbZb1rycxYa9mLwVlTcnEWIEHM7brMMiiGiKLlWdQLEKvkYU23k3LrXeVLEo0FIrMWhFNs/8urNR\\nv27HAz+/f8c3b99xe3+rE+ezGL5Px7IbjAMC1e5TNT2VUPX3Qpu03Tw9vegisksVayXKYEG9Y6gg\\nPuNK1TbrrEM2XDbpW+OfS61m0O2a6QgXDkxdwj7X3lMRllp5SonnosNrXQjdkDstX1HRTsNSdDHn\\n0rRVhMNBO+3WZOeD9vGlWWVftWnJsNdaNNMZPOMEw6IFy1yajrp27ubqOge32ECQZANDkmlnzGHF\\ngWr/4MDpGLeaBR8cQ7RRf6ZAp/UqVQc8jDosoZSKIzEvqashatu7p5g6Y9sDqVbwKgf7xqFCZmui\\npIpfi47BKTZfFYUDy3zl6fs/8nsf+M145ptjJN8f+NU3b/nm3QPnw4HDoFG/KjMaR1kqBbd7hr5H\\nj84pvHkYBm7Hkbsw8IxnFAjzrL9XCwUdZTcGnQvQnvn9MPKbmwf+/u03lg3rs6xSe3G6WG1krYV/\\nffrIv8wv/OHlCVkWpmPk3Vf3/Hf/7V/zV7/+C7795mtub85MLZOx7KoFb723wHS6a6kEX0wfPnKY\\nRm6PR+6nibODqWbq9ZnPOWlzX9bM+26clPXeWHFWw5EWXLI15xVROLBBtFms7mYbI7aubdsRrh2n\\nWbItzuzfU4riRiIQt7UKeZq8tzPo53W28OPXl+GRWyODc9Lb0/WaXsMYYoUUt4u2e7TsVKPjFZzh\\n9p+xpXablVMD7k2kC9HSW2v2AHYc0p0Rt/P7sTNsGKp3ruuqt8yqwxJseJnHcT+d+NWbr3hzd8fh\\neKRJjnaBFSXgamenSdH2eYuWtjpXdSE7W9RW7XbitntFM+qCl0p0il9nW3QSMuSC+KRG3KCVKrVL\\nwGLRZzAHsJPz0EXmWk3iNayURXjOK0957VzzTuGyVd02djUM0NSONFPAmaphJqViXFu4zlrcq0Wd\\nXvAqViQtUTXjpEJW1XBqXQM66b7JvDYp1Epes47/q8IyjuYcmkCbp1RYFoWIxjEwTANX42BLUWeD\\noNHqOADCGj0l510ars9Yaiat4LwW1JOxZKZp5HQYGa6R68uV/Dzr/LFe/LbWbq+zYZ8/fcQtC3/3\\n7lu+PX0F72/5+v6G8zThlfxMa9PX4EN2YwFlY4lY+UesBhK95xgHbkLgxnliBU/pxqfkBM6Gl/Tg\\nUHg3Hvnb+/f8zz/9TWeLdVjFuoSb8ZpL5v/+/nfw/W/54+c/UNLK4fTAT9488Fe//gt++fNvub+9\\nNdjQin1tn+3ID80JiWWyUqVPyhJ0sPI5Rs7OM0jFrYsWlIHoPFOI/MXNPbdh2Ay5VM0gWsRsQURy\\nmlGkZsQRFqkULybJYQNxzLlJtz82Tg6rFLgGOTWkoEHAes+KCN7WXpWq+HqXyNhqfX/u9WVGvcVo\\n4kXVlOO8yWu6zq7RYpL+o9HK2ny/UivVbRNDELHNYhGiNK2V2hdVFcPD7aY6722SeDWM2XeD2Xnt\\n7dXhiq2A5XaC/Q1yAacRtWxmvNOVnGKIb49H/vLte07DoGPacukb1Rw0xamwky/VaE4eJ6Ffp4QC\\nRNMv8Ua9e+1mdHFrU4WPDlxWZ1MKTgbNTpzyvJ1TrY5tzF2i5oSUQgNVwnZ3Xzmo/mnN69m3XtLK\\n46xj4jwmHuTVoYmDxRXy5HCDZ6xVZRZMd9xhsBmOZU5crjoKL+dKThqVH44jx8PI8TDhiuqyXK4L\\nl5dZT8M7psOkjKglMdjUnNaCH4JnjJGn9UpNNmFn0EaiyzUhwDRF0w8puJQ4ysjtMJCyUhWrwPWq\\n7eLBOcbzgRgC06BT5J8fn7i8mAOIyn/POYPzrKlweb5SUsZ77Ui9OU2k04mP9Qcu6wurQWO6zjVz\\ncQ5ySbxcXni5XfGD593Djc6rNQ52qdra3XU5dnWelt+XqqJhLevSmaMDYxwYnWcwJ7BFk94cqJIC\\ndPi3w1V4N5346njD7XhUQ9ODIPlRGKmfKw+F316e+H8f/8hVhMNh4u7+jvPpqIJoTruYFRv+UQC1\\n07KozXA3o9lZadrPcCJwcpGoaDNY1nxwka+GM//L17/mN+cHTj7YqEk9jlZs1G4UKgtVR79ZzJ5F\\nuPrKEoTktZmn1Np7ClaprDVrw1c2fr6DFNQJ1Fq7jcpFZx8UUTkFrglJ2sCnjUDglWXZe0r+3OvL\\nQCtV52amrBoWQ9A5gYGgKbFUYtA23VIL65x6ZyKWyk6+UbSaTKfFhN2ymle0RgPtmvP4KojpNGhA\\nuUWM0sPpjRPrpCU7PZzsIlbbLd0VOzukYdHDzqjfDCNf3dzxkzfvdMqPnWsvhdgmc4AX3ztI7QN7\\nVlCc4mXOuKbuTxyP/q9vwqCCQs5r0TO40qMGZccYV79op21KRQcxF+Uit0yjy4/urldPfIOD2rXM\\nOXPJSTfP7l4iikXmIDBohHsW3++5946aYb6ufHp84bf/9kc+P15Y1sw2Y1IsAtbW8TbEu5iGzuF4\\n4HA8EEalv01D5M3DmcM4aKTsPeuSeHm+cn2ekVI53hz4+u0tMWpAcXM+4XwgZ2G+XgnBVAFLVn/d\\nWvutMxYR4jBzmEZ7r9YFUs48PQlpLca4iMp9nxPzdVEoxytu7rxQU6YsWvhUFshOu6ctByBL5bvl\\nha/XK+8RGxierJM3U2OgFHWIVbbJVyoJrM+5NGNvhnMwQx5tOlCBThJoOkGbTda1M3jHV8cT7w9n\\nomkOST9P2yV97arRe3+65a/v3/GH6yP/z/NHoqhD6Nmr99sXaObtWsa9pbyuBRBNzdTmATRDGZzW\\nXVSD0/W91jquBxe4Gw68GQ8dcmohYXNCFZ0pnNF+DHqUrhH5Nm9YumPRWkAlWWG8IBQPc6gk1OFE\\nNKhcqMxemCOsAW6eMsO1EpIQ8IQKLttz+1H9b//6Ioa8YWfZpFK1cAguJ5akQvXBDHkqmet87Xic\\nA4ZhJOLJY9F5fbv0ZMNp2kNuF27Yk7PYveVANNzKjPMeBmALAKDZVDO6TkfVqQ7j/h1qsBqU0qlL\\nwLvDmW9u7nlzc0s0qKRhMZYoquETS10t6sdh+Lj+szoQ45Rt9YHdVbZ/bqsfnHXB1kjEICTDers2\\nex9PVqw7sHZxKbcz4vvXnsnTrtPhWGtlKcVgJW9Ft+bS9BdCFYai0FmMo06Uj4G8FtxceF4y5Xlm\\n+fTMy7yQcumiTGpMXWeSNMcWQ+B4c+J4PjEeB5Y54ZyqHXoHq+lyXC8LL09X1uuCi4Gb04Fvv77n\\nMCkue3tzolRR5slSqFX1WXTkn1L5vOiftUAqxTpMHePYojvIRbhcZ3IuBilWLpeFy2Uh50qMg/Ld\\nl4VShHyZyfOqBfjt1u7uGm2l8P184fv5wi9S5mysq1qKNtWUaDNbZYMMqnRnV1oNyAxnjJFhGJjG\\nkRAizrpfi0XyrRGuvURQnaA48JPTHe+O583h9GxNd1VjUulyF87jgd/cveOaV661svioMFIPunyn\\n3OmxdmGT7U07pB63N0AZo6hKz2SV1x16w2HLJmsVkq2bIQxbUNbOW5ozarpBdBsEBo24Fi7Znmtp\\nq51flRb4CdXD4oXsFBOI4qhOmENljsLLCHMU3hwqxxlicspXb4bc6nebjs7r15eZEGTpfnAO8Zlc\\nK/NyYVlnXq5Xlpxw1tpcLHI/jtr8UxRcp6TM+/HMg4jCIj4YFrdJuFpFjIaTbuwYnYRSZIermTGv\\nbpPQbM6gF1YN627uIkpja0iTO9kiJ4tAvNHuojh+fvOGb28fOAyKpTaJQ+ddD7qdtfsrRLsvhKh0\\nZ1sU4u3nu4Xd7+/uXKtz2g3rHb6qbkiVQHVlc1Syda/mlCkpW4RT+7CCzT/snIbbvqMORE15k/+v\\nCM4H/XJOL84251Ac8dOC9xfKADdvJh5ON9zfnUnXxNWdeB/OfH048d33H/njh498fH7meV64JhXW\\narRSnNPUNCXSvPLyclEZgGkk4zmeTjw/zXi2cWwvLwvzRWVmxzhyd3Pk26/eKAtpiAxxIKLMlBAw\\n+Vo1glhjCh5Oh0ExcZsmpN2bmlFU06yZ58TxODKOA7VUliXr1Bop3A6R6D1lWbk+L+TLQk3VMhmv\\nNaTdy+8i4k/rzPfXC4/Lwp1RdbtRM5hFG9eEUqBmjcSbgW+ReIg6oGIYFeePvZbgunFWiHCTJ0a0\\n+etnp3t+efeGd6cbo8u2NbFz2Q4LqCzbDIGf3bzhFEZSqfxzXBRiLJuh0jXsLQs0MgTSjykti6wb\\npNLXQ98HKn2sX7rYNwkPIYvq73vXRibuAx+29MfO2xktlbZv9Jv9384utuH0fh/3VBjrxnfRWFO4\\nEWVvLa5ypTBmiC4QQySKDspgkN6Y+B9KxvYffv//dYW/UssrjzqnZFGP6w8JdB0Erx19UitD9Sy3\\na48Y6Q/B4wLKjTZMbM8H91Z42Tzna/3uWrapNNJStl6wME9t0UkbCOF71GBwCnSoAGeUw2HiL95+\\nxTe3dyqq0zpLewzLzt3bwyrSo4F+MQ4ksH2fbQHtX3uopcFEEsBXTWNbtFTRaC0bLVFFl0xoq0UB\\n0rbQ68/Yn7Lij66LEO0doEiLcHQjBIGTi/yKM8dp4vFt5M3btx0nlTdKC82pMs9Xnl+e+fT0xA+f\\nPvPh42e+//iZ7z5+4vPTC0+XmbUUUqlkVylol19KmUvKZDxpLfzLv3iGqJOCStUCpxgjRIo6Led0\\n7qUPoTePOQfjqEYueKXAzSkpnmk3IAZPjEpbHKNGf3nQoRaHaeRwzByPB8Zx5PHzC8+XhcfLjHMq\\nsjWFoCqVKeNSsUxPX15cd6QNL273POXMdV25LKs1JG0pfhueUp0z7txrBtZ+XXgfCSESw0CMA0MI\\njDaNpxFzdRThxn8WHAcfeT+duBkmBh87NKnJrrQN9ipTbhQ7HwK3hyN///XPecjPfAyBYFBeo+A2\\nWGmfOetFaqS0D856kCaitSeTb1b1wF2mYNcgaESesUDQ2rS1DtdulNuyYrtfTevEIUbL1XPq261n\\n2LuH2M69w1Ptt2w8YAUnjqhVUNZBuI6Vu+wZqqbgzaH9yUO01xcx5L/94bue7lXD6Rz09vhiaVZT\\nQAzeU4rRr6pqLZzjyJp1gECzamo4tXOtGfJ2N12tdEytpWZbmLmloLVY1KLt0c3Ti/BaC8Vtk31g\\nD2m4DjO06PTG2vF//uYtb843XR1NP7riTNBYEMsgaCfVJWddC/W9R0I1SVb7nUYl65uGVw/ceYvw\\ne5SxTcapVqTJJRtLJJNy2oYv19fRPtu63o6//7Jn2TSmpUdTbXkr9jvh+TqcGA+OHx4mHu4fuLk5\\nMx4m/DDgw6ByprWwppXrcuXx8YkPP3zkj9994Hd/+I4/fPjIh0+PfHy58jIvXJfEmhNrcUhSFUkp\\nmXUtfJczo0E3IUJeVqP22X2uUKrCDKUIl+uKiE6wGobANI6KxRs0k6sW1xtDBicEb0qNRTd5jJHD\\nOPShE80hNicvxhAiFWrKkCqutNmfem/97pa3gKHdc29OqKzJ9krLPumGrUV+LXbZVmtrjAsQHDkM\\nZswD0QUG5wHTjOmruXcT4BzcDBPfnu84DaPVDPSnsoMt95/ZtohuE88YB35x+5Yxjfxx0OK+5NIh\\nBPF742z7JehnvzLgPVI14y9Ni7/i8URH76QUMDmTZoS3gKt9NWaV/ngDtcwWWzD348DJ3tPMQM+c\\n2l+3bAI7/07/FIVafPFkX5l94VMoHKsn1rBBtzun9uPXFzHkS1L96GZAAHCqF9LT0yUZTUe7MFNM\\n9Cnbxq7IWaNzbPG69nMRjVorXfGvifk02+5sobYoWrmiVnlOOj2+VGVIqMwkXWOlYYCbP8a+u1MM\\ndFsU8+Zw4m/efcNXt3ccRp1s0zerCGSdWC4mWtWae0Tza42gvaYCEhwyeLBuybqP1Jxs0QxuJyvg\\nTT1OedutiFmyDhHIedVhAmubWanwSsm1y7X269xxXVvDUHdg5qBwTusfVZXpRNTr5LqfwxqM9xuY\\nxkHbx9sxa8X5jZUUQ2AaJ+7v4DBE3t6e+dk37/j46ZHvPnzin373B373xw98+PTI8+IJOeuzyJUo\\nmZJVs3x2jtU5QtB2fSnqTr1preQKPldySTxfFGs+HiJv39yoUxFhXVXTo6rICKVUXi4zy2J88VDJ\\nWVhzRsQzxIF1TTw/KWR2PBx4/8ZznEYu1yvFtFLKNVHXghToxXK7tb5FsU7zvxYBHoAjjiEZW6VF\\nG4praaTnN8Pfraolr55G5RNCqfgQTS+k1XjsPcYi8kLvZhyc493xzF+9/wk349H2k9+smL23Z6oN\\nqoSOhTer+W48MRzghwxSivHO7flXCFXwWWE5GezkK5ZF76PURkXUfSxV1Q1H7zh5z9FF7V1w2hk+\\n+WByHKo2qAJxO1vZHe6PDJjb6nGO9vFm7o0m2EL0/e+6P/mL/rxlOMUJ2QnPZD5I4h0jEwEvbW+9\\nzsT3ry8zs/Pp2Ta03UQzCC5YxTooV9UyDUSk67F47zViFGFeV1XsK9VInNAxqmZUMEYgdm+9Mhma\\nJnrD2opBCjkbZchYGyXrbD+3u4N197Dd7v+2etnvmOAc9+OBn9++4eQD3vBqEVSh0KL/liyIE93M\\nxpV23kFwHX900B0L0CPK/vkWvcgu+4At49gkC2p3piUVSsqkNZFTRrI6smrNOv3q3I/+zebM9GTa\\n5twxDwyjxJKlRplkxwduaXTrpvWtgOUMu8/KohFzwCFEpnHk5nTUVNzBw82JHz4/84cfPvP94zMf\\nn16QNSlc0Z6XRW3V7pkz2OJ0OnA+qWTEmjIpayZyPA2aDeZKSqoW+fT8wpqKKSxG5jmxrpllzVzm\\nTPTWro9G22sp+Bh1UMRh5HBQnfExBobg+fThkfllxs0ZV7bEfYtezdCiioKIRpfHEPn7d9/w67df\\n8028IVZsHbXFvYcH2DFfNhy7UUHbMxWbD1BtgXv7L7jA6FQkKtiiq1VIufDxeuXb0wPehd3cAFt3\\nLZrehTrSskzZAsyRwEng6ZpgVnkDKYPeBxUwUhtRKpSAi64fv8GmPSMWqLmY5EZl9J6/e/sNv7l9\\nw1eHG57nmSKVaYj85f1XvD2cTAPImYHm9ZruIfaPE1H3ygZ08Mbt1lrbe90l77Ltdp/sSM6+50xo\\nrOaKy9rH0a23tM/509cXMeQq6B+Jpg+imHHDgcxgaRcAgmo+B9McaCyIKsKSdKJLf6DQPWHvdOiR\\nwBapWsCivyZiE4M0FU/ryrqoQS+lsmYrrGnstn8U7NMvDCxxFpGIU+WX6D3nYeDt6Uw0aKg7haqL\\nszVvqOqhnZg0B9QWjAmDmXBTu5J9pNOc3oaH6p/VGW2vmjFv3XY7ymFOmdUGbGi0binuj56d7ZXX\\n39tRONt/wQeG0ETNmhYKjXRAX5KCFee2yrwzZ0OjzO1ggrY+vFfM+zgNvH244XScuLs5M00jIQad\\nx3qdKW7r9m3nXUWsTrDVUNKqdMTLsnC5qsb53e2R82niMESOx4lSCpfrDDgOx5Hz6cDlZVaRq6Dz\\nRKtDAwsnNts1Mx3GLqsbY8BVIcfAGCOuVOqSiUm5/PttqtG4OstTiLyfjhxC4BwGHsYD/8NXv+Sb\\nN/dUP/Bcpe+Fvi5+/KR2B3f7SlyxwKcU8qrPH2ikPU4h8rPTLfdxZDI5gc/zhXMYlG1TW2bLbh2g\\nYm/W5LftOjYLbqcX8EwVhpdEnnWoSh1HG6TgNPtsImTSIFN2meG2pkTEKLQqzRu941c3D/z9u2/5\\n9uael3k2enPkbjx0mvOW/9ASBV2bbnsmr6Nh2f7oBd6doe2h+mtoxi6B5jL2z0iFszxHCdzmwJB3\\ndanmbP8jGfLD8dA3o2/4rdkjnRRCl4XUdVFtYTQYRY1fzqtqE0tV5NUaJ7w4ak+5WqRXrHCqSJ+0\\naKRNCkqZtCTSdSXNq/67FK4lc5VCQdAZ5G7zFeiDluC2CN9tugw4IThtPDlMA8WLMnKqdBU1h0a6\\nFWs37/9BdnbNRXDVQ4DirYmn4Z+wbZFu8OxnIr2BoReOZWteaM6qNcksqyoKSik/EiyzyEGUBdPa\\n5lvKr+/pCRG+CqP3TFEZGSGaIfeGu4oo/GpRlhT6OLValQGkFQ2btuTFJvxsDSK9cxflCx/HsTsO\\nQViXhafPTyx+MywtImrDAdpGevr4SC1CDAPP61Uj++8fmSaNnqfgOE0T0xgZhsAwjpzOauRFhJvb\\nEw9vbjWSxzLDKsoAKoW7mxPBO1KuxCwsS+Z6XViWFUlCLKrXsaXhzXU7om2M9+OB//79z/jp6czX\\nxxveHW/42c0bZIA/1hcuWbpKZrtWPWBzYpXG/KBFzsa4kJK3fTCvGg1iDK0qvB0n/qef/Ipfnh94\\nmA7gPP/w4d/4PF+JiEkiV6LftY05zEsqrCJ1a9jzrp2e5pdOPEGE6SVTLgt5XamHAzVGxFs2OsbN\\nhAld+E5CoHSNIf2ckjPLmlQKG8cpjNwME6c4cjqPvHr6jlZSA5qa6c4Y1y2y3hMKpN3iVwZb9j/t\\n64v2lv7vnfEWy2fw1lkAACAASURBVGydI4qOeXsrE+caOVdtZap+X3T9D2TIN5xbPX87tSrVKEem\\nbl3qzgtaDGrVRk033RZ92gNpuJYTrw7AbW3aGvVWIx82I1ItFVNDdl0Tc1pYc1JYJUtnDmxx92bA\\nGoShpMZNg7yd8eC9jg0LA37/0xa5eAcFnJgShoPOaHG+t+J3xxc2ZkiDjxqbRg+rmQwNXqmuZyet\\nZbprXddKyjqzdF4WjcZyUV503unNtHTR2fX2hW13oLVJ23k4y0Sic0jOOjBi132qsgMOJxmXQao6\\npyLakVilKrtIbCqSLfZ+vQ16sQfvfQCqOchNG7oNANDT3DoV63by6jQFyrry+OETWQpuXplMMlhq\\nwQ9BtTVSYPUeHxYuTxc+RS13j+PAeBip4nBBHdfgvTW3CWMcqcAslWQMk5QLy7xSUrZntWUrsj83\\ne6YVWFLm25s3/OruDYcQGWMgUTlIJOSqM1oblioKtThfejbUhpz09Wl7p0OLS2K5Lrru7fO9g2OM\\nfHW64evzLXfjAXEQ/besRWdY3g6TkgOano6tzwYfqFF3qD5rM2R7GEkN1SEJy1xYlqQ1jH1maQGU\\n7j39/jbowa6pquDYOidWo6julTu3AmWLbqXbxn2kK+xYKDsyf//9HkzbUWxziO1rkZalstnsbuzd\\n3mMDikiYS8XhOIhptzRxQbcd5k/zYX19EUMevN/RDqWPVBPjiFsHgRVLXCdkChvJXumVzjCkxnzp\\nT5o9jxrYIn6pvaouUjprIyX14vOiIkspZXLW83OihartLIyB0B2s0GKfBr20coizB1GlkkumVu1W\\nq05ZOkHagnJgkWtf7N53wam2eASN0BvjxFuk0zVWWvTppOsz6OKhL6BGqyy12tzLxLKuWkBOWh/I\\nBmvQLhFeLfUtNthFL93NoRS2oKPUNBrEUpVtUwlFh6Pk0NdCczDasauf1Oihrf28NbPox+o4Nf1n\\n6Q7b1eZUWjFpe7Uuv3aE4BRXfXl8Vi+TCgcwjNUx2ZLUqUvq/FPKZMtOZj/rBkalmEMMDDYaLsTI\\nYAXKYswp573Ox7wu1JRNTdF1jng3hn3N6USjP16fSbUy+sjNONHAPjXkovosLbCotdcPNBOySLwd\\n3+5J10MpmbSuzNeZlHO/NzhH9JHTMHIaRo7DiDgdq9ece8m1r6vNVrZ1qDd6bwO3xbTtJYApCXFR\\nGd9N492ep2tmjn4ddOOu/zVYZZlXmzJVtzXbo+wt0Gr3QKmEuwXSo2c7/b7Y3e4+7gz1Hn6xFdZf\\n7vXx2jU7W9uvr8UkgUU7wttnb/6gwcN/+voihnwYwrbYEBtwG0jZbfoJTh9e8F4nrIsa8bXq4vTi\\nGIvDF7FuNo1WN/zMKI1o6l2cp4qOEGsbvCsdlkLKqo3Q9BGSFQCt76PfyFbF7pi2YYDVFqazSSn6\\ncJTXvJbE03Il1NCnjniJvevMOW2S8INSwRx0imIrcgoguQ2MzuQ8ILUSc8UZP3rD6qwM6TCmjesD\\nXS3kV7y8t+RnlkWNeLJ/tzmpgDWl6L2te4MgG2NmF6Zot1yMTNOIG3SIhYh2jrrmBKvq5Uh1JhAl\\nljlUpGSNmr0eCxFqacqHOuKtm/EWeTujEBZNrXNTb3R0XXLY76vNkDnnkFK5Xq6dPnm0n0/eczsM\\nuBDAB2hG0YxccxzdueQKWVhctjmdmXyZbUiAPZvgyQLLywxr7jLI+5Q82MYWa0J5zol/eP6B//Lx\\ne95PJ+6nA4I664nIkLIqSbaHZvdMKZQB19bVLirX5NbWb1Z8/HKdSVln2FbbZ+1ade3oeQZr8lJB\\nMt/rGHo//S6woBvzboza03MbLdZXYQDCUnCzqRTW2rNThU2NrbUz6F3+2fZzWhPzZWZNybRhGklg\\nK/KyHUEP0bIH+j82Y72d/u4vfdH3wGvvB/oP98GlrTWEHbRH7xQHqMHhWxmBRk+U7b7Jf7AW/ZRU\\nhhSnIkkhRFsYXjWuRXHSGFXQH6ea1CLgciY7pT/FLKboBkjb0E55pGYUWrSsqZm3wcmNQ70ZxrIo\\nRr4sqzXFFMM8LSJt5PGWRrleutm67fbGzHjyfyiP/B+//0f+6eUD7jjBNOLGgWkYGbxnCJ6b44Hj\\nFDlMiqWPznOsnp+WIzc1MFXTXa9F9buPQl1HU1zUuKJx8QU1ks6ojBqROXyxKKDamDcraKY2szNl\\nbQqqwlJMF9v2bQsCWlTUFtc+UqA3rmhkOfrA6OLWqWeW4RXM1fTorbVaMV79Xi1Kl3Qh2Aao5rB3\\nWUW767bAlYGTLZtSwy+NgeFeu5v+xPr3pReh+sYPjvPpwM+/ec/D3S23t2eOpwPLsvbpVjfnGx3e\\nqx1r4DylCk8vF+Z5oaRC8I4hBqKDdVn4/PmZHz49cZ2TTn1qmefuzLwZ820Bqk7HS1p4sfFnvbDs\\nHDEJNSl1D0yDPqq0bjOqSVBapxOc0/2hXZ+VdVmZL1eVEzCH215VtOtXDAJorB/ZGZk2aKLfzR9H\\nji0j3H9fNFRXGMbjRRhSJV4TZU0U04wRgyDbHfKG7VfKpiho2HhaE+t1UZYT0gu2WxS+ffaGhe9W\\nhNu9y/6+Zdk/eq95p1eQy4/es0X9uw83Om57r6tiNSFDQtF9JkY26BIBTrPPP/f6Qoa8mOFR790u\\nKg6RSKu6wzBEvA8U0c0guxTNVSGmCtlScdsIDTveCn62dloq3go8KBe4TavPRi9TnRGjHrYIZx8s\\n7RZ4S+t8X85tvdriksJzLVyeF357/UR8c0N4ODMczxzcwOA9Y/DcD4nTMHAYAqdpJAicV89UhHEd\\nmXI0yqI1ngQj6Vi00Qo9ehIGT+SsRri1/xtcUU2nuxvyrDDSmjIFgWMgTEfqmsEG4zZj7J3H20Qb\\nMYlba1Du0JYzp5onxyVmvs8XgtNWVG+FI+c2/u0inlpcH7rdb6RFe016tWPi9iWNlWTvbxF/Sqov\\nnlI2+uTuqZmhEfmRMUf6s7NVZDWJwMPdLX/9F7/gp1+/5+3DHafzkXXVRjTvPLfnsxViKy5od2Mu\\nwtPzC9fLRQuaRe99WROPj4/Ul5WnAn7VNbZ/9c+3v2/xv2PAMcVADI5cc+d4u+AZUlUaac49ywhe\\n2+8VY1dapy8V72t3iNU0dtKisMq8rApJtEBSTNrYpHv73fqRndbb67rZ296zZYn9fdIesv1pka7H\\nEVMlzoWyKgGhDgPVVzXHfne8liS0oMka+dKama+LaduY8XOuO3J2H93XLOygFffqD2jG+M9c8Kt1\\n5Pbp3o8X2O7fbvd/esbSWTL29trWuL1JJ4F53CtHsr2+DP0w2dRoBz54imF549jmdzagPyhDLxeN\\nokVIUsiSEQkMVQcjqB5Ii5LbZncgftvw9qViQB5x1dqYBcxwZxMTkqbfA2g62dC59tibEVeDFOxn\\nFR06W5pP9Y5ahFW0gHhz9BzfH7n9yS2DUx7xMHiGwxFiIEXPcpzIKXF9ycy14rJnctH0ICrFK8Xt\\nGU/p5wFNzlEHQ1QkZTPi2gBRDJaphj2+olymRC6JGivx/kC4n4yKWKjmKBGlih6PRyiiEdOqgp+A\\nzsDMFYwN+nxT+V28IukDQTy+eh3s7J1+BY+vjlIiuRyUsmfPwykvlYZ7iqn20Q1Ai/ikb2YsKl+T\\nFq3XlJVB4tXV7lm/rv/eLhpvP5XNmAYfePtwz9/+9W/41c++4eHutk+n2dQxpZ+jM/W/bHDfuq4s\\ny8z8cuXp6YlPHz9xfXa96YliVRWnutPObJVmlpoFinO4qhDPXRz56fmWN4cDxfaAN835KavCYs5J\\n1zDs1DH1+hu3v0X/uvRVlmE1DZo5JSUdtFuNOtGctYlP6x3S72R7Rs3x7KPXBovB9oza+/vN3tl7\\nB4QsxGtmXZM1pWnm42h1IAvEelAnVj/RTDOlxPWiWQXQhxp36Yh2LqBZZAvC9lG0QRo/Nub/3mtz\\nu+xYLGzBiNP70j/fbavRI73W5rOAd73PQ6TS6NbbSMv/QIb8dDqxhz1wjhC1EaiKaHTccVC9RcUM\\ntQ+aJg8SOMtE1K4REBW+d83LuoB40TTGrJ0WP+2g9pxExLSAt/S+twi/SgOxB2Ln1Io4SMshrDAh\\nJpPZkOra0yofHH6o1JhY0sKcKnKtfH6xMVaDKtAhwn2O1HSz4WmvMgxLcdvnyraJerEQExVCNZWL\\nSJd6rQ1rT9rMooUhcFMg3oyE+wPMnpCLdlnS9b2I04jDEcuApKxzU4MnV03PSyp4HB/Ojv/rcOG/\\ncCG6gKtOJWRFR9sNPhCr47ac+Dq/JcqZoxiLYh+LmlQxUncw5g6esUJ3KZmU1BglV3E3kdNwo0JF\\n0qAd/oSi1/n1RsN0Fhl6b9h+EK7LhVqyngNm7F3rVdGjSaCvG6ogZct+RJSvn7OOnluSOhonbtdo\\npuuorc8ANAnlCpyGA391/46f37/lzelWSbQSaPNqxxqI2XFdGr5c2NMNle7q+3W31s8qhZxX5nnm\\netU5q8UcTHUaxRcppJLJol/VAqXN2Ldo3NEL27avOw97n021/0xDvE3SAn3sfnbURRuDihSiRIvw\\nW3pdd+tgtx6K6tVf5rUbcmUxqR5+rYW+Uzp+7TrW/wonb9G12xnxhlnL7j005yVmuOnrS/bHYXvO\\ne59QzYJUx+4aa8+q9m/uP/8zry/DIz+MGiGJYrJ6wRvVqDbjalrY0zB0uVqNNGAgcJRIsC7IxkBx\\n0BtQQN/cqHvNMFdpecxWQMi1WOt6E9xvDwz+JFX6r/hniyNbIP/qd3QRFKQYtdGE/UtxrMETSmDI\\nI8E5TlW0088Ertp6EGmdkBabtOCiLadmmGS7r7rAdqO3Gm++dXNmxRr9ccQfIm7wRAk6kELEWDd2\\n36JuWh8DbtA6RhgCQYSQgk6wd4EyRD6FghchWOSVsrb/IzBG5ci+dY6jrKzSxmzZJts9o3Yj+xSY\\nPcwCmlrbWLd5WZABDu9PBH8go12aVG/cdddlAqBh62JQU7JORme1FDi+ncjO5JJr61TcYIf9q+t+\\ntPb/Rm/dFZXXlMgB3HlgDEcbAAFdcxvX9XycYPNchek48fBwxk2e2esMTiceLxqhriTy6shrEzyr\\n/T61tfoqnReFVWou5KSG/HKdVUO7GSu3BVri/dZ7sT+iQQPt3y2g0Y1q/RoWdvc1bMcQHFk0UtX5\\n4Q5fUMh0SVRrThPr/2CXBe+SMw2aRB1pNiGxYkyg3o/iTSp72ynNTtv59VCIFrG5/kFbJt4jbr1B\\nVkOSP/UB21todr/dsq2yYGvQnJPfqys69yprbAf/82b8CxnycYqKWQmE3JpPNHrxXrUvolOw34lj\\njBNrWkk5IbXgRhX2GWvQ6RlFU6tWcOkKZs6CcZvzpze3pY0tJdvBKlZ0K1k6hthtObC/r9sNdX8S\\nqXvjy3b1P3vKFd04MivlLDhHiAMuWKONd13rIjiPS+2DpH9wXxQtMKnSkw4rEHTD1ji0jeqnXZ3Z\\nWDr6taQ2OxKG04BMgVIz6gudRbC+0zBrtmKZjd2yW4nDMU0HvFcGUovkhxjwxtv3EpB1UUgrDhQR\\n8uBY2IyHyspIj8ClmQ0R6wBtmVPtxkHb+DUdn5cZd/TcPtzgz1o8z0UoRRvGog9ae7HR8qqvoyJY\\nRRKDi4qpUqDC/XSDTFZz7IXHHz+XFhC0jMggO3s+NWsRNhvkI6fAcDxzw0HrNNXUF8GaWvTZShFI\\nEKsQpoly5/m9f2EpGSma2TRZ1loqaT1S02FH3TMaZ3WdgticBeg5llzJRju8LAvZqc/rQad3SAjU\\nGCnBU4KnD2vBdYhJ2kJoam5meJvJdG6Dw2ybIuKoWY1nsIArIgxZcGtGUlY4p+pErLa/+jbcB7cC\\ntahswNXqPc57hqCzS30IO7XC/UvsMcqWhe/gix6596h8+3571Z2zVJaNs0e/YdwCOtzZaaf1q0i7\\nViKeMao+EHi82xVj2Sia/97rywyWyNuEmtwmeoCNebKHNTgWPHktzLNGj0hliBERh6uOkLRg5LNo\\nJGXt4M4aRKRkak0aBTcVIXOVDbIQcSrhUKxFXDB8yt66uWU2Ye5/x0vK7mE7PUeVtVWjdDpO3JxP\\njMcj1GKyqFrMAd1sh2HAiXBIwUSK9IAaUb8+BdcQCLeLFBrI2+sMrnd02sWBteavubLkTK4Foicc\\nB9wUcV5ZCi1UqlVHgU3WYu6ts6RIMT0VpYcqm8CZZnzbrG1cnF7fEAeEig9eI1QKC1vE21JlV0WZ\\nQrVpw9jGqIqlN42ZWoS8JNY5sSyJtRYzRnYMe47OCalm1rQSqieGaF3FDYdEB1mXDZKqVVhK4bom\\nsumN91hKNproXkJARwVq25myhKygmBWLTjUzPIzc3h5IaO1ErOnR0zqXwbtArY60JiiFhOc/ucTv\\nxo8cgmYzYno80Tqk74dbvlkP3NbcVTtDswDO6bxaF/Q5yS6TWRJXEsup4n4y4VYHtWpjXoXLWPhH\\n/5GyZt5wNJhGqYlO7PPxaoxrg6YsGLGtoeP+WoJoQYndXydCdI5DiDgHJXtVgzRhvG1sY7uUlinq\\nAZt5bqPTcq5UXGcLjTFod3HY65vvjekWdO0AEPuM9pxd3+sb4qHvDg3aAZz4LcaiBXS2N4NHHBQp\\nnREGOu1Jj+N3c3EbXXL/xS60f/36IoZ8nlOHNFIyLMs7ciq9bd8H38eNlZL7wF2pIGZjBgnEDC5r\\nCult5iPeqfAUok2OZuSaWpo09T9rRW+j5/JO8+PHnlmP1v4ir/7sMUJbJOyes4OGJ4YYiMPAMI7U\\nnHVO4hCJY+gGLAad4HNwUQfQVte72jfx+u3rVZbQjAlGt+tROt2QNyw4WRvzuiayCIyO2mjSsEXz\\nOAgGGXmHD0qBct519cBqH9NmKO9HsmmHvWGa2SJtD4JnrZk5e+Z2761oVYsDm5LU2vZLNY1pacU2\\ni4JNJyYthvdTSWAQltK6lIGox89SKFmdjbf01Rv0RoWaW06sdYXZJa4pdfnaKkY3df2298fRFC33\\n+ZPUTctnzYmVTB0i/hBMSrUvks7mUdZItAwIKJ5UKnMpvMSVwReCU0ldsTU+xMhXfuCQr6w5vTZ+\\nzgIKr1Gq20FLIjYVKgr1LjCcD5CHrs/uxLOGkX88XPg0ZE7hWYeltPUoQvRRC4pY1zEKlYwxEhxQ\\nNdBy0gYJYwXzyvyog6YjntGKkv5mIr7/hvt84rDfb20XdtitQC36rKXYUA+9zzqSz5Nr5vN84fvL\\nE9M4aE3HqIxb1UAjpCZnu3+u3m2Kpg0zadh6f6/4Dh/13zPwqO3JKto/IOi1O6c1CEFgilAdLllk\\nVptz2kVtsj+rP319EUN+XVaNpIwu5Ew2E6nKK486GcPbjShV9Ri8d6ylmtChMLpAyOgoJDMW2i+g\\nBT4HxvE1ApEt7lpyx4lV7VA1uEvJ3XB0709PBtl8P/ZAm3f+UeplD8jRmnJ01YueED4G2oRucXCc\\nJgQdIIwTvI9MbmQcBnwJpjFim50/+Sg7t1206NQRYp/hzKC2e6kwxMq8LixrVvnTwZOd8s/7Rzjl\\nrAaLXDNFuxA9Kjlsx8M2Z/A6QNk7LViXUs24OWOUrPpco4ItS8pE77muiWQOtRTFkzquWduQ7m0I\\nid3+3r2YrXi45kwOULyYsqNo0buxJ5w6pIojl4xDpQRwypxqbeFORCfWCySfWVLSzFHaoA3j8rZC\\nnqMbKY3EtyJsY1SkrB3Dq68kydRsbI8QdFZnzdBYL1kQSSbsZlLAKINFoqN4KEif/lRr5eA9FzJP\\naWZJysF+XbB3BjM2aqU3zRObcj96wt3IIQaGfeAQD+A9vyszv/cXy7L8FhRVzZJDq0HtAqhpGogO\\npZBWp7IZ4rQbea7kZeX5j5+Ra1Gtk6KyG3dv7vjbn038VX7DGztnNZpbgdtJVSNedMRdkbI5zKJd\\nysU5np3wTx+/J9XEdDMwHUbGqI15kahsIcuAvHMajJiBVxnqYAbfRMS6HbU1JRqFtx4W375P+5nu\\ny+rQmQj2o+YcKgKHAalqwBvFeM+yatDlf+31ZTDywcPocYzdwUqt1DXjgu8UHB0om1mL6kt7F4jR\\nMwDiVPQ/ZMEli5Sg38QWaVXTbvE+4L01TLSiVBWLAPM2FccKVe1md5yblhJuBaDtpYa8xVdq5Fvx\\nVl/inV0bumlrUTzZeaXeieDNE5e0krL0KT3ArhdJNtEMNJV3RqHQKFyFpqqrHYrohqwUait0Gqac\\nS6UGh5uipoKN6NAjE09tzTUihOjUsBSlaeacqbl2bZMB3XiDD3jRVnScU+lZ75T9kARZTTM8eNaq\\nGUJaV51CEzJYkawHIlVIaVVju2vd1knnOotzKYU6BtzkCEPokAlomhuDRwKmsW6byXtzNqI6K45e\\npykijHFSOmSLxn68n5qnb6E4vDLg1Yroa8osNZFHRw6mK4RDSoaMdc2WPlNTBcGkGzCP1k+cqDPb\\nUDKT5Q2B6h2zZNaaSLUxPux+ucbIUSPbZmIqx1pHiw3DyHFUvDqia/Nw9x4JkefH35HKilBNXbJN\\njS+KQTuHVHp7v3O+DyYm6PBicZYFVRQyrStPOZFz6ppHVOF+Et5fnvlZSr2u1bp+G2xamwOyOkAp\\nqq2f0sqSM2tVnHyVlf/9u3/mppw5Hu+4PR6ZDkGnBsnQJZy9NQsGD14UJgp4dHSzMl9atadF8+Wa\\nkblCcoxxZPRR5RzmjM/aQR5d0L3h9HfOw8ibw9mcic0SnYvZC3NyDX5q8YfrFmiHm79+fRFDHoPf\\ncCPTUXFON6APWh1PRdOm4pxNCLL5ekTbhHZDi7I7LOAyT2Zz+LzicBqkW3qyE3hSCdtqjUDZNMib\\n13fNqfYvaDHxn8eptl3t+u80NgJmyJ1lDMrSUf2VNVm0bBFsFOWftzmg/bPFRs55vbb2YNtADTXw\\neuzinHZSNkdUN8XDZsSXpIyV2uSE2UWYLR1vx8FZiUHvT8BUDQWyFB2RZpOM2mIrrlIlA0o/FGr3\\nEK2LU2rrDShdl36bv1g7FNCuX8xB9qlN6N+TGXJ38iZ1oOcW7JwqmNHStZdNd7uvQ2k+0gqicaDU\\nqvIOxqjpbI4WGe4gLTGRr3Z/6u5+51xYc9KO2UkjajXk3hyTWOeyGETYzlPIJXcecQxW+zF81zun\\nGv7iGGxtZRSHL3VHQbTqaWNCeO80CAo62i5Gv1HwRKgUsoI2rOmKq5Yhi6PaNepxFIfzJpYl3non\\nqtYjxAWiwXHZmsuw5b+WxCUl5lpIdSM7SK2EtHJZF6vTND+524luy0D73hTLNHPW4yF9Atl3y4VP\\ni3AonjtXmUJzPIESC5VC9A3oqgSnRja4oBmE3WsvVlMRwRO45pn5siIJYhiZQuQYKuWHZ+rzooY8\\nqEMMDt4NJ35xfuAmDAwc0OFMjmEVgg2OeW2mrZ9ggwH+XcvzRQy5x1kaXRB00UbnGaLCDu3h1BCI\\noloO2hbdIm0ITnnZoQJFpTr0ZfiXyb3WVygkrxeAFXty01cppvjXG4L+f+be7EuS4zrz/Nnm7hGR\\na+0ASIKESDZbavX8/w/zNi/zNKfPtI7OkajWQooAqiorl4jwxdZ5uGYekQVAPW9QHFShKjMrM9zc\\n/Nq93/3u9zV6EazHxFmaLZnHud7I6WNyEJxgipOd1AmEi+3n1Ycz54wzFrFzrg2i+l2bdG8p0siT\\n5pWisVJaSShKArnqoQNZU8pJpyRGmeJcQsTX6bloxDDAaYUxWppIRQ5QXcv9XNq4u8AezkoGZ4wh\\nxFhLdtpEiwRjleRXyTQddQkcapVWyQVCyTJEZRTGueqonilFTA2Urmx9ayDW6q0GVhkfL/iY8Dmi\\nOtGsKaoybZSRGYXSXJvkmpYSWCoNtPVPck41JdM1Y6vZY25CXfWnVmZC2wrN/Leg14DT5FQF9gks\\nMbCUROpUtTCjHlrSsD2hP0q0h6yM+udFmo4iZaHXykiqibqzi9w7hbBvYorVIadR96TMqgWbyHub\\n+vwYLRrpSuCdFLM0YUvFh/2E1prOGQlzBVKITQFX9mM6VSoKTSkJHyvf3Bk6ZSr1tSYwGRlCq7De\\nudhc6xXFUP89p76DQJGqgdecPZagZNo1pAoVcl4jq7rPIktKkCTpSFmabaqTCkxIAAXjivSLFGty\\n0SqylMUIRufCfV7Y+4kcCpgFa+GiJA6PH1g+HSBXBp7W9Ebxh+0rbkwHKWOdyNbqjDSILSjbAvfK\\n1ZLreJYw/Hgo/3kwcr+sgUghcp8+R0LWlFAxpfqbqsGlFOGSa60Yup6t3tAVhy3VI7GW3EqJi9Ca\\nhkHN4M/0NvIPFQ99FaJv3WdgLW0kAzh9o9JKnCro0wZ9a764jlW3zzWKSQLmkAhjIfkKObi6geqD\\nn2sWdowyjdmC5wmaU+tmblinqvK2RlVcPCtKZB3bL+cUuBhXR5vFS0BPzmAUdMoIJKKlcrFaoKw2\\nnKBRpBwlW9EyWZpQBIrY97WMGbCdWQNHqZ6kxhqctbXpGjFGzCGMs6vGhLUWZaXxtToI5UzMDW6R\\nycU2ReljYvY1KOuM2fQoI0FQYqqSwyxnlhwoJWO0IYZU1yHUPSJ4e9c5oVUuC8661Y2mNdJijOuE\\nJLo2r1SttM6CTssiU66wSox4MtkZdC+TrVZZgpbvC4UYZEJ28QHXyWh9SgW0lNs5J3IdyCm56nHX\\nQGqdApXRpbDEuHrb5hwpxdKAAQOkFgthnbLtnWPT90Tj10a5KpoQ5AALOeNMj0bh41Jx/Swa69pi\\nrPh9UjN1Z6t+eKFWuad80jpDNkLzpNTDoK1bfWzXAb1VN6kySHStG9t8BKfnoR0CqUJuVApfKRl0\\nRlthcDVzd2FXFYxW5EoqoGSwhpISqmrR5PrcaWMEAkuF8ehZZpHbpWiMLlhbMLaIOFrlBCvEs/er\\n4YI/XL3m690NXYVUm53eGqhXTPM0MfssbJ/Hgc9eP49n5zRjrMVYKSdDDMJt1mq9WadxYmkIaFOx\\nbqOEG6osYa4eyQAAIABJREFUOldIoZR1CGIFlWqZ2BYjg/CWk0zXTUvgMC88jfJr9FFw6RZRqP9T\\nZ2O8Z9fQytsfW9j2dW0aUmmNcuYM/0ew7CJO8RIOha9N03ROrA9CTd7W35s4VStNlKm65VpBhWSE\\nfpdXGCM1H85wMpHwNTsvWTEYefg2XVfd5itO2FIvuWhSEgqb0hqVEpFcW0FUWqImZ8EZrdZ0nZEx\\nf6ij7ZpExiihQIbiWYwhRMkClVJCDbQWo61kyilSYkGp2N5GhfzlWmbvWXIkqHrt5WwOoD54TQNc\\nhsxOw18FTlIN9Z6FJH6tsUblHkOo2uKyJRqcVQ94LQe2TN3nOhchQTzEwDQvjDGwkIiVBUFp8JBw\\nD7UxUC0MU8VXtal1ZIU7SmpEdnkPwkyRazyOC14Fis7Mwa/aKC0jb5CX4OOqwisabQzOOTrrMOha\\nSZWV0dM2uNDiT9iIqlCaplWHyN5OdV6h7geh20nF1QyiqfcuxQZjnj059ZmN1c+3kQ/kKup9VVK0\\nLTFz9JG9jxxTIWhLtq5Wbqkit4qiMsoorJMp5JWL3pY3S6bdoMUQZMCwJYYtPTHWoQtEXxiPgeCr\\nr6jWkmGbFodaZFDcdANfbS/57faWN8MlW9dLolDh13XS/Cy6yNv6PIifPvNjr5/HWKIK9+is11JF\\nNEAEt7NKshFlKtaaylpaGqMxRbArpRSpFJaQOEyeZA2uUBkbMk0nDT0Zi54XzzTPHMaJ/X7i09PE\\nh/3MxzHwFAolqYrl1um0mmWvDL610SAc5XOYpJw+w9pxrt9DWY3uJNNs2gnK6RM+H1LNToWO6Ao4\\nLDo2PPmz26fUKZjXIL5u0Ib3tiBeKhQRm7xrZPFeHFS8VCU6m/XgNMbiOitZd83iFOrMKNtQW28V\\nc02S7SmDdcI4yhEo4gzed5ZiKq5rhHWUS8Epx5wmcWFCi2hXa/QYeR/GGHJCDldoF8Np5D2JbKlf\\npKJTiPbOWvcDVSFP4I9KQ8+nIG6dFQqskn0jFLE63xCl3uiVrrgrlZWjV9xa+uKZojSplvaidyIN\\nOO8Dx2lijJ5ZJVLR0tMRTzjR8lZgtD1Vfi1IF4Wxam3qliIDcw3ySqnx9RXz7PEobKeFHROl2UnJ\\nZ3LDlVlRA7kkAAbTOfHDzTIbEEtBVahTfsApS5YTRgnnXsv+O3VXqvtUbtmwNIlT6xuUKlRHIHjR\\nUUn5FLJOTNmCj0HWMssB35Kw4gOxKHyIHMeZ/XHmMHpGn1lMh97u2F1fMR1HQgioDKkePLZzMols\\nK79b69O0MFT2nKxrCEJndLbuO2R2IBcIS2aavJjOVAVX14FWhTTKsJZRCmcMX24v+ebill9vb7mw\\nEsRL68BTqbsqn6fla5K4xo//HzH1Zwnk725fVIqhYl6E3hWyDAmZaqSQ1vO/iC6HM0ItzBGrHaaI\\noP0hFb59zPyxeIb+iHG16ROEBSGGCTLBOIfI7D3zEpjnhadp5jAtHGbFEi0XObFFbmjLdM5f67xP\\nVXsXHqjCSIxCbow60884w7CNxvuAXhYcHZ3TuGrCm4o0XDKsgdzEKngEKFWnKkuVu9SnLFxrXTm8\\nksnL5Bhn4j1FxLM0xAJziIw+4peACokUMiWKBOpxnPDRV2Ery9ANDP2Gzjh8TEx+IWRpmskwk1kx\\nToF3LNY6tAU/B2LMUCK77YA2msl7lmUhp8K222C1WidQZQIzrXBk6wucuP7n6ocn4+gYo7gblYxy\\n0gRufpTNak1rjXOij14KNTsXrW7jDNqKIJm1gslrVRtUTpzrBcsvNcu0Kz5eqtCVFMiivpeCeE42\\nE28fItM0M+eAt4UcQ+WiSUDOLUAkmWJUqtANNdDUoNhsaZUqDL1Da1sby3qtJoZeZI3lEakMDh8o\\nQ17XTPQeTpu6TaPGLFVVbxx96fFhISdZD0VBVc3+eFYmqiT/Zh0CS5kQ5rUCNcqu2W4bvjPGYg3y\\nvjKUrNbGNbnBBoqUi6zZ4vEp0pWMDokYCz4VPh0mHg4T+3GWQ51CzAnfDVx/+RV/22357t//xKcP\\nH9k/HqRCtAbbOZxz0jOhzg5Qm+pKZD1ygRwz1oTqLVwwytRegiWHTC4BP0dMksnRzllcV1Ckmqhk\\nttbxy4tr/nD1ki83l/SN3ljve+PbU/eVuFvVyrBQUQW5T61SlQX98bD+swTyq8sdpsIMrgu42Up5\\n7MXPUiG4lTI1CFJOTUUFgzJ0WaFiJhb4lAJ/v8ysTSsyKfgTGyXlOpIuWGuMMim6RE8IQRzSfeai\\naAZtpfNfD5ITpFEz3YZ30LDQUnHzswB0nj+vyXllCqia0zapAESLRBgIVC67gqLP1NqgCeyXArGG\\nu9VtXuv11FmrgfoQtQGgmCJL8IzzwnGcUT7zSnV8LEesMdxudwyXHdpUaQQldD0hJiSMlnF70XnO\\ndT8VlJLPGdNxff2K7XbHMh95CA9M04FlETzbWCMHQRST4VQKxloZ/MqZUIS90MwE2uoqLZmyKZqU\\n9EoXFJgn46MkAp5MMoiEQ6wwSc4rm0fX6k6j6LoOHajYdEZpyaysta3WQLfmO4Vm35zzZ4GQs3tb\\nw3lTNkxNMsALAyMZMINF5o4E/w1VkkIpLYJvZKxV9H2DlGQiNKxQkRwqMUgTM1ZnHlMPKquMON1X\\nSCZm2fMhRpQxWFsDJ7VpmYRNE+rAE0V0ipyxZCOMi5yaSFZerQZl9F08s1JJOGfBtHUSTrb0V+y6\\nd5siYykFiybpyLlJQqlzHwIRGqYlc5g8h2nB2Z55DhyXwPePBw7TwuIlUx/6jq53bLqOzli6vqPb\\nDLjBcnVzw/5xz9P+Ca40F9strrcCA+VSSQasWTVU5UErhzQIm05X3N8YQ5wzYakWkEbXKlThOg3Z\\n4BNc2YG3u4H/evOad8MFO9PJDlGtdln5YXV9yvq8th31bI+dxZyfev0sgbzvHNrWZpYRZmbK0qmG\\nKqBjWhYuF5WrjZW1jj5bXNLomIla85QK/z56kb5MkVyEG94w8yaeL1IAlSGCohhXGTNKOMza0KNY\\nVFg76W0QY1UCKufL/PzPnxGiOOvAoCk4a3BODAZUGxhBdK21kaZlDoGUFUQLpY2iy/fOBQKap6Iw\\nsTD4jDZV0c0UrNHrGH5jUkh571kqrPQ0TizTwhALb+yGP/KARnFlB15vhd8aY8anxJwzfl6ItfLT\\nCP8710Au2ttSO+li6c2Gjb3EZy/l53Gph0jBOlMxTym5vQoSACq7ZqkTeTmf1gUl7lByUGZMtpja\\nbCxFKIRLHQRKtlCsyB6rdPKC1UZkBYxpLBbF0Hd1UYXhoUqpgdsJnRWkCaYa/1fWs5meNHJpm75r\\nh6dRbSBEmB3L4hmXhWMMpF7Rbbr6fVJt5sV1urDkjDUKY43AUblQDGAL0ZRqvye9lFRhJe+jHCB9\\nh7IKawy97TDWEpXiGBLMAY+hTwrTCeWxJPl+vlYMyyLPToiJ0okPgFIK5yyhJJQumKKwVtdn0lQN\\ndhme6qvmuVGKmIUf74yj6/uzZvXJXNthmPUik751HUt98DcXF1ze3NBfXBPQ7KcZZx3jHHn/eORf\\nPtxRUqEzhovtBrvpuOgdF5seZRVzjHSbge3Flpdv3zGNE58+vieahe7aomxe1Rzn4IX2usKnQj/U\\nuk6Ew8mdrD7VPiSWJcpkuTaiJWQLprPo6OgxvB2u+MZe8ldXr7ClQkZVybJp08h117hxNity4ok/\\nx8hXeuhPvH4eYwmfZaxes5bMnbMM7gpb3YK0Fiwuk+uUVrXSMhrlTS3XMg/Gcl/HXTdDh3MbTF8V\\nwtXp5Gu0MAMN8ZABCzLzPPJv//qvmMc9cZyl9Mnn2IqqJaNida9XzVThjH7YcOszJbWyfr7gnEMb\\nJzS30ppPVgJVkgDgrMZkhYpFqEml1QOKqOFBFf4ueIanhZfDwsusuegL285QnFx302b2Ubw4/bIw\\nTjOPh5m7pyM2FF6qji/cFps1h7uJwx8/8td5x9vLHaZo7sYD//hwx58ePhHrdYleSZKDrTb4qAEd\\nbbj7tz0oJQJnwVd6lyJ0Gdc5XGeqD2gmGY9GJj2N0YzaM19E0VyhrDhu613r1ltQBhBIIdQgFFLG\\nDh1mMxBk9ERuhdaYThICZyRnbmYLKWaylsBGiljluNhcymRrbRBqexrPbqP2Tey/vaf2gEkQkCwu\\nlVwldSfGaWJSkVSptSTJWDul2JjKTqgZvOs6rDGSndYekXEKszWi++IXOcxrEB86cc7qnGT6g7Fs\\nuy1ZDzwumocYUIc9/RDp+0hGtFec0mw3HWRFQDMtC4dl5hA8wQjDw1gZElJ9QRux0HNVs0RZUblE\\nK/puWHsWCiVNCET+N1dbxiZwJh3qTDNNXqea6rOgrOUXf/gdv/8//pbbyxs2JrIPCX+ceHgauX8c\\n69SrsHTsYLnaWd5cdtxcXaKtIQCvbxMfDgt7X5ix/Gr6FX2esMrzsDxyDDNT8OznidFPzHGRYbK1\\nClfrpGYKJ5CXUkS9MmbxEqgCdxipOgfTcdvt+F13xS/NBYOxq2HKKl8g3WYaiaKcOUqUOsHeYk4j\\ncsiPzi3K/OjrZwnk47xIluQEl2p4Vd91qGr5JiWviMxYbaETrrgvWYxmEfGZBRFegsyLmyvevn3F\\n63evaghVCDsknzVUM957vF+IPjJPIzEsRO/Z5MJr27O3QR6cfFq8s4Rc/g60RsR6Tq5mx6w0tBbb\\ndRUW0kULr7oyHxqvuTWDFKZi7HoVyGoPQVSKx1z4X8fI8u0jm4eJq43jYrDsOsvGiYKa0ZJtBu/x\\n88w0jtzdj3z7MPHvT57rBV5RTYFzwvnC1bHw67Djq3RJzpnrmLnf3/EP3+9l+q7IiZJLkUGrdT3U\\n6SLrdbdgLEukWKpmua3ejkBl55R6oCqu3hae3CXlq9MKN1jr2XASpaogClwkjjCB4zjj7w/1YayP\\nY9Xs0UavZiVKSW8hVPy88feNtcQH0V+nBmZthOXhtMbbW8r1ipPRTMFVzQrWQaCK9fsYmRaBBuaQ\\n8OOJQdT40dpIua6AnES8DVOf3SLZbqTQdbUqCYKvNtlWqoZ93zm2my296XHFcjcW7g8juShs57i4\\nyGyHzPE4UmLGKkW/6bGqkJeJp9FznAveW+xgCGnCp0DOWjIJpFqUyhlUlMaj0AzN6h8gUgJy73LJ\\nLMHLHqnXW5JAfSDMnJMmjzw72lhuXr/my2++4bIfGO8/st/fc8iKwxKYk+gTFZWxDrZO0amE1Zm+\\nN3SDyAnskjSfN8XB9orNo6X3Ax2Fh2PHkiNZwXEamZeRcR75cHzigz/yEJfanKVCIbLPc8kEnwhe\\n2EO2eu0WMmiBdAcsX2+ueJt3XCi3QqfQKBCyOvoMWGmwClAJFHIQnoKMYsVYT/jtD14/SyD3KYJR\\naKRMK7pmy1o6urligU3MSAyLjVCYUhSD1togy0WCuFGKq4sdv/rqS/7wN78nlVRlK9Vpwq4OwRyP\\nR/b7Jw5PhzpRlpmnERMK17rHuJ5jGzV+hledSqAWj87V3Nava13u+vdTg0MCia5TGdoIH9tqK0MM\\nuY4gK4DKhW9MhgKzgodc+MuUuDsspJzpdGGwlsEZBqsYOk1vFU4XShIDiRA8j08HPj5MfNgHehzU\\nAKJypgNus+MrteULNvgS2OnMt/S88Ir7/cgSI00E/zTRWPfaeeXS1ucMXvLrOpyvoVob9VrBp6DZ\\nv3h1amiuaw6iKX/qKbROdMky0TlPnsfHA8dpWfsVp8PlfFq1UgYbtxhWZoU2mif3tOqglwLaKoxR\\n9J3Dv/pqVVxs19gC+joF3ISbaiAfF89+nDkeZuaxUB4ri6Myd7SuuHzVxTHOy6EOnJyHMl3XobQi\\npSgB2Dl6vaHf7hi2G7abDZcXVxAV037h/mkiLDMKxcXFgLaWkuH+YU9agkA5ztZx9ETxCZ8sJfc4\\nbxj9yBgmtPKiKGqlx6Eqrlgqq8YYTekRN6ksWHob/1c+nDxTqYlJdU4qZKYpEIM0c0sWGMt1HbvL\\nK65ubukUHB8NcxSpjqWxe4ySrHgJ5MUQTCZsOpRRdFYE3XqtmY2SAbeLnv6o6JOupImNVCauY9ID\\nwW6Y9YZNlPsy1snQUupchtJyL5ISpk0QnEU+V0R2wCoomR7NF92Wy2gxqbKN1qPtBLmqCrkW5B6r\\nNY6c7frC+Qf+t6+fx1hiM2CslLimmuuGEpiip2gpnRuu3IJ7bCFAKbQzKKtJpdCbzEYpTB4ISYPu\\nuLp5yWleTAZsYkyk6hI/bLdsNhv6fkMB9k+PKGPZ54lPWXFlLF0dmQa1Bi4Fa76XK1eqYWmngkhA\\nsZZMg6oqhxrTWWxflQ5VG5U25PXAklNc54RWUo7VsIIGDkbzwcBxCfgq/uVDYr8ETpOkWaiHpZkL\\nyKj2skxM05FxnPib7YYb02Op2YGSTa5QNIeci27gV1cv+G+v3nK3jPgUCacrXINlG5Y4Bd9WCrY/\\nn+F6LbE4KxlblA2zxy/SoC4rTl5a9K/QjhzupjXbtCIhNmRhmomHqb6/02GzZjv1DZ+GR86ya6CN\\nPKqSaLCYNEANeTtQQpQrr8Jrp3nzdqdlND2lWPFxoR0+Pu55/+0HpiVSiiKrvC6LqsGgrUHjep++\\nr6yTDLjJWl+/esmrL7/gq69/yRe//BVv333Bi9uXLMcnPnz3HeOnP8tBgUhhDEMvYnMxUHKuDdmM\\nX2bGIgQaZzrcFi6U4nj/xP2nJ+4PT9iaVEizuo65KSQJqRWOMWeHt6Jmqm090lodlZX2mUg5sDx6\\nlqnOB+SCdortxY5N32FLJvqMto5u2DCOI6b2MUqGu/cf8dOeeHOJf/OCobPYnNDeY5Tg1/a4p2SY\\ncsTu9yS/kBQcxyd8iuQC4zQRq1JiTpkL0/HaXTAWz5iE95/WfaTQSaOSqoOKgu1ri4hxKUO/KPos\\n3rTPp39LHadV6x6SpnBLMk5V5xrq131V1++UnfBjr5+HR67ruG8uworQBm3KKvokjtpKVPKMBKg5\\nLELaNxayBixBF0YSCwarXc0Qz4c1hAqki2hEFy2qdlYjk1iqMDjDbrthd7GjzIEQCy/7LZ+W5ZTJ\\nUU7ZpzrBfeufoVLRpDHa6IelZuuFOsyABAkBjuQLchHBqZLFbzKnzCaLkJHKUm4lFHsD/0sV/qEU\\nloaXKSODIiiq7KNI/zaetJCwKSVV4wTJBi5tx9Y4cl4E69fCeVWV1lfqcMTtdstvX77hXx7vWVLm\\nzi/P7mPLtU7Zdzn7/YevFe76PLarIvovFX8UAbizaoSWYItBiDUySWicZLOrK09jdtRM5/P32g6h\\n55uxVIZCqveq3VElh3XNmnvX4ayTda6VUpu+y7UybA3mkAOLnziOI/vDiB8XUgvkZ9//BMuVNYC3\\nB/t01eX084zmy3e/5L/87X/n17/7HdeXl+y6jl4XgjW4qx3u3Ru+f9wzxYIyFuMUtrNsrOFi6FEh\\n4eeFj/s90+xFoldGSknzwrw/Mt8dWJ72xBZMKqNqPRRrtdMO1nZAFtUUAuthX86G7wtVjVGajXFJ\\npDkjaVuhJJj2R/74//4d435Pv9uxvRiwVrNMx6rgWVAl4MPEx0/3fHz/kV88PKJS5je3L8h1BiCk\\nzMN44FgK3u8ZxwnlZUhq9rPw0YHFhzqMGFlyJhXNxgxoa9DKognMhJU4sfhAitJXMUqjrUJZhPWU\\npaels17XY828M4I61ENQVcYPhVUPxihhv6z7dU2E/oMH6uz18xhLFJnAS0XKf8k6THUJOVmzaaMo\\nSrQ02oLbUtBJeOTUQY1YT7SUS52qy8LJpFSNEGlgiURphpxQOaLJdNaw3fZcX1+y7I+Mfl5deuoe\\nXuGDZzK2nJpbzXnkdBtO4a0dBu3U1VphiiGUZv4sLIwYIsss7jm96USsf5VGhScLf1GJP1cGQBtN\\nLkWx2Qxsdxu2uwHvl2qcLA9oToEcAzF0hNkRB8tVv6VXmjzP5FKFe0zNuOp6FaXZdT2/uL7lt7ev\\nePQLj34hrVffrvQUvNe1Wf9wwgWfffj87/WwCxWOaDIJLVNWSjjwKlVM2QqtzXYO21XN9vPv96w6\\n+PznnX9cPfvMKf9pQVY+b53l8nLHbrehH7qVMrkKeVW8PlVWi2jby5DScZw4jpOYjVdarS7ltGJn\\n8KeqFV5DhPL5+2sZm1a8evmSX//qa37/zV/hYgA/kqcDW53YXW253vVsrq+ZlaO4jjA+sVOZK6fp\\nlSL5wP7pwMPhiRilCT5Po0hBz57x8YlwGEX0Scn4OZyqqFOhU4/G2txv9m26JTGqHYl1Z6wbRGh/\\nqdREp2b3LZD/6z/8Ix+//5aXX77hi19+yfXtDX6Z5J5rIE30VqZe7x6PqA+FF9uBuy/fY4wExiVF\\nJp0J1pDLwpgSIQd89NK/qVskGvBZERLEIjCs0VqqcQy5KJYcajAWM+2c8joQZpzGdqJNpHPBpNNB\\nBshwYZYDjOYKpDXKGkoqhBwY44IqisF0dLh1W8og4o9l3z++t3+WQB5zwhmHVop5maWE1SLcY41a\\nFetSTsTq6qIAq2UBbDEMWHbKcUlhKOK5uE4sxkRnJUNWRbSLVc4YxIFeK3E12fQDMUS2mw23N9f8\\n+fv3/NkfuJ00j8mLktta+Z4ChCB+5+CJqqWv5N6r1KVWtaoydF0vAzbDBqUgThPRi3UdZOISCVPA\\n1kGh3nbCqkl10MeAaGlEMolUZOI1hsgvvrzit7/7Nb/53dcs88w4jszjxDIdWSbBS71fCPNMmiau\\nnzzq8Qgj6FKwQFcbr40iVRQ4ZbgeNvzN63d8mkf+/fjEmCKhbaasTtDE+VGmyhlsfhbq1wRDvv/J\\np5IVW045r1OrRVV9C1UbklQ4zjlc59j0HduhO6n3cR7ET4ySz1/nj4dqpgDrm1P130oDuu87Xr2+\\n4frqgu1mQ9d3WCeTkJI8nOznUhHFvVgbnePsmRYp44s67ZaGjz4vZGpSILXq2t+htNwdKJmHD9/x\\n8O//Qnp9hcPTq+qbajtwlltjuPmix1y9xF1cc3z/J9T+E918RIWFcUwwZRgPzPs9oxfKZ/Yev9/z\\n8OkevyzoupSK+uYbk4a6xM/OxHLaA2eQ1goPFHnv59d8qn0ypYrE5SQDUa6DXZcwZSH5CUpGOy2y\\nDoeJL243vLz8gsPxBfvjxLFE/vjwAWsLRct9sJst/eaKzcU15EKXM31Ows1PkvCFkNA+oBcP6yBX\\nIilJakyO5BApMVJCIvuEKkKzVFrRbRzdVqNMwniwGTolPqEqN/ZY7ecUs+4rpTXeT3ya9ny/PHE1\\n7HhlDTt12htrUfbMUOKzGZWz18/W7PS1XDmJEBlKyvTOMXSi/SCpVnWlxwjfUykchq5YOmXpiHIR\\nitWoIsVMtlYeiBwo4yMcD7DMYHpUCKhxouz3pHEiTCMlifznQ5j5n8mL+UgBW/fxTy3gc2r/+Sfq\\nk1vKOrAhD4VUH05bii3EpAhRONP9MMgEmXGoqmCSVWbS8HHIGJN5nWDCMQZEvRDYXVzw6tUr3rx5\\nRQyBeZpYppnxeGA8HBiPex4+fSIdDqj9kXex40XRvM+RJohlq+pfMRrqFBoKbHG8u7rmm5uX/OXw\\nyD89PZBSXKGl9kg+l/dtfy7Ph5rO4IPP+zhncX/9Fm2cvIi6Uz1LnahEho7tdsvN9SWXlzs+3R/g\\nMPI5dPO/499+Prz1zK3Paq6uLvj1L77g+vqSbugxzq2aMVRKYqoHUG6TqJUe2NQlTwDKZ7uonK7z\\n9AHpiLSHWWlNP3RcXm55dXvNX3/5it9sHZeHR0gLqrfoi8uT7IXpUCRUiRgibmNQZQCTCceC0gsp\\nJg6HI8dxZM6ZzljCIvtlnmZSjHUNTgdzgwLWM+ez6yjP7v2p2jqL3Ov6PjPOPtsr7W/OaHZbx+CU\\nzFxYQ2ctViUWVSt6rdhuO6wVWPDjYc+w7ek3A/1uSzEdk9ccvn/icDxKcjPPZ5x5caVK9f41wkUB\\nGaFXUHTG6yTc8UniiqHOJJiyShRkCl0RM3irxDyjNFGvdvKVXKEo0BXa6bue190tW+0YtH2+P9bl\\nLM8LzP9Mk52pag+nOriRS6ZU155c9SHIRbIxkU05yaMpMBlcUeL0XqrsqtJ1nDmvzTLZUwXmI+Xx\\ne9LTA2l7SyqKdJxYPr1n3I+Mj0fmw4HFLxziwiEFBt2xVR07ate6lUactt2PL+nz1xqXlDR/BPKQ\\n65eS1BBSxCiFdbayFsSUVpWC14VHV3jfZYrNXCfoQyfQTCoEY9hut1xeXbIZBpKzomZnrVQ2CNMH\\nBWleUI8HXnQvuEbzbdMPUQprlCj9mdaAa0G3cLnZ8vXtC+6mIw/eE6aRKYkbgmSNLVs4D+bP1+As\\nT372ubaWzonGi67641IZnAV9JaYQthhScljr2GwGrq4uuLq+oO8/nb7zZ6fEqoD3H96xH76vfuh4\\n+eKaX//iHZcXO2wN4k0FT6C7qj3eZIKrsbX3sVIcTxKc55nsCSFXZz9zLf+grsnucsfV9cDr2yt+\\n8fIl37y85I0u2PtPJJUgDmA6ssrozkmgWKJ40C4TGx3QGpI1BGMIqXAcZ+4enniaJoIVy7mweJZp\\nFvnYs4nLn1ytFXP8Ccjsx9Z3TedbjC/rXEZbinboDn3H0HdYZ4lF0zuxk0ubgbIIbbbvFMPQAzCl\\nQvaFoApzSYRwZBo9h8eRh/0T++ORaZoEI49V2KsWDEqD6YSdo63AH9oKNdR2lmXJzLOIfFlTPRO0\\nIlFIsYhwXLJ0RfSJ5DqyNDeVJDBtXsIoIEkPamMHtlbjMthy9pSs2yKfBfH2nP0nCuRWabTTaOeq\\ni08tSYOIbVIKPkZUknIsxSQLoBRBQ4kdOhZMxUkKcG6woEpElcrCUB0AaR5ZHj8Q9cCM5TiNPH78\\njvsPn7j7dODh0xPT4UiMEQPMJVNUYgAs0sA8zx5Effpsb55XQPVj7XzNORH8QkpZGkzLgk4JbSzK\\nWXxvSQMGAAAgAElEQVSUzDjYRGctfSn4KJSxQ1f4fpP4pBMTmVg0sejapARrDcNmYDMM9a2VWsrV\\naqH1HarfiS4ap6Q8rEZIq2mBMlKCqDX2CIulWMPbm1v+lsK344ExJcYprherlKqHHSd4Q322GFAb\\nmCe61RrXFFxebLm63Il8q64ZTc1knhNi5J4rLaP2u+2Wy4sdw6ZHW5E0Xp+HcjqQfkoAtDU418Hp\\nmgEl4OrmkndvX/LLN6/ZDoP0TkxjZbQSuDazm0tNiiuHfPmJoHh+4JQf+YSqglMXV5d88/tf8e5V\\nx+1guUwWDk88hojvOra7LUPIsMjQlbrY0hlDXhaW+09EFJuXLzBOtISNUszTwsdPD/zpwx1TiQy7\\nLSFCmDzJx2e85latff4GzzP1xnlvHaMT1HbWqF4ZONT7+tk6nL1CSMxLRNuBbtjSDxtiLnSdxRhw\\n5oUM6VRFxBhCHTzSLMeZD99/4MOHBx4fnjiOo4jmaUW0mmIUGEXXO4ZhwHUO42SeRRlW05cmopUB\\nH2Dx1Vs4IxobSoJ9CJk4ZmIOpOiE0aL0auJVtMCqSsmAVecszhjIBYcknnGOdaDOoszpMM/tWV6j\\n+mkNf+z1swRypyRr7Kyhd249iUOK5DZYV1gZAVoLPJEVBCVqbiplSgyUvMgos3EYLaJNpo4MU7Gs\\nogyl26C312g/UpZIeDowz57DFHg6LozHmRI8XclkpYgIhc+rKrCzonrPAYQ1KH12WK6d/fqXosC5\\nDjt0aCX2aRiFru8114sW17eGGiqOqvBJBeboGXPi4DXTrAixUFKpMqQWaxQlBmnkVj6v0aoOwsib\\nzlX8qQldtWtRSnSpS6fIrtRqAMinYNh3Ha+vrvibt19wiJ4nP7HkxsZhtVQr68WfFuoEV5y3RuX+\\n6Cpy9ebVS169uEWZxng47ViBVCq9L6fVOUhrLdROa3Gdw7qOkP1ntcGP7fwfVg5r8w6Fdha72fKb\\n3/+Wr3/zNV1n0cZUsao6wFN56OdG0OL2VBudSxUYM7o29M/Oth95S2etcXkf1jDsBt68veH22rE1\\nirIk/m1/z/jd94xL5qubG768ueXdzQ1u12EVmO0FJkSM96gCZprBQ0kB7SOHT5/4/vv3zNljnWEw\\nimnx+CCKjT/VKP7szdb/nYL3Dz75I9d5QlpO+NXn09cpRMb9xLd//sjhacG5rvGyZAI2yLAeRSCQ\\n0lyvlsAyzkzjLMNhi6/mFImldyzOEqxUmyZmnE+ij+8s1pkTixTq0FJ9g6VgVRb6o4wz433kMEbS\\nXpLQkCOvOodyBe0qKWKNXzVJqGyVog11BlYMJdSpOiu0Z6TKFqi2I2GV+/2J+/PzBHJtRD1PW5xx\\n4l1XB4FSkWaETH7JL5xgkhEJ9rpAip79+MhRHfGdg01Pp7VgadaidGNhVAaAG1DDJTx9ohyOxOOE\\nXxaWJRLmSBcC1yg2zvEIJCVKhh5wcLJdO3/eWtYLPwgd58uttcY4R+e6MzVBv054GqVXcSuf5OTX\\nVabXq8KhJOYU2fvM01JE5CtKC8E5UV+z1tDMpcWU1pPDQvILYZlJTYem+UEWqsazXJQuErmLqjDS\\nKg8gF2uN4XLY8LuXb3h/eOLb/SMfZk8o9QvPYKf1N4XAR2cr1HjnIiIk/8waw4ubK66vLtZsFxqs\\nIl+klBw9qTrgyNuuLKVSULbDbDYiJBWjlO3PHpDPX4oG4J/gHjn4h4st11+846uvf8GrNy8rxdys\\nErbtLq9TrUWCTE6JEETrfQ7iVKO0/mHgPgtyPwRY5EE21rC72PL67Su2Pegc8cw87vfc+SP3hwVf\\nEqNfeBz3vLi5JGmwtmMJC2FaKEtkPC6YzpKtkAwe7u748OmOzdYydB2d1hz8TKx+qJ+/zedvrqzv\\n+AeXpE4F4Q9hqhOD5fzZWHNNdboHOSXm48y3f3rPp/4BbYzonCTx1E3x1JFRlUwQY2KZF+mPpfTZ\\nsF4hOMVSFEuzG0wJHTJdZ9mi2FojUGejUtYhplIKOQXQqiqCntCCx+NCKFVeNyfCRcLsQLnzCy+1\\nQhE6MdpQrMiwqVRq5fW8lG/bv/nsPl/J/IN1b6+fJZAbLT82xEyKnk0neFjXWawT0H8eF0quMpJW\\nE0tmjhE/JbSGyU/8+eN3fGcij9sLir1gULCxBmMs0FzPZaQ2F03BkpeJNB1J80IcR8o4MswLvwTM\\nMDA5wz8WudkpF6EmZXCFFTpWpWVi6hRsymfbtEJaCpHm3fQbhq6nd0IttMbgg2cJQW50ET60zgkV\\nJQNtmWlKMEbDg9c8RAPKoLSc673rGYYe13dkpYlEQgyE8Ynx8Yn9/SOP9/f44568LCKVipSAsZzE\\n+ksosCS0q5rgUcoM4UpL6tApy1dXt/z29jXfPj2yjx/IocqwqoxuVbM6r1pOG1WGbNpHRfpVo+iM\\nYTc4ht6eTsH6jXSdvCxF3nMqBR+9CD/5wDTN4tRiO9TlNSYk8jRDkL7AyttuB8LpDtFwgEYjpYj5\\nwM2rW775b3/g+vYSGbps9MT6dUrJyV6bYu2b5lyqkFciZBFmW+34KkPnWVKl1Omfr29KxNFc57i8\\nuuLNu68oeWaZ9qQQuXx5zeZqy9sg4+J/3o/83Z/v+M1yy2+WifH+UYy0n0bS+yfuk2F4c8vwxQuS\\nLnz4eM/98cAvv7xFF8345MF7yhIg1MqhnNanBfPTmpV1+Kf+9fT/xrQ5feV6XcJYKWtmmes1NxRO\\nq5Z1KuIS+PjdXV3n9j7K+l7OBtzXzXauVSr7th239X7VgKl1rXU17HYbXr+85OXNlhBmuc9a5LJL\\nEZhnfDqS54hJBWd0tRXMzDFLgqgUZE2HpmsDP+ueYzUlN8gcANZQiqEU8V/IFE6OTLJPn8FZZ2v2\\ngwzx7PWzBPKLzWbV6qBA33V0XVfV8ESc3hhNwhCz6E0LSa/Q9z2LN3zUnk9hz59C5C9h5sPiMeM9\\nf7ITlxy5fvuO4foK2xnS/Qfy/pE8ziz7ieXugXj3AI9HzDhD9jyWwLEkDjlzTAEfq4tMqVCQMpi1\\n6Dl//fBv53leC/RKqxU+8stCqLQvgxK2iKY2WhSDdXRBMx0emZMib4oI3CtDzLr6HybRJa9KdIYC\\nOQq45hd4eM/y3feMd/dMD3v8ccEfR+I04YeAzwlfUh1RQgZalki2kawU5HZI1ccjSwZkjeWLyxv+\\n68u3/OW45/s8MeW84t8/WBZ1/qyrNXkX0TGN7Tv66wtK15GqN2cOkWISVR9LXklip1UaZxyzXwjV\\nJGOcZ4rVbK53MB8FN42pHqS1Wc2a87T/5D21w1kplNX86rdf84vf/5Yvf/1rOjUTgidmJ/6ZqTbT\\nEV5xm4FYs9DabA/VEzVUh5vTIXBaEzhPADg9vEpkjuM08+2//oX/6//8v3nz7iUvXl1xdfNOBKzi\\nwjweWMaJbjNwfXvJMHQcXeHP+YAKBtcr+ncXMCVsF2F+5NPjI3fLkc31jhdXVzx8PPDw4Ynp4Yk4\\neXGXOl+cdY2eZ4bn4ep5PG//9nSit57D55m4fFqtwX8VYGtfUIe7SFSBNvkm+hTS5WNnMJ6qScJ5\\nvbDWgqpRShXGQO80F0PHxhmchn4ztO8ISiz3yIVOGwJN7VCzhMgcEqmeZBrZk8PQ0W86StH1cBJj\\nEMnGoXm9Ukoj49Govs8ShHr4lWeLptY1/5zt1V4/SyBf0aD6prQSCU5njAy7tCaLEoL/HEI9tjVK\\nOfYZfI6Y4nkKC/t85OF4QM2PFL9nfLzj5osv2d7eMmx7tg9/ZvAzOsLd/R2Pd3fs7x/YL0lsonLg\\nU448lcxY5P3lcnLhnjVYBQ61xpUWpNsVtaxqde45f9VGbClAKqtOuELhrF1pecUorLJsosEEeJwf\\nOCRLMY7BVb/FrKt3Y0YjkEpjp5AKJSbyMhP3d/i79/j3d5SnEbUkOMzEaeRjv8eXgieLLdbGMe8s\\njyaTCaQiqnpqFcBfI4+way4HXsUXvDreMB3FogxUnXDMq5bIs0yiLpqqT2qmMGy27G5uuHn3Gntz\\nS3QDASVTqSmvGZs87DIPq5XBaEfGE3MhpMKSC/1uy4urjo/jkbIEShSlQr1mhD+4JbVkkgjgho7d\\n7RVffPNrvvj6V1xcXsGi8EYxuwu23QVp2GF7BylBCqCCsENUPfyUYOc+JXyBbCy661DNzq/IAdaM\\nSdaDhefBXAHJR+4/3DMeZh7uvuDdV295/cVLNrsB1ym07tlcdOyuRA9GqN6JYxSnJWcNXWdIKlGY\\nCYeR958+cT9PoBTTGHi6H3m63+PHSZyKnu3p54u1BnMlz+1zDtD509DC++fEwtNnn2fqp9d6XKiT\\nfDNAyfDDr15/zPk3+MFBIodDqeeCvHdnDLut4/piYLcRswvn7JpspSIxSNdr1u1OKUXIZc3G25Fi\\ntaE34neb0/M1bAJZMuBT0FnmTKhVSQvScDqsntMzawJU1/4/VUZ+9/SIre7SpoBViqFzKGURcaQs\\n5U2SZqectpkcYfGJaZ/ZzJEXpuCWhFlEhvL7uHA3Hfmnj9+j/uf/QClFpzV/ZQtfDD27oeefxpmP\\n1UOxaEXMBZ8Lh5TISjSYS4aiIsWDz5kpJ5SGjbI4xBGoLX37nzQ0nsdwJeey+PJZ0bZ2SmNth6s6\\nFkZJEzcX8fqzvaM/QomBj/6JMXfkfsNl0riYSaFQlEUhQ1R9Z6v0qRIM0QfCMjONB8LhgHo6st0f\\nYPKk40Q4jvxjgWvrCCXTb3r0yx1PX+74522hMwupCLfYarNubnJG1W47FwrfbXmp3jKPO1wIYmgc\\nAyEszIu4MsUosFYTFhOtjKYHn3nx7gve/uprvvzmG7YXG/xgGEvmQiuKLkRd0ywtOGkqhVQx/KIM\\nRVuKseS+5+b6Fd3FFYenPX7yqBBRfhFFubMgsz4e5fRwZq0Yri/5+q9/z8uvf4W9umacZrrNNcvl\\nJcfrG7YvXrC9vsRuOoievMyUaSQ9PYgtmF5kYk8pYskEZ9GXF/SuI8VPFdbKNB28NdBwAnyeB0bR\\nNB/DgX/6+z/yz3/8Z7q+48WbF7z78i2/+OWXvHn3movbHf3OYlBEvzBNR6ZpZJpmnuYDflmYfbU5\\nXBb288zD08y/f/fEfH9g2Y9ijlyhgM9F4M5Xrw2+NWSJZ+/3TIOHxmBqn22G5C17buH8+Uu1N3Be\\nrq+PWf3zWdV3YsnWg0a1ZKpUDSH5V6uRs5K+TN8Zbq423N5s2G0sSsv1i0mEZpqyQGFZEiOK6Lzk\\novCp4FOrQOWXsRoXC26JFGNppUXTthGNeiWTn7Eg9ognuKkdPqWcLq9tj3Ud/wNYBX6mQA5FtHdR\\ndBUzVhRC9MgEmeiEGFVwSjGlhJ8DISgWn4lLxmtFf32Dur7ipmS+SYmoTNU4kJunYmJIiQujeYye\\nf3qc+TB7DkGgBZXrBitFXFLqaZhTOLlnV3pjpLAgN8ZwNmSiqFN4clNU+1i9TsXJv096AT0pRYaq\\ntZ6zOOPMi8AEJmamPXzYB25y5mUKbPeFxydP9qC85qgsyjlK33OfHvkf/4/n3/757wnek0Og9xMv\\n5gfs3QNmP/JxXtiXwt5p9oPDEZlT4VIVuqHDXm2ZX+147yyuGmBrZMMJPz+vAy9Wi3nDEjLBXXAR\\nevpSsNbiw8K4zEzzIsEtJVQWI5H2K+eCD5HZL7x591dcvfiS0l9Qug1x6JgHy3F3QXd9zeb2Bf1m\\nqE4zmsV7nvZHjvcP3OePPM2GeKN5/esN4+w5PO2hKHYvbzEvr9BMWC37YQnh7BCpzetVDtZy+/Yd\\nv/wvf2B3eYlCk3QhKU3SHdntMLuX9Lcvubi+QJFJMbBMM8cP7xn1ex78B442M2499o3iq91rnh73\\nHB4fGbqB5BdUESVDmbwd5XCsMELTqD/tHE47qBSKUoQcuX94YJpn3n/3gc12w/ZiYHMxcHF5yWY7\\n0PcyPyDaHTsGt8WmTOcjtl9Y4iOH4z3h+ECYRb9ozfnKD2PF+m7WANqC7HNQZaWerl8l694y90o9\\nqJosJ6hr9fusAVi+6LMK5ScC2LOKeKWdyHO4qmtqhXHCatLG0PWGq+stN7eX7LYOozMpBdnnsZCi\\nVEPEBEmcgBQGtKqDjKJbdLpojVVZuOD5ZM9YgNxouTXLP2X3VdJAV5kNI7l9Pl/mzw/Huk/+U2Xk\\n1hjBupRYK6XqodhO0VwSMcS6IYX85xeY5mrsiiZvBvxug3OaWwWbnAnVuTylRNYaYsTMCyZnDsvC\\n3XHkyS8sdbKqxATVhGFpRrBFDhFVxZGauUFCTFxLhV5qpbZu1OcH5tlq12O2qEJWmYTIDhhtq9C8\\nNMlUqcpwIbI/RA57zzZHfgf8IhfiPEkDLxamAmboIXaE0TAd70Thzi+oGHmjFNvBUWbPg/d8mwOT\\n0oxKMRpNlyOuFDotetV221MuOo4FcUfRa48JKNL0zRmfEzmLNnkyGbYGo4wI7BsNiyLNUDYGG9Pq\\n19j3jr7v6JwjZ3AxYpaFi1fX9NsduWiU7VDdlrTZEK9eUF6+xr1+w7AbRKwKSNNMVPccjoq9WRhd\\noGwzl7pn+fCB+dMDShs2V5dsNhbjRjaDrY3lUEX+RXnOFGEGURTK9ly9eMvtm9dEH5jHkWnxXCuN\\nih4VIyUJdzkpgzEOTEcpltBNjHbkSR3Ys2F0F+hd4nYD2na1SWvJfsFpuL65Yp6PTOOeshqPC5RU\\nS8/VS1Ke3VK1PWoNWKQ57XMiTAemODEsPWMIbJctm6GvlWw9sJoDV8osS+BxjBx9YkmFqA2l71Ba\\n4AFr9OrKVRD2mMBjudrXSQ8gtWG2ttWVyFGsUrfnsECF11rTN+eC915MsotMaJ4y/vr1nAfp02sF\\nntYkSq0fe378nf0LxWrmoAwMg2G7c2y2TpQba6AVqSHZ6+RCiZkcZKS/QWFzkECesjBO2nFlY8Gk\\nUpv9bc1KfQdqRZ4aHq5W8TS1Qsjt1Cuw/rwmG33qw/wE9MXPZfXWd0J9y4UxeMpSKGQcHW0SMfiI\\n6h1KW9A9SygsPmNMx3ZwbHpH2TgGa9gqZKNV0SyfM3YYSDkxTRPTYSQcjmyB/XGk0wrtHFkLH/Qw\\nSdmba3mpVaUctlqtUhFbrdaCeDX1EWGrOsDSMHTZb5KjpJKZs+cQRia94OeAU47BWXqnUSlji+g0\\nzBn2k+e7pwPaKQat2Lk6rVAg50BIgeITKgVCiNwF4QHHFOljZDCWzeUlf1Saf9WKOwMqZ1KUqcOp\\nwKRgQqM7LfRFrUVUKGYxgTDiX6lKdTZRMpF7v98Tq2WaVpq+cziF+FMuHu+l4WpqpkEpAjUsC2VZ\\nUNWLVFEIwWPDLA0oDcY5lBnQ/QV2d0V3eV0zciMBZCnM2XCIwuIJWIrpsDaitQEtUsH9ZmB74TCu\\ncHU1sNttUBgJnEWcYKw6acsYvcX1NxijOU4jD3f33N8/cPGrRDcYzNGw3Cmeiqf4ka7vKShmHzge\\nj4xLYEpwSIopQSx1KtD2mG5AOXFZ7QbLyy/f4nrBrcWGTyzWaNmjsfRdtwbBnBKbvpeBLSX6OmL1\\nlokhQBOIU4ocM8d55Lv393y4O3D/NItRS+11lCyaRCUnrDVwdSE2blbG3S92AzcXW7QRrf/FS8M/\\nxUjygekQGI8LfpzX7Fsej4LtNJtdh27QSJGqmCxZqHVOnpkYOd4f8LMIUFUdwNOE54++Cm146lnT\\neI2A8kZOiNDZVyl5/mKJUHR1qiooFQlJzLUx4g2glUanyuRKWXjoKWFqk/ToA0sQAgQVvtFFGG2m\\nFAqJiECQlFL9eSUZVK4KAdZnqkGzdQapFj1qHcaTHEP6aeRSvYx/Koz/bBm5Yg5BqHepELRiiQpP\\nxGrJoIbtQCiwHwN39zOLF2qQMZphcHTOSgZbyxOKmCikqhOdcxbVxJTxKTPGxN57DkvAWcfG9owx\\noYyuuHhzKznhWk0qWgEGcJUzlWr5t74UVQfmh5heKycxBp8yJimyBtsZlDEEBQlFVJqsLBTF0Flu\\nrrboroPLC+63AzlGVE68y4mb1iYpQuNLJYqptI8MPnGdEg8UxijMij5KNh2iDKvMyuG1rVz5yljJ\\nUUwElK2KuOU0ch7CWupebXfEaqari8Amzhjpd1q3qr81r9VUMjFVaCYn0ZKpyo62LCyHDxwOET3d\\nUa6v0VfXHA4f0Q9/IXx7TT/0q8zv4TBxf/+Iv7+H/YEy7pnHJ54envj4/zH3Jk2uJMmW3mejuwMI\\nRNwhh6qXVa9fcxZhL0ghpbngijv+fS4ehaQ0+3VNmXmniMDgg41cqLkDNysft9mecodEBOLCATM1\\n1aPnHP3yyul8kQG5MXI5BYYhoY8W7zxxSTjrcLaXSs+0IjcXYvLUDHG68uFvf+L8/AWvFb/f/ZF/\\nOnY89GDLGXsO1PkTU5EG/GWc+Xy68Pxy4vn5hefXE+fLyBIDpuuZx5EwX9EqgSkUFQn5yr4bGPp9\\nG/7csjKjmJoHyBq0agWDpWpNtSJ8Urmgc8WWSoq62RVXpjlwmQIvp5EP18BrrIzKtMXZDKsae0pb\\ni+o8fW/YHxxvn3Y8HHbsh57Bma1pXasmhMA8L1yugXO9cE2ViVvmSa0cdp79045v3h1wVjJ7hWDE\\ngvWKQ6d3jpoKf/nnP/H88zPTZZJZvHf6mxu0cBfY71AddY+R/ysUjs3oSwFVkTOU3LzDvSSB3svB\\naI3GGamOjZIRgvNVrApSEruQWiFXxRgyceXal/bCNHijRYeRClXfzMVUkUBumlGWUrqhEDcyAEjF\\nsJm+rbV9ueu3qeY39P/jnPAb8cg1Viuyap7jG1e3bkpBlCKEzDhFrteAKloGIZQodKSoKTmjvacY\\nqEV8LXJtYp4iQTwukWkOXMeZ8zgTUsZoS2nE/tQEBALYyL+7NnNUc+LTiHmWLbJIVnrQiocDdyvw\\nLpi3jF0Zg22wgi7CG7XNBCqlxnev0hJSiIry4WDBdbDfMR4GKbGBR1V5pFBTQuUqmLUq5JJIMeNi\\nwYTEJS7kELAh0YeEipDSauYj4FCpVbyRjaboikJv/hBl85Ku26ZVtOy9KSxVBW+sUEVTwhqH1vYO\\nPRXIyGqoJTdqt+C33jmcKeS0YEh0eWSIhn3SdHPBnAIpnlDOScM1V/IccOPIw3LBlQVXruRw5tPL\\nR758+MzHy8S7t2+lCiDhjMFoS+87dFU47/DO3VSopUBtPuG5kONCmc/oeGUYDjwNlreDZXAAEUKG\\ncCWHIBbA04y+TtjrBT+dsOMr+XThdF1YtMFo8Bb2B48xDuc1u4Oh6zW+EzWuMRZjDWjQs2IKsm4F\\n5gOlNLk2b6KWVdYCJRWmJTAvkXlJnE4T5+vMeQycr4E5lWbvTFtfGYXGGoX1Brc37I8dT489T097\\n9ruBznucVsQYKalSE02/kTkviUvMjKUSG/SjtCRlw+PAw5s9x7cHkaFbI0MpGjyEboHcWvIS+bLr\\nGb0jmUBMq8jlF9j7r9Ba7rPsX89O5Wfc8axYqYfWGfrBcxg6Bu+wWoFq1ae1rSJv/bKUiDFTUrn1\\n0HJlSZKNbzh3+73Xmk4ZzNpdXYVmdY0nre+0TpKiwVWtMt1MsBUbkepeDLQehlXfvS+/uH6bwRJK\\n0VlHZywiOJHHeuca5CLNv3nMhDFSQsIVZJJ3qsSTmD0ZFGboUEbGYCWtW2ZbKVoLxS4XxsuV8+nM\\n+XyR8rJUpsY/XprHcKkFp5oLoJLhyzoXMHKAiEkXN7OkFSfnvsC7f4QtizXGSDBRwnhx2mBowSM0\\namWVkkwrS+csDIqoDVgjw4+RqThOK0oOGFUwqjSOasvqtaE6TdKaxcjIrV4llE44K3awISd8segq\\n5bP1Dts7sG0oRZZ+gBwrGmdkzF5IkZwSuqimcm2lohKlWkEOZVWlfM5ptfAsDF7Mj0JVzHOCDG7w\\neKcxnWM3PPDDrueb/Y7joWPoHJ0HaxNGrzyPSrKZY68IyjHZykkHbDD8nGdOXz7wt0+v7HYdTpvm\\nR29xxjJ0Pd7ZzUs8FzG1yilTU2aOCYXBFsubwbAvHd7t8N5hrGoVigx+VmuqZDTKW3S2dNWzo8eF\\nM+Pzwt9ePvPX88i7bx/54z++55tvH3g4DHTeyaFRUrNslcY+COxhdMGaSi5RfPYzKGWgiAw9hIBz\\nPSUr5jHyepl4Po18OU28nmYp+1Gb7FsSjeYnXkEhkMowwPFR8ebRcdw7jMqkOENNRKVZwsI0zVxe\\nFy6XyOm88Pw6MYVCah05rcBZw35wHPc9D4On0xqrFA6NUzKntZW3eK0hZcK4EEMm5xvGLEEBblj3\\nXfxev3wXt381hN/zse93pVYYZ+iGjjdPO94cduy8Q5fakpcCqrQZpZUUsrCuYqLkilWKUKr0FNqh\\noFsTMyODMQ7asLMObxy13PkEtZcg8KvsG92yxnt6ZQtmrOjt7U1Yq/oqn6OSBumvXb8Na6WVZLWU\\n1rmVzCMWmbSyTrMZS6TUhbdqYZgzPkrjJ+ZMrIVUIC8zGBlUnLUhK0g1M8ZMUIpoDJfLmWWeUKXg\\ntAxjFhe0JJzs1ca/8QdrrSRayYM4k/k735EGgLcOvMAT9+G7LQ/5HmQSdxhH9sNAZ3u876SZVJqM\\nuzS+qlHMoZCLlGouZdI5cb1qNIWsjVANtcAaGlGLrdl1qVKm1tWaszFNYhXuqreWh8MBnyEtkdO0\\nCEVq9dMGsUaIGatE/KO1bhmK8JOruQ3+cK4T+l4umCKiJhnMrHB9Jw1QJY1FmVRk2/g0y2HXk0tE\\nUTl4T+dda5axmZG1txNY98St8VPaNKOcpRLrO8vbx4GHXoYCoBS7fY91khlqhcjQl0ZFaz/VOItJ\\nNFy/ChbtxOfctXtVbdSauNG1ddu8fmIIxBDIIaKLwqLQJTONI0odORwGrJasWDLtDHn1x6+NdtqS\\nggwqa0wx1JApKaNMxVuD955OW2JRnK4Tf/3xmefzzGUKzCEKyaLZ6KpmOyteNA6lKt4pDjvP22Hz\\nLgkAACAASURBVMeBd08Db447nJUgH0MmLALp9F0n1LusmK6BT5+vvJwDIdWbAZhWDIPj4aHjzVPP\\nN8cdx11PZ8WTRsY4GpxzlJJFwZwz13Hh+dOZcZxlJq82oLJgEPeB7Y6SJ5/STam5rQbFJvS633l3\\nQYaKNAxdZzg+9Xz/zZFd70Wt2wwqSusd5ViIIbJMgbgkciyUVLBGEStMSbjoq6+Q0jJI3WvL0Tq8\\nXqf+SEO9VmGtFMoNJ9eyX4UtdWMLCQwjb0BdRwjW+0RRbffzSzniev0mgXya5zb2SdgcqikUYxNx\\nFIrIzUtC6UK3s6ioJEspMFGJtVKNaC2l3lGt61xYUuQ8RYox6A5ikjFenXOoooSB0RwHNwxcq40Z\\ncAseLbuoIrGVEqfdxDoJhV+sQW5BfYVrjFJ0xrDzHZ3323gypa2Mi0K696XANMXW5KosYWpD3RXk\\nSG8M3hmK21pEjV99G24gJBkRM4ScBNPLclAZY+iNFeVgLVymQt+wyFIFpkHXNrZMuN5U2WdidEbz\\nsZFS0WkJBLlBKGIdoDFGsEdrxSlQVamydK0oXaXRBuRS0arglRywto34o/HWZWR7m7jSNkJVNOe7\\nNhwgRFJK7DrHOz2gS2LJInTynaGSmZeZWjNziITUhgS3Q9EaRcyiZ1DVYpQwPbw12xizTcxVBY+u\\njS6YUyKGhbgs5BilKdUgQqXAeXkNKSfiNMmhXzJWabzWdG3kV61CyVzNqwpFXPtyRlfTbAIUKRZO\\nl4WPn8789PGVy5xauS9uiaWNUZPX0LjVWuOdYug1xwfH49Fz3HcMTu6vVlAGQkxNvVrIITNeIudL\\n5DIlpiA12rqwtYLdznF86HjYS2WldYMFGgsjU6CINfW8zMxL4vnlypdP5zb2jo2Ns7Ez7vbebQf+\\nMgO/S3PX77/7hq+fXkUjMDj2u45d77AbU4VtDoI2ol9JsTBPkRwEVqmlUrUWWCXXDaZZ4ddV6by3\\nHq/Mpm6uZa3YNZb1UFqfy+3Pu8Jhpc5v/uW/vJeyKRB+9fpNAvn5Osp9VFFPaiMez6u7mFIK47VM\\n9nGGanteKVxtZJwDc2LLHjZVZKlYqkxnqYpRaTSaHmnu+K7H2UKJhRwWSpR/t1JRWYQ5WyBXgke7\\nqtgVvUEqa323vs2q3uhDrUAW1Vb7UFZ/jc5ZHh8OPOx3aKeJKYsHg7Hg5H6dtdQKr6cXQpVGy+U6\\ntglFmhRmktH03pEHB0UaODEllpSISaaZ5CJBahg6UlM29mhxWjRKRD5GiWOcUfi7zKe3joJhSmzD\\nEqgVp8UhzhgZUyUFSaXmtEnoCwJPaG1R7XAqWdgrymhxNSwFjORCS0gC7eiMyQu27jDo1puoLYgX\\n0FV+3poZI43lqoQVsYTEEiO73uEGw3mcuISKG3q0rSxxoVzEROsyL0wxUqhYI5ainTXUZOh0J9VG\\nLQ0Oa0G84ZvrqEDJtgSzTjmRomTkKcXN5K3UwjB4uk6y4XERAdqSxUp25zsO/YC2Trw/amVaIs/n\\nC9MS0FZ6DqiK1eKznWNlvkR+/PDKhy9nXk4Tqd6zqVqzXt0S3FKhlIRzjsPB8fDg6Lym1Mw4Tnhr\\n8d6x8x6rNCFlSobzeeHzl5GXSyIUjbJOst9SULrinOZw8DwcHN4qUk7Mge2wFUStUJaFmALLsvD8\\nZeTTlyuvzxMPoYqwTqmNX37Lkm555zYGr33e6/9tOepWIcu1xQLqLVvX0PcdnXdslsjtl7Eebzqc\\nd4RQyWUmLJkSJTmUyk9EQHNuRGOltn9TaXFPHKyTgRIg4j4liRF1fd0NG19phOrudlV73U2vIh/a\\nnRoUGoLR7vs/p8ESD7uB1UtAa71BE6CJubEcQqFURSmKECvnJbPEQtUarQylVlIqWNdm/iFKuJAi\\nS050LRuMKaGNxVYJNiFEYvONXnnOutGzpKGpGqNCyuQGXW2f4XpySrLWQvp66N4tMvmwmgpRK9CG\\nXBRkhaoaY2ST5zYgwrQsse8t05iEGWJlg3ljSL2l8w7vPWnlnqfGP04JNS/M8cq4COVst9sJ6yRG\\nppRk4IB1mK7DeIcyBj30eG/pnMEqzTSOpKbITLmAliw+AaYWVFaUZjGwUi2NFqP93bAjV4G1SokY\\n2xwtS8Joh1WKnEX4ZZpfikKz05qDtW3UXNuIuVB0wTRef6kizV+zEVUrtUFHtRZCDHij6DvPh9cT\\nc5Jeg7KVmCMxBxSKHOWwG3OQakgpKAWL42gHHqoixYBTmqHvcFZMvHLOomRsDoe3ARKJlMRxr2TR\\nB8Q23iuGxDhNnK+jjCGMgayqeK5bQ1WFVDNhioSYuM4zU4ob7uqtFVGPslzHhc8vV378+MrreWGa\\nM0o58eivlUxucNHqAS32vp03PD32HB8kkA+dwxjZb1kpxhy5ThG9WDH7WhLna+D1tHC+BKalEosA\\nFNSC7wz7vefd2x3v3+zYDw5bC+O0yLAGKrkmcSzU4igYQmQcEy+XxPmamObMrihcC3S3aw3iDUP+\\nxaO3zLveBf5fXg1SaptVaVFd+s7Qd4bOG9apUwojbKhaxRsnBGKQoS+bg6KCOSXm1CiiRjQtm6Fd\\ng8l2Sjz+BToxmCJDmE1pFGalJOtvGV+ltAxb4FHd2HCCLtxV9euBgWqKKvXVwXV//TY8ct9tH4TQ\\nBKU8tKa5JDW/Dq01uSpKKXhr0bs2JzG1afTa4L1uWYBs7hATXZQG6TgFxmmh62QCempNzVxyG3ws\\nRV2nFb/v95jcPtQ2uUg3bwbWjEB9jVfdrso9y3M7SdcFJXUYJcuUEGeFsifTtzO5VsEzlaZ3DqtD\\ns2a1OOfYdx7jDdaaJvBYucQyd9BG4SmnGHA5o5QWT/aSNvVYqRWdE3mBlAImZmzKstibGf5KJ1Ny\\n7mwbJze6Iaze5nKSaa0p6lbCi3hhNQZqIgtUgyn09vO1kiG3NmZ8rTirG8NEb0KxWpujXUvMVzYZ\\nQF1Nw1gFL+CdwXeekESN2nmDBkKQxpVqVsGpTaJSBpJSMo9RVRK20TAz1huOh51USUjfoCqaR45s\\n9Jilb5CSCMpkXmfdMr6Ve51jQumKN0I9MwqBIVr1GJbIskRKzQJHaeHUkzUxVq7LwufXKx9fLnx6\\naQ3NIo3oVTaU77A9jYizhs5y2DvevxnY7yzeNagLhFpX6ybSSUtknAKX68Lr68x1iq1Xo9oSVmhT\\n2e0dT089b556doOjs/J5uWxvop+WmORmOjdPkdM58HoJjFNrcmYaFvz1Pvr1ELXWv/UO8uSr/bY9\\nv66oRfPlMRrTW3a7jmHX4bxtFa7a1qBCejzkloUXgVhK8/qZYiYkGaytqziB6kYVVEUSsH0Txa1y\\neqU0WtWWBDbR0DoCrt7dc12LqbpRndek8D7x/tq/6dfBld+ItSKZAVTGeSYEUXn5QWbeoTW6KNAW\\nCxgVGPYajIg+aqkbB3QtgXOV/89FpvB8/PSZ03VkHGfJroyGZL7ygFjdyHbG8z8e3pFT4ufrmZ/y\\nJAOGFW3+3u21//JDuOXgDedrP1+qoTU/aPM3mx2sc56GUGC1IZuCkQkLbSi1YMhKK/rec3x8YH/Y\\nySGUElqJUi82JkMKRih+tWC9F6imc3hVJEuwnlKkCkkpsIwBEzKHpKjlSEXofVZbjNMyabyutMhG\\nX2tNGGOtsFOMeKyXlsHnnNCrwKb9ski23lupAIqq21AApRQqBtl0TmOMlVFqrAffas5/f8Cs/hnS\\nC0EhzUprGn/ZkHLBDZb9zkOFGCLTvKD0DRJyd3DsShgwrRGljaLzjsfjQdw4SzNR0gIN5QapxBgJ\\nMTaHw9aYalCb1gpKRtciwrIGicm4vzaKUGuBt0KgpryxakCEV1PInM8LH59HPrxcOI+BmKCiWzOz\\n3uA74esKtKUUndcc9o63TwNvH3d4K6HPGC3VVhRhWNd5rLHEOXJ6XfjyOnK5xE29WGlU4TYt/nD0\\nHI8dnVcoMqXK1/udjFvTVWFVE9MEYX3M18jLy8TpMhNDkpFmglk0h0Pu0p67ILdWu+3xr9LwO5jl\\nPrzdHpZNaIzCDo7hMNDtOpQFUku6tBI77VohxWbZAORCbIdloTKl0qqSZudAbT0WgQFthb2Wilay\\n9NqIOnp7PcJRVxtMUtZMW63q2LpBSWsQV21t1vU+G224/Ctk8t8kkC8xY5rrToiNZN9Ov1w0IUKM\\nwlG1RmOcpbNOMtK7WqsidB7dPhS165hjksk/48QUItZ5OutwVtSRfRKL2pgiFMVOGX7vD/zPb3/P\\nx/HEaR4xaHJjstwLhuvdalmltltH/Su7NnlWRibvWG8Zdj3VQsmFaZ6hFqw1dM6jrcEYQy5wmQOx\\nFJy16BB5PD7ywz98hzGFOM+ESRZ1MUqann0v/uNxIB8PzDGSSgZleIg9MbWGUwiElkEqO6PUgqkZ\\n1wJhbSZlRhuccaQipX8thU7LwVIQTxqKWIHqKnbDGgi54o3FG4daBUtKJhRVBaUmUEWUm6bH2V48\\n0EvFdD3Od3jvMcpIs5ZbWdoiMFKFabx1dN6zhAXvDLvOA2IPnEvBO4PpNFMUxeewGxoDRzJiUyEp\\nsR1YFhjcwK7fYVzHftjx+PDA4/FRHPFUxWjJgFPzG49BAnmMsdEGNZ1z7LoiwdFbus7wtOt5t9/z\\n2uAJa8B6L1VoSKSCrAOj8EbTeY9SmrBkPl2f+enzmZ++jEyzlPbC7V/x4baZWmYpgjTZM8dDx5tj\\nz5tDj7eOofd0zmG0ML6WEIk5E2LhPCa+PC88nxYu19QmVK2BBsHpTeWhDUPWSmCHOcj0LGtsY2Ao\\nlBKqbi2VFLPg/qeJL68j1zlCkkk7tTTD723LrBGsbR91a+xJPG+BbA3c6777FZhB4Iqy+SQpryk1\\nMc8LoUqVb61FayuuqlkoqHGJUkGlTMjN6gOYEqSqNu3BCs1oZTAKvNL4RvEsdXVBbbJ/JTOHWT3p\\nV4rkSvRob/Ja5W/PVYrSsPOtbqmVUlJ7r//++k0CuTarx0o7nVasqrQhB4jHtkLw8K5zeCMcVepq\\n4CRVfCoFUzVWUl5yTEzTzPU6k3Nh6AectSKMoG4Zk27/7kE5vvUD/7B7IOeINzemBfxr5d4tM1d3\\nv+6/XxBL1bK+QqySoaQokIgGhq5Ha8uwvr4qLA3dvEu0Ep+S3TAQ0igLu6kiNU04VYAqMALOoJ0h\\nFvHC6JyRie4poZ3FhEhYRD1YVERMsSpVCduhFmELFWU2H5mqhCa5euKkWjBYDCJM0Ua1qfIVp42Y\\nVDVF4broBRoUcZW1O1z3SNcfwA10taL7HXnoyd6J6rNkTBWsuLIyfxpn3RaK6+hzJvhA5zqGvmNJ\\n4jr4cHyg33d4b6XKc6ICtqb5tmuZzJ5qJebMYB3e79gNT/jDN/QPb3g47HAPj9SaSTlSQ0QrK4MA\\nUqKkRM1SRXnvZR3XSsIwPBw5vk186waOb7/B928wSyaniVqCCJJqpaTCkiqpbXqrDWHJTFPg+TTx\\n48cLn15nrnPafPFX6Uitq8lVy9eUagyQNjsbTUqV6zURwszVF7wTNss2JLpkpiVxuQZO55lxioQk\\nCVVteK00r6VPYrQmzJlLDcSgW5ZpMDoDWYJbo+CVXAkhcxlnXi8L0yzsKVsLqonq/n4zfYWbtOB1\\nv/vuGSwr5Hn/1bVC3uptlNZYL0pl1eDaVG6qyhxym6jV5PgpU1KVHp0gQMRKs6xdm97t9amK1Rqv\\nDE6tnuOyH2ttz1gph01v0c7FrdK8J8mtWon1871DkoAq/veNmfZr128SyH2nCSGLB7JSaKtErdZO\\nX2sMxkrZris469BKeKwlJ8YQialAlkaUMZrdUCFlzuPI+XRmnqTBtd8J3S+myLIspJy2eYtGK3ba\\n8mg9e+vojcVpc1skd1nC+kbfX1tZp24n6sYlpW5+CaVmQg7C1kiReVoENzaaLneATAzJOWONxRiL\\n0hGt5QMMIXCZJpZ5ocZMp4SznWslxSgNzwaupXJTn1klisGkwTlpkMZZDpS1aZmRzDuV1HBB4RBr\\na6hVyvzcmnqlVEznG1XQbFx2YxS2KTpV47JrI+P5VJVQrLTBKEfXPzLs3tHtDqjH78SThcpkKtrI\\nZ2JrxJSMLmJqpmnNIgWYSjYZ7zp8l+mGhf3hSA0zpcK333a4DgavRX7tZKSZMeJgJ973clDbUtG+\\nikvg4S3D2x942HWyZnYddT6TxhNxGjFKHBhLlc9Eq8ajd55YIeaKsYldsby1O/w3muPxQLZ7bAjN\\nLkKw+t5ajFX4mJmAkCK1wJfniZ8/Xvjrx1dOc2SO+bbmVAvkG4h6VwFyz65RLEslp8hZRdlfjcK5\\nNofXgLGExLKIEVRdm0Cbtlq1ikr6GynBOUSul4jrtDTIG8asasVohbNG5uommKbCZVq4TonYBmJL\\nOCxtTdwy7O12/u6+brts41vf90ruv0vdk/Paz26N+JXhlVMRyw4SRkVizNv76WKiJMH3ZRaBKM+r\\nuoGn0ucpmz7DWEOnmvipvTAxNSs3eNAosbBe4eK1EFG3X7d7acyuX2aJtW4Yvqpfx6D1+m0k+gVR\\nTaYgs/KQN8khSrBaM6VqrpcrcY5cn084IwY3ORd+fnnlMs6QJC33Xcfh+EB1hiVGzucRZTSDcwzO\\nMIeFcZ65LjMhiblUKTefbKu0sFSq8MbvvZbX8q02vHzNgvT6NbVmD9wQANSt8aE1fd/z7vEJ5cSY\\nahrmVgZbtK7MYWnsi0KtUhGs1MwPHz8xXUYqlTRN1HlhX2T8HaY1ebWmakO1ilgkwIuJvXCtrzEy\\nLguXaeZ8mcgp41Oha3hdqYocCimsDURRoaVmXibZhKhKO+PpjahyraQ6m3fEWgtrRct6ErUUfPeW\\n4fCO/vAOZweM6UBbUePWSlGGoh1JG4JVuBLZ68hDjZg4Sq8C4aLHHIklUoylDkeM3vFUB1xYmJaF\\n8vwZZyOdL4QqdsVxmdn1Xdu4sO89qXHBVc70eqDDiE/84Q3dm7cMb99g4kh4+cDlpz/B5QWVFull\\nGI3uOzSGjGHOMOZK8BXjE0/HxKFRyEopaC2ykJDg+TwSvac3tjnraXJUfHo986efT/z85cp5anz3\\nUlCKZj2xYrs3ZNh7D1UYMrUWEpAU5CpWtlpLL6MRNiXjbD2KWqTZmnIWszHdxuohrCrx95Z6MqRK\\nvAo1USmFnRXOAaUQJhmNbKzBdxbnZLr89ZqavkFgxzX+bAG50noxt8ebbd0vMvH1ql89f+1ByWNs\\nVETFBr0TQmL+cuGcq9CYWwNTt0ox5SyZtdV8M3hSLJIgCvrRRiEiEA1iZaF12fD9rukybBO+3Qwt\\nmuNsLWAs1egmWmwJa3v9WUHVdz2AljjeEsJ2ALbIf0sX//76bQK5cVhXpayOqwJNQ1mphIolaXzn\\nKUXECqFVFKpWlpgJsQ0pVqBKZUpyIKRcMcbysBfWgfOWUMqGUQmeLidkXUn+FVRRDMZxdJ2U8Q3n\\n3Xig20Liq4X2FYtlDeYte6KyBbUlietcRXC6dfSTqBPl+bHAEsX7o+s8Q5Wm02lapJE3R8p1IuUk\\n4hgtTV7nHNY7YTqUNo4sSnNzSYlLTIzLwrQItGK03jyhvXN0zkkl4oR5onRjg5RCXIMybVWHiFWG\\nXluM1W3osBwYIrCQDYluw7RzwmmH9Y8Mu29EDFQRqXfDjkUJusN2HVUrriGTagQd2XUdnQZlneDB\\nh4orkItCzwl1nehKRxwvxPMrSr8gs48KqCqVWMrMcd5cG1PpW10riYBDEWPiy88/ySCIhyeicqjd\\nE0o7NJ7w/JEyXVBJhlhn7Sh+h+32uFyx80ycA2occXph5z0pRMI841zG2o5YeiqJkIWDX1NhHANf\\nThN/+3jiw8uV8zXI6MzWwFz37TpZaZ1yY7TieOxxVpFCani2rPGC2MrqpkjdGsQpUTIiIy+FWr0E\\nbLOW/sIJZw12Ruh7lUqIAdBYZ9ntHLVklimItF9pjDPYzopIqlH40AprFcpYbCm4VPBR2B9raqpW\\nXHndUF8lnOqrv91DmF/xx7fHb39DKXJRTIuinKFoqea1bhREhO1jDAy+UL3AuqkRKUpt0FcpSINZ\\n9r1psE41Cm8tO2NxWm+HydpTux/jt30GLXDf3+NWm+hfvAetMlkP7arW3/5zCuTO4lqA1MjmV0oJ\\nL9woUIaIwu8GMJ55nFgabmUpuN2enfXSNVeC4Snn2+I1jZxfW7ATnrg1MvBY4ahO5LexxnbyCjtj\\nZz1PXS8Kw1ao1VYuyYFft0aQXLe6SDICod2l9eOs0gQJKfF8uQqFSYO1GmqW4GmQHoFSpKqYlkhV\\nhmEYUMYwxUwsiIpVG7LWTEWCa86C5w3G0FdRc8ZaBAaZF6YcmXJmToUxpC3YGmfE8yFXaRxah7cG\\n6/WWUyigFsGBUxVcNTdamUWUqs4J7azmKgIrq9rBJZlVoRJypC+KWm0LIM03pXmxSDNRcTgMdMNB\\nXOZUJtWMdRlnM9aANhbtHNYLnFFDRn16IYUPZHUlxCpNwZjJJqKMUAxLzSxh4XRdBDM1hpgSzjez\\nKjQHDPM48fnTB469JxwfmB+f0IcH1P4dxj5Q3CPL6Zk8nkhhptoOs39kePseUwrqfKK+nlnyR0IM\\n9H1PVIqSIrrr6MsBrMZ3lRLOXKcLYVr4/Dzx85crf/t8IsT2nlR9Gzqtm4EbrepBnBv7zvDu/Y7D\\nzot5WuutKKUIRQZP69VkSf99IJdZok01a4RbVbN4EGlEOGac+JOXWrheR7SzdDvP8aFnGQOX80zv\\nxZ7WeUvXWc6XCaNjo9euPZRGf50z/hrRU/4qaglZQG3ZuWytG755C9K3jHT71nbWrYwPqK2iVVQM\\nVXWU2pOKJteAqh6jvRi8GYUhYJhlDTdYUinxIQopk6pI+XWbVqVU0ztoTWcdO+OEQ84aJ+qtqXmH\\nEukWmLdmdXut6yPr/ZQ1s1+nHLU3pQq2s8WUX16/zczO1lyYl4V5XoR1kcXE3VmL1p4lO1LS4j/R\\n0txaKguaw+NbAMZ5EuZALaiat+k1WSHDk4MINeYlEkIUS1frMJ1gpNckJetcpOnkteHgunbCqo32\\ntp6oajUg4nbSSlNQPggN6AJKrSShut1vDJG04o9BDhynFckkdoMnpsJ1ylSMUPGspdMadMLERKiF\\nag1qGFD55gOSUkYZQzJaeHSIejI6gz/0Qv1Tlss0MU8TcV6gFnSQDnhqsu6qxI+lVinlpyhClZwS\\n1qomJJH7ySUyLxPGgFGCPbu+a835ilWw5MQSA+O8oM0ztXaMY6TvnYiMlDTFnHMcDwcOuz1ud+A0\\nZYyZ8UYz7Pdo56nOUp0D29SlVDQT0zTy01/+xJ//47/w4cMHxunK+3d7nvYHdkNe8S9iSeSiqUqG\\nLCxR+iSuOLwfCCERpjOvn/6Gmv+BngVjKsYocinMU+CyVObkSHVHbiwenwtv+qH5t2v6bqCWSAoz\\n1lpSSijr6J3HDTsKwoD59NO/8PnLJ378+TPP54XrHBFHWwnGVNU43mw+QEVpKBVtK4dDx7fvD/zx\\n+0eeHga8MtiN/aBYsphu1Srsp5AiSwyg1sO/UQuVWAqjKxah9V6Dx2iZA1tKpu8HUIrL1WE7K0SF\\nklEO7IPneOjkvfSavu+Yp54chfi/2jwUCsZa4mlh+elCDiMllV/klvUOG76BxIp1+s/6Lbese3Um\\nhDuYZYXhVME6xcPRYfZHsH1r9FqGw3vefPMDh4cn5tMHTj/+X4zjn4lzFLGZamtFGVROt39TN42F\\n0qAdg3HstGmHSIsBbcqRghZ4Kyq357UKJLMmhqrBuHIAlBtyu1Vgm/VKcyf9FaIO8BsF8hITuoK3\\nDtUrTDCYHNsQA0PBERE3MbFsCMQcoZ3cNKk2zYDJKBH1aKtFHGE0OmqhiLVMOZVGuwqxQSzNa6MK\\nPqiUotOWg5Vmnli3rAvmBp3cHbLtWpswdwux/TUj1pen04z5yxcqteGAFWMkoHlreNh5lPVEZYlZ\\nkY2wTrQzdA2uSONIpJK1RmuLc0KnC/Pcsn9ZKMLOMChvxVCr+TEbLab6RinSslBVarCIHHbkKjRE\\nJHiUIswa67xs6lopSsQ8nZOJP8oY0jqsoLLWMKJYLILzbtxnLQKo2nDTKlEKiiblyOn0gp0XxlBJ\\ny4w2mqX2MOwp7MhavF3E80YGK4QQmMYLLy+fOJ0+A4X97j3Hg2booigqfSKkBFnKZjEz00IBjGKx\\n64879g9HHo9vePz9P+Ie3lKqZRqFsjleR6ZxZByvLNOFsAh91Psrx+MR33viPDOezlxPr5zPJ+Zl\\nZlkW4hIYvIfmdZ3izE8fnvnTj898eL4whyQq2nX5tICs65o0sDEcrNUcj55v3x/44bsn3j70PAyd\\nHBRGDMkosORIKuIlpI2MyFtis2bIhRCTeBVlYWxIpimQWr/ZEIuZmzZSxfWDwXqxe73OgZQSFZo9\\nc4MbasKa2vpZzSu9atGDNBm8dpZRqU3C/vVWUl89tsasG3zcgqRaab+3r69PXx9VGvqd5fh+R3Wa\\nqqWavk4JqydsvrIzRwqZtMzEKYgnkdbU2oaUOIVR7cDR0u8yLSuvWDyWXhlUUc3+WRhgtGqgBYYW\\n9G63thX1dZXg393+evPrY9v9/zrdcr1+k0CeU+veOy/sAxspOWG1JmVNyAaqR2tHKYXpmlGqlYvN\\nhrQUoVJ1RtNZTWfF+VAhizcpvZ3YJsqbGrOo7Yw2m/lTj+XBOpwWPuhOO1zzXcm08U1tJQlB/66l\\nsZ682/srTJDVa6FUxRIyry9XliiY7erZoY04xVlnOQ2e3eOR7nikWmHBJkrjZUvwmq5X4Z8qBcah\\nvfhq5yhloamgi0JZgU686ygpsSyREBYZYlClf5BMlEOl8ZmXMTBdFjovpXzIkbhEFGJhW7JqODko\\no9rwCSdmQsvSrIDXAbuVpWQyrXGqHAqR4+cUmGJoBl8Zp2XAQVaZLz/OaOskC5pnMIbx5WQJZAAA\\nIABJREFUOhB3B+Z+j+v29Psd1llQME/TVo0op+h3FmfE8bDrlDgOZvDasnMdqhPG0Ip5LrFs9gi7\\n3Z7vvvsjh4e3HN+8J7s918tCmF+Yp5HrdeR0fmWZLuRlZBlHSspY5zl0Gj/0zMvM8+dnPn/6yMvr\\nMyjTDrhK6ntU1YQQOZ2e+fNff+ZvH1+5xsDma1/FkkLwW9XcZ9Wm9LPW0HeWf/j2kR++e+L790cM\\nBacESrTrsOyqsFpTceLfoaQpF6xBG0XMmdkEppSZ8yL+KhusLOwgGWsn/ZyYYrOjlTQkNR56bgpi\\n1QaHkAoxLcjAHVHq5pTEJVMbbPOFadrtXwRhtWWsZY1mK6h8913rdY+X36HiW0VCe/+GwfHtNwdi\\nliqylEpZFpZp5pxGfBkZzy8sly/kGEVVa6T5DG0AimsH2y/oMiWLsKxrE68a4C8WtmssUGzsFWrz\\n/1/JE/XudW9Bu34dzNu/q9Z72zD4v79+k0Ae2niqNdONJZKbKc24ZEIW68mYI8sSWKal0X0avqsM\\nJRe8FQc5rStzjuLxLE1nUsuGUlOTDc5Rh56oDSlE4jxTcuZtf+Cf+gODEtxa13UprKzddlVAle0R\\n4XKz8Uf1VtKtk4ZkYdZSCFMgLLF1v1s8p9n3aph8x1PSPPV7HvYHfCeUyZzaTEMri8obyQi8dzjv\\ngYILDmtqGwgNqWRSLDgsFrHZzDpRVGHJiRCl2YnzhDny5WWiVHh5HZtdq1QNuXX4K2qrYLTRQpns\\nHNZZUhGPipTFXdJqLQpLq8nN1bJ3lun1lenlyucvJ67zwrxEllD47tDzj9+84b/+4XuqNtB5fN9x\\nfX5GGc3++MC1wFwNQQ+8/eO/oXs4SiXQnBHfffc7tDPM1xNpvpKiDFiwOhByJKQItYonutEsKUlD\\nuQhjwSiN0bY14D2X1xdefv6Z+Xzm+vKZ1y+f+fT5C+fzK4NVfPM4UJIIgZJxvHImVvh8vjAtiXGa\\nmZaFqjTWS+VSM8RUuZxHfv7pJ86XiaIszjZctYgR14anqpVOKKW0szL1/R++e8N/8cd3fPO0p3OW\\ncZmYloXzPKGVxlvP4Do6K/0L42ToR3GivK2qEmJsTpYBUyTrvmZx3DRVBFM0T6BIo89pqRBys7K1\\nTuO8GN1Z7bher6QUULk9V4OuRqrKKmPQVE6UJZLmdEd1vF2ruI62h9YIrbbdKBtzVUxuQVCtkMbt\\n5xVV0FrhvGW336GrIobEdZwpWcbzTeGFl5cPGBLaTAyswipRA8cUhN67Qqtt36pGSSwFjNP4OlBq\\nhioSfa3dNpatIOIzVWRPGdXqyY0LflN23sCG1WV1q0FYzW4Vkqz92vXb+JGjcFbUZlprUleIMbOE\\nTKiJOVUgURAeca3CS9ZtgcaYqCXhtMY5g1CwAqkN2KUg01NCYFmEZ10V9N6Tg9C6SsocteXfDkf+\\nu4d39MYQcxO1rNapW0ddbWZZaw389VJsGP66/urq09KifCnUVn7etKByUBilUFqEElo17w8jzI6a\\nC6U1pjSNhdAadhrBsql187Ney9mYMzGEdpBIY5EkNrLr82LOhFz4/Czjyawz22aojS72lfpMSRBX\\nSprLWqvNPrdWKc9l8tNqXwv7zvOHN4rd9wvKLpyXj5ATpiRUiJipw1wz/bVHdz1OJTwRVwOmGnya\\nydPMPEeyctjfv8ckTZoL0zSzjDOkmb7v0OyZSuL1+RPkK1ZHasuwjBLnu5gKJUBMAs3IDEVDjpHL\\nl0+cfvyATRM2L5gUsWHGX07Y8ZldWPhm98B/+f6B8XIhRvHvfkoXni8j8afPLEvk4+uFn09XihWx\\n0NB7DvsdfddRqiKlRTBmpL9Qaia3NbQO6rBKRFUiDFO8eez5/psH/vi7Nzw+9C2zFtfHMSxcl0W8\\nhKrYx4Zs6KqnV9Bh0cqgrRLaZhF74zlEQhb14gpXWGPYe09JcpD3RrVGaSEUEbKFGImzJCXalDad\\nSyxfU8lQFNWAUaLJ8NbQWytNWK2bY2jbMm1ttb/c9seWrt7vsFUZeQehr19r+MRG0GtV9GVc+NOP\\nXyipChVxTlznxLxkYs4oEsZWvK6iSK2QCsTafHNahrxW0aXSZhRI1eQBqyCWjNXtkGk+K6t30TY3\\nQK8xRd1ubYspDQ6qoMpKXZaTrH71vV/bhdxfv42ys5WCznnZ/IBWWXjeubDEDHFBm/pVkNBa6D8h\\nJyi5TYIRO9Pchh+IkVFliZE5yIzKGrOY8ztHTTK53KH5p+7Af3t4wz8dnvDGUGtpHGkJ5mFFxLfT\\n/u/xu68ebc2M9YPegn1zLdsKv7omLsJh74yMx9JaExaBKYxtbmlKtRO8BdP2c0uT3tfSDK2qapOS\\nSnNxi5s/OUqh2rQerRRLCM3vunCeg3ytlfTb4mk3dC9YuEF+svkKm/r4lj2tCkMU73Y7vp09/fsH\\n9kdLOkjZnZPhckl0KrFTE3U6Y1Wl15WhZrzWMh6vQI6BbpmoVdGPJzSZOE8slwvX68R5CmRrBX9O\\nideXE8t4QpWI9YbOd1hryTkyL5EpBuYiPineQekqy+XC6Tpz/fkTjyZyMAJHeK0xZSHaiDoY/vDu\\ngf/m+2/4+BHGeQEUA4nL9UT8/JF5Wji9nPhyvmB6sYjNXYdLe4bHRxEQlQylTYNCNrtqfGo5CAUi\\nMVrjnWHoHT98e+T33z3w7dsd1CpQWXNUDEn8XpQ2qCLujhPQlUgsieJkVqYxmpwLIUXmGLmGIKyM\\npkpd8z/dsGARPFlyzXIAKBmMkRaB4mg9qWTWXot4rIj4S6wpvHMM3rPzHqUqwYatMQi3RuUa0NTd\\nGtt6Lk1Tobb/1tW4cqtu11e5VqmcLzOf//yREAsxVsTAUIRlSotJX6cVHoXWlSXBkmTWQW42AUoB\\nRbc4dFehKpme5ZQ4fqrGLTT3GV57X0VF3l7/FlJuAqc1NlBp/kxrEL97U1aPgFL5tes3m9kZYmAO\\ni5gOVUgZLovmumRCyKQUZFGpKv2YXKhRUZmxRqOtQaNIQTLv6zhvXiIpy+CIOQTmZYFcyEmTZgns\\nDvh9v+N/e/sD//3hvTA7qHhtebCeB+vx2jDmLJj09n6qGx7WgqzwxdswgdpoRvWOxcK6wG4Zvnyu\\n8rM0MkvSeEdRmnGJ1CVitWHn/TYQthQ53XOthHnZSvIVUsmlkKtAJyHKWLbVdKcqJeVto0fFGMkx\\nomtlaMVqqbndz9c43JqZQ7vlNUVogfy2GNfKRb63orhMM//Ph498F7/l3f4t335bGFxF1ch8vRJD\\npsbCj+ef6a4n3nQPuOEJ1XmMt+hSYIq4kNFZkf6P/5tsNVedmNTCl+dXPrxesQ+P9A+PxFx5+fTK\\nx7/9zPV8Ec5uE7nk2ixe6y04HA57fN7R57+iesuDCvzueOSgDOl5ooxXVI70Bobe8aANZo7Eq3DG\\ntTKMl8Cnn5/500+feS0JtzP8u+/f8f5pYD/07Pqe49MerR2fXiP/8h8/kscZ3TQMq5LPVIVTGtsq\\nwt4ZHh8Gvv/myB9+98Tjg0c1JlAupcEXmU6BMkYcKmOhKMGylzhznSxzt2ff9TJGsVZKRhwlqU1g\\nJe6NMSauqXDVE9aIt9G+74Quq5WMKsyRqAIpBoyz1FzFSbGZSjltcZ1j33sehgFjDLuu5zAMqJKZ\\n3CjeJnUdynwHkXDXf9pKW9kz+m4xFthwZtUq5fv0Y12PpYp75OUSqMpQq1STuYp4ylBxpg2KTjJ5\\nLNfMnMSiODf4wxiJM0IH1duGVmic0ri25sua9NXcOkLrVCCpootS2x4uqGZx23QmCkrDZjeZfgve\\nq/OhmLYBv2618hs1O6tgq/MSSCnjnKMqx7xowpIIoZC3oIl8mE1ebdppqIDarETnEJlCIixBPKJz\\naX7RgpvrKnziXAuP1vFvuwf+h4dv+HfH7/im3wvVCymN3KrWWnm87SWIkVMre6ocMKw5wRqgWbON\\ne7l+e75as9t6t/jkt93Djm7oSCVzvo5UFN55RBhmQSmyNhRkAvdq45qzZHcp3zyUUxaBjVIbE755\\nZYt3eQqJuEQelOGHYU9ZpAF5SivYs3JZ15d+y3y23+8SKTnAWimpbu+FRkFMjJeRL58vvP/mkePj\\nI50t6BJwRhGXSIrib0HJXMqFdF5Qk2+Zk+b59YQtlUfXE8tMpDCqyNVmzq8vjNcrPzw5aoLTKbJM\\nE/M4M11GspJ1L0xgeY0G2uFX0VVxejnzu7c9RlWu4ysXC9YNOKtJqnCZZz7MM0/HB/b6xKdQ+fT5\\nC9cYqUpznUf+8vrK7CvfPh54OnoeHxzH/cD+cGD/sGe/H/j46crrpy+8fn5mmhMZGte5tAEkGnRE\\nW8HVv3M7fq8dP+ie97OhrzSGkAHVvN6zIZeOkAtzuXGec0nC3LKWLmt8rHgjtq5dsnTBM0QYo2YK\\ngSXCEiRjlUxaM/SWXbLCsqpFJjlF2GdLMoNYwCpFMaCcaWZr4pQ49J6971C6TaivWlSlMVNTuSU+\\ncAvSd+tNtQi/GWbdFt923dae/I/+6lvqpp5c9wNtnVYlVhDOKoyqkiwUqFqCf67S4K/NspZKY6s0\\nFlGTZjqtNsMsBcRaqEUIG6ZqaH5Faq021qlBSl5dVXprMkvgrtvfJd5Azc1qWCvp/bU5AL92/UbQ\\nimykksXIxyhFVavFZiKnSm0KwK0JQGssrp7VRYYmxJBYglDMlhgFWmlZRskZjeDeDsW+Kv7Q7fmf\\njt/y7x+/5/vuAd9GZKGU+INrhdd304JuBdv2Otak/L6Kur9uz+D2txa95Y92U+0P33mZUqIRQ6uq\\nQBmUStLlbzaw6wcKUGtmtWWIpRBWGuFKX9NC2yi5CMySxVAsLRFf4Q/dnn//8A3TPPHjeOZfLmfG\\nFgzWTKfCDff/lbvbFiktcN+14nVtVVRJvH448fr+iTfvvkc7MDiM1Tgvk3XEuzsT5sBlvKKTRRVF\\njoWPL1c8muAHdsUScuS1BF5s5bpcsTrzZlA8T1dePr0wXS7kEFA5b2dOUSt9dO1jyIYoITJfJ/EJ\\ncZZrzXw6nRj1zGA6xvnKx8uZv75emQqoXKnXkZ/OZ6acKUZzmiYuRPZvBv743RNPD46+s/R9z3B4\\nwO8G8VH5NPKXv3zherqQYqaqG5C1aRSUBhPRJeONYTf1HMfEe13ZLYpYDUrZmzq5CCc51sqUZRhJ\\nKbL2sUoYQ9qhMeiisdWQsyEWS6ieCc/MwlwTC4m8Ngm1pcPjqxfr4yw9qV01HDAcncGYpizWqjFm\\nmlDGgK+GLjuppItGhUKKmS4ojrZj8JqoLTFFaspboSQqi9qET9z2f1t3K5Fgy8jr7Zvu6YgZYbdF\\nrfHqNj9XMmZwWjN4S1elUq1ZqIm1KDQy5MQ2Cm5hlfTL00vDvTsUXePhGwUJod0KH7zRMVVjHbX7\\nKY1JIWMeW2W49j3v4vO219Ym6Ppgab9+5fpNAvljv2fvBtI+b69rXBJaTVt5UUrePBGUNnKCapp6\\nSrrwMll8IYQgwaD5Ra/YsVEyY3KwlnfV8Y/0/K9vf+C/2j/x3g9NPyPj0NYTUSqZG2al1L2DQnvw\\n7tdX308b9bb+nPa1rVnKPU6+/huK5bowvKs8PAxcp4kliumOZJEFXXPDTmWSOzWTrGqTaSrM0uQq\\nNVNSapiHYm7vTUlZqIvNzP/f+AP/y9Pv+N+/+ydSTfyfX36Gv/2//IfxTExiiL/upVIVZWXr1Jtk\\neH1/4G5DbRvs1nn3WhO+XDj/+ML0h2/RDx7Tezp/gDATl5F5PFNzRXuDt/KexJQZp8CyS3w5B/78\\n6QXfDvspJq458uap4/e/e4P3HacPz/zlLx/48vGZNC3IaEZ5tXbrVEsGZ9vLdaWic4Wicd3Au+/e\\n8fNfP/AfPn/kfI5Mi9D0IjB3itgVpmHPq48UwDlNd9zxOyPDM469p+89/b7jcDyirWeeEn/6l5/4\\n53/+K3/6T59QpeJbInN7z9aMsVBz5vr/MfdmzbIkuZ3fD+4ekctZ7lq3qou9sdmkZoZDyUwyk43p\\nUZ9ZT/oQkplspKFGJLuLXXvd7Sy5xOIOPQDuEXmqqNerKDt1z8mMjIzwBfgD+AMYBv5pHLg7HSnT\\nxO9+s+dX/bXVUXFhIKqUADlADsIzXBipkPNsyisKYbshbjtCn2wNzwWdMmWY0U1tKGx1WLTo6l7c\\nxRFsb1gHImWOhTFlBKEEq+2i2a75tOBTFss9AOuidF2u+NWrjnkuTM4oyt7EoSamZSzQmGt8ByyX\\nBFNaswcecWZYKbm5Yqp3OaOMArtkDUaKLrx1VaWL1p5tB5DNHatOkdyKsIlQotFT5zwz10Y06qWC\\nQ2CPeNXDQBSlWyVw1ecPweZ4metq5ctSXTSoJRFqzf6kBYRrwS6KWplp6ms/Pz6JIK8Rf0Eoo2XZ\\naS7sd4HzYDXKg3OYQxTczQRYEsM4j4zjyDCMThWywB+opeKTSEG4CpEXkvhNt+d36Zrfpxt+v3vG\\ns7ixAl2+btXNuFqqdSzWtqoKZ/v/8pd9bHGmND+6a1/xDMKmSv076t9Wb7kZjRweDuzvj9y8uOHV\\n7TWP55HjaWKeRiiRqJZGJH1nDTLUShtkUUpQtETIkXm0JsB5zs5gsR6R201Hn+FWEm/ilv/++jX/\\ncP2aF/0ODcrfvMjW9uu7r/jm+MiolTFfzdewGodLv2ZzJFWPi/8vqJCAXoTfb2/4m3hN9+7MMWeG\\nm44b7diEDWkT2caOuTuba2wa0aL0WkjbDukiyCPvppGT2oKOm8TrzYZXr264efmc93cnvv3xIz+9\\nvzf+e1FLLntiG1Epoy7YNRfOD0fu7088f77nZrPjxauXxL5nc33ifLb65jEGnl1veXl7xe2zKzbT\\nzkvLmgWXkgUm933PZtsTu47Hx4l3Hz7y3Q8f+cu/vuWnH+4ZztbJKdb7kVUIT2kp2eZugYmRtx8f\\nGF8M9L2yj50J6Box8+5Pi57yNaq1iQcg0SDq4C6NosgsMMWlgJMoosndFEJNNdeirQ2ZZWmay2J2\\nmFiw2IwWW58l2PVrXGXWQqnuBSmUTWYOliNhnDTroVuyNw9nhcprgB/Lci2yvEf2ZCW12EfGcjcq\\nMAKYXJiP0Z04BSjeXcldQNEV55wLOQQGhNEtNmOpWOZ3dV0Wr9aaYuAqJX5/dcvNdk8XTHZM88x5\\nGOmxRuIaxeoKh0As4jKGRmNsNoRU2bFYFa1An1RX7L9l/9vxaQR5Me2fc2Y4j/ZiEG62iWFnrhGd\\nLd3VqDxWeQ1VNGfyNDJXtDllQikk4Comq9CX4Cb1vI49X8iGP3TX/La/4Yvuyuqt1GSDFV8VxSlU\\nLsh1SSGup7RkhYa6LwdXnvy7PvOps0Wh1cA4H89MHx9Jt7d8+eqGx23i3Xzgfh6gWBas/Zjpp5qp\\nnU6DqFVBjMKALb7gtbyvJLHrIzddxzMSv4pb/rq/5t9fv+aLzZVVbQvCm/0N//GV8qe7DzwOIz/N\\n54tnqt72snKdCHZP9e+yesKqpOrnXqQNX+iG/GHmXSmc5pk8zuy3vTWBiFfEbUeXRhgnKxNLIUkx\\nvnwIFATT/yY4r/c9u6s9c+j4+vt7vnt7z+NxIGVd5QKsXGFttpbNUnJhOJz4+P6B65sd6dWWzf6a\\nl5sN+5sT8zAiWuhiYLvtuNpvubrao2CZpdOMl5BHApQQOU7KeBj46e09X3/7nm++fc/H94/MQ2lF\\n3qqwafeja1iAV1kQmAp3jwceHk5Mu5nr3Qa8uYRxlk1KepHC5fDlrQWjrw6KMlPr7IciyBxaBygV\\nadYvuiS+oRCKxz9csZd67WDXry33rD6IePau/czUpBgX5p1SUmF0AFLE3RpuLdZ+Ac2l4h+UuAT9\\nRIC5EHyMMmqWiqzWnQqz51Bnd8WIYqSHVYcqHLBlUWa3bHLAgrHqUrdg9ebnbFaH30+XIs83G677\\nzjLD58I0DDyMmY1GIymsGECRYIPn1F5xY6yyeCpMrGuhgcXVnnMTn186Pg1rJQUeD5YxVwpsu45N\\n6tn1kfBsT5c63r8/GIfcg3pm2uE9Ji0NePYdu42R267jhsRzTbzSnl+nK77o9nzW7bxCWbRmCFUW\\nN9lqi8gxmjUI0NJ6+9V+egsKBwuLLuO7YAc/lHa96s8LyKWwW81HUWX6eADe8rebW+b9nu+uIl/L\\nIydmNAqpj4gUyjRQslkftgis72dP5iYK+01H7HquQ8cLTXwWen7V7fj19pbPuz0v4sb6Y0aLpFOU\\nTiKvN9f88fo5b09H3t6PaKj4CCojIEhotT8qZepSeNfzbDxmAo+58MPjI7+9O/B3uyvifeHbYeDb\\nuwObTWS/67nZ79hsE31/zWYXKXlCi/tlux1pe8WzF8+ZRiucVkTQDPeHkR8/3PNPX7/nw/2JJMES\\nPHwOgtRaFYrlWNNQuXHlC3mcePfDBzQETvNzbvY9+02k7/bsNzuSWAyn660naOh6AoHUQdzYBrfm\\nvRP3D0d+evfA9z/d8+1PdxwPA+U8082+vsRQ5HqdNAWjtraX9HNjOTyOIz8cDrw7nXi1vzaedltj\\noS3Atv4E9yO7iysvQbsQKvqX5mZQQMNCi8PLGFywtZTFKqvoUFx4l8ByG9rOR712SBXQYSn1OpWa\\nOaoNoRYtnDUTJbKpjaUxYyIXbYomEZqrIoi0VoS5CkaxMcyO+DPFE6xsCczFLJOgQoj27DlCiYJ2\\nAZIV2iuloFMhjYWQLbFJpmLWkAfsJC6VW1NSphwJYUTVa7UEWnlsq2Vex9LlmFYLjEV5aZUP0ij2\\n7hXyfbayMFfHp2kskQK7TYeWrU9SaJS8q20EUaYBZmvPQdhYYu82w/Mhsp0VKT1TtyOkwg2JN3HH\\ntXRckbiSxFXouAodu5C8ySrNr0bVdNV35S8JFkTaBCtCBG7iYBQoV+YAi0JYB2b8dVbnrUF73Whm\\nQi8niAjDNHE4nLh+zLwOV/w+9bzvOg46M2hmHs2SMR9huEDp1aUTNkrqAxsi+9D5OFgJgqvYsQ2R\\nDe6nCgIJ800XoZfI6+0VLzY7gnwEoKw5BbJ+HGmbuR4tBuDjoU4dU5R/fbwjqPL2dOTz1y/44vmO\\n26ued+PM4Xji/uFkdS2SFZ+y/kLqyKh4OQZlmgrjbHkGx9PI6Tjy8DhwOgzIeaIbZ3AEWee4dVLy\\nOQp1wyyPxOH+kZwzx8cDV7d79ldbdttElwLJXXtdDHRpIKbYmgtP08w4TpyHmeN55O7+yOPDmdPj\\nwHAarLVZWb4PlqDZ5WguN6MVXOAWkBb+dP+B237LVUo863fsYmesqpUiqJdYGZj2e6nV9cSEcZMM\\ni/BXXe0HxYKWykI7VafJVdy4KgwVfPKrgbusF2mZ2ybcZZkXgYw01F+51EFnRAMJ+8FpfdkpvRHL\\nu6A+2+r72je5oiwEipjbpfqcBWk+7Cr0ccvFxlvAa64bGaMQSu3B6YJfKkpTpHj11iIEVYacOZTI\\njHjnJ5xpF4xx4rsKj80sQVtdmemr51F+Vrn2l8X4p3Kt5Mm0dApIsUzDUjI6z9bHMilXO2FOgAb6\\nlOhT5MUU+O37yPVktTTmXsixcB17Pg9XXE2BXp0qVJFNk7y6WuTqE+6lM507F8QKM+1jopOwGr91\\nRtXPh3J9Xnlyhj455wK5r94Zc+ZxHMiHgecbeBH3nGPHWTPnYlmYoxozQakNjo1KFyWQknGQNxLo\\nJdGH2BJLlvrntlokWKXEEqByoGII3G53XHW95x7Y65WKuPB0l7uvdEV1kFeToepnK83yp/OJ8zTz\\n4+HI/xQif9dv+WK7ZZsHftKR++PEGAoTpRXmQhZ3TVWY1tS3MI2Zcp7ZjpDOkQ/HzHCeKZMh9jay\\n6olXlT3gz3ExAwLT2Uo5nA8nHu52bPZbttue1EdCCsQAz/sNN5uebdeR50KZZsqYLcYzzEzHM28/\\nfOBwHmFWohYSdY2t/JxLivDPV5CyWkzuDxb49vRA/yGSUP747DVf7p/xrN/62r207ioNVJ6kAK5r\\nfNSkkirIngqSZmnqpWKoc3p53Xa7TxSUI3b/W1frJ4hYNUdq5qPt11DstXqOuee0SdqAu5OaWryQ\\nce0bTBlZq8W00mpm7cQV7K1zoujsVtrM4mLywmXVTSjVDBeFbKg+luDKrJCLUQ/XVO/aiLwpkYbK\\nLy3any0J1WYVLcvj3xLjn0iQ/3R/Z41si6Wgay2rGUc2ThXcdYHcmRDapQ3P9zu+HHv+7hy59RT6\\nww7uNjOy6UjdNd27gXR2KqGjDEN2NdGgTqZrXdYI05ZQFwPP+p6tp8kLy+KVmvu71vJiASdz4Rtz\\nwE5eoXYqepHmG2uvmqnAjHLIMz+cHvmr+ZbP4jV77dhLhy31pRkEtc2ab0rxpgBmylqgLDjK0Uq/\\nEv98sHIHlf4Z1RIXigQ2m56+7zz5QZvwM0FaEOdK1Ucs9TnBxrr6erVhdgRhRPmYJx7nmb85PvK7\\n8zN+W57xXBND2XPQzAcmPpaR+zxy1sJAYVIrgaqloFnZIWyzsM891+GK511Hnkf+l/M75lHJ7oNc\\nKlnY/5e/HYGxZKxWPrOUQhkmTuPE8OGegwSkS0g0n/yrl2/48vmOv7q5QYuSROg6oU+B3Gfeh0f+\\n1w+PfJNHhmJskmaZLYPY9oCyrAP19YSj2oX5alL6Pk/84/07/vLwgf/5VzOb15EX/WZBtbIstDW3\\nellfNCpcRd6hvieeOezvhXWThNqyrN7jyod+UYmvNglRahCgCVnFfOFztJsKvmDsdUP/NYU9IMaX\\nbuaE1t4jVI0lT6Iei3d5DdP0AnGXsrh8QpA2L/WzJgt8b5VFRoSaEi91/xoAQrCyso40glrtFVM2\\nYiU3gv0e8XiH1qyO5b7qRFc2TA16t9W6crtoWO21Xzg+iSA/nyz5fVYYh5k+WV/FvrMWYl0IpLTl\\nOA+M88w4T4xjooyJTelJEslkxjzwMJwpJSHZ/FHmZ8RMl0qhqmisUb38WJHra1AqcqYIAAAgAElE\\nQVSml8hnuyuuunuQQxvstjDVWqNVIWUTpKtrrVSD1kUiq00rLSOyZnW5LOdcMt8c7vn98ILf2Ypi\\nvVxDdXX49wfB0HXdQ1U6yLLca3U9M699v3nvv5hXSE2sOUE1aytzpSKkhnZWgqEeTTnWgXwisNaJ\\nRlEinSa6yQNsarzjqxx5ox2j7swv6sGq6p7R4PcXhJjMX79R4WE+cS2JhDTU2+Rg/dFl09Y5gfW5\\ndqYWPEsPkIwUSyaZA+R05nYr/N3zZ7ZJ1YKGASGHwnWJfNFdcRcGzmXwNaduFVzaMXXMLgRSTQlf\\nj54s9XtmVU5kjtlcbdVSqht+OVbP5JKjWks2nKtznUNd125dhy27cn1Vd5WxEpBQGTPaFKSshRPY\\nfowCSZmC9brsRiDbvBZ6990vuufiu/37alXIqiTsvWXPqY+tKWl/nmp1VwW5doPq6tpruCV1y4tT\\nj1c3oopkqaYBNRkOMdphJUhECZQo5C5YM/MCtVCYQHNtLu5ITIHUXJOqVHSxZKjnrMTX+vgkgnzf\\nbdriDBn2/Yar3c6q6nnabAxCPlnWIlpaZlitblhQzmVmzhPMM3lKDDkR1GqMpyKEslqgLCyKX9Jq\\ndfF0EnmzueZZvzEONJUbuwhvWV1zrSWX1xbxsDaN6i9NK7e/LBA0qfLD6cC74ci5zGxCt9Q/CbL4\\nGT0Sr6H6OHEO8LLJmhCvmzQsW1zzko1ZS52qWq/NJJY9K08GqW6I1WP774tQbK87mrIvW565YK34\\n8lwgm9KNCF0RdkVA0yIkXICvpXKQOn9K9q/IMbONieiUzxqL0gWWrlX3xfNczOMKCbWtVKDW2Hg8\\nnDgfB7YTXHUbuhCbrzhb6jBfbK74tnvgbR5aUGtZAKv5lvVdcPFzoYUu7teVYLLORm58XB5tIS5C\\nHFkQeiNA+O8V7rVaJiv3S7Ve7XK/PIJUIOKZ0VIRyup+RAISQZJJodozFEfJRBfNa/0il8MUnX+9\\nrKQFmPgT+OeqQqExbUQNPf9sTO0BV+Olq4mo3+SB4vVHXDNKkWUPCV7xtGbpCoTKJCskLfQs9yCB\\nxSIUGjC7HNtFmDflpRdq7OL4JIL8d5+9YVK1Kn15ZlO7qFjLGQtwTFZvpA+J0AVPhnEuafFkiBS5\\nPie6UbgSYZRMUWXvWWxWjUwoq0FcOoosC2C9oKMKbzZXfNZtuY6Ru7wm9FdkUE3TxT1Tjyc4pv3/\\nAjOp11nAFn+VXUWUD+PA2/OJu3Hg9dYaQQjBm0RgvrmyIOIcBVf5vgZtJWd/rd2FYPpCxQro+7nF\\nuciUWtJ1ldXahNFa+fzsaeq6W/+5ir47ynU0+HE48WE4WyC1blaneZkwCdSgdGuo62wO615uTakD\\nFgvpQ/TiaYZeGndcZamz7U+gLEKilh1uz0kVDtoUgikFq9j3w3DkTw8f+MvH9/z1izd0fXL+tbkH\\ntl3i8901t4cNcn64HCddKdGVVbh2DawXkazcWlWIJhH2KfHZ1TXPd/ulzEOTZoswbAHni2UoKw+M\\ngNYyxf6ep9yv0XBFwlDnYrln1cpusZuo1mG9jbYvgkAMSBA6FboSCVqsdkiwFPTaRYcqnJ9YK3Vu\\nm9JZASVt7+BuTm2gwdx9hSksayu4AMbdGRebV6zmiYTFqmnuKJ+7UtfGnNv4Bj+nst+qksmiPGZr\\nJ7nDqrQ2oLPeV/WGFbwQ/Xra2vrR9sLPj08iyA9e+EcV5xGb/zar1UseZuvkM87WoXvO1gKtoi3E\\nBjVKtKQShVg81V4LpWCddFCjMdVBc635hHLbxsbAV+A69Xy+veKL7TWPxzuyrBbZCgfUK1TKmOqy\\nkGqwVWX5XHOR+ApakoZMYBXgVDJ3w5mPpyMvtlfWIAErexrUfOFVoyug2bglzR+IUqllC8oTR5dm\\nzCKgrUiPZdeWoBbYixaXqAteXWE1aV3RwxNhsZivVWhdnoff49145v1wYsqFGM0FVC2DRaPRFq+0\\n8XXWhC4sAhEhxsib7TXPuzt+Gk9tY1/6cVcmckWnq7/bEqkmOcsc1qD4WDJvhxP/dPeBN9fPuer6\\n+lAAJAKvNnuedRs6FTLBsxr1cryacv25AG/C74KiWOfVqHOVimtra6H+Sc3KXJvjIvUR2neLLEi2\\nCWbxkg51f1QQ4DEWwNPKFxehDZfTB1cPUvvbQvWpC6FAykLBrBhJy31KqX7x5cJ1DBZQIBfzWsep\\nMsrqDFfFZADFZ71kxgQikTBD59MsoYIwH61K/QyrwKw6xREHkLK8Ti4k9e9ysJhRVKxyZ7TC7px0\\nQhVm2bamGtE3r7aHlcvaH0+erSm2XwyU2/FpfOSzpezaYklMqxohY5k5z9btHbD7nzObLMxTt0y4\\nD6b1ODeBkL11VSGab+qJi2AJzi+Bz3aIEzhU2ITI59trfru/5ZvzoyUE4OnIzdSpCMuuWa230pYZ\\njc3xVOs/+aVtmAKMJXPIE/fTaPzWhFdN0yaU2xCwKhEQDNGzcietn2+hehlPVv384pljKtB1iW3X\\nsY2RoczM1I1bMe2id56aeMtT2/mX23JZkB+ngbenA4/nM9ebvpUVrenL7VQAQpPtdQKFheUAkCTw\\n2+vnfHX4yNfHe84s9TiMu68X92p7YuWP/iVB2m7ElIuawcBxnnk3nKyrTlHceEAw8//V7opX/Y6r\\nkDiUBSmC+211/cr6Wy7HUtb/2kQ2dBxlYWUtCsm06Np6ak+x2i7rX3SZEmdpWHAz59kBUV0/0gRz\\nJDY3yuKn0QsUia7f8/03Y5mNoSJetc3mBavCMiyXI+H3sFYUJsCXE2tUYd3XU4AS8blXSljFjJYR\\nczaRNIzSsMpqEOs6KShjKFSXWxDrNGUWA42zXuqg+v3MWPmBRtlsw7bsqTbRcgE5FrT+ZEx+6fg0\\nPTsxdDHOEw/jyf1L9qAFvIP1DI7AkwqpZM5TgHwDrmn1dCJITyQRxQpOabawtxK8NKSjDZZJV8yn\\nVX3m1jU7YwsChMDnuyv+5uYF/3j/jikrJy0u8DyZoH62XdE26lLO8hL5VEFt/ts2z03SKzVrTjlT\\nOFIYe0hRYBbi5ADR+2eKeqanBN9cipa5Cb5mKrMEriQIIUVyKM6x9QXmSnTbbbjuNtymjsM8MaKI\\nc/hq4EdXG6qa1fXvprP0UjwtgsUE+fenAz893rPhueUTxJUJX8+uCMn99zV5JXgbPlVTcJHAH28/\\n418fP/Jf794y5qk2qWkbtSpbWVspbW5WAmCtjWTZdE2ZuSwurd5HdcaYe+XV/oo3+2tedBtO49BQ\\nLW4JqZQW0GrjshYY/lZQZ9X4vQe//jYl9v2GTdcZi6GNs6wAxvIMlgi0JOKEUr/XUuDrvkC11Sia\\ny9j2hIg1ZK6gJ1Tueg1ursenIn3Vy3lXkNk2coguFhXf6OKsq+r2WVsievmvP2krb/BEmIfVpNaG\\nDiUIc7JWibGYZSAWclsQGGpuHgdBa8aPal1vlv15CjNTsTjdxpML8XuatViFUbVS0xmL45gL0BOK\\nFKqFd7kIWPz7K8ld+e2Nf//z1duOTyLIf3j3ttGbFv+1l490wROxwvR9jFakJkd66ZGzPUhUYa+W\\n4BIVSpm9FrjxQrTYxtGaCeZItGWtuTCwVNziwrGiFWHfbfjy+ob/+OwV//n+PV8PR6pXTNC2yepk\\nOjEPWflmA44E1NCvMYhWftGK+NrrIBIYcuEwjsisdAiUVRlNL15kEXkX1m1TeSTdL11WO83qIruy\\nKMv9NSQMJA282ez54+0LPs4jx5JdwNSmxRXFrjHSIqib37a+d4FqDJ1NWrgfB765v+Oz3U3zqzZB\\nWj9RpVpDa0KMPvqKM2+gQ5C+5+Xuis/3N9w/3pPVZ6py2cSy6Kr0WVtTC+NjdfPwhEJpx6yF4zww\\nq6V2h6rO/f62seez7Q2/uXrO2/knplwF98IsapZRva600WkD1tLU61hWa00zo2ZmSmOL1Loy1dys\\nirleR9tEVIlh14xOWUU9i7E0roSd1qwHo6eGFTCoz9Jc2fWzFW3W8VWg1R5f5lkLrSRrQ/0slmYN\\nGLZ41mqdLVbHU4G2SHZBSDMWyA8BKSbIU+3SFaiCwD6WlwkRhCU2amF1xQCUpI4uR0IpdLOdW4kC\\n1iUprzj6gR6IRPaarHCbLvNblV5lvIkaeeEiyKB1+a4Rxi8fn0SQa1ELqnXG1S6+EUK0xJWixd0k\\n6vxJ812FsIjAiLBT60spLlGjsz9CbdXGU/Tl4yNWajL4l6uuB8qWcxciLzY7/sOz1zzmzGOZeT+P\\nTNj9XCA5X1brBJYl1bqm/9e1Iqt//Te58CozztkSS6biPRRDi8KvhYDtZW0BGWnRpwXB4pTMehON\\n3YJ4YLHetFkaLzdX/PH5a/50uOc4z5ycILuImacCXC6g7CIcK8pa3at/7nGe+Orxjr958YbXpSz3\\nUcdg9QXVRx/AnkO9J2rddBLoAry5uuH3Ny/57nxinpRZ1AKqKwVq97f4Rp8qoqoBl/GVRVgJjFp4\\nmEdOOZNVm5JuazJYduzvr1/w/zx+ZMjZ6upjyMxomKUJjKoYLxR7G1P7K7Sbowm6i03ugrR+TO3L\\nFhdBveL6D7hwRbTxDIEgsQlqE+Jh5UMHXSu+qjPK6rWVO6/xoFfuQLQK8WUv2Mvannu9WKpb53In\\nu9prz6QXz1f3XSoBjSwASlfPvfJD2dlt+lcKqX43hAKbSazUbRZSqZaSCdpaL6ZeJ2pg46p+i9dk\\nX0fnVtqpfW/xFxSXaZdzpk9fWB2fRJC/un5G77zx4o0R5pxRMZP/nCfuDyeO00CYJjZdoqOjzF0z\\nTyKRvfSWvosFPhHQUFyQR1qN53aY9jOzp9AVT3HHfNSCbwDf7LvY87fPPud+nrmfBx4fPxoaoi6M\\nRUhUgVPEqihVJOXiw3t4hoZA1kivvlI37TzPnM4DOmbzJyJNALfNhQvxslgSdYM3d4rvKyF40pV7\\nDaMh9BCjZdT6xtIiPNvs+eOrN/zXu594GAfO5/PF2DwFBo3m98RH2Z5QFgSHc6VPeebPpzveD0e+\\nnG/Zha6Nec2nre6Uak2EEFqwWqnVp/xbAnx+dcu/e/GGf7p7y5BnHr3GfKi+aWf6LPuj3qE2iVBd\\nCBdCvO57EaZSuJtGczuVQhftgVu8RYUXmyv+cPuSl++/5XGeODqyrTkAReQCZFz475tgW73mboeI\\nlVHoCVa7YxUgbFxuP7/Iwp2uFNxF6bKILV2Eg7UjiySJrWRrXWutf4Cj66dsFguihra+FS/ehitf\\ntwYVDP22WhdVYq+0jqwCrCvEugzJau9IVSxKLVnsM9LGMuVg9EPxPIaLc5YLNZfPaj2ghsQphTQp\\n+8HL+arSqTe6CebWER8z9XWZVOg0EYlWabVZy9qAk22JFaCq+7bOVwVldqKt1pWraX18GkSOdQ0v\\nxSh1JVst49QFznnmfB64PxzoJbHrrN1ZraNcuaqKF+mRincC0Xt8CpUZYt/WXCi+cCtADYR2rXVS\\niwkQy8uKUfjrZy/JQRlRvjo88HG0io3RTd7ZTbVQaOnllfEcN8Gi9ECQWLHESusvDpd6vwSxcrq5\\nMPqGMMMkuD9foThn1R3uIo62V+iM+ixzbune+KKD0rivbVOJsUBu+g3/8PILTnPhbvqBM6VZnzZY\\nCzZolny7vCLrTbLag1U4TihvxzP/+njHm901f9i8pFCpa4sXvgmMsDQHUBbhrnh1O4XrbsNvbl7w\\nP7z5NeH9d/zzw8dmlSjS3EpNEKyQUEt2aTOxQoassxS91rU3W5DYLcjShd02Jd7sbvgPt2+YCnx1\\nerAMTLErBdbWB8svuv6jDpz/rtZD9M3mimfdlq0ka3LRnmXl8lhZsSq07EN9ivBkmRjxWkc2xMaI\\nqTEWceHRBLtgY6+L0FkLcZp7c3lAARfe6jkMT9UVC+deF3fQcrsryLN6r6F1vbxWO+qa9ESvX/q+\\nn8UWVusZxMv42ietUTqgpcXJBByRFyuvW+9Lg3PN/VZqsJNVPECX71zmYnmuJyrYjzUwXY5PIshT\\n1xG9nKyI9cWzrtyCZhe27pcL0RBBVCGqCQoVQ9RFCqbzbCNFIq0AkB81/bUpdrHghFL5qfAzK6au\\nErFzXvY7/ubmJefagPXhjiEvrIQa7CFYYKWuoChw+9kN2/0GBPq+tzK6SPPxigghpoYIVAtvxsTn\\nureAzZwRKZ5NavdUXJBXd0gtZ6qlLBs3LEtBVJogl2B0zkwhC8Q+InHJGDUNF/jy+Qv+ehr4cT7z\\nzemR02wlW8svIoLF8vilRKL1sIrQyhG8PR/5OJ0burZzVlJuDYnbbjWhvJ5mQehS4uX+ir9//StG\\nDzz95XjgrNkBoFIrUK6FjLqgFKkW0aVIWLvHWiVLrYHmqhmX50sh8myz498//4yP08CH8cxDmchK\\nxWMXglsW7di+DViEBMYhf9b1/OHmBc+6jWWxlipxfE/ga1apLNGmkBrAXOQgq38Wb8hqQbdgpytP\\nhRW/+skc+z6uiHKl5v37tAlyqe4DxADBWqHiDJklBeJyJi4UXr3nXxThLGqOp4Dev09W62d5/iUW\\n7f+vY+JHjSvVXJJFAVbLxOdRFkCgQVtnoaf33x5J68StJ+vy5H8DjAOfKrNzu7PF4n/HmMgUZp0h\\nCqlLXF/trSiN1/3oJbKR6GntyizWUaRzQe5SytdiZRUs+qx5pC+oUg2vOVpb/M1ULS5CHxJvttfs\\nd1tmJnKZ+O44ci7WSDaqsyMIVo/Z/aApCV/85g3P3tyCwM31FdvtlpSS9S7EEHzXb7wGhEIuvPlp\\n5jc/FbaI1ecOxhcsOte92sReUXX0hAt2QL2GslSfXWjNlaMGHscjj3niFOBmd03aduaymBeEtU07\\nfhNe8ZBmju8ynAdyNtOymt6/pPzagq7BxbWUUJqJrVk4MnNkZk5L4Nn0pwtWYZlH/2y9XvaFH1aI\\neBs3/OHFGwMHAsfvv+btODBooZBbnXJT3ob8ltKr61IE+KpZi1dbPxFrFdZ5nWlivW9jiIgI267n\\nj88/4+1w5PvTHcMpM7hoqXu5zsySOOXK2IeqBspFhF0MfL694u+fv+G231wIIdpd+xVVkezrwznR\\nLYh48XxrsLOY7PV5QdpzKZVDbmCjlMXX+zMhXq9TOeeucZfM4/qdK0u5xpNqUEWrIP63jnqXvyAV\\nV4/WhHmVA/V9WfnY22e0WYQXCL+6bqjU1/WFDCSplOXZg7N76tpfPa8Xf28ypt79ArjW8TqllSn2\\nfaDrh3tyfKIytj0VnyjegWOaOUwnxjyStTDpzDTO6JytBZNeMecNFGmCfJLMRpW0EiCL+dJmzRfP\\nshkX00Xb77WYjn1k0cZN2UjgWhP/zdUrrkLPu3nmT3cf+frwwLvZKZQ4lQrLkNx2PS+f3fL69WtC\\nCvRdR+oSMQZfFIv/V7xWdFLh5Xni+cPMdvA2ZaUiR1ogsaJfQdyd4ijd342eMWfNA2bmebIqid2G\\n7x8+8s/DPV93E3/3h7/l9uXWkivzsoiTKsObPTe/+yv+/vyc0zQxz97JpQndBdqVXJimiXEaGaap\\nNdYORGNWuNumFljSDN3VNcdngZ82g9XeyAUmr1fhjXubEFVBolEOS7H6ciFF63KeMfdRNjrl86s9\\n/4HPUSn8l4/v+erxgYfJ3CImxLWNX6qaseGwFdKtUl+qYIOpKIMWJixgvgjAYF2sxJTEdtfzh+cv\\neZwHHn74C++mgXEBolzUqlkLBlw4ujDvRfjb56/5h1e/4te3z9mmzu+tSohyUXGzVczLaiyK1bqv\\nx1p+LeKwSnsXQBeuk+o6ogkjO60i/tVakOV8oL1eqY4t4QgDDBZM9fhBdcvIxeA3IdrcPGKv/oxF\\n8+QJ5elDswjNttXbuK1GoSYjVcm9FuyLeG2jqhVouMsvBctjGTRbOzgsVqeiKwNCmjCv4OViHvg5\\n4Ky39kvHpyljO8/eQFYukIg6XcsKEplJWYJ14Ga14dTJ9+dY2JZCKhDbQlowBVw+eJ10+LmGW7ia\\n6ohH1x80wSvCbez5q+0VnwXhJiSeb3r+fLrjx+M9h8m7HWFuoa5LXO323F5fE5K7iiq3XaRxov3i\\nBBF6FbZJ2QQlepLH2jRUQGqpNWr2nGvzimKaC8AWS1GjRhW/2GEc+HF45C868bov6JWQC2iR9l0W\\no+rpZMPrvGXKlgQDTzepLt2ehoFhHJnyxOk8ME/ea1S8nG6K1t8xmzC47TaM/Zavt6aw8zQzj5M1\\nEIjRFF+wOioBwSG6+SPFGpTEhKV3T4UyO4u/JKbTnq3c0JczkgeGYCheV1u5sT58Va1kOLCw0gJe\\nejUGShc4xsxDnEhxNI+an1d7ldqeVm6v9vxR3/Axj/zzw0d+OB05FutL+XO7vgIQ+30TIy+6Db++\\nuuG/ffUr/vb5a64326ZupKHZlfCmwZNl6Tr6rLx6WS3ztieeCL02r3V91f3SttgqEFzvo95XQ+hy\\noQjs7xqXqcHRupZWlnB7vsV1yUpGUNF/26urjbE+VriMtWVQUbff588SburFah7Iymq/vPwyAKqX\\nox7a/SoLUWU5pz7qxSM3RfrzZ2mf+bdNlE+U2TkMdF2Czpo+ICbkNrEn5hlRyDqTekuDHsaBTe5I\\nRCPvY3UMxlCYsbFKtdcTK7QCNE22Rg3QFkT9dVGUK73uqraWAM0omjNdKdx0G14+f8mX11c8O3b8\\n7z8YEs0+gRICISX6vmPTJUuICOb6sYQS56DWsp+e66yKISk1b+7iMxZDQqqod+u2/RFWiSuBtqdX\\nsgLhYhHlUpiLkkXI4p3YawMCr8pWO7WnEEhRCcXqd6aUWhq/LWJDWPM8M40dU55AlWmaKEXpYsdm\\ns6FLyYK4OYPi5qcyK3xdCnMujNPEeTibEoyRPhW6Tq2uupurtexpEQgRQigEDzSVIhQtTGPh8FD4\\n4Si8f0wczj2HuNSfaWi0CWAwt9SKHubuo+DfmbpIv4mEq4673cSP3ZGhgz6Ya89jYNYEIxdkVmKK\\n3N5e89+FvyKkwFktkD+XWr3wcmcu7hS46np+e/uc//HNr/n97UtebPfkJoVdcauXmbWuD47wrKZ7\\nzfat66flObByD1QL9omAqIJJfL2iXBRlC3HtClsr9yXo22rfO3PM6uQsaDx4gpCqLo2NkSbgzQpd\\nUIWKrDj/dlO6Ho/VHq55Im0P1xOq8hKsnLNviCWm4KcpzWqr5Ilq2dTPG/7yRD5PBFoDrlpLqSYi\\nIdoSfJ66JZfiZItyXjCnrp51mc+nxycR5M+ur40T7kKsdtHYxo6cM/M0sQtWgwWBOW15NnZsS6Jq\\n4aiWJJScqxlCcHJdFWR1GJ5m8+myOFdKsP62Srxbzl6jJgWZCpMOhBjZBfj9/oZ/6Xp+CNJaxAUX\\nNEIxn2+wBgUARVe8VS1ewlnRbA1zNRd301tHe0s6qxrd3AcLoanCSm8n5ZGiqsEzVuvcmuCqcegD\\npkB8o659hdVkXkUVEImEUK9prhWy+4VZu7RoXP7Yx2VsSyHPEyGmljyy9P8UiEKXOmKf6LfbhjYV\\nKCEyiXVcCSFYb8ZGlbHa61EKREuTPg8zP71/4JvvP/Cnb97x4XDmWDJTsJINzW8qq4BUbSBcsvs7\\nSwu4hQhdJ3z2as/tyx3PXmz4Osy8Dx/ZxgPXmx19jIQgaIHxPHN8GHh3d2A4zWYlFOU+D3zcdNwz\\nc85LbKNOgGCWaMT69X5xtSPut7yVmfF8x3Y6WBnfaIojqgn9GIQYYvtbBOvMLhAy7rYLDUGbgBFS\\nsXo60dfqUutEV26GBQ2EhqDrmvF4RliEeAt0+hoydpN6dmht1eguFrykRvWRh9pL19b4es+tVO8T\\nCC0XFuiCXZe/YL24uTxEWWoh+WdW0rjVK1Ifi9XXaH2jui8RdEW/nDVzZqYvPbEYEcFYmE+skPX9\\n+u3r6gVLkVme/5ctiE/lI++6ZbAwp34URZIVuc/JGprWh82S2ZHopkTBqD8dgmCdfCK12Gu95upY\\nzWPTylVJ+4TUU4pU94R/t7AS7CawUohsYufmU4Sg3CQLiIYWRKk+YfOLFacr5bws7CpIlkxrn8GV\\nw7MKPKt5HBezmAoJqolbmpVSF4LJ2uojX2t0r0Hhmy27b3vOMyFadm2IsdWwwp8/qzLlmXEabVFq\\noJPQ+n8abTt4sSCaizNnL0WcldiokYKIGh0zVCvCxiO65aH1vte518HvXYsnrihoRoMwz5nDceSH\\nt/d8/cNHvv3xnh8fjlbXx11CFcGJS7RWjMuzcUsQt5a81kiAzSawu+l4+fkVL55v6bdwP43c60gi\\nccVMJ7GN7TlP3A0n/vJ4z+PjSJ6VJMn862QOMTBLagk4rrHb+q1JJj+IMs9nvj+8t16kPjYxRBP2\\nxYRLdBddEu8a5fGWKMbOCh50VNTzHixrOnlLtY5AkujuL8uiNrefuQiMhGvXDRUxuwBaf3+tOlnd\\nTCgQ3KIpkU00Vyp+TfH7t/UQWop9nd8LZFEtpZWvuKJjm0Ntp1Z32SVy1cvF7Ii8BL+PbEKguZ7q\\nZ/TiCk2p+Y01N81cM9TXhd/QBiwNaymi5n2ovv4WSFWai7S6up5IsdVN/PLxSQR55QA3bmqsaSCW\\nbSnR0rFytgCWSKTXROqSpUar0Bn2IxHNF0w1iy6fdl0Bbz1A9a/K5bRJKWTx9PcqlKtf1oVqFxNp\\nYxtKg3AKBZGh+fpNMAAixGTDWxtHl2J+5qJqG9JNRg3r+V+iK4aozRXRSWo3K61okjM73ITOa4Gt\\ntv5rSn+DzBhKzy5IcjFBPk0jKXVIhyeBePDOxyvnzGk48+F8xzRNhBK4jjuutlu2fUJr4Cos5YmK\\n2iIfp9lqe8TZ3TImMGJ06iQw5+zUxrrQjSpoWfnqj2usmVwyIdSsYGux9XgY+fHtPf/1qx/49t0j\\nHx7P5JLJJTdXlrh7RnyO61JpSqNaCljwu++Fm6vEZy/3fP5qz34Tmcah3S1SLzAAACAASURBVOsc\\nFMniFpN9x+E88vZ44uvjIw+niTxDkLRaH4IkS1aTsHJD+JoRR+V/ngf+nAcrSdEEmLsfFKTkBS2D\\nC3or5RBjtNrlDhSKWhNzKdblKWDxp06sBHAvHZtkDdC3qafzhsJRhD4Eb+gR3IXkMSyx70xiDZGt\\nsp8Qqcwpk6g9gWvpebPdsEvJXKiSXOCbog7VvUJhzDNDnlsGdWXu1E0bROjFAooSHMRJGz7fQ8rc\\nSg64oNeatLeANw2rDj65kUMRV3pUAUv9nRVF1WNDWDPp7OCI1Tw3pVbt25p82GZTG+hrSmQVV1hA\\npe/n/w9J/okEuY2mrCLF1ZAvKBogps7MuKBIB122Aa/aXrCFmDw4WoVtU/I12OCHnVIu3C/VPRFq\\nYMM1euXwNi0svtkxGlYKHZt+QxGYyoTmobooV8FCIYXoGYkwTaMh3zmjBTabnq5LpBgIK4uiqR1h\\ntay0NRJQf0+CtM7kRaD2A6QuUhcuVr+9NGRQTUEEQgp0faLfJMwFZArCBFpBsyN+LUzjmcPhkbvD\\nHUOegcCjjuzPR3ZdQiRxvd2y7fu2QOeSOY8jx7MJv9hHerdo9t0GYUZKaDVsqiKc5olhnBjHkf1u\\nS9+nFugUTImKBLJmzsPEh7szX333nq++fc+7+wOH08B5GsnzxKzmCjI6YzKLI4ihMLyyZK0lEiKp\\nF/pNYL/vefGs59n1hpt9BxGGXNCQiL1ZI31M3Gy3iCrjNJJLZghzQ2mmSGzNqNhqq4kpohkp4r54\\nmtCQZnovR8kzpcxknVduLFmlnq+yfQXIhpIjHstwl0Wlt9XqhkGynRdmkk6kMpHms1mWPhfVFVNf\\nq6i2FiALLtxFtdUbUVeeULgOiV+lLf/p5jVf9Dt2MfGq37MPiaDKPM9UC3PWwk8PH/jq/j1/Hh4Z\\nPQaQ3P3RSeQmbfjD7Ut+dfWMXd83t0NWq6c/a+ExT3wzHjnkyXJGfF+WInRRrKl2CoRkSqj3/gXW\\n/1ZI6oX6WKyN4HtahGbRWOntwuQlG0o9r4AUoVdzXzmKs8DAWi//TDCu7YEKNRd517JCf+H4NNUP\\nczM6WgZmFa9aCiVn5nFmmmc7V5WrszBOiaJiTSMcvTQBvj5k0V71/83FUMVjXZQr8Vn7cF62XV5+\\nq9pTvOawLeDZEFrdkEANqBmlsZDzxOg11ksxNF43Vi1yX406rQX//WLr7MulTgQtyLU0Rl9pdP9Z\\n+h+qmQmiLT1aWConGgUy0iL59TpSL2X+niCgWTmfR8a5sJGZPG0om55NtyF3nQmvnM2aKMUCjMEp\\nWJjAicmSkCqlMjhjR4L3Hi0ZRCmSmcpEcUoiBQKRIJEYlcfjmbcfHvj2x0e+fXfPTx8PnIaBaR6Z\\nZ3MXZS+IFiSgoaDFXEdRLHhbfGxsBpSrfeLmJnFz03G9j2x6U+nnaXLTXQgpEKIQPLlrkzr2/das\\ngynwPg2GGDEFO5eZGiGrWYsVNFRU0cZHgrl9qGZ6sLhRnshlXlmcznzCfdeluiMUY2dWhsqSdVkL\\nr/kG8PkPvpeUkGckz1S/WJ17W8vLZ4sWWnPkVdkJQ67Fg3+2x5+p0km0FnV5triOm4virrEKk+d5\\n5uPxwF/u3/N/Hd9zrrEDUVSFPkRepA37EHmeNuw9kdCKNpjr4pxn3k4H/o/Hd/wwH4kBhhyYszGz\\nYoAk5r4K0V1H7mZKYmOaFHM5OY04huiuK9jkzKYUOglcp459TCSXRSlED7QGokQvmuWRJnHgqLCu\\ncCgXwvvnUmctzxsC/YXjkwjyPHvNYzzttWp7NSE+jROn05lxMGE+l8x26jgPW0pOtpGp5saSqr8O\\n2q2PSnlaIulVYC0BvfrhoFUxVqMIKlyvArV2eTFFqwzTTPYApR3aJm/OM8M4Mswjec6+MJKXCViE\\nbpO3lbDSGIYulLFYglW0s5vUKEz98rkyzWYiFu8RW4TSCvfbfV0IgtCwhm344EqqPbcJuMo0SSHS\\nhYRkIQ+ZnGZySI503FWEkkt233ewtn3JWvtpKWz7DX3XGar2MQoxtDE3/72iQdEIx2lgPit5yuRR\\nCRqJMRGC8Pb9I3/5/iPfvjtwGLKxQdpcg5e1cipbtjmytu2s695Y3MVaGd/se17eJvZ7V9JjIc+R\\nmCw4Hwh0IYHa/c9zZtdtuN5fISjnc2bXH+hrpyW1etRtGbkQV6pStWsGsWBfEUv+qmnvuEIsxQrJ\\nXdLyjHohLe3frlW0uDtlbms1SDT2T/Tx82qjoZLffQ/kkpsgz8WAFdC61xtLKRt/X2JTBvXH4h7W\\nDUcRYkh0qTfrVCpgsjXtrbWNRaPKMA18HE/8MBz4YTwyeNeg4vm4nQQOYeD31885zyORK9sbvl4o\\npvg/jEf+79N7/jQ+EBI8TIkhB7IaddkyoOtYmrWbHL4Fp/xGnLElNmYpCF2B23Hgdp7Zp8ibzZ7P\\nt1d8tr3iWdpyHbd0Bt1t7YtYwJlqEVa3Yd2B7j6RxWd+EditcqQpP3jqOq7HJxHkwzhSvNNJEBMC\\nyf3kwzhxOp85HI6UXMjFWsJJsVKQ7TG1Bgsrkl1w+drFYufav4Y8FvRtv9QPLfBIqx+ioibXuJqL\\n87iim7bmPrg7nzjl2eqNh0DJxQVt5DCMlNOROc/Eull1ZpwN2aSY6KW6MAygSHbKUlFbYB7IqoI1\\nqDJcd4y3kXlj0z2cMh9/eGB/VG5K4nq7MWFTIHihHwM+1S9nPOyUAl2qhQW0IfJczOGRS+Z0OnMe\\nB87jSCDxfH/Ls51ViNxutmw3G3ZdIiXji9M5WhXQktAqoHP28gzJF7bFSsY8M84jp2ngOA1Mc+Y8\\nDRyGM8M0c3gcebwbGU4ZihCIzHPhcB55PE3MJbQlr2o+6RSX2ju5WEJUcWZK1EgNoFVRvttEXj7b\\n8rvPn3F9lTjPoylGLUgQdmlDn3pSsufsgj+rwjBN6OlACIHzPDFZnjKqc4tRLNmfNPeXdyGw9RqM\\nqhfESgZbdquzT4Kgan5l9R62FiS3Gjh4jcUQIuplLSiFWaVR44pmsgS6rjPgk+cmWKpSr/EKkWhK\\nQrx8hCzbBEJTpNT95Iu3+JpZisWZdbuRYAjYhRnu9sNGiViUPGfePt7xzeGO74cTY7F63m2YXCGf\\nNHPME6c8kbF4mVmjBVTIJdvcMaOhUAjMKsyIZxW70qmC0fd5YeVOFbHG8FoVvhI9gztQuEmRV1c3\\nvN5ecdNtmLF7VdyqjJbxay0VLaBdxAP5VPfwKqy5AnP1qHuxuhtpwv4XkCqfqtXbcGaeZ9vYIdKn\\nRJdMJ06zmcPiDZilKBqFbgx03uqsoWZZUpovjmau6OKSWAvntpMWx0ppwn+1anX5tfoXzZ2CmeOi\\n5FI4z5MVlae6ZSyIaT6xQIodKSb3mxubJYbaVg1DCZ7BWQpQZEmMco+IuI++oJSNcNrBeS+UCOOU\\neX965J+//4b9OfCr/pp/133WBOVaiasHIIuo+efNEjTh4un3MzAOA4hlY4aY2PSBLvXs94vZExAT\\n3jF5/Ro3JEJk9oGrmzdZGLtRJOeizmfPjPPE4+nIYThzmgbGeeY8jZyGgWGY+PjxzPt3A8M5Y7lA\\ngZKV2emaIXXEaCg/EDy5KmK1PAqhmNWx7nJerYEUhGe317x5dc1vvnjOy5ueLiqncbQEtSDm2/fk\\nJCGg6pml7pKqNUhKzojAtk/sNx3HPlplz7kut1qkzYVfWLk3QmyskHUjFJHgTBQL7qkV32exrqpA\\ncgHqFeEqog+BmpLgqD86BNFVX1dP6xdTBiGa26FaLRVRar2rKrt9rzQGSd1UWml9hrZ7Z91UBozt\\nJz81Zx6HkY+nA/949yN/Pn7k/TzY+qCWUah70ai0o6+bWl4BbJ8EzFV2nidwLv2U1dlbC5O+AcDV\\nM1jtLAeFNct78ZVSgE6EV5sdv00bPtvuuO625k4ppfU5kCDkaA25p1yc3ilVnPi/2pS7q5IqZVj/\\nWqtcLB9e4opPj08iyMeSzX85Z7pQBYNNclGIKbFLxhlXVWYt7B4S3Vg9iXbY4OtK4DpKqP4QvRiF\\nNu01INlQ8M8GR1YfW7ikRkexO1AX5EWV0RfVynj2+7C0/P1mSwzBEZkigUbbM7QFtVu3CW1BtBY+\\nXbtDlDkUztvEcSuco2nyj4cjf3n7nn/8+htiidzfvOTL61uuOllyRXybFYVJzZyMwdCGZdyVpkSL\\nwuF0IsXIbrtj02+dfsZFB/BSWn6dsV/mJbiX1WrhkAu9U9vMGsnkKTPmidM4MuSJKRfuHx45DZYs\\nM5aZcbR2f6fjwMPdmY8fz4yjM3PAg1BmykfJ5hoSQRpDxGfSmwskqbx2Jfh8dylytdvw2y9f8rsv\\nX/HbX70k6IyW2YLSqoQYSJtoQlyFkgvn4Wx+4hDpup7gtMVpmtl2Pc+urvj85Wjp2vdHHg8TeXaF\\n6vdWlZpx9IP72+0OmylNRSws1NbqqtayZHfqiuq2qvJkgfZFYMXoyihY7R0DEE63zLb+UqcQAqKx\\n1f+vcaFlezTsuvLxLu4ZrbeB+aJ7qQHTasu6K7VAniY+HO758/17/svdT/xlPPBQZmYNtDihXz3X\\nHy2NoaXtlirSV4Yyt304Zqs71PZYRS04H74KyzXYuZAClR4I+xj5cnPN7/sr+mgMoWoJFpz+G4Qc\\n1Kulmi89NVdstXuXTmI/L4NQhVnD602B2vT+/wiRv7y9YZgmxnGizIWUkiWEiLBxHmyNENdFvy8z\\ncVD0tEDphsbFtWEzFJeOPb627NW10F7P1sWeWQbUXDSL/72VCMXQq0a8q/uyeUpZTN8uRbZed92u\\nZ3S/PM/UgGh01CzB2DMxutYtxbjZ1SVCQaMwbxIfrxKTQB6sGe+337/lq6+/4/544j5nzii/vrvl\\nj8/f8KzfOgKbHSlZGdnZvxMxM3ecZw7jmexlTMfZAmt9zoROES1kIC6xKdoKF8sItSzRzC4kIsJ5\\nVu7PR3o1tkpKiUkzp2ng/vjIeRoZ84wC0zi1hiP7bsNGesYyMZWCzBNBIymYW0nd5KUF62p99kJh\\nZnG4wRIMV0KAlAKbbSKFwPOrPb/98jW//vwZL5/t6bvkQdDeTPTzgGpGirLpekKIjW8PWPmB1GEy\\nyWIJu+2O26trXj9/yfu7R757e8efvn7H3XFinD3oWhegXgrJZnKrsSFy9mCxF4Ermj2GUZWAP2JF\\n+X7lnL1mO9oSlajIcJUgUAOrxmqq6Fzaf82arVeu36fVsl3twSe4Uh25JxEvMLZmudhZWQsPpyPf\\nPn7kXx7f8/145lCyo1tzR1RhXr+rsDBvMtX9YEluqXjGd60/o8JQvaH4mqmKMixjvhaXrdxBkymG\\n9pMI+xjZxkQfK7trma9qLc9iYjrjTDKpY7jQTBfrf60IaWSKOgstCLga21/G45+qjG2IkCxSX2Jx\\nV4OlYVdu7ZRrD00XFB7EC0Qzt9SCMcY9kNXAwsLcqC+wsloWZsZqDB1FVPRSTR+biNag2AX7+muK\\nOo9UK/PB3o0psN9t6DpDNkZzs+fLLgRti1ZtvSxYW+weqKMuNmGIwkOChzyjY2CaZz7c3/HN92/5\\n4cMdx5x5yBP59Mj/9u579mlDf23FW4sHhSz4lskBJEVwKl+ssQo1ulXaGgrvgqDzxKyeYCQ0posF\\nXu2nYDXmc555nCYIwlQywzRzmkYoHrCVwJQz98fB6F+hJ4RgyjwGrrYbisLdw4m748jjQRknIYSO\\ntF7bVdjIglbNF2lKp45biObXnKaJECJ9L1zvzcW13yl9ygzjicNRCXpF2nYkz9SktzVgbrDUEphS\\n17kwdT95U7QdfSpsusy2K+RR+ZjO9HFDF2sJYF8/K2vvl44mrIr72bVY8NLXemWL1A4+y+rWJlyq\\nAhNW5n1tgehW2II4q383NjfOavfwZNSXV1qyzvJqBU6IAZFejDJaaws1aiOBPnVsU88mdHbfvk46\\n58RnEW+irs3NuISzChqiAZwEYRZKgByNuTMjzCtq8SJ4DUOvffmyfn+RFM1KCgR2IbIN0XI6go+l\\nZ1rX8SiotRpUk2kULyGt6g6t6vNu/1sdwuWrl1mnPHl3fXwaHjnW0UdSsDtwP2rtApPzzDBNTSAX\\nlHkMkJ1bjm2cXBTq4uBnFtLPv7f5Ulgk+VrerxCHVVlzMx6WIGFYFIBiyGnM86pOt30mxchu1xNT\\ndCXjvmIxBDnPs7ULU72gYBoKF090UERKy0A9RuUuFM6TUiZ4PJ741+9+4Lu37/l4OFpVPlU+TGf+\\n88ef+HJ7w03oeLnbNnZARXvF71HcbxoksAnJfMoIm01sbqwamK4ZoqFWbHTFW8TQsOZMnicezyMh\\nGT/8PI2czxPTNKMlkyRRsnIcRvb7LV3f0YeOEjPbLvFsu+XhOHA+HHn3fuDjw8xxVGNz+Byul/uy\\nrGt/1tI2YAwQEyCFYSz0XeR6F3l+bQybbQ/oyPGYCZrZpR7ZdOZGKhD7ts3NFy5GW6xp6U1AOkND\\nxKwSLcJ5PnM6TRwOE9MMaCCSm7+3/fjMy8XLDZNRLcSKIYr752s9kaCmZNDVWNTgWQ2OS+UvV1eK\\nfUsu2dF1BVLSvq+VL6AC9RWKvHBDVNqr/6VLTEa1QOyMveFrpnFk1JTRtttw3e+4ShtL/nLA0Ytx\\n73MMDfgELYS8IOhaK6cm1bVevMEszlyERaX7KFdfuRaq83LxFDmgUm2grgl1gR2BjSdHicDkAKk9\\nPq581Z6vw6p3NgUh6++qK+vyuLBrFJam51zO8ZPjkwjyGiCqaNr6cdp7pRQsXhWYp5lpGhmHkZwT\\nRaOZ/mq6z1zW9uhxnYO+qNS2JVpK7JPocPvMaoRq2m/B6XSiXkOFtshVXMFoYSh5CRxVF4+IJR20\\nDSHMuTREUgDNhVxGdl3n2a3irdcMr1jesunxcxLuQ+ZejH1wPJ/58e4DX33/PR8PB8ZiQjwDY1He\\nTmf+z48/sQuR/7T5svHuS/Hgj60SVIVcjH2T89wa8ZqQMsZEipEUU7OEwF2quRaGdXOxWKenuShl\\nnhnzxN3hnmkQRCL9NnH/eOJ8Gshz5jyNbLqefW/dDQeZeLg/8y9fv+W7d/d8eDwxz9mSa9S/q2bd\\nOofbrCvjEElQLP/Q3UYRYuelbgm8vN3x+sWe57cbUgxsUmK/3bLbdP5vIgUbJwnBgs5rpMpSRMpv\\nZVk67m4oOXM6nPnqLz/xL9++4+u39xyH2carmnEOXC6D9RXxgjFDnHPf2XtaTEnmbAgdrWVf7TvN\\nVWfjUTncq9KOKIXZa+BT3SKavcOSZ8xKIKk2iyt69ixmyLkAtYB4VeKLe6U05VEpoGAJQkG1JQ61\\nZGGp1gLMZeY8j4zVXQJ0GP1UYkTVeAMRpTOz3ECPYO7IWQhFrWyBK6+p2A8a2xq17eR+7UXr0WqA\\nt5eXuFhdcyD0LMFa/6RndIrVNHKwU7ISVemKkzXU7rNITbBiUXZ17bRvrkBxLe217cl/6/gkgjxG\\n/1q/r+DRXgELTGlEOyWn5EHByP4c6GMgizJJtkQLlI5I78gEuAgGNIxc0Ukpy0DJLwxKNZEuEIdt\\nuOJVApsvTW2jqCrTXHwfX2gDRCzJYZ4md20s1wtAFrvOnIulCossC14sdVkFpii838JjJ0xiFKt3\\n9/d88/Yt745HhnlmBmaK9yO1+/zX8wPXDx1vtnu+2F9z028NyVSh4kHJUvnMwTZNwfyzIQY6iRSc\\nddDKFdhYzWX2lPlifPl55jzPjT54HE48HB4pczT/cgmcTiPTmIn/b3vv8SNJkqV5/kSUGXHuwSOy\\nKrNquma6e4DZ02D/f+xpscBiLzPdPd1VWRkZxMOZcTNVFbKH90RUPTJr9xiVgEki0pmZmqqQR7/3\\nvQixCFgijTW0B8fdtuN+ueX2cctqd+DgemXNG1AHcnwkXARGy56jdpOPBBuGjlGFlKvPJhVnl3Oe\\nn59wdTblZF5TFbK3qrpU+GsJhcnrI1BAgZFaqyaHGUJfqJJPsMIQPK53LBY7Pt+u+POHez4vNmxb\\nKWrLvRiSsDDDPht8Cw3PjeK0KUUbLEQ9NtGInTkUuI0Fasx8JanZrwhWowyGJm/9IaEqx8FohatK\\nypzMTorKZ2EtgtWmsneSFzFCYyRvgEgJonwK9Q70s0MIdK5n27esXUufIaFKCBZFcHol2GvKguum\\nYT6ZqOGTiurARqHA9i6wd57WRVwge9bpeA/cLCbzu0CiShif+6eC1Bo0rKJhIZPqv8meRJqBDo+J\\nkVlMfPrC6zKufRk+KI4iA0m2DPlBvRlGt/qr4xsJ8qfCNoPlh0AVxliCCVRFSV3XnGwCdSEHtTUy\\nWc5EGsG7UaELylOBGtUKysIcctJjCMakf18rgeGARJNItQbNKa5uoMs8ITryiVcSMC/+YC6xjxDV\\n8osIVptkRYbBcjEY+iKyrWDZQFsYvIft/sDN4pFPj49slS42EDXBMiR37vuWP29XXFZfqIqCWVVJ\\nJWCykOxwqOT2NdGssVlJJmpOQkuVYwwKCdMm1lrJ52OkdY591+Gc43DYczgc8K3P82u8QOwqU1Ir\\na1+JhRDZblu+3G/4+W5F54QrI8XgDTHTKOTKSN34if4Uo1jiAKYUyGRdW+aTiqvTGW+enXJxMuN0\\nNmHSlFSFlOtjJUnZB8eh9TgfaXxFXVZiBdkUbiDDBaXyUg9diDgX6Lqe5XLDx5slP3165MPdmm3n\\nkVTPSEijwi+K9znmEYl55cgW7nhfpiQndlAq5L8kojEvDUIYutbIlJksyFIIAaN0FOp1PGnyEJS1\\nU28ucYmj5ycSCHHgORnOzdiylXNdYLXLPAJj0T3nvSj8VXdg2R/oo8xQEpEVIqBaI4VSZVlxeXHG\\nrJhIi8LseWvRVwAXPLve0QZwOZaRT2xeyyHvK38ft7vNDoPOkUFgqrOioDYSkEmFjAlSmXsEG0On\\nCPieIPtbQ1tJig+29VhmjH7Mc2og75lfxBGejG8iyBOwNU1xinvmG1ZpVxghnpICjJ4yerCWUFq8\\nsfS+15CHZRoFKZFcu7SAmYoSlK9j+MwheRmfJIxyBxN54bAHCoMpbE4EBSJdiOw12Ul+qkFQlmVJ\\nURbSek0rV70PpIIYo1agjwHjwPUQfAkUBAO7MrJooCvl6r53fLy75/PjgsX+IJYWEi7pMWSxqWGW\\nu67l/3y84Xoy46JuuJrKkhfWUFcFdSn2Za/EVkW652Ig5pKQilpRTjlEovBpmBipy4KTegIWWt+x\\n33cUwEnTMK0bCgomVcl8NmW5a+nanipEem9oW8+nxYYPd2sWmxYXpKBCugMFTfiJMgzaWi/GKLGd\\nxGNdCIo9We31tOT0rOHkpOL1xTmvLs54djalKCpsUYApiFic9/T7luV+y7Zt6QM0Rcl8MuFifirh\\nsUIahJR5LUtsNLm0PWLoOsfD45Y/v7/l/c0jXxZbWmcJqpRJFMApnqoexrhlmmxFBYqaAaoag9eY\\nQFYdUtCZ4YAp9mzVM1DEjovqzfhs0VnNz4gUkzOQ4IADmiUQQp89k6jFZIGgNQFF5iNPNmQmmDNJ\\npZgc27UKYghBQw+FAmHVWNi1Bxbtjsf+gGMEwTXK8x6hAQ4xCGS2LLGzCbaekNSpQCzFQu9jZO09\\nHYZoSl0jjQ0N+BV5dqvNbax6LVrH4YlZh6Znk5L8miZTcwzY9cIkkjmFwCYyPFUkRp9jpOHI0joH\\nzcXjTVa95FLGinR4y6+Nb8S1IkQ52eLyCpZSZIe1Iw2GfFsE+VdFQwwWZw1dETFFpPSR0KWstjpO\\no+RjnsCYtPEg5JMbOPZYBO+cHR1Z0BiFDKcgKwmPtP3aBaeCXDdUDNpYImVGh42dQi4RjXEGsDZg\\ngnnSoNebyN5EVjawJRJ9ZH9ouV0u+en2jsftToqQTMxWeBLiaeGDkUo41wf+dfXAaVlzUk/wKPeM\\n9wTvhM60LHCK9RJuErU4CvvEHY6FHopgGLI3QtFrsEyrhvq0oCoF/ZF2njVSRVqYmod+x91izXrf\\nszv0tJ1j3XqcsZgyiYIon8EgNENIlYqyJsGELGAigmiqq4LL0xlXFydUlWE+qZlUpXg6yq3h9bmj\\nDzjX0Tuhguicx5ReoHImUmCZTibUTUVEGBr7EJTqVQD1213Ll/s1P39+5KebBx7XO/adI1KQ7IRB\\ngCtLYkoca1x9cDJSCCBx2Atc8mQ+ZTqtpUFJOuoBep8OuKyFeH9C25uKrYJ3uN7RdT6DA5I5moSE\\nUa6ZiFRnWg2zFNYKwoUohnQEa+TerE3G0Mj/1crrwSG1NLZgWpRK8PbklOkZCPgYshBPB35Ae4sg\\nN0XBrK6YT6fUJzNC2bBzhpmHMpINGhcj2yANS2RS9WZMCtjkm83hrnTP1hoKI6GSZKEbRKYURipU\\ny0TtMCI7k3oGFeAJex/TSR48Ovn9SK6ZNA8Dag2SVy0/WVWoJivaXx/fhmvFKx42Wba6ub3zlIVY\\nPYVS3UoMTIR9EQQraqKlKwyhkn2ZrIPBPxk98BM/afhdssYHXmDzZJ6SM5yvGWPmLDGIjPEh0EdP\\nFyQ2rYHGfAhtOQjA5Lfl5Fm6x7yYWS+DMfQmsrSetY1CHnRwLBYrPt7d8Xm5ZN91utghOTAj5TM8\\nekrC/MdmyVnV8Hp+xt57fBQucuccNZVgqKOEeYITdEWRrIkMRcuqTeYrpu4pkiewWKbVlKIxTJqa\\nuiwzDt75wKHr8X3LZuv4/HhgtWs5dE5CQwYGXmpdVD3QknYusiUaI8KXYQwmSjioLAqmTcnV2YxX\\nL865vJzho+dsOqOpaiRsJFDL3vfK0hcUFlpQlzUGR6MUAr0T4q8YDdaW4iuqgCRIgvFw6Li5W/Pz\\nlwU/3yy5X29pXSosd1mQ57BbVGjaiGAtHeYxGscYKIqCSVMwn1W8fHbO5dmM2aSW+9C8RueCEtCB\\nd8L77rzHGPEA+17a7603e5brA/vey+dqxXGGKhoxA4z+aVqXTCclTd5KLwAAIABJREFUk0k5nAIj\\n1moMhkhBjCXOQ9s59dayoY8GWATSaguxYnMln0lfwBiqsmRaVsyLilWMOMWZWGKuCq4xlNYyK0tK\\nW9BjWBH4HDpehJJTrO4LSXBufRAjJwKEkUgYwiwxiuEj2ApDodiCxN+e4I5EMCFSGqnsLGwqCEzX\\nN8O5NsJ/hBnkxCBPRs+txmF6+1iIp9kbhc7l3XZ8tV+Ob2ORx6HsGKSIxgXZiD4EyhBoqkoRH1E6\\nzvuICZKdNiFSB0NVWBpvmDqhnTQ6MyYOwHr5vBFEaizv42D4P5kmk46UbONsVaU4viG7xql/pIzk\\nwkU1eiSm7J2ELVJncWNRrLJU9XklprcRql4SawcTeKwDBysFHu1qx6fbe97f37HrRx5AHFyxdKBE\\nHYwsA2P44vb86+aRy/sJi74lmAnRSOPrGCJ1VUNR4Jyn5UCnfDipSMFqY4GiqijKEl9KQjQodWmI\\njqqsqYzciOscrnOUhaGqSlzvebjf8i9/ueHn2xWbJMBDIPjI6Exkzg9B1YzWJQ7WTSwkuVVEqe68\\nOJvx+sU5/+X3L3h2fsKkKTk4J/h0W6rgF0ik9B8Vg6CwFdO6YVrLdeuqxmDo+55J3dA0NSaW0vik\\nkCpF72G13fLh8z3/8eGO28WOzcHRuZCNCpk7zSWEOJCWobStWInnayIvccCnuonZtOT6cs7vXl/y\\n9vkFF6dT6rJg2NWSnxC20EDXdnSdFFBNJxNc51mvd9w9rPl537LoxTKPqpjNIFmTzUhRGmbTitcv\\nLnn17Jyr0wmF0TMAdM7Ru0DvIm0Li2XLp9sVrZcaA2PE2EnqPlVDSP2A/Ms9ahGKiIv5KW/bCx7a\\nPZvdo3hLkGPP6YmnxjIJkc1mS9f2PJiCj9HwT/UJVTllXtRK2xDYO0evoRiTLFq12GMqHgtIzoMU\\nf5bUrAcNxyavLObuXlWljVOCyXUjMHhcKQzpEUWUUDSp+XjKrkZDKrfWCvG0CIoGiinkxch6/zsU\\n5NkIHrvdtiDaIfnjvKdILnNkFJOSxFsdLFdtSYWlidJcYhR1z4nN7NaihvnYWM8W7PC+4fcpQUqG\\nOGbkS0hCNPnOT54uP5O1hbLHajOJOBzDrBRgoHENsv23RcTWEKsSnGO/P/DX+1s+rBYsDwd84hfX\\nDeijtnPLrjmDy27EovXRct+3/D+rO3YxMD0vOS2Uv0PDDTaKB5Ti5GnunHPSYky72qem2caIhRaC\\nPIeEawLehYxm2fnAbr/hYbnj/c0jN48b1ruOhIRLSdN034LLHgrBiqIk9QU1I8Fu1Upp6pJX12e8\\ne3nJ25fnPDufMW0ayrLgTAWHwAbleiFGTlydwzPZ88qWsVh2zpUq8OT5+ygGR9877h7WfPyy4MOX\\nBferHfveiyuuIRMUxeJj4iQfJS5NZCj5spho837FRKrSMp1WvHlxztuXF7x9cc7ZbMKkrp5QyZIt\\nc69UF4amkvoEGwN937LfbrlfrFjs9hyCk7L8qF5G1OYWyqA4aUrOTie8fnHG2xdXvLg45WQqnboM\\nCpeN4nkdWsfnLytWi4NUkYbklw3GBUa5vwtlAVSb1ETpLpVCD5O64e35NdFYylXN+92Kx+5AU5TU\\nRYmxBXskMbrZ7XnoepwtpJFJjGzm57TzK/7bybWGVgKtEm6lIjgT7YhrSr1vtXqbacnV+YRXz86Y\\n1JXef8h7rm871ss90z1MS6l5KIwwuCb5m1YlqNdWYwVxE0WJRRu1h4CKiyS8RzLHjObuiSX/lQD/\\nu2I/zJnzUSJSMvLpJxHkMSVjoskkUnoBylhwps0mhEQ/bSYVpV897yCqR1px/LsnCz1cZ8yqmBVF\\nEjQjV+3r54uI2xbCUCM8ICxkwYIWr6RGxjZEQufYq3cevaE/dCzWG35cPHCz27D3iZpUrYg4xNSS\\nnspGj95eUOti43t+3K/AFLzwJxL/Vq/Ie8n0pGsU1g6UsCEIv57xKlDlWRLmPAQV4CHges/+0LFv\\nO3aHju2+43G5426x5eZhRacuqYlaHo640ylPIf1bJdwxcF1bTfyrkMdgy5L5rOHZ+Zw/vL3i7ctL\\nnl3MKQsR3MaKEMmhC31OgBgqXbWk7Effgya9qhyPjyHQ9YHdrmW53vLTx3s+3i75stjS+wRpTW3i\\nFDmiCWGvAl1Ca+rVIXwvVslrJD4LTW05PWl4fnXC92+uePP8gqvzWW6/ZuwA90sr673Ne6gsLa63\\nuIPUXqw2e+5WO1aHDhdhoAJQ4aEEbk1dcH0549Xzc75/c8Wzi1POZlOqQpt4KB+QNdD1jtX6QPQr\\nuk7pmxVfn3DXxsj+OZ3VnDSVMEViMwmcJHFFkdZVxdXslKooacqS82rCp/2aXikhuijcTL0PtN6z\\nbls2GFqMgG1doI4l3zdnVEYMmh4S7f7T82nMKHcmNzyfVbx9dcE//O4l80kzhFpjwDnPbrvnpngk\\nPByYlRW1LSl8HAE0no4YpPCuHBuWBmJ2gGK2AdMFBuKAfKpHq5Qu/NQw/Xp8G/hhOTpUCb7mFc+s\\nrnkIQRMHUphhFQGQizCMle5AkLX9k6XTAyPzNprQkViOT99COs5JlOcCGP1Qo9adTYGLmIT1aNfo\\nNX0I7LsW6wSBAYAVPHZZFCIQ1QJMRRKxC4RFh9tC3wvL3nK75vPikU/rJdveaQgiZjZGafyKlDGT\\nhPjTsuSIFCP04ssB8r21EuKyxuBNyMnScZgm6sELwUvCzPf5eaVjvLizXuPNXed4XG75eLfk5n7N\\n/XrHoe9xijJJsSWheUkNsz3OdcSo92RKSquMkZorgSg8NIgHM503/PDmmj/97jkvz+dM6loQFUaS\\nfsHFJDeVffFrxasi0cgaGIzyKamREcgNmX3wHPY7lqstP3164OP9hsW+J1hxgKOiamJ0BGQ/O6fI\\nHo3RphRu4ohLkNtCk4pFabk4m/Dm5SV/ePeCZxdzTqcTQe/o61NoYiAFEw4TSUZHQqwpKocPFmd2\\n7F1k3wecA2sqTGWzIWGAojRMmpLrizk/vH3G799c8/rZuShANW6EjliNnyDrv9m2PG72rPadMBSm\\n+kkV5NYapnXJy2enPCvmTA7CXGq1uC2oNR6NVHjXheWsMczrhhfzU77s1nzaL7k97LlvW0qDBqQM\\nEwNtNHSakP+4PzAzK/7QLPndRCqYffLc4uBtpdBWwvwGZE5PTxrevb7m9dUp07rONAoGhCCtaYib\\nnsPOMK0qSgpMHDhUEo49ifUQDT1xoMk16V8UpFAUeZXI30ReGCXLYzCSNMCU5Za2iHyCdBqNbxRa\\nSdakWHJDBj+hEmTzxyhPbpFkZ/TiFgYjlo9oeLXnTT6aQ9IyKWK1XMdhDSCjHcZezmB1S0gkvSYf\\nxZTUMBrS0LZWeXpjVIRGST2tKWsVRklw20LLvdOHCBTQ7AN25ai2Hg6Rg4ts+z0/Lx/4afnAznVS\\n+pu4PfIuGKoDhaJ0cOOzQB9mflBVJhUADVA4Y23OByQoVFJi3nu6vmffHnA+iIu976irgqoocX3g\\ncbXnNhX0bA9s9z2H3hOiOqA235p4CYmrIiUAQyT6QCwDwUhCNvGzGGNpmoLptOR0XvPs4pRXl6ec\\nNpXmIkRJxZAStMM+EF6ekJtd6GnJ4ZWktJNJnrAYvevY7VpW6z3vPz/w6W7Fl8ct20PHoXc4H7KR\\nJVvE5lqGwkqSfmjtllxoQGPUpYbfJk3N1eWc37++4t2LC15cnzGtS+rSUNqnVlqiT0Y9FGsNxgrj\\nY1Kmhzaw2nqW+0g0FWVtFTIp+8UaqTq+PJ/y/OqEN88veXV9xvX5nElTZyNlDMM2iOfVtj3L1Zb1\\nZi8J91ERDwaqquDibMrrZ+f88Oycl21F0wViH1l2Wx66jglgEc+uqZosoApraL3j0Pc8HPYs2lYS\\n82jxHEJpUQO9gT3QxcDn9sD/sbzjv8cLFkHCYANtku54YzIxZMqZNWXFaVNyVhWEvqMLCtXUWHbw\\nEaKntFDboVAvC+expa+KOVqy55FpqNM86hJGa7RHqEYnkkeoUNugFpThKY8OGP5WrPybCHLnHBkP\\nHDS1YYyS+Yj7LsZjEIxmLLW/ITmBlMS2yeJqcEWeCmfzJDaerLB8cI0KvCHiQerCAiLsAyk+n1j1\\n5PN99DiFTw3vELerLAvqRhoRWKvXz0UXqaO4CE3jI+UhUG4DVQ/RBXZdx+ftig+bBV92a3ofRFAA\\nKSVukPZXVRSLZRc0zWLG9/PLSYnpR40HZ+UZkrWnh14t8Vy9GAcvxXlP23U4Z4Gezabj88Oam8ct\\ni00rIQel/ZXQrh4OvZYoar3PZLEa9XdMkQWuMdJjsakrLi8mnJ3VnJ40XM4mzBtL9D1tJ1Zxoc1J\\nyrIUYiu1VPPjpxOlSehc2ahSK3lxSTFutgfu7ld8+rLi/ZdH7pZbNocuw/ukgcjwbKT9aKUxQ5FM\\ngFEYTgS5lt9jmE0bri9P+P3ba3736pIXlyfMJrU2YhhKH2J+t36n4T2TCeUFGdR2noflnrvVgdXe\\nEUwhnkzyTrR+4Px0ytuXF7x7dcnL63NOZ1MmdUVhxVgauM4H7LVzgf2hZ7HasTkIb7zckyKHSsvF\\nuVz3hzfPeDWbcfrQ4/ya5WHPYrthtd1SxqAKGuqyAkSJl4UYEjvX82G3ZeMdHRCsxTEI89IYKuAA\\n+AgL3/M/tkvm1tDZQBdTaVUcBHn2itRQMTCbVMybksaC6zqNAJicCI4h4rzDhEiFFDZloZtXggyP\\nTcnZIlpqDQUblTFijSd3TM2vSI4BhSBcRb1y6Yji9yQvW7yugTvy6/FNBPmhPQg/RAgURUFVSlJj\\n6HiuoRYvDRdOzATjS2zQA2g1DsdQ0g5PBbgYrCncIPjQhHsdQhIxC5AkVIHsISSXKbGWFaONEBmw\\nukPcarD0C2u1/Zl2bCls5gcJMRUMSdGD2XfYXaDaC6nWwfSs+gN/WS34tN2wcU4taHGvpWpPLLqz\\nwjK1Na13vHc7TRyagY5T90suUBiNhBsOQQ6QCQFbSE9LrMEHp8k0J4mhSmBpvfO0rWNaFDwu93y5\\n3/L+ZsHjtmXXOQLCs20KFUQiRQW7rEiL6JMgFdiitHvTBgt6D4WVOZ82NVdXU354e87pvMYYaA87\\ntrsDviuZ1BPqqhY+GO+YzWZSOKJhE2BgCVTTaCitH4rTjNFS+BiJLvL4sOWvHxf8+4c7NodOWvqN\\nXFwXPUGtcklum1wYkjk5DAp7HEIaqTDHWnjx/JTfv73iP//+FefzhmktZGV63vX1ur+joC7ydGrS\\nOQmq9tDz8LDlr5/Ee1jvexI9rYTiDFVlOZ/X/P7VOd+/e86bF1dMm0pbt9nkrGSrUJSaXL/rA5tt\\nz+PqwKEPhOShYqlKy2xa8rvXV/zw7jm/e3nNxEXiw4L77Za/LL7wcb3kfr8TTp4kDjXckFgN8zro\\nP4zFIs23JUQo+Z7KGJpoORDpguOhi/xfqzuChQMBZzQHFaXZucgDQaYQIxSGs3nNfFJioqd3gVAk\\nGKMXYyIa2i4QnLSCK63FBHIf4ewUq4FWYZkGiwmGIhjJAyUFkjD/yoBAhOhlD1pSeK4XmZSatgf1\\nVmNUr2sEW/5qfJvGEr6HIA9Qa7ccVU/SNLd3mgwTtIE79BgnMbaQXKMIjLVTTF/SBKcwi1ox+RDr\\ni6NgvZP0T7bYGEokX/VLFAuWqBhPfa0gcOPwlhRFKAxVWVBVZbYUB/MqYgrV2H3AbBy2larKYCOL\\nvuXjdsndYcPOu6xoUrw3PfdZWfLfzq543kxZ9y17/zPLvuMQvSADkKKgke2gmyn7naSm1AYj8220\\nKk2ROVVRSY9KvYoLjq7teHzc8fHLkpvHDQ+rPbtDL/A7xuGQhB5KHlQKT1lxL8WPfbo1tb6gMIam\\ntLx+fsGb52e8fn7G2byiLCT/0DUViWK2LmspQLIF3nuqSUNZlTmRK17SoJxFG2tIIOHSkTBf33k2\\n25b7hw1//fjI54cNuzbgg+YzbBQrqjDaoEGpIQz5mXPnel203AdVjQmDWK/zacmr61PePj/nfN7Q\\nVKUaCuHJgU2BuxjNkxhuyg55H9lsWz7fLvnrh3tuHtdsDp16WlINayxMm5rr8wmvn5/y3esLrs9n\\nTBTSGENQIclonZLhI0pkv+9YbvYsdy3OI/j6aCgqy+X5jLcvL/j+7TNeXJ3R1CWuPfCwWfHh4TP3\\n7Y6172iVSMLHgDcINE83l09HLxqhusaAiRQEkQdm8K8KYAI4I96ox7P0SnRXaC2HqsIh5Drs83pS\\n8fLFJc+fXTCfzzJRWAJcxBA47DruHlbsFzvO9gZbxhwiq20JRmDDCYTubWSPZxpLzQeQSfMElazK\\nMRrlBiIrhHRKUsg1Bi0zTWFok171dxQjF0uiIHVOz8LSmLzZ81NpMdAgqJOFqc0R0paPv/pReeTz\\nm6yx9IaRFEmOaxz/YvTXcZ5aQjwKuUuWRBxceDnQw+Ed+Px1YQ3Qe8w2UO4CZS/ieed77g9bPu1W\\nrPsOF3xWAjnJFQ1Ta3k5mfLP59e8mMxZdC2f9lv+fbPgrtvjxo+geQThSNE5tlK1qXplFBhCG+4m\\nFIm2CzOG3nlWqwM3d2s+3a74eLviYb1j23Zq+aaYXszNeseTa0gKM9+Yrnt6gcxNYS3zWc3V2ZQ/\\nvL3k3csLnl3MFY6qDRcmtVQqgjbHldyDD4GqqkZ5iHQWBgvTpjnQryFE+k7QNovVltuHDZ/uVtw8\\nblnveqmgTAcNZVmMUkmaO7zkZzRZoI+NiRRDNsi5n9QF1xdzXlydcn02Y1IVCjNWUZVhDV8d9iix\\n6hgisYOu82x2HbcPaz7dLvnwZcFis6ftA0FDJGVhmE5KXlyd8vrZKa+en/Hs4oRZU2FUgaWeoUXS\\nOKNzFUKgbSWJfb/Ystp3OK9nz1rms4bn16d8/+45L6/POJs1WGPYdz33+x0/7Za0QYp0vF5UxPkQ\\nJhpXJiev12h+DP1eDJOBCrdCLPOeiI9wQIVk+s+KF2pUlhgjKJmyLpnPG55dX3J+dkbV1HnNjCpk\\n1zn60PG4bQmHnjNfZcQNURPUSrstTJtCt9tFL0R+w0o+lU2KLc+prHTm0qHIBpbPciSdDfW1+bXx\\nTQT5rJlmXRkSWgXpljNtJoQq0jtxd0xEeTdSY4OIN0H5h70kcWIKeQB6ONMhGsIe2sUjJrf6KVxv\\nZLRmVzL9LIpj+DmQYsVBscMJmaDuoW4IFwLGCcVoMMLbYVXzxxAwB4dZdjRdQR0MwXsedls+b9bc\\n7raC9IiDe53u04fIZdPwh/kZfzq7ZFrWzMuG//36LTvfs+wPujkU/6tvTJQI1gorYBJ4Ub2MgBec\\nrx01IgCBTkXD4dDzl/d3/Mf7ez7cbaTFXQzZ6kxttEaygIz44Wm8N3XUSXFyUXbCQlhVludXc/7L\\n9y/4/uW5CAabYs8lpoxA8yQ8lhanLCvhiWEQfElTxWQRG8Xtk8JLgbbtufuy5N9/uuHnuyUPu166\\nricFkIRyTJa3boakLLKVn4SgGQn5pIBlF1ojZF5vn5+rQK0hpAIWuccYkoU29mpkPwcnVZu7Xcft\\n44YPNwt+vl3yuNlzaBU5o5ZoNIbprOHVsxP+9P0rXl2fcj6f5Ov22pTaFCW2LMEWirpX1RFRQrAt\\nn26WfL5dsd23oBz2poCLE0luvn1xwXxSURlL6APtoWPbtqyDyzUUck+WXHHJsCcYcW/3RCrVhUkg\\n5iYmRgCPhTE0iEXekRQmEsYoCwnNWI0qW7CFZXZ2wsnJlPOzGWfnF9TNNGFuhn2KpYuGgzdsvaWm\\nUA8h3asqGGORbjfyOdEYDsEzjaKoSpIsURZNbKYQyX7qSAgZYyHI+kUDZVGSeqwm5fQENz0a36b5\\n8nZHVRbS2AB0pWLeyBhoyhpbBEobKTs5bL2XmfY2sLeebRm57qVrfB0LnsSqv4bp5GubLJjThCbY\\nVNCDFmO6qZRK1TijzSdN7iNGwbsm6xwlV7WWoiqpJ0kAieAIacEiHLYdh9st/aeduGI+cOhaflre\\n82W9pHOO1Cx4bNhaDJPC8seTc/7x7Bl1UWGwTIqK359c8qfDio3r+HG3zla5DTFXeqLWeFEI7rgs\\nCmxhVZlq7NcFtQ6lh2rf9dw+bPj5ZsH7L48sNi3eyHwUUYlWzRhSOggwEeip5D4JCPmbNXbglDCG\\npi44O53yw9tr3r045+X1CU1TaVzY5qKgIaAWh8S0Scus2HZlaUxCPOdbjSVYcZ2d91Lccrvk45cF\\nP988sljv2R6cNiUgm0spOZr4UdKnWZPK3UfqxDCEQFThD5h4iflfKB/MbNIoxNJkgyYlMIe1LyRv\\n5AKbTcvjYsuXhxWLzYHlpmW969i2PX0wmMJKWM2I53syr3n76pIf3j3n7YsLZnWpiXY5AC7KPFhj\\nKDyE4AT2GQyubVltD9wvtny8eeT9lyWP271ymHvxTIxh2hTM6oLaQnSOtcbqP/70mdu7BePgWWLL\\n8KBx7JjDxtJsgpzfSbms9DspChzQHtGIAJsAHVoUl7xjRYmlHWeNpalrvv/uHS9fPuf0dM7J2RRf\\nFxyMGeXL5G597SkuTnn7pwumtxte3G+JnVGmUrn/5IENu8EwjQV1tNJSIjn9I8s6Ce+QZI4qyyFB\\nUEh83mozk2TyJnTeWBiMxjdDrSSIV1kWOZTig/BTWGOwZaEWrweXWMkkDNMax64IrBs49QHvYj44\\noILkVzyQrDSyla2WGqnDYYIpJkSMXi8MSAtiwsCi8MOnGeyIJNaqsqKpa4GfDQaZHB7vub/dsPy0\\n4nC7ozaW6B1td+Bmt2TXtlq9GfMmSO55Ywte1xN+mJ/zdnaSG8CWxnJRT/jj6RVr1/HQtay9wwef\\nP3+wyAervEgEUNYrQVzA947WB9res1OUwqfbFZ/uVix3LX02I1IIYbAcB1eGYXPqg8cwtkVUwcUC\\nYwInswnXFye8fnnBD2+ueXY+Y9YMjZSz68nIKMmTrglLXWC5jQFamsI5ciuB6GHfdqzWO+4eNrz/\\nvODj/Yq7xRqXQpPZ1NbjFkcwWdRKZ6jglU5L+lyj0ERyh7PLb2A6KTk/nXB2MqVReGrKgOXgj5Gf\\niILN3+07Nps9dw8bbh9W3DwI6Vjbe9mDWoKe9klRCEb81fNzbSx9zfl8ijWKRTaiGKPThLd+xQS8\\nr6QD1WLNxztZ9y8Paxbblr2W+eejZCJVYalLyWt451guN7z/dM/HT/ccVjtJ5sU4LFmScHGwxgcT\\nbBBUY0E+3lMJgmcwwnVuYG4MzhYExb33JkoxEanBBOA9/eGAbw+YSc1uHWiV5CrhypMgl30kBGkn\\ndclJVWE67Xs73sP6HAkfbtR+HuTPCBGXnjcO3mJaYzVtSMo/h+O+Ety/QKPp+DYFQUnjhkBhpAw4\\nRCHNil6sl8IYDv0Bu+/xbUWMkiALRWRXeHZVwExqioPyZI8Cok8E+ZO5MPnvqY1b2ky5ptFookGv\\nF0JCN8SMQ00L55WHG2Py4psoPCp1VdLUDVKAHjJfcu8Dh13P+5/vuf24ot16LJ4YOoJrca4XpQHq\\nbmmTCUVfzMuSfzy95rv5KadVncMi6VG/P7nExcjP2zU/7dccolcLR4WLGb5mgiCLwDtV2Pa9Y7Pv\\nuHvc8vluzYdbQUB0PgzzRhQmDSVfGsP5BrshZoKohBJ5yodtKQqoioI3Ly754+9e8A/fv2SmnXoI\\nYWhHFoNYKrpmKdmMfh2O9qBMYlaAqmhAia8ci4UIm3/58xfu1gd2nSPVMsi9+pG8ibiY7sOQSrhT\\nk11rCwpb5qOdhbsdQizRB7BQFjBrSs7mDdOm0nZ7wjs0rlxOn0GE1WrP5y8L3n+65+ZhzWJ74OC8\\nhigGNTVsc6ljOJ1P+O7VNe9eXPHsbC6WOBEowFopM4+eHlHePZFoI23s2e96br888L/e3/PxfsOh\\nl/0bQGNkWWQNhoE1dHvHYrXhp093PC63FK3jVFZLw3zD+tmsXnWeRwo63WkSmtk7MbrDYuJhiTTG\\nMLElZtoQq5pYWKHI9Y6ld+x6IRNrN45/+5//yt3Hn3nz/IKzeUNdJk+1IMXRrU2NVCw+RmZtgT2U\\nqjZExgS9SRMBH7CloOg6wpA3SzF30v5XtT7KeSRjx8BQa6DyJWYDRc/mWON9Nb5RY4maopCEgXOe\\noBvMGkuvBExNXTFrGioqJq2hNMID7G3ENRbmJbOTOcXayaZPOCYdkSF2msVKPvBk1+3pvMT8+lTM\\nnCYwFREEBlhi0AOeOrWItg1DwjZJfjH/CTGwWu/5+eMD9/dbtvteBV2PCQ4bvPBPYzS5iQgWAz4G\\nzouKd5M5/3j+nGf1XO9wEFoAjSl5OT3hvz9/Q3/7gc1mQTvi60yWbUSqy5yXLj0hBg7bjofFlpv7\\nNXfLHYvNnvW+Zdc69TxM9mgwaU7iIIDkRkgQT7LloR8/Ci8A1JVUM75Vq/HVs3NOmkosMC39z4Iz\\neHxUGKb+y4VMcvpECecDZJS9UR1SI+GUtu1ZLPf8+f0Xfvz0yO16z8H5DLdD11DQHqPUUlALUVkT\\nM+8C6rFpqMkaS7RCkRoSras1+TCXxlIWAmntdi2FD7hSvKM8P/p67zyHzvHhZsFPn4QWYN85ugCY\\n1H0m6roOCn24f+HPwQtxW7RDIjaqgiwLw3w6EZqFCNiI6wNtkBL/zb6j7XUuzEhZyNakKCxVVVBW\\n2rC5EB6UrxsSjzEk+Q9pfkZnDUSgieyS9R8MFTMIO5kmpkXNSd1w2cyYn84pZxN8LUyZu95xvzvw\\nPx7uud3v8cEwO51w8WzO5bM55SBlSeExQGHFUVgukcIg46U5RjrSqXYkXaIMhiYY6lgIhpzk3ZN5\\n2jGDYorkP6giGQr75Pzons0GyxPx9ovxTQR5WRYDBjZxbwCFLQg2pXOlaKYuLHWM2r6LjHQpKGh8\\nSaG9HAcB+0thPuYniPkvg6uTrEiVcYP4T3/Pguipm+OjWOSYx7RmAAAbu0lEQVQ50kDaE1bbhiWu\\nDhFKu33P3cOGjx8WbLc9zsmuCMFRBCcY66yhv4LlhcCzyYQ/zi54PZkzTWX/BBXi8uoCw3nV8Kfz\\na97v1tx1B9b77ZONmkMUSGn5ofOst3vu7jd8edhy+7hjuT2w73thCszxueHdycomxaKzq6gTFkcb\\n0CRLVQWuqsHL8ynvXpxLKOXyhNNpTWGG6t7smTwJaRhF3tiRlTPcV4abPvmt3Gvb9jwuNvz1wwN/\\n/fTAzcNWce8xhfAH1zl5bjE9qzRpSI0W0nMloyoYxQMrL0lUBZ5j+noKQ4zCYX63otv3miuyFKWy\\nYRZKX2AEVrhvHe9vFtw8bljtO43ba2LsVw52jGhSX9d237Fe7ygNuaahyEixIamZNr+xhu7gJRa/\\nOrA7OO3WNMxn+tZaQ90UTJqKuiyzp2qNUj9ALlUnr0p2O+BJ2CSqKP8qlKDK01pDVAhiaY00AKkq\\nLuopl/WUy3rCrJlSNg3MakxV0oXA9WTPsj3Q9o5H57i6POHNy0u+e3mZuYJktVJgRJpSh0w1HDld\\nQtWOQodJUMgCZ4hhAl7kkxtDrt8Y5yjHhFm5NeU49JQ8rKQA9DUxge1/ZXwji3woPTVFMWwMIk2d\\nEmMWYsC4gHWMinOgcFDuI7ULFE4eONHAps00eG3xF8+ewwNfuXKQjYAhURFV+5NI5RUREaVEP2tm\\nvU6MBhKrYIxaPBBwPkiS6vOKLx9XsoBWSbWE2EOcz0EmY0Zbu8DwbnLKfz65ZlaUWnEaM3eHlA0b\\nTIhMrFjlP5yec9Nu+fmwHeCPaatECSH1vedxteXHD3f85cMDj5uW3if2jCemNpFU4JNMjcEbGJBA\\n6frynhzft0LAFLViszDw+vk53799xttnZ7KG3tP3hqFTDlmIR3jC/idV/7IjhFlS5ywHI6WYDFLD\\nZM96s+PjzSP/8udP3G8O7HovllAY3F5BTsjnpcKfNFeoYs5QNVKFcIrbJgU5KBUzYFCJBjof+Xy/\\n4X6xodJwRC4iKgvpM2oFwRWjoXOwaTsOfU9Iyjh5atljGW1iXRqvfOWPqy02RrbrvbJaFpKXIhVJ\\n6ZkcVZsuN3tu7jbcLQ/sU+EP45CZfFBhYdpUTKqSAqRJeu8hQGVLiV/HpNTNk3OWpms4d8Kz80RO\\n6q4LXjqDFTZQmpJpUXLWTHg+m/O8OeGsbGiMKFDrApU3MCmZ1JZ5aflhc8am7Vhv17y4PuP7N8/5\\n4eWVoEOChD2DHTE2hcQSKfc5f79jstmNitjQtU9JZQn7esSbEErySMSLxNK1HM7fsF+yoZkMo2w1\\nJmFuhyrrmDhYfjm+mSCHtL9VfyVUgHybbzxGKILYDcGKUKs9FCFQtR3WMWyQZFwMPspXGl6QMZGY\\nuYrj8KdBhur7x9cZDIeY43wuSIm++kwkqzS5x0EXqescm92en36658vnlSSYiGrhddjoSAUqgcFt\\nTNpkYkp+Pzvhj/NLXjQzIJLaF8SEmMhxN9kQzjvezE/4h/6Knw87ll1Lqy3mjAHnPMvlnpvHNTeP\\nG24ed2wPDhe1om40n8MMDp7M+DcxDL/I9prRn7KAE0KhSGRSF1ydTXl9fc7l2ZxYiiAQBsGR4tXD\\nLYpUJibF9tHnDCaoMhNhmTrLq7FEJNA7x2a95y8/3/Pnj/fcb1taH0mFVQKLHKymrMQ1txGCV0U7\\neBbGFKpYTN7L6f5ST1ijFZwpyZl0jNOwS0eyBkWhSIeawcKT/WAEf53j8inBlvYcKhCHebcYQoDt\\nzvHTpwWfb9cUxajyNMVuRzmE4Rmk7d/+0LPcdkINoUJGotxqRRpD1wcWyz3/9ufP3NwsmFQlwQdW\\nuwN3iw2uc9RRoLfERCQVs7GVlMLgSanhYgYPOSnUCjgpa16dnHPeTDipKmZFybSUfIpA9yL0ATqH\\naUpMaaGZ8N3lJdvg2VnH9emUaVUSepc9r6AFXll5mGRAaVewACYM4Q6MkWrdVG8RocXjjYAOCirp\\n1RntUHmeH8+ooTmyxONX5ycp6hiRmN7QBORvhVe+HY1tfoChw43ev1paJh/IKkjMzSlUOWGAy8RQ\\npyrcZq1vcrjDfPXZaTsqr/xoQ6HvjcMcy2WzcIzJPFfp1hO087chmqhoB6n28iHQ9R3Re9abPZ8/\\nL/hys2SzOgjxEYEYHISe1Gkn9/hLxE4EamO5Lhv++ewF76anNGUxShglaKTcb0BcWR+lfPy0rHg3\\nm/OPpxf8r9WCu3DA6eZdbvb820+33C23LHYt29YPnV7IIntkJWgo7BcbyQweSRIno0l/UtSgszqp\\nC15fn3GVut6MBFHeFulzbGqEYPTeclZArm/tGATxC+XcdoK6+fB5wY+fFtw87mhdwIchP5L3nTHZ\\nnDZqnSesY+IfSYJaWhJqgkwe9BfP+hRxRIa5xghDjdsQNTWebOmPrqIeZHrIrGVG92+SuyXKD7HW\\n+xBZu254SL2uYiN+/XwYCXWmHpvZQs6kX8nfkPzK7hC5ud/wuNiJYotRKGd7R+OCKpw4vFf3V0Lc\\n2/y5ab8M98nobxbDrKh4e3LOaV3T6GeVqpisUnwQIqHz2N5DVUJpOZ/PeOvP2dATdh2fP92zulsl\\nihr5PJuaeyRhLcnYMlperAJTNxD1JWMoJ32tQIs9QakZhufMPozRqtPxHh15H3lZR0aUTF2Skap0\\n/p5CK2M3Io42SyKxTzFCgmivMojA6o1Q24ZSCGyst3ik00yBTKodUT+mkRELJEE+0oAa10o3kbZp\\nMCrQjdHNPYRjUqcfFwNdlIIAKdePuW2d84623RMcPDxs+fHHe5aPO1yrDTOCw8Re8a4x33Ky4NIt\\nndiSd5M5//X8JdeThmBlh5jktYw8j0iK20szgwJ4Vtb8b2cX7NuO1jmWXkq3b5dbfjr0srkyI6BR\\nYp9RfJgoCVgjBTvjiR1IqVTIpfkx49eMrD61rGd1yZtnp5zNGurCqlczCCZGRoowwKVyf0ZJyZiv\\nrybqcA0j7Diu61ks9vz8ecH//PEL95sD+65/YgENfozJFYDpGRO3jS1CjtvrXxSVUjy1bJ8oLIb5\\n+Grvp2+GQMUQHnx6TGUTpLDPEP8fW3Gjq+u5ysZfjBKzZ3gbJKhtsrK//kw1TFATKym3LH6U5U/3\\nSO/Be5eMTVICzwKTYIZwJ8N9EQfm0q/STqMnN2oiyJrYCLUpuKxnzGppvdd6R6aV1iKjECPGeWzn\\nofKEqqCsS56dneBN5F++3PLX918UbTQIb5vohPPvBF03NSVlnHMVJuCHFUv3ZSwSSjVQRJCqjic7\\nAEhnyWQFmnJhTwAY+pZkxOYwpYkM8Ndfn69vIsh71w9dcVSzDpy+ZGFQGktlAoV2Qo0xYgIUXaCI\\nkcpbvI/a+OArTHOehKTlZctm6vCkTCK52krGaKODJi50c447yyPdSHrtJp/eYKLwrddVSV2VfLxd\\n8fHTirsvW6KTF/joIfZYLb/Py20gEd8bQavxZnLCP55dc1430kUJcfPzVMVkXam7rCXARnHLjSl4\\nUU/557MrMJb/e3VHiFLo4/Nth/yMSUiOjeKEb01wqMHi5MlXgWkOCekU006HNwZHWRVMJyVnJw1l\\nKddNCGr5wIBgsiVM0ntHiBIWSL0Z7BibP47NR1GEIQgvyO3dir9+lqrH+/WettckFmopJuHNKCQy\\ndinUKIeI0SR8spdTz9nsTYwkkrxllKgdhQoS58ugupN9q9/rswwUuKOd+dUpNsnM189JVuBQ3m8Y\\ni4khFR1HP41XVAV3zisMH5TCVCb/2mTDIxVbyVQO/lk0yYvVc6HGgJ7E9CmjMZqVrECgRApx1u2O\\nD6sH3p1fclI3GJzMv1ICB2MlhhwjpnVEYwkWbFUysSXPZyd8Wq25Xe/5uNkO1r9+tRqiSdTDlTFc\\nlBPezUuYTHNezBi1jFOxH9LADxOZqEGUHy0tzzgmnh9SgR1x2G95ZcxohZIyj/C3JPm3EeS9k0qy\\nwgycGFH7/jGsYYxBBHe0A/OgEaFuPBgfsVEtyjw7v67i0xyMuTHGOOEnrzXJupA35b9q/DJGsVt6\\n5Y9IWzcdOmtFmKxXez5/WnJ3u6FvgwqsQAw9RfQMdWejo6wPbw1clg3fTU/53eyc2tpBzuYnGhRO\\nzNZksvDEa2lshS0ifzi9pMPwc7vlzvus0MY86vk4jzX/yGXP2R9Gzr/hKwEjyrJI1mGK9cUIMXA6\\nnXBx2jCblJRW5syi9Av6urIQZZyw8xGUV0UsZMbrlX1/WUfnPOv1gbuHNT9+fODTw4aH9Z7W+bz+\\ng5jTnZBc5VG8OCe8DZD8PEUNGCBHv9IlRneV13G0RrK+SQGqpc0w58Plh/fkUIx+gBlfP31i9qBG\\nN5IvOHoexvc4UsTx62sOf5BCntGyk7wCVGk8DYF8PSySpK8snBYNp6ZgFkUot8HTRmFXSfsw02fo\\nbYXRHEWkQnm5XXM9nTPX3qoJimqsMA7mGLSLmM5h9hImsmXB1Ba8nM253+75FDbsnVeZMyoeMyZD\\nJ2traSqLb+TsCj48ZO9b2hKqNY6hMDajUJIyiGmjje2k9IS/IpNjUqb52EUV5P8fE823EuRdL4Km\\nMJRlFApTEJzm+DhEqcKzphQu4BReIW2idNhVgOYDyZNJSnOXvhrdpDZPqkkeoVjbY20Yxu8dLhqQ\\nqk6nGO3UxyZhY/f7jk+fHvn0ccFqsae0VmFNTgT5aD6SaJTrSFinsoZ3kznfzc54NpkjbqMZDl9S\\n+mPuhpg+XzZV+keIvKobWgw/7test8uB8dAM95yvQ7Jw04do3WscEBt5ifKcxOFfFCVMEGz36A65\\nOG14fj6lKcV9JYp1LRBNvVKM6vZKDDoplmF/D585ds0F4tlxc7vkxw8P/OXmkW3r6EMUaGc+UIMA\\nGpTF8EwwWMaD0Ev3EbKyHXTcU8s3x1C/Fp0mCouffp8TEqO9ZdL32ZgZXcaMBCrJ6xzCYCk8Bmmu\\nvoo1532Wbj7m3w1K7clTD4IkPddwK/m+4/CYTz6pRMjdzm3Bm+mc19WMZ7ah8z0H37MPPcFEZTQd\\nwAOpoCbBezNHEmC9JzqpsCytViYr14kxZuiLKxekOHg6H6ApsFXJ6+mc9emBm82aT9u94MV1/0Xd\\nTzGtXxCWxcR2opFqgXbGCFozAFAZobEtRvOKXivnT7K8Ia9dhmeqzEkRhHE+KjKs1N+KRX2bGLlB\\n48x2dFC1Gkr5ea3GnRVmogsZcDEqqY9AtIKyJKg6VeE2xN0Ng2UjIRx5b+J2SAImCet0nFIYNlVl\\njjVkiEJd2we5n/T6GAPWSCJtsTywftyxXnUEF1WjO4ieIhUNMYrLmuHw1Fiuqpp/urjk1WyeBWcK\\n+pgxfNKOrLZsXWmCMN0vBgic2JLvp2f8+bDji+uGw68bbqhcHEyILJDUAkO9n0jMfDaDDTWqwFOh\\nF41yCllhgzyfT7g4mVKWWn6v0ip1V4nJCo0hh9sStUByMaMqYzPe9D6y3zn++uGev3x84MPtmm0n\\nPDii3FPWcqx8yB1uRCmpe81YEI/mAjm46WANiafB+DCj34jgG0q+Bwt/9FwxL5p+fZKuffoZ+bUK\\nBtR7Hs72yGLWe86i2prROUhrlO40ZqWRC6yeWEPDG2JEz8NIYD3hTEjrYzgta37XTPh+dsLryZw3\\n1Zxn5RTvHX2QJgqDMpRQ5Tg34zWUEVSIChw0MmkaKArWxgGKDFJESTCGgOSHbADjAiYGXHS44KmM\\n4Xnd8J/Oztj2jse2EzSVQYjLotAXRCLBRm0cncKKEmoVhJNKipi8RW2CM9rTAkVWNRlGuYoszFED\\nMuazllExo72Rz38+9L8c3wa1wmD9jjW5AWIQTRxCpIhRO26j9odOaNQo3tf+xljgpslKsmnkFg1X\\nG145sqeGElkSGVbMi5Pi49IdSBvrMjyGNbDdt8RQsO0jsRPlEQgQvDRv0DcID4tYQemzI3BZN/yn\\n+Tm/m59xVtcqpuPoYCZZaxjtm3wf+ecY6Lxj13d0rmfTdVJJGwZh9Iv5l6fQzxvuKwnCv2EQjK6Q\\n/j/ckNyPeEFNVdBUZUYeJTVUJM4LL/cdgghuk+l7E26abCFiorIoRpbLHZ/vlvzHz3fcPG7ZHHot\\nZIKx6/yV4T0SwX/r2UYTi6z/AAEbDAAQZVRVJU1TUldiLQ4hKLXm9ECnmGcIMYcQ0vMloyPGhGUX\\nizEQf3GPgzD/+g/xyWt+6alm9T8WK8OCffX+YTo0XpsURNqTOj9BIXmlsbyZn/HD/IzvJnMuqobL\\nasqlneSG1J4w7IM4hFJS+CErF4bkq9cz3hppCO7Q588PKj+nsJIJSgVgIt46irrmrGn47uyMm8Oe\\nnsi689L4PCGg9IGtsdRGIISpN27iVHlCk6ZefHiqz4Z/aadnwzHNeVLCqQ5lMBzG+3LYu7+GGpPx\\njeCHI+xzSDSo8jfZ0CJwC+2gkxocJTfPxIRgCE8nBUZPneUuyXpKQnccl07zbOMIXTGSYdEkQa4b\\n2IgAdolfOY4Pgwjy3b4juIKIVCrGAnovvBY2NQ1ImuKrm66M4e1kzj+dX/NsMqcuhPnOjj4jjJTR\\n8DUpnqjUuh4XPLuu5WG/xcfIQ3fg837NPjhlgEnwMH2CkaQb7MJITIQFNkENY57vZMUPgiSFYMiT\\nmESGhNOs1BGosEgKpRDjBR/RatmIjyYjG9Qgl0/VALYPihHf9rz/9MBf3t/y4WFD28s9J4a8vENG\\nSaWnhkSyfAabOh26dP9PNB7prA/zYa2hLgtOTxsuzmeczBqhLVZTS/zJiPNROEuiFIt5FdTSOi7t\\n/ZAFvA+RrpXG113f570+PuImGQS6560dCnyyW2+S1xjzORpIwAQVlqqQY1IejAJGo7h+anyR50vP\\nWOaoIVJbw7uTU76bn3FZ1tQp1JcqX40BUwiNgobw8i6OeceoApfPj1bCjiZGDjHQArsQ6dVLzmcg\\n24eidMtohGLCi4c4rWteGPju9IS9D+z8TrHuhpjcQiM893VRiucfB2Mp+Vl2/L3G6p/cSLIEM/Xm\\nWGkOSiD32Y2MZnxkZMX0uuE6X49v1FgideIQTupxl5V0TiOGEkuljQ0McqiLYHHBYwOEWJBiwvLW\\nrybLoO2ejAryOOKhRjmE0woNExSDkEOFfOIhi38tapHu6gJLs5FMCBTU9KltQV3XVIWhdy2bnRey\\neEWIWBgJO7l2aeDlZMofzy75w/kVhRFXr6DIByl9po8hozhkkeVghOBxvqfrO9b9gYfDjs/7NVvv\\neOh7PnUHtrEXGKfzI3NLLQEVbBlOlzVbEtoDc0bGcJhB6EkMXVdAERoGpalVNE9RFIJCSpOgOHGD\\nCCHpgRufKNYIGh6Q4xOi43DoWS53/PjxkZ9vl3x53NM6FT/mK9x5JvsS63YcwhhHksfCRBSVWIS5\\nQBMV7KpcrDGURcG0KXl2ecKrZ2fSXGHeUJWl0tySN3dqLScWqJ71KCEN4ebXf0Ttwdnz5X7N3cOa\\nu/s1nR8hr9LRHrnhtjCczCrOTqacncxEaRpRHN6HoVWdV4WvlL+pB6lznrZzdJ22PxuVsed/Fslv\\nFVb6bKIKIgScLyiBedlwUlXU2kUpdaXKoR4z1HGohhlzDZNyP0Rkn6R3GnneMhrmlDjjJNyaGFDV\\n+w1K4WAwWC8FRbGwCuCPTKLlh/kZ+96z6FrWbTJA0iLLeysYGVFJYQbwXgwcbfHWhJKagkIhvBF5\\nwGjjCH00ZDZyTmRkH6T4ePbO9B2iY9SS+Xuq7LTKBx1CyJ2/Cdr7sEiwLiPtkLxwH2CkV6dNhzpZ\\njyZZlNm+zvviiRWlGyApCQN6uJ7qyGQhpsOfNtQ4bolaNr+G+43RcFo1nJ+ecHl2Stu3PK471k4K\\nf0a3M7qcbJC6qvjD1TXvzs45qSq60OUDGlWI98HR9T2d88KDEuS91haUtiTGSOd7tt2eL+2Wz4ct\\nH/cbdt6zDp5FcLQmELSfYCY3SgL8iVCLWXENlquGQYxRq+3p3MRRzBkGISPPV+J95HDosCEId3Zh\\nsWWpeRHyaxPs05DQAlEStwStWjxwe7/m45cl72/XLDYHDr3PBtFg4Xx1b/ocTyCAujdGy6t6U0I7\\ndWE5mVU0tXAEeS/zYAvpODObNJydCA3v1dmM83nDpK6E1zvxmoyvnw2HwYpO/UOTcI363M55JlXJ\\npCopC8tqe2B36Old7q+TvYu6KphPa15cnfDs8pTLsxOqctyHM/GwoJztg2fgvMP1jrZ3bLYHVps9\\nq21L23V0vcNrKbE1hqK0nJ7MODuZcjqbiNz1Aa8sgzbCiSk5LyaUWntgld5iOF/pjP3SwnwSch8f\\nmPxS4WhpsNTW0oZIP7IEs7eVDRNBvBROON2pDEVRcD6d8to7HlzHX9wSlwjr8t6PYkiODMUkd7L5\\nE6GIhjpa6oH1f2xzZ/jpEyv7yW6L47qp4ZHNYINnAMYv3i/j24RWrMH7gHNONqw2WiYGilhSlpp0\\nUrCz91F6T1ooPLlbtQgJFebEPE8DtpgnbqchCXIVWUkZ6F/Hnkz6SxK940keXjfekunD4bRueHV6\\nyqvrC+7XC3ZbCL170iwX5WnISRNraOqSdxdXXE/nFKqfTLbeJC7fe8eh79h3Pa1zOC9WdVmUNGUN\\nUXqirtoDt4c9nw47PrV72hjZx8jeRKErZdiOiYs9Ce0xxEx20VBCNSQuk3UpczlMkgQRhgrFZBkL\\nva9znt2uJfSOoiooq4oqGqGtJQl9mZtcbo94QLKWFucDm82Bm7slf/30wP22o3cxhw7M6HbS2mRO\\nbP17xuUaQ2qKkT9TBW/0IjBq7Ul5MqspSmHsJEaKAubTCeenc67OTzg7mdBUBaWVMpVCqz+jSbh1\\nkjGuIZDh2IdopHgniIJM4cdYl2rQi0IwKbm974YcgJFk5nRac3Ux5+3LS15en3N5OqeubG4SkhpY\\nyOdrXDcJctfTdR2HzvG43HD3uKYo16x2EfaRrpPmE2VpaZqSF89OePnskhdX5zJvzuPaHuc8JkSa\\nYDhZlhRd2siD30w6o2YIgZFs1Sz9xkaYSfqWFMLBREqDlMTbSGuCwJBtBBM0XGOG3RkjxkVwgVgK\\nn3Bd1VzFGW/6jo+rNVttjzdWLYVJLJriPljViimFnUMrMVE3mByCHTzafCuDVM4qbThqY+Gf1nXY\\nx095mL4e5usig+M4juM4juP4bQ37//+S4ziO4ziO4/h7HkdBfhzHcRzH8RsfR0F+HMdxHMfxGx9H\\nQX4cx3Ecx/EbH0dBfhzHcRzH8RsfR0F+HMdxHMfxGx9HQX4cx3Ecx/EbH0dBfhzHcRzH8RsfR0F+\\nHMdxHMfxGx9HQX4cx3Ecx/EbH0dBfhzHcRzH8RsfR0F+HMdxHMfxGx9HQX4cx3Ecx/EbH0dBfhzH\\ncRzH8RsfR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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fabdc077450>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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SGgHlQ7eAhOEfHkBrlnam225kOkO0cTpWmltEYKnk16hwx5zY25FWaE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\\n5JXLUuRLlixZ8srl/wBoXs5YfiswVQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fabdc193450>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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HNGRMg5k1IiJUWToFiyMSiUwQJBU0JVEcA4X94/b8koUTOCYsUDQsyZ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\\n77ryEsbvRQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fabd65950d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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TGY3lI3VlA1GAMpBAiKKNiUR0iIYDEYq1gU1UAIkcvLROc7VA1R82gD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\\nROkMnut9BfMAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fabd63b3790>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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VmFWhe0diXraspH27FX2ZoMjMp57O+PrZ31GTGPfP9WvVbshVRqQ7kA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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fabdc913990>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_net = solver.test_nets[0]\\n\",\n    \"for image_index in range(5):\\n\",\n    \"    plt.figure()\\n\",\n    \"    plt.imshow(transformer.deprocess(copy(test_net.blobs['data'].data[image_index, ...])))\\n\",\n    \"    gtlist = test_net.blobs['label'].data[image_index, ...].astype(np.int)\\n\",\n    \"    estlist = test_net.blobs['score'].data[image_index, ...] > 0\\n\",\n    \"    plt.title('GT: {} \\\\n EST: {}'.format(classes[np.where(gtlist)], classes[np.where(estlist)]))\\n\",\n    \"    plt.axis('off')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"description\": \"Multilabel classification on PASCAL VOC using a Python data layer.\",\n  \"example_name\": \"Multilabel Classification with Python Data Layer\",\n  \"include_in_docs\": true,\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.11\"\n  },\n  \"priority\": 5\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Caffe/pycaffe/caffenet.py",
    "content": "from __future__ import print_function\nfrom caffe import layers as L, params as P, to_proto\nfrom caffe.proto import caffe_pb2\n\n# helper function for common structures\n\ndef conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):\n    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n                                num_output=nout, pad=pad, group=group)\n    return conv, L.ReLU(conv, in_place=True)\n\ndef fc_relu(bottom, nout):\n    fc = L.InnerProduct(bottom, num_output=nout)\n    return fc, L.ReLU(fc, in_place=True)\n\ndef max_pool(bottom, ks, stride=1):\n    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n\ndef caffenet(lmdb, batch_size=256, include_acc=False):\n    data, label = L.Data(source=lmdb, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,\n        transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=True))\n\n    # the net itself\n    conv1, relu1 = conv_relu(data, 11, 96, stride=4)\n    pool1 = max_pool(relu1, 3, stride=2)\n    norm1 = L.LRN(pool1, local_size=5, alpha=1e-4, beta=0.75)\n    conv2, relu2 = conv_relu(norm1, 5, 256, pad=2, group=2)\n    pool2 = max_pool(relu2, 3, stride=2)\n    norm2 = L.LRN(pool2, local_size=5, alpha=1e-4, beta=0.75)\n    conv3, relu3 = conv_relu(norm2, 3, 384, pad=1)\n    conv4, relu4 = conv_relu(relu3, 3, 384, pad=1, group=2)\n    conv5, relu5 = conv_relu(relu4, 3, 256, pad=1, group=2)\n    pool5 = max_pool(relu5, 3, stride=2)\n    fc6, relu6 = fc_relu(pool5, 4096)\n    drop6 = L.Dropout(relu6, in_place=True)\n    fc7, relu7 = fc_relu(drop6, 4096)\n    drop7 = L.Dropout(relu7, in_place=True)\n    fc8 = L.InnerProduct(drop7, num_output=1000)\n    loss = L.SoftmaxWithLoss(fc8, label)\n\n    if include_acc:\n        acc = L.Accuracy(fc8, label)\n        return to_proto(loss, acc)\n    else:\n        return to_proto(loss)\n\ndef make_net():\n    with open('train.prototxt', 'w') as f:\n        print(caffenet('/path/to/caffe-train-lmdb'), file=f)\n\n    with open('test.prototxt', 'w') as f:\n        print(caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True), file=f)\n\nif __name__ == '__main__':\n    make_net()\n"
  },
  {
    "path": "Caffe/pycaffe/layers/pascal_multilabel_datalayers.py",
    "content": "# imports\nimport json\nimport time\nimport pickle\nimport scipy.misc\nimport skimage.io\nimport caffe\n\nimport numpy as np\nimport os.path as osp\n\nfrom xml.dom import minidom\nfrom random import shuffle\nfrom threading import Thread\nfrom PIL import Image\n\nfrom tools import SimpleTransformer\n\n\nclass PascalMultilabelDataLayerSync(caffe.Layer):\n\n    \"\"\"\n    This is a simple synchronous datalayer for training a multilabel model on\n    PASCAL.\n    \"\"\"\n\n    def setup(self, bottom, top):\n\n        self.top_names = ['data', 'label']\n\n        # === Read input parameters ===\n\n        # params is a python dictionary with layer parameters.\n        params = eval(self.param_str)\n\n        # Check the parameters for validity.\n        check_params(params)\n\n        # store input as class variables\n        self.batch_size = params['batch_size']\n\n        # Create a batch loader to load the images.\n        self.batch_loader = BatchLoader(params, None)\n\n        # === reshape tops ===\n        # since we use a fixed input image size, we can shape the data layer\n        # once. Else, we'd have to do it in the reshape call.\n        top[0].reshape(\n            self.batch_size, 3, params['im_shape'][0], params['im_shape'][1])\n        # Note the 20 channels (because PASCAL has 20 classes.)\n        top[1].reshape(self.batch_size, 20)\n\n        print_info(\"PascalMultilabelDataLayerSync\", params)\n\n    def forward(self, bottom, top):\n        \"\"\"\n        Load data.\n        \"\"\"\n        for itt in range(self.batch_size):\n            # Use the batch loader to load the next image.\n            im, multilabel = self.batch_loader.load_next_image()\n\n            # Add directly to the caffe data layer\n            top[0].data[itt, ...] = im\n            top[1].data[itt, ...] = multilabel\n\n    def reshape(self, bottom, top):\n        \"\"\"\n        There is no need to reshape the data, since the input is of fixed size\n        (rows and columns)\n        \"\"\"\n        pass\n\n    def backward(self, top, propagate_down, bottom):\n        \"\"\"\n        These layers does not back propagate\n        \"\"\"\n        pass\n\n\nclass BatchLoader(object):\n\n    \"\"\"\n    This class abstracts away the loading of images.\n    Images can either be loaded singly, or in a batch. The latter is used for\n    the asyncronous data layer to preload batches while other processing is\n    performed.\n    \"\"\"\n\n    def __init__(self, params, result):\n        self.result = result\n        self.batch_size = params['batch_size']\n        self.pascal_root = params['pascal_root']\n        self.im_shape = params['im_shape']\n        # get list of image indexes.\n        list_file = params['split'] + '.txt'\n        self.indexlist = [line.rstrip('\\n') for line in open(\n            osp.join(self.pascal_root, 'ImageSets/Main', list_file))]\n        self._cur = 0  # current image\n        # this class does some simple data-manipulations\n        self.transformer = SimpleTransformer()\n\n        print \"BatchLoader initialized with {} images\".format(\n            len(self.indexlist))\n\n    def load_next_image(self):\n        \"\"\"\n        Load the next image in a batch.\n        \"\"\"\n        # Did we finish an epoch?\n        if self._cur == len(self.indexlist):\n            self._cur = 0\n            shuffle(self.indexlist)\n\n        # Load an image\n        index = self.indexlist[self._cur]  # Get the image index\n        image_file_name = index + '.jpg'\n        im = np.asarray(Image.open(\n            osp.join(self.pascal_root, 'JPEGImages', image_file_name)))\n        im = scipy.misc.imresize(im, self.im_shape)  # resize\n\n        # do a simple horizontal flip as data augmentation\n        flip = np.random.choice(2)*2-1\n        im = im[:, ::flip, :]\n\n        # Load and prepare ground truth\n        multilabel = np.zeros(20).astype(np.float32)\n        anns = load_pascal_annotation(index, self.pascal_root)\n        for label in anns['gt_classes']:\n            # in the multilabel problem we don't care how MANY instances\n            # there are of each class. Only if they are present.\n            # The \"-1\" is b/c we are not interested in the background\n            # class.\n            multilabel[label - 1] = 1\n\n        self._cur += 1\n        return self.transformer.preprocess(im), multilabel\n\n\ndef load_pascal_annotation(index, pascal_root):\n    \"\"\"\n    This code is borrowed from Ross Girshick's FAST-RCNN code\n    (https://github.com/rbgirshick/fast-rcnn).\n    It parses the PASCAL .xml metadata files.\n    See publication for further details: (http://arxiv.org/abs/1504.08083).\n\n    Thanks Ross!\n\n    \"\"\"\n    classes = ('__background__',  # always index 0\n               'aeroplane', 'bicycle', 'bird', 'boat',\n               'bottle', 'bus', 'car', 'cat', 'chair',\n                         'cow', 'diningtable', 'dog', 'horse',\n                         'motorbike', 'person', 'pottedplant',\n                         'sheep', 'sofa', 'train', 'tvmonitor')\n    class_to_ind = dict(zip(classes, xrange(21)))\n\n    filename = osp.join(pascal_root, 'Annotations', index + '.xml')\n    # print 'Loading: {}'.format(filename)\n\n    def get_data_from_tag(node, tag):\n        return node.getElementsByTagName(tag)[0].childNodes[0].data\n\n    with open(filename) as f:\n        data = minidom.parseString(f.read())\n\n    objs = data.getElementsByTagName('object')\n    num_objs = len(objs)\n\n    boxes = np.zeros((num_objs, 4), dtype=np.uint16)\n    gt_classes = np.zeros((num_objs), dtype=np.int32)\n    overlaps = np.zeros((num_objs, 21), dtype=np.float32)\n\n    # Load object bounding boxes into a data frame.\n    for ix, obj in enumerate(objs):\n        # Make pixel indexes 0-based\n        x1 = float(get_data_from_tag(obj, 'xmin')) - 1\n        y1 = float(get_data_from_tag(obj, 'ymin')) - 1\n        x2 = float(get_data_from_tag(obj, 'xmax')) - 1\n        y2 = float(get_data_from_tag(obj, 'ymax')) - 1\n        cls = class_to_ind[\n            str(get_data_from_tag(obj, \"name\")).lower().strip()]\n        boxes[ix, :] = [x1, y1, x2, y2]\n        gt_classes[ix] = cls\n        overlaps[ix, cls] = 1.0\n\n    overlaps = scipy.sparse.csr_matrix(overlaps)\n\n    return {'boxes': boxes,\n            'gt_classes': gt_classes,\n            'gt_overlaps': overlaps,\n            'flipped': False,\n            'index': index}\n\n\ndef check_params(params):\n    \"\"\"\n    A utility function to check the parameters for the data layers.\n    \"\"\"\n    assert 'split' in params.keys(\n    ), 'Params must include split (train, val, or test).'\n\n    required = ['batch_size', 'pascal_root', 'im_shape']\n    for r in required:\n        assert r in params.keys(), 'Params must include {}'.format(r)\n\n\ndef print_info(name, params):\n    \"\"\"\n    Output some info regarding the class\n    \"\"\"\n    print \"{} initialized for split: {}, with bs: {}, im_shape: {}.\".format(\n        name,\n        params['split'],\n        params['batch_size'],\n        params['im_shape'])\n"
  },
  {
    "path": "Caffe/pycaffe/layers/pyloss.py",
    "content": "import caffe\nimport numpy as np\n\n\nclass EuclideanLossLayer(caffe.Layer):\n    \"\"\"\n    Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer\n    to demonstrate the class interface for developing layers in Python.\n    \"\"\"\n\n    def setup(self, bottom, top):\n        # check input pair\n        if len(bottom) != 2:\n            raise Exception(\"Need two inputs to compute distance.\")\n\n    def reshape(self, bottom, top):\n        # check input dimensions match\n        if bottom[0].count != bottom[1].count:\n            raise Exception(\"Inputs must have the same dimension.\")\n        # difference is shape of inputs\n        self.diff = np.zeros_like(bottom[0].data, dtype=np.float32)\n        # loss output is scalar\n        top[0].reshape(1)\n\n    def forward(self, bottom, top):\n        self.diff[...] = bottom[0].data - bottom[1].data\n        top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2.\n\n    def backward(self, top, propagate_down, bottom):\n        for i in range(2):\n            if not propagate_down[i]:\n                continue\n            if i == 0:\n                sign = 1\n            else:\n                sign = -1\n            bottom[i].diff[...] = sign * self.diff / bottom[i].num\n"
  },
  {
    "path": "Caffe/pycaffe/linreg.prototxt",
    "content": "name: 'LinearRegressionExample'\n# define a simple network for linear regression on dummy data\n# that computes the loss by a PythonLayer.\nlayer {\n  type: 'DummyData'\n  name: 'x'\n  top: 'x'\n  dummy_data_param {\n    shape: { dim: 10 dim: 3 dim: 2 }\n    data_filler: { type: 'gaussian' }\n  }\n}\nlayer {\n  type: 'DummyData'\n  name: 'y'\n  top: 'y'\n  dummy_data_param {\n    shape: { dim: 10 dim: 3 dim: 2 }\n    data_filler: { type: 'gaussian' }\n  }\n}\n# include InnerProduct layers for parameters\n# so the net will need backward\nlayer {\n  type: 'InnerProduct'\n  name: 'ipx'\n  top: 'ipx'\n  bottom: 'x'\n  inner_product_param {\n    num_output: 10\n    weight_filler { type: 'xavier' }\n  }\n}\nlayer {\n  type: 'InnerProduct'\n  name: 'ipy'\n  top: 'ipy'\n  bottom: 'y'\n  inner_product_param {\n    num_output: 10\n    weight_filler { type: 'xavier' }\n  }\n}\nlayer {\n  type: 'Python'\n  name: 'loss'\n  top: 'loss'\n  bottom: 'ipx'\n  bottom: 'ipy'\n  python_param {\n    # the module name -- usually the filename -- that needs to be in $PYTHONPATH\n    module: 'pyloss'\n    # the layer name -- the class name in the module\n    layer: 'EuclideanLossLayer'\n  }\n  # set loss weight so Caffe knows this is a loss layer.\n  # since PythonLayer inherits directly from Layer, this isn't automatically\n  # known to Caffe\n  loss_weight: 1\n}\n"
  },
  {
    "path": "Caffe/pycaffe/tools.py",
    "content": "import numpy as np\n\n\nclass SimpleTransformer:\n\n    \"\"\"\n    SimpleTransformer is a simple class for preprocessing and deprocessing\n    images for caffe.\n    \"\"\"\n\n    def __init__(self, mean=[128, 128, 128]):\n        self.mean = np.array(mean, dtype=np.float32)\n        self.scale = 1.0\n\n    def set_mean(self, mean):\n        \"\"\"\n        Set the mean to subtract for centering the data.\n        \"\"\"\n        self.mean = mean\n\n    def set_scale(self, scale):\n        \"\"\"\n        Set the data scaling.\n        \"\"\"\n        self.scale = scale\n\n    def preprocess(self, im):\n        \"\"\"\n        preprocess() emulate the pre-processing occuring in the vgg16 caffe\n        prototxt.\n        \"\"\"\n\n        im = np.float32(im)\n        im = im[:, :, ::-1]  # change to BGR\n        im -= self.mean\n        im *= self.scale\n        im = im.transpose((2, 0, 1))\n\n        return im\n\n    def deprocess(self, im):\n        \"\"\"\n        inverse of preprocess()\n        \"\"\"\n        im = im.transpose(1, 2, 0)\n        im /= self.scale\n        im += self.mean\n        im = im[:, :, ::-1]  # change to RGB\n\n        return np.uint8(im)\n\n\nclass CaffeSolver:\n\n    \"\"\"\n    Caffesolver is a class for creating a solver.prototxt file. It sets default\n    values and can export a solver parameter file.\n    Note that all parameters are stored as strings. Strings variables are\n    stored as strings in strings.\n    \"\"\"\n\n    def __init__(self, testnet_prototxt_path=\"testnet.prototxt\",\n                 trainnet_prototxt_path=\"trainnet.prototxt\", debug=False):\n\n        self.sp = {}\n\n        # critical:\n        self.sp['base_lr'] = '0.001'\n        self.sp['momentum'] = '0.9'\n\n        # speed:\n        self.sp['test_iter'] = '100'\n        self.sp['test_interval'] = '250'\n\n        # looks:\n        self.sp['display'] = '25'\n        self.sp['snapshot'] = '2500'\n        self.sp['snapshot_prefix'] = '\"snapshot\"'  # string withing a string!\n\n        # learning rate policy\n        self.sp['lr_policy'] = '\"fixed\"'\n\n        # important, but rare:\n        self.sp['gamma'] = '0.1'\n        self.sp['weight_decay'] = '0.0005'\n        self.sp['train_net'] = '\"' + trainnet_prototxt_path + '\"'\n        self.sp['test_net'] = '\"' + testnet_prototxt_path + '\"'\n\n        # pretty much never change these.\n        self.sp['max_iter'] = '100000'\n        self.sp['test_initialization'] = 'false'\n        self.sp['average_loss'] = '25'  # this has to do with the display.\n        self.sp['iter_size'] = '1'  # this is for accumulating gradients\n\n        if (debug):\n            self.sp['max_iter'] = '12'\n            self.sp['test_iter'] = '1'\n            self.sp['test_interval'] = '4'\n            self.sp['display'] = '1'\n\n    def add_from_file(self, filepath):\n        \"\"\"\n        Reads a caffe solver prototxt file and updates the Caffesolver\n        instance parameters.\n        \"\"\"\n        with open(filepath, 'r') as f:\n            for line in f:\n                if line[0] == '#':\n                    continue\n                splitLine = line.split(':')\n                self.sp[splitLine[0].strip()] = splitLine[1].strip()\n\n    def write(self, filepath):\n        \"\"\"\n        Export solver parameters to INPUT \"filepath\". Sorted alphabetically.\n        \"\"\"\n        f = open(filepath, 'w')\n        for key, value in sorted(self.sp.items()):\n            if not(type(value) is str):\n                raise TypeError('All solver parameters must be strings')\n            f.write('%s: %s\\n' % (key, value))\n"
  },
  {
    "path": "Caffe/siamese/convert_mnist_siamese_data.cpp",
    "content": "//\n// This script converts the MNIST dataset to the leveldb format used\n// by caffe to train siamese network.\n// Usage:\n//    convert_mnist_data input_image_file input_label_file output_db_file\n// The MNIST dataset could be downloaded at\n//    http://yann.lecun.com/exdb/mnist/\n#include <fstream>  // NOLINT(readability/streams)\n#include <string>\n\n#include \"glog/logging.h\"\n#include \"google/protobuf/text_format.h\"\n#include \"stdint.h\"\n\n#include \"caffe/proto/caffe.pb.h\"\n#include \"caffe/util/format.hpp\"\n#include \"caffe/util/math_functions.hpp\"\n\n#ifdef USE_LEVELDB\n#include \"leveldb/db.h\"\n\nuint32_t swap_endian(uint32_t val) {\n    val = ((val << 8) & 0xFF00FF00) | ((val >> 8) & 0xFF00FF);\n    return (val << 16) | (val >> 16);\n}\n\nvoid read_image(std::ifstream* image_file, std::ifstream* label_file,\n        uint32_t index, uint32_t rows, uint32_t cols,\n        char* pixels, char* label) {\n  image_file->seekg(index * rows * cols + 16);\n  image_file->read(pixels, rows * cols);\n  label_file->seekg(index + 8);\n  label_file->read(label, 1);\n}\n\nvoid convert_dataset(const char* image_filename, const char* label_filename,\n        const char* db_filename) {\n  // Open files\n  std::ifstream image_file(image_filename, std::ios::in | std::ios::binary);\n  std::ifstream label_file(label_filename, std::ios::in | std::ios::binary);\n  CHECK(image_file) << \"Unable to open file \" << image_filename;\n  CHECK(label_file) << \"Unable to open file \" << label_filename;\n  // Read the magic and the meta data\n  uint32_t magic;\n  uint32_t num_items;\n  uint32_t num_labels;\n  uint32_t rows;\n  uint32_t cols;\n\n  image_file.read(reinterpret_cast<char*>(&magic), 4);\n  magic = swap_endian(magic);\n  CHECK_EQ(magic, 2051) << \"Incorrect image file magic.\";\n  label_file.read(reinterpret_cast<char*>(&magic), 4);\n  magic = swap_endian(magic);\n  CHECK_EQ(magic, 2049) << \"Incorrect label file magic.\";\n  image_file.read(reinterpret_cast<char*>(&num_items), 4);\n  num_items = swap_endian(num_items);\n  label_file.read(reinterpret_cast<char*>(&num_labels), 4);\n  num_labels = swap_endian(num_labels);\n  CHECK_EQ(num_items, num_labels);\n  image_file.read(reinterpret_cast<char*>(&rows), 4);\n  rows = swap_endian(rows);\n  image_file.read(reinterpret_cast<char*>(&cols), 4);\n  cols = swap_endian(cols);\n\n  // Open leveldb\n  leveldb::DB* db;\n  leveldb::Options options;\n  options.create_if_missing = true;\n  options.error_if_exists = true;\n  leveldb::Status status = leveldb::DB::Open(\n      options, db_filename, &db);\n  CHECK(status.ok()) << \"Failed to open leveldb \" << db_filename\n      << \". Is it already existing?\";\n\n  char label_i;\n  char label_j;\n  char* pixels = new char[2 * rows * cols];\n  std::string value;\n\n  caffe::Datum datum;\n  datum.set_channels(2);  // one channel for each image in the pair\n  datum.set_height(rows);\n  datum.set_width(cols);\n  LOG(INFO) << \"A total of \" << num_items << \" items.\";\n  LOG(INFO) << \"Rows: \" << rows << \" Cols: \" << cols;\n  for (int itemid = 0; itemid < num_items; ++itemid) {\n    int i = caffe::caffe_rng_rand() % num_items;  // pick a random  pair\n    int j = caffe::caffe_rng_rand() % num_items;\n    read_image(&image_file, &label_file, i, rows, cols,\n        pixels, &label_i);\n    read_image(&image_file, &label_file, j, rows, cols,\n        pixels + (rows * cols), &label_j);\n    datum.set_data(pixels, 2*rows*cols);\n    if (label_i  == label_j) {\n      datum.set_label(1);\n    } else {\n      datum.set_label(0);\n    }\n    datum.SerializeToString(&value);\n    std::string key_str = caffe::format_int(itemid, 8);\n    db->Put(leveldb::WriteOptions(), key_str, value);\n  }\n\n  delete db;\n  delete [] pixels;\n}\n\nint main(int argc, char** argv) {\n  if (argc != 4) {\n    printf(\"This script converts the MNIST dataset to the leveldb format used\\n\"\n           \"by caffe to train a siamese network.\\n\"\n           \"Usage:\\n\"\n           \"    convert_mnist_data input_image_file input_label_file \"\n           \"output_db_file\\n\"\n           \"The MNIST dataset could be downloaded at\\n\"\n           \"    http://yann.lecun.com/exdb/mnist/\\n\"\n           \"You should gunzip them after downloading.\\n\");\n  } else {\n    google::InitGoogleLogging(argv[0]);\n    convert_dataset(argv[1], argv[2], argv[3]);\n  }\n  return 0;\n}\n#else\nint main(int argc, char** argv) {\n  LOG(FATAL) << \"This example requires LevelDB; compile with USE_LEVELDB.\";\n}\n#endif  // USE_LEVELDB\n"
  },
  {
    "path": "Caffe/siamese/create_mnist_siamese.sh",
    "content": "#!/usr/bin/env sh\n# This script converts the mnist data into leveldb format.\nset -e\n\nEXAMPLES=./build/examples/siamese\nDATA=./data/mnist\n\necho \"Creating leveldb...\"\n\nrm -rf ./examples/siamese/mnist_siamese_train_leveldb\nrm -rf ./examples/siamese/mnist_siamese_test_leveldb\n\n$EXAMPLES/convert_mnist_siamese_data.bin \\\n    $DATA/train-images-idx3-ubyte \\\n    $DATA/train-labels-idx1-ubyte \\\n    ./examples/siamese/mnist_siamese_train_leveldb\n$EXAMPLES/convert_mnist_siamese_data.bin \\\n    $DATA/t10k-images-idx3-ubyte \\\n    $DATA/t10k-labels-idx1-ubyte \\\n    ./examples/siamese/mnist_siamese_test_leveldb\n\necho \"Done.\"\n"
  },
  {
    "path": "Caffe/siamese/mnist_siamese.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setup\\n\",\n    \"\\n\",\n    \"Import Caffe and the usual modules.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"# Make sure that caffe is on the python path:\\n\",\n    \"caffe_root = '../../'  # this file is expected to be in {caffe_root}/examples/siamese\\n\",\n    \"import sys\\n\",\n    \"sys.path.insert(0, caffe_root + 'python')\\n\",\n    \"\\n\",\n    \"import caffe\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load the trained net\\n\",\n    \"\\n\",\n    \"Load the model definition and weights and set to CPU mode TEST phase computation with input scaling.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"MODEL_FILE = 'mnist_siamese.prototxt'\\n\",\n    \"# decrease if you want to preview during training\\n\",\n    \"PRETRAINED_FILE = 'mnist_siamese_iter_50000.caffemodel' \\n\",\n    \"caffe.set_mode_cpu()\\n\",\n    \"net = caffe.Net(MODEL_FILE, PRETRAINED_FILE, caffe.TEST)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load some MNIST test data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"TEST_DATA_FILE = '../../data/mnist/t10k-images-idx3-ubyte'\\n\",\n    \"TEST_LABEL_FILE = '../../data/mnist/t10k-labels-idx1-ubyte'\\n\",\n    \"n = 10000\\n\",\n    \"\\n\",\n    \"with open(TEST_DATA_FILE, 'rb') as f:\\n\",\n    \"    f.read(16) # skip the header\\n\",\n    \"    raw_data = np.fromstring(f.read(n * 28*28), dtype=np.uint8)\\n\",\n    \"\\n\",\n    \"with open(TEST_LABEL_FILE, 'rb') as f:\\n\",\n    \"    f.read(8) # skip the header\\n\",\n    \"    labels = np.fromstring(f.read(n), dtype=np.uint8)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Generate the Siamese features\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# reshape and preprocess\\n\",\n    \"caffe_in = raw_data.reshape(n, 1, 28, 28) * 0.00390625 # manually scale data instead of using `caffe.io.Transformer`\\n\",\n    \"out = net.forward_all(data=caffe_in)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Visualize the learned Siamese embedding\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": [\n       \"iVBORw0KGgoAAAANSUhEUgAAA54AAAIXCAYAAAD0R4FDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\n\",\n       \"AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXtwXOWZr/usvurWUktqGdmxaawEHEMuthGXITiIyMaJ\\n\",\n       \"wbEMFmCTDMkkoyqSyTnZMwdqpmYyzEyS2ruKue2ZqSTHO/vYGQbhCxdjwI637ViWMEEEMJhgB4MB\\n\",\n       \"gSRLsizJkiypuyX1+WP1Wlp971YvSd3y+1S5rF69Lt/6+lOrf/2+v/dVgsEggiAIgiAIgiAIgjBT\\n\",\n       \"WOZ6AIIgCIIgCIIgCML8RoSnIAiCIAiCIAiCMKOI8BQEQRAEQRAEQRBmFBGegiAIgiAIgiAIwowi\\n\",\n       \"wlMQBEEQBEEQBEGYUUR4CoIgCIIgCIIgCDNKRsJTUZQ8RVFaFUV5U1GUU4qi/HezBiYIgiAIgiAI\\n\",\n       \"giDMD5RM+3gqilIQDAZHFEWxAS8B/08wGHzJlNEJgiAIgiAIgiAIOU/GqbbBYHAk9KMDsAJ9mZ5T\\n\",\n       \"EARBEARBEARBmD9kLDwVRbEoivIm0A0cDQaDpzIfliAIgiAIgiAIgjBfMCPiORkMBlcAi4EvK4pS\\n\",\n       \"k/GoBEEQBEEQBEEQhHmDzawTBYPBi4qivAhUA03adkVRMjORCoIgCIIgCIIgCFlNMBhUEj2fkfBU\\n\",\n       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\"include_in_docs\": true,\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.9\"\n  },\n  \"priority\": 7\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "Caffe/siamese/mnist_siamese.prototxt",
    "content": "name: \"mnist_siamese\"\nlayer {\n  name: \"data\"\n  type: \"Input\"\n  top: \"data\"\n  input_param {\n    shape: { dim: 10000 dim: 1 dim: 28 dim: 28 }\n  }\n}\nlayer {\n  name: \"conv1\"\n  type: \"Convolution\"\n  bottom: \"data\"\n  top: \"conv1\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 20\n    kernel_size: 5\n    stride: 1\n  }\n}\nlayer {\n  name: \"pool1\"\n  type: \"Pooling\"\n  bottom: \"conv1\"\n  top: \"pool1\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 2\n    stride: 2\n  }\n}\nlayer {\n  name: \"conv2\"\n  type: \"Convolution\"\n  bottom: \"pool1\"\n  top: \"conv2\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 50\n    kernel_size: 5\n    stride: 1\n  }\n}\nlayer {\n  name: \"pool2\"\n  type: \"Pooling\"\n  bottom: \"conv2\"\n  top: \"pool2\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 2\n    stride: 2\n  }\n}\nlayer {\n  name: \"ip1\"\n  type: \"InnerProduct\"\n  bottom: \"pool2\"\n  top: \"ip1\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 500\n  }\n}\nlayer {\n  name: \"relu1\"\n  type: \"ReLU\"\n  bottom: \"ip1\"\n  top: \"ip1\"\n}\nlayer {\n  name: \"ip2\"\n  type: \"InnerProduct\"\n  bottom: \"ip1\"\n  top: \"ip2\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 10\n  }\n}\nlayer {\n  name: \"feat\"\n  type: \"InnerProduct\"\n  bottom: \"ip2\"\n  top: \"feat\"\n  param {\n    lr_mult: 1\n  }\n  param {\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 2\n  }\n}\n"
  },
  {
    "path": "Caffe/siamese/mnist_siamese_solver.prototxt",
    "content": "# The train/test net protocol buffer definition\nnet: \"examples/siamese/mnist_siamese_train_test.prototxt\"\n# test_iter specifies how many forward passes the test should carry out.\n# In the case of MNIST, we have test batch size 100 and 100 test iterations,\n# covering the full 10,000 testing images.\ntest_iter: 100\n# Carry out testing every 500 training iterations.\ntest_interval: 500\n# The base learning rate, momentum and the weight decay of the network.\nbase_lr: 0.01\nmomentum: 0.9\nweight_decay: 0.0000\n# The learning rate policy\nlr_policy: \"inv\"\ngamma: 0.0001\npower: 0.75\n# Display every 100 iterations\ndisplay: 100\n# The maximum number of iterations\nmax_iter: 50000\n# snapshot intermediate results\nsnapshot: 5000\nsnapshot_prefix: \"examples/siamese/mnist_siamese\"\n# solver mode: CPU or GPU\nsolver_mode: GPU\n"
  },
  {
    "path": "Caffe/siamese/mnist_siamese_train_test.prototxt",
    "content": "name: \"mnist_siamese_train_test\"\nlayer {\n  name: \"pair_data\"\n  type: \"Data\"\n  top: \"pair_data\"\n  top: \"sim\"\n  include {\n    phase: TRAIN\n  }\n  transform_param {\n    scale: 0.00390625\n  }\n  data_param {\n    source: \"examples/siamese/mnist_siamese_train_leveldb\"\n    batch_size: 64\n  }\n}\nlayer {\n  name: \"pair_data\"\n  type: \"Data\"\n  top: \"pair_data\"\n  top: \"sim\"\n  include {\n    phase: TEST\n  }\n  transform_param {\n    scale: 0.00390625\n  }\n  data_param {\n    source: \"examples/siamese/mnist_siamese_test_leveldb\"\n    batch_size: 100\n  }\n}\nlayer {\n  name: \"slice_pair\"\n  type: \"Slice\"\n  bottom: \"pair_data\"\n  top: \"data\"\n  top: \"data_p\"\n  slice_param {\n    slice_dim: 1\n    slice_point: 1\n  }\n}\nlayer {\n  name: \"conv1\"\n  type: \"Convolution\"\n  bottom: \"data\"\n  top: \"conv1\"\n  param {\n    name: \"conv1_w\"\n    lr_mult: 1\n  }\n  param {\n    name: \"conv1_b\"\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 20\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"pool1\"\n  type: \"Pooling\"\n  bottom: \"conv1\"\n  top: \"pool1\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 2\n    stride: 2\n  }\n}\nlayer {\n  name: \"conv2\"\n  type: \"Convolution\"\n  bottom: \"pool1\"\n  top: \"conv2\"\n  param {\n    name: \"conv2_w\"\n    lr_mult: 1\n  }\n  param {\n    name: \"conv2_b\"\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 50\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"pool2\"\n  type: \"Pooling\"\n  bottom: \"conv2\"\n  top: \"pool2\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 2\n    stride: 2\n  }\n}\nlayer {\n  name: \"ip1\"\n  type: \"InnerProduct\"\n  bottom: \"pool2\"\n  top: \"ip1\"\n  param {\n    name: \"ip1_w\"\n    lr_mult: 1\n  }\n  param {\n    name: \"ip1_b\"\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 500\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"relu1\"\n  type: \"ReLU\"\n  bottom: \"ip1\"\n  top: \"ip1\"\n}\nlayer {\n  name: \"ip2\"\n  type: \"InnerProduct\"\n  bottom: \"ip1\"\n  top: \"ip2\"\n  param {\n    name: \"ip2_w\"\n    lr_mult: 1\n  }\n  param {\n    name: \"ip2_b\"\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 10\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"feat\"\n  type: \"InnerProduct\"\n  bottom: \"ip2\"\n  top: \"feat\"\n  param {\n    name: \"feat_w\"\n    lr_mult: 1\n  }\n  param {\n    name: \"feat_b\"\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 2\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"conv1_p\"\n  type: \"Convolution\"\n  bottom: \"data_p\"\n  top: \"conv1_p\"\n  param {\n    name: \"conv1_w\"\n    lr_mult: 1\n  }\n  param {\n    name: \"conv1_b\"\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 20\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"pool1_p\"\n  type: \"Pooling\"\n  bottom: \"conv1_p\"\n  top: \"pool1_p\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 2\n    stride: 2\n  }\n}\nlayer {\n  name: \"conv2_p\"\n  type: \"Convolution\"\n  bottom: \"pool1_p\"\n  top: \"conv2_p\"\n  param {\n    name: \"conv2_w\"\n    lr_mult: 1\n  }\n  param {\n    name: \"conv2_b\"\n    lr_mult: 2\n  }\n  convolution_param {\n    num_output: 50\n    kernel_size: 5\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"pool2_p\"\n  type: \"Pooling\"\n  bottom: \"conv2_p\"\n  top: \"pool2_p\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 2\n    stride: 2\n  }\n}\nlayer {\n  name: \"ip1_p\"\n  type: \"InnerProduct\"\n  bottom: \"pool2_p\"\n  top: \"ip1_p\"\n  param {\n    name: \"ip1_w\"\n    lr_mult: 1\n  }\n  param {\n    name: \"ip1_b\"\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 500\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"relu1_p\"\n  type: \"ReLU\"\n  bottom: \"ip1_p\"\n  top: \"ip1_p\"\n}\nlayer {\n  name: \"ip2_p\"\n  type: \"InnerProduct\"\n  bottom: \"ip1_p\"\n  top: \"ip2_p\"\n  param {\n    name: \"ip2_w\"\n    lr_mult: 1\n  }\n  param {\n    name: \"ip2_b\"\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 10\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"feat_p\"\n  type: \"InnerProduct\"\n  bottom: \"ip2_p\"\n  top: \"feat_p\"\n  param {\n    name: \"feat_w\"\n    lr_mult: 1\n  }\n  param {\n    name: \"feat_b\"\n    lr_mult: 2\n  }\n  inner_product_param {\n    num_output: 2\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"loss\"\n  type: \"ContrastiveLoss\"\n  bottom: \"feat\"\n  bottom: \"feat_p\"\n  bottom: \"sim\"\n  top: \"loss\"\n  contrastive_loss_param {\n    margin: 1\n  }\n}\n"
  },
  {
    "path": "Caffe/siamese/readme.md",
    "content": "---\ntitle: Siamese Network Tutorial\ndescription: Train and test a siamese network on MNIST data.\ncategory: example\ninclude_in_docs: true\nlayout: default\npriority: 100\n---\n\n# Siamese Network Training with Caffe\nThis example shows how you can use weight sharing and a contrastive loss\nfunction to learn a model using a siamese network in Caffe.\n\nWe will assume that you have caffe successfully compiled. If not, please refer\nto the [Installation page](../../installation.html). This example builds on the\n[MNIST tutorial](mnist.html) so it would be a good idea to read that before\ncontinuing.\n\n*The guide specifies all paths and assumes all commands are executed from the\nroot caffe directory*\n\n## Prepare Datasets\n\nYou will first need to download and convert the data from the MNIST\nwebsite. To do this, simply run the following commands:\n\n    ./data/mnist/get_mnist.sh\n    ./examples/siamese/create_mnist_siamese.sh\n\nAfter running the script there should be two datasets,\n`./examples/siamese/mnist_siamese_train_leveldb`, and\n`./examples/siamese/mnist_siamese_test_leveldb`.\n\n## The Model\nFirst, we will define the model that we want to train using the siamese network.\nWe will use the convolutional net defined in\n`./examples/siamese/mnist_siamese.prototxt`. This model is almost\nexactly the same as the [LeNet model](mnist.html), the only difference is that\nwe have replaced the top layers that produced probabilities over the 10 digit\nclasses with a linear \"feature\" layer that produces a 2 dimensional vector.\n\n    layer {\n      name: \"feat\"\n      type: \"InnerProduct\"\n      bottom: \"ip2\"\n      top: \"feat\"\n      param {\n        name: \"feat_w\"\n        lr_mult: 1\n      }\n      param {\n        name: \"feat_b\"\n        lr_mult: 2\n      }\n      inner_product_param {\n        num_output: 2\n      }\n    }\n\n## Define the Siamese Network\n\nIn this section we will define the siamese network used for training. The\nresulting network is defined in\n`./examples/siamese/mnist_siamese_train_test.prototxt`.\n\n### Reading in the Pair Data\n\nWe start with a data layer that reads from the LevelDB database we created\nearlier. Each entry in this database contains the image data for a pair of\nimages (`pair_data`) and a binary label saying if they belong to the same class\nor different classes (`sim`).\n\n    layer {\n      name: \"pair_data\"\n      type: \"Data\"\n      top: \"pair_data\"\n      top: \"sim\"\n      include { phase: TRAIN }\n      transform_param {\n        scale: 0.00390625\n      }\n      data_param {\n        source: \"examples/siamese/mnist_siamese_train_leveldb\"\n        batch_size: 64\n      }\n    }\n\nIn order to pack a pair of images into the same blob in the database we pack one\nimage per channel. We want to be able to work with these two images separately,\nso we add a slice layer after the data layer. This takes the `pair_data` and\nslices it along the channel dimension so that we have a single image in `data`\nand its paired image in `data_p.`\n\n    layer {\n      name: \"slice_pair\"\n      type: \"Slice\"\n      bottom: \"pair_data\"\n      top: \"data\"\n      top: \"data_p\"\n      slice_param {\n        slice_dim: 1\n        slice_point: 1\n      }\n    }\n\n### Building the First Side of the Siamese Net\n\nNow we can specify the first side of the siamese net. This side operates on\n`data` and produces `feat`. Starting from the net in\n`./examples/siamese/mnist_siamese.prototxt` we add default weight fillers. Then\nwe name the parameters of the convolutional and inner product layers. Naming the\nparameters allows Caffe to share the parameters between layers on both sides of\nthe siamese net. In the definition this looks like:\n\n    ...\n    param { name: \"conv1_w\" ...  }\n    param { name: \"conv1_b\" ...  }\n    ...\n    param { name: \"conv2_w\" ...  }\n    param { name: \"conv2_b\" ...  }\n    ...\n    param { name: \"ip1_w\" ...  }\n    param { name: \"ip1_b\" ...  }\n    ...\n    param { name: \"ip2_w\" ...  }\n    param { name: \"ip2_b\" ...  }\n    ...\n\n### Building the Second Side of the Siamese Net\n\nNow we need to create the second path that operates on `data_p` and produces\n`feat_p`. This path is exactly the same as the first. So we can just copy and\npaste it. Then we change the name of each layer, input, and output by appending\n`_p` to differentiate the \"paired\" layers from the originals.\n\n### Adding the Contrastive Loss Function\n\nTo train the network we will optimize a contrastive loss function proposed in:\nRaia Hadsell, Sumit Chopra, and Yann LeCun \"Dimensionality Reduction by Learning\nan Invariant Mapping\". This loss function encourages matching pairs to be close\ntogether in feature space while pushing non-matching pairs apart. This cost\nfunction is implemented with the `CONTRASTIVE_LOSS` layer:\n\n    layer {\n        name: \"loss\"\n        type: \"ContrastiveLoss\"\n        contrastive_loss_param {\n            margin: 1.0\n        }\n        bottom: \"feat\"\n        bottom: \"feat_p\"\n        bottom: \"sim\"\n        top: \"loss\"\n    }\n\n## Define the Solver\n\nNothing special needs to be done to the solver besides pointing it at the\ncorrect model file. The solver is defined in\n`./examples/siamese/mnist_siamese_solver.prototxt`.\n\n## Training and Testing the Model\n\nTraining the model is simple after you have written the network definition\nprotobuf and solver protobuf files. Simply run\n`./examples/siamese/train_mnist_siamese.sh`:\n\n    ./examples/siamese/train_mnist_siamese.sh\n\n# Plotting the results\n\nFirst, we can draw the model and siamese networks by running the following\ncommands that draw the DAGs defined in the .prototxt files:\n\n    ./python/draw_net.py \\\n        ./examples/siamese/mnist_siamese.prototxt \\\n        ./examples/siamese/mnist_siamese.png\n\n    ./python/draw_net.py \\\n        ./examples/siamese/mnist_siamese_train_test.prototxt \\\n        ./examples/siamese/mnist_siamese_train_test.png\n\nSecond, we can load the learned model and plot the features using the iPython\nnotebook:\n\n    ipython notebook ./examples/siamese/mnist_siamese.ipynb\n\n"
  },
  {
    "path": "Caffe/siamese/train_mnist_siamese.sh",
    "content": "#!/usr/bin/env sh\nset -e\n\nTOOLS=./build/tools\n\n$TOOLS/caffe train --solver=examples/siamese/mnist_siamese_solver.prototxt $@\n"
  },
  {
    "path": "Caffe/web_demo/app.py",
    "content": "import os\nimport time\nimport cPickle\nimport datetime\nimport logging\nimport flask\nimport werkzeug\nimport optparse\nimport tornado.wsgi\nimport tornado.httpserver\nimport numpy as np\nimport pandas as pd\nfrom PIL import Image\nimport cStringIO as StringIO\nimport urllib\nimport exifutil\n\nimport caffe\n\nREPO_DIRNAME = os.path.abspath(os.path.dirname(os.path.abspath(__file__)) + '/../..')\nUPLOAD_FOLDER = '/tmp/caffe_demos_uploads'\nALLOWED_IMAGE_EXTENSIONS = set(['png', 'bmp', 'jpg', 'jpe', 'jpeg', 'gif'])\n\n# Obtain the flask app object\napp = flask.Flask(__name__)\n\n\n@app.route('/')\ndef index():\n    return flask.render_template('index.html', has_result=False)\n\n\n@app.route('/classify_url', methods=['GET'])\ndef classify_url():\n    imageurl = flask.request.args.get('imageurl', '')\n    try:\n        string_buffer = StringIO.StringIO(\n            urllib.urlopen(imageurl).read())\n        image = caffe.io.load_image(string_buffer)\n\n    except Exception as err:\n        # For any exception we encounter in reading the image, we will just\n        # not continue.\n        logging.info('URL Image open error: %s', err)\n        return flask.render_template(\n            'index.html', has_result=True,\n            result=(False, 'Cannot open image from URL.')\n        )\n\n    logging.info('Image: %s', imageurl)\n    result = app.clf.classify_image(image)\n    return flask.render_template(\n        'index.html', has_result=True, result=result, imagesrc=imageurl)\n\n\n@app.route('/classify_upload', methods=['POST'])\ndef classify_upload():\n    try:\n        # We will save the file to disk for possible data collection.\n        imagefile = flask.request.files['imagefile']\n        filename_ = str(datetime.datetime.now()).replace(' ', '_') + \\\n            werkzeug.secure_filename(imagefile.filename)\n        filename = os.path.join(UPLOAD_FOLDER, filename_)\n        imagefile.save(filename)\n        logging.info('Saving to %s.', filename)\n        image = exifutil.open_oriented_im(filename)\n\n    except Exception as err:\n        logging.info('Uploaded image open error: %s', err)\n        return flask.render_template(\n            'index.html', has_result=True,\n            result=(False, 'Cannot open uploaded image.')\n        )\n\n    result = app.clf.classify_image(image)\n    return flask.render_template(\n        'index.html', has_result=True, result=result,\n        imagesrc=embed_image_html(image)\n    )\n\n\ndef embed_image_html(image):\n    \"\"\"Creates an image embedded in HTML base64 format.\"\"\"\n    image_pil = Image.fromarray((255 * image).astype('uint8'))\n    image_pil = image_pil.resize((256, 256))\n    string_buf = StringIO.StringIO()\n    image_pil.save(string_buf, format='png')\n    data = string_buf.getvalue().encode('base64').replace('\\n', '')\n    return 'data:image/png;base64,' + data\n\n\ndef allowed_file(filename):\n    return (\n        '.' in filename and\n        filename.rsplit('.', 1)[1] in ALLOWED_IMAGE_EXTENSIONS\n    )\n\n\nclass ImagenetClassifier(object):\n    default_args = {\n        'model_def_file': (\n            '{}/models/bvlc_reference_caffenet/deploy.prototxt'.format(REPO_DIRNAME)),\n        'pretrained_model_file': (\n            '{}/models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'.format(REPO_DIRNAME)),\n        'mean_file': (\n            '{}/python/caffe/imagenet/ilsvrc_2012_mean.npy'.format(REPO_DIRNAME)),\n        'class_labels_file': (\n            '{}/data/ilsvrc12/synset_words.txt'.format(REPO_DIRNAME)),\n        'bet_file': (\n            '{}/data/ilsvrc12/imagenet.bet.pickle'.format(REPO_DIRNAME)),\n    }\n    for key, val in default_args.iteritems():\n        if not os.path.exists(val):\n            raise Exception(\n                \"File for {} is missing. Should be at: {}\".format(key, val))\n    default_args['image_dim'] = 256\n    default_args['raw_scale'] = 255.\n\n    def __init__(self, model_def_file, pretrained_model_file, mean_file,\n                 raw_scale, class_labels_file, bet_file, image_dim, gpu_mode):\n        logging.info('Loading net and associated files...')\n        if gpu_mode:\n            caffe.set_mode_gpu()\n        else:\n            caffe.set_mode_cpu()\n        self.net = caffe.Classifier(\n            model_def_file, pretrained_model_file,\n            image_dims=(image_dim, image_dim), raw_scale=raw_scale,\n            mean=np.load(mean_file).mean(1).mean(1), channel_swap=(2, 1, 0)\n        )\n\n        with open(class_labels_file) as f:\n            labels_df = pd.DataFrame([\n                {\n                    'synset_id': l.strip().split(' ')[0],\n                    'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]\n                }\n                for l in f.readlines()\n            ])\n        self.labels = labels_df.sort('synset_id')['name'].values\n\n        self.bet = cPickle.load(open(bet_file))\n        # A bias to prefer children nodes in single-chain paths\n        # I am setting the value to 0.1 as a quick, simple model.\n        # We could use better psychological models here...\n        self.bet['infogain'] -= np.array(self.bet['preferences']) * 0.1\n\n    def classify_image(self, image):\n        try:\n            starttime = time.time()\n            scores = self.net.predict([image], oversample=True).flatten()\n            endtime = time.time()\n\n            indices = (-scores).argsort()[:5]\n            predictions = self.labels[indices]\n\n            # In addition to the prediction text, we will also produce\n            # the length for the progress bar visualization.\n            meta = [\n                (p, '%.5f' % scores[i])\n                for i, p in zip(indices, predictions)\n            ]\n            logging.info('result: %s', str(meta))\n\n            # Compute expected information gain\n            expected_infogain = np.dot(\n                self.bet['probmat'], scores[self.bet['idmapping']])\n            expected_infogain *= self.bet['infogain']\n\n            # sort the scores\n            infogain_sort = expected_infogain.argsort()[::-1]\n            bet_result = [(self.bet['words'][v], '%.5f' % expected_infogain[v])\n                          for v in infogain_sort[:5]]\n            logging.info('bet result: %s', str(bet_result))\n\n            return (True, meta, bet_result, '%.3f' % (endtime - starttime))\n\n        except Exception as err:\n            logging.info('Classification error: %s', err)\n            return (False, 'Something went wrong when classifying the '\n                           'image. Maybe try another one?')\n\n\ndef start_tornado(app, port=5000):\n    http_server = tornado.httpserver.HTTPServer(\n        tornado.wsgi.WSGIContainer(app))\n    http_server.listen(port)\n    print(\"Tornado server starting on port {}\".format(port))\n    tornado.ioloop.IOLoop.instance().start()\n\n\ndef start_from_terminal(app):\n    \"\"\"\n    Parse command line options and start the server.\n    \"\"\"\n    parser = optparse.OptionParser()\n    parser.add_option(\n        '-d', '--debug',\n        help=\"enable debug mode\",\n        action=\"store_true\", default=False)\n    parser.add_option(\n        '-p', '--port',\n        help=\"which port to serve content on\",\n        type='int', default=5000)\n    parser.add_option(\n        '-g', '--gpu',\n        help=\"use gpu mode\",\n        action='store_true', default=False)\n\n    opts, args = parser.parse_args()\n    ImagenetClassifier.default_args.update({'gpu_mode': opts.gpu})\n\n    # Initialize classifier + warm start by forward for allocation\n    app.clf = ImagenetClassifier(**ImagenetClassifier.default_args)\n    app.clf.net.forward()\n\n    if opts.debug:\n        app.run(debug=True, host='0.0.0.0', port=opts.port)\n    else:\n        start_tornado(app, opts.port)\n\n\nif __name__ == '__main__':\n    logging.getLogger().setLevel(logging.INFO)\n    if not os.path.exists(UPLOAD_FOLDER):\n        os.makedirs(UPLOAD_FOLDER)\n    start_from_terminal(app)\n"
  },
  {
    "path": "Caffe/web_demo/exifutil.py",
    "content": "\"\"\"\nThis script handles the skimage exif problem.\n\"\"\"\n\nfrom PIL import Image\nimport numpy as np\n\nORIENTATIONS = {   # used in apply_orientation\n    2: (Image.FLIP_LEFT_RIGHT,),\n    3: (Image.ROTATE_180,),\n    4: (Image.FLIP_TOP_BOTTOM,),\n    5: (Image.FLIP_LEFT_RIGHT, Image.ROTATE_90),\n    6: (Image.ROTATE_270,),\n    7: (Image.FLIP_LEFT_RIGHT, Image.ROTATE_270),\n    8: (Image.ROTATE_90,)\n}\n\n\ndef open_oriented_im(im_path):\n    im = Image.open(im_path)\n    if hasattr(im, '_getexif'):\n        exif = im._getexif()\n        if exif is not None and 274 in exif:\n            orientation = exif[274]\n            im = apply_orientation(im, orientation)\n    img = np.asarray(im).astype(np.float32) / 255.\n    if img.ndim == 2:\n        img = img[:, :, np.newaxis]\n        img = np.tile(img, (1, 1, 3))\n    elif img.shape[2] == 4:\n        img = img[:, :, :3]\n    return img\n\n\ndef apply_orientation(im, orientation):\n    if orientation in ORIENTATIONS:\n        for method in ORIENTATIONS[orientation]:\n            im = im.transpose(method)\n    return im\n"
  },
  {
    "path": "Caffe/web_demo/readme.md",
    "content": "---\ntitle: Web demo\ndescription: Image classification demo running as a Flask web server.\ncategory: example\ninclude_in_docs: true\npriority: 10\n---\n\n# Web Demo\n\n## Requirements\n\nThe demo server requires Python with some dependencies.\nTo make sure you have the dependencies, please run `pip install -r examples/web_demo/requirements.txt`, and also make sure that you've compiled the Python Caffe interface and that it is on your `PYTHONPATH` (see [installation instructions](/installation.html)).\n\nMake sure that you have obtained the Reference CaffeNet Model and the ImageNet Auxiliary Data:\n\n    ./scripts/download_model_binary.py models/bvlc_reference_caffenet\n    ./data/ilsvrc12/get_ilsvrc_aux.sh\n\nNOTE: if you run into trouble, try re-downloading the auxiliary files.\n\n## Run\n\nRunning `python examples/web_demo/app.py` will bring up the demo server, accessible at `http://0.0.0.0:5000`.\nYou can enable debug mode of the web server, or switch to a different port:\n\n    % python examples/web_demo/app.py -h\n    Usage: app.py [options]\n\n    Options:\n      -h, --help            show this help message and exit\n      -d, --debug           enable debug mode\n      -p PORT, --port=PORT  which port to serve content on\n\n## How are the \"maximally accurate\" results generated?\n\nIn a nutshell: ImageNet predictions are made at the leaf nodes, but the organization of the project allows leaf nodes to be united via more general parent nodes, with 'entity' at the very top.\nTo give \"maximally accurate\" results, we \"back off\" from maximally specific predictions to maintain a high accuracy.\nThe `bet_file` that is loaded in the demo provides the graph structure and names of all relevant ImageNet nodes as well as measures of information gain between them.\nPlease see the \"Hedging your bets\" paper from [CVPR 2012](http://www.image-net.org/projects/hedging/) for further information.\n"
  },
  {
    "path": "Caffe/web_demo/requirements.txt",
    "content": "werkzeug\nflask\ntornado\nnumpy\npandas\npillow\npyyaml\n"
  },
  {
    "path": "Caffe/web_demo/templates/index.html",
    "content": "<!DOCTYPE html>\n<html lang=\"en\">\n  <head>\n    <meta charset=\"utf-8\">\n    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n    <meta name=\"description\" content=\"Caffe demos\">\n    <meta name=\"author\" content=\"BVLC (http://bvlc.eecs.berkeley.edu/)\">\n\n    <title>Caffe Demos</title>\n\n    <link href=\"//netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css\" rel=\"stylesheet\">\n\n    <script type=\"text/javascript\" src=\"//code.jquery.com/jquery-2.1.1.js\"></script>\n    <script src=\"//netdna.bootstrapcdn.com/bootstrap/3.1.1/js/bootstrap.min.js\"></script>\n\n    <!-- Script to instantly classify an image once it is uploaded. -->\n    <script type=\"text/javascript\">\n      $(document).ready(\n        function(){\n          $('#classifyfile').attr('disabled',true);\n          $('#imagefile').change(\n            function(){\n              if ($(this).val()){\n                $('#formupload').submit();\n              }\n            }\n          );\n        }\n      );\n    </script>\n\n    <style>\n    body {\n      font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n      line-height:1.5em;\n      color: #232323;\n      -webkit-font-smoothing: antialiased;\n    }\n\n    h1, h2, h3 {\n      font-family: Times, serif;\n      line-height:1.5em;\n      border-bottom: 1px solid #ccc;\n    }\n    </style>\n  </head>\n\n  <body>\n    <!-- Begin page content -->\n    <div class=\"container\">\n      <div class=\"page-header\">\n        <h1><a href=\"/\">Caffe Demos</a></h1>\n        <p>\n          The <a href=\"http://caffe.berkeleyvision.org\">Caffe</a> neural network library makes implementing state-of-the-art computer vision systems easy.\n        </p>\n      </div>\n\n      <div>\n        <h2>Classification</h2>\n        <a href=\"/classify_url?imageurl=http%3A%2F%2Fi.telegraph.co.uk%2Fmultimedia%2Farchive%2F02351%2Fcross-eyed-cat_2351472k.jpg\">Click for a Quick Example</a>\n      </div>\n\n      {% if has_result %}\n      {% if not result[0] %}\n      <!-- we have error in the result. -->\n      <div class=\"alert alert-danger\">{{ result[1] }} Did you provide a valid URL or a valid image file? </div>\n      {% else %}\n      <div class=\"media\">\n        <a class=\"pull-left\" href=\"#\"><img class=\"media-object\" width=\"192\" height=\"192\" src={{ imagesrc }}></a>\n        <div class=\"media-body\">\n          <div class=\"bs-example bs-example-tabs\">\n            <ul id=\"myTab\" class=\"nav nav-tabs\">\n              <li class=\"active\"><a href=\"#infopred\" data-toggle=\"tab\">Maximally accurate</a></li>\n              <li><a href=\"#flatpred\" data-toggle=\"tab\">Maximally specific</a></li>\n            </ul>\n            <div id=\"myTabContent\" class=\"tab-content\">\n              <div class=\"tab-pane fade in active\" id=\"infopred\">\n                <ul class=\"list-group\">\n                  {% for single_pred in result[2] %}\n                  <li class=\"list-group-item\">\n                  <span class=\"badge\">{{ single_pred[1] }}</span>\n                  <h4 class=\"list-group-item-heading\">\n                    <a href=\"https://www.google.com/#q={{ single_pred[0] }}\" target=\"_blank\">{{ single_pred[0] }}</a>\n                  </h4>\n                  </li>\n                  {% endfor %}\n                </ul>\n              </div>\n              <div class=\"tab-pane fade\" id=\"flatpred\">\n                <ul class=\"list-group\">\n                  {% for single_pred in result[1] %}\n                  <li class=\"list-group-item\">\n                  <span class=\"badge\">{{ single_pred[1] }}</span>\n                  <h4 class=\"list-group-item-heading\">\n                    <a href=\"https://www.google.com/#q={{ single_pred[0] }}\" target=\"_blank\">{{ single_pred[0] }}</a>\n                  </h4>\n                  </li>\n                  {% endfor %}\n                </ul>\n              </div>\n            </div>\n          </div>\n\n        </div>\n      </div>\n      <p> CNN took {{ result[3] }} seconds. </p>\n      {% endif %}\n      <hr>\n      {% endif %}\n\n      <form role=\"form\" action=\"classify_url\" method=\"get\">\n        <div class=\"form-group\">\n          <div class=\"input-group\">\n            <input type=\"text\" class=\"form-control\" name=\"imageurl\" id=\"imageurl\" placeholder=\"Provide an image URL\">\n            <span class=\"input-group-btn\">\n              <input class=\"btn btn-primary\" value=\"Classify URL\" type=\"submit\" id=\"classifyurl\"></input>\n            </span>\n          </div><!-- /input-group -->\n        </div>\n      </form>\n\n      <form id=\"formupload\" class=\"form-inline\" role=\"form\" action=\"classify_upload\" method=\"post\" enctype=\"multipart/form-data\">\n        <div class=\"form-group\">\n          <label for=\"imagefile\">Or upload an image:</label>\n          <input type=\"file\" name=\"imagefile\" id=\"imagefile\">\n        </div>\n        <!--<input type=\"submit\" class=\"btn btn-primary\" value=\"Classify File\" id=\"classifyfile\"></input>-->\n      </form>\n    </div>\n\n    <hr>\n    <div id=\"footer\">\n      <div class=\"container\">\n        <p>&copy; BVLC 2014</p>\n      </div>\n   </div>\n </body>\n</html>\n"
  },
  {
    "path": "DLbook/DeepLearningPapers.md",
    "content": "\n### Survey Review\n- Deep learning (2015), Y. LeCun, Y. Bengio and G. Hinton [[pdf]](https://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf) :sparkles:\n- Deep learning in neural networks: An overview (2015), J. Schmidhuber [[pdf]](http://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf) :sparkles:\n- Representation learning: A review and new perspectives (2013), Y. Bengio et al. [[pdf]](http://www.cl.uni-heidelberg.de/courses/ws14/deepl/BengioETAL12.pdf) :sparkles:\n\n### Theory Future\n- Distilling the knowledge in a neural network (2015), G. Hinton et al. [[pdf]](http://arxiv.org/pdf/1503.02531)\n- Deep neural networks are easily fooled: High confidence predictions for unrecognizable images (2015), A. Nguyen et al. [[pdf]](http://arxiv.org/pdf/1412.1897)\n- How transferable are features in deep neural networks? (2014), J. Yosinski et al. *(Bengio)* [[pdf]](http://papers.nips.cc/paper/5347-how-transferable-are-features-in-deep-neural-networks.pdf)\n- Why does unsupervised pre-training help deep learning (2010), E. Erhan et al. *(Bengio)* [[pdf]](http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf)\n- Understanding the difficulty of training deep feedforward neural networks (2010), X. Glorot and Y. Bengio [[pdf]](http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_GlorotB10.pdf)\n\n### Optimization Regularization\n- Taking the human out of the loop: A review of bayesian optimization (2016), B. Shahriari et al. [[pdf]](https://www.cs.ox.ac.uk/people/nando.defreitas/publications/BayesOptLoop.pdf)\n- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (2015), S. Loffe and C. Szegedy [[pdf]](http://arxiv.org/pdf/1502.03167) :sparkles:\n- Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015), K. He et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf) :sparkles:\n- Dropout: A simple way to prevent neural networks from overfitting (2014), N. Srivastava et al. *(Hinton)* [[pdf]](http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf) :sparkles:\n- Adam: A method for stochastic optimization (2014), D. Kingma and J. Ba [[pdf]](http://arxiv.org/pdf/1412.6980)\n- Regularization of neural networks using dropconnect (2013), L. Wan et al. *(LeCun)* [[pdf]](http://machinelearning.wustl.edu/mlpapers/paper_files/icml2013_wan13.pdf)\n- Improving neural networks by preventing co-adaptation of feature detectors (2012), G. Hinton et al. [[pdf]](http://arxiv.org/pdf/1207.0580.pdf) :sparkles:\n- Spatial pyramid pooling in deep convolutional networks for visual recognition (2014), K. He et al. [[pdf]](http://arxiv.org/pdf/1406.4729)\n- Random search for hyper-parameter optimization (2012) J. Bergstra and Y. Bengio [[pdf]](http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a)\n\n### NetworkModels\n- Deep residual learning for image recognition (2016), K. He et al. *(Microsoft)* [[pdf]](http://arxiv.org/pdf/1512.03385) :sparkles:\n- Going deeper with convolutions (2015), C. Szegedy et al. *(Google)* [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf) :sparkles:\n- Fast R-CNN (2015), R. Girshick [[pdf]](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf) :sparkles:\n- Very deep convolutional networks for large-scale image recognition (2014), K. Simonyan and A. Zisserman [[pdf]](http://arxiv.org/pdf/1409.1556) :sparkles:\n- Fully convolutional networks for semantic segmentation (2015), J. Long et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf) :sparkles:\n- OverFeat: Integrated recognition, localization and detection using convolutional networks (2014), P. Sermanet et al. *(LeCun)* [[pdf]](http://arxiv.org/pdf/1312.6229)\n- Visualizing and understanding convolutional networks (2014), M. Zeiler and R. Fergus [[pdf]](http://arxiv.org/pdf/1311.2901) :sparkles:\n- Maxout networks (2013), I. Goodfellow et al. *(Bengio)* [[pdf]](http://arxiv.org/pdf/1302.4389v4)\n- ImageNet classification with deep convolutional neural networks (2012), A. Krizhevsky et al. *(Hinton)* [[pdf]](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) :sparkles:\n- Large scale distributed deep networks (2012), J. Dean et al. [[pdf]](http://papers.nips.cc/paper/4687-large-scale-distributed-deep-networks.pdf) :sparkles:\n- Deep sparse rectifier neural networks (2011), X. Glorot et al. *(Bengio)* [[pdf]](http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2011_GlorotBB11.pdf)\n\n### Image\n- Imagenet large scale visual recognition challenge (2015), O. Russakovsky et al. [[pdf]](http://arxiv.org/pdf/1409.0575) :sparkles:\n- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015), S. Ren et al. [[pdf]](http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf)\n- DRAW: A recurrent neural network for image generation (2015), K. Gregor et al. [[pdf]](http://arxiv.org/pdf/1502.04623)\n- Rich feature hierarchies for accurate object detection and semantic segmentation (2014), R. Girshick et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf)\n- Learning and transferring mid-Level image representations using convolutional neural networks (2014), M. Oquab et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Oquab_Learning_and_Transferring_2014_CVPR_paper.pdf)\n- DeepFace: Closing the Gap to Human-Level Performance in Face Verification (2014), Y. Taigman et al. *(Facebook)* [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf)\n- Decaf: A deep convolutional activation feature for generic visual recognition (2013), J. Donahue et al. [[pdf]](http://arxiv.org/pdf/1310.1531)\n- Learning Hierarchical Features for Scene Labeling (2013), C. Farabet et al. *(LeCun)* [[pdf]](https://hal-enpc.archives-ouvertes.fr/docs/00/74/20/77/PDF/farabet-pami-13.pdf)\n- Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis (2011), Q. Le et al. [[pdf]](http://robotics.stanford.edu/~wzou/cvpr_LeZouYeungNg11.pdf)\n- Learning mid-level features for recognition (2010), Y. Boureau *(LeCun)* [[pdf]](http://ece.duke.edu/~lcarin/boureau-cvpr-10.pdf)\n\n### Caption\n- Show, attend and tell: Neural image caption generation with visual attention (2015), K. Xu et al. *(Bengio)* [[pdf]](http://arxiv.org/pdf/1502.03044) :sparkles:\n- Show and tell: A neural image caption generator (2015), O. Vinyals et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Vinyals_Show_and_Tell_2015_CVPR_paper.pdf) :sparkles:\n- Long-term recurrent convolutional networks for visual recognition and description (2015), J. Donahue et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf) :sparkles:\n- Deep visual-semantic alignments for generating image descriptions (2015), A. Karpathy and L. Fei-Fei [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Karpathy_Deep_Visual-Semantic_Alignments_2015_CVPR_paper.html) :sparkles:\n\n\n### Video HumanActivity\n- Large-scale video classification with convolutional neural networks (2014), A. Karpathy et al. *(FeiFei)* [[pdf]](vision.stanford.edu/pdf/karpathy14.pdf)\n- A survey on human activity recognition using wearable sensors (2013), O. Lara and M. Labrador [[pdf]](http://romisatriawahono.net/lecture/rm/survey/computer%20vision/Lara%20-%20Human%20Activity%20Recognition%20-%202013.pdf)\n- 3D convolutional neural networks for human action recognition (2013), S. Ji et al. [[pdf]](http://machinelearning.wustl.edu/mlpapers/paper_files/icml2010_JiXYY10.pdf)\n- Deeppose: Human pose estimation via deep neural networks (2014), A. Toshev and C. Szegedy [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Toshev_DeepPose_Human_Pose_2014_CVPR_paper.pdf)\n- Action recognition with improved trajectories (2013), H. Wang and C. Schmid [[pdf]](http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Wang_Action_Recognition_with_2013_ICCV_paper.pdf)\n\n### WordEmbedding\n- Glove: Global vectors for word representation (2014), J. Pennington et al. [[pdf]](http://llcao.net/cu-deeplearning15/presentation/nn-pres.pdf) :sparkles:\n- Sequence to sequence learning with neural networks (2014), I. Sutskever et al. [[pdf]](http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf)\n- Distributed representations of sentences and documents (2014), Q. Le and T. Mikolov [[pdf]](http://arxiv.org/pdf/1405.4053) *(Google)* :sparkles:\n- Distributed representations of words and phrases and their compositionality (2013), T. Mikolov et al. *(Google)* [[pdf]](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) :sparkles:\n- Efficient estimation of word representations in vector space (2013), T. Mikolov et al. *(Google)* [[pdf]](http://arxiv.org/pdf/1301.3781) :sparkles:\n- Word representations: a simple and general method for semi-supervised learning (2010), J. Turian *(Bengio)* [[pdf]](http://www.anthology.aclweb.org/P/P10/P10-1040.pdf)\n\n### MachineTranslation QnA\n- Towards ai-complete question answering: A set of prerequisite toy tasks (2015), J. Weston et al. [[pdf]](http://arxiv.org/pdf/1502.05698)\n- Neural machine translation by jointly learning to align and translate (2014), D. Bahdanau et al. *(Bengio)* [[pdf]](http://arxiv.org/pdf/1409.0473) :sparkles:\n- Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014), K. Cho et al. *(Bengio)* [[pdf]](http://arxiv.org/pdf/1406.1078)\n- A convolutional neural network for modelling sentences (2014), N. kalchbrenner et al. [[pdf]](http://arxiv.org/pdf/1404.2188v1)\n- Convolutional neural networks for sentence classification (2014), Y. Kim [[pdf]](http://arxiv.org/pdf/1408.5882)\n- The stanford coreNLP natural language processing toolkit (2014), C. Manning et al. [[pdf]](http://www.surdeanu.info/mihai/papers/acl2014-corenlp.pdf)\n- Recursive deep models for semantic compositionality over a sentiment treebank (2013), R. Socher et al. [[pdf]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.383.1327&rep=rep1&type=pdf) :sparkles:\n- Natural language processing (almost) from scratch (2011), R. Collobert et al. [[pdf]](http://arxiv.org/pdf/1103.0398)\n- Recurrent neural network based language model (2010), T. Mikolov et al. [[pdf]](http://www.fit.vutbr.cz/research/groups/speech/servite/2010/rnnlm_mikolov.pdf)\n\n### Speech Etc.\n- Speech recognition with deep recurrent neural networks (2013), A. Graves *(Hinton)* [[pdf]](http://arxiv.org/pdf/1303.5778.pdf)\n- Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups (2012), G. Hinton et al. [[pdf]](http://www.cs.toronto.edu/~asamir/papers/SPM_DNN_12.pdf) :sparkles:\n- Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition (2012) G. Dahl et al. [[pdf]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.337.7548&rep=rep1&type=pdf) :sparkles:\n\n### RL Robotics\n- Mastering the game of Go with deep neural networks and tree search, D. Silver et al. *(DeepMind)* [[pdf]](Mastering the game of Go with deep neural networks and tree search)\n- Human-level control through deep reinforcement learning (2015), V. Mnih et al. *(DeepMind)* [[pdf]](http://www.davidqiu.com:8888/research/nature14236.pdf) :sparkles:\n- Deep learning for detecting robotic grasps (2015), I. Lenz et al. [[pdf]](http://www.cs.cornell.edu/~asaxena/papers/lenz_lee_saxena_deep_learning_grasping_ijrr2014.pdf)\n- Playing atari with deep reinforcement learning (2013), V. Mnih et al. *(DeepMind)* [[pdf]](http://arxiv.org/pdf/1312.5602.pdf))\n\n### Unsupervised\n- Building high-level features using large scale unsupervised learning (2013), Q. Le et al. [[pdf]](http://arxiv.org/pdf/1112.6209) :sparkles:\n- Contractive auto-encoders: Explicit invariance during feature extraction (2011), S. Rifai et al. *(Bengio)* [[pdf]](http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf)\n- An analysis of single-layer networks in unsupervised feature learning (2011), A. Coates et al. [[pdf]](http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2011_CoatesNL11.pdf)\n- Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion (2010), P. Vincent et al. *(Bengio)* [[pdf]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484&rep=rep1&type=pdf)\n- A practical guide to training restricted boltzmann machines (2010), G. Hinton [[pdf]](http://www.csri.utoronto.ca/~hinton/absps/guideTR.pdf)\n- Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion (2010), P. Vincent et al. *(Bengio)* [[pdf]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484&rep=rep1&type=pdf)\n\n### Hardware Software\n- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (2016), M. Abadi et al. *(Google)* [[pdf]](http://arxiv.org/pdf/1603.04467)\n- MatConvNet: Convolutional neural networks for matlab (2015), A. Vedaldi and K. Lenc [[pdf]](http://arxiv.org/pdf/1412.4564)\n- Caffe: Convolutional architecture for fast feature embedding (2014), Y. Jia et al. [[pdf]](http://arxiv.org/pdf/1408.5093) :sparkles:\n- Theano: new features and speed improvements (2012), F. Bastien et al. *(Bengio)* [[pdf]](http://arxiv.org/pdf/1211.5590)\n\n## License\n[![CC0](http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0/)\n"
  },
  {
    "path": "Keras/README.md",
    "content": "# Keras 例子\naddition_rnn.py 用RNN拟合加法运算\n\nantirectifier.py 为Keras编写自定义图层\n\nbabi_memnn.py 在[bAbI](https://research.facebook.com/research/babi/)数据集上训练记忆网络以进行阅读理解\n\nbabi_rnn.py 在bAbI数据集上训练两分支递归网络以进行阅读理解\n\ncifar10_cnn.py 在CIFAR10小图像数据集上训练简单CNN\n\nconv_filter_visualization.py 通过梯度上升可视化VGG16的过滤器\n\ndeep_dream.py 为Keras编写深梦\n\nimage_ocr.py 训练卷积、循环和CTC logloss函数以执行OCR识别\n\nimdb_bidirectional_lstm.py 在IMDB情绪分类任务上训练双向LSTM\n\nimdb_cnn.py 使用Convolution1D进行文本分类\n\nimdb_cnn_lstm.py 在IMDB情绪分类任务上训练卷积、循环网络\n\nimdb_fasttext.py 在IMDB情绪分类任务上训练FastText模型\n\nimdb_lstm.py 在IMDB情绪分类任务上训练LSTM\n\nlstm_benchmark.py 在IMDB情绪分类任务上比较不同的LSTM实现\n\nlstm_text_generation.py 从尼采的书中生成文本\n\nmnist_cnn.py 在MNIST数据集上训练简单的ConvNet\n\nmnist_hierarchical_rnn.py 训练分层RNN以对MNIST数字进行分类\n\nmnist_irnn.py 使用MNIST数据集的IRNN实验\n\nmnist_mlp.py 在MNIST数据集上训练简单的多层感知器\n\nmnist_net2net.py 使用MNIST再现Net2Net实验\n\nmnist_siamese_graph.py 使用MNIST数据集训练一个多层感知器\n\nmnist_sklearn_wrapper.py 使用sklearn包装器\n\nmnist_swwae.py 使用MNIST数据集训练自动编码器\n\nmnist_transfer_cnn.py 转移学习\n\nneural_doodle.py 神经涂鸦\n\nneural_style_transfer.py 艺术画生成\n\npretrained_word_embeddings.py 在新闻组数据集上训练文本分类模型\n\nreuters_mlp.py 在路透社newswire主题分类任务上训练和评估简单的MLP\n\nstateful_lstm.py 使用有状态的RNN来有效地建模长序列\n\nvariational_autoencoder.py 构建变分自动编码器\n\nvariational_autoencoder_deconv.py 使用解卷积层与Keras构建变分自动编码器\n\n\n# Keras examples\n\n[addition_rnn.py](addition_rnn.py)\nImplementation of sequence to sequence learning for performing addition of two numbers (as strings).\n\n[antirectifier.py](antirectifier.py)\nDemonstrates how to write custom layers for Keras.\n\n[babi_memnn.py](babi_memnn.py)\nTrains a memory network on the bAbI dataset for reading comprehension.\n\n[babi_rnn.py](babi_rnn.py)\nTrains a two-branch recurrent network on the bAbI dataset for reading comprehension.\n\n[cifar10_cnn.py](cifar10_cnn.py)\nTrains a simple deep CNN on the CIFAR10 small images dataset.\n\n[conv_filter_visualization.py](conv_filter_visualization.py)\nVisualization of the filters of VGG16, via gradient ascent in input space.\n\n[deep_dream.py](deep_dream.py)\nDeep Dreams in Keras.\n\n[image_ocr.py](image_ocr.py)\nTrains a convolutional stack followed by a recurrent stack and a CTC logloss function to perform optical character recognition (OCR).\n\n[imdb_bidirectional_lstm.py](imdb_bidirectional_lstm.py)\nTrains a Bidirectional LSTM on the IMDB sentiment classification task.\n\n[imdb_cnn.py](imdb_cnn.py)\nDemonstrates the use of Convolution1D for text classification.\n\n[imdb_cnn_lstm.py](imdb_cnn_lstm.py)\nTrains a convolutional stack followed by a recurrent stack network on the IMDB sentiment classification task.\n\n[imdb_fasttext.py](imdb_fasttext.py)\nTrains a FastText model on the IMDB sentiment classification task.\n\n[imdb_lstm.py](imdb_lstm.py)\nTrains a LSTM on the IMDB sentiment classification task.\n\n[lstm_benchmark.py](lstm_benchmark.py)\nCompares different LSTM implementations on the IMDB sentiment classification task.\n\n[lstm_text_generation.py](lstm_text_generation.py)\nGenerates text from Nietzsche's writings.\n\n[mnist_cnn.py](mnist_cnn.py)\nTrains a simple convnet on the MNIST dataset.\n\n[mnist_hierarchical_rnn.py](mnist_hierarchical_rnn.py)\nTrains a Hierarchical RNN (HRNN) to classify MNIST digits.\n\n[mnist_irnn.py](mnist_irnn.py)\nReproduction of the IRNN experiment with pixel-by-pixel sequential MNIST in \"A Simple Way to Initialize Recurrent Networks of Rectified Linear Units\" by Le et al.\n\n[mnist_mlp.py](mnist_mlp.py)\nTrains a simple deep multi-layer perceptron on the MNIST dataset.\n\n[mnist_net2net.py](mnist_net2net.py)\nReproduction of the Net2Net experiment with MNIST in \"Net2Net: Accelerating Learning via Knowledge Transfer\".\n\n[mnist_siamese_graph.py](mnist_siamese_graph.py)\nTrains a Siamese multi-layer perceptron on pairs of digits from the MNIST dataset.\n\n[mnist_sklearn_wrapper.py](mnist_sklearn_wrapper.py)\nDemonstrates how to use the sklearn wrapper.\n\n[mnist_swwae.py](mnist_swwae.py)\nTrains a Stacked What-Where AutoEncoder built on residual blocks on the MNIST dataset.\n\n[mnist_transfer_cnn.py](mnist_transfer_cnn.py)\nTransfer learning toy example.\n\n[neural_doodle.py](neural_doodle.py)\nNeural doodle.\n\n[neural_style_transfer.py](neural_style_transfer.py)\nNeural style transfer.\n\n[pretrained_word_embeddings.py](pretrained_word_embeddings.py)\nLoads pre-trained word embeddings (GloVe embeddings) into a frozen Keras Embedding layer, and uses it to train a text classification model on the 20 Newsgroup dataset.\n\n[reuters_mlp.py](reuters_mlp.py)\nTrains and evaluate a simple MLP on the Reuters newswire topic classification task.\n\n[stateful_lstm.py](stateful_lstm.py)\nDemonstrates how to use stateful RNNs to model long sequences efficiently.\n\n[variational_autoencoder.py](variational_autoencoder.py)\nDemonstrates how to build a variational autoencoder.\n\n[variational_autoencoder_deconv.py](variational_autoencoder_deconv.py)\nDemonstrates how to build a variational autoencoder with Keras using deconvolution layers.\n"
  },
  {
    "path": "Keras/addition_rnn.py",
    "content": "# -*- coding: utf-8 -*-\n'''An implementation of sequence to sequence learning for performing addition\nInput: \"535+61\"\nOutput: \"596\"\nPadding is handled by using a repeated sentinel character (space)\n\nInput may optionally be inverted, shown to increase performance in many tasks in:\n\"Learning to Execute\"\nhttp://arxiv.org/abs/1410.4615\nand\n\"Sequence to Sequence Learning with Neural Networks\"\nhttp://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf\nTheoretically it introduces shorter term dependencies between source and target.\n\nTwo digits inverted:\n+ One layer LSTM (128 HN), 5k training examples = 99% train/test accuracy in 55 epochs\n\nThree digits inverted:\n+ One layer LSTM (128 HN), 50k training examples = 99% train/test accuracy in 100 epochs\n\nFour digits inverted:\n+ One layer LSTM (128 HN), 400k training examples = 99% train/test accuracy in 20 epochs\n\nFive digits inverted:\n+ One layer LSTM (128 HN), 550k training examples = 99% train/test accuracy in 30 epochs\n'''\n\nfrom __future__ import print_function\nfrom keras.models import Sequential\nfrom keras import layers\nimport numpy as np\nfrom six.moves import range\n\n\nclass CharacterTable(object):\n    \"\"\"Given a set of characters:\n    + Encode them to a one hot integer representation\n    + Decode the one hot integer representation to their character output\n    + Decode a vector of probabilities to their character output\n    \"\"\"\n    def __init__(self, chars):\n        \"\"\"Initialize character table.\n\n        # Arguments\n            chars: Characters that can appear in the input.\n        \"\"\"\n        self.chars = sorted(set(chars))\n        self.char_indices = dict((c, i) for i, c in enumerate(self.chars))\n        self.indices_char = dict((i, c) for i, c in enumerate(self.chars))\n\n    def encode(self, C, num_rows):\n        \"\"\"One hot encode given string C.\n\n        # Arguments\n            num_rows: Number of rows in the returned one hot encoding. This is\n                used to keep the # of rows for each data the same.\n        \"\"\"\n        x = np.zeros((num_rows, len(self.chars)))\n        for i, c in enumerate(C):\n            x[i, self.char_indices[c]] = 1\n        return x\n\n    def decode(self, x, calc_argmax=True):\n        if calc_argmax:\n            x = x.argmax(axis=-1)\n        return ''.join(self.indices_char[x] for x in x)\n\n\nclass colors:\n    ok = '\\033[92m'\n    fail = '\\033[91m'\n    close = '\\033[0m'\n\n# Parameters for the model and dataset.\nTRAINING_SIZE = 50000\nDIGITS = 3\nINVERT = True\n\n# Maximum length of input is 'int + int' (e.g., '345+678'). Maximum length of\n# int is DIGITS.\nMAxLEN = DIGITS + 1 + DIGITS\n\n# All the numbers, plus sign and space for padding.\nchars = '0123456789+ '\nctable = CharacterTable(chars)\n\nquestions = []\nexpected = []\nseen = set()\nprint('Generating data...')\nwhile len(questions) < TRAINING_SIZE:\n    f = lambda: int(''.join(np.random.choice(list('0123456789'))\n                    for i in range(np.random.randint(1, DIGITS + 1))))\n    a, b = f(), f()\n    # Skip any addition questions we've already seen\n    # Also skip any such that x+Y == Y+x (hence the sorting).\n    key = tuple(sorted((a, b)))\n    if key in seen:\n        continue\n    seen.add(key)\n    # Pad the data with spaces such that it is always MAxLEN.\n    q = '{}+{}'.format(a, b)\n    query = q + ' ' * (MAxLEN - len(q))\n    ans = str(a + b)\n    # Answers can be of maximum size DIGITS + 1.\n    ans += ' ' * (DIGITS + 1 - len(ans))\n    if INVERT:\n        # Reverse the query, e.g., '12+345  ' becomes '  543+21'. (Note the\n        # space used for padding.)\n        query = query[::-1]\n    questions.append(query)\n    expected.append(ans)\nprint('Total addition questions:', len(questions))\n\nprint('Vectorization...')\nx = np.zeros((len(questions), MAxLEN, len(chars)), dtype=np.bool)\ny = np.zeros((len(questions), DIGITS + 1, len(chars)), dtype=np.bool)\nfor i, sentence in enumerate(questions):\n    x[i] = ctable.encode(sentence, MAxLEN)\nfor i, sentence in enumerate(expected):\n    y[i] = ctable.encode(sentence, DIGITS + 1)\n\n# Shuffle (x, y) in unison as the later parts of x will almost all be larger\n# digits.\nindices = np.arange(len(y))\nnp.random.shuffle(indices)\nx = x[indices]\ny = y[indices]\n\n# Explicitly set apart 10% for validation data that we never train over.\nsplit_at = len(x) - len(x) // 10\n(x_train, x_val) = x[:split_at], x[split_at:]\n(y_train, y_val) = y[:split_at], y[split_at:]\n\nprint('Training Data:')\nprint(x_train.shape)\nprint(y_train.shape)\n\nprint('Validation Data:')\nprint(x_val.shape)\nprint(y_val.shape)\n\n# Try replacing GRU, or SimpleRNN.\nRNN = layers.LSTM\nHIDDEN_SIZE = 128\nBATCH_SIZE = 128\nLAYERS = 1\n\nprint('Build model...')\nmodel = Sequential()\n# \"Encode\" the input sequence using an RNN, producing an output of HIDDEN_SIZE.\n# Note: In a situation where your input sequences have a variable length,\n# use input_shape=(None, num_feature).\nmodel.add(RNN(HIDDEN_SIZE, input_shape=(MAxLEN, len(chars))))\n# As the decoder RNN's input, repeatedly provide with the last hidden state of\n# RNN for each time step. Repeat 'DIGITS + 1' times as that's the maximum\n# length of output, e.g., when DIGITS=3, max output is 999+999=1998.\nmodel.add(layers.RepeatVector(DIGITS + 1))\n# The decoder RNN could be multiple layers stacked or a single layer.\nfor _ in range(LAYERS):\n    # By setting return_sequences to True, return not only the last output but\n    # all the outputs so far in the form of (num_samples, timesteps,\n    # output_dim). This is necessary as TimeDistributed in the below expects\n    # the first dimension to be the timesteps.\n    model.add(RNN(HIDDEN_SIZE, return_sequences=True))\n\n# Apply a dense layer to the every temporal slice of an input. For each of step\n# of the output sequence, decide which character should be chosen.\nmodel.add(layers.TimeDistributed(layers.Dense(len(chars))))\nmodel.add(layers.Activation('softmax'))\nmodel.compile(loss='categorical_crossentropy',\n              optimizer='adam',\n              metrics=['accuracy'])\nmodel.summary()\n\n# Train the model each generation and show predictions against the validation\n# dataset.\nfor iteration in range(1, 200):\n    print()\n    print('-' * 50)\n    print('Iteration', iteration)\n    model.fit(x_train, y_train,\n              batch_size=BATCH_SIZE,\n              epochs=1,\n              validation_data=(x_val, y_val))\n    # Select 10 samples from the validation set at random so we can visualize\n    # errors.\n    for i in range(10):\n        ind = np.random.randint(0, len(x_val))\n        rowx, rowy = x_val[np.array([ind])], y_val[np.array([ind])]\n        preds = model.predict_classes(rowx, verbose=0)\n        q = ctable.decode(rowx[0])\n        correct = ctable.decode(rowy[0])\n        guess = ctable.decode(preds[0], calc_argmax=False)\n        print('Q', q[::-1] if INVERT else q)\n        print('T', correct)\n        if correct == guess:\n            print(colors.ok + '☑' + colors.close, end=\" \")\n        else:\n            print(colors.fail + '☒' + colors.close, end=\" \")\n        print(guess)\n        print('---')\n"
  },
  {
    "path": "Keras/antirectifier.py",
    "content": "'''The example demonstrates how to write custom layers for Keras.\n\nWe build a custom activation layer called 'Antirectifier',\nwhich modifies the shape of the tensor that passes through it.\nWe need to specify two methods: `compute_output_shape` and `call`.\n\nNote that the same result can also be achieved via a Lambda layer.\n\nBecause our custom layer is written with primitives from the Keras\nbackend (`K`), our code can run both on TensorFlow and Theano.\n'''\n\nfrom __future__ import print_function\nimport keras\nfrom keras.models import Sequential\nfrom keras import layers\nfrom keras.datasets import mnist\nfrom keras import backend as K\n\n\nclass Antirectifier(layers.Layer):\n    '''This is the combination of a sample-wise\n    L2 normalization with the concatenation of the\n    positive part of the input with the negative part\n    of the input. The result is a tensor of samples that are\n    twice as large as the input samples.\n\n    It can be used in place of a ReLU.\n\n    # Input shape\n        2D tensor of shape (samples, n)\n\n    # Output shape\n        2D tensor of shape (samples, 2*n)\n\n    # Theoretical justification\n        When applying ReLU, assuming that the distribution\n        of the previous output is approximately centered around 0.,\n        you are discarding half of your input. This is inefficient.\n\n        Antirectifier allows to return all-positive outputs like ReLU,\n        without discarding any data.\n\n        Tests on MNIST show that Antirectifier allows to train networks\n        with twice less parameters yet with comparable\n        classification accuracy as an equivalent ReLU-based network.\n    '''\n\n    def compute_output_shape(self, input_shape):\n        shape = list(input_shape)\n        assert len(shape) == 2  # only valid for 2D tensors\n        shape[-1] *= 2\n        return tuple(shape)\n\n    def call(self, inputs):\n        inputs -= K.mean(inputs, axis=1, keepdims=True)\n        inputs = K.l2_normalize(inputs, axis=1)\n        pos = K.relu(inputs)\n        neg = K.relu(-inputs)\n        return K.concatenate([pos, neg], axis=1)\n\n# global parameters\nbatch_size = 128\nnum_classes = 10\nepochs = 40\n\n# the data, shuffled and split between train and test sets\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\nx_train = x_train.reshape(60000, 784)\nx_test = x_test.reshape(10000, 784)\nx_train = x_train.astype('float32')\nx_test = x_test.astype('float32')\nx_train /= 255\nx_test /= 255\nprint(x_train.shape[0], 'train samples')\nprint(x_test.shape[0], 'test samples')\n\n# convert class vectors to binary class matrices\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\n\n# build the model\nmodel = Sequential()\nmodel.add(layers.Dense(256, input_shape=(784,)))\nmodel.add(Antirectifier())\nmodel.add(layers.Dropout(0.1))\nmodel.add(layers.Dense(256))\nmodel.add(Antirectifier())\nmodel.add(layers.Dropout(0.1))\nmodel.add(layers.Dense(10))\nmodel.add(layers.Activation('softmax'))\n\n# compile the model\nmodel.compile(loss='categorical_crossentropy',\n              optimizer='rmsprop',\n              metrics=['accuracy'])\n\n# train the model\nmodel.fit(x_train, y_train,\n          batch_size=batch_size,\n          epochs=epochs,\n          verbose=1,\n          validation_data=(x_test, y_test))\n\n# next, compare with an equivalent network\n# with2x bigger Dense layers and ReLU\n"
  },
  {
    "path": "Keras/babi_memnn.py",
    "content": "'''Trains a memory network on the bAbI dataset.\n\nReferences:\n- Jason Weston, Antoine Bordes, Sumit Chopra, Tomas Mikolov, Alexander M. Rush,\n  \"Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks\",\n  http://arxiv.org/abs/1502.05698\n\n- Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus,\n  \"End-To-End Memory Networks\",\n  http://arxiv.org/abs/1503.08895\n\nReaches 98.6% accuracy on task 'single_supporting_fact_10k' after 120 epochs.\nTime per epoch: 3s on CPU (core i7).\n'''\nfrom __future__ import print_function\n\nfrom keras.models import Sequential, Model\nfrom keras.layers.embeddings import Embedding\nfrom keras.layers import Input, Activation, Dense, Permute, Dropout, add, dot, concatenate\nfrom keras.layers import LSTM\nfrom keras.utils.data_utils import get_file\nfrom keras.preprocessing.sequence import pad_sequences\nfrom functools import reduce\nimport tarfile\nimport numpy as np\nimport re\n\n\ndef tokenize(sent):\n    '''Return the tokens of a sentence including punctuation.\n\n    >>> tokenize('Bob dropped the apple. Where is the apple?')\n    ['Bob', 'dropped', 'the', 'apple', '.', 'Where', 'is', 'the', 'apple', '?']\n    '''\n    return [x.strip() for x in re.split('(\\W+)?', sent) if x.strip()]\n\n\ndef parse_stories(lines, only_supporting=False):\n    '''Parse stories provided in the bAbi tasks format\n\n    If only_supporting is true, only the sentences\n    that support the answer are kept.\n    '''\n    data = []\n    story = []\n    for line in lines:\n        line = line.decode('utf-8').strip()\n        nid, line = line.split(' ', 1)\n        nid = int(nid)\n        if nid == 1:\n            story = []\n        if '\\t' in line:\n            q, a, supporting = line.split('\\t')\n            q = tokenize(q)\n            substory = None\n            if only_supporting:\n                # Only select the related substory\n                supporting = map(int, supporting.split())\n                substory = [story[i - 1] for i in supporting]\n            else:\n                # Provide all the substories\n                substory = [x for x in story if x]\n            data.append((substory, q, a))\n            story.append('')\n        else:\n            sent = tokenize(line)\n            story.append(sent)\n    return data\n\n\ndef get_stories(f, only_supporting=False, max_length=None):\n    '''Given a file name, read the file,\n    retrieve the stories,\n    and then convert the sentences into a single story.\n\n    If max_length is supplied,\n    any stories longer than max_length tokens will be discarded.\n    '''\n    data = parse_stories(f.readlines(), only_supporting=only_supporting)\n    flatten = lambda data: reduce(lambda x, y: x + y, data)\n    data = [(flatten(story), q, answer) for story, q, answer in data if not max_length or len(flatten(story)) < max_length]\n    return data\n\n\ndef vectorize_stories(data, word_idx, story_maxlen, query_maxlen):\n    X = []\n    Xq = []\n    Y = []\n    for story, query, answer in data:\n        x = [word_idx[w] for w in story]\n        xq = [word_idx[w] for w in query]\n        # let's not forget that index 0 is reserved\n        y = np.zeros(len(word_idx) + 1)\n        y[word_idx[answer]] = 1\n        X.append(x)\n        Xq.append(xq)\n        Y.append(y)\n    return (pad_sequences(X, maxlen=story_maxlen),\n            pad_sequences(Xq, maxlen=query_maxlen), np.array(Y))\n\ntry:\n    path = get_file('babi-tasks-v1-2.tar.gz', origin='https://s3.amazonaws.com/text-datasets/babi_tasks_1-20_v1-2.tar.gz')\nexcept:\n    print('Error downloading dataset, please download it manually:\\n'\n          '$ wget http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2.tar.gz\\n'\n          '$ mv tasks_1-20_v1-2.tar.gz ~/.keras/datasets/babi-tasks-v1-2.tar.gz')\n    raise\ntar = tarfile.open(path)\n\nchallenges = {\n    # QA1 with 10,000 samples\n    'single_supporting_fact_10k': 'tasks_1-20_v1-2/en-10k/qa1_single-supporting-fact_{}.txt',\n    # QA2 with 10,000 samples\n    'two_supporting_facts_10k': 'tasks_1-20_v1-2/en-10k/qa2_two-supporting-facts_{}.txt',\n}\nchallenge_type = 'single_supporting_fact_10k'\nchallenge = challenges[challenge_type]\n\nprint('Extracting stories for the challenge:', challenge_type)\ntrain_stories = get_stories(tar.extractfile(challenge.format('train')))\ntest_stories = get_stories(tar.extractfile(challenge.format('test')))\n\nvocab = set()\nfor story, q, answer in train_stories + test_stories:\n    vocab |= set(story + q + [answer])\nvocab = sorted(vocab)\n\n# Reserve 0 for masking via pad_sequences\nvocab_size = len(vocab) + 1\nstory_maxlen = max(map(len, (x for x, _, _ in train_stories + test_stories)))\nquery_maxlen = max(map(len, (x for _, x, _ in train_stories + test_stories)))\n\nprint('-')\nprint('Vocab size:', vocab_size, 'unique words')\nprint('Story max length:', story_maxlen, 'words')\nprint('Query max length:', query_maxlen, 'words')\nprint('Number of training stories:', len(train_stories))\nprint('Number of test stories:', len(test_stories))\nprint('-')\nprint('Here\\'s what a \"story\" tuple looks like (input, query, answer):')\nprint(train_stories[0])\nprint('-')\nprint('Vectorizing the word sequences...')\n\nword_idx = dict((c, i + 1) for i, c in enumerate(vocab))\ninputs_train, queries_train, answers_train = vectorize_stories(train_stories,\n                                                               word_idx,\n                                                               story_maxlen,\n                                                               query_maxlen)\ninputs_test, queries_test, answers_test = vectorize_stories(test_stories,\n                                                            word_idx,\n                                                            story_maxlen,\n                                                            query_maxlen)\n\nprint('-')\nprint('inputs: integer tensor of shape (samples, max_length)')\nprint('inputs_train shape:', inputs_train.shape)\nprint('inputs_test shape:', inputs_test.shape)\nprint('-')\nprint('queries: integer tensor of shape (samples, max_length)')\nprint('queries_train shape:', queries_train.shape)\nprint('queries_test shape:', queries_test.shape)\nprint('-')\nprint('answers: binary (1 or 0) tensor of shape (samples, vocab_size)')\nprint('answers_train shape:', answers_train.shape)\nprint('answers_test shape:', answers_test.shape)\nprint('-')\nprint('Compiling...')\n\n# placeholders\ninput_sequence = Input((story_maxlen,))\nquestion = Input((query_maxlen,))\n\n# encoders\n# embed the input sequence into a sequence of vectors\ninput_encoder_m = Sequential()\ninput_encoder_m.add(Embedding(input_dim=vocab_size,\n                              output_dim=64))\ninput_encoder_m.add(Dropout(0.3))\n# output: (samples, story_maxlen, embedding_dim)\n\n# embed the input into a sequence of vectors of size query_maxlen\ninput_encoder_c = Sequential()\ninput_encoder_c.add(Embedding(input_dim=vocab_size,\n                              output_dim=query_maxlen))\ninput_encoder_c.add(Dropout(0.3))\n# output: (samples, story_maxlen, query_maxlen)\n\n# embed the question into a sequence of vectors\nquestion_encoder = Sequential()\nquestion_encoder.add(Embedding(input_dim=vocab_size,\n                               output_dim=64,\n                               input_length=query_maxlen))\nquestion_encoder.add(Dropout(0.3))\n# output: (samples, query_maxlen, embedding_dim)\n\n# encode input sequence and questions (which are indices)\n# to sequences of dense vectors\ninput_encoded_m = input_encoder_m(input_sequence)\ninput_encoded_c = input_encoder_c(input_sequence)\nquestion_encoded = question_encoder(question)\n\n# compute a 'match' between the first input vector sequence\n# and the question vector sequence\n# shape: `(samples, story_maxlen, query_maxlen)`\nmatch = dot([input_encoded_m, question_encoded], axes=(2, 2))\nmatch = Activation('softmax')(match)\n\n# add the match matrix with the second input vector sequence\nresponse = add([match, input_encoded_c])  # (samples, story_maxlen, query_maxlen)\nresponse = Permute((2, 1))(response)  # (samples, query_maxlen, story_maxlen)\n\n# concatenate the match matrix with the question vector sequence\nanswer = concatenate([response, question_encoded])\n\n# the original paper uses a matrix multiplication for this reduction step.\n# we choose to use a RNN instead.\nanswer = LSTM(32)(answer)  # (samples, 32)\n\n# one regularization layer -- more would probably be needed.\nanswer = Dropout(0.3)(answer)\nanswer = Dense(vocab_size)(answer)  # (samples, vocab_size)\n# we output a probability distribution over the vocabulary\nanswer = Activation('softmax')(answer)\n\n# build the final model\nmodel = Model([input_sequence, question], answer)\nmodel.compile(optimizer='rmsprop', loss='categorical_crossentropy',\n              metrics=['accuracy'])\n\n# train\nmodel.fit([inputs_train, queries_train], answers_train,\n          batch_size=32,\n          epochs=120,\n          validation_data=([inputs_test, queries_test], answers_test))\n"
  },
  {
    "path": "Keras/babi_rnn.py",
    "content": "'''Trains two recurrent neural networks based upon a story and a question.\nThe resulting merged vector is then queried to answer a range of bAbI tasks.\n\nThe results are comparable to those for an LSTM model provided in Weston et al.:\n\"Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks\"\nhttp://arxiv.org/abs/1502.05698\n\nTask Number                  | FB LSTM Baseline | Keras QA\n---                          | ---              | ---\nQA1 - Single Supporting Fact | 50               | 100.0\nQA2 - Two Supporting Facts   | 20               | 50.0\nQA3 - Three Supporting Facts | 20               | 20.5\nQA4 - Two Arg. Relations     | 61               | 62.9\nQA5 - Three Arg. Relations   | 70               | 61.9\nQA6 - yes/No Questions       | 48               | 50.7\nQA7 - Counting               | 49               | 78.9\nQA8 - Lists/Sets             | 45               | 77.2\nQA9 - Simple Negation        | 64               | 64.0\nQA10 - Indefinite Knowledge  | 44               | 47.7\nQA11 - Basic Coreference     | 72               | 74.9\nQA12 - Conjunction           | 74               | 76.4\nQA13 - Compound Coreference  | 94               | 94.4\nQA14 - Time Reasoning        | 27               | 34.8\nQA15 - Basic Deduction       | 21               | 32.4\nQA16 - Basic Induction       | 23               | 50.6\nQA17 - Positional Reasoning  | 51               | 49.1\nQA18 - Size Reasoning        | 52               | 90.8\nQA19 - Path Finding          | 8                | 9.0\nQA20 - Agent's Motivations   | 91               | 90.7\n\nFor the resources related to the bAbI project, refer to:\nhttps://research.facebook.com/researchers/1543934539189348\n\nNotes:\n\n- With default word, sentence, and query vector sizes, the GRU model achieves:\n  - 100% test accuracy on QA1 in 20 epochs (2 seconds per epoch on CPU)\n  - 50% test accuracy on QA2 in 20 epochs (16 seconds per epoch on CPU)\nIn comparison, the Facebook paper achieves 50% and 20% for the LSTM baseline.\n\n- The task does not traditionally parse the question separately. This likely\nimproves accuracy and is a good example of merging two RNNs.\n\n- The word vector embeddings are not shared between the story and question RNNs.\n\n- See how the accuracy changes given 10,000 training samples (en-10k) instead\nof only 1000. 1000 was used in order to be comparable to the original paper.\n\n- Experiment with GRU, LSTM, and JZS1-3 as they give subtly different results.\n\n- The length and noise (i.e. 'useless' story components) impact the ability for\nLSTMs / GRUs to provide the correct answer. Given only the supporting facts,\nthese RNNs can achieve 100% accuracy on many tasks. Memory networks and neural\nnetworks that use attentional processes can efficiently search through this\nnoise to find the relevant statements, improving performance substantially.\nThis becomes especially obvious on QA2 and QA3, both far longer than QA1.\n'''\n\nfrom __future__ import print_function\nfrom functools import reduce\nimport re\nimport tarfile\n\nimport numpy as np\n\nfrom keras.utils.data_utils import get_file\nfrom keras.layers.embeddings import Embedding\nfrom keras import layers\nfrom keras.layers import recurrent\nfrom keras.models import Model\nfrom keras.preprocessing.sequence import pad_sequences\n\n\ndef tokenize(sent):\n    '''Return the tokens of a sentence including punctuation.\n\n    >>> tokenize('Bob dropped the apple. Where is the apple?')\n    ['Bob', 'dropped', 'the', 'apple', '.', 'Where', 'is', 'the', 'apple', '?']\n    '''\n    return [x.strip() for x in re.split('(\\W+)?', sent) if x.strip()]\n\n\ndef parse_stories(lines, only_supporting=False):\n    '''Parse stories provided in the bAbi tasks format\n\n    If only_supporting is true,\n    only the sentences that support the answer are kept.\n    '''\n    data = []\n    story = []\n    for line in lines:\n        line = line.decode('utf-8').strip()\n        nid, line = line.split(' ', 1)\n        nid = int(nid)\n        if nid == 1:\n            story = []\n        if '\\t' in line:\n            q, a, supporting = line.split('\\t')\n            q = tokenize(q)\n            substory = None\n            if only_supporting:\n                # Only select the related substory\n                supporting = map(int, supporting.split())\n                substory = [story[i - 1] for i in supporting]\n            else:\n                # Provide all the substories\n                substory = [x for x in story if x]\n            data.append((substory, q, a))\n            story.append('')\n        else:\n            sent = tokenize(line)\n            story.append(sent)\n    return data\n\n\ndef get_stories(f, only_supporting=False, max_length=None):\n    '''Given a file name, read the file, retrieve the stories,\n    and then convert the sentences into a single story.\n\n    If max_length is supplied,\n    any stories longer than max_length tokens will be discarded.\n    '''\n    data = parse_stories(f.readlines(), only_supporting=only_supporting)\n    flatten = lambda data: reduce(lambda x, y: x + y, data)\n    data = [(flatten(story), q, answer) for story, q, answer in data if not max_length or len(flatten(story)) < max_length]\n    return data\n\n\ndef vectorize_stories(data, word_idx, story_maxlen, query_maxlen):\n    xs = []\n    xqs = []\n    ys = []\n    for story, query, answer in data:\n        x = [word_idx[w] for w in story]\n        xq = [word_idx[w] for w in query]\n        # let's not forget that index 0 is reserved\n        y = np.zeros(len(word_idx) + 1)\n        y[word_idx[answer]] = 1\n        xs.append(x)\n        xqs.append(xq)\n        ys.append(y)\n    return pad_sequences(xs, maxlen=story_maxlen), pad_sequences(xqs, maxlen=query_maxlen), np.array(ys)\n\nRNN = recurrent.LSTM\nEMBED_HIDDEN_SIZE = 50\nSENT_HIDDEN_SIZE = 100\nQUERY_HIDDEN_SIZE = 100\nBATCH_SIZE = 32\nEPOCHS = 40\nprint('RNN / Embed / Sent / Query = {}, {}, {}, {}'.format(RNN,\n                                                           EMBED_HIDDEN_SIZE,\n                                                           SENT_HIDDEN_SIZE,\n                                                           QUERY_HIDDEN_SIZE))\n\ntry:\n    path = get_file('babi-tasks-v1-2.tar.gz', origin='https://s3.amazonaws.com/text-datasets/babi_tasks_1-20_v1-2.tar.gz')\nexcept:\n    print('Error downloading dataset, please download it manually:\\n'\n          '$ wget http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2.tar.gz\\n'\n          '$ mv tasks_1-20_v1-2.tar.gz ~/.keras/datasets/babi-tasks-v1-2.tar.gz')\n    raise\ntar = tarfile.open(path)\n# Default QA1 with 1000 samples\n# challenge = 'tasks_1-20_v1-2/en/qa1_single-supporting-fact_{}.txt'\n# QA1 with 10,000 samples\n# challenge = 'tasks_1-20_v1-2/en-10k/qa1_single-supporting-fact_{}.txt'\n# QA2 with 1000 samples\nchallenge = 'tasks_1-20_v1-2/en/qa2_two-supporting-facts_{}.txt'\n# QA2 with 10,000 samples\n# challenge = 'tasks_1-20_v1-2/en-10k/qa2_two-supporting-facts_{}.txt'\ntrain = get_stories(tar.extractfile(challenge.format('train')))\ntest = get_stories(tar.extractfile(challenge.format('test')))\n\nvocab = set()\nfor story, q, answer in train + test:\n    vocab |= set(story + q + [answer])\nvocab = sorted(vocab)\n\n# Reserve 0 for masking via pad_sequences\nvocab_size = len(vocab) + 1\nword_idx = dict((c, i + 1) for i, c in enumerate(vocab))\nstory_maxlen = max(map(len, (x for x, _, _ in train + test)))\nquery_maxlen = max(map(len, (x for _, x, _ in train + test)))\n\nx, xq, y = vectorize_stories(train, word_idx, story_maxlen, query_maxlen)\ntx, txq, ty = vectorize_stories(test, word_idx, story_maxlen, query_maxlen)\n\nprint('vocab = {}'.format(vocab))\nprint('x.shape = {}'.format(x.shape))\nprint('xq.shape = {}'.format(xq.shape))\nprint('y.shape = {}'.format(y.shape))\nprint('story_maxlen, query_maxlen = {}, {}'.format(story_maxlen, query_maxlen))\n\nprint('Build model...')\n\nsentence = layers.Input(shape=(story_maxlen,), dtype='int32')\nencoded_sentence = layers.Embedding(vocab_size, EMBED_HIDDEN_SIZE)(sentence)\nencoded_sentence = layers.Dropout(0.3)(encoded_sentence)\n\nquestion = layers.Input(shape=(query_maxlen,), dtype='int32')\nencoded_question = layers.Embedding(vocab_size, EMBED_HIDDEN_SIZE)(question)\nencoded_question = layers.Dropout(0.3)(encoded_question)\nencoded_question = RNN(EMBED_HIDDEN_SIZE)(encoded_question)\nencoded_question = layers.RepeatVector(story_maxlen)(encoded_question)\n\nmerged = layers.add([encoded_sentence, encoded_question])\nmerged = RNN(EMBED_HIDDEN_SIZE)(merged)\nmerged = layers.Dropout(0.3)(merged)\npreds = layers.Dense(vocab_size, activation='softmax')(merged)\n\nmodel = Model([sentence, question], preds)\nmodel.compile(optimizer='adam',\n              loss='categorical_crossentropy',\n              metrics=['accuracy'])\n\nprint('Training')\nmodel.fit([x, xq], y,\n          batch_size=BATCH_SIZE,\n          epochs=EPOCHS,\n          validation_split=0.05)\nloss, acc = model.evaluate([tx, txq], ty,\n                           batch_size=BATCH_SIZE)\nprint('Test loss / test accuracy = {:.4f} / {:.4f}'.format(loss, acc))\n"
  },
  {
    "path": "Keras/cifar10_cnn.py",
    "content": "'''Train a simple deep CNN on the CIFAR10 small images dataset.\n\nGPU run command with Theano backend (with TensorFlow, the GPU is automatically used):\n    THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatx=float32 python cifar10_cnn.py\n\nIt gets down to 0.65 test logloss in 25 epochs, and down to 0.55 after 50 epochs.\n(it's still underfitting at that point, though).\n'''\n\nfrom __future__ import print_function\nimport keras\nfrom keras.datasets import cifar10\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\n\nbatch_size = 32\nnum_classes = 10\nepochs = 200\ndata_augmentation = True\n\n# The data, shuffled and split between train and test sets:\n(x_train, y_train), (x_test, y_test) = cifar10.load_data()\nprint('x_train shape:', x_train.shape)\nprint(x_train.shape[0], 'train samples')\nprint(x_test.shape[0], 'test samples')\n\n# Convert class vectors to binary class matrices.\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\n\nmodel = Sequential()\n\nmodel.add(Conv2D(32, (3, 3), padding='same',\n                 input_shape=x_train.shape[1:]))\nmodel.add(Activation('relu'))\nmodel.add(Conv2D(32, (3, 3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Conv2D(64, (3, 3), padding='same'))\nmodel.add(Activation('relu'))\nmodel.add(Conv2D(64, (3, 3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Flatten())\nmodel.add(Dense(512))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(num_classes))\nmodel.add(Activation('softmax'))\n\n# initiate RMSprop optimizer\nopt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)\n\n# Let's train the model using RMSprop\nmodel.compile(loss='categorical_crossentropy',\n              optimizer=opt,\n              metrics=['accuracy'])\n\nx_train = x_train.astype('float32')\nx_test = x_test.astype('float32')\nx_train /= 255\nx_test /= 255\n\nif not data_augmentation:\n    print('Not using data augmentation.')\n    model.fit(x_train, y_train,\n              batch_size=batch_size,\n              epochs=epochs,\n              validation_data=(x_test, y_test),\n              shuffle=True)\nelse:\n    print('Using real-time data augmentation.')\n    # This will do preprocessing and realtime data augmentation:\n    datagen = ImageDataGenerator(\n        featurewise_center=False,  # set input mean to 0 over the dataset\n        samplewise_center=False,  # set each sample mean to 0\n        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n        samplewise_std_normalization=False,  # divide each input by its std\n        zca_whitening=False,  # apply ZCA whitening\n        rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)\n        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n        horizontal_flip=True,  # randomly flip images\n        vertical_flip=False)  # randomly flip images\n\n    # Compute quantities required for feature-wise normalization\n    # (std, mean, and principal components if ZCA whitening is applied).\n    datagen.fit(x_train)\n\n    # Fit the model on the batches generated by datagen.flow().\n    model.fit_generator(datagen.flow(x_train, y_train,\n                                     batch_size=batch_size),\n                        steps_per_epoch=x_train.shape[0] // batch_size,\n                        epochs=epochs,\n                        validation_data=(x_test, y_test))\n"
  },
  {
    "path": "Keras/conv_filter_visualization.py",
    "content": "'''Visualization of the filters of VGG16, via gradient ascent in input space.\n\nThis script can run on CPU in a few minutes (with the TensorFlow backend).\n\nResults example: http://i.imgur.com/4nj4KjN.jpg\n'''\nfrom __future__ import print_function\n\nfrom scipy.misc import imsave\nimport numpy as np\nimport time\nfrom keras.applications import vgg16\nfrom keras import backend as K\n\n# dimensions of the generated pictures for each filter.\nimg_width = 128\nimg_height = 128\n\n# the name of the layer we want to visualize\n# (see model definition at keras/applications/vgg16.py)\nlayer_name = 'block5_conv1'\n\n# util function to convert a tensor into a valid image\n\n\ndef deprocess_image(x):\n    # normalize tensor: center on 0., ensure std is 0.1\n    x -= x.mean()\n    x /= (x.std() + 1e-5)\n    x *= 0.1\n\n    # clip to [0, 1]\n    x += 0.5\n    x = np.clip(x, 0, 1)\n\n    # convert to RGB array\n    x *= 255\n    if K.image_data_format() == 'channels_first':\n        x = x.transpose((1, 2, 0))\n    x = np.clip(x, 0, 255).astype('uint8')\n    return x\n\n# build the VGG16 network with ImageNet weights\nmodel = vgg16.VGG16(weights='imagenet', include_top=False)\nprint('Model loaded.')\n\nmodel.summary()\n\n# this is the placeholder for the input images\ninput_img = model.input\n\n# get the symbolic outputs of each \"key\" layer (we gave them unique names).\nlayer_dict = dict([(layer.name, layer) for layer in model.layers[1:]])\n\n\ndef normalize(x):\n    # utility function to normalize a tensor by its L2 norm\n    return x / (K.sqrt(K.mean(K.square(x))) + 1e-5)\n\n\nkept_filters = []\nfor filter_index in range(0, 200):\n    # we only scan through the first 200 filters,\n    # but there are actually 512 of them\n    print('Processing filter %d' % filter_index)\n    start_time = time.time()\n\n    # we build a loss function that maximizes the activation\n    # of the nth filter of the layer considered\n    layer_output = layer_dict[layer_name].output\n    if K.image_data_format() == 'channels_first':\n        loss = K.mean(layer_output[:, filter_index, :, :])\n    else:\n        loss = K.mean(layer_output[:, :, :, filter_index])\n\n    # we compute the gradient of the input picture wrt this loss\n    grads = K.gradients(loss, input_img)[0]\n\n    # normalization trick: we normalize the gradient\n    grads = normalize(grads)\n\n    # this function returns the loss and grads given the input picture\n    iterate = K.function([input_img], [loss, grads])\n\n    # step size for gradient ascent\n    step = 1.\n\n    # we start from a gray image with some random noise\n    if K.image_data_format() == 'channels_first':\n        input_img_data = np.random.random((1, 3, img_width, img_height))\n    else:\n        input_img_data = np.random.random((1, img_width, img_height, 3))\n    input_img_data = (input_img_data - 0.5) * 20 + 128\n\n    # we run gradient ascent for 20 steps\n    for i in range(20):\n        loss_value, grads_value = iterate([input_img_data])\n        input_img_data += grads_value * step\n\n        print('Current loss value:', loss_value)\n        if loss_value <= 0.:\n            # some filters get stuck to 0, we can skip them\n            break\n\n    # decode the resulting input image\n    if loss_value > 0:\n        img = deprocess_image(input_img_data[0])\n        kept_filters.append((img, loss_value))\n    end_time = time.time()\n    print('Filter %d processed in %ds' % (filter_index, end_time - start_time))\n\n# we will stich the best 64 filters on a 8 x 8 grid.\nn = 8\n\n# the filters that have the highest loss are assumed to be better-looking.\n# we will only keep the top 64 filters.\nkept_filters.sort(key=lambda x: x[1], reverse=True)\nkept_filters = kept_filters[:n * n]\n\n# build a black picture with enough space for\n# our 8 x 8 filters of size 128 x 128, with a 5px margin in between\nmargin = 5\nwidth = n * img_width + (n - 1) * margin\nheight = n * img_height + (n - 1) * margin\nstitched_filters = np.zeros((width, height, 3))\n\n# fill the picture with our saved filters\nfor i in range(n):\n    for j in range(n):\n        img, loss = kept_filters[i * n + j]\n        stitched_filters[(img_width + margin) * i: (img_width + margin) * i + img_width,\n                         (img_height + margin) * j: (img_height + margin) * j + img_height, :] = img\n\n# save the result to disk\nimsave('stitched_filters_%dx%d.png' % (n, n), stitched_filters)\n"
  },
  {
    "path": "Keras/conv_lstm.py",
    "content": "\"\"\" This script demonstrates the use of a convolutional LSTM network.\nThis network is used to predict the next frame of an artificially\ngenerated movie which contains moving squares.\n\"\"\"\nfrom keras.models import Sequential\nfrom keras.layers.convolutional import Conv3D\nfrom keras.layers.convolutional_recurrent import ConvLSTM2D\nfrom keras.layers.normalization import BatchNormalization\nimport numpy as np\nimport pylab as plt\n\n# We create a layer which take as input movies of shape\n# (n_frames, width, height, channels) and returns a movie\n# of identical shape.\n\nseq = Sequential()\nseq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),\n                   input_shape=(None, 40, 40, 1),\n                   padding='same', return_sequences=True))\nseq.add(BatchNormalization())\n\nseq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),\n                   padding='same', return_sequences=True))\nseq.add(BatchNormalization())\n\nseq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),\n                   padding='same', return_sequences=True))\nseq.add(BatchNormalization())\n\nseq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),\n                   padding='same', return_sequences=True))\nseq.add(BatchNormalization())\n\nseq.add(Conv3D(filters=1, kernel_size=(3, 3, 3),\n               activation='sigmoid',\n               padding='same', data_format='channels_last'))\nseq.compile(loss='binary_crossentropy', optimizer='adadelta')\n\n\n# Artificial data generation:\n# Generate movies with 3 to 7 moving squares inside.\n# The squares are of shape 1x1 or 2x2 pixels,\n# which move linearly over time.\n# For convenience we first create movies with bigger width and height (80x80)\n# and at the end we select a 40x40 window.\n\ndef generate_movies(n_samples=1200, n_frames=15):\n    row = 80\n    col = 80\n    noisy_movies = np.zeros((n_samples, n_frames, row, col, 1), dtype=np.float)\n    shifted_movies = np.zeros((n_samples, n_frames, row, col, 1),\n                              dtype=np.float)\n\n    for i in range(n_samples):\n        # Add 3 to 7 moving squares\n        n = np.random.randint(3, 8)\n\n        for j in range(n):\n            # Initial position\n            xstart = np.random.randint(20, 60)\n            ystart = np.random.randint(20, 60)\n            # Direction of motion\n            directionx = np.random.randint(0, 3) - 1\n            directiony = np.random.randint(0, 3) - 1\n\n            # Size of the square\n            w = np.random.randint(2, 4)\n\n            for t in range(n_frames):\n                x_shift = xstart + directionx * t\n                y_shift = ystart + directiony * t\n                noisy_movies[i, t, x_shift - w: x_shift + w,\n                             y_shift - w: y_shift + w, 0] += 1\n\n                # Make it more robust by adding noise.\n                # The idea is that if during inference,\n                # the value of the pixel is not exactly one,\n                # we need to train the network to be robust and still\n                # consider it as a pixel belonging to a square.\n                if np.random.randint(0, 2):\n                    noise_f = (-1)**np.random.randint(0, 2)\n                    noisy_movies[i, t,\n                                 x_shift - w - 1: x_shift + w + 1,\n                                 y_shift - w - 1: y_shift + w + 1,\n                                 0] += noise_f * 0.1\n\n                # Shift the ground truth by 1\n                x_shift = xstart + directionx * (t + 1)\n                y_shift = ystart + directiony * (t + 1)\n                shifted_movies[i, t, x_shift - w: x_shift + w,\n                               y_shift - w: y_shift + w, 0] += 1\n\n    # Cut to a 40x40 window\n    noisy_movies = noisy_movies[::, ::, 20:60, 20:60, ::]\n    shifted_movies = shifted_movies[::, ::, 20:60, 20:60, ::]\n    noisy_movies[noisy_movies >= 1] = 1\n    shifted_movies[shifted_movies >= 1] = 1\n    return noisy_movies, shifted_movies\n\n# Train the network\nnoisy_movies, shifted_movies = generate_movies(n_samples=1200)\nseq.fit(noisy_movies[:1000], shifted_movies[:1000], batch_size=10,\n        epochs=300, validation_split=0.05)\n\n# Testing the network on one movie\n# feed it with the first 7 positions and then\n# predict the new positions\nwhich = 1004\ntrack = noisy_movies[which][:7, ::, ::, ::]\n\nfor j in range(16):\n    new_pos = seq.predict(track[np.newaxis, ::, ::, ::, ::])\n    new = new_pos[::, -1, ::, ::, ::]\n    track = np.concatenate((track, new), axis=0)\n\n\n# And then compare the predictions\n# to the ground truth\ntrack2 = noisy_movies[which][::, ::, ::, ::]\nfor i in range(15):\n    fig = plt.figure(figsize=(10, 5))\n\n    ax = fig.add_subplot(121)\n\n    if i >= 7:\n        ax.text(1, 3, 'Predictions !', fontsize=20, color='w')\n    else:\n        ax.text(1, 3, 'Inital trajectory', fontsize=20)\n\n    toplot = track[i, ::, ::, 0]\n\n    plt.imshow(toplot)\n    ax = fig.add_subplot(122)\n    plt.text(1, 3, 'Ground truth', fontsize=20)\n\n    toplot = track2[i, ::, ::, 0]\n    if i >= 2:\n        toplot = shifted_movies[which][i - 1, ::, ::, 0]\n\n    plt.imshow(toplot)\n    plt.savefig('%i_animate.png' % (i + 1))\n"
  },
  {
    "path": "Keras/deep_dream.py",
    "content": "'''Deep Dreaming in Keras.\n\nRun the script with:\n```\npython deep_dream.py path_to_your_base_image.jpg prefix_for_results\n```\ne.g.:\n```\npython deep_dream.py img/mypic.jpg results/dream\n```\n\nIt is preferable to run this script on GPU, for speed.\nIf running on CPU, prefer the TensorFlow backend (much faster).\n\nExample results: http://i.imgur.com/FX6ROg9.jpg\n'''\nfrom __future__ import print_function\n\nfrom keras.preprocessing.image import load_img, img_to_array\nimport numpy as np\nfrom scipy.misc import imsave\nfrom scipy.optimize import fmin_l_bfgs_b\nimport time\nimport argparse\n\nfrom keras.applications import vgg16\nfrom keras import backend as K\nfrom keras.layers import Input\n\nparser = argparse.ArgumentParser(description='Deep Dreams with Keras.')\nparser.add_argument('base_image_path', metavar='base', type=str,\n                    help='Path to the image to transform.')\nparser.add_argument('result_prefix', metavar='res_prefix', type=str,\n                    help='Prefix for the saved results.')\n\nargs = parser.parse_args()\nbase_image_path = args.base_image_path\nresult_prefix = args.result_prefix\n\n# dimensions of the generated picture.\nimg_height = 600\nimg_width = 600\n\n# some settings we found interesting\nsaved_settings = {\n    'bad_trip': {'features': {'block4_conv1': 0.05,\n                              'block4_conv2': 0.01,\n                              'block4_conv3': 0.01},\n                 'continuity': 0.1,\n                 'dream_l2': 0.8,\n                 'jitter': 5},\n    'dreamy': {'features': {'block5_conv1': 0.05,\n                            'block5_conv2': 0.02},\n               'continuity': 0.1,\n               'dream_l2': 0.02,\n               'jitter': 0},\n}\n# the settings we will use in this experiment\nsettings = saved_settings['dreamy']\n\n\ndef preprocess_image(image_path):\n    # util function to open, resize and format pictures\n    # into appropriate tensors\n    img = load_img(image_path, target_size=(img_height, img_width))\n    img = img_to_array(img)\n    img = np.expand_dims(img, axis=0)\n    img = vgg16.preprocess_input(img)\n    return img\n\n\ndef deprocess_image(x):\n    # util function to convert a tensor into a valid image\n    if K.image_data_format() == 'channels_first':\n        x = x.reshape((3, img_height, img_width))\n        x = x.transpose((1, 2, 0))\n    else:\n        x = x.reshape((img_height, img_width, 3))\n    # Remove zero-center by mean pixel\n    x[:, :, 0] += 103.939\n    x[:, :, 1] += 116.779\n    x[:, :, 2] += 123.68\n    # 'BGR'->'RGB'\n    x = x[:, :, ::-1]\n    x = np.clip(x, 0, 255).astype('uint8')\n    return x\n\nif K.image_data_format() == 'channels_first':\n    img_size = (3, img_height, img_width)\nelse:\n    img_size = (img_height, img_width, 3)\n# this will contain our generated image\ndream = Input(batch_shape=(1,) + img_size)\n\n# build the VGG16 network with our placeholder\n# the model will be loaded with pre-trained ImageNet weights\nmodel = vgg16.VGG16(input_tensor=dream,\n                    weights='imagenet', include_top=False)\nprint('Model loaded.')\n\n# get the symbolic outputs of each \"key\" layer (we gave them unique names).\nlayer_dict = dict([(layer.name, layer) for layer in model.layers])\n\n\ndef continuity_loss(x):\n    # continuity loss util function\n    assert K.ndim(x) == 4\n    if K.image_data_format() == 'channels_first':\n        a = K.square(x[:, :, :img_height - 1, :img_width - 1] -\n                     x[:, :, 1:, :img_width - 1])\n        b = K.square(x[:, :, :img_height - 1, :img_width - 1] -\n                     x[:, :, :img_height - 1, 1:])\n    else:\n        a = K.square(x[:, :img_height - 1, :img_width - 1, :] -\n                     x[:, 1:, :img_width - 1, :])\n        b = K.square(x[:, :img_height - 1, :img_width - 1, :] -\n                     x[:, :img_height - 1, 1:, :])\n    return K.sum(K.pow(a + b, 1.25))\n\n# define the loss\nloss = K.variable(0.)\nfor layer_name in settings['features']:\n    # add the L2 norm of the features of a layer to the loss\n    assert layer_name in layer_dict.keys(), 'Layer ' + layer_name + ' not found in model.'\n    coeff = settings['features'][layer_name]\n    x = layer_dict[layer_name].output\n    shape = layer_dict[layer_name].output_shape\n    # we avoid border artifacts by only involving non-border pixels in the loss\n    if K.image_data_format() == 'channels_first':\n        loss -= coeff * K.sum(K.square(x[:, :, 2: shape[2] - 2, 2: shape[3] - 2])) / np.prod(shape[1:])\n    else:\n        loss -= coeff * K.sum(K.square(x[:, 2: shape[1] - 2, 2: shape[2] - 2, :])) / np.prod(shape[1:])\n\n# add continuity loss (gives image local coherence, can result in an artful blur)\nloss += settings['continuity'] * continuity_loss(dream) / np.prod(img_size)\n# add image L2 norm to loss (prevents pixels from taking very high values, makes image darker)\nloss += settings['dream_l2'] * K.sum(K.square(dream)) / np.prod(img_size)\n\n# feel free to further modify the loss as you see fit, to achieve new effects...\n\n# compute the gradients of the dream wrt the loss\ngrads = K.gradients(loss, dream)\n\noutputs = [loss]\nif isinstance(grads, (list, tuple)):\n    outputs += grads\nelse:\n    outputs.append(grads)\n\nf_outputs = K.function([dream], outputs)\n\n\ndef eval_loss_and_grads(x):\n    x = x.reshape((1,) + img_size)\n    outs = f_outputs([x])\n    loss_value = outs[0]\n    if len(outs[1:]) == 1:\n        grad_values = outs[1].flatten().astype('float64')\n    else:\n        grad_values = np.array(outs[1:]).flatten().astype('float64')\n    return loss_value, grad_values\n\n\nclass Evaluator(object):\n    \"\"\"Loss and gradients evaluator.\n\n    This Evaluator class makes it possible\n    to compute loss and gradients in one pass\n    while retrieving them via two separate functions,\n    \"loss\" and \"grads\". This is done because scipy.optimize\n    requires separate functions for loss and gradients,\n    but computing them separately would be inefficient.\n    \"\"\"\n\n    def __init__(self):\n        self.loss_value = None\n        self.grad_values = None\n\n    def loss(self, x):\n        assert self.loss_value is None\n        loss_value, grad_values = eval_loss_and_grads(x)\n        self.loss_value = loss_value\n        self.grad_values = grad_values\n        return self.loss_value\n\n    def grads(self, x):\n        assert self.loss_value is not None\n        grad_values = np.copy(self.grad_values)\n        self.loss_value = None\n        self.grad_values = None\n        return grad_values\n\nevaluator = Evaluator()\n\n# Run scipy-based optimization (L-BFGS) over the pixels of the generated image\n# so as to minimize the loss\nx = preprocess_image(base_image_path)\nfor i in range(5):\n    print('Start of iteration', i)\n    start_time = time.time()\n\n    # Add a random jitter to the initial image.\n    # This will be reverted at decoding time\n    random_jitter = (settings['jitter'] * 2) * (np.random.random(img_size) - 0.5)\n    x += random_jitter\n\n    # Run L-BFGS for 7 steps\n    x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),\n                                     fprime=evaluator.grads, maxfun=7)\n    print('Current loss value:', min_val)\n    # Decode the dream and save it\n    x = x.reshape(img_size)\n    x -= random_jitter\n    img = deprocess_image(np.copy(x))\n    fname = result_prefix + '_at_iteration_%d.png' % i\n    imsave(fname, img)\n    end_time = time.time()\n    print('Image saved as', fname)\n    print('Iteration %d completed in %ds' % (i, end_time - start_time))\n"
  },
  {
    "path": "Keras/image_ocr.py",
    "content": "'''This example uses a convolutional stack followed by a recurrent stack\nand a CTC logloss function to perform optical character recognition\nof generated text images. I have no evidence of whether it actually\nlearns general shapes of text, or just is able to recognize all\nthe different fonts thrown at it...the purpose is more to demonstrate CTC\ninside of Keras.  Note that the font list may need to be updated\nfor the particular OS in use.\n\nThis starts off with 4 letter words.  For the first 12 epochs, the\ndifficulty is gradually increased using the TextImageGenerator class\nwhich is both a generator class for test/train data and a Keras\ncallback class. After 20 epochs, longer sequences are thrown at it\nby recompiling the model to handle a wider image and rebuilding\nthe word list to include two words separated by a space.\n\nThe table below shows normalized edit distance values. Theano uses\na slightly different CTC implementation, hence the different results.\n\n            Norm. ED\nEpoch |   TF   |   TH\n------------------------\n    10   0.027   0.064\n    15   0.038   0.035\n    20   0.043   0.045\n    25   0.014   0.019\n\nThis requires cairo and editdistance packages:\npip install cairocffi\npip install editdistance\n\nCreated by Mike Henry\nhttps://github.com/mbhenry/\n'''\nimport os\nimport itertools\nimport re\nimport datetime\nimport cairocffi as cairo\nimport editdistance\nimport numpy as np\nfrom scipy import ndimage\nimport pylab\nfrom keras import backend as K\nfrom keras.layers.convolutional import Conv2D, MaxPooling2D\nfrom keras.layers import Input, Dense, Activation\nfrom keras.layers import Reshape, Lambda\nfrom keras.layers.merge import add, concatenate\nfrom keras.models import Model\nfrom keras.layers.recurrent import GRU\nfrom keras.optimizers import SGD\nfrom keras.utils.data_utils import get_file\nfrom keras.preprocessing import image\nimport keras.callbacks\n\n\nOUTPUT_DIR = 'image_ocr'\n\nnp.random.seed(55)\n\n\n# this creates larger \"blotches\" of noise which look\n# more realistic than just adding gaussian noise\n# assumes greyscale with pixels ranging from 0 to 1\n\ndef speckle(img):\n    severity = np.random.uniform(0, 0.6)\n    blur = ndimage.gaussian_filter(np.random.randn(*img.shape) * severity, 1)\n    img_speck = (img + blur)\n    img_speck[img_speck > 1] = 1\n    img_speck[img_speck <= 0] = 0\n    return img_speck\n\n\n# paints the string in a random location the bounding box\n# also uses a random font, a slight random rotation,\n# and a random amount of speckle noise\n\ndef paint_text(text, w, h, rotate=False, ud=False, multi_fonts=False):\n    surface = cairo.ImageSurface(cairo.FORMAT_RGB24, w, h)\n    with cairo.Context(surface) as context:\n        context.set_source_rgb(1, 1, 1)  # White\n        context.paint()\n        # this font list works in Centos 7\n        if multi_fonts:\n            fonts = ['Century Schoolbook', 'Courier', 'STIX', 'URW Chancery L', 'FreeMono']\n            context.select_font_face(np.random.choice(fonts), cairo.FONT_SLANT_NORMAL,\n                                     np.random.choice([cairo.FONT_WEIGHT_BOLD, cairo.FONT_WEIGHT_NORMAL]))\n        else:\n            context.select_font_face('Courier', cairo.FONT_SLANT_NORMAL, cairo.FONT_WEIGHT_BOLD)\n        context.set_font_size(25)\n        box = context.text_extents(text)\n        border_w_h = (4, 4)\n        if box[2] > (w - 2 * border_w_h[1]) or box[3] > (h - 2 * border_w_h[0]):\n            raise IOError('Could not fit string into image. Max char count is too large for given image width.')\n\n        # teach the RNN translational invariance by\n        # fitting text box randomly on canvas, with some room to rotate\n        max_shift_x = w - box[2] - border_w_h[0]\n        max_shift_y = h - box[3] - border_w_h[1]\n        top_left_x = np.random.randint(0, int(max_shift_x))\n        if ud:\n            top_left_y = np.random.randint(0, int(max_shift_y))\n        else:\n            top_left_y = h // 2\n        context.move_to(top_left_x - int(box[0]), top_left_y - int(box[1]))\n        context.set_source_rgb(0, 0, 0)\n        context.show_text(text)\n\n    buf = surface.get_data()\n    a = np.frombuffer(buf, np.uint8)\n    a.shape = (h, w, 4)\n    a = a[:, :, 0]  # grab single channel\n    a = a.astype(np.float32) / 255\n    a = np.expand_dims(a, 0)\n    if rotate:\n        a = image.random_rotation(a, 3 * (w - top_left_x) / w + 1)\n    a = speckle(a)\n\n    return a\n\n\ndef shuffle_mats_or_lists(matrix_list, stop_ind=None):\n    ret = []\n    assert all([len(i) == len(matrix_list[0]) for i in matrix_list])\n    len_val = len(matrix_list[0])\n    if stop_ind is None:\n        stop_ind = len_val\n    assert stop_ind <= len_val\n\n    a = list(range(stop_ind))\n    np.random.shuffle(a)\n    a += list(range(stop_ind, len_val))\n    for mat in matrix_list:\n        if isinstance(mat, np.ndarray):\n            ret.append(mat[a])\n        elif isinstance(mat, list):\n            ret.append([mat[i] for i in a])\n        else:\n            raise TypeError('shuffle_mats_or_lists only supports '\n                            'numpy.array and list objects')\n    return ret\n\n\ndef text_to_labels(text, num_classes):\n    ret = []\n    for char in text:\n        if char >= 'a' and char <= 'z':\n            ret.append(ord(char) - ord('a'))\n        elif char == ' ':\n            ret.append(26)\n    return ret\n\n\n# only a-z and space..probably not to difficult\n# to expand to uppercase and symbols\n\ndef is_valid_str(in_str):\n    search = re.compile(r'[^a-z\\ ]').search\n    return not bool(search(in_str))\n\n\n# Uses generator functions to supply train/test with\n# data. Image renderings are text are created on the fly\n# each time with random perturbations\n\nclass TextImageGenerator(keras.callbacks.Callback):\n\n    def __init__(self, monogram_file, bigram_file, minibatch_size,\n                 img_w, img_h, downsample_factor, val_split,\n                 absolute_max_string_len=16):\n\n        self.minibatch_size = minibatch_size\n        self.img_w = img_w\n        self.img_h = img_h\n        self.monogram_file = monogram_file\n        self.bigram_file = bigram_file\n        self.downsample_factor = downsample_factor\n        self.val_split = val_split\n        self.blank_label = self.get_output_size() - 1\n        self.absolute_max_string_len = absolute_max_string_len\n\n    def get_output_size(self):\n        return 28\n\n    # num_words can be independent of the epoch size due to the use of generators\n    # as max_string_len grows, num_words can grow\n    def build_word_list(self, num_words, max_string_len=None, mono_fraction=0.5):\n        assert max_string_len <= self.absolute_max_string_len\n        assert num_words % self.minibatch_size == 0\n        assert (self.val_split * num_words) % self.minibatch_size == 0\n        self.num_words = num_words\n        self.string_list = [''] * self.num_words\n        tmp_string_list = []\n        self.max_string_len = max_string_len\n        self.Y_data = np.ones([self.num_words, self.absolute_max_string_len]) * -1\n        self.X_text = []\n        self.Y_len = [0] * self.num_words\n\n        # monogram file is sorted by frequency in english speech\n        with open(self.monogram_file, 'rt') as f:\n            for line in f:\n                if len(tmp_string_list) == int(self.num_words * mono_fraction):\n                    break\n                word = line.rstrip()\n                if max_string_len == -1 or max_string_len is None or len(word) <= max_string_len:\n                    tmp_string_list.append(word)\n\n        # bigram file contains common word pairings in english speech\n        with open(self.bigram_file, 'rt') as f:\n            lines = f.readlines()\n            for line in lines:\n                if len(tmp_string_list) == self.num_words:\n                    break\n                columns = line.lower().split()\n                word = columns[0] + ' ' + columns[1]\n                if is_valid_str(word) and \\\n                        (max_string_len == -1 or max_string_len is None or len(word) <= max_string_len):\n                    tmp_string_list.append(word)\n        if len(tmp_string_list) != self.num_words:\n            raise IOError('Could not pull enough words from supplied monogram and bigram files. ')\n        # interlace to mix up the easy and hard words\n        self.string_list[::2] = tmp_string_list[:self.num_words // 2]\n        self.string_list[1::2] = tmp_string_list[self.num_words // 2:]\n\n        for i, word in enumerate(self.string_list):\n            self.Y_len[i] = len(word)\n            self.Y_data[i, 0:len(word)] = text_to_labels(word, self.get_output_size())\n            self.X_text.append(word)\n        self.Y_len = np.expand_dims(np.array(self.Y_len), 1)\n\n        self.cur_val_index = self.val_split\n        self.cur_train_index = 0\n\n    # each time an image is requested from train/val/test, a new random\n    # painting of the text is performed\n    def get_batch(self, index, size, train):\n        # width and height are backwards from typical Keras convention\n        # because width is the time dimension when it gets fed into the RNN\n        if K.image_data_format() == 'channels_first':\n            X_data = np.ones([size, 1, self.img_w, self.img_h])\n        else:\n            X_data = np.ones([size, self.img_w, self.img_h, 1])\n\n        labels = np.ones([size, self.absolute_max_string_len])\n        input_length = np.zeros([size, 1])\n        label_length = np.zeros([size, 1])\n        source_str = []\n        for i in range(0, size):\n            # Mix in some blank inputs.  This seems to be important for\n            # achieving translational invariance\n            if train and i > size - 4:\n                if K.image_data_format() == 'channels_first':\n                    X_data[i, 0, 0:self.img_w, :] = self.paint_func('')[0, :, :].T\n                else:\n                    X_data[i, 0:self.img_w, :, 0] = self.paint_func('',)[0, :, :].T\n                labels[i, 0] = self.blank_label\n                input_length[i] = self.img_w // self.downsample_factor - 2\n                label_length[i] = 1\n                source_str.append('')\n            else:\n                if K.image_data_format() == 'channels_first':\n                    X_data[i, 0, 0:self.img_w, :] = self.paint_func(self.X_text[index + i])[0, :, :].T\n                else:\n                    X_data[i, 0:self.img_w, :, 0] = self.paint_func(self.X_text[index + i])[0, :, :].T\n                labels[i, :] = self.Y_data[index + i]\n                input_length[i] = self.img_w // self.downsample_factor - 2\n                label_length[i] = self.Y_len[index + i]\n                source_str.append(self.X_text[index + i])\n        inputs = {'the_input': X_data,\n                  'the_labels': labels,\n                  'input_length': input_length,\n                  'label_length': label_length,\n                  'source_str': source_str  # used for visualization only\n                  }\n        outputs = {'ctc': np.zeros([size])}  # dummy data for dummy loss function\n        return (inputs, outputs)\n\n    def next_train(self):\n        while 1:\n            ret = self.get_batch(self.cur_train_index, self.minibatch_size, train=True)\n            self.cur_train_index += self.minibatch_size\n            if self.cur_train_index >= self.val_split:\n                self.cur_train_index = self.cur_train_index % 32\n                (self.X_text, self.Y_data, self.Y_len) = shuffle_mats_or_lists(\n                    [self.X_text, self.Y_data, self.Y_len], self.val_split)\n            yield ret\n\n    def next_val(self):\n        while 1:\n            ret = self.get_batch(self.cur_val_index, self.minibatch_size, train=False)\n            self.cur_val_index += self.minibatch_size\n            if self.cur_val_index >= self.num_words:\n                self.cur_val_index = self.val_split + self.cur_val_index % 32\n            yield ret\n\n    def on_train_begin(self, logs={}):\n        self.build_word_list(16000, 4, 1)\n        self.paint_func = lambda text: paint_text(text, self.img_w, self.img_h,\n                                                  rotate=False, ud=False, multi_fonts=False)\n\n    def on_epoch_begin(self, epoch, logs={}):\n        # rebind the paint function to implement curriculum learning\n        if epoch >= 3 and epoch < 6:\n            self.paint_func = lambda text: paint_text(text, self.img_w, self.img_h,\n                                                      rotate=False, ud=True, multi_fonts=False)\n        elif epoch >= 6 and epoch < 9:\n            self.paint_func = lambda text: paint_text(text, self.img_w, self.img_h,\n                                                      rotate=False, ud=True, multi_fonts=True)\n        elif epoch >= 9:\n            self.paint_func = lambda text: paint_text(text, self.img_w, self.img_h,\n                                                      rotate=True, ud=True, multi_fonts=True)\n        if epoch >= 21 and self.max_string_len < 12:\n            self.build_word_list(32000, 12, 0.5)\n\n\n# the actual loss calc occurs here despite it not being\n# an internal Keras loss function\n\ndef ctc_lambda_func(args):\n    y_pred, labels, input_length, label_length = args\n    # the 2 is critical here since the first couple outputs of the RNN\n    # tend to be garbage:\n    y_pred = y_pred[:, 2:, :]\n    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)\n\n\n# For a real OCR application, this should be beam search with a dictionary\n# and language model.  For this example, best path is sufficient.\n\ndef decode_batch(test_func, word_batch):\n    out = test_func([word_batch])[0]\n    ret = []\n    for j in range(out.shape[0]):\n        out_best = list(np.argmax(out[j, 2:], 1))\n        out_best = [k for k, g in itertools.groupby(out_best)]\n        # 26 is space, 27 is CTC blank char\n        outstr = ''\n        for c in out_best:\n            if c >= 0 and c < 26:\n                outstr += chr(c + ord('a'))\n            elif c == 26:\n                outstr += ' '\n        ret.append(outstr)\n    return ret\n\n\nclass VizCallback(keras.callbacks.Callback):\n\n    def __init__(self, run_name, test_func, text_img_gen, num_display_words=6):\n        self.test_func = test_func\n        self.output_dir = os.path.join(\n            OUTPUT_DIR, run_name)\n        self.text_img_gen = text_img_gen\n        self.num_display_words = num_display_words\n        if not os.path.exists(self.output_dir):\n            os.makedirs(self.output_dir)\n\n    def show_edit_distance(self, num):\n        num_left = num\n        mean_norm_ed = 0.0\n        mean_ed = 0.0\n        while num_left > 0:\n            word_batch = next(self.text_img_gen)[0]\n            num_proc = min(word_batch['the_input'].shape[0], num_left)\n            decoded_res = decode_batch(self.test_func, word_batch['the_input'][0:num_proc])\n            for j in range(0, num_proc):\n                edit_dist = editdistance.eval(decoded_res[j], word_batch['source_str'][j])\n                mean_ed += float(edit_dist)\n                mean_norm_ed += float(edit_dist) / len(word_batch['source_str'][j])\n            num_left -= num_proc\n        mean_norm_ed = mean_norm_ed / num\n        mean_ed = mean_ed / num\n        print('\\nOut of %d samples:  Mean edit distance: %.3f Mean normalized edit distance: %0.3f'\n              % (num, mean_ed, mean_norm_ed))\n\n    def on_epoch_end(self, epoch, logs={}):\n        self.model.save_weights(os.path.join(self.output_dir, 'weights%02d.h5' % (epoch)))\n        self.show_edit_distance(256)\n        word_batch = next(self.text_img_gen)[0]\n        res = decode_batch(self.test_func, word_batch['the_input'][0:self.num_display_words])\n        if word_batch['the_input'][0].shape[0] < 256:\n            cols = 2\n        else:\n            cols = 1\n        for i in range(self.num_display_words):\n            pylab.subplot(self.num_display_words // cols, cols, i + 1)\n            if K.image_data_format() == 'channels_first':\n                the_input = word_batch['the_input'][i, 0, :, :]\n            else:\n                the_input = word_batch['the_input'][i, :, :, 0]\n            pylab.imshow(the_input.T, cmap='Greys_r')\n            pylab.xlabel('Truth = \\'%s\\'\\nDecoded = \\'%s\\'' % (word_batch['source_str'][i], res[i]))\n        fig = pylab.gcf()\n        fig.set_size_inches(10, 13)\n        pylab.savefig(os.path.join(self.output_dir, 'e%02d.png' % (epoch)))\n        pylab.close()\n\n\ndef train(run_name, start_epoch, stop_epoch, img_w):\n    # Input Parameters\n    img_h = 64\n    words_per_epoch = 16000\n    val_split = 0.2\n    val_words = int(words_per_epoch * (val_split))\n\n    # Network parameters\n    conv_filters = 16\n    kernel_size = (3, 3)\n    pool_size = 2\n    time_dense_size = 32\n    rnn_size = 512\n\n    if K.image_data_format() == 'channels_first':\n        input_shape = (1, img_w, img_h)\n    else:\n        input_shape = (img_w, img_h, 1)\n\n    fdir = os.path.dirname(get_file('wordlists.tgz',\n                                    origin='http://www.mythic-ai.com/datasets/wordlists.tgz', untar=True))\n\n    img_gen = TextImageGenerator(monogram_file=os.path.join(fdir, 'wordlist_mono_clean.txt'),\n                                 bigram_file=os.path.join(fdir, 'wordlist_bi_clean.txt'),\n                                 minibatch_size=32,\n                                 img_w=img_w,\n                                 img_h=img_h,\n                                 downsample_factor=(pool_size ** 2),\n                                 val_split=words_per_epoch - val_words\n                                 )\n    act = 'relu'\n    input_data = Input(name='the_input', shape=input_shape, dtype='float32')\n    inner = Conv2D(conv_filters, kernel_size, padding='same',\n                   activation=act, kernel_initializer='he_normal',\n                   name='conv1')(input_data)\n    inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max1')(inner)\n    inner = Conv2D(conv_filters, kernel_size, padding='same',\n                   activation=act, kernel_initializer='he_normal',\n                   name='conv2')(inner)\n    inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max2')(inner)\n\n    conv_to_rnn_dims = (img_w // (pool_size ** 2), (img_h // (pool_size ** 2)) * conv_filters)\n    inner = Reshape(target_shape=conv_to_rnn_dims, name='reshape')(inner)\n\n    # cuts down input size going into RNN:\n    inner = Dense(time_dense_size, activation=act, name='dense1')(inner)\n\n    # Two layers of bidirecitonal GRUs\n    # GRU seems to work as well, if not better than LSTM:\n    gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(inner)\n    gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(inner)\n    gru1_merged = add([gru_1, gru_1b])\n    gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)\n    gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)\n\n    # transforms RNN output to character activations:\n    inner = Dense(img_gen.get_output_size(), kernel_initializer='he_normal',\n                  name='dense2')(concatenate([gru_2, gru_2b]))\n    y_pred = Activation('softmax', name='softmax')(inner)\n    Model(inputs=input_data, outputs=y_pred).summary()\n\n    labels = Input(name='the_labels', shape=[img_gen.absolute_max_string_len], dtype='float32')\n    input_length = Input(name='input_length', shape=[1], dtype='int64')\n    label_length = Input(name='label_length', shape=[1], dtype='int64')\n    # Keras doesn't currently support loss funcs with extra parameters\n    # so CTC loss is implemented in a lambda layer\n    loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length])\n\n    # clipnorm seems to speeds up convergence\n    sgd = SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)\n\n    model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)\n\n    # the loss calc occurs elsewhere, so use a dummy lambda func for the loss\n    model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd)\n    if start_epoch > 0:\n        weight_file = os.path.join(OUTPUT_DIR, os.path.join(run_name, 'weights%02d.h5' % (start_epoch - 1)))\n        model.load_weights(weight_file)\n    # captures output of softmax so we can decode the output during visualization\n    test_func = K.function([input_data], [y_pred])\n\n    viz_cb = VizCallback(run_name, test_func, img_gen.next_val())\n\n    model.fit_generator(generator=img_gen.next_train(), steps_per_epoch=(words_per_epoch - val_words),\n                        epochs=stop_epoch, validation_data=img_gen.next_val(), validation_steps=val_words,\n                        callbacks=[viz_cb, img_gen], initial_epoch=start_epoch)\n\n\nif __name__ == '__main__':\n    run_name = datetime.datetime.now().strftime('%Y:%m:%d:%H:%M:%S')\n    train(run_name, 0, 20, 128)\n    # increase to wider images and start at epoch 20. The learned weights are reloaded\n    train(run_name, 20, 25, 512)\n"
  },
  {
    "path": "Keras/imdb_bidirectional_lstm.py",
    "content": "'''Train a Bidirectional LSTM on the IMDB sentiment classification task.\n\nOutput after 4 epochs on CPU: ~0.8146\nTime per epoch on CPU (Core i7): ~150s.\n'''\n\nfrom __future__ import print_function\nimport numpy as np\n\nfrom keras.preprocessing import sequence\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional\nfrom keras.datasets import imdb\n\n\nmax_features = 20000\n# cut texts after this number of words\n# (among top max_features most common words)\nmaxlen = 100\nbatch_size = 32\n\nprint('Loading data...')\n(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\nprint(len(x_train), 'train sequences')\nprint(len(x_test), 'test sequences')\n\nprint(\"Pad sequences (samples x time)\")\nx_train = sequence.pad_sequences(x_train, maxlen=maxlen)\nx_test = sequence.pad_sequences(x_test, maxlen=maxlen)\nprint('x_train shape:', x_train.shape)\nprint('x_test shape:', x_test.shape)\ny_train = np.array(y_train)\ny_test = np.array(y_test)\n\nmodel = Sequential()\nmodel.add(Embedding(max_features, 128, input_length=maxlen))\nmodel.add(Bidirectional(LSTM(64)))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(1, activation='sigmoid'))\n\n# try using different optimizers and different optimizer configs\nmodel.compile('adam', 'binary_crossentropy', metrics=['accuracy'])\n\nprint('Train...')\nmodel.fit(x_train, y_train,\n          batch_size=batch_size,\n          epochs=4,\n          validation_data=[x_test, y_test])\n"
  },
  {
    "path": "Keras/imdb_cnn.py",
    "content": "'''This example demonstrates the use of Convolution1D for text classification.\n\nGets to 0.89 test accuracy after 2 epochs.\n90s/epoch on Intel i5 2.4Ghz CPU.\n10s/epoch on Tesla K40 GPU.\n\n'''\n\nfrom __future__ import print_function\n\nfrom keras.preprocessing import sequence\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation\nfrom keras.layers import Embedding\nfrom keras.layers import Conv1D, GlobalMaxPooling1D\nfrom keras.datasets import imdb\n\n# set parameters:\nmax_features = 5000\nmaxlen = 400\nbatch_size = 32\nembedding_dims = 50\nfilters = 250\nkernel_size = 3\nhidden_dims = 250\nepochs = 2\n\nprint('Loading data...')\n(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\nprint(len(x_train), 'train sequences')\nprint(len(x_test), 'test sequences')\n\nprint('Pad sequences (samples x time)')\nx_train = sequence.pad_sequences(x_train, maxlen=maxlen)\nx_test = sequence.pad_sequences(x_test, maxlen=maxlen)\nprint('x_train shape:', x_train.shape)\nprint('x_test shape:', x_test.shape)\n\nprint('Build model...')\nmodel = Sequential()\n\n# we start off with an efficient embedding layer which maps\n# our vocab indices into embedding_dims dimensions\nmodel.add(Embedding(max_features,\n                    embedding_dims,\n                    input_length=maxlen))\nmodel.add(Dropout(0.2))\n\n# we add a Convolution1D, which will learn filters\n# word group filters of size filter_length:\nmodel.add(Conv1D(filters,\n                 kernel_size,\n                 padding='valid',\n                 activation='relu',\n                 strides=1))\n# we use max pooling:\nmodel.add(GlobalMaxPooling1D())\n\n# We add a vanilla hidden layer:\nmodel.add(Dense(hidden_dims))\nmodel.add(Dropout(0.2))\nmodel.add(Activation('relu'))\n\n# We project onto a single unit output layer, and squash it with a sigmoid:\nmodel.add(Dense(1))\nmodel.add(Activation('sigmoid'))\n\nmodel.compile(loss='binary_crossentropy',\n              optimizer='adam',\n              metrics=['accuracy'])\nmodel.fit(x_train, y_train,\n          batch_size=batch_size,\n          epochs=epochs,\n          validation_data=(x_test, y_test))\n"
  },
  {
    "path": "Keras/imdb_cnn_lstm.py",
    "content": "'''Train a recurrent convolutional network on the IMDB sentiment\nclassification task.\n\nGets to 0.8498 test accuracy after 2 epochs. 41s/epoch on K520 GPU.\n'''\nfrom __future__ import print_function\n\nfrom keras.preprocessing import sequence\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation\nfrom keras.layers import Embedding\nfrom keras.layers import LSTM\nfrom keras.layers import Conv1D, MaxPooling1D\nfrom keras.datasets import imdb\n\n# Embedding\nmax_features = 20000\nmaxlen = 100\nembedding_size = 128\n\n# Convolution\nkernel_size = 5\nfilters = 64\npool_size = 4\n\n# LSTM\nlstm_output_size = 70\n\n# Training\nbatch_size = 30\nepochs = 2\n\n'''\nNote:\nbatch_size is highly sensitive.\nOnly 2 epochs are needed as the dataset is very small.\n'''\n\nprint('Loading data...')\n(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\nprint(len(x_train), 'train sequences')\nprint(len(x_test), 'test sequences')\n\nprint('Pad sequences (samples x time)')\nx_train = sequence.pad_sequences(x_train, maxlen=maxlen)\nx_test = sequence.pad_sequences(x_test, maxlen=maxlen)\nprint('x_train shape:', x_train.shape)\nprint('x_test shape:', x_test.shape)\n\nprint('Build model...')\n\nmodel = Sequential()\nmodel.add(Embedding(max_features, embedding_size, input_length=maxlen))\nmodel.add(Dropout(0.25))\nmodel.add(Conv1D(filters,\n                 kernel_size,\n                 padding='valid',\n                 activation='relu',\n                 strides=1))\nmodel.add(MaxPooling1D(pool_size=pool_size))\nmodel.add(LSTM(lstm_output_size))\nmodel.add(Dense(1))\nmodel.add(Activation('sigmoid'))\n\nmodel.compile(loss='binary_crossentropy',\n              optimizer='adam',\n              metrics=['accuracy'])\n\nprint('Train...')\nmodel.fit(x_train, y_train,\n          batch_size=batch_size,\n          epochs=epochs,\n          validation_data=(x_test, y_test))\nscore, acc = model.evaluate(x_test, y_test, batch_size=batch_size)\nprint('Test score:', score)\nprint('Test accuracy:', acc)\n"
  },
  {
    "path": "Keras/imdb_fasttext.py",
    "content": "'''This example demonstrates the use of fasttext for text classification\n\nBased on Joulin et al's paper:\n\nBags of Tricks for Efficient Text Classification\nhttps://arxiv.org/abs/1607.01759\n\nResults on IMDB datasets with uni and bi-gram embeddings:\n    Uni-gram: 0.8813 test accuracy after 5 epochs. 8s/epoch on i7 cpu.\n    Bi-gram : 0.9056 test accuracy after 5 epochs. 2s/epoch on GTx 980M gpu.\n'''\n\nfrom __future__ import print_function\nimport numpy as np\n\nfrom keras.preprocessing import sequence\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import Embedding\nfrom keras.layers import GlobalAveragePooling1D\nfrom keras.datasets import imdb\n\n\ndef create_ngram_set(input_list, ngram_value=2):\n    \"\"\"\n    Extract a set of n-grams from a list of integers.\n\n    >>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=2)\n    {(4, 9), (4, 1), (1, 4), (9, 4)}\n\n    >>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=3)\n    [(1, 4, 9), (4, 9, 4), (9, 4, 1), (4, 1, 4)]\n    \"\"\"\n    return set(zip(*[input_list[i:] for i in range(ngram_value)]))\n\n\ndef add_ngram(sequences, token_indice, ngram_range=2):\n    \"\"\"\n    Augment the input list of list (sequences) by appending n-grams values.\n\n    Example: adding bi-gram\n    >>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]\n    >>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017}\n    >>> add_ngram(sequences, token_indice, ngram_range=2)\n    [[1, 3, 4, 5, 1337, 2017], [1, 3, 7, 9, 2, 1337, 42]]\n\n    Example: adding tri-gram\n    >>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]\n    >>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017, (7, 9, 2): 2018}\n    >>> add_ngram(sequences, token_indice, ngram_range=3)\n    [[1, 3, 4, 5, 1337], [1, 3, 7, 9, 2, 1337, 2018]]\n    \"\"\"\n    new_sequences = []\n    for input_list in sequences:\n        new_list = input_list[:]\n        for i in range(len(new_list) - ngram_range + 1):\n            for ngram_value in range(2, ngram_range + 1):\n                ngram = tuple(new_list[i:i + ngram_value])\n                if ngram in token_indice:\n                    new_list.append(token_indice[ngram])\n        new_sequences.append(new_list)\n\n    return new_sequences\n\n# Set parameters:\n# ngram_range = 2 will add bi-grams features\nngram_range = 1\nmax_features = 20000\nmaxlen = 400\nbatch_size = 32\nembedding_dims = 50\nepochs = 5\n\nprint('Loading data...')\n(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\nprint(len(x_train), 'train sequences')\nprint(len(x_test), 'test sequences')\nprint('Average train sequence length: {}'.format(np.mean(list(map(len, x_train)), dtype=int)))\nprint('Average test sequence length: {}'.format(np.mean(list(map(len, x_test)), dtype=int)))\n\nif ngram_range > 1:\n    print('Adding {}-gram features'.format(ngram_range))\n    # Create set of unique n-gram from the training set.\n    ngram_set = set()\n    for input_list in x_train:\n        for i in range(2, ngram_range + 1):\n            set_of_ngram = create_ngram_set(input_list, ngram_value=i)\n            ngram_set.update(set_of_ngram)\n\n    # Dictionary mapping n-gram token to a unique integer.\n    # Integer values are greater than max_features in order\n    # to avoid collision with existing features.\n    start_index = max_features + 1\n    token_indice = {v: k + start_index for k, v in enumerate(ngram_set)}\n    indice_token = {token_indice[k]: k for k in token_indice}\n\n    # max_features is the highest integer that could be found in the dataset.\n    max_features = np.max(list(indice_token.keys())) + 1\n\n    # Augmenting x_train and x_test with n-grams features\n    x_train = add_ngram(x_train, token_indice, ngram_range)\n    x_test = add_ngram(x_test, token_indice, ngram_range)\n    print('Average train sequence length: {}'.format(np.mean(list(map(len, x_train)), dtype=int)))\n    print('Average test sequence length: {}'.format(np.mean(list(map(len, x_test)), dtype=int)))\n\nprint('Pad sequences (samples x time)')\nx_train = sequence.pad_sequences(x_train, maxlen=maxlen)\nx_test = sequence.pad_sequences(x_test, maxlen=maxlen)\nprint('x_train shape:', x_train.shape)\nprint('x_test shape:', x_test.shape)\n\nprint('Build model...')\nmodel = Sequential()\n\n# we start off with an efficient embedding layer which maps\n# our vocab indices into embedding_dims dimensions\nmodel.add(Embedding(max_features,\n                    embedding_dims,\n                    input_length=maxlen))\n\n# we add a GlobalAveragePooling1D, which will average the embeddings\n# of all words in the document\nmodel.add(GlobalAveragePooling1D())\n\n# We project onto a single unit output layer, and squash it with a sigmoid:\nmodel.add(Dense(1, activation='sigmoid'))\n\nmodel.compile(loss='binary_crossentropy',\n              optimizer='adam',\n              metrics=['accuracy'])\n\nmodel.fit(x_train, y_train,\n          batch_size=batch_size,\n          epochs=epochs,\n          validation_data=(x_test, y_test))\n"
  },
  {
    "path": "Keras/imdb_lstm.py",
    "content": "'''Trains a LSTM on the IMDB sentiment classification task.\nThe dataset is actually too small for LSTM to be of any advantage\ncompared to simpler, much faster methods such as TF-IDF + LogReg.\nNotes:\n\n- RNNs are tricky. Choice of batch size is important,\nchoice of loss and optimizer is critical, etc.\nSome configurations won't converge.\n\n- LSTM loss decrease patterns during training can be quite different\nfrom what you see with CNNs/MLPs/etc.\n'''\nfrom __future__ import print_function\n\nfrom keras.preprocessing import sequence\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Embedding\nfrom keras.layers import LSTM\nfrom keras.datasets import imdb\n\nmax_features = 20000\nmaxlen = 80  # cut texts after this number of words (among top max_features most common words)\nbatch_size = 32\n\nprint('Loading data...')\n(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\nprint(len(x_train), 'train sequences')\nprint(len(x_test), 'test sequences')\n\nprint('Pad sequences (samples x time)')\nx_train = sequence.pad_sequences(x_train, maxlen=maxlen)\nx_test = sequence.pad_sequences(x_test, maxlen=maxlen)\nprint('x_train shape:', x_train.shape)\nprint('x_test shape:', x_test.shape)\n\nprint('Build model...')\nmodel = Sequential()\nmodel.add(Embedding(max_features, 128))\nmodel.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))\nmodel.add(Dense(1, activation='sigmoid'))\n\n# try using different optimizers and different optimizer configs\nmodel.compile(loss='binary_crossentropy',\n              optimizer='adam',\n              metrics=['accuracy'])\n\nprint('Train...')\nmodel.fit(x_train, y_train,\n          batch_size=batch_size,\n          epochs=15,\n          validation_data=(x_test, y_test))\nscore, acc = model.evaluate(x_test, y_test,\n                            batch_size=batch_size)\nprint('Test score:', score)\nprint('Test accuracy:', acc)\n"
  },
  {
    "path": "Keras/lstm_benchmark.py",
    "content": "'''Compare LSTM implementations on the IMDB sentiment classification task.\n\nimplementation=0 preprocesses input to the LSTM which typically results in\nfaster computations at the expense of increased peak memory usage as the\npreprocessed input must be kept in memory.\n\nimplementation=1 does away with the preprocessing, meaning that it might take\na little longer, but should require less peak memory.\n\nimplementation=2 concatenates the input, output and forget gate's weights\ninto one, large matrix, resulting in faster computation time as the GPU can\nutilize more cores, at the expense of reduced regularization because the same\ndropout is shared across the gates.\n\nNote that the relative performance of the different implementations can\nvary depending on your device, your model and the size of your data.\n'''\n\nimport time\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom keras.preprocessing import sequence\nfrom keras.models import Sequential\nfrom keras.layers import Embedding, Dense, LSTM, Dropout\nfrom keras.datasets import imdb\n\nmax_features = 20000\nmax_length = 80\nembedding_dim = 256\nbatch_size = 128\nepochs = 10\nmodes = [0, 1, 2]\n\nprint('Loading data...')\n(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=max_features)\nX_train = sequence.pad_sequences(X_train, max_length)\nX_test = sequence.pad_sequences(X_test, max_length)\n\n# Compile and train different models while meauring performance.\nresults = []\nfor mode in modes:\n    print('Testing mode: implementation={}'.format(mode))\n\n    model = Sequential()\n    model.add(Embedding(max_features, embedding_dim,\n                        input_length=max_length))\n    model.add(Dropout(0.2))\n    model.add(LSTM(embedding_dim,\n                   dropout=0.2,\n                   recurrent_dropout=0.2,\n                   implementation=mode))\n    model.add(Dense(1, activation='sigmoid'))\n    model.compile(loss='binary_crossentropy',\n                  optimizer='adam',\n                  metrics=['accuracy'])\n\n    start_time = time.time()\n    history = model.fit(X_train, y_train,\n                        batch_size=batch_size,\n                        epochs=epochs,\n                        validation_data=(X_test, y_test))\n    average_time_per_epoch = (time.time() - start_time) / epochs\n\n    results.append((history, average_time_per_epoch))\n\n# Compare models' accuracy, loss and elapsed time per epoch.\nplt.style.use('ggplot')\nax1 = plt.subplot2grid((2, 2), (0, 0))\nax1.set_title('Accuracy')\nax1.set_ylabel('Validation Accuracy')\nax1.set_xlabel('Epochs')\nax2 = plt.subplot2grid((2, 2), (1, 0))\nax2.set_title('Loss')\nax2.set_ylabel('Validation Loss')\nax2.set_xlabel('Epochs')\nax3 = plt.subplot2grid((2, 2), (0, 1), rowspan=2)\nax3.set_title('Time')\nax3.set_ylabel('Seconds')\nfor mode, result in zip(modes, results):\n    ax1.plot(result[0].epoch, result[0].history['val_acc'], label=mode)\n    ax2.plot(result[0].epoch, result[0].history['val_loss'], label=mode)\nax1.legend()\nax2.legend()\nax3.bar(np.arange(len(results)), [x[1] for x in results],\n        tick_label=modes, align='center')\nplt.tight_layout()\nplt.show()\n"
  },
  {
    "path": "Keras/lstm_text_generation.py",
    "content": "'''Example script to generate text from Nietzsche's writings.\n\nAt least 20 epochs are required before the generated text\nstarts sounding coherent.\n\nIt is recommended to run this script on GPU, as recurrent\nnetworks are quite computationally intensive.\n\nIf you try this script on new data, make sure your corpus\nhas at least ~100k characters. ~1M is better.\n'''\n\nfrom __future__ import print_function\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation\nfrom keras.layers import LSTM\nfrom keras.optimizers import RMSprop\nfrom keras.utils.data_utils import get_file\nimport numpy as np\nimport random\nimport sys\n\npath = get_file('nietzsche.txt', origin='https://s3.amazonaws.com/text-datasets/nietzsche.txt')\ntext = open(path).read().lower()\nprint('corpus length:', len(text))\n\nchars = sorted(list(set(text)))\nprint('total chars:', len(chars))\nchar_indices = dict((c, i) for i, c in enumerate(chars))\nindices_char = dict((i, c) for i, c in enumerate(chars))\n\n# cut the text in semi-redundant sequences of maxlen characters\nmaxlen = 40\nstep = 3\nsentences = []\nnext_chars = []\nfor i in range(0, len(text) - maxlen, step):\n    sentences.append(text[i: i + maxlen])\n    next_chars.append(text[i + maxlen])\nprint('nb sequences:', len(sentences))\n\nprint('Vectorization...')\nX = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)\ny = np.zeros((len(sentences), len(chars)), dtype=np.bool)\nfor i, sentence in enumerate(sentences):\n    for t, char in enumerate(sentence):\n        X[i, t, char_indices[char]] = 1\n    y[i, char_indices[next_chars[i]]] = 1\n\n\n# build the model: a single LSTM\nprint('Build model...')\nmodel = Sequential()\nmodel.add(LSTM(128, input_shape=(maxlen, len(chars))))\nmodel.add(Dense(len(chars)))\nmodel.add(Activation('softmax'))\n\noptimizer = RMSprop(lr=0.01)\nmodel.compile(loss='categorical_crossentropy', optimizer=optimizer)\n\n\ndef sample(preds, temperature=1.0):\n    # helper function to sample an index from a probability array\n    preds = np.asarray(preds).astype('float64')\n    preds = np.log(preds) / temperature\n    exp_preds = np.exp(preds)\n    preds = exp_preds / np.sum(exp_preds)\n    probas = np.random.multinomial(1, preds, 1)\n    return np.argmax(probas)\n\n# train the model, output generated text after each iteration\nfor iteration in range(1, 60):\n    print()\n    print('-' * 50)\n    print('Iteration', iteration)\n    model.fit(X, y,\n              batch_size=128,\n              epochs=1)\n\n    start_index = random.randint(0, len(text) - maxlen - 1)\n\n    for diversity in [0.2, 0.5, 1.0, 1.2]:\n        print()\n        print('----- diversity:', diversity)\n\n        generated = ''\n        sentence = text[start_index: start_index + maxlen]\n        generated += sentence\n        print('----- Generating with seed: \"' + sentence + '\"')\n        sys.stdout.write(generated)\n\n        for i in range(400):\n            x = np.zeros((1, maxlen, len(chars)))\n            for t, char in enumerate(sentence):\n                x[0, t, char_indices[char]] = 1.\n\n            preds = model.predict(x, verbose=0)[0]\n            next_index = sample(preds, diversity)\n            next_char = indices_char[next_index]\n\n            generated += next_char\n            sentence = sentence[1:] + next_char\n\n            sys.stdout.write(next_char)\n            sys.stdout.flush()\n        print()\n"
  },
  {
    "path": "Keras/mnist_acgan.py",
    "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nTrain an Auxiliary Classifier Generative Adversarial Network (ACGAN) on the\nMNIST dataset. See https://arxiv.org/abs/1610.09585 for more details.\n\nYou should start to see reasonable images after ~5 epochs, and good images\nby ~15 epochs. You should use a GPU, as the convolution-heavy operations are\nvery slow on the CPU. Prefer the TensorFlow backend if you plan on iterating,\nas the compilation time can be a blocker using Theano.\n\nTimings:\n\nHardware           | Backend | Time / Epoch\n-------------------------------------------\n CPU               | TF      | 3 hrs\n Titan X (maxwell) | TF      | 4 min\n Titan X (maxwell) | TH      | 7 min\n\nConsult https://github.com/lukedeo/keras-acgan for more information and\nexample output\n\"\"\"\nfrom __future__ import print_function\n\nfrom collections import defaultdict\ntry:\n    import cPickle as pickle\nexcept ImportError:\n    import pickle\nfrom PIL import Image\n\nfrom six.moves import range\n\nimport keras.backend as K\nfrom keras.datasets import mnist\nfrom keras import layers\nfrom keras.layers import Input, Dense, Reshape, Flatten, Embedding, Dropout\nfrom keras.layers.advanced_activations import LeakyReLU\nfrom keras.layers.convolutional import UpSampling2D, Conv2D\nfrom keras.models import Sequential, Model\nfrom keras.optimizers import Adam\nfrom keras.utils.generic_utils import Progbar\nimport numpy as np\n\nnp.random.seed(1337)\n\nK.set_image_data_format('channels_first')\n\n\ndef build_generator(latent_size):\n    # we will map a pair of (z, L), where z is a latent vector and L is a\n    # label drawn from P_c, to image space (..., 1, 28, 28)\n    cnn = Sequential()\n\n    cnn.add(Dense(1024, input_dim=latent_size, activation='relu'))\n    cnn.add(Dense(128 * 7 * 7, activation='relu'))\n    cnn.add(Reshape((128, 7, 7)))\n\n    # upsample to (..., 14, 14)\n    cnn.add(UpSampling2D(size=(2, 2)))\n    cnn.add(Conv2D(256, 5, padding='same',\n                   activation='relu',\n                   kernel_initializer='glorot_normal'))\n\n    # upsample to (..., 28, 28)\n    cnn.add(UpSampling2D(size=(2, 2)))\n    cnn.add(Conv2D(128, 5, padding='same',\n                   activation='relu',\n                   kernel_initializer='glorot_normal'))\n\n    # take a channel axis reduction\n    cnn.add(Conv2D(1, 2, padding='same',\n                   activation='tanh',\n                   kernel_initializer='glorot_normal'))\n\n    # this is the z space commonly refered to in GAN papers\n    latent = Input(shape=(latent_size, ))\n\n    # this will be our label\n    image_class = Input(shape=(1,), dtype='int32')\n\n    # 10 classes in MNIST\n    cls = Flatten()(Embedding(10, latent_size,\n                              embeddings_initializer='glorot_normal')(image_class))\n\n    # hadamard product between z-space and a class conditional embedding\n    h = layers.multiply([latent, cls])\n\n    fake_image = cnn(h)\n\n    return Model([latent, image_class], fake_image)\n\n\ndef build_discriminator():\n    # build a relatively standard conv net, with LeakyReLUs as suggested in\n    # the reference paper\n    cnn = Sequential()\n\n    cnn.add(Conv2D(32, 3, padding='same', strides=2,\n                   input_shape=(1, 28, 28)))\n    cnn.add(LeakyReLU())\n    cnn.add(Dropout(0.3))\n\n    cnn.add(Conv2D(64, 3, padding='same', strides=1))\n    cnn.add(LeakyReLU())\n    cnn.add(Dropout(0.3))\n\n    cnn.add(Conv2D(128, 3, padding='same', strides=2))\n    cnn.add(LeakyReLU())\n    cnn.add(Dropout(0.3))\n\n    cnn.add(Conv2D(256, 3, padding='same', strides=1))\n    cnn.add(LeakyReLU())\n    cnn.add(Dropout(0.3))\n\n    cnn.add(Flatten())\n\n    image = Input(shape=(1, 28, 28))\n\n    features = cnn(image)\n\n    # first output (name=generation) is whether or not the discriminator\n    # thinks the image that is being shown is fake, and the second output\n    # (name=auxiliary) is the class that the discriminator thinks the image\n    # belongs to.\n    fake = Dense(1, activation='sigmoid', name='generation')(features)\n    aux = Dense(10, activation='softmax', name='auxiliary')(features)\n\n    return Model(image, [fake, aux])\n\nif __name__ == '__main__':\n\n    # batch and latent size taken from the paper\n    epochs = 50\n    batch_size = 100\n    latent_size = 100\n\n    # Adam parameters suggested in https://arxiv.org/abs/1511.06434\n    adam_lr = 0.0002\n    adam_beta_1 = 0.5\n\n    # build the discriminator\n    discriminator = build_discriminator()\n    discriminator.compile(\n        optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),\n        loss=['binary_crossentropy', 'sparse_categorical_crossentropy']\n    )\n\n    # build the generator\n    generator = build_generator(latent_size)\n    generator.compile(optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),\n                      loss='binary_crossentropy')\n\n    latent = Input(shape=(latent_size, ))\n    image_class = Input(shape=(1,), dtype='int32')\n\n    # get a fake image\n    fake = generator([latent, image_class])\n\n    # we only want to be able to train generation for the combined model\n    discriminator.trainable = False\n    fake, aux = discriminator(fake)\n    combined = Model([latent, image_class], [fake, aux])\n\n    combined.compile(\n        optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),\n        loss=['binary_crossentropy', 'sparse_categorical_crossentropy']\n    )\n\n    # get our mnist data, and force it to be of shape (..., 1, 28, 28) with\n    # range [-1, 1]\n    (X_train, y_train), (X_test, y_test) = mnist.load_data()\n    X_train = (X_train.astype(np.float32) - 127.5) / 127.5\n    X_train = np.expand_dims(X_train, axis=1)\n\n    X_test = (X_test.astype(np.float32) - 127.5) / 127.5\n    X_test = np.expand_dims(X_test, axis=1)\n\n    num_train, num_test = X_train.shape[0], X_test.shape[0]\n\n    train_history = defaultdict(list)\n    test_history = defaultdict(list)\n\n    for epoch in range(epochs):\n        print('Epoch {} of {}'.format(epoch + 1, epochs))\n\n        num_batches = int(X_train.shape[0] / batch_size)\n        progress_bar = Progbar(target=num_batches)\n\n        epoch_gen_loss = []\n        epoch_disc_loss = []\n\n        for index in range(num_batches):\n            progress_bar.update(index)\n            # generate a new batch of noise\n            noise = np.random.uniform(-1, 1, (batch_size, latent_size))\n\n            # get a batch of real images\n            image_batch = X_train[index * batch_size:(index + 1) * batch_size]\n            label_batch = y_train[index * batch_size:(index + 1) * batch_size]\n\n            # sample some labels from p_c\n            sampled_labels = np.random.randint(0, 10, batch_size)\n\n            # generate a batch of fake images, using the generated labels as a\n            # conditioner. We reshape the sampled labels to be\n            # (batch_size, 1) so that we can feed them into the embedding\n            # layer as a length one sequence\n            generated_images = generator.predict(\n                [noise, sampled_labels.reshape((-1, 1))], verbose=0)\n\n            X = np.concatenate((image_batch, generated_images))\n            y = np.array([1] * batch_size + [0] * batch_size)\n            aux_y = np.concatenate((label_batch, sampled_labels), axis=0)\n\n            # see if the discriminator can figure itself out...\n            epoch_disc_loss.append(discriminator.train_on_batch(X, [y, aux_y]))\n\n            # make new noise. we generate 2 * batch size here such that we have\n            # the generator optimize over an identical number of images as the\n            # discriminator\n            noise = np.random.uniform(-1, 1, (2 * batch_size, latent_size))\n            sampled_labels = np.random.randint(0, 10, 2 * batch_size)\n\n            # we want to train the generator to trick the discriminator\n            # For the generator, we want all the {fake, not-fake} labels to say\n            # not-fake\n            trick = np.ones(2 * batch_size)\n\n            epoch_gen_loss.append(combined.train_on_batch(\n                [noise, sampled_labels.reshape((-1, 1))],\n                [trick, sampled_labels]))\n\n        print('\\nTesting for epoch {}:'.format(epoch + 1))\n\n        # evaluate the testing loss here\n\n        # generate a new batch of noise\n        noise = np.random.uniform(-1, 1, (num_test, latent_size))\n\n        # sample some labels from p_c and generate images from them\n        sampled_labels = np.random.randint(0, 10, num_test)\n        generated_images = generator.predict(\n            [noise, sampled_labels.reshape((-1, 1))], verbose=False)\n\n        X = np.concatenate((X_test, generated_images))\n        y = np.array([1] * num_test + [0] * num_test)\n        aux_y = np.concatenate((y_test, sampled_labels), axis=0)\n\n        # see if the discriminator can figure itself out...\n        discriminator_test_loss = discriminator.evaluate(\n            X, [y, aux_y], verbose=False)\n\n        discriminator_train_loss = np.mean(np.array(epoch_disc_loss), axis=0)\n\n        # make new noise\n        noise = np.random.uniform(-1, 1, (2 * num_test, latent_size))\n        sampled_labels = np.random.randint(0, 10, 2 * num_test)\n\n        trick = np.ones(2 * num_test)\n\n        generator_test_loss = combined.evaluate(\n            [noise, sampled_labels.reshape((-1, 1))],\n            [trick, sampled_labels], verbose=False)\n\n        generator_train_loss = np.mean(np.array(epoch_gen_loss), axis=0)\n\n        # generate an epoch report on performance\n        train_history['generator'].append(generator_train_loss)\n        train_history['discriminator'].append(discriminator_train_loss)\n\n        test_history['generator'].append(generator_test_loss)\n        test_history['discriminator'].append(discriminator_test_loss)\n\n        print('{0:<22s} | {1:4s} | {2:15s} | {3:5s}'.format(\n            'component', *discriminator.metrics_names))\n        print('-' * 65)\n\n        ROW_FMT = '{0:<22s} | {1:<4.2f} | {2:<15.2f} | {3:<5.2f}'\n        print(ROW_FMT.format('generator (train)',\n                             *train_history['generator'][-1]))\n        print(ROW_FMT.format('generator (test)',\n                             *test_history['generator'][-1]))\n        print(ROW_FMT.format('discriminator (train)',\n                             *train_history['discriminator'][-1]))\n        print(ROW_FMT.format('discriminator (test)',\n                             *test_history['discriminator'][-1]))\n\n        # save weights every epoch\n        generator.save_weights(\n            'params_generator_epoch_{0:03d}.hdf5'.format(epoch), True)\n        discriminator.save_weights(\n            'params_discriminator_epoch_{0:03d}.hdf5'.format(epoch), True)\n\n        # generate some digits to display\n        noise = np.random.uniform(-1, 1, (100, latent_size))\n\n        sampled_labels = np.array([\n            [i] * 10 for i in range(10)\n        ]).reshape(-1, 1)\n\n        # get a batch to display\n        generated_images = generator.predict(\n            [noise, sampled_labels], verbose=0)\n\n        # arrange them into a grid\n        img = (np.concatenate([r.reshape(-1, 28)\n                               for r in np.split(generated_images, 10)\n                               ], axis=-1) * 127.5 + 127.5).astype(np.uint8)\n\n        Image.fromarray(img).save(\n            'plot_epoch_{0:03d}_generated.png'.format(epoch))\n\n    pickle.dump({'train': train_history, 'test': test_history},\n                open('acgan-history.pkl', 'wb'))\n"
  },
  {
    "path": "Keras/mnist_cnn.py",
    "content": "'''Trains a simple convnet on the MNIST dataset.\n\nGets to 99.25% test accuracy after 12 epochs\n(there is still a lot of margin for parameter tuning).\n16 seconds per epoch on a GRID K520 GPU.\n'''\n\nfrom __future__ import print_function\nimport keras\nfrom keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras import backend as K\n\nbatch_size = 128\nnum_classes = 10\nepochs = 12\n\n# input image dimensions\nimg_rows, img_cols = 28, 28\n\n# the data, shuffled and split between train and test sets\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\nif K.image_data_format() == 'channels_first':\n    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n    input_shape = (1, img_rows, img_cols)\nelse:\n    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n    input_shape = (img_rows, img_cols, 1)\n\nx_train = x_train.astype('float32')\nx_test = x_test.astype('float32')\nx_train /= 255\nx_test /= 255\nprint('x_train shape:', x_train.shape)\nprint(x_train.shape[0], 'train samples')\nprint(x_test.shape[0], 'test samples')\n\n# convert class vectors to binary class matrices\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\n\nmodel = Sequential()\nmodel.add(Conv2D(32, kernel_size=(3, 3),\n                 activation='relu',\n                 input_shape=input_shape))\nmodel.add(Conv2D(64, (3, 3), activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\nmodel.add(Flatten())\nmodel.add(Dense(128, activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(num_classes, activation='softmax'))\n\nmodel.compile(loss=keras.losses.categorical_crossentropy,\n              optimizer=keras.optimizers.Adadelta(),\n              metrics=['accuracy'])\n\nmodel.fit(x_train, y_train,\n          batch_size=batch_size,\n          epochs=epochs,\n          verbose=1,\n          validation_data=(x_test, y_test))\nscore = model.evaluate(x_test, y_test, verbose=0)\nprint('Test loss:', score[0])\nprint('Test accuracy:', score[1])\n"
  },
  {
    "path": "Keras/mnist_hierarchical_rnn.py",
    "content": "\"\"\"This is an example of using Hierarchical RNN (HRNN) to classify MNIST digits.\n\nHRNNs can learn across multiple levels of temporal hiearchy over a complex sequence.\nUsually, the first recurrent layer of an HRNN encodes a sentence (e.g. of word vectors)\ninto a  sentence vector. The second recurrent layer then encodes a sequence of\nsuch vectors (encoded by the first layer) into a document vector. This\ndocument vector is considered to preserve both the word-level and\nsentence-level structure of the context.\n\n# References\n    - [A Hierarchical Neural Autoencoder for Paragraphs and Documents](https://arxiv.org/abs/1506.01057)\n        Encodes paragraphs and documents with HRNN.\n        Results have shown that HRNN outperforms standard\n        RNNs and may play some role in more sophisticated generation tasks like\n        summarization or question answering.\n    - [Hierarchical recurrent neural network for skeleton based action recognition](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298714)\n        Achieved state-of-the-art results on skeleton based action recognition with 3 levels\n        of bidirectional HRNN combined with fully connected layers.\n\nIn the below MNIST example the first LSTM layer first encodes every\ncolumn of pixels of shape (28, 1) to a column vector of shape (128,). The second LSTM\nlayer encodes then these 28 column vectors of shape (28, 128) to a image vector\nrepresenting the whole image. A final Dense layer is added for prediction.\n\nAfter 5 epochs: train acc: 0.9858, val acc: 0.9864\n\"\"\"\nfrom __future__ import print_function\n\nimport keras\nfrom keras.datasets import mnist\nfrom keras.models import Model\nfrom keras.layers import Input, Dense, TimeDistributed\nfrom keras.layers import LSTM\n\n# Training parameters.\nbatch_size = 32\nnum_classes = 10\nepochs = 5\n\n# Embedding dimensions.\nrow_hidden = 128\ncol_hidden = 128\n\n# The data, shuffled and split between train and test sets.\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\n# Reshapes data to 4D for Hierarchical RNN.\nx_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\nx_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\nx_train = x_train.astype('float32')\nx_test = x_test.astype('float32')\nx_train /= 255\nx_test /= 255\nprint('x_train shape:', x_train.shape)\nprint(x_train.shape[0], 'train samples')\nprint(x_test.shape[0], 'test samples')\n\n# Converts class vectors to binary class matrices.\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\n\nrow, col, pixel = x_train.shape[1:]\n\n# 4D input.\nx = Input(shape=(row, col, pixel))\n\n# Encodes a row of pixels using TimeDistributed Wrapper.\nencoded_rows = TimeDistributed(LSTM(row_hidden))(x)\n\n# Encodes columns of encoded rows.\nencoded_columns = LSTM(col_hidden)(encoded_rows)\n\n# Final predictions and model.\nprediction = Dense(num_classes, activation='softmax')(encoded_columns)\nmodel = Model(x, prediction)\nmodel.compile(loss='categorical_crossentropy',\n              optimizer='rmsprop',\n              metrics=['accuracy'])\n\n# Training.\nmodel.fit(x_train, y_train,\n          batch_size=batch_size,\n          epochs=epochs,\n          verbose=1,\n          validation_data=(x_test, y_test))\n\n# Evaluation.\nscores = model.evaluate(x_test, y_test, verbose=0)\nprint('Test loss:', scores[0])\nprint('Test accuracy:', scores[1])\n"
  },
  {
    "path": "Keras/mnist_irnn.py",
    "content": "'''This is a reproduction of the IRNN experiment\nwith pixel-by-pixel sequential MNIST in\n\"A Simple Way to Initialize Recurrent Networks of Rectified Linear Units\"\nby Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton\n\narxiv:1504.00941v2 [cs.NE] 7 Apr 2015\nhttp://arxiv.org/pdf/1504.00941v2.pdf\n\nOptimizer is replaced with RMSprop which yields more stable and steady\nimprovement.\n\nReaches 0.93 train/test accuracy after 900 epochs\n(which roughly corresponds to 1687500 steps in the original paper.)\n'''\n\nfrom __future__ import print_function\n\nimport keras\nfrom keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation\nfrom keras.layers import SimpleRNN\nfrom keras import initializers\nfrom keras.optimizers import RMSprop\n\nbatch_size = 32\nnum_classes = 10\nepochs = 200\nhidden_units = 100\n\nlearning_rate = 1e-6\nclip_norm = 1.0\n\n# the data, shuffled and split between train and test sets\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\nx_train = x_train.reshape(x_train.shape[0], -1, 1)\nx_test = x_test.reshape(x_test.shape[0], -1, 1)\nx_train = x_train.astype('float32')\nx_test = x_test.astype('float32')\nx_train /= 255\nx_test /= 255\nprint('x_train shape:', x_train.shape)\nprint(x_train.shape[0], 'train samples')\nprint(x_test.shape[0], 'test samples')\n\n# convert class vectors to binary class matrices\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\n\nprint('Evaluate IRNN...')\nmodel = Sequential()\nmodel.add(SimpleRNN(hidden_units,\n                    kernel_initializer=initializers.RandomNormal(stddev=0.001),\n                    recurrent_initializer=initializers.Identity(gain=1.0),\n                    activation='relu',\n                    input_shape=x_train.shape[1:]))\nmodel.add(Dense(num_classes))\nmodel.add(Activation('softmax'))\nrmsprop = RMSprop(lr=learning_rate)\nmodel.compile(loss='categorical_crossentropy',\n              optimizer=rmsprop,\n              metrics=['accuracy'])\n\nmodel.fit(x_train, y_train,\n          batch_size=batch_size,\n          epochs=epochs,\n          verbose=1,\n          validation_data=(x_test, y_test))\n\nscores = model.evaluate(x_test, y_test, verbose=0)\nprint('IRNN test score:', scores[0])\nprint('IRNN test accuracy:', scores[1])\n"
  },
  {
    "path": "Keras/mnist_mlp.py",
    "content": "'''Trains a simple deep NN on the MNIST dataset.\n\nGets to 98.40% test accuracy after 20 epochs\n(there is *a lot* of margin for parameter tuning).\n2 seconds per epoch on a K520 GPU.\n'''\n\nfrom __future__ import print_function\n\nimport keras\nfrom keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout\nfrom keras.optimizers import RMSprop\n\n\nbatch_size = 128\nnum_classes = 10\nepochs = 20\n\n# the data, shuffled and split between train and test sets\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\nx_train = x_train.reshape(60000, 784)\nx_test = x_test.reshape(10000, 784)\nx_train = x_train.astype('float32')\nx_test = x_test.astype('float32')\nx_train /= 255\nx_test /= 255\nprint(x_train.shape[0], 'train samples')\nprint(x_test.shape[0], 'test samples')\n\n# convert class vectors to binary class matrices\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\n\nmodel = Sequential()\nmodel.add(Dense(512, activation='relu', input_shape=(784,)))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(512, activation='relu'))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(10, activation='softmax'))\n\nmodel.summary()\n\nmodel.compile(loss='categorical_crossentropy',\n              optimizer=RMSprop(),\n              metrics=['accuracy'])\n\nhistory = model.fit(x_train, y_train,\n                    batch_size=batch_size,\n                    epochs=epochs,\n                    verbose=1,\n                    validation_data=(x_test, y_test))\nscore = model.evaluate(x_test, y_test, verbose=0)\nprint('Test loss:', score[0])\nprint('Test accuracy:', score[1])\n"
  },
  {
    "path": "Keras/mnist_net2net.py",
    "content": "'''This is an implementation of Net2Net experiment with MNIST in\n'Net2Net: Accelerating Learning via Knowledge Transfer'\nby Tianqi Chen, Ian Goodfellow, and Jonathon Shlens\n\narXiv:1511.05641v4 [cs.LG] 23 Apr 2016\nhttp://arxiv.org/abs/1511.05641\n\nNotes\n- What:\n  + Net2Net is a group of methods to transfer knowledge from a teacher neural\n    net to a student net,so that the student net can be trained faster than\n    from scratch.\n  + The paper discussed two specific methods of Net2Net, i.e. Net2WiderNet\n    and Net2DeeperNet.\n  + Net2WiderNet replaces a model with an equivalent wider model that has\n    more units in each hidden layer.\n  + Net2DeeperNet replaces a model with an equivalent deeper model.\n  + Both are based on the idea of 'function-preserving transformations of\n    neural nets'.\n- Why:\n  + Enable fast exploration of multiple neural nets in experimentation and\n    design process,by creating a series of wider and deeper models with\n    transferable knowledge.\n  + Enable 'lifelong learning system' by gradually adjusting model complexity\n    to data availability,and reusing transferable knowledge.\n\nExperiments\n- Teacher model: a basic CNN model trained on MNIST for 3 epochs.\n- Net2WiderNet experiment:\n  + Student model has a wider Conv2D layer and a wider FC layer.\n  + Comparison of 'random-padding' vs 'net2wider' weight initialization.\n  + With both methods, student model should immediately perform as well as\n    teacher model, but 'net2wider' is slightly better.\n- Net2DeeperNet experiment:\n  + Student model has an extra Conv2D layer and an extra FC layer.\n  + Comparison of 'random-init' vs 'net2deeper' weight initialization.\n  + Starting performance of 'net2deeper' is better than 'random-init'.\n- Hyper-parameters:\n  + SGD with momentum=0.9 is used for training teacher and student models.\n  + Learning rate adjustment: it's suggested to reduce learning rate\n    to 1/10 for student model.\n  + Addition of noise in 'net2wider' is used to break weight symmetry\n    and thus enable full capacity of student models. It is optional\n    when a Dropout layer is used.\n\nResults\n- Tested with 'Theano' backend and 'channels_first' image_data_format.\n- Running on GPU GeForce GTX 980M\n- Performance Comparisons - validation loss values during first 3 epochs:\n(1) teacher_model:             0.075    0.041    0.041\n(2) wider_random_pad:          0.036    0.034    0.032\n(3) wider_net2wider:           0.032    0.030    0.030\n(4) deeper_random_init:        0.061    0.043    0.041\n(5) deeper_net2deeper:         0.032    0.031    0.029\n'''\n\nfrom __future__ import print_function\nfrom six.moves import xrange\nimport numpy as np\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D, MaxPooling2D, Dense, Flatten\nfrom keras.optimizers import SGD\nfrom keras.datasets import mnist\n\nif keras.backend.image_data_format() == 'channels_first':\n    input_shape = (1, 28, 28)  # image shape\nelse:\n    input_shape = (28, 28, 1)  # image shape\nnum_class = 10  # number of class\n\n\n# load and pre-process data\ndef preprocess_input(x):\n    return x.reshape((-1, ) + input_shape) / 255.\n\n\ndef preprocess_output(y):\n    return keras.utils.to_categorical(y)\n\n(train_x, train_y), (validation_x, validation_y) = mnist.load_data()\ntrain_x, validation_x = map(preprocess_input, [train_x, validation_x])\ntrain_y, validation_y = map(preprocess_output, [train_y, validation_y])\nprint('Loading MNIST data...')\nprint('train_x shape:', train_x.shape, 'train_y shape:', train_y.shape)\nprint('validation_x shape:', validation_x.shape,\n      'validation_y shape', validation_y.shape)\n\n\n# knowledge transfer algorithms\ndef wider2net_conv2d(teacher_w1, teacher_b1, teacher_w2, new_width, init):\n    '''Get initial weights for a wider conv2d layer with a bigger filters,\n    by 'random-padding' or 'net2wider'.\n\n    # Arguments\n        teacher_w1: `weight` of conv2d layer to become wider,\n          of shape (filters1, num_channel1, kh1, kw1)\n        teacher_b1: `bias` of conv2d layer to become wider,\n          of shape (filters1, )\n        teacher_w2: `weight` of next connected conv2d layer,\n          of shape (filters2, num_channel2, kh2, kw2)\n        new_width: new `filters` for the wider conv2d layer\n        init: initialization algorithm for new weights,\n          either 'random-pad' or 'net2wider'\n    '''\n    assert teacher_w1.shape[0] == teacher_w2.shape[1], (\n        'successive layers from teacher model should have compatible shapes')\n    assert teacher_w1.shape[0] == teacher_b1.shape[0], (\n        'weight and bias from same layer should have compatible shapes')\n    assert new_width > teacher_w1.shape[0], (\n        'new width (filters) should be bigger than the existing one')\n\n    n = new_width - teacher_w1.shape[0]\n    if init == 'random-pad':\n        new_w1 = np.random.normal(0, 0.1, size=(n, ) + teacher_w1.shape[1:])\n        new_b1 = np.ones(n) * 0.1\n        new_w2 = np.random.normal(0, 0.1, size=(\n            teacher_w2.shape[0], n) + teacher_w2.shape[2:])\n    elif init == 'net2wider':\n        index = np.random.randint(teacher_w1.shape[0], size=n)\n        factors = np.bincount(index)[index] + 1.\n        new_w1 = teacher_w1[index, :, :, :]\n        new_b1 = teacher_b1[index]\n        new_w2 = teacher_w2[:, index, :, :] / factors.reshape((1, -1, 1, 1))\n    else:\n        raise ValueError('Unsupported weight initializer: %s' % init)\n\n    student_w1 = np.concatenate((teacher_w1, new_w1), axis=0)\n    if init == 'random-pad':\n        student_w2 = np.concatenate((teacher_w2, new_w2), axis=1)\n    elif init == 'net2wider':\n        # add small noise to break symmetry, so that student model will have\n        # full capacity later\n        noise = np.random.normal(0, 5e-2 * new_w2.std(), size=new_w2.shape)\n        student_w2 = np.concatenate((teacher_w2, new_w2 + noise), axis=1)\n        student_w2[:, index, :, :] = new_w2\n    student_b1 = np.concatenate((teacher_b1, new_b1), axis=0)\n\n    return student_w1, student_b1, student_w2\n\n\ndef wider2net_fc(teacher_w1, teacher_b1, teacher_w2, new_width, init):\n    '''Get initial weights for a wider fully connected (dense) layer\n       with a bigger nout, by 'random-padding' or 'net2wider'.\n\n    # Arguments\n        teacher_w1: `weight` of fc layer to become wider,\n          of shape (nin1, nout1)\n        teacher_b1: `bias` of fc layer to become wider,\n          of shape (nout1, )\n        teacher_w2: `weight` of next connected fc layer,\n          of shape (nin2, nout2)\n        new_width: new `nout` for the wider fc layer\n        init: initialization algorithm for new weights,\n          either 'random-pad' or 'net2wider'\n    '''\n    assert teacher_w1.shape[1] == teacher_w2.shape[0], (\n        'successive layers from teacher model should have compatible shapes')\n    assert teacher_w1.shape[1] == teacher_b1.shape[0], (\n        'weight and bias from same layer should have compatible shapes')\n    assert new_width > teacher_w1.shape[1], (\n        'new width (nout) should be bigger than the existing one')\n\n    n = new_width - teacher_w1.shape[1]\n    if init == 'random-pad':\n        new_w1 = np.random.normal(0, 0.1, size=(teacher_w1.shape[0], n))\n        new_b1 = np.ones(n) * 0.1\n        new_w2 = np.random.normal(0, 0.1, size=(n, teacher_w2.shape[1]))\n    elif init == 'net2wider':\n        index = np.random.randint(teacher_w1.shape[1], size=n)\n        factors = np.bincount(index)[index] + 1.\n        new_w1 = teacher_w1[:, index]\n        new_b1 = teacher_b1[index]\n        new_w2 = teacher_w2[index, :] / factors[:, np.newaxis]\n    else:\n        raise ValueError('Unsupported weight initializer: %s' % init)\n\n    student_w1 = np.concatenate((teacher_w1, new_w1), axis=1)\n    if init == 'random-pad':\n        student_w2 = np.concatenate((teacher_w2, new_w2), axis=0)\n    elif init == 'net2wider':\n        # add small noise to break symmetry, so that student model will have\n        # full capacity later\n        noise = np.random.normal(0, 5e-2 * new_w2.std(), size=new_w2.shape)\n        student_w2 = np.concatenate((teacher_w2, new_w2 + noise), axis=0)\n        student_w2[index, :] = new_w2\n    student_b1 = np.concatenate((teacher_b1, new_b1), axis=0)\n\n    return student_w1, student_b1, student_w2\n\n\ndef deeper2net_conv2d(teacher_w):\n    '''Get initial weights for a deeper conv2d layer by net2deeper'.\n\n    # Arguments\n        teacher_w: `weight` of previous conv2d layer,\n          of shape (filters, num_channel, kh, kw)\n    '''\n    filters, num_channel, kh, kw = teacher_w.shape\n    student_w = np.zeros((filters, filters, kh, kw))\n    for i in xrange(filters):\n        student_w[i, i, (kh - 1) / 2, (kw - 1) / 2] = 1.\n    student_b = np.zeros(filters)\n    return student_w, student_b\n\n\ndef copy_weights(teacher_model, student_model, layer_names):\n    '''Copy weights from teacher_model to student_model,\n     for layers with names listed in layer_names\n    '''\n    for name in layer_names:\n        weights = teacher_model.get_layer(name=name).get_weights()\n        student_model.get_layer(name=name).set_weights(weights)\n\n\n# methods to construct teacher_model and student_models\ndef make_teacher_model(train_data, validation_data, epochs=3):\n    '''Train a simple CNN as teacher model.\n    '''\n    model = Sequential()\n    model.add(Conv2D(64, 3, input_shape=input_shape,\n                     padding='same', name='conv1'))\n    model.add(MaxPooling2D(2, name='pool1'))\n    model.add(Conv2D(64, 3, padding='same', name='conv2'))\n    model.add(MaxPooling2D(2, name='pool2'))\n    model.add(Flatten(name='flatten'))\n    model.add(Dense(64, activation='relu', name='fc1'))\n    model.add(Dense(num_class, activation='softmax', name='fc2'))\n    model.compile(loss='categorical_crossentropy',\n                  optimizer=SGD(lr=0.01, momentum=0.9),\n                  metrics=['accuracy'])\n\n    train_x, train_y = train_data\n    history = model.fit(train_x, train_y,\n                        epochs=epochs,\n                        validation_data=validation_data)\n    return model, history\n\n\ndef make_wider_student_model(teacher_model, train_data,\n                             validation_data, init, epochs=3):\n    '''Train a wider student model based on teacher_model,\n       with either 'random-pad' (baseline) or 'net2wider'\n    '''\n    new_conv1_width = 128\n    new_fc1_width = 128\n\n    model = Sequential()\n    # a wider conv1 compared to teacher_model\n    model.add(Conv2D(new_conv1_width, 3, input_shape=input_shape,\n                     padding='same', name='conv1'))\n    model.add(MaxPooling2D(2, name='pool1'))\n    model.add(Conv2D(64, 3, padding='same', name='conv2'))\n    model.add(MaxPooling2D(2, name='pool2'))\n    model.add(Flatten(name='flatten'))\n    # a wider fc1 compared to teacher model\n    model.add(Dense(new_fc1_width, activation='relu', name='fc1'))\n    model.add(Dense(num_class, activation='softmax', name='fc2'))\n\n    # The weights for other layers need to be copied from teacher_model\n    # to student_model, except for widened layers\n    # and their immediate downstreams, which will be initialized separately.\n    # For this example there are no other layers that need to be copied.\n\n    w_conv1, b_conv1 = teacher_model.get_layer('conv1').get_weights()\n    w_conv2, b_conv2 = teacher_model.get_layer('conv2').get_weights()\n    new_w_conv1, new_b_conv1, new_w_conv2 = wider2net_conv2d(\n        w_conv1, b_conv1, w_conv2, new_conv1_width, init)\n    model.get_layer('conv1').set_weights([new_w_conv1, new_b_conv1])\n    model.get_layer('conv2').set_weights([new_w_conv2, b_conv2])\n\n    w_fc1, b_fc1 = teacher_model.get_layer('fc1').get_weights()\n    w_fc2, b_fc2 = teacher_model.get_layer('fc2').get_weights()\n    new_w_fc1, new_b_fc1, new_w_fc2 = wider2net_fc(\n        w_fc1, b_fc1, w_fc2, new_fc1_width, init)\n    model.get_layer('fc1').set_weights([new_w_fc1, new_b_fc1])\n    model.get_layer('fc2').set_weights([new_w_fc2, b_fc2])\n\n    model.compile(loss='categorical_crossentropy',\n                  optimizer=SGD(lr=0.001, momentum=0.9),\n                  metrics=['accuracy'])\n\n    train_x, train_y = train_data\n    history = model.fit(train_x, train_y,\n                        epochs=epochs,\n                        validation_data=validation_data)\n    return model, history\n\n\ndef make_deeper_student_model(teacher_model, train_data,\n                              validation_data, init, epochs=3):\n    '''Train a deeper student model based on teacher_model,\n       with either 'random-init' (baseline) or 'net2deeper'\n    '''\n    model = Sequential()\n    model.add(Conv2D(64, 3, input_shape=input_shape,\n                     padding='same', name='conv1'))\n    model.add(MaxPooling2D(2, name='pool1'))\n    model.add(Conv2D(64, 3, padding='same', name='conv2'))\n    # add another conv2d layer to make original conv2 deeper\n    if init == 'net2deeper':\n        prev_w, _ = model.get_layer('conv2').get_weights()\n        new_weights = deeper2net_conv2d(prev_w)\n        model.add(Conv2D(64, 3, padding='same',\n                         name='conv2-deeper', weights=new_weights))\n    elif init == 'random-init':\n        model.add(Conv2D(64, 3, padding='same', name='conv2-deeper'))\n    else:\n        raise ValueError('Unsupported weight initializer: %s' % init)\n    model.add(MaxPooling2D(2, name='pool2'))\n    model.add(Flatten(name='flatten'))\n    model.add(Dense(64, activation='relu', name='fc1'))\n    # add another fc layer to make original fc1 deeper\n    if init == 'net2deeper':\n        # net2deeper for fc layer with relu, is just an identity initializer\n        model.add(Dense(64, init='identity',\n                        activation='relu', name='fc1-deeper'))\n    elif init == 'random-init':\n        model.add(Dense(64, activation='relu', name='fc1-deeper'))\n    else:\n        raise ValueError('Unsupported weight initializer: %s' % init)\n    model.add(Dense(num_class, activation='softmax', name='fc2'))\n\n    # copy weights for other layers\n    copy_weights(teacher_model, model, layer_names=[\n                 'conv1', 'conv2', 'fc1', 'fc2'])\n\n    model.compile(loss='categorical_crossentropy',\n                  optimizer=SGD(lr=0.001, momentum=0.9),\n                  metrics=['accuracy'])\n\n    train_x, train_y = train_data\n    history = model.fit(train_x, train_y,\n                        epochs=epochs,\n                        validation_data=validation_data)\n    return model, history\n\n\n# experiments setup\ndef net2wider_experiment():\n    '''Benchmark performances of\n    (1) a teacher model,\n    (2) a wider student model with `random_pad` initializer\n    (3) a wider student model with `Net2WiderNet` initializer\n    '''\n    train_data = (train_x, train_y)\n    validation_data = (validation_x, validation_y)\n    print('\\nExperiment of Net2WiderNet ...')\n    print('\\nbuilding teacher model ...')\n    teacher_model, _ = make_teacher_model(train_data,\n                                          validation_data,\n                                          epochs=3)\n\n    print('\\nbuilding wider student model by random padding ...')\n    make_wider_student_model(teacher_model, train_data,\n                             validation_data, 'random-pad',\n                             epochs=3)\n    print('\\nbuilding wider student model by net2wider ...')\n    make_wider_student_model(teacher_model, train_data,\n                             validation_data, 'net2wider',\n                             epochs=3)\n\n\ndef net2deeper_experiment():\n    '''Benchmark performances of\n    (1) a teacher model,\n    (2) a deeper student model with `random_init` initializer\n    (3) a deeper student model with `Net2DeeperNet` initializer\n    '''\n    train_data = (train_x, train_y)\n    validation_data = (validation_x, validation_y)\n    print('\\nExperiment of Net2DeeperNet ...')\n    print('\\nbuilding teacher model ...')\n    teacher_model, _ = make_teacher_model(train_data,\n                                          validation_data,\n                                          epochs=3)\n\n    print('\\nbuilding deeper student model by random init ...')\n    make_deeper_student_model(teacher_model, train_data,\n                              validation_data, 'random-init',\n                              epochs=3)\n    print('\\nbuilding deeper student model by net2deeper ...')\n    make_deeper_student_model(teacher_model, train_data,\n                              validation_data, 'net2deeper',\n                              epochs=3)\n\n# run the experiments\nnet2wider_experiment()\nnet2deeper_experiment()\n"
  },
  {
    "path": "Keras/mnist_siamese_graph.py",
    "content": "'''Train a Siamese MLP on pairs of digits from the MNIST dataset.\n\nIt follows Hadsell-et-al.'06 [1] by computing the Euclidean distance on the\noutput of the shared network and by optimizing the contrastive loss (see paper\nfor mode details).\n\n[1] \"Dimensionality Reduction by Learning an Invariant Mapping\"\n    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n\nGets to 99.5% test accuracy after 20 epochs.\n3 seconds per epoch on a Titan X GPU\n'''\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nimport numpy as np\n\nimport random\nfrom keras.datasets import mnist\nfrom keras.models import Sequential, Model\nfrom keras.layers import Dense, Dropout, Input, Lambda\nfrom keras.optimizers import RMSprop\nfrom keras import backend as K\n\n\ndef euclidean_distance(vects):\n    x, y = vects\n    return K.sqrt(K.sum(K.square(x - y), axis=1, keepdims=True))\n\n\ndef eucl_dist_output_shape(shapes):\n    shape1, shape2 = shapes\n    return (shape1[0], 1)\n\n\ndef contrastive_loss(y_true, y_pred):\n    '''Contrastive loss from Hadsell-et-al.'06\n    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n    '''\n    margin = 1\n    return K.mean(y_true * K.square(y_pred) +\n                  (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))\n\n\ndef create_pairs(x, digit_indices):\n    '''Positive and negative pair creation.\n    Alternates between positive and negative pairs.\n    '''\n    pairs = []\n    labels = []\n    n = min([len(digit_indices[d]) for d in range(10)]) - 1\n    for d in range(10):\n        for i in range(n):\n            z1, z2 = digit_indices[d][i], digit_indices[d][i + 1]\n            pairs += [[x[z1], x[z2]]]\n            inc = random.randrange(1, 10)\n            dn = (d + inc) % 10\n            z1, z2 = digit_indices[d][i], digit_indices[dn][i]\n            pairs += [[x[z1], x[z2]]]\n            labels += [1, 0]\n    return np.array(pairs), np.array(labels)\n\n\ndef create_base_network(input_dim):\n    '''Base network to be shared (eq. to feature extraction).\n    '''\n    seq = Sequential()\n    seq.add(Dense(128, input_shape=(input_dim,), activation='relu'))\n    seq.add(Dropout(0.1))\n    seq.add(Dense(128, activation='relu'))\n    seq.add(Dropout(0.1))\n    seq.add(Dense(128, activation='relu'))\n    return seq\n\n\ndef compute_accuracy(predictions, labels):\n    '''Compute classification accuracy with a fixed threshold on distances.\n    '''\n    return labels[predictions.ravel() < 0.5].mean()\n\n\n# the data, shuffled and split between train and test sets\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\nx_train = x_train.reshape(60000, 784)\nx_test = x_test.reshape(10000, 784)\nx_train = x_train.astype('float32')\nx_test = x_test.astype('float32')\nx_train /= 255\nx_test /= 255\ninput_dim = 784\nepochs = 20\n\n# create training+test positive and negative pairs\ndigit_indices = [np.where(y_train == i)[0] for i in range(10)]\ntr_pairs, tr_y = create_pairs(x_train, digit_indices)\n\ndigit_indices = [np.where(y_test == i)[0] for i in range(10)]\nte_pairs, te_y = create_pairs(x_test, digit_indices)\n\n# network definition\nbase_network = create_base_network(input_dim)\n\ninput_a = Input(shape=(input_dim,))\ninput_b = Input(shape=(input_dim,))\n\n# because we re-use the same instance `base_network`,\n# the weights of the network\n# will be shared across the two branches\nprocessed_a = base_network(input_a)\nprocessed_b = base_network(input_b)\n\ndistance = Lambda(euclidean_distance,\n                  output_shape=eucl_dist_output_shape)([processed_a, processed_b])\n\nmodel = Model([input_a, input_b], distance)\n\n# train\nrms = RMSprop()\nmodel.compile(loss=contrastive_loss, optimizer=rms)\nmodel.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y,\n          batch_size=128,\n          epochs=epochs,\n          validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y))\n\n# compute final accuracy on training and test sets\npred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])\ntr_acc = compute_accuracy(pred, tr_y)\npred = model.predict([te_pairs[:, 0], te_pairs[:, 1]])\nte_acc = compute_accuracy(pred, te_y)\n\nprint('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))\nprint('* Accuracy on test set: %0.2f%%' % (100 * te_acc))\n"
  },
  {
    "path": "Keras/mnist_sklearn_wrapper.py",
    "content": "'''Example of how to use sklearn wrapper\n\nBuilds simple CNN models on MNIST and uses sklearn's GridSearchCV to find best model\n'''\n\nfrom __future__ import print_function\n\nimport keras\nfrom keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.wrappers.scikit_learn import KerasClassifier\nfrom keras import backend as K\nfrom sklearn.grid_search import GridSearchCV\n\n\nnum_classes = 10\n\n# input image dimensions\nimg_rows, img_cols = 28, 28\n\n# load training data and do basic data normalization\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\nif K.image_data_format() == 'channels_first':\n    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n    input_shape = (1, img_rows, img_cols)\nelse:\n    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n    input_shape = (img_rows, img_cols, 1)\n\nx_train = x_train.astype('float32')\nx_test = x_test.astype('float32')\nx_train /= 255\nx_test /= 255\n\n# convert class vectors to binary class matrices\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\n\n\ndef make_model(dense_layer_sizes, filters, kernel_size, pool_size):\n    '''Creates model comprised of 2 convolutional layers followed by dense layers\n\n    dense_layer_sizes: List of layer sizes.\n        This list has one number for each layer\n    filters: Number of convolutional filters in each convolutional layer\n    kernel_size: Convolutional kernel size\n    pool_size: Size of pooling area for max pooling\n    '''\n\n    model = Sequential()\n    model.add(Conv2D(filters, kernel_size,\n                     padding='valid',\n                     input_shape=input_shape))\n    model.add(Activation('relu'))\n    model.add(Conv2D(filters, kernel_size))\n    model.add(Activation('relu'))\n    model.add(MaxPooling2D(pool_size=pool_size))\n    model.add(Dropout(0.25))\n\n    model.add(Flatten())\n    for layer_size in dense_layer_sizes:\n        model.add(Dense(layer_size))\n    model.add(Activation('relu'))\n    model.add(Dropout(0.5))\n    model.add(Dense(num_classes))\n    model.add(Activation('softmax'))\n\n    model.compile(loss='categorical_crossentropy',\n                  optimizer='adadelta',\n                  metrics=['accuracy'])\n\n    return model\n\ndense_size_candidates = [[32], [64], [32, 32], [64, 64]]\nmy_classifier = KerasClassifier(make_model, batch_size=32)\nvalidator = GridSearchCV(my_classifier,\n                         param_grid={'dense_layer_sizes': dense_size_candidates,\n                                     # epochs is avail for tuning even when not\n                                     # an argument to model building function\n                                     'epochs': [3, 6],\n                                     'filters': [8],\n                                     'kernel_size': [3],\n                                     'pool_size': [2]},\n                         scoring='neg_log_loss',\n                         n_jobs=1)\nvalidator.fit(x_train, y_train)\n\nprint('The parameters of the best model are: ')\nprint(validator.best_params_)\n\n# validator.best_estimator_ returns sklearn-wrapped version of best model.\n# validator.best_estimator_.model returns the (unwrapped) keras model\nbest_model = validator.best_estimator_.model\nmetric_names = best_model.metrics_names\nmetric_values = best_model.evaluate(x_test, y_test)\nfor metric, value in zip(metric_names, metric_values):\n    print(metric, ': ', value)\n"
  },
  {
    "path": "Keras/mnist_swwae.py",
    "content": "'''Trains a stacked what-where autoencoder built on residual blocks on the\r\nMNIST dataset.  It exemplifies two influential methods that have been developed\r\nin the past few years.\r\n\r\nThe first is the idea of properly 'unpooling.' During any max pool, the\r\nexact location (the 'where') of the maximal value in a pooled receptive field\r\nis lost, however it can be very useful in the overall reconstruction of an\r\ninput image.  Therefore, if the 'where' is handed from the encoder\r\nto the corresponding decoder layer, features being decoded can be 'placed' in\r\nthe right location, allowing for reconstructions of much higher fidelity.\r\n\r\nReferences:\r\n[1]\r\n'Visualizing and Understanding Convolutional Networks'\r\nMatthew D Zeiler, Rob Fergus\r\nhttps://arxiv.org/abs/1311.2901v3\r\n\r\n[2]\r\n'Stacked What-Where Auto-encoders'\r\nJunbo Zhao, Michael Mathieu, Ross Goroshin, Yann LeCun\r\nhttps://arxiv.org/abs/1506.02351v8\r\n\r\nThe second idea exploited here is that of residual learning.  Residual blocks\r\nease the training process by allowing skip connections that give the network\r\nthe ability to be as linear (or non-linear) as the data sees fit.  This allows\r\nfor much deep networks to be easily trained.  The residual element seems to\r\nbe advantageous in the context of this example as it allows a nice symmetry\r\nbetween the encoder and decoder.  Normally, in the decoder, the final\r\nprojection to the space where the image is reconstructed is linear, however\r\nthis does not have to be the case for a residual block as the degree to which\r\nits output is linear or non-linear is determined by the data it is fed.\r\nHowever, in order to cap the reconstruction in this example, a hard softmax is\r\napplied as a bias because we know the MNIST digits are mapped to [0,1].\r\n\r\nReferences:\r\n[3]\r\n'Deep Residual Learning for Image Recognition'\r\nKaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\r\nhttps://arxiv.org/abs/1512.03385v1\r\n\r\n[4]\r\n'Identity Mappings in Deep Residual Networks'\r\nKaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\r\nhttps://arxiv.org/abs/1603.05027v3\r\n\r\n'''\r\nfrom __future__ import print_function\r\nimport numpy as np\r\n\r\nfrom keras.datasets import mnist\r\nfrom keras.models import Model\r\nfrom keras.layers import Activation\r\nfrom keras.layers import UpSampling2D, Conv2D, MaxPooling2D\r\nfrom keras.layers import Input, BatchNormalization, ELU\r\nimport matplotlib.pyplot as plt\r\nimport keras.backend as K\r\nfrom keras import layers\r\n\r\n\r\ndef convresblock(x, nfeats=8, ksize=3, nskipped=2, elu=True):\r\n    \"\"\"The proposed residual block from [4].\r\n\r\n    Running with elu=True will use ELU nonlinearity and running with\r\n    elu=False will use BatchNorm + RELU nonlinearity.  While ELU's are fast\r\n    due to the fact they do not suffer from BatchNorm overhead, they may\r\n    overfit because they do not offer the stochastic element of the batch\r\n    formation process of BatchNorm, which acts as a good regularizer.\r\n\r\n    # Arguments\r\n        x: 4D tensor, the tensor to feed through the block\r\n        nfeats: Integer, number of feature maps for conv layers.\r\n        ksize: Integer, width and height of conv kernels in first convolution.\r\n        nskipped: Integer, number of conv layers for the residual function.\r\n        elu: Boolean, whether to use ELU or BN+RELU.\r\n\r\n    # Input shape\r\n        4D tensor with shape:\r\n        `(batch, channels, rows, cols)`\r\n\r\n    # Output shape\r\n        4D tensor with shape:\r\n        `(batch, filters, rows, cols)`\r\n    \"\"\"\r\n    y0 = Conv2D(nfeats, ksize, padding='same')(x)\r\n    y = y0\r\n    for i in range(nskipped):\r\n        if elu:\r\n            y = ELU()(y)\r\n        else:\r\n            y = BatchNormalization(axis=1)(y)\r\n            y = Activation('relu')(y)\r\n        y = Conv2D(nfeats, 1, padding='same')(y)\r\n    return layers.add([y0, y])\r\n\r\n\r\ndef getwhere(x):\r\n    ''' Calculate the 'where' mask that contains switches indicating which\r\n    index contained the max value when MaxPool2D was applied.  Using the\r\n    gradient of the sum is a nice trick to keep everything high level.'''\r\n    y_prepool, y_postpool = x\r\n    return K.gradients(K.sum(y_postpool), y_prepool)\r\n\r\nif K.backend() == 'tensorflow':\r\n    raise RuntimeError('This example can only run with the '\r\n                       'Theano backend for the time being, '\r\n                       'because it requires taking the gradient '\r\n                       'of a gradient, which isn\\'t '\r\n                       'supported for all TF ops.')\r\n\r\n# This example assume 'channels_first' data format.\r\nK.set_image_data_format('channels_first')\r\n\r\n# input image dimensions\r\nimg_rows, img_cols = 28, 28\r\n\r\n# the data, shuffled and split between train and test sets\r\n(x_train, _), (x_test, _) = mnist.load_data()\r\n\r\nx_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\r\nx_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\r\nx_train = x_train.astype('float32')\r\nx_test = x_test.astype('float32')\r\nx_train /= 255\r\nx_test /= 255\r\nprint('x_train shape:', x_train.shape)\r\nprint(x_train.shape[0], 'train samples')\r\nprint(x_test.shape[0], 'test samples')\r\n\r\n# The size of the kernel used for the MaxPooling2D\r\npool_size = 2\r\n# The total number of feature maps at each layer\r\nnfeats = [8, 16, 32, 64, 128]\r\n# The sizes of the pooling kernel at each layer\r\npool_sizes = np.array([1, 1, 1, 1, 1]) * pool_size\r\n# The convolution kernel size\r\nksize = 3\r\n# Number of epochs to train for\r\nepochs = 5\r\n# Batch size during training\r\nbatch_size = 128\r\n\r\nif pool_size == 2:\r\n    # if using a 5 layer net of pool_size = 2\r\n    x_train = np.pad(x_train, [[0, 0], [0, 0], [2, 2], [2, 2]],\r\n                     mode='constant')\r\n    x_test = np.pad(x_test, [[0, 0], [0, 0], [2, 2], [2, 2]], mode='constant')\r\n    nlayers = 5\r\nelif pool_size == 3:\r\n    # if using a 3 layer net of pool_size = 3\r\n    x_train = x_train[:, :, :-1, :-1]\r\n    x_test = x_test[:, :, :-1, :-1]\r\n    nlayers = 3\r\nelse:\r\n    import sys\r\n    sys.exit('Script supports pool_size of 2 and 3.')\r\n\r\n# Shape of input to train on (note that model is fully convolutional however)\r\ninput_shape = x_train.shape[1:]\r\n# The final list of the size of axis=1 for all layers, including input\r\nnfeats_all = [input_shape[0]] + nfeats\r\n\r\n# First build the encoder, all the while keeping track of the 'where' masks\r\nimg_input = Input(shape=input_shape)\r\n\r\n# We push the 'where' masks to the following list\r\nwheres = [None] * nlayers\r\ny = img_input\r\nfor i in range(nlayers):\r\n    y_prepool = convresblock(y, nfeats=nfeats_all[i + 1], ksize=ksize)\r\n    y = MaxPooling2D(pool_size=(pool_sizes[i], pool_sizes[i]))(y_prepool)\r\n    wheres[i] = layers.Lambda(\r\n        getwhere, output_shape=lambda x: x[0])([y_prepool, y])\r\n\r\n# Now build the decoder, and use the stored 'where' masks to place the features\r\nfor i in range(nlayers):\r\n    ind = nlayers - 1 - i\r\n    y = UpSampling2D(size=(pool_sizes[ind], pool_sizes[ind]))(y)\r\n    y = layers.multiply([y, wheres[ind]])\r\n    y = convresblock(y, nfeats=nfeats_all[ind], ksize=ksize)\r\n\r\n# Use hard_simgoid to clip range of reconstruction\r\ny = Activation('hard_sigmoid')(y)\r\n\r\n# Define the model and it's mean square error loss, and compile it with Adam\r\nmodel = Model(img_input, y)\r\nmodel.compile('adam', 'mse')\r\n\r\n# Fit the model\r\nmodel.fit(x_train, x_train,\r\n          batch_size=batch_size,\r\n          epochs=epochs,\r\n          validation_data=(x_test, x_test))\r\n\r\n# Plot\r\nx_recon = model.predict(x_test[:25])\r\nx_plot = np.concatenate((x_test[:25], x_recon), axis=1)\r\nx_plot = x_plot.reshape((5, 10, input_shape[-2], input_shape[-1]))\r\nx_plot = np.vstack([np.hstack(x) for x in x_plot])\r\nplt.figure()\r\nplt.axis('off')\r\nplt.title('Test Samples: Originals/Reconstructions')\r\nplt.imshow(x_plot, interpolation='none', cmap='gray')\r\nplt.savefig('reconstructions.png')\r\n"
  },
  {
    "path": "Keras/mnist_transfer_cnn.py",
    "content": "'''Transfer learning toy example:\n\n1- Train a simple convnet on the MNIST dataset the first 5 digits [0..4].\n2- Freeze convolutional layers and fine-tune dense layers\n   for the classification of digits [5..9].\n\nRun on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python mnist_transfer_cnn.py\n\nGet to 99.8% test accuracy after 5 epochs\nfor the first five digits classifier\nand 99.2% for the last five digits after transfer + fine-tuning.\n'''\n\nfrom __future__ import print_function\n\nimport datetime\nimport keras\nfrom keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras import backend as K\n\nnow = datetime.datetime.now\n\nbatch_size = 128\nnum_classes = 5\nepochs = 5\n\n# input image dimensions\nimg_rows, img_cols = 28, 28\n# number of convolutional filters to use\nfilters = 32\n# size of pooling area for max pooling\npool_size = 2\n# convolution kernel size\nkernel_size = 3\n\nif K.image_data_format() == 'channels_first':\n    input_shape = (1, img_rows, img_cols)\nelse:\n    input_shape = (img_rows, img_cols, 1)\n\n\ndef train_model(model, train, test, num_classes):\n    x_train = train[0].reshape((train[0].shape[0],) + input_shape)\n    x_test = test[0].reshape((test[0].shape[0],) + input_shape)\n    x_train = x_train.astype('float32')\n    x_test = x_test.astype('float32')\n    x_train /= 255\n    x_test /= 255\n    print('x_train shape:', x_train.shape)\n    print(x_train.shape[0], 'train samples')\n    print(x_test.shape[0], 'test samples')\n\n    # convert class vectors to binary class matrices\n    y_train = keras.utils.to_categorical(train[1], num_classes)\n    y_test = keras.utils.to_categorical(test[1], num_classes)\n\n    model.compile(loss='categorical_crossentropy',\n                  optimizer='adadelta',\n                  metrics=['accuracy'])\n\n    t = now()\n    model.fit(x_train, y_train,\n              batch_size=batch_size,\n              epochs=epochs,\n              verbose=1,\n              validation_data=(x_test, y_test))\n    print('Training time: %s' % (now() - t))\n    score = model.evaluate(x_test, y_test, verbose=0)\n    print('Test score:', score[0])\n    print('Test accuracy:', score[1])\n\n\n# the data, shuffled and split between train and test sets\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\n# create two datasets one with digits below 5 and one with 5 and above\nx_train_lt5 = x_train[y_train < 5]\ny_train_lt5 = y_train[y_train < 5]\nx_test_lt5 = x_test[y_test < 5]\ny_test_lt5 = y_test[y_test < 5]\n\nx_train_gte5 = x_train[y_train >= 5]\ny_train_gte5 = y_train[y_train >= 5] - 5\nx_test_gte5 = x_test[y_test >= 5]\ny_test_gte5 = y_test[y_test >= 5] - 5\n\n# define two groups of layers: feature (convolutions) and classification (dense)\nfeature_layers = [\n    Conv2D(filters, kernel_size,\n           padding='valid',\n           input_shape=input_shape),\n    Activation('relu'),\n    Conv2D(filters, kernel_size),\n    Activation('relu'),\n    MaxPooling2D(pool_size=pool_size),\n    Dropout(0.25),\n    Flatten(),\n]\n\nclassification_layers = [\n    Dense(128),\n    Activation('relu'),\n    Dropout(0.5),\n    Dense(num_classes),\n    Activation('softmax')\n]\n\n# create complete model\nmodel = Sequential(feature_layers + classification_layers)\n\n# train model for 5-digit classification [0..4]\ntrain_model(model,\n            (x_train_lt5, y_train_lt5),\n            (x_test_lt5, y_test_lt5), num_classes)\n\n# freeze feature layers and rebuild model\nfor l in feature_layers:\n    l.trainable = False\n\n# transfer: train dense layers for new classification task [5..9]\ntrain_model(model,\n            (x_train_gte5, y_train_gte5),\n            (x_test_gte5, y_test_gte5), num_classes)\n"
  },
  {
    "path": "Keras/neural_doodle.py",
    "content": "'''Neural doodle with Keras\r\n\r\nScript Usage:\r\n    # Arguments:\r\n    ```\r\n    --nlabels:              # of regions (colors) in mask images\r\n    --style-image:          image to learn style from\r\n    --style-mask:           semantic labels for style image\r\n    --target-mask:          semantic labels for target image (your doodle)\r\n    --content-image:        optional image to learn content from\r\n    --target-image-prefix:  path prefix for generated target images\r\n    ```\r\n\r\n    # Example 1: doodle using a style image, style mask\r\n    and target mask.\r\n    ```\r\n    python neural_doodle.py --nlabels 4 --style-image Monet/style.png \\\r\n    --style-mask Monet/style_mask.png --target-mask Monet/target_mask.png \\\r\n    --target-image-prefix generated/monet\r\n    ```\r\n\r\n    # Example 2: doodle using a style image, style mask,\r\n    target mask and an optional content image.\r\n    ```\r\n    python neural_doodle.py --nlabels 4 --style-image Renoir/style.png \\\r\n    --style-mask Renoir/style_mask.png --target-mask Renoir/target_mask.png \\\r\n    --content-image Renoir/creek.jpg \\\r\n    --target-image-prefix generated/renoir\r\n    ```\r\n\r\nReferences:\r\n[Dmitry Ulyanov's blog on fast-neural-doodle](http://dmitryulyanov.github.io/feed-forward-neural-doodle/)\r\n[Torch code for fast-neural-doodle](https://github.com/DmitryUlyanov/fast-neural-doodle)\r\n[Torch code for online-neural-doodle](https://github.com/DmitryUlyanov/online-neural-doodle)\r\n[Paper Texture Networks: Feed-forward Synthesis of Textures and Stylized Images](http://arxiv.org/abs/1603.03417)\r\n[Discussion on parameter tuning](https://github.com/fchollet/keras/issues/3705)\r\n\r\nResources:\r\nExample images can be downloaded from\r\nhttps://github.com/DmitryUlyanov/fast-neural-doodle/tree/master/data\r\n'''\r\nfrom __future__ import print_function\r\nimport time\r\nimport argparse\r\nimport numpy as np\r\nfrom scipy.optimize import fmin_l_bfgs_b\r\nfrom scipy.misc import imread, imsave\r\n\r\nfrom keras import backend as K\r\nfrom keras.layers import Input, AveragePooling2D\r\nfrom keras.models import Model\r\nfrom keras.preprocessing.image import load_img, img_to_array\r\nfrom keras.applications import vgg19\r\n\r\n# Command line arguments\r\nparser = argparse.ArgumentParser(description='Keras neural doodle example')\r\nparser.add_argument('--nlabels', type=int,\r\n                    help='number of semantic labels'\r\n                    ' (regions in differnet colors)'\r\n                    ' in style_mask/target_mask')\r\nparser.add_argument('--style-image', type=str,\r\n                    help='path to image to learn style from')\r\nparser.add_argument('--style-mask', type=str,\r\n                    help='path to semantic mask of style image')\r\nparser.add_argument('--target-mask', type=str,\r\n                    help='path to semantic mask of target image')\r\nparser.add_argument('--content-image', type=str, default=None,\r\n                    help='path to optional content image')\r\nparser.add_argument('--target-image-prefix', type=str,\r\n                    help='path prefix for generated results')\r\nargs = parser.parse_args()\r\n\r\nstyle_img_path = args.style_image\r\nstyle_mask_path = args.style_mask\r\ntarget_mask_path = args.target_mask\r\ncontent_img_path = args.content_image\r\ntarget_img_prefix = args.target_image_prefix\r\nuse_content_img = content_img_path is not None\r\n\r\nnum_labels = args.nlabels\r\nnum_colors = 3  # RGB\r\n# determine image sizes based on target_mask\r\nref_img = imread(target_mask_path)\r\nimg_nrows, img_ncols = ref_img.shape[:2]\r\n\r\ntotal_variation_weight = 50.\r\nstyle_weight = 1.\r\ncontent_weight = 0.1 if use_content_img else 0\r\n\r\ncontent_feature_layers = ['block5_conv2']\r\n# To get better generation qualities, use more conv layers for style features\r\nstyle_feature_layers = ['block1_conv1', 'block2_conv1', 'block3_conv1',\r\n                        'block4_conv1', 'block5_conv1']\r\n\r\n\r\n# helper functions for reading/processing images\r\ndef preprocess_image(image_path):\r\n    img = load_img(image_path, target_size=(img_nrows, img_ncols))\r\n    img = img_to_array(img)\r\n    img = np.expand_dims(img, axis=0)\r\n    img = vgg19.preprocess_input(img)\r\n    return img\r\n\r\n\r\ndef deprocess_image(x):\r\n    if K.image_data_format() == 'channels_first':\r\n        x = x.reshape((3, img_nrows, img_ncols))\r\n        x = x.transpose((1, 2, 0))\r\n    else:\r\n        x = x.reshape((img_nrows, img_ncols, 3))\r\n    # Remove zero-center by mean pixel\r\n    x[:, :, 0] += 103.939\r\n    x[:, :, 1] += 116.779\r\n    x[:, :, 2] += 123.68\r\n    # 'BGR'->'RGB'\r\n    x = x[:, :, ::-1]\r\n    x = np.clip(x, 0, 255).astype('uint8')\r\n    return x\r\n\r\n\r\ndef kmeans(xs, k):\r\n    assert xs.ndim == 2\r\n    try:\r\n        from sklearn.cluster import k_means\r\n        _, labels, _ = k_means(xs.astype('float64'), k)\r\n    except ImportError:\r\n        from scipy.cluster.vq import kmeans2\r\n        _, labels = kmeans2(xs, k, missing='raise')\r\n    return labels\r\n\r\n\r\ndef load_mask_labels():\r\n    '''Load both target and style masks.\r\n    A mask image (nr x nc) with m labels/colors will be loaded\r\n    as a 4D boolean tensor: (1, m, nr, nc) for 'channels_first' or (1, nr, nc, m) for 'channels_last'\r\n    '''\r\n    target_mask_img = load_img(target_mask_path,\r\n                               target_size=(img_nrows, img_ncols))\r\n    target_mask_img = img_to_array(target_mask_img)\r\n    style_mask_img = load_img(style_mask_path,\r\n                              target_size=(img_nrows, img_ncols))\r\n    style_mask_img = img_to_array(style_mask_img)\r\n    if K.image_data_format() == 'channels_first':\r\n        mask_vecs = np.vstack([style_mask_img.reshape((3, -1)).T,\r\n                               target_mask_img.reshape((3, -1)).T])\r\n    else:\r\n        mask_vecs = np.vstack([style_mask_img.reshape((-1, 3)),\r\n                               target_mask_img.reshape((-1, 3))])\r\n\r\n    labels = kmeans(mask_vecs, num_labels)\r\n    style_mask_label = labels[:img_nrows *\r\n                              img_ncols].reshape((img_nrows, img_ncols))\r\n    target_mask_label = labels[img_nrows *\r\n                               img_ncols:].reshape((img_nrows, img_ncols))\r\n\r\n    stack_axis = 0 if K.image_data_format() == 'channels_first' else -1\r\n    style_mask = np.stack([style_mask_label == r for r in xrange(num_labels)],\r\n                          axis=stack_axis)\r\n    target_mask = np.stack([target_mask_label == r for r in xrange(num_labels)],\r\n                           axis=stack_axis)\r\n\r\n    return (np.expand_dims(style_mask, axis=0),\r\n            np.expand_dims(target_mask, axis=0))\r\n\r\n# Create tensor variables for images\r\nif K.image_data_format() == 'channels_first':\r\n    shape = (1, num_colors, img_nrows, img_ncols)\r\nelse:\r\n    shape = (1, img_nrows, img_ncols, num_colors)\r\n\r\nstyle_image = K.variable(preprocess_image(style_img_path))\r\ntarget_image = K.placeholder(shape=shape)\r\nif use_content_img:\r\n    content_image = K.variable(preprocess_image(content_img_path))\r\nelse:\r\n    content_image = K.zeros(shape=shape)\r\n\r\nimages = K.concatenate([style_image, target_image, content_image], axis=0)\r\n\r\n# Create tensor variables for masks\r\nraw_style_mask, raw_target_mask = load_mask_labels()\r\nstyle_mask = K.variable(raw_style_mask.astype('float32'))\r\ntarget_mask = K.variable(raw_target_mask.astype('float32'))\r\nmasks = K.concatenate([style_mask, target_mask], axis=0)\r\n\r\n# index constants for images and tasks variables\r\nSTYLE, TARGET, CONTENT = 0, 1, 2\r\n\r\n# Build image model, mask model and use layer outputs as features\r\n# image model as VGG19\r\nimage_model = vgg19.VGG19(include_top=False, input_tensor=images)\r\n\r\n# mask model as a series of pooling\r\nmask_input = Input(tensor=masks, shape=(None, None, None), name='mask_input')\r\nx = mask_input\r\nfor layer in image_model.layers[1:]:\r\n    name = 'mask_%s' % layer.name\r\n    if 'conv' in layer.name:\r\n        x = AveragePooling2D((3, 3), strides=(\r\n            1, 1), name=name, border_mode='same')(x)\r\n    elif 'pool' in layer.name:\r\n        x = AveragePooling2D((2, 2), name=name)(x)\r\nmask_model = Model(mask_input, x)\r\n\r\n# Collect features from image_model and task_model\r\nimage_features = {}\r\nmask_features = {}\r\nfor img_layer, mask_layer in zip(image_model.layers, mask_model.layers):\r\n    if 'conv' in img_layer.name:\r\n        assert 'mask_' + img_layer.name == mask_layer.name\r\n        layer_name = img_layer.name\r\n        img_feat, mask_feat = img_layer.output, mask_layer.output\r\n        image_features[layer_name] = img_feat\r\n        mask_features[layer_name] = mask_feat\r\n\r\n\r\n# Define loss functions\r\ndef gram_matrix(x):\r\n    assert K.ndim(x) == 3\r\n    features = K.batch_flatten(x)\r\n    gram = K.dot(features, K.transpose(features))\r\n    return gram\r\n\r\n\r\ndef region_style_loss(style_image, target_image, style_mask, target_mask):\r\n    '''Calculate style loss between style_image and target_image,\r\n    for one common region specified by their (boolean) masks\r\n    '''\r\n    assert 3 == K.ndim(style_image) == K.ndim(target_image)\r\n    assert 2 == K.ndim(style_mask) == K.ndim(target_mask)\r\n    if K.image_data_format() == 'channels_first':\r\n        masked_style = style_image * style_mask\r\n        masked_target = target_image * target_mask\r\n        num_channels = K.shape(style_image)[0]\r\n    else:\r\n        masked_style = K.permute_dimensions(\r\n            style_image, (2, 0, 1)) * style_mask\r\n        masked_target = K.permute_dimensions(\r\n            target_image, (2, 0, 1)) * target_mask\r\n        num_channels = K.shape(style_image)[-1]\r\n    s = gram_matrix(masked_style) / K.mean(style_mask) / num_channels\r\n    c = gram_matrix(masked_target) / K.mean(target_mask) / num_channels\r\n    return K.mean(K.square(s - c))\r\n\r\n\r\ndef style_loss(style_image, target_image, style_masks, target_masks):\r\n    '''Calculate style loss between style_image and target_image,\r\n    in all regions.\r\n    '''\r\n    assert 3 == K.ndim(style_image) == K.ndim(target_image)\r\n    assert 3 == K.ndim(style_masks) == K.ndim(target_masks)\r\n    loss = K.variable(0)\r\n    for i in xrange(num_labels):\r\n        if K.image_data_format() == 'channels_first':\r\n            style_mask = style_masks[i, :, :]\r\n            target_mask = target_masks[i, :, :]\r\n        else:\r\n            style_mask = style_masks[:, :, i]\r\n            target_mask = target_masks[:, :, i]\r\n        loss += region_style_loss(style_image,\r\n                                  target_image, style_mask, target_mask)\r\n    return loss\r\n\r\n\r\ndef content_loss(content_image, target_image):\r\n    return K.sum(K.square(target_image - content_image))\r\n\r\n\r\ndef total_variation_loss(x):\r\n    assert 4 == K.ndim(x)\r\n    if K.image_data_format() == 'channels_first':\r\n        a = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] -\r\n                     x[:, :, 1:, :img_ncols - 1])\r\n        b = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] -\r\n                     x[:, :, :img_nrows - 1, 1:])\r\n    else:\r\n        a = K.square(x[:, :img_nrows - 1, :img_ncols - 1, :] -\r\n                     x[:, 1:, :img_ncols - 1, :])\r\n        b = K.square(x[:, :img_nrows - 1, :img_ncols - 1, :] -\r\n                     x[:, :img_nrows - 1, 1:, :])\r\n    return K.sum(K.pow(a + b, 1.25))\r\n\r\n# Overall loss is the weighted sum of content_loss, style_loss and tv_loss\r\n# Each individual loss uses features from image/mask models.\r\nloss = K.variable(0)\r\nfor layer in content_feature_layers:\r\n    content_feat = image_features[layer][CONTENT, :, :, :]\r\n    target_feat = image_features[layer][TARGET, :, :, :]\r\n    loss += content_weight * content_loss(content_feat, target_feat)\r\n\r\nfor layer in style_feature_layers:\r\n    style_feat = image_features[layer][STYLE, :, :, :]\r\n    target_feat = image_features[layer][TARGET, :, :, :]\r\n    style_masks = mask_features[layer][STYLE, :, :, :]\r\n    target_masks = mask_features[layer][TARGET, :, :, :]\r\n    sl = style_loss(style_feat, target_feat, style_masks, target_masks)\r\n    loss += (style_weight / len(style_feature_layers)) * sl\r\n\r\nloss += total_variation_weight * total_variation_loss(target_image)\r\nloss_grads = K.gradients(loss, target_image)\r\n\r\n# Evaluator class for computing efficiency\r\noutputs = [loss]\r\nif isinstance(loss_grads, (list, tuple)):\r\n    outputs += loss_grads\r\nelse:\r\n    outputs.append(loss_grads)\r\n\r\nf_outputs = K.function([target_image], outputs)\r\n\r\n\r\ndef eval_loss_and_grads(x):\r\n    if K.image_data_format() == 'channels_first':\r\n        x = x.reshape((1, 3, img_nrows, img_ncols))\r\n    else:\r\n        x = x.reshape((1, img_nrows, img_ncols, 3))\r\n    outs = f_outputs([x])\r\n    loss_value = outs[0]\r\n    if len(outs[1:]) == 1:\r\n        grad_values = outs[1].flatten().astype('float64')\r\n    else:\r\n        grad_values = np.array(outs[1:]).flatten().astype('float64')\r\n    return loss_value, grad_values\r\n\r\n\r\nclass Evaluator(object):\r\n\r\n    def __init__(self):\r\n        self.loss_value = None\r\n        self.grads_values = None\r\n\r\n    def loss(self, x):\r\n        assert self.loss_value is None\r\n        loss_value, grad_values = eval_loss_and_grads(x)\r\n        self.loss_value = loss_value\r\n        self.grad_values = grad_values\r\n        return self.loss_value\r\n\r\n    def grads(self, x):\r\n        assert self.loss_value is not None\r\n        grad_values = np.copy(self.grad_values)\r\n        self.loss_value = None\r\n        self.grad_values = None\r\n        return grad_values\r\n\r\nevaluator = Evaluator()\r\n\r\n# Generate images by iterative optimization\r\nif K.image_data_format() == 'channels_first':\r\n    x = np.random.uniform(0, 255, (1, 3, img_nrows, img_ncols)) - 128.\r\nelse:\r\n    x = np.random.uniform(0, 255, (1, img_nrows, img_ncols, 3)) - 128.\r\n\r\nfor i in range(50):\r\n    print('Start of iteration', i)\r\n    start_time = time.time()\r\n    x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),\r\n                                     fprime=evaluator.grads, maxfun=20)\r\n    print('Current loss value:', min_val)\r\n    # save current generated image\r\n    img = deprocess_image(x.copy())\r\n    fname = target_img_prefix + '_at_iteration_%d.png' % i\r\n    imsave(fname, img)\r\n    end_time = time.time()\r\n    print('Image saved as', fname)\r\n    print('Iteration %d completed in %ds' % (i, end_time - start_time))\r\n"
  },
  {
    "path": "Keras/neural_style_transfer.py",
    "content": "'''Neural style transfer with Keras.\n\nRun the script with:\n```\npython neural_style_transfer.py path_to_your_base_image.jpg path_to_your_reference.jpg prefix_for_results\n```\ne.g.:\n```\npython neural_style_transfer.py img/tuebingen.jpg img/starry_night.jpg results/my_result\n```\nOptional parameters:\n```\n--iter, To specify the number of iterations the style transfer takes place (Default is 10)\n--content_weight, The weight given to the content loss (Default is 0.025)\n--style_weight, The weight given to the style loss (Default is 1.0)\n--tv_weight, The weight given to the total variation loss (Default is 1.0)\n```\n\nIt is preferable to run this script on GPU, for speed.\n\nExample result: https://twitter.com/fchollet/status/686631033085677568\n\n# Details\n\nStyle transfer consists in generating an image\nwith the same \"content\" as a base image, but with the\n\"style\" of a different picture (typically artistic).\n\nThis is achieved through the optimization of a loss function\nthat has 3 components: \"style loss\", \"content loss\",\nand \"total variation loss\":\n\n- The total variation loss imposes local spatial continuity between\nthe pixels of the combination image, giving it visual coherence.\n\n- The style loss is where the deep learning keeps in --that one is defined\nusing a deep convolutional neural network. Precisely, it consists in a sum of\nL2 distances between the Gram matrices of the representations of\nthe base image and the style reference image, extracted from\ndifferent layers of a convnet (trained on ImageNet). The general idea\nis to capture color/texture information at different spatial\nscales (fairly large scales --defined by the depth of the layer considered).\n\n - The content loss is a L2 distance between the features of the base\nimage (extracted from a deep layer) and the features of the combination image,\nkeeping the generated image close enough to the original one.\n\n# References\n    - [A Neural Algorithm of Artistic Style](http://arxiv.org/abs/1508.06576)\n'''\n\nfrom __future__ import print_function\nfrom keras.preprocessing.image import load_img, img_to_array\nfrom scipy.misc import imsave\nimport numpy as np\nfrom scipy.optimize import fmin_l_bfgs_b\nimport time\nimport argparse\n\nfrom keras.applications import vgg16\nfrom keras import backend as K\n\nparser = argparse.ArgumentParser(description='Neural style transfer with Keras.')\nparser.add_argument('base_image_path', metavar='base', type=str,\n                    help='Path to the image to transform.')\nparser.add_argument('style_reference_image_path', metavar='ref', type=str,\n                    help='Path to the style reference image.')\nparser.add_argument('result_prefix', metavar='res_prefix', type=str,\n                    help='Prefix for the saved results.')\nparser.add_argument('--iter', type=int, default=10, required=False,\n                    help='Number of iterations to run.')\nparser.add_argument('--content_weight', type=float, default=0.025, required=False,\n                    help='Content weight.')\nparser.add_argument('--style_weight', type=float, default=1.0, required=False,\n                    help='Style weight.')\nparser.add_argument('--tv_weight', type=float, default=1.0, required=False,\n                    help='Total Variation weight.')\n\nargs = parser.parse_args()\nbase_image_path = args.base_image_path\nstyle_reference_image_path = args.style_reference_image_path\nresult_prefix = args.result_prefix\niterations = args.iter\n\n# these are the weights of the different loss components\ntotal_variation_weight = args.tv_weight\nstyle_weight = args.style_weight\ncontent_weight = args.content_weight\n\n# dimensions of the generated picture.\nwidth, height = load_img(base_image_path).size\nimg_nrows = 400\nimg_ncols = int(width * img_nrows / height)\n\n# util function to open, resize and format pictures into appropriate tensors\n\n\ndef preprocess_image(image_path):\n    img = load_img(image_path, target_size=(img_nrows, img_ncols))\n    img = img_to_array(img)\n    img = np.expand_dims(img, axis=0)\n    img = vgg16.preprocess_input(img)\n    return img\n\n# util function to convert a tensor into a valid image\n\n\ndef deprocess_image(x):\n    if K.image_data_format() == 'channels_first':\n        x = x.reshape((3, img_nrows, img_ncols))\n        x = x.transpose((1, 2, 0))\n    else:\n        x = x.reshape((img_nrows, img_ncols, 3))\n    # Remove zero-center by mean pixel\n    x[:, :, 0] += 103.939\n    x[:, :, 1] += 116.779\n    x[:, :, 2] += 123.68\n    # 'BGR'->'RGB'\n    x = x[:, :, ::-1]\n    x = np.clip(x, 0, 255).astype('uint8')\n    return x\n\n# get tensor representations of our images\nbase_image = K.variable(preprocess_image(base_image_path))\nstyle_reference_image = K.variable(preprocess_image(style_reference_image_path))\n\n# this will contain our generated image\nif K.image_data_format() == 'channels_first':\n    combination_image = K.placeholder((1, 3, img_nrows, img_ncols))\nelse:\n    combination_image = K.placeholder((1, img_nrows, img_ncols, 3))\n\n# combine the 3 images into a single Keras tensor\ninput_tensor = K.concatenate([base_image,\n                              style_reference_image,\n                              combination_image], axis=0)\n\n# build the VGG16 network with our 3 images as input\n# the model will be loaded with pre-trained ImageNet weights\nmodel = vgg16.VGG16(input_tensor=input_tensor,\n                    weights='imagenet', include_top=False)\nprint('Model loaded.')\n\n# get the symbolic outputs of each \"key\" layer (we gave them unique names).\noutputs_dict = dict([(layer.name, layer.output) for layer in model.layers])\n\n# compute the neural style loss\n# first we need to define 4 util functions\n\n# the gram matrix of an image tensor (feature-wise outer product)\n\n\ndef gram_matrix(x):\n    assert K.ndim(x) == 3\n    if K.image_data_format() == 'channels_first':\n        features = K.batch_flatten(x)\n    else:\n        features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))\n    gram = K.dot(features, K.transpose(features))\n    return gram\n\n# the \"style loss\" is designed to maintain\n# the style of the reference image in the generated image.\n# It is based on the gram matrices (which capture style) of\n# feature maps from the style reference image\n# and from the generated image\n\n\ndef style_loss(style, combination):\n    assert K.ndim(style) == 3\n    assert K.ndim(combination) == 3\n    S = gram_matrix(style)\n    C = gram_matrix(combination)\n    channels = 3\n    size = img_nrows * img_ncols\n    return K.sum(K.square(S - C)) / (4. * (channels ** 2) * (size ** 2))\n\n# an auxiliary loss function\n# designed to maintain the \"content\" of the\n# base image in the generated image\n\n\ndef content_loss(base, combination):\n    return K.sum(K.square(combination - base))\n\n# the 3rd loss function, total variation loss,\n# designed to keep the generated image locally coherent\n\n\ndef total_variation_loss(x):\n    assert K.ndim(x) == 4\n    if K.image_data_format() == 'channels_first':\n        a = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] - x[:, :, 1:, :img_ncols - 1])\n        b = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] - x[:, :, :img_nrows - 1, 1:])\n    else:\n        a = K.square(x[:, :img_nrows - 1, :img_ncols - 1, :] - x[:, 1:, :img_ncols - 1, :])\n        b = K.square(x[:, :img_nrows - 1, :img_ncols - 1, :] - x[:, :img_nrows - 1, 1:, :])\n    return K.sum(K.pow(a + b, 1.25))\n\n# combine these loss functions into a single scalar\nloss = K.variable(0.)\nlayer_features = outputs_dict['block4_conv2']\nbase_image_features = layer_features[0, :, :, :]\ncombination_features = layer_features[2, :, :, :]\nloss += content_weight * content_loss(base_image_features,\n                                      combination_features)\n\nfeature_layers = ['block1_conv1', 'block2_conv1',\n                  'block3_conv1', 'block4_conv1',\n                  'block5_conv1']\nfor layer_name in feature_layers:\n    layer_features = outputs_dict[layer_name]\n    style_reference_features = layer_features[1, :, :, :]\n    combination_features = layer_features[2, :, :, :]\n    sl = style_loss(style_reference_features, combination_features)\n    loss += (style_weight / len(feature_layers)) * sl\nloss += total_variation_weight * total_variation_loss(combination_image)\n\n# get the gradients of the generated image wrt the loss\ngrads = K.gradients(loss, combination_image)\n\noutputs = [loss]\nif isinstance(grads, (list, tuple)):\n    outputs += grads\nelse:\n    outputs.append(grads)\n\nf_outputs = K.function([combination_image], outputs)\n\n\ndef eval_loss_and_grads(x):\n    if K.image_data_format() == 'channels_first':\n        x = x.reshape((1, 3, img_nrows, img_ncols))\n    else:\n        x = x.reshape((1, img_nrows, img_ncols, 3))\n    outs = f_outputs([x])\n    loss_value = outs[0]\n    if len(outs[1:]) == 1:\n        grad_values = outs[1].flatten().astype('float64')\n    else:\n        grad_values = np.array(outs[1:]).flatten().astype('float64')\n    return loss_value, grad_values\n\n# this Evaluator class makes it possible\n# to compute loss and gradients in one pass\n# while retrieving them via two separate functions,\n# \"loss\" and \"grads\". This is done because scipy.optimize\n# requires separate functions for loss and gradients,\n# but computing them separately would be inefficient.\n\n\nclass Evaluator(object):\n\n    def __init__(self):\n        self.loss_value = None\n        self.grads_values = None\n\n    def loss(self, x):\n        assert self.loss_value is None\n        loss_value, grad_values = eval_loss_and_grads(x)\n        self.loss_value = loss_value\n        self.grad_values = grad_values\n        return self.loss_value\n\n    def grads(self, x):\n        assert self.loss_value is not None\n        grad_values = np.copy(self.grad_values)\n        self.loss_value = None\n        self.grad_values = None\n        return grad_values\n\nevaluator = Evaluator()\n\n# run scipy-based optimization (L-BFGS) over the pixels of the generated image\n# so as to minimize the neural style loss\nif K.image_data_format() == 'channels_first':\n    x = np.random.uniform(0, 255, (1, 3, img_nrows, img_ncols)) - 128.\nelse:\n    x = np.random.uniform(0, 255, (1, img_nrows, img_ncols, 3)) - 128.\n\nfor i in range(iterations):\n    print('Start of iteration', i)\n    start_time = time.time()\n    x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),\n                                     fprime=evaluator.grads, maxfun=20)\n    print('Current loss value:', min_val)\n    # save current generated image\n    img = deprocess_image(x.copy())\n    fname = result_prefix + '_at_iteration_%d.png' % i\n    imsave(fname, img)\n    end_time = time.time()\n    print('Image saved as', fname)\n    print('Iteration %d completed in %ds' % (i, end_time - start_time))\n"
  },
  {
    "path": "Keras/pretrained_word_embeddings.py",
    "content": "'''This script loads pre-trained word embeddings (GloVe embeddings)\ninto a frozen Keras Embedding layer, and uses it to\ntrain a text classification model on the 20 Newsgroup dataset\n(classication of newsgroup messages into 20 different categories).\n\nGloVe embedding data can be found at:\nhttp://nlp.stanford.edu/data/glove.6B.zip\n(source page: http://nlp.stanford.edu/projects/glove/)\n\n20 Newsgroup data can be found at:\nhttp://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/news20.html\n'''\n\nfrom __future__ import print_function\n\nimport os\nimport sys\nimport numpy as np\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.utils import to_categorical\nfrom keras.layers import Dense, Input, Flatten\nfrom keras.layers import Conv1D, MaxPooling1D, Embedding\nfrom keras.models import Model\n\n\nBASE_DIR = ''\nGLOVE_DIR = BASE_DIR + '/glove.6B/'\nTEXT_DATA_DIR = BASE_DIR + '/20_newsgroup/'\nMAX_SEQUENCE_LENGTH = 1000\nMAX_NB_WORDS = 20000\nEMBEDDING_DIM = 100\nVALIDATION_SPLIT = 0.2\n\n# first, build index mapping words in the embeddings set\n# to their embedding vector\n\nprint('Indexing word vectors.')\n\nembeddings_index = {}\nf = open(os.path.join(GLOVE_DIR, 'glove.6B.100d.txt'))\nfor line in f:\n    values = line.split()\n    word = values[0]\n    coefs = np.asarray(values[1:], dtype='float32')\n    embeddings_index[word] = coefs\nf.close()\n\nprint('Found %s word vectors.' % len(embeddings_index))\n\n# second, prepare text samples and their labels\nprint('Processing text dataset')\n\ntexts = []  # list of text samples\nlabels_index = {}  # dictionary mapping label name to numeric id\nlabels = []  # list of label ids\nfor name in sorted(os.listdir(TEXT_DATA_DIR)):\n    path = os.path.join(TEXT_DATA_DIR, name)\n    if os.path.isdir(path):\n        label_id = len(labels_index)\n        labels_index[name] = label_id\n        for fname in sorted(os.listdir(path)):\n            if fname.isdigit():\n                fpath = os.path.join(path, fname)\n                if sys.version_info < (3,):\n                    f = open(fpath)\n                else:\n                    f = open(fpath, encoding='latin-1')\n                t = f.read()\n                i = t.find('\\n\\n')  # skip header\n                if 0 < i:\n                    t = t[i:]\n                texts.append(t)\n                f.close()\n                labels.append(label_id)\n\nprint('Found %s texts.' % len(texts))\n\n# finally, vectorize the text samples into a 2D integer tensor\ntokenizer = Tokenizer(num_words=MAX_NB_WORDS)\ntokenizer.fit_on_texts(texts)\nsequences = tokenizer.texts_to_sequences(texts)\n\nword_index = tokenizer.word_index\nprint('Found %s unique tokens.' % len(word_index))\n\ndata = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n\nlabels = to_categorical(np.asarray(labels))\nprint('Shape of data tensor:', data.shape)\nprint('Shape of label tensor:', labels.shape)\n\n# split the data into a training set and a validation set\nindices = np.arange(data.shape[0])\nnp.random.shuffle(indices)\ndata = data[indices]\nlabels = labels[indices]\nnum_validation_samples = int(VALIDATION_SPLIT * data.shape[0])\n\nx_train = data[:-num_validation_samples]\ny_train = labels[:-num_validation_samples]\nx_val = data[-num_validation_samples:]\ny_val = labels[-num_validation_samples:]\n\nprint('Preparing embedding matrix.')\n\n# prepare embedding matrix\nnum_words = min(MAX_NB_WORDS, len(word_index))\nembedding_matrix = np.zeros((num_words, EMBEDDING_DIM))\nfor word, i in word_index.items():\n    if i >= MAX_NB_WORDS:\n        continue\n    embedding_vector = embeddings_index.get(word)\n    if embedding_vector is not None:\n        # words not found in embedding index will be all-zeros.\n        embedding_matrix[i] = embedding_vector\n\n# load pre-trained word embeddings into an Embedding layer\n# note that we set trainable = False so as to keep the embeddings fixed\nembedding_layer = Embedding(num_words,\n                            EMBEDDING_DIM,\n                            weights=[embedding_matrix],\n                            input_length=MAX_SEQUENCE_LENGTH,\n                            trainable=False)\n\nprint('Training model.')\n\n# train a 1D convnet with global maxpooling\nsequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\nembedded_sequences = embedding_layer(sequence_input)\nx = Conv1D(128, 5, activation='relu')(embedded_sequences)\nx = MaxPooling1D(5)(x)\nx = Conv1D(128, 5, activation='relu')(x)\nx = MaxPooling1D(5)(x)\nx = Conv1D(128, 5, activation='relu')(x)\nx = MaxPooling1D(35)(x)\nx = Flatten()(x)\nx = Dense(128, activation='relu')(x)\npreds = Dense(len(labels_index), activation='softmax')(x)\n\nmodel = Model(sequence_input, preds)\nmodel.compile(loss='categorical_crossentropy',\n              optimizer='rmsprop',\n              metrics=['acc'])\n\nmodel.fit(x_train, y_train,\n          batch_size=128,\n          epochs=10,\n          validation_data=(x_val, y_val))\n"
  },
  {
    "path": "Keras/reuters_mlp.py",
    "content": "'''Trains and evaluate a simple MLP\non the Reuters newswire topic classification task.\n'''\nfrom __future__ import print_function\n\nimport numpy as np\nimport keras\nfrom keras.datasets import reuters\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation\nfrom keras.preprocessing.text import Tokenizer\n\nmax_words = 1000\nbatch_size = 32\nepochs = 5\n\nprint('Loading data...')\n(x_train, y_train), (x_test, y_test) = reuters.load_data(num_words=max_words,\n                                                         test_split=0.2)\nprint(len(x_train), 'train sequences')\nprint(len(x_test), 'test sequences')\n\nnum_classes = np.max(y_train) + 1\nprint(num_classes, 'classes')\n\nprint('Vectorizing sequence data...')\ntokenizer = Tokenizer(num_words=max_words)\nx_train = tokenizer.sequences_to_matrix(x_train, mode='binary')\nx_test = tokenizer.sequences_to_matrix(x_test, mode='binary')\nprint('x_train shape:', x_train.shape)\nprint('x_test shape:', x_test.shape)\n\nprint('Convert class vector to binary class matrix '\n      '(for use with categorical_crossentropy)')\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\nprint('y_train shape:', y_train.shape)\nprint('y_test shape:', y_test.shape)\n\nprint('Building model...')\nmodel = Sequential()\nmodel.add(Dense(512, input_shape=(max_words,)))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(num_classes))\nmodel.add(Activation('softmax'))\n\nmodel.compile(loss='categorical_crossentropy',\n              optimizer='adam',\n              metrics=['accuracy'])\n\nhistory = model.fit(x_train, y_train,\n                    batch_size=batch_size,\n                    epochs=epochs,\n                    verbose=1,\n                    validation_split=0.1)\nscore = model.evaluate(x_test, y_test,\n                       batch_size=batch_size, verbose=1)\nprint('Test score:', score[0])\nprint('Test accuracy:', score[1])\n"
  },
  {
    "path": "Keras/stateful_lstm.py",
    "content": "'''Example script showing how to use stateful RNNs\nto model long sequences efficiently.\n'''\nfrom __future__ import print_function\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom keras.models import Sequential\nfrom keras.layers import Dense, LSTM\n\n\n# since we are using stateful rnn tsteps can be set to 1\ntsteps = 1\nbatch_size = 25\nepochs = 25\n# number of elements ahead that are used to make the prediction\nlahead = 1\n\n\ndef gen_cosine_amp(amp=100, period=1000, x0=0, xn=50000, step=1, k=0.0001):\n    \"\"\"Generates an absolute cosine time series with the amplitude\n    exponentially decreasing\n\n    Arguments:\n        amp: amplitude of the cosine function\n        period: period of the cosine function\n        x0: initial x of the time series\n        xn: final x of the time series\n        step: step of the time series discretization\n        k: exponential rate\n    \"\"\"\n    cos = np.zeros(((xn - x0) * step, 1, 1))\n    for i in range(len(cos)):\n        idx = x0 + i * step\n        cos[i, 0, 0] = amp * np.cos(2 * np.pi * idx / period)\n        cos[i, 0, 0] = cos[i, 0, 0] * np.exp(-k * idx)\n    return cos\n\n\nprint('Generating Data...')\ncos = gen_cosine_amp()\nprint('Input shape:', cos.shape)\n\nexpected_output = np.zeros((len(cos), 1))\nfor i in range(len(cos) - lahead):\n    expected_output[i, 0] = np.mean(cos[i + 1:i + lahead + 1])\n\nprint('Output shape:', expected_output.shape)\n\nprint('Creating Model...')\nmodel = Sequential()\nmodel.add(LSTM(50,\n               input_shape=(tsteps, 1),\n               batch_size=batch_size,\n               return_sequences=True,\n               stateful=True))\nmodel.add(LSTM(50,\n               return_sequences=False,\n               stateful=True))\nmodel.add(Dense(1))\nmodel.compile(loss='mse', optimizer='rmsprop')\n\nprint('Training')\nfor i in range(epochs):\n    print('Epoch', i, '/', epochs)\n\n    # Note that the last state for sample i in a batch will\n    # be used as initial state for sample i in the next batch.\n    # Thus we are simultaneously training on batch_size series with\n    # lower resolution than the original series contained in cos.\n    # Each of these series are offset by one step and can be\n    # extracted with cos[i::batch_size].\n\n    model.fit(cos, expected_output,\n              batch_size=batch_size,\n              epochs=1,\n              verbose=1,\n              shuffle=False)\n    model.reset_states()\n\nprint('Predicting')\npredicted_output = model.predict(cos, batch_size=batch_size)\n\nprint('Plotting Results')\nplt.subplot(2, 1, 1)\nplt.plot(expected_output)\nplt.title('Expected')\nplt.subplot(2, 1, 2)\nplt.plot(predicted_output)\nplt.title('Predicted')\nplt.show()\n"
  },
  {
    "path": "Keras/variational_autoencoder.py",
    "content": "'''This script demonstrates how to build a variational autoencoder with Keras.\n\nReference: \"Auto-Encoding Variational Bayes\" https://arxiv.org/abs/1312.6114\n'''\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import norm\n\nfrom keras.layers import Input, Dense, Lambda\nfrom keras.models import Model\nfrom keras import backend as K\nfrom keras import metrics\nfrom keras.datasets import mnist\n\nbatch_size = 100\noriginal_dim = 784\nlatent_dim = 2\nintermediate_dim = 256\nepochs = 50\nepsilon_std = 1.0\n\nx = Input(batch_shape=(batch_size, original_dim))\nh = Dense(intermediate_dim, activation='relu')(x)\nz_mean = Dense(latent_dim)(h)\nz_log_var = Dense(latent_dim)(h)\n\n\ndef sampling(args):\n    z_mean, z_log_var = args\n    epsilon = K.random_normal(shape=(batch_size, latent_dim), mean=0.,\n                              stddev=epsilon_std)\n    return z_mean + K.exp(z_log_var / 2) * epsilon\n\n# note that \"output_shape\" isn't necessary with the TensorFlow backend\nz = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])\n\n# we instantiate these layers separately so as to reuse them later\ndecoder_h = Dense(intermediate_dim, activation='relu')\ndecoder_mean = Dense(original_dim, activation='sigmoid')\nh_decoded = decoder_h(z)\nx_decoded_mean = decoder_mean(h_decoded)\n\n\ndef vae_loss(x, x_decoded_mean):\n    xent_loss = original_dim * metrics.binary_crossentropy(x, x_decoded_mean)\n    kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)\n    return xent_loss + kl_loss\n\nvae = Model(x, x_decoded_mean)\nvae.compile(optimizer='rmsprop', loss=vae_loss)\n\n# train the VAE on MNIST digits\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\nx_train = x_train.astype('float32') / 255.\nx_test = x_test.astype('float32') / 255.\nx_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))\nx_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))\n\nvae.fit(x_train, x_train,\n        shuffle=True,\n        epochs=epochs,\n        batch_size=batch_size,\n        validation_data=(x_test, x_test))\n\n# build a model to project inputs on the latent space\nencoder = Model(x, z_mean)\n\n# display a 2D plot of the digit classes in the latent space\nx_test_encoded = encoder.predict(x_test, batch_size=batch_size)\nplt.figure(figsize=(6, 6))\nplt.scatter(x_test_encoded[:, 0], x_test_encoded[:, 1], c=y_test)\nplt.colorbar()\nplt.show()\n\n# build a digit generator that can sample from the learned distribution\ndecoder_input = Input(shape=(latent_dim,))\n_h_decoded = decoder_h(decoder_input)\n_x_decoded_mean = decoder_mean(_h_decoded)\ngenerator = Model(decoder_input, _x_decoded_mean)\n\n# display a 2D manifold of the digits\nn = 15  # figure with 15x15 digits\ndigit_size = 28\nfigure = np.zeros((digit_size * n, digit_size * n))\n# linearly spaced coordinates on the unit square were transformed through the inverse CDF (ppf) of the Gaussian\n# to produce values of the latent variables z, since the prior of the latent space is Gaussian\ngrid_x = norm.ppf(np.linspace(0.05, 0.95, n))\ngrid_y = norm.ppf(np.linspace(0.05, 0.95, n))\n\nfor i, yi in enumerate(grid_x):\n    for j, xi in enumerate(grid_y):\n        z_sample = np.array([[xi, yi]])\n        x_decoded = generator.predict(z_sample)\n        digit = x_decoded[0].reshape(digit_size, digit_size)\n        figure[i * digit_size: (i + 1) * digit_size,\n               j * digit_size: (j + 1) * digit_size] = digit\n\nplt.figure(figsize=(10, 10))\nplt.imshow(figure, cmap='Greys_r')\nplt.show()\n"
  },
  {
    "path": "Keras/variational_autoencoder_deconv.py",
    "content": "'''This script demonstrates how to build a variational autoencoder\nwith Keras and deconvolution layers.\n\nReference: \"Auto-Encoding Variational Bayes\" https://arxiv.org/abs/1312.6114\n'''\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import norm\n\nfrom keras.layers import Input, Dense, Lambda, Flatten, Reshape\nfrom keras.layers import Conv2D, Conv2DTranspose\nfrom keras.models import Model\nfrom keras import backend as K\nfrom keras import metrics\nfrom keras.datasets import mnist\n\n# input image dimensions\nimg_rows, img_cols, img_chns = 28, 28, 1\n# number of convolutional filters to use\nfilters = 64\n# convolution kernel size\nnum_conv = 3\n\nbatch_size = 100\nif K.image_data_format() == 'channels_first':\n    original_img_size = (img_chns, img_rows, img_cols)\nelse:\n    original_img_size = (img_rows, img_cols, img_chns)\nlatent_dim = 2\nintermediate_dim = 128\nepsilon_std = 1.0\nepochs = 5\n\nx = Input(batch_shape=(batch_size,) + original_img_size)\nconv_1 = Conv2D(img_chns,\n                kernel_size=(2, 2),\n                padding='same', activation='relu')(x)\nconv_2 = Conv2D(filters,\n                kernel_size=(2, 2),\n                padding='same', activation='relu',\n                strides=(2, 2))(conv_1)\nconv_3 = Conv2D(filters,\n                kernel_size=num_conv,\n                padding='same', activation='relu',\n                strides=1)(conv_2)\nconv_4 = Conv2D(filters,\n                kernel_size=num_conv,\n                padding='same', activation='relu',\n                strides=1)(conv_3)\nflat = Flatten()(conv_4)\nhidden = Dense(intermediate_dim, activation='relu')(flat)\n\nz_mean = Dense(latent_dim)(hidden)\nz_log_var = Dense(latent_dim)(hidden)\n\n\ndef sampling(args):\n    z_mean, z_log_var = args\n    epsilon = K.random_normal(shape=(batch_size, latent_dim),\n                              mean=0., stddev=epsilon_std)\n    return z_mean + K.exp(z_log_var) * epsilon\n\n# note that \"output_shape\" isn't necessary with the TensorFlow backend\n# so you could write `Lambda(sampling)([z_mean, z_log_var])`\nz = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])\n\n# we instantiate these layers separately so as to reuse them later\ndecoder_hid = Dense(intermediate_dim, activation='relu')\ndecoder_upsample = Dense(filters * 14 * 14, activation='relu')\n\nif K.image_data_format() == 'channels_first':\n    output_shape = (batch_size, filters, 14, 14)\nelse:\n    output_shape = (batch_size, 14, 14, filters)\n\ndecoder_reshape = Reshape(output_shape[1:])\ndecoder_deconv_1 = Conv2DTranspose(filters,\n                                   kernel_size=num_conv,\n                                   padding='same',\n                                   strides=1,\n                                   activation='relu')\ndecoder_deconv_2 = Conv2DTranspose(filters, num_conv,\n                                   padding='same',\n                                   strides=1,\n                                   activation='relu')\nif K.image_data_format() == 'channels_first':\n    output_shape = (batch_size, filters, 29, 29)\nelse:\n    output_shape = (batch_size, 29, 29, filters)\ndecoder_deconv_3_upsamp = Conv2DTranspose(filters,\n                                          kernel_size=(3, 3),\n                                          strides=(2, 2),\n                                          padding='valid',\n                                          activation='relu')\ndecoder_mean_squash = Conv2D(img_chns,\n                             kernel_size=2,\n                             padding='valid',\n                             activation='sigmoid')\n\nhid_decoded = decoder_hid(z)\nup_decoded = decoder_upsample(hid_decoded)\nreshape_decoded = decoder_reshape(up_decoded)\ndeconv_1_decoded = decoder_deconv_1(reshape_decoded)\ndeconv_2_decoded = decoder_deconv_2(deconv_1_decoded)\nx_decoded_relu = decoder_deconv_3_upsamp(deconv_2_decoded)\nx_decoded_mean_squash = decoder_mean_squash(x_decoded_relu)\n\n\ndef vae_loss(x, x_decoded_mean):\n    # NOTE: binary_crossentropy expects a batch_size by dim\n    # for x and x_decoded_mean, so we MUST flatten these!\n    x = K.flatten(x)\n    x_decoded_mean = K.flatten(x_decoded_mean)\n    xent_loss = img_rows * img_cols * metrics.binary_crossentropy(x, x_decoded_mean)\n    kl_loss = - 0.5 * K.mean(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)\n    return xent_loss + kl_loss\n\nvae = Model(x, x_decoded_mean_squash)\nvae.compile(optimizer='rmsprop', loss=vae_loss)\nvae.summary()\n\n# train the VAE on MNIST digits\n(x_train, _), (x_test, y_test) = mnist.load_data()\n\nx_train = x_train.astype('float32') / 255.\nx_train = x_train.reshape((x_train.shape[0],) + original_img_size)\nx_test = x_test.astype('float32') / 255.\nx_test = x_test.reshape((x_test.shape[0],) + original_img_size)\n\nprint('x_train.shape:', x_train.shape)\n\nvae.fit(x_train, x_train,\n        shuffle=True,\n        epochs=epochs,\n        batch_size=batch_size,\n        validation_data=(x_test, x_test))\n\n# build a model to project inputs on the latent space\nencoder = Model(x, z_mean)\n\n# display a 2D plot of the digit classes in the latent space\nx_test_encoded = encoder.predict(x_test, batch_size=batch_size)\nplt.figure(figsize=(6, 6))\nplt.scatter(x_test_encoded[:, 0], x_test_encoded[:, 1], c=y_test)\nplt.colorbar()\nplt.show()\n\n# build a digit generator that can sample from the learned distribution\ndecoder_input = Input(shape=(latent_dim,))\n_hid_decoded = decoder_hid(decoder_input)\n_up_decoded = decoder_upsample(_hid_decoded)\n_reshape_decoded = decoder_reshape(_up_decoded)\n_deconv_1_decoded = decoder_deconv_1(_reshape_decoded)\n_deconv_2_decoded = decoder_deconv_2(_deconv_1_decoded)\n_x_decoded_relu = decoder_deconv_3_upsamp(_deconv_2_decoded)\n_x_decoded_mean_squash = decoder_mean_squash(_x_decoded_relu)\ngenerator = Model(decoder_input, _x_decoded_mean_squash)\n\n# display a 2D manifold of the digits\nn = 15  # figure with 15x15 digits\ndigit_size = 28\nfigure = np.zeros((digit_size * n, digit_size * n))\n# linearly spaced coordinates on the unit square were transformed through the inverse CDF (ppf) of the Gaussian\n# to produce values of the latent variables z, since the prior of the latent space is Gaussian\ngrid_x = norm.ppf(np.linspace(0.05, 0.95, n))\ngrid_y = norm.ppf(np.linspace(0.05, 0.95, n))\n\nfor i, yi in enumerate(grid_x):\n    for j, xi in enumerate(grid_y):\n        z_sample = np.array([[xi, yi]])\n        z_sample = np.tile(z_sample, batch_size).reshape(batch_size, 2)\n        x_decoded = generator.predict(z_sample, batch_size=batch_size)\n        digit = x_decoded[0].reshape(digit_size, digit_size)\n        figure[i * digit_size: (i + 1) * digit_size,\n               j * digit_size: (j + 1) * digit_size] = digit\n\nplt.figure(figsize=(10, 10))\nplt.imshow(figure, cmap='Greys_r')\nplt.show()\n"
  },
  {
    "path": "MLyearning/README.md",
    "content": "#***Andrew Ng*** 机器学习新书\n\n>小润  \n>2016-12-05  \n\n第一周发布了前12章，这一周目前发布了第13章和14章，接下来几周将会发布更多  \n邮件订阅前往：[http://www.mlyearning.org/](http://www.mlyearning.org/)\n\n\n#感受\n\n每一章节内容精悍。 \n\n以猫的图片识别举例，从团队的开展进度为全局，教你为什么会入坑、怎么样排雷。 \n\n前12章告诉我们三个关键词：dev/test sets & metric（我理解为“开发集/测试集”和“度量标准”）。  \n\ntakeaways这里有相关的[小贴士](#12小贴士建立开发集和测试集)。 \n\n机器学习的开发节奏要把握：Idea -> Code -> Experiment -> Update the idea 的迭代。  \n\n有一个好的开发集/测试集和度量标准能够加速这个迭代过程。\n\n13章介绍了误差分析能够量化新改进想法的可行性，还可以产生新灵感，接下来的几章将展开实例。 \n\n14章给我们一个 trick ，制作一张误差原因占比图，既量化了最大提升度，又可能会分析出新的误差原因。  \n\n#TODO\n\n- [x] [01-12章](https://github.com/zhourunlai/deep-learning-demo/blob/master/MLyearning/Machine_Learning_Yearning_V0.5_01.pdf)\n- [x] [13章](https://github.com/zhourunlai/deep-learning-demo/blob/master/MLyearning/Machine_Learning_Yearning_V0.5_02.pdf)\n- [x] [14章](https://github.com/zhourunlai/deep-learning-demo/blob/master/MLyearning/Machine_Learning_Yearning_V0.5_03.pdf)\n- [ ] 后续章节\n\n\n#目录（草案） \n\n1. [为什么机器学习策略](#1为什么机器学习策略)\n2. [如何使用这本书来帮助你的团队](#2如何使用这本书来帮助你的团队)\n3. [前提和标记](#3前提和标记)\n4. [大规模驱动机器学习进程](#4大规模驱动机器学习进程)\n5. [你的开发集和测试集](#5你的开发集和测试集)\n6. [你的开发集和测试集应该来自相同分布](#6你的开发集和测试集应该来自相同分布)\n7. [开发集/测试集需要多大](#7开发集测试集需要多大)\n8. [为你的团队建立单数度量标准来进行优化](#8为你的团队建立单数度量标准来进行优化)\n9. [优化和满足指标](#9优化和满足指标)\n10. [有一个开发集和度量标准以加速迭代](#10有一个开发集和度量标准以加速迭代)\n11. [何时更改开发集/测试集和度量标准](#11何时更改开发集测试集和度量标准)\n12. [小贴士：建立开发集和测试集](#12小贴士建立开发集和测试集)\n13. [误差分析：着眼于开发集示例去评估想法](#13误差分析着眼于开发集示例去评估想法)\n14. [进行误差分析时并行评估多重方案](#14进行误差分析时并行评估多重方案)\n\n\n#1.为什么机器学习策略\n\n机器学习是无数重要应用的基础，包括网络搜索，反垃圾邮件，语音识别，产品推荐等。 我假设 你或你的团队正在开发一个机器学习应用程序，并且你想要快速进步。这本书将帮助你这样做。\n\n##示例：猫图片创业\n\n假设你正在建立一个为猫爱好者提供无尽猫图片的创业公司。你使用神经网络建立一个计算机视觉系统来检测图片中的猫。  \n\n![1.png](image/1.png)\n\n但悲剧的是，你的学习算法的准确性还不够好。你处在极大的压力中，为了提高你的猫探测器。你会做什么呢？ \n\n你的团队有很多想法，例如：  \n    1. 获取更多的数据：收集更多的猫的照片。  \n    2. 收集更多样化的训练集。例如，猫在不寻常位置的图片；有着不寻常着色的猫；在各种相机设置下拍摄的图片; ...  \n    3. 通过运行更多的梯度下降迭代，训练算法更长。  \n    4. 尝试更大的神经网络，具有更多的图层/隐藏单位/参数。  \n    5. 尝试更小的神经网络。  \n    6. 尝试添加正则化（例如L2正则化）。  \n    7. 更改神经网络架构（激活功能，隐藏单位数等）。  \n    8. ...  \n\n如果你选择这些可能的方向，你会建立领先的猫图片平台，并带领你的公司成功。如果你选择不好，你可能会浪费几个月。你会怎么进行呢？  \n\n这本书会告诉你怎么做。大多数机器学习问题留下的线索告诉你什么是有用的尝试，什么是没有用的尝试。学会阅读这些线索会节省你几个月甚至几年的开发时间。  \n\n\n#2.如何使用这本书来帮助你的团队\n\n读完这本书后，你将会对一个机器学习项目，如何把握技术方向有深刻的理解。但是你的队友可能不明白你为什么要推荐一个特定的方向。也许你希望你的团队定义一个单一的评估指标，但他们不能被信服。你要如何说服他们？  \n\n这就是为什么我把章节短：这样你就可以打印出来，并且让你的队友只读1到2页你需要他们知道的内容就足够了。  \n\n优先级的一些变化会对你的团队的生产力产生巨大的影响。通过帮助你的团队有一些的变化，我希望你能成为你的团队的超级英雄！\n\n![2.png](image/2.png)\n\n\n#3.前提和标记\n\n如果你已经上过机器学习的课程，如我的在 Coursera 机器学习 MOOC，或者如果你有应用监督学习的经验，你将能够理解这个本文。  \n\n我假设你熟悉***监督学习*** ：学习一个从 x 映射到 y 的函数，使用被标记过的训练示例（x，y）。监督学习算法包括线性回归，逻辑回归和神经网络。虽然已经有很多机器学习的形式，但是今天大多数机器学习的实际价值是来自监督学习。  \n\n我经常提到神经网络（也称为“深度学习”）。你只需要一个在这个文本中的基本了解。  \n\n如果你不熟悉这里提到的概念，看看这个在 Coursera 上机器学习课程的前3周视频， 访问[http://ml-class.org](http://ml-class.org) \n\n![3.png](image/3.png)\n\n\n#4.大规模驱动机器学习进程\n\n深层学习的许多想法（神经网络）已经存在了几十年了。为什么是这些想法现在才起飞？  \n\n最近进步的最大驱动因素有两个：  \n    1. 高可用数据。人们现在花更多的时间在数字设备（笔记本电脑，移动设备）上。他们的数字活动产生了大量的数据，我们可以把这些应用到我们的学习算法上。  \n    2. 大规模计算。我们几年前就能训练足够大的神经网络，即使利用如同今天那么庞大的数据集。\n\n详细来说，即使你积累更多的数据，通常是老学习算法的表现，如逻辑回归，“停滞”。这意味着它的学习曲线“平坦”，并且即使你给它更多的数据，算法也会停止改进：  \n\n![4.png](image/4.png)\n\n这就好像旧算法不知道如何处理我们现在拥有的所有数据。  \n\n如果你在同一个监督学习任务上训练一个小的神经网络（NN），你可能会获得略微更好的性能：  \n\n![5.png](image/5.png)\n\n在这里，“小NN”是指只有少量隐藏单位/层/参数的神经网络。最后，如果你训练越来越大的神经网络，你可以获得更好的性能：  \n\n![6.png](image/6.png)\n\n因此，你可以获得更好的表现，当你（i）训练一个非常大的神经网络时，你在上面的绿色曲线上；（ii）有大量的数据。  \n\n许多其他细节，如神经网络架构也很重要，这里也有有很多创新。但是今天来改进算法性能的一个更可靠方法仍然是（i）训练更大的网络和（ii）获得更多的数据。  \n\n如何完成（i）和（ii）的方法是惊人的复杂。这本书将讨论详细信息。我们将，从对传统算法和神经网络都有用的一般策略开始；直到为打造深度学习系统而建立最现代战略。 \n\n> 此图表显示NN在小数据集中表现更好。这种效果不太一致，比NNs在大数据集中表现良好的效果。在小数据系统中，取决于关于如何手工设计的功能，传统的算法可能或可能不会做得更好。对于如果你有20个训练示例，那么是否使用逻辑回归或神经网络也许并不重要；特征的手工工程将具有比较大的效果相比于算法的选择。但如果你有100万的示例，我更支持神经网络。\n\n\n#配置开发集和测试集\n\n\n#5.你的开发集和测试集\n\n让我们回到我们早期的猫图片示例：你运行一个移动应用程序，用户正在上传许多不同的东西的图片到你的应用程序上。你想自动找到猫图片。  \n\n你的团队从不同的网站下载猫的图片（正向示例）和不是猫的图片（反向示例）。他们将数据集的70％/ 30％分成训练集和测试集。使用这些数据，他们构建了一个对于这些训练集和测试集起到良好效果的猫检测器。  \n\n但是当你将这个分类器部署到移动应用程序时，您会发现性能是真的很差！  \n\n![7.png](image/7.png)\n\n发生了什么？  \n\n你弄明白了，用户上传的图片有不同于之前网站中构成你的训练集图片的外观：用户正在上传使用移动设备拍摄的图片，其趋向于较低的分辨率、模糊、不理想的照明度。自从你训练/测试集是由之前网站中的图像，您的算法没有足够一般化，对于你在意的智能手机里图像的实际分布。  \n\n在现代大数据时代之前，这是机器学习中使用的常见规则，一个随机的70％/ 30％分成你的训练集和测试集。这种做法可以起到效果，但这是一个坏主意，对于越来越多的训练分布（也就是之前我们例子中的网站图片）不同于实际分布（也就是我们例子中的手机图片）的应用程序中。  \n\n我们通常定义：  \n    1. 训练集 -- 使用它来运行你的学习算法。  \n    2. 开发集 -- 使用它来调整参数、选择特征、做出关于学习算法的其他决策。有时也被称为相应固定 交叉验证集。  \n    3. 测试集 -- 使用它来评估算法的性能，但不能做出关于学习算法或参数的任何决策。  \n\n一旦你定义好了一个开发集和测试集，你的团队会尝试很多想法，比如不同的学习算法参数，看看什么效果最好。开发集和测试集让你的团队快速了解你的算法的效果。  \n\n换句话说，开发集和测试集的目的是，为了打造机器学习系统，引导你的团队走向最重要的改变。  \n\n所以，你应该做到： 选择开发集和测试集，以反映你期望在未来获得的数据，并希望做得好。  \n\n换句话说，你的测试集不应该只是可用数据的30％，特别是如果你期望你的未来数据（移动应用程序图片）在性质上与您的训练集不同 （网站图像）。  \n\n如果您尚未启动移动应用，则可能还没有任何用户，因此可能无法获得准确反映您必须在其中做好的数据 未来。但你可能仍然尝试近似这一点。例如，请你的朋友发送手机图片给你。一旦你的应用程式启动后，你就可以使用实际的用户数据来更新你的开发集/测试集。  \n\n如果你在未来真的没有办法得到近似你期望得到的数据，也许你可以从使用网站图像开始。但你应该意识到导致系统不能很好地推广的风险。  \n\n这就需要判断：决定投入多少去开发卓越的开发集和测试集。但不要假定你的训练分布与你的测试分布是一样的。尝试挑选测试，以反映你最终想要表现得很好的实例，而不是你碰巧拥有训练的任何数据。  \n\n\n#6.你的开发集和测试集应该来自相同分布\n\n您的应用程式图片资料会根据您的最大分割成四个区域市场：（i）美国，（ii）中国，（iii）印度，和（iv）其他。 想出一个开发套件和测试 设置，我们可以随机分配这两个段到dev集，另外两个到 测试集，对吧？ 说美国和印度在开发套; 中国和其他在测试集。  \n\n![8.png](image/8.png)\n\n一旦你定义了开发和测试集，你的团队将专注于改进开发集的性能。因此，开发集应该反映你想要改进的任务：应用于所有四个地理位置，而不只是两个。  \n\n存在不同的开发集和测试集分布的第二个问题是：有可能你的团队会发生在开发集上起到好效果，而在测试集上起不到好效果的事。 我看到这以很多失望和浪费力气为结束。 避免这发生在你身上。  \n\n例如，假设您的团队开发了一个在开发集上工作良好的系统，但不是测试集。如果你的开发和测试集来自相同的分布，那么你会的有一个非常明确的诊断，什么地方出错了：你已经过度配合​​开发集。明显治愈是获得更多的开发数据。但是如果开发集和测试集来自不同的分布，那么你的明确选项就少了。几个事情可能会出错：  \n    1. 你已经对于开发集过拟合了。\n    2. 测试集比开发集更难。 所以你的算法和预期做得一样，可能没有进一步的重大改进。  \n    3. 测试集不一定更难，但只是不同与开发集。所以在开发集上起到好效果，而在测试集上起不到好效果。在这种情况下，你做的提高开发集性能的很多工作是白费力气。  \n\n在机器学习应用程序上工作已经够难了。具有不匹配的开发集和测试集引入了关于是否改进开发集分布的附加不确定性。具有不匹配的开发和测试集使得它更难找出什么起作用、什么不起作用，从而使得更难以确定工作的优先级。  \n\n如果你正在处理第三方基准测试问题，他们的创建者可能已经指定开发集和来自不同分布的测试集。幸运的是，并不是技能，将有一个对这种基准的性能有更大的影响，相比于开发集和测试集来自相同的分布。这是一个重要的研究问题，学习算法在一个分布上训练并且很好地推广到另一个分布。但如果你的目标是在特定的机器学习应用程序上取得进展，而不是做研究进展，我建议尝试选择开发集和测试集来自相同的分布。这将使您的团队更有效率。  \n\n\n#7.开发集/测试集需要多大\n\n开发集应该足够大，以检测您正在尝试的算法之间的差异。例如，如果分类器A具有90.0％的准确度并且分类器B具有90.1％的准确度，那么一组测试集的100个示例是不能检测到这0.1％的区别。与我看到的其他机器学习问题相比，100个示例的开发集是很小的。开发集的大小从1,000到10,000的示例很常见。有10,000的示例，你将有很好的机会去检测到0.1％的提高。  \n\n对于成熟和重要的应用程序，例如广告、网络搜索和产品推荐 -- 我也看到了很多积极的团队，甚至0.01％的提高，因为它直接影响公司的利润。在这种情况下，开发集可以确定远大于10,000，以便检测更小的改进。  \n\n测试集的大小怎么确定呢？它应该足够大，以给予整个系统的性能很高的可信度。一个流行的启发式方法是使用30％的数据为你的测试集。但在大数据的时代，我们现在有机器学习的问题有时超过十亿的示例，分配给开发集/测试集的数据的分数已经萎缩，即使开发集/测试集中的绝对数量的示例已经增加了。没有必要有过多的超出需要评估算法的性能的开发集/测试集。 \n\n> 在理论上，也可以测试算法的变化在开发集上是否具有统计学显着性差异。在实践中，大多数团队没有考虑这个（除非他们是出版学学术研究论文），我通常没有发现对测量临时进展有用的统计显着性测试。  \n\n\n#8.为你的团队建立单数度量标准来进行优化\n\n分类精度是一个单数的度量标准的例子 ：在运行你的分类器在开发集（或测试集），得到一个单一的数字，代表着被正确分类示例的分数。根据该度量，如果分类器A获得97％的精度，分类器B获得90％的精度，然后我们判断分类器A优越。  \n\n相比之下，精确率和召回率不是单数度量标准：它给出两个用于评估分类器的数字。拥有多个数字评估指标更难以比较算法。假设你的算法性能如下： \n\n![9.png](image/9.png)\n\n在这里，没有哪一个分类器明显优越，所以它不会立即指引你挑选一个。  \n\n在开发周期里，你的团队将尝试关于算法架构、模型参数、特征选择等的很多想法。具有 单数的评价指标，如允许你根据其对此度量的性能对所有模型进行排序的准确率，并快速决定什么是最好的。  \n\n如果你真的关心准确率和召回率，我建议使用一个标准的方法去把它们组合成一个数字。例如，可以取准确率和召回率的平均值，最终以单个数字。或者，您可以计算“F1值“，这是一种修改的计算其平均值的方法，并且比简单地取平均值更好。\n\n> 猫分类器的准确率是开发集（或测试集）中标记为猫的图像真是猫的分数。它的召回率是所有猫图像在开发集（或测试集）中的标记为猫的分数。在具有高准确率和高召回率之间通常存在折衷。如果你想了解更多关于F1值的信息，看到 [https://en.wikipedia.org/wiki/F1_score](https://en.wikipedia.org/wiki/F1_score)。 它是精度率和召回率之间的“几何平均”，计算为2 /（（1 /精度率）+（1 /召回率））。\n\n\n![10.png](image/10.png)\n\n拥有单数度量标准可加快您正在大量的分类器中进行选择而作出决策的能力。它给出了在所有这些之中明确的偏好排名，因此是一个明确的进步方向。  \n\n作为最后一个例子，假设你分别跟踪你的猫分类器在四个关键市场的准确性：（i）美国，（ii）中国，（iii）印度，和（iv）其他。给出四个指标。通过对这四个数字取平均值或加权平均值，最后得到单数度量。取平均值或加权平均值是最常见的一个将多个指标合并为一个的方法。\n\n\n#9.优化和满足指标\n\n这里有组合多个度量指标的另一种方法。  \n\n假设你关心学习算法的准确性和运行时间。你需要从这三个分类器中选择： \n\n![11.png](image/11.png)\n\n把准确率和运行时间放进一个单一的公式看起来是不寻常的，如：  \n\n    准确率 - 0.5 * 运行时间\n\n这里是你可以做的替代：首先，定义什么是“可接受”的运行时间。让我们假设任何运行在100ms的东西都是可以接受的。然后，最大化准确性，针对你的分类器而言可满足的运行时间标准。在这里，运行时间是一个“满意度量” - 你的分类器只需要对这个指标“足够好”，有意义地来说，它应该采取最多100ms。精度是“优化度量”。  \n\n如果你折衷N不同的标准，如二进制文件大小的模型（即对移动应用很重要，因为用户不想下载大型应用），运行时间， 和准确性，您可以考虑将N-1个条件设置为“满意”度量。你只需要他们满足一定的价值。然后将最后一个定义为“优化” 度量。 例如，为二进制文件大小和运行可接受的阈值，并且在给定这些约束的情况下尝试优化准确度。 \n\n作为最后一个例子，假设你正在构建一个使用麦克风的硬件设备，监听用户说一个特定的“唤醒词”，然后使系统唤醒。例如，Amazon Echo 监听“ Alexa ” ；苹果 Siri 监听“嘿，Siri”；Android 监听“ Okay Google ”；百度监听“ Hello Baidu ”。你关心关于假阳性率 - 即使在什么时候系统没有人说唤醒词却被唤醒的频率；以及假阴性率 - 当有人说唤醒词却没有醒来。\n\n以最小化假阴性率（优化度量），受到不超过每24小时一次假阳性（满意度量）。一旦你的团队对评估指标进行了优化，他们就能够做到 更快的进步。  \n\n\n#10.有一个开发集和度量标准以加速迭代\n\n很难事先知道什么方法最适合一个新问题。甚至经验丰富的机器学习研究人员通常会尝试许多数十个想法后，他们才发现一些令人满意的。在构建机器学习系统时，我会经常：  \n    1. 从关于如何构建系统的一些想法开始。\n    2. 贯彻执行编写代码。  \n    3. 进行了一项实验，它告诉我的想法有多好的效果。（通常我的前几个想法都    没有好效果！）基于这些学习，回归到生成更多的想法，并继续迭代。 \n\n![12.png](image/12.png)\n\n这是一个迭代过程。 你可以越快越好，这个循环，你会做得越快地进展。 这就是为什么有开发集/测试集/和一个度量指标是重要的：每次你尝试想法，测量你的想法的性能开发集，让你快速决定是否 朝正确的方向前进。  \n\n相反，假设您没有特定的开发集和度量。 所以每次你的团队开发一个新的猫分类器，你必须将它并入您的应用程序，并与应用程序运行几个小时，以了解是否新的分类器是一个改进。这将是 令人难以置信的慢！此外，如果您的团队将分类器的准确度从95.0％提高到95.1％，你可能无法通过使用应用程序检测到0.1％的改进。还有很多在你的系统将通过逐渐积累几十个这些0.1％的改进。有一个开发集和指标，你可以非常快速地检测哪些想法是成功地给你小（或大）的改进，因此让你快速决定什么想法不断完善，哪些丢弃。  \n\n\n#11.何时更改开发集/测试集和度量标准\n\n当开始一个新的项目，我试着快速选择开发集/测试集，因为这给团队定义明确的目标。  \n\n我通常要求我的团队少于一个星期拿出一个初始的开发/测试集和初始度量 - 不再比这更长。它更好地拿出一些不完美的东西快速前进，而不是过分考虑这一点。但这一周的时间表不适用于成熟的应用。例如，反垃圾邮件是一个成熟的深度学习应用程序。我有看到的团队工作在已经成熟的系统花费几个月来获得更好的开发集/测试集。  \n\n如果以后你意识到你的初始开发集/测试集或度量指标错了，那就快速更改它们。例如，如果您的开发集+度量标准使得分类器A优于分类器 B，但是你的团队认为分类器B实际上对你的产品更好，那么这可能是一个信号，你需要更改你的开发集/测试集或度量指标。  \n\n这里有三个错误地把分类器A排在更优位置的主要原因：\n\n    1 你需要不同于开发集/测试集做得很好的实际分布。  \n\n假设你的初始开发集/测试集主要是成年猫的图片。当你运行你的猫应用程序，你发现用户正在上传比预期更多的小猫图像。 所以，开发集/测试集的分布不代表实际分布，你需要做得好。在这个时候，更新开发集/测试集更具代表性。\n\n![13.png](image/13.png)\n\n    2 你已经对开发集过拟合了。  \n\n针对开发集地重复评估逐渐过拟合了开发集。当你完成开发，你将针对测试集评估你的系统。如果你发现你的开发集的性能比你的测试集更好，这是一个迹象，你已经对开发集过拟合了。在这种情况下，那就建立一个新的开发集吧。  \n\n如果您需要跟踪团队的进度，你还可以针对测试集每周一次或每月一次来定期评估您的系统。但是不要使用测试集来做任何关于算法的决策，包括是否把系统回滚到前一周。如果这样做，你将开始针对测试集过拟合，并且不能再依赖它给出系统性能的完全无偏估计（当你正在发表研究论文或者使用这个指标来做重要业务相关的决定时，你可以这么做）。  \n\n    3 度量指标衡量的是项目需要优化的内容。 \n\n假设对于你的猫应用程序，你的指标是分类精度。此指标目前将分类器A排在优于分类器B的位置。但是假设你试试两者的算法，你发现分类器A允许偶尔的色情图像通过。即使分类器A更准确，但偶尔会留下色情图像这个不良印象，意味着其性能是不可接受的。你会怎么做呢？  \n\n在这里，度量指标未能识别算法B实际上优于算法A的事实. 因此，你不能再信任这个指标来选择最好的 算法。现在是改变度量指标的时候了。例如，你可以将度量指标更改为严重惩罚色情图像。我强烈建议采取一项新指标，并使用新的指标来为团队明确定义一个新的目标，而不是太久没有一个值得信赖的指标，甚至回到手动选择分类的蹩脚方法。  \n\n在一个项目中更改开发集/测试集或度量指标是很常见。有一个初始的开发集/测试集和度量指标可以帮助你快速迭代。如果你发现开发集/测试集或度量不再指引你的团队走在正确的方向，这没什么大不了的！那就改变它们，并确保你的团队知道这个新的方向。\n\n#12.小贴士：建立开发集和测试集\n\n1. 从反映你希望得到什么数据的分布选择开发集和测试集。这可能来自不一样的训练数据的分布。  \n2. 如果可能从相同的分布中选择开发集和测试集。  \n3. 为你的团队选择单数度量指标进行优化。如果有多个是你关心的指标，考虑把他们合并到一个单一的公式里（例如平方误差指标），或者定义满意指标和优化指标。  \n4. 机器学习是一个高度迭代的过程：你之前可能尝试了很多种想法才找到一个令你满意的。   \n5. 建立开发集/测试集、单数度量指标可以帮助您快速评估算法的性能，因此迭代会更快。  \n6. 当一个全新的应用起步时，在不到一周的时间里，尝试建立开发集/测试集和度量指标。对于成熟的应用程序，这可能需要更长的时间。   \n7. 当你有大量的数据时，70％/30％的测试分布的传统启发式方法不适用于问题;开发集和测试集可以比数据的30％要低得多。  \n8. 你的开发集应足够大，以检测对于你的算法精度产生的有意义的变化，但并不一定要大很多。你的测试集应该足够大，给你系统的最终性能一个有信心的估计。   \n9. 如果说你的开发集和度量指标不再指引你的团队前往正确的方向，那就迅速改变它们：（i）如果你有过度拟合开发集，那就获得更多的开发集数据。（ⅱ）如果你关心的实际分布表明开发集/测试集的分布是不同的，那就获得新的开发集/测试集数据。（iii）如果你的指标不再度量对你而言重要的指标，那就改变度量指标。\n\n#基础的误差分析\n\n#13.误差分析：着眼于开发集示例去评估想法\n\n当你运行你的猫应用程序时，你注意到把狗误认为猫的几个示例。然而有些狗确实像猫！  \n\n![14.png](image/14.png)\n\n一个团队成员提议与甄别狗更好的第三方软件合并。这个更改势必会进行一个月的开发周期，这个团队成员依旧是满腔热血的。这个时候你应该让这个方案继续进行吗？  \n\n在为这个更改投入一个月之前，我推荐你应该首先评估这究竟能给系统的准确精度提高多少。然后你更能理性地决定是否这值得一个月开发周期的投入，还是转向其它的任务。  \n\n详细地说，这里有你能做的：  \n    1. 得到一个有100个使系统错误分类的开发集示例的样本。  \n    2. 人工检查一下这些示例，计数狗图像占多少。  \n\n检查误分类的过程称为“误差分析”。在这个例子中，如果你发现只有相对5%的误分类，而且无论如何优化这个问题你都无法去除这5%的误差。换句话说，5%是一个对于提议方案起作用的天花板（意味着最大可接受数目）。因此，如果你的整体系统目前是90%的精度（10%的误差），这个更改可能最好到90.5%（或者9.5%的误差，比原来的10%相对少了5%）。  \n\n对比来看，如果你发现50%的都分类错了，那么你能更有信心的说你的更改方案能起到巨大的作用。这能够使精度从90%增加到95%（对于误差相对减少了50%，从10%下降到5%）。  \n\n这个给误分类计数的步骤给我们一个很快的方法，去评估对于狗图像结合第三方软件的使用价值到底有多大。它提供了一个可以量化的基础，去决定是否去进行投入。  \n\n误差分析能够经常帮助你去弄明白另外的方向是多么的有希望。但是我已经看到许多工程师不情愿去执行误差分析。它经常对于先去执行想法感到更多的兴趣，而不是先去质疑这个想法是否值得去投入。这是个有共性的错误：你的团队花了一个月才意识到这个更改几乎没有产生好效果。  \n\n人工检查100个示例不需要花费很久。即使你每张图花一分钟，你也能2个小时不到就完成。这2个小时可能会避免你一个月的浪费，所以这个时间值得去这么做。  \n\n“误差分析”代表着去检查开发集中误分类的示例的过程，以便理解错误潜在的原因。这能既帮助你在这个例子中给项目排期，又能给新方向灵感，这一点我将接下来讨论。下面的几个章节里我将给读者展现执行误差分析的优秀实例。  \n\n#14.进行误差分析时并行评估多重方案\n\n你的团队对于改善猫分类器有如下几种方案：  \n    1. 修复把狗辨别为猫的问题。  \n    2. 修复把“庞大的猫”（狮子，黑豹等）辨别为家猫（宠物）的问题。  \n    3. 提高系统对于模糊图片处理的效果。  \n    4. ...  \n\n你能并行地、有效率地去评估这些方案。我通常创建一个表格，然后通过100个误分类的开发集示例去填写它，而且略记一下或许帮助我记住特殊的示例。用一个包含4个示例的小开发集来说明，你的表格会如下图所示：  \n\n![15.png](image/15.png)\n\n上图的图片3既有庞大的猫又有模糊图片的问题：它可能是与多个类别相关的一个例子。这是为什么图中底部的百分数没有达到100%的原因。  \n\n尽管我已经描述这个过程要先进行分类（狗、庞大的猫、模糊问题），然后给示例进行分类，实际上，一旦你开始处理这些示例，你将可能有发现新误差类别的灵感。例如，或许在处理了许多图片后，你意识到许多误差都来自被 Instagram 里的滤镜处理后的图片。你会退回去，在之前的表格里增加表示“ Instagram \"一栏。人工浏览这些算法出错的示例，自问作为人的你是如何去得到正确的标签的，这将经常给你提出新误差分类和解决方案以灵感。  \n\n最有帮助的误差分类是你能给它提出改进方案的那一个。例如，如果你有办法”消除“它的滤镜从而得到原始图片，那么 Instagram 分类会是最有帮助的。但是你不必限制你自己只关注那些你已经有改善方案的误差分类；这个过程的目的是为了增强你对于最值得关注领域的直觉。  \n\n误差分析是一个迭代过程。你甚至会最后没能得到任何分类。虽然也是浏览图片，但是你或许提不出误差类别的方案。然后在人工给这些图片分类之后，你或许有新分类的灵感，所以退回去以新分类的视角重新鉴别这些图片，重复这个过程。  \n\n假设你完成了对于这100个开发集示例的误差分析过程，最好得到下面这个：  \n\n![16.png](image/16.png)\n\n你现在知道了从事狗问题的方案最多只能改善8%。从事庞大的猫和模糊问题能够改善更多。你或许因此会挑后两个分类去从事解决它们。如果你的团队有足够的人去并行地从事多个方向，那么你就能让一些工程师去从事解决庞大的猫和模糊图片的其它问题了。  \n\n误差分析不是一个去告诉你哪一个是最优的方案的僵化的数学公式。你也要考虑你对不同的分类有多大的进展，以及对于每个方案你需要做多大的工作。  \n\n\n\n\n\n"
  },
  {
    "path": "README.md",
    "content": "# deeplearningDemo\n\n## Wunderlist\n\n- [ ] 1. [Machine Learning Yearning](https://github.com/zhourunlai/deep-learning-demo/blob/master/MLyearning/README.md)\n\n- [ ] 2. [Deep Learning Book](https://github.com/zhourunlai/deep-learning-demo/blob/master/DLbook/DeepLearningPapers.md)(感谢北京大学张志华团队的翻译工作，中文版点击[这里](https://github.com/exacity/deeplearningbook-chinese))\n\n\n\n***\n\n## 一、记录深度学习例子：\n\n| 名称 | 目录|\n|:-----|-----|\n| Caffe | [[dir]](https://github.com/zhourunlai/deeplearningDemo/tree/master/Caffe)|\n| TensorFlow | [[dir]](https://github.com/zhourunlai/deeplearningDemo/tree/master/TensorFlow)|\n| Theano | [[dir]](https://github.com/zhourunlai/deeplearningDemo/tree/master/Theano)|\n| Keras | [[dir]](https://github.com/zhourunlai/deeplearningDemo/tree/master/Keras)|\n\n\n\n***\n\n## 二、记录历程点滴:\n\n1. 掌握机器学习相关的概念及计算公式，包括有/无/半监督学习，强化学习，分类/回归/标注，聚类；训练集/验证集，交叉验证，测试集；数据预处理，正则化，归一化；损失函数，经验风险最小化，结构风险最小化，最优化算法；训练误差，泛化误差，欠拟合，过拟合；准确率，召回率，F1值，ROC和AUC；\n\n2. 掌握机器学习主流的模型及其[算法](https://github.com/zhourunlai/machine-learning-algorithm)，包括有生成方法：朴素贝叶斯、隐马尔可夫模型，判别方法：感知机、logistic回归、决策树、K近邻、支持向量机、提升方法、最大熵、条件随机场等；\n\n3. 安装 numpy, scipy, pandas, matplotlib, scikit-learn, xgboost 等 python 包，[实战](https://github.com/zhourunlai/machine-learning-in-action)项目：识别手写数字、画决策树、文本挖掘过滤垃圾邮件、情感倾向分析、波斯顿房价预测、基于协同过滤的推荐系统、图像分类等，上手 kaggle、KDD 比赛题或者阿里天池、滴滴Di-Tech、今日头条bytecup 比赛题；\n\n4. 了解大数据相关的知识，包括有Flume、Kafka，Storm，Hadoop，Spark等，知道Hadoop基金下的项目（Cassandra、HBase、Hive、Pig、ZooKeeper等）的应用场景，特别地要知道分布式计算框架的原理，从 HDFS、MapReducer 到 Streaming；\n\n5. 安装 spark-2.0.0-bin-hadoop2.7，掌握 [Hadoop Shell命令](https://spark.apache.org/docs/latest/spark-standalone.html)，两种模式下运行 [Spark 作业](https://spark.apache.org/docs/latest/spark-standalone.html)，了解 Spark SQL/Streaming/GraphX，掌握 [Spark MLlib 写机器学习算法](http://spark.apache.org/mllib/)；\n\n6. 深度学习相关的概念及计算公式，包括神经元模型、输入层、隐藏层、输出层、weight、bias、BP算法、目标函数（mean_squared_error、mean_absolute_percentage_error等）、激活函数（sigmoid、softmax、tanh、relu等）、优化算法（SGD、RMSprop、Adagrad、Adam等）、多层感知器、自动编码器、卷积神经网络CNN（卷积层Convolution2D、池化层MaxPooling2D）、递归神经网络RNN、LSTM、全连接网络等；\n\n7. 安装深度学习框架 TensorFlow/Theano 或其它，掌握 tf 的张量、图、会话的用法，了解分布式/使用GPU的方法，动手写经典的项目，学会使用 Vgg 16/19 和 ResNet 的模型并运用到自己的项目中；\n\n8. 安装更上层的[深度学习库 Keras](http://keras.io/)，更加快速、熟练的编写出各种种类的神经网络模型。\n\n\n\n***\n\nTODO:  \n\n1. Autoencoder：  \n    特点：1）数据相关的，2）有损的，3）从样本中自动学习的；    \n    作用：1）数据去噪，2）进行可视化而降维；  \n    类型：简单自编码器、稀疏自编码器、深度自编码器、卷积自编码器、序列到序列的自动编码器、变分自编码器；    \n    \n2. CNN：  \n    LeNet、AlexNet、GoogLeNet、VGG、ResNet  \n\n    [Neural Network Architectures](https://culurciello.github.io/tech/2016/06/04/nets.html)\n    \n    高级激活: LeakyReLU, PReLU, ELU, ParametricSoftplus, ThresholdedReLU, SReLU  \n\n    卷积: Convolution1D, Convolution2D, AtrousConvolution2D, SeparableConvolution2D, Deconvolution2D, Convolution3D, UpSampling1D, UpSampling2D, UpSampling3D, ZeroPadding1D, ZeroPadding2D, ZeroPadding3D  \n\n    内核: Dense, Activation, Dropout, SpatialDropout2D, SpatialDropout3D, Flatten, Reshape, Permute, RepeatVector, Merge, Highway, MaxoutDense  \n\n    嵌入: Embedding  \n\n    归一化: BatchNormalization  \n\n    池化: MaxPooling1D, MaxPooling2D, MaxPooling3D, AveragePooling1D, AveragePooling2D, AveragePooling3D, GlobalMaxPooling1D, GlobalAveragePooling1D, GlobalMaxPooling2D, GlobalAveragePooling2D  \n\n    循环: SimpleRNN, LSTM, GRU  \n\n    包装器:Bidirectional, TimeDistributed  \n\n3. RNN：  \n    [http://deeplearning.net/tutorial/rnnslu.html](http://deeplearning.net/tutorial/rnnslu.html)  \n\n4. LSTM：  \n    [http://deeplearning.net/tutorial/lstm.html](http://deeplearning.net/tutorial/lstm.html)  \n    \n5. GAN:  \n    [http://datascienceassn.org/sites/default/files/Generative%20Adversarial%20Nets.pdf](http://datascienceassn.org/sites/default/files/Generative%20Adversarial%20Nets.pdf)  \n    [https://github.com/255BITS/HyperGAN](https://github.com/255BITS/HyperGAN)\n\n![cnn](image/cnn.png)\n\n\n\n\n***\n\n## 三、记录开源资料：\n### 机器学习相关\n##### 网站：\n1. [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning)\n2. [dl](https://github.com/ty4z2008/Qix/blob/master/dl.md)\n3. [我爱机器学习](https://www.52ml.net/star)\n4. [寒小阳的博客](http://blog.csdn.net/han_xiaoyang?viewmode=contents)\n\n##### [书籍](https://www.douban.com/people/100617219/)：\n1. 统计学习方法、集体智慧编程、利用python进行数据分析、机器学习实战、机器学习西瓜书、Spark MLlib 机器学习\n2. 自然语言处理、计算广告、推荐系统、计算机视觉、大数据应用实践\n\n##### 课程：\n1. [Coursera Ng大牛的课程](https://www.coursera.org/learn/machine-learning)\n2. [小象学院邹博老师的课程](http://www.chinahadoop.cn/classroom/23/courses)\n\n### 深度学习相关\n##### 网站：\n1. [deeplearning.net](http://deeplearning.net/) 收藏夹必备，paper指南\n2. [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html)\n3. [UFLDL教程](http://deeplearning.stanford.edu/wiki/index.php/UFLDL%E6%95%99%E7%A8%8B)\n\n##### 书籍：\n1. [DeepLearningBook](http://www.deeplearningbook.org/) 亚马逊预售12月出，等不及花40元打\n\n##### 课程：\n1. [优达学城的deep-learning免费课程](https://cn.udacity.com/course/deep-learning--ud730)\n2. [深度学习2016暑假课程有PPT无字幕](http://videolectures.net/deeplearning2016_montreal/)\n3. [周莫烦的录制视频Youtebe和优酷均有](https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg)\n\n\n>Tips: \n①Follow 业界大牛的 [Twitter](https://twitter.com/ufozrl/following)，比如 Geoffrey Hinton (Google AI团队)、Aymeric Damien (Facebook AI实验室)、Yoshua Bengio (蒙特利尔大学终身教授) 、Andrew Ng (斯坦福大学副教授)、Li Feifei、Andrej Karpathy 等，掌握大神们的最新研究进展；\n②Reddit 上订阅一些主题如 [/r/deeplearning](https://www.reddit.com/r/deeplearning/)，可以知道业界最新的新闻动态，还有一些 discussion 如 WAYR([what_are_you_reading](https://www.reddit.com/r/MachineLearning/comments/4qyjiq/machine_learning_wayr_what_are_you_reading_week_1/)) 可以交流。\n\n\n\n***\n\n## 四、记录开发机\n\n1. 自己的 MacBook Pro 一训练数据CPU升到200%~300~就开始发热，甚至风扇开始转；\n\n2. 偶然听朋友建议到 [**SuperVessel**](https://crl.ptopenlab.com:8800/dashboard/auth/login/?next=/dashboard/project/instances/)上试试，装了GPU下的TF，但是必须在规定的VPN下才能SSH；  \n\n3. 接下来转到 [**AWS**](http://docs.aws.amazon.com/zh_cn/AWSEC2/latest/UserGuide/using_cluster_computing.html#gpu-instance-specifications)，可以自己搭建应用了， 现在有两种虚拟机 g2.2xlarge（单块CPU，4G显存）和 g2.8xlarge（4块CPU，4G显存），都是CUDA的。知乎上的教程[在AWS上配置深度学习主机](https://zhuanlan.zhihu.com/p/25066187)。\n\n4. 阿里云HPC 和 Ucloud 现也有带 Tesla 的物理机了。用前者低配版的训练 [neural-style](https://github.com/anishathalye/neural-style)，14分钟左右，```python neural_style.py --content content.jpg --styles style.jpg --output output.jpg --iteration 1000 --width 512```。用之前算一算数据量要付费多少，大了的话买虚拟机还不如自己搭一台工作站；      \n\n5. 等毕业了自己搭一台**工作站**吧... \n\n6. TPU是什么鬼\n\n\n\n***\n\n## 五、记录集群部署\n\n1. [Spark集群部署](https://zhuanlan.zhihu.com/p/23689558)  \n\n2. [分布式tensorflow部署与训练](http://blog.xiaorun.me/index.php/archives/375/)  \n\n使用 [git hook](https://dearb.me/archive/2015-03-30/automate-deploy-your-websites-with-git-hook/)，配合 [rsync](http://www.dahouduan.com/2014/11/19/rsync-daemon/)，本地开发机一次提交代码，使集群间指定目录代码一致，节省每台机器都复制粘贴代码的操作；这样跑分布式训练时，只需要在每台机器上带参数来运行代码就可以了\n\n\n\n***\n\n## 六、项目demo\n\n1. IMAGE相关：  \n    1.1 图像风格转换neural-style [anishathalye/neural-style](https://github.com/anishathalye/neural-style)  \n    1.2 素描自动上色 [pfnet/PaintsChainer](https://github.com/pfnet/PaintsChainer)  \n    1.3 图像描述 [iFighting/im2txt](https://github.com/iFighting/models/tree/master/im2txt)  \n    1.4 图片生成故事 [ryankiros/neural-storyteller](https://github.com/ryankiros/neural-storyteller)  \n    1.5 小度机器人  \n    1.6 生成明星脸  \n\n2. NLP相关：  \n    2.1 古诗词生成器  \n\n3. RNN相关：  \n    3.1 创作歌曲/歌曲风格转换  \n\n4. RL相关：  \n    4.1 愤怒的小鸟 [yenchenlin/DeepLearningFlappyBird](https://github.com/yenchenlin/DeepLearningFlappyBird)  \n    4.2 模拟自动驾驶 [kevinhughes27/TensorKart](https://github.com/kevinhughes27/TensorKart)  \n\n\n\n\n\n***\n\n## 七、调参trick\n\n1. [Theano调试技巧](https://zhuanlan.zhihu.com/p/24857032)\n\n"
  },
  {
    "path": "TensorFlow/LICENSE",
    "content": "The MIT License (MIT)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\nAll contributions by Aymeric Damien:\nCopyright (c) 2015, Aymeric Damien.\nAll rights reserved.\n\nAll other contributions:\nCopyright (c) 2015, the respective contributors.\nAll rights reserved.\n\nEach contributor holds copyright over their respective contributions.\nThe project versioning (Git) records all such contribution source information."
  },
  {
    "path": "TensorFlow/README.md",
    "content": "# TensorFlow Examples\nTensorFlow Tutorial with popular machine learning algorithms implementation. This tutorial was designed for easily diving into TensorFlow, through examples.\n\nIt is suitable for beginners who want to find clear and concise examples about TensorFlow. For readability, the tutorial includes both notebook and code with explanations.\n\nNote: If you are using older TensorFlow version (before 0.12), please have a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11)\n\n## Tutorial index\n\n#### 0 - Prerequisite\n- Introduction to Machine Learning ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/ml_introduction.ipynb))\n- Introduction to MNIST Dataset ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb))\n\n#### 1 - Introduction\n- Hello World ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/helloworld.py))\n- Basic Operations ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py))\n\n#### 2 - Basic Models\n- Nearest Neighbor ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py))\n- Linear Regression ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py))\n- Logistic Regression ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression.py))\n\n#### 3 - Neural Networks\n- Multilayer Perceptron ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/multilayer_perceptron.py))\n- Convolutional Neural Network ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py))\n- Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py))\n- Bidirectional Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py))\n- Dynamic Recurrent Neural Network (LSTM) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dynamic_rnn.py))\n- AutoEncoder ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py))\n\n#### 4 - Utilities\n- Save and Restore a model ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/save_restore_model.py))\n- Tensorboard - Graph and loss visualization ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_basic.py))\n- Tensorboard - Advanced visualization ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_advanced.py))\n\n#### 5 - Multi GPU\n- Basic Operations on multi-GPU ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py))\n\n## Dataset\nSome examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py).\nMNIST is a database of handwritten digits, for a quick description of that dataset, you can check [this notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb).\n\nOfficial Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)\n\n## More Examples\nThe following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api).\n\n### Tutorials\n- [TFLearn Quickstart](https://github.com/tflearn/tflearn/blob/master/tutorials/intro/quickstart.md). Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier.\n\n### Basics\n- [Linear Regression](https://github.com/tflearn/tflearn/blob/master/examples/basics/linear_regression.py). Implement a linear regression using TFLearn.\n- [Logical Operators](https://github.com/tflearn/tflearn/blob/master/examples/basics/logical.py). Implement logical operators with TFLearn (also includes a usage of 'merge').\n- [Weights Persistence](https://github.com/tflearn/tflearn/blob/master/examples/basics/weights_persistence.py). Save and Restore a model.\n- [Fine-Tuning](https://github.com/tflearn/tflearn/blob/master/examples/basics/finetuning.py). Fine-Tune a pre-trained model on a new task.\n- [Using HDF5](https://github.com/tflearn/tflearn/blob/master/examples/basics/use_hdf5.py). Use HDF5 to handle large datasets.\n- [Using DASK](https://github.com/tflearn/tflearn/blob/master/examples/basics/use_dask.py). Use DASK to handle large datasets.\n\n### Computer Vision\n- [Multi-layer perceptron](https://github.com/tflearn/tflearn/blob/master/examples/images/dnn.py). A multi-layer perceptron implementation for MNIST classification task.\n- [Convolutional Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_mnist.py). A Convolutional neural network implementation for classifying MNIST dataset.\n- [Convolutional Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_cifar10.py). A Convolutional neural network implementation for classifying CIFAR-10 dataset.\n- [Network in Network](https://github.com/tflearn/tflearn/blob/master/examples/images/network_in_network.py). 'Network in Network' implementation for classifying CIFAR-10 dataset.\n- [Alexnet](https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py). Apply Alexnet to Oxford Flowers 17 classification task.\n- [VGGNet](https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py). Apply VGG Network to Oxford Flowers 17 classification task.\n- [VGGNet Finetuning (Fast Training)](https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network_finetuning.py). Use a pre-trained VGG Network and retrain it on your own data, for fast training.\n- [RNN Pixels](https://github.com/tflearn/tflearn/blob/master/examples/images/rnn_pixels.py). Use RNN (over sequence of pixels) to classify images.\n- [Highway Network](https://github.com/tflearn/tflearn/blob/master/examples/images/highway_dnn.py). Highway Network implementation for classifying MNIST dataset.\n- [Highway Convolutional Network](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_highway_mnist.py). Highway Convolutional Network implementation for classifying MNIST dataset.\n- [Residual Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py). A bottleneck residual network applied to MNIST classification task.\n- [Residual Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py). A residual network applied to CIFAR-10 classification task.\n- [Google Inception (v3)](https://github.com/tflearn/tflearn/blob/master/examples/images/googlenet.py). Google's Inception v3 network applied to Oxford Flowers 17 classification task.\n- [Auto Encoder](https://github.com/tflearn/tflearn/blob/master/examples/images/autoencoder.py). An auto encoder applied to MNIST handwritten digits.\n\n### Natural Language Processing\n- [Recurrent Neural Network (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py). Apply an LSTM to IMDB sentiment dataset classification task.\n- [Bi-Directional RNN (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/bidirectional_lstm.py). Apply a bi-directional LSTM to IMDB sentiment dataset classification task.\n- [Dynamic RNN (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/dynamic_lstm.py). Apply a dynamic LSTM to classify variable length text from IMDB dataset.\n- [City Name Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_cityname.py). Generates new US-cities name, using LSTM network.\n- [Shakespeare Scripts Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py). Generates new Shakespeare scripts, using LSTM network.\n- [Seq2seq](https://github.com/tflearn/tflearn/blob/master/examples/nlp/seq2seq_example.py). Pedagogical example of seq2seq reccurent network. See [this repo](https://github.com/ichuang/tflearn_seq2seq) for full instructions.\n- [CNN Seq](https://github.com/tflearn/tflearn/blob/master/examples/nlp/cnn_sentence_classification.py). Apply a 1-D convolutional network to classify sequence of words from IMDB sentiment dataset.\n\n### Reinforcement Learning\n- [Atari Pacman 1-step Q-Learning](https://github.com/tflearn/tflearn/blob/master/examples/reinforcement_learning/atari_1step_qlearning.py). Teach a machine to play Atari games (Pacman by default) using 1-step Q-learning.\n\n### Others\n- [Recommender - Wide & Deep Network](https://github.com/tflearn/tflearn/blob/master/examples/others/recommender_wide_and_deep.py). Pedagogical example of wide & deep networks for recommender systems.\n\n### Notebooks\n- [Spiral Classification Problem](https://github.com/tflearn/tflearn/blob/master/examples/notebooks/spiral.ipynb). TFLearn implementation of spiral classification problem from Stanford CS231n.\n\n### Extending TensorFlow\n- [Layers](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/layers.py). Use TFLearn layers along with TensorFlow.\n- [Trainer](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/trainer.py). Use TFLearn trainer class to train any TensorFlow graph.\n- [Built-in Ops](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/builtin_ops.py). Use TFLearn built-in operations along with TensorFlow.\n- [Summaries](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/summaries.py). Use TFLearn summarizers along with TensorFlow.\n- [Variables](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/variables.py). Use TFLearn variables along with TensorFlow.\n\n\n## Dependencies\n```\ntensorflow 1.0alpha\nnumpy\nmatplotlib\ncuda\ntflearn (if using tflearn examples)\n```\nFor more details about TensorFlow installation, you can check [TensorFlow Installation Guide](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md)\n"
  },
  {
    "path": "TensorFlow/examples/1_Introduction/basic_operations.py",
    "content": "'''\nBasic Operations example using TensorFlow library.\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/TensorFlow-Examples/\n'''\n\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\n# Basic constant operations\n# The value returned by the constructor represents the output\n# of the Constant op.\na = tf.constant(2)\nb = tf.constant(3)\n\n# Launch the default graph.\nwith tf.Session() as sess:\n    print(\"a=2, b=3\")\n    print(\"Addition with constants: %i\" % sess.run(a+b))\n    print(\"Multiplication with constants: %i\" % sess.run(a*b))\n\n# Basic Operations with variable as graph input\n# The value returned by the constructor represents the output\n# of the Variable op. (define as input when running session)\n# tf Graph input\na = tf.placeholder(tf.int16)\nb = tf.placeholder(tf.int16)\n\n# Define some operations\nadd = tf.add(a, b)\nmul = tf.mul(a, b)\n\n# Launch the default graph.\nwith tf.Session() as sess:\n    # Run every operation with variable input\n    print(\"Addition with variables: %i\" % sess.run(add, feed_dict={a: 2, b: 3}))\n    print(\"Multiplication with variables: %i\" % sess.run(mul, feed_dict={a: 2, b: 3}))\n\n\n# ----------------\n# More in details:\n# Matrix Multiplication from TensorFlow official tutorial\n\n# Create a Constant op that produces a 1x2 matrix.  The op is\n# added as a node to the default graph.\n#\n# The value returned by the constructor represents the output\n# of the Constant op.\nmatrix1 = tf.constant([[3., 3.]])\n\n# Create another Constant that produces a 2x1 matrix.\nmatrix2 = tf.constant([[2.],[2.]])\n\n# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.\n# The returned value, 'product', represents the result of the matrix\n# multiplication.\nproduct = tf.matmul(matrix1, matrix2)\n\n# To run the matmul op we call the session 'run()' method, passing 'product'\n# which represents the output of the matmul op.  This indicates to the call\n# that we want to get the output of the matmul op back.\n#\n# All inputs needed by the op are run automatically by the session.  They\n# typically are run in parallel.\n#\n# The call 'run(product)' thus causes the execution of threes ops in the\n# graph: the two constants and matmul.\n#\n# The output of the op is returned in 'result' as a numpy `ndarray` object.\nwith tf.Session() as sess:\n    result = sess.run(product)\n    print(result)\n    # ==> [[ 12.]]\n"
  },
  {
    "path": "TensorFlow/examples/1_Introduction/helloworld.py",
    "content": "'''\nHelloWorld example using TensorFlow library.\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/TensorFlow-Examples/\n'''\n\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\n# Simple hello world using TensorFlow\n\n# Create a Constant op\n# The op is added as a node to the default graph.\n#\n# The value returned by the constructor represents the output\n# of the Constant op.\nhello = tf.constant('Hello, TensorFlow!')\n\n# Start tf session\nsess = tf.Session()\n\n# Run the op\nprint(sess.run(hello))\n"
  },
  {
    "path": "TensorFlow/examples/2_BasicModels/linear_regression.py",
    "content": "'''\nA linear regression learning algorithm example using TensorFlow library.\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/TensorFlow-Examples/\n'''\n\nfrom __future__ import print_function\n\nimport tensorflow as tf\nimport numpy\nimport matplotlib.pyplot as plt\nrng = numpy.random\n\n# Parameters\nlearning_rate = 0.01\ntraining_epochs = 1000\ndisplay_step = 50\n\n# Training Data\ntrain_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,\n                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])\ntrain_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,\n                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])\nn_samples = train_X.shape[0]\n\n# tf Graph Input\nX = tf.placeholder(\"float\")\nY = tf.placeholder(\"float\")\n\n# Set model weights\nW = tf.Variable(rng.randn(), name=\"weight\")\nb = tf.Variable(rng.randn(), name=\"bias\")\n\n# Construct a linear model\npred = tf.add(tf.mul(X, W), b)\n\n# Mean squared error\ncost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)\n# Gradient descent\noptimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n\n# Initializing the variables\ninit = tf.global_variables_initializer()\n\n# Launch the graph\nwith tf.Session() as sess:\n    sess.run(init)\n\n    # Fit all training data\n    for epoch in range(training_epochs):\n        for (x, y) in zip(train_X, train_Y):\n            sess.run(optimizer, feed_dict={X: x, Y: y})\n\n        # Display logs per epoch step\n        if (epoch+1) % display_step == 0:\n            c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})\n            print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(c), \\\n                \"W=\", sess.run(W), \"b=\", sess.run(b))\n\n    print(\"Optimization Finished!\")\n    training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})\n    print(\"Training cost=\", training_cost, \"W=\", sess.run(W), \"b=\", sess.run(b), '\\n')\n\n    # Graphic display\n    plt.plot(train_X, train_Y, 'ro', label='Original data')\n    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')\n    plt.legend()\n    plt.show()\n\n    # Testing example, as requested (Issue #2)\n    test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])\n    test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])\n\n    print(\"Testing... (Mean square loss Comparison)\")\n    testing_cost = sess.run(\n        tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]),\n        feed_dict={X: test_X, Y: test_Y})  # same function as cost above\n    print(\"Testing cost=\", testing_cost)\n    print(\"Absolute mean square loss difference:\", abs(\n        training_cost - testing_cost))\n\n    plt.plot(test_X, test_Y, 'bo', label='Testing data')\n    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')\n    plt.legend()\n    plt.show()\n"
  },
  {
    "path": "TensorFlow/examples/2_BasicModels/logistic_regression.py",
    "content": "'''\nA logistic regression learning algorithm example using TensorFlow library.\nThis example is using the MNIST database of handwritten digits\n(http://yann.lecun.com/exdb/mnist/)\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/TensorFlow-Examples/\n'''\n\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\n# Import MNIST data\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n\n# Parameters\nlearning_rate = 0.01\ntraining_epochs = 25\nbatch_size = 100\ndisplay_step = 1\n\n# tf Graph Input\nx = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784\ny = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes\n\n# Set model weights\nW = tf.Variable(tf.zeros([784, 10]))\nb = tf.Variable(tf.zeros([10]))\n\n# Construct model\npred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\n\n# Minimize error using cross entropy\ncost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))\n# Gradient Descent\noptimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n\n# Initializing the variables\ninit = tf.global_variables_initializer()\n\n# Launch the graph\nwith tf.Session() as sess:\n    sess.run(init)\n\n    # Training cycle\n    for epoch in range(training_epochs):\n        avg_cost = 0.\n        total_batch = int(mnist.train.num_examples/batch_size)\n        # Loop over all batches\n        for i in range(total_batch):\n            batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n            # Run optimization op (backprop) and cost op (to get loss value)\n            _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,\n                                                          y: batch_ys})\n            # Compute average loss\n            avg_cost += c / total_batch\n        # Display logs per epoch step\n        if (epoch+1) % display_step == 0:\n            print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost))\n\n    print(\"Optimization Finished!\")\n\n    # Test model\n    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n    # Calculate accuracy\n    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n    print(\"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))\n"
  },
  {
    "path": "TensorFlow/examples/2_BasicModels/nearest_neighbor.py",
    "content": "'''\nA nearest neighbor learning algorithm example using TensorFlow library.\nThis example is using the MNIST database of handwritten digits\n(http://yann.lecun.com/exdb/mnist/)\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/TensorFlow-Examples/\n'''\n\nfrom __future__ import print_function\n\nimport numpy as np\nimport tensorflow as tf\n\n# Import MNIST data\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n\n# In this example, we limit mnist data\nXtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates)\nXte, Yte = mnist.test.next_batch(200) #200 for testing\n\n# tf Graph Input\nxtr = tf.placeholder(\"float\", [None, 784])\nxte = tf.placeholder(\"float\", [784])\n\n# Nearest Neighbor calculation using L1 Distance\n# Calculate L1 Distance\ndistance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1)\n# Prediction: Get min distance index (Nearest neighbor)\npred = tf.arg_min(distance, 0)\n\naccuracy = 0.\n\n# Initializing the variables\ninit = tf.global_variables_initializer()\n\n# Launch the graph\nwith tf.Session() as sess:\n    sess.run(init)\n\n    # loop over test data\n    for i in range(len(Xte)):\n        # Get nearest neighbor\n        nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]})\n        # Get nearest neighbor class label and compare it to its true label\n        print(\"Test\", i, \"Prediction:\", np.argmax(Ytr[nn_index]), \\\n            \"True Class:\", np.argmax(Yte[i]))\n        # Calculate accuracy\n        if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):\n            accuracy += 1./len(Xte)\n    print(\"Done!\")\n    print(\"Accuracy:\", accuracy)\n"
  },
  {
    "path": "TensorFlow/examples/3_NeuralNetworks/autoencoder.py",
    "content": "# -*- coding: utf-8 -*-\n\n\"\"\" Auto Encoder Example.\nUsing an auto encoder on MNIST handwritten digits.\nReferences:\n    Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. \"Gradient-based\n    learning applied to document recognition.\" Proceedings of the IEEE,\n    86(11):2278-2324, November 1998.\nLinks:\n    [MNIST Dataset] http://yann.lecun.com/exdb/mnist/\n\"\"\"\nfrom __future__ import division, print_function, absolute_import\n\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Import MNIST data\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets(\"MNIST_data\", one_hot=True)\n\n# Parameters\nlearning_rate = 0.01\ntraining_epochs = 20\nbatch_size = 256\ndisplay_step = 1\nexamples_to_show = 10\n\n# Network Parameters\nn_hidden_1 = 256 # 1st layer num features\nn_hidden_2 = 128 # 2nd layer num features\nn_input = 784 # MNIST data input (img shape: 28*28)\n\n# tf Graph input (only pictures)\nX = tf.placeholder(\"float\", [None, n_input])\n\nweights = {\n    'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n    'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n    'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),\n    'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])),\n}\nbiases = {\n    'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),\n    'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),\n    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),\n    'decoder_b2': tf.Variable(tf.random_normal([n_input])),\n}\n\n\n# Building the encoder\ndef encoder(x):\n    # Encoder Hidden layer with sigmoid activation #1\n    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),\n                                   biases['encoder_b1']))\n    # Decoder Hidden layer with sigmoid activation #2\n    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),\n                                   biases['encoder_b2']))\n    return layer_2\n\n\n# Building the decoder\ndef decoder(x):\n    # Encoder Hidden layer with sigmoid activation #1\n    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),\n                                   biases['decoder_b1']))\n    # Decoder Hidden layer with sigmoid activation #2\n    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),\n                                   biases['decoder_b2']))\n    return layer_2\n\n# Construct model\nencoder_op = encoder(X)\ndecoder_op = decoder(encoder_op)\n\n# Prediction\ny_pred = decoder_op\n# Targets (Labels) are the input data.\ny_true = X\n\n# Define loss and optimizer, minimize the squared error\ncost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))\noptimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)\n\n# Initializing the variables\ninit = tf.global_variables_initializer()\n\n# Launch the graph\nwith tf.Session() as sess:\n    sess.run(init)\n    total_batch = int(mnist.train.num_examples/batch_size)\n    # Training cycle\n    for epoch in range(training_epochs):\n        # Loop over all batches\n        for i in range(total_batch):\n            batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n            # Run optimization op (backprop) and cost op (to get loss value)\n            _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs})\n        # Display logs per epoch step\n        if epoch % display_step == 0:\n            print(\"Epoch:\", '%04d' % (epoch+1),\n                  \"cost=\", \"{:.9f}\".format(c))\n\n    print(\"Optimization Finished!\")\n\n    # Applying encode and decode over test set\n    encode_decode = sess.run(\n        y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})\n    # Compare original images with their reconstructions\n    f, a = plt.subplots(2, 10, figsize=(10, 2))\n    for i in range(examples_to_show):\n        a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28)))\n        a[1][i].imshow(np.reshape(encode_decode[i], (28, 28)))\n    f.show()\n    plt.draw()\n    plt.waitforbuttonpress()\n"
  },
  {
    "path": "TensorFlow/examples/3_NeuralNetworks/bidirectional_rnn.py",
    "content": "'''\nA Bidirectional Recurrent Neural Network (LSTM) implementation example using TensorFlow library.\nThis example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\nLong Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/TensorFlow-Examples/\n'''\n\nfrom __future__ import print_function\n\nimport tensorflow as tf\nfrom tensorflow.contrib import rnn\nimport numpy as np\n\n# Import MNIST data\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n\n'''\nTo classify images using a bidirectional recurrent neural network, we consider\nevery image row as a sequence of pixels. Because MNIST image shape is 28*28px,\nwe will then handle 28 sequences of 28 steps for every sample.\n'''\n\n# Parameters\nlearning_rate = 0.001\ntraining_iters = 100000\nbatch_size = 128\ndisplay_step = 10\n\n# Network Parameters\nn_input = 28 # MNIST data input (img shape: 28*28)\nn_steps = 28 # timesteps\nn_hidden = 128 # hidden layer num of features\nn_classes = 10 # MNIST total classes (0-9 digits)\n\n# tf Graph input\nx = tf.placeholder(\"float\", [None, n_steps, n_input])\ny = tf.placeholder(\"float\", [None, n_classes])\n\n# Define weights\nweights = {\n    # Hidden layer weights => 2*n_hidden because of forward + backward cells\n    'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes]))\n}\nbiases = {\n    'out': tf.Variable(tf.random_normal([n_classes]))\n}\n\n\ndef BiRNN(x, weights, biases):\n\n    # Prepare data shape to match `bidirectional_rnn` function requirements\n    # Current data input shape: (batch_size, n_steps, n_input)\n    # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)\n\n    # Permuting batch_size and n_steps\n    x = tf.transpose(x, [1, 0, 2])\n    # Reshape to (n_steps*batch_size, n_input)\n    x = tf.reshape(x, [-1, n_input])\n    # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n    x = tf.split(x, n_steps, 0)\n\n    # Define lstm cells with tensorflow\n    # Forward direction cell\n    lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)\n    # Backward direction cell\n    lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)\n\n    # Get lstm cell output\n    try:\n        outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n                                              dtype=tf.float32)\n    except Exception: # Old TensorFlow version only returns outputs not states\n        outputs = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n                                        dtype=tf.float32)\n\n    # Linear activation, using rnn inner loop last output\n    return tf.matmul(outputs[-1], weights['out']) + biases['out']\n\npred = BiRNN(x, weights, biases)\n\n# Define loss and optimizer\ncost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n\n# Evaluate model\ncorrect_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\naccuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n\n# Initializing the variables\ninit = tf.global_variables_initializer()\n\n# Launch the graph\nwith tf.Session() as sess:\n    sess.run(init)\n    step = 1\n    # Keep training until reach max iterations\n    while step * batch_size < training_iters:\n        batch_x, batch_y = mnist.train.next_batch(batch_size)\n        # Reshape data to get 28 seq of 28 elements\n        batch_x = batch_x.reshape((batch_size, n_steps, n_input))\n        # Run optimization op (backprop)\n        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})\n        if step % display_step == 0:\n            # Calculate batch accuracy\n            acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})\n            # Calculate batch loss\n            loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})\n            print(\"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n                  \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n                  \"{:.5f}\".format(acc))\n        step += 1\n    print(\"Optimization Finished!\")\n\n    # Calculate accuracy for 128 mnist test images\n    test_len = 128\n    test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))\n    test_label = mnist.test.labels[:test_len]\n    print(\"Testing Accuracy:\", \\\n        sess.run(accuracy, feed_dict={x: test_data, y: test_label}))\n"
  },
  {
    "path": "TensorFlow/examples/3_NeuralNetworks/convolutional_network.py",
    "content": "'''\nA Convolutional Network implementation example using TensorFlow library.\nThis example is using the MNIST database of handwritten digits\n(http://yann.lecun.com/exdb/mnist/)\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/TensorFlow-Examples/\n'''\n\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\n# Import MNIST data\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n\n# Parameters\nlearning_rate = 0.001\ntraining_iters = 200000\nbatch_size = 128\ndisplay_step = 10\n\n# Network Parameters\nn_input = 784 # MNIST data input (img shape: 28*28)\nn_classes = 10 # MNIST total classes (0-9 digits)\ndropout = 0.75 # Dropout, probability to keep units\n\n# tf Graph input\nx = tf.placeholder(tf.float32, [None, n_input])\ny = tf.placeholder(tf.float32, [None, n_classes])\nkeep_prob = tf.placeholder(tf.float32) #dropout (keep probability)\n\n\n# Create some wrappers for simplicity\ndef conv2d(x, W, b, strides=1):\n    # Conv2D wrapper, with bias and relu activation\n    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')\n    x = tf.nn.bias_add(x, b)\n    return tf.nn.relu(x)\n\n\ndef maxpool2d(x, k=2):\n    # MaxPool2D wrapper\n    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],\n                          padding='SAME')\n\n\n# Create model\ndef conv_net(x, weights, biases, dropout):\n    # Reshape input picture\n    x = tf.reshape(x, shape=[-1, 28, 28, 1])\n\n    # Convolution Layer\n    conv1 = conv2d(x, weights['wc1'], biases['bc1'])\n    # Max Pooling (down-sampling)\n    conv1 = maxpool2d(conv1, k=2)\n\n    # Convolution Layer\n    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])\n    # Max Pooling (down-sampling)\n    conv2 = maxpool2d(conv2, k=2)\n\n    # Fully connected layer\n    # Reshape conv2 output to fit fully connected layer input\n    fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])\n    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])\n    fc1 = tf.nn.relu(fc1)\n    # Apply Dropout\n    fc1 = tf.nn.dropout(fc1, dropout)\n\n    # Output, class prediction\n    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])\n    return out\n\n# Store layers weight & bias\nweights = {\n    # 5x5 conv, 1 input, 32 outputs\n    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),\n    # 5x5 conv, 32 inputs, 64 outputs\n    'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),\n    # fully connected, 7*7*64 inputs, 1024 outputs\n    'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),\n    # 1024 inputs, 10 outputs (class prediction)\n    'out': tf.Variable(tf.random_normal([1024, n_classes]))\n}\n\nbiases = {\n    'bc1': tf.Variable(tf.random_normal([32])),\n    'bc2': tf.Variable(tf.random_normal([64])),\n    'bd1': tf.Variable(tf.random_normal([1024])),\n    'out': tf.Variable(tf.random_normal([n_classes]))\n}\n\n# Construct model\npred = conv_net(x, weights, biases, keep_prob)\n\n# Define loss and optimizer\ncost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n\n# Evaluate model\ncorrect_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\naccuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n\n# Initializing the variables\ninit = tf.global_variables_initializer()\n\n# Launch the graph\nwith tf.Session() as sess:\n    sess.run(init)\n    step = 1\n    # Keep training until reach max iterations\n    while step * batch_size < training_iters:\n        batch_x, batch_y = mnist.train.next_batch(batch_size)\n        # Run optimization op (backprop)\n        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,\n                                       keep_prob: dropout})\n        if step % display_step == 0:\n            # Calculate batch loss and accuracy\n            loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,\n                                                              y: batch_y,\n                                                              keep_prob: 1.})\n            print(\"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n                  \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n                  \"{:.5f}\".format(acc))\n        step += 1\n    print(\"Optimization Finished!\")\n\n    # Calculate accuracy for 256 mnist test images\n    print(\"Testing Accuracy:\", \\\n        sess.run(accuracy, feed_dict={x: mnist.test.images[:256],\n                                      y: mnist.test.labels[:256],\n                                      keep_prob: 1.}))\n"
  },
  {
    "path": "TensorFlow/examples/3_NeuralNetworks/dynamic_rnn.py",
    "content": "'''\nA Dynamic Recurrent Neural Network (LSTM) implementation example using\nTensorFlow library. This example is using a toy dataset to classify linear\nsequences. The generated sequences have variable length.\n\nLong Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/TensorFlow-Examples/\n'''\n\nfrom __future__ import print_function\n\nimport tensorflow as tf\nimport random\n\n\n# ====================\n#  TOY DATA GENERATOR\n# ====================\nclass ToySequenceData(object):\n    \"\"\" Generate sequence of data with dynamic length.\n    This class generate samples for training:\n    - Class 0: linear sequences (i.e. [0, 1, 2, 3,...])\n    - Class 1: random sequences (i.e. [1, 3, 10, 7,...])\n\n    NOTICE:\n    We have to pad each sequence to reach 'max_seq_len' for TensorFlow\n    consistency (we cannot feed a numpy array with inconsistent\n    dimensions). The dynamic calculation will then be perform thanks to\n    'seqlen' attribute that records every actual sequence length.\n    \"\"\"\n    def __init__(self, n_samples=1000, max_seq_len=20, min_seq_len=3,\n                 max_value=1000):\n        self.data = []\n        self.labels = []\n        self.seqlen = []\n        for i in range(n_samples):\n            # Random sequence length\n            len = random.randint(min_seq_len, max_seq_len)\n            # Monitor sequence length for TensorFlow dynamic calculation\n            self.seqlen.append(len)\n            # Add a random or linear int sequence (50% prob)\n            if random.random() < .5:\n                # Generate a linear sequence\n                rand_start = random.randint(0, max_value - len)\n                s = [[float(i)/max_value] for i in\n                     range(rand_start, rand_start + len)]\n                # Pad sequence for dimension consistency\n                s += [[0.] for i in range(max_seq_len - len)]\n                self.data.append(s)\n                self.labels.append([1., 0.])\n            else:\n                # Generate a random sequence\n                s = [[float(random.randint(0, max_value))/max_value]\n                     for i in range(len)]\n                # Pad sequence for dimension consistency\n                s += [[0.] for i in range(max_seq_len - len)]\n                self.data.append(s)\n                self.labels.append([0., 1.])\n        self.batch_id = 0\n\n    def next(self, batch_size):\n        \"\"\" Return a batch of data. When dataset end is reached, start over.\n        \"\"\"\n        if self.batch_id == len(self.data):\n            self.batch_id = 0\n        batch_data = (self.data[self.batch_id:min(self.batch_id +\n                                                  batch_size, len(self.data))])\n        batch_labels = (self.labels[self.batch_id:min(self.batch_id +\n                                                  batch_size, len(self.data))])\n        batch_seqlen = (self.seqlen[self.batch_id:min(self.batch_id +\n                                                  batch_size, len(self.data))])\n        self.batch_id = min(self.batch_id + batch_size, len(self.data))\n        return batch_data, batch_labels, batch_seqlen\n\n\n# ==========\n#   MODEL\n# ==========\n\n# Parameters\nlearning_rate = 0.01\ntraining_iters = 1000000\nbatch_size = 128\ndisplay_step = 10\n\n# Network Parameters\nseq_max_len = 20 # Sequence max length\nn_hidden = 64 # hidden layer num of features\nn_classes = 2 # linear sequence or not\n\ntrainset = ToySequenceData(n_samples=1000, max_seq_len=seq_max_len)\ntestset = ToySequenceData(n_samples=500, max_seq_len=seq_max_len)\n\n# tf Graph input\nx = tf.placeholder(\"float\", [None, seq_max_len, 1])\ny = tf.placeholder(\"float\", [None, n_classes])\n# A placeholder for indicating each sequence length\nseqlen = tf.placeholder(tf.int32, [None])\n\n# Define weights\nweights = {\n    'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))\n}\nbiases = {\n    'out': tf.Variable(tf.random_normal([n_classes]))\n}\n\n\ndef dynamicRNN(x, seqlen, weights, biases):\n\n    # Prepare data shape to match `rnn` function requirements\n    # Current data input shape: (batch_size, n_steps, n_input)\n    # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)\n\n    # Permuting batch_size and n_steps\n    x = tf.transpose(x, [1, 0, 2])\n    # Reshaping to (n_steps*batch_size, n_input)\n    x = tf.reshape(x, [-1, 1])\n    # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n    x = tf.split(0, seq_max_len, x)\n\n    # Define a lstm cell with tensorflow\n    lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden)\n\n    # Get lstm cell output, providing 'sequence_length' will perform dynamic\n    # calculation.\n    outputs, states = tf.nn.rnn(lstm_cell, x, dtype=tf.float32,\n                                sequence_length=seqlen)\n\n    # When performing dynamic calculation, we must retrieve the last\n    # dynamically computed output, i.e., if a sequence length is 10, we need\n    # to retrieve the 10th output.\n    # However TensorFlow doesn't support advanced indexing yet, so we build\n    # a custom op that for each sample in batch size, get its length and\n    # get the corresponding relevant output.\n\n    # 'outputs' is a list of output at every timestep, we pack them in a Tensor\n    # and change back dimension to [batch_size, n_step, n_input]\n    outputs = tf.pack(outputs)\n    outputs = tf.transpose(outputs, [1, 0, 2])\n\n    # Hack to build the indexing and retrieve the right output.\n    batch_size = tf.shape(outputs)[0]\n    # Start indices for each sample\n    index = tf.range(0, batch_size) * seq_max_len + (seqlen - 1)\n    # Indexing\n    outputs = tf.gather(tf.reshape(outputs, [-1, n_hidden]), index)\n\n    # Linear activation, using outputs computed above\n    return tf.matmul(outputs, weights['out']) + biases['out']\n\npred = dynamicRNN(x, seqlen, weights, biases)\n\n# Define loss and optimizer\ncost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\noptimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)\n\n# Evaluate model\ncorrect_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\naccuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n\n# Initializing the variables\ninit = tf.global_variables_initializer()\n\n# Launch the graph\nwith tf.Session() as sess:\n    sess.run(init)\n    step = 1\n    # Keep training until reach max iterations\n    while step * batch_size < training_iters:\n        batch_x, batch_y, batch_seqlen = trainset.next(batch_size)\n        # Run optimization op (backprop)\n        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,\n                                       seqlen: batch_seqlen})\n        if step % display_step == 0:\n            # Calculate batch accuracy\n            acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y,\n                                                seqlen: batch_seqlen})\n            # Calculate batch loss\n            loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y,\n                                             seqlen: batch_seqlen})\n            print(\"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n                  \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n                  \"{:.5f}\".format(acc))\n        step += 1\n    print(\"Optimization Finished!\")\n\n    # Calculate accuracy\n    test_data = testset.data\n    test_label = testset.labels\n    test_seqlen = testset.seqlen\n    print(\"Testing Accuracy:\", \\\n        sess.run(accuracy, feed_dict={x: test_data, y: test_label,\n                                      seqlen: test_seqlen}))\n"
  },
  {
    "path": "TensorFlow/examples/3_NeuralNetworks/multilayer_perceptron.py",
    "content": "'''\nA Multilayer Perceptron implementation example using TensorFlow library.\nThis example is using the MNIST database of handwritten digits\n(http://yann.lecun.com/exdb/mnist/)\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/TensorFlow-Examples/\n'''\n\nfrom __future__ import print_function\n\n# Import MNIST data\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n\nimport tensorflow as tf\n\n# Parameters\nlearning_rate = 0.001\ntraining_epochs = 15\nbatch_size = 100\ndisplay_step = 1\n\n# Network Parameters\nn_hidden_1 = 256 # 1st layer number of features\nn_hidden_2 = 256 # 2nd layer number of features\nn_input = 784 # MNIST data input (img shape: 28*28)\nn_classes = 10 # MNIST total classes (0-9 digits)\n\n# tf Graph input\nx = tf.placeholder(\"float\", [None, n_input])\ny = tf.placeholder(\"float\", [None, n_classes])\n\n\n# Create model\ndef multilayer_perceptron(x, weights, biases):\n    # Hidden layer with RELU activation\n    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n    layer_1 = tf.nn.relu(layer_1)\n    # Hidden layer with RELU activation\n    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n    layer_2 = tf.nn.relu(layer_2)\n    # Output layer with linear activation\n    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n    return out_layer\n\n# Store layers weight & bias\nweights = {\n    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\n}\nbiases = {\n    'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n    'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n    'out': tf.Variable(tf.random_normal([n_classes]))\n}\n\n# Construct model\npred = multilayer_perceptron(x, weights, biases)\n\n# Define loss and optimizer\ncost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n\n# Initializing the variables\ninit = tf.global_variables_initializer()\n\n# Launch the graph\nwith tf.Session() as sess:\n    sess.run(init)\n\n    # Training cycle\n    for epoch in range(training_epochs):\n        avg_cost = 0.\n        total_batch = int(mnist.train.num_examples/batch_size)\n        # Loop over all batches\n        for i in range(total_batch):\n            batch_x, batch_y = mnist.train.next_batch(batch_size)\n            # Run optimization op (backprop) and cost op (to get loss value)\n            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,\n                                                          y: batch_y})\n            # Compute average loss\n            avg_cost += c / total_batch\n        # Display logs per epoch step\n        if epoch % display_step == 0:\n            print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n                \"{:.9f}\".format(avg_cost))\n    print(\"Optimization Finished!\")\n\n    # Test model\n    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n    # Calculate accuracy\n    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n    print(\"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))\n"
  },
  {
    "path": "TensorFlow/examples/3_NeuralNetworks/recurrent_network.py",
    "content": "'''\nA Recurrent Neural Network (LSTM) implementation example using TensorFlow library.\nThis example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\nLong Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/TensorFlow-Examples/\n'''\n\nfrom __future__ import print_function\n\nimport tensorflow as tf\nfrom tensorflow.contrib import rnn\n\n# Import MNIST data\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n\n'''\nTo classify images using a recurrent neural network, we consider every image\nrow as a sequence of pixels. Because MNIST image shape is 28*28px, we will then\nhandle 28 sequences of 28 steps for every sample.\n'''\n\n# Parameters\nlearning_rate = 0.001\ntraining_iters = 100000\nbatch_size = 128\ndisplay_step = 10\n\n# Network Parameters\nn_input = 28 # MNIST data input (img shape: 28*28)\nn_steps = 28 # timesteps\nn_hidden = 128 # hidden layer num of features\nn_classes = 10 # MNIST total classes (0-9 digits)\n\n# tf Graph input\nx = tf.placeholder(\"float\", [None, n_steps, n_input])\ny = tf.placeholder(\"float\", [None, n_classes])\n\n# Define weights\nweights = {\n    'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))\n}\nbiases = {\n    'out': tf.Variable(tf.random_normal([n_classes]))\n}\n\n\ndef RNN(x, weights, biases):\n\n    # Prepare data shape to match `rnn` function requirements\n    # Current data input shape: (batch_size, n_steps, n_input)\n    # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)\n\n    # Permuting batch_size and n_steps\n    x = tf.transpose(x, [1, 0, 2])\n    # Reshaping to (n_steps*batch_size, n_input)\n    x = tf.reshape(x, [-1, n_input])\n    # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n    x = tf.split(x, n_steps, 0)\n\n    # Define a lstm cell with tensorflow\n    lstm_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)\n\n    # Get lstm cell output\n    outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)\n\n    # Linear activation, using rnn inner loop last output\n    return tf.matmul(outputs[-1], weights['out']) + biases['out']\n\npred = RNN(x, weights, biases)\n\n# Define loss and optimizer\ncost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n\n# Evaluate model\ncorrect_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\naccuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n\n# Initializing the variables\ninit = tf.global_variables_initializer()\n\n# Launch the graph\nwith tf.Session() as sess:\n    sess.run(init)\n    step = 1\n    # Keep training until reach max iterations\n    while step * batch_size < training_iters:\n        batch_x, batch_y = mnist.train.next_batch(batch_size)\n        # Reshape data to get 28 seq of 28 elements\n        batch_x = batch_x.reshape((batch_size, n_steps, n_input))\n        # Run optimization op (backprop)\n        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})\n        if step % display_step == 0:\n            # Calculate batch accuracy\n            acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})\n            # Calculate batch loss\n            loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})\n            print(\"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n                  \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n                  \"{:.5f}\".format(acc))\n        step += 1\n    print(\"Optimization Finished!\")\n\n    # Calculate accuracy for 128 mnist test images\n    test_len = 128\n    test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))\n    test_label = mnist.test.labels[:test_len]\n    print(\"Testing Accuracy:\", \\\n        sess.run(accuracy, feed_dict={x: test_data, y: test_label}))\n"
  },
  {
    "path": "TensorFlow/examples/4_Utils/save_restore_model.py",
    "content": "'''\nSave and Restore a model using TensorFlow.\nThis example is using the MNIST database of handwritten digits\n(http://yann.lecun.com/exdb/mnist/)\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/TensorFlow-Examples/\n'''\n\nfrom __future__ import print_function\n\n# Import MNIST data\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n\nimport tensorflow as tf\n\n# Parameters\nlearning_rate = 0.001\nbatch_size = 100\ndisplay_step = 1\nmodel_path = \"/tmp/model.ckpt\"\n\n# Network Parameters\nn_hidden_1 = 256 # 1st layer number of features\nn_hidden_2 = 256 # 2nd layer number of features\nn_input = 784 # MNIST data input (img shape: 28*28)\nn_classes = 10 # MNIST total classes (0-9 digits)\n\n# tf Graph input\nx = tf.placeholder(\"float\", [None, n_input])\ny = tf.placeholder(\"float\", [None, n_classes])\n\n\n# Create model\ndef multilayer_perceptron(x, weights, biases):\n    # Hidden layer with RELU activation\n    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n    layer_1 = tf.nn.relu(layer_1)\n    # Hidden layer with RELU activation\n    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n    layer_2 = tf.nn.relu(layer_2)\n    # Output layer with linear activation\n    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n    return out_layer\n\n# Store layers weight & bias\nweights = {\n    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\n}\nbiases = {\n    'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n    'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n    'out': tf.Variable(tf.random_normal([n_classes]))\n}\n\n# Construct model\npred = multilayer_perceptron(x, weights, biases)\n\n# Define loss and optimizer\ncost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n\n# Initializing the variables\ninit = tf.global_variables_initializer()\n\n# 'Saver' op to save and restore all the variables\nsaver = tf.train.Saver()\n\n# Running first session\nprint(\"Starting 1st session...\")\nwith tf.Session() as sess:\n    # Initialize variables\n    sess.run(init)\n\n    # Training cycle\n    for epoch in range(3):\n        avg_cost = 0.\n        total_batch = int(mnist.train.num_examples/batch_size)\n        # Loop over all batches\n        for i in range(total_batch):\n            batch_x, batch_y = mnist.train.next_batch(batch_size)\n            # Run optimization op (backprop) and cost op (to get loss value)\n            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,\n                                                          y: batch_y})\n            # Compute average loss\n            avg_cost += c / total_batch\n        # Display logs per epoch step\n        if epoch % display_step == 0:\n            print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n                \"{:.9f}\".format(avg_cost))\n    print(\"First Optimization Finished!\")\n\n    # Test model\n    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n    # Calculate accuracy\n    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n    print(\"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))\n\n    # Save model weights to disk\n    save_path = saver.save(sess, model_path)\n    print(\"Model saved in file: %s\" % save_path)\n\n# Running a new session\nprint(\"Starting 2nd session...\")\nwith tf.Session() as sess:\n    # Initialize variables\n    sess.run(init)\n\n    # Restore model weights from previously saved model\n    saver.restore(sess, model_path)\n    print(\"Model restored from file: %s\" % save_path)\n\n    # Resume training\n    for epoch in range(7):\n        avg_cost = 0.\n        total_batch = int(mnist.train.num_examples / batch_size)\n        # Loop over all batches\n        for i in range(total_batch):\n            batch_x, batch_y = mnist.train.next_batch(batch_size)\n            # Run optimization op (backprop) and cost op (to get loss value)\n            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,\n                                                          y: batch_y})\n            # Compute average loss\n            avg_cost += c / total_batch\n        # Display logs per epoch step\n        if epoch % display_step == 0:\n            print(\"Epoch:\", '%04d' % (epoch + 1), \"cost=\", \\\n                \"{:.9f}\".format(avg_cost))\n    print(\"Second Optimization Finished!\")\n\n    # Test model\n    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n    # Calculate accuracy\n    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n    print(\"Accuracy:\", accuracy.eval(\n        {x: mnist.test.images, y: mnist.test.labels}))\n"
  },
  {
    "path": "TensorFlow/examples/4_Utils/tensorboard_advanced.py",
    "content": "'''\nGraph and Loss visualization using Tensorboard.\nThis example is using the MNIST database of handwritten digits\n(http://yann.lecun.com/exdb/mnist/)\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/TensorFlow-Examples/\n'''\n\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\n# Import MNIST data\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n\n# Parameters\nlearning_rate = 0.01\ntraining_epochs = 25\nbatch_size = 100\ndisplay_step = 1\nlogs_path = '/tmp/tensorflow_logs/example'\n\n# Network Parameters\nn_hidden_1 = 256 # 1st layer number of features\nn_hidden_2 = 256 # 2nd layer number of features\nn_input = 784 # MNIST data input (img shape: 28*28)\nn_classes = 10 # MNIST total classes (0-9 digits)\n\n# tf Graph Input\n# mnist data image of shape 28*28=784\nx = tf.placeholder(tf.float32, [None, 784], name='InputData')\n# 0-9 digits recognition => 10 classes\ny = tf.placeholder(tf.float32, [None, 10], name='LabelData')\n\n\n# Create model\ndef multilayer_perceptron(x, weights, biases):\n    # Hidden layer with RELU activation\n    layer_1 = tf.add(tf.matmul(x, weights['w1']), biases['b1'])\n    layer_1 = tf.nn.relu(layer_1)\n    # Create a summary to visualize the first layer ReLU activation\n    tf.summary.histogram(\"relu1\", layer_1)\n    # Hidden layer with RELU activation\n    layer_2 = tf.add(tf.matmul(layer_1, weights['w2']), biases['b2'])\n    layer_2 = tf.nn.relu(layer_2)\n    # Create another summary to visualize the second layer ReLU activation\n    tf.summary.histogram(\"relu2\", layer_2)\n    # Output layer\n    out_layer = tf.add(tf.matmul(layer_2, weights['w3']), biases['b3'])\n    return out_layer\n\n# Store layers weight & bias\nweights = {\n    'w1': tf.Variable(tf.random_normal([n_input, n_hidden_1]), name='W1'),\n    'w2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]), name='W2'),\n    'w3': tf.Variable(tf.random_normal([n_hidden_2, n_classes]), name='W3')\n}\nbiases = {\n    'b1': tf.Variable(tf.random_normal([n_hidden_1]), name='b1'),\n    'b2': tf.Variable(tf.random_normal([n_hidden_2]), name='b2'),\n    'b3': tf.Variable(tf.random_normal([n_classes]), name='b3')\n}\n\n# Encapsulating all ops into scopes, making Tensorboard's Graph\n# Visualization more convenient\nwith tf.name_scope('Model'):\n    # Build model\n    pred = multilayer_perceptron(x, weights, biases)\n\nwith tf.name_scope('Loss'):\n    # Softmax Cross entropy (cost function)\n    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n\nwith tf.name_scope('SGD'):\n    # Gradient Descent\n    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n    # Op to calculate every variable gradient\n    grads = tf.gradients(loss, tf.trainable_variables())\n    grads = list(zip(grads, tf.trainable_variables()))\n    # Op to update all variables according to their gradient\n    apply_grads = optimizer.apply_gradients(grads_and_vars=grads)\n\nwith tf.name_scope('Accuracy'):\n    # Accuracy\n    acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n    acc = tf.reduce_mean(tf.cast(acc, tf.float32))\n\n# Initializing the variables\ninit = tf.global_variables_initializer()\n\n# Create a summary to monitor cost tensor\ntf.summary.scalar(\"loss\", loss)\n# Create a summary to monitor accuracy tensor\ntf.summary.scalar(\"accuracy\", acc)\n# Create summaries to visualize weights\nfor var in tf.trainable_variables():\n    tf.summary.histogram(var.name, var)\n# Summarize all gradients\nfor grad, var in grads:\n    tf.summary.histogram(var.name + '/gradient', grad)\n# Merge all summaries into a single op\nmerged_summary_op = tf.summary.merge_all()\n\n# Launch the graph\nwith tf.Session() as sess:\n    sess.run(init)\n\n    # op to write logs to Tensorboard\n    summary_writer = tf.summary.FileWriter(logs_path,\n                                            graph=tf.get_default_graph())\n\n    # Training cycle\n    for epoch in range(training_epochs):\n        avg_cost = 0.\n        total_batch = int(mnist.train.num_examples/batch_size)\n        # Loop over all batches\n        for i in range(total_batch):\n            batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n            # Run optimization op (backprop), cost op (to get loss value)\n            # and summary nodes\n            _, c, summary = sess.run([apply_grads, loss, merged_summary_op],\n                                     feed_dict={x: batch_xs, y: batch_ys})\n            # Write logs at every iteration\n            summary_writer.add_summary(summary, epoch * total_batch + i)\n            # Compute average loss\n            avg_cost += c / total_batch\n        # Display logs per epoch step\n        if (epoch+1) % display_step == 0:\n            print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost))\n\n    print(\"Optimization Finished!\")\n\n    # Test model\n    # Calculate accuracy\n    print(\"Accuracy:\", acc.eval({x: mnist.test.images, y: mnist.test.labels}))\n\n    print(\"Run the command line:\\n\" \\\n          \"--> tensorboard --logdir=/tmp/tensorflow_logs \" \\\n          \"\\nThen open http://0.0.0.0:6006/ into your web browser\")\n"
  },
  {
    "path": "TensorFlow/examples/4_Utils/tensorboard_basic.py",
    "content": "'''\nGraph and Loss visualization using Tensorboard.\nThis example is using the MNIST database of handwritten digits\n(http://yann.lecun.com/exdb/mnist/)\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/TensorFlow-Examples/\n'''\n\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\n# Import MNIST data\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n\n# Parameters\nlearning_rate = 0.01\ntraining_epochs = 25\nbatch_size = 100\ndisplay_step = 1\nlogs_path = '/tmp/tensorflow_logs/example'\n\n# tf Graph Input\n# mnist data image of shape 28*28=784\nx = tf.placeholder(tf.float32, [None, 784], name='InputData')\n# 0-9 digits recognition => 10 classes\ny = tf.placeholder(tf.float32, [None, 10], name='LabelData')\n\n# Set model weights\nW = tf.Variable(tf.zeros([784, 10]), name='Weights')\nb = tf.Variable(tf.zeros([10]), name='Bias')\n\n# Construct model and encapsulating all ops into scopes, making\n# Tensorboard's Graph visualization more convenient\nwith tf.name_scope('Model'):\n    # Model\n    pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\nwith tf.name_scope('Loss'):\n    # Minimize error using cross entropy\n    cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))\nwith tf.name_scope('SGD'):\n    # Gradient Descent\n    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\nwith tf.name_scope('Accuracy'):\n    # Accuracy\n    acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n    acc = tf.reduce_mean(tf.cast(acc, tf.float32))\n\n# Initializing the variables\ninit = tf.global_variables_initializer()\n\n# Create a summary to monitor cost tensor\ntf.summary.scalar(\"loss\", cost)\n# Create a summary to monitor accuracy tensor\ntf.summary.scalar(\"accuracy\", acc)\n# Merge all summaries into a single op\nmerged_summary_op = tf.summary.merge_all()\n\n# Launch the graph\nwith tf.Session() as sess:\n    sess.run(init)\n\n    # op to write logs to Tensorboard\n    summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())\n\n    # Training cycle\n    for epoch in range(training_epochs):\n        avg_cost = 0.\n        total_batch = int(mnist.train.num_examples/batch_size)\n        # Loop over all batches\n        for i in range(total_batch):\n            batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n            # Run optimization op (backprop), cost op (to get loss value)\n            # and summary nodes\n            _, c, summary = sess.run([optimizer, cost, merged_summary_op],\n                                     feed_dict={x: batch_xs, y: batch_ys})\n            # Write logs at every iteration\n            summary_writer.add_summary(summary, epoch * total_batch + i)\n            # Compute average loss\n            avg_cost += c / total_batch\n        # Display logs per epoch step\n        if (epoch+1) % display_step == 0:\n            print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost))\n\n    print(\"Optimization Finished!\")\n\n    # Test model\n    # Calculate accuracy\n    print(\"Accuracy:\", acc.eval({x: mnist.test.images, y: mnist.test.labels}))\n\n    print(\"Run the command line:\\n\" \\\n          \"--> tensorboard --logdir=/tmp/tensorflow_logs \" \\\n          \"\\nThen open http://0.0.0.0:6006/ into your web browser\")\n"
  },
  {
    "path": "TensorFlow/examples/5_MultiGPU/multigpu_basics.py",
    "content": "from __future__ import print_function\n'''\nBasic Multi GPU computation example using TensorFlow library.\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/TensorFlow-Examples/\n'''\n\n'''\nThis tutorial requires your machine to have 2 GPUs\n\"/cpu:0\": The CPU of your machine.\n\"/gpu:0\": The first GPU of your machine\n\"/gpu:1\": The second GPU of your machine\n'''\n\n\n\nimport numpy as np\nimport tensorflow as tf\nimport datetime\n\n# Processing Units logs\nlog_device_placement = True\n\n# Num of multiplications to perform\nn = 10\n\n'''\nExample: compute A^n + B^n on 2 GPUs\nResults on 8 cores with 2 GTX-980:\n * Single GPU computation time: 0:00:11.277449\n * Multi GPU computation time: 0:00:07.131701\n'''\n# Create random large matrix\nA = np.random.rand(10000, 10000).astype('float32')\nB = np.random.rand(10000, 10000).astype('float32')\n\n# Create a graph to store results\nc1 = []\nc2 = []\n\ndef matpow(M, n):\n    if n < 1: #Abstract cases where n < 1\n        return M\n    else:\n        return tf.matmul(M, matpow(M, n-1))\n\n'''\nSingle GPU computing\n'''\nwith tf.device('/gpu:0'):\n    a = tf.placeholder(tf.float32, [10000, 10000])\n    b = tf.placeholder(tf.float32, [10000, 10000])\n    # Compute A^n and B^n and store results in c1\n    c1.append(matpow(a, n))\n    c1.append(matpow(b, n))\n\nwith tf.device('/cpu:0'):\n  sum = tf.add_n(c1) #Addition of all elements in c1, i.e. A^n + B^n\n\nt1_1 = datetime.datetime.now()\nwith tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:\n    # Run the op.\n    sess.run(sum, {a:A, b:B})\nt2_1 = datetime.datetime.now()\n\n\n'''\nMulti GPU computing\n'''\n# GPU:0 computes A^n\nwith tf.device('/gpu:0'):\n    # Compute A^n and store result in c2\n    a = tf.placeholder(tf.float32, [10000, 10000])\n    c2.append(matpow(a, n))\n\n# GPU:1 computes B^n\nwith tf.device('/gpu:1'):\n    # Compute B^n and store result in c2\n    b = tf.placeholder(tf.float32, [10000, 10000])\n    c2.append(matpow(b, n))\n\nwith tf.device('/cpu:0'):\n  sum = tf.add_n(c2) #Addition of all elements in c2, i.e. A^n + B^n\n\nt1_2 = datetime.datetime.now()\nwith tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:\n    # Run the op.\n    sess.run(sum, {a:A, b:B})\nt2_2 = datetime.datetime.now()\n\n\nprint(\"Single GPU computation time: \" + str(t2_1-t1_1))\nprint(\"Multi GPU computation time: \" + str(t2_2-t1_2))\n"
  },
  {
    "path": "TensorFlow/input_data.py",
    "content": "\"\"\"Functions for downloading and reading MNIST data.\"\"\"\nfrom __future__ import print_function\nimport gzip\nimport os\nimport urllib\nimport numpy\nSOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'\ndef maybe_download(filename, work_directory):\n  \"\"\"Download the data from Yann's website, unless it's already here.\"\"\"\n  if not os.path.exists(work_directory):\n    os.mkdir(work_directory)\n  filepath = os.path.join(work_directory, filename)\n  if not os.path.exists(filepath):\n    filepath, _ = urllib.urlretrieve(SOURCE_URL + filename, filepath)\n    statinfo = os.stat(filepath)\n    print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')\n  return filepath\ndef _read32(bytestream):\n  dt = numpy.dtype(numpy.uint32).newbyteorder('>')\n  return numpy.frombuffer(bytestream.read(4), dtype=dt)\ndef extract_images(filename):\n  \"\"\"Extract the images into a 4D uint8 numpy array [index, y, x, depth].\"\"\"\n  print('Extracting', filename)\n  with gzip.open(filename) as bytestream:\n    magic = _read32(bytestream)\n    if magic != 2051:\n      raise ValueError(\n          'Invalid magic number %d in MNIST image file: %s' %\n          (magic, filename))\n    num_images = _read32(bytestream)\n    rows = _read32(bytestream)\n    cols = _read32(bytestream)\n    buf = bytestream.read(rows * cols * num_images)\n    data = numpy.frombuffer(buf, dtype=numpy.uint8)\n    data = data.reshape(num_images, rows, cols, 1)\n    return data\ndef dense_to_one_hot(labels_dense, num_classes=10):\n  \"\"\"Convert class labels from scalars to one-hot vectors.\"\"\"\n  num_labels = labels_dense.shape[0]\n  index_offset = numpy.arange(num_labels) * num_classes\n  labels_one_hot = numpy.zeros((num_labels, num_classes))\n  labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n  return labels_one_hot\ndef extract_labels(filename, one_hot=False):\n  \"\"\"Extract the labels into a 1D uint8 numpy array [index].\"\"\"\n  print('Extracting', filename)\n  with gzip.open(filename) as bytestream:\n    magic = _read32(bytestream)\n    if magic != 2049:\n      raise ValueError(\n          'Invalid magic number %d in MNIST label file: %s' %\n          (magic, filename))\n    num_items = _read32(bytestream)\n    buf = bytestream.read(num_items)\n    labels = numpy.frombuffer(buf, dtype=numpy.uint8)\n    if one_hot:\n      return dense_to_one_hot(labels)\n    return labels\nclass DataSet(object):\n  def __init__(self, images, labels, fake_data=False):\n    if fake_data:\n      self._num_examples = 10000\n    else:\n      assert images.shape[0] == labels.shape[0], (\n          \"images.shape: %s labels.shape: %s\" % (images.shape,\n                                                 labels.shape))\n      self._num_examples = images.shape[0]\n      # Convert shape from [num examples, rows, columns, depth]\n      # to [num examples, rows*columns] (assuming depth == 1)\n      assert images.shape[3] == 1\n      images = images.reshape(images.shape[0],\n                              images.shape[1] * images.shape[2])\n      # Convert from [0, 255] -> [0.0, 1.0].\n      images = images.astype(numpy.float32)\n      images = numpy.multiply(images, 1.0 / 255.0)\n    self._images = images\n    self._labels = labels\n    self._epochs_completed = 0\n    self._index_in_epoch = 0\n  @property\n  def images(self):\n    return self._images\n  @property\n  def labels(self):\n    return self._labels\n  @property\n  def num_examples(self):\n    return self._num_examples\n  @property\n  def epochs_completed(self):\n    return self._epochs_completed\n  def next_batch(self, batch_size, fake_data=False):\n    \"\"\"Return the next `batch_size` examples from this data set.\"\"\"\n    if fake_data:\n      fake_image = [1.0 for _ in xrange(784)]\n      fake_label = 0\n      return [fake_image for _ in xrange(batch_size)], [\n          fake_label for _ in xrange(batch_size)]\n    start = self._index_in_epoch\n    self._index_in_epoch += batch_size\n    if self._index_in_epoch > self._num_examples:\n      # Finished epoch\n      self._epochs_completed += 1\n      # Shuffle the data\n      perm = numpy.arange(self._num_examples)\n      numpy.random.shuffle(perm)\n      self._images = self._images[perm]\n      self._labels = self._labels[perm]\n      # Start next epoch\n      start = 0\n      self._index_in_epoch = batch_size\n      assert batch_size <= self._num_examples\n    end = self._index_in_epoch\n    return self._images[start:end], self._labels[start:end]\ndef read_data_sets(train_dir, fake_data=False, one_hot=False):\n  class DataSets(object):\n    pass\n  data_sets = DataSets()\n  if fake_data:\n    data_sets.train = DataSet([], [], fake_data=True)\n    data_sets.validation = DataSet([], [], fake_data=True)\n    data_sets.test = DataSet([], [], fake_data=True)\n    return data_sets\n  TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'\n  TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'\n  TEST_IMAGES = 't10k-images-idx3-ubyte.gz'\n  TEST_LABELS = 't10k-labels-idx1-ubyte.gz'\n  VALIDATION_SIZE = 5000\n  local_file = maybe_download(TRAIN_IMAGES, train_dir)\n  train_images = extract_images(local_file)\n  local_file = maybe_download(TRAIN_LABELS, train_dir)\n  train_labels = extract_labels(local_file, one_hot=one_hot)\n  local_file = maybe_download(TEST_IMAGES, train_dir)\n  test_images = extract_images(local_file)\n  local_file = maybe_download(TEST_LABELS, train_dir)\n  test_labels = extract_labels(local_file, one_hot=one_hot)\n  validation_images = train_images[:VALIDATION_SIZE]\n  validation_labels = train_labels[:VALIDATION_SIZE]\n  train_images = train_images[VALIDATION_SIZE:]\n  train_labels = train_labels[VALIDATION_SIZE:]\n  data_sets.train = DataSet(train_images, train_labels)\n  data_sets.validation = DataSet(validation_images, validation_labels)\n  data_sets.test = DataSet(test_images, test_labels)\n  return data_sets"
  },
  {
    "path": "TensorFlow/notebooks/0_Prerequisite/ml_introduction.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Machine Learning\\n\",\n    \"\\n\",\n    \"Prior to start browsing the examples, it may be useful that you get familiar with machine learning, as TensorFlow is mostly used for machine learning tasks (especially Neural Networks). You can find below a list of useful links, that can give you the basic knowledge required for this TensorFlow Tutorial.\\n\",\n    \"\\n\",\n    \"## Machine Learning\\n\",\n    \"\\n\",\n    \"- [An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples](https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer)\\n\",\n    \"- [A Gentle Guide to Machine Learning](https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/)\\n\",\n    \"- [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)\\n\",\n    \"- [Introduction to Machine Learning](http://alex.smola.org/drafts/thebook.pdf)\\n\",\n    \"\\n\",\n    \"## Deep Learning & Neural Networks\\n\",\n    \"\\n\",\n    \"- [An Introduction to Neural Networks](http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html)\\n\",\n    \"- [An Introduction to Image Recognition with Deep Learning](https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721)\\n\",\n    \"- [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html)\\n\",\n    \"\\n\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"IPython (Python 2.7)\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.11\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "TensorFlow/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"# MNIST Dataset Introduction\\n\",\n    \"\\n\",\n    \"Most examples are using MNIST dataset of handwritten digits. It has 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image, so each sample is represented as a matrix of size 28x28 with values from 0 to 1.\\n\",\n    \"\\n\",\n    \"## Overview\\n\",\n    \"\\n\",\n    \"![MNIST Digits](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\\n\",\n    \"\\n\",\n    \"## Usage\\n\",\n    \"In our examples, we are using TensorFlow [input_data.py](https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/examples/tutorials/mnist/input_data.py) script to load that dataset.\\n\",\n    \"It is quite useful for managing our data, and handle:\\n\",\n    \"\\n\",\n    \"- Dataset downloading\\n\",\n    \"\\n\",\n    \"- Loading the entire dataset into numpy array: \\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Import MNIST\\n\",\n    \"from tensorflow.examples.tutorials.mnist import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"/tmp/data/\\\", one_hot=True)\\n\",\n    \"\\n\",\n    \"# Load data\\n\",\n    \"X_train = mnist.train.images\\n\",\n    \"Y_train = mnist.train.labels\\n\",\n    \"X_test = mnist.test.images\\n\",\n    \"Y_test = mnist.test.labels\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- A `next_batch` function that can iterate over the whole dataset and return only the desired fraction of the dataset samples (in order to save memory and avoid to load the entire dataset).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Get the next 64 images array and labels\\n\",\n    \"batch_X, batch_Y = mnist.train.next_batch(64)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Link: http://yann.lecun.com/exdb/mnist/\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"IPython (Python 2.7)\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.11\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "TensorFlow/notebooks/1_Introduction/basic_operations.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Basic Operations example using TensorFlow library.\\n\",\n    \"# Author: Aymeric Damien\\n\",\n    \"# Project: https://github.com/aymericdamien/TensorFlow-Examples/\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Basic constant operations\\n\",\n    \"# The value returned by the constructor represents the output\\n\",\n    \"# of the Constant op.\\n\",\n    \"a = tf.constant(2)\\n\",\n    \"b = tf.constant(3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"a=2, b=3\\n\",\n      \"Addition with constants: 5\\n\",\n      \"Multiplication with constants: 6\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the default graph.\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    print \\\"a=2, b=3\\\"\\n\",\n    \"    print \\\"Addition with constants: %i\\\" % sess.run(a+b)\\n\",\n    \"    print \\\"Multiplication with constants: %i\\\" % sess.run(a*b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Basic Operations with variable as graph input\\n\",\n    \"# The value returned by the constructor represents the output\\n\",\n    \"# of the Variable op. (define as input when running session)\\n\",\n    \"# tf Graph input\\n\",\n    \"a = tf.placeholder(tf.int16)\\n\",\n    \"b = tf.placeholder(tf.int16)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Define some operations\\n\",\n    \"add = tf.add(a, b)\\n\",\n    \"mul = tf.mul(a, b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Addition with variables: 5\\n\",\n      \"Multiplication with variables: 6\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the default graph.\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    # Run every operation with variable input\\n\",\n    \"    print \\\"Addition with variables: %i\\\" % sess.run(add, feed_dict={a: 2, b: 3})\\n\",\n    \"    print \\\"Multiplication with variables: %i\\\" % sess.run(mul, feed_dict={a: 2, b: 3})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# ----------------\\n\",\n    \"# More in details:\\n\",\n    \"# Matrix Multiplication from TensorFlow official tutorial\\n\",\n    \"\\n\",\n    \"# Create a Constant op that produces a 1x2 matrix.  The op is\\n\",\n    \"# added as a node to the default graph.\\n\",\n    \"#\\n\",\n    \"# The value returned by the constructor represents the output\\n\",\n    \"# of the Constant op.\\n\",\n    \"matrix1 = tf.constant([[3., 3.]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Create another Constant that produces a 2x1 matrix.\\n\",\n    \"matrix2 = tf.constant([[2.],[2.]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.\\n\",\n    \"# The returned value, 'product', represents the result of the matrix\\n\",\n    \"# multiplication.\\n\",\n    \"product = tf.matmul(matrix1, matrix2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 12.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# To run the matmul op we call the session 'run()' method, passing 'product'\\n\",\n    \"# which represents the output of the matmul op.  This indicates to the call\\n\",\n    \"# that we want to get the output of the matmul op back.\\n\",\n    \"#\\n\",\n    \"# All inputs needed by the op are run automatically by the session.  They\\n\",\n    \"# typically are run in parallel.\\n\",\n    \"#\\n\",\n    \"# The call 'run(product)' thus causes the execution of threes ops in the\\n\",\n    \"# graph: the two constants and matmul.\\n\",\n    \"#\\n\",\n    \"# The output of the op is returned in 'result' as a numpy `ndarray` object.\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    result = sess.run(product)\\n\",\n    \"    print result\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"IPython (Python 2.7)\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2.0\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.8\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "TensorFlow/notebooks/1_Introduction/helloworld.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Simple hello world using TensorFlow\\n\",\n    \"\\n\",\n    \"# Create a Constant op\\n\",\n    \"# The op is added as a node to the default graph.\\n\",\n    \"#\\n\",\n    \"# The value returned by the constructor represents the output\\n\",\n    \"# of the Constant op.\\n\",\n    \"\\n\",\n    \"hello = tf.constant('Hello, TensorFlow!')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Start tf session\\n\",\n    \"sess = tf.Session()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Hello, TensorFlow!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Run graph\\n\",\n    \"print sess.run(hello)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"IPython (Python 2.7)\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2.0\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.8\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "TensorFlow/notebooks/2_BasicModels/linear_regression.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# A linear regression learning algorithm example using TensorFlow library.\\n\",\n    \"\\n\",\n    \"# Author: Aymeric Damien\\n\",\n    \"# Project: https://github.com/aymericdamien/TensorFlow-Examples/\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"import numpy\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"rng = numpy.random\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Parameters\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"training_epochs = 1000\\n\",\n    \"display_step = 50\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Training Data\\n\",\n    \"train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,\\n\",\n    \"                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])\\n\",\n    \"train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,\\n\",\n    \"                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])\\n\",\n    \"n_samples = train_X.shape[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# tf Graph Input\\n\",\n    \"X = tf.placeholder(\\\"float\\\")\\n\",\n    \"Y = tf.placeholder(\\\"float\\\")\\n\",\n    \"\\n\",\n    \"# Set model weights\\n\",\n    \"W = tf.Variable(rng.randn(), name=\\\"weight\\\")\\n\",\n    \"b = tf.Variable(rng.randn(), name=\\\"bias\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Construct a linear model\\n\",\n    \"pred = tf.add(tf.mul(X, W), b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Mean squared error\\n\",\n    \"cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)\\n\",\n    \"# Gradient descent\\n\",\n    \"optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Initializing the variables\\n\",\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 0050 cost= 0.195095107 W= 0.441748 b= -0.580876\\n\",\n      \"Epoch: 0100 cost= 0.181448311 W= 0.430319 b= -0.498661\\n\",\n      \"Epoch: 0150 cost= 0.169377610 W= 0.419571 b= -0.421336\\n\",\n      \"Epoch: 0200 cost= 0.158700854 W= 0.409461 b= -0.348611\\n\",\n      \"Epoch: 0250 cost= 0.149257123 W= 0.399953 b= -0.28021\\n\",\n      \"Epoch: 0300 cost= 0.140904188 W= 0.391011 b= -0.215878\\n\",\n      \"Epoch: 0350 cost= 0.133515999 W= 0.3826 b= -0.155372\\n\",\n      \"Epoch: 0400 cost= 0.126981199 W= 0.374689 b= -0.0984639\\n\",\n      \"Epoch: 0450 cost= 0.121201262 W= 0.367249 b= -0.0449408\\n\",\n      \"Epoch: 0500 cost= 0.116088994 W= 0.360252 b= 0.00539905\\n\",\n      \"Epoch: 0550 cost= 0.111567356 W= 0.35367 b= 0.052745\\n\",\n      \"Epoch: 0600 cost= 0.107568085 W= 0.34748 b= 0.0972751\\n\",\n      \"Epoch: 0650 cost= 0.104030922 W= 0.341659 b= 0.139157\\n\",\n      \"Epoch: 0700 cost= 0.100902475 W= 0.336183 b= 0.178547\\n\",\n      \"Epoch: 0750 cost= 0.098135538 W= 0.331033 b= 0.215595\\n\",\n      \"Epoch: 0800 cost= 0.095688373 W= 0.32619 b= 0.25044\\n\",\n      \"Epoch: 0850 cost= 0.093524046 W= 0.321634 b= 0.283212\\n\",\n      \"Epoch: 0900 cost= 0.091609895 W= 0.317349 b= 0.314035\\n\",\n      \"Epoch: 0950 cost= 0.089917004 W= 0.31332 b= 0.343025\\n\",\n      \"Epoch: 1000 cost= 0.088419855 W= 0.30953 b= 0.370291\\n\",\n      \"Optimization Finished!\\n\",\n      \"Training cost= 0.0884199 W= 0.30953 b= 0.370291 \\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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iIiEpGSBBEREYlISYKIiIhEpCRBREREIlKSICIizRxwwAFceumlnsbw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\\niEhEShJEREQkov8PMJtz3b7pz2EAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10eee3ad0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    # Fit all training data\\n\",\n    \"    for epoch in range(training_epochs):\\n\",\n    \"        for (x, y) in zip(train_X, train_Y):\\n\",\n    \"            sess.run(optimizer, feed_dict={X: x, Y: y})\\n\",\n    \"\\n\",\n    \"        #Display logs per epoch step\\n\",\n    \"        if (epoch+1) % display_step == 0:\\n\",\n    \"            c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})\\n\",\n    \"            print \\\"Epoch:\\\", '%04d' % (epoch+1), \\\"cost=\\\", \\\"{:.9f}\\\".format(c), \\\\\\n\",\n    \"                \\\"W=\\\", sess.run(W), \\\"b=\\\", sess.run(b)\\n\",\n    \"\\n\",\n    \"    print \\\"Optimization Finished!\\\"\\n\",\n    \"    training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})\\n\",\n    \"    print \\\"Training cost=\\\", training_cost, \\\"W=\\\", sess.run(W), \\\"b=\\\", sess.run(b), '\\\\n'\\n\",\n    \"\\n\",\n    \"    #Graphic display\\n\",\n    \"    plt.plot(train_X, train_Y, 'ro', label='Original data')\\n\",\n    \"    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')\\n\",\n    \"    plt.legend()\\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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48/qn///j5f9H/66Sfl5uae8TUSExNVWlqqTz/91Gff2rVr66Sm8mBT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\\nbl4AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Regression result\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "TensorFlow/notebooks/2_BasicModels/logistic_regression.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# A logistic regression learning algorithm example using TensorFlow library.\\n\",\n    \"# This example is using the MNIST database of handwritten digits \\n\",\n    \"# (http://yann.lecun.com/exdb/mnist/)\\n\",\n    \"\\n\",\n    \"# Author: Aymeric Damien\\n\",\n    \"# Project: https://github.com/aymericdamien/TensorFlow-Examples/\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting MNIST_data/train-images-idx3-ubyte.gz\\n\",\n      \"Extracting MNIST_data/train-labels-idx1-ubyte.gz\\n\",\n      \"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\\n\",\n      \"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"# Import MINST data\\n\",\n    \"from tensorflow.examples.tutorials.mnist import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"MNIST_data/\\\", one_hot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Parameters\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"training_epochs = 25\\n\",\n    \"batch_size = 100\\n\",\n    \"display_step = 1\\n\",\n    \"\\n\",\n    \"# tf Graph Input\\n\",\n    \"x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784\\n\",\n    \"y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes\\n\",\n    \"\\n\",\n    \"# Set model weights\\n\",\n    \"W = tf.Variable(tf.zeros([784, 10]))\\n\",\n    \"b = tf.Variable(tf.zeros([10]))\\n\",\n    \"\\n\",\n    \"# Construct model\\n\",\n    \"pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\\n\",\n    \"\\n\",\n    \"# Minimize error using cross entropy\\n\",\n    \"cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))\\n\",\n    \"# Gradient Descent\\n\",\n    \"optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\\n\",\n    \"\\n\",\n    \"# Initializing the variables\\n\",\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 0001 cost= 1.182138959\\n\",\n      \"Epoch: 0002 cost= 0.664778162\\n\",\n      \"Epoch: 0003 cost= 0.552686284\\n\",\n      \"Epoch: 0004 cost= 0.498628905\\n\",\n      \"Epoch: 0005 cost= 0.465469866\\n\",\n      \"Epoch: 0006 cost= 0.442537872\\n\",\n      \"Epoch: 0007 cost= 0.425462044\\n\",\n      \"Epoch: 0008 cost= 0.412185303\\n\",\n      \"Epoch: 0009 cost= 0.401311587\\n\",\n      \"Epoch: 0010 cost= 0.392326203\\n\",\n      \"Epoch: 0011 cost= 0.384736038\\n\",\n      \"Epoch: 0012 cost= 0.378137191\\n\",\n      \"Epoch: 0013 cost= 0.372363752\\n\",\n      \"Epoch: 0014 cost= 0.367308579\\n\",\n      \"Epoch: 0015 cost= 0.362704660\\n\",\n      \"Epoch: 0016 cost= 0.358588599\\n\",\n      \"Epoch: 0017 cost= 0.354823110\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    # Training cycle\\n\",\n    \"    for epoch in range(training_epochs):\\n\",\n    \"        avg_cost = 0.\\n\",\n    \"        total_batch = int(mnist.train.num_examples/batch_size)\\n\",\n    \"        # Loop over all batches\\n\",\n    \"        for i in range(total_batch):\\n\",\n    \"            batch_xs, batch_ys = mnist.train.next_batch(batch_size)\\n\",\n    \"            # Fit training using batch data\\n\",\n    \"            _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,\\n\",\n    \"                                                          y: batch_ys})\\n\",\n    \"            # Compute average loss\\n\",\n    \"            avg_cost += c / total_batch\\n\",\n    \"        # Display logs per epoch step\\n\",\n    \"        if (epoch+1) % display_step == 0:\\n\",\n    \"            print \\\"Epoch:\\\", '%04d' % (epoch+1), \\\"cost=\\\", \\\"{:.9f}\\\".format(avg_cost)\\n\",\n    \"\\n\",\n    \"    print \\\"Optimization Finished!\\\"\\n\",\n    \"\\n\",\n    \"    # Test model\\n\",\n    \"    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\\n\",\n    \"    # Calculate accuracy for 3000 examples\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\\n\",\n    \"    print \\\"Accuracy:\\\", accuracy.eval({x: mnist.test.images[:3000], y: mnist.test.labels[:3000]})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "TensorFlow/notebooks/2_BasicModels/nearest_neighbor.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# A nearest neighbor learning algorithm example using TensorFlow library.\\n\",\n    \"# This example is using the MNIST database of handwritten digits\\n\",\n    \"# (http://yann.lecun.com/exdb/mnist/)\\n\",\n    \"\\n\",\n    \"# Author: Aymeric Damien\\n\",\n    \"# Project: https://github.com/aymericdamien/TensorFlow-Examples/\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting MNIST_data/train-images-idx3-ubyte.gz\\n\",\n      \"Extracting MNIST_data/train-labels-idx1-ubyte.gz\\n\",\n      \"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\\n\",\n      \"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"# Import MINST data\\n\",\n    \"from tensorflow.examples.tutorials.mnist import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"MNIST_data/\\\", one_hot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# In this example, we limit mnist data\\n\",\n    \"Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates)\\n\",\n    \"Xte, Yte = mnist.test.next_batch(200) #200 for testing\\n\",\n    \"\\n\",\n    \"# tf Graph Input\\n\",\n    \"xtr = tf.placeholder(\\\"float\\\", [None, 784])\\n\",\n    \"xte = tf.placeholder(\\\"float\\\", [784])\\n\",\n    \"\\n\",\n    \"# Nearest Neighbor calculation using L1 Distance\\n\",\n    \"# Calculate L1 Distance\\n\",\n    \"distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1)\\n\",\n    \"# Prediction: Get min distance index (Nearest neighbor)\\n\",\n    \"pred = tf.arg_min(distance, 0)\\n\",\n    \"\\n\",\n    \"accuracy = 0.\\n\",\n    \"\\n\",\n    \"# Initializing the variables\\n\",\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test 0 Prediction: 7 True Class: 7\\n\",\n      \"Test 1 Prediction: 2 True Class: 2\\n\",\n      \"Test 2 Prediction: 1 True Class: 1\\n\",\n      \"Test 3 Prediction: 0 True Class: 0\\n\",\n      \"Test 4 Prediction: 4 True Class: 4\\n\",\n      \"Test 5 Prediction: 1 True Class: 1\\n\",\n      \"Test 6 Prediction: 4 True Class: 4\\n\",\n      \"Test 7 Prediction: 9 True Class: 9\\n\",\n      \"Test 8 Prediction: 8 True Class: 5\\n\",\n      \"Test 9 Prediction: 9 True Class: 9\\n\",\n      \"Test 10 Prediction: 0 True Class: 0\\n\",\n      \"Test 11 Prediction: 0 True Class: 6\\n\",\n      \"Test 12 Prediction: 9 True Class: 9\\n\",\n      \"Test 13 Prediction: 0 True Class: 0\\n\",\n      \"Test 14 Prediction: 1 True Class: 1\\n\",\n      \"Test 15 Prediction: 5 True Class: 5\\n\",\n      \"Test 16 Prediction: 4 True Class: 9\\n\",\n      \"Test 17 Prediction: 7 True Class: 7\\n\",\n      \"Test 18 Prediction: 3 True Class: 3\\n\",\n      \"Test 19 Prediction: 4 True Class: 4\\n\",\n      \"Test 20 Prediction: 9 True Class: 9\\n\",\n      \"Test 21 Prediction: 6 True Class: 6\\n\",\n      \"Test 22 Prediction: 6 True Class: 6\\n\",\n      \"Test 23 Prediction: 5 True Class: 5\\n\",\n      \"Test 24 Prediction: 4 True Class: 4\\n\",\n      \"Test 25 Prediction: 0 True Class: 0\\n\",\n      \"Test 26 Prediction: 7 True Class: 7\\n\",\n      \"Test 27 Prediction: 4 True Class: 4\\n\",\n      \"Test 28 Prediction: 0 True Class: 0\\n\",\n      \"Test 29 Prediction: 1 True Class: 1\\n\",\n      \"Test 30 Prediction: 3 True Class: 3\\n\",\n      \"Test 31 Prediction: 1 True Class: 1\\n\",\n      \"Test 32 Prediction: 3 True Class: 3\\n\",\n      \"Test 33 Prediction: 4 True Class: 4\\n\",\n      \"Test 34 Prediction: 7 True Class: 7\\n\",\n      \"Test 35 Prediction: 2 True Class: 2\\n\",\n      \"Test 36 Prediction: 7 True Class: 7\\n\",\n      \"Test 37 Prediction: 1 True Class: 1\\n\",\n      \"Test 38 Prediction: 2 True Class: 2\\n\",\n      \"Test 39 Prediction: 1 True Class: 1\\n\",\n      \"Test 40 Prediction: 1 True Class: 1\\n\",\n      \"Test 41 Prediction: 7 True Class: 7\\n\",\n      \"Test 42 Prediction: 4 True Class: 4\\n\",\n      \"Test 43 Prediction: 1 True Class: 2\\n\",\n      \"Test 44 Prediction: 3 True Class: 3\\n\",\n      \"Test 45 Prediction: 5 True Class: 5\\n\",\n      \"Test 46 Prediction: 1 True Class: 1\\n\",\n      \"Test 47 Prediction: 2 True Class: 2\\n\",\n      \"Test 48 Prediction: 4 True Class: 4\\n\",\n      \"Test 49 Prediction: 4 True Class: 4\\n\",\n      \"Test 50 Prediction: 6 True Class: 6\\n\",\n      \"Test 51 Prediction: 3 True Class: 3\\n\",\n      \"Test 52 Prediction: 5 True Class: 5\\n\",\n      \"Test 53 Prediction: 5 True Class: 5\\n\",\n      \"Test 54 Prediction: 6 True Class: 6\\n\",\n      \"Test 55 Prediction: 0 True Class: 0\\n\",\n      \"Test 56 Prediction: 4 True Class: 4\\n\",\n      \"Test 57 Prediction: 1 True Class: 1\\n\",\n      \"Test 58 Prediction: 9 True Class: 9\\n\",\n      \"Test 59 Prediction: 5 True Class: 5\\n\",\n      \"Test 60 Prediction: 7 True Class: 7\\n\",\n      \"Test 61 Prediction: 8 True Class: 8\\n\",\n      \"Test 62 Prediction: 9 True Class: 9\\n\",\n      \"Test 63 Prediction: 3 True Class: 3\\n\",\n      \"Test 64 Prediction: 7 True Class: 7\\n\",\n      \"Test 65 Prediction: 4 True Class: 4\\n\",\n      \"Test 66 Prediction: 6 True Class: 6\\n\",\n      \"Test 67 Prediction: 4 True Class: 4\\n\",\n      \"Test 68 Prediction: 3 True Class: 3\\n\",\n      \"Test 69 Prediction: 0 True Class: 0\\n\",\n      \"Test 70 Prediction: 7 True Class: 7\\n\",\n      \"Test 71 Prediction: 0 True Class: 0\\n\",\n      \"Test 72 Prediction: 2 True Class: 2\\n\",\n      \"Test 73 Prediction: 7 True Class: 9\\n\",\n      \"Test 74 Prediction: 1 True Class: 1\\n\",\n      \"Test 75 Prediction: 7 True Class: 7\\n\",\n      \"Test 76 Prediction: 3 True Class: 3\\n\",\n      \"Test 77 Prediction: 7 True Class: 2\\n\",\n      \"Test 78 Prediction: 9 True Class: 9\\n\",\n      \"Test 79 Prediction: 7 True Class: 7\\n\",\n      \"Test 80 Prediction: 7 True Class: 7\\n\",\n      \"Test 81 Prediction: 6 True Class: 6\\n\",\n      \"Test 82 Prediction: 2 True Class: 2\\n\",\n      \"Test 83 Prediction: 7 True Class: 7\\n\",\n      \"Test 84 Prediction: 8 True Class: 8\\n\",\n      \"Test 85 Prediction: 4 True Class: 4\\n\",\n      \"Test 86 Prediction: 7 True Class: 7\\n\",\n      \"Test 87 Prediction: 3 True Class: 3\\n\",\n      \"Test 88 Prediction: 6 True Class: 6\\n\",\n      \"Test 89 Prediction: 1 True Class: 1\\n\",\n      \"Test 90 Prediction: 3 True Class: 3\\n\",\n      \"Test 91 Prediction: 6 True Class: 6\\n\",\n      \"Test 92 Prediction: 9 True Class: 9\\n\",\n      \"Test 93 Prediction: 3 True Class: 3\\n\",\n      \"Test 94 Prediction: 1 True Class: 1\\n\",\n      \"Test 95 Prediction: 4 True Class: 4\\n\",\n      \"Test 96 Prediction: 1 True Class: 1\\n\",\n      \"Test 97 Prediction: 7 True Class: 7\\n\",\n      \"Test 98 Prediction: 6 True Class: 6\\n\",\n      \"Test 99 Prediction: 9 True Class: 9\\n\",\n      \"Test 100 Prediction: 6 True Class: 6\\n\",\n      \"Test 101 Prediction: 0 True Class: 0\\n\",\n      \"Test 102 Prediction: 5 True Class: 5\\n\",\n      \"Test 103 Prediction: 4 True Class: 4\\n\",\n      \"Test 104 Prediction: 9 True Class: 9\\n\",\n      \"Test 105 Prediction: 9 True Class: 9\\n\",\n      \"Test 106 Prediction: 2 True Class: 2\\n\",\n      \"Test 107 Prediction: 1 True Class: 1\\n\",\n      \"Test 108 Prediction: 9 True Class: 9\\n\",\n      \"Test 109 Prediction: 4 True Class: 4\\n\",\n      \"Test 110 Prediction: 8 True Class: 8\\n\",\n      \"Test 111 Prediction: 7 True Class: 7\\n\",\n      \"Test 112 Prediction: 3 True Class: 3\\n\",\n      \"Test 113 Prediction: 9 True Class: 9\\n\",\n      \"Test 114 Prediction: 7 True Class: 7\\n\",\n      \"Test 115 Prediction: 9 True Class: 4\\n\",\n      \"Test 116 Prediction: 9 True Class: 4\\n\",\n      \"Test 117 Prediction: 4 True Class: 4\\n\",\n      \"Test 118 Prediction: 9 True Class: 9\\n\",\n      \"Test 119 Prediction: 7 True Class: 2\\n\",\n      \"Test 120 Prediction: 5 True Class: 5\\n\",\n      \"Test 121 Prediction: 4 True Class: 4\\n\",\n      \"Test 122 Prediction: 7 True Class: 7\\n\",\n      \"Test 123 Prediction: 6 True Class: 6\\n\",\n      \"Test 124 Prediction: 7 True Class: 7\\n\",\n      \"Test 125 Prediction: 9 True Class: 9\\n\",\n      \"Test 126 Prediction: 0 True Class: 0\\n\",\n      \"Test 127 Prediction: 5 True Class: 5\\n\",\n      \"Test 128 Prediction: 8 True Class: 8\\n\",\n      \"Test 129 Prediction: 5 True Class: 5\\n\",\n      \"Test 130 Prediction: 6 True Class: 6\\n\",\n      \"Test 131 Prediction: 6 True Class: 6\\n\",\n      \"Test 132 Prediction: 5 True Class: 5\\n\",\n      \"Test 133 Prediction: 7 True Class: 7\\n\",\n      \"Test 134 Prediction: 8 True Class: 8\\n\",\n      \"Test 135 Prediction: 1 True Class: 1\\n\",\n      \"Test 136 Prediction: 0 True Class: 0\\n\",\n      \"Test 137 Prediction: 1 True Class: 1\\n\",\n      \"Test 138 Prediction: 6 True Class: 6\\n\",\n      \"Test 139 Prediction: 4 True Class: 4\\n\",\n      \"Test 140 Prediction: 6 True Class: 6\\n\",\n      \"Test 141 Prediction: 7 True Class: 7\\n\",\n      \"Test 142 Prediction: 2 True Class: 3\\n\",\n      \"Test 143 Prediction: 1 True Class: 1\\n\",\n      \"Test 144 Prediction: 7 True Class: 7\\n\",\n      \"Test 145 Prediction: 1 True Class: 1\\n\",\n      \"Test 146 Prediction: 8 True Class: 8\\n\",\n      \"Test 147 Prediction: 2 True Class: 2\\n\",\n      \"Test 148 Prediction: 0 True Class: 0\\n\",\n      \"Test 149 Prediction: 1 True Class: 2\\n\",\n      \"Test 150 Prediction: 9 True Class: 9\\n\",\n      \"Test 151 Prediction: 9 True Class: 9\\n\",\n      \"Test 152 Prediction: 5 True Class: 5\\n\",\n      \"Test 153 Prediction: 5 True Class: 5\\n\",\n      \"Test 154 Prediction: 1 True Class: 1\\n\",\n      \"Test 155 Prediction: 5 True Class: 5\\n\",\n      \"Test 156 Prediction: 6 True Class: 6\\n\",\n      \"Test 157 Prediction: 0 True Class: 0\\n\",\n      \"Test 158 Prediction: 3 True Class: 3\\n\",\n      \"Test 159 Prediction: 4 True Class: 4\\n\",\n      \"Test 160 Prediction: 4 True Class: 4\\n\",\n      \"Test 161 Prediction: 6 True Class: 6\\n\",\n      \"Test 162 Prediction: 5 True Class: 5\\n\",\n      \"Test 163 Prediction: 4 True Class: 4\\n\",\n      \"Test 164 Prediction: 6 True Class: 6\\n\",\n      \"Test 165 Prediction: 5 True Class: 5\\n\",\n      \"Test 166 Prediction: 4 True Class: 4\\n\",\n      \"Test 167 Prediction: 5 True Class: 5\\n\",\n      \"Test 168 Prediction: 1 True Class: 1\\n\",\n      \"Test 169 Prediction: 4 True Class: 4\\n\",\n      \"Test 170 Prediction: 9 True Class: 4\\n\",\n      \"Test 171 Prediction: 7 True Class: 7\\n\",\n      \"Test 172 Prediction: 2 True Class: 2\\n\",\n      \"Test 173 Prediction: 3 True Class: 3\\n\",\n      \"Test 174 Prediction: 2 True Class: 2\\n\",\n      \"Test 175 Prediction: 1 True Class: 7\\n\",\n      \"Test 176 Prediction: 1 True Class: 1\\n\",\n      \"Test 177 Prediction: 8 True Class: 8\\n\",\n      \"Test 178 Prediction: 1 True Class: 1\\n\",\n      \"Test 179 Prediction: 8 True Class: 8\\n\",\n      \"Test 180 Prediction: 1 True Class: 1\\n\",\n      \"Test 181 Prediction: 8 True Class: 8\\n\",\n      \"Test 182 Prediction: 5 True Class: 5\\n\",\n      \"Test 183 Prediction: 0 True Class: 0\\n\",\n      \"Test 184 Prediction: 2 True Class: 8\\n\",\n      \"Test 185 Prediction: 9 True Class: 9\\n\",\n      \"Test 186 Prediction: 2 True Class: 2\\n\",\n      \"Test 187 Prediction: 5 True Class: 5\\n\",\n      \"Test 188 Prediction: 0 True Class: 0\\n\",\n      \"Test 189 Prediction: 1 True Class: 1\\n\",\n      \"Test 190 Prediction: 1 True Class: 1\\n\",\n      \"Test 191 Prediction: 1 True Class: 1\\n\",\n      \"Test 192 Prediction: 0 True Class: 0\\n\",\n      \"Test 193 Prediction: 4 True Class: 9\\n\",\n      \"Test 194 Prediction: 0 True Class: 0\\n\",\n      \"Test 195 Prediction: 1 True Class: 3\\n\",\n      \"Test 196 Prediction: 1 True Class: 1\\n\",\n      \"Test 197 Prediction: 6 True Class: 6\\n\",\n      \"Test 198 Prediction: 4 True Class: 4\\n\",\n      \"Test 199 Prediction: 2 True Class: 2\\n\",\n      \"Done!\\n\",\n      \"Accuracy: 0.92\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    # loop over test data\\n\",\n    \"    for i in range(len(Xte)):\\n\",\n    \"        # Get nearest neighbor\\n\",\n    \"        nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]})\\n\",\n    \"        # Get nearest neighbor class label and compare it to its true label\\n\",\n    \"        print \\\"Test\\\", i, \\\"Prediction:\\\", np.argmax(Ytr[nn_index]), \\\\\\n\",\n    \"            \\\"True Class:\\\", np.argmax(Yte[i])\\n\",\n    \"        # Calculate accuracy\\n\",\n    \"        if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):\\n\",\n    \"            accuracy += 1./len(Xte)\\n\",\n    \"    print \\\"Done!\\\"\\n\",\n    \"    print \\\"Accuracy:\\\", accuracy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "TensorFlow/notebooks/3_NeuralNetworks/autoencoder.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\" Auto Encoder Example.\\n\",\n    \"Using an auto encoder on MNIST handwritten digits.\\n\",\n    \"References:\\n\",\n    \"    Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. \\\"Gradient-based\\n\",\n    \"    learning applied to document recognition.\\\" Proceedings of the IEEE,\\n\",\n    \"    86(11):2278-2324, November 1998.\\n\",\n    \"Links:\\n\",\n    \"    [MNIST Dataset] http://yann.lecun.com/exdb/mnist/\\n\",\n    \"\\\"\\\"\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from __future__ import division, print_function, absolute_import\\n\",\n    \"\\n\",\n    \"import tensorflow as tf\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"# Import MINST data\\n\",\n    \"from tensorflow.examples.tutorials.mnist import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"MNIST_data\\\", one_hot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Parameters\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"training_epochs = 20\\n\",\n    \"batch_size = 256\\n\",\n    \"display_step = 1\\n\",\n    \"examples_to_show = 10\\n\",\n    \"\\n\",\n    \"# Network Parameters\\n\",\n    \"n_hidden_1 = 256 # 1st layer num features\\n\",\n    \"n_hidden_2 = 128 # 2nd layer num features\\n\",\n    \"n_input = 784 # MNIST data input (img shape: 28*28)\\n\",\n    \"\\n\",\n    \"# tf Graph input (only pictures)\\n\",\n    \"X = tf.placeholder(\\\"float\\\", [None, n_input])\\n\",\n    \"\\n\",\n    \"weights = {\\n\",\n    \"    'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\\n\",\n    \"    'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\\n\",\n    \"    'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),\\n\",\n    \"    'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])),\\n\",\n    \"}\\n\",\n    \"biases = {\\n\",\n    \"    'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),\\n\",\n    \"    'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),\\n\",\n    \"    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),\\n\",\n    \"    'decoder_b2': tf.Variable(tf.random_normal([n_input])),\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Building the encoder\\n\",\n    \"def encoder(x):\\n\",\n    \"    # Encoder Hidden layer with sigmoid activation #1\\n\",\n    \"    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),\\n\",\n    \"                                   biases['encoder_b1']))\\n\",\n    \"    # Decoder Hidden layer with sigmoid activation #2\\n\",\n    \"    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),\\n\",\n    \"                                   biases['encoder_b2']))\\n\",\n    \"    return layer_2\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Building the decoder\\n\",\n    \"def decoder(x):\\n\",\n    \"    # Encoder Hidden layer with sigmoid activation #1\\n\",\n    \"    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),\\n\",\n    \"                                   biases['decoder_b1']))\\n\",\n    \"    # Decoder Hidden layer with sigmoid activation #2\\n\",\n    \"    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),\\n\",\n    \"                                   biases['decoder_b2']))\\n\",\n    \"    return layer_2\\n\",\n    \"\\n\",\n    \"# Construct model\\n\",\n    \"encoder_op = encoder(X)\\n\",\n    \"decoder_op = decoder(encoder_op)\\n\",\n    \"\\n\",\n    \"# Prediction\\n\",\n    \"y_pred = decoder_op\\n\",\n    \"# Targets (Labels) are the input data.\\n\",\n    \"y_true = X\\n\",\n    \"\\n\",\n    \"# Define loss and optimizer, minimize the squared error\\n\",\n    \"cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))\\n\",\n    \"optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)\\n\",\n    \"\\n\",\n    \"# Initializing the variables\\n\",\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"# Using InteractiveSession (more convenient while using Notebooks)\\n\",\n    \"sess = tf.InteractiveSession()\\n\",\n    \"sess.run(init)\\n\",\n    \"\\n\",\n    \"total_batch = int(mnist.train.num_examples/batch_size)\\n\",\n    \"# Training cycle\\n\",\n    \"for epoch in range(training_epochs):\\n\",\n    \"    # Loop over all batches\\n\",\n    \"    for i in range(total_batch):\\n\",\n    \"        batch_xs, batch_ys = mnist.train.next_batch(batch_size)\\n\",\n    \"        # Run optimization op (backprop) and cost op (to get loss value)\\n\",\n    \"        _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs})\\n\",\n    \"    # Display logs per epoch step\\n\",\n    \"    if epoch % display_step == 0:\\n\",\n    \"        print(\\\"Epoch:\\\", '%04d' % (epoch+1),\\n\",\n    \"              \\\"cost=\\\", \\\"{:.9f}\\\".format(c))\\n\",\n    \"\\n\",\n    \"print(\\\"Optimization Finished!\\\")\\n\",\n    \"\\n\",\n    \"# Applying encode and decode over test set\\n\",\n    \"encode_decode = sess.run(\\n\",\n    \"    y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})\\n\",\n    \"# Compare original images with their reconstructions\\n\",\n    \"f, a = plt.subplots(2, 10, figsize=(10, 2))\\n\",\n    \"for i in range(examples_to_show):\\n\",\n    \"    a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28)))\\n\",\n    \"    a[1][i].imshow(np.reshape(encode_decode[i], (28, 28)))\\n\",\n    \"f.show()\\n\",\n    \"plt.draw()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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hxdzy0y2p7H/+Mk1WmoKyAeow/ktFU41jnFxLEFwr0lAkMFUlN1PvjY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\\nRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x13b27b250>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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hxdzy0y2p7H/+Mk1WmoKyAeow/ktFU41jnFxLEFwr0lAkMFUlN1PvjY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\\n/wGTleGj2VhBawAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x14b541050>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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hxdzy0y2p7H/+Mk1WmoKyAeow/ktFU41jnFxLEFwr0lAkMFUlN1PvjY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\\npl2Nun2pVu1q1O1kafqt0G6HlDmjndHuftSubtq6TWh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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x13b379d90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.show()\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "TensorFlow/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"'''\\n\",\n    \"A Bidirectional Reccurent Neural Network (LSTM) implementation example using TensorFlow library.\\n\",\n    \"This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\\n\",\n    \"Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf\\n\",\n    \"\\n\",\n    \"Author: Aymeric Damien\\n\",\n    \"Project: https://github.com/aymericdamien/TensorFlow-Examples/\\n\",\n    \"'''\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.contrib import rnn\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"# Import MINST data\\n\",\n    \"from tensorflow.examples.tutorials.mnist import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"MNIST_data/\\\", one_hot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"'''\\n\",\n    \"To classify images using a bidirectional reccurent neural network, we consider\\n\",\n    \"every image row as a sequence of pixels. Because MNIST image shape is 28*28px,\\n\",\n    \"we will then handle 28 sequences of 28 steps for every sample.\\n\",\n    \"'''\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Parameters\\n\",\n    \"learning_rate = 0.001\\n\",\n    \"training_iters = 100000\\n\",\n    \"batch_size = 128\\n\",\n    \"display_step = 10\\n\",\n    \"\\n\",\n    \"# Network Parameters\\n\",\n    \"n_input = 28 # MNIST data input (img shape: 28*28)\\n\",\n    \"n_steps = 28 # timesteps\\n\",\n    \"n_hidden = 128 # hidden layer num of features\\n\",\n    \"n_classes = 10 # MNIST total classes (0-9 digits)\\n\",\n    \"\\n\",\n    \"# tf Graph input\\n\",\n    \"x = tf.placeholder(\\\"float\\\", [None, n_steps, n_input])\\n\",\n    \"y = tf.placeholder(\\\"float\\\", [None, n_classes])\\n\",\n    \"\\n\",\n    \"# Define weights\\n\",\n    \"weights = {\\n\",\n    \"    # Hidden layer weights => 2*n_hidden because of foward + backward cells\\n\",\n    \"    'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes]))\\n\",\n    \"}\\n\",\n    \"biases = {\\n\",\n    \"    'out': tf.Variable(tf.random_normal([n_classes]))\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def BiRNN(x, weights, biases):\\n\",\n    \"\\n\",\n    \"    # Prepare data shape to match `bidirectional_rnn` function requirements\\n\",\n    \"    # Current data input shape: (batch_size, n_steps, n_input)\\n\",\n    \"    # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)\\n\",\n    \"    \\n\",\n    \"    # Permuting batch_size and n_steps\\n\",\n    \"    x = tf.transpose(x, [1, 0, 2])\\n\",\n    \"    # Reshape to (n_steps*batch_size, n_input)\\n\",\n    \"    x = tf.reshape(x, [-1, n_input])\\n\",\n    \"    # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)\\n\",\n    \"    x = tf.split(x, n_steps, 0)\\n\",\n    \"\\n\",\n    \"    # Define lstm cells with tensorflow\\n\",\n    \"    # Forward direction cell\\n\",\n    \"    lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)\\n\",\n    \"    # Backward direction cell\\n\",\n    \"    lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)\\n\",\n    \"\\n\",\n    \"    # Get lstm cell output\\n\",\n    \"    try:\\n\",\n    \"        outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\\n\",\n    \"                                              dtype=tf.float32)\\n\",\n    \"    except Exception: # Old TensorFlow version only returns outputs not states\\n\",\n    \"        outputs = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\\n\",\n    \"                                        dtype=tf.float32)\\n\",\n    \"\\n\",\n    \"    # Linear activation, using rnn inner loop last output\\n\",\n    \"    return tf.matmul(outputs[-1], weights['out']) + biases['out']\\n\",\n    \"\\n\",\n    \"pred = BiRNN(x, weights, biases)\\n\",\n    \"\\n\",\n    \"# Define loss and optimizer\\n\",\n    \"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\\n\",\n    \"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\\n\",\n    \"\\n\",\n    \"# Evaluate model\\n\",\n    \"correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\\n\",\n    \"accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\\n\",\n    \"\\n\",\n    \"# Initializing the variables\\n\",\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iter 1280, Minibatch Loss= 1.557283, Training Accuracy= 0.49219\\n\",\n      \"Iter 2560, Minibatch Loss= 1.358445, Training Accuracy= 0.56250\\n\",\n      \"Iter 3840, Minibatch Loss= 1.043732, Training Accuracy= 0.64062\\n\",\n      \"Iter 5120, Minibatch Loss= 0.796770, Training Accuracy= 0.72656\\n\",\n      \"Iter 6400, Minibatch Loss= 0.626206, Training Accuracy= 0.72656\\n\",\n      \"Iter 7680, Minibatch Loss= 1.025919, Training Accuracy= 0.65625\\n\",\n      \"Iter 8960, Minibatch Loss= 0.744850, Training Accuracy= 0.76562\\n\",\n      \"Iter 10240, Minibatch Loss= 0.530111, Training Accuracy= 0.84375\\n\",\n      \"Iter 11520, Minibatch Loss= 0.383806, Training Accuracy= 0.86719\\n\",\n      \"Iter 12800, Minibatch Loss= 0.607816, Training Accuracy= 0.82812\\n\",\n      \"Iter 14080, Minibatch Loss= 0.410879, Training Accuracy= 0.89062\\n\",\n      \"Iter 15360, Minibatch Loss= 0.335351, Training Accuracy= 0.89844\\n\",\n      \"Iter 16640, Minibatch Loss= 0.428004, Training Accuracy= 0.91406\\n\",\n      \"Iter 17920, Minibatch Loss= 0.307468, Training Accuracy= 0.91406\\n\",\n      \"Iter 19200, Minibatch Loss= 0.249527, Training Accuracy= 0.92188\\n\",\n      \"Iter 20480, Minibatch Loss= 0.148163, Training Accuracy= 0.96094\\n\",\n      \"Iter 21760, Minibatch Loss= 0.445275, Training Accuracy= 0.83594\\n\",\n      \"Iter 23040, Minibatch Loss= 0.173083, Training Accuracy= 0.93750\\n\",\n      \"Iter 24320, Minibatch Loss= 0.373696, Training Accuracy= 0.87500\\n\",\n      \"Iter 25600, Minibatch Loss= 0.509869, Training Accuracy= 0.85938\\n\",\n      \"Iter 26880, Minibatch Loss= 0.198096, Training Accuracy= 0.92969\\n\",\n      \"Iter 28160, Minibatch Loss= 0.228221, Training Accuracy= 0.92188\\n\",\n      \"Iter 29440, Minibatch Loss= 0.280088, Training Accuracy= 0.89844\\n\",\n      \"Iter 30720, Minibatch Loss= 0.300495, Training Accuracy= 0.91406\\n\",\n      \"Iter 32000, Minibatch Loss= 0.171746, Training Accuracy= 0.95312\\n\",\n      \"Iter 33280, Minibatch Loss= 0.263745, Training Accuracy= 0.89844\\n\",\n      \"Iter 34560, Minibatch Loss= 0.177300, Training Accuracy= 0.93750\\n\",\n      \"Iter 35840, Minibatch Loss= 0.160621, Training Accuracy= 0.95312\\n\",\n      \"Iter 37120, Minibatch Loss= 0.321745, Training Accuracy= 0.91406\\n\",\n      \"Iter 38400, Minibatch Loss= 0.188322, Training Accuracy= 0.93750\\n\",\n      \"Iter 39680, Minibatch Loss= 0.104025, Training Accuracy= 0.96875\\n\",\n      \"Iter 40960, Minibatch Loss= 0.291053, Training Accuracy= 0.89062\\n\",\n      \"Iter 42240, Minibatch Loss= 0.131189, Training Accuracy= 0.95312\\n\",\n      \"Iter 43520, Minibatch Loss= 0.154949, Training Accuracy= 0.92969\\n\",\n      \"Iter 44800, Minibatch Loss= 0.150411, Training Accuracy= 0.93750\\n\",\n      \"Iter 46080, Minibatch Loss= 0.117008, Training Accuracy= 0.96094\\n\",\n      \"Iter 47360, Minibatch Loss= 0.181344, Training Accuracy= 0.96094\\n\",\n      \"Iter 48640, Minibatch Loss= 0.209197, Training Accuracy= 0.94531\\n\",\n      \"Iter 49920, Minibatch Loss= 0.159350, Training Accuracy= 0.96094\\n\",\n      \"Iter 51200, Minibatch Loss= 0.124001, Training Accuracy= 0.95312\\n\",\n      \"Iter 52480, Minibatch Loss= 0.165183, Training Accuracy= 0.94531\\n\",\n      \"Iter 53760, Minibatch Loss= 0.046438, Training Accuracy= 0.97656\\n\",\n      \"Iter 55040, Minibatch Loss= 0.199995, Training Accuracy= 0.91406\\n\",\n      \"Iter 56320, Minibatch Loss= 0.057071, Training Accuracy= 0.97656\\n\",\n      \"Iter 57600, Minibatch Loss= 0.177065, Training Accuracy= 0.92188\\n\",\n      \"Iter 58880, Minibatch Loss= 0.091666, Training Accuracy= 0.96094\\n\",\n      \"Iter 60160, Minibatch Loss= 0.069232, Training Accuracy= 0.96875\\n\",\n      \"Iter 61440, Minibatch Loss= 0.127353, Training Accuracy= 0.94531\\n\",\n      \"Iter 62720, Minibatch Loss= 0.095795, Training Accuracy= 0.96094\\n\",\n      \"Iter 64000, Minibatch Loss= 0.202651, Training Accuracy= 0.96875\\n\",\n      \"Iter 65280, Minibatch Loss= 0.118779, Training Accuracy= 0.95312\\n\",\n      \"Iter 66560, Minibatch Loss= 0.043173, Training Accuracy= 0.98438\\n\",\n      \"Iter 67840, Minibatch Loss= 0.152280, Training Accuracy= 0.95312\\n\",\n      \"Iter 69120, Minibatch Loss= 0.085301, Training Accuracy= 0.96875\\n\",\n      \"Iter 70400, Minibatch Loss= 0.093421, Training Accuracy= 0.96094\\n\",\n      \"Iter 71680, Minibatch Loss= 0.096358, Training Accuracy= 0.96875\\n\",\n      \"Iter 72960, Minibatch Loss= 0.053386, Training Accuracy= 0.98438\\n\",\n      \"Iter 74240, Minibatch Loss= 0.065237, Training Accuracy= 0.97656\\n\",\n      \"Iter 75520, Minibatch Loss= 0.228090, Training Accuracy= 0.92188\\n\",\n      \"Iter 76800, Minibatch Loss= 0.106751, Training Accuracy= 0.95312\\n\",\n      \"Iter 78080, Minibatch Loss= 0.187795, Training Accuracy= 0.94531\\n\",\n      \"Iter 79360, Minibatch Loss= 0.092611, Training Accuracy= 0.96094\\n\",\n      \"Iter 80640, Minibatch Loss= 0.137386, Training Accuracy= 0.96875\\n\",\n      \"Iter 81920, Minibatch Loss= 0.106634, Training Accuracy= 0.98438\\n\",\n      \"Iter 83200, Minibatch Loss= 0.111749, Training Accuracy= 0.94531\\n\",\n      \"Iter 84480, Minibatch Loss= 0.191184, Training Accuracy= 0.94531\\n\",\n      \"Iter 85760, Minibatch Loss= 0.063982, Training Accuracy= 0.96094\\n\",\n      \"Iter 87040, Minibatch Loss= 0.092380, Training Accuracy= 0.96875\\n\",\n      \"Iter 88320, Minibatch Loss= 0.089899, Training Accuracy= 0.97656\\n\",\n      \"Iter 89600, Minibatch Loss= 0.141107, Training Accuracy= 0.94531\\n\",\n      \"Iter 90880, Minibatch Loss= 0.075549, Training Accuracy= 0.96094\\n\",\n      \"Iter 92160, Minibatch Loss= 0.186539, Training Accuracy= 0.94531\\n\",\n      \"Iter 93440, Minibatch Loss= 0.079639, Training Accuracy= 0.97656\\n\",\n      \"Iter 94720, Minibatch Loss= 0.156895, Training Accuracy= 0.95312\\n\",\n      \"Iter 96000, Minibatch Loss= 0.088042, Training Accuracy= 0.97656\\n\",\n      \"Iter 97280, Minibatch Loss= 0.076670, Training Accuracy= 0.96875\\n\",\n      \"Iter 98560, Minibatch Loss= 0.051336, Training Accuracy= 0.97656\\n\",\n      \"Iter 99840, Minibatch Loss= 0.086923, Training Accuracy= 0.98438\\n\",\n      \"Optimization Finished!\\n\",\n      \"Testing Accuracy: 0.960938\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"    step = 1\\n\",\n    \"    # Keep training until reach max iterations\\n\",\n    \"    while step * batch_size < training_iters:\\n\",\n    \"        batch_x, batch_y = mnist.train.next_batch(batch_size)\\n\",\n    \"        # Reshape data to get 28 seq of 28 elements\\n\",\n    \"        batch_x = batch_x.reshape((batch_size, n_steps, n_input))\\n\",\n    \"        # Run optimization op (backprop)\\n\",\n    \"        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})\\n\",\n    \"        if step % display_step == 0:\\n\",\n    \"            # Calculate batch accuracy\\n\",\n    \"            acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})\\n\",\n    \"            # Calculate batch loss\\n\",\n    \"            loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})\\n\",\n    \"            print \\\"Iter \\\" + str(step*batch_size) + \\\", Minibatch Loss= \\\" + \\\\\\n\",\n    \"                  \\\"{:.6f}\\\".format(loss) + \\\", Training Accuracy= \\\" + \\\\\\n\",\n    \"                  \\\"{:.5f}\\\".format(acc)\\n\",\n    \"        step += 1\\n\",\n    \"    print \\\"Optimization Finished!\\\"\\n\",\n    \"\\n\",\n    \"    # Calculate accuracy for 128 mnist test images\\n\",\n    \"    test_len = 128\\n\",\n    \"    test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))\\n\",\n    \"    test_label = mnist.test.labels[:test_len]\\n\",\n    \"    print \\\"Testing Accuracy:\\\", \\\\\\n\",\n    \"        sess.run(accuracy, feed_dict={x: test_data, y: test_label})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "TensorFlow/notebooks/3_NeuralNetworks/convolutional_network.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"'''\\n\",\n    \"A Convolutional Network implementation example using TensorFlow library.\\n\",\n    \"This example is using the MNIST database of handwritten digits\\n\",\n    \"(http://yann.lecun.com/exdb/mnist/)\\n\",\n    \"\\n\",\n    \"Author: Aymeric Damien\\n\",\n    \"Project: https://github.com/aymericdamien/TensorFlow-Examples/\\n\",\n    \"'''\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"# Import MNIST data\\n\",\n    \"from tensorflow.examples.tutorials.mnist import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"MNIST_data/\\\", one_hot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Parameters\\n\",\n    \"learning_rate = 0.001\\n\",\n    \"training_iters = 200000\\n\",\n    \"batch_size = 128\\n\",\n    \"display_step = 10\\n\",\n    \"\\n\",\n    \"# Network Parameters\\n\",\n    \"n_input = 784 # MNIST data input (img shape: 28*28)\\n\",\n    \"n_classes = 10 # MNIST total classes (0-9 digits)\\n\",\n    \"dropout = 0.75 # Dropout, probability to keep units\\n\",\n    \"\\n\",\n    \"# tf Graph input\\n\",\n    \"x = tf.placeholder(tf.float32, [None, n_input])\\n\",\n    \"y = tf.placeholder(tf.float32, [None, n_classes])\\n\",\n    \"keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Create some wrappers for simplicity\\n\",\n    \"def conv2d(x, W, b, strides=1):\\n\",\n    \"    # Conv2D wrapper, with bias and relu activation\\n\",\n    \"    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')\\n\",\n    \"    x = tf.nn.bias_add(x, b)\\n\",\n    \"    return tf.nn.relu(x)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def maxpool2d(x, k=2):\\n\",\n    \"    # MaxPool2D wrapper\\n\",\n    \"    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],\\n\",\n    \"                          padding='SAME')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Create model\\n\",\n    \"def conv_net(x, weights, biases, dropout):\\n\",\n    \"    # Reshape input picture\\n\",\n    \"    x = tf.reshape(x, shape=[-1, 28, 28, 1])\\n\",\n    \"\\n\",\n    \"    # Convolution Layer\\n\",\n    \"    conv1 = conv2d(x, weights['wc1'], biases['bc1'])\\n\",\n    \"    # Max Pooling (down-sampling)\\n\",\n    \"    conv1 = maxpool2d(conv1, k=2)\\n\",\n    \"\\n\",\n    \"    # Convolution Layer\\n\",\n    \"    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])\\n\",\n    \"    # Max Pooling (down-sampling)\\n\",\n    \"    conv2 = maxpool2d(conv2, k=2)\\n\",\n    \"\\n\",\n    \"    # Fully connected layer\\n\",\n    \"    # Reshape conv2 output to fit fully connected layer input\\n\",\n    \"    fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])\\n\",\n    \"    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])\\n\",\n    \"    fc1 = tf.nn.relu(fc1)\\n\",\n    \"    # Apply Dropout\\n\",\n    \"    fc1 = tf.nn.dropout(fc1, dropout)\\n\",\n    \"\\n\",\n    \"    # Output, class prediction\\n\",\n    \"    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])\\n\",\n    \"    return out\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Store layers weight & bias\\n\",\n    \"weights = {\\n\",\n    \"    # 5x5 conv, 1 input, 32 outputs\\n\",\n    \"    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),\\n\",\n    \"    # 5x5 conv, 32 inputs, 64 outputs\\n\",\n    \"    'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),\\n\",\n    \"    # fully connected, 7*7*64 inputs, 1024 outputs\\n\",\n    \"    'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),\\n\",\n    \"    # 1024 inputs, 10 outputs (class prediction)\\n\",\n    \"    'out': tf.Variable(tf.random_normal([1024, n_classes]))\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"biases = {\\n\",\n    \"    'bc1': tf.Variable(tf.random_normal([32])),\\n\",\n    \"    'bc2': tf.Variable(tf.random_normal([64])),\\n\",\n    \"    'bd1': tf.Variable(tf.random_normal([1024])),\\n\",\n    \"    'out': tf.Variable(tf.random_normal([n_classes]))\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"# Construct model\\n\",\n    \"pred = conv_net(x, weights, biases, keep_prob)\\n\",\n    \"\\n\",\n    \"# Define loss and optimizer\\n\",\n    \"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\\n\",\n    \"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\\n\",\n    \"\\n\",\n    \"# Evaluate model\\n\",\n    \"correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\\n\",\n    \"accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\\n\",\n    \"\\n\",\n    \"# Initializing the variables\\n\",\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iter 1280, Minibatch Loss= 26574.855469, Training Accuracy= 0.25781\\n\",\n      \"Iter 2560, Minibatch Loss= 11454.494141, Training Accuracy= 0.49219\\n\",\n      \"Iter 3840, Minibatch Loss= 10070.515625, Training Accuracy= 0.55469\\n\",\n      \"Iter 5120, Minibatch Loss= 4008.586426, Training Accuracy= 0.78125\\n\",\n      \"Iter 6400, Minibatch Loss= 3148.004639, Training Accuracy= 0.80469\\n\",\n      \"Iter 7680, Minibatch Loss= 6740.440430, Training Accuracy= 0.71875\\n\",\n      \"Iter 8960, Minibatch Loss= 4103.991699, Training Accuracy= 0.80469\\n\",\n      \"Iter 10240, Minibatch Loss= 2631.275391, Training Accuracy= 0.85938\\n\",\n      \"Iter 11520, Minibatch Loss= 1428.798828, Training Accuracy= 0.91406\\n\",\n      \"Iter 12800, Minibatch Loss= 3909.772705, Training Accuracy= 0.78906\\n\",\n      \"Iter 14080, Minibatch Loss= 1423.095947, Training Accuracy= 0.88281\\n\",\n      \"Iter 15360, Minibatch Loss= 1524.569824, Training Accuracy= 0.89062\\n\",\n      \"Iter 16640, Minibatch Loss= 2234.539795, Training Accuracy= 0.86719\\n\",\n      \"Iter 17920, Minibatch Loss= 933.932800, Training Accuracy= 0.90625\\n\",\n      \"Iter 19200, Minibatch Loss= 2039.046021, Training Accuracy= 0.89062\\n\",\n      \"Iter 20480, Minibatch Loss= 674.179932, Training Accuracy= 0.95312\\n\",\n      \"Iter 21760, Minibatch Loss= 3778.958984, Training Accuracy= 0.82812\\n\",\n      \"Iter 23040, Minibatch Loss= 1038.217773, Training Accuracy= 0.91406\\n\",\n      \"Iter 24320, Minibatch Loss= 1689.513672, Training Accuracy= 0.89062\\n\",\n      \"Iter 25600, Minibatch Loss= 1800.954956, Training Accuracy= 0.85938\\n\",\n      \"Iter 26880, Minibatch Loss= 1086.292847, Training Accuracy= 0.90625\\n\",\n      \"Iter 28160, Minibatch Loss= 656.042847, Training Accuracy= 0.94531\\n\",\n      \"Iter 29440, Minibatch Loss= 1210.589844, Training Accuracy= 0.91406\\n\",\n      \"Iter 30720, Minibatch Loss= 1099.606323, Training Accuracy= 0.90625\\n\",\n      \"Iter 32000, Minibatch Loss= 1073.128174, Training Accuracy= 0.92969\\n\",\n      \"Iter 33280, Minibatch Loss= 518.844543, Training Accuracy= 0.95312\\n\",\n      \"Iter 34560, Minibatch Loss= 540.856689, Training Accuracy= 0.92188\\n\",\n      \"Iter 35840, Minibatch Loss= 353.990906, Training Accuracy= 0.97656\\n\",\n      \"Iter 37120, Minibatch Loss= 1488.962891, Training Accuracy= 0.91406\\n\",\n      \"Iter 38400, Minibatch Loss= 231.191864, Training Accuracy= 0.98438\\n\",\n      \"Iter 39680, Minibatch Loss= 171.154480, Training Accuracy= 0.98438\\n\",\n      \"Iter 40960, Minibatch Loss= 2092.023682, Training Accuracy= 0.90625\\n\",\n      \"Iter 42240, Minibatch Loss= 480.594299, Training Accuracy= 0.95312\\n\",\n      \"Iter 43520, Minibatch Loss= 504.128143, Training Accuracy= 0.96875\\n\",\n      \"Iter 44800, Minibatch Loss= 143.534485, Training Accuracy= 0.97656\\n\",\n      \"Iter 46080, Minibatch Loss= 325.875580, Training Accuracy= 0.96094\\n\",\n      \"Iter 47360, Minibatch Loss= 602.813049, Training Accuracy= 0.91406\\n\",\n      \"Iter 48640, Minibatch Loss= 794.595093, Training Accuracy= 0.94531\\n\",\n      \"Iter 49920, Minibatch Loss= 415.539032, Training Accuracy= 0.95312\\n\",\n      \"Iter 51200, Minibatch Loss= 146.016022, Training Accuracy= 0.96094\\n\",\n      \"Iter 52480, Minibatch Loss= 294.180786, Training Accuracy= 0.94531\\n\",\n      \"Iter 53760, Minibatch Loss= 50.955730, Training Accuracy= 0.99219\\n\",\n      \"Iter 55040, Minibatch Loss= 1026.607056, Training Accuracy= 0.92188\\n\",\n      \"Iter 56320, Minibatch Loss= 283.756134, Training Accuracy= 0.96875\\n\",\n      \"Iter 57600, Minibatch Loss= 691.538208, Training Accuracy= 0.95312\\n\",\n      \"Iter 58880, Minibatch Loss= 491.075073, Training Accuracy= 0.96094\\n\",\n      \"Iter 60160, Minibatch Loss= 571.951660, Training Accuracy= 0.95312\\n\",\n      \"Iter 61440, Minibatch Loss= 284.041168, Training Accuracy= 0.97656\\n\",\n      \"Iter 62720, Minibatch Loss= 1041.941528, Training Accuracy= 0.92969\\n\",\n      \"Iter 64000, Minibatch Loss= 664.833923, Training Accuracy= 0.93750\\n\",\n      \"Iter 65280, Minibatch Loss= 1582.112793, Training Accuracy= 0.88281\\n\",\n      \"Iter 66560, Minibatch Loss= 783.135376, Training Accuracy= 0.94531\\n\",\n      \"Iter 67840, Minibatch Loss= 245.942398, Training Accuracy= 0.96094\\n\",\n      \"Iter 69120, Minibatch Loss= 752.858948, Training Accuracy= 0.96875\\n\",\n      \"Iter 70400, Minibatch Loss= 623.243286, Training Accuracy= 0.94531\\n\",\n      \"Iter 71680, Minibatch Loss= 846.498230, Training Accuracy= 0.93750\\n\",\n      \"Iter 72960, Minibatch Loss= 586.516479, Training Accuracy= 0.95312\\n\",\n      \"Iter 74240, Minibatch Loss= 92.774963, Training Accuracy= 0.98438\\n\",\n      \"Iter 75520, Minibatch Loss= 644.039612, Training Accuracy= 0.95312\\n\",\n      \"Iter 76800, Minibatch Loss= 693.247681, Training Accuracy= 0.96094\\n\",\n      \"Iter 78080, Minibatch Loss= 466.491882, Training Accuracy= 0.96094\\n\",\n      \"Iter 79360, Minibatch Loss= 964.212341, Training Accuracy= 0.93750\\n\",\n      \"Iter 80640, Minibatch Loss= 230.451904, Training Accuracy= 0.97656\\n\",\n      \"Iter 81920, Minibatch Loss= 280.434570, Training Accuracy= 0.95312\\n\",\n      \"Iter 83200, Minibatch Loss= 213.208252, Training Accuracy= 0.97656\\n\",\n      \"Iter 84480, Minibatch Loss= 774.836060, Training Accuracy= 0.94531\\n\",\n      \"Iter 85760, Minibatch Loss= 164.687729, Training Accuracy= 0.96094\\n\",\n      \"Iter 87040, Minibatch Loss= 419.967407, Training Accuracy= 0.96875\\n\",\n      \"Iter 88320, Minibatch Loss= 160.920151, Training Accuracy= 0.96875\\n\",\n      \"Iter 89600, Minibatch Loss= 586.063599, Training Accuracy= 0.96094\\n\",\n      \"Iter 90880, Minibatch Loss= 345.598145, Training Accuracy= 0.96875\\n\",\n      \"Iter 92160, Minibatch Loss= 931.361145, Training Accuracy= 0.92188\\n\",\n      \"Iter 93440, Minibatch Loss= 170.107117, Training Accuracy= 0.97656\\n\",\n      \"Iter 94720, Minibatch Loss= 497.162750, Training Accuracy= 0.93750\\n\",\n      \"Iter 96000, Minibatch Loss= 906.600464, Training Accuracy= 0.94531\\n\",\n      \"Iter 97280, Minibatch Loss= 303.382202, Training Accuracy= 0.92969\\n\",\n      \"Iter 98560, Minibatch Loss= 509.161652, Training Accuracy= 0.97656\\n\",\n      \"Iter 99840, Minibatch Loss= 359.561981, Training Accuracy= 0.97656\\n\",\n      \"Iter 101120, Minibatch Loss= 136.516541, Training Accuracy= 0.97656\\n\",\n      \"Iter 102400, Minibatch Loss= 517.199341, Training Accuracy= 0.96875\\n\",\n      \"Iter 103680, Minibatch Loss= 487.793335, Training Accuracy= 0.95312\\n\",\n      \"Iter 104960, Minibatch Loss= 407.351929, Training Accuracy= 0.96094\\n\",\n      \"Iter 106240, Minibatch Loss= 70.495193, Training Accuracy= 0.98438\\n\",\n      \"Iter 107520, Minibatch Loss= 344.783508, Training Accuracy= 0.96094\\n\",\n      \"Iter 108800, Minibatch Loss= 242.682465, Training Accuracy= 0.95312\\n\",\n      \"Iter 110080, Minibatch Loss= 169.181458, Training Accuracy= 0.96094\\n\",\n      \"Iter 111360, Minibatch Loss= 152.638245, Training Accuracy= 0.98438\\n\",\n      \"Iter 112640, Minibatch Loss= 170.795868, Training Accuracy= 0.96875\\n\",\n      \"Iter 113920, Minibatch Loss= 133.262726, Training Accuracy= 0.98438\\n\",\n      \"Iter 115200, Minibatch Loss= 296.063293, Training Accuracy= 0.95312\\n\",\n      \"Iter 116480, Minibatch Loss= 254.247543, Training Accuracy= 0.96094\\n\",\n      \"Iter 117760, Minibatch Loss= 506.795715, Training Accuracy= 0.94531\\n\",\n      \"Iter 119040, Minibatch Loss= 446.006897, Training Accuracy= 0.96094\\n\",\n      \"Iter 120320, Minibatch Loss= 149.467377, Training Accuracy= 0.97656\\n\",\n      \"Iter 121600, Minibatch Loss= 52.783600, Training Accuracy= 0.98438\\n\",\n      \"Iter 122880, Minibatch Loss= 49.041794, Training Accuracy= 0.98438\\n\",\n      \"Iter 124160, Minibatch Loss= 184.371246, Training Accuracy= 0.97656\\n\",\n      \"Iter 125440, Minibatch Loss= 129.838501, Training Accuracy= 0.97656\\n\",\n      \"Iter 126720, Minibatch Loss= 288.006531, Training Accuracy= 0.96875\\n\",\n      \"Iter 128000, Minibatch Loss= 187.284653, Training Accuracy= 0.97656\\n\",\n      \"Iter 129280, Minibatch Loss= 197.969955, Training Accuracy= 0.96875\\n\",\n      \"Iter 130560, Minibatch Loss= 299.969818, Training Accuracy= 0.96875\\n\",\n      \"Iter 131840, Minibatch Loss= 537.602173, Training Accuracy= 0.96094\\n\",\n      \"Iter 133120, Minibatch Loss= 4.519302, Training Accuracy= 0.99219\\n\",\n      \"Iter 134400, Minibatch Loss= 133.264191, Training Accuracy= 0.97656\\n\",\n      \"Iter 135680, Minibatch Loss= 89.662292, Training Accuracy= 0.97656\\n\",\n      \"Iter 136960, Minibatch Loss= 107.774078, Training Accuracy= 0.96875\\n\",\n      \"Iter 138240, Minibatch Loss= 335.904572, Training Accuracy= 0.96094\\n\",\n      \"Iter 139520, Minibatch Loss= 457.494568, Training Accuracy= 0.96094\\n\",\n      \"Iter 140800, Minibatch Loss= 259.131531, Training Accuracy= 0.95312\\n\",\n      \"Iter 142080, Minibatch Loss= 152.205383, Training Accuracy= 0.96094\\n\",\n      \"Iter 143360, Minibatch Loss= 252.535828, Training Accuracy= 0.95312\\n\",\n      \"Iter 144640, Minibatch Loss= 109.477585, Training Accuracy= 0.96875\\n\",\n      \"Iter 145920, Minibatch Loss= 24.468613, Training Accuracy= 0.99219\\n\",\n      \"Iter 147200, Minibatch Loss= 51.722107, Training Accuracy= 0.97656\\n\",\n      \"Iter 148480, Minibatch Loss= 69.715233, Training Accuracy= 0.97656\\n\",\n      \"Iter 149760, Minibatch Loss= 405.289246, Training Accuracy= 0.92969\\n\",\n      \"Iter 151040, Minibatch Loss= 282.976379, Training Accuracy= 0.95312\\n\",\n      \"Iter 152320, Minibatch Loss= 134.991119, Training Accuracy= 0.97656\\n\",\n      \"Iter 153600, Minibatch Loss= 491.618103, Training Accuracy= 0.92188\\n\",\n      \"Iter 154880, Minibatch Loss= 154.299988, Training Accuracy= 0.99219\\n\",\n      \"Iter 156160, Minibatch Loss= 79.480019, Training Accuracy= 0.96875\\n\",\n      \"Iter 157440, Minibatch Loss= 68.093750, Training Accuracy= 0.99219\\n\",\n      \"Iter 158720, Minibatch Loss= 459.739685, Training Accuracy= 0.92188\\n\",\n      \"Iter 160000, Minibatch Loss= 168.076843, Training Accuracy= 0.94531\\n\",\n      \"Iter 161280, Minibatch Loss= 256.141846, Training Accuracy= 0.97656\\n\",\n      \"Iter 162560, Minibatch Loss= 236.400391, Training Accuracy= 0.94531\\n\",\n      \"Iter 163840, Minibatch Loss= 177.011261, Training Accuracy= 0.96875\\n\",\n      \"Iter 165120, Minibatch Loss= 48.583298, Training Accuracy= 0.97656\\n\",\n      \"Iter 166400, Minibatch Loss= 413.800293, Training Accuracy= 0.96094\\n\",\n      \"Iter 167680, Minibatch Loss= 209.587387, Training Accuracy= 0.96875\\n\",\n      \"Iter 168960, Minibatch Loss= 239.407318, Training Accuracy= 0.98438\\n\",\n      \"Iter 170240, Minibatch Loss= 183.567017, Training Accuracy= 0.96875\\n\",\n      \"Iter 171520, Minibatch Loss= 87.937515, Training Accuracy= 0.96875\\n\",\n      \"Iter 172800, Minibatch Loss= 203.777039, Training Accuracy= 0.98438\\n\",\n      \"Iter 174080, Minibatch Loss= 566.378052, Training Accuracy= 0.94531\\n\",\n      \"Iter 175360, Minibatch Loss= 325.170898, Training Accuracy= 0.95312\\n\",\n      \"Iter 176640, Minibatch Loss= 300.142212, Training Accuracy= 0.97656\\n\",\n      \"Iter 177920, Minibatch Loss= 205.370193, Training Accuracy= 0.95312\\n\",\n      \"Iter 179200, Minibatch Loss= 5.594437, Training Accuracy= 0.99219\\n\",\n      \"Iter 180480, Minibatch Loss= 110.732109, Training Accuracy= 0.98438\\n\",\n      \"Iter 181760, Minibatch Loss= 33.320297, Training Accuracy= 0.99219\\n\",\n      \"Iter 183040, Minibatch Loss= 6.885544, Training Accuracy= 0.99219\\n\",\n      \"Iter 184320, Minibatch Loss= 221.144806, Training Accuracy= 0.96875\\n\",\n      \"Iter 185600, Minibatch Loss= 365.337372, Training Accuracy= 0.94531\\n\",\n      \"Iter 186880, Minibatch Loss= 186.558258, Training Accuracy= 0.96094\\n\",\n      \"Iter 188160, Minibatch Loss= 149.720322, Training Accuracy= 0.98438\\n\",\n      \"Iter 189440, Minibatch Loss= 105.281998, Training Accuracy= 0.97656\\n\",\n      \"Iter 190720, Minibatch Loss= 289.980011, Training Accuracy= 0.96094\\n\",\n      \"Iter 192000, Minibatch Loss= 214.382278, Training Accuracy= 0.96094\\n\",\n      \"Iter 193280, Minibatch Loss= 461.044312, Training Accuracy= 0.93750\\n\",\n      \"Iter 194560, Minibatch Loss= 138.653076, Training Accuracy= 0.98438\\n\",\n      \"Iter 195840, Minibatch Loss= 112.004883, Training Accuracy= 0.98438\\n\",\n      \"Iter 197120, Minibatch Loss= 212.691467, Training Accuracy= 0.97656\\n\",\n      \"Iter 198400, Minibatch Loss= 57.642502, Training Accuracy= 0.97656\\n\",\n      \"Iter 199680, Minibatch Loss= 80.503563, Training Accuracy= 0.96875\\n\",\n      \"Optimization Finished!\\n\",\n      \"Testing Accuracy: 0.984375\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"    step = 1\\n\",\n    \"    # Keep training until reach max iterations\\n\",\n    \"    while step * batch_size < training_iters:\\n\",\n    \"        batch_x, batch_y = mnist.train.next_batch(batch_size)\\n\",\n    \"        # Run optimization op (backprop)\\n\",\n    \"        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,\\n\",\n    \"                                       keep_prob: dropout})\\n\",\n    \"        if step % display_step == 0:\\n\",\n    \"            # Calculate batch loss and accuracy\\n\",\n    \"            loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,\\n\",\n    \"                                                              y: batch_y,\\n\",\n    \"                                                              keep_prob: 1.})\\n\",\n    \"            print \\\"Iter \\\" + str(step*batch_size) + \\\", Minibatch Loss= \\\" + \\\\\\n\",\n    \"                  \\\"{:.6f}\\\".format(loss) + \\\", Training Accuracy= \\\" + \\\\\\n\",\n    \"                  \\\"{:.5f}\\\".format(acc)\\n\",\n    \"        step += 1\\n\",\n    \"    print \\\"Optimization Finished!\\\"\\n\",\n    \"\\n\",\n    \"    # Calculate accuracy for 256 mnist test images\\n\",\n    \"    print \\\"Testing Accuracy:\\\", \\\\\\n\",\n    \"        sess.run(accuracy, feed_dict={x: mnist.test.images[:256],\\n\",\n    \"                                      y: mnist.test.labels[:256],\\n\",\n    \"                                      keep_prob: 1.})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "TensorFlow/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"'''\\n\",\n    \"A Multilayer Perceptron implementation example using TensorFlow library.\\n\",\n    \"This example is using the MNIST database of handwritten digits\\n\",\n    \"(http://yann.lecun.com/exdb/mnist/)\\n\",\n    \"\\n\",\n    \"Author: Aymeric Damien\\n\",\n    \"Project: https://github.com/aymericdamien/TensorFlow-Examples/\\n\",\n    \"'''\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting MNIST_data/train-images-idx3-ubyte.gz\\n\",\n      \"Extracting MNIST_data/train-labels-idx1-ubyte.gz\\n\",\n      \"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\\n\",\n      \"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Import MINST data\\n\",\n    \"from tensorflow.examples.tutorials.mnist import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"MNIST_data/\\\", one_hot=True)\\n\",\n    \"\\n\",\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Parameters\\n\",\n    \"learning_rate = 0.001\\n\",\n    \"training_epochs = 15\\n\",\n    \"batch_size = 100\\n\",\n    \"display_step = 1\\n\",\n    \"\\n\",\n    \"# Network Parameters\\n\",\n    \"n_hidden_1 = 256 # 1st layer number of features\\n\",\n    \"n_hidden_2 = 256 # 2nd layer number of features\\n\",\n    \"n_input = 784 # MNIST data input (img shape: 28*28)\\n\",\n    \"n_classes = 10 # MNIST total classes (0-9 digits)\\n\",\n    \"\\n\",\n    \"# tf Graph input\\n\",\n    \"x = tf.placeholder(\\\"float\\\", [None, n_input])\\n\",\n    \"y = tf.placeholder(\\\"float\\\", [None, n_classes])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Create model\\n\",\n    \"def multilayer_perceptron(x, weights, biases):\\n\",\n    \"    # Hidden layer with RELU activation\\n\",\n    \"    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\\n\",\n    \"    layer_1 = tf.nn.relu(layer_1)\\n\",\n    \"    # Hidden layer with RELU activation\\n\",\n    \"    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\\n\",\n    \"    layer_2 = tf.nn.relu(layer_2)\\n\",\n    \"    # Output layer with linear activation\\n\",\n    \"    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\\n\",\n    \"    return out_layer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Store layers weight & bias\\n\",\n    \"weights = {\\n\",\n    \"    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\\n\",\n    \"    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\\n\",\n    \"    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\\n\",\n    \"}\\n\",\n    \"biases = {\\n\",\n    \"    'b1': tf.Variable(tf.random_normal([n_hidden_1])),\\n\",\n    \"    'b2': tf.Variable(tf.random_normal([n_hidden_2])),\\n\",\n    \"    'out': tf.Variable(tf.random_normal([n_classes]))\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"# Construct model\\n\",\n    \"pred = multilayer_perceptron(x, weights, biases)\\n\",\n    \"\\n\",\n    \"# Define loss and optimizer\\n\",\n    \"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\\n\",\n    \"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\\n\",\n    \"\\n\",\n    \"# Initializing the variables\\n\",\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 0001 cost= 173.056566575\\n\",\n      \"Epoch: 0002 cost= 44.054413928\\n\",\n      \"Epoch: 0003 cost= 27.455470655\\n\",\n      \"Epoch: 0004 cost= 19.008652363\\n\",\n      \"Epoch: 0005 cost= 13.654873594\\n\",\n      \"Epoch: 0006 cost= 10.059267435\\n\",\n      \"Epoch: 0007 cost= 7.436018432\\n\",\n      \"Epoch: 0008 cost= 5.587794416\\n\",\n      \"Epoch: 0009 cost= 4.209882509\\n\",\n      \"Epoch: 0010 cost= 3.203879515\\n\",\n      \"Epoch: 0011 cost= 2.319920681\\n\",\n      \"Epoch: 0012 cost= 1.676204545\\n\",\n      \"Epoch: 0013 cost= 1.248805338\\n\",\n      \"Epoch: 0014 cost= 1.052676844\\n\",\n      \"Epoch: 0015 cost= 0.890117338\\n\",\n      \"Optimization Finished!\\n\",\n      \"Accuracy: 0.9459\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    # Training cycle\\n\",\n    \"    for epoch in range(training_epochs):\\n\",\n    \"        avg_cost = 0.\\n\",\n    \"        total_batch = int(mnist.train.num_examples/batch_size)\\n\",\n    \"        # Loop over all batches\\n\",\n    \"        for i in range(total_batch):\\n\",\n    \"            batch_x, batch_y = mnist.train.next_batch(batch_size)\\n\",\n    \"            # Run optimization op (backprop) and cost op (to get loss value)\\n\",\n    \"            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,\\n\",\n    \"                                                          y: batch_y})\\n\",\n    \"            # Compute average loss\\n\",\n    \"            avg_cost += c / total_batch\\n\",\n    \"        # Display logs per epoch step\\n\",\n    \"        if epoch % display_step == 0:\\n\",\n    \"            print \\\"Epoch:\\\", '%04d' % (epoch+1), \\\"cost=\\\", \\\\\\n\",\n    \"                \\\"{:.9f}\\\".format(avg_cost)\\n\",\n    \"    print \\\"Optimization Finished!\\\"\\n\",\n    \"\\n\",\n    \"    # Test model\\n\",\n    \"    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\\n\",\n    \"    # Calculate accuracy\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \\\"float\\\"))\\n\",\n    \"    print \\\"Accuracy:\\\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "TensorFlow/notebooks/3_NeuralNetworks/recurrent_network.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"'''\\n\",\n    \"A Reccurent Neural Network (LSTM) implementation example using TensorFlow library.\\n\",\n    \"This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\\n\",\n    \"Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf\\n\",\n    \"\\n\",\n    \"Author: Aymeric Damien\\n\",\n    \"Project: https://github.com/aymericdamien/TensorFlow-Examples/\\n\",\n    \"'''\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.contrib import rnn\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"# Import MINST data\\n\",\n    \"from tensorflow.examples.tutorials.mnist import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"MNIST_data/\\\", one_hot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"'''\\n\",\n    \"To classify images using a reccurent neural network, we consider every image\\n\",\n    \"row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then\\n\",\n    \"handle 28 sequences of 28 steps for every sample.\\n\",\n    \"'''\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Parameters\\n\",\n    \"learning_rate = 0.001\\n\",\n    \"training_iters = 100000\\n\",\n    \"batch_size = 128\\n\",\n    \"display_step = 10\\n\",\n    \"\\n\",\n    \"# Network Parameters\\n\",\n    \"n_input = 28 # MNIST data input (img shape: 28*28)\\n\",\n    \"n_steps = 28 # timesteps\\n\",\n    \"n_hidden = 128 # hidden layer num of features\\n\",\n    \"n_classes = 10 # MNIST total classes (0-9 digits)\\n\",\n    \"\\n\",\n    \"# tf Graph input\\n\",\n    \"x = tf.placeholder(\\\"float\\\", [None, n_steps, n_input])\\n\",\n    \"y = tf.placeholder(\\\"float\\\", [None, n_classes])\\n\",\n    \"\\n\",\n    \"# Define weights\\n\",\n    \"weights = {\\n\",\n    \"    'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))\\n\",\n    \"}\\n\",\n    \"biases = {\\n\",\n    \"    'out': tf.Variable(tf.random_normal([n_classes]))\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def RNN(x, weights, biases):\\n\",\n    \"\\n\",\n    \"    # Prepare data shape to match `rnn` function requirements\\n\",\n    \"    # Current data input shape: (batch_size, n_steps, n_input)\\n\",\n    \"    # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)\\n\",\n    \"    \\n\",\n    \"    # Permuting batch_size and n_steps\\n\",\n    \"    x = tf.transpose(x, [1, 0, 2])\\n\",\n    \"    # Reshaping to (n_steps*batch_size, n_input)\\n\",\n    \"    x = tf.reshape(x, [-1, n_input])\\n\",\n    \"    # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)\\n\",\n    \"    x = tf.split(x, n_steps, 0)\\n\",\n    \"\\n\",\n    \"    # Define a lstm cell with tensorflow\\n\",\n    \"    lstm_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)\\n\",\n    \"\\n\",\n    \"    # Get lstm cell output\\n\",\n    \"    outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)\\n\",\n    \"\\n\",\n    \"    # Linear activation, using rnn inner loop last output\\n\",\n    \"    return tf.matmul(outputs[-1], weights['out']) + biases['out']\\n\",\n    \"\\n\",\n    \"pred = RNN(x, weights, biases)\\n\",\n    \"\\n\",\n    \"# Define loss and optimizer\\n\",\n    \"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\\n\",\n    \"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\\n\",\n    \"\\n\",\n    \"# Evaluate model\\n\",\n    \"correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\\n\",\n    \"accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\\n\",\n    \"\\n\",\n    \"# Initializing the variables\\n\",\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iter 1280, Minibatch Loss= 1.576423, Training Accuracy= 0.51562\\n\",\n      \"Iter 2560, Minibatch Loss= 1.450179, Training Accuracy= 0.53906\\n\",\n      \"Iter 3840, Minibatch Loss= 1.160066, Training Accuracy= 0.64844\\n\",\n      \"Iter 5120, Minibatch Loss= 0.898589, Training Accuracy= 0.73438\\n\",\n      \"Iter 6400, Minibatch Loss= 0.685712, Training Accuracy= 0.75781\\n\",\n      \"Iter 7680, Minibatch Loss= 1.085666, Training Accuracy= 0.64844\\n\",\n      \"Iter 8960, Minibatch Loss= 0.681488, Training Accuracy= 0.73438\\n\",\n      \"Iter 10240, Minibatch Loss= 0.557049, Training Accuracy= 0.82812\\n\",\n      \"Iter 11520, Minibatch Loss= 0.340857, Training Accuracy= 0.92188\\n\",\n      \"Iter 12800, Minibatch Loss= 0.596482, Training Accuracy= 0.78906\\n\",\n      \"Iter 14080, Minibatch Loss= 0.486564, Training Accuracy= 0.84375\\n\",\n      \"Iter 15360, Minibatch Loss= 0.302493, Training Accuracy= 0.90625\\n\",\n      \"Iter 16640, Minibatch Loss= 0.334277, Training Accuracy= 0.92188\\n\",\n      \"Iter 17920, Minibatch Loss= 0.222026, Training Accuracy= 0.90625\\n\",\n      \"Iter 19200, Minibatch Loss= 0.228581, Training Accuracy= 0.92188\\n\",\n      \"Iter 20480, Minibatch Loss= 0.150356, Training Accuracy= 0.96094\\n\",\n      \"Iter 21760, Minibatch Loss= 0.415417, Training Accuracy= 0.86719\\n\",\n      \"Iter 23040, Minibatch Loss= 0.159742, Training Accuracy= 0.94531\\n\",\n      \"Iter 24320, Minibatch Loss= 0.333764, Training Accuracy= 0.89844\\n\",\n      \"Iter 25600, Minibatch Loss= 0.379070, Training Accuracy= 0.88281\\n\",\n      \"Iter 26880, Minibatch Loss= 0.241612, Training Accuracy= 0.91406\\n\",\n      \"Iter 28160, Minibatch Loss= 0.200397, Training Accuracy= 0.93750\\n\",\n      \"Iter 29440, Minibatch Loss= 0.197994, Training Accuracy= 0.93750\\n\",\n      \"Iter 30720, Minibatch Loss= 0.330214, Training Accuracy= 0.89062\\n\",\n      \"Iter 32000, Minibatch Loss= 0.174626, Training Accuracy= 0.92969\\n\",\n      \"Iter 33280, Minibatch Loss= 0.202369, Training Accuracy= 0.93750\\n\",\n      \"Iter 34560, Minibatch Loss= 0.240835, Training Accuracy= 0.94531\\n\",\n      \"Iter 35840, Minibatch Loss= 0.207867, Training Accuracy= 0.93750\\n\",\n      \"Iter 37120, Minibatch Loss= 0.313306, Training Accuracy= 0.90625\\n\",\n      \"Iter 38400, Minibatch Loss= 0.089850, Training Accuracy= 0.96875\\n\",\n      \"Iter 39680, Minibatch Loss= 0.184803, Training Accuracy= 0.92188\\n\",\n      \"Iter 40960, Minibatch Loss= 0.236523, Training Accuracy= 0.92969\\n\",\n      \"Iter 42240, Minibatch Loss= 0.174834, Training Accuracy= 0.94531\\n\",\n      \"Iter 43520, Minibatch Loss= 0.127905, Training Accuracy= 0.93750\\n\",\n      \"Iter 44800, Minibatch Loss= 0.120045, Training Accuracy= 0.96875\\n\",\n      \"Iter 46080, Minibatch Loss= 0.068337, Training Accuracy= 0.98438\\n\",\n      \"Iter 47360, Minibatch Loss= 0.141118, Training Accuracy= 0.95312\\n\",\n      \"Iter 48640, Minibatch Loss= 0.182404, Training Accuracy= 0.92188\\n\",\n      \"Iter 49920, Minibatch Loss= 0.176778, Training Accuracy= 0.93750\\n\",\n      \"Iter 51200, Minibatch Loss= 0.098927, Training Accuracy= 0.97656\\n\",\n      \"Iter 52480, Minibatch Loss= 0.158776, Training Accuracy= 0.96094\\n\",\n      \"Iter 53760, Minibatch Loss= 0.031863, Training Accuracy= 0.99219\\n\",\n      \"Iter 55040, Minibatch Loss= 0.101799, Training Accuracy= 0.96094\\n\",\n      \"Iter 56320, Minibatch Loss= 0.176387, Training Accuracy= 0.96094\\n\",\n      \"Iter 57600, Minibatch Loss= 0.096277, Training Accuracy= 0.96875\\n\",\n      \"Iter 58880, Minibatch Loss= 0.137416, Training Accuracy= 0.94531\\n\",\n      \"Iter 60160, Minibatch Loss= 0.062801, Training Accuracy= 0.97656\\n\",\n      \"Iter 61440, Minibatch Loss= 0.036346, Training Accuracy= 0.98438\\n\",\n      \"Iter 62720, Minibatch Loss= 0.153030, Training Accuracy= 0.92969\\n\",\n      \"Iter 64000, Minibatch Loss= 0.117716, Training Accuracy= 0.95312\\n\",\n      \"Iter 65280, Minibatch Loss= 0.048387, Training Accuracy= 0.99219\\n\",\n      \"Iter 66560, Minibatch Loss= 0.070802, Training Accuracy= 0.97656\\n\",\n      \"Iter 67840, Minibatch Loss= 0.221085, Training Accuracy= 0.96875\\n\",\n      \"Iter 69120, Minibatch Loss= 0.184049, Training Accuracy= 0.93750\\n\",\n      \"Iter 70400, Minibatch Loss= 0.094883, Training Accuracy= 0.95312\\n\",\n      \"Iter 71680, Minibatch Loss= 0.087278, Training Accuracy= 0.96875\\n\",\n      \"Iter 72960, Minibatch Loss= 0.153267, Training Accuracy= 0.95312\\n\",\n      \"Iter 74240, Minibatch Loss= 0.161794, Training Accuracy= 0.94531\\n\",\n      \"Iter 75520, Minibatch Loss= 0.103779, Training Accuracy= 0.96875\\n\",\n      \"Iter 76800, Minibatch Loss= 0.165586, Training Accuracy= 0.96094\\n\",\n      \"Iter 78080, Minibatch Loss= 0.137721, Training Accuracy= 0.95312\\n\",\n      \"Iter 79360, Minibatch Loss= 0.124014, Training Accuracy= 0.96094\\n\",\n      \"Iter 80640, Minibatch Loss= 0.051460, Training Accuracy= 0.99219\\n\",\n      \"Iter 81920, Minibatch Loss= 0.185836, Training Accuracy= 0.96094\\n\",\n      \"Iter 83200, Minibatch Loss= 0.147694, Training Accuracy= 0.94531\\n\",\n      \"Iter 84480, Minibatch Loss= 0.061550, Training Accuracy= 0.98438\\n\",\n      \"Iter 85760, Minibatch Loss= 0.093457, Training Accuracy= 0.96875\\n\",\n      \"Iter 87040, Minibatch Loss= 0.094497, Training Accuracy= 0.98438\\n\",\n      \"Iter 88320, Minibatch Loss= 0.093934, Training Accuracy= 0.96094\\n\",\n      \"Iter 89600, Minibatch Loss= 0.061550, Training Accuracy= 0.96875\\n\",\n      \"Iter 90880, Minibatch Loss= 0.082452, Training Accuracy= 0.97656\\n\",\n      \"Iter 92160, Minibatch Loss= 0.087423, Training Accuracy= 0.97656\\n\",\n      \"Iter 93440, Minibatch Loss= 0.032694, Training Accuracy= 0.99219\\n\",\n      \"Iter 94720, Minibatch Loss= 0.069597, Training Accuracy= 0.97656\\n\",\n      \"Iter 96000, Minibatch Loss= 0.193636, Training Accuracy= 0.96094\\n\",\n      \"Iter 97280, Minibatch Loss= 0.134405, Training Accuracy= 0.96094\\n\",\n      \"Iter 98560, Minibatch Loss= 0.072992, Training Accuracy= 0.96875\\n\",\n      \"Iter 99840, Minibatch Loss= 0.041049, Training Accuracy= 0.99219\\n\",\n      \"Optimization Finished!\\n\",\n      \"Testing Accuracy: 0.960938\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"    step = 1\\n\",\n    \"    # Keep training until reach max iterations\\n\",\n    \"    while step * batch_size < training_iters:\\n\",\n    \"        batch_x, batch_y = mnist.train.next_batch(batch_size)\\n\",\n    \"        # Reshape data to get 28 seq of 28 elements\\n\",\n    \"        batch_x = batch_x.reshape((batch_size, n_steps, n_input))\\n\",\n    \"        # Run optimization op (backprop)\\n\",\n    \"        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})\\n\",\n    \"        if step % display_step == 0:\\n\",\n    \"            # Calculate batch accuracy\\n\",\n    \"            acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})\\n\",\n    \"            # Calculate batch loss\\n\",\n    \"            loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})\\n\",\n    \"            print \\\"Iter \\\" + str(step*batch_size) + \\\", Minibatch Loss= \\\" + \\\\\\n\",\n    \"                  \\\"{:.6f}\\\".format(loss) + \\\", Training Accuracy= \\\" + \\\\\\n\",\n    \"                  \\\"{:.5f}\\\".format(acc)\\n\",\n    \"        step += 1\\n\",\n    \"    print \\\"Optimization Finished!\\\"\\n\",\n    \"\\n\",\n    \"    # Calculate accuracy for 128 mnist test images\\n\",\n    \"    test_len = 128\\n\",\n    \"    test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))\\n\",\n    \"    test_label = mnist.test.labels[:test_len]\\n\",\n    \"    print \\\"Testing Accuracy:\\\", \\\\\\n\",\n    \"        sess.run(accuracy, feed_dict={x: test_data, y: test_label})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "TensorFlow/notebooks/4_Utils/save_restore_model.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"'''\\n\",\n    \"Save and Restore a model using TensorFlow.\\n\",\n    \"This example is using the MNIST database of handwritten digits\\n\",\n    \"(http://yann.lecun.com/exdb/mnist/)\\n\",\n    \"\\n\",\n    \"Author: Aymeric Damien\\n\",\n    \"Project: https://github.com/aymericdamien/TensorFlow-Examples/\\n\",\n    \"'''\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting MNIST_data/train-images-idx3-ubyte.gz\\n\",\n      \"Extracting MNIST_data/train-labels-idx1-ubyte.gz\\n\",\n      \"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\\n\",\n      \"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Import MINST data\\n\",\n    \"from tensorflow.examples.tutorials.mnist import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"MNIST_data/\\\", one_hot=True)\\n\",\n    \"\\n\",\n    \"import tensorflow as tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Parameters\\n\",\n    \"learning_rate = 0.001\\n\",\n    \"batch_size = 100\\n\",\n    \"display_step = 1\\n\",\n    \"model_path = \\\"/tmp/model.ckpt\\\"\\n\",\n    \"\\n\",\n    \"# Network Parameters\\n\",\n    \"n_hidden_1 = 256 # 1st layer number of features\\n\",\n    \"n_hidden_2 = 256 # 2nd layer number of features\\n\",\n    \"n_input = 784 # MNIST data input (img shape: 28*28)\\n\",\n    \"n_classes = 10 # MNIST total classes (0-9 digits)\\n\",\n    \"\\n\",\n    \"# tf Graph input\\n\",\n    \"x = tf.placeholder(\\\"float\\\", [None, n_input])\\n\",\n    \"y = tf.placeholder(\\\"float\\\", [None, n_classes])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Create model\\n\",\n    \"def multilayer_perceptron(x, weights, biases):\\n\",\n    \"    # Hidden layer with RELU activation\\n\",\n    \"    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\\n\",\n    \"    layer_1 = tf.nn.relu(layer_1)\\n\",\n    \"    # Hidden layer with RELU activation\\n\",\n    \"    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\\n\",\n    \"    layer_2 = tf.nn.relu(layer_2)\\n\",\n    \"    # Output layer with linear activation\\n\",\n    \"    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\\n\",\n    \"    return out_layer\\n\",\n    \"\\n\",\n    \"# Store layers weight & bias\\n\",\n    \"weights = {\\n\",\n    \"    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\\n\",\n    \"    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\\n\",\n    \"    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\\n\",\n    \"}\\n\",\n    \"biases = {\\n\",\n    \"    'b1': tf.Variable(tf.random_normal([n_hidden_1])),\\n\",\n    \"    'b2': tf.Variable(tf.random_normal([n_hidden_2])),\\n\",\n    \"    'out': tf.Variable(tf.random_normal([n_classes]))\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"# Construct model\\n\",\n    \"pred = multilayer_perceptron(x, weights, biases)\\n\",\n    \"\\n\",\n    \"# Define loss and optimizer\\n\",\n    \"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\\n\",\n    \"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\\n\",\n    \"\\n\",\n    \"# Initializing the variables\\n\",\n    \"init = tf.global_variables_initializer()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# 'Saver' op to save and restore all the variables\\n\",\n    \"saver = tf.train.Saver()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Starting 1st session...\\n\",\n      \"Epoch: 0001 cost= 187.778896380\\n\",\n      \"Epoch: 0002 cost= 42.367902536\\n\",\n      \"Epoch: 0003 cost= 26.488964058\\n\",\n      \"First Optimization Finished!\\n\",\n      \"Accuracy: 0.9075\\n\",\n      \"Model saved in file: /tmp/model.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Running first session\\n\",\n    \"print \\\"Starting 1st session...\\\"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    # Initialize variables\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    # Training cycle\\n\",\n    \"    for epoch in range(3):\\n\",\n    \"        avg_cost = 0.\\n\",\n    \"        total_batch = int(mnist.train.num_examples/batch_size)\\n\",\n    \"        # Loop over all batches\\n\",\n    \"        for i in range(total_batch):\\n\",\n    \"            batch_x, batch_y = mnist.train.next_batch(batch_size)\\n\",\n    \"            # Run optimization op (backprop) and cost op (to get loss value)\\n\",\n    \"            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,\\n\",\n    \"                                                          y: batch_y})\\n\",\n    \"            # Compute average loss\\n\",\n    \"            avg_cost += c / total_batch\\n\",\n    \"        # Display logs per epoch step\\n\",\n    \"        if epoch % display_step == 0:\\n\",\n    \"            print \\\"Epoch:\\\", '%04d' % (epoch+1), \\\"cost=\\\", \\\\\\n\",\n    \"                \\\"{:.9f}\\\".format(avg_cost)\\n\",\n    \"    print \\\"First Optimization Finished!\\\"\\n\",\n    \"\\n\",\n    \"    # Test model\\n\",\n    \"    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\\n\",\n    \"    # Calculate accuracy\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \\\"float\\\"))\\n\",\n    \"    print \\\"Accuracy:\\\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})\\n\",\n    \"\\n\",\n    \"    # Save model weights to disk\\n\",\n    \"    save_path = saver.save(sess, model_path)\\n\",\n    \"    print \\\"Model saved in file: %s\\\" % save_path\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Starting 2nd session...\\n\",\n      \"Model restored from file: /tmp/model.ckpt\\n\",\n      \"Epoch: 0001 cost= 18.292712951\\n\",\n      \"Epoch: 0002 cost= 13.404136196\\n\",\n      \"Epoch: 0003 cost= 9.855191723\\n\",\n      \"Epoch: 0004 cost= 7.276933088\\n\",\n      \"Epoch: 0005 cost= 5.564581285\\n\",\n      \"Epoch: 0006 cost= 4.165259939\\n\",\n      \"Epoch: 0007 cost= 3.139393926\\n\",\n      \"Second Optimization Finished!\\n\",\n      \"Accuracy: 0.9385\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Running a new session\\n\",\n    \"print \\\"Starting 2nd session...\\\"\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    # Initialize variables\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    # Restore model weights from previously saved model\\n\",\n    \"    load_path = saver.restore(sess, model_path)\\n\",\n    \"    print \\\"Model restored from file: %s\\\" % save_path\\n\",\n    \"\\n\",\n    \"    # Resume training\\n\",\n    \"    for epoch in range(7):\\n\",\n    \"        avg_cost = 0.\\n\",\n    \"        total_batch = int(mnist.train.num_examples / batch_size)\\n\",\n    \"        # Loop over all batches\\n\",\n    \"        for i in range(total_batch):\\n\",\n    \"            batch_x, batch_y = mnist.train.next_batch(batch_size)\\n\",\n    \"            # Run optimization op (backprop) and cost op (to get loss value)\\n\",\n    \"            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,\\n\",\n    \"                                                          y: batch_y})\\n\",\n    \"            # Compute average loss\\n\",\n    \"            avg_cost += c / total_batch\\n\",\n    \"        # Display logs per epoch step\\n\",\n    \"        if epoch % display_step == 0:\\n\",\n    \"            print \\\"Epoch:\\\", '%04d' % (epoch + 1), \\\"cost=\\\", \\\\\\n\",\n    \"                \\\"{:.9f}\\\".format(avg_cost)\\n\",\n    \"    print \\\"Second Optimization Finished!\\\"\\n\",\n    \"\\n\",\n    \"    # Test model\\n\",\n    \"    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\\n\",\n    \"    # Calculate accuracy\\n\",\n    \"    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \\\"float\\\"))\\n\",\n    \"    print \\\"Accuracy:\\\", accuracy.eval(\\n\",\n    \"        {x: mnist.test.images, y: mnist.test.labels})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "TensorFlow/notebooks/4_Utils/tensorboard_basic.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"'''\\n\",\n    \"Graph and Loss visualization using Tensorboard.\\n\",\n    \"This example is using the MNIST database of handwritten digits\\n\",\n    \"(http://yann.lecun.com/exdb/mnist/)\\n\",\n    \"\\n\",\n    \"Author: Aymeric Damien\\n\",\n    \"Project: https://github.com/aymericdamien/TensorFlow-Examples/\\n\",\n    \"'''\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"# Import MINST data\\n\",\n    \"from tensorflow.examples.tutorials.mnist import input_data\\n\",\n    \"mnist = input_data.read_data_sets(\\\"MNIST_data/\\\", one_hot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Parameters\\n\",\n    \"learning_rate = 0.01\\n\",\n    \"training_epochs = 25\\n\",\n    \"batch_size = 100\\n\",\n    \"display_step = 1\\n\",\n    \"logs_path = '/tmp/tensorflow_logs/example'\\n\",\n    \"\\n\",\n    \"# tf Graph Input\\n\",\n    \"# mnist data image of shape 28*28=784\\n\",\n    \"x = tf.placeholder(tf.float32, [None, 784], name='InputData')\\n\",\n    \"# 0-9 digits recognition => 10 classes\\n\",\n    \"y = tf.placeholder(tf.float32, [None, 10], name='LabelData')\\n\",\n    \"\\n\",\n    \"# Set model weights\\n\",\n    \"W = tf.Variable(tf.zeros([784, 10]), name='Weights')\\n\",\n    \"b = tf.Variable(tf.zeros([10]), name='Bias')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Construct model and encapsulating all ops into scopes, making\\n\",\n    \"# Tensorboard's Graph visualization more convenient\\n\",\n    \"with tf.name_scope('Model'):\\n\",\n    \"    # Model\\n\",\n    \"    pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\\n\",\n    \"with tf.name_scope('Loss'):\\n\",\n    \"    # Minimize error using cross entropy\\n\",\n    \"    cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))\\n\",\n    \"with tf.name_scope('SGD'):\\n\",\n    \"    # Gradient Descent\\n\",\n    \"    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\\n\",\n    \"with tf.name_scope('Accuracy'):\\n\",\n    \"    # Accuracy\\n\",\n    \"    acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\\n\",\n    \"    acc = tf.reduce_mean(tf.cast(acc, tf.float32))\\n\",\n    \"\\n\",\n    \"# Initializing the variables\\n\",\n    \"init = tf.global_variables_initializer()\\n\",\n    \"\\n\",\n    \"# Create a summary to monitor cost tensor\\n\",\n    \"tf.summary.scalar(\\\"loss\\\", cost)\\n\",\n    \"# Create a summary to monitor accuracy tensor\\n\",\n    \"tf.summary.scalar(\\\"accuracy\\\", acc)\\n\",\n    \"# Merge all summaries into a single op\\n\",\n    \"merged_summary_op = tf.summary.merge_all()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 0001 cost= 1.182138961\\n\",\n      \"Epoch: 0002 cost= 0.664609327\\n\",\n      \"Epoch: 0003 cost= 0.552565036\\n\",\n      \"Epoch: 0004 cost= 0.498541865\\n\",\n      \"Epoch: 0005 cost= 0.465393374\\n\",\n      \"Epoch: 0006 cost= 0.442491178\\n\",\n      \"Epoch: 0007 cost= 0.425474149\\n\",\n      \"Epoch: 0008 cost= 0.412152022\\n\",\n      \"Epoch: 0009 cost= 0.401320939\\n\",\n      \"Epoch: 0010 cost= 0.392305281\\n\",\n      \"Epoch: 0011 cost= 0.384732356\\n\",\n      \"Epoch: 0012 cost= 0.378109478\\n\",\n      \"Epoch: 0013 cost= 0.372409370\\n\",\n      \"Epoch: 0014 cost= 0.367236996\\n\",\n      \"Epoch: 0015 cost= 0.362727492\\n\",\n      \"Epoch: 0016 cost= 0.358627345\\n\",\n      \"Epoch: 0017 cost= 0.354815522\\n\",\n      \"Epoch: 0018 cost= 0.351413656\\n\",\n      \"Epoch: 0019 cost= 0.348314827\\n\",\n      \"Epoch: 0020 cost= 0.345429416\\n\",\n      \"Epoch: 0021 cost= 0.342749324\\n\",\n      \"Epoch: 0022 cost= 0.340224642\\n\",\n      \"Epoch: 0023 cost= 0.337897302\\n\",\n      \"Epoch: 0024 cost= 0.335720168\\n\",\n      \"Epoch: 0025 cost= 0.333691911\\n\",\n      \"Optimization Finished!\\n\",\n      \"Accuracy: 0.9143\\n\",\n      \"Run the command line:\\n\",\n      \"--> tensorboard --logdir=/tmp/tensorflow_logs \\n\",\n      \"Then open http://0.0.0.0:6006/ into your web browser\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Launch the graph\\n\",\n    \"with tf.Session() as sess:\\n\",\n    \"    sess.run(init)\\n\",\n    \"\\n\",\n    \"    # op to write logs to Tensorboard\\n\",\n    \"    summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())\\n\",\n    \"\\n\",\n    \"    # Training cycle\\n\",\n    \"    for epoch in range(training_epochs):\\n\",\n    \"        avg_cost = 0.\\n\",\n    \"        total_batch = int(mnist.train.num_examples/batch_size)\\n\",\n    \"        # Loop over all batches\\n\",\n    \"        for i in range(total_batch):\\n\",\n    \"            batch_xs, batch_ys = mnist.train.next_batch(batch_size)\\n\",\n    \"            # Run optimization op (backprop), cost op (to get loss value)\\n\",\n    \"            # and summary nodes\\n\",\n    \"            _, c, summary = sess.run([optimizer, cost, merged_summary_op],\\n\",\n    \"                                     feed_dict={x: batch_xs, y: batch_ys})\\n\",\n    \"            # Write logs at every iteration\\n\",\n    \"            summary_writer.add_summary(summary, epoch * total_batch + i)\\n\",\n    \"            # Compute average loss\\n\",\n    \"            avg_cost += c / total_batch\\n\",\n    \"        # Display logs per epoch step\\n\",\n    \"        if (epoch+1) % display_step == 0:\\n\",\n    \"            print \\\"Epoch:\\\", '%04d' % (epoch+1), \\\"cost=\\\", \\\"{:.9f}\\\".format(avg_cost)\\n\",\n    \"\\n\",\n    \"    print \\\"Optimization Finished!\\\"\\n\",\n    \"\\n\",\n    \"    # Test model\\n\",\n    \"    # Calculate accuracy\\n\",\n    \"    print \\\"Accuracy:\\\", acc.eval({x: mnist.test.images, y: mnist.test.labels})\\n\",\n    \"\\n\",\n    \"    print \\\"Run the command line:\\\\n\\\" \\\\\\n\",\n    \"          \\\"--> tensorboard --logdir=/tmp/tensorflow_logs \\\" \\\\\\n\",\n    \"          \\\"\\\\nThen open http://0.0.0.0:6006/ into your web browser\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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v9xpwlT3Q8tmN3mGG339KRr0pXT+Ua54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\\nX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pw3c67uyzz3ZBOH+OPjt16uRWU1Wg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\\n2viX37CbbrrDzu1zeuiXHs6NeWytfO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5naTPHvUoLsFCxaY/tRu4MCBkpaWZoIVNQhOAwLPP//8LPf78ssv5e23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58M6FRo4dLkyaNTJa1JydPky1btsn11w2R995/XeLiquY46WD7++TjBU7w3T//+X9y\\naa+/mnvNm/eJzHv/E7n33v+YLHgXdenkjGH79p1y3bW3meca2HWrFWxXsmRJWblilUyd+j95ccZM\\nOZp5VDRAJtTLoUMZMvKOe03QnR4X0xRcAZaCXJMjRzKt18ttkpiYbGW+i5YJD90nlSpVlKSkFJn4\\n0BOycuVqueH6IfL23FeczHCaIU9LbuYU4NTzvZrOd0D/W8x8NdjuzruGmu+fTCuD5Ny358mCBV/J\\niOFjrNfjo9LpLx1yHA9rlSNRnlVQ61denmX6058b/spzz71kfpbo9YG3XCddu3aWjIwM0SA6/bk5\\n5annJNN6Hei1QEowP+uK2vdVII7UQQABBBBAAAEEEEAAAQQQQAABBBBAIJwFCMALo9WtWrWqtGrV\\nSsqWLSv6G/i6zdG2bdusLYQ2ZZml1tUPG/R/YOemnDhxwvqgItE0yU0f+sHM7t27nVvVqFHDOfY+\\nKFOmjPmf4+763nV4HpoC+rr0VfR1pVlOctrqyV97X31qf+4PBI8ePWoyHOR0D199eZ/TceiY9XvI\\nfQ93Pft+7nOBHLvHp/c5efKkuY/d1jbQ+waaGcYei7uNjj833//ucdlj4bFwC+i6a9Gt7vy9Tt2v\\nA39rbNfxfi36mr39WvNkSSK+AABAAElEQVTXl7tNMK9lbR9sO/e9OUYAAQRCXWDFil9MEIm+F5k9\\ne4bUqVvbmdKbc16SceMeEQ0YmfTIU1bmsodyfM8QTH+aQWzChCfMfR997AH5618vdsbQsuV5ct55\\nTU3w1tixE+XjT+aYLR/175Lx1ti0/LNfXxltBQ7af0dpm/Yd2ppscTNnzpFLLu1ugqKcTkPsQN9n\\nPfvsC6IZsLRoYFtuSkGtiY5p1htvm2C0mtb2xXPmvOixdXDHju3kyiuuk507d8vcuR/Iv/71j9xM\\no1DW/ezThWa+8fFxMvvNF63XZhlnnG3btpLWbVrKo5OmyOOPT5W5F5xfaL5/dJBFba10zrq1tf6/\\nDd3e9zXrZ8PChd/oab9FA0k1kFfL9OefknbtWjt121hr27Fje7l3zIPy4ouvyxVX9skxK2UwP+v0\\nhkVxrRxoDhBAAAEEEEAAAQQQQAABBBBAAAEEECiCAgTghcGiazDblVdeKfHx8c4HWO5pHTx4UJYs\\nWSKff/65Oa2/KT5kyBApXbq0u1pAx8eOHZOnnnrK+p/gB3Pdx8qVK60PCGfLueeeKzfccEOO271o\\nIMeWLVtMG81GQAltAQ0GffXVV7OdhH4wef7558tFF11kgobclQNp765fvnx5GTFihHmdffbZZ7J4\\n8WJz+YorrpDmzZu7q+bqeOfOnfLSSy+ZNrGxsTJ06NAsr2X3/XLTuX4ArWOuUKGCCZ61vVq2bCl9\\n+/aV7du3yyuvvGK61OC7MWPGZHHyvt+aNWusD0tPb02nH9Lfc889ZrzPP/+8JCcne1f3+/yqq66S\\nZs2a+b3OhcIlMG/ePCtbzekt1f7+979L69Z/fvBoj1QD2R577DHRn7X62hg9erTPINgvvvjC+f7p\\n0qWLlUGkq92F86hBBjNmzJA9e/aYc/3797e2PWzkXPc+0J/pjz/+uAmmK1eunNxxxx1Zvo+82+jz\\nYNv56otzCCCAQKgK6M9czdKlZdSo2z2C7/Scvp+4y8ro9cXnX8uiRT9KQkKS1KwZr5d8lmD7++br\\nH8zfIZ07d5Ru3S7K0vf//d9lVha8j2XNmvXWv0WWmQC9bdt2mOca8DR8+K1Z/u3StGlj673VQJk2\\nbYZpq1n9QrXonN+a8540aFDXBLRt2vRrwFMpyDXRwKY5c941Y3v00Qc8gu/0ZNmyMfLIpPFyy8AR\\n8r/nX7H+3dknx4C0gCd6lived9+dHsF39nD69LnUzLVy5dgcf2GFtbLV8udRfW+5ZYRszsX3z1Lr\\ne09L796XeATf2SPs0eNieXP2O/LLL2tku7WdbU7bQgfzs64of1/ZzjwigAACCCCAAAIIIIAAAggg\\ngAACCCBQ1AQiitqEw22+Gqx0++23iwbh2dkjvOeoGfF69OghGhBhl0CyE9l13Y8REREBBUm429jH\\nGuCRm6L1mzZtKuPHj5f27dvnpil1Q1Tg8OHD8t1338nDDz9sAtDOZBral35g4118nfOuk93zRYsW\\nOZf37dtnZTVJdZ7bB3aGLvt5bh59jc/ur3r16k7AnWYl85Xd0n0v7WvZstMfQOn5unXrBv2BKUGw\\nbtnCf1y5cmVnkBrI7Kvs3bvXBE7oNQ2u1tezd9HX0I4dO5zT1apVc47dB2lpaaL92UWDvgMt+loO\\npgTbLph70QYBBBAoTAL6d7JmR9P3yl27dfY5tAoVyks/K8Oc/qzcsmWrzzr2yWD6078fliz5yXRx\\n/fVX+/z3gf674ZZbrzd1lv10Oih8zer15nnfvr39ZoS77G89TZ3FVhCN/R7InAihPzRj1ph7HjDv\\nu56Y/JDUt4LwclMKck327EkwWfo0+LFhw3o+h9miRTPR7Hjp6YdM5jiflVwnM3UL4RH3Sts2F0uX\\ni/5m2rkuh8BhMTPG5JRUn/+ecE+AtXJr5P2x/j+OiRP/LRMfHmv98sZ/ZMqUh6Vy5UrZ3ujkqZPm\\n+qVWFk1fRfts176Nr0tZzgX7s47vqyyUnEAAAQQQQAABBBBAAAEEEEAAAQQQQCDsBciAF8JLrFu1\\nXn755R4feCUlJcnGjRvNFq7169cX/dIPv7RoFi29vnDhQlMnJibGbG/pJjjnnHOcDEi61Wx6eroT\\n2Kf96G9y//bbb+4m5jghIcHcM8uFP05o23Xr1mW5rP9DW7N66XZUdtHtEqtUqeJ8KKfBgprBSevl\\nJmOX3R+PhU9Ag0I7duzorLt+OKyvWzuAR18XM2fOlDvv1KwUMVkm4N3eu4L2p1m1At2i1bu9v+f6\\nAZs7mEnHuXTpUvN96G6jGfY0w6S+lu2i3wO//PKL+R7Uc7Vr15bGjRt7fKin484uM6Vmr9QMkqtX\\nrzbdLl++PNusdBqEuGvXLnsI0qFDB+fYfdC2bVsr80NF9ymPY/3wu0GDBh7neFK4BTTY0i66nbf+\\njLX/LrDPr19/OghCn+trWb8HdWtxd9G1T0lJMae0vb5ufRV9LWofdtGf1/r3h6/vX7sOjwgggAAC\\nwQm4AzvKlSvrt5Omzc4117Zs3ioXX3yh33rB9HfBBe2cIED39rfeN6llBW1pWbt2gwkG1MBBLX/5\\ni+/3JHotNraixMVVleSkFLP1ZE7ZqbRNYSr69+HDE580QWejrC12Nfvg4YzDuRpiQa7JhvWbzdg6\\ndmrv972zvqdu06aFfLBrj6Qkp1q//FXd73x0y90B/QeaoD7NdPj23Fd8Zpnz20EBXChX/vT3jWZa\\n1EAs719O+9La4nT//gPWv6Vz/uUV1ir/F0zXQb/scveo4SbA1X7uftTvv8WLTgcHR5f2ve2zed+7\\n4fTr3t3W17EGk9oBz7n5WVcUv698+XEOAQQQQAABBBBAAAEEEEAAAQQQQACBoiRAAF4Ir7ZuA6hb\\ndmrR/4msmcM++ugjZ0Zffvml6DacGsSk9cxverdrZwLwNLjJu2hwz7333ms+gND+dNvBVatWeVcz\\nz7WuXbSuBvX5q2vX8/WoWZdmzZrlM6hvwIAB0qJFCzNuDWTq3bu3s/Wnr744FzoC9erVk06dOnkM\\nWLe21Exbuu2qBgvpl2ab69nzdBYUd2Vf7d3X8+tYt3P1zsSiwXC9evXy+OBOg5R8BSppAKsGwWrR\\nYLhgtnRtZ30P2wF4GuSkQXb2zwHveWuAlQb1adGAXQ2w9S4aVNW9e/dsA/+82/C88AtoELNmRtLt\\nZXXL8IyMDI9gOP257Z1BUV8vF154oRN0rbPU4Dv9Oa1FA199BYjq9+qKFStMHfsPPadb4Hbu7Dsz\\nk12PRwQQKBiBes+le9xo6+Cswe0eFXL5JL/7z+Vwikz1tm1b+Q2YUgTd+lRLamqa+beCv2zZppL1\\nR276s/71Yb3HOClVq1UxW5TafXg/VqtW1WSr+v1gunlPcvjwEfP3U/X4OO+qznMNhtKMawsXfiuH\\nDmXkuD2k07CQHMyf/4U19m+sgLWWctVVfz+jUeX3mujf1/Z72/OaNfE7Vn3ttGrZXD6YN1/SfGTM\\ntRtu3bpdbrxhqAk+bN26uTwz9THrvYPvICi7zdl4bN++rQnyXL9+k1x37W3WVs7DpW69c+TA/t9k\\nwYIv5Xlrq10tw4bdkuUXGMwFH3+wVj5Q8ulUhvVzwV/R1+pjjz9ova5PWP8+i/RZTbeD/u67xeZn\\nUXZBdacb5/5nXVH9vvKJzUkEEEAAAQQQQAABBBBAAAEEEEAAAQSKkABb0IbwYuuHJRpEoWX//v0e\\nwXf2tDRbnQbm2UUDKLwzHNnXvB81YCfQkpu67j71f5C7s4S5r73xxhseGe/cWyq663EcegL2B33e\\nI9cAMXfAnR2s5l3PX3vvenn5XL/X3Ntq2kFvGuCkmcMCKXYgk9a1A+MCaeeuU7NmTSeQSj/c2bzZ\\nd/YGHa87KEozYPrKCKj1dA6U8BLQ4IX4+HgzKX2d2Nkl7VlqUJ7+vWEXfW1o1lPN8uguO3fudJ5q\\nVj3vLHp6cevWrU47O9hbz+v2x3pvCgIIIIBA/ghUquQ/e63e0f53gmafC+TncW76O3bsuJlU1SqV\\nPQK3s8709L9VNJudbl8aGXn6n5+aGS2nou+VNNtaKJW9exNl7L8nmsCeCQ/d7/O9V27mUxBrYo/H\\nzgpnP/f3uG3bn+8NtI79Olu2bKVcdeUNZp379Oklz/9vSqEMvtMxlylTWma/+aIeWu/jt8jAgcOl\\ne7e/yxVXXOcE3/177N3SvEVTUyeQP1irQJQKro6/4LsdO3bJrbeMMAO59rp+OQb4aqCxltz+rDON\\nrD+K0veVPWceEUAAAQQQQAABBBBAAAEEEEAAAQQQKKoCBOCF8MqXKlXK+cDLe9sc97Q0y5Fm3tLg\\nH82W5R1g4a5b2I5//fXXwjYkxpPPAnFxf2ZE0YCgYAPV8nqYaWlpVgaZ0x8CaxBr//79nVv8+OOP\\nznF+H2gAVJs2bZzb6NafvooGWOnW0Fo00NXdxld9zoWXgK65exta75+l27Zt8wjG0O8zDc5wB9yp\\niNazS8OGDe1Dj0d3YGq/fv2cTIsaAG6/Bj0a8AQBBBBAIE8EAg2g138zBFJy01/JkiXMNqT6b4xA\\ni/7dFM5F/x69d8yDZorjH7jHZFg70/kW5JqcOnk6WDKnMbtXUQP4S5UqaWWN+0oG3TrSNB1xx20y\\nbvzoMw4+zGkcZ3L9oJWR8bZBp8er/Vx44QVy/Q1XW+/vrzTBk3ruoQlPyGefLdTDgAprFRDTWa20\\nePFP8o++15ogUc1QeeutN+Q4njP9WVeUvq9yxKQCAggggAACCCCAAAIIIIAAAggggAACYS5AAF4I\\nL7A7C1i5cuVk8ODBPmejARXjxo2T+++/Xx566CGf2736bFgITtoZFQrBUBhCAQm411wzKxaWD2s1\\nyM4eW+vWra0PnWuIvRXzrl275MCBAwUkJCaYznbRe6ene24tqANZt26dE2Cl25FWqlSpwMbHjQqH\\ngDsATwPp7Nevjk5fH1o0oPP//u//nMx27myOGkiwZ88ep56+5r2Lbm1rB+nplreaoVG3DrdLQQan\\n2vfkEQEEECgqAvpzN5ASaJBcbvrLzDxq/R2RYAVfBRbcp+N0/z3kKytvIHMpzHVmzZora9aslx49\\nLrYyOnfNk6EW5Jpk9wtd7sm4w/T0l7v+8+BjTuCh1qtzTu1C8/7dPW77WF+HD0+cbDLfNWnSyAqy\\ne0eefmaSDB8+SO66e5gsWvyZjBhxm6l+370TZPt2z4x/dj/ej6yVt0jhea4/ryY/8awMGzrKDKp7\\n9y7y3PQn/W5R6x75mf6sKyrfV24zjhFAAAEEEEAAAQQQQAABBBD4f/buBM6u+e4f+C+bJJLIRhZp\\niDVIrLFTa+21VbVVulGP8lS1pa2u6EY35fGvtbZqtSgt2tBaqi1FqRJbqCVIiSAhkYgs/Od79Hd7\\n5+bOTDKZO3Nn8v49r9uzb+9zbnI955PvjwABAgSWVwEBvE5852+66aYU4YfcImzxne98Jx122GFp\\ntdVWy7PbZThw4MAidBHdXFZ+ysMY1U6mWpeGeb311lsvjxouBwLxQmzixImlKx05cmQpGFSa2TAS\\nL5IjgBrPf7VPeTi1fLvWjkcQ6f777y82j+d1ww03LM4rV5WL5eXdvbb2OEu6XXzfIlQXLY6dw1R5\\n+3Asr4y3+eabV3XM68+ZM6eqY7bN6xl2LoHhw4eXuviOCo65+ml8P3JoLrqMjec5vzSOLo3jmYo2\\na9as0t8x/fv3TxH0rmyTJk0qVancYIMNGl5k9kzjxo0rhrHuQw89VFRfrdzONAECBAgsu8DTTz/T\\nKNTW1B633maLJapGtjT7y7/fn3zy6Wb/nI/uG+Oz1lpjGv4eGVCMR9XVV16e0dTpFvNjm/i7adVR\\nI5tdr14WRjemPz797OKcTzjh2OJ318KFixp+ry4qTvHl/1xvni4PIzZ3DbW+JwMGDCgdfuq/ny+N\\nNzcyflzj/z6L6nfR8m+JL33p5DR16pLtq7nj1GrZiw3dIcc5x2+gM848La28ytBGh4p/5BJdkx58\\n8P7F/Jtvuq3R8qYm3KumZDp2fnw3d9/twBQB2WinfPPL6XvfP3mJwnflZ740f9Ytj9+rcivjBAgQ\\nIECAAAECBAgQIECAAAECBJZXgZ7L64V3heuOMMU111yTPvCBD5ReePTq1asIwkXoLUJKU6ZMSTff\\nfPNi3Qq25fXHS4pddtmlyV3GC6aoZHbnnXcutk4sy6GQ8oVrrbVW2n///dPQof99IRKVvrSuIRAB\\noLj35d3LTp06NV133XVpxoz/vpDdcsstq15wdKcZ1RybahEAOvjgg5tavNTzoxvn3K3U6NGjU4SR\\nok2YMCFF95txLRF423HHHZfoBfdSn0DFBvGd22qrrdL1119fLIlw4BZbbFGqNhJdf+bucnNgsGIX\\npck49wsuuKA0XTkSxzruuOPSoEGDKheZrnOBeBEe3SVHFbv4rsUzERXqpk+fXnqeo1vZqEK09tpr\\nF2G56Lo4voMrr7xyw8vzqaUwXjz3OWyRLzuenfIKd/EMRovKkGPGjElPPPFEcdxHH320UVW8vL0h\\nAQIECLROIFedu//+B4tAW8+eParuaPKj/yrm926hUl5r9rfCCr0a/rxfoeHvjFfT67PnNFkJb/r0\\nl9LMma+mkSOHF79Thg4dXPzd8MIL09KYNar/g6GoOPXAAw8W5x7H6Qxt0qSHi9OM34t77HFQk6e8\\nx+7vK5Z96ujD05FHfrTJ9drvnqTUd8W+xXk89dSUJs8n/s6/6657iuX53PLKEWT7xeXnp9GjR6VP\\nfPx/iyqAx376i+mqX1+61CGnvM9aDmfPml3sfvvtt2qoEF399238/t19j13SVVdd2+KpZI+l+T62\\n7vuz/N2rFvFbWOGaa36XvvPtHxZrbbrphunU005q+EdMK7ewVePF7lVjD1MECBAgQIAAAQIECBAg\\nQIAAAQIECDQtIIDXtE2nWBLVh6LLwCOOOCKtvvrqpQBOnHy8DIgKcmPHji0CeD/96U+rht3a40Lj\\nJUa1FgGR448/frFF+UVGXhDdG5VXRsvzDTunQDyzp5xySrMn/573vKfo5rXZlZpYuGDBgiaWtG52\\nhOxyi2pyuUVANIJKL730UooqchFYiu9he7Tx48enG264oagEOG3atKJr6RySiz8X4kVptKiMGS9G\\nl6XlfS3LPmzb/gLx5+6aa65Z6kY2qt5FAG/y5Mmlk4nqd9HieYpqdXGvn3rqqeK5fuaZZ0rrxd8j\\nlS2CfDkwG9XxomJlbltvvXURwIvp+P7EcZr6eyBvY0iAQPsJrHnO4l2Xx9GfOvqdgHlTZ9LUdk2t\\nb35tBIYNWzmNGDEsTZs2vSFc/UoxXnmk+PP83n/8s5g9rqJiWeW6rdlfVDzdcqsJ6bprb2ioxPtY\\n2mHHbSt3W0w/+ujjxTBX4dtwo3Hpyit/m+5sCHNts231f2jx4otxXTNSdA9aXkmq6gHqZObKQ4ek\\nCPgMaKjyV61FGDLCiMOGrZJWHzO64e/ZIdVWK81rz3syduzaxXGj0ttRR32iamguQpH33fdA8Y++\\nImiXW/y33K+vvrT0DH7v+6ekAw84rOG/PaemSy7+RfpkMyHDvI+OHDb32yT+QcGSNPdqSZTaf50r\\nr/hN+t73ziwOfPIpJ6b3vnePVv0Wbe2fdcvz96r977YjEiBAgAABAgQIECBAgAABAgQIEKgPAV3Q\\n1sd9WKaziApyZ599djr//POLYEVUMCoPzMSLhQgFffGLX0zRdWUt2rPPPlt0gxmVjio/EfaICmJN\\ntQjbVX7K142Ax09+8pMiYFQ+33jXFIgqW+9///vT9ttv3+QFDhkypKiQuM8++6TKz5577llUomty\\n46VcEN1wxvMdLV4yrr/++qU9xHcrgka53X333Xm05sN4KRiVIqNFl6HxvYsW3/3cXW5MR6W8llpY\\n77vvvotZhu3ee+9dsz83Wjovy5ddIAJ4uUUAL56P/KxE5bvcXXkMYzpa/Jkd601pqKAaLZ7zqIBX\\n2cqDqdtss02jCnlx3Fwp8oUXXkivvvpq5eamCRAgQKCVAjkQEtXWrrnm+qp7ef75aUU4Ln67rLPO\\nO78X8opz5sxt+IcDc/Nk0W14hOmWZn/xd8O2277zG+OCCy4tdbVa2mnDSPyDiJ/8v3eq7G626UbF\\noo03Hl8Mr7ry2vTyS6+Ur16Mx98/v/rl1cX4ttttVTUMtthGdTBj513enX564Vnpxz/+7mKf00//\\nTtpkk/HF78grr7o4nXvu6enAA9/b6Kw78p6suuqIIkAXobl7/v6PRueVJ/761zuLUGQEPwcNblw1\\nrk+f/wbVYvl3T/16sdk551yU/nnfpLyLuhn269+vOJc777yn+Ac0TZ3YX/7yt6qL3KuqLHU181+P\\nP1kK351xxncb/jtnzyUO38Wfg3GPo2J7tNb+Wbe8fa/q6gFwMgQIECBAgAABAgQIECBAgAABAgQ6\\nSEAAr4Pga3HY6JbzoosuSt/61rcaXvz8OEUVrPz/OI7jRQWJj3/8421+6HhR9pe//CVdcskl6eKL\\nL676yd1hVjt4VA6L0GD+zJ07N73++uspupyNLjZPO+20osvEatua1zkF4lncfffdU1S5i8+Yhu4q\\ncxs8eHDaYIMN8mTVYYSBNt1006Lb1ej2svwTgbhRo/5bmaPqDpZi5oMPPljqhjO68YzqfRFwi08s\\ni2c1twguRdfP7dVyl59xvHvvvbc4z1deeaWhq7eZxSnES/fyAFa184rA47bbblt0p1vuWD5e2fVo\\ntf2YV58Cq666ahGsiLN78cUXiz9nc9W6qIYX3ZZHiyqJucvvqOQYVUfzc7TiiiumqHBX3iJUERXz\\ncougakzn70Z8F+IFZrT4O+Kee97pti6vb0iAAAECrReIQMiHD3l/sYMLf3pZiiBReZs79430pS+e\\nVMzaf/+90tCyamvX/nZi2uHde6fDDv2foivYWKm1+9u2oYLd4IYwVlTAu/SSy4s/7/N5xJ/9Z511\\nflGlb+DAldLmW2xaLIpQyq677lj8HfGtb/2goTr3O39X5O1u+9PtRYW8CIXvt99eeXaXGZb/t1m+\\nqI6+J/FbIFeq+8IXTkoR3ixvzzzzXPrG179bzPrfT3+yxVDkTjttnz7wgQOK9Y899ksNIfzXynfX\\n4eMREhw/fv2G3/Bz0le/8u0U35fK9teG8N1PL/hZMXvHhuvJzb3KEvU7jD97Lrro58UJfvWrx6d3\\n77DtEp9s/Hfc3nt9oPgzctJ/upWOjVvzZ93y9r1aYmQrEiBAgAABAgQIECBAgAABAgQIEOjCArqg\\n7aI3N7qk/PnPf56iStZxxx1XdCcYl7rKKqsU480F4lpD0q/fO5UElnbbCGicccYZqtstLVwnXz9C\\nYRH6yi3CXj/4wQ+KwGgEyCJMtuWW1bsli22qvbzM+2rLYWVwKAJ4V111VZOHiEp0jzzySNpss82a\\nXKctF4RjfMejCmaEquKlUYQC47yjRbefUSWnuRbrxvcwQlZa1xOIEGaEWqOb5AjVxXcrnuNo48aN\\nK11whC9iOrqVjXDd3//+99J6EdTL1fHyBhFELe/q+c4778yLqg4feOCBhsDFrovtp+rKZhIgQIBA\\niwLrrLtW+t///WRDleifpk//7xeK7hWjCtvUqc83VJ37afF3e/+GSl+fPvbIqpWfVl55aKNjtGZ/\\n/fqtmL73/ZPT/xz52YZq3Bemm26+LX3sY4cUf39cfNEvGiqpvlNB+OxzflQKfMffN1/56ufT3Xff\\nm26//a60047vTZ/7/DEpunC98cZb0i23/Lk4rxNP/GxDt+mrNjrHzjyxaNFbLZ5+R92TOLH99987\\nXX/djemBBx5K+773Q+nIhq5jo+vie++9v+G/Ka8szn233XZKO+/87tJ1NHVNcY+P++zRDf9A629F\\nAPPkk05LPzr923XzGyD+Ycl3T/1G2m/fQ0rP4GEf+UBxva+9Njtdd93E9OCkR4rrPProwxsqSP63\\nmnC+ePcqS3TMcH7Db9WmWgQq77nnne63v/OdH6ULGoKUr7wyo+rq8Zv4wovOaqhQueFiy7ulbqV5\\nrfmzLjZenr5XJSwjBAgQIECAAAECBAgQIECAAAECBJZjgeaTGcsxTL1fenQle/zxxxddGcVLjiuu\\nuCLdd999i512BHOuu+66ovJdvGyIdeupxflEgEhbvgQqA3TxDOywww7p1ltvLSBuvvnmIsTWUnis\\n1mqt6TozuuWM6nzt8V2L7/TGG29chKUiSBfhqocffrhgieMvSfeztTa0/44ViOdg7bXXLgJ4ERCN\\naqXRYv4666zT6ORi+k9/+lMR4LzttttKy8aOHVsaj5H8rDWa2cJEVDqdMmVKqdvkFla3mACBNhZ4\\n6uj+bbLHttpPm5yMnaRPHH5oWmXYyum73zk9/e53fyg+mWX33XdOX/jiZ0rdgef5zf22as3+JkzY\\npAiwnHD811N0+/i1r347HypFqO+kb3wxrbde479vBg0amK648uIU4Zi/3XF3+t5pZ5S2ieD4Kaec\\nmHbfY5fSvM4+En/njlljtSJ02KvX4v/5XQ/3JH5TntPQNe65516Ufnbpr4rQUrl7BPKO/J+PNQrR\\nRTXDplp0S/t/Z30vfeDgT6TovvaRhx9LG27UfIXrpvZVi/mjRo1Mf7zpmvT/GrpIvu7aGxoqOP6y\\n0WGGDVslfe3rJ6TtGrpBLm/uVblGx42vuebqxcHjz4vKFveod+//zp8+/aXKVRpN9+jeo2y6W8Mz\\n3v2d6YbvbXlrzZ91y9v3qtzLOAECBAgQIECAAAECBAgQIECAAIHlUWDxNwDLo0InvOYcYIr/p260\\nqFBULYDXGS4tQiEage23376hCsXtRcWWqMgWQaDddtutQ2HKq3pFVbvoLjd/98pPLM733HPPLZZF\\npbGo4rfyyiuXr1Kz8c0337wI4MUBcoAxxocMGdJu5xDH0+pXYK211mronvDORicYz0cEucvbiBEj\\nikB0POO5Sl6EBlZbbbXy1YqKpc8++2wxL/4OOvLII1O1KqjxAjQCfxFKjRZV9eJcNAIECBBoG4H4\\nM3rfffdMe+21W3rhhWlFpdPo9jC6hY2QW7W2z3t3T/Gp1lqzv9hPVI+6+ZbfpmkvvJhmzZ7dUDeq\\nW1qpodvZ4cNXqXaYYl50A3pWQ0Br5sxX08svvZLebvi/6A49glH5v2+a3LgTLjjmmCNSfKq1erkn\\nEVo67rhPNfy9/rGGbuvfqYgbz9PIkcNTnz59Gp16PCuf/dzRxafRgrKJtdZaI/3jvtvK5tTX6NCG\\nqosnnfSldOKJn2uoAPxSmvfGvNSzISAZXSYPGTK46sm6V1VZ2n1mhOGaerbiOf79xHeqNi7tiUVw\\n9A9/vKbJzVrzZ93y9r1qEs8CAgQIECBAgAABAgQIECBAgAABAsuBgABeJ73JUU1odsMLrvwyJLrz\\njNDPHXfcsdgV7bPPPo1eZNXipVacj0ZgWQTiudxjjz3S9ddfX+wmAkPbbLPNYpVbYmFz1SdaOofK\\nrjSbWj+qR06ePLlYHC8Z4zvWXDet6623XnrooYeK6mD33HNPw8vwvZradZvOj26lhw4dWnz/y3cc\\nwbwl+a7HtVWrHlG+L+OdW2DVVVctKtbkUF1cTVS7q3w+YnqNNdYoPfexXrx4HzRoUIyWWnQnm4PT\\nsf7IkSNLyypHtt5664aKP3cX34t//etfRTgkAhblrbXf59ZuV35s4wQIEOgKAj179kijR49qs0tp\\n7f5GNAS14rM0LcKC8dGaF2jPe7Liin0bfg+8U2Gs+bPqGksjINVZvz/L272qlyeuNX/WuVf1cvec\\nBwECBAgQIECAAAECBAgQIECAAIHaCQjg1c625nuOCmHvf//7ixBFBCf233//hm5ytkvPPPNMEc6L\\nakYRjijv4vXpp59u+Bf+09v03CLAs+eee6addtqpyW434/zuv//+oqpZmx7czrqUQHTdGl1fRrg0\\nAj433HBDOvjggxe7xscffzzddNNNVavR5ZX79++foqpePJ/lLUKqzz//fJPbxvo777xzEUJasGBB\\nsWlUC4tPc23LLbcsAnixzj//+c+iel97BITifLfYYot04403lk4vvm8bbrhhabq5kXCeOHFiEXSM\\nrkWrtaiIFkGqCPtpnU8gAm9R7W7GjBmlk19//fVL4+Uj48ePbxTAGz16dKPAazwvETDNraVujuO4\\nEdCL71xsO2nSpMW6Rp41a1bx/FZ+V/MxIjgYf5eNGzcuzyqGrd2u0U5MECBAgAABAgQIECBAgAAB\\nAgQIECBAgAABAgQIECBAgACBZRQQwFtGwI7c/N577y1CFdFNZwRuokW3l011fRnhi8suu2yJT7mp\\nMETeQfnypo6Z141hefijfH4+9/J5xpdPgXgWomLjr371qwLgkUceaegC68WGLswaV1N54403qlZ7\\nLFeLqm5Rta6y4l0EgeLTVIvnOsJ0UbUrtyWpJhfdQEfo7/XXXy+60Y2Q4AYbbJB3UdPhRhttVAQS\\nc4WzCE3FuTTXysN2UbmvpTZq1CgBvJaQ6nR5PNOrr7566c/g+G7E/azWorvZ+B5GWC7auuuu22i1\\nCHjHMx4t9rPmmms2Wl45EceeMGFC6TsX3dBGYDRafgZjGPObaxG2ywG81m7X3P4tI0CAAAECBAgQ\\nIECAAAECBAgQIECAAAECBAgQIECAAAECrRV4J7XV2q1t1+ECt9xyS7rgggvSlClTUg7fVJ7UvHnz\\niopFp512WopuNZtrOdgQ60TIqbnW1PGa2mb+/PnFovJjxD5y0KOp7czvGgLlQbjcdXK1Kxs7dmyK\\nLjOjlQdzyrevtl3lvKj6lUOi0Y3mkrbYJp7Jl156qdgkwkhLUk0u1osKfrk9+eSTebQ0LK+I15xB\\naYP/jLS0bnSNO2bMmNJmLVUlixWXttvZ8kqapQMZ6TQC8b3KLUJz5c9inh/DAQMGFF0ax3h8F6Ly\\nXHmLKqq5xfeiqf3kdWIYVfXyejNnzkxz584tFsd3ZklbeffPrd1uSY9lPQIECBAgQIAAAQIECBAg\\nQIAAAQIECBAgQIAAAQIECBAgsDQC3ebMmVO9z8Gl2UsdrnvyySfX4VnV/pSiEtbQoUOLcE2El559\\n9tlG3QnW/gwcYVkEltfndlnMbEuAAAECBCoFonpqtMoQaeV6pgkQIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAgQ6HwCuXhMZY+KTV3JOeeck6J4U3yigEwUflnS4i9RxCk+CxcuTAsWLCg+hx56aFHU\\nZtCgQcVQF7RNyXfS+ZMmTeqkZ+60CRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE\\nCBAg0LkElrz/t851Xc6WAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjU\\nVEAAr6a8dk6AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECXVVAAK+r3lnX\\nRYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQI1FRDAqymvnRMgQIAAAQIE\\nCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAVxUQwOuqd9Z1ESBAgAABAgQIECBAgAAB\\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBNBQTwaspr5wQIECBAgAABAgQIECBAgAABAgQIECBA\\ngAABAgQIECBAgAABAgQIECDQVQUE8LrqnXVdBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAgQIFBTAQG8mvLaOQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\\nBAh0VQEBvK56Z10XAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNRUQACv\\nprx2ToAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJdVUAAr6veWddFgAAB\\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAjUVEMCrKa+dEyBAgAABAgQIECBA\\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBXFRDA66p31nURIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAgQIECAAAECBAgQIECAQE0FBPBqymvnBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\\nBAgQIECAAAECBAgQINBVBQTwuuqddV0ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\\nAQIECBAgUFMBAbya8to5AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECHRV\\ngS4bwOvdu3dXvWeuq4sKeGa76I11WQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\\nAl1WoMsG8EaOHNllb5oL65oCntmueV9dFQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAQNcV6LIBvE022aTr3jVX1iUFPLNd8ra6KAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE\\nCBAgQIAAgS4s0KUDeGPGjOnCt86ldSWBeFYF8LrSHXUtBAgQIECAAAECBAgQIECAAAECBAgQIECA\\nAAECBAgQIECAAAECy4NAlw3gxc370Ic+lITwlofHuHNfYzyj8axqBAgQIECAAAECBAgQIECAAAEC\\nBAgQIECAAAECBAgQIECAAAECBAh0LoGenet0l+5s+/Tpkz7+8Y+n+++/v/i88MIL6c0331y6nVib\\nQA0EevfunUaOHFlUvVP5rgbAdkmAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECg\\nHQS6dAAv+0XAScgpaxgSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQFsI\\ndOkuaNsCyD4IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEA1AQG8airm\\nESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBFgQE8FoAspgAAQIECBAg\\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFQTEMCrpmIeAQIECBAgQIAAAQIECBAg\\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoQUAArwUgiwkQIECAAAECBAgQIECAAAECBAgQIECA\\nAAECBAgQIECAAAECBAgQIECAQDUBAbxqKuYRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\\nAAECBAgQIECAAIEWBATwWgCymAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\\nAgQIVBMQwKumYh4BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGhBQACv\\nBSCLCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBANQEBvGoq5hEgQIAA\\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRYEBPBaALKYAAECBAgQIECAAAEC\\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUExDAq6ZiHgECBAgQIECAAAECBAgQIECAAAEC\\nBAgQIECAAAECBAgQIECAAAECBAgQaEFAAK8FIIsJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\\nECBAgAABAgQIECBAgEA1AQG8airmESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\\nECBAgACBFgQE8FoAspgAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFQT\\nEMCrpmIeAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoQUAArwUgiwkQ\\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDUBAbxqKuYRIECAAAECBAgQ\\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEWBATwWgCymAABAgQIECBAgAABAgQIECBA\\ngAABAgQIECBAgAABAgQIECBAgAABAgQIVBMQwKumYh4BAgQIECBAgAABAgQIECBAgAABAgQIECBA\\ngAABAgQIECBAgAABAgQIEGhBQACvBSCLCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\\nAQIECBAgQIBANQEBvGoq5hEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\\ngRYEBPBaALKYAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUExDAq6Zi\\nHgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaEFAAK8FIIsJECBAgAAB\\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEA1AQG8airmESBAgAABAgQIECBAgAAB\\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBFgQE8FoAspgAAQIECBAgQIAAAQIECBAgQIAAAQIE\\nCBAgQIAAAQIECBAgQIAAAQIECFQTEMCrpmIeAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE\\nCBAgQIAAAQIECBBoQUAArwUgiwkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAQDUBAbxqKuYRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEWBATw\\nWgCymAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVBMQwKumYh4BAgQI\\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGhBQACvBSCLCRAgQIAAAQIECBAg\\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBANQEBvGoq5hEgQIAAAQIECBAgQIAAAQIECBAg\\nQIAAAQIECBAgQIAAAQIECBAgQIAAgRYEBPBaALKYAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\\nAAECBAgQIECAAAECBAhUExDAq6ZiHgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\\nAAECBAgQaEFAAK8FIIsJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEA1\\nAQG8airmESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBFgQE8FoAspgA\\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFQT6Pncc89Vm28eAQIECBAg\\nQIBAFxEYPXp0F7kSl0GAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIH6ElABr77uh7MhQIAA\\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgU4i0FNFlE5yp5wmAQIECBAgQIAA\\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNSVgAp4dXU7nAwBAgQIECBAgAABAgQIECBA\\ngAABAgQIECBAgAABAgQIECBAgAABAgQIdBYBAbzOcqecJwECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAgQIECAAAECBAjUlYAAXl3dDidDgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\\nAgQIECBAgAABAp1FQACvs9wp50mAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\\nAAECdSUggFdXt8PJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBnERDA\\n6yx3ynkSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQF0JCODV1e1wMgQI\\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQWQQE8DrLnXKeBAgQIECAAAEC\\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBXAgJ4dXU7nAwBAgQIECBAgAABAgQIECBA\\ngAABAgQIECBAgAABAgQIECBAgAABAgQIdBYBAbzOcqecJwECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAgQIECAAAECBAjUlYAAXl3dDidDgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\\nAgQIECBAgAABAp1FQACvs9wp50mAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\\nAAECdSUggFdXt8PJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBnERDA\\n6yx3ynkSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQF0JCODV1e1wMgQI\\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQWQQE8DrLnXKeBAgQIECAAAEC\\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBXAgJ4dXU7nAwBAgQIECBAgAABAgQIECBA\\ngAABAgQIECBAgAABAgQIECBAgAABAgQIdBYBAbzOcqecJwECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAgQIECAAAECBAjUlYAAXl3dDidDgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\\nAgQIECBAgAABAp1FQACvs9wp50mAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\\nAAECdSUggFdXt8PJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBnERDA\\n6yx3ynkSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQF0JCODV1e1wMgQI\\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQWQQE8DrLnXKeBAgQIECAAAEC\\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBXAgJ4dXU7nAwBAgQIECBAgAABAgQIECBA\\ngAABAgQIECBAgAABAgQIECBAgAABAgQIdBYBAbzOcqecJwECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAgQIECAAAECBAjUlYAAXl3dDidDgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\\nAgQIECBAgAABAp1FQACvs9wp50mAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\\nAAECdSUggFdXt8PJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBnERDA\\n6yx3ynkSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQF0J9Kyrs3EyBAgQ\\nIECAAAECBGoo8Pbbb9dw73ZNgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBbCnTr1q0td1eT\\nfQng1YTVTgkQIECAAAECBDpSIIJ2CxYsSD17+rnbkffBsQkQIECAAAECBAgQIECAAAECBAgQIECA\\nAAECBAgsi0BlgY358+enmFdPwTxvJJflDtuWAAECBAgQIECgbgTyj+9evXql+OH98ssvp8GDB6cV\\nVlihdI55ndIMIwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1LVADtstXLgwzZgxozjXeCeY\\n3/3l5R11EQJ4HSXvuAQIECBAgAABAm0mkH9cx7Bv377pzTffTHPmzCk+eVk+WOV0nm9IgAABAgQI\\nECBAgAABAgQIECBAgAABAgQIECBAgED9CFQG6/J0DPv161eqhBfv//Kyjjh7AbyOUHdMAgQIECBA\\ngACBNhPIgboYxicq3q200kpp7ty5RRAvL48Dlo+32QnYEQECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECNREoD9bFeO/evVP//v1Tz549S+/+Yn68ByxftyYn08ROBfCagDGbAAECBAgQIECg/gVy\\noC6G5Z8oOR0hvGh5fh4vZvofAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTqXiCH6mJYPv7W\\nW2+VpuMiYlm8F8zrtOeFCeC1p7ZjESBAgAABAgQItJlA/ICOlgN2eRg/tvN4+TAfOOZpBAgQIECA\\nAAECBAgQIECAAAECBAgQIECAAAECBAjUt0B5mC7GKz/du3dvdAGxPN4Flm/XaIUaTQjg1QjWbgkQ\\nIECAAAECBGonkEN05QG7HLyLYVTA69u3b+rRo0eq/OFdu7OyZwIECBAgQIAAAQIECBAgQIAAAQIE\\nCBAgQIAAAQIEaimwYMGCNHfu3LRw4cLiMBG2K38f2BEhvMYxwFpevX0TIECAAAECBAgQaEOBpkJ4\\nPXv2LLqfjRBe+Y/tNjy0XREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0AEC8Q5w4MCBKd4J\\n5gIduWhHB5xOcUgBvI6Sd1wCBAgQIECAAIFlFsg/puPHdf6suOKKy7xfOyBAgAABAgQIECBAgAAB\\nAgQIECBAgAABAgQIECBAoH4F4p1gfj/Y0UE8Abz6fU6cGQECBAgQIECAQBWBXPkuFuUAXvkw/tWL\\nRoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA1xXo0aNH1XeF+YrL3ynmebUa9qzVju2XAAEC\\nBAgQIECAQK0EcuAu9p/H879wqdUx7ZcAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgfoQ6N69\\ne1EBL86mW7duxTvDGM/Bu5jXXk0FvPaSdhwCBAgQIECAAIE2F8g/oMtDeG1+EDskQIAAAQIECBAg\\nQIAAAQIECBAgQIAAAQIECBAgQKDuBKJAR7Tyd4YdcZICeB2h7pgECBAgQIAAAQLLLJBDd5XDZd6x\\nHRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUPcC8Z4wQngd/b6wS3dBG7h33313euONN6o+\\nEAsWLEi9evVKa6yxRlpttdVSlCbUCBAgQIAAAQIEOpdA+Q/q/K9cOtcVOFsCBAgQIECAAAECBAgQ\\nIECAAAECBAgQIECAAAECBJZWIN4NRt4rvy9c2u3bav0uHcCbO3du+v73v58iaNdSi5tx7LHHpl13\\n3bXoF7il9S0nQIAAAQIECBCoL4GO/mFdXxrOhgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDX\\nFqiX94NduuRbhOp69OixRE9SJCLPPPPM9JWvfKUoTbhEG1mJAAECBAgQIECgXQXiR3S08mEeL59f\\nrOR/CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDosgKV7wnzdOWw1gBdugJeOV6E8U499dQ0\\ndOjQ0gvbRYsWpQcffDBdeOGFad68ecXqDz30ULr22mvTgQceWL65cQIECBAgQIAAgToTyD+c47Ri\\nvHy6zk7V6RAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUAOByveEMd2tW7caHKnpmTZgbwAA\\nQABJREFUXXbpCniVl/2ud70rDR8+PI0YMaL4jBo1Ku25557p8ssvT2ussUZp9auvvjotXLiwNG2E\\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUCiw3FfDiwqPiXbXWq1ev\\ndOKJJ6ajjz666H529uzZadasWWnIkCFFJZWHH364GPbp0yets846i+0ikpOPP/54mj9/furZs2ca\\nO3Zsiop70WbOnJmmTp1aHHvNNddMAwYMSPfdd1+69957S1Va1l133bTjjjs2213um2++me666640\\nefLkYrtIakZocIsttkiDBw9e7JzMIECAAAECBAgsjwKq4C2Pd901EyBAgAABAgQIECBAgAABAgQI\\nECBAgAABAgQILI8C9fJucLkK4DX3oEXYboUVVih1RZtLEc6dOzd94xvfSAsWLEgDBw5Ml1566WJB\\nuVjny1/+crFOBO8uvvjiIrwXx7vxxhuLCnsxfthhh6W///3vRVgvpnP7/e9/n84666z0f//3f2n0\\n6NF5dmn4pz/9KZ1++uml6cqRgw46KH30ox8thf4ql5smQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE\\nCBAgQIAAAQIECBAgQIAAgbYXWK66oG2Or7I6Xk5IRqCuR48exaYrrbRS1T6Cy9eJFXN4L8Z79+4d\\ng6L9/Oc/Xyx8l5dFl7dRhS+CfuXt5ptvbjZ8F+tGl7lnnHFG+WbGCRAgQIAAAQIECBAgQIAAAQIE\\nCBAgQIAAAQIECBAgQIAAAQIECBAgQKDGAstVAC+H6qqZRqW6efPmFYsicBfd0la2ynBc5fIlmY6w\\nXgTtrrzyyvSrX/0qHXDAAaXNotvbxx57rDQd53PeeeeVpidMmJAuvPDCdP3116dLLrmk6H42L4wq\\nedENrkaAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC7SOwXAXw+vTps5hq\\nVJ6LMFwE2nLbeeed04ABA/Jkmw179uyZLrjggrTddtulvn37pn79+qXDDz88bbbZZqVjPPLII6Xx\\nCAJGt7jRIrj3la98JQ0bNqyYHjp0aPra175W6uo2Zr7yyivFMv9DgAABAgQIECBAgAABAgQIECBA\\ngAABAgQIECBAgAABAgQIECBAgAABArUX6Fn7Q9THEd5666309a9/vVGXsBG+e/TRRxudYHQZ+8lP\\nfrLRvLaa+NSnPlUK0OV9Rne1EcC77777ilkzZ87Mi1Kc3/z580vTEbAbOXJkaTpX05s8eXKKLnTX\\nXXfd0jIjBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBbgeUmgBeMLXXR\\nOmrUqPSDH/ygqE5XC/bhw4dX3W2E56q1qJgXVfCiRYDwqKOOSgcddFDacccd04gRI1JU9Ft//fWL\\nT7XtzSNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB2gksVwG8qDYXXb/m\\nNnfu3DyaBg4cmM4+++yiq9fSzDYeiYp2S9N69eqVvvrVrxZdz8Z2b7/9dvr1r39dfGI6Kt7tt99+\\nadttt02xrkaAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC7Sew3ATworvW\\niy++OA0ZMqSk++CDD5bCba+99lrRHe24ceNKy+thZMMNN0znnntuuuyyy9Idd9zR6JSiot8Pf/jD\\nIjR42mmnqYTXSMcEAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEaivQvba7\\nr6+9RwW88jZ+/PgUn9x+8pOfFF295ul6GUbXuCeeeGK64oor0re//e304Q9/OI0cObJ0etE9bSx/\\n4YUXSvOMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBtBZarAF4lZQTy\\njjnmmNLs5557brEqc6WFDSPt3c3rokWL0pw5c9KsWbPSggUL0oorrpg23njjdMghh6Tzzz8/RdW7\\nfE4RwrvrrrvKT9c4AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNRQYLkO\\n4IXr6NGj00477VQiPvPMM9P8+fNL0+UjUWEuAnGVLdaPsFxbtyuvvDJ96EMfSoceemi69dZbF9t9\\ndJf7kY98pDR/+vTppXEjBAgQIECAAAECHSPwr3/9Kz344INpypQpHXMCjkqAAAECBAgQIECAAAEC\\nBAgQIECAAAECBAgQIECAQLsJLPcBvJCOEFv37u9QvPnmm+n3v/996QaUd1u7cOHC9Oc//7m0LEbe\\nfvvtdNZZZxUV6hotaIOJfv36lfZywQUXpNmzZ5emYySO/cADD5Tmla9fmmmEAAECBAgQIECg3QTi\\nH2vsueeeaaONNkof/ehHa/KPNNrtYhyIAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgywv84Q9/\\nSBdffHGaPHlyl7/WWl1gz1rtuDPtd9iwYWnfffdN1157bXHal112WXrPe96TBgwYkPr06ZO23Xbb\\nUgW68847Lz399NNpjTXWKLqG/fWvf12T8F2cyBZbbJEieBctgoGHHXZYERYcO3Zsimp3USHv+eef\\nL5bH/8T6GgECBAgQIECAQMcJ9OjRI/Xv3784gZEjR6byf8zRcWflyAQIECBAgAABAgQIECBAgAAB\\nAgQIECBAgAABAgQWF5g1a1aKHkGjRWEyrXUCXTqA99ZbbzVSiYpxTbWDDz44TZw4sQjTLViwIF1+\\n+eXpqKOOKlaPLmBvu+22lPf3xz/+sandFPObO06zG1YsjJe2p5xySjrppJOKJXH8Sy+9tGKtdyYP\\nPPDAFME8jQABAgQIECBAoD4E3njjjfo4EWdBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoEJg\\n2rRp6eabby564KxYZHIpBbp0F7QrrLBCik+06GK2uQokAwcOLCrMZb8or5i7fI0Keeeee24aPXp0\\nXlwa9uzZM33zm99Mm2yySTEvqp706tWr0fI80dTx+/btm1dJld3IbrbZZumcc85JEyZMKK1TPjJq\\n1KgioHf44YeXzzZOgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBksCLL76Y\\nfvvb36Zf/vKX6YYbbqhZr5+lAy4nI93mzJnTdFm45QRhaS4zun6NSnSLFi0qQn0jRoxoNti3NPtu\\nad3ohvbll18uusVtuG9ppZVWSoMGDWppM8sJECBAgAABAl1GIFcajt9j+RO/y+ITZbGjkvGYMWM6\\n7HrnzZuXttpqqzRp0qS07777Fv8BE/8QRCNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ0QK3\\n3HJLevbZZ6uexhZbbJHGjx9fdVm9zpwyZUpRKC0KqEXRtPjEu7n8ifOOgmlR/CwKqsUn1i1f3tK1\\n5XeS+V1kvI+M3lRjv5HbimGX7oK2JaDWLI9qeB3VevfunaLiXbShQ4d21Gk4LgECBAgQIECAQBsI\\nPPfcc+kf//hHeuqpp1L844o+ffqk1VZbLW255ZZpjTXWaPEI1bYfPnx42mCDDdLGG2/cqCpz5c5m\\nzpxZHPuxxx5LM2bMKP7DIH7nrr322mnTTTdNgwcPrtzENAECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAQCcXWHXVVYvCFhFCixahsn//+9+d/Ko6/vQF8Dr+HjgDAgQIECBAgACB5UjgpZdeSp/5zGfS\\nr371qyaveo899kjnnXdeWn311Rdb59VXX02f+9zn0iWXXLLYsjxjwIAB6dprr00777xznlUMo4Lg\\n6aefnk444YRG8ysnzj777PSpT32qCOZVLjNNgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQOQXW\\nX3/9FJ/coprb5ZdfXvQ8lecZLr2A/rCW3swWBAgQIECAAAECBFol8PTTTxelu5sL38WO//CHP6Qx\\nDV3pRle25S26uN19992bDd/F+rNnz0677LJLUeWufPuTTjqpxfBdrH/MMcekiy66qHxT4wQIECBA\\ngAABAgQIECBAgAABAgQIECBAgAABAl1MYNGiRV3sijrmcgTwOsbdUQkQIECAAAECBJYzgehm9oAD\\nDkjTp08vXfkXvvCF9PDDD6eoavfEE0+kCMiVt9122y1Fd7G53Xjjjemee+7Jk+nUU09Njz/+eHrl\\nlVfSs88+my6++OLSshg55ZRTSv9iacqUKelb3/pWafnhhx+e7r///vTyyy+nadOmpZtuuimtueaa\\npeVf+cpXGh27tMAIAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIlAV3QliiMECBAgAABAgQI\\nEKidQHQJW17RLqrgffCDHywdcODAgenkk09O2223XVHlLhZEWO8Xv/hF+vSnP12sN3fu3NL6P/rR\\nj9LnP//50vSQIUPSxz/+8TRu3Li05ZZbFvPvvvvu9Nprr6XBgwc3Cv7ts88+RRe3PXv+9z8Hhg8f\\nnmL9rbbaKj311FPF+lGxL7bVCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoLvDfN27Vl5tL\\ngAABAgQIECBAgMAyCixYsCCdccYZpb0ce+yxjcJ3pQUNI1H1LirbffnLXy5m//jHP05HHHFE6tu3\\nb1pxxRVLq0blvIULF6byEF0s3HTTTdORRx5ZrPfGG2+Uls+aNau07QMPPFBUzYvQXXlbeeWVU5zb\\nQw89VFTG69OnT/li4wQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVAgI4FWAmCRAgAABAgQI\\nECDQ1gIvvvhio65jI+TWXItKdhG8iwp4UY0uKtFtsMEGReAub3fRRRelBx98sOhmNireDR06tFgU\\ngbzzzz8/r1Yarr322qXxqVOnpnXWWSedffbZaccdd0yjR48uLfvsZz9bGjdCgAABAgQIECBAgAAB\\nAgQIECBAgAABAgQIECBAgEDzAt2bX2wpAQIECBAgQIAAAQLLKvD666+XdrHmmmumESNGlKarjUSY\\nbvXVVy8tyl3P7r777kUQLy+455570t57752ict3GG29chPH+8pe/FNXt8jp5GPuLYF9us2fPTh/5\\nyEfSaqutllZaaaX0mc98Jl133XUpwnkaAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJLJiCA\\nt2RO1iJAgAABAgQIECDQaoHyrlxHjRrVqCvZajvt1atX2nrrrRdbFEG522+/PX3oQx9abNmkSZPS\\nySefXFS0i0BedFv70ksvldbr1q1buuCCC9IPfvCD0rw8EmG8s846K+2///5FNbyoqBfd0GoECBAg\\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDQvIIDXvI+lBAgQIECAAAECBJZZYOHChaV9DBo0qDTe\\n1Mjbb7+dctW7ynUGDx6cfvnLX6Z///vf6Yorrkif+tSnKlcppqOL2vHjx6dp06aVlkf3tCeccEJ6\\n7bXX0sSJE9Pxxx+fhg0bVlqeR6Ky3oYbbpjuuuuuPMuQAAECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAAIEqAgJ4VVDMIkCAAAECBAgQINCWAuUBvLvvvjvNmjWr2d2/+eab6c4772x2nVVXXTV94AMf\\nSOecc06K/Ucg7ze/+U2KLm5zmz59eoogXmWLSnp77bVX+uEPf5hefPHFosva2267LR144IGNVv3i\\nF79Y7LvRTBMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJQEBPBKFEYIECBAgAABAgQI1EYg\\nwnI5GBehuH/+85/NHuiJJ55IjzzySLHOgAED0vDhw4vxCO9dc801RdAuQnq59ejRI8UxDjjggPTY\\nY481qooXVewWLVqUnn766XTVVVcVnziH8jZkyJCi69rY92WXXVZaFPuK7mk1AgQIECBAgAABAgQI\\nECBAgAABAgQIECBAgAABAgSqCwjgVXcxlwABAgQIECBAgECbCUSIbocddijt7xvf+EZasGBBabp8\\nJLqf/drXvlaatdNOO6WRI0emefPmpf/5n/9JBx10UHrf+96XnnnmmdI65SPRzeyECRPKZxXjP/7x\\nj4uKeVE179Zbb11seZ6x/fbb51FDAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB5UQg3jFprRMQ\\nwGudm60IECBAgAABAgQILLFAt27d0pFHHlla/4477ijCdHPmzCnNi5GoanfCCSeka6+9tjT/mGOO\\nSfEfPH369Enl4biPfexjacaMGaX18kjsI1fPi3mbbrppigp55QHA4447Lj366KN5k0bDW265pTQ9\\naNCg1L9//9K0EQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAga4jEO+f4p3TJz7xibTeeut1nQtr\\n5ysRXWxncIcjQIAAAQIECBBYPgW22WabFGG6s88+uwC45JJL0tVXX52++tWvpnHjxqUnn3wyffe7\\n303l3cNGtbrddtutBBbTefvoWnbo0KEpqumtv/76aYUVVkiTJ09OZ555ZqN9bL755sX2Ed4bNmxY\\nsSyOscEGG6Qjjjgivfvd705RoW/atGnp8ssvTxEOzG3//fdPvXr1ypOGBAgQIECAAAECBAgQIECA\\nAAECBAgQIECAAAECBAhUCAjgVYCYJECAAAECBAgQIFALgaiCF+G4t956K5177rnFIWbPnp1OPPHE\\nqoc7+OCD06WXXlpUr8sr7LjjjulHP/pROv744/Os9M1vfrM0Xjly6qmnpn333beYPWLEiHTFFVek\\nnXfeubTahRdemOJTre2zzz7pO9/5TrVF5hEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg8B8B\\nXdB6FAgQIECAAAECBAjUQKBv376L7TW6kj3nnHNSdPO63XbbLbY8Zmy22WbpN7/5TRGWi7Lfle3z\\nn/98+utf/5qiOl1Tba+99ioq2VWG+3baaaf01FNPFZX4mto2jn/VVVel6667TvW7ppDMJ0CAAAEC\\nBAgQIECAAAECBAgQIECAAAECBAgQIPAfgW5z5sx5mwYBAgQIECBAgACBziDw9tvv/HSNKnL5s2jR\\nohSfhQsXpgULFqQxY8Z0hkspznHmzJlpxowZKa4rKuQNHjw4DRkyZInPv+G3fHrllVfS/Pnzi20i\\n9Ddw4MDUv3//FvcR27z88stp7ty5xboRDozjx/YaAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\\ngXoXmDJlSlFQIt5z9ejRo/h079495U+cf7yDiwIZvXr1Kq1bvryla8zvJPO7yHgfeeihhxb7HTRo\\nUDHUBW1LipYTIECAAAECBAgQqJFABN7i09rWr1+/FJ/WtBVWWCGtuuqqrdnUNgQIECBAgAABAgQI\\nECBAgAABAgQIECBAgAABAgQI/EdAF7QeBQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\\nQIAAAQIECBAg0AoBAbxWoNmEAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\\nBAgI4HkGCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAKwQE8FqBZhMC\\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICCA5xkgQIAAAQIECBAgQIAA\\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKtEBDAawWaTQgQIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAgQIECAAAECBAgQIECAgACeZ4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE\\nCBAgQIAAAQIECLRCQACvFWg2IUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\\nAAECAnieAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0AoBAbxWoNmE\\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgI4HkGCBAgQIAAAQIECBAg\\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAKwQE8FqBZhMCBAgQIECAAAECBAgQIECAAAEC\\nBAgQIECAAAECBAgQIECAAAECBAgQICCA5xkgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\\nAQIECBAgQIAAAQKtEBDAawWaTQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAgACeZ4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLRCQACvFWg2\\nIUCAAAECBAgQqE+Bbt261eeJOSsCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBNpUoF7eDQrg\\ntelttTMCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQWF4EBPCWlzvtOgkQ\\nIECAAAECXUyg/F+0xHj+dLHLdDkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFQRyO8HK98b\\nVlm1prME8GrKa+cECBAgQIAAAQJtKZB/PJcPq4235THtiwABAgQIECBAgAABAgQIECBAgAABAgQI\\nECBAgACB+hOI94TV3hWWz2uPsxbAaw9lxyBAgAABAgQIEGgXgfxjul0O5iAECBAgQIAAAQIECBAg\\nQIAAAQIECBAgQIAAAQIECHSYQL28GxTA67BHwIEJECBAgAABAgTaQiB+WMene/fupX/h0hb7tQ8C\\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBOpXoPwdYUeG8QTw6vcZcWYECBAgQIAAAQLNCOTg\\nXfkwQngaAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJdXyAX6Ch/X9gRQbyeXZ/aFRIgQIAA\\nAQIECHRVgcof0/Ej+/7770+LFi1K73rXu9Jbb71V+rz99tspPtHysKu6uC4CBAgQIECAAAECBAgQ\\nIECAAAECBAgQIECAAAECnVkgB+ny+8B4D5g/U6dOTT169EgjR44sesjK6+Rt2vu6BfDaW9zxCBAg\\nQIAAAQIEllkg/3iOIF35D+o8Hj+4e/bsWQTx4od4BPFy6C4Pl/kk7IAAAQIECBAgQIAAAQIECBAg\\nQIAAAQIECBAgQIAAgZoJ5HeCMYx3fjGM94Dxye8Fy4dxInm6ZidVZccCeFVQzCJAgAABAgQIEKhf\\ngfjRnEN0eTx+cOcWP7gjcJd/eMe65Z9YL2+ftzEkQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\\nQKB+BOI9YLQcqMvDXAWvcpjXLTb6z3Z5vNZDAbxaC9s/AQIECBAgQIBATQTKf3THAWI6/9CO8Qjg\\nxXSE8aLlEF4x4X8IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKh7gRy8ixONd3/5PWB+N5iX\\nlw/b+6IE8Npb3PEIECBAgAABAgTaVCB+TEeLH9wRssvDCOCVT8c6Kt+FgkaAAAECBAgQIECAAAEC\\nBAgQIECAAAECBAgQIECgcwjkd4HlAbscxMvDvKyjrkgAr6PkHZcAAQIECBAgQKDVAvEjOsJ0+Qd3\\n+Y7yD+wcwMuhuzwsX9c4AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQL1LZDfCeb3gBG8i5an\\ny4d5frFCO/2PAF47QTsMAQIECBAgQIBA2wrED+nyEF6ezj+4Y1geuisfb9szsTcCBAgQIECAAAEC\\nBAgQIECAAAECBAgQIECAAAECBGolEO8Bc4vx8veBeVnlMK/fHkMBvPZQdgwCBAgQIECAAIGaCMQP\\n6RzCy8McvMvD8gML4ZVrGCdAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQ3wI5WJfPMqbLPzE/\\nr5OHed32GgrgtZe04xAgQIAAAQIECNREIH5I5/BdHCD/sI5hHs/BuzxdkxOxUwIECBAgQIAAAQIE\\nCBAgQIAAAQIECBAgQIAAAQIEaiJQ/p4vvwesnFeTAy/BTgXwlgDJKgQIECBAgAABAvUtUP7jOs40\\n/+jOZ125PM83JECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgcwnkd3952NFnL4DX0XfA8QkQ\\nIECAAAECBNpUIP/QzsPYea6A16YHsjMCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBNpFoPzd\\nX/l4uxy8hYMI4LUAZDEBAgQIECBAgEDnF6i3H+GdX9QVECBAgAABAgQIECBAgAABAgQIECBAgAAB\\nAgQIECAQAt0xECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAksvIIC3\\n9Ga2IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECSRe0HgICBBoJzJ8/\\nP82YMSPNmzcvDRw4MA0aNCjptq8RkQkCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\\nAAEChYAAXjs+CHPmzEn9+vVrxyM6FIElF4jQ3cSJE9Pjjz/eaKP+/funsWPHpv3222+xIN4DDzyQ\\nrr766mL9Y489Nq2yyiqNtjVBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoCsL\\ndNkA3ptvvpnOPPPM9Pbbb6fNN9887brrrk3ex9/97nfp4YcfLsJDhx9+eJPr3XHHHen2228vlh91\\n1FFFZbAmV65Y8LOf/Sw98cQTaeutt0577713xVKTBDpWYObMmenss89OUf2usr3++utpypQpi4Xv\\nKtczTYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGB5E+iyAbzevXunFVdcMU2f\\nPj09+eSTTQbwIqAX4buoThefCBtFxa9qLfYT6wwYMGCpwndvvfVWevbZZ4tdRpCpsr300ktp6tSp\\nxezx48enXr16Va5iuosLTJo0KS1atCitvPLKafTo0e1+tRFCzeG7PffcM8VzGN+hadOmpX//+9+p\\ne/furTqnjr6uVp20jQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgsoUCXDeDF\\n9Y8ZM6YI4D3//PNpwYIFVYNtL7zwQhGqy14Rstt4443zZGkYQb3nnnuumF599dVL85dkJMJL73vf\\n+9LkyZPThAkTFtskKuPdcMMNxfx11lmn6nkutpEZXUogAnDz5s0rqjW2dwAvgn/x3EeLY2+77bYl\\n23jWl/Z5L23cMNKR11V+HsYJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1EKg\\ndWWtanEmNdhnDg5FBbqo4lWt/etf/2o0u3I6L4wqddGtbbQI9i1t22CDDYoQXj6npd3e+gRqJTBj\\nxowU35Fo48aNq9Vh7JcAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAlxPo0gG8\\n8qBc7gK28g5G9blo73rXu4phVAKLaneVrXz78v1Wrpeno6pYrVutjlGr/UbIq5ptudOyHDuHyMr3\\ntzTjy3LspTlOW60blm1xznPnzi2d0tChQ0vjHTmyLNe1LNvGNS/r9kvq1trjLFy4cEkPYT0CBAgQ\\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEaC3TpLmgHDBiQhgwZkqLCV3mALptGl595\\n/tZbb51+/etfF93RTps2LY0cOTKvVgzzen379k2rrLJKadntt9+eHnroodS/f//04Q9/ON1yyy1F\\nV7NRMW+HHXZI73nPe4p1L7/88jRr1qy01lprpd122y3Nnj07/eIXvyiWzZkzp7S/Sy+9NPXo0SMN\\nHDgwHXLIIaX5eeTVV19Nf/vb39IjjzxS7C/OZ8SIESkq7G211VZ5taUaRpDrnnvuSU899VRRKfC1\\n115LK664Yho2bFjabrvt0tixY5dqf+F6ySWXFNvstNNOhc2f/vSn9MwzzxSV1r70pS+l3r17l/a5\\nLNcUDv/4xz/S1KlT0/z589Pw4cPTGmuskXbZZZd03333pX/+859p5ZVXTu9///tLxysfie3++te/\\npilTpqQ33ngjDRo0qNhHPA9xr8rb3//+92KfMW/nnXeu6pKfh1gn7v3aa68do1VbmDz22GPFslxd\\nMa4nukyOtuuuu6bokri8xXr33ntvcR7xXMe9i+cxntftt9++uGfl6zc3fscdd6QHH3ywcMvrRVfI\\ncV7R9thjj8Iyvg+//e1vi3l77rlnixUgW3NdcR0PP/xwuuuuu1IcL7qMjvu26qqrFtbxPa5sV155\\nZfHdXnfddVPcr4kTJ6ann346vf766+mwww5bzK5y+wjb3nTTTcXs+O7GtnGPo1pmBHI/+clPFst+\\n/vOfF/uM48RzVa2FT5x3nO9+++1XWqX8GJ/4xCdShB1vu+22ojvrl19+ufiexTbhGt+3ai2CenFe\\n8SzHPY/nPP5si+99/BmjqmY1NfMIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAu0j\\n0KUDeEEY4ZQcwIuQT7du3Uqyudpdr169igBbBGEi/BRV8SoDeM8991yxXVS/K99HhMdimwisXXPN\\nNWnSpEml/UeIKLcXX3wxzZw5swgExryoYpWDVnmdGMZ60SLEVtniOs4///wixJOXRWgsgkPxifM+\\n6KCDUp8+ffLiFocRCIrgYa4EmDeI+RFKi89mm22WDjjggLyoxWEEhvK1RaAsPhEayi3uQ26tvabY\\nRwS9IsxU3uK48Yl7GyGlGG+qMt7kyZPTL3/5y0ZV+eJ+xufxxx9P7373u0sByjjGJptsUoT1IqD4\\n+9//Pq255popnp3cIlAVAcy4/nhOKgN8eb08jOchO+V54R6faHFvy1sYRkAzQoPlLZ6Z+EQQdK+9\\n9kpbbLFF+eImx/OzW75C3I/c8nnEs5jPMwcF8zrVhkt7XbGPP/zhD0WwtHx/EWKNT9ynAw88sPiO\\nli+Pa47lEeaMZ2z69OmlxUtSJS5883VFcDLCf7mVX2cE6yI8G4HAplqcR+xrhRVWaLRK+THiOiLg\\nmF1jxRiP794555yTDj/88DR69OhG24f9BRdcUFxn+YII8MYnusyOkGyEJTUCBAgQIECAAAECBAgQ\\nIECAAAECBAgQIECAAAECBAgQIECAAIH2F+jyAbwIQkXlqAiyRECqvHpdhKyiRcW0nj17FhWzIkQT\\noZYIX+UWFepeeeWVYrKpalMRpInwXXThGcGsqMRWfqy8rzxcaaWV0rHHHltM3n///UWwKyYihNOv\\nX7+iCl5eN4YR5LnsssuKwE4cY++99y6qdMW5xfVFgCiqqd16663FsvJtmxuP6l4R6Irg0LbbbpvW\\nW2+9ogpchIWi0losi0pyESbbcMMNm9tV1WVxbWEbleCyc0xHW5ZrisptOXwXllFVcNSoUSnCcRFo\\niiqBEZxqqkWVs6uuuqoI30Vls6hoFwGruM9hGM/GX/7yl6KiWVQXjBZG+++/f/rZz35WhPRieVSp\\ny+13v/tdEb6LUF4EFsuDmnmd8mGcc37OzjvvvCKkOH78+OJcYr24rtwiRBgV3+J+dO/evThu3KsI\\nfsa1RCW3CKRdf/31xXluvPHGedMmhzvuuGPacssti+DY1VdfXaz33ve+t7hPMRFVGFvTlua6Yv9R\\n3S3uV3jFMzhhwoTiOxDXFUHHuCcREv3c5z5XhCorzynCp9Gial08Y3HeTVWTq9w2T0f4LizjOY39\\nRGXJtm5hHJX84hrjmYuwY4Qmo/JfhDb/+Mc/piOOOKLRYX/zm9+UwndR7S62iyqNETiMyo9xz6OS\\nYTz78exoBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC7SvQ5QN45YG56AK1PBSX\\nq77lLlaju88///nPRfeQUQErd5Oaq9/FrYlAX1Mtwj8f+9jHioBUU+vk+dHNbD6X6L42twiBlU/n\\n+VGpLYJIEco6+uijS5W2IigUgafYXwTSoivZqIi1JOGpqNqVq6lFt5kbbbRRPlxR9S6ChKeffnox\\nL4JCrQngRajq0EMPrVoNrrXXFNXv4lqjRZW7o446qhTMCr8IC0YAK8JL1VqEFiN4GBUK119//UZd\\n/UYVxOi+9KKLLiqq/0VFu1gnh+kioBUVASOUGKHHqIoXgcgIBEYXvtGi69lqXaZWnkuce3yiRagu\\nWlQvzM9FMeM//xPhrBwYjfMr79o2QlnxnOfqeLFunHNlNbby/cV4PGfxKa/IFs9NteNXbtvc9NJc\\nV4TnImQXLaq4RTgtt7jGI488Mp1xxhlFgDbu+b777psXNxpGV8dNdQ/baMUmJsIhvlf5fjSx2jLN\\njn1HwC4fI3cdfe211xZhuvjzKYLCuYJlVDzMXRRHCDR3Zx0nEV3ubrrppunMM88susd94IEHBPCW\\n6e7YmAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQOoF3Uj+t27ZTbBVBqBx4efbZ\\nZ0vnHJWjogvHaBG8ixaVryJ0F9WopjR0vZpb3i4CTRGaqdYiAPfBD36wFKSqtk5r50Ult3w+u+++\\ne9Vg1fbbb1+E8OLcIwy2JC0qtR1yyCHFJwJblS0qbUWoMNoLL7xQuXiJprfZZpuq4btluaboXjaq\\nGUaL/ef7W35CEU5qKiwZYaUI4UWLSoLVWlSHixYhxajEVt6im9cIQob1xIkTi8p1N954Y7FKBOEi\\nHNWWLbpTjWqE0eJZLQ/f5ePEc5u7IY3nOoKYnaHdeeedRRXCCDHGvaxsUZUuqvRFi2qK5d0X53Wj\\n+ltUMFyWFhULqz1Hy7LPym2bOkb5/SzvAjjCeLn75Grd38Y9j/BthBYru66tPLZpAgQIECBAgAAB\\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB2gh0+QBesOUQWQ7SxbxcTSwqpUXQLFpUIcthmFwd\\n7/+zdx9welV13sD/qWTSSCMhEHroSITQXliUItJEdi0QV4UFG4piQXAF3RWUFV1BAUEERHqRoiIg\\nIlgBFQXpNTQhpCdAen/zv+59eCaZmcxMMpOZZ77n/UyeW84995zveSYf982Pc/J6uQLexhtv3GjA\\nLgN4GRZqi5LbwZal7F95Xn5mOLBcuaw6xFPeb+gzx53Bu/zJMF5DJYNRWTIM1JpSvY1q9fOrM6YM\\nxZWletW+8lr5Wa4qV56Xn+W7c5vgxlYKzFBXWVb0zOBTbkWbJbcrzpXnMvSWW+s2Z+vZst3mfuYq\\ncbkaWpbcnrWxkuG/MqjV2sBkY223xfUMMKZflgwWlqsMrviuci5yxcIyOFldJ1eva+zZ6npNHbd1\\n+C7fnf1sqFRfnz17dqVK/u6U85lb5L5YFQouK2XQ9KCDDorcnlYhQIAAAQIECBAgQIAAAQIECBAg\\nQIAAAQIECBAgQIAAAQIECBBof4Ga34I2STOY9Mgjj0QGqTLgkoGXMvhTbj9b0ud2no8//niUAbwM\\nCZUroDW2olr5bFt9lgGwDJRdd911jb7mtddeK+7NnDmz0ToN3ShXzXviiSci23jjjTcit+DNUq7A\\n1dBzq3NtdcZUjjM9WhOcKp/P0FxuNdtYyVBXrrhW1q+ul4Gx3H42V2UrA5r7779/sR1tdb01cVz9\\n/lVtbZuByVwdsFwhcE28v63aSP/87mXJ38fG5qL8Lma9/G5XB9byWmcv1eHBFVf4yxUa83c+A5jp\\nM3jw4CIknKHZ3CK6sZBpZzfRfwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAZxHo\\nEgG86uBcroKXq8iVq+Fl4K66lCvMTZ8+vQj75IpbuQVolgzyrY1SrvqVYbiGVsFasU/Vq2iteG/F\\n8wwXXnPNNZXteDPQkyv5ZdArg0EZxmvt6ncrvqv6fHXGVI4vg1jV4aXq9ps6Lp+fO3fuannut99+\\nRQAv35VmDW2h2lQ/mnsv+1mWxlYULO+XgcQy4Fhe74if5Txk3/L3LX9WVTK015VK/n103HHHxV13\\n3VWEFDOAmNsL50+fPn1ihx12iNyWOo8VAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\\ngACB9hfoEgG83MYxA1IZZMrgXYa2MsyWoZWNNtqonnoGmHJr0smTJxer4JXhu9xedNSoUfXqttdJ\\nXV1d8aoMnB166KGrfG1zwzgZrrv66quLVQHT6MADDyzCibmdblluueWW+Nvf/laerrHP1RlTuQJa\\n9cpoLelY+mT4K7cm3m233Vb5aLkN74oVf//731cu5Xfr/vvvjz322KNybU0dVIfucsxNbXVchtpy\\nS+KOXqq/p+nWnIDr2vodXJuW+bs5bty4YhW8XCnw6aefLv5uyrnO383coviDH/xgZbvatdlX7yZA\\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIdDWBLhHAy8BdhnuefPLJmDJlSmXby1z9\\nrqEtHHN70QzgZbClV69exXcigz/VwbT2/KJkACdLhry22WabNdaPRx99tAiipcGxxx7brlt7rs6Y\\nBg0aVHhkGC235mxp2CzfneGl3O5z++23L9pq6R/PPfdcPPDAA8Vjw4cPL75XuUpZfqdWtU1sS99V\\nHQDM7WhzG9LGSrldbXVor7G6a/t6OuV3rwzDtnYu1vY42uv9+T1Po/zJ726G737xi18UKwf+5je/\\niSOOOKK9uuI9BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC/yfQvatIlKtr5dac\\nL730UjHsFbefLS3K67la3rRp04rL1dvYlvXa67MMq2VQ6fnnn19jr3355ZeLtjLQVa4qt2LjGfRp\\ni7I6Y6oOpD3zzDONdq+xvmdgLsurr74a5Va4jTbSwI0M/v3sZz8r7uQKih/72MciV07MMGBeb+y9\\nDTTVrEvrrbdeZavdJ554otFn8ruawdEsm2++eaP1OsqNDN+Vczl+/PiO0q2V+lH+brTmu7JSYy24\\n8Nhjj8Udd9wRv/3tb1d6KkPFu+66a5R/L6Xfmv7erfRSFwgQIECAAAECBAgQIECAAAECBAgQIECA\\nAAECBAgQIECAAAECBFYS6DIBvDKoMnPmzCKklAGW0aNHrwSSFzJUlatN5RatGdLKUj5fnKzhP3J7\\n27LMnz+/PKx8ZiAwA15ZfvnLX0a5LW6lwv8d5PaU995774qXGz0v3zt9+vRKcKu6cq4A+PDDDxeX\\n1nS4Z3XGtMUWW8S6665b9Ouee+6prGhY3fcXX3yx2G64+lp5vNNOOxWBtgzM3XnnneXllT7/+Mc/\\nFlt9rngjQ1Gvv/56sXrbu9/97lhnnXXikEMOKarle//yl7+s+Mgqz8u5aGj+c7venXfeuWgjV93L\\n73BD5Xe/+10Rwspg25gxYxqq0u7XmhpXdmbs2LFFn1555ZV46KGHGuxfztPtt98eGZ5dG6VccXDi\\nxIkN/u7laogTJkwourYmf0/y9/y+++4rAnj5O9pQKcOkOef5d5pCgAABAgQIECBAgAABAgQIECBA\\ngAABAgQIECBAgAABAgQIECDQvgJdJoC3/vrrF6G6DMjkT24p27dv3wa1c6vZDHllWbJkSRG0yvpt\\nVcotVbP9v//970XIpzrIk2HAMuCVq5xddNFF9QJzGRTMoM7VV18dv/rVryJXzmpOqQ4V3nbbbZGr\\nyeV4c5veDPJle2XYL0NQ1X1qTvtN1VmdMeX87L333kXzGZC84oorKisV5ja9jzzySNH3HEtDZcMN\\nNyxWD8t76X3jjTdWVsLLMU6dOrVYye7Xv/51XHPNNVFu65r1c6WxcuvZPfbYI0aMGJGXi21Bc+vi\\nLLkVbUvDYuV3IFc4LMNW1d7veMc7iu9vzsfFF19cbI9c3p83b17cfPPNxbjz/Ycffnjkqnkdoaxq\\nXNWGuXpghh4XLVpUdD3nL1ehvOyyy+LPf/5zXHvttcV2te09rvLvgvxu5Zav+buQJYOQGRpsq35V\\nbzed38PcMroMaOZqfPndffDBB4u+bLbZZsWnPwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBNpX4M2l19r3ve3+tlwhauONN66saLb11ls32YdcHe/JJ58s6mywwQZF+KnJB1bjZvYr\\nw4AZ8MkAUq6glgG1k08+udLq9ttvH29729uK+5MmTYrzzz8/cmW0XH2tOiB24IEHxg477FB5rqmD\\nXFUtw2q5alv5k0651W2WXr16RYbVcnWvDHvNmjUrBg4c2FSTLbq3OmPKldNyhb7HH3+8+Dz33HML\\ni9weNkv65dahs2fPbrBPBxxwQOGWocM0yJ8Mi2W4KuehbOODH/xgcT3Pq7eezRX49ttvv6Je+ce7\\n3vWuOO+88ypb0R5zzDHNXpVsu+22i1wFLt99zjnnFGPZf//9IwNqWfr16xfjxo0rwoI5ph//+MeV\\nMVaH/bJPucJfRymrGld+397//vfHddddV4QoM/SYAcYhQ4YUqwyWAdDcsvhDH/pQEYZt77Hl70kG\\nXDP8mqG3DN3l710ZhsvvWv5eZBB2TZY+ffrEUUcdVdhkKPSGG24oxp/f0+o5z/P87ikECBAgQIAA\\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLtL9BlVsBL2uoV33IL1KZKuZpZ1tlkk02aqrra\\n9zLM8773va+yIl+GwMrgUXXjuQraRz7ykWL1vlwFLlc+y/BdBoByBaxsY6+99qp+pMnjDD9lwOxf\\n/uVfItvLkuG7vJ5Wn/zkJysrzeW9DLyt6dLaMWV/jzjiiNhnn32KcFr2KwNyZd8/9alPVVaBy2sr\\nljTPQNe//uu/xtChQ4vbaZkBuAzubbvttnHssccWruWzufVsGbLKFQnTvbrkVqXZnywvtnAr2t13\\n371ecLIMEhaN/d8fGQr99Kc/HRlqy/7n96QMYmVgLUNY5furn1ubx80ZV26jmvO15557FvYZ9sxV\\nAPN3IOcmA4Uf/ehHK9sOt/d48vuT78/AaJbsX4bv8nr+3XDccccV21a3Rb/y9/oTn/hE5Gp4+b3M\\n389yzjMgm2Yf//jHK78DbdEHbRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDQu\\n0G35VobLGr/tTnsKZOAoV9nKkE0GjzJk1VjJOlk3S2432q1bt8aqNut6bveZW2pm8Cu3Ve3Zs/0X\\nR1ydMWUoKUNRGeYq+56r4qVRBqeOPPLIJh0yzJarjOVKc+W2qU0+0EY3c5XBHEuuqJar7DUUHixf\\nnfOVgcAM/g0YMGC1vwNlu23x2ZJx5faqaZDf61wFriOV/J7kdyoDoLkqXxlcba8+lo753cjvSFPf\\nj/bqk/cQIECgIwpMnjy56JYtujvi7OgTAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgdUTKBcR\\ny4xTc8oPfvCDYifQXOwoc0WZt2hu5iLzTPmTua5FixYVP7ngWWa1MmOUn+2fsmrOqLtonZzg9ddf\\nv1mjzy9Bhs3WVCkDRWuqvda009IxZeCuDGjlCnDVpQxx5bUMcq2q5Gp2ud3u2i4ZpMuf5pQM3uVP\\nZygtGVeGIPOnI5b8nuSW1GurtMRxbfXRewkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\\nAAECXUlg5b05u9LojbXTCjz77LPxne98J5544omVxpCp09tvv71In2bKNLfvVAgQIECAAAECBAgQ\\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILCmBayAt6ZFtdcuAhnAy+1Ar7vuuth2221jk002\\nKVa6y61LH3zwwZg4cWLRjz333HOtrljWLhheQoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\\nAQIECBAgQIDAWhEQwFsr7F66ugKHHHJIDBs2rFjp7sknn4z8qS65ne0ee+wR+++/f/VlxwQIECBA\\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEFhjAgJ4a4xSQ+0tsNtuu8Xo0aPjpZde\\nipdffjkmT54cAwcOLIJ5O+ywQ4wYMaK9u+R9BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAh0IQEBvC402bU41CFDhkT+7LTTTrU4PGMiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE\\nCBAgQIAAAQIECBAgQKADC3TvwH3TNQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\\nAQIECBAg0GEFrIDXYadGxwgQIECAAAECBGpdYPHixXHLLbfEwoULY/DgwXHggQfW+pCNjwABAgQI\\nECBAgAABAgQIECBAgAABAgQIECBAgEBNCQjg1dR0GgwBAgQIECBAgEBnEliwYEGcdNJJ8fzzz8fe\\ne+8d73jHO6JHjx6daQj6SoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBLC9iCtktPv8ETIECA\\nAAECBAisTYEM2/Xv37/owqBBg6Jbt25rszveTYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA\\nCwUE8FoIpjoBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgBATzfAwIE\\nCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0AoBAbxWoHmEAAECBAgQIECA\\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAj0rFWC//qv/6rVoRkXAQIECBAgUOMCp59+\\neo2P0PBaI/DII49E/rz22mvF4yNHjoy99tor1l9//VU29/LLL8ef//znmDx5clF34MCBscsuu8R2\\n223X5LMzZ86MP/3pT/H8888X9Xr37h077rhjjB07Nnr16tXks24SIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAgQ6AoCNRvA6wqTZ4wECBAgQIAAAQK1L/Dggw/G+9///koIbsURf+tb34oTTzwxevTo\\nseKtmDVrVnzxi1+Miy66aKV7eWGPPfaIa6+9NjbddNN695ctWxYXXHBBfPrTn653vTwZPnx4/OQn\\nP4m3v/3t5SWfBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLqkQM0G8Kwc0yW/zwZNgAABAgQI\\nEKgpgfvvvz923333Jsf0pS99Ke655564+eabo2fPN//n/eLFi+Pggw+Oe++9t9Hnc1W8bP+hhx6K\\nXFGvLGeeeWaccsop5elKn1OmTIl99tkn7r777thvv/1Wuu8CAQIECBAgQIAAAQIECBAgQIAAAQIE\\nCBAgQIAAga4i0L2rDNQ4CRAgQIAAAQIECHQmgTfeeCM+8IEPVLqcq9U99dRTkavTzZ8/Py677LLK\\nvV/84hdx7rnnVs7z4Pbbb6+E73LFugzpLVq0KDKY94c//CHyWpYM0333u98tjvOPF198sV747oor\\nroi5c+cW73366afjgAMOqNT92Mc+FvPmzaucOyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ\\n1QQE8LrajBsvAQIECBAgQIBApxC48cYbK9vOZvjuN7/5TWy99dZF39dZZ504+uiji5BdOZjcinbm\\nzJnlaSxcuLBynNvM7rXXXsUKeblV7d577x233npr5aAXj1UAAEAASURBVH6uhLdkyZLiPEN6ZTnj\\njDPiwx/+cNTV1RWXttpqq7j++usr4b3Zs2cL4JVYPgkQIECAAAECBAgQIECAAAECBAgQIECAAAEC\\nBLqkwJt7VHXJ4Rs0AQIECBAgQIAAgY4nkKvUXXnllZWOffvb366E4CoXlx8cdNBB8d73vjduuumm\\nYiW7O++8M4488sjqKsXx888/v9JWsTvttFPccsstxap46623XmQwL0sZxMvj5557rlgxr3pr28GD\\nB8d1111XhP26d+8e/fv3z6oKAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgS4pIIDXJafdoAkQ\\nIECAAAECBDqywIIFC4pAXfZx8803j1122aXB7nbr1i2OOuqoIoCXFaZNm1apN2jQoMpxbhX761//\\nOj772c/GdtttF3kvQ3WHHXZYpU55UB2ou/TSS+PRRx+Nr371q7HrrrvGiBEjIt+57777ltV9EiBA\\ngAABAgQIECBAgAABAgQIECBAgAABAgQIEOjSArag7dLTb/AECBAgQIAAAQIdUeDVV1+NJ554ouja\\nbrvtFrnlbGNl2223rdyq3nZ2//33jy984QuVez/5yU+KbWhzBbsxY8bEhRdeGK+88krlfnkwatSo\\nYpvZ8vyvf/1rvPvd746RI0fGuuuuG1/72tfi8ccfL2/7JECAAAECBAgQIECAAAECBAgQIECAAAEC\\nBAgQINClBQTwuvT0GzwBAgQIECBAgEBHFKjeBvaRRx6JpUuXNqubv/3tbytbyOZKdWeddVbccccd\\nRfCuuoFs85Of/GRstNFGceaZZ67U/hFHHBEPP/xwHH300dWPxaxZs+K0006LHXbYId71rnfF66+/\\nXu++EwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJdTUAAr6vNuPESIECAAAECBAh0eIHc6jW3\\nns2SYbfu3Rv/n+3z58+vjOfAAw+MHj16VM7zIK/dc889MWHChGIb2m984xv17n/5y18uQnj1Li4/\\n2XHHHeOyyy6LGTNmxL333hvnn39+pU9Z97bbbiu2sF28ePGKjzonQIAAAQIECBAgQIAAAQIECBAg\\nQIAAAQIECBAg0GUEGv+XvC5DYKAECBAgQIAAAQIEOpZAXV1d9OnTp+jU7373u5g+fXqjHbzvvvsa\\nvDd37tyYM2dO8ZMVNthgg3jHO94Rp556amRo73//938rz11++eWxaNGi4jy3sa1+Lres3XPPPeNT\\nn/pUPPfcc3HrrbdWnnvooYfi5Zdfrpw7IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINDVBATw\\nutqMGy8BAgQIECBAgECHF+jdu3dsueWWRT+nTJlSbCPbUKczKHfuuedWbu2+++7FcV5/y1veEv37\\n94+dd945Vlylbp111onjjjuu8txrr70Ws2fPLs4zaJfP5c/EiRMrdcqDQw45JPbZZ5/iNLekzf4p\\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLqqgABeV5154yZAgAABAgQIEOiwArnl7Oc///lK\\n/4466qh48MEHK+d5sGzZsviv//qveOKJJ4rrW221VYwZM6Y4zm1oM0CX5ZlnnolbbrmlOK7+45VX\\nXqmcZt2ePXsW58OGDatc/973vhdLly6tnOdBrqyX29KWJVfrUwgQIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAh0VQEBvK4688ZNgAABAgQIECDQoQXe9ra3xeGHH17p49ixYyMDcX/5y1/it7/9bRx4\\n4IFx9tlnV+5fcskl0atXr+I8t68dN25c5d573/veOOGEE+KPf/xj/P3vf48LL7wwtt1228r9/fff\\nvxLYO/jggyvXv/3tb0eueHfXXXfFo48+GjfccEPsuOOO8cgjjxR1Nt9888pKfZWHHBAgQIAAAQIE\\nCBAgQIAAAQIECBAgQIAAAQIECBDoQgLdlm9PtawLjddQCRAgQIAAAQIEalxg8uTJxQg322yzDj/S\\n+fPnRwbrchW7ww47LH72s59Frn5XljfeeKMI0v3yl78sLzX4efHFF8dHP/rRevdyhbzcTjbDdk2V\\nUaNGFaG86pXvLr300vjIRz7S1GPFvT//+c9Rbnu7ysoqECBAgAABAgQIECBAgAABAgQIECBAgAAB\\nAgQIEFgDAi+88ELRyogRI5rV2g9+8INiIYtczCJ3hcp/j6v+N7mmGsndovJn8eLFsWjRouLngx/8\\nYHTr1i0GDRpUfL75r3tNteQeAQIECBAgQIAAAQJtItCvX7+i3fKz+iUDBw6M2267La655prqy5Xj\\nI444Ih577LGVwndZIf9H/wUXXFBsP7vXXntVnikPBgwYEOecc048/fTTUR2+y/vHHnts/O1vf4tc\\nOa+h8qUvfSleeukl4buGcFwjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoUgJWwOtS022wBAgQ\\nIECAAIHaF+hMK+C1ZDaWLFkSObZ11lknFixYEBnO69+/f7ObmDlzZsydO7f4r3vyv9AZPnx48V/4\\nrKqB5Stmx2uvvRZ1dXUxb968GDp0aOQWtwoBAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBtSHQ\\n0VbA67k2ELyTAAECBAgQIECAAIGWCfTo0SM22GCDlj1UVXvw4MGRPy0tuTJfQ6vztbQd9QkQIECA\\nAAECBAgQIECAAAECBAgQIECAAAECBAjUooAtaGtxVo2JAAECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAgQIECAAAECBNpcQACvzYm9gAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\\nECBAgAABAgRqUUAArxZn1ZgIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\\noM0FBPDanNgLCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWBQTwanFW\\njYkAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE2lxAAK/Nib2AAAECBAgQ\\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGpRQACvFmfVmAgQIECAAAECBAgQIECA\\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgzQUE8Nqc2AsIECBAgAABAgQIECBAgAABAgQIECBA\\ngAABAgQIECBAgAABAgQIECBAoBYFetbioBob00N/nBq/ufGleGX8rJg3Z0lj1drs+pZjBsW7jtki\\ntnrr4DZ7h4YJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoH0EuswKeBm+\\n++FXH45nH35trYTvcjrz3d/93APxzEMz22d2vYUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\\nAQIECBAgQIAAAQIE2kygywTwfnPjP9oMsaUN3/rj51v6iPoECBAgQIAAAQIECBAgQIAAAQIECBAg\\nQIAAAQIECBAgQIAAAQIECBAg0MEEukwA79mHO86qc6+Mf6ODfQ10Z1UCixcvjgULFsSSJe2/dfGq\\n+uY+AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJrR6DLBPDWDm/Db503R4ir\\nYZmOeXX+/Pmx1157xZAhQ+KYY46JpUuXdsyO6hUBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\\nAgQIECBAgAABAu0qIIDXrtxe1lkFcgW8LPPmzYtly5Z11mHoNwECBAgQIECAAAECBAgQIECAAAEC\\nBAgQIECAAAECBAgQIECAAAECa1BAAG8NYmqqdgV69uxZGVy3bt0qxw4IECBAgAABAgQIECBAgAAB\\nAgQIECBAgAABAgQIECBAgAABAgQIEOi6AgJ4bTj3W+44pA1b1zQBAgQIECBAgAABAgQIECBAgAAB\\nAgQIECBAgAABAgQIECBAgAABAgQIrE0BAbw20n/74RvHVy75lzj6Szu20Rs0S4AAAQIECBAgQIAA\\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJrU+DNfTXXZi9q7N0Zvjv21LcWo9rvvZsWn5d/\\n65EONcolS5bEE088Ec8//3wsXLgw+vfvH295y1ti1KhR9fo5bdq0eP3112Pp0qWx3nrrxaBBg+rd\\nz5Nnn302unf/Z5Zz8803jxW3aJ00aVI8+eSTkW1lGTx4cOy0004xdOjQ4rz6j6wzY8aM6NGjR2Rb\\n+e6//OUv8cYbbxTtbrLJJkU/+/TpUzy2ePHieOSRR+LFF1+Mvn37xoIFC2L33XeP9ddfv7rZ4viV\\nV16JuXPnFu/Nd2e/89kcW5Yc/zbbbFMct+aP7Pujjz5ajDP7tfHGG8duu+0WvXr1ak1zniFAgAAB\\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoIMLCOCt4QmqDt+VTWcI77nHZsY9t71c\\nXlqrnw899FB86EMfihdeeGGlfhx33HFxxhlnRBlwu+KKK+KrX/1qUW+vvfaK22+/PXr2fPNr89Of\\n/rRoKytsuOGG8fDDD0ddXV1RP8NzX/7yl+Pyyy8vzlf84+yzz46PfexjlfBe3v/iF78YN9xwQ1H1\\nS1/6UnzrW99a8bEiCHjXXXcVYbtDDz00pk6dulKdb3/72/GpT32qEgacP39+HHTQQcWYM2z4H//x\\nH/H9739/pedy/P/zP/8T66yzzkr3GruQob/vfe97cfrpp69UJUOLP//5z2PMmDEr3XOBAAECBAgQ\\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHOLWAL2jU4fw2F77L5i0/7e4cJ3z3wwAOR\\nQbqGwnfZ1wsvvDA+/OEPR67gluX444+PsWPHFsf33ntv3HbbbcVx/vHaa6/F5z//+cr5lVdeWQnf\\n5b399tuvXvhuhx12KMJz5QNf+MIX4uKLLy5Pi89hw4ZVzhsK3+XNDNxloC1Xl2sofJd1Tj755Pj1\\nr3+dh5XSr1+/4nj27NkNhu/yZo4/Q4Hl+CsPN3KwbNmywqih8F0+kv3bc8894/7772+kBZcJECBA\\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOisAgJ4a2jmOkP4bs6cOfHxj3+8MuJc\\ngS6Dcnk9g3mbbbZZcS9XucuwXZZcCS5DaWX57Gc/WzyT52eddVYlAPe5z32u2Pq1rHfrrbfGU089\\nVZweffTRxRaxuZXsi8u3ii1XuMub3/zmN2PWrFnlY/U+c/W4O+64o9iGNvt455131gvwZeX3vOc9\\nMX78+GIMudVtrppXlmuuuaayvWx5rfzMtjMUl+3mlreXXHJJeStuuummyvgrFxs5yBUAr7322uJu\\n+uUWtGWbObaynHLKKc0O9ZXP+CRAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\\noGMLCOCtgflpLHx3UQda+S6HmQG2MhR35plnxic+8Yno1atXIbDNNtvEzTffXNG4/vrrI1d3y7Ld\\ndttF1s+SK7rltrRPPvlkZIAvS249m1vNVpenn366OM3tXjN8loG3shxyyCGRq99lmTdvXkyfPr28\\nVe/z6quvjr333ruy5W2u3PfDH/6wUicDbxdddFGMHDmyuDZgwICiH+WKfX/7299i7ty5lfrlQfbl\\nd7/7XWy//fbFpQwZfuADH4gf/ehHZZWoHn/l4goHua1tuUpf2ebmm29eafMzn/lMJfD4pz/9qQgK\\nrtCEUwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEOrGAAN7/Td4mW6/bqmls\\nKnx3720vt6rNtngow3S5mlyWDIuNGzdupddstdVW8aEPfai4/oc//CEyYFaW3Ja1DLZl2G6XXXYp\\nb8WPf/zjyKBddfn6179erCw3YcKEGDVqVPWt4niPPfYoPnM72FyBbsWSob4dd9xxxcux5ZZbVq4d\\nc8wxlS1vy4sZKMytabNk2w1tJbvPPvvEJptsUj5S+TzssMMig4hZ7rrrruL5ys0GDp5//vl47LHH\\niju5VW/19rl5sVu3bpUAXp6XocQ8VggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\\nBAgQ6PwCPTv/EFZ/BId/ZKt4zye2iUvPeCh+//N/NLvBzhK+ywHlSnC5IlyWXMUuV69bf/31i/Py\\njwzpXXXVVcVphtcWLFhQCbj16dMnzjvvvNhzzz3L6sXn5z//+ciV6RoqubLctGnTiuDf3XffHc88\\n80wMHz68qJqr8TVVFi5cGEuWLFmpytKlSyvXhg4dWjluyUFuEZtjzYBcdenXr1/su+++xSqBjb2/\\nun65mmBeO//88yOfX7RoUXWVYkva8kJukasQIECAAAECBAh0ToFXX301Jk6cuMrO9+jRY5V1VCBA\\ngAABAgTaRuCtb31r2zSsVQIECBAgQIAAAQIECBAgQIAAAQJNCHT5AF4ZvkujY0/95/8nXXNCeJ0p\\nfJdjy38I7Nnzzek+99xz83KLypgxY+LEE0+Ms846q/JcnjdUMuCWgb0Vt6ZtqG5rr2VAcE2W7HNz\\n/lG1fGd1gC9DjSeddFJ5yycBAgQIECBAgEAXFJg+fXq89NJLMXjw4Bg9enTNCIwfP74YSy2NKedq\\n5syZxVy19j/s6YgTbK464qw03Cdz1bBLR7xqrjrirDTcp3KuhPAa9nGVAAECBAgQIECAAAECBAgQ\\nIECg7QTeTGS13Ts6bMt7HbJRsfJddQebE8LrbOG7cnzV27EeddRRMXDgwPLWSp+5/Wxu51pdMvB2\\n3333VV8qVrXbfffd613Lk5/+9Kf1wnennHJKHHDAAZH/uJXtXnfddXHaaaet9NzavJCBunXXbd1W\\nxLmt75FHHtlo93PVvU033bTR+24QIECAAAECBAh0bIENNtgg8qep8tBDDxWhrgyq1eI//tfSmPI/\\nvJk8eXKMGDEiRo4c2dS0dsp75qrzTJu5MldrQ6DW/w5cG6beSYAAAQIECBAgQIAAAQIECBAg0LUF\\nunQA797bXy5m/+Nf26net6CpEF6j4buv/T3K9uo11oFOyhXwcsvY73//+8WqeC3p3kUXXRR/+tOf\\n6j3y4Q9/OB588MHo379/5Xpuw/q9732vOM9gWm4/u8UWW1Tu58H2229f77w9T+rq6hp8Xa6A19C2\\ntw1WXn6x9Mz7V199daNb8Tb2vOsECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE\\nCHRuge6du/ur3/sMzV20PDy3YskQXobtqktnDt/16dMnxo4dWwzn3nvvjSeeeKJ6aPWOGwqhZf3/\\n/M//LOplqG7cuHHF8YQJE+Kb3/xmvecXLlwYueJbln322Sc233zzevfzJLd7Wltl3rx5Db56xowZ\\ncdVVVxX3MlC4zjrrNFivvLj11luXh3HxxRfH0qVLK+fVBw15Vt93TIAAAQIECBAgQIAAAQIECBAg\\nQIAAAQIECBAgQIAAAQIECBAgQIBA5xTo8gG8nLbmhPA6c/iu/Gq+4x3vKA/jhBNOiNdff71yXh7c\\ndNNNxWp1jz/+eHkpcjva4447rnJ++eWXx4UXXhg77LBDcS1Xu8tQX0Mlt6xd8T1PP/10fOITn2io\\nertcu/322+Oee+6p965c/e7ss8+uXDv66KOjsZXyykobbrhhxeCGG26Im2++ubxV+cyx77nnnvGN\\nb3wjMpioECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQOwICeP83l02F8D72\\n3ztFuS1t9dTnynkdfdvZ6v4eeuihlVXw7r///hgzZkz85je/icmTJ8eTTz4ZJ510Uhx11FExderU\\n+PznP19Z0S1Xd3vggQeKpt7//vfH3nvvHb169Ypzzjmn0vwxxxwTs2fPLs4zuDZixIjiOFfI2223\\n3eLOO++MP/zhD/G///u/sfPOO1eey4Nsq73LwQcfHFdccUVMnz49nn/++Tj++OMr2+ZmX/7t3/5t\\nlV3q169fnH766ZV6Gdr7yle+EuPHj48c92233VYYP/bYY8UqgY8++milrgMCBAgQIECAAAECBAgQ\\nIECAAAECBAgQIECAAAECBAgQIECAAAECBDq/QM/OP4Q1N4IyTPfxr+1Ur9F/OXSjeud50tnCd9nn\\n3FL12muvjQMPPDBeeOGFImh32GGH5a16JbeYveiii6J79+6RK+GVW8/mtqy5kltez7LHHnvExz/+\\n8aJuBs7OPPPM+PrXv17c/853vhO77rprUS/vNRVoe/nll+Mtb3lLUXdNbNfa3DY++clPFu9c8Y8f\\n/ehHMXr06MrlbG/x4sWV81wtryxpmSvnfeELXyguffe73438WbGcdtppKwUPV6zjnAABAgQIECBA\\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBziVgBbwV5quxlfCqq3XG8F3Z/9w2NVe/O+OM\\nM8pL9T5PPfXUePDBB2PTTTeNDJqdf/75lfsZLBs1alTlPA8ynLfZZpsV13KlvJdeeqk43m677eLh\\nhx+Od7/73cV59R8XXHBB3HHHHZVLuU1tGWrL8F+W3r17R48ePSp1yoPq1fIGDBhQXq73mUHBLPnZ\\ns+fKGdODDjoofvrTn0b5rvLhPL/11ltj3Lhx5aXiM9vI1e6yDBs2LLp161Ycl3/kdrq5pW2uDLhi\\nGTt2bNHmF7/4xZWeW7GucwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEOpfA\\nyumkztX/NultYyvh5cs6c/iuxOrbt2987nOfi1wBLrdgzXDZwoULY/DgwfUCaxk0y7Bc/jRWcqvZ\\n3GK1oZKryOWKezNnzox58+YVVYYMGRJ9+vQpjufMmbPSY6ecckrkT2Nlk002iYaeq66fq/DlT2Ml\\nV/A74IAD4rnnnospU6ZUwn/Dhw+vN/7y+Vw5MLfPbarstNNORagwx7po0aJiW90MEA4cOLCpx9wj\\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKATCwjgNTJ5DYXwaiF8Vz3cDJZt\\nsMEG1Zfa5DiDffnTkUquuJcBuZEjR67RbnW0ca7RwWmMAAECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAgQIECAAIF6AragrcdR/6R6O9paC9/VH6kzAgQIECBAgAABAgQIECBAgAABAgQIECBA\\ngAABAgQIECBAgAABAgQIEGipgBXwViGWIbx5cxbFg7+ftIqabncWgbq6us7SVf0kQIAAAQIECBAg\\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKADC1gBrxmTI3zXDKROUGXOnDlFLydNmhS5Ba1C\\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB1RGwAt7q6Hm20wj06dMn7r77\\n7liwYEHkcY8ePTpN33WUAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGOKSCA\\n1zHnRa/aQGDEiBFt0KomCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoqgK2\\noO2qM2/cBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILBaAgJ4q8XXuof7\\n9LX9aevkPEWAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGOI9BlAnhbjhnc\\nYdQ32nJgh+mLjhAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA6wS6TABv\\nv/dt3DqhNnjqXcds3gatapIAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE\\n2lOgywTw3rr3evGJr4+JXAlvbW0Bm+/+/PfGxlZv7Tir8bXnl827CBAgQIAAAQIECBAgQIAAAQIE\\nCBAgQIAAAQIECBAgQIAAAQIECBAgUEsCPWtpMKsaS4bw8kchQIAAAQIECBAgQIAAAQIECBAgQIAA\\nAQIECBAgQIAAAQIECBAgQIAAAQKrK9BlVsBbXSjPEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\\nECBAgAABAgQIECBAgACBaoEutQJe9cAdEyBAgAABAgQIECBAoLkCs2bNitmzZzdZfc6cOTF//vyY\\nMWNGTJo0qcm6nelmjidLLY1p6tSpxTx17949unXr1pmmo8m+mqsmeTrUTXPVoaajyc6YqyZ5OtTN\\nnKshQ4Z0qD7pDAECBAgQIECAAAECBAgQIECAQNcQEMDrGvNslAQIECBAgAABAgQIrIZABvAmTpzY\\nZAsZwFu4cKEAXpNKHePm9OnTY+bMmbFs2bJYunRpx+jUGuhFLQaFzNUa+GK0UxPmqp2g18Branmu\\nBPDWwBdEEwQIECBAgAABAgQIECBAgAABAi0WEMBrMZkHCBAgQIAAAQIECBDoagIDBgxY5ZBz9bve\\nvXvH4MGDY8SIEaus31kqZFAjSy2NqVz1btiwYTF8+PDOMhWr7Ke5WiVRh6lgrjrMVKyyI+ZqlUQd\\npkI5Vx2mQzpCgAABAgQIECBAgAABAgQIECDQZQQE8LrMVBsoAQIECBAgQIAAAQKtFcgA3qpCeFOm\\nTIk+ffrE0KFDY+TIka19VYd7bvLkyUWfamlMOaBc/S7Dd7U0LnPV4X59Gu2QuWqUpsPdMFcdbkoa\\n7VA5V41WcIMAAQIECBAgQIAAAQIECBAgQIBAGwl0b6N2NUuAAAECBAgQIECAAAECBAgQIECAAAEC\\nBAgQIECAAAECBAgQIECAAAECBGpaQACvpqfX4AgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAgQIECgrQQE8NpKVrsECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE\\nCBAgUNMCAng1Pb0GR4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJtJSCA\\n11ay2iVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBmhYQwKvp6TU4AgQI\\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGgrAQG8tpLVLgECBAgQIECAAAEC\\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUtIAAXk1Pr8ERIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAgQIECAAAECBAgQIECAQFsJCOC1lax2CRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE\\nCBAgQIAAAQIECBAgQKCmBQTwanp6DY4AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\\nQIAAAQIE2kqgZ1s1rF0CBAgQIECAAAECBLqmwLJly2L8+BfilZcnxIKFC6NvXV1ssulGsfHGo6Jb\\nt24tRmlte9OmTo9HH3sienTvEfMXLIiNNtowtt56dHTv7r9DavEkeIAAAQIECBAgQIAAAQIECBAg\\nQIAAAQIECBAgQKBBAQG8BllcJECAAAECBAgQIECgNQKvvPJqfOTYT8e0aTNWenyDDdaP8y/4ThHE\\nW+lmIxda097ixYvjnO9dGNdcc+NKrdYtDwNe+uPzYqutRq90zwUCBAgQIECAAAECBAgQIECAAAEC\\nBAgQIECAAAECLRWw9ENLxdQnQIAAAQIECBAgQKBBgVxx7v3v+48ifLflVlvEued9K6697pI4/fRT\\non//fvHqq5PiyCOObTCc11CDrWkvV8s744yzKuG7k04+YXng7vtxwQ++E6NHbxbz5s2Lo4/6VEya\\nNKWhV7pGgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoEUCAngt4lKZAAECBAgQIECAAIHGBK68\\n8vpYuHzL2Z13HhNXXXVR7LXX7sVKc4e+653xyztujFHLt4DN+z/96a2NNVHvemvae/DBh+OWn/8y\\nevfuHTfddEWMG/eeGDNmh9h9913iuusvjUMOfWfRhzO/+d1YsmRJvfc5IUCAAAECBAgQIECAAAEC\\nBAgQIECAAAECBAgQINBSAQG8loqpT4AAAQIECBAgQIDASgLz58+P2267s7h+6ldOjJ49e9Sr07dv\\nXXzj66cW1/7w+3tXGX5rTXu5+t2NN/y8eMdJJ30mNt1s43p96NatW5x44vFFOO++++6PiRMn17vv\\nhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBLBQTwWiqmPgECBAgQIECAAAECKwksWbK0uLbt\\ntlvFqFEbrnQ/L2y08YbFVrR9+vRp8H71xda0t2DBgsgV8HL1u33327u6ucrxoEHrxpFH/lsRABw/\\n/vnKdQcECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEWiMggNcaNc8QIECAAAECBAgQIFBPYNKk\\nyTFz5mtRV1cXyxeaa7C8+uqkmD17TuTqdqsqrWlvwoSJMW3ajBg9erMYOHBAo6/YbvttinvjnxXA\\naxTJDQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgWYJ9GxWLZUIECBAgAABAgQIECDQhMAWW2wW\\n9//17ujRo/7Ws+UjuT3sZT++pjh9+9v3arReWX912hs79q1Ntp8BvSzTpk2P7FduTasQIECAAAEC\\nBAgQIECAAAECBAgQIECAAAECBAgQaI2AAF5r1DxDgAABAgQIECBAgMBKAk2F7y644Edx992/L7aH\\n/dd/PXSlZxu60Nr2hgwZ3FBzlWsZusvy+ONPxdKlS5sM61Ue6qIHD/xuUvz1j68Xo3/xr0/Hvxy6\\nUQzboG8X1TBsAgQIECBAgAABAgQIECBAgAABAgQIECBAgMDKAgJ4K5u4QoAAAQIECBAgQIDAGhKY\\nO3denHrq1+MPv7+vaPHc874Vw9Yb2urWm9PewoULm9V+nz59mlUvK2Wbq2p30aJFkT/z5s1bvtXu\\n7Ga33ZEr3n7VMzH+kX8G8CJej4237Rt9Bi7tyF1uVt/mzJlTzFN+1spc5cDzu5ellsZkroop7RR/\\nmKtOMU1FJ2t5rurq6jrPROgpAQIECBAgQIAAAQIECBAgQIBAzQgI4NXMVBoIAQIECBAgQIAAgY4l\\n8NBDj8YnjzuxElz74UXfjV122anVnWxue717927WO+bPn9+sellp2rRpMXHixCbrz5w5swhATZgw\\nIWolAFAGusqB59i6959ennbaz+nTp0fO19y5c2sqrJbzk6VWvn85FnOVCp2jmKvOMU/Zy1qeq9Gj\\nR3eeidBTAgQIECBAgAABAgQIECBAgACBmhEQwKuZqTQQAgQIECBAgAABAh1DYPHixZFbzl5+2bVF\\nh3ba6S3xzTP/O9Zbb1irOtjS9l544aXIbWa7devW5Pv2+H+7Nnv72XXWWSf69+/fZHuvv/560V6u\\nrNevX78m63aWmytuA1wrY8vgXYYLM6hWK3OV36lyVcdaGpO56ix/W0QRaPV71Tnmq9Z/rzrHLOgl\\nAQIECBAgQIAAAQIECBAgQIBALQkI4NXSbBoLAQIECBAgQIAAgbUsMG3ajPjgv390+YpxM4qenHb6\\nl+PQQ9+5yjBcY91uSXtl+ChXyluyZGn07NmjwWafevLZ4vo6zVwpLysPHTq0+Gmwwf+7mMGTqVOn\\nxqhRo2LLLbdsqmqnuVdXN2V5X+dW+vvPsbUuSFlppAMcZJiyb9++MWLEiBg5cmQH6NGa6UJuK5ml\\nVr5/ORZzlQqdo5irzjFP2ctan6vOMxN6SoAAAQIECBAgQIAAAQIECBAgUCsCAni1MpPGQYAAAQIE\\nCBAgQGAtC7z22usx7shjl2/t+VpsvfXoOOfcM1u96l0OpaXtDR8+LNZff3hMmjRleQBwenG8Ikmu\\njPe3B/5eXN5++21WvO2cAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQIsEureotsoECBAgQIAA\\nAQIECBBoQCCDbf9zxtlF+G7nncfEFVde2Ozw3ZIlS2LOnLkxf/6CSsutaa9nz56x2+5jY+HChXHz\\nzb+otFV98Oqrk+KWn/8yei9f/W7LLbeovuWYAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQIsF\\nBPBaTOYBAgQIECBAgAABAgRWFHj++Rfj7rt/v3xbu35x7nlnLt/+tXmLbWfQ7iunfiPetvchceml\\nV1WabU173bp1i3//wPuKNn50yZXxpz/9tdJeHsydOy++dPJ/F9cOP/zgGDpsSL37TggQIECAAAEC\\nBAgQIECAAAECBAgQIECAAAECBAi0VKB5/yrW0lbVJ0CAAAECBAgQIECgSwk89uiTxXhnz54T/3r4\\nB5evhPd6g+PP1e5yhbwLf3h29OjRo6izZMnS4rN3r16VZ1rb3pZbbRHHH//ROP/8S+LTx58U73rX\\ngbHvfnvHK6+8Gud//5JidbwMCX76Mx+LDOwpBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBFZH\\nQABvdfQ8S4AAAQIECBAgQIBAIVDXt64iMW3ajMpxQwd9q+rm/bq6PkW13Ba2LKvT3jHHfjDWGz6s\\n2BL31lt/FflTlne+c9846eQTlq/U17+85LMJgfU26BtP/316pcY/nnkjth07rHLugAABAgQIECBA\\ngAABAgQIECBAgAABAgQIECDQ1QUE8Lr6N8D4CRAgQIAAAQIECKwBgQy25U9LS65Cd9rpXy5+qp9t\\nbXvZRrZ52GEHxcEHHxATJ06KefPmRa/lq+sNHjwoBg1at/o1jlchMGxk33o15s5aVO/cCQECBAgQ\\nIECAAAECBAgQIECAAAECBAgQIECgqwsI4HX1b4DxEyBAgAABAgQIEKhRgZ49e8RGG21Yo6MzLAIE\\nCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgY4g0L0jdEIfCBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\\nAQIECBAgQIAAAQIECBAgQIBAZxMQwOtsM6a/BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAgQINAhBATwOsQ06AQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\\nAgQIdDYBAbzONmP6S4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIdQkAA\\nr0NMg04QIECAAAECBAgQIECg4wvMnb2o43dSDwkQIECAAAECBAgQIECAAAECBAgQIECAAAEC7Sgg\\ngNeO2F5FgAABAgQIECBAgACBziSwzdih9br70tOv1zt3QoAAAQIECBAgQIAAAQIECBAgQIAAAQIE\\nCBDo6gICeF39G2D8BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINAqAQG8\\nVrF5iAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgS6uoAAXlf/Bhg/AQIE\\nCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLRKQACvVWweIkCAAAECBAgQIECA\\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGuLiCA19W/AcZPgAABAgQIECBAgAABAgQIECBA\\ngAABAgQIECBAgAABAgQIECBAgAABAq0SEMBrFZuHCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE\\nCBAgQIAAAQIECBAgQKCrCwjgdfVvgPETIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\\nBAgQIECAQKsEerbqKQ8RIECAAAECBAgQIECgCwlMnz498qepMnPmzJgzZ05MmDAh+vfv31TVTnNv\\nwoQ59fo6b968GD9+fL1rnfFk6tSpMWPGjMjx5JzVSsnvXpZa+f7lWMxVKnSOYq46xzxlL2t5rjbc\\ncMPOMxF6SoAAAQIECBAgQIAAAQIECBAgUDMCAng1M5UGQoAAAQIECBAgQIBAWwksWLAgZs2a1WTz\\nixYtiiVLlhShrtmzZzdZt7PcnDdvfr2uzpuzKGphbHPnzi3mKT9rYTzlJGWgMEstjclclbPb8T/N\\nVcefo7KHtT5X5Th9EiBAgAABAgQIECBAgAABAgQIEGgvAQG89pL2HgIECBAgQIAAAQIEOq3AsGHD\\nYsCAAU32/6mnnoopU6bEBhtsEFtssUWTdTvLzRzGjWdNqXR36suLamJs/fr1i7q6ulhvvfVi/fXX\\nr4yvsx9kqCZLrXz/cizmKhU6RzFXnWOespe1PledZyb0lAABAgQIECBAgAABAgQIECBAoFYEBPBq\\nZSaNgwABAgQIECBAgACBNhPo3bt35E9TpVevXpE/ffv2XWVYr6l2Ovq9VQURO3r/s3+5QlxuPZtb\\ntdbCeErz/O5lqaUxmatydjv+p7nq+HNU9rDW56ocp08CBAgQIECAAAECBAgQIECAAAEC7SXQvb1e\\n5D0ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCWBATwamk2jYUAAQIE\\nCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE2k1AAK/dqL2IAAECBAgQIECAAAEC\\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGpJQACvlmbTWAgQIECAAAECBAgQIECAAAECBAgQ\\nIECAAAECBAgQIECAAAECBAgQIECg3QQE8NqN2osIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\\nECBAgAABAgQIECBAoJYEBPBqaTaNhQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\\ngAABAgTaTUAAr92ovYgAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEaklA\\nAK+WZtNYCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDdBATw2o3aiwgQ\\nIECAAAECBAgQIND5BDbccp16nX7ygWn1zp0QIECAAAECBAgQIECAAAECBAgQIECAAAECBLqygABe\\nV559YydAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBVgv0bPWTHiRAgAAB\\nAgQIECBAgMAqBKZNnR7/ePmV2HzzTWPQoHVXUbvh28uWLYvx41+IV16eEAsWLoy+dXWx6WYbx0Yb\\nbRjdunVr+KHlV/Pdjz72RPTo3iPmL1hQ1N9669HRvbv/DqlRNDcIECBAgAABAgQIECBAgAABAgQI\\nECBAgAABAgRaJCCA1yIulQkQIECAAAECBAgQaInArbf+Ks4776K48MKzY9fddm7Jo0Xdxx9/Kj57\\nwn/GzJmvrfTsllttEd/+9mmx8caj6t1bvHhxnPO9C+Oaa26sdz1P6paH9y798Xmx1VajV7rnAgEC\\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGWCgjgtVRMfQIECBAgQIAAAQIEmiUwZ87cuPbaf4bg\\nevZs+f/p8eKL/4ijPnxc8a4M233kIx+ODTccGVOmTI3zzr0onn3muTjyiGPjZz+/OkaMWK+ol6vl\\nnXHGWXHLz39ZnJ908gmx7bZbxfz58+Pss84vVtI7+qhPxU9/dlWsv/7wZo1DJQIECBAgQIAAAQIE\\nCBAgQIAAAQIECBAgQIAAAQKNCdh7qTEZ1wkQIECAAAECBAgQaLFArlSXAbn773+gCM9NmzajxW3k\\nAxmk++GFPy6effe7D46rr74oDjhgn9huu61jn33+JW686fLYf/+3x8LlW9LedNMtlXc8+ODDRfiu\\nd+/ey69fEePGvSfGjNkhdt99l7ju+kvjkEPfWTxz5je/G0uWLKk854AAAQIECBAgQIAAAQIECBAg\\nQIAAAQIECBAgQIBAawRavgxFa97iGQIECBAgQIAAAQIEal4gV7w75OAjioDbmhjskiVLI4N0xx//\\n0ejRo0e9Jrt16xbHffKYuPvu3xfXM7CX5cYbfl58nnTSZ2LTzTYujss/8pkTTzw+7vr17+K+++6P\\niRMnx6hRG5S3fRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBosYAAXovJPECAAAECBAgQIECA\\nQEMCffvWxbnnfStmzJgRvXr2imXL/9/JJ/13Q1XX6LVZs2YX7S1YsCByBbwM7e27394NvmPQoHXj\\nyCP/La688vrl29E+L4DXoJKLBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECzRWwBW1zpdQjQIAA\\nAQIECBAgQKBJgVxhbtddd4oDD9w/9tv/bcUWse8+/OAmn2nq5tChg4vV9K666icrVcsV76684vri\\n+iabbBT57gkTJkZueTt69GYxcOCAlZ4pL2y3/TbF4fhnny8v+SRAgAABAgQIECBAgAABAgQIECBA\\ngAABAgQIECDQKgEr4LWKzUMECBAgQIAAAQIECKxKIENy8+fNX1W1Bu9noO5DHz4yfvKTnxWr1b3+\\nxhsxbtx7Y/31h8ery4N2V1xxXdx552+jrq4uDjpo/3ptjB371pW2rK2ukAG9LNOmTY/sY75LIUCA\\nAAECBAgQIECAAAECBAgQIECAAAECBAgQINAaAQG81qh5hgABAgQIECBAgACBNhfYcMOR8aNLz4uP\\nHPuZuOXnvyx+VnzplVddGLmtbHUZMmRw9elKxxm6y/L440/F0qVLmwzrrfRwF7yw0dZ9YsKzCyoj\\nf+qB6bHt2GGVcwcECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAga4sIIDXlWff2AkQIECAAAECBAh0\\nYIHnnnshPnnciUUPe/ToEfu/4+2x2aYbxysTXo3bbr2zuP7vH/hYXHX1D2OLLf65ql1eXLhwYXFv\\nVX/06dNnVVUq92fNmhWzZ8+unDd0MGfOnJg/f37MmDEjJk2a1FCVTnlt3rx59fqdDp19fFOnTi3m\\nqXv37jW1AmJ+97J09vmp/sKZq2qNjn1srjr2/FT3rpbnasiQIdVDdUyAAAECBAgQIECAAAECBAgQ\\nIECgXQQE8NqF2UsIECBAgAABAgQIEGiJwPz5C+JTnzyxCNO9//2HxxdOPD569+5daeKkk06IM7/5\\n3bjjjrvj2GM+Hb+686bKvep6lYsNHGRYrrklA3gTJ05ssnoG8DL8V5sBvB6VsddCAG/69Okxc+bM\\nYgviXAWxVkotBvDMVef5dporc7W2BfLvQAG8tT0L3k+AAAECBAgQIECAAAECBAgQ6JoCAnhdc96N\\nmgABAgQIECBAgECHFvjb3/4e06bNiJ13HhMnnXzCStvEDhjQP7522n/GQw89uny1rynx5JPPxsCB\\n/YsxvfDCS0Wwqlu3bk2OcY//t+tK7Tb2wIABAxq7Vbmegb4M/w0ePDhGjBhRud7ZD+rqcjW/N1cV\\n7NevX6cfX/ndGDZsWAwfPryzT1Gl/xmAylJL3z9zVZneDn9grjr8FFU6WOtzVRmoAwIECBAgQIAA\\nAQIECBAgQIAAAQLtJCCA107QXkOAAAECBAgQIECAQPMFpk6dVlQ++JADGg3J9erVKw48aP+4/LJr\\nY3niLsotZTOUt2TJ0ujZ881V26rf/NTysF6WdapW1Ku+39BxBvBWFcKbMmVK0YehQ4fGyJEjG2qm\\nU16rq3tmeb/fDOClQy2Mb9ny70yG72phLOUXa/LkycVhLY0pB2Suyhnu+J/mquPPUdnDWp6rcow+\\nCRAgQIAAAQIECBAgQIAAAQIECLSXQPf2epH3ECBAgAABAgQIECBAoKUCPXs0HKIr2xlYtTLd8OHD\\nYv31hxcr4k2b9s+VwMp65WcGDv72wN+L0+2336a87JMAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\\nQIBAqwQE8FrF5iECBAgQIECAAAECBNaUwJIlS2LOnLkxf/6CSpODBq1bHN999++Xr2a3pHK9+mDp\\n0qVx112/q1zq2bNn7Lb72Fi4cGHcfPMvKterD159dVLc8vNfFlvFbrnlFtW3HBMgQIAAAQIECBAg\\nQIAAAQIECBAgQIAAAQIECBBosYAAXovJPECAAAECBAgQIECAwJoSyBXpvnLqN+Jtex8Sl156VaXZ\\nnXbasQjJ3XPPn+Pqq26IDNtVl3zuBz+4NJ588pno379fbLnVFtGtW7f49w+8r6j2o0uujD/96a/V\\nj8TcufPiSyf/d3Ht8MMPjqHDhtS774QAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBASwV6tvQB\\n9QkQIECAAAECBAgQINBcgSVL6gfnGnqurNO7V6/K7VwB73+++dX44olfjXPOuTCuv/7m+Pd/f3+M\\nHDkiJrw6MS6/7NqYOfO1ov53zvpGEcLLkwziHX/8R+P88y+JTx9/UrzrXQfGvvvtHa+88mqc//1L\\nitXxMrD36c98rAjsVV7ogAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEArBATwWoHmEQIECBAg\\nQIAAAQIEVi2QK9Jts82WkdvILk+7NfpAXV2f4l7v3r3r1dl3373j6msujjPP/G48+sgTcfbZ59e7\\nv/POY+KUU78Qm222Sb3rxxz7wVhv+LD4nzPOjltv/VXxU1Z45zv3jZNOPmF5YK9/ecknAQIECBAg\\nQIAAAQIECBAgQIAAAQIECBAgQIAAgVYLCOC1ms6DBAgQIECAAAECBAisSuDYj3wo8qexkiG9007/\\ncvHTUJ0M8F122QUxa9bsmDZteixatCj69OkTQ4YMrqx6t+Jz2eZhhx0UBx98QEycOCnmzZsXvZav\\nrjd48KDIlfUUAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmtKQABvTUlqhwABAgQIECBAgACB\\nNhMYMKB/5E9LSs+ePWKjjTZsySPqNiAwaut1Im5988aDf5gYB35g8+g74M0tg9+864gAAQIECBAg\\nQIAAAQIECBAgQIAAAQIECBAg0LUEunet4RotAQIECBAgQIAAAQIECKyOwD+eeSNeeub11WnCswQI\\nECBAgAABAgQIECBAgAABAgQIECBAgACBmhEQwKuZqTQQAgQIECBAgAABAgQIECBAgAABAgQIECBA\\ngAABAgQIECBAgAABAgQIEGhPAQG89tT2LgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\\nQIAAAQIECBCoGQEBvJqZSgMhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\\ngfYUEMBrT23vIkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGaERDAq5mp\\nNBACBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaE8BAbz21PYuAgQIECBA\\ngAABAgQIdDKBdYf27GQ91l0CBAgQIECAAAECBAgQIECAAAECBAgQIECAQPsJCOC1n7U3ESBAgAAB\\nAgQIECBAoNMJDBy2cgDvqQemd7px6DABAgQIECBAgAABAgQIECBAgAABAgQIECBAoC0EBPDaQlWb\\nBAgQIECAAAECBAgQqGGBJx+YVsOjMzQCBAgQIECAAAECBAgQIECAAAECBAgQIECAQPMFBPCab6Um\\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoCAjgVSgcECBAgAABAgQI\\nECBAgEBzBObNWdTtvJSFAABAAElEQVScauoQIECAAAECBAgQIECAAAECBAgQIECAAAECBGpeQACv\\n5qfYAAkQIECAAAECBAgQILBmBf7xzBtrtkGtESBAgAABAgQIECBAgAABAgQIECBAgAABAgQ6qYAA\\nXiedON0mQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgbUr0HPtvt7bCRAg\\nQIAAAQIECBAg0PEFXn311Zg4cWKTHZ0+fXq88cYbMX78+CbrdbabOZ5e6/SORQvq9/yhhx6qf6ET\\nneVczZw5M/Jz8uTJnajnTXe11r57OVpz1fScd6S75qojzUbTfanluRo9enTTg3eXAAECBAgQIECA\\nAAECBAgQIECAQBsIWAGvDVA1SYAAAQIECBAgQIAAgVoSOOizC2tpOMZCgAABAgQIECBAgAABAgQI\\nECBAgAABAgQIEFhjAlbAW2OUGiJAgAABAgQIECBAoFYFNthgg8ifpkquCJerquXqO29961ubqtpJ\\n7/2jXr878xhzNcNc+W7EiBExcuTIeuOqhZPOPDcr+purFUU67rm56rhzs2LPan2uVhyvcwIECBAg\\nQIAAAQIECBAgQIAAAQJtLWAFvLYW1j4BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\\ngAABAgQI1KSAAF5NTqtBESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBb\\nCwjgtbWw9gkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgJgUE8GpyWg2K\\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBNpaQACvrYW1T4AAAQIECBAg\\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQI1KSCAV5PTalAECBAgQIAAAQIECBAgQIAA\\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0NYCAnhtLax9AgQIECBAgAABAgQI1IDARlsOrDeKl555\\nvd65EwIECBAgQIAA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mi2fEaGhrN/ltbW0Uz4Gn2u5NPmWNVc3vX4L8Ljax+mv2urGyb2z0uQiMwtCBdcvPtPzeh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\\nAQQQQAABBBBAAAEEEEAAgUwFMglUc9+3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     \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Loss and accuracy Visualization\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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nakqI394t4sevik1r+2JfXaGDjrOueXW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\\nI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Cdg2FDgECBAgQIECAAAECBAgQIECA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\\nj6Rr/x9y5UKDWn4pfgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Image object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Graph Visualization\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "TensorFlow/notebooks/5_MultiGPU/multigpu_basics.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Basic Multi GPU computation example using TensorFlow library.\\n\",\n    \"\\n\",\n    \"# Author: Aymeric Damien\\n\",\n    \"# Project: https://github.com/aymericdamien/TensorFlow-Examples/\\n\",\n    \"\\n\",\n    \"# This tutorial requires your machine to have 2 GPUs\\n\",\n    \"# \\\"/cpu:0\\\": The CPU of your machine.\\n\",\n    \"# \\\"/gpu:0\\\": The first GPU of your machine\\n\",\n    \"# \\\"/gpu:1\\\": The second GPU of your machine\\n\",\n    \"# For this example, we are using 2 GTX-980\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import tensorflow as tf\\n\",\n    \"import datetime\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#Processing Units logs\\n\",\n    \"log_device_placement = True\\n\",\n    \"\\n\",\n    \"#num of multiplications to perform\\n\",\n    \"n = 10\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Example: compute A^n + B^n on 2 GPUs\\n\",\n    \"\\n\",\n    \"# Create random large matrix\\n\",\n    \"A = np.random.rand(1e4, 1e4).astype('float32')\\n\",\n    \"B = np.random.rand(1e4, 1e4).astype('float32')\\n\",\n    \"\\n\",\n    \"# Creates a graph to store results\\n\",\n    \"c1 = []\\n\",\n    \"c2 = []\\n\",\n    \"\\n\",\n    \"# Define matrix power\\n\",\n    \"def matpow(M, n):\\n\",\n    \"    if n < 1: #Abstract cases where n < 1\\n\",\n    \"        return M\\n\",\n    \"    else:\\n\",\n    \"        return tf.matmul(M, matpow(M, n-1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Single GPU computing\\n\",\n    \"\\n\",\n    \"with tf.device('/gpu:0'):\\n\",\n    \"    a = tf.constant(A)\\n\",\n    \"    b = tf.constant(B)\\n\",\n    \"    #compute A^n and B^n and store results in c1\\n\",\n    \"    c1.append(matpow(a, n))\\n\",\n    \"    c1.append(matpow(b, n))\\n\",\n    \"\\n\",\n    \"with tf.device('/cpu:0'):\\n\",\n    \"  sum = tf.add_n(c1) #Addition of all elements in c1, i.e. A^n + B^n\\n\",\n    \"\\n\",\n    \"t1_1 = datetime.datetime.now()\\n\",\n    \"with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:\\n\",\n    \"    # Runs the op.\\n\",\n    \"    sess.run(sum)\\n\",\n    \"t2_1 = datetime.datetime.now()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Multi GPU computing\\n\",\n    \"# GPU:0 computes A^n\\n\",\n    \"with tf.device('/gpu:0'):\\n\",\n    \"    #compute A^n and store result in c2\\n\",\n    \"    a = tf.constant(A)\\n\",\n    \"    c2.append(matpow(a, n))\\n\",\n    \"\\n\",\n    \"#GPU:1 computes B^n\\n\",\n    \"with tf.device('/gpu:1'):\\n\",\n    \"    #compute B^n and store result in c2\\n\",\n    \"    b = tf.constant(B)\\n\",\n    \"    c2.append(matpow(b, n))\\n\",\n    \"\\n\",\n    \"with tf.device('/cpu:0'):\\n\",\n    \"  sum = tf.add_n(c2) #Addition of all elements in c2, i.e. A^n + B^n\\n\",\n    \"\\n\",\n    \"t1_2 = datetime.datetime.now()\\n\",\n    \"with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:\\n\",\n    \"    # Runs the op.\\n\",\n    \"    sess.run(sum)\\n\",\n    \"t2_2 = datetime.datetime.now()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Single GPU computation time: 0:00:11.833497\\n\",\n      \"Multi GPU computation time: 0:00:07.085913\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print \\\"Single GPU computation time: \\\" + str(t2_1-t1_1)\\n\",\n    \"print \\\"Multi GPU computation time: \\\" + str(t2_2-t1_2)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2.0\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "TensorFlow/tensorflow_distributed_mnist_demo.py",
    "content": "'''\n@authou:zhourunlai\n@time:20161122\n1. For ps:\n    python tensorflow_distributed_mnist_demo.py --job_name=\"ps\" --task_index=0\n2. For worker:\n    python tensorflow_distributed_mnist_demo.py --job_name=\"worker\" --task_index=0\n    python tensorflow_distributed_mnist_demo.py --job_name=\"worker\" --task_index=1\n    python tensorflow_distributed_mnist_demo.py --job_name=\"worker\" --task_index=2\n    python tensorflow_distributed_mnist_demo.py --job_name=\"worker\" --task_index=3\n'''\nimport math\nimport tensorflow as tf\nfrom tensorflow.examples.tutorials.mnist import input_data\n\n# Flags for defining the tf.train.ClusterSpec\ntf.app.flags.DEFINE_string(\"ps_hosts\", \"deadbird-master:22222\",\n                           \"Comma-separated list of hostname:port pairs\")\ntf.app.flags.DEFINE_string(\"worker_hosts\", \"badboy-slave:2222,smartgirl-slave:22222,littleboy-slave:22222,fatboy-slave:222222\",\n                           \"Comma-separated list of hostname:port pairs\")\n\n# Flags for defining the tf.train.Server\ntf.app.flags.DEFINE_string(\"job_name\", \"\", \"One of 'ps', 'worker'\")\ntf.app.flags.DEFINE_integer(\"task_index\", 0, \"Index of task within the job\")\ntf.app.flags.DEFINE_integer(\"hidden_units\", 100,\n                            \"Number of units in the hidden layer of the NN\")\ntf.app.flags.DEFINE_string(\"data_dir\", \"/tmp/mnist-data\",\n                           \"Directory for storing mnist data\")\ntf.app.flags.DEFINE_integer(\"batch_size\", 100, \"Training batch size\")\n\nFLAGS = tf.app.flags.FLAGS\n\nIMAGE_PIXELS = 28\n\ndef main(_):\n  ps_hosts = FLAGS.ps_hosts.split(\",\")\n  worker_hosts = FLAGS.worker_hosts.split(\",\")\n\n  # Create a cluster from the parameter server and worker hosts.\n  cluster = tf.train.ClusterSpec({\"ps\": ps_hosts, \"worker\": worker_hosts})\n\n  # Create and start a server for the local task.\n  server = tf.train.Server(cluster,\n                           job_name=FLAGS.job_name,\n                           task_index=FLAGS.task_index)\n\n  if FLAGS.job_name == \"ps\":\n    server.join()\n  elif FLAGS.job_name == \"worker\":\n\n    # Assigns ops to the local worker by default.\n    with tf.device(tf.train.replica_device_setter(\n        worker_device=\"/job:worker/task:%d\" % FLAGS.task_index,\n        cluster=cluster)):\n\n      # Variables of the hidden layer\n      hid_w = tf.Variable(\n          tf.truncated_normal([IMAGE_PIXELS * IMAGE_PIXELS, FLAGS.hidden_units],\n                              stddev=1.0 / IMAGE_PIXELS), name=\"hid_w\")\n      hid_b = tf.Variable(tf.zeros([FLAGS.hidden_units]), name=\"hid_b\")\n\n      # Variables of the softmax layer\n      sm_w = tf.Variable(\n          tf.truncated_normal([FLAGS.hidden_units, 10],\n                              stddev=1.0 / math.sqrt(FLAGS.hidden_units)),\n          name=\"sm_w\")\n      sm_b = tf.Variable(tf.zeros([10]), name=\"sm_b\")\n\n      x = tf.placeholder(tf.float32, [None, IMAGE_PIXELS * IMAGE_PIXELS])\n      y_ = tf.placeholder(tf.float32, [None, 10])\n\n      hid_lin = tf.nn.xw_plus_b(x, hid_w, hid_b)\n      hid = tf.nn.relu(hid_lin)\n\n      y = tf.nn.softmax(tf.nn.xw_plus_b(hid, sm_w, sm_b))\n      loss = -tf.reduce_sum(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))\n\n      global_step = tf.Variable(0)\n\n      train_op = tf.train.AdagradOptimizer(0.01).minimize(\n          loss, global_step=global_step)\n\n      saver = tf.train.Saver()\n      summary_op = tf.merge_all_summaries()\n      init_op = tf.initialize_all_variables()\n\n    # Create a \"supervisor\", which oversees the training process.\n    sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),\n                             logdir=\"/tmp/train_logs\",\n                             init_op=init_op,\n                             summary_op=summary_op,\n                             saver=saver,\n                             global_step=global_step,\n                             save_model_secs=600)\n\n    mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)\n\n    # The supervisor takes care of session initialization, restoring from\n    # a checkpoint, and closing when done or an error occurs.\n    with sv.managed_session(server.target) as sess:\n      # Loop until the supervisor shuts down or 1000000 steps have completed.\n      step = 0\n      while not sv.should_stop() and step < 1000000:\n        # Run a training step asynchronously.\n        # See `tf.train.SyncReplicasOptimizer` for additional details on how to\n        # perform *synchronous* training.\n\n        batch_xs, batch_ys = mnist.train.next_batch(FLAGS.batch_size)\n        train_feed = {x: batch_xs, y_: batch_ys}\n\n        _, step = sess.run([train_op, global_step], feed_dict=train_feed)\n        if step % 100 == 0:\n            print \"Done step %d\" % step\n\n    # Ask for all the services to stop.\n    sv.stop()\n\nif __name__ == \"__main__\":\n  tf.app.run()\n"
  },
  {
    "path": "Theano/cnn.py",
    "content": "import theano\nfrom theano import tensor as T\nfrom theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams\nimport numpy as np\nfrom load import mnist\nfrom theano.tensor.nnet.conv import conv2d\nfrom theano.tensor.signal.downsample import max_pool_2d\n\nsrng = RandomStreams()\n\ndef floatX(X):\n    return np.asarray(X, dtype=theano.config.floatX)\n\ndef init_weights(shape):\n    return theano.shared(floatX(np.random.randn(*shape) * 0.01))\n\ndef rectify(X):\n    return T.maximum(X, 0.)\n\ndef softmax(X):\n    e_x = T.exp(X - X.max(axis=1).dimshuffle(0, 'x'))\n    return e_x / e_x.sum(axis=1).dimshuffle(0, 'x')\n\ndef dropout(X, p=0.):\n    if p > 0:\n        retain_prob = 1 - p\n        X *= srng.binomial(X.shape, p=retain_prob, dtype=theano.config.floatX)\n        X /= retain_prob\n    return X\n\ndef RMSprop(cost, params, lr=0.001, rho=0.9, epsilon=1e-6):\n    grads = T.grad(cost=cost, wrt=params)\n    updates = []\n    for p, g in zip(params, grads):\n        acc = theano.shared(p.get_value() * 0.)\n        acc_new = rho * acc + (1 - rho) * g ** 2\n        gradient_scaling = T.sqrt(acc_new + epsilon)\n        g = g / gradient_scaling\n        updates.append((acc, acc_new))\n        updates.append((p, p - lr * g))\n    return updates\n\ndef model(X, w, w2, w3, w4, p_drop_conv, p_drop_hidden):\n    l1a = rectify(conv2d(X, w, border_mode='full'))\n    l1 = max_pool_2d(l1a, (2, 2))\n    l1 = dropout(l1, p_drop_conv)\n\n    l2a = rectify(conv2d(l1, w2))\n    l2 = max_pool_2d(l2a, (2, 2))\n    l2 = dropout(l2, p_drop_conv)\n\n    l3a = rectify(conv2d(l2, w3))\n    l3b = max_pool_2d(l3a, (2, 2))\n    l3 = T.flatten(l3b, outdim=2)\n    l3 = dropout(l3, p_drop_conv)\n\n    l4 = rectify(T.dot(l3, w4))\n    l4 = dropout(l4, p_drop_hidden)\n\n    pyx = softmax(T.dot(l4, w_o))\n    return l1, l2, l3, l4, pyx\n\ntrX, teX, trY, teY = mnist(onehot=True)\n\ntrX = trX.reshape(-1, 1, 28, 28)\nteX = teX.reshape(-1, 1, 28, 28)\n\nX = T.ftensor4()\nY = T.fmatrix()\n\nw = init_weights((32, 1, 3, 3))\nw2 = init_weights((64, 32, 3, 3))\nw3 = init_weights((128, 64, 3, 3))\nw4 = init_weights((128 * 3 * 3, 625))\nw_o = init_weights((625, 10))\n\nnoise_l1, noise_l2, noise_l3, noise_l4, noise_py_x = model(X, w, w2, w3, w4, 0.2, 0.5)\nl1, l2, l3, l4, py_x = model(X, w, w2, w3, w4, 0., 0.)\ny_x = T.argmax(py_x, axis=1)\n\n\ncost = T.mean(T.nnet.categorical_crossentropy(noise_py_x, Y))\nparams = [w, w2, w3, w4, w_o]\nupdates = RMSprop(cost, params, lr=0.001)\n\ntrain = theano.function(inputs=[X, Y], outputs=cost, updates=updates, allow_input_downcast=True)\npredict = theano.function(inputs=[X], outputs=y_x, allow_input_downcast=True)\n\nfor i in range(100):\n    for start, end in zip(range(0, len(trX), 128), range(128, len(trX), 128)):\n        cost = train(trX[start:end], trY[start:end])\n    print np.mean(np.argmax(teY, axis=1) == predict(teX))\n"
  },
  {
    "path": "Theano/linear_regression.py",
    "content": "import theano\nfrom theano import tensor as T\nimport numpy as np\n\ntrX = np.linspace(-1, 1, 101)\ntrY = 2 * trX + np.random.randn(*trX.shape) * 0.33\n\nX = T.scalar()\nY = T.scalar()\n\ndef model(X, w):\n    return X * w\n\nw = theano.shared(np.asarray(0., dtype=theano.config.floatX))\ny = model(X, w)\n\ncost = T.mean(T.sqr(y - Y))\ngradient = T.grad(cost=cost, wrt=w)\nupdates = [[w, w - gradient * 0.01]]\n\ntrain = theano.function(inputs=[X, Y], outputs=cost, updates=updates, allow_input_downcast=True)\n\nfor i in range(100):\n    for x, y in zip(trX, trY):\n        train(x, y)\n        \nprint w.get_value() #something around 2\n\n"
  },
  {
    "path": "Theano/logistic_regression.p",
    "content": "import theano\nfrom theano import tensor as T\nimport numpy as np\nfrom load import mnist\n\ndef floatX(X):\n    return np.asarray(X, dtype=theano.config.floatX)\n\ndef init_weights(shape):\n    return theano.shared(floatX(np.random.randn(*shape) * 0.01))\n\ndef model(X, w):\n    return T.nnet.softmax(T.dot(X, w))\n\ntrX, teX, trY, teY = mnist(onehot=True)\n\nX = T.fmatrix()\nY = T.fmatrix()\n\nw = init_weights((784, 10))\n\npy_x = model(X, w)\ny_pred = T.argmax(py_x, axis=1)\n\ncost = T.mean(T.nnet.categorical_crossentropy(py_x, Y))\ngradient = T.grad(cost=cost, wrt=w)\nupdate = [[w, w - gradient * 0.05]]\n\ntrain = theano.function(inputs=[X, Y], outputs=cost, updates=update, allow_input_downcast=True)\npredict = theano.function(inputs=[X], outputs=y_pred, allow_input_downcast=True)\n\nfor i in range(100):\n    for start, end in zip(range(0, len(trX), 128), range(128, len(trX), 128)):\n        cost = train(trX[start:end], trY[start:end])\n    print i, np.mean(np.argmax(teY, axis=1) == predict(teX))\n\n"
  },
  {
    "path": "Theano/neural_networks.py",
    "content": "import theano\nfrom theano import tensor as T\nfrom theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams\nimport numpy as np\nfrom load import mnist\n\nsrng = RandomStreams()\n\ndef floatX(X):\n    return np.asarray(X, dtype=theano.config.floatX)\n\ndef init_weights(shape):\n    return theano.shared(floatX(np.random.randn(*shape) * 0.01))\n\ndef rectify(X):\n    return T.maximum(X, 0.)\n\ndef softmax(X):\n    e_x = T.exp(X - X.max(axis=1).dimshuffle(0, 'x'))\n    return e_x / e_x.sum(axis=1).dimshuffle(0, 'x')\n\ndef RMSprop(cost, params, lr=0.001, rho=0.9, epsilon=1e-6):\n    grads = T.grad(cost=cost, wrt=params)\n    updates = []\n    for p, g in zip(params, grads):\n        acc = theano.shared(p.get_value() * 0.)\n        acc_new = rho * acc + (1 - rho) * g ** 2\n        gradient_scaling = T.sqrt(acc_new + epsilon)\n        g = g / gradient_scaling\n        updates.append((acc, acc_new))\n        updates.append((p, p - lr * g))\n    return updates\n\ndef dropout(X, p=0.):\n    if p > 0:\n        retain_prob = 1 - p\n        X *= srng.binomial(X.shape, p=retain_prob, dtype=theano.config.floatX)\n        X /= retain_prob\n    return X\n\ndef model(X, w_h, w_h2, w_o, p_drop_input, p_drop_hidden):\n    X = dropout(X, p_drop_input)\n    h = rectify(T.dot(X, w_h))\n\n    h = dropout(h, p_drop_hidden)\n    h2 = rectify(T.dot(h, w_h2))\n\n    h2 = dropout(h2, p_drop_hidden)\n    py_x = softmax(T.dot(h2, w_o))\n    return h, h2, py_x\n\ntrX, teX, trY, teY = mnist(onehot=True)\n\nX = T.fmatrix()\nY = T.fmatrix()\n\nw_h = init_weights((784, 625))\nw_h2 = init_weights((625, 625))\nw_o = init_weights((625, 10))\n\nnoise_h, noise_h2, noise_py_x = model(X, w_h, w_h2, w_o, 0.2, 0.5)\nh, h2, py_x = model(X, w_h, w_h2, w_o, 0., 0.)\ny_x = T.argmax(py_x, axis=1)\n\ncost = T.mean(T.nnet.categorical_crossentropy(noise_py_x, Y))\nparams = [w_h, w_h2, w_o]\nupdates = RMSprop(cost, params, lr=0.001)\n\ntrain = theano.function(inputs=[X, Y], outputs=cost, updates=updates, allow_input_downcast=True)\npredict = theano.function(inputs=[X], outputs=y_x, allow_input_downcast=True)\n\nfor i in range(100):\n    for start, end in zip(range(0, len(trX), 128), range(128, len(trX), 128)):\n        cost = train(trX[start:end], trY[start:end])\n    print np.mean(np.argmax(teY, axis=1) == predict(teX))\n\n"
  }
]