{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 401 CNN\n", "\n", "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", "\n", "Dependencies:\n", "* torch: 0.1.11\n", "* torchvision\n", "* matplotlib" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", "from torch.autograd import Variable\n", "import torch.utils.data as Data\n", "import torchvision\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "torch.manual_seed(1) # reproducible" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Hyper Parameters\n", "EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch\n", "BATCH_SIZE = 50\n", "LR = 0.001 # learning rate\n", "DOWNLOAD_MNIST = True # set to False if you have downloaded" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Mnist digits dataset\n", "train_data = torchvision.datasets.MNIST(\n", " root='./mnist/',\n", " train=True, # this is training data\n", " transform=torchvision.transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to\n", " # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]\n", " download=DOWNLOAD_MNIST, # download it if you don't have it\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([60000, 28, 28])\n", "torch.Size([60000])\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot one example\n", "print(train_data.train_data.size()) # (60000, 28, 28)\n", "print(train_data.train_labels.size()) # (60000)\n", "plt.imshow(train_data.train_data[0].numpy(), cmap='gray')\n", "plt.title('%i' % train_data.train_labels[0])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)\n", "train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# convert test data into Variable, pick 2000 samples to speed up testing\n", "test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)\n", "test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1)).type(torch.FloatTensor)[:2000]/255. # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)\n", "test_y = test_data.test_labels[:2000]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class CNN(nn.Module):\n", " def __init__(self):\n", " super(CNN, self).__init__()\n", " self.conv1 = nn.Sequential( # input shape (1, 28, 28)\n", " nn.Conv2d(\n", " in_channels=1, # input height\n", " out_channels=16, # n_filters\n", " kernel_size=5, # filter size\n", " stride=1, # filter movement/step\n", " padding=2, # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1\n", " ), # output shape (16, 28, 28)\n", " nn.ReLU(), # activation\n", " nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14)\n", " )\n", " self.conv2 = nn.Sequential( # input shape (1, 28, 28)\n", " nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)\n", " nn.ReLU(), # activation\n", " nn.MaxPool2d(2), # output shape (32, 7, 7)\n", " )\n", " self.out = nn.Linear(32 * 7 * 7, 10) # fully connected layer, output 10 classes\n", "\n", " def forward(self, x):\n", " x = self.conv1(x)\n", " x = self.conv2(x)\n", " x = x.view(x.size(0), -1) # flatten the output of conv2 to (batch_size, 32 * 7 * 7)\n", " output = self.out(x)\n", " return output, x # return x for visualization" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CNN (\n", " (conv1): Sequential (\n", " (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", " (1): ReLU ()\n", " (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", " )\n", " (conv2): Sequential (\n", " (0): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", " (1): ReLU ()\n", " (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", " )\n", " (out): Linear (1568 -> 10)\n", ")\n" ] } ], "source": [ "cnn = CNN()\n", "print(cnn) # net architecture" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) # optimize all cnn parameters\n", "loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0623 | test accuracy: 0.98\n" ] }, { "data": { "image/png": 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J7l8BVPMM1c9b8BRR/HlMQVVCP9uiLqhC//4cEuPWUyHVHE3Qq5etMstGyM87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\n8jrZyoj1vMe+nEgRJVQz0O4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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0904 | test accuracy: 0.98\n" ] }, { "data": { "image/png": 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6WyqiinJAi2vqCh/n+C1iMlEpCDG2wye8HCthN92YsZVRgD4GqY8fml0MLROT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\nvks+W71lzGZQFTmZ6CSAv//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IkM3v9boaQerH+E3e2hBem1/fxPz6Jt46ZmcamlsY9sLC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\n1ye57QAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.1173 | test accuracy: 0.98\n" ] }, { "data": { "image/png": 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WrOR7hwxEMpRhTpJMyvKC64FvAkdhrYeuwWBwhhsz/hhwqVJqX2AM8GMR2Re4CnhJKTUU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\nT4j2z1+4LOiSD34Ywgtrz2X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0QEQWKqW0nxCF9S23gWnW4h92yjqEBadJZn0r3Z9C5vOh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\n8BsMBkOJYQy/wWAwlBjG8BsMBkOJ8f8BQJjmeczr570AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0559 | test accuracy: 0.98\n" ] }, { "data": { "image/png": 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GvwgJw8gAbCrPbJS9pPT5MRqmYwM6L0/d1VXQmUQ72cKr3MaFLKCRdcxhIpN5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\nyyyXt3mbwAQlvex2dVNSoufF/tKrv+6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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0859 | test accuracy: 0.98\n" ] }, { "data": { "image/png": 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VxmZjPcHJQAToAfwxg3bc0xu2elwkXO5gOUg7/rBipzftzGTZtEa8/frT9zfd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\nQzt+jUajyTP+PylDpLvHb7ZhAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0169 | test accuracy: 0.98\n" ] }, { "data": { "image/png": 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+dxXWaCqmMKmz2GuHrsN2UqV72XtXO7ajEC2NphFM0BjHbygpog7fVDRanrQ9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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0625 | test accuracy: 0.98\n" ] }, { "data": { "image/png": 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613LiDafQY2DHZMq7M/hZ8j3pppJqvzdBMYV/lsa+psRYtNK9p6qf1beXrNWC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\nPUHLD7kyO81fdBVc/cOs18U2bGXP6//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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0136 | test accuracy: 0.98\n" ] }, { "data": { "image/png": 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eufO88IfpH1ue92tmn2t4EVoZtCgduxjDn8/M/ByaNQp1nDzE12E4mRVWlTXr\nXVhT0z4s8sz9bPdlyC10jfaipbckbGPYIf0Zdkh/2toUa9c0csG4Kbww6zI/h+0pxvDnK2MfgxdT\naWXmADZj4dNJ2ur0aiRdOU3MysaErqjR9oKCAqG6piqjG7VOMIY/iJR2sSXNbLDBthYoK7Z1y7rt\nISqLCyxj+90Y73TV6e3auzvXvP5r19c4vW9a6Omc3eAF2LK5ifX16VXXdIsx/EHk1AdTXwMwPaUz\n3Bu2t0CpTWNZ3D4+2UnmqC+zyWc/73gszv3z6qpGZtQ28NChfWhpU3xv1lcsHG1ditKN8U5Xnd5N\ndRu56+hbKe9RkTDpSucap20HdYPXKy44ZypXXGu936S7iVxYWO7nEDtgDL9hBwlnr68t3nHNEIex\n5E7++J3OQt1iJ7Z/cJdOvHnMHgBUFhfylY0CLDphoE2tbY4loKP8Ycm9VPas5NOZHydMutK5xmnb\n2YyO4X7s6Z9a3qu7iTxkj5s8G68uxvDnId1aH4RHG9gQdzxdrgddnM5CU3ETMAsowboqmJ3Y/gIX\nKfc6chFelG+s7BmuDJYs6UrnmlT37Tsq8Z5RUOP7Yw37w9PPbXfObfRPkCN/jOHPIewoZlqRLteD\nLvdvfMjzNj8C5gLvAd+gVxXML2LlIrp3KmLRSXt3uMZypTF2f21Jh+2bt1NSVkJBYQErPl5ORY+O\nEgE616Rq2y5BcP+kMuxuDbeXm8heYwx/DmFXMTMeHddDqiQiKzoXaIZepoHFwEGRz9GqYJki6lJy\nREO91mWrPlvJo5dMprSyFBFh3AMTHF2TrO3fzv+9raEHhXjD/tC0c9udD5qwmpcYw5+j6ChmxqPj\nenjnzvMSJhDFY1XFya/wv16len+M+xJ27zQD/yNcFcxL7ET5eCEXkYoBw/dMaZh1rvHyvqAQb9jj\nCZqwmpcYw5+DOFXM9EKpMhVebthuGGdfKmIwMBY4DtgT8Do1LWj7JJkiSPH9hYXllrLK8YY9nqAJ\nq3mJMfxBIT7DVIdpZ1gedqqY6YVSZSp0N2x1DIfVikJn0/CSyNenhKuCOYntT0TQ9kkyRaYisqzY\np9/Vllm48YY93s+vK6z28vOfcOIpHTPCg1RqMZ68MvxjLwyxwWZeVLcuMH1iGn5MHmaYOlXM9ML1\nkCpeXzf8z6nh2MZ2JoWmJ30BjAJCQA8iVcEsYvtvof1S56Yz9bKcg6LUmWn8isjyknjDbhWWqSOs\n1qVrWYesNM6ZAAAgAElEQVRjQSu1GE9eGX67Rt/pPZnEE8VMF6SK1tANG3RrOJKNY6atluzhRUWv\nXCBb4vu9UMzcb2jvDseCHMoJeWL4ncz0sxW/tfrdYCdsMFsMRzxO9km80uwpo9RWmKSffnineQHZ\nSFVVxxl/0COC8sLwuzX6o38SSp/LJ4ZUiUbZhp2wwWw1HE72SbzaEI66tqz2Pqzwyw+v+4KPHWdQ\nErq8IugRQXlh+L1gQ0P4BRDF7xdBkBKNvEI3/E/XcPznobcCpwbpZJ8kUxvCfvnhneQFRPdm7BDk\nl4VuRFC6NXqiGMPvEL9dR1aJRk2Al9tEq0u6U9283t5NXewVRneCruHQMvrV1bY3zhtJXRXMSzK1\nIeyXOy1d8f1ByP5NhG5E0D79rs7A6IzhDyxWiUYbgJrYizZuA4uIgmSsVlU7Pu9zzHOAs3h4P9E1\nHOuWrU09S62ra/ftLXEu1o+ZTj2LOYqbAXiYI/gxj1NpZ8AuydSGcLa605yQKJbfT3Q2jieEZmu1\nVUVx6prSNjCGP6BYJRp1MHGXPtculr9bq6acc47gxSy1jO5sZ6dswnYaKKO726HZwk3inI7c9fbN\n2ymtKO1wzIk+T7YSnVlbxfPP+2AZjz48h/setM6LCQKb8LaesjH8Eea9fSKbNn5I/4GXsefgYESQ\nxCca5WcqUGLql69z3UYfDuENfk0rLTSyihIqKPLUoZYaN4lzOu6OVZ+tZMDwPTscc6LP4yUXnHOH\n5fGtXcqZ/pfL0pb9G43AccLzzyzkycfm8+gTej+/Fw65iqZ1O1+yu2vc01rdzMoV8xyNLxG+G34R\nOQG4l7DdmqSUsv7fzjD7DZvIutWv07RtZaaHsoMOiUaGdngxSy2jG8O4hIc5AkE4gXs9GJk9/Nbs\niTf60WNB1dnp3BB2yaQr+zcageMEu3kAsUZfl8LVJbbvSYWvhl9ECgnbrOMIu6nnicjzSqnP/OzX\nCaWdg5dhqZVoNPaxHR97PbmJNdvtievoips55eu5X7WbWZ5xzzn0PcCbRBavZqkHcj4Hcn7Sa3QL\nosxc1cjflqznqRG77/DX//eEvfIugcsL0pX9G43AySf8nvEPB5YopZYCiMjjwBggLYbfbuJWnwHj\n/RtMGvjitKrUF6UZP2eWqV4gn9f+0WJDz1m1I12fezqE7vKFdCXxVVWV8fgzF/rSdlDxexrSm3AY\nepQVkWM7EJELRWS+iMxfu3atp53nS7auwRovozh0fe7HVlfQhmLkrK/4/qyvfBG6ywSXVJ7LXcfc\nxl3H3MZ/HnrLkzaHvbqEx5YlrkcQG3XkxX5OUPmQR5jE95nMCL7lv2npM+Obu0qpicBEgIMPPtjM\njQxZTTo09lOxaXUDVdXehuj64W9/4+gBCbOUgxZ1NGSPmywjgnSIVek8KW7FuY0NfMB9XMAcGlnJ\n04xjAu96MeSk+G34VxLOP4rSJ3LMYGjHPcsP7XDsit3neN5Pp56bbW+wlZdmZrbZ0qZYuz3EmP/U\nMu/4vbTvEx/2E3T87XcdcxugH4GTLEs5CFFH8TjJBYhX6Xwqbp99JXPpx2EUUUI3BtBMIyGafI8s\n89vwzwMGisgAwgb/DMLh6RkncOGbDjJMqU5vhmku8IMP/pTymiG//lcaRpIap5m8UReJl+j42+2u\nCIb++8uEz5bOqCNd3fz4LFudFUC8SueJcTP+rdRTys5SpqV0ZRvrqWRXB0+ij6+GXykVEpGfA68S\nDud8SCm1yM8+dfEifDOq3eOJbk+dszjioGFXIRJgS6t1EZQtrcWUFzpLXCmjY0UlCHZxjHicZPKm\nKoDuNDZeJ8vXbgTOn1Y8wNquFUzSGoE/uNHN11kBxKt0/mdE+/OZSiD03cevlHoZeNnvfuIZe2Eo\n6XkvwzfNJvJOdESzhixdqtXWxJUHtfvejuvnrKJTWcQn7Y4FvThGPE4ig2JdJNe++dsO55346nX9\n7de88RtbEThNXTOfLexGN99KZyd+FZCqvGOmEggzvrnrF8YYG+IJenEMK4oLhI9GD9SODErlInES\nG2/H355tuj9+6+bHq3TGk6kEwqw2/PlUYCWXaW1sZPl55yElJbRt20avq6+mYsSI1DfaJOjFMRJR\nHjH60+77OdtiZ8k2ZYzBWWy8HX97uiNwkkk568g2+62bH6/S+X90/DnqJBB6TVYb/nQZ/cBtBOcY\nBeXl9H/8caSoiObly1lx+eW+GH63f+RKqYy+JLZ54BrxU5Hz/x31O08icLzS6NHZa9LVzXdDrKxD\nfFRPpshqw++GT+ddxMb6ObS1NdGwfgEHjkwcyRFEHZ9cQgoKoCA8q23bvJnSffZx1V6ijV23f+Sz\n6jZntRyD37HxVnsKTkiXRg/o6+Znmt2LR3ALUF4NV3sQB5K3hn/fYX/Xvra0cx/6DBhPKLTZxxHl\nNy11day4/HKav/6a3e680/Iaq1j/RPyepTwRd8ztH7m2HMP0j9t/PzZ50Uyvau6mIoix8VakS6Mn\nihcF19PFFpsR34nIOcMfdcscM2aV520XFWU+CiFXKa6pYcATT9C8YgW1Y8dSefTRvvTj5o/cjXxy\nMryquZuKICtyxqK7D5Eu2eZcJOcMf9QtY8gMg7QUQtuXbywoaqXmkHAsc2FFBQXlmalDmgq/5Bh0\nau4Oue1ZrsA6nDXXDKDuPkQ6XUJe4SRz3A9yzvAHUV45n7ArCw3QFipk2RlnQEEBqrWV6l9n+I95\n+scp3TNekqrm7uG/ejjp/dlmAKMvKqsx29mHSLdLSIdUSV1WmePb15bz4qH6tXfjy4cC3Iw66BbB\n6o9v9U2qfcVWyEHD7zctzRsoLumW+kKDLfo//rjte9IVBuo3qWru1lcm/30LogFMRrIXlZ19iHTJ\nNtshUfF0K2OdJix1XYzht4kx+sEhVRjoRkrpalM+orAw/W4mtxr+qQxg/KZ454JmLuqTXP737ysO\nZGubvcpPOu3CzhfVNW/8psM5O/sQTkJTnQitpft34kMeYQETEYTR/IXdONDzPgJl+Hv0e2mH/o0f\nREM4R56w0Lc+DB0JLVvItkevhYICpKCIsgl/obBXf9ftpgoDvZDTAFi0xx6u+9KmS/v9i8Nra6m/\n7dkd3y/69Q873OJ209iuAdzaVtLhZRD9GUUTouwafTv3RF9UbnAamppoRh4U0iXTHCjDX1DUI/VF\nGiSqpGUnhNPgHQVda6i4+imkrJKWhTPZ/vTvKb94oidt64SBHl5bS31rq612e1w/hXf+YLMiW5ce\n8Lc3k/Y3JOYl0K6/xg28e+d59vrDu9j8w2treadfP0cie3bxQj00W0JT7ZIumeZAGf5MYrJz/aOg\na4ybsagTUmj9a1c3txttLfbCIwuKu6UMA7Vr9AHqy7t0jMfXvddJfyn8+InwygBGxxyVOLgHPSE9\nu6RSD9UlW0JT7WJXptmpW8gY/ggmO9d/VNMWtj91G50v+IvlebtGP/aeIIeB+omXBjA667fCq430\nVOqh+Y4dmWZdt1Ak2qdddI8x/BHchIF287bKXU6iQi1suf98Op38Cwp7d5Rk+Ha2czffsrFjgxEG\n6pJEbqB0kWyloqunFL93EC+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M/SOdetjzoW+v78xH23dBldpLWCtubR+E8dGHK+jcuYRr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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0033 | test accuracy: 0.98\n" ] }, { "data": { "image/png": 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C07j6t/7vVaaM3ofDh4Qon9mmgOUqJ7v8oNGO9lfkv8maFzJl7Pg10jf4gxH+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\nknkG3nPaZ+/67tbMHvASiz/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.1449 | test accuracy: 0.98\n" ] }, { "data": { "image/png": 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VPHTRvdQ21CIinP2z8z3f61XQ41hVKfGTOLV75W8nsmH9vxi+xzf5xL7+f8nU\nJRMzjY3+78kRG+5HlIOKntUomN+9BcDqz+/PgKwtDhtJWgc/vSjvfSPGfoLvvfrDQKZ7FfTMVdWu\nnxxe8LnZcf/FqOmfVG6NsEncDZiDbhRy+dhWXW86BqMlTvj3P3gaa1Y9S/OWFcU2pSBhX1JKR/yI\nclDRiyIKJlv0wU6S1l+OHhFZcpcXN1n2qmroqGG+xwmScd2VsOXT/xf3M59pCMIE7mJnDvRze6d9\n1MQJf233eJpR26CUXlKlQByhibZdF5sYiJuTyUaSVtjkLq/88K3bXI9nr6q+85fvRm5LV8OG6G9h\nHS9xJxcwj42sYBZncz4vhHpm4oS/lCill1QpEIc/udBMN3Pj+YKqSQDcmBVssoV1PMhxXMA/2MhK\nbqdzrZ+wSVofbm1l9J/ftdb1LAhhXEmZpN0/6vYJxgpeZlcOp4oa+jCCbWyklWaqcuaQF6Ys2ga9\n8rcTefaJwbz3ZvhfYiU6MkV57bI11p+/ddNW2tvaAXxHwWRSRx8O5iJ+xZH8FvcSoZlJWm+dtCfX\nLGik2Rk7H7NXbuSUvy+lb01lp/vq1m8KZG9SKHe3T1A+Zi219Nn+vZbebMFnaZUsymLGH6VLZuiI\nc/Oen/CFVo0a8kCY0ESv1NbXbv88dNQwrpn7/cDPOpDzOJDznG+jOp0P2r0s331nXvyTzjfMeH37\nxzircKbRBK9wpH33F/Bizmvq6MtW1m//vpUm6ujb4Zp7+LQv/3/i1OiNV77K+rXzaG9vpumj+Rx4\n2G9DP7PYLhmNGipMmNDEKJj0zbug6Wbn2+t5r3UjaPeyUuvn62VDvm79poyX1s3uF/XqBz9/zv1c\nwrC1YZvpu8/HUA7hL1xHGy1sZCU11Hdy81zAi7TTyq84ypP/P3LhF5ETgDtIZbvfY4zJ8S+fYr+D\nfxF4rEmTc5d7KDQzV4pLEH9y5irBNt2bNoe6P2j3sjBdz+qoLehOsT1D97Ihv6W3h9Vb09pQdsSJ\nrSSsTN99PjJdi4JwAne4XldBlWf/f6TCLyKVpDLdjyNV3PAVEXnSGPNmFOPpzDrZHLF0KWvb2oBx\nXDIs/yzHC+lVgo0NyK7AmVWnFnT32K7mqQlewcn23eejo2sxN2n/fwOD814X9Yx/LLDIGLMYQEQe\nASYCkQi/kmxSom8Pr6sEv7PcVDRP3oVpyWI7ZDaOshlBuaT1ZTbgvV9ET6q5vWpshBZ1JNt3bwM3\n/78bUQsze/lBAAAcY0lEQVT/EOCDjO/LgUMiHjNWNIkr+YSd5faoXcPmrf0tWlQ8bM7Q/WzIx91u\nEqBh6Gh6r8rvRsnm1oFweYBk9CAJVpm++0rslP74Gq+5Hneyerdn8CZuc7dYuJWD9hKNo0lcwdjc\nVk2Pyni6d4Wd5V5+ytFFES6/TG+dVfAamzN0PxvyxWg3WelT9CGY/z5oglWm7z5fVI9FtmfwRi38\nK0h1rUsz1DmWySoClGbOFmrJUdE1zIw8c8+gTy/3PQTPEUPZBg4cGKzOTRdh2oqDaNu4kWVf/jJS\nU0P7li0MuPxy6g89tNO1YfcDbMxyS6FPbqGNXdshs3425G22m0waYRKsvPrubRO18L8C7CEiI0gJ\n/hdJNTXazlMzq7YXDxozZox59dVXPTVjycbkiJG2NSNPz/yD2OZKESpaJo2KHj0Y/sgjSFUV25Yt\nY/nFF7sKP3iLWMmFjVlu1MIVx4qimCGzs2Zex2nAA873rwL3ZJyvW7+JM2Ozxi65EqwKbbD6YQvr\neIBjrJVtiFT4jTGtIvIN4GlS4Zz3GWMWRjlmNsWO4VdyIxUVUJEKxWzftInavfd2ve47D5/EurN7\nMb11lm/xDzTLndQ5Ictmn1w34lhR2CrBEAWeQj4TipcEq7B4XVV43WuI3MdvjPkT8Keox1FKk5bG\nRpZffDHb3n+fnW+5Je+1heq8uIUy2prl2uyT60ZXdoUkiZBVLl3xkmAVFi+rCj97DSWzuZv21R8z\ncWWxTelAFJnGXZ3Gl/vQ3pIWzH50+9azdAM2Ahvnut+z1282BOruZGuWm1lKoW+3KhaetGfoZ2YS\n9YqinHhr6a20tW0Gru9wPIoql+AtwWoL6zC00Z1g0WFeVhV+9hpKRvjTvvqkESbTuFzZIfreCdrH\n1RbpUgpREWhF0atfZPbYJs6aPinR70wUVS7TFNqkXcHLvMufmOC8FH7OAXyFlz2P7WVV4WevoWSE\nP5evvtBKQGfk0bLXbzb4EOXkC1WuCphhSil4oVBRt+l3fsPdD16EwmzgX8i95FJMb50VadnmODZh\noxrby6rCz15DyQh/LgqtBJIyI28Gy16/ZFDsmbgN0nX33TZ1bfH0yo3MXNrEfeOG0tJuqK7oGN5b\nqDibjc3P919+r8N+xxdvP4ddDtg10LP8JsWlcym+/ttL6d6nh+s1W9jquk9jq45/HJuwfsaux18v\nxkKrCj97DSUv/FFH7dgK31wHPv+ZlVLAaxhmtrC/cvzuHc5HvaIAu1E9fpPi0rkUQbBVxz+OTdhc\n7MEJ7MEJ27/nyrANg9dibtAFhL8UaNvSiP0mgko2QWL966gtfFEevIZhxiHsceI3KS6o6NvEjzCW\nKl4TwkpG+NO++iSWV37q0dx1Nt4gVe5Lg/Oipxht/YoZhlnMJih+kuKiLJ/tFxuZsj0G2ivNXCxK\nRviT4qv3ynigldR25k+LbEvctC5ZwJaHroCKCqSiirrz76JywHBr1yeJYoZh2i6x7BW/SXGZuRRX\nPPe9TudLrYtXoSJu2T2ak0jJCH8ukroSmF1sA4pIRe9B1F/+GFLXQMuC2Wyd9UN6XDjN2vVx8fTK\njRw/OL+LIurErnzYLrHsFb9JcYX2For1Aksi6QSzSfye7hFGwZW88EexEvBb2G3C6d6rTPbZ0siM\n33cdX68bFb0zau5VdUMq8/+a+b0+Lo4dWDiSxk9v3SOu/BVrG7w13kjTb+M6nj/gINewzWI1QbFd\n+qFYL7CkkZ1g9jATrSSYuZGM/8MSRpSlltfVlU9sj2nezNbHbqL7BXdZu95f3oA3BtSKa1ZwZUXh\nNbufHrl+Rb/QPV787N95+CSmnvFH3+PGiZcXWGaYp9fwzm79N9G8xl8YbA/fdYLtUUcfLuSfAPRh\nRBSiv31nQoXfhdruQxk64lxaW92TecKyfYWQFSrqpf5/qWBaW9j8k/PodvK3qBziXnzN7/V9Hoqm\nt2aYF0mxonVslFj+xfID+bi9c816G20x/eC3eqrXyK3PvvRjX3aM3O36wheVINcbOs1gEqkyuWrf\nx01VVbwVA5PwM9vAGINUVVN/2UzP9/i9vtzx42ffsKWGnnXbOh13E32It0mO7R4BijcSKfy5Zr3W\nauErkSK5uuKUMzNeh8WLA906cvFiYBwA3Su28dWh//TsZ29bvYQf/O64Dsd2PnRt3numrTho++fJ\nQ+b7fgm0G/DgJQOK2yMgG7donB4BWzEmnUQKfymhPXeVOMk1S89F2AipzJeAHxZe93nX4/c8eFWH\n70nuEQCF4/VvHeQtpj+KctAecbVOhT8kfjaC9SURDV7yADZOmVA4R6BXP2jKPxsuNZISIdXV8Cr4\nEF05aDfc/Plu6G9FSPzUCgodLfS1z/gXpl794OfPBRvPEq1LFlA1/IBOx2wlbXnJA2i49qnCOQK5\n/p5CFm9bU9+bIwO6ecLS7cSLfV3vtQ+y32vjpLKyR87SzLnY+qF74bhc+MncjbIcdFBU+GMkHS0U\nmCCz0QTMYCt6dw5htZm05TkPIOYcgZE3/c7X9V6F9PZl4zw/c5fTAbz/Dvjpg1zZ0MCIxx5zPZf5\ns/fbuI7nb/myZxvCsveul7sez5VRu8MN83gkbphiloPORfGLZ8TMK387kWefGMx7b3b2K+Y7p7gz\noLbwyrKDMGcckzonK9aSIKfzANxmufnOFSRAw5M19f775aZFd/iMGQy94w5WT53q+xlhkYoKpCr1\nb5GvD7If0rkIxiSvhHfaDfMl/sqp/JqnCPD7UYBiloPORZeb8Rfyo+dzt0SZuNVVcUt88hNv7zfJ\nK+dzCuQB+Mkp6ISLC2hkBK4br83no8ZPH2Q/LGxqZsvqJuoG9Ar/rMU3er62srJHzlVAHG4YW+Wg\nbW4Ql5Twe4nvLyTe+XzyQWr7a4ev4PhN8sr5nPZ2Pr57MtUHnUTNQSe7XpPvXJKISnT9UD1oECMe\nfZRty5ezdNIkGo4+2spzd66r4g8HXcEfjhzOh1tbmfj3pXx11T0drsmO63/wa/dwzdzvhxo3n78/\nDjeMjXLQtjeIS0r4s+P73eL6o27Mkk0xq4Z6bQKSVGwlbbW8+ntaFsymfcNqtr04k8qh+9L9nI5u\nkm0vzsx5zi9HLF3qejzto8/l9/ZCVKLrlfbmZiq6pWajlfX1VPTIv+npZ4PXrZLph1nXxB3XH5cb\nJmw5aNsrk5IS/lLl2ScGRxLC6bUJSFenZuxEasZOzHtNwzX26tWsbWtzPZ720QclKtHdNHeu52ib\n5nfeYdWUKVBRgWlrY+B1+atm+tkMdqtkeknWNXHH9RezK5cfbK9MVPhjYK9RN0eybxC0CcglrS+z\nAX/ZmD2p5vaqsUHMLBsyffRBiEp0V0+d6ln46/bf39fLy8++hFsl02JTKl25bK9Myk74M33y2TPw\nUvPXB20C4lf0g95TjrQ0NlI9qGP4qlcXUFSiG/Umsdd9CbdKpjZY+O//8n+3PkNrSzv7jdqZy64a\n7+t+G125osb2yiSU8IvIacANwD7AWGPMqxnnrgbOB9qAi40xT4cZyyv5hB3y++RLrctX3E1Azm+d\nm/Oc1xVBUrttFYpEylW6OZt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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0121 | test accuracy: 0.98\n" ] }, { "data": { "image/png": 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po5VGVmastnU63g5B32z9xqnTjy2eX8w7gduSCuP4PXDAQeNZu+p1mptW+HYN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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 | train loss: 0.0233 | test accuracy: 0.99\n" ] }, { "data": { "image/png": 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nFUJf3VScaLt4VX6cQh/6kameYlSXd3bTPBP4pfljRSEodSZCcFfP+VHYpmTF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\n75eCZ6Qdvzrd6GI3N90ubqt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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# following function (plot_with_labels) is for visualization, can be ignored if not interested\n", "from matplotlib import cm\n", "try: from sklearn.manifold import TSNE; HAS_SK = True\n", "except: HAS_SK = False; print('Please install sklearn for layer visualization')\n", "def plot_with_labels(lowDWeights, labels):\n", " plt.cla()\n", " X, Y = lowDWeights[:, 0], lowDWeights[:, 1]\n", " for x, y, s in zip(X, Y, labels):\n", " c = cm.rainbow(int(255 * s / 9)); plt.text(x, y, s, backgroundcolor=c, fontsize=9)\n", " plt.xlim(X.min(), X.max()); plt.ylim(Y.min(), Y.max()); plt.title('Visualize last layer'); plt.show(); plt.pause(0.01)\n", "\n", "plt.ion()\n", "# training and testing\n", "for epoch in range(EPOCH):\n", " for step, (x, y) in enumerate(train_loader): # gives batch data, normalize x when iterate train_loader\n", " b_x = Variable(x) # batch x\n", " b_y = Variable(y) # batch y\n", "\n", " output = cnn(b_x)[0] # cnn output\n", " loss = loss_func(output, b_y) # cross entropy loss\n", " optimizer.zero_grad() # clear gradients for this training step\n", " loss.backward() # backpropagation, compute gradients\n", " optimizer.step() # apply gradients\n", "\n", " if step % 100 == 0:\n", " test_output, last_layer = cnn(test_x)\n", " pred_y = torch.max(test_output, 1)[1].data.squeeze()\n", " accuracy = sum(pred_y == test_y) / float(test_y.size(0))\n", " print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)\n", " if HAS_SK:\n", " # Visualization of trained flatten layer (T-SNE)\n", " tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n", " plot_only = 500\n", " low_dim_embs = tsne.fit_transform(last_layer.data.numpy()[:plot_only, :])\n", " labels = test_y.numpy()[:plot_only]\n", " plot_with_labels(low_dim_embs, labels)\n", "plt.ioff()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[7 2 1 0 4 1 4 9 5 9] prediction number\n", "[7 2 1 0 4 1 4 9 5 9] real number\n" ] } ], "source": [ "# print 10 predictions from test data\n", "test_output, _ = cnn(test_x[:10])\n", "pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()\n", "print(pred_y, 'prediction number')\n", "print(test_y[:10].numpy(), 'real number')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }