net-mnist-lenet5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import random\n",
    "import numpy as np\n",
    "\n",
    "random.seed(0)\n",
    "np.random.seed(0)\n",
    "torch.manual_seed(0)\n",
    "torch.cuda.manual_seed(0)\n",
    "torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import torchvision.datasets\n",
    "MNIST_train = torchvision.datasets.MNIST('./', download=True, train=True)\n",
    "MNIST_test = torchvision.datasets.MNIST('./', download=True, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\programs\\anaconda\\lib\\site-packages\\torchvision\\datasets\\mnist.py:58: UserWarning: train_data has been renamed data\n",
      "  warnings.warn(\"train_data has been renamed data\")\n",
      "D:\\programs\\anaconda\\lib\\site-packages\\torchvision\\datasets\\mnist.py:48: UserWarning: train_labels has been renamed targets\n",
      "  warnings.warn(\"train_labels has been renamed targets\")\n",
      "D:\\programs\\anaconda\\lib\\site-packages\\torchvision\\datasets\\mnist.py:63: UserWarning: test_data has been renamed data\n",
      "  warnings.warn(\"test_data has been renamed data\")\n",
      "D:\\programs\\anaconda\\lib\\site-packages\\torchvision\\datasets\\mnist.py:53: UserWarning: test_labels has been renamed targets\n",
      "  warnings.warn(\"test_labels has been renamed targets\")\n"
     ]
    }
   ],
   "source": [
    "X_train = MNIST_train.train_data\n",
    "y_train = MNIST_train.train_labels\n",
    "X_test = MNIST_test.test_data\n",
    "y_test = MNIST_test.test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10000)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_train), len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gzetQb6dp5KPhk5I21f7O7fR7V+irLe8bp8sCSXAGHZAEYQeSIOxAEoQdSIKwA0kQdiAJwg4k8X+zhHFo7nUhhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(5)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(X_train[0, :, :])\n",
    "plt.show()\n",
    "print(y_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X_train = X_train.unsqueeze(1).float()\n",
    "X_test = X_test.unsqueeze(1).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([60000, 1, 28, 28])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class LeNet5(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(LeNet5, self).__init__()\n",
    "\n",
    "        self.conv1 = torch.nn.Conv2d(\n",
    "            in_channels=1, out_channels=6, kernel_size=5, padding=2)\n",
    "        self.act1  = torch.nn.Tanh()\n",
    "        self.pool1 = torch.nn.AvgPool2d(kernel_size=2, stride=2)\n",
    "\n",
    "        self.conv2 = torch.nn.Conv2d(\n",
    "            in_channels=6, out_channels=16, kernel_size=5, padding=0)\n",
    "        self.act2  = torch.nn.Tanh()\n",
    "        self.pool2 = torch.nn.AvgPool2d(kernel_size=2, stride=2)\n",
    "\n",
    "        self.fc1   = torch.nn.Linear(5 * 5 * 16, 120)\n",
    "        self.act3  = torch.nn.Tanh()\n",
    "\n",
    "        self.fc2   = torch.nn.Linear(120, 84)\n",
    "        self.act4  = torch.nn.Tanh()\n",
    "\n",
    "        self.fc3   = torch.nn.Linear(84, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "\n",
    "        x = self.conv1(x)\n",
    "        x = self.act1(x)\n",
    "        x = self.pool1(x)\n",
    "\n",
    "        x = self.conv2(x)\n",
    "        x = self.act2(x)\n",
    "        x = self.pool2(x)\n",
    "\n",
    "        x = x.view(x.size(0), x.size(1) * x.size(2) * x.size(3))\n",
    "\n",
    "        x = self.fc1(x)\n",
    "        x = self.act3(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.act4(x)\n",
    "        x = self.fc3(x)\n",
    "\n",
    "        return x\n",
    "\n",
    "lenet5 = LeNet5()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "lenet5 = lenet5.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "loss = torch.nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(lenet5.parameters(), lr=1.0e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.9851)\n",
      "tensor(0.9865)\n",
      "tensor(0.9864)\n",
      "tensor(0.9876)\n",
      "tensor(0.9873)\n",
      "tensor(0.9890)\n",
      "tensor(0.9873)\n",
      "tensor(0.9882)\n",
      "tensor(0.9878)\n",
      "tensor(0.9889)\n",
      "tensor(0.9868)\n",
      "tensor(0.9876)\n",
      "tensor(0.9887)\n",
      "tensor(0.9888)\n",
      "tensor(0.9884)\n",
      "tensor(0.9884)\n",
      "tensor(0.9881)\n",
      "tensor(0.9895)\n",
      "tensor(0.9897)\n",
      "tensor(0.9892)\n"
     ]
    }
   ],
   "source": [
    "batch_size = 100\n",
    "\n",
    "test_accuracy_history = []\n",
    "test_loss_history = []\n",
    "\n",
    "X_test = X_test.to(device)\n",
    "y_test = y_test.to(device)\n",
    "\n",
    "for epoch in range(20):\n",
    "    order = np.random.permutation(len(X_train))\n",
    "    for start_index in range(0, len(X_train), batch_size):\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        batch_indexes = order[start_index:start_index+batch_size]\n",
    "\n",
    "        X_batch = X_train[batch_indexes].to(device)\n",
    "        y_batch = y_train[batch_indexes].to(device)\n",
    "\n",
    "        preds = lenet5.forward(X_batch)\n",
    "\n",
    "        loss_value = loss(preds, y_batch)\n",
    "        loss_value.backward()\n",
    "\n",
    "        optimizer.step()\n",
    "\n",
    "    test_preds = lenet5.forward(X_test)\n",
    "    test_loss_history.append(loss(test_preds, y_test).data.cpu())\n",
    "\n",
    "    accuracy = (test_preds.argmax(dim=1) == y_test).float().mean().data.cpu()\n",
    "    test_accuracy_history.append(accuracy)\n",
    "\n",
    "    print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-2.6069,  0.0958, -0.4017,  ..., 16.2245, -3.0743,  4.3835],\n",
       "        [ 1.8028, -0.2303, 18.4794,  ..., -3.5173,  2.2901, -4.0424],\n",
       "        [-3.3177, 18.4851, -0.3305,  ...,  1.2610,  0.5562, -3.0955],\n",
       "        ...,\n",
       "        [-4.8025, -1.5316, -2.4220,  ...,  0.1751,  0.9051,  3.1411],\n",
       "        [-2.1163, -2.3767, -8.8063,  ..., -6.7564,  5.3384, -1.2302],\n",
       "        [ 3.3568, -2.6211, -0.8461,  ..., -9.1101, -1.1871, -4.4427]],\n",
       "       device='cuda:0', grad_fn=<AddmmBackward>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lenet5.forward(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test_accuracy_history);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test_loss_history);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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