Basic/10_CNN_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision.datasets as dsets\n",
    "import torchvision.transforms as transforms\n",
    "from torch.autograd import Variable\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs = 5\n",
    "batch_size = 100\n",
    "learning_rate = 0.001\n",
    "\n",
    "train_dataset  = dsets.MNIST(root='./data/',\n",
    "                            train=True,\n",
    "                            transform=transforms.ToTensor(),\n",
    "                            download=True)\n",
    "\n",
    "test_dataset = dsets.MNIST(root='./data/',\n",
    "                          train=False,\n",
    "                          transform=transforms.ToTensor())\n",
    "\n",
    "train_loader  = torch.utils.data.DataLoader(dataset=train_dataset,\n",
    "                                           batch_size=batch_size,\n",
    "                                           shuffle=True)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n",
    "                                         batch_size=batch_size,\n",
    "                                         shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()\n",
    "        \n",
    "        self.layer1 = nn.Sequential(\n",
    "            nn.Conv2d(1, 16, kernel_size=5, padding=2), #1 * 28 * 28 -> 16 * 28 * 28\n",
    "            nn.BatchNorm2d(16), #16 * 28 * 28\n",
    "            nn.ReLU(),                 #16 * 28 * 28\n",
    "            nn.MaxPool2d(2))       # 16 * 14 * 14\n",
    "        self.layer2  = nn.Sequential(\n",
    "            nn.Conv2d(16, 32, kernel_size=5, padding=2), # 16 * 14 * 14 -> 32 * 14 * 14\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2))       # 32 * 7 * 7\n",
    "        self.fc = nn.Linear(32 * 7 * 7, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        out = self.layer1(x)\n",
    "        out = self.layer2(out)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.fc(out)\n",
    "        return out\n",
    "    \n",
    "cnn = CNN()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/5], lter [100/600] Loss: 0.1142\n",
      "Epoch [1/5], lter [200/600] Loss: 0.0885\n",
      "Epoch [1/5], lter [300/600] Loss: 0.0574\n",
      "Epoch [1/5], lter [400/600] Loss: 0.0797\n",
      "Epoch [1/5], lter [500/600] Loss: 0.0438\n",
      "Epoch [1/5], lter [600/600] Loss: 0.0585\n",
      "Epoch [2/5], lter [100/600] Loss: 0.0235\n",
      "Epoch [2/5], lter [200/600] Loss: 0.0240\n",
      "Epoch [2/5], lter [300/600] Loss: 0.0746\n",
      "Epoch [2/5], lter [400/600] Loss: 0.0513\n",
      "Epoch [2/5], lter [500/600] Loss: 0.0321\n",
      "Epoch [2/5], lter [600/600] Loss: 0.0608\n",
      "Epoch [3/5], lter [100/600] Loss: 0.0377\n",
      "Epoch [3/5], lter [200/600] Loss: 0.0199\n",
      "Epoch [3/5], lter [300/600] Loss: 0.0456\n",
      "Epoch [3/5], lter [400/600] Loss: 0.1003\n",
      "Epoch [3/5], lter [500/600] Loss: 0.0074\n",
      "Epoch [3/5], lter [600/600] Loss: 0.0085\n",
      "Epoch [4/5], lter [100/600] Loss: 0.0006\n",
      "Epoch [4/5], lter [200/600] Loss: 0.0088\n",
      "Epoch [4/5], lter [300/600] Loss: 0.0243\n",
      "Epoch [4/5], lter [400/600] Loss: 0.0225\n",
      "Epoch [4/5], lter [500/600] Loss: 0.0687\n",
      "Epoch [4/5], lter [600/600] Loss: 0.0051\n",
      "Epoch [5/5], lter [100/600] Loss: 0.0011\n",
      "Epoch [5/5], lter [200/600] Loss: 0.0031\n",
      "Epoch [5/5], lter [300/600] Loss: 0.0254\n",
      "Epoch [5/5], lter [400/600] Loss: 0.0049\n",
      "Epoch [5/5], lter [500/600] Loss: 0.0043\n",
      "Epoch [5/5], lter [600/600] Loss: 0.0059\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'plk/cnn.pkl'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-478e21b3c653>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'plk/cnn,pkl'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m         \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'plk/cnn.pkl'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\.conda\\envs\\cuda\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(obj, f, pickle_module, pickle_protocol)\u001b[0m\n\u001b[0;32m    222\u001b[0m         \u001b[1;33m>>\u001b[0m\u001b[1;33m>\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    223\u001b[0m     \"\"\"\n\u001b[1;32m--> 224\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_with_file_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"wb\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0m_save\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    225\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\cuda\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36m_with_file_like\u001b[1;34m(f, mode, body)\u001b[0m\n\u001b[0;32m    145\u001b[0m             \u001b[1;33m(\u001b[0m\u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mversion_info\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m3\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpathlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    146\u001b[0m         \u001b[0mnew_fd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 147\u001b[1;33m         \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    148\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    149\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'plk/cnn.pkl'"
     ]
    }
   ],
   "source": [
    "if os.path.isfile('pkl/cnn.pkl'):\n",
    "    cnn.load_state_dict(torch.load('pkl/cnn.pkl'))\n",
    "\n",
    "else:\n",
    "    criterion = nn.CrossEntropyLoss() # Loss\n",
    "    optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        for i, (images, labels) in enumerate(train_loader):\n",
    "            images = Variable(images)\n",
    "            labels = Variable(labels)\n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "            outputs = cnn(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            if (i+1) % 100 == 0:\n",
    "                print('Epoch [%d/%d], lter [%d/%d] Loss: %.4f'\n",
    "                     %(epoch+1, num_epochs, i+1, len(train_dataset)//batch_size, loss.data))\n",
    "                if not os.path.isfile('pkl/cnn,pkl'):\n",
    "                    torch.save(cnn.state_dict(), 'pkl/cnn.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Accuracy 10000 test images  = 98.000000 %\n"
     ]
    }
   ],
   "source": [
    "cnn.eval()\n",
    "correct = 0\n",
    "total = 0\n",
    "for images, labels in test_loader:\n",
    "    images = Variable(images)\n",
    "    outputs = cnn(images)\n",
    "    _, predicted = torch.max(outputs.data, 1)\n",
    "    total += labels.size(0)\n",
    "    correct += (predicted == labels).sum()\n",
    "    \n",
    "print('test Accuracy 10000 test images  = %f %%' % (100 * correct/total))"
   ]
  }
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