Intro_Pytorch.ipynb

{
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    "<img src=\"https://cdn-images-1.medium.com/max/2000/1*LLVL8xUiUOBE8WHgzAuY-Q.png\" width=\"600px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pytorch Intro | MNIST Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "torch.manual_seed(1)\n",
    "use_cuda = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda\" if use_cuda else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data and Dataloader\n",
    "<img src=\"https://cdn-images-1.medium.com/max/1200/1*9Mjoc_J0JR294YwHGXwCeg.jpeg\" width=\"300px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import datasets, transforms\n",
    "data_train = datasets.MNIST('data', train=True, download=True,\n",
    "                       transform=transforms.Compose([\n",
    "                           transforms.ToTensor(),\n",
    "                           transforms.Normalize((0.1307,), (0.3081,))\n",
    "                       ]))\n",
    "\n",
    "data_test = datasets.MNIST('data', train=False, transform=transforms.Compose([\n",
    "                           transforms.ToTensor(),\n",
    "                           transforms.Normalize((0.1307,), (0.3081,))\n",
    "                       ]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([60000, 28, 28])\n",
      "torch.Size([60000])\n"
     ]
    }
   ],
   "source": [
    "print(data_train.data.shape)\n",
    "print(data_train.targets.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(data_train, batch_size=64, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(data_test, batch_size=64, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 20, 5, 1)\n",
    "        self.conv2 = nn.Conv2d(20, 50, 5, 1)\n",
    "        self.fc1 = nn.Linear(4*4*50, 500)\n",
    "        self.fc2 = nn.Linear(500, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "        x = x.view(-1, 4*4*50)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        return F.log_softmax(x, dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Net()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Net(\n",
       "  (conv1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (conv2): Conv2d(20, 50, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (fc1): Linear(in_features=800, out_features=500, bias=True)\n",
       "  (fc2): Linear(in_features=500, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([[ 0.0272, -0.0286, -0.0184,  ...,  0.0236, -0.0228,  0.0153],\n",
       "        [-0.0234, -0.0054, -0.0145,  ...,  0.0223, -0.0192,  0.0007],\n",
       "        [ 0.0079, -0.0050, -0.0150,  ...,  0.0159,  0.0159,  0.0237],\n",
       "        ...,\n",
       "        [-0.0106,  0.0098,  0.0071,  ...,  0.0114, -0.0208,  0.0233],\n",
       "        [-0.0002,  0.0225, -0.0062,  ..., -0.0230,  0.0125,  0.0288],\n",
       "        [-0.0125,  0.0301, -0.0215,  ..., -0.0144,  0.0352, -0.0332]],\n",
       "       requires_grad=True)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fc1.weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Net(\n",
       "  (conv1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (conv2): Conv2d(20, 50, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (fc1): Linear(in_features=800, out_features=500, bias=True)\n",
       "  (fc2): Linear(in_features=500, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([[ 0.0272, -0.0286, -0.0184,  ...,  0.0236, -0.0228,  0.0153],\n",
       "        [-0.0234, -0.0054, -0.0145,  ...,  0.0223, -0.0192,  0.0007],\n",
       "        [ 0.0079, -0.0050, -0.0150,  ...,  0.0159,  0.0159,  0.0237],\n",
       "        ...,\n",
       "        [-0.0106,  0.0098,  0.0071,  ...,  0.0114, -0.0208,  0.0233],\n",
       "        [-0.0002,  0.0225, -0.0062,  ..., -0.0230,  0.0125,  0.0288],\n",
       "        [-0.0125,  0.0301, -0.0215,  ..., -0.0144,  0.0352, -0.0332]],\n",
       "       device='cuda:0', requires_grad=True)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fc1.weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train schedule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, device, train_loader, optimizer, epoch):\n",
    "    model.train()\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data) # The same as model.forward(data)\n",
    "        loss = F.nll_loss(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if batch_idx % 100 == 0:\n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "                100. * batch_idx / len(train_loader), loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(model, device, test_loader):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    with torch.no_grad():\n",
    "        for data, target in test_loader:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            output = model(data)\n",
    "            test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss\n",
    "            pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability\n",
    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "\n",
    "    test_loss /= len(test_loader.dataset)\n",
    "\n",
    "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
    "        test_loss, correct, len(test_loader.dataset),\n",
    "        100. * correct / len(test_loader.dataset)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.300039\n",
      "Train Epoch: 1 [6400/60000 (11%)]\tLoss: 1.716533\n",
      "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 0.555295\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss: 0.444822\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(1, 100 + 1):\n",
    "    train(model, device, train_loader, optimizer, epoch)\n",
    "    test(model, device, test_loader)\n",
    "\n",
    "torch.save(model.state_dict(), \"mnist_cnn.pt\")"
   ]
  }
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