pytorch_ipynb/cnn/cnn-basic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
    "- Author: Sebastian Raschka\n",
    "- GitHub Repository: https://github.com/rasbt/deeplearning-models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sebastian Raschka \n",
      "\n",
      "CPython 3.7.3\n",
      "IPython 7.6.1\n",
      "\n",
      "torch 1.1.0\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'Sebastian Raschka' -v -p torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Runs on CPU or GPU (if available)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Zoo -- Convolutional Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings and Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image batch dimensions: torch.Size([128, 1, 28, 28])\n",
      "Image label dimensions: torch.Size([128])\n"
     ]
    }
   ],
   "source": [
    "##########################\n",
    "### SETTINGS\n",
    "##########################\n",
    "\n",
    "# Device\n",
    "device = torch.device(\"cuda:3\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Hyperparameters\n",
    "random_seed = 1\n",
    "learning_rate = 0.05\n",
    "num_epochs = 10\n",
    "batch_size = 128\n",
    "\n",
    "# Architecture\n",
    "num_classes = 10\n",
    "\n",
    "\n",
    "##########################\n",
    "### MNIST DATASET\n",
    "##########################\n",
    "\n",
    "# Note transforms.ToTensor() scales input images\n",
    "# to 0-1 range\n",
    "train_dataset = datasets.MNIST(root='data', \n",
    "                               train=True, \n",
    "                               transform=transforms.ToTensor(),\n",
    "                               download=True)\n",
    "\n",
    "test_dataset = datasets.MNIST(root='data', \n",
    "                              train=False, \n",
    "                              transform=transforms.ToTensor())\n",
    "\n",
    "\n",
    "train_loader = DataLoader(dataset=train_dataset, \n",
    "                          batch_size=batch_size, \n",
    "                          shuffle=True)\n",
    "\n",
    "test_loader = DataLoader(dataset=test_dataset, \n",
    "                         batch_size=batch_size, \n",
    "                         shuffle=False)\n",
    "\n",
    "# Checking the dataset\n",
    "for images, labels in train_loader:  \n",
    "    print('Image batch dimensions:', images.shape)\n",
    "    print('Image label dimensions:', labels.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################\n",
    "### MODEL\n",
    "##########################\n",
    "\n",
    "\n",
    "class ConvNet(torch.nn.Module):\n",
    "\n",
    "    def __init__(self, num_classes):\n",
    "        super(ConvNet, self).__init__()\n",
    "        \n",
    "        # calculate same padding:\n",
    "        # (w - k + 2*p)/s + 1 = o\n",
    "        # => p = (s(o-1) - w + k)/2\n",
    "        \n",
    "        # 28x28x1 => 28x28x8\n",
    "        self.conv_1 = torch.nn.Conv2d(in_channels=1,\n",
    "                                      out_channels=8,\n",
    "                                      kernel_size=(3, 3),\n",
    "                                      stride=(1, 1),\n",
    "                                      padding=1) # (1(28-1) - 28 + 3) / 2 = 1\n",
    "        # 28x28x8 => 14x14x8\n",
    "        self.pool_1 = torch.nn.MaxPool2d(kernel_size=(2, 2),\n",
    "                                         stride=(2, 2),\n",
    "                                         padding=0) # (2(14-1) - 28 + 2) = 0                                       \n",
    "        # 14x14x8 => 14x14x16\n",
    "        self.conv_2 = torch.nn.Conv2d(in_channels=8,\n",
    "                                      out_channels=16,\n",
    "                                      kernel_size=(3, 3),\n",
    "                                      stride=(1, 1),\n",
    "                                      padding=1) # (1(14-1) - 14 + 3) / 2 = 1                 \n",
    "        # 14x14x16 => 7x7x16                             \n",
    "        self.pool_2 = torch.nn.MaxPool2d(kernel_size=(2, 2),\n",
    "                                         stride=(2, 2),\n",
    "                                         padding=0) # (2(7-1) - 14 + 2) = 0\n",
    "\n",
    "        self.linear_1 = torch.nn.Linear(7*7*16, num_classes)\n",
    "\n",
    "        # optionally initialize weights from Gaussian;\n",
    "        # Guassian weight init is not recommended and only for demonstration purposes\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, torch.nn.Conv2d) or isinstance(m, torch.nn.Linear):\n",
    "                m.weight.data.normal_(0.0, 0.01)\n",
    "                m.bias.data.zero_()\n",
    "                if m.bias is not None:\n",
    "                    m.bias.detach().zero_()\n",
    "        \n",
    "        \n",
    "    def forward(self, x):\n",
    "        out = self.conv_1(x)\n",
    "        out = F.relu(out)\n",
    "        out = self.pool_1(out)\n",
    "\n",
    "        out = self.conv_2(out)\n",
    "        out = F.relu(out)\n",
    "        out = self.pool_2(out)\n",
    "        \n",
    "        logits = self.linear_1(out.view(-1, 7*7*16))\n",
    "        probas = F.softmax(logits, dim=1)\n",
    "        return logits, probas\n",
    "\n",
    "    \n",
    "torch.manual_seed(random_seed)\n",
    "model = ConvNet(num_classes=num_classes)\n",
    "\n",
    "model = model.to(device)\n",
    "\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 001/010 | Batch 000/469 | Cost: 2.3026\n",
      "Epoch: 001/010 | Batch 050/469 | Cost: 2.3036\n",
      "Epoch: 001/010 | Batch 100/469 | Cost: 2.3001\n",
      "Epoch: 001/010 | Batch 150/469 | Cost: 2.3050\n",
      "Epoch: 001/010 | Batch 200/469 | Cost: 2.2984\n",
      "Epoch: 001/010 | Batch 250/469 | Cost: 2.2986\n",
      "Epoch: 001/010 | Batch 300/469 | Cost: 2.2983\n",
      "Epoch: 001/010 | Batch 350/469 | Cost: 2.2941\n",
      "Epoch: 001/010 | Batch 400/469 | Cost: 2.2962\n",
      "Epoch: 001/010 | Batch 450/469 | Cost: 2.2265\n",
      "Epoch: 001/010 training accuracy: 65.38%\n",
      "Time elapsed: 0.24 min\n",
      "Epoch: 002/010 | Batch 000/469 | Cost: 1.8989\n",
      "Epoch: 002/010 | Batch 050/469 | Cost: 0.6029\n",
      "Epoch: 002/010 | Batch 100/469 | Cost: 0.6099\n",
      "Epoch: 002/010 | Batch 150/469 | Cost: 0.4786\n",
      "Epoch: 002/010 | Batch 200/469 | Cost: 0.4518\n",
      "Epoch: 002/010 | Batch 250/469 | Cost: 0.3553\n",
      "Epoch: 002/010 | Batch 300/469 | Cost: 0.3167\n",
      "Epoch: 002/010 | Batch 350/469 | Cost: 0.2241\n",
      "Epoch: 002/010 | Batch 400/469 | Cost: 0.2259\n",
      "Epoch: 002/010 | Batch 450/469 | Cost: 0.3056\n",
      "Epoch: 002/010 training accuracy: 93.11%\n",
      "Time elapsed: 0.47 min\n",
      "Epoch: 003/010 | Batch 000/469 | Cost: 0.3313\n",
      "Epoch: 003/010 | Batch 050/469 | Cost: 0.1042\n",
      "Epoch: 003/010 | Batch 100/469 | Cost: 0.1328\n",
      "Epoch: 003/010 | Batch 150/469 | Cost: 0.2803\n",
      "Epoch: 003/010 | Batch 200/469 | Cost: 0.0975\n",
      "Epoch: 003/010 | Batch 250/469 | Cost: 0.1839\n",
      "Epoch: 003/010 | Batch 300/469 | Cost: 0.1774\n",
      "Epoch: 003/010 | Batch 350/469 | Cost: 0.1143\n",
      "Epoch: 003/010 | Batch 400/469 | Cost: 0.1753\n",
      "Epoch: 003/010 | Batch 450/469 | Cost: 0.1543\n",
      "Epoch: 003/010 training accuracy: 95.68%\n",
      "Time elapsed: 0.70 min\n",
      "Epoch: 004/010 | Batch 000/469 | Cost: 0.1057\n",
      "Epoch: 004/010 | Batch 050/469 | Cost: 0.1035\n",
      "Epoch: 004/010 | Batch 100/469 | Cost: 0.1851\n",
      "Epoch: 004/010 | Batch 150/469 | Cost: 0.1608\n",
      "Epoch: 004/010 | Batch 200/469 | Cost: 0.1458\n",
      "Epoch: 004/010 | Batch 250/469 | Cost: 0.1913\n",
      "Epoch: 004/010 | Batch 300/469 | Cost: 0.1295\n",
      "Epoch: 004/010 | Batch 350/469 | Cost: 0.1518\n",
      "Epoch: 004/010 | Batch 400/469 | Cost: 0.1717\n",
      "Epoch: 004/010 | Batch 450/469 | Cost: 0.0792\n",
      "Epoch: 004/010 training accuracy: 96.46%\n",
      "Time elapsed: 0.93 min\n",
      "Epoch: 005/010 | Batch 000/469 | Cost: 0.0905\n",
      "Epoch: 005/010 | Batch 050/469 | Cost: 0.1622\n",
      "Epoch: 005/010 | Batch 100/469 | Cost: 0.1934\n",
      "Epoch: 005/010 | Batch 150/469 | Cost: 0.1874\n",
      "Epoch: 005/010 | Batch 200/469 | Cost: 0.0742\n",
      "Epoch: 005/010 | Batch 250/469 | Cost: 0.1056\n",
      "Epoch: 005/010 | Batch 300/469 | Cost: 0.0997\n",
      "Epoch: 005/010 | Batch 350/469 | Cost: 0.0948\n",
      "Epoch: 005/010 | Batch 400/469 | Cost: 0.0575\n",
      "Epoch: 005/010 | Batch 450/469 | Cost: 0.1157\n",
      "Epoch: 005/010 training accuracy: 96.97%\n",
      "Time elapsed: 1.16 min\n",
      "Epoch: 006/010 | Batch 000/469 | Cost: 0.1326\n",
      "Epoch: 006/010 | Batch 050/469 | Cost: 0.1549\n",
      "Epoch: 006/010 | Batch 100/469 | Cost: 0.0784\n",
      "Epoch: 006/010 | Batch 150/469 | Cost: 0.0898\n",
      "Epoch: 006/010 | Batch 200/469 | Cost: 0.0991\n",
      "Epoch: 006/010 | Batch 250/469 | Cost: 0.0965\n",
      "Epoch: 006/010 | Batch 300/469 | Cost: 0.0477\n",
      "Epoch: 006/010 | Batch 350/469 | Cost: 0.0712\n",
      "Epoch: 006/010 | Batch 400/469 | Cost: 0.1109\n",
      "Epoch: 006/010 | Batch 450/469 | Cost: 0.0325\n",
      "Epoch: 006/010 training accuracy: 97.60%\n",
      "Time elapsed: 1.39 min\n",
      "Epoch: 007/010 | Batch 000/469 | Cost: 0.0665\n",
      "Epoch: 007/010 | Batch 050/469 | Cost: 0.0868\n",
      "Epoch: 007/010 | Batch 100/469 | Cost: 0.0427\n",
      "Epoch: 007/010 | Batch 150/469 | Cost: 0.0385\n",
      "Epoch: 007/010 | Batch 200/469 | Cost: 0.0611\n",
      "Epoch: 007/010 | Batch 250/469 | Cost: 0.0484\n",
      "Epoch: 007/010 | Batch 300/469 | Cost: 0.1288\n",
      "Epoch: 007/010 | Batch 350/469 | Cost: 0.0309\n",
      "Epoch: 007/010 | Batch 400/469 | Cost: 0.0359\n",
      "Epoch: 007/010 | Batch 450/469 | Cost: 0.0139\n",
      "Epoch: 007/010 training accuracy: 97.64%\n",
      "Time elapsed: 1.62 min\n",
      "Epoch: 008/010 | Batch 000/469 | Cost: 0.0939\n",
      "Epoch: 008/010 | Batch 050/469 | Cost: 0.1478\n",
      "Epoch: 008/010 | Batch 100/469 | Cost: 0.0769\n",
      "Epoch: 008/010 | Batch 150/469 | Cost: 0.0713\n",
      "Epoch: 008/010 | Batch 200/469 | Cost: 0.1272\n",
      "Epoch: 008/010 | Batch 250/469 | Cost: 0.0446\n",
      "Epoch: 008/010 | Batch 300/469 | Cost: 0.0525\n",
      "Epoch: 008/010 | Batch 350/469 | Cost: 0.1729\n",
      "Epoch: 008/010 | Batch 400/469 | Cost: 0.0672\n",
      "Epoch: 008/010 | Batch 450/469 | Cost: 0.0754\n",
      "Epoch: 008/010 training accuracy: 96.67%\n",
      "Time elapsed: 1.85 min\n",
      "Epoch: 009/010 | Batch 000/469 | Cost: 0.0988\n",
      "Epoch: 009/010 | Batch 050/469 | Cost: 0.0409\n",
      "Epoch: 009/010 | Batch 100/469 | Cost: 0.1046\n",
      "Epoch: 009/010 | Batch 150/469 | Cost: 0.0523\n",
      "Epoch: 009/010 | Batch 200/469 | Cost: 0.0815\n",
      "Epoch: 009/010 | Batch 250/469 | Cost: 0.0811\n",
      "Epoch: 009/010 | Batch 300/469 | Cost: 0.0416\n",
      "Epoch: 009/010 | Batch 350/469 | Cost: 0.0747\n",
      "Epoch: 009/010 | Batch 400/469 | Cost: 0.0467\n",
      "Epoch: 009/010 | Batch 450/469 | Cost: 0.0669\n",
      "Epoch: 009/010 training accuracy: 97.90%\n",
      "Time elapsed: 2.08 min\n",
      "Epoch: 010/010 | Batch 000/469 | Cost: 0.0257\n",
      "Epoch: 010/010 | Batch 050/469 | Cost: 0.0357\n",
      "Epoch: 010/010 | Batch 100/469 | Cost: 0.1469\n",
      "Epoch: 010/010 | Batch 150/469 | Cost: 0.0170\n",
      "Epoch: 010/010 | Batch 200/469 | Cost: 0.0493\n",
      "Epoch: 010/010 | Batch 250/469 | Cost: 0.0489\n",
      "Epoch: 010/010 | Batch 300/469 | Cost: 0.1348\n",
      "Epoch: 010/010 | Batch 350/469 | Cost: 0.0815\n",
      "Epoch: 010/010 | Batch 400/469 | Cost: 0.0552\n",
      "Epoch: 010/010 | Batch 450/469 | Cost: 0.0422\n",
      "Epoch: 010/010 training accuracy: 97.99%\n",
      "Time elapsed: 2.31 min\n",
      "Total Training Time: 2.31 min\n"
     ]
    }
   ],
   "source": [
    "def compute_accuracy(model, data_loader):\n",
    "    correct_pred, num_examples = 0, 0\n",
    "    for features, targets in data_loader:\n",
    "        features = features.to(device)\n",
    "        targets = targets.to(device)\n",
    "        logits, probas = model(features)\n",
    "        _, predicted_labels = torch.max(probas, 1)\n",
    "        num_examples += targets.size(0)\n",
    "        correct_pred += (predicted_labels == targets).sum()\n",
    "    return correct_pred.float()/num_examples * 100\n",
    "    \n",
    "\n",
    "start_time = time.time()    \n",
    "for epoch in range(num_epochs):\n",
    "    model = model.train()\n",
    "    for batch_idx, (features, targets) in enumerate(train_loader):\n",
    "        \n",
    "        features = features.to(device)\n",
    "        targets = targets.to(device)\n",
    "\n",
    "        ### FORWARD AND BACK PROP\n",
    "        logits, probas = model(features)\n",
    "        cost = F.cross_entropy(logits, targets)\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        cost.backward()\n",
    "        \n",
    "        ### UPDATE MODEL PARAMETERS\n",
    "        optimizer.step()\n",
    "        \n",
    "        ### LOGGING\n",
    "        if not batch_idx % 50:\n",
    "            print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \n",
    "                   %(epoch+1, num_epochs, batch_idx, \n",
    "                     len(train_loader), cost))\n",
    "    \n",
    "    model = model.eval()\n",
    "    print('Epoch: %03d/%03d training accuracy: %.2f%%' % (\n",
    "          epoch+1, num_epochs, \n",
    "          compute_accuracy(model, train_loader)))\n",
    "\n",
    "    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\n",
    "    \n",
    "print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 97.97%\n"
     ]
    }
   ],
   "source": [
    "with torch.set_grad_enabled(False): # save memory during inference\n",
    "    print('Test accuracy: %.2f%%' % (compute_accuracy(model, test_loader)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch       1.1.0\n",
      "numpy       1.16.4\n",
      "torchvision 0.3.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -iv"
   ]
  }
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