L08_logistic/code/softmax-regression-mnist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "STAT 479: Deep Learning (Spring 2019)  \n",
    "Instructor: Sebastian Raschka (sraschka@wisc.edu)  \n",
    "Course website: http://pages.stat.wisc.edu/~sraschka/teaching/stat479-ss2019/  \n",
    "GitHub repository: https://github.com/rasbt/stat479-deep-learning-ss19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sebastian Raschka \n",
      "\n",
      "CPython 3.6.8\n",
      "IPython 7.2.0\n",
      "\n",
      "torch 1.0.0\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": [
    "# Softmax Regression on MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementation of softmax regression (multinomial logistic regression)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader\n",
    "import torch.nn.functional as F\n",
    "import torch"
   ]
  },
  {
   "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([256, 1, 28, 28])\n",
      "Image label dimensions: torch.Size([256])\n"
     ]
    }
   ],
   "source": [
    "##########################\n",
    "### SETTINGS\n",
    "##########################\n",
    "\n",
    "# Device\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Hyperparameters\n",
    "random_seed = 123\n",
    "learning_rate = 0.1\n",
    "num_epochs = 25\n",
    "batch_size = 256\n",
    "\n",
    "# Architecture\n",
    "num_features = 784\n",
    "num_classes = 10\n",
    "\n",
    "\n",
    "##########################\n",
    "### MNIST DATASET\n",
    "##########################\n",
    "\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",
    "\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": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################\n",
    "### MODEL\n",
    "##########################\n",
    "\n",
    "class SoftmaxRegression(torch.nn.Module):\n",
    "\n",
    "    def __init__(self, num_features, num_classes):\n",
    "        super(SoftmaxRegression, self).__init__()\n",
    "        self.linear = torch.nn.Linear(num_features, num_classes)\n",
    "        \n",
    "        self.linear.weight.detach().zero_()\n",
    "        self.linear.bias.detach().zero_()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        logits = self.linear(x)\n",
    "        probas = F.softmax(logits, dim=1)\n",
    "        return logits, probas\n",
    "\n",
    "model = SoftmaxRegression(num_features=num_features,\n",
    "                          num_classes=num_classes)\n",
    "\n",
    "model.to(device)\n",
    "\n",
    "##########################\n",
    "### COST AND OPTIMIZER\n",
    "##########################\n",
    "\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# Manual seed for deterministic data loader\n",
    "torch.manual_seed(random_seed)\n",
    "\n",
    "\n",
    "def compute_accuracy(model, data_loader):\n",
    "    correct_pred, num_examples = 0, 0\n",
    "    \n",
    "    for features, targets in data_loader:\n",
    "        features = features.view(-1, 28*28).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",
    "        \n",
    "    return correct_pred.float() / num_examples * 100\n",
    "    \n",
    "\n",
    "start_time = time.time()\n",
    "epoch_costs = []\n",
    "for epoch in range(num_epochs):\n",
    "    avg_cost = 0.\n",
    "    for batch_idx, (features, targets) in enumerate(train_loader):\n",
    "        \n",
    "        features = features.view(-1, 28*28).to(device)\n",
    "        targets = targets.to(device)\n",
    "            \n",
    "        ### FORWARD AND BACK PROP\n",
    "        logits, probas = model(features)\n",
    "        \n",
    "        # note that the PyTorch implementation of\n",
    "        # CrossEntropyLoss works with logits, not\n",
    "        # probabilities\n",
    "        cost = F.cross_entropy(logits, targets)\n",
    "        optimizer.zero_grad()\n",
    "        cost.backward()\n",
    "        avg_cost += cost\n",
    "        avg_cost\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_dataset)//batch_size, cost))\n",
    "            \n",
    "    with torch.set_grad_enabled(False):\n",
    "        avg_cost = avg_cost/len(train_dataset)\n",
    "        epoch_costs.append(avg_cost)\n",
    "        print('Epoch: %03d/%03d training accuracy: %.2f%%' % (\n",
    "              epoch+1, num_epochs, \n",
    "              compute_accuracy(model, train_loader)))\n",
    "        print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "plt.plot(epoch_costs)\n",
    "plt.ylabel('Avg Cross Entropy Loss\\n(approximated by averaging over minibatches)')\n",
    "plt.xlabel('Epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 92.21%\n"
     ]
    }
   ],
   "source": [
    "print('Test accuracy: %.2f%%' % (compute_accuracy(model, test_loader)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for features, targets in test_loader:\n",
    "    break\n",
    "    \n",
    "fig, ax = plt.subplots(1, 4)\n",
    "for i in range(4):\n",
    "    ax[i].imshow(features[i].view(28, 28), cmap=matplotlib.cm.binary)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted labels tensor([7, 2, 1, 0], device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "_, predictions = model.forward(features[:4].view(-1, 28*28).to(device))\n",
    "predictions = torch.argmax(predictions, dim=1)\n",
    "print('Predicted labels', predictions)"
   ]
  }
 ],
 "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": "3.6.8"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}