pytorch_ipynb/mlp/mlp-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 3.6.8\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": [
    "# Model Zoo -- Multilayer Perceptron"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\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\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([64, 1, 28, 28])\n",
      "Image label dimensions: torch.Size([64])\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 = 0.1\n",
    "num_epochs = 10\n",
    "batch_size = 64\n",
    "\n",
    "# Architecture\n",
    "num_features = 784\n",
    "num_hidden_1 = 128\n",
    "num_hidden_2 = 256\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": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################\n",
    "### MODEL\n",
    "##########################\n",
    "\n",
    "class MultilayerPerceptron(torch.nn.Module):\n",
    "\n",
    "    def __init__(self, num_features, num_classes):\n",
    "        super(MultilayerPerceptron, self).__init__()\n",
    "        \n",
    "        ### 1st hidden layer\n",
    "        self.linear_1 = torch.nn.Linear(num_features, num_hidden_1)\n",
    "        # The following to lones are not necessary, \n",
    "        # but used here to demonstrate how to access the weights\n",
    "        # and use a different weight initialization.\n",
    "        # By default, PyTorch uses Xavier/Glorot initialization, which\n",
    "        # should usually be preferred.\n",
    "        self.linear_1.weight.detach().normal_(0.0, 0.1)\n",
    "        self.linear_1.bias.detach().zero_()\n",
    "        \n",
    "        ### 2nd hidden layer\n",
    "        self.linear_2 = torch.nn.Linear(num_hidden_1, num_hidden_2)\n",
    "        self.linear_2.weight.detach().normal_(0.0, 0.1)\n",
    "        self.linear_2.bias.detach().zero_()\n",
    "        \n",
    "        ### Output layer\n",
    "        self.linear_out = torch.nn.Linear(num_hidden_2, num_classes)\n",
    "        self.linear_out.weight.detach().normal_(0.0, 0.1)\n",
    "        self.linear_out.bias.detach().zero_()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        out = self.linear_1(x)\n",
    "        out = F.relu(out)\n",
    "        out = self.linear_2(out)\n",
    "        out = F.relu(out)\n",
    "        logits = self.linear_out(out)\n",
    "        probas = F.log_softmax(logits, dim=1)\n",
    "        return logits, probas\n",
    "\n",
    "    \n",
    "torch.manual_seed(random_seed)\n",
    "model = MultilayerPerceptron(num_features=num_features,\n",
    "                             num_classes=num_classes)\n",
    "\n",
    "model = model.to(device)\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": [
      "Epoch: 001/010 | Batch 000/938 | Cost: 2.4231\n",
      "Epoch: 001/010 | Batch 050/938 | Cost: 0.7213\n",
      "Epoch: 001/010 | Batch 100/938 | Cost: 0.3137\n",
      "Epoch: 001/010 | Batch 150/938 | Cost: 0.4774\n",
      "Epoch: 001/010 | Batch 200/938 | Cost: 0.3311\n",
      "Epoch: 001/010 | Batch 250/938 | Cost: 0.3082\n",
      "Epoch: 001/010 | Batch 300/938 | Cost: 0.3940\n",
      "Epoch: 001/010 | Batch 350/938 | Cost: 0.2292\n",
      "Epoch: 001/010 | Batch 400/938 | Cost: 0.0837\n",
      "Epoch: 001/010 | Batch 450/938 | Cost: 0.2238\n",
      "Epoch: 001/010 | Batch 500/938 | Cost: 0.2501\n",
      "Epoch: 001/010 | Batch 550/938 | Cost: 0.1543\n",
      "Epoch: 001/010 | Batch 600/938 | Cost: 0.2353\n",
      "Epoch: 001/010 | Batch 650/938 | Cost: 0.4138\n",
      "Epoch: 001/010 | Batch 700/938 | Cost: 0.2955\n",
      "Epoch: 001/010 | Batch 750/938 | Cost: 0.3486\n",
      "Epoch: 001/010 | Batch 800/938 | Cost: 0.1391\n",
      "Epoch: 001/010 | Batch 850/938 | Cost: 0.0675\n",
      "Epoch: 001/010 | Batch 900/938 | Cost: 0.2827\n",
      "Epoch: 001/010 training accuracy: 94.90%\n",
      "Time elapsed: 0.22 min\n",
      "Epoch: 002/010 | Batch 000/938 | Cost: 0.3237\n",
      "Epoch: 002/010 | Batch 050/938 | Cost: 0.2792\n",
      "Epoch: 002/010 | Batch 100/938 | Cost: 0.0915\n",
      "Epoch: 002/010 | Batch 150/938 | Cost: 0.2269\n",
      "Epoch: 002/010 | Batch 200/938 | Cost: 0.2248\n",
      "Epoch: 002/010 | Batch 250/938 | Cost: 0.1607\n",
      "Epoch: 002/010 | Batch 300/938 | Cost: 0.0551\n",
      "Epoch: 002/010 | Batch 350/938 | Cost: 0.2369\n",
      "Epoch: 002/010 | Batch 400/938 | Cost: 0.0538\n",
      "Epoch: 002/010 | Batch 450/938 | Cost: 0.0951\n",
      "Epoch: 002/010 | Batch 500/938 | Cost: 0.2423\n",
      "Epoch: 002/010 | Batch 550/938 | Cost: 0.2082\n",
      "Epoch: 002/010 | Batch 600/938 | Cost: 0.1623\n",
      "Epoch: 002/010 | Batch 650/938 | Cost: 0.1743\n",
      "Epoch: 002/010 | Batch 700/938 | Cost: 0.1987\n",
      "Epoch: 002/010 | Batch 750/938 | Cost: 0.0871\n",
      "Epoch: 002/010 | Batch 800/938 | Cost: 0.1662\n",
      "Epoch: 002/010 | Batch 850/938 | Cost: 0.1087\n",
      "Epoch: 002/010 | Batch 900/938 | Cost: 0.1574\n",
      "Epoch: 002/010 training accuracy: 96.62%\n",
      "Time elapsed: 0.45 min\n",
      "Epoch: 003/010 | Batch 000/938 | Cost: 0.1088\n",
      "Epoch: 003/010 | Batch 050/938 | Cost: 0.0994\n",
      "Epoch: 003/010 | Batch 100/938 | Cost: 0.0771\n",
      "Epoch: 003/010 | Batch 150/938 | Cost: 0.1926\n",
      "Epoch: 003/010 | Batch 200/938 | Cost: 0.1231\n",
      "Epoch: 003/010 | Batch 250/938 | Cost: 0.2532\n",
      "Epoch: 003/010 | Batch 300/938 | Cost: 0.0637\n",
      "Epoch: 003/010 | Batch 350/938 | Cost: 0.1193\n",
      "Epoch: 003/010 | Batch 400/938 | Cost: 0.0839\n",
      "Epoch: 003/010 | Batch 450/938 | Cost: 0.0830\n",
      "Epoch: 003/010 | Batch 500/938 | Cost: 0.0689\n",
      "Epoch: 003/010 | Batch 550/938 | Cost: 0.0504\n",
      "Epoch: 003/010 | Batch 600/938 | Cost: 0.1999\n",
      "Epoch: 003/010 | Batch 650/938 | Cost: 0.1190\n",
      "Epoch: 003/010 | Batch 700/938 | Cost: 0.2022\n",
      "Epoch: 003/010 | Batch 750/938 | Cost: 0.0598\n",
      "Epoch: 003/010 | Batch 800/938 | Cost: 0.0216\n",
      "Epoch: 003/010 | Batch 850/938 | Cost: 0.0174\n",
      "Epoch: 003/010 | Batch 900/938 | Cost: 0.0660\n",
      "Epoch: 003/010 training accuracy: 97.70%\n",
      "Time elapsed: 0.67 min\n",
      "Epoch: 004/010 | Batch 000/938 | Cost: 0.0067\n",
      "Epoch: 004/010 | Batch 050/938 | Cost: 0.0344\n",
      "Epoch: 004/010 | Batch 100/938 | Cost: 0.1624\n",
      "Epoch: 004/010 | Batch 150/938 | Cost: 0.0572\n",
      "Epoch: 004/010 | Batch 200/938 | Cost: 0.0306\n",
      "Epoch: 004/010 | Batch 250/938 | Cost: 0.1564\n",
      "Epoch: 004/010 | Batch 300/938 | Cost: 0.2233\n",
      "Epoch: 004/010 | Batch 350/938 | Cost: 0.0229\n",
      "Epoch: 004/010 | Batch 400/938 | Cost: 0.1174\n",
      "Epoch: 004/010 | Batch 450/938 | Cost: 0.1853\n",
      "Epoch: 004/010 | Batch 500/938 | Cost: 0.1418\n",
      "Epoch: 004/010 | Batch 550/938 | Cost: 0.1071\n",
      "Epoch: 004/010 | Batch 600/938 | Cost: 0.0354\n",
      "Epoch: 004/010 | Batch 650/938 | Cost: 0.0487\n",
      "Epoch: 004/010 | Batch 700/938 | Cost: 0.1886\n",
      "Epoch: 004/010 | Batch 750/938 | Cost: 0.1568\n",
      "Epoch: 004/010 | Batch 800/938 | Cost: 0.0702\n",
      "Epoch: 004/010 | Batch 850/938 | Cost: 0.0533\n",
      "Epoch: 004/010 | Batch 900/938 | Cost: 0.1500\n",
      "Epoch: 004/010 training accuracy: 98.01%\n",
      "Time elapsed: 0.89 min\n",
      "Epoch: 005/010 | Batch 000/938 | Cost: 0.0225\n",
      "Epoch: 005/010 | Batch 050/938 | Cost: 0.0168\n",
      "Epoch: 005/010 | Batch 100/938 | Cost: 0.1133\n",
      "Epoch: 005/010 | Batch 150/938 | Cost: 0.0691\n",
      "Epoch: 005/010 | Batch 200/938 | Cost: 0.0413\n",
      "Epoch: 005/010 | Batch 250/938 | Cost: 0.0840\n",
      "Epoch: 005/010 | Batch 300/938 | Cost: 0.0697\n",
      "Epoch: 005/010 | Batch 350/938 | Cost: 0.0901\n",
      "Epoch: 005/010 | Batch 400/938 | Cost: 0.0370\n",
      "Epoch: 005/010 | Batch 450/938 | Cost: 0.0514\n",
      "Epoch: 005/010 | Batch 500/938 | Cost: 0.1403\n",
      "Epoch: 005/010 | Batch 550/938 | Cost: 0.1164\n",
      "Epoch: 005/010 | Batch 600/938 | Cost: 0.0624\n",
      "Epoch: 005/010 | Batch 650/938 | Cost: 0.0280\n",
      "Epoch: 005/010 | Batch 700/938 | Cost: 0.0555\n",
      "Epoch: 005/010 | Batch 750/938 | Cost: 0.0432\n",
      "Epoch: 005/010 | Batch 800/938 | Cost: 0.0434\n",
      "Epoch: 005/010 | Batch 850/938 | Cost: 0.1074\n",
      "Epoch: 005/010 | Batch 900/938 | Cost: 0.0353\n",
      "Epoch: 005/010 training accuracy: 98.56%\n",
      "Time elapsed: 1.12 min\n",
      "Epoch: 006/010 | Batch 000/938 | Cost: 0.1073\n",
      "Epoch: 006/010 | Batch 050/938 | Cost: 0.0901\n",
      "Epoch: 006/010 | Batch 100/938 | Cost: 0.0736\n",
      "Epoch: 006/010 | Batch 150/938 | Cost: 0.0290\n",
      "Epoch: 006/010 | Batch 200/938 | Cost: 0.0399\n",
      "Epoch: 006/010 | Batch 250/938 | Cost: 0.0720\n",
      "Epoch: 006/010 | Batch 300/938 | Cost: 0.0863\n",
      "Epoch: 006/010 | Batch 350/938 | Cost: 0.0629\n",
      "Epoch: 006/010 | Batch 400/938 | Cost: 0.1095\n",
      "Epoch: 006/010 | Batch 450/938 | Cost: 0.0531\n",
      "Epoch: 006/010 | Batch 500/938 | Cost: 0.0680\n",
      "Epoch: 006/010 | Batch 550/938 | Cost: 0.1777\n",
      "Epoch: 006/010 | Batch 600/938 | Cost: 0.0525\n",
      "Epoch: 006/010 | Batch 650/938 | Cost: 0.0364\n",
      "Epoch: 006/010 | Batch 700/938 | Cost: 0.0836\n",
      "Epoch: 006/010 | Batch 750/938 | Cost: 0.1251\n",
      "Epoch: 006/010 | Batch 800/938 | Cost: 0.0638\n",
      "Epoch: 006/010 | Batch 850/938 | Cost: 0.0725\n",
      "Epoch: 006/010 | Batch 900/938 | Cost: 0.1785\n",
      "Epoch: 006/010 training accuracy: 98.88%\n",
      "Time elapsed: 1.34 min\n",
      "Epoch: 007/010 | Batch 000/938 | Cost: 0.0043\n",
      "Epoch: 007/010 | Batch 050/938 | Cost: 0.0928\n",
      "Epoch: 007/010 | Batch 100/938 | Cost: 0.0375\n",
      "Epoch: 007/010 | Batch 150/938 | Cost: 0.1094\n",
      "Epoch: 007/010 | Batch 200/938 | Cost: 0.0237\n",
      "Epoch: 007/010 | Batch 250/938 | Cost: 0.0398\n",
      "Epoch: 007/010 | Batch 300/938 | Cost: 0.0592\n",
      "Epoch: 007/010 | Batch 350/938 | Cost: 0.0340\n",
      "Epoch: 007/010 | Batch 400/938 | Cost: 0.0255\n",
      "Epoch: 007/010 | Batch 450/938 | Cost: 0.0472\n",
      "Epoch: 007/010 | Batch 500/938 | Cost: 0.0172\n",
      "Epoch: 007/010 | Batch 550/938 | Cost: 0.0860\n",
      "Epoch: 007/010 | Batch 600/938 | Cost: 0.1124\n",
      "Epoch: 007/010 | Batch 650/938 | Cost: 0.0783\n",
      "Epoch: 007/010 | Batch 700/938 | Cost: 0.0307\n",
      "Epoch: 007/010 | Batch 750/938 | Cost: 0.0686\n",
      "Epoch: 007/010 | Batch 800/938 | Cost: 0.0128\n",
      "Epoch: 007/010 | Batch 850/938 | Cost: 0.0684\n",
      "Epoch: 007/010 | Batch 900/938 | Cost: 0.0541\n",
      "Epoch: 007/010 training accuracy: 98.98%\n",
      "Time elapsed: 1.56 min\n",
      "Epoch: 008/010 | Batch 000/938 | Cost: 0.0237\n",
      "Epoch: 008/010 | Batch 050/938 | Cost: 0.0167\n",
      "Epoch: 008/010 | Batch 100/938 | Cost: 0.0446\n",
      "Epoch: 008/010 | Batch 150/938 | Cost: 0.0324\n",
      "Epoch: 008/010 | Batch 200/938 | Cost: 0.0068\n",
      "Epoch: 008/010 | Batch 250/938 | Cost: 0.0529\n",
      "Epoch: 008/010 | Batch 300/938 | Cost: 0.0278\n",
      "Epoch: 008/010 | Batch 350/938 | Cost: 0.0289\n",
      "Epoch: 008/010 | Batch 400/938 | Cost: 0.0099\n",
      "Epoch: 008/010 | Batch 450/938 | Cost: 0.0052\n",
      "Epoch: 008/010 | Batch 500/938 | Cost: 0.0116\n",
      "Epoch: 008/010 | Batch 550/938 | Cost: 0.0049\n",
      "Epoch: 008/010 | Batch 600/938 | Cost: 0.0214\n",
      "Epoch: 008/010 | Batch 650/938 | Cost: 0.0397\n",
      "Epoch: 008/010 | Batch 700/938 | Cost: 0.0494\n",
      "Epoch: 008/010 | Batch 750/938 | Cost: 0.0166\n",
      "Epoch: 008/010 | Batch 800/938 | Cost: 0.0482\n",
      "Epoch: 008/010 | Batch 850/938 | Cost: 0.0049\n",
      "Epoch: 008/010 | Batch 900/938 | Cost: 0.0482\n",
      "Epoch: 008/010 training accuracy: 99.25%\n",
      "Time elapsed: 1.78 min\n",
      "Epoch: 009/010 | Batch 000/938 | Cost: 0.0367\n",
      "Epoch: 009/010 | Batch 050/938 | Cost: 0.0624\n",
      "Epoch: 009/010 | Batch 100/938 | Cost: 0.0095\n",
      "Epoch: 009/010 | Batch 150/938 | Cost: 0.0166\n",
      "Epoch: 009/010 | Batch 200/938 | Cost: 0.0764\n",
      "Epoch: 009/010 | Batch 250/938 | Cost: 0.0056\n",
      "Epoch: 009/010 | Batch 300/938 | Cost: 0.0066\n",
      "Epoch: 009/010 | Batch 350/938 | Cost: 0.0060\n",
      "Epoch: 009/010 | Batch 400/938 | Cost: 0.0583\n",
      "Epoch: 009/010 | Batch 450/938 | Cost: 0.0170\n",
      "Epoch: 009/010 | Batch 500/938 | Cost: 0.0252\n",
      "Epoch: 009/010 | Batch 550/938 | Cost: 0.0140\n",
      "Epoch: 009/010 | Batch 600/938 | Cost: 0.0911\n",
      "Epoch: 009/010 | Batch 650/938 | Cost: 0.0469\n",
      "Epoch: 009/010 | Batch 700/938 | Cost: 0.0146\n",
      "Epoch: 009/010 | Batch 750/938 | Cost: 0.0573\n",
      "Epoch: 009/010 | Batch 800/938 | Cost: 0.0067\n",
      "Epoch: 009/010 | Batch 850/938 | Cost: 0.1513\n",
      "Epoch: 009/010 | Batch 900/938 | Cost: 0.0029\n",
      "Epoch: 009/010 training accuracy: 99.30%\n",
      "Time elapsed: 2.01 min\n",
      "Epoch: 010/010 | Batch 000/938 | Cost: 0.0207\n",
      "Epoch: 010/010 | Batch 050/938 | Cost: 0.0154\n",
      "Epoch: 010/010 | Batch 100/938 | Cost: 0.0135\n",
      "Epoch: 010/010 | Batch 150/938 | Cost: 0.0122\n",
      "Epoch: 010/010 | Batch 200/938 | Cost: 0.0354\n",
      "Epoch: 010/010 | Batch 250/938 | Cost: 0.0217\n",
      "Epoch: 010/010 | Batch 300/938 | Cost: 0.0102\n",
      "Epoch: 010/010 | Batch 350/938 | Cost: 0.0189\n",
      "Epoch: 010/010 | Batch 400/938 | Cost: 0.0015\n",
      "Epoch: 010/010 | Batch 450/938 | Cost: 0.0101\n",
      "Epoch: 010/010 | Batch 500/938 | Cost: 0.0950\n",
      "Epoch: 010/010 | Batch 550/938 | Cost: 0.0130\n",
      "Epoch: 010/010 | Batch 600/938 | Cost: 0.0328\n",
      "Epoch: 010/010 | Batch 650/938 | Cost: 0.0506\n",
      "Epoch: 010/010 | Batch 700/938 | Cost: 0.0182\n",
      "Epoch: 010/010 | Batch 750/938 | Cost: 0.0091\n",
      "Epoch: 010/010 | Batch 800/938 | Cost: 0.0075\n",
      "Epoch: 010/010 | Batch 850/938 | Cost: 0.0092\n",
      "Epoch: 010/010 | Batch 900/938 | Cost: 0.0413\n",
      "Epoch: 010/010 training accuracy: 99.00%\n",
      "Time elapsed: 2.23 min\n",
      "Total Training Time: 2.23 min\n"
     ]
    }
   ],
   "source": [
    "def compute_accuracy(net, data_loader):\n",
    "    net.eval()\n",
    "    correct_pred, num_examples = 0, 0\n",
    "    with torch.no_grad():\n",
    "        for features, targets in data_loader:\n",
    "            features = features.view(-1, 28*28).to(device)\n",
    "            targets = targets.to(device)\n",
    "            logits, probas = net(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.train()\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",
    "        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",
    "    with torch.set_grad_enabled(False):\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": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 97.54%\n"
     ]
    }
   ],
   "source": [
    "print('Test accuracy: %.2f%%' % (compute_accuracy(model, test_loader)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy       1.15.4\n",
      "torch       1.0.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -iv"
   ]
  }
 ],
 "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": "3.7.1"
  },
  "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
}