Chapter-wise code/Code - PyTorch/1. Intro to PyTorch/07. Validating_using_pytorch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import datasets, transforms\n",
    "import torch\n",
    "from torch import optim\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "import helper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a transform to normalize the data\n",
    "transform = transforms.Compose([transforms.ToTensor(),\n",
    "                                transforms.Normalize((0.5,), (0.5,))])\n",
    "# Download and load the training data\n",
    "trainset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
    "\n",
    "# Download and load the test data\n",
    "testset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=False, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = nn.Sequential(nn.Linear(784, 256),\n",
    "                      nn.ReLU(),\n",
    "                      nn.Linear(256, 128),\n",
    "                      nn.ReLU(),\n",
    "                      nn.Linear(128, 64),\n",
    "                      nn.ReLU(),\n",
    "                      nn.Linear(64, 10),\n",
    "                      nn.LogSoftmax(dim=1))\n",
    "\n",
    "criterion = nn.NLLLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.003)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/30..  Training Loss: 0.554..  Test Loss: 0.568..  Test Accuracy: 0.796\n",
      "Epoch: 2/30..  Training Loss: 0.532..  Test Loss: 0.548..  Test Accuracy: 0.805\n",
      "Epoch: 3/30..  Training Loss: 0.514..  Test Loss: 0.533..  Test Accuracy: 0.809\n",
      "Epoch: 4/30..  Training Loss: 0.497..  Test Loss: 0.517..  Test Accuracy: 0.812\n",
      "Epoch: 5/30..  Training Loss: 0.484..  Test Loss: 0.507..  Test Accuracy: 0.816\n",
      "Epoch: 6/30..  Training Loss: 0.472..  Test Loss: 0.497..  Test Accuracy: 0.820\n",
      "Epoch: 7/30..  Training Loss: 0.462..  Test Loss: 0.499..  Test Accuracy: 0.821\n",
      "Epoch: 8/30..  Training Loss: 0.452..  Test Loss: 0.483..  Test Accuracy: 0.826\n",
      "Epoch: 9/30..  Training Loss: 0.444..  Test Loss: 0.475..  Test Accuracy: 0.831\n",
      "Epoch: 10/30..  Training Loss: 0.436..  Test Loss: 0.467..  Test Accuracy: 0.831\n",
      "Epoch: 11/30..  Training Loss: 0.429..  Test Loss: 0.463..  Test Accuracy: 0.833\n",
      "Epoch: 12/30..  Training Loss: 0.422..  Test Loss: 0.457..  Test Accuracy: 0.837\n",
      "Epoch: 13/30..  Training Loss: 0.416..  Test Loss: 0.447..  Test Accuracy: 0.840\n",
      "Epoch: 14/30..  Training Loss: 0.410..  Test Loss: 0.448..  Test Accuracy: 0.839\n",
      "Epoch: 15/30..  Training Loss: 0.405..  Test Loss: 0.443..  Test Accuracy: 0.842\n",
      "Epoch: 16/30..  Training Loss: 0.400..  Test Loss: 0.439..  Test Accuracy: 0.845\n",
      "Epoch: 17/30..  Training Loss: 0.395..  Test Loss: 0.432..  Test Accuracy: 0.844\n",
      "Epoch: 18/30..  Training Loss: 0.390..  Test Loss: 0.430..  Test Accuracy: 0.846\n",
      "Epoch: 19/30..  Training Loss: 0.385..  Test Loss: 0.429..  Test Accuracy: 0.848\n",
      "Epoch: 20/30..  Training Loss: 0.381..  Test Loss: 0.428..  Test Accuracy: 0.847\n",
      "Epoch: 21/30..  Training Loss: 0.376..  Test Loss: 0.422..  Test Accuracy: 0.847\n",
      "Epoch: 22/30..  Training Loss: 0.372..  Test Loss: 0.410..  Test Accuracy: 0.854\n",
      "Epoch: 23/30..  Training Loss: 0.368..  Test Loss: 0.416..  Test Accuracy: 0.850\n",
      "Epoch: 24/30..  Training Loss: 0.364..  Test Loss: 0.411..  Test Accuracy: 0.852\n",
      "Epoch: 25/30..  Training Loss: 0.360..  Test Loss: 0.410..  Test Accuracy: 0.855\n",
      "Epoch: 26/30..  Training Loss: 0.356..  Test Loss: 0.402..  Test Accuracy: 0.855\n",
      "Epoch: 27/30..  Training Loss: 0.352..  Test Loss: 0.396..  Test Accuracy: 0.857\n",
      "Epoch: 28/30..  Training Loss: 0.349..  Test Loss: 0.398..  Test Accuracy: 0.858\n",
      "Epoch: 29/30..  Training Loss: 0.345..  Test Loss: 0.397..  Test Accuracy: 0.856\n",
      "Epoch: 30/30..  Training Loss: 0.342..  Test Loss: 0.393..  Test Accuracy: 0.862\n"
     ]
    }
   ],
   "source": [
    "epochs = 30 \n",
    "train_losses, test_losses = [], []\n",
    "for e in range(epochs):\n",
    "    running_loss = 0\n",
    "    for images, labels in trainloader:\n",
    "        images = images.view(images.shape[0], -1)\n",
    "        # training pass\n",
    "        optimizer.zero_grad()\n",
    "        output = model(images)\n",
    "        # calculate error by comparing output and actual label\n",
    "        loss = criterion(output, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss += loss.item()\n",
    "        # after for loop completion, run else part;\n",
    "    else:\n",
    "        # training part\n",
    "        test_loss = 0\n",
    "        accuracy = 0\n",
    "        with torch.no_grad():\n",
    "            for images, labels in testloader:\n",
    "                images = images.view(images.shape[0], -1)\n",
    "                log_ps = model(images)\n",
    "                test_loss += criterion(log_ps, labels)\n",
    "                \n",
    "                ps = torch.exp(log_ps)\n",
    "                top_p, top_class = ps.topk(1, dim=1)\n",
    "                equals = top_class == labels.view(*top_class.shape)\n",
    "                accuracy += torch.mean(equals.type(torch.FloatTensor))\n",
    "                \n",
    "        train_losses.append(running_loss/len(trainloader))\n",
    "        test_losses.append(test_loss/len(testloader))\n",
    "\n",
    "        print(\"Epoch: {}/{}.. \".format(e+1, epochs),\n",
    "              \"Training Loss: {:.3f}.. \".format(running_loss/len(trainloader)),\n",
    "              \"Test Loss: {:.3f}.. \".format(test_loss/len(testloader)),\n",
    "              \"Test Accuracy: {:.3f}\".format(accuracy/len(testloader)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f53406f92e8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 251,
       "width": 372
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_losses, label='Training loss')\n",
    "plt.plot(test_losses, label='Validation loss')\n",
    "plt.legend(frameon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}