8_Classifying Fashion-MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classifying Fashion-MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchvision import datasets, transforms\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "\n",
    "\n",
    "# Define a transform to normalize the data\n",
    "transform = transforms.Compose([transforms.ToTensor(),\n",
    "                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.5204751578920177\n",
      "Training loss: 0.38930828781969257\n",
      "Training loss: 0.3506451879959625\n",
      "Training loss: 0.33132812002701545\n",
      "Training loss: 0.3179758786599138\n"
     ]
    }
   ],
   "source": [
    "#Define your network architecture here\n",
    "class Classifier(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(784, 256)\n",
    "        self.fc2 = nn.Linear(256, 128)\n",
    "        self.fc3 = nn.Linear(128, 64)\n",
    "        self.fc4 = nn.Linear(64, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # make sure input tensor is flattened\n",
    "       \n",
    "        \n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = F.relu(self.fc3(x))\n",
    "        x = F.log_softmax(self.fc4(x), dim=1)\n",
    "        \n",
    "        return x\n",
    "model = Classifier()\n",
    "criterion = nn.NLLLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr = 0.003)\n",
    "\n",
    "epochs = 5\n",
    "\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",
    "        log_ps = model(images)\n",
    "        loss = criterion(log_ps, labels)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss += loss.item()\n",
    "    else:\n",
    "        print(f\"Training loss: {running_loss/len(trainloader)}\")\n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 197,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def view_classify(img, ps, version=\"MNIST\"):\n",
    "    ''' Function for viewing an image and it's predicted classes.\n",
    "    '''\n",
    "    ps = ps.data.numpy().squeeze()\n",
    "\n",
    "    fig, (ax1, ax2) = plt.subplots(figsize=(6,9), ncols=2)\n",
    "    ax1.imshow(img.resize_(1, 28, 28).numpy().squeeze())\n",
    "    ax1.axis('off')\n",
    "    ax2.barh(np.arange(10), ps)\n",
    "    ax2.set_aspect(0.1)\n",
    "    ax2.set_yticks(np.arange(10))\n",
    "    if version == \"MNIST\":\n",
    "        ax2.set_yticklabels(np.arange(10))\n",
    "    elif version == \"Fashion\":\n",
    "        ax2.set_yticklabels(['T-shirt/top',\n",
    "                            'Trouser',\n",
    "                            'Pullover',\n",
    "                            'Dress',\n",
    "                            'Coat',\n",
    "                            'Sandal',\n",
    "                            'Shirt',\n",
    "                            'Sneaker',\n",
    "                            'Bag',\n",
    "                            'Ankle Boot'], size='small');\n",
    "    ax2.set_title('Class Probability')\n",
    "    ax2.set_xlim(0, 1.1)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "\n",
    "\n",
    "# Test out your network!\n",
    "\n",
    "dataiter = iter(testloader)\n",
    "\n",
    "images, labels = dataiter.next()\n",
    "img = images[1]\n",
    "img = img.view(img.shape[0], -1)\n",
    "# TODO: Calculate the class probabilities (softmax) for img\n",
    "ps = torch.exp(model(img))\n",
    "\n",
    "# Plot the image and probabilities\n",
    "\n",
    "view_classify(img, ps, version='Fashion')"
   ]
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}