Part 4 - Fashion-MNIST (Exercises).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifying Fashion-MNIST\n",
    "\n",
    "Now it's your turn to build and train a neural network. You'll be using the [Fashion-MNIST dataset](https://github.com/zalandoresearch/fashion-mnist), a drop-in replacement for the MNIST dataset. MNIST is actually quite trivial with neural networks where you can easily achieve better than 97% accuracy. Fashion-MNIST is a set of 28x28 greyscale images of clothes. It's more complex than MNIST, so it's a better representation of the actual performance of your network, and a better representation of datasets you'll use in the real world.\n",
    "\n",
    "<img src='assets/fashion-mnist-sprite.png' width=500px>\n",
    "\n",
    "In this notebook, you'll build your own neural network. For the most part, you could just copy and paste the code from Part 3, but you wouldn't be learning. It's important for you to write the code yourself and get it to work. Feel free to consult the previous notebooks though as you work through this.\n",
    "\n",
    "First off, let's load the dataset through torchvision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchvision import datasets, transforms\n",
    "import helper\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": "markdown",
   "metadata": {},
   "source": [
    "Here we can see one of the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "image, label = next(iter(trainloader))\n",
    "helper.imshow(image[0,:]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the network\n",
    "\n",
    "Here you should define your network. As with MNIST, each image is 28x28 which is a total of 784 pixels, and there are 10 classes. You should include at least one hidden layer. We suggest you use ReLU activations for the layers and to return the logits or log-softmax from the forward pass. It's up to you how many layers you add and the size of those layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Define your network architecture here\n",
    "from torch import nn,optim\n",
    "import torch.nn.functional as F\n",
    "\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",
    "            x = x.view(x.shape[0],-1)\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the network\n",
    "\n",
    "Now you should create your network and train it. First you'll want to define [the criterion](http://pytorch.org/docs/master/nn.html#loss-functions) ( something like `nn.CrossEntropyLoss`) and [the optimizer](http://pytorch.org/docs/master/optim.html) (typically `optim.SGD` or `optim.Adam`).\n",
    "\n",
    "Then write the training code. Remember the training pass is a fairly straightforward process:\n",
    "\n",
    "* Make a forward pass through the network to get the logits \n",
    "* Use the logits to calculate the loss\n",
    "* Perform a backward pass through the network with `loss.backward()` to calculate the gradients\n",
    "* Take a step with the optimizer to update the weights\n",
    "\n",
    "By adjusting the hyperparameters (hidden units, learning rate, etc), you should be able to get the training loss below 0.4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Create the network, define the criterion and optimizer\n",
    "model = Classifier()\n",
    "criterion = nn.NLLLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.003)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.5135534927566677\n",
      "Training loss: 0.3892816776183368\n",
      "Training loss: 0.3555478405024705\n",
      "Training loss: 0.33317188291883926\n",
      "Training loss: 0.313291890868373\n"
     ]
    }
   ],
   "source": [
    "# TODO: Train the network here\n",
    "epochs = 5\n",
    "\n",
    "for e in range(epochs):\n",
    "    running_loss = 0\n",
    "    for images, labels in trainloader:\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)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 204,
       "width": 423
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import helper\n",
    "\n",
    "# Test out your network!\n",
    "\n",
    "dataiter = iter(testloader)\n",
    "images, labels = dataiter.next()\n",
    "img = images[0]\n",
    "# Convert 2D image to 1D vector\n",
    "img = img.resize_(1, 784)\n",
    "\n",
    "# TODO: Calculate the class probabilities (softmax) for img\n",
    "ps = torch.exp(model(img))\n",
    "\n",
    "# Plot the image and probabilities\n",
    "helper.view_classify(img.resize_(1, 28, 28), ps, version='Fashion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}