Introduction to PyTorch/Part 5 - Inference and Validation.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Inference and Validation\n",
    "\n",
    "Now that you have a trained network, you can use it for making predictions. This is typically called **inference**, a term borrowed from statistics. However, neural networks have a tendency to perform *too well* on the training data and aren't able to generalize to data that hasn't been seen before. This is called **overfitting** and it impairs inference performance. To test for overfitting while training, we measure the performance on data not in the training set called the **validation** set. We avoid overfitting through regularization such as dropout while monitoring the validation performance during training. In this notebook, I'll show you how to do this in PyTorch. \n",
    "\n",
    "As usual, let's start by loading the dataset through torchvision. You'll learn more about torchvision and loading data in a later part. This time we'll be taking advantage of the test set which you can get by setting `train=False` here:\n",
    "\n",
    "```python\n",
    "testset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=False, transform=transform)\n",
    "```\n",
    "\n",
    "The test set contains images just like the training set. Typically you'll see 10-20% of the original dataset held out for testing and validation with the rest being used for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# 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": "markdown",
   "metadata": {},
   "source": [
    "Here I'll create a model like normal, using the same one from my solution for part 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "        # make sure input tensor is flattened\n",
    "        x = x.view(x.shape[0], -1)\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of validation is to measure the model's performance on data that isn't part of the training set. Performance here is up to the developer to define though. Typically this is just accuracy, the percentage of classes the network predicted correctly. Other options are [precision and recall](https://en.wikipedia.org/wiki/Precision_and_recall#Definition_(classification_context)) and top-5 error rate. We'll focus on accuracy here. First I'll do a forward pass with one batch from the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 10])\n"
     ]
    }
   ],
   "source": [
    "model = Classifier()\n",
    "\n",
    "images, labels = next(iter(testloader))\n",
    "# Get the class probabilities\n",
    "ps = torch.exp(model(images))\n",
    "# Make sure the shape is appropriate, we should get 10 class probabilities for 64 examples\n",
    "print(ps.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the probabilities, we can get the most likely class using the `ps.topk` method. This returns the $k$ highest values. Since we just want the most likely class, we can use `ps.topk(1)`. This returns a tuple of the top-$k$ values and the top-$k$ indices. If the highest value is the fifth element, we'll get back 4 as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[5],\n",
      "        [5],\n",
      "        [5],\n",
      "        [5],\n",
      "        [5],\n",
      "        [5],\n",
      "        [0],\n",
      "        [5],\n",
      "        [8],\n",
      "        [5]])\n"
     ]
    }
   ],
   "source": [
    "top_p, top_class = ps.topk(1, dim=1)\n",
    "# Look at the most likely classes for the first 10 examples\n",
    "print(top_class[:10,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can check if the predicted classes match the labels. This is simple to do by equating `top_class` and `labels`, but we have to be careful of the shapes. Here `top_class` is a 2D tensor with shape `(64, 1)` while `labels` is 1D with shape `(64)`. To get the equality to work out the way we want, `top_class` and `labels` must have the same shape.\n",
    "\n",
    "If we do\n",
    "\n",
    "```python\n",
    "equals = top_class == labels\n",
    "```\n",
    "\n",
    "`equals` will have shape `(64, 64)`, try it yourself. What it's doing is comparing the one element in each row of `top_class` with each element in `labels` which returns 64 True/False boolean values for each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "equals = top_class == labels.view(*top_class.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to calculate the percentage of correct predictions. `equals` has binary values, either 0 or 1. This means that if we just sum up all the values and divide by the number of values, we get the percentage of correct predictions. This is the same operation as taking the mean, so we can get the accuracy with a call to `torch.mean`. If only it was that simple. If you try `torch.mean(equals)`, you'll get an error\n",
    "\n",
    "```\n",
    "RuntimeError: mean is not implemented for type torch.ByteTensor\n",
    "```\n",
    "\n",
    "This happens because `equals` has type `torch.ByteTensor` but `torch.mean` isn't implemented for tensors with that type. So we'll need to convert `equals` to a float tensor. Note that when we take `torch.mean` it returns a scalar tensor, to get the actual value as a float we'll need to do `accuracy.item()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 14.0625%\n"
     ]
    }
   ],
   "source": [
    "accuracy = torch.mean(equals.type(torch.FloatTensor))\n",
    "print(f'Accuracy: {accuracy.item()*100}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The network is untrained so it's making random guesses and we should see an accuracy around 10%. Now let's train our network and include our validation pass so we can measure how well the network is performing on the test set. Since we're not updating our parameters in the validation pass, we can speed up our code by turning off gradients using `torch.no_grad()`:\n",
    "\n",
    "```python\n",
    "# turn off gradients\n",
    "with torch.no_grad():\n",
    "    # validation pass here\n",
    "    for images, labels in testloader:\n",
    "        ...\n",
    "```\n",
    "\n",
    ">**Exercise:** Implement the validation loop below and print out the total accuracy after the loop. You can largely copy and paste the code from above, but I suggest typing it in because writing it out yourself is essential for building the skill. In general you'll always learn more by typing it rather than copy-pasting. You should be able to get an accuracy above 80%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/30..  Training Loss: 0.515..  Test Loss: 0.466..  Test Accuracy: 0.827\n",
      "Epoch: 2/30..  Training Loss: 0.390..  Test Loss: 0.392..  Test Accuracy: 0.859\n",
      "Epoch: 3/30..  Training Loss: 0.355..  Test Loss: 0.398..  Test Accuracy: 0.852\n",
      "Epoch: 4/30..  Training Loss: 0.331..  Test Loss: 0.427..  Test Accuracy: 0.850\n",
      "Epoch: 5/30..  Training Loss: 0.316..  Test Loss: 0.381..  Test Accuracy: 0.868\n",
      "Epoch: 6/30..  Training Loss: 0.300..  Test Loss: 0.356..  Test Accuracy: 0.872\n",
      "Epoch: 7/30..  Training Loss: 0.289..  Test Loss: 0.379..  Test Accuracy: 0.868\n",
      "Epoch: 8/30..  Training Loss: 0.279..  Test Loss: 0.451..  Test Accuracy: 0.845\n",
      "Epoch: 9/30..  Training Loss: 0.278..  Test Loss: 0.373..  Test Accuracy: 0.873\n",
      "Epoch: 10/30..  Training Loss: 0.262..  Test Loss: 0.371..  Test Accuracy: 0.874\n",
      "Epoch: 11/30..  Training Loss: 0.256..  Test Loss: 0.390..  Test Accuracy: 0.872\n",
      "Epoch: 12/30..  Training Loss: 0.253..  Test Loss: 0.374..  Test Accuracy: 0.875\n",
      "Epoch: 13/30..  Training Loss: 0.246..  Test Loss: 0.379..  Test Accuracy: 0.877\n",
      "Epoch: 14/30..  Training Loss: 0.242..  Test Loss: 0.400..  Test Accuracy: 0.874\n",
      "Epoch: 15/30..  Training Loss: 0.236..  Test Loss: 0.380..  Test Accuracy: 0.875\n",
      "Epoch: 16/30..  Training Loss: 0.233..  Test Loss: 0.375..  Test Accuracy: 0.882\n",
      "Epoch: 17/30..  Training Loss: 0.225..  Test Loss: 0.439..  Test Accuracy: 0.873\n",
      "Epoch: 18/30..  Training Loss: 0.226..  Test Loss: 0.386..  Test Accuracy: 0.885\n",
      "Epoch: 19/30..  Training Loss: 0.214..  Test Loss: 0.394..  Test Accuracy: 0.885\n",
      "Epoch: 20/30..  Training Loss: 0.216..  Test Loss: 0.391..  Test Accuracy: 0.877\n",
      "Epoch: 21/30..  Training Loss: 0.207..  Test Loss: 0.387..  Test Accuracy: 0.882\n",
      "Epoch: 22/30..  Training Loss: 0.206..  Test Loss: 0.385..  Test Accuracy: 0.883\n",
      "Epoch: 23/30..  Training Loss: 0.199..  Test Loss: 0.385..  Test Accuracy: 0.882\n",
      "Epoch: 24/30..  Training Loss: 0.202..  Test Loss: 0.431..  Test Accuracy: 0.875\n",
      "Epoch: 25/30..  Training Loss: 0.203..  Test Loss: 0.460..  Test Accuracy: 0.869\n",
      "Epoch: 26/30..  Training Loss: 0.188..  Test Loss: 0.436..  Test Accuracy: 0.883\n",
      "Epoch: 27/30..  Training Loss: 0.192..  Test Loss: 0.412..  Test Accuracy: 0.879\n",
      "Epoch: 28/30..  Training Loss: 0.186..  Test Loss: 0.427..  Test Accuracy: 0.885\n",
      "Epoch: 29/30..  Training Loss: 0.192..  Test Loss: 0.419..  Test Accuracy: 0.885\n",
      "Epoch: 30/30..  Training Loss: 0.192..  Test Loss: 0.417..  Test Accuracy: 0.884\n"
     ]
    }
   ],
   "source": [
    "model = Classifier()\n",
    "criterion = nn.NLLLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.003)\n",
    "\n",
    "epochs = 30\n",
    "steps = 0\n",
    "\n",
    "train_losses, test_losses = [], []\n",
    "for e in range(epochs):\n",
    "    running_loss = 0\n",
    "    for images, labels in trainloader:\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        log_ps = model(images)\n",
    "        loss = criterion(log_ps, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss += loss.item()\n",
    "        \n",
    "    else:\n",
    "        ## TODO: Implement the validation pass and print out the validation accuracy\n",
    "        test_loss = 0\n",
    "        accuracy = 0\n",
    "        \n",
    "        # Turn off gradients for validation, saves memory and computations\n",
    "        with torch.no_grad():\n",
    "            for images, labels in testloader:\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",
    "        \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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13e12386be0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 380
      },
      "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": "markdown",
   "metadata": {},
   "source": [
    "## Overfitting\n",
    "\n",
    "If we look at the training and validation losses as we train the network, we can see a phenomenon known as overfitting.\n",
    "\n",
    "<img src='assets/overfitting.png' width=450px>\n",
    "\n",
    "The network learns the training set better and better, resulting in lower training losses. However, it starts having problems generalizing to data outside the training set leading to the validation loss increasing. The ultimate goal of any deep learning model is to make predictions on new data, so we should strive to get the lowest validation loss possible. One option is to use the version of the model with the lowest validation loss, here the one around 8-10 training epochs. This strategy is called *early-stopping*. In practice, you'd save the model frequently as you're training then later choose the model with the lowest validation loss.\n",
    "\n",
    "The most common method to reduce overfitting (outside of early-stopping) is *dropout*, where we randomly drop input units. This forces the network to share information between weights, increasing it's ability to generalize to new data. Adding dropout in PyTorch is straightforward using the [`nn.Dropout`](https://pytorch.org/docs/stable/nn.html#torch.nn.Dropout) module.\n",
    "\n",
    "```python\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",
    "        # Dropout module with 0.2 drop probability\n",
    "        self.dropout = nn.Dropout(p=0.2)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # make sure input tensor is flattened\n",
    "        x = x.view(x.shape[0], -1)\n",
    "        \n",
    "        # Now with dropout\n",
    "        x = self.dropout(F.relu(self.fc1(x)))\n",
    "        x = self.dropout(F.relu(self.fc2(x)))\n",
    "        x = self.dropout(F.relu(self.fc3(x)))\n",
    "        \n",
    "        # output so no dropout here\n",
    "        x = F.log_softmax(self.fc4(x), dim=1)\n",
    "        \n",
    "        return x\n",
    "```\n",
    "\n",
    "During training we want to use dropout to prevent overfitting, but during inference we want to use the entire network. So, we need to turn off dropout during validation, testing, and whenever we're using the network to make predictions. To do this, you use `model.eval()`. This sets the model to evaluation mode where the dropout probability is 0. You can turn dropout back on by setting the model to train mode with `model.train()`. In general, the pattern for the validation loop will look like this, where you turn off gradients, set the model to evaluation mode, calculate the validation loss and metric, then set the model back to train mode.\n",
    "\n",
    "```python\n",
    "# turn off gradients\n",
    "with torch.no_grad():\n",
    "    \n",
    "    # set model to evaluation mode\n",
    "    model.eval()\n",
    "    \n",
    "    # validation pass here\n",
    "    for images, labels in testloader:\n",
    "        ...\n",
    "\n",
    "# set model back to train mode\n",
    "model.train()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Exercise:** Add dropout to your model and train it on Fashion-MNIST again. See if you can get a lower validation loss or higher accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TODO: Define your model with dropout added\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",
    "        self.dropout = nn.Dropout(p=0.2)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = x.view(x.shape[0], -1)\n",
    "        \n",
    "        x = self.dropout(F.relu(self.fc1(x)))\n",
    "        x = self.dropout(F.relu(self.fc2(x)))\n",
    "        x = self.dropout(F.relu(self.fc3(x)))\n",
    "        \n",
    "        x = F.log_softmax(self.fc4(x), dim=1)\n",
    "        \n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/30..  Training Loss: 0.609..  Test Loss: 0.471..  Test Accuracy: 0.831\n",
      "Epoch: 2/30..  Training Loss: 0.485..  Test Loss: 0.436..  Test Accuracy: 0.840\n",
      "Epoch: 3/30..  Training Loss: 0.446..  Test Loss: 0.449..  Test Accuracy: 0.844\n",
      "Epoch: 4/30..  Training Loss: 0.427..  Test Loss: 0.426..  Test Accuracy: 0.846\n",
      "Epoch: 5/30..  Training Loss: 0.418..  Test Loss: 0.430..  Test Accuracy: 0.852\n",
      "Epoch: 6/30..  Training Loss: 0.418..  Test Loss: 0.416..  Test Accuracy: 0.854\n",
      "Epoch: 7/30..  Training Loss: 0.402..  Test Loss: 0.389..  Test Accuracy: 0.859\n",
      "Epoch: 8/30..  Training Loss: 0.395..  Test Loss: 0.386..  Test Accuracy: 0.862\n",
      "Epoch: 9/30..  Training Loss: 0.393..  Test Loss: 0.388..  Test Accuracy: 0.865\n",
      "Epoch: 10/30..  Training Loss: 0.386..  Test Loss: 0.391..  Test Accuracy: 0.866\n",
      "Epoch: 11/30..  Training Loss: 0.382..  Test Loss: 0.393..  Test Accuracy: 0.864\n",
      "Epoch: 12/30..  Training Loss: 0.381..  Test Loss: 0.379..  Test Accuracy: 0.865\n",
      "Epoch: 13/30..  Training Loss: 0.375..  Test Loss: 0.371..  Test Accuracy: 0.870\n",
      "Epoch: 14/30..  Training Loss: 0.376..  Test Loss: 0.403..  Test Accuracy: 0.863\n",
      "Epoch: 15/30..  Training Loss: 0.370..  Test Loss: 0.376..  Test Accuracy: 0.875\n",
      "Epoch: 16/30..  Training Loss: 0.373..  Test Loss: 0.385..  Test Accuracy: 0.863\n",
      "Epoch: 17/30..  Training Loss: 0.369..  Test Loss: 0.380..  Test Accuracy: 0.873\n",
      "Epoch: 18/30..  Training Loss: 0.359..  Test Loss: 0.388..  Test Accuracy: 0.861\n",
      "Epoch: 19/30..  Training Loss: 0.361..  Test Loss: 0.389..  Test Accuracy: 0.868\n",
      "Epoch: 20/30..  Training Loss: 0.368..  Test Loss: 0.372..  Test Accuracy: 0.873\n",
      "Epoch: 21/30..  Training Loss: 0.354..  Test Loss: 0.377..  Test Accuracy: 0.869\n",
      "Epoch: 22/30..  Training Loss: 0.357..  Test Loss: 0.373..  Test Accuracy: 0.875\n",
      "Epoch: 23/30..  Training Loss: 0.349..  Test Loss: 0.378..  Test Accuracy: 0.873\n",
      "Epoch: 24/30..  Training Loss: 0.351..  Test Loss: 0.373..  Test Accuracy: 0.872\n",
      "Epoch: 25/30..  Training Loss: 0.352..  Test Loss: 0.369..  Test Accuracy: 0.871\n",
      "Epoch: 26/30..  Training Loss: 0.348..  Test Loss: 0.389..  Test Accuracy: 0.870\n",
      "Epoch: 27/30..  Training Loss: 0.353..  Test Loss: 0.376..  Test Accuracy: 0.877\n",
      "Epoch: 28/30..  Training Loss: 0.346..  Test Loss: 0.371..  Test Accuracy: 0.869\n",
      "Epoch: 29/30..  Training Loss: 0.348..  Test Loss: 0.393..  Test Accuracy: 0.862\n",
      "Epoch: 30/30..  Training Loss: 0.341..  Test Loss: 0.377..  Test Accuracy: 0.870\n"
     ]
    }
   ],
   "source": [
    "## TODO: Train your model with dropout, and monitor the training progress with the validation loss and accuracy\n",
    "model = Classifier()\n",
    "criterion = nn.NLLLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.003)\n",
    "\n",
    "epochs = 30\n",
    "steps = 0\n",
    "\n",
    "train_losses, test_losses = [], []\n",
    "for e in range(epochs):\n",
    "    running_loss = 0\n",
    "    for images, labels in trainloader:\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        log_ps = model(images)\n",
    "        loss = criterion(log_ps, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss += loss.item()\n",
    "        \n",
    "    else:\n",
    "        test_loss = 0\n",
    "        accuracy = 0\n",
    "        \n",
    "        # Turn off gradients for validation, saves memory and computations\n",
    "        with torch.no_grad():\n",
    "            model.eval()\n",
    "            for images, labels in testloader:\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",
    "        model.train()\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(train_losses[-1]),\n",
    "              \"Test Loss: {:.3f}.. \".format(test_losses[-1]),\n",
    "              \"Test Accuracy: {:.3f}\".format(accuracy/len(testloader)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13e124ea358>"
      ]
     },
     "execution_count": 14,
     "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": 250,
       "width": 380
      },
      "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": "markdown",
   "metadata": {},
   "source": [
    "## Inference\n",
    "\n",
    "Now that the model is trained, we can use it for inference. We've done this before, but now we need to remember to set the model in inference mode with `model.eval()`. You'll also want to turn off autograd with the `torch.no_grad()` context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 206,
       "width": 423
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import helper module (should be in the repo)\n",
    "import helper\n",
    "\n",
    "# Test out your network!\n",
    "\n",
    "model.eval()\n",
    "\n",
    "dataiter = iter(testloader)\n",
    "images, labels = dataiter.next()\n",
    "img = images[0]\n",
    "# Convert 2D image to 1D vector\n",
    "img = img.view(1, 784)\n",
    "\n",
    "# Calculate the class probabilities (softmax) for img\n",
    "with torch.no_grad():\n",
    "    output = model.forward(img)\n",
    "\n",
    "ps = torch.exp(output)\n",
    "\n",
    "# Plot the image and probabilities\n",
    "helper.view_classify(img.view(1, 28, 28), ps, version='Fashion')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next Up!\n",
    "\n",
    "In the next part, I'll show you how to save your trained models. In general, you won't want to train a model everytime you need it. Instead, you'll train once, save it, then load the model when you want to train more or use if for inference."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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