MNIST_digits_classification_softmax.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download the dataset\n",
    "- use **torchvision.dataset** to download the MNIST digits dataset. Make sure the downladed tensors are tansormed to Tensors.\n",
    "- generate train and test loaders of batch size 64 and assign it to variables **trainloader** and **testloader** respectively.\n",
    "\n",
    "#### Expected output:\n",
    "    dimension of first batch of trainloader: torch.Size([64, 1, 28, 28])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "dimension of first batch of trainloader: torch.Size([64, 1, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torchvision import datasets, transforms\n",
    "from torch import optim\n",
    "import helper\n",
    "\n",
    "# Download and load the training data\n",
    "trainset = datasets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor())\n",
    "testset = datasets.MNIST(root='./data', train=False, download=True, transform=transforms.ToTensor())\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
    "testloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
    "\n",
    "image, labels = next(iter(trainloader))\n",
    "print(\"dimension of first batch of trainloader:\", image.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- define the method named model_buid() to build a sequential model.\n",
    "  - parameters: \n",
    "       - **input_size** int: number of input features  \n",
    "       - **hidden_sizes** array of size 3: array of number of nodes in each of the three hidden layers  \n",
    "       - **output int**: number of nodes at output layer (or number of classes to classify)  \n",
    "  - returns model: sequential model  \n",
    "Apply relu activation between each layer and for the **final layer out apply logSoftmax.**  \n",
    "Logsoftmax log transformation of softmax output which is given by :$$\n",
    " \\sigma(x_i) =  \\log \\cfrac{e^{x_i}}{\\sum_{k=1}^{N}{e^{x_k}}}$$ where N is the number of classes  \n",
    " more information [here](https://pytorch.org/docs/stable/nn.html#torch.nn.LogSoftmax) \n",
    "  - using the function we defined, ** we initialize the model to have input size 784, three hidden layers to have 256, 128 and 64 nodes and finally an output layer of size 10 nodes.**\n",
    "  \n",
    "#### Expected output:  \n",
    "Sequential(  \n",
    "  (0): Linear(in_features=784, out_features=256, bias=True)  \n",
    "  (1): ReLU()  \n",
    "  (2): Linear(in_features=256, out_features=128, bias=True)  \n",
    "  (3): ReLU()  \n",
    "  (4): Linear(in_features=128, out_features=64, bias=True)  \n",
    "  (5): ReLU()  \n",
    "  (6): Linear(in_features=64, out_features=10, bias=True)  \n",
    "  (7): LogSoftmax()  \n",
    ")  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Sequential(\n  (0): Linear(in_features=784, out_features=256, bias=True)\n  (1): ReLU()\n  (2): Linear(in_features=256, out_features=128, bias=True)\n  (3): ReLU()\n  (4): Linear(in_features=128, out_features=64, bias=True)\n  (5): ReLU()\n  (6): Linear(in_features=64, out_features=10, bias=True)\n  (7): LogSoftmax()\n)\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(999)\n",
    "input_size = 784\n",
    "hidden_sizes = [256,128, 64]\n",
    "output_size = 10\n",
    "\n",
    "# Build a feed-forward network\n",
    "def build_model(input_size, hidden_sizes, output_size):\n",
    "    ####Start code here\n",
    "    model = nn.Sequential(nn.Linear(input_size, hidden_sizes[0]),  \n",
    "                   nn.ReLU(),\n",
    "                   nn.Linear(hidden_sizes[0], hidden_sizes[1]),\n",
    "                   nn.ReLU(),\n",
    "                   nn.Linear(hidden_sizes[1], hidden_sizes[2]),\n",
    "                   nn.ReLU(),\n",
    "                   nn.Linear(hidden_sizes[2], output_size),\n",
    "                   nn.LogSoftmax(dim=1))\n",
    "    #### End code\n",
    "    return model\n",
    "\n",
    "classifier = build_model(input_size,hidden_sizes,output_size)\n",
    "print(classifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform forward pass on single image\n",
    "- get the images and labels from first batch of trainloader\n",
    "- sample the first image from the batch and flatten the image tensor from (1 x 28 x 28) to (1, 784)\n",
    "- using the model we defined in the previous cell perfrom a forward pass on the flattened image and save the network output to variable **log_ps**\n",
    "- transform log transformed softmax output log_ps to exponential and assign it to ps (torch.exp())\n",
    "- return the top probability and its index from  **ps** (ps.topk())  \n",
    "Since the model is not trained yet, the output class probabilities are uniform.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "torch.manual_seed(999)\n",
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next() \n",
    "image =  images[0] ###sample first image from images\n",
    "image = image.view(image.shape[0], -1)\n",
    "with torch.no_grad():\n",
    "    log_ps = classifier(image)\n",
    "ps = torch.exp(log_ps)\n",
    "top_p, top_class = ps.topk(1, dim=1)\n",
    "\n",
    "helper.view_classify(image.view(1,28,28), ps)\n",
    "prob = top_p.item()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define criterion (loss function) and optimizer\n",
    "  - Define criterion to be [negative likelihood loss](https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss) since the network output is log transformed probabilities.\n",
    "  - Define optimizer to be [Adam](https://pytorch.org/docs/stable/optim.html#torch.optim.Adam) and set learning rate to 0.003 and pass the parameters of **classifier** model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.NLLLoss()\n",
    "optimizer = optim.Adam(classifier.parameters(),lr=0.003)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 1/5..  Training Loss: 0.249..  Test Loss: 0.093..  Test Accuracy: 0.972\n",
      "Epoch: 2/5..  Training Loss: 0.106..  Test Loss: 0.070..  Test Accuracy: 0.978\n",
      "Epoch: 3/5..  Training Loss: 0.079..  Test Loss: 0.053..  Test Accuracy: 0.984\n",
      "Epoch: 4/5..  Training Loss: 0.063..  Test Loss: 0.053..  Test Accuracy: 0.984\n",
      "Epoch: 5/5..  Training Loss: 0.054..  Test Loss: 0.036..  Test Accuracy: 0.989\n",
      "After training\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x648 with 2 Axes>",
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     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "torch.manual_seed(999)\n",
    "\n",
    "epochs = 5\n",
    "steps = 0\n",
    "\n",
    "for e in range(epochs):\n",
    "    \n",
    "    # Initializing loss\n",
    "    running_loss = 0\n",
    "    for images, labels in trainloader:\n",
    "        images = images.view(images.shape[0], -1)         # flatten the images\n",
    "        optimizer.zero_grad()   \n",
    "        log_ps = classifier(images)                       # perform forward pass\n",
    "        loss = criterion(log_ps,labels)                   # compute loss using the criterion by passing log_ps and labels\n",
    "        loss.backward()                                   # call .backward() on loss\n",
    "        optimizer.step()                                  # update parameters using .step() on optimizer\n",
    "        \n",
    "        running_loss += loss.item()\n",
    "        \n",
    "    # Compute loss on test set for each batch of training     \n",
    "    else:\n",
    "        # Initializing train loss and test loss\n",
    "        test_loss = 0\n",
    "        accuracy = 0\n",
    "        \n",
    "        \n",
    "        with torch.no_grad():\n",
    "            for images, labels in testloader:\n",
    "            \n",
    "                images =  images = images.view(images.shape[0], -1)           # flatten images\n",
    "                log_ps =   classifier(images)                                 # perform forward pass\n",
    "                test_loss += criterion(log_ps,labels)                         # update the test loss    \n",
    "                ps = torch.exp(log_ps)\n",
    "                top_p, top_class = ps.topk(1, dim=1)                          # get the top class for each sample\n",
    "                \n",
    "                equals = top_class == labels.view(*top_class.shape)\n",
    "                accuracy += torch.mean(equals.type(torch.FloatTensor))\n",
    "\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)))\n",
    "\n",
    "print(\"After training\")\n",
    "helper.view_classify(images[0].view(1,28,28), ps[0])\n",
    "with open(\"output.txt\", \"w\") as file:\n",
    "    file.write(\"{:.2f}\".format(prob)+\"\\n\")\n",
    "    file.write((\"{:.2f}\".format(running_loss)))"
   ]
  }
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12-final"
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