Hands_on_Deep_Learning/Deep_Neural_Networks_with_PyTorch/L25_1layer_neural_network_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Neural Networks with One Hidden Layer</h1>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Objective</h2><ul><li> How to classify handwritten digits using Neural Network.</li></ul> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Table of Contents</h2>\n",
    "<p>In this lab, you will use a single layer neural network to classify handwritten digits from the MNIST database.</p>\n",
    "\n",
    "<ul>\n",
    "    <li><a href=\"#Model\">Neural Network Module and Training Function</a></li>\n",
    "    <li><a href=\"#Makeup_Data\">Make Some Data</a></li>\n",
    "    <li><a href=\"#Train\">Define the Neural Network, Optimizer, and Train the  Model</a></li>\n",
    "    <li><a href=\"#Result\">Analyze Results</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Preparation</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll need the following libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the libraries we need for this lab\n",
    "\n",
    "# Using the following line code to install the torchvision library\n",
    "# !conda install -y torchvision\n",
    "\n",
    "import torch \n",
    "import torch.nn as nn\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.datasets as dsets\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the following helper functions for plotting the loss: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to plot accuracy and loss\n",
    "\n",
    "def plot_accuracy_loss(training_results): \n",
    "    plt.subplot(2, 1, 1)\n",
    "    plt.plot(training_results['training_loss'], 'r')\n",
    "    plt.ylabel('loss')\n",
    "    plt.title('training loss iterations')\n",
    "    plt.subplot(2, 1, 2)\n",
    "    plt.plot(training_results['validation_accuracy'])\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.xlabel('epochs')   \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the following function for printing the model parameters: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to plot model parameters\n",
    "\n",
    "def print_model_parameters(model):\n",
    "    count = 0\n",
    "    for ele in model.state_dict():\n",
    "        count += 1\n",
    "        if count % 2 != 0:\n",
    "            print (\"The following are the parameters for the layer \", count // 2 + 1)\n",
    "        if ele.find(\"bias\") != -1:\n",
    "            print(\"The size of bias: \", model.state_dict()[ele].size())\n",
    "        else:\n",
    "            print(\"The size of weights: \", model.state_dict()[ele].size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the neural network module or class: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to display data\n",
    "\n",
    "def show_data(data_sample):\n",
    "    plt.imshow(data_sample.numpy().reshape(28, 28), cmap='gray')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"Model\">Neural Network Module and Training Function</h2> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the neural network module or class: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a Neural Network class\n",
    "\n",
    "class Net(nn.Module):\n",
    "    \n",
    "    # Constructor\n",
    "    def __init__(self, D_in, H, D_out):\n",
    "        super(Net, self).__init__()\n",
    "        self.linear1 = nn.Linear(D_in, H)\n",
    "        self.linear2 = nn.Linear(H, D_out)\n",
    "\n",
    "    # Prediction    \n",
    "    def forward(self, x):\n",
    "        x = torch.sigmoid(self.linear1(x))  \n",
    "        x = self.linear2(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function to train the model. In this case, the function returns a Python dictionary to store the training loss and accuracy on the validation data. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a training function to train the model\n",
    "\n",
    "def train(model, criterion, train_loader, validation_loader, optimizer, epochs=100):\n",
    "    i = 0\n",
    "    useful_stuff = {'training_loss': [],'validation_accuracy': []}  \n",
    "    for epoch in range(epochs):\n",
    "        for i, (x, y) in enumerate(train_loader): \n",
    "            optimizer.zero_grad()\n",
    "            z = model(x.view(-1, 28 * 28))\n",
    "            loss = criterion(z, y)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "             #loss for every iteration\n",
    "            useful_stuff['training_loss'].append(loss.data.item())\n",
    "        correct = 0\n",
    "        for x, y in validation_loader:\n",
    "            #validation \n",
    "            z = model(x.view(-1, 28 * 28))\n",
    "            _, label = torch.max(z, 1)\n",
    "            correct += (label == y).sum().item()\n",
    "        accuracy = 100 * (correct / len(validation_dataset))\n",
    "        useful_stuff['validation_accuracy'].append(accuracy)\n",
    "    return useful_stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"Makeup_Data\">Make Some Data</h2> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the training dataset by setting the parameters <code>train</code> to <code>True</code> and convert it to a tensor by placing a transform object in the argument <code>transform</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create training dataset\n",
    "\n",
    "train_dataset = dsets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the testing dataset by setting the parameters <code>train</code> to <code>False</code> and convert it to a tensor by placing a transform object in the argument <code>transform</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create validating dataset\n",
    "\n",
    "validation_dataset = dsets.MNIST(root='./data', train=False, download=True, transform=transforms.ToTensor())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the criterion function:  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create criterion function\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the training-data loader and the validation-data loader objects: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create data loader for both train dataset and valdiate dataset\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=2000, shuffle=True)\n",
    "validation_loader = torch.utils.data.DataLoader(dataset=validation_dataset, batch_size=5000, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"Train\">Define the Neural Network, Optimizer, and Train the Model</h2> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the model with 100 neurons: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the model with 100 neurons\n",
    "\n",
    "input_dim = 28 * 28\n",
    "hidden_dim = 100\n",
    "output_dim = 10\n",
    "\n",
    "model = Net(input_dim, hidden_dim, output_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the model parameters: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following are the parameters for the layer  1\n",
      "The size of weights:  torch.Size([100, 784])\n",
      "The size of bias:  torch.Size([100])\n",
      "The following are the parameters for the layer  2\n",
      "The size of weights:  torch.Size([10, 100])\n",
      "The size of bias:  torch.Size([10])\n"
     ]
    }
   ],
   "source": [
    "# Print the parameters for model\n",
    "\n",
    "print_model_parameters(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the optimizer object with a learning rate of 0.01: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the learning rate and the optimizer\n",
    "\n",
    "learning_rate = 0.01\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model by using 100 epochs **(this process takes time)**: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the model\n",
    "\n",
    "training_results = train(model, criterion, train_loader, validation_loader, optimizer, epochs=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"Result\">Analyze Results</h2> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the training total loss or cost for every iteration and plot the training accuracy for every epoch:  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the accuracy and loss\n",
    "\n",
    "plot_accuracy_loss(training_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the first five misclassified samples:   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BJP4fyrRx4uBgvq0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EYQeSIOxAEv8HCSFtEUhVUVEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first five misclassified samples\n",
    "\n",
    "count = 0\n",
    "for x, y in validation_dataset:\n",
    "    z = model(x.reshape(-1, 28 * 28))\n",
    "    _,yhat = torch.max(z, 1)\n",
    "    if yhat != y:\n",
    "        show_data(x)\n",
    "        count += 1\n",
    "    if count >= 5:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Practice</h3> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use <code>nn.Sequential</code> to build exactly the same model as you just built. Use the function <train>train</train> to train the model and use the function <code>plot_accuracy_loss</code> to see the metrics. Also, try different epoch numbers. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Practice: Use nn.Sequential to build the same model. Use plot_accuracy_loss to print out the accuarcy and loss\n",
    "\n",
    "# Type your code here\n",
    "\n",
    "D_in = 28 * 28\n",
    "H = 100\n",
    "D_out = 10\n",
    "\n",
    "my_model = torch.nn.Sequential(\n",
    "    torch.nn.Linear(D_in, H),\n",
    "    torch.nn.Sigmoid(),\n",
    "    torch.nn.Linear(H, D_out),\n",
    ")\n",
    "\n",
    "optimizer = torch.optim.SGD(my_model.parameters(), lr=learning_rate)\n",
    "training_results = train(my_model, criterion, train_loader, validation_loader, optimizer, epochs=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the accuracy and loss\n",
    "\n",
    "plot_accuracy_loss(training_results)"
   ]
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}