handwritten_digit_recognition_CPU.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "handwritten_digit_recognition_CPU.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "MHvuWIUHdIpt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Handwritten Digit Recognition\n",
        "- Author = Amitrajit Bose\n",
        "- Dataset = MNIST\n",
        "- [Medium Article Link](https://medium.com/@amitrajit_bose/handwritten-digit-mnist-pytorch-977b5338e627)\n",
        "- Frameworks = PyTorch\n"
      ]
    },
    {
      "metadata": {
        "id": "oGjRmijsaXJ3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Necessary Imports"
      ]
    },
    {
      "metadata": {
        "id": "TOyGrPT5ASDc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Import necessary packages\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "\n",
        "import numpy as np\n",
        "import torch\n",
        "import torchvision\n",
        "import matplotlib.pyplot as plt\n",
        "from time import time"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XPuuTDEfAfDy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "from google.colab import drive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uLdtrS4zaeEs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Download The Dataset & Define The Transforms"
      ]
    },
    {
      "metadata": {
        "id": "sZD2NGz2Ak6w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "74eec0da-d867-406b-be2c-4013a2162bf1"
      },
      "cell_type": "code",
      "source": [
        "### Run this cell\n",
        "\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",
        "                              ])\n",
        "\n",
        "# Download and load the training data\n",
        "trainset = datasets.MNIST('drive/My Drive/mnist/MNIST_data/', download=True, train=True, transform=transform)\n",
        "valset = datasets.MNIST('drive/My Drive/mnist/MNIST_data/', download=True, train=False, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
        "valloader = torch.utils.data.DataLoader(valset, batch_size=64, shuffle=True)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GcAfrn2falkK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Exploring The Data"
      ]
    },
    {
      "metadata": {
        "id": "xOjlOyjcCezX",
        "colab_type": "code",
        "outputId": "fde25724-ea30-46f6-cf0f-4a7807c2ee0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "dataiter = iter(trainloader)\n",
        "images, labels = dataiter.next()\n",
        "print(type(images))\n",
        "print(images.shape)\n",
        "print(labels.shape)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'torch.Tensor'>\n",
            "torch.Size([64, 1, 28, 28])\n",
            "torch.Size([64])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "EuBvOWmGDHOq",
        "colab_type": "code",
        "outputId": "127e0264-be67-4f12-b03f-dac28b7280af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(images[0].numpy().squeeze(), cmap='gray_r');"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BFTvppJOmXQKwhJHDvLX2kCRvT/KSJA9L8qMkX0nyzt779kV990/yliTHJtmS5PYkFyY5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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 249,
              "height": 248
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "F9CppCcqDLtB",
        "colab_type": "code",
        "outputId": "0f59838b-90b6-4370-e23a-0e4a0ba90c2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "cell_type": "code",
      "source": [
        "figure = plt.figure()\n",
        "num_of_images = 60\n",
        "for index in range(1, num_of_images + 1):\n",
        "    plt.subplot(6, 10, index)\n",
        "    plt.axis('off')\n",
        "    plt.imshow(images[index].numpy().squeeze(), cmap='gray_r')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GKM9Vtt5664RGUh3KHZdy5qEpCvLZZ5/lvF7VM1ZeeeWyPWalfPTRR0DDaiKq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\nDkAoC6k5sZXlDmvdVjopWn755QH40pe+BED//v2BYm8AU4u2SNepRhC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wBwA23XTTxO9a\nv/XFL34x+wfQDSfkZmZmZmYROSHvAX0Lr0b1tbX6u4yUQmgevKqopFdyV6NkXHVBY3zbzNtqq60G\nhB3AGpnnrW/0Q4YMAcLc0HvvvRcoV8KnJFzz36We6gBpqrRx+umnZ9O4FtKuqjqVQw89FAj/665o\nNEGJqOrU/+pXvwLaIyHvCY3GqE59LXnUOm+Vv//970CowAQhGde82FtvvRWAL33pSy1uXXaUDGuk\nRMmw6kSnKR2FUJ1Jc8Z1W0o3i1RdpxWU/uo9d9111wVCnfZ2MXToUACmT5+eOF+P96c//WnHeUrG\n9Z6hWuzpz5ZLLrkk8ftWW20FwN57751Vs+vmhNzMzMzMLCIn5E1QcqFdBaFzCviNb3yjpW3Kw+c/\n/3kgrE6uTCgqHXPMMUCyIop2bWzHZDytkWRcq8CVlioRL2MynpaukpD+vau/0bzRytrtZXPzzTcn\nfld6qdSuKxqN0vxyzY2sZzRqfqCa7t/5zneAkCLXotGrMtGccX2+VKtEpJ06i1xLuV6qkNNdNRE9\n5gsvvLDjPCXC0r9/fwD222+/LJtYOnqf0OdIGdZi1UM16vU5maZkfM899+w471//+hcQ1q3Vu5eJ\nRqudkJuZmZmZzWeckDdBK/2r0bf+MleJEM1/16pk7Yo2a9asxPXGjRsHJKvOPPzww61oYmlozri+\nyaviiOYba81BGWkERXNB05Uv6kl3lV6ce+65AOy2225AsjZ10SnVll695r29LrbYYlWvP2nSpI6f\nx44dC4Q1F2krrbRSFk0sHSXhBx54YOL3WpQud1XJpmhUDeKaa64Bqifj2uthp512al3DIlH1Fb1X\n1kpFK02dOhUIo1KnnHIKMP9UH7n22msTv8dId7M0d+5cILwf3nLLLYnLNXr/zDPPALDCCisA4bUE\noTZ/etfSH/3oR0CojqZ56Q899BAQKp7F4ITczMzMzCyiBeqZ51kiLXkwqv1auVOnvoXdddddQEiT\nc9DIZNJM+0MjA9qVVMm5diaspNT0l7/8JZBrshOtPxpx3HHHAWEu7JVXXgk0V4u5G9H6Q8nWO++8\nA8BRRx0FwOzZswF4/fXXOzegRiUWVen54Q9/CMDhhx/ebLNa1h/aNVFt/sIXvgCEmvJaV6FV/aNH\njw6NrDGKsP/++wOh7nYGcu8PrZNQZRDN5WyEKiNsv/32ADzxxBNdXl8VOtTX6vs6tPz1ovRPc8b1\n3Na86AUXXBAIlXYgrEPQZTlqeX/oPVGfK6oo9PHHH89rUJXXhipoaORI89A1OqfRXY1AqjpNE0rx\n+fK5z83LVtVXqkLSTE3zbjS6mKWpPtFO3+n/m6rp6L1Wu7meddZZAAwfPjzc8f9/tqgij0ZLvv71\nrwPhveX+++8HQjU5jVw2UKEmswU+TsjNzMzMzCLyAbmZmZmZWUSestKAmTNnAmEbaw3NQyh9pwUo\nOQwVSeGG0DQd47zzzut0maauaAvwdpqi0Z3KrZ+1yEhbPOe4oUfh+kPlQbWZCYTNPzRcWGvKxjrr\nrAN0nvbRgJb1hzajUUk6PaallloKgN69ewOhfGjle2+txz9q1CgATjzxxJ40rVJu/fHee+8BMH78\neACmTJkChAXM3d5ZHf2Rtt566wFw3333AU1thNLy18ull14KwCGHHFL1cm04Vzn83kIt6w9tVb7L\nLrsAnReDa3qCFrxXvga23nprAFZccUUgTFnRpi4zZswAwsJQLYptQuHeT0UL4CEUkdA28LUWh2eg\nJVNWtLFT5eZYAAMHDgRCedyTTjoJgJ///OcAfPTRRx3X1bTH66+/HgilqPVeoWnF+oxRucMmPps9\nZcXMzMzMrB247GED9tprLyBsb11p9dVXB3JNxgtL6cPyyy/fcd4JJ5wAhFEELVC64YYbWty61lHC\npy16lRRCKEtV5q2um6XShZWL1PTztGnTgDC6Mnny5MTfanGPNp0q8sZBGv1RaqOFRpUjaRAS9MrN\na1QaUYvCtShRiVCRabG3yolpsXcraFOyMmwRrtJt6ZK4Cy20EABHH300kOloSKHpOZ/e5EfJpV4/\nG2ywQbe3pUILWswpjz32WI/bWVSVo0j6WYs725XeY/Va0mZRSsYrR+A1Ki/6LFEJVS3uVJnDInw2\nt/d/z8zMzMys4JyQ1+GNN94AwvbW1aj8TpmpLF1l0l0PpRLa3hbgggsuAODll1/OpnEFpk1/VJpM\nc9Y07xnCVsaWpI20NtpoIyBszpAuG6qNg3S6xRZbtKqJDRsxYgQQkj2Vg9xwww0B2GyzzYDk9ucj\nR44EwsYWKue29tprt6DFzVE5S62pef7551veBvWtnkdF3EBJ5ftUuvL9999PXK6E7tRTT21twyLT\nc1ulCaUHJQo7Ril1ut9++zV9W1Y8ffr0AULK/eGHHwJhIzCtuYFQflVrMrSma/HFFwdC+dmNN944\n51bXzwm5mZmZmVlETsi7oFXfgwcPBuAvf/lL4vJlllmm4+fDDjusdQ3LmOa9a2W60kpt0FBv6lS5\necdLL70EhLlt2gq9nWjDE229qw2StOFH37594zSshPRau/HGG6tero1ilMoWmaqp7L777onTrkyc\nODHxu+bdF5mSzRjJuEyYMAGAF154AQjzr/v37w+0ZCOdmpSEa4Os9PbfQ4YMAcKag/lVTxJx0UZr\n2kpdnztZ3LYVx7hx46qer5EQvQ9AWHf08MMPA6Eijyp7NVGxK3dOyM3MzMzMInJC3gV929Lq3LRB\ngwZ1/Jxe3V0mSieV6KiWuuZlaitwJX2qC6tEXFUVtPVxJc2d3nHHHXNpewyqeat5zmuuuSYADzzw\nANDQtt2l8PTTTwPh/6w50j2hJFwpqxKu9Pza+ZXqKRdZDnsKdKJKCN2l8LfffnviVGtYYo5cakT1\nkUceqXq50txZs2YB5f4MieWTTz4B4JprrgHCeh593qgWdTuqrNuvn+fOnRurOZlSzfB6qWpXZS17\n9YnWG1100UVAMZNxcUJuZmZmZhaRE/Iq9C1bdVDTu8Zp7rh2XCs7pZTa/UxzyVUhRfMyG6mPu+66\n6wJhhXMZU+PPPvsMCFUSlGCpoo6q0Vx99dUA9OrVni+n733ve0BIKVXfdcCAAYnrqd62KoYooehq\n18XurqPL9VrcbrvtGn8ABaWdPaGcIwOVa0ayUFkRQ7W6lcJX9hWE15x2e9So1be//W0gOXoZy1pr\nrQWEevN6XxWNqGmecz3z3bV7Zb2fPaoooffft956Cwj7Zmgn5bLSDsh33nln4vx2XLOUpp1wof3q\nkOszRzutdie9yyuE0TF9dpRhr4L2+O+ZmZmZmZVUe0Z6PaS50JXfQCt9//vfb2VzcqekV7uaqT7n\n8OHDgTDHsbs0c/vtt+/4WTsvlrn+9tixY4Gwk56SYT3+m266CWjfZFz0eHWqueS1dmRMPz+6SsjT\n19HufZr3pxrXxx13XKPNLrznnnuu42clvKLHXWRKsZq1zz77ADBs2DCg63mj2olR1ltvPSDUIdZO\nfZWVr4pCSZ3SalVhktdee63h26z3+aFa90rC9R6mEQb9D1pJtePTO16vssoqQO3qVJovDiEZHz16\ndOI6WufUbp/R1cSsbpQ3rVPTMckuu+wCwCuvvFL1+hqRr0zU99xzzzybmAsn5GZmZmZmEbV3tNek\nV199tcvLDzjggBa1pLUWXXRRAI488kggfCt99tlnAZg+fXrVv6tWSaWMc8ZFVUUuvvhiIHxb1xzy\nm2++GQj91e6UTmsNgVLJRmmkAUIapqoAqtyi+tvp+ent6Kmnnur4WSMEmiNc5pGlWjRvWfW49XtP\naoVrREWnRaR9LFSVSe8r6odatfcraY60dh+sl+aM61S7gsaYY3366acDcMYZZwBhbY6e+yussAIA\nt956a+LvNBKnHZABZs6cmbjOTjvtBMC5556bdbMtAo06K/nWZ/LJJ58MwDnnnAOECngaVdH7Z1k5\nITczMzMzi2iBylqWbSCTB6O5fpqXKJqvpsQw0orm7ifkBm31z60h8/5QDeipU6cCocbpqFGjgMKn\n/7k9PyZPngyEESRV56mV8J1//vmJ3/v06dPx8/7779/IXfdEYV8vGiWA0Kcbb7wxANOmTQOSowoZ\nyaw/NKdX8/0fffTRxOXa4VejH/vuuy9QuHrbhX1+VNK88+7qTF944YVA2DsiTcl4F8+r3PpDa5LO\nPPPMeX9cZ4WlapdrdHLgwIFASMa1G2OGCvv8qBwNUFWiTTfdFIAHH3wwr7ttpD/AxyANcUJuZmZm\nZhaRE/LyKew39kgy64/f/va3QJg7P2bMGAAOPfTQeXdUR7WQAvDzI6mw/bHkkkt2/Kw65F/84heB\nsAdA5ahCRgrbH5G4P5Jy6w89pzWPXXPCu0vI99prLwDWX3/9jst22GEHINR6z1Fhnx+V8+j79esH\nQP/+/YFQ4z4HTsg7c0JuZmZmZtYOnJCXT2G/sUeSWX+oCoB2/Ro/fnzTjYrIz4+kwvZHZYqlCkVD\nhw4FOtdXzlBh+yMS90eS+yOpFP2hhHzppZcGnJC3mBNyMzMzM7N24IS8fErxjb2F3B9J7o8k90eS\n+yPJ/ZHk/kgqRX+o8tsjjzwCOCFvMSfkZmZmZmbtwAl5+ZTiG3sLuT+S3B9J7o8k90eS+yPJ/ZHk\n/khyQt6ZE3IzMzMzs3bQbgm5mZmZmVmpOCE3MzMzM4vIB+RmZmZmZhH5gNzMzMzMLCIfkJuZmZmZ\nReQDcjMzMzOziHxAbmZmZmYWkQ/IzczMzMwi8gG5mZmZmVlEPiA3MzMzM4vIB+RmZmZmZhH5gNzM\nzMzMLCIfkJuZmZmZReQDcjMzMzOziHxAbmZmZmYWkQ/IzczMzMwi8gG5mZmZmVlEPiA3MzMzM4vI\nB+RmZmZmZhH5gNzMzMzMLCIfkJuZmZmZReQDcjMzMzOziHxAbmZmZmYWkQ/IzczMzMwi8gG5mZmZ\nmVlEPiB766ZNAAAAwklEQVQ3MzMzM4vIB+RmZmZmZhH5gNzMzMzMLCIfkJuZmZmZReQDcjMzMzOz\niHxAbmZmZmYWkQ/IzczMzMwi8gG5mZmZmVlEPiA3MzMzM4vIB+RmZmZmZhH5gNzMzMzMLCIfkJuZ\nmZmZReQDcjMzMzOziHxAbmZmZmYWkQ/IzczMzMwi8gG5mZmZmVlEPiA3MzMzM4vIB+RmZmZmZhH5\ngNzMzMzMLCIfkJuZmZmZReQDcjMzMzOziHxAbmZmZmYW0f8Bd1BSy8fzPFAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 60 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 370,
              "height": 248
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "lGyau0mOaP2m",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Defining The Neural Network"
      ]
    },
    {
      "metadata": {
        "id": "n-NR96UtFSkB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mlp_mnist.png)"
      ]
    },
    {
      "metadata": {
        "id": "3WJXInzQGcAy",
        "colab_type": "code",
        "outputId": "be31aea8-542d-46ab-9816-85bc418664f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "cell_type": "code",
      "source": [
        "from torch import nn\n",
        "\n",
        "# Layer details for the neural network\n",
        "input_size = 784\n",
        "hidden_sizes = [128, 64]\n",
        "output_size = 10\n",
        "\n",
        "# Build a feed-forward network\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], output_size),\n",
        "                      nn.LogSoftmax(dim=1))\n",
        "print(model)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sequential(\n",
            "  (0): Linear(in_features=784, out_features=128, bias=True)\n",
            "  (1): ReLU()\n",
            "  (2): Linear(in_features=128, out_features=64, bias=True)\n",
            "  (3): ReLU()\n",
            "  (4): Linear(in_features=64, out_features=10, bias=True)\n",
            "  (5): LogSoftmax()\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "oxSLypv2LOD-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "criterion = nn.NLLLoss()\n",
        "images, labels = next(iter(trainloader))\n",
        "images = images.view(images.shape[0], -1)\n",
        "\n",
        "logps = model(images)\n",
        "loss = criterion(logps, labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Pj4I2lLgLVWw",
        "colab_type": "code",
        "outputId": "f9032f51-5acb-431a-cc42-4de0abe331a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "cell_type": "code",
      "source": [
        "print('Before backward pass: \\n', model[0].weight.grad)\n",
        "\n",
        "loss.backward()\n",
        "\n",
        "print('After backward pass: \\n', model[0].weight.grad)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before backward pass: \n",
            " None\n",
            "After backward pass: \n",
            " tensor([[-0.0003, -0.0003, -0.0003,  ..., -0.0003, -0.0003, -0.0003],\n",
            "        [ 0.0008,  0.0008,  0.0008,  ...,  0.0008,  0.0008,  0.0008],\n",
            "        [-0.0037, -0.0037, -0.0037,  ..., -0.0037, -0.0037, -0.0037],\n",
            "        ...,\n",
            "        [-0.0005, -0.0005, -0.0005,  ..., -0.0005, -0.0005, -0.0005],\n",
            "        [ 0.0043,  0.0043,  0.0043,  ...,  0.0043,  0.0043,  0.0043],\n",
            "        [-0.0006, -0.0006, -0.0006,  ..., -0.0006, -0.0006, -0.0006]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "F0ZVHVbvI_yt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from torch import optim\n",
        "\n",
        "# Optimizers require the parameters to optimize and a learning rate\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dS9JqXhhLdkr",
        "colab_type": "code",
        "outputId": "4ec0a101-bc31-45ee-ea9b-39804b6cab16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "cell_type": "code",
      "source": [
        "print('Initial weights - ', model[0].weight)\n",
        "\n",
        "images, labels = next(iter(trainloader))\n",
        "images.resize_(64, 784)\n",
        "\n",
        "# Clear the gradients, do this because gradients are accumulated\n",
        "optimizer.zero_grad()\n",
        "\n",
        "# Forward pass, then backward pass, then update weights\n",
        "output = model(images)\n",
        "loss = criterion(output, labels)\n",
        "loss.backward()\n",
        "print('Gradient -', model[0].weight.grad)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Initial weights -  Parameter containing:\n",
            "tensor([[-0.0196, -0.0303, -0.0086,  ...,  0.0089,  0.0172,  0.0182],\n",
            "        [-0.0005, -0.0335, -0.0071,  ...,  0.0088, -0.0111, -0.0042],\n",
            "        [ 0.0294,  0.0252, -0.0253,  ...,  0.0180,  0.0155,  0.0208],\n",
            "        ...,\n",
            "        [ 0.0123, -0.0224,  0.0345,  ..., -0.0333, -0.0319,  0.0336],\n",
            "        [-0.0162, -0.0323,  0.0203,  ...,  0.0213, -0.0319, -0.0273],\n",
            "        [-0.0333, -0.0258,  0.0035,  ..., -0.0330, -0.0142,  0.0266]],\n",
            "       requires_grad=True)\n",
            "Gradient - tensor([[-0.0016, -0.0016, -0.0016,  ..., -0.0016, -0.0016, -0.0016],\n",
            "        [ 0.0016,  0.0016,  0.0016,  ...,  0.0016,  0.0016,  0.0016],\n",
            "        [ 0.0020,  0.0020,  0.0020,  ...,  0.0020,  0.0020,  0.0020],\n",
            "        ...,\n",
            "        [ 0.0014,  0.0014,  0.0014,  ...,  0.0014,  0.0014,  0.0014],\n",
            "        [ 0.0040,  0.0040,  0.0040,  ...,  0.0040,  0.0040,  0.0040],\n",
            "        [-0.0005, -0.0005, -0.0005,  ..., -0.0005, -0.0005, -0.0005]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "wy0KOQ95LgYN",
        "colab_type": "code",
        "outputId": "d742c1e9-50e2-47f0-ef0b-bdeea6064390",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "cell_type": "code",
      "source": [
        "# Take an update step and few the new weights\n",
        "optimizer.step()\n",
        "print('Updated weights - ', model[0].weight)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Updated weights -  Parameter containing:\n",
            "tensor([[-0.0196, -0.0303, -0.0086,  ...,  0.0089,  0.0172,  0.0182],\n",
            "        [-0.0005, -0.0335, -0.0071,  ...,  0.0088, -0.0111, -0.0042],\n",
            "        [ 0.0294,  0.0251, -0.0254,  ...,  0.0180,  0.0154,  0.0208],\n",
            "        ...,\n",
            "        [ 0.0123, -0.0224,  0.0345,  ..., -0.0333, -0.0319,  0.0336],\n",
            "        [-0.0163, -0.0323,  0.0203,  ...,  0.0213, -0.0320, -0.0273],\n",
            "        [-0.0333, -0.0258,  0.0035,  ..., -0.0330, -0.0142,  0.0266]],\n",
            "       requires_grad=True)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "wstRGu4FaJBe",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Core Training Of Neural Network"
      ]
    },
    {
      "metadata": {
        "id": "XCsoAdjdLjPb",
        "colab_type": "code",
        "outputId": "6e7a5f80-f945-4e5c-c538-8ef445b6ad3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "cell_type": "code",
      "source": [
        "optimizer = optim.SGD(model.parameters(), lr=0.003, momentum=0.9)\n",
        "time0 = time()\n",
        "epochs = 15\n",
        "for e in range(epochs):\n",
        "    running_loss = 0\n",
        "    for images, labels in trainloader:\n",
        "        # Flatten MNIST images into a 784 long vector\n",
        "        images = images.view(images.shape[0], -1)\n",
        "    \n",
        "        # Training pass\n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        output = model(images)\n",
        "        loss = criterion(output, labels)\n",
        "        \n",
        "        #This is where the model learns by backpropagating\n",
        "        loss.backward()\n",
        "        \n",
        "        #And optimizes its weights here\n",
        "        optimizer.step()\n",
        "        \n",
        "        running_loss += loss.item()\n",
        "    else:\n",
        "        print(\"Epoch {} - Training loss: {}\".format(e, running_loss/len(trainloader)))\n",
        "print(\"\\nTraining Time (in minutes) =\",(time()-time0)/60)\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0 - Training loss: 0.6322235897389937\n",
            "Epoch 1 - Training loss: 0.28710727898010824\n",
            "Epoch 2 - Training loss: 0.22280048086508505\n",
            "Epoch 3 - Training loss: 0.1789520422278691\n",
            "Epoch 4 - Training loss: 0.14983841985750046\n",
            "Epoch 5 - Training loss: 0.12802194330150257\n",
            "Epoch 6 - Training loss: 0.111843242423176\n",
            "Epoch 7 - Training loss: 0.09924472505048013\n",
            "Epoch 8 - Training loss: 0.09048181752771584\n",
            "Epoch 9 - Training loss: 0.08085540305974005\n",
            "Epoch 10 - Training loss: 0.07291485832682423\n",
            "Epoch 11 - Training loss: 0.06844451893637302\n",
            "Epoch 12 - Training loss: 0.06162214955525485\n",
            "Epoch 13 - Training loss: 0.0564850251327382\n",
            "Epoch 14 - Training loss: 0.05394477876169341\n",
            "\n",
            "Training Time (in minutes) = 2.2535348455111186\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "75j9X1b6ME5K",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def view_classify(img, ps):\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",
        "    ax2.set_yticklabels(np.arange(10))\n",
        "    ax2.set_title('Class Probability')\n",
        "    ax2.set_xlim(0, 1.1)\n",
        "    plt.tight_layout()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ie9Fffl_Mqp6",
        "colab_type": "code",
        "outputId": "2e93c6ea-0534-498d-e072-904f62591dfe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "cell_type": "code",
      "source": [
        "images, labels = next(iter(valloader))\n",
        "\n",
        "img = images[0].view(1, 784)\n",
        "# Turn off gradients to speed up this part\n",
        "with torch.no_grad():\n",
        "    logps = model(img)\n",
        "\n",
        "# Output of the network are log-probabilities, need to take exponential for probabilities\n",
        "ps = torch.exp(logps)\n",
        "probab = list(ps.numpy()[0])\n",
        "print(\"Predicted Digit =\", probab.index(max(probab)))\n",
        "view_classify(img.view(1, 28, 28), ps)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted Digit = 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QogAAAHoQogAAAHoQogAAAHoQogAAAHoQogAAAHoQogAAAHoQogAAAHoQogAAAHrYetwF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\nxDs+excAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 424,
              "height": 227
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "wAEvDtiaM6RQ"
      },
      "cell_type": "markdown",
      "source": [
        "### Model Evaluation"
      ]
    },
    {
      "metadata": {
        "id": "5sBPmaBONPkT",
        "colab_type": "code",
        "outputId": "25b032aa-737f-49d8-d1b5-76f6b9e6b7b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "correct_count, all_count = 0, 0\n",
        "for images,labels in valloader:\n",
        "  for i in range(len(labels)):\n",
        "    img = images[i].view(1, 784)\n",
        "    # Turn off gradients to speed up this part\n",
        "    with torch.no_grad():\n",
        "        logps = model(img)\n",
        "\n",
        "    # Output of the network are log-probabilities, need to take exponential for probabilities\n",
        "    ps = torch.exp(logps)\n",
        "    probab = list(ps.numpy()[0])\n",
        "    pred_label = probab.index(max(probab))\n",
        "    true_label = labels.numpy()[i]\n",
        "    if(true_label == pred_label):\n",
        "      correct_count += 1\n",
        "    all_count += 1\n",
        "\n",
        "print(\"Number Of Images Tested =\", all_count)\n",
        "print(\"\\nModel Accuracy =\", (correct_count/all_count))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number Of Images Tested = 10000\n",
            "\n",
            "Model Accuracy = 0.9751\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}