MNIST.ipynb

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            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
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          }
        },
        "b7857ae55463433790900791d51550bc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "0re21IYqjN9K"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ],
      "metadata": {
        "id": "W75C00SrjrQf"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 5\n",
        "batch_size = 4\n",
        "learning_rate = 0.001"
      ],
      "metadata": {
        "id": "Fhgj5FEnjz6k"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "transform = transforms.Compose([transforms.ToTensor()])"
      ],
      "metadata": {
        "id": "SGBiJWSpkAVr"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = torchvision.datasets.MNIST(root = './data', train = True, \n",
        "                                           download = True, transform = transform)\n",
        "test_dataset = torchvision.datasets.MNIST(root = './data', train = True, \n",
        "                                           download = True, transform = transform)"
      ],
      "metadata": {
        "id": "KqQoDA1uka5t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423,
          "referenced_widgets": [
            "272939dea7b046beb653661950d1cdbf",
            "cdc76da6988f4051a8292cfc59d2abfd",
            "5301698a847a41fbaa4f6176f4a9d262",
            "853048eda83747f6b0cc511e7b1b97c5",
            "1ce122d32ae7408d90bb02f8a813a815",
            "19bfd9ac47c54469959fae9e9bbaff95",
            "98e295599b454e96a1085d5e68bead79",
            "deba3f9f3e154adcaff02936495651fe",
            "af143cf6e3e64c03800086c7d9ac9ce0",
            "5c349f1e075c48f3aba6f77a880a6cb4",
            "808720b4017d466c99d9967bf2dd3558",
            "41f38ce7c0a342c99906f2168ce5c42e",
            "0a3cf1340eda403e9cc035c9509d1780",
            "c2b7c2800cc143858b1eb89fca754ced",
            "c209c19691fe4d8d8f4bb7f83970f175",
            "ed6a90059c684febbb6a41405b2a17f9",
            "5b33a59cac0246d29994d9521b476e5c",
            "6d12840db761499ba66d804f4bd38c90",
            "9c5b84444ede4ab2a8c0ef325cb5eaff",
            "6b34a1b5e35446feaf94e294183615ae",
            "97a310a5ea694362b76983c895ce717b",
            "0773104926244caa8a53e5660952cc26",
            "f47ae66f71e142058b6b89ff5c04a10c",
            "4f3543468e1a4d5283d812d79f8f9395",
            "383cacb45a8e46c8b4a7017f9e3ff886",
            "10fab8bc2af14ef99286e7ba8a85ce4c",
            "2967ce8f85a6457395c97e3acaec926d",
            "c2847032e5ce43138cc921a8990fdb2a",
            "34fc630d17d34a2bba026a9ccef9b670",
            "03812158773c4448ad509bba739411b9",
            "ecb7379defdb48edbef1d9a6108155dd",
            "f8be1b1391134fabaee8761002e14c80",
            "fe464f202faa414a8e9e2097aef58405",
            "e4013f0aa0184b30b52a5fa0e1062aab",
            "8c7d7110cfbe49b799e05d7843605193",
            "4ab18f7f479f4371820587f890af4d35",
            "4156ccf9d9f04d95a0afb591b435a7b2",
            "a2e38dd056a549c092fdff87911c989d",
            "482043c74d7348bf8bf198de33a803e3",
            "eae6fc348e774ffdbe067d56262a3886",
            "9e7d1a1f4d3f493dababaee485a1d7fa",
            "dda00fbc09354332876e0413b2b84358",
            "7a52b73b6ead4976b6685f342189bce3",
            "b7857ae55463433790900791d51550bc"
          ]
        },
        "outputId": "8191ec82-c7c3-491c-858e-3c6308f163e2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "272939dea7b046beb653661950d1cdbf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/28881 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "41f38ce7c0a342c99906f2168ce5c42e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f47ae66f71e142058b6b89ff5c04a10c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e4013f0aa0184b30b52a5fa0e1062aab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size = batch_size, shuffle = True )\n",
        "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size = batch_size)"
      ],
      "metadata": {
        "id": "vhOHXN-Hk9YX"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classes = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)"
      ],
      "metadata": {
        "id": "P4CmD729lqet"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def imshow(img):\n",
        "  img = (img / 2) + 0.5\n",
        "  npimg = img.numpy()\n",
        "  plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "  plt.show()\n",
        "\n",
        "data_iter = iter(train_loader)\n",
        "images, labels = data_iter.next()\n",
        "\n",
        "imshow(torchvision.utils.make_grid(images))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "id": "P8CM9tq-llVn",
        "outputId": "3e7a9494-c59e-4d96-a250-4ef7a996340a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class ConvNet(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(ConvNet, self).__init__()\n",
        "    self.conv1 = nn.Conv2d(1, 6, 5, stride= 1, padding= 0)   \n",
        "    self.pool = nn.MaxPool2d(2, 2)                           \n",
        "    self.conv2 = nn.Conv2d(6, 16, 5, stride= 1, padding= 0) \n",
        "    self.fc1 = nn.Linear(16*4*4, 120)\n",
        "    self.fc2 = nn.Linear(120, 84)\n",
        "    self.fc3 = nn.Linear(84, 10)\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = self.pool(F.relu(self.conv1(x)))\n",
        "    # conv1: [4, 1, 28, 28] to [4, 6, 24, 24]   -> Here 1 is the no of input channels of conv1 and 6 is no of output channels of conv1\n",
        "    # MaxPool: [4, 6, 24, 24] to [4, 6, 12, 12]\n",
        "    x = self.pool(F.relu(self.conv2(x)))\n",
        "    # conv2: [4, 6, 12, 12] to [4, 16, 8, 8]    -> Here 6 is the no of input channels of conv1 and 16 is no of output channels of conv1\n",
        "    # MaxPool: [4, 16, 8, 8] to [4, 16, 4, 4]     \n",
        "    x = x.view(-1, 16*4*4)  \n",
        "    x = F.relu(self.fc1(x)) #To flatten the 3D tensor to 1D, we pass it through fc1 with input size as 16*4*4 and output size as 120\n",
        "    x = F.relu(self.fc2(x)) #We further reduce 120 to 84\n",
        "    x = self.fc3(x) # Finally we reduce 84 to 10 (ie the no of classes)\n",
        "    return x"
      ],
      "metadata": {
        "id": "pUVAnR4mmXB5"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = ConvNet().to(device)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)"
      ],
      "metadata": {
        "id": "d3m_v0kIwkWS"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_total_steps = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "  for i, (images, labels) in enumerate(train_loader):\n",
        "    images = images.to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    #Forward Pass\n",
        "    outputs = model(images)\n",
        "    loss = criterion(outputs, labels)\n",
        "\n",
        "    #Backward Pass\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    if (i+1) % 2000 == 0:\n",
        "      print(f'Epoch [{epoch +1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss = {loss.item():.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gSw_iAR4w6Id",
        "outputId": "9be2dc45-f27a-405b-ba9f-f67a05c7384e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/5], Step [2000/15000], Loss = 2.2984\n",
            "Epoch [1/5], Step [4000/15000], Loss = 2.2755\n",
            "Epoch [1/5], Step [6000/15000], Loss = 2.2494\n",
            "Epoch [1/5], Step [8000/15000], Loss = 1.7576\n",
            "Epoch [1/5], Step [10000/15000], Loss = 0.4802\n",
            "Epoch [1/5], Step [12000/15000], Loss = 0.1581\n",
            "Epoch [1/5], Step [14000/15000], Loss = 0.3311\n",
            "Epoch [2/5], Step [2000/15000], Loss = 0.7128\n",
            "Epoch [2/5], Step [4000/15000], Loss = 0.0397\n",
            "Epoch [2/5], Step [6000/15000], Loss = 0.0146\n",
            "Epoch [2/5], Step [8000/15000], Loss = 0.4999\n",
            "Epoch [2/5], Step [10000/15000], Loss = 0.0723\n",
            "Epoch [2/5], Step [12000/15000], Loss = 0.0306\n",
            "Epoch [2/5], Step [14000/15000], Loss = 0.0777\n",
            "Epoch [3/5], Step [2000/15000], Loss = 0.0558\n",
            "Epoch [3/5], Step [4000/15000], Loss = 0.0051\n",
            "Epoch [3/5], Step [6000/15000], Loss = 0.0613\n",
            "Epoch [3/5], Step [8000/15000], Loss = 0.2554\n",
            "Epoch [3/5], Step [10000/15000], Loss = 0.3556\n",
            "Epoch [3/5], Step [12000/15000], Loss = 0.0042\n",
            "Epoch [3/5], Step [14000/15000], Loss = 0.0079\n",
            "Epoch [4/5], Step [2000/15000], Loss = 0.1358\n",
            "Epoch [4/5], Step [4000/15000], Loss = 0.0020\n",
            "Epoch [4/5], Step [6000/15000], Loss = 0.1733\n",
            "Epoch [4/5], Step [8000/15000], Loss = 0.0078\n",
            "Epoch [4/5], Step [10000/15000], Loss = 0.0637\n",
            "Epoch [4/5], Step [12000/15000], Loss = 0.0061\n",
            "Epoch [4/5], Step [14000/15000], Loss = 0.0308\n",
            "Epoch [5/5], Step [2000/15000], Loss = 0.0031\n",
            "Epoch [5/5], Step [4000/15000], Loss = 0.0275\n",
            "Epoch [5/5], Step [6000/15000], Loss = 0.0858\n",
            "Epoch [5/5], Step [8000/15000], Loss = 0.0112\n",
            "Epoch [5/5], Step [10000/15000], Loss = 0.0003\n",
            "Epoch [5/5], Step [12000/15000], Loss = 0.0199\n",
            "Epoch [5/5], Step [14000/15000], Loss = 0.1038\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Finished Training')\n",
        "PATH = './mnist.pth'\n",
        "torch.save(model.state_dict(), PATH)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nkAHXfanyKxD",
        "outputId": "6ad70561-e855-4918-81ed-c7df0e7938ea"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished Training\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "  n_correct = 0\n",
        "  n_samples = 0\n",
        "  n_class_correct = [0 for i in range(10)]\n",
        "  n_class_samples = [0 for i in range(10)]\n",
        "  for images, labels in test_loader:\n",
        "    images = images.to(device)\n",
        "    labels = labels.to(device)\n",
        "    outputs = model(images)\n",
        "    _, predicted = torch.max(outputs, 1)\n",
        "    n_samples += labels.size(0)\n",
        "    n_correct += (predicted == labels).sum().item()\n",
        "\n",
        "    for i in range(batch_size):\n",
        "      label = labels[i]\n",
        "      pred = predicted[i]\n",
        "      if (label == pred):\n",
        "        n_class_correct[label] += 1\n",
        "      n_class_samples[label] +=1\n",
        "  acc = 100.0 * n_correct / n_samples\n",
        "  print(f'Accuracy of the network: {acc} %')\n",
        "  for i in range(10):\n",
        "    acc = 100.0 * n_class_correct[i] / n_class_samples[i]\n",
        "    print(f'Accuracy of {classes[i]}: {acc} %')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3dv0jV7K4Ktn",
        "outputId": "d783bc94-dc2c-4855-e8c3-12f0d447a84f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the network: 97.325 %\n",
            "Accuracy of 0: 98.24413304068884 %\n",
            "Accuracy of 1: 98.82824087807772 %\n",
            "Accuracy of 2: 96.91171534071836 %\n",
            "Accuracy of 3: 98.62991355406949 %\n",
            "Accuracy of 4: 97.87743923313934 %\n",
            "Accuracy of 5: 94.65043349935436 %\n",
            "Accuracy of 6: 98.91855356539371 %\n",
            "Accuracy of 7: 95.96169193934557 %\n",
            "Accuracy of 8: 97.43633566911639 %\n",
            "Accuracy of 9: 95.41099344427634 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kDwKWZIE50Dz"
      },
      "execution_count": 13,
      "outputs": []
    }
  ]
}