pytorch-implementations/Logistic_MNIST_Model.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# !pip install pytorch-lightning"
      ],
      "metadata": {
        "id": "ouZdzwvum49J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N0ID8NN7wqRt"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.datasets as datasets\n",
        "from torch.utils.data import random_split\n",
        "# import pytorch_lightning as pl"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data load\n",
        "batch_size = 32\n",
        "\n",
        "train_dataset = datasets.MNIST(root='data', train=True, transform=transforms.ToTensor(), download=True)\n",
        "test_dataset = datasets.MNIST(root='data', train=False, transform=transforms.ToTensor(), download=True)\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)"
      ],
      "metadata": {
        "id": "gtwUIFbXB3HZ",
        "colab": {
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          "height": 423,
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        },
        "outputId": "85ea94b4-1bb9-4bda-a405-d0e51597dad2"
      },
      "execution_count": null,
      "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": {
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            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
            ]
          },
          "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",
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              "version_minor": 0,
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            "text/plain": [
              "  0%|          | 0/28881 [00:00<?, ?it/s]"
            ]
          },
          "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": {
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              "model_id": "61b8df10f0be49aba83a3c9b36661366",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
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          },
          "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": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f1c28c87b7904d278aeb06e74e773bb6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ]
          },
          "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": [
        "# Model\n",
        "class LogisticRegression(torch.nn.Module):\n",
        "    def __init__(self, input_dim, output_dim):\n",
        "        super(LogisticRegression, self).__init__()\n",
        "        self.linear = torch.nn.Linear(input_dim, output_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        outputs = self.linear(x)\n",
        "        return outputs"
      ],
      "metadata": {
        "id": "DPQ1_hU0CMX4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyperparameters and model initialization\n",
        "# Testing it with one epoch to and analyzing results.\n",
        "# Current settings provide 91.19% Training Accuracy and 91.52% Testing Accuracy \n",
        "num_epochs = 1\n",
        "input_dim = 784\n",
        "output_dim = 10\n",
        "lr_rate = 0.1\n",
        "\n",
        "model = LogisticRegression(input_dim, output_dim)\n"
      ],
      "metadata": {
        "id": "IQyOL1SUCTpL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loss\n",
        "criterion = torch.nn.CrossEntropyLoss()"
      ],
      "metadata": {
        "id": "RivmWNg7CkCY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Optimizer\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=lr_rate)"
      ],
      "metadata": {
        "id": "k-LXGCFSCnSe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train \n",
        "for epoch in range(int(num_epochs)):\n",
        "    for i, (images, labels) in enumerate(train_loader):\n",
        "        images = images.reshape(-1, input_dim)\n",
        "\n",
        "        outputs = model(images)\n",
        "\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "\n",
        "        optimizer.step()\n",
        "\n",
        "        if i%500==0:\n",
        "            # calculate Accuracy\n",
        "            correct = 0\n",
        "            total = 0\n",
        "            for images, labels in test_loader:\n",
        "                images = images.reshape(-1, input_dim)\n",
        "                outputs = model(images)\n",
        "                _, predicted = torch.max(outputs.data, 1)\n",
        "                total+= labels.size(0)\n",
        "\n",
        "                # for gpu, bring the predicted and labels back to cpu fro python operations to work\n",
        "                correct+= (predicted == labels).sum()\n",
        "\n",
        "            accuracy = 100 * correct/total\n",
        "            print(\"Epoch: {}. Iteration: {}. Loss: {}. Accuracy: {}.\".format(epoch+1, i, loss.item(), accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RWdcnti8C0TS",
        "outputId": "8341a250-8102-451a-cd56-fe943a4074a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1. Iteration: 0. Loss: 2.3075413703918457. Accuracy: 28.200000762939453.\n",
            "Epoch: 1. Iteration: 500. Loss: 0.311492383480072. Accuracy: 89.62000274658203.\n",
            "Epoch: 1. Iteration: 1000. Loss: 0.17686724662780762. Accuracy: 90.79000091552734.\n",
            "Epoch: 1. Iteration: 1500. Loss: 0.14751163125038147. Accuracy: 91.08999633789062.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    for images, labels in test_loader:\n",
        "        images = images.reshape(-1, input_dim)\n",
        "        outputs = model(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum()\n",
        "\n",
        "    print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "smIFtOvNGFxy",
        "outputId": "85547121-45ac-45bb-bf5b-f4cc58d6a0a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the model on the 10000 test images: 91.41999816894531 %\n"
          ]
        }
      ]
    }
  ]
}