MNIST.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/trytoYH/pytorch-practice/blob/main/MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This source code is from '한 줄씩 따라 해보는 파이토치 딥러닝 프로젝트 모음집' book."
      ],
      "metadata": {
        "id": "-o0wc59AZR4t"
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "byipppBqNVHc"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms\n",
        "\n",
        "from matplotlib import pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if torch.cuda.is_available():\n",
        "  device = torch.device('cuda:0')\n",
        "\n",
        "else:\n",
        "  device = torch.device('cpu')\n",
        "\n",
        "print('current device is ',device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XxqIZKU7O6Ca",
        "outputId": "9b93dfe6-20d1-4fe0-8959-1dbf843fd245"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current device is  cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 50\n",
        "epoch_num = 15\n",
        "learning_rate = 0.0001"
      ],
      "metadata": {
        "id": "z6zyZvrkPXqH"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#get MNIST data and set dataloader\n",
        "\n",
        "train_data = datasets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor())\n",
        "test_data = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())\n",
        "\n",
        "print('number of training data: ', len(train_data))\n",
        "print('number of test data: ', len(test_data))\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(dataset=test_data, batch_size=batch_size, shuffle=True)"
      ],
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        "id": "YmG3heqXPqF6",
        "outputId": "a54f7487-75ab-4929-9728-00c8ad74c529"
      },
      "execution_count": 4,
      "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"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
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          },
          "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": {
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          },
          "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]"
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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": {
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "number of training data:  60000\n",
            "number of test data:  10000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image, label = train_data[0]\n",
        "plt.imshow(image.squeeze().numpy(), cmap = 'gray')\n",
        "plt.title('label : %s' % label)\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "0Nu_0a86P4gM",
        "outputId": "6febaade-bfad-47e1-fe0d-4f8a34032040"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "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": [
        "# build CNN structure\n",
        "class CNN(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(CNN, self).__init__()\n",
        "    self.conv1 = nn.Conv2d(1, 32, 3, 1)\n",
        "    self.conv2 = nn.Conv2d(32, 64, 3, 1)\n",
        "    self.dropout1 = nn.Dropout(0.25)\n",
        "    self.dropout2 = nn.Dropout(0.5)\n",
        "    self.fc1 = nn.Linear(9216, 128)\n",
        "    self.fc2 = nn.Linear(128, 10)\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = self.conv1(x)\n",
        "    x = F.relu(x)\n",
        "    x = self.conv2(x)\n",
        "    x = F.relu(x)\n",
        "    x = F.max_pool2d(x, 2)\n",
        "    x = self.dropout1(x)\n",
        "    x = torch.flatten(x, 1)\n",
        "    x = self.fc1(x)\n",
        "    x = F.relu(x)\n",
        "    x = self.dropout2(x)\n",
        "    x = self.fc2(x)\n",
        "    output = F.log_softmax(x, dim=1)\n",
        "    return output"
      ],
      "metadata": {
        "id": "BkxICSqLR5Va"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# set optimizer and loss function\n",
        "\n",
        "model = CNN().to(device)\n",
        "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5zd9KFYEd1V3",
        "outputId": "0d2a33a3-0416-47ed-9712-ac5b28fc490d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CNN(\n",
            "  (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n",
            "  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
            "  (dropout1): Dropout(p=0.25, inplace=False)\n",
            "  (dropout2): Dropout(p=0.5, inplace=False)\n",
            "  (fc1): Linear(in_features=9216, out_features=128, bias=True)\n",
            "  (fc2): Linear(in_features=128, out_features=10, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model train\n",
        "\n",
        "model.train()\n",
        "i = 0\n",
        "for epoch in range(epoch_num):\n",
        "  for data, target in train_loader:\n",
        "    data = data.to(device)\n",
        "    target = target.to(device)\n",
        "    optimizer.zero_grad()\n",
        "    output = model(data)\n",
        "    loss = criterion(output, target)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    if i% 1000 == 0:\n",
        "      print('Train step: {}\\t Loss: {:.3f}'.format(i, loss.item()))\n",
        "    i += 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2NHWRn10f0vK",
        "outputId": "9e2c2d65-3ae5-4625-caa4-dd77056c14a4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train step: 0\t Loss: 2.302\n",
            "Train step: 1000\t Loss: 0.288\n",
            "Train step: 2000\t Loss: 0.086\n",
            "Train step: 3000\t Loss: 0.214\n",
            "Train step: 4000\t Loss: 0.045\n",
            "Train step: 5000\t Loss: 0.059\n",
            "Train step: 6000\t Loss: 0.035\n",
            "Train step: 7000\t Loss: 0.046\n",
            "Train step: 8000\t Loss: 0.042\n",
            "Train step: 9000\t Loss: 0.026\n",
            "Train step: 10000\t Loss: 0.177\n",
            "Train step: 11000\t Loss: 0.100\n",
            "Train step: 12000\t Loss: 0.067\n",
            "Train step: 13000\t Loss: 0.040\n",
            "Train step: 14000\t Loss: 0.046\n",
            "Train step: 15000\t Loss: 0.019\n",
            "Train step: 16000\t Loss: 0.019\n",
            "Train step: 17000\t Loss: 0.050\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model evaluation\n",
        "\n",
        "model.eval()\n",
        "correct = 0\n",
        "for data, target in test_loader:\n",
        "  data = data.to(device)\n",
        "  target = target.to(device)\n",
        "  output = model(data)\n",
        "  prediction = output.data.max(1)[1]\n",
        "  correct += prediction.eq(target.data).sum()\n",
        "\n",
        "print('Test set: AccuracyL :{:.2f}%'.format(100*correct/len(test_loader.dataset)))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O-jD3nD3h13s",
        "outputId": "d31c23ff-0826-4353-ff9b-4afbcae12746"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set: AccuracyL :99.11%\n"
          ]
        }
      ]
    }
  ]
}