cnn_mnist.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DXL64/MNIST-using-CNN/blob/main/cnn_mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pws5iGJF4o5c",
        "outputId": "769135c6-c098-4ab2-c4b9-6541529d32f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([10, 32, 30, 30])\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torchvision.datasets as datasets\n",
        "from tqdm import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "class ConvBlock(torch.nn.Module):\n",
        "  def __init__ (self, in_channels, out_channels, kernel_size):\n",
        "    super().__init__()\n",
        "    layers = [\n",
        "        torch.nn.Conv2d(in_channels, out_channels, kernel_size),\n",
        "        torch.nn.BatchNorm2d(out_channels),\n",
        "        torch.nn.ReLU(inplace = True)\n",
        "    ]\n",
        "    self.block = torch.nn.Sequential(*layers)\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.block(x)\n",
        "def testBlock():\n",
        "  block = ConvBlock(1,32,3)\n",
        "  x = torch.zeros(10, 1, 32, 32)\n",
        "  out = block(x)\n",
        "  print(out.shape)\n",
        "testBlock()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DcLKkjtO5pEv",
        "outputId": "d1c443f4-9fc7-44dc-f857-f50d8884effb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([10, 10])\n"
          ]
        }
      ],
      "source": [
        "class CNN(torch.nn.Module):\n",
        "  def __init__(self, channels, kernel_size, fc_in, num_class):\n",
        "    super().__init__()\n",
        "    layers = [\n",
        "        ConvBlock(1 if i == 0 else channels[i-1],\n",
        "              c,\n",
        "              kernel_size) \n",
        "              for i, c in enumerate(channels)\n",
        "    ]\n",
        "    layers.append(torch.nn.Flatten())\n",
        "    layers.append(torch.nn.Linear(fc_in, num_class))\n",
        "    layers.append(torch.nn.BatchNorm1d(num_class))\n",
        "    self.net = torch.nn.Sequential(*layers)\n",
        "\n",
        "  def forward(self, x):\n",
        "    out = self.net(x)\n",
        "    return out\n",
        "def testCNN():\n",
        "  net = CNN([16*(i+1) for i in range(10)], 3, 10240, 10)\n",
        "  x = torch.zeros(10, 1, 28, 28)\n",
        "  out = net(x)\n",
        "  print(out.shape)\n",
        "testCNN()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IZdV6WDRKBU9"
      },
      "outputs": [],
      "source": [
        "class M3(CNN):\n",
        "  def __init__(self):\n",
        "    super().__init__([16*(i+2) for i in range(10)], 3, 11264, 10)\n",
        "  def forward(self, x):\n",
        "    out = self.net(x)\n",
        "    return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RZ0gA-mVSsVG"
      },
      "outputs": [],
      "source": [
        "class M5(CNN):\n",
        "  def __init__(self):\n",
        "    super().__init__([32*(i+1) for i in range(5)], 5, 10240, 10)\n",
        "  def forward(self, x):\n",
        "    out = self.net(x)\n",
        "    return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hJ-SezwnS7bO"
      },
      "outputs": [],
      "source": [
        "class M7(CNN):\n",
        "  def __init__(self):\n",
        "    super().__init__([48*(i+1) for i in range(4)], 7, 3072, 10)\n",
        "  def forward(self, x):\n",
        "    out = self.net(x)\n",
        "    return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440,
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        "id": "gt0qppQb74fm",
        "outputId": "1bd4ff03-e330-4de5-d132-6ea9a6ced9fa"
      },
      "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": {
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              "model_id": "dbea08fc9bf44665baddc010b21cb1e0"
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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": {
            "text/plain": [
              "  0%|          | 0/28881 [00:00<?, ?it/s]"
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              "model_id": "8d5bea2551ce4bdfa304f3e7be4a9041"
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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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              "version_minor": 0,
              "model_id": "ccbbbd07636b462d9b2dbe91318539fb"
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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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              "version_minor": 0,
              "model_id": "f5cea66ea4024efea21407b220e98990"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['train', 'test'])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "def get_datasets():\n",
        "  from torchvision import transforms as T\n",
        "  train_transform = T.Compose([\n",
        "      T.ToTensor()\n",
        "  ])\n",
        "  test_transform = T.Compose([\n",
        "      T.ToTensor()\n",
        "  ])\n",
        "  train_ds = datasets.MNIST('./data', train = True, download = True,transform = train_transform)\n",
        "  test_ds = datasets.MNIST('./data', train = False, download = True, transform = test_transform)\n",
        "  return dict(train = train_ds, test = test_ds)\n",
        "\n",
        "ds = get_datasets()\n",
        "ds.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AWuuHObg-q8V",
        "outputId": "c815c510-6b63-4593-fb86-c59bec51eb7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([120, 1, 28, 28]) torch.Size([120])\n"
          ]
        }
      ],
      "source": [
        "def get_dataloaders(datasets, config):\n",
        "    train_loader = torch.utils.data.DataLoader(\n",
        "      datasets['train'], batch_size=120, shuffle=True, num_workers=2, drop_last=True\n",
        "    )\n",
        "    test_loader = torch.utils.data.DataLoader(\n",
        "      datasets['test'], batch_size=120, shuffle=False, num_workers=2, drop_last=False\n",
        "    )\n",
        "    return dict(train=train_loader, test=test_loader)\n",
        "\n",
        "class Config:\n",
        "  def __init__(self):\n",
        "    self.batch_size = 120\n",
        "    self.learning_rate = 1e-3\n",
        "    self.device = \"cuda\"\n",
        "    self.gamma = 0.98\n",
        "    self.n_epoch = 50\n",
        "\n",
        "config = Config()\n",
        "loaders = get_dataloaders(ds, config)\n",
        "\n",
        "for x,y in loaders['train']:\n",
        "  print(x.shape, y.shape)\n",
        "  break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kwRzeWJpM_X0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "0999fb06-259c-4e13-ae5e-d74ea15a3761"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-4975d29ea910>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mnet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mM5\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mnet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mloss_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCrossEntropyLoss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mto\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    897\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_complex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    898\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 899\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    900\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    901\u001b[0m     def register_backward_hook(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    569\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 570\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    571\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    572\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    569\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 570\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    571\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    572\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    569\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 570\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    571\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    572\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    591\u001b[0m             \u001b[0;31m# `with torch.no_grad():`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 593\u001b[0;31m                 \u001b[0mparam_applied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    594\u001b[0m             \u001b[0mshould_use_set_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mshould_use_set_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    895\u001b[0m                 return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None,\n\u001b[1;32m    896\u001b[0m                             non_blocking, memory_format=convert_to_format)\n\u001b[0;32m--> 897\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_complex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    898\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m    212\u001b[0m         \u001b[0;31m# This function throws if there's a driver initialization error, no GPUs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;31m# are found or any other error occurs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    215\u001b[0m         \u001b[0;31m# Some of the queued calls may reentrantly call _lazy_init();\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0;31m# we need to just return without initializing in that case.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: No CUDA GPUs are available"
          ]
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "net = M5()\n",
        "net = net.to(config.device)\n",
        "optimizer = torch.optim.Adam(net.parameters(), lr = config.learning_rate)\n",
        "loss_function = torch.nn.CrossEntropyLoss()\n",
        "lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma = config.gamma)\n",
        "\n",
        "total = config.n_epoch * len(loaders['train']) + len(loaders['test'])\n",
        "plotTrainX = []\n",
        "plotTrainY = []\n",
        "plotTestX = []\n",
        "plotTestY = []\n",
        "plotTest = [{0, 0}]\n",
        "with tqdm(total=total, position=0, leave=True) as pbar:\n",
        "  for epoch in range(config.n_epoch):\n",
        "    net.train()\n",
        "    total = 0\n",
        "    total_correct = 0\n",
        "    for step, (images, labels) in enumerate(loaders['train']) :\n",
        "      images, labels = images.to(config.device), labels.to(config.device)\n",
        "      out = net(images)\n",
        "      loss = loss_function(out, labels)\n",
        "\n",
        "      ypred = torch.argmax(out, dim=1)\n",
        "      batch_correct = torch.sum(ypred==labels)\n",
        "      total += len(labels)\n",
        "      total_correct += batch_correct.item()\n",
        "\n",
        "      optimizer.zero_grad()\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "      pbar.update()\n",
        "      pbar.set_description(f\"{epoch} - {step} loss = {loss.item():.4f}\")\n",
        "    plotTrainX.append(epoch)\n",
        "    plotTrainY.append(round(total_correct/total*100, 2))\n",
        "\n",
        "    with torch.no_grad():\n",
        "      total = 0\n",
        "      total_correct = 0\n",
        "      net.eval()\n",
        "      for step, (images, labels) in enumerate(loaders['test']) :\n",
        "        images, labels = images.to(config.device), labels.to(config.device)\n",
        "        out = net(images)\n",
        "        preds = torch.argmax(out, dim = 1)\n",
        "        batch_correct = torch.sum(preds == labels).item()\n",
        "        total += len(labels)\n",
        "        total_correct += batch_correct\n",
        "        pbar.update()\n",
        "        pbar.set_description(f\"{epoch} - {step} test_accuracy = {total_correct/total*100:.2f}\")\n",
        "      plotTestX.append(epoch)\n",
        "      plotTestY.append(round(total_correct/total*100, 2))\n",
        "\n",
        "    lr_scheduler.step()\n",
        "  print(plotTrainX, plotTrainY)\n",
        "  plt.plot(plotTrainX, plotTrainY)\n",
        "  plt.plot(plotTestX, plotTestY)\n",
        "  plt.show()"
      ]
    },
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