L5_CNN_CIFAR_10_Improved.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AdrianCobo/Deep-Learning-And-Artificial-Intelligence/blob/main/L5_CNN_CIFAR_10_Improved.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ojHKF5tIUe0V"
      },
      "source": [
        "Note 1: uses a much larger CNN\n",
        "\n",
        "Note 2: uses data augmentation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "qEmKSvKzUoiW"
      },
      "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 numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime # to calculate how much time does learn take per epoch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YJZB4njWUnxx",
        "outputId": "9494aa3a-c4ba-43bf-ef93-92b6b767450d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 170498071/170498071 [00:02<00:00, 62800198.26it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./cifar-10-python.tar.gz to .\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "# examples: https://pytorch.org/docs/stable/torchvision/transforms.html\n",
        "transformer_train = torchvision.transforms.Compose([\n",
        "    # torchvision.transforms.ColorJitter(\n",
        "    #   brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2),\n",
        "  transforms.RandomCrop(32, padding=4),\n",
        "  torchvision.transforms.RandomHorizontalFlip(p=0.5),\n",
        "  # torchvision.transforms.RandomRotation(degrees=15),\n",
        "  torchvision.transforms.RandomAffine(0, translate=(0.1, 0.1)),\n",
        "  # torchvision.transforms.RandomPerspective()\n",
        "  transforms.ToTensor(),\n",
        "])\n",
        "\n",
        "train_dataset = torchvision.datasets.CIFAR10(\n",
        "    root='.',\n",
        "    train=True,\n",
        "    transform=transformer_train,\n",
        "    download=True)\n",
        "test_dataset = torchvision.datasets.CIFAR10(\n",
        "    root='.',\n",
        "    train=False,\n",
        "    transform=transforms.ToTensor(),\n",
        "    download=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "k7erHuZPWQX7",
        "outputId": "7be8b8c0-03d7-44bf-852d-49bdf1d48c1f"
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      "outputs": [
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          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# behaves differently from MNIST / Fashion MNIST\n",
        "# it is a Numpy array!\n",
        "train_dataset.data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f08ggH-RsKEE",
        "outputId": "e8b8badc-dc6b-443f-917e-4b90036392a5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# we are working with color images now\n",
        "train_dataset.data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6K1UlDy4sQga",
        "outputId": "d07ba591-d26b-4295-cdc6-2323b1f23175"
      },
      "outputs": [
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              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# behaves differently from MNIST / Fashion MNIST\n",
        "# it is a list!\n",
        "train_dataset.targets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uSteqR6FsUem",
        "outputId": "8db5a9db-339e-4d5a-aa2d-5d15dc3b654f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of classes: 10\n"
          ]
        }
      ],
      "source": [
        "# number of classes\n",
        "K = len(set(train_dataset.targets))\n",
        "print(\"number of classes:\", K)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "wK--9SfksbIM"
      },
      "outputs": [],
      "source": [
        "# Data loader\n",
        "# Useful because it automatically generates batches in the training loop\n",
        "# and takes care of shuffling\n",
        "batch_size = 128\n",
        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
        "                                           batch_size=batch_size,\n",
        "                                           shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n",
        "                                          batch_size=batch_size,\n",
        "                                          shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jwVGkDAisdgr",
        "outputId": "9457cac9-48a9-4d3b-c6d4-2caeb35bdfbc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[[[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
            "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
            "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
            "          ...,\n",
            "          [0.4706, 0.4784, 0.5098,  ..., 0.0000, 0.0000, 0.0000],\n",
            "          [0.4667, 0.4667, 0.5020,  ..., 0.0000, 0.0000, 0.0000],\n",
            "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000]],\n",
            "\n",
            "         [[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
            "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
            "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
            "          ...,\n",
            "          [0.4157, 0.4235, 0.4588,  ..., 0.0000, 0.0000, 0.0000],\n",
            "          [0.4235, 0.4235, 0.4588,  ..., 0.0000, 0.0000, 0.0000],\n",
            "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000]],\n",
            "\n",
            "         [[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
            "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
            "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
            "          ...,\n",
            "          [0.4471, 0.4471, 0.4824,  ..., 0.0000, 0.0000, 0.0000],\n",
            "          [0.4431, 0.4235, 0.4588,  ..., 0.0000, 0.0000, 0.0000],\n",
            "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000]]]])\n",
            "torch.Size([1, 3, 32, 32])\n"
          ]
        }
      ],
      "source": [
        "# the data transformer mapped the data to (0, 1)\n",
        "# and also moved the color channel before height/width\n",
        "tmp_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
        "                                         batch_size=1,\n",
        "                                         shuffle=True)\n",
        "for x, y in tmp_loader:\n",
        "  print(x)\n",
        "  print(x.shape)\n",
        "  break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "hfJkRgg_sqd1"
      },
      "outputs": [],
      "source": [
        "# Define the model\n",
        "class CNN(nn.Module):\n",
        "  def __init__(self, K):\n",
        "    super(CNN, self).__init__()\n",
        "\n",
        "    # define the conv layers\n",
        "    self.conv1 = nn.Sequential(\n",
        "        nn.Conv2d(3, 32, kernel_size=3, padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.BatchNorm2d(32),\n",
        "        nn.Conv2d(32, 32, kernel_size=3, padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.BatchNorm2d(32),\n",
        "        nn.MaxPool2d(2),\n",
        "    )\n",
        "\n",
        "    self.conv2 = nn.Sequential(\n",
        "        nn.Conv2d(32, 64, kernel_size=3, padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.BatchNorm2d(64),\n",
        "        nn.Conv2d(64, 64, kernel_size=3, padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.BatchNorm2d(64),\n",
        "        nn.MaxPool2d(2),\n",
        "    )\n",
        "\n",
        "    self.conv3 = nn.Sequential(\n",
        "        nn.Conv2d(64, 128, kernel_size=3, padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.BatchNorm2d(128),\n",
        "        nn.Conv2d(128, 128, kernel_size=3, padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.BatchNorm2d(128),\n",
        "        nn.MaxPool2d(2),\n",
        "    )\n",
        "       \n",
        "    # p = padding\n",
        "    # Useful: https://pytorch.org/docs/stable/nn.html#torch.nn.MaxPool2d\n",
        "\n",
        "    # Easy to calculate output\n",
        "    # 32 > 16 > 8 > 4\n",
        "\n",
        "    # define the linear layers\n",
        "    self.fc1 = nn.Linear(128 * 4 * 4, 1024)\n",
        "    self.fc2 = nn.Linear(1024, K)\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = self.conv1(x)\n",
        "    x = self.conv2(x)\n",
        "    x = self.conv3(x)\n",
        "    x = x.view(x.size(0), -1)\n",
        "    x = F.dropout(x, p=0.5)\n",
        "    x = F.relu(self.fc1(x))\n",
        "    x = F.dropout(x, p=0.2)\n",
        "    x = self.fc2(x)\n",
        "    return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ZlrcnJYBuuVD"
      },
      "outputs": [],
      "source": [
        "# Instantiate the model\n",
        "model = CNN(K)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLNmALwsu16B",
        "outputId": "675dea30-fd3b-4862-b4e3-8992be85381e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda:0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CNN(\n",
              "  (conv1): Sequential(\n",
              "    (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (4): ReLU()\n",
              "    (5): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (conv2): Sequential(\n",
              "    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (4): ReLU()\n",
              "    (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (conv3): Sequential(\n",
              "    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (4): ReLU()\n",
              "    (5): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (fc1): Linear(in_features=2048, out_features=1024, bias=True)\n",
              "  (fc2): Linear(in_features=1024, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)\n",
        "model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "UVQt9cPEvA4N"
      },
      "outputs": [],
      "source": [
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "0Vj6oAwqvJvz"
      },
      "outputs": [],
      "source": [
        "# A function to encapsulate the training loop\n",
        "def batch_gd(model, criterion, optimizer, train_loader, test_loader, epochs):\n",
        "  train_losses = np.zeros(epochs)\n",
        "  test_losses = np.zeros(epochs)\n",
        "\n",
        "  for it in range(epochs):\n",
        "    model.train()\n",
        "    t0 = datetime.now()\n",
        "    train_loss = []\n",
        "    for inputs, targets in train_loader:\n",
        "      # move data to GPU\n",
        "      inputs, targets = inputs.to(device), targets.to(device)\n",
        "\n",
        "      # zero the parameter gradients\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      # Forward pass\n",
        "      outputs = model(inputs)\n",
        "      loss = criterion(outputs, targets)\n",
        "\n",
        "      # Backward and optimize\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "      train_loss.append(loss.item())\n",
        "\n",
        "    # Get train loss and test loss\n",
        "\n",
        "    train_loss = np.mean(train_loss) # a little misleading\n",
        "\n",
        "    model.eval()\n",
        "    test_loss = []\n",
        "    for inputs, targets in test_loader:\n",
        "      # move data to GPU\n",
        "      inputs, targets = inputs.to(device), targets.to(device)\n",
        "      outputs = model(inputs)\n",
        "      loss = criterion(outputs, targets)\n",
        "      test_loss.append(loss.item())\n",
        "    test_loss = np.mean(test_loss)\n",
        "\n",
        "    # Save losses\n",
        "    train_losses[it] = train_loss\n",
        "    test_losses[it] = test_loss\n",
        "\n",
        "    dt = datetime.now() - t0\n",
        "    print(f'Epoch {it+1}/{epochs}, Train Loss: {train_loss:.4f}, \\\n",
        "      Test Loss: {test_loss:.4f}, Duration: {dt}')\n",
        "    \n",
        "  return train_losses, test_losses"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ycxDyGaPvMMP",
        "outputId": "d0e8ff8a-3d6b-4bbd-c9f7-182ad21bbfde"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/80, Train Loss: 1.4588,       Test Loss: 1.1892, Duration: 0:00:37.396188\n",
            "Epoch 2/80, Train Loss: 1.0230,       Test Loss: 0.9496, Duration: 0:00:29.765747\n",
            "Epoch 3/80, Train Loss: 0.8581,       Test Loss: 0.8574, Duration: 0:00:31.632291\n",
            "Epoch 4/80, Train Loss: 0.7606,       Test Loss: 0.7781, Duration: 0:00:30.136510\n",
            "Epoch 5/80, Train Loss: 0.6966,       Test Loss: 0.8704, Duration: 0:00:30.451744\n",
            "Epoch 6/80, Train Loss: 0.6422,       Test Loss: 0.6838, Duration: 0:00:29.951607\n",
            "Epoch 7/80, Train Loss: 0.6088,       Test Loss: 0.6392, Duration: 0:00:30.658432\n",
            "Epoch 8/80, Train Loss: 0.5763,       Test Loss: 0.5981, Duration: 0:00:30.101795\n",
            "Epoch 9/80, Train Loss: 0.5468,       Test Loss: 0.6235, Duration: 0:00:30.632524\n",
            "Epoch 10/80, Train Loss: 0.5281,       Test Loss: 0.5466, Duration: 0:00:29.977257\n",
            "Epoch 11/80, Train Loss: 0.5137,       Test Loss: 0.5394, Duration: 0:00:30.802246\n",
            "Epoch 12/80, Train Loss: 0.4878,       Test Loss: 0.5252, Duration: 0:00:30.229041\n",
            "Epoch 13/80, Train Loss: 0.4707,       Test Loss: 0.5407, Duration: 0:00:30.917753\n",
            "Epoch 14/80, Train Loss: 0.4602,       Test Loss: 0.5627, Duration: 0:00:30.250531\n",
            "Epoch 15/80, Train Loss: 0.4452,       Test Loss: 0.5295, Duration: 0:00:30.743910\n",
            "Epoch 16/80, Train Loss: 0.4331,       Test Loss: 0.5095, Duration: 0:00:30.365853\n",
            "Epoch 17/80, Train Loss: 0.4277,       Test Loss: 0.4792, Duration: 0:00:30.427325\n",
            "Epoch 18/80, Train Loss: 0.4124,       Test Loss: 0.4912, Duration: 0:00:30.295296\n",
            "Epoch 19/80, Train Loss: 0.4006,       Test Loss: 0.5029, Duration: 0:00:30.383231\n",
            "Epoch 20/80, Train Loss: 0.3928,       Test Loss: 0.4651, Duration: 0:00:30.405533\n",
            "Epoch 21/80, Train Loss: 0.3852,       Test Loss: 0.4580, Duration: 0:00:30.023470\n",
            "Epoch 22/80, Train Loss: 0.3814,       Test Loss: 0.4741, Duration: 0:00:30.551407\n",
            "Epoch 23/80, Train Loss: 0.3694,       Test Loss: 0.4797, Duration: 0:00:30.121712\n",
            "Epoch 24/80, Train Loss: 0.3627,       Test Loss: 0.4359, Duration: 0:00:30.629300\n",
            "Epoch 25/80, Train Loss: 0.3542,       Test Loss: 0.4348, Duration: 0:00:30.080966\n",
            "Epoch 26/80, Train Loss: 0.3446,       Test Loss: 0.4661, Duration: 0:00:30.508791\n",
            "Epoch 27/80, Train Loss: 0.3406,       Test Loss: 0.4546, Duration: 0:00:29.928591\n",
            "Epoch 28/80, Train Loss: 0.3344,       Test Loss: 0.4164, Duration: 0:00:30.681293\n",
            "Epoch 29/80, Train Loss: 0.3279,       Test Loss: 0.4369, Duration: 0:00:30.225109\n",
            "Epoch 30/80, Train Loss: 0.3266,       Test Loss: 0.4241, Duration: 0:00:30.884781\n",
            "Epoch 31/80, Train Loss: 0.3165,       Test Loss: 0.4592, Duration: 0:00:30.192288\n",
            "Epoch 32/80, Train Loss: 0.3098,       Test Loss: 0.4508, Duration: 0:00:31.078846\n",
            "Epoch 33/80, Train Loss: 0.3098,       Test Loss: 0.4521, Duration: 0:00:30.064261\n",
            "Epoch 34/80, Train Loss: 0.3034,       Test Loss: 0.4230, Duration: 0:00:30.718050\n",
            "Epoch 35/80, Train Loss: 0.2995,       Test Loss: 0.4122, Duration: 0:00:30.344012\n",
            "Epoch 36/80, Train Loss: 0.2938,       Test Loss: 0.4022, Duration: 0:00:30.514100\n",
            "Epoch 37/80, Train Loss: 0.2850,       Test Loss: 0.4033, Duration: 0:00:30.503082\n",
            "Epoch 38/80, Train Loss: 0.2841,       Test Loss: 0.4540, Duration: 0:00:30.198712\n",
            "Epoch 39/80, Train Loss: 0.2778,       Test Loss: 0.4002, Duration: 0:00:30.535295\n",
            "Epoch 40/80, Train Loss: 0.2776,       Test Loss: 0.4319, Duration: 0:00:30.069612\n",
            "Epoch 41/80, Train Loss: 0.2724,       Test Loss: 0.4151, Duration: 0:00:30.676854\n",
            "Epoch 42/80, Train Loss: 0.2653,       Test Loss: 0.3990, Duration: 0:00:30.391826\n",
            "Epoch 43/80, Train Loss: 0.2658,       Test Loss: 0.4054, Duration: 0:00:30.723594\n",
            "Epoch 44/80, Train Loss: 0.2612,       Test Loss: 0.4138, Duration: 0:00:29.970634\n",
            "Epoch 45/80, Train Loss: 0.2617,       Test Loss: 0.4144, Duration: 0:00:30.767792\n",
            "Epoch 46/80, Train Loss: 0.2526,       Test Loss: 0.4009, Duration: 0:00:29.920989\n",
            "Epoch 47/80, Train Loss: 0.2503,       Test Loss: 0.4070, Duration: 0:00:30.570823\n",
            "Epoch 48/80, Train Loss: 0.2513,       Test Loss: 0.3951, Duration: 0:00:30.100947\n",
            "Epoch 49/80, Train Loss: 0.2463,       Test Loss: 0.3936, Duration: 0:00:30.612427\n",
            "Epoch 50/80, Train Loss: 0.2401,       Test Loss: 0.3965, Duration: 0:00:29.910781\n",
            "Epoch 51/80, Train Loss: 0.2421,       Test Loss: 0.4044, Duration: 0:00:30.374793\n",
            "Epoch 52/80, Train Loss: 0.2392,       Test Loss: 0.3825, Duration: 0:00:30.105226\n",
            "Epoch 53/80, Train Loss: 0.2345,       Test Loss: 0.3992, Duration: 0:00:30.005500\n",
            "Epoch 54/80, Train Loss: 0.2276,       Test Loss: 0.4041, Duration: 0:00:30.419980\n",
            "Epoch 55/80, Train Loss: 0.2337,       Test Loss: 0.4011, Duration: 0:00:29.985557\n",
            "Epoch 56/80, Train Loss: 0.2288,       Test Loss: 0.3827, Duration: 0:00:30.593521\n",
            "Epoch 57/80, Train Loss: 0.2242,       Test Loss: 0.4252, Duration: 0:00:29.945822\n",
            "Epoch 58/80, Train Loss: 0.2234,       Test Loss: 0.4045, Duration: 0:00:30.600354\n",
            "Epoch 59/80, Train Loss: 0.2217,       Test Loss: 0.3859, Duration: 0:00:30.072349\n",
            "Epoch 60/80, Train Loss: 0.2220,       Test Loss: 0.3972, Duration: 0:00:30.481153\n",
            "Epoch 61/80, Train Loss: 0.2156,       Test Loss: 0.3992, Duration: 0:00:29.865548\n",
            "Epoch 62/80, Train Loss: 0.2119,       Test Loss: 0.3990, Duration: 0:00:30.502093\n",
            "Epoch 63/80, Train Loss: 0.2077,       Test Loss: 0.4092, Duration: 0:00:29.928403\n",
            "Epoch 64/80, Train Loss: 0.2143,       Test Loss: 0.4179, Duration: 0:00:31.141558\n",
            "Epoch 65/80, Train Loss: 0.2088,       Test Loss: 0.4066, Duration: 0:00:31.368011\n",
            "Epoch 66/80, Train Loss: 0.2083,       Test Loss: 0.4041, Duration: 0:00:31.964147\n",
            "Epoch 67/80, Train Loss: 0.2042,       Test Loss: 0.3766, Duration: 0:00:31.343766\n",
            "Epoch 68/80, Train Loss: 0.2038,       Test Loss: 0.3935, Duration: 0:00:31.680150\n",
            "Epoch 69/80, Train Loss: 0.1982,       Test Loss: 0.3849, Duration: 0:00:31.129127\n",
            "Epoch 70/80, Train Loss: 0.2000,       Test Loss: 0.4133, Duration: 0:00:31.743398\n",
            "Epoch 71/80, Train Loss: 0.1966,       Test Loss: 0.4127, Duration: 0:00:31.176322\n",
            "Epoch 72/80, Train Loss: 0.1975,       Test Loss: 0.4403, Duration: 0:00:31.903404\n",
            "Epoch 73/80, Train Loss: 0.1961,       Test Loss: 0.3926, Duration: 0:00:31.184267\n",
            "Epoch 74/80, Train Loss: 0.1922,       Test Loss: 0.4356, Duration: 0:00:31.931576\n",
            "Epoch 75/80, Train Loss: 0.1924,       Test Loss: 0.4023, Duration: 0:00:31.701350\n",
            "Epoch 76/80, Train Loss: 0.1858,       Test Loss: 0.3948, Duration: 0:00:31.511075\n",
            "Epoch 77/80, Train Loss: 0.1915,       Test Loss: 0.4031, Duration: 0:00:31.860871\n",
            "Epoch 78/80, Train Loss: 0.1807,       Test Loss: 0.4213, Duration: 0:00:31.250708\n",
            "Epoch 79/80, Train Loss: 0.1896,       Test Loss: 0.4115, Duration: 0:00:31.806246\n",
            "Epoch 80/80, Train Loss: 0.1812,       Test Loss: 0.4328, Duration: 0:00:31.133708\n"
          ]
        }
      ],
      "source": [
        "train_losses, test_losses = batch_gd(\n",
        "    model, criterion, optimizer, train_loader, test_loader, epochs=80\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "QDhTG4STvs6B",
        "outputId": "9cb399ec-220c-4d11-e398-c762dbf92c99"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot the train loss and test loss per iteration\n",
        "plt.plot(train_losses, label='train loss')\n",
        "plt.plot(test_losses, label='test loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQY0Sg1qwBIE",
        "outputId": "93ab1297-6940-45f5-c411-39396b813909"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train acc: 0.9320, Test acc: 0.8814\n"
          ]
        }
      ],
      "source": [
        "# Accuracy\n",
        "\n",
        "model.eval() # model in evaluation mode\n",
        "n_correct = 0.\n",
        "n_total = 0.\n",
        "for inputs, targets in train_loader:\n",
        "  # move data to GPU\n",
        "  inputs, targets = inputs.to(device), targets.to(device)\n",
        "\n",
        "  # Forward pass\n",
        "  outputs = model(inputs)\n",
        "\n",
        "  # Get prediction\n",
        "  # torch.max return both max and argmax\n",
        "  _, predictions = torch.max(outputs, 1)\n",
        "\n",
        "  # update counts\n",
        "  n_correct += (predictions == targets).sum().item()\n",
        "  n_total += targets.shape[0]\n",
        "\n",
        "train_acc = n_correct / n_total\n",
        "\n",
        "n_correct = 0.\n",
        "n_total = 0.\n",
        "for inputs, targets in test_loader:\n",
        "  # move data to GPU\n",
        "  inputs, targets = inputs.to(device), targets.to(device)\n",
        "\n",
        "  # Forward pass\n",
        "  outputs = model(inputs)\n",
        "\n",
        "  # Get prediction\n",
        "  # torch.max return both max and argmax\n",
        "  _, predictions = torch.max(outputs, 1)\n",
        "\n",
        "  # update counts\n",
        "  n_correct += (predictions == targets).sum().item()\n",
        "  n_total += targets.shape[0]\n",
        "\n",
        "test_acc = n_correct / n_total\n",
        "\n",
        "print(f\"Train acc: {train_acc:.4f}, Test acc: {test_acc:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "UburuKWewKVa"
      },
      "outputs": [],
      "source": [
        "# plot confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import numpy as np\n",
        "import itertools\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "  \n",
        "  \"\"\"\n",
        "  This function prints and plots the confusion matrix.\n",
        "  Normalization can be applied by setting 'normalize=True'.\n",
        "  \"\"\"\n",
        "\n",
        "  if normalize:\n",
        "    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "    print(\"Normalized confusion matrix\")\n",
        "  else:\n",
        "    print('Confusion matrix, without normalization')\n",
        "  \n",
        "  print(cm)\n",
        "\n",
        "  plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "  plt.title(title)\n",
        "  plt.colorbar()\n",
        "  tick_marks = np.arange(len(classes))\n",
        "  plt.xticks(tick_marks, classes, rotation=45)\n",
        "  plt.yticks(tick_marks, classes)\n",
        "\n",
        "  fmt = '.2f' if normalize else 'd'\n",
        "  thresh = cm.max()/2\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "    plt.text(j,i,format(cm[i,j],fmt),\n",
        "             horizontalalignment=\"center\",\n",
        "             color=\"white\" if cm[i,j] > thresh else \"black\")\n",
        "  plt.tight_layout()\n",
        "  plt.ylabel('True label')\n",
        "  plt.xlabel('Prediceted label')\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        },
        "id": "P-eA0lm9wTUW",
        "outputId": "3532e2b4-ef71-480c-d45e-0f520a44a4ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[906   7  19   8   7   0   9   4  23  17]\n",
            " [  7 941   0   0   0   1   0   0   3  48]\n",
            " [ 48   1 786  21  38  13  62  20   5   6]\n",
            " [ 12   3  31 739  37  88  57  22   3   8]\n",
            " [  6   1  25  10 886  13  30  27   2   0]\n",
            " [  6   3  17 102  31 755  35  44   2   5]\n",
            " [  3   2  13   9   7   2 960   2   1   1]\n",
            " [ 11   1   5   7  34  14   7 918   1   2]\n",
            " [ 52   7   3   2   2   1   5   2 912  14]\n",
            " [  9  30   0   3   1   1   3   0   6 947]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# get all predictions in an array and plot confusion matrix\n",
        "\n",
        "x_test = test_dataset.data\n",
        "y_test = np.array(test_dataset.targets)\n",
        "p_test = np.array([])\n",
        "for inputs, targets in test_loader:\n",
        "  # move data to GPU\n",
        "  inputs, targets = inputs.to(device), targets.to(device)\n",
        "\n",
        "  # Forward pass\n",
        "  outputs = model(inputs)\n",
        "\n",
        "  # Get predictioin\n",
        "  _, predictions = torch.max(outputs, 1)\n",
        "\n",
        "  # update p_test\n",
        "  p_test = np.concatenate((p_test, predictions.cpu().numpy()))\n",
        "\n",
        "cm = confusion_matrix(y_test, p_test)\n",
        "plot_confusion_matrix(cm, list(range(10)))\n",
        "\n",
        "# confussion matrix tells us for each label, how many predictions correspond \n",
        "# to that label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "s9Jxj0bGwWQr"
      },
      "outputs": [],
      "source": [
        "# Label mapping\n",
        "labels = ''' airplane\n",
        "automobile\n",
        "bird\n",
        "cat\n",
        "deer\n",
        "dog\n",
        "frog\n",
        "horse\n",
        "ship\n",
        "truck'''.split(\"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "__td8Bm8wZIQ",
        "outputId": "4fef86e3-a743-4d2c-e14e-0d8213cff564"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3200x3200 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Show some miniclassified examples\n",
        "p_test = p_test.astype(np.uint8)\n",
        "misclassified_idx = np.where(p_test != y_test)[0]\n",
        "sample_idx = np.random.choice(misclassified_idx, 10, replace=False)\n",
        "plt.figure(figsize=(32,32))\n",
        "for j, i in enumerate(sample_idx):\n",
        "  plt.subplot(10, 1, j+1)\n",
        "  plt.axis('off')\n",
        "  plt.imshow(x_test[i].reshape(32,32,3))\n",
        "  plt.title(\"True label: %s Predicted: %s\" % (labels[y_test[i]], labels[p_test[i]]));"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3UeLqesxTk3",
        "outputId": "006ad0d0-bf34-484f-9ca7-db1f036a1847"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 32, 32, 32]             896\n",
            "              ReLU-2           [-1, 32, 32, 32]               0\n",
            "       BatchNorm2d-3           [-1, 32, 32, 32]              64\n",
            "            Conv2d-4           [-1, 32, 32, 32]           9,248\n",
            "              ReLU-5           [-1, 32, 32, 32]               0\n",
            "       BatchNorm2d-6           [-1, 32, 32, 32]              64\n",
            "         MaxPool2d-7           [-1, 32, 16, 16]               0\n",
            "            Conv2d-8           [-1, 64, 16, 16]          18,496\n",
            "              ReLU-9           [-1, 64, 16, 16]               0\n",
            "      BatchNorm2d-10           [-1, 64, 16, 16]             128\n",
            "           Conv2d-11           [-1, 64, 16, 16]          36,928\n",
            "             ReLU-12           [-1, 64, 16, 16]               0\n",
            "      BatchNorm2d-13           [-1, 64, 16, 16]             128\n",
            "        MaxPool2d-14             [-1, 64, 8, 8]               0\n",
            "           Conv2d-15            [-1, 128, 8, 8]          73,856\n",
            "             ReLU-16            [-1, 128, 8, 8]               0\n",
            "      BatchNorm2d-17            [-1, 128, 8, 8]             256\n",
            "           Conv2d-18            [-1, 128, 8, 8]         147,584\n",
            "             ReLU-19            [-1, 128, 8, 8]               0\n",
            "      BatchNorm2d-20            [-1, 128, 8, 8]             256\n",
            "        MaxPool2d-21            [-1, 128, 4, 4]               0\n",
            "           Linear-22                 [-1, 1024]       2,098,176\n",
            "           Linear-23                   [-1, 10]          10,250\n",
            "================================================================\n",
            "Total params: 2,396,330\n",
            "Trainable params: 2,396,330\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.01\n",
            "Forward/backward pass size (MB): 2.74\n",
            "Params size (MB): 9.14\n",
            "Estimated Total Size (MB): 11.90\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "from torchsummary import summary\n",
        "summary(model, (3, 32, 32))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM0MYEGF/zoqpvsC/Z4ol5o",
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}