CIFAR10_Image_Recognition.ipynb

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        "<a href=\"https://colab.research.google.com/github/Arijit-datascience/CIFAR10_Image_Recognition/blob/main/CIFAR10_Image_Recognition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
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      "metadata": {
        "id": "dSNqsLYMo5UN"
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      "source": [
        "\n",
        "<b>Advanced Convolutions</b>\n",
        "\n",
        "Two of the layers must use Depthwise Separable Convolution\n",
        "\n",
        "One of the layers must use Dilated Convolution\n",
        "\n",
        "Used GAP\n",
        "\n",
        "Used albumentation:\n",
        "1. Horizontal flip\n",
        "2. ShiftScaleRotate \n",
        "3. CoarseDropout (max_holes = 1, max_height=16px, max_width=1, min_holes = 1, min_height=16px, min_width=16px, fill_value=(mean of your dataset), mask_fill_value = None)\n",
        "4. Grayscale\n",
        "\n",
        "Parameters: 96,436\n",
        "\n",
        "Best Training Accuracy in 100 epochs: 88.42%\n",
        "\n",
        "Best Test Accuracy in 100 epochs: 89.35 %\n",
        "\n",
        "Total RF: 107\n",
        "\n",
        "Model meets all the mentioned objectives.\n",
        "\n",
        "Code is modularized and uploaded in git repository."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TYOIYkUdo1rD"
      },
      "source": [
        "import torch\n",
        "import torch.optim as optim\n",
        "\n",
        "from torchsummary import summary\n",
        "from torch.optim.lr_scheduler import StepLR, OneCycleLR\n",
        "\n",
        "import os\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "SFaOZjUspFet",
        "outputId": "fbaeae58-68ee-42bc-d3f8-02c2a38b641f"
      },
      "source": [
        "!git clone https://github.com/Arijit-datascience/CIFAR10_Image_Recognition\n",
        "!pip install albumentations==1.0.0"
      ],
      "execution_count": 2,
      "outputs": [
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            "Cloning into 'CIFAR10_Image_Recognition'...\n",
            "remote: Enumerating objects: 273, done.\u001b[K\n",
            "remote: Counting objects: 100% (273/273), done.\u001b[K\n",
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            "remote: Total 273 (delta 133), reused 158 (delta 62), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (273/273), 1.30 MiB | 9.53 MiB/s, done.\n",
            "Resolving deltas: 100% (133/133), done.\n",
            "Collecting albumentations==1.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b0/be/3db3cd8af771988748f69eace42047d5edebf01eaa7e1293f3b3f75f989e/albumentations-1.0.0-py3-none-any.whl (98kB)\n",
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            "\u001b[K     |████████████████████████████████| 38.2MB 77kB/s \n",
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            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==1.0.0) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==1.0.0) (1.3.1)\n",
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            "Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->scikit-image>=0.16.1->albumentations==1.0.0) (4.4.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==1.0.0) (1.15.0)\n",
            "Installing collected packages: opencv-python-headless, albumentations\n",
            "  Found existing installation: albumentations 0.1.12\n",
            "    Uninstalling albumentations-0.1.12:\n",
            "      Successfully uninstalled albumentations-0.1.12\n",
            "Successfully installed albumentations-1.0.0 opencv-python-headless-4.5.2.54\n"
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      "source": [
        "from CIFAR10_Image_Recognition.dataset import dataset as dataset\n",
        "from CIFAR10_Image_Recognition.models import model as models\n",
        "from CIFAR10_Image_Recognition.models import train as train\n",
        "from CIFAR10_Image_Recognition.models import test as test\n",
        "from CIFAR10_Image_Recognition.utils import plots as utils"
      ],
      "execution_count": 3,
      "outputs": []
    },
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      "source": [
        "norm_mean,norm_std = dataset.cifar10_mean_std()\n",
        "train_transform, test_transform = dataset.get_transforms(norm_mean,norm_std)\n",
        "\n",
        "# Extract and transform the data\n",
        "train_set,test_set = dataset.get_datasets(train_transform,test_transform)\n",
        "\n",
        "# Targets -> ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
        "classes = train_set.classes\n",
        "\n",
        "# Dataloader Arguments & Test/Train Dataloaders - Load part of ETL\n",
        "train_loader,test_loader = dataset.get_dataloaders(train_set,test_set)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "\n",
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n",
            "(0.492, 0.483, 0.447) (0.247, 0.243, 0.262)\n",
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "CUDA Available? True\n"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9mP4AoHEp7GD"
      },
      "source": [
        "## Data Exploration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "id": "38yj_Yc0p1GS",
        "outputId": "61c588b3-5c46-449c-92ad-dd10beaffac0"
      },
      "source": [
        "# Explore sample images\n",
        "utils.show_sample_images(train_loader, classes, .5, .25, num_of_images = 10)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
          "text": [
            "Displaying 10 images\n"
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          "output_type": "display_data",
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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Hzdh-0BsZPA"
      },
      "source": [
        "def seed_everything(seed):\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = True"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AieCc3hTqDZw"
      },
      "source": [
        "## Import Model and show model summary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V0e6fNdap1Bn",
        "outputId": "7b216f13-0dd7-4df2-ae3e-101c37f1d736"
      },
      "source": [
        "# Model summary\n",
        "use_cuda = torch.cuda.is_available()\n",
        "device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "print(device)\n",
        "model = models.Net().to(device)\n",
        "summary(model, input_size=(3, 32, 32))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 32, 32, 32]             864\n",
            "              ReLU-2           [-1, 32, 32, 32]               0\n",
            "       BatchNorm2d-3           [-1, 32, 32, 32]              64\n",
            "           Dropout-4           [-1, 32, 32, 32]               0\n",
            "            Conv2d-5           [-1, 64, 32, 32]          18,432\n",
            "              ReLU-6           [-1, 64, 32, 32]               0\n",
            "       BatchNorm2d-7           [-1, 64, 32, 32]             128\n",
            "           Dropout-8           [-1, 64, 32, 32]               0\n",
            "            Conv2d-9           [-1, 32, 32, 32]           2,080\n",
            "           Conv2d-10           [-1, 32, 16, 16]             288\n",
            "           Conv2d-11           [-1, 32, 16, 16]           9,216\n",
            "             ReLU-12           [-1, 32, 16, 16]               0\n",
            "      BatchNorm2d-13           [-1, 32, 16, 16]              64\n",
            "          Dropout-14           [-1, 32, 16, 16]               0\n",
            "           Conv2d-15           [-1, 32, 16, 16]             288\n",
            "           Conv2d-16           [-1, 64, 18, 18]           2,112\n",
            "             ReLU-17           [-1, 64, 18, 18]               0\n",
            "      BatchNorm2d-18           [-1, 64, 18, 18]             128\n",
            "          Dropout-19           [-1, 64, 18, 18]               0\n",
            "           Conv2d-20           [-1, 32, 18, 18]           2,080\n",
            "           Conv2d-21             [-1, 32, 9, 9]             288\n",
            "           Conv2d-22             [-1, 64, 7, 7]          18,432\n",
            "             ReLU-23             [-1, 64, 7, 7]               0\n",
            "      BatchNorm2d-24             [-1, 64, 7, 7]             128\n",
            "          Dropout-25             [-1, 64, 7, 7]               0\n",
            "           Conv2d-26             [-1, 64, 7, 7]          36,864\n",
            "             ReLU-27             [-1, 64, 7, 7]               0\n",
            "      BatchNorm2d-28             [-1, 64, 7, 7]             128\n",
            "          Dropout-29             [-1, 64, 7, 7]               0\n",
            "           Conv2d-30             [-1, 64, 7, 7]             576\n",
            "           Conv2d-31             [-1, 32, 9, 9]           2,080\n",
            "             ReLU-32             [-1, 32, 9, 9]               0\n",
            "      BatchNorm2d-33             [-1, 32, 9, 9]              64\n",
            "           Conv2d-34             [-1, 16, 9, 9]             528\n",
            "           Conv2d-35             [-1, 16, 5, 5]             144\n",
            "           Conv2d-36             [-1, 10, 5, 5]           1,440\n",
            "             ReLU-37             [-1, 10, 5, 5]               0\n",
            "      BatchNorm2d-38             [-1, 10, 5, 5]              20\n",
            "        AvgPool2d-39             [-1, 10, 1, 1]               0\n",
            "================================================================\n",
            "Total params: 96,436\n",
            "Trainable params: 96,436\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.01\n",
            "Forward/backward pass size (MB): 4.65\n",
            "Params size (MB): 0.37\n",
            "Estimated Total Size (MB): 5.03\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PwjyG7-bqLYq"
      },
      "source": [
        "## Train and Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4nk6CK4Or5Ct"
      },
      "source": [
        "def main(EPOCH, model, device, train_loader, test_loader, optimizer, scheduler, l1_factor):\n",
        "  train_loss_values = []\n",
        "  test_loss_values = []\n",
        "  train_acc_values = []\n",
        "  test_acc_values = []\n",
        "\n",
        "  for epoch in range(1, EPOCH + 1):\n",
        "      print('\\nEpoch {} : '.format(epoch))\n",
        "      # train the model\n",
        "      train_loss, train_acc = train.train(model, device, train_loader, optimizer, scheduler, epoch, l1_factor)\n",
        "      test_loss, test_acc, test_pred, target_pred, target_data = test.test(model, device, test_loader)\n",
        "\n",
        "      train_loss_values.append(train_loss)\n",
        "      test_loss_values.append(test_loss)\n",
        "\n",
        "      train_acc_values.append(train_acc)\n",
        "      test_acc_values.append(test_acc)\n",
        "\n",
        "  return train_loss_values, test_loss_values, train_acc_values, test_acc_values, test_pred, target_pred, target_data"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NXPKodhPp05P",
        "outputId": "5eec627a-99cf-445a-ae10-0201c397f77d"
      },
      "source": [
        "if __name__=='__main__':\n",
        "\n",
        "  EPOCH = 100\n",
        "  batch_size = 256\n",
        "  l1_factor = 0\n",
        "  l2_factor = 0.0001\n",
        "  lr_value = 0.01\n",
        "  mt_val = 0\n",
        "\n",
        "  cuda = torch.cuda.is_available()\n",
        "  device = torch.device(\"cuda\" if cuda else \"cpu\")\n",
        "\n",
        "  seed_everything(1)\n",
        "\n",
        "  model = models.Net().to(device)\n",
        "  optimizer = optim.SGD(model.parameters(), lr=lr_value, momentum=mt_val, weight_decay=l2_factor)\n",
        "  scheduler = OneCycleLR(optimizer, max_lr=0.1,epochs=EPOCH,steps_per_epoch=len(train_loader))\n",
        "  \n",
        "  print(\"------------------------------------------\")\n",
        "  train_loss, test_loss, train_acc, test_acc, test_pred, target_pred, target_data = main(EPOCH, model, device, train_loader, test_loader, optimizer, scheduler, l1_factor)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------\n",
            "\n",
            "Epoch 1 : \n",
            "Train set: Average loss: 1.5225, Accuracy: 34.65\n",
            "\n",
            "Test set: Average loss: 1.433, Accuracy: 48.32\n",
            "\n",
            "Epoch 2 : \n",
            "Train set: Average loss: 1.5894, Accuracy: 47.83\n",
            "\n",
            "Test set: Average loss: 1.192, Accuracy: 57.43\n",
            "\n",
            "Epoch 3 : \n",
            "Train set: Average loss: 1.0760, Accuracy: 55.00\n",
            "\n",
            "Test set: Average loss: 1.019, Accuracy: 64.76\n",
            "\n",
            "Epoch 4 : \n",
            "Train set: Average loss: 1.2204, Accuracy: 59.95\n",
            "\n",
            "Test set: Average loss: 0.903, Accuracy: 69.08\n",
            "\n",
            "Epoch 5 : \n",
            "Train set: Average loss: 0.8533, Accuracy: 63.23\n",
            "\n",
            "Test set: Average loss: 0.919, Accuracy: 68.77\n",
            "\n",
            "Epoch 6 : \n",
            "Train set: Average loss: 1.0025, Accuracy: 65.28\n",
            "\n",
            "Test set: Average loss: 0.811, Accuracy: 72.03\n",
            "\n",
            "Epoch 7 : \n",
            "Train set: Average loss: 0.9344, Accuracy: 66.85\n",
            "\n",
            "Test set: Average loss: 0.777, Accuracy: 73.43\n",
            "\n",
            "Epoch 8 : \n",
            "Train set: Average loss: 1.0671, Accuracy: 68.36\n",
            "\n",
            "Test set: Average loss: 0.716, Accuracy: 75.64\n",
            "\n",
            "Epoch 9 : \n",
            "Train set: Average loss: 0.9697, Accuracy: 69.50\n",
            "\n",
            "Test set: Average loss: 0.694, Accuracy: 76.15\n",
            "\n",
            "Epoch 10 : \n",
            "Train set: Average loss: 0.9277, Accuracy: 70.63\n",
            "\n",
            "Test set: Average loss: 0.663, Accuracy: 77.32\n",
            "\n",
            "Epoch 11 : \n",
            "Train set: Average loss: 0.8750, Accuracy: 71.48\n",
            "\n",
            "Test set: Average loss: 0.740, Accuracy: 74.67\n",
            "\n",
            "Epoch 12 : \n",
            "Train set: Average loss: 0.8348, Accuracy: 72.00\n",
            "\n",
            "Test set: Average loss: 0.630, Accuracy: 78.57\n",
            "\n",
            "Epoch 13 : \n",
            "Train set: Average loss: 0.8205, Accuracy: 72.70\n",
            "\n",
            "Test set: Average loss: 0.627, Accuracy: 78.47\n",
            "\n",
            "Epoch 14 : \n",
            "Train set: Average loss: 1.0720, Accuracy: 73.18\n",
            "\n",
            "Test set: Average loss: 0.618, Accuracy: 78.52\n",
            "\n",
            "Epoch 15 : \n",
            "Train set: Average loss: 0.7154, Accuracy: 73.87\n",
            "\n",
            "Test set: Average loss: 0.589, Accuracy: 79.84\n",
            "\n",
            "Epoch 16 : \n",
            "Train set: Average loss: 0.9311, Accuracy: 74.25\n",
            "\n",
            "Test set: Average loss: 0.587, Accuracy: 79.80\n",
            "\n",
            "Epoch 17 : \n",
            "Train set: Average loss: 0.7960, Accuracy: 74.62\n",
            "\n",
            "Test set: Average loss: 0.575, Accuracy: 79.83\n",
            "\n",
            "Epoch 18 : \n",
            "Train set: Average loss: 0.8704, Accuracy: 75.32\n",
            "\n",
            "Test set: Average loss: 0.578, Accuracy: 80.38\n",
            "\n",
            "Epoch 19 : \n",
            "Train set: Average loss: 0.7236, Accuracy: 75.48\n",
            "\n",
            "Test set: Average loss: 0.625, Accuracy: 78.87\n",
            "\n",
            "Epoch 20 : \n",
            "Train set: Average loss: 0.6059, Accuracy: 76.12\n",
            "\n",
            "Test set: Average loss: 0.598, Accuracy: 79.90\n",
            "\n",
            "Epoch 21 : \n",
            "Train set: Average loss: 0.7169, Accuracy: 76.53\n",
            "\n",
            "Test set: Average loss: 0.556, Accuracy: 81.22\n",
            "\n",
            "Epoch 22 : \n",
            "Train set: Average loss: 0.8604, Accuracy: 76.55\n",
            "\n",
            "Test set: Average loss: 0.536, Accuracy: 81.76\n",
            "\n",
            "Epoch 23 : \n",
            "Train set: Average loss: 0.8004, Accuracy: 77.01\n",
            "\n",
            "Test set: Average loss: 0.520, Accuracy: 82.38\n",
            "\n",
            "Epoch 24 : \n",
            "Train set: Average loss: 0.5429, Accuracy: 76.83\n",
            "\n",
            "Test set: Average loss: 0.521, Accuracy: 82.39\n",
            "\n",
            "Epoch 25 : \n",
            "Train set: Average loss: 0.5833, Accuracy: 77.44\n",
            "\n",
            "Test set: Average loss: 0.535, Accuracy: 81.56\n",
            "\n",
            "Epoch 26 : \n",
            "Train set: Average loss: 0.6304, Accuracy: 77.40\n",
            "\n",
            "Test set: Average loss: 0.509, Accuracy: 82.55\n",
            "\n",
            "Epoch 27 : \n",
            "Train set: Average loss: 0.5550, Accuracy: 77.86\n",
            "\n",
            "Test set: Average loss: 0.538, Accuracy: 81.87\n",
            "\n",
            "Epoch 28 : \n",
            "Train set: Average loss: 0.6003, Accuracy: 77.95\n",
            "\n",
            "Test set: Average loss: 0.518, Accuracy: 82.27\n",
            "\n",
            "Epoch 29 : \n",
            "Train set: Average loss: 0.5493, Accuracy: 77.94\n",
            "\n",
            "Test set: Average loss: 0.485, Accuracy: 83.29\n",
            "\n",
            "Epoch 30 : \n",
            "Train set: Average loss: 0.8727, Accuracy: 78.35\n",
            "\n",
            "Test set: Average loss: 0.497, Accuracy: 83.03\n",
            "\n",
            "Epoch 31 : \n",
            "Train set: Average loss: 0.6352, Accuracy: 78.50\n",
            "\n",
            "Test set: Average loss: 0.510, Accuracy: 82.70\n",
            "\n",
            "Epoch 32 : \n",
            "Train set: Average loss: 0.4838, Accuracy: 78.62\n",
            "\n",
            "Test set: Average loss: 0.490, Accuracy: 83.35\n",
            "\n",
            "Epoch 33 : \n",
            "Train set: Average loss: 0.7679, Accuracy: 78.92\n",
            "\n",
            "Test set: Average loss: 0.499, Accuracy: 82.97\n",
            "\n",
            "Epoch 34 : \n",
            "Train set: Average loss: 0.6656, Accuracy: 79.02\n",
            "\n",
            "Test set: Average loss: 0.491, Accuracy: 83.46\n",
            "\n",
            "Epoch 35 : \n",
            "Train set: Average loss: 0.5720, Accuracy: 79.17\n",
            "\n",
            "Test set: Average loss: 0.487, Accuracy: 83.53\n",
            "\n",
            "Epoch 36 : \n",
            "Train set: Average loss: 0.6452, Accuracy: 79.31\n",
            "\n",
            "Test set: Average loss: 0.491, Accuracy: 83.25\n",
            "\n",
            "Epoch 37 : \n",
            "Train set: Average loss: 0.5408, Accuracy: 79.29\n",
            "\n",
            "Test set: Average loss: 0.505, Accuracy: 83.29\n",
            "\n",
            "Epoch 38 : \n",
            "Train set: Average loss: 0.6572, Accuracy: 79.29\n",
            "\n",
            "Test set: Average loss: 0.500, Accuracy: 83.38\n",
            "\n",
            "Epoch 39 : \n",
            "Train set: Average loss: 0.5643, Accuracy: 79.71\n",
            "\n",
            "Test set: Average loss: 0.490, Accuracy: 83.75\n",
            "\n",
            "Epoch 40 : \n",
            "Train set: Average loss: 0.6627, Accuracy: 79.80\n",
            "\n",
            "Test set: Average loss: 0.472, Accuracy: 84.29\n",
            "\n",
            "Epoch 41 : \n",
            "Train set: Average loss: 0.6322, Accuracy: 80.03\n",
            "\n",
            "Test set: Average loss: 0.478, Accuracy: 83.42\n",
            "\n",
            "Epoch 42 : \n",
            "Train set: Average loss: 0.6993, Accuracy: 80.08\n",
            "\n",
            "Test set: Average loss: 0.452, Accuracy: 84.49\n",
            "\n",
            "Epoch 43 : \n",
            "Train set: Average loss: 0.7095, Accuracy: 80.31\n",
            "\n",
            "Test set: Average loss: 0.469, Accuracy: 84.24\n",
            "\n",
            "Epoch 44 : \n",
            "Train set: Average loss: 0.5335, Accuracy: 80.14\n",
            "\n",
            "Test set: Average loss: 0.500, Accuracy: 83.14\n",
            "\n",
            "Epoch 45 : \n",
            "Train set: Average loss: 0.3584, Accuracy: 80.27\n",
            "\n",
            "Test set: Average loss: 0.477, Accuracy: 84.23\n",
            "\n",
            "Epoch 46 : \n",
            "Train set: Average loss: 0.6820, Accuracy: 80.25\n",
            "\n",
            "Test set: Average loss: 0.478, Accuracy: 83.79\n",
            "\n",
            "Epoch 47 : \n",
            "Train set: Average loss: 0.6872, Accuracy: 80.48\n",
            "\n",
            "Test set: Average loss: 0.443, Accuracy: 84.84\n",
            "\n",
            "Epoch 48 : \n",
            "Train set: Average loss: 0.6842, Accuracy: 80.58\n",
            "\n",
            "Test set: Average loss: 0.449, Accuracy: 84.94\n",
            "\n",
            "Epoch 49 : \n",
            "Train set: Average loss: 0.3110, Accuracy: 80.82\n",
            "\n",
            "Test set: Average loss: 0.483, Accuracy: 83.82\n",
            "\n",
            "Epoch 50 : \n",
            "Train set: Average loss: 0.5039, Accuracy: 80.78\n",
            "\n",
            "Test set: Average loss: 0.476, Accuracy: 84.36\n",
            "\n",
            "Epoch 51 : \n",
            "Train set: Average loss: 0.6171, Accuracy: 80.93\n",
            "\n",
            "Test set: Average loss: 0.453, Accuracy: 84.39\n",
            "\n",
            "Epoch 52 : \n",
            "Train set: Average loss: 0.5804, Accuracy: 80.91\n",
            "\n",
            "Test set: Average loss: 0.461, Accuracy: 84.80\n",
            "\n",
            "Epoch 53 : \n",
            "Train set: Average loss: 1.1461, Accuracy: 81.16\n",
            "\n",
            "Test set: Average loss: 0.449, Accuracy: 84.48\n",
            "\n",
            "Epoch 54 : \n",
            "Train set: Average loss: 0.5454, Accuracy: 81.01\n",
            "\n",
            "Test set: Average loss: 0.446, Accuracy: 84.62\n",
            "\n",
            "Epoch 55 : \n",
            "Train set: Average loss: 0.4560, Accuracy: 81.37\n",
            "\n",
            "Test set: Average loss: 0.451, Accuracy: 84.48\n",
            "\n",
            "Epoch 56 : \n",
            "Train set: Average loss: 0.4754, Accuracy: 81.34\n",
            "\n",
            "Test set: Average loss: 0.451, Accuracy: 85.04\n",
            "\n",
            "Epoch 57 : \n",
            "Train set: Average loss: 0.5116, Accuracy: 81.48\n",
            "\n",
            "Test set: Average loss: 0.446, Accuracy: 85.02\n",
            "\n",
            "Epoch 58 : \n",
            "Train set: Average loss: 0.5361, Accuracy: 81.67\n",
            "\n",
            "Test set: Average loss: 0.442, Accuracy: 84.95\n",
            "\n",
            "Epoch 59 : \n",
            "Train set: Average loss: 0.7193, Accuracy: 81.66\n",
            "\n",
            "Test set: Average loss: 0.424, Accuracy: 85.69\n",
            "\n",
            "Epoch 60 : \n",
            "Train set: Average loss: 0.4909, Accuracy: 81.56\n",
            "\n",
            "Test set: Average loss: 0.429, Accuracy: 85.54\n",
            "\n",
            "Epoch 61 : \n",
            "Train set: Average loss: 0.5198, Accuracy: 82.10\n",
            "\n",
            "Test set: Average loss: 0.428, Accuracy: 85.44\n",
            "\n",
            "Epoch 62 : \n",
            "Train set: Average loss: 0.4067, Accuracy: 81.87\n",
            "\n",
            "Test set: Average loss: 0.415, Accuracy: 85.71\n",
            "\n",
            "Epoch 63 : \n",
            "Train set: Average loss: 0.5929, Accuracy: 82.03\n",
            "\n",
            "Test set: Average loss: 0.443, Accuracy: 84.73\n",
            "\n",
            "Epoch 64 : \n",
            "Train set: Average loss: 0.6035, Accuracy: 82.37\n",
            "\n",
            "Test set: Average loss: 0.431, Accuracy: 85.46\n",
            "\n",
            "Epoch 65 : \n",
            "Train set: Average loss: 0.3621, Accuracy: 82.34\n",
            "\n",
            "Test set: Average loss: 0.416, Accuracy: 85.69\n",
            "\n",
            "Epoch 66 : \n",
            "Train set: Average loss: 0.5801, Accuracy: 82.49\n",
            "\n",
            "Test set: Average loss: 0.427, Accuracy: 85.65\n",
            "\n",
            "Epoch 67 : \n",
            "Train set: Average loss: 0.5272, Accuracy: 82.62\n",
            "\n",
            "Test set: Average loss: 0.432, Accuracy: 85.35\n",
            "\n",
            "Epoch 68 : \n",
            "Train set: Average loss: 0.6505, Accuracy: 82.87\n",
            "\n",
            "Test set: Average loss: 0.420, Accuracy: 85.76\n",
            "\n",
            "Epoch 69 : \n",
            "Train set: Average loss: 0.4879, Accuracy: 82.70\n",
            "\n",
            "Test set: Average loss: 0.405, Accuracy: 86.14\n",
            "\n",
            "Epoch 70 : \n",
            "Train set: Average loss: 0.5477, Accuracy: 82.97\n",
            "\n",
            "Test set: Average loss: 0.407, Accuracy: 86.34\n",
            "\n",
            "Epoch 71 : \n",
            "Train set: Average loss: 0.4904, Accuracy: 83.22\n",
            "\n",
            "Test set: Average loss: 0.401, Accuracy: 86.43\n",
            "\n",
            "Epoch 72 : \n",
            "Train set: Average loss: 0.4151, Accuracy: 83.31\n",
            "\n",
            "Test set: Average loss: 0.418, Accuracy: 85.88\n",
            "\n",
            "Epoch 73 : \n",
            "Train set: Average loss: 0.5206, Accuracy: 83.31\n",
            "\n",
            "Test set: Average loss: 0.399, Accuracy: 86.72\n",
            "\n",
            "Epoch 74 : \n",
            "Train set: Average loss: 0.3698, Accuracy: 83.63\n",
            "\n",
            "Test set: Average loss: 0.381, Accuracy: 87.16\n",
            "\n",
            "Epoch 75 : \n",
            "Train set: Average loss: 0.4351, Accuracy: 83.84\n",
            "\n",
            "Test set: Average loss: 0.390, Accuracy: 86.80\n",
            "\n",
            "Epoch 76 : \n",
            "Train set: Average loss: 0.5039, Accuracy: 83.87\n",
            "\n",
            "Test set: Average loss: 0.405, Accuracy: 86.14\n",
            "\n",
            "Epoch 77 : \n",
            "Train set: Average loss: 0.5525, Accuracy: 84.00\n",
            "\n",
            "Test set: Average loss: 0.389, Accuracy: 86.64\n",
            "\n",
            "Epoch 78 : \n",
            "Train set: Average loss: 0.3883, Accuracy: 84.32\n",
            "\n",
            "Test set: Average loss: 0.388, Accuracy: 86.95\n",
            "\n",
            "Epoch 79 : \n",
            "Train set: Average loss: 0.5656, Accuracy: 84.45\n",
            "\n",
            "Test set: Average loss: 0.382, Accuracy: 87.32\n",
            "\n",
            "Epoch 80 : \n",
            "Train set: Average loss: 0.5782, Accuracy: 84.35\n",
            "\n",
            "Test set: Average loss: 0.376, Accuracy: 87.44\n",
            "\n",
            "Epoch 81 : \n",
            "Train set: Average loss: 0.4166, Accuracy: 84.90\n",
            "\n",
            "Test set: Average loss: 0.358, Accuracy: 87.76\n",
            "\n",
            "Epoch 82 : \n",
            "Train set: Average loss: 0.4933, Accuracy: 84.98\n",
            "\n",
            "Test set: Average loss: 0.372, Accuracy: 87.41\n",
            "\n",
            "Epoch 83 : \n",
            "Train set: Average loss: 0.6985, Accuracy: 85.55\n",
            "\n",
            "Test set: Average loss: 0.373, Accuracy: 87.41\n",
            "\n",
            "Epoch 84 : \n",
            "Train set: Average loss: 0.5999, Accuracy: 85.61\n",
            "\n",
            "Test set: Average loss: 0.358, Accuracy: 87.96\n",
            "\n",
            "Epoch 85 : \n",
            "Train set: Average loss: 0.4094, Accuracy: 86.05\n",
            "\n",
            "Test set: Average loss: 0.372, Accuracy: 87.46\n",
            "\n",
            "Epoch 86 : \n",
            "Train set: Average loss: 0.4537, Accuracy: 86.27\n",
            "\n",
            "Test set: Average loss: 0.357, Accuracy: 88.18\n",
            "\n",
            "Epoch 87 : \n",
            "Train set: Average loss: 0.3194, Accuracy: 86.24\n",
            "\n",
            "Test set: Average loss: 0.354, Accuracy: 88.25\n",
            "\n",
            "Epoch 88 : \n",
            "Train set: Average loss: 0.4309, Accuracy: 86.60\n",
            "\n",
            "Test set: Average loss: 0.353, Accuracy: 88.25\n",
            "\n",
            "Epoch 89 : \n",
            "Train set: Average loss: 0.4057, Accuracy: 86.89\n",
            "\n",
            "Test set: Average loss: 0.345, Accuracy: 88.59\n",
            "\n",
            "Epoch 90 : \n",
            "Train set: Average loss: 0.3082, Accuracy: 87.06\n",
            "\n",
            "Test set: Average loss: 0.337, Accuracy: 88.78\n",
            "\n",
            "Epoch 91 : \n",
            "Train set: Average loss: 0.3110, Accuracy: 87.33\n",
            "\n",
            "Test set: Average loss: 0.336, Accuracy: 88.61\n",
            "\n",
            "Epoch 92 : \n",
            "Train set: Average loss: 0.3397, Accuracy: 87.42\n",
            "\n",
            "Test set: Average loss: 0.337, Accuracy: 88.65\n",
            "\n",
            "Epoch 93 : \n",
            "Train set: Average loss: 0.4163, Accuracy: 87.76\n",
            "\n",
            "Test set: Average loss: 0.334, Accuracy: 88.76\n",
            "\n",
            "Epoch 94 : \n",
            "Train set: Average loss: 0.3698, Accuracy: 88.14\n",
            "\n",
            "Test set: Average loss: 0.335, Accuracy: 88.82\n",
            "\n",
            "Epoch 95 : \n",
            "Train set: Average loss: 0.3166, Accuracy: 88.14\n",
            "\n",
            "Test set: Average loss: 0.330, Accuracy: 89.10\n",
            "\n",
            "Epoch 96 : \n",
            "Train set: Average loss: 0.3296, Accuracy: 87.94\n",
            "\n",
            "Test set: Average loss: 0.331, Accuracy: 88.93\n",
            "\n",
            "Epoch 97 : \n",
            "Train set: Average loss: 0.4238, Accuracy: 88.12\n",
            "\n",
            "Test set: Average loss: 0.327, Accuracy: 89.10\n",
            "\n",
            "Epoch 98 : \n",
            "Train set: Average loss: 0.3652, Accuracy: 88.21\n",
            "\n",
            "Test set: Average loss: 0.325, Accuracy: 89.36\n",
            "\n",
            "Epoch 99 : \n",
            "Train set: Average loss: 0.2195, Accuracy: 88.38\n",
            "\n",
            "Test set: Average loss: 0.326, Accuracy: 89.35\n",
            "\n",
            "Epoch 100 : \n",
            "Train set: Average loss: 0.2496, Accuracy: 88.42\n",
            "\n",
            "Test set: Average loss: 0.325, Accuracy: 89.35\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hurXzCpKqXu-"
      },
      "source": [
        "## Validation accuracy and Loss curves"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "jA6W88-gp0yn",
        "outputId": "a06e9eda-8cd9-492c-c9d5-c2022ce734a3"
      },
      "source": [
        "utils.valid_accuracy_loss_plots(train_loss, train_acc, test_loss, test_acc)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M2WytilVqaaR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}