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      "cell_type": "markdown",
      "metadata": {
        "id": "aO-7t1Y7-hV4",
        "colab_type": "text"
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      "source": [
        "# Imports\n",
        "Importing necessary packages and modules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yVpodAMpoCi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from cuda import initialize_cuda\n",
        "from result import class_level_accuracy, plot_metric, save_and_show_result\n",
        "from model.train import train\n",
        "from model.evaluate import val\n",
        "from model.network import Net\n",
        "from model.utils import cross_entropy_loss, sgd_optimizer, model_summary\n",
        "from data.dataset import cifar10_dataset, cifar10_classes\n",
        "from data.downloader import download_cifar10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-7CMwrhPDw5b",
        "colab_type": "text"
      },
      "source": [
        "# Configuration\n",
        "\n",
        "Set various parameters and hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cq_Mo-qRD79h",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "class Args:\n",
        "\n",
        "    # Data Loading\n",
        "    # ============\n",
        "\n",
        "    train_batch_size = 64\n",
        "    val_batch_size = 64\n",
        "    num_workers = 4\n",
        "\n",
        "    # Regularization\n",
        "    # ==============\n",
        "    dropout = 0.1\n",
        "\n",
        "    # Training\n",
        "    # ========\n",
        "    random_seed = 1\n",
        "    epochs = 50\n",
        "    learning_rate = 0.01\n",
        "    momentum = 0.9"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffnfhydPLQk5",
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      "source": [
        "## Data Visualization\n",
        "Let's see how our data looks like. This information will help us decide the transformations that can be used on the dataset."
      ]
    },
    {
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      "source": [
        "# Download data\n",
        "cifar_dataset = download_cifar10()\n",
        "classes = cifar10_classes()\n",
        "sample_data, sample_targets = cifar_dataset.data, cifar_dataset.targets\n",
        "\n",
        "# Get image size\n",
        "image_size = np.transpose(sample_data[0], (2, 0, 1)).shape\n",
        "\n",
        "# Set number of images to display\n",
        "num_images = 4\n",
        "\n",
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        "fig.tight_layout()\n",
        "\n",
        "for i in range(num_images):\n",
        "    axs[i].axis('off')\n",
        "    axs[i].set_title(f'Label: {classes[sample_targets[i]]}')\n",
        "    axs[i].imshow(sample_data[i])"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /content/data/cifar10/cifar-10-python.tar.gz\n"
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            "Extracting /content/data/cifar10/cifar-10-python.tar.gz to /content/data/cifar10\n"
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ppYI+iZHXd6GA45tNV2ajoCYnuirZkxRokDAIVL0V9HWX5K/rC1dCu0CRBpkS6PHVLexn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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ve_XDp27GSN_",
        "colab_type": "text"
      },
      "source": [
        "## Set Seed and Get GPU Availability"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4Irg85hGRUR",
        "colab_type": "code",
        "outputId": "932a26f2-64ec-4b6e-f33d-5bf8b4f74b8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Initialize CUDA and set random seed\n",
        "cuda, device = initialize_cuda(Args.random_seed)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GPU Available? True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qFcWRAeY_MsB",
        "colab_type": "text"
      },
      "source": [
        "## Training and Validation Dataloaders\n",
        "This is the final step in data preparation. It sets the dataloader arguments and then creates the dataloader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D8Tjigo2poCl",
        "colab_type": "code",
        "outputId": "50b1ae30-6d84-4a72-84ed-058b1f24b555",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Create train data loader\n",
        "train_loader = cifar10_dataset(\n",
        "    Args.train_batch_size, cuda, Args.num_workers, train=True\n",
        ")\n",
        "\n",
        "# Create val data loader\n",
        "val_loader = cifar10_dataset(\n",
        "    Args.val_batch_size, cuda, Args.num_workers, train=False\n",
        ")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HSeDIqBq-6pC",
        "colab_type": "text"
      },
      "source": [
        "# Model Architecture and Summary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1NZ7kqezHG7",
        "colab_type": "code",
        "outputId": "d98c0677-d3ee-4131-bca6-40554b00bfa6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 952
        }
      },
      "source": [
        "model = Net(Args.dropout).to(device)  # Create model\n",
        "model_summary(model, image_size)  # Display model summary"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "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",
            "           Dropout-4           [-1, 32, 32, 32]               0\n",
            "            Conv2d-5           [-1, 64, 32, 32]          18,496\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",
            "         MaxPool2d-9           [-1, 64, 16, 16]               0\n",
            "           Conv2d-10           [-1, 32, 16, 16]           2,080\n",
            "           Conv2d-11           [-1, 32, 16, 16]           9,248\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, 64, 16, 16]          18,496\n",
            "             ReLU-16           [-1, 64, 16, 16]               0\n",
            "      BatchNorm2d-17           [-1, 64, 16, 16]             128\n",
            "          Dropout-18           [-1, 64, 16, 16]               0\n",
            "        MaxPool2d-19             [-1, 64, 8, 8]               0\n",
            "           Conv2d-20             [-1, 32, 8, 8]           2,080\n",
            "           Conv2d-21             [-1, 32, 8, 8]           9,248\n",
            "             ReLU-22             [-1, 32, 8, 8]               0\n",
            "      BatchNorm2d-23             [-1, 32, 8, 8]              64\n",
            "          Dropout-24             [-1, 32, 8, 8]               0\n",
            "           Conv2d-25             [-1, 32, 8, 8]             320\n",
            "           Conv2d-26             [-1, 64, 8, 8]           2,112\n",
            "             ReLU-27             [-1, 64, 8, 8]               0\n",
            "      BatchNorm2d-28             [-1, 64, 8, 8]             128\n",
            "          Dropout-29             [-1, 64, 8, 8]               0\n",
            "        MaxPool2d-30             [-1, 64, 4, 4]               0\n",
            "           Conv2d-31             [-1, 32, 4, 4]           2,080\n",
            "           Conv2d-32             [-1, 32, 4, 4]           9,248\n",
            "             ReLU-33             [-1, 32, 4, 4]               0\n",
            "      BatchNorm2d-34             [-1, 32, 4, 4]              64\n",
            "          Dropout-35             [-1, 32, 4, 4]               0\n",
            "           Conv2d-36             [-1, 64, 2, 2]          18,496\n",
            "             ReLU-37             [-1, 64, 2, 2]               0\n",
            "      BatchNorm2d-38             [-1, 64, 2, 2]             128\n",
            "          Dropout-39             [-1, 64, 2, 2]               0\n",
            "AdaptiveAvgPool2d-40             [-1, 64, 1, 1]               0\n",
            "           Linear-41                   [-1, 10]             650\n",
            "================================================================\n",
            "Total params: 94,218\n",
            "Trainable params: 94,218\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.01\n",
            "Forward/backward pass size (MB): 4.22\n",
            "Params size (MB): 0.36\n",
            "Estimated Total Size (MB): 4.59\n",
            "----------------------------------------------------------------\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HcyKrcQEGcdp",
        "colab_type": "text"
      },
      "source": [
        "# Model Training and Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pk83iZb2uaEA",
        "colab_type": "code",
        "outputId": "164ddb6c-b8cb-4e83-b186-9bea276dd60a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "losses = []\n",
        "accuracies = []\n",
        "incorrect_samples = []\n",
        "\n",
        "criterion = cross_entropy_loss()  # Create loss function\n",
        "optimizer = sgd_optimizer(model, Args.learning_rate, Args.momentum)  # Create optimizer\n",
        "\n",
        "for epoch in range(1, Args.epochs + 1):\n",
        "    print(f'Epoch {epoch}:')\n",
        "    train(model, train_loader, device, optimizer, criterion)\n",
        "    val(model, val_loader, device, criterion, losses, accuracies, incorrect_samples)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.85 Batch_ID=781 Accuracy=50.87: 100%|██████████| 782/782 [00:16<00:00, 47.49it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0176, Accuracy: 5984/10000 (59.84%)\n",
            "\n",
            "Epoch 2:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=1.07 Batch_ID=781 Accuracy=65.81: 100%|██████████| 782/782 [00:16<00:00, 46.28it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0137, Accuracy: 6946/10000 (69.46%)\n",
            "\n",
            "Epoch 3:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.46 Batch_ID=781 Accuracy=70.64: 100%|██████████| 782/782 [00:16<00:00, 47.92it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0125, Accuracy: 7193/10000 (71.93%)\n",
            "\n",
            "Epoch 4:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.92 Batch_ID=781 Accuracy=73.65: 100%|██████████| 782/782 [00:16<00:00, 47.53it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0115, Accuracy: 7421/10000 (74.21%)\n",
            "\n",
            "Epoch 5:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.40 Batch_ID=781 Accuracy=75.58: 100%|██████████| 782/782 [00:16<00:00, 47.45it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0106, Accuracy: 7661/10000 (76.61%)\n",
            "\n",
            "Epoch 6:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.82 Batch_ID=781 Accuracy=77.31: 100%|██████████| 782/782 [00:16<00:00, 47.64it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0100, Accuracy: 7786/10000 (77.86%)\n",
            "\n",
            "Epoch 7:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.32 Batch_ID=781 Accuracy=78.64: 100%|██████████| 782/782 [00:16<00:00, 46.48it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0102, Accuracy: 7751/10000 (77.51%)\n",
            "\n",
            "Epoch 8:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.57 Batch_ID=781 Accuracy=79.63: 100%|██████████| 782/782 [00:16<00:00, 53.67it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0098, Accuracy: 7884/10000 (78.84%)\n",
            "\n",
            "Epoch 9:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.66 Batch_ID=781 Accuracy=80.63: 100%|██████████| 782/782 [00:17<00:00, 45.77it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0092, Accuracy: 8008/10000 (80.08%)\n",
            "\n",
            "Epoch 10:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.41 Batch_ID=781 Accuracy=81.16: 100%|██████████| 782/782 [00:16<00:00, 46.90it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0090, Accuracy: 8020/10000 (80.20%)\n",
            "\n",
            "Epoch 11:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.78 Batch_ID=781 Accuracy=81.75: 100%|██████████| 782/782 [00:16<00:00, 47.31it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0090, Accuracy: 8060/10000 (80.60%)\n",
            "\n",
            "Epoch 12:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.63 Batch_ID=781 Accuracy=82.21: 100%|██████████| 782/782 [00:17<00:00, 45.07it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0089, Accuracy: 8051/10000 (80.51%)\n",
            "\n",
            "Epoch 13:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.73 Batch_ID=781 Accuracy=83.20: 100%|██████████| 782/782 [00:16<00:00, 46.67it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0090, Accuracy: 8073/10000 (80.73%)\n",
            "\n",
            "Epoch 14:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.74 Batch_ID=781 Accuracy=83.43: 100%|██████████| 782/782 [00:16<00:00, 47.14it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0086, Accuracy: 8150/10000 (81.50%)\n",
            "\n",
            "Epoch 15:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.38 Batch_ID=781 Accuracy=83.80: 100%|██████████| 782/782 [00:16<00:00, 46.16it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0085, Accuracy: 8143/10000 (81.43%)\n",
            "\n",
            "Epoch 16:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.87 Batch_ID=781 Accuracy=84.23: 100%|██████████| 782/782 [00:16<00:00, 46.49it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0083, Accuracy: 8218/10000 (82.18%)\n",
            "\n",
            "Epoch 17:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.64 Batch_ID=781 Accuracy=84.58: 100%|██████████| 782/782 [00:16<00:00, 46.87it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0084, Accuracy: 8198/10000 (81.98%)\n",
            "\n",
            "Epoch 18:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.58 Batch_ID=781 Accuracy=84.94: 100%|██████████| 782/782 [00:16<00:00, 46.39it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0090, Accuracy: 8103/10000 (81.03%)\n",
            "\n",
            "Epoch 19:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.62 Batch_ID=781 Accuracy=85.19: 100%|██████████| 782/782 [00:16<00:00, 46.51it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0081, Accuracy: 8276/10000 (82.76%)\n",
            "\n",
            "Epoch 20:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.27 Batch_ID=781 Accuracy=85.51: 100%|██████████| 782/782 [00:16<00:00, 55.09it/s]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0083, Accuracy: 8227/10000 (82.27%)\n",
            "\n",
            "Epoch 21:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.30 Batch_ID=781 Accuracy=85.79: 100%|██████████| 782/782 [00:16<00:00, 46.77it/s]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0081, Accuracy: 8270/10000 (82.70%)\n",
            "\n",
            "Epoch 22:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=1.31 Batch_ID=781 Accuracy=85.99: 100%|██████████| 782/782 [00:16<00:00, 47.84it/s]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0080, Accuracy: 8301/10000 (83.01%)\n",
            "\n",
            "Epoch 23:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.38 Batch_ID=781 Accuracy=86.18: 100%|██████████| 782/782 [00:16<00:00, 47.39it/s]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0081, Accuracy: 8300/10000 (83.00%)\n",
            "\n",
            "Epoch 24:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.96 Batch_ID=781 Accuracy=86.35: 100%|██████████| 782/782 [00:16<00:00, 47.57it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0080, Accuracy: 8298/10000 (82.98%)\n",
            "\n",
            "Epoch 25:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.56 Batch_ID=781 Accuracy=86.53: 100%|██████████| 782/782 [00:17<00:00, 43.86it/s]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0083, Accuracy: 8281/10000 (82.81%)\n",
            "\n",
            "Epoch 26:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.80 Batch_ID=781 Accuracy=87.11: 100%|██████████| 782/782 [00:18<00:00, 52.22it/s]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0078, Accuracy: 8355/10000 (83.55%)\n",
            "\n",
            "Epoch 27:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.35 Batch_ID=781 Accuracy=87.24: 100%|██████████| 782/782 [00:18<00:00, 51.44it/s]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0078, Accuracy: 8323/10000 (83.23%)\n",
            "\n",
            "Epoch 28:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.25 Batch_ID=781 Accuracy=87.29: 100%|██████████| 782/782 [00:18<00:00, 42.46it/s]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0080, Accuracy: 8323/10000 (83.23%)\n",
            "\n",
            "Epoch 29:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.48 Batch_ID=781 Accuracy=87.49: 100%|██████████| 782/782 [00:18<00:00, 41.51it/s]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0083, Accuracy: 8264/10000 (82.64%)\n",
            "\n",
            "Epoch 30:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.56 Batch_ID=781 Accuracy=87.68: 100%|██████████| 782/782 [00:18<00:00, 42.10it/s]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0079, Accuracy: 8350/10000 (83.50%)\n",
            "\n",
            "Epoch 31:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.63 Batch_ID=781 Accuracy=88.05: 100%|██████████| 782/782 [00:18<00:00, 43.38it/s]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0079, Accuracy: 8380/10000 (83.80%)\n",
            "\n",
            "Epoch 32:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.33 Batch_ID=781 Accuracy=87.98: 100%|██████████| 782/782 [00:18<00:00, 43.18it/s]\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0080, Accuracy: 8383/10000 (83.83%)\n",
            "\n",
            "Epoch 33:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.41 Batch_ID=781 Accuracy=88.21: 100%|██████████| 782/782 [00:18<00:00, 42.99it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0079, Accuracy: 8354/10000 (83.54%)\n",
            "\n",
            "Epoch 34:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.42 Batch_ID=781 Accuracy=88.38: 100%|██████████| 782/782 [00:17<00:00, 45.59it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0080, Accuracy: 8378/10000 (83.78%)\n",
            "\n",
            "Epoch 35:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.58 Batch_ID=781 Accuracy=88.53: 100%|██████████| 782/782 [00:16<00:00, 46.51it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0079, Accuracy: 8397/10000 (83.97%)\n",
            "\n",
            "Epoch 36:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.25 Batch_ID=781 Accuracy=88.64: 100%|██████████| 782/782 [00:16<00:00, 46.78it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0077, Accuracy: 8428/10000 (84.28%)\n",
            "\n",
            "Epoch 37:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.54 Batch_ID=781 Accuracy=88.76: 100%|██████████| 782/782 [00:16<00:00, 47.11it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0079, Accuracy: 8368/10000 (83.68%)\n",
            "\n",
            "Epoch 38:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.38 Batch_ID=781 Accuracy=88.74: 100%|██████████| 782/782 [00:16<00:00, 46.43it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0080, Accuracy: 8394/10000 (83.94%)\n",
            "\n",
            "Epoch 39:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.35 Batch_ID=781 Accuracy=89.17: 100%|██████████| 782/782 [00:16<00:00, 47.39it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0077, Accuracy: 8424/10000 (84.24%)\n",
            "\n",
            "Epoch 40:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.16 Batch_ID=781 Accuracy=88.97: 100%|██████████| 782/782 [00:16<00:00, 55.72it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0075, Accuracy: 8483/10000 (84.83%)\n",
            "\n",
            "Epoch 41:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.16 Batch_ID=781 Accuracy=89.43: 100%|██████████| 782/782 [00:16<00:00, 46.92it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0081, Accuracy: 8411/10000 (84.11%)\n",
            "\n",
            "Epoch 42:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.20 Batch_ID=781 Accuracy=89.56: 100%|██████████| 782/782 [00:16<00:00, 46.36it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0078, Accuracy: 8456/10000 (84.56%)\n",
            "\n",
            "Epoch 43:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.10 Batch_ID=781 Accuracy=89.60: 100%|██████████| 782/782 [00:17<00:00, 45.94it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0077, Accuracy: 8469/10000 (84.69%)\n",
            "\n",
            "Epoch 44:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.28 Batch_ID=781 Accuracy=89.69: 100%|██████████| 782/782 [00:16<00:00, 46.17it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0078, Accuracy: 8415/10000 (84.15%)\n",
            "\n",
            "Epoch 45:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.51 Batch_ID=781 Accuracy=89.79: 100%|██████████| 782/782 [00:16<00:00, 47.43it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0078, Accuracy: 8469/10000 (84.69%)\n",
            "\n",
            "Epoch 46:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.12 Batch_ID=781 Accuracy=89.88: 100%|██████████| 782/782 [00:16<00:00, 46.46it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0079, Accuracy: 8410/10000 (84.10%)\n",
            "\n",
            "Epoch 47:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.13 Batch_ID=781 Accuracy=90.07: 100%|██████████| 782/782 [00:16<00:00, 46.91it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0077, Accuracy: 8448/10000 (84.48%)\n",
            "\n",
            "Epoch 48:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.26 Batch_ID=781 Accuracy=90.19: 100%|██████████| 782/782 [00:16<00:00, 47.60it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0080, Accuracy: 8361/10000 (83.61%)\n",
            "\n",
            "Epoch 49:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.61 Batch_ID=781 Accuracy=90.11: 100%|██████████| 782/782 [00:16<00:00, 46.60it/s]\n",
            "  0%|          | 0/782 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0080, Accuracy: 8376/10000 (83.76%)\n",
            "\n",
            "Epoch 50:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.28 Batch_ID=781 Accuracy=90.36: 100%|██████████| 782/782 [00:16<00:00, 46.33it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set: Average loss: 0.0078, Accuracy: 8438/10000 (84.38%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2o99Jq1XTdo-",
        "colab_type": "text"
      },
      "source": [
        "## Plotting Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OvkqQXw-2buM",
        "colab_type": "text"
      },
      "source": [
        "Plot changes in validation loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YSZR02cuA067",
        "colab_type": "code",
        "outputId": "f4296e85-d279-4462-cecf-2e0dc9143bc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "plot_metric(losses, 'Loss')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GdM3kT7NWq9UoIu2aglGA0GLGN08ayMdb93D6b2bx3IeF1NbF/1qdIiIHUzDK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\n/x+q1E7iCUQe8wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OSctNrPG2afY",
        "colab_type": "text"
      },
      "source": [
        "Plot changes in validation accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMFYHCFVA7Z-",
        "colab_type": "code",
        "outputId": "5e904ffc-3a0d-476b-d890-5eddf7ac3c1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "plot_metric(accuracies, 'Accuracy')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uWmVNLX+atoKhXdvwzbE9o1itiEjzohZbFNw1dQUhd357znAqa2q56dn5TPzL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\nInFFwSYiInFFwSYiInFFwSYiInHl/wPRwn8NMvvvDwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "frMHF1KQt0Ff",
        "colab_type": "text"
      },
      "source": [
        "## Display Incorrect Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79CqMo3IPasc",
        "colab_type": "code",
        "outputId": "05b0a2f3-7ef3-4511-881b-56cbd5057b4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 723
        }
      },
      "source": [
        "save_and_show_result(incorrect_samples, classes)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2z/kGHc4DpVFK6QtLjFz/xiN/J2lPjEZZGT72yY8ln6UzvMZ7pJDCzAwlsvlF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\njajZim3pwgL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hHetE9Xh8cJTfu0hDuPNw/OcuQF5bzZJEXcVxOK5Z71EBgW1OcwXncoju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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "B7ufwZyTSKfk"
      },
      "source": [
        "## Result Analysis\n",
        "\n",
        "Displaying accuracy for each class in the entire validation dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pDNFuDEDpoDG",
        "colab_type": "code",
        "outputId": "cb59eb47-2419-45a4-d4b3-f504499f8ae4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "class_level_accuracy(model, val_loader, device, classes)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of plane : 90 %\n",
            "Accuracy of   car : 91 %\n",
            "Accuracy of  bird : 71 %\n",
            "Accuracy of   cat : 76 %\n",
            "Accuracy of  deer : 84 %\n",
            "Accuracy of   dog : 77 %\n",
            "Accuracy of  frog : 88 %\n",
            "Accuracy of horse : 82 %\n",
            "Accuracy of  ship : 91 %\n",
            "Accuracy of truck : 88 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBXFeKGvXJba",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}