05_CodingDrill/CodingDrill_01.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/satyajitghana/TSAI-DeepVision-EVA4.0/blob/master/05_CodingDrill/CodingDrill_01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eXuPMu9zb_s3",
        "colab_type": "text"
      },
      "source": [
        "# Coding Drill 01"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HR3_w_wrb7Bs",
        "colab_type": "text"
      },
      "source": [
        "## 1. Target\n",
        "\n",
        "- Get the Setup Right\n",
        "- Get a pretty decent accuracy, don't worry about `<=10K` params right now\n",
        "- Make sure the data loaders are working\n",
        "- Setup the basic training loop\n",
        "- Create a Baseline Model\n",
        "- 15 Epochs\n",
        "\n",
        "## 2. Result\n",
        "\n",
        "- Params: `13,808`\n",
        "- Best Train Accuracy: `99.22%`\n",
        "- Best Test Accuracy : `99.35%`\n",
        "\n",
        "## 3. Analysis\n",
        "\n",
        "- The model is not overfitting, but we need to reduce the number of params, since our target is `10K` Params\n",
        "- The model is underfitting since we have added BatchNorm and Dropout to the model, since we know they are beneficial for this network\n",
        "- We should add data augmentation to possibly improve the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x93nIsZUcGgi",
        "colab_type": "text"
      },
      "source": [
        "## BaseLine Model\n",
        "\n",
        "- Convolution Block 1\n",
        "    - `Conv2D:  1, 16, 3; Out: 26x26x16`\n",
        "    - `Conv2D: 16, 32, 3; Out: 24x24x32`\n",
        "\n",
        "- Transition Block\n",
        "    - `Conv2D: 32, 10, 1; Out: 24x24x10`\n",
        "    - `MaxPool2D: 2x2;    Out: 12x12x10`\n",
        "\n",
        "- Convolution Block 2\n",
        "    - `Conv2D: 10, 16, 3; Out: 10x10x16`\n",
        "    - `Conv2D: 16, 16, 3; Out: 8x8x16`\n",
        "    - `Conv2D: 16, 16, 3; Out: 6x6x16`\n",
        "    - `Conv2D: 16, 16, 3; Out: 6x6x16; Pad=1`\n",
        "\n",
        "- GAP\n",
        "    - `AvgPool2D: 6x6;    Out: 1x1x16`\n",
        "    - `Conv2D: 16, 10, 1; Out: 1x1x10`\n",
        "\n",
        "\n",
        "Legend\n",
        "- `Conv2D (input_channels), (output_channels), (kernel_size); Out: (output_dim)`\n",
        "- `MaxPool2D: (kernel_size); Out: (output_dim)`\n",
        "- `AvgPool2D: (kernel_size); Out: (output_dim)`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aO-7t1Y7-hV4",
        "colab_type": "text"
      },
      "source": [
        "# Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8kH16rnZ7wt_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ky3f_Odl-7um",
        "colab_type": "text"
      },
      "source": [
        "## Data Transformations\n",
        "\n",
        "We first start with defining our data transformations. We need to think what our data is and how can we augment it to correct represent images which it might not see otherwise. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YtssFUKb-jqx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train Phase transformations\n",
        "train_transforms = transforms.Compose([\n",
        "                                       transforms.ToTensor(),\n",
        "                                       transforms.Normalize((0.1307,), (0.3081,)) # The mean and std have to be sequences (e.g., tuples), therefore you should add a comma after the values. \n",
        "                                       # Note the difference between (0.1307) and (0.1307,)\n",
        "                                       ])\n",
        "\n",
        "# Test Phase transformations\n",
        "test_transforms = transforms.Compose([\n",
        "                                       transforms.ToTensor(),\n",
        "                                       transforms.Normalize((0.1307,), (0.3081,))\n",
        "                                       ])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQciFYo2B1mO",
        "colab_type": "text"
      },
      "source": [
        "# Dataset and Creating Train/Test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_4A84rlfDA23",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = datasets.MNIST('./data', train=True, download=True, transform=train_transforms)\n",
        "test = datasets.MNIST('./data', train=False, download=True, transform=test_transforms)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgldp_3-Dn0c",
        "colab_type": "text"
      },
      "source": [
        "# Dataloader Arguments & Test/Train Dataloaders\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8OLDR79DrHG",
        "colab_type": "code",
        "outputId": "5f928ceb-0d30-4278-e11c-f3751524d658",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "SEED = 1\n",
        "\n",
        "# CUDA?\n",
        "cuda = torch.cuda.is_available()\n",
        "print(\"CUDA Available?\", cuda)\n",
        "\n",
        "# For reproducibility\n",
        "torch.manual_seed(SEED)\n",
        "\n",
        "if cuda:\n",
        "    torch.cuda.manual_seed(SEED)\n",
        "\n",
        "# note about pin_memory\n",
        "# If you load your samples in the Dataset on CPU and would like to push it\n",
        "# during training to the GPU, you can speed up the host to device transfer by\n",
        "# enabling pin_memory. This lets your DataLoader allocate the samples in\n",
        "# page-locked memory, which speeds-up the transfer.\n",
        "\n",
        "# dataloader arguments - something you'll fetch these from cmdprmt\n",
        "dataloader_args = dict(shuffle=True, batch_size=128, num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n",
        "\n",
        "# train dataloader\n",
        "train_loader = torch.utils.data.DataLoader(train, **dataloader_args)\n",
        "\n",
        "# test dataloader\n",
        "test_loader = torch.utils.data.DataLoader(test, **dataloader_args)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CUDA Available? True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubQL3H6RJL3h",
        "colab_type": "text"
      },
      "source": [
        "# The model\n",
        "Let's start with the model we first saw"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7FXQlB9kH1ov",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn.functional as F\n",
        "dropout_value = 0.1\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        # Input Block\n",
        "        self.convblock1 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=1, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.ReLU(),\n",
        "            nn.BatchNorm2d(16),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 26\n",
        "\n",
        "        # CONVOLUTION BLOCK 1\n",
        "        self.convblock2 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.ReLU(),\n",
        "            nn.BatchNorm2d(32),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 24\n",
        "\n",
        "        # TRANSITION BLOCK 1\n",
        "        self.convblock3 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=32, out_channels=10, kernel_size=(1, 1), padding=0, bias=False),\n",
        "        ) # output_size = 24\n",
        "        self.pool1 = nn.MaxPool2d(2, 2) # output_size = 12\n",
        "\n",
        "        # CONVOLUTION BLOCK 2\n",
        "        self.convblock4 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=10, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.ReLU(),            \n",
        "            nn.BatchNorm2d(16),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 10\n",
        "        self.convblock5 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.ReLU(),            \n",
        "            nn.BatchNorm2d(16),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 8\n",
        "        self.convblock6 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.ReLU(),            \n",
        "            nn.BatchNorm2d(16),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 6\n",
        "        self.convblock7 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), padding=1, bias=False),\n",
        "            nn.ReLU(),            \n",
        "            nn.BatchNorm2d(16),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 6\n",
        "        \n",
        "        # OUTPUT BLOCK\n",
        "        self.gap = nn.Sequential(\n",
        "            nn.AvgPool2d(kernel_size=6)\n",
        "        ) # output_size = 1\n",
        "\n",
        "        self.convblock8 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=16, out_channels=10, kernel_size=(1, 1), padding=0, bias=False),\n",
        "        ) \n",
        "\n",
        "        self.dropout = nn.Dropout(dropout_value)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.convblock1(x)\n",
        "        x = self.convblock2(x)\n",
        "        x = self.convblock3(x)\n",
        "        x = self.pool1(x)\n",
        "        x = self.convblock4(x)\n",
        "        x = self.convblock5(x)\n",
        "        x = self.convblock6(x)\n",
        "        x = self.convblock7(x)\n",
        "        x = self.gap(x)        \n",
        "        x = self.convblock8(x)\n",
        "\n",
        "        x = x.view(-1, 10)\n",
        "        return F.log_softmax(x, dim=-1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M3-vp8X9LCWo",
        "colab_type": "text"
      },
      "source": [
        "# Model Params\n",
        "Can't emphasize on how important viewing Model Summary is. \n",
        "Unfortunately, there is no in-built model visualizer, so we have to take external help"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5skB97zIJQQe",
        "colab_type": "code",
        "outputId": "741dcac5-14ae-4ebc-9cbd-9650ccf188e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        }
      },
      "source": [
        "!pip install torchsummary\n",
        "from torchsummary import summary\n",
        "use_cuda = torch.cuda.is_available()\n",
        "device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "print(device)\n",
        "model = Net().to(device)\n",
        "summary(model, input_size=(1, 28, 28))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torchsummary in /usr/local/lib/python3.6/dist-packages (1.5.1)\n",
            "cuda\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 16, 26, 26]             144\n",
            "              ReLU-2           [-1, 16, 26, 26]               0\n",
            "       BatchNorm2d-3           [-1, 16, 26, 26]              32\n",
            "           Dropout-4           [-1, 16, 26, 26]               0\n",
            "            Conv2d-5           [-1, 32, 24, 24]           4,608\n",
            "              ReLU-6           [-1, 32, 24, 24]               0\n",
            "       BatchNorm2d-7           [-1, 32, 24, 24]              64\n",
            "           Dropout-8           [-1, 32, 24, 24]               0\n",
            "            Conv2d-9           [-1, 10, 24, 24]             320\n",
            "        MaxPool2d-10           [-1, 10, 12, 12]               0\n",
            "           Conv2d-11           [-1, 16, 10, 10]           1,440\n",
            "             ReLU-12           [-1, 16, 10, 10]               0\n",
            "      BatchNorm2d-13           [-1, 16, 10, 10]              32\n",
            "          Dropout-14           [-1, 16, 10, 10]               0\n",
            "           Conv2d-15             [-1, 16, 8, 8]           2,304\n",
            "             ReLU-16             [-1, 16, 8, 8]               0\n",
            "      BatchNorm2d-17             [-1, 16, 8, 8]              32\n",
            "          Dropout-18             [-1, 16, 8, 8]               0\n",
            "           Conv2d-19             [-1, 16, 6, 6]           2,304\n",
            "             ReLU-20             [-1, 16, 6, 6]               0\n",
            "      BatchNorm2d-21             [-1, 16, 6, 6]              32\n",
            "          Dropout-22             [-1, 16, 6, 6]               0\n",
            "           Conv2d-23             [-1, 16, 6, 6]           2,304\n",
            "             ReLU-24             [-1, 16, 6, 6]               0\n",
            "      BatchNorm2d-25             [-1, 16, 6, 6]              32\n",
            "          Dropout-26             [-1, 16, 6, 6]               0\n",
            "        AvgPool2d-27             [-1, 16, 1, 1]               0\n",
            "           Conv2d-28             [-1, 10, 1, 1]             160\n",
            "================================================================\n",
            "Total params: 13,808\n",
            "Trainable params: 13,808\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 1.06\n",
            "Params size (MB): 0.05\n",
            "Estimated Total Size (MB): 1.12\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1__x_SbrL7z3",
        "colab_type": "text"
      },
      "source": [
        "# Training and Testing\n",
        "\n",
        "Looking at logs can be boring, so we'll introduce **tqdm** progressbar to get cooler logs. \n",
        "\n",
        "Let's write train and test functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbkF2nN_LYIb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# this automatically selects tqdm for colab_notebook\n",
        "from tqdm.auto import tqdm, trange\n",
        "\n",
        "train_losses = []\n",
        "test_losses = []\n",
        "train_acc = []\n",
        "test_acc = []\n",
        "\n",
        "def train(model, device, train_loader, optimizer, epoch):\n",
        "  model.train()\n",
        "  pbar = tqdm(train_loader, ncols=\"80%\")\n",
        "  correct = 0\n",
        "  processed = 0\n",
        "  for batch_idx, (data, target) in enumerate(pbar):\n",
        "    # get samples\n",
        "    data, target = data.to(device), target.to(device)\n",
        "\n",
        "    # Init\n",
        "    optimizer.zero_grad()\n",
        "    # In PyTorch, we need to set the gradients to zero before starting to do \n",
        "    # backpropragation because PyTorch accumulates the gradients on subsequent \n",
        "    # backward passes. \n",
        "    # Because of this, when you start your training loop, ideally you should \n",
        "    # zero out the gradients so that you do the parameter update correctly.\n",
        "\n",
        "    # Predict\n",
        "    y_pred = model(data)\n",
        "\n",
        "    # Calculate loss\n",
        "    loss = F.nll_loss(y_pred, target)\n",
        "    train_losses.append(loss)\n",
        "\n",
        "    # Backpropagation\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    \n",
        "    # get the index of the max log-probability\n",
        "    pred = y_pred.argmax(dim=1, keepdim=True)\n",
        "    correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "    processed += len(data)\n",
        "\n",
        "    # Update pbar-tqdm\n",
        "    pbar.set_description(desc= f'Loss={loss.item()} Batch_id={batch_idx} Accuracy={100*correct/processed:0.2f}')\n",
        "    train_acc.append(100*correct/processed)\n",
        "\n",
        "def test(model, device, test_loader):\n",
        "    model.eval()\n",
        "    test_loss = 0\n",
        "    correct = 0\n",
        "    with torch.no_grad():\n",
        "        for data, target in test_loader:\n",
        "            data, target = data.to(device), target.to(device)\n",
        "            output = model(data)\n",
        "            # sum up batch loss\n",
        "            test_loss += F.nll_loss(output, target, reduction='sum').item()\n",
        "            # get the index of the max log-probability\n",
        "            pred = output.argmax(dim=1, keepdim=True)\n",
        "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "    test_loss /= len(test_loader.dataset)\n",
        "    test_losses.append(test_loss)\n",
        "\n",
        "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n",
        "        test_loss, correct, len(test_loader.dataset),\n",
        "        100. * correct / len(test_loader.dataset)))\n",
        "    \n",
        "    test_acc.append(100. * correct / len(test_loader.dataset))"
      ],
      "execution_count": 0,
      "outputs": []
    },
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        }
      },
      "source": [
        "model =  Net().to(device)\n",
        "\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
        "\n",
        "EPOCHS = 15\n",
        "for epoch in range(EPOCHS):\n",
        "    # Print Learning Rate\n",
        "    print(\"EPOCH:\", epoch+1)\n",
        "    # Train the model\n",
        "    train(model, device, train_loader, optimizer, epoch)\n",
        "    # Validate the model\n",
        "    test(model, device, test_loader)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "EPOCH: 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0766, Accuracy: 9802/10000 (98.02%)\n",
            "\n",
            "EPOCH: 2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0414, Accuracy: 9879/10000 (98.79%)\n",
            "\n",
            "EPOCH: 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0330, Accuracy: 9897/10000 (98.97%)\n",
            "\n",
            "EPOCH: 4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0321, Accuracy: 9904/10000 (99.04%)\n",
            "\n",
            "EPOCH: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0330, Accuracy: 9897/10000 (98.97%)\n",
            "\n",
            "EPOCH: 6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0321, Accuracy: 9898/10000 (98.98%)\n",
            "\n",
            "EPOCH: 7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8f6c6bdf881d473182207ef1dc688b46",
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0223, Accuracy: 9933/10000 (99.33%)\n",
            "\n",
            "EPOCH: 8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dfcea1355cef4e5c96ab869b4e7284e6",
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0260, Accuracy: 9920/10000 (99.20%)\n",
            "\n",
            "EPOCH: 9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "da39c60d6af6464391510381e1915295",
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            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0216, Accuracy: 9929/10000 (99.29%)\n",
            "\n",
            "EPOCH: 10\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0210, Accuracy: 9928/10000 (99.28%)\n",
            "\n",
            "EPOCH: 11\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "423984fa4ee64afb81021f0b1bdf1531",
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            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0273, Accuracy: 9916/10000 (99.16%)\n",
            "\n",
            "EPOCH: 12\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "3817908d5bdd46328c0090a779df64ab",
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              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0201, Accuracy: 9935/10000 (99.35%)\n",
            "\n",
            "EPOCH: 13\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d616741c00e7420fac92d039f5131332",
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              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0219, Accuracy: 9934/10000 (99.34%)\n",
            "\n",
            "EPOCH: 14\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8c40212417704b678d788e8a5720a5ad",
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              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0209, Accuracy: 9934/10000 (99.34%)\n",
            "\n",
            "EPOCH: 15\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9fa845d85e4242bca9827728a4c1dea9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0228, Accuracy: 9922/10000 (99.22%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "drokW8wWODKq",
        "colab_type": "text"
      },
      "source": [
        "# Plot the model's learning progress\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87RaqGSEOWDe",
        "colab_type": "code",
        "outputId": "2d469a60-c9cc-426c-b6f3-671599a61bd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        }
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.set()\n",
        "plt.style.use(\"dark_background\")\n",
        "\n",
        "fig, axs = plt.subplots(2,2,figsize=(15,10))\n",
        "axs[0, 0].plot(train_losses)\n",
        "axs[0, 0].set_title(\"Training Loss\")\n",
        "axs[1, 0].plot(train_acc[4000:])\n",
        "axs[1, 0].set_title(\"Training Accuracy\")\n",
        "axs[0, 1].plot(test_losses)\n",
        "axs[0, 1].set_title(\"Test Loss\")\n",
        "axs[1, 1].plot(test_acc)\n",
        "axs[1, 1].set_title(\"Test Accuracy\")\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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39y9zvIkTJ2L37t3IE7nJ0qjiIDMzQ4vWSlFzEBFR1elthE8QBKxbtw4nT54EAKxbtw5r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\n4oGPiIiIiIjIov4DPn8gJZdN6ScAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wjO3RK9UEnvF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}