EVA7_file_1.ipynb

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            "_view_count": null,
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            "_model_name": "LayoutModel",
            "justify_items": null,
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            "align_self": null,
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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/dhruv369/AI/blob/master/EVA7_file_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aO-7t1Y7-hV4"
      },
      "source": [
        "# Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8kH16rnZ7wt_"
      },
      "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": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ky3f_Odl-7um"
      },
      "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"
      },
      "source": [
        "# Train Phase transformations\n",
        "train_transforms = transforms.Compose([\n",
        "                                      #  transforms.Resize((28, 28)),\n",
        "                                      #  transforms.ColorJitter(brightness=0.10, contrast=0.1, saturation=0.10, hue=0.1),\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.Resize((28, 28)),\n",
        "                                      #  transforms.ColorJitter(brightness=0.10, contrast=0.1, saturation=0.10, hue=0.1),\n",
        "                                       transforms.ToTensor(),\n",
        "                                       transforms.Normalize((0.1307,), (0.3081,))\n",
        "                                       ])\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQciFYo2B1mO"
      },
      "source": [
        "# Dataset and Creating Train/Test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_4A84rlfDA23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492,
          "referenced_widgets": [
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            "0af4bc6e9ace464f9eb54e34047cc27b",
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          ]
        },
        "outputId": "1cfc9177-416c-4ffc-827c-216f5dde17ab"
      },
      "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": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
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              "model_id": "f678e40822b9485bbde216ceddb06d26",
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "095e5873783f4a23af2e8118b7744f67",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "93b73abe535944c6ba3572bdbd0590d8",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgldp_3-Dn0c"
      },
      "source": [
        "# Dataloader Arguments & Test/Train Dataloaders\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8OLDR79DrHG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08febe16-11af-46be-edba-cabefe6933ab"
      },
      "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",
        "# 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": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CUDA Available? True\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-TFjoFekE_va"
      },
      "source": [
        "# Data Statistics\n",
        "\n",
        "It is important to know your data very well. Let's check some of the statistics around our data and how it actually looks like"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWZPPo3yEHDW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "outputId": "a3db38f3-3061-42b7-b6ca-d2c11e5733fe"
      },
      "source": [
        "# We'd need to convert it into Numpy! Remember above we have converted it into tensors already\n",
        "train_data = train.train_data\n",
        "train_data = train.transform(train_data.numpy())\n",
        "\n",
        "print('[Train]')\n",
        "print(' - Numpy Shape:', train.train_data.cpu().numpy().shape)\n",
        "print(' - Tensor Shape:', train.train_data.size())\n",
        "print(' - min:', torch.min(train_data))\n",
        "print(' - max:', torch.max(train_data))\n",
        "print(' - mean:', torch.mean(train_data))\n",
        "print(' - std:', torch.std(train_data))\n",
        "print(' - var:', torch.var(train_data))\n",
        "\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "print(images.shape)\n",
        "print(labels.shape)\n",
        "\n",
        "# Let's visualize some of the images\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.imshow(images[0].numpy().squeeze(), cmap='gray_r')\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:62: UserWarning: train_data has been renamed data\n",
            "  warnings.warn(\"train_data has been renamed data\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Train]\n",
            " - Numpy Shape: (60000, 28, 28)\n",
            " - Tensor Shape: torch.Size([60000, 28, 28])\n",
            " - min: tensor(-0.4242)\n",
            " - max: tensor(2.8215)\n",
            " - mean: tensor(-0.0001)\n",
            " - std: tensor(1.0000)\n",
            " - var: tensor(1.0001)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([128, 1, 28, 28])\n",
            "torch.Size([128])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f63cc481850>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7l9lNaWYKuik"
      },
      "source": [
        "## MORE\n",
        "\n",
        "It is important that we view as many images as possible. This is required to get some idea on image augmentation later on"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hXXAg8hbK16u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "outputId": "d097d72f-d3d6-4715-f2cc-8bf70243da9a"
      },
      "source": [
        "figure = plt.figure()\n",
        "num_of_images = 60\n",
        "for index in range(1, num_of_images + 1):\n",
        "    plt.subplot(6, 10, index)\n",
        "    plt.axis('off')\n",
        "    plt.imshow(images[index].numpy().squeeze(), cmap='gray_r')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 60 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubQL3H6RJL3h"
      },
      "source": [
        "# The model\n",
        "Let's start with the model we first saw"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7FXQlB9kH1ov"
      },
      "source": [
        "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=32, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.ReLU()\n",
        "        ) # output_size = 26\n",
        "\n",
        "        # CONVOLUTION BLOCK 1\n",
        "        self.convblock2 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.ReLU()\n",
        "        ) # output_size = 24\n",
        "        self.convblock3 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.ReLU()\n",
        "        ) # output_size = 22\n",
        "\n",
        "        # TRANSITION BLOCK 1\n",
        "        self.pool1 = nn.MaxPool2d(2, 2) # output_size = 11\n",
        "        self.convblock4 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=128, out_channels=32, kernel_size=(1, 1), padding=0, bias=False),\n",
        "            nn.ReLU()\n",
        "        ) # output_size = 11\n",
        "\n",
        "        # CONVOLUTION BLOCK 2\n",
        "        self.convblock5 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.ReLU()\n",
        "        ) # output_size = 9\n",
        "        self.convblock6 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.ReLU()\n",
        "        ) # output_size = 7\n",
        "\n",
        "        # OUTPUT BLOCK\n",
        "        self.convblock7 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=128, out_channels=10, kernel_size=(1, 1), padding=0, bias=False),\n",
        "            nn.ReLU()\n",
        "        ) # output_size = 7\n",
        "        self.convblock8 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=10, out_channels=10, kernel_size=(7, 7), padding=0, bias=False),\n",
        "            # nn.ReLU() NEVER!\n",
        "        ) # output_size = 1\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.convblock8(x)\n",
        "        x = x.view(-1, 10)\n",
        "        return F.log_softmax(x, dim=-1)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M3-vp8X9LCWo"
      },
      "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": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "296735f6-6136-4463-c124-5135fe46ac05"
      },
      "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": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torchsummary in /usr/local/lib/python3.7/dist-packages (1.5.1)\n",
            "cuda\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 32, 26, 26]             288\n",
            "              ReLU-2           [-1, 32, 26, 26]               0\n",
            "            Conv2d-3           [-1, 64, 24, 24]          18,432\n",
            "              ReLU-4           [-1, 64, 24, 24]               0\n",
            "            Conv2d-5          [-1, 128, 22, 22]          73,728\n",
            "              ReLU-6          [-1, 128, 22, 22]               0\n",
            "         MaxPool2d-7          [-1, 128, 11, 11]               0\n",
            "            Conv2d-8           [-1, 32, 11, 11]           4,096\n",
            "              ReLU-9           [-1, 32, 11, 11]               0\n",
            "           Conv2d-10             [-1, 64, 9, 9]          18,432\n",
            "             ReLU-11             [-1, 64, 9, 9]               0\n",
            "           Conv2d-12            [-1, 128, 7, 7]          73,728\n",
            "             ReLU-13            [-1, 128, 7, 7]               0\n",
            "           Conv2d-14             [-1, 10, 7, 7]           1,280\n",
            "             ReLU-15             [-1, 10, 7, 7]               0\n",
            "           Conv2d-16             [-1, 10, 1, 1]           4,900\n",
            "================================================================\n",
            "Total params: 194,884\n",
            "Trainable params: 194,884\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 2.20\n",
            "Params size (MB): 0.74\n",
            "Estimated Total Size (MB): 2.94\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1__x_SbrL7z3"
      },
      "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"
      },
      "source": [
        "from tqdm import tqdm\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)\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 backpropragation because PyTorch accumulates the gradients on subsequent backward passes. \n",
        "    # Because of this, when you start your training loop, ideally you should 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",
        "    # Update pbar-tqdm\n",
        "    \n",
        "    pred = y_pred.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n",
        "    correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "    processed += len(data)\n",
        "\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",
        "            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\n",
        "            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\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": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "drokW8wWODKq"
      },
      "source": [
        "# Let's Train and test our model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMCFxeAKOB53",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79f729a5-189b-433a-a8f1-2cefae342a48"
      },
      "source": [
        "model =  Net().to(device)\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
        "EPOCHS = 20\n",
        "for epoch in range(EPOCHS):\n",
        "    print(\"EPOCH:\", epoch)\n",
        "    train(model, device, train_loader, optimizer, epoch)\n",
        "    test(model, device, test_loader)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EPOCH: 0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0/469 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n",
            "Loss=0.19667784869670868 Batch_id=468 Accuracy=65.61: 100%|██████████| 469/469 [00:20<00:00, 22.41it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.2273, Accuracy: 9312/10000 (93.12%)\n",
            "\n",
            "EPOCH: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.18412019312381744 Batch_id=468 Accuracy=95.11: 100%|██████████| 469/469 [00:20<00:00, 22.48it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.1059, Accuracy: 9686/10000 (96.86%)\n",
            "\n",
            "EPOCH: 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.16002525389194489 Batch_id=468 Accuracy=97.19: 100%|██████████| 469/469 [00:20<00:00, 22.38it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0722, Accuracy: 9777/10000 (97.77%)\n",
            "\n",
            "EPOCH: 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.02371351420879364 Batch_id=468 Accuracy=97.79: 100%|██████████| 469/469 [00:20<00:00, 22.43it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0512, Accuracy: 9847/10000 (98.47%)\n",
            "\n",
            "EPOCH: 4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.012967673130333424 Batch_id=468 Accuracy=98.20: 100%|██████████| 469/469 [00:20<00:00, 22.41it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0458, Accuracy: 9852/10000 (98.52%)\n",
            "\n",
            "EPOCH: 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.028913026675581932 Batch_id=468 Accuracy=98.44: 100%|██████████| 469/469 [00:20<00:00, 22.73it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0425, Accuracy: 9857/10000 (98.57%)\n",
            "\n",
            "EPOCH: 6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.06950093060731888 Batch_id=468 Accuracy=98.63: 100%|██████████| 469/469 [00:20<00:00, 22.52it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0413, Accuracy: 9864/10000 (98.64%)\n",
            "\n",
            "EPOCH: 7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.056731030344963074 Batch_id=468 Accuracy=98.77: 100%|██████████| 469/469 [00:20<00:00, 22.36it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0413, Accuracy: 9868/10000 (98.68%)\n",
            "\n",
            "EPOCH: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.008025695569813251 Batch_id=468 Accuracy=98.86: 100%|██████████| 469/469 [00:20<00:00, 22.47it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0411, Accuracy: 9876/10000 (98.76%)\n",
            "\n",
            "EPOCH: 9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.2428794503211975 Batch_id=468 Accuracy=98.91: 100%|██████████| 469/469 [00:20<00:00, 22.43it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0382, Accuracy: 9878/10000 (98.78%)\n",
            "\n",
            "EPOCH: 10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.11666304618120193 Batch_id=468 Accuracy=98.98: 100%|██████████| 469/469 [00:20<00:00, 22.54it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0343, Accuracy: 9897/10000 (98.97%)\n",
            "\n",
            "EPOCH: 11\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.006989303510636091 Batch_id=468 Accuracy=99.05: 100%|██████████| 469/469 [00:20<00:00, 22.48it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0376, Accuracy: 9878/10000 (98.78%)\n",
            "\n",
            "EPOCH: 12\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.08460808545351028 Batch_id=468 Accuracy=99.16: 100%|██████████| 469/469 [00:20<00:00, 22.47it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0381, Accuracy: 9879/10000 (98.79%)\n",
            "\n",
            "EPOCH: 13\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.012115214951336384 Batch_id=468 Accuracy=99.27: 100%|██████████| 469/469 [00:20<00:00, 22.45it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0341, Accuracy: 9891/10000 (98.91%)\n",
            "\n",
            "EPOCH: 14\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.038398560136556625 Batch_id=468 Accuracy=99.21: 100%|██████████| 469/469 [00:20<00:00, 22.41it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0363, Accuracy: 9891/10000 (98.91%)\n",
            "\n",
            "EPOCH: 15\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.008318337611854076 Batch_id=468 Accuracy=99.31: 100%|██████████| 469/469 [00:20<00:00, 22.53it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0362, Accuracy: 9882/10000 (98.82%)\n",
            "\n",
            "EPOCH: 16\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.007647501304745674 Batch_id=468 Accuracy=99.31: 100%|██████████| 469/469 [00:21<00:00, 22.29it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0329, Accuracy: 9891/10000 (98.91%)\n",
            "\n",
            "EPOCH: 17\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.007882175035774708 Batch_id=468 Accuracy=99.39: 100%|██████████| 469/469 [00:21<00:00, 22.16it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0391, Accuracy: 9876/10000 (98.76%)\n",
            "\n",
            "EPOCH: 18\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.04435967281460762 Batch_id=468 Accuracy=99.43: 100%|██████████| 469/469 [00:20<00:00, 22.52it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0349, Accuracy: 9895/10000 (98.95%)\n",
            "\n",
            "EPOCH: 19\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loss=0.005825518164783716 Batch_id=468 Accuracy=99.50: 100%|██████████| 469/469 [00:21<00:00, 22.17it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0366, Accuracy: 9884/10000 (98.84%)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87RaqGSEOWDe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "outputId": "bbcbe456-22fb-4f48-a4a6-477550265a36"
      },
      "source": [
        "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)\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\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Test Accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dwlJ75YTIafb"
      },
      "source": [
        "# Target\n",
        "# 99.4% validation accuracy\n",
        "# Less than 10k Parameters\n",
        "# Less than 15 Epochs\n",
        "# setp 1 :\n",
        "#     Total params: 194,884\n",
        "#     Best Training Accuracy: 99.50\n",
        "#     Best Test Accuracy: 98.84\n",
        "#comments \n",
        "#     Parameters are to high - reduce it\n",
        "#     Model overfit , resolved that as well\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}