AIF_lab9.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-f8_dQH1t3Zx"
      },
      "source": [
        "# NN & CNN\n",
        "In this laboratory we will be looking at Fully Connected Neural Networks and Convolutional Neural Networks using Pytorch.\n",
        "\n",
        "Make sure to change the Colab Runtime to GPU for this laboratory.\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YiodjuFPpc9f"
      },
      "source": [
        "# Load and Visualize the [Data](https://pytorch.org/vision/stable/datasets.html) - [MNIST Dataset](https://en.wikipedia.org/wiki/MNIST_database)\n",
        "Downloading may take a few moments, and you should see your progress as the data is loading. You may also choose to change the batch_size if you want to load more data at a time.\n",
        "\n",
        "This cell will create DataLoaders for each of our datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "FXhypvUvm2P-"
      },
      "outputs": [],
      "source": [
        "# import libraries\n",
        "import torch\n",
        "import numpy as np\n",
        "cuda = torch.device('cuda')\n",
        "import statistics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "kplDN_ZR-DcC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f31120e-bf06-46ed-f477-3b58029f674b"
      },
      "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/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 9912422/9912422 [00:00<00:00, 242729684.29it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/mnist/MNIST/raw/train-images-idx3-ubyte.gz to ./data/mnist/MNIST/raw\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\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/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 28881/28881 [00:00<00:00, 100194949.40it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/mnist/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/mnist/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1648877/1648877 [00:00<00:00, 48309499.97it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/mnist/MNIST/raw\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\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/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4542/4542 [00:00<00:00, 1357939.18it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/mnist/MNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "from torchvision.datasets import MNIST\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "train_transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0], [1])\n",
        "])\n",
        "\n",
        "valid_transform = train_transform\n",
        "\n",
        "train_set = MNIST('./data/mnist', train=True, download=True, transform=train_transform)\n",
        "valid_set = MNIST('./data/mnist', train=False, download=True, transform=valid_transform)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "5bSz2S6d-Vuf"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "train_loader = DataLoader(train_set, batch_size=128, num_workers=0, shuffle=True)\n",
        "valid_loader = DataLoader(valid_set, batch_size=512, num_workers=0, shuffle=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCsg_G83prRX"
      },
      "source": [
        "### Visualize a Batch of Training Data\n",
        "The first step in a classification task is to take a look at the data, make sure it is loaded in correctly, then make any initial observations about patterns in that data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "9ZrVf3eF-YiP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "7390f30f-6461-480e-d770-d1000e1bea65"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x400 with 20 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# obtain one batch of training images - 20 images\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = next(dataiter)\n",
        "images = images.numpy()\n",
        "\n",
        "# plot the images in the batch, along with the corresponding labels for 20 images\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "for idx in np.arange(20):\n",
        "    ax = fig.add_subplot(2, int(20/2), idx+1, xticks=[], yticks=[])\n",
        "    ax.imshow(np.squeeze(images[idx]), cmap='gray')\n",
        "    # print out the correct label for each image\n",
        "    # .item() gets the value contained in a Tensor\n",
        "    ax.set_title(str(labels[idx].item()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5sggH_fxpwOK"
      },
      "source": [
        "### View an Image in More Detail"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "gdoDa9xC-7lN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 984
        },
        "outputId": "8c40146b-7c12-481a-cc5b-15c413001630"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "img = np.squeeze(images[0]) # Take the 1st image from the dataset\n",
        "\n",
        "fig = plt.figure(figsize = (12,12))\n",
        "ax = fig.add_subplot(111)\n",
        "ax.imshow(img, cmap='gray')\n",
        "width, height = img.shape\n",
        "thresh = img.max()/2.5\n",
        "for x in range(width):\n",
        "    for y in range(height):\n",
        "        val = round(img[x][y],2) if img[x][y] !=0 else 0\n",
        "        ax.annotate(str(val), xy=(y,x),\n",
        "                    horizontalalignment='center',\n",
        "                    verticalalignment='center',\n",
        "                    color='white' if img[x][y]<thresh else 'black')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAkyu27Rqsqv"
      },
      "source": [
        "# Define the [Network Architecture](https://pytorch.org/docs/stable/nn.html)\n",
        "The architecture will be responsible for seeing as input a 784-dim Tensor of pixel values for each image, and producing a Tensor of length 10 (our number of classes) that indicates the class scores for an input image. This particular example uses two hidden layers and dropout to avoid overfitting.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DfBWV00hE2fW"
      },
      "source": [
        "\n",
        "### Simple Neural Network\n",
        "\n",
        "We will start with the basic example of a shallow NN: an input layer, two hidden layers and the output layer. We'll use dropout to avoid overfitting.\n",
        "\n",
        "Each MNIST training example consists of a 28x28 pixels image in grayscale (1 channel), that is turned into a 784-elements vector. The input layer has 784 neurons, we have a hidden layer of 64 neurons and a second hidden layer of 32 neurons. The output layer has 1 neuron for each one of the classes, in this case 10 neurons (10 digits - 0, 1 2, 3, etc).\n",
        "\n",
        "To implement our neural network, we create the class **BasicNN** and inherit the methods and properties from the Module class (**nn.Module**):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "pDMhQNsKqt9R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a306ca8-ad33-4039-fb68-7ade31ea0153"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BasicNN(\n",
            "  (fc1): Linear(in_features=784, out_features=64, bias=True)\n",
            "  (fc2): Linear(in_features=64, out_features=32, bias=True)\n",
            "  (fc3): Linear(in_features=32, out_features=10, bias=True)\n",
            "  (drop): Dropout(p=0.2, inplace=False)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class BasicNN(nn.Module):\n",
        "\n",
        "    def __init__(self, input_size, num_classes):\n",
        "\n",
        "        super(BasicNN, self).__init__()\n",
        "        # number of hidden nodes in each layer\n",
        "        hidden_size_1 = 64\n",
        "        hidden_size_2 = 32\n",
        "\n",
        "        # linear layer (784 -> hidden_size_1)\n",
        "        self.fc1 = nn.Linear(input_size, hidden_size_1)\n",
        "\n",
        "        # linear layer (hidden_size_1 -> hidden_size_2)\n",
        "        self.fc2 = nn.Linear(hidden_size_1, hidden_size_2)\n",
        "\n",
        "        # linear layer (hidden_size_2 -> num_classes)\n",
        "        self.fc3 = nn.Linear(hidden_size_2, num_classes)\n",
        "\n",
        "        # dropout layer (p=0.2)\n",
        "        # dropout prevents overfitting of data\n",
        "        self.drop = nn.Dropout(0.2)\n",
        "\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        # flatten image input\n",
        "        x = x.reshape(-1, 28 * 28)\n",
        "        # add hidden layer, with relu activation function\n",
        "        x = F.relu(self.fc1(x))\n",
        "        # add dropout layer\n",
        "        x = self.drop(x)\n",
        "        # add hidden layer, with relu activation function\n",
        "        x = F.relu(self.fc2(x))\n",
        "        # add dropout layer\n",
        "        x = self.drop(x)\n",
        "        # add output layer\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "# initialize the NN\n",
        "cuda = torch.device('cuda')\n",
        "model = BasicNN(input_size=28 * 28, num_classes=10)\n",
        "model.to(cuda)\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3wSWp-H0u0bU"
      },
      "source": [
        "### Train the Network\n",
        "The steps for training/learning from a batch of data are described in the comments below:\n",
        "\n",
        "* Clear the gradients of all optimized variables\n",
        "* Forward pass: compute predicted outputs by passing inputs to the model\n",
        "* Calculate the loss\n",
        "* Backward pass: compute gradient of the loss with respect to model parameters\n",
        "* Perform a single optimization step (update weights)\n",
        "* Update average training loss\n",
        "\n",
        "The following loop trains for 50 epochs; take a look at how the values for the training loss decrease over time. We want it to decrease while also avoiding overfitting the training data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "iMaCnOz3GvDl"
      },
      "outputs": [],
      "source": [
        "def train(model, loss_fn, optimizer):\n",
        "\n",
        "    # https://pytorch.org/docs/stable/nn.html#torch.nn.Module.train\n",
        "    # set the module in training mode\n",
        "    model.train()\n",
        "\n",
        "    train_batch_losses = []\n",
        "\n",
        "    for batch, labels in train_loader:\n",
        "\n",
        "        #send the training data to the GPU\n",
        "        batch = batch.to(cuda)\n",
        "        labels = labels.to(cuda)\n",
        "\n",
        "        #set all gradient to zero\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        #forward propagation\n",
        "        y_pred = model(batch)\n",
        "\n",
        "        #calculate loss\n",
        "        loss = loss_fn(y_pred, labels)\n",
        "\n",
        "        #backpropagation\n",
        "        loss.backward()\n",
        "\n",
        "        #update the parameters (weights and biases)\n",
        "        optimizer.step()\n",
        "\n",
        "        train_batch_losses.append(float(loss))\n",
        "\n",
        "        mean_loss = statistics.mean(train_batch_losses)\n",
        "\n",
        "    return mean_loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "kE-t9Wz2G4PI"
      },
      "outputs": [],
      "source": [
        "def validate(model, loss_fn, optimizer):\n",
        "\n",
        "    # set the model in validation mode\n",
        "    model.eval()\n",
        "\n",
        "    # save predictions for later\n",
        "    predictions = []\n",
        "\n",
        "    # stop tracking the parameters for backpropagation\n",
        "    with torch.no_grad():\n",
        "\n",
        "        validation_batch_losses = []\n",
        "\n",
        "        for batch, labels in valid_loader:\n",
        "\n",
        "            # send the validation data to the GPU\n",
        "            batch = batch.to(cuda)\n",
        "            labels = labels.to(cuda)\n",
        "\n",
        "            # forward propagation\n",
        "            labels_pred = model(batch)\n",
        "\n",
        "            # calculate the loss\n",
        "            loss = loss_fn(labels_pred, labels)\n",
        "\n",
        "            validation_batch_losses.append(float(loss))\n",
        "\n",
        "            mean_loss = statistics.mean(validation_batch_losses)\n",
        "\n",
        "    return mean_loss\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "YbJuQkGoG6jz"
      },
      "outputs": [],
      "source": [
        "def accuracy(model, loader):\n",
        "    correct = 0\n",
        "    total = 0\n",
        "\n",
        "    # set the model in validation mode to deactivate the dropout layer\n",
        "    model.eval()\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for batch, labels in loader:\n",
        "            batch = batch.to(cuda)\n",
        "            labels = labels.to(cuda)\n",
        "\n",
        "            labels_pred = model(batch)\n",
        "\n",
        "            _, predicted = torch.max(labels_pred.data, 1)\n",
        "\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "\n",
        "            return (100 * correct / total)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "XaA9aqKwG_de"
      },
      "outputs": [],
      "source": [
        "def training_stats(train_loss, train_accuracy, val_loss, val_accuracy):\n",
        "    print(('training loss: {:.3f} '\n",
        "           'training accuracy: {:.2f}% || '\n",
        "           'val. loss: {:.3f} '\n",
        "           'val. accuracy: {:.2f}%').format(train_loss, train_accuracy,\n",
        "                                            val_loss, val_accuracy))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1UfahN3jxhc"
      },
      "source": [
        "### Specify [Loss Function](https://pytorch.org/docs/stable/nn.html#loss-functions) and [Optimizer](https://pytorch.org/docs/stable/optim.html)\n",
        "It's recommended that you use cross-entropy loss for classification. If you look at the documentation (linked above), you can see that PyTorch's cross entropy function applies a softmax funtion to the output layer and then calculates the log loss."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "FWeaxEbcG_-C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98e63c27-3bb7-4f99-f15c-fa877fb92189"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch number  1\n",
            "training loss: 2.186 training accuracy: 39.84% || val. loss: 1.927 val. accuracy: 41.80%\n",
            "Epoch number  2\n",
            "training loss: 1.583 training accuracy: 75.00% || val. loss: 1.085 val. accuracy: 67.19%\n",
            "Epoch number  3\n",
            "training loss: 1.038 training accuracy: 80.47% || val. loss: 0.713 val. accuracy: 78.12%\n",
            "Epoch number  4\n",
            "training loss: 0.808 training accuracy: 87.50% || val. loss: 0.555 val. accuracy: 83.59%\n",
            "Epoch number  5\n",
            "training loss: 0.683 training accuracy: 86.72% || val. loss: 0.475 val. accuracy: 85.94%\n",
            "Epoch number  6\n",
            "training loss: 0.616 training accuracy: 83.59% || val. loss: 0.427 val. accuracy: 87.70%\n",
            "Epoch number  7\n",
            "training loss: 0.563 training accuracy: 85.94% || val. loss: 0.394 val. accuracy: 88.87%\n",
            "Epoch number  8\n",
            "training loss: 0.524 training accuracy: 86.72% || val. loss: 0.366 val. accuracy: 89.65%\n",
            "Epoch number  9\n",
            "training loss: 0.494 training accuracy: 86.72% || val. loss: 0.345 val. accuracy: 90.23%\n",
            "Epoch number  10\n",
            "training loss: 0.471 training accuracy: 90.62% || val. loss: 0.328 val. accuracy: 91.02%\n"
          ]
        }
      ],
      "source": [
        "# initialize the NN\n",
        "cuda = torch.device('cuda')\n",
        "model = BasicNN(input_size=28 * 28, num_classes=10)\n",
        "model.to(cuda)\n",
        "\n",
        "# specify loss function (categorical cross-entropy)\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "# specify optimizer (stochastic gradient descent) and learning rate = 0.01\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n",
        "epochs = 10\n",
        "\n",
        "train_losses = []\n",
        "valid_losses = []\n",
        "\n",
        "for epoch in range(0, epochs):\n",
        "\n",
        "    print('Epoch number ', epoch + 1)\n",
        "\n",
        "    train_loss = train(model, loss_fn, optimizer)\n",
        "    train_losses.append(train_loss)\n",
        "    train_accuracy = accuracy(model, train_loader)\n",
        "\n",
        "    valid_loss = validate(model, loss_fn, optimizer)\n",
        "    valid_losses.append(valid_loss)\n",
        "    valid_accuracy = accuracy(model, valid_loader)\n",
        "\n",
        "    training_stats(train_loss, train_accuracy, valid_loss, valid_accuracy)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JWloJIDSMrJk"
      },
      "source": [
        "### Visualize Sample Test Results\n",
        "This cell displays test images and their labels in this format: `predicted (ground-truth)`. The text will be green for accurately classified examples and red for incorrect predictions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "St6f_lm14mak",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "b1196ddf-2db4-4c85-b1c0-fd0db6580e7b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x400 with 20 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# obtain one batch of test images\n",
        "dataiter = iter(valid_loader)\n",
        "images, labels = next(dataiter)\n",
        "images = images.to(cuda)\n",
        "labels = labels.to(cuda)\n",
        "\n",
        "# get sample outputs\n",
        "output = model(images)\n",
        "# convert output probabilities to predicted class\n",
        "_, preds = torch.max(output, 1)\n",
        "# prep images for display\n",
        "images = images.detach().cpu().numpy()\n",
        "\n",
        "# plot the images in the batch, along with predicted and true labels\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "for idx in np.arange(20):\n",
        "    ax = fig.add_subplot(2, int(20/2), idx+1, xticks=[], yticks=[])\n",
        "    ax.imshow(np.squeeze(images[idx]), cmap='gray')\n",
        "    ax.set_title(\"{} ({})\".format(str(preds[idx].item()), str(labels[idx].item())),\n",
        "                 color=(\"green\" if preds[idx]==labels[idx] else \"red\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T5DY9YPyN2_x"
      },
      "source": [
        "# CNNs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qjNVQ_o7N4gM"
      },
      "source": [
        "## Convolutional and Pooling Layers\n",
        "\n",
        "A convolutional layer using pyTorch:\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "torch.nn.Conv2d(num_in_channels, num_out_channels, kernel_size)\n",
        "```\n",
        "\n",
        "num_in_channels is the number of channels of the input tensor. If the previous layer is the input layer, num_in_channels is the number of channels of the image (3 channels for RGB images), otherwise num_in_channels is equal to the number of feature maps of the previous layer.\n",
        "\n",
        "num_out_channels is the number of filters (feature extractor) that this layer will apply over the image or feature maps generated by the previous layer.\n",
        "\n",
        "So for instance, if we have an RGB image and we are going to apply 32 filters of 3x3:\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "torch.nn.Conv2d(3, 32, 3)\n",
        "```\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a0liSxSZN_4T"
      },
      "source": [
        "## A Simple Convolutional Neural Network\n",
        "\n",
        "In our convnet we'll use the next structure:\n",
        "\n",
        "*input -> convolution -> pooling-> convolution -> pooling -> convolution* (convolutional base)\n",
        "\n",
        "*fully connected -> fully connected -> output*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ra_zov1KN_Il"
      },
      "outputs": [],
      "source": [
        "class BasicCNN(nn.Module):\n",
        "\n",
        "    def __init__(self, num_channels, num_classes):\n",
        "        super(BasicCNN, self).__init__()\n",
        "        # nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)\n",
        "        # for grayscale (e.g. 28 x 28 x 1) image inputs we have 1 input channel\n",
        "        self.conv1 = nn.Conv2d(num_channels, 32, 3, stride=1, padding=0)\n",
        "        self.conv2 = nn.Conv2d(32, 64, 3, stride=1, padding=0)\n",
        "        self.conv3 = nn.Conv2d(64, 64, 3, stride=1, padding=0)\n",
        "        self.pool1 = nn.MaxPool2d(2)\n",
        "        self.pool2 = nn.MaxPool2d(2)\n",
        "        self.fc1 = nn.Linear(3*3*64, 64, bias=True)\n",
        "        self.fc2 = nn.Linear(64, 10)\n",
        "\n",
        "    def forward(self, X):\n",
        "        x = F.relu(self.conv1(X))\n",
        "        x = self.pool1(x)\n",
        "        x = F.relu(self.conv2(x))\n",
        "        x = self.pool2(x)\n",
        "        x = F.relu(self.conv3(x))\n",
        "        x = x.reshape(-1, 3*3*64)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6tigD15eODb9"
      },
      "source": [
        "**Input Image**\n",
        "\n",
        "28 x 28 x 1 pixel values\n",
        "\n",
        "**Convolution #1**\n",
        "\n",
        "32 kernels of 3x3; *Width/Height:* (28 - 3 + 2x0) / 1 + 1 = 26; *Output dimensions:* (32, 26, 26)\n",
        "\n",
        "**Max Pooling #1**\n",
        "\n",
        "filter size = 2, stride = 2; *Width/Height:* (26 - 2) / 2 + 1 = 13; *Output dimensions:* (32, 13, 13)\n",
        "\n",
        "**Convolution #2**\n",
        "\n",
        "64 kernels of 3x3; *Width/Height:* (13 - 3 + 2x0) / 1 + 1 = 11; *Output dimensions:* (64, 11, 11)\n",
        "\n",
        "**Max Pooling #2**\n",
        "\n",
        "filter size = 2, stride = 2; *Width/Height:* (11 - 2) / 2 + 1 = 5; *Output dimensions:* (64, 5, 5)\n",
        "\n",
        "**Convolution #3**\n",
        "\n",
        "64 kernels of 3x3; *Width/Height:* (5 - 3 + 2x0) / 1 + 1 = 3; *Output dimensions:* (64, 3, 3)\n",
        "\n",
        "So at the end of the last convolutional layer we get a tensor of dimension (64, 3, 3). And since now we are going to feed it to out fully connected classifier, we need to convert it into a vector, and for that we use the reshape method:\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "x = x.reshape(-1, 3*3*64)\n",
        "```\n",
        "\n",
        "### Useful output size calculation\n",
        "**Convolution & Pooling**\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "fO3Vd-NzOFLu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "299777ec-6c3f-42ef-870f-e29149cb3f4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BasicCNN(\n",
              "  (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n",
              "  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
              "  (conv3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))\n",
              "  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (fc1): Linear(in_features=576, out_features=64, bias=True)\n",
              "  (fc2): Linear(in_features=64, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "cuda = torch.device('cuda')\n",
        "\n",
        "model = BasicCNN(1, 10)\n",
        "model.to(cuda)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "JM9MVsRjOXUZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9af349c-6eca-4025-82b2-e8e6d8a4f599"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch number  1\n",
            "training loss: 0.653 training accuracy: 90.62% || val. loss: 0.216 val. accuracy: 93.75%\n",
            "Epoch number  2\n",
            "training loss: 0.116 training accuracy: 96.09% || val. loss: 0.072 val. accuracy: 97.66%\n",
            "Epoch number  3\n",
            "training loss: 0.077 training accuracy: 100.00% || val. loss: 0.053 val. accuracy: 98.83%\n",
            "Epoch number  4\n",
            "training loss: 0.058 training accuracy: 99.22% || val. loss: 0.047 val. accuracy: 98.63%\n",
            "Epoch number  5\n",
            "training loss: 0.047 training accuracy: 97.66% || val. loss: 0.056 val. accuracy: 98.44%\n",
            "Epoch number  6\n",
            "training loss: 0.039 training accuracy: 98.44% || val. loss: 0.039 val. accuracy: 99.02%\n",
            "Epoch number  7\n",
            "training loss: 0.035 training accuracy: 99.22% || val. loss: 0.065 val. accuracy: 97.07%\n",
            "Epoch number  8\n",
            "training loss: 0.030 training accuracy: 100.00% || val. loss: 0.035 val. accuracy: 98.44%\n",
            "Epoch number  9\n",
            "training loss: 0.026 training accuracy: 97.66% || val. loss: 0.041 val. accuracy: 98.83%\n",
            "Epoch number  10\n",
            "training loss: 0.023 training accuracy: 99.22% || val. loss: 0.034 val. accuracy: 98.63%\n"
          ]
        }
      ],
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.1)\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "epochs = 10\n",
        "\n",
        "train_losses = []\n",
        "valid_losses = []\n",
        "\n",
        "for epoch in range(0, epochs):\n",
        "\n",
        "    print('Epoch number ', epoch + 1)\n",
        "\n",
        "    train_loss = train(model, loss_fn, optimizer)\n",
        "    train_losses.append(train_loss)\n",
        "    train_accuracy = accuracy(model, train_loader)\n",
        "\n",
        "    valid_loss = validate(model, loss_fn, optimizer)\n",
        "    valid_losses.append(valid_loss)\n",
        "    valid_accuracy = accuracy(model, valid_loader)\n",
        "\n",
        "    training_stats(train_loss, train_accuracy, valid_loss, valid_accuracy)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "rVynt4KvS7Eo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "aba41d83-712e-4ba2-8425-d94412b73cb3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x400 with 20 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# obtain one batch of test images\n",
        "dataiter = iter(valid_loader)\n",
        "images, labels = next(dataiter)\n",
        "images = images.to(cuda)\n",
        "labels = labels.to(cuda)\n",
        "\n",
        "# get sample outputs\n",
        "output = model(images)\n",
        "# convert output probabilities to predicted class\n",
        "_, preds = torch.max(output, 1)\n",
        "# prep images for display\n",
        "images = images.detach().cpu().numpy()\n",
        "\n",
        "# plot the images in the batch, along with predicted and true labels\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "for idx in np.arange(20):\n",
        "    ax = fig.add_subplot(2, int(20/2), idx+1, xticks=[], yticks=[])\n",
        "    ax.imshow(np.squeeze(images[idx]), cmap='gray')\n",
        "    ax.set_title(\"{} ({})\".format(str(preds[idx].item()), str(labels[idx].item())),\n",
        "                 color=(\"green\" if preds[idx]==labels[idx] else \"red\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_sx-xprBZQwA"
      },
      "source": [
        "# Classifying [CIFAR10](https://en.wikipedia.org/wiki/CIFAR-10) dataset using CNNs\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "T7XdXadTTJQu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02812666-d297-4c69-a196-300c1d52b782"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./cifar10/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 170498071/170498071 [00:05<00:00, 29913429.72it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./cifar10/cifar-10-python.tar.gz to ./cifar10\n",
            "Files already downloaded and verified\n",
            "(50000, 32, 32, 3)\n",
            "(10000, 32, 32, 3)\n"
          ]
        }
      ],
      "source": [
        "from torchvision.datasets import CIFAR10\n",
        "\n",
        "dataset_transform = transforms.Compose([\n",
        "    transforms.ToTensor()\n",
        "])\n",
        "\n",
        "train_set = CIFAR10('./cifar10', train=True, download=True, transform=dataset_transform)\n",
        "valid_set = CIFAR10('./cifar10', train=False, download=True, transform=dataset_transform)\n",
        "\n",
        "print(train_set.data.shape)\n",
        "print(valid_set.data.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "44WQS9HrTXK0"
      },
      "outputs": [],
      "source": [
        "train_loader = DataLoader(train_set, batch_size=128, num_workers=0, shuffle=True)\n",
        "valid_loader = DataLoader(valid_set, batch_size=512, num_workers=0, shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "rkcE1SgUT1lT"
      },
      "outputs": [],
      "source": [
        "class BasicCNN(nn.Module):\n",
        "\n",
        "    def __init__(self, num_channels, num_classes):\n",
        "        super(BasicCNN, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(num_channels, 32, 3, stride=1, padding=0)\n",
        "        self.conv2 = nn.Conv2d(32, 64, 3, stride=1, padding=0)\n",
        "        self.conv3 = nn.Conv2d(64, 64, 3, stride=1, padding=0)\n",
        "        self.pool1 = nn.MaxPool2d(2)\n",
        "        self.pool2 = nn.MaxPool2d(2)\n",
        "        self.fc1 = nn.Linear(4*4*64, 64, bias=True)\n",
        "        self.fc2 = nn.Linear(64, 10)\n",
        "\n",
        "    def forward(self, X):\n",
        "        x = F.relu(self.conv1(X))\n",
        "        x = self.pool1(x)\n",
        "        x = F.relu(self.conv2(x))\n",
        "        x = self.pool2(x)\n",
        "        x = F.relu(self.conv3(x))\n",
        "        x = x.reshape(-1, 4*4*64)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        return x\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "NLMJpA_aTXp1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3f9cd94-daaa-4a2a-88bf-7dde18456650"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch number  1\n",
            "training loss: 2.140 training accuracy: 23.44% || val. loss: 2.116 val. accuracy: 25.20%\n",
            "Epoch number  2\n",
            "training loss: 1.817 training accuracy: 42.97% || val. loss: 1.649 val. accuracy: 41.02%\n",
            "Epoch number  3\n",
            "training loss: 1.614 training accuracy: 43.75% || val. loss: 1.531 val. accuracy: 41.80%\n",
            "Epoch number  4\n",
            "training loss: 1.445 training accuracy: 47.66% || val. loss: 1.393 val. accuracy: 46.29%\n",
            "Epoch number  5\n",
            "training loss: 1.320 training accuracy: 48.44% || val. loss: 1.328 val. accuracy: 53.91%\n",
            "Epoch number  6\n",
            "training loss: 1.219 training accuracy: 64.84% || val. loss: 1.171 val. accuracy: 57.81%\n",
            "Epoch number  7\n",
            "training loss: 1.134 training accuracy: 68.75% || val. loss: 1.115 val. accuracy: 62.11%\n",
            "Epoch number  8\n",
            "training loss: 1.056 training accuracy: 50.00% || val. loss: 1.306 val. accuracy: 52.93%\n",
            "Epoch number  9\n",
            "training loss: 0.987 training accuracy: 67.19% || val. loss: 1.111 val. accuracy: 59.57%\n",
            "Epoch number  10\n",
            "training loss: 0.931 training accuracy: 67.97% || val. loss: 1.043 val. accuracy: 63.87%\n",
            "Epoch number  11\n",
            "training loss: 0.869 training accuracy: 64.84% || val. loss: 1.002 val. accuracy: 65.43%\n",
            "Epoch number  12\n",
            "training loss: 0.821 training accuracy: 69.53% || val. loss: 1.002 val. accuracy: 61.91%\n",
            "Epoch number  13\n",
            "training loss: 0.781 training accuracy: 77.34% || val. loss: 1.021 val. accuracy: 61.52%\n",
            "Epoch number  14\n",
            "training loss: 0.731 training accuracy: 75.00% || val. loss: 0.953 val. accuracy: 66.60%\n",
            "Epoch number  15\n",
            "training loss: 0.693 training accuracy: 75.00% || val. loss: 0.996 val. accuracy: 66.21%\n",
            "Epoch number  16\n",
            "training loss: 0.665 training accuracy: 73.44% || val. loss: 0.928 val. accuracy: 69.14%\n",
            "Epoch number  17\n",
            "training loss: 0.621 training accuracy: 80.47% || val. loss: 0.961 val. accuracy: 67.77%\n",
            "Epoch number  18\n",
            "training loss: 0.585 training accuracy: 82.81% || val. loss: 0.939 val. accuracy: 69.92%\n",
            "Epoch number  19\n",
            "training loss: 0.551 training accuracy: 87.50% || val. loss: 0.983 val. accuracy: 68.95%\n",
            "Epoch number  20\n",
            "training loss: 0.521 training accuracy: 81.25% || val. loss: 1.113 val. accuracy: 68.16%\n",
            "Best validation accuracy 69.921875\n"
          ]
        }
      ],
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "cuda = torch.device('cuda')\n",
        "model = BasicCNN(3, 10)\n",
        "model.to(cuda)\n",
        "\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.1)\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "epochs = 20\n",
        "\n",
        "train_losses = []\n",
        "valid_losses = []\n",
        "\n",
        "best_valid_acc = 0\n",
        "\n",
        "for epoch in range(0, epochs):\n",
        "\n",
        "    print('Epoch number ', epoch + 1)\n",
        "\n",
        "    train_loss = train(model, loss_fn, optimizer)\n",
        "    train_losses.append(train_loss)\n",
        "    train_accuracy = accuracy(model, train_loader)\n",
        "\n",
        "    valid_loss = validate(model, loss_fn, optimizer)\n",
        "    valid_losses.append(valid_loss)\n",
        "    valid_accuracy = accuracy(model, valid_loader)\n",
        "    if best_valid_acc < valid_accuracy:\n",
        "        best_valid_acc = valid_accuracy\n",
        "\n",
        "    training_stats(train_loss, train_accuracy, valid_loss, valid_accuracy)\n",
        "print('Best validation accuracy', best_valid_acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "8wEXqOLlUCCP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "c2ca7fb0-116b-4916-b4b5-24ee6cd2912c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([3, 32, 32])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x400 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# obtain one batch of test images\n",
        "dataiter = iter(valid_loader)\n",
        "images, labels = next(dataiter)\n",
        "images = images.to(cuda)\n",
        "labels = labels.to(cuda)\n",
        "\n",
        "print(images[0].shape)\n",
        "\n",
        "# get sample outputs\n",
        "output = model(images)\n",
        "# convert output probabilities to predicted class\n",
        "_, preds = torch.max(output, 1)\n",
        "# prep images for display\n",
        "images = images.detach().cpu().numpy()\n",
        "\n",
        "# plot the images in the batch, along with predicted and true labels\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "for idx in np.arange(20):\n",
        "    ax = fig.add_subplot(2, int(20/2), idx+1, xticks=[], yticks=[])\n",
        "    # img = img.swapaxes(1,2)\n",
        "    ax.imshow(images[idx].swapaxes(0,2).swapaxes(0,1))\n",
        "    ax.set_title(\"{} ({})\".format(str(preds[idx].item()), str(labels[idx].item())),\n",
        "                 color=(\"green\" if preds[idx]==labels[idx] else \"red\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wxQflC1fYBXY"
      },
      "source": [
        "### CIFAR10 classes\n",
        "```\n",
        "airplane : 0\n",
        "automobile : 1\n",
        "bird : 2\n",
        "cat : 3\n",
        "deer : 4\n",
        "dog : 5\n",
        "frog : 6\n",
        "horse : 7\n",
        "ship : 8\n",
        "truck : 9\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B3v-jFaajCZ3"
      },
      "source": [
        "# Assignments - TO DO\n",
        "\n",
        "1. Create a **Fully Connected Neural Network** yourselves that obtains more than **95%** accuracy on **MNIST** data **after 10 epochs**\n",
        "\n",
        "2. Create a **Convolutional Neural Network** yourselves that obtains more than **67%** accuracy on **CIFAR10** data **after 10 epochs**\n",
        "\n",
        "Ideas: dropout layers, wider networks, deeper networks, imitating famous network architectures."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "CustomDeeperNN:\n",
        "- utilizes a fully connected neural network architecture where the hidden layers' sizes are defined explicitly:\n",
        "four hidden layers with sizes 512, 256, 128, and 64\n",
        "- uses the Adam optimizer with a learning rate of 0.0005\n",
        "BasicNN\n",
        "- is a simpler fully connected neural network with three layers where the hidden layers' sizes are predefined as 64 and 32\n",
        "- uses the SGD optimizer with a learning rate of 0.1\n",
        "\"\"\"\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torchvision.datasets import MNIST\n",
        "from torch.utils.data import DataLoader\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "# Device configuration\n",
        "cuda = torch.device('cuda')\n",
        "\n",
        "# Define the neural network architecture\n",
        "class CustomDeeperNN(nn.Module):\n",
        "    def __init__(self, input_size, hidden_sizes, output_size, dropout_rate=0.2):\n",
        "        super(CustomDeeperNN, self).__init__()\n",
        "        layers = []\n",
        "        previous_size = input_size\n",
        "        for size in hidden_sizes:\n",
        "            layers.append(nn.Linear(previous_size, size))\n",
        "            layers.append(nn.ReLU())\n",
        "            layers.append(nn.Dropout(dropout_rate))\n",
        "            previous_size = size\n",
        "        layers.append(nn.Linear(previous_size, output_size))\n",
        "        self.fc_layers = nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(x.size(0), -1)\n",
        "        x = self.fc_layers(x)\n",
        "        return x\n",
        "\n",
        "# Transformations for the MNIST dataset\n",
        "train_transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0], [1])\n",
        "])\n",
        "\n",
        "# Load the MNIST dataset\n",
        "train_set = MNIST('./data/mnist', train=True, download=True, transform=train_transform)\n",
        "valid_set = MNIST('./data/mnist', train=False, download=True, transform=train_transform)\n",
        "\n",
        "# DataLoaders\n",
        "train_loader = DataLoader(train_set, batch_size=128, num_workers=0, shuffle=True)\n",
        "valid_loader = DataLoader(valid_set, batch_size=512, num_workers=0, shuffle=False)\n",
        "\n",
        "# Model selection\n",
        "model = CustomDeeperNN(input_size=28 * 28, hidden_sizes=[512, 256, 128, 64], output_size=10, dropout_rate=0.3)\n",
        "model.to(cuda)\n",
        "\n",
        "# Use Adam optimizer with a smaller learning rate\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.0005)\n",
        "\n",
        "# Train for 10 epochs as before\n",
        "epochs = 10\n",
        "train_losses = []\n",
        "valid_losses = []\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    train_loss = train(model, loss_fn, optimizer)\n",
        "    train_losses.append(train_loss)\n",
        "    train_accuracy = accuracy(model, train_loader)\n",
        "\n",
        "    valid_loss = validate(model, loss_fn, optimizer)\n",
        "    valid_losses.append(valid_loss)\n",
        "    valid_accuracy = accuracy(model, valid_loader)\n",
        "\n",
        "    print(f'Epoch {epoch + 1}/{epochs}')\n",
        "    print(f'Training loss: {train_loss:.3f} Training accuracy: {train_accuracy:.2f}% || '\n",
        "          f'Validation loss: {valid_loss:.3f} Validation accuracy: {valid_accuracy:.2f}%')\n"
      ],
      "metadata": {
        "id": "jNK_Qpbnd-kk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5bb083f-20cf-48d2-e56b-ce48a9f36743"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "Training loss: 0.622 Training accuracy: 96.09% || Validation loss: 0.187 Validation accuracy: 94.73%\n",
            "Epoch 2/10\n",
            "Training loss: 0.208 Training accuracy: 96.09% || Validation loss: 0.118 Validation accuracy: 97.27%\n",
            "Epoch 3/10\n",
            "Training loss: 0.148 Training accuracy: 96.88% || Validation loss: 0.100 Validation accuracy: 97.85%\n",
            "Epoch 4/10\n",
            "Training loss: 0.116 Training accuracy: 99.22% || Validation loss: 0.087 Validation accuracy: 98.05%\n",
            "Epoch 5/10\n",
            "Training loss: 0.100 Training accuracy: 98.44% || Validation loss: 0.095 Validation accuracy: 97.85%\n",
            "Epoch 6/10\n",
            "Training loss: 0.085 Training accuracy: 99.22% || Validation loss: 0.081 Validation accuracy: 98.24%\n",
            "Epoch 7/10\n",
            "Training loss: 0.075 Training accuracy: 98.44% || Validation loss: 0.074 Validation accuracy: 98.05%\n",
            "Epoch 8/10\n",
            "Training loss: 0.065 Training accuracy: 100.00% || Validation loss: 0.067 Validation accuracy: 98.24%\n",
            "Epoch 9/10\n",
            "Training loss: 0.061 Training accuracy: 98.44% || Validation loss: 0.077 Validation accuracy: 97.66%\n",
            "Epoch 10/10\n",
            "Training loss: 0.054 Training accuracy: 99.22% || Validation loss: 0.070 Validation accuracy: 98.24%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "BASICNN:\n",
        "\n",
        "Convolutional layers:\n",
        "- has 3 convolutional layers: self.conv1, self.conv2, and self.conv3 (3x3 kernel, stride: 1 and padding: 0)\n",
        "- output channels: 32, 64, and 64\n",
        "Pooling Layers:\n",
        "- two max-pooling layers: self.pool1 and self.pool2 (2x2 kernel with a stride of 2)\n",
        "Fully Connected Layers:\n",
        "- has 2: self.fc1 and self.fc2:\n",
        "- self.fc1 - input size: 3x3x64; output size: 64\n",
        "- self.fc2 - input size: 64; output size: 10\n",
        "\n",
        "IMPROVEDCNN:\n",
        "Convolutional layers:\n",
        "- has 2 convolutional layers: self.conv1 and self.conv2 (3x3 kernel, stride: 1, padding: 1)\n",
        "- output channels: 32 and 64\n",
        "Pooling Layers:\n",
        "- uses max-pooling through self.pool after each convolutional layer (2x2 kernel and a stride of 2)\n",
        "Fully Connected Layers:\n",
        "- has 2: self.fc1 and self.fc2:\n",
        "- self.fc1 - input size: 64x8x8; output size: 512\n",
        "- self.fc2 - input size: 512; output size: 10\n",
        "\"\"\"\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torchvision.datasets import CIFAR10\n",
        "from torch.utils.data import DataLoader\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "])\n",
        "\n",
        "train_set = CIFAR10(root='./cifar10', train=True, download=False, transform=transform)\n",
        "valid_set = CIFAR10(root='./cifar10', train=False, download=False, transform=transform)\n",
        "\n",
        "train_loader = DataLoader(train_set, batch_size=128, shuffle=True)\n",
        "valid_loader = DataLoader(valid_set, batch_size=512, shuffle=False)\n",
        "\n",
        "# Improved CNN architecture\n",
        "class ImprovedCNN(nn.Module):\n",
        "    def __init__(self, num_channels, num_classes):\n",
        "        super(ImprovedCNN, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(num_channels, 32, 3, stride=1, padding=1)\n",
        "        self.conv2 = nn.Conv2d(32, 64, 3, stride=1, padding=1)\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.fc1 = nn.Linear(64 * 8 * 8, 512)\n",
        "        self.fc2 = nn.Linear(512, num_classes)\n",
        "\n",
        "    def forward(self, X):\n",
        "        x = self.pool(nn.functional.relu(self.conv1(X)))\n",
        "        x = self.pool(nn.functional.relu(self.conv2(x)))\n",
        "        x = x.view(-1, 64 * 8 * 8)\n",
        "        x = nn.functional.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "cuda = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = ImprovedCNN(3, 10).to(cuda)\n",
        "\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "def accuracy(loader):\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    model.eval()\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for data in loader:\n",
        "            inputs, labels = data[0].to(cuda), data[1].to(cuda)\n",
        "            outputs = model(inputs)\n",
        "            _, predicted = torch.max(outputs, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "\n",
        "    return (100 * correct / total)\n",
        "\n",
        "epochs = 10\n",
        "for epoch in range(epochs):\n",
        "    model.train()\n",
        "    running_loss = 0.0\n",
        "\n",
        "    for i, data in enumerate(train_loader, 0):\n",
        "        inputs, labels = data[0].to(cuda), data[1].to(cuda)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(inputs)\n",
        "        loss = loss_fn(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        running_loss += loss.item()\n",
        "\n",
        "    train_accuracy = accuracy(train_loader)\n",
        "    valid_accuracy = accuracy(valid_loader)\n",
        "\n",
        "    print(f'Epoch {epoch + 1}/{epochs}')\n",
        "    print(f'Training loss: {running_loss / len(train_loader):.3f} Training accuracy: {train_accuracy:.2f}% || '\n",
        "          f'Validation accuracy: {valid_accuracy:.2f}%')"
      ],
      "metadata": {
        "id": "_feftFRYlr_g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d67295e2-fc8a-4783-b9ed-a1812cccb857"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "Training loss: 1.685 Training accuracy: 52.53% || Validation accuracy: 52.32%\n",
            "Epoch 2/10\n",
            "Training loss: 1.221 Training accuracy: 59.43% || Validation accuracy: 57.85%\n",
            "Epoch 3/10\n",
            "Training loss: 1.028 Training accuracy: 68.87% || Validation accuracy: 65.11%\n",
            "Epoch 4/10\n",
            "Training loss: 0.879 Training accuracy: 73.49% || Validation accuracy: 68.04%\n",
            "Epoch 5/10\n",
            "Training loss: 0.761 Training accuracy: 77.77% || Validation accuracy: 69.90%\n",
            "Epoch 6/10\n",
            "Training loss: 0.651 Training accuracy: 81.16% || Validation accuracy: 71.13%\n",
            "Epoch 7/10\n",
            "Training loss: 0.550 Training accuracy: 86.30% || Validation accuracy: 71.90%\n",
            "Epoch 8/10\n",
            "Training loss: 0.435 Training accuracy: 89.58% || Validation accuracy: 72.47%\n",
            "Epoch 9/10\n",
            "Training loss: 0.336 Training accuracy: 92.08% || Validation accuracy: 72.40%\n",
            "Epoch 10/10\n",
            "Training loss: 0.241 Training accuracy: 95.47% || Validation accuracy: 72.57%\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}