LeNetArchitecture/LeNetArchitecture.ipynb

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  "metadata": {
    "colab": {
      "name": "LeNetArchitecture.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "qNXFM06HOnBy"
      },
      "source": [
        "#@ IMPORTING THE LIBRARIES AND DEPENDENCIES: \n",
        "# !pip install -U d2l\n",
        "%matplotlib inline \n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "from d2l import torch as d2l"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q2nnTsgboSzk"
      },
      "source": [
        "#@ INITIALIZING MODEL PARAMETERS: \n",
        "scale = 0.01                                                      # Initialization. \n",
        "W1 = torch.randn(size=(20, 1, 3, 3)) * scale                      # Initializing the Parameter. \n",
        "b1 = torch.zeros(20)                                              # Initializing the Tensor of zeros. \n",
        "W2 = torch.randn(size=(50, 20, 5, 5)) * scale                     # Initializing the Parameter. \n",
        "b2 = torch.zeros(50)                                              # Initializing the Tensor of zeros. \n",
        "W3 = torch.randn(size=(800, 128)) * scale                         # Initializing the Parameter. \n",
        "b3 = torch.zeros(128)                                             # Initializing the Tensor of zeros. \n",
        "W4 = torch.randn(size=(128, 10)) * scale                          # Initializing the Parameter. \n",
        "b4 = torch.zeros(10)                                              # Initializing the Tensor of zeros. \n",
        "params = [W1, b1, W2, b2, W3, b3, W4, b4]                         # Initializing a list of Parameters. "
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V5Z391gWrDnO"
      },
      "source": [
        "#@ DEFINING THE LENET MODEL: \n",
        "def lenet(X, params):                                                         # Initializing the LENET Model. \n",
        "  h1_conv = F.conv2d(input=X, weight=params[0], bias=params[1])               # Initializing the Convolutional Layer. \n",
        "  h1_activation = F.relu(h1_conv)                                             # Initializing the RELU Activation Function. \n",
        "  h1 = F.avg_pool2d(input=h1_activation, kernel_size=(2, 2), stride=(2, 2))   # Initializing the Average Pooling Layer. \n",
        "  h2_conv = F.conv2d(input=h1, weight=params[2], bias=params[3])              # Initializing the Convolutional Layer. \n",
        "  h2_activation = F.relu(h2_conv)                                             # Initializing the RELU Activation Function. \n",
        "  h2 = F.avg_pool2d(input=h2_activation, kernel_size=(2, 2), stride=(2, 2))   # Initializing the Average Pooling Layer. \n",
        "  h2 = h2.reshape(h2.shape[0], -1)                                            # Changing the shape. \n",
        "  h3_linear = torch.mm(h2, params[4] + params[5])                             # Initializing the Matrix Multiplication. \n",
        "  h3 = F.relu(h3_linear)                                                      # Initializing the RELU Activation Function. \n",
        "  y_hat = torch.mm(h3, params[6] + params[7])                                 # Initializing the Matrix Multiplication. \n",
        "  return y_hat\n",
        "#@ INITIALIZING CROSSENTROPY LOSS FUNCTION: \n",
        "loss = nn.CrossEntropyLoss(reduction=\"none\")                                  # Initializing the Cross Entropy Loss Function. "
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AYZHVTu1t-rH"
      },
      "source": [
        "#@ IMPLEMENTATION OF DATA SYNCHRONIZATION: \n",
        "def get_params(params, device):                                               # Initializing the Parameters.           \n",
        "  new_params = [p.clone().to(device) for p in params]\n",
        "  for p in new_params: \n",
        "    p.requires_grad_()\n",
        "  return new_params\n",
        "#@ IMPLEMENTATION DATA SYNCHRONIZATION: \n",
        "def allreduce(data):                                                          # Getting sum of the results. \n",
        "  for i in range(1, len(data)):\n",
        "    data[0][:] += data[i].to(data[0].device)\n",
        "  for i in range(1, len(data)):\n",
        "    data[i] = data[0].to(data[i].device)\n",
        "#@ IMPLEMENTATION OF DATA DISTRIBUTION: \n",
        "def split_data(X, y, devices):                                                # Data Distribution. \n",
        "  assert X.shape[0] == y.shape[0]\n",
        "  return (nn.parallel.scatter(X, devices), nn.parallel.scatter(y, devices))"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Drroj8Ku1ttI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "87450df1-7d25-4de3-f7b2-ca4aa0db83cf"
      },
      "source": [
        "#@ IMPLEMENTATION OF TRAINING FUNCTION: \n",
        "def train_batch(X, y, device_params, devices, lr):                                  # Function for Training Batches. \n",
        "  X_shards, y_shards = split_data(X, y, devices)                                    # Initializing the Data Distribution Function. \n",
        "  losses = [loss(lenet(X_shard, device_W), y_shard).sum() for \n",
        "            X_shard, y_shard, device_W in zip(X_shards, y_shards, device_params)] \n",
        "  for l in losses: \n",
        "    l.backward()                                                                    # Initializing Back Propagation. \n",
        "  with torch.no_grad():\n",
        "    for i in range(len(device_params[0])):\n",
        "      allreduce([device_params[c][i].grad for c in range(len(devices))])\n",
        "  for param in device_params: \n",
        "    d2l.sgd(param, lr, X.shape[0])\n",
        "#@ IMPLEMENTATION OF TRAINING FUNCTION: \n",
        "def train(num_gpus, batch_size, lr):                                                # Function for Training the Model. \n",
        "  train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)                   # Initializing the Training and Test Iterations. \n",
        "  devices = [d2l.try_gpu(i) for i in range(num_gpus)]\n",
        "  device_params = [get_params(params, d) for d in devices]                          # Initializing the Model Parameters. \n",
        "  num_epochs = 10                                                                   # Initializing the Training Epochs. \n",
        "  animator = d2l.Animator(\"epoch\", \"test acc\", xlim=[1, num_epochs])                # Initializing the Animation.\n",
        "  timer = d2l.Timer()                                                               # Initializing the Timer. \n",
        "  for epoch in range(num_epochs):\n",
        "    timer.start()\n",
        "    for X, y in train_iter: \n",
        "      train_batch(X, y, device_params, devices, lr)\n",
        "      torch.cuda.synchronize()\n",
        "    timer.stop()\n",
        "    animator.add(epoch + 1, (d2l.evaluate_accuracy_gpu(\n",
        "        lambda x: lenet(x, device_params[0]), test_iter, devices[0]), ))\n",
        "  print(f\"Test acc: {animator.Y[0][-1]:.2f}, {timer.avg():.1f} sec/epoch\"\n",
        "        f\"on {str(devices)}\")\n",
        "#@ EXPERIMENTATION:\n",
        "train(num_gpus=1, batch_size=256, lr=0.2)                                             # Training the Model. "
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test acc: 0.10, 6.8 sec/epochon [device(type='cuda', index=0)]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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