Regularized_Model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/daqichen/CIFAR10/blob/main/Regularized_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Regularized Model\n",
        "\n",
        "The following model differs from the baseline model by having a dropout layer after each VGG block and the final dense hidden layer. The dropout probability increases by 0.1 after each block. The dropout probability after the first VGG block is 0.2. The dropout probability after the second VGG block is 0.3. The dropout probability after the third VGG block is 0.4. The dropout probability after the final dense hidden layer is 0.5. The model is trained over 100 epochs using the Adam optimizer. It achieves a prediction accuracy of 80.93% on the validation set."
      ],
      "metadata": {
        "id": "BZ-GGRMRNmcw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e3NpgwrguKKs"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.datasets import cifar10\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aCMZl4XQgn8Y"
      },
      "outputs": [],
      "source": [
        "def data_loader(test=False):\n",
        "  # load dataset\n",
        "  (trainX, trainY), (testX, testY) = cifar10.load_data()\n",
        "  # normalize x values\n",
        "  trainX, testX = trainX.astype('float32')/255.0, testX.astype('float32')/255.0\n",
        "  # one hot encode y values\n",
        "  trainY, testY = to_categorical(trainY), to_categorical(testY)\n",
        "  if (test):\n",
        "    # return train set and test set\n",
        "    return trainX, trainY, testX, testY\n",
        "  else:\n",
        "    # randomly sample 20% of data from train set\n",
        "    mask_indices = np.random.choice(np.arange(trainX.shape[0]), size=int(trainX.shape[0] * 0.2), replace=False)\n",
        "    mask_arr = np.zeros(trainX.shape[0])\n",
        "    mask_arr[mask_indices] = 1\n",
        "    # create validation set using random sample\n",
        "    valX, valY = trainX[mask_arr == 1], trainY[mask_arr == 1]\n",
        "    # remove random sample from train set\n",
        "    trainX, trainY = trainX[mask_arr == 0], trainY[mask_arr == 0]\n",
        "    # return train set and validation set\n",
        "    return trainX, trainY, valX, valY"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1p9Y6ZE6zjM4"
      },
      "outputs": [],
      "source": [
        "# define ConvNet model\n",
        "def model(optimizer=Adam(), loss='categorical_crossentropy'):\n",
        "  # initialize model\n",
        "  model = Sequential()\n",
        "  # add layers to model\n",
        "  model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
        "  model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "  model.add(MaxPooling2D((2, 2)))\n",
        "  model.add(Dropout(0.2))\n",
        "  model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "  model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "  model.add(MaxPooling2D((2, 2)))\n",
        "  model.add(Dropout(0.3))\n",
        "  model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "  model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "  model.add(MaxPooling2D((2, 2)))\n",
        "  model.add(Dropout(0.4))\n",
        "  model.add(Flatten())\n",
        "  model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
        "  model.add(Dropout(0.5))\n",
        "  model.add(Dense(10, activation='softmax'))\n",
        "  # compile model\n",
        "  model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])\n",
        "  return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wgzd6BB9zbmN"
      },
      "outputs": [],
      "source": [
        "# display training metrics and plot learning curves\n",
        "def training_summary(history, loss, accuracy, test=False):\n",
        "  # define x-axis of plots\n",
        "  epochs = np.arange(len(history.history['loss'])) + 1\n",
        "  # determine if evaluating performance on test set or validation set\n",
        "  label = 'test' if test else 'validation'\n",
        "  plt.figure(figsize=(15,30))\n",
        "  # plot losses\n",
        "  plt.subplot(621)\n",
        "  plt.tight_layout()\n",
        "  plt.title('Cross-Entropy Loss', fontsize=16)\n",
        "  plt.xlabel('Epoch', fontsize=14)\n",
        "  plt.ylabel('Loss', fontsize=14)\n",
        "  plt.plot(epochs, history.history['loss'], color='blue', label='train')\n",
        "  plt.plot(epochs, history.history['val_loss'], color='orange', label=label)\n",
        "  plt.legend(loc='upper left')\n",
        "  # plot accuracies\n",
        "  plt.subplot(622)\n",
        "  plt.tight_layout()\n",
        "  plt.title('Classification Accuracy', fontsize=16)\n",
        "  plt.xlabel('Epoch', fontsize=14)\n",
        "  plt.ylabel('Accuracy', fontsize=14)\n",
        "  plt.plot(epochs, history.history['accuracy'], color='blue', label='train')\n",
        "  plt.plot(epochs, history.history['val_accuracy'], color='orange', label=label)\n",
        "  plt.legend(loc='upper left')\n",
        "  # print final loss and accuracy\n",
        "  print(f'{label} loss: {loss:.3f} - {label} accuracy: {accuracy:.3f}')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(model, examples, label_map, transform=lambda x: (255*x).astype('uint8')):\n",
        "  # make predictions based on example data\n",
        "  predictions = [label_map[np.argmax(prediction)] for prediction in model.predict(examples)]\n",
        "  # denormalize image data\n",
        "  examples = transform(examples)\n",
        "  for i in range(len(predictions)):\n",
        "    plt.subplot(6,2,i+3)\n",
        "    plt.tight_layout()\n",
        "    # remove axis labels and ticks\n",
        "    ax = plt.gca().axes\n",
        "    ax.xaxis.set_ticks([])\n",
        "    ax.yaxis.set_ticks([])\n",
        "    # display prediction\n",
        "    plt.xlabel(predictions[i], fontsize=16)\n",
        "    # display image\n",
        "    plt.imshow(examples[i])"
      ],
      "metadata": {
        "id": "6gV2zZZnfd1H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pU55X4X3zYDx"
      },
      "outputs": [],
      "source": [
        "# train the model and evaluate its performance\n",
        "def train(data_loader, model, optimizer=Adam(), loss='categorical_crossentropy', epochs=100, batch_size=64, test=False, verbose=False):\n",
        "  # load dataset\n",
        "  trainX, trainY, valX, valY = data_loader(test=test)\n",
        "  # define model\n",
        "  model = model(optimizer=optimizer, loss=loss)\n",
        "  # fit model\n",
        "  history = model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, validation_data=(valX, valY), verbose=verbose)\n",
        "  # evaluate model\n",
        "  loss, acc = model.evaluate(valX, valY)\n",
        "  # summarize training\n",
        "  training_summary(history, loss, acc, test=test)\n",
        "  # make predictions\n",
        "  label_map = {0:'airplane', 1:'automobile', 2:'bird', 3:'cat', 4:'deer', 5:'dog', 6:'frog', 7:'horse', 8:'ship', 9:'truck'}\n",
        "  predict(model, valX[:10], label_map)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "JBAYf4bLzT6g",
        "outputId": "c16eb4d0-fb3f-488b-98ff-4b1e32d831b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 3s 8ms/step - loss: 0.6589 - accuracy: 0.8093\n",
            "validation loss: 0.659 - validation accuracy: 0.809\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x2160 with 12 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "train(data_loader=data_loader, model=model)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "Regularized_Model.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}