Final_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/Final_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Final Model\n",
        "\n",
        "Since the regularized model with data augmentation and batch normalization achieved the highest prediction accuracy on the validation set, we decided to train it on the unpartitioned training set and evaluate its performance on the test set. The following model is trained over 150 epochs using the Adam optimizer. It achieves a prediction accuracy of 89.31% on the test 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 BatchNormalization, Conv2D, Dense, Dropout, Flatten, MaxPooling2D\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.preprocessing.image import ImageDataGenerator\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",
      "source": [
        "def data_generator(trainX, trainY, batch_size=64, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True):\n",
        "  # create data iterator\n",
        "  data_gen = ImageDataGenerator(width_shift_range=width_shift_range, height_shift_range=height_shift_range, horizontal_flip=horizontal_flip)\n",
        "  data_iter = data_gen.flow(trainX, trainY, batch_size=batch_size)\n",
        "  # calculate steps per epoch\n",
        "  steps_per_epoch = int(trainX.shape[0]/batch_size)\n",
        "  # return data iterator and steps per epoch\n",
        "  return data_iter, steps_per_epoch"
      ],
      "metadata": {
        "id": "dac4sX1e63ta"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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(BatchNormalization())\n",
        "  model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "  model.add(BatchNormalization())\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(BatchNormalization())\n",
        "  model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "  model.add(BatchNormalization())\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(BatchNormalization())\n",
        "  model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "  model.add(BatchNormalization())\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(BatchNormalization())\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",
        "  data_iter, steps_per_epoch = data_generator(trainX, trainY, batch_size=batch_size)\n",
        "  history = model.fit(data_iter, steps_per_epoch=steps_per_epoch, 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": "829e25f1-82db-410c-958b-7a39128eda46"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 4s 0us/step\n",
            "170508288/170498071 [==============================] - 4s 0us/step\n",
            "Epoch 1/150\n",
            "781/781 [==============================] - 58s 59ms/step - loss: 1.7561 - accuracy: 0.3937 - val_loss: 1.3294 - val_accuracy: 0.5275\n",
            "Epoch 2/150\n",
            "781/781 [==============================] - 48s 61ms/step - loss: 1.2534 - accuracy: 0.5510 - val_loss: 0.9883 - val_accuracy: 0.6525\n",
            "Epoch 3/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 1.0547 - accuracy: 0.6260 - val_loss: 0.8376 - val_accuracy: 0.7078\n",
            "Epoch 4/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.9474 - accuracy: 0.6704 - val_loss: 0.8394 - val_accuracy: 0.7035\n",
            "Epoch 5/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.8678 - accuracy: 0.7003 - val_loss: 0.7657 - val_accuracy: 0.7371\n",
            "Epoch 6/150\n",
            "781/781 [==============================] - 46s 58ms/step - loss: 0.8138 - accuracy: 0.7182 - val_loss: 0.7099 - val_accuracy: 0.7529\n",
            "Epoch 7/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.7526 - accuracy: 0.7402 - val_loss: 0.6614 - val_accuracy: 0.7737\n",
            "Epoch 8/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.7199 - accuracy: 0.7540 - val_loss: 0.5994 - val_accuracy: 0.7937\n",
            "Epoch 9/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.6874 - accuracy: 0.7644 - val_loss: 0.5917 - val_accuracy: 0.8041\n",
            "Epoch 10/150\n",
            "781/781 [==============================] - 46s 58ms/step - loss: 0.6537 - accuracy: 0.7786 - val_loss: 0.6231 - val_accuracy: 0.7857\n",
            "Epoch 11/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.6315 - accuracy: 0.7855 - val_loss: 0.5330 - val_accuracy: 0.8195\n",
            "Epoch 12/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.6178 - accuracy: 0.7905 - val_loss: 0.5572 - val_accuracy: 0.8067\n",
            "Epoch 13/150\n",
            "781/781 [==============================] - 46s 58ms/step - loss: 0.5914 - accuracy: 0.8007 - val_loss: 0.5646 - val_accuracy: 0.8088\n",
            "Epoch 14/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.5771 - accuracy: 0.8025 - val_loss: 0.6036 - val_accuracy: 0.7977\n",
            "Epoch 15/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.5627 - accuracy: 0.8098 - val_loss: 0.5037 - val_accuracy: 0.8299\n",
            "Epoch 16/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.5458 - accuracy: 0.8142 - val_loss: 0.5290 - val_accuracy: 0.8212\n",
            "Epoch 17/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.5364 - accuracy: 0.8176 - val_loss: 0.5134 - val_accuracy: 0.8287\n",
            "Epoch 18/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.5288 - accuracy: 0.8212 - val_loss: 0.4853 - val_accuracy: 0.8362\n",
            "Epoch 19/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.5109 - accuracy: 0.8268 - val_loss: 0.5183 - val_accuracy: 0.8235\n",
            "Epoch 20/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.5057 - accuracy: 0.8273 - val_loss: 0.4987 - val_accuracy: 0.8339\n",
            "Epoch 21/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.4994 - accuracy: 0.8314 - val_loss: 0.4927 - val_accuracy: 0.8343\n",
            "Epoch 22/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.4860 - accuracy: 0.8349 - val_loss: 0.4896 - val_accuracy: 0.8336\n",
            "Epoch 23/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.4770 - accuracy: 0.8385 - val_loss: 0.4988 - val_accuracy: 0.8351\n",
            "Epoch 24/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.4761 - accuracy: 0.8391 - val_loss: 0.4346 - val_accuracy: 0.8536\n",
            "Epoch 25/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.4685 - accuracy: 0.8408 - val_loss: 0.4679 - val_accuracy: 0.8407\n",
            "Epoch 26/150\n",
            "781/781 [==============================] - 45s 57ms/step - loss: 0.4637 - accuracy: 0.8424 - val_loss: 0.4941 - val_accuracy: 0.8326\n",
            "Epoch 27/150\n",
            "781/781 [==============================] - 45s 57ms/step - loss: 0.4589 - accuracy: 0.8435 - val_loss: 0.4671 - val_accuracy: 0.8436\n",
            "Epoch 28/150\n",
            "781/781 [==============================] - 44s 57ms/step - loss: 0.4474 - accuracy: 0.8484 - val_loss: 0.4110 - val_accuracy: 0.8579\n",
            "Epoch 29/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.4455 - accuracy: 0.8480 - val_loss: 0.4321 - val_accuracy: 0.8572\n",
            "Epoch 30/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.4391 - accuracy: 0.8514 - val_loss: 0.4657 - val_accuracy: 0.8460\n",
            "Epoch 31/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.4354 - accuracy: 0.8512 - val_loss: 0.3685 - val_accuracy: 0.8738\n",
            "Epoch 32/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.4307 - accuracy: 0.8520 - val_loss: 0.4806 - val_accuracy: 0.8428\n",
            "Epoch 33/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.4211 - accuracy: 0.8559 - val_loss: 0.3868 - val_accuracy: 0.8727\n",
            "Epoch 34/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.4247 - accuracy: 0.8561 - val_loss: 0.3752 - val_accuracy: 0.8701\n",
            "Epoch 35/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.4186 - accuracy: 0.8572 - val_loss: 0.5481 - val_accuracy: 0.8218\n",
            "Epoch 36/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.4089 - accuracy: 0.8604 - val_loss: 0.4185 - val_accuracy: 0.8616\n",
            "Epoch 37/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.4103 - accuracy: 0.8607 - val_loss: 0.4168 - val_accuracy: 0.8637\n",
            "Epoch 38/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.4122 - accuracy: 0.8603 - val_loss: 0.3867 - val_accuracy: 0.8730\n",
            "Epoch 39/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3975 - accuracy: 0.8648 - val_loss: 0.4274 - val_accuracy: 0.8626\n",
            "Epoch 40/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.4042 - accuracy: 0.8631 - val_loss: 0.3709 - val_accuracy: 0.8769\n",
            "Epoch 41/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.3978 - accuracy: 0.8643 - val_loss: 0.3984 - val_accuracy: 0.8715\n",
            "Epoch 42/150\n",
            "781/781 [==============================] - 45s 57ms/step - loss: 0.3921 - accuracy: 0.8672 - val_loss: 0.3904 - val_accuracy: 0.8756\n",
            "Epoch 43/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3960 - accuracy: 0.8650 - val_loss: 0.3852 - val_accuracy: 0.8719\n",
            "Epoch 44/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3915 - accuracy: 0.8678 - val_loss: 0.3751 - val_accuracy: 0.8792\n",
            "Epoch 45/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3854 - accuracy: 0.8690 - val_loss: 0.3655 - val_accuracy: 0.8789\n",
            "Epoch 46/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3836 - accuracy: 0.8697 - val_loss: 0.3796 - val_accuracy: 0.8735\n",
            "Epoch 47/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3814 - accuracy: 0.8723 - val_loss: 0.3829 - val_accuracy: 0.8723\n",
            "Epoch 48/150\n",
            "781/781 [==============================] - 47s 60ms/step - loss: 0.3804 - accuracy: 0.8697 - val_loss: 0.4111 - val_accuracy: 0.8679\n",
            "Epoch 49/150\n",
            "781/781 [==============================] - 46s 58ms/step - loss: 0.3774 - accuracy: 0.8704 - val_loss: 0.3452 - val_accuracy: 0.8858\n",
            "Epoch 50/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3765 - accuracy: 0.8715 - val_loss: 0.3920 - val_accuracy: 0.8730\n",
            "Epoch 51/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3633 - accuracy: 0.8770 - val_loss: 0.3538 - val_accuracy: 0.8840\n",
            "Epoch 52/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3684 - accuracy: 0.8742 - val_loss: 0.3559 - val_accuracy: 0.8800\n",
            "Epoch 53/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3633 - accuracy: 0.8767 - val_loss: 0.3451 - val_accuracy: 0.8850\n",
            "Epoch 54/150\n",
            "781/781 [==============================] - 46s 60ms/step - loss: 0.3613 - accuracy: 0.8764 - val_loss: 0.3811 - val_accuracy: 0.8760\n",
            "Epoch 55/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3621 - accuracy: 0.8765 - val_loss: 0.3867 - val_accuracy: 0.8762\n",
            "Epoch 56/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.3580 - accuracy: 0.8788 - val_loss: 0.3448 - val_accuracy: 0.8874\n",
            "Epoch 57/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3558 - accuracy: 0.8788 - val_loss: 0.4002 - val_accuracy: 0.8721\n",
            "Epoch 58/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3551 - accuracy: 0.8798 - val_loss: 0.3489 - val_accuracy: 0.8870\n",
            "Epoch 59/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3526 - accuracy: 0.8810 - val_loss: 0.3241 - val_accuracy: 0.8891\n",
            "Epoch 60/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3481 - accuracy: 0.8807 - val_loss: 0.3805 - val_accuracy: 0.8738\n",
            "Epoch 61/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3515 - accuracy: 0.8811 - val_loss: 0.3868 - val_accuracy: 0.8764\n",
            "Epoch 62/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3511 - accuracy: 0.8803 - val_loss: 0.3549 - val_accuracy: 0.8838\n",
            "Epoch 63/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3499 - accuracy: 0.8820 - val_loss: 0.3928 - val_accuracy: 0.8727\n",
            "Epoch 64/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.3470 - accuracy: 0.8808 - val_loss: 0.3228 - val_accuracy: 0.8939\n",
            "Epoch 65/150\n",
            "781/781 [==============================] - 47s 60ms/step - loss: 0.3430 - accuracy: 0.8831 - val_loss: 0.3300 - val_accuracy: 0.8908\n",
            "Epoch 66/150\n",
            "781/781 [==============================] - 46s 58ms/step - loss: 0.3401 - accuracy: 0.8836 - val_loss: 0.3581 - val_accuracy: 0.8831\n",
            "Epoch 67/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.3407 - accuracy: 0.8831 - val_loss: 0.3643 - val_accuracy: 0.8828\n",
            "Epoch 68/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3369 - accuracy: 0.8850 - val_loss: 0.4246 - val_accuracy: 0.8623\n",
            "Epoch 69/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.3361 - accuracy: 0.8849 - val_loss: 0.3689 - val_accuracy: 0.8799\n",
            "Epoch 70/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.3383 - accuracy: 0.8857 - val_loss: 0.3626 - val_accuracy: 0.8836\n",
            "Epoch 71/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3327 - accuracy: 0.8853 - val_loss: 0.3296 - val_accuracy: 0.8913\n",
            "Epoch 72/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.3284 - accuracy: 0.8889 - val_loss: 0.3817 - val_accuracy: 0.8728\n",
            "Epoch 73/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3347 - accuracy: 0.8869 - val_loss: 0.3514 - val_accuracy: 0.8836\n",
            "Epoch 74/150\n",
            "781/781 [==============================] - 43s 56ms/step - loss: 0.3313 - accuracy: 0.8868 - val_loss: 0.3269 - val_accuracy: 0.8917\n",
            "Epoch 75/150\n",
            "781/781 [==============================] - 44s 56ms/step - loss: 0.3275 - accuracy: 0.8876 - val_loss: 0.3580 - val_accuracy: 0.8894\n",
            "Epoch 76/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3248 - accuracy: 0.8892 - val_loss: 0.3864 - val_accuracy: 0.8728\n",
            "Epoch 77/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3300 - accuracy: 0.8876 - val_loss: 0.3567 - val_accuracy: 0.8847\n",
            "Epoch 78/150\n",
            "781/781 [==============================] - 44s 56ms/step - loss: 0.3244 - accuracy: 0.8879 - val_loss: 0.3916 - val_accuracy: 0.8712\n",
            "Epoch 79/150\n",
            "781/781 [==============================] - 44s 56ms/step - loss: 0.3230 - accuracy: 0.8901 - val_loss: 0.3376 - val_accuracy: 0.8917\n",
            "Epoch 80/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.3228 - accuracy: 0.8902 - val_loss: 0.3764 - val_accuracy: 0.8791\n",
            "Epoch 81/150\n",
            "781/781 [==============================] - 47s 60ms/step - loss: 0.3191 - accuracy: 0.8910 - val_loss: 0.3709 - val_accuracy: 0.8788\n",
            "Epoch 82/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.3186 - accuracy: 0.8905 - val_loss: 0.3464 - val_accuracy: 0.8909\n",
            "Epoch 83/150\n",
            "781/781 [==============================] - 44s 57ms/step - loss: 0.3225 - accuracy: 0.8887 - val_loss: 0.3522 - val_accuracy: 0.8858\n",
            "Epoch 84/150\n",
            "781/781 [==============================] - 46s 58ms/step - loss: 0.3181 - accuracy: 0.8913 - val_loss: 0.3661 - val_accuracy: 0.8826\n",
            "Epoch 85/150\n",
            "781/781 [==============================] - 44s 56ms/step - loss: 0.3150 - accuracy: 0.8917 - val_loss: 0.3430 - val_accuracy: 0.8902\n",
            "Epoch 86/150\n",
            "781/781 [==============================] - 44s 56ms/step - loss: 0.3173 - accuracy: 0.8920 - val_loss: 0.3396 - val_accuracy: 0.8936\n",
            "Epoch 87/150\n",
            "781/781 [==============================] - 44s 57ms/step - loss: 0.3133 - accuracy: 0.8924 - val_loss: 0.4095 - val_accuracy: 0.8696\n",
            "Epoch 88/150\n",
            "781/781 [==============================] - 44s 57ms/step - loss: 0.3150 - accuracy: 0.8926 - val_loss: 0.3455 - val_accuracy: 0.8873\n",
            "Epoch 89/150\n",
            "781/781 [==============================] - 45s 57ms/step - loss: 0.3165 - accuracy: 0.8922 - val_loss: 0.3145 - val_accuracy: 0.8975\n",
            "Epoch 90/150\n",
            "781/781 [==============================] - 47s 60ms/step - loss: 0.3068 - accuracy: 0.8947 - val_loss: 0.3431 - val_accuracy: 0.8895\n",
            "Epoch 91/150\n",
            "781/781 [==============================] - 45s 57ms/step - loss: 0.3109 - accuracy: 0.8933 - val_loss: 0.3193 - val_accuracy: 0.8985\n",
            "Epoch 92/150\n",
            "781/781 [==============================] - 45s 57ms/step - loss: 0.3087 - accuracy: 0.8948 - val_loss: 0.3571 - val_accuracy: 0.8848\n",
            "Epoch 93/150\n",
            "781/781 [==============================] - 44s 57ms/step - loss: 0.3106 - accuracy: 0.8928 - val_loss: 0.3790 - val_accuracy: 0.8806\n",
            "Epoch 94/150\n",
            "781/781 [==============================] - 44s 56ms/step - loss: 0.3086 - accuracy: 0.8941 - val_loss: 0.3507 - val_accuracy: 0.8857\n",
            "Epoch 95/150\n",
            "781/781 [==============================] - 44s 57ms/step - loss: 0.3067 - accuracy: 0.8965 - val_loss: 0.3700 - val_accuracy: 0.8843\n",
            "Epoch 96/150\n",
            "781/781 [==============================] - 46s 58ms/step - loss: 0.3016 - accuracy: 0.8966 - val_loss: 0.3257 - val_accuracy: 0.8961\n",
            "Epoch 97/150\n",
            "781/781 [==============================] - 44s 57ms/step - loss: 0.2991 - accuracy: 0.8969 - val_loss: 0.3641 - val_accuracy: 0.8875\n",
            "Epoch 98/150\n",
            "781/781 [==============================] - 44s 57ms/step - loss: 0.2999 - accuracy: 0.8983 - val_loss: 0.3419 - val_accuracy: 0.8916\n",
            "Epoch 99/150\n",
            "781/781 [==============================] - 47s 60ms/step - loss: 0.3028 - accuracy: 0.8971 - val_loss: 0.3409 - val_accuracy: 0.8912\n",
            "Epoch 100/150\n",
            "781/781 [==============================] - 46s 58ms/step - loss: 0.2971 - accuracy: 0.8984 - val_loss: 0.3421 - val_accuracy: 0.8894\n",
            "Epoch 101/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.2997 - accuracy: 0.8980 - val_loss: 0.4013 - val_accuracy: 0.8721\n",
            "Epoch 102/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.2986 - accuracy: 0.8979 - val_loss: 0.3832 - val_accuracy: 0.8793\n",
            "Epoch 103/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.2993 - accuracy: 0.8979 - val_loss: 0.3619 - val_accuracy: 0.8828\n",
            "Epoch 104/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.2963 - accuracy: 0.8972 - val_loss: 0.3533 - val_accuracy: 0.8842\n",
            "Epoch 105/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.3035 - accuracy: 0.8971 - val_loss: 0.3245 - val_accuracy: 0.8969\n",
            "Epoch 106/150\n",
            "781/781 [==============================] - 46s 58ms/step - loss: 0.2941 - accuracy: 0.8982 - val_loss: 0.3288 - val_accuracy: 0.8968\n",
            "Epoch 107/150\n",
            "781/781 [==============================] - 47s 60ms/step - loss: 0.2937 - accuracy: 0.8992 - val_loss: 0.3425 - val_accuracy: 0.8891\n",
            "Epoch 108/150\n",
            "781/781 [==============================] - 47s 60ms/step - loss: 0.2925 - accuracy: 0.8990 - val_loss: 0.3387 - val_accuracy: 0.8915\n",
            "Epoch 109/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.2928 - accuracy: 0.8994 - val_loss: 0.3453 - val_accuracy: 0.8894\n",
            "Epoch 110/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.2919 - accuracy: 0.8990 - val_loss: 0.3049 - val_accuracy: 0.9039\n",
            "Epoch 111/150\n",
            "781/781 [==============================] - 45s 57ms/step - loss: 0.2903 - accuracy: 0.8990 - val_loss: 0.3279 - val_accuracy: 0.8949\n",
            "Epoch 112/150\n",
            "781/781 [==============================] - 45s 57ms/step - loss: 0.2908 - accuracy: 0.8997 - val_loss: 0.3719 - val_accuracy: 0.8846\n",
            "Epoch 113/150\n",
            "781/781 [==============================] - 45s 57ms/step - loss: 0.2888 - accuracy: 0.9007 - val_loss: 0.3572 - val_accuracy: 0.8874\n",
            "Epoch 114/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.2883 - accuracy: 0.9013 - val_loss: 0.3443 - val_accuracy: 0.8910\n",
            "Epoch 115/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.2897 - accuracy: 0.9012 - val_loss: 0.3438 - val_accuracy: 0.8878\n",
            "Epoch 116/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.2847 - accuracy: 0.9021 - val_loss: 0.3253 - val_accuracy: 0.8962\n",
            "Epoch 117/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.2820 - accuracy: 0.9040 - val_loss: 0.3072 - val_accuracy: 0.9004\n",
            "Epoch 118/150\n",
            "781/781 [==============================] - 44s 57ms/step - loss: 0.2865 - accuracy: 0.9023 - val_loss: 0.3118 - val_accuracy: 0.8987\n",
            "Epoch 119/150\n",
            "781/781 [==============================] - 44s 56ms/step - loss: 0.2876 - accuracy: 0.9018 - val_loss: 0.3176 - val_accuracy: 0.8988\n",
            "Epoch 120/150\n",
            "781/781 [==============================] - 47s 60ms/step - loss: 0.2875 - accuracy: 0.9029 - val_loss: 0.3685 - val_accuracy: 0.8803\n",
            "Epoch 121/150\n",
            "781/781 [==============================] - 44s 57ms/step - loss: 0.2816 - accuracy: 0.9020 - val_loss: 0.3096 - val_accuracy: 0.8975\n",
            "Epoch 122/150\n",
            "781/781 [==============================] - 47s 60ms/step - loss: 0.2866 - accuracy: 0.9013 - val_loss: 0.3382 - val_accuracy: 0.8877\n",
            "Epoch 123/150\n",
            "781/781 [==============================] - 47s 60ms/step - loss: 0.2833 - accuracy: 0.9024 - val_loss: 0.3358 - val_accuracy: 0.8931\n",
            "Epoch 124/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.2790 - accuracy: 0.9041 - val_loss: 0.3252 - val_accuracy: 0.8969\n",
            "Epoch 125/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.2818 - accuracy: 0.9041 - val_loss: 0.3292 - val_accuracy: 0.8956\n",
            "Epoch 126/150\n",
            "781/781 [==============================] - 47s 60ms/step - loss: 0.2815 - accuracy: 0.9034 - val_loss: 0.3480 - val_accuracy: 0.8906\n",
            "Epoch 127/150\n",
            "781/781 [==============================] - 45s 57ms/step - loss: 0.2802 - accuracy: 0.9045 - val_loss: 0.3353 - val_accuracy: 0.8950\n",
            "Epoch 128/150\n",
            "781/781 [==============================] - 45s 57ms/step - loss: 0.2797 - accuracy: 0.9045 - val_loss: 0.3349 - val_accuracy: 0.8946\n",
            "Epoch 129/150\n",
            "781/781 [==============================] - 46s 58ms/step - loss: 0.2763 - accuracy: 0.9055 - val_loss: 0.3232 - val_accuracy: 0.8986\n",
            "Epoch 130/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.2797 - accuracy: 0.9041 - val_loss: 0.3214 - val_accuracy: 0.8952\n",
            "Epoch 131/150\n",
            "781/781 [==============================] - 47s 61ms/step - loss: 0.2774 - accuracy: 0.9047 - val_loss: 0.3253 - val_accuracy: 0.8968\n",
            "Epoch 132/150\n",
            "781/781 [==============================] - 47s 60ms/step - loss: 0.2793 - accuracy: 0.9037 - val_loss: 0.3094 - val_accuracy: 0.8990\n",
            "Epoch 133/150\n",
            "781/781 [==============================] - 46s 58ms/step - loss: 0.2709 - accuracy: 0.9064 - val_loss: 0.3755 - val_accuracy: 0.8870\n",
            "Epoch 134/150\n",
            "781/781 [==============================] - 47s 60ms/step - loss: 0.2730 - accuracy: 0.9062 - val_loss: 0.3133 - val_accuracy: 0.8998\n",
            "Epoch 135/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.2752 - accuracy: 0.9057 - val_loss: 0.3311 - val_accuracy: 0.8926\n",
            "Epoch 136/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.2727 - accuracy: 0.9056 - val_loss: 0.3271 - val_accuracy: 0.8960\n",
            "Epoch 137/150\n",
            "781/781 [==============================] - 45s 57ms/step - loss: 0.2693 - accuracy: 0.9080 - val_loss: 0.3476 - val_accuracy: 0.8876\n",
            "Epoch 138/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.2735 - accuracy: 0.9067 - val_loss: 0.3265 - val_accuracy: 0.8968\n",
            "Epoch 139/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.2749 - accuracy: 0.9076 - val_loss: 0.4032 - val_accuracy: 0.8740\n",
            "Epoch 140/150\n",
            "781/781 [==============================] - 47s 60ms/step - loss: 0.2696 - accuracy: 0.9063 - val_loss: 0.3050 - val_accuracy: 0.9009\n",
            "Epoch 141/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.2712 - accuracy: 0.9060 - val_loss: 0.3446 - val_accuracy: 0.8915\n",
            "Epoch 142/150\n",
            "781/781 [==============================] - 47s 60ms/step - loss: 0.2698 - accuracy: 0.9063 - val_loss: 0.3582 - val_accuracy: 0.8881\n",
            "Epoch 143/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.2683 - accuracy: 0.9064 - val_loss: 0.3294 - val_accuracy: 0.8975\n",
            "Epoch 144/150\n",
            "781/781 [==============================] - 44s 56ms/step - loss: 0.2701 - accuracy: 0.9080 - val_loss: 0.3511 - val_accuracy: 0.8872\n",
            "Epoch 145/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.2651 - accuracy: 0.9085 - val_loss: 0.3192 - val_accuracy: 0.8973\n",
            "Epoch 146/150\n",
            "781/781 [==============================] - 45s 58ms/step - loss: 0.2680 - accuracy: 0.9082 - val_loss: 0.3029 - val_accuracy: 0.9020\n",
            "Epoch 147/150\n",
            "781/781 [==============================] - 45s 57ms/step - loss: 0.2672 - accuracy: 0.9079 - val_loss: 0.3438 - val_accuracy: 0.8907\n",
            "Epoch 148/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.2665 - accuracy: 0.9072 - val_loss: 0.3159 - val_accuracy: 0.8981\n",
            "Epoch 149/150\n",
            "781/781 [==============================] - 46s 59ms/step - loss: 0.2604 - accuracy: 0.9112 - val_loss: 0.3306 - val_accuracy: 0.8958\n",
            "Epoch 150/150\n",
            "781/781 [==============================] - 44s 57ms/step - loss: 0.2649 - accuracy: 0.9101 - val_loss: 0.3380 - val_accuracy: 0.8931\n",
            "313/313 [==============================] - 2s 7ms/step - loss: 0.3380 - accuracy: 0.8931\n",
            "test loss: 0.338 - test accuracy: 0.893\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, epochs=150, test=True, verbose=True)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "Final_Model.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}