Fine_Tuning_DARTS_for_Image_Classification_Paper_Implementation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Fine-Tuning DARTS for Image Classification - Paper Implementation.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNiMHQoe53NB+p1xJT0xvON",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RohitGanji/fine-tuning-darts/blob/main/Fine_Tuning_DARTS_for_Image_Classification_Paper_Implementation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mSf6AQFF9Rlz"
      },
      "source": [
        "# Fine-Tuning DARTS for Image Classification - Paper Implementation\n",
        "\n",
        "The paper can be foud [here](https://arxiv.org/pdf/2006.09042.pdf)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AlrRhqXg5DG6"
      },
      "source": [
        "# Dataset Prepraration and Processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dh8pt6pzTx-c"
      },
      "source": [
        "## Data collection\n",
        "\n",
        "Let's import the **Fashion MNIST** dataset from `tensorflow.keras.datasets`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7DQsVsm-T3fK",
        "outputId": "97ba8964-3b7f-4f59-f394-2e1b548839bd"
      },
      "source": [
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "\n",
        "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n",
        "\n",
        "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "40960/29515 [=========================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "26435584/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "16384/5148 [===============================================================================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n",
            "4431872/4422102 [==============================] - 0s 0us/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0nEKBGBaXphZ"
      },
      "source": [
        "There are 70000 grayscale images split it train and test set in the ratio 6:1 with each image having a dimension of 28x28 pixels."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RNREhLg1VguM"
      },
      "source": [
        "## Data visualization\n",
        "\n",
        "Let's try to visualize the dataset. We'll be using `matplotlib.pyplot` for this process."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "wbf4NGCNV8Q_",
        "outputId": "0388e840-f8d2-4586-9ffe-9b51f0fefd5d"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "for i in range(9):\n",
        "  plt.subplot(330 + 1 + i)\n",
        "  plt.imshow(X_train[i], cmap=plt.get_cmap('gray'))\n",
        "plt.show()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CfspznobYXdB"
      },
      "source": [
        "## Data transformation\n",
        "\n",
        "The data needs to be converted to `float32` and reshaped to `(28, 28, 1)` for our model to work with it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2w5p8eUc87J"
      },
      "source": [
        "import numpy as np\n",
        "from tensorflow.keras.utils import to_categorical"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GDeLWbVgYjHH",
        "outputId": "585ffe5e-be3e-4cb5-cada-21b111ffc37b"
      },
      "source": [
        "X_train = X_train.astype(\"float32\").reshape(-1, 28, 28, 1) / 255.\n",
        "X_test = X_test.astype(\"float32\").reshape(-1, 28, 28, 1) / 255.\n",
        "\n",
        "X_train.shape, X_test.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 28, 28, 1), (10000, 28, 28, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eZZd9zhBcRQG",
        "outputId": "8c15a91c-5646-4fec-d1d1-9d0afc43dbad"
      },
      "source": [
        "num_classes = len(np.unique(y_train))\n",
        "\n",
        "y_train = to_categorical(y_train, num_classes)\n",
        "y_test = to_categorical(y_test, num_classes)\n",
        "\n",
        "y_train.shape, y_test.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 10), (10000, 10))"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zyAuFCyS5Hdt"
      },
      "source": [
        "# Dataset Splitting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqqDKIY2ZK7g"
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_generator = ImageDataGenerator(rescale=1/255.,\n",
        "                                     rotation_range=2.0,\n",
        "                                     shear_range=0.2,\n",
        "                                     zoom_range=0.2,\n",
        "                                     width_shift_range=0.2,\n",
        "                                     height_shift_range=0.2,\n",
        "                                     horizontal_flip=True,\n",
        "                                     validation_split=0.2)\n",
        "\n",
        "train_datagen = train_generator.flow(X_train, y_train,\n",
        "                                     batch_size=32,\n",
        "                                     shuffle=False,\n",
        "                                     subset='training')\n",
        "\n",
        "valid_datagen = train_generator.flow(X_train, y_train,\n",
        "                                     batch_size=32,\n",
        "                                     shuffle=False,\n",
        "                                     subset='validation')\n",
        "\n",
        "test_generator = ImageDataGenerator(rescale=1/255.)\n",
        "\n",
        "test_datagen = test_generator.flow(X_test, y_test,\n",
        "                                   batch_size=32,\n",
        "                                   shuffle=False)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NhXq8f3Z5JR8"
      },
      "source": [
        "# Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hHpz0tKQdt6s"
      },
      "source": [
        "## Building"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DEpJO4mF7eN9"
      },
      "source": [
        "import tensorflow as tf\n",
        "from keras import layers\n",
        "\n",
        "input_layer = layers.Input(shape=(28,28,1), name=\"Input_Layer\")\n",
        "\n",
        "conv2d_1 = layers.Conv2D(filters=32, kernel_size=3, padding=\"same\", name=\"Conv2D_1\")(input_layer)\n",
        "batch_norm_1 = layers.BatchNormalization(name=\"Batch_Normalization_1\")(conv2d_1)\n",
        "relu_1 = layers.Activation(activation=\"relu\", name=\"ReLU_1\")(batch_norm_1)\n",
        "\n",
        "conv2d_2 = layers.Conv2D(filters=32, kernel_size=3, padding=\"same\", name=\"Conv2D_2\")(relu_1)\n",
        "batch_norm_2 = layers.BatchNormalization(name=\"Batch_Normalization_2\")(conv2d_2)\n",
        "relu_2 = layers.Activation(activation=\"relu\", name=\"ReLU_2\")(batch_norm_2)\n",
        "\n",
        "conv2d_3 = layers.Conv2D(filters=32, kernel_size=3, padding=\"same\", name=\"Conv2D_3\")(relu_2)\n",
        "batch_norm_3 = layers.BatchNormalization(name=\"Batch_Normalization_3\")(conv2d_3)\n",
        "\n",
        "attention_1 = layers.Attention(name=\"Attention_1\")([batch_norm_2, batch_norm_3])\n",
        "attention_2 = layers.Attention(name=\"Attention_2\")([batch_norm_3, attention_1])\n",
        "attention_3 = layers.Attention(name=\"Attention_3\")([attention_1, attention_2])\n",
        "attention_4 = layers.Attention(name=\"Attention_4\")([attention_2, attention_3])\n",
        "\n",
        "pooling = layers.MaxPooling2D(pool_size=(3,3), name=\"MaxPooling2D\")(attention_4)\n",
        "flatten = layers.Flatten(name=\"Flatten\")(pooling)\n",
        "dense_1 = layers.Dense(64, activation=\"relu\", name=\"Dense_1\")(flatten)\n",
        "\n",
        "output_layer = layers.Dense(num_classes, activation=\"softmax\", name=\"Output_Layer\")(dense_1) \n",
        "\n",
        "model = tf.keras.Model(inputs=input_layer, outputs=output_layer)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBRnPOQzdyZi"
      },
      "source": [
        "## Compiling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WKAfFbZsffxt"
      },
      "source": [
        "model.compile(loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "             optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),\n",
        "             metrics=[\"accuracy\"])"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d2qQ3-Kvd28C"
      },
      "source": [
        "## Summary & Plotting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0aTjCFhId48",
        "outputId": "b1c893cc-098e-4589-cbb0-6c09a54dc350"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "Input_Layer (InputLayer)        [(None, 28, 28, 1)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv2D_1 (Conv2D)               (None, 28, 28, 32)   320         Input_Layer[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "Batch_Normalization_1 (BatchNor (None, 28, 28, 32)   128         Conv2D_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "ReLU_1 (Activation)             (None, 28, 28, 32)   0           Batch_Normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "Conv2D_2 (Conv2D)               (None, 28, 28, 32)   9248        ReLU_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "Batch_Normalization_2 (BatchNor (None, 28, 28, 32)   128         Conv2D_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "ReLU_2 (Activation)             (None, 28, 28, 32)   0           Batch_Normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "Conv2D_3 (Conv2D)               (None, 28, 28, 32)   9248        ReLU_2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "Batch_Normalization_3 (BatchNor (None, 28, 28, 32)   128         Conv2D_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "Attention_1 (Attention)         (None, 28, 28, 32)   0           Batch_Normalization_2[0][0]      \n",
            "                                                                 Batch_Normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "Attention_2 (Attention)         (None, 28, 28, 32)   0           Batch_Normalization_3[0][0]      \n",
            "                                                                 Attention_1[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "Attention_3 (Attention)         (None, 28, 28, 32)   0           Attention_1[0][0]                \n",
            "                                                                 Attention_2[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "Attention_4 (Attention)         (None, 28, 28, 32)   0           Attention_2[0][0]                \n",
            "                                                                 Attention_3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "MaxPooling2D (MaxPooling2D)     (None, 9, 9, 32)     0           Attention_4[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "Flatten (Flatten)               (None, 2592)         0           MaxPooling2D[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "Dense_1 (Dense)                 (None, 64)           165952      Flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "Output_Layer (Dense)            (None, 10)           650         Dense_1[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 185,802\n",
            "Trainable params: 185,610\n",
            "Non-trainable params: 192\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ORN9BSjyId0b",
        "outputId": "25e7016e-a26d-47f9-c25f-013252eea6a4"
      },
      "source": [
        "tf.keras.utils.plot_model(model)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ogIsDHsIfjFM"
      },
      "source": [
        "## Callbacks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r9Z1h2Rwfkvu"
      },
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "callbacks = [\n",
        "             EarlyStopping(monitor=\"val_accuracy\",\n",
        "                           min_delta=1e-4,\n",
        "                           patience=15,\n",
        "                           restore_best_weights=True)\n",
        "]"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UTjqKtkaIdyE"
      },
      "source": [
        "## Fitting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "txkhYjSQeA8p",
        "outputId": "e767a757-a262-41e9-8cf1-23410f776c9a"
      },
      "source": [
        "history = model.fit(X_train, y_train,\n",
        "                    epochs=1000, batch_size=128,\n",
        "                    # steps_per_epoch=len(X_train)//32,\n",
        "                    validation_data=(X_val, y_val),\n",
        "                    # validation_steps=len(X_val)//32,\n",
        "                    callbacks=callbacks)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1000\n",
            "422/422 [==============================] - 52s 52ms/step - loss: 0.6662 - accuracy: 0.7615 - val_loss: 1.1893 - val_accuracy: 0.5983\n",
            "Epoch 2/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.4198 - accuracy: 0.8485 - val_loss: 0.4137 - val_accuracy: 0.8528\n",
            "Epoch 3/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.3714 - accuracy: 0.8671 - val_loss: 0.3942 - val_accuracy: 0.8587\n",
            "Epoch 4/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.3417 - accuracy: 0.8760 - val_loss: 0.3573 - val_accuracy: 0.8697\n",
            "Epoch 5/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.3168 - accuracy: 0.8858 - val_loss: 0.3459 - val_accuracy: 0.8753\n",
            "Epoch 6/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.3051 - accuracy: 0.8896 - val_loss: 0.3478 - val_accuracy: 0.8757\n",
            "Epoch 7/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.2915 - accuracy: 0.8935 - val_loss: 0.3242 - val_accuracy: 0.8820\n",
            "Epoch 8/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.2818 - accuracy: 0.8959 - val_loss: 0.3219 - val_accuracy: 0.8820\n",
            "Epoch 9/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.2741 - accuracy: 0.8983 - val_loss: 0.3152 - val_accuracy: 0.8858\n",
            "Epoch 10/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.2630 - accuracy: 0.9031 - val_loss: 0.3172 - val_accuracy: 0.8863\n",
            "Epoch 11/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.2547 - accuracy: 0.9064 - val_loss: 0.3310 - val_accuracy: 0.8785\n",
            "Epoch 12/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.2538 - accuracy: 0.9071 - val_loss: 0.3071 - val_accuracy: 0.8880\n",
            "Epoch 13/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.2445 - accuracy: 0.9101 - val_loss: 0.3011 - val_accuracy: 0.8895\n",
            "Epoch 14/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.2407 - accuracy: 0.9103 - val_loss: 0.3048 - val_accuracy: 0.8903\n",
            "Epoch 15/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.2331 - accuracy: 0.9137 - val_loss: 0.3047 - val_accuracy: 0.8943\n",
            "Epoch 16/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.2240 - accuracy: 0.9166 - val_loss: 0.3012 - val_accuracy: 0.8923\n",
            "Epoch 17/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.2170 - accuracy: 0.9200 - val_loss: 0.2942 - val_accuracy: 0.8935\n",
            "Epoch 18/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.2129 - accuracy: 0.9216 - val_loss: 0.2944 - val_accuracy: 0.8888\n",
            "Epoch 19/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.2099 - accuracy: 0.9220 - val_loss: 0.2968 - val_accuracy: 0.8945\n",
            "Epoch 20/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.2047 - accuracy: 0.9248 - val_loss: 0.2902 - val_accuracy: 0.8938\n",
            "Epoch 21/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.1995 - accuracy: 0.9266 - val_loss: 0.2864 - val_accuracy: 0.8945\n",
            "Epoch 22/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.1925 - accuracy: 0.9293 - val_loss: 0.2868 - val_accuracy: 0.8928\n",
            "Epoch 23/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.1910 - accuracy: 0.9297 - val_loss: 0.2747 - val_accuracy: 0.8987\n",
            "Epoch 24/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1907 - accuracy: 0.9285 - val_loss: 0.2775 - val_accuracy: 0.8978\n",
            "Epoch 25/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1822 - accuracy: 0.9320 - val_loss: 0.2778 - val_accuracy: 0.9010\n",
            "Epoch 26/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1794 - accuracy: 0.9338 - val_loss: 0.2746 - val_accuracy: 0.9027\n",
            "Epoch 27/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1771 - accuracy: 0.9347 - val_loss: 0.2823 - val_accuracy: 0.8985\n",
            "Epoch 28/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1822 - accuracy: 0.9313 - val_loss: 0.2825 - val_accuracy: 0.8993\n",
            "Epoch 29/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.1679 - accuracy: 0.9379 - val_loss: 0.2742 - val_accuracy: 0.9033\n",
            "Epoch 30/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.1735 - accuracy: 0.9354 - val_loss: 0.2778 - val_accuracy: 0.9017\n",
            "Epoch 31/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1715 - accuracy: 0.9363 - val_loss: 0.2825 - val_accuracy: 0.9012\n",
            "Epoch 32/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1638 - accuracy: 0.9392 - val_loss: 0.2853 - val_accuracy: 0.8960\n",
            "Epoch 33/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.1619 - accuracy: 0.9399 - val_loss: 0.2858 - val_accuracy: 0.8990\n",
            "Epoch 34/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1579 - accuracy: 0.9422 - val_loss: 0.2825 - val_accuracy: 0.9018\n",
            "Epoch 35/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1560 - accuracy: 0.9429 - val_loss: 0.2812 - val_accuracy: 0.8998\n",
            "Epoch 36/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.1565 - accuracy: 0.9418 - val_loss: 0.2876 - val_accuracy: 0.8982\n",
            "Epoch 37/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1581 - accuracy: 0.9419 - val_loss: 0.2810 - val_accuracy: 0.9017\n",
            "Epoch 38/1000\n",
            "422/422 [==============================] - 22s 52ms/step - loss: 0.1522 - accuracy: 0.9444 - val_loss: 0.2745 - val_accuracy: 0.9033\n",
            "Epoch 39/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1497 - accuracy: 0.9443 - val_loss: 0.2879 - val_accuracy: 0.8975\n",
            "Epoch 40/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1458 - accuracy: 0.9471 - val_loss: 0.2966 - val_accuracy: 0.8965\n",
            "Epoch 41/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.1487 - accuracy: 0.9453 - val_loss: 0.2884 - val_accuracy: 0.8990\n",
            "Epoch 42/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.1405 - accuracy: 0.9482 - val_loss: 0.2838 - val_accuracy: 0.9058\n",
            "Epoch 43/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.1339 - accuracy: 0.9514 - val_loss: 0.2792 - val_accuracy: 0.9045\n",
            "Epoch 44/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1290 - accuracy: 0.9523 - val_loss: 0.2880 - val_accuracy: 0.9008\n",
            "Epoch 45/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1272 - accuracy: 0.9537 - val_loss: 0.2849 - val_accuracy: 0.9008\n",
            "Epoch 46/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1221 - accuracy: 0.9556 - val_loss: 0.2926 - val_accuracy: 0.9005\n",
            "Epoch 47/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1261 - accuracy: 0.9537 - val_loss: 0.2988 - val_accuracy: 0.9012\n",
            "Epoch 48/1000\n",
            "422/422 [==============================] - 21s 50ms/step - loss: 0.1269 - accuracy: 0.9539 - val_loss: 0.2877 - val_accuracy: 0.9025\n",
            "Epoch 49/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1223 - accuracy: 0.9547 - val_loss: 0.2950 - val_accuracy: 0.9030\n",
            "Epoch 50/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1219 - accuracy: 0.9561 - val_loss: 0.2926 - val_accuracy: 0.8970\n",
            "Epoch 51/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1170 - accuracy: 0.9575 - val_loss: 0.2842 - val_accuracy: 0.9085\n",
            "Epoch 52/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1132 - accuracy: 0.9588 - val_loss: 0.2923 - val_accuracy: 0.9023\n",
            "Epoch 53/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1144 - accuracy: 0.9579 - val_loss: 0.2942 - val_accuracy: 0.9032\n",
            "Epoch 54/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1079 - accuracy: 0.9610 - val_loss: 0.2899 - val_accuracy: 0.9050\n",
            "Epoch 55/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1035 - accuracy: 0.9628 - val_loss: 0.2849 - val_accuracy: 0.9042\n",
            "Epoch 56/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.1177 - accuracy: 0.9560 - val_loss: 0.2837 - val_accuracy: 0.9028\n",
            "Epoch 57/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1117 - accuracy: 0.9591 - val_loss: 0.2958 - val_accuracy: 0.9048\n",
            "Epoch 58/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.1087 - accuracy: 0.9599 - val_loss: 0.2901 - val_accuracy: 0.9005\n",
            "Epoch 59/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1036 - accuracy: 0.9627 - val_loss: 0.2929 - val_accuracy: 0.9057\n",
            "Epoch 60/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1075 - accuracy: 0.9604 - val_loss: 0.3029 - val_accuracy: 0.8975\n",
            "Epoch 61/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.1004 - accuracy: 0.9640 - val_loss: 0.2884 - val_accuracy: 0.9080\n",
            "Epoch 62/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.0975 - accuracy: 0.9652 - val_loss: 0.3067 - val_accuracy: 0.9023\n",
            "Epoch 63/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.0961 - accuracy: 0.9653 - val_loss: 0.3195 - val_accuracy: 0.9015\n",
            "Epoch 64/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.0907 - accuracy: 0.9681 - val_loss: 0.3041 - val_accuracy: 0.9037\n",
            "Epoch 65/1000\n",
            "422/422 [==============================] - 22s 51ms/step - loss: 0.0890 - accuracy: 0.9688 - val_loss: 0.2951 - val_accuracy: 0.9053\n",
            "Epoch 66/1000\n",
            "422/422 [==============================] - 21s 51ms/step - loss: 0.1001 - accuracy: 0.9631 - val_loss: 0.2952 - val_accuracy: 0.9028\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Ml8QOMbJYEp"
      },
      "source": [
        "## Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GghCmfMW5UUS",
        "outputId": "b66d3bea-c509-4b07-eb1c-242b1ca6f8f3"
      },
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 2s 7ms/step - loss: 0.3006 - accuracy: 0.9005\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3006191551685333, 0.9004999995231628]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBXEeGsXJbrQ"
      },
      "source": [
        "## Visualizing history"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "OOiLJW00Jfhy",
        "outputId": "a930435c-f4e6-4287-fe2c-e562fba11457"
      },
      "source": [
        "plt.plot(history.epoch, history.history['loss'], 'g', label='Training loss')\n",
        "plt.plot(history.epoch, history.history['val_loss'], 'b', label='Validation loss')\n",
        "plt.title('Training and Validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show();"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "NTIx_pMOIRrO",
        "outputId": "e22b8b31-b215-4bc9-9fa9-00cacac14546"
      },
      "source": [
        "plt.plot(history.epoch, history.history['accuracy'], 'g', label='Training Accuracy')\n",
        "plt.plot(history.epoch, history.history['val_accuracy'], 'b', label='Validation Accuracy')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show();"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}