ELU_Activation_Function_With_Optimizer_Experiment.ipynb

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      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Part1 Activation Function ELU"
      ],
      "metadata": {
        "id": "RDlKRG4xOb4R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U -q tensorflow tensorflow_datasets\n",
        "!apt install --allow-change-held-packages libcudnn8=8.1.0.77-1+cuda11.2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8w9PoY3yOohN",
        "outputId": "d2234413-7285-4a0d-9b45-05af81a64559"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following packages will be REMOVED:\n",
            "  libcudnn8-dev\n",
            "The following held packages will be changed:\n",
            "  libcudnn8\n",
            "The following packages will be DOWNGRADED:\n",
            "  libcudnn8\n",
            "0 upgraded, 0 newly installed, 1 downgraded, 1 to remove and 22 not upgraded.\n",
            "Need to get 430 MB of archives.\n",
            "After this operation, 1,153 MB disk space will be freed.\n",
            "Get:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64  libcudnn8 8.1.0.77-1+cuda11.2 [430 MB]\n",
            "Fetched 430 MB in 7s (62.4 MB/s)\n",
            "(Reading database ... 122518 files and directories currently installed.)\n",
            "Removing libcudnn8-dev (8.7.0.84-1+cuda11.8) ...\n",
            "update-alternatives: removing manually selected alternative - switching libcudnn to auto mode\n",
            "\u001b[1mdpkg:\u001b[0m \u001b[1;33mwarning:\u001b[0m downgrading libcudnn8 from 8.7.0.84-1+cuda11.8 to 8.1.0.77-1+cuda11.2\n",
            "(Reading database ... 122485 files and directories currently installed.)\n",
            "Preparing to unpack .../libcudnn8_8.1.0.77-1+cuda11.2_amd64.deb ...\n",
            "Unpacking libcudnn8 (8.1.0.77-1+cuda11.2) over (8.7.0.84-1+cuda11.8) ...\n",
            "Setting up libcudnn8 (8.1.0.77-1+cuda11.2) ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pathlib\n",
        "import librosa\n",
        "import json\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import models\n",
        "from IPython import display\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n"
      ],
      "metadata": {
        "id": "9uP3GVH4PTHM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WF9xPPa-7k4u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e84f19df-9392-42ed-ee3b-baa18c5dac51"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from http://storage.googleapis.com/download.tensorflow.org/data/mini_speech_commands.zip\n",
            "182082353/182082353 [==============================] - 1s 0us/step\n"
          ]
        }
      ],
      "source": [
        "DATASET_PATH = 'data/mini_speech_commands'\n",
        "\n",
        "data_dir = pathlib.Path(DATASET_PATH)\n",
        "if not data_dir.exists():\n",
        "  tf.keras.utils.get_file(\n",
        "      'mini_speech_commands.zip',\n",
        "      origin=\"http://storage.googleapis.com/download.tensorflow.org/data/mini_speech_commands.zip\",\n",
        "      extract=True,\n",
        "      cache_dir='.', cache_subdir='data')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "commands = np.array(tf.io.gfile.listdir(str(data_dir)))\n",
        "commands = commands[(commands != 'README.md') & (commands != '.DS_Store')]\n",
        "print('Commands:', commands)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rp5YxWMYQu7W",
        "outputId": "8d47cc50-d094-4089-919a-223e9929241c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Commands: ['right' 'up' 'no' 'down' 'yes' 'left' 'go' 'stop']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "JSON_PATH = \"data.json\"\n",
        "SAMPLES_TO_CONSIDER = 22050 # 1 sec. of audio\n",
        "\n",
        "# dictionary where we'll store mapping, labels, MFCCs and filenames   \n",
        "data = {\n",
        "    \"mapping\": [],\n",
        "    \"labels\": [],\n",
        "    \"MFCCs\": [],\n",
        "    \"files\": []\n",
        "}\n",
        "\n",
        "# loop through all sub-dirs\n",
        "for i, (dirpath, dirnames, filenames) in enumerate(os.walk(DATASET_PATH)):\n",
        "\n",
        "    # ensure we're at sub-folder level\n",
        "    if dirpath is not DATASET_PATH:\n",
        "\n",
        "        # save label (i.e., sub-folder name) in the mapping\n",
        "        label = dirpath.split(\"/\")[-1]\n",
        "        data[\"mapping\"].append(label)\n",
        "        print(\"\\nProcessing: '{}'\".format(label))\n",
        "\n",
        "        # process all audio files in sub-dir and store MFCCs\n",
        "        for f in filenames:\n",
        "            file_path = os.path.join(dirpath, f)\n",
        "\n",
        "            # load audio file and slice it to ensure length consistency among different files\n",
        "            signal, sample_rate = librosa.load(file_path)\n",
        "\n",
        "            # drop audio files with less than pre-decided number of samples\n",
        "            if len(signal) >= SAMPLES_TO_CONSIDER:\n",
        "\n",
        "                # ensure consistency of the length of the signal\n",
        "                signal = signal[:SAMPLES_TO_CONSIDER]\n",
        "\n",
        "                # extract MFCCs\n",
        "\n",
        "                num_mfcc = 13\n",
        "                n_fft = 2048\n",
        "                hop_length = 512\n",
        "                MFCCs = librosa.feature.mfcc(y=signal, n_mfcc=num_mfcc, hop_length=hop_length, n_fft=n_fft)\n",
        "                # MFCCs = librosa.feature.mfcc(y=signal, sample_rate, n_mfcc=num_mfcc, n_fft=n_fft,hop_length=hop_length)\n",
        "\n",
        "                # store data for analysed track\n",
        "                data[\"MFCCs\"].append(MFCCs.T.tolist())\n",
        "                data[\"labels\"].append(i-1)\n",
        "                data[\"files\"].append(file_path)\n",
        "                print(\"{}: {}\".format(file_path, i-1))\n",
        "\n",
        "# save data in json file\n",
        "with open(JSON_PATH, \"w\") as fp:\n",
        "    json.dump(data, fp, indent=4)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_C_bp-JGQwBy",
        "outputId": "9d4f5440-b33f-4c32-f966-db0c55ef70b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "\n",
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            "\n",
            "Processing: 'left'\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DATA_PATH = \"data.json\"\n",
        "\n",
        "# generate train, validation and test sets        \n",
        "def prepare_dataset(data_path, test_size=0.2, validation_size=0.2):\n",
        "    # load dataset\n",
        "    with open(data_path, \"r\") as fp:\n",
        "        data = json.load(fp)\n",
        "\n",
        "    X = np.array(data[\"MFCCs\"])\n",
        "    y = np.array(data[\"labels\"])\n",
        "    print(\"Training sets loaded!\")\n",
        "\n",
        "\n",
        "    # create train, validation, test split\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)\n",
        "    X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validation_size)\n",
        "\n",
        "    # add an axis to nd array\n",
        "    X_train = X_train[..., np.newaxis]\n",
        "    X_test = X_test[..., np.newaxis]\n",
        "    X_validation = X_validation[..., np.newaxis]\n",
        "\n",
        "    return X_train, y_train, X_validation, y_validation, X_test, y_test\n",
        "\n",
        "X_train, y_train, X_validation, y_validation, X_test, y_test = prepare_dataset(DATA_PATH)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TqcMYT0SuPel",
        "outputId": "6226a5fd-89ec-49c8-b479-13a97fcb1ec2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training sets loaded!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Optimizer: Ada"
      ],
      "metadata": {
        "id": "mFw5wsBMIvRk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create network\n",
        "\n",
        "#parameters \n",
        "LEARNING_RATE = 0.001\n",
        "\n",
        "\n",
        "def build_model(input_shape, loss=\"sparse_categorical_crossentropy\", learning_rate=0.0001):\n",
        "    \n",
        "    # build network architecture using convolutional layers\n",
        "    model = tf.keras.models.Sequential()\n",
        "\n",
        "    # 1st conv layer\n",
        "    model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='elu', input_shape=input_shape, kernel_regularizer=tf.keras.regularizers.l2(0.001)))\n",
        "    model.add(tf.keras.layers.BatchNormalization())\n",
        "    model.add(tf.keras.layers.MaxPooling2D((3, 3), strides=(2,2), padding='same'))\n",
        "\n",
        "    # 2nd conv layer\n",
        "    model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='elu',kernel_regularizer=tf.keras.regularizers.l2(0.001)))\n",
        "    model.add(tf.keras.layers.BatchNormalization())\n",
        "    model.add(tf.keras.layers.MaxPooling2D((3, 3), strides=(2,2), padding='same'))\n",
        "\n",
        "    # 3rd conv layer\n",
        "    model.add(tf.keras.layers.Conv2D(32, (2, 2), activation='elu', kernel_regularizer=tf.keras.regularizers.l2(0.001)))\n",
        "    model.add(tf.keras.layers.BatchNormalization())\n",
        "    model.add(tf.keras.layers.MaxPooling2D((2, 2), strides=(2,2), padding='same'))\n",
        "\n",
        "    # flatten output and feed into dense layer\n",
        "    model.add(tf.keras.layers.Flatten())\n",
        "    model.add(tf.keras.layers.Dense(64, activation='elu'))\n",
        "    tf.keras.layers.Dropout(0.3)\n",
        "\n",
        "    # softmax output layer\n",
        "    model.add(tf.keras.layers.Dense(8, activation='softmax'))\n",
        "\n",
        "    optimiser = tf.optimizers.Adamax(learning_rate=learning_rate)\n",
        "\n",
        "    # compile model\n",
        "    model.compile(optimizer=optimiser,\n",
        "                  loss=loss,\n",
        "                  metrics=[\"accuracy\"])\n",
        "\n",
        "    # print model parameters on console\n",
        "    model.summary()\n",
        "\n",
        "    return model\n",
        "\n",
        "input_shape = (X_train.shape[1], X_train.shape[2], 1)\n",
        "model = build_model(input_shape, learning_rate=LEARNING_RATE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W1ZIJvynIutx",
        "outputId": "6b342d08-9ff9-41b6-b531-c87bf6d45dc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 42, 11, 64)        640       \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 42, 11, 64)       256       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 21, 6, 64)        0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 19, 4, 32)         18464     \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 19, 4, 32)        128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 10, 2, 32)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 9, 1, 32)          4128      \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 9, 1, 32)         128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 5, 1, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 160)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                10304     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 8)                 520       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 34,568\n",
            "Trainable params: 34,312\n",
            "Non-trainable params: 256\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "EPOCHS = 40\n",
        "BATCH_SIZE = 16\n",
        "PATIENCE = 5\n",
        "\n",
        "def train(model, epochs, batch_size, patience, X_train, y_train, X_validation, y_validation):\n",
        "    earlystop_callback = tf.keras.callbacks.EarlyStopping(monitor=\"accuracy\", min_delta=0.001, patience=patience)\n",
        "\n",
        "    # train model\n",
        "    history = model.fit(X_train,\n",
        "                        y_train,\n",
        "                        epochs=epochs,\n",
        "                        batch_size=batch_size,\n",
        "                        validation_data=(X_validation, y_validation),\n",
        "                        callbacks=[earlystop_callback])\n",
        "    return history\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# train network\n",
        "history = train(model, EPOCHS, BATCH_SIZE, PATIENCE, X_train, y_train, X_validation, y_validation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SwHC5umXI4eh",
        "outputId": "94ada487-1eb5-4b7e-94a9-746cf6b3fa0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "288/288 [==============================] - 14s 41ms/step - loss: 1.4512 - accuracy: 0.5136 - val_loss: 1.0232 - val_accuracy: 0.6876\n",
            "Epoch 2/40\n",
            "288/288 [==============================] - 14s 47ms/step - loss: 0.8482 - accuracy: 0.7437 - val_loss: 0.8070 - val_accuracy: 0.7589\n",
            "Epoch 3/40\n",
            "288/288 [==============================] - 12s 43ms/step - loss: 0.6469 - accuracy: 0.8158 - val_loss: 0.6526 - val_accuracy: 0.8068\n",
            "Epoch 4/40\n",
            "288/288 [==============================] - 12s 41ms/step - loss: 0.5460 - accuracy: 0.8478 - val_loss: 0.5636 - val_accuracy: 0.8416\n",
            "Epoch 5/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.4722 - accuracy: 0.8663 - val_loss: 0.5410 - val_accuracy: 0.8451\n",
            "Epoch 6/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.4186 - accuracy: 0.8944 - val_loss: 0.5273 - val_accuracy: 0.8442\n",
            "Epoch 7/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.3831 - accuracy: 0.9022 - val_loss: 0.5126 - val_accuracy: 0.8512\n",
            "Epoch 8/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.3760 - accuracy: 0.9014 - val_loss: 0.4802 - val_accuracy: 0.8738\n",
            "Epoch 9/40\n",
            "288/288 [==============================] - 10s 36ms/step - loss: 0.3437 - accuracy: 0.9168 - val_loss: 0.5299 - val_accuracy: 0.8625\n",
            "Epoch 10/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.3096 - accuracy: 0.9262 - val_loss: 0.4889 - val_accuracy: 0.8564\n",
            "Epoch 11/40\n",
            "288/288 [==============================] - 11s 39ms/step - loss: 0.2954 - accuracy: 0.9299 - val_loss: 0.5277 - val_accuracy: 0.8477\n",
            "Epoch 12/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.2733 - accuracy: 0.9364 - val_loss: 0.4490 - val_accuracy: 0.8842\n",
            "Epoch 13/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.2654 - accuracy: 0.9388 - val_loss: 0.4476 - val_accuracy: 0.8790\n",
            "Epoch 14/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.2525 - accuracy: 0.9443 - val_loss: 0.4159 - val_accuracy: 0.8921\n",
            "Epoch 15/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.2363 - accuracy: 0.9523 - val_loss: 0.5130 - val_accuracy: 0.8660\n",
            "Epoch 16/40\n",
            "288/288 [==============================] - 11s 38ms/step - loss: 0.2328 - accuracy: 0.9488 - val_loss: 0.4543 - val_accuracy: 0.8764\n",
            "Epoch 17/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.2215 - accuracy: 0.9571 - val_loss: 0.4607 - val_accuracy: 0.8808\n",
            "Epoch 18/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.2148 - accuracy: 0.9551 - val_loss: 0.4150 - val_accuracy: 0.8973\n",
            "Epoch 19/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.2053 - accuracy: 0.9634 - val_loss: 0.4937 - val_accuracy: 0.8712\n",
            "Epoch 20/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1994 - accuracy: 0.9632 - val_loss: 0.4369 - val_accuracy: 0.8729\n",
            "Epoch 21/40\n",
            "288/288 [==============================] - 10s 33ms/step - loss: 0.2037 - accuracy: 0.9573 - val_loss: 0.4324 - val_accuracy: 0.8877\n",
            "Epoch 22/40\n",
            "288/288 [==============================] - 9s 31ms/step - loss: 0.1774 - accuracy: 0.9721 - val_loss: 0.4802 - val_accuracy: 0.8764\n",
            "Epoch 23/40\n",
            "288/288 [==============================] - 11s 38ms/step - loss: 0.1919 - accuracy: 0.9641 - val_loss: 0.4180 - val_accuracy: 0.8903\n",
            "Epoch 24/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1815 - accuracy: 0.9676 - val_loss: 0.4321 - val_accuracy: 0.8869\n",
            "Epoch 25/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1634 - accuracy: 0.9726 - val_loss: 0.4267 - val_accuracy: 0.8825\n",
            "Epoch 26/40\n",
            "288/288 [==============================] - 9s 31ms/step - loss: 0.1631 - accuracy: 0.9756 - val_loss: 0.4670 - val_accuracy: 0.8808\n",
            "Epoch 27/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1577 - accuracy: 0.9769 - val_loss: 0.4094 - val_accuracy: 0.8860\n",
            "Epoch 28/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1565 - accuracy: 0.9747 - val_loss: 0.4609 - val_accuracy: 0.8773\n",
            "Epoch 29/40\n",
            "288/288 [==============================] - 9s 33ms/step - loss: 0.1591 - accuracy: 0.9741 - val_loss: 0.4531 - val_accuracy: 0.8947\n",
            "Epoch 30/40\n",
            "288/288 [==============================] - 10s 33ms/step - loss: 0.1460 - accuracy: 0.9802 - val_loss: 0.4346 - val_accuracy: 0.8886\n",
            "Epoch 31/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1456 - accuracy: 0.9795 - val_loss: 0.4587 - val_accuracy: 0.8869\n",
            "Epoch 32/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1421 - accuracy: 0.9787 - val_loss: 0.4704 - val_accuracy: 0.8808\n",
            "Epoch 33/40\n",
            "288/288 [==============================] - 9s 33ms/step - loss: 0.1317 - accuracy: 0.9854 - val_loss: 0.5024 - val_accuracy: 0.8782\n",
            "Epoch 34/40\n",
            "288/288 [==============================] - 10s 33ms/step - loss: 0.1362 - accuracy: 0.9817 - val_loss: 0.4411 - val_accuracy: 0.8947\n",
            "Epoch 35/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1354 - accuracy: 0.9826 - val_loss: 0.4754 - val_accuracy: 0.8860\n",
            "Epoch 36/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1313 - accuracy: 0.9813 - val_loss: 0.4444 - val_accuracy: 0.8930\n",
            "Epoch 37/40\n",
            "288/288 [==============================] - 9s 33ms/step - loss: 0.1219 - accuracy: 0.9858 - val_loss: 0.4599 - val_accuracy: 0.8825\n",
            "Epoch 38/40\n",
            "288/288 [==============================] - 9s 33ms/step - loss: 0.1205 - accuracy: 0.9850 - val_loss: 0.4761 - val_accuracy: 0.8930\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate network on test set\n",
        "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
        "print(\"\\nTest loss: {}, test accuracy: {}\".format(test_loss, 100*test_acc))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4OPXmSrJEJC",
        "outputId": "846608c9-ddce-4df0-cc04-36d44880a04f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "45/45 [==============================] - 1s 15ms/step - loss: 0.4607 - accuracy: 0.8879\n",
            "\n",
            "Test loss: 0.46074172854423523, test accuracy: 88.78830075263977\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Optiizer: *Adamax*"
      ],
      "metadata": {
        "id": "-Yo9RfaDCc0e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create network\n",
        "\n",
        "#parameters \n",
        "LEARNING_RATE = 0.001\n",
        "\n",
        "\n",
        "def build_model(input_shape, loss=\"sparse_categorical_crossentropy\", learning_rate=0.0001):\n",
        "    \n",
        "    # build network architecture using convolutional layers\n",
        "    model = tf.keras.models.Sequential()\n",
        "\n",
        "    # 1st conv layer\n",
        "    model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='elu', input_shape=input_shape, kernel_regularizer=tf.keras.regularizers.l2(0.001)))\n",
        "    model.add(tf.keras.layers.BatchNormalization())\n",
        "    model.add(tf.keras.layers.MaxPooling2D((3, 3), strides=(2,2), padding='same'))\n",
        "\n",
        "    # 2nd conv layer\n",
        "    model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='elu',kernel_regularizer=tf.keras.regularizers.l2(0.001)))\n",
        "    model.add(tf.keras.layers.BatchNormalization())\n",
        "    model.add(tf.keras.layers.MaxPooling2D((3, 3), strides=(2,2), padding='same'))\n",
        "\n",
        "    # 3rd conv layer\n",
        "    model.add(tf.keras.layers.Conv2D(32, (2, 2), activation='elu', kernel_regularizer=tf.keras.regularizers.l2(0.001)))\n",
        "    model.add(tf.keras.layers.BatchNormalization())\n",
        "    model.add(tf.keras.layers.MaxPooling2D((2, 2), strides=(2,2), padding='same'))\n",
        "\n",
        "    # flatten output and feed into dense layer\n",
        "    model.add(tf.keras.layers.Flatten())\n",
        "    model.add(tf.keras.layers.Dense(64, activation='elu'))\n",
        "    tf.keras.layers.Dropout(0.3)\n",
        "\n",
        "    # softmax output layer\n",
        "    model.add(tf.keras.layers.Dense(8, activation='softmax'))\n",
        "\n",
        "    optimiser = tf.optimizers.Adamax(learning_rate=learning_rate)\n",
        "\n",
        "    # compile model\n",
        "    model.compile(optimizer=optimiser,\n",
        "                  loss=loss,\n",
        "                  metrics=[\"accuracy\"])\n",
        "\n",
        "    # print model parameters on console\n",
        "    model.summary()\n",
        "\n",
        "    return model\n",
        "\n",
        "input_shape = (X_train.shape[1], X_train.shape[2], 1)\n",
        "model = build_model(input_shape, learning_rate=LEARNING_RATE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P4dOwlCownU_",
        "outputId": "c80ff439-6ff7-4d58-9c95-51994656e592"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_3 (Conv2D)           (None, 42, 11, 64)        640       \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 42, 11, 64)       256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 21, 6, 64)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 19, 4, 32)         18464     \n",
            "                                                                 \n",
            " batch_normalization_4 (Batc  (None, 19, 4, 32)        128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPooling  (None, 10, 2, 32)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 9, 1, 32)          4128      \n",
            "                                                                 \n",
            " batch_normalization_5 (Batc  (None, 9, 1, 32)         128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPooling  (None, 5, 1, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 160)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 64)                10304     \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 8)                 520       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 34,568\n",
            "Trainable params: 34,312\n",
            "Non-trainable params: 256\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "EPOCHS = 40\n",
        "BATCH_SIZE = 16\n",
        "PATIENCE = 5\n",
        "\n",
        "def train(model, epochs, batch_size, patience, X_train, y_train, X_validation, y_validation):\n",
        "    earlystop_callback = tf.keras.callbacks.EarlyStopping(monitor=\"accuracy\", min_delta=0.001, patience=patience)\n",
        "\n",
        "    # train model\n",
        "    history = model.fit(X_train,\n",
        "                        y_train,\n",
        "                        epochs=epochs,\n",
        "                        batch_size=batch_size,\n",
        "                        validation_data=(X_validation, y_validation),\n",
        "                        callbacks=[earlystop_callback])\n",
        "    return history\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# train network\n",
        "history = train(model, EPOCHS, BATCH_SIZE, PATIENCE, X_train, y_train, X_validation, y_validation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sQHNl_oAuPyD",
        "outputId": "68014c5d-a290-4248-c51e-7cf8c60b125e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "288/288 [==============================] - 12s 35ms/step - loss: 1.3961 - accuracy: 0.5391 - val_loss: 1.0679 - val_accuracy: 0.6606\n",
            "Epoch 2/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.8575 - accuracy: 0.7420 - val_loss: 0.8271 - val_accuracy: 0.7537\n",
            "Epoch 3/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.6540 - accuracy: 0.8132 - val_loss: 0.7269 - val_accuracy: 0.7694\n",
            "Epoch 4/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.5465 - accuracy: 0.8528 - val_loss: 0.6768 - val_accuracy: 0.8033\n",
            "Epoch 5/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.4737 - accuracy: 0.8755 - val_loss: 0.5971 - val_accuracy: 0.8155\n",
            "Epoch 6/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.4289 - accuracy: 0.8898 - val_loss: 0.5624 - val_accuracy: 0.8338\n",
            "Epoch 7/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.4020 - accuracy: 0.8970 - val_loss: 0.5908 - val_accuracy: 0.8190\n",
            "Epoch 8/40\n",
            "288/288 [==============================] - 12s 41ms/step - loss: 0.3687 - accuracy: 0.9068 - val_loss: 0.6256 - val_accuracy: 0.8251\n",
            "Epoch 9/40\n",
            "288/288 [==============================] - 10s 33ms/step - loss: 0.3532 - accuracy: 0.9125 - val_loss: 0.6114 - val_accuracy: 0.8320\n",
            "Epoch 10/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.3184 - accuracy: 0.9218 - val_loss: 0.5173 - val_accuracy: 0.8642\n",
            "Epoch 11/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.3015 - accuracy: 0.9308 - val_loss: 0.4769 - val_accuracy: 0.8764\n",
            "Epoch 12/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.2912 - accuracy: 0.9312 - val_loss: 0.4593 - val_accuracy: 0.8686\n",
            "Epoch 13/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.2617 - accuracy: 0.9462 - val_loss: 0.5015 - val_accuracy: 0.8651\n",
            "Epoch 14/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.2471 - accuracy: 0.9473 - val_loss: 0.4775 - val_accuracy: 0.8712\n",
            "Epoch 15/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.2379 - accuracy: 0.9480 - val_loss: 0.4659 - val_accuracy: 0.8825\n",
            "Epoch 16/40\n",
            "288/288 [==============================] - 9s 31ms/step - loss: 0.2177 - accuracy: 0.9597 - val_loss: 0.4531 - val_accuracy: 0.8790\n",
            "Epoch 17/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.2106 - accuracy: 0.9615 - val_loss: 0.4556 - val_accuracy: 0.8860\n",
            "Epoch 18/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.2074 - accuracy: 0.9589 - val_loss: 0.4710 - val_accuracy: 0.8764\n",
            "Epoch 19/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1975 - accuracy: 0.9645 - val_loss: 0.4636 - val_accuracy: 0.8834\n",
            "Epoch 20/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.1889 - accuracy: 0.9663 - val_loss: 0.4629 - val_accuracy: 0.8782\n",
            "Epoch 21/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1885 - accuracy: 0.9671 - val_loss: 0.4862 - val_accuracy: 0.8729\n",
            "Epoch 22/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1842 - accuracy: 0.9669 - val_loss: 0.4573 - val_accuracy: 0.8808\n",
            "Epoch 23/40\n",
            "288/288 [==============================] - 10s 33ms/step - loss: 0.1706 - accuracy: 0.9732 - val_loss: 0.4659 - val_accuracy: 0.8903\n",
            "Epoch 24/40\n",
            "288/288 [==============================] - 9s 31ms/step - loss: 0.1654 - accuracy: 0.9776 - val_loss: 0.4428 - val_accuracy: 0.8886\n",
            "Epoch 25/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.1623 - accuracy: 0.9771 - val_loss: 0.4896 - val_accuracy: 0.8842\n",
            "Epoch 26/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1602 - accuracy: 0.9754 - val_loss: 0.4517 - val_accuracy: 0.8921\n",
            "Epoch 27/40\n",
            "288/288 [==============================] - 9s 33ms/step - loss: 0.1555 - accuracy: 0.9791 - val_loss: 0.4438 - val_accuracy: 0.8930\n",
            "Epoch 28/40\n",
            "288/288 [==============================] - 10s 33ms/step - loss: 0.1524 - accuracy: 0.9782 - val_loss: 0.4574 - val_accuracy: 0.8816\n",
            "Epoch 29/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1458 - accuracy: 0.9824 - val_loss: 0.4780 - val_accuracy: 0.8860\n",
            "Epoch 30/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1430 - accuracy: 0.9815 - val_loss: 0.4665 - val_accuracy: 0.8886\n",
            "Epoch 31/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.1413 - accuracy: 0.9826 - val_loss: 0.4575 - val_accuracy: 0.8886\n",
            "Epoch 32/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.1375 - accuracy: 0.9828 - val_loss: 0.4710 - val_accuracy: 0.8903\n",
            "Epoch 33/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1396 - accuracy: 0.9806 - val_loss: 0.4360 - val_accuracy: 0.8921\n",
            "Epoch 34/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.1332 - accuracy: 0.9848 - val_loss: 0.4580 - val_accuracy: 0.8912\n",
            "Epoch 35/40\n",
            "288/288 [==============================] - 9s 31ms/step - loss: 0.1334 - accuracy: 0.9817 - val_loss: 0.4681 - val_accuracy: 0.8903\n",
            "Epoch 36/40\n",
            "288/288 [==============================] - 11s 37ms/step - loss: 0.1347 - accuracy: 0.9830 - val_loss: 0.4583 - val_accuracy: 0.8860\n",
            "Epoch 37/40\n",
            "288/288 [==============================] - 11s 37ms/step - loss: 0.1307 - accuracy: 0.9824 - val_loss: 0.4550 - val_accuracy: 0.8938\n",
            "Epoch 38/40\n",
            "288/288 [==============================] - 11s 37ms/step - loss: 0.1217 - accuracy: 0.9865 - val_loss: 0.4509 - val_accuracy: 0.8947\n",
            "Epoch 39/40\n",
            "288/288 [==============================] - 9s 31ms/step - loss: 0.1195 - accuracy: 0.9887 - val_loss: 0.4768 - val_accuracy: 0.8825\n",
            "Epoch 40/40\n",
            "288/288 [==============================] - 11s 37ms/step - loss: 0.1256 - accuracy: 0.9843 - val_loss: 0.4820 - val_accuracy: 0.8912\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_history(history):\n",
        "    \"\"\"Plots accuracy/loss for training/validation set as a function of the epochs\n",
        "\n",
        "    :param history: Training history of model\n",
        "    :return:\n",
        "    \"\"\"\n",
        "\n",
        "    fig, axs = plt.subplots(2)\n",
        "\n",
        "    # create accuracy subplot\n",
        "    axs[0].plot(history.history[\"accuracy\"], label=\"accuracy\")\n",
        "    axs[0].plot(history.history['val_accuracy'], label=\"val_accuracy\")\n",
        "    axs[0].set_ylabel(\"Accuracy\")\n",
        "    axs[0].legend(loc=\"lower right\")\n",
        "    axs[0].set_title(\"Accuracy evaluation\")\n",
        "\n",
        "    # create loss subplot\n",
        "    axs[1].plot(history.history[\"loss\"], label=\"loss\")\n",
        "    axs[1].plot(history.history['val_loss'], label=\"val_loss\")\n",
        "    axs[1].set_xlabel(\"Epoch\")\n",
        "    axs[1].set_ylabel(\"Loss\")\n",
        "    axs[1].legend(loc=\"upper right\")\n",
        "    axs[1].set_title(\"Loss evaluation\")\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "\n",
        "# plot accuracy/loss for training/validation set as a function of the epochs\n",
        "plot_history(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "LGETq7Vb6mS_",
        "outputId": "3419683e-d3c5-47ae-b6c2-106b2550cf53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate network on test set\n",
        "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
        "print(\"\\nTest loss: {}, test accuracy: {}\".format(test_loss, 100*test_acc))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rbqIszYC8Zzh",
        "outputId": "fc2cdb89-41b5-495a-b88f-fe653d2d374a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "45/45 [==============================] - 1s 16ms/step - loss: 0.4190 - accuracy: 0.8983\n",
            "\n",
            "Test loss: 0.4189881682395935, test accuracy: 89.83286619186401\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Optimizer: Adagrad"
      ],
      "metadata": {
        "id": "MT6N_Vq_Cmtk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create network\n",
        "\n",
        "#parameters \n",
        "LEARNING_RATE = 0.001\n",
        "\n",
        "\n",
        "def build_model(input_shape, loss=\"sparse_categorical_crossentropy\", learning_rate=0.0001):\n",
        "    \n",
        "    # build network architecture using convolutional layers\n",
        "    model = tf.keras.models.Sequential()\n",
        "\n",
        "    # 1st conv layer\n",
        "    model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='elu', input_shape=input_shape, kernel_regularizer=tf.keras.regularizers.l2(0.001)))\n",
        "    model.add(tf.keras.layers.BatchNormalization())\n",
        "    model.add(tf.keras.layers.MaxPooling2D((3, 3), strides=(2,2), padding='same'))\n",
        "\n",
        "    # 2nd conv layer\n",
        "    model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='elu',kernel_regularizer=tf.keras.regularizers.l2(0.001)))\n",
        "    model.add(tf.keras.layers.BatchNormalization())\n",
        "    model.add(tf.keras.layers.MaxPooling2D((3, 3), strides=(2,2), padding='same'))\n",
        "\n",
        "    # 3rd conv layer\n",
        "    model.add(tf.keras.layers.Conv2D(32, (2, 2), activation='elu', kernel_regularizer=tf.keras.regularizers.l2(0.001)))\n",
        "    model.add(tf.keras.layers.BatchNormalization())\n",
        "    model.add(tf.keras.layers.MaxPooling2D((2, 2), strides=(2,2), padding='same'))\n",
        "\n",
        "    # flatten output and feed into dense layer\n",
        "    model.add(tf.keras.layers.Flatten())\n",
        "    model.add(tf.keras.layers.Dense(64, activation='elu'))\n",
        "    tf.keras.layers.Dropout(0.3)\n",
        "\n",
        "    # softmax output layer\n",
        "    model.add(tf.keras.layers.Dense(8, activation='softmax'))\n",
        "\n",
        "    optimiser = tf.optimizers.Adagrad(learning_rate=learning_rate)\n",
        "\n",
        "    # compile model\n",
        "    model.compile(optimizer=optimiser,\n",
        "                  loss=loss,\n",
        "                  metrics=[\"accuracy\"])\n",
        "\n",
        "    # print model parameters on console\n",
        "    model.summary()\n",
        "\n",
        "    return model\n",
        "\n",
        "input_shape = (X_train.shape[1], X_train.shape[2], 1)\n",
        "model = build_model(input_shape, learning_rate=LEARNING_RATE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ddc24f08-42c3-4a64-d3e7-daa32e7d31e1",
        "id": "bazd8iDiDHGB"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_6 (Conv2D)           (None, 42, 11, 64)        640       \n",
            "                                                                 \n",
            " batch_normalization_6 (Batc  (None, 42, 11, 64)       256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_6 (MaxPooling  (None, 21, 6, 64)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 19, 4, 32)         18464     \n",
            "                                                                 \n",
            " batch_normalization_7 (Batc  (None, 19, 4, 32)        128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_7 (MaxPooling  (None, 10, 2, 32)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_8 (Conv2D)           (None, 9, 1, 32)          4128      \n",
            "                                                                 \n",
            " batch_normalization_8 (Batc  (None, 9, 1, 32)         128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_8 (MaxPooling  (None, 5, 1, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 160)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 64)                10304     \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 8)                 520       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 34,568\n",
            "Trainable params: 34,312\n",
            "Non-trainable params: 256\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "EPOCHS = 40\n",
        "BATCH_SIZE = 16\n",
        "PATIENCE = 5\n",
        "\n",
        "def train(model, epochs, batch_size, patience, X_train, y_train, X_validation, y_validation):\n",
        "    earlystop_callback = tf.keras.callbacks.EarlyStopping(monitor=\"accuracy\", min_delta=0.001, patience=patience)\n",
        "\n",
        "    # train model\n",
        "    history = model.fit(X_train,\n",
        "                        y_train,\n",
        "                        epochs=epochs,\n",
        "                        batch_size=batch_size,\n",
        "                        validation_data=(X_validation, y_validation),\n",
        "                        callbacks=[earlystop_callback])\n",
        "    return history\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# train network\n",
        "history = train(model, EPOCHS, BATCH_SIZE, PATIENCE, X_train, y_train, X_validation, y_validation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b60bb73-38d7-4ecb-c1f0-fdefb4b967e7",
        "id": "qtCKlq0iDOAA"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "288/288 [==============================] - 11s 35ms/step - loss: 1.9166 - accuracy: 0.3433 - val_loss: 1.6296 - val_accuracy: 0.4526\n",
            "Epoch 2/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 1.5143 - accuracy: 0.5003 - val_loss: 1.4427 - val_accuracy: 0.5265\n",
            "Epoch 3/40\n",
            "288/288 [==============================] - 10s 36ms/step - loss: 1.3698 - accuracy: 0.5578 - val_loss: 1.3386 - val_accuracy: 0.5631\n",
            "Epoch 4/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 1.2714 - accuracy: 0.5987 - val_loss: 1.2604 - val_accuracy: 0.5849\n",
            "Epoch 5/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 1.1976 - accuracy: 0.6190 - val_loss: 1.1992 - val_accuracy: 0.6144\n",
            "Epoch 6/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 1.1309 - accuracy: 0.6506 - val_loss: 1.1413 - val_accuracy: 0.6336\n",
            "Epoch 7/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 1.0812 - accuracy: 0.6684 - val_loss: 1.0908 - val_accuracy: 0.6580\n",
            "Epoch 8/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 1.0419 - accuracy: 0.6808 - val_loss: 1.0607 - val_accuracy: 0.6675\n",
            "Epoch 9/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 1.0055 - accuracy: 0.7024 - val_loss: 1.0349 - val_accuracy: 0.6902\n",
            "Epoch 10/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.9634 - accuracy: 0.7117 - val_loss: 1.0040 - val_accuracy: 0.6980\n",
            "Epoch 11/40\n",
            "288/288 [==============================] - 9s 32ms/step - loss: 0.9385 - accuracy: 0.7248 - val_loss: 0.9712 - val_accuracy: 0.6919\n",
            "Epoch 12/40\n",
            "288/288 [==============================] - 9s 33ms/step - loss: 0.9026 - accuracy: 0.7337 - val_loss: 0.9523 - val_accuracy: 0.7137\n",
            "Epoch 13/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.8911 - accuracy: 0.7372 - val_loss: 0.9356 - val_accuracy: 0.7128\n",
            "Epoch 14/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.8675 - accuracy: 0.7466 - val_loss: 0.9127 - val_accuracy: 0.7232\n",
            "Epoch 15/40\n",
            "288/288 [==============================] - 9s 31ms/step - loss: 0.8447 - accuracy: 0.7522 - val_loss: 0.9094 - val_accuracy: 0.7267\n",
            "Epoch 16/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.8280 - accuracy: 0.7572 - val_loss: 0.8877 - val_accuracy: 0.7328\n",
            "Epoch 17/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.8142 - accuracy: 0.7655 - val_loss: 0.8709 - val_accuracy: 0.7389\n",
            "Epoch 18/40\n",
            "288/288 [==============================] - 11s 39ms/step - loss: 0.7958 - accuracy: 0.7668 - val_loss: 0.8769 - val_accuracy: 0.7302\n",
            "Epoch 19/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.7852 - accuracy: 0.7718 - val_loss: 0.8449 - val_accuracy: 0.7450\n",
            "Epoch 20/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.7745 - accuracy: 0.7744 - val_loss: 0.8406 - val_accuracy: 0.7424\n",
            "Epoch 21/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.7578 - accuracy: 0.7792 - val_loss: 0.8414 - val_accuracy: 0.7493\n",
            "Epoch 22/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.7465 - accuracy: 0.7914 - val_loss: 0.8197 - val_accuracy: 0.7607\n",
            "Epoch 23/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.7375 - accuracy: 0.7888 - val_loss: 0.8096 - val_accuracy: 0.7581\n",
            "Epoch 24/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.7345 - accuracy: 0.7888 - val_loss: 0.8028 - val_accuracy: 0.7633\n",
            "Epoch 25/40\n",
            "288/288 [==============================] - 10s 36ms/step - loss: 0.7208 - accuracy: 0.7892 - val_loss: 0.7923 - val_accuracy: 0.7650\n",
            "Epoch 26/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.7127 - accuracy: 0.7956 - val_loss: 0.7903 - val_accuracy: 0.7685\n",
            "Epoch 27/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.7021 - accuracy: 0.8030 - val_loss: 0.7818 - val_accuracy: 0.7676\n",
            "Epoch 28/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.6903 - accuracy: 0.8038 - val_loss: 0.7812 - val_accuracy: 0.7728\n",
            "Epoch 29/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.6841 - accuracy: 0.8067 - val_loss: 0.7691 - val_accuracy: 0.7737\n",
            "Epoch 30/40\n",
            "288/288 [==============================] - 9s 32ms/step - loss: 0.6817 - accuracy: 0.8078 - val_loss: 0.7627 - val_accuracy: 0.7781\n",
            "Epoch 31/40\n",
            "288/288 [==============================] - 9s 32ms/step - loss: 0.6670 - accuracy: 0.8152 - val_loss: 0.7611 - val_accuracy: 0.7789\n",
            "Epoch 32/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.6609 - accuracy: 0.8093 - val_loss: 0.7590 - val_accuracy: 0.7755\n",
            "Epoch 33/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.6617 - accuracy: 0.8115 - val_loss: 0.7481 - val_accuracy: 0.7781\n",
            "Epoch 34/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.6484 - accuracy: 0.8134 - val_loss: 0.7406 - val_accuracy: 0.7885\n",
            "Epoch 35/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.6442 - accuracy: 0.8217 - val_loss: 0.7365 - val_accuracy: 0.7868\n",
            "Epoch 36/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.6377 - accuracy: 0.8223 - val_loss: 0.7264 - val_accuracy: 0.7885\n",
            "Epoch 37/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.6346 - accuracy: 0.8221 - val_loss: 0.7220 - val_accuracy: 0.7894\n",
            "Epoch 38/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.6240 - accuracy: 0.8297 - val_loss: 0.7179 - val_accuracy: 0.7955\n",
            "Epoch 39/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.6127 - accuracy: 0.8280 - val_loss: 0.7192 - val_accuracy: 0.7894\n",
            "Epoch 40/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.6171 - accuracy: 0.8324 - val_loss: 0.7123 - val_accuracy: 0.7929\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate network on test set\n",
        "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
        "print(\"\\nTest loss: {}, test accuracy: {}\".format(test_loss, 100*test_acc))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8963ff0f-dbe4-4180-d1eb-c63d428c7319",
        "id": "ui0JbwSoDh69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "45/45 [==============================] - 1s 18ms/step - loss: 0.6988 - accuracy: 0.7841\n",
            "\n",
            "Test loss: 0.698760986328125, test accuracy: 78.41225862503052\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Optimizer:Nadam"
      ],
      "metadata": {
        "id": "YL4da5JVDkcA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create network\n",
        "\n",
        "#parameters \n",
        "LEARNING_RATE = 0.001\n",
        "\n",
        "\n",
        "def build_model(input_shape, loss=\"sparse_categorical_crossentropy\", learning_rate=0.0001):\n",
        "    \n",
        "    # build network architecture using convolutional layers\n",
        "    model = tf.keras.models.Sequential()\n",
        "\n",
        "    # 1st conv layer\n",
        "    model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='elu', input_shape=input_shape, kernel_regularizer=tf.keras.regularizers.l2(0.001)))\n",
        "    model.add(tf.keras.layers.BatchNormalization())\n",
        "    model.add(tf.keras.layers.MaxPooling2D((3, 3), strides=(2,2), padding='same'))\n",
        "\n",
        "    # 2nd conv layer\n",
        "    model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='elu',kernel_regularizer=tf.keras.regularizers.l2(0.001)))\n",
        "    model.add(tf.keras.layers.BatchNormalization())\n",
        "    model.add(tf.keras.layers.MaxPooling2D((3, 3), strides=(2,2), padding='same'))\n",
        "\n",
        "    # 3rd conv layer\n",
        "    model.add(tf.keras.layers.Conv2D(32, (2, 2), activation='elu', kernel_regularizer=tf.keras.regularizers.l2(0.001)))\n",
        "    model.add(tf.keras.layers.BatchNormalization())\n",
        "    model.add(tf.keras.layers.MaxPooling2D((2, 2), strides=(2,2), padding='same'))\n",
        "\n",
        "    # flatten output and feed into dense layer\n",
        "    model.add(tf.keras.layers.Flatten())\n",
        "    model.add(tf.keras.layers.Dense(64, activation='elu'))\n",
        "    tf.keras.layers.Dropout(0.3)\n",
        "\n",
        "    # softmax output layer\n",
        "    model.add(tf.keras.layers.Dense(8, activation='softmax'))\n",
        "\n",
        "    optimiser = tf.optimizers.Adagrad(learning_rate=learning_rate)\n",
        "\n",
        "    # compile model\n",
        "    model.compile(optimizer=optimiser,\n",
        "                  loss=loss,\n",
        "                  metrics=[\"accuracy\"])\n",
        "\n",
        "    # print model parameters on console\n",
        "    model.summary()\n",
        "\n",
        "    return model\n",
        "\n",
        "input_shape = (X_train.shape[1], X_train.shape[2], 1)\n",
        "model = build_model(input_shape, learning_rate=LEARNING_RATE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GQfF3ip8Dvdo",
        "outputId": "c59ad835-68a2-444a-ae61-216888f40a30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_9 (Conv2D)           (None, 42, 11, 64)        640       \n",
            "                                                                 \n",
            " batch_normalization_9 (Batc  (None, 42, 11, 64)       256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_9 (MaxPooling  (None, 21, 6, 64)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_10 (Conv2D)          (None, 19, 4, 32)         18464     \n",
            "                                                                 \n",
            " batch_normalization_10 (Bat  (None, 19, 4, 32)        128       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " max_pooling2d_10 (MaxPoolin  (None, 10, 2, 32)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_11 (Conv2D)          (None, 9, 1, 32)          4128      \n",
            "                                                                 \n",
            " batch_normalization_11 (Bat  (None, 9, 1, 32)         128       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " max_pooling2d_11 (MaxPoolin  (None, 5, 1, 32)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_3 (Flatten)         (None, 160)               0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 64)                10304     \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 8)                 520       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 34,568\n",
            "Trainable params: 34,312\n",
            "Non-trainable params: 256\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "EPOCHS = 40\n",
        "BATCH_SIZE = 16\n",
        "PATIENCE = 5\n",
        "\n",
        "def train(model, epochs, batch_size, patience, X_train, y_train, X_validation, y_validation):\n",
        "    earlystop_callback = tf.keras.callbacks.EarlyStopping(monitor=\"accuracy\", min_delta=0.001, patience=patience)\n",
        "\n",
        "    # train model\n",
        "    history = model.fit(X_train,\n",
        "                        y_train,\n",
        "                        epochs=epochs,\n",
        "                        batch_size=batch_size,\n",
        "                        validation_data=(X_validation, y_validation),\n",
        "                        callbacks=[earlystop_callback])\n",
        "    return history\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# train network\n",
        "history = train(model, EPOCHS, BATCH_SIZE, PATIENCE, X_train, y_train, X_validation, y_validation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6NO1vyJtDy3T",
        "outputId": "6e3076a5-2e5b-44a5-c57f-f83e274c970d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "288/288 [==============================] - 10s 31ms/step - loss: 1.9070 - accuracy: 0.3514 - val_loss: 1.6087 - val_accuracy: 0.4735\n",
            "Epoch 2/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 1.4815 - accuracy: 0.5153 - val_loss: 1.3913 - val_accuracy: 0.5413\n",
            "Epoch 3/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 1.3243 - accuracy: 0.5774 - val_loss: 1.2974 - val_accuracy: 0.5692\n",
            "Epoch 4/40\n",
            "288/288 [==============================] - 9s 31ms/step - loss: 1.2315 - accuracy: 0.6172 - val_loss: 1.2003 - val_accuracy: 0.6084\n",
            "Epoch 5/40\n",
            "288/288 [==============================] - 9s 32ms/step - loss: 1.1592 - accuracy: 0.6379 - val_loss: 1.1458 - val_accuracy: 0.6240\n",
            "Epoch 6/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 1.0891 - accuracy: 0.6667 - val_loss: 1.0983 - val_accuracy: 0.6397\n",
            "Epoch 7/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 1.0472 - accuracy: 0.6832 - val_loss: 1.0690 - val_accuracy: 0.6580\n",
            "Epoch 8/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 1.0099 - accuracy: 0.6961 - val_loss: 1.0223 - val_accuracy: 0.6701\n",
            "Epoch 9/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.9708 - accuracy: 0.7072 - val_loss: 1.0001 - val_accuracy: 0.6806\n",
            "Epoch 10/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.9437 - accuracy: 0.7161 - val_loss: 0.9739 - val_accuracy: 0.6928\n",
            "Epoch 11/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.9191 - accuracy: 0.7261 - val_loss: 0.9465 - val_accuracy: 0.6971\n",
            "Epoch 12/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.9033 - accuracy: 0.7350 - val_loss: 0.9211 - val_accuracy: 0.7041\n",
            "Epoch 13/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.8700 - accuracy: 0.7453 - val_loss: 0.9054 - val_accuracy: 0.7154\n",
            "Epoch 14/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.8533 - accuracy: 0.7442 - val_loss: 0.8912 - val_accuracy: 0.7128\n",
            "Epoch 15/40\n",
            "288/288 [==============================] - 9s 31ms/step - loss: 0.8389 - accuracy: 0.7535 - val_loss: 0.8752 - val_accuracy: 0.7276\n",
            "Epoch 16/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.8279 - accuracy: 0.7596 - val_loss: 0.8652 - val_accuracy: 0.7328\n",
            "Epoch 17/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.8031 - accuracy: 0.7677 - val_loss: 0.8465 - val_accuracy: 0.7293\n",
            "Epoch 18/40\n",
            "288/288 [==============================] - 10s 36ms/step - loss: 0.7962 - accuracy: 0.7677 - val_loss: 0.8405 - val_accuracy: 0.7406\n",
            "Epoch 19/40\n",
            "288/288 [==============================] - 8s 29ms/step - loss: 0.7824 - accuracy: 0.7827 - val_loss: 0.8303 - val_accuracy: 0.7415\n",
            "Epoch 20/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.7687 - accuracy: 0.7788 - val_loss: 0.8165 - val_accuracy: 0.7450\n",
            "Epoch 21/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.7553 - accuracy: 0.7840 - val_loss: 0.8108 - val_accuracy: 0.7528\n",
            "Epoch 22/40\n",
            "288/288 [==============================] - 9s 32ms/step - loss: 0.7373 - accuracy: 0.7929 - val_loss: 0.7996 - val_accuracy: 0.7537\n",
            "Epoch 23/40\n",
            "288/288 [==============================] - 9s 32ms/step - loss: 0.7357 - accuracy: 0.7940 - val_loss: 0.7902 - val_accuracy: 0.7589\n",
            "Epoch 24/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.7234 - accuracy: 0.7953 - val_loss: 0.7884 - val_accuracy: 0.7589\n",
            "Epoch 25/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.7125 - accuracy: 0.7984 - val_loss: 0.7798 - val_accuracy: 0.7615\n",
            "Epoch 26/40\n",
            "288/288 [==============================] - 8s 29ms/step - loss: 0.6964 - accuracy: 0.8062 - val_loss: 0.7657 - val_accuracy: 0.7659\n",
            "Epoch 27/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.6967 - accuracy: 0.8001 - val_loss: 0.7606 - val_accuracy: 0.7659\n",
            "Epoch 28/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.6866 - accuracy: 0.8027 - val_loss: 0.7516 - val_accuracy: 0.7737\n",
            "Epoch 29/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.6756 - accuracy: 0.8110 - val_loss: 0.7474 - val_accuracy: 0.7694\n",
            "Epoch 30/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.6669 - accuracy: 0.8149 - val_loss: 0.7386 - val_accuracy: 0.7737\n",
            "Epoch 31/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.6576 - accuracy: 0.8210 - val_loss: 0.7377 - val_accuracy: 0.7702\n",
            "Epoch 32/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.6530 - accuracy: 0.8154 - val_loss: 0.7288 - val_accuracy: 0.7807\n",
            "Epoch 33/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.6500 - accuracy: 0.8208 - val_loss: 0.7210 - val_accuracy: 0.7876\n",
            "Epoch 34/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.6417 - accuracy: 0.8239 - val_loss: 0.7209 - val_accuracy: 0.7842\n",
            "Epoch 35/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.6282 - accuracy: 0.8256 - val_loss: 0.7143 - val_accuracy: 0.7789\n",
            "Epoch 36/40\n",
            "288/288 [==============================] - 10s 36ms/step - loss: 0.6208 - accuracy: 0.8317 - val_loss: 0.7098 - val_accuracy: 0.7772\n",
            "Epoch 37/40\n",
            "288/288 [==============================] - 9s 30ms/step - loss: 0.6153 - accuracy: 0.8308 - val_loss: 0.7092 - val_accuracy: 0.7876\n",
            "Epoch 38/40\n",
            "288/288 [==============================] - 10s 34ms/step - loss: 0.6129 - accuracy: 0.8363 - val_loss: 0.7032 - val_accuracy: 0.7903\n",
            "Epoch 39/40\n",
            "288/288 [==============================] - 10s 35ms/step - loss: 0.6102 - accuracy: 0.8343 - val_loss: 0.6994 - val_accuracy: 0.7920\n",
            "Epoch 40/40\n",
            "288/288 [==============================] - 9s 32ms/step - loss: 0.6065 - accuracy: 0.8310 - val_loss: 0.6950 - val_accuracy: 0.7929\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate network on test set\n",
        "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
        "print(\"\\nTest loss: {}, test accuracy: {}\".format(test_loss, 100*test_acc))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UKdB7ugJD5MU",
        "outputId": "e6604788-1a61-4af1-affa-a9115b6bf2fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "45/45 [==============================] - 1s 17ms/step - loss: 0.7324 - accuracy: 0.7799\n",
            "\n",
            "Test loss: 0.7324416041374207, test accuracy: 77.99443006515503\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Graphical Representation"
      ],
      "metadata": {
        "id": "EwoGxs3yM-J_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot \n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# set width of bar\n",
        "barWidth = 0.25\n",
        "fig = plt.subplots(figsize =(6, 4))\n",
        "\n",
        "# set height of bar\n",
        "loss =  [0.4607, 0.418, 0.698, 0.732]\n",
        "accuracy = [88.788, 89.832, 78.412, 77.998]\n",
        "\n",
        "\n",
        "# Set position of bar on X axis\n",
        "br1 = np.arange(len(loss))\n",
        "br2 = [x + barWidth for x in br1]\n",
        "# br3 = [x + barWidth for x in br2]\n",
        "\n",
        "# Make the plot\n",
        "plt.bar(br1, loss, color ='r', width = barWidth,\n",
        "\t\tedgecolor ='grey', label ='Test loss')\n",
        "plt.bar(br2, accuracy, color ='g', width = barWidth,\n",
        "\t\tedgecolor ='grey', label ='Test Accuracy')\n",
        "# plt.bar(br3, CSE, color ='b', width = barWidth,\n",
        "# \t\tedgecolor ='grey', label ='CSE')\n",
        "\n",
        "# Adding Xticks\n",
        "plt.xlabel('Optimizer', fontweight ='bold', fontsize = 10)\n",
        "plt.ylabel('Percentage %', fontweight ='bold', fontsize = 10)\n",
        "plt.xticks([r + barWidth for r in range(len(loss))],\n",
        "\t\t['Adam', 'Adamax', 'Adagrad', 'Nadam'])\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "FOEhqceE_jnJ",
        "outputId": "8a517b1a-1b63-42f0-fd8b-06197e1f44b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define the data\n",
        "data = {\n",
        "    'Adam': (0.4607, 88.788),\n",
        "    'Adamax': (0.418, 89.832),\n",
        "    'Adagrad': (0.698, 78.412),\n",
        "    'Nadam': (0.732, 77.998)\n",
        "}\n",
        "\n",
        "# Create lists for the data\n",
        "loss = [data[key][0] for key in data.keys()]\n",
        "accuracy = [data[key][1] for key in data.keys()]\n",
        "\n",
        "# Create a bar chart of the loss\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.bar(data.keys(), loss)\n",
        "plt.title('Test Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.ylim(0, 2)\n",
        "\n",
        "# Create a bar chart of the accuracy\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.bar(data.keys(), accuracy)\n",
        "plt.title('Test Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim(0, 100)\n",
        "\n",
        "# Display the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "eQ6-rnJWNItI",
        "outputId": "635fd92d-8304-4ab3-f569-7295d0abb864"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}