LSTM_based_Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LSTM based Model.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1Pz-5I6NZ_QUa7v764s_iX0QJUK42f2-0",
      "authorship_tag": "ABX9TyMGrkuWuxvnlw4k6m/5gxAG",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rht6226/InnoHAR-NeuralNet/blob/master/LSTM_based_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xR6Aw-YD941f"
      },
      "source": [
        "# LSTM based model\r\n",
        "\r\n",
        "In this module we will be training a model which uses the Long Short Term Memory based architecture for Human Activity Recognition problem on UCI HAR Mobile Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nsJN6QuY1EJ9"
      },
      "source": [
        "# imports\r\n",
        "import numpy as np\r\n",
        "from keras.utils import to_categorical\r\n",
        "import json\r\n",
        "import matplotlib"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5G0Kmxn-YGf"
      },
      "source": [
        "## Loading the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bS9pFVPx-WWd"
      },
      "source": [
        "# Get class from file\r\n",
        "from drive.MyDrive.InnoHAR.DatasetLoader import UciMobileDataset"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YpvaOBf9-jN0",
        "outputId": "44fb753b-9344-4c2a-a253-f2d4e1f95941"
      },
      "source": [
        "uci = UciMobileDataset(filepath='/content/drive/MyDrive/InnoHAR/ProcessedDataset/')\r\n",
        "uci.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "UCI Human Activity Recognition Dataset: \n",
            "Shape of training data = X:(7352, 128, 9), y: (7352, 6)\n",
            "Shape of testing data = X:(2947, 128, 9), y: (2947, 6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fCd4qaVs-xlr"
      },
      "source": [
        "# Creating the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UJxQy9PS-xE9"
      },
      "source": [
        "# imports\r\n",
        "import keras\r\n",
        "from keras.models import Sequential\r\n",
        "from keras.layers import LSTM, Dense, Dropout\r\n",
        "from keras.utils.vis_utils import plot_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PTvSdFTo-r0M"
      },
      "source": [
        "# LSTM based Model defined layer by layer\r\n",
        "def get_lstm_model(X, y):\r\n",
        "    input_shape = (X.shape[1], X.shape[2])\r\n",
        "    n_outputs = y.shape[1]\r\n",
        "    model = Sequential()\r\n",
        "    model.add(LSTM(100, input_shape=input_shape))\r\n",
        "    model.add(Dropout(0.5))\r\n",
        "    model.add(Dense(100, activation='relu'))\r\n",
        "    model.add(Dense(n_outputs, activation='softmax'))\r\n",
        "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\r\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6tE5j02V_GZn",
        "outputId": "8e5dd1bc-b1c2-4640-85fe-5d3c08e3b56e"
      },
      "source": [
        "# Summarize the model\r\n",
        "\r\n",
        "model_lstm = get_lstm_model(X=uci.trainX, y=uci.trainy)\r\n",
        "model_lstm.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm (LSTM)                  (None, 100)               44000     \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 100)               10100     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 6)                 606       \n",
            "=================================================================\n",
            "Total params: 54,706\n",
            "Trainable params: 54,706\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "aStU4kO9_Vcf",
        "outputId": "b28b61c1-958a-4471-a010-585dba2c5e52"
      },
      "source": [
        "# Plotting the model\r\n",
        "plot_model(model_lstm, show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HsEsvXXg_yU0"
      },
      "source": [
        "## Training the Models\r\n",
        "\r\n",
        "Only one instance of a trained model cannot be used to determine the model performance. Hence we will be training Multiple instances of the same LSTM based model and then we will find out the average accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d0gZF-ee_rt8"
      },
      "source": [
        "def train_models(X=uci.trainX, y=uci.trainy, num_instances=50, verbose=1, epochs=15, batch_size=64, valid_data = (uci.testX, uci.testy)):\r\n",
        "    # Here we will store all the models\r\n",
        "    modelList = []\r\n",
        "\r\n",
        "    # Training multiple models at the same time for finding average accuracy\r\n",
        "    for i in range(num_instances):\r\n",
        "        print(\"\\n<========== Now training model - {} ...... =======>\".format(i))\r\n",
        "\r\n",
        "        model = get_lstm_model(X, y)\r\n",
        "        history = model.fit(X, y, epochs=epochs, verbose=1, batch_size=batch_size, validation_data=valid_data)\r\n",
        "\r\n",
        "        print(\"<==================================================>\\n\")\r\n",
        "\r\n",
        "        # Finding accuracy\r\n",
        "        _, accuracy = model.evaluate(valid_data[0], valid_data[1], batch_size=32, verbose=0)\r\n",
        "\r\n",
        "        modelList.append({\r\n",
        "            \"model\": model,\r\n",
        "            \"history\": history,\r\n",
        "            \"accuracy\": accuracy\r\n",
        "        })\r\n",
        "\r\n",
        "    return modelList"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlZOba_2_-R3",
        "outputId": "9ece221d-0e9b-4aea-ec57-1a2a152b23a6"
      },
      "source": [
        "epochs, batch_size = 15, 64\r\n",
        "modelList = train_models(epochs=epochs, batch_size=batch_size)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "<========== Now training model - 0 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 13ms/step - loss: 1.1410 - accuracy: 0.5058 - val_loss: 0.8765 - val_accuracy: 0.6159\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6995 - accuracy: 0.6974 - val_loss: 0.6848 - val_accuracy: 0.7404\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5708 - accuracy: 0.7832 - val_loss: 0.5666 - val_accuracy: 0.7866\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3981 - accuracy: 0.8619 - val_loss: 0.4830 - val_accuracy: 0.8392\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2569 - accuracy: 0.9106 - val_loss: 0.5175 - val_accuracy: 0.8310\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2106 - accuracy: 0.9240 - val_loss: 0.3739 - val_accuracy: 0.8887\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1713 - accuracy: 0.9377 - val_loss: 0.3814 - val_accuracy: 0.8850\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2340 - accuracy: 0.9183 - val_loss: 0.3849 - val_accuracy: 0.8958\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1756 - accuracy: 0.9336 - val_loss: 0.3031 - val_accuracy: 0.9040\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2821 - accuracy: 0.9074 - val_loss: 0.3640 - val_accuracy: 0.8877\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1735 - accuracy: 0.9343 - val_loss: 0.3515 - val_accuracy: 0.8951\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1467 - accuracy: 0.9448 - val_loss: 0.3817 - val_accuracy: 0.8843\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1444 - accuracy: 0.9442 - val_loss: 0.2844 - val_accuracy: 0.8989\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1632 - accuracy: 0.9365 - val_loss: 0.3236 - val_accuracy: 0.9016\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1258 - accuracy: 0.9486 - val_loss: 0.3596 - val_accuracy: 0.8972\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 1 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.3034 - accuracy: 0.4457 - val_loss: 1.1594 - val_accuracy: 0.4910\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 1.2122 - accuracy: 0.5175 - val_loss: 1.4950 - val_accuracy: 0.4062\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 1.1601 - accuracy: 0.5294 - val_loss: 1.0770 - val_accuracy: 0.5965\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.8351 - accuracy: 0.6415 - val_loss: 0.8187 - val_accuracy: 0.6807\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6425 - accuracy: 0.7387 - val_loss: 0.7578 - val_accuracy: 0.7391\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4833 - accuracy: 0.8161 - val_loss: 0.7675 - val_accuracy: 0.8022\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3960 - accuracy: 0.8570 - val_loss: 0.5995 - val_accuracy: 0.8507\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3289 - accuracy: 0.8876 - val_loss: 0.5294 - val_accuracy: 0.8734\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2529 - accuracy: 0.9173 - val_loss: 0.4744 - val_accuracy: 0.8711\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1817 - accuracy: 0.9358 - val_loss: 0.5051 - val_accuracy: 0.8819\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1584 - accuracy: 0.9407 - val_loss: 0.5775 - val_accuracy: 0.8846\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1757 - accuracy: 0.9357 - val_loss: 0.4780 - val_accuracy: 0.8819\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1634 - accuracy: 0.9353 - val_loss: 0.4778 - val_accuracy: 0.8843\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1466 - accuracy: 0.9397 - val_loss: 0.5087 - val_accuracy: 0.8884\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1597 - accuracy: 0.9389 - val_loss: 0.6301 - val_accuracy: 0.8707\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 2 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 12ms/step - loss: 1.2045 - accuracy: 0.4966 - val_loss: 0.9710 - val_accuracy: 0.5816\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.7686 - accuracy: 0.6695 - val_loss: 0.8410 - val_accuracy: 0.6654\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5587 - accuracy: 0.7651 - val_loss: 0.5710 - val_accuracy: 0.7964\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5032 - accuracy: 0.8063 - val_loss: 0.6365 - val_accuracy: 0.7496\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3450 - accuracy: 0.8721 - val_loss: 0.4499 - val_accuracy: 0.8734\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2361 - accuracy: 0.9177 - val_loss: 0.4222 - val_accuracy: 0.8795\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1877 - accuracy: 0.9342 - val_loss: 0.3925 - val_accuracy: 0.8741\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2649 - accuracy: 0.9040 - val_loss: 0.4468 - val_accuracy: 0.8704\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1933 - accuracy: 0.9325 - val_loss: 0.3680 - val_accuracy: 0.8789\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1982 - accuracy: 0.9306 - val_loss: 0.3974 - val_accuracy: 0.8768\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1417 - accuracy: 0.9423 - val_loss: 0.4758 - val_accuracy: 0.8839\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1275 - accuracy: 0.9467 - val_loss: 0.3779 - val_accuracy: 0.9002\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1374 - accuracy: 0.9449 - val_loss: 0.3480 - val_accuracy: 0.9016\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1280 - accuracy: 0.9497 - val_loss: 0.3255 - val_accuracy: 0.8989\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1214 - accuracy: 0.9532 - val_loss: 0.3348 - val_accuracy: 0.9104\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 3 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.1932 - accuracy: 0.4898 - val_loss: 0.8645 - val_accuracy: 0.6658\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.8640 - accuracy: 0.6235 - val_loss: 0.8294 - val_accuracy: 0.6417\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6247 - accuracy: 0.7281 - val_loss: 0.6483 - val_accuracy: 0.7394\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5007 - accuracy: 0.7971 - val_loss: 0.5646 - val_accuracy: 0.7896\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3630 - accuracy: 0.8625 - val_loss: 0.5025 - val_accuracy: 0.8358\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3945 - accuracy: 0.8526 - val_loss: 0.4320 - val_accuracy: 0.8493\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2235 - accuracy: 0.9219 - val_loss: 0.3789 - val_accuracy: 0.8839\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1837 - accuracy: 0.9339 - val_loss: 0.3458 - val_accuracy: 0.8897\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1695 - accuracy: 0.9378 - val_loss: 0.3664 - val_accuracy: 0.8951\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2096 - accuracy: 0.9259 - val_loss: 0.4915 - val_accuracy: 0.7978\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1819 - accuracy: 0.9357 - val_loss: 0.4443 - val_accuracy: 0.8758\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2224 - accuracy: 0.9255 - val_loss: 0.3956 - val_accuracy: 0.8683\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1486 - accuracy: 0.9418 - val_loss: 0.3926 - val_accuracy: 0.9019\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1580 - accuracy: 0.9408 - val_loss: 0.4026 - val_accuracy: 0.8890\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1664 - accuracy: 0.9399 - val_loss: 0.2458 - val_accuracy: 0.9158\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 4 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.1883 - accuracy: 0.5046 - val_loss: 1.0384 - val_accuracy: 0.5850\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.9110 - accuracy: 0.6147 - val_loss: 0.7839 - val_accuracy: 0.6888\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6724 - accuracy: 0.7093 - val_loss: 0.7083 - val_accuracy: 0.7038\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.7659 - accuracy: 0.6893 - val_loss: 0.8404 - val_accuracy: 0.6610\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6172 - accuracy: 0.7265 - val_loss: 0.6307 - val_accuracy: 0.7564\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4302 - accuracy: 0.8413 - val_loss: 0.4297 - val_accuracy: 0.8392\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3524 - accuracy: 0.8742 - val_loss: 0.3411 - val_accuracy: 0.8823\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2244 - accuracy: 0.9221 - val_loss: 0.3823 - val_accuracy: 0.8907\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1984 - accuracy: 0.9297 - val_loss: 0.3465 - val_accuracy: 0.8955\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2026 - accuracy: 0.9268 - val_loss: 0.3356 - val_accuracy: 0.8965\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1675 - accuracy: 0.9388 - val_loss: 0.3189 - val_accuracy: 0.8951\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1395 - accuracy: 0.9463 - val_loss: 0.2893 - val_accuracy: 0.9013\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1737 - accuracy: 0.9382 - val_loss: 0.3217 - val_accuracy: 0.9023\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1352 - accuracy: 0.9471 - val_loss: 0.4904 - val_accuracy: 0.8426\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1508 - accuracy: 0.9393 - val_loss: 0.2881 - val_accuracy: 0.9016\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 5 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.1883 - accuracy: 0.4995 - val_loss: 0.8659 - val_accuracy: 0.6454\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6982 - accuracy: 0.6930 - val_loss: 0.6819 - val_accuracy: 0.7329\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5288 - accuracy: 0.7909 - val_loss: 0.5157 - val_accuracy: 0.8096\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3686 - accuracy: 0.8652 - val_loss: 0.4463 - val_accuracy: 0.8548\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3307 - accuracy: 0.8838 - val_loss: 0.6059 - val_accuracy: 0.7574\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2750 - accuracy: 0.8999 - val_loss: 0.3080 - val_accuracy: 0.8985\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1791 - accuracy: 0.9331 - val_loss: 0.2265 - val_accuracy: 0.9053\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1589 - accuracy: 0.9410 - val_loss: 0.2546 - val_accuracy: 0.9118\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2420 - accuracy: 0.9195 - val_loss: 0.3030 - val_accuracy: 0.8850\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1581 - accuracy: 0.9425 - val_loss: 0.2336 - val_accuracy: 0.9094\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1509 - accuracy: 0.9425 - val_loss: 0.2728 - val_accuracy: 0.9063\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1434 - accuracy: 0.9406 - val_loss: 0.3203 - val_accuracy: 0.9033\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3045 - accuracy: 0.8942 - val_loss: 0.2479 - val_accuracy: 0.9063\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2633 - accuracy: 0.9139 - val_loss: 0.2513 - val_accuracy: 0.9030\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3206 - accuracy: 0.8896 - val_loss: 0.2863 - val_accuracy: 0.8992\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 6 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.2280 - accuracy: 0.4971 - val_loss: 1.1089 - val_accuracy: 0.5416\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.7786 - accuracy: 0.6627 - val_loss: 0.6684 - val_accuracy: 0.7258\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6198 - accuracy: 0.7376 - val_loss: 0.5766 - val_accuracy: 0.7808\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5592 - accuracy: 0.7817 - val_loss: 0.5615 - val_accuracy: 0.7923\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4384 - accuracy: 0.8162 - val_loss: 0.4789 - val_accuracy: 0.8025\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3458 - accuracy: 0.8606 - val_loss: 0.4045 - val_accuracy: 0.8507\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2620 - accuracy: 0.9110 - val_loss: 0.3321 - val_accuracy: 0.8863\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2022 - accuracy: 0.9283 - val_loss: 0.3951 - val_accuracy: 0.8714\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1878 - accuracy: 0.9324 - val_loss: 0.2689 - val_accuracy: 0.9114\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1553 - accuracy: 0.9388 - val_loss: 0.3627 - val_accuracy: 0.8948\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1607 - accuracy: 0.9365 - val_loss: 0.3269 - val_accuracy: 0.8901\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2726 - accuracy: 0.9131 - val_loss: 0.2623 - val_accuracy: 0.9104\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1881 - accuracy: 0.9366 - val_loss: 0.3403 - val_accuracy: 0.8985\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2064 - accuracy: 0.9280 - val_loss: 0.2621 - val_accuracy: 0.9114\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1436 - accuracy: 0.9468 - val_loss: 0.3013 - val_accuracy: 0.9080\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 7 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.1274 - accuracy: 0.5143 - val_loss: 0.8736 - val_accuracy: 0.5959\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.8067 - accuracy: 0.6439 - val_loss: 0.7631 - val_accuracy: 0.7068\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6700 - accuracy: 0.7134 - val_loss: 0.6153 - val_accuracy: 0.7594\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4403 - accuracy: 0.8339 - val_loss: 0.4580 - val_accuracy: 0.8371\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2735 - accuracy: 0.9066 - val_loss: 0.3972 - val_accuracy: 0.8626\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2475 - accuracy: 0.9149 - val_loss: 0.3943 - val_accuracy: 0.8819\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2098 - accuracy: 0.9263 - val_loss: 0.3951 - val_accuracy: 0.8656\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1981 - accuracy: 0.9312 - val_loss: 0.4582 - val_accuracy: 0.8531\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1591 - accuracy: 0.9438 - val_loss: 0.3692 - val_accuracy: 0.8962\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1462 - accuracy: 0.9446 - val_loss: 0.5826 - val_accuracy: 0.8456\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1735 - accuracy: 0.9347 - val_loss: 0.3181 - val_accuracy: 0.8836\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1536 - accuracy: 0.9415 - val_loss: 0.3426 - val_accuracy: 0.8941\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1276 - accuracy: 0.9486 - val_loss: 0.3535 - val_accuracy: 0.8999\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1231 - accuracy: 0.9502 - val_loss: 0.3951 - val_accuracy: 0.8935\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1242 - accuracy: 0.9501 - val_loss: 0.3737 - val_accuracy: 0.8931\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 8 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.2811 - accuracy: 0.4567 - val_loss: 1.1282 - val_accuracy: 0.5395\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.8292 - accuracy: 0.6411 - val_loss: 0.7791 - val_accuracy: 0.6688\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6110 - accuracy: 0.7428 - val_loss: 0.6643 - val_accuracy: 0.7448\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5226 - accuracy: 0.8132 - val_loss: 0.6311 - val_accuracy: 0.7974\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3724 - accuracy: 0.8700 - val_loss: 0.4520 - val_accuracy: 0.8470\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2657 - accuracy: 0.9102 - val_loss: 0.5555 - val_accuracy: 0.8303\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1951 - accuracy: 0.9289 - val_loss: 0.4060 - val_accuracy: 0.8782\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1909 - accuracy: 0.9305 - val_loss: 0.3196 - val_accuracy: 0.8867\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1489 - accuracy: 0.9449 - val_loss: 0.2839 - val_accuracy: 0.8938\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1697 - accuracy: 0.9325 - val_loss: 0.3864 - val_accuracy: 0.8826\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1395 - accuracy: 0.9456 - val_loss: 0.5773 - val_accuracy: 0.8371\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2327 - accuracy: 0.9202 - val_loss: 0.4109 - val_accuracy: 0.8480\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1619 - accuracy: 0.9421 - val_loss: 0.2947 - val_accuracy: 0.8992\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3222 - accuracy: 0.8900 - val_loss: 0.4807 - val_accuracy: 0.8524\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2450 - accuracy: 0.9079 - val_loss: 0.4377 - val_accuracy: 0.8806\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 9 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.1971 - accuracy: 0.4951 - val_loss: 1.0635 - val_accuracy: 0.5646\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.7113 - accuracy: 0.6965 - val_loss: 0.7398 - val_accuracy: 0.7119\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5258 - accuracy: 0.7764 - val_loss: 0.5918 - val_accuracy: 0.7784\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4435 - accuracy: 0.8358 - val_loss: 0.5341 - val_accuracy: 0.8222\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2651 - accuracy: 0.9066 - val_loss: 0.4782 - val_accuracy: 0.8673\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1907 - accuracy: 0.9316 - val_loss: 0.5134 - val_accuracy: 0.8873\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1717 - accuracy: 0.9359 - val_loss: 0.4571 - val_accuracy: 0.8907\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3017 - accuracy: 0.8904 - val_loss: 0.5694 - val_accuracy: 0.8670\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2130 - accuracy: 0.9266 - val_loss: 0.4668 - val_accuracy: 0.8833\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1890 - accuracy: 0.9313 - val_loss: 0.5138 - val_accuracy: 0.8846\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1504 - accuracy: 0.9430 - val_loss: 0.4895 - val_accuracy: 0.8863\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1583 - accuracy: 0.9388 - val_loss: 0.3820 - val_accuracy: 0.8972\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1374 - accuracy: 0.9464 - val_loss: 0.3888 - val_accuracy: 0.9125\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1346 - accuracy: 0.9474 - val_loss: 0.4386 - val_accuracy: 0.9050\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1279 - accuracy: 0.9480 - val_loss: 0.4404 - val_accuracy: 0.8884\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 10 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 14ms/step - loss: 1.1949 - accuracy: 0.4955 - val_loss: 1.0963 - val_accuracy: 0.5636\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.8062 - accuracy: 0.6574 - val_loss: 0.7334 - val_accuracy: 0.7112\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6184 - accuracy: 0.7424 - val_loss: 0.5561 - val_accuracy: 0.7689\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4555 - accuracy: 0.8256 - val_loss: 0.6576 - val_accuracy: 0.7536\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4050 - accuracy: 0.8517 - val_loss: 0.3773 - val_accuracy: 0.8761\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2121 - accuracy: 0.9237 - val_loss: 0.3115 - val_accuracy: 0.8941\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2558 - accuracy: 0.9106 - val_loss: 0.3079 - val_accuracy: 0.8887\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2020 - accuracy: 0.9295 - val_loss: 0.3548 - val_accuracy: 0.8870\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1672 - accuracy: 0.9393 - val_loss: 0.3392 - val_accuracy: 0.8924\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1685 - accuracy: 0.9397 - val_loss: 0.3323 - val_accuracy: 0.8700\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1499 - accuracy: 0.9442 - val_loss: 0.3695 - val_accuracy: 0.8941\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1356 - accuracy: 0.9448 - val_loss: 0.4034 - val_accuracy: 0.8812\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1387 - accuracy: 0.9440 - val_loss: 0.3700 - val_accuracy: 0.8958\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1387 - accuracy: 0.9436 - val_loss: 0.3803 - val_accuracy: 0.8968\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1499 - accuracy: 0.9410 - val_loss: 0.3426 - val_accuracy: 0.8989\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 11 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.2073 - accuracy: 0.4914 - val_loss: 1.1279 - val_accuracy: 0.5151\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.8373 - accuracy: 0.6281 - val_loss: 0.8099 - val_accuracy: 0.6600\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.7111 - accuracy: 0.6959 - val_loss: 0.7134 - val_accuracy: 0.6980\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5152 - accuracy: 0.7987 - val_loss: 0.5116 - val_accuracy: 0.8202\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4510 - accuracy: 0.8357 - val_loss: 0.4769 - val_accuracy: 0.8500\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3178 - accuracy: 0.8935 - val_loss: 0.7225 - val_accuracy: 0.7452\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2879 - accuracy: 0.8987 - val_loss: 0.2976 - val_accuracy: 0.8965\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1966 - accuracy: 0.9319 - val_loss: 0.2749 - val_accuracy: 0.9070\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1658 - accuracy: 0.9391 - val_loss: 0.3313 - val_accuracy: 0.8951\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1423 - accuracy: 0.9457 - val_loss: 0.3453 - val_accuracy: 0.9013\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1890 - accuracy: 0.9340 - val_loss: 0.3774 - val_accuracy: 0.8928\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1514 - accuracy: 0.9397 - val_loss: 0.2669 - val_accuracy: 0.9125\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1331 - accuracy: 0.9438 - val_loss: 0.3158 - val_accuracy: 0.8992\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1292 - accuracy: 0.9483 - val_loss: 0.2751 - val_accuracy: 0.9121\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1875 - accuracy: 0.9319 - val_loss: 0.3997 - val_accuracy: 0.8867\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 12 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.2014 - accuracy: 0.4913 - val_loss: 0.9469 - val_accuracy: 0.6288\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.9206 - accuracy: 0.6113 - val_loss: 0.7706 - val_accuracy: 0.6899\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6230 - accuracy: 0.7427 - val_loss: 0.7176 - val_accuracy: 0.6946\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4778 - accuracy: 0.8093 - val_loss: 0.5013 - val_accuracy: 0.8185\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3236 - accuracy: 0.8891 - val_loss: 0.5534 - val_accuracy: 0.8188\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2563 - accuracy: 0.9081 - val_loss: 0.3194 - val_accuracy: 0.8836\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1893 - accuracy: 0.9336 - val_loss: 0.2697 - val_accuracy: 0.8999\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2022 - accuracy: 0.9253 - val_loss: 0.2783 - val_accuracy: 0.8979\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1854 - accuracy: 0.9309 - val_loss: 0.2410 - val_accuracy: 0.9125\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1730 - accuracy: 0.9340 - val_loss: 0.3367 - val_accuracy: 0.8897\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1549 - accuracy: 0.9418 - val_loss: 0.2350 - val_accuracy: 0.9162\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1399 - accuracy: 0.9453 - val_loss: 0.2542 - val_accuracy: 0.9121\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1467 - accuracy: 0.9418 - val_loss: 0.2439 - val_accuracy: 0.9128\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1323 - accuracy: 0.9475 - val_loss: 0.2415 - val_accuracy: 0.9155\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1292 - accuracy: 0.9465 - val_loss: 0.2699 - val_accuracy: 0.9138\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 13 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.2195 - accuracy: 0.4846 - val_loss: 1.0185 - val_accuracy: 0.5826\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.7821 - accuracy: 0.6570 - val_loss: 0.7275 - val_accuracy: 0.7092\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6443 - accuracy: 0.7485 - val_loss: 0.7923 - val_accuracy: 0.7367\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4221 - accuracy: 0.8497 - val_loss: 0.4460 - val_accuracy: 0.8239\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2899 - accuracy: 0.9018 - val_loss: 0.3889 - val_accuracy: 0.8765\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2188 - accuracy: 0.9267 - val_loss: 0.4045 - val_accuracy: 0.8782\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1857 - accuracy: 0.9319 - val_loss: 0.4197 - val_accuracy: 0.8755\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1845 - accuracy: 0.9335 - val_loss: 0.4583 - val_accuracy: 0.8734\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1608 - accuracy: 0.9395 - val_loss: 0.4845 - val_accuracy: 0.8554\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1787 - accuracy: 0.9350 - val_loss: 0.2921 - val_accuracy: 0.8945\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1440 - accuracy: 0.9419 - val_loss: 0.3323 - val_accuracy: 0.9033\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1434 - accuracy: 0.9438 - val_loss: 0.3867 - val_accuracy: 0.8989\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1235 - accuracy: 0.9478 - val_loss: 0.2764 - val_accuracy: 0.9084\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1469 - accuracy: 0.9412 - val_loss: 0.5052 - val_accuracy: 0.8799\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1386 - accuracy: 0.9456 - val_loss: 0.4395 - val_accuracy: 0.8884\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 14 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.3048 - accuracy: 0.4285 - val_loss: 1.3134 - val_accuracy: 0.4147\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 1.0645 - accuracy: 0.5690 - val_loss: 0.9424 - val_accuracy: 0.6145\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.7420 - accuracy: 0.6751 - val_loss: 0.7546 - val_accuracy: 0.6787\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6097 - accuracy: 0.7579 - val_loss: 0.7239 - val_accuracy: 0.7394\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5452 - accuracy: 0.7928 - val_loss: 0.6008 - val_accuracy: 0.7879\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4154 - accuracy: 0.8527 - val_loss: 0.4273 - val_accuracy: 0.8578\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2875 - accuracy: 0.9021 - val_loss: 0.4181 - val_accuracy: 0.8663\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3178 - accuracy: 0.8828 - val_loss: 0.4680 - val_accuracy: 0.8470\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2111 - accuracy: 0.9227 - val_loss: 0.3258 - val_accuracy: 0.8894\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1826 - accuracy: 0.9309 - val_loss: 0.2809 - val_accuracy: 0.9019\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1531 - accuracy: 0.9392 - val_loss: 0.2977 - val_accuracy: 0.8985\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1715 - accuracy: 0.9380 - val_loss: 0.2666 - val_accuracy: 0.9077\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1521 - accuracy: 0.9403 - val_loss: 0.2808 - val_accuracy: 0.9040\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1482 - accuracy: 0.9444 - val_loss: 0.2559 - val_accuracy: 0.9074\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1384 - accuracy: 0.9463 - val_loss: 0.2312 - val_accuracy: 0.9097\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 15 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.1690 - accuracy: 0.5053 - val_loss: 1.6955 - val_accuracy: 0.4897\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.8605 - accuracy: 0.6400 - val_loss: 0.7303 - val_accuracy: 0.6899\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6551 - accuracy: 0.7194 - val_loss: 0.7753 - val_accuracy: 0.6953\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5068 - accuracy: 0.8030 - val_loss: 0.6456 - val_accuracy: 0.8039\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3115 - accuracy: 0.8970 - val_loss: 0.4890 - val_accuracy: 0.8504\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2504 - accuracy: 0.9136 - val_loss: 0.3044 - val_accuracy: 0.9009\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2600 - accuracy: 0.9144 - val_loss: 0.3153 - val_accuracy: 0.8839\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2240 - accuracy: 0.9217 - val_loss: 0.3500 - val_accuracy: 0.8918\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1905 - accuracy: 0.9309 - val_loss: 0.2848 - val_accuracy: 0.9077\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1621 - accuracy: 0.9385 - val_loss: 0.2615 - val_accuracy: 0.9199\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1485 - accuracy: 0.9422 - val_loss: 0.2272 - val_accuracy: 0.9175\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1410 - accuracy: 0.9423 - val_loss: 0.2302 - val_accuracy: 0.9135\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1451 - accuracy: 0.9419 - val_loss: 0.2488 - val_accuracy: 0.9155\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1440 - accuracy: 0.9399 - val_loss: 0.2346 - val_accuracy: 0.9138\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1981 - accuracy: 0.9261 - val_loss: 0.2216 - val_accuracy: 0.9128\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 16 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.2508 - accuracy: 0.4702 - val_loss: 1.0555 - val_accuracy: 0.5609\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.7732 - accuracy: 0.6691 - val_loss: 0.7424 - val_accuracy: 0.7187\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6579 - accuracy: 0.7447 - val_loss: 0.6263 - val_accuracy: 0.7944\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4581 - accuracy: 0.8304 - val_loss: 0.5455 - val_accuracy: 0.8286\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3016 - accuracy: 0.8962 - val_loss: 0.3875 - val_accuracy: 0.8666\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1962 - accuracy: 0.9270 - val_loss: 0.3547 - val_accuracy: 0.8870\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2061 - accuracy: 0.9266 - val_loss: 0.4195 - val_accuracy: 0.8636\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1679 - accuracy: 0.9343 - val_loss: 0.5650 - val_accuracy: 0.8466\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1798 - accuracy: 0.9328 - val_loss: 0.2910 - val_accuracy: 0.8992\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1616 - accuracy: 0.9392 - val_loss: 0.3825 - val_accuracy: 0.8985\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1763 - accuracy: 0.9370 - val_loss: 0.2650 - val_accuracy: 0.9091\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1937 - accuracy: 0.9320 - val_loss: 0.2873 - val_accuracy: 0.9043\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1425 - accuracy: 0.9465 - val_loss: 0.2558 - val_accuracy: 0.9030\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1301 - accuracy: 0.9472 - val_loss: 0.2887 - val_accuracy: 0.9040\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1208 - accuracy: 0.9510 - val_loss: 0.2743 - val_accuracy: 0.9036\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 17 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.2317 - accuracy: 0.4773 - val_loss: 0.9995 - val_accuracy: 0.5701\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.7270 - accuracy: 0.6674 - val_loss: 0.7383 - val_accuracy: 0.7021\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5828 - accuracy: 0.7573 - val_loss: 0.6612 - val_accuracy: 0.7370\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3887 - accuracy: 0.8611 - val_loss: 0.4624 - val_accuracy: 0.8290\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2415 - accuracy: 0.9153 - val_loss: 0.3844 - val_accuracy: 0.8799\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2246 - accuracy: 0.9195 - val_loss: 0.4127 - val_accuracy: 0.8683\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1939 - accuracy: 0.9287 - val_loss: 0.3391 - val_accuracy: 0.8938\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1719 - accuracy: 0.9343 - val_loss: 0.3162 - val_accuracy: 0.8951\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1539 - accuracy: 0.9374 - val_loss: 0.3054 - val_accuracy: 0.9057\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1340 - accuracy: 0.9444 - val_loss: 0.2737 - val_accuracy: 0.9104\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1337 - accuracy: 0.9446 - val_loss: 0.3165 - val_accuracy: 0.9050\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1482 - accuracy: 0.9396 - val_loss: 0.4268 - val_accuracy: 0.8836\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1340 - accuracy: 0.9445 - val_loss: 0.4110 - val_accuracy: 0.9043\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1363 - accuracy: 0.9453 - val_loss: 0.2385 - val_accuracy: 0.9141\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1345 - accuracy: 0.9474 - val_loss: 0.3453 - val_accuracy: 0.9070\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 18 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.1839 - accuracy: 0.5001 - val_loss: 1.0330 - val_accuracy: 0.5707\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.8408 - accuracy: 0.6389 - val_loss: 0.8348 - val_accuracy: 0.6736\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6662 - accuracy: 0.7198 - val_loss: 0.7078 - val_accuracy: 0.7642\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5054 - accuracy: 0.7912 - val_loss: 0.5829 - val_accuracy: 0.8022\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3535 - accuracy: 0.8706 - val_loss: 0.4178 - val_accuracy: 0.8470\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2729 - accuracy: 0.9036 - val_loss: 0.3225 - val_accuracy: 0.8717\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2014 - accuracy: 0.9287 - val_loss: 0.3166 - val_accuracy: 0.8962\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1754 - accuracy: 0.9365 - val_loss: 0.3392 - val_accuracy: 0.8870\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2329 - accuracy: 0.9203 - val_loss: 0.3546 - val_accuracy: 0.8860\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1876 - accuracy: 0.9287 - val_loss: 0.2483 - val_accuracy: 0.9033\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1383 - accuracy: 0.9438 - val_loss: 0.2870 - val_accuracy: 0.9087\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1245 - accuracy: 0.9479 - val_loss: 0.2886 - val_accuracy: 0.9030\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1250 - accuracy: 0.9489 - val_loss: 0.3098 - val_accuracy: 0.9080\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1260 - accuracy: 0.9497 - val_loss: 0.2938 - val_accuracy: 0.9084\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3050 - accuracy: 0.9066 - val_loss: 0.3207 - val_accuracy: 0.8914\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 19 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 13ms/step - loss: 1.2849 - accuracy: 0.4406 - val_loss: 1.1368 - val_accuracy: 0.4903\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.8193 - accuracy: 0.6513 - val_loss: 0.7205 - val_accuracy: 0.6932\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5811 - accuracy: 0.7601 - val_loss: 0.6340 - val_accuracy: 0.7706\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4245 - accuracy: 0.8436 - val_loss: 0.6575 - val_accuracy: 0.7991\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2737 - accuracy: 0.9071 - val_loss: 0.3970 - val_accuracy: 0.8731\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2395 - accuracy: 0.9172 - val_loss: 0.3822 - val_accuracy: 0.8565\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1884 - accuracy: 0.9304 - val_loss: 0.3148 - val_accuracy: 0.9094\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1857 - accuracy: 0.9323 - val_loss: 0.3056 - val_accuracy: 0.9080\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1991 - accuracy: 0.9267 - val_loss: 0.3241 - val_accuracy: 0.9135\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1505 - accuracy: 0.9426 - val_loss: 0.2832 - val_accuracy: 0.9104\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1404 - accuracy: 0.9474 - val_loss: 0.3319 - val_accuracy: 0.9091\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1489 - accuracy: 0.9411 - val_loss: 0.3411 - val_accuracy: 0.9053\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1535 - accuracy: 0.9415 - val_loss: 0.3326 - val_accuracy: 0.9094\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1362 - accuracy: 0.9422 - val_loss: 0.3541 - val_accuracy: 0.9046\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1269 - accuracy: 0.9470 - val_loss: 0.3725 - val_accuracy: 0.9084\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 20 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.1526 - accuracy: 0.5116 - val_loss: 0.9455 - val_accuracy: 0.5813\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.7288 - accuracy: 0.6868 - val_loss: 0.6648 - val_accuracy: 0.7309\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5193 - accuracy: 0.8007 - val_loss: 0.5460 - val_accuracy: 0.8110\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3327 - accuracy: 0.8904 - val_loss: 0.4517 - val_accuracy: 0.8351\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4086 - accuracy: 0.8645 - val_loss: 0.4160 - val_accuracy: 0.8558\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2249 - accuracy: 0.9251 - val_loss: 0.4824 - val_accuracy: 0.8504\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1679 - accuracy: 0.9366 - val_loss: 0.4003 - val_accuracy: 0.8748\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1756 - accuracy: 0.9366 - val_loss: 0.3754 - val_accuracy: 0.8887\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1508 - accuracy: 0.9436 - val_loss: 0.4129 - val_accuracy: 0.8795\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2311 - accuracy: 0.9219 - val_loss: 0.4035 - val_accuracy: 0.8575\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2486 - accuracy: 0.9143 - val_loss: 0.3539 - val_accuracy: 0.8928\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1804 - accuracy: 0.9321 - val_loss: 0.3230 - val_accuracy: 0.8989\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1370 - accuracy: 0.9457 - val_loss: 0.3198 - val_accuracy: 0.9111\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1310 - accuracy: 0.9504 - val_loss: 0.3253 - val_accuracy: 0.9009\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1281 - accuracy: 0.9504 - val_loss: 0.3987 - val_accuracy: 0.9125\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 21 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 1.1990 - accuracy: 0.4793 - val_loss: 1.0007 - val_accuracy: 0.5596\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.7494 - accuracy: 0.6791 - val_loss: 0.7615 - val_accuracy: 0.6990\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5298 - accuracy: 0.7961 - val_loss: 0.5259 - val_accuracy: 0.8171\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5050 - accuracy: 0.8109 - val_loss: 0.6689 - val_accuracy: 0.8130\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2852 - accuracy: 0.9029 - val_loss: 0.4918 - val_accuracy: 0.8636\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2341 - accuracy: 0.9211 - val_loss: 0.4083 - val_accuracy: 0.8758\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1838 - accuracy: 0.9331 - val_loss: 0.4507 - val_accuracy: 0.8948\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1704 - accuracy: 0.9340 - val_loss: 0.2669 - val_accuracy: 0.9046\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1360 - accuracy: 0.9433 - val_loss: 0.3453 - val_accuracy: 0.8945\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2041 - accuracy: 0.9300 - val_loss: 0.3417 - val_accuracy: 0.8901\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1834 - accuracy: 0.9321 - val_loss: 0.3823 - val_accuracy: 0.8853\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1680 - accuracy: 0.9324 - val_loss: 0.3850 - val_accuracy: 0.9006\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1492 - accuracy: 0.9429 - val_loss: 0.4393 - val_accuracy: 0.9080\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1288 - accuracy: 0.9461 - val_loss: 0.4274 - val_accuracy: 0.9077\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1188 - accuracy: 0.9517 - val_loss: 0.3207 - val_accuracy: 0.9043\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 22 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 13ms/step - loss: 1.1959 - accuracy: 0.4958 - val_loss: 1.1106 - val_accuracy: 0.5426\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.7824 - accuracy: 0.6629 - val_loss: 0.7627 - val_accuracy: 0.6817\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5986 - accuracy: 0.7443 - val_loss: 0.6218 - val_accuracy: 0.7448\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5028 - accuracy: 0.8104 - val_loss: 0.5885 - val_accuracy: 0.8151\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3986 - accuracy: 0.8573 - val_loss: 0.6584 - val_accuracy: 0.8022\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2422 - accuracy: 0.9207 - val_loss: 0.3738 - val_accuracy: 0.8938\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2150 - accuracy: 0.9240 - val_loss: 0.3763 - val_accuracy: 0.8673\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1745 - accuracy: 0.9331 - val_loss: 0.2905 - val_accuracy: 0.9053\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1747 - accuracy: 0.9351 - val_loss: 0.3466 - val_accuracy: 0.8884\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1499 - accuracy: 0.9433 - val_loss: 0.3955 - val_accuracy: 0.9016\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1346 - accuracy: 0.9461 - val_loss: 0.3635 - val_accuracy: 0.9016\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1346 - accuracy: 0.9475 - val_loss: 0.3793 - val_accuracy: 0.9013\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1716 - accuracy: 0.9357 - val_loss: 0.3023 - val_accuracy: 0.9046\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1230 - accuracy: 0.9521 - val_loss: 0.2850 - val_accuracy: 0.9043\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1332 - accuracy: 0.9471 - val_loss: 0.2694 - val_accuracy: 0.9019\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 23 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 13ms/step - loss: 1.1742 - accuracy: 0.5048 - val_loss: 0.9363 - val_accuracy: 0.6067\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.8202 - accuracy: 0.6517 - val_loss: 0.7541 - val_accuracy: 0.6922\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5731 - accuracy: 0.7692 - val_loss: 0.5767 - val_accuracy: 0.7743\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4298 - accuracy: 0.8494 - val_loss: 0.7864 - val_accuracy: 0.7085\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3677 - accuracy: 0.8598 - val_loss: 0.5192 - val_accuracy: 0.8185\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2440 - accuracy: 0.9079 - val_loss: 0.3817 - val_accuracy: 0.8809\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2621 - accuracy: 0.9110 - val_loss: 0.4687 - val_accuracy: 0.8741\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1868 - accuracy: 0.9320 - val_loss: 0.3418 - val_accuracy: 0.9036\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1647 - accuracy: 0.9359 - val_loss: 0.4510 - val_accuracy: 0.8375\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1617 - accuracy: 0.9387 - val_loss: 0.3406 - val_accuracy: 0.8989\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1650 - accuracy: 0.9400 - val_loss: 0.8448 - val_accuracy: 0.8334\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3857 - accuracy: 0.8791 - val_loss: 0.3489 - val_accuracy: 0.8836\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1677 - accuracy: 0.9407 - val_loss: 0.2626 - val_accuracy: 0.9104\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1540 - accuracy: 0.9411 - val_loss: 0.2582 - val_accuracy: 0.9084\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1415 - accuracy: 0.9441 - val_loss: 0.2724 - val_accuracy: 0.9111\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 24 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 13ms/step - loss: 1.3954 - accuracy: 0.4037 - val_loss: 1.2931 - val_accuracy: 0.4513\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 1.0391 - accuracy: 0.5385 - val_loss: 0.9049 - val_accuracy: 0.6152\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.7813 - accuracy: 0.6420 - val_loss: 0.7605 - val_accuracy: 0.7099\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6892 - accuracy: 0.6854 - val_loss: 0.7256 - val_accuracy: 0.6848\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6070 - accuracy: 0.7333 - val_loss: 0.6746 - val_accuracy: 0.7011\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.5247 - accuracy: 0.7599 - val_loss: 0.6017 - val_accuracy: 0.7496\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4602 - accuracy: 0.8003 - val_loss: 0.5094 - val_accuracy: 0.7896\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3672 - accuracy: 0.8566 - val_loss: 0.4491 - val_accuracy: 0.8300\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2604 - accuracy: 0.9018 - val_loss: 0.4131 - val_accuracy: 0.8836\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2011 - accuracy: 0.9249 - val_loss: 0.3309 - val_accuracy: 0.8880\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1825 - accuracy: 0.9340 - val_loss: 0.3446 - val_accuracy: 0.8853\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1797 - accuracy: 0.9309 - val_loss: 0.3515 - val_accuracy: 0.8904\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1575 - accuracy: 0.9374 - val_loss: 0.3053 - val_accuracy: 0.8938\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1570 - accuracy: 0.9384 - val_loss: 0.3111 - val_accuracy: 0.8982\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1444 - accuracy: 0.9393 - val_loss: 0.3041 - val_accuracy: 0.9023\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 25 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 13ms/step - loss: 1.1523 - accuracy: 0.5126 - val_loss: 0.8928 - val_accuracy: 0.6383\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7238 - accuracy: 0.6906 - val_loss: 0.8391 - val_accuracy: 0.6807\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6229 - accuracy: 0.7384 - val_loss: 0.7318 - val_accuracy: 0.7150\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.4812 - accuracy: 0.8059 - val_loss: 0.6171 - val_accuracy: 0.7740\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4027 - accuracy: 0.8565 - val_loss: 0.4408 - val_accuracy: 0.8493\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2555 - accuracy: 0.9172 - val_loss: 0.3619 - val_accuracy: 0.8806\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2679 - accuracy: 0.9072 - val_loss: 0.3194 - val_accuracy: 0.8992\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2154 - accuracy: 0.9236 - val_loss: 0.3525 - val_accuracy: 0.8945\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1669 - accuracy: 0.9374 - val_loss: 0.3378 - val_accuracy: 0.8965\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1493 - accuracy: 0.9393 - val_loss: 0.3484 - val_accuracy: 0.9097\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1525 - accuracy: 0.9415 - val_loss: 0.3233 - val_accuracy: 0.9030\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1606 - accuracy: 0.9355 - val_loss: 0.2863 - val_accuracy: 0.9118\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1654 - accuracy: 0.9403 - val_loss: 0.2245 - val_accuracy: 0.9104\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1286 - accuracy: 0.9471 - val_loss: 0.2456 - val_accuracy: 0.9145\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1286 - accuracy: 0.9445 - val_loss: 0.2708 - val_accuracy: 0.9138\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 26 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 17ms/step - loss: 1.2239 - accuracy: 0.4742 - val_loss: 1.0387 - val_accuracy: 0.5399\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.7434 - accuracy: 0.6767 - val_loss: 0.7208 - val_accuracy: 0.7051\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6060 - accuracy: 0.7458 - val_loss: 0.6199 - val_accuracy: 0.7513\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5170 - accuracy: 0.8029 - val_loss: 0.5805 - val_accuracy: 0.7988\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3002 - accuracy: 0.9011 - val_loss: 0.4774 - val_accuracy: 0.8568\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3684 - accuracy: 0.8604 - val_loss: 0.4382 - val_accuracy: 0.8690\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1938 - accuracy: 0.9291 - val_loss: 0.3710 - val_accuracy: 0.8785\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1805 - accuracy: 0.9328 - val_loss: 0.3523 - val_accuracy: 0.8968\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1525 - accuracy: 0.9402 - val_loss: 0.3953 - val_accuracy: 0.8914\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1685 - accuracy: 0.9384 - val_loss: 0.2848 - val_accuracy: 0.9036\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1658 - accuracy: 0.9374 - val_loss: 0.3068 - val_accuracy: 0.9033\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1537 - accuracy: 0.9391 - val_loss: 0.3172 - val_accuracy: 0.9074\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1398 - accuracy: 0.9431 - val_loss: 0.2832 - val_accuracy: 0.9277\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1269 - accuracy: 0.9484 - val_loss: 0.3329 - val_accuracy: 0.9104\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1544 - accuracy: 0.9400 - val_loss: 0.2985 - val_accuracy: 0.8999\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 27 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 13ms/step - loss: 1.2519 - accuracy: 0.4563 - val_loss: 1.0057 - val_accuracy: 0.5687\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7540 - accuracy: 0.6717 - val_loss: 0.7656 - val_accuracy: 0.6725\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6417 - accuracy: 0.7334 - val_loss: 0.7125 - val_accuracy: 0.7126\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4836 - accuracy: 0.8033 - val_loss: 0.5784 - val_accuracy: 0.7974\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.3149 - accuracy: 0.8927 - val_loss: 0.5195 - val_accuracy: 0.8602\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1924 - accuracy: 0.9283 - val_loss: 0.5041 - val_accuracy: 0.8812\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1893 - accuracy: 0.9304 - val_loss: 0.3560 - val_accuracy: 0.8717\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1495 - accuracy: 0.9436 - val_loss: 0.3154 - val_accuracy: 0.9060\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1536 - accuracy: 0.9404 - val_loss: 0.3118 - val_accuracy: 0.9026\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1340 - accuracy: 0.9460 - val_loss: 0.3974 - val_accuracy: 0.8979\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1851 - accuracy: 0.9275 - val_loss: 0.2723 - val_accuracy: 0.9040\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1602 - accuracy: 0.9378 - val_loss: 0.2582 - val_accuracy: 0.9063\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1291 - accuracy: 0.9467 - val_loss: 0.2373 - val_accuracy: 0.9087\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1210 - accuracy: 0.9498 - val_loss: 0.3480 - val_accuracy: 0.8989\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1167 - accuracy: 0.9491 - val_loss: 0.3707 - val_accuracy: 0.9067\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 28 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 13ms/step - loss: 1.2183 - accuracy: 0.4846 - val_loss: 0.9712 - val_accuracy: 0.5989\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7304 - accuracy: 0.6703 - val_loss: 0.7603 - val_accuracy: 0.6790\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7294 - accuracy: 0.7043 - val_loss: 0.8855 - val_accuracy: 0.6417\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7276 - accuracy: 0.7016 - val_loss: 0.6887 - val_accuracy: 0.7173\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.6181 - accuracy: 0.7607 - val_loss: 0.9582 - val_accuracy: 0.6359\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.6112 - accuracy: 0.7429 - val_loss: 0.5896 - val_accuracy: 0.7404\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5034 - accuracy: 0.7983 - val_loss: 0.5274 - val_accuracy: 0.8317\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3151 - accuracy: 0.8866 - val_loss: 0.4543 - val_accuracy: 0.8378\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2559 - accuracy: 0.9115 - val_loss: 0.3385 - val_accuracy: 0.9043\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2926 - accuracy: 0.8992 - val_loss: 0.3072 - val_accuracy: 0.9091\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2103 - accuracy: 0.9248 - val_loss: 0.2949 - val_accuracy: 0.9087\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1776 - accuracy: 0.9366 - val_loss: 0.3213 - val_accuracy: 0.9067\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2386 - accuracy: 0.9131 - val_loss: 0.3493 - val_accuracy: 0.8846\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.2050 - accuracy: 0.9227 - val_loss: 0.3053 - val_accuracy: 0.9009\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1696 - accuracy: 0.9350 - val_loss: 0.3161 - val_accuracy: 0.8941\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 29 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 13ms/step - loss: 1.2609 - accuracy: 0.4657 - val_loss: 1.0944 - val_accuracy: 0.5534\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.7732 - accuracy: 0.6695 - val_loss: 0.7974 - val_accuracy: 0.6878\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.6239 - accuracy: 0.7465 - val_loss: 0.6654 - val_accuracy: 0.7740\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4448 - accuracy: 0.8441 - val_loss: 0.4983 - val_accuracy: 0.8354\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3655 - accuracy: 0.8749 - val_loss: 0.4646 - val_accuracy: 0.8673\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2357 - accuracy: 0.9215 - val_loss: 0.3721 - val_accuracy: 0.8870\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1969 - accuracy: 0.9331 - val_loss: 0.3734 - val_accuracy: 0.8856\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2074 - accuracy: 0.9278 - val_loss: 0.4905 - val_accuracy: 0.8860\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1597 - accuracy: 0.9392 - val_loss: 0.3776 - val_accuracy: 0.9006\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1540 - accuracy: 0.9403 - val_loss: 0.4302 - val_accuracy: 0.9016\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1404 - accuracy: 0.9456 - val_loss: 0.4554 - val_accuracy: 0.8999\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1443 - accuracy: 0.9442 - val_loss: 0.4095 - val_accuracy: 0.9091\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1640 - accuracy: 0.9381 - val_loss: 0.3356 - val_accuracy: 0.8996\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1644 - accuracy: 0.9374 - val_loss: 0.3762 - val_accuracy: 0.9030\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1328 - accuracy: 0.9453 - val_loss: 0.3349 - val_accuracy: 0.9023\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 30 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 13ms/step - loss: 1.2236 - accuracy: 0.4810 - val_loss: 0.9709 - val_accuracy: 0.5775\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7325 - accuracy: 0.6801 - val_loss: 0.8111 - val_accuracy: 0.7044\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5878 - accuracy: 0.7527 - val_loss: 0.6609 - val_accuracy: 0.7207\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4682 - accuracy: 0.8101 - val_loss: 0.4581 - val_accuracy: 0.8395\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2720 - accuracy: 0.9067 - val_loss: 0.4347 - val_accuracy: 0.8592\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1917 - accuracy: 0.9314 - val_loss: 0.3207 - val_accuracy: 0.8918\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1577 - accuracy: 0.9407 - val_loss: 0.3637 - val_accuracy: 0.8717\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1585 - accuracy: 0.9430 - val_loss: 0.3173 - val_accuracy: 0.8873\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1455 - accuracy: 0.9437 - val_loss: 0.3752 - val_accuracy: 0.8799\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1344 - accuracy: 0.9472 - val_loss: 0.2414 - val_accuracy: 0.9213\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1348 - accuracy: 0.9467 - val_loss: 0.3648 - val_accuracy: 0.8897\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2951 - accuracy: 0.8925 - val_loss: 0.3685 - val_accuracy: 0.8894\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1883 - accuracy: 0.9319 - val_loss: 0.2226 - val_accuracy: 0.9097\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1442 - accuracy: 0.9425 - val_loss: 0.3331 - val_accuracy: 0.8975\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1341 - accuracy: 0.9474 - val_loss: 0.3842 - val_accuracy: 0.8992\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 31 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 13ms/step - loss: 1.1857 - accuracy: 0.4958 - val_loss: 0.9918 - val_accuracy: 0.5758\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7769 - accuracy: 0.6646 - val_loss: 0.7639 - val_accuracy: 0.7085\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5797 - accuracy: 0.7575 - val_loss: 0.6143 - val_accuracy: 0.7638\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4814 - accuracy: 0.8220 - val_loss: 0.5191 - val_accuracy: 0.8256\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4354 - accuracy: 0.8403 - val_loss: 0.3876 - val_accuracy: 0.8568\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2683 - accuracy: 0.9089 - val_loss: 0.3746 - val_accuracy: 0.8571\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1807 - accuracy: 0.9362 - val_loss: 0.2689 - val_accuracy: 0.8863\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2140 - accuracy: 0.9272 - val_loss: 0.2636 - val_accuracy: 0.9067\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1623 - accuracy: 0.9366 - val_loss: 0.2597 - val_accuracy: 0.9040\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1518 - accuracy: 0.9431 - val_loss: 0.2367 - val_accuracy: 0.9097\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1498 - accuracy: 0.9407 - val_loss: 0.2349 - val_accuracy: 0.9138\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1582 - accuracy: 0.9408 - val_loss: 0.2385 - val_accuracy: 0.9097\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1407 - accuracy: 0.9441 - val_loss: 0.2304 - val_accuracy: 0.9077\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1719 - accuracy: 0.9363 - val_loss: 0.3901 - val_accuracy: 0.8918\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1274 - accuracy: 0.9493 - val_loss: 0.3650 - val_accuracy: 0.8870\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 32 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 13ms/step - loss: 1.2478 - accuracy: 0.4672 - val_loss: 1.0416 - val_accuracy: 0.5786\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.8468 - accuracy: 0.6411 - val_loss: 0.7416 - val_accuracy: 0.6817\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.6065 - accuracy: 0.7312 - val_loss: 0.7838 - val_accuracy: 0.7156\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4941 - accuracy: 0.8120 - val_loss: 0.5404 - val_accuracy: 0.8113\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3205 - accuracy: 0.8823 - val_loss: 0.4918 - val_accuracy: 0.8575\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2104 - accuracy: 0.9225 - val_loss: 0.3837 - val_accuracy: 0.8918\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1791 - accuracy: 0.9365 - val_loss: 0.4046 - val_accuracy: 0.8918\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1628 - accuracy: 0.9378 - val_loss: 0.4293 - val_accuracy: 0.8541\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1640 - accuracy: 0.9373 - val_loss: 0.4375 - val_accuracy: 0.8768\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1428 - accuracy: 0.9449 - val_loss: 0.4141 - val_accuracy: 0.8968\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1431 - accuracy: 0.9415 - val_loss: 0.3125 - val_accuracy: 0.9050\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1581 - accuracy: 0.9429 - val_loss: 0.4434 - val_accuracy: 0.9036\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1325 - accuracy: 0.9464 - val_loss: 0.2698 - val_accuracy: 0.9087\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 0.1265 - accuracy: 0.9516 - val_loss: 0.4031 - val_accuracy: 0.8996\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1317 - accuracy: 0.9408 - val_loss: 0.4279 - val_accuracy: 0.9019\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 33 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 13ms/step - loss: 1.1767 - accuracy: 0.5044 - val_loss: 0.9725 - val_accuracy: 0.5948\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7692 - accuracy: 0.6782 - val_loss: 0.7797 - val_accuracy: 0.6525\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5365 - accuracy: 0.7972 - val_loss: 0.5122 - val_accuracy: 0.8171\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3539 - accuracy: 0.8783 - val_loss: 0.4277 - val_accuracy: 0.8453\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2545 - accuracy: 0.9178 - val_loss: 0.3895 - val_accuracy: 0.8785\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2188 - accuracy: 0.9227 - val_loss: 0.3905 - val_accuracy: 0.8772\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1810 - accuracy: 0.9357 - val_loss: 0.3341 - val_accuracy: 0.8941\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1557 - accuracy: 0.9434 - val_loss: 0.2808 - val_accuracy: 0.9080\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1884 - accuracy: 0.9329 - val_loss: 0.2877 - val_accuracy: 0.8809\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1562 - accuracy: 0.9416 - val_loss: 0.3015 - val_accuracy: 0.8823\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1413 - accuracy: 0.9437 - val_loss: 0.2656 - val_accuracy: 0.9030\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1432 - accuracy: 0.9438 - val_loss: 0.2806 - val_accuracy: 0.8996\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1283 - accuracy: 0.9457 - val_loss: 0.2841 - val_accuracy: 0.9023\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1308 - accuracy: 0.9455 - val_loss: 0.3029 - val_accuracy: 0.9040\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1382 - accuracy: 0.9422 - val_loss: 0.6576 - val_accuracy: 0.7553\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 34 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 18ms/step - loss: 1.2272 - accuracy: 0.4842 - val_loss: 0.9265 - val_accuracy: 0.5996\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7120 - accuracy: 0.6959 - val_loss: 0.7438 - val_accuracy: 0.7072\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.6437 - accuracy: 0.7354 - val_loss: 0.6367 - val_accuracy: 0.7540\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5148 - accuracy: 0.8078 - val_loss: 0.5563 - val_accuracy: 0.7808\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3706 - accuracy: 0.8704 - val_loss: 0.4396 - val_accuracy: 0.8517\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2729 - accuracy: 0.9091 - val_loss: 0.4626 - val_accuracy: 0.8568\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2301 - accuracy: 0.9200 - val_loss: 0.3328 - val_accuracy: 0.8846\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1811 - accuracy: 0.9343 - val_loss: 0.3690 - val_accuracy: 0.8870\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1597 - accuracy: 0.9376 - val_loss: 0.3678 - val_accuracy: 0.8935\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1792 - accuracy: 0.9343 - val_loss: 0.4206 - val_accuracy: 0.8646\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2110 - accuracy: 0.9267 - val_loss: 0.5068 - val_accuracy: 0.8867\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2001 - accuracy: 0.9256 - val_loss: 0.4073 - val_accuracy: 0.8636\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1666 - accuracy: 0.9359 - val_loss: 0.3776 - val_accuracy: 0.8877\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1544 - accuracy: 0.9380 - val_loss: 0.3115 - val_accuracy: 0.8985\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1542 - accuracy: 0.9382 - val_loss: 0.3171 - val_accuracy: 0.9019\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 35 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 13ms/step - loss: 1.2390 - accuracy: 0.4659 - val_loss: 1.0974 - val_accuracy: 0.5327\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7449 - accuracy: 0.6795 - val_loss: 0.7221 - val_accuracy: 0.7072\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5625 - accuracy: 0.7756 - val_loss: 0.6593 - val_accuracy: 0.7516\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4905 - accuracy: 0.8225 - val_loss: 0.5648 - val_accuracy: 0.7944\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3946 - accuracy: 0.8666 - val_loss: 0.5849 - val_accuracy: 0.8490\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2841 - accuracy: 0.9027 - val_loss: 0.5156 - val_accuracy: 0.8252\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2237 - accuracy: 0.9229 - val_loss: 0.4657 - val_accuracy: 0.8806\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1908 - accuracy: 0.9294 - val_loss: 0.4457 - val_accuracy: 0.8782\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1693 - accuracy: 0.9362 - val_loss: 0.4668 - val_accuracy: 0.8548\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1844 - accuracy: 0.9317 - val_loss: 0.4716 - val_accuracy: 0.8744\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1940 - accuracy: 0.9325 - val_loss: 0.4568 - val_accuracy: 0.8928\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1391 - accuracy: 0.9464 - val_loss: 0.4615 - val_accuracy: 0.8856\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1295 - accuracy: 0.9475 - val_loss: 0.3983 - val_accuracy: 0.8907\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1349 - accuracy: 0.9470 - val_loss: 0.4506 - val_accuracy: 0.8887\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2982 - accuracy: 0.8985 - val_loss: 0.4302 - val_accuracy: 0.8867\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 36 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 13ms/step - loss: 1.1863 - accuracy: 0.5016 - val_loss: 0.9855 - val_accuracy: 0.5955\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7278 - accuracy: 0.7024 - val_loss: 0.6823 - val_accuracy: 0.7346\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.6452 - accuracy: 0.7505 - val_loss: 0.5952 - val_accuracy: 0.7974\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3727 - accuracy: 0.8734 - val_loss: 0.4231 - val_accuracy: 0.8622\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3042 - accuracy: 0.9000 - val_loss: 0.3749 - val_accuracy: 0.8728\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2955 - accuracy: 0.8916 - val_loss: 0.3755 - val_accuracy: 0.8778\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1867 - accuracy: 0.9260 - val_loss: 0.3437 - val_accuracy: 0.8897\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1612 - accuracy: 0.9365 - val_loss: 0.3721 - val_accuracy: 0.8884\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1560 - accuracy: 0.9358 - val_loss: 0.4564 - val_accuracy: 0.8802\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1472 - accuracy: 0.9408 - val_loss: 0.3899 - val_accuracy: 0.8918\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1283 - accuracy: 0.9472 - val_loss: 0.4757 - val_accuracy: 0.8782\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1253 - accuracy: 0.9467 - val_loss: 0.4454 - val_accuracy: 0.9030\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1219 - accuracy: 0.9502 - val_loss: 0.4359 - val_accuracy: 0.9118\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1158 - accuracy: 0.9523 - val_loss: 0.4149 - val_accuracy: 0.9111\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1139 - accuracy: 0.9532 - val_loss: 0.4842 - val_accuracy: 0.9067\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 37 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 14ms/step - loss: 1.1812 - accuracy: 0.4950 - val_loss: 1.0938 - val_accuracy: 0.5534\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.6979 - accuracy: 0.7080 - val_loss: 0.6892 - val_accuracy: 0.7377\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5545 - accuracy: 0.7852 - val_loss: 0.6107 - val_accuracy: 0.7991\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3585 - accuracy: 0.8698 - val_loss: 0.5346 - val_accuracy: 0.8151\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3713 - accuracy: 0.8773 - val_loss: 0.4967 - val_accuracy: 0.8602\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2985 - accuracy: 0.8909 - val_loss: 0.4564 - val_accuracy: 0.8850\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1718 - accuracy: 0.9415 - val_loss: 0.3004 - val_accuracy: 0.8972\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1702 - accuracy: 0.9347 - val_loss: 0.3359 - val_accuracy: 0.8717\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1700 - accuracy: 0.9400 - val_loss: 0.3096 - val_accuracy: 0.8948\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1394 - accuracy: 0.9472 - val_loss: 0.3372 - val_accuracy: 0.8816\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1599 - accuracy: 0.9404 - val_loss: 0.3403 - val_accuracy: 0.8931\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1535 - accuracy: 0.9389 - val_loss: 0.2886 - val_accuracy: 0.8901\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1307 - accuracy: 0.9489 - val_loss: 0.3356 - val_accuracy: 0.8863\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1297 - accuracy: 0.9499 - val_loss: 0.3573 - val_accuracy: 0.8904\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1442 - accuracy: 0.9433 - val_loss: 0.2896 - val_accuracy: 0.9036\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 38 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 14ms/step - loss: 1.1848 - accuracy: 0.5029 - val_loss: 0.9948 - val_accuracy: 0.5935\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.8944 - accuracy: 0.6266 - val_loss: 0.7413 - val_accuracy: 0.7072\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7244 - accuracy: 0.6919 - val_loss: 0.8341 - val_accuracy: 0.6556\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7693 - accuracy: 0.6946 - val_loss: 0.8941 - val_accuracy: 0.6637\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.6439 - accuracy: 0.7515 - val_loss: 0.6535 - val_accuracy: 0.7598\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4558 - accuracy: 0.8277 - val_loss: 0.5911 - val_accuracy: 0.8035\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3096 - accuracy: 0.8965 - val_loss: 0.3842 - val_accuracy: 0.8755\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2159 - accuracy: 0.9257 - val_loss: 0.3974 - val_accuracy: 0.8765\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1859 - accuracy: 0.9332 - val_loss: 0.3954 - val_accuracy: 0.8928\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1793 - accuracy: 0.9359 - val_loss: 0.4242 - val_accuracy: 0.8514\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3197 - accuracy: 0.8862 - val_loss: 0.3372 - val_accuracy: 0.8945\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2755 - accuracy: 0.8909 - val_loss: 0.3744 - val_accuracy: 0.8911\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1734 - accuracy: 0.9400 - val_loss: 0.3726 - val_accuracy: 0.8826\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1616 - accuracy: 0.9381 - val_loss: 0.3549 - val_accuracy: 0.8958\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1546 - accuracy: 0.9446 - val_loss: 0.3452 - val_accuracy: 0.9070\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 39 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 14ms/step - loss: 1.1593 - accuracy: 0.5190 - val_loss: 1.0110 - val_accuracy: 0.6193\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7239 - accuracy: 0.6991 - val_loss: 0.6509 - val_accuracy: 0.7801\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4739 - accuracy: 0.8145 - val_loss: 0.5213 - val_accuracy: 0.8090\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3015 - accuracy: 0.8951 - val_loss: 0.5566 - val_accuracy: 0.8497\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4300 - accuracy: 0.8629 - val_loss: 0.4886 - val_accuracy: 0.8442\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2715 - accuracy: 0.9121 - val_loss: 0.4187 - val_accuracy: 0.8687\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2076 - accuracy: 0.9332 - val_loss: 0.3910 - val_accuracy: 0.8789\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1796 - accuracy: 0.9357 - val_loss: 0.5012 - val_accuracy: 0.8789\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1690 - accuracy: 0.9402 - val_loss: 0.3873 - val_accuracy: 0.8972\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1738 - accuracy: 0.9361 - val_loss: 0.4353 - val_accuracy: 0.8826\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1338 - accuracy: 0.9472 - val_loss: 0.4006 - val_accuracy: 0.8975\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1274 - accuracy: 0.9486 - val_loss: 0.4248 - val_accuracy: 0.8884\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1187 - accuracy: 0.9525 - val_loss: 0.4338 - val_accuracy: 0.8968\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1872 - accuracy: 0.9321 - val_loss: 0.4631 - val_accuracy: 0.8856\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1761 - accuracy: 0.9323 - val_loss: 0.3360 - val_accuracy: 0.8792\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 40 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 14ms/step - loss: 1.1553 - accuracy: 0.5110 - val_loss: 0.8973 - val_accuracy: 0.6478\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.8218 - accuracy: 0.6561 - val_loss: 0.7179 - val_accuracy: 0.7411\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5919 - accuracy: 0.7639 - val_loss: 0.6026 - val_accuracy: 0.7625\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4027 - accuracy: 0.8515 - val_loss: 0.4853 - val_accuracy: 0.8439\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4107 - accuracy: 0.8562 - val_loss: 0.4309 - val_accuracy: 0.8622\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2372 - accuracy: 0.9183 - val_loss: 0.3193 - val_accuracy: 0.8894\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1908 - accuracy: 0.9306 - val_loss: 0.4412 - val_accuracy: 0.8829\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1991 - accuracy: 0.9283 - val_loss: 0.3069 - val_accuracy: 0.8989\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2650 - accuracy: 0.9097 - val_loss: 0.5062 - val_accuracy: 0.8144\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2967 - accuracy: 0.8911 - val_loss: 0.4352 - val_accuracy: 0.8812\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2146 - accuracy: 0.9230 - val_loss: 0.4066 - val_accuracy: 0.8921\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1700 - accuracy: 0.9346 - val_loss: 0.3908 - val_accuracy: 0.8850\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1419 - accuracy: 0.9442 - val_loss: 0.3732 - val_accuracy: 0.8918\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1507 - accuracy: 0.9442 - val_loss: 0.3925 - val_accuracy: 0.8887\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1445 - accuracy: 0.9397 - val_loss: 0.3476 - val_accuracy: 0.9087\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 41 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 14ms/step - loss: 1.1402 - accuracy: 0.5156 - val_loss: 1.0290 - val_accuracy: 0.5752\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7491 - accuracy: 0.6766 - val_loss: 0.8286 - val_accuracy: 0.6559\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5827 - accuracy: 0.7485 - val_loss: 0.6211 - val_accuracy: 0.7452\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3678 - accuracy: 0.8702 - val_loss: 0.4076 - val_accuracy: 0.8415\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2895 - accuracy: 0.9051 - val_loss: 0.3238 - val_accuracy: 0.8863\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1758 - accuracy: 0.9372 - val_loss: 0.2559 - val_accuracy: 0.9040\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1534 - accuracy: 0.9415 - val_loss: 0.2669 - val_accuracy: 0.8982\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1704 - accuracy: 0.9361 - val_loss: 0.3424 - val_accuracy: 0.8918\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1468 - accuracy: 0.9433 - val_loss: 0.2604 - val_accuracy: 0.9063\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1530 - accuracy: 0.9389 - val_loss: 0.2580 - val_accuracy: 0.9043\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1349 - accuracy: 0.9470 - val_loss: 0.2090 - val_accuracy: 0.9121\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1365 - accuracy: 0.9436 - val_loss: 0.4834 - val_accuracy: 0.8286\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1559 - accuracy: 0.9414 - val_loss: 0.2658 - val_accuracy: 0.9155\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1372 - accuracy: 0.9474 - val_loss: 0.2295 - val_accuracy: 0.9220\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1368 - accuracy: 0.9467 - val_loss: 0.2943 - val_accuracy: 0.9158\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 42 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 14ms/step - loss: 1.2047 - accuracy: 0.4989 - val_loss: 1.0064 - val_accuracy: 0.5840\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7774 - accuracy: 0.6957 - val_loss: 0.6866 - val_accuracy: 0.7343\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5512 - accuracy: 0.7822 - val_loss: 0.6444 - val_accuracy: 0.7740\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4227 - accuracy: 0.8530 - val_loss: 0.4263 - val_accuracy: 0.8459\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3252 - accuracy: 0.8951 - val_loss: 0.4058 - val_accuracy: 0.8575\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2873 - accuracy: 0.9074 - val_loss: 0.4264 - val_accuracy: 0.8602\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2150 - accuracy: 0.9252 - val_loss: 0.3965 - val_accuracy: 0.8700\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1715 - accuracy: 0.9393 - val_loss: 0.3455 - val_accuracy: 0.8826\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2898 - accuracy: 0.9000 - val_loss: 0.4207 - val_accuracy: 0.8802\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2136 - accuracy: 0.9242 - val_loss: 0.3598 - val_accuracy: 0.8992\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1871 - accuracy: 0.9295 - val_loss: 0.4093 - val_accuracy: 0.8490\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1765 - accuracy: 0.9365 - val_loss: 0.3983 - val_accuracy: 0.8856\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1511 - accuracy: 0.9393 - val_loss: 0.3445 - val_accuracy: 0.9077\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1570 - accuracy: 0.9416 - val_loss: 0.2733 - val_accuracy: 0.9080\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2137 - accuracy: 0.9177 - val_loss: 0.2881 - val_accuracy: 0.8982\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 43 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 14ms/step - loss: 1.2791 - accuracy: 0.4580 - val_loss: 1.1150 - val_accuracy: 0.5283\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.8036 - accuracy: 0.6342 - val_loss: 0.7623 - val_accuracy: 0.6810\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.6522 - accuracy: 0.7055 - val_loss: 0.6778 - val_accuracy: 0.7438\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4293 - accuracy: 0.8459 - val_loss: 1.0130 - val_accuracy: 0.6753\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5114 - accuracy: 0.7942 - val_loss: 0.3684 - val_accuracy: 0.8717\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2361 - accuracy: 0.9197 - val_loss: 0.3319 - val_accuracy: 0.8806\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1909 - accuracy: 0.9316 - val_loss: 0.3214 - val_accuracy: 0.9030\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1719 - accuracy: 0.9340 - val_loss: 0.3827 - val_accuracy: 0.8918\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1745 - accuracy: 0.9362 - val_loss: 0.3720 - val_accuracy: 0.8775\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1537 - accuracy: 0.9433 - val_loss: 0.2530 - val_accuracy: 0.9175\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1782 - accuracy: 0.9340 - val_loss: 0.3633 - val_accuracy: 0.9013\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1509 - accuracy: 0.9403 - val_loss: 0.4565 - val_accuracy: 0.8731\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1519 - accuracy: 0.9444 - val_loss: 0.2888 - val_accuracy: 0.9006\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1514 - accuracy: 0.9429 - val_loss: 0.3443 - val_accuracy: 0.9016\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2127 - accuracy: 0.9280 - val_loss: 0.6748 - val_accuracy: 0.7703\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 44 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 14ms/step - loss: 1.1653 - accuracy: 0.5159 - val_loss: 0.9662 - val_accuracy: 0.5955\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7152 - accuracy: 0.6912 - val_loss: 0.8190 - val_accuracy: 0.7112\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5439 - accuracy: 0.7844 - val_loss: 0.5901 - val_accuracy: 0.7991\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5642 - accuracy: 0.7942 - val_loss: 0.5791 - val_accuracy: 0.7971\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3770 - accuracy: 0.8740 - val_loss: 0.4310 - val_accuracy: 0.8385\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2441 - accuracy: 0.9181 - val_loss: 0.4521 - val_accuracy: 0.8361\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2224 - accuracy: 0.9222 - val_loss: 0.3488 - val_accuracy: 0.8836\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1730 - accuracy: 0.9385 - val_loss: 0.3265 - val_accuracy: 0.8839\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1779 - accuracy: 0.9344 - val_loss: 0.6465 - val_accuracy: 0.7950\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2078 - accuracy: 0.9283 - val_loss: 0.4271 - val_accuracy: 0.8812\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1656 - accuracy: 0.9407 - val_loss: 0.3182 - val_accuracy: 0.8877\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1819 - accuracy: 0.9362 - val_loss: 0.4046 - val_accuracy: 0.8982\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1308 - accuracy: 0.9498 - val_loss: 0.4036 - val_accuracy: 0.8992\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1286 - accuracy: 0.9486 - val_loss: 0.3011 - val_accuracy: 0.9019\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1271 - accuracy: 0.9475 - val_loss: 0.2986 - val_accuracy: 0.9019\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 45 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 14ms/step - loss: 1.2069 - accuracy: 0.4917 - val_loss: 0.9006 - val_accuracy: 0.6111\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7712 - accuracy: 0.6797 - val_loss: 0.6898 - val_accuracy: 0.7211\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5817 - accuracy: 0.7575 - val_loss: 0.5783 - val_accuracy: 0.7594\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4585 - accuracy: 0.8183 - val_loss: 0.5358 - val_accuracy: 0.7900\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3262 - accuracy: 0.8795 - val_loss: 0.4043 - val_accuracy: 0.8605\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2628 - accuracy: 0.9079 - val_loss: 0.7191 - val_accuracy: 0.8161\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2965 - accuracy: 0.8901 - val_loss: 0.6293 - val_accuracy: 0.8134\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2502 - accuracy: 0.9129 - val_loss: 0.9487 - val_accuracy: 0.7024\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2706 - accuracy: 0.9037 - val_loss: 0.3158 - val_accuracy: 0.9046\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1864 - accuracy: 0.9343 - val_loss: 0.3629 - val_accuracy: 0.9013\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1824 - accuracy: 0.9336 - val_loss: 0.3042 - val_accuracy: 0.9135\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1594 - accuracy: 0.9411 - val_loss: 0.3199 - val_accuracy: 0.8975\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1875 - accuracy: 0.9274 - val_loss: 0.2977 - val_accuracy: 0.8935\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1426 - accuracy: 0.9461 - val_loss: 0.2985 - val_accuracy: 0.9104\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1297 - accuracy: 0.9493 - val_loss: 0.3107 - val_accuracy: 0.9152\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 46 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 14ms/step - loss: 1.2144 - accuracy: 0.4909 - val_loss: 1.0985 - val_accuracy: 0.5477\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7645 - accuracy: 0.6755 - val_loss: 0.7457 - val_accuracy: 0.7245\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7491 - accuracy: 0.6906 - val_loss: 0.6823 - val_accuracy: 0.7279\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5662 - accuracy: 0.7784 - val_loss: 0.6715 - val_accuracy: 0.7503\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4424 - accuracy: 0.8349 - val_loss: 0.6003 - val_accuracy: 0.8090\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2697 - accuracy: 0.9052 - val_loss: 0.4418 - val_accuracy: 0.8599\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2345 - accuracy: 0.9187 - val_loss: 0.4631 - val_accuracy: 0.8673\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2374 - accuracy: 0.9127 - val_loss: 0.4831 - val_accuracy: 0.8850\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1694 - accuracy: 0.9373 - val_loss: 0.4016 - val_accuracy: 0.8958\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1598 - accuracy: 0.9382 - val_loss: 0.5781 - val_accuracy: 0.8772\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2824 - accuracy: 0.8993 - val_loss: 0.3967 - val_accuracy: 0.8918\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1540 - accuracy: 0.9400 - val_loss: 0.3985 - val_accuracy: 0.8935\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1449 - accuracy: 0.9427 - val_loss: 0.4097 - val_accuracy: 0.9087\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1362 - accuracy: 0.9448 - val_loss: 0.3126 - val_accuracy: 0.8948\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1519 - accuracy: 0.9427 - val_loss: 0.3108 - val_accuracy: 0.9050\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 47 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 14ms/step - loss: 1.1893 - accuracy: 0.4937 - val_loss: 0.9795 - val_accuracy: 0.5959\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.6847 - accuracy: 0.6987 - val_loss: 0.6541 - val_accuracy: 0.7567\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.5468 - accuracy: 0.7826 - val_loss: 0.5364 - val_accuracy: 0.8049\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3691 - accuracy: 0.8687 - val_loss: 0.5261 - val_accuracy: 0.8405\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2749 - accuracy: 0.9055 - val_loss: 0.4383 - val_accuracy: 0.8568\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2237 - accuracy: 0.9200 - val_loss: 0.5420 - val_accuracy: 0.8246\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1751 - accuracy: 0.9368 - val_loss: 0.4112 - val_accuracy: 0.8955\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1877 - accuracy: 0.9304 - val_loss: 0.4460 - val_accuracy: 0.8789\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2385 - accuracy: 0.9128 - val_loss: 0.4063 - val_accuracy: 0.8551\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1780 - accuracy: 0.9362 - val_loss: 0.2962 - val_accuracy: 0.8918\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1602 - accuracy: 0.9416 - val_loss: 0.2689 - val_accuracy: 0.9121\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1594 - accuracy: 0.9434 - val_loss: 0.2712 - val_accuracy: 0.9060\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1379 - accuracy: 0.9444 - val_loss: 0.2773 - val_accuracy: 0.8979\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1231 - accuracy: 0.9509 - val_loss: 0.2528 - val_accuracy: 0.9067\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1276 - accuracy: 0.9472 - val_loss: 0.2494 - val_accuracy: 0.9155\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 48 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 14ms/step - loss: 1.2113 - accuracy: 0.4864 - val_loss: 1.0836 - val_accuracy: 0.5389\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7651 - accuracy: 0.6666 - val_loss: 0.7380 - val_accuracy: 0.7214\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.6692 - accuracy: 0.7499 - val_loss: 0.5613 - val_accuracy: 0.8056\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4409 - accuracy: 0.8432 - val_loss: 0.4898 - val_accuracy: 0.8334\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3359 - accuracy: 0.8923 - val_loss: 0.4129 - val_accuracy: 0.8700\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2210 - accuracy: 0.9248 - val_loss: 0.2929 - val_accuracy: 0.8901\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1976 - accuracy: 0.9300 - val_loss: 0.2951 - val_accuracy: 0.9006\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2022 - accuracy: 0.9301 - val_loss: 0.2966 - val_accuracy: 0.9084\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1658 - accuracy: 0.9382 - val_loss: 0.3074 - val_accuracy: 0.8999\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2034 - accuracy: 0.9301 - val_loss: 0.2584 - val_accuracy: 0.8996\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2077 - accuracy: 0.9275 - val_loss: 0.5416 - val_accuracy: 0.8487\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1798 - accuracy: 0.9363 - val_loss: 0.2796 - val_accuracy: 0.8992\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1675 - accuracy: 0.9376 - val_loss: 0.2662 - val_accuracy: 0.8975\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1443 - accuracy: 0.9470 - val_loss: 0.2643 - val_accuracy: 0.9101\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1292 - accuracy: 0.9490 - val_loss: 0.2802 - val_accuracy: 0.8972\n",
            "<==================================================>\n",
            "\n",
            "\n",
            "<========== Now training model - 49 ...... =======>\n",
            "Epoch 1/15\n",
            "115/115 [==============================] - 2s 14ms/step - loss: 1.2261 - accuracy: 0.4747 - val_loss: 0.9452 - val_accuracy: 0.6179\n",
            "Epoch 2/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.7479 - accuracy: 0.6745 - val_loss: 0.7359 - val_accuracy: 0.6888\n",
            "Epoch 3/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.6017 - accuracy: 0.7454 - val_loss: 0.5882 - val_accuracy: 0.7743\n",
            "Epoch 4/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.4613 - accuracy: 0.8255 - val_loss: 0.5110 - val_accuracy: 0.8161\n",
            "Epoch 5/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.3195 - accuracy: 0.8932 - val_loss: 0.4683 - val_accuracy: 0.8466\n",
            "Epoch 6/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2807 - accuracy: 0.9048 - val_loss: 0.4152 - val_accuracy: 0.8819\n",
            "Epoch 7/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2030 - accuracy: 0.9255 - val_loss: 0.4475 - val_accuracy: 0.8826\n",
            "Epoch 8/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1672 - accuracy: 0.9355 - val_loss: 0.3528 - val_accuracy: 0.8958\n",
            "Epoch 9/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1461 - accuracy: 0.9410 - val_loss: 0.3360 - val_accuracy: 0.8962\n",
            "Epoch 10/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1333 - accuracy: 0.9457 - val_loss: 0.3570 - val_accuracy: 0.9002\n",
            "Epoch 11/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1337 - accuracy: 0.9464 - val_loss: 0.3601 - val_accuracy: 0.8968\n",
            "Epoch 12/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1370 - accuracy: 0.9453 - val_loss: 0.3753 - val_accuracy: 0.9013\n",
            "Epoch 13/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2123 - accuracy: 0.9248 - val_loss: 0.6244 - val_accuracy: 0.7716\n",
            "Epoch 14/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.2399 - accuracy: 0.9108 - val_loss: 0.4261 - val_accuracy: 0.8873\n",
            "Epoch 15/15\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 0.1618 - accuracy: 0.9366 - val_loss: 0.3602 - val_accuracy: 0.9043\n",
            "<==================================================>\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j_Ix06OVBNrh"
      },
      "source": [
        "# Compare different instances\r\n",
        "\r\n",
        "We will compare the different instances of trained model to find the one which gives the best performance. We will also find the average accuracy for the trained models."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LKkaFA98AKvx"
      },
      "source": [
        "# Imports\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "from statistics import mean "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXc0M_STBR1Y"
      },
      "source": [
        "def plot_accuracies(modelList, modelName, figureSize=[8, 8]):\r\n",
        "    accuracies = []\r\n",
        "    for modelDict in modelList:\r\n",
        "        accuracies.append(modelDict['accuracy'])\r\n",
        "    mean_accuracy = mean(accuracies)\r\n",
        "    matplotlib.rcParams['figure.figsize'] = figureSize\r\n",
        "    plt.plot(accuracies)\r\n",
        "    plt.axhline(y=mean_accuracy, color='r', linestyle='-')\r\n",
        "    plt.title('Model accuracy for different instances of - {}'.format(modelName))\r\n",
        "    plt.ylabel('accuracy')\r\n",
        "    plt.xlabel('Instance #')\r\n",
        "    plt.legend(['Instance Accuracy', 'Mean Accuracy'], loc='lower right')\r\n",
        "    plt.show()\r\n",
        "    \r\n",
        "    print(\"\\n\\nThe average accuracy of this model was found out to be: {}\".format(mean_accuracy))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "9QPBfpN0BUbP",
        "outputId": "7131c156-106d-4b95-8c34-3d86c0fd7ceb"
      },
      "source": [
        "plot_accuracies(modelList, modelName='1D CNN Model')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "The average accuracy of this model was found out to be: 0.8959077036380768\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RnhSDbveR8GB"
      },
      "source": [
        "## Selecting Model closest to Mean\r\n",
        "\r\n",
        "here we will be selecting a model instance whose performance is the closest to the mean accuracy. This will help us in comparision between various models."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YwyqQJXAudyj"
      },
      "source": [
        "# Get functions from file\r\n",
        "from drive.MyDrive.InnoHAR.helpers import get_average_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGaRlq_1Ss00"
      },
      "source": [
        "model, history = get_average_model(modelList)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pt2jbMB6cp9G"
      },
      "source": [
        "## Save Model\r\n",
        "model.save('/content/drive/MyDrive/InnoHAR/SavedModels/LSTM.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lUW81JutclNb"
      },
      "source": [
        "## Plotting Accuracy and Losses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5UVpWLCkWyVP"
      },
      "source": [
        "# Get functions from file\r\n",
        "from drive.MyDrive.InnoHAR.helpers import plot_model_accuracy, plot_model_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oh3sQVsveEYg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "e2aed5a7-5e74-4322-b013-a7a946c09cfb"
      },
      "source": [
        "plot_model_accuracy('LSTM Model', history=history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYaTxyDWhdjt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "43dd9b63-11c3-442a-e099-97fb8ffc7e73"
      },
      "source": [
        "plot_model_loss('LSTM Model', history=history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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arRrFERFmWK774iIiEmTcDPENQIsKXzcvf+x3rLUvW2t7W2t7JycnB6S4/SLCPLRNjtdccRERCTpuhvhEYHT5KPV+wG5r7SYX6zms9NQEdaeLiEjQCffXhY0x44GBQJIxZj3wdyACwFr7IjAZGAqsAPYBV/irlprKSInn0183kldYQlyU3/7KREREvOK3RLLWXnKU5y1wvb9e35fS9y+/ujWX7i3qu1yNiIiIIyQGtrlNI9RFRCQYKcSroFWjOCLDPWQpxEVEJIgoxKsgzGNolxyvNdRFRCSoKMSrKCM1Xi1xEREJKgrxKkpPTWDj7gL2FhS7XYqIiAigEK+yjPIR6upSFxGRYKEQr6L9I9TVpS4iIsFCIV5FLRrEEh3hUUtcRESChkK8ijweQ7uUeLK0EYqIiAQJhbgXMrSGuoiIBBGFuBcyUhPYsqeQ3fkaoS4iIu5TiHtBg9tERCSYKMS9kJ7iTDNbphAXEZEgoBD3QrP6McRGhpGlEeoiIhIEFOJe8HgM6SnxGtwmIiJBQSHuJWeEulriIiLiPoW4lzJSE9iWW8jOvCK3SxERkTpOIe6l9PIR6upSFxERtynEvXRwIxSFuIiIuEsh7qUmidEkRIXrvriIiLhOIe4lYwztUjVCXURE3KcQr4b2qQlkbVVLXERE3KUQr4b01AR25BWxLbfQ7VJERKQOU4hXQ4ZGqIuISBBQiFfDgRHqmxXiIiLiHoV4NaQkRFEvOpzlui8uIiIuUohXgzGGjNQEbUkqIiKuUohXU0ZjZw11a63bpYiISB2lEK+mjJR4ducXk7NXI9RFRMQdCvFqOrj8qu6Li4iIOxTi1ZSuNdRFRMRlCvFqSoqPpEFshEJcRERcoxCvJmMM6akJCnEREXGNQrwGMlLjydIIdRERcYlCvAbapyawt7CEzXsK3C5FRETqIIV4DaRrhLqIiLhIIV4D+6eZaeU2ERFxg0K8BhrGRZIUH8kybYQiIiIuUIjXUHpKgjZCERERVyjEaygjNZ4VW/ZqhLqIiAScQryGMhonkFdUyoZd+W6XIiIidYxCfO/mGp1+cHCbutRFRCSw6naI//IqPNEJ9myq9iUyUrSGuoiIuKNuh3ibgWBL4dd3qn2JxNgIUhKiWKYQFxGRAKvbId6oLbQ6HuaMg7Kyal8mIzVB3ekiIhJwdTvEAXqOgp2rYc2P1b5Eemo8K7bmUlamEeoiIhI4CvGOZ0NUIsx5s9qXaJ+aQH5xKet3aoS6iIgEjkI8Mha6XgBLJkL+rmpd4uAa6rovLiIigaMQB+g5GkoKYMH71To9PTUegOVbFeIiIhI4CnGApt2hcddqd6nXi46gSWI0y7WGuoiIBJBCfL+el8Pm+bBxXrVOT09N0JakIiISUArx/bpeAGFRMHdctU7PSIlnZU4upRqhLiIiAaIQ3y+mAXQ6B+a/D8XejzLPaJxAYUkZa3fs80NxIiIiv6cQr6jnKCjcDYsnen1qhkaoi4hIgCnEK2p1PDRIq1aXenqKM0I9SyEuIiIBohCvyOOBHqMg+3vYvtKrU+OiwmlWP0aD20REJGAU4ofqfikYD8x9y+tTM1Lj1Z0uIiIBoxA/VL2mkH46zHsHSku8OjUjNYFVOXmUlFZ/MxUREZGqUohXpscoyN0MK77y6rT01ASKSsvI3q4R6iIi4n8K8cpknAFxKc4WpV5oXz5CXYPbREQkEBTilQmLgO6XwPIvYO/mKp/WLiUeY9DgNhERCQiF+OH0GAW2FH4dX+VTYiLDaNEgVhuhiIhIQCjEDycpHVr2d7rUbdWXUs1IjddGKCIiEhAK8SPpOQp2rIQ1M6p8SnpqAqu35VFUohHqIiLiXwrxI+l0DkTV82oFt4zUeErKLNnb8/xYmIiIiEL8yCLjoMtwWPQJFOyu0ilaQ11ERAJFIX40PUdDST4s+KBKh7dNjsejEeoiIhIACvGjadoDUrvAnDerdHh0RBitGsVprriIiPidQvxojHGmm22aB5vmV+mU9JR4linERUTEz/wa4saYwcaYZcaYFcaYuyp5vqUxZpoxZq4xZr4xZqg/66m2Y0ZAWFSVB7hlpCawZvs+CktK/VyYiIjUZX4LcWNMGPAcMAToBFxijOl0yGH3AhOstT2Ai4Hn/VVPjcQ2hI5nwfwJUFxw1MPTU+MpLbOsytEIdRER8R9/tsT7AiustaustUXAu8A5hxxjgXrlnycCG/1YT830HA0Fu2DpZ0c9tH1jjVAXERH/82eINwPWVfh6ffljFf0DGGmMWQ9MBv5c2YWMMVcbYzKNMZk5OTn+qPXo0k6E+q1gzhtHPbR1UhxhHkOWRqiLiIgfuT2w7RJgrLW2OTAUGGeM+V1N1tqXrbW9rbW9k5OTA14kAB6PM8Bt9XewY/URD40KDyOtUaxa4iIi4lf+DPENQIsKXzcvf6yiK4EJANbamUA0kOTHmmqm+6VgPDD3raMempGaQNZWtcRFRMR//BnivwDpxpjWxphInIFrEw85Zi1wCoAxpiNOiLvUX14Fic2g7Skw7x0oLTnioempCWRvz6OgWCPURUTEP/wW4tbaEuAG4EtgCc4o9EXGmPuNMWeXH3YbcJUx5ldgPDDGWi+2DHNDz9GwdyOs/PqIh2WkxmMtrFBrXERE/CTcnxe31k7GGbBW8bG/Vfh8MTDAnzX4XMZgiE1yVnDLOOOwh7UvX0M9a+teujRLDFR1IiJSh7g9sC30hEdC90tg+ReQu/Wwh6UlxRERZrSGuoiI+I1CvDp6jIayEvh1/GEPiQjz0DpJa6iLiIj/KMSrIzkDWvSDOePgCLfw01MT1BIXERG/UYhXV89RsD0L1s467CEZKQms3bGPfUVHHskuIiJSHQrx6up0LkTGH3FTlIzUeEAj1EVExD8U4tUVFQ9dhsOij6FgT6WHpKfuX0NdIS4iIr6nEK+JnqOheB8s/LDSp9MaxRIZ5tHgNhER8QuFeE006wUpnZw545UID/PQJjlOa6iLiIhfKMRrwhhnU5SNc2DzwkoPydAIdRER8ROFeE0dcxGERR52gFtGajwbduWTW6gR6iIi4lsK8ZqKawQdzoT570FJ4e+e3j+4TffFRUTE1xTivtBjFOTvhKWf/e6pjAMhri51ERHxLYW4L7Q5GRJbVjrArWXDWKLCPRrcJiIiPqcQ9wWPB3pcBqumw87s3zwV5jG0S4lnuRZ8ERERH1OI+0r3ywADc9/+3VMZqQm6Jy4iIj6nEPeV+i2g7SCY9zaUlf7mqfTUeDbtLmBPQbFLxYmISG2kEPelnqNhzwZY+c1vHs5I0Qh1ERHxPYW4L7UfCrGNfjfALUNrqIuIiB8oxH0pPBKOuRiWfQ65OQcebt4ghpiIMI1QFxERn1KI+1rPUVBWDPPfPfCQx2NIT43XXHEREfEphbivpXSE5n1gzjiw9sDD6SkJaomLiIhPKcT9oedo2LYM1v184KGM1Hi27i1k174iFwsTEZHaRCHuD53Pg4g4mHtwgJsGt4mIiK8pxP0hKgG6nA8LP4ZCpws9PTUeQF3qIiLiMwpxf+k5GorzYOFHADSrH0NcZJjmiouIiM8oxP2leR9I7nBgzrgxhvTUBHWni4iIzyjE/cUYZ4vSDZmwdQngDG7L2qqWuIiI+IZC3J+6XQyeCGe6Gc7gtm25RWzPLXS5MBERqQ0U4v4UlwQdhsKv46GkkHSNUBcRER9SiPtbj9GQvwOWTSajfIS6utRFRMQXFOL+1vZkqNcc5rxJ43rRJESFa5qZiIj4hELc3zxh0OMyWDkNs3sdGY01Ql1ERHxDIR4I3S9z/pz7tjNCfctebIV11UVERKpDIR4IDVpBm4Ew7206pMSyc1+xWuMiIlJjCvFA6TkKdq/jvMQsEqLDeXDSYrXGRUSkRhTigdLhLIhpQL0l73LzqRl8n7WNb5ZudbsqEREJYQrxQAmPgmMuhqWTGN0tnrbJcTzw2WKKSsrcrkxEREKUQjyQeo6CsmIiFk7gb8M6k719H2NnrHa7KhERCVEK8UBK7QzNesGccZyUnsQpHVJ4+usV5OzVMqwiIuI9hXig9boCcpbAoo+458yOFJaU8viXy9yuSkREQpBCPNC6XQJNusPnd9EmvpgrBrRmwux1LFi/2+3KREQkxCjEAy0sHIb9B/Ztg6n/4IZB7WgUF8l9ny7SlDMREfGKQtwNTbtDv+tg9ljqbcnkjjPak7lmJxN/3eh2ZSIiEkIU4m4ZeDcktoRPb+KCbil0aVaPRz5fyr6iErcrExGREKEQd0tUPJz5b9i2jLCZT/P3YZ3ZtLuAF79d5XZlIiISIhTibso4HTqfB989Tp/4HQzr1pSXvl3J+p373K5MRERCgELcbYP/BeHR8NnN3DW4PcbAw58vdbsqEREJAQpxtyWkwmn3Qfb3NMv+mGtPasuk+Zv4adV2tysTEZEgpxAPBj0vhxb9YMo9XNMrkaaJ0dz36WJKyzTlTEREDk8hHgw8HmfueGEuMdP+yt1DO7J40x4mZK5zuzIREQliCvFgkdIBjr8Z5r/HWXFL6ZPWgMe/XMbu/GK3KxMRkSClEA8mJ9wODdtiJt3CP4a0Yce+Ip75OsvtqkREJEhVKcSNMTcZY+oZx6vGmDnGmNP9XVydExENw56Cndl0znqRi3q3YOyMbFbm5LpdmYiIBKGqtsT/YK3dA5wONABGAY/4raq6rPWJ0P0ymPEMf+lRSkxEGA9+ttjtqkREJAhVNcRN+Z9DgXHW2kUVHhNfO/1BiE6k4de3c9OgNkxblsO0pVvdrkpERIJMVUN8tjFmCk6If2mMSQDK/FdWHRfbEM54GDZkMibqG9okxfHApMUUleivXEREDqpqiF8J3AX0sdbuAyKAK/xWlcAxI6DNyYR/8wAPDGrIqpw83pyZ7XZVIiISRKoa4scBy6y1u4wxI4F7gd3+K0swBs56AsqKGZD1KAPbJ/Ofr7PYllvodmUiIhIkqhriLwD7jDHdgNuAlcCbfqtKHA3bwEl3wpJPebjTOvKLSvn3lGVuVyUiIkGiqiFeYq21wDnAs9ba54AE/5UlB/T/M6R0psmPf+WqY5N595d1LNygThAREal6iO81xtyNM7VskjHGg3NfXPwtLMJZknXPRm72TKBBbCT3f7oY53cqERGpy6oa4hcBhTjzxTcDzYHH/FaV/FaLPtDnSqJmv8JDfYv5OXsHkxZscrsqERFxWZVCvDy43wYSjTFnAQXWWt0TD6RT/gYJjTlj1T/p0jiWhycvJb+o1O2qRETERVVddnUE8DNwITAC+MkYc4E/C5NDRCfCkEcxWxbyXJtZbNiVz8vfrXK7KhERcVFVu9PvwZkjfrm1djTQF/ir/8qSSnUcBu2H0mr+fxjdAV74dgUbd+W7XZWIiLikqiHusdZWXPdzuxfniq8YA0MfA08Yd9tXsNbyyOdL3a5KRERcUtUg/sIY86UxZowxZgwwCZjsv7LksBKbw6C/ErNmGk90XMHEXzfyS/YOt6sSEREXVHVg2x3Ay8Ax5R8vW2vv9GdhcgR9r4KmPRm64WkyEoq579NFlJVpypmISF1T5S5xa+2H1tpbyz8+9mdRchSeMBj2H0z+Dl5u+ikLN+zhg9nr3a5KREQC7IghbozZa4zZU8nHXmPMnqNd3Bgz2BizzBizwhhz12GOGWGMWWyMWWSMeae6b6TOaXIMHHc9aWs+YFST9Tz65VL2FhS7XZWIiATQEUPcWptgra1XyUeCtbbekc41xoQBzwFDgE7AJcaYTocckw7cDQyw1nYGbq7Ru6lrBt4F9VtyT9lL7MnN49lvVrhdkYiIBJA/R5j3BVZYa1dZa4uAd3HWXq/oKuA5a+1OgENGwMvRRMbBmU8SvXslz7aYzms/rmb1tjy3qxIRkQDxZ4g3A9ZV+Hp9+WMVZQAZxpgfjTGzjDGDK7uQMeZqY0ymMSYzJyfHT+WGqPRToctwTtvxNh3CN/PPSYvdrkhERALE7bne4UA6MBC4BHjFGFP/0IOstS9ba3tba3snJycHuMQQMPgRTEQML9Ufx9dLNvPtcv2iIyJSF/gzxDcALSp83bz8sYrWAxOttcXW2tXAcpxQF2/Ep8BpD9B09xz+VG8mD3y2mOLSMrerEhERP2P3rE8AACAASURBVPNniP8CpBtjWhtjIoGLgYmHHPMJTiscY0wSTve6FgSvjh6joGV/buEtdm7dwLiZa9yuSERE/MxvIW6tLQFuAL4ElgATrLWLjDH3G2POLj/sS2C7MWYxMA24w1q73V811WoeDwx7ivCSfTzTcAJPTl3O9txCt6sSERE/MtaG1kpfvXv3tpmZmW6XEbymPQzfPsKY4rto1vss/nleV7crEhGRGjDGzLbW9q7sObcHtomvnXArNErnybg3+fjnLBZvPOqaPCIiEqIU4rVNeBQMe4oGRRu5I+oT7v9sEaHW2yIiIlWjEK+N0o6HHqO43HzG7tVz+WLhZrcrEhERP1CI11an3Y+JbchTsa/x8KSFFBSXul2RiIj4mEK8toptiDnjYdqXZnHy3k/57/eauSciUtsoxGuzrhdA20HcHTmBD6b9rClnIiK1jEK8NjMGznyCyDDL//Eq7/681u2KRETEhxTitV3D1nhOvofTw2bDj09pOVYRkVpEIV4XHHcDm1uexfWlb7Fw8ktuVyMiIj6iEK8LPB5SRr7KbE9Xus6+B1Z+43ZFIiLiAwrxOsITGc3iE58nq6wZpe+OhE2/ul2SiIjUkEK8Djm3X0f+xN3stvHw9oWwM9vtkkREpAYU4nVIQnQEA3t347L82ykrLoC3hkOeNo0TEQlVCvE6ZvRxrVhS2owPMh6DXetg/MVQtM/tskREpBoU4nVMm+R4Tm6fzKNLGlF87suw/hf48I9QpmVZRURCjUK8DhozoDXbcgv5rKQ3DHkUlk2CyXeAdjsTEQkpCvE66IR2SbRJjuP1H7Oxfa+CATdD5qvw/b/dLk1ERLygEK+DPB7DFf3TmL9+N3PW7oJT/g5dR8A3D8Dct90uT0REqkghXked37M5CdHhjJ2RDR4PnPMctD4JPr0RVkx1uzwREakChXgdFRcVzkW9W/D5gk1s3l0A4ZFw0VuQ3BHeGw0b57pdooiIHIVCvA4bfVwapdby1qw1zgPR9eCy9yG2kbMYzI7V7hYoIiJHpBCvw1o2iuWUDqm88/NaCorLp5jVawIjP4SyEi0GIyIS5BTiddwfBqSxI6+Iib9uPPhgcgZc8i7s2QDvjNBiMCIiQUohXscd17YR7VMTGPtjNrbiPPGW/WD4f2HDbPjwSigtca9IERGplEK8jjPGMGZAGos37eGX7J2/fbLjMBj6GCybDJNv02IwIiJBRiEunNu9GYkxEbz+YyUD2fpeBcffCrPHwnePB7w2rxTlwfaVblchIhIwCnEhJjKMi/u24MtFm9mwK//3B5zyNzjmYpj2IMx9K/AFHk3edpj2MDzZBZ7tDVsWuV2RiEhAKMQFcKabAbw5M/v3TxoDZz8DbU6GiTdC1leBLO3wdq6ByX+BJzvDt4849/Ej42HaQ25XJiISEApxAaBZ/RjO6NyYd39eR35RJTuahUfCReMgtTNMuBw2zAl8kfttXuDsvPZ0D8h8DboMh+t/hkvGw3E3wNLP3K1PRCRAFOJywBUDWrM7v5hP5m2o/ICoBLjsA4hr5Ew927EqcMVZC6u/g3Hnw4vHw7LP4bjr4KZf4dznILm9c1y/P0FMQ/jmwcDVJiLiEoW4HNAnrQGdmtTj9R9X/3a6WUUJqTDyowqLwWzzb1FlpbD4f/DKIHhjmNMKP+VvcMsiOP1BSGz22+Oj68HxN8PKr2HNDP/WJiLiMoW4HLB/utnyLbnMXHmEldqS0uHSCbBnY/liMHm+L6a4ADJfh2f7wITRULALznoSbl4AJ9wGMfUPf26fqyA+Fb5+QNPiRKRWU4jLb5zdrSkN4yJ57cfsIx/Yoi8Mf9XZKOX9K3y3GEz+Lmdf86e6wmc3Oy3rC9+AGzKh9x8gIvro14iMhRPvgLUzYOU3vqlLRCQIKcTlN6Ijwri0b0u+XrqFtduPstxqx7Ng6OOQ9SVMurVmrd49G2HKvc40sa/vh8ZdYPREuGoadD4XPGHeXa/naEhs4eyRrta4iNRSCnH5nZH9WhFmTOXTzQ7V50o44XaY8wZ8+6j3L5azDD65Hp46BmY+BxlnwDXfwaiPoc1JzvS26giPgpPudHoKlk6q3jVERIKcQlx+p3FiNEO6NuG9zHXkFVahm3zQvdDtUpj+EMx5s2ovsu5nGH8pPNcXFn4IvcbAjXPhglehSbca1X9At0ugUTuY9k8oK/PNNUVEgohCXCo1pn8aewtK+GjO+qMfbAyc/TS0PQU+vRmWT6n8uLIyWPYFvDYYXj0N1vwIJ/4FblkIZz4ODdJ8+h4IC4eBd8PWxbDoI99eW0QkCCjEpVI9W9anW/NExs7IpqysCveUwyJgxBvOvez3L3d2P9uvpAjmvQMv9IfxF8GudTD4EWea2KB7IC7Jf2+k8/mQ0tlZxU07sYlILaMQl0rtn262MieP71dUcS54VAJc+j7EJcPbI2DTfOc+99Pd4ZM/OS32816Cm+Y5i7JExfv3TQB4PM4vCjtWwq/j/f96IiIBpBCXwxratQlJ8VGMrWx3s8PZvxiMLYOXToAv/8/pJr90AvxpBnS72Gm1B1L7odC0J3z7LygpDOxri4j4kUJcDisqPIyR/VoybVkOq3Jyq35iUjsY+SH0vByunApXTHZGnVd3pHlNGeMMvtu9Dma/4U4NIiJ+oBCXI7r02JZEhBnenLnGuxOb9XQGu7Xo45/CvNV2ELQaAN8/DkVHmf8uIhIiFOJyRCkJ0Qw7pinvZ65jb0Gx2+VU3/7WeO4W+OUVt6sREfEJhbgc1eX908grKuX9zCpMNwtmrfo70+B+eBIK9rhdjYhIjSnE5ai6tahPz5b1eWNmFaebBbNB90L+Tpj1gtuViIjUmEJcquSKAa1Zs30f05ZtdbuUmmnWEzqcBTOfhX073K5GRKRGFOJSJYO7NKZxvWjGzsh2u5SaO/keKNwLP/7H7UpERGpEIS5VEhHmYdRxrfg+axtZW/a6XU7NpHaCrhfATy/B3i1uVyMiUm0Kcamyi/u0IDLcUzta4wPvhtIi+OEJtysREak2hbhUWaP4KM7p1pSP5mxg974Qnm4G0Kgt9LgMMl9z1nIXEQlBCnHxypgBaeQXl/Je5lq3S6m5E//i/PndY+7WISJSTQpx8Urnpon0bd2QN2asoTTUp5vVbwG9roC5b8H2lW5XIyLiNYW4eO0PA9LYsCufqUtqwaCwE26DsEiY/ojblYiIeE0hLl47tWMqzerH8Lo3u5sFq4RUOPZqWPA+bFnsdjUiIl5RiIvXwsunm81atYMlm2rB8qUDbnb2Qp/+kNuViIh4RSEu1XJxnxZER3gY+2O226XUXGxDOO56WPIpbJzrdjUiIlWmEJdqqR8byXk9mvPJvA3syCtyu5ya63cdxDSAbx50uxIRkSpTiEu1jemfRmFJGe/+Ugumm0XXc7rVV0yFNTPdrkZEpEoU4lJt7RsnMKBdI8bNXENxaZnb5dRc36shLsVpjdsQnz4nInWCQlxqZEz/1mzaXcCURbVgullkLJx4O6z5AVZNc7saEZGjUohLjQzqkELLhrG1Y7oZQK8xkNhCrXERCQkKcamRMI9h9HGtyFyzkwXrd7tdTs2FR8FJf4ENs2HZ525XIyJyRApxqbELe7cgNjKsduxuBtDtUmjYBqb9E8pqwb1+Eam1FOJSY4kxEVzQqzmf/rqRnL2FbpdTc2HhMPD/YMtCWPyx29WIiByWQlx84vL+aRSVljH+51ow3Qygy3BI6QTTHoLSErerERGplEJcfKJtcjwnZSQzbtYaikpqQRe0xwMn3wPbV8D8d92uRkSkUgpx8ZkxA9LI2VvI5ws3uV2Kb3Q4E5r2gOn/gpJasCqdiNQ6CnHxmZPSk2mTFMfrtWE9dQBjYNC9sHstzHnD7WpERH5HIS4+4/EYLu+fxrx1u5i7dqfb5fhG21OgZX/47nEo2ud2NSIiv+HXEDfGDDbGLDPGrDDG3HWE44YbY6wxprc/6xH/G96rOQlR4bVnutn+1njuZvjlv25XIyLyG34LcWNMGPAcMAToBFxijOlUyXEJwE3AT/6qRQInPiqcC3u3YNL8TWzZU+B2Ob6RNgDaDoIfnoTCvW5XIyJygD9b4n2BFdbaVdbaIuBd4JxKjnsA+BdQS37iy+jjWlFqLW/PWuN2Kb4z6F7I3wGzXnC7EhGRA/wZ4s2AdRW+Xl/+2AHGmJ5AC2vtpCNdyBhztTEm0xiTmZOT4/tKxafSkuIY1D6Ft39aS0Fxqdvl+EazXtD+TJjxDOzb4XY1IiKAiwPbjDEe4AngtqMda6192Vrb21rbOzk52f/FSY1dfWIbtucV8ebMbLdL8Z1B9zjd6TOecbsSERHAvyG+AWhR4evm5Y/tlwB0AaYbY7KBfsBEDW6rHY5t04gT0pN4fvpK9hQUu12Ob6R2dlZy++lFyN3qdjUiIn4N8V+AdGNMa2NMJHAxMHH/k9ba3dbaJGttmrU2DZgFnG2tzfRjTRJAfzmjA7v2FfPf71a5XYrvDLwbSgrh+yfcrkRExH8hbq0tAW4AvgSWABOstYuMMfcbY8721+tK8OjaPJGhXRvz3x9Wsy23FmyMApDUDrpfApmvwu71blcjInWcsda6XYNXevfubTMz1VgPFSu25nL6k99yef80/j6ss9vl+MautfB0T+hxGQz7j9vVeM9apzehKA+Kcss/yj8vzK3k8TxnLEBCYzjpTgiLcPsdiNQpxpjZ1tpKbzWHB7oYqVvapcRzQa/mvD1rLVce35rmDWLdLqnm6reE3ldA5msw4CZn7/FAsBbyd8LeTZCXUx6uh4RtxfAtPCSgK/5ZVtWd2QxExkNknLPgza61cO6LzgYxErq2rYB926BlP7crkRpSiIvf3XRqBp/M3ch/pmbx2IXd3C7HN064DeaMczZHOf+lml3LWijYBXs3OwG9d3OFjwpf526G0qNsxBIR5wRuVHnwRsZDbCPnF4/I+AqPlz8XWeG43zyX4PwZEeOsWgfO0rPfPABxyXD6gwcfl9BQUgTLJjm/fK7+DjAw8kNod4rblUkNKMTF75rVj2Fkv1aMnbGaa05qQ7uUBLdLqrmExtD3Kme62fG3QEqH3x9jLRTsPhjARwrp0krGDEQlOq+T0BhaHVf+eRPnz7hkiEr4bQhHxPq3hXzCbU4PwMxnndc//mb/vZb4zs5smP0GzB3nfP8SWzqLFy38GD78I1z7PSQ2d7tKqSbdE5eA2J5byImPTuPEjGReGNnL7XJ8Y98OeOoYaNYD2p1aeUiX5P/+vKh6EJ/621A+8FH+dXxjiAzCWw9lZfDRH2Hhh3DO8864AAk+pSWQNcVpda+Y6vSaZAyG3n9wlhD2hMG2LHh5IKR0gjGTIDzS7arlMHRPXFzXKD6KK09ow9NfZzF//S6OaV7f7ZJqLrYhDLgRpv3T6Z6MiIN6TZwgbtarkpBu4oR3VLzblVefx+PcE9+3Ayb+2fk7aD/E7apkvz0bYc6bzseeDc4vgyf9BXqO/n1rOykdzn4GPrgCpv4dBj/sTs1SI2qJS8DsLSjmxEen0aVZIuOuPNbtcnyjrMzZbzy2kdO9XVcU7oU3hsHWJTD6fxog5aayMlj1DWS+Dss+B1vqbKHb+wqn9X202QSf3+ksYHThG9D53MDULF45UktcQ0wlYBKiI7huYDu+z9rGjJXb3C7HNzweaJBWtwIcnPd72QdO6+6dEbBlsdsV1T25Oc7Oek93h7eGw9pZ0P/PcONcGPURdBxWtemApz0AzXrD/26A7Sv9X7f4lFriElAFxaUMfGw6TepH89Gf+mM0wjm07VwDr53hfH7lFGcUfCjbsQpWfA0NW0NyR6jXNLhG4VsL2T8497qXfAplxZB2AvQa44R2eFT1rrtrHbx0ovN+r/wqOMdj1GFHaokrxCXgxv+8lrs/WsAro3tzWqdUt8uRmtqyCF4fAnEp8IcvIa6R2xVVT9ZU+OAPULj74GNRiZDc3pl9kNyx/M8OzviGQIb7vh3w67tOeG/PguhE6H6ZE97J7X3zGllT4e0LnOue+5xvrllXWevTfx8KcQkqJaVlnPbkd0SGeZh80wmEeYKopSPVs2YmjDvXGel8+aehNXjPWmfa3Fd/c+o/9wUo3OPc789ZCluXQs4S2Lf94DnRiU6YJ3eAlI4HP09o7Lsf3tbC+l+c4F70MZQUQPO+zr3uzuc5c/h9bdpD8O2/4Oxnoeco31+/tsvbDj+9ABvnOrebfPRvQaPTJaiEh3m49bQM/jx+LhN/3cB5PTRHNeS1Og4ueB3eGwkTRsEl74XGlKXiAvj0Jpj/LnQ8G8570Zl3D5B2/G+Pzc1xwnzrUifcc5Y6Xdpz3jh4THT98mA/pOUen1r1H+gFe2DBBGeg2paFzhoA3S9zwrtxV9+878M56U5Y9xNMvh2adIMmx/j39WqLvZudNSMyX4fifc6tjeJ9B/8t+ZFa4uKKsjLLWc/8wN7CYr6+dSCR4RpjWSvMGQcTb4AuF8D5rwT38qx7NsF7l8GG2TDw/+DEO7yv11pnAZUDrfYlkLPMCfv8nQePi65/sMVeseUen3Iw3DfOc1rdCz6A4jxofIwzr7vrBYEdOJm3DV48wbm/fs23Tq+DVG7XWvjxP86/+7Ji6HohHH9r5Ys/1YBa4hJ0PB7DHYPbc8Xrv/DeL2sZdVya2yWJL/Qc5YTa1/c5q7oNfji4Bobttz4T3r3MmSp30VtOy6k6jHGCOD4F2px08HFrnT3nD7Tcy/9c9BHMrnDPPaaB02Iv3geb5kF4DHQd7oR3057u/N3FJcGFr8PYM+GT65y/n2D8Hrpp2wpnZsD8dwED3S91VjAM1D4KFSjExTUDM5Lpm9aQp79ZwfBezYmN1D/HWuH4W5wgn/U8xCc7y7UGk3njnS70hMbOVKxUP+yuZwwkpDofbQYefNxayN1ySMt9qXP8kEfhmIsgJggWQmrZD069D6bcAzOfg/43uF1RcNiyCL7/tzNGISwS+vzRmdbn4rK1+qkprjHG8JfB7bngxZmMnZHNdQPbuV2S+IIxcPo/nW7Zr+93WuQ9R7tdFZSVOoPXZj7rTMu68I3Aj6Q35uAKfm1PDuxre+u462HtTOfvrFkvZ9xDXbVhNnz3b2cDmch46H+j8/cTn+J2ZVrsRdzVO60hgzqk8OL0lezeV+x2OeIrHg+c85yzctinN8HSSe7Wk7/LWZRm5rPQ5yoY9XHoToULFGPg3Oeduf8fXOEM7Ktr1syAcefBK4NgzY8w8G64eQGcdl9QBDgoxCUI3H56e/YUlPDSd1otqlYJj4QRb0LTHs786zUz3KkjZzn89xRYNR3OegrOfLxqK5mJM6htxJvOIL0Pr3R6M2o7a50Ff14b4qx/sHmBc2vhloUw8C5nv4AgohAX13VqWo+zuzXl9R+z2bq3wO1yxJei4uHS9yGxBbxzMWxeGNjXXz7FCfD8Xc789d5XBPb1a4Mmx8DQx2H1tzD9Eber8Z+yMqfH6JWT4a3zYdcaZ5zCTfOdQWtBurSyQlyCwq2nZVBcWsaz36xwuxTxtbhGzgCyyDhnje+d2f5/TWvhh6ecLvQGreDq6dCqv/9ft7bqOQq6j4TvHnVWdqtNykqdaX0vHg/vXur0Ogx7Gm6cB8deE/RL0CrEJSikJcUxok8Lxv+8lnU79rldjvha/ZYw8kNnf/Vx5/v3/mpxPnx0tbO9ZqdznKVg67fw3+vVFUMfg9Qu8NFVzlrroa60GOa+Bc/2Kb9VUOKsbXDDbOh1eWgsVoRCXILIjYPS8RjDk18td7sU8YfUTnDpBGef63cudOZo+9qejc59zAUTYNC9cOHYgKyaVSdExjr3x0uL4f0xUFLkdkXVU1wAP78CT/eE/13v/PsYMQ6umwXHjICw0Jq0pRCXoNE4MZox/dP4eN4Glm32ww94cV/Lfs7Urk3znSVafRkE636BlwfCtiy4+B1nBTYtUuJbjdo6m6NsyISv/up2Nd4pzHWWRv3PMc6ysvWaOOubX/MddDo7uFcXPILQrFpqrWtPakt8ZDiPT1nmdiniL+0Hw9nPOKPFP7nWGVBUU/PegbFDnU1B/jgVOpxZ82tK5TqdA/2uh59ehIUfuV3N0eXvgm8fg6e6wpR7neVuL//Muc2SflrI/6IXWv0GUus1iIvk6hPb8O+vljNn7U56tmzgdkniDz0uc1Z1m/p3iE2CIf+q3g/T0hJnMZJZz0HrE51WfpBNAaqVTrvP2WFt4p+dTVmS0t2u6PcK9jirzc163tmVLmMwnHA7tOjjdmU+pZa4BJ0/HN+apPhIHvtiGaG2QY94YcBNcNwN8PNL8P3j3p+fv9PZ/3rWc3DstTDyYwV4oIRFOOurh0fBe6OgKM/tig4qzi/vNu8G3z7iLHt7zfdw6Xu1LsBBIS5BKC4qnOtPbsfMVdv5YcU2t8sRfzEGTnvAWS/8mwdh9tiqn5uzzFlFK/sHp2t+yL9CbkBSyEts7ozmzlkKn93qTOtzU2mxsxXo0z2dbvOmPZyphReNq9VbqirEJShdemxLmtWP4bEv1Rqv1fYvz9ruNPjsFmd/7qNZ/iW8coozun3MZ8GxLntd1e4UZxWz+e/+dl/1QCorc+Z5P3csfHaz88vFmEnO2gRNe7hTUwApxCUoRYWHcfOp6cxfv5svFm52uxzxp7AIGPGGs8nGB1c6revKWAvfPwHvXASN2jitrJb9AlmpVObEO6DtIJj8F2dP9ECx1lmR7+UTnXne4dFwybtw5RRIOz5wdbhMIS5B6/yezWmXEs/jU5ZRUuqDEcwSvCLjnDnkDdJg/CXOetUVFe2DD//o7FPe5Xy44gtXt3+UCjxhTrd6XBJMGO2MVfC3NTOd9QD2rzdw/itw7Q/QfkjIjzb3lkJcglaYx3D76RmszMnjo7kb3C5H/C22odMFGpXgLM+6Y7Xz+O4Nzg/shR/CKX+D4a8G/VKYdU5ckrOwzp4N8Ml1/rs/vmk+vH0hvD7Y+fdx5hNwQ6azSEuIzvOuqbr5riVknNG5Md2aJ/KfqVkUltSBHZTqusTmMPIjKCl0NqFYOslZwGX7Sqer9ITb6lxLK2S06AunPwjLJsOMp3177e0rnZ3wXjoB1v3s7Cp241zoc2Wd35FOIS5BzRjDHWd0YMOufN6etdbtciQQUjrAZe/Dnk3OhhRR8c4CLu0Hu12ZHM2x1zqLwUy9D7J/rPn1dm+AiTc665sv+9yZ533Tr86uYuqNAcCE2sjf3r1728zMTLfLkAC79JVZLNu8l2//cjLxUZpKVCesmg6L/weD/qr536GkYI/Te1KUB9d+D/Ep3l8jbzv88ISzxrktg95/gBNvr961agFjzGxrbe/KnlNLXELCHWe0Z3teEa/9sNrtUiRQ2gyEs55UgIea6HrORikFu5wu8DIvboMV7oXp/3IWapn1PHQZDn+eDUMfrbMBfjQKcQkJPVo24PROqbzy3Sp25oXo7kkidUXjLs6gs+zvYdpDRz++uMBZIvU/3WD6Q9DmJPjTTDjvBWc/eDkshbiEjNvPaE9uUQkvfrvS7VJE5Gh6XAY9RjlL6i7/svJjSktgzpvwTC/48v+cddj/+A1c/LYzNkKOSiEuISMjNYHzejRj7IxsNu8ucLscETmaoY9Balf46GrYVWFgalkZLPoYnu/nbKKSkAqjJ8Lo/0HzXu7VG4IU4hJSbjk1gzJrefqbLLdLEZGjiYhxVuOzZTDhcmfqYNZUeGUgvD/GWSjmorfhj187XejiNYW4hJQWDWO5tG9LJvyyjuxtQbRzkohUrlFbOPd52DgHnu4Bbw93VnU790X40wzoeJbm/teAQlxCzg2D0okI8/DEV8vdLkVqmWWb9/LqD6u16Y6vdRzmzPEGGPIY3DAbul/itMSlRjThVkJOckIUfzg+jeemreTak9rSqWk9t0uSWmBvQTFXvvEL63fmEx3h4bJjNSrap075q/MhPqWWuISkq09sS2JMBI9PWeZ2KVJL3P/pYjbuyqdjk3r8c9IS1mzX7RoJfgpxCUmJMRFce1Jbvlm6lczsHW6XIyHui4WbeH/2eq4b2I5XL+9NmMdw24RfKS1Tt7oEN4W4hKwx/dNIToji0S+W6R6mVNvWPQXc/dECujZL5KZT02laP4b7zu5M5pqd/Pf7VW6XJ3JECnEJWTGRYdw4qB0/Z+9g+vIct8uREGSt5S8fzmdfUSlPXtSdiDDnR+J5PZpxRudU/j1lOcs273W5SpHDU4hLSLuoT0taNIzhsS+WUaauT/HSW7PWMH1ZDv83tCPtUuIPPG6M4aHzulIvJpxb3ptHUUmZi1WKHJ5CXEJaZLiHW0/LYPGmPUxasMntciSErMzJ5Z+Tl3BiRjKjj/v9SPRG8VE8dF5XFm/awzNaXEiClEJcQt7Z3ZrRPjWBJ75aTnGpWkxydMWlZdzy3jyiI8J47IJjMIdZbOT0zo0Z3rM5z01bwdy1OwNcpcjRKcQl5IV5DLef0Z7V2/L4YPZ6t8uREPDM11nMX7+bh8/rSmq96CMe+/ezO9G4XjS3TfiV/CIvttUUCQCFuNQKp3ZMoWfL+jz51XLmr9/ldjkSxGav2cmz01YwvGdzhnRtctTj60VH8PiF3Vi1LY9/fbE0ABWKVJ1CXGoFYwz3n9MFgHOf+5F/TFzE3oJil6uSYJNXWMKtE+bRtH4M/zi7U5XP698uiTH90xg7I5sfV2zzY4Ui3lGIS63RpVkiU287iVH9WvHGzGxOfeJbJi/YpDnkcsADny1m7Y59PDGiOwnREV6de+fgDrRJiuOO939lj35BlCChEJdapV50BPed04WPrxtAo7gornt7Dn8Y+wvrduxzuzRx2VeLt/DuL+u49qS29G3d0OvzYyLD+PeIbmzeU8D9ny72Q4Ui3lOIS63UvUV9Jt4wgHvP7MhPq3dw2pPf8uK3KzV6vY7K2VvIXR/Op1OTetxyhqo0vwAAGDxJREFUaka1r9OjZQOuP7kdH8xez5RFm31YoUj1KMSl1goP8/DHE9ow9daTODE9mUc+X8qwZ35g9hpNFapLrLXc9eF89haW8NTF3YkMr9mPvT8PSqdz03rc/dECtuUW+qhKkepRiEut17R+DC+P7s3Lo3qxJ7+Y4S/M4O6PFrB7n+5r1gXjf17H10u3ctfgDmSkJtT4epHhHp4Y0Z29BSXc8/ECjbkQVynEpc44/f/bu+/4qKr0j+OfJ5WEQAglhASkhBJCDdKLgqCgsARQbCtWtiAoCCu6rvvbXbe4q6KyVhBFXFhYREBEsYAiNSCQQICIQAIhoQRSIL2e3x8zuhHFpcydm5k879crL2buTO59Dknud+69557TKYLPpl/LxIGtWbrjGEOfX8/7SZm6E/ZiaWcK+fPq/Qx09i53lQ4R9ZhxQ3s+2XeKFYmZLluvUpdKQ1zVKnUD/XhyVCyrpgwgKiyYqUuSuPut7Rw5o3NHe5sK56hsAX4+PDe+Gz4+Pz4q2+WaOKgNvVqF8YdV+zieV+zSdSt1sTTEVa3UKTKU5ZP681R8J5LS87jhxQ38c91BSit0RK7zGWNYsOUIP5+XwOHTBXaXc9Fe/uIQScfy+OvYzkSE/vSobJfD10d4bnw3KqsMM5ft0Ql4lC00xFWt5esj3N2vFWtnXMv1sU15/rNvuGn2RhJSs+0urcYor6ziyZV7+cOqfWxLzSH+5c18vLfm98pOTM/lpc8PMTYuilFdIy3bTstGdfndyI5sOnSGhduOWrYdpS5EQ1zVek3r1+GVO3sw/75elFZUcfvcBH7z7m5yCsvsLs1WZ4vKuXf+dhZtS2fS4GjWPzqY6PAQfr1wJ//4+Gsqa+iRZ1FZBdOX7qZpvUD+OLqT5du7s/dVXNu+CX/7KIVUDzpTobyDhrhSTkM6hPPZI9cyaXA0KxMzGTprPUt3HKuVHd/SzhQy9rXNbE/L4dlbuvLYiBiahwWz9Fd9ubPPVby2/jD3vLWd7Bp4i9VfP0zhSHYhs27tTmjQpY3KdjlEhH/c3JVAP19mvLubCh2LQLmRhrhS1QQF+PLYiBg+fHgQ0U1CmLlsD7fPTeBQVu05wtp6OJsxr2wmt7CMRRP7Mr5ni+9eC/Tz5W9ju/DMLV3ZfiSHn720id3Has6EM59/fYpF29L5xaA29Itu5LbtRoTW4an4TiSm5zFnQ6rbtquUhrhSP6JDRD2W/qofT4/rwtcn87lx9gZmfXqAknLv7vj2n6/SmfDmNprUC2Tl5AEXHJ701p4teO/X/RERxr++lSXb091c6Q9lF5Qyc1kyMc7bv9xtdLdIRnZpxotrv2H/8XNu376qnTTElboAHx/hjt5XsW7GtYzqGslLnx9i+Isb2HjwtN2luVxlleFvH6Xw2HvJ9ItuxPIH+9OyUd2f/J4uzUNZ/dBA+rRpyOPLk3n8vT22fcgxxvD48mTOFZfz4u3dCfTzdXsNIsKfx3SmQXAA05cm6Z0Oyi00xJX6HxqHBPLCbd1Z+EAffESY8OZ2pi5J5HR+zbsefDkKSyv41b92MndDKnf3a8n8e3tR/yJn+AqrG8Db9/Vm8pBolnx1jFvnbCXThnuml+44xmf7TzFzRAdiIuq7ffvfalg3gH/c7Dh78+Lag7bVoWoPDXGlLtLAdo1ZM3UQDw9tx5rkkwydtZ5F24569P3BmXnF3PL6Vj7/+hR/Gt2Jp+I74+d7absFXx/h0eExzJ1wNWmnCxn1z41sOui+ObePZhfypw/2069NI+4f0Npt272Q62KacnuvFsz58jA7j+bYXY7ychriSl2COv6+TL++PR9NHURsZH1+t2IvN7++ha+OeN7OOjE9l/iXN5ORU8T8+3pzzxUOS3pDpwjenzKAJvUCufutbby6/pDlPfu/HZXN10eYdavrR2W7XE+OiiWyQRDTl+6msLTC7nKUF7M0xEVkhIgcEJFDIvL4j7w+XUT2i8geEVknIi2trEcpV2kbHsLiX/Rl1vhuZOQWM/71rdw3fzv7jp+1u7SL8sHu49w+N4GgAB+WP9ifa9s3ccl62zQJYcWDA7ipSzOe+fgAv164k/wS6yaaeW39YXal5/GXMZ2JbBBk2XYuVUigH8+N70Z6ThFPr0mxuxzlxSwLcRHxBV4BbgRigTtEJPa8tyUCPY0xXYFlwDNW1aOUq4kIN1/dnA2PDuGxETHsPJrLyH9u4qHFiTV2LHZjDLPXHuShxYl0bR7KygcH0M4FM3tVVzfQj5fuiOP3o2JZm5JF/MubOXgq36XbANiTkcfsdQf5WbdI4rtHuXz9V6pvm0Y8MKA1CxPS2fCN93WGVDWDlUfivYFDxphUY0wZsASIr/4GY8wXxpgi59MEoLmF9ShliaAAXyYNjmbjY9cxeUg0a/efYtjzX/LEimROnSuxu7zvlJRXMnVJEi+s/YZxPaJYOLEPjUICLdmWiPDAwNb8e2IfzpVUEP/KZlbvOe6y9ReXVTLtP0k0qRfIX+I7u2y9rvab4R1oG+4Yb0CnvlVWsDLEo4Bj1Z5nOJddyAPAGgvrUcpSoUH+PDo8hi9nDubOPlfx7o5jXPPMFzz9UQp5RfYO4ZqVX8LtcxNYtfs4M0d0YNb4bm65DatPm0asfmggMRH1mPLvRP6yer9LRjR7ek0KqacLeW58N0KDrR+V7XLV8fflhVu7c6aglD+s2mt3OcoL1YiObSJyF9ATePYCr/9SRHaIyI7Tp/W0lKrZwuvV4an4zqybPpibujRj7sZUBj3zBS9/ftCWTk4pJ84x9pUtHDiZz+t39eDBwW0RcV8HsIjQOiz5ZT/u7teSeZvS+Pm8bVd0e976A1m8s/UoDwxszYC2jV1YqTW6NA9lynVtWZl0nI+ST9hdjvIyYlXvURHpB/zRGDPc+fy3AMaYp8973zDgJeBaY0zW/1pvz549zY4dOyyoWClrfH3yHM998g1rU07ROCSAKUPackefq9xyJLwu5RQPL04kpI4fb97Ti85RoZZv86cs35XBEyuSCQ3y57W7rqbHVWGX9P05hWUMf3EDYcH+rJoykDr+7h/U5XKUV1Yx7tUtZOQW8ckj1xBez/VToyrvJSI7jTE9f+w1K4/EvwLaiUhrEQkAbgdWnVdYHDAHGH0xAa6UJ4qJqM+8e3ry3qT+RDcJ4Y8f7GforC95b2eGZTOBGWOYtzGVie/soE2TEN6fPND2AAcY16M5yycNINDPl9vmbOVfCUcv+jY0YwxPLE8mr6iMF2+L85gAB/D39eGF27pRWFbJE8uTa+WkOsoaloW4MaYCmAJ8AqQAS40x+0TkKREZ7Xzbs0AI8K6IJInIqgusTimPd3XLMJb8si8L7u9Ng2B/Zry7mxtnb+CTfSddulMvr6ziiRXJ/OXDFEZ0imDpr/oREVpzjvxiI+vzwZSBDGzbmN+v3Mtv3r244VqX7czg430nmXFDB2Ij7RuV7XK1Da/HzOEdWJuSxbs7M+wuR3kJy06nW0VPpytvUFVlWLP3JLM+PUDqmUK6t2jAzBEd6B99Zdd484rKmLRwF1tTs5k8JJoZ13eoMQOgnK+qyvDPzw8ye91BOkbUZ86Eq2nRMPhH33ssp4gbZ28kNrI+i3/RF98a2qb/parKcMcbCew7fo41UwddsL1KVWfX6XSl1AX4+Agjuzbj00eu4R83d+HUuRLufGMbE97cxp6My5vaM/V0AWNf3cLOo7k8f2s3Hh0eU2MDHBz/B9OGteete3qRkVvEqJc28cWBH15Vq6wyTF+ahADP39rNYwMcHG1+bnw3jDE8umy3Rw/Zq2oGDXGlbOTn68Ntva7ii98M5smRHdmbeZbRL29m0sKdlzSH+ZZDZxj76hbOFpfz71/0YVwPzxlyYUhMOB88NJDIBkHc//ZXzF578HvhNmfDYb46ksuf4jvRPMzzj1xbNAzm/34WS0JqDm9vOWJ3OcrD6el0pWqQ/JJy5m1MY97GVIrLK7nl6uZMHdaeqJ8YUnTx9nR+v3IvrRvX5a17e3nsKdriskqeWJHMisRMhsaE8/xt3TmWU8TYVzdzQ2wEL98Z59Zb46xkjGHigh1sOnSGDx8eRNvwELtLUjXYT51O1xBXqgbKLijllS8OszDhKAB39W3J5CHR3xthrbLK8PRHKczblMa17Zvw0p1xFz2FaE1ljOGdrUf58+r9RIUF4esjFJRU8Okj19AgOMDu8lwqK7+E4S9soEXDYN6b1B//S5w9TtUeGuJKeajMvGJmr/2GZTszCPL3ZeKgNkwc1BoRYeriRNZ9ncW9/Vvx5MiOlzyFaE2282gOkxbuIiu/lHfu7801Lpqgpab5cM8JJv97F1OGtGX69e1rdB8GZR8NcaU83KGsAp7/7AAfJZ8kLNifsLoBHM0u4o+jOzGhr3dO/pddUErqmUJ6tWpodymWmrYkkZVJx4kMrUN8XBRj46Jo7+JJaZRn0xBXykvsycjj2U8OkJx5lpfuiGNQO+88Qq1NyiqqWLP3BCsTM9lw8AyVVYbYZvUZ1yOK0d0iCa9fc+7xV/bQEFfKy1RVGT316oXOFJSyevdxViRmsjvjLD4CA9o2Zkz3KEZ0jqBuoJ/dJSobaIgrpZSHOXy6gPcTM1mRlMmxnGKC/H25oVNTxsRFMahtY6/qA2GVjNwiSsoraRvu2ZcnNMSVUspDGWPYeTSXFYmZrN5zgrPF5TQOCWBU10jG9YiiS1So19x6d6Uy84pJOJzN1tRsElKzycgtBmBwhyZMG9ae7i0a2Fzh5dEQV0opL1BWUcX6A1msSMxkXUoWZZVVtGlSl7HdoxgTF+WxYwRcrhNni0lIzWbr4WwSUnNIzykCICzYnz6tG9EvuhEFpRXM25hKblE5Q5xh3s3DwlxDXCmlvMzZ4nLWJJ9geWIm29NyAOjVKowxcVGM7NLM6+6rBzh1rqRaaGdzJNsR2qFB/vRp3ZB+0Y3o26YRHZrW+16fkYLSCt7ZeoS5G1LJKyrnuphwpg1rR9fmnhHmGuJKKeXFMnKLeD/J0SHuUFYB/r7CkA7hjOsRxZCYcLfMXW+FrPwSElJz2Ho4m22p2aSeKQSgXh2/7460+7ZpSMeI+hfV0bOgtIIFW47wxkZHmA+NCWfasPZ0aW7/NL0/RUNcKaVqAWMM+46fY0ViJu8nHedMQSn16/gxsmszxnSPolerhjX6robT+aVsS/vvkfbh087QDvSjd7Uj7Y7N6l/RRDj5JeXOME/jbHE5wzo6wrxzVM0Mcw1xpZSqZSoqq9hyOJuViZl8vO8kRWWVRDUIYkxcJGPjompEj+3sglK2peV8F9oHnZP+hAT60atV2Heh3Sky1JLZ6/JLynl7s+PI/FxJBcM6NmXasHY1Lsw1xJVSqhYrKqvg032nWJGYycaDp6ky0KJhEHUD/Aj09yXQz6fal/O5v+NxwHmvfffc/7/vDbiI7/P3FXKLytme9t+OaAdO5QMQHOBLr1YN6dvGcYq8c2R9t95Cd84Z5vOcYX5DbFOmDmtHp8iaEeYa4koppQDHdeYPdp8gMT2X0ooqyiqqKK2opLSiitJyx+Oyym8f//e1K40KEb5bR5C/Lz1bhX0X2l2iQmvEBDDnSsqZv+kI8zalkl9SwfBOTZk6tD2xkfVtrUtDXCml1GUzxlBRZZxB/8OQd3wQcAZ+eVW1150fDpxfdfx96NO6IV2iGhDgZ39oX8jZ4nLmb07jzU1p5JdUMKJTBFOHtaNjM3vCXENcKaWUukRni8t5a1Mab21KI7+0ghs7O8I8JsK9Ya4hrpRSSl2ms0XlvLk5jfnOML+pSwRTh7anQ4R7OgdqiCullFJXKK+ozHFkvvkIBaUVjOzSjKnD2lk+dayGuFJKKeUieUVlzNuYxvzNaRSVV3JTl2ZMG9qOdhaFuYa4Ukop5WK5hWXM25TK25uPUFReyaiukTx8XVuXh7mGuFJKKWWR3MIy3tiYyoItjjD/WddInh7XxWXzv/9UiOsM80oppdQVCKsbwMwRMUwc1IY3NqaSlJ5HcIB7xqvXEFdKKaVcoGHdAB4bEYMxxm1zvNfcu+2VUkopD+SuAAcNcaWUUspjaYgrpZRSHkpDXCmllPJQGuJKKaWUh9IQV0oppTyUhrhSSinloTTElVJKKQ+lIa6UUkp5KA1xpZRSykNpiCullFIeSkNcKaWU8lAa4koppZSH0hBXSimlPJSGuFJKKeWhNMSVUkopD6UhrpRSSnkoDXGllFLKQ4kxxu4aLomInAaOunCVjYEzLlxfTaXt9C7aTu+i7fQurm5nS2NMkx97weNC3NVEZIcxpqfddVhN2+ldtJ3eRdvpXdzZTj2drpRSSnkoDXGllFLKQ2mIw1y7C3ATbad30XZ6F22nd3FbO2v9NXGllFLKU+mRuFJKKeWhanWIi8gIETkgIodE5HG767GCiLQQkS9EZL+I7BORqXbXZCUR8RWRRBFZbXctVhGRBiKyTES+FpEUEelnd01WEJFHnL+ze0VksYjUsbsmVxCRt0QkS0T2VlvWUEQ+E5GDzn/D7KzRFS7Qzmedv7d7RGSFiDSws0ZX+LF2VntthogYEWls1fZrbYiLiC/wCnAjEAvcISKx9lZliQpghjEmFugLTPbSdn5rKpBidxEWmw18bIyJAbrhhe0VkSjgYaCnMaYz4Avcbm9VLvM2MOK8ZY8D64wx7YB1zuee7m1+2M7PgM7GmK7AN8Bv3V2UBd7mh+1ERFoANwDpVm681oY40Bs4ZIxJNcaUAUuAeJtrcjljzAljzC7n43wcO/woe6uyhog0B0YC8+yuxSoiEgpcA7wJYIwpM8bk2VuVZfyAIBHxA4KB4zbX4xLGmA1AznmL44EFzscLgDFuLcoCP9ZOY8ynxpgK59MEoLnbC3OxC/w8AV4AZgKWdjyrzSEeBRyr9jwDLw23b4lIKyAO2GZvJZZ5EccfTZXdhVioNXAamO+8bDBPROraXZSrGWMygedwHMWcAM4aYz61typLNTXGnHA+Pgk0tbMYN7kfWGN3EVYQkXgg0xiz2+pt1eYQr1VEJAR4D5hmjDlndz2uJiKjgCxjzE67a7GYH9ADeM0YEwcU4h2nXr/HeU04HseHlkigrojcZW9V7mEctwx59W1DIvI7HJf6Ftldi6uJSDDwBPB/7thebQ7xTKBFtefNncu8joj44wjwRcaY5XbXY5EBwGgROYLj0sh1IrLQ3pIskQFkGGO+PZuyDEeoe5thQJox5rQxphxYDvS3uSYrnRKRZgDOf7NsrscyInIvMAr4ufHOe5yjcXz43O3cHzUHdolIhBUbq80h/hXQTkRai0gAjk4zq2yuyeVERHBcP00xxjxvdz1WMcb81hjT3BjTCsfP8nNjjNcduRljTgLHRKSDc9FQYL+NJVklHegrIsHO3+GheGEHvmpWAfc4H98DvG9jLZYRkRE4LnmNNsYU2V2PFYwxycaYcGNMK+f+KAPo4fzbdblaG+LOzhVTgE9w7ByWGmP22VuVJQYAE3AcmSY5v26yuyh1RR4CFonIHqA78Deb63E555mGZcAuIBnHvsorRvsSkcXAVqCDiGSIyAPA34HrReQgjrMQf7ezRle4QDtfBuoBnzn3Ra/bWqQLXKCd7tu+d57NUEoppbxfrT0SV0oppTydhrhSSinloTTElVJKKQ+lIa6UUkp5KA1xpZRSykNpiCulXEZEBnvzDHJK1TQa4koppZSH0hBXqhYSkbtEZLtzwI05znnYC0TkBecc3utEpInzvd1FJKHaHNBhzuVtRWStiOwWkV0iEu1cfUi1+c4XOUdcU0pZQENcqVpGRDoCtwEDjDHdgUrg50BdYIcxphPwJfAH57e8AzzmnAM6udryRcArxphuOMY1/3YWrjhgGhALtMExaqBSygJ+dheglHK7ocDVwFfOg+QgHBNuVAH/cb5nIbDcOX95A2PMl87lC4B3RaQeEGWMWQFgjCkBcK5vuzEmw/k8CWgFbLK+WUrVPhriStU+Aiwwxvz2ewtFfn/e+y53TObSao8r0f2MUpbR0+lK1T7rgFtEJBxARBqKSEsc+4NbnO+5E9hkjDkL5IrIIOfyCcCXxph8IENExjjXEeicR1kp5Ub6CVmpWsYYs19EngQ+FREfoByYDBQCvZ2vZeG4bg6OqTFfd4Z0KnCfc/kEYI6IPOVcx3g3NkMphc5ippRyEpECY0yI3XUopS6enk5XSimlPJQeiSullFIeSo/ElVJKKQ+lIa6UUkp5KA1xpZRSykNpiCullFIeSkNcKaWU8lAa4koppZSH+n/cBweEk3hXFgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXlS28GOf5B7"
      },
      "source": [
        "## Plotting Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XRPnC1LlfhR1"
      },
      "source": [
        "# Get functions from file\r\n",
        "from drive.MyDrive.InnoHAR.helpers import plot_cm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7g3nHsARgBhB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "outputId": "a9a7c6b0-effd-454d-8b33-30b5b9708cc1"
      },
      "source": [
        "plot_cm(model, test_data=(uci.testX, uci.testy), title='Confusion Matrix for LSTM based Model')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NVxdV9N2gwy7"
      },
      "source": [
        "## Classification Report"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5nnNFRkAgpRD"
      },
      "source": [
        "# Get functions from file\r\n",
        "from drive.MyDrive.InnoHAR.helpers import print_classification_report"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1oKvSqkmg8s3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "967715a0-7add-479e-d40a-0ea6fdb1734c"
      },
      "source": [
        "print_classification_report(model, test_data=(uci.testX, uci.testy), title='LSTM Model')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classification Report for 'LSTM Model': \n",
            "\n",
            "                    precision    recall  f1-score   support\n",
            "\n",
            "           WALKING    0.91913   0.93952   0.92921       496\n",
            "  WALKING_UPSTAIRS    0.87576   0.91295   0.89397       471\n",
            "WALKING_DOWNSTAIRS    0.99520   0.98810   0.99164       420\n",
            "           SITTING    0.80802   0.78004   0.79378       491\n",
            "          STANDING    0.81181   0.82707   0.81937       532\n",
            "            LAYING    0.98837   0.94972   0.96866       537\n",
            "\n",
            "          accuracy                        0.89718      2947\n",
            "         macro avg    0.89972   0.89957   0.89944      2947\n",
            "      weighted avg    0.89777   0.89718   0.89727      2947\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}