01_Creating_the_ML_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6238f84b",
   "metadata": {},
   "source": [
    "# Building an ML Model for Deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9af1f65e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelBinarizer, MinMaxScaler\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.models import Sequential, load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d7f486f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 42\n",
    "EPOCHS = 888\n",
    "MODEL_PATH=\"./model/full_iris_model.h5\"\n",
    "SCALER_PATH=\"./model/iris_data_norm.pkl\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dd05956",
   "metadata": {},
   "source": [
    "## IRIS Dataset\n",
    "\n",
    "> `wget https://gist.githubusercontent.com/Thanatoz-1/9e7fdfb8189f0cdf5d73a494e4a6392a/raw/aaecbd14aeaa468cd749528f291aa8a30c2ea09e/iris_dataset.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4ef711bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "        target  \n",
       "0  Iris-setosa  \n",
       "1  Iris-setosa  \n",
       "2  Iris-setosa  \n",
       "3  Iris-setosa  \n",
       "4  Iris-setosa  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dataset = pd.read_csv(\"./data/iris_dataset.csv\")\n",
    "iris_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d624ed3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# separate features from labels\n",
    "X = iris_dataset.drop('target', axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3ee6788d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = iris_dataset['target']\n",
    "y.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bc4c737c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1-hot encoding labels\n",
    "encoder = LabelBinarizer()\n",
    "y = encoder.fit_transform(y)\n",
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9a2b5496",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create training / testing datasets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "                                        X, y,\n",
    "                                        test_size=0.2,\n",
    "                                        random_state=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "420f5670",
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize training data\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_norm = scaler.transform(X_train)\n",
    "X_test_norm = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86340f56",
   "metadata": {},
   "source": [
    "## Building the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "abefe174",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_model = Sequential([\n",
    "    Dense(units=4, activation='relu', input_shape=[4,]),\n",
    "    Dense(units=3, activation='softmax')\n",
    "])\n",
    "\n",
    "iris_model.compile(optimizer='adam',\n",
    "                  loss='categorical_crossentropy',\n",
    "                  metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "034d0540",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fitting the model\n",
    "early_stop = EarlyStopping(\n",
    "    monitor='val_loss',\n",
    "    min_delta=0.0001,\n",
    "    patience=10,\n",
    "    verbose=0,\n",
    "    mode='auto',\n",
    "    baseline=None,\n",
    "    restore_best_weights=True,\n",
    "    start_from_epoch=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa21bc2f",
   "metadata": {},
   "source": [
    "## Fitting the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "605465c7",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/888\n",
      "4/4 [==============================] - 1s 40ms/step - loss: 1.1879 - accuracy: 0.3250 - val_loss: 1.1359 - val_accuracy: 0.3667\n",
      "Epoch 2/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.1838 - accuracy: 0.3250 - val_loss: 1.1319 - val_accuracy: 0.3667\n",
      "Epoch 3/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.1788 - accuracy: 0.3250 - val_loss: 1.1279 - val_accuracy: 0.3667\n",
      "Epoch 4/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1742 - accuracy: 0.3250 - val_loss: 1.1241 - val_accuracy: 0.3667\n",
      "Epoch 5/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1705 - accuracy: 0.3250 - val_loss: 1.1205 - val_accuracy: 0.3667\n",
      "Epoch 6/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1660 - accuracy: 0.3250 - val_loss: 1.1171 - val_accuracy: 0.3667\n",
      "Epoch 7/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1616 - accuracy: 0.3250 - val_loss: 1.1138 - val_accuracy: 0.3667\n",
      "Epoch 8/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1581 - accuracy: 0.3250 - val_loss: 1.1106 - val_accuracy: 0.3667\n",
      "Epoch 9/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.1539 - accuracy: 0.3250 - val_loss: 1.1077 - val_accuracy: 0.3667\n",
      "Epoch 10/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1503 - accuracy: 0.3250 - val_loss: 1.1048 - val_accuracy: 0.3667\n",
      "Epoch 11/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1468 - accuracy: 0.3250 - val_loss: 1.1020 - val_accuracy: 0.3667\n",
      "Epoch 12/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1430 - accuracy: 0.3250 - val_loss: 1.0995 - val_accuracy: 0.3667\n",
      "Epoch 13/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.1400 - accuracy: 0.3250 - val_loss: 1.0970 - val_accuracy: 0.3667\n",
      "Epoch 14/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.1370 - accuracy: 0.3250 - val_loss: 1.0946 - val_accuracy: 0.3667\n",
      "Epoch 15/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 1.1338 - accuracy: 0.3250 - val_loss: 1.0923 - val_accuracy: 0.3667\n",
      "Epoch 16/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.1311 - accuracy: 0.3333 - val_loss: 1.0901 - val_accuracy: 0.3667\n",
      "Epoch 17/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1284 - accuracy: 0.3250 - val_loss: 1.0881 - val_accuracy: 0.3667\n",
      "Epoch 18/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1257 - accuracy: 0.3250 - val_loss: 1.0861 - val_accuracy: 0.3667\n",
      "Epoch 19/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1230 - accuracy: 0.3250 - val_loss: 1.0843 - val_accuracy: 0.3667\n",
      "Epoch 20/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1206 - accuracy: 0.3250 - val_loss: 1.0825 - val_accuracy: 0.3667\n",
      "Epoch 21/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.1181 - accuracy: 0.3250 - val_loss: 1.0808 - val_accuracy: 0.3667\n",
      "Epoch 22/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1157 - accuracy: 0.3250 - val_loss: 1.0792 - val_accuracy: 0.3667\n",
      "Epoch 23/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1135 - accuracy: 0.3250 - val_loss: 1.0776 - val_accuracy: 0.3667\n",
      "Epoch 24/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1115 - accuracy: 0.3250 - val_loss: 1.0761 - val_accuracy: 0.3667\n",
      "Epoch 25/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1092 - accuracy: 0.3250 - val_loss: 1.0747 - val_accuracy: 0.3667\n",
      "Epoch 26/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1075 - accuracy: 0.3250 - val_loss: 1.0733 - val_accuracy: 0.3667\n",
      "Epoch 27/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.1055 - accuracy: 0.3250 - val_loss: 1.0719 - val_accuracy: 0.3667\n",
      "Epoch 28/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.1035 - accuracy: 0.3250 - val_loss: 1.0707 - val_accuracy: 0.3667\n",
      "Epoch 29/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.1019 - accuracy: 0.3250 - val_loss: 1.0696 - val_accuracy: 0.3667\n",
      "Epoch 30/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.1003 - accuracy: 0.3250 - val_loss: 1.0684 - val_accuracy: 0.3667\n",
      "Epoch 31/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0988 - accuracy: 0.3250 - val_loss: 1.0674 - val_accuracy: 0.3667\n",
      "Epoch 32/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0972 - accuracy: 0.3250 - val_loss: 1.0664 - val_accuracy: 0.3667\n",
      "Epoch 33/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0956 - accuracy: 0.3250 - val_loss: 1.0655 - val_accuracy: 0.3667\n",
      "Epoch 34/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0945 - accuracy: 0.3250 - val_loss: 1.0646 - val_accuracy: 0.3667\n",
      "Epoch 35/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0929 - accuracy: 0.3250 - val_loss: 1.0637 - val_accuracy: 0.3667\n",
      "Epoch 36/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0921 - accuracy: 0.3250 - val_loss: 1.0629 - val_accuracy: 0.3667\n",
      "Epoch 37/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0905 - accuracy: 0.3250 - val_loss: 1.0620 - val_accuracy: 0.3667\n",
      "Epoch 38/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0891 - accuracy: 0.3250 - val_loss: 1.0613 - val_accuracy: 0.3667\n",
      "Epoch 39/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0882 - accuracy: 0.3250 - val_loss: 1.0605 - val_accuracy: 0.3667\n",
      "Epoch 40/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.0869 - accuracy: 0.3250 - val_loss: 1.0598 - val_accuracy: 0.3667\n",
      "Epoch 41/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0858 - accuracy: 0.3250 - val_loss: 1.0591 - val_accuracy: 0.3667\n",
      "Epoch 42/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0849 - accuracy: 0.3250 - val_loss: 1.0584 - val_accuracy: 0.3667\n",
      "Epoch 43/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0840 - accuracy: 0.3250 - val_loss: 1.0577 - val_accuracy: 0.3667\n",
      "Epoch 44/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0828 - accuracy: 0.3333 - val_loss: 1.0571 - val_accuracy: 0.3667\n",
      "Epoch 45/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0817 - accuracy: 0.3333 - val_loss: 1.0565 - val_accuracy: 0.3667\n",
      "Epoch 46/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0810 - accuracy: 0.3333 - val_loss: 1.0559 - val_accuracy: 0.3667\n",
      "Epoch 47/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0800 - accuracy: 0.3333 - val_loss: 1.0553 - val_accuracy: 0.3667\n",
      "Epoch 48/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0791 - accuracy: 0.3333 - val_loss: 1.0547 - val_accuracy: 0.3667\n",
      "Epoch 49/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0784 - accuracy: 0.3333 - val_loss: 1.0541 - val_accuracy: 0.3667\n",
      "Epoch 50/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0774 - accuracy: 0.3333 - val_loss: 1.0535 - val_accuracy: 0.3667\n",
      "Epoch 51/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0766 - accuracy: 0.3417 - val_loss: 1.0529 - val_accuracy: 0.3667\n",
      "Epoch 52/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0758 - accuracy: 0.3417 - val_loss: 1.0524 - val_accuracy: 0.3667\n",
      "Epoch 53/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.0752 - accuracy: 0.3417 - val_loss: 1.0519 - val_accuracy: 0.3667\n",
      "Epoch 54/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0744 - accuracy: 0.3500 - val_loss: 1.0514 - val_accuracy: 0.3667\n",
      "Epoch 55/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0736 - accuracy: 0.3500 - val_loss: 1.0509 - val_accuracy: 0.3667\n",
      "Epoch 56/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0731 - accuracy: 0.3583 - val_loss: 1.0504 - val_accuracy: 0.3667\n",
      "Epoch 57/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0722 - accuracy: 0.3583 - val_loss: 1.0499 - val_accuracy: 0.3667\n",
      "Epoch 58/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.0717 - accuracy: 0.3583 - val_loss: 1.0494 - val_accuracy: 0.3667\n",
      "Epoch 59/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.0709 - accuracy: 0.3583 - val_loss: 1.0489 - val_accuracy: 0.3667\n",
      "Epoch 60/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0702 - accuracy: 0.3583 - val_loss: 1.0483 - val_accuracy: 0.3667\n",
      "Epoch 61/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0696 - accuracy: 0.3583 - val_loss: 1.0478 - val_accuracy: 0.3667\n",
      "Epoch 62/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0690 - accuracy: 0.3583 - val_loss: 1.0473 - val_accuracy: 0.3667\n",
      "Epoch 63/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0684 - accuracy: 0.3583 - val_loss: 1.0468 - val_accuracy: 0.3667\n",
      "Epoch 64/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0678 - accuracy: 0.3583 - val_loss: 1.0463 - val_accuracy: 0.3667\n",
      "Epoch 65/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 1.0671 - accuracy: 0.3583 - val_loss: 1.0458 - val_accuracy: 0.3667\n",
      "Epoch 66/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 1.0665 - accuracy: 0.3583 - val_loss: 1.0452 - val_accuracy: 0.3667\n",
      "Epoch 67/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0660 - accuracy: 0.3583 - val_loss: 1.0447 - val_accuracy: 0.3667\n",
      "Epoch 68/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0653 - accuracy: 0.3583 - val_loss: 1.0442 - val_accuracy: 0.3667\n",
      "Epoch 69/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.0647 - accuracy: 0.3583 - val_loss: 1.0437 - val_accuracy: 0.3667\n",
      "Epoch 70/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0641 - accuracy: 0.3583 - val_loss: 1.0432 - val_accuracy: 0.3667\n",
      "Epoch 71/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.0635 - accuracy: 0.3667 - val_loss: 1.0426 - val_accuracy: 0.3667\n",
      "Epoch 72/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0630 - accuracy: 0.3667 - val_loss: 1.0422 - val_accuracy: 0.3667\n",
      "Epoch 73/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 1.0623 - accuracy: 0.3667 - val_loss: 1.0416 - val_accuracy: 0.3667\n",
      "Epoch 74/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0617 - accuracy: 0.3667 - val_loss: 1.0410 - val_accuracy: 0.3667\n",
      "Epoch 75/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0611 - accuracy: 0.3667 - val_loss: 1.0404 - val_accuracy: 0.3667\n",
      "Epoch 76/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.0605 - accuracy: 0.3750 - val_loss: 1.0399 - val_accuracy: 0.3667\n",
      "Epoch 77/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.0599 - accuracy: 0.3833 - val_loss: 1.0393 - val_accuracy: 0.3667\n",
      "Epoch 78/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0594 - accuracy: 0.3833 - val_loss: 1.0387 - val_accuracy: 0.3667\n",
      "Epoch 79/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0588 - accuracy: 0.3833 - val_loss: 1.0381 - val_accuracy: 0.3667\n",
      "Epoch 80/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0582 - accuracy: 0.3917 - val_loss: 1.0376 - val_accuracy: 0.3667\n",
      "Epoch 81/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0575 - accuracy: 0.3917 - val_loss: 1.0369 - val_accuracy: 0.3667\n",
      "Epoch 82/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 1.0569 - accuracy: 0.3917 - val_loss: 1.0363 - val_accuracy: 0.3667\n",
      "Epoch 83/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0563 - accuracy: 0.4000 - val_loss: 1.0357 - val_accuracy: 0.3667\n",
      "Epoch 84/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0557 - accuracy: 0.4000 - val_loss: 1.0350 - val_accuracy: 0.3667\n",
      "Epoch 85/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.0551 - accuracy: 0.4000 - val_loss: 1.0343 - val_accuracy: 0.3667\n",
      "Epoch 86/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.0545 - accuracy: 0.4000 - val_loss: 1.0337 - val_accuracy: 0.3667\n",
      "Epoch 87/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.0539 - accuracy: 0.4000 - val_loss: 1.0331 - val_accuracy: 0.3667\n",
      "Epoch 88/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0532 - accuracy: 0.4000 - val_loss: 1.0324 - val_accuracy: 0.3667\n",
      "Epoch 89/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0526 - accuracy: 0.4000 - val_loss: 1.0316 - val_accuracy: 0.3667\n",
      "Epoch 90/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.0519 - accuracy: 0.4000 - val_loss: 1.0309 - val_accuracy: 0.3667\n",
      "Epoch 91/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0513 - accuracy: 0.4000 - val_loss: 1.0302 - val_accuracy: 0.4000\n",
      "Epoch 92/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.0506 - accuracy: 0.4000 - val_loss: 1.0295 - val_accuracy: 0.4000\n",
      "Epoch 93/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0499 - accuracy: 0.4000 - val_loss: 1.0289 - val_accuracy: 0.4000\n",
      "Epoch 94/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.0493 - accuracy: 0.4000 - val_loss: 1.0281 - val_accuracy: 0.4000\n",
      "Epoch 95/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0486 - accuracy: 0.4000 - val_loss: 1.0274 - val_accuracy: 0.4000\n",
      "Epoch 96/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0479 - accuracy: 0.4000 - val_loss: 1.0266 - val_accuracy: 0.4000\n",
      "Epoch 97/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0472 - accuracy: 0.4000 - val_loss: 1.0258 - val_accuracy: 0.4000\n",
      "Epoch 98/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0465 - accuracy: 0.4000 - val_loss: 1.0250 - val_accuracy: 0.4000\n",
      "Epoch 99/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.0458 - accuracy: 0.4000 - val_loss: 1.0241 - val_accuracy: 0.4000\n",
      "Epoch 100/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0450 - accuracy: 0.4000 - val_loss: 1.0232 - val_accuracy: 0.4000\n",
      "Epoch 101/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0443 - accuracy: 0.4000 - val_loss: 1.0224 - val_accuracy: 0.4000\n",
      "Epoch 102/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0435 - accuracy: 0.4000 - val_loss: 1.0215 - val_accuracy: 0.4000\n",
      "Epoch 103/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.0428 - accuracy: 0.4000 - val_loss: 1.0206 - val_accuracy: 0.4000\n",
      "Epoch 104/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0420 - accuracy: 0.4000 - val_loss: 1.0197 - val_accuracy: 0.4000\n",
      "Epoch 105/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0413 - accuracy: 0.4000 - val_loss: 1.0187 - val_accuracy: 0.4000\n",
      "Epoch 106/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0404 - accuracy: 0.4000 - val_loss: 1.0178 - val_accuracy: 0.4000\n",
      "Epoch 107/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0397 - accuracy: 0.4000 - val_loss: 1.0168 - val_accuracy: 0.4000\n",
      "Epoch 108/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0388 - accuracy: 0.4000 - val_loss: 1.0159 - val_accuracy: 0.4000\n",
      "Epoch 109/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0380 - accuracy: 0.4000 - val_loss: 1.0149 - val_accuracy: 0.4000\n",
      "Epoch 110/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.0372 - accuracy: 0.4000 - val_loss: 1.0139 - val_accuracy: 0.4000\n",
      "Epoch 111/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.0363 - accuracy: 0.4000 - val_loss: 1.0129 - val_accuracy: 0.4000\n",
      "Epoch 112/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0356 - accuracy: 0.4000 - val_loss: 1.0118 - val_accuracy: 0.4000\n",
      "Epoch 113/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0347 - accuracy: 0.4000 - val_loss: 1.0109 - val_accuracy: 0.4000\n",
      "Epoch 114/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0337 - accuracy: 0.4000 - val_loss: 1.0099 - val_accuracy: 0.4000\n",
      "Epoch 115/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0328 - accuracy: 0.4083 - val_loss: 1.0089 - val_accuracy: 0.4000\n",
      "Epoch 116/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0321 - accuracy: 0.4083 - val_loss: 1.0077 - val_accuracy: 0.4000\n",
      "Epoch 117/888\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0311 - accuracy: 0.4083 - val_loss: 1.0068 - val_accuracy: 0.4000\n",
      "Epoch 118/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0301 - accuracy: 0.4083 - val_loss: 1.0056 - val_accuracy: 0.4000\n",
      "Epoch 119/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0292 - accuracy: 0.4083 - val_loss: 1.0044 - val_accuracy: 0.4000\n",
      "Epoch 120/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0283 - accuracy: 0.4000 - val_loss: 1.0033 - val_accuracy: 0.4000\n",
      "Epoch 121/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0273 - accuracy: 0.4000 - val_loss: 1.0022 - val_accuracy: 0.4000\n",
      "Epoch 122/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0263 - accuracy: 0.4083 - val_loss: 1.0011 - val_accuracy: 0.4000\n",
      "Epoch 123/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.0253 - accuracy: 0.4083 - val_loss: 1.0000 - val_accuracy: 0.4000\n",
      "Epoch 124/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.0243 - accuracy: 0.4083 - val_loss: 0.9988 - val_accuracy: 0.4000\n",
      "Epoch 125/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0235 - accuracy: 0.4000 - val_loss: 0.9974 - val_accuracy: 0.4000\n",
      "Epoch 126/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.0223 - accuracy: 0.4000 - val_loss: 0.9963 - val_accuracy: 0.4000\n",
      "Epoch 127/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0213 - accuracy: 0.4083 - val_loss: 0.9951 - val_accuracy: 0.4000\n",
      "Epoch 128/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0202 - accuracy: 0.4083 - val_loss: 0.9939 - val_accuracy: 0.4000\n",
      "Epoch 129/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.0192 - accuracy: 0.4083 - val_loss: 0.9926 - val_accuracy: 0.4000\n",
      "Epoch 130/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0181 - accuracy: 0.4083 - val_loss: 0.9914 - val_accuracy: 0.4000\n",
      "Epoch 131/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0170 - accuracy: 0.4083 - val_loss: 0.9901 - val_accuracy: 0.4000\n",
      "Epoch 132/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0159 - accuracy: 0.4083 - val_loss: 0.9888 - val_accuracy: 0.4000\n",
      "Epoch 133/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0148 - accuracy: 0.4083 - val_loss: 0.9875 - val_accuracy: 0.4000\n",
      "Epoch 134/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0137 - accuracy: 0.4083 - val_loss: 0.9862 - val_accuracy: 0.4000\n",
      "Epoch 135/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0126 - accuracy: 0.4083 - val_loss: 0.9850 - val_accuracy: 0.4000\n",
      "Epoch 136/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 1.0114 - accuracy: 0.4083 - val_loss: 0.9836 - val_accuracy: 0.4000\n",
      "Epoch 137/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 1.0102 - accuracy: 0.4083 - val_loss: 0.9822 - val_accuracy: 0.4000\n",
      "Epoch 138/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0091 - accuracy: 0.4083 - val_loss: 0.9808 - val_accuracy: 0.4000\n",
      "Epoch 139/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0079 - accuracy: 0.4083 - val_loss: 0.9794 - val_accuracy: 0.4000\n",
      "Epoch 140/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0067 - accuracy: 0.4083 - val_loss: 0.9779 - val_accuracy: 0.4000\n",
      "Epoch 141/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0054 - accuracy: 0.4083 - val_loss: 0.9766 - val_accuracy: 0.4000\n",
      "Epoch 142/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0042 - accuracy: 0.4000 - val_loss: 0.9751 - val_accuracy: 0.4000\n",
      "Epoch 143/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0030 - accuracy: 0.4083 - val_loss: 0.9738 - val_accuracy: 0.4000\n",
      "Epoch 144/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0017 - accuracy: 0.4083 - val_loss: 0.9723 - val_accuracy: 0.4000\n",
      "Epoch 145/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 1.0005 - accuracy: 0.4083 - val_loss: 0.9708 - val_accuracy: 0.4000\n",
      "Epoch 146/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.9992 - accuracy: 0.4083 - val_loss: 0.9694 - val_accuracy: 0.4000\n",
      "Epoch 147/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9979 - accuracy: 0.4083 - val_loss: 0.9679 - val_accuracy: 0.4000\n",
      "Epoch 148/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.9966 - accuracy: 0.4000 - val_loss: 0.9664 - val_accuracy: 0.4000\n",
      "Epoch 149/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9953 - accuracy: 0.4000 - val_loss: 0.9648 - val_accuracy: 0.4000\n",
      "Epoch 150/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9940 - accuracy: 0.4000 - val_loss: 0.9631 - val_accuracy: 0.4000\n",
      "Epoch 151/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9927 - accuracy: 0.4000 - val_loss: 0.9615 - val_accuracy: 0.4000\n",
      "Epoch 152/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.9914 - accuracy: 0.4000 - val_loss: 0.9599 - val_accuracy: 0.4000\n",
      "Epoch 153/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9899 - accuracy: 0.4000 - val_loss: 0.9584 - val_accuracy: 0.4000\n",
      "Epoch 154/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.9886 - accuracy: 0.4000 - val_loss: 0.9568 - val_accuracy: 0.4000\n",
      "Epoch 155/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9872 - accuracy: 0.4000 - val_loss: 0.9553 - val_accuracy: 0.4000\n",
      "Epoch 156/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.9858 - accuracy: 0.4000 - val_loss: 0.9537 - val_accuracy: 0.4000\n",
      "Epoch 157/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9844 - accuracy: 0.4000 - val_loss: 0.9520 - val_accuracy: 0.4000\n",
      "Epoch 158/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9830 - accuracy: 0.4000 - val_loss: 0.9504 - val_accuracy: 0.4000\n",
      "Epoch 159/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9816 - accuracy: 0.4000 - val_loss: 0.9487 - val_accuracy: 0.4000\n",
      "Epoch 160/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9802 - accuracy: 0.4000 - val_loss: 0.9471 - val_accuracy: 0.4000\n",
      "Epoch 161/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9787 - accuracy: 0.4000 - val_loss: 0.9454 - val_accuracy: 0.4000\n",
      "Epoch 162/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9772 - accuracy: 0.4000 - val_loss: 0.9437 - val_accuracy: 0.4000\n",
      "Epoch 163/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9759 - accuracy: 0.4000 - val_loss: 0.9421 - val_accuracy: 0.4000\n",
      "Epoch 164/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9744 - accuracy: 0.4000 - val_loss: 0.9404 - val_accuracy: 0.4000\n",
      "Epoch 165/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9729 - accuracy: 0.4000 - val_loss: 0.9387 - val_accuracy: 0.4000\n",
      "Epoch 166/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9714 - accuracy: 0.4000 - val_loss: 0.9371 - val_accuracy: 0.4000\n",
      "Epoch 167/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.9699 - accuracy: 0.4000 - val_loss: 0.9354 - val_accuracy: 0.4000\n",
      "Epoch 168/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9685 - accuracy: 0.4000 - val_loss: 0.9337 - val_accuracy: 0.4000\n",
      "Epoch 169/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9669 - accuracy: 0.4000 - val_loss: 0.9321 - val_accuracy: 0.4000\n",
      "Epoch 170/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9654 - accuracy: 0.4000 - val_loss: 0.9305 - val_accuracy: 0.4000\n",
      "Epoch 171/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9639 - accuracy: 0.4000 - val_loss: 0.9289 - val_accuracy: 0.4000\n",
      "Epoch 172/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9624 - accuracy: 0.4000 - val_loss: 0.9272 - val_accuracy: 0.4000\n",
      "Epoch 173/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9608 - accuracy: 0.4000 - val_loss: 0.9255 - val_accuracy: 0.4000\n",
      "Epoch 174/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9593 - accuracy: 0.4000 - val_loss: 0.9239 - val_accuracy: 0.4000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 175/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9577 - accuracy: 0.4000 - val_loss: 0.9223 - val_accuracy: 0.4000\n",
      "Epoch 176/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9562 - accuracy: 0.4000 - val_loss: 0.9207 - val_accuracy: 0.4000\n",
      "Epoch 177/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9546 - accuracy: 0.4000 - val_loss: 0.9190 - val_accuracy: 0.4000\n",
      "Epoch 178/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9530 - accuracy: 0.4000 - val_loss: 0.9171 - val_accuracy: 0.4000\n",
      "Epoch 179/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9514 - accuracy: 0.4000 - val_loss: 0.9152 - val_accuracy: 0.4000\n",
      "Epoch 180/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9498 - accuracy: 0.4000 - val_loss: 0.9134 - val_accuracy: 0.4000\n",
      "Epoch 181/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9482 - accuracy: 0.4000 - val_loss: 0.9116 - val_accuracy: 0.4000\n",
      "Epoch 182/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.9465 - accuracy: 0.4000 - val_loss: 0.9097 - val_accuracy: 0.4000\n",
      "Epoch 183/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9449 - accuracy: 0.4000 - val_loss: 0.9079 - val_accuracy: 0.4000\n",
      "Epoch 184/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.9432 - accuracy: 0.4000 - val_loss: 0.9061 - val_accuracy: 0.4000\n",
      "Epoch 185/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.9416 - accuracy: 0.4000 - val_loss: 0.9043 - val_accuracy: 0.4000\n",
      "Epoch 186/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9399 - accuracy: 0.4000 - val_loss: 0.9025 - val_accuracy: 0.4000\n",
      "Epoch 187/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9383 - accuracy: 0.4000 - val_loss: 0.9006 - val_accuracy: 0.4000\n",
      "Epoch 188/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9366 - accuracy: 0.4000 - val_loss: 0.8988 - val_accuracy: 0.4000\n",
      "Epoch 189/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9350 - accuracy: 0.4000 - val_loss: 0.8970 - val_accuracy: 0.4000\n",
      "Epoch 190/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9334 - accuracy: 0.4000 - val_loss: 0.8950 - val_accuracy: 0.4000\n",
      "Epoch 191/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9317 - accuracy: 0.4000 - val_loss: 0.8932 - val_accuracy: 0.4000\n",
      "Epoch 192/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9300 - accuracy: 0.4000 - val_loss: 0.8913 - val_accuracy: 0.4000\n",
      "Epoch 193/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.9284 - accuracy: 0.4000 - val_loss: 0.8894 - val_accuracy: 0.4000\n",
      "Epoch 194/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9267 - accuracy: 0.4000 - val_loss: 0.8876 - val_accuracy: 0.4000\n",
      "Epoch 195/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9250 - accuracy: 0.4000 - val_loss: 0.8857 - val_accuracy: 0.4000\n",
      "Epoch 196/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9233 - accuracy: 0.4000 - val_loss: 0.8839 - val_accuracy: 0.4000\n",
      "Epoch 197/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9217 - accuracy: 0.3917 - val_loss: 0.8819 - val_accuracy: 0.4000\n",
      "Epoch 198/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9200 - accuracy: 0.3917 - val_loss: 0.8800 - val_accuracy: 0.4000\n",
      "Epoch 199/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.9184 - accuracy: 0.3917 - val_loss: 0.8781 - val_accuracy: 0.4000\n",
      "Epoch 200/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.9166 - accuracy: 0.3917 - val_loss: 0.8762 - val_accuracy: 0.4000\n",
      "Epoch 201/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9149 - accuracy: 0.3917 - val_loss: 0.8743 - val_accuracy: 0.4000\n",
      "Epoch 202/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9132 - accuracy: 0.3917 - val_loss: 0.8724 - val_accuracy: 0.4000\n",
      "Epoch 203/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.9115 - accuracy: 0.3917 - val_loss: 0.8706 - val_accuracy: 0.4000\n",
      "Epoch 204/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9097 - accuracy: 0.3917 - val_loss: 0.8687 - val_accuracy: 0.4000\n",
      "Epoch 205/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9080 - accuracy: 0.3917 - val_loss: 0.8669 - val_accuracy: 0.4000\n",
      "Epoch 206/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.9063 - accuracy: 0.3917 - val_loss: 0.8650 - val_accuracy: 0.4000\n",
      "Epoch 207/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.9046 - accuracy: 0.3917 - val_loss: 0.8631 - val_accuracy: 0.4000\n",
      "Epoch 208/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9029 - accuracy: 0.3917 - val_loss: 0.8613 - val_accuracy: 0.4000\n",
      "Epoch 209/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9012 - accuracy: 0.3917 - val_loss: 0.8593 - val_accuracy: 0.4000\n",
      "Epoch 210/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8995 - accuracy: 0.3917 - val_loss: 0.8574 - val_accuracy: 0.4000\n",
      "Epoch 211/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8977 - accuracy: 0.3917 - val_loss: 0.8555 - val_accuracy: 0.4000\n",
      "Epoch 212/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8960 - accuracy: 0.3917 - val_loss: 0.8536 - val_accuracy: 0.4000\n",
      "Epoch 213/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8944 - accuracy: 0.3917 - val_loss: 0.8517 - val_accuracy: 0.4000\n",
      "Epoch 214/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8926 - accuracy: 0.3917 - val_loss: 0.8498 - val_accuracy: 0.4000\n",
      "Epoch 215/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8909 - accuracy: 0.3917 - val_loss: 0.8480 - val_accuracy: 0.4000\n",
      "Epoch 216/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8891 - accuracy: 0.3917 - val_loss: 0.8462 - val_accuracy: 0.4000\n",
      "Epoch 217/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8874 - accuracy: 0.3917 - val_loss: 0.8443 - val_accuracy: 0.4000\n",
      "Epoch 218/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8856 - accuracy: 0.3917 - val_loss: 0.8425 - val_accuracy: 0.4000\n",
      "Epoch 219/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.8839 - accuracy: 0.3917 - val_loss: 0.8406 - val_accuracy: 0.4000\n",
      "Epoch 220/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.8822 - accuracy: 0.3917 - val_loss: 0.8389 - val_accuracy: 0.4000\n",
      "Epoch 221/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8804 - accuracy: 0.3917 - val_loss: 0.8370 - val_accuracy: 0.4000\n",
      "Epoch 222/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.8786 - accuracy: 0.3917 - val_loss: 0.8351 - val_accuracy: 0.4000\n",
      "Epoch 223/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.8769 - accuracy: 0.3917 - val_loss: 0.8332 - val_accuracy: 0.4000\n",
      "Epoch 224/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8751 - accuracy: 0.3917 - val_loss: 0.8313 - val_accuracy: 0.4000\n",
      "Epoch 225/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.8734 - accuracy: 0.3917 - val_loss: 0.8294 - val_accuracy: 0.4000\n",
      "Epoch 226/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8716 - accuracy: 0.3917 - val_loss: 0.8276 - val_accuracy: 0.4000\n",
      "Epoch 227/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8699 - accuracy: 0.3917 - val_loss: 0.8257 - val_accuracy: 0.4000\n",
      "Epoch 228/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8681 - accuracy: 0.3917 - val_loss: 0.8239 - val_accuracy: 0.4000\n",
      "Epoch 229/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8664 - accuracy: 0.3917 - val_loss: 0.8221 - val_accuracy: 0.4000\n",
      "Epoch 230/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.8645 - accuracy: 0.3917 - val_loss: 0.8203 - val_accuracy: 0.4000\n",
      "Epoch 231/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.8628 - accuracy: 0.3917 - val_loss: 0.8184 - val_accuracy: 0.4000\n",
      "Epoch 232/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8611 - accuracy: 0.3917 - val_loss: 0.8166 - val_accuracy: 0.4000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 233/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8593 - accuracy: 0.3917 - val_loss: 0.8147 - val_accuracy: 0.4000\n",
      "Epoch 234/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8576 - accuracy: 0.3917 - val_loss: 0.8128 - val_accuracy: 0.4000\n",
      "Epoch 235/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8558 - accuracy: 0.3917 - val_loss: 0.8109 - val_accuracy: 0.4000\n",
      "Epoch 236/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8541 - accuracy: 0.3917 - val_loss: 0.8091 - val_accuracy: 0.4000\n",
      "Epoch 237/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8524 - accuracy: 0.3917 - val_loss: 0.8073 - val_accuracy: 0.4000\n",
      "Epoch 238/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8506 - accuracy: 0.3917 - val_loss: 0.8055 - val_accuracy: 0.4000\n",
      "Epoch 239/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.8488 - accuracy: 0.3917 - val_loss: 0.8037 - val_accuracy: 0.4000\n",
      "Epoch 240/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8471 - accuracy: 0.3917 - val_loss: 0.8019 - val_accuracy: 0.4000\n",
      "Epoch 241/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8453 - accuracy: 0.3917 - val_loss: 0.8001 - val_accuracy: 0.4000\n",
      "Epoch 242/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8436 - accuracy: 0.3917 - val_loss: 0.7983 - val_accuracy: 0.4000\n",
      "Epoch 243/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8419 - accuracy: 0.3917 - val_loss: 0.7965 - val_accuracy: 0.4000\n",
      "Epoch 244/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8401 - accuracy: 0.3917 - val_loss: 0.7947 - val_accuracy: 0.4000\n",
      "Epoch 245/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.8384 - accuracy: 0.4917 - val_loss: 0.7930 - val_accuracy: 0.6667\n",
      "Epoch 246/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8366 - accuracy: 0.6500 - val_loss: 0.7913 - val_accuracy: 0.6667\n",
      "Epoch 247/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8349 - accuracy: 0.6500 - val_loss: 0.7895 - val_accuracy: 0.6667\n",
      "Epoch 248/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8331 - accuracy: 0.6583 - val_loss: 0.7877 - val_accuracy: 0.6667\n",
      "Epoch 249/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8314 - accuracy: 0.6583 - val_loss: 0.7860 - val_accuracy: 0.6667\n",
      "Epoch 250/888\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 0.8297 - accuracy: 0.6667 - val_loss: 0.7842 - val_accuracy: 0.7000\n",
      "Epoch 251/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.8279 - accuracy: 0.6750 - val_loss: 0.7824 - val_accuracy: 0.7000\n",
      "Epoch 252/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8262 - accuracy: 0.6750 - val_loss: 0.7805 - val_accuracy: 0.7000\n",
      "Epoch 253/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8245 - accuracy: 0.6833 - val_loss: 0.7789 - val_accuracy: 0.7333\n",
      "Epoch 254/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8227 - accuracy: 0.6917 - val_loss: 0.7771 - val_accuracy: 0.7333\n",
      "Epoch 255/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8209 - accuracy: 0.6917 - val_loss: 0.7753 - val_accuracy: 0.7333\n",
      "Epoch 256/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.8192 - accuracy: 0.7000 - val_loss: 0.7736 - val_accuracy: 0.7333\n",
      "Epoch 257/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.8175 - accuracy: 0.7083 - val_loss: 0.7718 - val_accuracy: 0.7333\n",
      "Epoch 258/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8157 - accuracy: 0.7083 - val_loss: 0.7700 - val_accuracy: 0.7333\n",
      "Epoch 259/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.8140 - accuracy: 0.7083 - val_loss: 0.7682 - val_accuracy: 0.7333\n",
      "Epoch 260/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8123 - accuracy: 0.7167 - val_loss: 0.7664 - val_accuracy: 0.7333\n",
      "Epoch 261/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.8105 - accuracy: 0.7167 - val_loss: 0.7647 - val_accuracy: 0.7333\n",
      "Epoch 262/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8088 - accuracy: 0.7167 - val_loss: 0.7629 - val_accuracy: 0.7333\n",
      "Epoch 263/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.8071 - accuracy: 0.7167 - val_loss: 0.7611 - val_accuracy: 0.7333\n",
      "Epoch 264/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8054 - accuracy: 0.7250 - val_loss: 0.7593 - val_accuracy: 0.7333\n",
      "Epoch 265/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.8036 - accuracy: 0.7250 - val_loss: 0.7575 - val_accuracy: 0.7333\n",
      "Epoch 266/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8019 - accuracy: 0.7250 - val_loss: 0.7558 - val_accuracy: 0.7667\n",
      "Epoch 267/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.8002 - accuracy: 0.7250 - val_loss: 0.7540 - val_accuracy: 0.7667\n",
      "Epoch 268/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.7984 - accuracy: 0.7250 - val_loss: 0.7523 - val_accuracy: 0.7667\n",
      "Epoch 269/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7967 - accuracy: 0.7250 - val_loss: 0.7506 - val_accuracy: 0.7667\n",
      "Epoch 270/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.7951 - accuracy: 0.7333 - val_loss: 0.7489 - val_accuracy: 0.7667\n",
      "Epoch 271/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7933 - accuracy: 0.7333 - val_loss: 0.7472 - val_accuracy: 0.7667\n",
      "Epoch 272/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.7916 - accuracy: 0.7333 - val_loss: 0.7454 - val_accuracy: 0.7667\n",
      "Epoch 273/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.7899 - accuracy: 0.7333 - val_loss: 0.7438 - val_accuracy: 0.7667\n",
      "Epoch 274/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7882 - accuracy: 0.7333 - val_loss: 0.7420 - val_accuracy: 0.7667\n",
      "Epoch 275/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7865 - accuracy: 0.7417 - val_loss: 0.7404 - val_accuracy: 0.7667\n",
      "Epoch 276/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7848 - accuracy: 0.7417 - val_loss: 0.7387 - val_accuracy: 0.7667\n",
      "Epoch 277/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.7832 - accuracy: 0.7500 - val_loss: 0.7371 - val_accuracy: 0.7667\n",
      "Epoch 278/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.7814 - accuracy: 0.7500 - val_loss: 0.7354 - val_accuracy: 0.7667\n",
      "Epoch 279/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.7797 - accuracy: 0.7500 - val_loss: 0.7338 - val_accuracy: 0.7667\n",
      "Epoch 280/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7780 - accuracy: 0.7500 - val_loss: 0.7321 - val_accuracy: 0.7667\n",
      "Epoch 281/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7764 - accuracy: 0.7500 - val_loss: 0.7305 - val_accuracy: 0.7667\n",
      "Epoch 282/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7747 - accuracy: 0.7500 - val_loss: 0.7289 - val_accuracy: 0.7667\n",
      "Epoch 283/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.7730 - accuracy: 0.7500 - val_loss: 0.7272 - val_accuracy: 0.7667\n",
      "Epoch 284/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.7713 - accuracy: 0.7500 - val_loss: 0.7255 - val_accuracy: 0.7667\n",
      "Epoch 285/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7696 - accuracy: 0.7500 - val_loss: 0.7239 - val_accuracy: 0.7667\n",
      "Epoch 286/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7680 - accuracy: 0.7500 - val_loss: 0.7222 - val_accuracy: 0.7667\n",
      "Epoch 287/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7663 - accuracy: 0.7583 - val_loss: 0.7206 - val_accuracy: 0.7667\n",
      "Epoch 288/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7647 - accuracy: 0.7583 - val_loss: 0.7190 - val_accuracy: 0.7667\n",
      "Epoch 289/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7630 - accuracy: 0.7583 - val_loss: 0.7174 - val_accuracy: 0.7667\n",
      "Epoch 290/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.7613 - accuracy: 0.7583 - val_loss: 0.7157 - val_accuracy: 0.7667\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 291/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7597 - accuracy: 0.7583 - val_loss: 0.7141 - val_accuracy: 0.7667\n",
      "Epoch 292/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.7581 - accuracy: 0.7583 - val_loss: 0.7124 - val_accuracy: 0.8000\n",
      "Epoch 293/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.7564 - accuracy: 0.7583 - val_loss: 0.7108 - val_accuracy: 0.7667\n",
      "Epoch 294/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7547 - accuracy: 0.7583 - val_loss: 0.7092 - val_accuracy: 0.8000\n",
      "Epoch 295/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.7531 - accuracy: 0.7583 - val_loss: 0.7075 - val_accuracy: 0.8000\n",
      "Epoch 296/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.7515 - accuracy: 0.7583 - val_loss: 0.7059 - val_accuracy: 0.8000\n",
      "Epoch 297/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.7498 - accuracy: 0.7583 - val_loss: 0.7043 - val_accuracy: 0.8000\n",
      "Epoch 298/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.7482 - accuracy: 0.7583 - val_loss: 0.7028 - val_accuracy: 0.8000\n",
      "Epoch 299/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7466 - accuracy: 0.7583 - val_loss: 0.7012 - val_accuracy: 0.8000\n",
      "Epoch 300/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7450 - accuracy: 0.7583 - val_loss: 0.6996 - val_accuracy: 0.8000\n",
      "Epoch 301/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.7434 - accuracy: 0.7583 - val_loss: 0.6980 - val_accuracy: 0.8000\n",
      "Epoch 302/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7418 - accuracy: 0.7583 - val_loss: 0.6964 - val_accuracy: 0.8000\n",
      "Epoch 303/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.7401 - accuracy: 0.7583 - val_loss: 0.6949 - val_accuracy: 0.8000\n",
      "Epoch 304/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7385 - accuracy: 0.7583 - val_loss: 0.6933 - val_accuracy: 0.8000\n",
      "Epoch 305/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.7369 - accuracy: 0.7583 - val_loss: 0.6917 - val_accuracy: 0.8000\n",
      "Epoch 306/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.7353 - accuracy: 0.7583 - val_loss: 0.6902 - val_accuracy: 0.8000\n",
      "Epoch 307/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.7338 - accuracy: 0.7667 - val_loss: 0.6887 - val_accuracy: 0.8000\n",
      "Epoch 308/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.7321 - accuracy: 0.7667 - val_loss: 0.6871 - val_accuracy: 0.8000\n",
      "Epoch 309/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.7306 - accuracy: 0.7667 - val_loss: 0.6856 - val_accuracy: 0.8000\n",
      "Epoch 310/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.7290 - accuracy: 0.7667 - val_loss: 0.6841 - val_accuracy: 0.8000\n",
      "Epoch 311/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7274 - accuracy: 0.7667 - val_loss: 0.6826 - val_accuracy: 0.8000\n",
      "Epoch 312/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.7259 - accuracy: 0.7667 - val_loss: 0.6811 - val_accuracy: 0.8000\n",
      "Epoch 313/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7243 - accuracy: 0.7667 - val_loss: 0.6796 - val_accuracy: 0.8000\n",
      "Epoch 314/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.7228 - accuracy: 0.7667 - val_loss: 0.6781 - val_accuracy: 0.8000\n",
      "Epoch 315/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7212 - accuracy: 0.7667 - val_loss: 0.6766 - val_accuracy: 0.8000\n",
      "Epoch 316/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.7196 - accuracy: 0.7667 - val_loss: 0.6751 - val_accuracy: 0.8000\n",
      "Epoch 317/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.7181 - accuracy: 0.7667 - val_loss: 0.6736 - val_accuracy: 0.8000\n",
      "Epoch 318/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.7165 - accuracy: 0.7667 - val_loss: 0.6721 - val_accuracy: 0.8000\n",
      "Epoch 319/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.7150 - accuracy: 0.7667 - val_loss: 0.6706 - val_accuracy: 0.8000\n",
      "Epoch 320/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.7135 - accuracy: 0.7667 - val_loss: 0.6692 - val_accuracy: 0.8000\n",
      "Epoch 321/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.7120 - accuracy: 0.7667 - val_loss: 0.6677 - val_accuracy: 0.8000\n",
      "Epoch 322/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.7104 - accuracy: 0.7667 - val_loss: 0.6662 - val_accuracy: 0.8000\n",
      "Epoch 323/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.7089 - accuracy: 0.7667 - val_loss: 0.6648 - val_accuracy: 0.8000\n",
      "Epoch 324/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.7074 - accuracy: 0.7750 - val_loss: 0.6633 - val_accuracy: 0.8000\n",
      "Epoch 325/888\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.7059 - accuracy: 0.7750 - val_loss: 0.6618 - val_accuracy: 0.8000\n",
      "Epoch 326/888\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 0.7044 - accuracy: 0.7750 - val_loss: 0.6604 - val_accuracy: 0.8000\n",
      "Epoch 327/888\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 0.7029 - accuracy: 0.7750 - val_loss: 0.6590 - val_accuracy: 0.8000\n",
      "Epoch 328/888\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 0.7014 - accuracy: 0.7750 - val_loss: 0.6575 - val_accuracy: 0.8000\n",
      "Epoch 329/888\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 0.6999 - accuracy: 0.7750 - val_loss: 0.6561 - val_accuracy: 0.8000\n",
      "Epoch 330/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6984 - accuracy: 0.7833 - val_loss: 0.6547 - val_accuracy: 0.8000\n",
      "Epoch 331/888\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.6969 - accuracy: 0.7917 - val_loss: 0.6533 - val_accuracy: 0.8000\n",
      "Epoch 332/888\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6954 - accuracy: 0.7917 - val_loss: 0.6519 - val_accuracy: 0.8000\n",
      "Epoch 333/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6941 - accuracy: 0.7917 - val_loss: 0.6505 - val_accuracy: 0.8000\n",
      "Epoch 334/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6925 - accuracy: 0.7917 - val_loss: 0.6491 - val_accuracy: 0.8000\n",
      "Epoch 335/888\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.6911 - accuracy: 0.7917 - val_loss: 0.6477 - val_accuracy: 0.8000\n",
      "Epoch 336/888\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.6897 - accuracy: 0.7917 - val_loss: 0.6464 - val_accuracy: 0.8000\n",
      "Epoch 337/888\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.6882 - accuracy: 0.7917 - val_loss: 0.6449 - val_accuracy: 0.8000\n",
      "Epoch 338/888\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6867 - accuracy: 0.8083 - val_loss: 0.6436 - val_accuracy: 0.8000\n",
      "Epoch 339/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6853 - accuracy: 0.8083 - val_loss: 0.6422 - val_accuracy: 0.8000\n",
      "Epoch 340/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.6838 - accuracy: 0.8083 - val_loss: 0.6409 - val_accuracy: 0.8000\n",
      "Epoch 341/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6824 - accuracy: 0.8083 - val_loss: 0.6395 - val_accuracy: 0.8000\n",
      "Epoch 342/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6810 - accuracy: 0.8083 - val_loss: 0.6381 - val_accuracy: 0.8000\n",
      "Epoch 343/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.6796 - accuracy: 0.8083 - val_loss: 0.6367 - val_accuracy: 0.8000\n",
      "Epoch 344/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6781 - accuracy: 0.8083 - val_loss: 0.6354 - val_accuracy: 0.8000\n",
      "Epoch 345/888\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6767 - accuracy: 0.8083 - val_loss: 0.6341 - val_accuracy: 0.8000\n",
      "Epoch 346/888\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 0.6753 - accuracy: 0.8083 - val_loss: 0.6327 - val_accuracy: 0.8000\n",
      "Epoch 347/888\n",
      "4/4 [==============================] - 0s 25ms/step - loss: 0.6739 - accuracy: 0.8083 - val_loss: 0.6314 - val_accuracy: 0.8000\n",
      "Epoch 348/888\n",
      "4/4 [==============================] - 0s 31ms/step - loss: 0.6725 - accuracy: 0.8083 - val_loss: 0.6301 - val_accuracy: 0.8000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 349/888\n",
      "4/4 [==============================] - 0s 26ms/step - loss: 0.6712 - accuracy: 0.8083 - val_loss: 0.6287 - val_accuracy: 0.8000\n",
      "Epoch 350/888\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 0.6697 - accuracy: 0.8083 - val_loss: 0.6274 - val_accuracy: 0.8000\n",
      "Epoch 351/888\n",
      "4/4 [==============================] - 0s 20ms/step - loss: 0.6684 - accuracy: 0.8083 - val_loss: 0.6261 - val_accuracy: 0.8000\n",
      "Epoch 352/888\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 0.6670 - accuracy: 0.8083 - val_loss: 0.6247 - val_accuracy: 0.8000\n",
      "Epoch 353/888\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 0.6656 - accuracy: 0.8083 - val_loss: 0.6234 - val_accuracy: 0.8000\n",
      "Epoch 354/888\n",
      "4/4 [==============================] - 0s 22ms/step - loss: 0.6643 - accuracy: 0.8083 - val_loss: 0.6221 - val_accuracy: 0.8000\n",
      "Epoch 355/888\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 0.6629 - accuracy: 0.8083 - val_loss: 0.6208 - val_accuracy: 0.8000\n",
      "Epoch 356/888\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.6616 - accuracy: 0.8167 - val_loss: 0.6196 - val_accuracy: 0.8000\n",
      "Epoch 357/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6602 - accuracy: 0.8167 - val_loss: 0.6183 - val_accuracy: 0.8000\n",
      "Epoch 358/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6588 - accuracy: 0.8167 - val_loss: 0.6170 - val_accuracy: 0.8000\n",
      "Epoch 359/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6575 - accuracy: 0.8167 - val_loss: 0.6158 - val_accuracy: 0.8000\n",
      "Epoch 360/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.6562 - accuracy: 0.8167 - val_loss: 0.6145 - val_accuracy: 0.8000\n",
      "Epoch 361/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6548 - accuracy: 0.8167 - val_loss: 0.6133 - val_accuracy: 0.8000\n",
      "Epoch 362/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.6535 - accuracy: 0.8167 - val_loss: 0.6120 - val_accuracy: 0.8000\n",
      "Epoch 363/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6522 - accuracy: 0.8167 - val_loss: 0.6108 - val_accuracy: 0.8000\n",
      "Epoch 364/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.6509 - accuracy: 0.8167 - val_loss: 0.6096 - val_accuracy: 0.8000\n",
      "Epoch 365/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.6495 - accuracy: 0.8167 - val_loss: 0.6083 - val_accuracy: 0.8000\n",
      "Epoch 366/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6482 - accuracy: 0.8167 - val_loss: 0.6071 - val_accuracy: 0.8000\n",
      "Epoch 367/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6469 - accuracy: 0.8167 - val_loss: 0.6059 - val_accuracy: 0.8000\n",
      "Epoch 368/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6458 - accuracy: 0.8167 - val_loss: 0.6047 - val_accuracy: 0.8000\n",
      "Epoch 369/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6443 - accuracy: 0.8167 - val_loss: 0.6035 - val_accuracy: 0.8000\n",
      "Epoch 370/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6431 - accuracy: 0.8167 - val_loss: 0.6023 - val_accuracy: 0.8000\n",
      "Epoch 371/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6418 - accuracy: 0.8167 - val_loss: 0.6011 - val_accuracy: 0.8000\n",
      "Epoch 372/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6405 - accuracy: 0.8167 - val_loss: 0.5999 - val_accuracy: 0.8000\n",
      "Epoch 373/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6393 - accuracy: 0.8167 - val_loss: 0.5986 - val_accuracy: 0.8000\n",
      "Epoch 374/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.6380 - accuracy: 0.8167 - val_loss: 0.5975 - val_accuracy: 0.8000\n",
      "Epoch 375/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.6368 - accuracy: 0.8167 - val_loss: 0.5962 - val_accuracy: 0.8000\n",
      "Epoch 376/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.6355 - accuracy: 0.8167 - val_loss: 0.5951 - val_accuracy: 0.8000\n",
      "Epoch 377/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6343 - accuracy: 0.8250 - val_loss: 0.5938 - val_accuracy: 0.8000\n",
      "Epoch 378/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.6330 - accuracy: 0.8250 - val_loss: 0.5927 - val_accuracy: 0.8000\n",
      "Epoch 379/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6318 - accuracy: 0.8250 - val_loss: 0.5915 - val_accuracy: 0.8000\n",
      "Epoch 380/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.6306 - accuracy: 0.8250 - val_loss: 0.5903 - val_accuracy: 0.8000\n",
      "Epoch 381/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6293 - accuracy: 0.8250 - val_loss: 0.5891 - val_accuracy: 0.8000\n",
      "Epoch 382/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.6281 - accuracy: 0.8250 - val_loss: 0.5880 - val_accuracy: 0.8000\n",
      "Epoch 383/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.6269 - accuracy: 0.8250 - val_loss: 0.5869 - val_accuracy: 0.8000\n",
      "Epoch 384/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.6257 - accuracy: 0.8250 - val_loss: 0.5858 - val_accuracy: 0.8000\n",
      "Epoch 385/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6246 - accuracy: 0.8250 - val_loss: 0.5846 - val_accuracy: 0.8000\n",
      "Epoch 386/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6233 - accuracy: 0.8250 - val_loss: 0.5835 - val_accuracy: 0.8000\n",
      "Epoch 387/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6221 - accuracy: 0.8250 - val_loss: 0.5824 - val_accuracy: 0.8000\n",
      "Epoch 388/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.6209 - accuracy: 0.8250 - val_loss: 0.5813 - val_accuracy: 0.8000\n",
      "Epoch 389/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6197 - accuracy: 0.8250 - val_loss: 0.5802 - val_accuracy: 0.8000\n",
      "Epoch 390/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.6185 - accuracy: 0.8250 - val_loss: 0.5791 - val_accuracy: 0.8000\n",
      "Epoch 391/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.6174 - accuracy: 0.8250 - val_loss: 0.5780 - val_accuracy: 0.8000\n",
      "Epoch 392/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.6162 - accuracy: 0.8250 - val_loss: 0.5769 - val_accuracy: 0.8000\n",
      "Epoch 393/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.6151 - accuracy: 0.8250 - val_loss: 0.5759 - val_accuracy: 0.8000\n",
      "Epoch 394/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.6139 - accuracy: 0.8250 - val_loss: 0.5748 - val_accuracy: 0.8000\n",
      "Epoch 395/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6128 - accuracy: 0.8250 - val_loss: 0.5737 - val_accuracy: 0.8000\n",
      "Epoch 396/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.6116 - accuracy: 0.8250 - val_loss: 0.5727 - val_accuracy: 0.8000\n",
      "Epoch 397/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.6105 - accuracy: 0.8250 - val_loss: 0.5716 - val_accuracy: 0.8000\n",
      "Epoch 398/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.6093 - accuracy: 0.8250 - val_loss: 0.5705 - val_accuracy: 0.8000\n",
      "Epoch 399/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6082 - accuracy: 0.8250 - val_loss: 0.5694 - val_accuracy: 0.8000\n",
      "Epoch 400/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6071 - accuracy: 0.8250 - val_loss: 0.5684 - val_accuracy: 0.8000\n",
      "Epoch 401/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.6060 - accuracy: 0.8250 - val_loss: 0.5673 - val_accuracy: 0.8000\n",
      "Epoch 402/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.6050 - accuracy: 0.8250 - val_loss: 0.5663 - val_accuracy: 0.8333\n",
      "Epoch 403/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.6038 - accuracy: 0.8250 - val_loss: 0.5653 - val_accuracy: 0.8333\n",
      "Epoch 404/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.6027 - accuracy: 0.8250 - val_loss: 0.5643 - val_accuracy: 0.8333\n",
      "Epoch 405/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.6016 - accuracy: 0.8250 - val_loss: 0.5632 - val_accuracy: 0.8333\n",
      "Epoch 406/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.6005 - accuracy: 0.8250 - val_loss: 0.5622 - val_accuracy: 0.8333\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 407/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5995 - accuracy: 0.8250 - val_loss: 0.5611 - val_accuracy: 0.8333\n",
      "Epoch 408/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5984 - accuracy: 0.8250 - val_loss: 0.5601 - val_accuracy: 0.8333\n",
      "Epoch 409/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5973 - accuracy: 0.8250 - val_loss: 0.5591 - val_accuracy: 0.8333\n",
      "Epoch 410/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5963 - accuracy: 0.8250 - val_loss: 0.5581 - val_accuracy: 0.8333\n",
      "Epoch 411/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.5952 - accuracy: 0.8250 - val_loss: 0.5571 - val_accuracy: 0.8333\n",
      "Epoch 412/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5942 - accuracy: 0.8250 - val_loss: 0.5561 - val_accuracy: 0.8333\n",
      "Epoch 413/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5932 - accuracy: 0.8250 - val_loss: 0.5551 - val_accuracy: 0.8333\n",
      "Epoch 414/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5920 - accuracy: 0.8250 - val_loss: 0.5542 - val_accuracy: 0.8333\n",
      "Epoch 415/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5910 - accuracy: 0.8250 - val_loss: 0.5532 - val_accuracy: 0.8333\n",
      "Epoch 416/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5900 - accuracy: 0.8250 - val_loss: 0.5523 - val_accuracy: 0.8333\n",
      "Epoch 417/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5889 - accuracy: 0.8250 - val_loss: 0.5513 - val_accuracy: 0.8333\n",
      "Epoch 418/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5879 - accuracy: 0.8250 - val_loss: 0.5504 - val_accuracy: 0.8333\n",
      "Epoch 419/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5870 - accuracy: 0.8250 - val_loss: 0.5494 - val_accuracy: 0.8333\n",
      "Epoch 420/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5859 - accuracy: 0.8250 - val_loss: 0.5485 - val_accuracy: 0.8333\n",
      "Epoch 421/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5849 - accuracy: 0.8250 - val_loss: 0.5475 - val_accuracy: 0.8333\n",
      "Epoch 422/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5839 - accuracy: 0.8333 - val_loss: 0.5466 - val_accuracy: 0.8333\n",
      "Epoch 423/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5828 - accuracy: 0.8333 - val_loss: 0.5457 - val_accuracy: 0.8333\n",
      "Epoch 424/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5819 - accuracy: 0.8333 - val_loss: 0.5447 - val_accuracy: 0.8333\n",
      "Epoch 425/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.5808 - accuracy: 0.8333 - val_loss: 0.5438 - val_accuracy: 0.8333\n",
      "Epoch 426/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5799 - accuracy: 0.8333 - val_loss: 0.5429 - val_accuracy: 0.8333\n",
      "Epoch 427/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5789 - accuracy: 0.8333 - val_loss: 0.5420 - val_accuracy: 0.8333\n",
      "Epoch 428/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5779 - accuracy: 0.8250 - val_loss: 0.5411 - val_accuracy: 0.8333\n",
      "Epoch 429/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5770 - accuracy: 0.8333 - val_loss: 0.5401 - val_accuracy: 0.8333\n",
      "Epoch 430/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5760 - accuracy: 0.8333 - val_loss: 0.5392 - val_accuracy: 0.8333\n",
      "Epoch 431/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5750 - accuracy: 0.8333 - val_loss: 0.5382 - val_accuracy: 0.8333\n",
      "Epoch 432/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5740 - accuracy: 0.8333 - val_loss: 0.5374 - val_accuracy: 0.8333\n",
      "Epoch 433/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5730 - accuracy: 0.8250 - val_loss: 0.5365 - val_accuracy: 0.8333\n",
      "Epoch 434/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5721 - accuracy: 0.8250 - val_loss: 0.5355 - val_accuracy: 0.8333\n",
      "Epoch 435/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5712 - accuracy: 0.8250 - val_loss: 0.5346 - val_accuracy: 0.8333\n",
      "Epoch 436/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5703 - accuracy: 0.8250 - val_loss: 0.5337 - val_accuracy: 0.8333\n",
      "Epoch 437/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5693 - accuracy: 0.8250 - val_loss: 0.5328 - val_accuracy: 0.8333\n",
      "Epoch 438/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5683 - accuracy: 0.8250 - val_loss: 0.5320 - val_accuracy: 0.8333\n",
      "Epoch 439/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5674 - accuracy: 0.8250 - val_loss: 0.5311 - val_accuracy: 0.8333\n",
      "Epoch 440/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5665 - accuracy: 0.8250 - val_loss: 0.5302 - val_accuracy: 0.8333\n",
      "Epoch 441/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5656 - accuracy: 0.8250 - val_loss: 0.5293 - val_accuracy: 0.8333\n",
      "Epoch 442/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5646 - accuracy: 0.8250 - val_loss: 0.5284 - val_accuracy: 0.8333\n",
      "Epoch 443/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5637 - accuracy: 0.8250 - val_loss: 0.5275 - val_accuracy: 0.8333\n",
      "Epoch 444/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5628 - accuracy: 0.8250 - val_loss: 0.5267 - val_accuracy: 0.8333\n",
      "Epoch 445/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5619 - accuracy: 0.8250 - val_loss: 0.5258 - val_accuracy: 0.8333\n",
      "Epoch 446/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5610 - accuracy: 0.8250 - val_loss: 0.5250 - val_accuracy: 0.8333\n",
      "Epoch 447/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5601 - accuracy: 0.8250 - val_loss: 0.5241 - val_accuracy: 0.8333\n",
      "Epoch 448/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5592 - accuracy: 0.8250 - val_loss: 0.5232 - val_accuracy: 0.8333\n",
      "Epoch 449/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5583 - accuracy: 0.8250 - val_loss: 0.5224 - val_accuracy: 0.8333\n",
      "Epoch 450/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5574 - accuracy: 0.8250 - val_loss: 0.5215 - val_accuracy: 0.8333\n",
      "Epoch 451/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5565 - accuracy: 0.8250 - val_loss: 0.5207 - val_accuracy: 0.8333\n",
      "Epoch 452/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5557 - accuracy: 0.8333 - val_loss: 0.5199 - val_accuracy: 0.8333\n",
      "Epoch 453/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5548 - accuracy: 0.8333 - val_loss: 0.5191 - val_accuracy: 0.8333\n",
      "Epoch 454/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5539 - accuracy: 0.8333 - val_loss: 0.5183 - val_accuracy: 0.8333\n",
      "Epoch 455/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5531 - accuracy: 0.8333 - val_loss: 0.5175 - val_accuracy: 0.8333\n",
      "Epoch 456/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5522 - accuracy: 0.8417 - val_loss: 0.5167 - val_accuracy: 0.8333\n",
      "Epoch 457/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5513 - accuracy: 0.8417 - val_loss: 0.5159 - val_accuracy: 0.8333\n",
      "Epoch 458/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5505 - accuracy: 0.8417 - val_loss: 0.5151 - val_accuracy: 0.8333\n",
      "Epoch 459/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5497 - accuracy: 0.8417 - val_loss: 0.5143 - val_accuracy: 0.8333\n",
      "Epoch 460/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5488 - accuracy: 0.8500 - val_loss: 0.5136 - val_accuracy: 0.8333\n",
      "Epoch 461/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5480 - accuracy: 0.8500 - val_loss: 0.5128 - val_accuracy: 0.8333\n",
      "Epoch 462/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5471 - accuracy: 0.8500 - val_loss: 0.5120 - val_accuracy: 0.8333\n",
      "Epoch 463/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5463 - accuracy: 0.8500 - val_loss: 0.5112 - val_accuracy: 0.8333\n",
      "Epoch 464/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.5454 - accuracy: 0.8500 - val_loss: 0.5104 - val_accuracy: 0.8333\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 465/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5447 - accuracy: 0.8500 - val_loss: 0.5097 - val_accuracy: 0.8333\n",
      "Epoch 466/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5438 - accuracy: 0.8500 - val_loss: 0.5089 - val_accuracy: 0.8333\n",
      "Epoch 467/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5430 - accuracy: 0.8500 - val_loss: 0.5081 - val_accuracy: 0.8333\n",
      "Epoch 468/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5422 - accuracy: 0.8500 - val_loss: 0.5074 - val_accuracy: 0.8333\n",
      "Epoch 469/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5414 - accuracy: 0.8500 - val_loss: 0.5066 - val_accuracy: 0.8333\n",
      "Epoch 470/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5406 - accuracy: 0.8500 - val_loss: 0.5059 - val_accuracy: 0.8333\n",
      "Epoch 471/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5398 - accuracy: 0.8500 - val_loss: 0.5051 - val_accuracy: 0.8333\n",
      "Epoch 472/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5390 - accuracy: 0.8500 - val_loss: 0.5043 - val_accuracy: 0.8333\n",
      "Epoch 473/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5381 - accuracy: 0.8500 - val_loss: 0.5036 - val_accuracy: 0.8333\n",
      "Epoch 474/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5374 - accuracy: 0.8500 - val_loss: 0.5028 - val_accuracy: 0.8333\n",
      "Epoch 475/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5366 - accuracy: 0.8500 - val_loss: 0.5020 - val_accuracy: 0.8333\n",
      "Epoch 476/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5358 - accuracy: 0.8500 - val_loss: 0.5013 - val_accuracy: 0.8333\n",
      "Epoch 477/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5351 - accuracy: 0.8500 - val_loss: 0.5005 - val_accuracy: 0.8333\n",
      "Epoch 478/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5343 - accuracy: 0.8500 - val_loss: 0.4997 - val_accuracy: 0.8333\n",
      "Epoch 479/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5335 - accuracy: 0.8500 - val_loss: 0.4990 - val_accuracy: 0.8333\n",
      "Epoch 480/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5327 - accuracy: 0.8500 - val_loss: 0.4983 - val_accuracy: 0.8333\n",
      "Epoch 481/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5319 - accuracy: 0.8500 - val_loss: 0.4975 - val_accuracy: 0.8333\n",
      "Epoch 482/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5312 - accuracy: 0.8500 - val_loss: 0.4968 - val_accuracy: 0.8333\n",
      "Epoch 483/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5304 - accuracy: 0.8500 - val_loss: 0.4961 - val_accuracy: 0.8333\n",
      "Epoch 484/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5297 - accuracy: 0.8500 - val_loss: 0.4954 - val_accuracy: 0.8333\n",
      "Epoch 485/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5289 - accuracy: 0.8500 - val_loss: 0.4947 - val_accuracy: 0.8333\n",
      "Epoch 486/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5281 - accuracy: 0.8500 - val_loss: 0.4939 - val_accuracy: 0.8333\n",
      "Epoch 487/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5275 - accuracy: 0.8500 - val_loss: 0.4932 - val_accuracy: 0.8333\n",
      "Epoch 488/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5267 - accuracy: 0.8500 - val_loss: 0.4925 - val_accuracy: 0.8333\n",
      "Epoch 489/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.5259 - accuracy: 0.8500 - val_loss: 0.4918 - val_accuracy: 0.8333\n",
      "Epoch 490/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5252 - accuracy: 0.8500 - val_loss: 0.4912 - val_accuracy: 0.8667\n",
      "Epoch 491/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5245 - accuracy: 0.8500 - val_loss: 0.4905 - val_accuracy: 0.8667\n",
      "Epoch 492/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5237 - accuracy: 0.8500 - val_loss: 0.4898 - val_accuracy: 0.8667\n",
      "Epoch 493/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5230 - accuracy: 0.8500 - val_loss: 0.4891 - val_accuracy: 0.8667\n",
      "Epoch 494/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5223 - accuracy: 0.8500 - val_loss: 0.4884 - val_accuracy: 0.8667\n",
      "Epoch 495/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5216 - accuracy: 0.8500 - val_loss: 0.4877 - val_accuracy: 0.8667\n",
      "Epoch 496/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5208 - accuracy: 0.8500 - val_loss: 0.4870 - val_accuracy: 0.8667\n",
      "Epoch 497/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5201 - accuracy: 0.8500 - val_loss: 0.4863 - val_accuracy: 0.8667\n",
      "Epoch 498/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5194 - accuracy: 0.8500 - val_loss: 0.4856 - val_accuracy: 0.8667\n",
      "Epoch 499/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5187 - accuracy: 0.8500 - val_loss: 0.4850 - val_accuracy: 0.8667\n",
      "Epoch 500/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5180 - accuracy: 0.8500 - val_loss: 0.4843 - val_accuracy: 0.8667\n",
      "Epoch 501/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5172 - accuracy: 0.8500 - val_loss: 0.4836 - val_accuracy: 0.8667\n",
      "Epoch 502/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5166 - accuracy: 0.8500 - val_loss: 0.4829 - val_accuracy: 0.8667\n",
      "Epoch 503/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5160 - accuracy: 0.8500 - val_loss: 0.4823 - val_accuracy: 0.8667\n",
      "Epoch 504/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5152 - accuracy: 0.8500 - val_loss: 0.4816 - val_accuracy: 0.8667\n",
      "Epoch 505/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5145 - accuracy: 0.8500 - val_loss: 0.4810 - val_accuracy: 0.8667\n",
      "Epoch 506/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5138 - accuracy: 0.8500 - val_loss: 0.4803 - val_accuracy: 0.8667\n",
      "Epoch 507/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5131 - accuracy: 0.8500 - val_loss: 0.4797 - val_accuracy: 0.8667\n",
      "Epoch 508/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5125 - accuracy: 0.8500 - val_loss: 0.4790 - val_accuracy: 0.8667\n",
      "Epoch 509/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5118 - accuracy: 0.8583 - val_loss: 0.4784 - val_accuracy: 0.8667\n",
      "Epoch 510/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5111 - accuracy: 0.8583 - val_loss: 0.4777 - val_accuracy: 0.8667\n",
      "Epoch 511/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5105 - accuracy: 0.8583 - val_loss: 0.4770 - val_accuracy: 0.8667\n",
      "Epoch 512/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5098 - accuracy: 0.8583 - val_loss: 0.4765 - val_accuracy: 0.8667\n",
      "Epoch 513/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5091 - accuracy: 0.8583 - val_loss: 0.4759 - val_accuracy: 0.8667\n",
      "Epoch 514/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5085 - accuracy: 0.8583 - val_loss: 0.4752 - val_accuracy: 0.8667\n",
      "Epoch 515/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5079 - accuracy: 0.8583 - val_loss: 0.4745 - val_accuracy: 0.8667\n",
      "Epoch 516/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5071 - accuracy: 0.8583 - val_loss: 0.4739 - val_accuracy: 0.8667\n",
      "Epoch 517/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.5065 - accuracy: 0.8583 - val_loss: 0.4733 - val_accuracy: 0.8667\n",
      "Epoch 518/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5058 - accuracy: 0.8583 - val_loss: 0.4727 - val_accuracy: 0.8667\n",
      "Epoch 519/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5051 - accuracy: 0.8583 - val_loss: 0.4721 - val_accuracy: 0.8667\n",
      "Epoch 520/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5045 - accuracy: 0.8583 - val_loss: 0.4715 - val_accuracy: 0.8667\n",
      "Epoch 521/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5039 - accuracy: 0.8583 - val_loss: 0.4708 - val_accuracy: 0.8667\n",
      "Epoch 522/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5032 - accuracy: 0.8583 - val_loss: 0.4702 - val_accuracy: 0.8667\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 523/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5026 - accuracy: 0.8583 - val_loss: 0.4696 - val_accuracy: 0.8667\n",
      "Epoch 524/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.5019 - accuracy: 0.8583 - val_loss: 0.4690 - val_accuracy: 0.8667\n",
      "Epoch 525/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5013 - accuracy: 0.8583 - val_loss: 0.4683 - val_accuracy: 0.8667\n",
      "Epoch 526/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.5007 - accuracy: 0.8583 - val_loss: 0.4677 - val_accuracy: 0.8667\n",
      "Epoch 527/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.5001 - accuracy: 0.8583 - val_loss: 0.4671 - val_accuracy: 0.8667\n",
      "Epoch 528/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4994 - accuracy: 0.8583 - val_loss: 0.4665 - val_accuracy: 0.8667\n",
      "Epoch 529/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4988 - accuracy: 0.8583 - val_loss: 0.4659 - val_accuracy: 0.8667\n",
      "Epoch 530/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4982 - accuracy: 0.8583 - val_loss: 0.4653 - val_accuracy: 0.8667\n",
      "Epoch 531/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4976 - accuracy: 0.8583 - val_loss: 0.4647 - val_accuracy: 0.8667\n",
      "Epoch 532/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4970 - accuracy: 0.8583 - val_loss: 0.4641 - val_accuracy: 0.8667\n",
      "Epoch 533/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4963 - accuracy: 0.8583 - val_loss: 0.4635 - val_accuracy: 0.8667\n",
      "Epoch 534/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4957 - accuracy: 0.8583 - val_loss: 0.4629 - val_accuracy: 0.8667\n",
      "Epoch 535/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4951 - accuracy: 0.8583 - val_loss: 0.4623 - val_accuracy: 0.8667\n",
      "Epoch 536/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4945 - accuracy: 0.8583 - val_loss: 0.4617 - val_accuracy: 0.8667\n",
      "Epoch 537/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4939 - accuracy: 0.8583 - val_loss: 0.4611 - val_accuracy: 0.8667\n",
      "Epoch 538/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4933 - accuracy: 0.8583 - val_loss: 0.4605 - val_accuracy: 0.8667\n",
      "Epoch 539/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4927 - accuracy: 0.8583 - val_loss: 0.4600 - val_accuracy: 0.8667\n",
      "Epoch 540/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4921 - accuracy: 0.8583 - val_loss: 0.4594 - val_accuracy: 0.8667\n",
      "Epoch 541/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4915 - accuracy: 0.8583 - val_loss: 0.4588 - val_accuracy: 0.8667\n",
      "Epoch 542/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4909 - accuracy: 0.8583 - val_loss: 0.4582 - val_accuracy: 0.8667\n",
      "Epoch 543/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4904 - accuracy: 0.8583 - val_loss: 0.4576 - val_accuracy: 0.8667\n",
      "Epoch 544/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.4898 - accuracy: 0.8583 - val_loss: 0.4571 - val_accuracy: 0.8667\n",
      "Epoch 545/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4892 - accuracy: 0.8583 - val_loss: 0.4565 - val_accuracy: 0.8667\n",
      "Epoch 546/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4886 - accuracy: 0.8583 - val_loss: 0.4559 - val_accuracy: 0.8667\n",
      "Epoch 547/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4880 - accuracy: 0.8583 - val_loss: 0.4553 - val_accuracy: 0.8667\n",
      "Epoch 548/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.4875 - accuracy: 0.8583 - val_loss: 0.4548 - val_accuracy: 0.8667\n",
      "Epoch 549/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4869 - accuracy: 0.8583 - val_loss: 0.4542 - val_accuracy: 0.8667\n",
      "Epoch 550/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4863 - accuracy: 0.8583 - val_loss: 0.4537 - val_accuracy: 0.8667\n",
      "Epoch 551/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4857 - accuracy: 0.8583 - val_loss: 0.4532 - val_accuracy: 0.8667\n",
      "Epoch 552/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4852 - accuracy: 0.8583 - val_loss: 0.4526 - val_accuracy: 0.8667\n",
      "Epoch 553/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4846 - accuracy: 0.8583 - val_loss: 0.4521 - val_accuracy: 0.8667\n",
      "Epoch 554/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4841 - accuracy: 0.8583 - val_loss: 0.4516 - val_accuracy: 0.8667\n",
      "Epoch 555/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4835 - accuracy: 0.8583 - val_loss: 0.4510 - val_accuracy: 0.8667\n",
      "Epoch 556/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4830 - accuracy: 0.8583 - val_loss: 0.4505 - val_accuracy: 0.8667\n",
      "Epoch 557/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4824 - accuracy: 0.8583 - val_loss: 0.4500 - val_accuracy: 0.8667\n",
      "Epoch 558/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.4818 - accuracy: 0.8583 - val_loss: 0.4495 - val_accuracy: 0.8667\n",
      "Epoch 559/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4813 - accuracy: 0.8583 - val_loss: 0.4490 - val_accuracy: 0.8667\n",
      "Epoch 560/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4807 - accuracy: 0.8583 - val_loss: 0.4485 - val_accuracy: 0.8667\n",
      "Epoch 561/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4802 - accuracy: 0.8583 - val_loss: 0.4479 - val_accuracy: 0.8667\n",
      "Epoch 562/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4797 - accuracy: 0.8583 - val_loss: 0.4474 - val_accuracy: 0.8667\n",
      "Epoch 563/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4791 - accuracy: 0.8583 - val_loss: 0.4469 - val_accuracy: 0.8667\n",
      "Epoch 564/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4786 - accuracy: 0.8583 - val_loss: 0.4464 - val_accuracy: 0.8667\n",
      "Epoch 565/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4780 - accuracy: 0.8583 - val_loss: 0.4458 - val_accuracy: 0.8667\n",
      "Epoch 566/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4775 - accuracy: 0.8583 - val_loss: 0.4453 - val_accuracy: 0.8667\n",
      "Epoch 567/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4770 - accuracy: 0.8583 - val_loss: 0.4448 - val_accuracy: 0.8667\n",
      "Epoch 568/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4766 - accuracy: 0.8583 - val_loss: 0.4443 - val_accuracy: 0.8667\n",
      "Epoch 569/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.4759 - accuracy: 0.8583 - val_loss: 0.4438 - val_accuracy: 0.8667\n",
      "Epoch 570/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4754 - accuracy: 0.8583 - val_loss: 0.4432 - val_accuracy: 0.8667\n",
      "Epoch 571/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4749 - accuracy: 0.8583 - val_loss: 0.4427 - val_accuracy: 0.8667\n",
      "Epoch 572/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4744 - accuracy: 0.8583 - val_loss: 0.4422 - val_accuracy: 0.8667\n",
      "Epoch 573/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4739 - accuracy: 0.8583 - val_loss: 0.4417 - val_accuracy: 0.8667\n",
      "Epoch 574/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4733 - accuracy: 0.8583 - val_loss: 0.4412 - val_accuracy: 0.8667\n",
      "Epoch 575/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4728 - accuracy: 0.8583 - val_loss: 0.4407 - val_accuracy: 0.8667\n",
      "Epoch 576/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4723 - accuracy: 0.8583 - val_loss: 0.4402 - val_accuracy: 0.8667\n",
      "Epoch 577/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.4718 - accuracy: 0.8583 - val_loss: 0.4397 - val_accuracy: 0.8667\n",
      "Epoch 578/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4714 - accuracy: 0.8583 - val_loss: 0.4393 - val_accuracy: 0.8667\n",
      "Epoch 579/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4708 - accuracy: 0.8583 - val_loss: 0.4387 - val_accuracy: 0.8667\n",
      "Epoch 580/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4702 - accuracy: 0.8583 - val_loss: 0.4382 - val_accuracy: 0.8667\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 581/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4697 - accuracy: 0.8583 - val_loss: 0.4377 - val_accuracy: 0.8667\n",
      "Epoch 582/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4693 - accuracy: 0.8583 - val_loss: 0.4372 - val_accuracy: 0.8667\n",
      "Epoch 583/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.8583 - val_loss: 0.4368 - val_accuracy: 0.8667\n",
      "Epoch 584/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.8583 - val_loss: 0.4363 - val_accuracy: 0.8667\n",
      "Epoch 585/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4677 - accuracy: 0.8583 - val_loss: 0.4358 - val_accuracy: 0.8667\n",
      "Epoch 586/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4672 - accuracy: 0.8583 - val_loss: 0.4354 - val_accuracy: 0.8667\n",
      "Epoch 587/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4668 - accuracy: 0.8583 - val_loss: 0.4349 - val_accuracy: 0.8667\n",
      "Epoch 588/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.4664 - accuracy: 0.8583 - val_loss: 0.4344 - val_accuracy: 0.8667\n",
      "Epoch 589/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4658 - accuracy: 0.8583 - val_loss: 0.4339 - val_accuracy: 0.8667\n",
      "Epoch 590/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4653 - accuracy: 0.8583 - val_loss: 0.4334 - val_accuracy: 0.8667\n",
      "Epoch 591/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4648 - accuracy: 0.8583 - val_loss: 0.4329 - val_accuracy: 0.8667\n",
      "Epoch 592/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4643 - accuracy: 0.8583 - val_loss: 0.4325 - val_accuracy: 0.8667\n",
      "Epoch 593/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4638 - accuracy: 0.8667 - val_loss: 0.4320 - val_accuracy: 0.8667\n",
      "Epoch 594/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4633 - accuracy: 0.8667 - val_loss: 0.4315 - val_accuracy: 0.8667\n",
      "Epoch 595/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4629 - accuracy: 0.8583 - val_loss: 0.4310 - val_accuracy: 0.8667\n",
      "Epoch 596/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4624 - accuracy: 0.8667 - val_loss: 0.4305 - val_accuracy: 0.8667\n",
      "Epoch 597/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4619 - accuracy: 0.8667 - val_loss: 0.4301 - val_accuracy: 0.8667\n",
      "Epoch 598/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4615 - accuracy: 0.8667 - val_loss: 0.4296 - val_accuracy: 0.8667\n",
      "Epoch 599/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4609 - accuracy: 0.8667 - val_loss: 0.4292 - val_accuracy: 0.8667\n",
      "Epoch 600/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4605 - accuracy: 0.8667 - val_loss: 0.4287 - val_accuracy: 0.8667\n",
      "Epoch 601/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4600 - accuracy: 0.8667 - val_loss: 0.4283 - val_accuracy: 0.8667\n",
      "Epoch 602/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.4595 - accuracy: 0.8667 - val_loss: 0.4278 - val_accuracy: 0.8667\n",
      "Epoch 603/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4591 - accuracy: 0.8667 - val_loss: 0.4273 - val_accuracy: 0.8667\n",
      "Epoch 604/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4586 - accuracy: 0.8667 - val_loss: 0.4269 - val_accuracy: 0.8667\n",
      "Epoch 605/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.4581 - accuracy: 0.8667 - val_loss: 0.4264 - val_accuracy: 0.8667\n",
      "Epoch 606/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4577 - accuracy: 0.8750 - val_loss: 0.4260 - val_accuracy: 0.8667\n",
      "Epoch 607/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.4572 - accuracy: 0.8750 - val_loss: 0.4256 - val_accuracy: 0.8667\n",
      "Epoch 608/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4567 - accuracy: 0.8750 - val_loss: 0.4252 - val_accuracy: 0.8667\n",
      "Epoch 609/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4563 - accuracy: 0.8750 - val_loss: 0.4247 - val_accuracy: 0.9000\n",
      "Epoch 610/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4558 - accuracy: 0.8750 - val_loss: 0.4243 - val_accuracy: 0.9000\n",
      "Epoch 611/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4554 - accuracy: 0.8750 - val_loss: 0.4239 - val_accuracy: 0.9000\n",
      "Epoch 612/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4549 - accuracy: 0.8750 - val_loss: 0.4234 - val_accuracy: 0.9000\n",
      "Epoch 613/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4545 - accuracy: 0.8833 - val_loss: 0.4230 - val_accuracy: 0.9000\n",
      "Epoch 614/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4540 - accuracy: 0.8750 - val_loss: 0.4226 - val_accuracy: 0.9000\n",
      "Epoch 615/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4535 - accuracy: 0.8750 - val_loss: 0.4221 - val_accuracy: 0.9000\n",
      "Epoch 616/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4531 - accuracy: 0.8750 - val_loss: 0.4216 - val_accuracy: 0.9000\n",
      "Epoch 617/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4526 - accuracy: 0.8750 - val_loss: 0.4212 - val_accuracy: 0.9000\n",
      "Epoch 618/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4522 - accuracy: 0.8750 - val_loss: 0.4207 - val_accuracy: 0.8667\n",
      "Epoch 619/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4518 - accuracy: 0.8750 - val_loss: 0.4203 - val_accuracy: 0.8667\n",
      "Epoch 620/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4514 - accuracy: 0.8750 - val_loss: 0.4198 - val_accuracy: 0.8667\n",
      "Epoch 621/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4509 - accuracy: 0.8750 - val_loss: 0.4194 - val_accuracy: 0.8667\n",
      "Epoch 622/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4505 - accuracy: 0.8750 - val_loss: 0.4189 - val_accuracy: 0.8667\n",
      "Epoch 623/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4500 - accuracy: 0.8750 - val_loss: 0.4185 - val_accuracy: 0.8667\n",
      "Epoch 624/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4496 - accuracy: 0.8750 - val_loss: 0.4181 - val_accuracy: 0.8667\n",
      "Epoch 625/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.4492 - accuracy: 0.8750 - val_loss: 0.4177 - val_accuracy: 0.9000\n",
      "Epoch 626/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4487 - accuracy: 0.8750 - val_loss: 0.4173 - val_accuracy: 0.9000\n",
      "Epoch 627/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.4483 - accuracy: 0.8750 - val_loss: 0.4169 - val_accuracy: 0.9000\n",
      "Epoch 628/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.4479 - accuracy: 0.8750 - val_loss: 0.4164 - val_accuracy: 0.9000\n",
      "Epoch 629/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4474 - accuracy: 0.8750 - val_loss: 0.4160 - val_accuracy: 0.9000\n",
      "Epoch 630/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4470 - accuracy: 0.8750 - val_loss: 0.4156 - val_accuracy: 0.9000\n",
      "Epoch 631/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4466 - accuracy: 0.8750 - val_loss: 0.4152 - val_accuracy: 0.9000\n",
      "Epoch 632/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4461 - accuracy: 0.8750 - val_loss: 0.4148 - val_accuracy: 0.9000\n",
      "Epoch 633/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4457 - accuracy: 0.8750 - val_loss: 0.4144 - val_accuracy: 0.9000\n",
      "Epoch 634/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.4453 - accuracy: 0.8750 - val_loss: 0.4140 - val_accuracy: 0.9000\n",
      "Epoch 635/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4449 - accuracy: 0.8750 - val_loss: 0.4136 - val_accuracy: 0.9000\n",
      "Epoch 636/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4445 - accuracy: 0.8750 - val_loss: 0.4132 - val_accuracy: 0.9000\n",
      "Epoch 637/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4440 - accuracy: 0.8750 - val_loss: 0.4127 - val_accuracy: 0.9000\n",
      "Epoch 638/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4436 - accuracy: 0.8750 - val_loss: 0.4123 - val_accuracy: 0.9000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 639/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4432 - accuracy: 0.8750 - val_loss: 0.4119 - val_accuracy: 0.9000\n",
      "Epoch 640/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4428 - accuracy: 0.8750 - val_loss: 0.4115 - val_accuracy: 0.9000\n",
      "Epoch 641/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4424 - accuracy: 0.8750 - val_loss: 0.4111 - val_accuracy: 0.9000\n",
      "Epoch 642/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4420 - accuracy: 0.8750 - val_loss: 0.4107 - val_accuracy: 0.9000\n",
      "Epoch 643/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4416 - accuracy: 0.8750 - val_loss: 0.4103 - val_accuracy: 0.9000\n",
      "Epoch 644/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4411 - accuracy: 0.8833 - val_loss: 0.4099 - val_accuracy: 0.9000\n",
      "Epoch 645/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4407 - accuracy: 0.8833 - val_loss: 0.4095 - val_accuracy: 0.9000\n",
      "Epoch 646/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4403 - accuracy: 0.8833 - val_loss: 0.4091 - val_accuracy: 0.9000\n",
      "Epoch 647/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4400 - accuracy: 0.8833 - val_loss: 0.4087 - val_accuracy: 0.9000\n",
      "Epoch 648/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4395 - accuracy: 0.8750 - val_loss: 0.4084 - val_accuracy: 0.9000\n",
      "Epoch 649/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4391 - accuracy: 0.8833 - val_loss: 0.4080 - val_accuracy: 0.9000\n",
      "Epoch 650/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4387 - accuracy: 0.8833 - val_loss: 0.4076 - val_accuracy: 0.9000\n",
      "Epoch 651/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4383 - accuracy: 0.8833 - val_loss: 0.4072 - val_accuracy: 0.9000\n",
      "Epoch 652/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4379 - accuracy: 0.8833 - val_loss: 0.4068 - val_accuracy: 0.9000\n",
      "Epoch 653/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4375 - accuracy: 0.8833 - val_loss: 0.4065 - val_accuracy: 0.9000\n",
      "Epoch 654/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4371 - accuracy: 0.8833 - val_loss: 0.4061 - val_accuracy: 0.9000\n",
      "Epoch 655/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4367 - accuracy: 0.8833 - val_loss: 0.4057 - val_accuracy: 0.9000\n",
      "Epoch 656/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4364 - accuracy: 0.8917 - val_loss: 0.4054 - val_accuracy: 0.9000\n",
      "Epoch 657/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4360 - accuracy: 0.8917 - val_loss: 0.4050 - val_accuracy: 0.9000\n",
      "Epoch 658/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4355 - accuracy: 0.8833 - val_loss: 0.4046 - val_accuracy: 0.9000\n",
      "Epoch 659/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4352 - accuracy: 0.8833 - val_loss: 0.4042 - val_accuracy: 0.9000\n",
      "Epoch 660/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4348 - accuracy: 0.8833 - val_loss: 0.4038 - val_accuracy: 0.9000\n",
      "Epoch 661/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4344 - accuracy: 0.8833 - val_loss: 0.4034 - val_accuracy: 0.9000\n",
      "Epoch 662/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4340 - accuracy: 0.8833 - val_loss: 0.4031 - val_accuracy: 0.9000\n",
      "Epoch 663/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4336 - accuracy: 0.8833 - val_loss: 0.4027 - val_accuracy: 0.9000\n",
      "Epoch 664/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4332 - accuracy: 0.8917 - val_loss: 0.4023 - val_accuracy: 0.9000\n",
      "Epoch 665/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4330 - accuracy: 0.8833 - val_loss: 0.4019 - val_accuracy: 0.9000\n",
      "Epoch 666/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4325 - accuracy: 0.8833 - val_loss: 0.4015 - val_accuracy: 0.9000\n",
      "Epoch 667/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4321 - accuracy: 0.8833 - val_loss: 0.4011 - val_accuracy: 0.9000\n",
      "Epoch 668/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4317 - accuracy: 0.8917 - val_loss: 0.4008 - val_accuracy: 0.9000\n",
      "Epoch 669/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4313 - accuracy: 0.8917 - val_loss: 0.4004 - val_accuracy: 0.9000\n",
      "Epoch 670/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.4309 - accuracy: 0.8917 - val_loss: 0.4001 - val_accuracy: 0.9000\n",
      "Epoch 671/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4306 - accuracy: 0.8917 - val_loss: 0.3997 - val_accuracy: 0.9000\n",
      "Epoch 672/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4303 - accuracy: 0.8917 - val_loss: 0.3994 - val_accuracy: 0.9000\n",
      "Epoch 673/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4300 - accuracy: 0.8917 - val_loss: 0.3991 - val_accuracy: 0.9000\n",
      "Epoch 674/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4295 - accuracy: 0.8917 - val_loss: 0.3987 - val_accuracy: 0.9000\n",
      "Epoch 675/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4291 - accuracy: 0.8917 - val_loss: 0.3983 - val_accuracy: 0.9000\n",
      "Epoch 676/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.4288 - accuracy: 0.8917 - val_loss: 0.3979 - val_accuracy: 0.9000\n",
      "Epoch 677/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.4284 - accuracy: 0.8917 - val_loss: 0.3976 - val_accuracy: 0.9000\n",
      "Epoch 678/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4281 - accuracy: 0.8917 - val_loss: 0.3972 - val_accuracy: 0.9000\n",
      "Epoch 679/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4276 - accuracy: 0.8917 - val_loss: 0.3968 - val_accuracy: 0.9000\n",
      "Epoch 680/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4273 - accuracy: 0.8917 - val_loss: 0.3965 - val_accuracy: 0.9000\n",
      "Epoch 681/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4269 - accuracy: 0.8917 - val_loss: 0.3961 - val_accuracy: 0.9000\n",
      "Epoch 682/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4265 - accuracy: 0.8917 - val_loss: 0.3958 - val_accuracy: 0.9000\n",
      "Epoch 683/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4262 - accuracy: 0.8917 - val_loss: 0.3954 - val_accuracy: 0.9000\n",
      "Epoch 684/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4258 - accuracy: 0.8917 - val_loss: 0.3951 - val_accuracy: 0.9000\n",
      "Epoch 685/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4254 - accuracy: 0.8917 - val_loss: 0.3947 - val_accuracy: 0.9000\n",
      "Epoch 686/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4251 - accuracy: 0.8917 - val_loss: 0.3944 - val_accuracy: 0.9000\n",
      "Epoch 687/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4247 - accuracy: 0.8917 - val_loss: 0.3940 - val_accuracy: 0.9000\n",
      "Epoch 688/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4244 - accuracy: 0.8917 - val_loss: 0.3937 - val_accuracy: 0.9000\n",
      "Epoch 689/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4240 - accuracy: 0.8917 - val_loss: 0.3934 - val_accuracy: 0.9000\n",
      "Epoch 690/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4237 - accuracy: 0.8917 - val_loss: 0.3930 - val_accuracy: 0.9000\n",
      "Epoch 691/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4233 - accuracy: 0.8917 - val_loss: 0.3926 - val_accuracy: 0.9000\n",
      "Epoch 692/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4230 - accuracy: 0.8917 - val_loss: 0.3923 - val_accuracy: 0.9000\n",
      "Epoch 693/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4226 - accuracy: 0.8917 - val_loss: 0.3919 - val_accuracy: 0.9000\n",
      "Epoch 694/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4223 - accuracy: 0.8917 - val_loss: 0.3915 - val_accuracy: 0.9000\n",
      "Epoch 695/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4219 - accuracy: 0.8917 - val_loss: 0.3912 - val_accuracy: 0.9000\n",
      "Epoch 696/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4216 - accuracy: 0.8917 - val_loss: 0.3908 - val_accuracy: 0.9000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 697/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4212 - accuracy: 0.8917 - val_loss: 0.3905 - val_accuracy: 0.9000\n",
      "Epoch 698/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4209 - accuracy: 0.8917 - val_loss: 0.3901 - val_accuracy: 0.9000\n",
      "Epoch 699/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4205 - accuracy: 0.8917 - val_loss: 0.3898 - val_accuracy: 0.9000\n",
      "Epoch 700/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4202 - accuracy: 0.8917 - val_loss: 0.3894 - val_accuracy: 0.9000\n",
      "Epoch 701/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4198 - accuracy: 0.8917 - val_loss: 0.3891 - val_accuracy: 0.9000\n",
      "Epoch 702/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4195 - accuracy: 0.8917 - val_loss: 0.3887 - val_accuracy: 0.9000\n",
      "Epoch 703/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4192 - accuracy: 0.8917 - val_loss: 0.3884 - val_accuracy: 0.9000\n",
      "Epoch 704/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4188 - accuracy: 0.8917 - val_loss: 0.3881 - val_accuracy: 0.9000\n",
      "Epoch 705/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4185 - accuracy: 0.8917 - val_loss: 0.3877 - val_accuracy: 0.9000\n",
      "Epoch 706/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4181 - accuracy: 0.8917 - val_loss: 0.3874 - val_accuracy: 0.9000\n",
      "Epoch 707/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4177 - accuracy: 0.8917 - val_loss: 0.3871 - val_accuracy: 0.9000\n",
      "Epoch 708/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4174 - accuracy: 0.8917 - val_loss: 0.3867 - val_accuracy: 0.9000\n",
      "Epoch 709/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4171 - accuracy: 0.8917 - val_loss: 0.3864 - val_accuracy: 0.9000\n",
      "Epoch 710/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4167 - accuracy: 0.8917 - val_loss: 0.3860 - val_accuracy: 0.9000\n",
      "Epoch 711/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4164 - accuracy: 0.8917 - val_loss: 0.3857 - val_accuracy: 0.9000\n",
      "Epoch 712/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4161 - accuracy: 0.8917 - val_loss: 0.3854 - val_accuracy: 0.9000\n",
      "Epoch 713/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.4157 - accuracy: 0.8917 - val_loss: 0.3850 - val_accuracy: 0.9000\n",
      "Epoch 714/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.4156 - accuracy: 0.8917 - val_loss: 0.3847 - val_accuracy: 0.9000\n",
      "Epoch 715/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4150 - accuracy: 0.9000 - val_loss: 0.3843 - val_accuracy: 0.9000\n",
      "Epoch 716/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4147 - accuracy: 0.8917 - val_loss: 0.3840 - val_accuracy: 0.9000\n",
      "Epoch 717/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4144 - accuracy: 0.8917 - val_loss: 0.3836 - val_accuracy: 0.9000\n",
      "Epoch 718/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4141 - accuracy: 0.8917 - val_loss: 0.3833 - val_accuracy: 0.9000\n",
      "Epoch 719/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4137 - accuracy: 0.8917 - val_loss: 0.3829 - val_accuracy: 0.9000\n",
      "Epoch 720/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4134 - accuracy: 0.8917 - val_loss: 0.3826 - val_accuracy: 0.9000\n",
      "Epoch 721/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4131 - accuracy: 0.9000 - val_loss: 0.3823 - val_accuracy: 0.9000\n",
      "Epoch 722/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4128 - accuracy: 0.9000 - val_loss: 0.3820 - val_accuracy: 0.9000\n",
      "Epoch 723/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4124 - accuracy: 0.9000 - val_loss: 0.3816 - val_accuracy: 0.9000\n",
      "Epoch 724/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4121 - accuracy: 0.9000 - val_loss: 0.3813 - val_accuracy: 0.9000\n",
      "Epoch 725/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4117 - accuracy: 0.9000 - val_loss: 0.3810 - val_accuracy: 0.9000\n",
      "Epoch 726/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4114 - accuracy: 0.9000 - val_loss: 0.3806 - val_accuracy: 0.9000\n",
      "Epoch 727/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4111 - accuracy: 0.9000 - val_loss: 0.3803 - val_accuracy: 0.9000\n",
      "Epoch 728/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4108 - accuracy: 0.9000 - val_loss: 0.3799 - val_accuracy: 0.9000\n",
      "Epoch 729/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4104 - accuracy: 0.9000 - val_loss: 0.3796 - val_accuracy: 0.9000\n",
      "Epoch 730/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4101 - accuracy: 0.9000 - val_loss: 0.3793 - val_accuracy: 0.9000\n",
      "Epoch 731/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4098 - accuracy: 0.9000 - val_loss: 0.3789 - val_accuracy: 0.9000\n",
      "Epoch 732/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4095 - accuracy: 0.9000 - val_loss: 0.3786 - val_accuracy: 0.9000\n",
      "Epoch 733/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4091 - accuracy: 0.9000 - val_loss: 0.3783 - val_accuracy: 0.9000\n",
      "Epoch 734/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4088 - accuracy: 0.9000 - val_loss: 0.3780 - val_accuracy: 0.9000\n",
      "Epoch 735/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4085 - accuracy: 0.9167 - val_loss: 0.3777 - val_accuracy: 0.9000\n",
      "Epoch 736/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4082 - accuracy: 0.9250 - val_loss: 0.3774 - val_accuracy: 0.9000\n",
      "Epoch 737/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4079 - accuracy: 0.9250 - val_loss: 0.3771 - val_accuracy: 0.9000\n",
      "Epoch 738/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4076 - accuracy: 0.9250 - val_loss: 0.3768 - val_accuracy: 0.9000\n",
      "Epoch 739/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4072 - accuracy: 0.9250 - val_loss: 0.3764 - val_accuracy: 0.9000\n",
      "Epoch 740/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4070 - accuracy: 0.9250 - val_loss: 0.3761 - val_accuracy: 0.9000\n",
      "Epoch 741/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4066 - accuracy: 0.9250 - val_loss: 0.3757 - val_accuracy: 0.9000\n",
      "Epoch 742/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4063 - accuracy: 0.9250 - val_loss: 0.3754 - val_accuracy: 0.9000\n",
      "Epoch 743/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4060 - accuracy: 0.9250 - val_loss: 0.3751 - val_accuracy: 0.9000\n",
      "Epoch 744/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4056 - accuracy: 0.9250 - val_loss: 0.3747 - val_accuracy: 0.9000\n",
      "Epoch 745/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4053 - accuracy: 0.9250 - val_loss: 0.3744 - val_accuracy: 0.9000\n",
      "Epoch 746/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4050 - accuracy: 0.9167 - val_loss: 0.3740 - val_accuracy: 0.9000\n",
      "Epoch 747/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4047 - accuracy: 0.9167 - val_loss: 0.3737 - val_accuracy: 0.9000\n",
      "Epoch 748/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4045 - accuracy: 0.9167 - val_loss: 0.3734 - val_accuracy: 0.9000\n",
      "Epoch 749/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.4042 - accuracy: 0.9083 - val_loss: 0.3730 - val_accuracy: 0.9000\n",
      "Epoch 750/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.4039 - accuracy: 0.9083 - val_loss: 0.3728 - val_accuracy: 0.9000\n",
      "Epoch 751/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.4035 - accuracy: 0.9000 - val_loss: 0.3724 - val_accuracy: 0.9000\n",
      "Epoch 752/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4032 - accuracy: 0.9000 - val_loss: 0.3721 - val_accuracy: 0.9000\n",
      "Epoch 753/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.4028 - accuracy: 0.9083 - val_loss: 0.3718 - val_accuracy: 0.9000\n",
      "Epoch 754/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4026 - accuracy: 0.9083 - val_loss: 0.3715 - val_accuracy: 0.9000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 755/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4022 - accuracy: 0.9083 - val_loss: 0.3712 - val_accuracy: 0.9000\n",
      "Epoch 756/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4020 - accuracy: 0.9000 - val_loss: 0.3709 - val_accuracy: 0.9000\n",
      "Epoch 757/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4016 - accuracy: 0.9000 - val_loss: 0.3706 - val_accuracy: 0.9000\n",
      "Epoch 758/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.4013 - accuracy: 0.9000 - val_loss: 0.3703 - val_accuracy: 0.9000\n",
      "Epoch 759/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4011 - accuracy: 0.9000 - val_loss: 0.3699 - val_accuracy: 0.9000\n",
      "Epoch 760/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.4007 - accuracy: 0.9083 - val_loss: 0.3697 - val_accuracy: 0.9000\n",
      "Epoch 761/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.4004 - accuracy: 0.9250 - val_loss: 0.3694 - val_accuracy: 0.9000\n",
      "Epoch 762/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.4001 - accuracy: 0.9250 - val_loss: 0.3691 - val_accuracy: 0.9000\n",
      "Epoch 763/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3998 - accuracy: 0.9250 - val_loss: 0.3688 - val_accuracy: 0.9000\n",
      "Epoch 764/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3995 - accuracy: 0.9250 - val_loss: 0.3685 - val_accuracy: 0.9000\n",
      "Epoch 765/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3992 - accuracy: 0.9250 - val_loss: 0.3682 - val_accuracy: 0.9000\n",
      "Epoch 766/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3989 - accuracy: 0.9250 - val_loss: 0.3679 - val_accuracy: 0.9000\n",
      "Epoch 767/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.3986 - accuracy: 0.9250 - val_loss: 0.3677 - val_accuracy: 0.9000\n",
      "Epoch 768/888\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.3983 - accuracy: 0.9250 - val_loss: 0.3674 - val_accuracy: 0.9000\n",
      "Epoch 769/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.3979 - accuracy: 0.9250 - val_loss: 0.3671 - val_accuracy: 0.9000\n",
      "Epoch 770/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3978 - accuracy: 0.9250 - val_loss: 0.3669 - val_accuracy: 0.9000\n",
      "Epoch 771/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3974 - accuracy: 0.9250 - val_loss: 0.3666 - val_accuracy: 0.9000\n",
      "Epoch 772/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3970 - accuracy: 0.9250 - val_loss: 0.3663 - val_accuracy: 0.9000\n",
      "Epoch 773/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3967 - accuracy: 0.9250 - val_loss: 0.3660 - val_accuracy: 0.9000\n",
      "Epoch 774/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3964 - accuracy: 0.9250 - val_loss: 0.3657 - val_accuracy: 0.9000\n",
      "Epoch 775/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3961 - accuracy: 0.9250 - val_loss: 0.3655 - val_accuracy: 0.9000\n",
      "Epoch 776/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3959 - accuracy: 0.9250 - val_loss: 0.3651 - val_accuracy: 0.9000\n",
      "Epoch 777/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3956 - accuracy: 0.9250 - val_loss: 0.3649 - val_accuracy: 0.9000\n",
      "Epoch 778/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3953 - accuracy: 0.9250 - val_loss: 0.3647 - val_accuracy: 0.9000\n",
      "Epoch 779/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3950 - accuracy: 0.9250 - val_loss: 0.3644 - val_accuracy: 0.9000\n",
      "Epoch 780/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3947 - accuracy: 0.9250 - val_loss: 0.3641 - val_accuracy: 0.9000\n",
      "Epoch 781/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3944 - accuracy: 0.9250 - val_loss: 0.3638 - val_accuracy: 0.9000\n",
      "Epoch 782/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3941 - accuracy: 0.9250 - val_loss: 0.3635 - val_accuracy: 0.9000\n",
      "Epoch 783/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3938 - accuracy: 0.9250 - val_loss: 0.3632 - val_accuracy: 0.9000\n",
      "Epoch 784/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3935 - accuracy: 0.9250 - val_loss: 0.3629 - val_accuracy: 0.9000\n",
      "Epoch 785/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3932 - accuracy: 0.9250 - val_loss: 0.3625 - val_accuracy: 0.9000\n",
      "Epoch 786/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3929 - accuracy: 0.9250 - val_loss: 0.3622 - val_accuracy: 0.9000\n",
      "Epoch 787/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3926 - accuracy: 0.9250 - val_loss: 0.3619 - val_accuracy: 0.9000\n",
      "Epoch 788/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3923 - accuracy: 0.9250 - val_loss: 0.3616 - val_accuracy: 0.9000\n",
      "Epoch 789/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3920 - accuracy: 0.9250 - val_loss: 0.3614 - val_accuracy: 0.9000\n",
      "Epoch 790/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3917 - accuracy: 0.9250 - val_loss: 0.3611 - val_accuracy: 0.9000\n",
      "Epoch 791/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3915 - accuracy: 0.9250 - val_loss: 0.3608 - val_accuracy: 0.9000\n",
      "Epoch 792/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3912 - accuracy: 0.9250 - val_loss: 0.3605 - val_accuracy: 0.9000\n",
      "Epoch 793/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3909 - accuracy: 0.9250 - val_loss: 0.3603 - val_accuracy: 0.9000\n",
      "Epoch 794/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3906 - accuracy: 0.9250 - val_loss: 0.3600 - val_accuracy: 0.9000\n",
      "Epoch 795/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3904 - accuracy: 0.9250 - val_loss: 0.3597 - val_accuracy: 0.9000\n",
      "Epoch 796/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3900 - accuracy: 0.9250 - val_loss: 0.3594 - val_accuracy: 0.9000\n",
      "Epoch 797/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3897 - accuracy: 0.9250 - val_loss: 0.3591 - val_accuracy: 0.9000\n",
      "Epoch 798/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3894 - accuracy: 0.9250 - val_loss: 0.3588 - val_accuracy: 0.9000\n",
      "Epoch 799/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3891 - accuracy: 0.9250 - val_loss: 0.3586 - val_accuracy: 0.9000\n",
      "Epoch 800/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3888 - accuracy: 0.9250 - val_loss: 0.3583 - val_accuracy: 0.9000\n",
      "Epoch 801/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.3885 - accuracy: 0.9250 - val_loss: 0.3580 - val_accuracy: 0.9000\n",
      "Epoch 802/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3884 - accuracy: 0.9250 - val_loss: 0.3578 - val_accuracy: 0.9000\n",
      "Epoch 803/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3880 - accuracy: 0.9250 - val_loss: 0.3576 - val_accuracy: 0.9000\n",
      "Epoch 804/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3877 - accuracy: 0.9250 - val_loss: 0.3572 - val_accuracy: 0.9000\n",
      "Epoch 805/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3874 - accuracy: 0.9250 - val_loss: 0.3570 - val_accuracy: 0.9000\n",
      "Epoch 806/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3871 - accuracy: 0.9250 - val_loss: 0.3567 - val_accuracy: 0.9000\n",
      "Epoch 807/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3869 - accuracy: 0.9250 - val_loss: 0.3563 - val_accuracy: 0.9000\n",
      "Epoch 808/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3866 - accuracy: 0.9250 - val_loss: 0.3560 - val_accuracy: 0.9000\n",
      "Epoch 809/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3863 - accuracy: 0.9250 - val_loss: 0.3558 - val_accuracy: 0.9000\n",
      "Epoch 810/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3860 - accuracy: 0.9250 - val_loss: 0.3555 - val_accuracy: 0.9000\n",
      "Epoch 811/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3857 - accuracy: 0.9250 - val_loss: 0.3553 - val_accuracy: 0.9000\n",
      "Epoch 812/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3854 - accuracy: 0.9250 - val_loss: 0.3549 - val_accuracy: 0.9000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 813/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3852 - accuracy: 0.9250 - val_loss: 0.3546 - val_accuracy: 0.9000\n",
      "Epoch 814/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3849 - accuracy: 0.9250 - val_loss: 0.3544 - val_accuracy: 0.9000\n",
      "Epoch 815/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3846 - accuracy: 0.9250 - val_loss: 0.3540 - val_accuracy: 0.9000\n",
      "Epoch 816/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3843 - accuracy: 0.9250 - val_loss: 0.3537 - val_accuracy: 0.9000\n",
      "Epoch 817/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3840 - accuracy: 0.9250 - val_loss: 0.3535 - val_accuracy: 0.9000\n",
      "Epoch 818/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3838 - accuracy: 0.9250 - val_loss: 0.3532 - val_accuracy: 0.9000\n",
      "Epoch 819/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3835 - accuracy: 0.9250 - val_loss: 0.3529 - val_accuracy: 0.9000\n",
      "Epoch 820/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3832 - accuracy: 0.9250 - val_loss: 0.3526 - val_accuracy: 0.9000\n",
      "Epoch 821/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3829 - accuracy: 0.9250 - val_loss: 0.3523 - val_accuracy: 0.9000\n",
      "Epoch 822/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.3827 - accuracy: 0.9250 - val_loss: 0.3521 - val_accuracy: 0.9000\n",
      "Epoch 823/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3824 - accuracy: 0.9250 - val_loss: 0.3518 - val_accuracy: 0.9000\n",
      "Epoch 824/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3822 - accuracy: 0.9250 - val_loss: 0.3516 - val_accuracy: 0.9000\n",
      "Epoch 825/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3818 - accuracy: 0.9250 - val_loss: 0.3513 - val_accuracy: 0.9000\n",
      "Epoch 826/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3815 - accuracy: 0.9250 - val_loss: 0.3511 - val_accuracy: 0.9000\n",
      "Epoch 827/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3813 - accuracy: 0.9250 - val_loss: 0.3509 - val_accuracy: 0.9000\n",
      "Epoch 828/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3810 - accuracy: 0.9250 - val_loss: 0.3506 - val_accuracy: 0.9000\n",
      "Epoch 829/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3807 - accuracy: 0.9250 - val_loss: 0.3503 - val_accuracy: 0.9000\n",
      "Epoch 830/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3804 - accuracy: 0.9250 - val_loss: 0.3500 - val_accuracy: 0.9000\n",
      "Epoch 831/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3802 - accuracy: 0.9250 - val_loss: 0.3497 - val_accuracy: 0.9000\n",
      "Epoch 832/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3800 - accuracy: 0.9250 - val_loss: 0.3495 - val_accuracy: 0.9000\n",
      "Epoch 833/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3796 - accuracy: 0.9250 - val_loss: 0.3492 - val_accuracy: 0.9000\n",
      "Epoch 834/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.3794 - accuracy: 0.9250 - val_loss: 0.3489 - val_accuracy: 0.9000\n",
      "Epoch 835/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3791 - accuracy: 0.9250 - val_loss: 0.3486 - val_accuracy: 0.9000\n",
      "Epoch 836/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.3788 - accuracy: 0.9250 - val_loss: 0.3484 - val_accuracy: 0.9000\n",
      "Epoch 837/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3786 - accuracy: 0.9250 - val_loss: 0.3481 - val_accuracy: 0.9000\n",
      "Epoch 838/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3783 - accuracy: 0.9250 - val_loss: 0.3479 - val_accuracy: 0.9000\n",
      "Epoch 839/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3780 - accuracy: 0.9250 - val_loss: 0.3476 - val_accuracy: 0.9000\n",
      "Epoch 840/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3778 - accuracy: 0.9250 - val_loss: 0.3474 - val_accuracy: 0.9000\n",
      "Epoch 841/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3776 - accuracy: 0.9250 - val_loss: 0.3471 - val_accuracy: 0.9000\n",
      "Epoch 842/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3772 - accuracy: 0.9250 - val_loss: 0.3468 - val_accuracy: 0.9000\n",
      "Epoch 843/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3770 - accuracy: 0.9250 - val_loss: 0.3466 - val_accuracy: 0.9000\n",
      "Epoch 844/888\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.3768 - accuracy: 0.9250 - val_loss: 0.3462 - val_accuracy: 0.9000\n",
      "Epoch 845/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3764 - accuracy: 0.9250 - val_loss: 0.3460 - val_accuracy: 0.9000\n",
      "Epoch 846/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3762 - accuracy: 0.9250 - val_loss: 0.3457 - val_accuracy: 0.9000\n",
      "Epoch 847/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3759 - accuracy: 0.9250 - val_loss: 0.3455 - val_accuracy: 0.9000\n",
      "Epoch 848/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3756 - accuracy: 0.9250 - val_loss: 0.3452 - val_accuracy: 0.9000\n",
      "Epoch 849/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3754 - accuracy: 0.9250 - val_loss: 0.3449 - val_accuracy: 0.9000\n",
      "Epoch 850/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3752 - accuracy: 0.9250 - val_loss: 0.3447 - val_accuracy: 0.9000\n",
      "Epoch 851/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3749 - accuracy: 0.9250 - val_loss: 0.3445 - val_accuracy: 0.9000\n",
      "Epoch 852/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3746 - accuracy: 0.9250 - val_loss: 0.3442 - val_accuracy: 0.9000\n",
      "Epoch 853/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3743 - accuracy: 0.9250 - val_loss: 0.3440 - val_accuracy: 0.9000\n",
      "Epoch 854/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3741 - accuracy: 0.9250 - val_loss: 0.3437 - val_accuracy: 0.9000\n",
      "Epoch 855/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3738 - accuracy: 0.9250 - val_loss: 0.3434 - val_accuracy: 0.9000\n",
      "Epoch 856/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3737 - accuracy: 0.9250 - val_loss: 0.3431 - val_accuracy: 0.9000\n",
      "Epoch 857/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3733 - accuracy: 0.9250 - val_loss: 0.3429 - val_accuracy: 0.9000\n",
      "Epoch 858/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3731 - accuracy: 0.9250 - val_loss: 0.3426 - val_accuracy: 0.9000\n",
      "Epoch 859/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3728 - accuracy: 0.9250 - val_loss: 0.3423 - val_accuracy: 0.9000\n",
      "Epoch 860/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3725 - accuracy: 0.9250 - val_loss: 0.3421 - val_accuracy: 0.9000\n",
      "Epoch 861/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3722 - accuracy: 0.9250 - val_loss: 0.3419 - val_accuracy: 0.9000\n",
      "Epoch 862/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3720 - accuracy: 0.9250 - val_loss: 0.3416 - val_accuracy: 0.9000\n",
      "Epoch 863/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3717 - accuracy: 0.9250 - val_loss: 0.3414 - val_accuracy: 0.9000\n",
      "Epoch 864/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3715 - accuracy: 0.9250 - val_loss: 0.3411 - val_accuracy: 0.9000\n",
      "Epoch 865/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3712 - accuracy: 0.9250 - val_loss: 0.3409 - val_accuracy: 0.9000\n",
      "Epoch 866/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3710 - accuracy: 0.9250 - val_loss: 0.3406 - val_accuracy: 0.9000\n",
      "Epoch 867/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3708 - accuracy: 0.9250 - val_loss: 0.3404 - val_accuracy: 0.9000\n",
      "Epoch 868/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3704 - accuracy: 0.9250 - val_loss: 0.3401 - val_accuracy: 0.9000\n",
      "Epoch 869/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.3702 - accuracy: 0.9250 - val_loss: 0.3399 - val_accuracy: 0.9000\n",
      "Epoch 870/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3700 - accuracy: 0.9250 - val_loss: 0.3396 - val_accuracy: 0.9000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 871/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3697 - accuracy: 0.9250 - val_loss: 0.3393 - val_accuracy: 0.9000\n",
      "Epoch 872/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3695 - accuracy: 0.9250 - val_loss: 0.3391 - val_accuracy: 0.9000\n",
      "Epoch 873/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3692 - accuracy: 0.9250 - val_loss: 0.3388 - val_accuracy: 0.9000\n",
      "Epoch 874/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3689 - accuracy: 0.9250 - val_loss: 0.3386 - val_accuracy: 0.9000\n",
      "Epoch 875/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.3687 - accuracy: 0.9250 - val_loss: 0.3383 - val_accuracy: 0.9000\n",
      "Epoch 876/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3684 - accuracy: 0.9250 - val_loss: 0.3381 - val_accuracy: 0.9000\n",
      "Epoch 877/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3681 - accuracy: 0.9250 - val_loss: 0.3378 - val_accuracy: 0.9000\n",
      "Epoch 878/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3679 - accuracy: 0.9250 - val_loss: 0.3375 - val_accuracy: 0.9000\n",
      "Epoch 879/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3677 - accuracy: 0.9250 - val_loss: 0.3373 - val_accuracy: 0.9000\n",
      "Epoch 880/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3674 - accuracy: 0.9250 - val_loss: 0.3370 - val_accuracy: 0.9000\n",
      "Epoch 881/888\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.3672 - accuracy: 0.9250 - val_loss: 0.3368 - val_accuracy: 0.9000\n",
      "Epoch 882/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3669 - accuracy: 0.9250 - val_loss: 0.3365 - val_accuracy: 0.9000\n",
      "Epoch 883/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3666 - accuracy: 0.9250 - val_loss: 0.3363 - val_accuracy: 0.9000\n",
      "Epoch 884/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3664 - accuracy: 0.9250 - val_loss: 0.3360 - val_accuracy: 0.9000\n",
      "Epoch 885/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3661 - accuracy: 0.9250 - val_loss: 0.3357 - val_accuracy: 0.9000\n",
      "Epoch 886/888\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3659 - accuracy: 0.9250 - val_loss: 0.3355 - val_accuracy: 0.9000\n",
      "Epoch 887/888\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.3656 - accuracy: 0.9250 - val_loss: 0.3352 - val_accuracy: 0.9000\n",
      "Epoch 888/888\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.3654 - accuracy: 0.9250 - val_loss: 0.3350 - val_accuracy: 0.9000\n"
     ]
    }
   ],
   "source": [
    "history_iris_model = iris_model.fit(x=X_train_norm,\n",
    "         y=y_train,\n",
    "         epochs=EPOCHS,\n",
    "         validation_data=(X_test_norm, y_test),\n",
    "         callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c0045712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.334958016872406, 0.8999999761581421]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluate the model\n",
    "iris_model.evaluate(X_test_norm, y_test, verbose=0)\n",
    "# [0.334958016872406, 0.8999999761581421]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "92b75f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the validation accuracy\n",
    "def plot_accuracy_curves(history, title):\n",
    "    accuracy = history.history['accuracy']\n",
    "    val_accuracy = history.history['val_accuracy']\n",
    "    epochs = range(len(history.history['accuracy']))\n",
    "\n",
    "    # Plot accuracy\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.plot(epochs, accuracy, label='training_accuracy')\n",
    "    plt.plot(epochs, val_accuracy, label='val_accuracy')\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "05be3c4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_accuracy_curves(history_iris_model, \"IRIS Dataset :: Accuracy Curve\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c1c87a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the training loss\n",
    "def plot_loss_curves(history, title):\n",
    "    loss = history.history['loss']\n",
    "    val_loss = history.history['val_loss']\n",
    "    epochs = range(len(history.history['loss']))\n",
    "\n",
    "    # Plot accuracy\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.plot(epochs, loss, label='training_loss')\n",
    "    plt.plot(epochs, val_loss, label='val_loss')\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "64c250e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss_curves(history_iris_model, \"IRIS Dataset :: Loss Curve\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9c35aff",
   "metadata": {},
   "source": [
    "## Fit all Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aee06e2",
   "metadata": {},
   "source": [
    "After reaching a approx. 90% accuracy we can now add the testing data to our model training to increase the dataset variety the model was trained on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "79afb6a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_norm =scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "56b2e0d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_model_full = Sequential([\n",
    "    Dense(units=4, activation='relu', input_shape=[4,]),\n",
    "    Dense(units=3, activation='softmax')\n",
    "])\n",
    "\n",
    "iris_model_full.compile(optimizer='adam',\n",
    "                  loss='categorical_crossentropy',\n",
    "                  metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "397c21d6",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/888\n",
      "5/5 [==============================] - 1s 2ms/step - loss: 1.0736 - accuracy: 0.3200\n",
      "Epoch 2/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 1.0707 - accuracy: 0.3333\n",
      "Epoch 3/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0675 - accuracy: 0.3333\n",
      "Epoch 4/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0649 - accuracy: 0.3467\n",
      "Epoch 5/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0624 - accuracy: 0.3400\n",
      "Epoch 6/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0602 - accuracy: 0.3267\n",
      "Epoch 7/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0577 - accuracy: 0.3333\n",
      "Epoch 8/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 1.0554 - accuracy: 0.3533\n",
      "Epoch 9/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 1.0531 - accuracy: 0.3667\n",
      "Epoch 10/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0510 - accuracy: 0.4267\n",
      "Epoch 11/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 1.0486 - accuracy: 0.4800\n",
      "Epoch 12/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0465 - accuracy: 0.5000\n",
      "Epoch 13/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0447 - accuracy: 0.5133\n",
      "Epoch 14/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0422 - accuracy: 0.5133\n",
      "Epoch 15/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0401 - accuracy: 0.5467\n",
      "Epoch 16/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0378 - accuracy: 0.5733\n",
      "Epoch 17/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0357 - accuracy: 0.5867\n",
      "Epoch 18/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0333 - accuracy: 0.5867\n",
      "Epoch 19/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0309 - accuracy: 0.5867\n",
      "Epoch 20/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0286 - accuracy: 0.5867\n",
      "Epoch 21/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 1.0262 - accuracy: 0.6000\n",
      "Epoch 22/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 1.0237 - accuracy: 0.5933\n",
      "Epoch 23/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 1.0213 - accuracy: 0.6000\n",
      "Epoch 24/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0187 - accuracy: 0.5933\n",
      "Epoch 25/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0161 - accuracy: 0.5933\n",
      "Epoch 26/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0135 - accuracy: 0.5933\n",
      "Epoch 27/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0109 - accuracy: 0.5933\n",
      "Epoch 28/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 1.0081 - accuracy: 0.5933\n",
      "Epoch 29/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 1.0054 - accuracy: 0.5933\n",
      "Epoch 30/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 1.0027 - accuracy: 0.6000\n",
      "Epoch 31/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.9997 - accuracy: 0.6000\n",
      "Epoch 32/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.9968 - accuracy: 0.6000\n",
      "Epoch 33/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.9938 - accuracy: 0.6000\n",
      "Epoch 34/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.9908 - accuracy: 0.6000\n",
      "Epoch 35/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.9877 - accuracy: 0.6000\n",
      "Epoch 36/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.9847 - accuracy: 0.5933\n",
      "Epoch 37/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.9814 - accuracy: 0.5933\n",
      "Epoch 38/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.9782 - accuracy: 0.6000\n",
      "Epoch 39/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.9751 - accuracy: 0.5867\n",
      "Epoch 40/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.9717 - accuracy: 0.5867\n",
      "Epoch 41/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.9683 - accuracy: 0.5867\n",
      "Epoch 42/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.9650 - accuracy: 0.5867\n",
      "Epoch 43/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.9615 - accuracy: 0.5867\n",
      "Epoch 44/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.9580 - accuracy: 0.5867\n",
      "Epoch 45/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.9544 - accuracy: 0.5867\n",
      "Epoch 46/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.9508 - accuracy: 0.5933\n",
      "Epoch 47/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.9471 - accuracy: 0.6000\n",
      "Epoch 48/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.9434 - accuracy: 0.6000\n",
      "Epoch 49/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.9398 - accuracy: 0.5933\n",
      "Epoch 50/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.9359 - accuracy: 0.5933\n",
      "Epoch 51/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.9322 - accuracy: 0.5933\n",
      "Epoch 52/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.9283 - accuracy: 0.5933\n",
      "Epoch 53/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.9244 - accuracy: 0.5933\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 659/888\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2642 - accuracy: 0.9533\n",
      "Epoch 660/888\n",
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      "5/5 [==============================] - 0s 2ms/step - loss: 0.2328 - accuracy: 0.9667\n",
      "Epoch 755/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2324 - accuracy: 0.9667\n",
      "Epoch 756/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2321 - accuracy: 0.9667\n",
      "Epoch 757/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2318 - accuracy: 0.9667\n",
      "Epoch 758/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2315 - accuracy: 0.9667\n",
      "Epoch 759/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2312 - accuracy: 0.9667\n",
      "Epoch 760/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2309 - accuracy: 0.9667\n",
      "Epoch 761/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2306 - accuracy: 0.9667\n",
      "Epoch 762/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2303 - accuracy: 0.9667\n",
      "Epoch 763/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2300 - accuracy: 0.9667\n",
      "Epoch 764/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2296 - accuracy: 0.9667\n",
      "Epoch 765/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2293 - accuracy: 0.9667\n",
      "Epoch 766/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2290 - accuracy: 0.9667\n",
      "Epoch 767/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2287 - accuracy: 0.9667\n",
      "Epoch 768/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2283 - accuracy: 0.9667\n",
      "Epoch 769/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2280 - accuracy: 0.9667\n",
      "Epoch 770/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2278 - accuracy: 0.9667\n",
      "Epoch 771/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2274 - accuracy: 0.9667\n",
      "Epoch 772/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2271 - accuracy: 0.9667\n",
      "Epoch 773/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2268 - accuracy: 0.9667\n",
      "Epoch 774/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2265 - accuracy: 0.9667\n",
      "Epoch 775/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2262 - accuracy: 0.9667\n",
      "Epoch 776/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2259 - accuracy: 0.9667\n",
      "Epoch 777/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2256 - accuracy: 0.9667\n",
      "Epoch 778/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2253 - accuracy: 0.9667\n",
      "Epoch 779/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2250 - accuracy: 0.9667\n",
      "Epoch 780/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2247 - accuracy: 0.9667\n",
      "Epoch 781/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2244 - accuracy: 0.9667\n",
      "Epoch 782/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2240 - accuracy: 0.9667\n",
      "Epoch 783/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2238 - accuracy: 0.9667\n",
      "Epoch 784/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2235 - accuracy: 0.9667\n",
      "Epoch 785/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2231 - accuracy: 0.9667\n",
      "Epoch 786/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2228 - accuracy: 0.9667\n",
      "Epoch 787/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2225 - accuracy: 0.9667\n",
      "Epoch 788/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2222 - accuracy: 0.9667\n",
      "Epoch 789/888\n",
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      "Epoch 790/888\n",
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      "Epoch 791/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2213 - accuracy: 0.9667\n",
      "Epoch 792/888\n",
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      "Epoch 793/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2207 - accuracy: 0.9667\n",
      "Epoch 794/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2205 - accuracy: 0.9667\n",
      "Epoch 795/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2201 - accuracy: 0.9667\n",
      "Epoch 796/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2198 - accuracy: 0.9667\n",
      "Epoch 797/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2195 - accuracy: 0.9667\n",
      "Epoch 798/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2192 - accuracy: 0.9667\n",
      "Epoch 799/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2189 - accuracy: 0.9667\n",
      "Epoch 800/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2186 - accuracy: 0.9667\n",
      "Epoch 801/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2183 - accuracy: 0.9667\n",
      "Epoch 802/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2180 - accuracy: 0.9667\n",
      "Epoch 803/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2177 - accuracy: 0.9667\n",
      "Epoch 804/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2174 - accuracy: 0.9667\n",
      "Epoch 805/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2171 - accuracy: 0.9667\n",
      "Epoch 806/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2168 - accuracy: 0.9667\n",
      "Epoch 807/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2165 - accuracy: 0.9667\n",
      "Epoch 808/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2162 - accuracy: 0.9667\n",
      "Epoch 809/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2159 - accuracy: 0.9667\n",
      "Epoch 810/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2156 - accuracy: 0.9667\n",
      "Epoch 811/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2154 - accuracy: 0.9667\n",
      "Epoch 812/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2150 - accuracy: 0.9667\n",
      "Epoch 813/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2147 - accuracy: 0.9667\n",
      "Epoch 814/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2144 - accuracy: 0.9667\n",
      "Epoch 815/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2141 - accuracy: 0.9667\n",
      "Epoch 816/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2139 - accuracy: 0.9667\n",
      "Epoch 817/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2136 - accuracy: 0.9667\n",
      "Epoch 818/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2133 - accuracy: 0.9667\n",
      "Epoch 819/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2129 - accuracy: 0.9667\n",
      "Epoch 820/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2127 - accuracy: 0.9667\n",
      "Epoch 821/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2124 - accuracy: 0.9667\n",
      "Epoch 822/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2121 - accuracy: 0.9667\n",
      "Epoch 823/888\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2118 - accuracy: 0.9667\n",
      "Epoch 824/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2115 - accuracy: 0.9667\n",
      "Epoch 825/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2112 - accuracy: 0.9667\n",
      "Epoch 826/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2109 - accuracy: 0.9667\n",
      "Epoch 827/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2106 - accuracy: 0.9733\n",
      "Epoch 828/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2103 - accuracy: 0.9667\n",
      "Epoch 829/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2100 - accuracy: 0.9667\n",
      "Epoch 830/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2097 - accuracy: 0.9667\n",
      "Epoch 831/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2094 - accuracy: 0.9733\n",
      "Epoch 832/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2091 - accuracy: 0.9733\n",
      "Epoch 833/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2089 - accuracy: 0.9733\n",
      "Epoch 834/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2086 - accuracy: 0.9733\n",
      "Epoch 835/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2083 - accuracy: 0.9733\n",
      "Epoch 836/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2080 - accuracy: 0.9733\n",
      "Epoch 837/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2077 - accuracy: 0.9733\n",
      "Epoch 838/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2074 - accuracy: 0.9733\n",
      "Epoch 839/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2071 - accuracy: 0.9733\n",
      "Epoch 840/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2069 - accuracy: 0.9733\n",
      "Epoch 841/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2065 - accuracy: 0.9733\n",
      "Epoch 842/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2063 - accuracy: 0.9733\n",
      "Epoch 843/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2060 - accuracy: 0.9733\n",
      "Epoch 844/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2057 - accuracy: 0.9733\n",
      "Epoch 845/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2054 - accuracy: 0.9733\n",
      "Epoch 846/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2051 - accuracy: 0.9733\n",
      "Epoch 847/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2048 - accuracy: 0.9733\n",
      "Epoch 848/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2045 - accuracy: 0.9733\n",
      "Epoch 849/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2042 - accuracy: 0.9733\n",
      "Epoch 850/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2040 - accuracy: 0.9733\n",
      "Epoch 851/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2037 - accuracy: 0.9733\n",
      "Epoch 852/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2034 - accuracy: 0.9733\n",
      "Epoch 853/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2031 - accuracy: 0.9733\n",
      "Epoch 854/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2028 - accuracy: 0.9733\n",
      "Epoch 855/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2026 - accuracy: 0.9733\n",
      "Epoch 856/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2023 - accuracy: 0.9733\n",
      "Epoch 857/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2020 - accuracy: 0.9733\n",
      "Epoch 858/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2018 - accuracy: 0.9733\n",
      "Epoch 859/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2014 - accuracy: 0.9733\n",
      "Epoch 860/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2011 - accuracy: 0.9733\n",
      "Epoch 861/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2009 - accuracy: 0.9733\n",
      "Epoch 862/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2006 - accuracy: 0.9733\n",
      "Epoch 863/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.2003 - accuracy: 0.9733\n",
      "Epoch 864/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.2000 - accuracy: 0.9733\n",
      "Epoch 865/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.1998 - accuracy: 0.9733\n",
      "Epoch 866/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.1994 - accuracy: 0.9733\n",
      "Epoch 867/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.1992 - accuracy: 0.9733\n",
      "Epoch 868/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.1989 - accuracy: 0.9733\n",
      "Epoch 869/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.1986 - accuracy: 0.9733\n",
      "Epoch 870/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.1984 - accuracy: 0.9733\n",
      "Epoch 871/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.1981 - accuracy: 0.9733\n",
      "Epoch 872/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.1978 - accuracy: 0.9733\n",
      "Epoch 873/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.1975 - accuracy: 0.9733\n",
      "Epoch 874/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.1973 - accuracy: 0.9733\n",
      "Epoch 875/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.1969 - accuracy: 0.9733\n",
      "Epoch 876/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.1967 - accuracy: 0.9733\n",
      "Epoch 877/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.1965 - accuracy: 0.9733\n",
      "Epoch 878/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.1961 - accuracy: 0.9733\n",
      "Epoch 879/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.1958 - accuracy: 0.9733\n",
      "Epoch 880/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.1956 - accuracy: 0.9733\n",
      "Epoch 881/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.1953 - accuracy: 0.9733\n",
      "Epoch 882/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.1950 - accuracy: 0.9733\n",
      "Epoch 883/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.1948 - accuracy: 0.9733\n",
      "Epoch 884/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.1945 - accuracy: 0.9733\n",
      "Epoch 885/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.1942 - accuracy: 0.9733\n",
      "Epoch 886/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.1939 - accuracy: 0.9733\n",
      "Epoch 887/888\n",
      "5/5 [==============================] - 0s 2ms/step - loss: 0.1936 - accuracy: 0.9733\n",
      "Epoch 888/888\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.1934 - accuracy: 0.9733\n"
     ]
    }
   ],
   "source": [
    "history_iris_model_full = iris_model_full.fit(X_norm, y, epochs=EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4521f871",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.1275221109390259, 0.36000001430511475]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluate the model\n",
    "iris_model_full.evaluate(X_norm, y, verbose=0)\n",
    "# [0.1931973546743393, 0.9733333587646484]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8cc5cf20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the validation and training loss\n",
    "def plot_training_curves(history, title):\n",
    "    accuracy = history.history['accuracy']\n",
    "    loss = history.history['loss']\n",
    "    epochs = range(len(history.history['loss']))\n",
    "\n",
    "    # Plot accuracy\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.plot(epochs, accuracy, label='training_accuracy')\n",
    "    plt.plot(epochs, loss, label='training_loss')\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "05687999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot accuracy and loss curves\n",
    "plt.figure(figsize=(12, 6))\n",
    "plot_training_curves(history_iris_model_full, \"IRIS Dataset :: Training Curves\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2422f2cb",
   "metadata": {},
   "source": [
    "## Save the Trained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d3debc06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the full model with training weights\n",
    "iris_model_full.save(MODEL_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3b96f6ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./model/iris_data_norm.pkl']"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save data preprocessing\n",
    "joblib.dump(scaler, SCALER_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50233709",
   "metadata": {},
   "source": [
    "## Run Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e2bdabdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the saved model\n",
    "loaded_iris_model = load_model(MODEL_PATH)\n",
    "loaded_scaler = joblib.load(SCALER_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "26d64df4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.1931973546743393, 0.9733333587646484]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verify predictions are the same\n",
    "loaded_iris_model.evaluate(X_norm, y, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d19204c",
   "metadata": {},
   "source": [
    "## Prediction API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4fe6a0c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# simulate JSON API call\n",
    "flower_example = {\"sepal length (cm)\": 5.1,\n",
    "                  \"sepal width (cm)\": 3.5,\n",
    "                  \"petal length (cm)\":1.4,\n",
    "                  \"petal width (cm)\": 0.2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "926ffeac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# API function (return class index with highest probability)\n",
    "def return_prediction(model, scaler, json_request):\n",
    "    s_len = json_request[\"sepal length (cm)\"]\n",
    "    s_wi = json_request[\"sepal width (cm)\"]\n",
    "    p_len = json_request[\"petal length (cm)\"]\n",
    "    p_w = json_request[\"petal width (cm)\"]\n",
    "    \n",
    "    measures =[[s_len, s_wi, p_len, p_w]]\n",
    "    measures_norm = scaler.transform(measures)\n",
    "    \n",
    "    flower_class_probabilities = model.predict(measures_norm)\n",
    "    flower_class_index=np.argmax(flower_class_probabilities,axis=1)\n",
    "                           \n",
    "    return flower_class_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1c859544",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 23ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xiaodie/.local/lib/python3.10/site-packages/sklearn/base.py:409: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "return_prediction(loaded_iris_model, loaded_scaler, flower_example)\n",
    "# probabilities array([[9.987895e-01, 7.723020e-04, 4.383073e-04]], dtype=float32)\n",
    "# index array([0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "42549932",
   "metadata": {},
   "outputs": [],
   "source": [
    "# API function (return class name)\n",
    "def return_prediction(model, scaler, json_request):\n",
    "    s_len = json_request[\"sepal length (cm)\"]\n",
    "    s_wi = json_request[\"sepal width (cm)\"]\n",
    "    p_len = json_request[\"petal length (cm)\"]\n",
    "    p_w = json_request[\"petal width (cm)\"]\n",
    "    \n",
    "    classes = np.array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'])\n",
    "    measures =[[s_len, s_wi, p_len, p_w]]\n",
    "    measures_norm = scaler.transform(measures)\n",
    "    \n",
    "    flower_class_probabilities = model.predict(measures_norm)\n",
    "    flower_class_index=np.argmax(flower_class_probabilities,axis=1)\n",
    "                       \n",
    "    return classes[flower_class_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "93128b5a",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 20ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xiaodie/.local/lib/python3.10/site-packages/sklearn/base.py:409: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa'], dtype='<U15')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "return_prediction(loaded_iris_model, loaded_scaler, flower_example)\n",
    "# array(['Iris-setosa'], dtype='<U15')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "392ff636",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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