tensorflow2/004-batch-norm/batch-normalization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization in practice, an example with Keras and TensorFlow 2.0\n",
    "\n",
    "This is a notebook for the medium article [Batch Normalization in practice, an example with Keras and TensorFlow 2.0](https://medium.com/@bindiatwork/batch-normalization-in-practice-an-example-with-keras-and-tensorflow-2-0-b1ec28bde96f)\n",
    "\n",
    "Please check out article for instructions\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data into a DataFrame\n",
    "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "# Convert datatype to float\n",
    "df = df.astype(float)\n",
    "# append \"target\" and name it \"label\"\n",
    "df['label'] = iris.target\n",
    "# Use string label instead\n",
    "df['label'] = df.label.replace(dict(enumerate(iris.target_names)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>label</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>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>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>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>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>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",
       "    label  \n",
       "0  setosa  \n",
       "1  setosa  \n",
       "2  setosa  \n",
       "3  setosa  \n",
       "4  setosa  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing data for Keras and TensorFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# label -> one-hot encoding\n",
    "label = pd.get_dummies(df['label'], prefix='label')\n",
    "df = pd.concat([df, label], axis=1)\n",
    "# drop old label\n",
    "df.drop(['label'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</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>label_setosa</th>\n",
       "      <th>label_versicolor</th>\n",
       "      <th>label_virginica</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "   label_setosa  label_versicolor  label_virginica  \n",
       "0             1                 0                0  \n",
       "1             1                 0                0  \n",
       "2             1                 0                0  \n",
       "3             1                 0                0  \n",
       "4             1                 0                0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating X and y\n",
    "\n",
    "X = df[['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']]\n",
    "# Convert DataFrame into np array\n",
    "X = np.asarray(X)\n",
    "\n",
    "y = df[['label_setosa', 'label_versicolor', 'label_virginica']]\n",
    "# Convert DataFrame into np array\n",
    "y = np.asarray(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data set in a training set (80%) and a test set (20%)\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "  X,\n",
    "  y,\n",
    "  test_size=0.20\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(120, 4)\n",
      "(30, 4)\n",
      "(120, 3)\n",
      "(30, 3)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Batch normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, BatchNormalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 64)                320       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               8320      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 3)                 195       \n",
      "=================================================================\n",
      "Total params: 37,763\n",
      "Trainable params: 37,763\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Model without batch normalization\n",
    "model = Sequential([\n",
    "    Dense(64, input_shape=(4,), activation=\"relu\"),\n",
    "    Dense(128, activation='relu'),\n",
    "    Dense(128, activation='relu'),\n",
    "    Dense(64, activation='relu'),\n",
    "    Dense(64, activation='relu'),\n",
    "    Dense(3, activation='softmax')\n",
    "]);\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### With batch normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_6 (Dense)              (None, 64)                320       \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 128)               8320      \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 3)                 195       \n",
      "=================================================================\n",
      "Total params: 39,555\n",
      "Trainable params: 38,659\n",
      "Non-trainable params: 896\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Model with batch normalization\n",
    "model = Sequential([\n",
    "    Dense(64, input_shape=(4,), activation=\"relu\"),\n",
    "    BatchNormalization(),\n",
    "    Dense(128, activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    Dense(128, activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    Dense(64, activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    Dense(64, activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    Dense(3, activation='softmax')\n",
    "]);\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### With a customized batch normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.initializers import RandomNormal, Constant\n",
    "\n",
    "# Model with a customized batch normalization\n",
    "model = Sequential([\n",
    "    Dense(64, input_shape=(4,), activation=\"relu\"),\n",
    "    BatchNormalization(),\n",
    "    Dense(128, activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    Dense(128, activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    Dense(64, activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    Dense(64, activation='relu'),\n",
    "    BatchNormalization(\n",
    "        momentum=0.95, \n",
    "        epsilon=0.005,\n",
    "        beta_initializer=RandomNormal(mean=0.0, stddev=0.05), \n",
    "        gamma_initializer=Constant(value=0.9)\n",
    "    ),\n",
    "    Dense(3, activation='softmax')\n",
    "]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_12 (Dense)             (None, 64)                320       \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 128)               8320      \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_15 (Dense)             (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 3)                 195       \n",
      "=================================================================\n",
      "Total params: 39,555\n",
      "Trainable params: 38,659\n",
      "Non-trainable params: 896\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training\n",
    "\n",
    "Let's now compile and fit our model with batch normalisation\n",
    "\n",
    "First we compile our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer='adam', \n",
    "    loss='categorical_crossentropy', \n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After that, let's fit our model to the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 90 samples, validate on 30 samples\n",
      "Epoch 1/200\n",
      "90/90 - 4s - loss: 1.1598 - accuracy: 0.4000 - val_loss: 1.0651 - val_accuracy: 0.4333\n",
      "Epoch 2/200\n",
      "90/90 - 0s - loss: 0.3679 - accuracy: 0.9000 - val_loss: 1.0309 - val_accuracy: 0.4333\n",
      "Epoch 3/200\n",
      "90/90 - 0s - loss: 0.2004 - accuracy: 0.9667 - val_loss: 1.0061 - val_accuracy: 0.4667\n",
      "Epoch 4/200\n",
      "90/90 - 0s - loss: 0.1663 - accuracy: 0.9889 - val_loss: 0.9852 - val_accuracy: 0.5000\n",
      "Epoch 5/200\n",
      "90/90 - 0s - loss: 0.1428 - accuracy: 0.9556 - val_loss: 0.9692 - val_accuracy: 0.5000\n",
      "Epoch 6/200\n",
      "90/90 - 0s - loss: 0.0611 - accuracy: 1.0000 - val_loss: 0.9496 - val_accuracy: 0.5333\n",
      "Epoch 7/200\n",
      "90/90 - 0s - loss: 0.1876 - accuracy: 0.9333 - val_loss: 0.9333 - val_accuracy: 0.6333\n",
      "Epoch 8/200\n",
      "90/90 - 0s - loss: 0.0416 - accuracy: 1.0000 - val_loss: 0.9204 - val_accuracy: 0.7667\n",
      "Epoch 9/200\n",
      "90/90 - 0s - loss: 0.0859 - accuracy: 0.9667 - val_loss: 0.9112 - val_accuracy: 0.7667\n",
      "Epoch 10/200\n",
      "90/90 - 0s - loss: 0.1355 - accuracy: 0.9667 - val_loss: 0.9038 - val_accuracy: 0.7333\n",
      "Epoch 11/200\n",
      "90/90 - 0s - loss: 0.0436 - accuracy: 1.0000 - val_loss: 0.9012 - val_accuracy: 0.7667\n",
      "Epoch 12/200\n",
      "90/90 - 0s - loss: 0.0517 - accuracy: 0.9889 - val_loss: 0.8985 - val_accuracy: 0.7667\n",
      "Epoch 13/200\n",
      "90/90 - 0s - loss: 0.0387 - accuracy: 1.0000 - val_loss: 0.8985 - val_accuracy: 0.7333\n",
      "Epoch 14/200\n",
      "90/90 - 0s - loss: 0.0410 - accuracy: 1.0000 - val_loss: 0.8999 - val_accuracy: 0.6333\n",
      "Epoch 15/200\n",
      "90/90 - 0s - loss: 0.0270 - accuracy: 1.0000 - val_loss: 0.8980 - val_accuracy: 0.6333\n",
      "Epoch 16/200\n",
      "90/90 - 0s - loss: 0.2469 - accuracy: 0.9333 - val_loss: 0.9107 - val_accuracy: 0.4667\n",
      "Epoch 17/200\n",
      "90/90 - 0s - loss: 0.0410 - accuracy: 1.0000 - val_loss: 0.9194 - val_accuracy: 0.4333\n",
      "Epoch 18/200\n",
      "90/90 - 0s - loss: 0.0286 - accuracy: 1.0000 - val_loss: 0.9168 - val_accuracy: 0.5667\n",
      "Epoch 19/200\n",
      "90/90 - 0s - loss: 0.0469 - accuracy: 1.0000 - val_loss: 0.9094 - val_accuracy: 0.5667\n",
      "Epoch 20/200\n",
      "90/90 - 0s - loss: 0.0490 - accuracy: 0.9778 - val_loss: 0.8805 - val_accuracy: 0.7000\n",
      "Epoch 21/200\n",
      "90/90 - 0s - loss: 0.0786 - accuracy: 0.9556 - val_loss: 0.8565 - val_accuracy: 0.7000\n",
      "Epoch 22/200\n",
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      "Epoch 23/200\n",
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      "Epoch 24/200\n",
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      "Epoch 25/200\n",
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      "Epoch 26/200\n",
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      "Epoch 27/200\n",
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      "Epoch 28/200\n",
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      "Epoch 29/200\n",
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      "Epoch 30/200\n",
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      "Epoch 31/200\n",
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      "Epoch 32/200\n",
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      "Epoch 33/200\n",
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      "Epoch 34/200\n",
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      "Epoch 35/200\n",
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      "Epoch 36/200\n",
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      "Epoch 37/200\n",
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      "Epoch 38/200\n",
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      "Epoch 39/200\n",
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      "Epoch 40/200\n",
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      "Epoch 41/200\n",
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      "Epoch 42/200\n",
      "90/90 - 0s - loss: 0.0250 - accuracy: 1.0000 - val_loss: 0.6007 - val_accuracy: 0.7000\n",
      "Epoch 43/200\n",
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      "Epoch 44/200\n",
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      "Epoch 45/200\n",
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      "Epoch 46/200\n",
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      "Epoch 47/200\n",
      "90/90 - 0s - loss: 0.0693 - accuracy: 0.9667 - val_loss: 0.5771 - val_accuracy: 0.6333\n",
      "Epoch 48/200\n",
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      "Epoch 49/200\n",
      "90/90 - 0s - loss: 0.0211 - accuracy: 0.9889 - val_loss: 0.5303 - val_accuracy: 0.7000\n",
      "Epoch 50/200\n",
      "90/90 - 0s - loss: 0.0109 - accuracy: 1.0000 - val_loss: 0.5201 - val_accuracy: 0.7333\n",
      "Epoch 51/200\n",
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      "Epoch 52/200\n",
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      "Epoch 53/200\n",
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      "Epoch 54/200\n",
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      "Epoch 55/200\n",
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      "Epoch 56/200\n",
      "90/90 - 0s - loss: 0.0263 - accuracy: 0.9889 - val_loss: 0.4450 - val_accuracy: 0.8000\n",
      "Epoch 57/200\n",
      "90/90 - 0s - loss: 0.0859 - accuracy: 0.9778 - val_loss: 0.4250 - val_accuracy: 0.8000\n",
      "Epoch 58/200\n",
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      "Epoch 59/200\n",
      "90/90 - 0s - loss: 0.0224 - accuracy: 0.9889 - val_loss: 0.3940 - val_accuracy: 0.8333\n",
      "Epoch 60/200\n",
      "90/90 - 0s - loss: 0.0145 - accuracy: 1.0000 - val_loss: 0.3564 - val_accuracy: 0.9000\n",
      "Epoch 61/200\n",
      "90/90 - 0s - loss: 0.1633 - accuracy: 0.9667 - val_loss: 0.3256 - val_accuracy: 0.9000\n",
      "Epoch 62/200\n",
      "90/90 - 0s - loss: 0.0552 - accuracy: 0.9778 - val_loss: 0.2795 - val_accuracy: 0.9333\n",
      "Epoch 63/200\n",
      "90/90 - 0s - loss: 0.0166 - accuracy: 1.0000 - val_loss: 0.2564 - val_accuracy: 1.0000\n",
      "Epoch 64/200\n",
      "90/90 - 0s - loss: 0.0358 - accuracy: 0.9889 - val_loss: 0.2538 - val_accuracy: 0.9667\n",
      "Epoch 65/200\n",
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      "Epoch 66/200\n",
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      "Epoch 67/200\n",
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      "Epoch 68/200\n",
      "90/90 - 0s - loss: 0.0490 - accuracy: 1.0000 - val_loss: 0.1974 - val_accuracy: 0.9667\n",
      "Epoch 69/200\n",
      "90/90 - 0s - loss: 0.2388 - accuracy: 0.9667 - val_loss: 0.2043 - val_accuracy: 0.9667\n",
      "Epoch 70/200\n",
      "90/90 - 0s - loss: 0.0166 - accuracy: 1.0000 - val_loss: 0.2360 - val_accuracy: 0.9667\n",
      "Epoch 71/200\n",
      "90/90 - 0s - loss: 0.0158 - accuracy: 1.0000 - val_loss: 0.2691 - val_accuracy: 0.9333\n",
      "Epoch 72/200\n",
      "90/90 - 0s - loss: 0.0183 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9000\n",
      "Epoch 73/200\n",
      "90/90 - 0s - loss: 0.0124 - accuracy: 1.0000 - val_loss: 0.3212 - val_accuracy: 0.8667\n",
      "Epoch 74/200\n",
      "90/90 - 0s - loss: 0.0197 - accuracy: 1.0000 - val_loss: 0.3337 - val_accuracy: 0.8667\n",
      "Epoch 75/200\n",
      "90/90 - 0s - loss: 0.0235 - accuracy: 0.9889 - val_loss: 0.3341 - val_accuracy: 0.8667\n",
      "Epoch 76/200\n",
      "90/90 - 0s - loss: 0.0210 - accuracy: 1.0000 - val_loss: 0.3133 - val_accuracy: 0.8667\n",
      "Epoch 77/200\n",
      "90/90 - 0s - loss: 0.0630 - accuracy: 0.9889 - val_loss: 0.2871 - val_accuracy: 0.9000\n",
      "Epoch 78/200\n",
      "90/90 - 0s - loss: 0.0191 - accuracy: 1.0000 - val_loss: 0.2524 - val_accuracy: 0.9333\n",
      "Epoch 79/200\n",
      "90/90 - 0s - loss: 0.0245 - accuracy: 0.9889 - val_loss: 0.2346 - val_accuracy: 0.9667\n",
      "Epoch 80/200\n",
      "90/90 - 0s - loss: 0.0150 - accuracy: 1.0000 - val_loss: 0.2227 - val_accuracy: 0.9667\n",
      "Epoch 81/200\n",
      "90/90 - 0s - loss: 0.1076 - accuracy: 0.9667 - val_loss: 0.2103 - val_accuracy: 0.9667\n",
      "Epoch 82/200\n",
      "90/90 - 0s - loss: 0.0431 - accuracy: 0.9778 - val_loss: 0.2210 - val_accuracy: 0.9667\n",
      "Epoch 83/200\n",
      "90/90 - 0s - loss: 0.0349 - accuracy: 0.9778 - val_loss: 0.2119 - val_accuracy: 0.9667\n",
      "Epoch 84/200\n",
      "90/90 - 0s - loss: 0.0445 - accuracy: 0.9778 - val_loss: 0.1944 - val_accuracy: 0.9667\n",
      "Epoch 85/200\n",
      "90/90 - 0s - loss: 0.0424 - accuracy: 0.9778 - val_loss: 0.1805 - val_accuracy: 0.9667\n",
      "Epoch 86/200\n",
      "90/90 - 0s - loss: 0.0210 - accuracy: 1.0000 - val_loss: 0.1677 - val_accuracy: 0.9667\n",
      "Epoch 87/200\n",
      "90/90 - 0s - loss: 0.0358 - accuracy: 0.9889 - val_loss: 0.1514 - val_accuracy: 0.9667\n",
      "Epoch 88/200\n",
      "90/90 - 0s - loss: 0.0468 - accuracy: 0.9778 - val_loss: 0.1407 - val_accuracy: 0.9667\n",
      "Epoch 89/200\n",
      "90/90 - 0s - loss: 0.0203 - accuracy: 1.0000 - val_loss: 0.1344 - val_accuracy: 0.9667\n",
      "Epoch 90/200\n",
      "90/90 - 0s - loss: 0.1113 - accuracy: 0.9778 - val_loss: 0.1406 - val_accuracy: 0.9667\n",
      "Epoch 91/200\n",
      "90/90 - 0s - loss: 0.1013 - accuracy: 0.9667 - val_loss: 0.1698 - val_accuracy: 0.9667\n",
      "Epoch 92/200\n",
      "90/90 - 0s - loss: 0.2041 - accuracy: 0.9556 - val_loss: 0.1829 - val_accuracy: 0.9667\n",
      "Epoch 93/200\n",
      "90/90 - 0s - loss: 0.0398 - accuracy: 0.9778 - val_loss: 0.1677 - val_accuracy: 0.9667\n",
      "Epoch 94/200\n",
      "90/90 - 0s - loss: 0.0525 - accuracy: 0.9889 - val_loss: 0.1573 - val_accuracy: 0.9667\n",
      "Epoch 95/200\n",
      "90/90 - 0s - loss: 0.0332 - accuracy: 0.9889 - val_loss: 0.1613 - val_accuracy: 0.9667\n",
      "Epoch 96/200\n",
      "90/90 - 0s - loss: 0.0311 - accuracy: 1.0000 - val_loss: 0.1762 - val_accuracy: 0.9667\n",
      "Epoch 97/200\n",
      "90/90 - 0s - loss: 0.0910 - accuracy: 0.9778 - val_loss: 0.1732 - val_accuracy: 0.9667\n",
      "Epoch 98/200\n",
      "90/90 - 0s - loss: 0.0566 - accuracy: 0.9889 - val_loss: 0.1568 - val_accuracy: 0.9667\n",
      "Epoch 99/200\n",
      "90/90 - 0s - loss: 0.0270 - accuracy: 1.0000 - val_loss: 0.1382 - val_accuracy: 0.9667\n",
      "Epoch 100/200\n",
      "90/90 - 0s - loss: 0.0361 - accuracy: 0.9889 - val_loss: 0.1293 - val_accuracy: 0.9667\n",
      "Epoch 101/200\n",
      "90/90 - 0s - loss: 0.0326 - accuracy: 1.0000 - val_loss: 0.1242 - val_accuracy: 0.9667\n",
      "Epoch 102/200\n",
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      "Epoch 103/200\n",
      "90/90 - 0s - loss: 0.0456 - accuracy: 0.9889 - val_loss: 0.1102 - val_accuracy: 0.9667\n",
      "Epoch 104/200\n",
      "90/90 - 0s - loss: 0.0223 - accuracy: 1.0000 - val_loss: 0.1046 - val_accuracy: 0.9667\n",
      "Epoch 105/200\n",
      "90/90 - 0s - loss: 0.0144 - accuracy: 1.0000 - val_loss: 0.1005 - val_accuracy: 0.9667\n",
      "Epoch 106/200\n",
      "90/90 - 0s - loss: 0.0394 - accuracy: 0.9889 - val_loss: 0.0966 - val_accuracy: 0.9667\n",
      "Epoch 107/200\n",
      "90/90 - 0s - loss: 0.0493 - accuracy: 0.9889 - val_loss: 0.0970 - val_accuracy: 0.9667\n",
      "Epoch 108/200\n",
      "90/90 - 0s - loss: 0.0935 - accuracy: 0.9778 - val_loss: 0.0998 - val_accuracy: 0.9667\n",
      "Epoch 109/200\n",
      "90/90 - 0s - loss: 0.0322 - accuracy: 0.9889 - val_loss: 0.1070 - val_accuracy: 0.9667\n",
      "Epoch 110/200\n",
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      "Epoch 111/200\n",
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      "Epoch 112/200\n",
      "90/90 - 0s - loss: 0.0092 - accuracy: 1.0000 - val_loss: 0.0795 - val_accuracy: 0.9667\n",
      "Epoch 113/200\n",
      "90/90 - 0s - loss: 0.0195 - accuracy: 1.0000 - val_loss: 0.0855 - val_accuracy: 0.9667\n",
      "Epoch 114/200\n",
      "90/90 - 0s - loss: 0.0215 - accuracy: 1.0000 - val_loss: 0.0979 - val_accuracy: 0.9667\n",
      "Epoch 115/200\n",
      "90/90 - 0s - loss: 0.0130 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9667\n",
      "Epoch 116/200\n",
      "90/90 - 0s - loss: 0.2063 - accuracy: 0.9556 - val_loss: 0.1192 - val_accuracy: 0.9667\n",
      "Epoch 117/200\n",
      "90/90 - 0s - loss: 0.0521 - accuracy: 0.9889 - val_loss: 0.1466 - val_accuracy: 0.9667\n",
      "Epoch 118/200\n",
      "90/90 - 0s - loss: 0.1682 - accuracy: 0.9667 - val_loss: 0.2057 - val_accuracy: 0.9667\n",
      "Epoch 119/200\n",
      "90/90 - 0s - loss: 0.0267 - accuracy: 1.0000 - val_loss: 0.2257 - val_accuracy: 0.9667\n",
      "Epoch 120/200\n",
      "90/90 - 0s - loss: 0.0381 - accuracy: 0.9889 - val_loss: 0.2126 - val_accuracy: 0.9667\n",
      "Epoch 121/200\n",
      "90/90 - 0s - loss: 0.0255 - accuracy: 1.0000 - val_loss: 0.1857 - val_accuracy: 0.9667\n",
      "Epoch 122/200\n",
      "90/90 - 0s - loss: 0.0543 - accuracy: 0.9889 - val_loss: 0.1876 - val_accuracy: 0.9667\n",
      "Epoch 123/200\n",
      "90/90 - 0s - loss: 0.0182 - accuracy: 1.0000 - val_loss: 0.1928 - val_accuracy: 0.9667\n",
      "Epoch 124/200\n",
      "90/90 - 0s - loss: 0.0214 - accuracy: 1.0000 - val_loss: 0.1841 - val_accuracy: 0.9667\n",
      "Epoch 125/200\n",
      "90/90 - 0s - loss: 0.0827 - accuracy: 0.9889 - val_loss: 0.1600 - val_accuracy: 0.9667\n",
      "Epoch 126/200\n",
      "90/90 - 0s - loss: 0.0288 - accuracy: 1.0000 - val_loss: 0.1261 - val_accuracy: 0.9667\n",
      "Epoch 127/200\n",
      "90/90 - 0s - loss: 0.0431 - accuracy: 0.9889 - val_loss: 0.1233 - val_accuracy: 0.9667\n",
      "Epoch 128/200\n",
      "90/90 - 0s - loss: 0.0284 - accuracy: 0.9889 - val_loss: 0.1643 - val_accuracy: 0.9000\n",
      "Epoch 129/200\n",
      "90/90 - 0s - loss: 0.0154 - accuracy: 1.0000 - val_loss: 0.1734 - val_accuracy: 0.9000\n",
      "Epoch 130/200\n",
      "90/90 - 0s - loss: 0.0200 - accuracy: 1.0000 - val_loss: 0.1517 - val_accuracy: 0.9333\n",
      "Epoch 131/200\n",
      "90/90 - 0s - loss: 0.0218 - accuracy: 1.0000 - val_loss: 0.1347 - val_accuracy: 0.9333\n",
      "Epoch 132/200\n",
      "90/90 - 0s - loss: 0.0183 - accuracy: 1.0000 - val_loss: 0.1255 - val_accuracy: 0.9333\n",
      "Epoch 133/200\n",
      "90/90 - 0s - loss: 0.0180 - accuracy: 1.0000 - val_loss: 0.1302 - val_accuracy: 0.9333\n",
      "Epoch 134/200\n",
      "90/90 - 0s - loss: 0.0153 - accuracy: 1.0000 - val_loss: 0.1331 - val_accuracy: 0.9333\n",
      "Epoch 135/200\n",
      "90/90 - 0s - loss: 0.1565 - accuracy: 0.9556 - val_loss: 0.1096 - val_accuracy: 0.9333\n",
      "Epoch 136/200\n",
      "90/90 - 0s - loss: 0.0296 - accuracy: 1.0000 - val_loss: 0.0790 - val_accuracy: 0.9667\n",
      "Epoch 137/200\n",
      "90/90 - 0s - loss: 0.0339 - accuracy: 1.0000 - val_loss: 0.0729 - val_accuracy: 1.0000\n",
      "Epoch 138/200\n",
      "90/90 - 0s - loss: 0.0198 - accuracy: 1.0000 - val_loss: 0.0680 - val_accuracy: 1.0000\n",
      "Epoch 139/200\n",
      "90/90 - 0s - loss: 0.0207 - accuracy: 1.0000 - val_loss: 0.0663 - val_accuracy: 1.0000\n",
      "Epoch 140/200\n",
      "90/90 - 0s - loss: 0.1363 - accuracy: 0.9556 - val_loss: 0.0667 - val_accuracy: 1.0000\n",
      "Epoch 141/200\n",
      "90/90 - 0s - loss: 0.0339 - accuracy: 0.9889 - val_loss: 0.0729 - val_accuracy: 0.9667\n",
      "Epoch 142/200\n",
      "90/90 - 0s - loss: 0.0201 - accuracy: 1.0000 - val_loss: 0.0810 - val_accuracy: 0.9667\n",
      "Epoch 143/200\n",
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      "Epoch 144/200\n",
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      "Epoch 145/200\n",
      "90/90 - 0s - loss: 0.1094 - accuracy: 0.9556 - val_loss: 0.0918 - val_accuracy: 0.9667\n",
      "Epoch 146/200\n",
      "90/90 - 0s - loss: 0.0135 - accuracy: 1.0000 - val_loss: 0.0774 - val_accuracy: 0.9667\n",
      "Epoch 147/200\n",
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      "Epoch 148/200\n",
      "90/90 - 0s - loss: 0.0332 - accuracy: 0.9889 - val_loss: 0.0642 - val_accuracy: 0.9667\n",
      "Epoch 149/200\n",
      "90/90 - 0s - loss: 0.0122 - accuracy: 1.0000 - val_loss: 0.0521 - val_accuracy: 1.0000\n",
      "Epoch 150/200\n",
      "90/90 - 0s - loss: 0.0805 - accuracy: 0.9778 - val_loss: 0.0428 - val_accuracy: 1.0000\n",
      "Epoch 151/200\n",
      "90/90 - 0s - loss: 0.0171 - accuracy: 1.0000 - val_loss: 0.0499 - val_accuracy: 0.9667\n",
      "Epoch 152/200\n",
      "90/90 - 0s - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.0625 - val_accuracy: 0.9667\n",
      "Epoch 153/200\n",
      "90/90 - 0s - loss: 0.0168 - accuracy: 1.0000 - val_loss: 0.0762 - val_accuracy: 0.9667\n",
      "Epoch 154/200\n",
      "90/90 - 0s - loss: 0.0178 - accuracy: 1.0000 - val_loss: 0.0856 - val_accuracy: 0.9667\n",
      "Epoch 155/200\n",
      "90/90 - 0s - loss: 0.0064 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 0.9667\n",
      "Epoch 156/200\n",
      "90/90 - 0s - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.0729 - val_accuracy: 0.9667\n",
      "Epoch 157/200\n",
      "90/90 - 0s - loss: 0.0112 - accuracy: 1.0000 - val_loss: 0.0659 - val_accuracy: 0.9667\n",
      "Epoch 158/200\n",
      "90/90 - 0s - loss: 0.0073 - accuracy: 1.0000 - val_loss: 0.0595 - val_accuracy: 0.9667\n",
      "Epoch 159/200\n",
      "90/90 - 0s - loss: 0.0091 - accuracy: 1.0000 - val_loss: 0.0528 - val_accuracy: 0.9667\n",
      "Epoch 160/200\n",
      "90/90 - 0s - loss: 0.0098 - accuracy: 1.0000 - val_loss: 0.0458 - val_accuracy: 0.9667\n",
      "Epoch 161/200\n",
      "90/90 - 0s - loss: 0.0126 - accuracy: 1.0000 - val_loss: 0.0397 - val_accuracy: 0.9667\n",
      "Epoch 162/200\n",
      "90/90 - 0s - loss: 0.0066 - accuracy: 1.0000 - val_loss: 0.0366 - val_accuracy: 1.0000\n",
      "Epoch 163/200\n",
      "90/90 - 0s - loss: 0.1636 - accuracy: 0.9667 - val_loss: 0.0363 - val_accuracy: 1.0000\n",
      "Epoch 164/200\n",
      "90/90 - 0s - loss: 0.0129 - accuracy: 1.0000 - val_loss: 0.0397 - val_accuracy: 1.0000\n",
      "Epoch 165/200\n",
      "90/90 - 0s - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.0422 - val_accuracy: 1.0000\n",
      "Epoch 166/200\n",
      "90/90 - 0s - loss: 0.0086 - accuracy: 1.0000 - val_loss: 0.0451 - val_accuracy: 1.0000\n",
      "Epoch 167/200\n",
      "90/90 - 0s - loss: 0.0127 - accuracy: 1.0000 - val_loss: 0.0486 - val_accuracy: 0.9667\n",
      "Epoch 168/200\n",
      "90/90 - 0s - loss: 0.0381 - accuracy: 0.9778 - val_loss: 0.0491 - val_accuracy: 1.0000\n",
      "Epoch 169/200\n",
      "90/90 - 0s - loss: 0.0093 - accuracy: 1.0000 - val_loss: 0.0484 - val_accuracy: 0.9667\n",
      "Epoch 170/200\n",
      "90/90 - 0s - loss: 0.0233 - accuracy: 1.0000 - val_loss: 0.0461 - val_accuracy: 0.9667\n",
      "Epoch 171/200\n",
      "90/90 - 0s - loss: 0.0150 - accuracy: 1.0000 - val_loss: 0.0398 - val_accuracy: 0.9667\n",
      "Epoch 172/200\n",
      "90/90 - 0s - loss: 0.0281 - accuracy: 0.9889 - val_loss: 0.0377 - val_accuracy: 0.9667\n",
      "Epoch 173/200\n",
      "90/90 - 0s - loss: 0.0179 - accuracy: 1.0000 - val_loss: 0.0386 - val_accuracy: 0.9667\n",
      "Epoch 174/200\n",
      "90/90 - 0s - loss: 0.0256 - accuracy: 0.9889 - val_loss: 0.0377 - val_accuracy: 0.9667\n",
      "Epoch 175/200\n",
      "90/90 - 0s - loss: 0.0110 - accuracy: 1.0000 - val_loss: 0.0318 - val_accuracy: 1.0000\n",
      "Epoch 176/200\n",
      "90/90 - 0s - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.0287 - val_accuracy: 1.0000\n",
      "Epoch 177/200\n",
      "90/90 - 0s - loss: 0.0100 - accuracy: 1.0000 - val_loss: 0.0270 - val_accuracy: 1.0000\n",
      "Epoch 178/200\n",
      "90/90 - 0s - loss: 0.0220 - accuracy: 0.9889 - val_loss: 0.0271 - val_accuracy: 1.0000\n",
      "Epoch 179/200\n",
      "90/90 - 0s - loss: 0.0348 - accuracy: 0.9778 - val_loss: 0.0349 - val_accuracy: 0.9667\n",
      "Epoch 180/200\n",
      "90/90 - 0s - loss: 0.0061 - accuracy: 1.0000 - val_loss: 0.0452 - val_accuracy: 0.9667\n",
      "Epoch 181/200\n",
      "90/90 - 0s - loss: 0.0486 - accuracy: 0.9889 - val_loss: 0.0596 - val_accuracy: 0.9667\n",
      "Epoch 182/200\n",
      "90/90 - 0s - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.0774 - val_accuracy: 0.9667\n",
      "Epoch 183/200\n",
      "90/90 - 0s - loss: 0.0801 - accuracy: 0.9778 - val_loss: 0.0922 - val_accuracy: 0.9667\n",
      "Epoch 184/200\n",
      "90/90 - 0s - loss: 0.0224 - accuracy: 0.9889 - val_loss: 0.0967 - val_accuracy: 0.9333\n",
      "Epoch 185/200\n",
      "90/90 - 0s - loss: 0.1056 - accuracy: 0.9778 - val_loss: 0.0892 - val_accuracy: 0.9667\n",
      "Epoch 186/200\n",
      "90/90 - 0s - loss: 0.0954 - accuracy: 0.9667 - val_loss: 0.0768 - val_accuracy: 0.9667\n",
      "Epoch 187/200\n",
      "90/90 - 0s - loss: 0.0240 - accuracy: 0.9889 - val_loss: 0.0699 - val_accuracy: 0.9667\n",
      "Epoch 188/200\n",
      "90/90 - 0s - loss: 0.0138 - accuracy: 1.0000 - val_loss: 0.0700 - val_accuracy: 0.9667\n",
      "Epoch 189/200\n",
      "90/90 - 0s - loss: 0.0136 - accuracy: 1.0000 - val_loss: 0.0681 - val_accuracy: 0.9667\n",
      "Epoch 190/200\n",
      "90/90 - 0s - loss: 0.0550 - accuracy: 0.9778 - val_loss: 0.0600 - val_accuracy: 0.9667\n",
      "Epoch 191/200\n",
      "90/90 - 0s - loss: 0.0202 - accuracy: 1.0000 - val_loss: 0.0425 - val_accuracy: 0.9667\n",
      "Epoch 192/200\n",
      "90/90 - 0s - loss: 0.0332 - accuracy: 1.0000 - val_loss: 0.0351 - val_accuracy: 1.0000\n",
      "Epoch 193/200\n",
      "90/90 - 0s - loss: 0.0315 - accuracy: 0.9889 - val_loss: 0.0362 - val_accuracy: 1.0000\n",
      "Epoch 194/200\n",
      "90/90 - 0s - loss: 0.0241 - accuracy: 1.0000 - val_loss: 0.0381 - val_accuracy: 1.0000\n",
      "Epoch 195/200\n",
      "90/90 - 0s - loss: 0.0575 - accuracy: 0.9778 - val_loss: 0.0306 - val_accuracy: 1.0000\n",
      "Epoch 196/200\n",
      "90/90 - 0s - loss: 0.0602 - accuracy: 0.9889 - val_loss: 0.0264 - val_accuracy: 1.0000\n",
      "Epoch 197/200\n",
      "90/90 - 0s - loss: 0.0282 - accuracy: 0.9889 - val_loss: 0.0454 - val_accuracy: 0.9667\n",
      "Epoch 198/200\n",
      "90/90 - 0s - loss: 0.0458 - accuracy: 0.9889 - val_loss: 0.0596 - val_accuracy: 0.9667\n",
      "Epoch 199/200\n",
      "90/90 - 0s - loss: 0.0675 - accuracy: 0.9778 - val_loss: 0.0480 - val_accuracy: 0.9667\n",
      "Epoch 200/200\n",
      "90/90 - 0s - loss: 0.0218 - accuracy: 1.0000 - val_loss: 0.0395 - val_accuracy: 1.0000\n"
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    }
   ],
   "source": [
    "history = model.fit(\n",
    "    X_train, \n",
    "    y_train, \n",
    "    epochs=200, \n",
    "    validation_split=0.25, \n",
    "    batch_size=40, \n",
    "    verbose=2\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation\n",
    "\n",
    "Finally, we plot training and validation loss and accuracy to observe how the accuracy of our model improves over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "def plot_metric(history, metric):\n",
    "    train_metrics = history.history[metric]\n",
    "    val_metrics = history.history['val_'+metric]\n",
    "    epochs = range(1, len(train_metrics) + 1)\n",
    "    plt.plot(epochs, train_metrics)\n",
    "    plt.plot(epochs, val_metrics)\n",
    "    plt.title('Training and validation '+ metric)\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(metric)\n",
    "    plt.legend([\"train_\"+metric, 'val_'+metric])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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   "source": [
    "plot_metric(history, 'loss')"
   ]
  },
  {
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   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30/1 - 0s - loss: 0.1103 - accuracy: 0.9667\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.1103389710187912, 0.96666664]"
      ]
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     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "# Evaluate the model on the test set\n",
    "model.evaluate(X_test, y_test, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## That's it\n",
    "\n",
    "Thanks for reading\n",
    "\n",
    "This is a notebook for the medium article [Batch Normalization in practice: an example with Keras and TensorFlow 2.0](https://medium.com/@bindiatwork/batch-normalization-in-practice-an-example-with-keras-and-tensorflow-2-0-b1ec28bde96f)\n",
    "\n",
    "Please check out article for instructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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