tensorflow2/005-early-stopping/early-stopping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Early Stopping in Practice\n",
    "\n",
    "This is a notebook for the medium article [Early Stopping in Practice](https://towardsdatascience.com/a-practical-introduction-to-early-stopping-in-machine-learning-550ac88bc8fd)\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": 4,
   "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",
    "\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing data for Keras and TensorFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# label -> one-hot encoding\n",
    "label = pd.get_dummies(df['label'])\n",
    "label.columns = ['label_' + str(x) for x in label.columns]\n",
    "df = pd.concat([df, label], axis=1)\n",
    "\n",
    "# drop old label\n",
    "df.drop(['label'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data set in a training set (75%) and a test set (25%)\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "  X,\n",
    "  y,\n",
    "  test_size=0.25\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "def create_model(): \n",
    "    model = Sequential([\n",
    "        Dense(64, activation='relu', input_shape=(4,)),\n",
    "        Dense(128, activation='relu'),\n",
    "        Dense(128, activation='relu'),\n",
    "        Dense(128, activation='relu'),\n",
    "        Dense(64, activation='relu'),\n",
    "        Dense(64, activation='relu'),\n",
    "        Dense(64, activation='relu'),\n",
    "        Dense(3, activation='softmax'),\n",
    "    ])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(\n",
    "    optimizer='adam', \n",
    "    loss='categorical_crossentropy', \n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 84 samples, validate on 28 samples\n",
      "Epoch 1/200\n",
      "84/84 - 1s - loss: 1.0901 - accuracy: 0.3214 - val_loss: 1.0210 - val_accuracy: 0.7143\n",
      "Epoch 2/200\n",
      "84/84 - 0s - loss: 1.0163 - accuracy: 0.6905 - val_loss: 0.9427 - val_accuracy: 0.7143\n",
      "Epoch 3/200\n",
      "84/84 - 0s - loss: 0.9271 - accuracy: 0.7024 - val_loss: 0.8496 - val_accuracy: 0.7143\n",
      "Epoch 4/200\n",
      "84/84 - 0s - loss: 0.8421 - accuracy: 0.6786 - val_loss: 0.7493 - val_accuracy: 0.7143\n",
      "Epoch 5/200\n",
      "84/84 - 0s - loss: 0.7361 - accuracy: 0.7262 - val_loss: 0.6540 - val_accuracy: 0.8214\n",
      "Epoch 6/200\n",
      "84/84 - 0s - loss: 0.6338 - accuracy: 0.8690 - val_loss: 0.5392 - val_accuracy: 0.8214\n",
      "Epoch 7/200\n",
      "84/84 - 0s - loss: 0.5411 - accuracy: 0.6905 - val_loss: 0.4829 - val_accuracy: 0.7143\n",
      "Epoch 8/200\n",
      "84/84 - 0s - loss: 0.4804 - accuracy: 0.6786 - val_loss: 0.4137 - val_accuracy: 0.7500\n",
      "Epoch 9/200\n",
      "84/84 - 0s - loss: 0.3993 - accuracy: 0.7619 - val_loss: 0.3541 - val_accuracy: 0.9286\n",
      "Epoch 10/200\n",
      "84/84 - 0s - loss: 0.3421 - accuracy: 0.9762 - val_loss: 0.3170 - val_accuracy: 0.8929\n",
      "Epoch 11/200\n",
      "84/84 - 0s - loss: 0.3180 - accuracy: 0.8452 - val_loss: 0.2921 - val_accuracy: 0.9286\n",
      "Epoch 12/200\n",
      "84/84 - 0s - loss: 0.2641 - accuracy: 0.9405 - val_loss: 0.4158 - val_accuracy: 0.7143\n",
      "Epoch 13/200\n",
      "84/84 - 0s - loss: 0.3262 - accuracy: 0.7976 - val_loss: 0.2760 - val_accuracy: 0.8929\n",
      "Epoch 14/200\n",
      "84/84 - 0s - loss: 0.2688 - accuracy: 0.8452 - val_loss: 0.2377 - val_accuracy: 0.9286\n",
      "Epoch 15/200\n",
      "84/84 - 0s - loss: 0.2083 - accuracy: 0.9405 - val_loss: 0.2254 - val_accuracy: 0.8929\n",
      "Epoch 16/200\n",
      "84/84 - 0s - loss: 0.2218 - accuracy: 0.9286 - val_loss: 0.2348 - val_accuracy: 0.9286\n",
      "Epoch 17/200\n",
      "84/84 - 0s - loss: 0.1647 - accuracy: 0.9405 - val_loss: 0.2330 - val_accuracy: 0.8571\n",
      "Epoch 18/200\n",
      "84/84 - 0s - loss: 0.1201 - accuracy: 0.9762 - val_loss: 0.2794 - val_accuracy: 0.9286\n",
      "Epoch 19/200\n",
      "84/84 - 0s - loss: 0.1692 - accuracy: 0.9286 - val_loss: 0.2304 - val_accuracy: 0.8571\n",
      "Epoch 20/200\n",
      "84/84 - 0s - loss: 0.0998 - accuracy: 0.9762 - val_loss: 0.2344 - val_accuracy: 0.8571\n",
      "Epoch 21/200\n",
      "84/84 - 0s - loss: 0.0911 - accuracy: 0.9762 - val_loss: 0.2428 - val_accuracy: 0.9286\n",
      "Epoch 22/200\n",
      "84/84 - 0s - loss: 0.0875 - accuracy: 0.9881 - val_loss: 0.2378 - val_accuracy: 0.8571\n",
      "Epoch 23/200\n",
      "84/84 - 0s - loss: 0.0733 - accuracy: 0.9881 - val_loss: 0.2423 - val_accuracy: 0.8571\n",
      "Epoch 24/200\n",
      "84/84 - 0s - loss: 0.0678 - accuracy: 0.9881 - val_loss: 0.2647 - val_accuracy: 0.9286\n",
      "Epoch 25/200\n",
      "84/84 - 0s - loss: 0.0696 - accuracy: 0.9881 - val_loss: 0.2520 - val_accuracy: 0.8571\n",
      "Epoch 26/200\n",
      "84/84 - 0s - loss: 0.0656 - accuracy: 0.9881 - val_loss: 0.4071 - val_accuracy: 0.8571\n",
      "Epoch 27/200\n",
      "84/84 - 0s - loss: 0.2221 - accuracy: 0.8571 - val_loss: 0.2833 - val_accuracy: 0.8571\n",
      "Epoch 28/200\n",
      "84/84 - 0s - loss: 0.1029 - accuracy: 0.9524 - val_loss: 0.2646 - val_accuracy: 0.9286\n",
      "Epoch 29/200\n",
      "84/84 - 0s - loss: 0.0688 - accuracy: 0.9881 - val_loss: 0.2767 - val_accuracy: 0.8571\n",
      "Epoch 30/200\n",
      "84/84 - 0s - loss: 0.1970 - accuracy: 0.8929 - val_loss: 0.2489 - val_accuracy: 0.9286\n",
      "Epoch 31/200\n",
      "84/84 - 0s - loss: 0.1329 - accuracy: 0.9405 - val_loss: 0.5428 - val_accuracy: 0.8929\n",
      "Epoch 32/200\n",
      "84/84 - 0s - loss: 0.4213 - accuracy: 0.8571 - val_loss: 0.2279 - val_accuracy: 0.8929\n",
      "Epoch 33/200\n",
      "84/84 - 0s - loss: 0.1040 - accuracy: 0.9643 - val_loss: 0.5410 - val_accuracy: 0.8214\n",
      "Epoch 34/200\n",
      "84/84 - 0s - loss: 0.2798 - accuracy: 0.8571 - val_loss: 0.2412 - val_accuracy: 0.9286\n",
      "Epoch 35/200\n",
      "84/84 - 0s - loss: 0.1296 - accuracy: 0.9405 - val_loss: 0.3392 - val_accuracy: 0.8929\n",
      "Epoch 36/200\n",
      "84/84 - 0s - loss: 0.2339 - accuracy: 0.8929 - val_loss: 0.2215 - val_accuracy: 0.9286\n",
      "Epoch 37/200\n",
      "84/84 - 0s - loss: 0.0648 - accuracy: 0.9881 - val_loss: 0.2599 - val_accuracy: 0.8571\n",
      "Epoch 38/200\n",
      "84/84 - 0s - loss: 0.1295 - accuracy: 0.9405 - val_loss: 0.2194 - val_accuracy: 0.8571\n",
      "Epoch 39/200\n",
      "84/84 - 0s - loss: 0.0617 - accuracy: 0.9762 - val_loss: 0.2484 - val_accuracy: 0.9286\n",
      "Epoch 40/200\n",
      "84/84 - 0s - loss: 0.0908 - accuracy: 0.9643 - val_loss: 0.2387 - val_accuracy: 0.9286\n",
      "Epoch 41/200\n",
      "84/84 - 0s - loss: 0.0464 - accuracy: 0.9881 - val_loss: 0.2769 - val_accuracy: 0.8571\n",
      "Epoch 42/200\n",
      "84/84 - 0s - loss: 0.1031 - accuracy: 0.9524 - val_loss: 0.2329 - val_accuracy: 0.8571\n",
      "Epoch 43/200\n",
      "84/84 - 0s - loss: 0.0403 - accuracy: 0.9881 - val_loss: 0.2555 - val_accuracy: 0.9286\n",
      "Epoch 44/200\n",
      "84/84 - 0s - loss: 0.0440 - accuracy: 0.9881 - val_loss: 0.2436 - val_accuracy: 0.8929\n",
      "Epoch 45/200\n",
      "84/84 - 0s - loss: 0.0385 - accuracy: 0.9881 - val_loss: 0.2460 - val_accuracy: 0.8571\n",
      "Epoch 46/200\n",
      "84/84 - 0s - loss: 0.0388 - accuracy: 0.9762 - val_loss: 0.2613 - val_accuracy: 0.9286\n",
      "Epoch 47/200\n",
      "84/84 - 0s - loss: 0.0351 - accuracy: 0.9881 - val_loss: 0.2973 - val_accuracy: 0.9286\n",
      "Epoch 48/200\n",
      "84/84 - 0s - loss: 0.0465 - accuracy: 0.9881 - val_loss: 0.2741 - val_accuracy: 0.9286\n",
      "Epoch 49/200\n",
      "84/84 - 0s - loss: 0.0347 - accuracy: 0.9881 - val_loss: 0.2637 - val_accuracy: 0.8571\n",
      "Epoch 50/200\n",
      "84/84 - 0s - loss: 0.0411 - accuracy: 0.9762 - val_loss: 0.2681 - val_accuracy: 0.8571\n",
      "Epoch 51/200\n",
      "84/84 - 0s - loss: 0.0336 - accuracy: 0.9881 - val_loss: 0.2933 - val_accuracy: 0.9286\n",
      "Epoch 52/200\n",
      "84/84 - 0s - loss: 0.0324 - accuracy: 0.9881 - val_loss: 0.3002 - val_accuracy: 0.9286\n",
      "Epoch 53/200\n",
      "84/84 - 0s - loss: 0.0328 - accuracy: 0.9881 - val_loss: 0.2816 - val_accuracy: 0.8929\n",
      "Epoch 54/200\n",
      "84/84 - 0s - loss: 0.0292 - accuracy: 0.9881 - val_loss: 0.2865 - val_accuracy: 0.8929\n",
      "Epoch 55/200\n",
      "84/84 - 0s - loss: 0.0262 - accuracy: 0.9881 - val_loss: 0.3448 - val_accuracy: 0.9286\n",
      "Epoch 56/200\n",
      "84/84 - 0s - loss: 0.0501 - accuracy: 0.9881 - val_loss: 0.3622 - val_accuracy: 0.9286\n",
      "Epoch 57/200\n",
      "84/84 - 0s - loss: 0.0431 - accuracy: 0.9881 - val_loss: 0.2950 - val_accuracy: 0.8571\n",
      "Epoch 58/200\n",
      "84/84 - 0s - loss: 0.0437 - accuracy: 0.9762 - val_loss: 0.2996 - val_accuracy: 0.8571\n",
      "Epoch 59/200\n",
      "84/84 - 0s - loss: 0.0353 - accuracy: 0.9762 - val_loss: 0.3303 - val_accuracy: 0.9286\n",
      "Epoch 60/200\n",
      "84/84 - 0s - loss: 0.0343 - accuracy: 0.9881 - val_loss: 0.3091 - val_accuracy: 0.9286\n",
      "Epoch 61/200\n",
      "84/84 - 0s - loss: 0.0285 - accuracy: 0.9881 - val_loss: 0.3078 - val_accuracy: 0.9286\n",
      "Epoch 62/200\n",
      "84/84 - 0s - loss: 0.0440 - accuracy: 0.9881 - val_loss: 0.3945 - val_accuracy: 0.9286\n",
      "Epoch 63/200\n",
      "84/84 - 0s - loss: 0.0524 - accuracy: 0.9881 - val_loss: 0.3097 - val_accuracy: 0.8929\n",
      "Epoch 64/200\n",
      "84/84 - 0s - loss: 0.0214 - accuracy: 1.0000 - val_loss: 0.3520 - val_accuracy: 0.8929\n",
      "Epoch 65/200\n",
      "84/84 - 0s - loss: 0.0914 - accuracy: 0.9524 - val_loss: 0.3723 - val_accuracy: 0.9286\n",
      "Epoch 66/200\n",
      "84/84 - 0s - loss: 0.0787 - accuracy: 0.9762 - val_loss: 0.4314 - val_accuracy: 0.9286\n",
      "Epoch 67/200\n",
      "84/84 - 0s - loss: 0.0620 - accuracy: 0.9881 - val_loss: 0.3138 - val_accuracy: 0.8571\n",
      "Epoch 68/200\n",
      "84/84 - 0s - loss: 0.0622 - accuracy: 0.9762 - val_loss: 0.3152 - val_accuracy: 0.9286\n",
      "Epoch 69/200\n",
      "84/84 - 0s - loss: 0.0489 - accuracy: 0.9881 - val_loss: 0.4218 - val_accuracy: 0.9286\n",
      "Epoch 70/200\n",
      "84/84 - 0s - loss: 0.0755 - accuracy: 0.9762 - val_loss: 0.3168 - val_accuracy: 0.9286\n",
      "Epoch 71/200\n",
      "84/84 - 0s - loss: 0.0240 - accuracy: 0.9881 - val_loss: 0.3854 - val_accuracy: 0.8571\n",
      "Epoch 72/200\n",
      "84/84 - 0s - loss: 0.0914 - accuracy: 0.9643 - val_loss: 0.3319 - val_accuracy: 0.9286\n",
      "Epoch 73/200\n",
      "84/84 - 0s - loss: 0.0526 - accuracy: 0.9881 - val_loss: 0.3786 - val_accuracy: 0.9286\n",
      "Epoch 74/200\n",
      "84/84 - 0s - loss: 0.0595 - accuracy: 0.9881 - val_loss: 0.3259 - val_accuracy: 0.8929\n",
      "Epoch 75/200\n",
      "84/84 - 0s - loss: 0.0490 - accuracy: 0.9762 - val_loss: 0.2953 - val_accuracy: 0.9286\n",
      "Epoch 76/200\n",
      "84/84 - 0s - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.3389 - val_accuracy: 0.9286\n",
      "Epoch 77/200\n",
      "84/84 - 0s - loss: 0.0395 - accuracy: 0.9881 - val_loss: 0.3037 - val_accuracy: 0.9286\n",
      "Epoch 78/200\n",
      "84/84 - 0s - loss: 0.0261 - accuracy: 0.9881 - val_loss: 0.3085 - val_accuracy: 0.8929\n",
      "Epoch 79/200\n",
      "84/84 - 0s - loss: 0.0402 - accuracy: 0.9762 - val_loss: 0.2916 - val_accuracy: 0.9286\n",
      "Epoch 80/200\n",
      "84/84 - 0s - loss: 0.0296 - accuracy: 0.9881 - val_loss: 0.3391 - val_accuracy: 0.9286\n",
      "Epoch 81/200\n",
      "84/84 - 0s - loss: 0.0352 - accuracy: 0.9881 - val_loss: 0.2974 - val_accuracy: 0.9286\n",
      "Epoch 82/200\n",
      "84/84 - 0s - loss: 0.0233 - accuracy: 0.9881 - val_loss: 0.2932 - val_accuracy: 0.8571\n",
      "Epoch 83/200\n",
      "84/84 - 0s - loss: 0.0205 - accuracy: 1.0000 - val_loss: 0.3190 - val_accuracy: 0.9286\n",
      "Epoch 84/200\n",
      "84/84 - 0s - loss: 0.0236 - accuracy: 0.9881 - val_loss: 0.3243 - val_accuracy: 0.9286\n",
      "Epoch 85/200\n",
      "84/84 - 0s - loss: 0.0229 - accuracy: 0.9881 - val_loss: 0.2975 - val_accuracy: 0.8929\n",
      "Epoch 86/200\n",
      "84/84 - 0s - loss: 0.0225 - accuracy: 1.0000 - val_loss: 0.3885 - val_accuracy: 0.9286\n",
      "Epoch 87/200\n",
      "84/84 - 0s - loss: 0.1167 - accuracy: 0.9643 - val_loss: 0.5467 - val_accuracy: 0.8929\n",
      "Epoch 88/200\n",
      "84/84 - 0s - loss: 0.1801 - accuracy: 0.9167 - val_loss: 0.3650 - val_accuracy: 0.8571\n",
      "Epoch 89/200\n",
      "84/84 - 0s - loss: 0.2271 - accuracy: 0.9286 - val_loss: 0.2849 - val_accuracy: 0.9286\n",
      "Epoch 90/200\n",
      "84/84 - 0s - loss: 0.1654 - accuracy: 0.9524 - val_loss: 0.3927 - val_accuracy: 0.8929\n",
      "Epoch 91/200\n",
      "84/84 - 0s - loss: 0.1288 - accuracy: 0.9524 - val_loss: 0.4607 - val_accuracy: 0.8571\n",
      "Epoch 92/200\n",
      "84/84 - 0s - loss: 0.4151 - accuracy: 0.8452 - val_loss: 0.4100 - val_accuracy: 0.8929\n",
      "Epoch 93/200\n",
      "84/84 - 0s - loss: 0.1953 - accuracy: 0.9286 - val_loss: 0.1904 - val_accuracy: 0.9286\n",
      "Epoch 94/200\n",
      "84/84 - 0s - loss: 0.0915 - accuracy: 0.9524 - val_loss: 0.2715 - val_accuracy: 0.8929\n",
      "Epoch 95/200\n",
      "84/84 - 0s - loss: 0.1594 - accuracy: 0.9167 - val_loss: 0.2275 - val_accuracy: 0.9286\n",
      "Epoch 96/200\n",
      "84/84 - 0s - loss: 0.0772 - accuracy: 0.9643 - val_loss: 0.1818 - val_accuracy: 0.8929\n",
      "Epoch 97/200\n",
      "84/84 - 0s - loss: 0.0728 - accuracy: 0.9881 - val_loss: 0.2492 - val_accuracy: 0.8571\n",
      "Epoch 98/200\n",
      "84/84 - 0s - loss: 0.1210 - accuracy: 0.9524 - val_loss: 0.1991 - val_accuracy: 0.8929\n",
      "Epoch 99/200\n",
      "84/84 - 0s - loss: 0.0579 - accuracy: 0.9762 - val_loss: 0.1929 - val_accuracy: 0.9286\n",
      "Epoch 100/200\n",
      "84/84 - 0s - loss: 0.0481 - accuracy: 0.9881 - val_loss: 0.2190 - val_accuracy: 0.9286\n",
      "Epoch 101/200\n",
      "84/84 - 0s - loss: 0.0495 - accuracy: 0.9881 - val_loss: 0.2050 - val_accuracy: 0.9286\n",
      "Epoch 102/200\n",
      "84/84 - 0s - loss: 0.0330 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.8929\n",
      "Epoch 103/200\n",
      "84/84 - 0s - loss: 0.0339 - accuracy: 1.0000 - val_loss: 0.2124 - val_accuracy: 0.9286\n",
      "Epoch 104/200\n",
      "84/84 - 0s - loss: 0.0263 - accuracy: 1.0000 - val_loss: 0.2290 - val_accuracy: 0.9286\n",
      "Epoch 105/200\n",
      "84/84 - 0s - loss: 0.0236 - accuracy: 0.9881 - val_loss: 0.2420 - val_accuracy: 0.9286\n",
      "Epoch 106/200\n",
      "84/84 - 0s - loss: 0.0243 - accuracy: 0.9881 - val_loss: 0.2443 - val_accuracy: 0.9286\n",
      "Epoch 107/200\n",
      "84/84 - 0s - loss: 0.0193 - accuracy: 0.9881 - val_loss: 0.2404 - val_accuracy: 0.9286\n",
      "Epoch 108/200\n",
      "84/84 - 0s - loss: 0.0206 - accuracy: 1.0000 - val_loss: 0.2472 - val_accuracy: 0.9286\n",
      "Epoch 109/200\n",
      "84/84 - 0s - loss: 0.0180 - accuracy: 1.0000 - val_loss: 0.2587 - val_accuracy: 0.9286\n",
      "Epoch 110/200\n",
      "84/84 - 0s - loss: 0.0179 - accuracy: 0.9881 - val_loss: 0.2702 - val_accuracy: 0.9286\n",
      "Epoch 111/200\n",
      "84/84 - 0s - loss: 0.0162 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9286\n",
      "Epoch 112/200\n",
      "84/84 - 0s - loss: 0.0170 - accuracy: 1.0000 - val_loss: 0.3195 - val_accuracy: 0.9286\n",
      "Epoch 113/200\n",
      "84/84 - 0s - loss: 0.0259 - accuracy: 0.9881 - val_loss: 0.4327 - val_accuracy: 0.9286\n",
      "Epoch 114/200\n",
      "84/84 - 0s - loss: 0.1053 - accuracy: 0.9524 - val_loss: 0.3238 - val_accuracy: 0.9286\n",
      "Epoch 115/200\n",
      "84/84 - 0s - loss: 0.0166 - accuracy: 1.0000 - val_loss: 0.3203 - val_accuracy: 0.8929\n",
      "Epoch 116/200\n",
      "84/84 - 0s - loss: 0.0673 - accuracy: 0.9643 - val_loss: 0.2788 - val_accuracy: 0.8571\n",
      "Epoch 117/200\n",
      "84/84 - 0s - loss: 0.0282 - accuracy: 0.9762 - val_loss: 0.3326 - val_accuracy: 0.9286\n",
      "Epoch 118/200\n",
      "84/84 - 0s - loss: 0.0338 - accuracy: 0.9881 - val_loss: 0.3290 - val_accuracy: 0.9286\n",
      "Epoch 119/200\n",
      "84/84 - 0s - loss: 0.0220 - accuracy: 0.9881 - val_loss: 0.2949 - val_accuracy: 0.9286\n",
      "Epoch 120/200\n",
      "84/84 - 0s - loss: 0.0149 - accuracy: 0.9881 - val_loss: 0.2875 - val_accuracy: 0.9286\n",
      "Epoch 121/200\n",
      "84/84 - 0s - loss: 0.0131 - accuracy: 1.0000 - val_loss: 0.2770 - val_accuracy: 0.9286\n",
      "Epoch 122/200\n",
      "84/84 - 0s - loss: 0.0145 - accuracy: 1.0000 - val_loss: 0.2801 - val_accuracy: 0.9286\n",
      "Epoch 123/200\n",
      "84/84 - 0s - loss: 0.0130 - accuracy: 1.0000 - val_loss: 0.2929 - val_accuracy: 0.9286\n",
      "Epoch 124/200\n",
      "84/84 - 0s - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.3050 - val_accuracy: 0.9286\n",
      "Epoch 125/200\n",
      "84/84 - 0s - loss: 0.0143 - accuracy: 0.9881 - val_loss: 0.2926 - val_accuracy: 0.9286\n",
      "Epoch 126/200\n",
      "84/84 - 0s - loss: 0.0113 - accuracy: 1.0000 - val_loss: 0.2872 - val_accuracy: 0.9286\n",
      "Epoch 127/200\n",
      "84/84 - 0s - loss: 0.0129 - accuracy: 1.0000 - val_loss: 0.2852 - val_accuracy: 0.9286\n",
      "Epoch 128/200\n",
      "84/84 - 0s - loss: 0.0113 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9286\n",
      "Epoch 129/200\n",
      "84/84 - 0s - loss: 0.0115 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9286\n",
      "Epoch 130/200\n",
      "84/84 - 0s - loss: 0.0106 - accuracy: 1.0000 - val_loss: 0.3012 - val_accuracy: 0.9286\n",
      "Epoch 131/200\n",
      "84/84 - 0s - loss: 0.0100 - accuracy: 1.0000 - val_loss: 0.2986 - val_accuracy: 0.9286\n",
      "Epoch 132/200\n",
      "84/84 - 0s - loss: 0.0106 - accuracy: 1.0000 - val_loss: 0.2986 - val_accuracy: 0.9286\n",
      "Epoch 133/200\n",
      "84/84 - 0s - loss: 0.0101 - accuracy: 1.0000 - val_loss: 0.3466 - val_accuracy: 0.9286\n",
      "Epoch 134/200\n",
      "84/84 - 0s - loss: 0.0149 - accuracy: 0.9881 - val_loss: 0.3986 - val_accuracy: 0.9286\n",
      "Epoch 135/200\n",
      "84/84 - 0s - loss: 0.0333 - accuracy: 0.9881 - val_loss: 0.3292 - val_accuracy: 0.9286\n",
      "Epoch 136/200\n",
      "84/84 - 0s - loss: 0.0128 - accuracy: 1.0000 - val_loss: 0.2955 - val_accuracy: 0.8929\n",
      "Epoch 137/200\n",
      "84/84 - 0s - loss: 0.0312 - accuracy: 0.9762 - val_loss: 0.2958 - val_accuracy: 0.9286\n",
      "Epoch 138/200\n",
      "84/84 - 0s - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.3761 - val_accuracy: 0.9286\n",
      "Epoch 139/200\n",
      "84/84 - 0s - loss: 0.0254 - accuracy: 0.9881 - val_loss: 0.3527 - val_accuracy: 0.9286\n",
      "Epoch 140/200\n",
      "84/84 - 0s - loss: 0.0149 - accuracy: 0.9881 - val_loss: 0.3367 - val_accuracy: 0.9286\n",
      "Epoch 141/200\n",
      "84/84 - 0s - loss: 0.0225 - accuracy: 0.9881 - val_loss: 0.3987 - val_accuracy: 0.9286\n",
      "Epoch 142/200\n",
      "84/84 - 0s - loss: 0.0252 - accuracy: 0.9881 - val_loss: 0.5011 - val_accuracy: 0.8571\n",
      "Epoch 143/200\n",
      "84/84 - 0s - loss: 0.6659 - accuracy: 0.8929 - val_loss: 0.4318 - val_accuracy: 0.8571\n",
      "Epoch 144/200\n",
      "84/84 - 0s - loss: 0.1300 - accuracy: 0.9524 - val_loss: 0.4619 - val_accuracy: 0.8929\n",
      "Epoch 145/200\n",
      "84/84 - 0s - loss: 0.1021 - accuracy: 0.9524 - val_loss: 0.2317 - val_accuracy: 0.9286\n",
      "Epoch 146/200\n",
      "84/84 - 0s - loss: 0.0491 - accuracy: 0.9762 - val_loss: 0.3087 - val_accuracy: 0.8571\n",
      "Epoch 147/200\n",
      "84/84 - 0s - loss: 0.0779 - accuracy: 0.9643 - val_loss: 0.2055 - val_accuracy: 0.9286\n",
      "Epoch 148/200\n",
      "84/84 - 0s - loss: 0.0142 - accuracy: 1.0000 - val_loss: 0.2890 - val_accuracy: 0.9286\n",
      "Epoch 149/200\n",
      "84/84 - 0s - loss: 0.0593 - accuracy: 0.9762 - val_loss: 0.2878 - val_accuracy: 0.9286\n",
      "Epoch 150/200\n",
      "84/84 - 0s - loss: 0.0460 - accuracy: 0.9762 - val_loss: 0.1978 - val_accuracy: 0.9286\n",
      "Epoch 151/200\n",
      "84/84 - 0s - loss: 0.0176 - accuracy: 1.0000 - val_loss: 0.2065 - val_accuracy: 0.9286\n",
      "Epoch 152/200\n",
      "84/84 - 0s - loss: 0.0375 - accuracy: 0.9762 - val_loss: 0.1995 - val_accuracy: 0.9643\n",
      "Epoch 153/200\n",
      "84/84 - 0s - loss: 0.0217 - accuracy: 1.0000 - val_loss: 0.2458 - val_accuracy: 0.9286\n",
      "Epoch 154/200\n",
      "84/84 - 0s - loss: 0.0179 - accuracy: 1.0000 - val_loss: 0.2817 - val_accuracy: 0.9286\n",
      "Epoch 155/200\n",
      "84/84 - 0s - loss: 0.0282 - accuracy: 0.9762 - val_loss: 0.2561 - val_accuracy: 0.9286\n",
      "Epoch 156/200\n",
      "84/84 - 0s - loss: 0.0158 - accuracy: 1.0000 - val_loss: 0.2092 - val_accuracy: 0.9643\n",
      "Epoch 157/200\n",
      "84/84 - 0s - loss: 0.0339 - accuracy: 0.9881 - val_loss: 0.2346 - val_accuracy: 0.8929\n",
      "Epoch 158/200\n",
      "84/84 - 0s - loss: 0.0401 - accuracy: 0.9643 - val_loss: 0.2299 - val_accuracy: 0.9286\n",
      "Epoch 159/200\n",
      "84/84 - 0s - loss: 0.0132 - accuracy: 1.0000 - val_loss: 0.3051 - val_accuracy: 0.9286\n",
      "Epoch 160/200\n",
      "84/84 - 0s - loss: 0.0250 - accuracy: 0.9762 - val_loss: 0.3075 - val_accuracy: 0.9286\n",
      "Epoch 161/200\n",
      "84/84 - 0s - loss: 0.0212 - accuracy: 0.9881 - val_loss: 0.2428 - val_accuracy: 0.9286\n",
      "Epoch 162/200\n",
      "84/84 - 0s - loss: 0.0138 - accuracy: 1.0000 - val_loss: 0.2257 - val_accuracy: 0.9643\n",
      "Epoch 163/200\n",
      "84/84 - 0s - loss: 0.0228 - accuracy: 0.9881 - val_loss: 0.2294 - val_accuracy: 0.9286\n",
      "Epoch 164/200\n",
      "84/84 - 0s - loss: 0.0154 - accuracy: 1.0000 - val_loss: 0.3192 - val_accuracy: 0.9286\n",
      "Epoch 165/200\n",
      "84/84 - 0s - loss: 0.0325 - accuracy: 0.9881 - val_loss: 0.4519 - val_accuracy: 0.8929\n",
      "Epoch 166/200\n",
      "84/84 - 0s - loss: 0.0934 - accuracy: 0.9643 - val_loss: 0.3366 - val_accuracy: 0.9286\n",
      "Epoch 167/200\n",
      "84/84 - 0s - loss: 0.0117 - accuracy: 1.0000 - val_loss: 0.2394 - val_accuracy: 0.9286\n",
      "Epoch 168/200\n",
      "84/84 - 0s - loss: 0.0298 - accuracy: 0.9762 - val_loss: 0.3360 - val_accuracy: 0.9286\n",
      "Epoch 169/200\n",
      "84/84 - 0s - loss: 0.0912 - accuracy: 0.9762 - val_loss: 0.6236 - val_accuracy: 0.8929\n",
      "Epoch 170/200\n",
      "84/84 - 0s - loss: 0.3243 - accuracy: 0.9048 - val_loss: 0.4380 - val_accuracy: 0.8929\n",
      "Epoch 171/200\n",
      "84/84 - 0s - loss: 0.0730 - accuracy: 0.9762 - val_loss: 0.2131 - val_accuracy: 0.9286\n",
      "Epoch 172/200\n",
      "84/84 - 0s - loss: 0.0281 - accuracy: 0.9881 - val_loss: 0.2642 - val_accuracy: 0.8571\n",
      "Epoch 173/200\n",
      "84/84 - 0s - loss: 0.0533 - accuracy: 0.9643 - val_loss: 0.2466 - val_accuracy: 0.9286\n",
      "Epoch 174/200\n",
      "84/84 - 0s - loss: 0.0162 - accuracy: 1.0000 - val_loss: 0.3031 - val_accuracy: 0.9286\n",
      "Epoch 175/200\n",
      "84/84 - 0s - loss: 0.0566 - accuracy: 0.9762 - val_loss: 0.2698 - val_accuracy: 0.9286\n",
      "Epoch 176/200\n",
      "84/84 - 0s - loss: 0.0205 - accuracy: 0.9881 - val_loss: 0.2030 - val_accuracy: 0.9286\n",
      "Epoch 177/200\n",
      "84/84 - 0s - loss: 0.0435 - accuracy: 0.9762 - val_loss: 0.2221 - val_accuracy: 0.8929\n",
      "Epoch 178/200\n",
      "84/84 - 0s - loss: 0.0364 - accuracy: 0.9881 - val_loss: 0.2361 - val_accuracy: 0.9286\n",
      "Epoch 179/200\n",
      "84/84 - 0s - loss: 0.0168 - accuracy: 1.0000 - val_loss: 0.2830 - val_accuracy: 0.9286\n",
      "Epoch 180/200\n",
      "84/84 - 0s - loss: 0.0186 - accuracy: 0.9881 - val_loss: 0.2498 - val_accuracy: 0.9286\n",
      "Epoch 181/200\n",
      "84/84 - 0s - loss: 0.0120 - accuracy: 1.0000 - val_loss: 0.2230 - val_accuracy: 0.9286\n",
      "Epoch 182/200\n",
      "84/84 - 0s - loss: 0.0142 - accuracy: 1.0000 - val_loss: 0.2281 - val_accuracy: 0.9286\n",
      "Epoch 183/200\n",
      "84/84 - 0s - loss: 0.0120 - accuracy: 1.0000 - val_loss: 0.2542 - val_accuracy: 0.9286\n",
      "Epoch 184/200\n",
      "84/84 - 0s - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.2890 - val_accuracy: 0.9286\n",
      "Epoch 185/200\n",
      "84/84 - 0s - loss: 0.0127 - accuracy: 1.0000 - val_loss: 0.2895 - val_accuracy: 0.9286\n",
      "Epoch 186/200\n",
      "84/84 - 0s - loss: 0.0099 - accuracy: 1.0000 - val_loss: 0.2599 - val_accuracy: 0.9286\n",
      "Epoch 187/200\n",
      "84/84 - 0s - loss: 0.0085 - accuracy: 1.0000 - val_loss: 0.2543 - val_accuracy: 0.9286\n",
      "Epoch 188/200\n",
      "84/84 - 0s - loss: 0.0092 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9286\n",
      "Epoch 189/200\n",
      "84/84 - 0s - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.2891 - val_accuracy: 0.9286\n",
      "Epoch 190/200\n",
      "84/84 - 0s - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.3095 - val_accuracy: 0.9286\n",
      "Epoch 191/200\n",
      "84/84 - 0s - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9286\n",
      "Epoch 192/200\n",
      "84/84 - 0s - loss: 0.0064 - accuracy: 1.0000 - val_loss: 0.2842 - val_accuracy: 0.9286\n",
      "Epoch 193/200\n",
      "84/84 - 0s - loss: 0.0073 - accuracy: 1.0000 - val_loss: 0.2851 - val_accuracy: 0.9286\n",
      "Epoch 194/200\n",
      "84/84 - 0s - loss: 0.0060 - accuracy: 1.0000 - val_loss: 0.3067 - val_accuracy: 0.9286\n",
      "Epoch 195/200\n",
      "84/84 - 0s - loss: 0.0057 - accuracy: 1.0000 - val_loss: 0.3291 - val_accuracy: 0.9286\n",
      "Epoch 196/200\n",
      "84/84 - 0s - loss: 0.0063 - accuracy: 1.0000 - val_loss: 0.3166 - val_accuracy: 0.9286\n",
      "Epoch 197/200\n",
      "84/84 - 0s - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.2753 - val_accuracy: 0.9286\n",
      "Epoch 198/200\n",
      "84/84 - 0s - loss: 0.0306 - accuracy: 0.9762 - val_loss: 0.4704 - val_accuracy: 0.9286\n",
      "Epoch 199/200\n",
      "84/84 - 0s - loss: 0.2017 - accuracy: 0.9286 - val_loss: 0.7474 - val_accuracy: 0.8929\n",
      "Epoch 200/200\n",
      "84/84 - 0s - loss: 0.5269 - accuracy: 0.8690 - val_loss: 0.4781 - val_accuracy: 0.8929\n"
     ]
    }
   ],
   "source": [
    "# Fit the model to the training data\n",
    "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": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\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": 30,
   "metadata": {},
   "outputs": [
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  {
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   ],
   "source": [
    "plot_metric(history, 'loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EarlyStopping() with default settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "early_stopping = EarlyStopping() # by default, monitor='val_loss' and patience=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 84 samples, validate on 28 samples\n",
      "Epoch 1/200\n",
      "84/84 - 0s - loss: 0.0549 - accuracy: 0.9762 - val_loss: 0.2758 - val_accuracy: 0.9286\n",
      "Epoch 2/200\n",
      "84/84 - 0s - loss: 0.0752 - accuracy: 0.9643 - val_loss: 0.2399 - val_accuracy: 0.9286\n",
      "Epoch 3/200\n",
      "84/84 - 0s - loss: 0.0417 - accuracy: 0.9881 - val_loss: 0.1848 - val_accuracy: 0.9286\n",
      "Epoch 4/200\n",
      "84/84 - 0s - loss: 0.0246 - accuracy: 1.0000 - val_loss: 0.1790 - val_accuracy: 0.9286\n",
      "Epoch 5/200\n",
      "84/84 - 0s - loss: 0.0399 - accuracy: 0.9762 - val_loss: 0.1773 - val_accuracy: 0.9643\n",
      "Epoch 6/200\n",
      "84/84 - 0s - loss: 0.0290 - accuracy: 0.9881 - val_loss: 0.2036 - val_accuracy: 0.9286\n"
     ]
    }
   ],
   "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",
    "    callbacks=[early_stopping]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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   "source": [
    "plot_metric(history, 'loss')"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customizing Early Stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_early_stopping = EarlyStopping(\n",
    "    monitor='val_accuracy', \n",
    "    patience=8, \n",
    "    min_delta=0.001, \n",
    "    mode='max'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Train on 84 samples, validate on 28 samples\n",
      "Epoch 1/200\n",
      "84/84 - 0s - loss: 0.0181 - accuracy: 1.0000 - val_loss: 0.2464 - val_accuracy: 0.9286\n",
      "Epoch 2/200\n",
      "84/84 - 0s - loss: 0.0253 - accuracy: 0.9881 - val_loss: 0.2413 - val_accuracy: 0.9286\n",
      "Epoch 3/200\n",
      "84/84 - 0s - loss: 0.0184 - accuracy: 1.0000 - val_loss: 0.2083 - val_accuracy: 0.9286\n",
      "Epoch 4/200\n",
      "84/84 - 0s - loss: 0.0146 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9286\n",
      "Epoch 5/200\n",
      "84/84 - 0s - loss: 0.0166 - accuracy: 1.0000 - val_loss: 0.2169 - val_accuracy: 0.9286\n",
      "Epoch 6/200\n",
      "84/84 - 0s - loss: 0.0124 - accuracy: 1.0000 - val_loss: 0.2900 - val_accuracy: 0.9286\n",
      "Epoch 7/200\n",
      "84/84 - 0s - loss: 0.0337 - accuracy: 0.9881 - val_loss: 0.2882 - val_accuracy: 0.9286\n",
      "Epoch 8/200\n",
      "84/84 - 0s - loss: 0.0376 - accuracy: 0.9762 - val_loss: 0.4433 - val_accuracy: 0.8571\n",
      "Epoch 9/200\n",
      "84/84 - 0s - loss: 0.1473 - accuracy: 0.9524 - val_loss: 0.3199 - val_accuracy: 0.8929\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",
    "    callbacks=[custom_early_stopping]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
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