tensorflow2/002-3-ways-to-build-machine-learning-model-with-keras/3-ways-to-build-a-machine-learning-model-with-keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 ways to create a Machine Learning model with Keras and TensorFlow 2.0\n",
    "\n",
    "This is a notebook for the medium article [3 ways to create a Machine Learning model with Keras and TensorFlow 2.0](https://medium.com/@bindiatwork/3-ways-to-create-a-machine-learning-model-with-keras-and-tensorflow-2-0-de09323af4d3)\n",
    "\n",
    "Please check out article for instructions\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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": 29,
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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      "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing data for Keras and TensorFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 33,
   "metadata": {},
   "outputs": [
    {
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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_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",
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      "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  "
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     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 37,
   "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": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Sequential model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 5)                 25        \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                60        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 3)                 33        \n",
      "=================================================================\n",
      "Total params: 118\n",
      "Trainable params: 118\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Passing a list of layers to the constructor\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "model = Sequential([\n",
    "    Dense(5, activation='relu', input_shape=(4,)),\n",
    "    Dense(10, activation='relu'),\n",
    "    Dense(3, activation='softmax'),\n",
    "])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_3 (Dense)              (None, 5)                 25        \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                60        \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 3)                 33        \n",
      "=================================================================\n",
      "Total params: 118\n",
      "Trainable params: 118\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Adding layer via add() method\n",
    "model_seq = Sequential()\n",
    "model_seq.add(Dense(5, activation='relu', input_shape=(4,)))\n",
    "model_seq.add(Dense(10, activation='relu'))\n",
    "model_seq.add(Dense(3, activation='softmax'))\n",
    "\n",
    "model_seq.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Training a Sequential model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_seq.compile(\n",
    "    optimizer='rmsprop',\n",
    "    loss='categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 89 samples, validate on 23 samples\n",
      "Epoch 1/30\n",
      "89/89 [==============================] - 0s 837us/sample - loss: 1.0693 - accuracy: 0.3258 - val_loss: 1.0267 - val_accuracy: 0.3478\n",
      "Epoch 2/30\n",
      "89/89 [==============================] - 0s 431us/sample - loss: 1.0653 - accuracy: 0.3258 - val_loss: 1.0272 - val_accuracy: 0.3478\n",
      "Epoch 3/30\n",
      "89/89 [==============================] - 0s 450us/sample - loss: 1.0603 - accuracy: 0.3258 - val_loss: 1.0265 - val_accuracy: 0.3478\n",
      "Epoch 4/30\n",
      "89/89 [==============================] - 0s 420us/sample - loss: 1.0562 - accuracy: 0.3483 - val_loss: 1.0242 - val_accuracy: 0.5217\n",
      "Epoch 5/30\n",
      "89/89 [==============================] - 0s 465us/sample - loss: 1.0525 - accuracy: 0.3820 - val_loss: 1.0252 - val_accuracy: 0.6522\n",
      "Epoch 6/30\n",
      "89/89 [==============================] - 0s 431us/sample - loss: 1.0485 - accuracy: 0.5393 - val_loss: 1.0258 - val_accuracy: 0.6957\n",
      "Epoch 7/30\n",
      "89/89 [==============================] - 0s 461us/sample - loss: 1.0447 - accuracy: 0.5843 - val_loss: 1.0273 - val_accuracy: 0.7391\n",
      "Epoch 8/30\n",
      "89/89 [==============================] - 0s 438us/sample - loss: 1.0403 - accuracy: 0.5843 - val_loss: 1.0257 - val_accuracy: 0.7391\n",
      "Epoch 9/30\n",
      "89/89 [==============================] - 0s 458us/sample - loss: 1.0367 - accuracy: 0.5730 - val_loss: 1.0267 - val_accuracy: 0.6957\n",
      "Epoch 10/30\n",
      "89/89 [==============================] - 0s 473us/sample - loss: 1.0331 - accuracy: 0.5618 - val_loss: 1.0247 - val_accuracy: 0.6957\n",
      "Epoch 11/30\n",
      "89/89 [==============================] - 0s 487us/sample - loss: 1.0302 - accuracy: 0.5955 - val_loss: 1.0234 - val_accuracy: 0.6957\n",
      "Epoch 12/30\n",
      "89/89 [==============================] - 0s 504us/sample - loss: 1.0272 - accuracy: 0.6067 - val_loss: 1.0223 - val_accuracy: 0.6522\n",
      "Epoch 13/30\n",
      "89/89 [==============================] - 0s 493us/sample - loss: 1.0250 - accuracy: 0.6292 - val_loss: 1.0190 - val_accuracy: 0.6522\n",
      "Epoch 14/30\n",
      "89/89 [==============================] - 0s 483us/sample - loss: 1.0218 - accuracy: 0.6404 - val_loss: 1.0191 - val_accuracy: 0.6522\n",
      "Epoch 15/30\n",
      "89/89 [==============================] - 0s 481us/sample - loss: 1.0185 - accuracy: 0.6517 - val_loss: 1.0186 - val_accuracy: 0.6522\n",
      "Epoch 16/30\n",
      "89/89 [==============================] - 0s 465us/sample - loss: 1.0153 - accuracy: 0.6517 - val_loss: 1.0173 - val_accuracy: 0.6522\n",
      "Epoch 17/30\n",
      "89/89 [==============================] - 0s 476us/sample - loss: 1.0126 - accuracy: 0.6742 - val_loss: 1.0142 - val_accuracy: 0.6522\n",
      "Epoch 18/30\n",
      "89/89 [==============================] - 0s 477us/sample - loss: 1.0097 - accuracy: 0.6629 - val_loss: 1.0175 - val_accuracy: 0.6522\n",
      "Epoch 19/30\n",
      "89/89 [==============================] - 0s 537us/sample - loss: 1.0059 - accuracy: 0.6854 - val_loss: 1.0146 - val_accuracy: 0.6522\n",
      "Epoch 20/30\n",
      "89/89 [==============================] - 0s 493us/sample - loss: 1.0030 - accuracy: 0.6966 - val_loss: 1.0123 - val_accuracy: 0.6087\n",
      "Epoch 21/30\n",
      "89/89 [==============================] - 0s 477us/sample - loss: 0.9999 - accuracy: 0.6742 - val_loss: 1.0100 - val_accuracy: 0.5652\n",
      "Epoch 22/30\n",
      "89/89 [==============================] - 0s 498us/sample - loss: 0.9975 - accuracy: 0.6854 - val_loss: 1.0084 - val_accuracy: 0.5217\n",
      "Epoch 23/30\n",
      "89/89 [==============================] - 0s 492us/sample - loss: 0.9942 - accuracy: 0.6629 - val_loss: 1.0065 - val_accuracy: 0.5217\n",
      "Epoch 24/30\n",
      "89/89 [==============================] - 0s 460us/sample - loss: 0.9920 - accuracy: 0.6629 - val_loss: 1.0042 - val_accuracy: 0.5217\n",
      "Epoch 25/30\n",
      "89/89 [==============================] - 0s 468us/sample - loss: 0.9890 - accuracy: 0.6629 - val_loss: 1.0017 - val_accuracy: 0.5217\n",
      "Epoch 26/30\n",
      "89/89 [==============================] - 0s 471us/sample - loss: 0.9860 - accuracy: 0.6629 - val_loss: 1.0005 - val_accuracy: 0.5217\n",
      "Epoch 27/30\n",
      "89/89 [==============================] - 0s 472us/sample - loss: 0.9826 - accuracy: 0.6629 - val_loss: 1.0008 - val_accuracy: 0.5217\n",
      "Epoch 28/30\n",
      "89/89 [==============================] - 0s 459us/sample - loss: 0.9790 - accuracy: 0.6742 - val_loss: 1.0019 - val_accuracy: 0.5217\n",
      "Epoch 29/30\n",
      "89/89 [==============================] - 0s 454us/sample - loss: 0.9756 - accuracy: 0.6742 - val_loss: 1.0007 - val_accuracy: 0.5217\n",
      "Epoch 30/30\n",
      "89/89 [==============================] - 0s 417us/sample - loss: 0.9727 - accuracy: 0.6742 - val_loss: 1.0025 - val_accuracy: 0.5217\n"
     ]
    }
   ],
   "source": [
    "history = model_seq.fit(X_train, y_train,\n",
    "                    batch_size= 64,\n",
    "                    epochs= 30,\n",
    "                    validation_split=0.2\n",
    "                   )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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, 'bo--')\n",
    "    plt.plot(epochs, val_metrics, 'ro-')\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": 55,
   "metadata": {},
   "outputs": [
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   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38/1 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 139us/sample - loss: 0.9356 - accuracy: 0.7105\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.9538187102267617, 0.7105263]"
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     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "model_seq.evaluate(x = X_test,y = y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Functional API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_6 (InputLayer)         [(None, 4)]               0         \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 5)                 25        \n",
      "_________________________________________________________________\n",
      "dense_18 (Dense)             (None, 10)                60        \n",
      "_________________________________________________________________\n",
      "dense_19 (Dense)             (None, 3)                 33        \n",
      "=================================================================\n",
      "Total params: 118\n",
      "Trainable params: 118\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, Dense\n",
    "\n",
    "\n",
    "# This returns a tensor\n",
    "inputs = Input(shape=(4,))\n",
    "\n",
    "# a layer instance is callable on a tensor, and returns a tensor\n",
    "x = Dense(5, activation='relu')(inputs)\n",
    "x = Dense(10, activation='relu')(x)\n",
    "outputs = Dense(3, activation='softmax')(x)\n",
    "\n",
    "# This creates a model that includes\n",
    "# the Input layer and three Dense layers\n",
    "model_fun_api = Model(inputs=inputs, outputs=outputs)\n",
    "\n",
    "model_fun_api.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Training a Function API model\n",
    "Such model can be trained just like Keras Sequential model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 89 samples, validate on 23 samples\n",
      "Epoch 1/30\n",
      "89/89 [==============================] - 1s 10ms/sample - loss: 1.5887 - accuracy: 0.3596 - val_loss: 2.0215 - val_accuracy: 0.1739\n",
      "Epoch 2/30\n",
      "89/89 [==============================] - 0s 475us/sample - loss: 1.4872 - accuracy: 0.3596 - val_loss: 1.9218 - val_accuracy: 0.1739\n",
      "Epoch 3/30\n",
      "89/89 [==============================] - 0s 470us/sample - loss: 1.4204 - accuracy: 0.3596 - val_loss: 1.8256 - val_accuracy: 0.1739\n",
      "Epoch 4/30\n",
      "89/89 [==============================] - 0s 486us/sample - loss: 1.3627 - accuracy: 0.3596 - val_loss: 1.7535 - val_accuracy: 0.1739\n",
      "Epoch 5/30\n",
      "89/89 [==============================] - 0s 463us/sample - loss: 1.3181 - accuracy: 0.3596 - val_loss: 1.6901 - val_accuracy: 0.1739\n",
      "Epoch 6/30\n",
      "89/89 [==============================] - 0s 461us/sample - loss: 1.2784 - accuracy: 0.3596 - val_loss: 1.6330 - val_accuracy: 0.1739\n",
      "Epoch 7/30\n",
      "89/89 [==============================] - 0s 486us/sample - loss: 1.2424 - accuracy: 0.3596 - val_loss: 1.5754 - val_accuracy: 0.1739\n",
      "Epoch 8/30\n",
      "89/89 [==============================] - 0s 472us/sample - loss: 1.2073 - accuracy: 0.3596 - val_loss: 1.5209 - val_accuracy: 0.1739\n",
      "Epoch 9/30\n",
      "89/89 [==============================] - 0s 460us/sample - loss: 1.1739 - accuracy: 0.3596 - val_loss: 1.4684 - val_accuracy: 0.1739\n",
      "Epoch 10/30\n",
      "89/89 [==============================] - 0s 472us/sample - loss: 1.1420 - accuracy: 0.3596 - val_loss: 1.4187 - val_accuracy: 0.1739\n",
      "Epoch 11/30\n",
      "89/89 [==============================] - 0s 461us/sample - loss: 1.1141 - accuracy: 0.3596 - val_loss: 1.3836 - val_accuracy: 0.1739\n",
      "Epoch 12/30\n",
      "89/89 [==============================] - 0s 475us/sample - loss: 1.0916 - accuracy: 0.3596 - val_loss: 1.3465 - val_accuracy: 0.1739\n",
      "Epoch 13/30\n",
      "89/89 [==============================] - 0s 481us/sample - loss: 1.0727 - accuracy: 0.3596 - val_loss: 1.3212 - val_accuracy: 0.1739\n",
      "Epoch 14/30\n",
      "89/89 [==============================] - 0s 476us/sample - loss: 1.0536 - accuracy: 0.3596 - val_loss: 1.2813 - val_accuracy: 0.1739\n",
      "Epoch 15/30\n",
      "89/89 [==============================] - 0s 478us/sample - loss: 1.0321 - accuracy: 0.3596 - val_loss: 1.2491 - val_accuracy: 0.1739\n",
      "Epoch 16/30\n",
      "89/89 [==============================] - 0s 468us/sample - loss: 1.0134 - accuracy: 0.3596 - val_loss: 1.2152 - val_accuracy: 0.1739\n",
      "Epoch 17/30\n",
      "89/89 [==============================] - 0s 459us/sample - loss: 0.9941 - accuracy: 0.3596 - val_loss: 1.1812 - val_accuracy: 0.1739\n",
      "Epoch 18/30\n",
      "89/89 [==============================] - 0s 457us/sample - loss: 0.9756 - accuracy: 0.3596 - val_loss: 1.1490 - val_accuracy: 0.1739\n",
      "Epoch 19/30\n",
      "89/89 [==============================] - 0s 443us/sample - loss: 0.9584 - accuracy: 0.3596 - val_loss: 1.1203 - val_accuracy: 0.1739\n",
      "Epoch 20/30\n",
      "89/89 [==============================] - 0s 457us/sample - loss: 0.9434 - accuracy: 0.3596 - val_loss: 1.0942 - val_accuracy: 0.1739\n",
      "Epoch 21/30\n",
      "89/89 [==============================] - 0s 462us/sample - loss: 0.9293 - accuracy: 0.3596 - val_loss: 1.0719 - val_accuracy: 0.1739\n",
      "Epoch 22/30\n",
      "89/89 [==============================] - 0s 443us/sample - loss: 0.9178 - accuracy: 0.3596 - val_loss: 1.0521 - val_accuracy: 0.1739\n",
      "Epoch 23/30\n",
      "89/89 [==============================] - 0s 450us/sample - loss: 0.9074 - accuracy: 0.3596 - val_loss: 1.0350 - val_accuracy: 0.1739\n",
      "Epoch 24/30\n",
      "89/89 [==============================] - 0s 451us/sample - loss: 0.8973 - accuracy: 0.3596 - val_loss: 1.0159 - val_accuracy: 0.1739\n",
      "Epoch 25/30\n",
      "89/89 [==============================] - 0s 457us/sample - loss: 0.8877 - accuracy: 0.3596 - val_loss: 1.0002 - val_accuracy: 0.1739\n",
      "Epoch 26/30\n",
      "89/89 [==============================] - 0s 449us/sample - loss: 0.8789 - accuracy: 0.3596 - val_loss: 0.9843 - val_accuracy: 0.1739\n",
      "Epoch 27/30\n",
      "89/89 [==============================] - 0s 461us/sample - loss: 0.8689 - accuracy: 0.3708 - val_loss: 0.9581 - val_accuracy: 0.1739\n",
      "Epoch 28/30\n",
      "89/89 [==============================] - 0s 465us/sample - loss: 0.8568 - accuracy: 0.3933 - val_loss: 0.9344 - val_accuracy: 0.2174\n",
      "Epoch 29/30\n",
      "89/89 [==============================] - 0s 470us/sample - loss: 0.8458 - accuracy: 0.4045 - val_loss: 0.9170 - val_accuracy: 0.3043\n",
      "Epoch 30/30\n",
      "89/89 [==============================] - 0s 477us/sample - loss: 0.8373 - accuracy: 0.4719 - val_loss: 0.9029 - val_accuracy: 0.3043\n"
     ]
    }
   ],
   "source": [
    "# First configure model using model.compile()\n",
    "model_fun_api.compile(\n",
    "    optimizer='rmsprop',\n",
    "    loss='categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "# Then, train the model with fit()\n",
    "history = model_fun_api.fit(X_train, y_train,\n",
    "                    batch_size= 64,\n",
    "                    epochs= 30,\n",
    "                    validation_split=0.2\n",
    "                   )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Model Evaludation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metric(history, 'accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38/1 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 114us/sample - loss: 0.7776 - accuracy: 0.6053\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.8011450893000552, 0.6052632]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fun_api.evaluate(x = X_test,y = y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Multi-input and multi-output model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For the scenario with 2 inputs and 1 output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_17\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_structure (InputLayer)    [(None, 4)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_image (InputLayer)        [(None, 256)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_48 (Dense)                (None, 10)           50          input_structure[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_49 (Dense)                (None, 100)          25700       input_image[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_7 (Concatenate)     (None, 110)          0           dense_48[0][0]                   \n",
      "                                                                 dense_49[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "outputs (Dense)                 (None, 3)            333         concatenate_7[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 26,083\n",
      "Trainable params: 26,083\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.layers import concatenate\n",
    "\n",
    "# 2 inputs, one structured data, the other image data\n",
    "input_structure = Input(shape=(4,), name='input_structure')\n",
    "input_image = Input(shape=(256,), name='input_image')\n",
    "\n",
    "# middle layers\n",
    "x_1 = Dense(10, activation='relu')(input_structure)\n",
    "x_2 = Dense(100, activation='relu')(input_image)\n",
    "\n",
    "c = concatenate([x_1, x_2])\n",
    "\n",
    "outputs = Dense(3, activation='sigmoid', name='outputs')(c)\n",
    "\n",
    "model = Model(inputs=[input_structure, input_image], outputs=outputs)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For the scenario with 1 input and 2 outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_19\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_image (InputLayer)        [(None, 256)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_51 (Dense)                (None, 300)          77100       input_image[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "output_1 (Dense)                (None, 1)            301         dense_51[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "output_2 (Dense)                (None, 3)            903         dense_51[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 78,304\n",
      "Trainable params: 78,304\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "## 1 inputs, 2 output\n",
    "\n",
    "from tensorflow.keras.layers import concatenate\n",
    "\n",
    "# only one input\n",
    "input_image = Input(shape=(256,), name='input_image')\n",
    "\n",
    "# middle layer\n",
    "x = Dense(300, activation='relu')(input_image)\n",
    "\n",
    "# output layer\n",
    "output_1 = Dense(1, activation='sigmoid', name='output_1')(x)\n",
    "output_2 = Dense(3, activation='softmax', name='output_2')(x)\n",
    "\n",
    "model = Model(inputs=input_image, outputs=[output_1, output_2])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For the scenario with 2 inputs and 2 outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_21\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_structured (InputLayer)   [(None, 4)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_image (InputLayer)        [(None, 256)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_54 (Dense)                (None, 10)           50          input_structured[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dense_55 (Dense)                (None, 300)          77100       input_image[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_9 (Concatenate)     (None, 310)          0           dense_54[0][0]                   \n",
      "                                                                 dense_55[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "output_1 (Dense)                (None, 1)            311         concatenate_9[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "output_2 (Dense)                (None, 3)            933         concatenate_9[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 78,394\n",
      "Trainable params: 78,394\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "## 2 inputs, 2 outputs\n",
    "from tensorflow.keras.layers import concatenate\n",
    "\n",
    "# 2 inputs, one structured data, the other image data\n",
    "input_structured = Input(shape=(4,), name='input_structured')\n",
    "input_image = Input(shape=(256,), name='input_image')\n",
    "\n",
    "# middle layers\n",
    "x_1 = Dense(10, activation='relu')(input_structured)\n",
    "x_2 = Dense(300, activation='relu')(input_image)\n",
    "\n",
    "c = concatenate([x_1, x_2])\n",
    "\n",
    "# output layser\n",
    "output_1 = Dense(1, activation='sigmoid', name='output_1')(c)\n",
    "output_2 = Dense(3, activation='softmax', name='output_2')(c)\n",
    "\n",
    "model = Model(inputs=[input_structured, input_image], outputs=[output_1, output_2])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Shared layer in Functional API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_23\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_14 (InputLayer)           [(None, 4)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_58 (Dense)                (None, 4)            20          input_14[0][0]                   \n",
      "                                                                 dense_58[0][0]                   \n",
      "                                                                 dense_58[1][0]                   \n",
      "                                                                 dense_58[2][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_59 (Dense)                (None, 3)            15          dense_58[3][0]                   \n",
      "==================================================================================================\n",
      "Total params: 35\n",
      "Trainable params: 35\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "inputs = Input(shape=(4,))\n",
    "\n",
    "# a layer instance is callable on a tensor, and returns a tensor\n",
    "shared_layer = Dense(4, activation='relu')\n",
    "\n",
    "# Now we apply the layer three times\n",
    "x = shared_layer(inputs)\n",
    "x = shared_layer(x)\n",
    "x = shared_layer(x)\n",
    "x = shared_layer(x)\n",
    "\n",
    "predictions = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(inputs=inputs, outputs=predictions)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Model Subclassing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "class CustomModel(Model):\n",
    "    \n",
    "    def __init__(self, **kwargs):\n",
    "        super(CustomModel, self).__init__(**kwargs)\n",
    "        self.dense1 = Dense(5, activation='relu', )\n",
    "        self.dense2 = Dense(10, activation='relu')\n",
    "        self.dense3 = Dense(3, activation='softmax')\n",
    "        \n",
    "    def call(self, inputs):\n",
    "        x = self.dense1(inputs)\n",
    "        x = self.dense2(x)\n",
    "        return self.dense3(x)\n",
    "        \n",
    "\n",
    "my_custom_model = CustomModel(name='my_custom_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Training a Model Subclassing model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 89 samples, validate on 23 samples\n",
      "Epoch 1/30\n",
      "89/89 [==============================] - 1s 13ms/sample - loss: 1.5281 - accuracy: 0.3596 - val_loss: 1.6140 - val_accuracy: 0.1739\n",
      "Epoch 2/30\n",
      "89/89 [==============================] - 0s 571us/sample - loss: 1.3818 - accuracy: 0.3596 - val_loss: 1.4869 - val_accuracy: 0.1739\n",
      "Epoch 3/30\n",
      "89/89 [==============================] - 0s 711us/sample - loss: 1.3231 - accuracy: 0.3596 - val_loss: 1.4209 - val_accuracy: 0.1739\n",
      "Epoch 4/30\n",
      "89/89 [==============================] - 0s 598us/sample - loss: 1.2934 - accuracy: 0.3596 - val_loss: 1.3799 - val_accuracy: 0.1739\n",
      "Epoch 5/30\n",
      "89/89 [==============================] - 0s 575us/sample - loss: 1.2702 - accuracy: 0.3596 - val_loss: 1.3169 - val_accuracy: 0.3913\n",
      "Epoch 6/30\n",
      "89/89 [==============================] - 0s 597us/sample - loss: 1.2468 - accuracy: 0.5281 - val_loss: 1.2931 - val_accuracy: 0.4348\n",
      "Epoch 7/30\n",
      "89/89 [==============================] - 0s 603us/sample - loss: 1.2360 - accuracy: 0.5618 - val_loss: 1.2688 - val_accuracy: 0.4783\n",
      "Epoch 8/30\n",
      "89/89 [==============================] - 0s 562us/sample - loss: 1.2285 - accuracy: 0.5506 - val_loss: 1.2414 - val_accuracy: 0.6087\n",
      "Epoch 9/30\n",
      "89/89 [==============================] - 0s 566us/sample - loss: 1.2201 - accuracy: 0.5281 - val_loss: 1.2307 - val_accuracy: 0.6087\n",
      "Epoch 10/30\n",
      "89/89 [==============================] - 0s 560us/sample - loss: 1.2145 - accuracy: 0.5056 - val_loss: 1.2298 - val_accuracy: 0.6087\n",
      "Epoch 11/30\n",
      "89/89 [==============================] - 0s 823us/sample - loss: 1.2094 - accuracy: 0.5506 - val_loss: 1.2010 - val_accuracy: 0.5652\n",
      "Epoch 12/30\n",
      "89/89 [==============================] - 0s 668us/sample - loss: 1.2051 - accuracy: 0.4607 - val_loss: 1.2121 - val_accuracy: 0.6087\n",
      "Epoch 13/30\n",
      "89/89 [==============================] - 0s 605us/sample - loss: 1.1995 - accuracy: 0.5056 - val_loss: 1.2097 - val_accuracy: 0.6087\n",
      "Epoch 14/30\n",
      "89/89 [==============================] - 0s 562us/sample - loss: 1.1972 - accuracy: 0.5056 - val_loss: 1.2098 - val_accuracy: 0.6087\n",
      "Epoch 15/30\n",
      "89/89 [==============================] - 0s 592us/sample - loss: 1.1922 - accuracy: 0.5281 - val_loss: 1.1944 - val_accuracy: 0.6087\n",
      "Epoch 16/30\n",
      "89/89 [==============================] - 0s 578us/sample - loss: 1.1894 - accuracy: 0.5169 - val_loss: 1.1710 - val_accuracy: 0.5217\n",
      "Epoch 17/30\n",
      "89/89 [==============================] - 0s 579us/sample - loss: 1.1831 - accuracy: 0.3933 - val_loss: 1.1691 - val_accuracy: 0.5652\n",
      "Epoch 18/30\n",
      "89/89 [==============================] - 0s 603us/sample - loss: 1.1793 - accuracy: 0.4270 - val_loss: 1.1789 - val_accuracy: 0.6087\n",
      "Epoch 19/30\n",
      "89/89 [==============================] - 0s 582us/sample - loss: 1.1762 - accuracy: 0.4831 - val_loss: 1.1758 - val_accuracy: 0.6087\n",
      "Epoch 20/30\n",
      "89/89 [==============================] - 0s 607us/sample - loss: 1.1722 - accuracy: 0.4831 - val_loss: 1.1588 - val_accuracy: 0.5652\n",
      "Epoch 21/30\n",
      "89/89 [==============================] - 0s 545us/sample - loss: 1.1672 - accuracy: 0.4607 - val_loss: 1.1481 - val_accuracy: 0.5217\n",
      "Epoch 22/30\n",
      "89/89 [==============================] - 0s 592us/sample - loss: 1.1657 - accuracy: 0.4157 - val_loss: 1.1320 - val_accuracy: 0.4783\n",
      "Epoch 23/30\n",
      "89/89 [==============================] - 0s 551us/sample - loss: 1.1605 - accuracy: 0.3258 - val_loss: 1.1505 - val_accuracy: 0.5652\n",
      "Epoch 24/30\n",
      "89/89 [==============================] - 0s 577us/sample - loss: 1.1558 - accuracy: 0.4944 - val_loss: 1.1436 - val_accuracy: 0.5652\n",
      "Epoch 25/30\n",
      "89/89 [==============================] - 0s 560us/sample - loss: 1.1522 - accuracy: 0.4719 - val_loss: 1.1307 - val_accuracy: 0.5217\n",
      "Epoch 26/30\n",
      "89/89 [==============================] - 0s 548us/sample - loss: 1.1482 - accuracy: 0.4270 - val_loss: 1.1378 - val_accuracy: 0.5652\n",
      "Epoch 27/30\n",
      "89/89 [==============================] - 0s 573us/sample - loss: 1.1443 - accuracy: 0.4831 - val_loss: 1.1301 - val_accuracy: 0.5652\n",
      "Epoch 28/30\n",
      "89/89 [==============================] - 0s 552us/sample - loss: 1.1411 - accuracy: 0.4831 - val_loss: 1.1223 - val_accuracy: 0.5652\n",
      "Epoch 29/30\n",
      "89/89 [==============================] - 0s 568us/sample - loss: 1.1399 - accuracy: 0.4607 - val_loss: 1.1322 - val_accuracy: 0.6087\n",
      "Epoch 30/30\n",
      "89/89 [==============================] - 0s 555us/sample - loss: 1.1350 - accuracy: 0.4831 - val_loss: 1.1362 - val_accuracy: 0.6087\n"
     ]
    }
   ],
   "source": [
    "my_custom_model.compile(optimizer='rmsprop',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = my_custom_model.fit(X_train, y_train,\n",
    "                    batch_size= 64,\n",
    "                    epochs= 30,\n",
    "                    validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"my_custom_model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_66 (Dense)             multiple                  25        \n",
      "_________________________________________________________________\n",
      "dense_67 (Dense)             multiple                  60        \n",
      "_________________________________________________________________\n",
      "dense_68 (Dense)             multiple                  33        \n",
      "=================================================================\n",
      "Total params: 118\n",
      "Trainable params: 118\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "my_custom_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
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    }
   ],
   "source": [
    "plot_metric(history, 'accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38/1 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 141us/sample - loss: 1.1417 - accuracy: 0.4737\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.1410379598015232, 0.47368422]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_custom_model.evaluate(x = X_test,y = y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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