Dropout_regularisation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The task is to train a network to discrminate between sonar signals bounced off metal cylinder (M) \n",
    "\n",
    "#### and those bounced off roughly cylindrica rock (R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.read_csv('../Desktop/ML/Datasets/sonar_dataset.csv', header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(208, 61)"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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    "df.shape"
   ]
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   "execution_count": 4,
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   "outputs": [
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   "outputs": [
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    "df.columns"
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   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    111\n",
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    "df[60].value_counts()"
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   "outputs": [],
   "source": [
    "X = df.iloc[:, :-1]\n",
    "y = df.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     R\n",
       "198  0\n",
       "155  0\n",
       "191  0\n",
       "164  0\n",
       "40   1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding in the lables\n",
    "y = pd.get_dummies(y, drop_first=True)\n",
    "y.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R\n",
       "0    111\n",
       "1     97\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156, 60)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(52, 60)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(120, input_dim=60, activation='relu'),\n",
    "    keras.layers.Dense(60, activation='relu'),\n",
    "    keras.layers.Dense(30, activation='relu'),\n",
    "    keras.layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "20/20 [==============================] - 1s 3ms/step - loss: 0.6886 - accuracy: 0.5705\n",
      "Epoch 2/100\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.6418 - accuracy: 0.6859\n",
      "Epoch 3/100\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.5965 - accuracy: 0.7179\n",
      "Epoch 4/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.5476 - accuracy: 0.7244\n",
      "Epoch 5/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.5250 - accuracy: 0.7115\n",
      "Epoch 6/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.4535 - accuracy: 0.7949\n",
      "Epoch 7/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.3961 - accuracy: 0.8654\n",
      "Epoch 8/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.3721 - accuracy: 0.8269\n",
      "Epoch 9/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.3754 - accuracy: 0.8269\n",
      "Epoch 10/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.3427 - accuracy: 0.8526\n",
      "Epoch 11/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.3180 - accuracy: 0.8782\n",
      "Epoch 12/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.3308 - accuracy: 0.8462\n",
      "Epoch 13/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.3130 - accuracy: 0.8526\n",
      "Epoch 14/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.3006 - accuracy: 0.8718\n",
      "Epoch 15/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.2797 - accuracy: 0.8654\n",
      "Epoch 16/100\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.2619 - accuracy: 0.8718\n",
      "Epoch 17/100\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.2676 - accuracy: 0.8910\n",
      "Epoch 18/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.2007 - accuracy: 0.9103\n",
      "Epoch 19/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1810 - accuracy: 0.9359\n",
      "Epoch 20/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1684 - accuracy: 0.9359\n",
      "Epoch 21/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1910 - accuracy: 0.9231\n",
      "Epoch 22/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1894 - accuracy: 0.9167\n",
      "Epoch 23/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1395 - accuracy: 0.9487\n",
      "Epoch 24/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1051 - accuracy: 0.9744\n",
      "Epoch 25/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1082 - accuracy: 0.9551\n",
      "Epoch 26/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0948 - accuracy: 0.9808\n",
      "Epoch 27/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1010 - accuracy: 0.9615\n",
      "Epoch 28/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0959 - accuracy: 0.9679\n",
      "Epoch 29/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0605 - accuracy: 0.9936\n",
      "Epoch 30/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0524 - accuracy: 1.0000\n",
      "Epoch 31/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0559 - accuracy: 0.9936\n",
      "Epoch 32/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0396 - accuracy: 0.9936\n",
      "Epoch 33/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0508 - accuracy: 0.9936\n",
      "Epoch 34/100\n",
      "20/20 [==============================] - 0s 8ms/step - loss: 0.0322 - accuracy: 1.0000: 0s - loss: 0.0327 - accuracy: 1.\n",
      "Epoch 35/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0359 - accuracy: 0.9872\n",
      "Epoch 36/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0404 - accuracy: 0.9936\n",
      "Epoch 37/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0410 - accuracy: 0.9936\n",
      "Epoch 38/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0563 - accuracy: 0.9808\n",
      "Epoch 39/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0426 - accuracy: 0.9936\n",
      "Epoch 40/100\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.0394 - accuracy: 0.9872\n",
      "Epoch 41/100\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.0314 - accuracy: 0.9872\n",
      "Epoch 42/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0219 - accuracy: 1.0000\n",
      "Epoch 43/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0210 - accuracy: 1.0000\n",
      "Epoch 44/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0132 - accuracy: 1.0000\n",
      "Epoch 45/100\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.0102 - accuracy: 1.0000\n",
      "Epoch 46/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0097 - accuracy: 1.0000\n",
      "Epoch 47/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0062 - accuracy: 1.0000\n",
      "Epoch 48/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0067 - accuracy: 1.0000\n",
      "Epoch 49/100\n",
      "20/20 [==============================] - 0s 11ms/step - loss: 0.0051 - accuracy: 1.0000\n",
      "Epoch 50/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0043 - accuracy: 1.0000\n",
      "Epoch 51/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0040 - accuracy: 1.0000\n",
      "Epoch 52/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0037 - accuracy: 1.0000\n",
      "Epoch 53/100\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 0.0039 - accuracy: 1.0000\n",
      "Epoch 54/100\n",
      "20/20 [==============================] - ETA: 0s - loss: 0.0027 - accuracy: 1.00 - 0s 2ms/step - loss: 0.0036 - accuracy: 1.0000\n",
      "Epoch 55/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0032 - accuracy: 1.0000\n",
      "Epoch 56/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0029 - accuracy: 1.0000\n",
      "Epoch 57/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0031 - accuracy: 1.0000\n",
      "Epoch 58/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0026 - accuracy: 1.0000\n",
      "Epoch 59/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0025 - accuracy: 1.0000\n",
      "Epoch 60/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0023 - accuracy: 1.0000\n",
      "Epoch 61/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0022 - accuracy: 1.0000\n",
      "Epoch 62/100\n",
      "20/20 [==============================] - 0s 5ms/step - loss: 0.0021 - accuracy: 1.0000\n",
      "Epoch 63/100\n",
      "20/20 [==============================] - 0s 5ms/step - loss: 0.0020 - accuracy: 1.0000\n",
      "Epoch 64/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0019 - accuracy: 1.0000\n",
      "Epoch 65/100\n",
      "20/20 [==============================] - 0s 5ms/step - loss: 0.0018 - accuracy: 1.0000\n",
      "Epoch 66/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0017 - accuracy: 1.0000\n",
      "Epoch 67/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0018 - accuracy: 1.0000\n",
      "Epoch 68/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0016 - accuracy: 1.0000\n",
      "Epoch 69/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0015 - accuracy: 1.0000\n",
      "Epoch 70/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0015 - accuracy: 1.0000\n",
      "Epoch 71/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0015 - accuracy: 1.0000\n",
      "Epoch 72/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0015 - accuracy: 1.0000\n",
      "Epoch 73/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0013 - accuracy: 1.0000: 0s - loss: 0.0014 - accuracy: 1.00\n",
      "Epoch 74/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0013 - accuracy: 1.0000\n",
      "Epoch 75/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 1.0000\n",
      "Epoch 76/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0013 - accuracy: 1.0000\n",
      "Epoch 77/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0011 - accuracy: 1.0000\n",
      "Epoch 78/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0011 - accuracy: 1.0000\n",
      "Epoch 79/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0011 - accuracy: 1.0000\n",
      "Epoch 80/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0012 - accuracy: 1.0000\n",
      "Epoch 81/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0011 - accuracy: 1.0000\n",
      "Epoch 82/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.0010 - accuracy: 1.0000\n",
      "Epoch 83/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 9.3166e-04 - accuracy: 1.0000\n",
      "Epoch 84/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 8.4964e-04 - accuracy: 1.0000\n",
      "Epoch 85/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 8.2409e-04 - accuracy: 1.0000\n",
      "Epoch 86/100\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 8.3358e-04 - accuracy: 1.0000\n",
      "Epoch 87/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 7.7258e-04 - accuracy: 1.0000\n",
      "Epoch 88/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 7.6373e-04 - accuracy: 1.0000\n",
      "Epoch 89/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 7.1292e-04 - accuracy: 1.0000\n",
      "Epoch 90/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 7.0528e-04 - accuracy: 1.0000\n",
      "Epoch 91/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 7.0544e-04 - accuracy: 1.0000\n",
      "Epoch 92/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 6.5621e-04 - accuracy: 1.0000\n",
      "Epoch 93/100\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 6.3580e-04 - accuracy: 1.0000\n",
      "Epoch 94/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 6.1251e-04 - accuracy: 1.0000\n",
      "Epoch 95/100\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 6.0005e-04 - accuracy: 1.0000\n",
      "Epoch 96/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 5.7451e-04 - accuracy: 1.0000\n",
      "Epoch 97/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 5.6801e-04 - accuracy: 1.0000\n",
      "Epoch 98/100\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 5.4500e-04 - accuracy: 1.0000\n",
      "Epoch 99/100\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 5.2825e-04 - accuracy: 1.0000\n",
      "Epoch 100/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 5.1314e-04 - accuracy: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1296cb84790>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=100, batch_size=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 4ms/step - loss: 1.0738 - accuracy: 0.8462\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.0738253593444824, 0.8461538553237915]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred = np.round(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [1.]], dtype=float32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R\n",
       "12  1\n",
       "80  1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.96      0.86        26\n",
      "           1       0.95      0.73      0.83        26\n",
      "\n",
      "    accuracy                           0.85        52\n",
      "   macro avg       0.87      0.85      0.84        52\n",
      "weighted avg       0.87      0.85      0.84        52\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With Dropout layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1 = keras.Sequential([\n",
    "    keras.layers.Dense(120, input_dim=60, activation='relu'),\n",
    "    keras.layers.Dropout(0.5),\n",
    "    keras.layers.Dense(60, activation='relu'),\n",
    "    keras.layers.Dropout(0.5),\n",
    "    keras.layers.Dense(30, activation='relu'),\n",
    "    keras.layers.Dropout(0.5),\n",
    "    keras.layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "model_1.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "20/20 [==============================] - 2s 2ms/step - loss: 0.6785 - accuracy: 0.6154\n",
      "Epoch 2/100\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.7061 - accuracy: 0.5577\n",
      "Epoch 3/100\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 0.7037 - accuracy: 0.5449\n",
      "Epoch 4/100\n",
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      "Epoch 100/100\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1296fcb25b0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1.fit(X_train, y_train, epochs=100, batch_size=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 3ms/step - loss: 0.6901 - accuracy: 0.7885\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6900641918182373, 0.7884615659713745]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1.evaluate(X_test, y_test)"
   ]
  }
 ],
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