Chapter06/Activity6.01/Activity6_01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Activity 6.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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       "   aa_000  ab_000        ac_000  ad_000  ae_000  af_000  ag_000  ag_001  \\\n",
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      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tensorflow import random\n",
    "\n",
    "# Load the Data\n",
    "X = pd.read_csv(\"../data/aps_failure_training_feats.csv\")\n",
    "y = pd.read_csv(\"../data/aps_failure_training_target.csv\")\n",
    "\n",
    "# Use the head function to get a glimpse data\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "seed = 13\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize StandardScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "\n",
    "# Transform the training data\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_train = pd.DataFrame(X_train, columns=X_test.columns)\n",
    "\n",
    "# Transform the testing data\n",
    "X_test = sc.transform(X_test)\n",
    "X_test = pd.DataFrame(X_test, columns = X_train.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train a Neural Network and find its accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Import the relevant Keras libraries\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initiate the Model with Sequential Class\n",
    "np.random.seed(seed)\n",
    "random.set_seed(seed)\n",
    "model=Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the hidden dense layers and with dropout Layer\n",
    "model.add(Dense(units=64, activation='relu', kernel_initializer='uniform', input_dim=X_train.shape[1]))\n",
    "model.add(Dropout(rate=0.5))\n",
    "model.add(Dense(units=32, activation='relu', kernel_initializer='uniform'))\n",
    "model.add(Dropout(rate=0.4))\n",
    "model.add(Dense(units=16, activation='relu', kernel_initializer='uniform'))\n",
    "model.add(Dropout(rate=0.3))\n",
    "model.add(Dense(units=8, activation='relu', kernel_initializer='uniform'))\n",
    "model.add(Dropout(rate=0.2))\n",
    "model.add(Dense(units=4, activation='relu', kernel_initializer='uniform'))\n",
    "model.add(Dropout(rate=0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Output Dense Layer\n",
    "model.add(Dense(units=1,activation='sigmoid',kernel_initializer='uniform'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compile the Model\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 33600 samples, validate on 8400 samples\n",
      "Epoch 1/100\n",
      "33600/33600 [==============================] - 4s 121us/step - loss: 0.0941 - accuracy: 0.9829 - val_loss: 0.0487 - val_accuracy: 0.9842\n",
      "Epoch 2/100\n",
      "33600/33600 [==============================] - 4s 110us/step - loss: 0.0418 - accuracy: 0.9831 - val_loss: 0.0415 - val_accuracy: 0.9842\n",
      "Epoch 3/100\n",
      "33600/33600 [==============================] - 4s 110us/step - loss: 0.0388 - accuracy: 0.9831 - val_loss: 0.0392 - val_accuracy: 0.9842\n",
      "Epoch 4/100\n",
      "33600/33600 [==============================] - 3s 99us/step - loss: 0.0353 - accuracy: 0.9831 - val_loss: 0.0417 - val_accuracy: 0.9842\n",
      "Epoch 5/100\n",
      "33600/33600 [==============================] - 4s 118us/step - loss: 0.0372 - accuracy: 0.9831 - val_loss: 0.0405 - val_accuracy: 0.9842\n",
      "Epoch 6/100\n",
      "33600/33600 [==============================] - 4s 105us/step - loss: 0.0345 - accuracy: 0.9832 - val_loss: 0.0414 - val_accuracy: 0.9842\n",
      "Epoch 7/100\n",
      "33600/33600 [==============================] - 4s 104us/step - loss: 0.0320 - accuracy: 0.9865 - val_loss: 0.0396 - val_accuracy: 0.9908\n",
      "Epoch 8/100\n",
      "33600/33600 [==============================] - 4s 107us/step - loss: 0.0315 - accuracy: 0.9895 - val_loss: 0.0440 - val_accuracy: 0.9908\n",
      "Epoch 9/100\n",
      "33600/33600 [==============================] - 4s 108us/step - loss: 0.0315 - accuracy: 0.9899 - val_loss: 0.0392 - val_accuracy: 0.9913\n",
      "Epoch 10/100\n",
      "33600/33600 [==============================] - 4s 110us/step - loss: 0.0307 - accuracy: 0.9894 - val_loss: 0.0513 - val_accuracy: 0.9901\n",
      "Epoch 11/100\n",
      "33600/33600 [==============================] - 4s 106us/step - loss: 0.0308 - accuracy: 0.9896 - val_loss: 0.0397 - val_accuracy: 0.9894\n",
      "Epoch 12/100\n",
      "33600/33600 [==============================] - 4s 104us/step - loss: 0.0298 - accuracy: 0.9896 - val_loss: 0.0376 - val_accuracy: 0.9901\n",
      "Epoch 13/100\n",
      "33600/33600 [==============================] - 4s 114us/step - loss: 0.0299 - accuracy: 0.9905 - val_loss: 0.0440 - val_accuracy: 0.9904\n",
      "Epoch 14/100\n",
      "33600/33600 [==============================] - 4s 133us/step - loss: 0.0285 - accuracy: 0.9898 - val_loss: 0.0546 - val_accuracy: 0.9908\n",
      "Epoch 15/100\n",
      "33600/33600 [==============================] - 5s 135us/step - loss: 0.0284 - accuracy: 0.9903 - val_loss: 0.0534 - val_accuracy: 0.9901\n",
      "Epoch 16/100\n",
      "33600/33600 [==============================] - 4s 106us/step - loss: 0.0277 - accuracy: 0.9897 - val_loss: 0.0468 - val_accuracy: 0.9906\n",
      "Epoch 17/100\n",
      "33600/33600 [==============================] - 4s 113us/step - loss: 0.0290 - accuracy: 0.9910 - val_loss: 0.0430 - val_accuracy: 0.9917\n",
      "Epoch 18/100\n",
      "33600/33600 [==============================] - 4s 105us/step - loss: 0.0285 - accuracy: 0.9902 - val_loss: 0.0610 - val_accuracy: 0.9913\n",
      "Epoch 19/100\n",
      "33600/33600 [==============================] - 4s 106us/step - loss: 0.0258 - accuracy: 0.9913 - val_loss: 0.0513 - val_accuracy: 0.9908\n",
      "Epoch 20/100\n",
      "33600/33600 [==============================] - 4s 105us/step - loss: 0.0266 - accuracy: 0.9904 - val_loss: 0.0553 - val_accuracy: 0.9908\n",
      "Epoch 21/100\n",
      "33600/33600 [==============================] - 4s 125us/step - loss: 0.0263 - accuracy: 0.9907 - val_loss: 0.0627 - val_accuracy: 0.9923\n",
      "Epoch 22/100\n",
      "33600/33600 [==============================] - 4s 124us/step - loss: 0.0243 - accuracy: 0.9913 - val_loss: 0.0717 - val_accuracy: 0.9910\n",
      "Epoch 23/100\n",
      "33600/33600 [==============================] - 4s 108us/step - loss: 0.0262 - accuracy: 0.9908 - val_loss: 0.0747 - val_accuracy: 0.9914\n",
      "Epoch 24/100\n",
      "33600/33600 [==============================] - 4s 116us/step - loss: 0.0266 - accuracy: 0.9905 - val_loss: 0.0446 - val_accuracy: 0.9906\n",
      "Epoch 25/100\n",
      "33600/33600 [==============================] - 5s 137us/step - loss: 0.0260 - accuracy: 0.9912 - val_loss: 0.0463 - val_accuracy: 0.9912\n",
      "Epoch 26/100\n",
      "33600/33600 [==============================] - 5s 136us/step - loss: 0.0245 - accuracy: 0.9915 - val_loss: 0.0571 - val_accuracy: 0.9911\n",
      "Epoch 27/100\n",
      "33600/33600 [==============================] - 4s 113us/step - loss: 0.0252 - accuracy: 0.9919 - val_loss: 0.0477 - val_accuracy: 0.9908\n",
      "Epoch 28/100\n",
      "33600/33600 [==============================] - 4s 112us/step - loss: 0.0263 - accuracy: 0.9919 - val_loss: 0.0613 - val_accuracy: 0.9912\n",
      "Epoch 29/100\n",
      "33600/33600 [==============================] - 4s 117us/step - loss: 0.0245 - accuracy: 0.9911 - val_loss: 0.0506 - val_accuracy: 0.9918\n",
      "Epoch 30/100\n",
      "33600/33600 [==============================] - 4s 118us/step - loss: 0.0254 - accuracy: 0.9917 - val_loss: 0.0582 - val_accuracy: 0.9913\n",
      "Epoch 31/100\n",
      "33600/33600 [==============================] - 4s 111us/step - loss: 0.0254 - accuracy: 0.9925 - val_loss: 0.0560 - val_accuracy: 0.9912\n",
      "Epoch 32/100\n",
      "33600/33600 [==============================] - 4s 110us/step - loss: 0.0228 - accuracy: 0.9922 - val_loss: 0.0809 - val_accuracy: 0.9923\n",
      "Epoch 33/100\n",
      "33600/33600 [==============================] - 4s 106us/step - loss: 0.0251 - accuracy: 0.9914 - val_loss: 0.0639 - val_accuracy: 0.9921\n",
      "Epoch 34/100\n",
      "33600/33600 [==============================] - 4s 111us/step - loss: 0.0245 - accuracy: 0.9922 - val_loss: 0.0827 - val_accuracy: 0.9918\n",
      "Epoch 35/100\n",
      "33600/33600 [==============================] - 4s 117us/step - loss: 0.0239 - accuracy: 0.9922 - val_loss: 0.0567 - val_accuracy: 0.9907\n",
      "Epoch 36/100\n",
      "33600/33600 [==============================] - 4s 107us/step - loss: 0.0242 - accuracy: 0.9921 - val_loss: 0.0760 - val_accuracy: 0.9919\n",
      "Epoch 37/100\n",
      "33600/33600 [==============================] - 3s 104us/step - loss: 0.0251 - accuracy: 0.9919 - val_loss: 0.0536 - val_accuracy: 0.9912\n",
      "Epoch 38/100\n",
      "33600/33600 [==============================] - 3s 104us/step - loss: 0.0236 - accuracy: 0.9926 - val_loss: 0.0638 - val_accuracy: 0.9913\n",
      "Epoch 39/100\n",
      "33600/33600 [==============================] - 4s 114us/step - loss: 0.0229 - accuracy: 0.9927 - val_loss: 0.0714 - val_accuracy: 0.9911\n",
      "Epoch 40/100\n",
      "33600/33600 [==============================] - 4s 113us/step - loss: 0.0242 - accuracy: 0.9923 - val_loss: 0.0561 - val_accuracy: 0.9907\n",
      "Epoch 41/100\n",
      "33600/33600 [==============================] - 4s 119us/step - loss: 0.0234 - accuracy: 0.9926 - val_loss: 0.0600 - val_accuracy: 0.9912\n",
      "Epoch 42/100\n",
      "33600/33600 [==============================] - 4s 115us/step - loss: 0.0220 - accuracy: 0.9922 - val_loss: 0.0827 - val_accuracy: 0.9917\n",
      "Epoch 43/100\n",
      "33600/33600 [==============================] - 4s 114us/step - loss: 0.0225 - accuracy: 0.9928 - val_loss: 0.0731 - val_accuracy: 0.9918\n",
      "Epoch 44/100\n",
      "33600/33600 [==============================] - 4s 116us/step - loss: 0.0220 - accuracy: 0.9925 - val_loss: 0.0823 - val_accuracy: 0.9915\n",
      "Epoch 45/100\n",
      "33600/33600 [==============================] - 4s 112us/step - loss: 0.0233 - accuracy: 0.9920 - val_loss: 0.0826 - val_accuracy: 0.9918\n",
      "Epoch 46/100\n",
      "33600/33600 [==============================] - 4s 116us/step - loss: 0.0222 - accuracy: 0.9924 - val_loss: 0.0766 - val_accuracy: 0.9913\n",
      "Epoch 47/100\n",
      "33600/33600 [==============================] - 4s 110us/step - loss: 0.0221 - accuracy: 0.9930 - val_loss: 0.0675 - val_accuracy: 0.9911\n",
      "Epoch 48/100\n",
      "33600/33600 [==============================] - 4s 117us/step - loss: 0.0217 - accuracy: 0.9929 - val_loss: 0.0776 - val_accuracy: 0.9911\n",
      "Epoch 49/100\n",
      "33600/33600 [==============================] - 4s 113us/step - loss: 0.0224 - accuracy: 0.9929 - val_loss: 0.1058 - val_accuracy: 0.9908\n",
      "Epoch 50/100\n",
      "33600/33600 [==============================] - 4s 117us/step - loss: 0.0236 - accuracy: 0.9923 - val_loss: 0.0649 - val_accuracy: 0.9914\n",
      "Epoch 51/100\n",
      "33600/33600 [==============================] - 4s 112us/step - loss: 0.0214 - accuracy: 0.9926 - val_loss: 0.0906 - val_accuracy: 0.9905\n",
      "Epoch 52/100\n",
      "33600/33600 [==============================] - 4s 111us/step - loss: 0.0234 - accuracy: 0.9929 - val_loss: 0.0581 - val_accuracy: 0.9901\n",
      "Epoch 53/100\n",
      "33600/33600 [==============================] - 4s 106us/step - loss: 0.0238 - accuracy: 0.9924 - val_loss: 0.0743 - val_accuracy: 0.9908\n",
      "Epoch 54/100\n",
      "33600/33600 [==============================] - 4s 108us/step - loss: 0.0215 - accuracy: 0.9929 - val_loss: 0.1207 - val_accuracy: 0.9905\n",
      "Epoch 55/100\n",
      "33600/33600 [==============================] - 4s 109us/step - loss: 0.0227 - accuracy: 0.9930 - val_loss: 0.0833 - val_accuracy: 0.9908\n",
      "Epoch 56/100\n",
      "33600/33600 [==============================] - 4s 118us/step - loss: 0.0229 - accuracy: 0.9929 - val_loss: 0.0813 - val_accuracy: 0.9913\n",
      "Epoch 57/100\n",
      "33600/33600 [==============================] - 4s 108us/step - loss: 0.0206 - accuracy: 0.9934 - val_loss: 0.0938 - val_accuracy: 0.9913\n",
      "Epoch 58/100\n",
      "33600/33600 [==============================] - 4s 113us/step - loss: 0.0200 - accuracy: 0.9933 - val_loss: 0.0707 - val_accuracy: 0.9908\n",
      "Epoch 59/100\n",
      "33600/33600 [==============================] - 4s 124us/step - loss: 0.0226 - accuracy: 0.9926 - val_loss: 0.0820 - val_accuracy: 0.9915\n",
      "Epoch 60/100\n",
      "33600/33600 [==============================] - 3s 104us/step - loss: 0.0204 - accuracy: 0.9930 - val_loss: 0.1221 - val_accuracy: 0.9913\n",
      "Epoch 61/100\n",
      "33600/33600 [==============================] - 3s 102us/step - loss: 0.0212 - accuracy: 0.9933 - val_loss: 0.1111 - val_accuracy: 0.9915\n",
      "Epoch 62/100\n",
      "33600/33600 [==============================] - 3s 100us/step - loss: 0.0211 - accuracy: 0.9933 - val_loss: 0.0961 - val_accuracy: 0.9914\n",
      "Epoch 63/100\n",
      "33600/33600 [==============================] - 3s 101us/step - loss: 0.0206 - accuracy: 0.9927 - val_loss: 0.0970 - val_accuracy: 0.9914\n",
      "Epoch 64/100\n",
      "33600/33600 [==============================] - 3s 103us/step - loss: 0.0231 - accuracy: 0.9930 - val_loss: 0.0990 - val_accuracy: 0.9915\n",
      "Epoch 65/100\n",
      "33600/33600 [==============================] - 3s 99us/step - loss: 0.0205 - accuracy: 0.9936 - val_loss: 0.0811 - val_accuracy: 0.9906\n",
      "Epoch 66/100\n",
      "33600/33600 [==============================] - 3s 100us/step - loss: 0.0217 - accuracy: 0.9937 - val_loss: 0.1003 - val_accuracy: 0.9912\n",
      "Epoch 67/100\n",
      "33600/33600 [==============================] - 3s 98us/step - loss: 0.0209 - accuracy: 0.9930 - val_loss: 0.0634 - val_accuracy: 0.9892\n",
      "Epoch 68/100\n",
      "33600/33600 [==============================] - 3s 99us/step - loss: 0.0226 - accuracy: 0.9926 - val_loss: 0.1104 - val_accuracy: 0.9913\n",
      "Epoch 69/100\n",
      "33600/33600 [==============================] - 4s 109us/step - loss: 0.0185 - accuracy: 0.9939 - val_loss: 0.0654 - val_accuracy: 0.9908\n",
      "Epoch 70/100\n",
      "33600/33600 [==============================] - 3s 101us/step - loss: 0.0205 - accuracy: 0.9936 - val_loss: 0.0945 - val_accuracy: 0.9913\n",
      "Epoch 71/100\n",
      "33600/33600 [==============================] - 4s 105us/step - loss: 0.0211 - accuracy: 0.9935 - val_loss: 0.0757 - val_accuracy: 0.9905\n",
      "Epoch 72/100\n",
      "33600/33600 [==============================] - 4s 105us/step - loss: 0.0214 - accuracy: 0.9935 - val_loss: 0.1088 - val_accuracy: 0.9912\n",
      "Epoch 73/100\n",
      "33600/33600 [==============================] - 4s 114us/step - loss: 0.0212 - accuracy: 0.9934 - val_loss: 0.0915 - val_accuracy: 0.9906\n",
      "Epoch 74/100\n",
      "33600/33600 [==============================] - 4s 108us/step - loss: 0.0228 - accuracy: 0.9932 - val_loss: 0.1210 - val_accuracy: 0.9915\n",
      "Epoch 75/100\n",
      "33600/33600 [==============================] - 4s 106us/step - loss: 0.0207 - accuracy: 0.9936 - val_loss: 0.1255 - val_accuracy: 0.9910\n",
      "Epoch 76/100\n",
      "33600/33600 [==============================] - 4s 106us/step - loss: 0.0211 - accuracy: 0.9937 - val_loss: 0.0902 - val_accuracy: 0.9910\n",
      "Epoch 77/100\n",
      "33600/33600 [==============================] - 4s 105us/step - loss: 0.0197 - accuracy: 0.9935 - val_loss: 0.1467 - val_accuracy: 0.9905\n",
      "Epoch 78/100\n",
      "33600/33600 [==============================] - 3s 99us/step - loss: 0.0213 - accuracy: 0.9930 - val_loss: 0.1099 - val_accuracy: 0.9914\n",
      "Epoch 79/100\n",
      "33600/33600 [==============================] - 4s 118us/step - loss: 0.0206 - accuracy: 0.9935 - val_loss: 0.0958 - val_accuracy: 0.9908\n",
      "Epoch 80/100\n",
      "33600/33600 [==============================] - 3s 97us/step - loss: 0.0209 - accuracy: 0.9931 - val_loss: 0.1326 - val_accuracy: 0.9907\n",
      "Epoch 81/100\n",
      "33600/33600 [==============================] - 4s 105us/step - loss: 0.0200 - accuracy: 0.9931 - val_loss: 0.0982 - val_accuracy: 0.9919\n",
      "Epoch 82/100\n",
      "33600/33600 [==============================] - 3s 90us/step - loss: 0.0216 - accuracy: 0.9927 - val_loss: 0.1210 - val_accuracy: 0.9917\n",
      "Epoch 83/100\n",
      "33600/33600 [==============================] - 3s 87us/step - loss: 0.0174 - accuracy: 0.9946 - val_loss: 0.1356 - val_accuracy: 0.9913\n",
      "Epoch 84/100\n",
      "33600/33600 [==============================] - 3s 92us/step - loss: 0.0185 - accuracy: 0.9935 - val_loss: 0.1455 - val_accuracy: 0.9912\n",
      "Epoch 85/100\n",
      "33600/33600 [==============================] - 3s 89us/step - loss: 0.0203 - accuracy: 0.9939 - val_loss: 0.1048 - val_accuracy: 0.9918\n",
      "Epoch 86/100\n",
      "33600/33600 [==============================] - 3s 86us/step - loss: 0.0190 - accuracy: 0.9937 - val_loss: 0.0970 - val_accuracy: 0.9912\n",
      "Epoch 87/100\n",
      "33600/33600 [==============================] - 3s 84us/step - loss: 0.0191 - accuracy: 0.9943 - val_loss: 0.1309 - val_accuracy: 0.9910\n",
      "Epoch 88/100\n",
      "33600/33600 [==============================] - 3s 85us/step - loss: 0.0199 - accuracy: 0.9941 - val_loss: 0.1167 - val_accuracy: 0.9899\n",
      "Epoch 89/100\n",
      "33600/33600 [==============================] - 3s 83us/step - loss: 0.0189 - accuracy: 0.9939 - val_loss: 0.1143 - val_accuracy: 0.9923\n",
      "Epoch 90/100\n",
      "33600/33600 [==============================] - 3s 82us/step - loss: 0.0187 - accuracy: 0.9943 - val_loss: 0.1415 - val_accuracy: 0.9917\n",
      "Epoch 91/100\n",
      "33600/33600 [==============================] - 3s 88us/step - loss: 0.0205 - accuracy: 0.9942 - val_loss: 0.1028 - val_accuracy: 0.9914\n",
      "Epoch 92/100\n",
      "33600/33600 [==============================] - 3s 84us/step - loss: 0.0208 - accuracy: 0.9939 - val_loss: 0.1237 - val_accuracy: 0.9911\n",
      "Epoch 93/100\n",
      "33600/33600 [==============================] - 3s 84us/step - loss: 0.0193 - accuracy: 0.9941 - val_loss: 0.1384 - val_accuracy: 0.9917\n",
      "Epoch 94/100\n",
      "33600/33600 [==============================] - 3s 86us/step - loss: 0.0189 - accuracy: 0.9939 - val_loss: 0.0746 - val_accuracy: 0.9898\n",
      "Epoch 95/100\n",
      "33600/33600 [==============================] - 3s 84us/step - loss: 0.0178 - accuracy: 0.9941 - val_loss: 0.1366 - val_accuracy: 0.9919\n",
      "Epoch 96/100\n",
      "33600/33600 [==============================] - 3s 85us/step - loss: 0.0209 - accuracy: 0.9938 - val_loss: 0.1008 - val_accuracy: 0.9912\n",
      "Epoch 97/100\n",
      "33600/33600 [==============================] - 3s 86us/step - loss: 0.0181 - accuracy: 0.9941 - val_loss: 0.1279 - val_accuracy: 0.9914\n",
      "Epoch 98/100\n",
      "33600/33600 [==============================] - 3s 84us/step - loss: 0.0198 - accuracy: 0.9941 - val_loss: 0.1260 - val_accuracy: 0.9915\n",
      "Epoch 99/100\n",
      "33600/33600 [==============================] - 3s 85us/step - loss: 0.0184 - accuracy: 0.9941 - val_loss: 0.1173 - val_accuracy: 0.9914\n",
      "Epoch 100/100\n",
      "33600/33600 [==============================] - 3s 83us/step - loss: 0.0187 - accuracy: 0.9941 - val_loss: 0.0642 - val_accuracy: 0.9895\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x12ac6eeb8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the Model\n",
    "model.fit(X_train, y_train, epochs=100, batch_size=20, verbose=1, validation_split=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18000/18000 [==============================] - 0s 19us/step\n",
      "The loss on the test set is 0.0766 and the accuracy is 98.9833%\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
    "print(f'The loss on the test set is {test_loss:.4f} and the accuracy is {test_acc*100:.4f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the Null Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    17700\n",
       "1      300\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the value_count function to calculate distinct class values\n",
    "y_test['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9833333333333333"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the null accuracy\n",
    "y_test['class'].value_counts(normalize=True).loc[0]"
   ]
  }
 ],
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