acctTaining.py

# -*- coding: utf-8 -*-
"""accTraining-ourWays.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1HhlgE4LwvNFUv5VaMP7qH-MWMKpd2Oyk
"""

import keras
import numpy as np
import pandas as pd

df = pd.read_csv('drive/My Drive/hugefinal.csv')

from sklearn.utils import shuffle
df = shuffle(df)

df.reset_index(drop=True, inplace=True)

del df['index']

df.head()

x = df.iloc[:,1:151]
y = df.iloc[:,-1]

x.shape
x.head()

from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)
#classifier = Sequential() #First Hidden Layerclassifier.add(Dense(20, activation='relu', kernel_initializer='random_normal', input_dim=x_train.shape[1]))classifier.add(Dense(50, activation='relu', kernel_initializer='random_normal'))classifier.add(Dense(30, activation='relu', kernel_initializer='random_normal'))classifier.add(Dense(30, activation='relu',, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)

from keras import Sequential
from keras.layers import Dense

classifier = Sequential() 
#First Hidden Layer
classifier.add(Dense(20, activation='relu', kernel_initializer='random_normal', input_dim=x_train.shape[1]))
classifier.add(Dense(50, activation='relu', kernel_initializer='random_normal'))
classifier.add(Dense(30, activation='relu', kernel_initializer='random_normal'))
classifier.add(Dense(30, activation='relu', kernel_initializer='random_normal'))
classifier.add(Dense(1, activation='sigmoid', kernel_initializer='random_normal'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

classifier.fit(x_train, y_train, batch_size=50, epochs=50)

classifier.evaluate(x_test, y_test)

classifier.save('drive/My Drive/accOnlyModel.h5')