Hyperparameter.py

# -*- coding: utf-8 -*-
"""
Created on Mon Aug  5 13:30:38 2019

@author: krish.naik
"""

# Artificial Neural Network


# Part 1 - Data Preprocessing

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:13]
y = dataset.iloc[:, 13]

#Create dummy variables
geography=pd.get_dummies(X["Geography"],drop_first=True)
gender=pd.get_dummies(X['Gender'],drop_first=True)

## Concatenate the Data Frames

X=pd.concat([X,geography,gender],axis=1)

## Drop Unnecessary columns
X=X.drop(['Geography','Gender'],axis=1)

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

## Perform Hyperparameter Optimization

from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV

from keras.models import Sequential
from keras.layers import Dense, Activation, Embedding, Flatten, LeakyReLU, BatchNormalization, Dropout
from keras.activations import relu, sigmoid



def create_model(layers, activation):
    model = Sequential()
    for i, nodes in enumerate(layers):
        if i==0:
            model.add(Dense(nodes,input_dim=X_train.shape[1]))
            model.add(Activation(activation))
            model.add(Dropout(0.3))
        else:
            model.add(Dense(nodes))
            model.add(Activation(activation))
            model.add(Dropout(0.3))
            
    model.add(Dense(units = 1, kernel_initializer= 'glorot_uniform', activation = 'sigmoid')) # Note: no activation beyond this point
    
    model.compile(optimizer='adam', loss='binary_crossentropy',metrics=['accuracy'])
    return model
    
model = KerasClassifier(build_fn=create_model, verbose=0)


layers = [[20], [40, 20], [45, 30, 15]]
activations = ['sigmoid', 'relu']
param_grid = dict(layers=layers, activation=activations, batch_size = [128, 256], epochs=[30])
grid = GridSearchCV(estimator=model, param_grid=param_grid,cv=5)

grid_result = grid.fit(X_train, y_train)

[grid_result.best_score_,grid_result.best_params_]