Tune Network Weight Initialization.py

# -*- coding: utf-8 -*-
"""
Created on Thu Feb 17 19:15:22 2022

@author: ahmed
"""
# Use scikit-learn to grid search the weight initialization
import numpy
from sklearn.model_selection import GridSearchCV
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
# Function to create model, required for KerasClassifier
def create_model(init_mode='uniform'):
	# create model
	model = Sequential()
	model.add(Dense(12, input_dim=8, kernel_initializer=init_mode, activation='relu'))
	model.add(Dense(1, kernel_initializer=init_mode, activation='sigmoid'))
	# Compile model
	model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
	return model
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load dataset
dataset = numpy.loadtxt("pima-indians-diabetes.data.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = KerasClassifier(build_fn=create_model, epochs=100, batch_size=10, verbose=1)
# define the grid search parameters
init_mode = ['uniform', 'lecun_uniform', 'normal', 'zero', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform']
param_grid = dict(init_mode=init_mode)
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=None, cv=3)
grid_result = grid.fit(X, Y)
# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
    print("%f (%f) with: %r" % (mean, stdev, param))