GridSearchCV

learning rate and drop out rate.py

from keras.layers import Dropout

# Defining the model

def create_model(learning_rate,dropout_rate):
    model = Sequential()
    model.add(Dense(8,input_dim = 8,kernel_initializer = 'normal',activation = 'relu'))
    model.add(Dropout(dropout_rate))
    model.add(Dense(4,input_dim = 8,kernel_initializer = 'normal',activation = 'relu'))
    model.add(Dropout(dropout_rate))
    model.add(Dense(1,activation = 'sigmoid'))
    
    adam = Adam(lr = learning_rate)
    model.compile(loss = 'binary_crossentropy',optimizer = adam,metrics = ['accuracy'])
    return model

# Create the model

model = KerasClassifier(build_fn = create_model,verbose = 0,batch_size = 40,epochs = 10)

# Define the grid search parameters

learning_rate = [0.001,0.01,0.1]
dropout_rate = [0.0,0.1,0.2]

# Make a dictionary of the grid search parameters

param_grids = dict(learning_rate = learning_rate,dropout_rate = dropout_rate)

# Build and fit the GridSearchCV

grid = GridSearchCV(estimator = model,param_grid = param_grids,cv = KFold(),verbose = 10)
grid_result = grid.fit(X_standardized,y)

# Summarize the results

print('Best : {}, using {}'.format(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('{},{} with: {}'.format(mean, stdev, param))