Ultimate hyperparameter optimization

Ultimate hyperparameter optimization.py

def create_model(learning_rate,dropout_rate,activation_function,init,neuron1,neuron2):
    model = Sequential()
    model.add(Dense(neuron1,input_dim = 8,kernel_initializer = init,activation = activation_function))
    model.add(Dropout(dropout_rate))
    model.add(Dense(neuron2,input_dim = neuron1,kernel_initializer = init,activation = activation_function))
    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)

# Define the grid search parameters

batch_size = [10,20,40]
epochs = [10,50,100]
learning_rate = [0.001,0.01,0.1]
dropout_rate = [0.0,0.1,0.2]
activation_function = ['softmax','relu','tanh','linear']
init = ['uniform','normal','zero']
neuron1 = [4,8,16]
neuron2 = [2,4,8]

# Make a dictionary of the grid search parameters

param_grids = dict(batch_size = batch_size,epochs = epochs,learning_rate = learning_rate,dropout_rate = dropout_rate,
                   activation_function = activation_function,init = init,neuron1 = neuron1,neuron2 = neuron2)

# 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))