def createNetwork(loss):
regressor = Sequential()
regressor.add(Dense(units=158, activation='relu', input_dim=316))
regressor.add(Dense(units=158, activation='relu'))
regressor.add(Dense(units=1, activation='linear'))
regressor.compile(loss=loss,
optimizer='adam',
metrics=['mean_absolute_error'])
return regressor
regressor = KerasRegressor(build_fn=createNetwork)
parametros = {'loss': ['mean_absolute_error','mean_squared_error' , \
'mean_absolute_percentage_error' , \
'mean_squared_logarithmic_error', 'squared_hinge']}
grid = GridSearchCV(estimator=regressor, param_grid=parametros, cv=2)
grid = grid.fit(previsores, preco_real)
melhores_parametros = grid.best_params_
melhor_precissao = grid.best_score_
regressor_json = grid.to_json()
with open('previssor_carros.json', 'w') as json_file:
json_file.write(regressor_json)
regressor.save_weights('previssor_carros.h5') # -*- coding: utf-8 -*-
