#print('x_test number: ', X_test.shape[0])
#print('Y_train number: ', Y.shape[0])
#print('y_test number: ', Y_test)
regressor = SupervisedDBNRegression(hidden_layers_structure=[100],
learning_rate_rbm=0.01,
learning_rate=0.01,
n_epochs_rbm=10,
n_iter_backprop=100,
batch_size=16,
activation_function='relu')
regressor.fit(X, Y)
# Save the model
regressor.save('models/abalone_3.pkl')
# Restore it
#regressor = SupervisedDBNRegression.load('models/abalone_2.pkl')
# Test
data1 = pd.read_csv("abalone_test.csv")
data1['age'] = data1['rings'] + 1.5
data1.drop('rings', axis=1, inplace=True)
X1 = data1.drop(['age', 'sex'], axis=1)
Y1 = data1['age']
X1 = min_max_scaler.fit_transform(X1)
#print('X: ', X.shape[1])
#print('Y: ', Y)
#print('Y_train number: ', Y_train[0])
#print('y_test number: ', Y_test.shape[0])
#'''
# Training
regressor = SupervisedDBNRegression(hidden_layers_structure=[10, 100],
learning_rate_rbm=0.01,
learning_rate=0.01,
n_epochs_rbm=20,
n_iter_backprop=100,
batch_size=16,
activation_function='relu')
regressor.fit(X_train, Y_train)
# Save the model
regressor.save('models/taipei_data.pkl')
# Restore it
#regressor = SupervisedDBNRegression.load('models/model_regression.pkl')
# Test
X_test = min_max_scaler.transform(X_test)
Y_pred = regressor.predict(X_test)
print('Done.\nR-squared: %f\nMSE: %f\nMAPE: %f' %
(r2_score(Y_test, Y_pred), mean_squared_error(
Y_test, Y_pred), mean_absolute_percentage_error(Y_test, Y_pred)))
#'''