x_train = trainset[:, :-1]
y_train = trainset[:, -1:]
x_test = testset[:, :-1]
y_test = testset[:, -1:]
print('x_train.shape:' + str(x_train.shape))
print('y_train.shape:' + str(y_train.shape))
print('x_test.shape:' + str(x_test.shape))
print('y_test.shape' + str(y_test.shape))
# Build model
dbn = DBN(hidden_units, input_length, output_length, device=device)
# Train model
dbn.pretrain(x_train, epoch=epoch_pretrain, batch_size=batch_size)
dbn.finetune(x_train, y_train, epoch_finetune, batch_size, loss_function,
optimizer(dbn.parameters()))
# Make prediction and plot
y_predict = dbn.predict(x_test, batch_size)
y_real = scaler.inverse_transform(y_test.reshape(-1, 1)).flatten()
y_predict = scaler.inverse_transform(y_predict.reshape(-1, 1)).flatten()
plt.figure(1)
plt.plot(y_real, label='real')
plt.plot(y_predict, label='prediction')
plt.xlabel('MSE Error: {}'.format(mean_squared_error(y_real, y_predict)))
plt.legend()
plt.title('Prediction result')
plt.show()
