def main(): # Load data X, y = load_data('actuator', use_targets=False) X_seq, y_seq = data_to_seq(X, y, t_lag=32, t_future_shift=1, t_future_steps=1, t_sw_step=1) # Split train_end = int((45. / 100.) * len(X_seq)) test_end = int((90. / 100.) * len(X_seq)) X_train, y_train = X_seq[:train_end], y_seq[:train_end] X_test, y_test = X_seq[train_end:test_end], y_seq[train_end:test_end] X_valid, y_valid = X_seq[test_end:], y_seq[test_end:] data = { 'train': [X_train, y_train], 'valid': [X_valid, y_valid], 'test': [X_test, y_test], } # Re-format targets for set_name in data: y = data[set_name][1] y = y.reshape((-1, 1, np.prod(y.shape[1:]))) data[set_name][1] = [y[:,:,i] for i in range(y.shape[2])] # Model & training parameters nb_train_samples = data['train'][0].shape[0] input_shape = list(data['train'][0].shape[1:]) nb_outputs = len(data['train'][1]) gp_input_shape = (1,) batch_size = 128 epochs = 1 nn_params = { 'H_dim': 16, 'H_activation': 'tanh', 'dropout': 0.1, } gp_params = { 'cov': 'SEiso', 'hyp_lik': -2.0, 'hyp_cov': [[-0.7], [0.0]], 'opt': {}, } # Retrieve model config nn_configs = load_NN_configs(filename='lstm.yaml', input_shape=input_shape, output_shape=gp_input_shape, params=nn_params) gp_configs = load_GP_configs(filename='gp.yaml', nb_outputs=nb_outputs, batch_size=batch_size, nb_train_samples=nb_train_samples, params=gp_params) # Construct & compile the model model = assemble('GP-LSTM', [nn_configs['1H'], gp_configs['GP']]) loss = [gen_gp_loss(gp) for gp in model.output_gp_layers] model.compile(optimizer=Adam(1e-2), loss=loss) # Callbacks callbacks = [EarlyStopping(monitor='val_mse', patience=10)] # Train the model history = train(model, data, callbacks=callbacks, gp_n_iter=5, checkpoint='lstm', checkpoint_monitor='val_mse', epochs=epochs, batch_size=batch_size, verbose=2) store_training_loss(history=history, filepath="output/training_loss.csv") # Finetune the model model.finetune(*data['train'], batch_size=batch_size, gp_n_iter=1, verbose=0) # Test the model X_test, y_test = data['test'] y_preds = model.predict(X_test) rmse_predict = RMSE(y_test, y_preds) print('Test predict RMSE:', rmse_predict) store_predict_points(y_test, y_preds, 'output/test_mtl_prediction.csv')
verbose=0, save_best_only=True, save_weights_only=True, mode='auto', period=1) history = model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, validation_data=(X_valid, y_valid), callbacks=[earlystop, check_point], verbose=1) store_training_loss(history=history, filepath=output_dir + "/training_loss_epochs_" + str(EPOCHS) + "_lag" + str(time_step_lag) + ".csv") # Finetune the model # model.finetune(X_train, y_train, batch_size=BATCH_SIZE, gp_n_iter=10, verbose=1) # Test the model X_test = test_inputs['X'] y1_test = test_inputs['target_load'] y2_test = test_inputs['target_imf7'] y3_test = test_inputs['target_imf8'] y4_test = test_inputs['target_imf9'] y5_test = test_inputs['target_imf10'] y1_preds, y2_preds, y3_preds, y4_preds, y5_preds = model.predict(X_test)
save_weights_only=True, mode='auto', period=1) model.load_weights('output/model_checkpoint/weights-improvement-10.hdf5') history = model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, initial_epoch=10, validation_data=(X_valid, y_valid), callbacks=[earlystop, check_point], verbose=1) store_training_loss(history=history, filepath="output/training_loss_epochs_" + str(EPOCHS) + ".csv") # Finetune the model model.finetune(X_train, y_train, batch_size=BATCH_SIZE, gp_n_iter=10, verbose=1) # Test the model X_test = test_inputs['X'] y1_test = test_inputs['target_load'] y2_test = test_inputs['target_imf1'] y3_test = test_inputs['target_imf2']