main_normaliztion_lstm.py 文件源码

def train():
    model = build_stateful_lstm_model_with_normalization(BATCH_SIZE, TIME_STEP, INPUT_DIM, OUTPUT_DIM, dropout=0.1)

    # model.fit(x_train,y_train,validation_data=(x_train[:10],y_train[:10]),epochs=5,callbacks=[TensorBoard()],batch_size=1)

    for index, y_dat in enumerate(y):
        print('Run test on %s' % (index))
        model.fit(np.array([x[index]]), y_dat.reshape(1, 3),
                  validation_data=(np.array([x[index]]), y_dat.reshape(1, 3)), epochs=10, callbacks=[TensorBoard()])
        model.save(MODEL_PATH)
        x_pred = model.predict(np.array([x[index]]))
        print(x_pred)
        print(y_dat)

    model.save(MODEL_PATH)