co_lstm_predict_day.py 文件源码

def main():
    global_start_time = time.time()
    print('> Loading data... ')
    # mm_scaler, X_train, y_train, X_test, y_test = load_data()
    X_train, y_train, X_test, y_test = load_data()
    print('> Data Loaded. Compiling...')

    model = build_model()
    print(model.summary())

    # keras.callbacks.History????epochs?loss?val_loss
    hist = History()
    model.fit(X_train, y_train, batch_size=Conf.BATCH_SIZE, epochs=Conf.EPOCHS, shuffle=True,
              validation_split=0.05, callbacks=[hist])

    # ???????loss?val_loss
    print(hist.history['loss'])
    print(hist.history['val_loss'])

    # ?????loss?val_loss
    plot_loss(hist.history['loss'], hist.history['val_loss'])
    # predicted = predict_by_days(model, X_test, 20)
    predicted = predict_by_day(model, X_test)

    print('Training duration (s) : ', time.time() - global_start_time)

    # predicted = inverse_trans(mm_scaler, predicted)
    # y_test = inverse_trans(mm_scaler, y_test)

    # ????
    model_evaluation(pd.DataFrame(predicted), pd.DataFrame(y_test))

    # ???????
    model_visualization(y_test, predicted)