dense_lstm_test.py 文件源码

def test(path_test, input_size, hidden_size, batch_size, save_dir, model_name, maxlen):
    db = read_data(path_test)

    X = create_sequences(db[:-maxlen], win_size=maxlen, step=maxlen)
    X = np.reshape(X, (X.shape[0], X.shape[1], input_size))

    # build the model: 1 layer LSTM
    print('Build model...')
    model = Sequential()
    model.add(LSTM(hidden_size, return_sequences=False, input_shape=(maxlen, input_size)))
    model.add(Dense(maxlen))

    model.load_weights(save_dir + model_name)
    model.compile(loss='mse', optimizer='adam')

    prediction = model.predict(X, batch_size, verbose=1)
    prediction = prediction.flatten()
    # prediction_container = np.array(prediction).flatten()
    Y = db[maxlen:]
    plt.plot(prediction, label='prediction')
    plt.plot(Y, label='true')
    plt.legend()
    plt.show()