encdec_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, maxlen)
    y = create_sequences(db, maxlen, maxlen)
    X = np.reshape(X, (X.shape[0], X.shape[1], 1))
    y = np.reshape(y, (y.shape[0], y.shape[1], 1))

    # build the model: 1 layer LSTM
    print('Build model...')
    model = Sequential()
    # "Encode" the input sequence using an RNN, producing an output of HIDDEN_SIZE
    # note: in a situation where your input sequences have a variable length,
    # use input_shape=(None, nb_feature).
    model.add(LSTM(hidden_size, input_shape=(maxlen, input_size)))
    # For the decoder's input, we repeat the encoded input for each time step
    model.add(RepeatVector(maxlen))
    # The decoder RNN could be multiple layers stacked or a single layer
    model.add(LSTM(hidden_size, return_sequences=True))

    # For each of step of the output sequence, decide which character should be chosen
    model.add(TimeDistributed(Dense(1)))

    model.load_weights(save_dir + model_name)

    model.compile(loss='mae', optimizer='adam')
    model.summary()

    prediction = model.predict(X, batch_size, verbose=1, )
    prediction = prediction.flatten()
    # prediction_container = np.array(prediction).flatten()
    plt.plot(prediction.flatten()[:4000], label='prediction')
    plt.plot(y.flatten()[maxlen:4000 + maxlen], label='true')
    plt.legend()
    plt.show()

    store_prediction_and_ground_truth(model)