test_wrappers.py 文件源码

def test_Bidirectional():
    rnn = recurrent.SimpleRNN
    nb_sample = 2
    dim = 2
    timesteps = 2
    output_dim = 2
    for mode in ['sum', 'concat']:
        x = np.random.random((nb_sample, timesteps, dim))
        target_dim = 2 * output_dim if mode == 'concat' else output_dim
        y = np.random.random((nb_sample, target_dim))

        # test with Sequential model
        model = Sequential()
        model.add(wrappers.Bidirectional(rnn(output_dim),
                                         merge_mode=mode, input_shape=(timesteps, dim)))
        model.compile(loss='mse', optimizer='sgd')
        model.fit(x, y, nb_epoch=1, batch_size=1)

        # test config
        model.get_config()
        model = model_from_json(model.to_json())
        model.summary()

        # test stacked bidirectional layers
        model = Sequential()
        model.add(wrappers.Bidirectional(rnn(output_dim, return_sequences=True),
                                         merge_mode=mode, input_shape=(timesteps, dim)))
        model.add(wrappers.Bidirectional(rnn(output_dim), merge_mode=mode))
        model.compile(loss='mse', optimizer='sgd')
        model.fit(x, y, nb_epoch=1, batch_size=1)

        # test with functional API
        input = Input((timesteps, dim))
        output = wrappers.Bidirectional(rnn(output_dim), merge_mode=mode)(input)
        model = Model(input, output)
        model.compile(loss='mse', optimizer='sgd')
        model.fit(x, y, nb_epoch=1, batch_size=1)

        # Bidirectional and stateful
        input = Input(batch_shape=(1, timesteps, dim))
        output = wrappers.Bidirectional(rnn(output_dim, stateful=True), merge_mode=mode)(input)
        model = Model(input, output)
        model.compile(loss='mse', optimizer='sgd')
        model.fit(x, y, nb_epoch=1, batch_size=1)