33ff9dbce230068e65dedb2f41a00c39c70bef93,tests/keras/layers/test_wrappers.py,,test_Bidirectional,#,80

Before Change

            // test with Sequential model
            model = Sequential()
            model.add(wrappers.Bidirectional(rnn(3), merge_mode=mode, input_shape=(3, 2)))
            model.add(core.Activation("sigmoid"))
            model.compile(loss="mse", optimizer="sgd")
            model.fit(x, y, nb_epoch=1, batch_size=5)

After Change

    nb_sample = 1
    dim = 2
    timesteps = 2
    for mode in ["sum", "concat"]:
        x = np.random.random((nb_sample, timesteps, dim))
        output_dim = 2 if mode == "concat" else 1
        y = np.random.random((nb_sample, output_dim))

        // test with Sequential model
        model = Sequential()
        model.add(wrappers.Bidirectional(rnn(1),
                                         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(1, return_sequences=True),
                                         merge_mode=mode, input_shape=(timesteps, dim)))
        model.add(wrappers.Bidirectional(rnn(1), 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(1), merge_mode=mode)(input)
        model = Model(input, output)
        model.compile(loss="mse", optimizer="sgd")
        model.fit(x, y, nb_epoch=1, batch_size=1)


if __name__ == "__main__":
    pytest.main([__file__])

Instances