test_temporal_data_tasks.py 文件源码

def test_sequence_to_sequence():
    '''
    Apply a same Dense layer for each element of time dimension of the input
    and make predictions of the output sequence elements.
    This does not make use of the temporal structure of the sequence
    (see TimeDistributedDense for more details)
    '''
    (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=500,
                                                         nb_test=200,
                                                         input_shape=(3, 5),
                                                         output_shape=(3, 5),
                                                         classification=False)

    model = Sequential()
    model.add(TimeDistributedDense(y_train.shape[-1],
                                   input_shape=(X_train.shape[1], X_train.shape[2])))
    model.compile(loss='hinge', optimizer='rmsprop')
    history = model.fit(X_train, y_train, nb_epoch=20, batch_size=16,
                        validation_data=(X_test, y_test), verbose=0)
    assert(history.history['val_loss'][-1] < 0.8)