Beispiel #1
0
def test_trainable():
    gpus = deepspeech.get_available_gpus(
    )  # Support both Multi and Single-GPU tests
    base_configuration = DeepSpeech.get_configuration(
        'tests/models/base/configuration.yaml')
    base_configuration.model.pop('name')
    base_model = deepspeech_custom(is_gpu=len(gpus) > 0,
                                   **base_configuration.model)
    fname = 'weights.hdf5'
    base_model.save_weights(fname)

    extended_configuration = DeepSpeech.get_configuration(
        'tests/models/extended/configuration.yaml')
    extended_configuration.model.pop('name')
    extended_model = deepspeech_custom(is_gpu=len(gpus) > 0,
                                       **extended_configuration.model)
    weights_before_training = extended_model.get_weights()

    assert all(not extended_model.get_layer(name).trainable
               for name in ['base_1', 'base_2', 'base_3'])
    assert all(
        extended_model.get_layer(name).trainable
        for name in ['extension_1', 'extension_2'])
    assert all(not is_same(
        base_model.get_layer(name).get_weights(),
        extended_model.get_layer(name).get_weights())
               for name in ['base_1', 'base_2', 'base_3'])

    loss = DeepSpeech.get_loss()
    optimizer = DeepSpeech.get_optimizer(**extended_configuration.optimizer)
    parallel_model = DeepSpeech.distribute_model(extended_model, gpus)
    DeepSpeech.compile_model(parallel_model, optimizer, loss)
    extended_model.load_weights(fname, by_name=True)

    assert all(
        is_same(
            base_model.get_layer(name).get_weights(),
            extended_model.get_layer(name).get_weights())
        for name in ['base_1', 'base_2', 'base_3'])

    for i in range(10):  # Dummy training (10 epochs / 10 batch_size)
        X = np.random.rand(10, 100, 80)
        y = np.random.randint(0, 35, size=[10, 20], dtype=np.int32)
        parallel_model.train_on_batch(X, y)

    assert all(
        is_same(
            base_model.get_layer(name).get_weights(),
            extended_model.get_layer(name).get_weights())
        for name in ['base_1', 'base_2', 'base_3'
                     ]), "Freezed layers have to be unchangeable."
    assert not is_same(
        weights_before_training,
        extended_model.get_weights()), "The base model updates weights."
    assert is_close(extended_model.predict(X), parallel_model.predict(
        X)), "The results are the same (compiled model)."
    # assert is_same(extended_model.get_weights(), compiled_model.get_weights())    # Weights can not be compared (order changed)
    os.remove(fname)
Beispiel #2
0
def test_compile_model(config: Configuration):
    model = DeepSpeech.get_model(**config.model, is_gpu=False)
    optimizer = DeepSpeech.get_optimizer(**config.optimizer)
    loss = DeepSpeech.get_loss()
    compiled_model = DeepSpeech.compile_model(model, optimizer, loss, gpus=[])
    assert compiled_model._is_compiled