a0351c62548ebddf5c728ec525ad61a4f7827494,tests/hypermodel/test_preprocessor.py,,test_normalize,#,23

Before Change

def test_normalize():
    normalize = preprocessor.Normalization()
    x_train = np.random.rand(100, 32, 32, 3)
    dataset = tf.data.Dataset.from_tensor_slices(x_train)
    normalize.set_hp(kerastuner.HyperParameters())
    for x in dataset:
        normalize.update(x)
    normalize.finalize()
    normalize.set_config(normalize.get_config())

    weights = normalize.get_weights()
    normalize.clear_weights()
    normalize.set_weights(weights)

    for a in dataset:
        normalize.transform(a)

    def map_func(x):
        return tf.py_function(normalize.transform,
                              inp=[x],
                              Tout=(tf.float64,))

    new_dataset = dataset.map(map_func)
    for _ in new_dataset:
        pass
    assert isinstance(new_dataset, tf.data.Dataset)


def test_sequence():

After Change

def test_normalize():
    dataset = common.generate_data(dtype="dataset")
    new_dataset = run_preprocessor(preprocessor.Normalization(),
                                   dataset,
                                   common.generate_data(dtype="dataset"),
                                   dtype=tf.float32)
    assert isinstance(new_dataset, tf.data.Dataset)


def test_sequence():

Instances