def test_ngram_build_with_ngrams_return_tensor(): block = blocks.TextToNgramVector(ngrams=2) outputs = block.build(keras_tuner.HyperParameters(), tf.keras.Input(shape=(1, ), dtype=tf.string)) assert len(nest.flatten(outputs)) == 1
def test_ngram_get_config_has_all_attributes(): block = blocks.TextToNgramVector() config = block.get_config() assert test_utils.get_func_args( blocks.TextToNgramVector.__init__).issubset(config.keys())
def test_ngram_build_return_tensor(): block = blocks.TextToNgramVector() outputs = block.build(kerastuner.HyperParameters(), tf.keras.Input(shape=(1, ), dtype=tf.string)) assert len(nest.flatten(outputs)) == 1 assert isinstance(nest.flatten(outputs)[0], tf.Tensor)
def test_ngram_deserialize_to_ngram(): serialized_block = blocks.serialize(blocks.TextToNgramVector()) block = blocks.deserialize(serialized_block) assert isinstance(block, blocks.TextToNgramVector)