def test_dense_get_config_has_all_attributes(): block = blocks.DenseBlock() config = block.get_config() assert utils.get_func_args( blocks.DenseBlock.__init__).issubset(config.keys())
def test_dense_build_with_bn_return_tensor(): block = blocks.DenseBlock(use_batchnorm=True) outputs = block.build(keras_tuner.HyperParameters(), tf.keras.Input(shape=(32, ), dtype=tf.float32)) assert len(nest.flatten(outputs)) == 1
def test_dense_build_with_dropout_return_tensor(): block = blocks.DenseBlock(dropout=0.5) outputs = block.build( keras_tuner.HyperParameters(), keras.Input(shape=(32,), dtype=tf.float32) ) assert len(nest.flatten(outputs)) == 1
def test_dense_build_return_tensor(): block = blocks.DenseBlock() outputs = block.build(kerastuner.HyperParameters(), tf.keras.Input(shape=(32, ), dtype=tf.float32)) assert len(nest.flatten(outputs)) == 1 assert isinstance(nest.flatten(outputs)[0], tf.Tensor)
def test_dense_build_return_tensor(): block = blocks.DenseBlock( num_units=keras_tuner.engine.hyperparameters.Choice( "num_units", [10, 20])) outputs = block.build(keras_tuner.HyperParameters(), tf.keras.Input(shape=(32, ), dtype=tf.float32)) assert len(nest.flatten(outputs)) == 1
def test_dense_deserialize_to_dense(): serialized_block = blocks.serialize(blocks.DenseBlock()) block = blocks.deserialize(serialized_block) assert isinstance(block, blocks.DenseBlock)