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)
