def test_rnn_build_return_tensor():
block = blocks.RNNBlock()
outputs = block.build(
kerastuner.HyperParameters(),
tf.keras.Input(shape=(32, 10), dtype=tf.float32),
)
assert len(nest.flatten(outputs)) == 1
assert isinstance(nest.flatten(outputs)[0], tf.Tensor)
def test_rnn_input_shape_one_dim_error():
block = blocks.RNNBlock()
with pytest.raises(ValueError) as info:
block.build(
kerastuner.HyperParameters(),
tf.keras.Input(shape=(32, ), dtype=tf.float32),
)
assert "Expect the input tensor of RNNBlock" in str(info.value)
def test_rnn_deserialize_to_rnn():
serialized_block = blocks.serialize(blocks.RNNBlock())
block = blocks.deserialize(serialized_block)
assert isinstance(block, blocks.RNNBlock)
def test_rnn_get_config_has_all_attributes():
block = blocks.RNNBlock()
config = block.get_config()
assert test_utils.get_func_args(blocks.RNNBlock.__init__).issubset(config.keys())
