def test_custom_cell(self):
class CustomCell(tf.keras.layers.AbstractRNNCell):
@property
def output_size(self):
raise ValueError("assert_like_rnncell should not run code")
keras_utils.assert_like_rnncell("cell", CustomCell())
def __init__(self, cell, sampler, output_layer=None, **kwargs):
"""Initialize BasicDecoder.
Args:
cell: An `RNNCell` instance.
sampler: A `Sampler` instance.
output_layer: (Optional) An instance of `tf.layers.Layer`, i.e.,
`tf.layers.Dense`. Optional layer to apply to the RNN output prior
to storing the result or sampling.
**kwargs: Other keyward arguments for layer creation.
Raises:
TypeError: if `cell`, `helper` or `output_layer` have an incorrect
type.
"""
keras_utils.assert_like_rnncell("cell", cell)
if not isinstance(sampler, sampler_py.Sampler):
raise TypeError(
"sampler must be a Sampler, received: {}".format(sampler))
if (output_layer is not None
and not isinstance(output_layer, tf.keras.layers.Layer)):
raise TypeError(
"output_layer must be a Layer, received: {}".format(
output_layer))
self.cell = cell
self.sampler = sampler
self.output_layer = output_layer
super().__init__(**kwargs)
def __init__(
self,
cell,
beam_width,
output_layer=None,
length_penalty_weight=0.0,
coverage_penalty_weight=0.0,
reorder_tensor_arrays=True,
**kwargs
):
"""Initialize the BeamSearchDecoderMixin.
Args:
cell: An `RNNCell` instance.
beam_width: Python integer, the number of beams.
output_layer: (Optional) An instance of `tf.keras.layers.Layer`,
i.e., `tf.keras.layers.Dense`. Optional layer to apply to the RNN
output prior to storing the result or sampling.
length_penalty_weight: Float weight to penalize length. Disabled with
0.0.
coverage_penalty_weight: Float weight to penalize the coverage of
source sentence. Disabled with 0.0.
reorder_tensor_arrays: If `True`, `TensorArray`s' elements within the
cell state will be reordered according to the beam search path. If
the `TensorArray` can be reordered, the stacked form will be
returned. Otherwise, the `TensorArray` will be returned as is. Set
this flag to `False` if the cell state contains `TensorArray`s that
are not amenable to reordering.
**kwargs: Dict, other keyword arguments for parent class.
Raises:
TypeError: if `cell` is not an instance of `RNNCell`,
or `output_layer` is not an instance of `tf.keras.layers.Layer`.
"""
keras_utils.assert_like_rnncell("cell", cell)
if output_layer is not None and not isinstance(
output_layer, tf.keras.layers.Layer
):
raise TypeError(
"output_layer must be a Layer, received: %s" % type(output_layer)
)
self._cell = cell
self._output_layer = output_layer
self._reorder_tensor_arrays = reorder_tensor_arrays
self._start_tokens = None
self._end_token = None
self._batch_size = None
self._beam_width = beam_width
self._length_penalty_weight = length_penalty_weight
self._coverage_penalty_weight = coverage_penalty_weight
super().__init__(**kwargs)
def __init__(self,
cell: tf.keras.layers.Layer,
sampler: sampler_py.Sampler,
output_layer: Optional[tf.keras.layers.Layer] = None,
**kwargs):
"""Initialize BasicDecoder.
Args:
cell: A layer that implements the `tf.keras.layers.AbstractRNNCell`
interface.
sampler: A `Sampler` instance.
output_layer: (Optional) An instance of `tf.keras.layers.Layer`, i.e.,
`tf.keras.layers.Dense`. Optional layer to apply to the RNN output
prior to storing the result or sampling.
**kwargs: Other keyword arguments for layer creation.
"""
keras_utils.assert_like_rnncell("cell", cell)
self.cell = cell
self.sampler = sampler
self.output_layer = output_layer
super().__init__(**kwargs)
def test_non_cell(self):
with self.assertRaises(TypeError):
keras_utils.assert_like_rnncell("cell", tf.keras.layers.Dense(10))
def test_standard_cell(self):
keras_utils.assert_like_rnncell("cell", tf.keras.layers.LSTMCell(10))
def test_non_cell():
with pytest.raises(TypeError):
keras_utils.assert_like_rnncell("cell", tf.keras.layers.Dense(10))
