def _build(self, inputs):
"""Connects the Add module into the graph, with input Tensor `inputs`.
Args:
inputs: A Tensor of size `[batch_size, input_size1, ...]`.
Returns:
A Tensor of size `[batch_size, input_size1, ...]`.
Raises:
base.IncompatibleShapeError: If the input is not a >= 2D `Tensor`.
base.IncompatibleShapeError: If connecting the module into the graph
any time after the first time, and the inferred size of the input does
not match previous invocations.
base.IncompatibleShapeError: If the `output_shape` has been specified
but it does not match the input_shape`.
base.ParentNotBuiltError: If the module is a transposed and the original
untransposed module has not been built.
"""
input_shape = tuple(inputs.get_shape().as_list())
bias_shape = calculate_bias_shape(input_shape, self._bias_dims)
# Check always contains minibatched input.
if len(input_shape) < 2:
raise base.IncompatibleShapeError(
"Rank of input shape must be >=2 not: {}.".format(
len(input_shape)))
# Check previous input size is same as new input size.
if (self._input_shape is not None
and input_shape[1:] != self._input_shape[1:]):
raise base.IncompatibleShapeError("Input shape has changed.")
# If transposed, make sure that the original Module is built.
if callable(self._output_shape):
self._output_shape = self._output_shape()
if self._output_shape is None:
raise base.ParentNotBuiltError(
"Build the original untransposed module before building this one."
)
# If output_shape specified, check that it matches input_shape.
if (self._output_shape is not None
and self._output_shape[1:] != input_shape[1:]):
raise base.IncompatibleShapeError(
"Input shape must be {} not: {}.".format(
self._output_shape, input_shape[1]))
self._input_shape = input_shape
if "b" not in self._initializers:
self._initializers["b"] = create_bias_initializer(bias_shape)
dtype = inputs.dtype
self._b = tf.get_variable(
"b",
shape=bias_shape,
dtype=dtype,
initializer=self._initializers["b"],
partitioner=self._partitioners.get("b", None),
regularizer=self._regularizers.get("b", None))
outputs = inputs + self._b
return outputs
def _build(self, inputs, multiplier=1):
"""Connects the Add module into the graph, with input Tensor `inputs`.
Args:
inputs: A Tensor of size `[batch_size, input_size1, ...]`.
multiplier: A scalar or Tensor which the bias term is multiplied by
before adding it to `inputs`. Anything which works in the expression
`bias * multiplier` is acceptable here. This may be useful if you want
to add a bias in one place and subtract the same bias in another place
via `multiplier=-1`.
Returns:
A Tensor of size `[batch_size, input_size1, ...]`.
Raises:
base.IncompatibleShapeError: If the input is not a >= 2D `Tensor`.
base.IncompatibleShapeError: If connecting the module into the graph
any time after the first time, and the inferred size of the input does
not match previous invocations.
base.IncompatibleShapeError: If the `output_shape` has been specified
but it does not match the input_shape`.
base.ParentNotBuiltError: If the module is a transposed and the original
untransposed module has not been built.
"""
input_shape = tuple(inputs.get_shape().as_list())
bias_shape = calculate_bias_shape(input_shape, self._bias_dims)
# Check always contains minibatched input.
if len(input_shape) < 2:
raise base.IncompatibleShapeError(
"Rank of input shape must be >=2 not: {}.".format(len(input_shape)))
# Check previous input size is same as new input size.
if (self._input_shape is not None and
input_shape[1:] != self._input_shape[1:]):
raise base.IncompatibleShapeError("Input shape has changed.")
# If transposed, make sure that the original Module is built.
if callable(self._output_shape):
self._output_shape = self._output_shape()
if self._output_shape is None:
raise base.ParentNotBuiltError(
"Build the original untransposed module before building this one.")
# If output_shape specified, check that it matches input_shape.
if (self._output_shape is not None and
self._output_shape[1:] != input_shape[1:]):
raise base.IncompatibleShapeError(
"Input shape must be {} not: {}.".format(self._output_shape,
input_shape[1]))
self._input_shape = input_shape
dtype = inputs.dtype
if "b" not in self._initializers:
self._initializers["b"] = create_bias_initializer(bias_shape, dtype)
self._b = tf.get_variable(
"b",
shape=bias_shape,
dtype=dtype,
initializer=self._initializers["b"],
partitioner=self._partitioners.get("b", None),
regularizer=self._regularizers.get("b", None))
bias = self._b
if multiplier != 1:
bias = bias * multiplier # pylint: disable=g-no-augmented-assignment
outputs = inputs + bias
return outputs
