def _shape_tensor(self):
matrix_shape = array_ops.stack((self._num_rows, self._num_rows),
axis=0)
if self._batch_shape_arg is None:
return matrix_shape
return array_ops.concat((self._batch_shape_arg, matrix_shape), 0)
def _shape_tensor(self):
# Avoid messy broadcasting if possible.
if tensor_shape.TensorShape(self.shape).is_fully_defined():
return ops.convert_to_tensor(
tensor_shape.TensorShape(self.shape).as_list(), dtype=dtypes.int32, name="shape")
domain_dimension = sum(self._block_domain_dimension_tensors())
range_dimension = sum(self._block_range_dimension_tensors())
matrix_shape = array_ops.stack([domain_dimension, range_dimension])
batch_shape = self.operators[0][0].batch_shape_tensor()
for row in self.operators[1:]:
for operator in row:
batch_shape = array_ops.broadcast_dynamic_shape(
batch_shape, operator.batch_shape_tensor())
return prefer_static.concat((batch_shape, matrix_shape), 0)
def _shape_tensor(self):
# Avoid messy broadcasting if possible.
if tensor_shape.TensorShape(self.shape).is_fully_defined():
return ops.convert_to_tensor(
tensor_shape.TensorShape(self.shape).as_list(), dtype=dtypes.int32, name="shape")
domain_dimension = sum(self._block_domain_dimension_tensors())
range_dimension = sum(self._block_range_dimension_tensors())
matrix_shape = array_ops.stack([domain_dimension, range_dimension])
# Dummy Tensor of zeros. Will never be materialized.
zeros = array_ops.zeros(shape=self.operators[0].batch_shape_tensor())
for operator in self.operators[1:]:
zeros = zeros + array_ops.zeros(shape=operator.batch_shape_tensor())
batch_shape = array_ops.shape(zeros)
return array_ops.concat((batch_shape, matrix_shape), 0)
def _shape_tensor(self):
# Avoid messy broadcasting if possible.
if tensor_shape.TensorShape(self.shape).is_fully_defined():
return ops.convert_to_tensor(
tensor_shape.TensorShape(self.shape).as_list(), dtype=dtypes.int32, name="shape")
# Don't check the matrix dimensions. That would add unnecessary Asserts to
# the graph. Things will fail at runtime naturally if shapes are
# incompatible.
matrix_shape = array_ops.stack([
self.operators[0].range_dimension_tensor(),
self.operators[-1].domain_dimension_tensor()
])
# Dummy Tensor of zeros. Will never be materialized.
zeros = array_ops.zeros(shape=self.operators[0].batch_shape_tensor())
for operator in self.operators[1:]:
zeros += array_ops.zeros(shape=operator.batch_shape_tensor())
batch_shape = array_ops.shape(zeros)
return array_ops.concat((batch_shape, matrix_shape), 0)
def _shape_tensor(self):
matrix_shape = array_ops.stack((self._num_rows, self._num_rows),
axis=0)
batch_shape = array_ops.shape(self.multiplier)
return array_ops.concat((batch_shape, matrix_shape), 0)
