def _shape(self):
matrix_shape = tensor_shape.TensorShape(
(self._num_rows_static, self._num_columns_static))
if self._batch_shape_arg is None:
return matrix_shape
batch_shape = tensor_shape.TensorShape(self._batch_shape_static)
return batch_shape.concatenate(matrix_shape)
def _shape(self):
s_shape = _ops.TensorShape(self._spectrum.shape)
# Suppose _ops.TensorShape(spectrum.shape) = [a, b, c, d]
# block_depth = 2
# Then:
# batch_shape = [a, b]
# N = c*d
# and we want to return
# [a, b, c*d, c*d]
batch_shape = s_shape[:-self.block_depth]
# trailing_dims = [c, d]
trailing_dims = s_shape[-self.block_depth:]
if trailing_dims.is_fully_defined():
n = np.prod(trailing_dims.as_list())
else:
n = None
n_x_n = tensor_shape.TensorShape([n, n])
return batch_shape.concatenate(n_x_n)
def _shape(self):
# Get final matrix shape.
domain_dimension = self.operators[0].domain_dimension
range_dimension = self.operators[0].range_dimension
for operator in self.operators[1:]:
domain_dimension += operator.domain_dimension
range_dimension += operator.range_dimension
matrix_shape = tensor_shape.TensorShape(
[domain_dimension, range_dimension])
# Get broadcast batch shape.
# broadcast_shape checks for compatibility.
batch_shape = self.operators[0].batch_shape
for operator in self.operators[1:]:
batch_shape = common_shapes.broadcast_shape(
batch_shape, operator.batch_shape)
return batch_shape.concatenate(matrix_shape)
def _shape(self):
matrix_shape = tensor_shape.TensorShape(
(self._num_rows_static, self._num_rows_static))
batch_shape = _ops.TensorShape(self.multiplier.shape)
return batch_shape.concatenate(matrix_shape)