| 426 | self._states = states |
| 427 | |
| 428 | def compute_output_shape(self, input_shape): |
| 429 | if isinstance(input_shape, list): |
| 430 | input_shape = input_shape[0] |
| 431 | # Check whether the input shape contains any nested shapes. It could be |
| 432 | # (tensor_shape(1, 2), tensor_shape(3, 4)) or (1, 2, 3) which is from numpy |
| 433 | # inputs. |
| 434 | try: |
| 435 | input_shape = tensor_shape.as_shape(input_shape) |
| 436 | except (ValueError, TypeError): |
| 437 | # A nested tensor input |
| 438 | input_shape = nest.flatten(input_shape)[0] |
| 439 | |
| 440 | batch = input_shape[0] |
| 441 | time_step = input_shape[1] |
| 442 | if self.time_major: |
| 443 | batch, time_step = time_step, batch |
| 444 | |
| 445 | if _is_multiple_state(self.cell.state_size): |
| 446 | state_size = self.cell.state_size |
| 447 | else: |
| 448 | state_size = [self.cell.state_size] |
| 449 | |
| 450 | def _get_output_shape(flat_output_size): |
| 451 | output_dim = tensor_shape.as_shape(flat_output_size).as_list() |
| 452 | if self.return_sequences: |
| 453 | if self.time_major: |
| 454 | output_shape = tensor_shape.as_shape([time_step, batch] + output_dim) |
| 455 | else: |
| 456 | output_shape = tensor_shape.as_shape([batch, time_step] + output_dim) |
| 457 | else: |
| 458 | output_shape = tensor_shape.as_shape([batch] + output_dim) |
| 459 | return output_shape |
| 460 | |
| 461 | if getattr(self.cell, 'output_size', None) is not None: |
| 462 | # cell.output_size could be nested structure. |
| 463 | output_shape = nest.flatten(nest.map_structure( |
| 464 | _get_output_shape, self.cell.output_size)) |
| 465 | output_shape = output_shape[0] if len(output_shape) == 1 else output_shape |
| 466 | else: |
| 467 | # Note that state_size[0] could be a tensor_shape or int. |
| 468 | output_shape = _get_output_shape(state_size[0]) |
| 469 | |
| 470 | if self.return_state: |
| 471 | def _get_state_shape(flat_state): |
| 472 | state_shape = [batch] + tensor_shape.as_shape(flat_state).as_list() |
| 473 | return tensor_shape.as_shape(state_shape) |
| 474 | state_shape = nest.map_structure(_get_state_shape, state_size) |
| 475 | return generic_utils.to_list(output_shape) + nest.flatten(state_shape) |
| 476 | else: |
| 477 | return output_shape |
| 478 | |
| 479 | def compute_mask(self, inputs, mask): |
| 480 | # Time step masks must be the same for each input. |