Writes scalar summaries for metrics on every training batch. Performs profiling if current batch is in profiler_batches. Arguments: batch: Integer, index of batch within the current epoch. logs: Dict. Metric results for this batch.
(self, batch, logs=None)
| 1633 | self._set_default_writer(self._validation_run_name) |
| 1634 | |
| 1635 | def on_train_batch_end(self, batch, logs=None): |
| 1636 | """Writes scalar summaries for metrics on every training batch. |
| 1637 | |
| 1638 | Performs profiling if current batch is in profiler_batches. |
| 1639 | |
| 1640 | Arguments: |
| 1641 | batch: Integer, index of batch within the current epoch. |
| 1642 | logs: Dict. Metric results for this batch. |
| 1643 | """ |
| 1644 | if self.update_freq == 'epoch' and self._profile_batch is None: |
| 1645 | return |
| 1646 | |
| 1647 | # Don't output batch_size and batch number as TensorBoard summaries |
| 1648 | logs = logs or {} |
| 1649 | train_batches = self._total_batches_seen[self._train_run_name] |
| 1650 | if self.update_freq != 'epoch' and batch % self.update_freq == 0: |
| 1651 | self._log_metrics(logs, prefix='batch_', step=train_batches) |
| 1652 | |
| 1653 | self._increment_step(self._train_run_name) |
| 1654 | |
| 1655 | if context.executing_eagerly(): |
| 1656 | if self._is_tracing: |
| 1657 | self._log_trace() |
| 1658 | elif (not self._is_tracing and |
| 1659 | math_ops.equal(train_batches, self._profile_batch - 1)): |
| 1660 | self._enable_trace() |
| 1661 | |
| 1662 | def on_test_batch_end(self, batch, logs=None): |
| 1663 | if self.update_freq == 'epoch': |
nothing calls this directly
no test coverage detected