(self, value, aggregation=None, name=None)
| 1873 | return match[0] |
| 1874 | |
| 1875 | def _eager_add_metric(self, value, aggregation=None, name=None): |
| 1876 | # If the given metric is available in `metrics` list we just update state |
| 1877 | # on it, otherwise we create a new metric instance and |
| 1878 | # add it to the `metrics` list. |
| 1879 | metric_obj = getattr(value, '_metric_obj', None) |
| 1880 | if metric_obj: |
| 1881 | name = metric_obj.name |
| 1882 | |
| 1883 | match = self._get_existing_metric(name) |
| 1884 | if match: |
| 1885 | # Tensors that come from a Metric object already updated the Metric state. |
| 1886 | if not metric_obj: |
| 1887 | match(value) |
| 1888 | return |
| 1889 | |
| 1890 | if not metric_obj: |
| 1891 | assert aggregation is not None |
| 1892 | metric_obj, _ = base_layer_utils.create_mean_metric(value, name) |
| 1893 | self._metrics.append(metric_obj) |
| 1894 | |
| 1895 | def _symbolic_add_metric(self, value, aggregation=None, name=None): |
| 1896 | base_layer_utils.check_graph_consistency(value, method='add_metric') |
no test coverage detected