def _serialize_tf_metric(
metric: tf.keras.metrics.Metric) -> config.MetricConfig:
"""Serializes TF metric."""
cfg = metric_util.serialize_metric(metric)
return config.MetricConfig(class_name=cfg['class_name'],
config=json.dumps(cfg['config'],
sort_keys=True))
def _metric_keys_and_configs(
metrics: Dict[Text, List[_TFMetricOrLoss]], model_name: Text,
sub_key: Optional[metric_types.SubKey]
) -> Tuple[_KeysBySubKey, _ConfigsBySubKey, _ConfigsBySubKey]:
"""Returns metric keys, metric configs, and loss configs by sub key."""
metric_keys = collections.defaultdict(list)
metric_configs = collections.defaultdict(dict)
loss_configs = collections.defaultdict(dict)
for output_name, metrics_list in metrics.items():
for metric in metrics_list:
updated_sub_key = _verify_and_update_sub_key(
model_name, output_name, sub_key, metric)
if output_name not in metric_configs[updated_sub_key]:
metric_configs[updated_sub_key][output_name] = []
if output_name not in loss_configs[updated_sub_key]:
loss_configs[updated_sub_key][output_name] = []
metric_keys[updated_sub_key].append(
metric_types.MetricKey(name=metric.name,
model_name=model_name,
output_name=output_name,
sub_key=updated_sub_key))
if isinstance(metric, tf.keras.metrics.Metric):
metric_configs[updated_sub_key][output_name].append(
metric_util.serialize_metric(metric))
elif isinstance(metric, tf.keras.losses.Loss):
loss_configs[updated_sub_key][output_name].append(
metric_util.serialize_loss(metric))
return metric_keys, metric_configs, loss_configs
def _metric_keys_and_configs(
metrics: Dict[Text, List[_TFMetricOrLoss]], model_name: Text,
sub_key: Optional[metric_types.SubKey]
) -> Tuple[List[metric_types.MetricKey], Dict[Text, List[Dict[Text, Any]]],
Dict[Text, List[Dict[Text, Any]]]]:
"""Returns the metric keys, metric configs, and loss configs for metrics."""
metric_keys = []
metric_configs = {}
loss_configs = {}
for output_name, metrics_list in metrics.items():
metric_config_list = []
loss_config_list = []
for metric in metrics_list:
metric_keys.append(
metric_types.MetricKey(name=metric.name,
model_name=model_name,
output_name=output_name,
sub_key=_verify_and_update_sub_key(
model_name, output_name, sub_key,
metric)))
if isinstance(metric, tf.keras.metrics.Metric):
metric_config_list.append(metric_util.serialize_metric(metric))
elif isinstance(metric, tf.keras.losses.Loss):
loss_config_list.append(metric_util.serialize_loss(metric))
metric_configs[output_name] = metric_config_list
loss_configs[output_name] = loss_config_list
return metric_keys, metric_configs, loss_configs
def _private_tf_metric(
metric: tf.keras.metrics.Metric) -> tf.keras.metrics.Metric:
"""Creates a private version of given metric."""
cfg = metric_util.serialize_metric(metric)
if not cfg['config']['name'].startswith('_'):
cfg['config']['name'] = '_' + cfg['config']['name']
with tf.keras.utils.custom_object_scope(
{metric.__class__.__name__: metric.__class__}):
return tf.keras.metrics.deserialize(cfg)