def finalize_metric(metric: tf.keras.metrics.Metric, values):
# Note: the following call requires that `type(metric)` have a no argument
# __init__ method, which will restrict the types of metrics that can be
# used. This is somewhat limiting, but the pattern to use default
# arguments and export the values in `get_config()` (see
# `tf.keras.metrics.TopKCategoricalAccuracy`) works well.
keras_metric = None
try:
# This is some trickery to reconstruct a metric object in the current
# scope, so that the `tf.Variable`s get created when we desire.
keras_metric = type(metric).from_config(metric.get_config())
except TypeError as e:
# Re-raise the error with a more helpful message, but the previous stack
# trace.
raise TypeError(
'Caught exception trying to call `{t}.from_config()` with '
'config {c}. Confirm that {t}.__init__() has an argument for '
'each member of the config.\nException: {e}'.format(
t=type(metric), c=metric.config(), e=e))
assignments = []
for v, a in zip(keras_metric.variables, values):
assignments.append(v.assign(a))
with tf.control_dependencies(assignments):
return keras_metric.result()
def get_naive_forecasting_performance(dataset: tf.data.Dataset,
loss: tf.keras.losses.Loss,
metric: tf.keras.metrics.Metric) -> Tuple:
metric.reset_states()
losses = []
for sequence, y_true in dataset:
y_pred = sequence[..., -1]
metric.update_state(y_true, y_pred)
losses.append(loss(y_true, y_pred).numpy())
return sum(losses) / len(losses), metric.result().numpy()
def _check_keras_metric_config_constructable(metric: tf.keras.metrics.Metric):
"""Checks that a Keras metric is constructable from the `get_config()` method.
Args:
metric: A single `tf.keras.metrics.Metric`.
Raises:
TypeError: If the metric is not an instance of `tf.keras.metrics.Metric`, if
the metric is not constructable from the `get_config()` method.
"""
if not isinstance(metric, tf.keras.metrics.Metric):
raise TypeError(f'Metric {type(metric)} is not a `tf.keras.metrics.Metric` '
'to be constructable from the `get_config()` method.')
metric_type_str = type(metric).__name__
if not hasattr(tf.keras.metrics, metric_type_str):
_, init_fn = tf.__internal__.decorator.unwrap(metric.__init__)
init_args = inspect.getfullargspec(init_fn).args
init_args.remove('self')
get_config_args = metric.get_config().keys()
extra_args = [arg for arg in init_args if arg not in get_config_args]
if extra_args:
# TODO(b/197746608): Remove the suggestion of updating `get_config` if
# that code path is removed.
raise TypeError(
f'Metric {metric_type_str} is not constructable from the '
'`get_config()` method, because `__init__` takes extra arguments '
f'that are not included in the `get_config()`: {extra_args}. '
'Pass the metric constructor instead, or update the `get_config()` '
'in the metric class to include these extra arguments.\n'
'Example:\n'
'class CustomMetric(tf.keras.metrics.Metric):\n'
' def __init__(self, arg1):\n'
' self._arg1 = arg1\n\n'
' def get_config(self)\n'
' config = super().get_config()\n'
' config[\'arg1\'] = self._arg1\n'
' return config')