def _get_eval_ops(self, features, targets, metrics):
"""Method that builds model graph and returns evaluation ops.
Expected to be overriden by sub-classes that require custom support.
This implementation uses `model_fn` passed as parameter to constructor to
build model.
Args:
features: `Tensor` or `dict` of `Tensor` objects.
targets: `Tensor` or `dict` of `Tensor` objects.
metrics: Dict of metric ops to run. If None, the default metric functions
are used; if {}, no metrics are used. If model has one output (i.e.,
returning single predction), keys are `str`, e.g. `'accuracy'` - just a
name of the metric that will show up in the logs / summaries.
Otherwise, keys are tuple of two `str`, e.g. `('accuracy', 'classes')`
- name of the metric and name of `Tensor` in the predictions to run
this metric on. Metric ops should support streaming, e.g., returning
update_op and value tensors. See more details in
../../../../metrics/python/metrics/ops/streaming_metrics.py.
Returns:
metrics: `dict` of `Tensor` objects.
Raises:
ValueError: if `metrics` don't match `targets`.
"""
predictions, loss, _ = self._call_model_fn(features, targets, ModeKeys.EVAL)
result = {"loss": metrics_lib.streaming_mean(loss)}
weights = self._get_weight_tensor(features)
metrics = metrics or {}
if isinstance(targets, dict) and len(targets) == 1:
# Unpack single target into just tensor.
targets = targets[list(targets.keys())[0]]
for name, metric in six.iteritems(metrics):
if isinstance(name, tuple):
# Multi-head metrics.
if not isinstance(predictions, dict):
raise ValueError(
"Metrics passed provide (name, prediction), "
"but predictions are not dict. "
"Metrics: %s, Predictions: %s." % (metrics, predictions)
)
# Here are two options: targets are single Tensor or a dict.
if isinstance(targets, dict) and name[1] in targets:
# If targets are dict and the prediction name is in it, apply metric.
result[name[0]] = metrics_lib.run_metric(metric, predictions[name[1]], targets[name[1]], weights)
else:
# Otherwise pass the targets to the metric.
result[name[0]] = metrics_lib.run_metric(metric, predictions[name[1]], targets, weights)
else:
# Single head metrics.
if isinstance(predictions, dict):
raise ValueError(
"Metrics passed provide only name, no prediction, "
"but predictions are dict. "
"Metrics: %s, Targets: %s." % (metrics, targets)
)
result[name] = metrics_lib.run_metric(metric, predictions, targets, weights)
return result
def _run_metrics(predictions, targets, metrics, weights):
result = {}
targets = math_ops.cast(targets, predictions.dtype)
for name, metric in six.iteritems(metrics or {}):
result[name] = metrics_lib.run_metric(
metric, predictions, targets, weights=weights)
return result
def _get_eval_ops(self, features, targets, metrics):
"""Method that builds model graph and returns evaluation ops.
Expected to be overriden by sub-classes that require custom support.
This implementation uses `model_fn` passed as parameter to constructor to
build model.
Args:
features: `Tensor` or `dict` of `Tensor` objects.
targets: `Tensor` or `dict` of `Tensor` objects.
metrics: Dict of metric ops to run. If None, the default metric functions
are used; if {}, no metrics are used. If model has one output (i.e.,
returning single predction), keys are `str`, e.g. `'accuracy'` - just a
name of the metric that will show up in the logs / summaries.
Otherwise, keys are tuple of two `str`, e.g. `('accuracy', 'classes')`
- name of the metric and name of `Tensor` in the predictions to run
this metric on. Metric ops should support streaming, e.g., returning
update_op and value tensors. See more details in
../../../../metrics/python/metrics/ops/streaming_metrics.py.
Returns:
metrics: `dict` of `Tensor` objects.
Raises:
ValueError: if `metrics` don't match `targets`.
"""
predictions, loss, _ = self._call_model_fn(features, targets, ModeKeys.EVAL)
result = {'loss': metrics_lib.streaming_mean(loss)}
weights = self._get_weight_tensor(features)
metrics = metrics or {}
if isinstance(targets, dict) and len(targets) == 1:
# Unpack single target into just tensor.
targets = targets[list(targets.keys())[0]]
for name, metric in six.iteritems(metrics):
if isinstance(name, tuple):
# Multi-head metrics.
if not isinstance(predictions, dict):
raise ValueError(
'Metrics passed provide (name, prediction), '
'but predictions are not dict. '
'Metrics: %s, Predictions: %s.' % (metrics, predictions))
# Here are two options: targets are single Tensor or a dict.
if isinstance(targets, dict) and name[1] in targets:
# If targets are dict and the prediction name is in it, apply metric.
result[name[0]] = metrics_lib.run_metric(
metric, predictions[name[1]], targets[name[1]], weights)
else:
# Otherwise pass the targets to the metric.
result[name[0]] = metrics_lib.run_metric(
metric, predictions[name[1]], targets, weights)
else:
# Single head metrics.
if isinstance(predictions, dict):
raise ValueError(
'Metrics passed provide only name, no prediction, '
'but predictions are dict. '
'Metrics: %s, Targets: %s.' % (metrics, targets))
result[name] = metrics_lib.run_metric(
metric, predictions, targets, weights)
return result