def get_base_metric_name(metric, weighted=False):
"""Returns the metric name given the metric function.
Arguments:
metric: Metric function name or reference.
weighted: Boolean indicating if the metric for which we are adding
names is weighted.
Returns:
a metric name.
"""
metric_name_prefix = 'weighted_' if weighted else ''
if metric in ('accuracy', 'acc', 'crossentropy', 'ce'):
if metric in ('accuracy', 'acc'):
suffix = 'acc'
elif metric in ('crossentropy', 'ce'):
suffix = 'ce'
metric_name = metric_name_prefix + suffix
else:
metric_fn = metrics_module.get(metric)
# Get metric name as string
if hasattr(metric_fn, 'name'):
metric_name = metric_fn.name
else:
metric_name = metric_fn.__name__
metric_name = metric_name_prefix + metric_name
return metric_name
def get_base_metric_name(metric, weighted=False):
"""Returns the metric name given the metric function.
Arguments:
metric: Metric function name or reference.
weighted: Boolean indicating if the metric for which we are adding
names is weighted.
Returns:
a metric name.
"""
metric_name_prefix = 'weighted_' if weighted else ''
if metric in ('accuracy', 'acc', 'crossentropy', 'ce'):
if metric in ('accuracy', 'acc'):
suffix = 'acc'
elif metric in ('crossentropy', 'ce'):
suffix = 'ce'
metric_name = metric_name_prefix + suffix
else:
metric_fn = metrics_module.get(metric)
# Get metric name as string
if hasattr(metric_fn, 'name'):
metric_name = metric_fn.name
else:
metric_name = metric_fn.__name__
metric_name = metric_name_prefix + metric_name
return metric_name
def _get_metrics_info(metric, internal_output_shapes=None, loss_func=None):
if metric == 'accuracy' or metric == 'acc':
# custom handling of accuracy
# (because of class mode duality)
output_shape = internal_output_shapes
if output_shape[-1] == 1 or loss_func == losses.binary_crossentropy:
# case: binary accuracy
acc_fn = metrics_module.binary_accuracy
elif loss_func == losses.sparse_categorical_crossentropy:
# case: categorical accuracy with sparse targets
acc_fn = metrics_module.sparse_categorical_accuracy
else:
acc_fn = metrics_module.categorical_accuracy
metric_name = 'acc'
return metric_name, acc_fn
else:
metric_fn = metrics_module.get(metric)
metric_name = metric_fn.__name__
return metric_name, metric_fn
def _get_metrics_info(metric, internal_output_shapes=None, loss_func=None):
if metric == 'accuracy' or metric == 'acc':
# custom handling of accuracy
# (because of class mode duality)
output_shape = internal_output_shapes
if output_shape[-1] == 1 or loss_func == losses.binary_crossentropy:
# case: binary accuracy
acc_fn = metrics_module.binary_accuracy
elif loss_func == losses.sparse_categorical_crossentropy:
# case: categorical accuracy with sparse targets
acc_fn = metrics_module.sparse_categorical_accuracy
else:
acc_fn = metrics_module.categorical_accuracy
metric_name = 'acc'
return metric_name, acc_fn
else:
metric_fn = metrics_module.get(metric)
metric_name = metric_fn.__name__
return metric_name, metric_fn
