def _get_metric_object(self, metric, y_t, y_p):
"""Converts user-supplied metric to a `Metric` object.
Args:
metric: A string, function, or `Metric` object.
y_t: Sample of label.
y_p: Sample of output.
Returns:
A `Metric` object.
"""
if metric is None:
return None # Ok to have no metric for an output.
# Convenience feature for selecting b/t binary, categorical,
# and sparse categorical.
if metric not in ['accuracy', 'acc', 'crossentropy', 'ce']:
metric_obj = metrics_mod.get(metric)
else:
y_t_rank = len(y_t.shape.as_list())
y_p_rank = len(y_p.shape.as_list())
y_t_last_dim = y_t.shape.as_list()[-1]
y_p_last_dim = y_p.shape.as_list()[-1]
is_binary = y_p_last_dim == 1
is_sparse_categorical = (y_t_rank < y_p_rank
or y_t_last_dim == 1 and y_p_last_dim > 1)
if metric in ['accuracy', 'acc']:
if is_binary:
metric_obj = metrics_mod.binary_accuracy
elif is_sparse_categorical:
metric_obj = metrics_mod.sparse_categorical_accuracy
else:
metric_obj = metrics_mod.categorical_accuracy
else:
if is_binary:
metric_obj = metrics_mod.binary_crossentropy
elif is_sparse_categorical:
metric_obj = metrics_mod.sparse_categorical_crossentropy
else:
metric_obj = metrics_mod.categorical_crossentropy
if isinstance(metric_obj, losses_mod.Loss):
metric_obj._allow_sum_over_batch_size = True # pylint: disable=protected-access
if not isinstance(metric_obj, metrics_mod.Metric):
if isinstance(metric, six.string_types):
metric_name = metric
else:
metric_name = get_custom_object_name(metric)
if metric_name is None:
raise ValueError(
'Metric should be a callable, found: {}'.format(
metric))
metric_obj = metrics_mod.MeanMetricWrapper(metric_obj,
name=metric_name)
return metric_obj
def test_metric_wrappers_autograph(self):
def metric_fn(y_true, y_pred):
x = tf.constant(0.0)
for i in range(len(y_true)):
for j in range(len(y_true[i])):
if (tf.equal(y_true[i][j], y_pred[i][j])
and y_true[i][j] > 0):
x += 1.0
return x
mean_metric = metrics.MeanMetricWrapper(metric_fn)
sum_metric = metrics.SumOverBatchSizeMetricWrapper(metric_fn)
self.evaluate(tf.compat.v1.variables_initializer(
mean_metric.variables))
self.evaluate(tf.compat.v1.variables_initializer(sum_metric.variables))
y_true = tf.constant([[0, 0, 0, 1, 0], [0, 0, 1, 1, 1],
[1, 1, 1, 1, 0], [1, 1, 1, 0, 1]])
y_pred = tf.constant([[0, 0, 1, 1, 0], [1, 1, 1, 1, 1],
[0, 1, 0, 1, 0], [1, 1, 1, 1, 1]])
@tf.function
def tf_functioned_metric_fn(metric, y_true, y_pred):
return metric(y_true, y_pred)
metric_result = tf_functioned_metric_fn(mean_metric, y_true, y_pred)
self.assertAllClose(self.evaluate(metric_result), 10, 1e-2)
metric_result = tf_functioned_metric_fn(sum_metric, y_true, y_pred)
self.assertAllClose(self.evaluate(metric_result), 10, 1e-2)
def _get_metric_object(self, metric, y_t, y_p):
"""Converts user-supplied metric to a `Metric` object.
Args:
metric: A string, function, or `Metric` object.
y_t: Sample of label.
y_p: Sample of output.
Returns:
A `Metric` object.
"""
if metric is None:
return None # Ok to have no metric for an output.
# Convenience feature for selecting b/t binary, categorical,
# and sparse categorical.
if str(metric).lower() not in [
"accuracy", "acc", "crossentropy", "ce"
]:
metric_obj = metrics_mod.get(metric)
else:
y_t_rank = len(y_t.shape.as_list())
y_p_rank = len(y_p.shape.as_list())
y_t_last_dim = y_t.shape.as_list()[-1]
y_p_last_dim = y_p.shape.as_list()[-1]
is_binary = y_p_last_dim == 1
is_sparse_categorical = (y_t_rank < y_p_rank
or y_t_last_dim == 1 and y_p_last_dim > 1)
if str(metric).lower() in ["accuracy", "acc"]:
if is_binary:
metric_obj = metrics_mod.binary_accuracy
elif is_sparse_categorical:
metric_obj = metrics_mod.sparse_categorical_accuracy
else:
metric_obj = metrics_mod.categorical_accuracy
else:
if is_binary:
metric_obj = metrics_mod.binary_crossentropy
elif is_sparse_categorical:
metric_obj = metrics_mod.sparse_categorical_crossentropy
else:
metric_obj = metrics_mod.categorical_crossentropy
if isinstance(metric_obj, losses_mod.Loss):
metric_obj._allow_sum_over_batch_size = True
if not isinstance(metric_obj, metrics_mod.Metric):
if isinstance(metric, str):
metric_name = metric
else:
metric_name = get_custom_object_name(metric)
if metric_name is None:
raise ValueError(
f"Metric should be a callable, received: {metric}")
metric_obj = metrics_mod.MeanMetricWrapper(metric_obj,
name=metric_name)
return metric_obj
