def metrics(self, regularization_losses=None):
"""Creates metrics. See `base_head.Head` for details."""
keys = metric_keys.MetricKeys
with ops.name_scope('metrics', values=(regularization_losses, )):
# Mean metric.
eval_metrics = {}
eval_metrics[self._loss_mean_key] = metrics.Mean(
name=keys.LOSS_MEAN)
eval_metrics[self._accuracy_key] = (
metrics.SparseCategoricalAccuracy(name=keys.ACCURACY))
# TODO(b/118843532): create Keras metrics
# eval_metrics[self._precision_key] = metrics.Precision(name=keys.AUC)
# eval_metrics[self._recall_key] = metrics.Precision(name=keys.RECALL)
eval_metrics[self._prediction_mean_key] = metrics.Mean(
name=keys.PREDICTION_MEAN)
eval_metrics[self._label_mean_key] = metrics.Mean(
name=keys.LABEL_MEAN)
# TODO(b/118843532): create Keras metrics
# eval_metrics[self._accuracy_baseline_key] = (
# metrics.Mean(name=keys.ACCURACY_BASELINE))
# eval_metrics[self._auc_key] = metrics.Precision(name=keys.PRECISION)
# eval_metrics[self._auc_pr_key] = metrics.Precision(name=keys.AUC_PR)
if regularization_losses is not None:
eval_metrics[self._loss_regularization_key] = metrics.Mean(
name=keys.LOSS_REGULARIZATION)
for i, threshold in enumerate(self._thresholds):
eval_metrics[self._accuracy_keys[i]] = metrics.BinaryAccuracy(
name=self._accuracy_keys[i], threshold=threshold)
# TODO(b/118843532): create Keras metrics
# eval_metrics[self._precision_keys[i]] = (
# metrics.PRECISION_AT_THRESHOLD(
# name=self._precision_keys[i], threshold=threshold))
# eval_metrics[self._recall_keys[i]] = metrics.RECALL_AT_THRESHOLD(
# name=self._recall_keys[i], threshold=threshold)
return eval_metrics
def metrics(self, regularization_losses=None):
"""Creates metrics. See `base_head.Head` for details."""
keys = metric_keys.MetricKeys
with ops.name_scope('metrics', values=(regularization_losses,)):
# Mean metric.
eval_metrics = {}
eval_metrics[self._loss_mean_key] = metrics.Mean(name=keys.LOSS_MEAN)
if regularization_losses is not None:
eval_metrics[self._loss_regularization_key] = metrics.Mean(
name=keys.LOSS_REGULARIZATION)
# Accuracy metric.
eval_metrics[self._accuracy_key] = (
metrics.SparseCategoricalAccuracy(name=keys.ACCURACY))
return eval_metrics
def test_sparse_categorical_accuracy_mismatched_dims_dynamic(self):
with context.graph_mode(), self.cached_session() as sess:
acc_obj = metrics.SparseCategoricalAccuracy(name='my acc')
self.evaluate(variables.variables_initializer(acc_obj.variables))
t = array_ops.placeholder(dtypes.float32)
p = array_ops.placeholder(dtypes.float32)
w = array_ops.placeholder(dtypes.float32)
result_t = acc_obj(t, p, w)
result = sess.run(result_t,
feed_dict=({
t: [2, 1],
p: [[0.1, 0.1, 0.8], [0.05, 0, 0.95]],
w: [[0.5], [0.2]]
}))
self.assertAlmostEqual(result, 0.71, 2) # 2.5/2.7
def test_sparse_categorical_accuracy_mismatched_dims(self):
acc_obj = metrics.SparseCategoricalAccuracy(name='my acc')
# check config
self.assertEqual(acc_obj.name, 'my acc')
self.assertTrue(acc_obj.stateful)
self.assertEqual(len(acc_obj.variables), 2)
self.assertEqual(acc_obj.dtype, dtypes.float32)
self.evaluate(variables.variables_initializer(acc_obj.variables))
# verify that correct value is returned
update_op = acc_obj.update_state([2, 1], [[0.1, 0.1, 0.8], [0.05, 0.95, 0]])
self.evaluate(update_op)
result = self.evaluate(acc_obj.result())
self.assertEqual(result, 1) # 2/2
# check with sample_weight
result_t = acc_obj([2, 1], [[0.1, 0.1, 0.8], [0.05, 0, 0.95]],
[[0.5], [0.2]])
result = self.evaluate(result_t)
self.assertAlmostEqual(result, 0.93, 2) # 2.5/2.7