diff --git a/tensorflow/python/keras/metrics.py b/tensorflow/python/keras/metrics.py
index d853e927d21..c75cf5eb4eb 100644
--- a/tensorflow/python/keras/metrics.py
+++ b/tensorflow/python/keras/metrics.py
@@ -1556,7 +1556,7 @@ class SpecificityAtSensitivity(SensitivitySpecificityBase):
Args:
sensitivity: A scalar value in range `[0, 1]`.
num_thresholds: (Optional) Defaults to 200. The number of thresholds to
- use for matching the given specificity.
+ use for matching the given sensitivity.
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.
"""
@@ -1573,7 +1573,7 @@ class SpecificityAtSensitivity(SensitivitySpecificityBase):
self.true_positives, self.true_positives + self.false_negatives)
# Find the index of the threshold where the sensitivity is closest to the
- # given specificity.
+ # requested value.
min_index = math_ops.argmin(
math_ops.abs(sensitivities - self.value), axis=0)
min_index = math_ops.cast(min_index, dtypes.int32)
@@ -1592,6 +1592,79 @@ class SpecificityAtSensitivity(SensitivitySpecificityBase):
return dict(list(base_config.items()) + list(config.items()))
+@keras_export('keras.metrics.PrecisionAtRecall')
+class PrecisionAtRecall(SensitivitySpecificityBase):
+ """Computes the precision at a given recall.
+
+ This metric creates four local variables, `true_positives`, `true_negatives`,
+ `false_positives` and `false_negatives` that are used to compute the
+ precision at the given recall. The threshold for the given recall
+ value is computed and used to evaluate the corresponding precision.
+
+ If `sample_weight` is `None`, weights default to 1.
+ Use `sample_weight` of 0 to mask values.
+
+ Usage:
+
+ ```python
+ m = tf.keras.metrics.PrecisionAtRecall(0.8, num_thresholds=1)
+ m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
+ print('Final result: ', m.result().numpy()) # Final result: 1.0
+ ```
+
+ Usage with tf.keras API:
+
+ ```python
+ model = tf.keras.Model(inputs, outputs)
+ model.compile(
+ 'sgd',
+ loss='mse',
+ metrics=[tf.keras.metrics.PrecisionAtRecall()])
+ ```
+ """
+
+ def __init__(self, recall, num_thresholds=200, name=None, dtype=None):
+ """Creates a `PrecisionAtRecall` instance.
+
+ Args:
+ recall: A scalar value in range `[0, 1]`.
+ num_thresholds: (Optional) Defaults to 200. The number of thresholds to
+ use for matching the given recall.
+ name: (Optional) string name of the metric instance.
+ dtype: (Optional) data type of the metric result.
+ """
+ if recall < 0 or recall > 1:
+ raise ValueError('`recall` must be in the range [0, 1].')
+ self.recall = recall
+ self.num_thresholds = num_thresholds
+ super(PrecisionAtRecall, self).__init__(
+ value=recall,
+ num_thresholds=num_thresholds,
+ name=name,
+ dtype=dtype)
+
+ def result(self):
+ # Calculate recall at all the thresholds.
+ recalls = math_ops.div_no_nan(
+ self.true_positives, self.true_positives + self.false_negatives)
+
+ # Find the index of the threshold where the recall is closest to the
+ # requested value.
+ min_index = math_ops.argmin(
+ math_ops.abs(recalls - self.value), axis=0)
+ min_index = math_ops.cast(min_index, dtypes.int32)
+
+ # Compute precision at that index.
+ return math_ops.div_no_nan(
+ self.true_positives[min_index],
+ self.true_positives[min_index] + self.false_positives[min_index])
+
+ def get_config(self):
+ config = {'num_thresholds': self.num_thresholds, 'recall': self.recall}
+ base_config = super(PrecisionAtRecall, self).get_config()
+ return dict(list(base_config.items()) + list(config.items()))
+
+
@keras_export('keras.metrics.AUC')
class AUC(Metric):
"""Computes the approximate AUC (Area under the curve) via a Riemann sum.
diff --git a/tensorflow/python/keras/metrics_confusion_matrix_test.py b/tensorflow/python/keras/metrics_confusion_matrix_test.py
index 50179cb4a7e..e7189a13873 100644
--- a/tensorflow/python/keras/metrics_confusion_matrix_test.py
+++ b/tensorflow/python/keras/metrics_confusion_matrix_test.py
@@ -919,6 +919,103 @@ class SpecificityAtSensitivityTest(test.TestCase, parameterized.TestCase):
metrics.SpecificityAtSensitivity(0.4, num_thresholds=-1)
+@test_util.run_all_in_graph_and_eager_modes
+class PrecisionAtRecallTest(test.TestCase, parameterized.TestCase):
+
+ def test_config(self):
+ s_obj = metrics.PrecisionAtRecall(
+ 0.4, num_thresholds=100, name='precision_at_recall_1')
+ self.assertEqual(s_obj.name, 'precision_at_recall_1')
+ self.assertLen(s_obj.variables, 4)
+ self.assertEqual(s_obj.recall, 0.4)
+ self.assertEqual(s_obj.num_thresholds, 100)
+
+ # Check save and restore config
+ s_obj2 = metrics.PrecisionAtRecall.from_config(s_obj.get_config())
+ self.assertEqual(s_obj2.name, 'precision_at_recall_1')
+ self.assertLen(s_obj2.variables, 4)
+ self.assertEqual(s_obj2.recall, 0.4)
+ self.assertEqual(s_obj2.num_thresholds, 100)
+
+ def test_value_is_idempotent(self):
+ s_obj = metrics.PrecisionAtRecall(0.7)
+ y_pred = random_ops.random_uniform((10, 3),
+ maxval=1,
+ dtype=dtypes.float32,
+ seed=1)
+ y_true = random_ops.random_uniform((10, 3),
+ maxval=2,
+ dtype=dtypes.int64,
+ seed=1)
+ update_op = s_obj.update_state(y_true, y_pred)
+ self.evaluate(variables.variables_initializer(s_obj.variables))
+
+ # Run several updates.
+ for _ in range(10):
+ self.evaluate(update_op)
+
+ # Then verify idempotency.
+ initial_specificity = self.evaluate(s_obj.result())
+ for _ in range(10):
+ self.assertAlmostEqual(initial_specificity, self.evaluate(s_obj.result()),
+ 1e-3)
+
+ def test_unweighted_all_correct(self):
+ s_obj = metrics.PrecisionAtRecall(0.7)
+ inputs = np.random.randint(0, 2, size=(100, 1))
+ y_pred = constant_op.constant(inputs, dtype=dtypes.float32)
+ y_true = constant_op.constant(inputs)
+ self.evaluate(variables.variables_initializer(s_obj.variables))
+ result = s_obj(y_true, y_pred)
+ self.assertAlmostEqual(1, self.evaluate(result))
+
+ def test_unweighted_high_recall(self):
+ s_obj = metrics.PrecisionAtRecall(0.8)
+ pred_values = [0.0, 0.1, 0.2, 0.3, 0.5, 0.4, 0.5, 0.6, 0.8, 0.9]
+ label_values = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
+
+ # For a score between 0.4 and 0.5, we expect 0.8 precision, 0.8 recall.
+ y_pred = constant_op.constant(pred_values, dtype=dtypes.float32)
+ y_true = constant_op.constant(label_values)
+ self.evaluate(variables.variables_initializer(s_obj.variables))
+ result = s_obj(y_true, y_pred)
+ self.assertAlmostEqual(0.8, self.evaluate(result))
+
+ def test_unweighted_low_recall(self):
+ s_obj = metrics.PrecisionAtRecall(0.4)
+ pred_values = [0.0, 0.1, 0.2, 0.3, 0.4, 0.1, 0.15, 0.25, 0.26, 0.26]
+ label_values = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
+
+ y_pred = constant_op.constant(pred_values, dtype=dtypes.float32)
+ y_true = constant_op.constant(label_values)
+ self.evaluate(variables.variables_initializer(s_obj.variables))
+ result = s_obj(y_true, y_pred)
+ self.assertAlmostEqual(0.5, self.evaluate(result))
+
+ @parameterized.parameters([dtypes.bool, dtypes.int32, dtypes.float32])
+ def test_weighted(self, label_dtype):
+ s_obj = metrics.PrecisionAtRecall(0.4)
+ pred_values = [0.0, 0.1, 0.2, 0.3, 0.4, 0.01, 0.02, 0.25, 0.26, 0.26]
+ label_values = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
+ weight_values = [2, 2, 1, 1, 1, 1, 1, 2, 2, 2]
+
+ y_pred = constant_op.constant(pred_values, dtype=dtypes.float32)
+ y_true = math_ops.cast(label_values, dtype=label_dtype)
+ weights = constant_op.constant(weight_values)
+ self.evaluate(variables.variables_initializer(s_obj.variables))
+ result = s_obj(y_true, y_pred, sample_weight=weights)
+ self.assertAlmostEqual(2./3., self.evaluate(result))
+
+ def test_invalid_sensitivity(self):
+ with self.assertRaisesRegexp(
+ ValueError, r'`recall` must be in the range \[0, 1\].'):
+ metrics.PrecisionAtRecall(-1)
+
+ def test_invalid_num_thresholds(self):
+ with self.assertRaisesRegexp(ValueError, '`num_thresholds` must be > 0.'):
+ metrics.PrecisionAtRecall(0.4, num_thresholds=-1)
+
+
@test_util.run_all_in_graph_and_eager_modes
class AUCTest(test.TestCase):
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.-precision-at-recall.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.-precision-at-recall.pbtxt
new file mode 100644
index 00000000000..6e294ba5ef5
--- /dev/null
+++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.-precision-at-recall.pbtxt
@@ -0,0 +1,231 @@
+path: "tensorflow.keras.metrics.PrecisionAtRecall"
+tf_class {
+ is_instance: "<class \'tensorflow.python.keras.metrics.PrecisionAtRecall\'>"
+ is_instance: "<class \'tensorflow.python.keras.metrics.SensitivitySpecificityBase\'>"
+ is_instance: "<class \'tensorflow.python.keras.metrics.Metric\'>"
+ is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
+ is_instance: "<class \'tensorflow.python.module.module.Module\'>"
+ is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
+ is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
+ is_instance: "<type \'object\'>"
+ member {
+ name: "activity_regularizer"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "dtype"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "dynamic"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "inbound_nodes"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "input"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "input_mask"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "input_shape"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "input_spec"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "losses"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "metrics"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "name"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "name_scope"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "non_trainable_variables"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "non_trainable_weights"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "outbound_nodes"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "output"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "output_mask"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "output_shape"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "stateful"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "submodules"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "trainable"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "trainable_variables"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "trainable_weights"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "updates"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "variables"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "weights"
+ mtype: "<type \'property\'>"
+ }
+ member_method {
+ name: "__init__"
+ argspec: "args=[\'self\', \'recall\', \'num_thresholds\', \'name\', \'dtype\'], varargs=None, keywords=None, defaults=[\'200\', \'None\', \'None\'], "
+ }
+ member_method {
+ name: "add_loss"
+ argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+ }
+ member_method {
+ name: "add_metric"
+ argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+ }
+ member_method {
+ name: "add_update"
+ argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+ }
+ member_method {
+ name: "add_variable"
+ argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
+ }
+ member_method {
+ name: "add_weight"
+ argspec: "args=[\'self\', \'name\', \'shape\', \'aggregation\', \'synchronization\', \'initializer\', \'dtype\'], varargs=None, keywords=None, defaults=[\'()\', \'VariableAggregation.SUM\', \'VariableSynchronization.ON_READ\', \'None\', \'None\'], "
+ }
+ member_method {
+ name: "apply"
+ argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+ }
+ member_method {
+ name: "build"
+ argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "call"
+ argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=kwargs, defaults=None"
+ }
+ member_method {
+ name: "compute_mask"
+ argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+ }
+ member_method {
+ name: "compute_output_shape"
+ argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "compute_output_signature"
+ argspec: "args=[\'self\', \'input_signature\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "count_params"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "from_config"
+ argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_config"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_input_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_input_mask_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_input_shape_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_losses_for"
+ argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_output_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_output_mask_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_output_shape_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_updates_for"
+ argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_weights"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "reset_states"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "result"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "set_weights"
+ argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "update_state"
+ argspec: "args=[\'self\', \'y_true\', \'y_pred\', \'sample_weight\'], varargs=None, keywords=None, defaults=[\'None\'], "
+ }
+ member_method {
+ name: "with_name_scope"
+ argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
+ }
+}
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.pbtxt
index 3f7fe4a2f40..c4481a9233e 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.pbtxt
@@ -96,6 +96,10 @@ tf_module {
name: "Precision"
mtype: "<type \'type\'>"
}
+ member {
+ name: "PrecisionAtRecall"
+ mtype: "<type \'type\'>"
+ }
member {
name: "Recall"
mtype: "<type \'type\'>"
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.-precision-at-recall.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.-precision-at-recall.pbtxt
new file mode 100644
index 00000000000..6e294ba5ef5
--- /dev/null
+++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.-precision-at-recall.pbtxt
@@ -0,0 +1,231 @@
+path: "tensorflow.keras.metrics.PrecisionAtRecall"
+tf_class {
+ is_instance: "<class \'tensorflow.python.keras.metrics.PrecisionAtRecall\'>"
+ is_instance: "<class \'tensorflow.python.keras.metrics.SensitivitySpecificityBase\'>"
+ is_instance: "<class \'tensorflow.python.keras.metrics.Metric\'>"
+ is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
+ is_instance: "<class \'tensorflow.python.module.module.Module\'>"
+ is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
+ is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
+ is_instance: "<type \'object\'>"
+ member {
+ name: "activity_regularizer"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "dtype"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "dynamic"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "inbound_nodes"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "input"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "input_mask"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "input_shape"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "input_spec"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "losses"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "metrics"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "name"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "name_scope"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "non_trainable_variables"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "non_trainable_weights"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "outbound_nodes"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "output"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "output_mask"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "output_shape"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "stateful"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "submodules"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "trainable"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "trainable_variables"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "trainable_weights"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "updates"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "variables"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "weights"
+ mtype: "<type \'property\'>"
+ }
+ member_method {
+ name: "__init__"
+ argspec: "args=[\'self\', \'recall\', \'num_thresholds\', \'name\', \'dtype\'], varargs=None, keywords=None, defaults=[\'200\', \'None\', \'None\'], "
+ }
+ member_method {
+ name: "add_loss"
+ argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+ }
+ member_method {
+ name: "add_metric"
+ argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+ }
+ member_method {
+ name: "add_update"
+ argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+ }
+ member_method {
+ name: "add_variable"
+ argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
+ }
+ member_method {
+ name: "add_weight"
+ argspec: "args=[\'self\', \'name\', \'shape\', \'aggregation\', \'synchronization\', \'initializer\', \'dtype\'], varargs=None, keywords=None, defaults=[\'()\', \'VariableAggregation.SUM\', \'VariableSynchronization.ON_READ\', \'None\', \'None\'], "
+ }
+ member_method {
+ name: "apply"
+ argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+ }
+ member_method {
+ name: "build"
+ argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "call"
+ argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=kwargs, defaults=None"
+ }
+ member_method {
+ name: "compute_mask"
+ argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+ }
+ member_method {
+ name: "compute_output_shape"
+ argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "compute_output_signature"
+ argspec: "args=[\'self\', \'input_signature\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "count_params"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "from_config"
+ argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_config"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_input_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_input_mask_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_input_shape_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_losses_for"
+ argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_output_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_output_mask_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_output_shape_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_updates_for"
+ argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_weights"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "reset_states"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "result"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "set_weights"
+ argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "update_state"
+ argspec: "args=[\'self\', \'y_true\', \'y_pred\', \'sample_weight\'], varargs=None, keywords=None, defaults=[\'None\'], "
+ }
+ member_method {
+ name: "with_name_scope"
+ argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
+ }
+}
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.pbtxt
index 71e89765cb8..def1eb51bde 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.pbtxt
@@ -96,6 +96,10 @@ tf_module {
name: "Precision"
mtype: "<type \'type\'>"
}
+ member {
+ name: "PrecisionAtRecall"
+ mtype: "<type \'type\'>"
+ }
member {
name: "Recall"
mtype: "<type \'type\'>"
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.metrics.-precision-at-recall.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.metrics.-precision-at-recall.pbtxt
new file mode 100644
index 00000000000..1ba7673f021
--- /dev/null
+++ b/tensorflow/tools/api/golden/v2/tensorflow.metrics.-precision-at-recall.pbtxt
@@ -0,0 +1,231 @@
+path: "tensorflow.metrics.PrecisionAtRecall"
+tf_class {
+ is_instance: "<class \'tensorflow.python.keras.metrics.PrecisionAtRecall\'>"
+ is_instance: "<class \'tensorflow.python.keras.metrics.SensitivitySpecificityBase\'>"
+ is_instance: "<class \'tensorflow.python.keras.metrics.Metric\'>"
+ is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
+ is_instance: "<class \'tensorflow.python.module.module.Module\'>"
+ is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
+ is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
+ is_instance: "<type \'object\'>"
+ member {
+ name: "activity_regularizer"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "dtype"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "dynamic"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "inbound_nodes"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "input"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "input_mask"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "input_shape"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "input_spec"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "losses"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "metrics"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "name"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "name_scope"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "non_trainable_variables"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "non_trainable_weights"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "outbound_nodes"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "output"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "output_mask"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "output_shape"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "stateful"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "submodules"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "trainable"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "trainable_variables"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "trainable_weights"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "updates"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "variables"
+ mtype: "<type \'property\'>"
+ }
+ member {
+ name: "weights"
+ mtype: "<type \'property\'>"
+ }
+ member_method {
+ name: "__init__"
+ argspec: "args=[\'self\', \'recall\', \'num_thresholds\', \'name\', \'dtype\'], varargs=None, keywords=None, defaults=[\'200\', \'None\', \'None\'], "
+ }
+ member_method {
+ name: "add_loss"
+ argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+ }
+ member_method {
+ name: "add_metric"
+ argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+ }
+ member_method {
+ name: "add_update"
+ argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+ }
+ member_method {
+ name: "add_variable"
+ argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
+ }
+ member_method {
+ name: "add_weight"
+ argspec: "args=[\'self\', \'name\', \'shape\', \'aggregation\', \'synchronization\', \'initializer\', \'dtype\'], varargs=None, keywords=None, defaults=[\'()\', \'VariableAggregation.SUM\', \'VariableSynchronization.ON_READ\', \'None\', \'None\'], "
+ }
+ member_method {
+ name: "apply"
+ argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+ }
+ member_method {
+ name: "build"
+ argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "call"
+ argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=kwargs, defaults=None"
+ }
+ member_method {
+ name: "compute_mask"
+ argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+ }
+ member_method {
+ name: "compute_output_shape"
+ argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "compute_output_signature"
+ argspec: "args=[\'self\', \'input_signature\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "count_params"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "from_config"
+ argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_config"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_input_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_input_mask_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_input_shape_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_losses_for"
+ argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_output_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_output_mask_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_output_shape_at"
+ argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_updates_for"
+ argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_weights"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "reset_states"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "result"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "set_weights"
+ argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "update_state"
+ argspec: "args=[\'self\', \'y_true\', \'y_pred\', \'sample_weight\'], varargs=None, keywords=None, defaults=[\'None\'], "
+ }
+ member_method {
+ name: "with_name_scope"
+ argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
+ }
+}
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.metrics.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.metrics.pbtxt
index 4ae997c6bd4..5bd57dd7618 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.metrics.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.metrics.pbtxt
@@ -96,6 +96,10 @@ tf_module {
name: "Precision"
mtype: "<type \'type\'>"
}
+ member {
+ name: "PrecisionAtRecall"
+ mtype: "<type \'type\'>"
+ }
member {
name: "Recall"
mtype: "<type \'type\'>"