Implement RecallAtPrecision metric (in analogy to PrecisionAtRecall).
There are two differences: * Since precision is not monotonous function of recall, there might be multiple operating ranges with the required precision - the point with the maximal recall is selected. * For some score-label-distributions the required precision might not be achievable at all, in this case 0.0 for recall is returned. PiperOrigin-RevId: 297456035 Change-Id: Ie79e20014735c753282d1cd7699bd7475df15878
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
eacf534690
commit
cb1358d9ff
@ -1684,7 +1684,7 @@ class PrecisionAtRecall(SensitivitySpecificityBase):
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model.compile(
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'sgd',
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loss='mse',
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metrics=[tf.keras.metrics.PrecisionAtRecall()])
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metrics=[tf.keras.metrics.PrecisionAtRecall(recall=0.8)])
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```
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"""
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@ -1730,6 +1730,89 @@ class PrecisionAtRecall(SensitivitySpecificityBase):
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return dict(list(base_config.items()) + list(config.items()))
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@keras_export('keras.metrics.RecallAtPrecision')
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class RecallAtPrecision(SensitivitySpecificityBase):
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"""Computes the maximally achievable recall at a required precision.
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For a given score-label-distribution the required precision might not
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be achievable, in this case 0.0 is returned as recall.
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This metric creates four local variables, `true_positives`, `true_negatives`,
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`false_positives` and `false_negatives` that are used to compute the
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recall at the given precision. The threshold for the given precision
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value is computed and used to evaluate the corresponding recall.
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If `sample_weight` is `None`, weights default to 1.
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Use `sample_weight` of 0 to mask values.
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Usage:
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>>> m = tf.keras.metrics.RecallAtPrecision(0.8, num_thresholds=1)
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>>> _ = m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
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>>> m.result().numpy()
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0.5
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>>> m.reset_states()
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>>> _ = m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9],
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... sample_weight=[1, 0, 0, 1])
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>>> m.result().numpy()
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1.0
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Usage with tf.keras API:
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```python
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model = tf.keras.Model(inputs, outputs)
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model.compile(
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'sgd',
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loss='mse',
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metrics=[tf.keras.metrics.RecallAtPrecision(precision=0.8)])
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```
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"""
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def __init__(self, precision, num_thresholds=200, name=None, dtype=None):
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"""Creates a `RecallAtPrecision` instance.
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Args:
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precision: A scalar value in range `[0, 1]`.
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num_thresholds: (Optional) Defaults to 200. The number of thresholds to
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use for matching the given precision.
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name: (Optional) string name of the metric instance.
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dtype: (Optional) data type of the metric result.
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"""
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if precision < 0 or precision > 1:
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raise ValueError('`precision` must be in the range [0, 1].')
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self.precision = precision
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self.num_thresholds = num_thresholds
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super(RecallAtPrecision, self).__init__(
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value=precision,
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num_thresholds=num_thresholds,
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name=name,
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dtype=dtype)
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def result(self):
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# Calculate precision and recall at all the thresholds.
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# All recalls are computed, because they are not a monotoneous function of
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# precision and we want to search for the highest feasible recall.
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precisions = math_ops.div_no_nan(
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self.true_positives, self.true_positives + self.false_positives)
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recalls = math_ops.div_no_nan(
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self.true_positives, self.true_positives + self.false_negatives)
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# Find best recall where the precision is as good as required.
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feasible = array_ops.where(math_ops.greater_equal(precisions, self.value))
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feasible_exists = math_ops.greater(array_ops.size(feasible), 0)
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best_recall = control_flow_ops.cond(
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feasible_exists,
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lambda: math_ops.reduce_max(array_ops.gather(recalls, feasible)),
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lambda: 0.0)
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return best_recall
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def get_config(self):
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config = {'num_thresholds': self.num_thresholds,
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'precision': self.precision}
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base_config = super(RecallAtPrecision, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@keras_export('keras.metrics.AUC')
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class AUC(Metric):
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"""Computes the approximate AUC (Area under the curve) via a Riemann sum.
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@ -957,9 +957,9 @@ class PrecisionAtRecallTest(test.TestCase, parameterized.TestCase):
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self.evaluate(update_op)
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# Then verify idempotency.
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initial_specificity = self.evaluate(s_obj.result())
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initial_precision = self.evaluate(s_obj.result())
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for _ in range(10):
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self.assertAlmostEqual(initial_specificity, self.evaluate(s_obj.result()),
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self.assertAlmostEqual(initial_precision, self.evaluate(s_obj.result()),
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1e-3)
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def test_unweighted_all_correct(self):
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@ -1018,6 +1018,121 @@ class PrecisionAtRecallTest(test.TestCase, parameterized.TestCase):
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metrics.PrecisionAtRecall(0.4, num_thresholds=-1)
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@test_util.run_all_in_graph_and_eager_modes
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class RecallAtPrecisionTest(test.TestCase, parameterized.TestCase):
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def test_config(self):
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s_obj = metrics.RecallAtPrecision(
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0.4, num_thresholds=100, name='recall_at_precision_1')
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self.assertEqual(s_obj.name, 'recall_at_precision_1')
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self.assertLen(s_obj.variables, 4)
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self.assertEqual(s_obj.precision, 0.4)
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self.assertEqual(s_obj.num_thresholds, 100)
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# Check save and restore config
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s_obj2 = metrics.RecallAtPrecision.from_config(s_obj.get_config())
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self.assertEqual(s_obj2.name, 'recall_at_precision_1')
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self.assertLen(s_obj2.variables, 4)
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self.assertEqual(s_obj2.precision, 0.4)
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self.assertEqual(s_obj2.num_thresholds, 100)
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def test_value_is_idempotent(self):
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s_obj = metrics.RecallAtPrecision(0.7)
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y_pred = random_ops.random_uniform((10, 3),
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maxval=1,
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dtype=dtypes.float32,
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seed=1)
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y_true = random_ops.random_uniform((10, 3),
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maxval=2,
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dtype=dtypes.int64,
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seed=1)
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update_op = s_obj.update_state(y_true, y_pred)
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self.evaluate(variables.variables_initializer(s_obj.variables))
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# Run several updates.
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for _ in range(10):
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self.evaluate(update_op)
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# Then verify idempotency.
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initial_recall = self.evaluate(s_obj.result())
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for _ in range(10):
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self.assertAlmostEqual(initial_recall, self.evaluate(s_obj.result()),
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1e-3)
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def test_unweighted_all_correct(self):
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s_obj = metrics.RecallAtPrecision(0.7)
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inputs = np.random.randint(0, 2, size=(100, 1))
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y_pred = constant_op.constant(inputs, dtype=dtypes.float32)
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y_true = constant_op.constant(inputs)
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self.evaluate(variables.variables_initializer(s_obj.variables))
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result = s_obj(y_true, y_pred)
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self.assertAlmostEqual(1, self.evaluate(result))
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def test_unweighted_high_precision(self):
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s_obj = metrics.RecallAtPrecision(0.75)
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pred_values = [
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0.05, 0.1, 0.2, 0.3, 0.3, 0.35, 0.4, 0.45, 0.5, 0.6, 0.9, 0.95
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]
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label_values = [0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1]
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# precisions: [1/2, 6/11, 1/2, 5/9, 5/8, 5/7, 2/3, 3/5, 3/5, 2/3, 1/2, 1].
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# recalls: [1, 1, 5/6, 5/6, 5/6, 5/6, 2/3, 1/2, 1/2, 1/3, 1/6, 1/6].
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y_pred = constant_op.constant(pred_values, dtype=dtypes.float32)
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y_true = constant_op.constant(label_values)
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self.evaluate(variables.variables_initializer(s_obj.variables))
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result = s_obj(y_true, y_pred)
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# The precision 0.75 can be reached at thresholds 0.4<=t<0.45.
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self.assertAlmostEqual(0.5, self.evaluate(result))
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def test_unweighted_low_precision(self):
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s_obj = metrics.RecallAtPrecision(2.0 / 3)
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pred_values = [
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0.05, 0.1, 0.2, 0.3, 0.3, 0.35, 0.4, 0.45, 0.5, 0.6, 0.9, 0.95
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]
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label_values = [0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1]
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# precisions: [1/2, 6/11, 1/2, 5/9, 5/8, 5/7, 2/3, 3/5, 3/5, 2/3, 1/2, 1].
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# recalls: [1, 1, 5/6, 5/6, 5/6, 5/6, 2/3, 1/2, 1/2, 1/3, 1/6, 1/6].
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y_pred = constant_op.constant(pred_values, dtype=dtypes.float32)
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y_true = constant_op.constant(label_values)
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self.evaluate(variables.variables_initializer(s_obj.variables))
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result = s_obj(y_true, y_pred)
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# The precision 5/7 can be reached at thresholds 00.3<=t<0.35.
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self.assertAlmostEqual(5. / 6, self.evaluate(result))
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@parameterized.parameters([dtypes.bool, dtypes.int32, dtypes.float32])
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def test_weighted(self, label_dtype):
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s_obj = metrics.RecallAtPrecision(0.75)
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pred_values = [0.1, 0.2, 0.3, 0.5, 0.6, 0.9, 0.9]
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label_values = [0, 1, 0, 0, 0, 1, 1]
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weight_values = [1, 2, 1, 2, 1, 2, 1]
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y_pred = constant_op.constant(pred_values, dtype=dtypes.float32)
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y_true = math_ops.cast(label_values, dtype=label_dtype)
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weights = constant_op.constant(weight_values)
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self.evaluate(variables.variables_initializer(s_obj.variables))
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result = s_obj(y_true, y_pred, sample_weight=weights)
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self.assertAlmostEqual(0.6, self.evaluate(result))
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def test_unachievable_precision(self):
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s_obj = metrics.RecallAtPrecision(2.0 / 3)
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pred_values = [0.1, 0.2, 0.3, 0.9]
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label_values = [1, 1, 0, 0]
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y_pred = constant_op.constant(pred_values, dtype=dtypes.float32)
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y_true = constant_op.constant(label_values)
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self.evaluate(variables.variables_initializer(s_obj.variables))
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result = s_obj(y_true, y_pred)
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# The highest possible precision is 1/2 which is below the required
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# value, expect 0 recall.
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self.assertAlmostEqual(0, self.evaluate(result))
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def test_invalid_sensitivity(self):
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with self.assertRaisesRegexp(ValueError,
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r'`precision` must be in the range \[0, 1\].'):
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metrics.RecallAtPrecision(-1)
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def test_invalid_num_thresholds(self):
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with self.assertRaisesRegexp(ValueError, '`num_thresholds` must be > 0.'):
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metrics.RecallAtPrecision(0.4, num_thresholds=-1)
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@test_util.run_all_in_graph_and_eager_modes
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class AUCTest(test.TestCase):
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@ -2094,6 +2094,36 @@ class ResetStatesTest(keras_parameterized.TestCase):
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self.assertEqual(self.evaluate(s_obj.false_negatives), 25.)
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self.assertEqual(self.evaluate(s_obj.true_negatives), 25.)
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def test_reset_states_precision_at_recall(self):
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s_obj = metrics.PrecisionAtRecall(recall=0.5, num_thresholds=1)
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model = _get_model([s_obj])
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x = np.concatenate((np.ones((25, 4)), np.zeros((25, 4)), np.zeros((25, 4)),
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np.ones((25, 4))))
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y = np.concatenate((np.ones((25, 1)), np.zeros((25, 1)), np.ones((25, 1)),
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np.zeros((25, 1))))
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for _ in range(2):
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model.evaluate(x, y)
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self.assertEqual(self.evaluate(s_obj.true_positives), 25.)
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self.assertEqual(self.evaluate(s_obj.false_positives), 25.)
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self.assertEqual(self.evaluate(s_obj.false_negatives), 25.)
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self.assertEqual(self.evaluate(s_obj.true_negatives), 25.)
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def test_reset_states_recall_at_precision(self):
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s_obj = metrics.RecallAtPrecision(precision=0.5, num_thresholds=1)
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model = _get_model([s_obj])
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x = np.concatenate((np.ones((25, 4)), np.zeros((25, 4)), np.zeros((25, 4)),
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np.ones((25, 4))))
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y = np.concatenate((np.ones((25, 1)), np.zeros((25, 1)), np.ones((25, 1)),
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np.zeros((25, 1))))
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for _ in range(2):
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model.evaluate(x, y)
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self.assertEqual(self.evaluate(s_obj.true_positives), 25.)
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self.assertEqual(self.evaluate(s_obj.false_positives), 25.)
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self.assertEqual(self.evaluate(s_obj.false_negatives), 25.)
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self.assertEqual(self.evaluate(s_obj.true_negatives), 25.)
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def test_reset_states_auc(self):
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auc_obj = metrics.AUC(num_thresholds=3)
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model = _get_model([auc_obj])
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@ -0,0 +1,232 @@
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path: "tensorflow.keras.metrics.RecallAtPrecision"
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tf_class {
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is_instance: "<class \'tensorflow.python.keras.metrics.RecallAtPrecision\'>"
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is_instance: "<class \'tensorflow.python.keras.metrics.SensitivitySpecificityBase\'>"
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is_instance: "<class \'tensorflow.python.keras.metrics.Metric\'>"
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is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
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is_instance: "<class \'tensorflow.python.module.module.Module\'>"
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is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
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is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
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is_instance: "<class \'tensorflow.python.keras.utils.version_utils.LayerVersionSelector\'>"
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is_instance: "<type \'object\'>"
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member {
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name: "activity_regularizer"
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mtype: "<type \'property\'>"
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}
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member {
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name: "dtype"
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mtype: "<type \'property\'>"
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}
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member {
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name: "dynamic"
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mtype: "<type \'property\'>"
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}
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member {
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name: "inbound_nodes"
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mtype: "<type \'property\'>"
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}
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member {
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name: "input"
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mtype: "<type \'property\'>"
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}
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member {
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name: "input_mask"
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mtype: "<type \'property\'>"
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}
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member {
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name: "input_shape"
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mtype: "<type \'property\'>"
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}
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member {
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name: "input_spec"
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mtype: "<type \'property\'>"
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}
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member {
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name: "losses"
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mtype: "<type \'property\'>"
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}
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member {
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name: "metrics"
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mtype: "<type \'property\'>"
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}
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member {
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name: "name"
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mtype: "<type \'property\'>"
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}
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member {
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name: "name_scope"
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mtype: "<type \'property\'>"
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}
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member {
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name: "non_trainable_variables"
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mtype: "<type \'property\'>"
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}
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member {
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name: "non_trainable_weights"
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mtype: "<type \'property\'>"
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}
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member {
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name: "outbound_nodes"
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mtype: "<type \'property\'>"
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}
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member {
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name: "output"
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mtype: "<type \'property\'>"
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}
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member {
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name: "output_mask"
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mtype: "<type \'property\'>"
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}
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member {
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name: "output_shape"
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mtype: "<type \'property\'>"
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}
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member {
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name: "stateful"
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mtype: "<type \'property\'>"
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}
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member {
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name: "submodules"
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mtype: "<type \'property\'>"
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}
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member {
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name: "trainable"
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mtype: "<type \'property\'>"
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}
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member {
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name: "trainable_variables"
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mtype: "<type \'property\'>"
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}
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member {
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name: "trainable_weights"
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mtype: "<type \'property\'>"
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}
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member {
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name: "updates"
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mtype: "<type \'property\'>"
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}
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member {
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name: "variables"
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mtype: "<type \'property\'>"
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}
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member {
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name: "weights"
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mtype: "<type \'property\'>"
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}
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member_method {
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name: "__init__"
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argspec: "args=[\'self\', \'precision\', \'num_thresholds\', \'name\', \'dtype\'], varargs=None, keywords=None, defaults=[\'200\', \'None\', \'None\'], "
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}
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member_method {
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name: "add_loss"
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argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
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}
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member_method {
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name: "add_metric"
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argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
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}
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member_method {
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name: "add_update"
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argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
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}
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member_method {
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name: "add_variable"
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argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
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}
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member_method {
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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"
|
||||
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|
||||
}
|
||||
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"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
member_method {
|
||||
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|
||||
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
|
||||
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|
||||
member_method {
|
||||
name: "reset_states"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
|
||||
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|
||||
member_method {
|
||||
name: "result"
|
||||
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|
||||
}
|
||||
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"
|
||||
}
|
||||
}
|
||||
@ -104,6 +104,10 @@ tf_module {
|
||||
name: "Recall"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "RecallAtPrecision"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "RootMeanSquaredError"
|
||||
mtype: "<type \'type\'>"
|
||||
|
||||
@ -0,0 +1,232 @@
|
||||
path: "tensorflow.keras.metrics.RecallAtPrecision"
|
||||
tf_class {
|
||||
is_instance: "<class \'tensorflow.python.keras.metrics.RecallAtPrecision\'>"
|
||||
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\'>"
|
||||
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|
||||
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
|
||||
is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
|
||||
is_instance: "<class \'tensorflow.python.keras.utils.version_utils.LayerVersionSelector\'>"
|
||||
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|
||||
member {
|
||||
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|
||||
mtype: "<type \'property\'>"
|
||||
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|
||||
member {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
member {
|
||||
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|
||||
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|
||||
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|
||||
member {
|
||||
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|
||||
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|
||||
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|
||||
member {
|
||||
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|
||||
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|
||||
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|
||||
member {
|
||||
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|
||||
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|
||||
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|
||||
member {
|
||||
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|
||||
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|
||||
}
|
||||
member {
|
||||
name: "losses"
|
||||
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|
||||
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|
||||
member {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
member {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
member {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
member {
|
||||
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|
||||
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|
||||
}
|
||||
member {
|
||||
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|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'precision\', \'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 {
|
||||
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|
||||
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\'], "
|
||||
}
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
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|
||||
name: "count_params"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
name: "get_input_at"
|
||||
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|
||||
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|
||||
member_method {
|
||||
name: "get_input_mask_at"
|
||||
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|
||||
}
|
||||
member_method {
|
||||
name: "get_input_shape_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
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|
||||
name: "get_losses_for"
|
||||
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|
||||
}
|
||||
member_method {
|
||||
name: "get_output_at"
|
||||
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
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|
||||
name: "get_output_mask_at"
|
||||
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|
||||
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|
||||
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|
||||
name: "get_output_shape_at"
|
||||
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|
||||
}
|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
member_method {
|
||||
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|
||||
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
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|
||||
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|
||||
}
|
||||
member_method {
|
||||
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|
||||
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|
||||
}
|
||||
member_method {
|
||||
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|
||||
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|
||||
}
|
||||
member_method {
|
||||
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|
||||
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|
||||
}
|
||||
member_method {
|
||||
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|
||||
argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
}
|
||||
@ -104,6 +104,10 @@ tf_module {
|
||||
name: "Recall"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "RecallAtPrecision"
|
||||
mtype: "<type \'type\'>"
|
||||
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|
||||
member {
|
||||
name: "RootMeanSquaredError"
|
||||
mtype: "<type \'type\'>"
|
||||
|
||||
@ -0,0 +1,232 @@
|
||||
path: "tensorflow.metrics.RecallAtPrecision"
|
||||
tf_class {
|
||||
is_instance: "<class \'tensorflow.python.keras.metrics.RecallAtPrecision\'>"
|
||||
is_instance: "<class \'tensorflow.python.keras.metrics.SensitivitySpecificityBase\'>"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
member {
|
||||
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|
||||
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|
||||
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|
||||
member {
|
||||
name: "updates"
|
||||
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|
||||
}
|
||||
member {
|
||||
name: "variables"
|
||||
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|
||||
}
|
||||
member {
|
||||
name: "weights"
|
||||
mtype: "<type \'property\'>"
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'precision\', \'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"
|
||||
}
|
||||
}
|
||||
@ -104,6 +104,10 @@ tf_module {
|
||||
name: "Recall"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "RecallAtPrecision"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "RootMeanSquaredError"
|
||||
mtype: "<type \'type\'>"
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user