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Don't check for outputs if not specified by the user even if doctest expects an output respecting the user's choice.

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PiperOrigin-RevId: 304806114
Change-Id: I23c6ccae906da6b7517f3da296fd25b4403f2e8e
This commit is contained in:
Yash Katariya 2020-04-04 11:26:08 -07:00 committed by TensorFlower Gardener
parent ca4745d2ad
commit aedd99b97f
3 changed files with 83 additions and 77 deletions
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4
tensorflow/python/keras/engine/training.py
View File

@ -467,7 +467,7 @@ class Model(network.Network, version_utils.ModelVersionSelector):
>>> x = np.random.random((2, 3))
>>> y = np.random.randint(0, 2, (2, 2))
>>> _ = model.fit(x, y, verbose=0)
>>> model.fit(x, y)
>>> model.metrics_names
['loss', 'mae']
@ -478,7 +478,7 @@ class Model(network.Network, version_utils.ModelVersionSelector):
>>> model = tf.keras.models.Model(
... inputs=inputs, outputs=[output_1, output_2])
>>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])
>>> _ = model.fit(x, (y, y), verbose=0)
>>> model.fit(x, (y, y))
>>> model.metrics_names
['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
'out_1_acc']

150
tensorflow/python/keras/metrics.py
View File

@ -407,7 +407,7 @@ class Sum(Reduce):
Usage:
>>> m = tf.keras.metrics.Sum()
>>> _ = m.update_state([1, 3, 5, 7])
>>> m.update_state([1, 3, 5, 7])
>>> m.result().numpy()
16.0
@ -446,11 +446,11 @@ class Mean(Reduce):
Usage:
>>> m = tf.keras.metrics.Mean()
>>> _ = m.update_state([1, 3, 5, 7])
>>> m.update_state([1, 3, 5, 7])
>>> m.result().numpy()
4.0
>>> m.reset_states()
>>> _ = m.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0])
>>> m.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0])
>>> m.result().numpy()
2.0
@ -488,7 +488,7 @@ class MeanRelativeError(Mean):
Usage:
>>> m = tf.keras.metrics.MeanRelativeError(normalizer=[1, 3, 2, 3])
>>> _ = m.update_state([1, 3, 2, 3], [2, 4, 6, 8])
>>> m.update_state([1, 3, 2, 3], [2, 4, 6, 8])
>>> # metric = mean(|y_pred - y_true| / normalizer)
>>> # = mean([1, 1, 4, 5] / [1, 3, 2, 3]) = mean([1, 1/3, 2, 5/3])
@ -641,12 +641,12 @@ class Accuracy(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.Accuracy()
>>> _ = m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]])
>>> m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]])
>>> m.result().numpy()
0.75
>>> m.reset_states()
>>> _ = m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]],
>>> m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]],
... sample_weight=[1, 1, 0, 0])
>>> m.result().numpy()
0.5
@ -684,12 +684,12 @@ class BinaryAccuracy(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.BinaryAccuracy()
>>> _ = m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]])
>>> m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]])
>>> m.result().numpy()
0.75
>>> m.reset_states()
>>> _ = m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]],
>>> m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]],
... sample_weight=[1, 0, 0, 1])
>>> m.result().numpy()
0.5
@ -732,13 +732,13 @@ class CategoricalAccuracy(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.CategoricalAccuracy()
>>> _ = m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8],
>>> m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8],
... [0.05, 0.95, 0]])
>>> m.result().numpy()
0.5
>>> m.reset_states()
>>> _ = m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8],
>>> m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8],
... [0.05, 0.95, 0]],
... sample_weight=[0.7, 0.3])
>>> m.result().numpy()
@ -786,12 +786,12 @@ class SparseCategoricalAccuracy(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.SparseCategoricalAccuracy()
>>> _ = m.update_state([[2], [1]], [[0.1, 0.6, 0.3], [0.05, 0.95, 0]])
>>> m.update_state([[2], [1]], [[0.1, 0.6, 0.3], [0.05, 0.95, 0]])
>>> m.result().numpy()
0.5
>>> m.reset_states()
>>> _ = m.update_state([[2], [1]], [[0.1, 0.6, 0.3], [0.05, 0.95, 0]],
>>> m.update_state([[2], [1]], [[0.1, 0.6, 0.3], [0.05, 0.95, 0]],
... sample_weight=[0.7, 0.3])
>>> m.result().numpy()
0.3
@ -825,13 +825,13 @@ class TopKCategoricalAccuracy(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.TopKCategoricalAccuracy(k=1)
>>> _ = m.update_state([[0, 0, 1], [0, 1, 0]],
>>> m.update_state([[0, 0, 1], [0, 1, 0]],
... [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
>>> m.result().numpy()
0.5
>>> m.reset_states()
>>> _ = m.update_state([[0, 0, 1], [0, 1, 0]],
>>> m.update_state([[0, 0, 1], [0, 1, 0]],
... [[0.1, 0.9, 0.8], [0.05, 0.95, 0]],
... sample_weight=[0.7, 0.3])
>>> m.result().numpy()
@ -863,12 +863,12 @@ class SparseTopKCategoricalAccuracy(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.SparseTopKCategoricalAccuracy(k=1)
>>> _ = m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
>>> m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
>>> m.result().numpy()
0.5
>>> m.reset_states()
>>> _ = m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]],
>>> m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]],
... sample_weight=[0.7, 0.3])
>>> m.result().numpy()
0.3
@ -978,12 +978,12 @@ class FalsePositives(_ConfusionMatrixConditionCount):
Usage:
>>> m = tf.keras.metrics.FalsePositives()
>>> _ = m.update_state([0, 1, 0, 0], [0, 0, 1, 1])
>>> m.update_state([0, 1, 0, 0], [0, 0, 1, 1])
>>> m.result().numpy()
2.0
>>> m.reset_states()
>>> _ = m.update_state([0, 1, 0, 0], [0, 0, 1, 1], sample_weight=[0, 0, 1, 0])
>>> m.update_state([0, 1, 0, 0], [0, 0, 1, 1], sample_weight=[0, 0, 1, 0])
>>> m.result().numpy()
1.0
@ -1026,12 +1026,12 @@ class FalseNegatives(_ConfusionMatrixConditionCount):
Usage:
>>> m = tf.keras.metrics.FalseNegatives()
>>> _ = m.update_state([0, 1, 1, 1], [0, 1, 0, 0])
>>> m.update_state([0, 1, 1, 1], [0, 1, 0, 0])
>>> m.result().numpy()
2.0
>>> m.reset_states()
>>> _ = m.update_state([0, 1, 1, 1], [0, 1, 0, 0], sample_weight=[0, 0, 1, 0])
>>> m.update_state([0, 1, 1, 1], [0, 1, 0, 0], sample_weight=[0, 0, 1, 0])
>>> m.result().numpy()
1.0
@ -1074,12 +1074,12 @@ class TrueNegatives(_ConfusionMatrixConditionCount):
Usage:
>>> m = tf.keras.metrics.TrueNegatives()
>>> _ = m.update_state([0, 1, 0, 0], [1, 1, 0, 0])
>>> m.update_state([0, 1, 0, 0], [1, 1, 0, 0])
>>> m.result().numpy()
2.0
>>> m.reset_states()
>>> _ = m.update_state([0, 1, 0, 0], [1, 1, 0, 0], sample_weight=[0, 0, 1, 0])
>>> m.update_state([0, 1, 0, 0], [1, 1, 0, 0], sample_weight=[0, 0, 1, 0])
>>> m.result().numpy()
1.0
@ -1122,12 +1122,12 @@ class TruePositives(_ConfusionMatrixConditionCount):
Usage:
>>> m = tf.keras.metrics.TruePositives()
>>> _ = m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
>>> m.result().numpy()
2.0
>>> m.reset_states()
>>> _ = m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
>>> m.result().numpy()
1.0
@ -1186,24 +1186,24 @@ class Precision(Metric):
Usage:
>>> m = tf.keras.metrics.Precision()
>>> _ = m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
>>> m.result().numpy()
0.6666667
>>> m.reset_states()
>>> _ = m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
>>> m.result().numpy()
1.0
>>> # With top_k=2, it will calculate precision over y_true[:2] and y_pred[:2]
>>> m = tf.keras.metrics.Precision(top_k=2)
>>> _ = m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
>>> m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
>>> m.result().numpy()
0.0
>>> # With top_k=4, it will calculate precision over y_true[:4] and y_pred[:4]
>>> m = tf.keras.metrics.Precision(top_k=4)
>>> _ = m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
>>> m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
>>> m.result().numpy()
0.5
@ -1322,12 +1322,12 @@ class Recall(Metric):
Usage:
>>> m = tf.keras.metrics.Recall()
>>> _ = m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
>>> m.result().numpy()
0.6666667
>>> m.reset_states()
>>> _ = m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
>>> m.result().numpy()
1.0
@ -1532,12 +1532,12 @@ class SensitivityAtSpecificity(SensitivitySpecificityBase):
Usage:
>>> m = tf.keras.metrics.SensitivityAtSpecificity(0.5)
>>> _ = m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
>>> m.result().numpy()
0.5
>>> m.reset_states()
>>> _ = m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
... sample_weight=[1, 1, 2, 2, 1])
>>> m.result().numpy()
0.333333
@ -1608,12 +1608,12 @@ class SpecificityAtSensitivity(SensitivitySpecificityBase):
Usage:
>>> m = tf.keras.metrics.SpecificityAtSensitivity(0.5)
>>> _ = m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
>>> m.result().numpy()
0.66666667
>>> m.reset_states()
>>> _ = m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
... sample_weight=[1, 1, 2, 2, 2])
>>> m.result().numpy()
0.5
@ -1676,12 +1676,12 @@ class PrecisionAtRecall(SensitivitySpecificityBase):
Usage:
>>> m = tf.keras.metrics.PrecisionAtRecall(0.5)
>>> _ = m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
>>> m.result().numpy()
0.5
>>> m.reset_states()
>>> _ = m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
... sample_weight=[2, 2, 2, 1, 1])
>>> m.result().numpy()
0.33333333
@ -1747,12 +1747,12 @@ class RecallAtPrecision(SensitivitySpecificityBase):
Usage:
>>> m = tf.keras.metrics.RecallAtPrecision(0.8)
>>> _ = m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
>>> m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
>>> m.result().numpy()
0.5
>>> m.reset_states()
>>> _ = m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9],
>>> m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9],
... sample_weight=[1, 0, 0, 1])
>>> m.result().numpy()
1.0
@ -1864,7 +1864,7 @@ class AUC(Metric):
Usage:
>>> m = tf.keras.metrics.AUC(num_thresholds=3)
>>> _ = m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
>>> m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
>>> # threshold values are [0 - 1e-7, 0.5, 1 + 1e-7]
>>> # tp = [2, 1, 0], fp = [2, 0, 0], fn = [0, 1, 2], tn = [0, 2, 2]
>>> # recall = [1, 0.5, 0], fp_rate = [1, 0, 0]
@ -1873,7 +1873,7 @@ class AUC(Metric):
0.75
>>> m.reset_states()
>>> _ = m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9],
>>> m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9],
... sample_weight=[1, 0, 0, 1])
>>> m.result().numpy()
1.0
@ -2247,12 +2247,12 @@ class CosineSimilarity(MeanMetricWrapper):
>>> # result = mean(sum(l2_norm(y_true) . l2_norm(y_pred), axis=1))
>>> # = ((0. + 0.) + (0.5 + 0.5)) / 2
>>> m = tf.keras.metrics.CosineSimilarity(axis=1)
>>> _ = m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]])
>>> m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]])
>>> m.result().numpy()
0.49999997
>>> m.reset_states()
>>> _ = m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]],
>>> m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]],
... sample_weight=[0.3, 0.7])
>>> m.result().numpy()
0.6999999
@ -2284,12 +2284,12 @@ class MeanAbsoluteError(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.MeanAbsoluteError()
>>> _ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.result().numpy()
0.25
>>> m.reset_states()
>>> _ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
... sample_weight=[1, 0])
>>> m.result().numpy()
0.5
@ -2319,12 +2319,12 @@ class MeanAbsolutePercentageError(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.MeanAbsolutePercentageError()
>>> _ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.result().numpy()
250000000.0
>>> m.reset_states()
>>> _ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
... sample_weight=[1, 0])
>>> m.result().numpy()
500000000.0
@ -2356,12 +2356,12 @@ class MeanSquaredError(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.MeanSquaredError()
>>> _ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.result().numpy()
0.25
>>> m.reset_states()
>>> _ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
... sample_weight=[1, 0])
>>> m.result().numpy()
0.5
@ -2391,12 +2391,12 @@ class MeanSquaredLogarithmicError(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.MeanSquaredLogarithmicError()
>>> _ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.result().numpy()
0.12011322
>>> m.reset_states()
>>> _ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
... sample_weight=[1, 0])
>>> m.result().numpy()
0.24022643
@ -2431,12 +2431,12 @@ class Hinge(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.Hinge()
>>> _ = m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
>>> m.result().numpy()
1.3
>>> m.reset_states()
>>> _ = m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
... sample_weight=[1, 0])
>>> m.result().numpy()
1.1
@ -2467,12 +2467,12 @@ class SquaredHinge(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.SquaredHinge()
>>> _ = m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
>>> m.result().numpy()
1.86
>>> m.reset_states()
>>> _ = m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
... sample_weight=[1, 0])
>>> m.result().numpy()
1.46
@ -2503,12 +2503,12 @@ class CategoricalHinge(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.CategoricalHinge()
>>> _ = m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
>>> m.result().numpy()
1.4000001
>>> m.reset_states()
>>> _ = m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
... sample_weight=[1, 0])
>>> m.result().numpy()
1.2
@ -2535,12 +2535,12 @@ class RootMeanSquaredError(Mean):
Usage:
>>> m = tf.keras.metrics.RootMeanSquaredError()
>>> _ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.result().numpy()
0.5
>>> m.reset_states()
>>> _ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
... sample_weight=[1, 0])
>>> m.result().numpy()
0.70710677
@ -2597,12 +2597,12 @@ class LogCoshError(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.LogCoshError()
>>> _ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.result().numpy()
0.10844523
>>> m.reset_states()
>>> _ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
... sample_weight=[1, 0])
>>> m.result().numpy()
0.21689045
@ -2632,12 +2632,12 @@ class Poisson(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.Poisson()
>>> _ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.result().numpy()
0.49999997
>>> m.reset_states()
>>> _ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
... sample_weight=[1, 0])
>>> m.result().numpy()
0.99999994
@ -2667,12 +2667,12 @@ class KLDivergence(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.KLDivergence()
>>> _ = m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
>>> m.result().numpy()
0.45814306
>>> m.reset_states()
>>> _ = m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
... sample_weight=[1, 0])
>>> m.result().numpy()
0.9162892
@ -2719,12 +2719,12 @@ class MeanIoU(Metric):
>>> # iou = true_positives / (sum_row + sum_col - true_positives))
>>> # result = (1 / (2 + 2 - 1) + 1 / (2 + 2 - 1)) / 2 = 0.33
>>> m = tf.keras.metrics.MeanIoU(num_classes=2)
>>> _ = m.update_state([0, 0, 1, 1], [0, 1, 0, 1])
>>> m.update_state([0, 0, 1, 1], [0, 1, 0, 1])
>>> m.result().numpy()
0.33333334
>>> m.reset_states()
>>> _ = m.update_state([0, 0, 1, 1], [0, 1, 0, 1],
>>> m.update_state([0, 0, 1, 1], [0, 1, 0, 1],
... sample_weight=[0.3, 0.3, 0.3, 0.1])
>>> m.result().numpy()
0.23809525
@ -2839,12 +2839,12 @@ class MeanTensor(Metric):
Usage:
>>> m = tf.keras.metrics.MeanTensor()
>>> _ = m.update_state([0, 1, 2, 3])
>>> _ = m.update_state([4, 5, 6, 7])
>>> m.update_state([0, 1, 2, 3])
>>> m.update_state([4, 5, 6, 7])
>>> m.result().numpy()
array([2., 3., 4., 5.], dtype=float32)
>>> _ = m.update_state([12, 10, 8, 6], sample_weight= [0, 0.2, 0.5, 1])
>>> m.update_state([12, 10, 8, 6], sample_weight= [0, 0.2, 0.5, 1])
>>> m.result().numpy()
array([2. , 3.6363635, 4.8 , 5.3333335], dtype=float32)
"""
@ -2954,12 +2954,12 @@ class BinaryCrossentropy(MeanMetricWrapper):
Usage:
>>> m = tf.keras.metrics.BinaryCrossentropy()
>>> _ = m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
>>> m.result().numpy()
0.81492424
>>> m.reset_states()
>>> _ = m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
... sample_weight=[1, 0])
>>> m.result().numpy()
0.9162905
@ -3017,13 +3017,13 @@ class CategoricalCrossentropy(MeanMetricWrapper):
>>> # = [0.051, 2.302]
>>> # Reduced xent = (0.051 + 2.302) / 2
>>> m = tf.keras.metrics.CategoricalCrossentropy()
>>> _ = m.update_state([[0, 1, 0], [0, 0, 1]],
>>> m.update_state([[0, 1, 0], [0, 0, 1]],
... [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
>>> m.result().numpy()
1.1769392
>>> m.reset_states()
>>> _ = m.update_state([[0, 1, 0], [0, 0, 1]],
>>> m.update_state([[0, 1, 0], [0, 0, 1]],
... [[0.05, 0.95, 0], [0.1, 0.8, 0.1]],
... sample_weight=tf.constant([0.3, 0.7]))
>>> m.result().numpy()
@ -3089,13 +3089,13 @@ class SparseCategoricalCrossentropy(MeanMetricWrapper):
>>> # xent = [0.0513, 2.3026]
>>> # Reduced xent = (0.0513 + 2.3026) / 2
>>> m = tf.keras.metrics.SparseCategoricalCrossentropy()
>>> _ = m.update_state([1, 2],
>>> m.update_state([1, 2],
... [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
>>> m.result().numpy()
1.1769392
>>> m.reset_states()
>>> _ = m.update_state([1, 2],
>>> m.update_state([1, 2],
... [[0.05, 0.95, 0], [0.1, 0.8, 0.1]],
... sample_weight=tf.constant([0.3, 0.7]))
>>> m.result().numpy()

6
tensorflow/tools/docs/tf_doctest_lib.py
View File

@ -146,6 +146,12 @@ class TfDoctestOutputChecker(doctest.OutputChecker, object):
A bool, indicating if the check was successful or not.
"""
# If the docstring's output is empty and there is some output generated
# after running the snippet, return True. This is because if the user
# doesn't want to display output, respect that over what the doctest wants.
if not want and got:
return True
# Replace python's addresses with ellipsis (`...`) since it can change on
# each execution.
want = self._ADDRESS_RE.sub('at ...>', want)