diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index 5f68bbfb916..14c4dad34e5 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -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'] diff --git a/tensorflow/python/keras/metrics.py b/tensorflow/python/keras/metrics.py index 5cbd59c49cf..94f2bc99a90 100644 --- a/tensorflow/python/keras/metrics.py +++ b/tensorflow/python/keras/metrics.py @@ -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() diff --git a/tensorflow/tools/docs/tf_doctest_lib.py b/tensorflow/tools/docs/tf_doctest_lib.py index 2ba368e6fa2..96f2bc7341b 100644 --- a/tensorflow/tools/docs/tf_doctest_lib.py +++ b/tensorflow/tools/docs/tf_doctest_lib.py @@ -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)