Built-in metrics.

`class AUC`

: Computes the approximate AUC (Area under the curve) via a Riemann sum.

`class Accuracy`

: Calculates how often predictions equals labels.

`class BinaryAccuracy`

: Calculates how often predictions matches binary labels.

`class BinaryCrossentropy`

: Computes the crossentropy metric between the labels and predictions.

`class CategoricalAccuracy`

: Calculates how often predictions matches one-hot labels.

`class CategoricalCrossentropy`

: Computes the crossentropy metric between the labels and predictions.

`class CategoricalHinge`

: Computes the categorical hinge metric between `y_true`

and `y_pred`

.

`class CosineSimilarity`

: Computes the cosine similarity between the labels and predictions.

`class FalseNegatives`

: Calculates the number of false negatives.

`class FalsePositives`

: Calculates the number of false positives.

`class Hinge`

: Computes the hinge metric between `y_true`

and `y_pred`

.

`class KLDivergence`

: Computes Kullback-Leibler divergence metric between `y_true`

and `y_pred`

.

`class LogCoshError`

: Computes the logarithm of the hyperbolic cosine of the prediction error.

`class Mean`

: Computes the (weighted) mean of the given values.

`class MeanAbsoluteError`

: Computes the mean absolute error between the labels and predictions.

`class MeanAbsolutePercentageError`

: Computes the mean absolute percentage error between `y_true`

and `y_pred`

.

`class MeanIoU`

: Computes the mean Intersection-Over-Union metric.

`class MeanRelativeError`

: Computes the mean relative error by normalizing with the given values.

`class MeanSquaredError`

: Computes the mean squared error between `y_true`

and `y_pred`

.

`class MeanSquaredLogarithmicError`

: Computes the mean squared logarithmic error between `y_true`

and `y_pred`

.

`class MeanTensor`

: Computes the element-wise (weighted) mean of the given tensors.

`class Metric`

: Encapsulates metric logic and state.

`class Poisson`

: Computes the Poisson metric between `y_true`

and `y_pred`

.

`class Precision`

: Computes the precision of the predictions with respect to the labels.

`class PrecisionAtRecall`

: Computes best precision where recall is >= specified value.

`class Recall`

: Computes the recall of the predictions with respect to the labels.

`class RecallAtPrecision`

: Computes best recall where precision is >= specified value.

`class RootMeanSquaredError`

: Computes root mean squared error metric between `y_true`

and `y_pred`

.

`class SensitivityAtSpecificity`

: Computes best sensitivity where specificity is >= specified value.

`class SparseCategoricalAccuracy`

: Calculates how often predictions matches integer labels.

`class SparseCategoricalCrossentropy`

: Computes the crossentropy metric between the labels and predictions.

`class SparseTopKCategoricalAccuracy`

: Computes how often integer targets are in the top `K`

predictions.

`class SpecificityAtSensitivity`

: Computes best specificity where sensitivity is >= specified value.

`class SquaredHinge`

: Computes the squared hinge metric between `y_true`

and `y_pred`

.

`class Sum`

: Computes the (weighted) sum of the given values.

`class TopKCategoricalAccuracy`

: Computes how often targets are in the top `K`

predictions.

`class TrueNegatives`

: Calculates the number of true negatives.

`class TruePositives`

: Calculates the number of true positives.

`KLD(...)`

: Computes Kullback-Leibler divergence loss between `y_true`

and `y_pred`

.

`MAE(...)`

: Computes the mean absolute error between labels and predictions.

`MAPE(...)`

: Computes the mean absolute percentage error between `y_true`

and `y_pred`

.

`MSE(...)`

: Computes the mean squared error between labels and predictions.

`MSLE(...)`

: Computes the mean squared logarithmic error between `y_true`

and `y_pred`

.

`binary_accuracy(...)`

: Calculates how often predictions matches binary labels.

`binary_crossentropy(...)`

: Computes the binary crossentropy loss.

`categorical_accuracy(...)`

: Calculates how often predictions matches one-hot labels.

`categorical_crossentropy(...)`

: Computes the categorical crossentropy loss.

`cosine(...)`

: Computes the cosine similarity between labels and predictions.

`cosine_proximity(...)`

: Computes the cosine similarity between labels and predictions.

`deserialize(...)`

: Deserializes a serialized metric class/function instance.

`get(...)`

: Retrieves a Keras metric as a `function`

/`Metric`

class instance.

`hinge(...)`

: Computes the hinge loss between `y_true`

and `y_pred`

.

`kl_divergence(...)`

: Computes Kullback-Leibler divergence loss between `y_true`

and `y_pred`

.

`kld(...)`

: Computes Kullback-Leibler divergence loss between `y_true`

and `y_pred`

.

`kullback_leibler_divergence(...)`

: Computes Kullback-Leibler divergence loss between `y_true`

and `y_pred`

.

`mae(...)`

: Computes the mean absolute error between labels and predictions.

`mape(...)`

: Computes the mean absolute percentage error between `y_true`

and `y_pred`

.

`mean_absolute_error(...)`

: Computes the mean absolute error between labels and predictions.

`mean_absolute_percentage_error(...)`

: Computes the mean absolute percentage error between `y_true`

and `y_pred`

.

`mean_squared_error(...)`

: Computes the mean squared error between labels and predictions.

`mean_squared_logarithmic_error(...)`

: Computes the mean squared logarithmic error between `y_true`

and `y_pred`

.

`mse(...)`

: Computes the mean squared error between labels and predictions.

`msle(...)`

: Computes the mean squared logarithmic error between `y_true`

and `y_pred`

.

`poisson(...)`

: Computes the Poisson loss between y_true and y_pred.

`serialize(...)`

: Serializes metric function or `Metric`

instance.

`sparse_categorical_accuracy(...)`

: Calculates how often predictions matches integer labels.

`sparse_categorical_crossentropy(...)`

: Computes the sparse categorical crossentropy loss.

`sparse_top_k_categorical_accuracy(...)`

: Computes how often integer targets are in the top `K`

predictions.

`squared_hinge(...)`

: Computes the squared hinge loss between `y_true`

and `y_pred`

.

`top_k_categorical_accuracy(...)`

: Computes how often targets are in the top `K`

predictions.

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/compat/v1/keras/metrics