TensorFlow 1 version
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Built-in metrics.
Classes
class AUC: Computes the approximate AUC (Area under the curve) via a Riemann sum.
class Accuracy: Calculates how often predictions equal labels.
class BinaryAccuracy: Calculates how often predictions match 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.
Functions
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.
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.
log_cosh(...): Logarithm of the hyperbolic cosine of the prediction error.
logcosh(...): Logarithm of the hyperbolic cosine of the prediction error.
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.