About Keras
Getting started
Developer guides
Code examples
Keras 3 API documentation
Keras 2 API documentation
Models API
Layers API
Callbacks API
Optimizers
Metrics
Accuracy metrics
Probabilistic metrics
Regression metrics
Classification metrics based on True/False positives & negatives
Image segmentation metrics
Hinge metrics for "maximum-margin" classification
Losses
Data loading
Built-in small datasets
Keras Applications
Mixed precision
Utilities
KerasTuner: Hyperparam Tuning
KerasHub: Pretrained Models
search
►
Keras 2 API documentation
/ Metrics
Metrics
Accuracy metrics
Accuracy class
BinaryAccuracy class
CategoricalAccuracy class
SparseCategoricalAccuracy class
TopKCategoricalAccuracy class
SparseTopKCategoricalAccuracy class
Probabilistic metrics
BinaryCrossentropy class
CategoricalCrossentropy class
SparseCategoricalCrossentropy class
KLDivergence class
Poisson class
Regression metrics
MeanSquaredError class
RootMeanSquaredError class
MeanAbsoluteError class
MeanAbsolutePercentageError class
MeanSquaredLogarithmicError class
CosineSimilarity class
LogCoshError class
Classification metrics based on True/False positives & negatives
AUC class
Precision class
Recall class
TruePositives class
TrueNegatives class
FalsePositives class
FalseNegatives class
PrecisionAtRecall class
SensitivityAtSpecificity class
SpecificityAtSensitivity class
Image segmentation metrics
MeanIoU class
Hinge metrics for "maximum-margin" classification
Hinge class
SquaredHinge class
CategoricalHinge class
Metrics
Accuracy metrics
Probabilistic metrics
Regression metrics
Classification metrics based on True/False positives & negatives
Image segmentation metrics
Hinge metrics for "maximum-margin" classification