| activation_elu | Exponential Linear Unit. |
| activation_exponential | Exponential activation function. |
| activation_gelu | Gaussian error linear unit (GELU) activation function. |
| activation_hard_sigmoid | Hard sigmoid activation function. |
| activation_hard_silu | Hard SiLU activation function, also known as Hard Swish. |
| activation_leaky_relu | Leaky relu activation function. |
| activation_linear | Linear activation function (pass-through). |
| activation_log_softmax | Log-Softmax activation function. |
| activation_mish | Mish activation function. |
| activation_relu | Applies the rectified linear unit activation function. |
| activation_relu6 | Relu6 activation function. |
| activation_selu | Scaled Exponential Linear Unit (SELU). |
| activation_sigmoid | Sigmoid activation function. |
| activation_silu | Swish (or Silu) activation function. |
| activation_softmax | Softmax converts a vector of values to a probability... |
| activation_softplus | Softplus activation function. |
| activation_softsign | Softsign activation function. |
| activation_tanh | Hyperbolic tangent activation function. |
| active_property | Create an active property class method |
| adapt | Fits the state of the preprocessing layer to the data being... |
| application_convnext_base | Instantiates the ConvNeXtBase architecture. |
| application_convnext_large | Instantiates the ConvNeXtLarge architecture. |
| application_convnext_small | Instantiates the ConvNeXtSmall architecture. |
| application_convnext_tiny | Instantiates the ConvNeXtTiny architecture. |
| application_convnext_xlarge | Instantiates the ConvNeXtXLarge architecture. |
| application_densenet121 | Instantiates the Densenet121 architecture. |
| application_densenet169 | Instantiates the Densenet169 architecture. |
| application_densenet201 | Instantiates the Densenet201 architecture. |
| application_efficientnet_b0 | Instantiates the EfficientNetB0 architecture. |
| application_efficientnet_b1 | Instantiates the EfficientNetB1 architecture. |
| application_efficientnet_b2 | Instantiates the EfficientNetB2 architecture. |
| application_efficientnet_b3 | Instantiates the EfficientNetB3 architecture. |
| application_efficientnet_b4 | Instantiates the EfficientNetB4 architecture. |
| application_efficientnet_b5 | Instantiates the EfficientNetB5 architecture. |
| application_efficientnet_b6 | Instantiates the EfficientNetB6 architecture. |
| application_efficientnet_b7 | Instantiates the EfficientNetB7 architecture. |
| application_efficientnet_v2b0 | Instantiates the EfficientNetV2B0 architecture. |
| application_efficientnet_v2b1 | Instantiates the EfficientNetV2B1 architecture. |
| application_efficientnet_v2b2 | Instantiates the EfficientNetV2B2 architecture. |
| application_efficientnet_v2b3 | Instantiates the EfficientNetV2B3 architecture. |
| application_efficientnet_v2l | Instantiates the EfficientNetV2L architecture. |
| application_efficientnet_v2m | Instantiates the EfficientNetV2M architecture. |
| application_efficientnet_v2s | Instantiates the EfficientNetV2S architecture. |
| application_inception_resnet_v2 | Instantiates the Inception-ResNet v2 architecture. |
| application_inception_v3 | Instantiates the Inception v3 architecture. |
| application_mobilenet | Instantiates the MobileNet architecture. |
| application_mobilenet_v2 | Instantiates the MobileNetV2 architecture. |
| application_mobilenet_v3_large | Instantiates the MobileNetV3Large architecture. |
| application_mobilenet_v3_small | Instantiates the MobileNetV3Small architecture. |
| application_nasnet_large | Instantiates a NASNet model in ImageNet mode. |
| application_nasnet_mobile | Instantiates a Mobile NASNet model in ImageNet mode. |
| application_resnet101 | Instantiates the ResNet101 architecture. |
| application_resnet101_v2 | Instantiates the ResNet101V2 architecture. |
| application_resnet152 | Instantiates the ResNet152 architecture. |
| application_resnet152_v2 | Instantiates the ResNet152V2 architecture. |
| application_resnet50 | Instantiates the ResNet50 architecture. |
| application_resnet50_v2 | Instantiates the ResNet50V2 architecture. |
| application_vgg16 | Instantiates the VGG16 model. |
| application_vgg19 | Instantiates the VGG19 model. |
| application_xception | Instantiates the Xception architecture. |
| audio_dataset_from_directory | Generates a 'tf.data.Dataset' from audio files in a... |
| back-compat | Backward compatibility |
| bidirectional | layer_bidirectional |
| Callback | Define a custom 'Callback' class |
| callback_backup_and_restore | Callback to back up and restore the training state. |
| callback_csv_logger | Callback that streams epoch results to a CSV file. |
| callback_early_stopping | Stop training when a monitored metric has stopped improving. |
| callback_lambda | Callback for creating simple, custom callbacks on-the-fly. |
| callback_learning_rate_scheduler | Learning rate scheduler. |
| callback_model_checkpoint | Callback to save the Keras model or model weights at some... |
| callback_reduce_lr_on_plateau | Reduce learning rate when a metric has stopped improving. |
| callback_remote_monitor | Callback used to stream events to a server. |
| callback_swap_ema_weights | Swaps model weights and EMA weights before and after... |
| callback_tensorboard | Enable visualizations for TensorBoard. |
| callback_terminate_on_nan | Callback that terminates training when a NaN loss is... |
| clear_session | Resets all state generated by Keras. |
| clone_model | Clone a Functional or Sequential 'Model' instance. |
| compile.keras.src.models.model.Model | Configure a model for training. |
| config_backend | Publicly accessible method for determining the current... |
| config_disable_interactive_logging | Turn off interactive logging. |
| config_disable_traceback_filtering | Turn off traceback filtering. |
| config_dtype_policy | Returns the current default dtype policy object. |
| config_enable_interactive_logging | Turn on interactive logging. |
| config_enable_traceback_filtering | Turn on traceback filtering. |
| config_enable_unsafe_deserialization | Disables safe mode globally, allowing deserialization of... |
| config_epsilon | Return the value of the fuzz factor used in numeric... |
| config_floatx | Return the default float type, as a string. |
| config_image_data_format | Return the default image data format convention. |
| config_is_interactive_logging_enabled | Check if interactive logging is enabled. |
| config_is_traceback_filtering_enabled | Check if traceback filtering is enabled. |
| config_set_backend | Reload the backend (and the Keras package). |
| config_set_dtype_policy | Sets the default dtype policy globally. |
| config_set_epsilon | Set the value of the fuzz factor used in numeric expressions. |
| config_set_floatx | Set the default float dtype. |
| config_set_image_data_format | Set the value of the image data format convention. |
| Constraint | Define a custom 'Constraint' class |
| constraint_maxnorm | MaxNorm weight constraint. |
| constraint_minmaxnorm | MinMaxNorm weight constraint. |
| constraint_nonneg | Constrains the weights to be non-negative. |
| constraint_unitnorm | Constrains the weights incident to each hidden unit to have... |
| count_params | Count the total number of scalars composing the weights. |
| custom_metric | Custom metric function |
| dataset_boston_housing | Boston housing price regression dataset |
| dataset_cifar10 | CIFAR10 small image classification |
| dataset_cifar100 | CIFAR100 small image classification |
| dataset_fashion_mnist | Fashion-MNIST database of fashion articles |
| dataset_imdb | IMDB Movie reviews sentiment classification |
| dataset_mnist | MNIST database of handwritten digits |
| dataset_reuters | Reuters newswire topics classification |
| deserialize_keras_object | Retrieve the object by deserializing the config dict. |
| evaluate.keras.src.models.model.Model | Evaluate a Keras Model |
| export_savedmodel.keras.src.models.model.Model | Create a TF SavedModel artifact for inference (e.g. via... |
| fit.keras.src.models.model.Model | Train a model for a fixed number of epochs (dataset... |
| freeze_weights | Freeze and unfreeze weights |
| get_config | Layer/Model configuration |
| get_custom_objects | Get/set the currently registered custom objects. |
| get_file | Downloads a file from a URL if it not already in the cache. |
| get_layer | Retrieves a layer based on either its name (unique) or index. |
| get_registered_name | Returns the name registered to an object within the Keras... |
| get_registered_object | Returns the class associated with 'name' if it is registered... |
| get_source_inputs | Returns the list of input tensors necessary to compute... |
| get_weights | Layer/Model weights as R arrays |
| grapes-py_class-grapes | Make a python class constructor |
| grapes-set-active-grapes | Make an Active Binding |
| image_array_save | Saves an image stored as an array to a path or file object. |
| image_dataset_from_directory | Generates a 'tf.data.Dataset' from image files in a... |
| image_from_array | Converts a 3D array to a PIL Image instance. |
| image_load | Loads an image into PIL format. |
| imagenet_decode_predictions | Decodes the prediction of an ImageNet model. |
| imagenet_preprocess_input | Preprocesses a tensor or array encoding a batch of images. |
| image_smart_resize | Resize images to a target size without aspect ratio... |
| image_to_array | Converts a PIL Image instance to a matrix. |
| initializer_constant | Initializer that generates tensors with constant values. |
| initializer_glorot_normal | The Glorot normal initializer, also called Xavier normal... |
| initializer_glorot_uniform | The Glorot uniform initializer, also called Xavier uniform... |
| initializer_he_normal | He normal initializer. |
| initializer_he_uniform | He uniform variance scaling initializer. |
| initializer_identity | Initializer that generates the identity matrix. |
| initializer_lecun_normal | Lecun normal initializer. |
| initializer_lecun_uniform | Lecun uniform initializer. |
| initializer_ones | Initializer that generates tensors initialized to 1. |
| initializer_orthogonal | Initializer that generates an orthogonal matrix. |
| initializer_random_normal | Random normal initializer. |
| initializer_random_uniform | Random uniform initializer. |
| initializer_truncated_normal | Initializer that generates a truncated normal distribution. |
| initializer_variance_scaling | Initializer that adapts its scale to the shape of its input... |
| initializer_zeros | Initializer that generates tensors initialized to 0. |
| install_keras | Install Keras |
| keras | Main Keras module |
| keras3-package | keras3: R Interface to 'Keras' |
| keras_input | Create a Keras tensor (Functional API input). |
| keras_model | Keras Model (Functional API) |
| keras_model_sequential | Keras Model composed of a linear stack of layers |
| Layer | Define a custom 'Layer' class. |
| layer_activation | Applies an activation function to an output. |
| layer_activation_elu | Applies an Exponential Linear Unit function to an output. |
| layer_activation_leaky_relu | Leaky version of a Rectified Linear Unit activation layer. |
| layer_activation_parametric_relu | Parametric Rectified Linear Unit activation layer. |
| layer_activation_relu | Rectified Linear Unit activation function layer. |
| layer_activation_softmax | Softmax activation layer. |
| layer_activity_regularization | Layer that applies an update to the cost function based input... |
| layer_add | Performs elementwise addition operation. |
| layer_additive_attention | Additive attention layer, a.k.a. Bahdanau-style attention. |
| layer_alpha_dropout | Applies Alpha Dropout to the input. |
| layer_attention | Dot-product attention layer, a.k.a. Luong-style attention. |
| layer_average | Averages a list of inputs element-wise.. |
| layer_average_pooling_1d | Average pooling for temporal data. |
| layer_average_pooling_2d | Average pooling operation for 2D spatial data. |
| layer_average_pooling_3d | Average pooling operation for 3D data (spatial or... |
| layer_batch_normalization | Layer that normalizes its inputs. |
| layer_bidirectional | Bidirectional wrapper for RNNs. |
| layer_category_encoding | A preprocessing layer which encodes integer features. |
| layer_center_crop | A preprocessing layer which crops images. |
| layer_concatenate | Concatenates a list of inputs. |
| layer_conv_1d | 1D convolution layer (e.g. temporal convolution). |
| layer_conv_1d_transpose | 1D transposed convolution layer. |
| layer_conv_2d | 2D convolution layer. |
| layer_conv_2d_transpose | 2D transposed convolution layer. |
| layer_conv_3d | 3D convolution layer. |
| layer_conv_3d_transpose | 3D transposed convolution layer. |
| layer_conv_lstm_1d | 1D Convolutional LSTM. |
| layer_conv_lstm_2d | 2D Convolutional LSTM. |
| layer_conv_lstm_3d | 3D Convolutional LSTM. |
| layer_cropping_1d | Cropping layer for 1D input (e.g. temporal sequence). |
| layer_cropping_2d | Cropping layer for 2D input (e.g. picture). |
| layer_cropping_3d | Cropping layer for 3D data (e.g. spatial or spatio-temporal). |
| layer_dense | Just your regular densely-connected NN layer. |
| layer_depthwise_conv_1d | 1D depthwise convolution layer. |
| layer_depthwise_conv_2d | 2D depthwise convolution layer. |
| layer_discretization | A preprocessing layer which buckets continuous features by... |
| layer_dot | Computes element-wise dot product of two tensors. |
| layer_dropout | Applies dropout to the input. |
| layer_einsum_dense | A layer that uses 'einsum' as the backing computation. |
| layer_embedding | Turns positive integers (indexes) into dense vectors of fixed... |
| layer_feature_space | One-stop utility for preprocessing and encoding structured... |
| layer_flatten | Flattens the input. Does not affect the batch size. |
| layer_flax_module_wrapper | Keras Layer that wraps a Flax module. |
| layer_gaussian_dropout | Apply multiplicative 1-centered Gaussian noise. |
| layer_gaussian_noise | Apply additive zero-centered Gaussian noise. |
| layer_global_average_pooling_1d | Global average pooling operation for temporal data. |
| layer_global_average_pooling_2d | Global average pooling operation for 2D data. |
| layer_global_average_pooling_3d | Global average pooling operation for 3D data. |
| layer_global_max_pooling_1d | Global max pooling operation for temporal data. |
| layer_global_max_pooling_2d | Global max pooling operation for 2D data. |
| layer_global_max_pooling_3d | Global max pooling operation for 3D data. |
| layer_group_normalization | Group normalization layer. |
| layer_group_query_attention | Grouped Query Attention layer. |
| layer_gru | Gated Recurrent Unit - Cho et al. 2014. |
| layer_hashed_crossing | A preprocessing layer which crosses features using the... |
| layer_hashing | A preprocessing layer which hashes and bins categorical... |
| layer_identity | Identity layer. |
| layer_input | keras_input |
| layer_integer_lookup | A preprocessing layer that maps integers to (possibly... |
| layer_jax_model_wrapper | Keras Layer that wraps a JAX model. |
| layer_lambda | Wraps arbitrary expressions as a 'Layer' object. |
| layer_layer_normalization | Layer normalization layer (Ba et al., 2016). |
| layer_lstm | Long Short-Term Memory layer - Hochreiter 1997. |
| layer_masking | Masks a sequence by using a mask value to skip timesteps. |
| layer_maximum | Computes element-wise maximum on a list of inputs. |
| layer_max_pooling_1d | Max pooling operation for 1D temporal data. |
| layer_max_pooling_2d | Max pooling operation for 2D spatial data. |
| layer_max_pooling_3d | Max pooling operation for 3D data (spatial or... |
| layer_mel_spectrogram | A preprocessing layer to convert raw audio signals to Mel... |
| layer_minimum | Computes elementwise minimum on a list of inputs. |
| layer_multi_head_attention | Multi Head Attention layer. |
| layer_multiply | Performs elementwise multiplication. |
| layer_normalization | A preprocessing layer that normalizes continuous features. |
| layer_permute | Permutes the dimensions of the input according to a given... |
| layer_random_brightness | A preprocessing layer which randomly adjusts brightness... |
| layer_random_contrast | A preprocessing layer which randomly adjusts contrast during... |
| layer_random_crop | A preprocessing layer which randomly crops images during... |
| layer_random_flip | A preprocessing layer which randomly flips images during... |
| layer_random_rotation | A preprocessing layer which randomly rotates images during... |
| layer_random_translation | A preprocessing layer which randomly translates images during... |
| layer_random_zoom | A preprocessing layer which randomly zooms images during... |
| layer_repeat_vector | Repeats the input n times. |
| layer_rescaling | A preprocessing layer which rescales input values to a new... |
| layer_reshape | Layer that reshapes inputs into the given shape. |
| layer_resizing | A preprocessing layer which resizes images. |
| layer_rnn | Base class for recurrent layers |
| layer_separable_conv_1d | 1D separable convolution layer. |
| layer_separable_conv_2d | 2D separable convolution layer. |
| layer_simple_rnn | Fully-connected RNN where the output is to be fed back as the... |
| layer_spatial_dropout_1d | Spatial 1D version of Dropout. |
| layer_spatial_dropout_2d | Spatial 2D version of Dropout. |
| layer_spatial_dropout_3d | Spatial 3D version of Dropout. |
| layer_spectral_normalization | Performs spectral normalization on the weights of a target... |
| layer_string_lookup | A preprocessing layer that maps strings to (possibly encoded)... |
| layer_subtract | Performs elementwise subtraction. |
| layer_text_vectorization | A preprocessing layer which maps text features to integer... |
| layer_tfsm | Reload a Keras model/layer that was saved via... |
| layer_time_distributed | This wrapper allows to apply a layer to every temporal slice... |
| layer_torch_module_wrapper | Torch module wrapper layer. |
| layer_unit_normalization | Unit normalization layer. |
| layer_upsampling_1d | Upsampling layer for 1D inputs. |
| layer_upsampling_2d | Upsampling layer for 2D inputs. |
| layer_upsampling_3d | Upsampling layer for 3D inputs. |
| layer_zero_padding_1d | Zero-padding layer for 1D input (e.g. temporal sequence). |
| layer_zero_padding_2d | Zero-padding layer for 2D input (e.g. picture). |
| layer_zero_padding_3d | Zero-padding layer for 3D data (spatial or spatio-temporal). |
| LearningRateSchedule | Define a custom 'LearningRateSchedule' class |
| learning_rate_schedule_cosine_decay | A 'LearningRateSchedule' that uses a cosine decay with... |
| learning_rate_schedule_cosine_decay_restarts | A 'LearningRateSchedule' that uses a cosine decay schedule... |
| learning_rate_schedule_exponential_decay | A 'LearningRateSchedule' that uses an exponential decay... |
| learning_rate_schedule_inverse_time_decay | A 'LearningRateSchedule' that uses an inverse time decay... |
| learning_rate_schedule_piecewise_constant_decay | A 'LearningRateSchedule' that uses a piecewise constant decay... |
| learning_rate_schedule_polynomial_decay | A 'LearningRateSchedule' that uses a polynomial decay... |
| load_model | Loads a model saved via 'save_model()'. |
| load_model_weights | Load weights from a file saved via 'save_model_weights()'. |
| Loss | Subclass the base 'Loss' class |
| loss_binary_crossentropy | Computes the cross-entropy loss between true labels and... |
| loss_binary_focal_crossentropy | Computes focal cross-entropy loss between true labels and... |
| loss_categorical_crossentropy | Computes the crossentropy loss between the labels and... |
| loss_categorical_focal_crossentropy | Computes the alpha balanced focal crossentropy loss. |
| loss_categorical_hinge | Computes the categorical hinge loss between 'y_true' &... |
| loss_cosine_similarity | Computes the cosine similarity between 'y_true' & 'y_pred'. |
| loss_ctc | CTC (Connectionist Temporal Classification) loss. |
| loss_dice | Computes the Dice loss value between 'y_true' and 'y_pred'. |
| loss_hinge | Computes the hinge loss between 'y_true' & 'y_pred'. |
| loss_huber | Computes the Huber loss between 'y_true' & 'y_pred'. |
| loss_kl_divergence | Computes Kullback-Leibler divergence loss between 'y_true' &... |
| loss_log_cosh | Computes the logarithm of the hyperbolic cosine of the... |
| loss_mean_absolute_error | Computes the mean of absolute difference between labels and... |
| loss_mean_absolute_percentage_error | Computes the mean absolute percentage error between 'y_true'... |
| loss_mean_squared_error | Computes the mean of squares of errors between labels and... |
| loss_mean_squared_logarithmic_error | Computes the mean squared logarithmic error between 'y_true'... |
| loss_poisson | Computes the Poisson loss between 'y_true' & 'y_pred'. |
| loss_sparse_categorical_crossentropy | Computes the crossentropy loss between the labels and... |
| loss_squared_hinge | Computes the squared hinge loss between 'y_true' & 'y_pred'. |
| loss_tversky | Computes the Tversky loss value between 'y_true' and... |
| mark_active | active_property |
| Metric | Subclass the base 'Metric' class |
| metric_auc | Approximates the AUC (Area under the curve) of the ROC or PR... |
| metric_binary_accuracy | Calculates how often predictions match binary labels. |
| metric_binary_crossentropy | Computes the crossentropy metric between the labels and... |
| metric_binary_focal_crossentropy | Computes the binary focal crossentropy loss. |
| metric_binary_iou | Computes the Intersection-Over-Union metric for class 0... |
| metric_categorical_accuracy | Calculates how often predictions match one-hot labels. |
| metric_categorical_crossentropy | Computes the crossentropy metric between the labels and... |
| metric_categorical_focal_crossentropy | Computes the categorical focal crossentropy loss. |
| metric_categorical_hinge | Computes the categorical hinge metric between 'y_true' and... |
| metric_cosine_similarity | Computes the cosine similarity between the labels and... |
| metric_f1_score | Computes F-1 Score. |
| metric_false_negatives | Calculates the number of false negatives. |
| metric_false_positives | Calculates the number of false positives. |
| metric_fbeta_score | Computes F-Beta score. |
| metric_hinge | Computes the hinge metric between 'y_true' and 'y_pred'. |
| metric_huber | Computes Huber loss value. |
| metric_iou | Computes the Intersection-Over-Union metric for specific... |
| metric_kl_divergence | Computes Kullback-Leibler divergence metric between 'y_true'... |
| metric_log_cosh | Logarithm of the hyperbolic cosine of the prediction error. |
| metric_log_cosh_error | Computes the logarithm of the hyperbolic cosine of the... |
| metric_mean | Compute the (weighted) mean of the given values. |
| metric_mean_absolute_error | Computes the mean absolute error between the labels and... |
| metric_mean_absolute_percentage_error | Computes mean absolute percentage error between 'y_true' and... |
| metric_mean_iou | Computes the mean Intersection-Over-Union metric. |
| metric_mean_squared_error | Computes the mean squared error between 'y_true' and... |
| metric_mean_squared_logarithmic_error | Computes mean squared logarithmic error between 'y_true' and... |
| metric_mean_wrapper | Wrap a stateless metric function with the 'Mean' metric. |
| metric_one_hot_iou | Computes the Intersection-Over-Union metric for one-hot... |
| metric_one_hot_mean_iou | Computes mean Intersection-Over-Union metric for one-hot... |
| metric_poisson | Computes the Poisson metric between 'y_true' and 'y_pred'. |
| metric_precision | Computes the precision of the predictions with respect to the... |
| metric_precision_at_recall | Computes best precision where recall is >= specified value. |
| metric_r2_score | Computes R2 score. |
| metric_recall | Computes the recall of the predictions with respect to the... |
| metric_recall_at_precision | Computes best recall where precision is >= specified value. |
| metric_root_mean_squared_error | Computes root mean squared error metric between 'y_true' and... |
| metric_sensitivity_at_specificity | Computes best sensitivity where specificity is >= specified... |
| metric_sparse_categorical_accuracy | Calculates how often predictions match integer labels. |
| metric_sparse_categorical_crossentropy | Computes the crossentropy metric between the labels and... |
| metric_sparse_top_k_categorical_accuracy | Computes how often integer targets are in the top 'K'... |
| metric_specificity_at_sensitivity | Computes best specificity where sensitivity is >= specified... |
| metric_squared_hinge | Computes the hinge metric between 'y_true' and 'y_pred'. |
| metric_sum | Compute the (weighted) sum of the given values. |
| metric_top_k_categorical_accuracy | Computes how often targets are in the top 'K' predictions. |
| metric_true_negatives | Calculates the number of true negatives. |
| metric_true_positives | Calculates the number of true positives. |
| Model | Subclass the base Keras 'Model' Class |
| multi-assign | Assign values to names |
| new_callback_class | Callback |
| new_layer_class | Layer |
| new_learning_rate_schedule_class | LearningRateSchedule |
| new_loss_class | Loss |
| new_metric_class | Metric |
| new_model_class | Model |
| normalize | Normalizes an array. |
| op_abs | Compute the absolute value element-wise. |
| op_add | Add arguments element-wise. |
| op_all | Test whether all array elements along a given axis evaluate... |
| op_any | Test whether any array element along a given axis evaluates... |
| op_append | Append tensor 'x2' to the end of tensor 'x1'. |
| op_arange | Return evenly spaced values within a given interval. |
| op_arccos | Trigonometric inverse cosine, element-wise. |
| op_arccosh | Inverse hyperbolic cosine, element-wise. |
| op_arcsin | Inverse sine, element-wise. |
| op_arcsinh | Inverse hyperbolic sine, element-wise. |
| op_arctan | Trigonometric inverse tangent, element-wise. |
| op_arctan2 | Element-wise arc tangent of 'x1/x2' choosing the quadrant... |
| op_arctanh | Inverse hyperbolic tangent, element-wise. |
| op_argmax | Returns the indices of the maximum values along an axis. |
| op_argmin | Returns the indices of the minimum values along an axis. |
| op_argpartition | Performs an indirect partition along the given axis. |
| op_argsort | Returns the indices that would sort a tensor. |
| op_array | Create a tensor. |
| op_average | Compute the weighted average along the specified axis. |
| op_average_pool | Average pooling operation. |
| op_batch_normalization | Normalizes 'x' by 'mean' and 'variance'. |
| op_binary_crossentropy | Computes binary cross-entropy loss between target and output... |
| op_bincount | Count the number of occurrences of each value in a tensor of... |
| op_broadcast_to | Broadcast a tensor to a new shape. |
| op_cast | Cast a tensor to the desired dtype. |
| op_categorical_crossentropy | Computes categorical cross-entropy loss between target and... |
| op_ceil | Return the ceiling of the input, element-wise. |
| op_cholesky | Computes the Cholesky decomposition of a positive... |
| op_clip | Clip (limit) the values in a tensor. |
| op_concatenate | Join a sequence of tensors along an existing axis. |
| op_cond | Conditionally applies 'true_fn' or 'false_fn'. |
| op_conj | Returns the complex conjugate, element-wise. |
| op_conv | General N-D convolution. |
| op_convert_to_numpy | Convert a tensor to a NumPy array. |
| op_convert_to_tensor | Convert an array to a tensor. |
| op_conv_transpose | General N-D convolution transpose. |
| op_copy | Returns a copy of 'x'. |
| op_correlate | Compute the cross-correlation of two 1-dimensional tensors. |
| op_cos | Cosine, element-wise. |
| op_cosh | Hyperbolic cosine, element-wise. |
| op_count_nonzero | Counts the number of non-zero values in 'x' along the given... |
| op_cross | Returns the cross product of two (arrays of) vectors. |
| op_ctc_decode | Decodes the output of a CTC model. |
| op_ctc_loss | CTC (Connectionist Temporal Classification) loss. |
| op_cumprod | Return the cumulative product of elements along a given axis. |
| op_cumsum | Returns the cumulative sum of elements along a given axis. |
| op_custom_gradient | Decorator to define a function with a custom gradient. |
| op_depthwise_conv | General N-D depthwise convolution. |
| op_det | Computes the determinant of a square tensor. |
| op_diag | Extract a diagonal or construct a diagonal array. |
| op_diagonal | Return specified diagonals. |
| op_diff | Calculate the n-th discrete difference along the given axis. |
| op_digitize | Returns the indices of the bins to which each value in 'x'... |
| op_divide | Divide arguments element-wise. |
| op_divide_no_nan | Safe element-wise division which returns 0 where the... |
| op_dot | Dot product of two tensors. |
| op_dtype | Return the dtype of the tensor input as a standardized... |
| op_eig | Computes the eigenvalues and eigenvectors of a square matrix. |
| op_eigh | Computes the eigenvalues and eigenvectors of a complex... |
| op_einsum | Evaluates the Einstein summation convention on the operands. |
| op_elu | Exponential Linear Unit activation function. |
| op_empty | Return a tensor of given shape and type filled with... |
| op_equal | Returns '(x1 == x2)' element-wise. |
| op_erf | Computes the error function of 'x', element-wise. |
| op_erfinv | Computes the inverse error function of 'x', element-wise. |
| op_exp | Calculate the exponential of all elements in the input... |
| op_expand_dims | Expand the shape of a tensor. |
| op_expm1 | Calculate 'exp(x) - 1' for all elements in the tensor. |
| op_extract_sequences | Expands the dimension of last axis into sequences of... |
| op_eye | Return a 2-D tensor with ones on the diagonal and zeros... |
| op_fft | Computes the Fast Fourier Transform along last axis of input. |
| op_fft2 | Computes the 2D Fast Fourier Transform along the last two... |
| op_flip | Reverse the order of elements in the tensor along the given... |
| op_floor | Return the floor of the input, element-wise. |
| op_floor_divide | Returns the largest integer smaller or equal to the division... |
| op_fori_loop | For loop implementation. |
| op_full | Return a new tensor of given shape and type, filled with... |
| op_full_like | Return a full tensor with the same shape and type as the... |
| op_gelu | Gaussian Error Linear Unit (GELU) activation function. |
| op_get_item | Return 'x[key]'. |
| op_greater | Return the truth value of 'x1 > x2' element-wise. |
| op_greater_equal | Return the truth value of 'x1 >= x2' element-wise. |
| op_hard_sigmoid | Hard sigmoid activation function. |
| op_hard_silu | Hard SiLU activation function, also known as Hard Swish. |
| op_hstack | Stack tensors in sequence horizontally (column wise). |
| op_identity | Return the identity tensor. |
| op_imag | Return the imaginary part of the complex argument. |
| op_image_affine_transform | Applies the given transform(s) to the image(s). |
| op_image_crop | Crop 'images' to a specified 'height' and 'width'. |
| op_image_extract_patches | Extracts patches from the image(s). |
| op_image_hsv_to_rgb | Convert HSV images to RGB. |
| op_image_map_coordinates | Map the input array to new coordinates by interpolation. |
| op_image_pad | Pad 'images' with zeros to the specified 'height' and... |
| op_image_resize | Resize images to size using the specified interpolation... |
| op_image_rgb_to_grayscale | Convert RGB images to grayscale. |
| op_image_rgb_to_hsv | Convert RGB images to HSV. |
| op_in_top_k | Checks if the targets are in the top-k predictions. |
| op_inv | Computes the inverse of a square tensor. |
| op_irfft | Inverse real-valued Fast Fourier transform along the last... |
| op_isclose | Return whether two tensors are element-wise almost equal. |
| op_isfinite | Return whether a tensor is finite, element-wise. |
| op_isinf | Test element-wise for positive or negative infinity. |
| op_isnan | Test element-wise for NaN and return result as a boolean... |
| op_is_tensor | Check whether the given object is a tensor. |
| op_istft | Inverse Short-Time Fourier Transform along the last axis of... |
| op_leaky_relu | Leaky version of a Rectified Linear Unit activation function. |
| op_less | Return the truth value of 'x1 < x2' element-wise. |
| op_less_equal | Return the truth value of 'x1 <= x2' element-wise. |
| op_linspace | Return evenly spaced numbers over a specified interval. |
| op_log | Natural logarithm, element-wise. |
| op_log10 | Return the base 10 logarithm of the input tensor,... |
| op_log1p | Returns the natural logarithm of one plus the 'x',... |
| op_log2 | Base-2 logarithm of 'x', element-wise. |
| op_logaddexp | Logarithm of the sum of exponentiations of the inputs. |
| op_logical_and | Computes the element-wise logical AND of the given input... |
| op_logical_not | Computes the element-wise NOT of the given input tensor. |
| op_logical_or | Computes the element-wise logical OR of the given input... |
| op_logical_xor | Compute the truth value of x1 XOR x2, element-wise. |
| op_log_sigmoid | Logarithm of the sigmoid activation function. |
| op_log_softmax | Log-softmax activation function. |
| op_logspace | Returns numbers spaced evenly on a log scale. |
| op_logsumexp | Computes the logarithm of sum of exponentials of elements in... |
| op_lstsq | Return the least-squares solution to a linear matrix... |
| op_lu_factor | Computes the lower-upper decomposition of a square matrix. |
| op_map | Map a function over leading array axes. |
| op_matmul | Matrix product of two tensors. |
| op_max | Return the maximum of a tensor or maximum along an axis. |
| op_maximum | Element-wise maximum of 'x1' and 'x2'. |
| op_max_pool | Max pooling operation. |
| op_mean | Compute the arithmetic mean along the specified axes. |
| op_median | Compute the median along the specified axis. |
| op_meshgrid | Creates grids of coordinates from coordinate vectors. |
| op_min | Return the minimum of a tensor or minimum along an axis. |
| op_minimum | Element-wise minimum of 'x1' and 'x2'. |
| op_mod | Returns the element-wise remainder of division. |
| op_moments | Calculates the mean and variance of 'x'. |
| op_moveaxis | Move axes of a tensor to new positions. |
| op_multi_hot | Encodes integer labels as multi-hot vectors. |
| op_multiply | Multiply arguments element-wise. |
| op_nan_to_num | Replace NaN with zero and infinity with large finite numbers. |
| op_ndim | Return the number of dimensions of a tensor. |
| op_negative | Numerical negative, element-wise. |
| op_nonzero | Return the indices of the elements that are non-zero. |
| op_norm | Matrix or vector norm. |
| op_normalize | Normalizes 'x' over the specified axis. |
| op_not_equal | Return '(x1 != x2)' element-wise. |
| op_one_hot | Converts integer tensor 'x' into a one-hot tensor. |
| op_ones | Return a new tensor of given shape and type, filled with... |
| op_ones_like | Return a tensor of ones with the same shape and type of 'x'. |
| op_outer | Compute the outer product of two vectors. |
| op_pad | Pad a tensor. |
| op_power | First tensor elements raised to powers from second tensor,... |
| op_prod | Return the product of tensor elements over a given axis. |
| op_psnr | Peak Signal-to-Noise Ratio (PSNR) function. |
| op_qr | Computes the QR decomposition of a tensor. |
| op_quantile | Compute the q-th quantile(s) of the data along the specified... |
| op_ravel | Return a contiguous flattened tensor. |
| op_real | Return the real part of the complex argument. |
| op_reciprocal | Return the reciprocal of the argument, element-wise. |
| op_relu | Rectified linear unit activation function. |
| op_relu6 | Rectified linear unit activation function with upper bound of... |
| op_repeat | Repeat each element of a tensor after themselves. |
| op_reshape | Gives a new shape to a tensor without changing its data. |
| op_rfft | Real-valued Fast Fourier Transform along the last axis of the... |
| op_roll | Roll tensor elements along a given axis. |
| op_round | Evenly round to the given number of decimals. |
| op_rsqrt | Computes reciprocal of square root of x element-wise. |
| op_scan | Scan a function over leading array axes while carrying along... |
| op_scatter | Returns a tensor of shape 'shape' where 'indices' are set to... |
| op_scatter_update | Update inputs via updates at scattered (sparse) indices. |
| op_segment_max | Computes the max of segments in a tensor. |
| op_segment_sum | Computes the sum of segments in a tensor. |
| op_select | Return elements from 'choicelist', based on conditions in... |
| op_selu | Scaled Exponential Linear Unit (SELU) activation function. |
| op_separable_conv | General N-D separable convolution. |
| op_shape | Gets the shape of the tensor input. |
| op_sigmoid | Sigmoid activation function. |
| op_sign | Returns a tensor with the signs of the elements of 'x'. |
| op_silu | Sigmoid Linear Unit (SiLU) activation function, also known as... |
| op_sin | Trigonometric sine, element-wise. |
| op_sinh | Hyperbolic sine, element-wise. |
| op_size | Return the number of elements in a tensor. |
| op_slice | Return a slice of an input tensor. |
| op_slice_update | Update an input by slicing in a tensor of updated values. |
| op_slogdet | Compute the sign and natural logarithm of the determinant of... |
| op_softmax | Softmax activation function. |
| op_softplus | Softplus activation function. |
| op_softsign | Softsign activation function. |
| op_solve | Solves a linear system of equations given by a x = b. |
| op_solve_triangular | Solves a linear system of equations given by 'a %*% x = b'. |
| op_sort | Sorts the elements of 'x' along a given axis in ascending... |
| op_sparse_categorical_crossentropy | Computes sparse categorical cross-entropy loss. |
| op_split | Split a tensor into chunks. |
| op_sqrt | Return the non-negative square root of a tensor,... |
| op_square | Return the element-wise square of the input. |
| op_squeeze | Remove axes of length one from 'x'. |
| op_stack | Join a sequence of tensors along a new axis. |
| op_std | Compute the standard deviation along the specified axis. |
| op_stft | Short-Time Fourier Transform along the last axis of the... |
| op_stop_gradient | Stops gradient computation. |
| op_subtract | Subtract arguments element-wise. |
| op_sum | Sum of a tensor over the given axes. |
| op_svd | Computes the singular value decomposition of a matrix. |
| op_swapaxes | Interchange two axes of a tensor. |
| op_switch | Apply exactly one of the 'branches' given by 'index'. |
| op_take | Take elements from a tensor along an axis. |
| op_take_along_axis | Select values from 'x' at the 1-D 'indices' along the given... |
| op_tan | Compute tangent, element-wise. |
| op_tanh | Hyperbolic tangent, element-wise. |
| op_tensordot | Compute the tensor dot product along specified axes. |
| op_tile | Repeat 'x' the number of times given by 'repeats'. |
| optimizer_adadelta | Optimizer that implements the Adadelta algorithm. |
| optimizer_adafactor | Optimizer that implements the Adafactor algorithm. |
| optimizer_adagrad | Optimizer that implements the Adagrad algorithm. |
| optimizer_adam | Optimizer that implements the Adam algorithm. |
| optimizer_adamax | Optimizer that implements the Adamax algorithm. |
| optimizer_adam_w | Optimizer that implements the AdamW algorithm. |
| optimizer_ftrl | Optimizer that implements the FTRL algorithm. |
| optimizer_lion | Optimizer that implements the Lion algorithm. |
| optimizer_loss_scale | An optimizer that dynamically scales the loss to prevent... |
| optimizer_nadam | Optimizer that implements the Nadam algorithm. |
| optimizer_rmsprop | Optimizer that implements the RMSprop algorithm. |
| optimizer_sgd | Gradient descent (with momentum) optimizer. |
| op_top_k | Finds the top-k values and their indices in a tensor. |
| op_trace | Return the sum along diagonals of the tensor. |
| op_transpose | Returns a tensor with 'axes' transposed. |
| op_tri | Return a tensor with ones at and below a diagonal and zeros... |
| op_tril | Return lower triangle of a tensor. |
| op_triu | Return upper triangle of a tensor. |
| op_unstack | Unpacks the given dimension of a rank-R tensor into... |
| op_var | Compute the variance along the specified axes. |
| op_vdot | Return the dot product of two vectors. |
| op_vectorize | Turn a function into a vectorized function. |
| op_vectorized_map | Parallel map of function 'f' on the first axis of tensor(s)... |
| op_vstack | Stack tensors in sequence vertically (row wise). |
| op_where | Return elements chosen from 'x1' or 'x2' depending on... |
| op_while_loop | While loop implementation. |
| op_zeros | Return a new tensor of given shape and type, filled with... |
| op_zeros_like | Return a tensor of zeros with the same shape and type as 'x'. |
| pad_sequences | Pads sequences to the same length. |
| pipe | Pipe operator |
| plot.keras.src.models.model.Model | Plot a Keras model |
| plot.keras_training_history | Plot training history |
| pop_layer | Remove the last layer in a Sequential model |
| predict.keras.src.models.model.Model | Generates output predictions for the input samples. |
| predict_on_batch | Returns predictions for a single batch of samples. |
| process_utils | Preprocessing and postprocessing utilities |
| quantize_weights | Quantize the weights of a model. |
| random_beta | Draw samples from a Beta distribution. |
| random_binomial | Draw samples from a Binomial distribution. |
| random_categorical | Draws samples from a categorical distribution. |
| random_dropout | Randomly set some values in a tensor to 0. |
| random_gamma | Draw random samples from the Gamma distribution. |
| random_integer | Draw random integers from a uniform distribution. |
| random_normal | Draw random samples from a normal (Gaussian) distribution. |
| random_seed_generator | Generates variable seeds upon each call to a RNG-using... |
| random_shuffle | Shuffle the elements of a tensor uniformly at random along an... |
| random_truncated_normal | Draw samples from a truncated normal distribution. |
| random_uniform | Draw samples from a uniform distribution. |
| reexports | Objects exported from other packages |
| register_keras_serializable | Registers a custom object with the Keras serialization... |
| regularizer_l1 | A regularizer that applies a L1 regularization penalty. |
| regularizer_l1_l2 | A regularizer that applies both L1 and L2 regularization... |
| regularizer_l2 | A regularizer that applies a L2 regularization penalty. |
| regularizer_orthogonal | Regularizer that encourages input vectors to be orthogonal to... |
| reset_state | Reset the state for a model, layer or metric. |
| rnn_cell_gru | Cell class for the GRU layer. |
| rnn_cell_lstm | Cell class for the LSTM layer. |
| rnn_cell_simple | Cell class for SimpleRNN. |
| rnn_cells_stack | Wrapper allowing a stack of RNN cells to behave as a single... |
| save_model | Saves a model as a '.keras' file. |
| save_model_config | Save and load model configuration as JSON |
| save_model_weights | Saves all layer weights to a '.weights.h5' file. |
| serialize_keras_object | Retrieve the full config by serializing the Keras object. |
| set_random_seed | Sets all random seeds (Python, NumPy, and backend framework,... |
| shape | Tensor shape utility |
| split_dataset | Splits a dataset into a left half and a right half (e.g.... |
| summary.keras.src.models.model.Model | Print a summary of a Keras Model |
| test_on_batch | Test the model on a single batch of samples. |
| text_dataset_from_directory | Generates a 'tf.data.Dataset' from text files in a directory. |
| time_distributed | layer_time_distributed |
| timeseries_dataset_from_array | Creates a dataset of sliding windows over a timeseries... |
| to_categorical | Converts a class vector (integers) to binary class matrix. |
| train_on_batch | Runs a single gradient update on a single batch of data. |
| use_backend | Configure a Keras backend |
| with_custom_object_scope | Provide a scope with mappings of names to custom objects |
| zip_lists | Zip lists |
