# R Interface to 'Keras'

## Documentation for package ‘keras3’ version 1.0.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A C D E F G I K L M N O P Q R S T U W Z misc

## -- A --

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_hard_swish | 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 distribution.
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 passed
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_decode_predictions | Preprocessing and postprocessing utilities
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_nasnetlarge | Instantiates a NASNet model in ImageNet mode.
application_nasnetmobile | Instantiates a Mobile NASNet model in ImageNet mode.
application_preprocess_inputs | Preprocessing and postprocessing utilities
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.
as.integer.keras_shape | Tensor shape utility
as.list.keras_shape | Tensor shape utility
audio_dataset_from_directory | Generates a 'tf.data.Dataset' from audio files in a directory.

## -- C --

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 frequency.
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 evaluation.
callback_tensorboard | Enable visualizations for TensorBoard.
callback_terminate_on_nan | Callback that terminates training when a NaN loss is encountered.
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 backend.
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 lambdas.
config_epsilon | Return the value of the fuzz factor used in numeric expressions.
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 unit norm.
count_params | Count the total number of scalars composing the weights.
custom_metric | Custom metric function

## -- D --

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_imdb_word_index | IMDB Movie reviews sentiment classification
dataset_mnist | MNIST database of handwritten digits
dataset_reuters | Reuters newswire topics classification
dataset_reuters_word_index | Reuters newswire topics classification
deserialize_keras_object | Retrieve the object by deserializing the config dict.

## -- E --

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 TF-Serving).

## -- F --

feature_cross | One-stop utility for preprocessing and encoding structured data.
feature_custom | One-stop utility for preprocessing and encoding structured data.
feature_float | One-stop utility for preprocessing and encoding structured data.
feature_float_discretized | One-stop utility for preprocessing and encoding structured data.
feature_float_normalized | One-stop utility for preprocessing and encoding structured data.
feature_float_rescaled | One-stop utility for preprocessing and encoding structured data.
feature_integer_categorical | One-stop utility for preprocessing and encoding structured data.
feature_integer_hashed | One-stop utility for preprocessing and encoding structured data.
feature_string_categorical | One-stop utility for preprocessing and encoding structured data.
feature_string_hashed | One-stop utility for preprocessing and encoding structured data.
fit.keras.src.models.model.Model | Train a model for a fixed number of epochs (dataset iterations).
format.keras.src.models.model.Model | Print a summary of a Keras Model
format.keras_shape | Tensor shape utility
freeze_weights | Freeze and unfreeze weights
from_config | Layer/Model configuration

## -- G --

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 framework.
get_registered_object | Returns the class associated with 'name' if it is registered with Keras.
get_source_inputs | Returns the list of input tensors necessary to compute 'tensor'.
get_vocabulary | A preprocessing layer which maps text features to integer sequences.
get_weights | Layer/Model weights as R arrays

## -- I --

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 directory.
image_from_array | Converts a 3D array to a PIL Image instance.
image_load | Loads an image into PIL format.
image_smart_resize | Resize images to a target size without aspect ratio distortion.
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.
initializer_glorot_uniform | The Glorot uniform initializer, also called Xavier uniform initializer.
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 tensors.
initializer_zeros | Initializer that generates tensors initialized to 0.
install_keras | Install Keras

## -- K --

keras | Main Keras module
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

## -- L --

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 activity.
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 spatio-temporal).
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 ranges.
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 size.
layer_feature_space | One-stop utility for preprocessing and encoding structured data.
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 "hashing trick".
layer_hashing | A preprocessing layer which hashes and bins categorical features.
layer_identity | Identity layer.
layer_integer_lookup | A preprocessing layer that maps integers to (possibly encoded) indices.
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 spatio-temporal).
layer_mel_spectrogram | A preprocessing layer to convert raw audio signals to Mel spectrograms.
layer_minimum | Computes elementwise minimum on a list of inputs.
layer_multiply | Performs elementwise multiplication.
layer_multi_head_attention | Multi Head Attention layer.
layer_normalization | A preprocessing layer that normalizes continuous features.
layer_permute | Permutes the dimensions of the input according to a given pattern.
layer_random_brightness | A preprocessing layer which randomly adjusts brightness during training.
layer_random_contrast | A preprocessing layer which randomly adjusts contrast during training.
layer_random_crop | A preprocessing layer which randomly crops images during training.
layer_random_flip | A preprocessing layer which randomly flips images during training.
layer_random_rotation | A preprocessing layer which randomly rotates images during training.
layer_random_translation | A preprocessing layer which randomly translates images during training.
layer_random_zoom | A preprocessing layer which randomly zooms images during training.
layer_repeat_vector | Repeats the input n times.
layer_rescaling | A preprocessing layer which rescales input values to a new range.
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 new input.
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.
layer_string_lookup | A preprocessing layer that maps strings to (possibly encoded) indices.
layer_subtract | Performs elementwise subtraction.
layer_text_vectorization | A preprocessing layer which maps text features to integer sequences.
layer_tfsm | Reload a Keras model/layer that was saved via 'export_savedmodel()'.
layer_time_distributed | This wrapper allows to apply a layer to every temporal slice of an input.
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 optional warmup.
learning_rate_schedule_cosine_decay_restarts | A 'LearningRateSchedule' that uses a cosine decay schedule with restarts.
learning_rate_schedule_exponential_decay | A 'LearningRateSchedule' that uses an exponential decay schedule.
learning_rate_schedule_inverse_time_decay | A 'LearningRateSchedule' that uses an inverse time decay schedule.
learning_rate_schedule_piecewise_constant_decay | A 'LearningRateSchedule' that uses a piecewise constant decay schedule.
learning_rate_schedule_polynomial_decay | A 'LearningRateSchedule' that uses a polynomial decay schedule.
load_model | Loads a model saved via 'save_model()'.
load_model_config | Save and load model configuration as JSON
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 predicted labels.
loss_binary_focal_crossentropy | Computes focal cross-entropy loss between true labels and predictions.
loss_categorical_crossentropy | Computes the crossentropy loss between the labels and predictions.
loss_categorical_focal_crossentropy | Computes the alpha balanced focal crossentropy loss.
loss_categorical_hinge | Computes the categorical hinge loss between 'y_true' & 'y_pred'.
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' & 'y_pred'.
loss_log_cosh | Computes the logarithm of the hyperbolic cosine of the prediction error.
loss_mean_absolute_error | Computes the mean of absolute difference between labels and predictions.
loss_mean_absolute_percentage_error | Computes the mean absolute percentage error between 'y_true' and 'y_pred'.
loss_mean_squared_error | Computes the mean of squares of errors between labels and predictions.
loss_mean_squared_logarithmic_error | Computes the mean squared logarithmic error between 'y_true' and 'y_pred'.
loss_poisson | Computes the Poisson loss between 'y_true' & 'y_pred'.
loss_sparse_categorical_crossentropy | Computes the crossentropy loss between the labels and predictions.
loss_squared_hinge | Computes the squared hinge loss between 'y_true' & 'y_pred'.
loss_tversky | Computes the Tversky loss value between 'y_true' and 'y_pred'.

## -- M --

Metric | Subclass the base 'Metric' class
metric_auc | Approximates the AUC (Area under the curve) of the ROC or PR curves.
metric_binary_accuracy | Calculates how often predictions match binary labels.
metric_binary_crossentropy | Computes the crossentropy metric between the labels and predictions.
metric_binary_focal_crossentropy | Computes the binary focal crossentropy loss.
metric_binary_iou | Computes the Intersection-Over-Union metric for class 0 and/or 1.
metric_categorical_accuracy | Calculates how often predictions match one-hot labels.
metric_categorical_crossentropy | Computes the crossentropy metric between the labels and predictions.
metric_categorical_focal_crossentropy | Computes the categorical focal crossentropy loss.
metric_categorical_hinge | Computes the categorical hinge metric between 'y_true' and 'y_pred'.
metric_cosine_similarity | Computes the cosine similarity between the labels and predictions.
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 target classes.
metric_kl_divergence | Computes Kullback-Leibler divergence metric between 'y_true' and
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 prediction error.
metric_mean | Compute the (weighted) mean of the given values.
metric_mean_absolute_error | Computes the mean absolute error between the labels and predictions.
metric_mean_absolute_percentage_error | Computes mean absolute percentage error between 'y_true' and 'y_pred'.
metric_mean_iou | Computes the mean Intersection-Over-Union metric.
metric_mean_squared_error | Computes the mean squared error between 'y_true' and 'y_pred'.
metric_mean_squared_logarithmic_error | Computes mean squared logarithmic error between 'y_true' and 'y_pred'.
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 encoded labels.
metric_one_hot_mean_iou | Computes mean Intersection-Over-Union metric for one-hot encoded labels.
metric_poisson | Computes the Poisson metric between 'y_true' and 'y_pred'.
metric_precision | Computes the precision of the predictions with respect to the labels.
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 labels.
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 'y_pred'.
metric_sensitivity_at_specificity | Computes best sensitivity where specificity is >= specified value.
metric_sparse_categorical_accuracy | Calculates how often predictions match integer labels.
metric_sparse_categorical_crossentropy | Computes the crossentropy metric between the labels and predictions.
metric_sparse_top_k_categorical_accuracy | Computes how often integer targets are in the top 'K' predictions.
metric_specificity_at_sensitivity | Computes best specificity where sensitivity is >= specified value.
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

## -- N --

normalize | Normalizes an array.

## -- O --

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 underflow.
optimizer_nadam | Optimizer that implements the Nadam algorithm.
optimizer_rmsprop | Optimizer that implements the RMSprop algorithm.
optimizer_sgd | Gradient descent (with momentum) optimizer.
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 to 'TRUE'.
op_amax | Return the maximum of a tensor or maximum along an axis.
op_amin | Return the minimum of a tensor or minimum along an axis.
op_any | Test whether any array element along a given axis evaluates to 'TRUE'.
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 correctly.
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_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 tensor.
op_bincount | Count the number of occurrences of each value in a tensor of integers.
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 output tensor.
op_ceil | Return the ceiling of the input, element-wise.
op_cholesky | Computes the Cholesky decomposition of a positive semi-definite matrix.
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 'axis'.
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' belongs.
op_divide | Divide arguments element-wise.
op_divide_no_nan | Safe element-wise division which returns 0 where the denominator is 0.
op_dot | Dot product of two tensors.
op_eig | Computes the eigenvalues and eigenvectors of a square matrix.
op_eigh | Computes the eigenvalues and eigenvectors of a complex Hermitian.
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 uninitialized data.
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 tensor.
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 'sequence_length'.
op_eye | Return a 2-D tensor with ones on the diagonal and zeros elsewhere.
op_fft | Computes the Fast Fourier Transform along last axis of input.
op_fft2 | Computes the 2D Fast Fourier Transform along the last two axes of input.
op_flip | Reverse the order of elements in the tensor along the given axis.
op_floor | Return the floor of the input, element-wise.
op_floor_divide | Returns the largest integer smaller or equal to the division of inputs.
op_fori_loop | For loop implementation.
op_full | Return a new tensor of given shape and type, filled with 'fill_value'.
op_full_like | Return a full tensor with the same shape and type as the given tensor.
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_hard_swish | 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_map_coordinates | Map the input array to new coordinates by interpolation..
op_image_pad | Pad 'images' with zeros to the specified 'height' and 'width'.
op_image_resize | Resize images to size using the specified interpolation method.
op_image_rgb_to_grayscale | Convert RGB images to grayscale.
op_inv | Computes the inverse of a square tensor.
op_in_top_k | Checks if the targets are in the top-k predictions.
op_irfft | Inverse real-valued Fast Fourier transform along the last axis.
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 tensor.
op_istft | Inverse Short-Time Fourier Transform along the last axis of the input.
op_is_tensor | Check whether the given object is a tensor.
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, element-wise.
op_log1p | Returns the natural logarithm of one plus the 'x', element-wise.
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 tensors.
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 tensors.
op_logical_xor | Compute the truth value of x1 XOR x2, element-wise.
op_logspace | Returns numbers spaced evenly on a log scale.
op_logsumexp | Computes the logarithm of sum of exponentials of elements in a tensor.
op_log_sigmoid | Logarithm of the sigmoid activation function.
op_log_softmax | Log-softmax activation function.
op_lu_factor | Computes the lower-upper decomposition of a square matrix.
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_multiply | Multiply arguments element-wise.
op_multi_hot | Encodes integer labels as multi-hot vectors.
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_ones | Return a new tensor of given shape and type, filled with ones.
op_ones_like | Return a tensor of ones with the same shape and type of 'x'.
op_one_hot | Converts integer tensor 'x' into a one-hot tensor.
op_outer | Compute the outer product of two vectors.
op_pad | Pad a tensor.
op_pmax | Element-wise maximum of 'x1' and 'x2'.
op_pmin | Element-wise minimum of 'x1' and 'x2'.
op_power | First tensor elements raised to powers from second tensor, element-wise.
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 axis.
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 6.
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 input.
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_scatter | Returns a tensor of shape 'shape' where 'indices' are set to 'values'.
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 'condlist'.
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 Swish.
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 a matrix.
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 order.
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, element-wise.
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 input.
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_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 axis.
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'.
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 elsewhere.
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 rank-(R-1) tensors.
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) 'elements'.
op_vstack | Stack tensors in sequence vertically (row wise).
op_where | Return elements chosen from 'x1' or 'x2' depending on 'condition'.
op_while_loop | While loop implementation.
op_zeros | Return a new tensor of given shape and type, filled with zeros.
op_zeros_like | Return a tensor of zeros with the same shape and type as 'x'.

## -- P --

pad_sequences | Pads sequences to the same length.
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.
print.keras.src.models.model.Model | Print a summary of a Keras Model
print.keras_shape | Tensor shape utility
process_utils | Preprocessing and postprocessing utilities

## -- Q --

quantize_weights | Quantize the weights of a model.

## -- R --

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 function.
random_shuffle | Shuffle the elements of a tensor uniformly at random along an axis.
random_truncated_normal | Draw samples from a truncated normal distribution.
random_uniform | Draw samples from a uniform distribution.
register_keras_serializable | Registers a custom object with the Keras serialization framework.
regularizer_l1 | A regularizer that applies a L1 regularization penalty.
regularizer_l1_l2 | A regularizer that applies both L1 and L2 regularization penalties.
regularizer_l2 | A regularizer that applies a L2 regularization penalty.
regularizer_orthogonal | Regularizer that encourages input vectors to be orthogonal to each other.
reset_state | Reset the state for a model, layer or metric.
rnn_cells_stack | Wrapper allowing a stack of RNN cells to behave as a single cell.
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.

## -- S --

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_custom_objects | Get/set the currently registered custom objects.
set_random_seed | Sets all random seeds (Python, NumPy, and backend framework, e.g. TF).
set_vocabulary | A preprocessing layer which maps text features to integer sequences.
set_weights | Layer/Model weights as R arrays
shape | Tensor shape utility
split_dataset | Splits a dataset into a left half and a right half (e.g. train / test).
summary.keras.src.models.model.Model | Print a summary of a Keras Model

## -- T --

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.
timeseries_dataset_from_array | Creates a dataset of sliding windows over a timeseries provided as array.
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.

## -- U --

unfreeze_weights | Freeze and unfreeze weights
use_backend | Configure a Keras backend

## -- W --

with_custom_object_scope | Provide a scope with mappings of names to custom objects

## -- Z --

zip_lists | Zip lists

## -- misc --

[.keras_shape | Tensor shape utility