R Interface to 'Keras'

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Documentation for package ‘keras3’ version 0.2.0

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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 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'.

-- 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_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_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_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_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_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_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_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