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keras3 (version 1.2.0)

R Interface to 'Keras'

Description

Interface to 'Keras' , a high-level neural networks API. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.

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install.packages('keras3')

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1.2.0

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MIT + file LICENSE

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Maintainer

Tomasz Kalinowski

Last Published

September 5th, 2024

Functions in keras3 (1.2.0)

activation_exponential

Exponential activation function.
LearningRateSchedule

Define a custom LearningRateSchedule class
Loss

Subclass the base Loss class
activation_gelu

Gaussian error linear unit (GELU) activation function.
Metric

Subclass the base Metric class
Constraint

Define a custom Constraint class
activation_elu

Exponential Linear Unit.
Callback

Define a custom Callback class
Model

Subclass the base Keras Model Class
Layer

Define a custom Layer class.
activation_relu

Applies the rectified linear unit activation function.
activation_relu6

Relu6 activation function.
activation_mish

Mish activation function.
activation_hard_silu

Hard SiLU activation function, also known as Hard Swish.
activation_tanh

Hyperbolic tangent activation function.
activation_log_softmax

Log-Softmax activation function.
activation_hard_sigmoid

Hard sigmoid activation function.
activation_leaky_relu

Leaky relu 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.
activation_softplus

Softplus activation function.
activation_softsign

Softsign activation function.
activation_selu

Scaled Exponential Linear Unit (SELU).
activation_sigmoid

Sigmoid activation function.
activation_linear

Linear activation function (pass-through).
application_densenet169

Instantiates the Densenet169 architecture.
activation_silu

Swish (or Silu) activation function.
activation_softmax

Softmax converts a vector of values to a probability distribution.
application_convnext_large

Instantiates the ConvNeXtLarge architecture.
application_efficientnet_b0

Instantiates the EfficientNetB0 architecture.
application_densenet121

Instantiates the Densenet121 architecture.
application_efficientnet_b1

Instantiates the EfficientNetB1 architecture.
application_efficientnet_b3

Instantiates the EfficientNetB3 architecture.
application_convnext_small

Instantiates the ConvNeXtSmall architecture.
application_densenet201

Instantiates the Densenet201 architecture.
application_efficientnet_b2

Instantiates the EfficientNetB2 architecture.
application_convnext_tiny

Instantiates the ConvNeXtTiny architecture.
application_convnext_xlarge

Instantiates the ConvNeXtXLarge architecture.
application_efficientnet_b4

Instantiates the EfficientNetB4 architecture.
application_efficientnet_b7

Instantiates the EfficientNetB7 architecture.
application_efficientnet_v2b3

Instantiates the EfficientNetV2B3 architecture.
application_efficientnet_v2b1

Instantiates the EfficientNetV2B1 architecture.
application_efficientnet_v2b0

Instantiates the EfficientNetV2B0 architecture.
application_efficientnet_v2s

Instantiates the EfficientNetV2S architecture.
application_efficientnet_v2m

Instantiates the EfficientNetV2M architecture.
application_efficientnet_b5

Instantiates the EfficientNetB5 architecture.
application_efficientnet_v2b2

Instantiates the EfficientNetV2B2 architecture.
application_efficientnet_b6

Instantiates the EfficientNetB6 architecture.
application_efficientnet_v2l

Instantiates the EfficientNetV2L architecture.
application_inception_resnet_v2

Instantiates the Inception-ResNet v2 architecture.
application_nasnet_large

Instantiates a NASNet model in ImageNet mode.
application_resnet101

Instantiates the ResNet101 architecture.
application_mobilenet_v2

Instantiates the MobileNetV2 architecture.
application_mobilenet

Instantiates the MobileNet architecture.
application_nasnet_mobile

Instantiates a Mobile NASNet model in ImageNet mode.
application_resnet101_v2

Instantiates the ResNet101V2 architecture.
application_inception_v3

Instantiates the Inception v3 architecture.
bidirectional

layer_bidirectional
application_mobilenet_v3_large

Instantiates the MobileNetV3Large architecture.
audio_dataset_from_directory

Generates a tf.data.Dataset from audio files in a directory.
application_resnet152

Instantiates the ResNet152 architecture.
application_xception

Instantiates the Xception architecture.
application_mobilenet_v3_small

Instantiates the MobileNetV3Small architecture.
application_vgg16

Instantiates the VGG16 model.
application_resnet50

Instantiates the ResNet50 architecture.
application_nasnetlarge

Backward compatibility
application_resnet50_v2

Instantiates the ResNet50V2 architecture.
application_vgg19

Instantiates the VGG19 model.
application_resnet152_v2

Instantiates the ResNet152V2 architecture.
callback_model_checkpoint

Callback to save the Keras model or model weights at some frequency.
callback_early_stopping

Stop training when a monitored metric has stopped improving.
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_backup_and_restore

Callback to back up and restore the training state.
callback_lambda

Callback for creating simple, custom callbacks on-the-fly.
callback_learning_rate_scheduler

Learning rate scheduler.
callback_csv_logger

Callback that streams epoch results to a CSV file.
callback_tensorboard

Enable visualizations for TensorBoard.
config_backend

Publicly accessible method for determining the current backend.
config_enable_interactive_logging

Turn on interactive logging.
callback_terminate_on_nan

Callback that terminates training when a NaN loss is encountered.
compile.keras.src.models.model.Model

Configure a model for training.
clone_model

Clone a Functional or Sequential Model instance.
config_disable_traceback_filtering

Turn off traceback filtering.
config_disable_interactive_logging

Turn off interactive logging.
clear_session

Resets all state generated by Keras.
config_enable_traceback_filtering

Turn on traceback filtering.
config_dtype_policy

Returns the current default dtype policy object.
config_is_interactive_logging_enabled

Check if interactive logging is enabled.
config_floatx

Return the default float type, as a string.
config_image_data_format

Return the default image data format convention.
config_set_dtype_policy

Sets the default dtype policy globally.
config_set_backend

Reload the backend (and the Keras package).
count_params

Count the total number of scalars composing the weights.
constraint_unitnorm

Constrains the weights incident to each hidden unit to have unit norm.
custom_metric

Custom metric function
dataset_boston_housing

Boston housing price regression dataset
constraint_minmaxnorm

MinMaxNorm weight constraint.
dataset_cifar10

CIFAR10 small image classification
config_is_traceback_filtering_enabled

Check if traceback filtering is enabled.
config_enable_unsafe_deserialization

Disables safe mode globally, allowing deserialization of lambdas.
config_set_epsilon

Set the value of the fuzz factor used in numeric expressions.
config_set_floatx

Set the default float dtype.
constraint_maxnorm

MaxNorm weight constraint.
constraint_nonneg

Constrains the weights to be non-negative.
dataset_cifar100

CIFAR100 small image classification
config_epsilon

Return the value of the fuzz factor used in numeric expressions.
deserialize_keras_object

Retrieve the object by deserializing the config dict.
dataset_fashion_mnist

Fashion-MNIST database of fashion articles
evaluate.keras.src.models.model.Model

Evaluate a Keras Model
config_set_image_data_format

Set the value of the image data format convention.
export_savedmodel.keras.src.models.model.Model

Create a TF SavedModel artifact for inference (e.g. via TF-Serving).
dataset_imdb

IMDB Movie reviews sentiment classification
dataset_mnist

MNIST database of handwritten digits
fit.keras.src.models.model.Model

Train a model for a fixed number of epochs (dataset iterations).
freeze_weights

Freeze and unfreeze weights
dataset_reuters

Reuters newswire topics classification
%<-active%

Make an Active Binding
get_source_inputs

Returns the list of input tensors necessary to compute tensor.
get_registered_object

Returns the class associated with name if it is registered with Keras.
get_weights

Layer/Model weights as R arrays
%py_class%

Make a python class constructor
image_array_save

Saves an image stored as an array to a path or file object.
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_config

Layer/Model configuration
initializer_constant

Initializer that generates tensors with constant values.
image_load

Loads an image into PIL format.
image_to_array

Converts a PIL Image instance to a matrix.
initializer_glorot_normal

The Glorot normal initializer, also called Xavier normal initializer.
get_custom_objects

Get/set the currently registered custom objects.
imagenet_preprocess_input

Preprocesses a tensor or array encoding a batch of images.
get_file

Downloads a file from a URL if it not already in the cache.
initializer_glorot_uniform

The Glorot uniform initializer, also called Xavier uniform initializer.
image_smart_resize

Resize images to a target size without aspect ratio distortion.
imagenet_decode_predictions

Decodes the prediction of an ImageNet model.
initializer_identity

Initializer that generates the identity matrix.
initializer_random_uniform

Random uniform initializer.
initializer_orthogonal

Initializer that generates an orthogonal matrix.
initializer_he_normal

He normal initializer.
image_from_array

Converts a 3D array to a PIL Image instance.
initializer_lecun_normal

Lecun normal initializer.
initializer_random_normal

Random normal initializer.
image_dataset_from_directory

Generates a tf.data.Dataset from image files in a directory.
initializer_lecun_uniform

Lecun uniform initializer.
initializer_ones

Initializer that generates tensors initialized to 1.
initializer_he_uniform

He uniform variance scaling initializer.
initializer_truncated_normal

Initializer that generates a truncated normal distribution.
keras3-package

keras3: R Interface to 'Keras'
install_keras

Install Keras
keras_model_sequential

Keras Model composed of a linear stack of layers
keras_model

Keras Model (Functional API)
layer_activation

Applies an activation function to an output.
layer_activation_elu

Applies an Exponential Linear Unit function to an output.
keras

Main Keras module
initializer_variance_scaling

Initializer that adapts its scale to the shape of its input tensors.
initializer_zeros

Initializer that generates tensors initialized to 0.
keras_input

Create a Keras tensor (Functional API input).
layer_add

Performs elementwise addition operation.
layer_average

Averages a list of inputs element-wise..
layer_activation_parametric_relu

Parametric Rectified Linear Unit activation layer.
layer_attention

Dot-product attention layer, a.k.a. Luong-style attention.
layer_activity_regularization

Layer that applies an update to the cost function based input activity.
layer_activation_leaky_relu

Leaky version of a Rectified Linear Unit activation layer.
layer_activation_relu

Rectified Linear Unit activation function layer.
layer_activation_softmax

Softmax activation layer.
layer_additive_attention

Additive attention layer, a.k.a. Bahdanau-style attention.
layer_alpha_dropout

Applies Alpha Dropout to the input.
layer_bidirectional

Bidirectional wrapper for RNNs.
layer_average_pooling_1d

Average pooling for temporal data.
layer_concatenate

Concatenates a list of inputs.
layer_category_encoding

A preprocessing layer which encodes integer features.
layer_average_pooling_2d

Average pooling operation for 2D spatial data.
layer_batch_normalization

Layer that normalizes its inputs.
layer_center_crop

A preprocessing layer which crops images.
layer_average_pooling_3d

Average pooling operation for 3D data (spatial or spatio-temporal).
layer_conv_1d

1D convolution layer (e.g. temporal convolution).
layer_conv_1d_transpose

1D transposed convolution layer.
layer_conv_lstm_1d

1D Convolutional LSTM.
layer_cropping_2d

Cropping layer for 2D input (e.g. picture).
layer_cropping_1d

Cropping layer for 1D input (e.g. temporal sequence).
layer_conv_3d

3D convolution layer.
layer_conv_3d_transpose

3D transposed convolution layer.
layer_conv_2d_transpose

2D transposed convolution layer.
layer_cropping_3d

Cropping layer for 3D data (e.g. spatial or spatio-temporal).
layer_conv_lstm_3d

3D Convolutional LSTM.
layer_conv_lstm_2d

2D Convolutional LSTM.
layer_conv_2d

2D convolution layer.
layer_embedding

Turns positive integers (indexes) into dense vectors of fixed size.
layer_discretization

A preprocessing layer which buckets continuous features by ranges.
layer_depthwise_conv_2d

2D depthwise convolution layer.
layer_dot

Computes element-wise dot product of two tensors.
layer_depthwise_conv_1d

1D depthwise convolution layer.
layer_dropout

Applies dropout to the input.
layer_dense

Just your regular densely-connected NN layer.
layer_einsum_dense

A layer that uses einsum as the backing computation.
layer_flatten

Flattens the input. Does not affect the batch size.
layer_feature_space

One-stop utility for preprocessing and encoding structured data.
layer_global_max_pooling_1d

Global max pooling operation for temporal data.
layer_global_average_pooling_2d

Global average pooling operation for 2D data.
layer_global_average_pooling_1d

Global average pooling operation for temporal data.
layer_group_normalization

Group normalization layer.
layer_global_max_pooling_3d

Global max pooling operation for 3D data.
layer_flax_module_wrapper

Keras Layer that wraps a Flax module.
layer_gaussian_noise

Apply additive zero-centered Gaussian noise.
layer_global_average_pooling_3d

Global average pooling operation for 3D data.
layer_gaussian_dropout

Apply multiplicative 1-centered Gaussian noise.
layer_global_max_pooling_2d

Global max pooling operation for 2D data.
layer_hashed_crossing

A preprocessing layer which crosses features using the "hashing trick".
layer_group_query_attention

Grouped Query Attention layer.
layer_layer_normalization

Layer normalization layer (Ba et al., 2016).
layer_hashing

A preprocessing layer which hashes and bins categorical features.
layer_gru

Gated Recurrent Unit - Cho et al. 2014.
layer_identity

Identity layer.
layer_integer_lookup

A preprocessing layer that maps integers to (possibly encoded) indices.
layer_lambda

Wraps arbitrary expressions as a Layer object.
layer_jax_model_wrapper

Keras Layer that wraps a JAX model.
layer_input

keras_input
layer_max_pooling_1d

Max pooling operation for 1D temporal data.
layer_maximum

Computes element-wise maximum on a list of inputs.
layer_multi_head_attention

Multi Head Attention layer.
layer_max_pooling_2d

Max pooling operation for 2D spatial data.
layer_multiply

Performs elementwise multiplication.
layer_masking

Masks a sequence by using a mask value to skip timesteps.
layer_minimum

Computes elementwise minimum on a list of inputs.
layer_max_pooling_3d

Max pooling operation for 3D data (spatial or spatio-temporal).
layer_lstm

Long Short-Term Memory layer - Hochreiter 1997.
layer_mel_spectrogram

A preprocessing layer to convert raw audio signals to Mel spectrograms.
layer_random_crop

A preprocessing layer which randomly crops images during training.
layer_permute

Permutes the dimensions of the input according to a given pattern.
layer_repeat_vector

Repeats the input n times.
layer_random_rotation

A preprocessing layer which randomly rotates images during training.
layer_normalization

A preprocessing layer that normalizes continuous features.
layer_random_translation

A preprocessing layer which randomly translates images during training.
layer_random_contrast

A preprocessing layer which randomly adjusts contrast during training.
layer_random_zoom

A preprocessing layer which randomly zooms images during training.
layer_random_brightness

A preprocessing layer which randomly adjusts brightness during training.
layer_random_flip

A preprocessing layer which randomly flips images during training.
layer_rescaling

A preprocessing layer which rescales input values to a new range.
layer_reshape

Layer that reshapes inputs into the given shape.
layer_spatial_dropout_2d

Spatial 2D version of Dropout.
layer_simple_rnn

Fully-connected RNN where the output is to be fed back as the new input.
layer_resizing

A preprocessing layer which resizes images.
layer_rnn

Base class for recurrent layers
layer_spatial_dropout_1d

Spatial 1D version of Dropout.
layer_separable_conv_1d

1D separable convolution layer.
layer_separable_conv_2d

2D separable convolution layer.
layer_spatial_dropout_3d

Spatial 3D version of Dropout.
layer_torch_module_wrapper

Torch module wrapper layer.
layer_subtract

Performs elementwise subtraction.
layer_tfsm

Reload a Keras model/layer that was saved via export_savedmodel().
layer_unit_normalization

Unit normalization layer.
layer_upsampling_1d

Upsampling layer for 1D inputs.
layer_upsampling_2d

Upsampling layer for 2D inputs.
layer_time_distributed

This wrapper allows to apply a layer to every temporal slice of an input.
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_text_vectorization

A preprocessing layer which maps text features to integer sequences.
layer_zero_padding_1d

Zero-padding layer for 1D input (e.g. temporal sequence).
learning_rate_schedule_piecewise_constant_decay

A LearningRateSchedule that uses a piecewise constant decay schedule.
learning_rate_schedule_cosine_decay

A LearningRateSchedule that uses a cosine decay with optional warmup.
learning_rate_schedule_exponential_decay

A LearningRateSchedule that uses an exponential decay schedule.
learning_rate_schedule_polynomial_decay

A LearningRateSchedule that uses a polynomial decay schedule.
learning_rate_schedule_inverse_time_decay

A LearningRateSchedule that uses an inverse time decay schedule.
learning_rate_schedule_cosine_decay_restarts

A LearningRateSchedule that uses a cosine decay schedule with restarts.
layer_upsampling_3d

Upsampling layer for 3D inputs.
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).
load_model

Loads a model saved via save_model().
load_model_weights

Load weights from a file saved via save_model_weights().
loss_ctc

CTC (Connectionist Temporal Classification) loss.
loss_binary_focal_crossentropy

Computes focal cross-entropy loss between true labels and predictions.
loss_dice

Computes the Dice loss value between y_true and y_pred.
loss_categorical_hinge

Computes the categorical hinge loss between y_true & y_pred.
loss_binary_crossentropy

Computes the cross-entropy loss between true labels and predicted labels.
loss_categorical_crossentropy

Computes the crossentropy loss between the labels and predictions.
loss_cosine_similarity

Computes the cosine similarity between y_true & y_pred.
loss_categorical_focal_crossentropy

Computes the alpha balanced focal crossentropy loss.
loss_huber

Computes the Huber loss between y_true & 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_hinge

Computes the hinge loss between y_true & y_pred.
loss_mean_squared_logarithmic_error

Computes the mean squared logarithmic error between y_true and y_pred.
loss_mean_absolute_percentage_error

Computes the mean absolute percentage error between y_true and y_pred.
loss_mean_absolute_error

Computes the mean of absolute difference between labels and predictions.
loss_mean_squared_error

Computes the mean of squares of errors between labels and predictions.
loss_log_cosh

Computes the logarithm of the hyperbolic cosine of the prediction error.
loss_kl_divergence

Computes Kullback-Leibler divergence loss between y_true & y_pred.
metric_auc

Approximates the AUC (Area under the curve) of the ROC or PR curves.
mark_active

active_property
metric_categorical_accuracy

Calculates how often predictions match one-hot labels.
metric_binary_iou

Computes the Intersection-Over-Union metric for class 0 and/or 1.
metric_binary_accuracy

Calculates how often predictions match binary labels.
metric_binary_crossentropy

Computes the crossentropy metric between the labels and predictions.
metric_categorical_crossentropy

Computes the crossentropy metric between the labels and predictions.
metric_binary_focal_crossentropy

Computes the binary focal crossentropy loss.
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.
metric_false_positives

Calculates the number of false positives.
metric_false_negatives

Calculates the number of false negatives.
metric_iou

Computes the Intersection-Over-Union metric for specific target classes.
metric_categorical_hinge

Computes the categorical hinge metric between y_true and y_pred.
metric_hinge

Computes the hinge metric between y_true and y_pred.
metric_cosine_similarity

Computes the cosine similarity between the labels and predictions.
metric_categorical_focal_crossentropy

Computes the categorical focal crossentropy loss.
metric_huber

Computes Huber loss value.
metric_fbeta_score

Computes F-Beta score.
metric_f1_score

Computes F-1 Score.
metric_log_cosh_error

Computes the logarithm of the hyperbolic cosine of the prediction error.
metric_mean_squared_error

Computes the mean squared error between y_true and y_pred.
metric_mean_absolute_error

Computes the mean absolute error between the labels and predictions.
metric_mean_squared_logarithmic_error

Computes mean squared logarithmic error between y_true and y_pred.
metric_kl_divergence

Computes Kullback-Leibler divergence metric between y_true and
metric_mean_iou

Computes the mean Intersection-Over-Union metric.
metric_mean_absolute_percentage_error

Computes mean absolute percentage error between y_true and y_pred.
metric_log_cosh

Logarithm of the hyperbolic cosine of the prediction error.
metric_mean_wrapper

Wrap a stateless metric function with the Mean metric.
metric_mean

Compute the (weighted) mean of the given values.
metric_sensitivity_at_specificity

Computes best sensitivity where specificity is >= specified value.
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_precision_at_recall

Computes best precision where recall is >= specified value.
metric_precision

Computes the precision of the predictions with respect to the labels.
metric_poisson

Computes the Poisson metric between y_true and y_pred.
metric_one_hot_iou

Computes the Intersection-Over-Union metric for one-hot encoded labels.
metric_recall

Computes the recall of the predictions with respect to the labels.
metric_r2_score

Computes R2 score.
metric_one_hot_mean_iou

Computes mean Intersection-Over-Union metric for one-hot encoded labels.
metric_top_k_categorical_accuracy

Computes how often targets are in the top K predictions.
%<-%

Assign values to names
metric_squared_hinge

Computes the hinge metric between y_true and y_pred.
metric_true_positives

Calculates the number of true positives.
metric_sparse_top_k_categorical_accuracy

Computes how often integer targets are in the top K predictions.
metric_sparse_categorical_accuracy

Calculates how often predictions match integer labels.
metric_true_negatives

Calculates the number of true negatives.
metric_specificity_at_sensitivity

Computes best specificity where sensitivity is >= specified value.
metric_sum

Compute the (weighted) sum of the given values.
metric_sparse_categorical_crossentropy

Computes the crossentropy metric between the labels and predictions.
op_add

Add arguments element-wise.
normalize

Normalizes an array.
new_metric_class

Metric
op_abs

Compute the absolute value element-wise.
op_all

Test whether all array elements along a given axis evaluate to TRUE.
new_layer_class

Layer
new_callback_class

Callback
new_loss_class

Loss
new_model_class

Model
new_learning_rate_schedule_class

LearningRateSchedule