R Interface to 'Keras'

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Documentation for package ‘keras’ version 2.3.0.0

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A B C D E F G H I K L M N O P R S T U W X

keras-package R interface to Keras

-- A --

activation_elu Activation functions
activation_exponential Activation functions
activation_hard_sigmoid Activation functions
activation_linear Activation functions
activation_relu Activation functions
activation_selu Activation functions
activation_sigmoid Activation functions
activation_softmax Activation functions
activation_softplus Activation functions
activation_softsign Activation functions
activation_tanh Activation functions
adapt Fits the state of the preprocessing layer to the data being passed.
application_densenet Instantiates the DenseNet architecture.
application_densenet121 Instantiates the DenseNet architecture.
application_densenet169 Instantiates the DenseNet architecture.
application_densenet201 Instantiates the DenseNet architecture.
application_inception_resnet_v2 Inception-ResNet v2 model, with weights trained on ImageNet
application_inception_v3 Inception V3 model, with weights pre-trained on ImageNet.
application_mobilenet MobileNet model architecture.
application_mobilenet_v2 MobileNetV2 model architecture
application_nasnet Instantiates a NASNet model.
application_nasnetlarge Instantiates a NASNet model.
application_nasnetmobile Instantiates a NASNet model.
application_resnet50 ResNet50 model for Keras.
application_vgg VGG16 and VGG19 models for Keras.
application_vgg16 VGG16 and VGG19 models for Keras.
application_vgg19 VGG16 and VGG19 models for Keras.
application_xception Xception V1 model for Keras.

-- B --

backend Keras backend tensor engine
bidirectional Bidirectional wrapper for RNNs.

-- C --

callback_csv_logger Callback that streams epoch results to a csv file
callback_early_stopping Stop training when a monitored quantity has stopped improving.
callback_lambda Create a custom callback
callback_learning_rate_scheduler Learning rate scheduler.
callback_model_checkpoint Save the model after every epoch.
callback_progbar_logger Callback that prints metrics to stdout.
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_tensorboard TensorBoard basic visualizations
callback_terminate_on_naan Callback that terminates training when a NaN loss is encountered.
clone_model Clone a model instance.
compile.keras.engine.training.Model Configure a Keras model for training
constraints Weight constraints
constraint_maxnorm Weight constraints
constraint_minmaxnorm Weight constraints
constraint_nonneg Weight constraints
constraint_unitnorm Weight constraints
count_params Count the total number of scalars composing the weights.
create_layer Create a Keras Layer
create_wrapper Create a Keras Wrapper
custom_metric Model performance metrics

-- 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
densenet_preprocess_input Instantiates the DenseNet architecture.

-- E --

evaluate.keras.engine.training.Model Evaluate a Keras model
evaluate_generator Evaluates the model on a data generator.
export_savedmodel.keras.engine.training.Model Export a Saved Model

-- F --

fit.keras.engine.training.Model Train a Keras model
fit_generator Fits the model on data yielded batch-by-batch by a generator.
fit_image_data_generator Fit image data generator internal statistics to some sample data.
fit_text_tokenizer Update tokenizer internal vocabulary based on a list of texts or list of sequences.
flow_images_from_data Generates batches of augmented/normalized data from image data and labels
flow_images_from_dataframe Takes the dataframe and the path to a directory and generates batches of augmented/normalized data.
flow_images_from_directory Generates batches of data from images in a directory (with optional augmented/normalized data)
freeze_weights Freeze and unfreeze weights
from_config Layer/Model configuration

-- G --

generator_next Retrieve the next item from a generator
get_config Layer/Model configuration
get_file Downloads a file from a URL if it not already in the cache.
get_input_at Retrieve tensors for layers with multiple nodes
get_input_mask_at Retrieve tensors for layers with multiple nodes
get_input_shape_at Retrieve tensors for layers with multiple nodes
get_layer Retrieves a layer based on either its name (unique) or index.
get_output_at Retrieve tensors for layers with multiple nodes
get_output_mask_at Retrieve tensors for layers with multiple nodes
get_output_shape_at Retrieve tensors for layers with multiple nodes
get_vocabulary Get the vocabulary for text vectorization layers
get_weights Layer/Model weights as R arrays

-- H --

hdf5_matrix Representation of HDF5 dataset to be used instead of an R array

-- I --

imagenet_decode_predictions Decodes the prediction of an ImageNet model.
imagenet_preprocess_input Preprocesses a tensor or array encoding a batch of images.
image_array_resize 3D array representation of images
image_array_save 3D array representation of images
image_data_generator Generate batches of image data with real-time data augmentation. The data will be looped over (in batches).
image_load Loads an image into PIL format.
image_to_array 3D array representation of images
implementation Keras implementation
inception_resnet_v2_preprocess_input Inception-ResNet v2 model, with weights trained on ImageNet
inception_v3_preprocess_input Inception V3 model, with weights pre-trained on ImageNet.
initializer_constant Initializer that generates tensors initialized to a constant value.
initializer_glorot_normal Glorot normal initializer, also called Xavier normal initializer.
initializer_glorot_uniform 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 a random orthogonal matrix.
initializer_random_normal Initializer that generates tensors with a normal distribution.
initializer_random_uniform Initializer that generates tensors with a uniform distribution.
initializer_truncated_normal Initializer that generates a truncated normal distribution.
initializer_variance_scaling Initializer capable of adapting its scale to the shape of weights.
initializer_zeros Initializer that generates tensors initialized to 0.
install_keras Install Keras and the TensorFlow backend
is_keras_available Check if Keras is Available

-- K --

keras R interface to Keras
KerasCallback Base R6 class for Keras callbacks
KerasConstraint Base R6 class for Keras constraints
KerasLayer Base R6 class for Keras layers
KerasWrapper Base R6 class for Keras wrappers
keras_array Keras array object
keras_model Keras Model
keras_model_custom Create a Keras custom model
keras_model_sequential Keras Model composed of a linear stack of layers
k_abs Element-wise absolute value.
k_all Bitwise reduction (logical AND).
k_any Bitwise reduction (logical OR).
k_arange Creates a 1D tensor containing a sequence of integers.
k_argmax Returns the index of the maximum value along an axis.
k_argmin Returns the index of the minimum value along an axis.
k_backend Active Keras backend
k_batch_dot Batchwise dot product.
k_batch_flatten Turn a nD tensor into a 2D tensor with same 1st dimension.
k_batch_get_value Returns the value of more than one tensor variable.
k_batch_normalization Applies batch normalization on x given mean, var, beta and gamma.
k_batch_set_value Sets the values of many tensor variables at once.
k_bias_add Adds a bias vector to a tensor.
k_binary_crossentropy Binary crossentropy between an output tensor and a target tensor.
k_cast Casts a tensor to a different dtype and returns it.
k_cast_to_floatx Cast an array to the default Keras float type.
k_categorical_crossentropy Categorical crossentropy between an output tensor and a target tensor.
k_clear_session Destroys the current TF graph and creates a new one.
k_clip Element-wise value clipping.
k_concatenate Concatenates a list of tensors alongside the specified axis.
k_constant Creates a constant tensor.
k_conv1d 1D convolution.
k_conv2d 2D convolution.
k_conv2d_transpose 2D deconvolution (i.e. transposed convolution).
k_conv3d 3D convolution.
k_conv3d_transpose 3D deconvolution (i.e. transposed convolution).
k_cos Computes cos of x element-wise.
k_count_params Returns the static number of elements in a Keras variable or tensor.
k_ctc_batch_cost Runs CTC loss algorithm on each batch element.
k_ctc_decode Decodes the output of a softmax.
k_ctc_label_dense_to_sparse Converts CTC labels from dense to sparse.
k_cumprod Cumulative product of the values in a tensor, alongside the specified axis.
k_cumsum Cumulative sum of the values in a tensor, alongside the specified axis.
k_depthwise_conv2d Depthwise 2D convolution with separable filters.
k_dot Multiplies 2 tensors (and/or variables) and returns a _tensor_.
k_dropout Sets entries in 'x' to zero at random, while scaling the entire tensor.
k_dtype Returns the dtype of a Keras tensor or variable, as a string.
k_elu Exponential linear unit.
k_epsilon Fuzz factor used in numeric expressions.
k_equal Element-wise equality between two tensors.
k_eval Evaluates the value of a variable.
k_exp Element-wise exponential.
k_expand_dims Adds a 1-sized dimension at index 'axis'.
k_eye Instantiate an identity matrix and returns it.
k_flatten Flatten a tensor.
k_floatx Default float type
k_foldl Reduce elems using fn to combine them from left to right.
k_foldr Reduce elems using fn to combine them from right to left.
k_function Instantiates a Keras function
k_gather Retrieves the elements of indices 'indices' in the tensor 'reference'.
k_get_session TF session to be used by the backend.
k_get_uid Get the uid for the default graph.
k_get_value Returns the value of a variable.
k_get_variable_shape Returns the shape of a variable.
k_gradients Returns the gradients of 'variables' w.r.t. 'loss'.
k_greater Element-wise truth value of (x > y).
k_greater_equal Element-wise truth value of (x >= y).
k_hard_sigmoid Segment-wise linear approximation of sigmoid.
k_identity Returns a tensor with the same content as the input tensor.
k_image_data_format Default image data format convention ('channels_first' or 'channels_last').
k_int_shape Returns the shape of tensor or variable as a list of int or NULL entries.
k_in_test_phase Selects 'x' in test phase, and 'alt' otherwise.
k_in_top_k Returns whether the 'targets' are in the top 'k' 'predictions'.
k_in_train_phase Selects 'x' in train phase, and 'alt' otherwise.
k_is_keras_tensor Returns whether 'x' is a Keras tensor.
k_is_placeholder Returns whether 'x' is a placeholder.
k_is_sparse Returns whether a tensor is a sparse tensor.
k_is_tensor Returns whether 'x' is a symbolic tensor.
k_l2_normalize Normalizes a tensor wrt the L2 norm alongside the specified axis.
k_learning_phase Returns the learning phase flag.
k_less Element-wise truth value of (x < y).
k_less_equal Element-wise truth value of (x <= y).
k_local_conv1d Apply 1D conv with un-shared weights.
k_local_conv2d Apply 2D conv with un-shared weights.
k_log Element-wise log.
k_logsumexp Computes log(sum(exp(elements across dimensions of a tensor))).
k_manual_variable_initialization Sets the manual variable initialization flag.
k_map_fn Map the function fn over the elements elems and return the outputs.
k_max Maximum value in a tensor.
k_maximum Element-wise maximum of two tensors.
k_mean Mean of a tensor, alongside the specified axis.
k_min Minimum value in a tensor.
k_minimum Element-wise minimum of two tensors.
k_moving_average_update Compute the moving average of a variable.
k_ndim Returns the number of axes in a tensor, as an integer.
k_normalize_batch_in_training Computes mean and std for batch then apply batch_normalization on batch.
k_not_equal Element-wise inequality between two tensors.
k_ones Instantiates an all-ones tensor variable and returns it.
k_ones_like Instantiates an all-ones variable of the same shape as another tensor.
k_one_hot Computes the one-hot representation of an integer tensor.
k_permute_dimensions Permutes axes in a tensor.
k_placeholder Instantiates a placeholder tensor and returns it.
k_pool2d 2D Pooling.
k_pool3d 3D Pooling.
k_pow Element-wise exponentiation.
k_print_tensor Prints 'message' and the tensor value when evaluated.
k_prod Multiplies the values in a tensor, alongside the specified axis.
k_random_binomial Returns a tensor with random binomial distribution of values.
k_random_normal Returns a tensor with normal distribution of values.
k_random_normal_variable Instantiates a variable with values drawn from a normal distribution.
k_random_uniform Returns a tensor with uniform distribution of values.
k_random_uniform_variable Instantiates a variable with values drawn from a uniform distribution.
k_relu Rectified linear unit.
k_repeat Repeats a 2D tensor.
k_repeat_elements Repeats the elements of a tensor along an axis.
k_reset_uids Reset graph identifiers.
k_reshape Reshapes a tensor to the specified shape.
k_resize_images Resizes the images contained in a 4D tensor.
k_resize_volumes Resizes the volume contained in a 5D tensor.
k_reverse Reverse a tensor along the specified axes.
k_rnn Iterates over the time dimension of a tensor
k_round Element-wise rounding to the closest integer.
k_separable_conv2d 2D convolution with separable filters.
k_set_epsilon Fuzz factor used in numeric expressions.
k_set_floatx Default float type
k_set_image_data_format Default image data format convention ('channels_first' or 'channels_last').
k_set_learning_phase Sets the learning phase to a fixed value.
k_set_session TF session to be used by the backend.
k_set_value Sets the value of a variable, from an R array.
k_shape Returns the symbolic shape of a tensor or variable.
k_sigmoid Element-wise sigmoid.
k_sign Element-wise sign.
k_sin Computes sin of x element-wise.
k_softmax Softmax of a tensor.
k_softplus Softplus of a tensor.
k_softsign Softsign of a tensor.
k_sparse_categorical_crossentropy Categorical crossentropy with integer targets.
k_spatial_2d_padding Pads the 2nd and 3rd dimensions of a 4D tensor.
k_spatial_3d_padding Pads 5D tensor with zeros along the depth, height, width dimensions.
k_sqrt Element-wise square root.
k_square Element-wise square.
k_squeeze Removes a 1-dimension from the tensor at index 'axis'.
k_stack Stacks a list of rank 'R' tensors into a rank 'R+1' tensor.
k_std Standard deviation of a tensor, alongside the specified axis.
k_stop_gradient Returns 'variables' but with zero gradient w.r.t. every other variable.
k_sum Sum of the values in a tensor, alongside the specified axis.
k_switch Switches between two operations depending on a scalar value.
k_tanh Element-wise tanh.
k_temporal_padding Pads the middle dimension of a 3D tensor.
k_tile Creates a tensor by tiling 'x' by 'n'.
k_to_dense Converts a sparse tensor into a dense tensor and returns it.
k_transpose Transposes a tensor and returns it.
k_truncated_normal Returns a tensor with truncated random normal distribution of values.
k_update Update the value of 'x' to 'new_x'.
k_update_add Update the value of 'x' by adding 'increment'.
k_update_sub Update the value of 'x' by subtracting 'decrement'.
k_var Variance of a tensor, alongside the specified axis.
k_variable Instantiates a variable and returns it.
k_zeros Instantiates an all-zeros variable and returns it.
k_zeros_like Instantiates an all-zeros variable of the same shape as another tensor.

-- L --

layer_activation Apply an activation function to an output.
layer_activation_elu Exponential Linear Unit.
layer_activation_leaky_relu Leaky version of a Rectified Linear Unit.
layer_activation_parametric_relu Parametric Rectified Linear Unit.
layer_activation_relu Rectified Linear Unit activation function
layer_activation_selu Scaled Exponential Linear Unit.
layer_activation_softmax Softmax activation function.
layer_activation_thresholded_relu Thresholded Rectified Linear Unit.
layer_activity_regularization Layer that applies an update to the cost function based input activity.
layer_add Layer that adds a list of inputs.
layer_alpha_dropout Applies Alpha Dropout to the input.
layer_attention Creates attention layer
layer_average Layer that averages a list of inputs.
layer_average_pooling_1d Average pooling for temporal data.
layer_average_pooling_2d Average pooling operation for spatial data.
layer_average_pooling_3d Average pooling operation for 3D data (spatial or spatio-temporal).
layer_batch_normalization Batch normalization layer (Ioffe and Szegedy, 2014).
layer_concatenate Layer that concatenates a list of inputs.
layer_conv_1d 1D convolution layer (e.g. temporal convolution).
layer_conv_2d 2D convolution layer (e.g. spatial convolution over images).
layer_conv_2d_transpose Transposed 2D convolution layer (sometimes called Deconvolution).
layer_conv_3d 3D convolution layer (e.g. spatial convolution over volumes).
layer_conv_3d_transpose Transposed 3D convolution layer (sometimes called Deconvolution).
layer_conv_lstm_2d 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_cudnn_gru Fast GRU implementation backed by CuDNN.
layer_cudnn_lstm Fast LSTM implementation backed by CuDNN.
layer_dense Add a densely-connected NN layer to an output
layer_dense_features Constructs a DenseFeatures.
layer_depthwise_conv_2d Depthwise separable 2D convolution.
layer_dot Layer that computes a dot product between samples in two tensors.
layer_dropout Applies Dropout to the input.
layer_embedding Turns positive integers (indexes) into dense vectors of fixed size.
layer_flatten Flattens an input
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 spatial 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 spatial data.
layer_global_max_pooling_3d Global Max pooling operation for 3D data.
layer_gru Gated Recurrent Unit - Cho et al.
layer_input Input layer
layer_lambda Wraps arbitrary expression as a layer
layer_locally_connected_1d Locally-connected layer for 1D inputs.
layer_locally_connected_2d Locally-connected layer for 2D inputs.
layer_lstm Long Short-Term Memory unit - Hochreiter 1997.
layer_masking Masks a sequence by using a mask value to skip timesteps.
layer_maximum Layer that computes the maximum (element-wise) a list of inputs.
layer_max_pooling_1d Max pooling operation for temporal data.
layer_max_pooling_2d Max pooling operation for spatial data.
layer_max_pooling_3d Max pooling operation for 3D data (spatial or spatio-temporal).
layer_minimum Layer that computes the minimum (element-wise) a list of inputs.
layer_multiply Layer that multiplies (element-wise) a list of inputs.
layer_permute Permute the dimensions of an input according to a given pattern
layer_repeat_vector Repeats the input n times.
layer_reshape Reshapes an output to a certain shape.
layer_separable_conv_1d Depthwise separable 1D convolution.
layer_separable_conv_2d Separable 2D convolution.
layer_simple_rnn Fully-connected RNN where the output is to be fed back to 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_subtract Layer that subtracts two inputs.
layer_text_vectorization Text vectorization 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).
load_model_hdf5 Save/Load models using HDF5 files
load_model_tf Save/Load models using SavedModel format
load_model_weights_hdf5 Save/Load model weights using HDF5 files
load_model_weights_tf Save model weights in the SavedModel format
load_text_tokenizer Save a text tokenizer to an external file
loss_binary_crossentropy Model loss functions
loss_categorical_crossentropy Model loss functions
loss_categorical_hinge Model loss functions
loss_cosine_proximity Model loss functions
loss_cosine_similarity Model loss functions
loss_hinge Model loss functions
loss_kullback_leibler_divergence Model loss functions
loss_logcosh Model loss functions
loss_mean_absolute_error Model loss functions
loss_mean_absolute_percentage_error Model loss functions
loss_mean_squared_error Model loss functions
loss_mean_squared_logarithmic_error Model loss functions
loss_poisson Model loss functions
loss_sparse_categorical_crossentropy Model loss functions
loss_squared_hinge Model loss functions

-- M --

make_sampling_table Generates a word rank-based probabilistic sampling table.
metric_binary_accuracy Model performance metrics
metric_binary_crossentropy Model performance metrics
metric_categorical_accuracy Model performance metrics
metric_categorical_crossentropy Model performance metrics
metric_cosine_proximity Model performance metrics
metric_hinge Model performance metrics
metric_kullback_leibler_divergence Model performance metrics
metric_mean_absolute_error Model performance metrics
metric_mean_absolute_percentage_error Model performance metrics
metric_mean_squared_error Model performance metrics
metric_mean_squared_logarithmic_error Model performance metrics
metric_poisson Model performance metrics
metric_sparse_categorical_crossentropy Model performance metrics
metric_sparse_top_k_categorical_accuracy Model performance metrics
metric_squared_hinge Model performance metrics
metric_top_k_categorical_accuracy Model performance metrics
mobilenet_decode_predictions MobileNet model architecture.
mobilenet_load_model_hdf5 MobileNet model architecture.
mobilenet_preprocess_input MobileNet model architecture.
mobilenet_v2_decode_predictions MobileNetV2 model architecture
mobilenet_v2_load_model_hdf5 MobileNetV2 model architecture
mobilenet_v2_preprocess_input MobileNetV2 model architecture
model_from_json Model configuration as JSON
model_from_saved_model Load a Keras model from the Saved Model format
model_from_yaml Model configuration as YAML
model_to_json Model configuration as JSON
model_to_saved_model Export to Saved Model format
model_to_yaml Model configuration as YAML
multi_gpu_model Replicates a model on different GPUs.

-- N --

nasnet_preprocess_input Instantiates a NASNet model.
normalize Normalize a matrix or nd-array

-- O --

optimizer_adadelta Adadelta optimizer.
optimizer_adagrad Adagrad optimizer.
optimizer_adam Adam optimizer
optimizer_adamax Adamax optimizer
optimizer_nadam Nesterov Adam optimizer
optimizer_rmsprop RMSProp optimizer
optimizer_sgd Stochastic gradient descent optimizer

-- P --

pad_sequences Pads sequences to the same length
plot.keras_training_history Plot training history
pop_layer Remove the last layer in a model
predict.keras.engine.training.Model Generate predictions from a Keras model
predict_classes Generates probability or class probability predictions for the input samples.
predict_generator Generates predictions for the input samples from a data generator.
predict_on_batch Returns predictions for a single batch of samples.
predict_proba Generates probability or class probability predictions for the input samples.

-- R --

regularizer_l1 L1 and L2 regularization
regularizer_l1_l2 L1 and L2 regularization
regularizer_l2 L1 and L2 regularization
reset_states Reset the states for a layer

-- S --

save_model_hdf5 Save/Load models using HDF5 files
save_model_tf Save/Load models using SavedModel format
save_model_weights_hdf5 Save/Load model weights using HDF5 files
save_model_weights_tf Save model weights in the SavedModel format
save_text_tokenizer Save a text tokenizer to an external file
sequences_to_matrix Convert a list of sequences into a matrix.
serialize_model Serialize a model to an R object
set_vocabulary Sets vocabulary (and optionally document frequency) data for the layer
set_weights Layer/Model weights as R arrays
skipgrams Generates skipgram word pairs.
summary.keras.engine.training.Model Print a summary of a Keras model

-- T --

test_on_batch Single gradient update or model evaluation over one batch of samples.
texts_to_matrix Convert a list of texts to a matrix.
texts_to_sequences Transform each text in texts in a sequence of integers.
texts_to_sequences_generator Transforms each text in texts in a sequence of integers.
text_hashing_trick Converts a text to a sequence of indexes in a fixed-size hashing space.
text_one_hot One-hot encode a text into a list of word indexes in a vocabulary of size n.
text_tokenizer Text tokenization utility
text_to_word_sequence Convert text to a sequence of words (or tokens).
timeseries_generator Utility function for generating batches of temporal data.
time_distributed Apply a layer to every temporal slice of an input.
to_categorical Converts a class vector (integers) to binary class matrix.
train_on_batch Single gradient update or model evaluation over one batch of samples.

-- U --

unfreeze_weights Freeze and unfreeze weights
unserialize_model Serialize a model to an R object
use_backend Select a Keras implementation and backend
use_implementation Select a Keras implementation and backend

-- W --

with_custom_object_scope Provide a scope with mappings of names to custom objects

-- X --

xception_preprocess_input Xception V1 model for Keras.