Learn R Programming

⚠️There's a newer version (2.13.0) of this package.Take me there.

keras (version 2.11.0)

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.

Copy Link

Version

Install

install.packages('keras')

Monthly Downloads

22,418

Version

2.11.0

License

MIT + file LICENSE

Maintainer

Tomasz Kalinowski

Last Published

December 20th, 2022

Functions in keras (2.11.0)

(Deprecated) Base R6 class for Keras wrappers

Fits the state of the preprocessing layer to the data being passed

application_densenet

Instantiates the DenseNet architecture.

application_efficientnet

Instantiates the EfficientNetB0 architecture

activation_relu

Activation functions

(Deprecated) Base R6 class for Keras layers

KerasConstraint

(Deprecated) Base R6 class for Keras constraints

(Deprecated) Create a custom Layer

(Deprecated) Base R6 class for Keras callbacks

application_mobilenet_v3

Instantiates the MobileNetV3Large architecture

application_xception

Instantiates the Xception architecture

Keras backend tensor engine

application_mobilenet

MobileNet model architecture.

application_resnet

Instantiates the ResNet architecture

application_mobilenet_v2

MobileNetV2 model architecture

application_vgg

VGG16 and VGG19 models for Keras.

application_inception_resnet_v2

Inception-ResNet v2 model, with weights trained on ImageNet

application_nasnet

Instantiates a NASNet model.

application_inception_v3

Inception V3 model, with weights pre-trained on ImageNet.

callback_reduce_lr_on_plateau

Reduce learning rate when a metric has stopped improving.

callback_lambda

Create a custom callback

callback_model_checkpoint

Save the model after every epoch.

callback_learning_rate_scheduler

Learning rate scheduler.

Bidirectional wrapper for RNNs

callback_progbar_logger

Callback that prints metrics to stdout.

callback_remote_monitor

Callback used to stream events to a server.

callback_terminate_on_naan

Callback that terminates training when a NaN loss is encountered.

callback_tensorboard

TensorBoard basic visualizations

Custom metric function

Weight constraints

Count the total number of scalars composing the weights.

(Deprecated) Create a Keras Wrapper

Clone a model instance.

dataset_cifar100

CIFAR100 small image classification

callback_backup_and_restore

Callback to back up and restore the training state

compile.keras.engine.training.Model

Configure a Keras model for training

IMDB Movie reviews sentiment classification

MNIST database of handwritten digits

evaluate_generator

(Deprecated) Evaluates the model on a data generator.

Layer/Model configuration

Retrieve the next item from a generator

export_savedmodel.keras.engine.training.Model

Export a Saved Model

callback_csv_logger

Callback that streams epoch results to a csv file

fit.keras.engine.training.Model

Train a Keras model

(Deprecated) Fits the model on data yielded batch-by-batch by a generator.

Retrieve tensors for layers with multiple nodes

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

callback_early_stopping

Stop training when a monitored quantity has stopped improving.

create_layer_wrapper

Create a Keras Layer wrapper

Create a Keras Layer

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.

Make a python class constructor

Make an Active Binding

dataset_fashion_mnist

Fashion-MNIST database of fashion articles

evaluate.keras.engine.training.Model

Evaluate a Keras model

dataset_reuters

Reuters newswire topics classification

initializer_constant

Initializer that generates tensors initialized to a constant value.

initializer_glorot_normal

Glorot normal initializer, also called Xavier normal initializer.

Retrieves a layer based on either its name (unique) or index.

Layer/Model weights as R arrays

imagenet_preprocess_input

Preprocesses a tensor or array encoding a batch of images.

initializer_he_uniform

He uniform variance scaling initializer.

initializer_lecun_uniform

LeCun uniform initializer.

initializer_ones

Initializer that generates tensors initialized to 1.

initializer_identity

Initializer that generates the identity matrix.

Keras implementation

initializer_orthogonal

Initializer that generates a random orthogonal matrix.

flow_images_from_dataframe

Takes the dataframe and the path to a directory and generates batches of augmented/normalized data.

initializer_random_normal

Initializer that generates tensors with a normal distribution.

image_dataset_from_directory

Create a dataset from a directory

initializer_lecun_normal

LeCun normal initializer.

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

image_data_generator

Generate batches of image data with real-time data augmentation. The data will be looped over (in batches).

Bitwise reduction (logical AND).

Bitwise reduction (logical OR).

initializer_random_uniform

Initializer that generates tensors with a uniform distribution.

initializer_glorot_uniform

Glorot uniform initializer, also called Xavier uniform initializer.

flow_images_from_data

Generates batches of augmented/normalized data from image data and labels

Loads an image into PIL format.

k_clear_session

Destroys the current TF graph and creates a new one.

initializer_zeros

Initializer that generates tensors initialized to 0.

initializer_he_normal

He normal initializer.

initializer_truncated_normal

Initializer that generates a truncated normal distribution.

Element-wise value clipping.

k_conv2d_transpose

2D deconvolution (i.e. transposed convolution).

is_keras_available

Check if Keras is Available

initializer_variance_scaling

Initializer capable of adapting its scale to the shape of weights.

Batchwise dot product.

k_batch_flatten

Turn a nD tensor into a 2D tensor with same 1st dimension.

Element-wise absolute value.

k_batch_get_value

Returns the value of more than one tensor variable.

k_categorical_crossentropy

Categorical crossentropy between an output tensor and a target tensor.

k_cast_to_floatx

Cast an array to the default Keras float type.

Returns the index of the minimum value along an axis.

Active Keras backend

Concatenates a list of tensors alongside the specified axis.

Creates a constant tensor.

3D convolution.

k_depthwise_conv2d

Depthwise 2D convolution with separable filters.

Multiplies 2 tensors (and/or variables) and returns a tensor.

Computes cos of x element-wise.

k_conv3d_transpose

3D deconvolution (i.e. transposed convolution).

Default float type

Reduce elems using fn to combine them from left to right.

k_image_data_format

Default image data format convention ('channels_first' or 'channels_last').

Install TensorFlow and Keras, including all Python dependencies

Creates a 1D tensor containing a sequence of integers.

TF session to be used by the backend.

Retrieves the elements of indices indices in the tensor reference.

flow_images_from_directory

Generates batches of data from images in a directory (with optional augmented/normalized data)

dataset_cifar10

CIFAR10 small image classification

k_batch_set_value

Sets the values of many tensor variables at once.

k_in_test_phase

Selects x in test phase, and alt otherwise.

imagenet_decode_predictions

Decodes the prediction of an ImageNet model.

Freeze and unfreeze weights

dataset_boston_housing

Boston housing price regression dataset

3D array representation of images

Returns the static number of elements in a Keras variable or tensor.

Adds a bias vector to a tensor.

Casts a tensor to a different dtype and returns it.

1D convolution.

Fuzz factor used in numeric expressions.

Exponential linear unit.

Returns the index of the maximum value along an axis.

2D convolution.

k_binary_crossentropy

Binary crossentropy between an output tensor and a target tensor.

Sets entries in x to zero at random, while scaling the entire tensor.

k_ctc_batch_cost

Runs CTC loss algorithm on each batch element.

k_is_placeholder

Returns whether x is a placeholder.

Returns whether a tensor is a sparse tensor.

Maximum value in a tensor.

Map the function fn over the elements elems and return the outputs.

Returns the dtype of a Keras tensor or variable, as a string.

k_normalize_batch_in_training

Computes mean and std for batch then apply batch_normalization on batch.

Element-wise inequality between two tensors.

Apply 2D conv with un-shared weights.

Adds a 1-sized dimension at index axis.

Apply 1D conv with un-shared weights.

Element-wise exponential.

Element-wise truth value of (x <= y).

Returns a tensor with the same content as the input tensor.

Segment-wise linear approximation of sigmoid.

Cumulative product of the values in a tensor, alongside the specified axis.

k_batch_normalization

Applies batch normalization on x given mean, var, beta and gamma.

Decodes the output of a softmax.

k_ctc_label_dense_to_sparse

Converts CTC labels from dense to sparse.

Reduce elems using fn to combine them from right to left.

Instantiate an identity matrix and returns it.

Flatten a tensor.

Returns the gradients of variables w.r.t. loss.

Instantiates a Keras function

k_get_variable_shape

Returns the shape of a variable.

Returns whether x is a symbolic tensor.

Element-wise log.

Mean of a tensor, alongside the specified axis.

Instantiates a placeholder tensor and returns it.

Element-wise maximum of two tensors.

Repeats a 2D tensor.

Instantiates an all-ones variable of the same shape as another tensor.

k_permute_dimensions

Permutes axes in a tensor.

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_moving_average_update

Compute the moving average of a variable.

Reverse a tensor along the specified axes.

Element-wise square root.

Element-wise square.

Iterates over the time dimension of a tensor

Returns the number of axes in a tensor, as an integer.

k_set_learning_phase

Sets the learning phase to a fixed value.

Stacks a list of rank R tensors into a rank R+1 tensor.

Sets the value of a variable, from an R array.

Removes a 1-dimension from the tensor at index axis.

Returns whether the targets are in the top k predictions.

Get the uid for the default graph.

Returns the value of a variable.

Cumulative sum of the values in a tensor, alongside the specified axis.

Element-wise truth value of (x > y).

Element-wise equality between two tensors.

Evaluates the value of a variable.

k_greater_equal

Element-wise truth value of (x >= y).

Minimum value in a tensor.

Normalizes a tensor wrt the L2 norm alongside the specified axis.

k_is_keras_tensor

Returns whether x is a Keras tensor.

Returns the shape of tensor or variable as a list of int or NULL entries.

k_repeat_elements

Repeats the elements of a tensor along an axis.

Transposes a tensor and returns it.

Instantiates an all-zeros variable of the same shape as another tensor.

Instantiates an all-zeros variable and returns it.

k_truncated_normal

Returns a tensor with truncated random normal distribution of values.

Returns the symbolic shape of a tensor or variable.

Element-wise sigmoid.

Multiplies the values in a tensor, alongside the specified axis.

Rectified linear unit.

Creates a tensor by tiling x by n.

Element-wise minimum of two tensors.

Converts a sparse tensor into a dense tensor and returns it.

k_random_uniform_variable

Instantiates a variable with values drawn from a uniform distribution.

Prints message and the tensor value when evaluated.

k_manual_variable_initialization

Sets the manual variable initialization flag.

Element-wise exponentiation.

(Deprecated) Computes log(sum(exp(elements across dimensions of a tensor))).

k_in_train_phase

Selects x in train phase, and alt otherwise.

Reset graph identifiers.

k_learning_phase

Returns the learning phase flag.

Softsign of a tensor.

Element-wise truth value of (x < y).

Computes the one-hot representation of an integer tensor.

layer_activation_parametric_relu

Parametric Rectified Linear Unit.

layer_activation_leaky_relu

Leaky version of a Rectified Linear Unit.

Instantiates an all-ones tensor variable and returns it.

k_random_binomial

Returns a tensor with random binomial distribution of values.

k_resize_images

Resizes the images contained in a 4D tensor.

k_resize_volumes

Resizes the volume contained in a 5D tensor.

Softmax of a tensor.

Update the value of x to new_x.

layer_attention

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

k_sparse_categorical_crossentropy

Categorical crossentropy with integer targets.

Computes sin of x element-wise.

k_temporal_padding

Pads the middle dimension of a 3D tensor.

layer_alpha_dropout

Applies Alpha Dropout to the input.

Element-wise sign.

Layer that averages a list of inputs.

k_random_normal

Returns a tensor with normal distribution of values.

Unstack rank R tensor into a list of rank R-1 tensors.

Instantiates a variable and returns it.

Keras array object

Element-wise tanh.

layer_additive_attention

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

Variance of a tensor, alongside the specified axis.

Reshapes a tensor to the specified shape.

Softplus of a tensor.

layer_activation_relu

Rectified Linear Unit activation function

layer_concatenate

Layer that concatenates a list of inputs.

layer_activation_selu

Scaled Exponential Linear Unit.

Sum of the values in a tensor, alongside the specified axis.

Switches between two operations depending on a scalar value.

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

Update the value of x by adding increment.

layer_average_pooling_3d

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

Element-wise rounding to the closest integer.

layer_batch_normalization

Batch normalization layer (Ioffe and Szegedy, 2014).

k_separable_conv2d

2D convolution with separable filters.

k_spatial_3d_padding

Pads 5D tensor with zeros along the depth, height, width dimensions.

k_spatial_2d_padding

Pads the 2nd and 3rd dimensions of a 4D tensor.

3D convolution layer (e.g. spatial convolution over volumes).

keras_model_sequential

Keras Model composed of a linear stack of layers

layer_activation_thresholded_relu

Thresholded Rectified Linear Unit.

keras_model_custom

(Deprecated) Create a Keras custom model

layer_conv_2d_transpose

Transposed 2D convolution layer (sometimes called Deconvolution).

layer_activation_softmax

Softmax activation function.

layer_activation

Apply an activation function to an output.

layer_activation_elu

Exponential Linear Unit.

Update the value of x by subtracting decrement.

layer_activity_regularization

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

Layer that adds a list of inputs.

Standard deviation of a tensor, alongside the specified axis.

Add a densely-connected NN layer to an output

layer_cudnn_lstm

(Deprecated) Fast LSTM implementation backed by CuDNN.

layer_category_encoding

A preprocessing layer which encodes integer features.

layer_cropping_1d

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

R interface to Keras

k_stop_gradient

Returns variables but with zero gradient w.r.t. every other variable.

layer_conv_lstm_2d

Convolutional LSTM.

layer_conv_lstm_3d

3D Convolutional LSTM

layer_center_crop

Crop the central portion of the images to target height and width

layer_cudnn_gru

(Deprecated) Fast GRU implementation backed by CuDNN.

layer_cropping_3d

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

layer_cropping_2d

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

layer_dense_features

Constructs a DenseFeatures.

layer_gaussian_dropout

Apply multiplicative 1-centered Gaussian noise.

Main Keras module

layer_average_pooling_1d

Average pooling for temporal data.

layer_gaussian_noise

Apply additive zero-centered Gaussian noise.

Flattens an input

layer_embedding

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

layer_depthwise_conv_2d

Depthwise separable 2D convolution.

layer_discretization

A preprocessing layer which buckets continuous features by ranges.

layer_average_pooling_2d

Average pooling operation for spatial data.

layer_conv_1d_transpose

Transposed 1D convolution layer (sometimes called Deconvolution).

2D convolution layer (e.g. spatial convolution over images).

layer_conv_3d_transpose

Transposed 3D convolution layer (sometimes called Deconvolution).

layer_depthwise_conv_1d

Depthwise 1D convolution

layer_global_max_pooling_3d

Global Max pooling operation for 3D data.

layer_global_max_pooling_2d

Global max pooling operation for spatial data.

layer_global_average_pooling_3d

Global Average pooling operation for 3D data.

layer_conv_lstm_1d

1D Convolutional LSTM

layer_global_max_pooling_1d

Global max pooling operation for temporal data.

Applies Dropout to the input.

Layer that computes a dot product between samples in two tensors.

layer_global_average_pooling_1d

Global average pooling operation for temporal data.

layer_global_average_pooling_2d

Global average pooling operation for spatial data.

Gated Recurrent Unit - Cho et al.

Cell class for the GRU layer

A preprocessing layer which hashes and bins categorical features.

layer_layer_normalization

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

layer_locally_connected_1d

Locally-connected layer for 1D inputs.

layer_integer_lookup

A preprocessing layer which maps integer features to contiguous ranges.

Wraps arbitrary expression as a layer

layer_locally_connected_2d

Locally-connected layer for 2D inputs.

layer_max_pooling_2d

Max pooling operation for spatial data.

layer_max_pooling_1d

Max pooling operation for temporal data.

Long Short-Term Memory unit - Hochreiter 1997.

Permute the dimensions of an input according to a given pattern

Layer that multiplies (element-wise) a list of inputs.

layer_random_crop

Randomly crop the images to target height and width

layer_lstm_cell

Cell class for the LSTM layer

layer_random_contrast

Adjust the contrast of an image or images by a random factor

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

layer_multi_head_attention

MultiHeadAttention layer

Layer that computes the minimum (element-wise) a list of inputs.

layer_random_brightness

A preprocessing layer which randomly adjusts brightness during training

layer_normalization

A preprocessing layer which normalizes continuous features.

layer_max_pooling_3d

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

Layer that computes the maximum (element-wise) a list of inputs.

layer_random_flip

Randomly flip each image horizontally and vertically

layer_random_zoom

A preprocessing layer which randomly zooms images during training.

layer_random_width

Randomly vary the width of a batch of images during training

layer_random_height

Randomly vary the height of a batch of images during training

Image resizing layer

Reshapes an output to a certain shape.

layer_random_rotation

Randomly rotate each image

layer_random_translation

Randomly translate each image during training

layer_rescaling

Multiply inputs by scale and adds offset

layer_repeat_vector

Repeats the input n times.

layer_simple_rnn

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

layer_separable_conv_2d

Separable 2D convolution.

layer_spatial_dropout_3d

Spatial 3D version of Dropout.

layer_string_lookup

A preprocessing layer which maps string features to integer indices.

layer_simple_rnn_cell

Cell class for SimpleRNN

layer_separable_conv_1d

Depthwise separable 1D convolution.

layer_spatial_dropout_2d

Spatial 2D version of Dropout.

layer_spatial_dropout_1d

Spatial 1D version of Dropout.

layer_stacked_rnn_cells

Wrapper allowing a stack of RNN cells to behave as a single cell

Base class for recurrent layers

layer_zero_padding_1d

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

layer_zero_padding_3d

Zero-padding layer for 3D data (spatial or spatio-temporal).

layer_upsampling_2d

Upsampling layer for 2D inputs.

learning_rate_schedule_cosine_decay

A LearningRateSchedule that uses a cosine decay schedule

layer_upsampling_1d

Upsampling layer for 1D inputs.

layer_text_vectorization

A preprocessing layer which maps text features to integer sequences.

layer_unit_normalization

Unit normalization layer

layer_upsampling_3d

Upsampling layer for 3D inputs.

layer_zero_padding_2d

Zero-padding layer for 2D input (e.g. picture).

Layer that subtracts two inputs.

learning_rate_schedule_inverse_time_decay

A LearningRateSchedule that uses an inverse time decay schedule

metric_accuracy

Calculates how often predictions equal labels

learning_rate_schedule_piecewise_constant_decay

A LearningRateSchedule that uses a piecewise constant decay schedule

loss_cosine_proximity

(Deprecated) loss_cosine_proximity

make_sampling_table

Generates a word rank-based probabilistic sampling table.

learning_rate_schedule_polynomial_decay

A LearningRateSchedule that uses a polynomial decay schedule

learning_rate_schedule_exponential_decay

A LearningRateSchedule that uses an exponential decay schedule

learning_rate_schedule_cosine_decay_restarts

A LearningRateSchedule that uses a cosine decay schedule with restarts

metric-or-Metric

metric-or-Metric

metric_categorical_hinge

Computes the categorical hinge metric between y_true and y_pred

metric_categorical_crossentropy

Computes the crossentropy metric between the labels and predictions

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

metric_cosine_similarity

Computes the cosine similarity between the labels and predictions

metric_false_positives

Calculates the number of false positives

metric_categorical_accuracy

Calculates how often predictions match one-hot labels

metric_binary_accuracy

Calculates how often predictions match binary labels

metric_binary_crossentropy

Computes the crossentropy metric between the labels and predictions

metric_false_negatives

Calculates the number of false negatives

metric_cosine_proximity

(Deprecated) metric_cosine_proximity

metric_mean_relative_error

Computes the mean relative error by normalizing with the given values

metric_mean_iou

Computes the mean Intersection-Over-Union metric

Computes the hinge metric between y_true and y_pred

metric_mean_squared_logarithmic_error

Computes the mean squared logarithmic error

metric_mean_absolute_error

Computes the mean absolute error between the labels and predictions

metric_mean_squared_error

Computes the mean squared error between labels and predictions

metric_mean_absolute_percentage_error

Computes the mean absolute percentage error between y_true and y_pred

metric_logcosh_error

Computes the logarithm of the hyperbolic cosine of the prediction error

Computes the (weighted) mean of the given values

metric_kullback_leibler_divergence

Computes Kullback-Leibler divergence

metric_mean_tensor

Computes the element-wise (weighted) mean of the given tensors

metric_precision_at_recall

Computes best precision where recall 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

Computes the Poisson metric between y_true and y_pred

metric_mean_wrapper

Wraps a stateless metric function with the Mean metric

Computes the recall of the predictions with respect to the labels

metric_sparse_categorical_accuracy

Calculates how often predictions match integer labels

metric_precision

Computes the precision of the predictions with respect to the labels

metric_sensitivity_at_specificity

Computes best sensitivity where specificity is >= specified value

metric_specificity_at_sensitivity

Computes best specificity where sensitivity is >= specified value

Computes the (weighted) sum of the given values

metric_true_negatives

Calculates the number of true negatives

metric_sparse_categorical_crossentropy

Computes the crossentropy metric between the labels and predictions

model_from_saved_model

Load a Keras model from the Saved Model format

metric_squared_hinge

Computes the squared hinge metric

Model configuration as JSON

model_to_saved_model

(Deprecated) Export to Saved Model format

new_learning_rate_schedule_class

Create a new learning rate schedule type

Model configuration as YAML

metric_sparse_top_k_categorical_accuracy

Computes how often integer targets are in the top K predictions

metric_top_k_categorical_accuracy

Computes how often targets are in the top K predictions

Define new keras types

metric_true_positives

Calculates the number of true positives

Optimizer that implements the Adam algorithm

optimizer_adadelta

Optimizer that implements the Adadelta algorithm

multi_gpu_model

(Deprecated) Replicates a model on different GPUs.

optimizer_adagrad

Optimizer that implements the Adagrad algorithm

Normalize a matrix or nd-array

Assign values to names

Pads sequences to the same length

Remove the last layer in a model

optimizer_nadam

Optimizer that implements the Nadam algorithm

plot.keras.engine.training.Model

Plot a Keras model

optimizer_rmsprop

Optimizer that implements the RMSprop algorithm

Gradient descent (with momentum) optimizer

Optimizer that implements the FTRL algorithm

optimizer_adamax

Optimizer that implements the Adamax algorithm

plot.keras_training_history

Plot training history

L1 and L2 regularization

save_model_hdf5

Save/Load models using HDF5 files

(Deprecated) Generates probability or class probability predictions for the input samples.

predict.keras.engine.training.Model

Generate predictions from a Keras model

Save/Load models using SavedModel format

Reset the states for a layer

regularizer_orthogonal

A regularizer that encourages input vectors to be orthogonal to each other

predict_on_batch

Returns predictions for a single batch of samples.

Objects exported from other packages

predict_generator

(Deprecated) Generates predictions for the input samples from a data generator.

save_text_tokenizer

Save a text tokenizer to an external file

text_dataset_from_directory

Generate a tf.data.Dataset from text files in a directory

Generates skipgram word pairs.

summary.keras.engine.training.Model

Print a summary of a Keras model

sequential_model_input_layer

sequential_model_input_layer

serialize_model

Serialize a model to an R object

sequences_to_matrix

Convert a list of sequences into a matrix.

save_model_weights_tf

Save model weights in the SavedModel format

save_model_weights_hdf5

Save/Load model weights using HDF5 files

text_hashing_trick

Converts a text to a sequence of indexes in a fixed-size hashing space.

timeseries_dataset_from_array

Creates a dataset of sliding windows over a timeseries provided as array

time_distributed

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

Text tokenization utility

Converts a class vector (integers) to binary class matrix.

timeseries_generator

Utility function for generating batches of temporal data.

One-hot encode a text into a list of word indexes in a vocabulary of size n.

texts_to_matrix

Convert a list of texts to a matrix.

texts_to_sequences_generator

Transforms each text in texts in a sequence of integers.

text_to_word_sequence

Convert text to a sequence of words (or tokens).

texts_to_sequences

Transform each text in texts in a sequence of integers.

Single gradient update or model evaluation over one batch of samples.

with_custom_object_scope

Provide a scope with mappings of names to custom objects

use_implementation

Select a Keras implementation and backend