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keras (version 2.11.1)

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

install.packages('keras')

Monthly Downloads

30,713

Version

2.11.1

License

MIT + file LICENSE

Maintainer

Tomasz Kalinowski

Last Published

March 20th, 2023

Functions in keras (2.11.1)

application_densenet

Instantiates the DenseNet architecture.

activation_relu

Activation functions

(Deprecated) Base R6 class for Keras wrappers

application_efficientnet

Instantiates the EfficientNetB0 architecture

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

(Deprecated) Base R6 class for Keras callbacks

KerasConstraint

(Deprecated) Base R6 class for Keras constraints

(Deprecated) Create a custom Layer

(Deprecated) Base R6 class for Keras layers

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_v3

Instantiates the MobileNetV3Large architecture

application_resnet

Instantiates the ResNet architecture

application_mobilenet

MobileNet model architecture.

application_xception

Instantiates the Xception architecture

Keras backend tensor engine

application_vgg

VGG16 and VGG19 models for Keras.

application_mobilenet_v2

MobileNetV2 model architecture

application_nasnet

Instantiates a NASNet model.

Bidirectional wrapper for RNNs

callback_progbar_logger

Callback that prints metrics to stdout.

callback_model_checkpoint

Save the model after every epoch.

callback_early_stopping

Stop training when a monitored quantity has stopped improving.

callback_csv_logger

Callback that streams epoch results to a csv file

callback_reduce_lr_on_plateau

Reduce learning rate when a metric has stopped improving.

callback_learning_rate_scheduler

Learning rate scheduler.

callback_backup_and_restore

Callback to back up and restore the training state

callback_remote_monitor

Callback used to stream events to a server.

callback_lambda

Create a custom callback

Create a Keras Layer

Weight constraints

Count the total number of scalars composing the weights.

callback_tensorboard

TensorBoard basic visualizations

(Deprecated) Create a Keras Wrapper

Custom metric function

compile.keras.engine.training.Model

Configure a Keras model for training

callback_terminate_on_naan

Callback that terminates training when a NaN loss is encountered.

Clone a model instance.

create_layer_wrapper

Create a Keras Layer wrapper

dataset_reuters

Reuters newswire topics classification

evaluate.keras.engine.training.Model

Evaluate a Keras model

export_savedmodel.keras.engine.training.Model

Export a Saved Model

dataset_boston_housing

Boston housing price regression dataset

dataset_cifar100

CIFAR100 small image classification

evaluate_generator

(Deprecated) Evaluates the model on a data generator.

IMDB Movie reviews sentiment classification

dataset_cifar10

CIFAR10 small image classification

MNIST database of handwritten digits

fit.keras.engine.training.Model

Train a Keras model

flow_images_from_directory

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

Retrieve the next item from a generator

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

dataset_fashion_mnist

Fashion-MNIST database of fashion articles

Freeze and unfreeze weights

flow_images_from_data

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

Layer/Model configuration

fit_text_tokenizer

Update tokenizer internal vocabulary based on a list of texts or list of sequences.

fit_image_data_generator

Fit image data generator internal statistics to some sample data.

flow_images_from_dataframe

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

Loads an image into PIL format.

Make an Active Binding

Retrieve tensors for layers with multiple nodes

image_dataset_from_directory

Create a dataset from a directory

Make a python class constructor

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

Layer/Model weights as R arrays

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).

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

initializer_glorot_uniform

Glorot uniform initializer, also called Xavier uniform initializer.

initializer_identity

Initializer that generates the identity matrix.

imagenet_decode_predictions

Decodes the prediction of an ImageNet model.

initializer_constant

Initializer that generates tensors initialized to a constant value.

initializer_glorot_normal

Glorot normal initializer, also called Xavier normal initializer.

imagenet_preprocess_input

Preprocesses a tensor or array encoding a batch of images.

initializer_he_uniform

He uniform variance scaling initializer.

3D array representation of images

Keras implementation

initializer_he_normal

He normal initializer.

initializer_ones

Initializer that generates tensors initialized to 1.

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_random_normal

Initializer that generates tensors with a normal distribution.

initializer_lecun_uniform

LeCun uniform initializer.

initializer_lecun_normal

LeCun normal initializer.

Install TensorFlow and Keras, including all Python dependencies

initializer_zeros

Initializer that generates tensors initialized to 0.

initializer_orthogonal

Initializer that generates a random orthogonal matrix.

initializer_random_uniform

Initializer that generates tensors with a uniform distribution.

Batchwise dot product.

Returns the index of the minimum value along an axis.

Active Keras backend

is_keras_available

Check if Keras is Available

Returns the index of the maximum value along an axis.

Bitwise reduction (logical OR).

Creates a 1D tensor containing a sequence of integers.

Element-wise absolute value.

Bitwise reduction (logical AND).

k_batch_flatten

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

Adds a bias vector to a tensor.

k_batch_set_value

Sets the values of many tensor variables at once.

k_categorical_crossentropy

Categorical crossentropy between an output tensor and a target tensor.

k_binary_crossentropy

Binary crossentropy between an output tensor and a target tensor.

Element-wise value clipping.

k_clear_session

Destroys the current TF graph and creates a new one.

Casts a tensor to a different dtype and returns it.

k_batch_normalization

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

k_cast_to_floatx

Cast an array to the default Keras float type.

k_batch_get_value

Returns the value of more than one tensor variable.

3D convolution.

k_conv2d_transpose

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

Concatenates a list of tensors alongside the specified axis.

1D convolution.

k_conv3d_transpose

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

Computes cos of x element-wise.

Creates a constant tensor.

k_depthwise_conv2d

Depthwise 2D convolution with separable filters.

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

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

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

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

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

k_ctc_batch_cost

Runs CTC loss algorithm on each batch element.

2D convolution.

Element-wise exponential.

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

Element-wise equality between two tensors.

Default float type

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

Fuzz factor used in numeric expressions.

Instantiate an identity matrix and returns it.

Exponential linear unit.

Returns the value of a variable.

Get the uid for the default graph.

Decodes the output of a softmax.

Evaluates the value of a variable.

Adds a 1-sized dimension at index axis.

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

Flatten a tensor.

Instantiates a Keras function

Returns whether x is a symbolic tensor.

TF session to be used by the backend.

Retrieves the elements of indices indices in the tensor reference.

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

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

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

k_get_variable_shape

Returns the shape of a variable.

k_image_data_format

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

k_in_test_phase

Selects x in test phase, and alt otherwise.

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

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

Segment-wise linear approximation of sigmoid.

Returns whether a tensor is a sparse tensor.

k_is_placeholder

Returns whether x is a placeholder.

k_manual_variable_initialization

Sets the manual variable initialization flag.

Element-wise minimum of two tensors.

Minimum value in a tensor.

k_ctc_label_dense_to_sparse

Converts CTC labels from dense to sparse.

Maximum value in a tensor.

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

Instantiates an all-ones tensor variable and returns it.

Element-wise maximum of two tensors.

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

Mean of a tensor, alongside the specified axis.

Apply 1D conv with un-shared weights.

k_random_uniform

Returns a tensor with uniform distribution of values.

Element-wise exponentiation.

k_random_normal_variable

Instantiates a variable with values drawn from a normal distribution.

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

Element-wise inequality between two tensors.

k_normalize_batch_in_training

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

k_random_uniform_variable

Instantiates a variable with values drawn from a uniform distribution.

k_random_binomial

Returns a tensor with random binomial distribution of values.

k_random_normal

Returns a tensor with normal distribution of values.

Iterates over the time dimension of a tensor

Instantiates a placeholder tensor and returns it.

k_greater_equal

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

Reverse a tensor along the specified axes.

k_spatial_3d_padding

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

Returns whether the targets are in the top k predictions.

k_spatial_2d_padding

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

k_in_train_phase

Selects x in train phase, and alt otherwise.

Reset graph identifiers.

Prints message and the tensor value when evaluated.

Reshapes a tensor to the specified shape.

k_separable_conv2d

2D convolution with separable filters.

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

Softsign of a tensor.

Computes the one-hot representation of an integer tensor.

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

Element-wise sign.

Rectified linear unit.

k_learning_phase

Returns the learning phase flag.

Repeats a 2D tensor.

k_set_learning_phase

Sets the learning phase to a fixed value.

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

Element-wise log.

Apply 2D conv with un-shared weights.

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

k_moving_average_update

Compute the moving average of a variable.

k_is_keras_tensor

Returns whether x is a Keras tensor.

Computes sin of x element-wise.

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

Element-wise rounding to the closest integer.

k_stop_gradient

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

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

k_repeat_elements

Repeats the elements of a tensor along an axis.

k_resize_images

Resizes the images contained in a 4D tensor.

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

k_permute_dimensions

Permutes axes in a tensor.

Instantiates an all-zeros variable and returns it.

Switches between two operations depending on a scalar value.

k_resize_volumes

Resizes the volume contained in a 5D tensor.

Element-wise square root.

Returns the symbolic shape of a tensor or variable.

k_sparse_categorical_crossentropy

Categorical crossentropy with integer targets.

layer_activation_selu

Scaled Exponential Linear Unit.

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

layer_activation_relu

Rectified Linear Unit activation function

Variance of a tensor, alongside the specified axis.

Softplus of a tensor.

Softmax of a tensor.

Element-wise sigmoid.

Standard deviation of a tensor, alongside the specified axis.

Element-wise square.

k_truncated_normal

Returns a tensor with truncated random normal distribution of values.

Transposes a tensor and returns it.

layer_activation_elu

Exponential Linear Unit.

Update the value of x by adding increment.

layer_activation

Apply an activation function to an output.

Update the value of x by subtracting decrement.

k_temporal_padding

Pads the middle dimension of a 3D tensor.

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

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

Element-wise tanh.

Update the value of x to new_x.

Creates a tensor by tiling x by n.

Layer that adds a list of inputs.

layer_activity_regularization

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

Instantiates a variable and returns it.

R interface to Keras

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

Main Keras module

keras_model_sequential

Keras Model composed of a linear stack of layers

Keras array object

layer_activation_leaky_relu

Leaky version of a Rectified Linear Unit.

layer_average_pooling_1d

Average pooling for temporal data.

keras_model_custom

(Deprecated) Create a Keras custom model

layer_concatenate

Layer that concatenates a list of inputs.

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

layer_average_pooling_2d

Average pooling operation for spatial data.

layer_batch_normalization

Batch normalization layer (Ioffe and Szegedy, 2014).

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 that averages a list of inputs.

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

layer_conv_2d_transpose

Transposed 2D convolution layer (sometimes called Deconvolution).

layer_activation_softmax

Softmax activation function.

layer_activation_thresholded_relu

Thresholded Rectified Linear Unit.

layer_conv_1d_transpose

Transposed 1D convolution layer (sometimes called Deconvolution).

layer_activation_parametric_relu

Parametric Rectified Linear Unit.

layer_average_pooling_3d

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

layer_category_encoding

A preprocessing layer which encodes integer features.

layer_center_crop

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

layer_cropping_1d

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

layer_cropping_2d

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

layer_conv_3d_transpose

Transposed 3D convolution layer (sometimes called Deconvolution).

Flattens an input

layer_embedding

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

layer_conv_lstm_3d

3D Convolutional LSTM

layer_gaussian_dropout

Apply multiplicative 1-centered Gaussian noise.

A preprocessing layer which hashes and bins categorical features.

layer_conv_lstm_2d

Convolutional LSTM.

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

layer_gaussian_noise

Apply additive zero-centered Gaussian noise.

layer_global_max_pooling_2d

Global max pooling operation for spatial data.

Applies Dropout to the input.

layer_conv_lstm_1d

1D Convolutional LSTM

layer_global_max_pooling_3d

Global Max pooling operation for 3D data.

layer_cropping_3d

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

layer_locally_connected_2d

Locally-connected layer for 2D inputs.

layer_cudnn_gru

(Deprecated) Fast GRU implementation backed by CuDNN.

layer_max_pooling_1d

Max pooling operation for temporal data.

layer_depthwise_conv_2d

Depthwise separable 2D convolution.

layer_discretization

A preprocessing layer which buckets continuous features by ranges.

Cell class for the GRU layer

Gated Recurrent Unit - Cho et al.

layer_depthwise_conv_1d

Depthwise 1D convolution

layer_lstm_cell

Cell class for the LSTM layer

layer_max_pooling_2d

Max pooling operation for spatial data.

layer_global_max_pooling_1d

Global max pooling operation for temporal data.

layer_dense_features

Constructs a DenseFeatures.

layer_random_brightness

A preprocessing layer which randomly adjusts brightness during training

Permute the dimensions of an input according to a given pattern

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

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

layer_max_pooling_3d

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

layer_global_average_pooling_3d

Global Average pooling operation for 3D data.

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

layer_random_rotation

Randomly rotate each image

layer_random_translation

Randomly translate each image during training

layer_global_average_pooling_2d

Global average pooling operation for spatial data.

Add a densely-connected NN layer to an output

layer_cudnn_lstm

(Deprecated) Fast LSTM implementation backed by CuDNN.

Long Short-Term Memory unit - Hochreiter 1997.

layer_layer_normalization

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

layer_locally_connected_1d

Locally-connected layer for 1D inputs.

layer_random_contrast

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

layer_random_crop

Randomly crop the images to target height and width

layer_repeat_vector

Repeats the input n times.

Wraps arbitrary expression as a layer

layer_random_flip

Randomly flip each image horizontally and vertically

layer_normalization

A preprocessing layer which normalizes continuous features.

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

layer_rescaling

Multiply inputs by scale and adds offset

layer_string_lookup

A preprocessing layer which maps string features to integer indices.

layer_stacked_rnn_cells

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

layer_spatial_dropout_1d

Spatial 1D version of Dropout.

layer_simple_rnn_cell

Cell class for SimpleRNN

layer_global_average_pooling_1d

Global average pooling operation for temporal data.

layer_upsampling_2d

Upsampling layer for 2D inputs.

layer_random_height

Randomly vary the height of a batch of images during training

Base class for recurrent layers

Reshapes an output to a certain shape.

Image resizing layer

layer_separable_conv_1d

Depthwise separable 1D convolution.

layer_upsampling_3d

Upsampling layer for 3D inputs.

layer_zero_padding_1d

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

layer_random_width

Randomly vary the width of a batch of images during training

layer_integer_lookup

A preprocessing layer which maps integer features to contiguous ranges.

metric-or-Metric

metric-or-Metric

layer_upsampling_1d

Upsampling layer for 1D inputs.

layer_unit_normalization

Unit normalization layer

loss_cosine_proximity

(Deprecated) loss_cosine_proximity

Layer that subtracts two inputs.

learning_rate_schedule_inverse_time_decay

A LearningRateSchedule that uses an inverse time decay schedule

layer_spatial_dropout_3d

Spatial 3D version of Dropout.

make_sampling_table

Generates a word rank-based probabilistic sampling table.

layer_text_vectorization

A preprocessing layer which maps text features to integer sequences.

layer_multi_head_attention

MultiHeadAttention layer

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

layer_spatial_dropout_2d

Spatial 2D version of Dropout.

layer_separable_conv_2d

Separable 2D convolution.

layer_zero_padding_2d

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

metric_binary_crossentropy

Computes the crossentropy metric between the labels and predictions

learning_rate_schedule_cosine_decay

A LearningRateSchedule that uses a cosine decay schedule

learning_rate_schedule_piecewise_constant_decay

A LearningRateSchedule that uses a piecewise constant decay schedule

layer_zero_padding_3d

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

layer_random_zoom

A preprocessing layer which randomly zooms images during training.

metric_accuracy

Calculates how often predictions equal labels

metric_cosine_proximity

(Deprecated) metric_cosine_proximity

metric_categorical_crossentropy

Computes the crossentropy metric between the labels and predictions

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

layer_simple_rnn

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

metric_categorical_accuracy

Calculates how often predictions match one-hot labels

metric_kullback_leibler_divergence

Computes Kullback-Leibler divergence

metric_logcosh_error

Computes the logarithm of the hyperbolic cosine of the prediction error

Computes the (weighted) mean of the given values

Computes the hinge metric between y_true and y_pred

metric_mean_absolute_percentage_error

Computes the mean absolute percentage error between y_true and y_pred

metric_mean_absolute_error

Computes the mean absolute error between the labels and predictions

metric_false_negatives

Calculates the number of false negatives

learning_rate_schedule_cosine_decay_restarts

A LearningRateSchedule that uses a cosine decay schedule with restarts

metric_mean_squared_error

Computes the mean squared error between labels and predictions

learning_rate_schedule_polynomial_decay

A LearningRateSchedule that uses a polynomial decay schedule

metric_binary_accuracy

Calculates how often predictions match binary labels

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

metric_mean_squared_logarithmic_error

Computes the mean squared logarithmic error

learning_rate_schedule_exponential_decay

A LearningRateSchedule that uses an exponential decay schedule

metric_mean_iou

Computes the mean Intersection-Over-Union metric

metric_mean_relative_error

Computes the mean relative error by normalizing with the given values

Computes the Poisson metric between y_true and y_pred

Computes the recall of the predictions with respect to the labels

metric_precision_at_recall

Computes best precision where recall is >= specified value

metric_false_positives

Calculates the number of false positives

metric_recall_at_precision

Computes best recall where precision is >= specified value

metric_precision

Computes the precision of the predictions with respect to the labels

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

model_from_saved_model

Load a Keras model from the Saved Model format

metric_squared_hinge

Computes the squared hinge metric

metric_specificity_at_sensitivity

Computes best specificity where sensitivity is >= specified value

Computes the (weighted) sum of the given values

metric_top_k_categorical_accuracy

Computes how often targets are in the top K predictions

metric_true_positives

Calculates the number of true positives

metric_true_negatives

Calculates the number of true negatives

Assign values to names

Define new keras types

new_learning_rate_schedule_class

Create a new learning rate schedule type

Model configuration as JSON

multi_gpu_model

(Deprecated) Replicates a model on different GPUs.

Normalize a matrix or nd-array

optimizer_adadelta

Optimizer that implements the Adadelta algorithm

Model configuration as YAML

model_to_saved_model

(Deprecated) Export to Saved Model format

optimizer_adagrad

Optimizer that implements the Adagrad algorithm

metric_mean_wrapper

Wraps a stateless metric function with the Mean metric

metric_sparse_top_k_categorical_accuracy

Computes how often integer targets are in the top K predictions

metric_mean_tensor

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

metric_sparse_categorical_crossentropy

Computes the crossentropy metric between the labels and predictions

Optimizer that implements the Adam algorithm

plot.keras_training_history

Plot training history

plot.keras.engine.training.Model

Plot a Keras model

Remove the last layer in a model

Pads sequences to the same length

predict_generator

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

Optimizer that implements the FTRL algorithm

predict.keras.engine.training.Model

Generate predictions from a Keras model

optimizer_adamax

Optimizer that implements the Adamax algorithm

save_model_hdf5

Save/Load models using HDF5 files

Gradient descent (with momentum) optimizer

Reset the states for a layer

Save/Load models using SavedModel format

optimizer_nadam

Optimizer that implements the Nadam algorithm

regularizer_orthogonal

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

L1 and L2 regularization

Objects exported from other packages

serialize_model

Serialize a model to an R object

sequential_model_input_layer

sequential_model_input_layer

save_model_weights_tf

Save model weights in the SavedModel format

Text tokenization utility

texts_to_sequences

Transform each text in texts in a sequence of integers.

timeseries_dataset_from_array

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

save_model_weights_hdf5

Save/Load model weights using HDF5 files

time_distributed

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

Generates skipgram word pairs.

summary.keras.engine.training.Model

Print a summary of a Keras model

texts_to_matrix

Convert a list of texts to a matrix.

optimizer_rmsprop

Optimizer that implements the RMSprop algorithm

texts_to_sequences_generator

Transforms each text in texts in a sequence of integers.

predict_on_batch

Returns predictions for a single batch of samples.

text_dataset_from_directory

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

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

save_text_tokenizer

Save a text tokenizer to an external file

sequences_to_matrix

Convert a list of sequences into a matrix.

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

use_implementation

Select a Keras implementation and backend

text_to_word_sequence

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

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

text_hashing_trick

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

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

timeseries_generator

Utility function for generating batches of temporal data.

with_custom_object_scope

Provide a scope with mappings of names to custom objects