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keras (version 2.9.0)

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('keras')

Monthly Downloads

17,691

Version

2.9.0

License

MIT + file LICENSE

Maintainer

Tomasz Kalinowski

Last Published

May 23rd, 2022

Functions in keras (2.9.0)

(Deprecated) Base R6 class for Keras wrappers

(Deprecated) Base R6 class for Keras layers

application_inception_resnet_v2

Inception-ResNet v2 model, with weights trained on ImageNet

application_vgg

VGG16 and VGG19 models for Keras.

application_resnet

Instantiates the ResNet architecture

application_inception_v3

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

fit.keras.engine.training.Model

Train a Keras model

callback_tensorboard

TensorBoard basic visualizations

callback_remote_monitor

Callback used to stream events to a server.

export_savedmodel.keras.engine.training.Model

Export a Saved Model

callback_csv_logger

Callback that streams epoch results to a csv file

Bidirectional wrapper for RNNs

Custom metric function

callback_early_stopping

Stop training when a monitored quantity has stopped improving.

create_layer_wrapper

Create a Keras Layer wrapper

dataset_boston_housing

Boston housing price regression dataset

callback_lambda

Create a custom callback

flow_images_from_dataframe

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

(Deprecated) Create a Keras Wrapper

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

Make an Active Binding

Layer/Model configuration

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

Loads an image into PIL format.

3D array representation of images

flow_images_from_directory

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

Install TensorFlow and Keras, including all Python dependencies

is_keras_available

Check if Keras is Available

Bitwise reduction (logical OR).

Creates a 1D tensor containing a sequence of integers.

dataset_cifar10

CIFAR10 small image classification

dataset_cifar100

CIFAR100 small image classification

initializer_identity

Initializer that generates the identity matrix.

initializer_ones

Initializer that generates tensors initialized to 1.

initializer_lecun_normal

LeCun normal initializer.

fit_text_tokenizer

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

Element-wise absolute value.

initializer_lecun_uniform

LeCun uniform initializer.

Bitwise reduction (logical AND).

flow_images_from_data

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

Exponential linear unit.

Concatenates a list of tensors alongside the specified axis.

Element-wise value clipping.

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

application_nasnet

Instantiates a NASNet model.

callback_model_checkpoint

Save the model after every epoch.

callback_learning_rate_scheduler

Learning rate scheduler.

application_mobilenet_v3

Instantiates the MobileNetV3Large architecture

Adds a 1-sized dimension at index axis.

Computes cos of x element-wise.

Instantiate an identity matrix and returns it.

Returns the value of a variable.

Layer/Model weights as R arrays

Create a Keras Layer

dataset_reuters

Reuters newswire topics classification

MNIST database of handwritten digits

Count the total number of scalars composing the weights.

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

initializer_glorot_normal

Glorot normal initializer, also called Xavier normal initializer.

Make a python class constructor

initializer_glorot_uniform

Glorot uniform initializer, also called Xavier uniform initializer.

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

initializer_zeros

Initializer that generates tensors initialized to 0.

initializer_variance_scaling

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

k_ctc_label_dense_to_sparse

Converts CTC labels from dense to sparse.

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

k_get_variable_shape

Returns the shape of a variable.

fit_image_data_generator

Fit image data generator internal statistics to some sample data.

Batchwise dot product.

Active Keras backend

imagenet_preprocess_input

Preprocesses a tensor or array encoding a batch of images.

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

Retrieve tensors for layers with multiple nodes

imagenet_decode_predictions

Decodes the prediction of an ImageNet model.

initializer_random_uniform

Initializer that generates tensors with a uniform distribution.

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

k_learning_phase

Returns the learning phase flag.

Element-wise exponential.

Evaluates the value of a variable.

TF session to be used by the backend.

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

application_mobilenet_v2

MobileNetV2 model architecture

callback_reduce_lr_on_plateau

Reduce learning rate when a metric has stopped improving.

application_mobilenet

MobileNet model architecture.

activation_relu

Activation functions

Adds a bias vector to a tensor.

callback_progbar_logger

Callback that prints metrics to stdout.

Get the uid for the default graph.

initializer_truncated_normal

Initializer that generates a truncated normal distribution.

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

Apply 2D conv with un-shared weights.

Apply 1D conv with un-shared weights.

Instantiates an all-ones tensor variable and returns it.

k_batch_normalization

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

Returns the index of the maximum value along an axis.

Returns the index of the minimum value along an axis.

Computes sin of x element-wise.

k_sparse_categorical_crossentropy

Categorical crossentropy with integer targets.

Softmax of a tensor.

k_batch_set_value

Sets the values of many tensor variables at once.

Creates a constant tensor.

k_in_test_phase

Selects x in test phase, and alt otherwise.

k_binary_crossentropy

Binary crossentropy between an output tensor and a target tensor.

Weight constraints

IMDB Movie reviews sentiment classification

dataset_fashion_mnist

Fashion-MNIST database of fashion articles

initializer_he_normal

He normal initializer.

compile.keras.engine.training.Model

Configure a Keras model for training

Fuzz factor used in numeric expressions.

1D convolution.

Returns whether the targets are in the top k predictions.

3D convolution.

Element-wise log.

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

initializer_he_uniform

He uniform variance scaling initializer.

Element-wise equality between two tensors.

layer_activation_elu

Exponential Linear Unit.

Switches between two operations depending on a scalar value.

Element-wise tanh.

k_spatial_2d_padding

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

layer_attention

Creates attention layer

layer_activation_leaky_relu

Leaky version of a Rectified Linear Unit.

layer_alpha_dropout

Applies Alpha Dropout to the input.

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

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

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

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

layer_conv_3d_transpose

Transposed 3D convolution layer (sometimes called Deconvolution).

k_moving_average_update

Compute the moving average of a variable.

Repeats a 2D tensor.

Element-wise sigmoid.

Rectified linear unit.

Element-wise minimum of two tensors.

initializer_orthogonal

Initializer that generates a random orthogonal matrix.

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

k_conv3d_transpose

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

Casts a tensor to a different dtype and returns it.

initializer_random_normal

Initializer that generates tensors with a normal distribution.

Element-wise sign.

k_in_train_phase

Selects x in train phase, and alt otherwise.

k_manual_variable_initialization

Sets the manual variable initialization flag.

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

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

k_depthwise_conv2d

Depthwise 2D convolution with separable filters.

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

Softsign of a tensor.

Softplus of a tensor.

Returns whether a tensor is a sparse tensor.

k_image_data_format

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

k_cast_to_floatx

Cast an array to the default Keras float type.

Element-wise inequality between two tensors.

Computes the one-hot representation of an integer tensor.

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

k_conv2d_transpose

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

2D convolution.

Flatten a tensor.

application_efficientnet

Instantiates the EfficientNetB0 architecture

application_densenet

Instantiates the DenseNet architecture.

application_xception

Instantiates the Xception architecture

layer_cudnn_gru

(Deprecated) Fast GRU implementation backed by CuDNN.

Keras backend tensor engine

Instantiates a placeholder tensor and returns it.

k_permute_dimensions

Permutes axes in a tensor.

k_truncated_normal

Returns a tensor with truncated random normal distribution of values.

Add a densely-connected NN layer to an output

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

callback_terminate_on_naan

Callback that terminates training when a NaN loss is encountered.

k_repeat_elements

Repeats the elements of a tensor along an axis.

Reset graph identifiers.

layer_conv_lstm_1d

1D Convolutional LSTM

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

layer_category_encoding

A preprocessing layer which encodes integer features.

Instantiates a variable and returns it.

Clone a model instance.

evaluate_generator

(Deprecated) Evaluates the model on a data generator.

evaluate.keras.engine.training.Model

Evaluate a Keras model

Freeze and unfreeze weights

Retrieve the next item from a generator

Returns the symbolic shape of a tensor or variable.

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

k_random_normal_variable

Instantiates a variable with values drawn from a normal distribution.

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

Returns whether x is a symbolic tensor.

Default float type

k_greater_equal

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

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

keras_model_sequential

Keras Model composed of a linear stack of layers

layer_cudnn_lstm

(Deprecated) Fast LSTM implementation backed by CuDNN.

Segment-wise linear approximation of sigmoid.

Instantiates an all-zeros variable and returns it.

layer_global_average_pooling_1d

Global average pooling operation for temporal data.

layer_activation_selu

Scaled Exponential Linear Unit.

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

layer_global_max_pooling_1d

Global max pooling operation for temporal data.

layer_discretization

A preprocessing layer which buckets continuous features by ranges.

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.

Standard deviation of a tensor, alongside the specified axis.

Mean of a tensor, alongside the specified axis.

k_normalize_batch_in_training

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

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

Minimum value in a tensor.

image_dataset_from_directory

Create a dataset from a directory

Keras implementation

image_data_generator

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

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

layer_global_max_pooling_2d

Global max pooling operation for spatial data.

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

layer_max_pooling_1d

Max pooling operation for temporal data.

layer_multi_head_attention

MultiHeadAttention layer

R interface to Keras

k_random_normal

Returns a tensor with normal distribution of values.

k_resize_images

Resizes the images contained in a 4D tensor.

Reshapes a tensor to the specified shape.

Update the value of x by subtracting decrement.

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

Cell class for the GRU layer

Prints message and the tensor value when evaluated.

Element-wise exponentiation.

Variance of a tensor, alongside the specified axis.

layer_activation

Apply an activation function to an output.

layer_batch_normalization

Batch normalization layer (Ioffe and Szegedy, 2014).

Image resizing layer

Base class for recurrent layers

layer_activation_thresholded_relu

Thresholded Rectified Linear Unit.

Reverse a tensor along the specified axes.

k_resize_volumes

Resizes the volume contained in a 5D tensor.

initializer_constant

Initializer that generates tensors initialized to a constant value.

k_set_learning_phase

Sets the learning phase to a fixed value.

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

k_separable_conv2d

2D convolution with separable filters.

Element-wise square.

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.

layer_spatial_dropout_3d

Spatial 3D version of Dropout.

layer_lstm_cell

Cell class for the LSTM layer

layer_activation_softmax

Softmax activation function.

layer_activity_regularization

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

Layer that adds a list of inputs.

layer_additive_attention

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

keras_model_custom

(Deprecated) Create a Keras custom model

layer_conv_lstm_2d

Convolutional LSTM.

layer_random_flip

Randomly flip each image horizontally and vertically

layer_random_height

Randomly vary the height of a batch of images during training

k_clear_session

Destroys the current TF graph and creates a new one.

k_categorical_crossentropy

Categorical crossentropy between an output tensor and a target tensor.

k_ctc_batch_cost

Runs CTC loss algorithm on each batch element.

k_temporal_padding

Pads the middle dimension of a 3D tensor.

layer_random_crop

Randomly crop the images to target height and width

layer_locally_connected_1d

Locally-connected layer for 1D inputs.

layer_locally_connected_2d

Locally-connected layer for 2D inputs.

Applies Dropout to the input.

layer_simple_rnn_cell

Cell class for SimpleRNN

layer_simple_rnn

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

learning_rate_schedule_cosine_decay

A LearningRateSchedule that uses a cosine decay schedule

Main Keras module

Keras array object

Creates a tensor by tiling x by n.

layer_activation_parametric_relu

Parametric Rectified Linear Unit.

layer_average_pooling_2d

Average pooling operation for spatial data.

layer_activation_relu

Rectified Linear Unit activation function

learning_rate_schedule_cosine_decay_restarts

A LearningRateSchedule that uses a cosine decay schedule with restarts

learning_rate_schedule_inverse_time_decay

A LearningRateSchedule that uses an inverse time decay schedule

learning_rate_schedule_exponential_decay

A LearningRateSchedule that uses an exponential decay schedule

layer_stacked_rnn_cells

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

layer_concatenate

Layer that concatenates a list of inputs.

layer_dense_features

Constructs a DenseFeatures.

layer_gaussian_noise

Apply additive zero-centered Gaussian noise.

layer_center_crop

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

layer_average_pooling_3d

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

layer_embedding

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

Gated Recurrent Unit - Cho et al.

layer_global_max_pooling_3d

Global Max pooling operation for 3D data.

Decodes the output of a softmax.

layer_random_brightness

A preprocessing layer which randomly adjusts brightness during training

Computes the hinge metric between y_true and y_pred

metric_false_positives

Calculates the number of false positives

A preprocessing layer which hashes and bins categorical features.

Wraps arbitrary expression as a layer

layer_string_lookup

A preprocessing layer which maps string features to integer indices.

layer_unit_normalization

Unit normalization layer

layer_layer_normalization

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

Layer that subtracts two inputs.

layer_random_contrast

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

layer_rescaling

Multiply inputs by scale and adds offset

layer_random_zoom

A preprocessing layer which randomly zooms images during training.

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

Long Short-Term Memory unit - Hochreiter 1997.

metric_logcosh_error

Computes the logarithm of the hyperbolic cosine of the prediction error

metric_kullback_leibler_divergence

Computes Kullback-Leibler divergence

Retrieves the elements of indices indices in the tensor reference.

Element-wise maximum of two tensors.

k_is_placeholder

Returns whether x is a placeholder.

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

Instantiates a Keras function

Maximum value in a tensor.

k_is_keras_tensor

Returns whether x is a Keras tensor.

make_sampling_table

Generates a word rank-based probabilistic sampling table.

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

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

Reshapes an output to a certain shape.

layer_text_vectorization

A preprocessing layer which maps text features to integer sequences.

metric_sparse_categorical_accuracy

Calculates how often predictions match integer labels

metric_sparse_categorical_crossentropy

Computes the crossentropy metric between the labels and predictions

metric_binary_accuracy

Calculates how often predictions match binary labels

Computes the recall of the predictions with respect to the labels

metric-or-Metric

metric-or-Metric

metric_binary_crossentropy

Computes the crossentropy metric between the labels and predictions

layer_repeat_vector

Repeats the input n times.

metric_mean_absolute_percentage_error

Computes the mean absolute percentage error between y_true and y_pred

metric_categorical_hinge

Computes the categorical hinge metric between y_true and y_pred

layer_random_rotation

Randomly rotate each image

layer_separable_conv_2d

Separable 2D convolution.

Computes the (weighted) mean of the given values

k_random_binomial

Returns a tensor with random binomial distribution of values.

metric_mean_absolute_error

Computes the mean absolute error between the labels and predictions

metric_true_negatives

Calculates the number of true negatives

metric_recall_at_precision

Computes best recall where precision is >= specified value

metric_squared_hinge

Computes the squared hinge metric

layer_conv_2d_transpose

Transposed 2D convolution layer (sometimes called Deconvolution).

Stochastic gradient descent optimizer

metric_top_k_categorical_accuracy

Computes how often targets are in the top K predictions

Pads sequences to the same length

optimizer_adadelta

Adadelta optimizer.

Computes the (weighted) sum of the given values

optimizer_adagrad

Adagrad optimizer.

layer_separable_conv_1d

Depthwise separable 1D convolution.

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

metric_categorical_accuracy

Calculates how often predictions match one-hot labels

Model configuration as JSON

regularizer_orthogonal

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

Reset the states for a layer

layer_upsampling_1d

Upsampling layer for 1D inputs.

loss_cosine_proximity

(Deprecated) loss_cosine_proximity

save_model_weights_hdf5

Save/Load model weights using HDF5 files

save_text_tokenizer

Save a text tokenizer to an external file

save_model_weights_tf

Save model weights in the SavedModel format

sequences_to_matrix

Convert a list of sequences into a matrix.

Flattens an input

layer_cropping_3d

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

learning_rate_schedule_piecewise_constant_decay

A LearningRateSchedule that uses a piecewise constant decay schedule

layer_gaussian_dropout

Apply multiplicative 1-centered Gaussian noise.

layer_cropping_2d

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

layer_global_average_pooling_3d

Global Average pooling operation for 3D data.

layer_upsampling_2d

Upsampling layer for 2D inputs.

layer_global_average_pooling_2d

Global average pooling operation for spatial data.

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.

metric_mean_tensor

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

metric_true_positives

Calculates the number of true positives

metric_cosine_proximity

(Deprecated) metric_cosine_proximity

metric_mean_iou

Computes the mean Intersection-Over-Union metric

new_learning_rate_schedule_class

Create a new learning rate schedule type

Normalize a matrix or nd-array

model_from_saved_model

Load a Keras model from the Saved Model format

layer_max_pooling_2d

Max pooling operation for spatial data.

metric_sparse_top_k_categorical_accuracy

Computes how often integer targets are in the top K predictions

layer_max_pooling_3d

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

metric_accuracy

Calculates how often predictions equal labels

Iterates over the time dimension of a tensor

metric_mean_relative_error

Computes the mean relative error by normalizing with the given values

metric_categorical_crossentropy

Computes the crossentropy metric between the labels and predictions

metric_mean_squared_error

Computes the mean squared error between labels and predictions

predict.keras.engine.training.Model

Generate predictions from a Keras model

text_dataset_from_directory

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

text_hashing_trick

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

predict_generator

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

metric_root_mean_squared_error

Computes root mean squared error metric between y_true and y_pred

Save/Load models using SavedModel format

layer_normalization

A preprocessing layer which normalizes continuous features.

Element-wise rounding to the closest integer.

time_distributed

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

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

timeseries_generator

Utility function for generating batches of temporal data.

timeseries_dataset_from_array

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

text_to_word_sequence

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

k_spatial_3d_padding

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

layer_conv_1d_transpose

Transposed 1D convolution layer (sometimes called Deconvolution).

Element-wise square root.

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

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

metric_sensitivity_at_specificity

Computes best sensitivity where specificity is >= specified value

model_to_saved_model

(Deprecated) Export to Saved Model format

Model configuration as YAML

Assign values to names

layer_zero_padding_3d

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

Transposes a tensor and returns it.

Layer that averages a list of inputs.

Update the value of x to new_x.

Update the value of x by adding increment.

layer_average_pooling_1d

Average pooling for temporal data.

multi_gpu_model

(Deprecated) Replicates a model on different GPUs.

layer_cropping_1d

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

optimizer_nadam

Nesterov Adam optimizer

optimizer_rmsprop

RMSProp optimizer

learning_rate_schedule_polynomial_decay

A LearningRateSchedule that uses a polynomial decay schedule

Permute the dimensions of an input according to a given pattern

layer_zero_padding_2d

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

save_model_hdf5

Save/Load models using HDF5 files

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

serialize_model

Serialize a model to an R object

sequential_model_input_layer

sequential_model_input_layer

use_implementation

Select a Keras implementation and backend

Computes the Poisson metric between y_true and y_pred

metric_specificity_at_sensitivity

Computes best specificity where sensitivity is >= specified value

metric_cosine_similarity

Computes the cosine similarity between the labels and predictions

metric_false_negatives

Calculates the number of false negatives

metric_precision

Computes the precision of the predictions with respect to the labels

layer_integer_lookup

A preprocessing layer which maps integer features to contiguous ranges.

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

layer_conv_lstm_3d

3D Convolutional LSTM

layer_depthwise_conv_2d

Depthwise separable 2D convolution.

layer_spatial_dropout_2d

Spatial 2D version of Dropout.

Text tokenization utility

metric_mean_wrapper

Wraps a stateless metric function with the Mean metric

metric_mean_squared_logarithmic_error

Computes the mean squared logarithmic error

plot.keras_training_history

Plot training history

optimizer_adamax

Adamax optimizer

texts_to_matrix

Convert a list of texts to a matrix.

predict_on_batch

Returns predictions for a single batch of samples.

Remove the last layer in a model

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

layer_depthwise_conv_1d

Depthwise 1D convolution

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

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

layer_random_translation

Randomly translate each image during training

with_custom_object_scope

Provide a scope with mappings of names to custom objects

layer_spatial_dropout_1d

Spatial 1D version of Dropout.

layer_upsampling_3d

Upsampling layer for 3D inputs.

layer_zero_padding_1d

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

texts_to_sequences

Transform each text in texts in a sequence of integers.

layer_random_width

Randomly vary the width of a batch of images during training

summary.keras.engine.training.Model

Print a summary of a Keras model

Define new keras types

metric_precision_at_recall

Computes best precision where recall is >= specified value

Objects exported from other packages

plot.keras.engine.training.Model

Plot a Keras model

Generates skipgram word pairs.

L1 and L2 regularization

texts_to_sequences_generator

Transforms each text in texts in a sequence of integers.

KerasConstraint

(Deprecated) Base R6 class for Keras constraints

(Deprecated) Base R6 class for Keras callbacks

(Deprecated) Create a custom Layer