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keras (version 2.7.0)

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

2.7.0

License

MIT + file LICENSE

Maintainer

Tomasz Kalinowski

Last Published

November 9th, 2021

Functions in keras (2.7.0)

(Deprecated) Base R6 class for Keras wrappers

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

KerasConstraint

(Deprecated) Base R6 class for Keras constraints

(Deprecated) Base R6 class for Keras layers

(Deprecated) Base R6 class for Keras callbacks

activation_relu

Activation functions

Create a custom Layer

application_densenet

Instantiates the DenseNet architecture.

application_efficientnet

Instantiates the EfficientNetB0 architecture

application_mobilenet

MobileNet model architecture.

application_mobilenet_v3

Instantiates the MobileNetV3Large architecture

compile.keras.engine.training.Model

Configure a Keras model for training

application_mobilenet_v2

MobileNetV2 model architecture

Weight constraints

application_inception_v3

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

callback_remote_monitor

Callback used to stream events to a server.

Count the total number of scalars composing the weights.

callback_tensorboard

TensorBoard basic visualizations

application_inception_resnet_v2

Inception-ResNet v2 model, with weights trained on ImageNet

application_resnet

Instantiates the ResNet architecture

application_nasnet

Instantiates a NASNet model.

Bidirectional wrapper for RNNs

Create a Keras Layer

dataset_cifar10

CIFAR10 small image classification

application_xception

Instantiates the Xception architecture

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.

dataset_cifar100

CIFAR100 small image classification

Freeze and unfreeze weights

Retrieve the next item from a generator

Retrieve tensors for layers with multiple nodes

Layer/Model weights as R arrays

callback_progbar_logger

Callback that prints metrics to stdout.

callback_reduce_lr_on_plateau

Reduce learning rate when a metric has stopped improving.

Keras backend tensor engine

callback_model_checkpoint

Save the model after every epoch.

callback_learning_rate_scheduler

Learning rate scheduler.

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

create_layer_wrapper

Create a Keras Layer wrapper

fit.keras.engine.training.Model

Train a Keras model

(Deprecated) Create a Keras Wrapper

export_savedmodel.keras.engine.training.Model

Export a Saved Model

callback_csv_logger

Callback that streams epoch results to a csv file

image_data_generator

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

Custom metric function

callback_early_stopping

Stop training when a monitored quantity has stopped improving.

callback_lambda

Create a custom callback

application_vgg

VGG16 and VGG19 models for Keras.

dataset_boston_housing

Boston housing price regression dataset

Install TensorFlow and Keras, including all Python dependencies

image_dataset_from_directory

Create a dataset from a directory

Element-wise absolute value.

is_keras_available

Check if Keras is Available

Make a python class constructor

initializer_he_normal

He normal initializer.

initializer_he_uniform

He uniform variance scaling initializer.

callback_terminate_on_naan

Callback that terminates training when a NaN loss is encountered.

evaluate.keras.engine.training.Model

Evaluate a Keras model

Clone a model instance.

initializer_orthogonal

Initializer that generates a random orthogonal matrix.

evaluate_generator

Evaluates the model on a data generator.

initializer_glorot_uniform

Glorot uniform initializer, also called Xavier uniform initializer.

initializer_glorot_normal

Glorot normal initializer, also called Xavier normal initializer.

dataset_fashion_mnist

Fashion-MNIST database of fashion articles

fit_text_tokenizer

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

IMDB Movie reviews sentiment classification

initializer_random_normal

Initializer that generates tensors with a 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.

dataset_reuters

Reuters newswire topics classification

MNIST database of handwritten digits

Bitwise reduction (logical OR).

flow_images_from_dataframe

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

Layer/Model configuration

flow_images_from_directory

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

imagenet_decode_predictions

Decodes the prediction of an ImageNet model.

imagenet_preprocess_input

Preprocesses a tensor or array encoding a batch of images.

3D array representation of images

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

Loads an image into PIL format.

initializer_identity

Initializer that generates the identity matrix.

Returns the index of the maximum value along an axis.

initializer_lecun_normal

LeCun normal initializer.

flow_images_from_data

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

initializer_lecun_uniform

LeCun uniform initializer.

Make an Active Binding

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

Active Keras backend

k_batch_get_value

Returns the value of more than one tensor variable.

initializer_ones

Initializer that generates tensors initialized to 1.

k_batch_flatten

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

Keras implementation

3D convolution.

Batchwise dot product.

Adds a bias vector to a tensor.

Creates a 1D tensor containing a sequence of integers.

k_binary_crossentropy

Binary crossentropy between an output tensor and a target tensor.

initializer_constant

Initializer that generates tensors initialized to a constant value.

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.

Returns the index of the minimum value along an axis.

initializer_random_uniform

Initializer that generates tensors with a uniform distribution.

initializer_truncated_normal

Initializer that generates a truncated normal distribution.

k_ctc_batch_cost

Runs CTC loss algorithm on each batch element.

Bitwise reduction (logical AND).

Casts a tensor to a different dtype and returns it.

k_cast_to_floatx

Cast an array to the default Keras float type.

Concatenates a list of tensors alongside the specified axis.

1D convolution.

Element-wise value clipping.

Creates a constant tensor.

k_conv3d_transpose

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

k_ctc_label_dense_to_sparse

Converts CTC labels from dense to sparse.

Decodes the output of a softmax.

Computes cos of x element-wise.

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

k_batch_normalization

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

Instantiates a Keras function

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

k_batch_set_value

Sets the values of many tensor variables at once.

Flatten a tensor.

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

Default float type

Retrieves the elements of indices indices in the tensor reference.

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

2D convolution.

Fuzz factor used in numeric expressions.

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

k_depthwise_conv2d

Depthwise 2D convolution with separable filters.

Evaluates the value of a variable.

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

Element-wise exponential.

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

Reset graph identifiers.

Instantiates an all-ones tensor variable and returns it.

k_image_data_format

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

k_repeat_elements

Repeats the elements of a tensor along an axis.

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

Computes sin of x element-wise.

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

k_conv2d_transpose

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

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

TF session to be used by the backend.

Exponential linear unit.

k_is_keras_tensor

Returns whether x is a Keras tensor.

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

Maximum value in a tensor.

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

Get the uid for the default graph.

k_is_placeholder

Returns whether x is a placeholder.

Softmax of a tensor.

k_greater_equal

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

Element-wise maximum of two tensors.

k_random_binomial

Returns a tensor with random binomial distribution of values.

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

Element-wise sigmoid.

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

Returns whether a tensor is a sparse tensor.

Returns whether x is a symbolic tensor.

Apply 1D conv with un-shared weights.

Segment-wise linear approximation of sigmoid.

Apply 2D conv with un-shared weights.

k_learning_phase

Returns the learning phase flag.

k_normalize_batch_in_training

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

k_temporal_padding

Pads the middle dimension of a 3D tensor.

Element-wise sign.

layer_dense_features

Constructs a DenseFeatures.

layer_activation_softmax

Softmax activation function.

layer_additive_attention

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

Creates a tensor by tiling x by n.

layer_activation_thresholded_relu

Thresholded Rectified Linear Unit.

layer_alpha_dropout

Applies Alpha Dropout to the input.

layer_depthwise_conv_2d

Depthwise separable 2D convolution.

k_in_test_phase

Selects x in test phase, and alt otherwise.

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

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

Returns whether the targets are in the top k predictions.

layer_global_max_pooling_3d

Global Max pooling operation for 3D data.

Element-wise equality between two tensors.

Adds a 1-sized dimension at index axis.

k_manual_variable_initialization

Sets the manual variable initialization flag.

Mean of a tensor, alongside the specified axis.

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

k_random_uniform_variable

Instantiates a variable with values drawn from a uniform distribution.

k_random_uniform

Returns a tensor with uniform distribution of values.

Standard deviation of a tensor, alongside the specified axis.

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

Element-wise log.

Element-wise exponentiation.

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

Minimum value in a tensor.

Prints message and the tensor value when evaluated.

Reverse a tensor along the specified axes.

k_resize_volumes

Resizes the volume contained in a 5D tensor.

k_truncated_normal

Returns a tensor with truncated random normal distribution of values.

Gated Recurrent Unit - Cho et al.

Long Short-Term Memory unit - Hochreiter 1997.

Instantiates a placeholder tensor and returns it.

Computes the one-hot representation of an integer tensor.

Repeats a 2D tensor.

k_permute_dimensions

Permutes axes in a tensor.

Element-wise inequality between two tensors.

Rectified linear unit.

Returns the symbolic shape of a tensor or variable.

Softsign of a tensor.

Softplus of a tensor.

Update the value of x to new_x.

Iterates over the time dimension of a tensor

Element-wise tanh.

layer_activation_parametric_relu

Parametric Rectified Linear Unit.

Layer that averages a list of inputs.

layer_activation_leaky_relu

Leaky version of a Rectified Linear Unit.

Switches between two operations depending on a scalar value.

layer_attention

Creates attention layer

Keras array object

layer_category_encoding

A preprocessing layer which encodes integer features.

layer_lstm_cell

Cell class for the LSTM layer

Reshapes an output to a certain shape.

Instantiate an identity matrix and returns it.

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

k_in_train_phase

Selects x in train phase, and alt otherwise.

Returns the value of a variable.

k_get_variable_shape

Returns the shape of a variable.

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

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

Update the value of x by adding increment.

Element-wise square.

Image resizing layer

Element-wise rounding to the closest integer.

Element-wise minimum of two tensors.

k_moving_average_update

Compute the moving average of a variable.

k_random_normal

Returns a tensor with normal distribution of values.

Update the value of x by subtracting decrement.

layer_discretization

A preprocessing layer which buckets continuous features by ranges.

layer_center_crop

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

layer_conv_lstm_2d

Convolutional LSTM.

layer_conv_lstm_3d

3D Convolutional LSTM

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

k_sparse_categorical_crossentropy

Categorical crossentropy with integer targets.

Reshapes a tensor to the specified shape.

metric_categorical_accuracy

Calculates how often predictions match one-hot labels

k_random_normal_variable

Instantiates a variable with values drawn from a normal distribution.

layer_activation

Apply an activation function to an output.

layer_activation_elu

Exponential Linear Unit.

k_spatial_2d_padding

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

layer_integer_lookup

A preprocessing layer which maps integer features to contiguous ranges.

layer_locally_connected_2d

Locally-connected layer for 2D inputs.

layer_locally_connected_1d

Locally-connected layer for 1D inputs.

k_resize_images

Resizes the images contained in a 4D tensor.

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

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.

k_separable_conv2d

2D convolution with separable filters.

layer_cropping_3d

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

Flattens an input

Computes the (weighted) mean of the given values

layer_cudnn_gru

Fast GRU implementation backed by CuDNN.

metric_categorical_crossentropy

Computes the crossentropy metric between the labels and predictions

k_set_learning_phase

Sets the learning phase to a fixed value.

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

keras_model_custom

Create a Keras custom model

Instantiates an all-zeros variable and returns it.

metric_top_k_categorical_accuracy

Computes how often targets are in the top K predictions

metric_mean_absolute_error

Computes the mean absolute error between the labels and predictions

metric_precision

Computes the precision of the predictions with respect to the labels

metric_precision_at_recall

Computes best precision where recall is >= specified value

layer_random_flip

Randomly flip each image horizontally and vertically

layer_separable_conv_2d

Separable 2D convolution.

layer_random_height

Randomly vary the height of a batch of images during training

layer_simple_rnn

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

layer_upsampling_2d

Upsampling layer for 2D inputs.

layer_upsampling_1d

Upsampling layer for 1D inputs.

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

metric_true_negatives

Calculates the number of true negatives

optimizer_adamax

Adamax optimizer

keras_model_sequential

Keras Model composed of a linear stack of layers

Element-wise square root.

Transposes a tensor and returns it.

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

k_spatial_3d_padding

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

R interface to Keras

predict_generator

Generates predictions for the input samples from a data generator.

layer_gaussian_dropout

Apply multiplicative 1-centered Gaussian noise.

layer_conv_2d_transpose

Transposed 2D convolution layer (sometimes called Deconvolution).

Main Keras module

layer_upsampling_3d

Upsampling layer for 3D inputs.

layer_global_max_pooling_1d

Global max pooling operation for temporal data.

layer_cudnn_lstm

Fast LSTM implementation backed by CuDNN.

layer_global_average_pooling_2d

Global average pooling operation for spatial data.

Add a densely-connected NN layer to an output

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

layer_global_average_pooling_3d

Global Average pooling operation for 3D data.

layer_zero_padding_1d

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

metric_cosine_similarity

Computes the cosine similarity between the labels and predictions

predict_on_batch

Returns predictions for a single batch of samples.

Wraps arbitrary expression as a layer

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.

metric_false_negatives

Calculates the number of false negatives

Variance of a tensor, alongside the specified axis.

text_hashing_trick

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

text_dataset_from_directory

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

layer_activity_regularization

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

metric_mean_absolute_percentage_error

Computes the mean absolute percentage error between y_true and y_pred

metric_mean_iou

Computes the mean Intersection-Over-Union metric

Instantiates a variable and returns it.

Computes the recall of the predictions with respect to the labels

Layer that adds a list of inputs.

layer_conv_lstm_1d

1D Convolutional LSTM

layer_conv_3d_transpose

Transposed 3D convolution layer (sometimes called Deconvolution).

layer_global_max_pooling_2d

Global max pooling operation for spatial data.

layer_layer_normalization

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

layer_normalization

A preprocessing layer which normalizes continuous features.

Permute the dimensions of an input according to a given pattern

metric_recall_at_precision

Computes best recall where precision is >= specified value

optimizer_adadelta

Adadelta optimizer.

Applies Dropout to the input.

layer_activation_selu

Scaled Exponential Linear Unit.

layer_activation_relu

Rectified Linear Unit activation function

layer_average_pooling_1d

Average pooling for temporal data.

layer_separable_conv_1d

Depthwise separable 1D convolution.

Base class for recurrent layers

layer_stacked_rnn_cells

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

layer_max_pooling_1d

Max pooling operation for temporal data.

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

layer_multi_head_attention

MultiHeadAttention layer

layer_random_width

Randomly vary the width of a batch of images during training

layer_random_zoom

A preprocessing layer which randomly zooms images during training.

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

layer_simple_rnn_cell

Cell class for SimpleRNN

Stochastic gradient descent optimizer

optimizer_adagrad

Adagrad optimizer.

layer_string_lookup

A preprocessing layer which maps string features to integer indices.

loss_cosine_proximity

(Deprecated) loss_cosine_proximity

layer_max_pooling_3d

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

layer_random_contrast

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

layer_max_pooling_2d

Max pooling operation for spatial data.

layer_embedding

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

metric_binary_accuracy

Calculates how often predictions match binary labels

layer_conv_1d_transpose

Transposed 1D convolution layer (sometimes called Deconvolution).

layer_average_pooling_2d

Average pooling operation for spatial data.

layer_random_crop

Randomly crop the images to target height and width

layer_spatial_dropout_1d

Spatial 1D version of Dropout.

metric_binary_crossentropy

Computes the crossentropy metric between the labels and predictions

make_sampling_table

Generates a word rank-based probabilistic sampling table.

metric_false_positives

Calculates the number of false positives

metric-or-Metric

metric-or-Metric

layer_cropping_1d

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

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

Computes the hinge metric between y_true and y_pred

metric_kullback_leibler_divergence

Computes Kullback-Leibler divergence

metric_mean_relative_error

Computes the mean relative error by normalizing with the given values

metric_mean_squared_error

Computes the mean squared error between labels and predictions

metric_sparse_top_k_categorical_accuracy

Computes how often integer targets are in the top K predictions

Layer that subtracts two inputs.

layer_cropping_2d

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

layer_gaussian_noise

Apply additive zero-centered Gaussian noise.

Pads sequences to the same length

L1 and L2 regularization

metric_specificity_at_sensitivity

Computes best specificity where sensitivity is >= specified value

metric_logcosh_error

Computes the logarithm of the hyperbolic cosine of the prediction error

layer_global_average_pooling_1d

Global average pooling operation for temporal data.

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).

layer_text_vectorization

A preprocessing layer which maps text features to integer sequences.

Reset the states for a layer

metric_mean_tensor

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

metric_mean_squared_logarithmic_error

Computes the mean squared logarithmic error

metric_sparse_categorical_accuracy

Calculates how often predictions match integer labels

Cell class for the GRU layer

metric_mean_wrapper

Wraps a stateless metric function with the Mean metric

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

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

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

metric_sparse_categorical_crossentropy

Computes the crossentropy metric between the labels and predictions

A preprocessing layer which hashes and bins categorical features.

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.

timeseries_generator

Utility function for generating batches of temporal data.

layer_random_rotation

Randomly rotate each image

Objects exported from other packages

metric_accuracy

Calculates how often predictions equal labels

layer_repeat_vector

Repeats the input n times.

layer_spatial_dropout_2d

Spatial 2D version of Dropout.

layer_random_translation

Randomly translate each image during training

layer_spatial_dropout_3d

Spatial 3D version of Dropout.

layer_rescaling

Multiply inputs by scale and adds offset

Computes the Poisson metric between y_true and y_pred

metric_squared_hinge

Computes the squared hinge metric

sequential_model_input_layer

sequential_model_input_layer

Computes the (weighted) sum of the given values

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

optimizer_rmsprop

RMSProp optimizer

optimizer_nadam

Nesterov Adam optimizer

serialize_model

Serialize a model to an R object

save_model_weights_hdf5

Save/Load model weights using HDF5 files

save_model_weights_tf

Save model weights in the SavedModel format

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

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

Model configuration as JSON

metric_sensitivity_at_specificity

Computes best sensitivity where specificity is >= specified value

metric_root_mean_squared_error

Computes root mean squared error metric between y_true and y_pred

metric_cosine_proximity

(Deprecated) metric_cosine_proximity

metric_categorical_hinge

Computes the categorical hinge metric between y_true and y_pred

metric_true_positives

Calculates the number of true positives

texts_to_sequences

Transform each text in texts in a sequence of integers.

model_from_saved_model

Load a Keras model from the Saved Model format

texts_to_sequences_generator

Transforms each text in texts in a sequence of integers.

use_implementation

Select a Keras implementation and backend

model_to_saved_model

Export to Saved Model format

multi_gpu_model

Replicates a model on different GPUs.

Normalize a matrix or nd-array

plot.keras_training_history

Plot training history

save_text_tokenizer

Save a text tokenizer to an external file

Remove the last layer in a model

Model configuration as YAML

Assign values to names

predict.keras.engine.training.Model

Generate predictions from a Keras model

save_model_hdf5

Save/Load models using HDF5 files

sequences_to_matrix

Convert a list of sequences into a matrix.

summary.keras.engine.training.Model

Print a summary of a Keras model

Generates skipgram word pairs.

Save/Load models using SavedModel format

with_custom_object_scope

Provide a scope with mappings of names to custom objects

Text tokenization utility

time_distributed

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

timeseries_dataset_from_array

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

texts_to_matrix

Convert a list of texts to a matrix.