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keras (version 2.6.1)

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.packages('keras')

Monthly Downloads

83,375

Version

2.6.1

License

MIT + file LICENSE

Maintainer

Tomasz Kalinowski

Last Published

September 30th, 2021

Functions in keras (2.6.1)

Create a custom Layer

application_inception_resnet_v2

Inception-ResNet v2 model, with weights trained on ImageNet

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

(Deprecated) Base R6 class for Keras layers

(Deprecated) Base R6 class for Keras wrappers

activation_relu

Activation functions

application_densenet

Instantiates the DenseNet architecture.

(Deprecated) Base R6 class for Keras callbacks

KerasConstraint

(Deprecated) Base R6 class for Keras constraints

Bidirectional wrapper for RNNs.

application_vgg

VGG16 and VGG19 models for Keras.

application_resnet50

ResNet50 model for Keras.

application_xception

Xception V1 model for Keras.

Keras backend tensor engine

application_mobilenet

MobileNet model architecture.

application_inception_v3

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

callback_progbar_logger

Callback that prints metrics to stdout.

callback_reduce_lr_on_plateau

Reduce learning rate when a metric has stopped improving.

callback_tensorboard

TensorBoard basic visualizations

callback_remote_monitor

Callback used to stream events to a server.

callback_learning_rate_scheduler

Learning rate scheduler.

callback_model_checkpoint

Save the model after every epoch.

callback_csv_logger

Callback that streams epoch results to a csv file

application_mobilenet_v2

MobileNetV2 model architecture

application_nasnet

Instantiates a NASNet model.

callback_lambda

Create a custom callback

callback_early_stopping

Stop training when a monitored quantity has stopped improving.

Custom metric function

create_layer_wrapper

Create a Keras Layer wrapper

dataset_cifar100

CIFAR100 small image classification

dataset_cifar10

CIFAR10 small image classification

dataset_boston_housing

Boston housing price regression dataset

dataset_reuters

Reuters newswire topics classification

MNIST database of handwritten digits

fit_image_data_generator

Fit image data generator internal statistics to some sample data.

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

Freeze and unfreeze weights

callback_terminate_on_naan

Callback that terminates training when a NaN loss is encountered.

fit_text_tokenizer

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

flow_images_from_data

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

export_savedmodel.keras.engine.training.Model

Export a Saved Model

Retrieve tensors for layers with multiple nodes

(Deprecated) Create a Keras Wrapper

fit.keras.engine.training.Model

Train a Keras model

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

Clone a model instance.

image_dataset_from_directory

Create a dataset from a directory

Retrieve the next item from a generator

compile.keras.engine.training.Model

Configure a Keras model for training

Layer/Model configuration

initializer_ones

Initializer that generates tensors initialized to 1.

Bitwise reduction (logical AND).

Bitwise reduction (logical OR).

initializer_orthogonal

Initializer that generates a random orthogonal matrix.

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

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

image_data_generator

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

Loads an image into PIL format.

Count the total number of scalars composing the weights.

IMDB Movie reviews sentiment classification

dataset_fashion_mnist

Fashion-MNIST database of fashion articles

Create a Keras Layer

initializer_identity

Initializer that generates the identity matrix.

initializer_he_uniform

He uniform variance scaling initializer.

Weight constraints

evaluate.keras.engine.training.Model

Evaluate a Keras model

Adds a bias vector to a tensor.

k_batch_set_value

Sets the values of many tensor variables at once.

is_keras_available

Check if Keras is Available

Element-wise absolute value.

evaluate_generator

Evaluates the model on a data generator.

imagenet_preprocess_input

Preprocesses a tensor or array encoding a batch of images.

Keras implementation

Make a python class constructor

initializer_lecun_normal

LeCun normal initializer.

Layer/Model weights as R arrays

initializer_he_normal

He normal initializer.

initializer_glorot_uniform

Glorot uniform initializer, also called Xavier uniform initializer.

initializer_random_uniform

Initializer that generates tensors with a uniform distribution.

initializer_random_normal

Initializer that generates tensors with a normal distribution.

1D convolution.

flow_images_from_dataframe

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

k_binary_crossentropy

Binary crossentropy between an output tensor and a target tensor.

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

Casts a tensor to a different dtype and returns it.

initializer_constant

Initializer that generates tensors initialized to a constant value.

Returns the index of the minimum value along an axis.

initializer_glorot_normal

Glorot normal initializer, also called Xavier normal initializer.

Active Keras backend

Element-wise value clipping.

Computes cos of x element-wise.

k_clear_session

Destroys the current TF graph and creates a new one.

2D convolution.

k_conv3d_transpose

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

initializer_lecun_uniform

LeCun uniform initializer.

initializer_zeros

Initializer that generates tensors initialized to 0.

3D convolution.

k_conv2d_transpose

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

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.

Fuzz factor used in numeric expressions.

Exponential linear unit.

Install TensorFlow and Keras, including all Python dependencies

Decodes the output of a softmax.

k_batch_normalization

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

k_ctc_label_dense_to_sparse

Converts CTC labels from dense to sparse.

k_batch_get_value

Returns the value of more than one tensor variable.

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

k_depthwise_conv2d

Depthwise 2D convolution with separable filters.

Element-wise exponential.

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

Batchwise dot product.

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.

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

3D array representation of images

imagenet_decode_predictions

Decodes the prediction of an ImageNet model.

flow_images_from_directory

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

Instantiate an identity matrix and returns it.

Adds a 1-sized dimension at index axis.

Flatten a tensor.

k_manual_variable_initialization

Sets the manual variable initialization flag.

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

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

TF session to be used by the backend.

Segment-wise linear approximation of sigmoid.

Retrieves the elements of indices indices in the tensor reference.

initializer_truncated_normal

Initializer that generates a truncated normal distribution.

initializer_variance_scaling

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

k_batch_flatten

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

Instantiates a Keras function

Concatenates a list of tensors alongside the specified axis.

Get the uid for the default graph.

Returns the value of a variable.

Creates a constant tensor.

k_image_data_format

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

Returns whether the targets are in the top k predictions.

k_random_binomial

Returns a tensor with random binomial distribution of values.

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

k_get_variable_shape

Returns the shape of a variable.

Returns the index of the maximum value along an axis.

Creates a 1D tensor containing a sequence of integers.

Element-wise equality between two tensors.

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

k_cast_to_floatx

Cast an array to the default Keras float type.

k_ctc_batch_cost

Runs CTC loss algorithm on each batch element.

k_categorical_crossentropy

Categorical crossentropy between an output tensor and a target tensor.

Returns the symbolic shape of a tensor or variable.

k_is_placeholder

Returns whether x is a placeholder.

k_random_normal

Returns a tensor with normal distribution of values.

k_in_test_phase

Selects x in test phase, and alt otherwise.

Returns whether x is a symbolic tensor.

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

k_learning_phase

Returns the learning phase flag.

Update the value of x by subtracting decrement.

Standard deviation of a tensor, alongside the specified axis.

k_stop_gradient

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

Element-wise sigmoid.

Returns whether a tensor is a sparse tensor.

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

Variance of a tensor, alongside the specified axis.

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

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

k_in_train_phase

Selects x in train phase, and alt otherwise.

Instantiates a placeholder tensor and returns it.

Default float type

Evaluates the value of a variable.

keras_model_sequential

Keras Model composed of a linear stack of layers

layer_activation

Apply an activation function to an output.

layer_activation_selu

Scaled Exponential Linear Unit.

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

Maximum value in a tensor.

Apply 1D conv with un-shared weights.

Mean of a tensor, alongside the specified axis.

Element-wise maximum of two tensors.

layer_activation_softmax

Softmax activation function.

layer_center_crop

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

layer_conv_3d_transpose

Transposed 3D convolution layer (sometimes called Deconvolution).

layer_category_encoding

A preprocessing layer which encodes integer features.

k_random_uniform_variable

Instantiates a variable with values drawn from a uniform distribution.

layer_conv_lstm_2d

Convolutional LSTM.

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

layer_discretization

A preprocessing layer which buckets continuous features by ranges.

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

k_greater_equal

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

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

k_permute_dimensions

Permutes axes in a tensor.

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.

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.

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

layer_string_lookup

A preprocessing layer which maps string features to integer indices.

layer_gaussian_noise

Apply additive zero-centered Gaussian noise.

layer_repeat_vector

Repeats the input n times.

layer_rescaling

Multiply inputs by scale and adds offset

layer_global_average_pooling_1d

Global average pooling operation for temporal data.

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

Minimum value in a tensor.

Rectified linear unit.

k_resize_volumes

Resizes the volume contained in a 5D tensor.

k_resize_images

Resizes the images contained in a 4D tensor.

Element-wise rounding to the closest integer.

Prints message and the tensor value when evaluated.

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

Repeats a 2D tensor.

Element-wise minimum of two tensors.

Softsign of a tensor.

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

k_sparse_categorical_crossentropy

Categorical crossentropy with integer targets.

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

Layer that subtracts two inputs.

layer_zero_padding_1d

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

Computes the one-hot representation of an integer tensor.

Element-wise log.

Instantiates an all-ones tensor variable and returns it.

layer_zero_padding_2d

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

Element-wise square root.

k_repeat_elements

Repeats the elements of a tensor along an axis.

Element-wise square.

Creates a tensor by tiling x by n.

Computes the hinge metric between y_true and y_pred

k_temporal_padding

Pads the middle dimension of a 3D tensor.

Element-wise tanh.

metric_kullback_leibler_divergence

Computes Kullback-Leibler divergence

Element-wise exponentiation.

Reset graph identifiers.

metric_mean_absolute_error

Computes the mean absolute error between the labels and predictions

metric_sensitivity_at_specificity

Computes best sensitivity where specificity is >= specified value

metric_mean_absolute_percentage_error

Computes the mean absolute percentage error between y_true and y_pred

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

layer_attention

Creates attention layer

k_moving_average_update

Compute the moving average of a variable.

k_separable_conv2d

2D convolution with separable filters.

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

metric_sparse_categorical_accuracy

Calculates how often predictions match integer labels

Softmax of a tensor.

Softplus of a tensor.

R interface to Keras

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

Layer that averages a list of inputs.

Normalize a matrix or nd-array

Layer that adds a list of inputs.

Pads sequences to the same length

Iterates over the time dimension of a tensor

Reverse a tensor along the specified axes.

optimizer_adadelta

Adadelta optimizer.

layer_alpha_dropout

Applies Alpha Dropout to the input.

Keras array object

Main Keras module

layer_activation_elu

Exponential Linear Unit.

layer_concatenate

Layer that concatenates a list of inputs.

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

k_set_learning_phase

Sets the learning phase to a fixed value.

k_spatial_2d_padding

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

Save/Load models using SavedModel format

layer_activation_leaky_relu

Leaky version of a Rectified Linear Unit.

layer_batch_normalization

Batch normalization layer (Ioffe and Szegedy, 2014).

layer_average_pooling_3d

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

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

Transposes a tensor and returns it.

k_spatial_3d_padding

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

save_model_weights_hdf5

Save/Load model weights using HDF5 files

texts_to_sequences_generator

Transforms each text in texts in a sequence of integers.

texts_to_sequences

Transform each text in texts in a sequence of integers.

keras_model_custom

Create a Keras custom model

k_truncated_normal

Returns a tensor with truncated random normal distribution of values.

Computes sin of x element-wise.

Element-wise sign.

Reshapes a tensor to the specified shape.

layer_global_max_pooling_3d

Global Max pooling operation for 3D data.

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

layer_cudnn_lstm

Fast LSTM implementation backed by CuDNN.

layer_conv_2d_transpose

Transposed 2D convolution layer (sometimes called Deconvolution).

Gated Recurrent Unit - Cho et al.

layer_max_pooling_1d

Max pooling operation for temporal data.

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

layer_random_zoom

A preprocessing layer which randomly zooms images during training.

layer_upsampling_2d

Upsampling layer for 2D inputs.

layer_random_width

Randomly vary the width of a batch of images during training

layer_activation_parametric_relu

Parametric Rectified Linear Unit.

layer_average_pooling_1d

Average pooling for temporal data.

layer_activation_relu

Rectified Linear Unit activation function

layer_average_pooling_2d

Average pooling operation for spatial data.

layer_cropping_3d

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

Add a densely-connected NN layer to an output

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

layer_cudnn_gru

Fast GRU implementation backed by CuDNN.

Flattens an input

Update the value of x to new_x.

Update the value of x by adding increment.

Switches between two operations depending on a scalar value.

layer_global_average_pooling_3d

Global Average pooling operation for 3D data.

layer_integer_lookup

A preprocessing layer which maps integer features to contiguous ranges.

layer_global_average_pooling_2d

Global average pooling operation for spatial data.

layer_global_max_pooling_1d

Global max pooling operation for temporal data.

layer_gaussian_dropout

Apply multiplicative 1-centered Gaussian noise.

layer_max_pooling_2d

Max pooling operation for spatial data.

layer_global_max_pooling_2d

Global max pooling operation for spatial data.

layer_conv_1d_transpose

Transposed 1D convolution layer (sometimes called Deconvolution).

layer_random_contrast

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

layer_max_pooling_3d

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

layer_random_crop

Randomly crop the images to target height and width

layer_dense_features

Constructs a DenseFeatures.

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

layer_text_vectorization

A preprocessing layer which maps text features to integer sequences.

make_sampling_table

Generates a word rank-based probabilistic sampling table.

loss_cosine_proximity

(Deprecated) loss_cosine_proximity

layer_upsampling_1d

Upsampling layer for 1D inputs.

layer_zero_padding_3d

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

layer_upsampling_3d

Upsampling layer for 3D inputs.

layer_depthwise_conv_2d

Depthwise separable 2D convolution.

Implements categorical feature hashing, also known as "hashing trick"

metric_false_negatives

Calculates the number of false negatives

metric_false_positives

Calculates the number of false positives

metric_mean_wrapper

Wraps a stateless metric function with the Mean metric

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

layer_layer_normalization

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

layer_random_height

Randomly vary the height of a batch of images during training

layer_random_flip

Randomly flip each image horizontally and vertically

layer_locally_connected_1d

Locally-connected layer for 1D inputs.

Instantiates a variable and returns it.

Wraps arbitrary expression as a layer

Instantiates an all-zeros variable and returns it.

layer_activation_thresholded_relu

Thresholded Rectified Linear Unit.

metric_mean_iou

Computes the mean Intersection-Over-Union metric

layer_separable_conv_2d

Separable 2D convolution.

layer_spatial_dropout_1d

Spatial 1D version of Dropout.

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_specificity_at_sensitivity

Computes best specificity where sensitivity is >= specified value

Computes the recall of the predictions with respect to the labels

metric_squared_hinge

Computes the squared hinge metric

layer_activity_regularization

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

layer_normalization

A preprocessing layer which normalizes continuous features.

Permute the dimensions of an input according to a given pattern

layer_random_translation

Randomly translate each image during training

layer_random_rotation

Randomly rotate each image

layer_cropping_1d

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

layer_cropping_2d

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

layer_embedding

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

Applies Dropout to the input.

metric_mean_relative_error

Computes the mean relative error by normalizing with the given values

layer_locally_connected_2d

Locally-connected layer for 2D inputs.

predict_on_batch

Returns predictions for a single batch of samples.

layer_multi_head_attention

MultiHeadAttention layer

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

Long Short-Term Memory unit - Hochreiter 1997.

Image resizing layer

layer_spatial_dropout_2d

Spatial 2D version of Dropout.

Reshapes an output to a certain shape.

layer_spatial_dropout_3d

Spatial 3D version of Dropout.

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

metric-or-Metric

metric-or-Metric

serialize_model

Serialize a model to an R object

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

Generates skipgram word pairs.

metric_binary_accuracy

Calculates how often predictions match binary labels

metric_true_negatives

Calculates the number of true negatives

metric_mean_squared_error

Computes the mean squared error between labels and predictions

metric_mean_squared_logarithmic_error

Computes the mean squared logarithmic error

metric_cosine_similarity

Computes the cosine similarity between the labels and predictions

metric_cosine_proximity

(Deprecated) metric_cosine_proximity

metric_logcosh_error

Computes the logarithm of the hyperbolic cosine of the prediction error

Computes the (weighted) mean of the given values

metric_true_positives

Calculates the number of true positives

texts_to_matrix

Convert a list of texts to a matrix.

Text tokenization utility

metric_sparse_categorical_crossentropy

Computes the crossentropy metric between the labels and predictions

metric_accuracy

Calculates how often predictions equal labels

metric_categorical_accuracy

Calculates how often predictions match one-hot labels

metric_binary_crossentropy

Computes the crossentropy metric between the labels and predictions

metric_recall_at_precision

Computes best recall where precision is >= specified value

optimizer_adagrad

Adagrad optimizer.

Objects exported from other packages

metric_sparse_top_k_categorical_accuracy

Computes how often integer targets are in the top K predictions

model_to_saved_model

Export to Saved Model format

Model configuration as YAML

metric_root_mean_squared_error

Computes root mean squared error metric between y_true and y_pred

L1 and L2 regularization

layer_simple_rnn

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

Reset the states for a layer

time_distributed

Apply a layer to every temporal slice of an input.

save_model_hdf5

Save/Load models using HDF5 files

layer_separable_conv_1d

Depthwise separable 1D convolution.

use_implementation

Select a Keras implementation and backend

timeseries_generator

Utility function for generating batches of temporal data.

with_custom_object_scope

Provide a scope with mappings of names to custom objects

Computes the Poisson metric between y_true and y_pred

optimizer_rmsprop

RMSProp optimizer

metric_precision

Computes the precision of the predictions with respect to the labels

save_model_weights_tf

Save model weights in the SavedModel format

Stochastic gradient descent optimizer

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

text_to_word_sequence

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

save_text_tokenizer

Save a text tokenizer to an external file

multi_gpu_model

Replicates a model on different GPUs.

Computes the (weighted) sum of the given values

metric_top_k_categorical_accuracy

Computes how often targets are in the top K predictions

Assign values to names

optimizer_adamax

Adamax optimizer

Model configuration as JSON

model_from_saved_model

Load a Keras model from the Saved Model format

optimizer_nadam

Nesterov Adam optimizer

plot.keras_training_history

Plot training history

Remove the last layer in a model

predict_generator

Generates predictions for the input samples from a data generator.

sequential_model_input_layer

sequential_model_input_layer

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.

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

summary.keras.engine.training.Model

Print a summary of a Keras model

predict.keras.engine.training.Model

Generate predictions from a Keras model

sequences_to_matrix

Convert a list of sequences into a matrix.