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R interface to Keras

Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras has the following key features:

  • Allows the same code to run on CPU or on GPU, seamlessly.

  • User-friendly API which makes it easy to quickly prototype deep learning models.

  • Built-in support for convolutional networks (for computer vision), recurrent networks (for sequence processing), and any combination of both.

  • Supports arbitrary network architectures: multi-input or multi-output models, layer sharing, model sharing, etc. This means that Keras is appropriate for building essentially any deep learning model, from a memory network to a neural Turing machine.

See the package website at https://rstudio.github.io/keras for complete documentation.

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install.packages('keras')

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Version

0.3.5

License

MIT + file LICENSE

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Maintainer

JJ Allaire

Last Published

August 15th, 2023

Functions in keras (0.3.5)

backend

Keras backend tensor engine
bidirectional

Bidirectional wrapper for RNNs.
application_vgg

VGG16 and VGG19 models for Keras.
application_xception

Xception V1 model for Keras.
application_inception_v3

Inception V3 model, with weights pre-trained on ImageNet.
application_resnet50

ResNet50 model for Keras.
callback_csv_logger

Callback that streams epoch results to a csv file
callback_early_stopping

Stop training when a monitored quantity has stopped improving.
KerasCallback

Base R6 class for Keras callbacks
activation_relu

Activation functions
callback_lambda

Create a custom callback
callback_learning_rate_scheduler

Learning rate scheduler.
callback_reduce_lr_on_plateau

Reduce learning rate when a metric has stopped improving.
callback_remote_monitor

Callback used to stream events to a server.
count_params

Count the total number of scalars composing the weights.
callback_tensorboard

TensorBoard basic visualizations
compile

Configure a Keras model for training
dataset_imdb

IMDB Movie reviews sentiment classification
dataset_mnist

MNIST database of handwritten digits
callback_model_checkpoint

Save the model after every epoch.
callback_progbar_logger

Callback that prints metrics to stdout.
dataset_reuters

Reuters newswire topics classification
evaluate_generator

Evaluates the model on a data generator.
fit

Train a Keras model
initializer_constant

Initializer that generates tensors initialized to a constant value.
constraint_maxnorm

MaxNorm weight constraint
constraint_minmaxnorm

MinMaxNorm weight constraint
constraint_nonneg

NonNeg weight constraint
constraint_unitnorm

UnitNorm weight constraint
dataset_cifar10

CIFAR10 small image classification
dataset_cifar100

CIFAR100 small image classification
get_file

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

Retrieve tensors for layers with multiple nodes
initializer_glorot_normal

Glorot normal initializer, also called Xavier normal initializer.
initializer_he_uniform

He uniform variance scaling initializer.
initializer_identity

Initializer that generates the identity matrix.
keras_model

Keras Model
dataset_boston_housing

Boston housing price regression dataset
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
imagenet_decode_predictions

Decodes the prediction of an ImageNet model.
keras_model_sequential

Keras Model composed of a linear stack of layers
layer_concatenate

Layer that concatenates a list of inputs.
layer_conv_1d

1D convolution layer (e.g. temporal convolution).
layer_cropping_3d

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

Initializer that generates a random orthogonal matrix.
initializer_random_normal

Initializer that generates tensors with a normal distribution.
layer_activation

Apply an activation function to an output.
layer_activation_elu

Exponential Linear Unit.
layer_dense

Add a densely-connected NN layer to an output
layer_global_average_pooling_3d

Global Average pooling operation for 3D data.
layer_global_max_pooling_1d

Global max pooling operation for temporal data.
layer_locally_connected_2d

Locally-connected layer for 2D inputs.
layer_lstm

Long-Short Term Memory unit - Hochreiter 1997.
layer_reshape

Reshapes an output to a certain shape.
layer_separable_conv_2d

Depthwise separable 2D convolution.
optimizer_adam

Adam optimizer
hdf5_matrix

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

Generate minibatches of image data with real-time data augmentation.
initializer_random_uniform

Initializer that generates tensors with a uniform distribution.
initializer_truncated_normal

Initializer that generates a truncated normal distribution.
imagenet_preprocess_input

Preprocesses a tensor encoding a batch of images.
initializer_glorot_uniform

Glorot uniform initializer, also called Xavier uniform initializer.
initializer_he_normal

He normal initializer.
layer_activation_leaky_relu

Leaky version of a Rectified Linear Unit.
evaluate

Evaluate a Keras model
fit_generator

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.
get_layer

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

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

Layer/Model configuration
image_load

Loads an image into PIL format.
image_to_array

Converts a PIL Image instance to a 3d-array.
initializer_variance_scaling

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

Adamax optimizer
reexports

Objects exported from other packages
regularizer_l1

L1 and L2 regularization
text_tokenizer

Text tokenization utility
initializer_zeros

Initializer that generates tensors initialized to 0.
layer_average_pooling_1d

Average pooling for temporal data.
layer_average_pooling_2d

Average pooling operation for spatial data.
layer_conv_3d

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

Thresholded Rectified Linear Unit.
layer_activity_regularization

Layer that applies an update to the cost function based input activity.
layer_average_pooling_3d

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

Batch normalization layer (Ioffe and Szegedy, 2014).
texts_to_matrix

Convert a list of texts to a matrix.
train_on_batch

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

Parametric Rectified Linear Unit.
layer_add

Layer that adds a list of inputs.
layer_average

Layer that averages a list of inputs.
layer_dot

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

Applies Dropout to the input.
layer_global_max_pooling_2d

Global max pooling operation for spatial data.
layer_global_max_pooling_3d

Global Max pooling operation for 3D data.
layer_max_pooling_2d

Max pooling operation for spatial data.
layer_max_pooling_3d

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

Permute the dimensions of an input according to a given pattern
get_weights

Layer/Model weights as R arrays
initializer_ones

Initializer that generates tensors initialized to 1.
layer_conv_2d

2D convolution layer (e.g. spatial convolution over images).
layer_cropping_1d

Cropping layer for 1D input (e.g. temporal sequence).
layer_cropping_2d

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

Apply multiplicative 1-centered Gaussian noise.
layer_gaussian_noise

Apply additive zero-centered Gaussian noise.
initializer_lecun_uniform

LeCun uniform initializer.
layer_gru

Gated Recurrent Unit - Cho et al.
layer_input

Input layer
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_repeat_vector

Repeats the input n times.
metric_binary_accuracy

Model performance metrics
model_to_json

Model configuration as JSON
%>%

Pipe operator
reset_states

Reset the states for a layer
save_model_hdf5

Save/Load models using HDF5 files
text_one_hot

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 word indices.
layer_conv_2d_transpose

Transposed convolution layer (sometimes called Deconvolution).
layer_embedding

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

Flattens an input
layer_lambda

Wraps arbitrary expression as a layer
layer_locally_connected_1d

Locally-connected layer for 1D inputs.
layer_conv_lstm_2d

Convolutional LSTM.
layer_global_average_pooling_1d

Global average pooling operation for temporal data.
layer_global_average_pooling_2d

Global average pooling operation for spatial data.
layer_maximum

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

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

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

Stochastic gradient descent optimizer
pad_sequences

Pads each sequence to the same length (length of the longest sequence).
predict.tensorflow.keras.engine.training.Model

Generate predictions from a Keras model
layer_masking

Masks a sequence by using a mask value to skip timesteps.
layer_max_pooling_1d

Max pooling operation for temporal data.
layer_upsampling_1d

Upsampling layer for 1D inputs.
layer_upsampling_2d

Upsampling layer for 2D inputs.
loss_mean_squared_error

Model loss functions
make_sampling_table

Generates a word rank-based probabilistic sampling table.
optimizer_nadam

Nesterov Adam optimizer
optimizer_rmsprop

RMSProp optimizer
pop_layer

Remove the last layer in a model
save_model_weights_hdf5

Save/Load model weights using HDF5 files
sequences_to_matrix

Convert a list of sequences into a matrix.
skipgrams

Generates skipgram word pairs.
time_distributed

Apply a layer to every temporal slice of an input.
to_categorical

Converts a class vector (integers) to binary class matrix.
summary.tensorflow.keras.engine.training.Model

Print a summary of a Keras model
layer_spatial_dropout_2d

Spatial 2D version of Dropout.
layer_spatial_dropout_3d

Spatial 3D version of Dropout.
model_to_yaml

Model configuration as YAML
normalize

Normalize a matrix or nd-array
optimizer_adadelta

Adadelta optimizer.
predict_generator

Generates predictions for the input samples from a data generator.
optimizer_adagrad

Adagrad optimizer.
predict_on_batch

Returns predictions for a single batch of samples.
predict_proba

Generates probability or class probability predictions for the input samples.
texts_to_sequences

Transform each text in texts in a sequence of integers.
texts_to_sequences_generator

Transforms each text in texts in a sequence of integers.