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keras (version 2.0.5)

R Interface to 'Keras'

Description

Interface to 'Keras', a high-level neural networks API which runs on top of 'TensorFlow'. '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')

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Version

2.0.5

License

MIT + file LICENSE

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Repository

https://github.com/rstudio/keras

Maintainer

JJ Allaire

Last Published

July 30th, 2017

Functions in keras (2.0.5)

Base R6 class for Keras callbacks

Base R6 class for Keras layers

application_mobilenet

MobileNet model architecture.

application_resnet50

ResNet50 model for Keras.

application_vgg

VGG16 and VGG19 models for Keras.

application_xception

Xception V1 model for Keras.

Keras backend tensor engine

Bidirectional wrapper for RNNs.

activation_relu

Activation functions

application_inception_v3

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

callback_model_checkpoint

Save the model after every epoch.

callback_progbar_logger

Callback that prints metrics to stdout.

constraint_minmaxnorm

MinMaxNorm weight constraint

constraint_nonneg

NonNeg weight constraint

callback_tensorboard

TensorBoard basic visualizations

callback_terminate_on_naan

Callback that terminates training when a NaN loss is encountered.

Create a Keras Layer

dataset_boston_housing

Boston housing price regression dataset

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.

Configure a Keras model for training

IMDB Movie reviews sentiment classification

MNIST database of handwritten digits

Loads an image into PIL format.

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

callback_csv_logger

Callback that streams epoch results to a csv file

callback_early_stopping

Stop training when a monitored quantity has stopped improving.

constraint_unitnorm

UnitNorm weight constraint

Count the total number of scalars composing the weights.

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

callback_lambda

Create a custom callback

callback_learning_rate_scheduler

Learning rate scheduler.

dataset_cifar10

CIFAR10 small image classification

fit_image_data_generator

Fit image data generator internal statistics to some sample data.

flow_images_from_directory

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

Layer/Model configuration

initializer_glorot_normal

Glorot normal initializer, also called Xavier normal initializer.

dataset_cifar100

CIFAR100 small image classification

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

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

evaluate_generator

Evaluates the model on a data generator.

Train a Keras model

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

Retrieve tensors for layers with multiple nodes

initializer_glorot_uniform

Glorot uniform initializer, also called Xavier uniform initializer.

initializer_random_uniform

Initializer that generates tensors with a uniform distribution.

initializer_truncated_normal

Initializer that generates a truncated normal distribution.

image_data_generator

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

initializer_he_normal

He normal initializer.

initializer_he_uniform

He uniform variance scaling initializer.

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_2d

Average pooling operation for spatial data.

layer_average_pooling_3d

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

constraint_maxnorm

MaxNorm weight constraint

dataset_reuters

Reuters newswire topics classification

Evaluate a Keras model

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

initializer_identity

Initializer that generates the identity matrix.

initializer_lecun_normal

LeCun normal initializer.

initializer_lecun_uniform

LeCun uniform initializer.

initializer_ones

Initializer that generates tensors initialized to 1.

layer_activation_leaky_relu

Leaky version of a Rectified Linear Unit.

layer_activation_parametric_relu

Parametric Rectified Linear Unit.

initializer_variance_scaling

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

initializer_zeros

Initializer that generates tensors initialized to 0.

Layer that adds a list of inputs.

layer_conv_2d_transpose

Transposed 2D convolution layer (sometimes called Deconvolution).

layer_cropping_3d

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

Add a densely-connected NN layer to an output

layer_alpha_dropout

Applies Alpha Dropout to the input.

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

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

layer_embedding

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

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

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

imagenet_decode_predictions

Decodes the prediction of an ImageNet model.

imagenet_preprocess_input

Preprocesses a tensor encoding a batch of images.

keras_model_sequential

Keras Model composed of a linear stack of layers

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

layer_upsampling_1d

Upsampling layer for 1D inputs.

layer_upsampling_2d

Upsampling layer for 2D inputs.

loss_mean_squared_error

Model loss functions

layer_activation

Apply an activation function to an output.

layer_activation_elu

Exponential Linear Unit.

layer_batch_normalization

Batch normalization layer (Ioffe and Szegedy, 2014).

layer_concatenate

Layer that concatenates a list of inputs.

Layer/Model weights as R arrays

Keras implementation

initializer_constant

Initializer that generates tensors initialized to a constant value.

initializer_orthogonal

Initializer that generates a random orthogonal matrix.

make_sampling_table

Generates a word rank-based probabilistic sampling table.

Stochastic gradient descent optimizer

Pads each sequence to the same length (length of the longest sequence).

sequences_to_matrix

Convert a list of sequences into a matrix.

initializer_random_normal

Initializer that generates tensors with a normal distribution.

Layer that averages a list of inputs.

layer_average_pooling_1d

Average pooling for temporal data.

layer_conv_3d_transpose

Transposed 3D convolution layer (sometimes called Deconvolution).

layer_conv_lstm_2d

Convolutional LSTM.

Generates skipgram word pairs.

Text tokenization utility

texts_to_matrix

Convert a list of texts to a matrix.

Flattens an input

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_max_pooling_2d

Max pooling operation for spatial data.

layer_max_pooling_3d

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

layer_upsampling_3d

Upsampling layer for 3D inputs.

layer_zero_padding_1d

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

metric_binary_accuracy

Model performance metrics

Model configuration as JSON

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

Applies Dropout to the input.

Gated Recurrent Unit - Cho et al.

layer_gaussian_dropout

Apply multiplicative 1-centered Gaussian noise.

layer_gaussian_noise

Apply additive zero-centered Gaussian noise.

Wraps arbitrary expression as a layer

layer_locally_connected_1d

Locally-connected layer for 1D inputs.

Permute the dimensions of an input according to a given pattern

layer_repeat_vector

Repeats the input n times.

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_cropping_1d

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

layer_cropping_2d

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

layer_global_max_pooling_2d

Global max pooling operation for spatial data.

layer_global_max_pooling_3d

Global Max pooling operation for 3D data.

Remove the last layer in a model

predict.keras.engine.training.Model

Generate predictions from a Keras model

Generates probability or class probability predictions for the input samples.

Objects exported from other packages

summary.keras.engine.training.Model

Print a summary of a Keras model

layer_locally_connected_2d

Locally-connected layer for 2D inputs.

Long-Short Term Memory unit - Hochreiter 1997.

layer_spatial_dropout_2d

Spatial 2D version of Dropout.

layer_spatial_dropout_3d

Spatial 3D version of Dropout.

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).

optimizer_adamax

Adamax optimizer

L1 and L2 regularization

Reset the states for a layer

texts_to_sequences

Transform each text in texts in a sequence of integers.

optimizer_adadelta

Adadelta optimizer.

optimizer_adagrad

Adagrad optimizer.

plot.keras_training_history

Plot training history

text_hashing_trick

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

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

layer_max_pooling_1d

Max pooling operation for temporal data.

Reshapes an output to a certain shape.

texts_to_sequences_generator

Transforms each text in texts in a sequence of integers.

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

predict_generator

Generates predictions for the input samples from a data generator.

predict_on_batch

Returns predictions for a single batch of samples.

time_distributed

Apply a layer to every temporal slice of an input.

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

layer_separable_conv_2d

Depthwise separable 2D convolution.

Model configuration as YAML

Normalize a matrix or nd-array

optimizer_nadam

Nesterov Adam optimizer

optimizer_rmsprop

RMSProp optimizer

save_model_hdf5

Save/Load models using HDF5 files

save_model_weights_hdf5

Save/Load model weights using HDF5 files

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.