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keras (version 2.1.3)

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

2.1.3

License

MIT + file LICENSE

Maintainer

JJ Allaire

Last Published

January 17th, 2018

Functions in keras (2.1.3)

Base R6 class for Keras callbacks

Base R6 class for Keras layers

application_resnet50

ResNet50 model for Keras.

application_vgg

VGG16 and VGG19 models for Keras.

application_inception_v3

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

application_mobilenet

MobileNet model architecture.

activation_relu

Activation functions

application_inception_resnet_v2

Inception-ResNet v2 model, with weights trained on ImageNet

application_xception

Xception V1 model for Keras.

Keras backend tensor engine

callback_learning_rate_scheduler

Learning rate scheduler.

callback_model_checkpoint

Save the model after every epoch.

callback_early_stopping

Stop training when a monitored quantity has stopped improving.

callback_lambda

Create a custom callback

Configure a Keras model for training

constraint_maxnorm

MaxNorm weight constraint

Bidirectional wrapper for RNNs.

callback_csv_logger

Callback that streams epoch results to a csv file

Create a Keras Layer

dataset_boston_housing

Boston housing price regression dataset

constraint_minmaxnorm

MinMaxNorm weight constraint

constraint_nonneg

NonNeg weight constraint

dataset_fashion_mnist

Fashion-MNIST database of fashion articles

IMDB Movie reviews sentiment classification

callback_progbar_logger

Callback that prints metrics to stdout.

callback_reduce_lr_on_plateau

Reduce learning rate when a metric has stopped improving.

constraint_unitnorm

UnitNorm weight constraint

Count the total number of scalars composing the weights.

export_savedmodel.keras.engine.training.Model

Export a Saved Model

Train a Keras model

initializer_identity

Initializer that generates the identity matrix.

initializer_lecun_normal

LeCun normal initializer.

callback_remote_monitor

Callback used to stream events to a server.

callback_tensorboard

TensorBoard basic visualizations

callback_terminate_on_naan

Callback that terminates training when a NaN loss is encountered.

MNIST database of handwritten digits

dataset_reuters

Reuters newswire topics 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

Install Keras and the TensorFlow backend

is_keras_available

Check if Keras is Available

k_batch_flatten

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

dataset_cifar10

CIFAR10 small image classification

dataset_cifar100

CIFAR100 small image classification

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.

Clone a model instance.

evaluate.keras.engine.training.Model

Evaluate a Keras model

evaluate_generator

Evaluates the model on a data generator.

Loads an image into PIL format.

3D array representation of images

initializer_glorot_normal

Glorot normal initializer, also called Xavier normal initializer.

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

Retrieve tensors for layers with multiple nodes

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

k_batch_get_value

Returns the value of more than one tensor variable.

Casts a tensor to a different dtype and returns it.

k_cast_to_floatx

Cast an array to the default Keras float type.

3D convolution.

Layer/Model weights as R arrays

initializer_he_normal

He normal initializer.

initializer_he_uniform

He uniform variance scaling initializer.

Keras implementation

initializer_constant

Initializer that generates tensors initialized to a constant value.

initializer_orthogonal

Initializer that generates a random orthogonal matrix.

initializer_random_normal

Initializer that generates tensors with a normal distribution.

initializer_glorot_uniform

Glorot uniform initializer, also called Xavier uniform initializer.

Element-wise absolute value.

Bitwise reduction (logical AND).

Retrieve the next item from a generator

Layer/Model configuration

imagenet_decode_predictions

Decodes the prediction of an ImageNet model.

initializer_variance_scaling

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

initializer_zeros

Initializer that generates tensors initialized to 0.

k_batch_normalization

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

imagenet_preprocess_input

Preprocesses a tensor encoding a batch of images.

initializer_lecun_uniform

LeCun uniform initializer.

initializer_ones

Initializer that generates tensors initialized to 1.

Bitwise reduction (logical OR).

Creates a 1D tensor containing a sequence of integers.

Returns the index of the maximum value along an axis.

Returns the index of the minimum value along an axis.

Adds a bias vector to a tensor.

k_binary_crossentropy

Binary crossentropy between an output tensor and a target tensor.

k_categorical_crossentropy

Categorical crossentropy between an output tensor and a target tensor.

Element-wise value clipping.

Concatenates a list of tensors alongside the specified axis.

k_ctc_label_dense_to_sparse

Converts CTC labels from dense to sparse.

2D convolution.

k_conv2d_transpose

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

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

k_depthwise_conv2d

Depthwise 2D convolution with separable filters.

Evaluates the value of a variable.

Element-wise exponential.

Flatten a tensor.

k_batch_set_value

Sets the values of many tensor variables at once.

k_conv3d_transpose

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

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

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

k_in_test_phase

Selects x in test phase, and alt otherwise.

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

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

Exponential linear unit.

Returns whether the targets are in the top k predictions.

Apply 2D conv with un-shared weights.

Element-wise log.

k_moving_average_update

Compute the moving average of a variable.

Instantiates a Keras function

Retrieves the elements of indices indices in the tensor reference.

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

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

Computes the one-hot representation of an integer tensor.

Instantiates an all-ones tensor variable and returns it.

k_random_binomial

Returns a tensor with random binomial distribution of values.

k_clear_session

Destroys the current TF graph and creates a new one.

k_ctc_batch_cost

Runs CTC loss algorithm on each batch element.

Decodes the output of a softmax.

Computes cos of x element-wise.

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

Adds a 1-sized dimension at index axis.

flow_images_from_directory

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

Freeze and unfreeze weights

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.

Default float type

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

k_image_data_format

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

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

k_is_keras_tensor

Returns whether x is a Keras tensor.

k_is_placeholder

Returns whether x is a placeholder.

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

Element-wise maximum of two tensors.

Mean of a tensor, alongside the specified axis.

k_random_normal

Returns a tensor with normal distribution of values.

Element-wise sign.

Computes sin of x element-wise.

Standard deviation of a tensor, alongside the specified axis.

Maximum value in a tensor.

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

k_permute_dimensions

Permutes axes in a tensor.

Element-wise exponentiation.

Repeats a 2D tensor.

k_repeat_elements

Repeats the elements of a tensor along an axis.

Reverse a tensor along the specified axes.

Fuzz factor used in numeric expressions.

Element-wise equality between two tensors.

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

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

k_stop_gradient

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

Instantiates a variable and returns it.

Instantiates an all-zeros variable and returns it.

layer_activation_parametric_relu

Parametric Rectified Linear Unit.

layer_activation_thresholded_relu

Thresholded Rectified Linear Unit.

layer_concatenate

Layer that concatenates a list of inputs.

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

k_in_train_phase

Selects x in train phase, and alt otherwise.

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

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

Instantiate an identity matrix and returns it.

TF session to be used by the backend.

Get the uid for the default graph.

k_greater_equal

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

Segment-wise linear approximation of sigmoid.

Returns whether a tensor is a sparse tensor.

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

k_random_uniform_variable

Instantiates a variable with values drawn from a uniform distribution.

Rectified linear unit.

Reset graph identifiers.

Reshapes a tensor to the specified shape.

initializer_random_uniform

Initializer that generates tensors with a uniform distribution.

initializer_truncated_normal

Initializer that generates a truncated normal distribution.

Active Keras backend

Softsign of a tensor.

k_sparse_categorical_crossentropy

Categorical crossentropy with integer targets.

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

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.

k_resize_images

Resizes the images contained in a 4D tensor.

k_resize_volumes

Resizes the volume contained in a 5D tensor.

Element-wise tanh.

k_temporal_padding

Pads the middle dimension of a 3D tensor.

Batchwise dot product.

Creates a constant tensor.

1D convolution.

Returns the value of a variable.

k_get_variable_shape

Returns the shape of a variable.

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

layer_gaussian_noise

Apply additive zero-centered Gaussian noise.

layer_activation_elu

Exponential Linear Unit.

layer_activation_leaky_relu

Leaky version of a Rectified Linear Unit.

layer_alpha_dropout

Applies Alpha Dropout to the input.

Layer that averages a list of inputs.

layer_global_average_pooling_1d

Global average pooling operation for temporal data.

Wraps arbitrary expression as a layer

layer_conv_3d_transpose

Transposed 3D convolution layer (sometimes called Deconvolution).

Iterates over the time dimension of a tensor

k_spatial_2d_padding

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

k_spatial_3d_padding

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

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.

Assign values to names

multi_gpu_model

Replicates a model on different GPUs.

Switches between two operations depending on a scalar value.

Transposes a tensor and returns it.

k_truncated_normal

Returns a tensor with truncated random normal distribution of values.

predict.keras.engine.training.Model

Generate predictions from a Keras model

predict_generator

Generates predictions for the input samples from a data generator.

Generates skipgram word pairs.

summary.keras.engine.training.Model

Print a summary of a Keras model

Apply 1D conv with un-shared weights.

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

k_manual_variable_initialization

Sets the manual variable initialization flag.

Applies Dropout to the input.

layer_embedding

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

layer_locally_connected_1d

Locally-connected layer for 1D inputs.

layer_locally_connected_2d

Locally-connected layer for 2D inputs.

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

layer_conv_2d_transpose

Transposed 2D convolution layer (sometimes called Deconvolution).

Flattens an input

texts_to_sequences_generator

Transforms each text in texts in a sequence of integers.

time_distributed

Apply a layer to every temporal slice of an input.

Instantiates a placeholder tensor and returns it.

k_set_learning_phase

Sets the learning phase to a fixed value.

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

Softmax of a tensor.

layer_gaussian_dropout

Apply multiplicative 1-centered Gaussian noise.

layer_global_max_pooling_1d

Global max pooling operation for temporal data.

layer_global_max_pooling_2d

Global max pooling operation for spatial data.

k_learning_phase

Returns the learning phase flag.

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

Minimum value in a tensor.

Softplus of a tensor.

Element-wise square root.

Element-wise square.

Update the value of x by subtracting decrement.

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

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

Update the value of x to new_x.

layer_max_pooling_3d

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

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

Model configuration as JSON

Model configuration as YAML

Update the value of x by adding increment.

keras_model_sequential

Keras Model composed of a linear stack of layers

layer_activation

Apply an activation function to an output.

layer_average_pooling_1d

Average pooling for temporal data.

Element-wise minimum of two tensors.

k_normalize_batch_in_training

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

Element-wise inequality between two tensors.

Prints message and the tensor value when evaluated.

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

Element-wise rounding to the closest integer.

k_separable_conv2d

2D convolution with separable filters.

Returns the symbolic shape of a tensor or variable.

Element-wise sigmoid.

Creates a tensor by tiling x by n.

layer_average_pooling_2d

Average pooling operation for spatial data.

layer_cropping_2d

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

layer_cropping_3d

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

layer_global_average_pooling_2d

Global average pooling operation for spatial data.

Variance of a tensor, alongside the specified axis.

Keras array object

layer_activity_regularization

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

Layer that adds a list of inputs.

layer_max_pooling_1d

Max pooling operation for temporal data.

layer_max_pooling_2d

Max pooling operation for spatial data.

Layer that subtracts two inputs.

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

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

R interface to Keras

layer_global_average_pooling_3d

Global Average pooling operation for 3D data.

Reshapes an output to a certain shape.

layer_separable_conv_2d

Depthwise separable 2D convolution.

layer_spatial_dropout_2d

Spatial 2D version of Dropout.

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_cudnn_gru

Fast GRU implementation backed by CuDNN.

layer_upsampling_1d

Upsampling layer for 1D inputs.

optimizer_rmsprop

RMSProp optimizer

Stochastic gradient descent optimizer

save_model_weights_hdf5

Save/Load model weights using HDF5 files

layer_conv_lstm_2d

Convolutional LSTM.

layer_cropping_1d

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

Add a densely-connected NN layer to an output

layer_global_max_pooling_3d

Global Max pooling operation for 3D data.

Gated Recurrent Unit - Cho et al.

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

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

layer_zero_padding_1d

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

layer_zero_padding_2d

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

layer_cudnn_lstm

Fast LSTM implementation backed by CuDNN.

layer_spatial_dropout_3d

Spatial 3D version of Dropout.

make_sampling_table

Generates a word rank-based probabilistic sampling table.

metric_binary_accuracy

Model performance metrics

optimizer_adamax

Adamax optimizer

optimizer_nadam

Nesterov Adam optimizer

text_to_word_sequence

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

Text tokenization utility

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

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

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

Long-Short Term Memory unit - Hochreiter 1997.

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

Permute the dimensions of an input according to a given pattern

layer_repeat_vector

Repeats the input n times.

optimizer_adagrad

Adagrad optimizer.

Reset the states for a layer

save_model_hdf5

Save/Load models using HDF5 files

texts_to_matrix

Convert a list of texts to a matrix.

texts_to_sequences

Transform each text in texts in a sequence of integers.

save_text_tokenizer

Save a text tokenizer to an external file

sequences_to_matrix

Convert a list of sequences into a matrix.

serialize_model

Serialize a model to an R object

layer_upsampling_2d

Upsampling layer for 2D inputs.

layer_upsampling_3d

Upsampling layer for 3D inputs.

layer_zero_padding_3d

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

loss_mean_squared_error

Model loss functions

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

Normalize a matrix or nd-array

optimizer_adadelta

Adadelta optimizer.

plot.keras_training_history

Plot training history

Remove the last layer in a model

predict_on_batch

Returns predictions for a single batch of samples.

Generates probability or class probability predictions for the input samples.

use_implementation

Select a Keras implementation and backend

with_custom_object_scope

Provide a scope with mappings of names to custom objects

Objects exported from other packages

L1 and L2 regularization

text_hashing_trick

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

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