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keras (version 2.1.6)

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

30,713

Version

2.1.6

License

MIT + file LICENSE

Maintainer

JJ Allaire

Last Published

April 29th, 2018

Functions in keras (2.1.6)

Create a Keras Layer

Count the total number of scalars composing the weights.

Weight constraints

callback_remote_monitor

Callback used to stream events to a server.

Retrieve tensors for layers with multiple nodes

initializer_constant

Initializer that generates tensors initialized to a constant value.

IMDB Movie reviews sentiment classification

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

callback_reduce_lr_on_plateau

Reduce learning rate when a metric has stopped improving.

dataset_boston_housing

Boston housing price regression dataset

dataset_reuters

Reuters newswire topics classification

MNIST database of handwritten digits

3D array representation of images

initializer_lecun_normal

LeCun normal initializer.

Keras implementation

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.

initializer_identity

Initializer that generates the identity matrix.

Loads an image into PIL format.

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

callback_tensorboard

TensorBoard basic visualizations

Clone a model instance.

callback_terminate_on_naan

Callback that terminates training when a NaN loss is encountered.

evaluate.keras.engine.training.Model

Evaluate a Keras model

Bidirectional wrapper for RNNs.

initializer_orthogonal

Initializer that generates a random orthogonal matrix.

dataset_cifar10

CIFAR10 small image classification

dataset_cifar100

CIFAR100 small image classification

Configure a Keras model for training

Element-wise absolute value.

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

evaluate_generator

Evaluates the model on a data generator.

Retrieve the next item from a generator

Layer/Model configuration

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

Layer/Model weights as R arrays

image_data_generator

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

initializer_random_normal

Initializer that generates tensors with a normal distribution.

Bitwise reduction (logical AND).

Creates a 1D tensor containing a sequence of integers.

initializer_lecun_uniform

LeCun uniform initializer.

Bitwise reduction (logical OR).

Train a Keras model

initializer_ones

Initializer that generates tensors initialized to 1.

Install Keras and the TensorFlow backend

Adds a bias vector to a tensor.

k_binary_crossentropy

Binary crossentropy between an output tensor and a target tensor.

Creates a constant tensor.

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

is_keras_available

Check if Keras is Available

Returns the index of the maximum value along an axis.

fit_image_data_generator

Fit image data generator internal statistics to some sample data.

1D convolution.

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

imagenet_decode_predictions

Decodes the prediction of an ImageNet model.

k_batch_normalization

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

imagenet_preprocess_input

Preprocesses a tensor or array encoding a batch of images.

initializer_he_normal

He normal initializer.

k_batch_set_value

Sets the values of many tensor variables at once.

Returns the index of the minimum value along an axis.

initializer_he_uniform

He uniform variance scaling initializer.

k_conv2d_transpose

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

k_categorical_crossentropy

Categorical crossentropy between an output tensor and a target tensor.

2D convolution.

k_clear_session

Destroys the current TF graph and creates a new one.

k_depthwise_conv2d

Depthwise 2D convolution with separable filters.

initializer_variance_scaling

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

Computes cos of x element-wise.

3D convolution.

initializer_zeros

Initializer that generates tensors initialized to 0.

k_conv3d_transpose

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

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

Exponential linear unit.

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

Adds a 1-sized dimension at index axis.

k_batch_flatten

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

Instantiate an identity matrix and returns it.

TF session to be used by the backend.

initializer_glorot_normal

Glorot normal initializer, also called Xavier normal initializer.

Get the uid for the default graph.

Casts a tensor to a different dtype and returns it.

k_batch_get_value

Returns the value of more than one tensor variable.

Instantiates a Keras function

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

k_in_train_phase

Selects x in train phase, and alt otherwise.

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

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

Returns whether a tensor is a sparse tensor.

Retrieves the elements of indices indices in the tensor reference.

k_cast_to_floatx

Cast an array to the default Keras float type.

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

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

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

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

Element-wise maximum of two tensors.

initializer_glorot_uniform

Glorot uniform initializer, also called Xavier uniform initializer.

Mean of a tensor, alongside the specified axis.

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

k_ctc_label_dense_to_sparse

Converts CTC labels from dense to sparse.

k_image_data_format

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

k_normalize_batch_in_training

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

k_is_keras_tensor

Returns whether x is a Keras tensor.

Element-wise inequality between two tensors.

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

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

Repeats a 2D tensor.

Fuzz factor used in numeric expressions.

initializer_random_uniform

Initializer that generates tensors with a uniform distribution.

initializer_truncated_normal

Initializer that generates a truncated normal distribution.

Batchwise dot product.

Active Keras backend

k_is_placeholder

Returns whether x is a placeholder.

Element-wise equality between two tensors.

k_moving_average_update

Compute the moving average of a variable.

Returns the value of a variable.

Element-wise value clipping.

Concatenates a list of tensors alongside the specified axis.

k_learning_phase

Returns the learning phase flag.

k_ctc_batch_cost

Runs CTC loss algorithm on each batch element.

k_get_variable_shape

Returns the shape of a variable.

k_repeat_elements

Repeats the elements of a tensor along an axis.

Decodes the output of a softmax.

Evaluates the value of a variable.

Reverse a tensor along the specified axes.

Element-wise exponential.

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

Flatten a tensor.

k_in_test_phase

Selects x in test phase, and alt otherwise.

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

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

Default float type

Returns whether the targets are in the top k predictions.

Apply 2D conv with un-shared weights.

k_permute_dimensions

Permutes axes in a tensor.

Minimum value in a tensor.

Element-wise log.

Iterates over the time dimension of a tensor

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

k_random_binomial

Returns a tensor with random binomial distribution of values.

Element-wise minimum of two tensors.

k_manual_variable_initialization

Sets the manual variable initialization flag.

k_random_normal

Returns a tensor with normal distribution of values.

Returns the symbolic shape of a tensor or variable.

Element-wise sigmoid.

Element-wise square root.

k_set_learning_phase

Sets the learning phase to a fixed value.

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

Element-wise square.

Element-wise sign.

Transposes a tensor and returns it.

Computes sin of x element-wise.

Computes the one-hot representation of an integer tensor.

Instantiates an all-ones tensor variable and returns it.

Element-wise exponentiation.

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_greater_equal

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

Segment-wise linear approximation of sigmoid.

k_random_uniform_variable

Instantiates a variable with values drawn from a uniform distribution.

Standard deviation of a tensor, alongside the specified axis.

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

Apply 1D conv with un-shared weights.

k_stop_gradient

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

layer_activation_parametric_relu

Parametric Rectified Linear Unit.

k_truncated_normal

Returns a tensor with truncated random normal distribution of values.

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

Creates a tensor by tiling x by n.

Reset graph identifiers.

layer_activation_softmax

Softmax activation function.

Instantiates a placeholder tensor and returns it.

Maximum value in a tensor.

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

keras_model_sequential

Keras Model composed of a linear stack of layers

Layer that adds a list of inputs.

layer_alpha_dropout

Applies Alpha Dropout to the input.

layer_activation

Apply an activation function to an output.

layer_conv_3d_transpose

Transposed 3D convolution layer (sometimes called Deconvolution).

layer_activation_thresholded_relu

Thresholded Rectified Linear Unit.

layer_cropping_3d

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

layer_conv_lstm_2d

Convolutional LSTM.

layer_activity_regularization

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

layer_depthwise_conv_2d

Depthwise separable 2D convolution.

k_resize_images

Resizes the images contained in a 4D tensor.

Prints message and the tensor value when evaluated.

k_resize_volumes

Resizes the volume contained in a 5D tensor.

Rectified linear unit.

layer_cudnn_gru

Fast GRU implementation backed by CuDNN.

Reshapes a tensor to the specified shape.

Applies Dropout to the input.

layer_embedding

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

Softmax of a tensor.

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

Wraps arbitrary expression as a layer

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

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.

Permute the dimensions of an input according to a given pattern

layer_global_max_pooling_3d

Global Max pooling operation for 3D data.

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

layer_upsampling_1d

Upsampling layer for 1D inputs.

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

Instantiates a variable and returns it.

layer_repeat_vector

Repeats the input n times.

Element-wise rounding to the closest integer.

k_separable_conv2d

2D convolution with separable filters.

Gated Recurrent Unit - Cho et al.

Softplus of a tensor.

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

Softsign of a tensor.

layer_max_pooling_1d

Max pooling operation for temporal data.

layer_upsampling_2d

Upsampling layer for 2D inputs.

Instantiates an all-zeros variable and returns it.

Switches between two operations depending on a scalar value.

metric_binary_accuracy

Model performance metrics

Update the value of x to new_x.

Update the value of x by adding increment.

Layer that averages a list of inputs.

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

R interface to Keras

k_sparse_categorical_crossentropy

Categorical crossentropy with integer targets.

Element-wise tanh.

layer_max_pooling_2d

Max pooling operation for spatial data.

loss_mean_squared_error

Model loss functions

layer_separable_conv_2d

Separable 2D convolution.

layer_simple_rnn

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

optimizer_nadam

Nesterov Adam optimizer

make_sampling_table

Generates a word rank-based probabilistic sampling table.

Model configuration as JSON

layer_average_pooling_1d

Average pooling for temporal data.

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

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

layer_cudnn_lstm

Fast LSTM implementation backed by CuDNN.

layer_global_max_pooling_1d

Global max pooling operation for temporal data.

Add a densely-connected NN layer to an output

optimizer_rmsprop

RMSProp optimizer

layer_global_max_pooling_2d

Global max pooling operation for spatial data.

layer_max_pooling_3d

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

Keras array object

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

predict_generator

Generates predictions for the input samples from a data generator.

layer_average_pooling_3d

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

layer_average_pooling_2d

Average pooling operation for spatial data.

k_temporal_padding

Pads the middle dimension of a 3D tensor.

Update the value of x by subtracting decrement.

Remove the last layer in a model

optimizer_adadelta

Adadelta optimizer.

predict_on_batch

Returns predictions for a single batch of samples.

predict.keras.engine.training.Model

Generate predictions from a Keras model

optimizer_adagrad

Adagrad optimizer.

Variance of a tensor, alongside the specified axis.

layer_cropping_1d

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

layer_cropping_2d

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

summary.keras.engine.training.Model

Print a summary of a Keras model

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

text_hashing_trick

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

layer_spatial_dropout_3d

Spatial 3D version of Dropout.

time_distributed

Apply a layer to every temporal slice of an input.

Layer that subtracts two inputs.

layer_activation_elu

Exponential Linear Unit.

timeseries_generator

Utility function for generating batches of 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).

text_to_word_sequence

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

multi_gpu_model

Replicates a model on different GPUs.

Normalize a matrix or nd-array

L1 and L2 regularization

save_model_hdf5

Save/Load models using HDF5 files

Reset the states for a layer

save_model_weights_hdf5

Save/Load model weights using HDF5 files

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

layer_gaussian_noise

Apply additive zero-centered Gaussian noise.

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

layer_global_average_pooling_1d

Global average pooling operation for temporal data.

layer_activation_leaky_relu

Leaky version of a Rectified Linear Unit.

layer_global_average_pooling_2d

Global average pooling operation for spatial data.

layer_global_average_pooling_3d

Global Average pooling operation for 3D data.

Long Short-Term Memory unit - Hochreiter 1997.

layer_batch_normalization

Batch normalization layer (Ioffe and Szegedy, 2014).

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

Reshapes an output to a certain shape.

layer_separable_conv_1d

Depthwise separable 1D convolution.

layer_concatenate

Layer that concatenates a list of inputs.

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

layer_conv_2d_transpose

Transposed 2D convolution layer (sometimes called Deconvolution).

texts_to_sequences

Transform each text in texts in a sequence of integers.

layer_upsampling_3d

Upsampling layer for 3D inputs.

texts_to_sequences_generator

Transforms each text in texts in a sequence of integers.

Flattens an input

layer_gaussian_dropout

Apply multiplicative 1-centered Gaussian noise.

Stochastic gradient descent optimizer

layer_zero_padding_1d

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

Pads sequences to the same length

Generates probability or class probability predictions for the input samples.

layer_locally_connected_1d

Locally-connected layer for 1D inputs.

layer_locally_connected_2d

Locally-connected layer for 2D inputs.

Objects exported from other packages

serialize_model

Serialize a model to an R object

use_implementation

Select a Keras implementation and backend

Generates skipgram word pairs.

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

with_custom_object_scope

Provide a scope with mappings of names to custom objects

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

layer_spatial_dropout_1d

Spatial 1D version of Dropout.

layer_spatial_dropout_2d

Spatial 2D version of Dropout.

Model configuration as YAML

Assign values to names

optimizer_adamax

Adamax optimizer

plot.keras_training_history

Plot training history

save_text_tokenizer

Save a text tokenizer to an external file

sequences_to_matrix

Convert a list of sequences into a matrix.

Text tokenization utility

texts_to_matrix

Convert a list of texts to a matrix.

application_densenet

Instantiates the DenseNet architecture.

application_inception_resnet_v2

Inception-ResNet v2 model, with weights trained on ImageNet

KerasConstraint

Base R6 class for Keras constraints

activation_relu

Activation functions

application_resnet50

ResNet50 model for Keras.

application_nasnet

Instantiates a NASNet model.

Base R6 class for Keras callbacks

application_inception_v3

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

application_mobilenet

MobileNet model architecture.

callback_model_checkpoint

Save the model after every epoch.

callback_csv_logger

Callback that streams epoch results to a csv file

callback_early_stopping

Stop training when a monitored quantity has stopped improving.

callback_lambda

Create a custom callback

Base R6 class for Keras layers

Keras backend tensor engine

application_vgg

VGG16 and VGG19 models for Keras.

callback_progbar_logger

Callback that prints metrics to stdout.

application_xception

Xception V1 model for Keras.

callback_learning_rate_scheduler

Learning rate scheduler.