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keras (version 2.1.5)

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

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Version

2.1.5

License

MIT + file LICENSE

Maintainer

JJ Allaire

Last Published

March 25th, 2018

Functions in keras (2.1.5)

application_mobilenet

MobileNet model architecture.

application_inception_resnet_v2

Inception-ResNet v2 model, with weights trained on ImageNet

application_nasnet

Instantiates a NASNet model.

activation_relu

Activation functions

application_resnet50

ResNet50 model for Keras.

Base R6 class for Keras layers

application_inception_v3

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

application_densenet

Instantiates the DenseNet architecture.

Base R6 class for Keras callbacks

KerasConstraint

Base R6 class for Keras constraints

callback_progbar_logger

Callback that prints metrics to stdout.

callback_model_checkpoint

Save the model after every epoch.

callback_terminate_on_naan

Callback that terminates training when a NaN loss is encountered.

dataset_boston_housing

Boston housing price regression dataset

Bidirectional wrapper for RNNs.

Create a Keras Layer

application_vgg

VGG16 and VGG19 models for Keras.

callback_lambda

Create a custom callback

callback_csv_logger

Callback that streams epoch results to a csv file

callback_reduce_lr_on_plateau

Reduce learning rate when a metric has stopped improving.

dataset_fashion_mnist

Fashion-MNIST database of fashion articles

Keras backend tensor engine

callback_remote_monitor

Callback used to stream events to a server.

callback_learning_rate_scheduler

Learning rate scheduler.

Weight constraints

application_xception

Xception V1 model for Keras.

fit_text_tokenizer

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

Count the total number of scalars composing the weights.

Clone a model instance.

flow_images_from_data

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

callback_tensorboard

TensorBoard basic visualizations

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

Configure a Keras model for training

MNIST database of handwritten digits

flow_images_from_directory

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

Layer/Model weights as R arrays

IMDB Movie reviews sentiment classification

export_savedmodel.keras.engine.training.Model

Export a Saved Model

Freeze and unfreeze weights

Keras implementation

dataset_reuters

Reuters newswire topics classification

evaluate.keras.engine.training.Model

Evaluate a Keras model

Retrieve the next item from a generator

evaluate_generator

Evaluates the model on a data generator.

Layer/Model configuration

initializer_constant

Initializer that generates tensors initialized to a constant value.

dataset_cifar10

CIFAR10 small image classification

callback_early_stopping

Stop training when a monitored quantity has stopped improving.

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

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

dataset_cifar100

CIFAR100 small image classification

initializer_identity

Initializer that generates the identity matrix.

Retrieve tensors for layers with multiple nodes

image_data_generator

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

Train a Keras model

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

initializer_he_normal

He normal initializer.

fit_image_data_generator

Fit image data generator internal statistics to some sample data.

imagenet_decode_predictions

Decodes the prediction of an ImageNet model.

initializer_orthogonal

Initializer that generates a random orthogonal matrix.

initializer_random_normal

Initializer that generates tensors with a normal distribution.

initializer_random_uniform

Initializer that generates tensors with a uniform distribution.

initializer_glorot_normal

Glorot normal initializer, also called Xavier normal initializer.

imagenet_preprocess_input

Preprocesses a tensor or array encoding a batch of images.

initializer_he_uniform

He uniform variance scaling initializer.

initializer_truncated_normal

Initializer that generates a truncated normal distribution.

initializer_lecun_uniform

LeCun uniform initializer.

Loads an image into PIL format.

k_batch_flatten

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

initializer_ones

Initializer that generates tensors initialized to 1.

Element-wise absolute value.

Bitwise reduction (logical AND).

k_batch_get_value

Returns the value of more than one tensor variable.

3D array representation of images

initializer_lecun_normal

LeCun normal initializer.

Adds a bias vector to a tensor.

k_binary_crossentropy

Binary crossentropy between an output tensor and a target tensor.

Install Keras and the TensorFlow backend

initializer_variance_scaling

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

k_batch_set_value

Sets the values of many tensor variables at once.

Casts a tensor to a different dtype and returns it.

Element-wise value clipping.

k_cast_to_floatx

Cast an array to the default Keras float type.

initializer_zeros

Initializer that generates tensors initialized to 0.

is_keras_available

Check if Keras is Available

Concatenates a list of tensors alongside the specified axis.

Bitwise reduction (logical OR).

Returns the index of the maximum value along an axis.

k_ctc_batch_cost

Runs CTC loss algorithm on each batch element.

k_batch_normalization

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

Creates a 1D tensor containing a sequence of integers.

Decodes the output of a softmax.

Returns the index of the minimum value along an axis.

Creates a constant tensor.

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

k_categorical_crossentropy

Categorical crossentropy between an output tensor and a target tensor.

Adds a 1-sized dimension at index axis.

1D convolution.

Returns whether a tensor is a sparse tensor.

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

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

Instantiate an identity matrix and returns it.

k_clear_session

Destroys the current TF graph and creates a new one.

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

k_moving_average_update

Compute the moving average of a variable.

3D convolution.

TF session to be used by the backend.

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

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

k_conv3d_transpose

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

Get the uid for the default graph.

2D convolution.

Repeats a 2D tensor.

Fuzz factor used in numeric expressions.

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

k_repeat_elements

Repeats the elements of a tensor along an axis.

k_conv2d_transpose

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

Instantiates a Keras function

Element-wise equality between two tensors.

k_image_data_format

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

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

Retrieves the elements of indices indices in the tensor reference.

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

k_is_keras_tensor

Returns whether x is a Keras tensor.

k_depthwise_conv2d

Depthwise 2D convolution with separable filters.

k_greater_equal

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

k_is_placeholder

Returns whether x is a placeholder.

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

Apply 1D conv with un-shared weights.

Segment-wise linear approximation of sigmoid.

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

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

k_manual_variable_initialization

Sets the manual variable initialization flag.

initializer_glorot_uniform

Glorot uniform initializer, also called Xavier uniform initializer.

Maximum value in a tensor.

Exponential linear unit.

k_normalize_batch_in_training

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

k_in_test_phase

Selects x in test phase, and alt otherwise.

Active Keras backend

Flatten a tensor.

Element-wise inequality between two tensors.

Batchwise dot product.

Returns whether the targets are in the top k predictions.

Default float type

Apply 2D conv with un-shared weights.

Computes cos of x element-wise.

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

Element-wise rounding to the closest integer.

k_random_normal_variable

Instantiates a variable with values drawn from a normal distribution.

k_learning_phase

Returns the learning phase flag.

k_separable_conv2d

2D convolution with separable filters.

Returns the value of a variable.

k_random_uniform

Returns a tensor with uniform distribution of values.

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

Returns the symbolic shape of a tensor or variable.

k_resize_images

Resizes the images contained in a 4D tensor.

k_get_variable_shape

Returns the shape of a variable.

Reverse a tensor along the specified axes.

k_resize_volumes

Resizes the volume contained in a 5D tensor.

Iterates over the time dimension of a tensor

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

k_spatial_2d_padding

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

Element-wise exponentiation.

Softsign of a tensor.

Element-wise sigmoid.

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

k_ctc_label_dense_to_sparse

Converts CTC labels from dense to sparse.

k_sparse_categorical_crossentropy

Categorical crossentropy with integer targets.

Standard deviation of a tensor, alongside the specified axis.

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

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

k_stop_gradient

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

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

Element-wise log.

Update the value of x to new_x.

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

Evaluates the value of a variable.

Minimum value in a tensor.

Update the value of x by adding increment.

R interface to Keras

Element-wise minimum of two tensors.

k_spatial_3d_padding

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

layer_activation_elu

Exponential Linear Unit.

Element-wise exponential.

Layer that adds a list of inputs.

layer_alpha_dropout

Applies Alpha Dropout to the input.

Element-wise tanh.

k_in_train_phase

Selects x in train phase, and alt otherwise.

layer_cropping_3d

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

layer_activation_leaky_relu

Leaky version of a Rectified Linear Unit.

k_temporal_padding

Pads the middle dimension of a 3D tensor.

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

layer_cudnn_gru

Fast GRU implementation backed by CuDNN.

layer_average_pooling_2d

Average pooling operation for spatial data.

Element-wise maximum of two tensors.

Mean of a tensor, alongside the specified axis.

Creates a tensor by tiling x by n.

Prints message and the tensor value when evaluated.

layer_average_pooling_3d

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

Instantiates a placeholder tensor and returns it.

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

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

Computes the one-hot representation of an integer tensor.

k_set_learning_phase

Sets the learning phase to a fixed value.

Reset graph identifiers.

layer_cropping_1d

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

layer_depthwise_conv_2d

Depthwise separable 2D convolution.

k_random_binomial

Returns a tensor with random binomial distribution of values.

layer_cropping_2d

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

Reshapes a tensor to the specified shape.

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

layer_gaussian_noise

Apply additive zero-centered Gaussian noise.

k_random_normal

Returns a tensor with normal distribution of values.

Element-wise sign.

layer_global_max_pooling_3d

Global Max pooling operation for 3D data.

layer_global_average_pooling_1d

Global average pooling operation for temporal data.

Element-wise square root.

k_truncated_normal

Returns a tensor with truncated random normal distribution of values.

Computes sin of x element-wise.

Gated Recurrent Unit - Cho et al.

layer_activation_parametric_relu

Parametric Rectified Linear Unit.

Long Short-Term Memory unit - Hochreiter 1997.

Element-wise square.

Layer that averages a list of inputs.

layer_activation_softmax

Softmax activation function.

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

layer_batch_normalization

Batch normalization layer (Ioffe and Szegedy, 2014).

layer_average_pooling_1d

Average pooling for temporal data.

Transposes a tensor and returns it.

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

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

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

Softmax of a tensor.

Switches between two operations depending on a scalar value.

Instantiates an all-ones tensor variable and returns it.

Softplus of a tensor.

Instantiates a variable and returns it.

layer_concatenate

Layer that concatenates a list of inputs.

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

Flattens an input

Instantiates an all-zeros variable and returns it.

layer_max_pooling_3d

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

k_permute_dimensions

Permutes axes in a tensor.

layer_gaussian_dropout

Apply multiplicative 1-centered Gaussian noise.

Keras array object

layer_global_average_pooling_2d

Global average pooling operation for spatial data.

k_random_uniform_variable

Instantiates a variable with values drawn from a uniform distribution.

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

Update the value of x by subtracting decrement.

Rectified linear unit.

layer_spatial_dropout_3d

Spatial 3D version of Dropout.

Variance of a tensor, alongside the specified axis.

layer_conv_2d_transpose

Transposed 2D convolution layer (sometimes called Deconvolution).

layer_locally_connected_1d

Locally-connected layer for 1D inputs.

keras_model_sequential

Keras Model composed of a linear stack of layers

Layer that subtracts two inputs.

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

layer_locally_connected_2d

Locally-connected layer for 2D inputs.

layer_activation

Apply an activation function to an output.

layer_simple_rnn

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

layer_global_average_pooling_3d

Global Average pooling operation for 3D data.

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

Pads sequences to the same length

layer_zero_padding_2d

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

layer_max_pooling_1d

Max pooling operation for temporal data.

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

layer_spatial_dropout_1d

Spatial 1D version of Dropout.

layer_zero_padding_3d

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

layer_max_pooling_2d

Max pooling operation for spatial data.

layer_upsampling_1d

Upsampling layer for 1D inputs.

layer_spatial_dropout_2d

Spatial 2D version of Dropout.

Stochastic gradient descent optimizer

layer_separable_conv_2d

Depthwise separable 2D convolution.

Model configuration as YAML

layer_upsampling_2d

Upsampling layer for 2D inputs.

layer_activation_thresholded_relu

Thresholded Rectified Linear Unit.

layer_conv_lstm_2d

Convolutional LSTM.

metric_binary_accuracy

Model performance metrics

layer_upsampling_3d

Upsampling layer for 3D inputs.

layer_cudnn_lstm

Fast LSTM implementation backed by CuDNN.

Model configuration as JSON

layer_activity_regularization

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

layer_zero_padding_1d

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

multi_gpu_model

Replicates a model on different GPUs.

Add a densely-connected NN layer to an output

layer_conv_3d_transpose

Transposed 3D convolution layer (sometimes called Deconvolution).

optimizer_adadelta

Adadelta optimizer.

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

text_to_word_sequence

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

Wraps arbitrary expression as a layer

Applies Dropout to the input.

optimizer_adagrad

Adagrad optimizer.

Permute the dimensions of an input according to a given pattern

layer_embedding

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

save_text_tokenizer

Save a text tokenizer to an external file

Remove the last layer in a model

layer_global_max_pooling_1d

Global max pooling operation for temporal data.

Normalize a matrix or nd-array

sequences_to_matrix

Convert a list of sequences into a matrix.

predict_generator

Generates predictions for the input samples from a data generator.

layer_global_max_pooling_2d

Global max pooling operation for spatial data.

predict.keras.engine.training.Model

Generate predictions from a Keras model

predict_on_batch

Returns predictions for a single batch of samples.

use_implementation

Select a Keras implementation and backend

loss_mean_squared_error

Model loss functions

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

Assign values to names

make_sampling_table

Generates a word rank-based probabilistic sampling table.

with_custom_object_scope

Provide a scope with mappings of names to custom objects

time_distributed

Apply a layer to every temporal slice of an input.

L1 and L2 regularization

summary.keras.engine.training.Model

Print a summary of a Keras model

optimizer_adamax

Adamax optimizer

Reset the states for a layer

timeseries_generator

Utility function for generating batches of temporal data.

layer_repeat_vector

Repeats the input n times.

text_hashing_trick

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

optimizer_nadam

Nesterov Adam optimizer

plot.keras_training_history

Plot training history

Reshapes an output to a certain shape.

texts_to_sequences

Transform each text in texts in a sequence of integers.

optimizer_rmsprop

RMSProp optimizer

serialize_model

Serialize a model to an R object

layer_separable_conv_1d

Depthwise separable 1D convolution.

texts_to_sequences_generator

Transforms each text in texts in a sequence of integers.

save_model_weights_hdf5

Save/Load model weights using HDF5 files

Generates probability or class probability predictions for the input samples.

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

Generates skipgram word pairs.

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

Text tokenization utility

Objects exported from other packages

texts_to_matrix

Convert a list of texts to a matrix.

save_model_hdf5

Save/Load models using HDF5 files