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keras (version 2.2.4.1)

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

17,912

Version

2.2.4.1

License

MIT + file LICENSE

Maintainer

Daniel Falbel

Last Published

April 5th, 2019

Functions in keras (2.2.4.1)

dataset_boston_housing

Boston housing price regression dataset

callback_csv_logger

Callback that streams epoch results to a csv file

Keras backend tensor engine

callback_reduce_lr_on_plateau

Reduce learning rate when a metric has stopped improving.

flow_images_from_dataframe

Takes the dataframe and the path to a directory and generates batches of augmented/normalized data.

flow_images_from_data

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

callback_remote_monitor

Callback used to stream events to a server.

KerasConstraint

Base R6 class for Keras constraints

Bidirectional wrapper for RNNs.

Base R6 class for Keras callbacks

application_vgg

VGG16 and VGG19 models for Keras.

dataset_cifar10

CIFAR10 small image classification

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

dataset_fashion_mnist

Fashion-MNIST database of fashion articles

application_xception

Xception V1 model for Keras.

callback_progbar_logger

Callback that prints metrics to stdout.

dataset_cifar100

CIFAR100 small image classification

callback_model_checkpoint

Save the model after every epoch.

flow_images_from_directory

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

callback_early_stopping

Stop training when a monitored quantity has stopped improving.

Freeze and unfreeze weights

application_inception_resnet_v2

Inception-ResNet v2 model, with weights trained on ImageNet

fit_image_data_generator

Fit image data generator internal statistics to some sample data.

fit_text_tokenizer

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

callback_terminate_on_naan

Callback that terminates training when a NaN loss is encountered.

application_resnet50

ResNet50 model for Keras.

callback_tensorboard

TensorBoard basic visualizations

Weight constraints

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

image_data_generator

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

Retrieve the next item from a generator

Count the total number of scalars composing the weights.

dataset_reuters

Reuters newswire topics classification

Retrieve tensors for layers with multiple nodes

initializer_he_normal

He normal initializer.

initializer_he_uniform

He uniform variance scaling initializer.

initializer_random_uniform

Initializer that generates tensors with a uniform distribution.

Create a Keras Wrapper

Create a Keras Layer

application_inception_v3

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

initializer_truncated_normal

Initializer that generates a truncated normal distribution.

k_batch_flatten

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

Layer/Model configuration

k_batch_get_value

Returns the value of more than one tensor variable.

callback_lambda

Create a custom callback

Element-wise absolute value.

callback_learning_rate_scheduler

Learning rate scheduler.

Bitwise reduction (logical AND).

k_categorical_crossentropy

Categorical crossentropy between an output tensor and a target tensor.

k_clear_session

Destroys the current TF graph and creates a new one.

Keras implementation

initializer_constant

Initializer that generates tensors initialized to a constant value.

k_ctc_batch_cost

Runs CTC loss algorithm on each batch element.

IMDB Movie reviews sentiment classification

evaluate.keras.engine.training.Model

Evaluate a Keras model

Decodes the output of a softmax.

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

Exponential linear unit.

k_batch_normalization

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

k_batch_set_value

Sets the values of many tensor variables at once.

MNIST database of handwritten digits

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

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

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

fit.keras.engine.training.Model

Train a Keras model

evaluate_generator

Evaluates the model on a data generator.

k_greater_equal

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

Install Keras and the TensorFlow backend

export_savedmodel.keras.engine.training.Model

Export a Saved Model

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

Layer/Model weights as R arrays

initializer_glorot_normal

Glorot normal initializer, also called Xavier normal initializer.

initializer_glorot_uniform

Glorot uniform initializer, also called Xavier uniform initializer.

imagenet_decode_predictions

Decodes the prediction of an ImageNet model.

imagenet_preprocess_input

Preprocesses a tensor or array encoding a batch of images.

initializer_lecun_uniform

LeCun uniform initializer.

initializer_ones

Initializer that generates tensors initialized to 1.

Segment-wise linear approximation of sigmoid.

Returns the index of the maximum value along an axis.

Loads an image into PIL format.

3D array representation of images

is_keras_available

Check if Keras is Available

2D convolution.

initializer_identity

Initializer that generates the identity matrix.

k_conv2d_transpose

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

Casts a tensor to a different dtype and returns it.

Element-wise log.

initializer_orthogonal

Initializer that generates a random orthogonal matrix.

initializer_random_normal

Initializer that generates tensors with a normal distribution.

Bitwise reduction (logical OR).

initializer_lecun_normal

LeCun normal initializer.

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

k_ctc_label_dense_to_sparse

Converts CTC labels from dense to sparse.

Mean of a tensor, alongside the specified axis.

Creates a 1D tensor containing a sequence of integers.

Computes cos of x element-wise.

k_cast_to_floatx

Cast an array to the default Keras float type.

Minimum value in a tensor.

Returns the index of the minimum value along an axis.

Creates a constant tensor.

k_permute_dimensions

Permutes axes in a tensor.

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

Evaluates the value of a variable.

Element-wise exponential.

Returns the value of a variable.

k_get_variable_shape

Returns the shape of a variable.

initializer_zeros

Initializer that generates tensors initialized to 0.

initializer_variance_scaling

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

Batchwise dot product.

Active Keras backend

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

1D convolution.

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

Adds a bias vector to a tensor.

Instantiates a placeholder tensor and returns it.

k_depthwise_conv2d

Depthwise 2D convolution with separable filters.

Instantiates a Keras function

Retrieves the elements of indices indices in the tensor reference.

k_binary_crossentropy

Binary crossentropy between an output tensor and a target tensor.

Element-wise value clipping.

Concatenates a list of tensors alongside the specified axis.

Adds a 1-sized dimension at index axis.

Instantiate an identity matrix and returns it.

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

k_learning_phase

Returns the learning phase flag.

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

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

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

Rectified linear unit.

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

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

3D convolution.

Element-wise minimum of two tensors.

Repeats a 2D tensor.

k_in_test_phase

Selects x in test phase, and alt otherwise.

k_conv3d_transpose

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

k_moving_average_update

Compute the moving average of a variable.

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.

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

Maximum value in a tensor.

Element-wise maximum of two tensors.

k_random_normal

Returns a tensor with normal distribution of values.

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

k_random_binomial

Returns a tensor with random binomial distribution of values.

Returns whether the targets are in the top k predictions.

Fuzz factor used in numeric expressions.

Flatten a tensor.

Reshapes a tensor to the specified shape.

Element-wise equality between two tensors.

k_resize_images

Resizes the images contained in a 4D tensor.

Default float type

Computes sin of x element-wise.

k_random_normal_variable

Instantiates a variable with values drawn from a normal distribution.

TF session to be used by the backend.

k_manual_variable_initialization

Sets the manual variable initialization flag.

Softmax of a tensor.

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

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

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

k_normalize_batch_in_training

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

Softplus of a tensor.

k_image_data_format

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

Get the uid for the default graph.

k_random_uniform

Returns a tensor with uniform distribution of values.

Softsign of a tensor.

k_random_uniform_variable

Instantiates a variable with values drawn from a uniform distribution.

k_separable_conv2d

2D convolution with separable filters.

Returns whether a tensor is a sparse tensor.

Returns whether x is a symbolic tensor.

k_temporal_padding

Pads the middle dimension of a 3D tensor.

Creates a tensor by tiling x by n.

Instantiates an all-ones tensor variable and returns it.

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

k_set_learning_phase

Sets the learning phase to a fixed value.

keras_model_custom

Create a Keras custom model

Element-wise exponentiation.

k_is_keras_tensor

Returns whether x is a Keras tensor.

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

Prints message and the tensor value when evaluated.

k_repeat_elements

Repeats the elements of a tensor along an axis.

Standard deviation of a tensor, alongside the specified axis.

Reset graph identifiers.

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

Transposes a tensor and returns it.

R interface to Keras

k_is_placeholder

Returns whether x is a placeholder.

Keras array object

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

Returns the symbolic shape of a tensor or variable.

keras_model_sequential

Keras Model composed of a linear stack of layers

layer_cudnn_lstm

Fast LSTM implementation backed by CuDNN.

Iterates over the time dimension of a tensor

Element-wise rounding to the closest integer.

layer_activation

Apply an activation function to an output.

k_spatial_3d_padding

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

Apply 1D conv with un-shared weights.

Layer that adds a list of inputs.

Element-wise square root.

layer_alpha_dropout

Applies Alpha Dropout to the input.

Apply 2D conv with un-shared weights.

k_sparse_categorical_crossentropy

Categorical crossentropy with integer targets.

Element-wise inequality between two tensors.

Computes the one-hot representation of an integer tensor.

k_spatial_2d_padding

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

Element-wise square.

k_stop_gradient

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

Switches between two operations depending on a scalar value.

Element-wise tanh.

Add a densely-connected NN layer to an output

layer_conv_2d_transpose

Transposed 2D convolution layer (sometimes called Deconvolution).

Update the value of x by adding increment.

k_resize_volumes

Resizes the volume contained in a 5D tensor.

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

Variance of a tensor, alongside the specified axis.

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

Instantiates a variable and returns it.

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

layer_activation_selu

Scaled Exponential Linear Unit.

layer_activation_softmax

Softmax activation function.

layer_depthwise_conv_2d

Depthwise separable 2D convolution.

Reverse a tensor along the specified axes.

layer_average_pooling_2d

Average pooling operation for spatial data.

layer_average_pooling_3d

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

Update the value of x by subtracting decrement.

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

layer_activation_elu

Exponential Linear Unit.

Instantiates an all-zeros variable and returns it.

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

layer_activation_leaky_relu

Leaky version of a Rectified Linear Unit.

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

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

layer_locally_connected_1d

Locally-connected layer for 1D inputs.

layer_global_max_pooling_1d

Global max pooling operation for temporal data.

layer_locally_connected_2d

Locally-connected layer for 2D inputs.

layer_global_max_pooling_2d

Global max pooling operation for spatial data.

layer_gaussian_noise

Apply additive zero-centered Gaussian noise.

layer_upsampling_1d

Upsampling layer for 1D inputs.

layer_global_average_pooling_1d

Global average pooling operation for temporal data.

Reshapes an output to a certain shape.

Long Short-Term Memory unit - Hochreiter 1997.

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

layer_upsampling_2d

Upsampling layer for 2D inputs.

Model configuration as YAML

Element-wise sigmoid.

Element-wise sign.

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

Assign values to names

layer_separable_conv_1d

Depthwise separable 1D convolution.

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

layer_spatial_dropout_3d

Spatial 3D version of Dropout.

optimizer_nadam

Nesterov Adam optimizer

layer_separable_conv_2d

Separable 2D convolution.

layer_activation_parametric_relu

Parametric Rectified Linear Unit.

Layer that subtracts two inputs.

loss_mean_squared_error

Model loss functions

layer_simple_rnn

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

make_sampling_table

Generates a word rank-based probabilistic sampling table.

layer_zero_padding_2d

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

plot.keras_training_history

Plot training history

layer_cropping_3d

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

layer_zero_padding_3d

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

layer_cudnn_gru

Fast GRU implementation backed by CuDNN.

L1 and L2 regularization

Reset the states for a layer

layer_activation_relu

Rectified Linear Unit activation function

Applies Dropout to the input.

optimizer_rmsprop

RMSProp optimizer

layer_batch_normalization

Batch normalization layer (Ioffe and Szegedy, 2014).

predict_generator

Generates predictions for the input samples from a data generator.

predict_on_batch

Returns predictions for a single batch of samples.

serialize_model

Serialize a model to an R object

layer_embedding

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

Generates skipgram word pairs.

layer_max_pooling_1d

Max pooling operation for temporal data.

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 words (or tokens).

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

k_truncated_normal

Returns a tensor with truncated random normal distribution of values.

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

layer_concatenate

Layer that concatenates a list of inputs.

Update the value of x to new_x.

use_implementation

Select a Keras implementation and backend

layer_max_pooling_2d

Max pooling operation for spatial data.

with_custom_object_scope

Provide a scope with mappings of names to custom objects

layer_spatial_dropout_1d

Spatial 1D version of Dropout.

layer_spatial_dropout_2d

Spatial 2D version of Dropout.

metric_binary_accuracy

Model performance metrics

Model configuration as JSON

layer_activation_thresholded_relu

Thresholded Rectified Linear Unit.

layer_cropping_1d

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

optimizer_adadelta

Adadelta optimizer.

layer_activity_regularization

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

Layer that averages a list of inputs.

layer_average_pooling_1d

Average pooling for temporal data.

optimizer_adagrad

Adagrad optimizer.

layer_cropping_2d

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

Remove the last layer in a model

predict.keras.engine.training.Model

Generate predictions from a Keras model

layer_conv_3d_transpose

Transposed 3D convolution layer (sometimes called Deconvolution).

layer_conv_lstm_2d

Convolutional LSTM.

Flattens an input

save_text_tokenizer

Save a text tokenizer to an external file

sequences_to_matrix

Convert a list of sequences into a matrix.

layer_gaussian_dropout

Apply multiplicative 1-centered Gaussian noise.

layer_global_average_pooling_2d

Global average pooling operation for spatial data.

layer_global_max_pooling_3d

Global Max pooling operation for 3D data.

Gated Recurrent Unit - Cho et al.

layer_global_average_pooling_3d

Global Average pooling operation for 3D data.

Wraps arbitrary expression as a layer

layer_max_pooling_3d

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

Permute the dimensions of an input according to a given pattern

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

layer_repeat_vector

Repeats the input n times.

layer_upsampling_3d

Upsampling layer for 3D inputs.

layer_zero_padding_1d

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

multi_gpu_model

Replicates a model on different GPUs.

Stochastic gradient descent optimizer

Normalize a matrix or nd-array

Pads sequences to the same length

save_model_hdf5

Save/Load models using HDF5 files

save_model_weights_hdf5

Save/Load model weights using HDF5 files

summary.keras.engine.training.Model

Print a summary of a Keras model

text_hashing_trick

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

time_distributed

Apply a layer to every temporal slice of an input.

optimizer_adamax

Adamax optimizer

Generates probability or class probability predictions for the input samples.

timeseries_generator

Utility function for generating batches of temporal data.

Objects exported from other packages

Text tokenization utility

texts_to_matrix

Convert a list of texts to a matrix.

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.

application_nasnet

Instantiates a NASNet model.

Base R6 class for Keras layers

Base R6 class for Keras wrappers

application_mobilenet

MobileNet model architecture.

application_mobilenet_v2

MobileNetV2 model architecture

activation_relu

Activation functions

application_densenet

Instantiates the DenseNet architecture.

Clone a model instance.

compile.keras.engine.training.Model

Configure a Keras model for training