Type:	Package
Title:	R Interface to 'Keras'
Version:	2.15.0
Description:	Interface to 'Keras' https://keras.io, 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.
Encoding:	UTF-8
License:	MIT + file LICENSE
URL:	https://tensorflow.rstudio.com/, https://github.com/rstudio/keras/tree/r2
BugReports:	https://github.com/rstudio/keras/issues
Depends:	R (≥ 3.6)
Imports:	generics (≥ 0.0.1), reticulate (≥ 1.31), tensorflow (≥ 2.13.0.9000), tfruns (≥ 1.0), magrittr, zeallot, glue, methods, R6, rlang
Suggests:	ggplot2, testthat (≥ 2.1.0), knitr, rmarkdown, callr, tfdatasets, withr, png, jpeg
RoxygenNote:	7.3.1
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2024-04-19 18:01:27 UTC; tomasz
Author:	Tomasz Kalinowski [ctb, cph, cre], Daniel Falbel [ctb, cph], JJ Allaire [aut, cph], François Chollet [aut, cph], RStudio [ctb, cph, fnd], Google [ctb, cph, fnd], Yuan Tang [ctb, cph], Wouter Van Der Bijl [ctb, cph], Martin Studer [ctb, cph], Sigrid Keydana [ctb]
Maintainer:	Tomasz Kalinowski <tomasz@posit.co>
Repository:	CRAN
Date/Publication:	2024-04-20 05:42:42 UTC

R interface to Keras

Description

Keras is a high-level neural networks API, developed with a focus on enabling fast experimentation. Keras has the following key features:

Details

• Allows the same code to run on CPU or on GPU, seamlessly.

• User-friendly API which makes it easy to quickly prototype deep learning models.

• Built-in support for convolutional networks (for computer vision), recurrent networks (for sequence processing), and any combination of both.

• Supports arbitrary network architectures: multi-input or multi-output models, layer sharing, model sharing, etc. This means that Keras is appropriate for building essentially any deep learning model, from a memory network to a neural Turing machine.

• Is capable of running on top of multiple back-ends including TensorFlow, CNTK, or Theano.

See the package website at https://tensorflow.rstudio.com for complete documentation.

Author(s)

Maintainer: Tomasz Kalinowski tomasz@posit.co [contributor, copyright holder]

Authors:

• JJ Allaire [copyright holder]

• François Chollet [copyright holder]

Other contributors:

• Daniel Falbel daniel@rstudio.com [contributor, copyright holder]

• RStudio [contributor, copyright holder, funder]

• Google [contributor, copyright holder, funder]

• Yuan Tang terrytangyuan@gmail.com (ORCID) [contributor, copyright holder]

• Wouter Van Der Bijl [contributor, copyright holder]

• Martin Studer [contributor, copyright holder]

• Sigrid Keydana [contributor]

See Also

Useful links:

• https://tensorflow.rstudio.com/

• https://github.com/rstudio/keras/tree/r2

• Report bugs at https://github.com/rstudio/keras/issues

Pipe operator

Description

See %>% for more details.

Usage

lhs %>% rhs

Assign values to names

Description

See %<-% for more details.

Usage

x %<-% value

Make an Active Binding

Description

Make an Active Binding

Usage

sym %<-active% value

Arguments

Details

Active bindings defined in a %py_class% are converted to ⁠@property⁠ decorated methods.

Value

value, invisibly

See Also

makeActiveBinding()

Examples

set.seed(1234)
x %<-active% function(value) {
  message("Evaluating function of active binding")
  if(missing(value))
    runif(1)
  else
   message("Received: ", value)
}
x
x
x <- "foo"
x <- "foo"
x
rm(x) # cleanup

Make a python class constructor

Description

Make a python class constructor

Usage

spec %py_class% body

Arguments

Value

The python class constructor, invisibly. Note, the same constructor is also assigned in the parent frame.

Examples

## Not run: 
MyClass %py_class% {
  initialize <- function(x) {
    print("Hi from MyClass$initialize()!")
    self$x <- x
  }
  my_method <- function() {
    self$x
  }
}

my_class_instance <- MyClass(42)
my_class_instance$my_method()

MyClass2(MyClass) %py_class% {
  "This will be a __doc__ string for MyClass2"

  initialize <- function(...) {
    "This will be the __doc__ string for the MyClass2.__init__() method"
    print("Hi from MyClass2$initialize()!")
    super$initialize(...)
  }
}

my_class_instance2 <- MyClass2(42)
my_class_instance2$my_method()

reticulate::py_help(MyClass2) # see the __doc__ strings and more!

# In addition to `self`, there is also `private` available.
# This is an R environment unique to each class instance, where you can
# store objects that you don't want converted to Python, but still want
# available from methods. You can also assign methods to private, and
# `self` and `private` will be available in private methods.

MyClass %py_class% {

  initialize <- function(x) {
    print("Hi from MyClass$initialize()!")
    private$y <- paste("A Private field:", x)
  }

  get_private_field <- function() {
    private$y
  }

  private$a_private_method <- function() {
    cat("a_private_method() was called.\n")
    cat("private$y is ", sQuote(private$y), "\n")
  }

  call_private_method <- function()
    private$a_private_method()

  # equivalent of @property decorator in python
  an_active_property %<-active% function(x = NULL) {
    if(!is.null(x)) {
      cat("`an_active_property` was assigned", x, "\n")
      return(x)
    } else {
      cat("`an_active_property` was accessed\n")
      return(42)
    }
  }
}

inst1 <- MyClass(1)
inst2 <- MyClass(2)
inst1$get_private_field()
inst2$get_private_field()
inst1$call_private_method()
inst2$call_private_method()
inst1$an_active_property
inst1$an_active_property <- 11

## End(Not run)

Activation functions

Description

relu(...): Applies the rectified linear unit activation function.

elu(...): Exponential Linear Unit.

selu(...): Scaled Exponential Linear Unit (SELU).

hard_sigmoid(...): Hard sigmoid activation function.

linear(...): Linear activation function (pass-through).

sigmoid(...): Sigmoid activation function, sigmoid(x) = 1 / (1 + exp(-x)).

softmax(...): Softmax converts a vector of values to a probability distribution.

softplus(...): Softplus activation function, softplus(x) = log(exp(x) + 1).

softsign(...): Softsign activation function, softsign(x) = x / (abs(x) + 1).

tanh(...): Hyperbolic tangent activation function.

exponential(...): Exponential activation function.

gelu(...): Applies the Gaussian error linear unit (GELU) activation function.

swish(...): Swish activation function, swish(x) = x * sigmoid(x).

Usage

activation_relu(x, alpha = 0, max_value = NULL, threshold = 0)

activation_elu(x, alpha = 1)

activation_selu(x)

activation_hard_sigmoid(x)

activation_linear(x)

activation_sigmoid(x)

activation_softmax(x, axis = -1)

activation_softplus(x)

activation_softsign(x)

activation_tanh(x)

activation_exponential(x)

activation_gelu(x, approximate = FALSE)

activation_swish(x)

Arguments

Details

Activations functions can either be used through layer_activation(), or through the activation argument supported by all forward layers.

• activation_selu() to be used together with the initialization "lecun_normal".

• activation_selu() to be used together with the dropout variant "AlphaDropout".

Value

Tensor with the same shape and dtype as x.

References

• activation_swish(): Searching for Activation Functions

• activation_gelu(): Gaussian Error Linear Units (GELUs)

• activation_selu(): Self-Normalizing Neural Networks

• activation_elu(): Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

See Also

https://www.tensorflow.org/api_docs/python/tf/keras/activations

Fits the state of the preprocessing layer to the data being passed

Description

Fits the state of the preprocessing layer to the data being passed

Usage

adapt(object, data, ..., batch_size = NULL, steps = NULL)

Arguments

Details

After calling adapt on a layer, a preprocessing layer's state will not update during training. In order to make preprocessing layers efficient in any distribution context, they are kept constant with respect to any compiled tf.Graphs that call the layer. This does not affect the layer use when adapting each layer only once, but if you adapt a layer multiple times you will need to take care to re-compile any compiled functions as follows:

• If you are adding a preprocessing layer to a keras.Model, you need to call compile(model) after each subsequent call to adapt().

• If you are calling a preprocessing layer inside tfdatasets::dataset_map(), you should call dataset_map() again on the input tf.data.Dataset after each adapt().

• If you are using a tensorflow::tf_function() directly which calls a preprocessing layer, you need to call tf_function again on your callable after each subsequent call to adapt().

keras_model example with multiple adapts:

layer <- layer_normalization(axis=NULL)
adapt(layer, c(0, 2))
model <- keras_model_sequential(layer)
predict(model, c(0, 1, 2)) # [1] -1  0  1

adapt(layer, c(-1, 1))
compile(model)  # This is needed to re-compile model.predict!
predict(model, c(0, 1, 2)) # [1] 0 1 2

tf.data.Dataset example with multiple adapts:

layer <- layer_normalization(axis=NULL)
adapt(layer, c(0, 2))
input_ds <- tfdatasets::range_dataset(0, 3)
normalized_ds <- input_ds %>%
  tfdatasets::dataset_map(layer)
str(reticulate::iterate(normalized_ds))
# List of 3
#  $ :tf.Tensor([-1.], shape=(1,), dtype=float32)
#  $ :tf.Tensor([0.], shape=(1,), dtype=float32)
#  $ :tf.Tensor([1.], shape=(1,), dtype=float32)
adapt(layer, c(-1, 1))
normalized_ds <- input_ds %>%
  tfdatasets::dataset_map(layer) # Re-map over the input dataset.
str(reticulate::iterate(normalized_ds$as_numpy_iterator()))
# List of 3
#  $ : num [1(1d)] -1
#  $ : num [1(1d)] 0
#  $ : num [1(1d)] 1

See Also

• https://www.tensorflow.org/guide/keras/preprocessing_layers#the_adapt_method

Instantiates the DenseNet architecture.

Description

Instantiates the DenseNet architecture.

Usage

application_densenet(
  blocks,
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000
)

application_densenet121(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000
)

application_densenet169(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000
)

application_densenet201(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000
)

densenet_preprocess_input(x, data_format = NULL)

Arguments

Details

Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set image_data_format='channels_last' in your Keras config at ~/.keras/keras.json.

The model and the weights are compatible with TensorFlow, Theano, and CNTK. The data format convention used by the model is the one specified in your Keras config file.

Instantiates the EfficientNetB0 architecture

Description

Instantiates the EfficientNetB0 architecture

Usage

application_efficientnet_b0(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  ...
)

application_efficientnet_b1(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  ...
)

application_efficientnet_b2(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  ...
)

application_efficientnet_b3(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  ...
)

application_efficientnet_b4(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  ...
)

application_efficientnet_b5(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  ...
)

application_efficientnet_b6(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  ...
)

application_efficientnet_b7(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  ...
)

Arguments

Details

Reference:

• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

EfficientNet models expect their inputs to be float tensors of pixels with values in the ⁠[0-255]⁠ range.

Note

Each Keras Application typically expects a specific kind of input preprocessing. For EfficientNet, input preprocessing is included as part of the model (as a Rescaling layer), and thus a calling a preprocessing function is not necessary.

See Also

• https://www.tensorflow.org/api_docs/python/tf/keras/applications/efficientnet/EfficientNetB0

• https://keras.io/api/applications/

Inception-ResNet v2 model, with weights trained on ImageNet

Description

Inception-ResNet v2 model, with weights trained on ImageNet

Usage

application_inception_resnet_v2(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000,
  classifier_activation = "softmax",
  ...
)

inception_resnet_v2_preprocess_input(x)

Arguments

Details

Do note that the input image format for this model is different than for the VGG16 and ResNet models (299x299 instead of 224x224).

The inception_resnet_v2_preprocess_input() function should be used for image preprocessing.

Value

A Keras model instance.

Reference

• Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning(https://arxiv.org/abs/1512.00567)

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

Description

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

Usage

application_inception_v3(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000,
  classifier_activation = "softmax",
  ...
)

inception_v3_preprocess_input(x)

Arguments

Details

Do note that the input image format for this model is different than for the VGG16 and ResNet models (299x299 instead of 224x224).

The inception_v3_preprocess_input() function should be used for image preprocessing.

Value

A Keras model instance.

Reference

• Rethinking the Inception Architecture for Computer Vision

MobileNet model architecture.

Description

MobileNet model architecture.

Usage

application_mobilenet(
  input_shape = NULL,
  alpha = 1,
  depth_multiplier = 1L,
  dropout = 0.001,
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  ...
)

mobilenet_preprocess_input(x)

mobilenet_decode_predictions(preds, top = 5)

mobilenet_load_model_hdf5(filepath)

Arguments

Details

The mobilenet_preprocess_input() function should be used for image preprocessing. To load a saved instance of a MobileNet model use the mobilenet_load_model_hdf5() function. To prepare image input for MobileNet use mobilenet_preprocess_input(). To decode predictions use mobilenet_decode_predictions().

Value

application_mobilenet() and mobilenet_load_model_hdf5() return a Keras model instance. mobilenet_preprocess_input() returns image input suitable for feeding into a mobilenet model. mobilenet_decode_predictions() returns a list of data frames with variables class_name, class_description, and score (one data frame per sample in batch input).

Reference

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

MobileNetV2 model architecture

Description

MobileNetV2 model architecture

Usage

application_mobilenet_v2(
  input_shape = NULL,
  alpha = 1,
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  pooling = NULL,
  classes = 1000,
  classifier_activation = "softmax",
  ...
)

mobilenet_v2_preprocess_input(x)

mobilenet_v2_decode_predictions(preds, top = 5)

mobilenet_v2_load_model_hdf5(filepath)

Arguments

Value

application_mobilenet_v2() and mobilenet_v2_load_model_hdf5() return a Keras model instance. mobilenet_v2_preprocess_input() returns image input suitable for feeding into a mobilenet v2 model. mobilenet_v2_decode_predictions() returns a list of data frames with variables class_name, class_description, and score (one data frame per sample in batch input).

Reference

• MobileNetV2: Inverted Residuals and Linear Bottlenecks

See Also

application_mobilenet

Instantiates the MobileNetV3Large architecture

Description

Instantiates the MobileNetV3Large architecture

Usage

application_mobilenet_v3_large(
  input_shape = NULL,
  alpha = 1,
  minimalistic = FALSE,
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  classes = 1000L,
  pooling = NULL,
  dropout_rate = 0.2,
  classifier_activation = "softmax",
  include_preprocessing = TRUE
)

application_mobilenet_v3_small(
  input_shape = NULL,
  alpha = 1,
  minimalistic = FALSE,
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  classes = 1000L,
  pooling = NULL,
  dropout_rate = 0.2,
  classifier_activation = "softmax",
  include_preprocessing = TRUE
)

Arguments

Details

Reference:

• Searching for MobileNetV3 (ICCV 2019)

The following table describes the performance of MobileNets v3:

MACs stands for Multiply Adds

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Value

A keras Model instance

Note

Each Keras application typically expects a specific kind of input preprocessing. For ModelNetV3, by default input preprocessing is included as a part of the model (as a Rescaling layer), and thus a preprocessing function is not necessary. In this use case, ModelNetV3 models expect their inputs to be float tensors of pixels with values in the ⁠[0-255]⁠ range. At the same time, preprocessing as a part of the model (i.e. Rescaling layer) can be disabled by setting include_preprocessing argument to FALSE. With preprocessing disabled ModelNetV3 models expect their inputs to be float tensors of pixels with values in the ⁠[-1, 1]⁠ range.

See Also

• https://www.tensorflow.org/api_docs/python/tf/keras/applications/MobileNetV3Large

• https://www.tensorflow.org/api_docs/python/tf/keras/applications/MobileNetV3Small

• https://keras.io/api/applications/

Instantiates a NASNet model.

Description

Note that only TensorFlow is supported for now, therefore it only works with the data format image_data_format='channels_last' in your Keras config at ⁠~/.keras/keras.json⁠.

Usage

application_nasnet(
  input_shape = NULL,
  penultimate_filters = 4032L,
  num_blocks = 6L,
  stem_block_filters = 96L,
  skip_reduction = TRUE,
  filter_multiplier = 2L,
  include_top = TRUE,
  weights = NULL,
  input_tensor = NULL,
  pooling = NULL,
  classes = 1000,
  default_size = NULL
)

application_nasnetlarge(
  input_shape = NULL,
  include_top = TRUE,
  weights = NULL,
  input_tensor = NULL,
  pooling = NULL,
  classes = 1000
)

application_nasnetmobile(
  input_shape = NULL,
  include_top = TRUE,
  weights = NULL,
  input_tensor = NULL,
  pooling = NULL,
  classes = 1000
)

nasnet_preprocess_input(x)

Arguments

Instantiates the ResNet architecture

Description

Instantiates the ResNet architecture

Usage

application_resnet50(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000,
  ...
)

application_resnet101(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000,
  ...
)

application_resnet152(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000,
  ...
)

application_resnet50_v2(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000,
  classifier_activation = "softmax",
  ...
)

application_resnet101_v2(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000,
  classifier_activation = "softmax",
  ...
)

application_resnet152_v2(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000,
  classifier_activation = "softmax",
  ...
)

resnet_preprocess_input(x)

resnet_v2_preprocess_input(x)

Arguments

Details

Reference:

• Deep Residual Learning for Image Recognition (CVPR 2015)

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call tf.keras.applications.resnet.preprocess_input on your inputs before passing them to the model. resnet.preprocess_input will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling.

See Also

• https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet50/ResNet50

• https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet/ResNet101

• https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet/ResNet152

• https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet_v2/ResNet50V2

• https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet_v2/ResNet101V2

• https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet_v2/ResNet152V2

• https://keras.io/api/applications/

Examples

## Not run: 
library(keras)

# instantiate the model
model <- application_resnet50(weights = 'imagenet')

# load the image
img_path <- "elephant.jpg"
img <- image_load(img_path, target_size = c(224,224))
x <- image_to_array(img)

# ensure we have a 4d tensor with single element in the batch dimension,
# the preprocess the input for prediction using resnet50
x <- array_reshape(x, c(1, dim(x)))
x <- imagenet_preprocess_input(x)

# make predictions then decode and print them
preds <- model %>% predict(x)
imagenet_decode_predictions(preds, top = 3)[[1]]

## End(Not run)

VGG16 and VGG19 models for Keras.

Description

VGG16 and VGG19 models for Keras.

Usage

application_vgg16(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000,
  classifier_activation = "softmax"
)

application_vgg19(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000,
  classifier_activation = "softmax"
)

Arguments

Details

Optionally loads weights pre-trained on ImageNet.

The imagenet_preprocess_input() function should be used for image preprocessing.

Value

Keras model instance.

Reference

- Very Deep Convolutional Networks for Large-Scale Image Recognition

Examples

## Not run: 
library(keras)

model <- application_vgg16(weights = 'imagenet', include_top = FALSE)

img_path <- "elephant.jpg"
img <- image_load(img_path, target_size = c(224,224))
x <- image_to_array(img)
x <- array_reshape(x, c(1, dim(x)))
x <- imagenet_preprocess_input(x)

features <- model %>% predict(x)

## End(Not run)

Instantiates the Xception architecture

Description

Instantiates the Xception architecture

Usage

application_xception(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000,
  classifier_activation = "softmax",
  ...
)

xception_preprocess_input(x)

Arguments

Details

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

The default input image size for this model is 299x299.

Reference

• Xception: Deep Learning with Depthwise Separable Convolutions (CVPR 2017)

Note

Each Keras Application typically expects a specific kind of input preprocessing. For Xception, call xception_preprocess_input() on your inputs before passing them to the model. xception_preprocess_input() will scale input pixels between -1 and 1.

See Also

• https://www.tensorflow.org/api_docs/python/tf/keras/applications/xception/Xception

• https://keras.io/api/applications/

Keras backend tensor engine

Description

Obtain a reference to the keras.backend Python module used to implement tensor operations.

Usage

backend(convert = TRUE)

Arguments

Value

Reference to Keras backend python module.

Note

See the documentation here https://keras.io/backend/ for additional details on the available functions.

Bidirectional wrapper for RNNs

Description

Bidirectional wrapper for RNNs

Usage

bidirectional(
  object,
  layer,
  merge_mode = "concat",
  weights = NULL,
  backward_layer = NULL,
  ...
)

Arguments

See Also

• https://www.tensorflow.org/api_docs/python/tf/keras/layers/Bidirectional

• https://keras.io/api/layers/recurrent_layers/bidirectional/

Other layer wrappers: time_distributed()

Callback to back up and restore the training state

Description

Callback to back up and restore the training state

Usage

callback_backup_and_restore(backup_dir, ...)

Arguments

Details

BackupAndRestore callback is intended to recover training from an interruption that has happened in the middle of a fit(model) execution, by backing up the training states in a temporary checkpoint file (with the help of a tf.train.CheckpointManager), at the end of each epoch. Each backup overwrites the previously written checkpoint file, so at any given time there is at most one such checkpoint file for backup/restoring purpose.

If training restarts before completion, the training state (which includes the Model weights and epoch number) is restored to the most recently saved state at the beginning of a new fit() run. At the completion of a fit() run, the temporary checkpoint file is deleted.

Note that the user is responsible to bring jobs back after the interruption. This callback is important for the backup and restore mechanism for fault tolerance purpose, and the model to be restored from an previous checkpoint is expected to be the same as the one used to back up. If user changes arguments passed to compile or fit, the checkpoint saved for fault tolerance can become invalid.

Note:

1. This callback is not compatible with eager execution disabled.

2. A checkpoint is saved at the end of each epoch. After restoring, fit() redoes any partial work during the unfinished epoch in which the training got restarted (so the work done before the interruption doesn't affect the final model state).

3. This works for both single worker and multi-worker modes. When fit() is used with tf.distribute, it supports tf.distribute.MirroredStrategy, tf.distribute.MultiWorkerMirroredStrategy, tf.distribute.TPUStrategy, and tf.distribute.experimental.ParameterServerStrategy.

See Also

• https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/BackupAndRestore

Callback that streams epoch results to a csv file

Description

Supports all values that can be represented as a string

Usage

callback_csv_logger(filename, separator = ",", append = FALSE)

Arguments

See Also

Other callbacks: callback_early_stopping(), callback_lambda(), callback_learning_rate_scheduler(), callback_model_checkpoint(), callback_progbar_logger(), callback_reduce_lr_on_plateau(), callback_remote_monitor(), callback_tensorboard(), callback_terminate_on_naan()

Stop training when a monitored quantity has stopped improving.

Description

Stop training when a monitored quantity has stopped improving.

Usage

callback_early_stopping(
  monitor = "val_loss",
  min_delta = 0,
  patience = 0,
  verbose = 0,
  mode = c("auto", "min", "max"),
  baseline = NULL,
  restore_best_weights = FALSE
)

Arguments

See Also

Other callbacks: callback_csv_logger(), callback_lambda(), callback_learning_rate_scheduler(), callback_model_checkpoint(), callback_progbar_logger(), callback_reduce_lr_on_plateau(), callback_remote_monitor(), callback_tensorboard(), callback_terminate_on_naan()

Create a custom callback

Description

This callback is constructed with anonymous functions that will be called at the appropriate time. Note that the callbacks expects positional arguments, as:

Usage

callback_lambda(
  on_epoch_begin = NULL,
  on_epoch_end = NULL,
  on_batch_begin = NULL,
  on_batch_end = NULL,
  on_train_batch_begin = NULL,
  on_train_batch_end = NULL,
  on_train_begin = NULL,
  on_train_end = NULL,
  on_predict_batch_begin = NULL,
  on_predict_batch_end = NULL,
  on_predict_begin = NULL,
  on_predict_end = NULL,
  on_test_batch_begin = NULL,
  on_test_batch_end = NULL,
  on_test_begin = NULL,
  on_test_end = NULL
)

Arguments

Details

• on_epoch_begin and on_epoch_end expect two positional arguments: epoch, logs

• ⁠on_batch_*⁠, ⁠on_train_batch_*⁠, ⁠on_predict_batch_*⁠ and ⁠on_test_batch_*⁠, expect two positional arguments: batch, logs

• ⁠on_train_*⁠, ⁠on_test_*⁠ and ⁠on_predict_*⁠ expect one positional argument: logs

See Also

Other callbacks: callback_csv_logger(), callback_early_stopping(), callback_learning_rate_scheduler(), callback_model_checkpoint(), callback_progbar_logger(), callback_reduce_lr_on_plateau(), callback_remote_monitor(), callback_tensorboard(), callback_terminate_on_naan()

Learning rate scheduler.

Description

Learning rate scheduler.

Usage

callback_learning_rate_scheduler(schedule)

Arguments

See Also

Other callbacks: callback_csv_logger(), callback_early_stopping(), callback_lambda(), callback_model_checkpoint(), callback_progbar_logger(), callback_reduce_lr_on_plateau(), callback_remote_monitor(), callback_tensorboard(), callback_terminate_on_naan()

Save the model after every epoch.

Description

filepath can contain named formatting options, which will be filled the value of epoch and keys in logs (passed in on_epoch_end). For example: if filepath is ⁠weights.{epoch:02d}-{val_loss:.2f}.hdf5⁠, then the model checkpoints will be saved with the epoch number and the validation loss in the filename.

Usage

callback_model_checkpoint(
  filepath,
  monitor = "val_loss",
  verbose = 0,
  save_best_only = FALSE,
  save_weights_only = FALSE,
  mode = c("auto", "min", "max"),
  period = NULL,
  save_freq = "epoch"
)

Arguments

For example

if filepath is ⁠weights.{epoch:02d}-{val_loss:.2f}.hdf5⁠,: then the model checkpoints will be saved with the epoch number and the validation loss in the filename.

See Also

Other callbacks: callback_csv_logger(), callback_early_stopping(), callback_lambda(), callback_learning_rate_scheduler(), callback_progbar_logger(), callback_reduce_lr_on_plateau(), callback_remote_monitor(), callback_tensorboard(), callback_terminate_on_naan()

Callback that prints metrics to stdout.

Description

Callback that prints metrics to stdout.

Usage

callback_progbar_logger(count_mode = "samples", stateful_metrics = NULL)

Arguments

See Also

Other callbacks: callback_csv_logger(), callback_early_stopping(), callback_lambda(), callback_learning_rate_scheduler(), callback_model_checkpoint(), callback_reduce_lr_on_plateau(), callback_remote_monitor(), callback_tensorboard(), callback_terminate_on_naan()

Reduce learning rate when a metric has stopped improving.

Description

Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. This callback monitors a quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced.

Usage

callback_reduce_lr_on_plateau(
  monitor = "val_loss",
  factor = 0.1,
  patience = 10,
  verbose = 0,
  mode = c("auto", "min", "max"),
  min_delta = 1e-04,
  cooldown = 0,
  min_lr = 0
)

Arguments

See Also

Other callbacks: callback_csv_logger(), callback_early_stopping(), callback_lambda(), callback_learning_rate_scheduler(), callback_model_checkpoint(), callback_progbar_logger(), callback_remote_monitor(), callback_tensorboard(), callback_terminate_on_naan()

Callback used to stream events to a server.

Description

Callback used to stream events to a server.

Usage

callback_remote_monitor(
  root = "https://localhost:9000",
  path = "/publish/epoch/end/",
  field = "data",
  headers = NULL,
  send_as_json = FALSE
)

Arguments

Details

Events are sent to root + '/publish/epoch/end/' by default. Calls are HTTP POST, with a data argument which is a JSON-encoded dictionary of event data. If send_as_json is set to True, the content type of the request will be application/json. Otherwise the serialized JSON will be send within a form

See Also

Other callbacks: callback_csv_logger(), callback_early_stopping(), callback_lambda(), callback_learning_rate_scheduler(), callback_model_checkpoint(), callback_progbar_logger(), callback_reduce_lr_on_plateau(), callback_tensorboard(), callback_terminate_on_naan()

TensorBoard basic visualizations

Description

This callback writes a log for TensorBoard, which allows you to visualize dynamic graphs of your training and test metrics, as well as activation histograms for the different layers in your model.

Usage

callback_tensorboard(
  log_dir = NULL,
  histogram_freq = 0,
  batch_size = NULL,
  write_graph = TRUE,
  write_grads = FALSE,
  write_images = FALSE,
  embeddings_freq = 0,
  embeddings_layer_names = NULL,
  embeddings_metadata = NULL,
  embeddings_data = NULL,
  update_freq = "epoch",
  profile_batch = 0
)

Arguments

Details

TensorBoard is a visualization tool provided with TensorFlow.

You can find more information about TensorBoard here.

When using a backend other than TensorFlow, TensorBoard will still work (if you have TensorFlow installed), but the only feature available will be the display of the losses and metrics plots.

See Also

Other callbacks: callback_csv_logger(), callback_early_stopping(), callback_lambda(), callback_learning_rate_scheduler(), callback_model_checkpoint(), callback_progbar_logger(), callback_reduce_lr_on_plateau(), callback_remote_monitor(), callback_terminate_on_naan()

Callback that terminates training when a NaN loss is encountered.

Description

Callback that terminates training when a NaN loss is encountered.

Usage

callback_terminate_on_naan()

See Also

Other callbacks: callback_csv_logger(), callback_early_stopping(), callback_lambda(), callback_learning_rate_scheduler(), callback_model_checkpoint(), callback_progbar_logger(), callback_reduce_lr_on_plateau(), callback_remote_monitor(), callback_tensorboard()

Clone a model instance.

Description

Model cloning is similar to calling a model on new inputs, except that it creates new layers (and thus new weights) instead of sharing the weights of the existing layers.

Usage

clone_model(model, input_tensors = NULL, clone_function = NULL)

Arguments

Configure a Keras model for training

Description

Configure a Keras model for training

Usage

## S3 method for class 'keras.engine.training.Model'
compile(
  object,
  optimizer = NULL,
  loss = NULL,
  metrics = NULL,
  loss_weights = NULL,
  weighted_metrics = NULL,
  run_eagerly = NULL,
  steps_per_execution = NULL,
  ...,
  target_tensors = NULL,
  sample_weight_mode = NULL
)

Arguments

See Also

Other model functions: evaluate.keras.engine.training.Model(), evaluate_generator(), fit.keras.engine.training.Model(), fit_generator(), get_config(), get_layer(), keras_model(), keras_model_sequential(), multi_gpu_model(), pop_layer(), predict.keras.engine.training.Model(), predict_generator(), predict_on_batch(), predict_proba(), summary.keras.engine.training.Model(), train_on_batch()

Weight constraints

Description

Functions that impose constraints on weight values.

Usage

constraint_maxnorm(max_value = 2, axis = 0)

constraint_nonneg()

constraint_unitnorm(axis = 0)

constraint_minmaxnorm(min_value = 0, max_value = 1, rate = 1, axis = 0)

Arguments

Details

• constraint_maxnorm() constrains the weights incident to each hidden unit to have a norm less than or equal to a desired value.

• constraint_nonneg() constraints the weights to be non-negative

• constraint_unitnorm() constrains the weights incident to each hidden unit to have unit norm.

• constraint_minmaxnorm() constrains the weights incident to each hidden unit to have the norm between a lower bound and an upper bound.

Custom constraints

You can implement your own constraint functions in R. A custom constraint is an R function that takes weights (w) as input and returns modified weights. Note that keras backend() tensor functions (e.g. k_greater_equal()) should be used in the implementation of custom constraints. For example:

nonneg_constraint <- function(w) {
  w * k_cast(k_greater_equal(w, 0), k_floatx())
}

layer_dense(units = 32, input_shape = c(784),
            kernel_constraint = nonneg_constraint)

Note that models which use custom constraints cannot be serialized using save_model_hdf5(). Rather, the weights of the model should be saved and restored using save_model_weights_hdf5().

See Also

Dropout: A Simple Way to Prevent Neural Networks from Overfitting Srivastava, Hinton, et al. 2014

KerasConstraint

Count the total number of scalars composing the weights.

Description

Count the total number of scalars composing the weights.

Usage

count_params(object)

Arguments

Value

An integer count

See Also

Other layer methods: get_config(), get_input_at(), get_weights(), reset_states()

Create a Keras Layer

Description

Create a Keras Layer

Usage

create_layer(layer_class, object, args = list())

Arguments

Value

A Keras layer

Note

The object parameter can be missing, in which case the layer is created without a connection to an existing graph.

Create a Keras Layer wrapper

Description

Create a Keras Layer wrapper

Usage

create_layer_wrapper(Layer, modifiers = NULL, convert = TRUE)

Arguments

Value

An R function that behaves similarly to the builtin keras ⁠layer_*⁠ functions. When called, it will create the class instance, and also optionally call it on a supplied argument object if it is present. This enables keras layers to compose nicely with the pipe (⁠%>%⁠).

The R function will arguments taken from the initialize (or ⁠__init__⁠) method of the Layer.

If Layer is an R6 object, this will delay initializing the python session, so it is safe to use in an R package.

(Deprecated) Create a Keras Wrapper

Description

R6 classes that inherit from keras$layers$Wrapper can now be instantiated directly by create_layer

Usage

create_wrapper(wrapper_class, object, args = list())

Arguments

Value

A Keras wrapper

Note

The object parameter can be missing, in which case the layer is created without a connection to an existing graph.

Custom metric function

Description

Custom metric function

Usage

custom_metric(name, metric_fn)

Arguments

Details

You can provide an arbitrary R function as a custom metric. Note that the y_true and y_pred parameters are tensors, so computations on them should use backend tensor functions.

Use the custom_metric() function to define a custom metric. Note that a name ('mean_pred') is provided for the custom metric function: this name is used within training progress output.

If you want to save and load a model with custom metrics, you should also specify the metric in the call the load_model_hdf5(). For example: load_model_hdf5("my_model.h5", c('mean_pred' = metric_mean_pred)).

Alternatively, you can wrap all of your code in a call to with_custom_object_scope() which will allow you to refer to the metric by name just like you do with built in keras metrics.

Documentation on the available backend tensor functions can be found at https://tensorflow.rstudio.com/reference/keras/#backend.

Alternative ways of supplying custom metrics:

• ⁠custom_metric():⁠ Arbitrary R function.

• metric_mean_wrapper(): Wrap an arbitrary R function in a Metric instance.

• subclass keras$metrics$Metric: see ?Metric for example.

See Also

Other metrics: metric_accuracy(), metric_auc(), metric_binary_accuracy(), metric_binary_crossentropy(), metric_categorical_accuracy(), metric_categorical_crossentropy(), metric_categorical_hinge(), metric_cosine_similarity(), metric_false_negatives(), metric_false_positives(), metric_hinge(), metric_kullback_leibler_divergence(), metric_logcosh_error(), metric_mean(), metric_mean_absolute_error(), metric_mean_absolute_percentage_error(), metric_mean_iou(), metric_mean_relative_error(), metric_mean_squared_error(), metric_mean_squared_logarithmic_error(), metric_mean_tensor(), metric_mean_wrapper(), metric_poisson(), metric_precision(), metric_precision_at_recall(), metric_recall(), metric_recall_at_precision(), metric_root_mean_squared_error(), metric_sensitivity_at_specificity(), metric_sparse_categorical_accuracy(), metric_sparse_categorical_crossentropy(), metric_sparse_top_k_categorical_accuracy(), metric_specificity_at_sensitivity(), metric_squared_hinge(), metric_sum(), metric_top_k_categorical_accuracy(), metric_true_negatives(), metric_true_positives()

Boston housing price regression dataset

Description

Dataset taken from the StatLib library which is maintained at Carnegie Mellon University.

Usage

dataset_boston_housing(
  path = "boston_housing.npz",
  test_split = 0.2,
  seed = 113L
)

Arguments

Value

Lists of training and test data: ⁠train$x, train$y, test$x, test$y⁠.

Samples contain 13 attributes of houses at different locations around the Boston suburbs in the late 1970s. Targets are the median values of the houses at a location (in k$).

See Also

Other datasets: dataset_cifar10(), dataset_cifar100(), dataset_fashion_mnist(), dataset_imdb(), dataset_mnist(), dataset_reuters()

CIFAR10 small image classification

Description

Dataset of 50,000 32x32 color training images, labeled over 10 categories, and 10,000 test images.

Usage

dataset_cifar10()

Value

Lists of training and test data: ⁠train$x, train$y, test$x, test$y⁠.

The x data is an array of RGB image data with shape (num_samples, 3, 32, 32).

The y data is an array of category labels (integers in range 0-9) with shape (num_samples).

See Also

Other datasets: dataset_boston_housing(), dataset_cifar100(), dataset_fashion_mnist(), dataset_imdb(), dataset_mnist(), dataset_reuters()

CIFAR100 small image classification

Description

Dataset of 50,000 32x32 color training images, labeled over 100 categories, and 10,000 test images.

Usage

dataset_cifar100(label_mode = c("fine", "coarse"))

Arguments

Value

Lists of training and test data: ⁠train$x, train$y, test$x, test$y⁠.

The x data is an array of RGB image data with shape (num_samples, 3, 32, 32).

The y data is an array of category labels with shape (num_samples).

See Also

Other datasets: dataset_boston_housing(), dataset_cifar10(), dataset_fashion_mnist(), dataset_imdb(), dataset_mnist(), dataset_reuters()

Fashion-MNIST database of fashion articles

Description

Dataset of 60,000 28x28 grayscale images of the 10 fashion article classes, along with a test set of 10,000 images. This dataset can be used as a drop-in replacement for MNIST. The class labels are encoded as integers from 0-9 which correspond to T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt,

Usage

dataset_fashion_mnist()

Details

Dataset of 60,000 28x28 grayscale images of 10 fashion categories, along with a test set of 10,000 images. This dataset can be used as a drop-in replacement for MNIST. The class labels are:

• 0 - T-shirt/top

• 1 - Trouser

• 2 - Pullover

• 3 - Dress

• 4 - Coat

• 5 - Sandal

• 6 - Shirt

• 7 - Sneaker

• 8 - Bag

• 9 - Ankle boot

Value

Lists of training and test data: ⁠train$x, train$y, test$x, test$y⁠, where x is an array of grayscale image data with shape (num_samples, 28, 28) and y is an array of article labels (integers in range 0-9) with shape (num_samples).

See Also

Other datasets: dataset_boston_housing(), dataset_cifar10(), dataset_cifar100(), dataset_imdb(), dataset_mnist(), dataset_reuters()

IMDB Movie reviews sentiment classification

Description

Dataset of 25,000 movies reviews from IMDB, labeled by sentiment (positive/negative). Reviews have been preprocessed, and each review is encoded as a sequence of word indexes (integers). For convenience, words are indexed by overall frequency in the dataset, so that for instance the integer "3" encodes the 3rd most frequent word in the data. This allows for quick filtering operations such as: "only consider the top 10,000 most common words, but eliminate the top 20 most common words".

Usage

dataset_imdb(
  path = "imdb.npz",
  num_words = NULL,
  skip_top = 0L,
  maxlen = NULL,
  seed = 113L,
  start_char = 1L,
  oov_char = 2L,
  index_from = 3L
)

dataset_imdb_word_index(path = "imdb_word_index.json")

Arguments

Details

As a convention, "0" does not stand for a specific word, but instead is used to encode any unknown word.

Value

Lists of training and test data: ⁠train$x, train$y, test$x, test$y⁠.

The x data includes integer sequences. If the num_words argument was specific, the maximum possible index value is num_words-1. If the maxlen argument was specified, the largest possible sequence length is maxlen.

The y data includes a set of integer labels (0 or 1).

The dataset_imdb_word_index() function returns a list where the names are words and the values are integer.

See Also

Other datasets: dataset_boston_housing(), dataset_cifar10(), dataset_cifar100(), dataset_fashion_mnist(), dataset_mnist(), dataset_reuters()

MNIST database of handwritten digits

Description

Dataset of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images.

Usage

dataset_mnist(path = "mnist.npz")

Arguments

Value

Lists of training and test data: ⁠train$x, train$y, test$x, test$y⁠, where x is an array of grayscale image data with shape (num_samples, 28, 28) and y is an array of digit labels (integers in range 0-9) with shape (num_samples).

See Also

Other datasets: dataset_boston_housing(), dataset_cifar10(), dataset_cifar100(), dataset_fashion_mnist(), dataset_imdb(), dataset_reuters()

Reuters newswire topics classification

Description

Dataset of 11,228 newswires from Reuters, labeled over 46 topics. As with dataset_imdb() , each wire is encoded as a sequence of word indexes (same conventions).

Usage

dataset_reuters(
  path = "reuters.npz",
  num_words = NULL,
  skip_top = 0L,
  maxlen = NULL,
  test_split = 0.2,
  seed = 113L,
  start_char = 1L,
  oov_char = 2L,
  index_from = 3L
)

dataset_reuters_word_index(path = "reuters_word_index.pkl")

Arguments

Value

Lists of training and test data: ⁠train$x, train$y, test$x, test$y⁠ with same format as dataset_imdb(). The dataset_reuters_word_index() function returns a list where the names are words and the values are integer. e.g. word_index[["giraffe"]] might return 1234.

See Also

Other datasets: dataset_boston_housing(), dataset_cifar10(), dataset_cifar100(), dataset_fashion_mnist(), dataset_imdb(), dataset_mnist()

(Deprecated) Evaluates the model on a data generator.

Description

The generator should return the same kind of data as accepted by test_on_batch().

Usage

evaluate_generator(
  object,
  generator,
  steps,
  max_queue_size = 10,
  workers = 1,
  callbacks = NULL
)

Arguments

Value

Named list of model test loss (or losses for models with multiple outputs) and model metrics.

See Also

Other model functions: compile.keras.engine.training.Model(), evaluate.keras.engine.training.Model(), fit.keras.engine.training.Model(), fit_generator(), get_config(), get_layer(), keras_model(), keras_model_sequential(), multi_gpu_model(), pop_layer(), predict.keras.engine.training.Model(), predict_generator(), predict_on_batch(), predict_proba(), summary.keras.engine.training.Model(), train_on_batch()

Evaluate a Keras model

Description

Evaluate a Keras model

Usage

## S3 method for class 'keras.engine.training.Model'
evaluate(
  object,
  x = NULL,
  y = NULL,
  batch_size = NULL,
  verbose = "auto",
  sample_weight = NULL,
  steps = NULL,
  callbacks = NULL,
  ...
)

Arguments

Value

Named list of model test loss (or losses for models with multiple outputs) and model metrics.

See Also

Other model functions: compile.keras.engine.training.Model(), evaluate_generator(), fit.keras.engine.training.Model(), fit_generator(), get_config(), get_layer(), keras_model(), keras_model_sequential(), multi_gpu_model(), pop_layer(), predict.keras.engine.training.Model(), predict_generator(), predict_on_batch(), predict_proba(), summary.keras.engine.training.Model(), train_on_batch()

Export a Saved Model

Description

Serialize a model to disk.

Usage

## S3 method for class 'keras.engine.training.Model'
export_savedmodel(
  object,
  export_dir_base,
  overwrite = TRUE,
  versioned = !overwrite,
  remove_learning_phase = TRUE,
  as_text = FALSE,
  ...
)

Arguments

Value

The path to the exported directory, as a string.

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

Description

The generator is run in parallel to the model, for efficiency. For instance, this allows you to do real-time data augmentation on images on CPU in parallel to training your model on GPU.

Usage

fit_generator(
  object,
  generator,
  steps_per_epoch,
  epochs = 1,
  verbose = getOption("keras.fit_verbose", default = 1),
  callbacks = NULL,
  view_metrics = getOption("keras.view_metrics", default = "auto"),
  validation_data = NULL,
  validation_steps = NULL,
  class_weight = NULL,
  max_queue_size = 10,
  workers = 1,
  initial_epoch = 0
)

Arguments

 - (inputs, targets)
 - (inputs, targets, sample_weights)

Value

Training history object (invisibly)

See Also

Other model functions: compile.keras.engine.training.Model(), evaluate.keras.engine.training.Model(), evaluate_generator(), fit.keras.engine.training.Model(), get_config(), get_layer(), keras_model(), keras_model_sequential(), multi_gpu_model(), pop_layer(), predict.keras.engine.training.Model(), predict_generator(), predict_on_batch(), predict_proba(), summary.keras.engine.training.Model(), train_on_batch()

Fit image data generator internal statistics to some sample data.

Description

Required for featurewise_center, featurewise_std_normalization and zca_whitening.

Usage

fit_image_data_generator(object, x, augment = FALSE, rounds = 1, seed = NULL)

Arguments

See Also

Other image preprocessing: flow_images_from_data(), flow_images_from_dataframe(), flow_images_from_directory(), image_load(), image_to_array()

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

Description

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

Usage

fit_text_tokenizer(object, x)

Arguments

Note

Required before using texts_to_sequences(), texts_to_matrix(), or sequences_to_matrix().

See Also

Other text tokenization: save_text_tokenizer(), sequences_to_matrix(), text_tokenizer(), texts_to_matrix(), texts_to_sequences(), texts_to_sequences_generator()

Train a Keras model

Description

Trains the model for a fixed number of epochs (iterations on a dataset).

Usage

## S3 method for class 'keras.engine.training.Model'
fit(
  object,
  x = NULL,
  y = NULL,
  batch_size = NULL,
  epochs = 10,
  verbose = getOption("keras.fit_verbose", default = "auto"),
  callbacks = NULL,
  view_metrics = getOption("keras.view_metrics", default = "auto"),
  validation_split = 0,
  validation_data = NULL,
  shuffle = TRUE,
  class_weight = NULL,
  sample_weight = NULL,
  initial_epoch = 0,
  steps_per_epoch = NULL,
  validation_steps = NULL,
  ...
)

Arguments

Value

A history object that contains all information collected during training.

See Also

Other model functions: compile.keras.engine.training.Model(), evaluate.keras.engine.training.Model(), evaluate_generator(), fit_generator(), get_config(), get_layer(), keras_model(), keras_model_sequential(), multi_gpu_model(), pop_layer(), predict.keras.engine.training.Model(), predict_generator(), predict_on_batch(), predict_proba(), summary.keras.engine.training.Model(), train_on_batch()

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

Description

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

Usage

flow_images_from_data(
  x,
  y = NULL,
  generator = image_data_generator(),
  batch_size = 32,
  shuffle = TRUE,
  sample_weight = NULL,
  seed = NULL,
  save_to_dir = NULL,
  save_prefix = "",
  save_format = "png",
  subset = NULL
)

Arguments

Details

Yields batches indefinitely, in an infinite loop.

Yields

⁠(x, y)⁠ where x is an array of image data and y is a array of corresponding labels. The generator loops indefinitely.

See Also

Other image preprocessing: fit_image_data_generator(), flow_images_from_dataframe(), flow_images_from_directory(), image_load(), image_to_array()

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

Description

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

Usage

flow_images_from_dataframe(
  dataframe,
  directory = NULL,
  x_col = "filename",
  y_col = "class",
  generator = image_data_generator(),
  target_size = c(256, 256),
  color_mode = "rgb",
  classes = NULL,
  class_mode = "categorical",
  batch_size = 32,
  shuffle = TRUE,
  seed = NULL,
  save_to_dir = NULL,
  save_prefix = "",
  save_format = "png",
  subset = NULL,
  interpolation = "nearest",
  drop_duplicates = NULL
)

Arguments

Details

Yields batches indefinitely, in an infinite loop.

Yields

⁠(x, y)⁠ where x is an array of image data and y is a array of corresponding labels. The generator loops indefinitely.

Note

This functions requires that pandas (Python module) is installed in the same environment as tensorflow and keras.

If you are using r-tensorflow (the default environment) you can install pandas by running reticulate::virtualenv_install("pandas", envname = "r-tensorflow") or reticulate::conda_install("pandas", envname = "r-tensorflow") depending on the kind of environment you are using.

See Also

Other image preprocessing: fit_image_data_generator(), flow_images_from_data(), flow_images_from_directory(), image_load(), image_to_array()

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

Description

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

Usage

flow_images_from_directory(
  directory,
  generator = image_data_generator(),
  target_size = c(256, 256),
  color_mode = "rgb",
  classes = NULL,
  class_mode = "categorical",
  batch_size = 32,
  shuffle = TRUE,
  seed = NULL,
  save_to_dir = NULL,
  save_prefix = "",
  save_format = "png",
  follow_links = FALSE,
  subset = NULL,
  interpolation = "nearest"
)

Arguments

Details

Yields batches indefinitely, in an infinite loop.

Yields

⁠(x, y)⁠ where x is an array of image data and y is a array of corresponding labels. The generator loops indefinitely.

See Also

Other image preprocessing: fit_image_data_generator(), flow_images_from_data(), flow_images_from_dataframe(), image_load(), image_to_array()

Freeze and unfreeze weights

Description

Freeze weights in a model or layer so that they are no longer trainable.

Usage

freeze_weights(object, from = NULL, to = NULL, which = NULL)

unfreeze_weights(object, from = NULL, to = NULL, which = NULL)

Arguments

Note

The from and to layer arguments are both inclusive.

When applied to a model, the freeze or unfreeze is a global operation over all layers in the model (i.e. layers not within the specified range will be set to the opposite value, e.g. unfrozen for a call to freeze).

Models must be compiled again after weights are frozen or unfrozen.

Examples

## Not run: 
conv_base <- application_vgg16(
  weights = "imagenet",
  include_top = FALSE,
  input_shape = c(150, 150, 3)
)

# freeze it's weights
freeze_weights(conv_base)

conv_base

# create a composite model that includes the base + more layers
model <- keras_model_sequential() %>%
  conv_base() %>%
  layer_flatten() %>%
  layer_dense(units = 256, activation = "relu") %>%
  layer_dense(units = 1, activation = "sigmoid")

# compile
model %>% compile(
  loss = "binary_crossentropy",
  optimizer = optimizer_rmsprop(lr = 2e-5),
  metrics = c("accuracy")
)

model
print(model, expand_nested = TRUE)



# unfreeze weights from "block5_conv1" on
unfreeze_weights(conv_base, from = "block5_conv1")

# compile again since we froze or unfroze weights
model %>% compile(
  loss = "binary_crossentropy",
  optimizer = optimizer_rmsprop(lr = 2e-5),
  metrics = c("accuracy")
)

conv_base
print(model, expand_nested = TRUE)

# freeze only the last 5 layers
freeze_weights(conv_base, from = -5)
conv_base
# equivalently, also freeze only the last 5 layers
unfreeze_weights(conv_base, to = -6)
conv_base

# Freeze only layers of a certain type, e.g, BatchNorm layers
batch_norm_layer_class_name <- class(layer_batch_normalization())[1]
is_batch_norm_layer <- function(x) inherits(x, batch_norm_layer_class_name)

model <- application_efficientnet_b0()
freeze_weights(model, which = is_batch_norm_layer)
model
# equivalent to:
for(layer in model$layers) {
  if(is_batch_norm_layer(layer))
    layer$trainable <- FALSE
  else
    layer$trainable <- TRUE
}

## End(Not run)

Retrieve the next item from a generator

Description

Use to retrieve items from generators (e.g. image_data_generator()). Will return either the next item or NULL if there are no more items.

Usage

generator_next(generator, completed = NULL)

Arguments

Layer/Model configuration

Description

A layer config is an object returned from get_config() that contains the configuration of a layer or model. The same layer or model can be reinstantiated later (without its trained weights) from this configuration using from_config(). The config does not include connectivity information, nor the class name (those are handled externally).

Usage

get_config(object)

from_config(config, custom_objects = NULL)

Arguments

Value

get_config() returns an object with the configuration, from_config() returns a re-instantiation of the object.

Note

Objects returned from get_config() are not serializable. Therefore, if you want to save and restore a model across sessions, you can use the model_to_json() function (for model configuration only, not weights) or the save_model_tf() function to save the model configuration and weights to the filesystem.

See Also

Other model functions: compile.keras.engine.training.Model(), evaluate.keras.engine.training.Model(), evaluate_generator(), fit.keras.engine.training.Model(), fit_generator(), get_layer(), keras_model(), keras_model_sequential(), multi_gpu_model(), pop_layer(), predict.keras.engine.training.Model(), predict_generator(), predict_on_batch(), predict_proba(), summary.keras.engine.training.Model(), train_on_batch()

Other layer methods: count_params(), get_input_at(), get_weights(), reset_states()

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

Description

Passing the MD5 hash will verify the file after download as well as if it is already present in the cache.

Usage

get_file(
  fname,
  origin,
  file_hash = NULL,
  cache_subdir = "datasets",
  hash_algorithm = "auto",
  extract = FALSE,
  archive_format = "auto",
  cache_dir = NULL,
  untar = FALSE
)

Arguments

Value

Path to the downloaded file

Retrieve tensors for layers with multiple nodes

Description

Whenever you are calling a layer on some input, you are creating a new tensor (the output of the layer), and you are adding a "node" to the layer, linking the input tensor to the output tensor. When you are calling the same layer multiple times, that layer owns multiple nodes indexed as 1, 2, 3. These functions enable you to retrieve various tensor properties of layers with multiple nodes.

Usage

get_input_at(object, node_index)

get_output_at(object, node_index)

get_input_shape_at(object, node_index)

get_output_shape_at(object, node_index)

get_input_mask_at(object, node_index)

get_output_mask_at(object, node_index)

Arguments

Value

A tensor (or list of tensors if the layer has multiple inputs/outputs).

See Also

Other layer methods: count_params(), get_config(), get_weights(), reset_states()

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

Description

Indices are based on order of horizontal graph traversal (bottom-up) and are 1-based. If name and index are both provided, index will take precedence.

Usage

get_layer(object, name = NULL, index = NULL)

Arguments

Value

A layer instance.

See Also

Other model functions: compile.keras.engine.training.Model(), evaluate.keras.engine.training.Model(), evaluate_generator(), fit.keras.engine.training.Model(), fit_generator(), get_config(), keras_model(), keras_model_sequential(), multi_gpu_model(), pop_layer(), predict.keras.engine.training.Model(), predict_generator(), predict_on_batch(), predict_proba(), summary.keras.engine.training.Model(), train_on_batch()

Layer/Model weights as R arrays

Description

Layer/Model weights as R arrays

Usage

get_weights(object, trainable = NA)

set_weights(object, weights)

Arguments

Note

You can access the Layer/Model as tf.Tensors or tf.Variables at object$weights, object$trainable_weights, or object$non_trainable_weights

See Also

Other model persistence: model_to_json(), model_to_yaml(), save_model_hdf5(), save_model_tf(), save_model_weights_hdf5(), serialize_model()

Other layer methods: count_params(), get_config(), get_input_at(), reset_states()

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

Description

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

Usage

hdf5_matrix(datapath, dataset, start = 0, end = NULL, normalizer = NULL)

Arguments

Details

Providing start and end allows use of a slice of the dataset.

Optionally, a normalizer function (or lambda) can be given. This will be called on every slice of data retrieved.

Value

An array-like HDF5 dataset.

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

Description

Deprecated: image_data_generator is not recommended for new code. Prefer loading images with image_dataset_from_directory and transforming the output TF Dataset with preprocessing layers. For more information, see the tutorials for loading images and augmenting images, as well as the preprocessing layer guide.

Usage

image_data_generator(
  featurewise_center = FALSE,
  samplewise_center = FALSE,
  featurewise_std_normalization = FALSE,
  samplewise_std_normalization = FALSE,
  zca_whitening = FALSE,
  zca_epsilon = 1e-06,
  rotation_range = 0,
  width_shift_range = 0,
  height_shift_range = 0,
  brightness_range = NULL,
  shear_range = 0,
  zoom_range = 0,
  channel_shift_range = 0,
  fill_mode = "nearest",
  cval = 0,
  horizontal_flip = FALSE,
  vertical_flip = FALSE,
  rescale = NULL,
  preprocessing_function = NULL,
  data_format = NULL,
  validation_split = 0
)

Arguments

Create a dataset from a directory

Description

Generates a tf.data.Dataset from image files in a directory.

Usage

image_dataset_from_directory(
  directory,
  labels = "inferred",
  label_mode = "int",
  class_names = NULL,
  color_mode = "rgb",
  batch_size = 32,
  image_size = c(256, 256),
  shuffle = TRUE,
  seed = NULL,
  validation_split = NULL,
  subset = NULL,
  interpolation = "bilinear",
  follow_links = FALSE,
  crop_to_aspect_ratio = FALSE,
  ...
)

Arguments

Details

If your directory structure is:

main_directory/
...class_a/
......a_image_1.jpg
......a_image_2.jpg
...class_b/
......b_image_1.jpg
......b_image_2.jpg

Then calling image_dataset_from_directory(main_directory, labels='inferred') will return a tf.data.Dataset that yields batches of images from the subdirectories class_a and class_b, together with labels 0 and 1 (0 corresponding to class_a and 1 corresponding to class_b).

Supported image formats: jpeg, png, bmp, gif. Animated gifs are truncated to the first frame.

Value

A tf.data.Dataset object. If label_mode is NULL, it yields float32 tensors of shape ⁠(batch_size, image_size[1], image_size[2], num_channels)⁠, encoding images (see below for rules regarding num_channels).

Otherwise, it yields pairs of ⁠(images, labels)⁠, where images has shape ⁠(batch_size, image_size[1], image_size[2], num_channels)⁠, and labels follows the format described below.

Rules regarding labels format:

• if label_mode is int, the labels are an int32 tensor of shape (batch_size).

• if label_mode is binary, the labels are a float32 tensor of 1s and 0s of shape ⁠(batch_size, 1)⁠.

• if label_mode is categorial, the labels are a float32 tensor of shape ⁠(batch_size, num_classes)⁠, representing a one-hot encoding of the class index.

Rules regarding number of channels in the yielded images:

• if color_mode is grayscale, there's 1 channel in the image tensors.

• if color_mode is rgb, there are 3 channel in the image tensors.

• if color_mode is rgba, there are 4 channel in the image tensors.

See Also

https://www.tensorflow.org/api_docs/python/tf/keras/utils/image_dataset_from_directory

Loads an image into PIL format.

Description

Loads an image into PIL format.

Usage

image_load(
  path,
  grayscale = FALSE,
  color_mode = "rgb",
  target_size = NULL,
  interpolation = "nearest"
)

Arguments

Value

A PIL Image instance.

See Also

Other image preprocessing: fit_image_data_generator(), flow_images_from_data(), flow_images_from_dataframe(), flow_images_from_directory(), image_to_array()

3D array representation of images

Description

3D array that represents an image with dimensions (height,width,channels) or (channels,height,width) depending on the data_format.

Usage

image_to_array(img, data_format = c("channels_last", "channels_first"))

image_array_resize(
  img,
  height,
  width,
  data_format = c("channels_last", "channels_first")
)

image_array_save(
  img,
  path,
  data_format = NULL,
  file_format = NULL,
  scale = TRUE
)

Arguments

See Also

Other image preprocessing: fit_image_data_generator(), flow_images_from_data(), flow_images_from_dataframe(), flow_images_from_directory(), image_load()

Decodes the prediction of an ImageNet model.

Description

Decodes the prediction of an ImageNet model.

Usage

imagenet_decode_predictions(preds, top = 5)

Arguments

Value

List of data frames with variables class_name, class_description, and score (one data frame per sample in batch input).

Preprocesses a tensor or array encoding a batch of images.

Description

Preprocesses a tensor or array encoding a batch of images.

Usage

imagenet_preprocess_input(x, data_format = NULL, mode = "caffe")

Arguments

Value

Preprocessed tensor or array.

Keras implementation

Description

Obtain a reference to the Python module used for the implementation of Keras.

Usage

implementation()

Details

There are currently two Python modules which implement Keras:

• keras ("keras")

• tensorflow.keras ("tensorflow")

This function returns a reference to the implementation being currently used by the keras package. The default implementation is "keras". You can override this by setting the KERAS_IMPLEMENTATION environment variable to "tensorflow".

Value

Reference to the Python module used for the implementation of Keras.

Initializer that generates tensors initialized to a constant value.

Description

Initializer that generates tensors initialized to a constant value.

Usage

initializer_constant(value = 0)

Arguments

See Also

Other initializers: initializer_glorot_normal(), initializer_glorot_uniform(), initializer_he_normal(), initializer_he_uniform(), initializer_identity(), initializer_lecun_normal(), initializer_lecun_uniform(), initializer_ones(), initializer_orthogonal(), initializer_random_normal(), initializer_random_uniform(), initializer_truncated_normal(), initializer_variance_scaling(), initializer_zeros()

Glorot normal initializer, also called Xavier normal initializer.

Description

It draws samples from a truncated normal distribution centered on 0 with stddev = sqrt(2 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor.

Usage

initializer_glorot_normal(seed = NULL)

Arguments

References

Glorot & Bengio, AISTATS 2010 https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf

See Also

Other initializers: initializer_constant(), initializer_glorot_uniform(), initializer_he_normal(), initializer_he_uniform(), initializer_identity(), initializer_lecun_normal(), initializer_lecun_uniform(), initializer_ones(), initializer_orthogonal(), initializer_random_normal(), initializer_random_uniform(), initializer_truncated_normal(), initializer_variance_scaling(), initializer_zeros()

Glorot uniform initializer, also called Xavier uniform initializer.

Description

It draws samples from a uniform distribution within ⁠-limit, limit⁠ where limit is sqrt(6 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor.

Usage

initializer_glorot_uniform(seed = NULL)

Arguments

References

Glorot & Bengio, AISTATS 2010 https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf

See Also

Other initializers: initializer_constant(), initializer_glorot_normal(), initializer_he_normal(), initializer_he_uniform(), initializer_identity(), initializer_lecun_normal(), initializer_lecun_uniform(), initializer_ones(), initializer_orthogonal(), initializer_random_normal(), initializer_random_uniform(), initializer_truncated_normal(), initializer_variance_scaling(), initializer_zeros()

He normal initializer.

Description

It draws samples from a truncated normal distribution centered on 0 with stddev = sqrt(2 / fan_in) where fan_in is the number of input units in the weight tensor.

Usage

initializer_he_normal(seed = NULL)

Arguments

References

He et al., https://arxiv.org/abs/1502.01852

See Also

Other initializers: initializer_constant(), initializer_glorot_normal(), initializer_glorot_uniform(), initializer_he_uniform(), initializer_identity(), initializer_lecun_normal(), initializer_lecun_uniform(), initializer_ones(), initializer_orthogonal(), initializer_random_normal(), initializer_random_uniform(), initializer_truncated_normal(), initializer_variance_scaling(), initializer_zeros()

He uniform variance scaling initializer.

Description

It draws samples from a uniform distribution within ⁠-limit, limit⁠ where ⁠limit`` is ⁠sqrt(6 / fan_in)where fan_in' is the number of input units in the weight tensor.

Usage

initializer_he_uniform(seed = NULL)

Arguments

References

He et al., https://arxiv.org/abs/1502.01852

See Also

Other initializers: initializer_constant(), initializer_glorot_normal(), initializer_glorot_uniform(), initializer_he_normal(), initializer_identity(), initializer_lecun_normal(), initializer_lecun_uniform(), initializer_ones(), initializer_orthogonal(), initializer_random_normal(), initializer_random_uniform(), initializer_truncated_normal(), initializer_variance_scaling(), initializer_zeros()

Initializer that generates the identity matrix.

Description

Only use for square 2D matrices.

Usage

initializer_identity(gain = 1)

Arguments

See Also

Other initializers: initializer_constant(), initializer_glorot_normal(), initializer_glorot_uniform(), initializer_he_normal(), initializer_he_uniform(), initializer_lecun_normal(), initializer_lecun_uniform(), initializer_ones(), initializer_orthogonal(), initializer_random_normal(), initializer_random_uniform(), initializer_truncated_normal(), initializer_variance_scaling(), initializer_zeros()

LeCun normal initializer.

Description

It draws samples from a truncated normal distribution centered on 0 with stddev <- sqrt(1 / fan_in) where fan_in is the number of input units in the weight tensor..

Usage

initializer_lecun_normal(seed = NULL)

Arguments

References

• Self-Normalizing Neural Networks

• Efficient Backprop, LeCun, Yann et al. 1998

See Also

Other initializers: initializer_constant(), initializer_glorot_normal(), initializer_glorot_uniform(), initializer_he_normal(), initializer_he_uniform(), initializer_identity(), initializer_lecun_uniform(), initializer_ones(), initializer_orthogonal(), initializer_random_normal(), initializer_random_uniform(), initializer_truncated_normal(), initializer_variance_scaling(), initializer_zeros()

LeCun uniform initializer.

Description

It draws samples from a uniform distribution within ⁠-limit, limit⁠ where limit is sqrt(3 / fan_in) where fan_in is the number of input units in the weight tensor.

Usage

initializer_lecun_uniform(seed = NULL)

Arguments

References

LeCun 98, Efficient Backprop,

See Also

Other initializers: initializer_constant(), initializer_glorot_normal(), initializer_glorot_uniform(), initializer_he_normal(), initializer_he_uniform(), initializer_identity(), initializer_lecun_normal(), initializer_ones(), initializer_orthogonal(), initializer_random_normal(), initializer_random_uniform(), initializer_truncated_normal(), initializer_variance_scaling(), initializer_zeros()

Initializer that generates tensors initialized to 1.

Description

Initializer that generates tensors initialized to 1.

Usage

initializer_ones()

See Also

Other initializers: initializer_constant(), initializer_glorot_normal(), initializer_glorot_uniform(), initializer_he_normal(), initializer_he_uniform(), initializer_identity(), initializer_lecun_normal(), initializer_lecun_uniform(), initializer_orthogonal(), initializer_random_normal(), initializer_random_uniform(), initializer_truncated_normal(), initializer_variance_scaling(), initializer_zeros()

Initializer that generates a random orthogonal matrix.

Description

Initializer that generates a random orthogonal matrix.

Usage

initializer_orthogonal(gain = 1, seed = NULL)

Arguments

References

Saxe et al., https://arxiv.org/abs/1312.6120

See Also

Other initializers: initializer_constant(), initializer_glorot_normal(), initializer_glorot_uniform(), initializer_he_normal(), initializer_he_uniform(), initializer_identity(), initializer_lecun_normal(), initializer_lecun_uniform(), initializer_ones(), initializer_random_normal(), initializer_random_uniform(), initializer_truncated_normal(), initializer_variance_scaling(), initializer_zeros()

Initializer that generates tensors with a normal distribution.

Description

Initializer that generates tensors with a normal distribution.

Usage

initializer_random_normal(mean = 0, stddev = 0.05, seed = NULL)

Arguments

See Also

Other initializers: initializer_constant(), initializer_glorot_normal(), initializer_glorot_uniform(), initializer_he_normal(), initializer_he_uniform(), initializer_identity(), initializer_lecun_normal(), initializer_lecun_uniform(), initializer_ones(), initializer_orthogonal(), initializer_random_uniform(), initializer_truncated_normal(), initializer_variance_scaling(), initializer_zeros()

Initializer that generates tensors with a uniform distribution.

Description

Initializer that generates tensors with a uniform distribution.

Usage

initializer_random_uniform(minval = -0.05, maxval = 0.05, seed = NULL)

Arguments

See Also

Other initializers: initializer_constant(), initializer_glorot_normal(), initializer_glorot_uniform(), initializer_he_normal(), initializer_he_uniform(), initializer_identity(), initializer_lecun_normal(), initializer_lecun_uniform(), initializer_ones(), initializer_orthogonal(), initializer_random_normal(), initializer_truncated_normal(), initializer_variance_scaling(), initializer_zeros()

Initializer that generates a truncated normal distribution.

Description

These values are similar to values from an initializer_random_normal() except that values more than two standard deviations from the mean are discarded and re-drawn. This is the recommended initializer for neural network weights and filters.

Usage

initializer_truncated_normal(mean = 0, stddev = 0.05, seed = NULL)

Arguments

See Also

Other initializers: initializer_constant(), initializer_glorot_normal(), initializer_glorot_uniform(), initializer_he_normal(), initializer_he_uniform(), initializer_identity(), initializer_lecun_normal(), initializer_lecun_uniform(), initializer_ones(), initializer_orthogonal(), initializer_random_normal(), initializer_random_uniform(), initializer_variance_scaling(), initializer_zeros()

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

Description

With distribution="normal", samples are drawn from a truncated normal distribution centered on zero, with stddev = sqrt(scale / n) where n is:

• number of input units in the weight tensor, if mode = "fan_in"

• number of output units, if mode = "fan_out"

• average of the numbers of input and output units, if mode = "fan_avg"

Usage

initializer_variance_scaling(
  scale = 1,
  mode = c("fan_in", "fan_out", "fan_avg"),
  distribution = c("normal", "uniform", "truncated_normal", "untruncated_normal"),
  seed = NULL
)

Arguments

Details

With distribution="uniform", samples are drawn from a uniform distribution within ⁠-limit, limit⁠, with limit = sqrt(3 * scale / n).

See Also

Other initializers: initializer_constant(), initializer_glorot_normal(), initializer_glorot_uniform(), initializer_he_normal(), initializer_he_uniform(), initializer_identity(), initializer_lecun_normal(), initializer_lecun_uniform(), initializer_ones(), initializer_orthogonal(), initializer_random_normal(), initializer_random_uniform(), initializer_truncated_normal(), initializer_zeros()

Initializer that generates tensors initialized to 0.

Description

Initializer that generates tensors initialized to 0.

Usage

initializer_zeros()

See Also

Other initializers: initializer_constant(), initializer_glorot_normal(), initializer_glorot_uniform(), initializer_he_normal(), initializer_he_uniform(), initializer_identity(), initializer_lecun_normal(), initializer_lecun_uniform(), initializer_ones(), initializer_orthogonal(), initializer_random_normal(), initializer_random_uniform(), initializer_truncated_normal(), initializer_variance_scaling()

Install TensorFlow and Keras, including all Python dependencies

Description

This function will install Tensorflow and all Keras dependencies. This is a thin wrapper around tensorflow::install_tensorflow(), with the only difference being that this includes by default additional extra packages that keras expects, and the default version of tensorflow installed by install_keras() may at times be different from the default installed install_tensorflow(). The default version of tensorflow installed by install_keras() is "2.15".

Usage

install_keras(
  method = c("auto", "virtualenv", "conda"),
  conda = "auto",
  version = "default",
  tensorflow = version,
  extra_packages = NULL,
  ...
)

Arguments

Details

The default additional packages are: tensorflow-hub, tensorflow-datasets, scipy, requests, pyyaml, Pillow, h5py, pandas, pydot, with their versions potentially constrained for compatibility with the requested tensorflow version.

See Also

tensorflow::install_tensorflow()

Check if Keras is Available

Description

Probe to see whether the Keras Python package is available in the current system environment.

Usage

is_keras_available(version = NULL)

Arguments

Value

Logical indicating whether Keras (or the specified minimum version of Keras) is available.

Examples

## Not run: 
# testthat utilty for skipping tests when Keras isn't available
skip_if_no_keras <- function(version = NULL) {
  if (!is_keras_available(version))
    skip("Required keras version not available for testing")
}

# use the function within a test
test_that("keras function works correctly", {
  skip_if_no_keras()
  # test code here
})

## End(Not run)

Element-wise absolute value.

Description

Element-wise absolute value.

Usage

k_abs(x)

Arguments

Value

A tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Bitwise reduction (logical AND).

Description

Bitwise reduction (logical AND).

Usage

k_all(x, axis = NULL, keepdims = FALSE)

Arguments

Value

A uint8 tensor (0s and 1s).

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Bitwise reduction (logical OR).

Description

Bitwise reduction (logical OR).

Usage

k_any(x, axis = NULL, keepdims = FALSE)

Arguments

Value

A uint8 tensor (0s and 1s).

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Creates a 1D tensor containing a sequence of integers.

Description

The function arguments use the same convention as Theano's arange: if only one argument is provided, it is in fact the "stop" argument. The default type of the returned tensor is 'int32' to match TensorFlow's default.

Usage

k_arange(start, stop = NULL, step = 1, dtype = "int32")

Arguments

Value

An integer tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Returns the index of the maximum value along an axis.

Description

Returns the index of the maximum value along an axis.

Usage

k_argmax(x, axis = -1)

Arguments

Value

A tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Returns the index of the minimum value along an axis.

Description

Returns the index of the minimum value along an axis.

Usage

k_argmin(x, axis = -1)

Arguments

Value

A tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Active Keras backend

Description

Active Keras backend

Usage

k_backend()

Value

The name of the backend Keras is currently using.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Batchwise dot product.

Description

batch_dot is used to compute dot product of x and y when x and y are data in batch, i.e. in a shape of (batch_size). batch_dot results in a tensor or variable with less dimensions than the input. If the number of dimensions is reduced to 1, we use expand_dims to make sure that ndim is at least 2.

Usage

k_batch_dot(x, y, axes)

Arguments

Value

A tensor with shape equal to the concatenation of x's shape (less the dimension that was summed over) and y's shape (less the batch dimension and the dimension that was summed over). If the final rank is 1, we reshape it to ⁠(batch_size, 1)⁠.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

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

Description

In other words, it flattens each data samples of a batch.

Usage

k_batch_flatten(x)

Arguments

Value

A tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Returns the value of more than one tensor variable.

Description

Returns the value of more than one tensor variable.

Usage

k_batch_get_value(ops)

Arguments

Value

A list of arrays.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

See Also

k_batch_set_value()

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

Description

i.e. returns output <- (x - mean) / (sqrt(var) + epsilon) * gamma + beta

Usage

k_batch_normalization(x, mean, var, beta, gamma, axis = -1, epsilon = 0.001)

Arguments

Value

A tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Sets the values of many tensor variables at once.

Description

Sets the values of many tensor variables at once.

Usage

k_batch_set_value(lists)

Arguments

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

See Also

k_batch_get_value()

Adds a bias vector to a tensor.

Description

Adds a bias vector to a tensor.

Usage

k_bias_add(x, bias, data_format = NULL)

Arguments

Value

Output tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Binary crossentropy between an output tensor and a target tensor.

Description

Binary crossentropy between an output tensor and a target tensor.

Usage

k_binary_crossentropy(target, output, from_logits = FALSE)

Arguments

Value

A tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Casts a tensor to a different dtype and returns it.

Description

You can cast a Keras variable but it still returns a Keras tensor.

Usage

k_cast(x, dtype)

Arguments

Value

Keras tensor with dtype dtype.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Cast an array to the default Keras float type.

Description

Cast an array to the default Keras float type.

Usage

k_cast_to_floatx(x)

Arguments

Value

The same array, cast to its new type.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Categorical crossentropy between an output tensor and a target tensor.

Description

Categorical crossentropy between an output tensor and a target tensor.

Usage

k_categorical_crossentropy(target, output, from_logits = FALSE, axis = -1)

Arguments

Value

Output tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Destroys the current TF graph and creates a new one.

Description

Useful to avoid clutter from old models / layers.

Usage

k_clear_session()

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Element-wise value clipping.

Description

Element-wise value clipping.

Usage

k_clip(x, min_value = NULL, max_value = NULL)

Arguments

Value

A tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Concatenates a list of tensors alongside the specified axis.

Description

Concatenates a list of tensors alongside the specified axis.

Usage

k_concatenate(tensors, axis = -1)

Arguments

Value

A tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Creates a constant tensor.

Description

Creates a constant tensor.

Usage

k_constant(value, dtype = NULL, shape = NULL, name = NULL)

Arguments

Value

A Constant Tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

1D convolution.

Description

1D convolution.

Usage

k_conv1d(
  x,
  kernel,
  strides = 1,
  padding = "valid",
  data_format = NULL,
  dilation_rate = 1
)

Arguments

Value

A tensor, result of 1D convolution.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

2D convolution.

Description

2D convolution.

Usage

k_conv2d(
  x,
  kernel,
  strides = c(1, 1),
  padding = "valid",
  data_format = NULL,
  dilation_rate = c(1, 1)
)

Arguments

Value

A tensor, result of 2D convolution.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

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

Description

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

Usage

k_conv2d_transpose(
  x,
  kernel,
  output_shape,
  strides = c(1, 1),
  padding = "valid",
  data_format = NULL
)

Arguments

Value

A tensor, result of transposed 2D convolution.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

3D convolution.

Description

3D convolution.

Usage

k_conv3d(
  x,
  kernel,
  strides = c(1, 1, 1),
  padding = "valid",
  data_format = NULL,
  dilation_rate = c(1, 1, 1)
)

Arguments

Value

A tensor, result of 3D convolution.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

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

Description

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

Usage

k_conv3d_transpose(
  x,
  kernel,
  output_shape,
  strides = c(1, 1, 1),
  padding = "valid",
  data_format = NULL
)

Arguments

Value

A tensor, result of transposed 3D convolution.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Computes cos of x element-wise.

Description

Computes cos of x element-wise.

Usage

k_cos(x)

Arguments

Value

A tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

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

Description

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

Usage

k_count_params(x)

Arguments

Value

Integer, the number of elements in x, i.e., the product of the array's static dimensions.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Runs CTC loss algorithm on each batch element.

Description

Runs CTC loss algorithm on each batch element.

Usage

k_ctc_batch_cost(y_true, y_pred, input_length, label_length)

Arguments

Value

Tensor with shape (samples,1) containing the CTC loss of each element.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Decodes the output of a softmax.

Description

Can use either greedy search (also known as best path) or a constrained dictionary search.

Usage

k_ctc_decode(
  y_pred,
  input_length,
  greedy = TRUE,
  beam_width = 100L,
  top_paths = 1
)

Arguments

Value

If greedy is TRUE, returns a list of one element that contains the decoded sequence. If FALSE, returns the top_paths most probable decoded sequences. Important: blank labels are returned as -1. Tensor (top_paths) that contains the log probability of each decoded sequence.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Converts CTC labels from dense to sparse.

Description

Converts CTC labels from dense to sparse.

Usage

k_ctc_label_dense_to_sparse(labels, label_lengths)

Arguments

Value

A sparse tensor representation of the labels.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

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

Description

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

Usage

k_cumprod(x, axis = 1)

Arguments

Value

A tensor of the cumulative product of values of x along axis.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

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

Description

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

Usage

k_cumsum(x, axis = 1)

Arguments

Value

A tensor of the cumulative sum of values of x along axis.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Depthwise 2D convolution with separable filters.

Description

Depthwise 2D convolution with separable filters.

Usage

k_depthwise_conv2d(
  x,
  depthwise_kernel,
  strides = c(1, 1),
  padding = "valid",
  data_format = NULL,
  dilation_rate = c(1, 1)
)

Arguments

Value

Output tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

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

Description

When attempting to multiply a nD tensor with a nD tensor, it reproduces the Theano behavior. (e.g. ⁠(2, 3) * (4, 3, 5) -> (2, 4, 5)⁠)

Usage

k_dot(x, y)

Arguments

Value

A tensor, dot product of x and y.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

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

Description

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

Usage

k_dropout(x, level, noise_shape = NULL, seed = NULL)

Arguments

Value

A tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

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

Description

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

Usage

k_dtype(x)

Arguments

Value

String, dtype of x.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Exponential linear unit.

Description

Exponential linear unit.

Usage

k_elu(x, alpha = 1)

Arguments

Value

A tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Fuzz factor used in numeric expressions.

Description

Fuzz factor used in numeric expressions.

Usage

k_epsilon()

k_set_epsilon(e)

Arguments

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Element-wise equality between two tensors.

Description

Element-wise equality between two tensors.

Usage

k_equal(x, y)

Arguments

Value

A bool tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Evaluates the value of a variable.

Description

Evaluates the value of a variable.

Usage

k_eval(x)

Arguments

Value

An R array.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Element-wise exponential.

Description

Element-wise exponential.

Usage

k_exp(x)

Arguments

Value

A tensor.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Adds a 1-sized dimension at index axis.

Description

Adds a 1-sized dimension at index axis.

Usage

k_expand_dims(x, axis = -1)

Arguments

Value

A tensor with expanded dimensions.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Instantiate an identity matrix and returns it.

Description

Instantiate an identity matrix and returns it.

Usage

k_eye(size, dtype = NULL, name = NULL)

Arguments

Value

A Keras variable, an identity matrix.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Flatten a tensor.

Description

Flatten a tensor.

Usage

k_flatten(x)

Arguments

Value

A tensor, reshaped into 1-D

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Default float type

Description

Default float type

Usage

k_floatx()

k_set_floatx(floatx)

Arguments

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

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

Description

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

Usage

k_foldl(fn, elems, initializer = NULL, name = NULL)

Arguments

Value

Tensor with same type and shape as initializer.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

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

Description

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

Usage

k_foldr(fn, elems, initializer = NULL, name = NULL)

Arguments

Value

Tensor with same type and shape as initializer.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Instantiates a Keras function

Description

Instantiates a Keras function

Usage

k_function(inputs, outputs, updates = NULL, ...)

Arguments

Value

Output values as R arrays.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Retrieves the elements of indices indices in the tensor reference.

Description

Retrieves the elements of indices indices in the tensor reference.

Usage

k_gather(reference, indices)

Arguments

Value

A tensor of same type as reference.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

TF session to be used by the backend.

Description

If a default TensorFlow session is available, we will return it. Else, we will return the global Keras session. If no global Keras session exists at this point: we will create a new global session. Note that you can manually set the global session via k_set_session().

Usage

k_get_session()

k_set_session(session)

Arguments

Value

A TensorFlow session

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Get the uid for the default graph.

Description

Get the uid for the default graph.

Usage

k_get_uid(prefix = "")

Arguments

Value

A unique identifier for the graph.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Returns the value of a variable.

Description

Returns the value of a variable.

Usage

k_get_value(x)

Arguments

Value

An R array.

Keras Backend

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.

Returns the shape of a variable.

Description

Returns the shape of a variable.

Usage

k_get_variable_shape(x)