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Overview

The tfhub package provides R wrappers to TensorFlow Hub.

TensorFlow Hub is a library for reusable machine learning modules.

TensorFlow Hub is a library for the publication, discovery, and consumption of reusable parts of machine learning models. A module is a self-contained piece of a TensorFlow graph, along with its weights and assets, that can be reused across different tasks in a process known as transfer learning. Transfer learning can:

Installation

You can install the released version of tfhub from CRAN with:

install.packages("tfhub")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("rstudio/tfhub")

After installing the tfhub package you need to install the TensorFlow Hub python module:

library(tfhub)
install_tfhub()

Loading modules

Modules can be loaded from URL’s and local paths using hub_load()

module <- hub_load("https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/2")

Module’s behave like functions and can be called with Tensors eg:

input <- tf$random$uniform(shape = shape(1,224,224,3), minval = 0, maxval = 1)
output <- module(input)

Using with Keras

The easiest way to get started with tfhub is using layer_hub. A Keras layer that loads a TensorFlow Hub module and prepares it for using with your model.

library(tfhub)
library(keras)

input <- layer_input(shape = c(32, 32, 3))

output <- input %>%
  # we are using a pre-trained MobileNet model!
  layer_hub(handle = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/2") %>%
  layer_dense(units = 10, activation = "softmax")

model <- keras_model(input, output)

model %>%
  compile(
    loss = "sparse_categorical_crossentropy",
    optimizer = "adam",
    metrics = "accuracy"
  )

We can then fit our model in the CIFAR10 dataset:

cifar <- dataset_cifar10()
cifar$train$x <- tf$image$resize(cifar$train$x/255, size = shape(224,224))

model %>%
  fit(
    x = cifar$train$x,
    y = cifar$train$y,
    validation_split = 0.2,
    batch_size = 128
  )

Using with tfdatasets

tfhub can also be used with tfdatasets:

Using with recipes

tfhub adds a step_pretrained_text_embedding that can be used with the recipes package.

An example can be found here.

tfhub.dev

tfhub.dev is a gallery of pre-trained model ready to be used with TensorFlow Hub.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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