R interface to TensorFlow Hub

We’re happy to announce that the primary model of tfhub is now on CRAN. tfhub is an R interface to TensorFlow Hub – a library for the publication, discovery, and consumption of reusable components of machine studying fashions. A module is a self-contained piece of a TensorFlow graph, together with its weights and belongings, that may be reused throughout completely different duties in a course of often known as switch studying.

The CRAN model of tfhub might be put in with:

After putting in the R bundle it’s good to set up the TensorFlow Hub python bundle. You are able to do it by working:

Getting began

The important perform of tfhub is layer_hub which works identical to a keras layer however permits you to load a whole pre-trained deep studying mannequin.

For instance you may:

library(tfhub)
layer_mobilenet <- layer_hub(
  deal with = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4"
)

It will obtain the MobileNet mannequin pre-trained on the ImageNet dataset. tfhub fashions are cached regionally and don’t have to be downloaded the subsequent time you utilize the identical mannequin.

Now you can use layer_mobilenet as a ordinary Keras layer. For instance you may outline a mannequin:

library(keras)
enter <- layer_input(form = c(224, 224, 3))
output <- layer_mobilenet(enter)
mannequin <- keras_model(enter, output)
abstract(mannequin)

Mannequin: "mannequin"
____________________________________________________________________
Layer (kind)                  Output Form               Param #    
====================================================================
input_2 (InputLayer)          [(None, 224, 224, 3)]      0          
____________________________________________________________________
keras_layer_1 (KerasLayer)    (None, 1001)               3540265    
====================================================================
Whole params: 3,540,265
Trainable params: 0
Non-trainable params: 3,540,265
____________________________________________________________________

This mannequin can now be used to foretell Imagenet labels for a picture. For instance, let’s see the outcomes for the well-known Grace Hopper’s photograph:

img <- image_load("https://blogs.rstudio.com/tensorflow/posts/pictures/grace-hopper.jpg", target_size = c(224,224)) %>% 
  image_to_array()
img <- img/255
dim(img) <- c(1, dim(img))
pred <- predict(mannequin, img)
imagenet_decode_predictions(pred[,-1,drop=FALSE])[[1]]

  class_name class_description    rating
1  n03763968  military_uniform 9.760404
2  n02817516          bearskin 5.922512
3  n04350905              go well with 5.729345
4  n03787032       mortarboard 5.400651
5  n03929855       pickelhaube 5.008665

TensorFlow Hub additionally affords many different pre-trained picture, textual content and video fashions.
All attainable fashions might be discovered on the TensorFlow hub web site.

You’ll find extra examples of layer_hub utilization within the following articles on the TensorFlow for R web site:

Utilization with Recipes and the Characteristic Spec API

tfhub additionally affords recipes steps to make
it simpler to make use of pre-trained deep studying fashions in your machine studying workflow.

For instance, you may outline a recipe that makes use of a pre-trained textual content embedding mannequin with:

rec <- recipe(obscene ~ comment_text, information = practice) %>%
  step_pretrained_text_embedding(
    comment_text,
    deal with = "https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim-with-oov/1"
  ) %>%
  step_bin2factor(obscene)

You may see a whole working instance right here.

You too can use tfhub with the brand new Characteristic Spec API carried out in tfdatasets. You may see a whole instance right here.

We hope our readers have enjoyable experimenting with Hub fashions and/or can put them to good use. Should you run into any issues, tell us by creating a difficulty within the tfhub repository

Reuse

Textual content and figures are licensed beneath Inventive Commons Attribution CC BY 4.0. The figures which were reused from different sources do not fall beneath this license and might be acknowledged by a notice of their caption: “Determine from …”.

Quotation

For attribution, please cite this work as

Falbel (2019, Dec. 18). Posit AI Weblog: tfhub: R interface to TensorFlow Hub. Retrieved from https://blogs.rstudio.com/tensorflow/posts/2019-12-18-tfhub-0.7.0/

BibTeX quotation

@misc{tfhub,
  creator = {Falbel, Daniel},
  title = {Posit AI Weblog: tfhub: R interface to TensorFlow Hub},
  url = {https://blogs.rstudio.com/tensorflow/posts/2019-12-18-tfhub-0.7.0/},
  yr = {2019}
}