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 property, that may be reused throughout totally different duties in a course of often called switch studying.

The CRAN model of tfhub will be put in with:

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

Getting began

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

For instance you’ll be able to:

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

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

Now you can use layer_mobilenet as a regular Keras layer. For instance you’ll be able to 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 picture:

img <- image_load("https://blogs.rstudio.com/tensorflow/posts/photographs/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 presents many different pre-trained picture, textual content and video fashions.
All potential fashions will 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 Function Spec API

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

For instance, you’ll be able to 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 Function Spec API applied 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. When you run into any issues, tell us by creating a difficulty within the tfhub repository

Reuse

Textual content and figures are licensed beneath Artistic Commons Attribution CC BY 4.0. The figures which have been reused from different sources do not fall beneath this license and will 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,
  writer = {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/},
  12 months = {2019}
}