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 totally different duties in a course of often known as switch studying.
The CRAN model of tfhub could 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 operating:
Getting began
The important operate of tfhub is layer_hub
which works identical to a keras layer however permits you to load an entire pre-trained deep studying mannequin.
For instance you may:
It will obtain the MobileNet mannequin pre-trained on the ImageNet dataset. tfhub fashions are cached domestically and don’t should be downloaded the following time you employ the identical mannequin.
Now you can use layer_mobilenet
as a traditional Keras layer. For instance you may outline a mannequin:
Mannequin: "mannequin"
____________________________________________________________________
Layer (sort) 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:
class_name class_description rating
1 n03763968 military_uniform 9.760404
2 n02817516 bearskin 5.922512
3 n04350905 swimsuit 5.729345
4 n03787032 mortarboard 5.400651
5 n03929855 pickelhaube 5.008665
TensorFlow Hub additionally provides many different pre-trained picture, textual content and video fashions.
All potential fashions could be discovered on the TensorFlow hub web site.
Yow will discover 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 provides 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 = prepare) %>%
step_pretrained_text_embedding(
comment_text,
deal with = "https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim-with-oov/1"
) %>%
step_bin2factor(obscene)
You’ll be able to see an entire operating instance right here.
You can even use tfhub with the brand new Function Spec API carried out in tfdatasets. You’ll be able to see an entire 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 could be acknowledged by a be aware 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/}, yr = {2019} }