Using a Tensorflow model in the Browser

So the model from yesterday performs pretty well. Next step is to get it running in the browser for a simple demo app. As text is typed the model will output the parts of speech.

Converting TensorFlow models to TensorFlow.js is very straightforward. First install tensorflowjs at the command line using pip. 

pip install tensorflowjs

Then run a single command to convert your model. You can find the details here. There are plenty of options that you can set, but the vanilla version worked fine for me. 

tensorflowjs_converter data/model ./output_dir --input_format=tf_saved_model

Next I needed a bit of JavaScript to load the model into the page and make a prediction. You can see that I have stopped this in the Chrome debugger so that I could fiddle with the shape of the tensor for prediction. 


So I just chose some numbers to fill the tensor and run a prediction:



Just to check that everything is working as I expect I ran the same tensor into the python version of the model:



So both of the models return 38. But what does that mean? That brings me to my next task. I need to write the text preprocessing that I used in the python as javascript. 

Hopefully soon the need for this tricky - and hard to maintain - step in the process will be obviated by using TensorFlow Preprocessing Layers. You can find details here. Not available in TensorFlow.js as at time of writing, but they look really great especially for text models. 

I originally found out about Preprocessing Layers by watching this talk on youtube. It is an excellent talk and there are more new ideas in there which I will come back to in subsequent posts. 

And this talk from TensorFlow Fall 2020 is excellent too. It covers a wide range of new features in TF.  

So my next task is to write the preprocessing (and prediction decoding) logic for the model in JavaScript. 

Comments

Post a Comment

Popular posts from this blog

Execute Jupyter notebooks line by line in VS Code

Normal notebook execution is cell by cell in Jupyter notebooks in the browser. This frequently results in cells needing to be split and rejoined at various points in the dev process. VS Code allows line by line execution of code within those cells. This feels a lot like running SQL code in MS SQL Analyzer and can be a very natural way to work. In addition VS Code gives you a nice view of the data returned from a cell or line execution with its Data Viewer. In WSL Ubuntu to get the line by line stuff working you will need to install the Jupyter Extension and the Python extension. Once these are in place the process of getting VS Code to find your correct python environment is very straightforward. You can find more about line by line debugging here: https://github.com/microsoft/vscode-jupyter/wiki/Setting-Up-Run-by-Line-and-Debugging-for-Notebooks Once the python extension is installed VS Code will find your virtual environments and you pyenv environments.
Read more

Using TensorFlow Serving

Having converted the front end to React the next step is to get a model making predictions from the server. TensorFlow Serving seems like the best way to do this. Instructions are here  for using Serving with Docker which is the recommended approach.  Some tips on docker: After you have run docker pull to get the TensorFlow Serving container you will want to list your containers: docker ps To run a container use docker run as per the instructions.  docker run -p 8501:8501 --mount type=bind,source=$(pwd)/data/model/pos_checkpoint,target=/models/pos_checkpoint -e MODEL_NAME=pos_checkpoint -t tensorflow/serving Then use docker stop XX where XX is enough of the tag from docker ps to identify that container.  You will need to put your SavedModel into an integer folder for the version.  The command to run a prediction using the REST endpoint is: curl -d '{"instances":[1,2,3]}' -X POST http://localhost:8501/v1/models/pos_checkpoint:predict That will error because the ...
Read more

Text Summarisation with BERT

Hugging Face provides a wide range of pretrained models for lots of machine learning tasks. Summarising the chapters of our novel - Bleak House - would be an interesting challenge. This could also be a very useful mnemonic technique if you were reviewing a long piece of work.  So here is the Hugging Face instructions on this. pip install transformers Then in python: from transformers import pipeline summarizer = pipeline("summarizer") Give it the text to summarize along with some params summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False) BERT is a large model and takes about 15 minutes to download for me. However it is then a pip package and so will be available next time you try to run it. 
Read more