This is a sample template repo showing how to use tensorflow.js with react native.
Here is the app in action.
In order to use this repo, you need to have a tfjs model. You can get a smaple artifact from this repo's release.
Please download it from this repos's release and place the contents in assets/model_tfjs
(base) deploying-mobile-app % tree assets/model_tfjs
assets/model_tfjs
├── classes.json
├── classes.txt
├── group1-shard1of1.bin
└── model.json
If you want to bring in your own model, please use a command like below to convert a keras h5 artifact.
The below command converts artifacts/model_tf_keras.h5 to tfjs and saves it to artifacts/model_tfjs.
The model is also 16 bit quanitzed.
The model is converted to tfjs_graph_model which is an optimized version of the graph.
The model is broken into 100MB shards.
pip install tensorflowjs==2.3.0
tensorflowjs_converter \
--input_format=keras \
--output_format=tfjs_graph_model \
--split_weights_by_layer \
--weight_shard_size_bytes=99999999 \
--quantize_float16=* \
artifacts/model_tf_keras.h5 artifacts/model_tfjs
yarn global add expo-cli
# you can open iOS, Android, or web from here, or run them directly with the commands below.
yarn start
# you can open in android simulator or device driectly
yarn android
# you can open in ios simulator or device directly
yarn ios
In the future, it is possible that packages may be out of date.
The below upgrade will upgrade expo and its dependencies
expo upgrade
The below command command can be used to interactively update dependencies.
yarn upgrade-interactive
If you are interested in a guided exploration of this content, consider Manning's Live project Deploying a Deep Learning Model on Web and Mobile Applications