The document discusses Keras.js, a library that allows running Keras models in the browser. Keras.js can load Keras models saved in HDF5 format along with weights and metadata files. It supports running models on the GPU or CPU via WebGL. The document provides an example of using Keras.js to load a model, make a prediction on input data, and log the output. It also links to a GitHub repository with a machine learning rock-paper-scissors demo built with Keras.js.

1. 101
for Keras.js
2017/8/1
ARCANA Meetup #27@

2. Keras
WebAPI
Processing + Flask

3. N

12. Keras.js

13. Keras.js
Tensor ow
Keras (GPU !)
Keras
JSON
HDF5
Node.js (CPU )
https://github.com/transcranial/keras-js

14. Keras.js encoder.py
HDF5
Keras.js

15. const model = new KerasJS.Model({
filepaths: {
model: 'url/path/to/model.json',
weights: 'url/path/to/model_weights.buf',
metadata: 'url/path/to/model_metadata.json'
},
gpu: true
})

16. model.ready().then(
function () {
const inputData = {
'input': new Float32Array(testData)
};
console.log(inputData);
return model.predict(inputData)
}).then(
function (outputData) {
console.log(outputData);
}
).catch(
function (err) {
console.log(err);
}
);

18. https://github.com/endoh0509/MachineLearningRPS