deeplearn-graph-serializer

deeplearn-graph-serializer

WARNING: This is an unofficial serialization/deserialization library for DeepLearnJs graphs, it is currently obsolete and only compatible with DeepLearnJs versions less than 0.6.

Usage

Require or import this module in your code:

const GraphSerializer = require('deeplearn-graph-serializer')

After a graph is created its structure and values can be exported using GraphSerializer.graphToJson:

const graphJson = GraphSerializer.graphToJson(graph)
console.log(JSON.stringify(graphJson))

A network can be recreated from JSON using GraphSerializer.jsonToGraph:

const net = GraphSerializer.jsonToGraph(graphJson)
const { graph, placeholders, variables, tensors } = net

The GraphSerializer.jsonToGraph method returns a graph object, any tensors created, placeholder references to tensors by name, and variable data by name.

Advanced Usage

The GraphSerializer.graphToJson method normally returns a JSON that references tensors starting from id 0. To preserve the tensor id we can pass false as the second parameter to GraphSerializer.graphToJson. In this way it is possible to serialize a collection of graphs that reference shared tensors:

const graph1 = new Graph()
const v = graph1.variable('v',dl.variable(dl.scalar(2)))
const graph2 = new Graph()
const result = graph2.multiply(v,v)
graph2.variable('result',result)

const graph1Json = GraphSerializer.graphToJson(graph1, false)
const graph2Json = GraphSerializer.graphToJson(graph2, false)

Then when using GraphSerializer.jsonToGraph we would pass a deserialized tensors object as the second parameter, this allows the deserializer to chain tensor references from other graphs:

let deserial =  GraphSerializer.jsonToGraph(graph1Json)
deserial.variables.v.assign(dl.scalar(3))
deserial =  GraphSerializer.jsonToGraph(graph2Json, deserial.tensors)
const session = new Session(deserial.graph, math)
const squared = await session.eval(deserial.variables.result).val(0)
console.log(squared) // = 9