问TensorFlow JS -将最小/最大值与模型一起保存，并在预测数据旁边重新加载

EN

//saving tensor normalisedFeature to later access min/max used 

    function downloadJ() {

            let values = {
                normalisedFeature
            }
            let json = JSON.stringify(values);
            //Convert JSON string to BLOB.
            json = [json];
            let blob1 = new Blob(json, { data:"text/json;charset=utf-8" }); 
            

            let url = window.URL || window.webkitURL;
            link = url.createObjectURL(blob1);
            let a = document.createElement("a");
            a.download = "tValues.json";
            a.href = link;
            document.body.appendChild(a);
            a.click();
            document.body.removeChild(a);
        }


//loading up tensor saved values
let normalisedFeatureJ = {};
    $.ajax({
        url: "model/tValues.json",
        async: false,
        dataType: 'json',
        success: function(data) {
            normalisedFeatureJ = (data);
        }
    });
console.log(Object.values(normalisedFeatureJ));

//tried dataSync();, looping, parsing etc. Can't get anything to let me access min/max


//json file looks like: 
{"normalisedFeature":
    {"tensor": {"isDisposedInternal":false,"shape":[10000,17],"dtype":"float32","size":170000,"strides":[17],"dataId":{},"id":28,"rankType":"2"},
            "min":{"isDisposedInternal":false,"shape":[],"dtype":"float32","size":1,"strides":[],"dataId":{},"id":6,"rankType":"0"},
                "max":{"isDisposedInternal":false,"shape":[],"dtype":"float32","size":1,"strides":[],"dataId":{},"id":16,"rankType":"0"}}}



//normalise and denormalise functions using tensor maths
function normalise(tensor, previousMin = null, previousMax = null) {
        const min = previousMin || tensor.min();
        console.log("tensor min for normalised is :" + tensor.min());
        const max = previousMax || tensor.max();
        console.log("tensor max for normalised is :" + tensor.max());

        const normalisedTensor = tensor.sub(min).div(max.sub(min));
       // const normalisedTensor = (tensor-min)/(max-min);
        return {
            tensor: normalisedTensor,
            min,
            max
        };
    }

    function denormalise(tensor, min, max) {
        console.log("tensor min for denormalised is :" + min);
        console.log("tensor max for denormalised is :" + max);
        const denormalisedTensor = tensor.mul(max.sub(min)).add(min);
        return denormalisedTensor;
    }