在 tensorflow.js 中加载保存模型后使用自定义模型进行错误预测

在编译和训练我的自定义模型后，我保存它并得到两个文件，例如 .bin 和 .json。此外，我在另一个页面上加载了该自定义模型，我将图像作为输入，用于训练该模型并根据加载的自定义模型获取这些图像的预测。

因为它对某些图像工作正常，但对其他图像返回错误的预测。

这是我的代码：

        $("#predict-button").click(async function(){
        let image= $('#selected-image').get(0);
        let image1 = $('#selected-image1').get(0);
        console.log('image:::',image);
        console.log('image1:::',image1);
        let tensorarr = [];
        let tensor1 = preprocessImage(image,$("#model-selector").val());
        tensorarr.push(tensor1);
        let tensor2 = preprocessImage(image1,$("#model-selector").val());
        tensorarr.push(tensor2);
        let resize_image = [];
        let resize;
        for(var i=0; i<tensorarr.length; i++)
        {
            resize = tf.reshape(tensorarr[i], [1, 224, 224, 3],'resize');
            console.log('resize:::',resize);
            resize_image.push(resize);
        }
        // Labels
        const label = ['Shelf','Rack'];
        const setLabel = Array.from(new Set(label));
        let ysarr =[];
        const ys = tf.oneHot(tf.tensor1d(label.map((a) => setLabel.findIndex(e => e === a)), 'int32'), 10)
        console.log('ys:::'+ys);
        const y = tf.reshape(ys, [-1]);
        y.print();
        const d = y.slice([0], [10]);
        d.print();
        ysarr.push(d);
        const e = y.slice([10], [10]);
        e.print();
        ysarr.push(e);
        console.log('ysarr',ysarr);
        model.add(tf.layers.conv2d({
            inputShape: [224, 224 , 3],
            kernelSize: 5,
            filters: 8,
            strides: 1,
            activation: 'relu',
            kernelInitializer: 'VarianceScaling'
        }));

        model.add(tf.layers.maxPooling2d({poolSize: 2, strides: 2}));
        model.add(tf.layers.maxPooling2d({poolSize: 2, strides: 2}));
        model.add(tf.layers.flatten({}));
        model.add(tf.layers.dense({units: 64, activation: 'relu'}));
        model.add(tf.layers.dense({units: 10, activation: 'softmax'}));
        model.compile({
            loss: 'meanSquaredError',
            optimizer : 'sgd'
        })
        console.log('model:::'+model);
        // Train the model using the data.
        let tesnor_dim =[];
        let tensr;
        for(var j=0; j<2; j++){
            console.log('resize_image',resize_image);
            tensr = tf.expandDims(ysarr[j], 0);
            tesnor_dim.push(tensr);
            console.log('tesnor_dim',tesnor_dim);
            console.log('before resize_image[j]',resize_image[j]);
            console.log('before tesnor_dim[j]',tesnor_dim[j]);
            await model.fit(resize_image[j], tesnor_dim[j], {epochs: 100}).then((loss) => {
                console.log('resize_image.get[j]',resize_image[j]);
                console.log('tesnor_dim[j]',tesnor_dim[j]);
                console.log('loss',loss);
                const t = model.predict(resize_image[j]);
                console.log('Prediction:::'+t);
                pred = t.argMax(1).dataSync(); // get the class of highest probability
                const labelsPred = Array.from(pred).map(e => setLabel[e]);
                console.log('labelsPred:::'+labelsPred);

            }).catch((e) => {
                console.log(e.message);
            })
            }     
                const saveResults = model.save('downloads://my-model-1');
                console.log(saveResults);   
            });