After compiling and training my custom model, I saved it and got two files such as .bin and .json. Further, I loaded that custom model on another page where I'm giving images as input which I used for training of that model and getting the prediction for those images based on the loaded custom model.
Since it works fine for some of the images but returning the wrong prediction for other images.
This is my code:
$("#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);
});
