TensorFlowJS Exception - Cannot start training because another fit() call is ongoing

Question

I used tf.expandDims() to add dimensions. Since I'm able to go into model.fit(), but got stuck due to this error Cannot start training because another fit() call is ongoing. and Cannot read property 'length' of undefined. You can find my code here

// 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);
    model.fit(resize_image[j], tesnor_dim[j], {epochs: 100}).then((loss) => {
         console.log('resize_image[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);
                //const saveResults = model.save('downloads://my-model-1');
                //console.log(saveResults);
            }).catch((e) => {
                console.log(e.message);
            })
            }

Answer 1

When multiple fit are called on the same model, they have to be done sequentially. It means that the second call has to start only when the first one has completed. Using async and await will prevent your second call to happen unless the first has completed.

loss = await model.fit(resize_image[j], tesnor_dim[j], {epochs: 100})
// continue rest of processing

Answer 2

you may use this code,

await model.fit(resize_image[j], tesnor_dim[j], {epochs: 100}).then((loss) => {
     console.log('resize_image[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);
            //const saveResults = model.save('downloads://my-model-1');
            //console.log(saveResults);
        }).catch((e) => {
            console.log(e.message);
        })
        }