javascript - tensorflow.js throws puzzling error when calling tf.grad() for gradient ascent

Question

For a course I'm taking, I am to implement deep dream for feature visualization. I chose to realize this project in the browser. As for my background, I am new to Machine Learning and Tensorflow.js.

Mostly, I have followed this guide for the Python API of Tensorflow:
https://www.tensorflow.org/tutorials/generative/deepdream#calculate_loss

So far, I was able to implement all the steps until Gradient Ascent. I had a feeling that calculating the gradients would be a challenge, since the Python API has this convenient tf.GradientTape() construct, which Tensorflow.js does not have. From my understanding I have to use tf.grad() or tf.grads() instead.
This is my loss function:

function calc_loss(model, img_tensor) {
    const activations = [].concat(model.predict(img_tensor));

    const losses = activations.map( v => tf.mean(v))

    const means = losses.reduce((acc, val) => {
        acc = tf.add(acc, val);
        return acc;
    })
    return tf.sum(means)        // unsure if tf.sum() is needed here
}

I pass it the feature extraction model and a tensor4d and it returns a tensor with 1 value.

The (partial) gradient ascent function I used:

function gradient_ascent(model, img_tensor) {
    
    const img_batch = img_tensor.expandDims(0);

    const loss_function = (input) => calc_loss(model, input);

    const grad_function = tf.grad(loss_function);
    return grad_function(img_batch)
}

The error it throws:

Uncaught (in promise) TypeError: x is undefined
    clone http://127.0.0.1:8080/tf.2.8.2.js:17122
    saved http://127.0.0.1:8080/tf.2.8.2.js:17373
    saveTensorsForBackwardMode http://127.0.0.1:8080/tf.2.8.2.js:17372
    kernelFunc http://127.0.0.1:8080/tf.2.8.2.js:17277
    runKernelFunc http://127.0.0.1:8080/tf.2.8.2.js:17324
    scopedRun http://127.0.0.1:8080/tf.2.8.2.js:17094
    runKernelFunc http://127.0.0.1:8080/tf.2.8.2.js:17318
    runKernel http://127.0.0.1:8080/tf.2.8.2.js:17171
    batchNorm_ http://127.0.0.1:8080/tf.2.8.2.js:26574
    f2 http://127.0.0.1:8080/tf.2.8.2.js:18338
    batchNorm4d_ http://127.0.0.1:8080/tf.2.8.2.js:26746
    f2 http://127.0.0.1:8080/tf.2.8.2.js:18338
    batchNormalization http://127.0.0.1:8080/tf.2.8.2.js:72769
    normalizeInference http://127.0.0.1:8080/tf.2.8.2.js:72966
    call http://127.0.0.1:8080/tf.2.8.2.js:72971
    tidy http://127.0.0.1:8080/tf.2.8.2.js:17080
    scopedRun http://127.0.0.1:8080/tf.2.8.2.js:17094
    tidy http://127.0.0.1:8080/tf.2.8.2.js:17075
    tidy http://127.0.0.1:8080/tf.2.8.2.js:24132
    call http://127.0.0.1:8080/tf.2.8.2.js:72942
    apply http://127.0.0.1:8080/tf.2.8.2.js:56063
    nameScope http://127.0.0.1:8080/tf.2.8.2.js:53015
    apply http://127.0.0.1:8080/tf.2.8.2.js:56019
    execute http://127.0.0.1:8080/tf.2.8.2.js:59585
    batchOuts http://127.0.0.1:8080/tf.2.8.2.js:63644
    tidy http://127.0.0.1:8080/tf.2.8.2.js:17080
    scopedRun http://127.0.0.1:8080/tf.2.8.2.js:17094
    tidy http://127.0.0.1:8080/tf.2.8.2.js:17075
    tidy http://127.0.0.1:8080/tf.2.8.2.js:24132
    _loop2 http://127.0.0.1:8080/tf.2.8.2.js:63620
    predictLoop http://127.0.0.1:8080/tf.2.8.2.js:63652
    tidy http://127.0.0.1:8080/tf.2.8.2.js:17080
    scopedRun http://127.0.0.1:8080/tf.2.8.2.js:17094
    tidy http://127.0.0.1:8080/tf.2.8.2.js:17075
    tidy http://127.0.0.1:8080/tf.2.8.2.js:24132
    predictLoop http://127.0.0.1:8080/tf.2.8.2.js:63601
    predict http://127.0.0.1:8080/tf.2.8.2.js:63704
    calc_loss http://127.0.0.1:8080/utils.js:103
    loss_function http://127.0.0.1:8080/utils.js:124
    gradients http://127.0.0.1:8080/tf.2.8.2.js:29870
    tidy http://127.0.0.1:8080/tf.2.8.2.js:17080
    scopedRun http://127.0.0.1:8080/tf.2.8.2.js:17094
    tidy http://127.0.0.1:8080/tf.2.8.2.js:17075
    y http://127.0.0.1:8080/tf.2.8.2.js:17798
    scopedRun http://127.0.0.1:8080/tf.2.8.2.js:17094
    gradients http://127.0.0.1:8080/tf.2.8.2.js:17793
    grad http://127.0.0.1:8080/tf.2.8.2.js:29869
    tidy http://127.0.0.1:8080/tf.2.8.2.js:17080
    scopedRun http://127.0.0.1:8080/tf.2.8.2.js:17094
    tidy http://127.0.0.1:8080/tf.2.8.2.js:17075
    grad http://127.0.0.1:8080/tf.2.8.2.js:29868
    gradient_ascent http://127.0.0.1:8080/utils.js:127
    handleTest http://127.0.0.1:8080/script.js:74
    promise callback*handleTest/< http://127.0.0.1:8080/script.js:69
    promise callback*handleTest http://127.0.0.1:8080/script.js:68
    EventListener.handleEvent* http://127.0.0.1:8080/script.js:126

What I've tried:

• I have replaced the loss function and the function to calculate the gradients with inputGradientAscent() from this repo (tfjs-examples), but got a similar error. Instead of x is undefined, it show _this2.gamma is undefined.
• I didn't gain any insight by using the debugger.
• I've used tf.js version 2.0, 2.4, 2.7 and now 2.8.2, with the same result.

Answer 1

等待大神答复