AttributeError: 'Net' object has no attribute 'backward_from_layer'

[zcboluo]

Hi,

I am a beginner in deep learning and programming. When I run "find_fooling_image.py" in "./caffe/ascent"
,an error has occurred:
AttributeError: 'Net' object has no attribute 'backward_from_layer'

I don't know how to overcome it? Can you help me?

Thanks!

[zcboluo]

I have seen the closed issue about"no attribute 'backward_from_layer'"and sovled this problem.

But another problem has occurred:

---

grad: 0.0 0.0 0.0
Grad 0, failed
Result: no convergence

---

the grad is always 0, it may because:
code: " diffs = net.blobs[push_layer].diff * 0 " in the "find_fooling_image.py"

or it may result from that I used the model 'bvlc_reference_caffenet.caffemodel' instead, because I couldn't download the trained model from 'http://yosinski.cs.cornell.edu/yos_140311__caffenet_iter_450000'.
Where can I download it?

[zcboluo]

@yosinski I am waiting for you help. Thank you very much!

[yosinski]

Hi @zcboluo, sorry for the inactive URL. Try this one instead:
http://c.yosinski.com/caffenet-yos-weights

Does that work? If not, make sure you're using a network definition prototext containing the force_backward: true line like as in ours:
https://github.com/Evolving-AI-Lab/fooling/blob/ascent/caffe/ascent/deploy_1_forcebackward.prototxt#L7

[zcboluo]

@yosinski Thank you very much. The model works. But I have a new problem:
When I put the generated picture back in the model, the results is different.
These is the code ():

---

    im=caffe.io.load_image('291_maj_Xpm.png')  
    tmp = transformer.preprocess('data', im)   # converts rgb -> bgr

    X0 = tmp[newaxis,:]
    X = minimum(255.0, maximum(0.0, X0 + mn4d)) - mn4d     # Crop all values to [0,255]
    out = net.forward_all(data = X)
    acts = net.blobs['prob'].data

    iimax = unravel_index(acts.argmax(), acts.shape)[1:]   # chop off batch idx of 0
    push_label = labels[push_idx]
    print  'Push idx: %d, val: %g (%s)\n      Max idx: %d, val: %g (%s)' % (push_idx, acts[0][push_idx],     push_label, iimax[0], acts.max(), labels[iimax[0]])

---

However, when replace 'im' with 'best_X'(return from the fuction 'find_image'). The result is the same.

Why ‘im’ is different from 'best_X'? So I wonder if there is any mistake I have not noticed.

[zcboluo]

@yosinski
I found that the code ‘X = minimum(255.0, maximum(0.0, X + mn4d)) - mn4d # Crop all values to [0,255]’ didn't really make 'X' in [0,255]. I wonde if this is the reason?