swamiviv / guided_perturbations Goto Github PK

    #Modify output to backprop gradient based on network output
    sh = out.squeeze().shape
    conf_softmax = np.zeros((1,sh[0],sh[1],sh[2]))        
    if args.type == 'one-hot':
        for i in np.arange(sh[0]):       # should be sh[1] ? 
            for j in np.arange(sh[1]):   # should be sh[2] ?                                                    
                conf_softmax[0,out_argmax[i,j],i,j]=1.0  
    elif args.type== 'same':
        conf_softmax = copy.deepcopy(out[None,:,:,:])