问题描述
我正在尝试优化一个函数，该函数采用权重的输入列表，并使用这些权重更改图像的像素，然后计算一些得分（NSS得分）。 我们的目标是找到能够获得最高NSS得分的最佳输入权重。 为此，我们使用了Scipy最小化功能
# Our input initial Weights,which we want to find the highest values of them
weights_init= [i for i in range(82)]
weights_init[0] = 0
min_result= minimize(global_stimuli_optimization,weights_init,method='L-BFGS-B',options={'disp': True})
global_stimuli_optimization函数采用权重als输入并进行一些处理，然后计算一些分数（NSS）
def global_stimuli_optimization(weights):
... some attributs ...
for index,boundary in enumerate (boundaries):
... Some processing ...
all_stimuli = [all_stimuli_sorted for _,all_stimuli_sorted in sorted(zip(indices_sorted,all_stimuli_sorted))]
# we create a model
model = MySaliencyMapModel(all_stimuli)
# we calculate a score
nss_score = np.nanmean(model.NSSs(mit_stimuli,mit_fixations))
print('Weights : ',weights,' NSS :',nss_score)
return - nss_score
优化功能运行良好，但仅执行1次迭代，然后停止
输出：
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skippeds
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
82 0 1 0 0 0 0.000D+00 -6.932D-01
F = -0.693229780993317
CONVERGENCE: norM_OF_PROJECTED_GRADIENT_<=_PGTOL
Cauchy time 0.000E+00 seconds.
Subspace minimization time 0.000E+00 seconds.
Line search time 0.000E+00 seconds.
Total User time 0.000E+00 seconds.
是否有人知道它会在第一次迭代后停止？
解决方法
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