【发布时间】：2020-09-26 21:39:00
【问题描述】：
我在进行神经风格迁移时正在使用fmin_l_bfgs_b 函数并继续获取
TypeError: 'numpy.float32' object is not callable
错误块的详细信息是：
TypeError Traceback (most recent call last)
<ipython-input-10-4699dceebbd9> in <module>()
----> 1 generate_art('/content/nst_images/5.jpg', '/content/nst_images/a.jpg',1, img_height=400)
4 frames
<ipython-input-9-67cbbed20548> in generate_art(content_image_path, style_image_path, iterations, img_height)
38 for i in range(iterations):
39
---> 40 x, min_val, info = fmin_l_bfgs_b(evaluator.loss(x,img_height,img_width,fetch_loss_and_grads),x, fprime=evaluator.grads, maxfun=20)
41 img = x.copy().reshape((img_height, img_width, 3))
42 img = deprocess_image(img)
/usr/local/lib/python3.6/dist-packages/scipy/optimize/lbfgsb.py in fmin_l_bfgs_b(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, iprint, maxfun, maxiter, disp, callback, maxls)
197
198 res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,
--> 199 **opts)
200 d = {'grad': res['jac'],
201 'task': res['message'],
/usr/local/lib/python3.6/dist-packages/scipy/optimize/lbfgsb.py in _minimize_lbfgsb(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, **unknown_options)
343 # until the completion of the current minimization iteration.
344 # Overwrite f and g:
--> 345 f, g = func_and_grad(x)
346 elif task_str.startswith(b'NEW_X'):
347 # new iteration
/usr/local/lib/python3.6/dist-packages/scipy/optimize/lbfgsb.py in func_and_grad(x)
293 else:
294 def func_and_grad(x):
--> 295 f = fun(x, *args)
296 g = jac(x, *args)
297 return f, g
/usr/local/lib/python3.6/dist-packages/scipy/optimize/optimize.py in function_wrapper(*wrapper_args)
325 def function_wrapper(*wrapper_args):
326 ncalls[0] += 1
--> 327 return function(*(wrapper_args + args))
328
329 return ncalls, function_wrapper
TypeError: 'numpy.float32' object is not callable
关于如何解决此问题的任何建议？谢谢！
【问题讨论】：
-
evaluator.loss的类型是什么 -
我会说：使用
print()和print(type(...))检查变量中的内容。并检查您是否以正确的顺序使用变量。也许您将数字放在函数名称的位置，它会尝试将此数字用作函数 - 即。1()
标签： python numpy deep-learning scipy