for step in range(config.total_step):

    
    # Extract multiple(5) conv feature vectors
    target_features = vgg(target)  # 每一次输入到网络中的是同样一张图片，反传优化的目标是输入的target
    content_features = vgg(Variable(content))
    style_features = vgg(Variable(style))

    style_loss = 0
    content_loss = 0
    for f1, f2, f3 in zip(target_features, content_features, style_features):
      # Compute content loss (target and content image)
      content_loss += torch.mean((f1 - f2)**2) # square 可以进行直接加-操作？可以，并且mean对所有的元素进行均值化造作

      # Reshape conv features
      _, c, h, w = f1.size() # channel height width
      f1 = f1.view(c, h * w) # reshape a vector
      f3 = f3.view(c, h * w) # reshape a vector

      # Compute gram matrix 
      f1 = torch.mm(f1, f1.t())
      f3 = torch.mm(f3, f3.t())

      # Compute style loss (target and style image)
      style_loss += torch.mean((f1 - f3)**2) / (c * h * w)  # 总共元素的数目？

# -*-coding: utf-8 -*-
import torch
from torch.autograd import Variable

# dtype = torch.FloatTensor
dtype = torch.cuda.FloatTensor # Uncomment this to run on GPU

# N is batch size; D_in is input dimension;
# H is hidden dimension; D_out is output dimension.
N, D_in, H, D_out = 64, 1000, 100, 10

# Randomly initialize weights
w1 = torch.randn(D_in, H).type(dtype) # 两个权重矩阵
w2 = torch.randn(D_in, H).type(dtype)
# operate with +-*/ and **
w3 = w1-2*w2
w4 = w3**2
w5 = w4/w1


# operate the Variable with +-*/ and **
w6 = Variable(torch.randn(N, D_in).type(dtype))
w7 = Variable(torch.randn(N, D_in).type(dtype))
w8 = w6 + w7
w9 = w6*w7
w10 = w9**2
print(1)