diff --git a/styleTransfer.py b/styleTransfer.py
new file mode 100644
index 0000000..9d32f18
--- /dev/null
+++ b/styleTransfer.py
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+#--------------------------导包
+import torch
+from torch.autograd import Variable
+import torchvision
+from torchvision import transforms, models
+import copy
+from PIL import Image
+import matplotlib.pyplot as plt
+ 
+#--------------------------1.数据预处理，加载数据
+transform = transforms.Compose([transforms.Resize([224,224]),
+                               transforms.ToTensor()])
+ 
+def loadimg(path = None):
+    img = Image.open(path)
+    img = transform(img)
+    img = img.unsqueeze(0)
+    return img
+ 
+content_img = loadimg('images/1.jpg')  #入参是自己存放图片的位置
+content_img = Variable(content_img).cuda()
+style_img = loadimg('images/2.jpg')
+style_img = Variable(style_img).cuda()
+ 
+#--------------------------2.定义内容损失和风格损失
+class Content_loss(torch.nn.Module):
+    def __init__(self,weight,target):
+        super(Content_loss,self).__init__()
+        self.weight = weight
+        self.target = target.detach()*weight
+        self.loss_fn = torch.nn.MSELoss()
+        
+    def forward(self,in_put):
+        self.loss = self.loss_fn(in_put*self.weight,self.target)
+        return in_put
+    
+    def backward(self):
+        self.loss.backward(retain_graph = True)
+        return self.loss
+ 
+class Gram_matrix(torch.nn.Module):
+    def forward(self,in_put):
+        a,b,c,d = in_put.size()
+        feature = in_put.view(a*b,c*d)
+        gram = torch.mm(feature,feature.t())
+        return gram.div(a*b*c*d)
+    
+class Style_loss(torch.nn.Module):
+    def __init__(self,weight,target):
+        super(Style_loss,self).__init__()
+        self.weight = weight
+        self.target = target.detach()*weight
+        self.loss_fn = torch.nn.MSELoss()
+        self.gram = Gram_matrix()
+        
+    def forward(self,in_put):
+        self.Gram = self.gram(in_put.clone())
+        self.Gram.mul_(self.weight)
+        self.loss = self.loss_fn(self.Gram,self.target)
+        return in_put
+    
+    def backward(self):
+        self.loss.backward(retain_graph = True)
+        return self.loss
+ 
+#--------------------------3.模型搭建
+cnn = models.vgg16(pretrained = True).features  #迁移VGG16架构的特征提取部分
+#指定整个卷积过程中分别在哪一层提取内容和风格
+content_layer = ["Conv_3"]
+style_layer = ["Conv_1","Conv_2","Conv_3","Conv_4"]
+#定义保存内容损失和风格损失的列表
+content_losses = []
+style_losses = []
+#指定内容损失和风格损失对最后得到的融合图片的影响权重
+content_weight = 1
+style_weight = 1000
+ 
+#搭建图像风格迁移模型的代码如下：
+new_model = torch.nn.Sequential() #建立空的模型
+model = copy.deepcopy(cnn)
+#deepcopy深复制，将被复制对象完全再复制一遍作为独立的新个体单独存在，改变原有被复制对象不会对已经复制出来的新对象产生影响。
+#copy浅复制，并不会产生一个独立的对象单独存在，他只是将原有的数据块打上一个新标签
+#所以当其中一个标签被改变的时候，数据块就会发生变化，另一个标签也会随之改变。
+gram = Gram_matrix()
+ 
+use_gpu = torch.cuda.is_available()
+if use_gpu:
+    model = model.cuda()
+    new_model = new_model.cuda()
+    gram = gram.cuda()
+    
+index = 1
+#只使用迁移模型特征提取部分的前8层
+for layer in list(model)[:8]:
+    if isinstance(layer,torch.nn.Conv2d):
+        name = "Conv_" + str(index)
+        #使用add_module方法向空的模型加入指定的层次模块
+        new_model.add_module(name,layer)
+        if name in content_layer:
+            target = new_model(content_img).clone()
+            content_loss = Content_loss(content_weight,target)
+            new_model.add_module("content_loss_"+str(index),content_loss)
+            content_losses.append(content_loss)
+            
+        if name in style_layer:
+            target = new_model(style_img).clone()
+            target = gram(target)
+            style_loss = Style_loss(style_weight,target)
+            new_model.add_module("style_loss_"+str(index),style_loss)
+            style_losses.append(style_loss)
+    
+    if isinstance(layer,torch.nn.ReLU):
+        name = "ReLU_"+str(index)
+        new_model.add_module(name,layer)
+        index = index + 1
+        
+    if isinstance(layer,torch.nn.MaxPool2d):
+        name = "MaxPool_"+str(index)
+        new_model.add_module(name,layer)
+#构造优化器      
+input_img = content_img.clone()
+parameter = torch.nn.Parameter(input_img.data)
+optimizer = torch.optim.LBFGS([parameter])
+ 
+#--------------------------４.模型训练和参数优化
+epoch_n = 300
+epoch = [0]
+while epoch[0] <= epoch_n:
+ 
+    def closure():
+        optimizer.zero_grad()
+        style_score = 0
+        content_score = 0
+        parameter.data.clamp_(0,1)
+        new_model(parameter)
+        for sl in style_losses:
+            style_score += sl.backward()
+ 
+        for cl in content_losses:
+            content_score += cl.backward()
+ 
+        epoch[0] += 1
+        if epoch[0] % 50 == 0:
+            print('Epoch:{} Style_loss: {:4f} Content_loss: {:.4f}'.format(epoch[0], style_score.data.item(),
+                                                                           content_score.data.item()))
+        return style_score + content_score
+ 
+    optimizer.step(closure)
+    
+#--------------------------5.对风格迁移图片输出
+output = parameter.data
+unloader = transforms.ToPILImage()  # 重新转化成PIL图像格式
+ 
+plt.ion()
+plt.figure()
+def imshow(tensor, title=None):
+    image = tensor.clone().cpu()  # 克隆tensor，改变时不影响被克隆的tensor
+    image = image.view(3, 224, 224)  # 转换维度
+    image = unloader(image)
+    plt.imshow(image)
+    if title is not None:
+        plt.title(title)
+    plt.pause(0.001) # 稍作停顿，以便更新图表
+imshow(output, title='Output Image')
+ 
+# 设置sphinx_gallery_thumbnail_number = 4
+plt.ioff()
+plt.show()
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