diff --git a/2_visual_weights.py b/2_visual_weights.py
new file mode 100644
index 00000000..9b273a37
--- /dev/null
+++ b/2_visual_weights.py
@@ -0,0 +1,69 @@
+# coding: utf-8
+import os
+import torch
+import torchvision.utils as vutils
+from tensorboardX import SummaryWriter
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool1 = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.pool2 = nn.MaxPool2d(2, 2)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool1(F.relu(self.conv1(x)))
+        x = self.pool2(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+    # 定义权值初始化
+    def initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                torch.nn.init.xavier_normal_(m.weight.data)
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                torch.nn.init.normal_(m.weight.data, 0, 0.01)
+                m.bias.data.zero_()
+
+
+net = Net()     # 创建一个网络
+pretrained_dict = torch.load(os.path.join("..", "2_model", "net_params.pkl"))
+net.load_state_dict(pretrained_dict)
+
+writer = SummaryWriter(log_dir=os.path.join("..", "..", "Result", "visual_weights"))
+params = net.state_dict()
+for k, v in params.items():
+    if 'conv' in k and 'weight' in k:
+
+        c_int = v.size()[1]     # 输入层通道数
+        c_out = v.size()[0]     # 输出层通道数
+
+        # 以feature map为单位，绘制一组卷积核，一张feature map对应的卷积核个数为输入通道数
+        for j in range(c_out):
+            print(k, v.size(), j)
+            kernel_j = v[j, :, :, :].unsqueeze(1)       # 压缩维度，为make_grid制作输入
+            kernel_grid = vutils.make_grid(kernel_j, normalize=True, scale_each=True, nrow=c_int)   # 1*输入通道数, w, h
+            writer.add_image(k+'_split_in_channel', kernel_grid, global_step=j)     # j 表示feature map数
+
+        # 将一个卷积层的卷积核绘制在一起，每一行是一个feature map的卷积核
+        k_w, k_h = v.size()[-1], v.size()[-2]
+        kernel_all = v.view(-1, 1, k_w, k_h)
+        kernel_grid = vutils.make_grid(kernel_all, normalize=True, scale_each=True, nrow=c_int)  # 1*输入通道数, w, h
+        writer.add_image(k + '_all', kernel_grid, global_step=666)
+writer.close()
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