diff --git a/2024.04.26_torchviz_example/torchviz_example.py b/2024.04.26_torchviz_example/torchviz_example.py
new file mode 100644
index 0000000..5d5d1b4
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+++ b/2024.04.26_torchviz_example/torchviz_example.py
@@ -0,0 +1,50 @@
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/40353
+"""
+
+import torch
+import torchviz
+
+
+# 简单网络的例子
+class SimpleNet(torch.nn.Module):
+    def __init__(self):
+        super(SimpleNet, self).__init__()
+        self.fc1 = torch.nn.Linear(10, 6)
+        self.fc2 = torch.nn.Linear(6, 2)
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+model = SimpleNet()
+input = torch.randn(1, 10)
+output = model(input)
+graph = torchviz.make_dot(output, params=dict(model.named_parameters()))
+graph.render("Simple_net_graph") # 保存计算图为 PDF 文件
+
+
+# 卷积网络的例子
+class ConvolutionalNeuralNetwork(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.convolutional_layer_1 = torch.nn.Conv2d(in_channels=1, out_channels=10, kernel_size=3, stride=1, padding=1)
+        self.convolutional_layer_2 = torch.nn.Conv2d(in_channels=10, out_channels=10, kernel_size=3, stride=1, padding=1)
+        self.pooling_layer = torch.nn.MaxPool2d(kernel_size=2, stride=2)
+        self.hidden_layer_1 = torch.nn.Linear(in_features=10*7*7, out_features=10)
+        self.hidden_layer_2 = torch.nn.Linear(in_features=10, out_features=10)
+        self.output_layer = torch.nn.Linear(in_features=10, out_features=1)
+    def forward(self, x):
+        channel_output_1 = torch.nn.functional.relu(self.pooling_layer(self.convolutional_layer_1(x)))
+        channel_output_2 = torch.nn.functional.relu(self.pooling_layer(self.convolutional_layer_2(channel_output_1)))
+        channel_output_2 = torch.flatten(channel_output_2, 1)
+        hidden_output_1 = torch.nn.functional.relu(self.hidden_layer_1(channel_output_2))
+        hidden_output_2 = torch.nn.functional.relu(self.hidden_layer_2(hidden_output_1))
+        output = self.output_layer(hidden_output_2)
+        return output
+model = ConvolutionalNeuralNetwork()
+input = torch.randn(15, 1, 28, 28)
+output = model(input)
+graph = torchviz.make_dot(output, params=dict(model.named_parameters()))
+graph.render("CNN_graph") # 保存计算图为 PDF 文件
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