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1. 定义模型

import torchimport torch.nn as nnimport torch.nn.functional as Fclass Net_model(nn.Module):    def __init__(self):        super(Net_model, self).__init__()        self.conv1 = nn.Conv2d(1,6,5) # 卷积        		# in_channels, out_channels, kernel_size, stride=1,                # padding=0, dilation=1, groups=1,                # bias=True, padding_mode='zeros'        self.conv2 = nn.Conv2d(6,16,5)        self.fc1 = nn.Linear(16*5*5, 120) # FC层        self.fc2 = nn.Linear(120, 84)        self.fc3 = nn.Linear(84, 10)    def forward(self, x):        x = self.conv1(x)        x = F.relu(x)        x = F.max_pool2d(x, (2,2))        x = self.conv2(x)        x = F.relu(x)        x = F.max_pool2d(x, 2)        x = x.view(-1, self.num_flat_features(x)) # 展平        x = self.fc1(x)        x = F.relu(x)        x = self.fc2(x)        x = F.relu(x)        x = self.fc3(x)        return x    def num_flat_features(self, x):        size = x.size()[1:] # 除了batch 维度外的维度        num_features = 1        for s in size:            num_features *= s        return num_featuresmodel = Net_model()print(model)

输出：

Net_model(  (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))  (fc1): Linear(in_features=400, out_features=120, bias=True)  (fc2): Linear(in_features=120, out_features=84, bias=True)  (fc3): Linear(in_features=84, out_features=10, bias=True))

1.1 绘制模型

from torchviz import make_dotvis_graph = make_dot(model(input),params=dict(model.named_parameters()))vis_graph.view()

1.2 模型参数

params = list(model.parameters())print(len(params))for i in range(len(params)):    print(params[i].size())

输出：

10torch.Size([6, 1, 5, 5])torch.Size([6])torch.Size([16, 6, 5, 5])torch.Size([16])torch.Size([120, 400])torch.Size([120])torch.Size([84, 120])torch.Size([84])torch.Size([10, 84])torch.Size([10])

2. 前向传播

input = torch.randn(1,1,32,32)out  = model(input)print(out)

输出：

tensor([[-0.1100,  0.0273,  0.1260,  0.0713, -0.0744, -0.1442, -0.0068, -0.0965,         -0.0601, -0.0463]], grad_fn=
   
    )
   

3. 反向传播

# 清零梯度缓存器model.zero_grad()out.backward(torch.randn(1,10)) # 使用随机的梯度反向传播

4. 计算损失

output = model(input)target = torch.randn(10) # 举例用target = target.view(1,-1) # 形状匹配 outputcriterion = nn.MSELoss() # 定义损失类型loss = criterion(output, target)print(loss)# tensor(0.5048, grad_fn=
   
    )
   

• 测试 .zero_grad() 清零梯度缓存作用

model.zero_grad()print(model.conv1.bias.grad)loss.backward()print(model.conv1.bias.grad)

输出：

tensor([0., 0., 0., 0., 0., 0.])tensor([-0.0067,  0.0114,  0.0033, -0.0013,  0.0076,  0.0010])

5. 更新参数

learning_rate = 0.01for f in model.parameters():    f.data.sub_(f.grad.data*learning_rate)

6. 完整简洁代码

criterion = nn.MSELoss() # 定义损失类型import torch.optim as optimoptimizer = optim.SGD(model.parameters(), lr=0.1)# 优化目标，学习率# 循环执行以下内容 训练optimizer.zero_grad() # 清空梯度缓存output = model(input) # 输入，输出，前向传播loss = criterion(output, target) # 计算损失loss.backward() # 反向传播optimizer.step() # 更新参数

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