pytorch 自定义权重变量初始化

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
# 定义模型
class TheModelClass(nn.Module):
    def __init__(self):
        super(TheModelClass, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

        for m in self.modules():
            if isinstance(m,nn.Conv2d):
                m.weight.data.fill_(7)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(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

# 初始化模型
model = TheModelClass()

# 初始化优化器
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 模型自定义初始化
for m in model.modules():
    if  isinstance(m,nn.Conv2d):
        b=torch.ones(m.weight.size())*15
        b=torch.Tensor(b)
        m.weight=torch.nn.Parameter(b)
        print(m.weight)