训练多个分类器，如何将每个分类器训练时的参数初始化pytorch代码

以下是几种常见的分类器参数初始化的 PyTorch 代码：

1. 随机初始化：

import torch.nn as nn

# 初始化分类器1的参数
classifier_1 = nn.Sequential(
    nn.Linear(input_size, hidden_size),
    nn.ReLU(),
    nn.Linear(hidden_size, num_classes)
)
for m in classifier_1.modules():
    if isinstance(m, nn.Linear):
        nn.init.normal_(m.weight)
        nn.init.zeros_(m.bias)

# 初始化分类器2的参数
classifier_2 = nn.Sequential(
    nn.Linear(input_size, hidden_size),
    nn.ReLU(),
    nn.Linear(hidden_size, num_classes)
)
for m in classifier_2.modules():
    if isinstance(m, nn.Linear):
        nn.init.normal_(m.weight)
        nn.init.zeros_(m.bias)

2. 预训练模型初始化：

import torch.nn as nn

# 加载预训练模型
pretrained_model = torch.load('path/to/pretrained_model.pth')

# 使用预训练模型初始化分类器1的参数
classifier_1 = nn.Sequential(
    nn.Linear(input_size, hidden_size),
    nn.ReLU(),
    nn.Linear(hidden_size, num_classes)
)
classifier_1[0].weight.data.copy_(pretrained_model['classifier_1']['0.weight'])
classifier_1[0].bias.data.copy_(pretrained_model['classifier_1']['0.bias'])
classifier_1[2].weight.data.copy_(pretrained_model['classifier_1']['2.weight'])
classifier_1[2].bias.data.copy_(pretrained_model['classifier_1']['2.bias'])

# 使用预训练模型初始化分类器2的参数
classifier_2 = nn.Sequential(
    nn.Linear(input_size, hidden_size),
    nn.ReLU(),
    nn.Linear(hidden_size, num_classes)
)
classifier_2[0].weight.data.copy_(pretrained_model['classifier_2']['0.weight'])
classifier_2[0].bias.data.copy_(pretrained_model['classifier_2']['0.bias'])
classifier_2[2].weight.data.copy_(pretrained_model['classifier_2']['2.weight'])
classifier_2[2].bias.data.copy_(pretrained_model['classifier_2']['2.bias'])

3. 共享参数初始化：

import torch.nn as nn

# 共享参数的初始化
linear1 = nn.Linear(input_size, hidden_size)
nn.init.normal_(linear1.weight)
nn.init.zeros_(linear1.bias)
linear2 = nn.Linear(hidden_size, num_classes)
nn.init.normal_(linear2.weight)
nn.init.zeros_(linear2.bias)

# 初始化分类器1的参数
classifier_1 = nn.Sequential(
    linear1,
    nn.ReLU(),
    linear2
)

# 初始化分类器2的参数
classifier_2 = nn.Sequential(
    linear1,
    nn.ReLU(),
    linear2
)

以上是三种常见的分类器参数初始化的 PyTorch 代码，需要根据具体的需求进行选择和修改。