[LAB 02 · 参数迁移：冻结、局部与全量微调](/course/notebooks/4bd450d54870432db91c80a4df563b8e.html)

# 2. “冻住”到底冻住哪些参数？ · 实现代码
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同源参数的冻结/局部/全量/scratch与BN模式辨析
展开完整实现代码
```
MODES=['head','late','all','scratch']
def make_target_model(mode):
    assert mode in MODES
    torch.manual_seed(SEED)
    model=make_cnn()
    if mode!='scratch':model.load_state_dict(source_state)
    torch.manual_seed(SEED+1)
    model.logits=nn.Linear(32,2)
    for name,param in model.named_parameters():
        param.requires_grad_(mode in ('all','scratch') or name.startswith('logits.') or (mode=='late' and name.startswith(('conv2.','hidden.'))))
    return model
scope_table=[]
for mode in MODES:
    model=make_target_model(mode)
    scope_table.append({'mode':mode,'trainable':sum(p.numel() for p in model.parameters() if p.requires_grad),'total':sum(p.numel() for p in model.parameters())})
print(json.dumps(scope_table,indent=2))

```