1、model.named_parameters(),迭代打印model.named_parameters()将会打印每一次迭代元素的名字和paramforname, paraminmodel.named_parameters():print(name,param.requires_grad) param.requires_grad=False2、model.parameters(),迭代打印model.parameters()将会打印每一次迭代元素的param而不会打印名字,这是他和named_p...
结果 (bbn) jyzhang@admin2-X10DAi:~/test$ python net.py True False 1 2 3 parameters() 总述 model.parameters()返回的是一个生成器,该生成器中只保存了可学习、可被优化器更新的参数的具体的参数,可通过循环迭代打印参数。(参见代码示例一) 与model.named_parameters()相比,model.parameters()不会保存参...
for i in model.named_parameters(): print(type(i)) for j in i: print(j) break 1. 2. 3. 4. 5. 6. # 选取全连接层部分的打印结果 <class 'tuple'> fc.weight Parameter containing: tensor([[-0.0240, -0.0264, 0.0397, ..., -0.0377, 0.0322, -0.0025], [ 0.0402, 0.0193, 0.0344, ....
model.named_parameters() defget_parameter_names(model,forbidden_layer_types):"""Returns the names of the model parameters that are not inside a forbidden layer."""result=[]forname,childinmodel.named_children():result+=[f"{name}.{n}"forninget_parameter_names(child,forbidden_layer_types)ifno...
named_parameters(): print(f"Layer: {name} | Size: {param.size()} | Values : {param[:2]} \n") 除了named_parameters() 这个方法外,很多时候我们也会使用 named_modules(). # 下面的这个函数使用 named_modules() 找出当前model中的全连接网络 # 延伸:这串代码是大模型使用lora进行轻量化时的...
forname, parameterinmodel.named_parameters():# 打印每一层,及每一层的参数print(name)# 每一层的参数默认都requires_grad=True的,参数是可以学习的print(parameter.requires_grad)# 如果只想训练第11层transformer的参数的话:if'11'inname: parameter.requires_grad =Trueelse:...
_modules()] In [16]: model_children = [x for x in model.children()] In [17]: model_named_children = [x for x in model.named_children()] In [18]: model_parameters = [x for x in model.parameters()] In [19]: model_named_parameters = [x for x in model.named_parameters()...
for name, param in quantized_model.named_parameters(): logger.info(f"Parameter Name: {name}, Data Type: {param.dtype}, Shape: {param.shape}") The results are as follows: 2024-08-14 15:13:33 [INFO] Save tuning history to F:\Beam-Guided-TFDPRNN-PTQ\nc_workspace\2024-08-14_15-09...
打印网络结构(不带节点名称): for ele in model.modules(): print(ele) 打印named_parameters(): for (name, param) in...:pytorch model.named_parameters() ,model.parameters() ,model.state_dict().items() 打印模型状态: import torch...model = torch.nn.BatchNorm2d((10, 3, 112, 112)) prin...