pytorch中的named_parameters()和parameters()

更新时间：2023年09月12日 08:57:16 作者：Hanawh

这篇文章主要介绍了pytorch中的named_parameters()和parameters()使用方式,具有很好的参考价值,希望对大家有所帮助,如有错误或未考虑完全的地方,望不吝赐教

pytorch named_parameters()和parameters()

nn.Module

nn.Module里面关于参数有两个很重要的属性named_parameters()和parameters()，前者给出网络层的名字和参数的迭代器，而后者仅仅是参数的迭代器。

import torchvision.models as models
model = models.resnet18()
for param in model.named_parameters():
    print(param[0])
'''
conv1.weight
bn1.weight
bn1.bias
layer1.0.conv1.weight
layer1.0.bn1.weight
layer1.0.bn1.bias
layer1.0.conv2.weight
layer1.0.bn2.weight
layer1.0.bn2.bias
layer1.1.conv1.weight
layer1.1.bn1.weight
layer1.1.bn1.bias
layer1.1.conv2.weight
layer1.1.bn2.weight
layer1.1.bn2.bias
layer2.0.conv1.weight
layer2.0.bn1.weight
layer2.0.bn1.bias
layer2.0.conv2.weight
layer2.0.bn2.weight
layer2.0.bn2.bias
layer2.0.downsample.0.weight
layer2.0.downsample.1.weight
layer2.0.downsample.1.bias
layer2.1.conv1.weight
layer2.1.bn1.weight
layer2.1.bn1.bias
layer2.1.conv2.weight
layer2.1.bn2.weight
layer2.1.bn2.bias
layer3.0.conv1.weight
layer3.0.bn1.weight
layer3.0.bn1.bias
layer3.0.conv2.weight
layer3.0.bn2.weight
layer3.0.bn2.bias
layer3.0.downsample.0.weight
layer3.0.downsample.1.weight
layer3.0.downsample.1.bias
layer3.1.conv1.weight
layer3.1.bn1.weight
layer3.1.bn1.bias
layer3.1.conv2.weight
layer3.1.bn2.weight
layer3.1.bn2.bias
layer4.0.conv1.weight
layer4.0.bn1.weight
layer4.0.bn1.bias
layer4.0.conv2.weight
layer4.0.bn2.weight
layer4.0.bn2.bias
layer4.0.downsample.0.weight
layer4.0.downsample.1.weight
layer4.0.downsample.1.bias
layer4.1.conv1.weight
layer4.1.bn1.weight
layer4.1.bn1.bias
layer4.1.conv2.weight
layer4.1.bn2.weight
layer4.1.bn2.bias
fc.weight
fc.bias
'''

模型参数：named_parameters()、parameters()、state_dict()区别

torch中存在3个功能极其类似的方法，它们分别是model.parameters()、model.named_parameters()、model.state_dict()，

下面就具体来说说这三个函数的差异：

一、model.parameters()和model.named_parameters()差别

named_parameters()返回的list中，每个元组（与list相似，只是数据不可修改）打包了2个内容，分别是layer-name和layer-param（网络层的名字和参数的迭代器）；
parameters()只有后者layer-param（参数的迭代器）

1、model.named_parameters()里的网络层名字

import torchvision.models as models
model = models.resnet18()
for param_tuple in model.named_parameters():
    name, param = param_tuple
    print("name = ", name)
    print("-" * 100)

打印结果：

name = conv1.weight
----------------------------------------------------------------------------------------------------
name = bn1.weight
----------------------------------------------------------------------------------------------------
name = bn1.bias
----------------------------------------------------------------------------------------------------
name = layer1.0.conv1.weight
----------------------------------------------------------------------------------------------------
name = layer1.0.bn1.weight
----------------------------------------------------------------------------------------------------
name = layer1.0.bn1.bias
----------------------------------------------------------------------------------------------------
name = layer1.0.conv2.weight
----------------------------------------------------------------------------------------------------
name = layer1.0.bn2.weight
----------------------------------------------------------------------------------------------------
name = layer1.0.bn2.bias
----------------------------------------------------------------------------------------------------
name = layer1.1.conv1.weight
----------------------------------------------------------------------------------------------------
name = layer1.1.bn1.weight
----------------------------------------------------------------------------------------------------
name = layer1.1.bn1.bias
----------------------------------------------------------------------------------------------------
name = layer1.1.conv2.weight
----------------------------------------------------------------------------------------------------
name = layer1.1.bn2.weight
----------------------------------------------------------------------------------------------------
name = layer1.1.bn2.bias
----------------------------------------------------------------------------------------------------
name = layer2.0.conv1.weight
----------------------------------------------------------------------------------------------------
name = layer2.0.bn1.weight
----------------------------------------------------------------------------------------------------
name = layer2.0.bn1.bias
----------------------------------------------------------------------------------------------------
name = layer2.0.conv2.weight
----------------------------------------------------------------------------------------------------
name = layer2.0.bn2.weight
----------------------------------------------------------------------------------------------------
name = layer2.0.bn2.bias
----------------------------------------------------------------------------------------------------
name = layer2.0.downsample.0.weight
----------------------------------------------------------------------------------------------------
name = layer2.0.downsample.1.weight
----------------------------------------------------------------------------------------------------
name = layer2.0.downsample.1.bias
----------------------------------------------------------------------------------------------------
name = layer2.1.conv1.weight
----------------------------------------------------------------------------------------------------
name = layer2.1.bn1.weight
----------------------------------------------------------------------------------------------------
name = layer2.1.bn1.bias
----------------------------------------------------------------------------------------------------
name = layer2.1.conv2.weight
----------------------------------------------------------------------------------------------------
name = layer2.1.bn2.weight
----------------------------------------------------------------------------------------------------
name = layer2.1.bn2.bias
----------------------------------------------------------------------------------------------------
name = layer3.0.conv1.weight
----------------------------------------------------------------------------------------------------
name = layer3.0.bn1.weight
----------------------------------------------------------------------------------------------------
name = layer3.0.bn1.bias
----------------------------------------------------------------------------------------------------
name = layer3.0.conv2.weight
----------------------------------------------------------------------------------------------------
name = layer3.0.bn2.weight
----------------------------------------------------------------------------------------------------
name = layer3.0.bn2.bias
----------------------------------------------------------------------------------------------------
name = layer3.0.downsample.0.weight
----------------------------------------------------------------------------------------------------
name = layer3.0.downsample.1.weight
----------------------------------------------------------------------------------------------------
name = layer3.0.downsample.1.bias
----------------------------------------------------------------------------------------------------
name = layer3.1.conv1.weight
----------------------------------------------------------------------------------------------------
name = layer3.1.bn1.weight
----------------------------------------------------------------------------------------------------
name = layer3.1.bn1.bias
----------------------------------------------------------------------------------------------------
name = layer3.1.conv2.weight
----------------------------------------------------------------------------------------------------
name = layer3.1.bn2.weight
----------------------------------------------------------------------------------------------------
name = layer3.1.bn2.bias
----------------------------------------------------------------------------------------------------
name = layer4.0.conv1.weight
----------------------------------------------------------------------------------------------------
name = layer4.0.bn1.weight
----------------------------------------------------------------------------------------------------
name = layer4.0.bn1.bias
----------------------------------------------------------------------------------------------------
name = layer4.0.conv2.weight
----------------------------------------------------------------------------------------------------
name = layer4.0.bn2.weight
----------------------------------------------------------------------------------------------------
name = layer4.0.bn2.bias
----------------------------------------------------------------------------------------------------
name = layer4.0.downsample.0.weight
----------------------------------------------------------------------------------------------------
name = layer4.0.downsample.1.weight
----------------------------------------------------------------------------------------------------
name = layer4.0.downsample.1.bias
----------------------------------------------------------------------------------------------------
name = layer4.1.conv1.weight
----------------------------------------------------------------------------------------------------
name = layer4.1.bn1.weight
----------------------------------------------------------------------------------------------------
name = layer4.1.bn1.bias
----------------------------------------------------------------------------------------------------
name = layer4.1.conv2.weight
----------------------------------------------------------------------------------------------------
name = layer4.1.bn2.weight
----------------------------------------------------------------------------------------------------
name = layer4.1.bn2.bias
----------------------------------------------------------------------------------------------------
name = fc.weight
----------------------------------------------------------------------------------------------------
name = fc.bias
----------------------------------------------------------------------------------------------------

Process finished with exit code 0

2、model.named_parameters()里的网络层名字、参数

import torchvision.models as models
model = models.resnet18()
for param_tuple in model.named_parameters():
    name, param = param_tuple
    print("name = ", name)
    print("-" * 100)
    print("param_tuple = ", param_tuple)
    print("*" * 200)

打印结果：

C:\Program_Files_AI\Anaconda3531\python.exe C:/Users/Admin/OneDrive/WorkSpace_AI/0-基于知识库的智能问答系统-华控智加/01-意图识别/test.py
name = conv1.weight
----------------------------------------------------------------------------------------------------
param_tuple = ('conv1.weight', Parameter containing:
tensor([[[[-1.4115e-05, 2.9187e-02, 2.9325e-03, ..., -4.2247e-02,
1.7490e-02, -4.5253e-02],
[-2.4594e-02, -3.0836e-02, 3.8604e-02, ..., 3.5473e-02,
-4.7046e-03, -2.9440e-02],
[ 2.4811e-02, 1.2679e-02, 1.0070e-02, ..., -8.3476e-03,
1.7960e-02, -1.7406e-02],
...,
[-1.3021e-02, 2.9023e-02, -6.1800e-02, ..., -5.2802e-02,
-4.7817e-02, -2.2377e-02],
[-3.8513e-03, -1.0603e-02, -3.9712e-02, ..., 5.1941e-03,
8.2868e-03, -8.3469e-03],
[ 3.8993e-03, 3.2017e-02, -3.6292e-02, ..., -2.0210e-02,
-4.0358e-02, 1.7709e-02]],

[[-1.0894e-03, 1.5720e-02, 7.0129e-03, ..., -1.2024e-02,
1.8644e-02, 1.7892e-02],
[-2.3866e-02, 9.1136e-03, 3.5243e-02, ..., -1.6756e-02,
1.4441e-03, 4.7943e-02],
[-2.0514e-03, 4.3022e-02, 2.6358e-02, ..., -2.3662e-02,
-7.8241e-04, 1.0167e-02],
...

[[-4.6689e-02, -1.1407e-03, 1.8674e-02, ..., 1.2649e-03,
-2.9532e-02, 6.4535e-04],
[ 1.4171e-03, -1.9274e-02, -8.6811e-03, ..., 2.4428e-02,
6.9516e-03, 4.3715e-02],
[ 1.9982e-02, 1.3124e-02, 9.1508e-03, ..., 2.5405e-02,
-1.3132e-02, 4.0835e-02],
...,
[-3.4174e-03, 1.8623e-02, -1.4386e-02, ..., 1.0627e-03,
-5.1297e-04, 2.2055e-02],
[ 2.7333e-02, 2.4858e-02, -5.4305e-02, ..., -1.2139e-02,
1.7735e-03, -3.4184e-03],
[ 1.1412e-03, 1.5794e-02, -2.0699e-02, ..., -1.7846e-02,
3.7425e-02, -1.6059e-02]]],

...,

[[[-2.7389e-02, -3.8327e-02, -2.9043e-02, ..., -7.6396e-03,
-1.6519e-02, 3.9659e-02],
[ 2.8740e-03, -1.0621e-02, -9.2430e-03, ..., 2.2581e-02,
5.1526e-03, -2.0006e-02],
[ 1.3575e-02, 1.5290e-02, -1.7260e-02, ..., 6.3830e-03,
-1.9759e-02, 1.5501e-02],
...,
[ 1.6091e-02, 2.4038e-02, 2.4507e-02, ..., -4.5613e-02,
-3.6233e-02, 2.1632e-02],
[-1.1573e-02, -3.6514e-02, 4.1576e-02, ..., 1.8090e-02,
-2.3350e-02, -8.7074e-03],
[-1.5837e-02, -3.1353e-02, 1.8726e-02, ..., 9.3698e-03,
3.0781e-02, 1.0976e-02]],

[[-2.7063e-02, 8.7158e-03, 2.7193e-03, ..., -1.6670e-03,
-4.3033e-03, 7.2011e-04],
[ 2.7870e-03, 1.4264e-02, -5.0581e-02, ..., 2.5463e-02,
7.6864e-03, -4.9655e-02],
[ 2.6030e-03, 2.5918e-02, 2.9615e-02, ..., 3.0676e-02,
-2.7723e-02, -7.3628e-03],
...,
[ 2.5969e-02, -1.4247e-02, 1.2516e-02, ..., 5.9602e-03,
-3.2843e-02, 3.5822e-02],
[ 1.2845e-02, -2.0035e-02, 9.9398e-04, ..., -3.1800e-02,
5.7984e-03, 2.8756e-02],
[ 2.3458e-02, 3.8193e-02, -2.3754e-03, ..., -1.3867e-02,
8.0831e-03, -3.2438e-02]],
...

[[-9.9291e-03, -5.6023e-03, -1.7064e-02, ..., 8.8544e-03,
-5.8145e-03, 2.3248e-02],
[ 1.2148e-02, -1.0730e-02, -1.2682e-02, ..., 9.4389e-03,
1.2149e-02, 3.8613e-03],
[ 3.5913e-02, -5.2048e-04, -8.7133e-02, ..., -2.0969e-03,
-5.4117e-03, 5.4637e-05],
...,
[ 4.0351e-03, -1.3189e-02, 3.1229e-02, ..., 3.2340e-02,
-2.8351e-02, 1.0634e-02],
[ 2.6041e-02, -3.0633e-04, -1.2732e-02, ..., 2.9417e-02,
-7.3859e-03, 1.7207e-02],
[ 6.9960e-04, 3.8486e-03, 1.0397e-02, ..., 1.4535e-03,
-3.6449e-02, 3.4848e-02]]]], requires_grad=True))
********************************************************************************************************************************************************************************************************
name = bn1.weight
----------------------------------------------------------------------------------------------------
param_tuple = ('bn1.weight', Parameter containing:
tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], requires_grad=True))
********************************************************************************************************************************************************************************************************
name = bn1.bias
----------------------------------------------------------------------------------------------------
param_tuple = ('bn1.bias', Parameter containing:
tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
requires_grad=True))
********************************************************************************************************************************************************************************************************

********************************************************************************************************************************************************************************************************
name = layer1.0.conv2.weight
----------------------------------------------------------------------------------------------------
param_tuple = ('layer1.0.conv2.weight', Parameter containing:
tensor([[[[-8.6159e-02, 1.8507e-04, 5.4006e-03],
[-6.3063e-03, 3.9225e-03, -6.3141e-02],
[-7.0145e-02, -3.9266e-02, 1.9724e-03]],

[[ 4.6454e-02, 2.1519e-02, 5.3696e-02],
[ 1.1086e-02, 1.6269e-01, -7.0579e-02],
[-1.1220e-01, -4.9811e-02, -7.5515e-02]],

[[ 5.5275e-02, -8.2407e-02, -8.9807e-02],
[ 5.8418e-02, 4.4029e-02, 3.0584e-03],
[ 5.2371e-02, -1.5983e-02, 5.1494e-02]],

...,

[[ 7.3441e-02, 4.5401e-02, -1.9175e-02],
[-6.2500e-02, -8.5905e-03, -7.4856e-02],
[-1.6170e-02, 3.7529e-02, -5.1231e-02]],

[[ 7.7501e-04, -5.7506e-02, 1.8422e-01],
[ 2.4594e-02, 1.7378e-02, 4.0000e-02],
[-8.6796e-02, -6.0548e-02, 2.6795e-02]],

[[ 5.3264e-02, -8.6190e-02, 4.2443e-02],
[-6.8029e-03, -1.6581e-02, 7.8568e-02],
[ 3.2037e-02, -7.3002e-02, 4.9353e-02]]]], requires_grad=True))
...，
********************************************************************************************************************************************************************************************************
name = layer4.1.bn2.bias
----------------------------------------------------------------------------------------------------
param_tuple = ('layer4.1.bn2.bias', Parameter containing:
tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
...,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0.], requires_grad=True))
********************************************************************************************************************************************************************************************************
name = fc.weight
----------------------------------------------------------------------------------------------------
param_tuple = ('fc.weight', Parameter containing:
tensor([[-0.0125, 0.0437, -0.0014, ..., -0.0230, 0.0280, 0.0249],
[-0.0105, 0.0242, 0.0291, ..., 0.0153, 0.0366, -0.0236],
[-0.0315, 0.0306, -0.0216, ..., 0.0387, 0.0403, 0.0056],
...,
[-0.0068, -0.0222, -0.0027, ..., -0.0243, 0.0260, 0.0065],
[ 0.0213, 0.0167, -0.0379, ..., -0.0140, 0.0037, -0.0372],
[ 0.0180, 0.0101, -0.0341, ..., -0.0295, -0.0146, 0.0416]],
requires_grad=True))
********************************************************************************************************************************************************************************************************
name = fc.bias
----------------------------------------------------------------------------------------------------
param_tuple = ('fc.bias', Parameter containing:
tensor([ 3.5711e-02, 3.2682e-02, 7.5932e-03, -3.1623e-02, -9.6316e-03,
-2.4051e-02, -1.0393e-02, 2.3210e-02, -3.6044e-02, 2.3099e-02,
-3.5723e-02, -3.9482e-02, 4.8526e-03, -3.2688e-02, 3.7720e-03,
-2.2014e-02, -4.0935e-02, 4.0533e-02, -4.1172e-02, 3.9513e-02,
-3.0332e-02, 3.2777e-02, 1.3342e-02, 2.3394e-02, 8.2328e-03,
1.3757e-02, -1.7578e-02, -2.7165e-02, 3.8495e-03, -3.2116e-02,
7.9903e-03, 9.9640e-04, -8.3106e-03, 2.5033e-02, -3.0446e-02,
-1.8282e-02, -3.8420e-03, -8.6129e-03, -4.2712e-03, 1.7169e-02,
...,
-5.3570e-05, -3.7353e-02, -9.8633e-03, -9.1069e-03, 3.2688e-02,
2.2457e-02, 7.6379e-03, -3.6287e-02, -1.0444e-02, 2.1669e-02,
2.5270e-02, -4.3881e-02, 2.1960e-02, 2.6293e-02, -3.5049e-02,
-2.0074e-02, -9.7686e-03, -2.3766e-02, -5.0265e-03, -2.1095e-02,
2.0981e-02, -3.5132e-02, 8.6407e-03, 1.8453e-02, 2.4282e-02,
3.8392e-02, -1.7470e-02, 3.6958e-02, -3.7590e-02, -4.1951e-02,
-1.8246e-02, 9.0818e-03, 3.8774e-02, 7.3408e-03, 1.7728e-02,
3.5547e-02, -7.2857e-03, -2.7015e-02, -8.6983e-03, -2.3785e-02],
requires_grad=True))
********************************************************************************************************************************************************************************************************

Process finished with exit code 0

二、model.named_parameters()和model.state_dict()差别

它们的差异主要体现在3方面：

返回值类型不同
存储的模型参数的种类不同
返回的值的require_grad属性不同

named_parameters()	state_dict()
将layer_name : layer_param的键值信息打包成一个元祖然后再存到list当中	将layer_name : layer_param的键值信息存储为dict形式
只保存可学习、可被更新的参数，model.buffer()中的参数不包含在model.named_parameters()中	存储的是该model中包含的所有layer中的所有参数
require_grad属性都是True	存储的模型参数tensor的require_grad属性都是False

为何model.parameters()迭代出来的所有参数的require_grad属性都是True，因为它们在被创建时，默认的require_grad就是True。

这也符合逻辑，即，使用nn.Parameter()创建的变量是模型参数，本就是要参与学习和更新的

总结

以上为个人经验，希望能给大家一个参考，也希望大家多多支持脚本之家。

您可能感兴趣的文章:

Python返回数组/List长度的实例
今天小编就为大家分享一篇Python返回数组/List长度的实例，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
2018-06-06
利用Python实现快捷操作文件和文件夹
shutil是Python标准库中的一个模块,提供了许多用于文件和文件夹操作的高级接口,本文主要详细介绍了Python如何使用shutil实现快捷操作文件和文件夹,需要的可以参考下
2024-02-02
python画折线图的程序
这篇文章主要为大家详细介绍了python画折线图的方法，一个画折线图的程序具有一定的参考价值，感兴趣的小伙伴们可以参考一下
2018-07-07
Python matplotlib图例放在外侧保存时显示不完整问题解决
这篇文章主要介绍了Python matplotlib图例放在外侧保存时显示不完整问题解决，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧
2020-07-07
Python使用PIL进行JPEG图像压缩的简易教程
本文介绍了如何使用Python编程语言和wxPython图形用户界面库进行JPEG图像的压缩,通过添加滑块控件,我们可以调整压缩质量,并将压缩后的照片另存为原来的名称加上后缀"压缩+质量数字"的新文件,需要的朋友可以参考下
2023-09-09
Python执行时间计算方法以及优化总结
python脚本运行时间远远大于python脚本中统计的计算时间，所以本文将为大家分享就几个Python执行时间计算方法以及优化，感兴趣的可以了解一下
2022-08-08
Python中海象运算符:=的实现
海象运算符(:=)是Python3.8引入的新特性,用于在表达式中同时完成赋值和返回值操作,本文就来介绍一下Python中海象运算符:=的实现,感兴趣的可以了解一下
2025-02-02
python3反转字符串的3种方法(小结)
这篇文章主要介绍了python3反转字符串的3种方法(小结)，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧
2019-11-11
python np.arange 步长0.1的问题需要特别注意
这篇文章主要介绍了python np.arange 步长0.1的问题需要特别注意，具有很好的参考价值，希望对大家有所帮助。如有错误或未考虑完全的地方，望不吝赐教
2022-05-05
python中提高pip install速度
本文给大家分享了如何提高pip install速度的方法,其实就是将默认源替换为国内高速的源,非常的简单实用,有需要的小伙伴可以参考下
2020-02-02