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实现车牌识别的示例代码
这篇文章主要介绍了python实现车牌识别的示例代码，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
2019-08-08
Python工程师面试必备25条知识点
这篇文章主要为大家详细介绍了Python工程师面试必备25条知识点，具有一定的参考价值，感兴趣的小伙伴们可以参考一下
2018-01-01
Python中hash()函数之哈希值的奥秘详解
hash()是Python 中的一个内置函数,用于计算对象的哈希值,哈希值是一个整数,用于唯一标识对象,下面这篇文章主要给大家介绍了关于Python中hash()函数之哈希值奥秘的相关资料,需要的朋友可以参考下
2024-06-06
详解python-opencv 常用函数
这篇文章主要介绍了python-opencv 常用函数,主要包括读取图像保存图像和缩放图像的相关知识，需要的朋友可以参考下
2022-08-08
python之线程通过信号pyqtSignal刷新ui的方法
今天小编就为大家分享一篇python之线程通过信号pyqtSignal刷新ui的方法，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
2019-01-01
简单了解Django ContentType内置组件
这篇文章主要介绍了简单了解Django ContentType内置组件,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,需要的朋友可以参考下
2019-07-07
Python基本文件操作实用指南
文件操作是我们开发中必不可少的一项需求,下面这篇文章主要给大家介绍了关于Python基本文件操作的相关资料,文中通过实例代码介绍的非常详细,需要的朋友可以参考下
2022-06-06
Python动态创建类实例详解
这篇文章主要为大家介绍了Python动态创建类实例详解,有需要的朋友可以借鉴参考下,希望能够有所帮助,祝大家多多进步,早日升职加薪
2023-12-12
关于numpy两个array叠加操作详解
numpy是Python用来科学计算的一个非常重要的库,numpy主要用来处理一些矩阵对象,可以说numpy让Python有了Matlab的味道,下面这篇文章主要给大家介绍了关于numpy两个array叠加操作的相关资料,需要的朋友可以参考下
2022-08-08
python实现感知器算法详解
这篇文章主要为大家详细介绍了python实现感知器算法，文中示例代码介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们可以参考一下
2017-12-12