PyTorch教程参数访问#yyds干货盘点#

怀抱观古今，寝食展戏谑。这篇文章主要讲述PyTorch教程参数访问#yyds干货盘点#相关的知识，希望能为你提供帮助。
为什么要单独摘出来参数讲一下。
因为在训练的过程中，我们的目标就是找到让损失函数最小化的参数值。经过训练之后我们需要将这些参数拿出来做预测，或者在其他地方使用。
所以为了以后方便，我们现在就要摘出来单独讲一下：

访问参数，用于调试、诊断和可视化。
参数初始化。
在不同模型组件间共享参数。

import torch from torch import nnnet = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 1)) X = torch.rand(size=(2, 4)) net(X)

这还是简易实现了一个多层感知机，然后弄了一个X做输入。
当通过Sequential类定义模型时，我们可以通过索引来访问模型的任意层。

print(net)

可以看到输出为：

> > Sequential( (0): Linear(in_features=4, out_features=8, bias=True) (1): ReLU() (2): Linear(in_features=8, out_features=1, bias=True) )

我们可以通过前边的序号得到想要的层。

print(net[0]) print(net[1])print(net[2].state_dict())

> > Linear(in_features=4, out_features=8, bias=True) ReLU()OrderedDict([(weight, tensor([[-0.0264, -0.0906,0.3497,0.3284, -0.0173,0.0124,0.0136,0.0782]])), (bias, tensor([0.2243]))])

不出意外，我们看到了前两层是什么。
至于第三个输出，我们可以看到，这个层包含两个参数。

[ ( weight, tensor([[-0.0264, -0.0906,0.3497,0.3284, -0.0173,0.0124,0.0136,0.0782]]) ), ( bias, tensor([0.2243]) ) ]

print(type(net[2].bias)) print(type(net[0].weight))

> > < class torch.nn.parameter.Parameter> < class torch.nn.parameter.Parameter>

可以看到每个参数都表示为参数（parameter）类的一个实例。

print(net[2].bias) print(net[0].weight)

Parameter containing: tensor([-0.1431,0.1381, -0.2775,0.0038, -0.0269,0.0631, -0.1791,0.1291], requires_grad=True) Parameter containing: tensor([[-0.4736,0.2223, -0.0059,0.4146], [-0.1052,0.2813, -0.2315,0.2931], [-0.4990, -0.1991, -0.1453,0.0369], [-0.4676,0.0669, -0.0069, -0.4932], [-0.4223,0.0659, -0.3783, -0.1145], [-0.0460,0.2386, -0.1586,0.2148], [-0.0085, -0.3642,0.0265,0.0487], [ 0.2703, -0.2903,0.1822, -0.3782]], requires_grad=True)

相应的层序号+方法调用，提取网络的偏置或参数。

print(*[(name, param.shape) for name, param in net[0].named_parameters()])print(*[(name, param.shape) for name, param in net.named_parameters()])print(*net.named_parameters(),end="\\n",sep=\\n)# 这里*是一个解包器，用于输出列表的每一个元素

> > (weight, torch.Size([8, 4])) (bias, torch.Size([8]))(0.weight, torch.Size([8, 4])) (0.bias, torch.Size([8])) (2.weight, torch.Size([1, 8])) (2.bias, torch.Size([1]))(0.weight, Parameter containing: tensor([[ 0.3700,0.3270, -0.3741, -0.1365], [ 0.2200,0.0786,0.1241, -0.2834], [ 0.3143,0.3718,0.3278,0.0949], [ 0.1565,0.4639, -0.1515, -0.4962], [ 0.3102, -0.0025, -0.0099, -0.4132], [ 0.1754, -0.1320, -0.3762, -0.1371], [-0.3860, -0.0369,0.3743, -0.0892], [ 0.0280, -0.2877, -0.1884,0.2915]], requires_grad=True)) (0.bias, Parameter containing: tensor([ 0.4722, -0.4143,0.0858, -0.2280,0.4349,0.3954,0.0971, -0.1192], requires_grad=True)) (2.weight, Parameter containing: tensor([[ 0.0984,0.0207, -0.1292,0.0530, -0.0693,0.0413, -0.2231, -0.3125]], requires_grad=True)) (2.bias, Parameter containing: tensor([0.1844], requires_grad=True))

关于解包器看这里：Python * ** 打包解包详解 - 掘金 (juejin.cn)
我把三个输出分开了。

第一个是解包net的第0层的参数参数名称和参数形状
第二个是解包net所有层的参数名称和参数形状
第三个是解包net的参数列表

还可以这样获取参数列表：

print(net.state_dict()[2.bias].data) print(net.state_dict()[0.weight])

> > tensor([0.1844]) tensor([[ 0.3700,0.3270, -0.3741, -0.1365], [ 0.2200,0.0786,0.1241, -0.2834], [ 0.3143,0.3718,0.3278,0.0949], [ 0.1565,0.4639, -0.1515, -0.4962], [ 0.3102, -0.0025, -0.0099, -0.4132], [ 0.1754, -0.1320, -0.3762, -0.1371], [-0.3860, -0.0369,0.3743, -0.0892], [ 0.0280, -0.2877, -0.1884,0.2915]])

【PyTorch教程参数访问#yyds干货盘点#】后边不管加不加.data都可以直接输出参数的值。

def block1(): return nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 4), nn.ReLU())def block2(): net = nn.Sequential() for i in range(4): # 在这里嵌套 net.add_module(fblock {i}, block1()) net[2] = nn.Linear(4,4) return netX = torch.rand(size=(2, 4)) rgnet = nn.Sequential(block2(), nn.Linear(4, 1)) rgnet(X)

定义一个嵌套的网络。随手画了个图，长这样。

文章图片

print(rgnet)

输出一下这个网路哦，可以看到其结构如下：

> > Sequential( (0): Sequential( (block 0): Sequential( (0): Linear(in_features=4, out_features=8, bias=True) (1): ReLU() (2): Linear(in_features=8, out_features=4, bias=True) (3): ReLU() ) (block 1): Linear(in_features=4, out_features=4, bias=True) (block 2): Sequential( (0): Linear(in_features=4, out_features=8, bias=True) (1): ReLU() (2): Linear(in_features=8, out_features=4, bias=True) (3): ReLU() ) ) (1): Linear(in_features=4, out_features=1, bias=True) )

比如：