在实际开发中，我们有时需要指定一层神经网络的参数或者冻结他们。在torch中，我们都可以轻易实现。本文以简单的cnn为例，讲述指定一层网络的权值和冻结一层网络的权值。
一.指定权值
如果有这样一个神经网络
import torch.nn as nn
import torch.nn.functional as Fclass Network(nn.Module):def __init__(self):super(Network, self).__init__()self.conv1 = nn.Conv2d(in_channels=1,out_channels=6,kernel_size=5) # out_channels 6 filtersself.conv2 = nn.Conv2d(in_channels=6,out_channels=12,kernel_size=5) # 可以用 out_featuresself.fc1 = nn.Linear(in_features= 12*4*4,out_features=120)self.fc2 = nn.Linear(in_features=120,out_features=60)self.out = nn.Linear(in_features=60,out_features=10)def forward(self,t):# input layert = t # conv layer 1t = self.conv1(t)t = F.relu(t)t = F.max_pool2d(t,kernel_size=2,stride=2)# conv layer 2t = self.conv2(t)t = F.relu(t)t = F.max_pool2d(t,kernel_size=2,stride=2)# linear layerst = t.reshape(-1,12*4*4)t = self.fc1(t)t = F.relu(t)t = self.fc2(t)t = F.relu(t)t = self.out(t)t = F.softmax(t, axis = 1)return t 由于一层神经网络，如conv1，为Network类的一个属性（attribute），而conv1是nn.Conv2d的一个实例(instance)。在nn.Conv2d中转到定义往下翻，可以看到他的属性：
class Conv2d(_ConvNd):
# 定义里面往下翻
"""
Attributes:weight (Tensor): the learnable weights of the module of shape:math:`(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}},`:math:`\text{kernel\_size[0]}, \text{kernel\_size[1]})`.The values of these weights are sampled from:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where:math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}`bias (Tensor): the learnable bias of the module of shape(out_channels). If :attr:`bias` is ``True``,then the values of these weights aresampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where:math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}`
"""我们先在notebook中看conv1
network.conv1可以看到，他属于nn.Conv2d类
Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))再引用属性
network.conv1.weightOutput exceeds the size limit. Open the full output data in a text editor
Parameter containing:
tensor([[[[-0.0017, 0.0833, 0.1076, -0.1180, -0.0225],[ 0.1380, 0.0366, 0.0529, 0.0363, -0.1543],[ 0.1068, 0.1220, -0.1522, 0.0381, 0.0512],[ 0.1212, -0.0746, -0.1923, -0.1805, 0.0160],[ 0.0523, 0.0680, -0.1754, 0.1557, -0.0219]]],[[[ 0.1577, 0.1780, -0.0353, 0.0785, -0.1649],[-0.0598, -0.1900, -0.1578, 0.0462, 0.1965],[ 0.0830, -0.0344, 0.0268, -0.0406, -0.0303],
...[[[ 0.1930, -0.1314, 0.1577, 0.0067, -0.1153],[ 0.1750, 0.1161, -0.0069, -0.0044, 0.1100],[-0.1456, 0.0942, -0.0637, 0.0621, 0.1154],[ 0.0873, 0.1486, 0.1285, 0.0642, 0.0339],[-0.0197, -0.0500, 0.0105, 0.0326, -0.0809]]]], requires_grad=True)调用
network.conv1.weight.shape输出
torch.Size([6, 1, 5, 5])有时候要拿到张量，这样写拿权重的张量
network.conv1.weight.data理解了这些，我们就可以改参数了，为了我写的方便（doge)，我们改个简单的全连接层。如果要改我们的卷积层，要先初始化一个torch.Size([6, 1, 5, 5])大小的张量，道理都一样。
in_feaure = torch.tensor([1,2,3,4],dtype = torch.float32)
weight_matrix = torch.tensor([[1,2,3,4],[2,3,4,5],[3,4,5,6]
],dtype = torch.float32)fc = nn.Linear(in_features=4,out_features=3,bias=False)我们直接使用weight_matrix换掉权值，注意改的是weight.data而不是weight。
# 冻结
fc.weight.data = nn.Parameter(weight_matrix, requires_grad = False)# 不冻结
fc.weight.data = nn.Parameter(weight_matrix)由于fc.weight属性要改的话只能是nn.Parameter或者none，所以要先转类型。
二.改冻结
由于fc.weight类型为nn.Parameter，转到nn.Parameter定义，看到Args，所以我们可以直接改requires_grad。
class Parameter(torch.Tensor):
#往下看"""Args:data (Tensor): parameter tensor.requires_grad (bool, optional): if the parameter requires gradient. See:ref:`locally-disable-grad-doc` for more details. Default: `True`"""fc.weight.requires_grad = False