PyTorch：查看模型参数
本文目录
-
- 一、模型构建
- 二、state_dict()
- 三、named_parameters()
一、模型构建
首先随机构建一个网络模型，随后的state_dict()以及named_parameters都是在模型之后运行的
import torch
import torch.nn as nn
from torch.nn.modules.activation import ReLU
from torch.nn.modules.batchnorm import BatchNorm1d
torch.manual_seed(0)
class Model(nn.Module):
def __init__(self, in_channel=2, out_channel=4):
super(Model, self).__init__()
self.l1 = nn.Sequential(
nn.Linear(in_channel, out_channel),
nn.BatchNorm1d(out_channel),
nn.ReLU())
def forward(self, x):
y = self.l1(x)
return y
model = Model()
input = torch.randn(2, 2)
y = model(input)
print(y)
'''
tensor([[0.9999, 0.0000, 0.0000, 1.0000],
[0.0000, 1.0000, 0.9960, 0.0000]], grad_fn=)
'''
二、state_dict()
model.state_dict()
返回一个字典，里面包含了整个模型参数，包括buffer
Returns a dictionary containing a whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names.
print(model.state_dict().keys())
'''
odict_keys(['l1.0.weight', 'l1.0.bias', 'l1.1.weight', 'l1.1.bias', 'l1.1.running_mean', 'l1.1.running_var', 'l1.1.num_batches_tracked'])
'''
print(model.state_dict())
'''
OrderedDict([('l1.0.weight', tensor([[-0.0053, 0.3793],
[-0.5820, -0.5204],
[-0.2723, 0.1896],
[-0.0140, 0.5607]])),
('l1.0.bias', tensor([-0.0628, 0.1871, -0.2137, -0.1390])),
('l1.1.weight', tensor([1., 1., 1., 1.])),
('l1.1.bias', tensor([0., 0., 0., 0.])),
('l1.1.running_mean', tensor([ 0.0021, 0.0166, -0.0129, -0.0015])),
('l1.1.running_var', tensor([0.9108, 0.9844, 0.9002, 0.9231])),
('l1.1.num_batches_tracked', tensor(1))])
'''
for param in model.state_dict():
print(param, "\r\t\t\t\t", model.state_dict()[param])
'''
l1.0.weight tensor([[-0.0053, 0.3793],
[-0.5820, -0.5204],
[-0.2723, 0.1896],
[-0.0140, 0.5607]])
l1.0.bias tensor([-0.0628, 0.1871, -0.2137, -0.1390])
l1.1.weight tensor([1., 1., 1., 1.])
l1.1.bias tensor([0., 0., 0., 0.])
l1.1.running_mean tensor([ 0.0021, 0.0166, -0.0129, -0.0015])
l1.1.running_var tensor([0.9108, 0.9844, 0.9002, 0.9231])
l1.1.num_batches_tracked tensor(1)
'''
三、named_parameters()
named_parameters(prefix=''， recurse=True)
这个方法和上面的state_dict()相同，都是含有模型的参数，不过这个方法返回的是一个迭代器
- prefix：在参数名字前面加上前缀
- recurse：如果为True，产生的参数包括当前模型以及子模型，如果为False，只包含当前模型
返回一个迭代器包括所有的模型参数，包括参数名以及参数值
(string, Parameter) – Tuple containing the name and parameter
Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
注意：这里还有一个parameters()方法，二者的唯一区别是paramters()方法只包含参数的迭代
print(model.parameters())
print(model.named_parameters())
'''
'''
for param in model.parameters():
print(param)
'''
Parameter containing: tensor([[-0.0053, 0.3793],
[-0.5820, -0.5204],
[-0.2723, 0.1896],
[-0.0140, 0.5607]], requires_grad=True)
Parameter containing: tensor([-0.0628, 0.1871, -0.2137, -0.1390], requires_grad=True)
Parameter containing: tensor([1., 1., 1., 1.], requires_grad=True)
Parameter containing: tensor([0., 0., 0., 0.], requires_grad=True)
'''
for name, param in model.named_parameters(prefix="xxxxxxx"):
print(name, param)
'''
xxxxxxx.l1.0.weight
Parameter containing: tensor([[-0.0053, 0.3793],
[-0.5820, -0.5204],
[-0.2723, 0.1896],
[-0.0140, 0.5607]], requires_grad=True)
xxxxxxx.l1.0.bias
Parameter containing: tensor([-0.0628, 0.1871, -0.2137, -0.1390], requires_grad=True)
xxxxxxx.l1.1.weight
Parameter containing: tensor([1., 1., 1., 1.], requires_grad=True)
xxxxxxx.l1.1.bias
Parameter containing: tensor([0., 0., 0., 0.], requires_grad=True)
'''
print("----------------model parameters---------------------")
for name, param in model.named_parameters(prefix=""):
if 'bias' in name:
print(name, param.size())
'''
l1.0.bias torch.Size([4])
l1.1.bias torch.Size([4])
'''