看了pytorch官网parameters和Module部分。做个部分测试并记录。详细建议去官网查询

一、nn.Parameter类

是Tensor的子类。

唯一区别：在和Module一起使用时。当parameter赋值给module的属性时，会自动将parameter加入到模型的参数列表中。而tensor不会。

二、nn.Module是一个python容器。

是所有神经网络模块的基类。

Module可以包含其他的Module模块，支持用树结构来嵌入他们。

可以直接将模块赋值给Module的普通属性

"""可以在__init__构建函数中添加模块"""
class NeuralNet(nn.Module):
    def __init__(self):
        super(NeuralNet,self).__init__()
        linear = nn.Linear(512,3)
        self.linear1 = linear
        self.linear2 = linear
        self.test1 = nn.Parameter(torch.tensor([1,2,3],dtype=torch.float),requires_grad=True)
        self.test2 = torch.tensor([4,5,6],dtype=torch.float)
        self.add_module('linear3',nn.Linear(512,4)) #add_module方法等同于直接给model的属性赋值。

 

model.children(): 返回模型的所有子模块的迭代器

model.modules():返回模型的所有模块（不仅仅是子模块，还包含当前模块）

model.named_children():返回当前子模块的迭代器。名字：模块

model.named_modules():

测试代码：

model = NeuralNet()
print("\n*****children ****\n")
for i in model.children():
    print(i)
print("\n*****modules ****\n")
for i in model.modules():
    print(i)
print("\n*****named children ****\n")
for i in model.named_children():
    print(i)
print("\n*****named modules ****\n")
for i in model.named_modules():
    print(i)

测试结果：

*****children ****

Linear(in_features=512, out_features=3, bias=True)
Linear(in_features=512, out_features=4, bias=True)
*****modules ****

NeuralNet(
  (linear1): Linear(in_features=512, out_features=3, bias=True)
  (linear2): Linear(in_features=512, out_features=3, bias=True)
  (linear3): Linear(in_features=512, out_features=4, bias=True)
)
Linear(in_features=512, out_features=3, bias=True)
Linear(in_features=512, out_features=4, bias=True)
*****named children ****

('linear1', Linear(in_features=512, out_features=3, bias=True))
('linear3', Linear(in_features=512, out_features=4, bias=True))
*****named modules ****

('', NeuralNet(
  (linear1): Linear(in_features=512, out_features=3, bias=True)
  (linear2): Linear(in_features=512, out_features=3, bias=True)
  (linear3): Linear(in_features=512, out_features=4, bias=True)
))
('linear1', Linear(in_features=512, out_features=3, bias=True))
('linear3', Linear(in_features=512, out_features=4, bias=True))

 

model.buffers:返回模型所有缓存参数的迭代器（缓存参数：不需要训练的），

model.parameters:返回模型所有参数的迭代器

module.named_buffers:

module.named_parameters:

module.parameters常和optimizer()一起使用，来制定需要优化的参数。

测试代码：

print("\n*****buffers ****\n")
for i in model.buffers():
    print(i)
print("\n*****named buffers ****\n")
for i in model.named_buffers():
    print(i)
print("\n*****parameters ****\n")
for i in model.parameters():
    print(i)
print("\n*****named parameters ****\n")
for i in model.named_parameters():
    print(i)

测试结果：

*****buffers ****


*****named buffers ****


*****parameters ****

Parameter containing:
tensor([1., 2., 3.], requires_grad=True)
Parameter containing:
tensor([[-0.0403, -0.0027,  0.0287,  ..., -0.0113, -0.0403, -0.0343],
        [-0.0389, -0.0086, -0.0035,  ...,  0.0246, -0.0127, -0.0313],
        [ 0.0275, -0.0339, -0.0109,  ...,  0.0304,  0.0250,  0.0273]],
       requires_grad=True)
Parameter containing:
tensor([-0.0349,  0.0117, -0.0313], requires_grad=True)
Parameter containing:
tensor([[-0.0166, -0.0328,  0.0354,  ..., -0.0193, -0.0410,  0.0034],
        [-0.0350, -0.0207, -0.0372,  ..., -0.0130, -0.0279,  0.0041],
        [-0.0320,  0.0410,  0.0157,  ..., -0.0358,  0.0145,  0.0272],
        [ 0.0214,  0.0276,  0.0103,  ...,  0.0165, -0.0265, -0.0109]],
       requires_grad=True)
Parameter containing:
tensor([ 0.0035,  0.0182, -0.0215,  0.0390], requires_grad=True)

*****named parameters ****

('test1', Parameter containing:
tensor([1., 2., 3.], requires_grad=True))
('linear1.weight', Parameter containing:
tensor([[-0.0403, -0.0027,  0.0287,  ..., -0.0113, -0.0403, -0.0343],
        [-0.0389, -0.0086, -0.0035,  ...,  0.0246, -0.0127, -0.0313],
        [ 0.0275, -0.0339, -0.0109,  ...,  0.0304,  0.0250,  0.0273]],
       requires_grad=True))
('linear1.bias', Parameter containing:
tensor([-0.0349,  0.0117, -0.0313], requires_grad=True))
('linear3.weight', Parameter containing:
tensor([[-0.0166, -0.0328,  0.0354,  ..., -0.0193, -0.0410,  0.0034],
        [-0.0350, -0.0207, -0.0372,  ..., -0.0130, -0.0279,  0.0041],
        [-0.0320,  0.0410,  0.0157,  ..., -0.0358,  0.0145,  0.0272],
        [ 0.0214,  0.0276,  0.0103,  ...,  0.0165, -0.0265, -0.0109]],
       requires_grad=True))
('linear3.bias', Parameter containing:
tensor([ 0.0035,  0.0182, -0.0215,  0.0390], requires_grad=True))

注意parameters和tensor的区别：parameters被自动加入到模型的参数列表中。而tensor没有。

 

model.cpu():所有模型parameters和buffers移到cpu

model.cuda():所有模型parameters和buffers移到gpu

model.train():设置模型到训练模式。只对部分模型有影响（包含Dropout，BatchNorm）。

model.eval():设置模型到测试模式。