Parameter-pytorch

import torch
from torch import nn
                         #net[0],  net[1]  ,net[2]
net=nn.Sequential(nn.Linear(4,8),nn.ReLU(),nn.Linear(8,1))
X=torch.rand(size=(2,4))

Ausgabe:

print(X)

tensor([[0.2999, 0.0203, 0.3420, 0.7971],[0.8667, 0.5062, 0.8066, 0.2629]])

print(net(X))

tensor([[0.2376],[0.3675]], grad_fn=<AddmmBackward0>)

#输出每一层里面的权重
#net是nn.Sequantial,Sequantial 可简单认为是python的一个list。
print(net[2].state_dict()) #state:状态

OrderedDict([('weight', tensor([[-0.2090,  0.2708,  0.3308,  0.2240,  0.3504, -0.2484, -0.2670,  0.2866]])), ('bias', tensor([0.1088]))])

print(type(net[2].bias))
print(net[2].bias)
print(net[2].bias.data)   #访问数值
print(net[2].weight.grad==None)

<class 'torch.nn.parameter.Parameter'>
Parameter containing:
tensor([-0.3492], requires_grad=True)
tensor([-0.3492])
True

#一次性访问所有参数
print(*[(name,param.shape) for name,param in net[0].named_parameters()])
print(*[(name,param.shape) for name,param in net.named_parameters()])

('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]))

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):
        net.add_module(f'block{i},',block1())
    return net

rgnet=nn.Sequential(block2(),nn.Linear(4,1))
rgnet(X)
print(rgnet)

Ausgabe:

Sequential(
  (0): Sequential(
    (block0,): 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()
    )
    (block1,): 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()
    )
    (block2,): 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()
    )
    (block3,): 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)
)

#内置初始化：

def init_normal(m):
    if type(m)==nn.Linear:
        nn.init.normal_(m.weight,mean=0,std=0.01)
        nn.init.zeros_(m.bias)

net.apply(init_normal)
print(net[0].weight.data[0],net[0].bias.data[0])

Ausgabe:

tensor([ 0.0069,  0.0174,  0.0089, -0.0098]) tensor(0.)

def init_constant(m):
    if type(m)==nn.Linear:
        nn.init.constant_(m.weight,1)
        nn.init.zeros_(m.bias)

net.apply(init_constant)
net[0].weight.data[0],net[0].bias.data[0]

#对不同的层使用不同的初始化函数：
def xavier(m):
    if type(m)==nn.Linear:
        nn.init.xavier_uniform_(m.weight)
def init_42(m):
    if type(m)==nn.Linear:
        nn.init.constant_(m.weight,42)

net[0].apply(xavier)
net[2].apply(init_42)
print(net[0].weight.data[0])
print(net[2].weight.data)

#自定义初始化：
def my_init(m):
    if type(m)==nn.Linear:
        print(
            "init",
            *[(name,param.shape) for name,param in m.named_parameters()][0]
        )
        nn.init.uniform_(m.weight,-10,-10)
        m.weight.data*=m.weight.data.abs()>=5

net.apply(my_init)
net[0].weight[:2]

net[0].weight.data[:]+=1
net[0].weight.data[0,0]=42
net[0].weight.data[0]

#共享层数，参数绑定。
shared=nn.Linear(8,8)
net=nn.Sequential(nn.Linear(4,8),nn.ReLU(),shared,nn.ReLU(),shared,nn.ReLU(),nn.Linear(8,1))
net(X)

print(net[2].weight.data[0]==net[4].weight.data[0])
net[2].weight.data[0,0]=100
print(net[2].weight.data[0]==net[4].weight.data[0])

D:\anaconda3\envs\pythonProject6\python.exe C:/Users/Dell/PycharmProjects/pythonProject6/Parameters.py
tensor([[0.3637, 0.2694, 0.3710, 0.3239],
        [0.6063, 0.0185, 0.6333, 0.1290]])
tensor([[0.2764],
        [0.2935]], grad_fn=<AddmmBackward0>)
OrderedDict([('weight', tensor([[-0.1440,  0.2390,  0.3281,  0.2522,  0.2373,  0.3361, -0.1059,  0.2527]])), ('bias', tensor([0.1953]))])
<class 'torch.nn.parameter.Parameter'>
Parameter containing:
tensor([0.1953], requires_grad=True)
tensor([0.1953])
True
('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]))
Sequential(
  (0): Sequential(
    (block0,): 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()
    )
    (block1,): 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()
    )
    (block2,): 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()
    )
    (block3,): 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)
)
tensor([ 0.0046,  0.0317, -0.0041,  0.0032]) tensor(0.)
tensor([-0.5886, -0.4940,  0.6852,  0.1372])
tensor([[42., 42., 42., 42., 42., 42., 42., 42.]])
init weight torch.Size([8, 4])
init weight torch.Size([1, 8])
tensor([True, True, True, True, True, True, True, True])
tensor([True, True, True, True, True, True, True, True])

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