网站开发安全机制,北京做网站多少钱合理,网站流量宝,制作网页计算器文章目录 1. 重要类2. add_modules3. Apply(fn)4. register_buffer5. nn.Parametersister_parameters6. 后续测试 1. 重要类
nn.module -- 所有神经网络的父类#xff0c;自定义神经网络需要继承此类#xff0c;并且自定义__init__,forward函数即可#xff1a;
#!/usr… 文章目录 1. 重要类2. add_modules3. Apply(fn)4. register_buffer5. nn.Parameters®ister_parameters6. 后续测试 1. 重要类
nn.module -- 所有神经网络的父类自定义神经网络需要继承此类并且自定义__init__,forward函数即可
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# FileName :MyModelNet.py
# Time :2024/11/20 13:38
# Author :Jason Zhang
import torch
from torch import nnclass NeuralNetwork(nn.Module):def __init__(self):super(NeuralNetwork,self).__init__()self.flatten nn.Flatten()self.linear_relu_stack nn.Sequential(nn.Linear(28 * 28, 512),nn.ReLU(),nn.Linear(512, 512),nn.ReLU(),nn.Linear(512, 10))def forward(self, x):x self.flatten(x)logits self.linear_relu_stack(x)return logitsif __name__ __main__:run_code 0x_row 28x_column 28x_total x_row * x_columnx torch.arange(x_total, dtypetorch.float).reshape((1, x_row, x_column))my_net NeuralNetwork()y my_net(x)print(fy.shape{y.shape})print(my_net)结果
y.shapetorch.Size([1, 10])
NeuralNetwork((flatten): Flatten(start_dim1, end_dim-1)(linear_relu_stack): Sequential((0): Linear(in_features784, out_features512, biasTrue)(1): ReLU()(2): Linear(in_features512, out_features512, biasTrue)(3): ReLU()(4): Linear(in_features512, out_features10, biasTrue))
)2. add_modules
通过add_modules在旧的网络里面添加新的网络
重点 用nn.ModuleList自带的insert,新的网络继承自老网络中直接用按位置插入python
import torch
from torch import nn
from pytorch_model_summary import summarytorch.manual_seed(2323)class MyModel(nn.Module):def __init__(self):super(MyModel, self).__init__()self.flatten nn.Flatten()self.block nn.ModuleList([nn.Linear(28 * 28, 512),nn.ReLU(),nn.Linear(512, 10)])def forward(self, x):x self.flatten(x)for layer in self.block:x layer(x)return xclass MyNewNet(MyModel):def __init__(self):super(MyNewNet, self).__init__()self.block.insert(2, nn.Linear(512, 256)) # 插入新层self.block.insert(3, nn.ReLU()) # 插入新的激活函数self.block.insert(4, nn.Linear(256, 512)) # 插入另一层self.block.insert(5, nn.ReLU()) # 插入激活函数if __name__ __main__:# 测试原始模型my_model MyModel()print(Original Model:)print(summary(my_model, torch.ones((1, 28, 28))))# 测试新模型my_new_model MyNewNet()print(\nNew Model:)print(summary(my_new_model, torch.ones((1, 28, 28))))结果
Original Model:
-----------------------------------------------------------------------Layer (type) Output Shape Param # Tr. Param #
Flatten-1 [1, 784] 0 0Linear-2 [1, 512] 401,920 401,920ReLU-3 [1, 512] 0 0Linear-4 [1, 10] 5,130 5,130Total params: 407,050
Trainable params: 407,050
Non-trainable params: 0
-----------------------------------------------------------------------New Model:
-----------------------------------------------------------------------Layer (type) Output Shape Param # Tr. Param #
Flatten-1 [1, 784] 0 0Linear-2 [1, 512] 401,920 401,920ReLU-3 [1, 512] 0 0Linear-4 [1, 256] 131,328 131,328ReLU-5 [1, 256] 0 0Linear-6 [1, 512] 131,584 131,584ReLU-7 [1, 512] 0 0Linear-8 [1, 10] 5,130 5,130Total params: 669,962
Trainable params: 669,962
Non-trainable params: 0
-----------------------------------------------------------------------3. Apply(fn)
模型权重weight,bias 的初始化
python
import torch.nn as nn
import torchclass MyAwesomeModel(nn.Module):def __init__(self):super(MyAwesomeModel, self).__init__()self.fc1 nn.Linear(3, 4)self.fc2 nn.Linear(4, 5)self.fc3 nn.Linear(5, 6)# 定义初始化函数
torch.no_grad()
def init_weights(m):print(m)if type(m) nn.Linear:m.weight.fill_(1.0)print(m.weight)# 创建神经网络实例
model MyAwesomeModel()# 应用初始化权值函数到神经网络上
model.apply(init_weights)结果
Linear(in_features3, out_features4, biasTrue)
Parameter containing:
tensor([[1., 1., 1.],[1., 1., 1.],[1., 1., 1.],[1., 1., 1.]], requires_gradTrue)
Linear(in_features4, out_features5, biasTrue)
Parameter containing:
tensor([[1., 1., 1., 1.],[1., 1., 1., 1.],[1., 1., 1., 1.],[1., 1., 1., 1.],[1., 1., 1., 1.]], requires_gradTrue)
Linear(in_features5, out_features6, biasTrue)
Parameter containing:
tensor([[1., 1., 1., 1., 1.],[1., 1., 1., 1., 1.],[1., 1., 1., 1., 1.],[1., 1., 1., 1., 1.],[1., 1., 1., 1., 1.],[1., 1., 1., 1., 1.]], requires_gradTrue)
MyAwesomeModel((fc1): Linear(in_features3, out_features4, biasTrue)(fc2): Linear(in_features4, out_features5, biasTrue)(fc3): Linear(in_features5, out_features6, biasTrue)
)Process finished with exit code 0
4. register_buffer
将模型中添加常数项。比如加1
python:
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# FileName :RegisterBuffer.py
# Time :2024/11/23 19:21
# Author :Jason Zhang
import torch
from torch import nnclass MyNet(nn.Module):def __init__(self):super(MyNet, self).__init__()self.register_buffer(my_buffer_a, torch.ones(2, 3))def forward(self, x):x x self.my_buffer_areturn xif __name__ __main__:run_code 0my_test MyNet()in_x torch.arange(6).reshape((2, 3))y my_test(in_x)print(fx\n{in_x})print(fy\n{y})结果
x
tensor([[0, 1, 2],[3, 4, 5]])
y
tensor([[1., 2., 3.],[4., 5., 6.]])5. nn.Parametersregister_parameters
python
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# FileName :ParameterTest.py
# Time :2024/11/23 19:37
# Author :Jason Zhang
import torch
from torch import nnclass MyModule(nn.Module):def __init__(self, in_size, out_size):self.in_size in_sizeself.out_size out_sizesuper(MyModule, self).__init__()self.test torch.rand(self.in_size, self.out_size)self.linear nn.Linear(self.in_size, self.out_size)def forward(self, x):x self.linear(x)return xclass MyModuleRegister(nn.Module):def __init__(self, in_size, out_size):self.in_size in_sizeself.out_size out_sizesuper(MyModuleRegister, self).__init__()self.test torch.rand(self.in_size, self.out_size)self.linear nn.Linear(self.in_size, self.out_size)def forward(self, x):x self.linear(x)return xclass MyModulePara(nn.Module):def __init__(self, in_size, out_size):self.in_size in_sizeself.out_size out_sizesuper(MyModulePara, self).__init__()self.test nn.Parameter(torch.rand(self.in_size, self.out_size))self.linear nn.Linear(self.in_size, self.out_size)def forward(self, x):x self.linear(x)return xif __name__ __main__:run_code 0test_in 4test_out 6my_test MyModule(test_in, test_out)my_test_para MyModulePara(test_in, test_out)test_list list(my_test.named_parameters())test_list_para list(my_test_para.named_parameters())my_test_register MyModuleRegister(test_in, test_out)para_register nn.Parameter(torch.rand(test_in, test_out))my_test_register.register_parameter(para_add_register, para_register)test_list_para_register list(my_test_register.named_parameters())print(f* * 50)print(ftest_list\n{test_list})print(f* * 50)print(f* * 50)print(ftest_list_para\n{test_list_para})print(f* * 50)print(f* * 50)print(ftest_list_para_register\n{test_list_para_register})print(f* * 50)结果
**************************************************
test_list
[(linear.weight, Parameter containing:
tensor([[ 0.3805, -0.3368, 0.2348, 0.4525],[-0.4557, -0.3344, 0.1368, -0.3471],[-0.3961, 0.3302, 0.1904, -0.0111],[ 0.4542, -0.3325, -0.3782, 0.0376],[ 0.2083, -0.3113, -0.3447, -0.1503],[ 0.0343, 0.0410, -0.4216, -0.4793]], requires_gradTrue)), (linear.bias, Parameter containing:
tensor([-0.3465, -0.4510, 0.4919, 0.1967, -0.1366, -0.2496],requires_gradTrue))]
**************************************************
**************************************************
test_list_para
[(test, Parameter containing:
tensor([[0.1353, 0.9934, 0.0462, 0.2103, 0.3410, 0.0814],[0.7509, 0.2573, 0.8030, 0.0952, 0.1381, 0.5360],[0.1972, 0.1241, 0.5597, 0.2691, 0.3226, 0.0660],[0.3333, 0.8031, 0.9226, 0.4290, 0.3660, 0.6159]], requires_gradTrue)), (linear.weight, Parameter containing:
tensor([[-0.0633, -0.4030, -0.4962, 0.1928],[-0.1707, 0.2259, 0.0373, -0.0317],[ 0.4523, 0.2439, -0.1376, -0.3323],[ 0.3215, 0.1283, 0.0729, 0.3912],[ 0.0262, -0.1087, 0.4721, -0.1661],[-0.1055, -0.2199, -0.4974, -0.3444]], requires_gradTrue)), (linear.bias, Parameter containing:
tensor([ 0.3702, -0.0142, -0.2098, -0.0910, -0.2323, -0.0546],requires_gradTrue))]
**************************************************
**************************************************
test_list_para_register
[(para_add_register, Parameter containing:
tensor([[0.2428, 0.1388, 0.6612, 0.4215, 0.0215, 0.2618],[0.4234, 0.0160, 0.8947, 0.4784, 0.4403, 0.4800],[0.8845, 0.1469, 0.6894, 0.7050, 0.5911, 0.7702],[0.7694, 0.0491, 0.3583, 0.4451, 0.2282, 0.4293]], requires_gradTrue)), (linear.weight, Parameter containing:
tensor([[ 0.1358, -0.4704, -0.4181, -0.4504],[ 0.0903, 0.3235, -0.3164, -0.4163],[ 0.1342, 0.3108, 0.0612, -0.2910],[ 0.3527, 0.3397, -0.0414, -0.0408],[-0.4877, 0.1925, -0.2912, -0.2239],[-0.0081, -0.1730, 0.0921, -0.4210]], requires_gradTrue)), (linear.bias, Parameter containing:
tensor([-0.2194, 0.2233, -0.4950, -0.3260, -0.0206, -0.0197],requires_gradTrue))]
**************************************************6. 后续测试
register_moduleget_submoduleget_parameter
