代码
最编程
2024-04-10 22:40:30
...
# 本书链接https://tangshusen.me/Dive-into-DL-PyTorch/#/chapter03_DL-basics/3.8_mlp
# 5.9 含并行连结的网络(GoogLeNet)
# 注释:黄文俊
# E-mail:hurri_cane@qq.com
import time
import torch
from torch import nn, optim
import torch.nn.functional as F
import sys
sys.path.append("..")
import d2lzh_pytorch as d2l
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class Inception(nn.Module):
# c1 - c4为每条线路里的层的输出通道数
def __init__(self, in_c, c1, c2, c3, c4):
super(Inception, self).__init__()
# 线路1,单1 x 1卷积层
self.p1_1 = nn.Conv2d(in_c, c1, kernel_size=1)
# 线路2,1 x 1卷积层后接3 x 3卷积层
self.p2_1 = nn.Conv2d(in_c, c2[0], kernel_size=1)
self.p2_2 = nn.Conv2d(c2[0], c2[1], kernel_size=3, padding=1)
# 线路3,1 x 1卷积层后接5 x 5卷积层
self.p3_1 = nn.Conv2d(in_c, c3[0], kernel_size=1)
self.p3_2 = nn.Conv2d(c3[0], c3[1], kernel_size=5, padding=2)
# 线路4,3 x 3最大池化层后接1 x 1卷积层
self.p4_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
self.p4_2 = nn.Conv2d(in_c, c4, kernel_size=1)
def forward(self, x):
p1 = F.relu(self.p1_1(x))
p2 = F.relu(self.p2_2(F.relu(self.p2_1(x))))
p3 = F.relu(self.p3_2(F.relu(self.p3_1(x))))
p4 = F.relu(self.p4_2(self.p4_1(x)))
return torch.cat((p1, p2, p3, p4), dim=1) # 在通道维上连结输出
# 第一模块使用一个64通道的7×77×7卷积层。
b1 = nn.Sequential(nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
# 第二模块使用2个卷积层:首先是64通道的1×11×1卷积层,然后是将通道增大3倍的3×33×3卷积层。
b2 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1),
nn.Conv2d(64, 192, kernel_size=3, padding=1),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b3 = nn.Sequential(Inception(192, 64, (96, 128), (16, 32), 32),
Inception(256, 128, (128, 192), (32, 96), 64),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b4 = nn.Sequential(Inception(480, 192, (96, 208), (16, 48), 64),
Inception(512, 160, (112, 224), (24, 64), 64),
Inception(512, 128, (128, 256), (24, 64), 64),
Inception(512, 112, (144, 288), (32, 64), 64),
Inception(528, 256, (160, 320), (32, 128), 128),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
b5 = nn.Sequential(Inception(832, 256, (160, 320), (32, 128), 128),
Inception(832, 384, (192, 384), (48, 128), 128),
d2l.GlobalAvgPool2d())
net = nn.Sequential(b1, b2, b3, b4, b5, d2l.FlattenLayer(), nn.Linear(1024, 10))
X = torch.rand(1, 1, 96, 96)
for blk in net.children():
X = blk(X)
print('output shape: ', X.shape)
batch_size = 512
# 如出现“out of memory”的报错信息,可减小batch_size或resize
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=96)
lr, num_epochs = 0.001, 5
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
d2l.train_ch5(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)
print("*"*50)
推荐阅读
-
[信号处理] 雷达信号处理的代码和报告(Matlab 实现)
-
使用 NPOI 导入和导出 Excel 的 ASP.Net MVC 示例代码
-
操作系统原理和源代码示例:Linux 实现时间管理和定时器源代码
-
如何在 php 代码中实现万年历
-
C语言的10种基本算法,学习C语言必须要有源代码(珍藏版)
-
C 编程语言基础入门经典 100 题(1-10) - Simple_c 简单代码
-
HTML_CSS 学习:列表相关属性 - 相关代码:
-
静态页面代码高亮的一些尝试
-
如何在低代码平台中引用 JavaScript?
-
基于 Java 的食品推荐管理系统的设计与实施(源代码 + lw + 部署文档 + 说明等)