深度学习实战基础案例——卷积神经网络(CNN)基于MobileNetV3的肺炎识别|第3例

本文主要是介绍深度学习实战基础案例——卷积神经网络(CNN)基于MobileNetV3的肺炎识别|第3例,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!

文章目录

  • 前言
  • 一、数据集介绍
  • 二、前期工作
  • 三、数据集读取
  • 四、构建CA注意力模块
  • 五、构建模型
  • 六、开始训练

前言

Google公司继MobileNetV2之后,在2019年发表了它的改进版本MobileNetV3。而MobileNetV3共有两个版本,分别是MobileNetV3-Large和MobileNetV2-Small。改进后的MobileNetV3,在ImageNet数据集的分类精度上,它的MobileNetV3-Large版本相较于MobileNetV2提升了大概3.2%的精度同时延迟减少了20%,而MobileNetV3-Small则提升了6.6%的精度,减少了大概23%的延迟。

今天,我们用MobileNetV3来进行肺炎的识别,同时我们用CA注意力机制替换了原模型中的SE注意力模块。


我的环境:

  • 基础环境:python3.7
  • 编译器:jupyter notebook
  • 深度学习框架:pytorch

一、数据集介绍

ChestXRay2017数据集共包含5856张胸腔X射线透视图,诊断结果(即分类标签)主要分为正常和肺炎,其中肺炎又可以细分为:细菌性肺炎和病毒性肺炎。

胸腔X射线图像选自广州市妇幼保健中心的1至5岁儿科患者的回顾性研究。所有胸腔X射线成像都是患者常规临床护理的一部分。

为了分析胸腔X射线图像,首先对所有胸腔X光片进行了筛查,去除所有低质量或不可读的扫描,从而保证图片质量。然后由两名专业医师对图像的诊断进行分级,最后为降低图像诊断错误, 还由第三位专家检查了测试集。

主要分为train和test两大子文件夹,分别用于模型的训练和测试。在每个子文件内又分为了NORMAL(正常)和PNEUMONIA(肺炎)两大类。

在PNEUMONIA文件夹内含有细菌性和病毒性肺炎两类,可以通过图片的命名格式进行判别。
在这里插入图片描述

二、前期工作

from torch import nn
import torch.utils.data as Data
from torchvision.transforms import transforms
import torchvision
import torchsummary# 设置device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

三、数据集读取

data_transform = {"train": transforms.Compose([transforms.RandomResizedCrop(224),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]),"val": transforms.Compose([transforms.Resize((224, 224)),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])}train_data=torchvision.datasets.ImageFolder(root=r"ChestXRay2017/chest_xray/train",transform=data_transform["train"])
train_dataloader=Data.DataLoader(train_data,batch_size=48,shuffle=True)test_data=torchvision.datasets.ImageFolder(root=r"ChestXRay2017/chest_xray/test",transform=data_transform["val"])
test_dataloader=Data.DataLoader(test_data,batch_size=48,shuffle=True)

四、构建CA注意力模块

我们都知道注意力机制在各种计算机视觉任务中都是有帮助,如图像分类和图像分割。其中最为经典和被熟知的便是SENet,它通过简单地squeeze每个2维特征图,进而有效地构建通道之间的相互依赖关系。
在这里插入图片描述

SE Block虽然近2年来被广泛使用;然而,它只考虑通过建立通道之间的关系来重新衡量每个通道的重要性,而忽略了位置信息,但是位置信息对于生成空间选择性attention maps是很重要的。因此就有人引入了一种新的注意块,它不仅仅考虑了通道间的关系还考虑了特征空间的位置信息,即CA(Coordinate Attention)注意力机制。

在这里插入图片描述

class h_swish(nn.Module):def __init__(self, inplace=True):super(h_swish, self).__init__()self.relu6 = nn.ReLU6()def forward(self, x):return x * self.relu6(x + 3) / 6class CoordAtt(nn.Module):def __init__(self, inp, oup, groups=32):super(CoordAtt, self).__init__()self.pool_h = nn.AdaptiveAvgPool2d((None, 1))self.pool_w = nn.AdaptiveAvgPool2d((1, None))mip = max(8, inp // groups)self.conv1 = nn.Conv2d(inp, mip, kernel_size=1, stride=1, padding=0)self.bn1 = nn.BatchNorm2d(mip)self.conv2 = nn.Conv2d(mip, oup, kernel_size=1, stride=1, padding=0)self.conv3 = nn.Conv2d(mip, oup, kernel_size=1, stride=1, padding=0)self.relu = h_swish()def forward(self, x):identity = xn,c,h,w = x.size()x_h = self.pool_h(x)x_w = self.pool_w(x).permute(0, 1, 3, 2)y = torch.cat([x_h, x_w], dim=2)y = self.conv1(y)y = self.bn1(y)y = self.relu(y)x_h, x_w = torch.split(y, [h, w], dim=2)x_w = x_w.permute(0, 1, 3, 2)x_h = self.conv2(x_h).sigmoid()x_w = self.conv3(x_w).sigmoid()x_h = x_h.expand(-1, -1, h, w)x_w = x_w.expand(-1, -1, h, w)y = identity * x_w * x_h# y=x_w * x_hreturn yclass CA_SA(nn.Module):def __init__(self,inchannel,outchannel):super(CA_SA, self).__init__()self.CA=CoordAtt(inchannel,outchannel)self.SA=Spatial_Attention_Module(7)def forward(self,x):y=self.CA(x)z=self.SA(x)return x*y*z

五、构建模型

import torch.nn as nn
import torch
import torchsummarydevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')# 定义h-swith激活函数
class HardSwish(nn.Module):def __init__(self, inplace=True):super(HardSwish, self).__init__()self.relu6 = nn.ReLU6()def forward(self, x):return x * self.relu6(x + 3) / 6# DW卷积
def ConvBNActivation(in_channels, out_channels, kernel_size, stride, activate):# 通过设置padding达到当stride=2时,hw减半的效果。此时不与kernel_size有关,所实现的公式为: padding=(kernel_size-1)//2# 当kernel_size=3,padding=1时: stride=2 hw减半, stride=1 hw不变# 当kernel_size=5,padding=2时: stride=2 hw减半, stride=1 hw不变# 从而达到了使用 stride 来控制hw的效果, 不用去关心kernel_size的大小,控制单一变量return nn.Sequential(nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,padding=(kernel_size - 1) // 2, groups=in_channels),nn.BatchNorm2d(out_channels),nn.ReLU6() if activate == 'relu' else HardSwish())class Inceptionnext(nn.Module):def __init__(self, in_channels, out_channels, kernel_size, stride, activate):super(Inceptionnext, self).__init__()gc = int(in_channels * 1 / 4)  # channel number of a convolution branch# self.dwconv_hw = nn.Conv2D(gc, gc, kernel_size,stride=stride,padding=(kernel_size-1)//2,groups=gc)self.dwconv_hw1 = nn.Conv2d(gc, gc, (1, kernel_size), stride=stride, padding=(0, (kernel_size - 1) // 2),groups=gc)self.dwconv_hw2 = nn.Conv2d(gc, gc, (kernel_size, 1), stride=stride, padding=((kernel_size - 1) // 2, 0),groups=gc)self.dwconv_hw = nn.Sequential(nn.Conv2d(gc, gc, (1, kernel_size), stride=stride, padding=(0, (kernel_size - 1) // 2), groups=gc),nn.Conv2d(gc, gc, (kernel_size, 1), stride=stride, padding=((kernel_size - 1) // 2, 0), groups=gc))# self.dwconv_hw = nn.Sequential(#     nn.Conv2d(gc,gc//2,kernel_size=1,stride=1),#     nn.Conv2d(gc//2, gc//2, (1, kernel_size), stride=stride, padding=(0, (kernel_size - 1) // 2), groups=gc//2),#     nn.Conv2d(gc//2, gc//2, (kernel_size, 1), stride=stride, padding=((kernel_size - 1) // 2, 0), groups=gc//2)#     )self.dwconv_w = nn.Conv2d(gc, gc, kernel_size=(1, 11), stride=stride, padding=(0, 11 // 2), groups=gc)self.dwconv_h = nn.Conv2d(gc, gc, kernel_size=(11, 1), stride=stride, padding=(11 // 2, 0), groups=gc)self.batch2d = nn.BatchNorm2d(out_channels)self.activate = nn.ReLU6() if activate == 'relu' else HardSwish()self.split_indexes = (gc, gc, gc, in_channels - 3 * gc)self.cheap=nn.Sequential(nn.Conv2d(gc // 2, gc // 2, (1, 3), stride=stride, padding=(0, (3 - 1) // 2),groups=gc//2),nn.Conv2d(gc // 2, gc // 2, (3, 1), stride=stride, padding=((3 - 1) // 2, 0), groups=gc//2))def forward(self, x):# B, C, H, W = x.shapex_hw, x_w, x_h, x_id = torch.split(x, self.split_indexes, dim=1)x = torch.cat((self.dwconv_hw(x_hw),self.dwconv_w(x_w),self.dwconv_h(x_h),x_id),dim=1)# x = torch.cat(#     (torch.cat((self.dwconv_hw(x_hw),self.cheap(self.dwconv_hw(x_hw))),dim=1),#      self.dwconv_w(x_w),#      self.dwconv_h(x_h),#      x_id),#     dim=1)x = self.batch2d(x)x = self.activate(x)return x# PW卷积(接全连接层)
def Conv1x1BN(in_channels, out_channels):return nn.Sequential(nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),nn.BatchNorm2d(out_channels))class SqueezeAndExcite(nn.Module):def __init__(self, in_channels, out_channels, se_kernel_size, divide=4):super(SqueezeAndExcite, self).__init__()mid_channels = in_channels // divide   # 维度变为原来的1/4# 将当前的channel平均池化成1self.pool = nn.AvgPool2d(kernel_size=se_kernel_size,stride=1)# 两个全连接层 最后输出每层channel的权值self.SEblock = nn.Sequential(nn.Linear(in_features=in_channels, out_features=mid_channels),nn.ReLU6(),nn.Linear(in_features=mid_channels, out_features=out_channels),HardSwish(),)def forward(self, x):a=x.shapeb, c, h, w = a[0],a[1],a[2],a[3]out = self.pool(x)       # 不管当前的 h,w 为多少, 全部池化为1out = out.reshape([b, -1])    # 打平处理,与全连接层相连# 获取注意力机制后的权重out = self.SEblock(out)# out是每层channel的权重,需要扩维才能与原特征矩阵相乘out = out.reshape([b, c, 1, 1])  # 增维return out * x# # 普通的1x1卷积
# class Conv1x1BNActivation(nn.Module):
#     def __init__(self,inchannel,outchannel,activate):
#         super(Conv1x1BNActivation, self).__init__()
#         self.first=nn.Sequential(
#             nn.Conv2d(inchannel,outchannel//2,kernel_size=1,stride=1),
#             nn.Conv2d(outchannel//2,outchannel//2,kernel_size=3,stride=1,padding=1,groups=outchannel//2)
#                                 )
#         self.second=nn.Conv2d(outchannel//2,outchannel//2,kernel_size=3,stride=1,padding=1,groups=outchannel//2)
#         self.BN=nn.BatchNorm2d(outchannel)
#         self.act=nn.ReLU6() if activate == 'relu' else HardSwish()
#     def forward(self,x):
#         x=self.first(x)
#         y=torch.cat((x,self.second(x)),dim=1)
#         y=self.BN(y)
#         y=self.act(y)
#         return y
def Conv1x1BNActivation(in_channels,out_channels,activate):return nn.Sequential(nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),nn.BatchNorm2d(out_channels),nn.ReLU6() if activate == 'relu' else HardSwish())class SEInvertedBottleneck(nn.Module):def __init__(self, in_channels, mid_channels, out_channels, kernel_size, stride, activate, use_se,se_kernel_size=1):super(SEInvertedBottleneck, self).__init__()self.stride = strideself.use_se = use_seself.in_channels = in_channelsself.out_channels = out_channels# mid_channels = (in_channels * expansion_factor)# 普通1x1卷积升维操作self.conv = Conv1x1BNActivation(in_channels, mid_channels, activate)# DW卷积 维度不变,但可通过stride改变尺寸 groups=in_channelsif stride == 1:self.depth_conv = Inceptionnext(mid_channels, mid_channels, kernel_size, stride, activate)else:self.depth_conv = ConvBNActivation(mid_channels, mid_channels, kernel_size, stride, activate)# self.depth_conv = ConvBNActivation(mid_channels, mid_channels, kernel_size,stride,activate)# 注意力机制的使用判断if self.use_se:# self.SEblock = SqueezeAndExcite(mid_channels, mid_channels, se_kernel_size)# self.SEblock = CBAM.CBAMBlock("FC", 5, channels=mid_channels, ratio=9)self.SEblock = CoordAtt(mid_channels,mid_channels)# self.SEblock = CAblock.CA_SA(mid_channels, mid_channels)# PW卷积 降维操作self.point_conv = Conv1x1BN(mid_channels, out_channels)# shortcut的使用判断if self.stride == 1:self.shortcut = Conv1x1BN(in_channels, out_channels)def forward(self, x):# DW卷积out = self.depth_conv(self.conv(x))# 当 use_se=True 时使用注意力机制if self.use_se:out = self.SEblock(out)# PW卷积out = self.point_conv(out)# 残差操作# 第一种: 只看步长,步长相同shape不一样的输入输出使用1x1卷积使其相加# out = (out + self.shortcut(x)) if self.stride == 1 else out# 第二种: 同时满足步长与输入输出的channel, 不使用1x1卷积强行升维out = (out + x) if self.stride == 1 and self.in_channels == self.out_channels else outreturn outclass MobileNetV3(nn.Module):def __init__(self, num_classes=8, type='large'):super(MobileNetV3, self).__init__()self.type = type# 224x224x3 conv2d 3 -> 16 SE=False HS s=2self.first_conv = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=2, padding=1),nn.BatchNorm2d(16),HardSwish(),)# torch.Size([1, 16, 112, 112])# MobileNetV3_Large 网络结构if type == 'large':self.large_bottleneck = nn.Sequential(# torch.Size([1, 16, 112, 112]) 16 -> 16 -> 16 SE=False RE s=1SEInvertedBottleneck(in_channels=16, mid_channels=16, out_channels=16, kernel_size=3, stride=1,activate='relu', use_se=False),# torch.Size([1, 16, 112, 112]) 16 -> 64 -> 24 SE=False RE s=2SEInvertedBottleneck(in_channels=16, mid_channels=64, out_channels=24, kernel_size=3, stride=2,activate='relu', use_se=False),# torch.Size([1, 24, 56, 56])   24 -> 72 -> 24 SE=False RE s=1SEInvertedBottleneck(in_channels=24, mid_channels=72, out_channels=24, kernel_size=3, stride=1,activate='relu', use_se=False),# torch.Size([1, 24, 56, 56])   24 -> 72 -> 40 SE=True RE s=2SEInvertedBottleneck(in_channels=24, mid_channels=72, out_channels=40, kernel_size=5, stride=2,activate='relu', use_se=True, se_kernel_size=28),# torch.Size([1, 40, 28, 28])   40 -> 120 -> 40 SE=True RE s=1SEInvertedBottleneck(in_channels=40, mid_channels=120, out_channels=40, kernel_size=5, stride=1,activate='relu', use_se=True, se_kernel_size=28),# torch.Size([1, 40, 28, 28])   40 -> 120 -> 40 SE=True RE s=1SEInvertedBottleneck(in_channels=40, mid_channels=120, out_channels=40, kernel_size=5, stride=1,activate='relu', use_se=True, se_kernel_size=28),# torch.Size([1, 40, 28, 28])   40 -> 240 -> 80 SE=False HS s=1SEInvertedBottleneck(in_channels=40, mid_channels=240, out_channels=80, kernel_size=3, stride=1,activate='hswish', use_se=False),# torch.Size([1, 80, 28, 28])   80 -> 200 -> 80 SE=False HS s=1SEInvertedBottleneck(in_channels=80, mid_channels=200, out_channels=80, kernel_size=3, stride=1,activate='hswish', use_se=False),# torch.Size([1, 80, 28, 28])   80 -> 184 -> 80 SE=False HS s=2SEInvertedBottleneck(in_channels=80, mid_channels=184, out_channels=80, kernel_size=3, stride=2,activate='hswish', use_se=False),# torch.Size([1, 80, 14, 14])   80 -> 184 -> 80 SE=False HS s=1SEInvertedBottleneck(in_channels=80, mid_channels=184, out_channels=80, kernel_size=3, stride=1,activate='hswish', use_se=False),# torch.Size([1, 80, 14, 14])   80 -> 480 -> 112 SE=True HS s=1SEInvertedBottleneck(in_channels=80, mid_channels=480, out_channels=112, kernel_size=3, stride=1,activate='hswish', use_se=True, se_kernel_size=14),# torch.Size([1, 112, 14, 14])  112 -> 672 -> 112 SE=True HS s=1SEInvertedBottleneck(in_channels=112, mid_channels=672, out_channels=112, kernel_size=3, stride=1,activate='hswish', use_se=True, se_kernel_size=14),# torch.Size([1, 112, 14, 14])  112 -> 672 -> 160 SE=True HS s=2SEInvertedBottleneck(in_channels=112, mid_channels=672, out_channels=160, kernel_size=5, stride=2,activate='hswish', use_se=True, se_kernel_size=7),# torch.Size([1, 160, 7, 7])    160 -> 960 -> 160 SE=True HS s=1SEInvertedBottleneck(in_channels=160, mid_channels=960, out_channels=160, kernel_size=5, stride=1,activate='hswish', use_se=True, se_kernel_size=7),# torch.Size([1, 160, 7, 7])    160 -> 960 -> 160 SE=True HS s=1SEInvertedBottleneck(in_channels=160, mid_channels=960, out_channels=160, kernel_size=5, stride=1,activate='hswish', use_se=True, se_kernel_size=7),)# torch.Size([1, 160, 7, 7])# 相比MobileNetV2,尾部结构改变,,变得更加的高效self.large_last_stage = nn.Sequential(nn.Conv2d(in_channels=160, out_channels=960, kernel_size=1, stride=1),nn.BatchNorm2d(960),HardSwish(),nn.AvgPool2d(kernel_size=7, stride=1),nn.Conv2d(in_channels=960, out_channels=1280, kernel_size=1, stride=1),HardSwish(),)# MobileNetV3_Small 网络结构if type == 'small':self.small_bottleneck = nn.Sequential(# torch.Size([1, 16, 112, 112]) 16 -> 16 -> 16 SE=False RE s=2SEInvertedBottleneck(in_channels=16, mid_channels=16, out_channels=16, kernel_size=3, stride=2,activate='relu', use_se=True, se_kernel_size=56),# torch.Size([1, 16, 56, 56])   16 -> 72 -> 24 SE=False RE s=2SEInvertedBottleneck(in_channels=16, mid_channels=72//2, out_channels=24, kernel_size=3, stride=2,activate='relu', use_se=False),# torch.Size([1, 24, 28, 28])   24 -> 88 -> 24 SE=False RE s=1SEInvertedBottleneck(in_channels=24, mid_channels=88//2, out_channels=24, kernel_size=3, stride=1,activate='relu', use_se=False),# torch.Size([1, 24, 28, 28])   24 -> 96 -> 40 SE=True RE s=2SEInvertedBottleneck(in_channels=24, mid_channels=96//2, out_channels=40, kernel_size=5, stride=2,activate='hswish', use_se=True, se_kernel_size=14),# torch.Size([1, 40, 14, 14])   40 -> 240 -> 40 SE=True RE s=1SEInvertedBottleneck(in_channels=40, mid_channels=240//2, out_channels=40, kernel_size=5, stride=1,activate='hswish', use_se=True, se_kernel_size=14),# torch.Size([1, 40, 14, 14])   40 -> 240 -> 40 SE=True RE s=1SEInvertedBottleneck(in_channels=40, mid_channels=240//2, out_channels=40, kernel_size=5, stride=1,activate='hswish', use_se=True, se_kernel_size=14),# torch.Size([1, 40, 14, 14])   40 -> 120 -> 48 SE=True RE s=1SEInvertedBottleneck(in_channels=40, mid_channels=120//2, out_channels=48, kernel_size=5, stride=1,activate='hswish', use_se=True, se_kernel_size=14),# torch.Size([1, 48, 14, 14])   48 -> 144 -> 48 SE=True RE s=1SEInvertedBottleneck(in_channels=48, mid_channels=144//2, out_channels=48, kernel_size=5, stride=1,activate='hswish', use_se=True, se_kernel_size=14),# torch.Size([1, 48, 14, 14])   48 -> 288 -> 96 SE=True RE s=2SEInvertedBottleneck(in_channels=48, mid_channels=288//2, out_channels=96, kernel_size=5, stride=2,activate='hswish', use_se=True, se_kernel_size=7),# torch.Size([1, 96, 7, 7])     96 -> 576 -> 96 SE=True RE s=1SEInvertedBottleneck(in_channels=96, mid_channels=576//2, out_channels=96, kernel_size=5, stride=1,activate='hswish', use_se=True, se_kernel_size=7),# torch.Size([1, 96, 7, 7])     96 -> 576 -> 96 SE=True RE s=1SEInvertedBottleneck(in_channels=96, mid_channels=576//2, out_channels=96, kernel_size=5, stride=1,activate='hswish', use_se=True, se_kernel_size=7),)# torch.Size([1, 96, 7, 7])# 相比MobileNetV2,尾部结构改变,,变得更加的高效self.small_last_stage = nn.Sequential(nn.Conv2d(in_channels=96, out_channels=576, kernel_size=1, stride=1),nn.BatchNorm2d(576),HardSwish(),nn.AvgPool2d(kernel_size=7, stride=1),nn.Conv2d(in_channels=576, out_channels=1280, kernel_size=1, stride=1),HardSwish(),)self.dorpout = nn.Dropout(0.5)self.classifier =nn.Linear(in_features=1280, out_features=num_classes)# self.init_params()def forward(self, x):x = self.first_conv(x)  # torch.Size([1, 16, 112, 112])if self.type == 'large':x = self.large_bottleneck(x)  # torch.Size([1, 160, 7, 7])x = self.large_last_stage(x)  # torch.Size([1, 1280, 1, 1])if self.type == 'small':x = self.small_bottleneck(x)  # torch.Size([1, 96, 7, 7])x = self.small_last_stage(x)  # torch.Size([1, 1280, 1, 1])x = x.reshape((x.shape[0], -1))  # torch.Size([1, 1280])x = self.dorpout(x)x = self.classifier(x)  # torch.Size([1, 5])return x
if __name__ == '__main__':models = MobileNetV3(8,type='large').to(device)input = torch.randn(size=[1, 3, 224, 224]).to(device)out = models(input)print(out.shape)torchsummary.summary(models,input_size=(3,224,224))

六、开始训练

import numpy
models = MobileNetV3(8,type='large').to('cuda')
# 设置优化器
optim = torch.optim.Adam(lr=0.001, params=models.parameters())
# 设置损失函数
loss_fn = torch.nn.CrossEntropyLoss().to('cuda')
bestacc=0
for epoch in range(20):train_data=0acc_data=0loss_data=0models.train()for batch_id, data in enumerate(train_dataloader):x_data,label=datapredicts=models(x_data.to('cuda'))loss=loss_fn(predicts, label.to('cuda'))acc=numpy.sum(numpy.argmax(predicts.cpu().detach().numpy(), axis=1)==label.numpy())train_data+=len(x_data)acc_data+=accloss_data+=loss# callbacks.step(loss)loss.backward()optim.step()optim.zero_grad()accuracy=acc_data/train_dataall_loss=loss_data/batch_idprint(f"train:eopch:{epoch} train: acc:{accuracy} loss:{all_loss.item()}",end=' ')if epoch+1:models.eval()test_data=0acc_data=0for batch_id, data in enumerate(test_dataloader):x_data,label=datapredicts=models(x_data.to('cuda'))acc=numpy.sum(numpy.argmax(predicts.cpu().detach().numpy(), axis=1)==label.numpy())test_data+=len(x_data)acc_data+=accaccuracy=acc_data/test_dataprint(f"test: acc:{accuracy}")if accuracy > bestacc:torch.save(models.state_dict(), "best.pth")bestacc = accuracyprint("Done")

在这里插入图片描述

这篇关于深度学习实战基础案例——卷积神经网络(CNN)基于MobileNetV3的肺炎识别|第3例的文章就介绍到这儿,希望我们推荐的文章对编程师们有所帮助!


原文地址:
本文来自互联网用户投稿,该文观点仅代表作者本人,不代表本站立场。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如若转载,请注明出处:http://www.chinasem.cn/article/158471

相关文章

PostgreSQL的扩展dict_int应用案例解析

《PostgreSQL的扩展dict_int应用案例解析》dict_int扩展为PostgreSQL提供了专业的整数文本处理能力,特别适合需要精确处理数字内容的搜索场景,本文给大家介绍PostgreS... 目录PostgreSQL的扩展dict_int一、扩展概述二、核心功能三、安装与启用四、字典配置方法

深度解析Java DTO(最新推荐)

《深度解析JavaDTO(最新推荐)》DTO(DataTransferObject)是一种用于在不同层(如Controller层、Service层)之间传输数据的对象设计模式,其核心目的是封装数据,... 目录一、什么是DTO?DTO的核心特点:二、为什么需要DTO?(对比Entity)三、实际应用场景解析

从原理到实战深入理解Java 断言assert

《从原理到实战深入理解Java断言assert》本文深入解析Java断言机制,涵盖语法、工作原理、启用方式及与异常的区别,推荐用于开发阶段的条件检查与状态验证,并强调生产环境应使用参数验证工具类替代... 目录深入理解 Java 断言(assert):从原理到实战引言:为什么需要断言?一、断言基础1.1 语

深度解析Java项目中包和包之间的联系

《深度解析Java项目中包和包之间的联系》文章浏览阅读850次,点赞13次,收藏8次。本文详细介绍了Java分层架构中的几个关键包:DTO、Controller、Service和Mapper。_jav... 目录前言一、各大包1.DTO1.1、DTO的核心用途1.2. DTO与实体类(Entity)的区别1

Python中re模块结合正则表达式的实际应用案例

《Python中re模块结合正则表达式的实际应用案例》Python中的re模块是用于处理正则表达式的强大工具,正则表达式是一种用来匹配字符串的模式,它可以在文本中搜索和匹配特定的字符串模式,这篇文章主... 目录前言re模块常用函数一、查看文本中是否包含 A 或 B 字符串二、替换多个关键词为统一格式三、提

Java MQTT实战应用

《JavaMQTT实战应用》本文详解MQTT协议,涵盖其发布/订阅机制、低功耗高效特性、三种服务质量等级(QoS0/1/2),以及客户端、代理、主题的核心概念,最后提供Linux部署教程、Sprin... 目录一、MQTT协议二、MQTT优点三、三种服务质量等级四、客户端、代理、主题1. 客户端(Clien

在Spring Boot中集成RabbitMQ的实战记录

《在SpringBoot中集成RabbitMQ的实战记录》本文介绍SpringBoot集成RabbitMQ的步骤,涵盖配置连接、消息发送与接收,并对比两种定义Exchange与队列的方式:手动声明(... 目录前言准备工作1. 安装 RabbitMQ2. 消息发送者(Producer)配置1. 创建 Spr

深度解析Python装饰器常见用法与进阶技巧

《深度解析Python装饰器常见用法与进阶技巧》Python装饰器(Decorator)是提升代码可读性与复用性的强大工具,本文将深入解析Python装饰器的原理,常见用法,进阶技巧与最佳实践,希望可... 目录装饰器的基本原理函数装饰器的常见用法带参数的装饰器类装饰器与方法装饰器装饰器的嵌套与组合进阶技巧

深度解析Spring Boot拦截器Interceptor与过滤器Filter的区别与实战指南

《深度解析SpringBoot拦截器Interceptor与过滤器Filter的区别与实战指南》本文深度解析SpringBoot中拦截器与过滤器的区别,涵盖执行顺序、依赖关系、异常处理等核心差异,并... 目录Spring Boot拦截器(Interceptor)与过滤器(Filter)深度解析:区别、实现

深度解析Spring AOP @Aspect 原理、实战与最佳实践教程

《深度解析SpringAOP@Aspect原理、实战与最佳实践教程》文章系统讲解了SpringAOP核心概念、实现方式及原理,涵盖横切关注点分离、代理机制(JDK/CGLIB)、切入点类型、性能... 目录1. @ASPect 核心概念1.1 AOP 编程范式1.2 @Aspect 关键特性2. 完整代码实