好网站设计公司,哪些网站是增值网,logo设计网站平台,wordpress图片广告代码#x1f368; 本文为#x1f517;365天深度学习训练营 中的学习记录博客#x1f356; 原作者#xff1a;K同学啊 | 接辅导、项目定制 文章目录 前言1 我的环境2 代码实现与执行结果2.1 前期准备2.1.1 引入库2.1.2 设置GPU#xff08;如果设备上支持GPU就使用GPU,否则使用C… 本文为365天深度学习训练营 中的学习记录博客 原作者K同学啊 | 接辅导、项目定制 文章目录 前言1 我的环境2 代码实现与执行结果2.1 前期准备2.1.1 引入库2.1.2 设置GPU如果设备上支持GPU就使用GPU,否则使用CPU2.1.3 导入数据2.1.4 可视化数据2.1.4 图像数据变换2.1.4 划分数据集2.1.4 加载数据2.1.4 查看数据 2.2 构建CNN网络模型2.3 训练模型2.3.1 训练模型2.3.2 编写训练函数2.3.3 编写测试函数2.3.4 正式训练 2.4 结果可视化2.4 指定图片进行预测2.6 保存并加载模型 3 知识点详解3.1 torch.utils.data.DataLoader()参数详解3.2 torch.squeeze()与torch.unsqueeze()详解3.3 拔高尝试--更改优化器为Adam3.4 拔高尝试--更改优化器为Adam增加dropout层3.5 拔高尝试--更改优化器为Adam增加dropout层保存最好的模型 总结 前言
本文将采用pytorch框架创建CNN网络实现猴痘病识别。讲述实现代码与执行结果并浅谈涉及知识点。 关键字 torch.utils.data.DataLoader()参数详解torch.squeeze()与torch.unsqueeze()详解拔高尝试–更改优化器为Adam增加dropout层保存最好的模型
1 我的环境
电脑系统Windows 11语言环境python 3.8.6编译器pycharm2020.2.3深度学习环境 torch 1.9.1cu111 torchvision 0.10.1cu111显卡NVIDIA GeForce RTX 4070
2 代码实现与执行结果
2.1 前期准备
2.1.1 引入库
import torch
import torch.nn as nn
from torchvision import transforms, datasets
import time
from pathlib import Path
from PIL import Image
from torchinfo import summary
import torch.nn.functional as F
import matplotlib.pyplot as pltplt.rcParams[font.sans-serif] [SimHei] # 用来正常显示中文标签
plt.rcParams[axes.unicode_minus] False # 用来正常显示负号
plt.rcParams[figure.dpi] 100 # 分辨率
import warningswarnings.filterwarnings(ignore) # 忽略一些warning内容无需打印2.1.2 设置GPU如果设备上支持GPU就使用GPU,否则使用CPU
前期准备-设置GPU
# 如果设备上支持GPU就使用GPU,否则使用CPUdevice torch.device(cuda if torch.cuda.is_available() else cpu)print(Using {} device.format(device))输出
Using cuda device2.1.3 导入数据 [猴痘病数据]https://pan.baidu.com/s/11r_uOUV0ToMNXQtxahb0yg?pwd7qtp) 前期工作-导入数据
data_dir rD:\DeepLearning\data\monkeypox_recognition
data_dir Path(data_dir)data_paths list(data_dir.glob(*))
classeNames [str(path).split(\\)[-1] for path in data_paths]
print(classeNames)输出
[Monkeypox, Others]2.1.4 可视化数据
前期工作-可视化数据
cloudyPath Path(data_dir)/Monkeypox
image_files list(p.resolve() for p in cloudyPath.glob(*) if p.suffix in [.jpg, .png, .jpeg])
plt.figure(figsize(10, 6))
for i in range(len(image_files[:12])):image_file image_files[i]ax plt.subplot(3, 4, i 1)img Image.open(str(image_file))plt.imshow(img)plt.axis(off)
# 显示图片
plt.tight_layout()
plt.show()2.1.4 图像数据变换
前期工作-图像数据变换
total_datadir data_dir# 关于transforms.Compose的更多介绍可以参考https://blog.csdn.net/qq_38251616/article/details/124878863
train_transforms transforms.Compose([transforms.Resize([224, 224]), # 将输入图片resize成统一尺寸transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换为tensor并归一化到[0,1]之间transforms.Normalize( # 标准化处理--转换为标准正太分布高斯分布使模型更容易收敛mean[0.485, 0.456, 0.406],std[0.229, 0.224, 0.225]) # 其中 mean[0.485,0.456,0.406]与std[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])
total_data datasets.ImageFolder(total_datadir, transformtrain_transforms)
print(total_data)
print(total_data.class_to_idx)输出
Dataset ImageFolderNumber of datapoints: 2142Root location: D:\DeepLearning\data\monkeypox_recognitionStandardTransform
Transform: Compose(Resize(size[224, 224], interpolationbilinear, max_sizeNone, antialiasNone)ToTensor()Normalize(mean[0.485, 0.456, 0.406], std[0.229, 0.224, 0.225]))
{Monkeypox: 0, Others: 1}2.1.4 划分数据集
前期工作-划分数据集
train_size int(0.8 * len(total_data)) # train_size表示训练集大小通过将总体数据长度的80%转换为整数得到
test_size len(total_data) - train_size # test_size表示测试集大小是总体数据长度减去训练集大小。
# 使用torch.utils.data.random_split()方法进行数据集划分。该方法将总体数据total_data按照指定的大小比例[train_size, test_size]随机划分为训练集和测试集
# 并将划分结果分别赋值给train_dataset和test_dataset两个变量。
train_dataset, test_dataset torch.utils.data.random_split(total_data, [train_size, test_size])
print(train_dataset{}\ntest_dataset{}.format(train_dataset, test_dataset))
print(train_size{}\ntest_size{}.format(train_size, test_size))输出
train_datasettorch.utils.data.dataset.Subset object at 0x00000231AFD1B550
test_datasettorch.utils.data.dataset.Subset object at 0x00000231AFD1B430
train_size1713
test_size4292.1.4 加载数据
前期工作-加载数据
batch_size 32train_dl torch.utils.data.DataLoader(train_dataset,batch_sizebatch_size,shuffleTrue,num_workers1)
test_dl torch.utils.data.DataLoader(test_dataset,batch_sizebatch_size,shuffleTrue,num_workers1)2.1.4 查看数据
前期工作-查看数据
for X, y in test_dl:print(Shape of X [N, C, H, W]: , X.shape)print(Shape of y: , y.shape, y.dtype)break输出
Shape of X [N, C, H, W]: torch.Size([32, 3, 224, 224])
Shape of y: torch.Size([32]) torch.int642.2 构建CNN网络模型 构建CNN网络
class Network_bn(nn.Module):def __init__(self):super(Network_bn, self).__init__()nn.Conv2d()函数第一个参数in_channels是输入的channel数量第二个参数out_channels是输出的channel数量第三个参数kernel_size是卷积核大小第四个参数stride是步长默认为1第五个参数padding是填充大小默认为0self.conv1 nn.Conv2d(in_channels3, out_channels12, kernel_size5, stride1, padding0)self.bn1 nn.BatchNorm2d(12)self.conv2 nn.Conv2d(in_channels12, out_channels12, kernel_size5, stride1, padding0)self.bn2 nn.BatchNorm2d(12)self.pool nn.MaxPool2d(2, 2)self.conv4 nn.Conv2d(in_channels12, out_channels24, kernel_size5, stride1, padding0)self.bn4 nn.BatchNorm2d(24)self.conv5 nn.Conv2d(in_channels24, out_channels24, kernel_size5, stride1, padding0)self.bn5 nn.BatchNorm2d(24)self.fc1 nn.Linear(24 * 50 * 50, len(classeNames))def forward(self, x):x F.relu(self.bn1(self.conv1(x)))x F.relu(self.bn2(self.conv2(x)))x self.pool(x)x F.relu(self.bn4(self.conv4(x)))x F.relu(self.bn5(self.conv5(x)))x self.pool(x)x x.view(-1, 24 * 50 * 50)x self.fc1(x)return xmodel Network_bn().to(device)
print(model)
summary(model) 输出
Network_bn((conv1): Conv2d(3, 12, kernel_size(5, 5), stride(1, 1))(bn1): BatchNorm2d(12, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(12, 12, kernel_size(5, 5), stride(1, 1))(bn2): BatchNorm2d(12, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(pool): MaxPool2d(kernel_size2, stride2, padding0, dilation1, ceil_modeFalse)(conv4): Conv2d(12, 24, kernel_size(5, 5), stride(1, 1))(bn4): BatchNorm2d(24, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv5): Conv2d(24, 24, kernel_size(5, 5), stride(1, 1))(bn5): BatchNorm2d(24, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(fc1): Linear(in_features60000, out_features2, biasTrue)
)Layer (type:depth-idx) Param #Network_bn --
├─Conv2d: 1-1 912
├─BatchNorm2d: 1-2 24
├─Conv2d: 1-3 3,612
├─BatchNorm2d: 1-4 24
├─MaxPool2d: 1-5 --
├─Conv2d: 1-6 7,224
├─BatchNorm2d: 1-7 48
├─Conv2d: 1-8 14,424
├─BatchNorm2d: 1-9 48
├─Linear: 1-10 120,002Total params: 146,318
Trainable params: 146,318
Non-trainable params: 02.3 训练模型
2.3.1 训练模型
训练模型--设置超参数
loss_fn nn.CrossEntropyLoss() # 创建损失函数计算实际输出和真实相差多少交叉熵损失函数事实上它就是做图片分类任务时常用的损失函数
learn_rate 1e-4 # 学习率
opt torch.optim.SGD(model.parameters(), lrlearn_rate) # 作用是定义优化器用来训练时候优化模型参数其中SGD表示随机梯度下降用于控制实际输出y与真实y之间的相差有多大
2.3.2 编写训练函数
训练模型--编写训练函数
# 训练循环
def train(dataloader, model, loss_fn, optimizer):size len(dataloader.dataset) # 训练集的大小一共60000张图片num_batches len(dataloader) # 批次数目187560000/32train_loss, train_acc 0, 0 # 初始化训练损失和正确率for X, y in dataloader: # 加载数据加载器得到里面的 X图片数据和 y真实标签X, y X.to(device), y.to(device) # 用于将数据存到显卡# 计算预测误差pred model(X) # 网络输出loss loss_fn(pred, y) # 计算网络输出和真实值之间的差距targets为真实值计算二者差值即为损失# 反向传播optimizer.zero_grad() # 清空过往梯度loss.backward() # 反向传播计算当前梯度optimizer.step() # 根据梯度更新网络参数# 记录acc与losstrain_acc (pred.argmax(1) y).type(torch.float).sum().item()train_loss loss.item()train_acc / sizetrain_loss / num_batchesreturn train_acc, train_loss2.3.3 编写测试函数
训练模型--编写测试函数
# 测试函数和训练函数大致相同但是由于不进行梯度下降对网络权重进行更新所以不需要传入优化器
def test(dataloader, model, loss_fn):size len(dataloader.dataset) # 测试集的大小一共10000张图片num_batches len(dataloader) # 批次数目31310000/32312.5向上取整test_loss, test_acc 0, 0# 当不进行训练时停止梯度更新节省计算内存消耗with torch.no_grad(): # 测试时模型参数不用更新所以 no_grad整个模型参数正向推就ok不反向更新参数for imgs, target in dataloader:imgs, target imgs.to(device), target.to(device)# 计算losstarget_pred model(imgs)loss loss_fn(target_pred, target)test_loss loss.item()test_acc (target_pred.argmax(1) target).type(torch.float).sum().item()#统计预测正确的个数test_acc / sizetest_loss / num_batchesreturn test_acc, test_loss
2.3.4 正式训练
训练模型--正式训练
epochs 20
train_loss []
train_acc []
test_loss []
test_acc []for epoch in range(epochs):model.train()epoch_train_acc, epoch_train_loss train(train_dl, model, loss_fn, opt)model.eval()epoch_test_acc, epoch_test_loss test(test_dl, model, loss_fn)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)template (Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%Test_loss:{:.3f})print(template.format(epoch 1, epoch_train_acc * 100, epoch_train_loss, epoch_test_acc * 100, epoch_test_loss))
print(Done)输出
Epoch: 1, duration:7062ms, Train_acc:59.7%, Train_loss:0.679, Test_acc:62.5%Test_loss:0.651
Epoch: 2, duration:5408ms, Train_acc:68.9%, Train_loss:0.580, Test_acc:66.4%Test_loss:0.650
Epoch: 3, duration:5328ms, Train_acc:74.2%, Train_loss:0.543, Test_acc:67.8%Test_loss:0.630
Epoch: 4, duration:5345ms, Train_acc:77.6%, Train_loss:0.493, Test_acc:68.5%Test_loss:0.575
Epoch: 5, duration:5340ms, Train_acc:77.8%, Train_loss:0.471, Test_acc:74.6%Test_loss:0.565
Epoch: 6, duration:5295ms, Train_acc:81.8%, Train_loss:0.435, Test_acc:73.2%Test_loss:0.555
Epoch: 7, duration:5309ms, Train_acc:83.1%, Train_loss:0.418, Test_acc:74.8%Test_loss:0.517
Epoch: 8, duration:5268ms, Train_acc:85.4%, Train_loss:0.392, Test_acc:76.9%Test_loss:0.504
Epoch: 9, duration:5395ms, Train_acc:85.8%, Train_loss:0.374, Test_acc:77.6%Test_loss:0.490
Epoch:10, duration:5346ms, Train_acc:87.9%, Train_loss:0.356, Test_acc:76.0%Test_loss:0.498
Epoch:11, duration:5297ms, Train_acc:88.3%, Train_loss:0.336, Test_acc:77.6%Test_loss:0.464
Epoch:12, duration:5291ms, Train_acc:89.6%, Train_loss:0.323, Test_acc:78.1%Test_loss:0.470
Epoch:13, duration:5259ms, Train_acc:89.7%, Train_loss:0.311, Test_acc:78.6%Test_loss:0.475
Epoch:14, duration:5343ms, Train_acc:90.3%, Train_loss:0.295, Test_acc:80.0%Test_loss:0.443
Epoch:15, duration:5363ms, Train_acc:90.5%, Train_loss:0.295, Test_acc:78.1%Test_loss:0.442
Epoch:16, duration:5305ms, Train_acc:91.2%, Train_loss:0.284, Test_acc:79.5%Test_loss:0.427
Epoch:17, duration:5279ms, Train_acc:91.5%, Train_loss:0.270, Test_acc:79.5%Test_loss:0.421
Epoch:18, duration:5356ms, Train_acc:92.8%, Train_loss:0.259, Test_acc:80.7%Test_loss:0.426
Epoch:19, duration:5284ms, Train_acc:91.9%, Train_loss:0.251, Test_acc:80.2%Test_loss:0.427
Epoch:20, duration:5274ms, Train_acc:92.6%, Train_loss:0.255, Test_acc:80.4%Test_loss:0.440
Done2.4 结果可视化
训练模型--结果可视化
epochs_range range(epochs)plt.figure(figsize(12, 3))
plt.subplot(1, 2, 1)plt.plot(epochs_range, train_acc, labelTraining Accuracy)
plt.plot(epochs_range, test_acc, labelTest Accuracy)
plt.legend(loclower right)
plt.title(Training and Validation Accuracy)plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, labelTraining Loss)
plt.plot(epochs_range, test_loss, labelTest Loss)
plt.legend(locupper right)
plt.title(Training and Validation Loss)
plt.show()2.4 指定图片进行预测
def predict_one_image(image_path, model, transform, classes):test_img Image.open(image_path).convert(RGB)plt.imshow(test_img) # 展示预测的图片plt.show()test_img transform(test_img)img test_img.to(device).unsqueeze(0)model.eval()output model(img)_, pred torch.max(output, 1)pred_class classes[pred]print(f预测结果是{pred_class})classes list(total_data.class_to_idx)
# 预测训练集中的某张照片
predict_one_image(image_pathstr(Path(data_dir)/Monkeypox/M01_01_00.jpg),modelmodel,transformtrain_transforms,classesclasses)输出
预测结果是Monkeypox2.6 保存并加载模型 保存并加载模型
# 模型保存
PATH ./model.pth # 保存的参数文件名
torch.save(model.state_dict(), PATH)# 将参数加载到model当中
model.load_state_dict(torch.load(PATH, map_locationdevice))3 知识点详解
3.1 torch.utils.data.DataLoader()参数详解
torch.utils.data.DataLoader 是 PyTorch 中用于加载和管理数据的一个实用工具类。它允许你以小批次的方式迭代你的数据集这对于训练神经网络和其他机器学习任务非常有用。DataLoader 构造函数接受多个参数下面是一些常用的参数及其解释 dataset必需参数这是你的数据集对象通常是 torch.utils.data.Dataset 的子类它包含了你的数据样本。 batch_size可选参数指定每个小批次中包含的样本数。默认值为 1。 shuffle可选参数如果设置为 True则在每个 epoch 开始时对数据进行洗牌以随机打乱样本的顺序。这对于训练数据的随机性很重要以避免模型学习到数据的顺序性。默认值为 False。 num_workers可选参数用于数据加载的子进程数量。通常将其设置为大于 0 的值可以加快数据加载速度特别是当数据集很大时。默认值为 0表示在主进程中加载数据。 pin_memory可选参数如果设置为 True则数据加载到 GPU 时会将数据存储在 CUDA 的锁页内存中这可以加速数据传输到 GPU。默认值为 False。drop_last可选参数如果设置为 True则在最后一个小批次可能包含样本数小于 batch_size 时丢弃该小批次。这在某些情况下很有用以确保所有小批次具有相同的大小。默认值为 False。 timeout可选参数如果设置为正整数它定义了每个子进程在等待数据加载器传递数据时的超时时间以秒为单位。这可以用于避免子进程卡住的情况。默认值为 0表示没有超时限制。worker_init_fn可选参数一个可选的函数用于初始化每个子进程的状态。这对于设置每个子进程的随机种子或其他初始化操作很有用。
3.2 torch.squeeze()与torch.unsqueeze()详解
torch.squeeze() 对数据的维度进行压缩去掉维数为1的的维度 函数原型
torch.squeeze(input, dimNone, *, outNone)关键参数 ● input (Tensor)输入Tensor ● dim (int, optional)如果给定输入将只在这个维度上被压缩 实战案例 x torch.zeros(2, 1, 2, 1, 2)x.size()
torch.Size([2, 1, 2, 1, 2])y torch.squeeze(x)y.size()
torch.Size([2, 2, 2])y torch.squeeze(x, 0)y.size()
torch.Size([2, 1, 2, 1, 2])y torch.squeeze(x, 1)y.size()
torch.Size([2, 2, 1, 2])torch.unsqueeze() 对数据维度进行扩充。给指定位置加上维数为一的维度 函数原型
torch.unsqueeze(input, dim)关键参数说明 ●input (Tensor)输入Tensor ●dim (int)插入单例维度的索引 实战案例 x torch.tensor([1, 2, 3, 4])torch.unsqueeze(x, 0)
tensor([[ 1, 2, 3, 4]])torch.unsqueeze(x, 1)
tensor([[ 1],[ 2],[ 3],[ 4]])3.3 拔高尝试–更改优化器为Adam
opt torch.optim.Adam(model.parameters(), lrlearn_rate)训练过程如下
Epoch: 1, duration:8545ms, Train_acc:62.6%, Train_loss:0.822, Test_acc:61.3%Test_loss:0.622
Epoch: 2, duration:5357ms, Train_acc:77.5%, Train_loss:0.462, Test_acc:80.0%Test_loss:0.448
Epoch: 3, duration:5594ms, Train_acc:85.0%, Train_loss:0.340, Test_acc:79.3%Test_loss:0.417
Epoch: 4, duration:5536ms, Train_acc:89.3%, Train_loss:0.263, Test_acc:80.4%Test_loss:0.485
Epoch: 5, duration:5387ms, Train_acc:91.8%, Train_loss:0.226, Test_acc:77.2%Test_loss:0.524
Epoch: 6, duration:5337ms, Train_acc:95.1%, Train_loss:0.175, Test_acc:84.6%Test_loss:0.388
Epoch: 7, duration:5445ms, Train_acc:96.0%, Train_loss:0.136, Test_acc:86.9%Test_loss:0.384
Epoch: 8, duration:5413ms, Train_acc:97.1%, Train_loss:0.108, Test_acc:86.7%Test_loss:0.368
Epoch: 9, duration:5402ms, Train_acc:97.8%, Train_loss:0.094, Test_acc:85.5%Test_loss:0.374
Epoch:10, duration:5360ms, Train_acc:98.6%, Train_loss:0.077, Test_acc:87.4%Test_loss:0.370
Epoch:11, duration:5327ms, Train_acc:99.1%, Train_loss:0.057, Test_acc:86.2%Test_loss:0.389
Epoch:12, duration:5432ms, Train_acc:99.5%, Train_loss:0.044, Test_acc:84.1%Test_loss:0.500
Epoch:13, duration:5385ms, Train_acc:99.5%, Train_loss:0.043, Test_acc:86.2%Test_loss:0.399
Epoch:14, duration:5419ms, Train_acc:99.8%, Train_loss:0.031, Test_acc:86.9%Test_loss:0.400
Epoch:15, duration:5375ms, Train_acc:99.8%, Train_loss:0.025, Test_acc:86.9%Test_loss:0.380
Epoch:16, duration:5373ms, Train_acc:99.9%, Train_loss:0.023, Test_acc:87.6%Test_loss:0.374
Epoch:17, duration:5383ms, Train_acc:99.8%, Train_loss:0.023, Test_acc:88.8%Test_loss:0.390
Epoch:18, duration:5398ms, Train_acc:99.9%, Train_loss:0.021, Test_acc:88.1%Test_loss:0.425
Epoch:19, duration:5491ms, Train_acc:99.9%, Train_loss:0.020, Test_acc:87.6%Test_loss:0.393
Epoch:20, duration:5405ms, Train_acc:99.9%, Train_loss:0.015, Test_acc:87.2%Test_loss:0.400acc与loss图如下测试精度最高达到88.8%
3.4 拔高尝试–更改优化器为Adam增加dropout层
在更改优化器为Adam代码的基础上修改网络模型结构提升测试精度
class Network_bn(nn.Module):def __init__(self):super(Network_bn, self).__init__()nn.Conv2d()函数第一个参数in_channels是输入的channel数量第二个参数out_channels是输出的channel数量第三个参数kernel_size是卷积核大小第四个参数stride是步长默认为1第五个参数padding是填充大小默认为0self.conv1 nn.Conv2d(in_channels3, out_channels12, kernel_size5, stride1, padding0)self.bn1 nn.BatchNorm2d(12)self.conv2 nn.Conv2d(in_channels12, out_channels12, kernel_size5, stride1, padding0)self.bn2 nn.BatchNorm2d(12)self.pool nn.MaxPool2d(2, 2)self.conv4 nn.Conv2d(in_channels12, out_channels24, kernel_size5, stride1, padding0)self.bn4 nn.BatchNorm2d(24)self.conv5 nn.Conv2d(in_channels24, out_channels24, kernel_size5, stride1, padding0)self.bn5 nn.BatchNorm2d(24)self.dropout nn.Dropout(p0.5)self.fc1 nn.Linear(24 * 50 * 50, len(classeNames))def forward(self, x):x F.relu(self.bn1(self.conv1(x)))x F.relu(self.bn2(self.conv2(x)))x self.pool(x)x F.relu(self.bn4(self.conv4(x)))x F.relu(self.bn5(self.conv5(x)))x self.pool(x)x self.dropout(x)x x.view(-1, 24 * 50 * 50)x self.fc1(x)return x
训练过程如下
Epoch: 1, duration:7376ms, Train_acc:62.3%, Train_loss:0.812, Test_acc:69.7%Test_loss:0.576
Epoch: 2, duration:5362ms, Train_acc:75.0%, Train_loss:0.522, Test_acc:77.4%Test_loss:0.470
Epoch: 3, duration:5439ms, Train_acc:84.5%, Train_loss:0.369, Test_acc:78.3%Test_loss:0.418
Epoch: 4, duration:5418ms, Train_acc:87.6%, Train_loss:0.305, Test_acc:84.4%Test_loss:0.369
Epoch: 5, duration:5422ms, Train_acc:90.6%, Train_loss:0.253, Test_acc:83.0%Test_loss:0.377
Epoch: 6, duration:5418ms, Train_acc:92.1%, Train_loss:0.215, Test_acc:87.6%Test_loss:0.334
Epoch: 7, duration:5414ms, Train_acc:92.4%, Train_loss:0.196, Test_acc:86.5%Test_loss:0.343
Epoch: 8, duration:5373ms, Train_acc:95.6%, Train_loss:0.140, Test_acc:84.4%Test_loss:0.454
Epoch: 9, duration:5403ms, Train_acc:96.4%, Train_loss:0.125, Test_acc:87.2%Test_loss:0.331
Epoch:10, duration:5402ms, Train_acc:96.8%, Train_loss:0.111, Test_acc:87.9%Test_loss:0.300
Epoch:11, duration:5448ms, Train_acc:98.0%, Train_loss:0.081, Test_acc:89.5%Test_loss:0.293
Epoch:12, duration:5394ms, Train_acc:98.4%, Train_loss:0.073, Test_acc:89.0%Test_loss:0.314
Epoch:13, duration:5444ms, Train_acc:98.5%, Train_loss:0.064, Test_acc:88.6%Test_loss:0.352
Epoch:14, duration:5400ms, Train_acc:98.0%, Train_loss:0.074, Test_acc:89.7%Test_loss:0.322
Epoch:15, duration:5396ms, Train_acc:98.9%, Train_loss:0.052, Test_acc:89.7%Test_loss:0.329
Epoch:16, duration:5519ms, Train_acc:99.0%, Train_loss:0.045, Test_acc:86.2%Test_loss:0.397
Epoch:17, duration:5374ms, Train_acc:99.2%, Train_loss:0.038, Test_acc:90.2%Test_loss:0.302
Epoch:18, duration:5464ms, Train_acc:99.3%, Train_loss:0.037, Test_acc:91.1%Test_loss:0.314
Epoch:19, duration:5668ms, Train_acc:98.6%, Train_loss:0.054, Test_acc:88.3%Test_loss:0.341
Epoch:20, duration:5540ms, Train_acc:99.6%, Train_loss:0.029, Test_acc:90.2%Test_loss:0.308
acc与loss图如下测试精度最高达到91.1%
3.5 拔高尝试–更改优化器为Adam增加dropout层保存最好的模型
在更改优化器为Adam增加dropout层代码的基础上在训练模型阶段增加部分代码保存最好的模型预测前加载模型 训练模型--正式训练epochs 20train_loss []train_acc []test_loss []test_acc []best_test_acc0PATH ./model.pth # 保存的参数文件名for epoch in range(epochs):milliseconds_t1 int(time.time() * 1000)model.train()epoch_train_acc, epoch_train_loss train(train_dl, model, loss_fn, opt)model.eval()epoch_test_acc, epoch_test_loss test(test_dl, model, loss_fn)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)milliseconds_t2 int(time.time() * 1000)template (Epoch:{:2d}, duration:{}ms, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%Test_loss:{:.3f})if best_test_acc epoch_test_acc:best_test_acc epoch_test_acc# 模型保存torch.save(model.state_dict(), PATH)template (Epoch:{:2d}, duration:{}ms, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%Test_loss:{:.3f},save model.pth)print(template.format(epoch 1, milliseconds_t2-milliseconds_t1, epoch_train_acc * 100, epoch_train_loss, epoch_test_acc * 100, epoch_test_loss))print(Done)训练记录如下
Epoch: 1, duration:7342ms, Train_acc:61.6%, Train_loss:0.837, Test_acc:68.3%Test_loss:0.796,save model.pth
Epoch: 2, duration:5390ms, Train_acc:75.4%, Train_loss:0.524, Test_acc:77.6%Test_loss:0.472,save model.pth
Epoch: 3, duration:5348ms, Train_acc:82.4%, Train_loss:0.417, Test_acc:84.6%Test_loss:0.411,save model.pth
Epoch: 4, duration:5377ms, Train_acc:85.1%, Train_loss:0.351, Test_acc:82.3%Test_loss:0.424
Epoch: 5, duration:5362ms, Train_acc:85.7%, Train_loss:0.326, Test_acc:83.7%Test_loss:0.426
Epoch: 6, duration:5371ms, Train_acc:88.3%, Train_loss:0.270, Test_acc:84.6%Test_loss:0.353
Epoch: 7, duration:5426ms, Train_acc:92.6%, Train_loss:0.199, Test_acc:89.0%Test_loss:0.320,save model.pth
Epoch: 8, duration:5432ms, Train_acc:89.7%, Train_loss:0.256, Test_acc:83.0%Test_loss:0.478
Epoch: 9, duration:5447ms, Train_acc:93.1%, Train_loss:0.189, Test_acc:85.5%Test_loss:0.395
Epoch:10, duration:5630ms, Train_acc:95.2%, Train_loss:0.133, Test_acc:87.4%Test_loss:0.316
Epoch:11, duration:5469ms, Train_acc:95.2%, Train_loss:0.127, Test_acc:87.2%Test_loss:0.352
Epoch:12, duration:5366ms, Train_acc:96.3%, Train_loss:0.106, Test_acc:90.0%Test_loss:0.328,save model.pth
Epoch:13, duration:5543ms, Train_acc:97.3%, Train_loss:0.085, Test_acc:88.6%Test_loss:0.284
Epoch:14, duration:5500ms, Train_acc:97.4%, Train_loss:0.084, Test_acc:89.3%Test_loss:0.299
Epoch:15, duration:5398ms, Train_acc:97.9%, Train_loss:0.068, Test_acc:90.2%Test_loss:0.269,save model.pth
Epoch:16, duration:5436ms, Train_acc:98.4%, Train_loss:0.056, Test_acc:88.8%Test_loss:0.282
Epoch:17, duration:5447ms, Train_acc:99.1%, Train_loss:0.050, Test_acc:87.6%Test_loss:0.325
Epoch:18, duration:5483ms, Train_acc:97.7%, Train_loss:0.067, Test_acc:89.5%Test_loss:0.294
Epoch:19, duration:5431ms, Train_acc:98.7%, Train_loss:0.046, Test_acc:90.2%Test_loss:0.298
Epoch:20, duration:5553ms, Train_acc:99.4%, Train_loss:0.032, Test_acc:90.7%Test_loss:0.278,save model.pth总结
通过本文的学习pytorch实现猴痘病识别并通过改变优化器的方式以及增加dropout层提升了原有模型的测试精度并保存训练过程中最好的模型。
