邢台网站优化定制,免费网站源码大全下载,wordpress 图标,wordpress自带搜索吗#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 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 设置超参数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动态学习率3.1.1 torch.optim.lr_scheduler.StepLR3.1.2 lr_scheduler.LambdaLR3.1.3 lr_scheduler.MultiStepLR 3.2 拔高尝试 总结 前言
本文将采用pytorch框架创建CNN网络实现运动鞋识别。讲述实现代码与执行结果并浅谈涉及知识点。 关键字torch动态学习率
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
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(./train/*))classeNames [str(path).split(\\)[-1] for path in data_paths]print(classeNames)输出
[adidas, nike]2.1.4 可视化数据
前期工作-可视化数据
subfolder Path(data_dir)/train/nike
image_files list(p.resolve() for p in subfolder.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# 关于transforms.Compose的更多介绍可以参考https://blog.csdn.net/qq_38251616/article/details/124878863train_transforms transforms.Compose([transforms.Resize([224, 224]), # 将输入图片resize成统一尺寸# transforms.RandomHorizontalFlip(), # 随机水平翻转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。])test_transform 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。])train_dataset datasets.ImageFolder(Path(data_dir)/train, transformtrain_transforms)test_dataset datasets.ImageFolder(Path(data_dir)/test, transformtrain_transforms)print(train_dataset.class_to_idx)输出
{adidas: 0, nike: 1}2.1.4 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) 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)
)2.3 训练模型
2.3.1 设置超参数
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
optimizer1 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
optimizer2 torch.optim.Adam(model.parameters(), lrlearn_rate)
#测试集acc 84.2%
lr_opt optimizer1
model_opt optimizer1
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 40train_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)# 更新学习率使用自定义学习率时使用adjust_learning_rate(lr_opt, epoch, learn_rate)model.train()epoch_train_acc, epoch_train_loss train(train_dl, model, loss_fn, model_opt)# scheduler.step() # 更新学习率调用官方动态学习率接口时使用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)# 获取当前的学习率lr lr_opt.state_dict()[param_groups][0][lr]milliseconds_t2 int(time.time() * 1000)template (Epoch:{:2d}, duration:{}ms, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%Test_loss:{:.3f}, Lr:{:.2E})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}, Lr:{:.2E},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, lr))print(Done)输出
Epoch: 1, duration:5323ms, Train_acc:52.2%, Train_loss:0.745, Test_acc:50.0%Test_loss:0.700, Lr:1.00E-04,save model.pth
Epoch: 2, duration:3125ms, Train_acc:62.9%, Train_loss:0.651, Test_acc:57.9%Test_loss:0.667, Lr:1.00E-04,save model.pth
Epoch: 3, duration:3130ms, Train_acc:66.9%, Train_loss:0.614, Test_acc:67.1%Test_loss:0.584, Lr:9.20E-05,save model.pth
Epoch: 4, duration:3136ms, Train_acc:70.5%, Train_loss:0.567, Test_acc:72.4%Test_loss:0.568, Lr:9.20E-05,save model.pth
Epoch: 5, duration:3346ms, Train_acc:76.1%, Train_loss:0.531, Test_acc:72.4%Test_loss:0.537, Lr:8.46E-05
Epoch: 6, duration:3087ms, Train_acc:77.5%, Train_loss:0.510, Test_acc:72.4%Test_loss:0.531, Lr:8.46E-05
Epoch: 7, duration:3215ms, Train_acc:77.7%, Train_loss:0.492, Test_acc:78.9%Test_loss:0.511, Lr:7.79E-05,save model.pth
Epoch: 8, duration:3520ms, Train_acc:82.3%, Train_loss:0.467, Test_acc:77.6%Test_loss:0.504, Lr:7.79E-05
Epoch: 9, duration:3662ms, Train_acc:83.1%, Train_loss:0.442, Test_acc:81.6%Test_loss:0.494, Lr:7.16E-05,save model.pth
Epoch:10, duration:3410ms, Train_acc:85.7%, Train_loss:0.427, Test_acc:80.3%Test_loss:0.464, Lr:7.16E-05
Epoch:11, duration:3486ms, Train_acc:86.3%, Train_loss:0.413, Test_acc:81.6%Test_loss:0.469, Lr:6.59E-05
Epoch:12, duration:3356ms, Train_acc:87.6%, Train_loss:0.394, Test_acc:78.9%Test_loss:0.452, Lr:6.59E-05
Epoch:13, duration:3453ms, Train_acc:87.6%, Train_loss:0.391, Test_acc:81.6%Test_loss:0.494, Lr:6.06E-05
Epoch:14, duration:3226ms, Train_acc:87.8%, Train_loss:0.385, Test_acc:80.3%Test_loss:0.450, Lr:6.06E-05
Epoch:15, duration:3290ms, Train_acc:89.0%, Train_loss:0.368, Test_acc:82.9%Test_loss:0.486, Lr:5.58E-05,save model.pth
Epoch:16, duration:3247ms, Train_acc:90.4%, Train_loss:0.359, Test_acc:81.6%Test_loss:0.443, Lr:5.58E-05
Epoch:17, duration:3195ms, Train_acc:90.6%, Train_loss:0.358, Test_acc:81.6%Test_loss:0.452, Lr:5.13E-05
Epoch:18, duration:3294ms, Train_acc:90.6%, Train_loss:0.342, Test_acc:82.9%Test_loss:0.436, Lr:5.13E-05
Epoch:19, duration:3305ms, Train_acc:91.2%, Train_loss:0.338, Test_acc:81.6%Test_loss:0.452, Lr:4.72E-05
Epoch:20, duration:3241ms, Train_acc:91.8%, Train_loss:0.332, Test_acc:81.6%Test_loss:0.418, Lr:4.72E-05
Epoch:21, duration:3371ms, Train_acc:93.0%, Train_loss:0.320, Test_acc:81.6%Test_loss:0.459, Lr:4.34E-05
Epoch:22, duration:3279ms, Train_acc:92.8%, Train_loss:0.317, Test_acc:81.6%Test_loss:0.475, Lr:4.34E-05
Epoch:23, duration:3279ms, Train_acc:93.4%, Train_loss:0.310, Test_acc:82.9%Test_loss:0.438, Lr:4.00E-05
Epoch:24, duration:3225ms, Train_acc:93.0%, Train_loss:0.313, Test_acc:81.6%Test_loss:0.437, Lr:4.00E-05
Epoch:25, duration:3293ms, Train_acc:94.0%, Train_loss:0.304, Test_acc:81.6%Test_loss:0.439, Lr:3.68E-05
Epoch:26, duration:3273ms, Train_acc:94.0%, Train_loss:0.297, Test_acc:81.6%Test_loss:0.414, Lr:3.68E-05
Epoch:27, duration:3249ms, Train_acc:94.2%, Train_loss:0.296, Test_acc:80.3%Test_loss:0.413, Lr:3.38E-05
Epoch:28, duration:3266ms, Train_acc:94.8%, Train_loss:0.288, Test_acc:84.2%Test_loss:0.425, Lr:3.38E-05,save model.pth
Epoch:29, duration:3248ms, Train_acc:94.4%, Train_loss:0.288, Test_acc:81.6%Test_loss:0.400, Lr:3.11E-05
Epoch:30, duration:3243ms, Train_acc:94.6%, Train_loss:0.291, Test_acc:81.6%Test_loss:0.445, Lr:3.11E-05
Epoch:31, duration:3250ms, Train_acc:96.4%, Train_loss:0.278, Test_acc:81.6%Test_loss:0.465, Lr:2.86E-05
Epoch:32, duration:3193ms, Train_acc:95.2%, Train_loss:0.275, Test_acc:81.6%Test_loss:0.438, Lr:2.86E-05
Epoch:33, duration:3283ms, Train_acc:95.2%, Train_loss:0.270, Test_acc:81.6%Test_loss:0.402, Lr:2.63E-05
Epoch:34, duration:3542ms, Train_acc:94.8%, Train_loss:0.280, Test_acc:81.6%Test_loss:0.407, Lr:2.63E-05
Epoch:35, duration:3592ms, Train_acc:95.8%, Train_loss:0.269, Test_acc:81.6%Test_loss:0.442, Lr:2.42E-05
Epoch:36, duration:3592ms, Train_acc:95.4%, Train_loss:0.267, Test_acc:80.3%Test_loss:0.413, Lr:2.42E-05
Epoch:37, duration:3588ms, Train_acc:95.0%, Train_loss:0.265, Test_acc:81.6%Test_loss:0.432, Lr:2.23E-05
Epoch:38, duration:3736ms, Train_acc:95.0%, Train_loss:0.267, Test_acc:81.6%Test_loss:0.438, Lr:2.23E-05
Epoch:39, duration:3431ms, Train_acc:95.2%, Train_loss:0.265, Test_acc:82.9%Test_loss:0.400, Lr:2.05E-05
Epoch:40, duration:3417ms, Train_acc:95.8%, Train_loss:0.270, Test_acc:81.6%Test_loss:0.379, Lr:2.05E-05
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})指定图片进行预测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)/test/adidas/1.jpg),modelmodel,transformtrain_transforms,classesclasses)如果使用效果最好的模型就先加载保存好的模型再调用预测代码 # 将参数加载到model当中model.load_state_dict(torch.load(PATH, map_locationdevice))输出
预测结果是adidas2.6 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动态学习率
3.1.1 torch.optim.lr_scheduler.StepLR
函数原型
torch.optim.lr_scheduler.StepLR(optimizer, step_size, gamma0.1, last_epoch-1)关键参数详解
optimizer(Optimizer)是之前定义好的需要优化的优化器的实例名step_size(int)是学习率衰减的周期每经过每个epoch做一次学习率decaygamma(float)学习率衰减的乘法因子。Default0.1
用法示例
optimizer torch.optim.SGD(net.parameters(), lr0.001 )
scheduler torch.optim.lr_scheduler.StepLR(optimizer, step_size5, gamma0.1)3.1.2 lr_scheduler.LambdaLR
根据自己定义的函数更新学习率。 函数原型
torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda, last_epoch-1, verboseFalse)关键参数详解
optimizer(Optimizer)是之前定义好的需要优化的优化器的实例名lr_lambda(function)更新学习率的函数 用法示例
lambda1 lambda epoch: (0.92 ** (epoch // 2) # 第二组参数的调整方法
optimizer torch.optim.SGD(model.parameters(), lrlearn_rate)
scheduler torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambdalambda1) #选定调整方法3.1.3 lr_scheduler.MultiStepLR
在特定的 epoch 中调整学习率 函数原型
torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones, gamma0.1, last_epoch-1, verboseFalse)关键参数详解
optimizer(Optimizer)是之前定义好的需要优化的优化器的实例名milestones(list)是一个关于epoch数值的list表示在达到哪个epoch范围内开始变化必须是升序排列gamma(float)学习率衰减的乘法因子。Default0.1 用法示例
optimizer torch.optim.SGD(net.parameters(), lr0.001 )
scheduler torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones[2,6,15], #调整学习率的epoch数gamma0.1)更多的官方动态学习率设置方式可参考https://pytorch.org/docs/stable/optim.html 调用官方接口示例
model [Parameter(torch.randn(2, 2, requires_gradTrue))]
optimizer SGD(model, 0.1)
scheduler ExponentialLR(optimizer, gamma0.9)for epoch in range(20):for input, target in dataset:optimizer.zero_grad()output model(input)loss loss_fn(output, target)loss.backward()optimizer.step()scheduler.step()3.2 拔高尝试
尝试变更dropout失活比例为0.5测试集acc提升至86.8%,实际上也不能确定是因为变更比例导致的效果提升因为每次运行效果都有不同有好有坏。
Epoch: 1, duration:4820ms, Train_acc:53.6%, Train_loss:1.133, Test_acc:52.6%Test_loss:0.712, Lr:1.00E-04,save model.pth
Epoch: 2, duration:3271ms, Train_acc:73.5%, Train_loss:0.547, Test_acc:59.2%Test_loss:0.697, Lr:1.00E-04,save model.pth
Epoch: 3, duration:3566ms, Train_acc:81.5%, Train_loss:0.396, Test_acc:77.6%Test_loss:0.485, Lr:9.20E-05,save model.pth
Epoch: 4, duration:3309ms, Train_acc:89.4%, Train_loss:0.287, Test_acc:77.6%Test_loss:0.470, Lr:9.20E-05
Epoch: 5, duration:3411ms, Train_acc:94.2%, Train_loss:0.219, Test_acc:82.9%Test_loss:0.418, Lr:8.46E-05,save model.pth
Epoch: 6, duration:3239ms, Train_acc:98.2%, Train_loss:0.172, Test_acc:81.6%Test_loss:0.385, Lr:8.46E-05
Epoch: 7, duration:3282ms, Train_acc:98.8%, Train_loss:0.127, Test_acc:85.5%Test_loss:0.374, Lr:7.79E-05,save model.pth
Epoch: 8, duration:3277ms, Train_acc:99.2%, Train_loss:0.115, Test_acc:82.9%Test_loss:0.332, Lr:7.79E-05
Epoch: 9, duration:3440ms, Train_acc:99.6%, Train_loss:0.091, Test_acc:86.8%Test_loss:0.356, Lr:7.16E-05,save model.pth
Epoch:10, duration:3570ms, Train_acc:100.0%, Train_loss:0.078, Test_acc:81.6%Test_loss:0.411, Lr:7.16E-05
Epoch:11, duration:3418ms, Train_acc:99.6%, Train_loss:0.072, Test_acc:84.2%Test_loss:0.370, Lr:6.59E-05
Epoch:12, duration:3291ms, Train_acc:100.0%, Train_loss:0.064, Test_acc:85.5%Test_loss:0.339, Lr:6.59E-05
Epoch:13, duration:3273ms, Train_acc:100.0%, Train_loss:0.054, Test_acc:85.5%Test_loss:0.321, Lr:6.06E-05
Epoch:14, duration:3365ms, Train_acc:100.0%, Train_loss:0.049, Test_acc:85.5%Test_loss:0.336, Lr:6.06E-05
Epoch:15, duration:3321ms, Train_acc:100.0%, Train_loss:0.046, Test_acc:84.2%Test_loss:0.311, Lr:5.58E-05
Epoch:16, duration:3273ms, Train_acc:100.0%, Train_loss:0.041, Test_acc:84.2%Test_loss:0.336, Lr:5.58E-05
Epoch:17, duration:3315ms, Train_acc:100.0%, Train_loss:0.038, Test_acc:85.5%Test_loss:0.350, Lr:5.13E-05
Epoch:18, duration:3380ms, Train_acc:100.0%, Train_loss:0.034, Test_acc:82.9%Test_loss:0.314, Lr:5.13E-05
Epoch:19, duration:3275ms, Train_acc:100.0%, Train_loss:0.034, Test_acc:84.2%Test_loss:0.378, Lr:4.72E-05
Epoch:20, duration:3264ms, Train_acc:100.0%, Train_loss:0.031, Test_acc:82.9%Test_loss:0.342, Lr:4.72E-05
Epoch:21, duration:3267ms, Train_acc:100.0%, Train_loss:0.029, Test_acc:84.2%Test_loss:0.299, Lr:4.34E-05
Epoch:22, duration:3243ms, Train_acc:100.0%, Train_loss:0.028, Test_acc:84.2%Test_loss:0.320, Lr:4.34E-05
Epoch:23, duration:3319ms, Train_acc:100.0%, Train_loss:0.025, Test_acc:82.9%Test_loss:0.335, Lr:4.00E-05
Epoch:24, duration:3230ms, Train_acc:100.0%, Train_loss:0.025, Test_acc:84.2%Test_loss:0.351, Lr:4.00E-05
Epoch:25, duration:3251ms, Train_acc:100.0%, Train_loss:0.025, Test_acc:84.2%Test_loss:0.329, Lr:3.68E-05
Epoch:26, duration:3275ms, Train_acc:100.0%, Train_loss:0.023, Test_acc:84.2%Test_loss:0.304, Lr:3.68E-05
Epoch:27, duration:3244ms, Train_acc:100.0%, Train_loss:0.022, Test_acc:82.9%Test_loss:0.324, Lr:3.38E-05
Epoch:28, duration:3319ms, Train_acc:100.0%, Train_loss:0.022, Test_acc:85.5%Test_loss:0.308, Lr:3.38E-05
Epoch:29, duration:3287ms, Train_acc:100.0%, Train_loss:0.020, Test_acc:85.5%Test_loss:0.353, Lr:3.11E-05
Epoch:30, duration:3230ms, Train_acc:100.0%, Train_loss:0.019, Test_acc:85.5%Test_loss:0.346, Lr:3.11E-05
Epoch:31, duration:3285ms, Train_acc:100.0%, Train_loss:0.019, Test_acc:85.5%Test_loss:0.335, Lr:2.86E-05
Epoch:32, duration:3255ms, Train_acc:100.0%, Train_loss:0.019, Test_acc:85.5%Test_loss:0.337, Lr:2.86E-05
Epoch:33, duration:3307ms, Train_acc:100.0%, Train_loss:0.018, Test_acc:85.5%Test_loss:0.334, Lr:2.63E-05
Epoch:34, duration:3281ms, Train_acc:100.0%, Train_loss:0.017, Test_acc:85.5%Test_loss:0.323, Lr:2.63E-05
Epoch:35, duration:3249ms, Train_acc:100.0%, Train_loss:0.016, Test_acc:85.5%Test_loss:0.314, Lr:2.42E-05
Epoch:36, duration:3287ms, Train_acc:100.0%, Train_loss:0.017, Test_acc:84.2%Test_loss:0.368, Lr:2.42E-05
Epoch:37, duration:3337ms, Train_acc:100.0%, Train_loss:0.016, Test_acc:85.5%Test_loss:0.328, Lr:2.23E-05
Epoch:38, duration:3367ms, Train_acc:100.0%, Train_loss:0.016, Test_acc:84.2%Test_loss:0.375, Lr:2.23E-05
Epoch:39, duration:3244ms, Train_acc:100.0%, Train_loss:0.015, Test_acc:84.2%Test_loss:0.329, Lr:2.05E-05
Epoch:40, duration:3277ms, Train_acc:100.0%, Train_loss:0.015, Test_acc:85.5%Test_loss:0.295, Lr:2.05E-05尝试变更学习率优化器及模型优化器为SGD和Adam的四种组合测试集acc几乎无变化 尝试变更初始学习率尝试变更学习率不动态更新测试集acc无提升
总结
通过本文学习到几种动态学习率的设置与调用要想得到一个比较好的模型效果对模型相关参数进行不同的尝试获取一个相对适配该案例的参数。
