wordpress可以制作什么网站,百度做广告多少钱,保定php网站制作,网站宣传册怎么做的#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 目录 0. 总结1. 设置GPU2. 导入数据及处理部分3. 划分数据集4. 模型构建部分5. 设置超参数#xff1a;定义损失函数#xff0c;学习率 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 目录 0. 总结1. 设置GPU2. 导入数据及处理部分3. 划分数据集4. 模型构建部分5. 设置超参数定义损失函数学习率以及根据学习率定义优化器等6. 训练函数7. 测试函数7. 正式训练9. 结果可视化10. 模型的保存11. 使用训练好的模型进行预测 0. 总结
数据导入及处理部分本次数据导入没有使用torchvision自带的数据集需要将原始数据进行处理包括数据导入查看数据分类情况定义transforms进行数据类型转换等操作。
划分数据集划定训练集测试集后再使用torch.utils.data中的DataLoader()分别加载上一步处理好的训练及测试数据查看批处理维度.
模型构建部分resnet-50
设置超参数在这之前需要定义损失函数学习率动态学习率以及根据学习率定义优化器例如SGD随机梯度下降用来在训练中更新参数最小化损失函数。
定义训练函数函数的传入的参数有四个分别是设置好的DataLoader(),定义好的模型损失函数优化器。函数内部初始化损失准确率为0接着开始循环使用DataLoader()获取一个批次的数据对这个批次的数据带入模型得到预测值然后使用损失函数计算得到损失值。接下来就是进行反向传播以及使用优化器优化参数梯度清零放在反向传播之前或者是使用优化器优化之后都是可以的一般是默认放在反向传播之前。
定义测试函数函数传入的参数相比训练函数少了优化器只需传入设置好的DataLoader(),定义好的模型损失函数。此外除了处理批次数据时无需再设置梯度清零、返向传播以及优化器优化参数其余部分均和训练函数保持一致。
训练过程定义训练次数有几次就使用整个数据集进行几次训练初始化四个空list分别存储每次训练及测试的准确率及损失。使用model.train()开启训练模式调用训练函数得到准确率及损失。使用model.eval()将模型设置为评估模式调用测试函数得到准确率及损失。接着就是将得到的训练及测试的准确率及损失存储到相应list中并合并打印出来得到每一次整体训练后的准确率及损失。
结果可视化
模型的保存调取及使用。在PyTorch中通常使用 torch.save(model.state_dict(), ‘model.pth’) 保存模型的参数使用 model.load_state_dict(torch.load(‘model.pth’)) 加载参数。
需要改进优化的地方在保证整体流程没有问题的情况下继续细化细节研究比如一些函数的原理及作用如何提升训练集准确率等问题。
import torch
import torch.nn as nn
import torchvision
from torchvision import datasets,transforms
from torch.utils.data import DataLoader
import torchvision.models as models
import torch.nn.functional as Fimport os,PIL,pathlib
import matplotlib.pyplot as plt
import warningswarnings.filterwarnings(ignore) # 忽略警告信息plt.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1. 设置GPU
device torch.device(cuda if torch.cuda.is_available() else cpu)
devicedevice(typecuda)2. 导入数据及处理部分
# 获取数据分布情况
path_dir ./data/bird_photos/
path_dir pathlib.Path(path_dir)paths list(path_dir.glob(*))
# classNames [str(path).split(\\)[-1] for path in paths] # [Bananaquit, Black Skimmer, Black Throated Bushtiti, Cockatoo]
classNames [path.parts[-1] for path in paths]
classNames[Bananaquit, Black Skimmer, Black Throated Bushtiti, Cockatoo]# 定义transforms 并处理数据
train_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 装换为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], # 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。std [0.229,0.224,0.225])
])
test_transforms transforms.Compose([transforms.Resize([224,224]),transforms.ToTensor(),transforms.Normalize(mean [0.485,0.456,0.406],std [0.229,0.224,0.225])
])
total_data datasets.ImageFolder(./data/bird_photos/,transform train_transforms)
total_dataDataset ImageFolderNumber of datapoints: 565Root location: ./data/bird_photos/StandardTransform
Transform: Compose(Resize(size[224, 224], interpolationbilinear, max_sizeNone, antialiasTrue)RandomHorizontalFlip(p0.5)ToTensor()Normalize(mean[0.485, 0.456, 0.406], std[0.229, 0.224, 0.225]))total_data.class_to_idx{Bananaquit: 0,Black Skimmer: 1,Black Throated Bushtiti: 2,Cockatoo: 3}3. 划分数据集
# 划分数据集
train_size int(len(total_data) * 0.8)
test_size len(total_data) - train_sizetrain_dataset,test_dataset torch.utils.data.random_split(total_data,[train_size,test_size])
train_dataset,test_dataset(torch.utils.data.dataset.Subset at 0x23b545994b0,torch.utils.data.dataset.Subset at 0x23b54599300)# 定义DataLoader用于数据集的加载batch_size 32train_dl torch.utils.data.DataLoader(train_dataset,batch_size batch_size,shuffle True,num_workers 1
)
test_dl torch.utils.data.DataLoader(test_dataset,batch_size batch_size,shuffle True,num_workers 1
)# 观察数据维度
for X,y in test_dl:print(Shape of X [N,C,H,W]: ,X.shape)print(Shape of y: , y.shape,y.dtype)breakShape of X [N,C,H,W]: torch.Size([32, 3, 224, 224])
Shape of y: torch.Size([32]) torch.int644. 模型构建部分
import torch
import torch.nn as nn
import torch.nn.functional as Fclass ConvBlock(nn.Module):def __init__(self, in_channels, filters, kernel_size, strides1):super(ConvBlock, self).__init__()filters1, filters2, filters3 filtersself.conv1 nn.Conv2d(in_channels, filters1, kernel_size1, stridestrides)self.bn1 nn.BatchNorm2d(filters1)self.conv2 nn.Conv2d(filters1, filters2, kernel_sizekernel_size, padding1)self.bn2 nn.BatchNorm2d(filters2)self.conv3 nn.Conv2d(filters2, filters3, kernel_size1)self.bn3 nn.BatchNorm2d(filters3)self.shortcut nn.Sequential()if in_channels ! filters3 or strides ! 1:self.shortcut nn.Sequential(nn.Conv2d(in_channels, filters3, kernel_size1, stridestrides),nn.BatchNorm2d(filters3))def forward(self, x):shortcut self.shortcut(x)x F.relu(self.bn1(self.conv1(x)))x F.relu(self.bn2(self.conv2(x)))x self.bn3(self.conv3(x))x shortcutx F.relu(x)return xclass ResNet50(nn.Module):def __init__(self, num_classes1000):super(ResNet50, self).__init__()self.pad nn.ZeroPad2d(3)self.conv1 nn.Conv2d(3, 64, kernel_size7, stride2, padding3)self.bn1 nn.BatchNorm2d(64)self.maxpool nn.MaxPool2d(kernel_size3, stride2, padding1)# Define block layers with appropriate strides and filter sizesself.layer1 self._build_layer(64, [64, 64, 256], blocks3, strides1)self.layer2 self._build_layer(256, [128, 128, 512], blocks4, strides2)self.layer3 self._build_layer(512, [256, 256, 1024], blocks6, strides2)self.layer4 self._build_layer(1024, [512, 512, 2048], blocks3, strides2)self.avgpool nn.AdaptiveAvgPool2d((1, 1))self.fc nn.Linear(2048, num_classes)def _build_layer(self, in_channels, filters, blocks, strides1):layers []# Add the first block with potential downsampling (by strides)layers.append(ConvBlock(in_channels, filters, kernel_size3, stridesstrides))in_channels filters[-1]# Add the additional blocks in this layer without downsamplingfor _ in range(1, blocks):layers.append(ConvBlock(in_channels, filters, kernel_size3, strides1))return nn.Sequential(*layers)def forward(self, x):x self.pad(x)x F.relu(self.bn1(self.conv1(x)))x self.maxpool(x)x self.layer1(x)x self.layer2(x)x self.layer3(x)x self.layer4(x)x self.avgpool(x)x torch.flatten(x, 1)x self.fc(x)return x# Ensure the model uses the correct device
device torch.device(cuda if torch.cuda.is_available() else cpu)
model ResNet50()
model.to(device)ResNet50((pad): ZeroPad2d((3, 3, 3, 3))(conv1): Conv2d(3, 64, kernel_size(7, 7), stride(2, 2), padding(3, 3))(bn1): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(maxpool): MaxPool2d(kernel_size3, stride2, padding1, dilation1, ceil_modeFalse)(layer1): Sequential((0): ConvBlock((conv1): Conv2d(64, 64, kernel_size(1, 1), stride(1, 1))(bn1): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(64, 64, kernel_size(3, 3), stride(1, 1), padding(1, 1))(bn2): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(64, 256, kernel_size(1, 1), stride(1, 1))(bn3): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(shortcut): Sequential((0): Conv2d(64, 256, kernel_size(1, 1), stride(1, 1))(1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)))(1): ConvBlock((conv1): Conv2d(256, 64, kernel_size(1, 1), stride(1, 1))(bn1): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(64, 64, kernel_size(3, 3), stride(1, 1), padding(1, 1))(bn2): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(64, 256, kernel_size(1, 1), stride(1, 1))(bn3): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(shortcut): Sequential())(2): ConvBlock((conv1): Conv2d(256, 64, kernel_size(1, 1), stride(1, 1))(bn1): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(64, 64, kernel_size(3, 3), stride(1, 1), padding(1, 1))(bn2): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(64, 256, kernel_size(1, 1), stride(1, 1))(bn3): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(shortcut): Sequential()))(layer2): Sequential((0): ConvBlock((conv1): Conv2d(256, 128, kernel_size(1, 1), stride(2, 2))(bn1): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(128, 128, kernel_size(3, 3), stride(1, 1), padding(1, 1))(bn2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(128, 512, kernel_size(1, 1), stride(1, 1))(bn3): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(shortcut): Sequential((0): Conv2d(256, 512, kernel_size(1, 1), stride(2, 2))(1): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)))(1): ConvBlock((conv1): Conv2d(512, 128, kernel_size(1, 1), stride(1, 1))(bn1): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(128, 128, kernel_size(3, 3), stride(1, 1), padding(1, 1))(bn2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(128, 512, kernel_size(1, 1), stride(1, 1))(bn3): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(shortcut): Sequential())(2): ConvBlock((conv1): Conv2d(512, 128, kernel_size(1, 1), stride(1, 1))(bn1): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(128, 128, kernel_size(3, 3), stride(1, 1), padding(1, 1))(bn2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(128, 512, kernel_size(1, 1), stride(1, 1))(bn3): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(shortcut): Sequential())(3): ConvBlock((conv1): Conv2d(512, 128, kernel_size(1, 1), stride(1, 1))(bn1): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(128, 128, kernel_size(3, 3), stride(1, 1), padding(1, 1))(bn2): BatchNorm2d(128, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(128, 512, kernel_size(1, 1), stride(1, 1))(bn3): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(shortcut): Sequential()))(layer3): Sequential((0): ConvBlock((conv1): Conv2d(512, 256, kernel_size(1, 1), stride(2, 2))(bn1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(256, 256, kernel_size(3, 3), stride(1, 1), padding(1, 1))(bn2): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(256, 1024, kernel_size(1, 1), stride(1, 1))(bn3): BatchNorm2d(1024, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(shortcut): Sequential((0): Conv2d(512, 1024, kernel_size(1, 1), stride(2, 2))(1): BatchNorm2d(1024, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)))(1): ConvBlock((conv1): Conv2d(1024, 256, kernel_size(1, 1), stride(1, 1))(bn1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(256, 256, kernel_size(3, 3), stride(1, 1), padding(1, 1))(bn2): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(256, 1024, kernel_size(1, 1), stride(1, 1))(bn3): BatchNorm2d(1024, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(shortcut): Sequential())(2): ConvBlock((conv1): Conv2d(1024, 256, kernel_size(1, 1), stride(1, 1))(bn1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(256, 256, kernel_size(3, 3), stride(1, 1), padding(1, 1))(bn2): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(256, 1024, kernel_size(1, 1), stride(1, 1))(bn3): BatchNorm2d(1024, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(shortcut): Sequential())(3): ConvBlock((conv1): Conv2d(1024, 256, kernel_size(1, 1), stride(1, 1))(bn1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(256, 256, kernel_size(3, 3), stride(1, 1), padding(1, 1))(bn2): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(256, 1024, kernel_size(1, 1), stride(1, 1))(bn3): BatchNorm2d(1024, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(shortcut): Sequential())(4): ConvBlock((conv1): Conv2d(1024, 256, kernel_size(1, 1), stride(1, 1))(bn1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(256, 256, kernel_size(3, 3), stride(1, 1), padding(1, 1))(bn2): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(256, 1024, kernel_size(1, 1), stride(1, 1))(bn3): BatchNorm2d(1024, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(shortcut): Sequential())(5): ConvBlock((conv1): Conv2d(1024, 256, kernel_size(1, 1), stride(1, 1))(bn1): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(256, 256, kernel_size(3, 3), stride(1, 1), padding(1, 1))(bn2): BatchNorm2d(256, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(256, 1024, kernel_size(1, 1), stride(1, 1))(bn3): BatchNorm2d(1024, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(shortcut): Sequential()))(layer4): Sequential((0): ConvBlock((conv1): Conv2d(1024, 512, kernel_size(1, 1), stride(2, 2))(bn1): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(512, 512, kernel_size(3, 3), stride(1, 1), padding(1, 1))(bn2): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(512, 2048, kernel_size(1, 1), stride(1, 1))(bn3): BatchNorm2d(2048, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(shortcut): Sequential((0): Conv2d(1024, 2048, kernel_size(1, 1), stride(2, 2))(1): BatchNorm2d(2048, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)))(1): ConvBlock((conv1): Conv2d(2048, 512, kernel_size(1, 1), stride(1, 1))(bn1): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(512, 512, kernel_size(3, 3), stride(1, 1), padding(1, 1))(bn2): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(512, 2048, kernel_size(1, 1), stride(1, 1))(bn3): BatchNorm2d(2048, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(shortcut): Sequential())(2): ConvBlock((conv1): Conv2d(2048, 512, kernel_size(1, 1), stride(1, 1))(bn1): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv2): Conv2d(512, 512, kernel_size(3, 3), stride(1, 1), padding(1, 1))(bn2): BatchNorm2d(512, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(conv3): Conv2d(512, 2048, kernel_size(1, 1), stride(1, 1))(bn3): BatchNorm2d(2048, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(shortcut): Sequential()))(avgpool): AdaptiveAvgPool2d(output_size(1, 1))(fc): Linear(in_features2048, out_features1000, biasTrue)
)5. 设置超参数定义损失函数学习率以及根据学习率定义优化器等
# loss_fn nn.CrossEntropyLoss() # 创建损失函数# learn_rate 1e-3 # 初始学习率
# def adjust_learning_rate(optimizer,epoch,start_lr):
# # 每两个epoch 衰减到原来的0.98
# lr start_lr * (0.92 ** (epoch//2))
# for param_group in optimizer.param_groups:
# param_group[lr] lr# optimizer torch.optim.Adam(model.parameters(),lrlearn_rate)# 调用官方接口示例
loss_fn nn.CrossEntropyLoss()learn_rate 1e-4
lambda1 lambda epoch:(0.92**(epoch//2))optimizer torch.optim.Adam(model.parameters(),lr learn_rate)
scheduler torch.optim.lr_scheduler.LambdaLR(optimizer,lr_lambdalambda1) # 选定调整方法6. 训练函数
# 训练函数
def train(dataloader,model,loss_fn,optimizer):size len(dataloader.dataset) # 训练集大小num_batches len(dataloader) # 批次数目train_loss,train_acc 0,0for X,y in dataloader:X,y X.to(device),y.to(device)# 计算预测误差pred model(X)loss loss_fn(pred,y)# 反向传播optimizer.zero_grad()loss.backward()optimizer.step()# 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_loss7. 测试函数
# 测试函数
def test(dataloader,model,loss_fn):size len(dataloader.dataset)num_batches len(dataloader)test_acc,test_loss 0,0with torch.no_grad():for X,y in dataloader:X,y X.to(device),y.to(device)# 计算losspred model(X)loss loss_fn(pred,y)test_acc (pred.argmax(1)y).type(torch.float).sum().item()test_loss loss.item()test_acc / sizetest_loss / num_batchesreturn test_acc,test_loss7. 正式训练
import copyepochs 40train_acc []
train_loss []
test_acc []
test_loss []best_acc 0.0for epoch in range(epochs):# 更新学习率——使用自定义学习率时使用# adjust_learning_rate(optimizer,epoch,learn_rate)model.train()epoch_train_acc,epoch_train_loss train(train_dl,model,loss_fn,optimizer)scheduler.step() # 更新学习率——调用官方动态学习率时使用model.eval()epoch_test_acc,epoch_test_loss test(test_dl,model,loss_fn)# 保存最佳模型到 best_modelif epoch_test_acc best_acc:best_acc epoch_test_accbest_model copy.deepcopy(model)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)# 获取当前学习率lr optimizer.state_dict()[param_groups][0][lr]template (Epoch:{:2d},Train_acc:{:.1f}%,Train_loss:{:.3f},Test_acc:{:.1f}%,Test_loss:{:.3f},Lr:{:.2E})print(template.format(epoch1,epoch_train_acc*100,epoch_train_loss,epoch_test_acc*100,epoch_test_loss,lr))print(Done)Epoch: 1,Train_acc:35.4%,Train_loss:3.431,Test_acc:23.9%,Test_loss:3.033,Lr:1.00E-04
Epoch: 2,Train_acc:69.2%,Train_loss:0.899,Test_acc:26.5%,Test_loss:3.017,Lr:9.20E-05
Epoch: 3,Train_acc:74.1%,Train_loss:0.652,Test_acc:59.3%,Test_loss:1.278,Lr:9.20E-05
Epoch: 4,Train_acc:78.3%,Train_loss:0.587,Test_acc:67.3%,Test_loss:1.353,Lr:8.46E-05
Epoch: 5,Train_acc:82.5%,Train_loss:0.521,Test_acc:75.2%,Test_loss:0.829,Lr:8.46E-05
Epoch: 6,Train_acc:86.3%,Train_loss:0.433,Test_acc:66.4%,Test_loss:1.308,Lr:7.79E-05
Epoch: 7,Train_acc:90.3%,Train_loss:0.340,Test_acc:67.3%,Test_loss:1.600,Lr:7.79E-05
Epoch: 8,Train_acc:89.2%,Train_loss:0.374,Test_acc:71.7%,Test_loss:1.014,Lr:7.16E-05
Epoch: 9,Train_acc:88.7%,Train_loss:0.305,Test_acc:77.0%,Test_loss:0.841,Lr:7.16E-05
Epoch:10,Train_acc:90.7%,Train_loss:0.309,Test_acc:79.6%,Test_loss:1.094,Lr:6.59E-05
Epoch:11,Train_acc:91.8%,Train_loss:0.318,Test_acc:72.6%,Test_loss:0.976,Lr:6.59E-05
Epoch:12,Train_acc:93.6%,Train_loss:0.243,Test_acc:73.5%,Test_loss:1.209,Lr:6.06E-05
Epoch:13,Train_acc:94.7%,Train_loss:0.159,Test_acc:71.7%,Test_loss:0.947,Lr:6.06E-05
Epoch:14,Train_acc:98.0%,Train_loss:0.076,Test_acc:80.5%,Test_loss:0.707,Lr:5.58E-05
Epoch:15,Train_acc:97.8%,Train_loss:0.083,Test_acc:79.6%,Test_loss:0.923,Lr:5.58E-05
Epoch:16,Train_acc:98.2%,Train_loss:0.059,Test_acc:82.3%,Test_loss:0.650,Lr:5.13E-05
Epoch:17,Train_acc:98.5%,Train_loss:0.072,Test_acc:76.1%,Test_loss:0.828,Lr:5.13E-05
Epoch:18,Train_acc:98.0%,Train_loss:0.175,Test_acc:78.8%,Test_loss:0.834,Lr:4.72E-05
Epoch:19,Train_acc:94.2%,Train_loss:0.173,Test_acc:61.9%,Test_loss:2.606,Lr:4.72E-05
Epoch:20,Train_acc:96.2%,Train_loss:0.123,Test_acc:77.9%,Test_loss:0.959,Lr:4.34E-05
Epoch:21,Train_acc:96.5%,Train_loss:0.166,Test_acc:76.1%,Test_loss:1.266,Lr:4.34E-05
Epoch:22,Train_acc:96.9%,Train_loss:0.196,Test_acc:85.0%,Test_loss:0.698,Lr:4.00E-05
Epoch:23,Train_acc:98.7%,Train_loss:0.082,Test_acc:82.3%,Test_loss:0.626,Lr:4.00E-05
Epoch:24,Train_acc:96.0%,Train_loss:0.136,Test_acc:81.4%,Test_loss:0.805,Lr:3.68E-05
Epoch:25,Train_acc:98.5%,Train_loss:0.101,Test_acc:83.2%,Test_loss:0.576,Lr:3.68E-05
Epoch:26,Train_acc:98.5%,Train_loss:0.062,Test_acc:80.5%,Test_loss:0.597,Lr:3.38E-05
Epoch:27,Train_acc:99.6%,Train_loss:0.039,Test_acc:83.2%,Test_loss:0.574,Lr:3.38E-05
Epoch:28,Train_acc:99.3%,Train_loss:0.080,Test_acc:85.0%,Test_loss:0.758,Lr:3.11E-05
Epoch:29,Train_acc:99.6%,Train_loss:0.059,Test_acc:84.1%,Test_loss:0.608,Lr:3.11E-05
Epoch:30,Train_acc:98.9%,Train_loss:0.054,Test_acc:82.3%,Test_loss:0.753,Lr:2.86E-05
Epoch:31,Train_acc:98.9%,Train_loss:0.035,Test_acc:83.2%,Test_loss:0.617,Lr:2.86E-05
Epoch:32,Train_acc:98.7%,Train_loss:0.046,Test_acc:78.8%,Test_loss:0.847,Lr:2.63E-05
Epoch:33,Train_acc:98.9%,Train_loss:0.028,Test_acc:82.3%,Test_loss:0.746,Lr:2.63E-05
Epoch:34,Train_acc:99.6%,Train_loss:0.032,Test_acc:79.6%,Test_loss:0.629,Lr:2.42E-05
Epoch:35,Train_acc:99.3%,Train_loss:0.027,Test_acc:82.3%,Test_loss:0.597,Lr:2.42E-05
Epoch:36,Train_acc:99.6%,Train_loss:0.029,Test_acc:87.6%,Test_loss:0.488,Lr:2.23E-05
Epoch:37,Train_acc:99.6%,Train_loss:0.026,Test_acc:87.6%,Test_loss:0.552,Lr:2.23E-05
Epoch:38,Train_acc:99.1%,Train_loss:0.029,Test_acc:79.6%,Test_loss:0.572,Lr:2.05E-05
Epoch:39,Train_acc:99.8%,Train_loss:0.107,Test_acc:84.1%,Test_loss:0.704,Lr:2.05E-05
Epoch:40,Train_acc:99.1%,Train_loss:0.094,Test_acc:76.1%,Test_loss:0.772,Lr:1.89E-05
Done9. 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,label Training Accuracy)
plt.plot(epochs_range,test_acc,label Test Accuracy)
plt.legend(loc lower right)
plt.title(Training and Validation Accuracy)plt.subplot(1,2,2)
plt.plot(epochs_range,train_loss,label Test Accuracy)
plt.plot(epochs_range,test_loss,label Test Loss)
plt.legend(loc lower right)
plt.title(Training and validation Loss)
plt.show()
10. 模型的保存
# 自定义模型保存
# 状态字典保存
torch.save(model.state_dict(),./模型参数/J1_resnet50_model_state_dict.pth) # 仅保存状态字典# 加载状态字典到模型
best_model ResNet50().to(device) # 定义官方vgg16模型用来加载参数best_model.load_state_dict(torch.load(./模型参数/J1_resnet50_model_state_dict.pth)) # 加载状态字典到模型All keys matched successfully11. 使用训练好的模型进行预测
# 指定路径图片预测
from PIL import Image
import torchvision.transforms as transformsclasses list(total_data.class_to_idx) # classes list(total_data.class_to_idx)def predict_one_image(image_path,model,transform,classes):test_img Image.open(image_path).convert(RGB)# plt.imshow(test_img) # 展示待预测的图片test_img transform(test_img)img test_img.to(device).unsqueeze(0)model.eval()output model(img)print(output) # 观察模型预测结果的输出数据_,pred torch.max(output,1)pred_class classes[pred]print(f预测结果是:{pred_class})# 预测训练集中的某张照片
predict_one_image(image_path./data/bird_photos/Bananaquit/007.jpg,model model,transform test_transforms,classes classes)tensor([[13.8683, 4.9143, 4.8488, 0.3971, -4.3269, -6.6820, -5.4773, -4.8104,-6.3061, -6.0159, -4.1786, -5.2478, -4.7984, -4.5901, -5.0879, -4.8318,-5.1802, -4.8213, -4.1029, -4.8857, -5.7887, -4.4304, -5.2544, -4.4558,-3.9174, -4.3528, -5.6769, -5.0493, -4.5999, -6.1482, -4.0200, -3.2741,-4.6080, -6.3622, -3.1413, -4.3438, -5.2021, -4.2925, -5.5243, -4.7640,-4.9537, -5.5491, -5.6634, -3.9548, -4.3976, -5.2634, -5.1851, -4.0045,-6.0956, -4.2346, -4.1718, -4.4907, -4.1201, -4.3656, -4.0707, -5.3282,-5.3788, -4.4176, -4.8130, -5.5919, -5.5178, -5.1131, -4.6648, -4.9041,-5.3978, -5.4855, -5.0189, -4.8627, -5.7097, -4.2608, -5.3921, -4.4241,-5.8152, -5.2379, -5.2549, -5.9455, -4.4937, -5.2863, -5.2548, -5.5048,-4.9770, -5.0247, -4.8115, -5.1978, -5.0874, -4.8657, -4.7004, -6.1652,-7.1067, -5.3773, -5.5511, -4.6870, -5.7534, -4.9669, -4.2131, -5.3542,-4.9979, -5.4464, 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-5.3559, -5.2414, -5.0133, -3.9686, -5.3160, -4.1124]],devicecuda:0, grad_fnAddmmBackward0)
预测结果是:Bananaquitclasses[Bananaquit, Black Skimmer, Black Throated Bushtiti, Cockatoo]
