深度学习每周学习总结J1（ResNet-50算法实战与解析 - 鸟类识别）

• 🍨 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

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")
device

device(type='cuda')

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_data

Dataset ImageFolderNumber of datapoints: 565Root location: ./data/bird_photos/StandardTransform
Transform: Compose(Resize(size=[224, 224], interpolation=bilinear, max_size=None, antialias=True)RandomHorizontalFlip(p=0.5)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)break

Shape of X [N,C,H,W]:  torch.Size([32, 3, 224, 224])
Shape of y:  torch.Size([32]) torch.int64

4. 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, strides=1):super(ConvBlock, self).__init__()filters1, filters2, filters3 = filtersself.conv1 = nn.Conv2d(in_channels, filters1, kernel_size=1, stride=strides)self.bn1 = nn.BatchNorm2d(filters1)self.conv2 = nn.Conv2d(filters1, filters2, kernel_size=kernel_size, padding=1)self.bn2 = nn.BatchNorm2d(filters2)self.conv3 = nn.Conv2d(filters2, filters3, kernel_size=1)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_size=1, stride=strides),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_classes=1000):super(ResNet50, self).__init__()self.pad = nn.ZeroPad2d(3)self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)self.bn1 = nn.BatchNorm2d(64)self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)# Define block layers with appropriate strides and filter sizesself.layer1 = self._build_layer(64, [64, 64, 256], blocks=3, strides=1)self.layer2 = self._build_layer(256, [128, 128, 512], blocks=4, strides=2)self.layer3 = self._build_layer(512, [256, 256, 1024], blocks=6, strides=2)self.layer4 = self._build_layer(1024, [512, 512, 2048], blocks=3, strides=2)self.avgpool = nn.AdaptiveAvgPool2d((1, 1))self.fc = nn.Linear(2048, num_classes)def _build_layer(self, in_channels, filters, blocks, strides=1):layers = []# Add the first block with potential downsampling (by strides)layers.append(ConvBlock(in_channels, filters, kernel_size=3, strides=strides))in_channels = filters[-1]# Add the additional blocks in this layer without downsamplingfor _ in range(1, blocks):layers.append(ConvBlock(in_channels, filters, kernel_size=3, strides=1))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, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)(layer1): Sequential((0): ConvBlock((conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(shortcut): Sequential((0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(1): ConvBlock((conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(shortcut): Sequential())(2): ConvBlock((conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(shortcut): Sequential()))(layer2): Sequential((0): ConvBlock((conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(2, 2))(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(shortcut): Sequential((0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2))(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(1): ConvBlock((conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(shortcut): Sequential())(2): ConvBlock((conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(shortcut): Sequential())(3): ConvBlock((conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(shortcut): Sequential()))(layer3): Sequential((0): ConvBlock((conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(2, 2))(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1))(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(shortcut): Sequential((0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2))(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(1): ConvBlock((conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1))(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(shortcut): Sequential())(2): ConvBlock((conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1))(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(shortcut): Sequential())(3): ConvBlock((conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1))(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(shortcut): Sequential())(4): ConvBlock((conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1))(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(shortcut): Sequential())(5): ConvBlock((conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1))(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(shortcut): Sequential()))(layer4): Sequential((0): ConvBlock((conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(2, 2))(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1))(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(shortcut): Sequential((0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2))(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(1): ConvBlock((conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1))(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1))(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(shortcut): Sequential())(2): ConvBlock((conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1))(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1))(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(shortcut): Sequential()))(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))(fc): Linear(in_features=2048, out_features=1000, bias=True)
)

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(),lr=learn_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_lambda=lambda1) # 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_loss

7. 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_loss

7. 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(epoch+1,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
Done

9. 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 successfully>

11. 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, -5.2461, -4.8821, -5.5314, -5.8627, -4.4703, -4.0505,-4.3990, -6.0654, -4.9767, -4.8933, -5.0749, -5.0969, -4.7121, -4.7877,-5.9138, -5.0609, -5.6325, -4.4758, -5.3832, -5.3308, -5.1663, -5.3744,-4.6568, -5.5547, -4.7015, -4.5332, -4.7048, -4.0575, -4.9354, -5.9719,-4.3700, -4.3375, -4.3835, -5.3360, -4.6949, -4.7043, -3.0128, -5.4335,-4.1490, -5.5024, -5.1213, -4.5514, -4.3888, -5.1336, -4.9035, -2.9031,-4.2921, -5.1654, -4.5989, -5.7990, -5.1267, -4.8723, -5.2425, -5.2648,-4.7031, -4.4419, -4.9500, -5.4625, -4.4614, -6.0281, -4.5982, -5.2243,-5.2080, -4.5609, -4.2382, -5.6643, -5.3306, -4.9993, -5.4132, -5.1988,-4.8110, -4.3645, -4.7883, -4.4945, -4.1371, -6.1615, -4.3348, -6.0694,-5.0832, -5.7544, -7.4370, -5.0871, -4.8265, -4.9912, -5.4170, -4.7337,-4.8869, -4.2867, -6.0734, -5.3458, -4.6136, -4.6427, -5.0670, -5.2674,-5.3138, -6.1891, -4.5351, -5.4272, -4.0822, -3.8868, -5.3524, -4.5716,-5.2943, -5.4524, -5.8091, -4.6113, -6.2336, -3.9732, -5.5085, -4.9551,-5.8190, -4.7829, -6.0425, -4.4670, -5.0961, -4.8832, -4.9884, -5.3502,-4.7928, -4.6363, -5.2002, -5.2305, -6.3564, -4.8475, -5.7222, -4.9972,-4.8803, -4.9031, -4.7486, -5.1633, -4.4830, -5.8250, -4.9781, -6.4707,-5.1635, -6.2186, -4.6911, -6.1466, -4.3560, -4.0647, -5.6964, -4.6971,-4.5650, -5.2661, -4.4342, -6.5263, -5.0083, -4.1184, -4.4144, -4.3142,-3.9152, -4.6833, -3.9448, -4.7297, -4.7736, -4.5994, -5.2097, -3.7666,-4.4968, -4.7199, -3.7594, -4.8945, -4.8153, -4.3126, -5.4928, -4.2669,-4.4810, -5.1798, -6.5569, -6.4180, -6.2858, -5.0941, -4.5702, -5.1153,-5.6136, -5.2451, -4.9205, -5.2890, -4.1699, -4.1866, -4.2302, -4.9986,-4.8075, -5.6134, -3.6785, -5.4610, -5.5478, -6.9582, -4.6375, -5.1448,-4.6614, -5.0536, -4.4895, -5.3882, -4.4433, -5.9130, -5.5548, -5.1081,-5.9129, -5.2355, -5.0405, -4.8093, -4.5147, -6.5717, -4.7978, -6.3876,-4.6591, -6.3384, -5.6406, -5.2991, -3.7750, -4.6432, -5.0256, -5.9266,-4.7254, -5.3511, -4.1279, -5.8108, -5.6086, -4.8933, -4.4658, -4.2604,-4.2203, -6.0904, -4.4941, -4.3299, -4.4338, -4.5120, -5.5362, -5.6916,-4.3631, -5.7946, -4.5226, -5.0334, -5.3108, -4.6992, -5.8546, -5.3448,-4.7411, -4.6165, -5.0561, -3.7938, -4.5349, -6.3409, -5.6717, -5.2238,-5.4950, -5.6322, -5.9336, -4.7374, -5.2193, -4.7341, -5.5894, -4.9538,-4.9265, -4.3776, -5.9501, -4.6434, -4.7603, -3.4144, -4.9672, -4.5454,-4.5984, -4.3427, -5.4971, -4.6948, -4.5390, -4.1352, -5.3147, -5.4603,-5.7149, -4.4007, -5.0655, -5.0953, -3.7071, -5.5659, -4.5470, -4.9357,-4.5709, -4.9261, -4.6197, -4.9192, -5.8589, -5.4089, -4.1649, -5.2944,-5.5576, -5.7486, -4.8553, -4.2282, -4.0010, -6.0522, -5.6085, -5.3450,-5.6549, -4.8746, -4.5044, -5.1996, -5.0918, -4.1159, -4.2053, -4.7821,-5.6509, -6.5732, -4.7520, -5.1714, -5.1409, -5.1455, -6.2418, -5.0210,-5.5984, -3.9693, -5.4372, -5.1220, -5.2193, -4.5396, -4.4132, -5.4393,-4.9878, -5.6195, -4.7220, -5.4855, -4.8570, -4.3293, -5.4886, -5.1216,-6.0905, -5.6525, -4.9218, -5.5533, -4.9065, -5.2826, -4.3511, -4.3716,-4.7577, -5.2228, -4.3568, -5.2613, -7.0981, -4.7413, -4.1612, -5.1176,-5.5265, -4.8816, -5.5066, -3.1811, -5.5949, -5.1209, -5.3330, -4.5807,-5.0738, -6.2544, -5.7920, -4.8863, -5.4842, -4.1021, -4.2780, -5.6989,-4.9062, -4.9241, -4.6026, -3.8766, -4.2304, -5.3913, -3.9213, -4.8042,-3.8525, -4.7163, -5.0682, -5.0261, -5.7655, -6.6659, -5.0076, -4.4935,-4.9612, -5.2125, -5.8215, -5.3559, -4.8470, -4.5751, -5.2267, -5.7606,-5.1162, -5.7072, -4.0790, -5.3081, -4.5741, -5.2311, -5.0682, -3.9425,-4.0050, -5.5297, -4.4969, -4.0187, -5.7901, -4.9107, -5.2692, -4.6438,-5.0574, -5.8231, -5.3713, -5.0068, -4.3541, -4.4603, -4.4939, -4.8495,-5.5300, -4.9960, -3.5204, -4.3364, -6.1883, -4.1293, -5.0230, -4.7546,-5.8216, -4.1616, -5.9442, -5.3490, -3.8164, -5.5285, -4.4659, -3.8073,-4.0800, -4.3338, -4.8140, -5.4170, -5.5654, -3.5721, -5.4222, -5.4133,-5.3061, -5.6210, -5.0620, -4.9480, -5.4236, -5.0383, -4.5365, -6.5556,-4.9221, -5.6572, -5.3458, -4.0751, -3.5474, -5.1584, -5.4831, -5.0386,-5.1952, -5.7538, -5.2630, -4.6680, -5.3158, -4.4897, -5.2653, -5.6185,-5.2754, -4.9732, -3.6142, -3.3807, -5.9308, -3.3762, -4.5909, -4.8592,-5.4307, -5.8186, -3.1528, -5.8814, -4.3251, -5.4645, -5.7290, -5.5545,-6.9818, -4.6826, -5.2847, -5.2316, -5.3749, -5.5513, -5.0912, -6.0087,-5.0094, -5.2834, -5.9248, -4.2985, -5.6460, -4.0210, -5.0835, -5.0239,-4.2798, -4.0711, -3.0299, -4.4249, -3.8580, -5.4938, -6.1500, -6.2026,-5.5107, -4.6297, -5.2490, -4.1693, -5.5621, -5.3462, -4.4608, -5.4131,-4.8175, -4.7294, -5.3795, -4.7759, -4.9308, -4.6114, -4.8512, -5.6910,-4.5780, -6.0506, -4.5082, -5.0415, -5.1643, -4.3208, -5.7007, -5.4402,-5.0097, -3.7647, -5.9034, -4.9685, -4.2927, -3.3002, -6.0967, -4.7784,-5.0862, -4.9967, -5.5023, -4.9356, -5.8725, -4.7621, -4.8660, -5.6542,-4.1783, -5.8236, -4.4004, -5.2087, -6.2577, -5.9747, -3.9065, -4.9602,-4.9865, -6.0940, -5.1763, -4.1279, -4.7915, -4.5835, -4.4909, -5.0032,-4.4306, -5.1279, -5.9237, -5.6016, -4.1580, -5.8993, -5.5826, -5.1257,-5.2317, -5.4553, -5.0349, -5.3762, -4.8476, -5.7886, -4.9892, -5.3761,-6.2784, -4.3499, -5.6478, -5.4893, -3.0633, -4.5597, -5.5055, -5.4887,-5.8181, -5.6683, -6.3172, -4.5253, -5.2920, -4.5857, -5.5727, -4.5398,-4.7678, -5.5095, -3.8584, -5.2461, -5.6084, -4.6665, -5.4107, -4.8152,-5.4975, -5.0608, -4.3795, -6.9169, -5.0226, -5.4908, -5.5358, -5.0029,-5.5313, -4.9354, -4.8460, -4.9901, -4.7630, -4.8377, -5.0652, -5.9628,-5.9703, -5.4385, -4.6380, -5.6674, -5.3192, -4.4605, -3.6204, -5.3540,-7.2942, -4.0140, -5.5212, -4.0053, -3.5999, -5.2319, -5.9446, -5.6247,-4.3006, -5.1588, -4.5104, -3.9837, -5.1592, -4.2998, -6.2257, -5.3139,-4.5336, -5.6970, -4.5432, -5.0471, -5.1169, -5.7729, -5.3915, -3.4912,-4.6369, -5.2365, -6.0601, -5.3651, -5.0499, -5.1211, -4.7336, -6.7476,-5.6456, -5.0389, -4.7289, -5.5121, -2.9521, -4.6852, -5.8838, -5.6147,-4.9222, -4.7334, -5.0473, -5.3684, -4.3785, -4.4092, -5.7249, -5.1093,-4.9247, -4.0973, -5.2757, -5.0103, -5.3298, -5.2246, -5.2376, -5.0409,-5.3682, -4.4103, -5.2739, -5.7753, -5.3121, -5.3229, -4.7973, -5.3419,-4.0869, -5.5489, -6.0253, -4.6384, -4.7001, -5.3798, -5.4072, -4.6274,-5.2741, -6.1363, -4.6067, -5.3099, -5.0059, -5.1699, -4.9767, -5.2467,-5.0942, -4.4623, -5.6662, -4.6116, -5.3887, -4.7994, -4.1454, -2.2531,-5.6628, -5.2896, -4.7683, -6.1704, -5.5401, -7.0299, -5.1669, -5.5821,-4.7684, -5.2925, -4.6315, -4.8803, -5.1687, -5.2941, -4.9299, -4.8190,-5.4344, -4.7551, -4.7198, -5.1019, -5.7492, -6.1550, -5.6996, -4.4132,-6.2275, -5.4198, -4.8401, -5.1504, -5.1971, -5.7264, -4.6849, -5.0585,-3.8531, -3.6110, -4.3900, -4.0146, -5.6333, -5.7491, -4.2836, -4.9333,-5.2104, -4.4378, -4.6586, -6.6317, -5.3482, -4.9060, -4.0507, -4.3850,-4.3232, -4.4060, -4.6808, -6.1481, -5.0341, -5.0642, -4.2104, -4.4937,-5.8942, -5.7710, -5.0782, -4.6604, -6.3730, -7.0023, -5.4708, -6.7699,-4.6094, -5.3579, -6.3511, -5.2445, -3.5320, -4.9982, -5.1565, -5.1640,-5.4871, -3.6589, -3.9923, -5.0592, -4.5019, -5.9001, -4.1611, -4.6703,-5.1029, -2.9716, -5.3578, -5.1678, -4.0690, -6.2209, -4.3308, -4.6219,-5.7228, -3.6582, -4.6460, -5.5739, -4.9900, -5.2182, -5.1610, -4.2914,-3.4296, -4.7841, -5.4836, -4.0942, -5.3494, -4.3294, -6.1656, -5.4277,-5.9730, -3.6229, -5.0981, -5.5474, -5.6498, -4.2508, -3.6384, -4.9230,-5.5150, -3.9948, -5.5786, -4.2315, -4.3435, -5.1986, -4.8257, -4.2392,-4.6649, -5.5955, -5.2190, -4.8896, -5.6084, -5.1639, -5.6644, -4.2302,-4.6478, -5.2653, -5.4640, -4.5797, -6.1926, -4.3489, -5.2554, -4.2098,-5.5954, -4.5482, -4.8853, -5.3986, -4.9047, -4.2353, -4.8603, -5.6714,-5.5819, -5.5323, -4.5244, -3.7890, -5.6494, -5.6447, -4.6866, -4.5489,-5.4129, -5.2079, -5.7100, -5.2953, -4.0829, -5.3427, -4.6995, -5.5088,-5.3798, -5.3735, -4.4773, -5.0795, -5.4732, -5.3964, -5.0798, -4.6644,-5.7256, -6.7874, -4.4255, -4.7243, -4.0838, -4.5000, -5.7769, -5.8769,-5.8356, -5.6986, -4.8260, -4.9483, -3.8793, -4.4843, -4.7780, -2.8808,-5.0341, -5.5861, -4.7410, -4.6428, -6.2251, -4.2188, -3.2221, -5.4640,-5.5350, -3.3220, -5.3559, -5.2414, -5.0133, -3.9686, -5.3160, -4.1124]],device='cuda:0', grad_fn=<AddmmBackward0>)
预测结果是:Bananaquit

classes

['Bananaquit', 'Black Skimmer', 'Black Throated Bushtiti', 'Cockatoo']