365天深度学习训练营 第P6周：好莱坞明星识别

• 🍨 本文为🔗365天深度学习训练营 内部限免文章（版权归 K同学啊 所有）
• 🍦 参考文章地址： 🔗第P6周：好莱坞明星识别 | 365天深度学习训练营
• 🍖 作者：K同学啊 | 接辅导、程序定制

我的环境：

• 语言环境：Python 3.6.8
• 编译器：jupyter notebook
• 深度学习环境：
  • torch==0.13.1、cuda==11.3
  • torchvision==1.12.1、cuda==11.3

一、前期工作

1. 设置 GPU

import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from torchvision import transforms, datasetsif __name__=='__main__':''' 设置GPU '''device = torch.device("cuda" if torch.cuda.is_available() else "cpu")print("Using {} device\n".format(device))

Using cuda device

2. 导入数据

import os, PIL, pathlib
data_dir = 'D:/jupyter notebook/DL-100-days/datasets/48-data/'
data_dir = pathlib.Path(data_dir)
data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[5] for path in data_paths]
print(classeNames)

['Angelina Jolie','Brad Pitt','Denzel Washington','Hugh Jackman','Jennifer Lawrence','Johnny Depp','Kate Winslet','Leonardo DiCaprio','Megan Fox','Natalie Portman','Nicole Kidman','Robert Downey Jr','Sandra Bullock','Scarlett Johansson','Tom Cruise','Tom Hanks','Will Smith']

train_transforms = transforms.Compose([transforms.Resize([224,224]),# resize输入图片transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换成tensortransforms.Normalize(mean = [0.485, 0.456, 0.406],std = [0.229,0.224,0.225]) # 从数据集中随机抽样计算得到
])total_data = datasets.ImageFolder(data_dir,transform=train_transforms)
total_data

Dataset ImageFolderNumber of datapoints: 1800Root location: hlwStandardTransform
Transform: Compose(Resize(size=[224, 224], interpolation=PIL.Image.BILINEAR)ToTensor()Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))

3. 划分数据集

train_size = int(0.8*len(total_data))
test_size = len(total_data) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(total_data,[train_size,test_size])

batch_size = 32
train_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)

二、调用vgg-16模型

from torchvision.models import vgg16model = vgg16(pretrained = True).to(device)
for param in model.parameters():param.requires_grad = Falsemodel.classifier._modules['6'] = nn.Linear(4096,len(classNames))model.to(device)

# 查看要训练的层
params_to_update = model.parameters()
# params_to_update = []
for name,param in model.named_parameters():if param.requires_grad == True:
#         params_to_update.append(param)print("\t",name)

三、训练模型

1. 设置超参数

# 优化器设置
optimizer = torch.optim.Adam(params_to_update, lr=1e-4)#要训练什么参数/
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.92)#学习率每5个epoch衰减成原来的1/10
loss_fn = nn.CrossEntropyLoss()

2. 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900张图片num_batches = len(dataloader)   # 批次数目，29（900/32）train_loss, train_acc = 0, 0  # 初始化训练损失和正确率for X, y in dataloader:  # 获取图片及其标签X, y = X.to(device), y.to(device)# 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()  # grad属性归零loss.backward()        # 反向传播optimizer.step()       # 每一步自动更新# 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

3. 编写测试函数

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，8（255/32=8，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():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()test_acc  /= sizetest_loss /= num_batchesreturn test_acc, test_loss

4. 正式训练

epochs     = 20
train_loss = []
train_acc  = []
test_loss  = []
test_acc   = []
best_acc = 0
filename='checkpoint.pth'for epoch in range(epochs):model.train()epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)scheduler.step()#学习率衰减model.eval()epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)# 保存最优模型if epoch_test_acc > best_acc:best_acc = epoch_train_accstate = {'state_dict': model.state_dict(),#字典里key就是各层的名字，值就是训练好的权重'best_acc': best_acc,'optimizer' : optimizer.state_dict(),}torch.save(state, filename)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')
print('best_acc：',best_acc)

Epoch: 1, Train_acc:12.2%, Train_loss:2.701, Test_acc:13.9%，Test_loss:2.544
Epoch: 2, Train_acc:20.8%, Train_loss:2.386, Test_acc:20.6%，Test_loss:2.377
Epoch: 3, Train_acc:26.1%, Train_loss:2.228, Test_acc:22.5%，Test_loss:2.274
…
Epoch:19, Train_acc:51.6%, Train_loss:1.528, Test_acc:35.8%，Test_loss:1.864
Epoch:20, Train_acc:53.9%, Train_loss:1.499, Test_acc:35.3%，Test_loss:1.852
Done
best_acc： 0.37430555555555556

四、结果可视化

1.Loss 与 Accuracy 图

import matplotlib.pyplot as plt
#隐藏警告
import warnings
warnings.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='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()