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我的环境 语言环境#xff1a;Python 3.8.12 编译器#xff1a;jupyter notebook 深度学习环境#xff1a;torch 1.12.0cu113 一、前期准备
1.设置GPU
im… 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
我的环境 语言环境Python 3.8.12 编译器jupyter notebook 深度学习环境torch 1.12.0cu113 一、前期准备
1.设置GPU
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets
import os,PIL,pathlib,warningswarnings.filterwarnings(ignore) #忽略警告信息device torch.device(cuda if torch.cuda.is_available() else cpu)
device
device(typecuda)
2.导入数据
import os,PIL,random,pathlibdata_dir F:/jupyter lab/DL-100-days/datasets/Hollywood_stars_photos/
data_dir pathlib.Path(data_dir)data_paths list(data_dir.glob(*))
classeNames [str(path).split(\\)[5] for path in data_paths]
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]
# 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.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。
])total_data datasets.ImageFolder(F:/jupyter lab/DL-100-days/datasets/Hollywood_stars_photos/,transformtrain_transforms)
total_data
Dataset ImageFolderNumber of datapoints: 1800Root location: F:/jupyter lab/DL-100-days/datasets/Hollywood_stars_photos/StandardTransform
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]))
total_data.class_to_idx
{Angelina Jolie: 0,Brad Pitt: 1,Denzel Washington: 2,Hugh Jackman: 3,Jennifer Lawrence: 4,Johnny Depp: 5,Kate Winslet: 6,Leonardo DiCaprio: 7,Megan Fox: 8,Natalie Portman: 9,Nicole Kidman: 10,Robert Downey Jr: 11,Sandra Bullock: 12,Scarlett Johansson: 13,Tom Cruise: 14,Tom Hanks: 15,Will Smith: 16}
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])
train_dataset, test_dataset
(torch.utils.data.dataset.Subset at 0x187391e5a60,torch.utils.data.dataset.Subset at 0x187391e5b20)
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)
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
二、调用官方的VGG16模型
from torchvision.models import vgg16device cuda if torch.cuda.is_available() else cpu
print(Using {} device.format(device))# 加载预训练模型并且对模型进行微调
model vgg16(pretrained True).to(device) # 加载预训练的vgg16模型for param in model.parameters():param.requires_grad False # 冻结模型的参数这样子在训练的时候只训练最后一层的参数# 修改classifier模块的第6层即(6): Linear(in_features4096, out_features2, biasTrue)
# 注意查看我们下方打印出来的模型
model.classifier._modules[6] nn.Linear(4096,len(classeNames)) # 修改vgg16模型中最后一层全连接层输出目标类别个数
model.to(device)
model
Using cuda device
VGG((features): Sequential((0): Conv2d(3, 64, kernel_size(3, 3), stride(1, 1), padding(1, 1))(1): ReLU(inplaceTrue)(2): Conv2d(64, 64, kernel_size(3, 3), stride(1, 1), padding(1, 1))(3): ReLU(inplaceTrue)(4): MaxPool2d(kernel_size2, stride2, padding0, dilation1, ceil_modeFalse)(5): Conv2d(64, 128, kernel_size(3, 3), stride(1, 1), padding(1, 1))(6): ReLU(inplaceTrue)(7): Conv2d(128, 128, kernel_size(3, 3), stride(1, 1), padding(1, 1))(8): ReLU(inplaceTrue)(9): MaxPool2d(kernel_size2, stride2, padding0, dilation1, ceil_modeFalse)(10): Conv2d(128, 256, kernel_size(3, 3), stride(1, 1), padding(1, 1))(11): ReLU(inplaceTrue)(12): Conv2d(256, 256, kernel_size(3, 3), stride(1, 1), padding(1, 1))(13): ReLU(inplaceTrue)(14): Conv2d(256, 256, kernel_size(3, 3), stride(1, 1), padding(1, 1))(15): ReLU(inplaceTrue)(16): MaxPool2d(kernel_size2, stride2, padding0, dilation1, ceil_modeFalse)(17): Conv2d(256, 512, kernel_size(3, 3), stride(1, 1), padding(1, 1))(18): ReLU(inplaceTrue)(19): Conv2d(512, 512, kernel_size(3, 3), stride(1, 1), padding(1, 1))(20): ReLU(inplaceTrue)(21): Conv2d(512, 512, kernel_size(3, 3), stride(1, 1), padding(1, 1))(22): ReLU(inplaceTrue)(23): MaxPool2d(kernel_size2, stride2, padding0, dilation1, ceil_modeFalse)(24): Conv2d(512, 512, kernel_size(3, 3), stride(1, 1), padding(1, 1))(25): ReLU(inplaceTrue)(26): Conv2d(512, 512, kernel_size(3, 3), stride(1, 1), padding(1, 1))(27): ReLU(inplaceTrue)(28): Conv2d(512, 512, kernel_size(3, 3), stride(1, 1), padding(1, 1))(29): ReLU(inplaceTrue)(30): MaxPool2d(kernel_size2, stride2, padding0, dilation1, ceil_modeFalse))(avgpool): AdaptiveAvgPool2d(output_size(7, 7))(classifier): Sequential((0): Linear(in_features25088, out_features4096, biasTrue)(1): ReLU(inplaceTrue)(2): Dropout(p0.5, inplaceFalse)(3): Linear(in_features4096, out_features4096, biasTrue)(4): ReLU(inplaceTrue)(5): Dropout(p0.5, inplaceFalse)(6): Linear(in_features4096, out_features17, biasTrue))
)
三、训练循环
1.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num_batches len(dataloader) # 批次数目, (size/batch_size向上取整)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
2.编写测试函数
def test (dataloader, model, loss_fn):size len(dataloader.dataset) # 测试集的大小num_batches len(dataloader) # 批次数目, (size/batch_size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
3.设置动态学习率
# 调用官方动态学习率接口时使用
learn_rate 1e-3 # 初始学习率
lambda1 lambda epoch: 0.92 ** (epoch // 4)
optimizer torch.optim.SGD(model.parameters(), lrlearn_rate)
scheduler torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambdalambda1) #选定调整方法 4.正式训练
import copyloss_fn nn.CrossEntropyLoss() # 创建损失函数
epochs 40train_loss []
train_acc []
test_loss []
test_acc []best_acc 0 # 设置一个最佳准确率作为最佳模型的判别指标for 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接口时使用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)# 获取当前的学习率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))# 保存最佳模型到文件中
PATH ./best_model123.pth # 保存的参数文件名
torch.save(model.state_dict(), PATH)print(Done)
Epoch: 1, Train_acc:11.9%, Train_loss:2.810, Test_acc:14.4%, Test_loss:2.639, Lr:1.00E-03
Epoch: 2, Train_acc:17.4%, Train_loss:2.590, Test_acc:15.3%, Test_loss:2.512, Lr:1.00E-03
Epoch: 3, Train_acc:17.8%, Train_loss:2.483, Test_acc:17.5%, Test_loss:2.400, Lr:1.00E-03
Epoch: 4, Train_acc:21.0%, Train_loss:2.409, Test_acc:20.8%, Test_loss:2.344, Lr:9.20E-04
Epoch: 5, Train_acc:22.1%, Train_loss:2.331, Test_acc:23.9%, Test_loss:2.289, Lr:9.20E-04
...........
Epoch:36, Train_acc:42.2%, Train_loss:1.763, Test_acc:38.9%, Test_loss:1.858, Lr:4.72E-04
Epoch:37, Train_acc:43.8%, Train_loss:1.744, Test_acc:39.2%, Test_loss:1.872, Lr:4.72E-04
Epoch:38, Train_acc:44.0%, Train_loss:1.747, Test_acc:39.4%, Test_loss:1.872, Lr:4.72E-04
Epoch:39, Train_acc:43.9%, Train_loss:1.744, Test_acc:39.7%, Test_loss:1.859, Lr:4.72E-04
Epoch:40, Train_acc:44.4%, Train_loss:1.731, Test_acc:39.7%, Test_loss:1.885, Lr:4.34E-04
Done
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from datetime import datetime
current_time datetime.now() # 获取当前时间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.xlabel(current_time) # 打卡请带上时间戳否则代码截图无效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.对指定图片进行预测
from PIL import Image 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) # 展示预测的图片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})
# 预测训练集中的某张照片
predict_one_image(image_pathF:/jupyter lab/DL-100-days/datasets/Hollywood_stars_photos/Angelina Jolie/001_fe3347c0.jpg, modelmodel, transformtrain_transforms, classesclasses)
预测结果是Angelina Jolie 3.模型评估
best_model.eval()
epoch_test_acc, epoch_test_loss test(test_dl, best_model, loss_fn)
epoch_test_acc, epoch_test_loss
(0.3972222222222222, 1.8640953699747722)
# 查看是否与我们记录的最高准确率一致
epoch_test_acc
0.3972222222222222
五、学习心得
1.本次使用pytorch深度学习环境对官方的VGG-16模型进行调用并且保存最佳模型权重。VGG-16模型的最大特点是深度。除此之外其卷积层都采用3x3的卷积核和步长为1的卷积操作同时卷积层后都有ReLU激活函数从而降低过拟合风险。
2.训练过程中发现训练和测试的acc都过低约为20%通过调整动态学习率予以调整初始学习率增大一个数量级之后此问题得到一定的解决。
3.下一步将自行搭建VGG-16模型。
