Pytorch-图像分类和CNN模型的迁移学习
导包：
1 import torch 2 import torch.nn as nn 3 import torch.nn.functional as F 4 import torch.optim as optim 5 from torchvision import datasets, transforms
关于torchvision：
- torchvision是独立于pytorch的关于图像操作的一些方便工具库。
- torchvision的详细介绍在：https://pypi.org/project/torchvision/0.1.8/
torchvision主要包括一下几个包：
- vision.datasets : 几个常用视觉数据集，可以下载和加载；
- vision.models : 流行的模型，例如 AlexNet, VGG, and ResNet 以及 与训练好的参数；
- vision.transforms : 常用的图像操作，例如：随机切割，旋转等；
- vision.utils : 用于把形似 (3 x H x W) 的张量保存到硬盘中，给一个mini-batch的图像可以产生一个图像格网；
设置参数：
1 #设置超参数 2 torch.manual_seed(53113) #cpu随机种子 3 batch_size = test_batch_size = 32 4 5 #设置GPU参数 6 use_cuda = torch.cuda.is_available() 7 device = torch.device("cuda" if use_cuda else "cpu") 8 kwargs = {'num_workers': 0, 'pin_memory': True} if use_cuda else {}
1.数据预处理
torch.utils.data.DataLoader在训练模型时使用到此函数，用来把训练数据分成多个batch，此函数每次抛出一个batch数据，直至把所有的数据都抛出，也就是个数据迭代器。
DataLoader中的transform参数：接受一个图像返回变换后的图像的函数，相当于图像先预处理下，常用的操作如 ToTensor, RandomCrop，Normalize等，他们可以通过transforms.Compose被组合在一起。
-
.ToTensor()将shape为(H, W, C)的nump.ndarray或img转为shape为(C, H, W)的tensor，其将每一个数值归一化到[0,1]，其归一化方法比较简单，直接除以255即可。
- .Normalize作用就是.ToTensor将输入归一化到(0,1)后，再使用公式”(x-mean)/std”，将每个元素分布到(-1,1)
1 train_loader = torch.utils.data.DataLoader( 2 datasets.MNIST('./mnist_data', #数据集 3 train=True, #如果true，从training.pt创建数据集 4 download=True, #如果ture，从网上自动下载 5 6 transform=transforms.Compose([ 7 transforms.ToTensor(), 8 transforms.Normalize((0.1307,), (0.3081,)) # 所有图片像素均值和方差 9 ])), 10 batch_size = batch_size, 11 shuffle=True, 12 **kwargs) #kwargs是上面gpu的设置
1 test_loader = torch.utils.data.DataLoader( 2 datasets.MNIST('./mnist_data', 3 train=False, #如果False，从test.pt创建数据集 4 transform=transforms.Compose([ 5 transforms.ToTensor(), 6 transforms.Normalize((0.1307,), (0.3081,)) 7 ])), 8 batch_size=test_batch_size, 9 shuffle=True, 10 **kwargs)
查看一下：
1 print(train_loader.dataset[0][0].shape) #torch.Size([1, 28, 28])
2.创建模型
1 class Net(nn.Module): 2 def __init__(self): 3 super(Net, self).__init__() 4 self.conv1 = nn.Conv2d(1, 20, 5, 1) #(in_channels, out_channels, kernel_size, stride=1) 5 self.conv2 = nn.Conv2d(20, 50, 5, 1) #上个卷积网络的out_channels，就是下一个网络的in_channels，所以这里是20 6 7 self.fc1 = nn.Linear(4*4*50, 500) 8 self.fc2 = nn.Linear(500, 10) #10分类 9 10 def forward(self, x): #手写数字的输入维度，(N,1,28,28), N为batch_size 11 x = F.relu(self.conv1(x)) # x = (N,20,24,24) 12 x = F.max_pool2d(x, 2, 2) # x = (N,20,12,12) 13 x = F.relu(self.conv2(x)) # x = (N,50,8,8) 14 x = F.max_pool2d(x, 2, 2) # x = (N,50,4,4) 15 x = x.view(-1, 4*4*50) # x = (N,4*4*50) 16 x = F.relu(self.fc1(x)) # x = (N,4*4*50)*(4*4*50, 500)=(N,500) 17 x = self.fc2(x) # x = (N,500)*(500, 10)=(N,10) 18 return F.log_softmax(x, dim=1) #带log的softmax分类，每张图片返回10个概率
模型初始化：
1 lr = 0.01 2 momentum = 0.5 3 model = Net().to(device) #模型初始化 4 optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum) #定义优化器
3.训练函数
1 def train(model, device, train_loader, optimizer, epoch, log_interval=100): 2 model.train() 3 for batch_idx, (data, target) in enumerate(train_loader): 4 data, target = data.to(device), target.to(device) 5 optimizer.zero_grad() 6 output = model(data) #输出的维度[N,10] 这里的data是函数的forward参数x 7 loss = F.nll_loss(output, target) #这里loss求的是平均数，除以了batch 8 loss.backward() 9 optimizer.step() 10 if batch_idx % log_interval == 0: 11 print("Train Epoch: {} [{}/{} ({:0f}%)]\tLoss: {:.6f}".format( 12 epoch, 13 batch_idx * len(data), #100*32 14 len(train_loader.dataset), #60000 15 100. * batch_idx / len(train_loader), #len(train_loader)=60000/32=1875 16 loss.item() 17 ))
4.测试函数
1 def test(model, device, test_loader): 2 model.eval() 3 test_loss = 0 4 correct = 0 5 with torch.no_grad(): 6 for data, target in test_loader: 7 data, target = data.to(device), target.to(device) 8 output = model(data) 9 test_loss += F.nll_loss(output, target, reduction='sum').item() #reduction='sum'代表batch的每个元素loss累加求和，默认是mean求平均 10 11 pred = output.argmax(dim=1, keepdim=True) #pred.shape=torch.Size([32, 1]) 12 13 correct += pred.eq(target.view_as(pred)).sum().item() #target.shape=torch.Size([32]) 14 15 test_loss /= len(test_loader.dataset) 16 17 print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( 18 test_loss, correct, len(test_loader.dataset), 19 100. * correct / len(test_loader.dataset)))
执行：
1 epochs = 2 2 for epoch in range(1, epochs + 1): 3 train(model, device, train_loader, optimizer, epoch) 4 test(model, device, test_loader) 5 6 save_model = True 7 if (save_model): 8 torch.save(model.state_dict(),"mnist_cnn.pt") #词典格式，model.state_dict()只保存模型参数
训练结果：
1 Train Epoch: 1 [0/60000 (0.000000%)] Loss: 2.297938 2 Train Epoch: 1 [3200/60000 (5.333333%)] Loss: 0.570356 3 Train Epoch: 1 [6400/60000 (10.666667%)] Loss: 0.207343 4 Train Epoch: 1 [9600/60000 (16.000000%)] Loss: 0.094465 5 Train Epoch: 1 [12800/60000 (21.333333%)] Loss: 0.178536 6 Train Epoch: 1 [16000/60000 (26.666667%)] Loss: 0.041227 7 Train Epoch: 1 [19200/60000 (32.000000%)] Loss: 0.136767 8 Train Epoch: 1 [22400/60000 (37.333333%)] Loss: 0.051781 9 Train Epoch: 1 [25600/60000 (42.666667%)] Loss: 0.112557 10 Train Epoch: 1 [28800/60000 (48.000000%)] Loss: 0.058771 11 Train Epoch: 1 [32000/60000 (53.333333%)] Loss: 0.085873 12 Train Epoch: 1 [35200/60000 (58.666667%)] Loss: 0.188629 13 Train Epoch: 1 [38400/60000 (64.000000%)] Loss: 0.092433 14 Train Epoch: 1 [41600/60000 (69.333333%)] Loss: 0.075023 15 Train Epoch: 1 [44800/60000 (74.666667%)] Loss: 0.038028 16 Train Epoch: 1 [48000/60000 (80.000000%)] Loss: 0.038069 17 Train Epoch: 1 [51200/60000 (85.333333%)] Loss: 0.052910 18 Train Epoch: 1 [54400/60000 (90.666667%)] Loss: 0.012891 19 Train Epoch: 1 [57600/60000 (96.000000%)] Loss: 0.033460 20 21 Test set: Average loss: 0.0653, Accuracy: 9799/10000 (98%) 22 23 Train Epoch: 2 [0/60000 (0.000000%)] Loss: 0.057514 24 Train Epoch: 2 [3200/60000 (5.333333%)] Loss: 0.030869 25 Train Epoch: 2 [6400/60000 (10.666667%)] Loss: 0.091362 26 Train Epoch: 2 [9600/60000 (16.000000%)] Loss: 0.059315 27 Train Epoch: 2 [12800/60000 (21.333333%)] Loss: 0.031055 28 Train Epoch: 2 [16000/60000 (26.666667%)] Loss: 0.012735 29 Train Epoch: 2 [19200/60000 (32.000000%)] Loss: 0.104735 30 Train Epoch: 2 [22400/60000 (37.333333%)] Loss: 0.132139 31 Train Epoch: 2 [25600/60000 (42.666667%)] Loss: 0.010015 32 Train Epoch: 2 [28800/60000 (48.000000%)] Loss: 0.012915 33 Train Epoch: 2 [32000/60000 (53.333333%)] Loss: 0.038762 34 Train Epoch: 2 [35200/60000 (58.666667%)] Loss: 0.015236 35 Train Epoch: 2 [38400/60000 (64.000000%)] Loss: 0.163834 36 Train Epoch: 2 [41600/60000 (69.333333%)] Loss: 0.064514 37 Train Epoch: 2 [44800/60000 (74.666667%)] Loss: 0.007881 38 Train Epoch: 2 [48000/60000 (80.000000%)] Loss: 0.074057 39 Train Epoch: 2 [51200/60000 (85.333333%)] Loss: 0.209342 40 Train Epoch: 2 [54400/60000 (90.666667%)] Loss: 0.018052 41 Train Epoch: 2 [57600/60000 (96.000000%)] Loss: 0.012391 42 43 Test set: Average loss: 0.0460, Accuracy: 9851/10000 (99%)
5.CNN模型的迁移学习
很多时候当我们需要训练一个新的图像分类任务，我们不会完全从一个随机的模型开始训练，而是利用_预训练_的模型来加速训练的过程。我们经常使用在ImageNet上的预训练模型。
以下两种方法做迁移学习：
- fine tuning：从一个预训练模型开始，我们改变一些模型的架构，然后继续训练整个模型的参数；
- feature extraction：不再改变预训练模型的参数，而是只更新我们改变过的部分模型参数。我们之所以叫它feature extraction是因为我们把预训练的CNN模型当做一个特征提取模型，利用提取出来的特征做来完成我们的训练任务；
以下是构建和训练迁移学习模型的基本步骤：
- 初始化预训练模型；
- 把最后一层的输出层改变成我们想要分的类别总数；
- 定义一个optimizer来更新参数；
- 模型训练；
导包：
1 import numpy as np 2 import torchvision 3 from torchvision import datasets, transforms, models 4 5 import matplotlib.pyplot as plt 6 import time 7 import os 8 import copy
9
10input_size = 224
我们会使用hymenoptera_data数据集，下载，然后放在当前代码目录下。这个数据集包括两类图片，bees 和 ants，这些数据都被处理成了可以使用ImageFolder <https://pytorch.org/docs/stable/torchvision/datasets.html#torchvision.datasets.ImageFolder>来读取的格式。我们只需要把data_dir设置成数据的根目录，然后把model_name设置成我们想要使用的预训练模型： [resnet, alexnet, vgg, squeezenet, densenet, inception]
5.1查看数据
1 data_dir = "./hymenoptera_data" 2 batch_size = 32 3 4 #os.path.join() 连接路径，相当于.../data_dir/train 5 all_imgs = datasets.ImageFolder(os.path.join(data_dir, "train"), 6 transforms.Compose([ 7 transforms.RandomResizedCrop(input_size), #把每张图片变成resnet需要输入的维度224 8 transforms.RandomHorizontalFlip(), 9 transforms.ToTensor(), 10 ])) 11 loader = torch.utils.data.DataLoader(all_imgs, batch_size=batch_size, shuffle=True, num_workers=0) #训练数据分batch，变成tensor迭代器 12 13 img = next(iter(loader))[0] #这个img是一个batch的tensor 14 print(img.shape) #torch.Size([32, 3, 224, 224])
1 unloader = transforms.ToPILImage() #.ToPILImage() 把tensor或数组转换成图像 2 3 plt.ion() #交互模式，默认是交互模式，可以不写 4 5 def imshow(tensor, title=None): 6 image = tensor.cpu().clone() # we clone the tensor to not do changes on it 7 image = image.squeeze(0) 8 9 image = unloader(image) #tensor转换成图像 10 plt.imshow(image) 11 if title is not None: 12 plt.title(title) 13 plt.pause(1) #可以去掉看看，只是延迟显示作用 14 15 plt.figure() 16 imshow(img[8], title='Image') 17 imshow(img[9], title='Image')
关于torchvision.transforms.ToPILImage()见：https://blog.csdn.net/qq_37385726/article/details/81811466
关于plt.ion()和plt.ioff()见：https://blog.csdn.net/SZuoDao/article/details/52973621
Tip：查看对应文件夹的图片label；
1 print(all_imgs.class_to_idx) # {'ants': 0, 'bees': 1}
查看所有图片的路径和对应的label；
1 print(all_imgs.imgs)
输出列表的其中一个元素为('./hymenoptera_data\\train\\ants\\0013035.jpg', 0)
5.2数据预处理（把训练集和验证集分batch转换成迭代器）
1 data_transforms = { 2 "train": transforms.Compose([ 3 transforms.RandomResizedCrop(input_size), 4 transforms.RandomHorizontalFlip(), 5 transforms.ToTensor(), 6 transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 7 ]), 8 "val": transforms.Compose([ 9 transforms.Resize(input_size), 10 transforms.CenterCrop(input_size), 11 transforms.ToTensor(), 12 transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 13 ]), 14 } 15 print("Initializing Datasets and Dataloaders...") 16 17 # Create training and validation datasets 18 image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']} 19 # Create training and validation dataloaders 20 dataloaders_dict = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True, num_workers=0) for x in ['train', 'val']} #把迭代器存放到字典里作为value，key是train和val，后面调用key即可 21 22 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
测试一下：
1 inputs, labels=next(iter(dataloaders_dict["train"])) #一个batch 2 print(inputs.shape) #torch.Size([32, 3, 224, 224]) 3 print(labels) 4 # tensor([0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 5 # 1, 0, 0, 0, 1, 1, 1, 0]) 6 7 for inputs, labels in dataloaders_dict["train"]: 8 print(labels.size()) #最后一个batch不足32 9 # torch.Size([32]) 10 # torch.Size([32]) 11 # torch.Size([32]) 12 # torch.Size([32]) 13 # torch.Size([32]) 14 # torch.Size([32]) 15 # torch.Size([32]) 16 # torch.Size([20])
5.3加载resnet模型并修改全连接层
1 model_name = "resnet" 2 num_classes = 2 3 num_epochs = 10 4 feature_extract = True #只更新修改的层 5 6 def set_parameter_requires_grad(model, feature_extracting): 7 if feature_extracting: 8 for param in model.parameters(): 9 param.requires_grad = False #提取的参数梯度不更新 10 11 def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True): 12 if model_name == "resnet": 13 model_ft = models.resnet18(pretrained=use_pretrained) #如果True，从imagenet上返回预训练的模型和参数 14 set_parameter_requires_grad(model_ft, feature_extract) #提取的参数梯度不更新 15 16 num_ftrs = model_ft.fc.in_features #model_ft.fc是resnet的最后全连接层，(fc): Linear(in_features=512, out_features=1000, bias=True)，num_ftrs值为512 17 model_ft.fc = nn.Linear(num_ftrs, num_classes) #out_features=1000 改为 num_classes=2 18 19 input_size = 224 #resnet18网络输入图片维度是224，resnet34，50，101，152也是 20 21 return model_ft, input_size 22 23 model_ft, input_size = initialize_model(model_name, num_classes, feature_extract, use_pretrained=True) 24 # print(model_ft)
看下有哪些参数：
1 for name,param in model_ft.named_parameters(): 2 print(name)
1 conv1.weight 2 bn1.weight 3 bn1.bias 4 layer1.0.conv1.weight 5 layer1.0.bn1.weight 6 layer1.0.bn1.bias 7 layer1.0.conv2.weight 8 layer1.0.bn2.weight 9 layer1.0.bn2.bias 10 layer1.1.conv1.weight 11 layer1.1.bn1.weight 12 layer1.1.bn1.bias 13 layer1.1.conv2.weight 14 layer1.1.bn2.weight 15 layer1.1.bn2.bias 16 layer2.0.conv1.weight 17 layer2.0.bn1.weight 18 layer2.0.bn1.bias 19 layer2.0.conv2.weight 20 layer2.0.bn2.weight 21 layer2.0.bn2.bias 22 layer2.0.downsample.0.weight 23 layer2.0.downsample.1.weight 24 layer2.0.downsample.1.bias 25 layer2.1.conv1.weight 26 layer2.1.bn1.weight 27 layer2.1.bn1.bias 28 layer2.1.conv2.weight 29 layer2.1.bn2.weight 30 layer2.1.bn2.bias 31 layer3.0.conv1.weight 32 layer3.0.bn1.weight 33 layer3.0.bn1.bias 34 layer3.0.conv2.weight 35 layer3.0.bn2.weight 36 layer3.0.bn2.bias 37 layer3.0.downsample.0.weight 38 layer3.0.downsample.1.weight 39 layer3.0.downsample.1.bias 40 layer3.1.conv1.weight 41 layer3.1.bn1.weight 42 layer3.1.bn1.bias 43 layer3.1.conv2.weight 44 layer3.1.bn2.weight 45 layer3.1.bn2.bias 46 layer4.0.conv1.weight 47 layer4.0.bn1.weight 48 layer4.0.bn1.bias 49 layer4.0.conv2.weight 50 layer4.0.bn2.weight 51 layer4.0.bn2.bias 52 layer4.0.downsample.0.weight 53 layer4.0.downsample.1.weight 54 layer4.0.downsample.1.bias 55 layer4.1.conv1.weight 56 layer4.1.bn1.weight 57 layer4.1.bn1.bias 58 layer4.1.conv2.weight 59 layer4.1.bn2.weight 60 layer4.1.bn2.bias 61 fc.weight 62 fc.bias
5.4查看需要更新的参数、定义优化器
1 model_ft = model_ft.to(device) 2 3 print("Params to learn:") 4 if feature_extract: 5 params_to_update = [] #需要更新的参数存放在此 6 for name,param in model_ft.named_parameters(): 7 if param.requires_grad == True: #这里全连接层之前的层param.requires_grad == Flase，后面加的全连接层param.requires_grad == True 8 params_to_update.append(param) 9 print("\t",name) 10 else: #否则，所有的参数都会更新 11 for name,param in model_ft.named_parameters(): 12 if param.requires_grad == True: 13 print("\t",name) 14 15 optimizer_ft = optim.SGD(params_to_update, lr=0.001, momentum=0.9) #定义优化器 16 criterion = nn.CrossEntropyLoss() #定义损失函数
执行结果：
1 Params to learn: 2 fc.weight 3 fc.bias
5.5定义训练模型
训练和测试合在一起了
1 def train_model(model, dataloaders, criterion, optimizer, num_epochs=5): 2 since = time.time() 3 val_acc_history = [] 4 best_model_wts = copy.deepcopy(model.state_dict()) #深拷贝上面resnet模型参数 5 best_acc = 0. 6 7 for epoch in range(num_epochs): 8 print("Epoch {}/{}".format(epoch, num_epochs-1)) 9 print("-"*10) 10 11 for phase in ["train", "val"]: 12 running_loss = 0. 13 running_corrects = 0. 14 if phase == "train": 15 model.train() 16 else: 17 model.eval() 18 19 for inputs, labels in dataloaders[phase]: 20 inputs = inputs.to(device) #inputs.shape = torch.Size([32, 3, 224, 224]) 21 labels = labels.to(device) #labels.shape = torch.Size([32]) 22 23 with torch.autograd.set_grad_enabled(phase=="train"): #torch.autograd.set_grad_enabled梯度管理器，可设置为打开或关闭，phase=="train"值为True或False 24 outputs = model(inputs) #outputs.shape = torch.Size([32, 10]) 25 loss = criterion(outputs, labels) 26 27 _, preds = torch.max(outputs, 1) #返回每一行最大的数和索引，prds的位置是索引的位置，或者preds = outputs.argmax(dim=1) 28 29 if phase == "train": 30 optimizer.zero_grad() 31 loss.backward() 32 optimizer.step() 33 34 running_loss += loss.item() * inputs.size(0) #交叉熵损失函数是平均过的 35 running_corrects += torch.sum(preds.view(-1) == labels.view(-1)).item() #.view(-1)展开到一维，并自己计算 36 37 38 epoch_loss = running_loss / len(dataloaders[phase].dataset) 39 epoch_acc = running_corrects / len(dataloaders[phase].dataset) 40 41 print("{} Loss: {} Acc: {}".format(phase, epoch_loss, epoch_acc)) 42 if phase == "val" and epoch_acc > best_acc: 43 best_acc = epoch_acc 44 best_model_wts = copy.deepcopy(model.state_dict()) #模型变好，就拷贝更新后的模型参数 45 46 if phase == "val": 47 val_acc_history.append(epoch_acc) #记录每个epoch验证集的准确率 48 49 print() 50 51 time_elapsed = time.time() - since 52 print("Training compete in {}m {}s".format(time_elapsed // 60, time_elapsed % 60)) 53 print("Best val Acc: {}".format(best_acc)) 54 55 model.load_state_dict(best_model_wts) #把最新的参数复制到model中 56 return model, val_acc_history
调用一下：
1 # Train and evaluate 2 model_ft, ohist = train_model(model_ft, dataloaders_dict, criterion, optimizer_ft, num_epochs=num_epochs)
执行结果：
1 Epoch 0/9 2 ---------- 3 train Loss: 0.6792222046461261 Acc: 0.5491803278688525 4 val Loss: 0.6042880532788295 Acc: 0.6797385620915033 5 6 Epoch 1/9 7 ---------- 8 train Loss: 0.5260111435514981 Acc: 0.7254098360655737 9 val Loss: 0.4606282469493891 Acc: 0.8366013071895425 10 11 Epoch 2/9 12 ---------- 13 train Loss: 0.3967628830769023 Acc: 0.8811475409836066 14 val Loss: 0.33848777238060446 Acc: 0.9084967320261438 15 16 Epoch 3/9 17 ---------- 18 train Loss: 0.3282915304918758 Acc: 0.8852459016393442 19 val Loss: 0.2889009240795584 Acc: 0.9150326797385621 20 21 Epoch 4/9 22 ---------- 23 train Loss: 0.2884497346936679 Acc: 0.9139344262295082 24 val Loss: 0.2592071742793314 Acc: 0.9215686274509803 25 26 Epoch 5/9 27 ---------- 28 train Loss: 0.26097508507673856 Acc: 0.8975409836065574 29 val Loss: 0.24648226690448188 Acc: 0.9150326797385621 30 31 Epoch 6/9 32 ---------- 33 train Loss: 0.2270883551386536 Acc: 0.9385245901639344 34 val Loss: 0.23724308084039128 Acc: 0.9150326797385621 35 36 Epoch 7/9 37 ---------- 38 train Loss: 0.20939802268489463 Acc: 0.9467213114754098 39 val Loss: 0.23119436038864982 Acc: 0.9150326797385621 40 41 Epoch 8/9 42 ---------- 43 train Loss: 0.21726583628380886 Acc: 0.9180327868852459 44 val Loss: 0.221941787919967 Acc: 0.9150326797385621 45 46 Epoch 9/9 47 ---------- 48 train Loss: 0.19981201164057996 Acc: 0.9385245901639344 49 val Loss: 0.2194489004954793 Acc: 0.9150326797385621 50 51 Training compete in 0.0m 37.531731367111206s 52 Best val Acc: 0.9215686274509803
不使用预训练模型，所有参数都参加训练
1 scratch_model,_ = initialize_model(model_name, 2 num_classes, 3 feature_extract=False, #所有参数都训练 4 use_pretrained=False)# 不要imagenet的参数 5 scratch_model = scratch_model.to(device) 6 scratch_optimizer = optim.SGD(scratch_model.parameters(), lr=0.001, momentum=0.9) 7 scratch_criterion = nn.CrossEntropyLoss() 8 _,scratch_hist = train_model(scratch_model, dataloaders_dict, scratch_criterion, scratch_optimizer, num_epochs=num_epochs)
执行结果：
1 Epoch 0/9 2 ---------- 3 train Loss: 0.8219864260954935 Acc: 0.5450819672131147 4 val Loss: 0.695075081454383 Acc: 0.5424836601307189 5 6 Epoch 1/9 7 ---------- 8 train Loss: 0.7687111731435432 Acc: 0.5040983606557377 9 val Loss: 0.7560343270987467 Acc: 0.46405228758169936 10 11 Epoch 2/9 12 ---------- 13 train Loss: 0.6719165463916591 Acc: 0.5819672131147541 14 val Loss: 0.6266151779617359 Acc: 0.6013071895424836 15 16 Epoch 3/9 17 ---------- 18 train Loss: 0.6333085493963273 Acc: 0.6147540983606558 19 val Loss: 0.6167325887804717 Acc: 0.6601307189542484 20 21 Epoch 4/9 22 ---------- 23 train Loss: 0.5848636265660896 Acc: 0.6434426229508197 24 val Loss: 0.5851604537247053 Acc: 0.673202614379085 25 26 Epoch 5/9 27 ---------- 28 train Loss: 0.5586931158284671 Acc: 0.6844262295081968 29 val Loss: 0.5588414598913753 Acc: 0.7450980392156863 30 31 Epoch 6/9 32 ---------- 33 train Loss: 0.5667437266130917 Acc: 0.680327868852459 34 val Loss: 0.5625949673403322 Acc: 0.6928104575163399 35 36 Epoch 7/9 37 ---------- 38 train Loss: 0.5877759007156872 Acc: 0.639344262295082 39 val Loss: 0.6133050057623122 Acc: 0.7254901960784313 40 41 Epoch 8/9 42 ---------- 43 train Loss: 0.581167609965215 Acc: 0.680327868852459 44 val Loss: 0.5674625876682257 Acc: 0.7254901960784313 45 46 Epoch 9/9 47 ---------- 48 train Loss: 0.5575579023752056 Acc: 0.6680327868852459 49 val Loss: 0.5709076671818503 Acc: 0.6993464052287581 50 51 Training compete in 0.0m 50.611786127090454s 52 Best val Acc: 0.7450980392156863
演示不同训练模型的性能
1 plt.title("Validation Accuracy vs. Number of Training Epochs") 2 plt.xlabel("Training Epochs") 3 plt.ylabel("Validation Accuracy") 4 plt.plot(range(1,num_epochs+1),ohist,label="Pretrained") 5 plt.plot(range(1,num_epochs+1),scratch_hist,label="Scratch") 6 plt.ylim((0,1.)) 7 plt.xticks(np.arange(1, num_epochs+1, 1.0)) 8 plt.legend() 9 plt.show()
