5.2 项目实战-猫狗分类(引入vgg16模型,只训练全连接层)
# 只训练全连接层
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms, models
from torch.utils.data import DataLoader
# 数据预处理,增强数据
transform = transforms.Compose([
transforms.RandomResizedCrop(224),# 对图像进行随机的crop以后再resize成固定大小
transforms.RandomRotation(20), # 随机旋转角度
transforms.RandomHorizontalFlip(p=0.5), # 随机水平翻转
transforms.ToTensor()
])
# 读取数据
root = '项目实战/猫狗识别/image'
train_dataset = datasets.ImageFolder(root + '/train', transform)
test_dataset = datasets.ImageFolder(root + '/test', transform)
# 导入数据
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=8, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=8, shuffle=True)
classes = train_dataset.classes
classes_index = train_dataset.class_to_idx
print(classes)
print(classes_index)
## ['cat', 'dog']
## {'cat': 0, 'dog': 1}
model = models.vgg16(pretrained = True)
# 前面说过了,如果我们想只训练模型的全连接层,就要先设置参数不更新!
for param in model.parameters():
param.requires_grad = False
# 构建新的全连接层
model.classifier = torch.nn.Sequential(torch.nn.Linear(25088, 100),
torch.nn.ReLU(),
torch.nn.Dropout(p=0.5),
torch.nn.Linear(100, 2))
LR = 0.0003
# 定义代价函数
entropy_loss = nn.CrossEntropyLoss()
# 定义优化器
optimizer = optim.Adam(model.parameters(), LR)
def train():
model.train()
for i, data in enumerate(train_loader):
# 获得数据和对应的标签
inputs, labels = data
# 获得模型预测结果,(64,10)
out = model(inputs)
# 交叉熵代价函数out(batch,C),labels(batch)
loss = entropy_loss(out, labels)
# 梯度清0
optimizer.zero_grad()
# 计算梯度
loss.backward()
# 修改权值
optimizer.step()
def test():
model.eval()
correct = 0
for i, data in enumerate(test_loader):
# 获得数据和对应的标签
inputs, labels = data
# 获得模型预测结果
out = model(inputs)
# 获得最大值,以及最大值所在的位置
_, predicted = torch.max(out, 1)
# 预测正确的数量
correct += (predicted == labels).sum()
print("Test acc: {0}".format(correct.item() / len(test_dataset)))
correct = 0
for i, data in enumerate(train_loader):
# 获得数据和对应的标签
inputs, labels = data
# 获得模型预测结果
out = model(inputs)
# 获得最大值,以及最大值所在的位置
_, predicted = torch.max(out, 1)
# 预测正确的数量
correct += (predicted == labels).sum()
print("Train acc: {0}".format(correct.item() / len(train_dataset)))
# 训练3个周期
for epoch in range(0, 3):
print('epoch:',epoch)
train()
test()
## epoch: 0
## Test acc: 0.855
## Train acc: 0.865
## epoch: 1
## Test acc: 0.785
## Train acc: 0.8475
## epoch: 2
## Test acc: 0.87
## Train acc: 0.865
torch.save(model.state_dict(), 'model/猫狗分类只训练全连接层')
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