知识蒸馏:教师学生模型,通过软目标实现从教师到学生模型的知识传递

单独训练教师模型
教师模型采用三个全连接层,隐藏层的神经元为1200–>2400–>1200,为防止过拟合,加入Dropout
模型训练采用交叉熵损失,Adam优化器,学习率为1e-4,训练批次epoch=10,batch-size=64
import torch
from torch import nn
from tqdm import tqdm
import torchvision
from torchvision import transforms
from torch.utils.data import DataLoader
class TeacherModel(nn.Module):
def __init__(self, in_channel=1, num_classes=10):
super(TeacherModel, self).__init__()
self.relu = nn.ReLU()
self.fc1 = nn.Linear(784, 1200)
self.fc2 = nn.Linear(1200, 2400)
self.fc3 = nn.Linear(2400, 1200)
self.fc4 = nn.Linear(1200, num_classes)
self.dropout = nn.Dropout(p=0.5)
def forward(self, x):
x = x.view(-1, 784)
x = self.fc1(x)
x = self.dropout(x)
x = self.relu(x)
x = self.fc2(x)
x = self.dropout(x)
x = self.relu(x)
x = self.fc3(x)
x = self.dropout(x)
x = self.relu(x)
x = self.fc4(x)
return x
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
train_dataset = torchvision.datasets.MNIST(
root = "./dataset/",
train=True,
transform=transforms.ToTensor(),
download=True
)
test_dataset = torchvision.datasets.MNIST(
root = "./dataset/",
train = False,
transform=transforms.ToTensor(),
download=True
)
train_loader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)
if __name__