基于paddle2.0写一个用于知识蒸馏的代码

好的，下面是一个基于PaddlePaddle 2.0的知识蒸馏代码示例：

import paddle
import paddle.nn.functional as F
from paddle.vision.transforms import Compose, Normalize
from paddle.vision.datasets import MNIST
from paddle.io import DataLoader

# 定义数据预处理
transform = Compose([Normalize(mean=[127.5], std=[127.5], data_format='CHW')])

# 加载训练集和测试集
train_dataset = MNIST(mode='train', transform=transform)
test_dataset = MNIST(mode='test', transform=transform)

# 定义超参数
batch_size = 64
learning_rate = 0.001
epochs = 10

# 定义模型
class LeNet(paddle.nn.Layer):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = paddle.nn.Conv2D(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)
        self.pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
        self.conv2 = paddle.nn.Conv2D(in_channels=6, out_channels=16, kernel_size=5, stride=1, padding=0)
        self.pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
        self.fc1 = paddle.nn.Linear(in_features=16*5*5, out_features=120)
        self.fc2 = paddle.nn.Linear(in_features=120, out_features=84)
        self.fc3 = paddle.nn.Linear(in_features=84, out_features=10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = self.pool2(x)
        x = paddle.flatten(x, start_axis=1, stop_axis=-1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        x = F.relu(x)
        x = self.fc3(x)
        return x

# 定义教师模型
teacher_model = LeNet()
teacher_model.train()
# 使用MNIST训练教师模型
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
optimizer = paddle.optimizer.Adam(parameters=teacher_model.parameters(), learning_rate=learning_rate)
for epoch in range(epochs):
    for batch_id, data in enumerate(train_loader()):
        x_data = data[0]
        y_data = paddle.to_tensor(data[1])
        y_data = F.one_hot(y_data, num_classes=10)
        y_data = paddle.reshape(y_data, [-1, 10])
        logits = teacher_model(x_data)
        loss = F.softmax_with_cross_entropy(logits, y_data)
        avg_loss = paddle.mean(loss)
        if batch_id % 100 == 0:
            print("epoch: {}, batch_id: {}, loss is: {}".format(epoch, batch_id, avg_loss.numpy()))
        avg_loss.backward()
        optimizer.step()
        optimizer.clear_grad()

# 定义学生模型
student_model = LeNet()
student_model.train()
# 定义蒸馏损失函数
def distillation_loss(T, teacher_logits, student_logits):
    T = paddle.to_tensor(T)
    teacher_soft = F.softmax(teacher_logits / T, axis=-1)
    student_soft = F.softmax(student_logits / T, axis=-1)
    loss = F.kl_div(teacher_soft, student_soft, reduction='batchmean') * T * T
    return loss

# 定义蒸馏训练函数
def train_distillation(model, T, train_loader, optimizer):
    for batch_id, data in enumerate(train_loader()):
        x_data = data[0]
        y_data = paddle.to_tensor(data[1])
        y_data = F.one_hot(y_data, num_classes=10)
        y_data = paddle.reshape(y_data, [-1, 10])
        logits = model(x_data)
        teacher_logits = teacher_model(x_data)
        loss = distillation_loss(T, teacher_logits, logits) + F.softmax_with_cross_entropy(logits, y_data)
        avg_loss = paddle.mean(loss)
        if batch_id % 100 == 0:
            print("batch_id: {}, loss is: {}".format(batch_id, avg_loss.numpy()))
        avg_loss.backward()
        optimizer.step()
        optimizer.clear_grad()

# 使用蒸馏训练学生模型
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
optimizer = paddle.optimizer.Adam(parameters=student_model.parameters(), learning_rate=learning_rate)
T = 5.0
for epoch in range(epochs):
    train_distillation(student_model, T, train_loader, optimizer)

# 评估模型
test_loader = DataLoader(test_dataset, batch_size=batch_size)
student_model.eval()
acc = paddle.metric.accuracy()
for batch_id, data in enumerate(test_loader()):
    x_data = data[0]
    y_data = paddle.to_tensor(data[1])
    logits = student_model(x_data)
    pred = F.softmax(logits)
    acc.update(pred, y_data)
print("Test accuracy: {:.2f}%".format(acc.accumulate() * 100))

以上代码演示了如何使用PaddlePaddle 2.0实现知识蒸馏。其中，我们先使用MNIST数据集训练一个教师模型，然后使用蒸馏训练方法训练一个学生模型。在蒸馏训练中，我们使用教师模型的输出作为软标签，同时加入交叉熵损失，最终得到的损失函数就是蒸馏损失函数。使用蒸馏训练得到的学生模型可以达到与教师模型相近的准确率，同时具有更小的模型体积和计算开销。