写一个分类网络知识蒸馏的代码
时间: 2023-12-01 21:10:35 浏览: 36
一个简单的分类网络知识蒸馏的代码可能如下:import torch# 定义一个分类网络
class ClassificationNetwork(torch.nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(ClassificationNetwork, self).__init__()
self.input_layer = torch.nn.Linear(input_dim, hidden_dim)
self.hidden_layer = torch.nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = self.input_layer(x)
x = torch.nn.functional.relu(x)
x = self.hidden_layer(x)
return x# 定义一个知识蒸馏层
class KnowledgeDistillationLayer(torch.nn.Module):
def __init__(self, teacher_model):
super(KnowledgeDistillationLayer, self).__init__()
self.teacher_model = teacher_model
def forward(self, x, teacher_logits):
student_logits = self.teacher_model(x)
distillation_loss = torch.nn.functional.mse_loss(student_logits, teacher_logits)
return distillation_loss# 定义一个模型
model = ClassificationNetwork(input_dim, hidden_dim, output_dim)# 加载教师模型
teacher_model = torch.load("teacher_model.pt")# 定义知识蒸馏层
knowledge_distillation_layer = KnowledgeDistillationLayer(teacher_model)# 将知识蒸馏层加入模型
model.add_module("Knowledge Distillation Layer", knowledge_distillation_layer)# 训练模型
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
for x, y in train_loader:
teacher_logits = teacher_model(x)
output = model(x, teacher_logits)
loss = torch.nn.functional.cross_entropy(output, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()