[英]Pytorch: How to train a network with two loss functions?
我想先用重建损失预训练一个网络,然后通过交叉熵损失对其进行微调。 但似乎我必须在这两个阶段定义两个网络。 如何实现?
class Net():
def __init__(self,pretrain):
self.pretrain = pretrain
def encoder(self,x):
# do something here
return x
def decoder(self,x):
# do something here
return x
def forward(self):
e_x = self.encoder(x)
if self.pretrain:
return decoder(e_x)
else:
return e_x
def train(x,y):
pretrain = True
if pretrain:
network = Net(pretrain=True)
output = network(x)
loss = MSE(x,output)
else:
network = Net(pretrain=False)
output = network(x)
loss = crossentropy(output,y)
loss.backward()
您可以通过简单地定义两个损失函数来实现这一点,而 loss.backward 将适用于 go。 请参阅此处的相关讨论
MSE = torch.nn.MSELoss()
crossentropy = torch.nn.CrossEntropyLoss()
def train(x,y):
pretrain = True
if pretrain:
network = Net(pretrain=True)
output = network(x)
loss = MSE(x,output)
else:
network = Net(pretrain=False)
output = network(x)
loss = crossentropy(output,y)
loss.backward()
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.