Dont know how to apply centerloss to my own project

[1653091]

I want to use ResNet50 as my model and change Fully Connection layer to classify images.

#Create model:
model = models.resnet50(pretrained=True)
fc_inputs = model.fc.in_features
class FClayer(nn.Module):
def init(self):
super(FClayer, self).init()
self.fc1 = nn.Linear(fc_inputs, 2)
self.relu1 = nn.ReLU()
self.fc2 = nn.Linear(2, num_classes)
def forward(self, x):
x = self.relu1(self.fc1(x))
y = self.fc2(x)
return x, y
model.fc = FClayer()
...
#In train function:
features, outputs = model(inputs)
alpha = 1
loss = loss_criterion(outputs, labels) + (loss_criterion_cent(features, labels) * alpha)
optimizer.zero_grad()
optimizer_cent.zero_grad()
loss.backward()
optimizer.step()
for param in loss_criterion_cent.parameters():
param.grad.data *= (1./alpha)
optimizer_cent.step()

I dont know why when i was training, at the very first epochs, the accuracy had increased, but soon later, nothing happended. Here is my accuracy plot curve chart:

Do I have something wrong?

[shijianjian]

Your question is quite ambiguous. But I think it was probably caused by a high LR or a high loss weight which makes gradient descent optimization failed.

[RaduFilip16]

Have you found a solution to your problem?