-
Notifications
You must be signed in to change notification settings - Fork 11.7k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
The loss of Step 2 #27
Comments
Doesn't seem normal. Does it go down afterwards? You can try a smaller learning rate and see if that improves the training. |
The rpn_loss and mrcnn_loss are normal while the loss(l1_loss) is jumps a high value(like epoch 40: loss = 1.9,while epoch 41:loss =13.1,other loss are normal).And I try a smaller learing rate(lr = 0.001,0.0001),but it is also in this situation. |
Even I'm facing the same issue after first stage training. Not yet
completed the second stage.
…On Tue, Nov 7, 2017, 9:47 PM Frédéric Branchaud-Charron < ***@***.***> wrote:
Yeah I have a similar problem. All the losses are small but this one.
[image: selection_115]
<https://user-images.githubusercontent.com/8976546/32504072-ea0da892-c3ac-11e7-94ed-13be7962b0e2.png>
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub
<#27 (comment)>,
or mute the thread
<https://github.com/notifications/unsubscribe-auth/AHMgs1CzfZlkJg-F2od8JCg1SMzaVVOkks5s0IKfgaJpZM4QTD8g>
.
|
@Dref360 Did you change anything at around step 7? The main losses to pay attention to are the individual losses like rpn_class_loss, mrcnn_bbox_loss, ..etc. You'd want to see nice graphs on those like the ones posted by @Dref360 above. The total loss is the sum of the individual losses plus the It might be a good idea to divide the L1 regularization by the number of weights to get a mean rather than a sum, and that should remove that unexpected behavior. I'll look into doing that this weekend. |
Gamma and beta parameters shouldn't be included in regularization loss. (batch norm isn't updated by the backprop) |
@leicaand Good catch. I pushed the fix. Thanks. I also pushed an update to divide the weight regularization by the number of weights so the loss is the mean of the L2 rather than the sum. This removes the confusing jump in the total loss in the graphs. |
I am confused about this issue. |
Batchnorm has 4 different weights, running mean and std is updated by moving average operation while beta and gamma are updated via gradient. If you want to skip those aren't updated during bp, you should exclude 'moving_mean' and 'moving_variance' but not 'beta' and 'gamma' |
During my training process, the loss of step 1 suddenly jumps from a low value (like 2.1) to a high value (like 13) in step 2. is that a normal situation?
The text was updated successfully, but these errors were encountered: