-
Notifications
You must be signed in to change notification settings - Fork 61
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 can not decrese #9
Comments
I think it would be better if you could use some kind of plot (e.g. tensorboard ) to visualize the convergence and test some samples, also try to do some parameter tunning. If the model still couldn't generate satisfactory samples, probably adding more layers and use more advance structures may help. |
than you very much! |
Not sure what the problem is, I guess it is very likely becaues of version issues. You can set a stop at stft to see what is going wrong.
On 11/07/2018 11:12, yuanyuan0209 wrote:
But I am also having another problem and want to ask if you solved it. I have tried different approaches but all to no available
.
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub, or mute the thread.
|
it occurs when i use your code .it likes below but i test the code l'ibrosa; it does not have problem。can you give me some advice |
@yuanyuan0209 is your loss is decreased? can your share your successful results? |
hi, have you solve the problem? I meet the same question |
I meet the same problem. |
i have no idea, so I changed the code to reproduce the model
发自我的 iPad
…------------------ Original ------------------
From: ZBang <[email protected]>
Date: Tue,Jun 16,2020 1:39 PM
To: zhr1201/CNN-for-single-channel-speech-enhancement <[email protected]>
Cc: lvyilan23 <[email protected]>, Comment <[email protected]>
Subject: Re: [zhr1201/CNN-for-single-channel-speech-enhancement] The loss can not decrese (#9)
I meet the same problem.
if you hava some ideas,please tell me
thanks!
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub, or unsubscribe.
|
Thank you for sharing!
![a](https://user-images.githubusercontent.com/32134929/47974483-c8278a80-e0e4-11e8-8432-75897c95494a.png)
I used the 4620 utterances from the training set of the TIMIT corpus as train set, and 119 types noise as train set. Besides, the 10 utterances from the test set of the TIMIT corpus and other 10 types noise as validation set. I have trained this model for more than 10 hours, but the loss seems to be invariant.
I do not know why. I will be honoured if you help me.
The text was updated successfully, but these errors were encountered: