Comments (3)
Glad Neurolab can help you
I dont know about papers.
But some people use neurolab in his research.
I would be grateful if you would inform me a link to your publication
You may use groups for your questions
https://groups.google.com/forum/#!forum/py-neurolab
Original comment by [email protected]
on 4 May 2014 at 9:45
- Changed state: Done
from neurolab.
Hello,
You can read my publication in
http://wsl.softwarelivre.org/2014/cantor-ambiente-integrado-de-desenvolvimento-v
oltado-a-computacao-cientifica-utilizando-python-saraiva-wsl-2014-.pdf the
language is in Portuguese.
The paper subject is Cantor, a IDE to scientific programming using several
mathematical software backends. In this paper I describe Python backend. I used
neurolab to show a typical scientific application using Python and Cantor.
Let me know if you will write a paper about neurolab. If you desire, I can help
you with the writing.
(Some day I will try to implement the Levenberg-Marquardt training algorithm to
neurolab) =)
Original comment by [email protected]
on 31 May 2014 at 5:39
from neurolab.
Hi!
Nice work! But, I have not plans write publication. May be later...
About Levenberg-Marquardt, I thought about it. Scipy has implement of this
algorithm in
scipy.optimize.leastsq (wrapper around MINPACK’s lmdif and lmder). Most easy
and powerfull way is use this implement (like neurolab.train.train_bgfs use
scipy.optimize.fmin_bfgs). But need function for calculate Jacobian
(http://en.wikipedia.org/wiki/Jacobian_matrix_and_determinant) for multilayer
network (like neurolab.tool.ff_grad for calculate gradient of net with back
propogation method).
It's not easy, but if you will write this function, I add Levenberg-Marquardt
train support.
Let me know, If you will find some literature abut calculate Jacobian fof
multilayer network.
Good luck.
Original comment by [email protected]
on 2 Jun 2014 at 4:59
from neurolab.
Related Issues (20)
- Failing to add Levenberg-Marquardt-training HOT 5
- Problem when using a modified network property HOT 1
- feedforward network not learning HOT 12
- PureLin in outputl layer does not work HOT 2
- Setup issue HOT 3
- strange result HOT 13
- Cannot install successfully for Python 3.2 HOT 5
- Learning Rate is not present HOT 2
- Multiprocessing can not pickle unbound function HOT 6
- cannot save nn HOT 2
- output norm and different resutls HOT 1
- Parameters Ignored in Training Function Construction HOT 2
- Training Fails for Non-Default Activation Functions HOT 1
- Linear Activation Leads to NaN minmax HOT 2
- Support for weight decay (regularization parameter) HOT 2
- Missing newelm example in doc HOT 3
- fmin_bfgs() got an unexpected keyword argument 'lr', train func does not take lr as parameter HOT 2
- Added regularization and cross-entropy error to Neurolab HOT 5
- 0.3.5 version not available in pypi HOT 1
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