Comments (2)
I recently added regularization capabilities to the Neurolab library. It now
supports l2 and l1 regularization of the network weights, biases, or both.
You can find neurolab source code with my modifications here:
https://github.com/kwecht/NeuroLab/code
I am now hoping to incorporate these changes into the standard Neurolab
distribution.
Original comment by [email protected]
on 22 Dec 2014 at 9:12
from neurolab.
I added regularization to Neurolob v 0.3.5.
Example
http://nbviewer.ipython.org/urls/neurolab.googlecode.com/svn/trunk/example/Neuro
lab%20-%20Regularization.ipynb
Original comment by [email protected]
on 26 Jan 2015 at 4:35
- Changed state: Fixed
from neurolab.
Related Issues (20)
- 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
- Citing neurolab HOT 3
- 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
- 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
- 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
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from neurolab.