Comments (12)
@vsoch @nick-youngblut Just uploaded 0.6.1 to PyPI, containing your changes. Let me know how that works for you.
from nolearn.
In regards to this, I'm trying to add nolearn to conda-forge, and nolearn
depends on the gdbn
package, which itself depends on gnumpy
, which is py2 only and hasn't been maintained since 2013. Maybe there's a way to remove the gdbn
dependency?
from nolearn.
Hmm, well gnumpy could be modified for Python 3, no? In that the source code is available for gdbn and gnumpy, we might just want to contact the authors and get permission to do so. If you look at the bio here he also recommends using a different library provided by Google, jax.
from nolearn.
hey @georgedahl! I'm hoping it's okay that we are pinging you here - there is some interest to use nolearn, and the dependencies (gnumpy
and gdbn
) are not being maintained. Could we talk about some options for moving forward? Here are some ideas:
- discuss how Jax (you mentioned in your bio) could be used instead of nolearn entirely
- use an entirely different library for
gdbn
- it looks like it's providing activation functions, and a build function for a net. - create a GitHub release for
gdbn
that includes a refactoredgnumpy
updated for Python 3 - update nolearn to use it. - something else!
Thanks for your help! I'm not actively using this library, but I'm an RSE at Stanford and many of our researchers might, so I'm happy to help however I can.
from nolearn.
@dnouri AFAICT, gdbn is not required to use nolearn with lasagne. Would it make sense to have a nolearn release that gets rid of the gdbn dependency?
from nolearn.
There is even commentary in this module to use lasagne, as dbn isn't supported. How about instead of having as an install requires, move to
# Fix this to whatever version is needed
GDBN_REQUIRES = (
('gdbn', {'min_version': None}),
)
...
extras_require={
'gbdn': [GDBN_REQUIRES],
}
Then default install won't include it, but if you do something like pip install nolearn[gdbn]
then you can get it.
from nolearn.
Sorry for the late reply. Do you mind making a pull request that contains your suggested change?
from nolearn.
Yes I can definitely give it a try!
from nolearn.
here you go #337
from nolearn.
I'm good to close this issue if @nick-youngblut is also happy! I am not actively working on the analysis I was previously, but I'll definitely come back and give it a try in the future.
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I'm also fine with closing the issue
from nolearn.
Make it so! /insert Picard
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Related Issues (20)
- RememberBestWeights does not honor the verbose parameter HOT 2
- A replayable fit() method - diff/patch attached HOT 1
- remove('trainable') Lasagne's command doesn't work in nolearn HOT 6
- flip_filters and pad parameter not used by NeuralNet's class HOT 5
- OSError: could not read bytes when trying to fetch mldata HOT 2
- CUDA error, possibly related to network size? HOT 2
- Trained on GPU, inference on CPU doesn't make sense
- Install nolearn with Lasagne dependance not working HOT 2
- Bug in calculating average scores
- nolearn is not installing
- Bug when using Lasagne `mask_input` parameter
- 'NeuralNet' object has no attribute 'layers_' HOT 1
- Weights sum up to zero
- Future issue with sklearn.cross_validation
- Dependency on both backends in requirements.txt switches off GPU support HOT 3
- Enable to reproduce the last value of trainning when predicting CNN
- enable to reproduce loss value of training when predicting CNN HOT 1
- TypeError: Failed to instantiate <class 'lasagne.layers.pool.MaxPool2DLayer'> with args {'name': 'pool1', 'ds': (2, 2), 'incoming': <lasagne.layers.conv.Conv2DLayer object at 0x7ff765fa29e8>}. Maybe parameter names have changed?
- nolearn now on conda-forge HOT 1
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