They use theano, but I was talking about sklearn-theano, which is
something else ;)
On 05/07/2015 01:40 PM, zhenjiang zech xu wrote:
Just checked. It is the other way around. lasagne and keras uses theano.
On Thu, May 7, 2015 at 8:50 AM, Andreas Mueller <t3k...@gmail.com
<mailto:t3k...@gmail.com>> wrote:
There is also keras https://github.com/fchollet/keras and lasagne
https://github.com/Lasagne/Lasagne
I think sklearn-theano wraps some of lasagne?
On 05/07/2015 10:34 AM, Michael Eickenberg wrote:
\begin{plug}
And in a similar vein there is also
http://sklearn-theano.github.io for those who want to leverage
existing, pre-trained networks as feature extractors for their
scikit-learn pipeline, which can, e.g. be followed by a simple
logistic regression to fine-tune to new types of objects.
\end{plug}
Michael
On Thu, May 7, 2015 at 3:45 PM, Boyuan Deng
<bryanhsud...@gmail.com <mailto:bryanhsud...@gmail.com>> wrote:
FYI, in April a project called *"***scikit-neuralnetwork"
came into existence. It's a library wrapping Pylearn2 and
providing a scikit-learn compatible interface.
https://github.com/aigamedev/scikit-neuralnetwork
As others have stated, there are already some nice Python
neural network libraries and we don't really need to reinvent
a wheel. Pylean2 is mostly research-oriented and provides
building blocks. I think sknn is able to fill that gap and
enable sklearn-like experience with neural networks.
Personally I hope in the future our community can officially
help with sknn and feature it as an "add-on" to sklearn
(because Pylearn2 may never go into stable release). In that
way sklearn can definitely be the dominating Python machine
learning library.
On 05/07/2015 07:10 AM, Joel Nothman wrote:
What Sebastian and Ronnie said. Plus: there are multiple
off-the-shelf neural net pull requests in the process of
review, notably those by Issam Laradji for GSoC 2014.
Extreme Learning Machines and Multilayer Perceptrons should
be merged Real Soon Now.
On 7 May 2015 at 14:58, Ronnie Ghose <ronnie.gh...@gmail.com
<mailto:ronnie.gh...@gmail.com>> wrote:
neural nets are already well supported in other python
libraries and don't fit the current transformer model
that scikit-learn uses
On Thu, May 7, 2015 at 12:55 AM, Sebastian Raschka
<se.rasc...@gmail.com <mailto:se.rasc...@gmail.com>> wrote:
I am not one of the core developers, just a typical
user, but although I think that neural nets would be
a nice addition, I have to admit that I wouldn't
count them as top priority. I think that in
applications, neural networks require far more
flexibility for tweaking than the "classic"
off-the-shelve learning algorithms currently
implemented in scikit-learn. I think that it really
requires a lot of planning to implement them in a
way that allows a user certain flexibility. To me,
neural nets are more of an "research tool" in
contrast to the currently implemented algos in
scikit-learn. I really would like to see some way of
implementing frameworks for neural networks in some
useful way in scikit-learn, but I can understand
that it would really require a different API, a lot
of planning, and a lot of work. Also, there are many
attempts to implement neural nets already, like
pylearn2, lasagne, and all the other theano wrappers
> On May 6, 2015, at 11:15 PM, 赵孽
<snakehunt2...@gmail.com
<mailto:snakehunt2...@gmail.com>> wrote:
>
> I used to seeking neural network algorithms in
sklearn, but I just found a RBF in it.
> There are plenty of Neural Network algorithms, why
dose we only support RBF which is not even a typical
neural network ?
> I thought the neural networks should be the
largest family amount sklearn algoritms, but it is
far smaller than embedings, far smaller than SVMs.
>
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