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> 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>
> 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> 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
>>> > 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> 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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