Why would autoencoders be naturally batch? I think historically one of
their early uses was for Online PCA, but I may be wrong.
Vlad
On Wed, Jun 26, 2013 at 11:51 PM, Issam wrote:
> Hi @Olivier, you are absolutely right, scipy.optimize.fmin_l_bfgs_b
> would not be suitable for MLP because some p
Hi @Olivier, you are absolutely right, scipy.optimize.fmin_l_bfgs_b
would not be suitable for MLP because some practitioners would want
on-line updating (partial_fit()) rather than batch. However, what's your
opinion about the use of 'fmin_l_bfgs_b' in naturally BATCH processing
algorithms like
2013/6/26 Issam :
> Hi @Hannes, how about using scipy.optimize.fmin_l_bfgs_b for optimizing
> the weights? I found it to be very efficient and fast (I even found it
> to be faster than MATLAB's minFunc), it's also widely used for neural
> networks type of optimization like in Prof. Andrew's courses
Hi @Hannes, how about using scipy.optimize.fmin_l_bfgs_b for optimizing
the weights? I found it to be very efficient and fast (I even found it
to be faster than MATLAB's minFunc), it's also widely used for neural
networks type of optimization like in Prof. Andrew's courses and even in
Deep Lear
Thanks! that does sound very easy. I'll get into Cython soon!
I have pushed a draft version of the Sparse Autoencoder to scikit's
github. Hopefully I have sent the pull request correctly :).
Thanks!
On 6/26/2013 2:28 AM, Robert Layton wrote:
The basics of cython are, and I'm not kidding her
Before things diverge completely, please also have a look at
https://github.com/temporaer/scikit-learn/tree/mlperceptron
and the discussions at
https://github.com/larsmans/scikit-learn/pull/5
where I tried to refactor larsmans' code and the gradient descent into
activity and weight layers, and
2013/6/26 Robert Layton :
> The basics of cython are, and I'm not kidding here, quite easy to learn.
> Steps:
> 1) Rename .py file to .pyc
You mean .pyx.
> 2) Put "int" in front of all object declarations that will be integers,
> "float" in front of things that are floats. (If you know java/C/C++
I strongly recommend reading Jake's blog entries on Cython (Memoryviews in
particular) [1] and Wes' blog [2],[3].
Another great resource is the ball_tree.pyx code in
/sklearn/neighbors/ball_tree.pyx .
when you compile the pyx file to c using cython you should use the flag
"-a" - it will generate a
