Binary Classifier - Log loss function

[andrewcz]

Hi,
Fantastic Library!
I was just wondering, i am trying to use the library for a binary classifier experiment using the log loss function to train the model. This is for a university experiment around benchmarking different models. Would you have time to provide an example of how to use the library to achieve the above goal.
Also showing a visualization in how the algorithm learns and decreases the error.
Many thanks,
Best,
Andrew

[jermainewang]

Hi Andrew!

Thanks for your interests in Minerva! Our current status is: we have move
our development efforts to another project called MXNet
https://github.com/dmlc/mxnet, which has better maintenance. To implement
the log loss function you mentioned, either directly used MXNet’s symbolic
layers https://mxnet.readthedocs.org/en/latest/python/symbol.html . Or use
our latest-developed python interface Minpy https://github.com/dmlc/minpy.
You will find exactly what you want in this example:
https://github.com/dmlc/minpy/blob/master/examples/lr.py . The project is
still under development and our next goal is to have full examples
implemented from this course: http://cs231n.stanford.edu/syllabus.html .
Please have a look at these links. I think you will find what you want. And
please tell me if you are interested in any of them.

Best regards,
Minjie
​

On Wed, Mar 9, 2016 at 12:23 AM, andrewcz notifications@github.com wrote:

Hi,
Fantastic Library!
I was just wondering, i am trying to use the library for a binary
classifier experiment using the log loss function to train the model. This
is for a university experiment around benchmarking different models. Would
you have time to provide an example of how to use the library to achieve
the above goal.
Also showing a visualization in how the algorithm learns and decreases the
error.
Many thanks,
Best,
Andrew

—
Reply to this email directly or view it on GitHub
#52.

Minjie Wang
New York University | Computer Science
715 Broadway, New York, NY, 10009