[jira] [Updated] (SYSTEMML-618) Deep Learning DML Library

["Mike Dusenberry (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SYSTEMML-618?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Mike Dusenberry updated SYSTEMML-618:
-------------------------------------
    Description: 
This issue tracks the creation of an experimental, layers-based library in pure PyDML & DML that contains layer abstractions with simple forward/backward APIs for affine, convolution (start with 2D), max-pooling, non-linearities (relu, sigmoid, softmax, etc.), dropout, loss functions, other layers, optimizers, and gradient checks.

*SystemML-NN*: [https://github.com/dusenberrymw/systemml-nn|https://github.com/dusenberrymw/systemml-nn]
* Layers:
** Core:
*** Affine
*** Spatial Convolution
*** LSTM
*** Max Pooling
*** RNN
** Nonlinearities:
*** ReLU
*** Sigmoid
*** Softmax
*** Tanh
** Loss:
*** Cross-entropy loss
*** L1 loss
*** L2 loss
*** Log ("Logistic") loss
** Regularization:
*** Dropout
*** L1 reg
*** L2 reg

  was:This issue tracks the creation of an experimental, layers-based library in pure DML that contains layer abstractions with simple forward/backward APIs for affine, convolution (start with 2D), max-pooling, non-linearities (relu, sigmoid, softmax, etc.), dropout, loss functions, other layers, optimizers, and gradient checks.


> Deep Learning DML Library
> -------------------------
>
>                 Key: SYSTEMML-618
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-618
>             Project: SystemML
>          Issue Type: New Feature
>            Reporter: Mike Dusenberry
>            Assignee: Mike Dusenberry
>
> This issue tracks the creation of an experimental, layers-based library in pure PyDML & DML that contains layer abstractions with simple forward/backward APIs for affine, convolution (start with 2D), max-pooling, non-linearities (relu, sigmoid, softmax, etc.), dropout, loss functions, other layers, optimizers, and gradient checks.
> *SystemML-NN*: [https://github.com/dusenberrymw/systemml-nn|https://github.com/dusenberrymw/systemml-nn]
> * Layers:
> ** Core:
> *** Affine
> *** Spatial Convolution
> *** LSTM
> *** Max Pooling
> *** RNN
> ** Nonlinearities:
> *** ReLU
> *** Sigmoid
> *** Softmax
> *** Tanh
> ** Loss:
> *** Cross-entropy loss
> *** L1 loss
> *** L2 loss
> *** Log ("Logistic") loss
> ** Regularization:
> *** Dropout
> *** L1 reg
> *** L2 reg



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