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Posted to issues@systemml.apache.org by "Mike Dusenberry (JIRA)" <ji...@apache.org> on 2017/03/29 23:02:41 UTC
[jira] [Updated] (SYSTEMML-618) Deep Learning DML Library
[ 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 a layers-based *deep learning library* in pure DML.
The library contains layers with simple {{forward}} (function evaluation) and {{backward}} (gradient computation) functions for affine, convolution (start with 2D), max-pooling, non-linearities (relu, sigmoid, softmax, etc.), dropout, loss functions, other layers, optimizers, and gradient checks.
*Examples*: Please see example *scripts* and *notebooks* in the {{examples}} folder: [https://github.com/apache/incubator-systemml/tree/master/scripts/staging/SystemML-NN/examples].
*SystemML-NN*: [https://github.com/apache/incubator-systemml/tree/master/scripts/staging/SystemML-NN]
* Layers:
** Core:
*** Affine
*** Batch Normalization
*** Spatial Convolution
*** LSTM
*** Spatial Max Pooling
*** RNN
*** Spatial Batch Normalization
** Nonlinearities:
*** ReLU
*** Sigmoid
*** Softmax
*** Tanh
** Loss:
*** Cross-entropy loss
*** L1 loss
*** L2 loss
*** Log ("Logistic") loss
** Regularization:
*** Dropout
*** L1 reg
*** L2 reg
* Optimizers:
** Adagrad
** Adam
** RMSprop
** SGD
** SGD w/ Momentum
** SGD w/ Nesterov Momentum
* Tests:
** Gradient Checks
** Unit Tests
was:
This issue tracks the creation of a layers-based *deep learning library* in pure DML.
The library contains layers with simple {{forward}} (function evaluation) and {{backward}} (gradient computation) functions for affine, convolution (start with 2D), max-pooling, non-linearities (relu, sigmoid, softmax, etc.), dropout, loss functions, other layers, optimizers, and gradient checks.
*Examples*: Please see example *scripts* and *notebooks* in the {{examples}} folder: [https://github.com/apache/incubator-systemml/tree/master/scripts/staging/SystemML-NN/examples].
*SystemML-NN*: [https://github.com/apache/incubator-systemml/tree/master/scripts/staging/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
* Optimizers:
** Adagrad
** Adam
** RMSprop
** SGD
** SGD w/ Momentum
** SGD w/ Nesterov Momentum
* Tests:
** 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 a layers-based *deep learning library* in pure DML.
> The library contains layers with simple {{forward}} (function evaluation) and {{backward}} (gradient computation) functions for affine, convolution (start with 2D), max-pooling, non-linearities (relu, sigmoid, softmax, etc.), dropout, loss functions, other layers, optimizers, and gradient checks.
> *Examples*: Please see example *scripts* and *notebooks* in the {{examples}} folder: [https://github.com/apache/incubator-systemml/tree/master/scripts/staging/SystemML-NN/examples].
> *SystemML-NN*: [https://github.com/apache/incubator-systemml/tree/master/scripts/staging/SystemML-NN]
> * Layers:
> ** Core:
> *** Affine
> *** Batch Normalization
> *** Spatial Convolution
> *** LSTM
> *** Spatial Max Pooling
> *** RNN
> *** Spatial Batch Normalization
> ** Nonlinearities:
> *** ReLU
> *** Sigmoid
> *** Softmax
> *** Tanh
> ** Loss:
> *** Cross-entropy loss
> *** L1 loss
> *** L2 loss
> *** Log ("Logistic") loss
> ** Regularization:
> *** Dropout
> *** L1 reg
> *** L2 reg
> * Optimizers:
> ** Adagrad
> ** Adam
> ** RMSprop
> ** SGD
> ** SGD w/ Momentum
> ** SGD w/ Nesterov Momentum
> * Tests:
> ** Gradient Checks
> ** Unit Tests
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