[jira] [Commented] (SYSTEMML-547) Implement built-in functions for max and average pooling

["Niketan Pansare (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SYSTEMML-547?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15304638#comment-15304638 ] 

Niketan Pansare commented on SYSTEMML-547:
------------------------------------------

Resolved in commit https://github.com/apache/incubator-systemml/commit/c334c2c85bc9cbb343e63b5b28ff3a1c5098c7fa

> Implement built-in functions for max and average pooling
> --------------------------------------------------------
>
>                 Key: SYSTEMML-547
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-547
>             Project: SystemML
>          Issue Type: New Feature
>          Components: Parser, Runtime
>            Reporter: Niketan Pansare
>            Assignee: Nakul Jindal
>            Priority: Minor
>             Fix For: SystemML 0.10
>
>   Original Estimate: 168h
>  Remaining Estimate: 168h
>
> pool2d(input, pool_size, stride_length, border_mode="valid", pool_mode="max")
> Performs downscaling of the input matrix.
> The arguments to this function are:
> 1. input is a 2-dimensional matrix.
> 2. pool_size is a required integer parameter.
> 3. stride_length is an optional Int parameter. The default value is 1.
> 4. border_mode is an optional String parameter. The valid values are "same" and "valid".
> 5. pool_mode is an optional String parameter. The valid values are "max" and "avg". We can later add additional operators here (such as sum).
> For detailed documentation, see Theano's pool_2d function: https://github.com/Theano/Theano/blob/master/theano/tensor/signal/pool.py#L40
> An an example, our pool2d(input=X, pool_size=2, stride_length=1, border_mode="valid", pool_mode="avg") invocation is similar to Theano's 
> pool_2d(X, ds=(2,2), st=(1,1), ignore_border=True, padding=(0, 0), mode="average_exc_pad")
> Since padding=(0,0) is the most common padding (probably the only one most people will use), I thought of simplifying the interface by borrowing concepts from TensorFlow's functions max_pool and avg_pool. See https://www.tensorflow.org/versions/r0.7/api_docs/python/nn.html#avg_pool
> The above example will translate into following TensorFlow code:
> tf.nn.avg_pool(X, pool_size=(1,2,2,1), strides=(1,1,1,1), padding="VALID")
> Another good reference to understanding pooling operation is http://cs231n.github.io/convolutional-networks/#pool
> [~mwdusenb@us.ibm.com], [~nakul02], [~prithvi_r_s], [~reinwald@us.ibm.com]



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