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https://issues.apache.org/jira/browse/MADLIB-413?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16082476#comment-16082476
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ASF GitHub Bot commented on MADLIB-413:
---------------------------------------
GitHub user cooper-sloan opened a pull request:
https://github.com/apache/incubator-madlib/pull/149
MLP: Multilayer Perceptron
JIRA: MADLIB-413
Add train and predict for multilayer perceptron.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/cooper-sloan/incubator-madlib mlp_phase1
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/incubator-madlib/pull/149.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #149
----
commit 3693c70178ea74fb3cb742715c4091ddcc265bdc
Author: Cooper Sloan <cooper.sl...@gmail.com>
Date: 2017-06-17T00:41:07Z
MLP: Multilayer Perceptron
JIRA: MADLIB-413
Add train and predict for multilayer perceptron.
----
> Neural Networks - MLP
> ---------------------
>
> Key: MADLIB-413
> URL: https://issues.apache.org/jira/browse/MADLIB-413
> Project: Apache MADlib
> Issue Type: New Feature
> Components: Module: Neural Networks
> Reporter: Caleb Welton
> Assignee: Cooper Sloan
> Fix For: v1.12
>
>
> Multilayer perceptron with backpropagation
> Modules:
> * mlp_classification
> * mlp_regression
> Interface
> {code}
> source_table VARCHAR
> output_table VARCHAR
> independent_varname VARCHAR -- Column name for input features, should be a
> Real Valued array
> dependent_varname VARCHAR, -- Column name for target values, should be Real
> Valued array of size 1 or greater
> hidden_layer_sizes INTEGER[], -- Number of units per hidden layer (can be
> empty or null, in which case, no hidden layers)
> optimizer_params VARCHAR, -- Specified below
> weights VARCHAR, -- Column name for weights. Weights the loss for each input
> vector. Column should contain positive real value
> activation_function VARCHAR, -- One of 'sigmoid' (default), 'tanh', 'relu',
> or any prefix (eg. 't', 's')
> grouping_cols
> )
> {code}
> where
> {code}
> optimizer_params: -- eg "step_size=0.5, n_tries=5"
> {
> step_size DOUBLE PRECISION, -- Learning rate
> n_iterations INTEGER, -- Number of iterations per try
> n_tries INTEGER, -- Total number of training cycles, with random
> initializations to avoid local minima.
> tolerance DOUBLE PRECISION, -- Maximum distance between weights before
> training stops (or until it reaches n_iterations)
> }
> {code}
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