[GitHub] incubator-madlib pull request #162: MLP: Multilayer Perceptron Phase 2

[njayaram2]

Github user njayaram2 commented on a diff in the pull request:

    https://github.com/apache/incubator-madlib/pull/162#discussion_r132058113
  
    --- Diff: src/ports/postgres/modules/convex/mlp.sql_in ---
    @@ -226,26 +268,52 @@ the parameter is ignored.
     
     
     <pre class="syntax">
    -  'step_size = &lt;value>,
    +  'learning_rate_init = &lt;value>,
        n_iterations = &lt;value>,
        n_tries = &lt;value>,
        tolerance = &lt;value>'
     </pre>
     \b Optimizer Parameters
     <DL class="arglist">
     
    -<DT>step_size</dt>
    -<DD>Default: [0.001].
    +<DT>learning_rate_init</dt>
    +<DD>Default: 0.001.
     Also known as the learning rate. A small value is usually desirable to
     ensure convergence, while a large value provides more room for progress 
during
     training. Since the best value depends on the condition number of the 
data, in
     practice one often tunes this parameter.
     </DD>
     
    +<DT>learning_rate_policy</dt>
    +<DD>Default: constant.
    +One of 'constant', 'exp', 'inv' or 'step' or any prefix of these.
    +'constant': learning_rate = learning_rate_init
    +'exp': learning_rate = learning_rate_init * gamma^(iter)
    +'inv': learning_rate = learning_rate_init * (iter+1)^(-power)
    +'step': learning_rate = learning_rate_init * 
gamma^(floor(iter/iterations_per_step))
    +Where iter is the current iteration of SGD.
    +</DD>
    +
    +<DT>gamma</dt>
    +<DD>Default: 0.1.
    +Decay rate for learning rate when learning_rate_policy is 'exp' or 'step'.
    +</DD>
    +
    +<DT>power</dt>
    +<DD>Default: 0.5.
    +Exponent for learning_rate_policy = 'inv'
    +</DD>
    +
    +<DT>iterations_per_step</dt>
    +<DD>Default: 100.
    +Number of iterations to run before decreasing the learning rate by
    +a factor of gamma.  Valid for learning rate policy = 'step'
    +</DD>
    --- End diff --
    
    Documentation for `lambda` missing here?


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