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https://issues.apache.org/jira/browse/MADLIB-1223?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16432762#comment-16432762
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Frank McQuillan commented on MADLIB-1223:
-----------------------------------------
This works for me
{code}
load mnist data into mnist_train table and run preprocessor to create
mnist_train_packed table
{code}
{code}
madlib=# drop table mnist_train;
DROP TABLE
{code}
{code}
madlib=# DROP TABLE IF EXISTS mnist_result, mnist_result_summary,
mnist_result_standardization;
DROP TABLE
madlib=#
madlib=# SELECT madlib.mlp_classification(
madlib(# 'mnist_train_packed', -- Packed table from preprocessor
madlib(# 'mnist_result', -- Destination table
madlib(# 'independent_varname', -- Independent
madlib(# 'dependent_varname', -- Dependent
madlib(# ARRAY[5], -- Hidden layer sizes
madlib(# 'learning_rate_init=0.1,
madlib'# n_iterations=1,
madlib'# learning_rate_policy=const,
madlib'# lambda=0.0001, -- Regularization
madlib'# tolerance=0',
madlib(# 'tanh', -- Activation function
madlib(# '', -- No weights
madlib(# FALSE, -- No warmstart
madlib(# FALSE); -- Verbose
NOTICE: Table doesn't have 'DISTRIBUTED BY' clause -- Using column(s) named
'coeff' as the Greenplum Database data distribution key for this table.
HINT: The 'DISTRIBUTED BY' clause determines the distribution of data. Make
sure column(s) chosen are the optimal data distribution key to minimize skew.
NOTICE: Table doesn't have 'DISTRIBUTED BY' clause -- Using column named
'source_table' as the Greenplum Database data distribution key for this table.
HINT: The 'DISTRIBUTED BY' clause determines the distribution of data. Make
sure column(s) chosen are the optimal data distribution key to minimize skew.
NOTICE: Table doesn't have 'DISTRIBUTED BY' clause. Creating a NULL policy
entry.
NOTICE: Table doesn't have 'DISTRIBUTED BY' clause. Creating a NULL policy
entry.
mlp_classification
--------------------
(1 row)
{code}
{code}
madlib=# DROP TABLE IF EXISTS mnist_test_prediction;
DROP TABLE
madlib=# SELECT madlib.mlp_predict(
madlib(# 'mnist_result',
madlib(# 'mnist_test',
madlib(# 'id',
madlib(# 'mnist_test_prediction',
madlib(# 'response');
NOTICE: Table doesn't have 'DISTRIBUTED BY' clause. Creating a NULL policy
entry.
mlp_predict
-------------
(1 row)
{code}
so accepting this JIRA
> MLP regression predict fails if input table does not exist
> ----------------------------------------------------------
>
> Key: MADLIB-1223
> URL: https://issues.apache.org/jira/browse/MADLIB-1223
> Project: Apache MADlib
> Issue Type: Bug
> Components: Module: Neural Networks
> Reporter: Nikhil
> Priority: Major
> Fix For: v1.14
>
>
> If a model is trained with mlp regression and then the input table is
> dropped, mlp predict fails for that model.
> Ideally the predict function should not depend on the existence of the
> training data.
> The predict code for regression only needs to know if the dependent varname
> type is an array or not. This information can be potentially stored in the
> model's summary table.
> We also need to make sure that the predict function is backwards compatible.
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