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Posted to issues@madlib.apache.org by "Nandish Jayaram (JIRA)" <ji...@apache.org> on 2019/04/26 21:15:00 UTC
[jira] [Created] (MADLIB-1333) DL: Add new function for
preprocessing images for validation dataset
Nandish Jayaram created MADLIB-1333:
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Summary: DL: Add new function for preprocessing images for validation dataset
Key: MADLIB-1333
URL: https://issues.apache.org/jira/browse/MADLIB-1333
Project: Apache MADlib
Issue Type: Improvement
Components: Deep Learning
Reporter: Nandish Jayaram
Function to prepare the validation dataset for deep learning with madlib
* This function assumes that the pre processor for training data has already been run.
* mini-batch x and y.
* 1-hot encode class levels (for 1-hot) - want to make sure don't miss any class levels (in the case that validation data set by itself does not have all class values that are in the training dataset). This value will be read from the output of the summary table for pre processor for training data.
* normalizing: use the same normalizing constant that was used while creating batched training data, found in its summary table.
* rename x and y so that the column names for training data and validation data are the same.
* applies to fit() and evaluate()
Proposed Interface:
Rename `minibatch_preprocessor_dl` to `training_preprocessor_dl`. Interface is the same as in master currently:
{code:java}
training_preprocessor_dl( source_table, -- training dataset
output_table,
dependent_varname,
independent_varname,
buffer_size, -- Optional
normalizing_const, -- Optional
num_classes -- Optional
)
{code}
New function for preparing validation data for evaluation:
{code:java}
validation_preprocessor_dl(
source_table, -- validation dataset
output_table,
dependent_varname,
independent_varname,
training_preprocessor_table, -- i.e., from training_preprocessor_dl
buffer_size -- Optional
)
{code}
Acceptance:
1. Input validation check to ensure `training_preprocessor_table` is not null.
2. Run `validation_preprocessor_dl` on the exact same data set as `training_preprocessor_dl` and ensure that respective output tables are the same element-by-element. This test may only be verifiable if there was exactly one image in the input table.
3. Make the `buffer_size` in `validation_preprocessor_dl` <1 and ensure fails with nice error message.
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