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https://issues.apache.org/jira/browse/MADLIB-1441?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Frank McQuillan closed MADLIB-1441.
-----------------------------------
Resolution: Fixed
https://github.com/apache/madlib/pull/503
> DL - Add support for custom loss functions
> -------------------------------------------
>
> Key: MADLIB-1441
> URL: https://issues.apache.org/jira/browse/MADLIB-1441
> Project: Apache MADlib
> Issue Type: New Feature
> Components: Deep Learning
> Reporter: Ekta Khanna
> Priority: Major
> Fix For: v1.18.0
>
>
> Keras supports custom loss functions to be passed in as compile_params to the
> fit function. For MADlib deep_learning module to support custom functions, we
> have the following functions are affected:
> 1. madlib_keras_fit()
> 2. madlib_keras_fit_multiple_model()
> 3. madlib_keras_evaluate()
> Currently the compile_param passed in are like
> {{optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True),
> loss='categorical_crossentropy', metrics=['mae']}}
> For a custom function we would just pass in as follows:
> {{optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss=custom_fn1,
> metrics=['mae']}}
>
> object table definition with a dill object of custom functions is as follows:
> ||id||name||description||function||
> |1|custom_fn1|test function|\x80026...|
> |1|custom_fn2|test function2|\x8ab26...|
>
> For fit_multiple, we want to support multiple configs in mst_table with a mix
> of custom and keras loss functions:
> ||mst_key||model_id||compile_params||fit_params||
> |2|1|loss='custom_fn1', optimizer=....|batch_size=16, epochs=1|
> |3|1|loss='custom_fn2', optimizer=....|batch_size=16, epochs=1|
> |1|1|loss='categorical_crossentropy', optimizer=....|batch_size=16, epochs=1|
>
> h4. API Changes
> # *Fit*: add a new optional param for passing in the object_table name
> object_table (optional) VARCHAR: Name of the table containing Python objects
> in the case that custom loss functions or custom metrics are specified in the
> parameter `compile_params
> {code:java}
> madlib_keras_fit(
> source_table,
> model,
> model_arch_table,
> model_id,
> compile_params,
> fit_params,
> num_iterations,
> use_gpus,
> validation_table,
> metrics_compute_frequency,
> warm_start,
> name,
> description,
> object_table -- new parameter
> ){code}
> # *Fit_multiple*: No change to the {{madlib_keras_fit_multiple_model()}}
> function. Reads object_table information from the model_selection table.
> A summary table named <model>_summary is also created, which has the
> following new columns:
> |model_selection_table |Name of the table containing model selection
> parameters to be tried.|
> |object_table| Name of the object table containing the serialized Python
> objects for custom loss functions and custom metrics (read from the
> mst_summary table).|
> # *Evaluate*: No change to the {{madlib_keras_evaluate()}} function. Reads
> object_table information from the model table. Output table adds a new column
> |loss_type|Type of loss used that was used in the training step
> If a custom loss or metric is used, we should give the name of it. Otherwise
> list the built-in one used|
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