Ekta Khanna created MADLIB-1441:
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Summary: 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
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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