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new 1fa020b clarify warm start with model selection in user docs
1fa020b is described below
commit 1fa020b08c2a4a8971d1957674794894d6c71783
Author: Frank McQuillan <fmcquil...@pivotal.io>
AuthorDate: Tue Jan 7 17:33:42 2020 -0800
clarify warm start with model selection in user docs
---
.../modules/deep_learning/madlib_keras_fit_multiple_model.sql_in | 4 +++-
1 file changed, 3 insertions(+), 1 deletion(-)
diff --git
a/src/ports/postgres/modules/deep_learning/madlib_keras_fit_multiple_model.sql_in
b/src/ports/postgres/modules/deep_learning/madlib_keras_fit_multiple_model.sql_in
index fbf3497..0468942 100644
---
a/src/ports/postgres/modules/deep_learning/madlib_keras_fit_multiple_model.sql_in
+++
b/src/ports/postgres/modules/deep_learning/madlib_keras_fit_multiple_model.sql_in
@@ -84,7 +84,7 @@ for the training data. For example, you may only want
to train models on segments that reside on hosts that are GPU enabled.
You can set up the models and hyperparameters to try with the
-<a href="group__grp__keras__model__selection.html">Setup
+<a href="group__grp__keras__setup__model__selection.html">Setup
Model Selection</a> utility to define the unique combinations
of model architectures, compile and fit parameters.
@@ -1320,6 +1320,8 @@ set the 'warm_start' parameter to TRUE in the fit
function.
Transfer learning uses initial model state (weights) stored in the
'model_arch_table' - in this case set the
'warm_start' parameter to FALSE in the fit function.
+4. Here are some more details on how warm start works. These details are
mostly applicable when implementing autoML algorithms on top of MADlib's model
selection. In short, the 'model_selection_table' dictates which models get
trained and output to the 'model_output_table' and associated summary and info
tables. When 'warm_start' is TRUE, models are built for each 'mst_key' in the
'model_selection_table'. If there are prior runs for an 'mst_key' then the
weights from that run will be [...]
+
@anchor background
@par Technical Background
