kaknikhil commented on a change in pull request #425: DL: Add training for
multiple models
URL: https://github.com/apache/madlib/pull/425#discussion_r309941439
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File path: src/ports/postgres/modules/deep_learning/madlib_keras.py_in
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@@ -455,17 +495,23 @@ def fit_transition(state, dependent_var,
independent_var, model_architecture,
# Once done with all images on a segment, we update weights
# with the total number of images here instead of the merge function.
# The merge function only deals with aggregating them.
- updated_weights = [ total_images * w for w in updated_weights ]
- # In GPDB, each segment would have a keras session, so clear
- # them after the last buffer is processed.
- clear_keras_session()
-
+ if not is_cerebro:
+ updated_weights = [ total_images * w for w in updated_weights ]
+
+ # In GPDB, each segment would have a keras session, so clear
+ # them at the end of final iteration and also every final buffer for
cerebro
+ if is_final or is_cerebro:
+ K.clear_session()
+ sess.close()
+ del SD['segment_model']
+ del SD['sess']
+
+ all_ops_len = len([n.name for n in
tf.get_default_graph().as_graph_def().node])
Review comment:
Why do we need this variable `all_ops_len` ? I only see this being written
to but never read from.
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