jingyimei commented on a change in pull request #393: DL: Add support for
reporting various metrics in fit/evaluate
URL: https://github.com/apache/madlib/pull/393#discussion_r286256856
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File path: src/ports/postgres/modules/deep_learning/madlib_keras.py_in
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@@ -638,21 +611,21 @@ def internal_keras_eval_transition(state, dependent_var,
independent_var,
SD = kwargs['SD']
device_name = get_device_name_and_set_cuda_env(gpus_per_host,
current_seg_id)
- agg_loss, agg_accuracy, agg_image_count = state
+ agg_loss, agg_metric, agg_image_count = state
if not agg_image_count:
if not is_platform_pg():
set_keras_session(gpus_per_host, segments_per_host)
model = model_from_json(model_architecture)
model_shapes = madlib_keras_serializer.get_model_shapes(model)
- _, _, _, model_weights = madlib_keras_serializer.deserialize_weights(
+ _, model_weights = madlib_keras_serializer.deserialize_weights(
model_data, model_shapes)
model.set_weights(model_weights)
with K.tf.device(device_name):
compile_model(model, compile_params)
SD['segment_model'] = model
# These should already be 0, but just in case make sure
- agg_accuracy = 0
+ agg_metric = 0
Review comment:
metrics is not specified and metrics=[] are addressed, so that they won't be
passed as part of compile params. But for case metrics=[''], keras will throw
out error saying unknown metric. I would let keras handle this, instead of us.
Thoughts? @kaknikhil
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