From 9b01c955be590524e4b81d1d6bfde69fac3734e1 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 14 Nov 2023 10:12:23 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/icing_fcn.py | 20 ++++++++------------
1 file changed, 8 insertions(+), 12 deletions(-)
diff --git a/modules/deeplearning/icing_fcn.py b/modules/deeplearning/icing_fcn.py
index b51306f6..a267ed40 100644
--- a/modules/deeplearning/icing_fcn.py
+++ b/modules/deeplearning/icing_fcn.py
@@ -1198,11 +1198,7 @@ def run_average_models(ckpt_dir_s_path, day_night='NIGHT', l1b_andor_l2='BOTH',
for k, w in enumerate(m):
model_lyrs[k].append(w)
for lyr in model_lyrs:
- nda = np.stack(lyr, axis=-1)
- print(nda.shape)
- avg = np.mean(nda, axis=-1)
- print(avg.shape)
- avg_model_weights.append(nda)
+ avg_model_weights.append(np.mean(np.stack(lyr, axis=-1), axis=-1))
# -- Make a new model for the averaged weights
new_model = IcingIntensityFCN(day_night=day_night, l1b_or_l2=l1b_andor_l2, use_flight_altitude=use_flight_altitude)
@@ -1211,13 +1207,13 @@ def run_average_models(ckpt_dir_s_path, day_night='NIGHT', l1b_andor_l2='BOTH',
new_model.build_evaluation()
# -- Save the averaged weights to a new the model
- # if not os.path.exists(modeldir):
- # os.mkdir(modeldir)
- # ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=new_model.model)
- # ckpt_manager = tf.train.CheckpointManager(ckpt, modeldir, max_to_keep=3)
- #
- # new_model.model.set_weights(avg_weights)
- # ckpt_manager.save()
+ if not os.path.exists(modeldir):
+ os.mkdir(modeldir)
+ ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=new_model.model)
+ ckpt_manager = tf.train.CheckpointManager(ckpt, modeldir, max_to_keep=3)
+
+ new_model.model.set_weights(avg_model_weights)
+ ckpt_manager.save()
return
--
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