From 26372c0e887b529a1365f1f78490c6711cb6cd80 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Fri, 17 Sep 2021 08:35:27 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/icing_cnn.py | 13 +++++++++----
1 file changed, 9 insertions(+), 4 deletions(-)
diff --git a/modules/deeplearning/icing_cnn.py b/modules/deeplearning/icing_cnn.py
index 311db230..be8cdeb5 100644
--- a/modules/deeplearning/icing_cnn.py
+++ b/modules/deeplearning/icing_cnn.py
@@ -35,6 +35,7 @@ TRIPLET = False
CONV3D = False
NOISE_TRAINING = False
+NOISE_STDDEV = 0.01
img_width = 16
@@ -244,10 +245,12 @@ class IcingIntensityNN:
data = []
for param in train_params:
nda = self.get_parameter_data(param, nd_idxs, is_training)
- if NOISE_TRAINING and is_training:
- nda = normalize(nda, param, mean_std_dct, add_noise=True, noise_scale=0.01, seed=42)
- else:
- nda = normalize(nda, param, mean_std_dct)
+ # Manual Corruption Process. Better: see use of tf.keras.layers.GaussianNoise
+ # if NOISE_TRAINING and is_training:
+ # nda = normalize(nda, param, mean_std_dct, add_noise=True, noise_scale=0.01, seed=42)
+ # else:
+ # nda = normalize(nda, param, mean_std_dct)
+ nda = normalize(nda, param, mean_std_dct)
if DO_ZERO_OUT and is_training:
try:
zero_out_params.index(param)
@@ -842,6 +845,8 @@ class IcingIntensityNN:
f.close()
def build_model(self):
+ if NOISE_TRAINING:
+ self.inputs[0] = tf.keras.layers.GaussianNoise(stddev=NOISE_STDDEV)(self.inputs[0])
flat = self.build_cnn()
# flat_1d = self.build_1d_cnn()
# flat = tf.keras.layers.concatenate([flat, flat_1d, flat_anc])
--
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