From 3b6fa107732a00932664f3f18bee9bc8fb292a02 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 14 Sep 2021 16:05:42 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/icing_cnn.py | 22 +++++++++++-----------
1 file changed, 11 insertions(+), 11 deletions(-)
diff --git a/modules/deeplearning/icing_cnn.py b/modules/deeplearning/icing_cnn.py
index e384b00d..c7213d1c 100644
--- a/modules/deeplearning/icing_cnn.py
+++ b/modules/deeplearning/icing_cnn.py
@@ -13,6 +13,7 @@ from icing.pirep_goes import normalize, make_for_full_domain_predict
LOG_DEVICE_PLACEMENT = False
+# Manual (data, label) caching, but has been replaced with tf.data.dataset.cache()
CACHE_DATA_IN_MEM = False
PROC_BATCH_SIZE = 4096
@@ -37,8 +38,8 @@ NOISE_TRAINING = False
img_width = 16
-mean_std_file = homedir+'data/icing/mean_std_no_ice.pkl'
-# mean_std_file = homedir+'data/icing/mean_std_l1b_no_ice.pkl'
+# mean_std_file = homedir+'data/icing/mean_std_no_ice.pkl'
+mean_std_file = homedir+'data/icing/mean_std_l1b_no_ice.pkl'
f = open(mean_std_file, 'rb')
mean_std_dct = pickle.load(f)
f.close()
@@ -47,13 +48,13 @@ f.close()
# train_params = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
# 'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_acha', 'cld_opd_acha']
# -- DAY L2 -------------
-train_params = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
-# 'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'cld_cwp_dcomp', 'iwc_dcomp', 'lwc_dcomp']
- 'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
+#train_params = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
+## 'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'cld_cwp_dcomp', 'iwc_dcomp', 'lwc_dcomp']
+# 'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
# -- DAY L1B --------------------------------
-# train_params = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
-# 'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom',
-# 'refl_0_47um_nom', 'refl_0_65um_nom', 'refl_0_86um_nom', 'refl_1_38um_nom', 'refl_1_60um_nom']
+train_params = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
+ 'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom',
+ 'refl_0_47um_nom', 'refl_0_65um_nom', 'refl_0_86um_nom', 'refl_1_38um_nom', 'refl_1_60um_nom']
# -- NIGHT L1B -------------------------------
# train_params = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
# 'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom']
@@ -223,9 +224,8 @@ class IcingIntensityNN:
if not is_training:
h5f = self.h5f_tst
- key = frozenset(idxs)
-
if CACHE_DATA_IN_MEM:
+ key = frozenset(idxs)
tup = self.in_mem_data_cache.get(key)
if tup is not None:
return tup[0], tup[1]
@@ -436,7 +436,7 @@ class IcingIntensityNN:
momentum = 0.99
# num_filters = 16
- num_filters = 24
+ num_filters = 30
conv = tf.keras.layers.Conv2D(num_filters, 5, strides=[1, 1], padding=padding, activation=activation)(self.inputs[0])
conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
--
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