From 0617bd07c4591f4b59b426cb0d8f80b3cacf442d Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 9 Aug 2021 15:27:31 -0500
Subject: [PATCH] new method
---
modules/deeplearning/icing_cnn.py | 41 ++++++++++++++++++++++---------
1 file changed, 30 insertions(+), 11 deletions(-)
diff --git a/modules/deeplearning/icing_cnn.py b/modules/deeplearning/icing_cnn.py
index 335b407f..d73948e4 100644
--- a/modules/deeplearning/icing_cnn.py
+++ b/modules/deeplearning/icing_cnn.py
@@ -38,19 +38,19 @@ 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()
# train_params = ['cld_height_acha', 'cld_geo_thick', 'supercooled_cloud_fraction', 'cld_temp_acha', 'cld_press_acha',
# 'cld_reff_acha', 'cld_opd_acha', 'conv_cloud_fraction', 'cld_emiss_acha']
-# train_params = ['cld_height_acha', 'cld_geo_thick', 'supercooled_cloud_fraction', 'cld_temp_acha', 'cld_press_acha',
-# 'cld_reff_dcomp', 'cld_opd_dcomp', 'cld_cwp_dcomp', 'iwc_dcomp', 'lwc_dcomp', 'conv_cloud_fraction', 'cld_emiss_acha']
-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 = ['cld_height_acha', 'cld_geo_thick', 'supercooled_cloud_fraction', 'cld_temp_acha', 'cld_press_acha',
+ 'cld_reff_dcomp', 'cld_opd_dcomp', 'cld_cwp_dcomp', 'iwc_dcomp', 'lwc_dcomp', 'conv_cloud_fraction', 'cld_emiss_acha']
+# 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']
@@ -418,8 +418,8 @@ class IcingIntensityNN:
activation = tf.nn.leaky_relu
momentum = 0.99
- num_filters = 16
- # num_filters = 12
+ # num_filters = 16
+ num_filters = 12
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)
@@ -803,7 +803,7 @@ class IcingIntensityNN:
self.h5f_tst.close()
- def do_evaluate(self, ckpt_dir, ll, cc):
+ def do_evaluate(self, ckpt_dir, ll, cc, prob_thresh=0.5):
ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=self.model)
ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
@@ -821,7 +821,7 @@ class IcingIntensityNN:
preds = np.concatenate(pred_s)
if NumClasses == 2:
- preds = np.where(preds > 0.6, 1, 0)
+ preds = np.where(preds > prob_thresh, 1, 0)
else:
preds = np.argmax(preds, axis=1)
print(preds.shape[0], np.sum(preds == 1))
@@ -871,6 +871,25 @@ class IcingIntensityNN:
return filename, ice_lons, ice_lats
+def run_restore_static(filename_tst, ckpt_dir_s):
+ cm_s = []
+ for ckpt_dir in ckpt_dir_s:
+ nn = IcingIntensityNN()
+ nn.run_restore(filename_tst, ckpt_dir)
+ cm_s.append(tf.math.confusion_matrix(nn.test_labels, nn.test_preds))
+ num = len(cm_s)
+ cm_avg = cm_s[0]
+ for k in range(num-1):
+ cm_avg += cm_s[k+1]
+ cm_avg /= num
+
+ return cm_avg
+
+
+def run_evaluate_static(filename, ckpt_dir_s):
+ nn = IcingIntensityNN()
+
+
if __name__ == "__main__":
nn = IcingIntensityNN()
nn.run('matchup_filename')
--
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