From 0d655e543f84dcb48070cf850be8d93662f0e583 Mon Sep 17 00:00:00 2001
From: rink <rink@ssec.wisc.edu>
Date: Tue, 10 Nov 2020 14:17:50 -0600
Subject: [PATCH] snapshot..
---
modules/deeplearning/cloudheight.py | 8 ++++++++
1 file changed, 8 insertions(+)
diff --git a/modules/deeplearning/cloudheight.py b/modules/deeplearning/cloudheight.py
index 0210c863..ea45158f 100644
--- a/modules/deeplearning/cloudheight.py
+++ b/modules/deeplearning/cloudheight.py
@@ -215,12 +215,14 @@ class CloudHeightNN:
self.accuracy_2 = None
self.accuracy_3 = None
self.accuracy_4 = None
+ self.accuracy_5 = None
self.num_0 = 0
self.num_1 = 0
self.num_2 = 0
self.num_3 = 0
self.num_4 = 0
+ self.num_5 = 0
self.learningRateSchedule = None
self.num_data_samples = None
@@ -628,6 +630,7 @@ class CloudHeightNN:
self.accuracy_2 = tf.keras.metrics.MeanAbsoluteError(name='acc_2')
self.accuracy_3 = tf.keras.metrics.MeanAbsoluteError(name='acc_3')
self.accuracy_4 = tf.keras.metrics.MeanAbsoluteError(name='acc_4')
+ self.accuracy_5 = tf.keras.metrics.MeanAbsoluteError(name='acc_5')
def build_predict(self):
_, pred = tf.nn.top_k(self.logits)
@@ -695,6 +698,10 @@ class CloudHeightNN:
self.num_4 += np.sum(m)
self.accuracy_4(labels[m], pred[m])
+ m = np.logical_and(labels >= 0.01, labels < 0.5)
+ self.num_5 += np.sum(m)
+ self.accuracy_5(labels[m], pred[m])
+
def do_training(self, ckpt_dir=None):
if ckpt_dir is None:
@@ -813,6 +820,7 @@ class CloudHeightNN:
print('acc_2', self.num_2, self.accuracy_2.result())
print('acc_3', self.num_3, self.accuracy_3.result())
print('acc_4', self.num_4, self.accuracy_4.result())
+ print('acc_5', self.num_5, self.accuracy_5.result())
def run(self, matchup_dict, train_dict=None, valid_dict=None):
self.setup_pipeline(matchup_dict, train_dict=train_dict, valid_test_dict=valid_dict)
--
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