From e4cdb4e990c67c6b412350e14ae048d75fe4f0b6 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 22 Jan 2024 10:02:09 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/cloud_fraction_fcn_abi_hkm_refl.py | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/modules/deeplearning/cloud_fraction_fcn_abi_hkm_refl.py b/modules/deeplearning/cloud_fraction_fcn_abi_hkm_refl.py
index f8fc9ef8..8d8b095b 100644
--- a/modules/deeplearning/cloud_fraction_fcn_abi_hkm_refl.py
+++ b/modules/deeplearning/cloud_fraction_fcn_abi_hkm_refl.py
@@ -588,7 +588,7 @@ class SRCNN:
labels = tf.squeeze(labels, axis=[3])
with tf.GradientTape() as tape:
# pred = self.model([inputs], training=True)
- pred = self.model(inputs, training=True)
+ pred = self.model({'2km':inputs[0], 'hkm':inputs[1]}, training=True)
loss = self.loss(labels, pred)
total_loss = loss
if len(self.model.losses) > 0:
@@ -608,7 +608,7 @@ class SRCNN:
def test_step(self, inputs, labels):
labels = tf.squeeze(labels, axis=[3])
# pred = self.model([inputs], training=False)
- pred = self.model(inputs, training=False)
+ pred = self.model({'2km':inputs[0], 'hkm':inputs[1]}, training=False)
t_loss = self.loss(labels, pred)
self.test_loss(t_loss)
@@ -618,7 +618,7 @@ class SRCNN:
# decorator commented out because pred.numpy(): pred not evaluated yet.
def predict(self, inputs, labels):
# pred = self.model([inputs], training=False)
- pred = self.model(inputs, training=False)
+ pred = self.model({'2km':inputs[0], 'hkm':inputs[1]}, training=False)
# t_loss = self.loss(tf.squeeze(labels, axis=[3]), pred)
t_loss = self.loss(labels, pred)
--
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