From e1951bb41e6d2dc92811b4dcb23eca85203dfafc Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 23 Jan 2024 15:04:28 -0600
Subject: [PATCH] snapshot...
---
.../cloud_fraction_fcn_abi_hkm_refl.py | 18 ++++++++++--------
1 file changed, 10 insertions(+), 8 deletions(-)
diff --git a/modules/deeplearning/cloud_fraction_fcn_abi_hkm_refl.py b/modules/deeplearning/cloud_fraction_fcn_abi_hkm_refl.py
index 6d56d830..d4b23053 100644
--- a/modules/deeplearning/cloud_fraction_fcn_abi_hkm_refl.py
+++ b/modules/deeplearning/cloud_fraction_fcn_abi_hkm_refl.py
@@ -611,12 +611,13 @@ class SRCNN:
self.train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
self.test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
- @tf.function(input_signature=[tf.TensorSpec(None, tf.float32), tf.TensorSpec(None, tf.float32)])
+ # @tf.function(input_signature=[tf.TensorSpec(None, tf.float32), tf.TensorSpec(None, tf.float32)])
+ @tf.function
def train_step(self, inputs, labels):
labels = tf.squeeze(labels, axis=[3])
with tf.GradientTape() as tape:
- # pred = self.model([inputs], training=True)
- pred = self.model({'2km': inputs[0], 'hkm': inputs[1]}, training=True)
+ # pred = self.model(inputs, training=True)
+ pred = self.model({'input_1': inputs[0], 'input_2': inputs[1]}, training=True)
loss = self.loss(labels, pred)
total_loss = loss
if len(self.model.losses) > 0:
@@ -632,11 +633,12 @@ class SRCNN:
return loss
- @tf.function(input_signature=[tf.TensorSpec(None, tf.float32), tf.TensorSpec(None, tf.float32)])
+ # @tf.function(input_signature=[tf.TensorSpec(None, tf.float32), tf.TensorSpec(None, tf.float32)])
+ @tf.function
def test_step(self, inputs, labels):
labels = tf.squeeze(labels, axis=[3])
- # pred = self.model([inputs], training=False)
- pred = self.model({'2km': inputs[0], 'hkm': inputs[1]}, training=False)
+ # pred = self.model(inputs, training=False)
+ pred = self.model({'input_1': inputs[0], 'input_2': inputs[1]}, training=False)
t_loss = self.loss(labels, pred)
self.test_loss(t_loss)
@@ -645,8 +647,8 @@ class SRCNN:
# @tf.function(input_signature=[tf.TensorSpec(None, tf.float32), tf.TensorSpec(None, tf.float32)])
# decorator commented out because pred.numpy(): pred not evaluated yet.
def predict(self, inputs, labels):
- # pred = self.model([inputs], training=False)
- pred = self.model({'2km': inputs[0], 'hkm': inputs[1]}, training=False)
+ # pred = self.model(inputs, training=False)
+ pred = self.model({'input_1': inputs[0], 'input_2': inputs[1]}, training=False)
# t_loss = self.loss(tf.squeeze(labels, axis=[3]), pred)
t_loss = self.loss(labels, pred)
--
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