snapshot...

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)