snapshot...

modules/deeplearning/icing.py

@@ -13,18 +13,15 @@ from icing.pirep_goes import split_data, normalize
 LOG_DEVICE_PLACEMENT = False
 
 CACHE_DATA_IN_MEM = True
-CACHE_GFS = True
 
 PROC_BATCH_SIZE = 60
 PROC_BATCH_BUFFER_SIZE = 50000
 NumLabels = 1
-BATCH_SIZE = 256
+BATCH_SIZE = 512
 NUM_EPOCHS = 200
 
-
 TRACK_MOVING_AVERAGE = False
 
-DAY_NIGHT = 'ANY'
 
 TRIPLET = False
 CONV3D = False
@@ -159,7 +156,7 @@ class IcingIntensityNN:
         self.inputs.append(self.X_img)
         #self.inputs.append(self.X_prof)
 
-        self.DISK_CACHE = True
+        self.DISK_CACHE = False
 
         if datapath is not None:
             self.DISK_CACHE = False
@@ -380,7 +377,7 @@ class IcingIntensityNN:
         self.logits = logits
 
     def build_training(self):
-        self.loss = tf.keras.losses.BinaryCrossentropy(from_logits=True)  # for two-class only
+        self.loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)  # for two-class only
         #self.loss = tf.keras.losses.SparseCategoricalCrossentropy()  # For multi-class
 
         # decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)