snapshot...

modules/deeplearning/icing.py

@@ -45,7 +45,7 @@ abi_half_width = {'08': 12, '14': 12, '02': 48, '11': 12, '13': 12, '15': 12, '1
 #abi_half_width = {'08': 6, '14': 6, '02': 24, '11': 6, '13': 6, '15': 6, '16': 6, '09': 6, '10': 6}
 #abi_half_width = {'08': 3, '14': 3, '02': 12, '11': 3, '13': 3, '15': 3, '16': 3, '09': 3, '10': 3}
 abi_stride = {'08': 1, '14': 1, '02': 4, '11': 1, '13': 1, '15': 1, '16': 1, '09': 1, '10': 1}
-img_width = 24
+img_width = 16
 #img_width = 12
 #img_width = 6
 
@@ -204,10 +204,14 @@ class IcingIntensityNN:
             # nda = do_normalize(nda)
             data.append(nda)
         data = np.stack(data)
-        data = np.transpose(data, axes=(1,0))
+        data = data.astype(np.float32)
+        data = np.transpose(data, axes=(1, 0))
+
         label = self.h5f['icing_intensity'][nd_keys]
+        label = label.astype(np.int32)
         label = np.where(label == -1, 0, label)
-        # binary
+
+        # binary, two class
         label = np.where(label != 0, 1, label)
 
         # TODO: Implement in memory cache
@@ -228,7 +232,7 @@ class IcingIntensityNN:
 
     @tf.function(input_signature=[tf.TensorSpec(None, tf.int32)])
     def data_function(self, indexes):
-        out = tf.numpy_function(self.get_in_mem_data_batch, [indexes], [tf.float64, tf.float64, tf.int32])
+        out = tf.numpy_function(self.get_in_mem_data_batch, [indexes], [tf.float32, tf.float32, tf.int32])
         return out
 
     def get_train_dataset(self, indexes):