minor

modules/deeplearning/icing_fcn.py

@@ -256,11 +256,6 @@ class IcingIntensityFCN:
         data = []
         for param in self.train_params:
             nda = self.get_parameter_data(param, nd_idxs, is_training)
-            # Manual Corruption Process. Better: see use of tf.keras.layers.GaussianNoise
-            # if NOISE_TRAINING and is_training:
-            #     nda = normalize(nda, param, mean_std_dct, add_noise=True, noise_scale=0.01, seed=42)
-            # else:
-            #     nda = normalize(nda, param, mean_std_dct)
             nda = normalize(nda, param, mean_std_dct)
             if DO_ZERO_OUT and is_training:
                 try: