diff --git a/modules/deeplearning/icing_fcn.py b/modules/deeplearning/icing_fcn.py
index 07aa8adfaf53ee2420b457d4d2849ca6efcb108c..2cb9642766a08e3c0a229085f3922ad8994ab87a 100644
--- a/modules/deeplearning/icing_fcn.py
+++ b/modules/deeplearning/icing_fcn.py
@@ -30,7 +30,7 @@ TRIPLET = False
 CONV3D = False
 
 NOISE_TRAINING = True
-NOISE_STDDEV = 0.10
+NOISE_STDDEV = 0.01
 DO_AUGMENT = True
 
 img_width = 16
@@ -566,7 +566,7 @@ class IcingIntensityFCN:
         # activation = tf.nn.elu
         activation = tf.nn.leaky_relu
 
-        num_filters = len(self.train_params) * 10
+        num_filters = len(self.train_params) * 16
 
         input_2d = self.inputs[0]
         conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding=padding, activation=None)(input_2d)
@@ -651,11 +651,11 @@ class IcingIntensityFCN:
 
         conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_2', padding=padding)
 
-        conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_3', padding=padding)
+        # conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_3', padding=padding)
 
-        conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_4', padding=padding)
+        # conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_4', padding=padding)
 
-        conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_5', padding=padding)
+        # conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_5', padding=padding)
 
         print(conv.shape)
 
@@ -681,7 +681,7 @@ class IcingIntensityFCN:
         initial_learning_rate = 0.002
         decay_rate = 0.95
         steps_per_epoch = int(self.num_data_samples/BATCH_SIZE)  # one epoch
-        decay_steps = int(steps_per_epoch / 2)
+        decay_steps = int(steps_per_epoch)
         print('initial rate, decay rate, steps/epoch, decay steps: ', initial_learning_rate, decay_rate, steps_per_epoch, decay_steps)
 
         self.learningRateSchedule = tf.keras.optimizers.schedules.ExponentialDecay(initial_learning_rate, decay_steps, decay_rate)