diff --git a/modules/deeplearning/cnn_l1b_l2_16.py b/modules/deeplearning/cnn_l1b_l2_16.py
index 79cba6dba6b9aceb0bf41fc7c55366ae7196eafc..0531a21beb099084b5f820b6d04667a7e46dcb03 100644
--- a/modules/deeplearning/cnn_l1b_l2_16.py
+++ b/modules/deeplearning/cnn_l1b_l2_16.py
@@ -414,9 +414,9 @@ class UNET:
 
         conv = input_layer
 
-        # conv = build_residual_block_1x1(input_layer, num_filters, activation, 'Residual_Block_1', padding=padding)
+        conv = build_residual_block_1x1(input_layer, num_filters, activation, 'Residual_Block_1', padding=padding)
 
-        # 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_2', padding=padding)
 
         # conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_3', padding=padding)
 
@@ -467,8 +467,8 @@ class UNET:
         conv = tf.keras.layers.BatchNormalization()(conv)
         print(conv.shape)
 
-        skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
-        skip = tf.keras.layers.BatchNormalization()(skip)
+        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(conv)
+        conv = tf.keras.layers.BatchNormalization()(conv)
 
         conv = conv + skip
         conv = tf.keras.layers.LeakyReLU()(conv)
@@ -476,50 +476,46 @@ class UNET:
         # -----------------------------------------------------------------------------------------------------------
 
         skip = conv
-        num_filters *= 2
+
         conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
-        conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
         conv = tf.keras.layers.BatchNormalization()(conv)
         
-        skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
-        skip = tf.keras.layers.MaxPool2D(padding=padding)(skip)
-        skip = tf.keras.layers.BatchNormalization()(skip)
+        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(conv)
+        conv = tf.keras.layers.BatchNormalization()(conv)
         
         conv = conv + skip
         conv = tf.keras.layers.LeakyReLU()(conv)
         print('2d: ', conv.shape)
-        # # ----------------------------------------------------------------------------------------------------------
-        #
+        # ----------------------------------------------------------------------------------------------------------
+
         skip = conv
-        num_filters *= 2
+
         conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
-        conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
         conv = tf.keras.layers.BatchNormalization()(conv)
         
-        skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
-        skip = tf.keras.layers.MaxPool2D(padding=padding)(skip)
-        skip = tf.keras.layers.BatchNormalization()(skip)
+        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(conv)
+        conv = tf.keras.layers.BatchNormalization()(conv)
         
         conv = conv + skip
         conv = tf.keras.layers.LeakyReLU()(conv)
         print('3d: ', conv.shape)
-        #
-        # return conv
-        # -----------------------------------------------------------------------------------------------------------
 
-        skip = conv
-        num_filters *= 2
-        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
-        conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
-        conv = tf.keras.layers.BatchNormalization()(conv)
-        
-        skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
-        skip = tf.keras.layers.MaxPool2D(padding=padding)(skip)
-        skip = tf.keras.layers.BatchNormalization()(skip)
-        
-        conv = conv + skip
-        conv = tf.keras.layers.LeakyReLU()(conv)
-        print('4d: ', conv.shape)
+        return conv
+
+        # -----------------------------------------------------------------------------------------------------------
+        # skip = conv
+        # num_filters *= 2
+        # conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+        # conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
+        # conv = tf.keras.layers.BatchNormalization()(conv)
+        #
+        # skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
+        # skip = tf.keras.layers.MaxPool2D(padding=padding)(skip)
+        # skip = tf.keras.layers.BatchNormalization()(skip)
+        #
+        # conv = conv + skip
+        # conv = tf.keras.layers.LeakyReLU()(conv)
+        # print('4d: ', conv.shape)
 
         return conv