diff --git a/modules/deeplearning/espcn_l1b_l2.py b/modules/deeplearning/espcn_l1b_l2.py
index 8a85149516edb452fe01f2d971f063a06280fa37..3c98cebad81dcd2cdab15a19f1848357c3173ca6 100644
--- a/modules/deeplearning/espcn_l1b_l2.py
+++ b/modules/deeplearning/espcn_l1b_l2.py
@@ -72,8 +72,6 @@ y_134_2 = y_134[2:133:2]
 
 slc_x = slice(3, 131)
 slc_y = slice(3, 131)
-#slc_x_2 = slice(3, 131, 2)
-#slc_y_2 = slice(3, 131, 2)
 slc_x_2 = slice(2, 133, 2)
 slc_y_2 = slice(2, 133, 2)
 
@@ -384,23 +382,25 @@ class ESPCN:
 
         conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', scale=scale)
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', scale=scale)
+        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', scale=scale)
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', scale=scale)
+        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', scale=scale)
 
-        # conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, kernel_initializer=kernel_initializer)(conv_b)
+        conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation, kernel_initializer=kernel_initializer)(conv_b)
 
         conv = conv + conv_b
-        # conv = conv_b
         print(conv.shape)
 
-        conv = tf.keras.layers.Conv2D(IMG_DEPTH * (factor ** 2), 3, padding='same', activation=activation)(conv)
+        conv = tf.keras.layers.Conv2D(IMG_DEPTH * (factor ** 2), 3, padding=padding, activation=activation)(conv)
+        print(conv.shape)
+
+        conv = tf.keras.layers.Conv2D(IMG_DEPTH * (factor ** 2), 3, padding=padding, activation=activation)(conv)
         print(conv.shape)
 
         conv = tf.nn.depth_to_space(conv, factor)
         print(conv.shape)
 
-        self.logits = tf.keras.layers.Conv2D(IMG_DEPTH, kernel_size=1, strides=1, padding=padding, activation=activation, name='regression')(conv)
+        self.logits = tf.keras.layers.Conv2D(IMG_DEPTH, kernel_size=1, strides=1, padding=padding, name='regression')(conv)
 
         print(self.logits.shape)