diff --git a/modules/deeplearning/espcn.py b/modules/deeplearning/espcn.py
index 6ddb3f14d8b04f9980751a5bbad5d2a1b25299c0..d189b523ffc337a6856032abfa7aafefaecfd5e3 100644
--- a/modules/deeplearning/espcn.py
+++ b/modules/deeplearning/espcn.py
@@ -397,7 +397,7 @@ class ESPCN:
         if do_batch_norm:
             conv = tf.keras.layers.BatchNormalization()(conv)
 
-        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding=padding, activation=activation)(conv)
         print(conv.shape)
 
         if do_drop_out:
@@ -430,20 +430,21 @@ class ESPCN:
         if do_batch_norm:
             conv = tf.keras.layers.BatchNormalization()(conv)
 
-        conv = tf.keras.layers.Conv2D(num_filters // 2, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
-        print(conv.shape)
-
-        # conv = tf.keras.layers.Conv2D(4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+        # conv = tf.keras.layers.Conv2D(num_filters // 2, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
         # print(conv.shape)
 
-        conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=2, padding=padding, activation=activation)(conv)
+        conv = tf.keras.layers.Conv2D(4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
         print(conv.shape)
 
-        conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+        # conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=2, padding=padding, activation=activation)(conv)
+        conv = tf.keras.layers.Conv2DTranspose(1, kernel_size=3, strides=2, padding=padding, activation=activation)(conv)
         print(conv.shape)
 
-        conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
-        print(conv.shape)
+        # conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+        # print(conv.shape)
+        #
+        # conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+        # print(conv.shape)
 
         #self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='probability', activation=tf.nn.sigmoid)(conv)
         self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='probability')(conv)