snapshot..

modules/deeplearning/srcnn_l1b_l2.py

@@ -106,12 +106,12 @@ y_2 = y_67
 
 
 def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME',
-                                kernel_initializer='he_uniform', scale=None,
+                                kernel_initializer='he_uniform', scale=None, kernel_size=3,
                                 do_drop_out=True, drop_rate=0.5, do_batch_norm=False):
 
     with tf.name_scope(block_name):
-        skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, padding=padding, kernel_initializer=kernel_initializer, activation=activation)(conv)
-        skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, padding=padding, activation=None)(skip)
+        skip = tf.keras.layers.Conv2D(num_filters, kernel_size=kernel_size, padding=padding, kernel_initializer=kernel_initializer, activation=activation)(conv)
+        skip = tf.keras.layers.Conv2D(num_filters, kernel_size=kernel_size, padding=padding, activation=None)(skip)
 
         if scale is not None:
             skip = tf.keras.layers.Lambda(lambda x: x * scale)(skip)