snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -75,7 +75,7 @@ s = np.arange(0, 64, 0.5)
 
 def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME',
                                 kernel_initializer='he_uniform', scale=None,
-                                do_drop_out=False, drop_rate=0.5, do_batch_norm=False):
+                                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)