import tensorflow as tf


    tf.summary.image("input_image", image, max_outputs=2)
    tf.summary.image("ground_truth", tf.cast(annotation, tf.uint8), max_outputs=2)
    tf.summary.image("pred_annotation", tf.cast(pred_annotation, tf.uint8), max_outputs=2)

   # loss0 = tf.reduce_mean(tf.abs(z))
    loss = tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square((image - logits)),[1,2,3])))
   # loss2 = tf.reduce_mean(tf.square((image - logits)*mask2))
   # loss = loss1 + loss2 + loss0
   # loss = tf.reduce_mean(tf.squared_difference(logits ,annotation ))
    loss_summary = tf.summary.scalar("entropy", loss)
    
