for step in range(NUM_BATCHES):
    img, lbl = sess.run([batch_images, batch_labels])
    _, loss_value = sess.run([train_op, cost], feed_dict={X: img, Y: lbl, p_keep_conv: 0.8, p_keep_hidden: 0.5})
    print("Step %d, loss %1.5f" % (step, loss_value))
    sys.stdout.flush()
    tf.summary.scalar('loss', loss_value)
    summary_writer.add_summary(sess.run(tf.summary.merge_all()), step)