When I use predict_2.py with a BP neural network model, why can not get a accuracy like train accuracy?

[Wchenghui]

this is code:
def predictint(imvalue):

     with tf.Graph().as_default():
      def addlayer(input_data,insize,outsize,act_function=None):
            W=tf.Variable(tf.random_normal([insize,outsize]))
            b=tf.Variable(tf.zeros([outsize]))+0.1
            out_data=tf.matmul(input_data,W)+b
            if act_function==None:
	         return out_data
            elif act_function=="relu":
	         return tf.nn.relu(out_data)
            elif act_function=="softmax":
	          return tf.nn.softmax(out_data)
           else:
	          return tf.nn.sigmoid(out_data)
           x_input=tf.placeholder(tf.float32,[None,784])
          #y_input=tf.placeholder(tf.float32,[None,10])
           l1=addlayer(x_input,784,64,act_function="relu")
           l2=addlayer(l1,64,10,act_function="softmax")	    
          init_op = tf.initialize_all_variables()
          saver = tf.train.Saver()	    
    
              with tf.Session() as sess:
	         sess.run(init_op)
	         saver.restore(sess, "./model.ckpt")
                 prediction=tf.argmax(l2,1)
	         return prediction.eval(feed_dict={x_input: [imvalue]}, session=sess)