def METHOD_NAME(graph_def,
             input_node_name,
             output_node_name,
             batch_size, 
             total_batch,
             class_num,
             eval_image_dir,
             eval_image_list):
    """dump activation of classification network graph_def's accuracy, need evaluation dataset"""
    # Initialization
    text = open(eval_image_list)
    line = text.readlines()
    base = 0
    sum_acc = 0
    tf.reset_default_graph()
    with tf.Session() as sess:
        sess.graph.as_default()
        tf.import_graph_def(graph_def,name = '')
        input_x = sess.graph.get_tensor_by_name(input_node_name)
        output = sess.graph.get_tensor_by_name(output_node_name)
        input_y = tf.placeholder(tf.float32,shape = [None,class_num])
        prediction = tf.argmax(tf.reshape(output, [batch_size, class_num]),1)
        label_prediction = tf.argmax(input_y,1)
        correct_prediction = tf.equal(prediction,label_prediction)
        accuracy = tf.reduce_mean(tf.cast(correct_prediction,'float'))
        tensors = []
        tensors_name = []
        for node in graph_def.node:
            print(node.name)
            tmp_tensor = sess.graph.get_tensor_by_name(node.name+":0")
            tensors.append(tmp_tensor)
            tensors_name.append(node.name+":0")
        print(tensors_name)
        tensors.append(input_x)
        tensors.append(output)
        tensors_name.append("input_x")
        tensors_name.append("output")
        print("Start Evaluation for {} Batches...".format(total_batch))
        progress = ProgressBar()
        for i in progress(range(0,total_batch)):
            batch_xs,batch_ys = load_image(base,line,batch_size,class_num,eval_image_dir)
            base +=  batch_size
            #  results = sess.run(node_names,{input_x:batch_xs,input_y:batch_ys})
            results = sess.run(tensors,{input_x:batch_xs,input_y:batch_ys})
            for name,res in zip(tensors_name,results):
                filename = os.path.join(FLAGS.output_dir, name.replace("/","_"))
                print("output: " , os.path.join(FLAGS.output_dir, name.replace("/","_")))
                res.tofile(filename+".bin")
                np.savetxt(filename+".txt", res, fmt="%s", delimiter=",")