在我的TensorFlow代码中,我希望我的网络从其中一个获取输入 StagingArea 对象,这取决于我是想进行培训还是测试。 我编写的图形构造代码的一部分如下:

with tf.device("/gpu:0"):
      for i in range(numgpus):
          with tf.variable_scope(tf.get_variable_scope(), reuse=i>0) as vscope:
              with tf.device('/gpu:{}'.format(i)):
                  with tf.name_scope('GPU-Tower-{}'.format(i)) as scope:
                      phase = tf.get_variable("phase", [], initializer=tf.zeros_initializer(),dtype=tf.uint8, trainable=False)
                      phaseassigntest = phase.assign(1)
                      phaseassigntrain = phase.assign(0)
                      phasetest = tf.equal(phase, 0)
                      is_training = tf.cond(phasetest, lambda: tf.constant(True), lambda: tf.constant(False))

                      trainstagingarea = tf.contrib.staging.StagingArea([tf.float32, tf.int32], shapes=[[trainbatchsize, 3, 221, 221], [trainbatchsize]], capacity=20)
                      putoptrain = trainstagingarea.put(train_iterator.get_next())
                      trainputop.append(putoptrain)
                      getoptrain = trainstagingarea.get()
                      traingetop.append(getoptrain)
                      trainclearop = trainstagingarea.clear()
                      trainstageclear.append(trainclearop)
                      trainsizeop = trainstagingarea.size()
                      trainstagesize.append(trainsizeop)

                      valstagingarea = tf.contrib.staging.StagingArea([tf.float32, tf.int32], shapes=[[valbatchsize, 3, 221, 221], [valbatchsize]], capacity=20)
                      putopval = valstagingarea.put(val_iterator.get_next())
                      valputop.append(putopval)
                      getopval = valstagingarea.get()
                      valgetop.append(getopval)
                      valclearop = valstagingarea.clear()
                      valstageclear.append(valclearop)
                      valsizeop = valstagingarea.size()
                      valstagesize.append(valsizeop)


                      #elem = valgetop[i]
                      elem = tf.cond(is_training,lambda: traingetop[i],lambda: valgetop[i])

                      img = elem[0]
                      label = elem[1]
                      labelonehot = tf.one_hot(label, depth=numclasses)
                      net, networksummaries =  overfeataccurate(img,numclasses=numclasses, phase=is_training)

tf.cond 确保网络由其中一个供电 阶段性区域 物体。一个用于培训,另一个用于验证。

with tf.Session(graph=g,config=config) as sess:
    sess.run(init_op)
    sess.run(tf.local_variables_initializer())
    sess.run(val_initialize)
    for i in range(20):
        sess.run(valputop)
        print(sess.run(valstagesize))
    writer = tf.summary.FileWriter('.', graph=tf.get_default_graph())
    epoch = 0
    iter = 0
    print("Performing Validation")
    sess.run(phaseassigntest)
    saver = tf.train.Saver()
    while(epoch<10):
        time_init = time.time()
        while True:
            try:
                [val_accu, _, summaries] = sess.run([towervalidation, towervalidationupdateop,validation_summary_op])
                print(val_accu)

何时而不是 tf.cond() 我直接分配 elem = valgetop[i] ,代码运行良好。

基于我是想进行培训还是测试,给我的网络提供信息的正确方式是什么?

注释 numgpus 到1。