问TensorFlow：图表已完成，无法修改？

# ==========  STEP1: 读取数据  ========== #

mnist = input\_data.read\_data\_sets(FLAGS.data\_dir, one\_hot=True)    # 读取数据

# ==========  STEP2: 声明集群  ========== #

# 构建集群ClusterSpec和服务声明

ps\_hosts = FLAGS.ps\_hosts.split(",")

worker\_hosts = FLAGS.worker\_hosts.split(",")

cluster = tf.train.ClusterSpec({"ps":ps\_hosts, "worker":worker\_hosts})    # 构建集群名单

server = tf.train.Server(cluster, job\_name=FLAGS.job\_name, task\_index=FLAGS.task\_id)    # 声明服务

n\_workers = len(worker\_hosts)    # worker机的数量

# ==========  STEP3: ps机内容  ========== #

# 分工，对于ps机器不需要执行训练过程，只需要管理变量。server.join()会一直停在这条语句上。

if FLAGS.job\_name == "ps":

    with tf.device("/cpu:0"):

        server.join()

# ==========  STEP4: worker机内容  ========== #

# 下面定义worker机需要进行的操作

is\_chief = (FLAGS.task\_id == 0)    # 选取task\_id=0的worker机作为chief

# 通过replica\_device\_setter函数来指定每一个运算的设备。

# replica\_device\_setter会自动将所有参数分配到参数服务器上，将计算分配到当前的worker机上。

device\_setter = tf.train.replica\_device\_setter(

    worker\_device="/job:worker/task:%d" % FLAGS.task\_id,

    cluster=cluster)

# 这一台worker机器需要做的计算内容

with tf.device(device\_setter):

    # 输入数据

    x = tf.placeholder(name="x-input",shape=[None,28\*28],dtype=tf.float32)    # 输入样本像素为28\*28

    # x\_shape = x.get\_shape().as\_list()

    # length = x\_shape[1]

    # x\_reshaped = tf.reshape(x, [-1,length])

    y\_ = tf.placeholder(name="y-input", shape=[None,10],dtype=tf.float32)      # MNIST是十分类

    # 第一层（隐藏层）

    with tf.variable\_scope("layer1"):

        weight1 = tf.get\_variable(name="weight1", shape=[28\*28, 10], initializer=tf.glorot\_normal\_initializer())

        biases1 = tf.get\_variable(name="biases1", shape=[10], initializer=tf.glorot\_normal\_initializer())

        layer1 = tf.nn.relu(tf.matmul(x, weight1) + biases1, name="layer1")

    # 第二层（输出层）

    with tf.variable\_scope("layer2"):

        weight2 = tf.get\_variable(name="weight2", shape=[10, 10], initializer=tf.glorot\_normal\_initializer())

        biases2 = tf.get\_variable(name="biases2", shape=[10], initializer=tf.glorot\_normal\_initializer())

        y = tf.add(tf.matmul(layer1, weight2), biases2, name="y")

    pred = tf.argmax(y, axis=1, name="pred")

    global\_step = tf.contrib.framework.get\_or\_create\_global\_step()    # 必须手动声明global\_step否则会报错

    # 损失和优化

    cross\_entropy = tf.nn.sparse\_softmax\_cross\_entropy\_with\_logits(logits=y, labels=tf.argmax(y\_, axis=1))

    loss = tf.reduce\_mean(cross\_entropy)

    with tf.name\_scope('train'):

        optimizer = tf.train.GradientDescentOptimizer(0.01)

    with tf.name\_scope('gradient'):

        gradient\_all = optimizer.compute\_gradients(loss,weight2)

        gradients\_node=tf.gradients(loss,weight2)

        grads\_holder = [(tf.placeholder(tf.float32,shape=g.get\_shape()), v) 

                        for (g, v) in gradient\_all]

    # \*\*通过tf.train.SyncReplicasOptimizer函数实现函数同步更新\*\*

    opt = tf.train.SyncReplicasOptimizer(

        tf.train.GradientDescentOptimizer(0.01),

        replicas\_to\_aggregate=n\_workers,

        total\_num\_replicas=n\_workers

    )

    sync\_replicas\_hook = opt.make\_session\_run\_hook(is\_chief)

    train\_op = opt.apply\_gradients(grads\_holder, global\_step=global\_step)

    if is\_chief:

        train\_op = tf.no\_op()

    hooks = [sync\_replicas\_hook, tf.train.StopAtStepHook(last\_step=10000)]    # 把同步更新的hook加进来

    config = tf.ConfigProto(

        allow\_soft\_placement=True,    # 设置成True，那么当运行设备不满足要求时，会自动分配GPU或者CPU。

        log\_device\_placement=False,   # 设置为True时，会打印出TensorFlow使用了哪种操作

    )

    # ==========  STEP5: 打开会话  ========== #

    # 对于分布式训练，打开会话时不采用tf.Session()，而采用tf.train.MonitoredTrainingSession()

    # 详情参考：https://www.cnblogs.com/estragon/p/10034511.html

    with tf.train.MonitoredTrainingSession(

            master=server.target,

            is\_chief=is\_chief,

            # checkpoint\_dir=MODEL\_DIR,

            hooks=hooks,

            # save\_checkpoint\_secs=10,

            config=config) as sess:

        print("session started!")

        start\_time = time.time()

        step = 0

        while not sess.should\_stop(): 

            # for step in range(THREAD\_STEPS):   

            xs,ys= mnist.train.next\_batch(BATCH\_SIZE)    # batch\_size=32

            #求每个梯度

            grads = sess.run(gradients\_node, feed\_dict={x:xs, y\_: ys})

            grads=np.array(grads)

            grads=grads.reshape((10,10))

            print(grads)

            grad\_abs=np.abs(grads)

            variance = np.var(grad\_abs, axis=1)

            print('variance:',variance)

            #取方差最大的几组值

            topk\_var=tf.constant(variance)

            k=1

            output1 = tf.nn.top\_k(topk\_var, k)

            with tf.Session() as sess1:

                print(sess1.run(output1))

                a=output1.indices[-1]

                # print(sess1.run(a))  #a是所在TOPK个方差最大的索引值

                x=a.eval()

                a=int(x)

            g\_a=grads[a,:]

                # print('g\_a=',g\_a) 

                # print('\n')

            #取方差最大的一组值中的前几个大的梯度值，设置梯度阈值

            g\_a\_abs=np.abs(g\_a)        

            k=3

            output2 = tf.nn.top\_k(g\_a\_abs, k)

            with tf.Session() as sess2:

                print(sess2.run(output2))

                b=output2.indices[-1]

                # print(sess2.run(b))  #a是所在TOPK个方差最大的索引值

                x=b.eval()

                b=int(x) 

                threshold=g\_a\_abs[b]

            grad\_end=np.where(grad\_abs<threshold,0,grads)

            grad\_end=[grad\_end]

            grad\_var={}

            for i in range(len(grads\_holder)):

                    k = grads\_holder[i][0]

                    if k is not None:

                          # grad\_var[k] =np.var([tf.reshape(g, [-1]) for g in grads])

                          grad\_var[k] =[g[i][0] for g in grad\_end]

            \_, loss\_value, global\_step\_value = sess.run([train\_op, loss, global\_step], feed\_dict=grad\_var)

            if step > 0 and step % 100 == 0:

                duration = time.time() - start\_time

                sec\_per\_batch = duration / global\_step\_value

                print("After %d training steps(%d global steps), loss on training batch is %g (%.3f sec/batch)" % (step, global\_step\_value, loss\_value, sec\_per\_batch))

                print('Training elapsed time:%f s' % duration)

            step += 1

tf.app.run()