how to write summary in distributied tensorflow?

I have tried servral methods to write summary in distributied tensorflow setting, but all of them failed. my last try failed with

InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float

Following is my code:

if arguments.job_name == "ps":

    server.join()

elif arguments.job_name == "worker":

    is_chief = (arguments.task_index == 0)

    with tf.device(tf.train.replica_device_setter(worker_device="/job:worker/task:%d" % arguments.task_index,

                                                  cluster=cluster)):

        x = tf.placeholder(tf.float32, shape=(None, feature_num), name="input_x")

        y = tf.placeholder(tf.float32, shape=(None, 1), name="input_y")

        w = tf.get_variable("weight", (feature_num, 1), initializer=tf.random_normal_initializer())

        b = tf.get_variable("bais", (1, 1), initializer=tf.constant_initializer(0.0))

        p_y = tf.add(tf.matmul(x, w), b)

        loss = tf.reduce_sum(tf.square(y - p_y), name="loss")

        global_step = tf.contrib.framework.get_or_create_global_step()

        opt = tf.train.GradientDescentOptimizer(learning_rate=0.0000001)

        training_op = opt.minimize(loss, global_step=global_step)

        tf.summary.scalar("loss",loss)

        summary_op = tf.summary.merge_all()

        summary_hook = tf.train.SummarySaverHook(save_secs=1, output_dir="./summary", summary_op=summary_op)



    with tf.train.MonitoredTrainingSession(master=server.target,

                                           is_chief=is_chief,

                                           hooks=[summary_hook],save_summaries_secs=None,save_summaries_steps=None,

                                           checkpoint_dir="./model",save_checkpoint_secs=5) as mon_sess:



        for (x_data, y_data) in dataSet:

            if not mon_sess.should_stop():

                print("============training...=====================")

                mon_sess.run(training_op, feed_dict={x: x_data, y: y_data})

                mon_sess.run(summary_op, feed_dict={x: x_data, y: y_data})

        print("finised!!!!")

can anybody give help?

edited Jan 2 at 1:13

asked Jan 1 at 12:29

jianjunwu

add a comment |

I have tried servral methods to write summary in distributied tensorflow setting, but all of them failed. my last try failed with

InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float

Following is my code:

if arguments.job_name == "ps":

    server.join()

elif arguments.job_name == "worker":

    is_chief = (arguments.task_index == 0)

    with tf.device(tf.train.replica_device_setter(worker_device="/job:worker/task:%d" % arguments.task_index,

                                                  cluster=cluster)):

        x = tf.placeholder(tf.float32, shape=(None, feature_num), name="input_x")

        y = tf.placeholder(tf.float32, shape=(None, 1), name="input_y")

        w = tf.get_variable("weight", (feature_num, 1), initializer=tf.random_normal_initializer())

        b = tf.get_variable("bais", (1, 1), initializer=tf.constant_initializer(0.0))

        p_y = tf.add(tf.matmul(x, w), b)

        loss = tf.reduce_sum(tf.square(y - p_y), name="loss")

        global_step = tf.contrib.framework.get_or_create_global_step()

        opt = tf.train.GradientDescentOptimizer(learning_rate=0.0000001)

        training_op = opt.minimize(loss, global_step=global_step)

        tf.summary.scalar("loss",loss)

        summary_op = tf.summary.merge_all()

        summary_hook = tf.train.SummarySaverHook(save_secs=1, output_dir="./summary", summary_op=summary_op)



    with tf.train.MonitoredTrainingSession(master=server.target,

                                           is_chief=is_chief,

                                           hooks=[summary_hook],save_summaries_secs=None,save_summaries_steps=None,

                                           checkpoint_dir="./model",save_checkpoint_secs=5) as mon_sess:



        for (x_data, y_data) in dataSet:

            if not mon_sess.should_stop():

                print("============training...=====================")

                mon_sess.run(training_op, feed_dict={x: x_data, y: y_data})

                mon_sess.run(summary_op, feed_dict={x: x_data, y: y_data})

        print("finised!!!!")

can anybody give help?

edited Jan 2 at 1:13

asked Jan 1 at 12:29

jianjunwu

add a comment |

I have tried servral methods to write summary in distributied tensorflow setting, but all of them failed. my last try failed with

InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float

Following is my code:

if arguments.job_name == "ps":

    server.join()

elif arguments.job_name == "worker":

    is_chief = (arguments.task_index == 0)

    with tf.device(tf.train.replica_device_setter(worker_device="/job:worker/task:%d" % arguments.task_index,

                                                  cluster=cluster)):

        x = tf.placeholder(tf.float32, shape=(None, feature_num), name="input_x")

        y = tf.placeholder(tf.float32, shape=(None, 1), name="input_y")

        w = tf.get_variable("weight", (feature_num, 1), initializer=tf.random_normal_initializer())

        b = tf.get_variable("bais", (1, 1), initializer=tf.constant_initializer(0.0))

        p_y = tf.add(tf.matmul(x, w), b)

        loss = tf.reduce_sum(tf.square(y - p_y), name="loss")

        global_step = tf.contrib.framework.get_or_create_global_step()

        opt = tf.train.GradientDescentOptimizer(learning_rate=0.0000001)

        training_op = opt.minimize(loss, global_step=global_step)

        tf.summary.scalar("loss",loss)

        summary_op = tf.summary.merge_all()

        summary_hook = tf.train.SummarySaverHook(save_secs=1, output_dir="./summary", summary_op=summary_op)



    with tf.train.MonitoredTrainingSession(master=server.target,

                                           is_chief=is_chief,

                                           hooks=[summary_hook],save_summaries_secs=None,save_summaries_steps=None,

                                           checkpoint_dir="./model",save_checkpoint_secs=5) as mon_sess:



        for (x_data, y_data) in dataSet:

            if not mon_sess.should_stop():

                print("============training...=====================")

                mon_sess.run(training_op, feed_dict={x: x_data, y: y_data})

                mon_sess.run(summary_op, feed_dict={x: x_data, y: y_data})

        print("finised!!!!")

can anybody give help?

edited Jan 2 at 1:13

asked Jan 1 at 12:29

jianjunwu

I have tried servral methods to write summary in distributied tensorflow setting, but all of them failed. my last try failed with

InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float

Following is my code:

if arguments.job_name == "ps":

    server.join()

elif arguments.job_name == "worker":

    is_chief = (arguments.task_index == 0)

    with tf.device(tf.train.replica_device_setter(worker_device="/job:worker/task:%d" % arguments.task_index,

                                                  cluster=cluster)):

        x = tf.placeholder(tf.float32, shape=(None, feature_num), name="input_x")

        y = tf.placeholder(tf.float32, shape=(None, 1), name="input_y")

        w = tf.get_variable("weight", (feature_num, 1), initializer=tf.random_normal_initializer())

        b = tf.get_variable("bais", (1, 1), initializer=tf.constant_initializer(0.0))

        p_y = tf.add(tf.matmul(x, w), b)

        loss = tf.reduce_sum(tf.square(y - p_y), name="loss")

        global_step = tf.contrib.framework.get_or_create_global_step()

        opt = tf.train.GradientDescentOptimizer(learning_rate=0.0000001)

        training_op = opt.minimize(loss, global_step=global_step)

        tf.summary.scalar("loss",loss)

        summary_op = tf.summary.merge_all()

        summary_hook = tf.train.SummarySaverHook(save_secs=1, output_dir="./summary", summary_op=summary_op)



    with tf.train.MonitoredTrainingSession(master=server.target,

                                           is_chief=is_chief,

                                           hooks=[summary_hook],save_summaries_secs=None,save_summaries_steps=None,

                                           checkpoint_dir="./model",save_checkpoint_secs=5) as mon_sess:



        for (x_data, y_data) in dataSet:

            if not mon_sess.should_stop():

                print("============training...=====================")

                mon_sess.run(training_op, feed_dict={x: x_data, y: y_data})

                mon_sess.run(summary_op, feed_dict={x: x_data, y: y_data})

        print("finised!!!!")

can anybody give help?

tensorflow distributed

edited Jan 2 at 1:13

asked Jan 1 at 12:29

jianjunwu

edited Jan 2 at 1:13

asked Jan 1 at 12:29

jianjunwu

edited Jan 2 at 1:13

asked Jan 1 at 12:29

jianjunwu

asked Jan 1 at 12:29

jianjunwu

asked Jan 1 at 12:29

jianjunwu

add a comment |

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