InvalidArgumentError when using summary in Tensorflow v1.2.1

Question

I have wrote a simple code to try out the Tensorflow summarize feature. The code is below.

import tensorflow as tf
import numpy as np

graph = tf.Graph()


with graph.as_default():
    x = tf.placeholder(tf.float32, [1, 2], name='x')
    W = tf.ones([2, 1], tf.float32, name='W')
    b = tf.constant([1.5], dtype=tf.float32, shape=(1, 1), name='bias')
    y_ = tf.add(tf.matmul(x, W, name='mul'), b, name='add')
tf.summary.scalar('y', y_)

with tf.Session(graph=graph) as session:
    merged = tf.summary.merge_all()
    fw = tf.summary.FileWriter("/tmp/tensorflow/logs", graph=graph)

    tf.global_variables_initializer().run()
    x_var = np.array([1., 1.], np.float32).reshape([1, 2])
    print(x_var)
    summary, y = session.run([merged, y_], feed_dict={x: x_var})
    fw.add_summary(summary, 0)
    print(y)

    fw.close()

Basically, it tries to implement y=Wx + b.

The code works if I remove all the summary related code. But if I add the summary related code, I got below error:

InvalidArgumentError (see above for traceback): tags and values not the same shape: [] != [1,1] (tag 'y')
     [[Node: y = ScalarSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](y/tags, add)]]

I tried in both normal python, and IPython.

David S. · Accepted Answer · 2017-07-17 09:29:27Z

4

Tags and values do not have the same shape. You are passing x_var which is a vector and the summary takes a scalar value. You can simply use tf.reduce_mean to solve this problem:

with graph.as_default():
    x = tf.placeholder(tf.float32, [None, 2], name='x')
    W = tf.ones([2, 1], tf.float32, name='W')
    b = tf.constant([1.5], dtype=tf.float32, shape=(1, 1), name='bias')
    y_ = tf.add(tf.matmul(x, W, name='mul'), b, name='add')
    tf.summary.scalar('y', tf.reduce_mean(y_))

This will create a scalar value.

edited Jul 17, 2017 at 9:29

David S.

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answered Jul 17, 2017 at 9:17

Dat Tran

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2 Comments

David S. Over a year ago

With the help of your answer and this post, I finally get the code work. I also updated your answer.

David S. Over a year ago

The keys are, input x should NOT be fixed size; use reduce_* on the output y.

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