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.
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.