1

I tried to add some additional measurements to my training code for a CNN by utilising the functions from the tf.metrics submodule, such as tf.metrics.accuracy(y_labels, y_predicted) and equivalents for precision or recall. This is done in contrast to most of their tutorials where they suggest the convoluted:

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

Whereas my implementation replaces this line with:

accuracy = tf.metrics.accuracy(y_labels, y_predicted)

Now, even though I do the sess.run(tf.initialize_all_variables()) within my with tf.Session() as sess: block, I still get the following error when trying to use tf.metrics.accuracy function:

FailedPreconditionError (see above for traceback): Attempting to use uninitialized value performance/accuracy/count
 [[Node: performance/accuracy/count/read = Identity[T=DT_FLOAT, _class=["loc:@performance/accuracy/count"], _device="/job:localhost/replica:0/task:0/cpu:0"](performance/accuracy/count)]]

Most notably, replacing the accuracy = tf.metrics.accuracy(y_labels, y_predicted) line with accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) fixes the problem, however, I would like to implement other metrics such as precision, recall, etc. without doing it by hand.

Bruno KM
  • 768
  • 10
  • 20

2 Answers2

2

TL;DR: Add the following line at the beginning of your session:

sess.run(tf.local_variables_initializer())

The confusion arises from the name of the (as frankyjuang points out) deprecated tf.initialize_all_variables() function. This function was deprecated in part because it is misnamed: it doesn't actually initialize all variables, and instead it only initializes global (not local) variables. According to the documentation for the tf.metrics.accuracy() function (emphasis added):

The accuracy function creates two local variables, total and count that are used to compute the frequency with which predictions matches labels.

Therefore you need to add an explicit initialization step for the local variables, which can be done using tf.local_variables_initializer(), as suggested above.

mrry
  • 125,488
  • 26
  • 399
  • 400
-1

sess.run(tf.initialize_all_variables()) is deprecated.

Use sess.run(tf.global_variables_initializer()) instead to resolve your issue.

Reference

According to doc of tf.initialize_all_variables,

THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-02. Instructions for updating: Use tf.global_variables_initializer instead.

YLJ
  • 2,940
  • 2
  • 18
  • 29
  • I tried tf.global_variables_initializer() as well. Thankfully, as mrry pointed out, initialize_all_variables doesn't actually initialize all the variables as I thought would be the case. tf.local_variables_initializer() works fine for this use case. – Bruno KM Jul 11 '17 at 08:26