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.
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
accuracyfunction creates two local variables,totalandcountthat are used to compute the frequency with whichpredictionsmatcheslabels.
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.