logging - Printing extra training metrics with Tensorflow Estimator

Question

Is there a way to let Tensorflow print extra training metrics (e.g. batch accuracy) when using the Estimator API?

One can add summaries and view the result in Tensorboard (see another post), but I was wondering if there is an elegant way to get the scalar summary values printed while training. This already happens for training loss, e.g.:

loss = 0.672677, step = 2901 (52.995 sec)

but it would be nice to have e.g.

loss = 0.672677, accuracy = 0.54678, step = 2901 (52.995 sec)

without to much trouble. I am aware that most of the time it is more useful to plot test set accuracy (I am already doing this with a validation monitor), but in this case I am also interested in training batch accuracy.

Answer 1

From what I've read it is not possible to change it by passing parameter. You can try to do by creating a logging hook and passing it into to estimator run.

In the body of model_fn function for your estimator:

logging_hook = tf.train.LoggingTensorHook({"loss" : loss, 
    "accuracy" : accuracy}, every_n_iter=10)

# Rest of the function

return tf.estimator.EstimatorSpec(
    ...params...
    training_hooks = [logging_hook])

EDIT:

To see the output you must also set logging verbosity high enough (unless its your default): tf.logging.set_verbosity(tf.logging.INFO)