Printing extra training metrics with Tensorflow Estimator

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(
    training_hooks = [logging_hook])


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

You can also use the TensorBoard to see some graphics of the desired metrics. To do that, add the metric to a TensorFlow summary like this:

accuracy = tf.metrics.accuracy(labels=labels, predictions=predictions["classes"])
tf.summary.scalar('accuracy', accuracy[1])

The cool thing when you use the tf.estimator.Estimator is that you don't need to add the summaries to a FileWriter, since it's done automatically (merging and saving them periodically by default - on average every 100 steps).

Don't forget to change this line as well, based on the accuracy parameter you just added:

eval_metric_ops = { "accuracy": accuracy }
return tf.estimator.EstimatorSpec(
    mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)

In order to see the TensorBoard you need to open a new terminal and type:

tensorboard --logdir={$MODEL_DIR}

After that you will be able to see the graphics in your browser at localhost:6006.