How to add regularizations in TensorFlow?

Question

I found in many available neural network code implemented using TensorFlow that regularization terms are often implemented by manually adding an additional term to loss value.

My questions are:

1. Is there a more elegant or recommended way of regularization than doing it manually?

2. I also find that get_variable has an argument regularizer. How should it be used? According to my observation, if we pass a regularizer to it (such as tf.contrib.layers.l2_regularizer, a tensor representing regularized term will be computed and added to a graph collection named tf.GraphKeys.REGULARIZATOIN_LOSSES. Will that collection be automatically used by TensorFlow (e.g. used by optimizers when training)? Or is it expected that I should use that collection by myself?

Answer 1

As you say in the second point, using the regularizer argument is the recommended way. You can use it in get_variable, or set it once in your variable_scope and have all your variables regularized.

The losses are collected in the graph, and you need to manually add them to your cost function like this.

  reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)   reg_constant = 0.01  # Choose an appropriate one.   loss = my_normal_loss + reg_constant * sum(reg_losses) 

Hope that helps!

Answer 2

A few aspects of the existing answer were not immediately clear to me, so here is a step-by-step guide:

1. Define a regularizer. This is where the regularization constant can be set, e.g.:

   regularizer = tf.contrib.layers.l2_regularizer(scale=0.1) 

2. Create variables via:

       weights = tf.get_variable(         name="weights",         regularizer=regularizer,         ...     ) 

   Equivalently, variables can be created via the regular weights = tf.Variable(...) constructor, followed by tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, weights).

3. Define some loss term and add the regularization term:

   reg_variables = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) reg_term = tf.contrib.layers.apply_regularization(regularizer, reg_variables) loss += reg_term 

   Note: It looks like tf.contrib.layers.apply_regularization is implemented as an AddN, so more or less equivalent to sum(reg_variables).