How to add regularizations in TensorFlow?

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!

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).

I'll provide a simple correct answer since I didn't find one. You need two simple steps, the rest is done by tensorflow magic:

1. Add regularizers when creating variables or layers:

   tf.layers.dense(x, kernel_regularizer=tf.contrib.layers.l2_regularizer(0.001))# ortf.get_variable('a', regularizer=tf.contrib.layers.l2_regularizer(0.001))

2. Add the regularization term when defining loss:

   loss = ordinary_loss + tf.losses.get_regularization_loss()