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:
Is there a more elegant or recommended way of regularization than doing it manually?
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?
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!