How to add regularizations in TensorFlow?
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 q are:
Is there a more elegant or recommended way of regularization than doing it manually?
I also find that
get_variablehas 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!
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