TensorFlow tf.nn.l2_loss() can help us to calculate the l2 loss of a deep learning model, which is a good way to void over-fitting problem. In this tutorial, we will introduce how to use this function to compute l2 loss for tensorflow beginners.
Suppose a neural model contain m weights, L2 loss can be defined as:
where n is the demension of weight wi.
tf.nn.l2_loss( t, name=None )
As to tf.nn.l2_loss() it will compute l2 loss fo a tensor, which is:
output = sum(t ** 2) / 2
Here is an example:
import numpy as np import tensorflow as tf x = tf.Variable(np.array([[1, 2, 3, 4],[5, 6, 7, 8]]), dtype = tf.float32) l2 = tf.nn.l2_loss(x)
Output the l2 loss
init = tf.global_variables_initializer() init_local = tf.local_variables_initializer() with tf.Session() as sess: sess.run([init, init_local]) print(sess.run([l2]))
The l2 loss is: [102.0]
However, you will use a penalty λ in deep learning model. You can learn more in this tutorial:
Moreover, as a neural model, there exist a serial of weights, how to get these weights easily and compute their l2 loss, you can refoer to this tutorial.