How to do weight initialization by Xavier rule in Tensorflow 2.0?

Question

TF 2.0 happened to get rid of contrib library. Therefore, all the goodies like tf.contrib.conv2d or tf.contrib.layers.variance_scaling_initializer are gone. That said, what do you think would be the best way to do Xavier initialization in TF2.0 without using Keras (or initializing with some numpy hack)?

Namely, I am sticking to tf.nn.conv2d and for that function I am the one providing the weights:

filters = tf.Variable(tf.zeros([3, 3, 32, 64]))
??? # something should happen hear, i guess
net = tf.nn.conv2d(input, filters)

Note: Just in case you are using the first version of TF you can just go with:

filters = tf.get_variable("w", shape=[3,3, 32, 64],
           initializer=tf.contrib.layers.xavier_initializer()) 
# no tf.contrib in 2.0, no tf.get_variable in 2.0

Answer 1

In tensorflow 2.0 you have a package tf.initializer with all the Keras-like initializers you need.

The Xavier initializer is the same as the Glorot Uniform initializer. Thus, to create a (3,3) variable with values sampled from that initializer you can just:

shape = (3,3)
initializer = tf.initializers.GlorotUniform()
var = tf.Variable(initializer(shape=shape))

Answer 2

Just use glorot uniform initializer which is the same as xavier initializer.

Source: https://www.tensorflow.org/api_docs/python/tf/glorot_uniform_initializer

Also here is an example to prove that they are the same:

tf.reset_default_graph()
tf.set_random_seed(42)
xavier_var = tf.get_variable("w_xavier", shape=[3, 3], initializer=tf.contrib.layers.xavier_initializer())
sess = tf.Session()
sess.run(tf.global_variables_initializer())
print(sess.run(xavier_var))
# [[ 0.27579927 -0.6790426  -0.6128938 ]
#  [-0.49439836 -0.36137486 -0.7235348 ]
#  [-0.23143482 -0.3394227  -0.34756017]]
tf.reset_default_graph()
tf.set_random_seed(42)
glorot_var = tf.get_variable("w_glorot", shape=[3, 3], initializer=tf.glorot_uniform_initializer())
sess = tf.Session()
sess.run(tf.global_variables_initializer())
print(sess.run(glorot_var))
# [[ 0.27579927 -0.6790426  -0.6128938 ]
#  [-0.49439836 -0.36137486 -0.7235348 ]
#  [-0.23143482 -0.3394227  -0.34756017]]

In addition, if you want to the glorot uniform initializer with tf.Variable you can do:

tf.reset_default_graph()
tf.set_random_seed(42)
normal_var = tf.Variable(tf.glorot_uniform_initializer()((3, 3)))
sess = tf.Session()
sess.run(tf.global_variables_initializer())
print(sess.run(normal_var))
# [[ 0.27579927 -0.6790426  -0.6128938 ]
#  [-0.49439836 -0.36137486 -0.7235348 ]
#  [-0.23143482 -0.3394227  -0.34756017]]