significance of "trainable" and "training" flag in tf.layers.batch_normalization

Question

What is the significance of "trainable" and "training" flag in tf.layers.batch_normalization? How are these two different during training and prediction?

Answer 1

The batch norm has two phases:

1. Training:
   -  Normalize layer activations using `moving_avg`, `moving_var`, `beta` and `gamma` 
     (`training`* should be `True`.)
   -  update the `moving_avg` and `moving_var` statistics. 
     (`trainable` should be `True`)
2. Inference:
   -  Normalize layer activations using `beta` and `gamma`.
      (`training` should be `False`)

Example code to illustrate few cases:

#random image
img = np.random.randint(0,10,(2,2,4)).astype(np.float32)

# batch norm params initialized
beta = np.ones((4)).astype(np.float32)*1 # all ones 
gamma = np.ones((4)).astype(np.float32)*2 # all twos
moving_mean = np.zeros((4)).astype(np.float32) # all zeros
moving_var = np.ones((4)).astype(np.float32) # all ones

#Placeholders for input image
_input = tf.placeholder(tf.float32, shape=(1,2,2,4), name='input')

#batch Norm
out = tf.layers.batch_normalization(
       _input,
       beta_initializer=tf.constant_initializer(beta),
       gamma_initializer=tf.constant_initializer(gamma),
       moving_mean_initializer=tf.constant_initializer(moving_mean),
       moving_variance_initializer=tf.constant_initializer(moving_var),
       training=False, trainable=False)


update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
init_op = tf.global_variables_initializer()

 ## 2. Run the graph in a session 

 with tf.Session() as sess:

    # init the variables   
    sess.run(init_op)

    for i in range(2):
        ops, o = sess.run([update_ops, out], feed_dict={_input: np.expand_dims(img, 0)})
        print('beta', sess.run('batch_normalization/beta:0'))
        print('gamma', sess.run('batch_normalization/gamma:0'))
        print('moving_avg',sess.run('batch_normalization/moving_mean:0'))
        print('moving_variance',sess.run('batch_normalization/moving_variance:0'))
        print('out', np.round(o))
        print('')

When training=False and trainable=False:

  img = [[[4., 5., 9., 0.]...
  out = [[ 9. 11. 19.  1.]... 
  The activation is scaled/shifted using gamma and beta.

When training=True and trainable=False:

  out = [[ 2.  2.  3. -1.] ...
  The activation is normalized using `moving_avg`, `moving_var`, `gamma` and `beta`. 
  The averages are not updated.

When traning=True and trainable=True:

  The out is same as above, but the `moving_avg` and `moving_var` gets updated to new values.

  moving_avg [0.03249997 0.03499997 0.06499994 0.02749997]
  moving_variance [1.0791667 1.1266665 1.0999999 1.0925]