Party Normalization - Tensor

Question

I looked at a few BN examples, but still got a little confused. Therefore, I use this function, which calls the function here;

https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.batch_norm.md

from tensorflow.contrib.layers.python.layers import batch_norm as batch_norm
import tensorflow as tf

def bn(x,is_training,name):
    bn_train = batch_norm(x, decay=0.9, center=True, scale=True,
    updates_collections=None,
    is_training=True,
    reuse=None, 
    trainable=True,
    scope=name)
    bn_inference = batch_norm(x, decay=1.00, center=True, scale=True,
    updates_collections=None,
    is_training=False,
    reuse=True, 
    trainable=False,
    scope=name)
    z = tf.cond(is_training, lambda: bn_train, lambda: bn_inference)
    return z

This next part is a toy where I just check that the function reuses the means and deviations calculated at the training stage for two functions. Running this part of the code in test mode, i.e. is_training=False, the current average / variance calculated during the training phase changes, which is visible when we print the BN variables that I receive from the callbnParams

if __name__ == "__main__":
    print("Example")

    import os
    import numpy as np
    import scipy.stats as stats
    np.set_printoptions(suppress=True,linewidth=200,precision=3)
    np.random.seed(1006)
    import pdb
    path = "batchNorm/"
    if not os.path.exists(path):
        os.mkdir(path)
    savePath = path + "bn.model"

    nFeats = 2
    X = tf.placeholder(tf.float32,[None,nFeats])
    is_training = tf.placeholder(tf.bool,name="is_training")
    Y = bn(X,is_training=is_training,name="bn")
    mvn = stats.multivariate_normal([0,100])
    bs = 4
    load = 0
    train = 1
    saver = tf.train.Saver()
    def bnCheck(batch,mu,std):
        # Checking calculation
        return (x - mu)/(std + 0.001)
    with tf.Session() as sess:
        if load == 1:
            saver.restore(sess,savePath)
        else:
            tf.global_variables_initializer().run()
        #### TRAINING #####
        if train == 1:
            for i in xrange(100):
                x = mvn.rvs(bs)
                y = Y.eval(feed_dict={X:x, is_training.name: True})

        def bnParams():
            beta, gamma, mean, var = [v.eval() for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,scope="bn")]
            return beta, gamma, mean, var

        beta, gamma, mean, var = bnParams()
        #### TESTING #####
        for i in xrange(10):
            x = mvn.rvs(1).reshape(1,-1)
            check = bnCheck(x,mean,np.sqrt(var))
            y = Y.eval(feed_dict={X:x, is_training.name: False})
            print("x = {0}, y = {1}, check = {2}".format(x,y,check))
            beta, gamma, mean, var = bnParams()
            print("BN Params: Beta {0} Gamma {1} mean {2} var{3} \n".format(beta,gamma,mean,var))

        saver.save(sess,savePath)

The first three iterations of the test cycle are as follows:

x = [[  -1.782  100.941]], y = [[-1.843  1.388]], check = [[-1.842  1.387]]
BN Params: Beta [ 0.  0.] Gamma [ 1.  1.] mean [ -0.2   99.93] var[ 0.818  0.589] 

x = [[  -1.245  101.126]], y = [[-1.156  1.557]], check = [[-1.155  1.557]]
BN Params: Beta [ 0.  0.] Gamma [ 1.  1.] mean [  -0.304  100.05 ] var[ 0.736  0.53 ] 

x = [[ -0.107  99.349]], y = [[ 0.23  -0.961]], check = [[ 0.23 -0.96]]
BN Params: Beta [ 0.  0.] Gamma [ 1.  1.] mean [ -0.285  99.98 ] var[ 0.662  0.477] 

BP, . / . ?

EDIT: , / ;

updates_collections, reuse, trainable

Answer 1

bn . :

def bn(x,is_training,name):
    return batch_norm(x, decay=0.9, center=True, scale=True,
    updates_collections=None,
    is_training=is_training,
    reuse=None,
    trainable=True,
    scope=name)

is_training - bool 0-D , , .. . , is_training, , .

EDIT: , True/False number.

Answer 2

slim.batch_norm slim.learning.create_train_op tf.train.GradientDecentOptimizer(lr).minimize(loss) . , !