Explain what is batch normalization in TF learn

This recipe explains what what is batch normalization in TF learn

Recipe Objective

This recipe explains what is batch normalization in TFLayer.

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Batch Normalization

Its syntax is: tflearn.layers.normalization.batch_normalization (incoming, beta=0.0, gamma=1.0, epsilon=1e-05, decay=0.9, stddev=0.002, trainable=True, restore=True, reuse=False, scope=None, name='BatchNormalization')

def Batch_Bormalization(incoming, beta=0.0, gamma=1.0, epsilon=1e-5, decay=0.9, stddev=0.002, trainable=True, restore=True, reuse=False, scope=None, name="BatchNormalization"):

    ip_shape = utils.get_incoming_shape(incoming)
    ip_ndim = len(ip_shape)

    gamma_init = tf.random_normal_initializer(mean=gamma, stddev=stddev)

    with tf.variable_scope(scope, default_name=name, values=[incoming], reuse=reuse) as scope:
       name = scope.name
       b = vs.variable('beta', shape=[input_shape[-1]], initializer=tf.constant_initializer(beta), trainable=trainable, restore=restore)
       g = vs.variable('gamma', shape=[input_shape[-1]], initializer=gamma_init, trainable=trainable, restore=restore)

       tf.add_to_collection(tf.GraphKeys.LAYER_VARIABLES + '/' + name, b)
       tf.add_to_collection(tf.GraphKeys.LAYER_VARIABLES + '/' + name, g)
       if not restore:
          tf.add_to_collection(tf.GraphKeys.EXCL_RESTORE_VARS, b)
          tf.add_to_collection(tf.GraphKeys.EXCL_RESTORE_VARS, g)

       axis = list(range(ip_ndim - 1))

       mean = vs.variable('mean', ip_shape[-1:], initializer=tf.zeros_initializer(), trainable=False, restore=restore)
       variance = vs.variable('variance', ip_shape[-1:], initializer=tf.constant_initializer(1.), trainable=False, restore=restore)

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