<s> from __future__ import division , print_function , absolute_import <EOL> import tensorflow as tf <EOL> from tensorflow . python . training import moving_averages <EOL> import tflearn <EOL> from . . import utils <EOL> from . . import variables as vs <EOL> def batch_normalization ( incoming , beta = <NUM_LIT:0.0> , gamma = <NUM_LIT:1.0> , epsilon = <NUM_LIT> , <EOL> decay = <NUM_LIT> , trainable = True , restore = True , <EOL> stddev = <NUM_LIT> , name = "<STR_LIT>" ) : <EOL> """<STR_LIT>""" <EOL> input_shape = utils . get_incoming_shape ( incoming ) <EOL> input_ndim = len ( input_shape ) <EOL> gamma_init = tf . random_normal_initializer ( mean = gamma , stddev = stddev ) <EOL> with tf . name_scope ( name ) as scope : <EOL> beta = vs . variable ( scope + '<STR_LIT>' , shape = [ input_shape [ - <NUM_LIT:1> ] ] , <EOL> initializer = tf . constant_initializer ( beta ) , <EOL> trainable = trainable , restore = restore ) <EOL> gamma = vs . variable ( scope + '<STR_LIT>' , shape = [ input_shape [ - <NUM_LIT:1> ] ] , <EOL> initializer = gamma_init , trainable = trainable , <EOL> restore = restore ) <EOL> tf . add_to_collection ( tf . GraphKeys . LAYER_VARIABLES + '<STR_LIT:/>' + scope , beta ) <EOL> tf . add_to_collection ( tf . GraphKeys . LAYER_VARIABLES + '<STR_LIT:/>' + scope , gamma ) <EOL> if not restore : <EOL> tf . add_to_collection ( tf . GraphKeys . EXCL_RESTORE_VARS , beta ) <EOL> tf . add_to_collection ( tf . GraphKeys . EXCL_RESTORE_VARS , gamma ) <EOL> axis = list ( range ( input_ndim - <NUM_LIT:1> ) ) <EOL> moving_mean = vs . variable ( scope + '<STR_LIT>' , <EOL> input_shape [ - <NUM_LIT:1> : ] , <EOL> initializer = tf . zeros_initializer , <EOL> trainable = False , <EOL> restore = restore ) <EOL> moving_variance = vs . variable ( scope + '<STR_LIT>' , <EOL> input_shape [ - <NUM_LIT:1> : ] , <EOL> initializer = tf . ones_initializer , <EOL> trainable = False , <EOL> restore = restore ) <EOL> def update_mean_var ( ) : <EOL> mean , variance = tf . nn . moments ( incoming , axis ) <EOL> update_moving_mean = moving_averages . assign_moving_average ( <EOL> moving_mean , mean , decay ) <EOL> update_moving_variance = moving_averages . assign_moving_average ( <EOL> moving_variance , variance , decay ) <EOL> with tf . control_dependencies ( <EOL> [ update_moving_mean , update_moving_variance ] ) : <EOL> return tf . identity ( mean ) , tf . identity ( variance ) <EOL> is_training = tflearn . get_training_mode ( ) <EOL> mean , var = tf . python . control_flow_ops . cond ( <EOL> is_training , update_mean_var , lambda : ( moving_mean , moving_variance ) ) <EOL> try : <EOL> inference = tf . nn . batch_normalization ( <EOL> incoming , mean , var , beta , gamma , epsilon ) <EOL> inference . set_shape ( input_shape ) <EOL> except Exception as e : <EOL> inference = tf . nn . batch_norm_with_global_normalization ( <EOL> incoming , mean , var , beta , gamma , epsilon , <EOL> scale_after_normalization = True , <EOL> ) <EOL> inference . set_shape ( input_shape ) <EOL> inference . scope = scope <EOL> inference . beta = beta <EOL> inference . gamma = gamma <EOL> return inference <EOL> def local_response_normalization ( incoming , depth_radius = <NUM_LIT:5> , bias = <NUM_LIT:1.0> , <EOL> alpha = <NUM_LIT> , beta = <NUM_LIT> , <EOL> name = "<STR_LIT>" ) : <EOL> """<STR_LIT>""" <EOL> with tf . name_scope ( name ) as scope : <EOL> inference = tf . nn . lrn ( incoming , depth_radius = depth_radius , <EOL> bias = bias , alpha = alpha , <EOL> beta = beta , name = name ) <EOL> inference . scope = scope <EOL> return inference </s>
