<s> from __future__ import division , print_function , unicode_literals <EOL> from collections import OrderedDict <EOL> from brainstorm . layers . base_layer import Layer <EOL> from brainstorm . structure . buffer_structure import ( BufferStructure , <EOL> StructureTemplate ) <EOL> from brainstorm . structure . construction import ConstructionWrapper <EOL> from brainstorm . utils import flatten_all_but_last <EOL> def BatchNorm ( name = None , decay = <NUM_LIT> , epsilon = <NUM_LIT> ) : <EOL> """<STR_LIT>""" <EOL> return ConstructionWrapper . create ( BatchNormLayerImpl , <EOL> name = name , <EOL> decay = decay , <EOL> epsilon = epsilon ) <EOL> class BatchNormLayerImpl ( Layer ) : <EOL> expected_inputs = { '<STR_LIT:default>' : StructureTemplate ( '<STR_LIT:T>' , '<STR_LIT:B>' , '<STR_LIT>' ) } <EOL> expected_kwargs = { '<STR_LIT>' , '<STR_LIT>' } <EOL> def setup ( self , kwargs , in_shapes ) : <EOL> self . epsilon = kwargs . get ( '<STR_LIT>' , <NUM_LIT> ) <EOL> self . decay = kwargs . get ( '<STR_LIT>' , <NUM_LIT> ) <EOL> assert <NUM_LIT:0.0> <= self . decay <= <NUM_LIT:1.0> , "<STR_LIT>" <EOL> outputs = OrderedDict ( ) <EOL> outputs [ '<STR_LIT:default>' ] = in_shapes [ '<STR_LIT:default>' ] <EOL> parameters = OrderedDict ( ) <EOL> buf = BufferStructure ( self . in_shapes [ '<STR_LIT:default>' ] . feature_shape [ - <NUM_LIT:1> ] ) <EOL> parameters [ '<STR_LIT>' ] = buf <EOL> parameters [ '<STR_LIT>' ] = buf <EOL> parameters [ '<STR_LIT>' ] = buf <EOL> parameters [ '<STR_LIT>' ] = buf <EOL> internals = OrderedDict ( ) <EOL> internals [ '<STR_LIT>' ] = buf <EOL> internals [ '<STR_LIT>' ] = self . in_shapes [ '<STR_LIT:default>' ] <EOL> internals [ '<STR_LIT>' ] = self . in_shapes [ '<STR_LIT:default>' ] <EOL> return outputs , parameters , internals <EOL> def forward_pass ( self , buffers , training_pass = True ) : <EOL> _h = self . handler <EOL> sigma_b , centered , x_hat = buffers . internals <EOL> gamma , beta , mu , sigma = buffers . parameters <EOL> inputs = flatten_all_but_last ( buffers . inputs . default ) <EOL> centered = flatten_all_but_last ( centered ) <EOL> x_hat = flatten_all_but_last ( x_hat ) <EOL> out = flatten_all_but_last ( buffers . outputs . default ) <EOL> m = inputs . shape [ <NUM_LIT:0> ] <EOL> if training_pass : <EOL> mu_b = sigma_b <EOL> _h . sum_t ( inputs , <NUM_LIT:0> , mu_b ) <EOL> _h . mult_st ( - <NUM_LIT:1.0> / m , mu_b , mu_b ) <EOL> _h . mult_st ( self . decay , mu , mu ) <EOL> _h . mult_add_st ( <NUM_LIT:1.0> - self . decay , mu_b , mu ) <EOL> mu = mu_b <EOL> _h . add_mv ( inputs , mu . reshape ( ( <NUM_LIT:1> , mu . size ) ) , centered ) <EOL> if training_pass : <EOL> sigma2 = sigma_b <EOL> centered2 = x_hat <EOL> _h . mult_tt ( centered , centered , centered2 ) <EOL> _h . sum_t ( centered2 , <NUM_LIT:0> , sigma2 ) <EOL> _h . mult_st ( <NUM_LIT:1.0> / m , sigma2 , sigma2 ) <EOL> _h . add_st ( self . epsilon , sigma2 , sigma2 ) <EOL> _h . sqrt_t ( sigma2 , sigma_b ) <EOL> _h . mult_st ( self . decay , sigma , sigma ) <EOL> _h . mult_add_st ( <NUM_LIT:1.0> - self . decay , sigma_b , sigma ) <EOL> sigma = sigma_b <EOL> _h . divide_mv ( centered , sigma . reshape ( ( <NUM_LIT:1> , sigma . size ) ) , x_hat ) <EOL> _h . mult_mv ( x_hat , gamma . reshape ( ( <NUM_LIT:1> , gamma . size ) ) , out ) <EOL> _h . add_mv ( out , beta . reshape ( ( <NUM_LIT:1> , beta . size ) ) , out ) <EOL> def backward_pass ( self , buffers ) : <EOL> _h = self . handler <EOL> sigma_b , centered , x_hat = buffers . internals <EOL> gamma = buffers . parameters . gamma <EOL> dgamma = buffers . gradients . gamma <EOL> dbeta = buffers . gradients . beta <EOL> x_hat = flatten_all_but_last ( x_hat ) <EOL> outdeltas = flatten_all_but_last ( buffers . output_deltas . default ) <EOL> indeltas = flatten_all_but_last ( buffers . input_deltas . default ) <EOL> m = outdeltas . shape [ <NUM_LIT:0> ] <EOL> big_tmp = _h . allocate ( x_hat . shape ) <EOL> small_tmp = _h . allocate ( gamma . shape ) <EOL> tmp = big_tmp <EOL> dgamma_tmp = small_tmp <EOL> _h . mult_tt ( outdeltas , x_hat , tmp ) <EOL> _h . sum_t ( tmp , axis = <NUM_LIT:0> , out = dgamma_tmp ) <EOL> _h . add_tt ( dgamma_tmp , dgamma , dgamma ) <EOL> _h . mult_st ( <NUM_LIT:1> / m , dgamma_tmp , dgamma_tmp ) <EOL> term1 = big_tmp <EOL> _h . mult_mv ( x_hat , dgamma_tmp . reshape ( ( <NUM_LIT:1> , gamma . size ) ) , term1 ) <EOL> dbeta_tmp = small_tmp <EOL> _h . sum_t ( outdeltas , axis = <NUM_LIT:0> , out = dbeta_tmp ) <EOL> _h . add_tt ( dbeta_tmp , dbeta , dbeta ) <EOL> _h . mult_st ( <NUM_LIT:1> / m , dbeta_tmp , dbeta_tmp ) <EOL> term2 = big_tmp <EOL> term3 = big_tmp <EOL> _h . subtract_tt ( outdeltas , term1 , term2 ) <EOL> _h . subtract_mv ( term2 , dbeta_tmp . reshape ( ( <NUM_LIT:1> , dbeta . size ) ) , term3 ) <EOL> coeff = small_tmp <EOL> _h . divide_tt ( gamma , sigma_b , coeff ) <EOL> term4 = big_tmp <EOL> _h . mult_mv ( term3 , coeff . reshape ( ( <NUM_LIT:1> , coeff . size ) ) , term4 ) <EOL> _h . add_tt ( term4 , indeltas , indeltas ) </s>
