<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 LayerValidationError , flatten_time , flatten_time_and_features <EOL> def Recurrent ( size , activation = '<STR_LIT>' , name = None ) : <EOL> """<STR_LIT>""" <EOL> return ConstructionWrapper . create ( RecurrentLayerImpl , size = size , <EOL> name = name , activation = activation ) <EOL> class RecurrentLayerImpl ( Layer ) : <EOL> expected_inputs = { '<STR_LIT:default>' : StructureTemplate ( '<STR_LIT:T>' , '<STR_LIT:B>' , '<STR_LIT>' ) } <EOL> expected_kwargs = { '<STR_LIT:size>' , '<STR_LIT>' } <EOL> def setup ( self , kwargs , in_shapes ) : <EOL> self . activation = kwargs . get ( '<STR_LIT>' , '<STR_LIT>' ) <EOL> self . size = kwargs . get ( '<STR_LIT:size>' , self . in_shapes [ '<STR_LIT:default>' ] . feature_size ) <EOL> if not isinstance ( self . size , int ) : <EOL> raise LayerValidationError ( '<STR_LIT>' . <EOL> format ( self . size ) ) <EOL> in_size = self . in_shapes [ '<STR_LIT:default>' ] . feature_size <EOL> outputs = OrderedDict ( ) <EOL> outputs [ '<STR_LIT:default>' ] = BufferStructure ( '<STR_LIT:T>' , '<STR_LIT:B>' , self . size , <EOL> context_size = <NUM_LIT:1> ) <EOL> parameters = OrderedDict ( ) <EOL> parameters [ '<STR_LIT>' ] = BufferStructure ( self . size , in_size ) <EOL> parameters [ '<STR_LIT:R>' ] = BufferStructure ( self . size , self . size ) <EOL> parameters [ '<STR_LIT>' ] = BufferStructure ( self . size ) <EOL> internals = OrderedDict ( ) <EOL> internals [ '<STR_LIT>' ] = BufferStructure ( '<STR_LIT:T>' , '<STR_LIT:B>' , self . size , context_size = <NUM_LIT:1> ) <EOL> internals [ '<STR_LIT>' ] = BufferStructure ( '<STR_LIT:T>' , '<STR_LIT:B>' , self . size , context_size = <NUM_LIT:1> , <EOL> is_backward_only = True ) <EOL> internals [ '<STR_LIT>' ] = BufferStructure ( '<STR_LIT:T>' , '<STR_LIT:B>' , self . size , context_size = <NUM_LIT:1> , <EOL> is_backward_only = True ) <EOL> return outputs , parameters , internals <EOL> def forward_pass ( self , buffers , training_pass = True ) : <EOL> _h = self . handler <EOL> W , R , bias = buffers . parameters <EOL> inputs = buffers . inputs . default <EOL> outputs = buffers . outputs . default <EOL> Ha = buffers . internals . Ha <EOL> flat_inputs = flatten_time_and_features ( inputs ) <EOL> flat_H = flatten_time ( Ha [ : - <NUM_LIT:1> ] ) <EOL> _h . dot_mm ( flat_inputs , W , flat_H , transb = True ) <EOL> _h . add_mv ( flat_H , bias . reshape ( ( <NUM_LIT:1> , self . size ) ) , flat_H ) <EOL> for t in range ( inputs . shape [ <NUM_LIT:0> ] ) : <EOL> _h . dot_add_mm ( outputs [ t - <NUM_LIT:1> ] , R , Ha [ t ] , transb = True ) <EOL> _h . act_func [ self . activation ] ( Ha [ t ] , outputs [ t ] ) <EOL> def backward_pass ( self , buffers ) : <EOL> _h = self . handler <EOL> W , R , bias = buffers . parameters <EOL> dW , dR , dbias = buffers . gradients <EOL> inputs = buffers . inputs . default <EOL> outputs = buffers . outputs . default <EOL> dinputs = buffers . input_deltas . default <EOL> doutputs = buffers . output_deltas . default <EOL> Ha , dHa , dHb = buffers . internals <EOL> _h . copy_to ( doutputs , dHb ) <EOL> T = inputs . shape [ <NUM_LIT:0> ] - <NUM_LIT:1> <EOL> _h . act_func_deriv [ self . activation ] ( Ha [ T ] , outputs [ T ] , dHb [ T ] , dHa [ T ] ) <EOL> for t in range ( T - <NUM_LIT:1> , - <NUM_LIT:1> , - <NUM_LIT:1> ) : <EOL> _h . dot_add_mm ( dHa [ t + <NUM_LIT:1> ] , R , dHb [ t ] ) <EOL> _h . act_func_deriv [ self . activation ] ( Ha [ t ] , outputs [ t ] , <EOL> dHb [ t ] , dHa [ t ] ) <EOL> flat_inputs = flatten_time_and_features ( inputs ) <EOL> flat_dinputs = flatten_time_and_features ( dinputs ) <EOL> flat_dHa = flatten_time ( dHa [ : - <NUM_LIT:1> ] ) <EOL> _h . dot_add_mm ( flat_dHa , W , flat_dinputs ) <EOL> _h . dot_add_mm ( flat_dHa , flat_inputs , dW , transa = True ) <EOL> dbias_tmp = _h . allocate ( dbias . shape ) <EOL> _h . sum_t ( flat_dHa , axis = <NUM_LIT:0> , out = dbias_tmp ) <EOL> _h . add_tt ( dbias , dbias_tmp , dbias ) <EOL> flat_outputs = flatten_time ( outputs [ : - <NUM_LIT:2> ] ) <EOL> flat_dHa = flatten_time ( dHa [ <NUM_LIT:1> : - <NUM_LIT:1> ] ) <EOL> _h . dot_add_mm ( flat_dHa , flat_outputs , dR , transa = True ) <EOL> _h . dot_add_mm ( dHa [ <NUM_LIT:0> ] , outputs [ - <NUM_LIT:1> ] , dR , transa = True ) </s>
