<s> import numpy as np <EOL> import time <EOL> from operator import add <EOL> import numpy as np <EOL> import random <EOL> from toolbox import * <EOL> from models import * <EOL> if __name__ == "<STR_LIT:__main__>" : <EOL> hp = Parameters ( ) <EOL> with hp : <EOL> batch_size = <NUM_LIT> <EOL> test_batch_size = <NUM_LIT> <EOL> load_model = False <EOL> save_model = True <EOL> debug = False <EOL> train_perm = False <EOL> walkforward = False <EOL> dynamic_eval = False <EOL> Model = lm_lstm . LM_lstm <EOL> seq_size = <NUM_LIT:20> <EOL> warmup_size = <NUM_LIT:1> <EOL> init_scale = <NUM_LIT> <EOL> learning_rate = <NUM_LIT> <EOL> lr_halflife = <NUM_LIT> <EOL> optimizer = sgdgc <EOL> description = '<STR_LIT>' <EOL> data_path = '<STR_LIT>' <EOL> data = tokentext ( path = data_path + '<STR_LIT>' , name = '<STR_LIT>' , batch_size = batch_size , n_train = <NUM_LIT:0> ) <EOL> visualize_tokens ( - <NUM_LIT:1> , data [ '<STR_LIT>' ] [ <NUM_LIT:0> : min ( len ( data [ '<STR_LIT>' ] ) , <NUM_LIT> ) ] / float ( data [ '<STR_LIT>' ] ) , data [ '<STR_LIT>' ] ) <EOL> model = Model ( data , hp ) <EOL> print ( "<STR_LIT>" % ( model . id , learning_rate , init_scale , batch_size , seq_size , description ) ) <EOL> if walkforward : <EOL> n_ws = len ( data [ '<STR_LIT>' ] ) / walkstep_size / batch_size <EOL> it_lr = learning_rate <EOL> for walkstep in xrange ( <NUM_LIT:0> , n_ws ) : <EOL> begin = time . time ( ) <EOL> min_validation = <NUM_LIT> <EOL> for it in range ( ws_iterations ) : <EOL> begin_inner = time . time ( ) <EOL> tr_outputs = None <EOL> for i in xrange ( <NUM_LIT:0> , walkstep_size ) : <EOL> batch_idx = walkstep * walkstep_size + i <EOL> outputs = model . train ( batch_idx , it_lr ) <EOL> outputs = map ( lambda x : x / float ( walkstep_size * batch_size ) , outputs ) <EOL> if i == <NUM_LIT:0> : <EOL> tr_outputs = outputs <EOL> else : <EOL> tr_outputs = map ( add , tr_outputs , outputs ) <EOL> prev_va_outputs = [ <NUM_LIT:0.> ] * <NUM_LIT:100> <EOL> for i in xrange ( <NUM_LIT:0> , walkstep * walkstep_size ) : <EOL> outputs = model . validate ( i ) <EOL> outputs = map ( lambda x : x / ( walkstep * walkstep_size * batch_size ) , outputs ) <EOL> prev_va_outputs = ( map ( add , prev_va_outputs , outputs ) if i != <NUM_LIT:0> else outputs ) <EOL> print ( "<STR_LIT>" % ( it , <EOL> tr_outputs [ model . outidx [ '<STR_LIT>' ] ] , prev_va_outputs [ model . outidx [ '<STR_LIT>' ] ] , <EOL> tr_outputs [ model . outidx [ '<STR_LIT>' ] ] , prev_va_outputs [ model . outidx [ '<STR_LIT>' ] ] , <EOL> tr_outputs [ model . outidx [ '<STR_LIT>' ] ] , <EOL> time . time ( ) - begin_inner ) ) <EOL> if prev_va_outputs [ model . outidx [ '<STR_LIT>' ] ] < min_validation : <EOL> min_validation = prev_va_outputs [ model . outidx [ '<STR_LIT>' ] ] <EOL> elif prev_va_outputs [ model . outidx [ '<STR_LIT>' ] ] > min_validation * ( <NUM_LIT:1.> + ws_validstop ) : <EOL> break <EOL> te_outputs = model . test_epoch ( ) <EOL> if model . type == '<STR_LIT>' : <EOL> print ( "<STR_LIT>" % ( walkstep , <EOL> tr_outputs [ model . outidx [ '<STR_LIT>' ] ] , te_outputs [ model . outidx [ '<STR_LIT>' ] ] , <EOL> tr_outputs [ model . outidx [ '<STR_LIT>' ] ] , te_outputs [ model . outidx [ '<STR_LIT>' ] ] , <EOL> tr_outputs [ model . outidx [ '<STR_LIT>' ] ] , <EOL> time . time ( ) - begin ) ) <EOL> else : <EOL> print ( "<STR_LIT>" % ( walkstep , <EOL> tr_outputs [ model . outidx [ '<STR_LIT>' ] ] , te_outputs [ model . outidx [ '<STR_LIT>' ] ] , <EOL> tr_outputs [ model . outidx [ '<STR_LIT>' ] ] , te_outputs [ model . outidx [ '<STR_LIT>' ] ] , <EOL> tr_outputs [ model . outidx [ '<STR_LIT>' ] ] , <EOL> time . time ( ) - begin ) ) <EOL> y_samples = model . decode ( <NUM_LIT> * ( model . n_t + <NUM_LIT:1> ) ) <EOL> y_samples = np . transpose ( y_samples , ( <NUM_LIT:1> , <NUM_LIT:0> , <NUM_LIT:2> ) ) . reshape ( ( - <NUM_LIT:1> , y_samples . shape [ <NUM_LIT:2> ] ) ) <EOL> visualize ( walkstep + <NUM_LIT:1> , y_samples , data [ '<STR_LIT>' ] ) <EOL> ws_iterations = int ( ws_iterations * n_stepdecay ) <EOL> model . save ( ) <EOL> elif dynamic_eval : <EOL> it_lr = float ( learning_rate ) <EOL> va_outputs = model . validation_epoch ( ) <EOL> te_outputs = model . test_epoch ( ) <EOL> dyn_va_outputs = model . dyn_validation_epoch ( it_lr ) <EOL> model . load ( ) <EOL> dyn_te_outputs = model . dyn_test_epoch ( it_lr ) <EOL> if model . type == '<STR_LIT>' : <EOL> print ( "<STR_LIT>" % ( np . exp ( va_outputs [ model . outidx [ '<STR_LIT>' ] ] ) , <EOL> np . exp ( te_outputs [ model . outidx [ '<STR_LIT>' ] ] ) , <EOL> np . exp ( dyn_va_outputs [ model . outidx [ '<STR_LIT>' ] ] ) , <EOL> np . exp ( dyn_te_outputs [ model . outidx [ '<STR_LIT>' ] ] ) ) ) <EOL> else : <EOL> n_iterations = <NUM_LIT> <EOL> freq_save = <NUM_LIT:2> <EOL> freq_sample = <NUM_LIT:2> <EOL> it_lr = float ( learning_rate ) <EOL> rnd_offset = np . random . permutation ( seq_size ) <EOL> for it in range ( n_iterations ) : <EOL> begin = time . time ( ) <EOL> model . permuteData ( data ) <EOL> tr_outputs = model . train_epoch ( it_lr , rnd_offset [ it % seq_size ] ) <EOL> va_outputs = model . validation_epoch ( ) <EOL> if model . type == '<STR_LIT>' : <EOL> print ( "<STR_LIT>" % ( it , <EOL> np . exp ( tr_outputs [ model . outidx [ '<STR_LIT>' ] ] ) , <EOL> np . exp ( va_outputs [ model . outidx [ '<STR_LIT>' ] ] ) , <EOL> tr_outputs [ model . outidx [ '<STR_LIT>' ] ] , <EOL> time . time ( ) - begin ) ) <EOL> if hp . save_model and it % freq_save == <NUM_LIT:0> : <EOL> model . save ( ) <EOL> if lr_halflife != <NUM_LIT:0> : <EOL> it_lr = float ( it_lr * np . power ( <NUM_LIT:0.5> , <NUM_LIT:1.> / lr_halflife ) ) </s>
