<s> from __future__ import print_function <EOL> import numpy as np <EOL> import tensorflow as tf <EOL> import argparse <EOL> import time <EOL> import os <EOL> from six . moves import cPickle <EOL> from utils import TextLoader <EOL> from model import Model <EOL> def main ( ) : <EOL> parser = argparse . ArgumentParser ( ) <EOL> parser . add_argument ( '<STR_LIT>' , type = str , default = '<STR_LIT>' , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = str , default = '<STR_LIT>' , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = int , default = <NUM_LIT> , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = int , default = <NUM_LIT:2> , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = str , default = '<STR_LIT>' , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = int , default = <NUM_LIT:50> , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = int , default = <NUM_LIT:50> , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = int , default = <NUM_LIT:50> , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = int , default = <NUM_LIT:1000> , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = float , default = <NUM_LIT> , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = float , default = <NUM_LIT> , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = float , default = <NUM_LIT> , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = str , default = None , <EOL> help = """<STR_LIT>""" ) <EOL> args = parser . parse_args ( ) <EOL> train ( args ) <EOL> def train ( args ) : <EOL> data_loader = TextLoader ( args . data_dir , args . batch_size , args . seq_length ) <EOL> args . vocab_size = data_loader . vocab_size <EOL> if args . init_from is not None : <EOL> assert os . path . isdir ( args . init_from ) , "<STR_LIT>" % args . init_from <EOL> assert os . path . isfile ( os . path . join ( args . init_from , "<STR_LIT>" ) ) , "<STR_LIT>" % args . init_from <EOL> assert os . path . isfile ( os . path . join ( args . init_from , "<STR_LIT>" ) ) , "<STR_LIT>" % args . init_from <EOL> ckpt = tf . train . get_checkpoint_state ( args . init_from ) <EOL> assert ckpt , "<STR_LIT>" <EOL> assert ckpt . model_checkpoint_path , "<STR_LIT>" <EOL> with open ( os . path . join ( args . init_from , '<STR_LIT>' ) ) as f : <EOL> saved_model_args = cPickle . load ( f ) <EOL> need_be_same = [ "<STR_LIT>" , "<STR_LIT>" , "<STR_LIT>" , "<STR_LIT>" ] <EOL> for checkme in need_be_same : <EOL> assert vars ( saved_model_args ) [ checkme ] == vars ( args ) [ checkme ] , "<STR_LIT>" % checkme <EOL> with open ( os . path . join ( args . init_from , '<STR_LIT>' ) ) as f : <EOL> saved_chars , saved_vocab = cPickle . load ( f ) <EOL> assert saved_chars == data_loader . chars , "<STR_LIT>" <EOL> assert saved_vocab == data_loader . vocab , "<STR_LIT>" <EOL> with open ( os . path . join ( args . save_dir , '<STR_LIT>' ) , '<STR_LIT:wb>' ) as f : <EOL> cPickle . dump ( args , f ) <EOL> with open ( os . path . join ( args . save_dir , '<STR_LIT>' ) , '<STR_LIT:wb>' ) as f : <EOL> cPickle . dump ( ( data_loader . chars , data_loader . vocab ) , f ) <EOL> model = Model ( args ) <EOL> with tf . Session ( ) as sess : <EOL> tf . initialize_all_variables ( ) . run ( ) <EOL> saver = tf . train . Saver ( tf . all_variables ( ) ) <EOL> if args . init_from is not None : <EOL> saver . restore ( sess , ckpt . model_checkpoint_path ) <EOL> for e in range ( args . num_epochs ) : <EOL> sess . run ( tf . assign ( model . lr , args . learning_rate * ( args . decay_rate ** e ) ) ) <EOL> data_loader . reset_batch_pointer ( ) <EOL> state = model . initial_state . eval ( ) <EOL> for b in range ( data_loader . num_batches ) : <EOL> start = time . time ( ) <EOL> x , y = data_loader . next_batch ( ) <EOL> feed = { model . input_data : x , model . targets : y , model . initial_state : state } <EOL> train_loss , state , _ = sess . run ( [ model . cost , model . final_state , model . train_op ] , feed ) <EOL> end = time . time ( ) <EOL> print ( "<STR_LIT>" . format ( e * data_loader . num_batches + b , <EOL> args . num_epochs * data_loader . num_batches , <EOL> e , train_loss , end - start ) ) <EOL> if ( e * data_loader . num_batches + b ) % args . save_every == <NUM_LIT:0> : <EOL> checkpoint_path = os . path . join ( args . save_dir , '<STR_LIT>' ) <EOL> saver . save ( sess , checkpoint_path , global_step = e * data_loader . num_batches + b ) <EOL> print ( "<STR_LIT>" . format ( checkpoint_path ) ) <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> main ( ) </s>
