<s> import numpy as np <EOL> import tensorflow as tf <EOL> import argparse <EOL> import time <EOL> import os <EOL> import cPickle <EOL> from mnist_data import * <EOL> from model import CPPNVAE <EOL> '''<STR_LIT>''' <EOL> def main ( ) : <EOL> parser = argparse . ArgumentParser ( ) <EOL> parser . add_argument ( '<STR_LIT>' , type = int , default = <NUM_LIT:100> , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = int , default = <NUM_LIT:1> , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = int , default = <NUM_LIT:1> , <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 = 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 = float , default = <NUM_LIT> , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = float , default = <NUM_LIT> , <EOL> help = '<STR_LIT>' ) <EOL> args = parser . parse_args ( ) <EOL> return train ( args ) <EOL> def train ( args ) : <EOL> learning_rate = args . learning_rate <EOL> learning_rate_d = args . learning_rate_d <EOL> learning_rate_vae = args . learning_rate_vae <EOL> batch_size = args . batch_size <EOL> training_epochs = args . training_epochs <EOL> display_step = args . display_step <EOL> checkpoint_step = args . checkpoint_step <EOL> beta1 = args . beta1 <EOL> keep_prob = args . keep_prob <EOL> dirname = '<STR_LIT>' <EOL> if not os . path . exists ( dirname ) : <EOL> os . makedirs ( dirname ) <EOL> with open ( os . path . join ( dirname , '<STR_LIT>' ) , '<STR_LIT:w>' ) as f : <EOL> cPickle . dump ( args , f ) <EOL> mnist = read_data_sets ( ) <EOL> n_samples = mnist . num_examples <EOL> cppnvae = CPPNVAE ( batch_size = batch_size , learning_rate = learning_rate , learning_rate_d = learning_rate_d , learning_rate_vae = learning_rate_vae , beta1 = beta1 , keep_prob = keep_prob ) <EOL> ckpt = tf . train . get_checkpoint_state ( dirname ) <EOL> if ckpt : <EOL> cppnvae . load_model ( dirname ) <EOL> counter = <NUM_LIT:0> <EOL> for epoch in range ( training_epochs ) : <EOL> avg_d_loss = <NUM_LIT:0.> <EOL> avg_q_loss = <NUM_LIT:0.> <EOL> avg_vae_loss = <NUM_LIT:0.> <EOL> mnist . shuffle_data ( ) <EOL> total_batch = int ( n_samples / batch_size ) <EOL> for i in range ( total_batch ) : <EOL> batch_images = mnist . next_batch ( batch_size ) <EOL> d_loss , g_loss , vae_loss , n_operations = cppnvae . partial_train ( batch_images ) <EOL> assert ( vae_loss < <NUM_LIT> ) <EOL> assert ( d_loss < <NUM_LIT> ) <EOL> assert ( g_loss < <NUM_LIT> ) <EOL> if ( counter + <NUM_LIT:1> ) % display_step == <NUM_LIT:0> : <EOL> print "<STR_LIT>" , '<STR_LIT>' % ( ( i + <NUM_LIT:1> ) * batch_size ) , "<STR_LIT>" , '<STR_LIT>' % ( epoch ) , "<STR_LIT>" , "<STR_LIT>" . format ( d_loss ) , "<STR_LIT>" , "<STR_LIT>" . format ( g_loss ) , "<STR_LIT>" , "<STR_LIT>" . format ( vae_loss ) , "<STR_LIT>" , '<STR_LIT>' % ( n_operations ) <EOL> counter += <NUM_LIT:1> <EOL> avg_d_loss += d_loss / n_samples * batch_size <EOL> avg_q_loss += g_loss / n_samples * batch_size <EOL> avg_vae_loss += vae_loss / n_samples * batch_size <EOL> if epoch >= <NUM_LIT:0> : <EOL> print "<STR_LIT>" , '<STR_LIT>' % ( epoch ) , "<STR_LIT>" , "<STR_LIT>" . format ( avg_d_loss ) , "<STR_LIT>" , "<STR_LIT>" . format ( avg_q_loss ) , "<STR_LIT>" , "<STR_LIT>" . format ( avg_vae_loss ) <EOL> if epoch >= <NUM_LIT:0> and epoch % checkpoint_step == <NUM_LIT:0> : <EOL> checkpoint_path = os . path . join ( '<STR_LIT>' , '<STR_LIT>' ) <EOL> cppnvae . save_model ( checkpoint_path , epoch ) <EOL> print "<STR_LIT>" . format ( checkpoint_path ) <EOL> cppnvae . save_model ( checkpoint_path , <NUM_LIT:0> ) <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> main ( ) </s>
