def train_model(name, g_train, d_train, sampler, generator, samples_per_epoch, nb_epoch, z_dim=100, verbose=1, callbacks=[], validation_data=None, nb_val_samples=None, saver=None):
 self = {}
 epoch = 0
 counter = 0
 out_labels = ['g_loss', 'd_loss', 'd_loss_fake', 'd_loss_legit', 'time']
 callback_metrics = (out_labels + [('val_' + n) for n in out_labels])
 history = cbks.History()
 callbacks = (([cbks.BaseLogger()] + callbacks) + [history])
 if verbose:
  callbacks += [cbks.ProgbarLogger()]
 callbacks = cbks.CallbackList(callbacks)
 callbacks._set_params({'nb_epoch': nb_epoch, 'nb_sample': samples_per_epoch, 'verbose': verbose, 'metrics': callback_metrics})
 callbacks.on_train_begin()
 while (epoch < nb_epoch):
  callbacks.on_epoch_begin(epoch)
  samples_seen = 0
  batch_index = 0
  while (samples_seen < samples_per_epoch):
   (z, x) = next(generator)
   batch_logs = {}
   if (type(x) is list):
    batch_size = len(x[0])
   elif (type(x) is dict):
    batch_size = len(list(x.values())[0])
   else:
    batch_size = len(x)
   batch_logs['batch'] = batch_index
   batch_logs['size'] = batch_size
   callbacks.on_batch_begin(batch_index, batch_logs)
   t1 = time.time()
   d_losses = d_train(x, z, counter)
   (z, x) = next(generator)
   (g_loss, samples, xs) = g_train(x, z, counter)
   outs = (((g_loss,) + d_losses) + ((time.time() - t1),))
   counter += 1
   if ((batch_index % 100) == 0):
    join_image = np.zeros_like(np.concatenate([samples[:64], xs[:64]], axis=0))
    for (j, (i1, i2)) in enumerate(zip(samples[:64], xs[:64])):
     join_image[(j * 2)] = i1
     join_image[((j * 2) + 1)] = i2
    save_images(join_image, [(8 * 2), 8], ('./outputs/samples_%s/train_%s_%s.png' % (name, epoch, batch_index)))
    (samples, xs) = sampler(z, x)
    join_image = np.zeros_like(np.concatenate([samples[:64], xs[:64]], axis=0))
    for (j, (i1, i2)) in enumerate(zip(samples[:64], xs[:64])):
     join_image[(j * 2)] = i1
     join_image[((j * 2) + 1)] = i2
    save_images(join_image, [(8 * 2), 8], ('./outputs/samples_%s/test_%s_%s.png' % (name, epoch, batch_index)))
   for (l, o) in zip(out_labels, outs):
    batch_logs[l] = o
   callbacks.on_batch_end(batch_index, batch_logs)
   epoch_logs = {}
   batch_index += 1
   samples_seen += batch_size
  if (saver is not None):
   saver(epoch)
  callbacks.on_epoch_end(epoch, epoch_logs)
  epoch += 1
 callbacks.on_train_end()

def train_model ( name , g_train , d_train , sampler , generator , samples_per_epoch , nb_epoch , z_dim = 100 , verbose = 1 , callbacks = [ ] , validation_data = None , nb_val_samples = None , saver = None ) : self = { } epoch = 0 counter = 0 out_labels = [ 'g_loss' , 'd_loss' , 'd_loss_fake' , 'd_loss_legit' , 'time' ] callback_metrics = ( out_labels + [ ( 'val_' + n ) for n in out_labels ] ) history = cbks . History ( ) callbacks = ( ( [ cbks . BaseLogger ( ) ] + callbacks ) + [ history ] ) if verbose : callbacks += [ cbks . ProgbarLogger ( ) ] callbacks = cbks . CallbackList ( callbacks ) callbacks . _set_params ( { 'nb_epoch' : nb_epoch , 'nb_sample' : samples_per_epoch , 'verbose' : verbose , 'metrics' : callback_metrics } ) callbacks . on_train_begin ( ) while ( epoch < nb_epoch ) : callbacks . on_epoch_begin ( epoch ) samples_seen = 0 batch_index = 0 while ( samples_seen < samples_per_epoch ) : ( z , x ) = next ( generator ) batch_logs = { } if ( type ( x ) is list ) : batch_size = len ( x [ 0 ] ) elif ( type ( x ) is dict ) : batch_size = len ( list ( x . values ( ) ) [ 0 ] ) else : batch_size = len ( x ) batch_logs [ 'batch' ] = batch_index batch_logs [ 'size' ] = batch_size callbacks . on_batch_begin ( batch_index , batch_logs ) t1 = time . time ( ) d_losses = d_train ( x , z , counter ) ( z , x ) = next ( generator ) ( g_loss , samples , xs ) = g_train ( x , z , counter ) outs = ( ( ( g_loss , ) + d_losses ) + ( ( time . time ( ) - t1 ) , ) ) counter += 1 if ( ( batch_index % 100 ) == 0 ) : join_image = np . zeros_like ( np . concatenate ( [ samples [ : 64 ] , xs [ : 64 ] ] , axis = 0 ) ) for ( j , ( i1 , i2 ) ) in enumerate ( zip ( samples [ : 64 ] , xs [ : 64 ] ) ) : join_image [ ( j * 2 ) ] = i1 join_image [ ( ( j * 2 ) + 1 ) ] = i2 save_images ( join_image , [ ( 8 * 2 ) , 8 ] , ( './outputs/samples_%s/train_%s_%s.png' % ( name , epoch , batch_index ) ) ) ( samples , xs ) = sampler ( z , x ) join_image = np . zeros_like ( np . concatenate ( [ samples [ : 64 ] , xs [ : 64 ] ] , axis = 0 ) ) for ( j , ( i1 , i2 ) ) in enumerate ( zip ( samples [ : 64 ] , xs [ : 64 ] ) ) : join_image [ ( j * 2 ) ] = i1 join_image [ ( ( j * 2 ) + 1 ) ] = i2 save_images ( join_image , [ ( 8 * 2 ) , 8 ] , ( './outputs/samples_%s/test_%s_%s.png' % ( name , epoch , batch_index ) ) ) for ( l , o ) in zip ( out_labels , outs ) : batch_logs [ l ] = o callbacks . on_batch_end ( batch_index , batch_logs ) epoch_logs = { } batch_index += 1 samples_seen += batch_size if ( saver is not None ) : saver ( epoch ) callbacks . on_epoch_end ( epoch , epoch_logs ) epoch += 1 callbacks . on_train_end ( )
train model .