old_contents: import os
  
  from keras.callbacks import ModelCheckpoint, EarlyStopping
  from common import CsvHistory
  
  def train(train, dev, model, model_dir, batch_size):
      
      if not os.path.exists(model_dir):
           os.makedirs(model_dir)  
      es = EarlyStopping(patience = 5)
      saver = ModelCheckpoint(model_dir + '/model.weights', monitor = 'val_loss', save_best_only = True)
      csv = CsvHistory(model_dir + '/history.csv')
      return model.fit([train[0], train[1]], train[2], batch_size=batch_size, 
                       nb_epoch = 1000, validation_data = ([dev[0], dev[1]], dev[2]), 
                       callbacks = [saver, es, csv])
          
  
new_contents: import os
  
  from keras.callbacks import ModelCheckpoint, EarlyStopping
  from common import CsvHistory
  
  def train(train, dev, model, model_dir, batch_size):
      
      if not os.path.exists(model_dir):
           os.makedirs(model_dir)  
      es = EarlyStopping(patience = 2)
      saver = ModelCheckpoint(model_dir + '/model.weights', monitor = 'val_loss', save_best_only = True)
      csv = CsvHistory(model_dir + '/history.csv')
      return model.fit([train[0], train[1]], train[2], batch_size=batch_size, 
                       nb_epoch = 1000, validation_data = ([dev[0], dev[1]], dev[2]), 
                       callbacks = [saver, es, csv])
          
  
subject: reduce patience for classify