text: import numpy
  import os
  
  from nmt import train
  
  def main(job_id, params):
      print params
      basedir = '/data/lisatmp3/firatorh/nmt/europarlv7'
      validerr = train(saveto=params['model'][0],
                                          reload_=params['reload'][0],
                                          dim_word=params['dim_word'][0],
                                          dim=params['dim'][0],
                                          n_words=params['n-words'][0],
                                          n_words_src=params['n-words'][0],
                                          decay_c=params['decay-c'][0],
                                          clip_c=params['clip-c'][0],
                                          lrate=params['learning-rate'][0],
                                          optimizer=params['optimizer'][0],
                                          maxlen=15,
                                          batch_size=32,
                                          valid_batch_size=32,
  					datasets=['%s/europarl-v7.fr-en.fr.tok'%basedir,
  					'%s/europarl-v7.fr-en.en.tok'%basedir],
  					valid_datasets=['%s/newstest2011.fr.tok'%basedir,
  					'%s/newstest2011.en.tok'%basedir],
  					dictionaries=['%s/europarl-v7.fr-en.fr.tok.pkl'%basedir,
  					'%s/europarl-v7.fr-en.en.tok.pkl'%basedir],
                                          validFreq=500000,
                                          dispFreq=1,
                                          saveFreq=100,
                                          sampleFreq=50,
                                          use_dropout=params['use-dropout'][0])
      return validerr
  
  if __name__ == '__main__':
      basedir = '/data/lisatmp3/firatorh/nmt/europarlv7'
      main(0, {
          'model': ['%s/models/model_session3.npz'%basedir],
          'dim_word': [150],
          'dim': [124],
          'n-words': [3000],
          'optimizer': ['adadelta'],
          'decay-c': [0.],
          'clip-c': [1.],
          'use-dropout': [False],
          'learning-rate': [0.0001],
          'reload': [False]})
  
  
  