old_contents: import json
  from imaginet.commands import train, evaluate, compressed
  from funktional.util import linear, clipped_rectify, CosineDistance
  dataset = 'flickr8k'
  epochs = 1
  
  
  train(dataset=dataset,
        datapath='/home/gchrupala/repos/neuraltalk/',
        model_path='.',
        epochs=epochs,
        min_df=10,
        max_norm=5.0,
        scale=True,
        batch_size=64,
        shuffle=True,
        size_embed=256,
        size_hidden=1024,
        depth=3,
        tokenize=compressed,
        validate_period=64*1000)
  
  for epoch in range(1, epochs+1):
      
      scores = evaluate(dataset=dataset,
                        datapath='/home/gchrupala/repos/neuraltalk/',
                        model_path='.',
                        model_name='model.{}.pkl.gz'.format(epoch))
      json.dump(scores, open('scores.{}.json'.format(epoch),'w'))
  
new_contents: import json
  from imaginet.commands import train, evaluate, compressed
  from funktional.util import linear, clipped_rectify, CosineDistance
  dataset = 'flickr8k'
  datapath = "."
  epochs = 1
  
  
  train(dataset=dataset,
        datapath=datapath,
        model_path='.',
        epochs=epochs,
        min_df=10,
        max_norm=5.0,
        scale=True,
        batch_size=64,
        shuffle=True,
        size_embed=256,
        size_hidden=1024,
        depth=3,
        tokenize=compressed,
        validate_period=64*1000)
  
  for epoch in range(1, epochs+1):
      
      scores = evaluate(dataset=dataset,
                        datapath=datapath,
                        model_path='.',
                        model_name='model.{}.pkl.gz'.format(epoch))
      json.dump(scores, open('scores.{}.json'.format(epoch),'w'))
  
subject: Change path in simple_example.py