old_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  c
  
  from deepy.networks import AutoEncoder
  from deepy.layers import RNN, Dense
  from deepy.trainers import SGDTrainer, LearningRateAnnealer
  
  from util import get_data, VECTOR_SIZE, SEQUENCE_LENGTH
  
  HIDDEN_SIZE = 50
  
  model_path = os.path.join(os.path.dirname(__file__), "models", "rnn1.gz")
  
  if __name__ == '__main__':
      model = AutoEncoder(input_dim=VECTOR_SIZE, input_tensor=3)
      model.stack_encoders(RNN(hidden_size=HIDDEN_SIZE, input_type="sequence", output_type="one"))
      model.stack_decoders(RNN(hidden_size=HIDDEN_SIZE, input_type="one", output_type="sequence", steps=SEQUENCE_LENGTH),
                           Dense(VECTOR_SIZE, 'softmax'))
  
      trainer = SGDTrainer(model)
  
      annealer = LearningRateAnnealer(trainer)
  
      trainer.run(get_data(), controllers=[annealer])
  
      model.save_params(model_path)
  
  
new_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  
  from deepy.networks import AutoEncoder
  from deepy.layers import RNN, Dense
  from deepy.trainers import SGDTrainer, LearningRateAnnealer
  
  from util import get_data, VECTOR_SIZE, SEQUENCE_LENGTH
  
  HIDDEN_SIZE = 50
  
  model_path = os.path.join(os.path.dirname(__file__), "models", "rnn1.gz")
  
  if __name__ == '__main__':
      model = AutoEncoder(input_dim=VECTOR_SIZE, input_tensor=3)
      model.stack_encoders(RNN(hidden_size=HIDDEN_SIZE, input_type="sequence", output_type="one"))
      model.stack_decoders(RNN(hidden_size=HIDDEN_SIZE, input_type="one", output_type="sequence", steps=SEQUENCE_LENGTH),
                           Dense(VECTOR_SIZE, 'softmax'))
  
      trainer = SGDTrainer(model)
  
      annealer = LearningRateAnnealer(trainer)
  
      trainer.run(get_data(), controllers=[annealer])
  
      model.save_params(model_path)
  
  
subject: Fix auto-encoder