old_contents: # Original script developed by Harshal Priyadarshi https://github.com/harpribot
  # Edited for purpose of this project.
  # Script to initiate training process of the LSTM network with attention disabled.
  
  # Import required libraries; Point system directory back to parent folder to allow import files below:
  import os
  import sys
  sys.path.append(os.path.abspath(os.path.dirname(__file__) + '/' + '..'))
  from Models import lstmSimple
  from PreProcessingScript import checkpoint
  
  
  print("LSTM Network training process initiated with attention disabled...")
  
  
  # Get the review summary file
  try:
  	docSum_file = 'Data/rewsum.csv'
  	print("Source data loaded.")
  except NameError:
  	raise ValueError("No source data found. Please check if 'Data/rewsum.csv' exists!")
  
  
  # Initialise Checkpointer to ensure checkpointing, No. of steps per checkpoint: 100
  checkpointSys = checkpoint.checkpointSys('simple', 'lstm', 'noAttention')
  checkpointSys.steps_per_checkpoint(100)
  checkpointSys.steps_per_prediction(100)
  
  
  # Do using LSTM cell - with attention mechanism disabled
  out_file = 'Results/Simple-LSTM/NoAttention/no-attention.csv'
  checkpointSys.set_result_location(out_file)
  lstm_net = lstmSimple.LstmSimple(docSum_file, checkpointSys, attention=True)
  #Set the parameters for the model and training.
          #parameter train_batch_size: The batch size of examples used for batch training
          #parameter test_batch_size: The batch size of test examples used for testing
          #parameter memory_dim: The length of the hidden vector produced by the encoder
          #parameter learning_rate: The learning rate for Stochastic Gradient Descent
  lstm_net.set_parameters(train_batch_size=128, test_batch_size=128, memory_dim=128, learning_rate=0.05)
  lstm_net.begin_session()
  lstm_net.form_model_graph()
  lstm_net.fit()
  lstm_net.predict()
  lstm_net.store_test_predictions()
  
new_contents: # Original script developed by Harshal Priyadarshi https://github.com/harpribot
  # Edited for purpose of this project.
  # Script to initiate training process of the LSTM network with attention disabled.
  
  # Import required libraries; Point system directory back to parent folder to allow import files below:
  import os
  import sys
  sys.path.append(os.path.abspath(os.path.dirname(__file__) + '/' + '..'))
  from Models import lstmSimple
  from PreProcessingScript import checkpoint
  
  
  print("LSTM Network training process initiated with attention disabled...")
  
  
  # Get the review summary file
  try:
  	docSum_file = 'Data/rewsum.csv'
  	print("Source data loaded.")
  except NameError:
  	raise ValueError("No source data found. Please check if 'Data/rewsum.csv' exists!")
  
  
  # Initialise Checkpointer to ensure checkpointing, No. of steps per checkpoint: 100
  checkpointSys = checkpoint.checkpointSys('simple', 'lstm', 'noAttention')
  checkpointSys.steps_per_checkpoint(100)
  checkpointSys.steps_per_prediction(100)
  
  
  # Do using LSTM cell - with attention mechanism disabled
  out_file = 'Results/Simple-LSTM/NoAttention/no-attention.csv'
  checkpointSys.set_result_location(out_file)
  lstm_net = lstmSimple.LstmSimple(docSum_file, checkpointSys, attention=False)
  #Set the parameters for the model and training.
          #parameter train_batch_size: The batch size of examples used for batch training
          #parameter test_batch_size: The batch size of test examples used for testing
          #parameter memory_dim: The length of the hidden vector produced by the encoder
          #parameter learning_rate: The learning rate for Stochastic Gradient Descent
  lstm_net.set_parameters(train_batch_size=128, test_batch_size=128, memory_dim=128, learning_rate=0.05)
  lstm_net.begin_session()
  lstm_net.form_model_graph()
  lstm_net.fit()
  lstm_net.predict()
  lstm_net.store_test_predictions()
  
subject: Update trainLSTM-noATTN.py