old_contents: import platform
  
  """
  Default paramters for experiemnt
  """
  
  
  class ExpConfig:
  
      # phase 
      PHASE = 'test'
      VISUALIZE = True
  
      # input and output
      DATA_BASE_DIR = '/mnt/90kDICT32px'
      DATA_PATH = '/mnt/train_shuffled_words.txt' # path containing data file names and labels. Format: 
      MODEL_DIR = 'train' # the directory for saving and loading model parameters (structure is not stored)
      LOG_PATH = 'log.txt'
      OUTPUT_DIR = 'results' # output directory
      STEPS_PER_CHECKPOINT = 400 # checkpointing (print perplexity, save model) per how many steps
  
      # Optimization
      NUM_EPOCH = 1000
      BATCH_SIZE = 64
      INITIAL_LEARNING_RATE = 0.001 # initial learning rate, note the we use AdaDelta, so the initial value doe not matter much
  
      # Network parameters
      TARGET_EMBEDDING_SIZE = 10 # embedding dimension for each target
      ATTN_USE_LSTM = True # whether or not use LSTM attention decoder cell
      ATTN_NUM_HIDDEN=128 # number of hidden units in attention decoder cell
      ATTN_NUM_LAYERS = 2 # number of layers in attention decoder cell
                          # (Encoder number of hidden units will be ATTN_NUM_HIDDEN*ATTN_NUM_LAYERS)
      LOAD_MODEL = True
      TARGET_VOCAB_SIZE = 26+10+3 # 0: PADDING, 1: GO, 2: EOS, >2: 0-9, a-z
  
new_contents: import platform
  
  """
  Default paramters for experiemnt
  """
  
  
  class ExpConfig:
  
      # phase 
      PHASE = 'test'
      VISUALIZE = True
  
      # input and output
      DATA_BASE_DIR = '/mnt/90kDICT32px'
      DATA_PATH = '/mnt/train_shuffled_words.txt' # path containing data file names and labels. Format: 
      MODEL_DIR = 'train' # the directory for saving and loading model parameters (structure is not stored)
      LOG_PATH = 'log.txt'
      OUTPUT_DIR = 'results' # output directory
      STEPS_PER_CHECKPOINT = 400 # checkpointing (print perplexity, save model) per how many steps
  
      # Optimization
      NUM_EPOCH = 1000
      BATCH_SIZE = 64
      INITIAL_LEARNING_RATE = 1.0 # initial learning rate, note the we use AdaDelta, so the initial value doe not matter much
  
      # Network parameters
      TARGET_EMBEDDING_SIZE = 10 # embedding dimension for each target
      ATTN_USE_LSTM = True # whether or not use LSTM attention decoder cell
      ATTN_NUM_HIDDEN=128 # number of hidden units in attention decoder cell
      ATTN_NUM_LAYERS = 2 # number of layers in attention decoder cell
                          # (Encoder number of hidden units will be ATTN_NUM_HIDDEN*ATTN_NUM_LAYERS)
      LOAD_MODEL = True
      TARGET_VOCAB_SIZE = 26+10+3 # 0: PADDING, 1: GO, 2: EOS, >2: 0-9, a-z
  
subject: increase initial lr