original_docstring: Trains a CompressiveTransformer model on the config-specified dataset.
  
      Optionally Tokenizes and restructures the input data to be used with a batch generator.
      Any additional preprocessing (removal of certain words, replacement of words, etc.)
      ought to be done beforehand if required using a custom preprocess.initial_cleanup for each
      respective dataset used.
      
docstring: Trains a CompressiveTransformer model on the config-specified dataset.
  Optionally Tokenizes and restructures the input data to be used with a batch generator.
  Any additional preprocessing (removal of certain words, replacement of words, etc.)
  ought to be done beforehand if required using a custom preprocess.initial_cleanup for each
  respective dataset used.
docstring_tokens: Trains a CompressiveTransformer model on the config - specified dataset . Optionally Tokenizes and restructures the input data to be used with a batch generator . Any additional preprocessing ( removal of certain words replacement of words etc . ) ought to be done beforehand if required using a custom preprocess . initial_cleanup for each respective dataset used .
code: def train(config_path: str = 'default',
            **kwargs):
      """Trains a CompressiveTransformer model on the config-specified dataset.
  
      Optionally Tokenizes and restructures the input data to be used with a batch generator.
      Any additional preprocessing (removal of certain words, replacement of words, etc.)
      ought to be done beforehand if required using a custom preprocess.initial_cleanup for each
      respective dataset used.
      """
      config = get_config(config_path)
  
      # ### Setup ### #
      if config.tokenize:
          tokenizer = Tokenizer(input_paths=config.input_paths,
                                tokenizer_output_path=config.tokenizer_output_path if config.save_tokens else None,
                                vocab_size=config.vocab_size,
                                lowercase=config.lowercase)
          encodings = tokenizer.encode_files(input_paths=config.input_paths,
                                             tokens_output_dir=config.tokens_output_dir if config.save_tokens else None,
                                             return_encodings=not config.load_tokens,
                                             tqdm=tqdm)
          if config.load_tokens:
              tokens_paths = [os.path.join(config.tokens_output_dir, filename) for
                              filename in os.listdir(config.tokens_output_dir)]
  
              tokens = []
              for path in tqdm(tokens_paths):
                  with open(path, 'rb') as file:
                      t = pickle.load(file)
                  tokens.append(t)
          else:
              tokens = [encoding.ids for encoding in encodings]
              del encodings
  
          training_data = [t for toks in tqdm(tokens) for t in toks]
          del tokens
  
          if config.save_tokens:
              os.makedirs(os.path.split(config.processed_path)[0], exist_ok=True)
  
              with open(config.processed_path, 'wb') as file:
                  pickle.dump(training_data, file)
  
      if not config.tokenize or config.load_tokens:
          with open(config.processed_path, 'rb') as file:
              training_data = pickle.load(file)
  
      config.train_steps = config.train_steps or len(training_data)
      config.steps_per_epoch = config.steps_per_epoch or config.train_steps//config.sequence_length - 1
  
      if config.continue_training and os.path.exists(config.model_output_path):
          ct = CompressiveTransformer.load(config.model_output_path, compile=False)
      else:
          ct = CompressiveTransformer(d_layers=config.d_layers,
                                      sequence_length=config.sequence_length,
                                      d_model=config.d_model,
                                      memory_size=config.memory_size,
                                      compressed_memory_size=config.compressed_memory_size,
                                      d_k=config.d_k,
                                      d_heads=config.d_heads,
                                      output_size=config.output_size,
                                      batch_size=config.batch_size,
                                      vocab_size=config.vocab_size,
                                      use_relative_encoding=False)
  
      ct.compile(optimizer='Adam',
                 loss='categorical_crossentropy',
                 metrics=['accuracy'],
                 metrics_reconstruction_loss=True)
  
      if config.verbose:
          print(ct.summary())
  
      generator = next_token_batch_generator(ct=ct,
                                             data=training_data,
                                             data_path=None,
                                             epoch_steps=config.train_steps,
                                             sequence_length=config.sequence_length,
                                             batch_size=config.batch_size,
                                             vocab_size=config.vocab_size)
  
      callbacks = [WriteLogsToFile(filepath=config.train_logs_output_path, overwrite_old_file=False),
                   PrintAttentionReconstructionLoss(),
                   # ClearCompressedMemory(),
                   SaveModel(filepath=config.model_output_path,
                             save_every_n_batches=config.save_interval)]
  
      # PG-19
      ct.fit_generator(generator(),
                       steps_per_epoch=config.steps_per_epoch,
                       epochs=config.epochs,
                       callbacks=callbacks,
                       shuffle=False)
code_tokens: def train ( config_path : str = 'default' , ** kwargs ) : config = get_config ( config_path ) if config . tokenize : tokenizer = Tokenizer ( input_paths = config . input_paths , tokenizer_output_path = config . tokenizer_output_path if config . save_tokens else None , vocab_size = config . vocab_size , lowercase = config . lowercase ) encodings = tokenizer . encode_files ( input_paths = config . input_paths , tokens_output_dir = config . tokens_output_dir if config . save_tokens else None , return_encodings = not config . load_tokens , tqdm = tqdm ) if config . load_tokens : tokens_paths = [ os . path . join ( config . tokens_output_dir , filename ) for filename in os . listdir ( config . tokens_output_dir ) ] tokens = [ ] for path in tqdm ( tokens_paths ) : with open ( path , 'rb' ) as file : t = pickle . load ( file ) tokens . append ( t ) else : tokens = [ encoding . ids for encoding in encodings ] del encodings training_data = [ t for toks in tqdm ( tokens ) for t in toks ] del tokens if config . save_tokens : os . makedirs ( os . path . split ( config . processed_path ) [ 0 ] , exist_ok = True ) with open ( config . processed_path , 'wb' ) as file : pickle . dump ( training_data , file ) if not config . tokenize or config . load_tokens : with open ( config . processed_path , 'rb' ) as file : training_data = pickle . load ( file ) config . train_steps = config . train_steps or len ( training_data ) config . steps_per_epoch = config . steps_per_epoch or config . train_steps // config . sequence_length - 1 if config . continue_training and os . path . exists ( config . model_output_path ) : ct = CompressiveTransformer . load ( config . model_output_path , compile = False ) else : ct = CompressiveTransformer ( d_layers = config . d_layers , sequence_length = config . sequence_length , d_model = config . d_model , memory_size = config . memory_size , compressed_memory_size = config . compressed_memory_size , d_k = config . d_k , d_heads = config . d_heads , output_size = config . output_size , batch_size = config . batch_size , vocab_size = config . vocab_size , use_relative_encoding = False ) ct . compile ( optimizer = 'Adam' , loss = 'categorical_crossentropy' , metrics = [ 'accuracy' ] , metrics_reconstruction_loss = True ) if config . verbose : print ( ct . summary ( ) ) generator = next_token_batch_generator ( ct = ct , data = training_data , data_path = None , epoch_steps = config . train_steps , sequence_length = config . sequence_length , batch_size = config . batch_size , vocab_size = config . vocab_size ) callbacks = [ WriteLogsToFile ( filepath = config . train_logs_output_path , overwrite_old_file = False ) , PrintAttentionReconstructionLoss ( ) , SaveModel ( filepath = config . model_output_path , save_every_n_batches = config . save_interval ) ] ct . fit_generator ( generator ( ) , steps_per_epoch = config . steps_per_epoch , epochs = config . epochs , callbacks = callbacks , shuffle = False )
short_docstring: Trains a CompressiveTransformer model on the config-specified dataset.
short_docstring_tokens: Trains a CompressiveTransformer model on the config - specified dataset .
comment: """Trains a CompressiveTransformer model on the config-specified dataset.
  
      Optionally Tokenizes and restructures the input data to be used with a batch generator.
      Any additional preprocessing (removal of certain words, replacement of words, etc.)
      ought to be done beforehand if required using a custom preprocess.initial_cleanup for each
      respective dataset used.
      """$SEP$# ### Setup ### #$SEP$# ClearCompressedMemory(),$SEP$# PG-19