class_docstr: Logs statistics about training run into Pandas dataframes.
  Records three separate dataframes: train, eval, epoch.
  
  Parameters
  ----------
  batch_size: int
      batch_size of data
  frequent: int
      How many training mini-batches between calculations.
      Defaults to calculating every 50 batches.
      (Eval data is stored once per epoch over the entire
      eval data set.)
class_docstr_tok: ["Logs","statistics","about","training","run","into","Pandas","dataframes",".","Records","three","separate","dataframes",":","train",",","eval",",","epoch",".","Parameters","--","--","--","--","--","batch_size",":","int","batch_size","of","data","frequent",":","int","How","many","training","mini-batches","between","calculations",".","Defaults","to","calculating","every","50","batches",".","(","Eval","data","is","stored","once","per","epoch","over","the","entire","eval","data","set",".",")"]
code: def __init__(self, batch_size, frequent=50):
      self.batch_size = batch_size
      self.frequent = frequent
      self._dataframes = {
          "train": pd.DataFrame(),
          "eval": pd.DataFrame(),
          "epoch": pd.DataFrame(),
      }
      self.last_time = time.time()
      self.start_time = datetime.datetime.now()
      self.last_epoch_time = datetime.datetime.now()
  
imports: import time
label_desc: Use image node_id 1 to create a new PandasLogger object from inherited base classes: object with example: obj = PandasLogger(batch_size, frequent)