def __init__(self, logdir, step=None, log=None, report=None, reset=None):
    """Execute operations in a loop and coordinate logging and checkpoints.

    The step, log, report, and report arguments will get created if not
    provided. Reset is used to indicate switching to a new phase, so that the
    model can start a new computation in case its computation is split over
    multiple training steps.

    Args:
      logdir: Will contain checkpoints and summaries for each phase.
      step: Variable of the global step (optional).
      log: Tensor indicating to the model to compute summary tensors.
      report: Tensor indicating to the loop to report the current mean score.
      reset: Tensor indicating to the model to start a new computation.
    """
    self._logdir = logdir
    self._step = (
        tf.Variable(0, False, name='global_step') if step is None else step)
    self._log = tf.placeholder(tf.bool) if log is None else log
    self._report = tf.placeholder(tf.bool) if report is None else report
    self._reset = tf.placeholder(tf.bool) if reset is None else reset
    self._phases = []
def __init__ ( self , logdir , step = None , log = None , report = None , reset = None ) : self . _logdir = logdir self . _step = ( tf . Variable ( 0 , False , name = 'global_step' ) if step is None else step ) self . _log = tf . placeholder ( tf . bool ) if log is None else log self . _report = tf . placeholder ( tf . bool ) if report is None else report self . _reset = tf . placeholder ( tf . bool ) if reset is None else reset self . _phases = [ ]
Execute operations in a loop and coordinate logging and checkpoints.

    The step, log, report, and report arguments will get created if not
    provided. Reset is used to indicate switching to a new phase, so that the
    model can start a new computation in case its computation is split over
    multiple training steps.

    Args:
      logdir: Will contain checkpoints and summaries for each phase.
      step: Variable of the global step (optional).
      log: Tensor indicating to the model to compute summary tensors.
      report: Tensor indicating to the loop to report the current mean score.
      reset: Tensor indicating to the model to start a new computation.