def finalize_stats()

in tensorflow_fold/blocks/plan.py [0:0]

• 29 lines of code
• 16 McCabe index (conditional complexity)

  def finalize_stats(self):
    """Finalizes metrics and losses. Gets/creates global_step if unset."""
    if self.has_finalized_stats:
      raise RuntimeError('finalize_stats() has already been called')
    self._finalized = True
    if self.compute_summaries:
      for name, tensor in six.iteritems(self.metrics):
        if tensor.get_shape().ndims == 0:
          tf.summary.scalar(name, tensor)
        else:
          tf.summary.histogram(name, tensor)
    if self.losses:
      loss_dtype = next(six.itervalues(self.losses)).dtype
      if not loss_dtype.is_floating:
        raise TypeError('invalid loss dtype %r, must be a floating point type'
                        % loss_dtype)
      if self.compute_summaries:
        self._batch_size_ph = tf.placeholder(loss_dtype, [], name='batch_size')
    loss_sums = []
    for name, tensor in six.iteritems(self.losses):
      loss_sums.append(tf.reduce_sum(tensor))
      if self.compute_summaries:
        tf.summary.scalar(name, loss_sums[-1] / self._batch_size_ph)
    if loss_sums:
      self._loss_total = tf.add_n(loss_sums)
      # computing a loss total summary is redundant if there is only one loss
      if self.compute_summaries and len(loss_sums) > 1:
        tf.summary.scalar('loss_total', self.loss_total / self._batch_size_ph)
    if self.compute_summaries:
      self._summaries = tf.summary.merge_all()
      if self._summaries is None: self._summaries = tf.constant('')