message: Don't divide steps by NUM_TPUS if passed from command line
diff: @@ -239,7 +239,6 @@ def model_fn(features, labels, mode, params=None):
  # pi_tensor is one_hot when generated from sgfs (for supervised learning)
  # and soft-max when using self-play records. argmax normalizes the two.
  policy_target_top_1 = tf.argmax(pi_tensor, axis=1)
  - policy_output_top_1 = tf.argmax(policy_output, axis=1)
  policy_output_in_top1 = tf.to_float(
  tf.nn.in_top_k(policy_output, policy_target_top_1, k=1))
  @@ -251,7 +250,6 @@ def model_fn(features, labels, mode, params=None):
  policy_output,
  tf.one_hot(policy_target_top_1, tf.shape(policy_output)[1]))
  - # TODO(sethtroisi): For V10 add tf.variable_scope for tf.metrics.mean's
  with tf.variable_scope("metrics"):
  metric_ops = {
  'policy_cost': tf.metrics.mean(policy_cost),
  @@ -485,6 +483,8 @@ def train(
  total_examples = sum(map(count_examples, tf_records))
  steps = total_examples // FLAGS.train_batch_size
  + if FLAGS.use_tpu:
  + steps //= FLAGS.num_tpu_cores
  if FLAGS.use_tpu:
  def input_fn(params):
  @@ -494,7 +494,6 @@ def train(
  random_rotation=True)
  # TODO: get hooks working again with TPUestimator.
  hooks = []
  - steps //= FLAGS.num_tpu_cores
  else:
  def input_fn():
  return preprocessing.get_input_tensors(
  