message: Log policy entropy
diff: @@ -123,7 +123,7 @@ class DualNetworkTrainer():
  training_stats.report(
  {k: tensor_values[k] for k in (
  'policy_cost', 'value_cost', 'l2_cost',
  - 'combined_cost')})
  + 'combined_cost', 'policy_entropy')})
  if should_log(i):
  after_weights = self.sess.run(weight_tensors)
  weight_update_summaries = compute_update_ratio(
  @@ -344,6 +344,9 @@ def train_ops(input_tensors, output_tensors, **hparams):
  l2_cost = hparams['l2_strength'] * tf.add_n([tf.nn.l2_loss(v)
  for v in tf.trainable_variables() if not 'bias' in v.name])
  combined_cost = policy_cost + value_cost + l2_cost
  + policy_output = output_tensors['policy_output']
  + policy_entropy = -tf.reduce_mean(tf.reduce_sum(
  + policy_output * tf.log(policy_output), axis=1))
  boundaries = list(map(int, [1e6, 2 * 1e6]))
  values = [1e-2, 1e-3, 1e-4]
  learning_rate = tf.train.piecewise_constant(
  @@ -359,6 +362,7 @@ def train_ops(input_tensors, output_tensors, **hparams):
  'value_cost': value_cost,
  'l2_cost': l2_cost,
  'combined_cost': combined_cost,
  + 'policy_entropy': policy_entropy,
  'global_step': global_step,
  'train_op': train_op,
  }
  