message: Update with fixes
diff: @@ -32,9 +32,10 @@ def get_default_hyperparams() -> Dict:
  In other words, returns a dict whose paramaters come from the AGZ paper:
  {
  - k: ???
  + k: number of filters (AlphaGoZero used 256)
  fc_width: ???
  - num_shared_layers: ???
  + num_shared_layers: number of shared residual blocks. AGZ used both 19
  + and 39. Here we use 19 because it's faster to train.
  l2_strength: The L2 regularization parameter. Note AGZ paper has this
  set to 10^-4 for self-play learning.
  }
  @@ -96,10 +97,10 @@ class DualNetwork(object):
  def my_res_layer(inputs):
  int_layer1 = my_batchn(my_conv2d(inputs))
  initial_output = tf.nn.relu(int_layer1)
  + int_layer2 = my_batchn(my_conv2d(initial_output))
  + output = tf.nn.relu(inputs + int_layer2)
  return output
  - initial_output = tf.nn.relu(my_batchn(my_conv2d(x)))
  -
  # the shared stack
  shared_output = initial_output
  for i in range(self.num_shared_layers):
  @@ -151,7 +152,11 @@ class DualNetwork(object):
  self.saver = tf.train.Saver()
  def initialize_logging(self, tensorboard_logdir):
  - """Initializes a logging-summary writer"""
  + """Initializes a logging-summary writer.
  +
  + This method initializes a writer to write for writing stats to
  + tensorboard.
  + """
  self.summary_writer = tf.summary.FileWriter(os.path.join(
  tensorboard_logdir, "training"), self.session.graph)
  