message: Fiddle with batch norm / bias / update ops
diff: @@ -59,7 +59,7 @@ class DualNetwork(object):
  self.outcome = outcome = tf.placeholder(tf.float32, [None])
  my_conv2d = functools.partial(tf.layers.conv2d,
  - filters=self.k, kernel_size=[3, 3], padding="same", use_bias=False)
  + filters=self.k, kernel_size=[3, 3], padding="same")
  def my_res_layer(inputs):
  int_layer1 = tf.layers.batch_normalization(my_conv2d(inputs))
  @@ -69,7 +69,7 @@ class DualNetwork(object):
  return output
  initial_output = tf.nn.relu(tf.layers.batch_normalization(
  - my_conv2d(x), renorm=True))
  + my_conv2d(x)))
  # the shared stack
  shared_output = initial_output
  @@ -105,8 +105,10 @@ class DualNetwork(object):
  # Combined loss + regularization
  self.dual_cost = self.mse_cost + self.log_likelihood_cost + self.l2_cost
  learning_rate = tf.train.exponential_decay(1e-2, global_step, 10 ** 6, 0.5)
  - self.dual_train_step = tf.train.MomentumOptimizer(learning_rate, 0.9).minimize(
  - self.dual_cost, global_step=global_step)
  + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
  + with tf.control_dependencies(update_ops):
  + self.dual_train_step = tf.train.MomentumOptimizer(
  + learning_rate, 0.9).minimize(self.dual_cost, global_step=global_step)
  # misc ops
  self.weight_summaries = tf.summary.merge([
  