message: Add names to the output tensors for graph exports.
diff: @@ -166,7 +166,8 @@ class DualNetwork():
  def get_inference_input():
  return {
  'pos_tensor': tf.placeholder(tf.float32,
  - [None, go.N, go.N, features.NEW_FEATURES_PLANES]),
  + [None, go.N, go.N, features.NEW_FEATURES_PLANES],
  + name='pos_tensor'),
  'pi_tensor': tf.placeholder(tf.float32,
  [None, go.N * go.N + 1]),
  'value_tensor': tf.placeholder(tf.float32, [None]),
  @@ -223,7 +224,8 @@ def dual_net(input_tensors, batch_size, train_mode, **hparams):
  training=train_mode)
  my_conv2d = functools.partial(tf.layers.conv2d,
  - filters=hparams['k'], kernel_size=[3, 3], padding="same")
  + filters=hparams['k'],
  + kernel_size=[3, 3], padding="same")
  def my_res_layer(inputs, train_mode):
  int_layer1 = my_batchn(my_conv2d(inputs))
  @@ -248,7 +250,7 @@ def dual_net(input_tensors, batch_size, train_mode, **hparams):
  tf.reshape(policy_conv, [batch_size, go.N * go.N * 2]),
  go.N * go.N + 1)
  - policy_output = tf.nn.softmax(logits)
  + policy_output = tf.nn.softmax(logits, name='policy_output')
  # value head
  value_conv = tf.nn.relu(my_batchn(
  @@ -257,8 +259,9 @@ def dual_net(input_tensors, batch_size, train_mode, **hparams):
  value_fc_hidden = tf.nn.relu(tf.layers.dense(
  tf.reshape(value_conv, [batch_size, go.N * go.N]),
  hparams['fc_width']))
  - value_output = value_output = tf.nn.tanh(
  - tf.reshape(tf.layers.dense(value_fc_hidden, 1), [batch_size]))
  + value_output = tf.nn.tanh(
  + tf.reshape(tf.layers.dense(value_fc_hidden, 1), [batch_size]),
  + name='value_output')
  return {
  'logits': logits,
  'policy_output': policy_output,
  