message: remove relu6 for now
diff: @@ -368,16 +368,14 @@ def model_inference_fn(features, training):
  data_format="channels_last",
  use_bias=False)
  - my_relu = tf.nn.relu6 if FLAGS.quantize else tf.nn.relu
  -
  def my_res_layer(inputs):
  int_layer1 = my_batchn(my_conv2d(inputs))
  - initial_output = my_relu(int_layer1)
  + initial_output = tf.nn.relu(int_layer1)
  int_layer2 = my_batchn(my_conv2d(initial_output))
  - output = my_relu(inputs + int_layer2)
  + output = tf.nn.relu(inputs + int_layer2)
  return output
  - initial_output = my_relu(my_batchn(my_conv2d(features)))
  + initial_output = tf.nn.relu(my_batchn(my_conv2d(features)))
  # the shared stack
  shared_output = initial_output
  @@ -386,7 +384,7 @@ def model_inference_fn(features, training):
  # policy head
  policy_conv = my_conv2d(shared_output, filters=2, kernel_size=1)
  - policy_conv = my_relu(my_batchn(policy_conv, center=False, scale=False))
  + policy_conv = tf.nn.relu(my_batchn(policy_conv, center=False, scale=False))
  logits = tf.layers.dense(
  tf.reshape(policy_conv, [-1, 2 * go.N * go.N]),
  go.N * go.N + 1)
  @@ -395,9 +393,9 @@ def model_inference_fn(features, training):
  # value head
  value_conv = my_conv2d(shared_output, filters=1, kernel_size=1)
  - value_conv = my_relu(my_batchn(value_conv, center=False, scale=False))
  + value_conv = tf.nn.relu(my_batchn(value_conv, center=False, scale=False))
  - value_fc_hidden = my_relu(tf.layers.dense(
  + value_fc_hidden = tf.nn.relu(tf.layers.dense(
  tf.reshape(value_conv, [-1, go.N * go.N]),
  FLAGS.fc_width))
  value_output = tf.nn.tanh(
  