Discriminator architecture based on InfoGAN.

    Args:
      x: input images, shape [bs, h, w, channels]
      is_training: boolean, are we in train or eval model.
      reuse: boolean, should params be re-used.

    Returns:
      out_logit: the output logits (before sigmoid).

def discriminator(self, x, is_training, reuse=False):
    """Discriminator architecture based on InfoGAN.

    Args:
      x: input images, shape [bs, h, w, channels]
      is_training: boolean, are we in train or eval model.
      reuse: boolean, should params be re-used.

    Returns:
      out_logit: the output logits (before sigmoid).
    """
    hparams = self.hparams
    with tf.variable_scope(
        "discriminator", reuse=reuse,
        initializer=tf.random_normal_initializer(stddev=0.02)):
      batch_size, height, width = common_layers.shape_list(x)[:3]
      # Mapping x from [bs, h, w, c] to [bs, 1]
      net = tf.layers.conv2d(x, 64, (4, 4), strides=(2, 2),
                             padding="SAME", name="d_conv1")
      # [bs, h/2, w/2, 64]
      net = lrelu(net)
      net = tf.layers.conv2d(net, 128, (4, 4), strides=(2, 2),
                             padding="SAME", name="d_conv2")
      # [bs, h/4, w/4, 128]
      if hparams.discriminator_batchnorm:
        net = tf.layers.batch_normalization(net, training=is_training,
                                            momentum=0.999, name="d_bn2")
      net = lrelu(net)
      size = height * width
      net = tf.reshape(net, [batch_size, size * 8])  # [bs, h * w * 8]
      net = tf.layers.dense(net, 1024, name="d_fc3")  # [bs, 1024]
      if hparams.discriminator_batchnorm:
        net = tf.layers.batch_normalization(net, training=is_training,
                                            momentum=0.999, name="d_bn3")
      net = lrelu(net)
      return net