def single_discriminator(x, filters=128, kernel_size=8,
                         strides=4, pure_mean=False):
  """A simple single-layer convolutional discriminator."""
  with tf.variable_scope("discriminator"):
    net = layers().Conv2D(
        filters, kernel_size, strides=strides, padding="SAME", name="conv1")(x)
    if pure_mean:
      net = tf.reduce_mean(net, [1, 2])
    else:
      net = mean_with_attention(net, "mean_with_attention")
    return net
def single_discriminator ( x , filters = 128 , kernel_size = 8 , strides = 4 , pure_mean = False ) : with tf . variable_scope ( "discriminator" ) : net = layers ( ) . Conv2D ( filters , kernel_size , strides = strides , padding = "SAME" , name = "conv1" ) ( x ) if pure_mean : net = tf . reduce_mean ( net , [ 1 , 2 ] ) else : net = mean_with_attention ( net , "mean_with_attention" ) return net
A simple single-layer convolutional discriminator.