adversary.py 文件源码

def adversarial_discriminator(net, layers, scope='adversary', leaky=False):
    if leaky:
        activation_fn = tflearn.activations.leaky_relu
    else:
        activation_fn = tf.nn.relu
    with ExitStack() as stack:
        stack.enter_context(tf.variable_scope(scope))
        stack.enter_context(
            slim.arg_scope(
                [slim.fully_connected],
                activation_fn=activation_fn,
                weights_regularizer=slim.l2_regularizer(2.5e-5)))
        for dim in layers:
            net = slim.fully_connected(net, dim)
        net = slim.fully_connected(net, 2, activation_fn=None)
    return net