unrolled_gan.py 文件源码

def discriminator(img, name, target):
    size = 64
    with tf.variable_scope(name):
        # img = ly.conv2d(img, num_outputs=size, kernel_size=3,
        #                 stride=2, activation_fn=None, biases_initializer=None)
        # bias = slim.model_variable('conv_bias', shape=(
        #     size, ), initializer=tf.zeros_initializer)
        # img += bias
        # img = lrelu(img)
        img = ly.conv2d(img, num_outputs=size, kernel_size=3,
                        stride=2, activation_fn=lrelu, normalizer_fn=ly.batch_norm)
        img = ly.conv2d(img, num_outputs=size * 2, kernel_size=3,
                        stride=2, activation_fn=lrelu, normalizer_fn=ly.batch_norm)
        img = ly.conv2d(img, num_outputs=size * 4, kernel_size=3,
                        stride=2, activation_fn=lrelu, normalizer_fn=ly.batch_norm)
        img = tf.reshape(img, (2 * batch_size, -1))
        weights = slim.model_variable('weights', shape=[img.get_shape().as_list()[-1], 1],
                                      initializer=ly.xavier_initializer())
        bias = slim.model_variable('bias', shape=(
            1,), initializer=tf.zeros_initializer)
        logit = fully_connected(img, weights, bias)
        fake_logit = logit[:FLAGS.batch_size]
        true_logit = logit[FLAGS.batch_size:]
        d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
            fake_logit, tf.zeros_like(fake_logit)))
        d_loss_true = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
            true_logit, tf.ones_like(true_logit)))
        f = tf.reduce_mean(d_loss_fake + d_loss_true)

    return f, logit, d_loss_true, d_loss_fake