gan.py 文件源码

def discriminator(self, opts, input_, is_training,
                      prefix='DISCRIMINATOR', reuse=False):
        """Discriminator function, suitable for simple toy experiments.

        """
        num_filters = opts['d_num_filters']

        with tf.variable_scope(prefix, reuse=reuse):
            h0 = ops.conv2d(opts, input_, num_filters, scope='h0_conv')
            h0 = ops.batch_norm(opts, h0, is_training, reuse, scope='bn_layer1')
            h0 = ops.lrelu(h0)
            h1 = ops.conv2d(opts, h0, num_filters * 2, scope='h1_conv')
            h1 = ops.batch_norm(opts, h1, is_training, reuse, scope='bn_layer2')
            h1 = ops.lrelu(h1)
            h2 = ops.conv2d(opts, h1, num_filters * 4, scope='h2_conv')
            h2 = ops.batch_norm(opts, h2, is_training, reuse, scope='bn_layer3')
            h2 = ops.lrelu(h2)
            h3 = ops.linear(opts, h2, 1, scope='h3_lin')

        return h3