model_mmd.py 文件源码

def discriminator(self, image, y=None, reuse=False):
        if reuse:
            tf.get_variable_scope().reuse_variables()

        s = self.output_size
        if np.mod(s, 16) == 0:
            h0 = lrelu(conv2d(image, self.df_dim, name='d_h0_conv'))
            h1 = lrelu(self.d_bn1(conv2d(h0, self.df_dim*2, name='d_h1_conv')))
            h2 = lrelu(self.d_bn2(conv2d(h1, self.df_dim*4, name='d_h2_conv')))
            h3 = lrelu(self.d_bn3(conv2d(h2, self.df_dim*8, name='d_h3_conv')))
            h4 = linear(tf.reshape(h3, [self.batch_size, -1]), 1, 'd_h3_lin')

            return tf.nn.sigmoid(h4), h4
        else:
            h0 = lrelu(conv2d(image, self.df_dim, name='d_h0_conv'))
            h1 = lrelu(self.d_bn1(conv2d(h0, self.df_dim*2, name='d_h1_conv')))
            h2 = linear(tf.reshape(h1, [self.batch_size, -1]), 1, 'd_h2_lin')
            if not self.config.use_kernel:
              return tf.nn.sigmoid(h2), h2
            else:
              return tf.nn.sigmoid(h2), h2, h1, h0