model_mmd_fm.py 文件源码

def discriminator_k(self, image, reuse=False):
        if reuse:
            tf.get_variable_scope().reuse_variables()
        #1024, 512, 128
        h0 = tf.nn.sigmoid(linear(image, 512, 'dk_h0_lin', stddev=self.config.init))
        h1 = tf.nn.sigmoid(linear(h0, 256, 'dk_h1_lin', stddev=self.config.init))
        h2 = tf.nn.sigmoid(linear(h1, 256, 'dk_h2_lin', stddev=self.config.init))
        h3 = tf.nn.sigmoid(linear(h2, 128, 'dk_h3_lin', stddev=self.config.init))
        h4 = tf.nn.relu(linear(h3, 64, 'dk_h4_lin', stddev=self.config.init))
        if self.config.use_gan:
          h5 = linear(h4, 1, 'dk_h5_lin', stddev=self.config.init)
          return image, h0, h1, h2, h3, h4, h5
        elif self.config.use_layer_kernel:
          return image, h0, h1, h2, h3, h4
        elif self.config.use_scale_kernel:
          return tf.concat(1, [image, (28.0 * 28.0/512.0) * h0, (28 * 28.0/256.0) * h1, (28.0 * 28.0/256.0) * h2, (28.0 * 28.0/128.0) * h3,
 (28.0 * 28.0/64.0) * h4])

        else:
          return tf.concat(1, [image, h0, h1, h2, h3, h4])