vae.py 文件源码

def _encoder(self, x, is_training=None):
        net = self.arch['encoder']
        for i, (o, k, s) in enumerate(zip(net['output'], net['kernel'], net['stride'])):
            x = conv2d_nchw_layernorm(
                x, o, k, s, lrelu,
                name='Conv2d-{}'.format(i)
            )
        x = slim.flatten(x)
        z_mu = tf.layers.dense(x, self.arch['z_dim'])
        z_lv = tf.layers.dense(x, self.arch['z_dim'])
        return z_mu, z_lv