vaegan.py 文件源码

def _encoder(self, x, is_training):
        n_layer = len(self.arch['encoder']['output'])
        subnet = self.arch['encoder']
        with slim.arg_scope(
                [slim.batch_norm],
                scale=True,
                updates_collections=None,
                decay=0.9, epsilon=1e-5,
                is_training=is_training,
                reuse=None):
            with slim.arg_scope(
                    [slim.conv2d],
                    weights_regularizer=slim.l2_regularizer(subnet['l2-reg']),
                    normalizer_fn=slim.batch_norm,
                    activation_fn=lrelu):

                for i in range(n_layer):
                    x = slim.conv2d(
                        x,
                        subnet['output'][i],
                        subnet['kernel'][i],
                        subnet['stride'][i])

        x = slim.flatten(x)

        with slim.arg_scope(
            [slim.fully_connected],
            num_outputs=self.arch['z_dim'],
            weights_regularizer=slim.l2_regularizer(subnet['l2-reg']),
            normalizer_fn=None,
            activation_fn=None):
            z_mu = slim.fully_connected(x)
            z_lv = slim.fully_connected(x)
        return z_mu, z_lv