gvae.py 文件源码

def _classifier(self, x, is_training):
        n_layer = len(self.arch['classifier']['output'])
        subnet = self.arch['classifier']

        with slim.arg_scope(
            [slim.batch_norm],
            scale=True, scope='BN',
            updates_collections=None,
            # decay=0.9, epsilon=1e-5,  # [TODO] Test these hyper-parameters
            is_training=is_training):
            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])
                    tf.summary.image(
                        'down-sample{:d}'.format(i),
                        tf.transpose(x[:, :, :, 0:3], [2, 1, 0, 3]))

        x = slim.flatten(x)

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