vaegan.py 文件源码

def _generator(self, z, is_training):
        subnet = self.arch['generator']
        n_layer = len(subnet['output'])
        h, w, c = subnet['hwc']
        with slim.arg_scope(
            [slim.batch_norm],
            scale=True,
            updates_collections=None,
            decay=0.9, epsilon=1e-5,
            is_training=is_training,
            scope='BN'):

            x = slim.fully_connected(
                z,
                h * w * c,
                normalizer_fn=slim.batch_norm,
                activation_fn=tf.nn.relu)
            x = tf.reshape(x, [-1, h, w, c])

            with slim.arg_scope(
                    [slim.conv2d_transpose],
                    weights_regularizer=slim.l2_regularizer(subnet['l2-reg']),
                    normalizer_fn=slim.batch_norm,
                    activation_fn=tf.nn.relu):

                for i in range(n_layer -1):
                    x = slim.conv2d_transpose(
                        x,
                        subnet['output'][i],
                        subnet['kernel'][i],
                        subnet['stride'][i])

                # Don't apply BN for the last layer of G
                x = slim.conv2d_transpose(
                    x,
                    subnet['output'][-1],
                    subnet['kernel'][-1],
                    subnet['stride'][-1],
                    normalizer_fn=None,
                    activation_fn=tf.nn.tanh)
        return x