nice.py 文件源码

def __call__(self, inputs, steps):
        def fn(zv, x):
            """
            Transition for training, without Metropolis-Hastings.
            `z` is the input state.
            `v` is created as a dummy variable to allow output of v_, for training p(v).
            :param x: variable only for specifying the number of steps
            :return: next state `z_`, and the corresponding auxiliary variable `v_`.
            """
            z, v = zv
            v = tf.random_normal(shape=tf.stack([tf.shape(z)[0], self.network.v_dim]))
            z_, v_ = self.network.forward([z, v])
            return z_, v_

        elems = tf.zeros([steps])
        return tf.scan(fn, elems, inputs, back_prop=True)