Following this blog.

One way of recoving the functionality in tensorflow is the edward.copy trick.

For instance, consider the hard-coded “function” y(x) in tensorflow:

import numpy as np
import tensorflow as tf

x = tf.constant(2.0)
y = 3 * x

For making the y a function of an abstract x, we employ the edward.copy trick:

import edward as ed

def y_fn(x_var, x=x, y=y):
    """Function version of `y`, with the `x_var` an abstract argument."""
    y_var = ed.copy(y, {x: x_var},
                    scope='functionality')
    return y_var

This makes the functionality forsooth. E.g.,

xs = tf.constant([float(i) for i in range(100)])
ys = tf.map_fn(y_fn, xs)

with tf.Session() as sess:
    writer = tf.summary.FileWriter('./logdir', sess.graph)
    ys_value = sess.run(ys)
    print(ys_value)

Since tf.map_fn accept a function as its first argument, y_fn does be so. In addition, by visualizing the computational graph with TensorBoard, we find that tf.map_fn does map y_fn as a function, without any redundancy like re-copying.