python - 如何在 tf.while_loop 中使用 tf.scatter_update

Question

我有 tf.while_loop ，其中它基于 tf.Variable 内的元素进行调节。问题是当我使用 tf.scatter_update 时，我收到以下错误消息(注意:当我使用 tf.add 时它工作正常):

---> 11   var = tf.scatter_update(var, [0], tf.add(var, tf.constant([1.0])))

AttributeError: 'Tensor' object has no attribute '_lazy_read'

简化的代码如下(注意:我不能使用tf.add，因为我只想更新变量张量内的一个元素，所以我必须使用tf.scatter_update):

def func(var1, cons):
  var1, _ = tf.while_loop(cond, body, [var1, x], return_same_structure=True)
  with tf.control_dependencies([var1, _]):
    return var1

def cond(var, cons):
  return tf.reduce_all(tf.less(var,cons))

def body(var, cons):
  var = tf.scatter_update(var, [0], tf.add(var, tf.constant([1.0])))
  # Works fine when using --> var = tf.add(var, tf.constant([1.0]))
  return (var, cons)

with tf.Session() as sess:
  x = tf.constant([10.0])
  m = tf.Variable([2.0])
  b = func(m, x)
  init = tf.initialize_all_variables()
  sess.run(init)
  print sess.run(b)

Answer 1

尝试tf.get_variable。

import tensorflow as tf

def func(var1, cons):
    var1, _ = tf.while_loop(cond, body, [var1, cons], return_same_structure=True)
    with tf.control_dependencies([var1, _]):
        return var1

def cond(var, cons):
    return tf.reduce_all(tf.less(var,cons))

def body(var, cons):
    var = tf.get_variable(name="m",initializer=[2.0])
    var = tf.scatter_update(var, [0], tf.add(var, tf.constant([1.0])))
    # var = tf.add(var, tf.constant([1.0]))
    return (var, cons)

with tf.Session() as sess:
    x = tf.constant([10.0])
    m = tf.Variable([2.0],name='m')
    b = func(m, x)
    init = tf.initialize_all_variables()
    sess.run(init)
    print(sess.run(b))

[10.]