Tensorflow 中的 while_loop 错误 [英] while_loop error in Tensorflow

问题描述

我尝试在 Tensorflow 中使用 while_loop，但是当我尝试从 while 循环中的 callable 返回目标 output 时，它给了我每次都会增加形状的错误.

I tried to use while_loop in Tensorflow, but when I try to return the target output from callable in while loop, it gives me an error because the shape is increased every time.

输出应包含基于数据值(输入数组)的(0 或 1)个值.如果数据值大于5返回1，否则返回0.返回值必须添加到输出

The output should be contains (0 or 1) values based on data value (input array). If data value is large than 5 return 1 else return 0. The returned value must be added into output

这是代码::

import numpy as np
import tensorflow as tf

data = np.random.randint(10, size=(30))
data = tf.constant(data, dtype= tf.float32)

global output
output= tf.constant([], dtype= tf.float32)
i = tf.constant(0)
c = lambda i: tf.less(i, 30)


def b(i):
   i= tf.add(i,1)
   cond= tf.cond(tf.greater(data[i-1], tf.constant(5.)), lambda: tf.constant(1.0), lambda: tf.constant([0.0]))
   output =tf.expand_dims(cond, axis = i-1)
   return i, output

r,out = tf.while_loop(c, b, [i])
print(out)
sess=  tf.Session()
sess.run(out) 

错误::

r, out = tf.while_loop(c, b, [i])

r, out = tf.while_loop(c, b, [i])

ValueError: 两个结构的元素数量不同.

ValueError: The two structures don't have the same number of elements.

第一个结构(1个元素):[tf.Tensor 'while/Identity:0' shape=()dtype=int32]

First structure (1 elements): [tf.Tensor 'while/Identity:0' shape=() dtype=int32]

第二个结构(2个元素):[tf.Tensor 'while/Add:0' shape=()dtype=int32, tf.Tensor 'while/ExpandDims:0' shape=unknowndtype=float32>]

Second structure (2 elements): [tf.Tensor 'while/Add:0' shape=() dtype=int32, tf.Tensor 'while/ExpandDims:0' shape=unknown dtype=float32>]

我使用 tensorflow-1.1.3 和 python-3.5

如何更改代码以提供目标结果?

How can I change my code to gives me the target result?

:

我根据@mrry 的答案编辑了代码，但我仍然遇到输出错误答案的问题输出是数字求和

I edit the code based on @mrry answer, but I still have an issue that the output is incorrect answer the output is numbers summation

a = tf.ones([10,4])
print(a)
a = tf.reduce_sum(a, axis = 1)
i =tf.constant(0)
c = lambda i, _:tf.less(i,10)

def Smooth(x):
   return tf.add(x,2)

summ = tf.constant(0.)
def b(i,_):
   global summ
   summ = tf.add(summ, tf.cast(Smooth(a[i]), tf.float32))
   i= tf.add(i,1)
   return i, summ

r, smooth_l1 = tf.while_loop(c, b, [i, smooth_l1])

print(smooth_l1)

sess = tf.Session()
print(sess.run(smooth_l1))

输出是6.0(错误).

推荐答案

tf.while_loop() 函数要求以下四个列表的长度相同，每个元素的类型相同:

The tf.while_loop() function requires that the following four lists have the same length, and the same type for each element:

• cond 函数的参数列表(在本例中为 c).
• body 函数的参数列表(在本例中为 b).
• body 函数的返回值列表.
• 表示循环变量的 loop_vars 列表.

• The list of arguments to the cond function (c in this case).
• The list of arguments to the body function (b in this case).
• The list of return values from the body function.
• The list of loop_vars representing the loop variables.

因此，如果你的循环体有两个输出，你必须给b和c添加一个对应的参数，给loop_vars添加一个对应的元素>:

Therefore, if your loop body has two outputs, you must add a corresponding argument to b and c, and a corresponding element to loop_vars:

c = lambda i, _: tf.less(i, 30)

def b(i, _):
  i = tf.add(i, 1)
  cond = tf.cond(tf.greater(data[i-1], tf.constant(5.)),
                 lambda: tf.constant(1.0),
                 lambda: tf.constant([0.0]))

  # NOTE: This line fails with a shape error, because the output of `cond` has
  # a rank of either 0 or 1, but axis may be as large as 28.
  output = tf.expand_dims(cond, axis=i-1)
  return i, output

# NOTE: Use a shapeless `tf.placeholder_with_default()` because the shape
# of the output will vary from one iteration to the next.
r, out = tf.while_loop(c, b, [i, tf.placeholder_with_default(0., None)])

正如评论中所指出的，循环体(特别是对 tf.expand_dims() 的调用)似乎不正确，该程序无法按原样运行，但希望这足以让您入门.

As noted in the comments, the body of the loop (specifically the call to tf.expand_dims()) seems to be incorrect and this program won't work as-is, but hopefully this is enough to get you started.

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