Question

我正在尝试学习TensorFlow，所以我想写一个斐波那契序列（其中的一部分，ofc）。

本练习的灵感来自IBM认知课程。

这是我的代码：

#import stuff
import tensorflow as tf

# define the first 2 terms of the sequence
a = tf.Variable(0)
b = tf.Variable(1)

# define the next term. By definition it is the sum of the previous ones
newterm = tf.add(a,b)     # or newterm = a+b

# define the update operations. I want a to become b, and b to become the new term
update1 = tf.assign(a, b)
update2 = tf.assign(b, newterm)

# initialize variables
init = tf.global_variables_initializer()

# run
with tf.Session() as sess:
    sess.run(init)
    fibonacci = [a.eval(), b.eval()]
    for i in range(10):
         new, up1, up2 = sess.run([newterm, update1, update2])
         fibonacci.append(new)
    print(fibonacci)

然而，这会打印[0, 1, 2, 4, 8, 12, 24, 48, 96, 144, 240, 480]。我真的不明白我做错了什么。我只是创建下一个字词，然后将a与b和b相同，与newterm相同。

Answer 1

Tensorflow使用静态图描述计算。首先必须定义图形，然后执行它。

图表执行从您放入sess.run([var1,var2, ..., vaN])调用的节点开始：变量的顺序无意义。 Tensorflow图评估从随机节点开始，并跟随从叶到根的每个节点。

由于您要强制执行某个执行顺序，因此必须使用tf.control_dependencies将排序约束引入图形中，从而按特定顺序执行操作。

看看我如何更改代码以使其正常工作，应该非常清楚。

import tensorflow as tf

# define the first 2 terms of the sequence
a = tf.Variable(0)
b = tf.Variable(1)

# you have to force the order of assigments:
# first execute newterm, then execute update1 and than update2

# define the next term. By definition it is the sum of the previous ones
newterm = tf.add(a,b)     # or newterm = a+b

with tf.control_dependencies([newterm]):
    update1 = tf.assign(a, b)

    # thus, execute update2 after update1 and newterm
    # have been executed

    with tf.control_dependencies([update1]):
        update2 = tf.assign(b, newterm)

# initialize variables
init = tf.global_variables_initializer()

# run
with tf.Session() as sess:
    sess.run(init)
    fibonacci = [a.eval(), b.eval()]
    for i in range(10):
         next, up1, up2 = sess.run([newterm, update1, update2])
         fibonacci.append(next)
    print(fibonacci)

TensorFlow：我的斐波纳契序列有什么问题？

1 个答案: