python - tensorflow 随机值在单次运行中是否保证相同？

Question

对于tf.random_uniform和类似的随机操作我知道“随机操作是有状态的，并且每次评估时都会创建新的随机值。”，因此在调用 session.run() 时我得到不同的值。两次:

# Each time we run these ops, different results are generated
sess = tf.Session()
print(sess.run(norm))
print(sess.run(norm))

我的问题是，如果我的图表两次引用一个随机操作，是否可以保证这两个“调用”会在单个 run() 中看到相同的值？例如

rnd_source = tf.random_normal(...)
x1 = rnd_source + 0.
x2 = rnd_source * 1.
sess.run([x1, x2])

如果不能保证 x1和x2将具有相同的值，是否有一种简单的方法将随机值存储在张量(而不是 tf.Variable)中以确保随机操作仅被评估一次？如果保证 x1将具有与 x2 相同的值，有没有办法强制在一次运行中重新评估随机操作以获得新的随机值？

Answer 1

你已经在不知不觉中做到了这一点。只需将值分配给张量，然后使用该值即可:

rnd_source = tf.random_normal((1,))
m = rnd_source

现在，在每次运行时，m 都会计算出正态分布的单次绘制，然后您可以从中绘制其他图形:

In [27]: for i in range(10):
    ...:     a, b, c, d, e = sess.run( [m*1, m+0, m+1, m+2, m+3 ]  )
    ...:     print(a, b, c, d, e)

[-2.1935725] [-2.1935725] [-1.1935725] [-0.19357252] [0.8064275]
[-0.5607107] [-0.5607107] [0.43928927] [1.4392893] [2.4392893]
[0.17031813] [0.17031813] [1.1703181] [2.1703181] [3.1703181]
[0.05647242] [0.05647242] [1.0564724] [2.0564723] [3.0564723]
[-0.2119268] [-0.2119268] [0.7880732] [1.7880732] [2.7880733]
[-0.07041783] [-0.07041783] [0.9295822] [1.9295821] [2.929582]
[-0.9486307] [-0.9486307] [0.05136931] [1.0513693] [2.0513692]
[1.3629643] [1.3629643] [2.3629642] [3.3629642] [4.362964]
[1.6997207] [1.6997207] [2.6997209] [3.6997209] [4.699721]
[1.480969] [1.480969] [2.480969] [3.480969] [4.480969]

现在，每次完成训练循环时，您都会从分布中获得一个新值，但是，使用 m 创建图形的其余部分，这将是一致的......

为了进一步澄清，让我们添加新节点...

In [28]: n = m+0
In [29]: o = m+1

现在，

In [31]: for i in range(10):
    ...:     a, b = sess.run([n, o])
    ...:     print(a, b)
    ...:
[0.32054538] [1.3205454]
[-0.6587958] [0.34120423]
[-0.8067821] [0.19321787]
[-0.29313084] [0.7068691]
[-1.1867933] [-0.18679333]
[1.4355402] [2.4355402]
[0.45581594] [1.4558159]
[-1.9583491] [-0.9583491]
[-1.2682568] [-0.26825678]
[1.534502] [2.534502]