为什么TensorFlow 2和1的RNG不同？

Question

import numpy as np
np.random.seed(1)
import random
random.seed(2)
import tensorflow as tf
tf.compat.v1.set_random_seed(3)  # graph-level seed
if tf.__version__[0] == '2':
    tf.random.set_seed(4)  # global seed
else:
    tf.set_random_seed(4)  # global seed

from tensorflow.keras.initializers import glorot_uniform as GlorotUniform
from tensorflow.keras import backend as K

init = GlorotUniform(seed=5)(shape=(4, 4))
print(K.eval(init))

[[-0.75889236  0.5744677   0.82025963 -0.26889956]
 [ 0.0180248  -0.24747121 -0.0666492   0.23440498]
 [ 0.61886185  0.05548459  0.39713246  0.126324  ]
 [ 0.6639387  -0.58397514  0.39671892  0.67872125]]  # TF 2

[[ 0.2515846  -0.41902617 -0.7859829   0.41573995]
 [ 0.8099498  -0.6861247  -0.46198446 -0.7579694 ]
 [ 0.29976922  0.0310365   0.5031274   0.314076  ]
 [-0.62062943 -0.01889879  0.7725797  -0.65635633]]  # TF 1

---

为什么不同？这在两个版本之间造成了严重的再现性问题，并且在同一版本的的（TF2）图与Eager图中出现了此问题或其他问题。更重要的是，TF1的RNG序列可以在TF2中使用吗

Answer 1

有足够的挖掘-是的。TL；博士：

• TF2在TF1中的行为：from tensorflow.python.keras.initializers import GlorotUniformV2 as GlorotUniform
• TF2中的TF1行为：from tensorflow.python.keras.initializers import GlorotUniform

TF2基本上执行引擎盖下的第一个项目符号GlorotUniform实际上是GlorotUniformV2

---

一些细节：

已找到文档-但代码本身终止于某个pywrapped编译代码（TF1TF2出于某种原因，Github拒绝显示TF2的gen_stateless_random_ops和TF1的gen_random_ops，但您可以在本地安装中找到这两个代码）：

tensorflow.python.ops.gen_random_ops.truncated_normal Outputs random values from a truncated normal distribution.

The generated values follow a normal distribution with mean 0 and standard deviation 1, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked.

---

tensorflow.python.ops.gen_stateless_random_ops.truncated_normal Outputs deterministic pseudorandom values from a truncated normal distribution.

The generated values follow a normal distribution with mean 0 and standard deviation 1, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked.

The outputs are a deterministic function of shape and seed.

第一个和第二个最终分别是GlorotUniform和GlorotUniformV2的路径。TF2的from tensorflow.keras.initializers从init_ops_v2（即V2）导入，而TF1从init_ops导入