ios - 为什么我们总是在tf.compat.v1.random.set_random_seed(1234)中使用seed =1234。有什么具体原因吗？

Question

<分区>

Answer 1

是的，让所有操作生成的随机序列在 session 中可重复。顺便说一句，seed=1234 是随机的。您可以选择任何值。

例如:

a = tf.random.uniform([1])
b = tf.random.normal([1])

# Repeatedly running this block with the same graph will generate the 
# different sequences of 'a' and 'b' across sessions.
print("Session 1")
with tf.Session() as sess1:
  print(sess1.run(a))  # generates 'A1'
  print(sess1.run(a))  # generates 'A2'
  print(sess1.run(b))  # generates 'B1'
  print(sess1.run(b))  # generates 'B2'

print("Session 2")
with tf.Session() as sess2:
  print(sess2.run(a))  # generates 'A3'
  print(sess2.run(a))  # generates 'A4'
  print(sess2.run(b))  # generates 'B3'
  print(sess2.run(b))  # generates 'B4'

输出:

Session 1
[0.35214436]
[0.61644566]
[-0.2290629]
[0.8414659]
Session 2
[0.62713313]
[0.5924448]
[-0.5366475]
[-0.36064562]

但是，在使用 tf.random.set_random_seed(1234) 设置图级种子后:

tf.random.set_random_seed(1234)
a = tf.random.uniform([1])
b = tf.random.normal([1])

# Repeatedly running this block with the same graph will generate the same
# sequences of 'a' and 'b'.
print("Session 1")
with tf.Session() as sess1:
  print(sess1.run(a))  # generates 'A1'
  print(sess1.run(a))  # generates 'A2'
  print(sess1.run(b))  # generates 'B1'
  print(sess1.run(b))  # generates 'B2'

print("Session 2")
with tf.Session() as sess2:
  print(sess2.run(a))  # generates 'A1'
  print(sess2.run(a))  # generates 'A2'
  print(sess2.run(b))  # generates 'B1'
  print(sess2.run(b))  # generates 'B2'

输出:

Session 1
[0.53202796]
[0.91749656]
[-1.3118125]
[-0.44506428]
Session 2
[0.53202796]
[0.91749656]
[-1.3118125]
[-0.44506428]

您还可以设置操作级种子，如 a = tf.random.uniform([1], seed=1)。从官方文档 here 中阅读更多相关信息。