TensorFlow隨機張量:tf.set_random_seed函數(shù)

2018-01-20 11:32 更新

tf.set_random_seed 函數(shù)

set_random_seed(seed)

定義在:tensorflow/python/framework/random_seed.py.

請參閱指南:生成常量,序列和隨機值>隨機張量

設置圖形級隨機seed.

可以從兩個seed中獲得依賴隨機seed的操作:圖形級seed和操作級seed.本節(jié)是介紹如何設置圖形級別的seed.

它與操作級別seed的交互如下:

  1. 如果既沒有設置圖層級也沒有設置操作級別的seed:則使用隨機seed進行該操作.
  2. 如果設置了圖形級seed,但操作seed沒有設置:系統(tǒng)確定性地選擇與圖形級seed結合的操作seed,以便獲得唯一的隨機序列.
  3. 如果未設置圖形級seed,但設置了操作seed:使用默認的圖層seed和指定的操作seed來確定隨機序列.
  4. 如果圖層級seed和操作seed都被設置:則兩個seed將一起用于確定隨機序列.

為了說明用戶可見的效果,請考慮以下示例:

要在會話中生成不同的序列,請不要設置圖層級別seed或操作級別seed:

a = tf.random_uniform([1])
b = tf.random_normal([1])

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'

要為會話中的操作生成相同的可重復序列,請為操作設置seed:

a = tf.random_uniform([1], seed=1)
b = tf.random_normal([1])

# Repeatedly running this block with the same graph will generate the same
# sequence of values for 'a', but different sequences of values for '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 'B3'
  print(sess2.run(b))  # generates 'B4'

要使所有操作生成的隨機序列在會話中可重復,請設置圖形級別seed:

tf.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'

函數(shù)參數(shù)

  • seed:整數(shù).


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