设置图级随机seed。依赖于随机seed的操作实际上从两个seed中获取:图级和操作级seed。 这将设置图级别的seed。其与操作级seed的相互作用如下:
为了说明用户可见的效果,请考虑以下示例:
要跨会话生成不同的序列,既不设置图级别也不设置op级别的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'
要为跨会话生成一个可操作的序列,请为op设置seed:为了使所有op产生的随机序列在会话之间是可重复的,请设置一个图级别的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'
为了使所有op产生的随机序列在会话之间是可重复的,请设置一个图级别的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 different
# 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'
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