1、tf.ones(shape,type=tf.float32,name=None)
tf.ones([
2
,
3
], int32) ==> [[
1
,
1
,
1
], [
1
,
1
,
1
]]
2、tf.zeros(shape,type=tf.float32,name=None)
tf.zeros([
2
,
3
], int32) ==> [[
0
,
0
,
0
], [
0
,
0
,
0
]]
3、tf.ones_like(tensor,dype=None,name=None)
新建一个与给定的tensor类型大小一致的tensor，其所有元素为1。
# 'tensor' is [[1, 2, 3], [4, 5, 6]]
tf.ones_like(tensor) ==> [[
1
,
1
,
1
], [
1
,
1
,
1
]]
4、tf.zeros_like(tensor,dype=None,name=None)
新建一个与给定的tensor类型大小一致的tensor，其所有元素为0。
# 'tensor' is [[1, 2, 3], [4, 5, 6]]
tf.ones_like(tensor) ==>
[[
0
,
0
,
0
], [
0
,
0
,
0
]]
5、tf.fill(dim,value,name=None)
创建一个形状大小为dim的tensor，其初始值为value
# Output tensor has shape [2, 3].
fill([2, 3], 9) ==> [[9, 9, 9]
[9, 9, 9]]
6、tf.constant(value,dtype=None,shape=None,name='Const')
创建一个常量tensor，先给出value，可以设定其shape
# Constant 1-D Tensor populated with value list.
tensor = tf.constant([
1
,
2
,
3
,
4
,
5
,
6
,
7
]) => [
1
2
3
4
5
6
7
]
# Constant 2-D tensor populated with scalar value -1.
tensor = tf.constant(-
1.0
, shape=[
2
,
3
]) => [[-
1.
-
1.
-
1.
] [-
1.
-
1.
-
1.
]
7、tf.linspace(start,stop,num,name=None)
返回一个tensor，该tensor中的数值在start到stop区间之间取等差数列（包含start和stop），如果num>1则差值为(stop-start)/(num-1)，以保证最后一个元素的值为stop。
其中，start和stop必须为tf.float32或tf.float64。num的类型为int。
tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0]
8、tf.range(start,limit=None,delta=1,name='range')
返回一个tensor等差数列，该tensor中的数值在start到limit之间，不包括limit，delta是等差数列的差值。
start，limit和delta都是int32类型。
# 'start' is 3
# 'limit' is 18
# 'delta' is 3
tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]
# 'limit' is 5 start is 0
tf.range(start, limit) ==> [0, 1, 2, 3, 4]
9、tf.random_normal(shape,mean=0.0,stddev=1.0,dtype=tf.float32,seed=None,name=None)
返回一个tensor其中的元素的值服从正态分布。
seed
: A Python integer. Used to create a random seed for the distribution.See
set_random_seed
for behavior。
10、tf.truncated_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)
返回一个tensor其中的元素服从截断正态分布（？概念不懂，留疑）
11、tf.random_uniform(shape,minval=0,maxval=None,dtype=tf.float32,seed=None,name=None)
返回一个形状为shape的tensor，其中的元素服从minval和maxval之间的均匀分布。
12、tf.random_shuffle(value,seed=None,name=None)
对value（是一个tensor）的第一维进行随机化。
[[1,2], [[2,3],
[2,3], ==> [1,2],
[3,4]] [3,4]]
13、tf.set_random_seed(seed)
设置产生随机数的种子。
To generate different sequences across sessions, set neither graph-level nor op-level seeds:
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'
To generate the same repeatable sequence for an op across sessions, set the seed for the op:
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'
To make the random sequences generated by all ops be repeatable across sessions, set a graph-level 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'
