# tensorflow--变量/序列/随机数-汇总

## 一、常值张量（ Constant Value Tensors ）

TensorFlow提供了一些可以用来生成常量的操作。

## 三、随机张量（ Random Tensors ）

TensorFlow有几个OPS创建随机张量具有不同的分布。随机OPS是有状态的，每次评估时都创建新的随机值。

# Create a tensor of shape [2, 3] consisting of random normal values, with mean
# -1 and standard deviation 4.
norm = tf.random_normal([2, 3], mean=-1, stddev=4)

# Shuffle the first dimension of a tensor
c = tf.constant([[1, 2], [3, 4], [5, 6]])
shuff = tf.random_shuffle(c)

# Each time we run these ops, different results are generated
sess = tf.Session()
print(sess.run(norm))
print(sess.run(norm))

# Set an op-level seed to generate repeatable sequences across sessions.
norm = tf.random_normal([2, 3], seed=1234)
sess = tf.Session()
print(sess.run(norm))
print(sess.run(norm))
sess = tf.Session()
print(sess.run(norm))
print(sess.run(norm))


# Use random uniform values in [0, 1) as the initializer for a variable of shape
# [2, 3]. The default type is float32.
var = tf.Variable(tf.random_uniform([2, 3]), name="var")
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
print(sess.run(var))