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# Constants, Sequences, and Random Values
Note: Functions taking `Tensor` arguments can also take anything accepted by
`tf.convert_to_tensor`.
[TOC]
## Constant Value Tensors
TensorFlow provides several operations that you can use to generate constants.
* `tf.zeros`
* `tf.zeros_like`
* `tf.ones`
* `tf.ones_like`
* `tf.fill`
* `tf.constant`
## Sequences
* `tf.linspace`
* `tf.range`
## Random Tensors
TensorFlow has several ops that create random tensors with different
distributions. The random ops are stateful, and create new random values each
time they are evaluated.
The `seed` keyword argument in these functions acts in conjunction with
the graph-level random seed. Changing either the graph-level seed using
`tf.set_random_seed` or the
op-level seed will change the underlying seed of these operations. Setting
neither graph-level nor op-level seed, results in a random seed for all
operations.
See `tf.set_random_seed`
for details on the interaction between operation-level and graph-level random
seeds.
### Examples:
```python
# 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))
```
Another common use of random values is the initialization of variables. Also see
the @{$variables$Variables How To}.
```python
# 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))
```
* `tf.random_normal`
* `tf.truncated_normal`
* `tf.random_uniform`
* `tf.random_shuffle`
* `tf.random_crop`
* `tf.multinomial`
* `tf.random_gamma`
* `tf.set_random_seed`
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