# tf.random.truncated_normal

Outputs random values from a truncated normal distribution.

The values are drawn from a normal distribution with specified mean and standard deviation, discarding and re-drawing any samples that are more than two standard deviations from the mean.

#### Examples:

````tf.random.truncated_normal(shape=[2])`
`<tf.Tensor: shape=(2,), dtype=float32, numpy=array([..., ...], dtype=float32)>`
```
````tf.random.truncated_normal(shape=[2], mean=3, stddev=1, dtype=tf.float32)`
`<tf.Tensor: shape=(2,), dtype=float32, numpy=array([..., ...], dtype=float32)>`
```

`shape` A 1-D integer Tensor or Python array. The shape of the output tensor.
`mean` A 0-D Tensor or Python value of type `dtype`. The mean of the truncated normal distribution.
`stddev` A 0-D Tensor or Python value of type `dtype`. The standard deviation of the normal distribution, before truncation.
`dtype` The type of the output. Restricted to floating-point types: `tf.half`, `tf.float`, `tf.double`, etc.
`seed` A Python integer. Used to create a random seed for the distribution. See `tf.random.set_seed` for more information.
`name` A name for the operation (optional).

A tensor of the specified shape filled with random truncated normal values.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]
{"lastModified": "Last updated 2024-01-23 UTC."}