# tf.initializers.lecun_uniform

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/ops/init_ops.py#L1336-L1360) |

LeCun uniform initializer.

#### View aliases


**Main aliases**

[`tf.keras.initializers.lecun_uniform`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/LecunUniform)
**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.initializers.lecun_uniform`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/initializers/lecun_uniform), [`tf.compat.v1.keras.initializers.lecun_uniform`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/keras/initializers/lecun_uniform)

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    tf.initializers.lecun_uniform(
        seed=None
    )

It draws samples from a uniform distribution within \[-limit, limit\]
where `limit` is `sqrt(3 / fan_in)`
where `fan_in` is the number of input units in the weight tensor.

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| ## Arguments ||
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| `seed` | A Python integer. Used to seed the random generator. |

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| ## Returns ||
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| An initializer. ||

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#### References:

- Self-Normalizing Neural Networks, [Klambauer et al.,
  2017](https://papers.nips.cc/paper/6698-self-normalizing-neural-networks)

([pdf](https://papers.nips.cc/paper/6698-self-normalizing-neural-networks.pdf))

- Efficient Backprop, [Lecun et al., 1998](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)