# tf.compat.v1.initializers.lecun_normal

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/init_ops.py#L1682-L1707) |

LeCun normal initializer.

    tf.compat.v1.initializers.lecun_normal(
        seed=None
    )

It draws samples from a truncated normal distribution centered on 0
with standard deviation (after truncation) given by
`stddev = sqrt(1 / fan_in)` where `fan_in` is the number of
input units in the weight tensor.

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| ## Args ||
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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 ||
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| <br /> - 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) ||

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