tfp.experimental.nn.initializers.he_normal
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He normal initializer.
tfp.experimental.nn.initializers.he_normal(
seed=None
)
It draws samples from a truncated normal distribution centered on 0
with standard deviation (after truncation) given by
stddev = sqrt(2 / fan_in) where fan_in is the number of
input units in the weight tensor.
Returns |
|---|
init_fn
|
A python callable which takes a shape Tensor, dtype and an
optional scalar int number of batch dims and returns a randomly
initialized Tensor with the specified shape and dtype.
|
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Last updated 2023-11-21 UTC.
[null,null,["Last updated 2023-11-21 UTC."],[],[],null,["# tfp.experimental.nn.initializers.he_normal\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/probability/blob/v0.23.0/tensorflow_probability/python/experimental/nn/initializers/initializers.py#L84-L109) |\n\nHe normal initializer. \n\n tfp.experimental.nn.initializers.he_normal(\n seed=None\n )\n\nIt draws samples from a truncated normal distribution centered on 0\nwith standard deviation (after truncation) given by\n`stddev = sqrt(2 / fan_in)` where `fan_in` is the number of\ninput units in the weight tensor.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------|-----------------------------------------------------------------------------------------------------------------------|\n| `seed` | PRNG seed; see [`tfp.random.sanitize_seed`](../../../../tfp/random/sanitize_seed) for details. Default value: `None`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|-----------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `init_fn` | A python `callable` which takes a shape `Tensor`, dtype and an optional scalar `int` number of batch dims and returns a randomly initialized `Tensor` with the specified shape and dtype. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| References ---------- ||\n|---|---|\n| [He et al., 2015](https://www.cv-foundation.org/openaccess/content_iccv_2015/html/He_Delving_Deep_into_ICCV_2015_paper.html) ([pdf](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf)) ||\n\n\u003cbr /\u003e"]]
View source on GitHub