jax.nn.initializers
module#
Common neural network layer initializers, consistent with definitions used in Keras and Sonnet.
Initializers#
This module provides common neural network layer initializers, consistent with definitions used in Keras and Sonnet.
An initializer is a function that takes three arguments:
(key, shape, dtype)
and returns an array with dimensions shape
and
data type dtype
. Argument key
is a jax.random.PRNGKey
random
key used when generating random numbers to initialize the array.
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Builds an initializer that returns arrays full of a constant |
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Builds an initializer for delta orthogonal kernels. |
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Builds a Glorot normal initializer (aka Xavier normal initializer). |
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Builds a Glorot uniform initializer (aka Xavier uniform initializer). |
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Builds a He normal initializer (aka Kaiming normal initializer). |
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Builds a He uniform initializer (aka Kaiming uniform initializer). |
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Builds a Lecun normal initializer. |
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Builds a Lecun uniform initializer. |
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Builds an initializer that returns real normally-distributed random arrays. |
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An initializer that returns a constant array full of ones. |
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Builds an initializer that returns uniformly distributed orthogonal matrices. |
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Builds an initializer that returns truncated-normal random arrays. |
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Builds an initializer that returns real uniformly-distributed random arrays. |
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Initializer that adapts its scale to the shape of the weights tensor. |
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An initializer that returns a constant array full of zeros. |