Class LayerNormImpl¶
Defined in File normalization.h
Page Contents
Inheritance Relationships¶
Base Type¶
public torch::nn::Cloneable< LayerNormImpl >
(Template Class Cloneable)
Class Documentation¶
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class LayerNormImpl : public torch::nn::Cloneable<LayerNormImpl>¶
Applies Layer Normalization over a mini-batch of inputs as described in the paper
Layer Normalization
_ .See https://pytorch.org/docs/main/nn.html#torch.nn.LayerNorm to learn about the exact behavior of this module.
See the documentation for
torch::nn::LayerNormOptions
class to learn what constructor arguments are supported for this module.Example:
LayerNorm model(LayerNormOptions({2, 2}).elementwise_affine(false).eps(2e-5));
Public Functions
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inline LayerNormImpl(std::vector<int64_t> normalized_shape)¶
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explicit LayerNormImpl(LayerNormOptions options_)¶
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virtual void reset() override¶
reset()
must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.
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void reset_parameters()¶
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virtual void pretty_print(std::ostream &stream) const override¶
Pretty prints the
LayerNorm
module into the givenstream
.
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Tensor forward(const Tensor &input)¶
Applies layer normalization over a mini-batch of inputs as described in the paper
Layer Normalization
_ .The mean and standard-deviation are calculated separately over the last certain number dimensions which have to be of the shape specified by input
normalized_shape
.Layer Normalization
: https://arxiv.org/abs/1607.06450
Public Members
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LayerNormOptions options¶
The options with which this module was constructed.
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Tensor weight¶
The learned weight.
Initialized to ones if the
elementwise_affine
option is set totrue
upon construction.
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Tensor bias¶
The learned bias.
Initialized to zeros
elementwise_affine
option is set totrue
upon construction.
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inline LayerNormImpl(std::vector<int64_t> normalized_shape)¶