torch::nn::RNNImpl Class Reference

A multi-layer Elman RNN module with Tanh or ReLU activation. More...

#include <rnn.h>

Inheritance diagram for torch::nn::RNNImpl:

Public Member Functions
	RNNImpl (int64_t input_size, int64_t hidden_size)
	RNNImpl (const RNNOptions &options)
void	pretty_print (std::ostream &stream) const override
	Pretty prints the RNN module into the given stream.
RNNOutput	forward (const Tensor &input, Tensor state={})
	Applies the RNN module to an input sequence and input state. More...
Public Member Functions inherited from torch::nn::detail::RNNImplBase< RNNImpl >
	RNNImplBase (const RNNOptionsBase &options_, optional< CuDNNMode > cudnn_mode=nullopt, int64_t number_of_gates=1)
void	reset () override
	Initializes the parameters of the RNN module.
void	to (torch::Device device, torch::Dtype dtype, bool non_blocking=false) override
	Overrides nn::Module::to() to call flatten_parameters() after the original operation. More...
void	to (torch::Dtype dtype, bool non_blocking=false) override
	Recursively casts all parameters to the given dtype. More...
void	to (torch::Device device, bool non_blocking=false) override
	Recursively moves all parameters to the given device. More...
void	pretty_print (std::ostream &stream) const override
	Pretty prints the RNN module into the given stream.
void	flatten_parameters ()
	Modifies the internal storage of weights for optimization purposes. More...
Public Member Functions inherited from torch::nn::Cloneable< RNNImpl >
std::shared_ptr< Module >	clone (const optional< Device > &device=nullopt) const override
	Performs a recursive "deep copy" of the Module, such that all parameters and submodules in the cloned module are different from those in the original module. More...
Public Member Functions inherited from torch::nn::Module
	Module (std::string name)
	Tells the base Module about the name of the submodule.
	Module ()
	Constructs the module without immediate knowledge of the submodule's name. More...
const std::string &	name () const noexcept
	Returns the name of the Module. More...
void	apply (const ModuleApplyFunction &function)
	Applies the function to the Module and recursively to every submodule. More...
void	apply (const ConstModuleApplyFunction &function) const
	Applies the function to the Module and recursively to every submodule. More...
void	apply (const NamedModuleApplyFunction &function, const std::string &name_prefix=std::string())
	Applies the function to the Module and recursively to every submodule. More...
void	apply (const ConstNamedModuleApplyFunction &function, const std::string &name_prefix=std::string()) const
	Applies the function to the Module and recursively to every submodule. More...
void	apply (const ModulePointerApplyFunction &function) const
	Applies the function to the Module and recursively to every submodule. More...
void	apply (const NamedModulePointerApplyFunction &function, const std::string &name_prefix=std::string()) const
	Applies the function to the Module and recursively to every submodule. More...
std::vector< Tensor >	parameters (bool recurse=true) const
	Returns the parameters of this Module and if recurse is true, also recursively of every submodule. More...
OrderedDict< std::string, Tensor >	named_parameters (bool recurse=true) const
	Returns an OrderedDict with the parameters of this Module along with their keys, and if recurse is true also recursively of every submodule. More...
std::vector< Tensor >	buffers (bool recurse=true) const
	Returns the buffers of this Module and if recurse is true, also recursively of every submodule. More...
OrderedDict< std::string, Tensor >	named_buffers (bool recurse=true) const
	Returns an OrderedDict with the buffers of this Module along with their keys, and if recurse is true also recursively of every submodule. More...
std::vector< std::shared_ptr< Module > >	modules (bool include_self=true) const
	Returns the submodules of this Module (the entire submodule hierarchy) and if include_self is true, also inserts a shared_ptr to this module in the first position. More...
OrderedDict< std::string, std::shared_ptr< Module > >	named_modules (const std::string &name_prefix=std::string(), bool include_self=true) const
	Returns an OrderedDict of he submodules of this Module (the entire submodule hierarchy) and thei keys, and if include_self is true, also inserts a shared_ptr to this module in the first position. More...
std::vector< std::shared_ptr< Module > >	children () const
	Returns the direct submodules of this Module.
OrderedDict< std::string, std::shared_ptr< Module > >	named_children () const
	Returns an OrderedDict of the direct submodules of this Module and their keys. More...
virtual void	train (bool on=true)
	Enables "training" mode.
void	eval ()
	Calls train(false) to enable "eval" mode. More...
virtual bool	is_training () const noexcept
	True if the module is in training mode. More...
virtual void	zero_grad ()
	Recursively zeros out the grad value of each registered parameter.
template<typename ModuleType >
ModuleType::ContainedType *	as () noexcept
	Attempts to cast this Module to the given ModuleType. More...
template<typename ModuleType >
const ModuleType::ContainedType *	as () const noexcept
	Attempts to cast this Module to the given ModuleType. More...
template<typename ModuleType , typename = torch::detail::disable_if_module_holder_t<ModuleType>>
ModuleType *	as () noexcept
	Attempts to cast this Module to the given ModuleType. More...
template<typename ModuleType , typename = torch::detail::disable_if_module_holder_t<ModuleType>>
const ModuleType *	as () const noexcept
	Attempts to cast this Module to the given ModuleType. More...
virtual void	save (serialize::OutputArchive &archive) const
	Serializes the Module into the given OutputArchive.
virtual void	load (serialize::InputArchive &archive)
	Deserializes the Module from the given InputArchive.

Data Fields
RNNOptions	options
Data Fields inherited from torch::nn::detail::RNNImplBase< RNNImpl >
RNNOptionsBase	options
	The RNN's options.
std::vector< Tensor >	w_ih
	The weights for input x hidden gates.
std::vector< Tensor >	w_hh
	The weights for hidden x hidden gates.
std::vector< Tensor >	b_ih
	The biases for input x hidden gates.
std::vector< Tensor >	b_hh
	The biases for hidden x hidden gates.

Additional Inherited Members
Public Types inherited from torch::nn::detail::RNNImplBase< RNNImpl >
enum	CuDNNMode
	These must line up with the CUDNN mode codes: https://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnRNNMode_t.
Public Types inherited from torch::nn::Module
using	ModuleApplyFunction = std::function< void(Module &)>
using	ConstModuleApplyFunction = std::function< void(const Module &)>
using	NamedModuleApplyFunction = std::function< void(const std::string &, Module &)>
using	ConstNamedModuleApplyFunction = std::function< void(const std::string &, const Module &)>
using	ModulePointerApplyFunction = std::function< void(const std::shared_ptr< Module > &)>
using	NamedModulePointerApplyFunction = std::function< void(const std::string &, const std::shared_ptr< Module > &)>
Protected Types inherited from torch::nn::detail::RNNImplBase< RNNImpl >
using	RNNFunctionSignature = std::tuple< Tensor, Tensor >(const Tensor &, const Tensor &, TensorList, bool, int64_t, double, bool, bool, bool)
	The function signature of rnn_relu, rnn_tanh and gru.
Protected Member Functions inherited from torch::nn::detail::RNNImplBase< RNNImpl >
RNNOutput	generic_forward (std::function< RNNFunctionSignature > function, const Tensor &input, Tensor state)
	A generic forward() used for RNN and GRU (but not LSTM!). More...
std::vector< Tensor >	flat_weights () const
	Returns a flat vector of all weights, with layer weights following each other sequentially in (w_ih, w_hh, b_ih, b_hh) order. More...
bool	any_parameters_alias () const
	Very simple check if any of the parameters (weights, biases) are the same.
Protected Member Functions inherited from torch::nn::Module
Tensor &	register_parameter (std::string name, Tensor tensor, bool requires_grad=true)
	Registers a parameter with this Module. More...
Tensor &	register_buffer (std::string name, Tensor tensor)
	Registers a buffer with this Module. More...
template<typename ModuleType >
std::shared_ptr< ModuleType >	register_module (std::string name, std::shared_ptr< ModuleType > module)
	Registers a submodule with this Module. More...
template<typename ModuleType >
std::shared_ptr< ModuleType >	register_module (std::string name, ModuleHolder< ModuleType > module_holder)
	Registers a submodule with this Module. More...
Protected Attributes inherited from torch::nn::detail::RNNImplBase< RNNImpl >
int64_t	number_of_gates_
	The number of gate weights/biases required by the RNN subclass.
optional< CuDNNMode >	cudnn_mode_
	The cuDNN RNN mode, if this RNN subclass has any.
std::vector< Tensor >	flat_weights_
	The cached result of the latest flat_weights() call.

Detailed Description

A multi-layer Elman RNN module with Tanh or ReLU activation.

See https://pytorch.org/docs/master/nn.html#torch.nn.RNN to learn about the exact behavior of this module.

Definition at line 177 of file rnn.h.

Member Function Documentation

RNNOutput torch::nn::RNNImpl::forward	(	const Tensor &	input,
		Tensor	state = {}
	)

Applies the RNN module to an input sequence and input state.

The input should follow a (sequence, batch, features) layout unless batch_first is true, in which case the layout should be (batch, sequence, features).

Definition at line 225 of file rnn.cpp.

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The documentation for this class was generated from the following files:

• torch/csrc/api/include/torch/nn/modules/rnn.h
• torch/csrc/api/src/nn/modules/rnn.cpp