torchrecurrent.ResLSTMCell#

class torchrecurrent.ResLSTMCell(input_size, hidden_size, bias=True, recurrent_bias=True, nonlinearity='tanh', gate_nonlinearity='sigmoid', kernel_init=<function xavier_uniform_>, recurrent_kernel_init=<function xavier_uniform_>, peephole_kernel_init=<function normal_>, proj_init=<function xavier_uniform_>, residual_init=<function xavier_uniform_>, bias_init=<function zeros_>, recurrent_bias_init=<function zeros_>, device=None, dtype=None)[source]#

A residual long short-term memory (ResLSTM) cell.

[arXiv]

\[\begin{split}\begin{aligned} \mathbf{i}(t) &= \sigma\bigl( \mathbf{W}_{ih}^{i}\mathbf{x}(t) + \mathbf{b}_{ih}^{i} + \mathbf{W}_{hh}^{i}\mathbf{h}(t-1) + \mathbf{b}_{hh}^{i} + \mathbf{p}^{i}\odot\mathbf{c}(t-1)\bigr), \\ \mathbf{f}(t) &= \sigma\bigl( \mathbf{W}_{ih}^{f}\mathbf{x}(t) + \mathbf{b}_{ih}^{f} + \mathbf{W}_{hh}^{f}\mathbf{h}(t-1) + \mathbf{b}_{hh}^{f} + \mathbf{p}^{f}\odot\mathbf{c}(t-1)\bigr), \\ \mathbf{g}(t) &= \phi\bigl( \mathbf{W}_{ih}^{g}\mathbf{x}(t) + \mathbf{b}_{ih}^{g} + \mathbf{W}_{hh}^{g}\mathbf{h}(t-1) + \mathbf{b}_{hh}^{g}\bigr), \\ \mathbf{c}(t) &= \mathbf{f}(t)\odot\mathbf{c}(t-1) + \mathbf{i}(t)\odot\mathbf{g}(t), \\ \mathbf{o}(t) &= \sigma\bigl( \mathbf{W}_{ih}^{o}\mathbf{x}(t) + \mathbf{b}_{ih}^{o} + \mathbf{W}_{hh}^{o}\mathbf{h}(t-1) + \mathbf{b}_{hh}^{o} + \mathbf{p}^{o}\odot\mathbf{c}(t)\bigr), \\ \mathbf{h}(t) &= \mathbf{o}(t)\odot\bigl( \mathbf{W}_{p}\,\phi(\mathbf{c}(t)) + \mathbf{W}_{r}\mathbf{x}(t) \bigr), \end{aligned}\end{split}\]

where \(\sigma\) is the sigmoid function, \(\phi\) is a pointwise nonlinearity (e.g., tanh), and \(\odot\) denotes element-wise multiplication. The projection \(\mathbf{W}_{p}\) maps the cell output back to the hidden size, and the residual projection \(\mathbf{W}_{r}\) maps the input \(\mathbf{x}(t)\) to the hidden size so that the spatial shortcut term matches dimensions. The output gate \(\mathbf{o}(t)\) controls both the projected cell output and the shortcut path.

Parameters:

  • input_size – The number of expected features in the input x.

  • hidden_size – The number of features in the hidden and cell states.

  • bias – If False, the layer does not use input-side biases. Default: True.

  • recurrent_bias – If False, the layer does not use recurrent biases. Default: True.

  • nonlinearity – Nonlinearity \(\phi\) for the candidate and cell output. Default: torch.tanh().

  • gate_nonlinearity – Activation for the gates. Default: torch.sigmoid().

  • kernel_init – Initializer for W_{ih}^*. Default: torch.nn.init.xavier_uniform_().

  • recurrent_kernel_init – Initializer for W_{hh}^*. Default: torch.nn.init.xavier_uniform_().

  • peephole_kernel_init – Initializer for peephole weights p. Default: torch.nn.init.normal_().

  • proj_init – Initializer for the projection W_p. Default: torch.nn.init.xavier_uniform_().

  • residual_init – Initializer for the residual projection W_r. Default: torch.nn.init.xavier_uniform_().

  • bias_init – Initializer for input-side biases when bias=True. Default: torch.nn.init.zeros_().

  • recurrent_bias_init – Initializer for recurrent biases when recurrent_bias=True. Default: torch.nn.init.zeros_().

  • device – The desired device of parameters.

  • dtype – The desired floating point type of parameters.

Inputs: input, (h_0, c_0)

  • input of shape (batch, input_size) or (input_size,): tensor containing input features.

  • h_0 of shape (batch, hidden_size) or (hidden_size,): initial hidden state.

  • c_0 of shape (batch, hidden_size) or (hidden_size,): initial cell state.

If (h_0, c_0) is not provided, both default to zero.

Outputs: (h_1, c_1)

  • h_1 of shape (batch, hidden_size) or (hidden_size,): next hidden state.

  • c_1 of shape (batch, hidden_size) or (hidden_size,): next cell state.

Variables:

  • weight_ih – the learnable input–hidden weights, of shape (4*hidden_size, input_size).

  • weight_hh – the learnable hidden–hidden weights, of shape (4*hidden_size, hidden_size).

  • weight_proj – the learnable projection weights W_p, of shape (hidden_size, hidden_size).

  • weight_res – the learnable residual projection weights W_r, of shape (hidden_size, input_size).

  • weight_ph – the learnable peephole weights, of shape (3*hidden_size,).

  • bias_ih – the learnable input–hidden biases, of shape (4*hidden_size) if bias=True.

  • bias_hh – the learnable hidden–hidden biases, of shape (4*hidden_size) if recurrent_bias=True.

Examples:

>>> cell = ResLSTMCell(10, 20)
>>> x = torch.randn(5, 3, 10)   # (time_steps, batch, input_size)
>>> h = torch.zeros(3, 20)
>>> c = torch.zeros(3, 20)
>>> out_h = []
>>> for t in range(x.size(0)):
...     h, c = cell(x[t], (h, c))
...     out_h.append(h)
>>> out_h = torch.stack(out_h, dim=0)  # (time_steps, batch, hidden_size)
__init__(input_size, hidden_size, bias=True, recurrent_bias=True, nonlinearity='tanh', gate_nonlinearity='sigmoid', kernel_init=<function xavier_uniform_>, recurrent_kernel_init=<function xavier_uniform_>, peephole_kernel_init=<function normal_>, proj_init=<function xavier_uniform_>, residual_init=<function xavier_uniform_>, bias_init=<function zeros_>, recurrent_bias_init=<function zeros_>, device=None, dtype=None)[source]#

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__(input_size, hidden_size[, bias, ...])

Initialize internal Module state, shared by both nn.Module and ScriptModule.

add_module(name, module)

Add a child module to the current module.

apply(fn)

Apply fn recursively to every submodule (as returned by .children()) as well as self.

bfloat16()

Casts all floating point parameters and buffers to bfloat16 datatype.

buffers([recurse])

Return an iterator over module buffers.

children()

Return an iterator over immediate children modules.

compile(*args, **kwargs)

Compile this Module's forward using torch.compile().

cpu()

Move all model parameters and buffers to the CPU.

cuda([device])

Move all model parameters and buffers to the GPU.

double()

Casts all floating point parameters and buffers to double datatype.

eval()

Set the module in evaluation mode.

extra_repr()

Return the extra representation of the module.

float()

Casts all floating point parameters and buffers to float datatype.

forward(inp[, state])

Define the computation performed at every call.

get_buffer(target)

Return the buffer given by target if it exists, otherwise throw an error.

get_extra_state()

Return any extra state to include in the module's state_dict.

get_parameter(target)

Return the parameter given by target if it exists, otherwise throw an error.

get_submodule(target)

Return the submodule given by target if it exists, otherwise throw an error.

half()

Casts all floating point parameters and buffers to half datatype.

ipu([device])

Move all model parameters and buffers to the IPU.

load_state_dict(state_dict[, strict, assign])

Copy parameters and buffers from state_dict into this module and its descendants.

modules([remove_duplicate])

Return an iterator over all modules in the network.

mtia([device])

Move all model parameters and buffers to the MTIA.

named_buffers([prefix, recurse, ...])

Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

named_children()

Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

named_modules([memo, prefix, remove_duplicate])

Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

named_parameters([prefix, recurse, ...])

Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

parameters([recurse])

Return an iterator over module parameters.

register_backward_hook(hook)

Register a backward hook on the module.

register_buffer(name, tensor[, persistent])

Add a buffer to the module.

register_forward_hook(hook, *[, prepend, ...])

Register a forward hook on the module.

register_forward_pre_hook(hook, *[, ...])

Register a forward pre-hook on the module.

register_full_backward_hook(hook[, prepend])

Register a backward hook on the module.

register_full_backward_pre_hook(hook[, prepend])

Register a backward pre-hook on the module.

register_load_state_dict_post_hook(hook)

Register a post-hook to be run after module's load_state_dict() is called.

register_load_state_dict_pre_hook(hook)

Register a pre-hook to be run before module's load_state_dict() is called.

register_module(name, module)

Alias for add_module().

register_parameter(name, param)

Add a parameter to the module.

register_state_dict_post_hook(hook)

Register a post-hook for the state_dict() method.

register_state_dict_pre_hook(hook)

Register a pre-hook for the state_dict() method.

requires_grad_([requires_grad])

Change if autograd should record operations on parameters in this module.

reset_parameters()

Default initializer behavior by naming convention.

set_extra_state(state)

Set extra state contained in the loaded state_dict.

set_submodule(target, module[, strict])

Set the submodule given by target if it exists, otherwise throw an error.

share_memory()

See torch.Tensor.share_memory_().

state_dict(*args[, destination, prefix, ...])

Return a dictionary containing references to the whole state of the module.

to(*args, **kwargs)

Move and/or cast the parameters and buffers.

to_empty(*, device[, recurse])

Move the parameters and buffers to the specified device without copying storage.

train([mode])

Set the module in training mode.

type(dst_type)

Casts all parameters and buffers to dst_type.

uses_double_state()

xpu([device])

Move all model parameters and buffers to the XPU.

zero_grad([set_to_none])

Reset gradients of all model parameters.

Attributes

T_destination

call_super_init

dump_patches

weight_ih

weight_hh

weight_proj

weight_res

weight_ph

bias_ih

bias_hh

input_size

hidden_size

bias

recurrent_bias

training