torchrecurrent.PeepholeLSTMCell#
- class torchrecurrent.PeepholeLSTMCell(input_size, hidden_size, bias=True, recurrent_bias=True, nonlinearity=<built-in method tanh of type object>, gate_nonlinearity=<built-in method sigmoid of type object>, kernel_init=<function xavier_uniform_>, recurrent_kernel_init=<function xavier_uniform_>, peephole_kernel_init=<function normal_>, bias_init=<function zeros_>, recurrent_bias_init=<function zeros_>, device=None, dtype=None)[source]#
A Peephole LSTM cell with learnable peephole connections.
[JMLR]
\[\begin{split}\mathbf{z}(t) &= \tanh\Bigl( \mathbf{W}_{ih}^{z}\,\mathbf{x}(t) + \mathbf{b}_{ih}^{z} + \mathbf{W}_{hh}^{z}\,\mathbf{h}(t-1) + \mathbf{b}_{hh}^{z} \Bigr), \\[6pt] \mathbf{i}(t) &= \sigma\Bigl( \mathbf{W}_{ih}^{i}\,\mathbf{x}(t) + \mathbf{b}_{ih}^{i} + \mathbf{W}_{hh}^{i}\,\mathbf{h}(t-1) + \mathbf{b}_{hh}^{i} + \mathbf{p}^{i}\circ\mathbf{c}(t-1) \Bigr), \\[6pt] \mathbf{f}(t) &= \sigma\Bigl( \mathbf{W}_{ih}^{f}\,\mathbf{x}(t) + \mathbf{b}_{ih}^{f} + \mathbf{W}_{hh}^{f}\,\mathbf{h}(t-1) + \mathbf{b}_{hh}^{f} + \mathbf{p}^{f}\circ\mathbf{c}(t-1) \Bigr), \\[6pt] \mathbf{c}(t) &= \mathbf{f}(t)\,\circ\,\mathbf{c}(t-1) + \mathbf{i}(t)\,\circ\,\mathbf{z}(t), \\[6pt] \mathbf{o}(t) &= \sigma\Bigl( \mathbf{W}_{ih}^{o}\,\mathbf{x}(t) + \mathbf{b}_{ih}^{o} + \mathbf{W}_{hh}^{o}\,\mathbf{h}(t-1) + \mathbf{b}_{hh}^{o} + \mathbf{p}^{o}\circ\mathbf{c}(t) \Bigr), \\[6pt] \mathbf{h}(t) &= \mathbf{o}(t)\,\circ\,\tanh\bigl(\mathbf{c}(t)\bigr)\end{split}\]- Parameters:
input_size – The number of expected features in the input
x
.hidden_size – The number of features in the hidden state
h
.bias – If
False
, the layer does not use input-side biasb_{ih}
. Default:True
.recurrent_bias – If
False
, the layer does not use recurrent biasb_{hh}
. Default:True
.nonlinearity – Activation for the cell candidate
z
. Default:torch.tanh()
.gate_nonlinearity – Activation for input/forget/output 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_()
.bias_init – Initializer for
b_{ih}
whenbias=True
. Default:torch.nn.init.zeros_()
.recurrent_bias_init – Initializer for
b_{hh}
whenrecurrent_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,)
: Tensor containing the initial hidden state.c_0 of shape
(batch, hidden_size)
or(hidden_size,)
: Tensor containing the 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,)
: Tensor containing the next hidden state.c_1 of shape
(batch, hidden_size)
or(hidden_size,)
: Tensor containing the 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_ph – The learnable peephole weights (for i, f, o), of shape
(3*hidden_size,)
.bias_ih – The learnable input–hidden biases, of shape
(4*hidden_size)
.bias_hh – The learnable hidden–hidden biases, of shape
(4*hidden_size)
.
Examples:
>>> cell = PeepholeLSTMCell(10, 20) >>> x = torch.randn(5, 3, 10) # (time_steps, batch, input_size) >>> h = torch.zeros(3, 20) >>> c = torch.zeros(3, 20) >>> outs = [] >>> for t in range(x.size(0)): ... h, c = cell(x[t], (h, c)) ... outs.append(h) >>> outs = torch.stack(outs, dim=0) # (time_steps, batch, hidden_size)
- __init__(input_size, hidden_size, bias=True, recurrent_bias=True, nonlinearity=<built-in method tanh of type object>, gate_nonlinearity=<built-in method sigmoid of type object>, kernel_init=<function xavier_uniform_>, recurrent_kernel_init=<function xavier_uniform_>, peephole_kernel_init=<function normal_>, 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])Run one step of the recurrent cell.
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.init_weights
()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
()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.
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
()Return True if forward returns (h, c), else just h.
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_ph
bias_ih
bias_hh
input_size
hidden_size
bias
recurrent_bias
training