torchrecurrent.NASCell#
- class torchrecurrent.NASCell(input_size, hidden_size, bias=True, recurrent_bias=True, kernel_init=<function xavier_uniform_>, recurrent_kernel_init=<function xavier_uniform_>, bias_init=<function zeros_>, recurrent_bias_init=<function zeros_>, device=None, dtype=None)[source]#
A Neural Architecture Search (NAS) cell.
[arXiv]
\[\begin{split}\mathbf{g}(t) &= \mathbf{W}_{ih}\,\mathbf{x}(t) + \mathbf{b}_{ih} + \mathbf{W}_{hh}\,\mathbf{h}(t-1) + \mathbf{b}_{hh}, \\[6pt] [g_0,\dots,g_7] &= \mathrm{chunk}_8\bigl(\mathbf{g}(t)\bigr), \\[6pt] o_k(t) &= \begin{cases} \sigma(g_k), & k\in\{0,2,5,7\},\\ \mathrm{ReLU}(g_k), & k\in\{1,3\},\\ \tanh(g_k), & k\in\{4,6\}, \end{cases} \\[6pt] \ell_1(t) &= \tanh\bigl(o_0\,\circ\,o_1\bigr),\quad \ell_2(t) = \tanh\bigl(o_2 + o_3\bigr), \\[3pt] \ell_3(t) &= \tanh\bigl(o_4\,\circ\,o_5\bigr),\quad \ell_4(t) = \sigma\bigl(o_6 + o_7\bigr), \\[6pt] \tilde{c}(t) &= \tanh\bigl(\ell_1 + c(t-1)\bigr),\quad c(t) = \ell_1\,\circ\,\ell_2, \\[3pt] \ell_5(t) &= \tanh\bigl(\ell_3 + \ell_4\bigr),\quad h(t) = \tanh\bigl(c(t)\,\circ\,\ell_5(t)\bigr).\end{split}\]- Parameters:
input_size – The number of expected features in the input
xhidden_size – The number of features in the hidden and cell states
bias – If
False, the layer does not use input-side biasb_{ih}. Default:Truerecurrent_bias – If
False, the layer does not use recurrent biasb_{hh}. Default:Truekernel_init – Initializer for
W_{ih}. Default:torch.nn.init.xavier_uniform_()recurrent_kernel_init – Initializer for
W_{hh}. Default:torch.nn.init.xavier_uniform_()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 featuresh_0 of shape
(batch, hidden_size)or(hidden_size,): initial hidden statec_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 statec_1 of shape
(batch, hidden_size)or(hidden_size,): next cell state
- Variables:
weight_ih – the learnable input–hidden weights, of shape
(8*hidden_size, input_size)weight_hh – the learnable hidden–hidden weights, of shape
(8*hidden_size, hidden_size)bias_ih – the learnable input–hidden biases, of shape
(8*hidden_size)bias_hh – the learnable hidden–hidden biases, of shape
(8*hidden_size)
Examples:
>>> cell = NASCell(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, kernel_init=<function xavier_uniform_>, recurrent_kernel_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
fnrecursively to every submodule (as returned by.children()) as well as self.bfloat16()Casts all floating point parameters and buffers to
bfloat16datatype.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
doubledatatype.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
floatdatatype.forward(inp[, state])Run one step of the recurrent cell.
get_buffer(target)Return the buffer given by
targetif 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
targetif it exists, otherwise throw an error.get_submodule(target)Return the submodule given by
targetif it exists, otherwise throw an error.half()Casts all floating point parameters and buffers to
halfdatatype.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_dictinto 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
targetif 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_destinationcall_super_initdump_patchesweight_ihweight_hhbias_ihbias_hhinput_sizehidden_sizebiasrecurrent_biastraining