URLSTMCell

URLSTMCell#

class braintrace.nn.URLSTMCell#

Update-Reset LSTM (URLSTM) cell.

A variant of LSTM that uses update and reset gates for more flexible control over the cell state dynamics.

Parameters:

  • in_size (brainstate.typing.Size) – The dimension of the input vector.

  • out_size (brainstate.typing.Size) – The number of hidden units in the node.

  • w_init (Callable or ArrayLike, optional) – The input weight initializer. Default is XavierNormal().

  • state_init (Callable or ArrayLike, optional) – The state initializer. Default is ZeroInit().

  • activation (str or Callable, optional) – The activation function. It can be a string or a callable function. Default is ‘tanh’.

  • name (str or None, optional) – The name of the module. Default is None.

Examples

>>> import braintrace
>>> import brainstate
>>>
>>> # Create a URLSTM cell
>>> urlstm_cell = braintrace.nn.URLSTMCell(in_size=128, out_size=256)
>>> urlstm_cell.init_state(batch_size=16)
>>>
>>> # Process a sequence of inputs
>>> x = brainstate.random.randn(16, 128)
>>> h = urlstm_cell(x)
>>> print(h.shape)
(16, 256)
init_state(batch_size=None, **kwargs)#

State initialization function.

reset_state(batch_size=None, **kwargs)#

State resetting function.

update(x)#

Advance the cell by one time step.

Updates both the cell state c and the hidden state h in place.

Parameters:

x (ArrayLike) – Input for the current step, of shape (..., in_size).

Returns:

ArrayLike – The updated hidden state h, of shape (..., out_size).

URLSTMCell.__init__(in_size, out_size, w_init=XavierNormal(scale=1.0, unit=1), state_init=ZeroInit(unit=1), activation='tanh', name=None)#