Method __init__

bindsnet/models/models.py:465–584

Constructor for class ``LocallyConnectedNetwork``. Uses ``DiehlAndCookNodes`` to avoid multiple spikes per timestep in the output layer population. :param n_inpt: Number of input neurons. Matches the 1D size of the input data. :param input_shape: Two-dimensional sha

(
        self,
        n_inpt: int,
        input_shape: List[int],
        kernel_size: Union[int, Tuple[int, int]],
        stride: Union[int, Tuple[int, int]],
        n_filters: int,
        inh: float = 25.0,
        dt: float = 1.0,
        nu: Optional[Union[float, Sequence[float]]] = (1e-4, 1e-2),
        reduction: Optional[callable] = None,
        theta_plus: float = 0.05,
        tc_theta_decay: float = 1e7,
        wmin: float = 0.0,
        wmax: float = 1.0,
        norm: Optional[float] = 0.2,
        exc_thresh: float = -52.0,
    )

    """

    def __init__(
        self,
        n_inpt: int,
        input_shape: List[int],
        kernel_size: Union[int, Tuple[int, int]],
        stride: Union[int, Tuple[int, int]],
        n_filters: int,
        inh: float = 25.0,
        dt: float = 1.0,
        nu: Optional[Union[float, Sequence[float]]] = (1e-4, 1e-2),
        reduction: Optional[callable] = None,
        theta_plus: float = 0.05,
        tc_theta_decay: float = 1e7,
        wmin: float = 0.0,
        wmax: float = 1.0,
        norm: Optional[float] = 0.2,
        exc_thresh: float = -52.0,
    ) -> None:
        # language=rst
        """
        Constructor for class ``LocallyConnectedNetwork``. Uses ``DiehlAndCookNodes`` to
        avoid multiple spikes per timestep in the output layer population.

        :param n_inpt: Number of input neurons. Matches the 1D size of the input data.
        :param input_shape: Two-dimensional shape of input population.
        :param kernel_size: Size of input windows. Integer or two-tuple of integers.
        :param stride: Length of horizontal, vertical stride across input space. Integer
            or two-tuple of integers.
        :param n_filters: Number of locally connected filters per input region. Integer
            or two-tuple of integers.
        :param inh: Strength of synapse weights from output layer back onto itself.
        :param dt: Simulation time step.
        :param nu: Single or pair of learning rates for pre- and post-synaptic events,
            respectively.
        :param reduction: Method for reducing parameter updates along the minibatch
            dimension.
        :param wmin: Minimum allowed weight on ``Input`` to ``DiehlAndCookNodes``
            synapses.
        :param wmax: Maximum allowed weight on ``Input`` to ``DiehlAndCookNodes``
            synapses.
        :param theta_plus: On-spike increment of ``DiehlAndCookNodes`` membrane
            threshold potential.
        :param tc_theta_decay: Time constant of ``DiehlAndCookNodes`` threshold
            potential decay.
        :param norm: ``Input`` to ``DiehlAndCookNodes`` layer connection weights
            normalization constant.
        """
        super().__init__(dt=dt)

        kernel_size = _pair(kernel_size)
        stride = _pair(stride)

        self.n_inpt = n_inpt
        self.input_shape = input_shape
        self.kernel_size = kernel_size
        self.stride = stride
        self.n_filters = n_filters
        self.inh = inh