Method _get_inputs

bindsnet/network/network.py:211–250

Fetches outputs from network layers to use as input to downstream layers. :param layers: Layers to update inputs for. Defaults to all network layers. :return: Inputs to all layers for the current iteration.

(self, layers: Iterable = None)

        return torch.load(virtual_file)

    def _get_inputs(self, layers: Iterable = None) -> Dict[str, torch.Tensor]:
        # language=rst
        """
        Fetches outputs from network layers to use as input to downstream layers.

        :param layers: Layers to update inputs for. Defaults to all network layers.
        :return: Inputs to all layers for the current iteration.
        """
        inputs = {}

        if layers is None:
            layers = self.layers

        # Loop over network connections.
        for c in self.connections:
            if c[1] in layers:
                # Fetch source and target populations.
                source = self.connections[c].source
                target = self.connections[c].target

                if not c[1] in inputs:
                    if isinstance(target, CSRMNodes):
                        inputs[c[1]] = torch.zeros(
                            self.batch_size,
                            target.res_window_size,
                            *target.shape,
                            device=target.s.device,
                        )
                    else:
                        inputs[c[1]] = torch.zeros(
                            self.batch_size, *target.shape, device=target.s.device
                        )

                # Add to input: source's spikes multiplied by connection weights.
                if isinstance(target, CSRMNodes):
                    inputs[c[1]] += self.connections[c].compute_window(source.s)
                else:
                    inputs[c[1]] += self.connections[c].compute(source.s)

        return inputs

    def run(
        self, inputs: Dict[str, torch.Tensor], time: int, one_step=False, **kwargs