Method run

bindsnet/network/network.py:252–465

Simulate network for given inputs and time. :param inputs: Dictionary of ``Tensor``s of shape ``[time, *input_shape]`` or ``[time, batch_size, *input_shape]``. :param time: Simulation time. :param one_step: Whether to run the network in "feed-f

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

        return inputs

    def run(
        self, inputs: Dict[str, torch.Tensor], time: int, one_step=False, **kwargs
    ) -> None:
        # language=rst
        """
        Simulate network for given inputs and time.

        :param inputs: Dictionary of ``Tensor``s of shape ``[time, *input_shape]`` or
                      ``[time, batch_size, *input_shape]``.
        :param time: Simulation time.
        :param one_step: Whether to run the network in "feed-forward" mode, where inputs
            propagate all the way through the network in a single simulation time step.
            Layers are updated in the order they are added to the network.

        Keyword arguments:

        :param Dict[str, torch.Tensor] clamp: Mapping of layer names to boolean masks if
            neurons should be clamped to spiking. The ``Tensor``s have shape
            ``[n_neurons]`` or ``[time, n_neurons]``.
        :param Dict[str, torch.Tensor] unclamp: Mapping of layer names to boolean masks
            if neurons should be clamped to not spiking. The ``Tensor``s should have
            shape ``[n_neurons]`` or ``[time, n_neurons]``.
        :param Dict[str, torch.Tensor] injects_v: Mapping of layer names to boolean
            masks if neurons should be added voltage. The ``Tensor``s should have shape
            ``[n_neurons]`` or ``[time, n_neurons]``.
        :param Union[float, torch.Tensor] reward: Scalar value used in reward-modulated
            learning.
        :param Dict[Tuple[str], torch.Tensor] masks: Mapping of connection names to
            boolean masks determining which weights to clamp to zero.
        :param Bool progress_bar: Show a progress bar while running the network.

        **Example:**

        .. code-block:: python

            import torch
            import matplotlib.pyplot as plt

            from bindsnet.network import Network
            from bindsnet.network.nodes import Input
            from bindsnet.network.monitors import Monitor

            # Build simple network.
            network = Network()
            network.add_layer(Input(500), name='I')
            network.add_monitor(Monitor(network.layers['I'], state_vars=['s']), 'I')

            # Generate spikes by running Bernoulli trials on Uniform(0, 0.5) samples.
            spikes = torch.bernoulli(0.5 * torch.rand(500, 500))

            # Run network simulation.
            network.run(inputs={'I' : spikes}, time=500)

            # Look at input spiking activity.
            spikes = network.monitors['I'].get('s')
            plt.matshow(spikes, cmap='binary')
            plt.xticks(()); plt.yticks(());
            plt.xlabel('Time'); plt.ylabel('Neuron index')