multilayered

MultiLayered (Module)

Source code in evotorch/neuroevolution/net/multilayered.py

class MultiLayered(nn.Module):
    def __init__(self, *layers: nn.Module):
        super().__init__()
        self._submodules = nn.ModuleList(layers)

    def forward(self, x: torch.Tensor, h: Optional[dict] = None):
        if h is None:
            h = {}

        new_h = {}

        for i, layer in enumerate(self._submodules):
            layer_h = h.get(i, None)
            if layer_h is None:
                layer_result = layer(x)
            else:
                layer_result = layer(x, h[i])

            if isinstance(layer_result, tuple):
                if len(layer_result) == 2:
                    x, layer_new_h = layer_result
                else:
                    raise ValueError(
                        f"The layer number {i} returned a tuple of length {len(layer_result)}."
                        f" A tensor or a tuple of two elements was expected."
                    )
            elif isinstance(layer_result, torch.Tensor):
                x = layer_result
                layer_new_h = None
            else:
                raise TypeError(
                    f"The layer number {i} returned an object of type {type(layer_result)}."
                    f" A tensor or a tuple of two elements was expected."
                )

            if layer_new_h is not None:
                new_h[i] = layer_new_h

        if len(new_h) == 0:
            return x
        else:
            return x, new_h

    def __iter__(self):
        return self._submodules.__iter__()

    def __getitem__(self, i):
        return self._submodules[i]

    def append(self, module: nn.Module):
        self._submodules.append(module)

forward(self, x, h=None)

Defines the computation performed at every call.

Should be overridden by all subclasses.

.. note:: Although the recipe for forward pass needs to be defined within this function, one should call the :class:Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Source code in evotorch/neuroevolution/net/multilayered.py

def forward(self, x: torch.Tensor, h: Optional[dict] = None):
    if h is None:
        h = {}

    new_h = {}

    for i, layer in enumerate(self._submodules):
        layer_h = h.get(i, None)
        if layer_h is None:
            layer_result = layer(x)
        else:
            layer_result = layer(x, h[i])

        if isinstance(layer_result, tuple):
            if len(layer_result) == 2:
                x, layer_new_h = layer_result
            else:
                raise ValueError(
                    f"The layer number {i} returned a tuple of length {len(layer_result)}."
                    f" A tensor or a tuple of two elements was expected."
                )
        elif isinstance(layer_result, torch.Tensor):
            x = layer_result
            layer_new_h = None
        else:
            raise TypeError(
                f"The layer number {i} returned an object of type {type(layer_result)}."
                f" A tensor or a tuple of two elements was expected."
            )

        if layer_new_h is not None:
            new_h[i] = layer_new_h

    if len(new_h) == 0:
        return x
    else:
        return x, new_h