hxtorch.spiking.modules.BatchDropout

class hxtorch.spiking.modules.BatchDropout(size: int, dropout: float, experiment: Experiment, func: Union[Callable, torch.autograd.Function] = <function batch_dropout>)

Bases: hxtorch.spiking.modules.hx_module.HXFunctionalModule

Batch dropout layer

Caveat: In-place operations on TensorHandles are not supported. Must be placed after a neuron layer, i.e. Neuron.

__init__(size: int, dropout: float, experiment: Experiment, func: Union[Callable, torch.autograd.Function] = <function batch_dropout>) → None

Initialize BatchDropout layer. This layer disables spiking neurons in the previous spiking Neuron layer with a probability of dropout. Note, size has to be equal to the size in the corresponding spiking layer. The spiking mask is maintained for the whole batch.

Parameters

• size – Size of the population this dropout layer is applied to.

• dropout – Probability that a neuron in the precessing layer gets disabled during training.

• experiment – Experiment to append layer to.

• func – Callable function implementing the module’s forward functionality or a torch.autograd.Function implementing the module’s forward and backward operation. Defaults to batch_dropout.

• execution_instance – Execution instance to place to.

Methods

__init__(size, dropout, experiment[, func])	Initialize BatchDropout layer.
extra_repr()	Add additional information
set_mask()	Creates a new random dropout mask, applied to the spiking neurons in the previous module.

Attributes

mask	Getter for spike mask.

extra_repr() → str

Add additional information

property mask

Getter for spike mask.

Returns

Returns the current spike mask.

output_type

alias of hxtorch.spiking.handle.NeuronHandle

set_mask() → None

Creates a new random dropout mask, applied to the spiking neurons in the previous module. If module.eval() dropout will be disabled.

Returns

Returns a random boolean spike mask of size self.size.