hxtorch.perceptron.nn.ExpandedConv1d

class hxtorch.perceptron.nn.ExpandedConv1d(in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int]], stride: int = 1, padding: Union[int, Tuple[int, int]] = 0, dilation: Union[int, Tuple] = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros', num_sends: Optional[int] = None, wait_between_events: int = 5, mock: bool = False, *, input_transform: Optional[Callable[[torch.Tensor], torch.Tensor]] = None, weight_transform: Optional[Callable[[torch.Tensor], torch.Tensor]] = <function clamp_weight_>, num_expansions: Optional[int] = None)

Bases: hxtorch.perceptron.nn.Conv1d

Unrolls the weight matrix for execution on hardware. This maximizes the use of the synapses array.

Caveat: Fixed-pattern noise cannot be individually compensated for during training, because the same weights are used at different locations!

__init__(in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int]], stride: int = 1, padding: Union[int, Tuple[int, int]] = 0, dilation: Union[int, Tuple] = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros', num_sends: Optional[int] = None, wait_between_events: int = 5, mock: bool = False, *, input_transform: Optional[Callable[[torch.Tensor], torch.Tensor]] = None, weight_transform: Optional[Callable[[torch.Tensor], torch.Tensor]] = <function clamp_weight_>, num_expansions: Optional[int] = None)
Parameters

  • in_channels – Number of channels in the input

  • out_channels – Number of channels produced by the convolution

  • kernel_size – Size of the convolving kernel

  • stride – Stride of the convolution

  • padding – Zero-padding added to both sides of the input

  • padding_mode – ‘zeros’, ‘reflect’, ‘replicate’ or ‘circular’

  • dilation – Spacing between kernel elements

  • groups – Number of blocked connections from input channels to output channels

  • bias – If True, adds a learnable bias to the output

  • num_sends – Number of sends of the input. Values greater than 1 result in higher output to the neurons and increases the s/n ratio. For None this is automatically adjusted during initialization.

  • wait_between_events – Wait time between two successive vector inputs, in FPGA clock cycles. Shorter wait time can lead to saturation of the synaptic input.

  • mock – Enable mock mode.

  • input_transform – Function that receives the input and returns a tensor to be used as input to the chip.

  • weight_transform – Function that receives the weight and returns a tensor to be used as weight matrix on the chip.

  • num_expansions – Number of enrolled kernels in a single operation

Methods

__init__(in_channels, out_channels, kernel_size)

param in_channels

Number of channels in the input

extra_repr()

Set the extra representation of the module

Attributes

bias: Optional[torch.Tensor]
dilation: Tuple[int, …]
extra_repr() → str

Set the extra representation of the module

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

groups: int
in_channels: int
kernel_size: Tuple[int, …]
out_channels: int
output_padding: Tuple[int, …]
padding: Union[str, Tuple[int, …]]
padding_mode: str
stride: Tuple[int, …]
training: bool
transposed: bool
weight: torch.Tensor