快速上手

SNNGrow的代码风格和Pytorch保持一致，您可以使用简单的代码构建脉冲神经网络:

from snngrow.base import utils
from snngrow.base.neuron import LIFNode
import torch

x = torch.randn(2, 3, 5, 5)

net = torch.nn.Sequential(
    nn.Conv2d(1, 32, kernel_size=3),
    LIFNode(),
    nn.Flatten(),
    nn.Linear(54, 1)
)

y = net(x)
utils.reset(net)

如果使用脉冲计算模式，构建网络的例子如下:

import torch
import torch.nn as nn
from snngrow.base.neuron.LIFNode import LIFNode
from snngrow.base.surrogate import Sigmoid
import snngrow.base.nn as snngrow_nn
class SimpleNet(nn.Module):
    def __init__(self, T):
        super(SimpleNet, self).__init__()
        self.T = T
        self.surrogate = Sigmoid.Sigmoid(spike_out=True)
        self.classifier = nn.Sequential(
            nn.Flatten(),
            nn.Linear(28 * 28, 512),
            LIFNode(T=T, spike_out=True, surrogate_function=self.surrogate),
            snngrow_nn.Linear(512, 512, spike_in=True),
            LIFNode(T=T, spike_out=True, surrogate_function=self.surrogate),
            snngrow_nn.Linear(512, 128, spike_in=True),
            nn.Linear(128, 10)
        )