Building Networks with Instance Variables: Synaptic Conductance-based LIF Neuron
Building a fully-connected network using a Synaptic Conductance-based neuron model. Using instance variables are only required when calling the built-in backprop methods in snntorch.backprop.
Example:
import torch
import torch.nn as nn
import snntorch as snn
alpha = 0.9
beta = 0.85
batch_size = 128
num_inputs = 784
num_hidden = 1000
num_outputs = 10
num_steps = 100
# Define Network
class Net(nn.Module):
def __init__(self):
super().__init__()
# initialize layers
snn.LIF.clear_instances() # boilerplate
self.fc1 = nn.Linear(num_inputs, num_hidden)
self.lif1 = snn.Synaptic(alpha=alpha, beta=beta, num_inputs=num_hidden, batch_size=batch_size, init_hidden=True)
self.fc2 = nn.Linear(num_hidden, num_outputs)
self.lif2 = snn.Synaptic(alpha=alpha, beta=beta, num_inputs=num_outputs, batch_size=batch_size, init_hidden=True)
# move the time-loop into the training-loop
def forward(self, x):
cur1 = self.fc1(x)
self.lif1.spk1, self.lif1.syn1, self.lif1.mem1 = self.lif1(cur1, self.lif1.syn, self.lif1.mem)
cur2 = self.fc2(self.lif1.spk)
self.lif2.spk, self.lif2.syn, self.lif2.mem = self.lif2(cur2, self.lif2.syn, self.lif2.mem)
return self.lif2.spk, self.lif2.mem
net = Net().to(device)
for step in range(num_steps):
spk_out, mem_out = net(data.view(batch_size, -1))