def test_izhikevich_feedforward_layer(spiking_method):
layer = Izhikevich(spiking_method)
data = torch.randn(10, 5, 4)
out, s = layer(data)
assert out.shape == (10, 5, 4)
for x in s:
assert x.shape == (5, 4)
def test_izhikevich_feedforward_layer_backward_iteration(spiking_method):
# Tests that gradient variables can be used in subsequent applications
model = Izhikevich(spiking_method)
data = torch.ones(10, 6)
out, s = model(data)
out, _ = model(out, s)
loss = out.sum()
loss.backward()
def __init__(self, spiking_method: IzhikevichSpikingBehavior):
super(SNNetwork, self).__init__()
self.spiking_method = spiking_method
self.l0 = Izhikevich(spiking_method)
self.l1 = Izhikevich(spiking_method)
self.s0 = self.s1 = None
def test_izhikevich_feedforward_layer_backward(spiking_method):
model = Izhikevich(spiking_method)
data = torch.ones(10, 12)
out, _ = model(data)
loss = out.sum()
loss.backward()
def test_izhikevich_in_time(spiking_method):
layer = Izhikevich(spiking_method)
data = torch.randn(10, 5, 2)
out, _ = layer(data)
assert out.shape == (10, 5, 2)