def __init__(self):
super().__init__()
# initialize layers
snn.LIF.clear_instances() # boilerplate
self.fc1 = nn.Linear(1, 1)
self.lif1 = snn.Synaptic(alpha=0.5,
beta=0.5,
num_inputs=1,
batch_size=1,
init_hidden=True)
self.lif2 = snn.Alpha(alpha=0.6,
beta=0.5,
num_inputs=1,
batch_size=1,
hidden_init=True)
def test_fire():
synaptic = snn.Synaptic(alpha=0.5, beta=0.5)
input_large = torch.Tensor([synaptic.threshold * 10])
assert synaptic.fire(input_large)[0] == 1
def test_instances():
snn.SpikingNeuron.instances = []
snn.Synaptic(alpha=0.5, beta=0.5)
snn.Alpha(alpha=0.5, beta=0.4)
assert len(snn.SpikingNeuron.instances) == 2
def synaptic_hidden_reset_none_instance():
return snn.Synaptic(alpha=0.5,
beta=0.5,
init_hidden=True,
reset_mechanism="none")
def synaptic_hidden_instance():
return snn.Synaptic(alpha=0.5, beta=0.5, init_hidden=True)
def synaptic_reset_none_instance():
return snn.Synaptic(alpha=0.5, beta=0.5, reset_mechanism="none")
def synaptic_reset_zero_instance():
return snn.Synaptic(alpha=0.5, beta=0.5, reset_mechanism="zero")
def synaptic_instance():
return snn.Synaptic(alpha=0.5, beta=0.5)