def __init__(self, model="super"):
super(LSNNPolicy, self).__init__()
self.state_dim = 4
self.input_features = 16
self.hidden_features = 128
self.output_features = 2
# self.affine1 = torch.nn.Linear(self.state_dim, self.input_features)
self.constant_current_encoder = ConstantCurrentLIFEncoder(40)
self.lif_layer = LSNNCell(
2 * self.state_dim,
self.hidden_features,
p=LSNNParameters(method=model, alpha=100.0),
)
self.dropout = torch.nn.Dropout(p=0.5)
self.readout = LICell(self.hidden_features, self.output_features)
self.saved_log_probs = []
self.rewards = []
def __init__(
self,
input_features,
output_features,
seq_length,
is_lsnn,
dt=0.01,
model="super",
):
super(MemoryNet, self).__init__()
self.input_features = input_features
self.output_features = output_features
self.seq_length = seq_length
self.is_lsnn = is_lsnn
if is_lsnn:
p = LSNNParameters(method=model)
self.layer = LSNNRecurrentCell(input_features, input_features, p, dt=dt)
else:
p = LIFParameters(method=model)
self.layer = LIFRecurrentCell(input_features, input_features, dt=dt)
self.dropout = torch.nn.Dropout(p=0.2)
self.readout = LILinearCell(input_features, output_features)
def __init__(self, input_features, output_features, args):
super(MemoryNet, self).__init__()
self.input_features = input_features
self.output_features = output_features
self.seq_length = args.seq_length
self.optimizer = args.optimizer
self.learning_rate = args.learning_rate
self.regularization_factor = args.regularization_factor
self.regularization_target = args.regularization_target / (
self.seq_length * args.seq_repetitions
)
self.log("Neuron model", args.neuron_model)
p_lsnn = LSNNParameters(
method=args.model,
v_th=torch.as_tensor(0.5),
tau_adapt_inv=torch.as_tensor(1 / 1200.0),
beta=torch.as_tensor(1.8),
)
p_lif = LIFParameters(
method=args.model,
v_th=torch.as_tensor(0.5),
)
p_li = LIParameters()
if args.neuron_model == "lsnn":
self.capture_b = False
self.layer = LSNNRecurrentCell(input_features, input_features, p=p_lsnn)
elif args.neuron_model == "lsnnlif":
self.layer = LSNNLIFNet(
input_features, p_lsnn=p_lsnn, p_lif=p_lif, dt=args.dt
)
self.capture_b = True
else:
self.layer = LIFRecurrentCell(
input_features, input_features, p=p_lif, dt=args.dt
)
self.capture_b = False
self.readout = LILinearCell(input_features, output_features, p=p_li)
self.scheduler = None