Lava 软件框架文档将 Leaky-Integrate-And-Fire 神经元描述为：
class LIF(AbstractProcess):
"""Leaky-Integrate-and-Fire neural process with activation input and spike
output ports a_in and s_out.
Realizes the following abstract behavior:
u[t] = u[t-1] * (1-du) + a_in
v[t] = v[t-1] * (1-dv) + u[t] + bias
s_out = v[t] > vth
v[t] = v[t] - s_out*vth
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
shape = kwargs.get("shape", (1,))
self.a_in = InPort(shape=shape)
self.s_out = OutPort(shape=shape)
self.u = Var(shape=shape, init=0)
self.v = Var(shape=shape, init=0)
self.du = Var(shape=(1,), init=kwargs.pop("du", 0))
self.dv = Var(shape=(1,), init=kwargs.pop("dv", 0))
self.bias = Var(shape=shape, init=kwargs.pop("b", 0))
self.vth = Var(shape=(1,), init=kwargs.pop("vth", 10))
并且可以使用以下方法创建一个在
t=6def two_lif_neurons():
# Instantiate Lava processes to build network
from lava.proc.dense.process import Dense
from lava.proc.lif.process import LIF
lif1 = LIF(u=0, du=3, dv=0, bias=2)
dense = Dense()
lif2 = LIF()
# Connect processes via their directional input and output ports
lif1.out_ports.s_out.connect(dense.in_ports.s_in)
dense.out_ports.a_out.connect(lif2.in_ports.a_in)
# Execute process lif1 and all processes connected to it for fixed number of steps
from lava.magma.core.run_conditions import RunSteps
from lava.magma.core.run_configs import Loihi1SimCfg
for t in range(1, 10):
lif1.run(condition=RunSteps(num_steps=1), run_cfg=Loihi1SimCfg())
if t == 6:
# print the output spike
print(lif1.s_out)
lif1.stop()
相关线位于
t=6print(lif1.s_out)所以我想打印
lif1.s_out'__abstractmethods__',
'__class__',
'__delattr__',
'__dict__',
'__dir__',
'__doc__',
'__eq__',
'__format__',
'__ge__',
'__getattribute__',
'__gt__',
'__hash__',
'__init__',
'__init_subclass__',
'__le__',
'__lt__',
'__module__',
'__ne__',
'__new__',
'__reduce__',
'__reduce_ex__',
'__repr__',
'__setattr__',
'__sizeof__',
'__slots__',
'__str__',
'__subclasshook__',
'__weakref__',
'_abc_impl',
'_add_inputs',
'_add_outputs',
'_connect_backward',
'_connect_forward',
'_name',
'_process',
'_validate_ports',
'concat_with',
'connect',
'connect_from',
'flatten',
'get_dst_ports',
'get_incoming_virtual_ports',
'get_outgoing_virtual_ports',
'get_src_ports',
'in_connections',
'name',
'out_connections',
'process',
'reshape',
'shape',
'size',
'transpose']
我查看了
port_indense()lif1如何在英特尔 Lava 神经形态计算框架中打印 lif 神经元输出尖峰的布尔值？
可以在这里找到示例
在开始模拟神经元之前，可以添加一个监视器来监视和存储神经元的行为。然后，您可以读出神经元做了什么。
# Instantiate Lava processes to build network
from lava.magma.core.run_conditions import RunSteps
from lava.magma.core.run_configs import Loihi1SimCfg
from lava.proc.dense.process import Dense
from lava.proc.lif.process import LIF
from lava.proc.monitor.process import Monitor
from pprint import pprint
import numpy as np
lif1 = LIF(bias=2, vth=1)
dense = Dense(shape=(1, 1), weights=np.ones((1, 1)))
lif2 = LIF(vth=np.inf, dv=0, du=1)
mon_lif_1_v = Monitor()
mon_lif_2_v = Monitor()
mon_spike = Monitor()
mon_lif_1_v.probe(lif1.v, 20)
mon_lif_2_v.probe(lif2.v, 20)
mon_spike.probe(lif1.s_out, 20)
# This is used to get the name of the process that is used for monitoring.
mon_lif_1_v_process = list(mon_lif_1_v.get_data())[0]
mon_lif_2_v_process = list(mon_lif_2_v.get_data())[0]
mon_spike_process = list(mon_spike.get_data())[0]
# Note it follows the order of declaration/initialisation of the neuron/dense it follows.
print(f"mon_lif_1_v_process={mon_lif_1_v_process}")
print(f"mon_lif_2_v_process={mon_lif_2_v_process}")
print(f"mon_spike_process={mon_spike_process}")
# Connect processes via their directional input and output ports
lif1.out_ports.s_out.connect(dense.in_ports.s_in)
dense.out_ports.a_out.connect(lif2.in_ports.a_in)
for run in range(10):
t = run
# Execute process lif1 and all processes connected to it for fixed number of steps
lif1.run(condition=RunSteps(num_steps=1), run_cfg=Loihi1SimCfg())
# Print the currents that have accumulated in the post synaptic neuron (lif2)
print(
f'lif1.v={mon_lif_1_v.get_data()[mon_lif_1_v_process]["v"][t]},lif1.s_out={mon_spike.get_data()[mon_spike_process]["s_out"][t]}, lif2.v={mon_lif_2_v.get_data()[mon_lif_2_v_process]["v"][t]}'
)
# Change and Print the weights of the synapse (dense) to 2 from its initial state of 1
dense.weights.set(np.ones((1, 1)) * 2)
print(dense.weights)
for run in range(10):
t = run + 10
# Run the simulation for 10 more timesteps
lif1.run(condition=RunSteps(num_steps=1), run_cfg=Loihi1SimCfg())
# Show that the voltage increase reflects the increase in the synaptic weights
print(
f'lif1.v={mon_lif_1_v.get_data()[mon_lif_1_v_process]["v"][t]},lif1.s_out={mon_spike.get_data()[mon_spike_process]["s_out"][t]}, lif2.v={mon_lif_2_v.get_data()[mon_lif_2_v_process]["v"][t]}'
)
print(f"voltage_list after={voltage_list}")
lif1.stop()
我正在尝试在 loihi2 中运行类似的部署代码，但由于某种原因，我的代码总是卡在运行网络的部分：
from lava.magma.core.run_conditions import RunSteps
from lava.magma.core.run_configs import Loihi1SimCfg
# Import monitoring Process.
from lava.proc.monitor.process import Monitor
# Configurations for execution.
num_steps = 1
rcfg = Loihi1SimCfg(select_tag='rate_neurons')
run_cond = RunSteps(num_steps=num_steps)
# Instantiating network and IO processes.
network_balanced = EINetwork(**network_params_balanced)
state_monitor = Monitor()
state_monitor.probe(target=network_balanced.state, num_steps=num_steps)
# Here it gets stuck:
network_balanced.run(run_cfg=rcfg, condition=run_cond)
我让它运行了好几个小时，但它永远不会结束。有人知道我如何解决这个问题吗？ 我有一台 i5 第十代 1.10GHz 的电脑。是不是电脑速度的问题？
提前非常感谢！