def __init__(self, netParams):
super(Network, self).__init__()
# initialize slayer
slayer = spikeLayer(netParams['neuron'], netParams['simulation'])
self.slayer = slayer
# define network functions
self.fc1 = slayer.dense((34 * 34 * 2), 512)
self.fc2 = slayer.dense(512, 10)
def __init__(self, netParams):
super(Network, self).__init__()
# initialize slayer
slayer = spikeLayer(netParams['neuron'], netParams['simulation'])
self.slayer = slayer
# define network functions
self.fc1 = slayer.dense(Nin, Nhid)
self.fc2 = slayer.dense(Nhid, Nout)
def __init__(self, netParams):
super(Network, self).__init__()
# initialize slayer
slayer = spikeLayer(netParams['neuron'], netParams['simulation'])
self.slayer = slayer
# define network functions
self.conv1 = slayer.conv(2, 16, 5, padding=2, weightScale=10)
self.conv2 = slayer.conv(16, 32, 3, padding=1, weightScale=50)
self.pool1 = slayer.pool(4)
self.pool2 = slayer.pool(2)
self.pool3 = slayer.pool(2)
self.fc1 = slayer.dense((8 * 8 * 32), 512)
self.fc2 = slayer.dense(512, 11)
self.drop = slayer.dropout(0.1)
def __init__(self,
params,
input_size,
hidden_size,
output_size,
quantize=True):
super(SlayerLoihiMLP, self).__init__()
self.hidden_size = hidden_size
self.output_size = output_size
self.quantize = quantize
self.slayer = spikeLayer(params["neuron"], params["simulation"])
self.fc1 = self.slayer.dense(input_size,
hidden_size,
quantize=self.quantize)
self.fc2 = self.slayer.dense(hidden_size,
output_size,
quantize=self.quantize)
def __init__(
self,
params,
tact_input_size,
vis_input_size,
tact_output_size,
vis_output_size,
output_size,
):
super(SlayerLoihiMM, self).__init__()
self.tact_output_size = tact_output_size
self.vis_output_size = vis_output_size
self.output_size = output_size
self.slayer = spikeLayer(params["neuron"], params["simulation"])
self.tact_fc = self.slayer.dense(tact_input_size, tact_output_size)
self.vis_fc = self.slayer.dense(vis_input_size, vis_output_size)
self.combi = self.slayer.dense(tact_output_size + vis_output_size,
output_size)
net_params = SlayerParams('test_files/Loihi/network.yaml')
if verbose is True:
print('Neuron Type:', 'LOIHI')
print('vThMant :', 80)
print('vDecay :', 128)
print('iDecay :', 1024)
print('refDelay:', 1)
# Network structure
Ns = int(net_params['simulation']['tSample'] / net_params['simulation']['Ts'])
Nin = int(net_params['layer'][0]['dim'])
Nhid = int(net_params['layer'][1]['dim'])
Nout = int(net_params['layer'][2]['dim'])
slayer = spikeLayer(net_params['neuron'], net_params['simulation']).to(device)
if verbose is True: print('Neuron Threshold =', slayer.neuron['theta'])
# define network functions
fc1 = slayer.dense(Nin, Nhid).to(device)
fc2 = slayer.dense(Nhid, Nout).to(device)
# load input spikes
spikeAER = np.loadtxt('test_files/Loihi/snnData/spikeIn.txt')
spikeAER[:, 0] /= net_params['simulation']['Ts']
spikeAER[:, 1] -= 1
# spikeAER = np.fliplr(np.loadtxt('loihiInputSpikes.txt'))
spikeAER[:,
0] += 49 # 49 not 50 because the input spikes seem to be transmitted without delay