def __init__(self, net_params):
super(Network, self).__init__()
# initialize slayer
slayer = spikeLayer(net_params['neuron'], net_params['simulation'])
self.slayer = slayer
# define network functions
self.fc1 = slayer.dense(Nin, Nhid)
self.fc2 = slayer.dense(Nhid, Nout)
def __init__(self, net_params):
super(Network, self).__init__()
# initialize slayer
slayer = spikeLayer(net_params['neuron'], net_params['simulation'], fullRefKernel=True)
self.slayer = slayer
# define network functions
self.fc1 = slayer.dense(Nin, Nout)
W1 = np.loadtxt('test_files/snnData/w1Initial.txt')
self.fc1.weight = torch.nn.Parameter(torch.FloatTensor(W1.reshape((Nout, Nin , 1, 1, 1))).to(self.fc1.weight.device), requires_grad = True)
# testing against known snn data ##############################################
verbose = True if (('-v' in sys.argv) or ('--verbose' in sys.argv)) else False
net_params = SlayerParams(CURRENT_TEST_DIR + "/test_files/snnData/network.yaml")
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'])
device = torch.device('cuda')
# device = torch.device('cuda:1')
# initialize slayer
# slayer = spikeLayer(net_params['neuron'], net_params['simulation'])
slayer = spikeLayer(net_params['neuron'], net_params['simulation'], fullRefKernel = True).to(device)
# 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/snnData/spikeIn.txt')
spikeAER[:,0] /= net_params['simulation']['Ts']
spikeAER[:,1] -= 1
spikeData = np.zeros((Nin, Ns))
for (tID, nID) in np.rint(spikeAER).astype(int):
if tID < Ns : spikeData[nID, tID] = 1/net_params['simulation']['Ts']
spikeIn = torch.FloatTensor(spikeData.reshape((1, Nin, 1, 1, Ns))).to(device)
device = torch.device('cuda')
net_params = SlayerParams(CURRENT_TEST_DIR + "/test_files/parameters.yaml")
Ns = int(net_params['simulation']['tSample'] / net_params['simulation']['Ts'])
spikeData = np.zeros((2, Ns))
spikeData[0, 100] = 1 / net_params['simulation']['Ts']
spikeData[0, 120] = 1 / net_params['simulation']['Ts']
spikeData[0, 870] = 1 / net_params['simulation']['Ts']
spikeData[1, 153] = 1 / net_params['simulation']['Ts']
spikeData[1, 680] = 1 / net_params['simulation']['Ts']
spike = torch.FloatTensor(spikeData.reshape((1, 1, 1, 2, Ns))).to(device)
slayer = spikeLayer(net_params['neuron'], net_params['simulation']).to(device)
a = slayer.psp(spike)
print('neuron type: ', slayer.neuron['type'])
print('Ts : ', slayer.simulation['Ts'])
print('spikeData : ', spikeData.shape)
print('spike : ', spike.shape)
print('psp : ', a.shape)
###############################################################################
# testing dense layer #########################################################
fcLayer1 = slayer.dense(512, 10)
fcLayer2 = slayer.dense((34, 34), 10)
fcLayer3 = slayer.dense((34, 34, 2), 10)
