def __init__(self, netParams):
super(Network, self).__init__()
# initialize slayer
slayer = snn.loihi(netParams['neuron'], netParams['simulation'])
self.slayer = slayer
# define network functions
self.conv1 = torch.nn.utils.weight_norm(slayer.conv(2,
8,
3,
padding=1,
weightScale=20),
name='weight')
self.conv2 = torch.nn.utils.weight_norm(slayer.conv(8,
16,
3,
padding=1,
weightScale=100),
name='weight')
self.conv3 = torch.nn.utils.weight_norm(slayer.conv(16,
32,
3,
padding=1,
weightScale=100),
name='weight')
self.fc1 = torch.nn.utils.weight_norm(slayer.dense((10 * 10 * 32),
512),
name='weight')
self.fc2 = torch.nn.utils.weight_norm(slayer.dense(512, 5),
name='weight')
self.pool1 = slayer.pool(2)
self.pool2 = slayer.pool(2)
def __init__(self, path):
super(emgFeature, self).__init__()
netParams = snn.params(path + '/network.yaml')
# initialize slayer
slayer = snn.loihi(netParams['neuron'], netParams['simulation'])
self.slayer = slayer
# define network functions
self.fc1 = slayer.dense(16, 128)
self.fc2 = slayer.dense(128, 128)
self.fc3 = slayer.dense(128, 5)
self.delay1 = slayer.delay(128)
self.delay2 = slayer.delay(128)
self.fc1.weight.data = torch.FloatTensor(
np.load(path + '/Trained/fc1Weights.npy').reshape(
self.fc1.weight.shape))
self.fc2.weight.data = torch.FloatTensor(
np.load(path + '/Trained/fc2Weights.npy').reshape(
self.fc2.weight.shape))
self.fc3.weight.data = torch.FloatTensor(
np.load(path + '/Trained/fc3Weights.npy').reshape(
self.fc3.weight.shape))
self.delay1.delay.data = torch.FloatTensor(
np.load(path + '/Trained/delay1.npy').reshape(
self.delay1.delay.shape))
self.delay2.delay.data = torch.FloatTensor(
np.load(path + '/Trained/delay2.npy').reshape(
self.delay2.delay.shape))
def __init__(self, netParams):
super(Network, self).__init__()
# initialize slayer
slayer = snn.loihi(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(fusionNet, self).__init__()
# initialize slayer
slayer = snn.loihi(netParams['neuron'], netParams['simulation'])
self.slayer = slayer
# define network functions
self.fc1 = slayer.dense(512 + 128, 5)
self.fc1.weight.data = torch.FloatTensor(np.load('Trained/fc1Weights.npy').reshape(self.fc1.weight.shape))
def __init__(self, netParams):
super(Network, self).__init__()
# initialize slayer
slayer = snn.loihi(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, emgPath, dvsPath):
super(Network, self).__init__()
# initialize slayer
slayer = snn.loihi(netParams['neuron'], netParams['simulation'])
self.slayer = slayer
# define network functions
self.fc1 = torch.nn.utils.weight_norm(slayer.dense(512 + 128, 5), name='weight')
self.emgNet = emgFeature(emgPath)
self.dvsNet = dvsFeature(dvsPath)
def __init__(self, netParams):
super(Network, self).__init__()
# initialize slayer
slayer = snn.loihi(netParams['neuron'], netParams['simulation'])
self.slayer = slayer
# define network functions
self.fc1 = torch.nn.utils.weight_norm(slayer.dense(16, 128),
name='weight')
self.fc2 = torch.nn.utils.weight_norm(slayer.dense(128, 128),
name='weight')
self.fc3 = torch.nn.utils.weight_norm(slayer.dense(128, 5),
name='weight')
self.delay1 = slayer.delay(128)
self.delay2 = slayer.delay(128)
def __init__(self, netParams):
super(Network, self).__init__()
# initialize slayer
slayer = snn.loihi(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, path):
super(dvsFeature, self).__init__()
netParams = snn.params(path + '/network.yaml')
# initialize slayer
slayer = snn.loihi(netParams['neuron'], netParams['simulation'])
self.slayer = slayer
# define network functions
self.conv1 = slayer.conv( 2, 8, 3, padding=1, weightScale=20)
self.conv2 = slayer.conv( 8, 16, 3, padding=1, weightScale=100)
self.conv3 = slayer.conv(16, 32, 3, padding=1, weightScale=100)
self.fc1 = slayer.dense((10*10*32), 512)
self.fc2 = slayer.dense(512, 5)
self.pool1 = slayer.pool(2)
self.pool2 = slayer.pool(2)
self.fc1 .weight.data = torch.FloatTensor(np.load(path + '/Trained/fc1Weights.npy' ).reshape(self.fc1 .weight.shape))
self.fc2 .weight.data = torch.FloatTensor(np.load(path + '/Trained/fc2Weights.npy' ).reshape(self.fc2 .weight.shape))
self.conv1.weight.data = torch.FloatTensor(np.load(path + '/Trained/conv1Weights.npy').reshape(self.conv1.weight.shape))
self.conv2.weight.data = torch.FloatTensor(np.load(path + '/Trained/conv2Weights.npy').reshape(self.conv2.weight.shape))
self.conv3.weight.data = torch.FloatTensor(np.load(path + '/Trained/conv3Weights.npy').reshape(self.conv3.weight.shape))
self.pool1.weight.data = torch.FloatTensor(np.load(path + '/Trained/pool1Weights.npy').reshape(self.pool1.weight.shape))
self.pool2.weight.data = torch.FloatTensor(np.load(path + '/Trained/pool2Weights.npy').reshape(self.pool2.weight.shape))