netParams = snn.params('network.yaml')
# Define the cuda device to run the code on.
device = torch.device('cuda')
# deviceIds = [2, 3]
# Create network instance.
net = Network(netParams).to(device)
# net = torch.nn.DataParallel(Network(netParams).to(device), device_ids=deviceIds)
# Create snn loss instance.
error = snn.loss(netParams, spikeLayer).to(device)
# Define optimizer module.
# optimizer = torch.optim.Adam(net.parameters(), lr = 0.01, amsgrad = True)
optimizer = optimizer.Nadam(net.parameters(), lr=0.01, amsgrad=True)
# Dataset and dataLoader instances.
trainingSet = IBMGestureDataset(
datasetPath=netParams['training']['path']['in'],
sampleFile=netParams['training']['path']['train'],
samplingTime=netParams['simulation']['Ts'],
sampleLength=netParams['simulation']['tSample'])
trainLoader = DataLoader(dataset=trainingSet,
batch_size=4,
shuffle=True,
num_workers=1)
testingSet = IBMGestureDataset(
datasetPath=netParams['training']['path']['in'],
sampleFile=netParams['training']['path']['test'],
netParams = snn.params('network.yaml')
# Define the cuda device to run the code on.
device = torch.device('cuda')
# deviceIds = [1, 2]
# Create network instance.
net = Network(netParams, 'emg', 'dvsCropped').to(device)
# net = torch.nn.DataParallel(Network(netParams).to(device), device_ids=deviceIds)
# Create snn loss instance.
error = snn.loss(netParams, snn.loihi).to(device)
# Define optimizer module.
optimizer = optim.Nadam(net.parameters(), lr=0.01)
# Dataset and dataLoader instances.
trainingSet = fusionDataset(
samples=np.loadtxt('train.txt').astype(int),
samplingTime=netParams['simulation']['Ts'],
sampleLength=netParams['simulation']['tSample'],
# sampleLength=2000,
)
testingSet = fusionDataset(
samples=np.loadtxt('test.txt').astype(int),
samplingTime=netParams['simulation']['Ts'],
sampleLength=netParams['simulation']['tSample'],
)
