def __init__(self, vgg_name, T, bias=False):
super(CatVGG, self).__init__()
self.snn = catSNN.spikeLayer(T)
self.T = T
self.bias = bias
self.features = self._make_layers(cfg[vgg_name])
self.classifier = self.snn.dense((1, 1, 512), 10)
def __init__(self, vgg_name, T, is_noise=False, bias=True):
super(CatVGG_o, self).__init__()
self.snn = catSNN.spikeLayer(T)
self.T = T
self.is_noise = is_noise
self.bias = bias
self.features = self._make_layers(cfg[vgg_name], is_noise)
self.classifier = self.snn.dense((1, 1, 1024), 10, bias=True)
def __init__(self, vgg_name, T, bias=False):
super(CatVGG, self).__init__()
self.snn = catSNN.spikeLayer(T)
self.T = T
self.bias=bias
self.features = self._make_layers(cfg[vgg_name])
#self.classifier = nn.Linear(512, 10, bias=False)
#((1,1,512),10)
self.classifier1 = self.snn.dense((7,7,512),4096, bias=self.bias)
self.classifier2 = self.snn.dense(4096,4096, bias=self.bias)
self.classifier3 = self.snn.dense(4096,1000, bias=self.bias)
def __init__(self, T):
super(CatNet, self).__init__()
self.T = T
snn = spikeLayer(T)
self.snn = snn
self.conv1 = snn.conv(1, 16, (3, 1), 1, (1, 0), bias=True)
self.conv2 = snn.conv(16, 24, (3, 1), 1, (1, 0), bias=True)
self.pool1 = snn.pool((2, 1))
self.pool2 = snn.pool((2, 1))
self.fc1 = snn.dense((1, 45, 24), 2, bias=True)
def __init__(self, T):
super(CatNet, self).__init__()
self.T = T
snn = spikeLayer(T)
self.snn = snn
self.conv1 = snn.conv(1, 64, 5, 1, 0, bias=True)
self.conv2 = snn.conv(64, 64, 5, 1, 0, bias=True)
self.conv3 = snn.conv(64, 128, 5, 1, 0, bias=True)
self.pool1 = snn.pool(2)
self.pool2 = snn.pool(2)
self.fc1 = snn.dense((4, 4, 128), 128, bias=True)
self.fc2 = snn.dense(128, 2, bias=True)
def __init__(self, T):
super(CatNet, self).__init__()
self.T = T
snn = spikeLayer(T)
self.snn = snn
self.conv1 = snn.conv(1, 32, 3, 1, 1, bias=True)
self.conv2 = snn.conv(32, 32, 3, 1, 1, bias=True)
self.conv3 = snn.conv(32, 32, 3, 1, 1, bias=True)
self.pool1 = snn.pool(2)
self.conv4 = snn.conv(32, 16, 1, 1, 0, bias=True)
self.conv5 = snn.conv(16, 16, 3, 1, 1, bias=True)
self.conv6 = snn.conv(16, 32, 1, 1, 0, bias=True)
self.fc1 = snn.dense((14, 14, 32), 128, bias=True)
self.fc2 = snn.dense(128, 10, bias=True)
def __init__(self, T):
super(CatNet, self).__init__()
self.T = T
snn = spikeLayer(T)
self.snn = snn
self.conv1 = snn.conv(1, 32, 3, 1, 0, bias=True)
self.conv2 = snn.conv(32, 32, 3, 1, 0, bias=True)
self.conv3 = snn.conv(32, 32, 4, 2, 1, bias=True)
self.conv4 = snn.conv(32, 64, 3, 1, 0, bias=True)
self.conv5 = snn.conv(64, 64, 3, 1, 0, bias=True)
self.conv6 = snn.conv(64, 64, 4, 2, 1, bias=True)
self.conv7 = snn.conv(64, 128, 3, 1, 0, bias=True)
self.fc1 = snn.dense((2, 2, 128), 10, bias=True)
def __init__(self, T):
super(CatNet, self).__init__()
self.snn = catSNN.spikeLayer(T)
self.T = T
self.model = self._make_layers()
self.fc = self.snn.dense(512, 10, bias=True)
