def __init__(self, v_threshold=1.0, v_reset=0.0):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(1, 128, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(128),
neuron.PLIFNode(v_threshold=v_threshold, v_reset=v_reset),
nn.MaxPool2d(2, 2), # 14 * 14
nn.Conv2d(128, 128, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(128),
neuron.IFNode(v_threshold=v_threshold, v_reset=v_reset),
nn.MaxPool2d(2, 2) # 7 * 7
)
self.fc = nn.Sequential(
nn.Flatten(),
layer.Dropout(0.7),
nn.Linear(128 * 7 * 7, 128 * 3 * 3, bias=False),
neuron.PLIFNode(v_threshold=v_threshold, v_reset=v_reset),
layer.Dropout(0.7),
nn.Linear(128 * 3 * 3, 128, bias=False),
neuron.PLIFNode(v_threshold=v_threshold, v_reset=v_reset),
nn.Linear(128, 100, bias=False),
neuron.PLIFNode(v_threshold=v_threshold, v_reset=v_reset)
)
self.boost = nn.AvgPool1d(10, 10)
def __init__(self, v_threshold=1.0, v_reset=0.0):
super().__init__()
self.train_times = 0
self.max_test_acccuracy = 0
self.conv = nn.Sequential(
nn.Conv2d(3, 256, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(256),
neuron.IFNode(v_threshold=v_threshold, v_reset=v_reset),
nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(256),
neuron.IFNode(v_threshold=v_threshold, v_reset=v_reset),
nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(256),
neuron.IFNode(v_threshold=v_threshold, v_reset=v_reset),
nn.MaxPool2d(2, 2), # 16 * 16
nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(256),
neuron.IFNode(v_threshold=v_threshold, v_reset=v_reset),
nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(256),
neuron.IFNode(v_threshold=v_threshold, v_reset=v_reset),
nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(256),
neuron.IFNode(v_threshold=v_threshold, v_reset=v_reset),
nn.MaxPool2d(2, 2) # 8 * 8
)
self.fc = nn.Sequential(
nn.Flatten(), layer.Dropout(0.5),
nn.Linear(256 * 8 * 8, 128 * 4 * 4, bias=False),
neuron.PLIFNode(v_threshold=v_threshold, v_reset=v_reset),
nn.Linear(128 * 4 * 4, 100, bias=False),
neuron.PLIFNode(v_threshold=v_threshold, v_reset=v_reset))
self.boost = nn.AvgPool1d(10, 10)