def __init__(self):
super(__class__, self).__init__()
classes = 2
self.sharedNet = SharedNet().double()
self.newbatch = nn_spd.NewBatchNormSPD(50).double()
self.re = nn_spd.ReEig()
self.logeig = nn_spd.LogEig()
self.linear = nn.Linear(50**2, classes, bias=True)
self.dropout1 = nn.Dropout(p=0.3)
self.dropout2 = nn.Dropout(p=0.3)
self.dropout3 = nn.Dropout(p=0.5)
self.linear.weight.data.normal_(0, 0.005)
def __init__(self):
super(__class__, self).__init__()
dim = 62
dim1 = 58
dim2 = 54
dim3 = 50
self.re = nn_spd.ReEig()
self.bimap1 = nn_spd.BiMap(1, 1, dim, dim1)
self.bimap2 = nn_spd.BiMap(1, 1, dim1, dim2)
self.bimap3 = nn_spd.BiMap(1, 1, dim2, dim3)
self.logeig = nn_spd.LogEig()
self.dropout1 = nn.Dropout(p=0.3)
self.dropout2 = nn.Dropout(p=0.3)
def __init__(self):
super(__class__, self).__init__()
dim = 62
dim1 = 58
dim2 = 54
dim3 = 50
classes = 2
self.re = nn_spd.ReEig()
self.bimap1 = nn_spd.BiMap(1, 1, dim, dim1)
self.batchnorm1 = nn_spd.BatchNormSPD(dim1)
self.bimap2 = nn_spd.BiMap(1, 1, dim1, dim2)
self.batchnorm2 = nn_spd.BatchNormSPD(dim2)
self.bimap3 = nn_spd.BiMap(1, 1, dim2, dim3)
self.batchnorm3 = nn_spd.BatchNormSPD(dim3)
self.logeig = nn_spd.LogEig()
self.linear = nn.Linear(dim3**2, classes, bias=True)
self.dropout1 = nn.Dropout(p=0.3)
self.dropout2 = nn.Dropout(p=0.3)
self.dropout3 = nn.Dropout(p=0.5)