def __init__(self):
torch.nn.Module.__init__(self)
self.conv1 = torchwriter.conv3x3(nInChans, nOutChans)
self.conv2 = torchwriter.conv3x3(nOutChans, nOutChans)
self.sin = torch.sin
# number of classes = nOutChans
self.linear = x = torch.nn.Linear(nOutChans, nOutChans)
def __init__(self):
torch.nn.Module.__init__(self)
self.conv1 = torchwriter.conv3x3(nInChans, nOutChans)
self.conv2 = torchwriter.conv3x3(nOutChans, nOutChans)
self.sin = torch.sin
# for softmax dim -1 is correct for [sample][class],
# gives class probabilities for each sample.
self.softmax = torch.nn.Softmax(dim=-1)
def __init__(self):
torch.nn.Module.__init__(self)
self.conv1 = torchwriter.conv3x3(nChans, oChans)
self.conv2 = torchwriter.conv3x3(oChans, oChans)
self.weights = torch.nn.Parameter(torch.rand(10, 5))
self.relu = torch.nn.functional.relu
self.matmul = torch.matmul
def __init__(self):
torch.nn.Module.__init__(self)
self.sin = torch.sin
self.conv1 = torchwriter.conv3x3(nInChans, nOutChans)
self.conv2 = torchwriter.conv3x3(nOutChans, nOutChans)
self.bn2 = torch.nn.BatchNorm2d(nOutChans, eps=0.1)
self.conv3 = torchwriter.conv3x3(nOutChans, nOutChans)
self.bn3 = torch.nn.BatchNorm2d(nOutChans, eps=0.1)
def __init__(self):
torch.nn.Module.__init__(self)
self.conv1 = torchwriter.conv3x3(nInChans, nOutChans)
self.conv2 = torchwriter.conv3x3(nOutChans, nOutChans)
torch.nn.init.constant_(self.conv2.weight, 0.02)
torch.nn.init.constant_(self.conv1.weight, 0.02)
self.sin = torch.sin
self.pad = torch.nn.functional.pad
# for softmax dim -1 is correct for [sample][class],
# gives class probabilities for each sample.
self.softmax = torch.nn.Softmax(dim=-1)
def __init__(self):
torch.nn.Module.__init__(self)
self.sin = torch.sin
self.conv1 = torchwriter.conv3x3(nInChans, nOutChans)
self.in2 = torch.nn.InstanceNorm2d(nOutChans,
eps=0.1,
affine=True,
momentum=0)
# Force random initialization
np.random.seed(0)
self.in2.weight.data = torch.tensor(
np.random.rand(nOutChans).astype(np.float32))
def __init__(self):
torch.nn.Module.__init__(self)
self.conv1 = torchwriter.conv3x3(nChans, nChans)
self.conv2 = torchwriter.conv3x3(nChans, nChans)
self.relu = torch.nn.functional.relu
def __init__(self):
torch.nn.Module.__init__(self)
self.conv1 = torchwriter.conv3x3(nInChans, nOutChans)
self.conv2 = torchwriter.conv3x3(nInChans, nOutChans)