Short cut

I am trying to modify the Binary Neural network but my network gets deeper, and I have a vanishing gradient problem. I tried to put batch normalization, and also use ReLU function but no luck, I used also dirac initialization but also no luck. So, I decided to use the Residual Block instead but the problem persist saying

The size of tensor a (160) must match the size of tensor b (192) at non-singleton dimension 1

I tried a different way, I even printed every tensor and the tensor change through the network but I was not able to catch the error

```
class BinConv2d(nn.Module):
def __init__(self, input_channels, output_channels,
kernel_size=-1, stride=-1, padding=-1, dropout=0):
super(BinConv2d, self).__init__()
self.layer_type = 'BinConv2d'
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dropout_ratio = dropout
self.bn = nn.BatchNorm2d(input_channels, eps=1e-4, momentum=0.1, affine=True)
self.bn.weight.data = self.bn.weight.data.zero_().add(1.0)
if dropout!=0:
self.dropout = nn.Dropout(dropout)
self.conv = nn.Conv2d(input_channels, output_channels,
kernel_size=kernel_size, stride=stride, padding=padding)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
res = x
x = self.bn(x)
x, mean = BinActive(x)
if self.dropout_ratio!=0:
x = self.dropout(x)
x = self.conv(x)
x += res
x = self.relu(x)
return x
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.xnor = nn.Sequential(
nn.Conv2d(3, 192, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(192, eps=1e-4, momentum=0.1, affine=False),
nn.ReLU(inplace=True),
BinConv2d(192, 160, kernel_size=1, stride=1, padding=0),
BinConv2d(160, 96, kernel_size=1, stride=1, padding=0),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
BinConv2d( 96, 192, kernel_size=5, stride=1, padding=2, dropout=0.5),
BinConv2d(192, 192, kernel_size=1, stride=1, padding=0),
BinConv2d(192, 192, kernel_size=1, stride=1, padding=0),
nn.AvgPool2d(kernel_size=3, stride=2, padding=1),
BinConv2d(192, 192, kernel_size=3, stride=1, padding=1, dropout=0.5),
BinConv2d(192, 192, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(192, eps=1e-4, momentum=0.1, affine=False),
nn.Conv2d(192, 10, kernel_size=1, stride=1, padding=0),
nn.ReLU(inplace=True),
nn.AvgPool2d(kernel_size=8, stride=1, padding=0),
)
def forward(self, x):
for m in self.modules():
if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
if hasattr(m.weight, 'data'):
m.weight.data.clamp_(min=0.01)
x = self.xnor(x)
x = x.view(x.size(0), 10)
return x
```

Thanks