```
class convNet(nn.Module):
#constructor
def __init__(self):
super(convNet, self).__init__()
#defining layers in convnet
#input size=1*657*1625
self.conv1 = nn.Conv2d(1,16, kernel_size=3,stride=1,padding=1)
self.conv2 = nn.Conv2d(16,32, kernel_size=3,stride=1,padding=1)
self.conv3 = nn.Conv2d(32,64, kernel_size=3,stride=1,padding=1)
#Parallel rectangle and square convolution
self.Pconv1=nn.Conv2d(64,32, kernel_size=(3,3),stride=1,padding=(1,1))
self.Pconv2=nn.Conv2d(64,32, kernel_size=(3,7),stride=1,padding=(1,3))
self.Pconv3=nn.Conv2d(64,32, kernel_size=(7,3),stride=1,padding=(3,1))
#auxilary convolution
self.conv6 = nn.Conv2d(32,8, kernel_size=3,stride=1,padding=1)
self.conv7 = nn.Conv2d(8,1, kernel_size=3,stride=1,padding=1)
def forward(self, x):
x = nnFunctions.leaky_relu(self.conv1(x))
x = nnFunctions.leaky_relu(self.conv2(x))
x = nnFunctions.leaky_relu(self.conv3(x))
x=nnFunctions.leaky_relu(self.Pconv1(x))+nnFunctions.leaky_relu(self.Pconv2(x))+nnFunctions.leaky_relu(self.Pconv3(x))
x=nnFunctions.leaky_relu(self.conv6(x))
x=nnFunctions.leaky_relu(self.conv7(x))
return x
```

The above is my convNet class which takes input of `410x1x512x1024`

dimension data and outputs a `410x1x512x1024`

dimension data.

The data is `410`

images grayscale so `1`

channel and dimension of `512x1024.`

I use loss functions:

`criterion = nn.SmoothL1Loss()`

The following is my train function:

```
def train(train_loader,net,criterion,epochs,total_samples,learning_rate):
prev_loss=0
optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9)
for epoch in range(int(epochs)): # loop over the dataset multiple times
running_loss = 0.0
for i,data in enumerate(train_loader):
inputs,labels=data
# wrap them in Variable
inputs, labels = Variable(inputs).cuda(), Variable(labels).cuda()
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.data[0]
**print (i,running_loss)**
print('Finished Training')
return net
```

The `print(i,running_loss``)`

in the train function prints some value for 3 batches and then just outputs nan for every batch afterwards.

Kindly help