My code is a bit lengthy, but what i am wondering is where do i change the channel input for the network, the network i am training takes in 1 channel and the images i have,have 4 channels.This is the error i get when i try to train it
RuntimeError                              Traceback (most recent call last)
<ipython-input-336-8e1b70ff88e8> in <module>()
     16 for epoch in range(0, num_epochs):
     17     # train for one epoch
---> 18     curr_loss = train(train_loader, model, criterion, epoch, num_epochs)
     19 
     20     # store best loss and save a model checkpoint

<ipython-input-334-a6512205648d> in train(train_loader, model, criterion, epoch, num_epochs)
     12         # compute output
     13         optimizer.zero_grad()
---> 14         outputs = model(images)
     15 
     16         # measure loss

/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    323         for hook in self._forward_pre_hooks.values():
    324             hook(self, input)
--> 325         result = self.forward(*input, **kwargs)
    326         for hook in self._forward_hooks.values():
    327             hook_result = hook(self, input, result)

<ipython-input-332-5c12062f256d> in forward(self, x)
     66 
     67     def forward(self, x):
---> 68         block1 = self.conv_block1_64(x)
     69         pool1 = self.pool1(block1)
     70 

/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    323         for hook in self._forward_pre_hooks.values():
    324             hook(self, input)
--> 325         result = self.forward(*input, **kwargs)
    26         for hook in self._forward_hooks.values():
    32 7             hook_result = hook(self, input, result)

<ipython-input-332-5c12062f256d> in forward(self, x)
     10 
     11     def forward(self, x):
---> 12         out = self.activation(self.conv(x))
     13         out = self.activation(self.conv2(out))
     14 

/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    323         for hook in self._forward_pre_hooks.values():
    324             hook(self, input)
--> 325         result = self.forward(*input, **kwargs)
     326         for hook in self._forward_hooks.values():
     327             hook_result = hook(self, input, result)

     /usr/local/lib/python3.5/dist-packages/torch/nn/modules/conv.py in forward(self, input)
        275     def forward(self, input):
         276         return F.conv2d(input, self.weight, self.bias, self.stride,
    --> 277                         self.padding, self.dilation, self.groups)
        278 
        279 

/usr/local/lib/python3.5/dist-packages/torch/nn/functional.py in conv2d(input, weight, bias, stride, padding,     dilation, groups)
     88                 _pair(0), groups, torch.backends.cudnn.benchmark,
     89                 torch.backends.cudnn.deterministic, torch.backends.cudnn.enabled)
---> 90     return f(input, weight, bias)
     91 
     92 

RuntimeError: Given groups=1, weight[64, 1, 3, 3], so expected input[8, 4, 256, 256] to have 1 channels,but    got 4 channels instead

Any suggestions would be helpful,
If you need the code,kindly let me know