Hi there,
I am new to deep learning and PyTorch and trying to build a simple CNN model to do a regression task on DNA sequence inputs.
Here is my CNN model:
Build CNN
class CNN(nn.Module):
def init(self):
super().init()
self.CNN_conv_layer1 = nn.Conv2d(in_channels=8, out_channels=32, kernel_size=1, stride=2)
#self.CNN_conv_layer2 = nn.Conv2d(16, 32, 1, 2)
self.CNN_max_pool1 = nn.MaxPool2d(2, 2)
self.CNN_conv_layer3 = nn.Conv2d(32, 64, 1, 2)
#self.CNN_conv_layer4 = nn.Conv2d(64, 128, 1, 2)
#self.CNN_max_pool2 = nn.MaxPool2d(2, 2)
# flatten and batch normalization
#self.CNN_flatten = torch.flatten()
self.CNN_BatchNorm = nn.BatchNorm1d(525, momentum=0.5) #263->525
self.CNN_fc1 = nn.Linear(525, 1) #263->525
#self.CNN_relu1 = nn.ReLU()
self.CNN_relu1 = nn.LeakyReLU()
self.CNN_fc2 = nn.Linear(1,1)
#pass data through CNN
def forward(self, x):
output = self.CNN_conv_layer1(x)
#output = self.CNN_conv_layer2(output)
output = self.CNN_max_pool1(output)
output = self.CNN_conv_layer3(output)
#output = self.CNN_conv_layer4(output)
#output = self.CNN_max_pool2(output)
#output = output.reshape(#reshape size, how to define the size to be compatible with next net)
#output = self.CNN_flatten(output, 1)#
output = self.CNN_BatchNorm(output)
output = output.view(output.size(0), -1)
#print(output.size)
output = self.CNN_fc1(output)
output = self.CNN_relu1(output)
output = self.CNN_fc2(output)
return output
And here is my training loop:
def train(net, dataloader, epochs=200, lr=0.0001, momentum=0.9, decay=0.0, verbose=1):
‘’’ Trains a neural network. Returns a 2d numpy array, where every list
represents the losses per epoch.
‘’’
losses_per_epoch = []
criterion = nn.MSELoss()
optimizer = optim.SGD(net.parameters(), lr=lr, weight_decay=decay)
for epoch in range(epochs):
#print(epoch)
sum_loss = 0.0
losses = []
for i, data in enumerate(train_dataloader, 0):
# get the inputs; data is a list of [inputs, labels]
#inputs, labels = data[0].to(device), data[1].to(device)
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
#loss = criterion(outputs, labels.float().unsqueeze(1))
loss.backward()
optimizer.step()
# print statistics
losses.append(loss.item())
sum_loss += loss.item()
if i % 100 == 99: # print every 100 mini-batches
if verbose:
print('[%d, %5d] train loss: %.3f' % (epoch + 1, i + 1, sum_loss / 20))
sum_loss = 0.0
# print(len(losses))
losses_per_epoch.append(np.mean(losses))
return losses_per_epoch
train(net=net, dataloader=train_dataloader)
print(f"Using {device} device")
I got a continuous error:
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/loss.py:529: UserWarning: Using a target size (torch.Size([8])) that is different to the input size (torch.Size([64, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
return F.mse_loss(input, target, reduction=self.reduction)
[1, 100] train loss: 57.159
[1, 200] train loss: 57.104
[1, 300] train loss: 56.298
[1, 400] train loss: 61.532
[1, 500] train loss: 59.678
[1, 600] train loss: 57.460
RuntimeError Traceback (most recent call last)
in ()
37 return losses_per_epoch
38
—> 39 train(net=net, dataloader=train_dataloader)
40 print(f"Using {device} device")
5 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias)
442 _pair(0), self.dilation, self.groups)
443 return F.conv2d(input, weight, bias, self.stride,
→ 444 self.padding, self.dilation, self.groups)
445
446 def forward(self, input: Tensor) → Tensor:
RuntimeError: Given groups=1, weight of size [32, 8, 1, 1], expected input[1, 7, 4200, 4] to have 8 channels, but got 7 channels instead
When I change the input channels to 7, another error will be dropped as:
RuntimeError: Given groups=1, weight of size [32, 7, 1, 1], expected input[1, 8, 4200, 4] to have 7 channels, but got 8 channels instead
I am totally confused and have no clues to fix it.
May I ask if anyone can help me on this?
Thank you!!!
Best regards,
Chao
