Using GPU: True

Epoch 1/10
----------
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-13-cd4c03781ec7> in <module>()
      8 exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1) #decay
      9                                                             # 0.1 every 5 epochs
---> 10 model_ft = train_model(net, criterion, optimizer, exp_lr_scheduler, num_epochs=10)

2 frames
<ipython-input-12-e791953bf1c6> in forward(self, x)
     17         # If the size is a square you can only specify a single number
     18         x = F.max_pool2d(F.relu(self.conv2(x)), 2)
---> 19         x = x.view(x.size(0), 3 * 224 * 224)
     20         x = F.relu(self.fc1(x))
     21         x = F.relu(self.fc2(x))

I have this problem but I don’t know how to solve this. Could someone help me, please?
That’s my Net
class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        # 1 input image channel, 6 output channels, 3x3 square convolution
        # kernel
        self.conv1 = nn.Conv2d(3, 6, 3)
        self.conv2 = nn.Conv2d(6, 16, 3)
        # an affine operation: y = Wx + b
        self.fc1 = nn.Linear(3 * 224 * 224, 144)  # 6*6 from image dimension
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        # If the size is a square you can only specify a single number
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(x.size(0), 3 * 224 * 224)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]  # all dimensions except the batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features