import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):

    super().__init__()

    self.conv1 = nn.Conv2d(3, 202, 3)

    self.pool = nn.MaxPool2d(2, 2)

    self.conv2 = nn.Conv2d(202, 22, 3)

    self.pool = nn.MaxPool2d(2, 2)

    self.conv3 = nn.Conv2d(22, 220, 3)

    self.pool = nn.MaxPool2d(2, 2)

    self.conv4 = nn.Conv2d(220, 200, 3)

    self.fc1 = nn.Linear(200*3*3, 202)

    self.fc2 = nn.Linear(202, 200)

    self.fc3 = nn.Linear(200, 120)

def forward(self, x):

    x = self.pool(F.relu(self.conv1(x)))

    x = self.pool(F.relu(self.conv2(x)))

    x = x.view(-1, 200 * 3 * 3)

    x = F.relu(self.fc1(x))

    x = F.relu(self.fc2(x))

    x = self.fc3(x)

    return x

net = Net()
for epoch in range(2):  # loop over the dataset multiple times
running_loss = 0.0

for i, data in enumerate(trainloader, 0):

    # get the inputs; data is a list of [inputs, labels]

    inputs, labels = data

    # 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.item()

    if i % 2000 == 1999:    # print every 2000 mini-batches

        print('[%d, %5d] loss: %.3f' %

              (epoch + 1, i + 1, running_loss / 2000))

        running_loss = 0.0

print(‘Finished Training’)
RuntimeError                              Traceback (most recent call last)
 in ()
11
12         # forward + backward + optimize
—> 13         outputs = net(inputs)
14         loss = criterion(outputs, labels)
15         loss.backward()
1 frames
 in forward(self, x)
20         x = self.pool(F.relu(self.conv1(x)))
21         x = self.pool(F.relu(self.conv2(x)))
—> 22         x = x.view(-1, 200 * 3 * 3)
23         x = F.relu(self.fc1(x))
24         x = F.relu(self.fc2(x))
RuntimeError: shape ‘[-1, 1800]’ is invalid for input of size 43120
Please help me figure out this error