Hi, this is my first time writing a Neural Network using PyTorch and I encountered the following error
'Linear' object has no attribute 'log_softmax'

Here’s my code:
class ConvNet(nn.Module):
  def __init__(self):
    super(ConvNet, self).__init__()

    self.conv1 = nn.Conv2d(in_channels=3,       # 224 x 224 -> 222 x 222
                           out_channels = 64,
                           kernel_size = 3)
    self.maxpool1 = nn.MaxPool2d(kernel_size=2)   # 222 x 222 -> 111 x 111
    self.batchnorm1 = nn.BatchNorm2d(64)
    self.dropout1 = nn.Dropout(p = 0.5)

    self.conv2 = nn.Conv2d(in_channels = 64,         # 111 x 111 -> 106 x 106
                           out_channels = 128,
                           kernel_size = 5)
    self.maxpool2 = nn.MaxPool2d(kernel_size = 2)  # 106 x 106 -> 53 x 53
    self.batchnorm2 = nn.BatchNorm2d(128)
    self.dropout2 = nn.Dropout(p = 0.3)

    self.conv3 = nn.Conv2d(in_channels = 128,     # 53 x 53 -> 46 x 46
                           out_channels = 256,
                           kernel_size = 7)
    self.maxpool3 = nn.MaxPool2d(kernel_size=2)  # 46 x 46 -> 23 x 23
    self.batchnorm3 = nn.BatchNorm2d(256)
    self.dropout3 = nn.Dropout(p = 0.2)

    self.fc1 = nn.Linear(256*23*23, 1000)
    self.dropout4 = nn.Dropout(p = 0.1)

    self.fc2 = nn.Linear(1000, 2)

  def forward(self, x):
    x = self.conv1(x)
    x = self.maxpool1(x)
    x = self.batchnorm1(x)
    x = self.dropout1(x)
    x = F.relu(x)

    x = self.conv2(x)
    x = self.maxpool2(x)
    x = self.batchnorm2(x)
    x = self.dropout2(x)
    x = F.relu(x)

    x = self.conv3(x)
    x = self.maxpool3(x)
    x = self.batchnorm3(x)
    x = self.dropout3(x)
    x = F.relu(x)

    print(x.shape)
    x = x.view(x.size(0), -1)
    print(x.shape)

    x = self.fc1(x)
    x = self.dropout4(x)
    x = F.relu(x)
    x = self.fc2

    return x

Here’s the code for my training loop
In_epochs = 10
lr = 0.001
cec_loss = nn.CrossEntropyLoss()
params = model.parameters()
optimizer = optim.Adam(params = params, lr = 0.001)

n_epochs = 3
n_iterations = 0

losses = []
for e in range(n_epochs):
  counter = 0
  for i, (images, labels) in enumerate(train_loader):
    images = Variable(images)
    labels = Variable(labels)
    output = model(images)

    model.zero_grad()
    loss = cec_loss(output, labels)
    loss.backward()

    optimizer.step()
    n_iterations +=1 
    counter += 1

    if counter % 100 == 0:
      print(loss.item())