I am training a VGG16 with images of size 224x224 with 4 classes.
device = torch.device("cuda" if torch.cuda.is_available()
else "cpu")
print(device)
model = models.vgg16(pretrained=True)
for param in model.parameters():
param.requires_grad = False
model.fc = nn.Sequential(nn.Linear(2048, 512),
nn.ReLU(),
nn.Linear(512, 4),
nn.LogSoftmax(dim=1))
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.fc.parameters(), lr=0.0001)
model.to(device)
And I am training it for 50 epochs:
epochs = 50
steps = 0
running_loss = 0
print_every = 105
train_losses, test_losses, accuracy = [], [], []
for epoch in range(epochs):
for inputs, labels in trainloader:
#print('working')
steps += 1
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
logps = model.forward(inputs)
loss = criterion(logps, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if steps % print_every == 0:
test_loss = 0
accuracy = 0
model.eval()
with torch.no_grad():
for inputs, labels in testloader:
#print('validating')
inputs, labels = inputs.to(device), labels.to(device)
logps = model.forward(inputs)
batch_loss = criterion(logps, labels)
test_loss += batch_loss.item()
ps = torch.exp(logps)
top_p, top_class = ps.topk(1, dim=1)
equals = top_class == labels.view(*top_class.shape)
accuracy += torch.mean(equals.type(torch.FloatTensor)).item()
train_losses.append(running_loss/len(trainloader))
test_losses.append(test_loss/len(testloader))
print(f"Epoch {epoch+1}/{epochs}.. "
f"Train loss: {running_loss/print_every:.3f}.. "
f"Test loss: {test_loss/len(testloader):.3f}.. "
f"Test accuracy: {accuracy/len(testloader):.3f}")
running_loss = 0
model.train()
However, this error message pops up:
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
What am I doing wrong? Thanks!