Hi everybody,
I’ve been trying to debug what is happening but don’t know what’s wrong.
If you need more info let me know.
Regards!
epochs = 10
steps = 0
running_loss = 0
print_every = 5
for epoch in range(epochs):
for inputs, labels in train_loader:
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:
val_loss = 0
accuracy = 0
model.eval()
with torch.no_grad():
for inputs, labels in val_loader:
inputs, labels = inputs.to(device), labels.to(device)
logps = model.forward(inputs)
batch_loss = criterion(logps, labels)
val_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.tpye(torch.FloatTensor)).item()
print('Epoch {}/{}'.format(epoch + 1, epochs))
print('Train loss: {}'.format(running_loss/print_every))
print('Val loss: {}'.format(val_loss/len(val_loader)))
print('Val accuracy: {}'.format(accuracy/len(val_loader)))
running_loss = 0
model.train()