Epoch: 0 – Training Loss: 0.057411
Epoch: 1 –  Training Loss: 0.056986
Epoch: 2 – Training Loss: 0.057286
Epoch: 3 –  Training Loss: 0.057266
Anyone knows why it is sooo slow ??
My data set have 1248 images for training
CODE:
model = models.densenet121(pretrained = True)
manter os features parameters
for param in model.parameters():
param.requires_grad = False
criterion = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=0.003)
for e in range(epochs):
# keep track of training and validation loss
running_loss = 0.0
running_corrects = 0.0
for inputs, label in (dataloaders['train']):
    model.train()
    # IF GPU is availible
    if train_on_gpu:
        inputs, label = inputs.cuda(), label.cuda()
    
    optimizer.zero_grad()
    with torch.set_grad_enabled(True):
        logps = model(inputs)
        _, preds = torch.max(logps, 1) # tecnica nova de validacao
        loss = criterion(logps, label)
        loss.backward()
        optimizer.step()
    
        running_loss += loss.item()
        running_corrects += torch.sum(preds == label.data)