I’m trying to train a dataset with AlexNet model. The task is multiclass classification (15 classes). I am wondering why I am getting very low accuracy.
I tried different learning rate but has not been improved.
Here is the snippet for the training.
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.9)
def train_valid_model():
num_epochs=5
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
for phase in ['train', 'valid', 'test']:
if phase == 'train':
model.train()
else:
model.eval()
train_loss = 0.0
total_train = 0
correct_train = 0
for t_image, target, image_path in dataLoaders[phase]:
t_image = t_image.to(device)
target = target.to(device)
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
outputs = model(t_image)
outputs = F.softmax(outputs, dim=1)
loss = criterion(outputs,target)
if phase == 'train':
loss.backward()
optimizer.step()
_, predicted = torch.max(outputs.data, 1)
train_loss += loss.item()* t_image.size(0)
correct_train += (predicted == target).sum().item()
epoch_loss = train_loss / len(dataLoaders[phase].dataset)
epoch_acc = 100 * correct_train / len(dataLoaders[phase].dataset)
print('{} Loss: {:.4f} {} Acc: {:.4f}'.format(phase, epoch_loss, phase, epoch_acc))
Epoch 0/4
train Loss: 2.7026 train Acc: 17.2509
valid Loss: 2.6936 valid Acc: 28.7632
test Loss: 2.6936 test Acc: 28.7632
Epoch 1/4
train Loss: 2.6425 train Acc: 17.8019
valid Loss: 2.6357 valid Acc: 28.7632
test Loss: 2.6355 test Acc: 28.7632