Using SGD on MNIST dataset with Pytorch, loss not decreasing
I tried to use SGD on MNIST dataset with batch size of 32, but the loss does not decrease at all.
I checked my model, loss function and read documentation but couldn't figure out what I've done wrong.
I defined my neural network as below
class classification(nn.Module):
def __init__(self):
    super(classification, self).__init__()
    
    # construct layers for a neural network
    self.classifier1 = nn.Sequential(
        nn.Linear(in_features=28*28, out_features=20*20),
        nn.Sigmoid(),
    ) 
    self.classifier2 = nn.Sequential(
        nn.Linear(in_features=20*20, out_features=10*10),
        nn.Sigmoid(),
    ) 
    self.classifier3 = nn.Sequential(
        nn.Linear(in_features=10*10, out_features=10),
        nn.LogSoftmax(dim=1),
    ) 
    
    
def forward(self, inputs):                 # [batchSize, 1, 28, 28]
    x = inputs.view(inputs.size(0), -1)    # [batchSize, 28*28]
    x = self.classifier1(x)                # [batchSize, 20*20]
    x = self.classifier2(x)                # [batchSize, 10*10]
    out = self.classifier3(x)              # [batchSize, 10]
    
    return out

And I defined my training process as below

classifier = classification().to(&quot;cuda&quot;)
#optimizer
optimizer = torch.optim.SGD(classifier.parameters(), lr=learning_rate_value)
#loss function
criterion = nn.NLLLoss()
batch_size=32
epoch = 30
#array to save loss history
loss_train_arr=np.zeros(epoch)

#used DataLoader to make split batch
batched_train = torch.utils.data.DataLoader(training_set, batch_size, shuffle=True)

for i in range(epoch):
    
    loss_train=0
    
    #train and compute loss, accuracy
    for img, label in batched_train:
        img=img.to(device)
        label=label.to(device)

        optimizer.zero_grad()
        predicted = classifier(img)
        
        label_predicted = torch.argmax(predicted,dim=1)
        loss = criterion(predicted, label)
        loss.backward
        optimizer.step()
        loss_train += loss.item()
        
    loss_train_arr[i]=loss_train/(len(batched_train.dataset)/batch_size)


I am using a model with LogSoftmax layer, so my loss function seems right. But the loss does not decrease at all.

If the code you posted is the exact code you use, the problem is that you don't actually call backward on the loss (missing parentheses ()).
