Hello, I have very basic problem with training classification MLP network - I’m trying to train a network for simple classification task on randomly generated dataset with a bit imbalanced classes (59 observations of class 0 and 140 of class 1), and I can’t seem to teach the NN to distinguish between them, it always just simply predicts all the classes to class 1. I tried to use different weighting of classes, but it didn’t help. I know that the dataset is small, but unfortunately that was the task given by the teacher, and I shouldn’t make the dataset bigger. Here is my code:

```
class NeuralNet(nn.Module):
def __init__(self, n_input, n_hidden1, n_hidden2, output_shape):
super().__init__()
self.hidden2 = nn.Linear(n_hidden1, n_hidden2)
self.hidden = nn.Linear(n_input, n_hidden1)
self.outer = nn.Linear(n_hidden1, output_shape)
self.sigmoid = nn.Sigmoid()
self.softmax = nn.Softmax(dim=1)
def forward(self, data):
step1 = self.hidden(data)
step2 = self.sigmoid(step1)
step3 = self.hidden2(step2)
step4 = self.sigmoid(step3)
step5 = self.outer(step4)
step6 = self.softmax(step5)
return step6
model = NeuralNet(4, 5, 5, 2)
model = model.float()
num_epoch = 100
learning_rate = 0.001
loss_function = F.binary_cross_entropy
optimizer = optim.SGD(model.parameters(), lr=learning_rate)#, weight_decay=1e-4, momentum=0.9)
#training loop:
train_scores = []
test_scores = []
for epoch in range(num_epoch):
scores = []
model.train()
for x_batch, y_batch in train_dataloader:
y_pred = model(x_batch).squeeze()
y_batch = y_batch.squeeze().float()
loss = loss_function(y_pred, y_batch, weight=torch.tensor([0.6, 0.4]))
loss.backward()
optimizer.step()
optimizer.zero_grad()
scores.append(loss.item())
model.eval()
train_loss = np.mean(scores)
with torch.no_grad():
test_loss = sum(loss_function(model(xb).squeeze(), yb.squeeze().float()) for xb, yb in test_dataloader)
test_loss = test_loss/len(test_dataloader)
#print(f"Epoch {epoch} train loss: {train_loss}, test loss: {test_loss}")
train_scores.append(train_loss)
test_scores.append(test_loss)
```