PyTorch Neural Net not learning - machine-learning

Question

I am new to PyTorch and I'm trying to build a simple neural net for classification. The problem is the network doesn't learn at all. I tried various learning rate ranging from 0.3 to 1e-8 and I also tried training it for a longer duration. My data is small with only 120 training examples and the batch size is 16. Here is the code
Define network
model = nn.Sequential(nn.Linear(4999, 1000),
nn.ReLU(),
nn.Linear(1000,200),
nn.ReLU(),
nn.Linear(200,1),
nn.Sigmoid())
Loss and optimizer
import torch.optim as optim
optimizer = optim.SGD(model.parameters(), lr=0.01)
criterion = nn.BCELoss(reduction="mean")
Training
num_epochs = 100
for epoch in range(num_epochs):
cumulative_loss = 0
for i, data in enumerate(batch_gen(X_train, y_train, batch_size=16)):
inputs, labels = data
inputs = torch.from_numpy(inputs).float()
labels = torch.from_numpy(labels).float()
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
cumulative_loss += loss.item()
if i%5 == 0 and i != 0:
print(f"epoch {epoch} batch {i} => Loss: {cumulative_loss/5}")
print("Finished Training!!")
Any help is appreciated!

Answer 1

The reason your loss doesn't seem to decrease every epoch is because you're not printing it every epoch. You're actually printing it every 5th batch. And the loss does not decrease a lot per batch.
Try the following. Here, loss every epoch will be printed.
num_epochs = 100
for epoch in range(num_epochs):
cumulative_loss = 0
for i, data in enumerate(batch_gen(X_train, y_train, batch_size=16)):
inputs, labels = data
inputs = torch.from_numpy(inputs).float()
labels = torch.from_numpy(labels).float()
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
cumulative_loss += loss.item()
print(f"epoch {epoch} => Loss: {cumulative_loss}")
print("Finished Training!!")
One reason that your loss doesn't decrease could be because your neural-net isn't deep enough to learn anything. So, trying add more layers.
model = nn.Sequential(nn.Linear(4999, 3000),
nn.ReLU(),
nn.Linear(3000,200),
nn.ReLU(),
nn.Linear(2000,1000),
nn.ReLU(),
nn.Linear(500,250),
nn.ReLU(),
nn.Linear(250,1),
nn.Sigmoid())
Also, I just noticed you're passing data that has very high dimensionality. You have 4999 features/columns and only 120 training examples/rows. Converging a model with so less data is next to impossible (considering you have very high dimensional data).
I'd suggest you try finding more rows or perform dimensionality reduction on your input data (like PCA) to reduce the feature space (to maybe 50/100 or lesser features) and then try again. Chances are that your model still won't converge but it's worth a try.