FMNIST-train-loop

FMNIST-train-loop.py

m = RunManager()

# get all runs from params using RunBuilder class
for run in RunBuilder.get_runs(params):

    # if params changes, following line of code should reflect the changes too
    network = Network()
    loader = torch.utils.data.DataLoader(train_set, batch_size = run.batch_size)
    optimizer = optim.Adam(network.parameters(), lr=run.lr)

    m.begin_run(run, network, loader)
    for epoch in range(epochs):
      
      m.begin_epoch()
      for batch in loader:
        
        images = batch[0]
        labels = batch[1]
        preds = network(images)
        loss = F.cross_entropy(preds, labels)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        m.track_loss(loss)
        m.track_num_correct(preds, labels)

      m.end_epoch()
    m.end_run()

# when all runs are done, save results to files
m.save('results')