ASL.py

EPOCHS = 150


for epoch in range(EPOCHS):
  model.train()
  running_loss = 0.0
  for batch_idx, (data, target) in \
  enumerate(zip(inputs_train, np.expand_dims(outputs_train, axis=1))):
    # Get Samples
    data = torch.from_numpy(np.array(data, dtype=np.float32))
    target = torch.from_numpy(np.array(target, dtype=np.int64))
    if cuda:
      data, target = data.cuda(), target.cuda()
    
    # Init
    optimizer.zero_grad()

    # Predict
    y_pred = model(data) 

    # Calculate loss
    loss = criterion(y_pred, target) #;F.cross_entropy(y_pred, target)
    running_loss += loss.cpu().data
    # Backpropagation
    loss.backward()
    optimizer.step()
    

    # Display
    if (batch_idx == inputs_train.shape[0]-1):
      model.eval()
      output = model(torch.from_numpy(np.array(inputs_train, dtype=np.float32)))
      pred = output.data.max(1)[1]
      d = pred.eq(torch.from_numpy(outputs_train))
      accuracy = d.sum().item()/d.size().numel()
      print('\rTrain Epoch: {}/{} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tAccuracy: {}/{}={:.1f}%'.format(
              epoch+1,
              EPOCHS,
              batch_idx+1, 
              inputs_train.shape[0],
              100. * (batch_idx+1) / inputs_train.shape[0], 
              running_loss/inputs_train.shape[0], 
              d.sum(), d.size().numel(), accuracy*100,
              end=''))