(grad)
| 64 | ) |
| 65 | |
| 66 | def train_one_iter(grad): |
| 67 | grad_tensor = flow.tensor( |
| 68 | grad, |
| 69 | dtype=flow.float32, |
| 70 | requires_grad=False, |
| 71 | device=flow.device(device), |
| 72 | ) |
| 73 | loss = flow.sum(x * grad_tensor) |
| 74 | loss.backward() |
| 75 | ftrl.step() |
| 76 | ftrl.zero_grad() |
| 77 | |
| 78 | for i in range(train_iters): |
| 79 | train_one_iter(random_grad_seq[i]) |