64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
# To be deleted.
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import copy
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import torch
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from xlayers.super_core import SuperSequential, SuperMLPv1
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from xlayers.super_core import SuperSimpleNorm
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from xlayers.super_core import SuperLinear
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def optimize_fn(xs, ys, device="cpu", max_iter=2000, max_lr=0.1):
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xs = torch.FloatTensor(xs).view(-1, 1).to(device)
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ys = torch.FloatTensor(ys).view(-1, 1).to(device)
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model = SuperSequential(
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SuperSimpleNorm(xs.mean().item(), xs.std().item()),
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SuperLinear(1, 200),
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torch.nn.LeakyReLU(),
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SuperLinear(200, 100),
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torch.nn.LeakyReLU(),
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SuperLinear(100, 1),
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).to(device)
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model.train()
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optimizer = torch.optim.Adam(
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model.parameters(), lr=max_lr, amsgrad=True
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)
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loss_func = torch.nn.MSELoss()
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lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
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optimizer,
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milestones=[
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int(max_iter * 0.25),
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int(max_iter * 0.5),
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int(max_iter * 0.75),
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],
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gamma=0.3,
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)
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best_loss, best_param = None, None
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for _iter in range(max_iter):
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preds = model(xs)
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optimizer.zero_grad()
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loss = loss_func(preds, ys)
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loss.backward()
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optimizer.step()
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lr_scheduler.step()
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if best_loss is None or best_loss > loss.item():
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best_loss = loss.item()
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best_param = copy.deepcopy(model.state_dict())
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# print('loss={:}, best-loss={:}'.format(loss.item(), best_loss))
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model.load_state_dict(best_param)
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return model, loss_func, best_loss
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def evaluate_fn(model, xs, ys, loss_fn, device="cpu"):
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with torch.no_grad():
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inputs = torch.FloatTensor(xs).view(-1, 1).to(device)
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ys = torch.FloatTensor(ys).view(-1, 1).to(device)
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preds = model(inputs)
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loss = loss_fn(preds, ys)
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preds = preds.view(-1).cpu().numpy()
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return preds, loss.item()
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