文章目录
- Link
- [Task: Few-shot Classification](#Task: Few-shot Classification)
- Baseline
-
- [Simple---transfer learning](#Simple—transfer learning)
- [Medium --- FO-MAML](#Medium — FO-MAML)
- [Strong --- MAML](#Strong — MAML)
Link
Task: Few-shot Classification
The Omniglot dataset
- background set: 30 alphabets
- evaluation set: 20 alphabets
- Problem setup: 5-way 1-shot classification
Omniglot数据集
- 背景集:30个字母
- 评估集:20个字母
- 问题设置:5-way 1-shot分类
Definition of support set and query set
Baseline
Simple---transfer learning
直接把sample code运行即可
- traing:
对随机选择的5个任务进行正常分类训练验证/测试 - validation / testing:
对五个 Support Images 进行微调，并对Query Images进行推理
Slover首先从训练集中选择5个任务，然后对选择的5个任务进行正常分类训练。在推理中，模型在支持集support set图像上微调inner_train_step步骤，然后在查询集Query Set图像上进行推理。
为了与元学习Slover保持一致，基本Slover具有与元学习Slover完全相同的输入输出格式
python
def BaseSolver(
model,
optimizer,
x,
n_way,
k_shot,
q_query,
loss_fn,
inner_train_step=1,
inner_lr=0.4,
train=True,
return_labels=False,
):
criterion, task_loss, task_acc = loss_fn, [], []
labels = []
for meta_batch in x:
# Get data
support_set = meta_batch[: n_way * k_shot]
query_set = meta_batch[n_way * k_shot :]
if train:
""" training loop """
# Use the support set to calculate loss
labels = create_label(n_way, k_shot).to(device)
logits = model.forward(support_set)
loss = criterion(logits, labels)
task_loss.append(loss)
task_acc.append(calculate_accuracy(logits, labels))
else:
""" validation / testing loop """
# First update model with support set images for `inner_train_step` steps
fast_weights = OrderedDict(model.named_parameters())
for inner_step in range(inner_train_step):
# Simply training
train_label = create_label(n_way, k_shot).to(device)
logits = model.functional_forward(support_set, fast_weights)
loss = criterion(logits, train_label)
grads = torch.autograd.grad(loss, fast_weights.values(), create_graph=True)
# Perform SGD
fast_weights = OrderedDict(
(name, param - inner_lr * grad)
for ((name, param), grad) in zip(fast_weights.items(), grads)
)
if not return_labels:
""" validation """
val_label = create_label(n_way, q_query).to(device)
logits = model.functional_forward(query_set, fast_weights)
loss = criterion(logits, val_label)
task_loss.append(loss)
task_acc.append(calculate_accuracy(logits, val_label))
else:
""" testing """
logits = model.functional_forward(query_set, fast_weights)
labels.extend(torch.argmax(logits, -1).cpu().numpy())
if return_labels:
return labels
batch_loss = torch.stack(task_loss).mean()
task_acc = np.mean(task_acc)
if train:
# Update model
model.train()
optimizer.zero_grad()
batch_loss.backward()
optimizer.step()
return batch_loss, task_accMedium --- FO-MAML
FOMAML（First-Order MAML）是MAML（Model-Agnostic Meta-Learning）的一种简化版本。MAML是一种元学习算法，旨在通过训练模型使其能够在少量新数据上快速适应新任务。FOMAML通过忽略二阶导数来简化MAML的计算过程，从而提高计算效率。它在许多情况下表现良好，尤其是在计算资源有限的情况下。然而，它也可能在某些任务上表现不如完整的MAML。
MAML的核心思想是通过在多个任务上进行训练，使得模型能够在面对新任务时，只需少量数据就能快速收敛到一个好的参数配置。具体来说，MAML的训练过程包括两个层次的优化：
-
内层优化（Inner Loop）：在每个任务上进行少量的梯度更新，以适应该任务。
-
外层优化（Outer Loop）：在所有任务上进行梯度更新，以优化模型的初始参数，使得模型在面对新任务时能够快速适应。
python
""" Inner Loop Update """
grads = torch.autograd.grad(loss, fast_weights.values(), create_graph=False) # create_graph=False：这个参数表示在计算梯度时不创建计算图。在FOMAML中，我们只关心一阶导数，因此不需要创建计算图
fast_weights = OrderedDict(
(name, param - inner_lr * grad)
for ((name, param), grad) in zip(fast_weights.items(), grads)
)
""" Outer Loop Update """
# TODO: Finish the outer loop update
# raise NotimplementedError
meta_batch_loss.backward()
optimizer.step()Strong --- MAML
python
""" Inner Loop Update """
grads = torch.autograd.grad(loss, fast_weights.values(), create_graph=True)
fast_weights = OrderedDict(
(name, param - inner_lr * grad)
for ((name, param), grad) in zip(fast_weights.items(), grads)
)
""" Outer Loop Update """
# TODO: Finish the outer loop update
# raise NotimplementedError
meta_batch_loss.backward()
optimizer.step()