无模型元学习算法——MAML元学习算法

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

class MAML(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, num_layers):
        super(MAML, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x, h):
        out, h = self.lstm(x, h)
        out = self.fc(out[:,-1,:])
        return out, h

def train(model, optimizer, train_data, num_updates=5):
    for i, task in enumerate(train_data):
        x, y = task
        x = x.unsqueeze(0)
        y = y.unsqueeze(0)
        h = None
        for j in range(num_updates):
            optimizer.zero_grad()
            outputs, h = model(x, h)
            loss = nn.CrossEntropyLoss()(outputs, y)
            loss.backward()
            optimizer.step()
        if i % 10 == 0:
            print("Training task {}: loss = {}".format(i, loss.item()))

def test(model, test_data):
    num_correct = 0
    num_total = 0
    for task in test_data:
        x, y = task
        x = x.unsqueeze(0)
        y = y.unsqueeze(0)
        h = None
        outputs, h = model(x, h)
        _, predicted = torch.max(outputs.data, 1)
        num_correct += (predicted == y).sum().item()
        num_total += y.size(1)
    acc = num_correct / num_total
    print("Test accuracy: {}".format(acc))

# Load the mini-ImageNet dataset
train_data = DataLoader(...)
test_data = DataLoader(...)

input_size = ...
hidden_size = ...
output_size = ...
num_layers = ...

# Initialize the MAML model
model = MAML(input_size, hidden_size, output_size, num_layers)

# Define the optimizer
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Train the MAML model
for epoch in range(10):
    train(model, optimizer, train_data)
    test(model, test_data)