使用pytorch编写一个3分类网络模型，数据 集为4维特征，请使用带有特征融合的元学习方法

以下是使用 PyTorch 编写一个带特征融合的元学习方法的3分类网络模型的代码：

import torch
import torch.nn as nn
import torch.optim as optim

class MetaLearner(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(MetaLearner, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.norm1 = nn.BatchNorm1d(hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.norm2 = nn.BatchNorm1d(hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.fc1(x)
        x = self.norm1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.norm2(x)
        x = self.relu(x)
        x = self.fc3(x)
        x = self.sigmoid(x)
        return x


class Classifier(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Classifier, self).__init__()
        self.meta_learner = MetaLearner(input_size, hidden_size, output_size)
        self.fc4 = nn.Linear(input_size, hidden_size)
        self.fc5 = nn.Linear(hidden_size, hidden_size)
        self.fc6 = nn.Linear(hidden_size, output_size)
        self.softmax = nn.Softmax()

    def forward(self, x, x_train, y_train):
        x_train = x_train.detach()
        y_pred_train = self.meta_learner(x_train)
        y_pred_train = (torch.logical_and(y_train, y_pred_train)).float()
        combined_features = torch.cat((x, y_pred_train), dim=1)
        x = self.fc4(combined_features)
        x = self.fc5(x)
        x = self.fc6(x)
        x = self.softmax(x)
        return x


def train_classifier(x_train, y_train, x_val, y_val):
    input_size = x_train.shape[1]
    hidden_size = 10
    output_size = 3
    lr = 0.01
    num_epochs = 100

    classifier = Classifier(input_size, hidden_size, output_size)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(classifier.parameters(), lr=lr)

    for epoch in range(num_epochs):
        optimizer.zero_grad()

        y_pred = classifier(x_train, x_train, y_train)
        loss = criterion(y_pred, y_train)
        loss.backward()
        optimizer.step()

        if epoch % 10 == 0:
            with torch.no_grad():
                val_pred = classifier(x_val, x_train, y_train)
                _, val_pred = torch.max(val_pred, 1)
                val_acc = torch.sum(val_pred == y_val) / y_val.shape[0]
                print(f"Epoch {epoch}, Train Loss: {loss:.4f}, Val Acc: {val_acc:.4f}")

    return classifier

请注意，这只是一个简单的示例代码，您需要根据您的数据和模型结构进行修改。

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