从训练网络开始写内容：Python代码示例

这篇文章提供了一个使用PyTorch训练神经网络的代码示例。代码展示了如何定义网络、优化器、训练和验证过程，以及如何计算损失和准确率。

import torch
import torch.optim as optim
import torch.nn.functional as F
import torch.nn as nn

# 代码中的网络定义、数据加载等部分省略

network = MyNetwork()
optimizer = optim.AdamW(network.parameters(), lr=0.001, weight_decay=1)

num_epochs = 100
for epoch in range(num_epochs):
    running_loss = 0.0
    correct = 0
    total = 0

    # 训练阶段
    network.train()
    for i, input_tensor in enumerate(train_tensors):
        optimizer.zero_grad()

        output = network(input_tensor)

        loss = custom_loss(output, tensor_list[i])

        loss.backward()
        optimizer.step()

        # 统计准确率
        target_similarity = F.cosine_similarity(output, tensor_list[i].unsqueeze(0), dim=1)

        other_similarities = []
        for j, tensor in enumerate(tensor_list):
            if j != i:
                similarity = F.cosine_similarity(output, tensor.unsqueeze(0), dim=1)
                other_similarities.append(similarity)

        other_similarities = torch.cat(other_similarities)

        labels = [torch.tensor([1, 0, 0, 0]), torch.tensor([0, 1, 0, 0]), torch.tensor([0, 0, 1, 0]), torch.tensor([1, 1, 1, 1])]
        label_index = torch.argmax(tensor_list[i])
        label = labels[label_index]

        if target_similarity > torch.max(other_similarities):
            predicted_index = torch.argmax(output)
            if torch.all(torch.eq(label, labels[predicted_index])):
                correct += 1

        total += 1

        running_loss += loss.item()

    # 打印训练信息
    print('Epoch: %d, Loss: %.3f, Training Accuracy: %.2f%%' % (epoch+1, running_loss, 100 * correct / total))

    # 验证阶段
    network.eval()
    val_correct = 0
    val_total = 0

    with torch.no_grad():
        for j, val_input_tensor in enumerate(val_tensors):
            val_output = network(val_input_tensor)

            # 计算相似度
            val_target_similarity = F.cosine_similarity(val_output, tensor_list[j].unsqueeze(0), dim=1)

            val_other_similarities = []
            for k, tensor in enumerate(tensor_list):
                if k != j:
                    similarity = F.cosine_similarity(val_output, tensor.unsqueeze(0), dim=1)
                    val_other_similarities.append(similarity)

            val_other_similarities = torch.cat(val_other_similarities)

            val_labels = [torch.tensor([1, 0, 0, 0]), torch.tensor([0, 1, 0, 0]), torch.tensor([0, 0, 1, 0]), torch.tensor([1, 1, 1, 1])]
            val_label_index = torch.argmax(tensor_list[j])
            val_label = val_labels[val_label_index]

            if val_target_similarity > torch.max(val_other_similarities):
                val_predicted_index = torch.argmax(val_output)
                if torch.all(torch.eq(val_label, val_labels[val_predicted_index])):
                    val_correct += 1

            val_total += 1

    # 打印验证信息
    print('Epoch: %d, Validation Accuracy: %.2f%%' % (epoch + 1, 100 * val_correct / val_total))

这段代码中，首先定义了网络和优化器。然后进行了多个epoch的训练和验证。在每个epoch的训练阶段，通过反向传播和优化器更新网络权重，并计算训练集上的损失和准确率。在每个epoch的验证阶段，计算验证集上的准确率。

希望这次的回答能够满足你的需求！如果还有任何问题，请随时提问。