pytorch实现L1正则化的交叉熵损失函数

在PyTorch中，可以使用以下代码实现L1正则化的交叉熵损失函数：

import torch
import torch.nn as nn

def l1_regularization(parameters, lambda_=0.01):
    """Compute L1 regularization loss.
    :param parameters: Model parameters
    :param lambda_: Regularization strength
    :return: L1 regularization loss
    """
    l1_reg = 0
    for param in parameters:
        l1_reg += torch.norm(param, p=1)
    return lambda_ * l1_reg

def cross_entropy_loss(output, target):
    """Compute cross entropy loss.
    :param output: Model output
    :param target: Ground truth target
    :return: Cross entropy loss
    """
    criterion = nn.CrossEntropyLoss()
    return criterion(output, target)

def l1_cross_entropy_loss(output, target, parameters, lambda_=0.01):
    """Compute L1 regularization and cross entropy loss.
    :param output: Model output
    :param target: Ground truth target
    :param parameters: Model parameters
    :param lambda_: Regularization strength
    :return: L1 regularization and cross entropy loss
    """
    l1_loss = l1_regularization(parameters, lambda_)
    ce_loss = cross_entropy_loss(output, target)
    return l1_loss + ce_loss

在训练模型时，可以将这个损失函数传递给优化器。