test_y = test_data.targets[:500].numpy()

这看起来像是在将 PyTorch 的 Tensor 类型转换为 NumPy 数组类型。可以推断出 `test_data` 是一个 PyTorch 的数据集对象，其中包含了测试数据和测试标签，而 `test_data.targets` 则是测试数据对应的标签。这里的代码将前 500 个测试数据的标签取出来，并将其转换为 NumPy 数组类型，存储在变量 `test_y` 中。

相关问题

import numpy import scipy.special class NeuralNetwork(): def __init__(self,inputnodes,hiddennodes,outputnodes,learningrate): self.inodes=inputnodes self.hnodes=hiddennodes self.onodes=outputnodes self.lr=learningrate self.wih=numpy.random.normal(0.0,pow(self.hnodes,-0.5),(self.hnodes,self.inodes)) self.who=numpy.random.normal(0.0,pow(self.onodes,-0.5),(self.onodes,self.hnodes)) self.activation_function=lambda x:scipy.special.expit(x) pass def train(self,input_list,target_list): inputs=numpy.array(input_list,ndmin=2).T targets=numpy.array(target_list,ndmin=2).T hidden_inputs=numpy.dot(self.wih,inputs) hidden_outputs=self.activation_function(hidden_inputs) final_inputs=numpy.dot(self.who,hidden_outputs) final_outputs=self.activation_function(final_inputs) output_errors=targets-final_outputs hidden_errors=numpy.dot(self.who.T,output_errors) self.who+=self.lr*numpy.dot((output_errors*final_outputs*(1.0-final_outputs)),numpy.transpose(hidden_outputs)) self.wih+=self.lr*numpy.dot((hidden_errors*hidden_outputs*(1.0-hidden_outputs)),numpy.transpose(inputs)) pass def query(self,input_list): inputs=numpy.array(input_list,ndmin=2).T hidden_inputs=numpy.dot(self.wih,inputs) hidden_outputs=self.activation_function(hidden_inputs) final_inputs=numpy.dot(self.who,hidden_outputs) final_outputs=self.activation_function(final_inputs) return final_outputs print('n')写一下注释

以下是对代码的注释：

import numpy
import scipy.special

class NeuralNetwork():
    def __init__(self,inputnodes,hiddennodes,outputnodes,learningrate):
        # 初始化神经网络的输入层、隐藏层、输出层的节点数以及学习率
        self.inodes = inputnodes
        self.hnodes = hiddennodes
        self.onodes = outputnodes
        self.lr = learningrate
        
        # 初始化输入层到隐藏层和隐藏层到输出层的权重
        # 对权重进行随机初始化，取值范围为均值为0，标准差为节点数的负平方根
        self.wih = numpy.random.normal(0.0, pow(self.hnodes, -0.5), (self.hnodes, self.inodes))
        self.who = numpy.random.normal(0.0, pow(self.onodes, -0.5), (self.onodes, self.hnodes))
        
        # 定义激活函数为 sigmoid 函数
        self.activation_function = lambda x: scipy.special.expit(x)
    
    def train(self,input_list,target_list):
        # 将输入列表和目标列表转换为二维数组
        inputs = numpy.array(input_list, ndmin=2).T
        targets = numpy.array(target_list, ndmin=2).T
        
        # 计算隐藏层的输入和输出
        hidden_inputs = numpy.dot(self.wih, inputs)
        hidden_outputs = self.activation_function(hidden_inputs)
        
        # 计算输出层的输入和输出
        final_inputs = numpy.dot(self.who, hidden_outputs)
        final_outputs = self.activation_function(final_inputs)
        
        # 计算输出层误差和隐藏层误差
        output_errors = targets - final_outputs
        hidden_errors = numpy.dot(self.who.T, output_errors)
        
        # 更新隐藏层到输出层和输入层到隐藏层的权重
        self.who += self.lr * numpy.dot((output_errors * final_outputs * (1.0 - final_outputs)), numpy.transpose(hidden_outputs))
        self.wih += self.lr * numpy.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)), numpy.transpose(inputs))
    
    def query(self,input_list):
        # 将输入列表转换为二维数组
        inputs = numpy.array(input_list, ndmin=2).T
        
        # 计算隐藏层的输入和输出
        hidden_inputs = numpy.dot(self.wih, inputs)
        hidden_outputs = self.activation_function(hidden_inputs)
        
        # 计算输出层的输入和输出
        final_inputs = numpy.dot(self.who, hidden_outputs)
        final_outputs = self.activation_function(final_inputs)
        
        # 返回最终输出
        return final_outputs

# 创建神经网络实例
input_nodes = 3
hidden_nodes = 3
output_nodes = 3
learning_rate = 0.3
n = NeuralNetwork(input_nodes,hidden_nodes,output_nodes,learning_rate)

# 使用训练数据进行训练
training_data = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]]
for data in training_data:
    n.train(data, data)

# 使用测试数据进行测试
test_data = [0.2, 0.5, 0.8]
print(n.query(test_data))

这个神经网络实现了一个简单的自编码器，训练数据和测试数据都是由一些简单的数字组成。

LDAM损失函数pytorch代码如下：class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s if weight is not None: weight = torch.FloatTensor(weight).cuda() self.weight = weight self.cls_num_list = cls_num_list def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0)) # 0,1 batch_m = batch_m.view((16, 1)) # size=(batch_size, 1) (-1,1) x_m = x - batch_m output = torch.where(index, x_m, x) if self.weight is not None: output = output * self.weight[None, :] target = torch.flatten(target) # 将 target 转换成 1D Tensor logit = output * self.s return F.cross_entropy(logit, target, weight=self.weight) 模型部分参数如下：# 设置全局参数 model_lr = 1e-5 BATCH_SIZE = 16 EPOCHS = 50 DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') use_amp = True use_dp = True classes = 7 resume = None CLIP_GRAD = 5.0 Best_ACC = 0 #记录最高得分 use_ema=True model_ema_decay=0.9998 start_epoch=1 seed=1 seed_everything(seed) # 数据增强 mixup mixup_fn = Mixup( mixup_alpha=0.8, cutmix_alpha=1.0, cutmix_minmax=None, prob=0.1, switch_prob=0.5, mode='batch', label_smoothing=0.1, num_classes=classes) # 读取数据集 dataset_train = datasets.ImageFolder('/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/train', transform=transform) dataset_test = datasets.ImageFolder("/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/valid", transform=transform_test)# 导入数据 train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True,drop_last=True) test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False) 帮我用pytorch实现模型在模型训练中使用LDAM损失函数

好的，以下是使用LDAM损失函数训练模型的Pytorch代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torchvision import datasets, transforms
from ldam_loss import LDAMLoss

# 定义模型
class MyModel(nn.Module):
    def __init__(self, num_classes):
        super(MyModel, self).__init__()
        self.num_classes = num_classes
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.classifier = nn.Sequential(
            nn.Linear(128 * 4 * 4, 256),
            nn.ReLU(inplace=True),
            nn.Linear(256, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

# 设置超参数
model_lr = 1e-4
BATCH_SIZE = 16
EPOCHS = 50
DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
use_amp = True
use_dp = True
classes = 7
resume = None
CLIP_GRAD = 5.0
Best_ACC = 0
use_ema = True
model_ema_decay = 0.9998
start_epoch = 1
seed = 1

# 设置随机种子
def seed_everything(seed):
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
seed_everything(seed)

# 定义数据增强
transform = transforms.Compose([
    transforms.Resize(224),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(10),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])
transform_test = transforms.Compose([
    transforms.Resize(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

# 定义数据集
dataset_train = datasets.ImageFolder('/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/train', transform=transform)
dataset_test = datasets.ImageFolder("/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/valid", transform=transform_test)

# 定义数据加载器
train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)
test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False)

# 定义模型及优化器
model = MyModel(num_classes=classes).to(DEVICE)
optimizer = torch.optim.Adam(model.parameters(), lr=model_lr)

# 使用LDAM损失函数
cls_num_list = [dataset_train.targets.count(i) for i in range(classes)]
criterion = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, weight=None, s=30)

# 训练模型
for epoch in range(start_epoch, EPOCHS+1):
    model.train()
    for i, (data, target) in enumerate(train_loader):
        data, target = data.to(DEVICE), target.to(DEVICE)
        mixup_data, mixup_target = mixup_fn(data, target)  # 数据增强
        optimizer.zero_grad()
        output = model(mixup_data)
        loss = criterion(output, mixup_target)
        if use_dp:
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD)
        else:
            with amp.scale_loss(loss, optimizer) as scaled_loss:
                scaled_loss.backward()
                torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), CLIP_GRAD)
        optimizer.step()
    if use_ema:
        ema_model = ModelEMA(model, decay=model_ema_decay)
        ema_model.update(model)
    else:
        ema_model = None
    test_acc = test(model, test_loader, DEVICE)
    if test_acc > Best_ACC:
        Best_ACC = test_acc
        save_checkpoint({
            'epoch': epoch,
            'state_dict': model.state_dict(),
            'optimizer': optimizer.state_dict(),
            'Best_ACC': Best_ACC,
        }, is_best=True)