import torch train_data = torch.tensor([[[ 58.]], [[367.]], [[324.]], [[620.]], [[146.]], [[681.]], [[582.]], [[432.]], [[ 87.]], [[415.]], [[443.]], [[680.]], [[ 0.]], [[230.]], [[484.]], [[497.]], [[324.]], [[620.]], [[681.]], [[ 84.]], [[484.]], [[448.]], [[144.]], [[536.]], [[680.]], [[ 0.]]], dtype = torch.float32) class POEM_LSTM(torch.nn.Module): def __init__(self, input_size, hidden_size, num_layers): super(POEM_LSTM, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.LstmLayer = torch.nn.LSTM(input_size=self.input_size, hidden_size=self.hidden_size, num_layers=self.num_layers, bias= False) self.LinearLayer = torch.nn.Linear(in_features=self.hidden_size, out_features=self.input_size) def forward(self, x): LstmLayerOutput, _ = self.LstmLayer(x) # h_c = (h_t, c_t) FinalOutput = self.LinearLayer(LstmLayerOutput) #需要对输出进行圆整，因为onehot为0~681的整数 return FinalOutput poem_lstm = POEM_LSTM(1,3,2) #网络模型实例化 loss = torch.nn.MSELoss() opt = torch.optim.Adam(poem_lstm.parameters(), lr = 0.001) for i in range(200): # input = train_data[0] for j in range(0,26): opt.zero_grad() # 每个iteration梯度清0 output= poem_lstm(torch.tensor([[j]],dtype=torch.float32)) l_loss = loss(output, train_data[j]) l_loss.backward() opt.step() if i == 199: # print(poem_lstm.state_dict()) print(output) print(train_data[j]) print(l_loss) print('\n')

from torch.utils.data import DataLoader train_data = torchvision.datasets.CIFAR10(root="./data", train=True, transform=torchvision.transforms.ToTensor(), download=True) test_data = torchvision.datasets.CIFAR10(root="./data", train=False, transform=torchvision.transforms.ToTensor(), download=True)

这段代码是用 PyTorch 中的 DataLoader 模块加载 CIFAR10 数据集，并将训练集和测试集分别存储在 train_data 和 test_data 中。CIFAR10 是一个经典的图像分类数据集，包含 10 个类别的 60000 张 32x32 像素的彩色...

'''Training script. ''' import os from tqdm import tqdm import torch import torch.nn as nn from torch.utils.data import DataLoader from torch.optim import Adam, lr_scheduler from torchsummary import summary from torchvision import transforms import torch.distributed as dist import torch.multiprocessing as mp from models.resnet50 import ResNet50 from runtime_args import args from load_dataset import LoadDataset from plot import plot_loss_acc from helpers import calculate_accuracy device = torch.device("cuda:0" if torch.cuda.is_available() and args.device == 'gpu' else 'cpu') if not os.path.exists(args.graphs_folder) : os.mkdir(args.graphs_folder) model_save_folder = 'resnet_cbam/' if args.use_cbam else 'resnet/' if not os.path.exists(model_save_folder) : os.mkdir(model_save_folder) def train(gpu, args): '''Init models and dataloaders and train/validate model. ''' rank = args.rank * args.gpus + gpu world_size = args.gpus * args.nodes dist.init_process_group(backend='nccl', init_method='env://', world_size=world_size, rank=rank) model = ResNet50(image_depth=args.img_depth, num_classes=args.num_classes, use_cbam=args.use_cbam) torch.cuda.set_device(gpu) model.cuda(gpu) optimizer = Adam(model.parameters(), lr=args.learning_rate) lr_decay = lr_scheduler.ExponentialLR(optimizer, gamma=args.decay_rate) criterion = torch.nn.CrossEntropyLoss().cuda(gpu) summary(model, (3, 224, 224)) model = nn.parallel.DistributedDataParallel(model, device_ids=[gpu]) train_dataset = LoadDataset(dataset_folder_path=args.data_folder, image_size=args.img_size, image_depth=args.img_depth, train=True, transform=transforms.ToTensor()) test_dataset = LoadDataset(dataset_folder_path=args.data_folder, image_size=args.img_size, image_depth=args.img_depth, train=False, transform=transforms.ToTensor()) train_sampler = torch.utils.data.distributed.DistributedSample

train_sampler = torch.utils.data.distributed.DistributedSampler(dataset) data_loader = torch.utils.data.DataLoader( dataset, batch_size=batch_size, sampler=train_sampler, num_workers=workers, pin...

from data_process import get_data import torch from sklearn.model_selection import train_test_split from LeNet5 import LeNet5 X, y = get_data() # 获取数据【0.025，0.035】100*0.2 = 20 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y) # 数据拆分 print(X_train.shape) #(1075, 227, 227, 1) 0 1 2 3 --- (1075, 1, 227, 227) 0 3 1 2 X_train_tensor = torch.tensor(X_train, dtype=torch.float32).permute(0, 3, 1, 2) # 将数据转成模型要求的形式 print(X_train_tensor.shape) X_test_tensor = torch.tensor(X_test, dtype=torch.float32).permute(0, 3, 1, 2) y_train_tensor = torch.tensor(y_train, dtype=torch.int64) train_ds = torch.utils.data.TensorDataset(X_train_tensor, y_train_tensor) # 将数据转为tensordata类型 train_dl = torch.utils.data.DataLoader(train_ds, batch_size=128, shuffle=True) # 对数据进行分批及打乱操作 network = LeNet5() # 实例化得到一个leNet-5网络模型 loss_fn = torch.nn.CrossEntropyLoss() # 损失函数（交差熵） optimizer = torch.optim.SGD(network.parameters(), lr=0.01) # 优化器 # 模型训练 for epoch in range(1): for image, label in train_dl: y_pre = network(image) # 模型计算(前向传播) loss = loss_fn(y_pre, label) # 计算损失值 network.zero_grad() # 将网络中的所有梯度清零 loss.backward() # 计算梯度项（反向求导） optimizer.step() # 参数优化（模型训练） print('第{}轮训练，当前批次的训练损失值为：{}'.format(epoch, loss.item())) predicted = network(X_test_tensor) # 模型预测 result = predicted.data.numpy().argmax(axis=1) # 预测标签 acc_test = (result == y_test).mean() # 模型测试精度 print(acc_test) torch.save(network.state_dict(), 'leNet5-1.pt') # 保存模型参数

1. 数据准备部分：使用get_data函数获取数据，然后使用train_test_split将数据集划分为训练集和测试集，并将数据转换成模型要求的格式； 2. 模型定义部分：实例化一个LeNet-5网络模型； 3. 损失函数和优化器的...

下面的这段python代码，哪里有错误，修改一下：import numpy as np import matplotlib.pyplot as plt import pandas as pd import torch import torch.nn as nn from torch.autograd import Variable from sklearn.preprocessing import MinMaxScaler training_set = pd.read_csv('CX2-36_1971.csv') training_set = training_set.iloc[:, 1:2].values def sliding_windows(data, seq_length): x = [] y = [] for i in range(len(data) - seq_length): _x = data[i:(i + seq_length)] _y = data[i + seq_length] x.append(_x) y.append(_y) return np.array(x), np.array(y) sc = MinMaxScaler() training_data = sc.fit_transform(training_set) seq_length = 1 x, y = sliding_windows(training_data, seq_length) train_size = int(len(y) * 0.8) test_size = len(y) - train_size dataX = Variable(torch.Tensor(np.array(x))) dataY = Variable(torch.Tensor(np.array(y))) trainX = Variable(torch.Tensor(np.array(x[1:train_size]))) trainY = Variable(torch.Tensor(np.array(y[1:train_size]))) testX = Variable(torch.Tensor(np.array(x[train_size:len(x)]))) testY = Variable(torch.Tensor(np.array(y[train_size:len(y)]))) class LSTM(nn.Module): def init(self, num_classes, input_size, hidden_size, num_layers): super(LSTM, self).init() self.num_classes = num_classes self.num_layers = num_layers self.input_size = input_size self.hidden_size = hidden_size self.seq_length = seq_length self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, num_classes) def forward(self, x): h_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) c_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) # Propagate input through LSTM ula, (h_out, _) = self.lstm(x, (h_0, c_0)) h_out = h_out.view(-1, self.hidden_size) out = self.fc(h_out) return out num_epochs = 2000 learning_rate = 0.001 input_size = 1 hidden_size = 2 num_layers = 1 num_classes = 1 lstm = LSTM(num_classes, input_size, hidden_size, num_layers) criterion = torch.nn.MSELoss() # mean-squared error for regression optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate) # optimizer = torch.optim.SGD(lstm.parameters(), lr=learning_rate) runn = 10 Y_predict = np.zeros((runn, len(dataY))) # Train the model for i in range(runn): print('Run: ' + str(i + 1)) for epoch in range(num_epochs): outputs = lstm(trainX) optimizer.zero_grad() # obtain the loss function loss = criterion(outputs, trainY) loss.backward() optimizer.step() if epoch % 100 == 0: print("Epoch: %d, loss: %1.5f" % (epoch, loss.item())) lstm.eval() train_predict = lstm(dataX) data_predict = train_predict.data.numpy() dataY_plot = dataY.data.numpy() data_predict = sc.inverse_transform(data_predict) dataY_plot = sc.inverse_transform(dataY_plot) Y_predict[i,:] = np.transpose(np.array(data_predict)) Y_Predict = np.mean(np.array(Y_predict)) Y_Predict_T = np.transpose(np.array(Y_Predict))

testX = Variable(torch.Tensor(np.array(x[train_size:len(x)]))) testY = Variable(torch.Tensor(np.array(y[train_size:len(y)]))) # 定义 LSTM 模型 class LSTM(nn.Module): def __init__(self, num_classes, ...

import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset import scipy.io import numpy as np from sklearn.model_selection import train_test_split # 1. 数据加载与预处理 data = scipy.io.loadmat('newdata1_8.mat') train_x = torch.tensor(data['train_x'], dtype=torch.float32) train_y = torch.tensor(data['train_y'], dtype=torch.float32) test_x = torch.tensor(data['test_x'], dtype=torch.float32) test_y = torch.tensor(data['test_y'], dtype=torch.float32) # 数据标准化 mean = train_x.mean(dim=0) std = train_x.std(dim=0) train_x = (train_x - mean) / (std + 1e-8) test_x = (test_x - mean) / (std + 1e-8) # 划分验证集 train_x, val_x, train_y, val_y = train_test_split( train_x.numpy(), train_y.numpy(), test_size=0.2, random_state=42 ) train_x, val_x = torch.tensor(train_x), torch.tensor(val_x) train_y, val_y = torch.tensor(train_y), torch.tensor(val_y) # 创建DataLoader batch_size = 128 train_dataset = TensorDataset(train_x, train_y) val_dataset = TensorDataset(val_x, val_y) test_dataset = TensorDataset(test_x, test_y) train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=batch_size) test_loader = DataLoader(test_dataset, batch_size=batch_size) # 1. 模型定义（替换方案一的模型部分） class ResidualBlock(nn.Module): def init(self, in_channels, out_channels, stride=1): super().init() self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1) self.bn1 = nn.BatchNorm1d(out_channels) self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, padding=1) self.bn2 = nn.BatchNorm1d(out_channels) self.shortcut = nn.Sequential() if stride != 1 or in_channels != out_channels: self.shortcut = nn.Sequential( nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=stride), nn.BatchNorm1d(out_channels) ) def forward(self, x): residual = self.shortcut(x) x = nn.ReLU()(self.bn1(self.conv1(x))) x = self.bn2(self.conv2(x)) x += residual return nn.ReLU()(x) class FatigueResNet(nn.Module): def init(self, num_classes=3): super().init() self.in_channels = 64 self.conv1 = nn.Conv1d(1, 64, kernel_size=7, stride=2, padding=3) self.bn1 = nn.BatchNorm1d(64) self.layer1 = self.make_layer(64, 2, stride=1) self.layer2 = self.make_layer(128, 2, stride=2) self.avgpool = nn.AdaptiveAvgPool1d(1) self.fc = nn.Linear(128, num_classes) def make_layer(self, out_channels, blocks, stride=1): layers = [ResidualBlock(self.in_channels, out_channels, stride)] self.in_channels = out_channels for _ in range(1, blocks): layers.append(ResidualBlock(out_channels, out_channels)) return nn.Sequential(*layers) def forward(self, x): x = x.unsqueeze(1) x = nn.ReLU()(self.bn1(self.conv1(x))) x = self.layer1(x) x = self.layer2(x) x = self.avgpool(x) x = torch.flatten(x, 1) return self.fc(x) # 2. 训练配置（使用混合精度加速） from torch.amp import GradScaler, autocast with autocast(device_type='cuda'): device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = FatigueResNet().to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=1e-3) scaler = GradScaler() scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1) # 3. 修改训练循环（使用混合精度） for epoch in range(50): model.train() train_loss = 0 for inputs, labels in train_loader: inputs, labels = inputs.to(device), labels.to(device) optimizer.zero_grad() with autocast(device_type='cuda'): outputs = model(inputs) loss = criterion(outputs, labels.argmax(dim=1)) scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() train_loss += loss.item() # 验证和测试部分与方案一相同 # 验证 model.eval() val_correct = 0 with torch.no_grad(): for inputs, labels in val_loader: inputs, labels = inputs.to(device), labels.to(device) outputs = model(inputs) preds = outputs.argmax(dim=1) targets = labels.argmax(dim=1) val_correct += (preds == targets).sum().item() val_acc = val_correct / len(val_dataset) scheduler.step(val_acc) print(f'Epoch {epoch+1}: Train Loss={train_loss/len(train_loader):.4f}, Val Acc={val_acc:.4f}') # 保存最佳模型 if val_acc > best_val_acc: best_val_acc = val_acc torch.save(model.state_dict(), 'best_mlp_model.pth') # 5. 测试评估 model.load_state_dict(torch.load('best_mlp_model.pth')) model.eval() test_correct = 0 with torch.no_grad(): for inputs, labels in test_loader: inputs, labels = inputs.to(device), labels.to(device) outputs = model(inputs) preds = outputs.argmax(dim=1) targets = labels.argmax(dim=1) test_correct += (preds == targets).sum().item() test_acc = test_correct / len(test_dataset) print(f'Test Accuracy: {test_acc:.4f}') 此代码报错很多，请帮我修改

from torch.optim import Adam from torchvision.models import resnet18 # 自定义1D ResNet适配器 class ResNet1D(nn.Module): def __init__(self, num_classes): super().__init__() # 使用ImageNet预训练模型...

def load_train_data(src_root, batch_size): train_data=sio.loadmat(src_root) # load mat文件 X_train=train_data[list(train_data.keys())[3]] #原时域信号 X_train=torch.FloatTensor(X_train) X_train=torch.reshape(X_train, [-1,1,2048]) y_train=torch.LongTensor(train_data[list(train_data.keys())[4]]) #标签 y_train=y_train.reshape(-1) # 2D >>> 1D train_dataset = Data.TensorDataset(X_train, y_train) dataloader_source = Data.DataLoader( dataset=train_dataset, # torch TensorDataset format batch_size=batch_size, # mini batch size shuffle=True, # random shuffle for training num_workers=0, # subprocesses for loading data ) return dataloader_source 详细解释一下该代码及其用法，用新手的方式

y_train=torch.LongTensor(train_data[list(train_data.keys())[4]]) # 第5个变量是标签 y_train=y_train.reshape(-1) # 从二维[[1],[0],...]转为一维[1,0,...] 5. **创建数据集** train_dataset = Data....

import os import torch from torch import nn from d2l import torch as d2l 读取数据集首先，下载并提取路径../data/aclImdb中的IMDb评论数据集。 #@save d2l.DATA_HUB['aclImdb'] = ( 'http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz', '01ada507287d82875905620988597833ad4e0903')data_dir = d2l.download_extract('aclImdb', 'aclImdb') #@save def read_imdb(data_dir, is_train): """读取IMDb评论数据集文本序列和标签""" data, labels = [], [] for label in ('pos', 'neg'): folder_name = os.path.join(data_dir, 'train' if is_train else 'test', label) for file in os.listdir(folder_name): with open(os.path.join(folder_name, file), 'rb') as f: review = f.read().decode('utf-8').replace('\n', '') data.append(review) labels.append(1 if label == 'pos' else 0) return data, labels train_data = read_imdb(data_dir, is_train=True) print('训练集数目：', len(train_data[0])) for x, y in zip(train_data[0][:3], train_data[1][:3]): print('标签：', y, 'review:', x[0:60]) train_tokens = d2l.tokenize(train_data[0], token='word') vocab = d2l.Vocab(train_tokens, min_freq=5, reserved_tokens=['']) d2l.set_figsize() d2l.plt.xlabel('# tokens per review') d2l.plt.ylabel('count') d2l.plt.hist([len(line) for line in train_tokens], bins=range(0, 1000, 50)); num_steps = 500 # 序列长度 train_features = torch.tensor([d2l.truncate_pad( vocab[line], num_steps, vocab['']) for line in train_tokens]) print(train_features.shape) train_iter = d2l.load_array((train_features, torch.tensor(train_data[1])), 64) for X, y in train_iter: print('X:', X.shape, ', y:', y.shape) break print('小批量数目：', len(train_iter)) #@save def load_data_imdb(batch_size, num_steps=500): """返回数据迭代器和IMDb评论数据集的词表""" data_dir = d2l.download_extract('aclImdb', 'aclImdb') train_data = read_imdb(data_dir, True) test_data = read_imdb(data_dir, False) train_tokens = d2l.tokenize(train_data[0], token='word') test_tokens = d2l.tokenize(test_data[0], token='word') vocab = d2l.Vocab(train_tokens, min_freq=5) train_features = torch.tensor([d2l.truncate_pad( vocab[line], num_steps, vocab[''])

torch.utils.data.TensorDataset(train_features, torch.tensor(train_data[1])), batch_size, shuffle=True) test_iter = torch.utils.data.DataLoader( torch.utils.data.TensorDataset(test_features, torch....

接下来我会给出一段代码，请理解这段代码的逻辑，并且仿照其将其改变为医疗图像分析代码如下： from future import print_function, division import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.autograd import Variable from torch.utils.data import Dataset from torchvision import transforms, datasets, models from Dataloader import DogCatDataSet # 配置参数 random_state = 1 torch.manual_seed(random_state) # 设置随机数种子，确保结果可重复 torch.cuda.manual_seed(random_state) torch.cuda.manual_seed_all(random_state) np.random.seed(random_state) # random.seed(random_state) epochs = 30 # 训练次数 batch_size = 16 # 批处理大小 num_workers = 4 # 多线程的数目 use_gpu = torch.cuda.is_available() # 对加载的图像作归一化处理，并裁剪为[224x224x3]大小的图像 data_transform = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), ]) train_dataset = DogCatDataSet(img_dir="/mnt/d/深度学习1/train", transform=data_transform) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4) test_dataset = DogCatDataSet(img_dir="/mnt/d/深度学习1/test", transform=data_transform) test_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4) # 加载resnet18 模型， net = models.resnet18(pretrained=False) num_ftrs = net.fc.in_features net.fc = nn.Linear(num_ftrs, 2) if use_gpu: net = net.cuda() print(net) # 定义loss和optimizer cirterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.0001, momentum=0.9) # 开始训练 net.train() for epoch in range(epochs): running_loss = 0.0 train_correct = 0 train_total = 0 for i, data in enumerate(train_loader, 0): inputs, train_labels = data if use_gpu: inputs, labels = Variable(inputs.cuda()), Variable(train_labels.cuda()) else: inputs, labels = Variable(inputs), Variable(train_labels) # inputs, labels = Variable(inputs), Variable(train_labels) optimi

class_counts = torch.bincount(torch.tensor(train_dataset.targets)) class_weights = 1. / class_counts samples_weights = class_weights[train_dataset.targets] sampler = WeightedRandomSampler( weights=...

在处理类别不平衡问题上我的代码是否需要进一步修改from torch.optim import AdamW from torch.utils.data import Subset from torchmetrics.classification import BinaryAccuracy, BinaryF1Score from collections import defaultdict import torch from torch.utils.data import DataLoader from torch.nn.utils.rnn import pad_sequence class_counts = [60, 21] total_samples = sum(class_counts) class_weights = [total_samples / (len(class_counts) * count) for count in class_counts] device = torch.device("cuda" if torch.cuda.is_available() else "cpu") class_weights = torch.tensor(class_weights, dtype=torch.float32).to(device) def train_model(fold, train_idx, val_idx): # 初始化模型 model = AttentionTransformer() criterion = torch.nn.CrossEntropyLoss(weight=class_weights) optimizer = AdamW(model.parameters(), lr=1e-4) # 数据加载 train_subset = Subset(dataset, train_idx) val_subset = Subset(dataset, val_idx) # 处理类别不平衡 class_counts = torch.bincount(torch.tensor([label for _, label in train_subset])) sample_weights = torch.tensor([1 / class_counts[label] for _, label in train_subset]) sampler = WeightedRandomSampler(sample_weights, len(train_idx)) train_loader = DataLoader(train_subset, batch_size=16, sampler=sampler) val_loader = DataLoader(val_subset, batch_size=16)

class_counts = torch.bincount(train_labels) class_weights = 1. / class_counts # 归一化处理（可选） class_weights = class_weights / class_weights.sum() - 需验证是否从训练集统计类别分布 - 建议使用...

import torch import torch.nn as nn from torchtext.datasets import AG_NEWS from torchtext.data.utils import get_tokenizer from torchtext.vocab import build_vocab_from_iterator # 数据预处理 tokenizer = get_tokenizer('basic_english') train_iter = AG_NEWS(split='train') counter = Counter() for (label, line) in train_iter: counter.update(tokenizer(line)) vocab = build_vocab_from_iterator([counter], specials=["<unk>"]) word2idx = dict(vocab.stoi) # 设定超参数 embedding_dim = 64 hidden_dim = 128 num_epochs = 10 batch_size = 64 # 定义模型 class RNN(nn.Module): def init(self, vocab_size, embedding_dim, hidden_dim): super(RNN, self).init() self.embedding = nn.Embedding(vocab_size, embedding_dim) self.rnn = nn.RNN(embedding_dim, hidden_dim, batch_first=True) self.fc = nn.Linear(hidden_dim, 4) def forward(self, x): x = self.embedding(x) out, _ = self.rnn(x) out = self.fc(out[:, -1, :]) return out # 初始化模型、优化器和损失函数 model = RNN(len(vocab), embedding_dim, hidden_dim) optimizer = torch.optim.Adam(model.parameters()) criterion = nn.CrossEntropyLoss() # 定义数据加载器 train_iter = AG_NEWS(split='train') train_data = [] for (label, line) in train_iter: label = torch.tensor([int(label)-1]) line = torch.tensor([word2idx[word] for word in tokenizer(line)]) train_data.append((line, label)) train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True) # 开始训练 for epoch in range(num_epochs): total_loss = 0.0 for input, target in train_loader: model.zero_grad() output = model(input) loss = criterion(output, target.squeeze()) loss.backward() optimizer.step() total_loss += loss.item() * input.size(0) print("Epoch: {}, Loss: {:.4f}".format(epoch+1, total_loss/len(train_data)))改错

train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True) # 开始训练 for epoch in range(num_epochs): total_loss = 0.0 for input, target in train_loader: optimizer...

这段python代码什么意思:import torch import torchvision import torchvision.transforms as transforms import PIL train_transform = transforms.Compose( [transforms.RandomHorizontalFlip(p=0.5), transforms.RandomAffine(degrees=(-5, 5), translate=(0.1, 0.1), scale=(0.9, 1.1), interpolation=PIL.Image.BILINEAR), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) test_transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=train_transform) train_set, val_set = torch.utils.data.random_split(dataset, [40000, 10000]) train_loader = torch.utils.data.DataLoader(train_set, batch_size=128, shuffle=True, num_workers=8) val_loader = torch.utils.data.DataLoader(val_set, batch_size=128, shuffle=False, num_workers=8) test_set = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=test_transform) test_loader = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False, num_workers=8) classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

接着使用 torch.utils.data.random_split() 函数将数据集划分为训练集和验证集，划分的比例为 4:1。 4. 定义了数据加载器。这里使用了 torch.utils.data.DataLoader() 函数来定义数据加载器，分别对训练集、验证集...

import torch import torch.nn as nn import torchvision import torchvision.transforms as transforms from torch.utils.data import DataLoader import matplotlib.pyplot as plt import torch.nn.functional as F from sklearn.metrics import accuracy_score train_dataset=torchvision.datasets.FashionMNIST(root='E:\savething\fashion-mnist-master-data-fashion\data\fashion',train=True,transform=transforms.ToTensor(),download=False) test_dataset=torchvision.datasets.FashionMNIST(root='E:\savething\fashion-mnist-master-data-fashion\data\fashion',train=False,transform=transforms.ToTensor(),download=False) train_loader = DataLoader(train_dataset, batch_size=256, shuffle=True) test_loader = DataLoader(test_dataset, batch_size=256, shuffle=False)为什么无法载入数据

首先，他们导入了必要的库，比如torch、torchvision，还有数据加载相关的模块。然后他们尝试加载训练集和测试集，指定了root路径，transform为ToTensor，download设置为False。接着用DataLoader来载入数据。可能的...

张量移动到GPU# 准备数据集 train_data = torchvision.datasets.CIFAR10( root="./data_taolu", train=True, transform=torchvision.transforms.ToTensor(), download=True ) test_data = torchvision.datasets.CIFAR10( root="./data_taolu", train=False, transform=torchvision.transforms.ToTensor(), download=True ) # 数据统计 train_data_size = len(train_data) test_data_size = len(test_data) print(f"训练数据集长度：{train_data_size}") print(f"测试数据集长度：{test_data_size}") # 数据加载器 train_dataloader = DataLoader(train_data, batch_size=64, shuffle=True) test_dataloader = DataLoader(test_data, batch_size=64) # 模型初始化 model = Jzh() # 确保模型类已修改（见下方model.py）

from torch.utils.data import DataLoader # 定义设备 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") # 准备数据集 train_data = torchvision....

def load_graphdata_channel1(graph_signal_matrix_filename, num_of_hours, num_of_days, num_of_weeks, DEVICE, batch_size, shuffle=True): ''' 从图信号矩阵文件路径中提取文件名（去掉后缀），获取文件所在目录路径。然后构造一个新的文件名，包含小时数、天数、周数等信息。最后打印加载文件的路径 ''' file = os.path.basename(graph_signal_matrix_filename).split('.')[0] dirpath = os.path.dirname(graph_signal_matrix_filename) filename = os.path.join(dirpath, file + '_r' + str(num_of_hours) + '_d' + str(num_of_days) + '_w' + str(num_of_weeks)) +'_astcgn' print('load file:', filename) file_data = np.load(filename + '.npz') train_x = file_data['train_x'] # (10181, 307, 3, 12) train_x = train_x[:, :, 0:1, :] train_target = file_data['train_target'] # (10181, 307, 12) val_x = file_data['val_x'] val_x = val_x[:, :, 0:1, :] val_target = file_data['val_target'] test_x = file_data['test_x'] test_x = test_x[:, :, 0:1, :] test_target = file_data['test_target'] mean = file_data['mean'][:, :, 0:1, :] # (1, 1, 3, 1) std = file_data['std'][:, :, 0:1, :] # (1, 1, 3, 1) # ------- train_loader ------- train_x_tensor = torch.from_numpy(train_x).type(torch.FloatTensor).to(DEVICE) # (B, N, F, T) train_target_tensor = torch.from_numpy(train_target).type(torch.FloatTensor).to(DEVICE) # (B, N, T) train_dataset = torch.utils.data.TensorDataset(train_x_tensor, train_target_tensor) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=shuffle) # ------- val_loader ------- val_x_tensor = torch.from_numpy(val_x).type(torch.FloatTensor).to(DEVICE) # (B, N, F, T) val_target_tensor = torch.from_numpy(val_target).type(torch.FloatTensor).to(DEVICE) # (B, N, T) val_dataset = torch.utils.data.TensorDataset(val_x_tensor, val_target_tensor) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False) 解释下这段代码

X_train_tensor = torch.from_numpy(X_train).float() X_test_tensor = torch.from_numpy(X_test).float() - **自定义Dataset**：继承torch.utils.data.Dataset类，封装数据与标签： python class ...

from model import Model import numpy as np import torch from torchvision.datasets import mnist from torch.nn import CrossEntropyLoss from torch.optim import SGD from torch.utils.data import DataLoader from torchvision.transforms import ToTensor if name == 'main': batch_size = 256 train_dataset = mnist.MNIST(root='./train', train=True, transform=ToTensor()) test_dataset = mnist.MNIST(root='./test', train=False, transform=ToTensor()) train_loader = DataLoader(train_dataset, batch_size=batch_size) test_loader = DataLoader(test_dataset, batch_size=batch_size) model = Model() sgd = SGD(model.parameters(), lr=1e-1) cost = CrossEntropyLoss() epoch = 100 for _epoch in range(epoch): model.train() for idx, (train_x, train_label) in enumerate(train_loader): label_np = np.zeros((train_label.shape[0], 10)) sgd.zero_grad() predict_y = model(train_x.float()) loss = cost(predict_y, train_label.long()) if idx % 10 == 0: print('idx: {}, loss: {}'.format(idx, loss.sum().item())) loss.backward() sgd.step() correct = 0 _sum = 0 model.eval() for idx, (test_x, test_label) in enumerate(test_loader): predict_y = model(test_x.float()).detach() predict_ys = np.argmax(predict_y, axis=-1) label_np = test_label.numpy() _ = predict_ys == test_label correct += np.sum(_.numpy(), axis=-1) _sum += _.shape[0] print('accuracy: {:.2f}'.format(correct / _sum)) torch.save(model, 'models/mnist_{:.2f}.pkl'.format(correct / _sum))

首先，它导入了所需的库和模块，包括了模型定义(Model)、数据集(mnist)、数据加载器(DataLoader)、损失函数(CrossEntropyLoss)、优化器(SGD)以及数据转换(ToTensor)。然后，在if __name__ == '__main...

Spring Data for Pivotal GemFire 是 Spring Data 生态的一部分，专门用于简化基于 Pivotal GemFire（一种分布式内存数据网格，Data Grid）

Spring Data for Pivotal GemFire 是 Spring Data 生态的一部分，专门用于简化基于 Pivotal GemFire（一种分布式内存数据网格，Data Grid）的应用开发。以下从版本特性、核心功能、适用场景等方面进行详细分析：

广州国际汽车展览会网络营销策划方案.pptx

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from torch.utils.data import DataLoader train_data = torchvision.datasets.CIFAR10(root="./data", train=True, transform=torchvision.transforms.ToTensor(), download=True) test_data = torchvision.datasets.CIFAR10(root="./data", train=False, transform=torchvision.transforms.ToTensor(), download=True)

Spring Data for Pivotal GemFire 是 Spring Data 生态的一部分，专门用于简化基于 Pivotal GemFire（一种分布式内存数据网格，Data Grid）

广州国际汽车展览会网络营销策划方案.pptx

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