解释每一句代码def train(train_loader, model, optimizer, epoch, best_loss): model.train() loss_record2, loss_record3, loss_record4 = AvgMeter(), AvgMeter(), AvgMeter() accum = 0 for i, pack in enumerate(train_loader, start=1): # ---- data prepare ---- images, gts = pack images = Variable(images).cuda() gts = Variable(gts).cuda() # ---- forward ---- lateral_map_4, lateral_map_3, lateral_map_2 = model(images) # ---- loss function ---- loss4 = structure_loss(lateral_map_4, gts) loss3 = structure_loss(lateral_map_3, gts) loss2 = structure_loss(lateral_map_2, gts) loss = 0.5 * loss2 + 0.3 * loss3 + 0.2 * loss4 # ---- backward ---- loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_norm) optimizer.step() optimizer.zero_grad() # ---- recording loss ---- loss_record2.update(loss2.data, opt.batchsize) loss_record3.update(loss3.data, opt.batchsize) loss_record4.update(loss4.data, opt.batchsize) # ---- train visualization ---- if i % 20 == 0 or i == total_step: print('{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], ' '[lateral-2: {:.4f}, lateral-3: {:0.4f}, lateral-4: {:0.4f}]'. format(datetime.now(), epoch, opt.epoch, i, total_step, loss_record2.show(), loss_record3.show(), loss_record4.show()))

keras 自定义loss model.add_loss的使用详解

在构建模型时，为每一层赋予唯一的名称，可以方便地通过 model.load_weights 函数加载预训练权重，即使网络结构有所改变。通过设置 by_name=True 参数，Keras 将根据层名匹配权重，而不是按照默认的顺序加载。...

Neural-network.rar_train neural

在“train_neural”标签下，我们可以推测这些代码主要涉及神经网络的训练过程： 1. **数据预处理（Data Preprocessing）**: 数据清洗、归一化、标准化等，使数据适合输入神经网络。 2. **模型构建（Model ...

def train_and_evaluate(model: torch.nn.Module, optimizer: torch.optim.Optimizer, train_loader: DataLoader, valid_loader: DataLoader, num_epochs: int, device: str): """训练和评估函数""" best_valid_loss = float("inf") for epoch in range(num_epochs): train_loss = train(model, optimizer, train_loader, device) valid_loss = evaluate(model, valid_loader, device) print(f"Epoch [{epoch + 1}/{num_epochs}], Train Loss: {train_loss:.4f}, Valid Loss: {valid_loss:.4f}") if valid_loss < best_valid_loss: best_valid_loss = valid_loss torch.save(model.state_dict(), "best_model.pt") model.load_state_dict(torch.load("best_model.pt")) test_loss = evaluate(model, test_loader, device) print(f"Test Loss: {test_loss:.4f}")

这段代码是一个训练和评估模型的函数。它包含了以下参数： - model: 要训练的PyTorch模型。 - optimizer: 优化器，用于更新模型参数。 - train_loader: 训练数据的DataLoader。 - valid_loader: 验证数据的...

def train(train_dataset, val_dataset, batch_size, epochs, learning_rate, wt_decay, print_cost=True, isPlot=True): # 加载数据集并分割batch train_loader = data.DataLoader(train_dataset, batch_size) # 构建模型 model = FaceCNN() # 加载模型 # model = torch.load('./model/model-best.pth') model.to(device) # 损失函数和优化器 compute_loss = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=learning_rate, weight_decay=wt_decay) loss_values = [] train_acc_values = [] val_acc_values = [] for epoch in range(epochs): loss = 0 model.train() model = model.to(device) for images, labels in train_loader: optimizer.zero_grad() # 梯度清零 outputs = model.forward(images.to(device)) loss = compute_loss(outputs, labels.to(device)) loss.backward() # 更新参数 optimizer.step() # 打印损失值 if print_cost: print('epoch{}: train_loss:'.format(epoch + 1), loss.item()) # 评估模型准确率 if epoch != 0: model.eval() acc_train = validate(model, train_dataset, batch_size) acc_val = validate(model, val_dataset, batch_size) print('acc_train: %.1f %%' % (acc_train * 100)) print('acc_val: %.1f %%' % (acc_val * 100)) train_acc_values.append(acc_train) val_dataset.append(acc_val) loss_values.append(loss.item()) if isPlot: plt.plot(loss_values, label='损失') plt.plot(train_acc_values, label='训练准确率') plt.plot(val_acc_values, label='验证准确率') plt.xlabel('Epoch') plt.ylabel('Value') plt.legend() plt.show() return model

这段代码是用来训练一个模型的函数。它接受训练集、验证集、批次大小、训练轮数、学习率、权重衰减等参数。函数首先加载训练集并构建一个模型对象。然后，定义了损失函数和优化器。接下来，函数通过循环迭代每个训练...

解释代码def train(train_loader, model, optimizer, epoch, best_loss): model.train() loss_record2, loss_record3, loss_record4 = AvgMeter(), AvgMeter(), AvgMeter() accum = 0 for i, pack in enumerate(train_loader, start=1): # ---- data prepare ---- images, gts = pack images = Variable(images).cuda() gts = Variable(gts).cuda() # ---- forward ---- lateral_map_4, lateral_map_3, lateral_map_2 = model(images) # ---- loss function ---- loss4 = structure_loss(lateral_map_4, gts) loss3 = structure_loss(lateral_map_3, gts) loss2 = structure_loss(lateral_map_2, gts) loss = 0.5 * loss2 + 0.3 * loss3 + 0.2 * loss4 # ---- backward ---- loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_norm) optimizer.step() optimizer.zero_grad() # ---- recording loss ---- loss_record2.update(loss2.data, opt.batchsize) loss_record3.update(loss3.data, opt.batchsize) loss_record4.update(loss4.data, opt.batchsize) # ---- train visualization ---- if i % 20 == 0 or i == total_step: print('{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], ' '[lateral-2: {:.4f}, lateral-3: {:0.4f}, lateral-4: {:0.4f}]'. format(datetime.now(), epoch, opt.epoch, i, total_step, loss_record2.show(), loss_record3.show(), loss_record4.show())) print('lr: ', optimizer.param_groups[0]['lr']) save_path = 'snapshots/{}/'.format(opt.train_save) os.makedirs(save_path, exist_ok=True) if (epoch+1) % 1 == 0: meanloss = test(model, opt.test_path) if meanloss < best_loss: print('new best loss: ', meanloss) best_loss = meanloss torch.save(model.state_dict(), save_path + 'TransFuse-%d.pth' % epoch) print('[Saving Snapshot:]', save_path + 'TransFuse-%d.pth'% epoch) return best_loss

这段代码是用于训练一个图像分割模型的主函数。首先，将模型设置为训练模式，然后对于每个批次的数据进行循环。从数据包中获取图像和标注，并将它们转换为可变变量并送入GPU中。然后，通过模型进行前向传播，得到三...

def train(train_loader, model, optimizer, epoch, best_loss): model.train() loss_record2, loss_record3, loss_record4 = AvgMeter(), AvgMeter(), AvgMeter() accum = 0 for i, pack in enumerate(train_loader, start=1): # ---- data prepare ---- images, gts = pack images = Variable(images).cuda() gts = Variable(gts).cuda() # ---- forward ---- lateral_map_4, lateral_map_3, lateral_map_2 = model(images) # ---- loss function ---- loss4 = structure_loss(lateral_map_4, gts) loss3 = structure_loss(lateral_map_3, gts) loss2 = structure_loss(lateral_map_2, gts) loss = 0.5 * loss2 + 0.3 * loss3 + 0.2 * loss4 # ---- backward ---- loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_norm) optimizer.step() optimizer.zero_grad() # ---- recording loss ---- loss_record2.update(loss2.data, opt.batchsize) loss_record3.update(loss3.data, opt.batchsize) loss_record4.update(loss4.data, opt.batchsize) # ---- train visualization ---- if i % 400 == 0 or i == total_step: print('{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], ' '[lateral-2: {:.4f}, lateral-3: {:0.4f}, lateral-4: {:0.4f}]'. format(datetime.now(), epoch, opt.epoch, i, total_step, loss_record2.show(), loss_record3.show(), loss_record4.show())) print('lr: ', optimizer.param_groups[0]['lr']) save_path = 'snapshots/{}/'.format(opt.train_save) os.makedirs(save_path, exist_ok=True) if (epoch+1) % 1 == 0: meanloss = test(model, opt.test_path) if meanloss < best_loss: print('new best loss: ', meanloss) best_loss = meanloss torch.save(model.state_dict(), save_path + 'TransFuse-%d.pth' % epoch) print('[Saving Snapshot:]', save_path + 'TransFuse-%d.pth'% epoch) return best_loss

这段代码是一个训练函数，接受一个训练数据集(train_loader)，一个模型(model)，一个优化器(optimizer)，当前的训练周期(epoch)和最佳loss(best_loss)。第一行定义了函数名和参数，其中"loss_record2, loss_record...

def train(): # 训练 print('Start training ===========================================>') best_epo = -1 max_pck = -1 cur_lr = learning_rate print('Learning Rate: {}'.format(learning_rate)) for epoch in range(1, epochs + 1): print('Epoch[{}/{}] ==============>'.format(epoch, epochs)) model.train() train_loss = [] for step, (img, label, img_name, w, h) in enumerate(train_loader): label = torch.stack([label] * 6, dim=1) # bz * 6 * 21 * 46 * 46 if cuda: img = img.cuda() label = label.cuda() optimizer.zero_grad() pred_maps = model(img) # (FloatTensor.cuda) size:(bz,6,21,46,46) loss = sum_mse_loss(pred_maps, label) # total loss loss.backward() optimizer.step() if step % 100 == 0: print('STEP: {} LOSS {}'.format(step, loss.item())) loss_final = sum_mse_loss(pred_maps[:, -1, ...].cpu(), label[:, -1, ...].cpu()) train_loss.append(loss_final) # save sample image save_images(label[:, -1, ...].cpu(), pred_maps[:, -1, ...].cpu(), epoch, img_name, save_dir) # eval model after one epoch eval_loss, cur_pck = eval(epoch, mode='valid') print('EPOCH {} Valid PCK {}'.format(epoch, cur_pck)) print('EPOCH {} TRAIN_LOSS {}'.format(epoch, sum(train_loss)/len(train_loss))) print('EPOCH {} VALID_LOSS {}'.format(epoch, eval_loss)) if cur_pck > max_pck: torch.save(model.state_dict(), os.path.join(save_dir, 'best_model.pth')) max_pck = cur_pck best_epo = epoch print('Current Best EPOCH is : {}\n**********\n'.format(best_epo)) torch.save(model.state_dict(), os.path.join(save_dir, 'final_epoch.pth')) if epoch % lr_decay_epoch == 0: cur_lr /= 2 update_lr(optimizer, cur_lr) print('Train Done!') print('Best epoch is {}'.format(best_epo))

train()是一个函数，具体实现的内容需要根据上下文和代码细节来确定。一般来说，train()函数用于训练机器学习模型，包括读取数据、构建模型、设置优化器和损失函数、进行训练迭代等步骤。在训练过程中，train()函数...

采用def train_one_epoch，def train这种结构代码怎么设置动态学习率

如果你的代码采用了类似于def train_one_epoch和def train这样的结构，你可以在train函数中通过以下方式来设置动态学习率： 1. 在初始化optimizer时，设置初始学习率为一个变量，比如init_lr。 python ...

net = LeNet5() paddle.summary(net,(-1,1,img_size,img_size)) from paddle.metric import Accuracy save_dir = "model/lenet_2" epoch = 5 lr = 0.01 weight_decay = 5e-4 batch_size = 64 model = paddle.Model(net) optim = paddle.optimizer.Adam(learning_rate=lr,parameter=model.parameters(),weight_decay=weight_decay) model.prepare(optim,paddle.nn.CrossEntropyloss(),paddle.nn.Accuracy()) model.fit(train_dataset,epochs=epoch,batch_size=batch_size,save_dir=save_dir,verbose=1) best_model_path = "model/lenet_2/final.pdparams" net = LeNet5() model = paddle.Model(net) model.load(best_model_path) model.prepare(optim,paddle.nn.CrossEntropyloss(),Accuracy()) results = model.evaluate(test_dataset,batch_size=batch_size,verbose=1) print(results)在pytorch中如何表示

在 PyTorch 中，上述代码可以表示为： python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader class LeNet5(nn.Module): def __init__(self): ...

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： python import torch import torch.nn as nn import torch.nn.functional as F import numpy as np from torchvision import datasets, transforms from ...

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) 帮我用pytorch实现模型在模型训练中使用LDAM损失函数

loss.backward() nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD) # 防止梯度爆炸 optimizer.step() model.eval() with torch.no_grad(): total = 0 correct = 0 for data, target in test_...

现在我有三张DGL异质图，train_g_data = { ('drug', 'interacts', 'drug'): (train_edge_idx[0], train_edge_idx[1]), ('drug', 'interacts_reverse', 'drug'): (train_edge_idx[1], train_edge_idx[0]), ('drug', 'acts_on', 'protein'): (drug_protein[0], drug_protein[1]), } train_g = dgl.heterograph(train_g_data)，val_g和test_g也是类似的结构，图的特征也已经添加，请你用python写一段代码，用DGL中的graphSAGE模型训练，用以预测interacts的属性DDI_feat即药物相互作用分数，这是一个回归任务，并解释一下原理

best_model = train(model, optimizer, loss_fn, train_loader, val_loader, device, epochs=100) test_loss = evaluate(best_model, loss_fn, test_loader, device) print('Test Loss {:.4f}'.format(test_loss)) ...

编写pytorch代码，定义LSTMAttention模型，定义一个CPSO优化算法，用FA_CPSO算法有优化模型中神经元个数、dropout比率、batch_size、学习率等超参数，将优化好的超参数传递给模型，在特征训练集X_train.csv和标签训练集y_train.csv上训练模型，将最优的参数设置给模型，在特征测试集X_test.csv和标签测试集y_test.csv上测试模型，进一步优化模型，将优化后的参数设置给模型，并输出测试损失，绘制测试集的预测值和实际值，计算测试集的均方根误差，在预测集上进行预测，设定标签数据最大值的85%为警戒线，绘制预测集的实际值到达的时间和预测值到达的时间

loss.backward() optimizer.step() # 计算测试误差 y_pred = model(torch.tensor(X_test.values).float()) test_loss = mean_squared_error(y_test, y_pred.detach().numpy()) return test_loss # 定义参数...

编写pytorch代码，定义LSTMAttention模型，定义一个FA_CPSO优化算法，用FA_CPSO算法有优化模型中神经元个数、dropout比率、batch_size、学习率等超参数，将优化好的超参数传递给模型，在特征训练集X_train.csv和标签训练集y_train.csv上训练模型，将最优的参数设置给模型，在特征测试集X_test.csv和标签测试集y_test.csv上测试模型，进一步优化模型，将优化后的参数设置给模型，并输出测试损失，绘制测试集的预测值和实际值，计算测试集的均方根误差，在预测集上进行预测，设定标签数据最大值的85%为警戒线，绘制预测集的实际值到达的时间和预测值到达的时间

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=best_batch_size, shuffle=True) for epoch in range(100): for i, (inputs, labels) in enumerate(train_loader): inputs = inputs.to...

构建一个全连接网络，网络中只允许存在Linear层(层数不得超过5)，可以调节的超参数为learning_rate和epoch(eopch不得超过50)。使用FashionMNIST数据集训练并测试网络的精度，将最优(在测试集上的精度)的模型保存为model.pth文件，

print(f'Epoch {epoch+1}/{max_epochs}, Loss: {running_loss / len(train_loader):.4f}, Accuracy: {accuracy:.2f}%') if accuracy > best_accuracy: best_accuracy = accuracy torch.save(net.state_dict(), ...

可以将seg.train转化成未封装好的代码吗

def train(model: torch.nn.Module, optimizer: torch.optim.Optimizer, data_loader: DataLoader, device: str): """训练函数""" model.train() loss_func = torch.nn.BCELoss(reduction="none") total_loss =...

给出对定义好的UNET网络model的训练代码，其中训练集原始图像"data\train\trainvol""，标签"data\train\trainseg"，验证集原始图像"data\val\valvol"，标签"data\val\valseg"

def train(model, criterion, optimizer, train_loader, val_loader, num_epochs): best_loss = float('inf') for epoch in range(num_epochs): model.train() train_loss = 0.0 for data, target in train_...

用于训练pkuseg的train函数的未封装原代码

print(f'Epoch: [{epoch+1}/{config.epoch}] Step: [{i+1}/{len(train_loader)}] Loss: {loss.item():.6f}') # 在开发集上评估模型 f1 = evaluate(model, dev_loader) print(f'Epoch: [{epoch+1}/{config.epoch...

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