for epoch in range(1, num_epochs + 1): train(model, device, train_loader, optimizer, epoch) test_loss, test_acc, pred, target = test(model, device, test_loader) Recall = recall_score(target, pred, average='macro') Precision = precision_score(target, pred, average='macro') F1_score = f1_score(target, pred, average='macro') kappa_score=cohen_kappa_score(target, pred) test_Loss_list.append(test_loss) test_Accuracy_list.append(test_acc) Recall_list.append(Recall) Precision_list.append(Precision) F1_score_list.append(F1_score) kappa_list.append(kappa_score) Loss.append(test_Loss_list) Accuracy.append(test_Accuracy_list) Recall_test.append(Recall_list) Precision_test.append(Precision_list) F1_score_test.append(F1_score_list) kappa_test.append(kappa_list)

pytorch_practice_code:练习pytorch的基本代码。深度学习最重要的工具之一

for epoch in range(num_epochs): for inputs, labels in dataloader: optimizer.zero_grad() # 清零梯度 outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() # 反向传播 optimizer....

pytorch 模型的train模式与eval模式实例

for epoch in range(num_epochs): model.train() # 进入训练模式 for data, target in train_loader: optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() ...

for epoch in range(num_epochs): model.train() train_loss = 0 train_mae = 0 for batch_data, batch_target in train_dataloader: optimizer.zero_grad() output = model(batch

print(f"Epoch {epoch+1}/{num_epochs}, Train Loss: {train_loss:.4f}, Train MAE: {train_mae:.4f}, Eval Loss: {eval_loss:.4f}, Eval MAE: {eval_mae:.4f}") if eval_loss < best_eval_loss: best_eval_loss ...

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}")

- num_epochs: 训练的总轮数。 - device: 训练使用的设备（GPU或CPU）。在训练过程中，函数会迭代每个epoch，并在每个epoch完成后打印训练和验证损失。如果当前的验证损失比之前最佳的验证损失更小，就会保存...

ef train(model, train_loader, criterion, optimizer, num_epochs): total_step = len(train_loader) for epoch in range(num_epochs): for i, (features, labels) in enumerate(train_loader): outputs = model(features.permute(0, 2, 1)) loss = criterion(outputs, labels.unsqueeze(1)) optimizer.zero_grad() loss.backward() optimizer.step() if (i + 1) % 100 == 0: print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))

在每个 epoch 中，它首先使用 train_loader 加载训练数据集中的数据，然后对于每个 batch，它计算模型的输出，并计算损失函数值。然后使用 optimizer 进行反向传播和优化器更新。在每个 100 个 batch 的时候，它会...

for epoch in range(num_epochs): model.train() for images, labels in train_loader: optimizer.zero_grad() outputs = model(images) loss = criterion(outputs, labels) loss.backward() optimizer.step()出现错误TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'PIL.Image.Image'>

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn) 这个 collate_fn 首先将 PIL.Image.Image 转换成 numpy 数组，然后再将其转换成 ...

解释一下这个代码num_epochs = 500 batch_size = 2048 num_samples = x_train_tensor.size(0) num_batches = num_samples // batch_size for epoch in range(num_epochs): for i in range(num_batches): start_idx = i * batch_size end_idx = (i + 1) * batch_size inputs = x_train_tensor[start_idx:end_idx] labels = y_train_tensor[start_idx:end_idx] optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs.squeeze(), labels) loss.backward() optimizer.step()

train the model for 10 epochs num_epochs = 10 for epoch in range(num_epochs): # train for one epoch, printing every 10 iterations torchvision.models.detection.train_one_epoch(model, optimizer, data_loader, device=torch.device('cuda')) # update the learning rate lr_scheduler.step() # evaluate on the test dataset every epoch torchvision.models.detection.evaluate(model, data_loader, device=torch.device('cuda'))详细写出上述代码中的train_one_epoch和evaluate函数，给出代码注释

print(f"Epoch [{epoch+1}/{num_epochs}], Batch [{i}/{len(data_loader)}], Train Loss: {losses.item():.4f}") # 计算平均训练损失 train_loss /= len(data_loader) # 返回平均训练损失 return train_loss ...

model = RotatE(num_entities=num_entities, num_relations=num_relations, embedding_dim=embedding_dim) optimizer = torch.optim.Adam(model.parameters(), lr=0.001) for epoch in range(num_epochs): for pos_heads, pos_relations, pos_tails, neg_heads, neg_relations, neg_tails in train_data_loader: optimizer.zero_grad() loss = model.nssa_loss(pos_heads, pos_relations, pos_tails, neg_heads, neg_relations, neg_tails) loss.backward() optimizer.step() print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))对示例中的数据集给出示范

print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item())) 在这个示例中，batch_size 和 num_epochs 都是训练的超参数。pos_heads, pos_relations, pos_tails, neg_heads, neg_...

for epoch in range(num_epochs): for i, (batch_features, batch_labels) in enumerate(train_dataset): logits = net(batch_features) loss = loss_fn(logits, batch_labels) optimizer.clear_gradients() loss.backward() optimizer.step() print("Epoch [{}/{}], Loss: {:.4f}".format(epoch+1, num_epochs, loss.asnumpy()))这段代码会报错，基于mindspore写的

for epoch in range(n_epochs): running_loss = 0.0 correct_train = 0 correct_val = 0 # 训练集 for i, (inputs, labels) in enumerate(train_loader, 0): optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() _, predicted = torch.max(outputs.data, 1) correct_train += (predicted == labels).sum().item() train_acc.append(correct_train / len(train_dataset)) train_loss.append(running_loss / len(train_loader))

这是一个用 Python 语言写出的 for 循环。它的作用是在一个神经网络训练过程中迭代地进行多次训练，即一个 epoch。在循环开始时，定义了三个变量：running_loss、correct_train 和 correct_val，都初始化为 0。在...

# 模型训练 for epoch in range(num_epochs): total_loss = 0.0 for batch_idx, (head, tail, relation) in enumerate(train_loader): head, tail, relation = head.to(device), tail.to(device), relation.to(device) optimizer.zero_grad() loss = model(head, tail, relation) loss.backward() optimizer.step() total_loss += loss.item() print("Epoch {}, Loss {:.4f}".format(epoch+1, total_loss/len(train_loader)))报错AttributeError: 'tuple' object has no attribute 'to'

for batch_idx, batch_data in enumerate(train_loader): head, tail, relation = batch_data[0].to(device), batch_data[1].to(device), batch_data[2].to(device) 这样就可以将元组中的每个张量都转移到GPU...

def train(net, train_features, train_labels, test_features, test_labels, num_epochs, learning_rate, weight_decay, batch_size): train_ls, test_ls = [], [] train_iter = d2l.load_array((train_features, train_labels), batch_size) optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=weight_decay) for epoch in range(num_epochs): for X, y in train_iter: optimizer.zero_grad() l = loss(net(X), y) l.backward() optimizer.step() train_ls.append(log_rmse(net, train_features, train_labels)) if test_labels is not None: test_ls.append(log_rmse(net, test_features, test_labels)) return train_ls, test_ls 逐行解释一下代码

6. for epoch in range(num_epochs):：对于每个训练轮次，执行以下操作： 7. for X, y in train_iter:：对于每个小批量数据，执行以下操作： 8. optimizer.zero_grad()：将优化器的梯度缓存清零，以便进行...

# Train VAE model vae.train() for epoch in range(num_epochs): for i, (images, _) in enumerate(train_loader): # Flatten images images = images.view(images.size(0), -1) # Forward pass x_hat, z_mean, z_logvar = vae(images) # Compute loss loss = loss_fn(x_hat, images, z_mean, z_logvar) # Backward pass optimizer.zero_grad() loss.backward() optimizer.step() # Print loss every 100 batches if i % 100 == 0: print('Epoch [{}/{}], Batch [{}/{}], Loss: {:.4f}' .format(epoch + 1, num_epochs, i + 1, len(train_loader), loss.item())) torch.save(vae.state_dict(),'model/vae.pkl')修改代码降低损失

1. 调整学习率：尝试减小学习率，增加训练次数，这有可能会使模型更加收敛。 2. 增加训练数据：增加训练数据量可以帮助模型更好地学习数据分布，从而提高模型的泛化能力。 3. 调整损失函数：尝试使用其他的损失...

描述这段代码 #定义训练网络函数，网络，损失评价，训练集 def train(net, trainloader, criterion, optimizer, num_epochs, device, num_print, lr_scheduler=None, testloader=None): net.train() record_train = list() record_test = list() for epoch in range(num_epochs): print("========== epoch: [{}/{}] ==========".format(epoch + 1, num_epochs)) total, correct, train_loss = 0, 0, 0 start = time.time() for i, (X, y) in enumerate(trainloader): X, y = X.to(device), y.to(device) output = net(X) loss = criterion(output, y) optimizer.zero_grad() loss.backward() optimizer.step() train_loss += loss.item() total += y.size(0) correct += (output.argmax(dim=1) == y).sum().item() train_acc = 100.0 * correct / total if (i + 1) % num_print == 0: print("step: [{}/{}], train_loss: {:.3f} | train_acc: {:6.3f}% | lr: {:.6f}" \ .format(i + 1, len(trainloader), train_loss / (i + 1), \ train_acc, get_cur_lr(optimizer))) if lr_scheduler is not None: lr_scheduler.step() print("--- cost time: {:.4f}s ---".format(time.time() - start)) if testloader is not None: record_test.append(test(net, testloader, criterion, device)) record_train.append(train_acc) return record_train, record_test def get_cur_lr(optimizer): for param_group in optimizer.param_groups: return param_group['lr'] #定义保存网络参数的函数 def save(net,path): torch.save(net.state_dict(), path)

这段代码定义了一个训练神经网络的函数...最后返回训练准确率和测试准确率分别随epoch变化的记录列表record_train和record_test。还定义了一个保存网络参数的函数save，用于将训练好的网络模型参数保存到指定路径。

解释下面这段代码def do_train( model, data_loader, criterion, optimizer, scheduler, metric ): model.train() global_step = 0 tic_train = time.time() log_steps=100 for epoch in range(num_train_epochs): losses = [] for step,sample in enumerate(data_loader): print(sample) # 表示从样本中获取 input_ids 和 token_type_ids。 input_ids = sample["input_ids"] token_type_ids = sample["token_type_ids"] # 表示使用模型进行前向计算，得到预测结果。 outputs = model(input_ids=input_ids, token_type_ids=token_type_ids) print(outputs)

在函数内部，首先将模型设置为训练模式（model.train()）。然后开始迭代训练数据，每次迭代都计算模型的损失并更新模型的参数。具体来说，它会进行以下循环： - 对于每个 epoch，都会创建一个空的 losses 列表。 - ...

# 训练RNN for epoch in range(num_epochs): # 设置合适的迭代次数 # ... (在这里编写你的训练步骤，如分批处理数据、前向传播、反向传播等)python代码

validation_loss = calculate_validation_loss(test_data, rnn_model, device) # 使用测试集评估 print(f"Epoch {epoch}: Train Loss: {loss.item()}, Validation Loss: {validation_loss}") 这里的...

def train(dev, data_loader_train, data_loader_test, in_dim, h_dim, n_layers, n_classes, n_epochs, l_rate): model_trained = RNN(in_dim, h_dim, n_layers, n_classes).to(dev) criterion = nn.CrossEntropyLoss().to(dev) optimizer = torch.optim.Adam(model_trained.parameters(), lr=l_rate) rate_list = [] # 损失函数采用交叉熵、优化器采用的是Adam、训练过程中，逐epoch逐step,输出训练得到的损失函数loss情况,同时每个epoch结束，用测试数据进行测试，计算当前模型的分类准确率 # total_step = len(data_loader_train) for epoch in range(n_epochs): for i, (images, labels) in enumerate(data_loader_train): image = images.float().to(dev) image = image.permute(0, 2, 1) labels = labels.long().to(dev) outputs = model_trained(image) loss = criterion(outputs, labels) optimizer.zero_grad() loss.backward() optimizer.step() # if (i + 1) % 5 == 0: # print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' # .format(epoch + 1, num_epochs, i + 1, total_step, loss.item())) rate_correct = test(model_trained, data_loader_test, epoch) # 每迭代一轮，都测试并输出当前模型的识别准确率 rate_list.append(rate_correct) torch.save(model_trained.state_dict(), 'savemodel.dic') return rate_list

这是一个使用PyTorch实现的RNN（循环神经...函数在训练过程中会输出每个epoch的损失函数loss，并使用测试数据计算当前模型的分类准确率。最后，函数会返回每个epoch的分类准确率的列表，并将训练好的模型保存到本地。

for epoch in range(resume_epoch, num_epochs)

在这个代码片段中，for循环用于遍历从resume_epoch到num_epochs的每一个epoch。在每个epoch中，通过调用train_dataloader来获取训练数据的每个batch，并使用训练步骤(training_step)来计算出一个out。然后通过...

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