def evaluate(model, loader, criterion): model.eval() total_loss = 0 all_labels, all_probs = [], [] with torch.no_grad(): for x, y in loader: x, y = x.to(device), y.to(device) outputs = model(x) loss = criterion(outputs, y) total_loss += loss.item() # 获取概率 probs = torch.softmax(outputs, dim=1) all_probs.extend(probs.cpu().numpy()) all_labels.extend(y.cpu().numpy()) all_labels = np.array(all_labels) all_probs = np.array(all_probs) n_classes = all_probs.shape[1] # 计算 AUROC if n_classes == 2: auroc = roc_auc_score(all_labels, all_probs[:, 1]) else: auroc = roc_auc_score( all_labels, all_probs, multi_class='ovr', average='weighted' ) # 计算 AUPRC if n_classes == 2: # 二分类：直接使用正类概率 auprc = average_precision_score(all_labels, all_probs[:, 1]) else: # 多分类：对每个类别计算 PR 曲线面积，再加权平均 from sklearn.preprocessing import label_binarize y_true_bin = label_binarize(all_labels, classes=np.arange(n_classes)) auprc_list = [] for i in range(n_classes): precision, recall, _ = precision_recall_curve( y_true_bin[:, i], all_probs[:, i] ) auprc_i = auc(recall, precision) # 注意顺序：recall在前，precision在后 auprc_list.append(auprc_i) # 按类别样本数加权平均 class_counts = np.sum(y_true_bin, axis=0) auprc = np.average(auprc_list, weights=class_counts) # 其他指标 all_preds = np.argmax(all_probs, axis=1) metrics = { 'accuracy': accuracy_score(all_labels, all_preds), 'f1': f1_score(all_labels, all_preds, average='weighted'), 'auroc': auroc, 'auprc': auprc } return total_loss / len(loader), metrics修改为将预测的结果输出到一个文件夹里

def evaluate(config, model, data_iter, test=False): model.eval() loss_total = 0 predict_all = np.array([], dtype=int) labels_all = np.array([], dtype=int) with torch.no_grad(): for texts, labels in data_iter: outputs = model(texts) loss = F.cross_entropy(outputs, labels) loss_total += loss labels = labels.data.cpu().numpy() predic = torch.max(outputs.data, 1)[1].cpu().numpy() labels_all = np.append(labels_all, labels) predict_all = np.append(predict_all, predic) acc = metrics.accuracy_score(labels_all, predict_all) if test: report = metrics.classification_report(labels_all, predict_all, target_names=config.class_list, digits=4) confusion = metrics.confusion_matrix(labels_all, predict_all) return acc, loss_total / len(data_iter), report, confusion return acc, loss_total / len(data_iter)

这是一个用于模型评估的函数，输入参数包括配置文件config、模型model、数据迭代器data_iter以及一个布尔值test，表示是否进行测试。函数首先将模型设为评估模式（eval()），然后在数据迭代器上进行循环，对每个文本...

def vali_test(config, model, data_iter, LOSS): t = time.time() model.eval() loss_total = 0 output_all = torch.FloatTensor([]).to(config.device) labels_all = torch.FloatTensor([]).to(config.device) with torch.no_grad(): for texts, labels in data_iter: outputs = model(texts) loss = LOSS(outputs, labels) loss_total += loss labels_all = torch.cat((labels_all, labels), 0) output_all = torch.cat((output_all, outputs), 0) loss = loss_total / len(data_iter) print(' ' * 22 + ' | loss: {:.4f}'.format(loss.item()), end='') output_result = evaluate(output_all, labels_all, config.classify_type) print(' | time: {:.4f}s'.format(get_time_dif(t))) return loss.item(), output_result

loss_total 用于累计损失值，output_all 用于存储模型的输出结果，labels_all 用于存储真实的标签。然后，在没有梯度计算的情况下，遍历数据迭代器 data_iter 获取每个batch的输入数据 texts 和标签 ...

解释代码def evaluate_1step_pred(args, model, test_dataset): # Turn on evaluation mode which disables dropout. model.eval() total_loss = 0 with torch.no_grad(): hidden = model.init_hidden(args.eval_batch_size) for nbatch, i in enumerate(range(0, test_dataset.size(0) - 1, args.bptt)): inputSeq, targetSeq = get_batch(args,test_dataset, i) outSeq, hidden = model.forward(inputSeq, hidden) loss = criterion(outSeq.view(args.batch_size,-1), targetSeq.view(args.batch_size,-1)) hidden = model.repackage_hidden(hidden) total_loss+= loss.item() return total_loss / nbatch

首先通过 model.eval() 将模型置于评估模式，禁用了 dropout。然后使用 torch.no_grad() 将梯度计算关闭，提高代码运行效率。在循环中，使用 get_batch() 函数获取输入序列和目标序列，并使用 model.forward() 函数...

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}")

- 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DataLoader。 - num_epochs: 训练的总轮数。 - device: ...

这个代码里用所有的数据输入GCN模型，得到output，然后根据idx_train，idx_val,idx_test分别测试训练、验证和测试精度，但这些数据都已经被模型学习了，会不会存在不合理的情况？之前用unet验证时都是把三个数据集分开的，代码如下：def train(epoch): t = time.time() model.train() optimizer.zero_grad() output = model(features, adj) loss_train = torch.nn.functional.binary_cross_entropy(output[idx_train], labels[idx_train]) # 使用二分类交叉熵损失 acc_train = accuracy(output[idx_train], labels[idx_train]) loss_train.backward() optimizer.step() if not args.fastmode: # Evaluate validation set performance separately, # deactivates dropout during validation run. model.eval() output = model(features, adj) loss_val = torch.nn.functional.binary_cross_entropy(output[idx_val], labels[idx_val]) acc_val = accuracy(output[idx_val], labels[idx_val]) print('Epoch: {:04d}'.format(epoch+1), 'loss_train: {:.4f}'.format(loss_train.item()), 'acc_train: {:.4f}'.format(acc_train.item()), 'loss_val: {:.4f}'.format(loss_val.item()), 'acc_val: {:.4f}'.format(acc_val.item()), 'time: {:.4f}s'.format(time.time() - t)) def test(): model.eval() output = model(features, adj) loss_test = torch.nn.functional.binary_cross_entropy(output[idx_test], labels[idx_test]) acc_test = accuracy(output[idx_test], labels[idx_test]) print("Test set results:", "loss= {:.4f}".format(loss_test.item()), "accuracy= {:.4f}".format(acc_test.item())) # Train model t_total = time.time() for epoch in range(args.epochs): train(epoch) print("Optimization Finished!") print("Total time elapsed: {:.4f}s".format(time.time() - t_total)) # Testing test()

在训练过程中，模型是通过学习训练集的数据来进行优化的，因此在测试过程中使用这些数据进行评估是不合理的，因为模型已经看到了这些数据。为了避免这种情况，通常会将数据集分成三个部分：训练集、验证集和测试集。...

请解释一下这段代码的含义：def validate(val_loader, model, criterion, args): batch_time = AverageMeter('Time', ':6.3f') losses = AverageMeter('Loss', ':.4f') top1 = AverageMeter('Acc@1', ':6.2f') top5 = AverageMeter('Acc@5', ':6.2f') progress = ProgressMeter( len(val_loader), [losses, top1, top5], prefix='Test: ') # switch to evaluate mode model.eval() total_logits = torch.empty((0, args.num_classes)).cuda() total_labels = torch.empty(0, dtype=torch.long).cuda()

1. def validate(val_loader, model, criterion, args):：这是一个函数定义，它接受四个参数 val_loader（验证数据的数据加载器）、 model（模型）、 criterion（损失函数）、 args（包含一些设置参数的...

for e in range(1, epoch + 1): print('[{}/{}] Training'.format(e, epoch)) # train train_loss, train_acc = model.train_model(train_loader, criterion, optimizer) # evaluate test_loss, test_acc = model.evaluate(test_loader, criterion) # 用于判断是否保存模型 is_best = test_acc > best_acc # 记录当前最好的acc best_acc = max(test_acc, best_acc) # 保存模型的文件名 name = 'checkpoint' + '.pth' save_checkpoint({ 'epoch': e, 'state_dict': model.model.state_dict(), 'train_acc': train_acc, 'test_acc': test_acc, 'best_acc': best_acc, 'optimizer': optimizer.state_dict() }, is_best, checkpoint=save_path, filename=name) print('Now acc:') print(test_acc) print('Best acc:') print(best_acc)

这段代码中，train_loader 和 test_loader 是训练集和测试集的 DataLoader 对象，criterion 是损失函数，optimizer 是优化器，save_path 是保存模型的路径。save_checkpoint 函数用于保存模型文件，is_...

import argparse import logging import os import random import sys import torch import torch.nn as nn import torch.nn.functional as F import torchvision.transforms as transforms import torchvision.transforms.functional as TF from pathlib import Path from torch import optim from torch.utils.data import DataLoader, random_split from tqdm import tqdm # 删除 wandb 导入 from evaluate import evaluate from unet import UNet from utils.data_loading import BasicDataset, CarvanaDataset from utils.dice_score import dice_loss dir_img = Path('./data/imgs/') dir_mask = Path('./data/masks/') dir_checkpoint = Path('./checkpoints/') def train_model( model, device, epochs: int = 5, batch_size: int = 1, learning_rate: float = 1e-5, val_percent: float = 0.1, save_checkpoint: bool = True, img_scale: float = 0.5, amp: bool = False, weight_decay: float = 1e-8, momentum: float = 0.999, gradient_clipping: float = 1.0, ): # 1. Create dataset try: dataset = CarvanaDataset(dir_img, dir_mask, img_scale) except (AssertionError, RuntimeError, IndexError): dataset = BasicDataset(dir_img, dir_mask, img_scale) # 2. Split into train/validation n_val = int(len(dataset) * val_percent) n_train = len(dataset) - n_val train_set, val_set = random_split(dataset, [n_train, n_val], generator=torch.Generator().manual_seed(0)) # 3. Create data loaders loader_args = dict(batch_size=batch_size, num_workers=os.cpu_count(), pin_memory=True) train_loader = DataLoader(train_set, shuffle=True, loader_args) val_loader = DataLoader(val_set, shuffle=False, drop_last=True, loader_args) # 删除 wandb 初始化，改为直接打印配置 logging.info(f'''Starting training: Epochs: {epochs} Batch size: {batch_size} Learning rate: {learning_rate} Training size: {n_train} Validation size: {n_val} Checkpoints: {save_chec

def train_model(model, train_loader, val_loader, criterion, optimizer, epochs): best_loss = float('inf') for epoch in range(epochs): # 训练阶段 model.train() train_loss = 0.0 for inputs, ...

找出这段代码错误import tensorflow as tf from tensorflow.keras import layers class GCNModel(tf.keras.Model): def init(self, hidden_dim, output_dim): super(GCNModel, self).init() self.gc1 = GraphConvolution(hidden_dim) self.gc2 = GraphConvolution(output_dim) self.relu = layers.ReLU() self.dropout = layers.Dropout(0.5) self.dense = layers.Dense(1) def call(self, inputs): x, adj = inputs x = self.gc1(x, adj) x = self.relu(x) x = self.dropout(x) x = self.gc2(x, adj) x = self.dense(tf.reduce_mean(x, axis=1)) return x loss_fn = tf.keras.losses.MeanSquaredError() metrics = [tf.keras.metrics.MeanAbsoluteError(), tf.keras.metrics.RootMeanSquaredError()] optimizer = tf.keras.optimizers.Adam(learning_rate=0.01) model = GCNModel(hidden_dim=64, output_dim=32) model.compile(optimizer=optimizer, loss=loss_fn, metrics=metrics) history=model.fit((train_features,train_adj),train_labels,epochs=50,validation_data=((val_features, val_adj), val_labels)) test_scores = model.evaluate((test_features, test_adj), test_labels, verbose=0)

这段代码的错误是缺少GraphConvolution类的定义和导入。需要在代码开头添加类的定义和导入。如果已经定义了GraphConvolution类，则可能是没有导入GraphConvolution类所在的模块。

帮我找出下面代码的错误loss_fn = tf.keras.losses.MeanSquaredError() metrics = [tf.k optimizer = tf.keras.optimizers.Adam(learning_rate=0.01) eras.metrics.MeanAbsoluteError(), tf.keras.metrics.RootMeanSquaredError()] model = GCNModel(hidden_dim=64, output_dim=32) model.compile(optimizer=optimizer, loss=loss_fn, metrics=metrics) history=model.fit((train_features,train_adj),train_labels,epochs=50,validation_data=((val_features, val_adj), val_labels)) test_scores = model.evaluate((test_features, test_adj), test_labels, verbose=0)

这段代码的错误在于第三行中的"tf.k"，应该改为"tf.keras"。正确的是： loss_fn = tf.keras.losses.MeanSquaredError() ...test_scores = model.evaluate((test_features, test_adj), test_labels, verbose=0)

def test(config, model, test_iter): # test model.load_state_dict(torch.load(config.save_path)) model.eval() start_time = time.time() test_acc, test_loss, test_report, test_confusion = evaluate(config, model, test_iter, test=True) msg = 'Test Loss: {0:>5.2}, Test Acc: {1:>6.2%}' print(msg.format(test_loss, test_acc)) print("Precision, Recall and F1-Score...") print(test_report) print("Confusion Matrix...") print(test_confusion) time_dif = get_time_dif(start_time) print("Time usage:", time_dif)怎么实现的

labels_all = np.append(labels_all, labels) predict_all = np.append(predict_all, predic) acc = metrics.accuracy_score(labels_all, predict_all) if test: report = metrics.classification_report...

lstm_model = LSTM(input_dim=1, output_dim=pre_len, hidden_dim=train_window) loss_function = nn.MSELoss() optimizer = torch.optim.Adam(lstm_model.parameters(), lr=0.001) epochs = 10 Train = True # 训练还是预测 if Train: losss = [] lstm_model.train() # 训练模式 start_time = time.time() # 计算起始时间 for i in range(epochs): for seq, labels in train_inout_seq: lstm_model.train() optimizer.zero_grad() y_pred = lstm_model(seq) single_loss = loss_function(y_pred, labels) single_loss.backward() optimizer.step() print(f'epoch: {i:3} loss: {single_loss.item():10.8f}') losss.append(single_loss.detach().numpy()) torch.save(lstm_model.state_dict(), 'save_model.pth') print(f"模型已保存,用时:{(time.time() - start_time) / 60:.4f} min") plt.plot(losss) # 设置图表标题和坐标轴标签 plt.title('Training Error') plt.xlabel('Epoch') plt.ylabel('Error') # 保存图表到本地 plt.savefig('training_error.png') else: # 加载模型进行预测 lstm_model.load_state_dict(torch.load('save_model.pth')) lstm_model.eval() # 评估模式 results = [] reals = [] losss = [] test_inout_seq = create_inout_sequences(test_data_normalized, train_window, pre_len) for seq, labels in train_inout_seq: pred = lstm_model(seq)[0].item() results.append(pred) mae = calculate_mae(pred, labels.detach().numpy()) # MAE误差计算绝对值(预测值 - 真实值) reals.append(labels.detach().numpy()) losss.append(mae) print("模型预测结果：", results) print("预测误差MAE:", losss) plt.style.use('ggplot') # 创建折线图 plt.plot(results, label='real', color='blue') # 实际值 plt.plot(reals, label='forecast', color='red', linestyle='--') # 预测值 # 增强视觉效果 plt.grid(True) plt.title('real vs forecast') plt.xlabel('time') plt.ylabel('value') plt.legend() plt.savefig('test——results.png') 将以上代码改为先训练模型，再进行预测

val_loss = evaluate(model, val_loader, criterion) if val_loss < best_loss: torch.save(model.state_dict(), 'best_model.pth') best_loss = val_loss #### 2. 预测部分 python def predict(model_...

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： ... total += labels.size(0) correct += (predicted == labels).sum().item() accuracy = correct / total print(f"Accuracy on test set: {accuracy:.4f}")

import tensorflow as tf from tensorflow import keras import numpy as np # 加载 IMDB 数据集 imdb = keras.datasets.imdb (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000) # 将整数序列填充为相同的长度 maxlen = 256 train_data = keras.preprocessing.sequence.pad_sequences(train_data, value=0, padding='post', maxlen=maxlen) test_data = keras.preprocessing.sequence.pad_sequences(test_data, value=0, padding='post', maxlen=maxlen) # 构建模型 model = keras.Sequential() model.add(keras.layers.Embedding(10000, 16)) model.add(keras.layers.GlobalAveragePooling1D()) model.add(keras.layers.Dense(16, activation=tf.nn.relu)) model.add(keras.layers.Dense(1, activation=tf.sigmoid)) # 编译模型 model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # 训练模型 history = model.fit(train_data, train_labels, epochs=40, batch_size=512, validation_data=(test_data, test_labels), verbose=1) # 在测试数据上评估模型 results = model.evaluate(test_data, test_labels) print('Test loss:', results[0]) print('Test accuracy:', results[1])优化代码

loss, accuracy = model.evaluate(test_data, test_labels) print(f'Test loss: {loss}, Test accuracy: {accuracy}') 综上所述，我们可以对代码进行如下的优化： from tensorflow import keras import ...

请解释这段代码step = 0 for epoch in range(10): # Switch the model to training mode model.train() for idx, (input_ids, token_type_ids, length, labels) in enumerate(train_loader): logits = model(input_ids, token_type_ids) loss = paddle.mean(loss_fn(logits, labels)) loss.backward() optimizer.step() optimizer.clear_grad() step += 1 print("epoch:%d - step:%d - loss: %f" % (epoch, step, loss)) evaluate(model, metric, dev_loader) paddle.save(model.state_dict(), './ernie_result/model_%d.pdparams' % step)

这段代码是用来训练一个模型的，...此外，在每个 epoch 结束时，代码会调用 evaluate 函数对模型在验证集上的性能进行评估，并将当前模型参数保存到指定路径中。最后，整个训练过程会重复 10 次，即迭代 10 个 epoch。

def evaluate_model(model, test_data,vectorizer): test_vectors = [] for text in test_data['sms']: tokens = bert_tokenize(text) test_vectors.append(" ".join(tokens)) test_vectors = vectorizer.transform(test_vectors) pred_probs = model.predict_proba(test_vectors)[:, 1] fpr, tpr, thresholds = roc_curve(test_data['target'], pred_probs) auc_score = roc_auc_score(test_data['target'], pred_probs) return fpr, tpr, auc_score怎么算出KS值

def evaluate_model(model, test_data,vectorizer): test_vectors = [] for text in test_data['sms']: tokens = bert_tokenize(text) test_vectors.append(" ".join(tokens)) test_vectors = vectorizer....

class MYLSTM: def init(self, seed): self.seed=seed self.valid_ratio=valid_ratio self.mydataset=MyDataset(Dataset)

### 自定义 LSTM 类的实现 ... val_loss = evaluate_model(model, val_loader, criterion, device) print(f"Epoch {epoch+1}, Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}") ---

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class MYLSTM: def init(self, seed): self.seed=seed self.valid_ratio=valid_ratio self.mydataset=MyDataset(Dataset)