kaggle100/100 [==============================] - 38s 385ms/step - loss: 0.3392 - acc: 0.8556 - val_loss: 0.4208 - val_acc: 0.8357 Epoch 99/100 100/100 [==============================] - 38s 384ms/step - loss: 0.3345 - acc: 0.8528 - val_loss: 0.4641 - val_acc: 0.8185 Epoch 100/100 100/100 [==============================] - 38s 381ms/step - loss: 0.3374 - acc: 0.8516 - val_loss: 0.3796 - val_acc: 0.8382 测试集准确度：0.8894用python的ptint函数输出上面结果猫狗识别

### 猫狗分类模型的训练过程和测试结果输出 在猫狗分类模型的训练过程中，可以使用 `print` 函数将训练和验证阶段的关键指标（如 `loss`、`acc`、`val_loss` 和 `val_acc`）输出到控制台。以下是实现此功能的完整代码示例： ```python import torch import torch.nn as nn from torch.utils.data import DataLoader # 假设定义了以下变量 BATCH_SIZE = 32 EPOCHS = 10 criterion = nn.CrossEntropyLoss() def train(model, device, train_loader, optimizer, epoch): model.train() train_loss = 0.0 correct = 0 for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() optimizer.step() train_loss += loss.item() * data.size(0) pred = output.argmax(dim=1, keepdim=True) correct += pred.eq(target.view_as(pred)).sum().item() train_loss /= len(train_loader.dataset) train_acc = correct / len(train_loader.dataset) print("Train set: Epoch {} average loss: {:.4f}, accuracy: {}/{} ({:.4f}%)".format( epoch, train_loss, correct, len(train_loader.dataset), 100. * train_acc)) return train_loss, 100. * train_acc def val(model, device, val_loader, epoch): model.eval() val_loss = 0.0 correct = 0 with torch.no_grad(): for data, target in val_loader: data, target = data.to(device), target.to(device) output = model(data) val_loss += criterion(output, target).item() * data.size(0) pred = output.argmax(dim=1, keepdim=True) correct += pred.eq(target.view_as(pred)).sum().item() val_loss /= len(val_loader.dataset) val_acc = correct / len(val_loader.dataset) print("\nValidation set: Epoch {} average loss: {:.4f}, accuracy: {}/{} ({:.4f}%)".format( epoch, val_loss, correct, len(val_loader.dataset), 100. * val_acc)) return val_loss, 100. * val_acc # 模拟主函数 if __name__ == "__main__": # 假设定义了模型、设备、数据加载器和优化器 model = None # 替换为实际模型 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") train_loader = DataLoader(...) # 替换为实际训练数据加载器 val_loader = DataLoader(...) # 替换为实际验证数据加载器 optimizer = torch.optim.Adam(model.parameters(), lr=0.001) for epoch in range(1, EPOCHS + 1): train_loss, train_acc = train(model, device, train_loader, optimizer, epoch) val_loss, val_acc = val(model, device, val_loader, epoch) ``` 上述代码中，`train` 函数负责训练阶段的损失和准确率计算，并通过 `print` 输出训练集的平均损失和准确率[^1]。`val` 函数则负责验证阶段的损失和准确率计算，并通过 `print` 输出验证集的平均损失和准确率[^1]。 ### 注意事项 - 在计算 `train_loss` 和 `val_loss` 时，确保乘以 `data.size(0)`（即每个批次的大小），以便正确计算整个数据集的平均损失[^1]。 - 使用 `argmax` 函数获取预测值的最大索引，并与目标值进行比较以计算准确率[^1]。 ### 输出示例 假设训练和验证完成一个 epoch 后，控制台输出如下： ``` Train set: Epoch 1 average loss: 0.6932, accuracy: 875/1000 (87.5000%) Validation set: Epoch 1 average loss: 0.6875, accuracy: 880/1000 (88.0000%) ```