在deepseek 回答中，继续深挖。
迭代和实验：深度学习是高度实验性的。系统地改变超参数（学习率、批大小、层数等），并使用 TensorBoard 或 Weights & Biases 等工具来跟踪和可视化你的实验结果。
Q：【deepseek】作为人工智能专家，TensorBoard 或 Weights & Biases 等工具来跟踪和可视化你的实验结果。具体怎么DO？
A：【deepseek】整理记录
太好了！作为人工智能专家，我非常推荐使用这些工具。它们能彻底改变你的实验管理方式。我来详细讲解 TensorBoard 和 Weights & Biases (W&B) 的使用方法。
TensorBoard
TensorBoard 是 TensorFlow 生态的原生可视化工具，但也完美支持 PyTorch。
安装与基本使用
(dl_env) ubuntu@ubuntu-Inspiron-N4050:~$ pip install tensorboard
在 PyTorch 中的集成
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import torchvision
import numpy as np
#补充
#[W824 10:23:53.141795619 NNPACK.cpp:56] Could not initialize NNPACK! Reason: Unsupported hardware.
#NNPACK 是 Facebook 开发的一个加速神经网络计算的库，但它需要特定的 CPU 指令集（如 ARM NEON 或 x86 AVX2）。您的硬件可能不支持这些指令，所以 PyTorch 无法初始化 NNPACK，但它会自动回退到其他的计算后端。
# 1. 定义模型（必须在使用前定义！）
class SimpleCNN(nn.Module):def __init__(self):super(SimpleCNN, self).__init__()self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)self.pool = nn.MaxPool2d(2, 2)self.fc1 = nn.Linear(64 * 7 * 7, 128)self.fc2 = nn.Linear(128, 10)self.relu = nn.ReLU()self.dropout = nn.Dropout(0.5)def forward(self, x):x = self.pool(self.relu(self.conv1(x)))x = self.pool(self.relu(self.conv2(x)))x = x.view(-1, 64 * 7 * 7)x = self.relu(self.fc1(x))x = self.dropout(x)x = self.fc2(x)return x# 2. 创建模型实例
model = SimpleCNN()
print("模型创建成功")# 3. 创建 SummaryWriter
writer = SummaryWriter('runs/mnist_experiment')# 4. 准备数据
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,), (0.5,))
])# 下载数据
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
test_loader = DataLoader(test_dataset, batch_size=1000, shuffle=False)# 5. 记录模型结构（现在model已经定义了！）
dummy_input = torch.randn(1, 1, 28, 28) # 创建虚拟输入
writer.add_graph(model, dummy_input)
print("模型图已记录到TensorBoard")# 6. 记录一些训练数据样本
dataiter = iter(train_loader)
images, labels = next(dataiter)# 创建图像网格
img_grid = torchvision.utils.make_grid(images)
writer.add_image('mnist_images', img_grid)
print("训练样本图像已记录")# 7. 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)# 8. 训练函数
def train_model():model.train()running_loss = 0.0correct = 0total = 0for epoch in range(3): # 只训练3个epoch作为演示for i, (images, labels) in enumerate(train_loader):# 清零梯度
optimizer.zero_grad()# 前向传播outputs = model(images)loss = criterion(outputs, labels)# 反向传播和优化
loss.backward()optimizer.step()running_loss += loss.item()_, predicted = torch.max(outputs.data, 1)total += labels.size(0)correct += (predicted == labels).sum().item()# 每100个batch记录一次if i % 100 == 99:avg_loss = running_loss / 100accuracy = 100 * correct / total# 记录到TensorBoardwriter.add_scalar('training_loss', avg_loss, epoch * len(train_loader) + i)writer.add_scalar('training_accuracy', accuracy, epoch * len(train_loader) + i)print(f'Epoch [{epoch+1}], Batch [{i+1}], Loss: {avg_loss:.4f}, Accuracy: {accuracy:.2f}%')running_loss = 0.0correct = 0total = 0# 记录每个epoch结束后的学习率writer.add_scalar('learning_rate', optimizer.param_groups[0]['lr'], epoch)# 记录模型权重和梯度的直方图for name, param in model.named_parameters():writer.add_histogram(f'{name}/weights', param, epoch)if param.grad is not None:writer.add_histogram(f'{name}/gradients', param.grad, epoch)# 9. 评估函数
def evaluate_model():model.eval()correct = 0total = 0test_loss = 0with torch.no_grad():for images, labels in test_loader:outputs = model(images)loss = criterion(outputs, labels)test_loss += loss.item()_, predicted = torch.max(outputs.data, 1)total += labels.size(0)correct += (predicted == labels).sum().item()accuracy = 100 * correct / totalavg_loss = test_loss / len(test_loader)writer.add_scalar('test_accuracy', accuracy, 0)writer.add_scalar('test_loss', avg_loss, 0)print(f'测试集准确率: {accuracy:.2f}%')return accuracy, avg_loss# 10. 运行训练和评估
print("开始训练...")
train_model()print("开始评估...")
test_accuracy, test_loss = evaluate_model()# 11. 记录超参数和最终指标
writer.add_hparams({'lr': 0.001, 'batch_size': 64,