做网站需要的带宽上行还是下行做移动端网站设计

知识点回顾：

1. 彩色和灰度图片测试和训练的规范写法：封装在函数中
2. 展平操作：除第一个维度batchsize外全部展平
3. dropout操作：训练阶段随机丢弃神经元，测试阶段eval模式关闭dropout

# 先继续之前的代码
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader , Dataset # DataLoader 是 PyTorch 中用于加载数据的工具
from torchvision import datasets, transforms # torchvision 是一个用于计算机视觉的库，datasets 和 transforms 是其中的模块
import matplotlib.pyplot as plt
import warnings
# 忽略警告信息
warnings.filterwarnings("ignore")
# 设置随机种子，确保结果可复现
torch.manual_seed(42)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"使用设备: {device}")# 1. 数据预处理
transform = transforms.Compose([transforms.ToTensor(),  # 转换为张量并归一化到[0,1]transforms.Normalize((0.1307,), (0.3081,))  # MNIST数据集的均值和标准差
])# 2. 加载MNIST数据集
train_dataset = datasets.MNIST(root='./data',train=True,download=True,transform=transform
)test_dataset = datasets.MNIST(root='./data',train=False,transform=transform
)# 3. 创建数据加载器
batch_size = 64  # 每批处理64个样本
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)# 4. 定义模型、损失函数和优化器
class MLP(nn.Module):def __init__(self):super(MLP, self).__init__()self.flatten = nn.Flatten()  # 将28x28的图像展平为784维向量self.layer1 = nn.Linear(784, 128)  # 第一层：784个输入，128个神经元self.relu = nn.ReLU()  # 激活函数self.layer2 = nn.Linear(128, 10)  # 第二层：128个输入，10个输出（对应10个数字类别）def forward(self, x):x = self.flatten(x)  # 展平图像x = self.layer1(x)   # 第一层线性变换x = self.relu(x)     # 应用ReLU激活函数x = self.layer2(x)   # 第二层线性变换，输出logitsreturn x# 初始化模型
model = MLP()
model = model.to(device)  # 将模型移至GPU（如果可用）# from torchsummary import summary  # 导入torchsummary库
# print("\n模型结构信息：")
# summary(model, input_size=(1, 28, 28))  # 输入尺寸为MNIST图像尺寸criterion = nn.CrossEntropyLoss()  # 交叉熵损失函数，适用于多分类问题
optimizer = optim.Adam(model.parameters(), lr=0.001)  # Adam优化器# 5. 训练模型（记录每个 iteration 的损失）
def train(model, train_loader, test_loader, criterion, optimizer, device, epochs):model.train()  # 设置为训练模式# 新增：记录每个 iteration 的损失all_iter_losses = []  # 存储所有 batch 的损失iter_indices = []     # 存储 iteration 序号（从1开始）for epoch in range(epochs):running_loss = 0.0correct = 0total = 0for batch_idx, (data, target) in enumerate(train_loader):# enumerate() 是 Python 内置函数，用于遍历可迭代对象（如列表、元组）并同时获取索引和值。# batch_idx：当前批次的索引（从 0 开始）# (data, target)：当前批次的样本数据和对应的标签，是一个元组，这是因为dataloader内置的getitem方法返回的是一个元组，包含数据和标签。# 只需要记住这种固定写法即可data, target = data.to(device), target.to(device)  # 移至GPU(如果可用)optimizer.zero_grad()  # 梯度清零output = model(data)  # 前向传播loss = criterion(output, target)  # 计算损失loss.backward()  # 反向传播optimizer.step()  # 更新参数# 记录当前 iteration 的损失（注意：这里直接使用单 batch 损失，而非累加平均）iter_loss = loss.item()all_iter_losses.append(iter_loss)iter_indices.append(epoch * len(train_loader) + batch_idx + 1)  # iteration 序号从1开始# 统计准确率和损失running_loss += loss.item() #将loss转化为标量值并且累加到running_loss中，计算总损失_, predicted = output.max(1) # output：是模型的输出（logits），形状为 [batch_size, 10]（MNIST 有 10 个类别）# 获取预测结果，max(1) 返回每行（即每个样本）的最大值和对应的索引，这里我们只需要索引total += target.size(0) # target.size(0) 返回当前批次的样本数量，即 batch_size，累加所有批次的样本数，最终等于训练集的总样本数correct += predicted.eq(target).sum().item() # 返回一个布尔张量，表示预测是否正确，sum() 计算正确预测的数量，item() 将结果转换为 Python 数字# 每100个批次打印一次训练信息（可选：同时打印单 batch 损失）if (batch_idx + 1) % 100 == 0:print(f'Epoch: {epoch+1}/{epochs} | Batch: {batch_idx+1}/{len(train_loader)} 'f'| 单Batch损失: {iter_loss:.4f} | 累计平均损失: {running_loss/(batch_idx+1):.4f}')# 测试、打印 epoch 结果epoch_train_loss = running_loss / len(train_loader)epoch_train_acc = 100. * correct / totalepoch_test_loss, epoch_test_acc = test(model, test_loader, criterion, device)print(f'Epoch {epoch+1}/{epochs} 完成 | 训练准确率: {epoch_train_acc:.2f}% | 测试准确率: {epoch_test_acc:.2f}%')# 绘制所有 iteration 的损失曲线plot_iter_losses(all_iter_losses, iter_indices)# 保留原 epoch 级曲线（可选）# plot_metrics(train_losses, test_losses, train_accuracies, test_accuracies, epochs)return epoch_test_acc  # 返回最终测试准确率# 6. 测试模型（不变）
def test(model, test_loader, criterion, device):model.eval()  # 设置为评估模式test_loss = 0correct = 0total = 0with torch.no_grad():  # 不计算梯度，节省内存和计算资源for data, target in test_loader:data, target = data.to(device), target.to(device)output = model(data)test_loss += criterion(output, target).item()_, predicted = output.max(1)total += target.size(0)correct += predicted.eq(target).sum().item()avg_loss = test_loss / len(test_loader)accuracy = 100. * correct / totalreturn avg_loss, accuracy  # 返回损失和准确率# 7. 绘制每个 iteration 的损失曲线
def plot_iter_losses(losses, indices):plt.figure(figsize=(10, 4))plt.plot(indices, losses, 'b-', alpha=0.7, label='Iteration Loss')plt.xlabel('Iteration（Batch序号）')plt.ylabel('损失值')plt.title('每个 Iteration 的训练损失')plt.legend()plt.grid(True)plt.tight_layout()plt.show()# 8. 执行训练和测试（设置 epochs=2 验证效果）
epochs = 2  
print("开始训练模型...")
final_accuracy = train(model, train_loader, test_loader, criterion, optimizer, device, epochs)
print(f"训练完成！最终测试准确率: {final_accuracy:.2f}%")

 

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import numpy as np# 设置中文字体支持
plt.rcParams["font.family"] = ["SimHei"]
plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题# 1. 数据预处理
transform = transforms.Compose([transforms.ToTensor(),                # 转换为张量transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))  # 标准化处理
])# 2. 加载CIFAR-10数据集
train_dataset = datasets.CIFAR10(root='./data',train=True,download=True,transform=transform
)test_dataset = datasets.CIFAR10(root='./data',train=False,transform=transform
)# 3. 创建数据加载器
batch_size = 64
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)# 4. 定义MLP模型（适应CIFAR-10的输入尺寸）
class MLP(nn.Module):def __init__(self):super(MLP, self).__init__()self.flatten = nn.Flatten()  # 将3x32x32的图像展平为3072维向量self.layer1 = nn.Linear(3072, 512)  # 第一层：3072个输入，512个神经元self.relu1 = nn.ReLU()self.dropout1 = nn.Dropout(0.2)  # 添加Dropout防止过拟合self.layer2 = nn.Linear(512, 256)  # 第二层：512个输入，256个神经元self.relu2 = nn.ReLU()self.dropout2 = nn.Dropout(0.2)self.layer3 = nn.Linear(256, 10)  # 输出层：10个类别def forward(self, x):# 第一步：将输入图像展平为一维向量x = self.flatten(x)  # 输入尺寸: [batch_size, 3, 32, 32] → [batch_size, 3072]# 第一层全连接 + 激活 + Dropoutx = self.layer1(x)   # 线性变换: [batch_size, 3072] → [batch_size, 512]x = self.relu1(x)    # 应用ReLU激活函数x = self.dropout1(x) # 训练时随机丢弃部分神经元输出# 第二层全连接 + 激活 + Dropoutx = self.layer2(x)   # 线性变换: [batch_size, 512] → [batch_size, 256]x = self.relu2(x)    # 应用ReLU激活函数x = self.dropout2(x) # 训练时随机丢弃部分神经元输出# 第三层（输出层）全连接x = self.layer3(x)   # 线性变换: [batch_size, 256] → [batch_size, 10]return x  # 返回未经过Softmax的logits# 检查GPU是否可用
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")# 初始化模型
model = MLP()
model = model.to(device)  # 将模型移至GPU（如果可用）criterion = nn.CrossEntropyLoss()  # 交叉熵损失函数
optimizer = optim.Adam(model.parameters(), lr=0.001)  # Adam优化器# 5. 训练模型（记录每个 iteration 的损失）
def train(model, train_loader, test_loader, criterion, optimizer, device, epochs):model.train()  # 设置为训练模式# 记录每个 iteration 的损失all_iter_losses = []  # 存储所有 batch 的损失iter_indices = []     # 存储 iteration 序号for epoch in range(epochs):running_loss = 0.0correct = 0total = 0for batch_idx, (data, target) in enumerate(train_loader):data, target = data.to(device), target.to(device)  # 移至GPUoptimizer.zero_grad()  # 梯度清零output = model(data)  # 前向传播loss = criterion(output, target)  # 计算损失loss.backward()  # 反向传播optimizer.step()  # 更新参数# 记录当前 iteration 的损失iter_loss = loss.item()all_iter_losses.append(iter_loss)iter_indices.append(epoch * len(train_loader) + batch_idx + 1)# 统计准确率和损失running_loss += iter_loss_, predicted = output.max(1)total += target.size(0)correct += predicted.eq(target).sum().item()# 每100个批次打印一次训练信息if (batch_idx + 1) % 100 == 0:print(f'Epoch: {epoch+1}/{epochs} | Batch: {batch_idx+1}/{len(train_loader)} 'f'| 单Batch损失: {iter_loss:.4f} | 累计平均损失: {running_loss/(batch_idx+1):.4f}')# 计算当前epoch的平均训练损失和准确率epoch_train_loss = running_loss / len(train_loader)epoch_train_acc = 100. * correct / total# 测试阶段model.eval()  # 设置为评估模式test_loss = 0correct_test = 0total_test = 0with torch.no_grad():for data, target in test_loader:data, target = data.to(device), target.to(device)output = model(data)test_loss += criterion(output, target).item()_, predicted = output.max(1)total_test += target.size(0)correct_test += predicted.eq(target).sum().item()epoch_test_loss = test_loss / len(test_loader)epoch_test_acc = 100. * correct_test / total_testprint(f'Epoch {epoch+1}/{epochs} 完成 | 训练准确率: {epoch_train_acc:.2f}% | 测试准确率: {epoch_test_acc:.2f}%')# 绘制所有 iteration 的损失曲线plot_iter_losses(all_iter_losses, iter_indices)return epoch_test_acc  # 返回最终测试准确率# 6. 绘制每个 iteration 的损失曲线
def plot_iter_losses(losses, indices):plt.figure(figsize=(10, 4))plt.plot(indices, losses, 'b-', alpha=0.7, label='Iteration Loss')plt.xlabel('Iteration（Batch序号）')plt.ylabel('损失值')plt.title('每个 Iteration 的训练损失')plt.legend()plt.grid(True)plt.tight_layout()plt.show()# 7. 执行训练和测试
epochs = 20  # 增加训练轮次以获得更好效果
print("开始训练模型...")
final_accuracy = train(model, train_loader, test_loader, criterion, optimizer, device, epochs)
print(f"训练完成！最终测试准确率: {final_accuracy:.2f}%")# # 保存模型
# torch.save(model.state_dict(), 'cifar10_mlp_model.pth')
# # print("模型已保存为: cifar10_mlp_model.pth")

# 7. 执行训练和测试
epochs = 20  # 增加训练轮次以获得更好效果
print("开始训练模型...")
final_accuracy = train(model, train_loader, test_loader, criterion, optimizer, device, epochs)
print(f"训练完成！最终测试准确率: {final_accuracy:.2f}%")

 @浙大疏锦行