深度学习之神经网络框架搭建及模型优化

神经网络框架搭建及模型优化

目录 神经网络框架搭建及模型优化1 数据及配置1.1 配置1.2 数据1.3 函数导入1.4 数据函数1.5 数据打包 2 神经网络框架搭建2.1 框架确认2.2 函数搭建2.3 框架上传 3 模型优化3.1 函数理解3.2 训练模型和测试模型代码 4 最终代码测试4.1 SGD优化算法4.2 Adam优化算法4.3 多次迭代

1 数据及配置

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1.1 配置

需要安装PyTorch,下载安装torch、torchvision、torchaudio，GPU需下载cuda版本，CPU可直接下载

cuda版本较大，最后通过控制面板pip install +存储地址离线下载， CPU版本需再下载安装VC_redist.x64.exe，可下载上述三个后运行，通过报错网址直接下载安装 1.2 数据

使用的是 torchvision.datasets.MNIST的手写数据，包括特征数据和结果类别

1.3 函数导入 import torch from torch import nn from torch.utils.data import DataLoader from torchvision import datasets from torchvision.transforms import ToTensor 1.4 数据函数 train_data = datasets.MNIST( root='data', # 数据集存储的根目录 train=True, # 加载训练集 download=True, # 如果数据集不存在，自动下载 transform=ToTensor() # 将图像转换为张量 ) root 指定数据集存储的根目录。如果数据集不存在，会自动下载到这个目录。train 决定加载训练集还是测试集。True 表示加载训练集，False 表示加载测试集。download 如果数据集不在 root 指定的目录中，是否自动下载数据集。True 表示自动下载。transform 对加载的数据进行预处理或转换。通常用于将数据转换为模型所需的格式，如将图像转换为张量。 1.5 数据打包

train_dataloader = DataLoader(train_data, batch_size=64)

train_data, 打包数据batch_size=64，打包个数

代码展示：

import torch print(torch.__version__) import torch from torch import nn from torch.utils.data import DataLoader from torchvision import datasets from torchvision.transforms import ToTensor train_data = datasets.MNIST( root = 'data', train = True, download = True, transform = ToTensor() ) test_data = datasets.MNIST( root = 'data', train = False, download = True, transform = ToTensor() ) print(len(train_data)) print(len(test_data)) from matplotlib import pyplot as plt figure = plt.figure() for i in range(9): img,label = train_data[i+59000] figure.add_subplot(3,3,i+1) plt.title(label) plt.axis('off') plt.imshow(img.squeeze(),cmap='gray') a = img.squeeze() plt.show() train_dataloader = DataLoader(train_data, batch_size=64) test_dataloader= DataLoader(test_data, batch_size=64)

运行结果：

调试查看:

2 神经网络框架搭建

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2.1 框架确认

在搭建神经网络框架前，需先确认建立怎样的框架，目前并没有理论的指导，凭经验建立框架如下：

输入层：输入的图像数据（28*28）个神经元。 中间层1：全连接层，128个神经元， 中间层2：全连接层，256个神经元， 输出层：全连接层，10个神经元，对应10个类别。 需注意，中间层需使用激励函数激活，对累加数进行非线性的映射，以及forward前向传播过程的函数名不可更改，

2.2 函数搭建 nn.Flatten() ， 将输入展平为一维向量nn.Linear(28*28, 128) ，全连接层，需注意每个连接层的输入输出需前后对应torch.sigmoid(x)，对中间层的输出应用Sigmoid激活函数 # 定义一个神经网络类，继承自 nn.Module class NeuralNetwork(nn.Module): def __init__(self): super().__init__() # 调用父类 nn.Module 的构造函数 # 定义网络层 self.flatten = nn.Flatten() # 将输入展平为一维向量，适用于将图像数据（如28x28）展平为784维 self.hidden1 = nn.Linear(28*28, 128) # 第一个全连接层，输入维度为784（28*28），输出维度为128 self.hidden2 = nn.Linear(128, 256) # 第二个全连接层，输入维度为128，输出维度为256 self.out = nn.Linear(256, 10) # 输出层，输入维度为256，输出维度为10（对应10个类别） # 定义前向传播过程 def forward(self, x): x = self.flatten(x) # 将输入数据展平 x = self.hidden1(x) # 通过第一个全连接层 x = torch.sigmoid(x) # 对第一个全连接层的输出应用Sigmoid激活函数 x = self.hidden2(x) # 通过第二个全连接层 x = torch.sigmoid(x) # 对第二个全连接层的输出应用Sigmoid激活函数 x = self.out(x) # 通过输出层 return x # 返回最终的输出 2.3 框架上传 device = ‘cuda’ if torch.cuda.is_available() else ‘mps’ if torch.backends.mps.is_available() else ‘cpu’，确认设备， 检查是否有可用的GPU设备，如果有则使用GPU，否则使用CPUmodel = NeuralNetwork().to(device)，框架上传到GPU/CPU

模型输出展示：

3 模型优化

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3.1 函数理解 optimizer = torch.optim.Adam(model.parameters(), lr=0.001)，定义优化器： Adam(）使用Adam优化算法，也可为SGD等优化算法model.parameters()为优化模型的参数，lr为学习率/梯度下降步长为0.001 loss_fn = nn.CrossEntropyLoss(pre,y)，定义损失函数，使用交叉熵损失函数，适用于分类任务 pre,预测结果y,真实结果loss_fn.item()，当前损失值 model.train() ，将模型设置为训练模式，模型参数是可变的x, y = x.to(device), y.to(device)，将数据移动到指定设备（GPU或CPU）反向传播：清零梯度，计算梯度，更新模型参数 optimizer.zero_grad() ，清零梯度缓存 loss.backward()， 计算梯度 optimizer.step() ， 更新模型参数 model.eval()，将模型设置为评估模式，模型参数是不可变with torch.no_grad()，禁用梯度计算，在测试过程中不需要计算梯度 3.2 训练模型和测试模型代码 optimizer = torch.optim.Adam(model.parameters(),lr=0.001) loss_fn = nn.CrossEntropyLoss() def train(dataloader,model,loss_fn,optimizer): model.train() batch_size_num = 1 for x,y in dataloader: x,y = x.to(device),y.to(device) pred = model.forward(x) loss = loss_fn(pred,y) optimizer.zero_grad() loss.backward() optimizer.step() loss_value = loss.item() if batch_size_num %100 ==0: print(f'loss: {loss_value:>7f} [number: {batch_size_num}]') batch_size_num +=1 train(train_dataloader,model,loss_fn,optimizer) def test(dataloader,model,loss_fn): size = len(dataloader.dataset) num_batches = len(dataloader) model.eval() test_loss,correct = 0,0 with torch.no_grad(): for x,y in dataloader: x,y = x.to(device),y.to(device) pred = model.forward(x) test_loss += loss_fn(pred,y).item() correct +=(pred.argmax(1) == y).type(torch.float).sum().item() a = (pred.argmax(1)==y) b = (pred.argmax(1)==y).type(torch.float) test_loss /=num_batches correct /= size print(f'test result: \n Accuracy: {(100*correct)}%, Avg loss:{test_loss}') 4 最终代码测试

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4.1 SGD优化算法

torch.optim.SGD(model.parameters(),lr=0.01)

代码展示：

import torch print(torch.__version__) import torch from torch import nn from torch.utils.data import DataLoader from torchvision import datasets from torchvision.transforms import ToTensor train_data = datasets.MNIST( root = 'data', train = True, download = True, transform = ToTensor() ) test_data = datasets.MNIST( root = 'data', train = False, download = True, transform = ToTensor() ) print(len(train_data)) print(len(test_data)) from matplotlib import pyplot as plt figure = plt.figure() for i in range(9): img,label = train_data[i+59000] figure.add_subplot(3,3,i+1) plt.title(label) plt.axis('off') plt.imshow(img.squeeze(),cmap='gray') a = img.squeeze() plt.show() train_dataloader = DataLoader(train_data, batch_size=64) test_dataloader= DataLoader(test_data, batch_size=64) device = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu' print(f'Using {device} device') class NeuralNetwork(nn.Module): def __init__(self): super().__init__() self.flatten = nn.Flatten() self.hidden1 = nn.Linear(28*28,128) self.hidden2 = nn.Linear(128, 256) self.out = nn.Linear(256,10) def forward(self,x): x = self.flatten(x) x = self.hidden1(x) x = torch.sigmoid(x) x = self.hidden2(x) x = torch.sigmoid(x) x = self.out(x) return x model = NeuralNetwork().to(device) # print(model) optimizer = torch.optim.SGD(model.parameters(),lr=0.01) loss_fn = nn.CrossEntropyLoss() def train(dataloader,model,loss_fn,optimizer): model.train() batch_size_num = 1 for x,y in dataloader: x,y = x.to(device),y.to(device) pred = model.forward(x) loss = loss_fn(pred,y) optimizer.zero_grad() loss.backward() optimizer.step() loss_value = loss.item() if batch_size_num %100 ==0: print(f'loss: {loss_value:>7f} [number: {batch_size_num}]') batch_size_num +=1 def test(dataloader,model,loss_fn): size = len(dataloader.dataset) num_batches = len(dataloader) model.eval() test_loss,correct = 0,0 with torch.no_grad(): for x,y in dataloader: x,y = x.to(device),y.to(device) pred = model.forward(x) test_loss += loss_fn(pred,y).item() correct +=(pred.argmax(1) == y).type(torch.float).sum().item() a = (pred.argmax(1)==y) b = (pred.argmax(1)==y).type(torch.float) test_loss /=num_batches correct /= size print(f'test result: \n Accuracy: {(100*correct)}%, Avg loss:{test_loss}') # train(train_dataloader,model,loss_fn,optimizer) test(test_dataloader,model,loss_fn)

运行结果：

4.2 Adam优化算法

自适应算法，torch.optim.Adam(model.parameters(),lr=0.01)

运行结果：

4.3 多次迭代

代码展示：

import torch print(torch.__version__) import torch from torch import nn from torch.utils.data import DataLoader from torchvision import datasets from torchvision.transforms import ToTensor train_data = datasets.MNIST( root = 'data', train = True, download = True, transform = ToTensor() ) test_data = datasets.MNIST( root = 'data', train = False, download = True, transform = ToTensor() ) print(len(train_data)) print(len(test_data)) from matplotlib import pyplot as plt figure = plt.figure() for i in range(9): img,label = train_data[i+59000] figure.add_subplot(3,3,i+1) plt.title(label) plt.axis('off') plt.imshow(img.squeeze(),cmap='gray') a = img.squeeze() plt.show() train_dataloader = DataLoader(train_data, batch_size=64) test_dataloader= DataLoader(test_data, batch_size=64) device = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu' print(f'Using {device} device') class NeuralNetwork(nn.Module): def __init__(self): super().__init__() self.flatten = nn.Flatten() self.hidden1 = nn.Linear(28*28,128) self.hidden2 = nn.Linear(128, 256) self.out = nn.Linear(256,10) def forward(self,x): x = self.flatten(x) x = self.hidden1(x) x = torch.sigmoid(x) x = self.hidden2(x) x = torch.sigmoid(x) x = self.out(x) return x model = NeuralNetwork().to(device) # print(model) optimizer = torch.optim.Adam(model.parameters(),lr=0.01) loss_fn = nn.CrossEntropyLoss() def train(dataloader,model,loss_fn,optimizer): model.train() batch_size_num = 1 for x,y in dataloader: x,y = x.to(device),y.to(device) pred = model.forward(x) loss = loss_fn(pred,y) optimizer.zero_grad() loss.backward() optimizer.step() loss_value = loss.item() if batch_size_num %100 ==0: print(f'loss: {loss_value:>7f} [number: {batch_size_num}]') batch_size_num +=1 def test(dataloader,model,loss_fn): size = len(dataloader.dataset) num_batches = len(dataloader) model.eval() test_loss,correct = 0,0 with torch.no_grad(): for x,y in dataloader: x,y = x.to(device),y.to(device) pred = model.forward(x) test_loss += loss_fn(pred,y).item() correct +=(pred.argmax(1) == y).type(torch.float).sum().item() a = (pred.argmax(1)==y) b = (pred.argmax(1)==y).type(torch.float) test_loss /=num_batches correct /= size print(f'test result: \n Accuracy: {(100*correct)}%, Avg loss:{test_loss}') # train(train_dataloader,model,loss_fn,optimizer) test(test_dataloader,model,loss_fn) # e = 30 for i in range(e): print(f'e: {i+1}\n------------------') train(train_dataloader, model, loss_fn, optimizer) print('done') test(test_dataloader, model, loss_fn)

运行结果：