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用Pytorch定义并训练一个简单的卷积神经网络

用Pytorch定义并训练一个简单的卷积神经网络

作者: LabVIEW_Python | 来源:发表于2023-01-03 10:40 被阅读0次

    用Pytorch定义并训练一个简单的全连接网络完整展示了使用PyTorch定义模型,载入数据集,训练模型并评估模型的全流程,本文将介绍用Pytorch定义并训练一个简单的卷积神经网络。

    首先,请学习卷积神经网络的基础知识和基础组件

    其次,本文针对MNIST数据集定义的卷积神经网络如下:

    # Optional:Define CNN
    class CNN(nn.Module):
        def __init__(self, in_channels=1, num_classes=10):
            super().__init__()
            self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=8, kernel_size=3, stride=1, padding=1) # same convolution
            self.pool = nn.MaxPool2d(kernel_size=(2,2), stride=(2,2)) # Downsampling by 2
            self.conv2 = nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3, stride=1, padding=1)
            self.fc1 = nn.Linear(16*7*7, num_classes)
    
        def forward(self, x):
            x = self.pool(F.relu(self.conv1(x)))
            x = self.pool(F.relu(self.conv2(x)))
            x = x.reshape(x.shape[0], -1)
            x = self.fc1(x)
            return x
    
    • 卷积层用于提取空间特征,其参数:
      • in_channels指输入Tensor有几个通道
      • out_channels在有些地方也叫滤波器数量,例如:TensorFlow的Conv2D的参数名就叫filters。每个滤波器都在输入Tensor上提取特征
      • kernel_size:定义滤波器Kernel大小
      • stride(步幅)定义滤波器每次移动的像素个数
      • padding(填充)定义在边界周围用零填充
      • kernel_size, stride和padding让我们可以控制输出体积的空间大小,由下面的公式决定:
    卷积尺寸输出公式

    所以,代码中kernel_size = 3, stride=1, padding=1现实了输出尺寸与输入尺寸同样大小(same convolution)

    • 池化层(Pooling): 在Conv 层之间周期性地插入一个 Pooling 层用于逐步减小特征空间的大小,以减少网络中的参数量和计算量,从而也可以控制过拟合。池化层kernel_size=(2,2), stride=(2,2)实现将长宽缩小一半,如下图所示:

      池化层
    • 全连接层(Fully connected):用于分类(classifier)。

    训练过程的代码,基本可以完整复用全连接网络的,完整代码如下所示:

    import torch
    import torch.nn as nn
    import torch.optim as optim
    import torch.nn.functional as F
    from torch.utils.data import DataLoader
    import torchvision.datasets as datasets
    import torchvision.transforms as transforms
    
    # Step1: Define Fully connected network
    class NN(nn.Module):
        def __init__(self, num_features, num_classes=10):
            super().__init__()
            self.fc1 =nn.Linear(num_features, 50)
            self.fc2 = nn.Linear(50, num_classes)
    
        def forward(self, x):
            x = F.relu(self.fc1(x))
            x = self.fc2(x)
            return x
    
    # Optional:Define CNN
    class CNN(nn.Module):
        def __init__(self, in_channels=1, num_classes=10):
            super().__init__()
            self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=8, kernel_size=3, stride=1, padding=1) # same convolution
            self.pool = nn.MaxPool2d(kernel_size=(2,2), stride=(2,2)) # Downsampling by 2
            self.conv2 = nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3, stride=1, padding=1)
            self.fc1 = nn.Linear(16*7*7, num_classes)
    
        def forward(self, x):
            x = self.pool(F.relu(self.conv1(x)))
            x = self.pool(F.relu(self.conv2(x)))
            x = x.reshape(x.shape[0], -1)
            x = self.fc1(x)
            return x
    
    # Set device & Hyperparameters
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    num_features = 784
    num_classes = 10
    learning_rate = 1e-3
    batch_size = 64
    num_epochs = 3
    
    # Step2: Load data
    train_dataset = datasets.MNIST(root="dataset/", train=True, transform=transforms.ToTensor(), download=True)
    train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
    
    test_dataset = datasets.MNIST(root="dataset/", train=False, transform=transforms.ToTensor(), download=True)
    test_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=False)
    
    # Step3: Initialize network
    # model = NN(num_features, num_classes).to(device)
    model = CNN().to(device)
    
    # Step4: define Loss and optimizer
    loss_fn = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)
    
    # Step5: Train Network
    for epoch in range(num_epochs):
        losses=[]
        for batch_idx, (data, targets) in enumerate(train_dataloader):
            data = data.to(device=device)
            targets = targets.to(device=device)
            # data = data.reshape(data.shape[0], -1)
    
            # forward
            preds = model(data)
            loss = loss_fn(preds, targets)
            losses.append(loss)
            
            # backward
            optimizer.zero_grad()
            loss.backward()
    
            # GSD
            optimizer.step()
        print(f"Epoch:{epoch}, loss is {sum(losses)/len(losses)}.")
    
    # Step6: Chekc accuracy on test dataset
    num_correct = 0
    num_samples = 0
    model.eval()
    
    with torch.no_grad():
        for data, targets in test_dataloader:
            data = data.to(device)
            targets = targets.to(device)
            # data = data.reshape(data.shape[0], -1)
    
            preds = model(data)
            _, results = preds.max(1)
            # print(preds.shape, results.shape, targets.shape)
            num_correct += (results == targets).sum()
            num_samples += results.size(0)
        print(f"The accuracy on test dataset is : {float(num_correct)/float(num_samples)*100:.2f}%")
    

    运行结果如下:

    Epoch:0, loss is 0.36820390820503235.
    Epoch:1, loss is 0.11146386712789536.
    Epoch:2, loss is 0.07877714186906815.
    The accuracy on test dataset is : 98.09%

    由此可见,卷积神经网络的分类效果远好于全连接网络

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