PyTorch 04：用PyTorch实现Fashion-MNIST

傷城~ 2023-03-13 15:29 56阅读 0赞

好了，我们来实战演练。你将使用 [Fashion-MNIST 数据集][Fashion-MNIST]。对于神经网络来说，MNIST 的规模很小，可以轻松地达到 97% 的准确率。Fashion-MNIST 包括很多 28x28 灰色服饰图像。它比 MNIST 更复杂，也更贴近实际现实需求。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x1dGVyZXNh_size_16_color_FFFFFF_t_70]

你要在这个 notebook 里构建神经网络。其实你可以用 Part 3 notebook 里的代码，但是这样就学不到任何知识了。只有自己编写代码并确保代码能运行，才能实现最佳学习效果。当然，你可以参考 Part 3 notebook。

首先，通过 torchvision 加载数据集。

import torch
    from torchvision import datasets, transforms
    import helper
    
    # Define a transform to normalize the data
    transform = transforms.Compose([transforms.ToTensor(),
                                    transforms.Normalize((0.5,), (0.5,))])
    # Download and load the training data
    trainset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=True, transform=transform)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
    
    # Download and load the test data
    testset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=False, transform=transform)
    testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True)

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz

Processing…

Done!

下面是一个图像示例。

image, label = next(iter(trainloader))
    helper.imshow(image[0,:]);

![在这里插入图片描述][20200515181631420.png]

## 构建网络 ##

你要先定义网络。和 MNIST 一样，每个图像是 28x28 = 784 像素，并且有 10 个类别。你应该至少包含一个隐藏层。建议对层级使用 ReLU 激活函数，并返回前向传播的对数或 log-softmax。你可以决定添加多少个层级以及这些层级的大小。

# TODO: Define your network architecture here
    from torch import nn, optim
    import torch.nn.functional as F
    
    class Classifier_fashion(nn.Module):
        def __init__(self):
            super().__init__()
            self.layer1 = nn.Linear(784,256)
            self.layer2 = nn.Linear(256,128)
            self.layer3 = nn.Linear(128,64)
            self.layer4 = nn.Linear(64,10)
        def forward(self, x):
            x = x.view(x.shape[0],-1)
            
            x = F.relu(self.layer1(x))
            x = F.relu(self.layer2(x))
            x = F.relu(self.layer3(x))
            x = F.log_softmax(self.layer4(x), dim=1)
            
            return x

# 训练网络 #

现在，来试试创建并训练网络。首先，你需要定义[条件][Link 1]（例如 `nn.CrossEntropyLoss`）和[优化器][Link 2]（通常是 `optim.SGD` 或 `optim.Adam`）。

然后编写训练代码。你可以参考这个流程：

\*通过网络进行正向传递以获取logits  
\*使用 logits 计算损失

*  通过 `loss.backward()` 在网络中反向传播以计算梯度
 *  使用优化器更新权重

通过调整参数（隐藏单元、学习速率等），你应该能够使训练损失低于 0.4。

# TODO: Create the network, define the criterion and optimizer
    model = Classifier_fashion()
    criterion = nn.NLLLoss()
    optimizer = optim.Adam(model.parameters(),lr=0.003)

# TODO: Train the network here
    epochs = 5
    
    for e in range(epochs):
        running_loss = 0
        for images, labels in trainloader:
            log_ps = model(images)
            loss = criterion(log_ps, labels)
            
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            
            running_loss += loss.item()
        print("Trainning loss:{}".format(running_loss/len(trainloader)))

Trainning loss:0.521082957575062
    Trainning loss:0.3933480036442976
    Trainning loss:0.358206258828579
    Trainning loss:0.3316666531219665
    Trainning loss:0.31710777687492653

%matplotlib inline
    %config InlineBackend.figure_format = 'retina'
    
    import helper
    
    # Test out your network!
    
    dataiter = iter(testloader)
    images, labels = dataiter.next()
    img = images[0]
    # Convert 2D image to 1D vector
    img = img.resize_(1, 784)
    
    # TODO: Calculate the class probabilities (softmax) for img
    ps = torch.exp(model(img))
    
    # Plot the image and probabilities
    helper.view_classify(img.resize_(1, 28, 28), ps, version='Fashion')

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x1dGVyZXNh_size_16_color_FFFFFF_t_70 1]

[Fashion-MNIST]: https://github.com/zalandoresearch/fashion-mnist
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x1dGVyZXNh_size_16_color_FFFFFF_t_70]: /images/20230312/e53900ae760c4f939ed9b90e69eb2eee.png
[20200515181631420.png]: /images/20230312/f03e582fff0b4fd7857ad366422730a1.png
[Link 1]: http://pytorch.org/docs/master/nn.html#loss-functions
[Link 2]: http://pytorch.org/docs/master/optim.html
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x1dGVyZXNh_size_16_color_FFFFFF_t_70 1]: /images/20230312/66299c230951457d99d1ad8ae6b48168.png