26_Pytorch多分类，Softmax多分类实战，利用神经网络进行分类

短命女 2022-10-29 09:20 222阅读 0赞

此文为学习博文整理出

11.21.Pytorch多分类问题  
1.21.1.PyTorch:Softmax多分类实战  
1.21.1.1.MNIST数据集  
1.21.1.2.Softmax分类  
1.21.1.3.PyTorch实战  
1.21.2.利用神经网络进行分类

## 1.21.Pytorch多分类问题 ##

### 1.21.1.PyTorch:Softmax多分类实战 ###

多分类一种比较常用的做法是在最后一层加softmax归一化，值最大的维度所对应的位置则作为该样本对应的类。本文采用PyTorch框架，选用经典图像数据集mnist学习一波多分类。

#### 1.21.1.1.MNIST数据集 ####

MNIST 数据集(手写数字数据集)来自美国国家标准与技术研究所, National Institute of Standards and Technology (NIST). 训练集 (training set) 由来自 250 个不同人手写的数字构成, 其中 50% 是高中学生, 50% 来自人口普查局 (the Census Bureau) 的工作人员. 测试集(test set) 也是同样比例的手写数字数据。MNIST数据集下载地址:http://yann.lecun.com/exdb/mnist/。手写数字的MNIST数据库包括60,000个的训练集样本，以及10,000个测试集样本。  
![在这里插入图片描述][20210211105511121.png]  
其中：  
train-images-idx3-ubyte.gz (训练数据集图片)  
train-labels-idx1-ubyte.gz （训练数据集标记类别）  
t10k-images-idx3-ubyte.gz: （测试数据集）  
t10k-labels-idx1-ubyte.gz（测试数据集标记类别）

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70]  
MNIST数据集是经典图像数据集，包括10个类别(0到9)。每一张图片拉成向量表示，如下图784维向量作为第一层输入特征。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 1]

#### 1.21.1.2.Softmax分类 ####

softmax函数的本质就是将一个K 维的任意实数向量压缩（映射）成另一个K维的实数向量，其中向量中的每个元素取值都介于（0，1）之间，并且压缩后的K个值相加等于1(变成了概率分布)。在选用Softmax做多分类时，可以根据值的大小来进行多分类的任务，如取权重最大的一维。softmax介绍和公式网上很多，这里不介绍了。下面使用Pytorch定义一个多层网络(4个隐藏层，最后一层softmax概率归一化)，输出层为10正好对应10类。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 2]

#### 1.21.1.3.PyTorch实战 ####

# -*- coding: UTF-8 -*-
    
    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    import torch.optim as optim
    from torchvision import datasets, transforms
    from torch.autograd import Variable
    
    # Training settings
    batch_size = 64
    
    # MNIST Dataset
    train_dataset = datasets.MNIST(root='./mnist_data/',
                                   train=True,
                                   transform=transforms.ToTensor(),
                                   download=True)
    
    test_dataset = datasets.MNIST(root='./mnist_data/',
                                  train=False,
                                  transform=transforms.ToTensor())
    
    # Data Loader (Input Pipeline)
    train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                               batch_size=batch_size,
                                               shuffle=True)
    
    test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                              batch_size=batch_size,
                                              shuffle=False)
    
    
    class Net(nn.Module):
        def __init__(self):
            super(Net, self).__init__()
            self.l1 = nn.Linear(784, 520)
            self.l2 = nn.Linear(520, 320)
            self.l3 = nn.Linear(320, 240)
            self.l4 = nn.Linear(240, 120)
            self.l5 = nn.Linear(120, 10)
    
        def forward(self, x):
            # Flatten the data (n, 1, 28, 28) --> (n, 784)
            x = x.view(-1, 784)
            x = F.relu(self.l1(x))
            x = F.relu(self.l2(x))
            x = F.relu(self.l3(x))
            x = F.relu(self.l4(x))
            return F.log_softmax(self.l5(x), dim=1)
            #return self.l5(x)
    
    
    model = Net()
    
    optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
    
    
    def train(epoch):
        # 每次输入barch_idx个数据
        for batch_idx, (data, target) in enumerate(train_loader):
            data, target = Variable(data), Variable(target)
    
            optimizer.zero_grad()
            output = model(data)
            # loss
            loss = F.nll_loss(output, target)
            loss.backward()
            # update
            optimizer.step()
            if batch_idx % 200 == 0:
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                    epoch, batch_idx * len(data), len(train_loader.dataset),
                    100. * batch_idx / len(train_loader), loss.item()))
    
    
    def test():
        test_loss = 0
        correct = 0
        # 测试集
        for data, target in test_loader:
            data, target = Variable(data, volatile=True), Variable(target)
            output = model(data)
            # sum up batch loss
            test_loss += F.nll_loss(output, target).item()
            # get the index of the max
            pred = output.data.max(1, keepdim=True)[1]
            correct += pred.eq(target.data.view_as(pred)).cpu().sum()
    
        test_loss /= len(test_loader.dataset)
        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
            test_loss, correct, len(test_loader.dataset),
            100. * correct / len(test_loader.dataset)
        ))
    
    
    for epoch in range(1, 6):
        train(epoch)
        test()

**输出结果：**

Python 3.7.4 (default, Aug  9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)] on win32
    Django 3.0.6
    runfile('E:/workspace/pytorch-learn/26_多分类问题/01_多分类问题.py', wdir='E:/workspace/pytorch-learn/26_多分类问题')
    Train Epoch: 1 [0/60000 (0%)]	Loss: 2.299828
    Train Epoch: 1 [12800/60000 (21%)]	Loss: 2.296097
    Train Epoch: 1 [25600/60000 (43%)]	Loss: 2.286291
    Train Epoch: 1 [38400/60000 (64%)]	Loss: 2.258982
    Train Epoch: 1 [51200/60000 (85%)]	Loss: 2.001041
    E:/workspace/pytorch-learn/26_多分类问题/01_多分类问题.py:81: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.
      data, target = Variable(data, volatile=True), Variable(target)
    Test set: Average loss: 0.0223, Accuracy: 5924/10000 (59%)
    Train Epoch: 2 [0/60000 (0%)]	Loss: 1.392825
    Train Epoch: 2 [12800/60000 (21%)]	Loss: 0.917865
    Train Epoch: 2 [25600/60000 (43%)]	Loss: 0.554404
    Train Epoch: 2 [38400/60000 (64%)]	Loss: 0.556347
    Train Epoch: 2 [51200/60000 (85%)]	Loss: 0.422638
    Test set: Average loss: 0.0065, Accuracy: 8784/10000 (88%)
    Train Epoch: 3 [0/60000 (0%)]	Loss: 0.348750
    Train Epoch: 3 [12800/60000 (21%)]	Loss: 0.396100
    Train Epoch: 3 [25600/60000 (43%)]	Loss: 0.404045
    Train Epoch: 3 [38400/60000 (64%)]	Loss: 0.275161
    Train Epoch: 3 [51200/60000 (85%)]	Loss: 0.526218
    Test set: Average loss: 0.0052, Accuracy: 8978/10000 (90%)
    Train Epoch: 4 [0/60000 (0%)]	Loss: 0.422416
    Train Epoch: 4 [12800/60000 (21%)]	Loss: 0.269215
    Train Epoch: 4 [25600/60000 (43%)]	Loss: 0.182410
    Train Epoch: 4 [38400/60000 (64%)]	Loss: 0.150055
    Train Epoch: 4 [51200/60000 (85%)]	Loss: 0.224126
    Test set: Average loss: 0.0036, Accuracy: 9333/10000 (93%)
    Train Epoch: 5 [0/60000 (0%)]	Loss: 0.149385
    Train Epoch: 5 [12800/60000 (21%)]	Loss: 0.271054
    Train Epoch: 5 [25600/60000 (43%)]	Loss: 0.340432
    Train Epoch: 5 [38400/60000 (64%)]	Loss: 0.311231
    Train Epoch: 5 [51200/60000 (85%)]	Loss: 0.127134
    Test set: Average loss: 0.0026, Accuracy: 9511/10000 (95%)

### 1.21.2.利用神经网络进行分类 ###

本文就是用最简单的途径来看看神经网络是怎么进行事物的分类。具体的实现如下：

# -*- coding: UTF-8 -*-
    
    import torch
    import torch.nn.functional as F
    import matplotlib as plt
    from torch.autograd import Variable
    
    #创建一些假数据
    n_data = torch.ones(100, 2)         # 数据的基本形态
    x0 = torch.normal(2*n_data, 1)      # 类型0 x data (tensor), shape=(100, 2)
    y0 = torch.zeros(100)               # 类型0 y data (tensor), shape=(100, 1)
    x1 = torch.normal(-2*n_data, 1)     # 类型1 x data (tensor), shape=(100, 1)
    y1 = torch.ones(100)                # 类型1 y data (tensor), shape=(100, 1)
    
    # 注意 x, y 数据的数据形式是一定要像下面一样 (torch.cat 是在合并数据)
    x = torch.cat((x0, x1), 0).type(torch.FloatTensor)    # FloatTensor = 32-bit floating
    y = torch.cat((y0, y1), ).type(torch.LongTensor)      # LongTensor = 64-bit integer
    
    # torch只能在Variable上训练,所以把它们变成Variable
    x, y = Variable(x), Variable(y)
    
    
    # 建立一个神经网络我们可以直接运用torch中的体系，先定义所有的层属性（init()）,然后再一层层搭建（forward(x)）
    # 层与层的关系链接。这个和我们在前面的regression的时候的神经网络基本没差。建立关系的时候，我们会用到激活函数。
    
    
    # 建立神经网络
    class Net(torch.nn.Module):    # 继承torch的Module
        def __init__(self, n_feature, n_hidden, n_output):
            super(Net, self).__init__()                              #继承__init__功能
            self.hidden = torch.nn.Linear(n_feature, n_hidden)     # 隐藏层线性输出
            self.out = torch.nn.Linear(n_hidden, n_output)         # 输出层线性输出
    
        def forward(self, x):
            # 正向传播输入值，神经网络分析输出值
            x = F.relu(self.hidden(x))                               # 激活函数（隐藏层的线性值）
            x = self.out(x)                                           # 输出值, 但是这个不是预测值, 预测值还需要再另外计算
            return x
    
    
    net = Net(n_feature=2, n_hidden=10, n_output=2)                # 几个类别就几个 output
    print(net)
    # 训练网络
    # optimizer是训练的工具
    optimizer = torch.optim.SGD(net.parameters(), lr=0.02)         #传入net的所有参数，学习率
    # 算误差的时候, 注意真实值!不是! one-hot 形式的, 而是1D Tensor, (batch,)
    # 但是预测值是2D tensor (batch, n_classes)
    loss_func = torch.nn.CrossEntropyLoss()
    
    for t in range(200):
        out = net(x)         # 喂给 net 训练数据 x, 输出分析值
    
        loss = loss_func(out, y)     # 计算两者的误差
        print(loss)
    
        optimizer.zero_grad()        # 清空上一步的残余更新参数值
        loss.backward()              # 误差反向传播, 计算参数更新值
        optimizer.step()             # 将参数更新值施加到 net 的 parameters上

**输出结果：**

runfile('E:/workspace/pytorch-learn/26_多分类问题/03_pytorch之区分类型.py', wdir='E:/workspace/pytorch-learn/26_多分类问题')
    Net(
      (hidden): Linear(in_features=2, out_features=10, bias=True)
      (out): Linear(in_features=10, out_features=2, bias=True)
    )
    tensor(1.2678, grad_fn=<NllLossBackward>)
    tensor(1.1538, grad_fn=<NllLossBackward>)
    tensor(1.0548, grad_fn=<NllLossBackward>)
    tensor(0.9684, grad_fn=<NllLossBackward>)
    tensor(0.8926, grad_fn=<NllLossBackward>)
    xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
    tensor(0.2143, grad_fn=<NllLossBackward>)
    tensor(0.2082, grad_fn=<NllLossBackward>)
    tensor(0.2023, grad_fn=<NllLossBackward>)
    tensor(0.1968, grad_fn=<NllLossBackward>)

### 再如案例 ###

import  torch
    import  torch.nn as nn
    import  torch.nn.functional as F
    import  torch.optim as optim
    from    torchvision import datasets, transforms
    
    
    batch_size=200
    learning_rate=0.01
    epochs=10
    
    train_loader = torch.utils.data.DataLoader(
        datasets.MNIST('../data', train=True, download=True,
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.1307,), (0.3081,))
                       ])),
        batch_size=batch_size, shuffle=True)
    test_loader = torch.utils.data.DataLoader(
        datasets.MNIST('../data', train=False, transform=transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.1307,), (0.3081,))
        ])),
        batch_size=batch_size, shuffle=True)
    
    
    
    w1, b1 = torch.randn(200, 784, requires_grad=True),\
             torch.zeros(200, requires_grad=True)
    w2, b2 = torch.randn(200, 200, requires_grad=True),\
             torch.zeros(200, requires_grad=True)
    w3, b3 = torch.randn(10, 200, requires_grad=True),\
             torch.zeros(10, requires_grad=True)
    
    torch.nn.init.kaiming_normal_(w1)
    torch.nn.init.kaiming_normal_(w2)
    torch.nn.init.kaiming_normal_(w3)
    
    
    def forward(x):
        x = x@w1.t() + b1
        x = F.relu(x)
        x = x@w2.t() + b2
        x = F.relu(x)
        x = x@w3.t() + b3
        x = F.relu(x)
        return x
    
    
    
    optimizer = optim.SGD([w1, b1, w2, b2, w3, b3], lr=learning_rate)
    criteon = nn.CrossEntropyLoss()
    
    for epoch in range(epochs):
    
        for batch_idx, (data, target) in enumerate(train_loader):
            data = data.view(-1, 28*28)
    
            logits = forward(data)
            loss = criteon(logits, target)
    
            optimizer.zero_grad()
            loss.backward()
            # print(w1.grad.norm(), w2.grad.norm())
            optimizer.step()
    
            if batch_idx % 100 == 0:
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                    epoch, batch_idx * len(data), len(train_loader.dataset),
                           100. * batch_idx / len(train_loader), loss.item()))
    
    
        test_loss = 0
        correct = 0
        for data, target in test_loader:
            data = data.view(-1, 28 * 28)
            logits = forward(data)
            test_loss += criteon(logits, target).item()
    
            pred = logits.data.max(1)[1]
            correct += pred.eq(target.data).sum()
    
        test_loss /= len(test_loader.dataset)
        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
            test_loss, correct, len(test_loader.dataset),
            100. * correct / len(test_loader.dataset)))

[20210211105511121.png]: /images/20221024/9ff3ae9558cc473e82525649f6d4117f.png
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 2]: /images/20221024/f85e827760804acd9950d75c5230e688.png