基于Pytorch的神经网络之Classfication

1.引言

我们上次介绍了神经网络主要功能之一预测，本篇大部分内容与回归相似，有看不懂的点可以看看我回归 Regression ，今天介绍一下神经网络的另一种功能：分类。

2.网络搭建

2.1 准备工作

还是先引用我们所需要的库，和回归所需的一样。

import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
n_data = torch.ones(2000, 2)
x0 = torch.normal(2*n_data, 1)      # class0 x data (tensor), shape=(100, 2)
y0 = torch.zeros(2000)               # class0 y data (tensor), shape=(100, 1)
x1 = torch.normal(-2*n_data, 1)     # class1 x data (tensor), shape=(100, 2)
y1 = torch.ones(2000)                # class1 y data (tensor), shape=(100, 1)
x = torch.cat((x0, x1), 0).type(torch.FloatTensor)  # shape (200, 2) FloatTensor = 32-bit floating
y = torch.cat((y0, y1), ).type(torch.LongTensor)    # shape (200,) LongTensor = 64-bit integer

然后我们生成一些二维数据来代表许多不同的点，我们就是要对这些点进行分类。

2.2 搭建网络

主体部分和回归没有什么区别，只是需要修改几个小部分就行了：因为我们的点是二维数据所以输入层有两个神经元，最终结果也是二维数据所以输出层也有两个神经元，损失函数我们选取交叉熵(CrossEntropy)。

class Net(torch.nn.Module):
    def __init__(self,n_feature,n_hidden,n_output):
        super(Net,self).__init__()
        self.hidden=torch.nn.Linear(n_feature,n_hidden)
        self.predict=torch.nn.Linear(n_hidden,n_output)
    def forward(self,x):
        x=F.relu(self.hidden(x))
        x=self.predict(x)
        return x
net=Net(2,20,2)
print(net)
optimizer=torch.optim.Adam(net.parameters(),lr=0.001)
loss_func=torch.nn.CrossEntropyLoss()

2.3 训练网络

训练网络与回归一样，就不赘述了。

for t in range(200):
    out = net(x) 

    loss = loss_func(out, y)  

    optimizer.zero_grad()  
    loss.backward() 
    optimizer.step() 

最后效果是这样的：

3.完整代码

import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
n_data = torch.ones(2000, 2)
x0 = torch.normal(2*n_data, 1)      # class0 x data (tensor), shape=(100, 2)
y0 = torch.zeros(2000)               # class0 y data (tensor), shape=(100, 1)
x1 = torch.normal(-2*n_data, 1)     # class1 x data (tensor), shape=(100, 2)
y1 = torch.ones(2000)                # class1 y data (tensor), shape=(100, 1)
x = torch.cat((x0, x1), 0).type(torch.FloatTensor)  # shape (200, 2) FloatTensor = 32-bit floating
y = torch.cat((y0, y1), ).type(torch.LongTensor)    # shape (200,) LongTensor = 64-bit integer

class Net(torch.nn.Module):
    def __init__(self,n_feature,n_hidden,n_output):
        super(Net,self).__init__()
        self.hidden=torch.nn.Linear(n_feature,n_hidden)
        self.predict=torch.nn.Linear(n_hidden,n_output)
    def forward(self,x):
        x=F.relu(self.hidden(x))
        x=self.predict(x)
        return x
net=Net(2,20,2)
print(net)
optimizer=torch.optim.Adam(net.parameters(),lr=0.001)
loss_func=torch.nn.CrossEntropyLoss()
plt.ion()
for t in range(200):
    out = net(x)

    loss = loss_func(out, y)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step() 

    if t % 2 == 0:
        # plot and show learning process
        plt.cla()
        prediction = torch.max(out, 1)[1]
        pred_y = prediction.data.numpy()
        target_y = y.data.numpy()
        plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=pred_y, s=10, lw=0, cmap='RdYlGn')
        accuracy = float((pred_y == target_y).astype(int).sum()) / float(target_y.size)
        plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 10, 'color': 'red'})
        plt.pause(0.1)

plt.ioff()
plt.show()