classify.py

#!/user/bin/env python
# -*- coding:utf-8 -*-
import torch
import matplotlib.pyplot as plt
import torch
import torch.nn.functional as F
n_data=torch.ones(100,2)
x0=torch.normal(2*n_data,1)
y0=torch.zeros(100)
x1=torch.normal(-2*n_data,1)
y1=torch.ones(100)
x=torch.cat((x0,x1),0).type(torch.FloatTensor)
y=torch.cat((y0,y1),).type(torch.LongTensor)
# plt.scatter(x.data.numpy()[:,0],x.data.numpy()[:,1],c=y.data.numpy(), s=100, lw=0, cmap='RdYlGn')
# plt.show()


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.out=torch.nn.Linear(n_hidden,n_output)

    def forward(self,x):
        x=F.relu(self.hidden(x))
        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)
print(net)

#训练网络
optimizer=torch.optim.SGD(net.parameters(),lr=0.02)
loss_func=torch.nn.CrossEntropyLoss()#CrossEntropyLoss()名字为交叉熵损失函数，不用于one_hotted编码形式
plt.ion()
for t in range(100):
    out=net(x)
    loss=loss_func(out,y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    # 接着上面来
    if t % 2 == 0:
        plt.cla()
        # 过了一道 softmax 的激励函数后的最大概率才是预测值
        prediction = torch.max(F.softmax(out), 1)[1]
        pred_y = prediction.data.numpy().squeeze()
        target_y = y.data.numpy()
        plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=pred_y, s=100, lw=0, cmap='RdYlGn')
        accuracy = sum(pred_y == target_y)/200.  # 预测中有多少和真实值一样
        plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 20, 'color':  'red'})
        plt.pause(0.1)
plt.ioff()
plt.show()
print('lalala')