同上的分类神经网络

import torch
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt

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)

x,y = Variable(x),Variable(y)

# method 1
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)

    # 前向传递
    def forward(self,x):
        x = F.relu(self.hidden(x))
        x = self.predict(x)
        return x

net1 = Net(2,10,2)

这里通过net1快速搭建网络net2

# method 2
net2 = torch.nn.Sequential(
    torch.nn.Linear(2,10),
    torch.nn.ReLU(),
    torch.nn.Linear(10,2),
)

print(net1)
print(net2)

plt.ion()
plt.show()

optimizer = torch.optim.SGD(net2.parameters(),lr=0.02)
loss_func = torch.nn.CrossEntropyLoss() # 预测标签误差

for t in range(100):
    out = net2(x)
    loss = loss_func(out,y)

    optimizer.zero_grad() # 将梯度降为0
    loss.backward() # 反向传递过程
    optimizer.step() # 优化梯度

    if t % 2 == 0:
        plt.cla()
        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.
        plt.text(1.5,-4,'Accuracy=%.2f'%accuracy,fontdict={'size':20,'color':'red'})
        plt.pause(0.1)

plt.ioff()
plt.show()