目录

1.快速搭建神经网络

（1）代码

（2）运行结果

1.快速搭建神经网络

（1）代码

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),0).type(torch.LongTensor)

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

# plt.scatter(x.data.numpy()[:,0],x.data.numpy()[:,1],c= y.data.numpy(),s=100,lw =0,cmap =\'RdYlGn\')
# plt.show()


# Net __init__()
# methord 1
class Net(torch.nn.Module):#继承module
    def __init__(self,n_features,n_hidden,n_output):
        super(Net,self).__init__()#官方步骤，继承
        self.hidden = torch.nn.Linear(n_features,n_hidden)
        self.predict = torch.nn.Linear(n_hidden,n_output)#预测


    def forward(self,x):
        # 前向传递过程，搭建神经网络
        x = F.relu(self.hidden(x))#一个function
        x = self.predict(x)
        return x

net = Net(2,10,2)      #输入、隐藏层、输出分别为1，10，1
#哪个位置为1就是其对应分类


#methord 2【新的快速搭建方法】
net2 = torch.nn.Sequential(
    #一层一层磊神经层
    torch.nn.Linear(2,10),
    torch.nn.ReLU(),#层的类
    torch.nn.Linear(10,2),

)
print(net)
print(net2)

（2）运行结果

Net(
  (hidden): Linear(in_features=2, out_features=10, bias=True)
  (predict): Linear(in_features=10, out_features=2, bias=True)
)
Sequential(
  (0): Linear(in_features=2, out_features=10, bias=True)
  (1): ReLU()
  (2): Linear(in_features=10, out_features=2, bias=True)
)

Process finished with exit code 0