半年前用numpy写了个鸢尾花分类200行。。每一步计算都是手写的 

现在用pytorch简单写一遍，pytorch语法解释请看上一篇

1 import pandas as pd 2 import torch.nn as nn 3 import torch 4  5  6 class MyNet(nn.Module): 7     def __init__(self): 8         super(MyNet, self).__init__() 9         self.fc = nn.Sequential(10             nn.Linear(4, 3),11             nn.Sigmoid(),12             nn.Linear(3, 3),13             nn.Sigmoid(),14             nn.Linear(3, 1),15         )16         self.mls = nn.MSELoss()17         self.opt = torch.optim.Adam(params=self.parameters(), lr=0.001)18 19     def get_data(self):20         inputs = []21         labels = []22         with open('flower.csv') as file:23             df = pd.read_csv(file, header=None)24             x = df.iloc[:, 0:4].values25             y = df.iloc[:, 4].values26             for i in range(len(x)):27                 inputs.append(x[i])28             for j in range(len(y)):29                 a = []30                 a.append(y[j])31                 labels.append(a)32 33         return inputs, labels34 35     def forward(self, inputs):36         out = self.fc(inputs)37         return out38 39     def train(self, x, label):40         out = self.forward(x)41         loss = self.mls(out, label)42         self.opt.zero_grad()43         loss.backward()44         self.opt.step()45 46     def test(self, x):47         return self.fc(x)48 49 50 if __name__ == '__main__':51     net = MyNet()52     inputs, labels = net.get_data()53     for i in range(1000):54         for index, input in enumerate(inputs):55             # 这里不加.float()会报错，可能是数据格式的问题吧56             input = torch.from_numpy(input).float()57             label = torch.Tensor(labels[index])58             net.train(input, label)59     # 简单测试一下60     c = torch.Tensor([[5.6, 2.7, 4.2, 1.3]])61     print(net.test(c))

运行结果趋近于0.5  正确，单纯练一下pytorch，就没有分训练集，测试集

1 tensor([[0.5392]], grad_fn=
     
      )
     

不用手写反向传播和梯度下降 是多么幸福一件事~