pytorch解决鸢尾花分类

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  python构建bp神经网络_鸢尾花分类

现在用pytorch简单写一遍，pytorch语法解释请看上一篇pytorch搭建简单网络

 import pandas as pd
 import torch.nn as nn
 import torch

 class MyNet(nn.Module):
     def __init__(self):
         super(MyNet, self).__init__()
         self.fc = nn.Sequential(
             nn.Linear(4, 3),
             nn.Sigmoid(),
             nn.Linear(3, 3),
             nn.Sigmoid(),
             nn.Linear(3, 1),
         )
         self.mls = nn.MSELoss()
         self.opt = torch.optim.Adam(params=self.parameters(), lr=0.001)

     def get_data(self):
         inputs = []
         labels = []
         with open('flower.csv') as file:
             df = pd.read_csv(file, header=None)
             x = df.iloc[:, 0:4].values
             y = df.iloc[:, 4].values
             for i in range(len(x)):
                 inputs.append(x[i])
             for j in range(len(y)):
                 a = []
                 a.append(y[j])
                 labels.append(a)

         return inputs, labels

     def forward(self, inputs):
         out = self.fc(inputs)
         return out

     def train(self, x, label):
         out = self.forward(x)
         loss = self.mls(out, label)
         self.opt.zero_grad()
         loss.backward()
         self.opt.step()

     def test(self, x):
         return self.fc(x)

 if __name__ == '__main__':
     net = MyNet()
     inputs, labels = net.get_data()
     for i in range(1000):
         for index, input in enumerate(inputs):
             # 这里不加.float()会报错，可能是数据格式的问题吧
             input = torch.from_numpy(input).float()
             label = torch.Tensor(labels[index])
             net.train(input, label)
     # 简单测试一下
     c = torch.Tensor([[5.6, 2.7, 4.2, 1.3]])
     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

 tensor([[0.5392]], grad_fn=<AddmmBackward>)

不用手写反向传播和梯度下降 是多么幸福一件事~