pytorch搭建簡單網絡

pytorch搭建一個簡單神經網絡

import torch
import torch.nn as nn

# 定義數據
# x:輸入數據
# y:標簽
x = torch.Tensor([[0.2, 0.4], [0.2, 0.3], [0.3, 0.4]])
y = torch.Tensor([[0.6], [0.5], [0.7]])


class MyNet(nn.Module):
    def __init__(self):
        # 調用基類構造函數
        super(MyNet, self).__init__()
        # 容器，使用時順序調用各個層
        self.fc = nn.Sequential(
            # 定義三層
            # 輸入層
            nn.Linear(2, 4),
            # 激活函數
            nn.Sigmoid(),
            # 隱藏層
            nn.Linear(4, 4),
            nn.Sigmoid(),
            # 輸出層
            nn.Linear(4, 1),
        )
        # 優化器
        # params:優化對象
        # lr:學習率
        self.opt = torch.optim.Adam(params=self.parameters(), lr=0.001)
        # 損失函數,均方差
        self.mls = torch.nn.MSELoss()

    def forward(self, inputs):
        # 前向傳播
        return self.fc(inputs)

    def train(self, x, y):
        # 訓練
        # 得到輸出結果
        out = self.forward(x)
        # 計算誤差
        loss = self.mls(out, y)
        # print('loss', loss)
        # 梯度置零
        self.opt.zero_grad()
        # 誤差反向傳播
        loss.backward()
        # 更新權重
        self.opt.step()

    def test(self, x):
        # 測試，就是前向傳播的過程
        return self.forward(x)


net = MyNet()
for i in range(10000):
    net.train(x, y)
x = torch.Tensor([[0.4, 0.1]])
out = net.test(x)
print(out)  # 輸出結果 tensor([[0.5205]], grad_fn=<AddmmBackward>)

訓練集較少，可能結果不是很好，主要是結構，畢竟剛開始接觸這個pytorch