07 处理多维特征输入

import numpy as np
import torch
import matplotlib.pyplot as plt

# 载入数据集
# xy是载入的数组，touch.from_numpy把载入的数组转化为张量
xy = np.loadtxt('diabetes.csv', delimiter=',', dtype=np.float32)              # 一般用32位浮点数
x_data = torch.from_numpy(xy[:, :-1])       # 切片操作，对于多维来说，是用逗号分隔开
y_data = torch.from_numpy(xy[:, [-1]])

class Model(torch.nn.Module):
    def __init__(self):
        super(Model,self).__init__()
        self.linear1 = torch.nn.Linear(8,6)        #八位空间的向量映射到6维（空间维度变换）
        self.linear2 = torch.nn.Linear(6,4)
        self.linear3 = torch.nn.Linear(4,1)
        # self.linear4 = torch.nn.Linear(2,1)
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self,x):
        x = self.sigmoid(self.linear1(x))
        x = self.sigmoid(self.linear2(x))
        x = self.sigmoid(self.linear3(x))
        return x


model =Model()

# 优化器和损失函数
criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(),lr=0.1)

for epoch in range(1000):
    y_pred = model(x_data)
    loss = criterion(y_pred,y_data)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if epoch%10 == 0:
       # y_pred_label = torch.where(y_pred >= 0.5, torch.tensor([1.0]), torch.tensor(0.0))
      #  acc = torch.eq(y_pred_label,y_data).sum().item()/y_data.size(0)
        #print("loss=", loss.item(), "acc=", acc)
        print("loss=", loss.item())