PyTorch学习笔记（5）

多维输入的情况

需要自行下载数据集diabetes.csv.gz
根据视频

import torch
import numpy as np

xy = np.loadtxt('diabetes.csv.gz', delimiter=',', dtype=np.float32)
x_data = torch.from_numpy(xy[:-1, :-1])
y_data = torch.from_numpy(xy[:-1, [-1]])
x_test = torch.from_numpy(xy[[-1], :-1])
y_test = torch.from_numpy(xy[[-1], [-1]])

class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear1 = torch.nn.Linear(8, 6)
        self.linear2 = torch.nn.Linear(6, 4)
        self.linear3 = torch.nn.Linear(4, 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()
criterion = torch.nn.BCELoss(size_average=True)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

for epoch in range(1000):
    y_pre = model(x_data)
    loss = criterion(y_pre, y_data)
    print(epoch,'loss=',loss.item())
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

y_pre= model(x_test)
print("y_pre = ", y_pre.item())
print("y_test = ", y_test.item())