处理多维数据输入

1，代码如下：

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

xy = np.loadtxt('deep_learn/diabetes.csv.gz',delimiter=',',dtype=np.float32)

print(xy)

x_data = torch.from_numpy(xy[:,:-1])
y_data = torch.from_numpy(xy[:,[-1]])

class Model(torch.nn.Module):
    def __init__(self) -> None:
        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(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(),lr=0.1)

epoch_list = []
loss_list = []

for epoch in range(100):
    # Forward
    y_pred = model(x_data)
    loss = criterion(y_pred,y_data)
    print(epoch,loss.item())
    epoch_list.append(epoch)
    loss_list.append(loss.item())

    # Backward
    optimizer.zero_grad()
    loss.backward()
    
    # Update
    optimizer.step()
 
plt.plot(epoch_list, loss_list)
plt.ylabel('loss')
plt.xlabel('epoch')
plt.show()