什么是矩阵:
空间转换,从N维映射到M维空间的线性变换
神经网络:非线性空间变换
# Multiple_Dimension_Input
# 处理多维数据输入
import numpy as np
import torch
xy = np.loadtxt('data/diabetes.csv.gz', delimiter=',', dtype=np.float32)
x_data = torch.from_numpy(xy[:, :-1])
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)
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)
# 训练
for epoch in range(100):
# 前馈
y_pred = model(x_data)
# 损失
loss = criterion(y_pred, y_data)
print(epoch, loss.item())
# 梯度清零
optimizer.zero_grad()
# 反向传播
loss.backward()
# 更新优化参数
optimizer.step()