快速搭建神经网络

# class Net(torch.nn.Module):     # 继承 torch 的 Module
#     def __init__(self, n_feature, n_hidden, n_output):
#         super(Net, self).__init__()     # 继承 __init__ 功能
#         self.hidden = torch.nn.Linear(n_feature, n_hidden)   # 隐藏层线性输出
#         self.out = torch.nn.Linear(n_hidden, n_output)       # 输出层线性输出
#
#     def forward(self, x):
#         # 正向传播输入值, 神经网络分析出输出值
#         x = F.relu(self.hidden(x))      # 激励函数(隐藏层的线性值)
#         x = self.out(x)                 # 输出值, 但是这个不是预测值, 预测值还需要再另外计算
#         return x
# net = Net(n_feature=2, n_hidden=10, n_output=2) # 几个类别就几个 output

"""
Net (
  (hidden): Linear (1 -> 10)
  (predict): Linear (10 -> 1)
)

net = torch.nn.Sequential(
    torch.nn.Linear(2, 10),
    torch.nn.ReLU(),
    torch.nn.Linear(10, 2)
)

"""
Sequential (
  (0): Linear (1 -> 10)
  (1): ReLU ()
  (2): Linear (10 -> 1)
)
"""