教程Python代碼如下：

import torch

import torch.nn as nn

import torch.nn.functional as F

# option 1 (create nn modules)

class NeuralNet(nn.Module):

def __init__(self, input_size, hidden_size):

super(NeuralNet, self).__init__()

self.linear1 = nn.Linear(input_size, hidden_size)

self.relu = nn.ReLU()

self.linear2 = nn.Linear(hidden_size, 1)

self.sigmoid = nn.Sigmoid()

def forward(self, x):

out = self.linear1(x)

out = self.relu(out)

out = self.linear2(out)

out = self.sigmoid(out)

return out

# option 2 (use activation functions directly in forward pass)

class NeuralNet(nn.Module):

def __init__(self, input_size, hidden_size):

super(NeuralNet, self).__init__()

self.linear1 = nn.Linear(input_size, hidden_size)

self.linear2 = nn.Linear(hidden_size, 1)

def forward(self, x):

out = torch.relu(self.linear1(x))

out = torch.sigmoid(self.linear2(out))

return out