Neural networks are necessary when a heuristic approach is required to solve a problem. Let's explore a basic neural network using the following example: 

import torch
import torch.nn.functional as F

# replace following class code with an easy sequential network
class Net(torch.nn.Module):
 def __init__(self, n_feature, n_hidden, n_output):
 super(Net, self).__init__()
 self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer
 self.predict = torch.nn.Linear(n_hidden, n_output) # output layer

def forward(self, x):
 x = F.relu(self.hidden(x)) # activation function for hidden layer
 x = self.predict(x) # linear output
 return x

net1 = Net(1, 10, 1)

Following is the easiest and fastest way to build your network:

 net2 = torch.nn.Sequential(
 torch.nn.Linear(1, 10),
 torch.nn.ReLU(),
 torch.nn.Linear(10, 1)
)

print(net1) # net1 architecture
"""
Net (
 (hidden): Linear (1 -> 10)
 (predict): Linear (10 -> 1)
)
"""

print(net2) # net2 architecture
"""
Sequential (
 (0): Linear (1 -> 10)
 (1): ReLU ()
 (2): Linear (10 -> 1)
)
"""

The output of the preceding code is as follows:

Net(
  (hidden): Linear(in_features=1, out_features=10, bias=True)
  (predict): Linear(in_features=10, out_features=1, bias=True)
)
Sequential(
  (0): Linear(in_features=1, out_features=10, bias=True)
  (1): ReLU()
  (2): Linear(in_features=10, out_features=1, bias=True)
)

Out[1]:
'
Sequential (
  (0): Linear (1 -> 10)
  (1): ReLU ()
  (2): Linear (10 -> 1)
)
'