Classes in the torch.nn module
Torch.nn contains various classes and modules. Some of them are:

Parameters
This torch.nn.Parameter() subclass can store learnable initial states and hidden states of models.

Containers
This container class uses nn.Container() subclass to create complex neural networks.
Examples  torch.nn.Sequential() is used to combine different layers, torch.nn.ParameterDict() to store the parameters in a dictionary, etc.

Layers
We can configure various trainable layers in a neural network using torch.nn.
Examples  Padding layers add value to the sides of a tensor, Recurrent layers, Sparse layers, etc.

Functions
Torch.nn module contains various loss functions to evaluate the error between the input and the target values. Example  torch.nn.L1Loss() is used to calculate the mean absolute error between input and output, torch.nn.CrossEntropyLoss(), etc.
Torch.nn also contains functions to calculate the distance between two parameters, such as torch.nn.CosineSimilarity() to calculate the cosine similarity between two variables.
Please refer to this link to learn more about these classes and modules.
Implementation of torch.nn functions
Let's build some neural network architectures using the torch.nn module.
import torch
import torch.nn.functional as F
#Sample 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)) #relu activation function for hidden layer
x = self.predict(x) #output
return x
network_1 = Net(2, 11, 2) #example
print(network_1)
Output
Net(
(hidden): Linear(in_features=2, out_features=11, bias=True)
(predict): Linear(in_features=11, out_features=2, bias=True)
)
Another example:
#faster way to build neural network
network_2 = torch.nn.Sequential(
torch.nn.Linear(2, 11),
torch.nn.ReLU(),
torch.nn.Linear(11, 2)
)
print(network_2)
Output
Sequential(
(0): Linear(in_features=2, out_features=11, bias=True)
(1): ReLU()
(2): Linear(in_features=11, out_features=2, bias=True)
)
Let us see how to use the crossentropy loss function using the torch.nn module.
from torch import nn
loss_fun = nn.CrossEntropyLoss()
input = torch.tensor([[0.1,0.2,0.3,0.4]],dtype=torch.float) #sample input
target = torch.tensor([0], dtype=torch.long) #sample output
loss_fun(input, target)
Output
tensor(1.5425)
Now calculate the cosine similarity between two random tensors using the torch.nn module.
import torch
import torch.nn.functional as F
tensor1 = torch.randn(50) #random tensor of size=50
tensor2 = torch.randn(50) #random tensor of size=50
cosine_similarity_value = F.cosine_similarity(tensor1, tensor2, dim=0, eps=1e6)
#eps is a small value to avoid division by zero
print(cosine_similarity_value)
Output
tensor(0.0819)
Frequently Asked Questions
1. What is the torch.nn module in Python?
The torch.nn module helps in developing and building neural networks quickly.
2. What is nn.linear in PyTorch?
nn.linear(n,m) module takes n inputs to create a singlelayer feedforward network with m outputs.
3. What is PyTorch?
PyTorch is a library in Python that helps in developing neural networks conveniently.
4. How can you calculate crossentropy loss using the torch.nn module?
We can use the torch.nn.CrossEntropyLoss() function of torch.nn for calculation.
5. What does nn.sequential do?
It helps to run layers sequentially quickly.
Conclusion
This article discussed the torch.nn module in PyTorch, its uses, and the implementation of various functions and methods present in it.
Check out this article  Padding In Convolutional Neural Network
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