Activation functions are a crucial component of neural networks. They introduce non-linearities to our model, enabling it to learn complex patterns. PyTorch provides a variety of activation functions you can use in our models.
import torch import torch.nn as nn import torch.nn.functional as F
Here are some of the common activation functions:
This is the most commonly used activation function. It returns the input for all positive values of input, and returns 0 for all negative values of input.
relu = nn.ReLU() x = torch.tensor([-1.0, 1.0, 0.0]) output = relu(x) print(output) # Returns tensor([0., 1., 0.])
This activation function squashes the input to a range between 0 and 1. It is often used in the output layer of a binary classification problem.
sigmoid = nn.Sigmoid() x = torch.tensor([-1.0, 1.0, 0.0]) output = sigmoid(x) print(output) # Returns tensor([0.2689, 0.7311, 0.5000])
This activation function squashes the input to a range between -1 and 1.
tanh = nn.Tanh() x = torch.tensor([-1.0, 1.0, 0.0]) output = tanh(x) print(output) # Returns tensor([-0.7616, 0.7616, 0.0000])
In a neural network, these activation functions are usually applied after linear transformations. Here’s an example of a simple network with a ReLU activation:
class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.fc1 = nn.Linear(10, 20) self.fc2 = nn.Linear(20, 1) def forward(self, x): x = F.relu(self.fc1(x)) x = self.fc2(x) return x net = Net()
In this network, we apply a ReLU activation function after the first linear layer. Note that we didn’t have to create an instance of nn.ReLU. Instead, we used the F.relu function from torch.nn.functional, which is a stateless version of the same function.
And that’s it! We now understand what activation functions are, how they’re used in PyTorch, and how to include them in your own neural networks.