Can different layers use different activation functions?

Yes. Different layers in the same neural network can use different activation functions.

This is common because different layers may serve different purposes.

Hidden layers

Hidden layers often use nonlinear activations such as:

• ReLU
• Leaky ReLU
• GELU
• Swish/SiLU
• Tanh

The choice depends on the architecture and training behavior.

Output layer

The output layer often uses an activation that matches the task.

For example:

• Regression: linear activation
• Binary classification: sigmoid
• Multiclass classification: softmax
• Multilabel classification: independent sigmoid outputs

Can hidden layers also use different activations?

Yes.

For example, one part of a model may use ReLU while another specialized component uses tanh or sigmoid.

Some architectures deliberately combine activation functions because different components have different mathematical roles.

Should you mix activation functions randomly?

No.

Using different activations should be based on the purpose of each layer, the architecture, or validation results.

Mixing them without a reason can make the model harder to understand and tune.

Simple example

A classification network could use:

Input → ReLU hidden layer → ReLU hidden layer → Softmax output

Here, the hidden layers use ReLU to learn nonlinear features, while the output layer uses softmax to produce class probabilities.

For guidance on selecting them, see “How do you choose an activation function for a neural network?”.

In short, different layers can use different activation functions, and the activation should match the role of each layer rather than being identical throughout the network.