Activation Function vs Softmax
Softmax is a specific type of activation function, but not all activation functions are Softmax. Here’s a detailed comparison:
1️⃣ Activation Function
🔹 Purpose:
- Controls how neurons process and pass information to the next layer.
- Introduces non-linearity, enabling neural networks to learn complex patterns.
- Applied in hidden layers and sometimes output layers.
🔹 Examples:
- ReLU → Used in hidden layers for deep learning.
- Sigmoid → Used for binary classification.
- Tanh → Used for normalizing between -1 and 1.
- Softmax → Used in multi-class classification (special case).
🔹 Example in PyTorch:
pythonCopy codeimport torch.nn.functional as F
x = torch.tensor([-1.0, 0.0, 2.0])
relu_output = F.relu(x)
print(relu_output) # tensor([0., 0., 2.])
2️⃣ Softmax Function (A Special Activation Function)
🔹 Purpose:
- Converts raw scores (logits) into probabilities that sum to 1.
- Used only in the output layer for multi-class classification.
🔹 Formula: σ(xi)=exi∑jexj\sigma(x_i) = \frac{e^{x_i}}{\sum_{j} e^{x_j}}σ(xi)=∑jexjexi
Each output is scaled between 0 and 1, making it interpretable as a probability.
🔹 Example in PyTorch:
import torch
import torch.nn.functional as F
logits = torch.tensor([2.0, 1.0, 0.1])
softmax_output = F.softmax(logits, dim=0)
print(softmax_output) # Probabilities sum to 1
🔑 Key Differences
| Feature | Activation Function | Softmax |
|---|---|---|
| Purpose | Transforms neuron output | Converts logits to probabilities |
| Affects | Hidden & output layers | Output layer only |
| Type | Can be ReLU, Sigmoid, Tanh, etc. | A specific activation function |
| Range of Values | Varies (e.g., ReLU: [0, ∞], Tanh: [-1,1]) | [0,1] (probabilities) |
| Usage | Hidden layers, binary classification | Multi-class classification |
🛠️ When to Use Each?
- Use a general activation function (ReLU, Tanh) in hidden layers to introduce non-linearity.
- Use Softmax in the output layer when dealing with multi-class classification.