Activation Functions for Humans: Sigmoid, Tanh, and ReLU (Deep Dive)
🤖 What Are Activation Functions?
Activation functions are what make neural networks non-linear and capable of learning complex patterns. They control whether a neuron "fires" and how strong its output signal is, based on the weighted input.
In simpler terms:
> Think of them like dimmer switches for your brain cells — deciding how much light (signal) should come through. >
In technical terms: Given an input value , the activation function transforms it to produce the neuron's output .
🔁 What Role Do They Play in Training?
Activation functions are used during forward propagation after computing:
Then:
Where is the activation function.
They allow the network to:
- Learn complex patterns
- Avoid collapsing into just a linear regression model
- Enable gradient descent to work during backpropagation
🧪 Refresher: What is Gradient Descent?
Gradient descent is how neural networks learn. Imagine you’re walking downhill in fog trying to find the lowest point (lowest error).
Steps:
- Compute how steep the slope is (gradient)
- Take a small step downhill
- Repeat
If the slope (gradient) is near-zero, your steps get smaller — that’s the vanishing gradient problem.
🧠 Comparing Activation Functions
1. Sigmoid Function
- Output: 0 to 1
- Looks like an S-curve
- Used often in the output layer for binary classification
Pros:
- Gives probability-like outputs
- Intuitive interpretation
Cons:
- Not zero-centered → gradients can zigzag
- Vanishing gradient for very large/small
🧠 Veer Notes: “If we move the weight to the right, the slope increases, but the change is so minimal in extremes that it becomes bad for lots of calculations.” ✔️
2. Tanh Function (Hyperbolic Tangent)
- Output: 1 to 1
- Also an S-curve
- Often used in hidden layers
Pros:
- Zero-centered → faster learning
- Stronger gradients than sigmoid
Cons:
- Still suffers from vanishing gradients (less than sigmoid)
🧠 Veer Notes: “Since it goes from -1 to 1 we get a more detailed view of success vs. failure. It's useful in hidden layers.” ✔️
3. ReLU (Rectified Linear Unit)
- Output: 0 to ∞
- Very simple: zero if input is negative, identity if positive
Pros:
- Fast to compute
- Doesn’t squash gradients → avoids vanishing
- Works well in deep networks
Cons:
- Dying ReLU problem: if too often, neuron outputs 0 forever
🧠 Veer Notes: “It doesn’t respond when results are negative, but if they’re positive, it just keeps going. That’s why it’s strong — but it can die.” ✔️
4. Leaky ReLU
- Same as ReLU, but negative inputs get a small slope instead of flat zero
Pros:
- Fixes dying ReLU
- Lets gradient flow even for negative
🧠 Veer Notes: “So that’s why you use Leaky ReLU — it keeps neurons alive that would otherwise die when they get negative inputs.” ✔️
📊 Summary Table
| Function | Output Range | Zero-Centered | Vanishing Gradients? | Best Used For |
|---|---|---|---|---|
| Sigmoid | (0, 1) | ❌ No | ✅ Yes | Output layer (binary classification) |
| Tanh | (-1, 1) | ✅ Yes | ⚠️ Sometimes | Hidden layers (classic networks) |
| ReLU | (0, ∞) | ❌ No | ❌ Rarely | Hidden layers (modern deep nets) |
| Leaky ReLU | (~−∞, ∞) | ❌ No | ❌ No | Deeper nets with risk of dying ReLUs |
🏁 Final Analogy (Veer-Style 🧢)
- Sigmoid = Like deciding how much you agree (
0 = no, 1 = yes) - Tanh = Like saying how strongly you agree or disagree (-
1 = no way, +1 = absolutely) - ReLU = Like only listening to good news. Bad input? Silent. Good input? Amplify it.
- Leaky ReLU = Like having a tiny backup mic for when the good news guy gets too quiet.
💬 Your Takeaway
> All activation functions help train the model by shaping how information flows. > > > Sigmoid is great for binary answers. Tanh adds balance for inner reasoning. > > ReLU says “yes loudly” or “nothing at all,” and Leaky ReLU is its backup plan. > > The better the activation, the more efficient your learning becomes — just like switching from a spoon to a shovel when digging your ideas deeper. >