Visual Roadmap – Activation Function Relevancy in Neural Network
Activation Function Selection Guide (Decision Tree – Text Version)
START | |--> Is it the **output layer**? | | | |--> YES | | | | | |--> Is it a **binary classification** (e.g., yes/no)? | | | | | |--> YES → Use **SIGMOID** | | | | | |--> NO → Use **SOFTMAX** (for multi-class classification) | | | |--> NO (It's a hidden layer) | |--> Are the inputs **centered around zero** (include both positive and negative values)? | |--> YES → Use **TANH** | |--> NO → Use **RELU**
Summary of When to Use Each:
| Function | Use When… | Example Use Case |
|---|---|---|
| ReLU | Hidden layers in deep networks, inputs are non-negative | Image recognition |
| Sigmoid | Output layer for binary classification | Spam detection |
| Softmax | Output layer for multi-class classification | Handwritten digit recognition |
| Tanh | Hidden layers when data is centered (negatives + positives) | Sentiment analysis |
Activation Function Decision Chart
## Activation Function Selection Guide
### Is this the **output layer**?
– **Yes**
– Is this a **binary classification** (Yes/No)?
– Yes → Use **`Sigmoid`**
– No → Use **`Softmax`** (for multi-class output)– **No (it’s a hidden layer)**
– Are inputs **centered around zero** (both positive & negative)?
– Yes → Use **`Tanh`**
– No → Use **`ReLU`**
Summary Table
| Activation Function | Use When… | Example Use Case |
|———————|————————————————–|——————————–|
| `ReLU` | Hidden layers with non-negative input values | Image recognition, deep CNNs |
| `Sigmoid` | Binary output layer | Spam detection, medical tests |
| `Softmax` | Multi-class output layer | Digit recognition (0–9) |
| `Tanh` | Hidden layers with zero-centered data | Sentiment analysis, text data |