Activation Function vs Loss Function
Both activation functions and loss functions are essential in neural networks, but they serve different roles.
1️⃣ Activation Function
🔹 Purpose:
- Adds non-linearity to the network.
- Determines the output of a neuron in hidden or output layers.
- Helps the network learn complex patterns.
🔹 Examples:
- ReLU (Rectified Linear Unit) → Used in hidden layers.
- Sigmoid → Used in binary classification outputs.
- Softmax → Used in multi-class classification outputs.
- Tanh → Used in hidden layers.
🔹 Example in PyTorch:
import torch.nn.functional as F
x = torch.tensor([1.0, -2.0, 3.0])
relu_output = F.relu(x) # Applies ReLU activation
print(relu_output) # tensor([1., 0., 3.])
2️⃣ Loss Function
🔹 Purpose:
- Measures how far the predicted output is from the actual target.
- Guides the training process by minimizing error using backpropagation.
🔹 Examples:
- Mean Squared Error (MSE) → For regression tasks.
- Cross-Entropy Loss → For classification tasks.
- Binary Cross-Entropy → For binary classification.
🔹 Example in PyTorch:
import torch
loss_fn = torch.nn.CrossEntropyLoss()
predictions = torch.tensor([[2.0, 1.0, 0.1]]) # Logits (before softmax)
target = torch.tensor([0]) # Correct class
loss = loss_fn(predictions, target)
print(loss) # Loss value
🔑 Key Differences
| Feature | Activation Function | Loss Function |
|---|---|---|
| Purpose | Transforms neuron output | Measures error |
| Used in | Hidden & output layers | Optimization process |
| Affects | Network non-linearity & learning | Gradient updates (backpropagation) |
| Examples | ReLU, Sigmoid, Tanh, Softmax | MSE, Cross-Entropy, Binary Cross-Entropy |
| Applied to | Each neuron | Entire model output |
🛠️ When to Use Each?
- Use an activation function in hidden and output layers to introduce non-linearity.
- Use a loss function to evaluate model performance and adjust weights during training.
🚀 Final Thought
✅ Activation functions help the model make predictions.
✅ Loss functions evaluate how good or bad those predictions are.
Let me know if you need more details! 🚀