Choosing the right activation function for the output layer in a neural network is crucial as it determines the nature of the predictions your model makes. Here’s a straightforward guide to help you decide between using a linear unit or a sigmoid activation in the output layer.
Use a Linear Unit When:
- Your output is continuous: If you’re predicting real-valued numbers (e.g., price of a house, temperature, stock prices), a linear activation function is the best choice. It allows the output to take any value within the range of real numbers, which is essential for regression tasks.
- No inherent boundaries: Linear activation doesn’t impose constraints like upper or lower limits, making it ideal for scenarios where the output values can be large or negative.
Use a Sigmoid Function When:
- Your output is probabilistic: Sigmoid activation squashes the output into a range between 0 and 1, making it perfect for binary classification tasks where you need to model the probability of a single class.
- You’re working with binary labels: If your target variable is either 0 or 1, sigmoid ensures your model outputs values interpretable as probabilities.
Why the Choice Matters Using the wrong activation function can limit your model’s performance. For instance, using a sigmoid function in regression can constrain your output to [0, 1], which doesn’t make sense for predicting something like temperature. Similarly, using a linear activation for binary classification won’t provide probabilistic outputs, complicating the decision threshold.
In summary, the rule of thumb is:
- Go linear for regression tasks.
- Go sigmoid for binary classification.
Choosing wisely ensures your model aligns with the task at hand and delivers meaningful predictions.