Discover why ReLU activation function is unsuitable for the output layer in image classification models and learn about better alternatives like Softmax and Sigmoid.
Table of Contents
Question
Which of the following activation function can not be used in the output layer of an image classification model?
A. ReLu
B. Softmax
C. Sigmoid
D. None
Answer
A. ReLu
Explanation
Understand Activation Functions in Image Classification Models
The choice of activation function for the output layer of an image classification model is crucial for obtaining accurate predictions. Among the options presented, ReLU (Rectified Linear Unit) is the one that cannot be used in the output layer of an image classification model. Let’s explore why this is the case and discuss more suitable alternatives.
Why ReLU is Unsuitable for the Output Layer
ReLU (Rectified Linear Unit) is a popular activation function for hidden layers in neural networks, but it has limitations that make it inappropriate for the output layer of image classification models:
- Unbounded output: ReLU outputs values in the range [0, ∞), which doesn’t align with the probability distribution required for classification tasks.
- Lack of normalization: ReLU doesn’t normalize outputs, making it difficult to interpret results as class probabilities.
- Non-differentiability at zero: This can cause issues during backpropagation for the final layer.
Better Alternatives for the Output Layer
For image classification tasks, more suitable activation functions for the output layer include:
- Softmax: Ideal for multi-class classification problems, as it outputs a probability distribution over all classes.
- Sigmoid: Suitable for binary classification tasks, producing outputs between 0 and 1.
These functions provide normalized outputs that can be interpreted as probabilities, making them more appropriate for classification tasks.
While ReLU is an excellent choice for hidden layers in neural networks, it is not suitable for the output layer of image classification models. Instead, Softmax (for multi-class problems) or Sigmoid (for binary classification) are preferred due to their ability to produce normalized, probability-like outputs.
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