Suppose we use a neural network to classify images of handwritten digits (0-9). Which of the following activation functions is most suitable at the output layer? Softmax ReLU Tanh Sigmoid
Question
Suppose we use a neural network to classify images of handwritten digits (0-9). Which of the following activation functions is most suitable at the output layer? Softmax ReLU Tanh Sigmoid
Solution
The most suitable activation function at the output layer for a neural network classifying images of handwritten digits (0-9) would be the Softmax function.
Here's why:
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The task at hand is a multi-class classification problem, where an image can belong to one of 10 classes (digits 0-9).
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The Softmax function is specifically designed for multi-class classification problems in neural networks. It generates a probability distribution over n output classes, and the sum of all the probabilities will be 1. This means that the output can be interpreted as the probability of the input image being a certain digit.
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On the other hand, ReLU (Rectified Linear Unit), Tanh (Hyperbolic Tangent), and Sigmoid are more commonly used in the hidden layers of a neural network.
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While Sigmoid could also output probabilities, it is more suited for binary classification problems.
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ReLU is not suitable as it does not output probabilities and Tanh, while it does output in a range (-1, 1), it does not provide a probability distribution over classes.
So, in this case, Softmax would be the most suitable activation function for the output layer.
Similar Questions
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