Contents
Why is cross-entropy better than MSE for classification?
Practical understanding: First, Cross-entropy (or softmax loss, but cross-entropy works better) is a better measure than MSE for classification, because the decision boundary in a classification task is large (in comparison with regression). For regression problems, you would almost always use the MSE.
Why does tanh work better than sigmoid?
tanh function is symmetric about the origin, where the inputs would be normalized and they are more likely to produce outputs (which are inputs to next layer)and also, they are on an average close to zero. These are the main reasons why tanh is preferred and performs better than sigmoid (logistic).
Does tanh as output activation work with cross-entropy loss?
Yes we can, as long as we use some normalizor (e.g. softmax) to ensure that the final output values are in between 0 and 1 and add up to 1. If you’re doing binary classification and only use one output value, only normalizing it to be between 0 and 1 will do.
What is the range of activation which can be obtained when using the hyperbolic tangent function?
Tanh or hyperbolic tangent Activation Function tanh is also like logistic sigmoid but better. The range of the tanh function is from (-1 to 1). tanh is also sigmoidal (s – shaped).
Why is MSE bad for classification?
There are two reasons why Mean Squared Error(MSE) is a bad choice for binary classification problems: If we use maximum likelihood estimation(MLE), assuming that the data is from a normal distribution(a wrong assumption, by the way), we get the MSE as a Cost function for optimizing our model.
What activation function should I use in the case of only in hidden layers?
The rectified linear activation function, or ReLU activation function, is perhaps the most common function used for hidden layers. It is common because it is both simple to implement and effective at overcoming the limitations of other previously popular activation functions, such as Sigmoid and Tanh.
What is hyperbolic tangent used for?
Hyperbolic functions can be used to describe the shape of electrical lines freely hanging between two poles or any idealized hanging chain or cable supported only at its ends and hanging under its own weight.