I am trying to train and predict SVHN dataset (VGG architecture). I get very high validate/test accuracy by just getting the largest output class. However, the output weights are of large positive and negative numbers. Are they supposed to parsed as exp(output)/sum(exp(output)) to be converted to probability? Thank you!
Category Data Science
The numbers to which you are referring are probably the logits of the NN. The logits have to be transformed using a activation function which could be:
Softmax ( which you showed ) for classification tasks.
Linear activation function for regression tasks.
Softmax activation function :
The mathematical notion is as follows:
$\Large S( y_i ) = \frac{ e^{y_i}}{ \sum e^{y_i} }$
It converts the logits to class probabilities that sum to 1. It is widely used in multiclass classification problems.
CNNs for classification need to produce class probabilities which are produced by a softmax activation function at the output layer.
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