The softmax layer (softmax(wx + b) is a classifier, that is trained on the last fully-connected layer, and backpropagates a gradient so that the rest of the network is trained as well.
SVM is a different classifier, that they connected to the same input (x, the output of the last fully-connected layer) and that they trained (without backpropagation I think). There is sometimes confusion in the literature between the softmax operation itself (exp(x) / exp(x).sum(), that converts unnormalized log-probabilities into a probability vector) and the "softmax layer", or "logistic regression layer" (softmax(Wx+b)). On Thu, Nov 24, 2016, Beatriz G. wrote: > Hi Everyone, I am trying to build a cnn based in imagenet. The paper which > I am following sais that the architecture is formed by convolutional layers > and fully connected layers, and in the last layer, i.e. output layer is > followed by softmax. Then, it sais that after extracting the features from > the last fully connected layer, uses a SVM as a classifier. > > I do not know if the input of the classifier is the output of the softmax. > > And I thought that the softmax was a classifier, and I must be wrong > > > Regards. > > -- > > --- > You received this message because you are subscribed to the Google Groups > "theano-users" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > For more options, visit https://groups.google.com/d/optout. -- Pascal -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
