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 theano-users+unsubscr...@googlegroups.com.
> 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 theano-users+unsubscr...@googlegroups.com.
For more options, visit https://groups.google.com/d/optout.
