Thank you very much guys, that is much clear for me.
Regards.
El jueves, 24 de noviembre de 2016, 23:37:55 (UTC+1), Kyle Kastner escribió:
>
> Also, normally people refer to the "last fully connected layer" as the
> layer before the "softmax layer" when using pretrained weights. This
> is becau
Also, normally people refer to the "last fully connected layer" as the
layer before the "softmax layer" when using pretrained weights. This
is because the weights associated with that last softmax layer are
intrinsically linked to the training and the softmax, while lower
layers may have more abstr
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 l
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 fro
