Hi all,
I have 2 different network models which I'm trying to connect so they will work together.
Let's say that we have a picture of a dog
For example, NM1 predicts that it's a cat on the picture with a probability 0.52 and that it's a cat with a probability 0.48.
NM2 predicts that it's a dog with a probability 0.6 and that it's a cat with a probability 0.4.
NM1 - will predict wrong
NM2 - will predict correctly
NM1 + NM2 - connection will predict correctly (because 0.48 + 0.6 > 0.52 + 0.4)
That's the best way I can explain what result I'm looking for.
So, I have 2 different network models and in the end, I connect last layers from NM1 and NM2 with ConcatLayer.
layer {
name: "concat"
bottom: "fc8"
bottom: "fc8N"
top: "out"
type: "Concat"
concat_param {
axis: 1
}
}
And it's even working. Network is training, however very slow.
Thus, my question is, does it work like it supposed too?
Or maybe I just don't understand what concat layer do and it's connecting layers in some other way?
Thank you for your answers!