I have designed a Convolutional Neural Network in Keras for image classification with several convolution/max-pooling layers, one densely connected hidden layer and softmax activation on the final layer. I want to replace softmax with an SVM or Random Forest in the final layer to see if that yields a better accuracy. Is there any way to do it in Keras?
1 Answers
2
votes
In order to have (kind of) SVM simply use a hinge loss instead of log loss. Putting RF does not make sense, as you need a differentiable model to be a part of neural net (unless all you want to do is to train a network, and later chop off its final part and use it as a feature detector which is just fed into RF, but this is not a valid approach in general).
