CODEWITHSUNDEEP
pythonkerasneural-networkbias-neuron
Robin
Robin

Reputation: 81

Train bias in neural network as weighted sum of seperate inputs

I am currently trying to implement the min-max relevance model from page 217 in this paper: https://reader.elsevier.com/reader/sd/pii/S0031320316303582?token=3C705E0F2F8518D919BAA293EC6ABA570F1CCB83ACB67C60419737F55BDFEC9013FA2FCF3ACC4CE1887E5387315E70E8

The problem is, that I need to train a bias, which is added to a layer and itself is given as a sum of weights*inputs + bias. The latter weights should be trained.

So, I have a neural network with one hidden layer. The bias for the hidden layer is constructed like an linear regression, just input and output layer. The bias gets its own input values. I guess I have to use the functional api, but how do I add the LR output as bias-term in the hidden layer?

Upvotes: 1

Views: 87

Answers (1)

Robin
Robin

Reputation: 81

Got it, just stack/concatenate the a layer for the bias with a layer for the neurons and then add them up with a non-trainable layer.

Upvotes: 0

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