I want to make a model which predicts the future response of the input signal, the architecture of my network is [3, 5, 1]:
My questions are:
The primary reason why bias is required in neural networks is that, without bias weights, your model would have very limited movement when looking for a solution.
same bias value per layer but different bias value per layer; different bias values across all neurons in the NN.
Usually we have one bias value per neuron (except input layer), i.e. you have to have a bias vector per layer with the length of the vector being the number of neurons in that layer. The biases are (almost always) individual to each neuron. The exception is in some modern neural networks with weight sharing.
Perceptron Bias Term The addition of the bias term is helpful because it serves as another model parameter (in addition to weights) that can be tuned to make the model's performance on training data as good as possible. The default input value for the bias weight is 1 and the weight value is adjustable.
So, I think it'd clear most of this up if we were to step back and discuss the role the bias unit is meant to play in a NN.
A bias unit is meant to allow units in your net to learn an appropriate threshold (i.e. after reaching a certain total input, start sending positive activation), since normally a positive total input means a positive activation.
For example if your bias unit has a weight of -2 with some neuron x, then neuron x will provide a positive activation if all other input adds up to be greater then -2.
So, with that as background, your answers:
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