How to caculate numWeightElements in network?

I have a problem that when I code:

net=configure(net,inputs,targets)

When targets is 1 output I Think function to caculation numWeightElements(NWE) is:

NWE=m*n+n+n+o;(1)

Where:

- m: number of input layers

- n: number of hidden layers

- o: number of output layers

But when it is 2 output

Example: for 2 output

inputs: 25x550

outputs: 2x550

m = 25;

n =5 ;

o = 2;

Equation(1)is = 137 and It is not truth when I checked net.numWeightElements is 142

Can anyone help me explain this?

Thanks

LXT

Answer 1

[Ayush Aniket]

The discrepancy in the calculation of the number of weight elements net.numWeightElements arises from the structure of the neural network and the connections between layers. Let's break down the calculation for a neural network with one hidden layer and two outputs:

1. Inputs to Hidden Layer:

• Number of inputs (m) = 25
• Number of neurons in the hidden layer (n) = 5
• Weights from inputs to hidden layer: m * n = 25 * 5 = 125
• Biases for hidden layer neurons: n = 5

2. Hidden Layer to Output Layer:

• Number of outputs (o) = 2
• Weights from hidden layer to output layer: n * o = 5 * 2 = 10
• Biases for output layer neurons: o = 2

The total number of weight elements is the sum of all weights and biases:

Total Weights = (m * n) + n + (n * o) + o = (25 * 5) + 5 + (5 * 2) + 2 = 125 + 5 + 10 + 2 = 142

This matches the value you observed (net.numWeightElements = 142). The original equation you used didn't account for all biases and connections between layers.

[Le Xuan Thang]

It've been 4 since 2019 :D. Btw, thank you so much. Its correct.