I'm studying the multi-layer perceptron algorithm and I'm translating python code to golang.
I have 2 matrices. Let's call this matrix M1:
[[0 0 1 1]
[0 1 0 1]]
Let's call this matrix M2:
[[ 0.00041597 0.02185088 -0.00362142]
[-0.00057384 -0.02866677 0.00488404]
[-0.00056316 -0.02705587 0.00410378]
[ 0.00048268 0.01692128 -0.00262183]]
I'm implementing the dotProduct(M1,M2) in python and it gives me this result
[[ -8.04778516e-05 -1.01345901e-02 1.48194623e-03]
[ -9.11603819e-05 -1.17454886e-02 2.26221011e-03]]
I'm doing it in golang with the same inputs matrix(M1,M2) but the golang code returns this matrix:
[[-8.047785157755936e-05 -0.010134590118173147 0.0014819462317188985]
[-9.116038191682538e-05 -0.011745488603430228 0.0022622101145935328]]
In python I'm using numpy's dot operation:
resultMatrix = M1.dot(M2)
In go, I'm using this package to work with matrix in go
The problem here is because I calculate others dotProcut calculos with golang and it are all ok
I make N tests with other values, i'm using this package(the same dotProduct method) in others parts of this my code and all has been ok
My Go code at line 128
Tutorial Python code at line 61
Matrix golang package method that implemets the golang dotProduct at line 30
The code in python is not mine, and because this, the code it's written in Portuguese, but my go code is written in English
In python i know that's right because all the neural network works well, but in go I'm not sure
i read the matrix go package method many times and dont get the "bug code implementation", some one know where I'm wrong?
Well, actually the results are pretty much the same. The thing that might confuse you is that formatting is different but still Python's -1.01345901e-02 = -0.0101345901 (see Scientific notation and particularly its E-notation" section) which is pretty close to Go's -0.010134590118173147 and just to make it clear let's align them
Python -1.01345901e-02
Go -0.010134590118173147
So if you have any problems in your code, they probably come from some other source than matrix multiplication.