I dont knwo how to use my input vector with values inside a deep learning model.
Imagine that I developed a library with many fuctions that have versions inside github. I have the analysis of wich function and the runtime that each spend to run:
• Previous Version of functions with its runtime
• Current Version of functions with its runtime
• Difference between the time (previou runtime - currente runtime)
• Name of function
I want to predict which function makes my program fail. For Example:
GIT PREVIOUS VERSION
Total runtime = 100s
Function A = 40
Function B = 50
Function C = 10
GIT CURRENT VERSION
Total runtime = 110s
Function A = 0s
Function B = 0s
Function C = 20s
New Function Z = 39s
New Function X = 52s
In the example above, I have the following analysis:
DIFFERENCE RUNTIME: PREVIOUS – CURRENT
Function A = 40 - 0 = 40s
Function B = 50 - 0 = 50s
Function C = 10 - 20 = -10s
Function Z = 0 - 39 = -39s
Function X = 0 - 52 = -52s
Using the DIFFERENCE RUNTIME I can estimate that the problem are the function C, because the function Z and function X are equivalence with function A and Function B.
wIth Python I have the vector that I use as input to predict wich function has the problem:
• X_diff
• X_prev
• X_curr