How can I calculate confidence level of my ML Model for each Image of ResNet9
Question:
I made the ML Model using the Resnet9 algorithm. In this model, I have 38 classes.
After the prediction, It returns the tuple like this:
It is returning negative values. In the above case, the highest value is -204.0966 means it has the highest chance of being the correct one.
I want to make it in percentage to see what the confidence(Accuracy) of that image. If anyone wants to see the code please tell me.
Answers:
You should apply the softmax function, which maps vector values to values that sum to 1 (it’s a way to get a probability distribution) and multiplies the result by 100 to get percentages:
import torch
torch.nn.functional.softmax(model_output) * 100
I made the ML Model using the Resnet9 algorithm. In this model, I have 38 classes.
After the prediction, It returns the tuple like this:
It is returning negative values. In the above case, the highest value is -204.0966 means it has the highest chance of being the correct one.
I want to make it in percentage to see what the confidence(Accuracy) of that image. If anyone wants to see the code please tell me.
You should apply the softmax function, which maps vector values to values that sum to 1 (it’s a way to get a probability distribution) and multiplies the result by 100 to get percentages:
import torch
torch.nn.functional.softmax(model_output) * 100