Log loss vs accuracy for deciding between different learning rates?
According to me, it is not correct to co-relate loss with accuracy.


  Loss is used to optimize the hypothesis such that we can get best
  weights whereas accuracy is used to identify how well model is doing in term
  of correctly predicting the values.


Model internally takes the reference of predict_proba() and returns 1 if probability is > .5 otherwise 0. For example if returned predict_proba() is (.49, .51), model will return 1 as an classification output.

Now consider a use case where some trained model is used for test-data prediction. Assumed such model has 100% accuracy but predict_proba() value close to (.49,.51) or (.51,.49) i.e. having very low confidence level.

In such case logloss is quite high, even though it's having 100% accuracy.

If our criteria for model selection is "accuracy value" then we are selecting the bad model.