Activation　functions　play　an　important　role　in　deep　learning　and　its　choice　has　a　significant　effect　on　the　training　and　performance　of　a　model.　In　this　study,　a　new　variant　of　Exponential　Linear　Unit　(ELU)　activation　called　Transformed　Exponential　Linear　Unit　(TELU)　is　proposed.　An　empirical　evaluation　is　done　to　determine　the　effectiveness　of　the　new　activation　function　using　state-of-the-art　deep　learning　architectures.　From　the　experiments,　TELU　activation　function　tends　to　work　better　than　the　conventional　activations　functions　on　deep　models　across　a　number　of　benchmarking　datasets.　TELU　achieves　superior　classification　accuracy　on　Cifar-10,　SVHN　and　Caltech-101　dataset　on　state-of-the-art　deep　learning　models.　Additionally,　it　shows　superior　AUROC,　MCC,　and　F1-score　on　the　STL-10　dataset.　This　proves　that　TELU　can　be　successfully　applied　in　deep　learning　for　image　classification.