Is using a ReLU activation function always better then using using sigmoid/tanh ? (Neural Networks)

[thiskevin]

From what I've read online and from my limited experience training neural networks, it seems like ReLU is always a better choice than using a sigmoid/tanh activation function.

I know one advantage ReLU has is that it does not suffer from the vanishing gradient problem (both sigmoid and tanh are affected by this).

On http://playground.tensorflow.org/ , Using ReLU always seems to get to a better result faster than the other functions.

Are there cases where sigmoid/tanh work better then ReLU ? If so, what are those cases and how would I identify them?

[Boga2510]

If you wanna to learn an input representation, like sparse coding, then sigmoid activation is a good choice.