Is it possible to customize the activation function in scikit-learn's MLPClassifier? Scikit-learn lists these as the implemented activation functions for it's multi-layer perceptron classifier:

‘identity’, no-op activation, useful to implement linear bottleneck, returns f(x) = x
‘logistic’, the logistic sigmoid function, returns f(x) = 1 / (1 + exp(-x)).
‘tanh’, the hyperbolic tan function, returns f(x) = tanh(x).
‘relu’, the rectified linear unit function, returns f(x) = max(0, x)


Does Anyone know if it is possible to implement a custom activation function? If not, can someone point me to a library where this is possible? 

Thanks!
Although @Emre's comment that sklearn wasn't specifically made for the task is correct, nothing stops you from introducing another activation function by implementing it in sklearn/neural_network/_base.py. You should be able to find the file in your installation.

To this end, you would define your single-parameter function and add it in the ACTVATIONS list. Refer to the existing implementations for some guidance how to do it.