Predict_proba for Binary classifier in Tensorflow I'm working in binary classifier problem, where I have used Tensorflow low level API's.
Last layer wrapped with Sigmoidal Function and just returning a  single value.

For my prediction I just set a standard threshold value of 0.5 and hence if it's >0.5, I'm predicting as 1, otherwise 0.

I'm also looking for a confidence level of each prediction. Is it possible to get the same with this architecture, or does it need some modification?
Since a neural net that ends with a sigmoid activation outputs probabilities, you can take the output of the network as is. 

If you're referring to scikit-learn's predict_proba, it is equivalent to taking the sigmoid-activated output of the model in tensorflow. In fact that's exactly what scikit-learn does.

E.g. if tour model outputs $0.8$ for class $1$, you would classify this as $1$ (since $0.8 &gt; 0.5$), with a probability of $0.8$. S