How to use False Positives metric in Tensorflow 2.0?

Question

In Tensorflow 2.0, I'm trying to build a model that classifies my objects onto two categories: positives and negatives.

I want to use tf.keras.metrics.FalsePositives and tf.keras.metrics.FalseNegatives metrics to see how the model improves with every epoch. Both of these metrics have assertions stipulating: [predictions must be >= 0] and [predictions must be <= 1].

The problem is that an untrained model can generate an arbitrary number as a prediction. But even a trained model can sometimes produce an output slightly above 1 or slightly below 0.

Is there any way to disable these assertions?

Alternatively, is there any suitable activation function that forces the model outputs into [0, 1] range without causing any problems with the learning rate?

Answer 1

The sigmoid activation function is a suitable alternative if outputs must be in the range [0, 1] as it also ranges from 0 t0 1.