# What activation function should I use for multi variable binary TRUE output?

I have a multi vairable binary target variable like this with multiple TRUE values.

    Y1  Y2  Y3  Y4
1   0   0   1   1
2   1   1   1   1
3   1   0   1   0
4   1   0   0   1
5   0   1   1   0
6   1   1   1   0
7   0   0   0   1
8   1   1   1   0
9   1   1   1   0
10  0   1   1   0
11  0   0   0   1
12  1   1   1   1


How should I prepare my output neural network such a way that I can predict multiple true variable?

I have tried using softmax activation function in the output layer for a multi variable single TRUE classification problems whose actual value is like this

Actual output value= [0, 0, 0, 1]
Predicted output value= [0.1,   0.2,    0.1,    0.6]


Then I take argmax of the predicted array which gives output 3 which result in [0, 0, 0, 1]. But what method should I follow for a classification problem with multiple true value output.

You could simply replace the softmax in the output layer with sigmoid, and train it with the binary cross entropy loss.