I have a network with 3 features and 4 vector outputs. How is MSE and accuracy metric calculated?
It’s not advised to calculate accuracy for continuous values. For such values you would want to calculate a measure of how close the predicted values are to the true values. This task of prediction of continuous values is known as regression. And generally R-squared value is used to measure the performance of the model.
If the predicted output is of continuous values then mean square error is the right option 
For example:
Predicted o/p vector1-----> [2,4,8] and
Actual o/p vector1 -------> [2,3.5,6]
1.Mean square error is  sqrt((2-2)^2+(4-3.5)^2+(8-6)^2 )
2.Mean absolute error..etc.
(2)if the output is of classes then accuracy is the right metric to decide on model performance
Predicted o/p vector1-----> [0,1,1]
Actual o/p vector1 -------> [1,0,1]
Then accuracy calculation can be done with following:
1.Classification Accuracy
2.Logarithmic Loss
3.Confusion Matrix
4.Area under Curve
5.F1 Score
I understand how it works when you have one column output but could not understand how it is done for 4 column outputs.