Regression with -1,1 target range - Should we use a tanh activation in the last 1 unit dense layer?












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Say in a regression problem the target range to be between [0,1] or [-1,1], and say the last layer of the network is as 



xx = tf.layers.dense(inputs=xx, units=1, name='Prediction')


Should we use a sigmoid or tanh activation function after xx respectively to enforce the output of the 1 unit dense layer to be between [0,1] or [-1,1]?



To mention that most of the regression examples that I have seen they do not add an activation function after xx, and leave it to be open and take whatever value it ends up taking.






regression activation-function






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    $begingroup$


    Say in a regression problem the target range to be between [0,1] or [-1,1], and say the last layer of the network is as 



    xx = tf.layers.dense(inputs=xx, units=1, name='Prediction')


    Should we use a sigmoid or tanh activation function after xx respectively to enforce the output of the 1 unit dense layer to be between [0,1] or [-1,1]?



    To mention that most of the regression examples that I have seen they do not add an activation function after xx, and leave it to be open and take whatever value it ends up taking.






    regression activation-function






    share









    $endgroup$















      0












      0








      0





      $begingroup$


      Say in a regression problem the target range to be between [0,1] or [-1,1], and say the last layer of the network is as 



      xx = tf.layers.dense(inputs=xx, units=1, name='Prediction')


      Should we use a sigmoid or tanh activation function after xx respectively to enforce the output of the 1 unit dense layer to be between [0,1] or [-1,1]?



      To mention that most of the regression examples that I have seen they do not add an activation function after xx, and leave it to be open and take whatever value it ends up taking.






      regression activation-function






      share









      $endgroup$




      Say in a regression problem the target range to be between [0,1] or [-1,1], and say the last layer of the network is as 



      xx = tf.layers.dense(inputs=xx, units=1, name='Prediction')


      Should we use a sigmoid or tanh activation function after xx respectively to enforce the output of the 1 unit dense layer to be between [0,1] or [-1,1]?



      To mention that most of the regression examples that I have seen they do not add an activation function after xx, and leave it to be open and take whatever value it ends up taking.






      regression activation-function




      regression activation-function





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      asked 4 mins ago









      SoyolSoyol

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