The most used loss function in tensorflow for a binary classification?












3












$begingroup$


I am working on a binary classification problem using CNN model, the model designed using tensorflow framework, in most github projects that I saw, they use "softmax cross entropy with logits" v1 and v2 as loss function, my questions is:



1- why this loss method is the most used one?



2- what is the type of this function because of the name I am confused about its type.



3- what is the equation for the function? in tensorflow website the equation is not available.






tensorflow cnn image-classification







share|improve this question









$endgroup$

















    3












    $begingroup$


    I am working on a binary classification problem using CNN model, the model designed using tensorflow framework, in most github projects that I saw, they use "softmax cross entropy with logits" v1 and v2 as loss function, my questions is:



    1- why this loss method is the most used one?



    2- what is the type of this function because of the name I am confused about its type.



    3- what is the equation for the function? in tensorflow website the equation is not available.






    tensorflow cnn image-classification







    share|improve this question









    $endgroup$















      3












      3








      3





      $begingroup$


      I am working on a binary classification problem using CNN model, the model designed using tensorflow framework, in most github projects that I saw, they use "softmax cross entropy with logits" v1 and v2 as loss function, my questions is:



      1- why this loss method is the most used one?



      2- what is the type of this function because of the name I am confused about its type.



      3- what is the equation for the function? in tensorflow website the equation is not available.






      tensorflow cnn image-classification







      share|improve this question









      $endgroup$




      I am working on a binary classification problem using CNN model, the model designed using tensorflow framework, in most github projects that I saw, they use "softmax cross entropy with logits" v1 and v2 as loss function, my questions is:



      1- why this loss method is the most used one?



      2- what is the type of this function because of the name I am confused about its type.



      3- what is the equation for the function? in tensorflow website the equation is not available.






      tensorflow cnn image-classification




      tensorflow cnn image-classification






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked 21 hours ago









      honar.cshonar.cs

      14410




      14410






















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          $begingroup$

          I think there is some confusion here. Softmax is usually an activation function which you will use in your output layer, and cross-entropy is the loss function that you will use.



          Softmax



          This activation function outputs the probability for each class, it is defined as



          $sigma(bf{y_i}) = frac{e^{bf{y_i}}}{sum_{j}{e^{bf{y_j}}}}$.



          For example, in a problem with 2 class labels. Then if we have some outputs from our neural network like $y$=[3, 4] then we can get the probability of each output classes by using the softmax function on these outputs.



          import numpy as np
          

          
x = [3, 4]
          
np.exp(x)/np.sum(np.exp(x))



          [0.26894142, 0.73105858]




          You will see that these are probabilities and do sum to 1.



          Then we can get the class of the input by seeing which probability is higher. In this case class 2 has a probability of 73.11%, so the predicted class label, $hat{y} = 1$.



          Cross-entropy



          Cross-entropy is a loss function that is used for classification tasks. For binary classification it is defined as



          $H(p, q) = -ylog(p) - (1-y)log(1-p)$.



          Let's assume that the real class of the above example is 0, $y=0$. Then we made a mistake and you can see that



          $H(p, q) = -0log(0.26894142) - (1-0)log(1-0.26894142) = 0.313$.



          That is the loss that is used for backpropagation.






          share|improve this answer









          $endgroup$













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            $begingroup$

            I think there is some confusion here. Softmax is usually an activation function which you will use in your output layer, and cross-entropy is the loss function that you will use.



            Softmax



            This activation function outputs the probability for each class, it is defined as



            $sigma(bf{y_i}) = frac{e^{bf{y_i}}}{sum_{j}{e^{bf{y_j}}}}$.



            For example, in a problem with 2 class labels. Then if we have some outputs from our neural network like $y$=[3, 4] then we can get the probability of each output classes by using the softmax function on these outputs.



            import numpy as np
            

            
x = [3, 4]
            
np.exp(x)/np.sum(np.exp(x))



            [0.26894142, 0.73105858]




            You will see that these are probabilities and do sum to 1.



            Then we can get the class of the input by seeing which probability is higher. In this case class 2 has a probability of 73.11%, so the predicted class label, $hat{y} = 1$.



            Cross-entropy



            Cross-entropy is a loss function that is used for classification tasks. For binary classification it is defined as



            $H(p, q) = -ylog(p) - (1-y)log(1-p)$.



            Let's assume that the real class of the above example is 0, $y=0$. Then we made a mistake and you can see that



            $H(p, q) = -0log(0.26894142) - (1-0)log(1-0.26894142) = 0.313$.



            That is the loss that is used for backpropagation.






            share|improve this answer









            $endgroup$


















              1












              $begingroup$

              I think there is some confusion here. Softmax is usually an activation function which you will use in your output layer, and cross-entropy is the loss function that you will use.



              Softmax



              This activation function outputs the probability for each class, it is defined as



              $sigma(bf{y_i}) = frac{e^{bf{y_i}}}{sum_{j}{e^{bf{y_j}}}}$.



              For example, in a problem with 2 class labels. Then if we have some outputs from our neural network like $y$=[3, 4] then we can get the probability of each output classes by using the softmax function on these outputs.



              import numpy as np
              

              
x = [3, 4]
              
np.exp(x)/np.sum(np.exp(x))



              [0.26894142, 0.73105858]




              You will see that these are probabilities and do sum to 1.



              Then we can get the class of the input by seeing which probability is higher. In this case class 2 has a probability of 73.11%, so the predicted class label, $hat{y} = 1$.



              Cross-entropy



              Cross-entropy is a loss function that is used for classification tasks. For binary classification it is defined as



              $H(p, q) = -ylog(p) - (1-y)log(1-p)$.



              Let's assume that the real class of the above example is 0, $y=0$. Then we made a mistake and you can see that



              $H(p, q) = -0log(0.26894142) - (1-0)log(1-0.26894142) = 0.313$.



              That is the loss that is used for backpropagation.






              share|improve this answer









              $endgroup$
















                1












                1








                1





                $begingroup$

                I think there is some confusion here. Softmax is usually an activation function which you will use in your output layer, and cross-entropy is the loss function that you will use.



                Softmax



                This activation function outputs the probability for each class, it is defined as



                $sigma(bf{y_i}) = frac{e^{bf{y_i}}}{sum_{j}{e^{bf{y_j}}}}$.



                For example, in a problem with 2 class labels. Then if we have some outputs from our neural network like $y$=[3, 4] then we can get the probability of each output classes by using the softmax function on these outputs.



                import numpy as np
                

                
x = [3, 4]
                
np.exp(x)/np.sum(np.exp(x))



                [0.26894142, 0.73105858]




                You will see that these are probabilities and do sum to 1.



                Then we can get the class of the input by seeing which probability is higher. In this case class 2 has a probability of 73.11%, so the predicted class label, $hat{y} = 1$.



                Cross-entropy



                Cross-entropy is a loss function that is used for classification tasks. For binary classification it is defined as



                $H(p, q) = -ylog(p) - (1-y)log(1-p)$.



                Let's assume that the real class of the above example is 0, $y=0$. Then we made a mistake and you can see that



                $H(p, q) = -0log(0.26894142) - (1-0)log(1-0.26894142) = 0.313$.



                That is the loss that is used for backpropagation.






                share|improve this answer









                $endgroup$



                I think there is some confusion here. Softmax is usually an activation function which you will use in your output layer, and cross-entropy is the loss function that you will use.



                Softmax



                This activation function outputs the probability for each class, it is defined as



                $sigma(bf{y_i}) = frac{e^{bf{y_i}}}{sum_{j}{e^{bf{y_j}}}}$.



                For example, in a problem with 2 class labels. Then if we have some outputs from our neural network like $y$=[3, 4] then we can get the probability of each output classes by using the softmax function on these outputs.



                import numpy as np
                

                
x = [3, 4]
                
np.exp(x)/np.sum(np.exp(x))



                [0.26894142, 0.73105858]




                You will see that these are probabilities and do sum to 1.



                Then we can get the class of the input by seeing which probability is higher. In this case class 2 has a probability of 73.11%, so the predicted class label, $hat{y} = 1$.



                Cross-entropy



                Cross-entropy is a loss function that is used for classification tasks. For binary classification it is defined as



                $H(p, q) = -ylog(p) - (1-y)log(1-p)$.



                Let's assume that the real class of the above example is 0, $y=0$. Then we made a mistake and you can see that



                $H(p, q) = -0log(0.26894142) - (1-0)log(1-0.26894142) = 0.313$.



                That is the loss that is used for backpropagation.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 20 hours ago









                JahKnowsJahKnows

                4,932625




                4,932625






























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