What is one hot encoding in tensorflow?












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$begingroup$


I am currently doing a course in tensorflow in which they used tf.one_hot(indices, depth). Now I don't understand how these indices change into that binary sequence.



Can somebody please explain to me the exact process???










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    9












    $begingroup$


    I am currently doing a course in tensorflow in which they used tf.one_hot(indices, depth). Now I don't understand how these indices change into that binary sequence.



    Can somebody please explain to me the exact process???










    share|improve this question











    $endgroup$















      9












      9








      9


      2



      $begingroup$


      I am currently doing a course in tensorflow in which they used tf.one_hot(indices, depth). Now I don't understand how these indices change into that binary sequence.



      Can somebody please explain to me the exact process???










      share|improve this question











      $endgroup$




      I am currently doing a course in tensorflow in which they used tf.one_hot(indices, depth). Now I don't understand how these indices change into that binary sequence.



      Can somebody please explain to me the exact process???







      machine-learning python neural-network deep-learning tensorflow






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      share|improve this question













      share|improve this question




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      edited yesterday







      thanatoz

















      asked Apr 12 '18 at 9:42









      thanatozthanatoz

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          2 Answers
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          14












          $begingroup$

          Suppose you have a categorical feature in your dataset (e.g. color). And your samples can be either red, yellow or blue. In order to pass this argument to a ML algorithm, you first need to encode it so that instead of strings you have numbers.



          The easiest way to do such a thing is to create a mapping where:



          red --> 1

          yellow --> 2

          blue --> 3



          and replace each string with its mapped value.



          However this might create unwanted side effects in our ML model as when dealing with numbers it might think that blue > yellow (because 3 > 2) or that red + yellow = blue (because 1 + 2 = 3). The model has no way of knowing that these data were categorical and then were mapped as integers.



          The solution to this problem is one-hot encoding where we create N new features, where N is the number of unique values in the original feature. In our exampel N would be equal to 3, because we have 3 unique colors (red, yellow and blue).



          Each of these features be binary and would correspond to one of these unique values. In our example the first feature would be a binary feature telling us if that sample is red or not, the second would be the same thing for yellow and the third for blue.



          An example of such a transformation is illustrated below:





          Note, that because this approach increases the dimensionality of the dataset, if we have a feature that takes many unique values, we may want to use a more sparse encoding (like the one I presented above).






          share|improve this answer









          $endgroup$





















            2












            $begingroup$

            depth: A scalar defining the depth of the one hot dimension.



            indices: A Tensor of indices.



            This the example given in tensorflow documentation.

            1. Only Specifying indices and depth(Default Values of on_value is 1 and off_value is 0)



             indices = [0, 1, 2]
            depth = 3
            tf.one_hot(indices, depth) # output: [3 x 3]
            # [[1., 0., 0.],
            # [0., 1., 0.],
            # [0., 0., 1.]]



            1. Specifying on_value and off_value



            indices = [0, 2, -1, 1]
            depth = 3
            tf.one_hot(indices, depth, on_value=5.0, off_value=0.0, axis=-1)
            ##output: [4 x 3]
            # [[5.0, 0.0, 0.0], # one_hot(0)
            # [0.0, 0.0, 5.0], # one_hot(2)
            # [0.0, 0.0, 0.0], # one_hot(-1)
            # [0.0, 5.0, 0.0]] # one_hot(1)


            You can also see the code on GitHub






            share|improve this answer









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              14












              $begingroup$

              Suppose you have a categorical feature in your dataset (e.g. color). And your samples can be either red, yellow or blue. In order to pass this argument to a ML algorithm, you first need to encode it so that instead of strings you have numbers.



              The easiest way to do such a thing is to create a mapping where:



              red --> 1

              yellow --> 2

              blue --> 3



              and replace each string with its mapped value.



              However this might create unwanted side effects in our ML model as when dealing with numbers it might think that blue > yellow (because 3 > 2) or that red + yellow = blue (because 1 + 2 = 3). The model has no way of knowing that these data were categorical and then were mapped as integers.



              The solution to this problem is one-hot encoding where we create N new features, where N is the number of unique values in the original feature. In our exampel N would be equal to 3, because we have 3 unique colors (red, yellow and blue).



              Each of these features be binary and would correspond to one of these unique values. In our example the first feature would be a binary feature telling us if that sample is red or not, the second would be the same thing for yellow and the third for blue.



              An example of such a transformation is illustrated below:





              Note, that because this approach increases the dimensionality of the dataset, if we have a feature that takes many unique values, we may want to use a more sparse encoding (like the one I presented above).






              share|improve this answer









              $endgroup$


















                14












                $begingroup$

                Suppose you have a categorical feature in your dataset (e.g. color). And your samples can be either red, yellow or blue. In order to pass this argument to a ML algorithm, you first need to encode it so that instead of strings you have numbers.



                The easiest way to do such a thing is to create a mapping where:



                red --> 1

                yellow --> 2

                blue --> 3



                and replace each string with its mapped value.



                However this might create unwanted side effects in our ML model as when dealing with numbers it might think that blue > yellow (because 3 > 2) or that red + yellow = blue (because 1 + 2 = 3). The model has no way of knowing that these data were categorical and then were mapped as integers.



                The solution to this problem is one-hot encoding where we create N new features, where N is the number of unique values in the original feature. In our exampel N would be equal to 3, because we have 3 unique colors (red, yellow and blue).



                Each of these features be binary and would correspond to one of these unique values. In our example the first feature would be a binary feature telling us if that sample is red or not, the second would be the same thing for yellow and the third for blue.



                An example of such a transformation is illustrated below:





                Note, that because this approach increases the dimensionality of the dataset, if we have a feature that takes many unique values, we may want to use a more sparse encoding (like the one I presented above).






                share|improve this answer









                $endgroup$
















                  14












                  14








                  14





                  $begingroup$

                  Suppose you have a categorical feature in your dataset (e.g. color). And your samples can be either red, yellow or blue. In order to pass this argument to a ML algorithm, you first need to encode it so that instead of strings you have numbers.



                  The easiest way to do such a thing is to create a mapping where:



                  red --> 1

                  yellow --> 2

                  blue --> 3



                  and replace each string with its mapped value.



                  However this might create unwanted side effects in our ML model as when dealing with numbers it might think that blue > yellow (because 3 > 2) or that red + yellow = blue (because 1 + 2 = 3). The model has no way of knowing that these data were categorical and then were mapped as integers.



                  The solution to this problem is one-hot encoding where we create N new features, where N is the number of unique values in the original feature. In our exampel N would be equal to 3, because we have 3 unique colors (red, yellow and blue).



                  Each of these features be binary and would correspond to one of these unique values. In our example the first feature would be a binary feature telling us if that sample is red or not, the second would be the same thing for yellow and the third for blue.



                  An example of such a transformation is illustrated below:





                  Note, that because this approach increases the dimensionality of the dataset, if we have a feature that takes many unique values, we may want to use a more sparse encoding (like the one I presented above).






                  share|improve this answer









                  $endgroup$



                  Suppose you have a categorical feature in your dataset (e.g. color). And your samples can be either red, yellow or blue. In order to pass this argument to a ML algorithm, you first need to encode it so that instead of strings you have numbers.



                  The easiest way to do such a thing is to create a mapping where:



                  red --> 1

                  yellow --> 2

                  blue --> 3



                  and replace each string with its mapped value.



                  However this might create unwanted side effects in our ML model as when dealing with numbers it might think that blue > yellow (because 3 > 2) or that red + yellow = blue (because 1 + 2 = 3). The model has no way of knowing that these data were categorical and then were mapped as integers.



                  The solution to this problem is one-hot encoding where we create N new features, where N is the number of unique values in the original feature. In our exampel N would be equal to 3, because we have 3 unique colors (red, yellow and blue).



                  Each of these features be binary and would correspond to one of these unique values. In our example the first feature would be a binary feature telling us if that sample is red or not, the second would be the same thing for yellow and the third for blue.



                  An example of such a transformation is illustrated below:





                  Note, that because this approach increases the dimensionality of the dataset, if we have a feature that takes many unique values, we may want to use a more sparse encoding (like the one I presented above).







                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered Apr 12 '18 at 10:05









                  Djib2011Djib2011

                  2,61231125




                  2,61231125























                      2












                      $begingroup$

                      depth: A scalar defining the depth of the one hot dimension.



                      indices: A Tensor of indices.



                      This the example given in tensorflow documentation.

                      1. Only Specifying indices and depth(Default Values of on_value is 1 and off_value is 0)



                       indices = [0, 1, 2]
                      depth = 3
                      tf.one_hot(indices, depth) # output: [3 x 3]
                      # [[1., 0., 0.],
                      # [0., 1., 0.],
                      # [0., 0., 1.]]



                      1. Specifying on_value and off_value



                      indices = [0, 2, -1, 1]
                      depth = 3
                      tf.one_hot(indices, depth, on_value=5.0, off_value=0.0, axis=-1)
                      ##output: [4 x 3]
                      # [[5.0, 0.0, 0.0], # one_hot(0)
                      # [0.0, 0.0, 5.0], # one_hot(2)
                      # [0.0, 0.0, 0.0], # one_hot(-1)
                      # [0.0, 5.0, 0.0]] # one_hot(1)


                      You can also see the code on GitHub






                      share|improve this answer









                      $endgroup$


















                        2












                        $begingroup$

                        depth: A scalar defining the depth of the one hot dimension.



                        indices: A Tensor of indices.



                        This the example given in tensorflow documentation.

                        1. Only Specifying indices and depth(Default Values of on_value is 1 and off_value is 0)



                         indices = [0, 1, 2]
                        depth = 3
                        tf.one_hot(indices, depth) # output: [3 x 3]
                        # [[1., 0., 0.],
                        # [0., 1., 0.],
                        # [0., 0., 1.]]



                        1. Specifying on_value and off_value



                        indices = [0, 2, -1, 1]
                        depth = 3
                        tf.one_hot(indices, depth, on_value=5.0, off_value=0.0, axis=-1)
                        ##output: [4 x 3]
                        # [[5.0, 0.0, 0.0], # one_hot(0)
                        # [0.0, 0.0, 5.0], # one_hot(2)
                        # [0.0, 0.0, 0.0], # one_hot(-1)
                        # [0.0, 5.0, 0.0]] # one_hot(1)


                        You can also see the code on GitHub






                        share|improve this answer









                        $endgroup$
















                          2












                          2








                          2





                          $begingroup$

                          depth: A scalar defining the depth of the one hot dimension.



                          indices: A Tensor of indices.



                          This the example given in tensorflow documentation.

                          1. Only Specifying indices and depth(Default Values of on_value is 1 and off_value is 0)



                           indices = [0, 1, 2]
                          depth = 3
                          tf.one_hot(indices, depth) # output: [3 x 3]
                          # [[1., 0., 0.],
                          # [0., 1., 0.],
                          # [0., 0., 1.]]



                          1. Specifying on_value and off_value



                          indices = [0, 2, -1, 1]
                          depth = 3
                          tf.one_hot(indices, depth, on_value=5.0, off_value=0.0, axis=-1)
                          ##output: [4 x 3]
                          # [[5.0, 0.0, 0.0], # one_hot(0)
                          # [0.0, 0.0, 5.0], # one_hot(2)
                          # [0.0, 0.0, 0.0], # one_hot(-1)
                          # [0.0, 5.0, 0.0]] # one_hot(1)


                          You can also see the code on GitHub






                          share|improve this answer









                          $endgroup$



                          depth: A scalar defining the depth of the one hot dimension.



                          indices: A Tensor of indices.



                          This the example given in tensorflow documentation.

                          1. Only Specifying indices and depth(Default Values of on_value is 1 and off_value is 0)



                           indices = [0, 1, 2]
                          depth = 3
                          tf.one_hot(indices, depth) # output: [3 x 3]
                          # [[1., 0., 0.],
                          # [0., 1., 0.],
                          # [0., 0., 1.]]



                          1. Specifying on_value and off_value



                          indices = [0, 2, -1, 1]
                          depth = 3
                          tf.one_hot(indices, depth, on_value=5.0, off_value=0.0, axis=-1)
                          ##output: [4 x 3]
                          # [[5.0, 0.0, 0.0], # one_hot(0)
                          # [0.0, 0.0, 5.0], # one_hot(2)
                          # [0.0, 0.0, 0.0], # one_hot(-1)
                          # [0.0, 5.0, 0.0]] # one_hot(1)


                          You can also see the code on GitHub







                          share|improve this answer












                          share|improve this answer



                          share|improve this answer










                          answered Apr 12 '18 at 12:05









                          VallieVallie

                          314




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