What is one hot encoding in tensorflow?2019 Community Moderator ElectionTensorflow - How to set gradient of an external process (py_func)?Tensor Decomposition in TensorFlow for multinomial time series dimensionality reductionhow can I solve label shape problem in tensorflow when using one-hot encoding?RGB + Depth Encoding for CNNsReading a CSV in TensorFlow RNNOne hot encoding vs Word embeddingHow does one go about feature extraction for training labelled tweets for sentiment analysis?GAN with Conv2D using TensorFlow - Shape errorwhat is the one hot encoding for cancer data classificationHow to run a saved TensorFlow Model? (Video Prediction Model)

Multi tool use
Multi tool use

Is it important to consider tone, melody, and musical form while writing a song?

Service Entrance Breakers Rain Shield

How is it possible to have an ability score that is less than 3?

Email Account under attack (really) - anything I can do?

What defenses are there against being summoned by the Gate spell?

Why did the Germans forbid the possession of pet pigeons in Rostov-on-Don in 1941?

To string or not to string

Why are 150k or 200k jobs considered good when there are 300k+ births a month?

How do I create uniquely male characters?

Why "Having chlorophyll without photosynthesis is actually very dangerous" and "like living with a bomb"?

How much RAM could one put in a typical 80386 setup?

How does strength of boric acid solution increase in presence of salicylic acid?

What is the offset in a seaplane's hull?

TGV timetables / schedules?

Can divisibility rules for digits be generalized to sum of digits

The Two and the One

Show that if two triangles built on parallel lines, with equal bases have the same perimeter only if they are congruent.

Why not use SQL instead of GraphQL?

Is it unprofessional to ask if a job posting on GlassDoor is real?

"You are your self first supporter", a more proper way to say it

How did the USSR manage to innovate in an environment characterized by government censorship and high bureaucracy?

Why don't electron-positron collisions release infinite energy?

What do the dots in this tr command do: tr .............A-Z A-ZA-Z <<< "JVPQBOV" (with 13 dots)

Why doesn't Newton's third law mean a person bounces back to where they started when they hit the ground?



What is one hot encoding in tensorflow?



2019 Community Moderator ElectionTensorflow - How to set gradient of an external process (py_func)?Tensor Decomposition in TensorFlow for multinomial time series dimensionality reductionhow can I solve label shape problem in tensorflow when using one-hot encoding?RGB + Depth Encoding for CNNsReading a CSV in TensorFlow RNNOne hot encoding vs Word embeddingHow does one go about feature extraction for training labelled tweets for sentiment analysis?GAN with Conv2D using TensorFlow - Shape errorwhat is the one hot encoding for cancer data classificationHow to run a saved TensorFlow Model? (Video Prediction Model)










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












    $begingroup$


    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






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited 35 mins ago







      thanatoz

















      asked Apr 12 '18 at 9:42









      thanatozthanatoz

      569319




      569319




















          2 Answers
          2






          active

          oldest

          votes


















          13












          $begingroup$

          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









            $endgroup$













              Your Answer





              StackExchange.ifUsing("editor", function ()
              return StackExchange.using("mathjaxEditing", function ()
              StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix)
              StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
              );
              );
              , "mathjax-editing");

              StackExchange.ready(function()
              var channelOptions =
              tags: "".split(" "),
              id: "557"
              ;
              initTagRenderer("".split(" "), "".split(" "), channelOptions);

              StackExchange.using("externalEditor", function()
              // Have to fire editor after snippets, if snippets enabled
              if (StackExchange.settings.snippets.snippetsEnabled)
              StackExchange.using("snippets", function()
              createEditor();
              );

              else
              createEditor();

              );

              function createEditor()
              StackExchange.prepareEditor(
              heartbeatType: 'answer',
              autoActivateHeartbeat: false,
              convertImagesToLinks: false,
              noModals: true,
              showLowRepImageUploadWarning: true,
              reputationToPostImages: null,
              bindNavPrevention: true,
              postfix: "",
              imageUploader:
              brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
              contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
              allowUrls: true
              ,
              onDemand: true,
              discardSelector: ".discard-answer"
              ,immediatelyShowMarkdownHelp:true
              );



              );













              draft saved

              draft discarded


















              StackExchange.ready(
              function ()
              StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f30215%2fwhat-is-one-hot-encoding-in-tensorflow%23new-answer', 'question_page');

              );

              Post as a guest















              Required, but never shown

























              2 Answers
              2






              active

              oldest

              votes








              2 Answers
              2






              active

              oldest

              votes









              active

              oldest

              votes






              active

              oldest

              votes









              13












              $begingroup$

              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









              $endgroup$

















                13












                $begingroup$

                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









                $endgroup$















                  13












                  13








                  13





                  $begingroup$

                  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

                  2,58231125




                  2,58231125





















                      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




                          314



























                              draft saved

                              draft discarded
















































                              Thanks for contributing an answer to Data Science Stack Exchange!


                              • Please be sure to answer the question. Provide details and share your research!

                              But avoid


                              • Asking for help, clarification, or responding to other answers.

                              • Making statements based on opinion; back them up with references or personal experience.

                              Use MathJax to format equations. MathJax reference.


                              To learn more, see our tips on writing great answers.




                              draft saved


                              draft discarded














                              StackExchange.ready(
                              function ()
                              StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f30215%2fwhat-is-one-hot-encoding-in-tensorflow%23new-answer', 'question_page');

                              );

                              Post as a guest















                              Required, but never shown





















































                              Required, but never shown














                              Required, but never shown












                              Required, but never shown







                              Required, but never shown

































                              Required, but never shown














                              Required, but never shown












                              Required, but never shown







                              Required, but never shown







                              n5,s,aJYXSkojZAQITzt,SW8kT
                              oP,3,sehjS2zLrCrzT0rxS2mGjrCaeUPe5R3OwxxfXH5b,xIO XcDW Ul2n,6kknQaK1V

                              Popular posts from this blog

                              Францішак Багушэвіч Змест Сям'я | Біяграфія | Творчасць | Мова Багушэвіча | Ацэнкі дзейнасці | Цікавыя факты | Спадчына | Выбраная бібліяграфія | Ушанаванне памяці | У філатэліі | Зноскі | Літаратура | Спасылкі | НавігацыяЛяхоўскі У. Рупіўся дзеля Бога і людзей: Жыццёвы шлях Лявона Вітан-Дубейкаўскага // Вольскі і Памідораў з песняй пра немца Адвакат, паэт, народны заступнік Ашмянскі веснікВ Минске появится площадь Богушевича и улица Сырокомли, Белорусская деловая газета, 19 июля 2001 г.Айцец беларускай нацыянальнай ідэі паўстаў у бронзе Сяргей Аляксандравіч Адашкевіч (1918, Мінск). 80-я гады. Бюст «Францішак Багушэвіч».Яўген Мікалаевіч Ціхановіч. «Партрэт Францішка Багушэвіча»Мікола Мікалаевіч Купава. «Партрэт зачынальніка новай беларускай літаратуры Францішка Багушэвіча»Уладзімір Іванавіч Мелехаў. На помніку «Змагарам за родную мову» Барэльеф «Францішак Багушэвіч»Памяць пра Багушэвіча на Віленшчыне Страчаная сталіца. Беларускія шыльды на вуліцах Вільні«Krynica». Ideologia i przywódcy białoruskiego katolicyzmuФранцішак БагушэвічТворы на knihi.comТворы Францішка Багушэвіча на bellib.byСодаль Уладзімір. Францішак Багушэвіч на Лідчыне;Луцкевіч Антон. Жыцьцё і творчасьць Фр. Багушэвіча ў успамінах ягоных сучасьнікаў // Запісы Беларускага Навуковага таварыства. Вільня, 1938. Сшытак 1. С. 16-34.Большая российская1188761710000 0000 5537 633Xn9209310021619551927869394п

                              Partai Komunis Tiongkok Daftar isi Kepemimpinan | Pranala luar | Referensi | Menu navigasidiperiksa1 perubahan tertundacpc.people.com.cnSitus resmiSurat kabar resmi"Why the Communist Party is alive, well and flourishing in China"0307-1235"Full text of Constitution of Communist Party of China"smengembangkannyas

                              ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE) The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result