How to pass 2 features to LSTM , one of them is one-hot-encoded with Keras?












0












$begingroup$


I have a very simple LSTM model 



model = Sequential()

model.add(LSTM(64, input_shape=(seq_length, X_train.shape[2]) , return_sequences=True))

model.add(Dense(y_cat_train.shape[2], activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

model.fit(X_train, y_cat_train, epochs=100, batch_size=10, verbose=2)


The input X_train has 2 feature , one is categorical (values 1-4) and the other is numeric (values 1-100). There are 4 classes in y_test that I one-hot-encoded with keras's to_categorical .




  1. Should I encode the categorical input feature as well ? If I do , how can I pass it along with the other feature ? (e.g. now a timestep looks like this for example: [1,44])

  2. Later , I would like to take make a sampling , meaning I need to take the predicted y_hat<t-1> and pass it as x<t> . I will have to pass the second numeric feature (1-100) along with it. How can it be done ?


EDIT : note that I do not want my numeric feature to become categorical since there is importance to the values (meaning 2<10<90 etc)






python keras lstm rnn







share|improve this question











$endgroup$




bumped to the homepage by Community♦ 16 mins ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.




















    0












    $begingroup$


    I have a very simple LSTM model 



    model = Sequential()
    
model.add(LSTM(64, input_shape=(seq_length, X_train.shape[2]) , return_sequences=True))
    
model.add(Dense(y_cat_train.shape[2], activation='softmax'))
    
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    
model.fit(X_train, y_cat_train, epochs=100, batch_size=10, verbose=2)


    The input X_train has 2 feature , one is categorical (values 1-4) and the other is numeric (values 1-100). There are 4 classes in y_test that I one-hot-encoded with keras's to_categorical .




    1. Should I encode the categorical input feature as well ? If I do , how can I pass it along with the other feature ? (e.g. now a timestep looks like this for example: [1,44])

    2. Later , I would like to take make a sampling , meaning I need to take the predicted y_hat<t-1> and pass it as x<t> . I will have to pass the second numeric feature (1-100) along with it. How can it be done ?


    EDIT : note that I do not want my numeric feature to become categorical since there is importance to the values (meaning 2<10<90 etc)






    python keras lstm rnn







    share|improve this question











    $endgroup$




    bumped to the homepage by Community♦ 16 mins ago


    This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.


















      0












      0








      0





      $begingroup$


      I have a very simple LSTM model 



      model = Sequential()
      
model.add(LSTM(64, input_shape=(seq_length, X_train.shape[2]) , return_sequences=True))
      
model.add(Dense(y_cat_train.shape[2], activation='softmax'))
      
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
      
model.fit(X_train, y_cat_train, epochs=100, batch_size=10, verbose=2)


      The input X_train has 2 feature , one is categorical (values 1-4) and the other is numeric (values 1-100). There are 4 classes in y_test that I one-hot-encoded with keras's to_categorical .




      1. Should I encode the categorical input feature as well ? If I do , how can I pass it along with the other feature ? (e.g. now a timestep looks like this for example: [1,44])

      2. Later , I would like to take make a sampling , meaning I need to take the predicted y_hat<t-1> and pass it as x<t> . I will have to pass the second numeric feature (1-100) along with it. How can it be done ?


      EDIT : note that I do not want my numeric feature to become categorical since there is importance to the values (meaning 2<10<90 etc)






      python keras lstm rnn







      share|improve this question











      $endgroup$




      I have a very simple LSTM model 



      model = Sequential()
      
model.add(LSTM(64, input_shape=(seq_length, X_train.shape[2]) , return_sequences=True))
      
model.add(Dense(y_cat_train.shape[2], activation='softmax'))
      
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
      
model.fit(X_train, y_cat_train, epochs=100, batch_size=10, verbose=2)


      The input X_train has 2 feature , one is categorical (values 1-4) and the other is numeric (values 1-100). There are 4 classes in y_test that I one-hot-encoded with keras's to_categorical .




      1. Should I encode the categorical input feature as well ? If I do , how can I pass it along with the other feature ? (e.g. now a timestep looks like this for example: [1,44])

      2. Later , I would like to take make a sampling , meaning I need to take the predicted y_hat<t-1> and pass it as x<t> . I will have to pass the second numeric feature (1-100) along with it. How can it be done ?


      EDIT : note that I do not want my numeric feature to become categorical since there is importance to the values (meaning 2<10<90 etc)






      python keras lstm rnn




      python keras lstm rnn






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Jan 10 at 22:53







      M.F

















      asked Jan 10 at 21:18









      M.FM.F

      167




      167





      bumped to the homepage by Community♦ 16 mins ago


      This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.







      bumped to the homepage by Community♦ 16 mins ago


      This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
























          1 Answer
          1






          active

          oldest

          votes


















          0












          $begingroup$

          (1) Yes, it is a common practice to encode the categorical feature by one-hot encoding, for example, encode [1,44] as [1,0,0,0,44], encode [2,44] as [0,1,0,0,44], etc.



          (2) Same as (1), just concatenate the one-hot encoded categorical feature and the numerical feature(s).






          share|improve this answer









          $endgroup$













          • $begingroup$
            So it will be treated as one feature ? The categorical part is actuallyy<t-1> so X<t> = [y<t-1>,feature2] - won't concatenating lose some of the importance of one of the features ?
            $endgroup$
            – M.F
            Jan 11 at 15:40













          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%2f43801%2fhow-to-pass-2-features-to-lstm-one-of-them-is-one-hot-encoded-with-keras%23new-answer', 'question_page');
          }
          );

          Post as a guest















          Required, but never shown

























          1 Answer
          1






          active

          oldest

          votes








          1 Answer
          1






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          0












          $begingroup$

          (1) Yes, it is a common practice to encode the categorical feature by one-hot encoding, for example, encode [1,44] as [1,0,0,0,44], encode [2,44] as [0,1,0,0,44], etc.



          (2) Same as (1), just concatenate the one-hot encoded categorical feature and the numerical feature(s).






          share|improve this answer









          $endgroup$













          • $begingroup$
            So it will be treated as one feature ? The categorical part is actuallyy<t-1> so X<t> = [y<t-1>,feature2] - won't concatenating lose some of the importance of one of the features ?
            $endgroup$
            – M.F
            Jan 11 at 15:40


















          0












          $begingroup$

          (1) Yes, it is a common practice to encode the categorical feature by one-hot encoding, for example, encode [1,44] as [1,0,0,0,44], encode [2,44] as [0,1,0,0,44], etc.



          (2) Same as (1), just concatenate the one-hot encoded categorical feature and the numerical feature(s).






          share|improve this answer









          $endgroup$













          • $begingroup$
            So it will be treated as one feature ? The categorical part is actuallyy<t-1> so X<t> = [y<t-1>,feature2] - won't concatenating lose some of the importance of one of the features ?
            $endgroup$
            – M.F
            Jan 11 at 15:40
















          0












          0








          0





          $begingroup$

          (1) Yes, it is a common practice to encode the categorical feature by one-hot encoding, for example, encode [1,44] as [1,0,0,0,44], encode [2,44] as [0,1,0,0,44], etc.



          (2) Same as (1), just concatenate the one-hot encoded categorical feature and the numerical feature(s).






          share|improve this answer









          $endgroup$



          (1) Yes, it is a common practice to encode the categorical feature by one-hot encoding, for example, encode [1,44] as [1,0,0,0,44], encode [2,44] as [0,1,0,0,44], etc.



          (2) Same as (1), just concatenate the one-hot encoded categorical feature and the numerical feature(s).







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Jan 11 at 2:35









          user12075user12075

          1,276515




          1,276515












          • $begingroup$
            So it will be treated as one feature ? The categorical part is actuallyy<t-1> so X<t> = [y<t-1>,feature2] - won't concatenating lose some of the importance of one of the features ?
            $endgroup$
            – M.F
            Jan 11 at 15:40




















          • $begingroup$
            So it will be treated as one feature ? The categorical part is actuallyy<t-1> so X<t> = [y<t-1>,feature2] - won't concatenating lose some of the importance of one of the features ?
            $endgroup$
            – M.F
            Jan 11 at 15:40


















          $begingroup$
          So it will be treated as one feature ? The categorical part is actuallyy<t-1> so X<t> = [y<t-1>,feature2] - won't concatenating lose some of the importance of one of the features ?
          $endgroup$
          – M.F
          Jan 11 at 15:40






          $begingroup$
          So it will be treated as one feature ? The categorical part is actuallyy<t-1> so X<t> = [y<t-1>,feature2] - won't concatenating lose some of the importance of one of the features ?
          $endgroup$
          – M.F
          Jan 11 at 15:40




















          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%2f43801%2fhow-to-pass-2-features-to-lstm-one-of-them-is-one-hot-encoded-with-keras%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







          -

          Popular posts from this blog

          Ponta tanko

          Image
          Usona ponta tanko M60A1 Ponta tanko estas, simile kiel kirasita savveturilo, inter specialaj kirasitaj transportiloj. Kiel pontaj tankoj eblas konsideri ĉiujn memmovigaj rimedoj ebligantaj superi obstaklojn ĝis larĝeco 24 metroj rekte en spaco de bataloperacoj. Ties ĉefa tasko estas ebligi al la propraj taĉmentoj daŭrigi en rapida antaŭenirado sen senbezona reteniĝo. El tio pli-malpli rezultas taktikaj-teknikaj postuloj, kiujn ili devas plenumi. Precipe temas pri kapablo operacii komune kun unue-sinsekvaj taĉmentoj kaj transpontigi obstaklojn dum kelke da minutoj. This page is only for reference, If you need detailed information, please check here
          Read more

          Tantalo (mitologio)

          Image
          Ĉi tiu artikolo temas pri mitologio. Por aliaj signifoj vidu la paĝon Tantalo (apartigilo) . Tantalo , laŭ Gioacchino Assereto. Laŭ la helena mitologio, Tantalo (el la greka Τάνταλος , ŝancelanta aŭ tre malfeliĉa ) estis filo de Zeŭso kaj la oceanidino Pluto, laŭ la plej divastigata versio. Li estis reĝo de Frigio [1] aŭ de la monto Sipilo, en Lidio (Malgrandazio) [2] kaj post sia morto li suferis eternan torturadon ĉe la Tartaro, la plej profunda parto de Hadeso, rezervita por la plej perversaj homoj. Tamen, kelkaj rakontoj asertas ke li estis reĝo de Argo aŭ de Korinto [3] , aŭ ke li migris norden el la monto Sipilo por akiri la tronon de Paflagonio, el kie Ilo forpelis lin pro tio, ke Tantalo forkaptis kaj seksperfortis lian fraton Ganimedon [4] , kaj tiel ofendis la diojn. Verŝajne li ankaŭ regis urbon en okcidenta Anatolio, kiun oni nomis Tantalis [5] pro li, aŭ Sipilo , pro la monto sur kies flanko ankoraŭ staras ĝiaj ruinaĵoj [6] . Laŭ la geografiisto Strabo,...
          Read more

          Erzsébet Schaár

          Image
          Erzsébet Schaár Memportreto de Erzsébet Schaár en Pécs Personaj informoj Naskiĝo 27-an de julio 1908 ( 1908-07-27 ) en Budafok Morto 29-an de aŭgusto 1975 ( 1975-08-29 ) (67-jara) en Budapeŝto Ŝtataneco Hungario Profesio skulptisto Familianoj Edz(in)o Tibor Vilt v   •   d   •   r Erzsébet Schaár [erĵEbet ŝAr], laŭ hungarlingve kutima sinsekvo Schaár Erzsébet estis hungara skulptisto. Ŝia edzo estis Tibor Vilt. Déryné Széppataki Róza en Miskolc fare de Erzsébet Schaár Erzsébet Schaár [1] naskiĝis la 27-an de julio 1908 en Budafok, ŝi mortis la 29-an de aŭgusto 1975 en Budapeŝto. Enhavo 1 Biografio 2 Elektitaj solaj ekspozicioj 3 Elektitaj kolektivaj ekspozicioj 4 Elektitaj skulptaĵoj 5 Elektitaj disĉiploj 6 Fontoj 7 Referencoj Biografio | Erzsébet Schaár studis en Industriarta Lernejo, poste en Reĝa Belarta Altlernejo (1924-1926). Ŝia ĉefinstruisto estis Zsig...
          Read more