Is the Keras Embedding layer dependent on the target label?How does Keras 'Embedding' layer work?Should word embedding vectors be normalized before being used as inputs?deep learning - word embedding with parts of speechRandomly initialized embedding matrixHow to use Keras pre-trained 'Embedding' layer?How the embedding layer is trained in Keras Embedding layerDimension reduction - word embeddings as inputs for a time series model (LSTM)What is difference between keras embedding layer and word2vec?Learning image embeddings using VGG and Word2VecCan an embedding layer be replaced by a fully connected layer?Autoencoder keeping constant vector as predict in keras

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Is the Keras Embedding layer dependent on the target label?


How does Keras 'Embedding' layer work?Should word embedding vectors be normalized before being used as inputs?deep learning - word embedding with parts of speechRandomly initialized embedding matrixHow to use Keras pre-trained 'Embedding' layer?How the embedding layer is trained in Keras Embedding layerDimension reduction - word embeddings as inputs for a time series model (LSTM)What is difference between keras embedding layer and word2vec?Learning image embeddings using VGG and Word2VecCan an embedding layer be replaced by a fully connected layer?Autoencoder keeping constant vector as predict in keras






.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;








1












$begingroup$


I learned how to 'use' the Keras Embedding layer, but I am not able to find any more specific information about the actual behavior and training process of this layer. For now, I understand that the Keras Embedding layer maps distinct categorical features to n-dimensional vectors, which allows us to find, for example, how similar two features are.



What I do not understand is how these vectors in the embedding layer are trained. Here is an explanation where there is information that these vectors are not computed with any operation, but working only as a lookup table, but I always thought that they are somehow "trained" to find similarities between distinct features.



If they are trained, are they trained from target labels, or from the order in which they appear (similar to GloVe, word2vec, etc.) or from both?



I have the following example of two pairs of rows in a dataset. y is the model target label and X are the features encoded to integers to be used in the embedding layer:



#pair 1 
dataset_y_row1 = [1]
dataset_y_row2 = [0]
dataset_X_row1 = [3,5,8,45,2]
dataset_X_row2 = [3,5,8,45,2]

#pair 2
dataset_y_row3 = [1]
dataset_y_row4 = [1]
dataset_X_row3 = [3,5,8,45,2]
dataset_X_row4 = [3,5,45,8,2]


My questions are the following:



  1. Will the embedding layer see any difference between rows 1 and 2 (i.e. is
    it 'target-label-sensitive')?

  2. Will the embedding layer see any difference between rows 3 and 4 (i.e. is it sensitive to order of features like word2vec, GloVe, etc.)?









share|cite|improve this question











$endgroup$


















    1












    $begingroup$


    I learned how to 'use' the Keras Embedding layer, but I am not able to find any more specific information about the actual behavior and training process of this layer. For now, I understand that the Keras Embedding layer maps distinct categorical features to n-dimensional vectors, which allows us to find, for example, how similar two features are.



    What I do not understand is how these vectors in the embedding layer are trained. Here is an explanation where there is information that these vectors are not computed with any operation, but working only as a lookup table, but I always thought that they are somehow "trained" to find similarities between distinct features.



    If they are trained, are they trained from target labels, or from the order in which they appear (similar to GloVe, word2vec, etc.) or from both?



    I have the following example of two pairs of rows in a dataset. y is the model target label and X are the features encoded to integers to be used in the embedding layer:



    #pair 1 
    dataset_y_row1 = [1]
    dataset_y_row2 = [0]
    dataset_X_row1 = [3,5,8,45,2]
    dataset_X_row2 = [3,5,8,45,2]

    #pair 2
    dataset_y_row3 = [1]
    dataset_y_row4 = [1]
    dataset_X_row3 = [3,5,8,45,2]
    dataset_X_row4 = [3,5,45,8,2]


    My questions are the following:



    1. Will the embedding layer see any difference between rows 1 and 2 (i.e. is
      it 'target-label-sensitive')?

    2. Will the embedding layer see any difference between rows 3 and 4 (i.e. is it sensitive to order of features like word2vec, GloVe, etc.)?









    share|cite|improve this question











    $endgroup$














      1












      1








      1





      $begingroup$


      I learned how to 'use' the Keras Embedding layer, but I am not able to find any more specific information about the actual behavior and training process of this layer. For now, I understand that the Keras Embedding layer maps distinct categorical features to n-dimensional vectors, which allows us to find, for example, how similar two features are.



      What I do not understand is how these vectors in the embedding layer are trained. Here is an explanation where there is information that these vectors are not computed with any operation, but working only as a lookup table, but I always thought that they are somehow "trained" to find similarities between distinct features.



      If they are trained, are they trained from target labels, or from the order in which they appear (similar to GloVe, word2vec, etc.) or from both?



      I have the following example of two pairs of rows in a dataset. y is the model target label and X are the features encoded to integers to be used in the embedding layer:



      #pair 1 
      dataset_y_row1 = [1]
      dataset_y_row2 = [0]
      dataset_X_row1 = [3,5,8,45,2]
      dataset_X_row2 = [3,5,8,45,2]

      #pair 2
      dataset_y_row3 = [1]
      dataset_y_row4 = [1]
      dataset_X_row3 = [3,5,8,45,2]
      dataset_X_row4 = [3,5,45,8,2]


      My questions are the following:



      1. Will the embedding layer see any difference between rows 1 and 2 (i.e. is
        it 'target-label-sensitive')?

      2. Will the embedding layer see any difference between rows 3 and 4 (i.e. is it sensitive to order of features like word2vec, GloVe, etc.)?









      share|cite|improve this question











      $endgroup$




      I learned how to 'use' the Keras Embedding layer, but I am not able to find any more specific information about the actual behavior and training process of this layer. For now, I understand that the Keras Embedding layer maps distinct categorical features to n-dimensional vectors, which allows us to find, for example, how similar two features are.



      What I do not understand is how these vectors in the embedding layer are trained. Here is an explanation where there is information that these vectors are not computed with any operation, but working only as a lookup table, but I always thought that they are somehow "trained" to find similarities between distinct features.



      If they are trained, are they trained from target labels, or from the order in which they appear (similar to GloVe, word2vec, etc.) or from both?



      I have the following example of two pairs of rows in a dataset. y is the model target label and X are the features encoded to integers to be used in the embedding layer:



      #pair 1 
      dataset_y_row1 = [1]
      dataset_y_row2 = [0]
      dataset_X_row1 = [3,5,8,45,2]
      dataset_X_row2 = [3,5,8,45,2]

      #pair 2
      dataset_y_row3 = [1]
      dataset_y_row4 = [1]
      dataset_X_row3 = [3,5,8,45,2]
      dataset_X_row4 = [3,5,45,8,2]


      My questions are the following:



      1. Will the embedding layer see any difference between rows 1 and 2 (i.e. is
        it 'target-label-sensitive')?

      2. Will the embedding layer see any difference between rows 3 and 4 (i.e. is it sensitive to order of features like word2vec, GloVe, etc.)?






      neural-networks keras word-embeddings embeddings






      share|cite|improve this question















      share|cite|improve this question













      share|cite|improve this question




      share|cite|improve this question








      edited Jun 9 at 19:49









      Mihai Chelaru

      2311 silver badge7 bronze badges




      2311 silver badge7 bronze badges










      asked Jun 9 at 16:20









      Jan MusilJan Musil

      354 bronze badges




      354 bronze badges




















          1 Answer
          1






          active

          oldest

          votes


















          3












          $begingroup$

          Embeddings layer for vocabulary of size $m$, that encodes each word into embeddings vector of size $k$ is a shorthand for having the words one-hot encoded using into $m$ features and then putting dense layer with $k$ units over it. Word2vec and GloVe are specialized algorithms for learning the embeddings, but the end product is a matrix of weights that is multiplied by the one-hot encoded words.



          If you are interested in detailed, yet accessible introductory source on word embeddingss, check the series of blog post by Sebastian Ruder .



          To answer your question, one would need to consider what is your network architecture and the data. Algorithms like word2vec and GloVe are trained on language data, to predict things like next word in a sequence. On another hand, if you use the embeddingss layer that is trained from the scratch and used as a part of larger network, that has some utilitarian purpose (e.g. spam detection, sentiment classification), then the layers work as any other dense layers, so they serve purpose of automatic feature engineering. In the latter case, you would expect to see more specialised embeddingss, that would learn features related to the objective of your network.






          share|cite|improve this answer











          $endgroup$








          • 1




            $begingroup$
            okay, thanks just ask to "but the end product is a matrix of weights that is multiplied by the one-hot encoded words." This is related to word2vec and glove, or also to the first part of paragraph (keras Embedding layer). Does it mean that Embedding vector of size m can be just simulated by using one hot encoded layer as input, and dense layer with m neurons? So vector for each one-hot encoded feature should be just it's m weights going from this input feature to dense layer neurons?
            $endgroup$
            – Jan Musil
            Jun 9 at 18:29










          • $begingroup$
            @JanMusil as I said, embeddingss are dense layers, so they are matrices of weights to be multiplied by the features, it applies to all the embeddings.
            $endgroup$
            – Tim
            Jun 9 at 19:38













          Your Answer








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          1 Answer
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          1 Answer
          1






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          3












          $begingroup$

          Embeddings layer for vocabulary of size $m$, that encodes each word into embeddings vector of size $k$ is a shorthand for having the words one-hot encoded using into $m$ features and then putting dense layer with $k$ units over it. Word2vec and GloVe are specialized algorithms for learning the embeddings, but the end product is a matrix of weights that is multiplied by the one-hot encoded words.



          If you are interested in detailed, yet accessible introductory source on word embeddingss, check the series of blog post by Sebastian Ruder .



          To answer your question, one would need to consider what is your network architecture and the data. Algorithms like word2vec and GloVe are trained on language data, to predict things like next word in a sequence. On another hand, if you use the embeddingss layer that is trained from the scratch and used as a part of larger network, that has some utilitarian purpose (e.g. spam detection, sentiment classification), then the layers work as any other dense layers, so they serve purpose of automatic feature engineering. In the latter case, you would expect to see more specialised embeddingss, that would learn features related to the objective of your network.






          share|cite|improve this answer











          $endgroup$








          • 1




            $begingroup$
            okay, thanks just ask to "but the end product is a matrix of weights that is multiplied by the one-hot encoded words." This is related to word2vec and glove, or also to the first part of paragraph (keras Embedding layer). Does it mean that Embedding vector of size m can be just simulated by using one hot encoded layer as input, and dense layer with m neurons? So vector for each one-hot encoded feature should be just it's m weights going from this input feature to dense layer neurons?
            $endgroup$
            – Jan Musil
            Jun 9 at 18:29










          • $begingroup$
            @JanMusil as I said, embeddingss are dense layers, so they are matrices of weights to be multiplied by the features, it applies to all the embeddings.
            $endgroup$
            – Tim
            Jun 9 at 19:38















          3












          $begingroup$

          Embeddings layer for vocabulary of size $m$, that encodes each word into embeddings vector of size $k$ is a shorthand for having the words one-hot encoded using into $m$ features and then putting dense layer with $k$ units over it. Word2vec and GloVe are specialized algorithms for learning the embeddings, but the end product is a matrix of weights that is multiplied by the one-hot encoded words.



          If you are interested in detailed, yet accessible introductory source on word embeddingss, check the series of blog post by Sebastian Ruder .



          To answer your question, one would need to consider what is your network architecture and the data. Algorithms like word2vec and GloVe are trained on language data, to predict things like next word in a sequence. On another hand, if you use the embeddingss layer that is trained from the scratch and used as a part of larger network, that has some utilitarian purpose (e.g. spam detection, sentiment classification), then the layers work as any other dense layers, so they serve purpose of automatic feature engineering. In the latter case, you would expect to see more specialised embeddingss, that would learn features related to the objective of your network.






          share|cite|improve this answer











          $endgroup$








          • 1




            $begingroup$
            okay, thanks just ask to "but the end product is a matrix of weights that is multiplied by the one-hot encoded words." This is related to word2vec and glove, or also to the first part of paragraph (keras Embedding layer). Does it mean that Embedding vector of size m can be just simulated by using one hot encoded layer as input, and dense layer with m neurons? So vector for each one-hot encoded feature should be just it's m weights going from this input feature to dense layer neurons?
            $endgroup$
            – Jan Musil
            Jun 9 at 18:29










          • $begingroup$
            @JanMusil as I said, embeddingss are dense layers, so they are matrices of weights to be multiplied by the features, it applies to all the embeddings.
            $endgroup$
            – Tim
            Jun 9 at 19:38













          3












          3








          3





          $begingroup$

          Embeddings layer for vocabulary of size $m$, that encodes each word into embeddings vector of size $k$ is a shorthand for having the words one-hot encoded using into $m$ features and then putting dense layer with $k$ units over it. Word2vec and GloVe are specialized algorithms for learning the embeddings, but the end product is a matrix of weights that is multiplied by the one-hot encoded words.



          If you are interested in detailed, yet accessible introductory source on word embeddingss, check the series of blog post by Sebastian Ruder .



          To answer your question, one would need to consider what is your network architecture and the data. Algorithms like word2vec and GloVe are trained on language data, to predict things like next word in a sequence. On another hand, if you use the embeddingss layer that is trained from the scratch and used as a part of larger network, that has some utilitarian purpose (e.g. spam detection, sentiment classification), then the layers work as any other dense layers, so they serve purpose of automatic feature engineering. In the latter case, you would expect to see more specialised embeddingss, that would learn features related to the objective of your network.






          share|cite|improve this answer











          $endgroup$



          Embeddings layer for vocabulary of size $m$, that encodes each word into embeddings vector of size $k$ is a shorthand for having the words one-hot encoded using into $m$ features and then putting dense layer with $k$ units over it. Word2vec and GloVe are specialized algorithms for learning the embeddings, but the end product is a matrix of weights that is multiplied by the one-hot encoded words.



          If you are interested in detailed, yet accessible introductory source on word embeddingss, check the series of blog post by Sebastian Ruder .



          To answer your question, one would need to consider what is your network architecture and the data. Algorithms like word2vec and GloVe are trained on language data, to predict things like next word in a sequence. On another hand, if you use the embeddingss layer that is trained from the scratch and used as a part of larger network, that has some utilitarian purpose (e.g. spam detection, sentiment classification), then the layers work as any other dense layers, so they serve purpose of automatic feature engineering. In the latter case, you would expect to see more specialised embeddingss, that would learn features related to the objective of your network.







          share|cite|improve this answer














          share|cite|improve this answer



          share|cite|improve this answer








          edited Jun 9 at 19:39

























          answered Jun 9 at 17:19









          TimTim

          62.2k9 gold badges138 silver badges235 bronze badges




          62.2k9 gold badges138 silver badges235 bronze badges







          • 1




            $begingroup$
            okay, thanks just ask to "but the end product is a matrix of weights that is multiplied by the one-hot encoded words." This is related to word2vec and glove, or also to the first part of paragraph (keras Embedding layer). Does it mean that Embedding vector of size m can be just simulated by using one hot encoded layer as input, and dense layer with m neurons? So vector for each one-hot encoded feature should be just it's m weights going from this input feature to dense layer neurons?
            $endgroup$
            – Jan Musil
            Jun 9 at 18:29










          • $begingroup$
            @JanMusil as I said, embeddingss are dense layers, so they are matrices of weights to be multiplied by the features, it applies to all the embeddings.
            $endgroup$
            – Tim
            Jun 9 at 19:38












          • 1




            $begingroup$
            okay, thanks just ask to "but the end product is a matrix of weights that is multiplied by the one-hot encoded words." This is related to word2vec and glove, or also to the first part of paragraph (keras Embedding layer). Does it mean that Embedding vector of size m can be just simulated by using one hot encoded layer as input, and dense layer with m neurons? So vector for each one-hot encoded feature should be just it's m weights going from this input feature to dense layer neurons?
            $endgroup$
            – Jan Musil
            Jun 9 at 18:29










          • $begingroup$
            @JanMusil as I said, embeddingss are dense layers, so they are matrices of weights to be multiplied by the features, it applies to all the embeddings.
            $endgroup$
            – Tim
            Jun 9 at 19:38







          1




          1




          $begingroup$
          okay, thanks just ask to "but the end product is a matrix of weights that is multiplied by the one-hot encoded words." This is related to word2vec and glove, or also to the first part of paragraph (keras Embedding layer). Does it mean that Embedding vector of size m can be just simulated by using one hot encoded layer as input, and dense layer with m neurons? So vector for each one-hot encoded feature should be just it's m weights going from this input feature to dense layer neurons?
          $endgroup$
          – Jan Musil
          Jun 9 at 18:29




          $begingroup$
          okay, thanks just ask to "but the end product is a matrix of weights that is multiplied by the one-hot encoded words." This is related to word2vec and glove, or also to the first part of paragraph (keras Embedding layer). Does it mean that Embedding vector of size m can be just simulated by using one hot encoded layer as input, and dense layer with m neurons? So vector for each one-hot encoded feature should be just it's m weights going from this input feature to dense layer neurons?
          $endgroup$
          – Jan Musil
          Jun 9 at 18:29












          $begingroup$
          @JanMusil as I said, embeddingss are dense layers, so they are matrices of weights to be multiplied by the features, it applies to all the embeddings.
          $endgroup$
          – Tim
          Jun 9 at 19:38




          $begingroup$
          @JanMusil as I said, embeddingss are dense layers, so they are matrices of weights to be multiplied by the features, it applies to all the embeddings.
          $endgroup$
          – Tim
          Jun 9 at 19:38

















          draft saved

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