Using text classification for system calls












0












$begingroup$


I'm working on a project in which I should classify System calls sequences, my dataset is represented as sequences of integers (from 1 to 340). To do the classification I have inspired from Text classification projects. I'm trying to use on of them but I found a problem in my dataset shape, the code is:



df = pd.read_csv("data.txt") 
#df_test = pd.read_csv("validation.txt")

#split arrays into train and test data (cross validation)
train_text, test_text, train_y, test_y = train_test_split(df,df,test_size =
0.2)

#train_text, train_y = (df,df)
#test_text, test_y = (df_test, df_test)
MAX_NB_WORDS = 5700

texts_train = train_text.astype(str)
texts_test = test_text.astype(str)


tokenizer = Tokenizer(nb_words=MAX_NB_WORDS, char_level=False)
tokenizer.fit_on_texts(texts_train)
sequences = tokenizer.texts_to_sequences(texts_train)
sequences_test = tokenizer.texts_to_sequences(texts_test)

word_index = tokenizer.word_index
#print('Found %s unique tokens.' % len(word_index))
type(tokenizer.word_index), len(tokenizer.word_index)
index_to_word = dict((i, w) for w, i in tokenizer.word_index.items())
" ".join([index_to_word[i] for i in sequences[0]])

seq_lens = [len(s) for s in sequences]
#print("average length: %0.1f" % np.mean(seq_lens))
#print("max length: %d" % max(seq_lens))

MAX_SEQUENCE_LENGTH = 100
# pad sequences with 0s
x_train = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH) # former des
sequence de meme taille 150, en ajoutant des 0
x_test = pad_sequences(sequences_test, maxlen=MAX_SEQUENCE_LENGTH)
#print('Shape of data train:', x_train.shape) #it gives (1,100)
#print('Shape of data test tensor:', x_test.shape)
y_train = train_y
y_test = test_y
#if np.any(y_train):
#y_train = to_categorical(y_train)
print('Shape of label tensor:', y_train.shape)
EMBEDDING_DIM = 50
N_CLASSES = 2
# input: a sequence of MAX_SEQUENCE_LENGTH integers
sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='float32')

embedding_layer = Embedding(MAX_NB_WORDS, EMBEDDING_DIM,
input_length=MAX_SEQUENCE_LENGTH,
trainable=True)
embedded_sequences = embedding_layer(sequence_input)

average = GlobalAveragePooling1D()(embedded_sequences)
predictions = Dense(N_CLASSES, activation='softmax')(average)

model = Model(sequence_input, predictions)
model.compile(loss='categorical_crossentropy',
optimizer='adam', metrics=['acc'])
model.fit(x_train, y_train, validation_split=0.1,
nb_epoch=10, batch_size=100)
output_test = model.predict(x_test)
print("test auc:", roc_auc_score(y_test,output_test[:,1]))


I got the error: ValueError Error when checking target expected dense_1 to have shape(2,), but got array with shape(1,)



Any suggestion, cause I don't know how to proceed .
Thank you










share|improve this question









$endgroup$

















    0












    $begingroup$


    I'm working on a project in which I should classify System calls sequences, my dataset is represented as sequences of integers (from 1 to 340). To do the classification I have inspired from Text classification projects. I'm trying to use on of them but I found a problem in my dataset shape, the code is:



    df = pd.read_csv("data.txt") 
    #df_test = pd.read_csv("validation.txt")

    #split arrays into train and test data (cross validation)
    train_text, test_text, train_y, test_y = train_test_split(df,df,test_size =
    0.2)

    #train_text, train_y = (df,df)
    #test_text, test_y = (df_test, df_test)
    MAX_NB_WORDS = 5700

    texts_train = train_text.astype(str)
    texts_test = test_text.astype(str)


    tokenizer = Tokenizer(nb_words=MAX_NB_WORDS, char_level=False)
    tokenizer.fit_on_texts(texts_train)
    sequences = tokenizer.texts_to_sequences(texts_train)
    sequences_test = tokenizer.texts_to_sequences(texts_test)

    word_index = tokenizer.word_index
    #print('Found %s unique tokens.' % len(word_index))
    type(tokenizer.word_index), len(tokenizer.word_index)
    index_to_word = dict((i, w) for w, i in tokenizer.word_index.items())
    " ".join([index_to_word[i] for i in sequences[0]])

    seq_lens = [len(s) for s in sequences]
    #print("average length: %0.1f" % np.mean(seq_lens))
    #print("max length: %d" % max(seq_lens))

    MAX_SEQUENCE_LENGTH = 100
    # pad sequences with 0s
    x_train = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH) # former des
    sequence de meme taille 150, en ajoutant des 0
    x_test = pad_sequences(sequences_test, maxlen=MAX_SEQUENCE_LENGTH)
    #print('Shape of data train:', x_train.shape) #it gives (1,100)
    #print('Shape of data test tensor:', x_test.shape)
    y_train = train_y
    y_test = test_y
    #if np.any(y_train):
    #y_train = to_categorical(y_train)
    print('Shape of label tensor:', y_train.shape)
    EMBEDDING_DIM = 50
    N_CLASSES = 2
    # input: a sequence of MAX_SEQUENCE_LENGTH integers
    sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='float32')

    embedding_layer = Embedding(MAX_NB_WORDS, EMBEDDING_DIM,
    input_length=MAX_SEQUENCE_LENGTH,
    trainable=True)
    embedded_sequences = embedding_layer(sequence_input)

    average = GlobalAveragePooling1D()(embedded_sequences)
    predictions = Dense(N_CLASSES, activation='softmax')(average)

    model = Model(sequence_input, predictions)
    model.compile(loss='categorical_crossentropy',
    optimizer='adam', metrics=['acc'])
    model.fit(x_train, y_train, validation_split=0.1,
    nb_epoch=10, batch_size=100)
    output_test = model.predict(x_test)
    print("test auc:", roc_auc_score(y_test,output_test[:,1]))


    I got the error: ValueError Error when checking target expected dense_1 to have shape(2,), but got array with shape(1,)



    Any suggestion, cause I don't know how to proceed .
    Thank you










    share|improve this question









    $endgroup$















      0












      0








      0





      $begingroup$


      I'm working on a project in which I should classify System calls sequences, my dataset is represented as sequences of integers (from 1 to 340). To do the classification I have inspired from Text classification projects. I'm trying to use on of them but I found a problem in my dataset shape, the code is:



      df = pd.read_csv("data.txt") 
      #df_test = pd.read_csv("validation.txt")

      #split arrays into train and test data (cross validation)
      train_text, test_text, train_y, test_y = train_test_split(df,df,test_size =
      0.2)

      #train_text, train_y = (df,df)
      #test_text, test_y = (df_test, df_test)
      MAX_NB_WORDS = 5700

      texts_train = train_text.astype(str)
      texts_test = test_text.astype(str)


      tokenizer = Tokenizer(nb_words=MAX_NB_WORDS, char_level=False)
      tokenizer.fit_on_texts(texts_train)
      sequences = tokenizer.texts_to_sequences(texts_train)
      sequences_test = tokenizer.texts_to_sequences(texts_test)

      word_index = tokenizer.word_index
      #print('Found %s unique tokens.' % len(word_index))
      type(tokenizer.word_index), len(tokenizer.word_index)
      index_to_word = dict((i, w) for w, i in tokenizer.word_index.items())
      " ".join([index_to_word[i] for i in sequences[0]])

      seq_lens = [len(s) for s in sequences]
      #print("average length: %0.1f" % np.mean(seq_lens))
      #print("max length: %d" % max(seq_lens))

      MAX_SEQUENCE_LENGTH = 100
      # pad sequences with 0s
      x_train = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH) # former des
      sequence de meme taille 150, en ajoutant des 0
      x_test = pad_sequences(sequences_test, maxlen=MAX_SEQUENCE_LENGTH)
      #print('Shape of data train:', x_train.shape) #it gives (1,100)
      #print('Shape of data test tensor:', x_test.shape)
      y_train = train_y
      y_test = test_y
      #if np.any(y_train):
      #y_train = to_categorical(y_train)
      print('Shape of label tensor:', y_train.shape)
      EMBEDDING_DIM = 50
      N_CLASSES = 2
      # input: a sequence of MAX_SEQUENCE_LENGTH integers
      sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='float32')

      embedding_layer = Embedding(MAX_NB_WORDS, EMBEDDING_DIM,
      input_length=MAX_SEQUENCE_LENGTH,
      trainable=True)
      embedded_sequences = embedding_layer(sequence_input)

      average = GlobalAveragePooling1D()(embedded_sequences)
      predictions = Dense(N_CLASSES, activation='softmax')(average)

      model = Model(sequence_input, predictions)
      model.compile(loss='categorical_crossentropy',
      optimizer='adam', metrics=['acc'])
      model.fit(x_train, y_train, validation_split=0.1,
      nb_epoch=10, batch_size=100)
      output_test = model.predict(x_test)
      print("test auc:", roc_auc_score(y_test,output_test[:,1]))


      I got the error: ValueError Error when checking target expected dense_1 to have shape(2,), but got array with shape(1,)



      Any suggestion, cause I don't know how to proceed .
      Thank you










      share|improve this question









      $endgroup$




      I'm working on a project in which I should classify System calls sequences, my dataset is represented as sequences of integers (from 1 to 340). To do the classification I have inspired from Text classification projects. I'm trying to use on of them but I found a problem in my dataset shape, the code is:



      df = pd.read_csv("data.txt") 
      #df_test = pd.read_csv("validation.txt")

      #split arrays into train and test data (cross validation)
      train_text, test_text, train_y, test_y = train_test_split(df,df,test_size =
      0.2)

      #train_text, train_y = (df,df)
      #test_text, test_y = (df_test, df_test)
      MAX_NB_WORDS = 5700

      texts_train = train_text.astype(str)
      texts_test = test_text.astype(str)


      tokenizer = Tokenizer(nb_words=MAX_NB_WORDS, char_level=False)
      tokenizer.fit_on_texts(texts_train)
      sequences = tokenizer.texts_to_sequences(texts_train)
      sequences_test = tokenizer.texts_to_sequences(texts_test)

      word_index = tokenizer.word_index
      #print('Found %s unique tokens.' % len(word_index))
      type(tokenizer.word_index), len(tokenizer.word_index)
      index_to_word = dict((i, w) for w, i in tokenizer.word_index.items())
      " ".join([index_to_word[i] for i in sequences[0]])

      seq_lens = [len(s) for s in sequences]
      #print("average length: %0.1f" % np.mean(seq_lens))
      #print("max length: %d" % max(seq_lens))

      MAX_SEQUENCE_LENGTH = 100
      # pad sequences with 0s
      x_train = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH) # former des
      sequence de meme taille 150, en ajoutant des 0
      x_test = pad_sequences(sequences_test, maxlen=MAX_SEQUENCE_LENGTH)
      #print('Shape of data train:', x_train.shape) #it gives (1,100)
      #print('Shape of data test tensor:', x_test.shape)
      y_train = train_y
      y_test = test_y
      #if np.any(y_train):
      #y_train = to_categorical(y_train)
      print('Shape of label tensor:', y_train.shape)
      EMBEDDING_DIM = 50
      N_CLASSES = 2
      # input: a sequence of MAX_SEQUENCE_LENGTH integers
      sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='float32')

      embedding_layer = Embedding(MAX_NB_WORDS, EMBEDDING_DIM,
      input_length=MAX_SEQUENCE_LENGTH,
      trainable=True)
      embedded_sequences = embedding_layer(sequence_input)

      average = GlobalAveragePooling1D()(embedded_sequences)
      predictions = Dense(N_CLASSES, activation='softmax')(average)

      model = Model(sequence_input, predictions)
      model.compile(loss='categorical_crossentropy',
      optimizer='adam', metrics=['acc'])
      model.fit(x_train, y_train, validation_split=0.1,
      nb_epoch=10, batch_size=100)
      output_test = model.predict(x_test)
      print("test auc:", roc_auc_score(y_test,output_test[:,1]))


      I got the error: ValueError Error when checking target expected dense_1 to have shape(2,), but got array with shape(1,)



      Any suggestion, cause I don't know how to proceed .
      Thank you







      python deep-learning keras tensorflow nlp






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked yesterday









      KikioKikio

      214




      214






















          1 Answer
          1






          active

          oldest

          votes


















          0












          $begingroup$

          The problem seems to arise in this line.



          predictions = Dense(N_CLASSES, activation='softmax')(average) 


          Here, N_CLASSES has the value of 2. Meaning you have 2 classes. But the y_train vector has shape ( 1, ).




          • You need to convert the y_train vector to a one hot vector. Here's any example.


          If y_train = [ [ 0 ] , [ 1 ] ] then its one hot vector would be [ [ 1 , 0 ] , [ 0 , 1 ] ]. The one hot vector has shape ( 2 , ) which is needed in the above code snippet.
          So make this edit.



          y_train = keras.utils.to_categorical( y_train , N_CLASSES )





          share|improve this answer








          New contributor




          Shubham Panchal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
          Check out our Code of Conduct.






          $endgroup$













          • $begingroup$
            Thanks for your answer. I tried this an I had this error: ValueError: Input arrays should have the same number of samples as target arrays. Found 1 input samples and 0 target samples
            $endgroup$
            – Kikio
            yesterday










          • $begingroup$
            Make sure that you have the number of labels equal to the number of samples.
            $endgroup$
            – Shubham Panchal
            yesterday










          • $begingroup$
            I have two classes which means there are two labels, or something else ? Sorry but I'm a beginner
            $endgroup$
            – Kikio
            yesterday










          • $begingroup$
            No, you will count only the number of labels and not the number of one hot numbers
            $endgroup$
            – Shubham Panchal
            22 hours ago











          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%2f45874%2fusing-text-classification-for-system-calls%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












          $begingroup$

          The problem seems to arise in this line.



          predictions = Dense(N_CLASSES, activation='softmax')(average) 


          Here, N_CLASSES has the value of 2. Meaning you have 2 classes. But the y_train vector has shape ( 1, ).




          • You need to convert the y_train vector to a one hot vector. Here's any example.


          If y_train = [ [ 0 ] , [ 1 ] ] then its one hot vector would be [ [ 1 , 0 ] , [ 0 , 1 ] ]. The one hot vector has shape ( 2 , ) which is needed in the above code snippet.
          So make this edit.



          y_train = keras.utils.to_categorical( y_train , N_CLASSES )





          share|improve this answer








          New contributor




          Shubham Panchal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
          Check out our Code of Conduct.






          $endgroup$













          • $begingroup$
            Thanks for your answer. I tried this an I had this error: ValueError: Input arrays should have the same number of samples as target arrays. Found 1 input samples and 0 target samples
            $endgroup$
            – Kikio
            yesterday










          • $begingroup$
            Make sure that you have the number of labels equal to the number of samples.
            $endgroup$
            – Shubham Panchal
            yesterday










          • $begingroup$
            I have two classes which means there are two labels, or something else ? Sorry but I'm a beginner
            $endgroup$
            – Kikio
            yesterday










          • $begingroup$
            No, you will count only the number of labels and not the number of one hot numbers
            $endgroup$
            – Shubham Panchal
            22 hours ago
















          0












          $begingroup$

          The problem seems to arise in this line.



          predictions = Dense(N_CLASSES, activation='softmax')(average) 


          Here, N_CLASSES has the value of 2. Meaning you have 2 classes. But the y_train vector has shape ( 1, ).




          • You need to convert the y_train vector to a one hot vector. Here's any example.


          If y_train = [ [ 0 ] , [ 1 ] ] then its one hot vector would be [ [ 1 , 0 ] , [ 0 , 1 ] ]. The one hot vector has shape ( 2 , ) which is needed in the above code snippet.
          So make this edit.



          y_train = keras.utils.to_categorical( y_train , N_CLASSES )





          share|improve this answer








          New contributor




          Shubham Panchal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
          Check out our Code of Conduct.






          $endgroup$













          • $begingroup$
            Thanks for your answer. I tried this an I had this error: ValueError: Input arrays should have the same number of samples as target arrays. Found 1 input samples and 0 target samples
            $endgroup$
            – Kikio
            yesterday










          • $begingroup$
            Make sure that you have the number of labels equal to the number of samples.
            $endgroup$
            – Shubham Panchal
            yesterday










          • $begingroup$
            I have two classes which means there are two labels, or something else ? Sorry but I'm a beginner
            $endgroup$
            – Kikio
            yesterday










          • $begingroup$
            No, you will count only the number of labels and not the number of one hot numbers
            $endgroup$
            – Shubham Panchal
            22 hours ago














          0












          0








          0





          $begingroup$

          The problem seems to arise in this line.



          predictions = Dense(N_CLASSES, activation='softmax')(average) 


          Here, N_CLASSES has the value of 2. Meaning you have 2 classes. But the y_train vector has shape ( 1, ).




          • You need to convert the y_train vector to a one hot vector. Here's any example.


          If y_train = [ [ 0 ] , [ 1 ] ] then its one hot vector would be [ [ 1 , 0 ] , [ 0 , 1 ] ]. The one hot vector has shape ( 2 , ) which is needed in the above code snippet.
          So make this edit.



          y_train = keras.utils.to_categorical( y_train , N_CLASSES )





          share|improve this answer








          New contributor




          Shubham Panchal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
          Check out our Code of Conduct.






          $endgroup$



          The problem seems to arise in this line.



          predictions = Dense(N_CLASSES, activation='softmax')(average) 


          Here, N_CLASSES has the value of 2. Meaning you have 2 classes. But the y_train vector has shape ( 1, ).




          • You need to convert the y_train vector to a one hot vector. Here's any example.


          If y_train = [ [ 0 ] , [ 1 ] ] then its one hot vector would be [ [ 1 , 0 ] , [ 0 , 1 ] ]. The one hot vector has shape ( 2 , ) which is needed in the above code snippet.
          So make this edit.



          y_train = keras.utils.to_categorical( y_train , N_CLASSES )






          share|improve this answer








          New contributor




          Shubham Panchal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
          Check out our Code of Conduct.









          share|improve this answer



          share|improve this answer






          New contributor




          Shubham Panchal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
          Check out our Code of Conduct.









          answered yesterday









          Shubham PanchalShubham Panchal

          1512




          1512




          New contributor




          Shubham Panchal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
          Check out our Code of Conduct.





          New contributor





          Shubham Panchal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
          Check out our Code of Conduct.






          Shubham Panchal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
          Check out our Code of Conduct.












          • $begingroup$
            Thanks for your answer. I tried this an I had this error: ValueError: Input arrays should have the same number of samples as target arrays. Found 1 input samples and 0 target samples
            $endgroup$
            – Kikio
            yesterday










          • $begingroup$
            Make sure that you have the number of labels equal to the number of samples.
            $endgroup$
            – Shubham Panchal
            yesterday










          • $begingroup$
            I have two classes which means there are two labels, or something else ? Sorry but I'm a beginner
            $endgroup$
            – Kikio
            yesterday










          • $begingroup$
            No, you will count only the number of labels and not the number of one hot numbers
            $endgroup$
            – Shubham Panchal
            22 hours ago


















          • $begingroup$
            Thanks for your answer. I tried this an I had this error: ValueError: Input arrays should have the same number of samples as target arrays. Found 1 input samples and 0 target samples
            $endgroup$
            – Kikio
            yesterday










          • $begingroup$
            Make sure that you have the number of labels equal to the number of samples.
            $endgroup$
            – Shubham Panchal
            yesterday










          • $begingroup$
            I have two classes which means there are two labels, or something else ? Sorry but I'm a beginner
            $endgroup$
            – Kikio
            yesterday










          • $begingroup$
            No, you will count only the number of labels and not the number of one hot numbers
            $endgroup$
            – Shubham Panchal
            22 hours ago
















          $begingroup$
          Thanks for your answer. I tried this an I had this error: ValueError: Input arrays should have the same number of samples as target arrays. Found 1 input samples and 0 target samples
          $endgroup$
          – Kikio
          yesterday




          $begingroup$
          Thanks for your answer. I tried this an I had this error: ValueError: Input arrays should have the same number of samples as target arrays. Found 1 input samples and 0 target samples
          $endgroup$
          – Kikio
          yesterday












          $begingroup$
          Make sure that you have the number of labels equal to the number of samples.
          $endgroup$
          – Shubham Panchal
          yesterday




          $begingroup$
          Make sure that you have the number of labels equal to the number of samples.
          $endgroup$
          – Shubham Panchal
          yesterday












          $begingroup$
          I have two classes which means there are two labels, or something else ? Sorry but I'm a beginner
          $endgroup$
          – Kikio
          yesterday




          $begingroup$
          I have two classes which means there are two labels, or something else ? Sorry but I'm a beginner
          $endgroup$
          – Kikio
          yesterday












          $begingroup$
          No, you will count only the number of labels and not the number of one hot numbers
          $endgroup$
          – Shubham Panchal
          22 hours ago




          $begingroup$
          No, you will count only the number of labels and not the number of one hot numbers
          $endgroup$
          – Shubham Panchal
          22 hours ago


















          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%2f45874%2fusing-text-classification-for-system-calls%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

          How to label and detect the document text images

          Vallis Paradisi

          Tabula Rosettana