title: Converting python SVM text classifier to Tensorflow model
body: <p>I have written a python code for <strong>text classifier using SVM (Multi-class)</strong>, now I want to run this code in the android application. <strong>TensorFlow-lite</strong> is useful in this scenario from what I have read, how should I proceed to work to convert my python code to TensorFlow-lite code? what should steps that I need to follow? </p>
  
  <p>Below is the code for SVM Classifier,</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  import tensorflow as tf
  from collections import Counter
  from sklearn import model_selection, preprocessing, linear_model, naive_bayes, metrics, svm
  from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
  from sklearn.feature_extraction.text import TfidfTransformer
  from sklearn.svm import SVC
  
  column_names = ['text', 'labels'] 
  data = pd.read_csv("newdataset.csv", names = column_names, index_col = False)
  
  train_x, test_x, train_y, test_y = model_selection.train_test_split(data.text,data.labels,test_size = 0.5 ,random_state = 0)
  
  count_vect = CountVectorizer(analyzer='word', token_pattern=r'\w{1,}',max_features=100)
  count_vect.fit(data.text)
  xtrain_count =  count_vect.transform(train_x)
  xtest_count =  count_vect.transform(test_x)
  
  tfidf_vect = TfidfTransformer()
  xtrain_tfidf =  tfidf_vect.fit_transform(xtrain_count)
  xtest_tfidf =  tfidf_vect.fit_transform(xtest_count)
  
  clf = svm.SVC(kernel='linear')
  clf.fit(xtrain_tfidf, train_y)
  
  predicted = clf.predict(xtest_tfidf)
  
  from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
  
  print(confusion_matrix(test_y,predicted))
  print(classification_report(test_y,predicted))
  print(accuracy_score(test_y,predicted))
  </code></pre>