title: How to convert predicted sequence back to text in keras?
body: <p>I have a sequence to sequence learning model which works fine and able to predict some outputs. The problem is I have no idea how to convert the output back to text sequence.</p>
  
  <p>This is my code.</p>
  
  <pre><code>from keras.preprocessing.text import Tokenizer,base_filter
  from keras.preprocessing.sequence import pad_sequences
  from keras.models import Sequential
  from keras.layers import Dense
  
  txt1="""What makes this problem difficult is that the sequences can vary in length,
  be comprised of a very large vocabulary of input symbols and may require the model 
  to learn the long term context or dependencies between symbols in the input sequence."""
  
  #txt1 is used for fitting 
  tk = Tokenizer(nb_words=2000, filters=base_filter(), lower=True, split=" ")
  tk.fit_on_texts(txt1)
  
  #convert text to sequence
  t= tk.texts_to_sequences(txt1)
  
  #padding to feed the sequence to keras model
  t=pad_sequences(t, maxlen=10)
  
  model = Sequential()
  model.add(Dense(10,input_dim=10))
  model.add(Dense(10,activation='softmax'))
  model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy'])
  
  #predicting new sequcenc
  pred=model.predict(t)
  
  #Convert predicted sequence to text
  pred=??
  </code></pre>