How to convert predicted sequence back to text in keras?

Question

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.

This is my 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=?? 

Answer 1

You can use directly the inverse tokenizer.sequences_to_texts function.

text = tokenizer.sequences_to_texts(<list-of-integer-equivalent-encodings>) 

I have tested the above and it works as expected.

PS.: Take extra care to make the argument be the list of the integer encodings and not the One Hot ones.