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

Answer 1

I had to resolve the same problem, so here is how I ended up doing it (inspired by @Ben Usemans reversed dictionary).

# Importing library
from keras.preprocessing.text import Tokenizer

# My texts
texts = ['These are two crazy sentences', 'that I want to convert back and forth']

# Creating a tokenizer
tokenizer = Tokenizer(lower=True)

# Building word indices
tokenizer.fit_on_texts(texts)

# Tokenizing sentences
sentences = tokenizer.texts_to_sequences(texts)

>sentences
>[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11, 12, 13]]

# Creating a reverse dictionary
reverse_word_map = dict(map(reversed, tokenizer.word_index.items()))

# Function takes a tokenized sentence and returns the words
def sequence_to_text(list_of_indices):
    # Looking up words in dictionary
    words = [reverse_word_map.get(letter) for letter in list_of_indices]
    return(words)

# Creating texts 
my_texts = list(map(sequence_to_text, sentences))

>my_texts
>[['these', 'are', 'two', 'crazy', 'sentences'], ['that', 'i', 'want', 'to', 'convert', 'back', 'and', 'forth']]