Keras .predict with word embeddings back to string

I'm following the tutorial here: https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html, using a different data set. I'm trying to predict the label for a new random string.

I'm doing labelling a bit different:

encoder = LabelEncoder()
encoder.fit(labels)
encoded_Y = encoder.transform(labels)
dummy_y = np_utils.to_categorical(encoded_Y)

And then trying to predict like:

string = "I am a cat"
query = tokenizer.texts_to_sequences(string)
query = pad_sequences(query, maxlen=50)

prediction = model.predict(query)
print(prediction)

I get back an array of arrays like below (perhaps the word embeddings?). What are those and how can I translate them back to a string?

[[ 0.03039312  0.02099193  0.02320454  0.02183384  0.01965107  0.01830118
   0.0170384   0.01979697  0.01764384  0.02244077  0.0162186   0.02672437
   0.02190582  0.01630476  0.01388928  0.01655456  0.011678    0.02256939
   0.02161663  0.01649982  0.02086013  0.0161493   0.01821378  0.01440909
   0.01879989  0.01217389  0.02032642  0.01405699  0.01393504  0.01957162
   0.01818203  0.01698637  0.02639499  0.02102267  0.01956343  0.01588933
   0.01635705  0.01391534  0.01587612  0.01677094  0.01908684  0.02032183
   0.01798265  0.02017053  0.01600159  0.01576616  0.01373934  0.01596323
   0.01386674  0.01532488  0.01638312  0.0172212   0.01432543  0.01893282
   0.02020231]