Decode your predictions

Your LSTM model has already been trained (details in the previous exercise success message) so that you don't have to wait. It's time to define a function that decodes its predictions. The trained model will be passed as a default parameter to this function.

Since you are predicting on a model that uses the softmax function, numpy's argmax() can be used to obtain the index/position representing the most probable next word out of the output vector of probabilities.

The tokenizer you previously created and fitted, is loaded for you. You will be making use of its internal index_word dictionary to turn the model's next word prediction (which is an integer) into the actual written word it represents.

You're very close to experimenting with your model!

Use texts_to_sequences() to turn the test_text parameter into a sequence of numbers.
Get the model's next word prediction by passing in test_seq . The index/position representing the word with the highest probability is obtained by calling .argmax(axis=1)[0] on the numpy array of predictions.
Return the word that maps to the prediction using the tokenizer's index_word dictionary.

Exercise

Decode your predictions

Instructions