Part 2: Text reversing model - Encoder

You will now implement the rest of the encoder of the text reversing model. The encoder feeds on the one-hot vectors produced by the words2onehot() function you implemented previously.

Here you will be implementing the encoder() function. The encoder() function takes in a set of one-hot vectors and converts them to a list of word ids.

For this exercise, the words2onehot() function and the word2index dictionary (having the words We, like and dogs) have been provided.

Convert onehot to an array of word IDs using np.argmax() function and return the word IDs.
Define a list of words with words We, like, dogs.
Convert the list of words to onehot vectors using the words2onehot() function. Remember that words2onehot() takes a list of words and a Python dictionary as its arguments.
Get the context vector of the onehot vectors using the encoder() function.

Exercise

Part 2: Text reversing model - Encoder

Instructions