Gfyuki

So there is a function in Dino_Name_Generator at Deeplearning.ai notebook

def sample(parameters, char_to_ix, seed):  

    # Retrieve parameters and relevant shapes from "parameters" dictionary

    Waa, Wax, Wya, by, b = parameters['Waa'], parameters['Wax'],parameters['Wya'], parameters['by'], parameters['b']

    vocab_size = by.shape[0]

    n_a = Waa.shape[1]



    ### START CODE HERE ###

    # Step 1: Create the one-hot vector x for the first character (initializing the sequence generation). (≈1 line)

    x = np.zeros((vocab_size, 1))



    # Step 1': Initialize a_prev as zeros (≈1 line)

    a_prev = np.zeros((n_a, 1))



    # Create an empty list of indices, this is the list which will contain the list of indices of the characters to generate (≈1 line)

    indices = 



    # Idx is a flag to detect a newline character, we initialize it to -1

    idx = -1 



    # Loop over time-steps t. At each time-step, sample a character from a probability distribution and append 

    # its index to "indices". We'll stop if we reach 50 characters (which should be very unlikely with a well 

    # trained model), which helps debugging and prevents entering an infinite loop. 

    counter = 0

    newline_character = char_to_ix['n']



    while (idx != newline_character and counter != 50):



        # Step 2: Forward propagate x using the equations (1), (2) and (3)

        a = np.tanh(np.dot(Wax, x) + np.dot(Waa, a_prev) + b)

        z = np.dot(Wya, a) + by

        y = softmax(z)



        # for grading purposes

        np.random.seed(counter+seed) 



        # Step 3: Sample the index of a character within the vocabulary from the probability distribution y

        idx = np.random.choice(vocab_size, size=None, p = y.ravel())



        # Append the index to "indices"

        indices.append(idx)



        # Step 4: Overwrite the input character as the one corresponding to the sampled index.

        x = np.zeros((vocab_size, 1))

        x[[idx]] = 1



        # Update "a_prev" to be "a"

        a_prev = a



        # for grading purposes

        seed += 1

        counter +=1





    ### END CODE HERE ###



    if (counter == 50):

        indices.append(char_to_ix['n'])



    return indices

Can someone please help and explain what benefit of returned indices over normal char_to_integer indices?

I want to understand the text processing in the link carried out before feeding into the network.

From the link you provided:

Sample a sequence of characters according to a sequence of probability
distributions output of the RNN

Arguments:
parameters -- python dictionary containing the parameters Waa, Wax, Wya, by, and b.
char_to_ix -- python dictionary mapping each character to an index.

Returns:
indices -- a list of length n containing the indices of the sampled characters

You are returning the indices from a dictionary you gave as argument. Why should you use char_to_integer indices.