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 n_a = Waa.shape ### 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.