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

        # 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"

        # 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):

    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.


1 Answer 1


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.

  • $\begingroup$ I want to understand how this returned indices are transforming text data entered for feeding it into the network? $\endgroup$
    – thanatoz
    Commented Sep 20, 2018 at 21:55

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.