CHARACTER LEVEL LANGUAGE MODEL (RNN)

## CHARACTER LEVEL LANGUAGE MODEL (RNN)


import numpy as np
from utils import *
import random
data = open('dinos.txt', 'r').read()
data= data.lower()
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)
print('There are %d total characters and %d unique characters in your data.' % (data_size, vocab_size))

### GRADED FUNCTION: clip

def clip(gradients, maxValue):
    '''
    Clips the gradients' values between minimum and maximum.

    Arguments:
    gradients -- a dictionary containing the gradients "dWaa", "dWax", "dWya", "db", "dby"
    maxValue -- everything above this number is set to this number, and everything less than -maxValue is set to -maxValue

    Returns:
    gradients -- a dictionary with the clipped gradients.
    '''

    dWaa, dWax, dWya, db, dby = gradients['dWaa'], gradients['dWax'], gradients['dWya'], gradients['db'], gradients['dby']

    ### START CODE HERE ###
    # clip to mitigate exploding gradients, loop over [dWax, dWaa, dWya, db, dby]. (≈2 lines)
    for gradient in [dWax, dWaa, dWya, db, dby]:
        np.clip(gradient,-maxValue,maxValue,gradient)
    ### END CODE HERE ###

    gradients = {"dWaa": dWaa, "dWax": dWax, "dWya": dWya, "db": db, "dby": dby}

    return gradients

    # GRADED FUNCTION: sample

def sample(parameters, char_to_ix, seed):
    """
    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.
    seed -- used for grading purposes. Do not worry about it.

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

    # 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(range(vocab_size), 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

    ## Function Test
    np.random.seed(2)
_, n_a = 20, 100
Wax, Waa, Wya = np.random.randn(n_a, vocab_size), np.random.randn(n_a, n_a), np.random.randn(vocab_size, n_a)
b, by = np.random.randn(n_a, 1), np.random.randn(vocab_size, 1)
parameters = {"Wax": Wax, "Waa": Waa, "Wya": Wya, "b": b, "by": by}


indices = sample(parameters, char_to_ix, 0)
print("Sampling:")
print("list of sampled indices:", indices)
print("list of sampled characters:", [ix_to_char[i] for i in indices])


#def rnn_forward(X, Y, a_prev, parameters):
#   """ Performs the forward propagation through the RNN and computes the cross-entropy loss.
#    It returns the loss' value as well as a "cache" storing values to be used in the backpropagation."""
#    return loss, cache

#def rnn_backward(X, Y, parameters, cache):
#    """ Performs the backward propagation through time to compute the gradients of the loss with respect
#    to the parameters. It returns also all the hidden states."""
#    return gradients, a

#def update_parameters(parameters, gradients, learning_rate):
#    """ Updates parameters using the Gradient Descent Update Rule."""


# GRADED FUNCTION: optimize

def optimize(X, Y, a_prev, parameters, learning_rate = 0.01):
    """
    Execute one step of the optimization to train the model.

    Arguments:
    X -- list of integers, where each integer is a number that maps to a character in the vocabulary.
    Y -- list of integers, exactly the same as X but shifted one index to the left.
    a_prev -- previous hidden state.
    parameters -- python dictionary containing:
                        Wax -- Weight matrix multiplying the input, numpy array of shape (n_a, n_x)
                        Waa -- Weight matrix multiplying the hidden state, numpy array of shape (n_a, n_a)
                        Wya -- Weight matrix relating the hidden-state to the output, numpy array of shape (n_y, n_a)
                        b --  Bias, numpy array of shape (n_a, 1)
                        by -- Bias relating the hidden-state to the output, numpy array of shape (n_y, 1)
    learning_rate -- learning rate for the model.

    Returns:
    loss -- value of the loss function (cross-entropy)
    gradients -- python dictionary containing:
                        dWax -- Gradients of input-to-hidden weights, of shape (n_a, n_x)
                        dWaa -- Gradients of hidden-to-hidden weights, of shape (n_a, n_a)
                        dWya -- Gradients of hidden-to-output weights, of shape (n_y, n_a)
                        db -- Gradients of bias vector, of shape (n_a, 1)
                        dby -- Gradients of output bias vector, of shape (n_y, 1)
    a[len(X)-1] -- the last hidden state, of shape (n_a, 1)
    """

    ### START CODE HERE ###

    # Forward propagate through time (≈1 line)
    loss, cache = rnn_forward(X, Y, a_prev, parameters)

    # Backpropagate through time (≈1 line)
    gradients, a = rnn_backward(X, Y, parameters, cache)

    # Clip your gradients between -5 (min) and 5 (max) (≈1 line)
    gradients = clip(gradients,5)

    # Update parameters (≈1 line)
    parameters = update_parameters(parameters, gradients, learning_rate)

    ### END CODE HERE ###

    return loss, gradients, a[len(X)-1]

## GRADED FUNCTION: model

def model(data, ix_to_char, char_to_ix, num_iterations = 35000, n_a = 50, dino_names = 7, vocab_size = 27):
    """
    Trains the model and generates dinosaur names.

    Arguments:
    data -- text corpus
    ix_to_char -- dictionary that maps the index to a character
    char_to_ix -- dictionary that maps a character to an index
    num_iterations -- number of iterations to train the model for
    n_a -- number of units of the RNN cell
    dino_names -- number of dinosaur names you want to sample at each iteration.
    vocab_size -- number of unique characters found in the text, size of the vocabulary

    Returns:
    parameters -- learned parameters
    """

    # Retrieve n_x and n_y from vocab_size
    n_x, n_y = vocab_size, vocab_size

    # Initialize parameters
    parameters = initialize_parameters(n_a, n_x, n_y)

    # Initialize loss (this is required because we want to smooth our loss, don't worry about it)
    loss = get_initial_loss(vocab_size, dino_names)

    # Build list of all dinosaur names (training examples).
    with open("dinos.txt") as f:
        examples = f.readlines()
    examples = [x.lower().strip() for x in examples]

    # Shuffle list of all dinosaur names
    np.random.seed(0)
    np.random.shuffle(examples)

    # Initialize the hidden state of your LSTM
    a_prev = np.zeros((n_a, 1))

    # Optimization loop
    for j in range(num_iterations):

        ### START CODE HERE ###

        # Use the hint above to define one training example (X,Y) (≈ 2 lines)
        index = j % len(examples)
        X = [None] + [char_to_ix[ch] for ch in examples[index]]
        Y = X[1:] + [char_to_ix["\n"]]

        # Perform one optimization step: Forward-prop -> Backward-prop -> Clip -> Update parameters
        # Choose a learning rate of 0.01
        curr_loss, gradients, a_prev = optimize(X,Y,a_prev,parameters,learning_rate=0.01)

        ### END CODE HERE ###

        # Use a latency trick to keep the loss smooth. It happens here to accelerate the training.
        loss = smooth(loss, curr_loss)

        # Every 2000 Iteration, generate "n" characters thanks to sample() to check if the model is learning properly
        if j % 2000 == 0:

            print('Iteration: %d, Loss: %f' % (j, loss) + '\n')

            # The number of dinosaur names to print
            seed = 0
            for name in range(dino_names):

                # Sample indices and print them
                sampled_indices = sample(parameters, char_to_ix, seed)
                print_sample(sampled_indices, ix_to_char)

                seed += 1  # To get the same result for grading purposed, increment the seed by one.

            print('\n')

    return parameters


    ## RUNNING THE MODEL
    parameters = model(data, ix_to_char, char_to_ix)