band-batch-generator.py

#Our batch generator - the bias allows us to control how much of the context is from genre and how much is from score
def gen_batch(genres, scores, size, bias):
    #Initialise the numpy arrays - the xs are the target words and the ys the inidividual contexts.
    xs = np.ndarray(shape=(size), dtype=np.int32)
    ys = np.ndarray(shape=(size, 1), dtype=np.int32)
    for idx in range(size):
        b = np.random.randint(10)
        if b < bias:
            genre = np.random.randint(len(genres))
            g1 = np.random.randint(len(genres[genre_lookup[genre]]))
            g2 = np.random.randint(len(genres[genre_lookup[genre]]))
            #Probably an inefficient way to make sure we don't choose the same artist
            while g1 == g2:
                g2 = np.random.randint(len(genres[genre_lookup[genre]]))
            #Adding the artist unique integer id
            xs[idx] = genres[genre_lookup[genre]][g1]
            ys[idx][0] = genres[genre_lookup[genre]][g2]
        else:
            score = np.random.randint(len(scores))
            s1 = np.random.randint(len(scores[score]))
            s2 = np.random.randint(len(scores[score]))
            while s1 == s2:
                s2 = np.random.randint(len(scores[score]))
            xs[idx] = scores[score][s1]
            ys[idx][0] = scores[score][s2]
    return xs, ys

#A useful dictionary to go from unique integer to artist name
artist_decode = dict(zip(artist_lookup.values(), artist_lookup.keys()))