my_answers.py

import numpy as np

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
import keras


# TODO: fill out the function below that transforms the input series 
# and window-size into a set of input/output pairs for use with our RNN model
def window_transform_series(series, window_size):
    # containers for input/output pairs
    X = []
    y = []

    X = [series[i:i+window_size] for i in range(0, len(series) - window_size)]
    y = [series[i+window_size] for i in range(0, len(series) - window_size)]
    # reshape each 
    X = np.asarray(X)
    X.shape = (np.shape(X)[0:2])
    y = np.asarray(y)
    y.shape = (len(y),1)

    return X,y

# TODO: build an RNN to perform regression on our time series input/output data
def build_part1_RNN(window_size):

    model = Sequential()
    model.add(LSTM(5, input_shape=(window_size,1) ))
    #model.add(Dropout(0.5))
    model.add(Dense(1))
    return model

### TODO: return the text input with only ascii lowercase and the punctuation given below included.
def cleaned_text(text):
    import string
    punctuation = ['!', ',', '.', ':', ';', '?']
    a = string.ascii_letters
    not_replace_list = punctuation + [a[i] for i in range(len(a))]
    chars = sorted(list(set(text)))
    for i in range(len(chars)):
        if chars[i] not in not_replace_list:
            text = text.replace(chars[i], ' ')
    return text

### TODO: fill out the function below that transforms the input text and window-size into a set of input/output pairs for use with our RNN model
def window_transform_text(text, window_size, step_size):
    # containers for input/output pairs
    inputs = []
    outputs = []

    inputs = [text[i:i+window_size] for i in range(0, len(text) - window_size, step_size)]
    outputs = [text[i+window_size] for i in range(0, len(text) - window_size, step_size)]


    return inputs,outputs

# TODO build the required RNN model: 
# a single LSTM hidden layer with softmax activation, categorical_crossentropy loss 
def build_part2_RNN(window_size, num_chars):

    model = Sequential()
    model.add(LSTM(200, input_shape=(window_size, num_chars)))
    #model.add(Dropout(0.5))
    model.add(Dense(num_chars, activation='softmax'))
    return model