my_answers.py

import numpy as np

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import LSTM,Activation
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 = []

    for i in range(len(series) - window_size):
        X.append(series[i:i+window_size])
        y.append(series[i+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(Dense(1))
    return model


### TODO: return the text input with only ascii lowercase and the punctuation given below included.
def cleaned_text(text):
    text = list(text)
    punctuation = ['!', ',', '.', ':', ';', '?']
    for i in range(len(text)):
        if (text[i] not in punctuation) and (ord(text[i])<97 or ord(text[i])>122):
            text[i] = ' '
    return ''.join(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 = []

    for i in range(0, len(text) - window_size, step_size):
        inputs.append(text[i:i+window_size])
        outputs.append(text[i+window_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(Dense(num_chars))
    model.add(Activation(activation='softmax'))
    return model