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my_answers.py
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my_answers.py
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import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.layers import LSTM
import keras
import string
# Implemented: The function below 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 = []
last_element_start = len(series) - window_size
for ele in range(0, last_element_start, 1):
current_series_last_element = ele+window_size
X.append(series[ele : current_series_last_element])
y.append(series[current_series_last_element])
# 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
# Implemented: 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, activation='linear'))
return model
### Implemented: return the text input with only ascii lowercase and the punctuation given below included.
def cleaned_text(text):
punctuation = ['!', ',', '.', ':', ';', '?']
stringlower = string.ascii_lowercase + ' '
chartokeep = stringlower + ''.join(punctuation)
for charele in text:
if charele not in chartokeep:
text = text.replace(charele, ' ')
return text
### Implemented: The function below 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 = []
last_element_start = len(text) - window_size
for ele in range(0, last_element_start, step_size):
current_series_last_element = ele+window_size
inputs.append(text[ele : current_series_last_element])
outputs.append(text[current_series_last_element])
return inputs,outputs
# Implemented: 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, activation='linear'))
model.add(Activation('softmax'))
return model