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shakespeare_utils.py
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# Load Packages
from __future__ import print_function
from tensorflow.keras.callbacks import LambdaCallback
from tensorflow.keras.models import Model, load_model, Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout, Input, Masking
from tensorflow.keras.layers import LSTM
from tensorflow.keras.utils import get_file
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np
import random
import sys
import io
def build_data(text, Tx = 40, stride = 3):
"""
Create a training set by scanning a window of size Tx over the text corpus, with stride 3.
Arguments:
text -- string, corpus of Shakespearian poem
Tx -- sequence length, number of time-steps (or characters) in one training example
stride -- how much the window shifts itself while scanning
Returns:
X -- list of training examples
Y -- list of training labels
"""
X = []
Y = []
### START CODE HERE ### (≈ 3 lines)
for i in range(0, len(text) - Tx, stride):
X.append(text[i: i + Tx])
Y.append(text[i + Tx])
### END CODE HERE ###
print('number of training examples:', len(X))
return X, Y
def vectorization(X, Y, n_x, char_indices, Tx = 40):
"""
Convert X and Y (lists) into arrays to be given to a recurrent neural network.
Arguments:
X --
Y --
Tx -- integer, sequence length
Returns:
x -- array of shape (m, Tx, len(chars))
y -- array of shape (m, len(chars))
"""
m = len(X)
x = np.zeros((m, Tx, n_x), dtype=np.bool)
y = np.zeros((m, n_x), dtype=np.bool)
for i, sentence in enumerate(X):
for t, char in enumerate(sentence):
x[i, t, char_indices[char]] = 1
y[i, char_indices[Y[i]]] = 1
return x, y
def sample(preds, temperature=1.0):
# helper function to sample an index from a probability array
preds = np.asarray(preds).astype('float64')
preds = np.log(preds) / temperature
exp_preds = np.exp(preds)
preds = exp_preds / np.sum(exp_preds)
probas = np.random.multinomial(1, preds, 1)
out = np.random.choice(range(len(chars)), p = probas.ravel())
return out
#return np.argmax(probas)
def on_epoch_end(epoch, logs):
# Function invoked at end of each epoch. Prints generated text.
None
#start_index = random.randint(0, len(text) - Tx - 1)
#generated = ''
#sentence = text[start_index: start_index + Tx]
#sentence = '0'*Tx
#usr_input = input("Write the beginning of your poem, the Shakespearian machine will complete it.")
# zero pad the sentence to Tx characters.
#sentence = ('{0:0>' + str(Tx) + '}').format(usr_input).lower()
#generated += sentence
#
#sys.stdout.write(usr_input)
#for i in range(400):
"""
#x_pred = np.zeros((1, Tx, len(chars)))
for t, char in enumerate(sentence):
if char != '0':
x_pred[0, t, char_indices[char]] = 1.
preds = model.predict(x_pred, verbose=0)[0]
next_index = sample(preds, temperature = 1.0)
next_char = indices_char[next_index]
generated += next_char
sentence = sentence[1:] + next_char
sys.stdout.write(next_char)
sys.stdout.flush()
if next_char == '\n':
continue
# Stop at the end of a line (4 lines)
print()
"""
print("Loading text data...")
text = io.open('shakespeare.txt', encoding='utf-8').read().lower()
#print('corpus length:', len(text))
Tx = 40
chars = sorted(list(set(text)))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))
#print('number of unique characters in the corpus:', len(chars))
print("Creating training set...")
X, Y = build_data(text, Tx, stride = 3)
print("Vectorizing training set...")
x, y = vectorization(X, Y, n_x = len(chars), char_indices = char_indices)
print("Loading model...")
model = load_model('models/model_shakespeare_kiank_350_epoch.h5')
def generate_output():
generated = ''
#sentence = text[start_index: start_index + Tx]
#sentence = '0'*Tx
usr_input = input("Write the beginning of your poem, the Shakespeare machine will complete it. Your input is: ")
# zero pad the sentence to Tx characters.
sentence = ('{0:0>' + str(Tx) + '}').format(usr_input).lower()
generated += usr_input
sys.stdout.write("\n\nHere is your poem: \n\n")
sys.stdout.write(usr_input)
for i in range(400):
x_pred = np.zeros((1, Tx, len(chars)))
for t, char in enumerate(sentence):
if char != '0':
x_pred[0, t, char_indices[char]] = 1.
preds = model.predict(x_pred, verbose=0)[0]
next_index = sample(preds, temperature = 1.0)
next_char = indices_char[next_index]
generated += next_char
sentence = sentence[1:] + next_char
sys.stdout.write(next_char)
sys.stdout.flush()
if next_char == '\n':
continue