"word level text generation keras" Code Answer's
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word level text generation keras
import string
# load doc into memory
def load_doc(filename):
# open the file as read only
file = open(filename, 'r')
# read all text
text = file.read()
# close the file
file.close()
return text
# turn a doc into clean tokens
def clean_doc(doc):
# replace '--' with a space ' '
doc = doc.replace('--', ' ')
# split into tokens by white space
tokens = doc.split()
# remove punctuation from each token
table = str.maketrans('', '', string.punctuation)
tokens = [w.translate(table) for w in tokens]
# remove remaining tokens that are not alphabetic
tokens = [word for word in tokens if word.isalpha()]
# make lower case
tokens = [word.lower() for word in tokens]
return tokens
# save tokens to file, one dialog per line
def save_doc(lines, filename):
data = '\n'.join(lines)
file = open(filename, 'w')
file.write(data)
file.close()
# load document
in_filename = 'republic_clean.txt'
doc = load_doc(in_filename)
print(doc[:200])
# clean document
tokens = clean_doc(doc)
print(tokens[:200])
print('Total Tokens: %d' % len(tokens))
print('Unique Tokens: %d' % len(set(tokens)))
# organize into sequences of tokens
length = 50 + 1
sequences = list()
for i in range(length, len(tokens)):
# select sequence of tokens
seq = tokens[i-length:i]
# convert into a line
line = ' '.join(seq)
# store
sequences.append(line)
print('Total Sequences: %d' % len(sequences))
# save sequences to file
out_filename = 'republic_sequences.txt'
save_doc(sequences, out_filename)
Source: machinelearningmastery.com
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