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traindata.py
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traindata.py
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#!/usr/bin/python
# -*- coding: utf8 -*-
import collections
import helper
import numpy as np
#import project_tests as tests
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Model
from keras.layers import GRU, Input, Dense, TimeDistributed, Activation, RepeatVector, Bidirectional
from keras.layers.embeddings import Embedding
from keras.optimizers import Adam
from keras.losses import sparse_categorical_crossentropy
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
def create_tokenizer(lines):
tokenizer = Tokenizer()
tokenizer.fit_on_texts(lines)
return tokenizer
# max sentence length
def max_length(lines):
return max(len(line.split()) for line in lines)
# encode and pad sequences
def encode_sequences(tokenizer, length, lines):
# integer encode sequences
X = tokenizer.texts_to_sequences(lines)
# pad sequences with 0 values
X = pad_sequences(X, maxlen=length, padding='post')
return X
# one hot encode target sequence
def encode_output(sequences, vocab_size):
ylist = list()
for sequence in sequences:
encoded = to_categorical(sequence, num_classes=vocab_size)
ylist.append(encoded)
y = array(ylist)
y = y.reshape(sequences.shape[0], sequences.shape[1], vocab_size)
return y
def tokenize(x):
"""
Tokenize x
:param x: List of sentences/strings to be tokenized
:return: Tuple of (tokenized x data, tokenizer used to tokenize x)
"""
# TODO: Implement
x_tk = Tokenizer(char_level = False)
x_tk.fit_on_texts(x)
return x_tk.texts_to_sequences(x), x_tk
def pad(x, length=None):
"""
Pad x
:param x: List of sequences.
:param length: Length to pad the sequence to. If None, use length of longest sequence in x.
:return: Padded numpy array of sequences
"""
# TODO: Implement
if length is None:
length = max([len(sentence) for sentence in x])
return pad_sequences(x, maxlen = length, padding = 'post')
def preprocess(x, y):
"""
Preprocess x and y
:param x: Feature List of sentences
:param y: Label List of sentences
:return: Tuple of (Preprocessed x, Preprocessed y, x tokenizer, y tokenizer)
"""
preprocess_x, x_tk = tokenize(x)
preprocess_y, y_tk = tokenize(y)
preprocess_x = pad(preprocess_x)
preprocess_y = pad(preprocess_y)
# Keras's sparse_categorical_crossentropy function requires the labels to be in 3 dimensions
preprocess_y = preprocess_y.reshape(*preprocess_y.shape, 1)
return preprocess_x, preprocess_y, x_tk, y_tk
def logits_to_text(logits, tokenizer):
"""
Turn logits from a neural network into text using the tokenizer
:param logits: Logits from a neural network
:param tokenizer: Keras Tokenizer fit on the labels
:return: String that represents the text of the logits
"""
index_to_words = {id: word for word, id in tokenizer.word_index.items()}
index_to_words[0] = '<PAD>'
return ' '.join([index_to_words[prediction] for prediction in np.argmax(logits, 1)])
# Load no accent data
non_accent_sentences = helper.load_data('data/data_noaccent_clean.txt')
# Load accent data
accent_sentences = helper.load_data('data/data_clean.txt')
print('Dataset Loaded')
# add space at begin and end in the sentence
non_accent_sentences = [" "+sentence +" " for sentence in non_accent_sentences]
accent_sentences = [" "+sentence +" " for sentence in accent_sentences]
split_non_accent_words = [" "+word.strip()+" " for sentence in non_accent_sentences for word in sentence.split()]
split_accent_words = [" "+word.strip()+" " for sentence in accent_sentences for word in sentence.split()]
words_dict = dict()
for _row in range(len(split_non_accent_words)):
for _col in range(len(split_non_accent_words[_row])):
word = split_non_accent_words[_row][_col]
if(word not in words_dict):
words_dict.append(word,{})
words_dict[word].add(split_accent_words[_row][_col])
unique_words = {}
for key,val in words_dict:
if len(val) == 1:
unique_words.add(key)
# dumplicate_data= set(split_non_accent_words).intersection(split_accent_words)
# unique_words =set([x for x in split_non_accent_words if x not in dumplicate_data])
# with open("logs/dumplicate_data.txt",'w+',encoding='utf-8') as f:
# for item in dumplicate_data:
# f.writelines(item)
with open("logs/unique_words.txt",'w+',encoding='utf-8') as f:
for item in unique_words:
f.writelines(item)
# for sentence in non_accent_sentences:
nonaccent_words_counter = collections.Counter(split_non_accent_words)
accent_words_counter = collections.Counter(split_accent_words)
#thống kê dữ liệu
print('{} non accent words'.format(len(split_non_accent_words)))
print('{} non accent unique words'.format(len(nonaccent_words_counter)))
with open("logs/non_accent_counter.txt",'w+') as f:
for k,v in nonaccent_words_counter.most_common():
f.write( "{} {}\n".format(k,v) )
print('{} accent words'.format(len(split_accent_words)))
print('{} accent unique words'.format(len(accent_words_counter)))
with open("logs/accent_counter.txt",'w+',encoding='utf-8') as f:
for k,v in accent_words_counter.most_common():
f.write( "{} {}\n".format(k.encode("utf-8").decode("utf-8"),v) )
# def simple_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size):
# """
# Build and train a basic RNN on x and y
# :param input_shape: Tuple of input shape
# :param output_sequence_length: Length of output sequence
# :param english_vocab_size: Number of unique English words in the dataset
# :param french_vocab_size: Number of unique French words in the dataset
# :return: Keras model built, but not trained
# """
# # TODO: Build the layers
# learning_rate = 1e-3
# input_seq = Input(input_shape[1:])
# rnn = GRU(64, return_sequences = True)(input_seq)
# logits = TimeDistributed(Dense(french_vocab_size))(rnn)
# model = Model(input_seq, Activation('softmax')(logits))
# model.compile(loss = sparse_categorical_crossentropy,
# optimizer = Adam(learning_rate),
# metrics = ['accuracy'])
# return model
# preproc_english_sentences, preproc_french_sentences, english_tokenizer, french_tokenizer =preprocess(non_accent_sentences, accent_sentences)
# max_english_sequence_length = preproc_english_sentences.shape[1]
# max_french_sequence_length = preproc_french_sentences.shape[1]
# english_vocab_size = len(english_tokenizer.word_index)
# french_vocab_size = len(french_tokenizer.word_index)
# # Reshaping the input to work with a basic RNN
# tmp_x = pad(preproc_english_sentences, max_french_sequence_length)
# tmp_x = tmp_x.reshape((-1, preproc_french_sentences.shape[-2], 1))
# # Train the neural network
# simple_rnn_model = simple_model(
# tmp_x.shape,
# max_french_sequence_length,
# english_vocab_size,
# french_vocab_size)
# simple_rnn_model.fit(tmp_x, preproc_french_sentences, batch_size=16, epochs=10, validation_split=0.2)
# # Print prediction(s)
# print(logits_to_text(simple_rnn_model.predict(tmp_x[:1])[0], french_tokenizer))
# # serialize model to YAML
# model_yaml = simple_rnn_model.to_yaml()
# with open("model.yaml", "w") as yaml_file:
# yaml_file.write(model_yaml)
# # serialize weights to HDF5
# simple_rnn_model.save_weights("model.h5")
# print("Saved model to disk")