Turn sequence of words into a fix-length representation vector
This is a version to refactor all the seq2vec structures and use customed layers in yklz.
pip install seq2vec
or clone the repo, then install:
git clone --recursive https://github.com/Yoctol/seq2vec.git
python setup.py install
Simple hash:
from seq2vec import Seq2VecHash
transformer = Seq2VecHash(vector_length=100)
seqs = [
['我', '有', '一個', '蘋果'],
['我', '有', 'pineapple'],
]
result = transformer.transform(seqs)
print(result)
'''
array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0.]])
'''Sequence-to-sequence auto-encoder:
-
LSTM to LSTM auto-encoder with word embedding (RNN to RNN architecture)
from seq2vec.word2vec import GensimWord2vec from seq2vec import Seq2VecR2RWord # load Gensim word2vec from word2vec_model_path word2vec = GensimWord2vec(word2vec_model_path) transformer = Seq2VecR2RWord( word2vec_model=word2vec, max_length=20, latent_size=300, encoding_size=300, learning_rate=0.05 ) train_seq = [ ['我', '有', '一個', '蘋果'], ['我', '有', '筆'], ['一個', '鳳梨'], ] test_seq = [ ['我', '愛', '吃', '鳳梨'], ] transformer.fit(train_seq) result = transformer.transform(test_seq)
-
CNN to LSTM auto-encoder with word embedding (CNN to RNN architecture)
from seq2vec.word2vec import GensimWord2vec from seq2vec import Seq2VecC2RWord # load Gensim word2vec from word2vec_model_path word2vec = GensimWord2vec(word2vec_model_path) transformer = Seq2VecC2RWord( word2vec_model=word2vec, max_length=20, latent_size=300, conv_size=5, channel_size=10, learning_rate=0.05, ) train_seq = [ ['我', '有', '一個', '蘋果'], ['我', '有', '筆'], ['一個', '鳳梨'], ] test_seq = [ ['我', '愛', '吃', '鳳梨'], ] transformer.fit(train_seq) result = transformer.transform(test_seq)
-
CNN to LSTM auto-encoder with char embedding (CNN to RNN architecture)
from seq2vec.word2vec import GensimWord2vec from seq2vec import Seq2VecC2RChar # load Gensim word2vec from word2vec_model_path word2vec = GensimWord2vec(word2vec_model_path) transformer = Seq2VecC2RChar( word2vec_model=word2vec, max_index=1000, max_length=20, embedding_size=200, latent_size=200, learning_rate=0.05, channel_size=10, conv_size=5 ) train_seq = [ ['我', '有', '一個', '蘋果'], ['我', '有', '筆'], ['一個', '鳳梨'], ] test_seq = [ ['我', '愛', '吃', '鳳梨'], ] transformer.fit(train_seq) result = transformer.transform(test_seq)
-
LSTM to LSTM auto-encoder with hash word embedding (RNN to RNN architecture)
from seq2vec import Seq2VecR2RHash
transformer = Seq2VecR2RHash(
max_index=1000,
max_length=10,
latent_size=20,
embedding_size=200,
encoding_size=300,
learning_rate=0.05
)
train_seq = [
['我', '有', '一個', '蘋果'],
['我', '有', '筆'],
['一個', '鳳梨'],
]
test_seq = [
['我', '愛', '吃', '鳳梨'],
]
transformer.fit(train_seq)
result = transformer.transform(test_seq)We provide an example with LSTM to LSTM auto-encoder (word embedding).
Use the following training method while lack of memory is an issue for you.
The file should be a tokenized txt file splitted by whitespace with a sequence per line.
from seq2vec.word2vec import GensimWord2vec
from seq2vec.model import Seq2VecR2RWord
from seq2vec.transformer import WordEmbeddingTransformer
from seq2vec.util import DataGenterator
word2vec = GensimWord2vec(word2vec_model_path)
max_length = 20
transformer = Seq2VecR2RWord(
word2vec_model=word2vec,
max_length=max_length,
latent_size=200,
encoding_size=300,
learning_rate=0.05
)
train_data = DataGenterator(
corpus_for_training_path,
transformer.input_transformer,
transformer.output_transformer,
batch_size=128
)
test_data = DataGenterator(
corpus_for_validation_path,
transformer.input_transformer,
transformer.output_transformer,
batch_size=128
)
transformer.fit_generator(
train_data,
test_data,
epochs=10,
batch_number=1250 # The number of batch per epoch
)
transformer.save_model(model_path) # save your model
# You can reload your model and retrain it.
transformer.load_model(model_path)
transformer.fit_generator(
train_data,
test_data,
epochs=10,
batch_number=1250 # The number of batch per epoch
)You can customize your seq2vec model easily with our framework.
import keras
from seq2vec.model import TrainableSeq2VecBase
class YourSeq2Vec(TrainableSeq2VecBase):
def __init__(self
max_length,
latent_size,
learning_rate
):
# initialize your setting and set input_transformer
# and output_transformer
# Input and output transformers transform data from
# raw sequence into Keras Layer input format
# See seq2vec.transformer for more detail
self.input_transformer = YourInputTransformer()
self.output_transformer = YourOutputTransformer()
# add your customized layer
self.custom_objects = {}
self.custom_objects[customized_class_name] = customized_class
super(YourSeq2Vec, self).__init__(
max_length,
latent_size,
learning_rate
)
def create_model(self):
# create and compile your model in this function
# You should return your model and encoder here
# encoder is the one encoded input sequences
model.compile(loss)
return model, encoder
def load_model(self, file_path):
# load your seq2vec model here and set its attribute values
self.model = self.load_customed_model(file_path)pylint --rcfile=./yoctol-pylintrc/.pylintrc seq2vec
python -m unittest
