def batch_generator(sentences, taggings, steps, training=True):
global word_dict
while True:
for i in range(steps):
batch_sentences = sentences[BATCH_SIZE *
i:min(BATCH_SIZE *
(i + 1), len(sentences))]
batch_taggings = taggings[BATCH_SIZE *
i:min(BATCH_SIZE *
(i + 1), len(taggings))]
word_input, _ = get_batch_input(batch_sentences,
1,
word_dict, {},
word_ignore_case=True,
char_ignore_case=False)
if not training:
yield word_input, batch_taggings
continue
sentence_len = word_input.shape[1]
for j in range(len(batch_taggings)):
batch_taggings[j] = batch_taggings[j] + [0] * (
sentence_len - len(batch_taggings[j]))
batch_taggings[j] = [[tag] for tag in batch_taggings[j]]
batch_taggings = numpy.asarray(batch_taggings)
yield word_input, batch_taggings
if not training:
break
def train_batch_generator(batch_size=32, training=True):
while True:
sentences = []
if training: #use training set
data = sentences_train
batch_ix = random.sample(range(len(data)), batch_size)
for ix in batch_ix:
text = data[ix]
sentences.append(get_word_list_eng(text))
onehot_labels = onehot_train[batch_ix, :]
else: #use validation set
data = sentences_dev
batch_ix = random.sample(range(len(data)), batch_size)
for ix in batch_ix:
text = data[ix]
sentences.append(get_word_list_eng(text))
onehot_labels = onehot_dev[batch_ix, :]
word_input, char_input = get_batch_input(
sentences=sentences,
max_word_len=max_word_len,
word_dict=word_dict,
char_dict=char_dict,
word_ignore_case=True,
char_ignore_case=False,
)
yield [word_input, char_input], onehot_labels
def batch_generator(self, sentences, taggings, steps, training=True):
while True:
for i in range(steps):
batch_sentences = sentences[self.BATCH_SIZE *
i:min(self.BATCH_SIZE *
(i + 1), len(sentences))]
batch_taggings = taggings[self.BATCH_SIZE *
i:min(self.BATCH_SIZE *
(i + 1), len(taggings))]
word_input, char_input = get_batch_input(
batch_sentences,
self.max_word_len,
self.word_dict,
self.char_dict,
word_ignore_case=True,
char_ignore_case=False)
if not training:
yield [word_input, char_input], batch_taggings
continue
sentence_len = word_input.shape[1]
for j in range(len(batch_taggings)):
batch_taggings[j] = batch_taggings[j] + [0] * (
sentence_len - len(batch_taggings[j]))
batch_taggings = self.to_categorical_tensor(
numpy.asarray(batch_taggings), len(self.TAGS))
yield [word_input, char_input], batch_taggings
if not training:
break
def batch_generator(sentences, taggings, steps, training=True):
global word_dict, char_dict, max_word_len
while True:
for i in range(steps):
batch_sentences = sentences[BATCH_SIZE * i:min(BATCH_SIZE * (i + 1), len(sentences))]
output_sentences = copy.deepcopy(batch_sentences)
batch_taggings = taggings[BATCH_SIZE * i:min(BATCH_SIZE * (i + 1), len(taggings))]
word_input, char_input = get_batch_input(
batch_sentences,
max_word_len,
word_dict,
char_dict,
word_ignore_case=True,
char_ignore_case=False
)
if not training:
# yield [word_input, char_input], batch_taggings # original
yield [word_input, char_input], batch_taggings, output_sentences # 加一个原始text输出
# yield [word_input], batch_taggings, output_sentences # 去掉字符级
continue
pass
sentence_len = word_input.shape[1]
for j in range(len(batch_taggings)):
batch_taggings[j] = batch_taggings[j] + [0] * (sentence_len - len(batch_taggings[j]))
batch_taggings[j] = [[tag] for tag in batch_taggings[j]]
batch_taggings = numpy.asarray(batch_taggings)
yield [word_input, char_input], batch_taggings
# yield [word_input, char_input], batch_taggings, output_sentences
if not training:
break
def test_exceed_len(self):
sentences = [
['All', 'work', 'and', 'no', 'play'],
['makes', 'Jack', 'a', 'dull', 'boy', '.'],
]
word_embd_input, char_embd_input = get_batch_input(
sentences,
max_word_len=2,
word_dict={},
char_dict={},
)
self.assertEqual(word_embd_input.shape, (2, 6))
self.assertEqual(char_embd_input.shape, (2, 6, 2))
def test_batch_generator(batch_size=1):
index = 0
while index < test_num:
sentences = []
index += 1
sentences.append(get_word_list_eng(single_sentence))
word_input, char_input = get_batch_input(
sentences=sentences,
max_word_len=max_word_len,
word_dict=word_dict,
char_dict=char_dict,
word_ignore_case=True,
char_ignore_case=False,
)
yield [word_input, char_input]
def test_batch_generator(batch_size=32):
index = 0
while index < test_num:
sentences = []
batch_ix = range(index, min(index + batch_size, test_num))
index += batch_size
for ix in batch_ix:
text = sentences_test[ix]
sentences.append(get_word_list_eng(text))
word_input, char_input = get_batch_input(
sentences=sentences,
max_word_len=max_word_len,
word_dict=word_dict,
char_dict=char_dict,
word_ignore_case=True,
char_ignore_case=False,
)
yield [word_input, char_input]
def test_shape(self):
sentences = [
['All', 'work', 'and', 'no', 'play'],
['makes', 'Jack', 'a', 'dull', 'boy', '.'],
]
word_embd_input, char_embd_input = get_batch_input(
sentences,
max_word_len=5,
word_dict={},
char_dict={},
)
self.assertEqual(word_embd_input.shape, (2, 6))
self.assertEqual(char_embd_input.shape, (2, 6, 5))
for sentence_index in range(2):
for word_index in range(6):
if word_index < len(sentences[sentence_index]):
self.assertEqual(
1,
word_embd_input[sentence_index, word_index],
(sentence_index, word_index),
)
for char_index in range(5):
if char_index < len(
sentences[sentence_index][word_index]):
self.assertEqual(
1,
char_embd_input[sentence_index, word_index,
char_index].tolist(),
(sentence_index, word_index),
)
else:
self.assertEqual(
0,
char_embd_input[sentence_index, word_index,
char_index].tolist(),
(sentence_index, word_index),
)
else:
self.assertEqual(
0,
word_embd_input[sentence_index, word_index],
(sentence_index, word_index),
)
def batch_generator(sentences, taggings, steps, training=True):
global word_dict, char_dict, max_word_len
while True:
for i in range(steps):
batch_sentences = sentences[BATCH_SIZE * i:min(BATCH_SIZE * (i + 1), len(sentences))]
batch_taggings = taggings[BATCH_SIZE * i:min(BATCH_SIZE * (i + 1), len(taggings))]
word_input, char_input = get_batch_input(
batch_sentences,
max_word_len,
word_dict,
char_dict,
word_ignore_case=True,
char_ignore_case=False
)
if not training:
yield [word_input, char_input], batch_taggings
continue
sentence_len = word_input.shape[1]
for j in range(len(batch_taggings)):
batch_taggings[j] = batch_taggings[j] + [0] * (sentence_len - len(batch_taggings[j]))
batch_taggings = keras.utils.to_categorical(numpy.asarray(batch_taggings), len(TAGS))
yield [word_input, char_input], batch_taggings
if not training:
break
def test_mapping(self):
sentences = [
['All', 'work', 'and', 'no', 'play'],
['makes', 'Jack', 'a', 'dull', 'boy', '.'],
]
word_embd_input, char_embd_input = get_batch_input(
sentences,
max_word_len=5,
word_dict={
'All': 2,
'Work': 0
},
char_dict={'a': 3},
word_ignore_case=False,
char_ignore_case=False,
)
self.assertEqual(word_embd_input.shape, (2, 6))
self.assertEqual(char_embd_input.shape, (2, 6, 5))
for sentence_index in range(2):
for word_index in range(6):
if word_index < len(sentences[sentence_index]):
if sentences[sentence_index][word_index] == 'All':
self.assertEqual(
2,
word_embd_input[sentence_index, word_index],
(sentence_index, word_index),
)
else:
self.assertEqual(
1,
word_embd_input[sentence_index, word_index],
(sentence_index, word_index),
)
for char_index in range(5):
if char_index < len(
sentences[sentence_index][word_index]):
if sentences[sentence_index][word_index][
char_index] == 'a':
self.assertEqual(
3,
char_embd_input[sentence_index, word_index,
char_index].tolist(),
(sentence_index, word_index),
)
else:
self.assertEqual(
1,
char_embd_input[sentence_index, word_index,
char_index].tolist(),
(sentence_index, word_index),
)
else:
self.assertEqual(
0,
char_embd_input[sentence_index, word_index,
char_index].tolist(),
(sentence_index, word_index),
)
else:
self.assertEqual(
0,
word_embd_input[sentence_index, word_index],
(sentence_index, word_index),
)
