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dnn.py
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# -*- coding: UTF-8 -*-
import math
import time
import tensorflow as tf
import numpy as np
from dnn_base import DNNBase
from preprocess_data import PreprocessData
from config import TrainMode
class DNN(DNNBase):
def __init__(self, type = 'mlp', batch_size = 10, batch_length = 224,
mode = TrainMode.Batch, task = 'cws', is_seg = False,
is_embed = False):
tf.reset_default_graph()
DNNBase.__init__(self)
# 参数初始化
self.dtype = tf.float32
self.skip_window_left = 0
self.skip_window_right = 1
self.window_size = self.skip_window_left + self.skip_window_right + 1
# self.vocab_size = 4000
self.embed_size = 100
self.hidden_units = 150
if task == 'cws':
self.tags = [0, 1, 2, 3]
elif task == 'ner':
self.tags = [0, 1, 2]
else:
raise Exception('task name error')
self.is_embed = is_embed
self.tags_count = len(self.tags)
self.concat_embed_size = self.window_size * self.embed_size
self.learning_rate = 0.01
self.lam = 0.0001
self.batch_length = batch_length
self.batch_size = batch_size
self.mode = mode
self.type = type
self.is_seg = is_seg
self.dropout_rate = 0.2
# 数据初始化
pre = PreprocessData('emr_ner', self.mode, force_generate = True)
self.character_batches = pre.character_batches
self.label_batches = pre.label_batches
self.lengths = pre.lengths
self.dictionary = pre.dictionary
self.vocab_size = len(self.dictionary)
# 模型定义和初始化
self.sess = tf.Session()
initializer = tf.contrib.layers.xavier_initializer(dtype = self.dtype)
if not self.is_embed:
self.embeddings = tf.Variable(
tf.truncated_normal([self.vocab_size, self.embed_size],
stddev = 1.0 / math.sqrt(self.embed_size),
dtype = self.dtype), name = 'embeddings')
# self.embeddings = tf.get_variable('embeddings', [self.vocab_size, self.embed_size], dtype=self.dtype,
# initializer=initializer)
else:
self.embeddings = tf.Variable(np.load('corpus/embed/embeddings.npy'),
dtype = self.dtype, name = 'embeddings')
self.input = tf.placeholder(tf.int32, shape = [None, self.window_size])
self.label_index_correct = tf.placeholder(tf.int32, shape = [None, 2])
self.label_index_current = tf.placeholder(tf.int32, shape = [None, 2])
# self.w = tf.Variable(
# tf.truncated_normal([self.tags_count, self.hidden_units], stddev=1.0 / math.sqrt(self.concat_embed_size),
# dtype=self.dtype), name='w')
self.w = tf.get_variable('w', [self.tags_count, self.hidden_units],
dtype = self.dtype, initializer = initializer)
# self.transition = tf.Variable(tf.random_uniform([self.tags_count, self.tags_count], -0.2, 0.2, dtype=self.dtype))
# self.transition_init = tf.Variable(tf.random_uniform([self.tags_count], -0.2, 0.2, dtype=self.dtype))
self.transition = tf.get_variable('transition',
[self.tags_count, self.tags_count],
dtype = self.dtype,
initializer = initializer)
self.transition_init = tf.get_variable('transition_init', [self.tags_count],
dtype = self.dtype,
initializer = initializer)
self.transition_holder = tf.placeholder(self.dtype,
shape = self.transition.get_shape())
self.transition_init_holder = tf.placeholder(self.dtype,
shape = self.transition_init.get_shape())
# self.optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)
self.optimizer = tf.train.AdagradOptimizer(0.02)
# self.optimizer = tf.train.MomentumOptimizer(0.01,0.9)
# self.optimizer = tf.train.AdamOptimizer(0.0001)#,beta1=0.1,beta2=0.001)
self.update_transition = self.transition.assign(
tf.add((1 - self.learning_rate * self.lam) * self.transition,
self.learning_rate * self.transition_holder))
self.update_transition_init = self.transition_init.assign(
tf.add((1 - self.learning_rate * self.lam) * self.transition_init,
self.learning_rate * self.transition_init_holder))
self.look_up = tf.reshape(
tf.nn.embedding_lookup(self.embeddings, self.input),
[-1, self.concat_embed_size])
self.params = [self.w, self.embeddings]
if type == 'mlp':
self.b = tf.Variable(tf.zeros([self.tags_count, 1], dtype = self.dtype),
name = 'b')
self.params.append(self.b)
self.input_embeds = tf.transpose(
tf.reshape(tf.nn.embedding_lookup(self.embeddings, self.input),
[-1, self.concat_embed_size]))
self.hidden_w = tf.Variable(
tf.random_uniform([self.hidden_units, self.concat_embed_size],
4.0 / math.sqrt(self.concat_embed_size),
4 / math.sqrt(self.concat_embed_size),
dtype = self.dtype), name = 'hidden_w')
self.hidden_b = tf.Variable(
tf.zeros([self.hidden_units, 1], dtype = self.dtype), name = 'hidden_b')
self.word_scores = tf.matmul(self.w,
tf.sigmoid(tf.matmul(self.hidden_w,
self.input_embeds) + self.hidden_b)) + self.b
self.params += [self.hidden_w, self.hidden_b]
self.loss = tf.reduce_sum(
tf.gather_nd(self.word_scores, self.label_index_current) -
tf.gather_nd(self.word_scores,
self.label_index_correct)) + tf.contrib.layers.apply_regularization(
tf.contrib.layers.l2_regularizer(self.lam), self.params)
elif type == 'lstm':
self.lstm = tf.nn.rnn_cell.BasicLSTMCell(self.hidden_units)
self.b = tf.Variable(
tf.zeros([self.tags_count, 1, 1], dtype = self.dtype), name = 'b')
self.params.append(self.b)
if self.mode == TrainMode.Batch:
if not self.is_seg:
self.input = tf.placeholder(tf.int32, shape = [self.batch_size,
self.batch_length,
self.window_size])
self.input_embeds = tf.reshape(
tf.nn.embedding_lookup(self.embeddings, self.input),
[self.batch_size, self.batch_length, self.concat_embed_size])
self.input_embeds = tf.layers.dropout(self.input_embeds,
self.dropout_rate)
self.lstm_output, self.lstm_out_state = tf.nn.dynamic_rnn(self.lstm,
self.input_embeds,
dtype = self.dtype)
self.params += [v for v in tf.global_variables() if
v.name.startswith('rnn')]
self.word_scores = tf.tensordot(self.w,
tf.transpose(self.lstm_output),
[[1], [0]]) + self.b
self.label_index_correct = tf.placeholder(tf.int32, shape = [None, 3])
self.label_index_current = tf.placeholder(tf.int32, shape = [None, 3])
self.transition_correct_holder = tf.placeholder(tf.int32, [None, 2])
self.transition_current_holder = tf.placeholder(tf.int32, [None, 2])
self.transition_init_correct_holder = tf.placeholder(tf.int32,
[None, 1])
self.transition_init_current_holder = tf.placeholder(tf.int32,
[None, 1])
self.loss_scores = tf.reduce_sum(
tf.gather_nd(self.word_scores, self.label_index_current) -
tf.gather_nd(self.word_scores,
self.label_index_correct)) + tf.reduce_sum(
tf.gather_nd(self.transition,
self.transition_current_holder) - tf.gather_nd(
self.transition,
self.transition_correct_holder))
self.loss_scores_with_init = self.loss_scores + tf.reduce_sum(
tf.gather_nd(self.transition_init,
self.transition_init_current_holder) - tf.gather_nd(
self.transition_init,
self.transition_init_correct_holder))
self.regularization = tf.contrib.layers.apply_regularization(
tf.contrib.layers.l2_regularizer(self.lam),
self.params + [self.transition])
self.regularization_with_init = tf.contrib.layers.apply_regularization(
tf.contrib.layers.l2_regularizer(self.lam),
self.params + [self.transition, self.transition_init])
self.loss = self.loss_scores / self.batch_size + self.regularization
self.loss_with_init = self.loss_scores_with_init / self.batch_size + self.regularization_with_init
else:
self.input_embeds = tf.reshape(
tf.nn.embedding_lookup(self.embeddings, self.input),
[1, -1, self.concat_embed_size])
self.lstm_output, self.lstm_out_state = tf.nn.dynamic_rnn(self.lstm,
self.input_embeds,
dtype = self.dtype)
self.word_scores = tf.matmul(self.w, tf.transpose(
self.lstm_output[-1, :, :])) + self.b[:, :, -1]
if self.is_seg == False:
gvs = self.optimizer.compute_gradients(self.loss)
cliped_grad = [
(tf.clip_by_norm(grad, 5) if grad is not None else grad, var) for
grad, var in gvs]
self.train = self.optimizer.apply_gradients(
cliped_grad) # self.optimizer.minimize(self.loss)
if self.is_seg == False and self.type == 'lstm':
gvs2 = self.optimizer.compute_gradients(self.loss_with_init)
cliped_grad2 = [
(tf.clip_by_norm(grad2, 5) if grad2 is not None else grad2, var2) for
grad2, var2 in gvs2]
self.train_with_init = self.optimizer.apply_gradients(cliped_grad2)
# self.train_with_init = self.optimizer.minimize(self.loss_with_init)
self.saver = tf.train.Saver(max_to_keep = 100)
# self.saver.restore(self.sess, 'tmp/lstm-bbbmodel6.ckpt')
self.sentence_index = 0
def train_exe(self):
tf.global_variables_initializer().run(session = self.sess)
self.sess.graph.finalize()
epochs = 50
last_time = time.time()
if self.mode == TrainMode.Sentence:
for i in range(epochs):
print('epoch:%d' % i)
for sentence_index, (sentence, labels, length) in enumerate(
zip(self.character_batches, self.label_batches, self.lengths)):
# self.train_sentence(sentence[:length], labels[:length])
self.train_sentence(sentence, labels)
self.sentence_index = sentence_index
if sentence_index > 0 and sentence_index % 8000 == 0:
print(sentence_index)
print(time.time() - last_time)
last_time = time.time()
if (i + 1) % 10 == 0:
if self.type == 'mlp':
if self.is_embed:
self.saver.save(self.sess,
'tmp/mlp/mlp-ner-embed-model{0}.ckpt'.format(
i + 1))
else:
self.saver.save(self.sess,
'tmp/mlp/mlp-ner-model{0}.ckpt'.format(i + 1))
elif self.type == 'lstm':
if self.is_embed:
self.saver.save(self.sess,
'tmp/lstm/lstm-ner-embed-model{0}.ckpt'.format(
i + 1))
else:
self.saver.save(self.sess,
'tmp/lstm/lstm-ner-model{0}.ckpt'.format(i + 1))
elif self.mode == TrainMode.Batch:
for i in range(epochs):
self.step = i
print('epoch:%d' % i)
for batch_index, (character_batch, label_batch, lengths) in enumerate(
zip(self.character_batches, self.label_batches, self.lengths)):
self.train_batch(character_batch, label_batch, lengths)
if batch_index > 0 and batch_index % 100 == 0:
print(batch_index)
print(time.time() - last_time)
last_time = time.time()
if (i + 1) % 10 == 0:
if self.is_embed:
self.saver.save(self.sess,
'tmp/lstm/lstm-ner-embed-model{0}.ckpt'.format(
i + 1))
else:
self.saver.save(self.sess,
'tmp/lstm/lstm-ner-model{0}.ckpt'.format(i + 1))
def train_sentence(self, sentence, labels):
scores = self.sess.run(self.word_scores, feed_dict = {self.input: sentence})
current_labels = self.viterbi(scores,
self.transition.eval(session = self.sess),
self.transition_init.eval(
session = self.sess), labels = labels,
size = 3)
diff_tags = np.subtract(labels, current_labels)
update_index = np.where(diff_tags != 0)[0]
update_length = len(update_index)
if update_length == 0:
return
update_labels_pos = np.stack([labels[update_index], update_index],
axis = -1)
update_labels_neg = np.stack([current_labels[update_index], update_index],
axis = -1)
feed_dict = {self.input: sentence,
self.label_index_current: update_labels_neg,
self.label_index_correct: update_labels_pos}
self.sess.run(self.train, feed_dict)
# 更新转移矩阵
transition_update, transition_init_update, update_init = self.generate_transition_update(
labels, current_labels)
self.sess.run(self.update_transition,
feed_dict = {self.transition_holder: transition_update})
if update_init:
self.sess.run(self.update_transition_init, feed_dict = {
self.transition_init_holder: transition_init_update})
def train_batch(self, sentence_batches, label_batches, lengths):
scores = self.sess.run(self.word_scores,
feed_dict = {self.input: sentence_batches})
transition = self.transition.eval(session = self.sess)
transition_init = self.transition_init.eval(session = self.sess)
update_labels_pos = None
update_labels_neg = None
current_labels = []
trans_pos_indices = []
trans_neg_indices = []
trans_init_pos_indices = []
trans_init_neg_indices = []
for i in range(self.batch_size):
current_label = self.viterbi(scores[:, :lengths[i], i], transition,
transition_init)
# current_label = self.viterbi(scores[:, :lengths[i], i], transition, transition_init, is_constraint=True,
# labels=label_batches[i, :lengths[i]])
# current_label = self.viterbi_new(scores[:, :lengths[i], i], transition, transition_init,
# label_batches[i, :lengths[i]])
current_labels.append(current_label)
diff_tag = np.subtract(label_batches[i, :lengths[i]], current_label)
update_index = np.where(diff_tag != 0)[0]
update_length = len(update_index)
if update_length == 0:
continue
update_label_pos = np.stack([label_batches[i, update_index], update_index,
i * np.ones([update_length])], axis = -1)
update_label_neg = np.stack([current_label[update_index], update_index,
i * np.ones([update_length])], axis = -1)
if update_labels_pos is not None:
np.concatenate((update_labels_pos, update_label_pos))
np.concatenate((update_labels_neg, update_label_neg))
else:
update_labels_pos = update_label_pos
update_labels_neg = update_label_neg
trans_pos_index, trans_neg_index, trans_init_pos, trans_init_neg, update_init = self.generate_transition_update_index(
label_batches[i, :lengths[i]], current_labels[i])
trans_pos_indices.extend(trans_pos_index)
trans_neg_indices.extend(trans_neg_index)
if update_init:
trans_init_pos_indices.append(trans_init_pos)
trans_init_neg_indices.append(trans_init_neg)
if update_labels_pos is not None and update_labels_neg is not None:
feed_dict = {self.input: sentence_batches,
self.label_index_current: update_labels_neg,
self.label_index_correct: update_labels_pos,
self.transition_current_holder: trans_neg_indices,
self.transition_correct_holder: trans_pos_indices}
# self.sess.run(self.train, feed_dict)
if len(trans_init_pos_indices) == 0:
self.sess.run(self.train, feed_dict)
else:
feed_dict[self.transition_init_correct_holder] = trans_init_pos_indices
feed_dict[self.transition_init_current_holder] = trans_init_neg_indices
self.sess.run(self.train_with_init, feed_dict)
def seg(self, sentence, model_path = 'tmp/mlp-model0.ckpt', debug = False,
ner = False, trans = False):
tf.global_variables_initializer().run(session = self.sess)
self.saver.restore(self.sess, model_path)
if not trans:
s = self.sentence2index(sentence)
else:
if isinstance(sentence, np.ndarray):
s = sentence.tolist()
else:
s = sentence
seq = self.index2seq(s)
sentence_scores = self.sess.run(self.word_scores,
feed_dict = {self.input: seq})
transition_init = self.transition_init.eval(session = self.sess)
transition = self.transition.eval(session = self.sess)
if debug:
print(transition)
embeds = self.sess.run(self.look_up, feed_dict = {self.input: seq})
print(sentence_scores)
if self.type == 'lstm':
output = self.sess.run(self.lstm_output, feed_dict = {self.input: seq})
print(output[-1, :, 10])
print(self.transition_init.eval(session = self.sess))
current_labels = self.viterbi(sentence_scores, transition, transition_init)
if not ner:
return self.tags2words(sentence, current_labels), current_labels
else:
# return self.tags2entities(sentence, current_labels), current_labels
return None, current_labels
# return self.tags2category_entities(sentence, current_labels), current_labels
if __name__ == '__main__':
mlp = DNN('mlp', mode = TrainMode.Sentence, task = 'ner')
mlp.train_exe()
mlp_embed = DNN('mlp', mode = TrainMode.Sentence, task = 'ner',
is_embed = True)
mlp_embed.train_exe()
lstm = DNN('lstm', task = 'ner')
lstm.train_exe()
lstm_embed = DNN('lstm', task = 'ner', is_embed = True)
lstm_embed.train_exe()