TensorFlow (RNN)深度学习 双向LSTM(BiLSTM)+CRF 实现 sequence labeling 序列标注问题 源码下载

http://blog.csdn.net/scotfield_msn/article/details/60339415

在TensorFlow (RNN)深度学习下 双向LSTM(BiLSTM)+CRF 实现 sequence labeling

双向LSTM+CRF跑序列标注问题

源码下载


去年底样子一直在做NLP相关task,是个关于序列标注问题。这 sequence labeling属于NLP的经典问题了，开始尝试用HMM，哦不，用CRF做baseline，by the way, 用的CRF++。

关于CRF的理论就不再啰嗦了，街货。顺便提下，CRF比HMM在理论上以及实际效果上都要好不少。但我要说的是CRF跑我这task还是不太乐观。P值0.6样子，R低的离谱，所以F1很不乐观。mentor告诉我说是特征不足，师兄说是这个task本身就比较难做，F1低算是正常了。

CRF做完baseline后，一直在着手用BiLSTM+CRF跑 sequence labeling，奈何项目繁多，没有多余的精力去按照正常的计划做出来。后来还是一点一点的，按照大牛们的步骤以及参考现有的代码，把 BiLSTM+CRF的实现拿下了。后来发现，跑出来的效果也不太理想……可能是这个task确实变态……抑或模型还要加强吧~

这里对比下CRF与LSTM的cell，先说RNN吧，RNN其实是比CNN更适合做序列问题的模型，RNN隐层当前时刻的输入有一部分是前一时刻的隐层输出，这使得他能通过循环反馈连接看到前面的信息，将一段序列的前面的context capture 过来参与此刻的计算，并且还具备非线性的拟合能力，这都是CRF无法超越的地方。而LSTM的cell很好的将RNN的梯度弥散问题优化解决了，他对门卫gate说：老兄，有的不太重要的信息，你该忘掉就忘掉吧，免得占用现在的资源。而双向LSTM就更厉害了，不仅看得到过去，还能将未来的序列考虑进来，使得上下文信息充分被利用。而CRF，他不像LSTM能够考虑长远的上下文信息，它更多地考虑整个句子的局部特征的线性加权组合(通过特征模板扫描整个句子)，特别的一点，他计算的是联合概率，优化了整个序列，而不是拼接每个时刻的最优值。那么，将BILSTM与CRF一起就构成了还比较不错的组合，这目前也是学术界的流行做法~

另外针对目前的跑通结果提几个改进点：

1.+CNN，通过CNN的卷积操作去提取英文单词的字母细节。

2.+char representation,作用与上相似，提取更细粒度的细节。

3.考虑将特定的人工提取的规则融入到NN模型中去。

好了，叨了不少。codes time:

完整代码以及相关预处理的数据请移步github: scofiled's github/bilstm+crf

注明：codes参考的是chilynn

requirements:

ubuntu14

python2.7

tensorflow 0.8

numpy

pandas0.15

BILSTM_CRF.py

[python] view plain copy







import math

import helper

import numpy as np

import tensorflow as tf

from tensorflow.models.rnn import rnn, rnn_cell

class BILSTM_CRF(object):

def __init__(self, num_chars, num_classes, num_steps=200, num_epochs=100, embedding_matrix=None, is_training=True, is_crf=True, weight=False):

# Parameter

self.max_f1 = 0

self.learning_rate = 0.002

self.dropout_rate = 0.5

self.batch_size = 128

self.num_layers = 1

self.emb_dim = 100

self.hidden_dim = 100

self.num_epochs = num_epochs

self.num_steps = num_steps

self.num_chars = num_chars

self.num_classes = num_classes

# placeholder of x, y and weight

self.inputs = tf.placeholder(tf.int32, [None, self.num_steps])

self.targets = tf.placeholder(tf.int32, [None, self.num_steps])

self.targets_weight = tf.placeholder(tf.float32, [None, self.num_steps])

self.targets_transition = tf.placeholder(tf.int32, [None])

# char embedding

if embedding_matrix != None:

self.embedding = tf.Variable(embedding_matrix, trainable=False, name="emb", dtype=tf.float32)

else:

self.embedding = tf.get_variable("emb", [self.num_chars, self.emb_dim])

self.inputs_emb = tf.nn.embedding_lookup(self.embedding, self.inputs)

self.inputs_emb = tf.transpose(self.inputs_emb, [1, 0, 2])

self.inputs_emb = tf.reshape(self.inputs_emb, [-1, self.emb_dim])

self.inputs_emb = tf.split(0, self.num_steps, self.inputs_emb)

# lstm cell

lstm_cell_fw = tf.nn.rnn_cell.BasicLSTMCell(self.hidden_dim)

lstm_cell_bw = tf.nn.rnn_cell.BasicLSTMCell(self.hidden_dim)

# dropout

if is_training:

lstm_cell_fw = tf.nn.rnn_cell.DropoutWrapper(lstm_cell_fw, output_keep_prob=(1 - self.dropout_rate))

lstm_cell_bw = tf.nn.rnn_cell.DropoutWrapper(lstm_cell_bw, output_keep_prob=(1 - self.dropout_rate))

lstm_cell_fw = tf.nn.rnn_cell.MultiRNNCell([lstm_cell_fw] * self.num_layers)

lstm_cell_bw = tf.nn.rnn_cell.MultiRNNCell([lstm_cell_bw] * self.num_layers)

# get the length of each sample

self.length = tf.reduce_sum(tf.sign(self.inputs), reduction_indices=1)

self.length = tf.cast(self.length, tf.int32)

# forward and backward

self.outputs, _, _ = rnn.bidirectional_rnn(

lstm_cell_fw,

lstm_cell_bw,

self.inputs_emb,

dtype=tf.float32,

sequence_length=self.length

)

# softmax

self.outputs = tf.reshape(tf.concat(1, self.outputs), [-1, self.hidden_dim * 2])

self.softmax_w = tf.get_variable("softmax_w", [self.hidden_dim * 2, self.num_classes])

self.softmax_b = tf.get_variable("softmax_b", [self.num_classes])

self.logits = tf.matmul(self.outputs, self.softmax_w) + self.softmax_b

if not is_crf:

pass

else:

self.tags_scores = tf.reshape(self.logits, [self.batch_size, self.num_steps, self.num_classes])

self.transitions = tf.get_variable("transitions", [self.num_classes + 1, self.num_classes + 1])

dummy_val = -1000

class_pad = tf.Variable(dummy_val * np.ones((self.batch_size, self.num_steps, 1)), dtype=tf.float32)

self.observations = tf.concat(2, [self.tags_scores, class_pad])

begin_vec = tf.Variable(np.array([[dummy_val] * self.num_classes + [0] for _ in range(self.batch_size)]), trainable=False, dtype=tf.float32)

end_vec = tf.Variable(np.array([[0] + [dummy_val] * self.num_classes for _ in range(self.batch_size)]), trainable=False, dtype=tf.float32)

begin_vec = tf.reshape(begin_vec, [self.batch_size, 1, self.num_classes + 1])

end_vec = tf.reshape(end_vec, [self.batch_size, 1, self.num_classes + 1])

self.observations = tf.concat(1, [begin_vec, self.observations, end_vec])

self.mask = tf.cast(tf.reshape(tf.sign(self.targets),[self.batch_size * self.num_steps]), tf.float32)

# point score

self.point_score = tf.gather(tf.reshape(self.tags_scores, [-1]), tf.range(0, self.batch_size * self.num_steps) * self.num_classes + tf.reshape(self.targets,[self.batch_size * self.num_steps]))

self.point_score *= self.mask

# transition score

self.trans_score = tf.gather(tf.reshape(self.transitions, [-1]), self.targets_transition)

# real score

self.target_path_score = tf.reduce_sum(self.point_score) + tf.reduce_sum(self.trans_score)

# all path score

self.total_path_score, self.max_scores, self.max_scores_pre = self.forward(self.observations, self.transitions, self.length)

# loss

self.loss = - (self.target_path_score - self.total_path_score)

# summary

self.train_summary = tf.scalar_summary("loss", self.loss)

self.val_summary = tf.scalar_summary("loss", self.loss)

self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.loss)

def logsumexp(self, x, axis=None):

x_max = tf.reduce_max(x, reduction_indices=axis, keep_dims=True)

x_max_ = tf.reduce_max(x, reduction_indices=axis)

return x_max_ + tf.log(tf.reduce_sum(tf.exp(x - x_max), reduction_indices=axis))

def forward(self, observations, transitions, length, is_viterbi=True, return_best_seq=True):

length = tf.reshape(length, [self.batch_size])

transitions = tf.reshape(tf.concat(0, [transitions] * self.batch_size), [self.batch_size, 6, 6])

observations = tf.reshape(observations, [self.batch_size, self.num_steps + 2, 6, 1])

observations = tf.transpose(observations, [1, 0, 2, 3])

previous = observations[0, :, :, :]

max_scores = []

max_scores_pre = []

alphas = [previous]

for t in range(1, self.num_steps + 2):

previous = tf.reshape(previous, [self.batch_size, 6, 1])

current = tf.reshape(observations[t, :, :, :], [self.batch_size, 1, 6])

alpha_t = previous + current + transitions

if is_viterbi:

max_scores.append(tf.reduce_max(alpha_t, reduction_indices=1))

max_scores_pre.append(tf.argmax(alpha_t, dimension=1))

alpha_t = tf.reshape(self.logsumexp(alpha_t, axis=1), [self.batch_size, 6, 1])

alphas.append(alpha_t)

previous = alpha_t

alphas = tf.reshape(tf.concat(0, alphas), [self.num_steps + 2, self.batch_size, 6, 1])

alphas = tf.transpose(alphas, [1, 0, 2, 3])

alphas = tf.reshape(alphas, [self.batch_size * (self.num_steps + 2), 6, 1])

last_alphas = tf.gather(alphas, tf.range(0, self.batch_size) * (self.num_steps + 2) + length)

last_alphas = tf.reshape(last_alphas, [self.batch_size, 6, 1])

max_scores = tf.reshape(tf.concat(0, max_scores), (self.num_steps + 1, self.batch_size, 6))

max_scores_pre = tf.reshape(tf.concat(0, max_scores_pre), (self.num_steps + 1, self.batch_size, 6))

max_scores = tf.transpose(max_scores, [1, 0, 2])

max_scores_pre = tf.transpose(max_scores_pre, [1, 0, 2])

return tf.reduce_sum(self.logsumexp(last_alphas, axis=1)), max_scores, max_scores_pre

def train(self, sess, save_file, X_train, y_train, X_val, y_val):

saver = tf.train.Saver()

char2id, id2char = helper.loadMap("char2id")

label2id, id2label = helper.loadMap("label2id")

merged = tf.merge_all_summaries()

summary_writer_train = tf.train.SummaryWriter('loss_log/train_loss', sess.graph)

summary_writer_val = tf.train.SummaryWriter('loss_log/val_loss', sess.graph)

num_iterations = int(math.ceil(1.0 * len(X_train) / self.batch_size))

cnt = 0

for epoch in range(self.num_epochs):

# shuffle train in each epoch

sh_index = np.arange(len(X_train))

np.random.shuffle(sh_index)

X_train = X_train[sh_index]

y_train = y_train[sh_index]

print "current epoch: %d" % (epoch)

for iteration in range(num_iterations):

# train

X_train_batch, y_train_batch = helper.nextBatch(X_train, y_train, start_index=iteration * self.batch_size, batch_size=self.batch_size)

y_train_weight_batch = 1 + np.array((y_train_batch == label2id['B']) | (y_train_batch == label2id['E']), float)

transition_batch = helper.getTransition(y_train_batch)

_, loss_train, max_scores, max_scores_pre, length, train_summary =\

sess.run([

self.optimizer,

self.loss,

self.max_scores,

self.max_scores_pre,

self.length,

self.train_summary

],

feed_dict={

self.targets_transition:transition_batch,

self.inputs:X_train_batch,

self.targets:y_train_batch,

self.targets_weight:y_train_weight_batch

})

predicts_train = self.viterbi(max_scores, max_scores_pre, length, predict_size=self.batch_size)

if iteration % 10 == 0:

cnt += 1

precision_train, recall_train, f1_train = self.evaluate(X_train_batch, y_train_batch, predicts_train, id2char, id2label)

summary_writer_train.add_summary(train_summary, cnt)

print "iteration: %5d, train loss: %5d, train precision: %.5f, train recall: %.5f, train f1: %.5f" % (iteration, loss_train, precision_train, recall_train, f1_train)

# validation

if iteration % 100 == 0:

X_val_batch, y_val_batch = helper.nextRandomBatch(X_val, y_val, batch_size=self.batch_size)

y_val_weight_batch = 1 + np.array((y_val_batch == label2id['B']) | (y_val_batch == label2id['E']), float)

transition_batch = helper.getTransition(y_val_batch)

loss_val, max_scores, max_scores_pre, length, val_summary =\

sess.run([

self.loss,

self.max_scores,

self.max_scores_pre,

self.length,

self.val_summary

],

feed_dict={

self.targets_transition:transition_batch,

self.inputs:X_val_batch,

self.targets:y_val_batch,

self.targets_weight:y_val_weight_batch

})

predicts_val = self.viterbi(max_scores, max_scores_pre, length, predict_size=self.batch_size)

precision_val, recall_val, f1_val = self.evaluate(X_val_batch, y_val_batch, predicts_val, id2char, id2label)

summary_writer_val.add_summary(val_summary, cnt)

print "iteration: %5d, valid loss: %5d, valid precision: %.5f, valid recall: %.5f, valid f1: %.5f" % (iteration, loss_val, precision_val, recall_val, f1_val)

if f1_val > self.max_f1:

self.max_f1 = f1_val

save_path = saver.save(sess, save_file)

print "saved the best model with f1: %.5f" % (self.max_f1)

def test(self, sess, X_test, X_test_str, output_path):

char2id, id2char = helper.loadMap("char2id")

label2id, id2label = helper.loadMap("label2id")

num_iterations = int(math.ceil(1.0 * len(X_test) / self.batch_size))

print "number of iteration: " + str(num_iterations)

with open(output_path, "wb") as outfile:

for i in range(num_iterations):

print "iteration: " + str(i + 1)

results = []

X_test_batch = X_test[i * self.batch_size : (i + 1) * self.batch_size]

X_test_str_batch = X_test_str[i * self.batch_size : (i + 1) * self.batch_size]

if i == num_iterations - 1 and len(X_test_batch) < self.batch_size:

X_test_batch = list(X_test_batch)

X_test_str_batch = list(X_test_str_batch)

last_size = len(X_test_batch)

X_test_batch += [[0 for j in range(self.num_steps)] for i in range(self.batch_size - last_size)]

X_test_str_batch += [['x' for j in range(self.num_steps)] for i in range(self.batch_size - last_size)]

X_test_batch = np.array(X_test_batch)

X_test_str_batch = np.array(X_test_str_batch)

results = self.predictBatch(sess, X_test_batch, X_test_str_batch, id2label)

results = results[:last_size]

else:

X_test_batch = np.array(X_test_batch)

results = self.predictBatch(sess, X_test_batch, X_test_str_batch, id2label)

for i in range(len(results)):

doc = ''.join(X_test_str_batch[i])

outfile.write(doc + "<@>" +" ".join(results[i]).encode("utf-8") + "\n")

def viterbi(self, max_scores, max_scores_pre, length, predict_size=128):

best_paths = []

for m in range(predict_size):

path = []

last_max_node = np.argmax(max_scores[m][length[m]])

# last_max_node = 0

for t in range(1, length[m] + 1)[::-1]:

last_max_node = max_scores_pre[m][t][last_max_node]

path.append(last_max_node)

path = path[::-1]

best_paths.append(path)

return best_paths

def predictBatch(self, sess, X, X_str, id2label):

results = []

length, max_scores, max_scores_pre = sess.run([self.length, self.max_scores, self.max_scores_pre], feed_dict={self.inputs:X})

predicts = self.viterbi(max_scores, max_scores_pre, length, self.batch_size)

for i in range(len(predicts)):

x = ''.join(X_str[i]).decode("utf-8")

y_pred = ''.join([id2label[val] for val in predicts[i] if val != 5 and val != 0])

entitys = helper.extractEntity(x, y_pred)

results.append(entitys)

return results

def evaluate(self, X, y_true, y_pred, id2char, id2label):

precision = -1.0

recall = -1.0

f1 = -1.0

hit_num = 0

pred_num = 0

true_num = 0

for i in range(len(y_true)):

x = ''.join([str(id2char[val].encode("utf-8")) for val in X[i]])

y = ''.join([str(id2label[val].encode("utf-8")) for val in y_true[i]])

y_hat = ''.join([id2label[val] for val in y_pred[i] if val != 5])

true_labels = helper.extractEntity(x, y)

pred_labels = helper.extractEntity(x, y_hat)

hit_num += len(set(true_labels) & set(pred_labels))

pred_num += len(set(pred_labels))

true_num += len(set(true_labels))

if pred_num != 0:

precision = 1.0 * hit_num / pred_num

if true_num != 0:

recall = 1.0 * hit_num / true_num

if precision > 0 and recall > 0:

f1 = 2.0 * (precision * recall) / (precision + recall)

return precision, recall, f1

util.py

[python] view plain copy







#encoding:utf-8

import re

import os

import csv

import time

import pickle

import numpy as np

import pandas as pd

def getEmbedding(infile_path="embedding"):

char2id, id_char = loadMap("char2id")

row_index = 0

with open(infile_path, "rb") as infile:

for row in infile:

row = row.strip()

row_index += 1

if row_index == 1:

num_chars = int(row.split()[0])

emb_dim = int(row.split()[1])

emb_matrix = np.zeros((len(char2id.keys()), emb_dim))

continue

items = row.split()

char = items[0]

emb_vec = [float(val) for val in items[1:]]

if char in char2id:

emb_matrix[char2id[char]] = emb_vec

return emb_matrix

def nextBatch(X, y, start_index, batch_size=128):

last_index = start_index + batch_size

X_batch = list(X[start_index:min(last_index, len(X))])

y_batch = list(y[start_index:min(last_index, len(X))])

if last_index > len(X):

left_size = last_index - (len(X))

for i in range(left_size):

index = np.random.randint(len(X))

X_batch.append(X[index])

y_batch.append(y[index])

X_batch = np.array(X_batch)

y_batch = np.array(y_batch)

return X_batch, y_batch

def nextRandomBatch(X, y, batch_size=128):

X_batch = []

y_batch = []

for i in range(batch_size):

index = np.random.randint(len(X))

X_batch.append(X[index])

y_batch.append(y[index])

X_batch = np.array(X_batch)

y_batch = np.array(y_batch)

return X_batch, y_batch

# use "0" to padding the sentence

def padding(sample, seq_max_len):

for i in range(len(sample)):

if len(sample[i]) < seq_max_len:

sample[i] += [0 for _ in range(seq_max_len - len(sample[i]))]

return sample

def prepare(chars, labels, seq_max_len, is_padding=True):

X = []

y = []

tmp_x = []

tmp_y = []

for record in zip(chars, labels):

c = record[0]

l = record[1]

# empty line

if c == -1:

if len(tmp_x) <= seq_max_len:

X.append(tmp_x)

y.append(tmp_y)

tmp_x = []

tmp_y = []

else:

tmp_x.append(c)

tmp_y.append(l)

if is_padding:

X = np.array(padding(X, seq_max_len))

else:

X = np.array(X)

y = np.array(padding(y, seq_max_len))

return X, y

def extractEntity(sentence, labels):

entitys = []

re_entity = re.compile(r'BM*E')

m = re_entity.search(labels)

while m:

entity_labels = m.group()

start_index = labels.find(entity_labels)

entity = sentence[start_index:start_index + len(entity_labels)]

labels = list(labels)

# replace the "BM*E" with "OO*O"

labels[start_index: start_index + len(entity_labels)] = ['O' for i in range(len(entity_labels))]

entitys.append(entity)

labels = ''.join(labels)

m = re_entity.search(labels)

return entitys

def loadMap(token2id_filepath):

if not os.path.isfile(token2id_filepath):

print "file not exist, building map"

buildMap()

token2id = {}

id2token = {}

with open(token2id_filepath) as infile:

for row in infile:

row = row.rstrip().decode("utf-8")

token = row.split('\t')[0]

token_id = int(row.split('\t')[1])

token2id[token] = token_id

id2token[token_id] = token

return token2id, id2token

def saveMap(id2char, id2label):

with open("char2id", "wb") as outfile:

for idx in id2char:

outfile.write(id2char[idx] + "\t" + str(idx) + "\r\n")

with open("label2id", "wb") as outfile:

for idx in id2label:

outfile.write(id2label[idx] + "\t" + str(idx) + "\r\n")

print "saved map between token and id"

def buildMap(train_path="train.in"):

df_train = pd.read_csv(train_path, delimiter='\t', quoting=csv.QUOTE_NONE, skip_blank_lines=False, header=None, names=["char", "label"])

chars = list(set(df_train["char"][df_train["char"].notnull()]))

labels = list(set(df_train["label"][df_train["label"].notnull()]))

char2id = dict(zip(chars, range(1, len(chars) + 1)))

label2id = dict(zip(labels, range(1, len(labels) + 1)))

id2char = dict(zip(range(1, len(chars) + 1), chars))

id2label = dict(zip(range(1, len(labels) + 1), labels))

id2char[0] = "<PAD>"

id2label[0] = "<PAD>"

char2id["<PAD>"] = 0

label2id["<PAD>"] = 0

id2char[len(chars) + 1] = "<NEW>"

char2id["<NEW>"] = len(chars) + 1

saveMap(id2char, id2label)

return char2id, id2char, label2id, id2label

def getTrain(train_path, val_path, train_val_ratio=0.99, use_custom_val=False, seq_max_len=200):

char2id, id2char, label2id, id2label = buildMap(train_path)

df_train = pd.read_csv(train_path, delimiter='\t', quoting=csv.QUOTE_NONE, skip_blank_lines=False, header=None, names=["char", "label"])

# map the char and label into id

df_train["char_id"] = df_train.char.map(lambda x : -1 if str(x) == str(np.nan) else char2id[x])

df_train["label_id"] = df_train.label.map(lambda x : -1 if str(x) == str(np.nan) else label2id[x])

# convert the data in maxtrix

X, y = prepare(df_train["char_id"], df_train["label_id"], seq_max_len)

# shuffle the samples

num_samples = len(X)

indexs = np.arange(num_samples)

np.random.shuffle(indexs)

X = X[indexs]

y = y[indexs]

if val_path != None:

X_train = X

y_train = y

X_val, y_val = getTest(val_path, is_validation=True, seq_max_len=seq_max_len)

else:

# split the data into train and validation set

X_train = X[:int(num_samples * train_val_ratio)]

y_train = y[:int(num_samples * train_val_ratio)]

X_val = X[int(num_samples * train_val_ratio):]

y_val = y[int(num_samples * train_val_ratio):]

print "train size: %d, validation size: %d" %(len(X_train), len(y_val))

return X_train, y_train, X_val, y_val

def getTest(test_path="test.in", is_validation=False, seq_max_len=200):

char2id, id2char = loadMap("char2id")

label2id, id2label = loadMap("label2id")

df_test = pd.read_csv(test_path, delimiter='\t', quoting=csv.QUOTE_NONE, skip_blank_lines=False, header=None, names=["char", "label"])

def mapFunc(x, char2id):

if str(x) == str(np.nan):

return -1

elif x.decode("utf-8") not in char2id:

return char2id["<NEW>"]

else:

return char2id[x.decode("utf-8")]

df_test["char_id"] = df_test.char.map(lambda x:mapFunc(x, char2id))

df_test["label_id"] = df_test.label.map(lambda x : -1 if str(x) == str(np.nan) else label2id[x])

if is_validation:

X_test, y_test = prepare(df_test["char_id"], df_test["label_id"], seq_max_len)

return X_test, y_test

else:

df_test["char"] = df_test.char.map(lambda x : -1 if str(x) == str(np.nan) else x)

X_test, _ = prepare(df_test["char_id"], df_test["char_id"], seq_max_len)

X_test_str, _ = prepare(df_test["char"], df_test["char_id"], seq_max_len, is_padding=False)

print "test size: %d" %(len(X_test))

return X_test, X_test_str

def getTransition(y_train_batch):

transition_batch = []

for m in range(len(y_train_batch)):

y = [5] + list(y_train_batch[m]) + [0]

for t in range(len(y)):

if t + 1 == len(y):

continue

i = y[t]

j = y[t + 1]

if i == 0:

break

transition_batch.append(i * 6 + j)

transition_batch = np.array(transition_batch)

return transition_batch

train.py

[python] view plain copy







import time

import helper

import argparse

import numpy as np

import pandas as pd

import tensorflow as tf

from BILSTM_CRF import BILSTM_CRF

# python train.py train.in model -v validation.in -c char_emb -e 10 -g 2

parser = argparse.ArgumentParser()

parser.add_argument("train_path", help="the path of the train file")

parser.add_argument("save_path", help="the path of the saved model")

parser.add_argument("-v","--val_path", help="the path of the validation file", default=None)

parser.add_argument("-e","--epoch", help="the number of epoch", default=100, type=int)

parser.add_argument("-c","--char_emb", help="the char embedding file", default=None)

parser.add_argument("-g","--gpu", help="the id of gpu, the default is 0", default=0, type=int)

args = parser.parse_args()

train_path = args.train_path

save_path = args.save_path

val_path = args.val_path

num_epochs = args.epoch

emb_path = args.char_emb

gpu_config = "/cpu:0"

#gpu_config = "/gpu:"+str(args.gpu)

num_steps = 200 # it must consist with the test

start_time = time.time()

print "preparing train and validation data"

X_train, y_train, X_val, y_val = helper.getTrain(train_path=train_path, val_path=val_path, seq_max_len=num_steps)

char2id, id2char = helper.loadMap("char2id")

label2id, id2label = helper.loadMap("label2id")

num_chars = len(id2char.keys())

num_classes = len(id2label.keys())

if emb_path != None:

embedding_matrix = helper.getEmbedding(emb_path)

else:

embedding_matrix = None

print "building model"

config = tf.ConfigProto(allow_soft_placement=True)

with tf.Session(config=config) as sess:

with tf.device(gpu_config):

initializer = tf.random_uniform_initializer(-0.1, 0.1)

with tf.variable_scope("model", reuse=None, initializer=initializer):

model = BILSTM_CRF(num_chars=num_chars, num_classes=num_classes, num_steps=num_steps, num_epochs=num_epochs, embedding_matrix=embedding_matrix, is_training=True)

print "training model"

tf.initialize_all_variables().run()

model.train(sess, save_path, X_train, y_train, X_val, y_val)

print "final best f1 is: %f" % (model.max_f1)

end_time = time.time()

print "time used %f(hour)" % ((end_time - start_time) / 3600)

test.py

[python] view plain copy







import time

import helper

import argparse

import numpy as np

import pandas as pd

import tensorflow as tf

from BILSTM_CRF import BILSTM_CRF

# python test.py model test.in test.out -c char_emb -g 2

parser = argparse.ArgumentParser()

parser.add_argument("model_path", help="the path of model file")

parser.add_argument("test_path", help="the path of test file")

parser.add_argument("output_path", help="the path of output file")

parser.add_argument("-c","--char_emb", help="the char embedding file", default=None)

parser.add_argument("-g","--gpu", help="the id of gpu, the default is 0", default=0, type=int)

args = parser.parse_args()

model_path = args.model_path

test_path = args.test_path

output_path = args.output_path

gpu_config = "/cpu:0"

emb_path = args.char_emb

num_steps = 200 # it must consist with the train

start_time = time.time()

print "preparing test data"

X_test, X_test_str = helper.getTest(test_path=test_path, seq_max_len=num_steps)

char2id, id2char = helper.loadMap("char2id")

label2id, id2label = helper.loadMap("label2id")

num_chars = len(id2char.keys())

num_classes = len(id2label.keys())

if emb_path != None:

embedding_matrix = helper.getEmbedding(emb_path)

else:

embedding_matrix = None

print "building model"

config = tf.ConfigProto(allow_soft_placement=True)

with tf.Session(config=config) as sess:

with tf.device(gpu_config):

initializer = tf.random_uniform_initializer(-0.1, 0.1)

with tf.variable_scope("model", reuse=None, initializer=initializer):

model = BILSTM_CRF(num_chars=num_chars, num_classes=num_classes, num_steps=num_steps, embedding_matrix=embedding_matrix, is_training=False)

print "loading model parameter"

saver = tf.train.Saver()

saver.restore(sess, model_path)

print "testing"

model.test(sess, X_test, X_test_str, output_path)

end_time = time.time()

print "time used %f(hour)" % ((end_time - start_time) / 3600)



相关预处理的数据请参考github: scofiled's github/bilstm+crf