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main.py
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from config.globalConfig import *
from config.msraConfig import Config
from dataset.msraDataset import MsraDataset
from utils.get_batch import BatchGenerator
from models.bilstm_crf import BilstmCrfModel
import tensorflow as tf
import os
import numpy as np
from utils.tmp import find_all_tag,get_labels,get_multi_metric,mean,get_binary_metric
labels_list = ['ns','nt','nr']
def train(config,model,save_path,trainBatchGen,valBatchGen):
globalStep = tf.Variable(0, name="globalStep", trainable=False)
save_path = os.path.join(save_path,"best_validation")
saver = tf.train.Saver()
with tf.Session() as sess:
# 定义trainOp
# 定义优化函数，传入学习速率参数
optimizer = tf.train.AdamOptimizer(config.trainConfig.learning_rate)
# 计算梯度,得到梯度和变量
gradsAndVars = optimizer.compute_gradients(model.loss)
# 将梯度应用到变量下，生成训练器
trainOp = optimizer.apply_gradients(gradsAndVars, global_step=globalStep)
sess.run(tf.global_variables_initializer())
best_f_beta_val = 0.0 #最佳验证集的f1值
for epoch in range(1,config.trainConfig.epoch+1):
for trainX_batch,trainY_batch,train_seqlen in trainBatchGen.next_batch(config.trainConfig.batch_size):
feed_dict = {
model.inputX : trainX_batch, #[batch,max_len]
model.inputY : trainY_batch, #[batch,max_len]
model.seq_lens : train_seqlen, #[batch]
}
_, loss, pre = sess.run([trainOp,model.loss,model.viterbi_sequence],feed_dict)
currentStep = tf.train.global_step(sess, globalStep)
true_idx2label = [get_labels(label,idx2label,seq_len) for label,seq_len in zip(trainY_batch,train_seqlen)]
pre_idx2label = [get_labels(label,idx2label,seq_len) for label,seq_len in zip(pre,train_seqlen)]
precision,recall,f1 = get_multi_metric(true_idx2label,pre_idx2label,train_seqlen,labels_list)
if currentStep % 100 == 0:
print("[train] step:{} loss:{:.4f} precision:{:.4f} recall:{:.4f} f1:{:.4f}".format(currentStep,loss,precision,recall,f1))
if currentStep % 100 == 0:
#要计算所有验证样本的
losses = []
f_betas = []
precisions = []
recalls = []
for valX_batch,valY_batch,val_seqlen in valBatchGen.next_batch(config.trainConfig.batch_size):
feed_dict = {
model.inputX : valX_batch, #[batch,max_len]
model.inputY : valY_batch, #[batch,max_len]
model.seq_lens : val_seqlen, #[batch]
}
val_loss, val_pre = sess.run([model.loss,model.viterbi_sequence],feed_dict)
val_true_idx2label = [get_labels(label,idx2label,seq_len) for label,seq_len in zip(valY_batch,val_seqlen)]
val_pre_idx2label = [get_labels(label,idx2label,seq_len) for label,seq_len in zip(val_pre,val_seqlen)]
val_precision,val_recall,val_f1 = get_multi_metric(val_true_idx2label,val_pre_idx2label,val_seqlen,labels_list)
losses.append(val_loss)
f_betas.append(val_f1)
precisions.append(val_precision)
recalls.append(val_recall)
if mean(f_betas) > best_f_beta_val:
# 保存最好结果
best_f_beta_val = mean(f_betas)
last_improved = currentStep
saver.save(sess=sess, save_path=save_path)
improved_str = '*'
else:
improved_str = ''
print("[val] loss:{:.4f} precision:{:.4f} recall:{:.4f} f1:{:.4f} {}".format(
mean(losses),mean(precisions),mean(recalls),mean(f_betas),improved_str
))
def test(config,model,save_path,testBatchGen):
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state('checkpoint/msra/')
path = ckpt.model_checkpoint_path
saver.restore(sess, path) # 读取保存的模型
precisions = []
recalls = []
f1s = []
for testX_batch,testY_batch,test_seqlen in testBatchGen.next_batch(config.trainConfig.batch_size):
feed_dict = {
model.inputX : testX_batch, #[batch,max_len]
model.inputY : testY_batch, #[batch,max_len]
model.seq_lens : test_seqlen, #[batch]
}
test_pre = sess.run([model.viterbi_sequence],feed_dict) #这里有点奇怪，和train、val出来的数据相比多了一个[]
test_pre = test_pre[0]
test_true_idx2label = [get_labels(label,idx2label,seq_len) for label,seq_len in zip(testY_batch,test_seqlen)]
test_pre_idx2label = [get_labels(label,idx2label,seq_len) for label,seq_len in zip(test_pre,test_seqlen)]
precision,recall,f1 = get_multi_metric(test_true_idx2label,test_pre_idx2label,test_seqlen,labels_list)
precisions.append(precision)
recalls.append(recall)
f1s.append(f1)
print("[test] precision:{:.4f} recall:{:.4f} f1:{:.4f}".format(
mean(precisions),mean(recalls),mean(f1s)))
def predict(word2idx,idx2word,idx2label):
max_len = 60
input_list = []
input_len = []
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state('checkpoint/msra/')
path = ckpt.model_checkpoint_path
saver.restore(sess, path) # 读取保存的模型
while True:
print("请输入一句话：")
line = input()
if line == 'q':
break
line_len = len(line)
input_len.append(line_len)
word_list = [word2idx[word] if word in word2idx else word2idx['unknow'] for word in line]
if line_len < max_len:
word_list =word_list + [0]*(max_len-line_len)
else:
word_list = word_list[:max_len]
input_list.append(word_list) #需要增加一个维度
input_list = np.array(input_list)
input_label = np.zeros((input_list.shape[0],input_list.shape[1])) #标签占位
input_len = np.array(input_len)
feed_dict = {
model.inputX : input_list, #[batch,max_len]
model.inputY : input_label, #[batch,max_len]
model.seq_lens : input_len, #[batch]
}
pred_label = sess.run([model.viterbi_sequence],feed_dict)
pred_label = pred_label[0]
# 将预测标签id还原为真实标签
pred_idx2label = [get_labels(label,idx2label,seq_len) for label,seq_len in zip(pred_label,input_len)]
for line,pre,s_len in zip(input_list,pred_idx2label,input_len):
res = find_all_tag(pre,s_len)
for k in res:
for v in res[k]:
if v:
print(k,"".join([idx2word[word] for word in line[v[0]:v[0]+v[1]]]))
input_list = []
input_len = []
if __name__ == "__main__":
config = Config()
msraDataset = MsraDataset(config)
word2idx = msraDataset.get_word2idx()
idx2word = msraDataset.get_idx2word()
label2idx = msraDataset.get_label2idx()
idx2label = msraDataset.get_idx2label()
embedding_pre = msraDataset.get_embedding()
x_train,y_train,z_train = msraDataset.get_train_data()
x_val,y_val,z_val = msraDataset.get_val_data()
x_test,y_test,z_test = msraDataset.get_test_data()
print("====验证是否得到相关数据===")
print("word2idx:",len(word2idx))
print("idx2word:",len(idx2word))
print("label2idx:",len(label2idx))
print("idx2label:",len(idx2label))
print("embedding_pre:",embedding_pre.shape)
print(x_train.shape,y_train.shape,z_train.shape)
print(x_val.shape,y_val.shape,z_val.shape)
print(x_test.shape,y_test.shape,z_test.shape)
print("======打印相关参数======")
print("batch_size:",config.trainConfig.batch_size)
print("learning_rate:",config.trainConfig.learning_rate)
print("embedding_dim:",config.msraConfig.embedding_dim)
is_train,is_val,is_test = True,True,True
model = BilstmCrfModel(config,embedding_pre)
if is_train:
trainBatchGen = BatchGenerator(x_train,y_train,z_train,shuffle=True)
if is_val:
valBatchGen = BatchGenerator(x_val,y_val,z_val,shuffle=False)
if is_test:
testBatchGen = BatchGenerator(x_test,y_test,z_test,shuffle=False)
dataset = "msra"
if dataset == "msra":
save_path = os.path.join(PATH,'checkpoint/msra/')
if not os.path.exists(save_path):
os.makedirs(save_path)
#train(config,model,save_path,trainBatchGen,valBatchGen)
#test(config,model,save_path,testBatchGen)
predict(word2idx,idx2word,idx2label)