def test(self, sess, t_x, t_y, idx2tag, idx2char, outpath=None, ensemble=None, batch_size=200, tag_num=1):
t_y = toolbox.unpad_zeros(t_y)
gold = toolbox.decode_tags(t_y, idx2tag, self.tag_scheme)
chars = toolbox.decode_chars(t_x[0], idx2char)
gold_out = toolbox.generate_output(chars, gold, self.tag_scheme)
pt_holder = None
if self.graphic:
pt_holder = self.input_p[0]
prediction = self.predict(data=t_x, sess=sess, model=self.input_v[0] + self.output[0], index=0, pt_h=pt_holder,
pt=self.pixels, ensemble=ensemble, batch_size=batch_size)
prediction = toolbox.decode_tags(prediction, idx2tag, self.tag_scheme)
prediction_out = toolbox.generate_output(chars, prediction, self.tag_scheme)
scores = toolbox.evaluator(prediction_out, gold_out, metric='All', verbose=True, tag_num=tag_num)
print 'Best scores: '
print 'Segmentation F1-score: %f' % scores[2]
print 'Segmentation Precision: %f' % scores[0]
print 'Segmentation Recall: %f' % scores[1]
print 'Segmentation True Negative Rate: %f' % scores[6]
print 'Segmentation Boundary-F1-score: %f\n' % scores[10]
print 'Joint POS tagging F-score: %f' % scores[5]
print 'Joint POS tagging Precision: %f' % scores[3]
print 'Joint POS tagging Recall: %f' % scores[4]
print 'Joint POS True Negative Rate: %f' % scores[7]
print 'Joint POS tagging Boundary-F1-score: %f\n' % scores[13]
if outpath is not None:
final_out = prediction_out[0]
toolbox.printer(final_out, outpath)
def test(self,
sess,
t_x,
t_y,
idx2tag,
idx2char,
outpath=None,
ensemble=None,
batch_size=200):
t_y = toolbox.unpad_zeros(t_y)
gold = toolbox.decode_tags(t_y, idx2tag, self.tag_scheme)
chars = toolbox.decode_chars(t_x[0], idx2char)
gold_out = toolbox.generate_output(chars, gold, self.tag_scheme)
pt_holder = None
if self.graphic:
pt_holder = self.input_p[0]
prediction = self.predict(data=t_x,
sess=sess,
model=self.input_v[0] + self.output[0],
index=0,
pt_h=pt_holder,
pt=self.pixels,
ensemble=ensemble,
batch_size=batch_size)
prediction = toolbox.decode_tags(prediction, idx2tag, self.tag_scheme)
prediction_out = toolbox.generate_output(chars, prediction,
self.tag_scheme)
scores = toolbox.evaluator(prediction_out,
gold_out,
tag_scheme=self.tag_scheme,
verbose=True)
scores = np.asarray(scores)
scores_f = scores[:, 1]
best_idx = int(np.argmax(scores_f))
c_score = scores[0]
print 'Best scores: '
print 'Segmentation F-score: %f' % c_score[0]
print 'Segmentation Precision: %f' % c_score[2]
print 'Segmentation Recall: %f\n' % c_score[3]
print 'Joint POS tagging F-score: %f' % c_score[1]
print 'Joint POS tagging Precision: %f' % c_score[4]
print 'Joint POS tagging Recall: %f' % c_score[5]
if outpath is not None:
if self.tag_scheme == 'parallel':
final_out = prediction_out[best_idx + 1]
elif self.tag_scheme == 'mul':
final_out = prediction_out[best_idx]
else:
final_out = prediction_out[0]
toolbox.printer(final_out, outpath)
def train(self, t_x, t_y, v_x, v_y, idx2tag, idx2char, sess, epochs, trained_model, lr=0.05, decay=0.05, decay_step=1):
lr_r = lr
best_epoch = 0
best_score = 0
best_seg = 0
best_pos = 0
v_y = toolbox.merge_bucket(v_y)
v_y = toolbox.unpad_zeros(v_y)
gold = toolbox.decode_tags(v_y, idx2tag, self.tag_scheme)
input_chars = toolbox.merge_bucket([v_x[0]])
chars = toolbox.decode_chars(input_chars[0], idx2char)
gold_out = toolbox.generate_output(chars, gold, self.tag_scheme)
for epoch in range(epochs):
print 'epoch: %d' % (epoch + 1)
t = time()
if epoch % decay_step == 0 and decay > 0:
lr_r = lr/(1 + decay*(epoch/decay_step))
data_list = t_x + t_y
samples = zip(*data_list)
random.shuffle(samples)
for sample in samples:
c_len = len(sample[0][0])
idx = self.bucket_dit[c_len]
real_batch_size = self.real_batches[idx]
model = self.input_v[idx] + self.output_[idx]
pt_holder = None
if self.graphic:
pt_holder = self.input_p[idx]
Batch.train(sess=sess[0], model=model, batch_size=real_batch_size, config=self.train_step[idx], lr=self.l_rate, lrv=lr_r, dr=self.drop_out, drv=self.drop_out_v, data=list(sample), pt_h=pt_holder, pixels=self.pixels, verbose=False)
predictions = []
for v_b_x in zip(*v_x):
c_len = len(v_b_x[0][0])
idx = self.bucket_dit[c_len]
pt_holder = None
if self.graphic:
pt_holder = self.input_p[idx]
b_prediction = self.predict(data=v_b_x, sess=sess, model=self.input_v[idx] + self.output[idx], index=idx, pt_h=pt_holder, pt=self.pixels, batch_size=100)
b_prediction = toolbox.decode_tags(b_prediction, idx2tag, self.tag_scheme)
predictions.append(b_prediction)
predictions = zip(*predictions)
predictions = toolbox.merge_bucket(predictions)
prediction_out = toolbox.generate_output(chars, predictions, self.tag_scheme)
scores = toolbox.evaluator(prediction_out, gold_out, tag_scheme=self.tag_scheme)
scores = np.asarray(scores)
c_score = np.max(scores[:,1])*np.max(scores[:,0])
if c_score > best_score and epoch > 4:
best_epoch = epoch + 1
best_score = c_score
best_seg = np.max(scores[:,0])
best_pos = np.max(scores[:,1])
self.saver.save(sess[0], trained_model, write_meta_graph=False)
print 'Time consumed: %d seconds' % int(time() - t)
print 'Training is finished!'
print 'Best segmentation score: %f' % best_seg
print 'Best POS tag score: %f' % best_pos
print 'Best epoch: %d' % best_epoch
def train(self, t_x, t_y, v_x, v_y, idx2tag, idx2char, sess, epochs, trained_model, lr=0.05, decay=0.05,
decay_step=1, tag_num=1):
lr_r = lr
best_epoch, best_score, best_seg, best_pos, c_tag, c_seg, c_score = {}, {}, {}, {}, {}, {}, {}
pindex = 0
metric = self.metric
for m in self.all_metrics:
best_epoch[m] = 0
best_score[m] = 0
best_seg[m] = 0
best_pos[m] = 0
c_tag[m] = 0
c_seg[m] = 0
c_score[m] = 0
v_y = toolbox.merge_bucket(v_y)
v_y = toolbox.unpad_zeros(v_y)
gold = toolbox.decode_tags(v_y, idx2tag, self.tag_scheme)
input_chars = toolbox.merge_bucket([v_x[0]])
chars = toolbox.decode_chars(input_chars[0], idx2char)
gold_out = toolbox.generate_output(chars, gold, self.tag_scheme)
for epoch in range(epochs):
print 'epoch: %d' % (epoch + 1)
t = time()
if epoch % decay_step == 0 and decay > 0:
lr_r = lr/(1 + decay*(epoch/decay_step))
data_list = t_x + t_y
samples = zip(*data_list)
random.shuffle(samples)
for sample in samples:
c_len = len(sample[0][0])
idx = self.bucket_dit[c_len]
real_batch_size = self.real_batches[idx]
model = self.input_v[idx] + self.output_[idx]
pt_holder = None
if self.graphic:
pt_holder = self.input_p[idx]
Batch.train(sess=sess[0], model=model, batch_size=real_batch_size, config=self.train_step[idx],
lr=self.l_rate, lrv=lr_r, dr=self.drop_out, drv=self.drop_out_v, data=list(sample),
pt_h=pt_holder, pixels=self.pixels, verbose=False)
predictions = []
for v_b_x in zip(*v_x):
c_len = len(v_b_x[0][0])
idx = self.bucket_dit[c_len]
pt_holder = None
if self.graphic:
pt_holder = self.input_p[idx]
b_prediction = self.predict(data=v_b_x, sess=sess, model=self.input_v[idx] + self.output[idx],
index=idx, pt_h=pt_holder, pt=self.pixels, batch_size=200)
b_prediction = toolbox.decode_tags(b_prediction, idx2tag, self.tag_scheme)
predictions.append(b_prediction)
predictions = zip(*predictions)
predictions = toolbox.merge_bucket(predictions)
prediction_out = toolbox.generate_output(chars, predictions, self.tag_scheme)
scores = toolbox.evaluator(prediction_out, gold_out, metric=metric, verbose=True, tag_num=tag_num)
scores = np.asarray(scores)
#Score_seg * Score_seg&tag
c_seg['Precision'] = scores[0]
c_seg['Recall'] = scores[1]
c_seg['F1-score'] = scores[2]
c_seg['True-Negative-Rate'] = scores[6]
c_seg['Boundary-F1-score'] = scores[10]
if self.tag_scheme != 'seg':
c_tag['Precision'] = scores[3]
c_tag['Recall'] = scores[4]
c_tag['F1-score'] = scores[5]
c_tag['True-Negative-Rate'] = scores[7]
c_tag['Boundary-F1-score'] = scores[13]
else:
c_tag['Precision'] = 1
c_tag['Recall'] = 1
c_tag['F1-score'] = 1
c_tag['True-Negative-Rate'] = 1
c_tag['Boundary-F1-score'] = 1
if metric == 'All':
for m in self.all_metrics:
print 'Segmentation ' + m + ': %f' % c_seg[m]
print 'POS Tagging ' + m + ': %f\n' % c_tag[m]
pindex = trained_model.rindex('/') + 1
else:
print 'Segmentation ' + metric + ': %f' % c_seg[metric]
if self.tag_scheme != 'seg':
print 'POS Tagging ' + metric + ': %f\n' % c_tag[metric]
for m in self.all_metrics:
c_score[m] = c_seg[m] * c_tag[m]
if metric == 'All':
for m in self.all_metrics:
if c_score[m] > best_score[m] and epoch > 4:
best_epoch[m] = epoch + 1
best_score[m] = c_score[m]
best_seg[m] = c_seg[m]
best_pos[m] = c_tag[m]
self.saver.save(sess[0], trained_model[:pindex] + m + '_' + trained_model[pindex:],
write_meta_graph=False)
elif c_score[metric] > best_score[metric] and epoch > 4:
best_epoch[metric] = epoch + 1
best_score[metric] = c_score[metric]
best_seg[metric] = c_seg[metric]
best_pos[metric] = c_tag[metric]
self.saver.save(sess[0], trained_model, write_meta_graph=False)
print 'Time consumed: %d seconds' % int(time() - t)
print 'Training is finished!'
if metric == 'All':
for m in self.all_metrics:
print 'Best segmentation ' + m + ': %f' % best_seg[m]
print 'Best POS Tagging ' + m + ': %f' % best_pos[m]
print 'Best epoch: %d\n' % best_epoch[m]
else:
print 'Best segmentation ' + metric + ': %f' % best_seg[metric]
print 'Best POS Tagging ' + metric + ': %f' % best_pos[metric]
print 'Best epoch: %d\n' % best_epoch[metric]
def train(self, t_x, t_y, v_x, v_y, idx2tag, idx2char, sess,
epochs, trained_model, lr=0.05, decay=0.05, decay_step=1, tag_num=1):
"""
:param t_x: b_train_x
:param t_y: b_train_y
:param v_x: b_dev_x
:param v_y: b_dev_y
:param idx2tag:
:param idx2char:
:param sess:
:param epochs: 训练轮数
:param trained_model: 训练好的模型参数
:param lr: 学习率
:param decay: 学习率衰减率
:param decay_step:
:param tag_num: 标签种类个数
"""
log_dir = "./train_log"
shutil.rmtree(log_dir)
train_writer = tf.summary.FileWriter(log_dir, sess[0].graph)
lr_r = lr
best_epoch, best_score, best_seg, best_pos, c_tag, c_seg, c_score = {}, {}, {}, {}, {}, {}, {}
pindex = 0
metric = self.metric
# 每种衡量标准下都有对应的最佳结果
for m in self.all_metrics:
best_epoch[m] = 0
best_score[m] = 0
best_seg[m] = 0
best_pos[m] = 0
c_tag[m] = 0
c_seg[m] = 0
c_score[m] = 0
v_y = toolbox.merge_bucket(v_y)
v_y = toolbox.unpad_zeros(v_y)
gold = toolbox.decode_tags(v_y, idx2tag, self.tag_scheme)
# 0 是字符本身，1 是偏旁部首，2、3 分别是 2gram 和 3gram
input_chars = toolbox.merge_bucket([v_x[0]])
chars = toolbox.decode_chars(input_chars[0], idx2char)
# 正确答案，实际上直接读取 dev.txt 即可得到，不知为何还要这么麻烦通过各种 ID 转换获取
gold_out = toolbox.generate_output(chars, gold, self.tag_scheme)
for epoch in range(epochs):
print 'epoch: %d' % (epoch + 1)
t = time()
# 在 decay_step 轮之后，衰减学习率
if epoch % decay_step == 0 and decay > 0:
lr_r = lr / (1 + decay * (epoch / decay_step))
# data_list: shape=(5,bucket 数量，bucket 中句子个数，句子长度)
data_list = t_x + t_y
# samples: shape=(bucket 数量，5, bucket 中句子个数，句子长度)，相当于置换了 data_list 中的 shape[0] 和 shape[1]
samples = zip(*data_list)
random.shuffle(samples)
# 遍历每一个 bucket
for sample in samples:
# sample: shape=(5, bucket 中句子个数，句子长度)
# 当前 bucket 中的句子长度
c_len = len(sample[0][0])
# 当前 bucket 的序号
idx = self.bucket_dit[c_len]
real_batch_size = self.real_batches[idx]
# 当前 bucket 的模型的输入和输出（注意每个 bucket 都有一个单独的模型）
model_placeholders = self.input_v[idx] + self.output_[idx] + self.lm_groundtruthes[idx]
pt_holder = None
if self.graphic:
pt_holder = self.input_p[idx]
# sess[0] 是 main_sess, sess[1] 是 decode_sess(如果使用 CRF 的话)
# 训练当前的 bucket，这个函数里面才真正地为模型填充了数据并运行(以 real_batch_size 为单位，将 bucket 中的句子依次喂给模型)
# 被 sess.run 的是 config=self.train_step[idx]，train_step[idx] 就会触发 BP 更新参数了
Batch.train(sess=sess[0], placeholders=model_placeholders, batch_size=real_batch_size,
train_step=self.train_steps[idx],loss=self.losses[idx],
lr=self.l_rate, lrv=lr_r, dr=self.drop_out, drv=self.drop_out_v, data=list(sample),
# debug_variable=[self.lm_output[idx], self.lm_output_[idx], self.output[idx], self.output_[idx]],
pt_h=pt_holder, pixels=self.pixels, verbose=False,
merged_summary=self.merged_summary, log_writer=train_writer,
single_summary=self.summaries[idx], epoch_index=epoch)
predictions = []
# 遍历每个 bucket, 用开发集测试准确率
for v_b_x in zip(*v_x):
# v_b_x: shape=(4,bucket 中句子个数，句子长度)
c_len = len(v_b_x[0][0])
idx = self.bucket_dit[c_len]
pt_holder = None
if self.graphic:
pt_holder = self.input_p[idx]
b_prediction = self.predict(data=v_b_x, sess=sess, model=self.input_v[idx] + self.output[idx],
index=idx, pt_h=pt_holder, pt=self.pixels, batch_size=100)
b_prediction = toolbox.decode_tags(b_prediction, idx2tag, self.tag_scheme)
predictions.append(b_prediction)
predictions = zip(*predictions)
predictions = toolbox.merge_bucket(predictions)
prediction_out = toolbox.generate_output(chars, predictions, self.tag_scheme)
scores = toolbox.evaluator(prediction_out, gold_out, metric=metric, verbose=True, tag_num=tag_num)
scores = np.asarray(scores)
# Score_seg * Score_seg&tag
c_seg['Precision'] = scores[0]
c_seg['Recall'] = scores[1]
c_seg['F1-score'] = scores[2]
c_seg['True-Negative-Rate'] = scores[6]
c_seg['Boundary-F1-score'] = scores[10]
if self.tag_scheme != 'seg':
c_tag['Precision'] = scores[3]
c_tag['Recall'] = scores[4]
c_tag['F1-score'] = scores[5]
c_tag['True-Negative-Rate'] = scores[7]
c_tag['Boundary-F1-score'] = scores[13]
else:
c_tag['Precision'] = 1
c_tag['Recall'] = 1
c_tag['F1-score'] = 1
c_tag['True-Negative-Rate'] = 1
c_tag['Boundary-F1-score'] = 1
if metric == 'All':
for m in self.all_metrics:
print 'Segmentation ' + m + ': %f' % c_seg[m]
print 'POS Tagging ' + m + ': %f\n' % c_tag[m]
pindex = trained_model.rindex('/') + 1
else:
print 'Segmentation ' + metric + ': %f' % c_seg[metric]
if self.tag_scheme != 'seg':
print 'POS Tagging ' + metric + ': %f\n' % c_tag[metric]
for m in self.all_metrics:
c_score[m] = c_seg[m] * c_tag[m]
if metric == 'All':
for m in self.all_metrics:
if c_score[m] > best_score[m] and epoch > 4:
best_epoch[m] = epoch + 1
best_score[m] = c_score[m]
best_seg[m] = c_seg[m]
best_pos[m] = c_tag[m]
self.saver.save(sess[0], trained_model[:pindex] + m + '_' + trained_model[pindex:],
write_meta_graph=False)
elif c_score[metric] > best_score[metric] and epoch > 4:
best_epoch[metric] = epoch + 1
best_score[metric] = c_score[metric]
best_seg[metric] = c_seg[metric]
best_pos[metric] = c_tag[metric]
self.saver.save(sess[0], trained_model, write_meta_graph=False)
print 'Time consumed: %d seconds' % int(time() - t)
print 'Training is finished!'
if metric == 'All':
for m in self.all_metrics:
print 'Best segmentation ' + m + ': %f' % best_seg[m]
print 'Best POS Tagging ' + m + ': %f' % best_pos[m]
print 'Best epoch: %d\n' % best_epoch[m]
else:
print 'Best segmentation ' + metric + ': %f' % best_seg[metric]
print 'Best POS Tagging ' + metric + ': %f' % best_pos[metric]
print 'Best epoch: %d\n' % best_epoch[metric]
