print 'Longest sentence by character is %d. ' % max_step_c
print 'Longest sentence by word is %d. ' % max_step_w
print 'Longest word is %d. ' % max_w_len
# b_train_x: shape=(4, bucket 数量，)
b_train_x, b_train_y = toolbox.buckets(train_x,
train_y,
size=args.bucket_size)
b_dev_x, b_dev_y = toolbox.buckets(dev_x, dev_y, size=args.bucket_size)
# 在句子在末尾填充 0 从而使得每个 bucket 内的句子长度保持一致
# b_train_x: shape=(4, bucket 数量，每个 bucket 内的句子数量，句子长度)
# b_train_y: shape=(1, bucket 数量，每个 bucket 内的句子数量，句子长度)，1表示每个字对应1个标签
b_train_x, b_train_y, b_buckets, b_counts = toolbox.pad_bucket(
b_train_x, b_train_y)
# b_buckets：每一个 bucket 中句子的长度（已经对齐过，bucket 中所有句子的长度一致）
# b_counts：每一个 bucket 中句子的个数
b_dev_x, b_dev_y, b_buckets, _ = toolbox.pad_bucket(b_dev_x,
b_dev_y,
bucket_len_c=b_buckets)
print 'Training set: %d instances; Dev set: %d instances.' % (len(
train_x[0]), len(dev_x[0]))
nums_tags = toolbox.get_nums_tags(tag2idx, args.tag_scheme)
# 用来对session进行参数配置，allow_soft_placement 表示如果你指定的设备不存在，允许TF自动分配设备
config = tf.ConfigProto(allow_soft_placement=True)
gpu_config = "/gpu:" + str(args.gpu)
print 'Initialization....'
t = time()
limit=args.sent_limit,
sent_seg=args.sent_seg,
is_space=is_space,
ignore_space=args.ignore_space)
train_x += train_gram
dev_x += dev_gram
nums_grams = []
for dic in gram2idx:
nums_grams.append(len(dic.keys()))
max_len = max(max_len_train, max_len_dev)
b_train_x, b_train_y = toolbox.buckets(train_x,
train_y,
size=args.bucket_size)
b_train_x, b_train_y, b_lens, b_count = toolbox.pad_bucket(
b_train_x, b_train_y, max_len)
b_dev_x = [toolbox.pad_zeros(dev_x_i, max_len) for dev_x_i in dev_x]
b_dev_y_gold = [
line.strip() for line in codecs.open(
path + '/tag_dev_gold.txt', 'r', encoding='utf-8')
]
nums_tag = len(tag2idx)
config = tf.ConfigProto(allow_soft_placement=True)
gpu_config = "/gpu:" + str(args.gpu)
transducer = None
transducer_graph = None