def get_validation_data(input_texts, target_texts, char2id, maxlen=400):
# 数据生成器
X, Y = [], []
for i in range(len(input_texts)):
X.append(str2id(input_texts[i], char2id, maxlen))
Y.append(str2id(target_texts[i], char2id, maxlen))
X = np.array(padding(X, char2id))
Y = np.array(padding(Y, char2id))
return [X, Y], None
def data_generator(input_texts, target_texts, char2id, batch_size, maxlen=400):
# 数据生成器
while True:
X, Y = [], []
for i in range(len(input_texts)):
X.append(str2id(input_texts[i], char2id, maxlen))
Y.append(str2id(target_texts[i], char2id, maxlen))
if len(X) == batch_size:
X = np.array(padding(X, char2id))
Y = np.array(padding(Y, char2id))
yield [X, Y], None
X, Y = [], []
def get_validation_data(input_texts,
target_texts,
char2id,
input_pinyins,
output_pinyins,
pingyin2id,
maxlen=400):
# 数据生成器
X, Y = [], []
X_p, Y_p = [], []
for i in range(len(input_texts)):
X.append(str2id(input_texts[i], char2id, maxlen))
Y.append(str2id(target_texts[i], char2id, maxlen))
X_p.append(str2id(input_pinyins[i], pingyin2id, maxlen))
Y_p.append(str2id(output_pinyins[i], pingyin2id, maxlen))
X = np.array(padding(X, char2id))
Y = np.array(padding(Y, char2id))
X_p = np.array(padding(X_p, pingyin2id))
Y_p = np.array(padding(Y_p, pingyin2id))
return [X, Y, X_p, Y_p], None
def data_generator(input_texts,
target_texts,
char2id,
input_pinyins,
output_pinyins,
pingyin2id,
batch_size,
maxlen=400):
# 数据生成器
while True:
X, Y = [], []
X_p, Y_p = [], []
for i in range(len(input_texts)):
X.append(str2id(input_texts[i], char2id, maxlen))
Y.append(str2id(target_texts[i], char2id, maxlen))
X_p.append(str2id(input_pinyins[i], pingyin2id, maxlen))
Y_p.append(str2id(output_pinyins[i], pingyin2id, maxlen))
if len(X) == batch_size:
X = np.array(padding(X, char2id))
Y = np.array(padding(Y, char2id))
X_p = np.array(padding(X_p, pingyin2id))
Y_p = np.array(padding(Y_p, pingyin2id))
yield [X, Y, X_p, Y_p], None
X, Y, X_p, Y_p = [], [], [], []
def gen_target(input_text,
model,
char2id,
id2char,
pinyin2id,
id2pinyin,
maxlen=400,
topk=3,
max_target_len=50):
"""beam search解码
每次只保留topk个最优候选结果；如果topk=1，那么就是贪心搜索
"""
xid = np.array([str2id(input_text, char2id, maxlen)] * topk) # 输入转id
yid = np.array([[char2id[GO_TOKEN]]] * topk) # 解码均以GO开始
scores = [0] * topk # 候选答案分数
for i in range(max_target_len): # 强制要求target不超过maxlen字
proba = model.predict([xid, yid])[:, i, :] # 预测
log_proba = np.log(proba + 1e-6) # 取对数，方便计算
arg_topk = log_proba.argsort(axis=1)[:, -topk:] # 每一项选出topk
_yid = [] # 暂存的候选目标序列
_scores = [] # 暂存的候选目标序列得分
if i == 0:
for j in range(topk):
_yid.append(list(yid[j]) + [arg_topk[0][j]])
_scores.append(scores[j] + log_proba[0][arg_topk[0][j]])
else:
for j in range(len(xid)):
for k in range(topk): # 遍历topk*topk的组合
_yid.append(list(yid[j]) + [arg_topk[j][k]])
_scores.append(scores[j] + log_proba[j][arg_topk[j][k]])
_arg_topk = np.argsort(_scores)[-topk:] # 从中选出新的topk
_yid = [_yid[k] for k in _arg_topk]
_scores = [_scores[k] for k in _arg_topk]
yid = []
scores = []
for k in range(len(xid)):
if _yid[k][-1] == char2id[EOS_TOKEN]: # 找到<end>就返回
return id2str(_yid[k][1:-1], id2char)
else:
yid.append(_yid[k])
scores.append(_scores[k])
yid = np.array(yid)
# 如果maxlen字都找不到EOS，直接返回
return id2str(yid[np.argmax(scores)][1:-1], id2char)
