def sentence2vec(w2v_model, s, max_length):
if isinstance(s, str):
words = word_tokenize(remove_punc(s.lower()))
else:
words = s
vec = []
if len(words) > max_length:
words = words[:max_length]
for word in words:
if word in w2v_model.wv.vocab:
vec.append(w2v_model.wv[word])
dim = len(vec[0])
# print("dim", dim)
print("len(vec)", len(vec))
for i in range(max_length - len(vec)):
vec.append(np.zeros(dim))
return np.array(vec)
def get_train_data(data_type, w2v_model, qa_file, doc_file, to_file_path,
args):
logger.info("preprocessing...")
ns_amount = args.ns_amount
questions = []
answers = []
# 计算每个question的向量
input_length = 0
with open(qa_file, "r") as f:
lines = f.readlines()
for i, line in enumerate(lines):
line = line.strip().lower()
if line != "" and i % 2 == 0:
words = word_tokenize(remove_punc(line))
input_length = max(len(words), input_length)
questions.append(words)
elif line != "" and i % 2 == 1:
arr = line.strip().split(" ")
ans = []
for a in arr:
if a != "":
ans.append(int(a) - 1) # 因为原始数据从1开始计数，这里减去1。改为从0开始。
answers.append(ans)
question_vecs = []
for q_words in questions:
question_vecs.append(sentence2vec(w2v_model, q_words, input_length))
print("len(question_vecs)", len(question_vecs))
# 计算每个document的向量
docs = []
output_length = 0
with open(doc_file, "r") as f:
lines = f.readlines()
for i, line in enumerate(lines):
line = line.strip().lower()
if line != "":
words = word_tokenize(remove_punc(line))
output_length = max(len(words), output_length)
docs.append(words)
doc_vecs = []
output_length = args.output_length
for d_words in docs:
doc_vecs.append(sentence2vec(w2v_model, d_words, output_length))
print("len(doc_vecs)", len(doc_vecs))
logger.info("input_length:%d, output_length:%d" %
(input_length, output_length))
# 计算每个doc出现的频率
doc_count = {}
for ans in answers:
for a in ans:
if a in doc_count.keys():
doc_count[a] += 1
else:
doc_count[a] = 1
# 计算每个doc的weight
doc_weight = {}
t_max = 0
for k in doc_count.keys():
t_max = max(t_max, doc_count[k])
for k in doc_count.keys():
doc_weight[k] = doc_count[k] / t_max
total = len(question_vecs)
train_num = int(total * 0.9)
logger.info("train_num:%d, total:%d" % (train_num, total))
# 打乱数据
qa_index = list(range(total))
random.shuffle(qa_index)
step = 0
while step * 200 <= train_num:
# [q_encoder_input, r_decoder_input, w_decoder_input, weight_data_r, weight_data_w]
q_encoder_input = []
r_decoder_input = []
w_decoder_input = []
weight_data_r = []
weight_data_w = []
y_data = []
qid_list = []
label_list = []
aid_list = []
logger.info("step: %d" % step)
end = min(train_num, (step + 1) * 200)
for ss in range(step * 200, end):
i = qa_index[ss]
logger.info("question: %d" % i)
qid_list.append(i)
label_list.append(1)
y = [1] + [0] * ns_amount
y_data.append(y)
# question
q_encoder_input.append(question_vecs[i])
# 每个question一个正确答案
aid = answers[i][0]
aid_list.append(aid)
r_decoder_input.append(doc_vecs[aid])
weight_data_r.append(doc_weight[aid])
# 10个un-related答案
aids = get_randoms(list(doc_weight.keys()), [aid], 10)
w_decoder = []
w_weight = []
for aid in aids:
w_decoder.append(doc_vecs[aid])
w_weight.append(doc_weight[aid])
w_decoder = np.array(w_decoder).reshape(output_length,
args.input_dim, ns_amount)
w_weight = np.array(w_weight).reshape((1, ns_amount))
w_decoder_input.append(w_decoder)
weight_data_w.append(w_weight)
for aaid in aids:
qid_list.append(i)
label_list.append(0)
aid_list.append(aaid)
# 这些答案都是unrelated
y = [0] * (1 + ns_amount)
y_data.append(y)
# question
q_encoder_input.append(question_vecs[i])
r_decoder_input.append(doc_vecs[aaid])
weight_data_r.append(doc_weight[aaid])
# 10个un-related答案
aids = get_randoms(list(doc_weight.keys()), [aid], 10)
w_decoder = []
w_weight = []
for aid in aids:
w_decoder.append(doc_vecs[aid])
w_weight.append(doc_weight[aid])
w_decoder = np.array(w_decoder).reshape(
output_length, args.input_dim, ns_amount)
w_weight = np.array(w_weight).reshape((1, ns_amount))
w_decoder_input.append(w_decoder)
weight_data_w.append(w_weight)
logger.info("loading weights: ckpt/nn_weights_%s.h5" % data_type)
model = negative_samples(input_length=input_length,
input_dim=args.input_dim,
output_length=output_length,
output_dim=args.output_dim,
hidden_dim=args.hidden_dim,
ns_amount=ns_amount,
learning_rate=args.learning_rate,
drop_rate=args.drop_rate)
model.load_weights("ckpt/nn_weights_%s.h5" % data_type)
new_dnn_model = Model(inputs=model.input,
outputs=model.get_layer('dropout_con').output)
logger.info("predicting...")
res = new_dnn_model.predict([
q_encoder_input, r_decoder_input, w_decoder_input, weight_data_r,
weight_data_w
])
# print(res)
with open(to_file_path, "a") as f:
for i in range(len(res)):
row = res[i]
feature_str = ''
for j in range(len(row)):
feature_str = feature_str + (" %d:%.9f" % (j + 1, row[j]))
label = label_list[i]
id = qid_list[i]
doc_id = aid_list[i]
line = "%d qid:%d%s # doc-%d \n" % (label, id, feature_str,
doc_id)
f.write(line)
print("saved to:", to_file_path)
logger.info("step:%d added" % step)
step += 1
logger.info("saved to: %s" % to_file_path)
def get_train_data(data_type,
w2v_model,
qa_file,
doc_file,
to_file_path,
args,
step=0):
logger.info("preprocessing...")
questions = []
answers = []
# 计算每个question的向量
input_length = 0
with open(qa_file, "r") as f:
lines = f.readlines()
for i, line in enumerate(lines):
line = line.strip().lower()
if line != "" and i % 2 == 0:
words = word_tokenize(remove_punc(line))
input_length = max(len(words), input_length)
questions.append(words)
elif line != "" and i % 2 == 1:
arr = line.strip().split(" ")
ans = []
for a in arr:
if a != "":
ans.append(int(a) - 1) # 因为原始数据从1开始计数，这里减去1。改为从0开始。
answers.append(ans)
input_length = args.input_length
question_vecs = []
for q_words in questions:
question_vecs.append(sentence2vec(w2v_model, q_words, input_length))
print("len(question_vecs)", len(question_vecs))
# 计算每个document的向量
docs = []
output_length = 0
with open(doc_file, "r") as f:
lines = f.readlines()
for i, line in enumerate(lines):
line = line.strip().lower()
if line != "":
words = word_tokenize(remove_punc(line))
output_length = max(len(words), output_length)
docs.append(words)
doc_vecs = []
output_length = args.output_length
for d_words in docs:
doc_vecs.append(sentence2vec(w2v_model, d_words, output_length))
print("len(doc_vecs)", len(doc_vecs))
logger.info("input_length:%d, output_length:%d" %
(input_length, output_length))
# 计算每个doc出现的频率
doc_count = {}
for ans in answers:
for a in ans:
if a in doc_count.keys():
doc_count[a] += 1
else:
doc_count[a] = 1
# 计算每个doc的weight
doc_weight = {}
t_max = 0
for k in doc_count.keys():
t_max = max(t_max, doc_count[k])
for k in doc_count.keys():
doc_weight[k] = doc_count[k] / t_max
model = DNN(args.input_lenth,
args.input_dim,
filters_num=args.filters_num,
kernel_val=(args.kernel_val),
pool_s=args.pool_s,
pool_stride=args.pool_stride,
hidden1_dim=args.hidden1_dim,
hidden2_dim=args.hidden2_dim,
activation=args.activation)
model.load_weights("ckpt/dnn_weights_v2_%s.h5" % data_type)
new_dnn_model = Model(inputs=model.input,
outputs=model.get_layer('hidden_layer').output)
total = len(question_vecs)
train_num = int(total * 0.9)
for i in range(train_num):
q_encoder_input = []
r_decoder_input = []
label_list = []
aid_list = []
logger.info("get all documents for question: %d" % i)
print("get all documents for question: %d" % i)
# qid_list.append(i)
# label_list.append(1)
cur_answers = answers[i]
doc_list_ordered = [a for a in cur_answers]
for aid in list(doc_weight.keys()):
if aid not in doc_list_ordered:
doc_list_ordered.append(aid)
print("len(doc_list_ordered):", len(doc_list_ordered))
print("len(cur_answers):", len(cur_answers))
for aid in doc_list_ordered:
aid_list.append(aid)
if aid in cur_answers:
label_list.append(1)
else:
label_list.append(0)
# question
q_encoder_input.append(question_vecs[i])
r_decoder_input.append(doc_vecs[aid])
logger.info("predicting question: %d" % i)
print("predicting question: %d" % i)
res = new_dnn_model.predict([q_encoder_input, r_decoder_input])
# print(res)
with open(to_file_path, "a") as f:
for j in range(len(res)):
row = res[j]
feature_str = ''
for k in range(len(row)):
feature_str = feature_str + (" %d:%.9f" % (k + 1, row[k]))
label = label_list[j]
doc_id = aid_list[j]
line = "%d qid:%d%s # doc-%d \n" % (label, i, feature_str,
doc_id)
f.write(line)
print("saved to:", to_file_path)
logger.info("total:%d" % total)
logger.info("saved to: %s" % to_file_path)
def train(w2v_model, qa_file, doc_file, to_model_file, to_ckpt_file, args):
logger.info("preprocessing...")
ns_amount = args.ns_amount
questions = []
answers = []
# question vector
input_length = 0
with open(qa_file, "r") as f:
lines = f.readlines()
for i, line in enumerate(lines):
line = line.strip().lower()
if line != "" and i % 2 == 0:
words = word_tokenize(remove_punc(line))
input_length = max(len(words), input_length)
questions.append(words)
elif line != "" and i % 2 == 1:
arr = line.strip().split(" ")
ans = []
for a in arr:
if a != "":
ans.append(
int(a) - 1
) # the index starts from 1 in the QA_list file, make it start from 0.
answers.append(ans)
question_vecs = []
for q_words in questions:
question_vecs.append(sentence2vec(w2v_model, q_words, input_length))
print("len(question_vecs)", len(question_vecs))
# document vector
docs = []
output_length = 0
with open(doc_file, "r") as f:
lines = f.readlines()
for i, line in enumerate(lines):
line = line.strip().lower()
if line != "":
words = word_tokenize(remove_punc(line))
output_length = max(len(words), output_length)
docs.append(words)
doc_vecs = []
output_length = args.output_length
for d_words in docs:
doc_vecs.append(sentence2vec(w2v_model, d_words, output_length))
print("len(doc_vecs)", len(doc_vecs))
logger.info("input_length:%d, output_length:%d" %
(input_length, output_length))
# weights for each doc
doc_count = {}
for ans in answers:
for a in ans:
if a in doc_count.keys():
doc_count[a] += 1
else:
doc_count[a] = 1
doc_weight = {}
t_max = 0
for k in doc_count.keys():
t_max = max(t_max, doc_count[k])
for k in doc_count.keys():
doc_weight[k] = doc_count[k] / t_max
# [q_encoder_input, r_decoder_input, w_decoder_input, weight_data_r, weight_data_w]
q_encoder_input = []
r_decoder_input = []
w_decoder_input = []
weight_data_r = []
weight_data_w = []
y_data = []
total = len(question_vecs)
qa_index = list(range(total))
random.shuffle(qa_index)
for i in qa_index:
y = [1] + [0] * ns_amount
y_data.append(y)
# question
q_encoder_input.append(question_vecs[i])
aid = answers[i][0]
r_decoder_input.append(doc_vecs[aid])
weight_data_r.append(doc_weight[aid])
aids = get_randoms(list(doc_weight.keys()), [aid], 10)
w_decoder = []
w_weight = []
for aid in aids:
w_decoder.append(doc_vecs[aid])
w_weight.append(doc_weight[aid])
w_decoder = np.array(w_decoder).reshape(output_length, args.input_dim,
ns_amount)
w_weight = np.array(w_weight).reshape((1, ns_amount))
w_decoder_input.append(w_decoder)
weight_data_w.append(w_weight)
y_data = np.array(y_data).reshape(total, (1 + ns_amount))
train_num = int(total * 0.9)
model = negative_samples(input_length=input_length,
input_dim=args.input_dim,
output_length=output_length,
output_dim=args.output_dim,
hidden_dim=args.hidden_dim,
ns_amount=ns_amount,
learning_rate=args.learning_rate,
drop_rate=args.drop_rate)
print(model.summary())
print("start training...")
logger.info("start training...")
model.fit([
q_encoder_input[:train_num], r_decoder_input[:train_num],
w_decoder_input[:train_num], weight_data_r[:train_num],
weight_data_w[:train_num]
],
y_data[:train_num],
batch_size=args.batch_size,
epochs=args.epochs,
verbose=1,
validation_data=([
q_encoder_input[train_num:], r_decoder_input[train_num:],
w_decoder_input[train_num:], weight_data_r[train_num:],
weight_data_w[train_num:]
], y_data[train_num:]))
res = model.evaluate([
q_encoder_input[train_num:], r_decoder_input[train_num:],
w_decoder_input[train_num:], weight_data_r[train_num:],
weight_data_w[train_num:]
],
y_data[train_num:],
verbose=1)
print("training over.")
logger.info("training over")
print(model.metrics_names)
print(res)
print(model.summary())
model.save(to_model_file)
print("saved model to:", )
model.save_weights(to_ckpt_file)
print("saved weights to:", to_ckpt_file)
if __name__ == '__main__':
qa_path = "%s/QA_list.txt" % "twitter"
qa_list = []
# 读取 question 和 answer
input_length = 0
qid = 0
with open(qa_path, "r") as f:
lines = f.readlines()
for i, line in enumerate(lines):
line = line.strip().lower()
if line != "" and i % 2 == 0:
words = word_tokenize(remove_punc(line))
input_length = max(len(words), input_length)
elif line != "" and i % 2 == 1:
arr = line.strip().split(" ")
ans = []
for a in arr:
if a != "":
ans.append(int(a) - 1) # 因为原始数据从1开始计数，这里减去1。改为从0开始。
qa_list.append({"qid": qid, "question": words, "answers": ans})
qid += 1
qa_index = list(range(len(qa_list)))
random.shuffle(qa_index)
parser = argparse.ArgumentParser(description='Test for argparse')
parser.add_argument('--data_type',
help='data_type',
def get_train_data(data_type,
w2v_model,
qa_file,
doc_file,
to_file_path,
args,
step=0):
logger.info("preprocessing...")
questions = []
answers = []
# 计算每个question的向量
input_length = 0
with open(qa_file, "r") as f:
lines = f.readlines()
for i, line in enumerate(lines):
if i >= 2000: # 数据太大，内存不够
break
line = line.strip().lower()
if line != "" and i % 2 == 0:
words = word_tokenize(remove_punc(line))
input_length = max(len(words), input_length)
questions.append(words)
elif line != "" and i % 2 == 1:
arr = line.strip().split(" ")
ans = []
for a in arr:
if a != "":
ans.append(int(a) - 1) # 因为原始数据从1开始计数，这里减去1。改为从0开始。
answers.append(ans)
input_length = args.input_length
question_vecs = []
for q_words in questions:
question_vecs.append(sentence2vec(w2v_model, q_words, input_length))
print("len(question_vecs)", len(question_vecs))
# 计算每个document的向量
docs = []
output_length = 0
with open(doc_file, "r") as f:
lines = f.readlines()
for i, line in enumerate(lines):
line = line.strip().lower()
if line != "":
words = word_tokenize(remove_punc(line))
output_length = max(len(words), output_length)
docs.append(words)
doc_vecs = []
output_length = args.output_length
for d_words in docs:
doc_vecs.append(sentence2vec(w2v_model, d_words, output_length))
print("len(doc_vecs)", len(doc_vecs))
logger.info("input_length:%d, output_length:%d" %
(input_length, output_length))
# 计算每个doc出现的频率
doc_count = {}
for ans in answers:
for a in ans:
if a in doc_count.keys():
doc_count[a] += 1
else:
doc_count[a] = 1
# 计算每个doc的weight
doc_weight = {}
t_max = 0
for k in doc_count.keys():
t_max = max(t_max, doc_count[k])
for k in doc_count.keys():
doc_weight[k] = doc_count[k] / t_max
total = len(question_vecs)
train_num = int(total * 0.9)
logger.info("train_num:%d, total:%d" % (train_num, total))
# 打乱数据
qa_index = list(range(total))
random.shuffle(qa_index)
# [q_encoder_input, r_decoder_input, w_decoder_input, weight_data_r, weight_data_w]
q_encoder_input = []
r_decoder_input = []
y_data = []
label_list = []
qid_list = []
aid_list = []
total = len(question_vecs)
for i in range(total):
# question
q_encoder_input.append(question_vecs[i])
qid_list.append(i)
# 每个question一个正确答案
aid = answers[i][0]
r_decoder_input.append(doc_vecs[aid])
y_data.append([1, 0])
label_list.append(1)
aid_list.append(aid)
# 10个un-related答案
aids = get_randoms(list(doc_weight.keys()), [aid], 10)
for aaid in aids:
q_encoder_input.append(question_vecs[i])
r_decoder_input.append(doc_vecs[aaid])
y_data.append([0, 1])
label_list.append(0)
qid_list.append(i)
aid_list.append(aaid)
y_data = np.array(y_data)
train_num = int(total * 0.9) * 11
model = DNN(args.input_lenth,
args.input_dim,
filters_num=args.filters_num,
kernel_val=(args.kernel_val),
pool_s=args.pool_s,
pool_stride=args.pool_stride,
hidden1_dim=args.hidden1_dim,
hidden2_dim=args.hidden2_dim,
activation=args.activation)
model.load_weights("ckpt/dnn_weights_v2_%s.h5" % data_type)
new_dnn_model = Model(inputs=model.input,
outputs=model.get_layer('hidden_layer').output)
logger.info("predicting...")
res = new_dnn_model.predict(
[r_decoder_input[:train_num], q_encoder_input[:train_num]])
print("len(res)", len(res))
print("train_num", train_num)
print("len(r_decoder_input[:train_num])", len(r_decoder_input[:train_num]))
with open(to_file_path, "w") as f:
for i in range(len(res)):
row = res[i]
feature_str = ''
for j in range(len(row)):
feature_str = feature_str + (" %d:%.9f" % (j + 1, row[j]))
label = label_list[i]
id = qid_list[i]
doc_id = aid_list[i]
line = "%d qid:%d%s # doc-%d \n" % (label, id, feature_str, doc_id)
f.write(line)
print("saved to:", to_file_path)
logger.info("total:%d" % total)
logger.info("saved to: %s" % to_file_path)
def get_train_data(data_type,
w2v_model,
ckpt_path,
qa_file,
doc_file,
to_file_path,
args,
step=0):
if os.path.exists(to_file_path):
logger.info("file exists: %s" % to_file_path)
return
logger.info("preprocessing...")
ns_amount = 10
questions = []
answers = []
# 计算每个question的向量
input_length = 0
with open(qa_file, "r", encoding="utf-8") as f:
lines = f.readlines()
for i, line in enumerate(lines):
if i >= 2000:
break
line = line.strip().lower()
if line != "" and i % 2 == 0:
words = word_tokenize(remove_punc(line))
input_length = max(len(words), input_length)
questions.append(words)
elif line != "" and i % 2 == 1:
arr = line.strip().split(" ")
ans = []
for a in arr:
if a != "":
ans.append(int(a) - 1) # 因为原始数据从1开始计数，这里减去1。改为从0开始。
answers.append(ans)
question_vecs = []
for q_words in questions:
question_vecs.append(sentence2vec(w2v_model, q_words, input_length))
print("len(question_vecs)", len(question_vecs))
# 计算每个document的向量
docs = []
output_length = 0
with open(doc_file, "r", encoding="utf-8") as f:
lines = f.readlines()
for i, line in enumerate(lines):
line = line.strip().lower()
if line != "":
words = word_tokenize(remove_punc(line))
output_length = max(len(words), output_length)
docs.append(words)
doc_vecs = []
output_length = 1000
for d_words in docs:
doc_vecs.append(sentence2vec(w2v_model, d_words, output_length))
print("len(doc_vecs)", len(doc_vecs))
logger.info("input_length:%d, output_length:%d" %
(input_length, output_length))
# 计算每个doc出现的频率
doc_count = {}
for ii in range(len(docs)):
doc_count[ii] = 0
for ans in answers:
for a in ans:
if a in doc_count.keys():
doc_count[a] += 1
# 计算每个doc的weight
doc_weight = {}
t_max = 0
for k in doc_count.keys():
t_max = max(t_max, doc_count[k])
for k in doc_count.keys():
doc_weight[k] = doc_count[k] / t_max
logger.info("loading weights...")
model = negative_samples(input_length=input_length,
input_dim=args.input_dim,
output_length=output_length,
output_dim=args.output_dim,
hidden_dim=args.hidden_dim,
ns_amount=ns_amount,
learning_rate=args.learning_rate,
drop_rate=args.drop_rate)
model.load_weights(ckpt_path)
new_dnn_model = Model(inputs=model.input,
outputs=model.get_layer('dropout_con').output)
total = len(question_vecs)
train_num = int(total * 0.9)
qid_list = []
# 打乱数据
qa_index = list(range(total))
# random.shuffle(qa_index)
for ss in range(train_num, total):
i = qa_index[ss]
# [q_encoder_input, r_decoder_input, w_decoder_input, weight_data_r, weight_data_w]
q_encoder_input = []
r_decoder_input = []
w_decoder_input = []
weight_data_r = []
weight_data_w = []
logger.info("get all documents for question: %d" % i)
print("get all documents for question: %d" % i)
# qid_list.append(i)
# label_list.append(1)
cur_answers = answers[i]
doc_list_ordered = [a for a in cur_answers]
for aid in list(doc_weight.keys()):
if aid not in doc_list_ordered:
doc_list_ordered.append(aid)
label_list = []
aid_list = []
print("len(doc_list_ordered):", len(doc_list_ordered))
print("len(cur_answers):", len(cur_answers))
for aid in doc_list_ordered:
aid_list.append(aid)
if aid in cur_answers:
label_list.append(1)
else:
label_list.append(0)
# question
q_encoder_input.append(question_vecs[i])
r_decoder_input.append(doc_vecs[aid])
weight_data_r.append(doc_weight[aid])
# 10个un-related答案
aids = get_randoms(list(doc_weight.keys()), cur_answers, ns_amount)
w_decoder = []
w_weight = []
for aid in aids:
w_decoder.append(doc_vecs[aid])
w_weight.append(doc_weight[aid])
w_decoder = np.array(w_decoder).reshape(output_length,
args.input_dim, ns_amount)
w_weight = np.array(w_weight).reshape((1, ns_amount))
w_decoder_input.append(w_decoder)
weight_data_w.append(w_weight)
logger.info("now:%d , predicting question: %d" % (ss, i))
print("now:%d , predicting question: %d" % (ss, i))
start = 0
end = len(q_encoder_input)
for cur in range(0, end, 1000):
print("cur:%d / %d" % (cur, end))
a = q_encoder_input[cur:cur + 1000]
b = r_decoder_input[cur:cur + 1000]
c = w_decoder_input[cur:cur + 1000]
d = weight_data_r[cur:cur + 1000]
e = weight_data_w[cur:cur + 1000]
res = new_dnn_model.predict([a, b, c, d, e])
# print(res)
with open(to_file_path, "a") as f:
for j in range(len(res)):
row = res[j]
feature_str = ''
for k in range(len(row)):
feature_str = feature_str + (" %d:%.9f" %
(k + 1, row[k]))
label = label_list[j]
doc_id = aid_list[j]
line = "%d qid:%d%s # doc-%d \n" % (label, i, feature_str,
doc_id)
f.write(line)
print("saved to:", to_file_path)
logger.info("total:%d" % total)
logger.info("saved to: %s" % to_file_path)
def train(w2v_model, qa_file, doc_file, to_model_file, to_ckpt_file, args):
logger.info("preprocessing...")
questions = []
answers = []
# 计算每个question的向量
input_length = 0
with open(qa_file, "r") as f:
lines = f.readlines()
for i, line in enumerate(lines):
if i >= 2000: # 数据太大，内存不够
break
line = line.strip().lower()
if line != "" and i % 2 == 0:
words = word_tokenize(remove_punc(line))
input_length = max(len(words), input_length)
questions.append(words)
elif line != "" and i % 2 == 1:
arr = line.strip().split(" ")
ans = []
for a in arr:
if a != "":
ans.append(int(a) - 1) # 因为原始数据从1开始计数，这里减去1。改为从0开始。
answers.append(ans)
input_length = args.input_length
question_vecs = []
for q_words in questions:
question_vecs.append(sentence2vec(w2v_model, q_words, input_length))
print("len(question_vecs)", len(question_vecs))
# 计算每个document的向量
docs = []
output_length = 0
with open(doc_file, "r") as f:
lines = f.readlines()
for i, line in enumerate(lines):
line = line.strip().lower()
if line != "":
words = word_tokenize(remove_punc(line))
output_length = max(len(words), output_length)
docs.append(words)
doc_vecs = []
output_length = args.output_length
for d_words in docs:
doc_vecs.append(sentence2vec(w2v_model, d_words, output_length))
print("len(doc_vecs)", len(doc_vecs))
logger.info("input_length:%d, output_length:%d" %
(input_length, output_length))
# 计算每个doc出现的频率
doc_count = {}
for ans in answers:
for a in ans:
if a in doc_count.keys():
doc_count[a] += 1
else:
doc_count[a] = 1
# 计算每个doc的weight
doc_weight = {}
t_max = 0
for k in doc_count.keys():
t_max = max(t_max, doc_count[k])
for k in doc_count.keys():
doc_weight[k] = doc_count[k] / t_max
# [q_encoder_input, r_decoder_input, w_decoder_input, weight_data_r, weight_data_w]
q_encoder_input = []
r_decoder_input = []
y_data = []
total = len(question_vecs)
for i in range(total):
# question
q_encoder_input.append(question_vecs[i])
# 每个question一个正确答案
aid = answers[i][0]
r_decoder_input.append(doc_vecs[aid])
y_data.append([1, 0])
# 10个un-related答案
aids = get_randoms(list(doc_weight.keys()), [aid], 10)
for aaid in aids:
q_encoder_input.append(question_vecs[i])
r_decoder_input.append(doc_vecs[aaid])
y_data.append([0, 1])
y_data = np.array(y_data)
train_num = int(total * 0.9) * 11
model = DNN(args.input_lenth,
args.input_dim,
filters_num=args.filters_num,
kernel_val=(args.kernel_val),
pool_s=args.pool_s,
pool_stride=args.pool_stride,
hidden1_dim=args.hidden1_dim,
hidden2_dim=args.hidden2_dim,
activation=args.activation)
print("start training...")
logger.info("start training...")
model.fit([r_decoder_input[:train_num], q_encoder_input[:train_num]],
y_data[:train_num],
batch_size=args.batch_size,
epochs=args.epochs,
verbose=1,
validation_data=([
r_decoder_input[train_num:], q_encoder_input[train_num:]
], y_data[train_num:]))
res = model.evaluate(
[r_decoder_input[train_num:], q_encoder_input[train_num:]],
y_data[train_num:],
verbose=1)
print("training over.")
logger.info("training over")
print(model.metrics_names)
print(res)
print(model.summary())
model.save(to_model_file)
print("saved model to:", )
model.save_weights(to_ckpt_file)
print("saved weights to:", to_ckpt_file)
