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a2_predict.py
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# -*- coding: utf-8 -*-
#prediction using model.
#process--->1.load data(X:list of lint,y:int). 2.create session. 3.feed data. 4.predict
import sys
reload(sys)
sys.setdefaultencoding('utf8')
import tensorflow as tf
import numpy as np
from a07_Transformer import Transformer
from data_util_zhihu import load_data_predict,load_final_test_data,create_voabulary,create_voabulary_label
from tflearn.data_utils import pad_sequences #to_categorical
import os
import codecs
#configuration
FLAGS=tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer("num_classes",1999+3,"number of label") #3 ADDITIONAL TOKEN: _GO,_END,_PAD
tf.app.flags.DEFINE_float("learning_rate",0.01,"learning rate")
tf.app.flags.DEFINE_integer("batch_size", 128, "Batch size for training/evaluating.") #批处理的大小 32-->128 #16
tf.app.flags.DEFINE_integer("decay_steps", 6000, "how many steps before decay learning rate.") #6000批处理的大小 32-->128
tf.app.flags.DEFINE_float("decay_rate", 1.0, "Rate of decay for learning rate.") #0.87一次衰减多少
tf.app.flags.DEFINE_string("ckpt_dir","checkpoint_transformer/","checkpoint location for the model")
tf.app.flags.DEFINE_integer("sequence_length",25,"max sentence length") #100-->25
tf.app.flags.DEFINE_integer("embed_size",512,"embedding size")
tf.app.flags.DEFINE_boolean("is_training",False,"is traning.true:tranining,false:testing/inference")
#tf.app.flags.DEFINE_string("cache_path","text_cnn_checkpoint/data_cache.pik","checkpoint location for the model")
#train-zhihu4-only-title-all.txt
tf.app.flags.DEFINE_string("traning_data_path","train-zhihu4-only-title-all.txt","path of traning data.") #O.K.train-zhihu4-only-title-all.txt-->training-data/test-zhihu4-only-title.txt--->'training-data/train-zhihu5-only-title-multilabel.txt'
tf.app.flags.DEFINE_string("word2vec_model_path","zhihu-word2vec-title-desc.bin-512","word2vec's vocabulary and vectors") #zhihu-word2vec.bin-100-->zhihu-word2vec-multilabel-minicount15.bin-100
tf.app.flags.DEFINE_boolean("multi_label_flag",True,"use multi label or single label.") #set this false. becase we are using it is a sequence of token here.
tf.app.flags.DEFINE_float("l2_lambda", 0.0001, "l2 regularization")
tf.app.flags.DEFINE_string("predict_target_file","checkpoint_transformer/zhihu_result_transformer.csv","target file path for final prediction")
tf.app.flags.DEFINE_string("predict_source_file",'test-zhihu-forpredict-title-desc-v6.txt',"target file path for final prediction") #test-zhihu-forpredict-v4only-title.txt
tf.app.flags.DEFINE_integer("decoder_sent_length",25,"length of decoder inputs")
tf.app.flags.DEFINE_integer("d_model",512,"hidden size")
tf.app.flags.DEFINE_integer("d_k",64,"hidden size")
tf.app.flags.DEFINE_integer("d_v",64,"hidden size")
tf.app.flags.DEFINE_integer("h",8,"hidden size")
tf.app.flags.DEFINE_integer("num_layer",1,"hidden size") #6
#1.load data(X:list of lint,y:int). 2.create session. 3.feed data. 4.training (5.validation) ,(6.prediction)
# 1.load data with vocabulary of words and labels
_GO="_GO"
_END="_END"
_PAD="_PAD"
def main(_):
# 1.load data with vocabulary of words and labels
vocabulary_word2index, vocabulary_index2word = create_voabulary(word2vec_model_path=FLAGS.word2vec_model_path,name_scope="transformer") # simple='simple'
vocab_size = len(vocabulary_word2index)
print("transformer.vocab_size:", vocab_size)
vocabulary_word2index_label, vocabulary_index2word_label = create_voabulary_label(name_scope="transformer",use_seq2seq=True)
questionid_question_lists=load_final_test_data(FLAGS.predict_source_file)
test= load_data_predict(vocabulary_word2index,vocabulary_word2index_label,questionid_question_lists)
testX=[]
question_id_list=[]
for tuple in test:
question_id,question_string_list=tuple
question_id_list.append(question_id)
testX.append(question_string_list)
# 2.Data preprocessing: Sequence padding
print("start padding....")
testX2 = pad_sequences(testX, maxlen=FLAGS.sequence_length, value=0.) # padding to max length
print("end padding...")
# 3.create session.
config=tf.ConfigProto()
config.gpu_options.allow_growth=True
with tf.Session(config=config) as sess:
# 4.Instantiate Model
model=Transformer(FLAGS.num_classes, FLAGS.learning_rate, FLAGS.batch_size, FLAGS.decay_steps, FLAGS.decay_rate, FLAGS.sequence_length,
vocab_size, FLAGS.embed_size,FLAGS.d_model,FLAGS.d_k,FLAGS.d_v,FLAGS.h,FLAGS.num_layer,FLAGS.is_training,decoder_sent_length=FLAGS.decoder_sent_length,l2_lambda=FLAGS.l2_lambda)
saver=tf.train.Saver()
if os.path.exists(FLAGS.ckpt_dir+"checkpoint"):
print("Restoring Variables from Checkpoint")
saver.restore(sess,tf.train.latest_checkpoint(FLAGS.ckpt_dir))
else:
print("Can't find the checkpoint.going to stop")
return
# 5.feed data, to get logits
number_of_training_data=len(testX2);print("number_of_training_data:",number_of_training_data)
index=0
predict_target_file_f = codecs.open(FLAGS.predict_target_file, 'a', 'utf8')
#decoder_input=np.reshape(np.array([vocabulary_word2index_label[_GO]]+[vocabulary_word2index_label[_PAD]]*(FLAGS.decoder_sent_length-1)),[-1,FLAGS.decoder_sent_length])
decoder_input=np.full((FLAGS.batch_size,FLAGS.decoder_sent_length),vocabulary_word2index_label[_PAD])
decoder_input[:,0:1]=vocabulary_word2index_label[_GO] #set all values in first column to _GO
for start, end in zip(range(0, number_of_training_data, FLAGS.batch_size),range(FLAGS.batch_size, number_of_training_data+1, FLAGS.batch_size)):
predictions,logits=sess.run([model.predictions,model.logits],
feed_dict={model.input_x:testX2[start:end],
model.decoder_input:decoder_input,
model.dropout_keep_prob:1
})
####################################################################################
#for j in range(FLAGS.decoder_sent_length):
# predict = sess.run(model.predictions, #model.loss_val,--->loss, model.train_op
# feed_dict={model.input_x:testX2[start:end],
# model.decoder_input:decoder_input,
# #model.input_y_label: input_y_label,
# model.dropout_keep_prob: 1.0,
# })
# decoder_input[:,j] = predict[:,j]
####################################################################################
print("===============>",start,"predict:",predict)
# 6. get lable using logtis
for _,logit in enumerate(logits):
predicted_labels=get_label_using_logits(logit,predictions,vocabulary_index2word_label,vocabulary_word2index_label)
print(index," ;predicted_labels:",predicted_labels)
# 7. write question id and labels to file system.
write_question_id_with_labels(question_id_list[index],predicted_labels,predict_target_file_f)
index=index+1
predict_target_file_f.close()
def get_label_using_logits(logits, predictions,vocabulary_index2word_label,vocabulary_word2index_label, top_number=5):
print("logits:",logits.shape) #(6, 2002)
result_list=[]
for i,row in enumerate(logits):
#print("i,",i,"row:",row)
if i!=len(logits)-1: #not include result from last column, which usually it should be <END> TOKEN.
label=process_each_row_get_lable(row,vocabulary_index2word_label,vocabulary_word2index_label,result_list)
result_list.append(label)
return result_list
def process_each_row_get_lable(row,vocabulary_index2word_label,vocabulary_word2index_label,result_list):
"""
:param row: it is a list.length is number of labels. e.g. 2002
:param vocabulary_index2word_label
:param result_list
:return: a lable
"""
label_list=list(np.argsort(row))
label_list.reverse()
#print("label_list:",label_list) # a list,length is number of labels.
for i,index in enumerate(label_list): # if index is not exists, and not _PAD,_END, then it is the label we want.
#print(i,"index:",index)
flag1=vocabulary_index2word_label[index] not in result_list
flag2=index!=vocabulary_word2index_label[_PAD]
flag3=index!=vocabulary_word2index_label[_END]
if flag1 and flag2 and flag3:
#print("going to return ")
return vocabulary_index2word_label[index]
# write question id and labels to file system.
def write_question_id_with_labels(question_id,labels_list,f):
labels_string=",".join(labels_list)
f.write(question_id+","+labels_string+"\n")
if __name__ == "__main__":
tf.app.run()