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a1_dual_bilstm_cnn_predict_ensemble.py
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# -*- coding: utf-8 -*-
import sys
reload(sys)
sys.setdefaultencoding('utf-8') #gb2312
#training the model.
#process--->1.load data(X:list of lint,y:int). 2.create session. 3.feed data. 4.training (5.validation) ,(6.prediction)
import tensorflow as tf
import numpy as np
from a1_dual_bilstm_cnn_model import DualBilstmCnnModel
from data_util_test import load_vocabulary,load_test_data,get_label_by_logits,write_predict_result
import os
#import word2vec
#configuration
FLAGS=tf.app.flags.FLAGS
#tf.app.flags.DEFINE_string("tokenize_style",'char',"tokenize sentence in char,word,or pinyin.default is char") #to tackle miss typed words
#tf.app.flags.DEFINE_string("ckpt_dir","dual_bilstm_char_checkpoint/","checkpoint location for the model")
#tf.app.flags.DEFINE_string("model","dual_bilstm","which model to use:dual_bilstm_cnn,dual_bilstm,dual_cnn.default is:dual_bilstm_cnn")
#tf.app.flags.DEFINE_string("name_scope","cnn","name scope value.")
tf.app.flags.DEFINE_integer("embed_size",64,"embedding size") #128
tf.app.flags.DEFINE_integer("hidden_size",128,"embedding size") #128
tf.app.flags.DEFINE_integer("num_filters", 10, "number of filters") #32
tf.app.flags.DEFINE_integer("sentence_len",21,"max sentence length. length should be divide by 3, which is used by k max pooling.") #40
tf.app.flags.DEFINE_string("similiarity_strategy",'additive',"similiarity strategy: additive or multiply. default is additive") #to tackle miss typed words
tf.app.flags.DEFINE_string("max_pooling_style",'chunk_max_pooling',"max_pooling_style:max_pooling,k_max_pooling,chunk_max_pooling. default: chunk_max_pooling") #extract top k feature instead of max feature(max pooling)
tf.app.flags.DEFINE_integer("top_k", 3, "value of top k")
tf.app.flags.DEFINE_string("traning_data_path","./data/atec_nlp_sim_train.csv","path of traning data.")
tf.app.flags.DEFINE_integer("vocab_size",60000,"maximum vocab size.") #80000
tf.app.flags.DEFINE_float("learning_rate",0.0001,"learning rate")
tf.app.flags.DEFINE_integer("batch_size", 4, "Batch size for training/evaluating.")
tf.app.flags.DEFINE_integer("decay_steps", 1000, "how many steps before decay learning rate.")
tf.app.flags.DEFINE_float("decay_rate", 1.0, "Rate of decay for learning rate.")
tf.app.flags.DEFINE_boolean("is_training",False,"is traning.true:tranining,false:testing/inference")
tf.app.flags.DEFINE_integer("num_epochs",10,"number of epochs to run.")
tf.app.flags.DEFINE_integer("validate_every", 1, "Validate every validate_every epochs.")
tf.app.flags.DEFINE_boolean("use_pretrained_embedding",False,"whether to use embedding or not.")
tf.app.flags.DEFINE_string("word2vec_model_path","word2vec.bin","word2vec's vocabulary and vectors")
tf.app.flags.DEFINE_float("dropout_keep_prob", 1.0, "dropout keep probability")
filter_sizes=[2,3,4]
#def main(_):
def predict_bilstm(inpath,tokenize_style,ckpt_dir,model_name,name_scope,graph):
logits_result=None
with graph:
print("1.load vocabulary...")
vocabulary_word2index, vocabulary_index2label= load_vocabulary(FLAGS.traning_data_path,FLAGS.vocab_size,
name_scope=name_scope,tokenize_style=tokenize_style)
vocab_size = len(vocabulary_word2index);print(model_name+".vocab_size:",vocab_size);num_classes=len(vocabulary_index2label);print("num_classes:",num_classes)
print("2.load data....")
lineno_list, X1, X2,BLUESCORE=load_test_data(inpath, vocabulary_word2index, FLAGS.sentence_len, tokenize_style=tokenize_style)
length_data_mining_features = len(BLUESCORE[0])
print("length_data_mining_features:",length_data_mining_features)
print("3.construct model...")
#2.create session.
config=tf.ConfigProto()
config.gpu_options.allow_growth=True
with tf.Session(config=config) as sess:
#Instantiate Model
model=DualBilstmCnnModel(filter_sizes,FLAGS.num_filters,num_classes, FLAGS.learning_rate, FLAGS.batch_size, FLAGS.decay_steps,
FLAGS.decay_rate,FLAGS.sentence_len,vocab_size,FLAGS.embed_size,FLAGS.hidden_size,FLAGS.is_training,model=model_name,
similiarity_strategy=FLAGS.similiarity_strategy,top_k=FLAGS.top_k,max_pooling_style=FLAGS.max_pooling_style,
length_data_mining_features=length_data_mining_features)
#Initialize Save
saver=tf.train.Saver()
if os.path.exists(ckpt_dir+"checkpoint"):
print(model_name+".Restoring Variables from Checkpoint.")
saver.restore(sess, tf.train.latest_checkpoint(ckpt_dir))
else:
print(model_name+".Not able to find Checkpoint. Going to stop now...")
iii=0
iii/0
#3.feed data & training
number_of_test_data=len(X1)
print(model_name+".number_of_test_data:",number_of_test_data)
batch_size=FLAGS.batch_size
iteration=0
divide_equally=(number_of_test_data%batch_size==0)
steps=0
if divide_equally:
steps=int(number_of_test_data/batch_size)
else:
steps=int(number_of_test_data/batch_size)+1
print("steps:",steps)
start=0
end=0
logits_result=np.zeros((number_of_test_data,len(vocabulary_index2label)))
for i in range(steps):
print("i:",i)
start=i*batch_size
if i!=steps or divide_equally:
end=(i+1)*batch_size
feed_dict = {model.input_x1: X1[start:end],model.input_x2: X2[start:end],model.input_bluescores:BLUESCORE[start:end],
model.dropout_keep_prob: FLAGS.dropout_keep_prob,
model.iter: iteration,model.tst: not FLAGS.is_training}
print(i*batch_size,end)
else:
end=number_of_test_data-(batch_size*int(number_of_test_data%batch_size))
feed_dict = {model.input_x1: X1[start:end],model.input_x2: X2[start:end],
model.dropout_keep_prob: FLAGS.dropout_keep_prob,
model.iter: iteration,model.tst: not FLAGS.is_training}
print("start:",i*batch_size,";end:",end)
logits_batch=sess.run(model.logits,feed_dict) #[batch_size,num_classes]
logits_result[start:end]=logits_batch
print("logits_result:",logits_result)
return logits_result,lineno_list,vocabulary_index2label
#label_list=get_label_by_logits(logits,vocabulary_index2label)
#write_predict_result(lineno_list[start:end],label_list,file_object)
if __name__ == "__main__":
#tf.app.run()
pass