# coding=utf-8
from keras import Input, Model
from keras.layers import Embedding, Conv1D, Dropout, MaxPooling1D, Flatten, merge, Dense
import globalvar as gl
from preprocess.data_preprocessor import preprocess, generateWord2VectorMatrix, loadEmbeddingsIndex
#全局变量
gl.set_train_rela_files("train_case_rela.txt")
gl.set_train_ques_file("train_case_ques.txt")
gl.set_train_label_file("train_case_label.txt")
gl.set_test_rela_files("test_case_rela.txt")
gl.set_test_ques_file("test_case_ques.txt")
gl.set_test_label_file("test_case_label.txt")
gl.set_preprocessWordVector_files("TencentPreTrain.txt")
gl.set_preprocessWordVector_path("/data/zjy/")
gl.set_MAX_NB_WORDS(30)
gl.set_EMBEDDING_DIM(200)
gl.set_LSTM_DIM(150)
train_rela_files = gl.get_train_rela_files()
train_ques_file = gl.get_train_ques_file()
train_label_file = gl.get_train_label_file()
test_rela_files = gl.get_test_rela_files()
test_ques_file = gl.get_test_ques_file()
test_label_file = gl.get_test_label_file()
preprocessWordVector_files = gl.get_preprocessWordVector_files()
preprocessWordVector_path = gl.get_preprocessWordVector_path()
MAX_NB_WORDS = gl.get_MAX_NB_WORDS()
EMBEDDING_DIM = gl.get_EMBEDDING_DIM()
# coding=utf-8
import io
import numpy as np
from keras.utils import np_utils
from keras.preprocessing.sequence import pad_sequences
import globalvar as gl
from model.classification_model import test_cnn
#全局变量
from preprocess.data_preprocessor import tokenize, loadEmbeddingsIndex, generateWord2VectorMatrix
gl.set_train_rela_files("train_case_rela_fenlei_label.txt")
gl.set_train_ques_file("train_case_ques_fenlei.txt")
gl.set_test_rela_files("test_case_rela_fenlei_label.txt")
gl.set_test_ques_file("test_case_ques.txt")
gl.set_preprocessWordVector_files("TencentPreTrain.txt")
gl.set_preprocessWordVector_path("/data/zjy/")
gl.set_MAX_NB_WORDS(50)
gl.set_EMBEDDING_DIM(200)
gl.set_LSTM_DIM(150)
train_rela_files = gl.get_train_rela_files()
train_ques_file = gl.get_train_ques_file()
test_rela_files = gl.get_test_rela_files()
test_ques_file = gl.get_test_ques_file()