Jirayut558

def test_model():
    # define model

    train, infenc, infdec = ende_embedding_model(n_in, n_out, 256)
    # load weights
    infenc.load_weights("model/model_enc.h5")
    infdec.load_weights("model/model_dec.h5")

    # start prediction
    while True:

Jirayut558

def train():
    data = "corpus.th.csv"
    dataX = []
    dataY = []
    X1 = []
    X2 = []
    Y = []
    vecsize = encoded_length

    dataX,dataY = load_csv_data(data)

Jirayut558

def embedding_model():
    # define word embedding
    vocab_list = [(k, wv_model.wv[k]) for k, v in wv_model.wv.vocab.items()]
    embeddings_matrix = np.zeros((len(wv_model.wv.vocab.items()) + 1, wv_model.vector_size))
    for i in range(len(vocab_list)):
        word = vocab_list[i][0]
        embeddings_matrix[i + 1] = vocab_list[i][1]

    embedding_layer = Embedding(input_dim=len(embeddings_matrix),
                                output_dim=EMBEDDING_DIM,

Jirayut558

def word_index(listword):
    dataset = []
    for sentence in listword:
        tmp = []
        for w in sentence:
            tmp.append(word2idx(w))
        dataset.append(tmp)
    return np.array(dataset)
    
def word2idx(word):

Jirayut558

X1 = word_index(X1)
X2 = word_index(X2)
Y = word_index(Y)
Y = to_categorical(Y, num_classes=max_word+1)

Jirayut558

def padding_sequence(listsentence,maxseq):
    dataset = []
    for s in listsentence:
        n = maxseq - len(s)
        if n>0:
            dataset.append(s+(["<EOS>"]*n))
        elif n<0:
            dataset.append(s[0:maxseq])
        else:
            dataset.append(s)

Jirayut558

def preparingword(listword):
    word =[]
    for w in listword:
        word.append(wordcut(w))
    return word
def wordcut(sentence):
    return deepcut.tokenize(sentence)

Jirayut558

def load_data(datafile):
    dataX = []
    dataY = []
    data = open(datafile, "r").read().lower()
    for i in data.split("\n\n"):
        a = i.split("\n")
        question = a[0]
        answer = a[1]
        dataX.append(question)
        dataY.append(answer)

Jirayut558

def load_data(datafile):
    dataX = []
    dataY = []
    data = open(datafile, "r").read().lower()
    for i in data.split("\n\n"):
        a = i.split("\n")
        question = a[0]
        answer = a[1]
        dataX.append(question)
        dataY.append(answer)

Jirayut558

# -*- coding: utf-8 -*-
import logging
import os.path
import sys
import multiprocessing
from gensim.corpora import WikiCorpus
from gensim.models import Word2Vec
from gensim.models.word2vec import LineSentence
 
if __name__ == '__main__':