def __init__(self):
self.model = NMT_Model.NMT_Model()
self.srcVocab = Corpus.Vocabulary()
self.trgVocab = Corpus.Vocabulary()
self.srcVocab.loadDict(Config.srcVocabF)
self.trgVocab.loadDict(Config.trgVocabF)
self.trainData = Corpus.BiCorpus(self.srcVocab, self.trgVocab,
Config.trainSrcF, Config.trainTrgF)
self.valData = Corpus.BiCorpus(self.srcVocab, self.trgVocab,
Config.valSrcF, Config.valTrgF)
self.buckets = self.trainData.getBuckets()
self.networkBucket = {}
self.bestValCE = 999999
self.bestBleu = 0
self.badValCount = 0
self.maxBadVal = 5
self.learningRate = Config.LearningRate
self.inputSrc = tf.placeholder(
tf.int32,
shape=[Config.MaxLength, Config.BatchSize],
name='srcInput')
self.maskSrc = tf.placeholder(
tf.float32,
shape=[Config.MaxLength, Config.BatchSize],
name='srcMask')
self.inputTrg = tf.placeholder(
tf.int32,
shape=[Config.MaxLength, Config.BatchSize],
name='trgInput')
self.maskTrg = tf.placeholder(
tf.float32,
shape=[Config.MaxLength, Config.BatchSize],
name='trgMask')
self.optimizer = tf.train.AdamOptimizer()
self.createBucketNetworks()
def __init__(self):
self.model = NMT_Model.NMT_Model()
self.srcVocab = Corpus.Vocabulary()
self.trgVocab = Corpus.Vocabulary()
self.srcVocab.loadDict(Config.srcVocabF)
self.trgVocab.loadDict(Config.trgVocabF)
self.trainData = Corpus.BiCorpus(self.srcVocab, self.trgVocab,
Config.trainSrcF, Config.trainTrgF)
self.valData = Corpus.BiCorpus(self.srcVocab, self.trgVocab,
Config.valSrcF, Config.valTrgF)
self.valBleuData = Corpus.ValCorpus(self.srcVocab, self.trgVocab,
Config.valFile, Config.refCount)
self.decoder = NMT_Decoder.NMT_Decoder(self.model, self.srcVocab,
self.trgVocab)
self.networkBucket = {}
self.exampleNetwork = self.getNetwork(1, 1)
self.bestValCE = 999999
self.bestBleu = 0
self.badValCount = 0
self.maxBadVal = 5
self.learningRate = Config.LearningRate
if os.path.isfile(Config.initModelF):
self.model.loadModel(Config.initModelF)