def __init__(self):
self.model = RNNLM_Model.LM_Model()
self.trainData = Corpus.MonoCorpus(Config.trgVocabF, Config.trainTrgF)
self.valData = Corpus.MonoCorpus(Config.trgVocabF, Config.valTrgF)
self.networkBucket = {}
self.exampleNetwork = self.getNetwork(Config.BucketGap)
if os.path.isfile(Config.initModelF):
self.model.loadModel(Config.initModelF)
def __init__(self):
self.model = RnnlmModel.LM_Model()
self.trainData = Corpus.MonoCorpus(Config.trgVocabF, Config.trainTrgF)
self.valData = Corpus.MonoCorpus(Config.trgVocabF, Config.valTrgF)
self.networkBucket = {}
self.inputTrg = tf.placeholder(
tf.int32, shape=[Config.MaxLength, Config.BatchSize], name='input')
self.maskTrg = tf.placeholder(
tf.float32,
shape=[Config.MaxLength, Config.BatchSize],
name='inputMask')
self.optimizer = tf.train.AdamOptimizer()
self.createBucketNetworks(Config.MaxLength)
