Created by: peterzsj6

class BaseModel(object): def init(self, hidden_size, src_vocab_size, tar_vocab_size, batch_size, num_layers=1, init_scale=0.1, dropout=None, beam_start_token=1, beam_end_token=2, beam_max_step_num=100):

    self.hidden_size = hidden_size
    self.src_vocab_size = VOCAB_SIZE
    self.tar_vocab_size = VOCAB_SIZE
    self.batch_size = batch_size
    self.num_layers = num_layers
    self.init_scale = init_scale
    self.dropout = dropout
    self.beam_start_token = beam_start_token
    self.beam_end_token = beam_end_token
    self.beam_max_step_num = beam_max_step_num
    self.src_embeder = lambda x: fluid.embedding(
        input=x,
        size=[self.src_vocab_size, self.hidden_size],
        dtype='float32',
        is_sparse=False,
        param_attr=fluid.ParamAttr(
            name='source_embedding',
            initializer=uniform_initializer(init_scale)))

    self.tar_embeder = lambda x: fluid.embedding(
        input=x,
        size=[self.tar_vocab_size, self.hidden_size],
        dtype='float32',
        is_sparse=False,
        param_attr=fluid.ParamAttr(
            name='target_embedding',
            initializer=uniform_initializer(init_scale)))

def _build_data(self):
    self.src = fluid.data(name="src", shape=[None, None], dtype='int64')
    self.src_sequence_length = fluid.data(
        name="src_sequence_length", shape=[None], dtype='int32')

    self.tar = fluid.data(name="tar", shape=[None, None], dtype='int64')
    self.tar_sequence_length = fluid.data(
        name="tar_sequence_length", shape=[None], dtype='int32')
    

     self.label = fluid.data(
        name="label", shape=[None, None, 1], dtype='int64')