def __init__(self, config, vocab):
self._config = config
self._vocab = vocab
self._name = self._config.model.get('errche_decoder_name')
# vocabulary size of the target sequence
self._targ_voc = self._vocab.errche_vocab_size_targ
# index of the END token in the vocabulary
self._id_end = self._config.dataset.get('id_end')
# embeding dim of the target sequence
self._embeding_dim_targ = self._config.model.get('errche_embeding_dims_target')
# dim of encoder
self._rnn_encoder_dim = self._config.model.get('errche_rnn_encoder_dim')
# dim of decoder
self._rnn_decoder_dim = self._config.model.get('errche_rnn_decoder_dim')
# dim of attention
self._att_dim = self._config.model.get('att_dim')
assert self._rnn_encoder_dim * 2 == self._rnn_decoder_dim, \
"Encoder BiRnn out dim is the double encoder dim and it must be equal with decoder dim"
self._tiles = 1 if self._config.model.decoding == 'greedy' else self._config.model.beam_size
self._embedding_table_traget = tf.get_variable(
"targ_vocab_embeding", dtype=tf.float32, shape=[self._targ_voc, self._embeding_dim_targ],
initializer=embedding_initializer())
self._start_token = tf.squeeze(
input=self._embedding_table_traget[0, :],
name='targ_start_flage')
def __init__(self, config, vocab, trainable):
self._config = config
self._vocab = vocab
self._name = self._config.model.get('errche_encoder_name')
# dim of the encoder, due to the birnn, the output of the encoder is 2*encoder_dim
self._rnn_encoder_dim = self._config.model.get(
'errche_rnn_encoder_dim')
self._embeding_dim_source = self._config.model.get(
'errche_embeding_dims_source')
# vocabulary size of the source sequecn
self._source_voc = self._vocab.errche_vocab_size_source
self.is_training = trainable
self._embedding_table_source = tf.get_variable(
"source_vocab_embeding",
dtype=tf.float32,
shape=[self._source_voc, self._embeding_dim_source],
initializer=embedding_initializer())
def __init__(self, config, vocab):
self._config = config
self._vocab = vocab
self._name = self._config.model.get('decoder_name')
self._vocabsize = self._vocab.vocab_size
self._id_end = self._config.dataset.get('id_end')
self._embeding_dim = self._config.model.get('embeding_dims')
self._encoder_dim = self._config.model.get('rnn_encoder_dim')
self._decoder_dim = self._config.model.get('rnn_decoder_dim')
self._att_dim = self._config.model.get('att_dim')
assert self._encoder_dim * 2 == self._decoder_dim, \
"Encoder bilstm out dim is the double encoder dim and it must be equal with decoder dim"
self._tiles = 1 if self._config.model.decoding == 'greedy' else self._config.model.beam_size
self._vocab_embeding = tf.get_variable(
"vocab_embeding",
dtype=tf.float32,
shape=[self._vocabsize, self._embeding_dim],
initializer=embedding_initializer())
self._start_token = tf.squeeze(input=self._vocab_embeding[0, :],
name='start_flage')