def __init__(self, embs, encoder=None, pooling=None, combiner='sum', **kwargs): super(Encoders, self).__init__(**kwargs) self.embs = embs self.encoder = util.get_encoder(encoder) pooling = pooling or 'att' self.pooling = mt.layers.Pooling(pooling) self.output_dim = embs[0].output_dim if combiner != 'concat' else embs[0].output_dim * (len(embs)) self.combiner = combiner assert self.combiner == 'sum'
def __init__(self, encoder, seqs_encoder=None, pooling=None, use_seq_position=False, **kwargs):
super(SeqsEncoder, self).__init__(**kwargs)
self.encoder = keras.layers.TimeDistributed(encoder, name=encoder.name)
self.seqs_encoder = util.get_encoder(seqs_encoder)
pooling = pooling or 'din'
self.pooling_name = pooling
self.pooling = util.get_att_pooling(pooling)
# position emb seems not help
self.use_seq_position = use_seq_position or FLAGS.use_seq_position
if self.use_seq_position:
self.position_emb = keras.layers.Embedding(500, FLAGS.emb_size)
def __init__(self, emb=None, encoder=None, pooling=None, **kwargs):
super(Encoder, self).__init__(**kwargs)
self.emb = emb
pooling = pooling or 'att'
self.pooling = mt.layers.Pooling(pooling)
self.output_dim = None
if emb is not None:
self.output_dim = emb.output_dim
self.encoder = util.get_encoder(encoder) if isinstance(encoder, str) or encoder is None else encoder
if not self.output_dim:
self.output_dim = self.encoder.output_dim
def __init__(self):
super(Model, self).__init__()
self.mode = 'train'
self.input_ = {}
def _emb(vocab_name, emb_name=None):
return util.create_emb(vocab_name, emb_name)
self.uemb = _emb('uid')
self.demb = _emb('did')
self.cat_emb = _emb('cat')
self.scat_emb = _emb('sub_cat')
self.entity_emb = _emb('entity')
self.entity_type_emb = _emb('entity_type')
self.word_emb = _emb('word')
self.hour_emb = Embedding(24, FLAGS.emb_size, name='hour_emb')
self.weekday_emb = Embedding(7, FLAGS.emb_size, name='weekday_emb')
self.fresh_hour_emb = Embedding(300,
FLAGS.emb_size,
name='fresh_hour_emb') # 7 * 24
self.fresh_day_emb = Embedding(50,
FLAGS.emb_size,
name='fresh_day_emb')
self.position_emb = Embedding(300, FLAGS.emb_size, name='position_emb')
self.title_lookup = melt.layers.LookupArray(FLAGS.title_lookup)
self.doc_lookup = melt.layers.LookupArray(FLAGS.doc_lookup)
self.title_encoder = TitleEncoder(self.word_emb)
self.titles_encoder = TitlesEncoder(self.title_encoder)
self.sum_pooling = melt.layers.SumPooling()
self.mean_pooling = melt.layers.MeanPooling()
self.pooling = melt.layers.Pooling(FLAGS.pooling)
self.feat_pooling = melt.layers.Pooling(FLAGS.feat_pooling)
self.his_simple_pooling = melt.layers.Pooling(FLAGS.his_simple_pooling)
self.dense = Dense(
1) if not FLAGS.use_multi_dropout else melt.layers.MultiDorpout(
1, drop_rate=0.3)
self.batch_norm = BatchNormalization()
self.dropout = keras.layers.Dropout(FLAGS.dropout)
# --arch-mlp-bot="13-512-256-64-16" --arch-mlp-top="512-256-1"
activation = FLAGS.activation
mlp_dims = [FLAGS.emb_size *
2, FLAGS.emb_size] if not FLAGS.big_mlp else [
FLAGS.emb_size * 4, FLAGS.emb_size * 2, FLAGS.emb_size
]
self.dense_mlp = melt.layers.MLP(mlp_dims,
activation=activation,
drop_rate=FLAGS.mlp_dropout,
name='dense_mlp')
mlp_dims = [512, 256, 64] if not FLAGS.big_mlp else [1024, 512, 256]
self.mlp = melt.layers.MLP(mlp_dims,
activation=activation,
drop_rate=FLAGS.mlp_dropout,
name='mlp')
self.his_encoder = util.get_encoder(FLAGS.his_encoder)
self.his_dense = keras.layers.Dense(FLAGS.hidden_size)
self.his_pooling = util.get_att_pooling(FLAGS.his_pooling)
self.his_pooling2 = util.get_att_pooling(FLAGS.his_pooling2)
self.cur_dense = keras.layers.Dense(FLAGS.hidden_size)
if FLAGS.his_strategy.startswith('bst'):
self.transformer = melt.layers.transformer.Encoder(
num_layers=1,
d_model=FLAGS.hidden_size,
num_heads=FLAGS.num_heads,
dff=FLAGS.hidden_size,
maximum_position_encoding=None,
activation=FLAGS.transformer_activation,
rate=FLAGS.transformer_dropout)
self.fusion = melt.layers.SemanticFusion(drop_rate=0.1)
if FLAGS.feat_pooling == 'cin':
from deepctr.layers.interaction import CIN
self.cin = CIN((
128,
128,
), 'relu', True, 0, 1024)
self.feat_pooling = self.cin
if FLAGS.aux_loss_rate or FLAGS.lm_target:
vsize = gezi.get('vocab_sizes')['vid'][0]
# hidden_size = FLAGS.hidden_size if FLAGS.his_encoder in ['lstm', 'gru'] else int(FLAGS.hidden_size / 2)
hidden_size = int(FLAGS.hidden_size / 2)
self.sampled_weight = self.add_weight(
name='sampled_weight',
shape=(vsize, hidden_size),
#initializer = keras.initializers.RandomUniform(minval=-10, maxval=10, seed=None),
dtype=tf.float32,
trainable=True)
self.sampled_bias = self.add_weight(
name='sampled_bias',
shape=(vsize, ),
#initializer = keras.initializers.RandomUniform(minval=-10, maxval=10, seed=None),
dtype=tf.float32,
trainable=True)
self.softmax_loss_function = melt.seq2seq.gen_sampled_softmax_loss_function(
5,
vsize,
weights=self.sampled_weight,
biases=self.sampled_bias,
log_uniform_sample=True,
is_predict=False,
sample_seed=1234)
def __init__(self, **kwargs):
super(Model, self).__init__(**kwargs)
self.mode = 'train'
self.input_ = {}
def _emb(vocab_name, emb_name=None):
return util.create_emb(vocab_name, emb_name)
self.uemb = _emb('uid')
self.demb = _emb('did')
self.cat_emb = _emb('cat')
self.scat_emb = _emb('sub_cat')
self.entity_emb = _emb('entity')
self.entity_type_emb = _emb('entity_type')
if not FLAGS.bert_dir or not FLAGS.bert_only:
self.word_emb = _emb('word')
self.hour_emb = Embedding(24, FLAGS.emb_size, name='hour_emb')
self.weekday_emb = Embedding(7, FLAGS.emb_size, name='weekday_emb')
self.fresh_hour_emb = Embedding(300,
FLAGS.emb_size,
name='fresh_hour_emb') # 7 * 24
self.fresh_day_emb = Embedding(50,
FLAGS.emb_size,
name='fresh_day_emb')
self.position_emb = Embedding(300, FLAGS.emb_size, name='position_emb')
# self.title_lookup = mt.layers.LookupArray(FLAGS.title_lookup, name='title_lookup')
self.doc_lookup = mt.layers.LookupArray(FLAGS.doc_lookup,
name='doc_lookup')
if _is_ok('enti'):
self.entities_encoder = Encoders(
[self.entity_emb, self.entity_type_emb],
None,
FLAGS.pooling,
name='entities_encoder')
self.his_entities_encoder = SeqsEncoder(
self.entities_encoder,
None,
FLAGS.seqs_pooling,
name='his_entities_encoder')
if not FLAGS.bert_dir or not FLAGS.bert_only:
if _is_ok('^cur_title&') or _is_ok('abstract') or _is_ok('body'):
if FLAGS.share_words_encoder:
words_encoder = Encoder(self.word_emb,
FLAGS.seqs_encoder,
FLAGS.pooling,
name='words_encoder')
else:
words_encoder = None
if _is_ok('^cur_title&'):
self.title_encoder = words_encoder or Encoder(
self.word_emb,
FLAGS.seqs_encoder,
FLAGS.pooling,
name='title_encoder')
self.titles_encoder = SeqsEncoder(self.title_encoder,
FLAGS.seqs_encoder,
FLAGS.seqs_pooling,
name='titles_encoder')
self.titles_encoder2 = SeqsEncoder(self.title_encoder,
FLAGS.seqs_encoder,
FLAGS.seqs_pooling2,
name='titles_encoder2')
if _is_ok('^abstract&'):
self.abstract_encoder = words_encoder or Encoder(
self.word_emb, FLAGS.seqs_encoder, name='abstract_encoder')
self.abstracts_encoder = SeqsEncoder(self.abstract_encoder,
FLAGS.seqs_encoder,
FLAGS.seqs_pooling,
name='abstracts_encoder')
if _is_ok('^body&'):
self.body_encoder = words_encoder or Encoder(
self.word_emb, None, FLAGS.pooling, name='body_encoder')
self.bodies_encoder = SeqsEncoder(self.body_encoder,
FLAGS.seqs_encoder,
FLAGS.seqs_pooling,
name='bodies_encoder')
if FLAGS.bert_dir:
# tpu 会做静态检查 bert_encoder会认为没有weight失败
# Weights for model bert_encoder have not yet been created. Weights are created when the Model is first called on inputs or `build()` is called with an `input_shape` tpu
# max_input_length = None if not gezi.get('tpu') else FLAGS.max_bert_input_length
max_input_length = None
bert_encoder = mt.models.Bert(
FLAGS.bert_dir,
FLAGS.emb_size,
max_input_length=max_input_length,
return_sequences=FLAGS.bert_pooling_seqs,
name='bert_encoder')
self.bert_title_encoder = bert_encoder
self.bert_abstract_encoder = bert_encoder
self.bert_body_encoder = bert_encoder
if FLAGS.bert_pooling_seqs:
if FLAGS.share_words_encoder:
bert_words_encoder = Encoder(None,
bert_encoder,
FLAGS.pooling,
name='words_encoder')
else:
bert_words_encoder = None
if _is_ok('bert_title'):
self.bert_title_encoder = bert_words_encoder or Encoder(
None,
bert_encoder,
FLAGS.pooling,
name='bert_title_encoder')
if _is_ok('bert_abstract'):
self.bert_abstract_encoder = bert_words_encoder or Encoder(
None,
bert_encoder,
FLAGS.pooling,
name='bert_abstract_encoder')
if _is_ok('bert_body'):
self.bert_body_encoder = bert_words_encoder or Encoder(
None,
bert_encoder,
FLAGS.pooling,
name='bert_body_encoder')
if _is_ok('bert_title'):
self.bert_titles_encoder = SeqsEncoder(
self.bert_title_encoder,
FLAGS.seqs_encoder,
FLAGS.seqs_pooling,
name='bert_titles_encoder')
if _is_ok('bert_abstract'):
self.bert_abstracts_encoder = SeqsEncoder(
self.bert_abstract_encoder,
FLAGS.seqs_encoder,
FLAGS.seqs_pooling,
name='bert_abstracts_encoder')
if _is_ok('bert_body'):
self.bert_bodies_encoder = SeqsEncoder(
self.bert_body_encoder,
FLAGS.seqs_encoder,
FLAGS.seqs_pooling,
name='bert_bodies_encoder')
self.sum_pooling = mt.layers.SumPooling()
self.mean_pooling = mt.layers.MeanPooling()
self.pooling = mt.layers.Pooling(FLAGS.pooling)
self.feat_pooling = mt.layers.Pooling(FLAGS.feat_pooling,
name='feat_pooling')
self.his_simple_pooling = mt.layers.Pooling(FLAGS.his_simple_pooling)
# self.his_entity_pooling = mt.layers.Pooling('att', name='his_entity_pooling')
self.his_entity_pooling = util.get_att_pooling(
'din', name='his_entity_pooling')
self.his_cat_pooling = mt.layers.Pooling('att', name='his_cat_pooling')
self.his_scat_din_pooling = util.get_att_pooling(
'din', name='his_scat_din_pooling')
self.dense = Dense(
1) if not FLAGS.use_multi_dropout else mt.layers.MultiDropout(
1, drop_rate=0.3)
self.batch_norm = BatchNormalization()
self.dropout = keras.layers.Dropout(FLAGS.dropout)
# --arch-mlp-bot="13-512-256-64-16" --arch-mlp-top="512-256-1"
activation = FLAGS.activation
mlp_dims = [FLAGS.emb_size *
2, FLAGS.emb_size] if not FLAGS.big_mlp else [
FLAGS.emb_size * 4, FLAGS.emb_size * 2, FLAGS.emb_size
]
self.dense_mlp = mt.layers.MLP(mlp_dims,
activation=activation,
drop_rate=FLAGS.mlp_dropout,
name='dense_mlp')
mlp_dims = [512, 256, 64] if not FLAGS.big_mlp else [1024, 512, 256]
self.mlp = mt.layers.MLP(mlp_dims,
activation=activation,
drop_rate=FLAGS.mlp_dropout,
batch_norm=FLAGS.mlp_bn,
name='mlp')
self.his_encoder = util.get_encoder(FLAGS.his_encoder)
self.his_dense = keras.layers.Dense(FLAGS.hidden_size)
self.his_pooling = util.get_att_pooling(FLAGS.his_pooling)
self.his_pooling2 = util.get_att_pooling(FLAGS.his_pooling2)
self.cur_dense = keras.layers.Dense(FLAGS.hidden_size)
if FLAGS.his_strategy.startswith('bst'):
self.transformer = mt.layers.transformer.Encoder(
num_layers=1,
d_model=FLAGS.hidden_size,
num_heads=FLAGS.num_heads,
dff=FLAGS.hidden_size,
maximum_position_encoding=None,
activation=FLAGS.transformer_activation,
rate=FLAGS.transformer_dropout)
self.fusion = mt.layers.SemanticFusion(drop_rate=0.1)
if FLAGS.feat_pooling == 'cin':
from deepctr.layers.interaction import CIN
self.cin = CIN((
128,
128,
), 'relu', True, 0, 1024)
self.feat_pooling = self.cin
if FLAGS.aux_loss_rate or FLAGS.lm_target:
vsize = gezi.get('vocab_sizes')['vid'][0]
# hidden_size = FLAGS.hidden_size if FLAGS.his_encoder in ['lstm', 'gru'] else int(FLAGS.hidden_size / 2)
hidden_size = int(FLAGS.hidden_size / 2)
self.sampled_weight = self.add_weight(
name='sampled_weight',
shape=(vsize, hidden_size),
# initializer = keras.initializers.RandomUniform(minval=-10, maxval=10, seed=None),
dtype=tf.float32,
trainable=True)
self.sampled_bias = self.add_weight(
name='sampled_bias',
shape=(vsize, ),
# initializer = keras.initializers.RandomUniform(minval=-10, maxval=10, seed=None),
dtype=tf.float32,
trainable=True)
self.softmax_loss_function = mt.seq2seq.gen_sampled_softmax_loss_function(
5,
vsize,
weights=self.sampled_weight,
biases=self.sampled_bias,
log_uniform_sample=True,
is_predict=False,
sample_seed=1234)