def _create_params(self):
### embed
self.dict_data_embed_op = {}
list_names = self.item_slot_names + self.user_slot_names + ['pos']
for name in list_names:
vob_size = self.npz_config['embedding_size'][name] + 1
self.dict_data_embed_op[name] = default_embedding(
[vob_size, self.embed_size], 'embed_' + name)
self.user_feature_fc_op = default_fc(self.hidden_size,
act='relu',
name='user_feature_fc')
self.item_fc_op = default_fc(self.hidden_size,
act='relu',
name='item_fc')
self.item_gru_fc_op = default_fc(self.hidden_size * 3,
act='relu',
name='item_gru_fc')
self.item_gru_forward_op = default_drnn(self.hidden_size,
name='item_gru_forward')
self.item_gru_backward_op = default_drnn(self.hidden_size,
name='item_gru_backward',
is_reverse=True)
self.out_click_fc1_op = default_fc(self.hidden_size,
act='relu',
name='out_click_fc1')
self.out_click_fc2_op = default_fc(2,
act='softmax',
name='out_click_fc2')
def __init__(self, nf, name=''):
super(MultiHeadAttention, self).__init__()
self._nf = nf
self._safe_eps = 1e-5
self.Q_fc_op = default_fc(nf,
num_flatten_dims=2,
act='relu',
name='%s_Q_fc' % name)
self.K_fc_op = default_fc(nf,
num_flatten_dims=2,
act='relu',
name='%s_K_fc' % name)
self.V_fc_op = default_fc(nf,
num_flatten_dims=2,
act='relu',
name='%s_V_fc' % name)
def _create_params(self):
### embed
self.dict_data_embed_op = {}
list_names = self.item_slot_names + self.user_slot_names + ['pos']
for name in list_names:
vob_size = self.npz_config['embedding_size'][name] + 1
self.dict_data_embed_op[name] = default_embedding(
[vob_size, self.embed_size], 'embed_' + name)
self.user_feature_fc_op = default_fc(self.hidden_size,
act='relu',
name='user_feature_fc')
self.item_fc_op = default_fc(self.hidden_size,
act='relu',
name='item_fc')
self.atten_item_fc_op = default_fc(self.hidden_size,
act='relu',
name='atten_item_fc')
self.candidate_encode_fc_op = default_fc(self.hidden_size,
act='relu',
name='candidate_encode_fc')
self.item_gru_fc_op = default_fc(self.hidden_size * 3,
act='relu',
name='item_gru_fc')
self.item_gru_op = default_drnn(self.hidden_size, name='item_gru')
self.hidden_fc_op = default_fc(self.hidden_size, name='hidden_fc')
if self._attention_type == 'concat_fc':
self.atten_fc_op = default_fc(1, act=None, name='atten_fc')
def _create_params(self):
"""create all params here"""
### embed
self.dict_data_embed_op = {}
list_names = self.item_slot_names + self.user_slot_names + ['pos']
for name in list_names:
vob_size = self.npz_config['embedding_size'][name] + 1
self.dict_data_embed_op[name] = default_embedding(
[vob_size, self.embed_size], 'embed_' + name)
### embed fc
self.user_feature_fc_op = default_fc(self.hidden_size,
act='relu',
name='user_feature_fc')
self.item_fc_op = default_fc(self.hidden_size,
act='relu',
name='item_fc')
self.input_embed_fc_op = default_fc(self.hidden_size,
act='relu',
name='input_embed_fc')
### blocks
self.atten_ops = []
self.atten_norm_ops = []
self.ffn_fc1_ops = []
self.ffn_fc2_ops = []
self.ffn_norm_ops = []
for block_id in range(self._num_blocks):
# attention
self.atten_ops.append(
MultiHeadAttention(self.hidden_size,
name="blk%d_atten" % block_id))
self.atten_norm_ops.append(
default_batch_norm(name="blk%d_norm" % block_id))
# feed forward
self.ffn_fc1_ops.append(
default_fc(self.hidden_size * 2,
act='relu',
name='blk%d_ffn_fc1' % block_id))
self.ffn_fc2_ops.append(
default_fc(self.hidden_size,
act='relu',
name='blk%d_ffn_fc2' % block_id))
self.ffn_norm_ops.append(
default_batch_norm(name='blk%d_ffn_norm' % block_id))
### output
self.output_fc1_op = default_fc(self.hidden_size,
act='relu',
name='output_fc1')
self.output_fc2_op = default_fc(2, act='softmax', name='output_fc2')