def build(self, input_map):
# get the inputs
with tf.variable_scope('inputs'):
usrid, prdid, input_x, sen_len, doc_len = \
(input_map['usr'], input_map['prd'],
input_map['content'],
input_map['sen_len'], input_map['doc_len'])
usr = lookup(self.usr_emb, usrid)
prd = lookup(self.prd_emb, prdid)
input_x = lookup(self.wrd_emb, input_x)
self.usr, self.prd, self.input_x = usr, prd, input_x
feature = self.dnsc(input_x, self.max_sen_len, self.max_doc_len,
sen_len, doc_len, [usr, prd])
return feature
def build(self, input_map):
# get the inputs
with tf.variable_scope('inputs'):
prdid, input_x, sen_len, doc_len = \
(input_map['prd'], input_map['content'],
input_map['sen_len'], input_map['doc_len'])
prd = lookup(self.prd_emb, prdid, name='cur_prd_embedding')
input_x = lookup(self.wrd_emb, input_x, name='cur_wrd_embedding')
logit = self.dnsc(input_x, self.max_sen_len, self.max_doc_len, sen_len,
doc_len, [prd])
logit = tf.layers.dense(logit,
self.cls_cnt,
kernel_initializer=self.w_init,
bias_initializer=self.b_init)
return logit
def build(self, input_map, seperate_encoder=False):
# get the inputs
with tf.variable_scope('inputs'):
input_x, sen_len, doc_len, task_label = \
(input_map['content'], input_map['sen_len'],
input_map['doc_len'], input_map['task_label'])
if not seperate_encoder:
task_label = tf.zeros_like(task_label)
input_x = lookup(self.wrd_emb, input_x)
feature = self.dnsc(input_x, self.max_sen_len, self.max_doc_len,
sen_len, doc_len, task_label)
return feature
def build(self, input_map):
# get the inputs
with tf.variable_scope('inputs'):
usrid, prdid, input_x, sen_len, doc_len = \
(input_map['usr'], input_map['prd'],
input_map['content'],
input_map['sen_len'], input_map['doc_len'])
usr = lookup(self.usr_emb, usrid, name='cur_usr_embedding')
prd = lookup(self.prd_emb, prdid, name='cur_prd_embedding')
input_x = lookup(self.wrd_emb, input_x, name='cur_wrd_embedding')
logits, d_hats = [], []
for scope, identities in zip(['user_block', 'product_block'],
[[usr], [prd]]):
with tf.variable_scope(scope):
logit = self.dnsc(input_x, self.max_sen_len, self.max_doc_len,
sen_len, doc_len, identities)
logits.append(logit)
with tf.variable_scope('result'):
d_hats.append(
tf.layers.dense(
logit,
self.cls_cnt,
kernel_initializer=self.w_init,
bias_initializer=self.b_init))
with tf.variable_scope('result'):
logits = tf.concat(logits, axis=1, name='huapa_output')
d_hat = tf.layers.dense(
logits,
self.cls_cnt,
kernel_initializer=self.w_init,
bias_initializer=self.b_init)
return d_hat
def build(self, input_map):
transform = partial(tf.layers.dense,
use_bias=False,
kernel_initializer=self.w_init,
bias_initializer=self.b_init)
dense = partial(tf.layers.dense,
kernel_initializer=self.w_init,
bias_initializer=self.b_init)
lstm_cell = partial(tf.nn.rnn_cell.LSTMCell,
self.hidden_size // 2,
forget_bias=0.,
initializer=self.w_init)
def pad_context(context, input_x):
""" padding content with context embedding """
tiled_context = transform(context, self.emb_dim)
tiled_context = tf.tile(tiled_context[:, None, None, :],
[1, self.max_doc_len, 1, 1])
input_x = tf.reshape(
input_x,
[-1, self.max_doc_len, self.max_sen_len, self.emb_dim])
input_x = tf.concat([tiled_context, input_x], axis=2)
input_x = tf.reshape(input_x,
[-1, self.max_sen_len + 1, self.emb_dim])
return input_x
# get the inputs
with tf.variable_scope('inputs'):
usrid, prdid, input_x, input_y, sen_len, doc_len, docid = \
(input_map['usr'], input_map['prd'],
input_map['content'], input_map['rating'],
input_map['sen_len'], input_map['doc_len'],
input_map['docid'])
usr = lookup(self.usr_emb, usrid)
prd = lookup(self.prd_emb, prdid)
input_x = lookup(self.wrd_emb, input_x)
nscua_input_x = pad_context(usr, input_x)
nscpa_input_x = pad_context(prd, input_x)
nscua_input_x = input_x
nscpa_input_x = input_x
# sen_len = tf.where(
# tf.equal(sen_len, 0), tf.zeros_like(sen_len), sen_len + 1)
# self.max_sen_len += 1
# build the process of model
sen_embs, doc_embs = [], []
sen_cell_fw = lstm_cell()
sen_cell_bw = lstm_cell()
for scope, identities, input_x, attention_type in zip(
['user_block', 'product_block'], [[usr], [prd]],
[nscua_input_x, nscpa_input_x], ['additive', 'additive']):
with tf.variable_scope(scope):
sen_emb = nsc_sentence_layer(
input_x,
self.max_sen_len,
self.max_doc_len,
sen_len,
identities,
self.hidden_size,
self.emb_dim,
self.sen_hop_cnt,
bidirectional_lstm=True,
lstm_cells=[sen_cell_fw, sen_cell_bw],
auged=False,
attention_type=attention_type)
sen_embs.append(sen_emb)
sen_embs = tf.concat(sen_embs, axis=-1)
# padding doc with user and product embeddings
doc_aug_usr = transform(usr, 2 * self.hidden_size)
nscua_sen_embs = tf.concat([doc_aug_usr[:, None, :], sen_embs], axis=1)
doc_aug_prd = transform(prd, 2 * self.hidden_size)
nscpa_sen_embs = tf.concat([doc_aug_prd[:, None, :], sen_embs], axis=1)
# none_sen_embs = tf.pad(sen_embs, [[0, 0], [1, 0], [0, 0]])
nscua_sen_embs = sen_embs
nscpa_sen_embs = sen_embs
# self.max_doc_len += 1
# doc_len = doc_len + 1
doc_cell_fw = lstm_cell()
doc_cell_bw = lstm_cell()
for scope, identities, input_x, attention_type in zip(
['user_block', 'product_block'], [[usr], [prd]],
[nscua_sen_embs, nscpa_sen_embs], ['additive', 'additive']):
with tf.variable_scope(scope):
doc_emb = nsc_document_layer(
input_x,
self.max_doc_len,
doc_len,
identities,
self.hidden_size,
self.doc_hop_cnt,
bidirectional_lstm=True,
lstm_cells=[doc_cell_fw, doc_cell_bw],
auged=False,
attention_type=attention_type)
doc_embs.append(doc_emb)
with tf.variable_scope('result'):
doc_emb = tf.concat(doc_embs, axis=1, name='dhuapa_output')
logit = dense(doc_emb, self.cls_cnt)
return logit