def net(self, input, is_infer=False):
""" network"""
text = input[0]
pos_tag = input[1]
neg_tag = input[2]
text_emb = fluid.embedding(input=text,
size=[self.vocab_text_size, self.emb_dim],
param_attr="text_emb")
text_emb = fluid.layers.squeeze(input=text_emb, axes=[1])
pos_tag_emb = fluid.embedding(input=pos_tag,
size=[self.vocab_tag_size, self.emb_dim],
param_attr="tag_emb")
pos_tag_emb = fluid.layers.squeeze(input=pos_tag_emb, axes=[1])
neg_tag_emb = fluid.embedding(input=neg_tag,
size=[self.vocab_tag_size, self.emb_dim],
param_attr="tag_emb")
neg_tag_emb = fluid.layers.squeeze(input=neg_tag_emb, axes=[1])
conv_1d = fluid.nets.sequence_conv_pool(input=text_emb,
num_filters=self.hid_dim,
filter_size=self.win_size,
act="tanh",
pool_type="max",
param_attr="cnn")
text_hid = fluid.layers.fc(input=conv_1d,
size=self.emb_dim,
param_attr="text_hid")
cos_pos = nn.cos_sim(pos_tag_emb, text_hid)
mul_text_hid = fluid.layers.sequence_expand_as(x=text_hid,
y=neg_tag_emb)
mul_cos_neg = nn.cos_sim(neg_tag_emb, mul_text_hid)
cos_neg_all = fluid.layers.sequence_reshape(input=mul_cos_neg,
new_dim=self.neg_size)
#choose max negtive cosine
cos_neg = nn.reduce_max(cos_neg_all, dim=1, keep_dim=True)
#calculate hinge loss
loss_part1 = nn.elementwise_sub(
tensor.fill_constant_batch_size_like(input=cos_pos,
shape=[-1, 1],
value=self.margin,
dtype='float32'), cos_pos)
loss_part2 = nn.elementwise_add(loss_part1, cos_neg)
loss_part3 = nn.elementwise_max(
tensor.fill_constant_batch_size_like(input=loss_part2,
shape=[-1, 1],
value=0.0,
dtype='float32'), loss_part2)
avg_cost = nn.mean(loss_part3)
less = tensor.cast(cf.less_than(cos_neg, cos_pos), dtype='float32')
correct = nn.reduce_sum(less)
self._cost = avg_cost
if is_infer:
self._infer_results["correct"] = correct
self._infer_results["cos_pos"] = cos_pos
else:
self._metrics["correct"] = correct
self._metrics["cos_pos"] = cos_pos
def network(vocab_text_size,
vocab_tag_size,
emb_dim=10,
hid_dim=1000,
win_size=5,
margin=0.1,
neg_size=5):
""" network definition """
text = io.data(name="text", shape=[1], lod_level=1, dtype='int64')
pos_tag = io.data(name="pos_tag", shape=[1], lod_level=1, dtype='int64')
neg_tag = io.data(name="neg_tag", shape=[1], lod_level=1, dtype='int64')
text_emb = nn.embedding(input=text,
size=[vocab_text_size, emb_dim],
param_attr="text_emb")
pos_tag_emb = nn.embedding(input=pos_tag,
size=[vocab_tag_size, emb_dim],
param_attr="tag_emb")
neg_tag_emb = nn.embedding(input=neg_tag,
size=[vocab_tag_size, emb_dim],
param_attr="tag_emb")
conv_1d = fluid.nets.sequence_conv_pool(input=text_emb,
num_filters=hid_dim,
filter_size=win_size,
act="tanh",
pool_type="max",
param_attr="cnn")
text_hid = fluid.layers.fc(input=conv_1d,
size=emb_dim,
param_attr="text_hid")
cos_pos = nn.cos_sim(pos_tag_emb, text_hid)
mul_text_hid = fluid.layers.sequence_expand_as(x=text_hid, y=neg_tag_emb)
mul_cos_neg = nn.cos_sim(neg_tag_emb, mul_text_hid)
cos_neg_all = fluid.layers.sequence_reshape(input=mul_cos_neg,
new_dim=neg_size)
#choose max negtive cosine
cos_neg = nn.reduce_max(cos_neg_all, dim=1, keep_dim=True)
#calculate hinge loss
loss_part1 = nn.elementwise_sub(
tensor.fill_constant_batch_size_like(input=cos_pos,
shape=[-1, 1],
value=margin,
dtype='float32'), cos_pos)
loss_part2 = nn.elementwise_add(loss_part1, cos_neg)
loss_part3 = nn.elementwise_max(
tensor.fill_constant_batch_size_like(input=loss_part2,
shape=[-1, 1],
value=0.0,
dtype='float32'), loss_part2)
avg_cost = nn.mean(loss_part3)
less = tensor.cast(cf.less_than(cos_neg, cos_pos), dtype='float32')
correct = nn.reduce_sum(less)
return avg_cost, correct, cos_pos
def train(self):
user_data = io.data(name="user", shape=[1], dtype="int64", lod_level=1)
pos_item_data = io.data(name="p_item",
shape=[1],
dtype="int64",
lod_level=1)
neg_item_data = io.data(name="n_item",
shape=[1],
dtype="int64",
lod_level=1)
user_emb = nn.embedding(input=user_data,
size=self.emb_shape,
param_attr="emb.item")
pos_item_emb = nn.embedding(input=pos_item_data,
size=self.emb_shape,
param_attr="emb.item")
neg_item_emb = nn.embedding(input=neg_item_data,
size=self.emb_shape,
param_attr="emb.item")
user_enc = self.user_encoder.forward(user_emb)
pos_item_enc = self.item_encoder.forward(pos_item_emb)
neg_item_enc = self.item_encoder.forward(neg_item_emb)
user_hid = nn.fc(input=user_enc,
size=self.hidden_size,
param_attr='user.w',
bias_attr="user.b")
pos_item_hid = nn.fc(input=pos_item_enc,
size=self.hidden_size,
param_attr='item.w',
bias_attr="item.b")
neg_item_hid = nn.fc(input=neg_item_enc,
size=self.hidden_size,
param_attr='item.w',
bias_attr="item.b")
cos_pos = nn.cos_sim(user_hid, pos_item_hid)
cos_neg = nn.cos_sim(user_hid, neg_item_hid)
hinge_loss = self.pairwise_hinge_loss.forward(cos_pos, cos_neg)
avg_cost = nn.mean(hinge_loss)
correct = self.get_correct(cos_neg, cos_pos)
return [user_data, pos_item_data,
neg_item_data], cos_pos, avg_cost, correct
def pred_net(self, query_fields, pos_title_fields, neg_title_fields):
q_slots = [
io.data(name="q%d" % i, shape=[1], lod_level=1, dtype='int64')
for i in range(len(self.query_encoders))
]
pt_slots = [
io.data(name="pt%d" % i, shape=[1], lod_level=1, dtype='int64')
for i in range(len(self.title_encoders))
]
# lookup embedding for each slot
q_embs = [
nn.embedding(input=query, size=self.emb_shape, param_attr="emb")
for query in q_slots
]
pt_embs = [
nn.embedding(input=title, size=self.emb_shape, param_attr="emb")
for title in pt_slots
]
# encode each embedding field with encoder
q_encodes = [
self.query_encoder[i].forward(emb) for i, emb in enumerate(q_embs)
]
pt_encodes = [
self.title_encoders[i].forward(emb)
for i, emb in enumerate(pt_embs)
]
# concat multi view for query, pos_title, neg_title
q_concat = nn.concat(q_encodes)
pt_concat = nn.concat(pt_encodes)
# projection of hidden layer
q_hid = nn.fc(q_concat,
size=self.hidden_size,
param_attr='q_fc.w',
bias_attr='q_fc.b')
pt_hid = nn.fc(pt_concat,
size=self.hidden_size,
param_attr='t_fc.w',
bias_attr='t_fc.b')
# cosine of hidden layers
cos = nn.cos_sim(q_hid, pt_hid)
return cos
def train_net(self):
# input fields for query, pos_title, neg_title
q_slots = [
io.data(name="q%d" % i, shape=[1], lod_level=1, dtype='int64')
for i in range(len(self.query_encoders))
]
pt_slots = [
io.data(name="pt%d" % i, shape=[1], lod_level=1, dtype='int64')
for i in range(len(self.title_encoders))
]
nt_slots = [
io.data(name="nt%d" % i, shape=[1], lod_level=1, dtype='int64')
for i in range(len(self.title_encoders))
]
# lookup embedding for each slot
q_embs = [
nn.embedding(input=query, size=self.emb_shape, param_attr="emb")
for query in q_slots
]
pt_embs = [
nn.embedding(input=title, size=self.emb_shape, param_attr="emb")
for title in pt_slots
]
nt_embs = [
nn.embedding(input=title, size=self.emb_shape, param_attr="emb")
for title in nt_slots
]
# encode each embedding field with encoder
q_encodes = [
self.query_encoders[i].forward(emb) for i, emb in enumerate(q_embs)
]
pt_encodes = [
self.title_encoders[i].forward(emb)
for i, emb in enumerate(pt_embs)
]
nt_encodes = [
self.title_encoders[i].forward(emb)
for i, emb in enumerate(nt_embs)
]
# concat multi view for query, pos_title, neg_title
q_concat = nn.concat(q_encodes)
pt_concat = nn.concat(pt_encodes)
nt_concat = nn.concat(nt_encodes)
# projection of hidden layer
q_hid = nn.fc(q_concat,
size=self.hidden_size,
param_attr='q_fc.w',
bias_attr='q_fc.b')
pt_hid = nn.fc(pt_concat,
size=self.hidden_size,
param_attr='t_fc.w',
bias_attr='t_fc.b')
nt_hid = nn.fc(nt_concat,
size=self.hidden_size,
param_attr='t_fc.w',
bias_attr='t_fc.b')
# cosine of hidden layers
cos_pos = nn.cos_sim(q_hid, pt_hid)
cos_neg = nn.cos_sim(q_hid, nt_hid)
# pairwise hinge_loss
loss_part1 = nn.elementwise_sub(
tensor.fill_constant_batch_size_like(input=cos_pos,
shape=[-1, 1],
value=self.margin,
dtype='float32'), cos_pos)
loss_part2 = nn.elementwise_add(loss_part1, cos_neg)
loss_part3 = nn.elementwise_max(
tensor.fill_constant_batch_size_like(input=loss_part2,
shape=[-1, 1],
value=0.0,
dtype='float32'), loss_part2)
avg_cost = nn.mean(loss_part3)
correct = self.get_correct(cos_neg, cos_pos)
return q_slots + pt_slots + nt_slots, avg_cost, correct