def _build_newsencoder(self, embedding_layer):
"""The main function to create news encoder of LSTUR.
Args:
embedding_layer (object): a word embedding layer.
Return:
object: the news encoder of LSTUR.
"""
hparams = self.hparams
sequences_input_title = keras.Input(shape=(hparams.title_size, ),
dtype="int32")
embedded_sequences_title = embedding_layer(sequences_input_title)
y = layers.Dropout(hparams.dropout)(embedded_sequences_title)
y = layers.Conv1D(
hparams.filter_num,
hparams.window_size,
activation=hparams.cnn_activation,
padding="same",
bias_initializer=keras.initializers.Zeros(),
kernel_initializer=keras.initializers.glorot_uniform(
seed=self.seed),
)(y)
print(y)
y = layers.Dropout(hparams.dropout)(y)
y = layers.Masking()(
OverwriteMasking()([y, ComputeMasking()(sequences_input_title)]))
pred_title = AttLayer2(hparams.attention_hidden_dim, seed=self.seed)(y)
print(pred_title)
model = keras.Model(sequences_input_title,
pred_title,
name="news_encoder")
return model
def _build_userencoder(self, newsencoder):
"""The main function to create user encoder of NAML.
Args:
newsencoder (object): the news encoder of NAML.
Return:
object: the user encoder of NAML.
"""
hparams = self.hparams
his_input_title_body_verts = keras.Input(
shape=(hparams.his_size,
hparams.title_size + hparams.body_size + 2),
dtype="int32",
)
click_news_presents = layers.TimeDistributed(newsencoder)(
his_input_title_body_verts)
user_present = AttLayer2(hparams.attention_hidden_dim,
seed=self.seed)(click_news_presents)
model = keras.Model(his_input_title_body_verts,
user_present,
name="user_encoder")
return model
def _build_bodyencoder(self, embedding_layer):
"""build body encoder of NAML news encoder.
Args:
embedding_layer (object): a word embedding layer.
Return:
object: the body encoder of NAML.
"""
hparams = self.hparams
sequences_input_body = keras.Input(shape=(hparams.body_size, ),
dtype="int32")
embedded_sequences_body = embedding_layer(sequences_input_body)
y = layers.Dropout(hparams.dropout)(embedded_sequences_body)
y = layers.Conv1D(
hparams.filter_num,
hparams.window_size,
activation=hparams.cnn_activation,
padding="same",
bias_initializer=keras.initializers.Zeros(),
kernel_initializer=keras.initializers.glorot_uniform(
seed=self.seed),
)(y)
y = layers.Dropout(hparams.dropout)(y)
pred_body = AttLayer2(hparams.attention_hidden_dim, seed=self.seed)(y)
pred_body = layers.Reshape((1, hparams.filter_num))(pred_body)
model = keras.Model(sequences_input_body,
pred_body,
name="body_encoder")
return model
def _build_newsencoder(self, embedding_layer):
"""The main function to create news encoder of NRMS.
Args:
embedding_layer (object): a word embedding layer.
Return:
object: the news encoder of NRMS.
"""
hparams = self.hparams
sequences_input_title = keras.Input(shape=(hparams.title_size, ),
dtype="int32")
embedded_sequences_title = embedding_layer(sequences_input_title)
y = layers.Dropout(hparams.dropout)(embedded_sequences_title)
y = SelfAttention(hparams.head_num, hparams.head_dim,
seed=self.seed)([y, y, y])
y = layers.Dropout(hparams.dropout)(y)
pred_title = AttLayer2(hparams.attention_hidden_dim, seed=self.seed)(y)
model = keras.Model(sequences_input_title,
pred_title,
name="news_encoder")
return model
def _build_newsencoder(self, embedding_layer):
"""The main function to create news encoder of NAML.
news encoder in composed of title encoder, body encoder, vert encoder and subvert encoder
Args:
embedding_layer (object): a word embedding layer.
Return:
object: the news encoder of NAML.
"""
hparams = self.hparams
input_title_body_verts = keras.Input(shape=(hparams.title_size +
hparams.body_size + 2, ),
dtype="int32")
sequences_input_title = layers.Lambda(
lambda x: x[:, :hparams.title_size])(input_title_body_verts)
sequences_input_body = layers.Lambda(
lambda x: x[:, hparams.title_size:hparams.title_size + hparams.
body_size])(input_title_body_verts)
input_vert = layers.Lambda(
lambda x: x[:, hparams.title_size + hparams.body_size:hparams.
title_size + hparams.body_size + 1, ])(
input_title_body_verts)
input_subvert = layers.Lambda(
lambda x: x[:, hparams.title_size + hparams.body_size + 1:])(
input_title_body_verts)
title_repr = self._build_titleencoder(embedding_layer)(
sequences_input_title)
body_repr = self._build_bodyencoder(embedding_layer)(
sequences_input_body)
vert_repr = self._build_vertencoder()(input_vert)
subvert_repr = self._build_subvertencoder()(input_subvert)
concate_repr = layers.Concatenate(axis=-2)(
[title_repr, body_repr, vert_repr, subvert_repr])
news_repr = AttLayer2(hparams.attention_hidden_dim,
seed=self.seed)(concate_repr)
model = keras.Model(input_title_body_verts,
news_repr,
name="news_encoder")
return model
def _build_userencoder(self, titleencoder):
"""The main function to create user encoder of NRMS.
Args:
titleencoder (object): the news encoder of NRMS.
Return:
object: the user encoder of NRMS.
"""
hparams = self.hparams
his_input_title = keras.Input(
shape=(hparams.his_size, hparams.title_size), dtype="int32"
)
click_title_presents = layers.TimeDistributed(titleencoder)(his_input_title)
y = SelfAttention(hparams.head_num, hparams.head_dim, seed=self.seed)(
[click_title_presents] * 3
)
user_present = AttLayer2(hparams.attention_hidden_dim, seed=self.seed)(y)
model = keras.Model(his_input_title, user_present, name="user_encoder")
return model