def build_graph(self, **kwargs):
user_sparse_inputs = {uf['feat']: Input(shape=(1,), dtype=tf.float32) for uf in
self.user_sparse_feature_columns}
item_sparse_inputs = {uf['feat']: Input(shape=(1,), dtype=tf.float32) for uf in
self.item_sparse_feature_columns}
seq_inputs = Input(shape=(self.seq_len,), dtype=tf.int32)
pos_inputs = Input(shape=(1,), dtype=tf.int32)
neg_inputs = Input(shape=(self.neg_len,), dtype=tf.int32)
model = Model(inputs=[seq_inputs, pos_inputs, neg_inputs],
outputs=self.call([seq_inputs, pos_inputs, neg_inputs]))
user_input = user_sparse_inputs.update({'seq_item': seq_inputs})
model.__setattr__("user_input", user_input)
model.__setattr__("item_input", item_sparse_inputs)
model.__setattr__("embed", self.embed)
return model
# def model_test():
# user_features = [{'feat': 'user_id', 'feat_num': 100, 'feat_len': 1, 'embed_dim': 8},
# {'feat': 'seq_item', 'feat_num': 100, 'feat_len': 10, 'embed_dim': 64},
# {'feat': 'pos_item', 'feat_num': 100, 'feat_len': 1, 'embed_dim': 64},
# {'feat': 'neg_item', 'feat_num': 100, 'feat_len': 100, 'embed_dim': 64}]
# item_features = [{'feat': 'item_id', 'feat_num': 100, 'feat_len': 1, 'embed_dim': 32}]
# model = SASRec(user_features, item_features, att_hidden_unit=64)
# m = model.build_graph()
# m.summary()
#
#
# model_test()
def DSSM(user_feature_columns,
item_feature_columns,
user_dnn_hidden_units=(256, 256, 128),
item_dnn_hidden_units=(256, 256, 128),
user_dnn_dropout=(0, 0, 0),
item_dnn_dropout=(0, 0, 0),
out_dnn_activation='tanh',
gamma=20,
dnn_use_bn=False,
seed=1024,
metric='cos'):
features_columns = user_feature_columns + item_feature_columns
# 构建 embedding_dict
embedding_dict = build_embedding_dict(features_columns)
# user 特征 处理
user_features = build_input_features(user_feature_columns)
user_inputs_list = list(user_features.values())
user_sparse_embedding_list, user_dense_value_list = input_from_feature_columns(
user_features, user_feature_columns, embedding_dict)
user_dnn_input = combined_dnn_input(user_sparse_embedding_list,
user_dense_value_list)
# item 特征 处理
item_features = build_input_features(item_feature_columns)
item_inputs_list = list(item_features.values())
item_sparse_embedding_list, item_dense_value_list = input_from_feature_columns(
item_features, item_feature_columns, embedding_dict)
item_dnn_input = combined_dnn_input(item_sparse_embedding_list,
item_dense_value_list)
# user tower
for i in range(len(user_dnn_hidden_units)):
if i == len(user_dnn_hidden_units) - 1:
user_dnn_out = Dense(units=user_dnn_hidden_units[i],
activation=out_dnn_activation,
name='user_embed_out')(user_dnn_input)
break
user_dnn_input = Dense(units=user_dnn_hidden_units[i],
activation=out_dnn_activation,
name='dnn_user_' + str(i))(user_dnn_input)
# item tower
for i in range(len(item_dnn_hidden_units)):
if i == len(item_dnn_hidden_units) - 1:
item_dnn_out = Dense(units=item_dnn_hidden_units[i],
activation=out_dnn_activation,
name='item_embed_out')(item_dnn_input)
break
item_dnn_input = Dense(units=item_dnn_hidden_units[i],
activation=out_dnn_activation,
name='dnn_item_' + str(i))(item_dnn_input)
score = Similarity(type_sim=metric, gamma=gamma,
name='dssm_out')([user_dnn_out, item_dnn_out])
output = score
model = Model(inputs=user_inputs_list + item_inputs_list, outputs=output)
model.__setattr__("user_input", user_inputs_list)
model.__setattr__("item_input", item_inputs_list)
model.__setattr__("user_embedding", user_dnn_out)
model.__setattr__("item_embedding", item_dnn_out)
return model
def YouTubeNet(sparse_input_length=1,
dense_input_length=1,
sparse_seq_input_length=50,
embedding_dim=64,
neg_sample_num=10,
user_hidden_unit_list=[128, 64]):
# 1. Input layer
user_id_input_layer = Input(shape=(sparse_input_length, ),
name="user_id_input_layer")
gender_input_layer = Input(shape=(sparse_input_length, ),
name="gender_input_layer")
age_input_layer = Input(shape=(sparse_input_length, ),
name="age_input_layer")
occupation_input_layer = Input(shape=(sparse_input_length, ),
name="occupation_input_layer")
zip_input_layer = Input(shape=(sparse_input_length, ),
name="zip_input_layer")
user_click_item_seq_input_layer = Input(
shape=(sparse_seq_input_length, ),
name="user_click_item_seq_input_layer")
user_click_item_seq_length_input_layer = Input(
shape=(sparse_input_length, ),
name="user_click_item_seq_length_input_layer")
pos_item_sample_input_layer = Input(shape=(sparse_input_length, ),
name="pos_item_sample_input_layer")
neg_item_sample_input_layer = Input(shape=(neg_sample_num, ),
name="neg_item_sample_input_layer")
# 2. Embedding layer
user_id_embedding_layer = Embedding(
6040 + 1,
embedding_dim,
mask_zero=True,
name='user_id_embedding_layer')(user_id_input_layer)
gender_embedding_layer = Embedding(
2 + 1, embedding_dim, mask_zero=True,
name='gender_embedding_layer')(gender_input_layer)
age_embedding_layer = Embedding(
7 + 1, embedding_dim, mask_zero=True,
name='age_embedding_layer')(age_input_layer)
occupation_embedding_layer = Embedding(
21 + 1,
embedding_dim,
mask_zero=True,
name='occupation_embedding_layer')(occupation_input_layer)
zip_embedding_layer = Embedding(
3439 + 1, embedding_dim, mask_zero=True,
name='zip_embedding_layer')(zip_input_layer)
item_id_embedding_layer = Embedding(3706 + 1,
embedding_dim,
mask_zero=True,
name='item_id_embedding_layer')
pos_item_sample_embedding_layer = item_id_embedding_layer(
pos_item_sample_input_layer)
neg_item_sample_embedding_layer = item_id_embedding_layer(
neg_item_sample_input_layer)
user_click_item_seq_embedding_layer = item_id_embedding_layer(
user_click_item_seq_input_layer)
user_click_item_seq_embedding_layer = SequencePoolingLayer(sequence_mask_length=sparse_seq_input_length)\
([user_click_item_seq_embedding_layer, user_click_item_seq_length_input_layer])
### ********** ###
# user part
### ********** ###
# 3. Concat "sparse" embedding & "sparse_seq" embedding
user_embedding_layer = concatenate([
user_id_embedding_layer, gender_embedding_layer, age_embedding_layer,
occupation_embedding_layer, zip_embedding_layer,
user_click_item_seq_embedding_layer
],
axis=-1)
for i, u in enumerate(user_hidden_unit_list):
user_embedding_layer = Dense(
u, activation="relu",
name="FC_{0}".format(i + 1))(user_embedding_layer)
#user_embedding_layer = Dropout(0.3)(user_embedding_layer)
### ********** ###
# item part
### ********** ###
item_embedding_layer = concatenate([pos_item_sample_embedding_layer, neg_item_sample_embedding_layer], \
axis=1)
item_embedding_layer = tf.transpose(item_embedding_layer, [0, 2, 1])
# Output
dot_output = tf.matmul(user_embedding_layer, item_embedding_layer)
dot_output = tf.nn.softmax(
dot_output) # 输出11个值，index为0的值是正样本，负样本的索引位置为[1-10]
user_inputs_list = [user_id_input_layer, gender_input_layer, age_input_layer, \
occupation_input_layer, zip_input_layer, \
user_click_item_seq_input_layer, user_click_item_seq_length_input_layer]
item_inputs_list = [
pos_item_sample_input_layer, neg_item_sample_input_layer
]
model = Model(inputs=user_inputs_list + item_inputs_list,
outputs=dot_output)
#print(model.summary())
#tf.keras.utils.plot_model(model, to_file='YouTubeNet_model.png', show_shapes=True)
model.__setattr__("user_input", user_inputs_list)
model.__setattr__("user_embedding", user_embedding_layer)
model.__setattr__("item_input", pos_item_sample_input_layer)
model.__setattr__("item_embedding", pos_item_sample_embedding_layer)
return model
def mind(sparse_input_length=1,
dense_input_length=1,
sparse_seq_input_length=50,
embedding_dim=64,
neg_sample_num=10,
user_hidden_unit_list=[128, 64],
k_max=5,
p=1,
dynamic_k=True):
# 1. Input layer
user_id_input_layer = Input(shape=(sparse_input_length, ),
name="user_id_input_layer")
gender_input_layer = Input(shape=(sparse_input_length, ),
name="gender_input_layer")
age_input_layer = Input(shape=(sparse_input_length, ),
name="age_input_layer")
occupation_input_layer = Input(shape=(sparse_input_length, ),
name="occupation_input_layer")
zip_input_layer = Input(shape=(sparse_input_length, ),
name="zip_input_layer")
user_click_item_seq_input_layer = Input(
shape=(sparse_seq_input_length, ),
name="user_click_item_seq_input_layer")
user_click_item_seq_length_input_layer = Input(
shape=(sparse_input_length, ),
name="user_click_item_seq_length_input_layer")
pos_item_sample_input_layer = Input(shape=(sparse_input_length, ),
name="pos_item_sample_input_layer")
neg_item_sample_input_layer = Input(shape=(neg_sample_num, ),
name="neg_item_sample_input_layer")
# 2. Embedding layer
user_id_embedding_layer = Embedding(
6040 + 1,
embedding_dim,
mask_zero=True,
name='user_id_embedding_layer')(user_id_input_layer)
gender_embedding_layer = Embedding(
2 + 1, embedding_dim, mask_zero=True,
name='gender_embedding_layer')(gender_input_layer)
age_embedding_layer = Embedding(
7 + 1, embedding_dim, mask_zero=True,
name='age_embedding_layer')(age_input_layer)
occupation_embedding_layer = Embedding(
21 + 1,
embedding_dim,
mask_zero=True,
name='occupation_embedding_layer')(occupation_input_layer)
zip_embedding_layer = Embedding(
3439 + 1, embedding_dim, mask_zero=True,
name='zip_embedding_layer')(zip_input_layer)
item_id_embedding_layer = Embedding(3706 + 1,
embedding_dim,
mask_zero=True,
name='item_id_embedding_layer')
pos_item_sample_embedding_layer = item_id_embedding_layer(
pos_item_sample_input_layer)
neg_item_sample_embedding_layer = item_id_embedding_layer(
neg_item_sample_input_layer)
user_click_item_seq_embedding_layer = item_id_embedding_layer(
user_click_item_seq_input_layer)
### ********** ###
# 3. user part
### ********** ###
# 3.1 pooling layer
user_click_item_seq_embedding_layer_pooling = SequencePoolingLayer()\
([user_click_item_seq_embedding_layer, user_click_item_seq_length_input_layer])
print("user_click_item_seq_embedding_layer_pooling",
user_click_item_seq_embedding_layer_pooling)
# 3.2 capsule layer
high_capsule = CapsuleLayer(input_units=embedding_dim,
out_units=embedding_dim, max_len=sparse_seq_input_length,
k_max=k_max)\
([user_click_item_seq_embedding_layer, user_click_item_seq_length_input_layer])
print("high_capsule: ", high_capsule)
# 3.3 Concat "sparse" embedding & "sparse_seq" embedding, and tile embedding
other_user_embedding_layer = concatenate([user_id_embedding_layer, gender_embedding_layer, \
age_embedding_layer, occupation_embedding_layer, \
zip_embedding_layer, user_click_item_seq_embedding_layer_pooling],
axis=-1)
other_user_embedding_layer = tf.tile(other_user_embedding_layer,
[1, k_max, 1])
print("other_user_embedding_layer: ", other_user_embedding_layer)
# 3.4 user dnn part
user_deep_input = concatenate([other_user_embedding_layer, high_capsule],
axis=-1)
print("user_deep_input: ", user_deep_input)
for i, u in enumerate(user_hidden_unit_list):
user_deep_input = Dense(u,
activation="relu",
name="FC_{0}".format(i + 1))(user_deep_input)
#user_deep_input = Dropout(0.3)(user_deep_input)
print("user_deep_input: ", user_deep_input)
if dynamic_k:
user_embedding_final = LabelAwareAttention(k_max=k_max, pow_p=p, )(\
[user_deep_input, pos_item_sample_embedding_layer, user_click_item_seq_length_input_layer])
else:
user_embedding_final = LabelAwareAttention(k_max=k_max, pow_p=p, )(\
[user_deep_input, pos_item_sample_embedding_layer])
user_embedding_final = tf.expand_dims(user_embedding_final, 1)
print("user_embedding_final: ", user_embedding_final)
### ********** ###
# 4. item part
### ********** ###
item_embedding_layer = concatenate([pos_item_sample_embedding_layer, neg_item_sample_embedding_layer], \
axis=1)
item_embedding_layer = tf.transpose(item_embedding_layer, [0, 2, 1])
print("item_embedding_layer: ", item_embedding_layer)
### ********** ###
# 5. Output
### ********** ###
dot_output = tf.matmul(user_embedding_final, item_embedding_layer)
dot_output = tf.nn.softmax(
dot_output) # 输出11个值，index为0的值是正样本，负样本的索引位置为[1-10]
print(dot_output)
user_inputs_list = [user_id_input_layer, gender_input_layer, age_input_layer, \
occupation_input_layer, zip_input_layer, \
user_click_item_seq_input_layer, user_click_item_seq_length_input_layer]
item_inputs_list = [
pos_item_sample_input_layer, neg_item_sample_input_layer
]
model = Model(inputs=user_inputs_list + item_inputs_list,
outputs=dot_output)
#print(model.summary())
#tf.keras.utils.plot_model(model, to_file='MIND_model.png', show_shapes=True)
model.__setattr__("user_input", user_inputs_list)
model.__setattr__("user_embedding", user_deep_input)
model.__setattr__("item_input", pos_item_sample_input_layer)
model.__setattr__("item_embedding", pos_item_sample_embedding_layer)
return model
