def NFFM(
feature_metas,
biinteraction_mode='all',
embedding_initializer='glorot_uniform',
embedding_regularizer=tf.keras.regularizers.l2(1e-5),
fm_fixed_embedding_dim=None,
linear_use_bias=True,
linear_kernel_initializer=tf.keras.initializers.RandomNormal(stddev=1e-4, seed=1024),
linear_kernel_regularizer=tf.keras.regularizers.l2(1e-5),
dnn_hidden_units=(128, 64, 1),
dnn_activations=('relu', 'relu', None),
dnn_use_bias=True,
dnn_use_bn=False,
dnn_dropout=0,
dnn_kernel_initializers='glorot_uniform',
dnn_bias_initializers='zeros',
dnn_kernel_regularizers=tf.keras.regularizers.l2(1e-5),
dnn_bias_regularizers=None,
name='NFFM'):
assert isinstance(feature_metas, FeatureMetas)
with tf.name_scope(name):
features = Features(metas=feature_metas)
# Linear Part
with tf.name_scope('Linear'):
linear_output = features.get_linear_logit(use_bias=linear_use_bias,
kernel_initializer=linear_kernel_initializer,
kernel_regularizer=linear_kernel_regularizer,
embedding_group='dot_embedding',
slots_filter=None)
# Interaction Part
with tf.name_scope('Interaction'):
fm_embedded_dict = features.get_embedded_dict(
group_name='embedding',
fixed_embedding_dim=fm_fixed_embedding_dim,
embedding_initializer=embedding_initializer,
embedding_regularizer=embedding_regularizer,
slots_filter=None
)
fm_dim_groups = group_embedded_by_dim(fm_embedded_dict)
interactions = list()
for fm_group in fm_dim_groups.values():
group_interaction = BiInteraction(mode=biinteraction_mode)(fm_group)
interactions.append(group_interaction)
interactions = tf.concat(interactions, axis=1)
dnn_output = DNN(
units=dnn_hidden_units,
use_bias=dnn_use_bias,
activations=dnn_activations,
use_bn=dnn_use_bn,
dropout=dnn_dropout,
kernel_initializers=dnn_kernel_initializers,
bias_initializers=dnn_bias_initializers,
kernel_regularizers=dnn_kernel_regularizers,
bias_regularizers=dnn_bias_regularizers
)(interactions)
# Output
output = tf.add_n([linear_output, dnn_output])
output = tf.keras.activations.sigmoid(output)
model = tf.keras.Model(inputs=features.get_inputs_list(), outputs=output)
return model
def AFM(feature_metas,
linear_slots,
fm_slots,
embedding_initializer='glorot_uniform',
embedding_regularizer=tf.keras.regularizers.l2(1e-5),
fm_fixed_embedding_dim=None,
linear_use_bias=True,
linear_kernel_initializer=tf.keras.initializers.RandomNormal(
stddev=1e-4, seed=1024),
linear_kernel_regularizer=tf.keras.regularizers.l2(1e-5),
dnn_hidden_units=(128, 64, 1),
dnn_activations=('relu', 'relu', None),
dnn_use_bias=True,
dnn_use_bn=False,
dnn_dropout=0,
dnn_kernel_initializers='glorot_uniform',
dnn_bias_initializers='zeros',
dnn_kernel_regularizers=tf.keras.regularizers.l2(1e-5),
dnn_bias_regularizers=None,
name='AFM'):
assert isinstance(feature_metas, FeatureMetas)
with tf.name_scope(name):
features = Features(metas=feature_metas)
# Linear Part
with tf.name_scope('Linear'):
linear_output = features.get_linear_logit(
use_bias=linear_use_bias,
kernel_initializer=linear_kernel_initializer,
kernel_regularizer=linear_kernel_regularizer,
embedding_group='dot_embedding',
slots_filter=linear_slots)
# Interaction
with tf.name_scope('Interaction'):
fm_embedded_dict = features.get_embedded_dict(
group_name='embedding',
fixed_embedding_dim=fm_fixed_embedding_dim,
embedding_initializer=embedding_initializer,
embedding_regularizer=embedding_regularizer,
slots_filter=fm_slots)
# print(fm_embedded_dict)
fm_dim_groups = group_embedded_by_dim(fm_embedded_dict)
fms = [
AttentionBasedPoolingLayer()(group)
for group in fm_dim_groups.values() if len(group) > 1
]
print("fms", fms)
dnn_inputs = tf.concat(fms, axis=1)
print("dnn_inputs", dnn_inputs)
dnn_output = DNN(
units=dnn_hidden_units,
use_bias=dnn_use_bias,
activations=dnn_activations,
use_bn=dnn_use_bn,
dropout=dnn_dropout,
kernel_initializers=dnn_kernel_initializers,
bias_initializers=dnn_bias_initializers,
kernel_regularizers=dnn_kernel_regularizers,
bias_regularizers=dnn_bias_regularizers)(dnn_inputs)
# Output
output = tf.add_n([linear_output, dnn_output])
output = tf.keras.activations.sigmoid(output)
model = tf.keras.Model(inputs=features.get_inputs_list(),
outputs=output)
return model
def WideAndDeep(
feature_metas,
wide_slots,
deep_slots,
embedding_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=1e-4),
embedding_regularizer=tf.keras.regularizers.l2(1e-5),
wide_use_bias=True,
wide_kernel_initializer=tf.keras.initializers.RandomNormal(stddev=1e-4, seed=1024),
wide_kernel_regularizer=tf.keras.regularizers.l2(1e-5),
deep_fixed_embedding_dim=None,
deep_hidden_units=(128, 64, 1),
deep_activations=('relu', 'relu', None),
deep_use_bias=True,
deep_use_bn=False,
deep_dropout=0,
deep_kernel_initializers='glorot_uniform',
deep_bias_initializers='zeros',
deep_kernel_regularizers=tf.keras.regularizers.l2(1e-5),
deep_bias_regularizers=None,
name='Wide&Deep'):
assert isinstance(feature_metas, FeatureMetas)
with tf.name_scope(name):
features = Features(metas=feature_metas)
# Wide Part
with tf.name_scope('Wide'):
wide_output = features.get_linear_logit(embedding_group='dot_embedding',
use_bias=wide_use_bias,
kernel_initializer=wide_kernel_initializer,
kernel_regularizer=wide_kernel_regularizer,
slots_filter=wide_slots)
# Deep Part
with tf.name_scope('Deep'):
deep_inputs = features.gen_concated_feature(embedding_group='embedding',
fixed_embedding_dim=deep_fixed_embedding_dim,
embedding_initializer=embedding_initializer,
embedding_regularizer=embedding_regularizer,
slots_filter=deep_slots)
deep_output = DNN(
units=deep_hidden_units,
use_bias=deep_use_bias,
activations=deep_activations,
use_bn=deep_use_bn,
dropout=deep_dropout,
kernel_initializers=deep_kernel_initializers,
bias_initializers=deep_bias_initializers,
kernel_regularizers=deep_kernel_regularizers,
bias_regularizers=deep_bias_regularizers
)(deep_inputs)
# Output
output = tf.add_n([wide_output, deep_output])
output = tf.keras.activations.sigmoid(output)
model = tf.keras.Model(inputs=features.get_inputs_list(), outputs=output)
return model
def DeepFM(
feature_metas,
linear_slots,
fm_slots,
dnn_slots,
embedding_initializer='glorot_uniform',
embedding_regularizer=tf.keras.regularizers.l2(1e-5),
fm_fixed_embedding_dim=None,
linear_use_bias=True,
#按照正态分布生成随机张量的初始化器。
linear_kernel_initializer=tf.keras.initializers.RandomNormal(
stddev=1e-4, seed=1024),
linear_kernel_regularizer=tf.keras.regularizers.l2(1e-5),
dnn_hidden_units=(128, 64, 1),
dnn_activations=('relu', 'relu', None),
dnn_use_bias=True,
dnn_use_bn=False,
dnn_dropout=0,
dnn_kernel_initializers='glorot_uniform',
dnn_bias_initializers='zeros',
dnn_kernel_regularizers=tf.keras.regularizers.l2(1e-5),
dnn_bias_regularizers=None,
name='DeepFM'):
# 检测是否是feature__metas类型是否是FeatrueMetas;
assert isinstance(feature_metas, FeatureMetas)
with tf.name_scope(name):
features = Features(metas=feature_metas)
#print(features.shape)
# Linear Part
with tf.name_scope('Linear'):
#
linear_output = features.get_linear_logit(
use_bias=linear_use_bias,
kernel_initializer=linear_kernel_initializer,
kernel_regularizer=linear_kernel_regularizer,
embedding_group='dot_embedding',
slots_filter=linear_slots)
# FM Part
with tf.name_scope('FM'):
fm_embedded_dict = features.get_embedded_dict(
group_name='embedding',
fixed_embedding_dim=fm_fixed_embedding_dim,
embedding_initializer=embedding_initializer,
embedding_regularizer=embedding_regularizer,
slots_filter=fm_slots)
fm_dim_groups = group_embedded_by_dim(fm_embedded_dict)
fms = [
FM()(group) for group in fm_dim_groups.values()
if len(group) > 1
]
fm_output = tf.add_n(fms)
# DNN Part
with tf.name_scope('DNN'):
dnn_inputs = features.gen_concated_feature(
embedding_group='embedding',
fixed_embedding_dim=fm_fixed_embedding_dim,
embedding_initializer=embedding_initializer,
embedding_regularizer=embedding_regularizer,
slots_filter=dnn_slots)
dnn_output = DNN(
units=dnn_hidden_units,
use_bias=dnn_use_bias,
activations=dnn_activations,
use_bn=dnn_use_bn,
dropout=dnn_dropout,
kernel_initializers=dnn_kernel_initializers,
bias_initializers=dnn_bias_initializers,
kernel_regularizers=dnn_kernel_regularizers,
bias_regularizers=dnn_bias_regularizers)(dnn_inputs)
# Output 将计算矩阵进行拼接
output = tf.add_n([linear_output, fm_output, dnn_output])
output = tf.keras.activations.sigmoid(output)
model = tf.keras.Model(inputs=features.get_inputs_list(),
outputs=output)
return model