目录
1 keras实现Deepfm
demo
2 deepctr模版
3 其他实现方式
ctr_Kera
模型
数据集
预处理
执行步骤
4何为focal loss
参考
1 keras实现Deepfm
假设我们有两种 field 的特征，连续型和离散型，连续型 field 一般不做处理沿用原值，离散型一般会做One-hot编码。离散型又能进一步分为单值型和多值型，单值型在Onehot后的稀疏向量中，只有一个特征为1，其余都是0，而多值型在Onehot后，有多于1个特征为1，其余是0。
DeepFM模型本质
-
将Wide & Deep 部分的wide部分由 人工特征工程+LR 转换为FM模型，避开了人工特征工程；
-
FM模型与deep part共享feature embedding。
为什么要用FM代替线性部分（wide）呢？
因为线性模型有个致命的缺点：无法提取高阶的组合特征。 FM通过隐向量latent vector做内积来表示组合特征，从理论上解决了低阶和高阶组合特征提取的问题。但是实际应用中受限于计算复杂度，一般也就只考虑到2阶交叉特征。
DeepFM包含两部分：神经网络部分与因子分解机部分，分别负责低阶特征的提取和高阶特征的提取。这两部分共享同样的输入。
不需要预训练 FM 得到隐向量；
不需要人工特征工程；
能同时学习低阶和高阶的组合特征；
FM 模块和 Deep 模块共享 Feature Embedding 部分，可以更快的训练，以及更精确的训练学习。
https://blog.csdn.net/m0_51933492/article/details/126888136
# FM的一阶特征运算
#将预处理完的类别特征列合并
cat_columns = [movie_ind_col, user_ind_col, user_genre_ind_col, item_genre_ind_col]
#数值型特征不用经过独热编码和特征稠密化，转换成可以输入神经网络的dense类型直接输入网络
【推荐系统】DeepFM模型_deepfm多分类求auc_—Xi—的博客-CSDN博客
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
movieAvgRating (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
movieGenre1 (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
movieGenre2 (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
movieGenre3 (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
movieId (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
movieRatingCount (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
movieRatingStddev (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
releaseYear (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
userAvgRating (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
userGenre1 (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
userGenre2 (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
userGenre3 (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
userGenre4 (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
userGenre5 (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
userId (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
userRatedMovie1 (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
userRatingCount (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
userRatingStddev (InputLayer) [(None,)] 0
__________________________________________________________________________________________________
dense_features_16 (DenseFeature (None, 10) 190 movieAvgRating[0][0]
movieGenre1[0][0]
movieGenre2[0][0]
movieGenre3[0][0]
movieId[0][0]
movieRatingCount[0][0]
movieRatingStddev[0][0]
releaseYear[0][0]
userAvgRating[0][0]
userGenre1[0][0]
userGenre2[0][0]
userGenre3[0][0]
userGenre4[0][0]
userGenre5[0][0]
userId[0][0]
userRatedMovie1[0][0]
userRatingCount[0][0]
userRatingStddev[0][0]
__________________________________________________________________________________________________
dense_features_17 (DenseFeature (None, 10) 10010 movieAvgRating[0][0]
movieGenre1[0][0]
movieGenre2[0][0]
movieGenre3[0][0]
movieId[0][0]
movieRatingCount[0][0]
movieRatingStddev[0][0]
releaseYear[0][0]
userAvgRating[0][0]
userGenre1[0][0]
userGenre2[0][0]
userGenre3[0][0]
userGenre4[0][0]
userGenre5[0][0]
userId[0][0]
userRatedMovie1[0][0]
userRatingCount[0][0]
userRatingStddev[0][0]
__________________________________________________________________________________________________
dense_features_18 (DenseFeature (None, 10) 190 movieAvgRating[0][0]
movieGenre1[0][0]
movieGenre2[0][0]
movieGenre3[0][0]
movieId[0][0]
movieRatingCount[0][0]
movieRatingStddev[0][0]
releaseYear[0][0]
userAvgRating[0][0]
userGenre1[0][0]
userGenre2[0][0]
userGenre3[0][0]
userGenre4[0][0]
userGenre5[0][0]
userId[0][0]
userRatedMovie1[0][0]
userRatingCount[0][0]
userRatingStddev[0][0]
__________________________________________________________________________________________________
dense_features_19 (DenseFeature (None, 10) 300010 movieAvgRating[0][0]
movieGenre1[0][0]
movieGenre2[0][0]
movieGenre3[0][0]
movieId[0][0]
movieRatingCount[0][0]
movieRatingStddev[0][0]
releaseYear[0][0]
userAvgRating[0][0]
userGenre1[0][0]
userGenre2[0][0]
userGenre3[0][0]
userGenre4[0][0]
userGenre5[0][0]
userId[0][0]
userRatedMovie1[0][0]
userRatingCount[0][0]
userRatingStddev[0][0]
__________________________________________________________________________________________________
dense_features_20 (DenseFeature (None, 7) 0 movieAvgRating[0][0]
movieGenre1[0][0]
movieGenre2[0][0]
movieGenre3[0][0]
movieId[0][0]
movieRatingCount[0][0]
movieRatingStddev[0][0]
releaseYear[0][0]
userAvgRating[0][0]
userGenre1[0][0]
userGenre2[0][0]
userGenre3[0][0]
userGenre4[0][0]
userGenre5[0][0]
userId[0][0]
userRatedMovie1[0][0]
userRatingCount[0][0]
userRatingStddev[0][0]
__________________________________________________________________________________________________
dense_18 (Dense) (None, 64) 704 dense_features_16[0][0]
__________________________________________________________________________________________________
dense_19 (Dense) (None, 64) 704 dense_features_17[0][0]
__________________________________________________________________________________________________
dense_20 (Dense) (None, 64) 704 dense_features_18[0][0]
__________________________________________________________________________________________________
dense_21 (Dense) (None, 64) 704 dense_features_19[0][0]
__________________________________________________________________________________________________
dense_22 (Dense) (None, 64) 512 dense_features_20[0][0]
__________________________________________________________________________________________________
reshape_10 (Reshape) (None, 10, 64) 0 dense_18[0][0]
__________________________________________________________________________________________________
reshape_11 (Reshape) (None, 10, 64) 0 dense_19[0][0]
__________________________________________________________________________________________________
reshape_12 (Reshape) (None, 10, 64) 0 dense_20[0][0]
__________________________________________________________________________________________________
reshape_13 (Reshape) (None, 10, 64) 0 dense_21[0][0]
__________________________________________________________________________________________________
reshape_14 (Reshape) (None, 10, 64) 0 dense_22[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 50, 64) 0 reshape_10[0][0]
reshape_11[0][0]
reshape_12[0][0]
reshape_13[0][0]
reshape_14[0][0]
__________________________________________________________________________________________________
dense_features_14 (DenseFeature (None, 31040) 0 movieAvgRating[0][0]
movieGenre1[0][0]
movieGenre2[0][0]
movieGenre3[0][0]
movieId[0][0]
movieRatingCount[0][0]
movieRatingStddev[0][0]
releaseYear[0][0]
userAvgRating[0][0]
userGenre1[0][0]
userGenre2[0][0]
userGenre3[0][0]
userGenre4[0][0]
userGenre5[0][0]
userId[0][0]
userRatedMovie1[0][0]
userRatingCount[0][0]
userRatingStddev[0][0]
__________________________________________________________________________________________________
dense_features_15 (DenseFeature (None, 7) 0 movieAvgRating[0][0]
movieGenre1[0][0]
movieGenre2[0][0]
movieGenre3[0][0]
movieId[0][0]
movieRatingCount[0][0]
movieRatingStddev[0][0]
releaseYear[0][0]
userAvgRating[0][0]
userGenre1[0][0]
userGenre2[0][0]
userGenre3[0][0]
userGenre4[0][0]
userGenre5[0][0]
userId[0][0]
userRatedMovie1[0][0]
userRatingCount[0][0]
userRatingStddev[0][0]
__________________________________________________________________________________________________
reduce_layer (ReduceLayer) (None, 64) 0 concatenate_2[0][0]
__________________________________________________________________________________________________
multiply_1 (Multiply) (None, 50, 64) 0 concatenate_2[0][0]
concatenate_2[0][0]
__________________________________________________________________________________________________
flatten_2 (Flatten) (None, 3200) 0 concatenate_2[0][0]
__________________________________________________________________________________________________
dense_16 (Dense) (None, 1) 31041 dense_features_14[0][0]
__________________________________________________________________________________________________
dense_17 (Dense) (None, 1) 8 dense_features_15[0][0]
__________________________________________________________________________________________________
multiply (Multiply) (None, 64) 0 reduce_layer[0][0]
reduce_layer[0][0]
__________________________________________________________________________________________________
reduce_layer_1 (ReduceLayer) (None, 64) 0 multiply_1[0][0]
__________________________________________________________________________________________________
dense_23 (Dense) (None, 32) 102432 flatten_2[0][0]
__________________________________________________________________________________________________
add_2 (Add) (None, 1) 0 dense_16[0][0]
dense_17[0][0]
__________________________________________________________________________________________________
subtract (Subtract) (None, 64) 0 multiply[0][0]
reduce_layer_1[0][0]
__________________________________________________________________________________________________
dense_24 (Dense) (None, 16) 528 dense_23[0][0]
__________________________________________________________________________________________________
concatenate_3 (Concatenate) (None, 81) 0 add_2[0][0]
subtract[0][0]
dense_24[0][0]
__________________________________________________________________________________________________
dense_25 (Dense) (None, 1) 82 concatenate_3[0][0]
==================================================================================================
Total params: 447,819
Trainable params: 447,819
Non-trainable params: 0
__________________________________________________________________________________________________
None
TensorFlow的plot_model功能_plot_model函数_buptwhq的博客-CSDN博客
from keras.utils.vis_utils import plot_model
...
plot_model(model, to_file="model.png", show_shapes=True, show_layer_names=False, rankdir='TB')
8. keras - 绘制网络结构_keras 设计网络_Micheal超的博客-CSDN博客
问题一：什么是特征交互，为什么要进行特征交互？
二阶特征交互：通过对主流应用市场的研究，我们发现人们经常在用餐时间下载送餐的应用程序，这就表明应用类别和时间戳之间的（阶数-2）交互作用是CTR预测的一个信号。
三阶或者高阶特征交互：我们还发现男性青少年喜欢射击游戏和RPG游戏，这意味着应用类别、用户性别和年龄的(阶数-3)交互是CTR的另一个信号。
根据谷歌的W&D模型的应用， 作者发现同时考虑低阶和高阶的交互特征，比单独考虑其中之一有更多的改进
问题二：为啥人工特征工程有挑战性？
一些特征工程比较容易理解，就比如上面提到的那两个， 这时候往往我们都能很容易的设计或者组合那样的特征。 然而，其他大部分特征交互都隐藏在数据中，难以先验识别（比如经典的关联规则 "尿布和啤酒 "就是从数据中挖掘出来的，而不是由专家发现的），只能由机器学习自动捕捉，即使是对于容易理解的交互，专家们似乎也不可能详尽地对它们进行建模，特别是当特征的数量很大的时候.。
demo
【CTR模型】TensorFlow2.0 的 xDeepFM 实现与实战(附代码+数据)_tensorflow ctr 模型_VariableX的博客-CSDN博客
python相减substract_浅谈keras中的Merge层(实现层的相加、相减、相乘实例)_林祈墨的博客-CSDN博客
# coding:utf-8
from keras.layers import *
from keras.models import Model
from MyMeanPooling import MyMeanPool
from MySumLayer import MySumLayer
from MyFlatten import MyFlatten
from keras.utils import plot_model
# numeric fields
in_score = Input(shape=[1], name="score") # None*1
in_sales = Input(shape=[1], name="sales") # None*1
# single value categorical fields
in_gender = Input(shape=[1], name="gender") # None*1
in_age = Input(shape=[1], name="age") # None*1
# multiple value categorical fields
in_interest = Input(shape=[3], name="interest") # None*3, 最长长度3
in_topic = Input(shape=[4], name="topic") # None*4, 最长长度4
'''First Order Embeddings'''
numeric = Concatenate()([in_score, in_sales]) # None*2
dense_numeric = Dense(1)(numeric) # None*1
emb_gender_1d = Reshape([1])(Embedding(3, 1)(in_gender)) # None*1, 性别取值3种
emb_age_1d = Reshape([1])(Embedding(10, 1)(in_age)) # None*1, 年龄取值10种
emb_interest_1d = Embedding(11, 1, mask_zero=True)(in_interest) # None*3*1
emb_interest_1d = MyMeanPool(axis=1)(emb_interest_1d) # None*1
emb_topic_1d = Embedding(22, 1, mask_zero=True)(in_topic) # None*4*1
emb_topic_1d = MyMeanPool(axis=1)(emb_topic_1d) # None*1
'''compute'''
y_first_order = Add()([dense_numeric,
emb_gender_1d,
emb_age_1d,
emb_interest_1d,
emb_topic_1d]) # None*1
latent = 8
'''Second Order Embeddings'''
emb_score_Kd = RepeatVector(1)(Dense(latent)(in_score)) # None * 1 * K
emb_sales_Kd = RepeatVector(1)(Dense(latent)(in_sales)) # None * 1 * K
emb_gender_Kd = Embedding(3, latent)(in_gender) # None * 1 * K
emb_age_Kd = Embedding(10, latent)(in_age) # None * 1 * K
emb_interest_Kd = Embedding(11, latent, mask_zero=True)(in_interest) # None * 3 * K
emb_interest_Kd = RepeatVector(1)(MyMeanPool(axis=1)(emb_interest_Kd)) # None * 1 * K
emb_topic_Kd = Embedding(22, latent, mask_zero=True)(in_topic) # None * 4 * K
emb_topic_Kd = RepeatVector(1)(MyMeanPool(axis=1)(emb_topic_Kd)) # None * 1 * K
emb = Concatenate(axis=1)([emb_score_Kd,
emb_sales_Kd,
emb_gender_Kd,
emb_age_Kd,
emb_interest_Kd,
emb_topic_Kd]) # None * 6 * K
'''compute'''
summed_features_emb = MySumLayer(axis=1)(emb) # None * K
summed_features_emb_square = Multiply()([summed_features_emb,summed_features_emb]) # None * K
squared_features_emb = Multiply()([emb, emb]) # None * 9 * K
squared_sum_features_emb = MySumLayer(axis=1)(squared_features_emb) # Non * K
sub = Subtract()([summed_features_emb_square, squared_sum_features_emb]) # None * K
sub = Lambda(lambda x:x*0.5)(sub) # None * K
y_second_order = MySumLayer(axis=1)(sub) # None * 1
'''deep parts'''
y_deep = MyFlatten()(emb) # None*(6*K)
y_deep = Dropout(0.5)(Dense(128, activation='relu')(y_deep))
y_deep = Dropout(0.5)(Dense(64, activation='relu')(y_deep))
y_deep = Dropout(0.5)(Dense(32, activation='relu')(y_deep))
y_deep = Dropout(0.5)(Dense(1, activation='relu')(y_deep))
'''deepFM'''
y = Concatenate(axis=1)([y_first_order, y_second_order,y_deep])
y = Dense(1, activation='sigmoid')(y)
model = Model(inputs=[in_score, in_sales,
in_gender, in_age,
in_interest, in_topic],
outputs=[y])
plot_model(model, 'model.png', show_shapes=True)in_interest本身one-hot
2 deepctr模版
DeepCTR & DeepMatch简单实用指南_tf.Print(**)的博客-CSDN博客
文档：
Welcome to DeepCTR’s documentation! — DeepCTR 0.9.3 documentation
模型保存：
FAQ — DeepCTR 0.9.3 documentation
deepctr.models.deepfm module — DeepCTR 0.9.3 documentation
model = DeepFM()
model.save_weights('DeepFM_w.h5')
model.load_weights('DeepFM_w.h5')sparse_features = ['C' + str(i) for i in range(1, 27)]
#稀疏，离散特征
dense_features = ['I' + str(i) for i in range(1, 14)]
#连续，全连接def get_feature_names(feature_columns):
features = build_input_features(feature_columns)
return list(features.keys())
def build_input_features(feature_columns, prefix=''):
input_features = OrderedDict()
# 字典
for fc in feature_columns:
if isinstance(fc, SparseFeat):
input_features[fc.name] = Input(
shape=(1,), name=prefix + fc.name, dtype=fc.dtype)
elif isinstance(fc, DenseFeat):
input_features[fc.name] = Input(
shape=(fc.dimension,), name=prefix + fc.name, dtype=fc.dtype)
elif isinstance(fc, VarLenSparseFeat):
input_features[fc.name] = Input(shape=(fc.maxlen,), name=prefix + fc.name,
dtype=fc.dtype)
if fc.weight_name is not None:
input_features[fc.weight_name] = Input(shape=(fc.maxlen, 1), name=prefix + fc.weight_name,
dtype="float32")
if fc.length_name is not None:
input_features[fc.length_name] = Input((1,), name=prefix + fc.length_name, dtype='int32')
else:
raise TypeError("Invalid feature column type,got", type(fc))
return input_features
class DenseFeat(namedtuple('DenseFeat', ['name', 'dimension', 'dtype', 'transform_fn'])):
""" Dense feature
Args:
name: feature name.
dimension: dimension of the feature, default = 1.
dtype: dtype of the feature, default="float32".
transform_fn: If not `None` , a function that can be used to transform
values of the feature. the function takes the input Tensor as its
argument, and returns the output Tensor.
(e.g. lambda x: (x - 3.0) / 4.2).
"""
__slots__ = ()
def __new__(cls, name, dimension=1, dtype="float32", transform_fn=None):
return super(DenseFeat, cls).__new__(cls, name, dimension, dtype, transform_fn)
def __hash__(self):
return self.name.__hash__()
# def __eq__(self, other):
# if self.name == other.name:
# return True
# return False
# def __repr__(self):
# return 'DenseFeat:'+self.name
class SparseFeat(namedtuple('SparseFeat',
['name', 'vocabulary_size', 'embedding_dim', 'use_hash', 'vocabulary_path', 'dtype', 'embeddings_initializer',
'embedding_name',
'group_name', 'trainable'])):
__slots__ = ()
def __new__(cls, name, vocabulary_size, embedding_dim=4, use_hash=False, vocabulary_path=None, dtype="int32", embeddings_initializer=None,
embedding_name=None,
group_name=DEFAULT_GROUP_NAME, trainable=True):
if embedding_dim == "auto":
embedding_dim = 6 * int(pow(vocabulary_size, 0.25))
if embeddings_initializer is None:
embeddings_initializer = RandomNormal(mean=0.0, stddev=0.0001, seed=2020)
if embedding_name is None:
embedding_name = name
return super(SparseFeat, cls).__new__(cls, name, vocabulary_size, embedding_dim, use_hash, vocabulary_path, dtype,
embeddings_initializer,
embedding_name, group_name, trainable)使用例子
python:sklearn标签编码(LabelEncoder)_sklearn labelencoder_jenny_paofu的博客-CSDN博客
from sklearn import preprocessing
data=['电脑','手机','手机','手表']
enc=preprocessing.LabelEncoder()
enc=enc.fit(['电脑','手机','手表'])#训练LabelEncoder,将电脑，手表，手机编码为0,1,2
data=enc.transform(data)#使用训练好的LabelEncoder对原数据进行编码，也叫归一化
print(data)#[2 0 0 1]
import pandas as pd
from sklearn.metrics import log_loss, roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
from deepctr.models import DeepFM
from deepctr.feature_column import SparseFeat, DenseFeat, get_feature_names
if __name__ == "__main__":
data = pd.read_csv('./criteo_sample.txt')
sparse_features = ['C' + str(i) for i in range(1, 27)]
dense_features = ['I' + str(i) for i in range(1, 14)]
data[sparse_features] = data[sparse_features].fillna('-1', )
data[dense_features] = data[dense_features].fillna(0, )
target = ['label']
# 1.Label Encoding for sparse features,and do simple Transformation for dense features
for feat in sparse_features:
lbe = LabelEncoder()
data[feat] = lbe.fit_transform(data[feat])
mms = MinMaxScaler(feature_range=(0, 1))
data[dense_features] = mms.fit_transform(data[dense_features])
# 2.count #unique features for each sparse field,and record dense feature field name
fixlen_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4 )
for i,feat in enumerate(sparse_features)] + [DenseFeat(feat, 1,)
for feat in dense_features]
dnn_feature_columns = fixlen_feature_columns
linear_feature_columns = fixlen_feature_columns
feature_names = get_feature_names(linear_feature_columns + dnn_feature_columns)
# 3.generate input data for model
train, test = train_test_split(data, test_size=0.2, random_state=2018)
train_model_input = {name:train[name] for name in feature_names}
test_model_input = {name:test[name] for name in feature_names}
# 4.Define Model,train,predict and evaluate
model = DeepFM(linear_feature_columns, dnn_feature_columns, task='binary')
model.compile("adam", "binary_crossentropy",
metrics=['binary_crossentropy'], )
history = model.fit(train_model_input, train[target].values,
batch_size=256, epochs=10, verbose=2, validation_split=0.2, )
pred_ans = model.predict(test_model_input, batch_size=256)
print("test LogLoss", round(log_loss(test[target].values, pred_ans), 4))
print("test AUC", round(roc_auc_score(test[target].values, pred_ans), 4))fm
输入：
model = Model(inputs=inputs_list, outputs=output)
return model输入样式
def input_from_feature_columns(features, feature_columns, l2_reg, seed, prefix='', seq_mask_zero=True,
support_dense=True, support_group=False):
sparse_feature_columns = list(
filter(lambda x: isinstance(x, SparseFeat), feature_columns)) if feature_columns else []
varlen_sparse_feature_columns = list(
filter(lambda x: isinstance(x, VarLenSparseFeat), feature_columns)) if feature_columns else []
embedding_matrix_dict = create_embedding_matrix(feature_columns, l2_reg, seed, prefix=prefix,
seq_mask_zero=seq_mask_zero)
group_sparse_embedding_dict = embedding_lookup(embedding_matrix_dict, features, sparse_feature_columns)
dense_value_list = get_dense_input(features, feature_columns)
if not support_dense and len(dense_value_list) > 0:
raise ValueError("DenseFeat is not supported in dnn_feature_columns")
sequence_embed_dict = varlen_embedding_lookup(embedding_matrix_dict, features, varlen_sparse_feature_columns)
group_varlen_sparse_embedding_dict = get_varlen_pooling_list(sequence_embed_dict, features,
varlen_sparse_feature_columns)
group_embedding_dict = mergeDict(group_sparse_embedding_dict, group_varlen_sparse_embedding_dict)
if not support_group:
group_embedding_dict = list(chain.from_iterable(group_embedding_dict.values()))
return group_embedding_dict, dense_value_list
def build_input_features(feature_columns, prefix=''):
input_features = OrderedDict()
for fc in feature_columns:
if isinstance(fc, SparseFeat):
input_features[fc.name] = Input(
shape=(1,), name=prefix + fc.name, dtype=fc.dtype)
elif isinstance(fc, DenseFeat):
input_features[fc.name] = Input(
shape=(fc.dimension,), name=prefix + fc.name, dtype=fc.dtype)
elif isinstance(fc, VarLenSparseFeat):
input_features[fc.name] = Input(shape=(fc.maxlen,), name=prefix + fc.name,
dtype=fc.dtype)
if fc.weight_name is not None:
input_features[fc.weight_name] = Input(shape=(fc.maxlen, 1), name=prefix + fc.weight_name,
dtype="float32")
if fc.length_name is not None:
input_features[fc.length_name] = Input((1,), name=prefix + fc.length_name, dtype='int32')
else:
raise TypeError("Invalid feature column type,got", type(fc))
return embedding
深度学习框架Keras中的embedding简单理解 - 简书
def create_embedding_dict(sparse_feature_columns, varlen_sparse_feature_columns, seed, l2_reg,
prefix='sparse_', seq_mask_zero=True):
sparse_embedding = {}
for feat in sparse_feature_columns:
emb = Embedding(feat.vocabulary_size, feat.embedding_dim,
embeddings_initializer=feat.embeddings_initializer,
embeddings_regularizer=l2(l2_reg),
name=prefix + '_emb_' + feat.embedding_name)
emb.trainable = feat.trainable
sparse_embedding[feat.embedding_name] = emb
if varlen_sparse_feature_columns and len(varlen_sparse_feature_columns) > 0:
for feat in varlen_sparse_feature_columns:
# if feat.name not in sparse_embedding:
emb = Embedding(feat.vocabulary_size, feat.embedding_dim,
embeddings_initializer=feat.embeddings_initializer,
embeddings_regularizer=l2(
l2_reg),
name=prefix + '_seq_emb_' + feat.name,
mask_zero=seq_mask_zero)
emb.trainable = feat.trainable
sparse_embedding[feat.embedding_name] = emb
return # -*- coding:utf-8 -*-
"""
Author:
Weichen Shen, weichenswc@163.com
Reference:
[1] Guo H, Tang R, Ye Y, et al. Deepfm: a factorization-machine based neural network for ctr prediction[J]. arXiv preprint arXiv:1703.04247, 2017.(https://arxiv.org/abs/1703.04247)
"""
from itertools import chain
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense
from ..feature_column import build_input_features, get_linear_logit, DEFAULT_GROUP_NAME, input_from_feature_columns
from ..layers.core import PredictionLayer, DNN
from ..layers.interaction import FM
from ..layers.utils import concat_func, add_func, combined_dnn_input
def DeepFM(linear_feature_columns, dnn_feature_columns, fm_group=(DEFAULT_GROUP_NAME,), dnn_hidden_units=(256, 128, 64),
l2_reg_linear=0.00001, l2_reg_embedding=0.00001, l2_reg_dnn=0, seed=1024, dnn_dropout=0,
dnn_activation='relu', dnn_use_bn=False, task='binary'):
"""Instantiates the DeepFM Network architecture.
:param linear_feature_columns: An iterable containing all the features used by the linear part of the model.
:param dnn_feature_columns: An iterable containing all the features used by the deep part of the model.
:param fm_group: list, group_name of features that will be used to do feature interactions.
:param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN
:param l2_reg_linear: float. L2 regularizer strength applied to linear part
:param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
:param l2_reg_dnn: float. L2 regularizer strength applied to DNN
:param seed: integer ,to use as random seed.
:param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
:param dnn_activation: Activation function to use in DNN
:param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN
:param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:return: A Keras model instance.
"""
features = build_input_features(
linear_feature_columns + dnn_feature_columns)
inputs_list = list(features.values())
linear_logit = get_linear_logit(features, linear_feature_columns, seed=seed, prefix='linear',
l2_reg=l2_reg_linear)
group_embedding_dict, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding,
seed, support_group=True)
fm_logit = add_func([FM()(concat_func(v, axis=1))
for k, v in group_embedding_dict.items() if k in fm_group])
dnn_input = combined_dnn_input(list(chain.from_iterable(
group_embedding_dict.values())), dense_value_list)
dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input)
dnn_logit = Dense(1, use_bias=False)(dnn_output)
final_logit = add_func([linear_logit, fm_logit, dnn_logit])
output = PredictionLayer(task)(final_logit)
model = Model(inputs=inputs_list, outputs=output)
return model
3 其他实现方式
GitHub - as472780551/ctr_Keras: LR, Wide&Deep, DCN, NFM, DeepFM, NFFM
ctr_Keras
很简单的ctr模型实现，欢迎指出bug&提出宝贵意见！
模型
LR
FNN：http://www0.cs.ucl.ac.uk/staff/w.zhang/rtb-papers/deep-ctr.pdf
Wide&Deep：https://arxiv.org/abs/1606.07792
IPNN：https://arxiv.org/abs/1611.00144
DCN：https://arxiv.org/abs/1708.05123
NFM：https://www.comp.nus.edu.sg/~xiangnan/papers/sigir17-nfm.pdf
DeepFM：https://arxiv.org/abs/1703.04247
NFFM：腾讯赛冠军模型
数据集
kaggle-criteo-2014 dataset
Kaggle Display Advertising Challenge Dataset - Criteo Engineering
数据集按9:1划分，请自行划分
| train | test |
|---|---|
| 41256556 | 4584063 |
预处理
连续型特征(13)：缺失值补0，离散分桶
离散型特征(26)：过滤频率低于10的特征值
执行步骤
运行preprocess.py生成train.csv和test.csv文件
python preprocess.py
运行相应的ctr模型代码文件，如
python lr.py代码：
Embedding理解及keras中Embedding参数详解，代码案例说明_小时不识月123的博客-CSDN博客
from keras.layers.embeddings import Embedding
keras.layers.Embedding(input_dim, #词汇表大小，就是你的文本里你感兴趣词的数量
output_dim, #词向量的维度
embeddings_initializer='uniform',# Embedding矩阵的初始化方法
embeddings_regularizer=None,# Embedding matrix 的正则化方法
activity_regularizer=None,
embeddings_constraint=None, # Embedding matrix 的约束函数
mask_zero=False, #是否把 0 看作"padding" 值，取值为True时，接下来的所有层都必须支持 masking，词汇表的索引要从1开始（因为文档填充用的是0，如果词汇表索引从0开始会产生混淆，input_dim
=vocabulary + 1）
input_length=None)# 输入序列的长度，就是文档经过padding后的向量的长度。
'''
函数输入：尺寸为（batch_size, input_length）的2D张量，
batch_size就是你的mini batch里的样本量，
input_length就是你的文档转化成索引向量（每个词用词索引表示的向量）后的维数。
函数输出：尺寸为（batch_size, input_length,output_dim）的3D张量，
上面说了，output_dim就是词向量的维度，就是词转化为向量，这个向量的维度，
比如word2vec把“哈哈”转化为向量[1.01,2.9,3]，那么output_dim就是3.
'''
embedding
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
@author:
@contact:
@time:
@context:
"""
from keras.layers.embeddings import Embedding
from keras.models import Sequential
import numpy as np
#我们随机生成第一层输入，即每个样本存储于单独的list，此list里的每个特征或者说元素用正整数索引表示，同时所有样本构成list
input_array = np.random.randint(1000, size=(32, 10))
'''
[[250 219 228 56 572 110 467 214 173 342]
[678 13 994 406 678 995 966 398 732 715]
...
[426 519 254 180 235 707 887 962 834 269]
[775 380 706 784 842 369 514 265 797 976]
[666 832 821 953 369 836 656 808 562 263]]
'''
model = Sequential()
model.add(Embedding(1000, 64, input_length=10))#词汇表里词999,词向量的维度64,输入序列的长度10
# keras.layers.Embedding(input_dim, output_dim, input_length)#词汇表大小,词向量的维度,输入序列的长度
print(model.input_shape)
print(model.output_shape)
'''
(None, 10) #其中 None的取值是batch_size
(None, 10, 64)
input_shape:函数输入,尺寸为（batch_size, 10）的2D张量(矩阵的意思)
output_shape:函数输出,尺寸为（batch_size, 10,64）的3D张量
'''
model.compile('rmsprop', 'mse')
output_array = model.predict(input_array)
assert output_array.shape == (32, 10, 64)
print(output_array)
print(len(output_array))
print(len(output_array[1]))
print(len(output_array[1][1]))
'''
[
[[] [] [] ... [] [] []]
[[] [] [] ... [] [] []]
...
[[] [] [] ... [] [] []]
]
32:最外层维数32，32个样本
10：第二层维数10，每个样本用10个词表示
64：最内层维数64，每个词用64维向量表示
'''
for cat in cat_columns:
input = Input(shape=(1,))
cat_field_input.append(input)
nums = pd.concat((train[cat], test[cat])).max() + 1
embed = Embedding(nums, 1, input_length=1, trainable=True)(input)def base_model(cat_columns, train, test):
cat_num = len(cat_columns)
field_cnt = cat_num
cat_field_input = []
field_embedding = []
lr_embedding = []
for cat in cat_columns:
input = Input(shape=(1,))
cat_field_input.append(input)
nums = pd.concat((train[cat], test[cat])).max() + 1
embed = Embedding(nums, 1, input_length=1, trainable=True)(input)
reshape = Reshape((1,))(embed)
lr_embedding.append(reshape)
# fm embeddings
field = []
embed = Embedding(nums, 10, input_length=1, trainable=True)(input)
reshape = Reshape((10,))(embed)
field_embedding.append(reshape)
# fm embeddings
#######fm layer##########
fm_square = Lambda(lambda x: K.square(x))(add(field_embedding))
square_fm = add([Lambda(lambda x:K.square(x))(embed)
for embed in field_embedding])
inner_product = subtract([fm_square, square_fm])
inner_product = Lambda(lambda x: K.sum(
x / 2, axis=-1, keepdims=True))(inner_product)
#######dnn layer##########
embed_layer = concatenate(field_embedding, axis=-1)
embed_layer = Dense(64)(embed_layer)
embed_layer = BatchNormalization()(embed_layer)
embed_layer = Activation('relu')(embed_layer)
embed_layer = Dense(64)(embed_layer)
embed_layer = BatchNormalization()(embed_layer)
embed_layer = Activation('relu')(embed_layer)
embed_layer = Dense(1)(embed_layer)
########linear layer##########
lr_layer = add(lr_embedding + [embed_layer, inner_product])
preds = Activation('sigmoid')(lr_layer)
opt = Adam(0.001)
model = Model(inputs=cat_field_input, outputs=preds)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['acc'])
print(model.summary())
return model4何为focal loss
focal loss 是随网络RetinaNet一起提出的一个令人惊艳的损失函数 paper 下载，主要针对的是解决正负样本比例严重偏斜所产生的模型难以训练的问题。
在keras中使用此函数作为损失函数，只需在编译模型时指定损失函数为focal loss：
model.compile(loss=[binary_focal_loss(alpha=.25, gamma=2)], metrics=["accuracy"], optimizer=optimizer)
参考
- 深度学习推荐系统作业：deepFM介绍和代码实现_deepfm代码_J_co的博客-CSDN博客
- CTR算法总结_Roger-Liu的博客-CSDN博客
- DeepCTR模型优缺点对比_deepctr训练加速_ChristineC_的博客-CSDN博客
- deepFm的keras实现_deepfm keras_xxaxtt的博客-CSDN博客
- 如何用keras实现deepFM_DemonHunter211的博客-CSDN博客
- 最通俗的deepFM理解及keras实现_keras实现deepfm_Mr_不想起床的博客-CSDN博客
- 用Keras实现一个DeepFM_deepfm 基于libsvm实现_蕉叉熵的博客-CSDN博客
- DeepCTR：易用可扩展的深度学习点击率预测算法包 - 知乎
- python:sklearn标签编码(LabelEncoder)_sklearn labelencoder_jenny_paofu的博客-CSDN博客
- 【推荐系统】DeepFM模型_deepfm多分类求auc_—Xi—的博客-CSDN博客
- GitHub - shenweichen/DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
- 理解：推荐算法之DeepFM_Aliert的博客-CSDN博客
