导库
from tensorflow.keras import *
from tensorflow.keras.layers import *
from tensorflow.keras.models import *
from tensorflow.keras.callbacks import *
import tensorflow.keras.backend as K
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.datasets import load_breast_cancer
from tqdm import tqdm
import pandas as pd
import numpy as np
FM特征组合层
# FM 特征组合层
class crossLayer(layers.Layer):
def __init__(self, input_dim, output_dim=10, **kwargs):
super(crossLayer, self).__init__(**kwargs)
self.input_dim = input_dim
self.output_dim = output_dim
self.kernel = self.add_weight(name='kernel',
shape=(self.input_dim, self.output_dim),
initializer='glorot_uniform',
trainable=True)
def call(self, x):
a = K.pow(K.dot(x, self.kernel), 2)
b = K.dot(K.pow(x, 2), K.pow(self.kernel, 2))
return 0.5 * K.mean(a - b, 1, keepdims=True)
定义FM模型
# 定义FM模型
def FM(feature_dim):
inputs = Input(shape=(feature_dim,))
# 一阶特征
linear = Dense(units=1,
kernel_regularizer=regularizers.l2(0.01),
bias_regularizer=regularizers.l2(0.01))(inputs)
# 二阶特征
cross = crossLayer(feature_dim)(inputs)
add = Add()([linear, cross])
pred = Dense(units=1, activation="sigmoid")(add)
model = Model(inputs=inputs, outputs=pred)
model.summary()
model.compile(loss='binary_crossentropy',
optimizer=optimizers.Adam(),
metrics=['binary_accuracy'])
return model
读取数据
# 读取数据
print('loading data...')
X, y = load_breast_cancer(return_X_y = True)
划分数据集
# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2021)
构建模型
# 构建模型
model = FM(X.shape[1])
训练模型
# 训练模型
model.fit(X_train, y_train, epochs=10, batch_size=128, validation_data=(X_test, y_test))
