深度推荐模型——FM

import tensorflow as tf
import numpy as np
from tensorflow import keras
from utils import *

EPOCH = 10
STEP_PRINT = 200
STOP_STEP = 2000

LEARNING_RATE = 1e-4
BATCH_SIZE = 32
LAMDA = 1e-3

VEC_DIM = 10
base, test = loadData()
FEAT_NUM = base.shape[1] - 1
K = tf.keras.backend


class CrossLayer(keras.layers.Layer):
    def __init__(self, feat_num, vec_dim, **kwargs):
        self.feat_num = feat_num
        self.vec_dim = vec_dim
        super(CrossLayer, self).__init__(**kwargs)

    def build(self, input_shape):
        self.V = self.add_weight(name='V', shape=(self.feat_num, self.vec_dim), initializer='uniform', trainable=True)
        super(CrossLayer, self).build(input_shape)

    def call(self, inputs, **kwargs):
        a = K.pow(K.dot(inputs, self.V), 2)
        b = K.dot(K.pow(inputs, 2), K.pow(self.V, 2))
        return 0.5 * K.mean(a - b, 1, keepdims=True)


def run():
    val_x, val_y = getAllData(test)
    train_x, train_y = getAllData(base)

    inputs = keras.Input((val_x.shape[1],))
    liner = keras.layers.Dense(1)(inputs)
    cross = CrossLayer(FEAT_NUM,VEC_DIM)(inputs)
    add = keras.layers.Add()([liner,cross])
    predictions = keras.layers.Activation('sigmoid')(add)


    model = keras.Model(inputs,predictions)
    model.compile(loss='binary_crossentropy', optimizer=tf.train.AdamOptimizer(0.001), metrics=[keras.metrics.AUC()])
    tbCallBack = keras.callbacks.TensorBoard(log_dir='./logs',  # log 目录
                                             histogram_freq=0,  # 按照何等频率（epoch）来计算直方图，0为不计算
                                             #                  batch_size=32,     # 用多大量的数据计算直方图
                                             write_graph=True,  # 是否存储网络结构图
                                             write_grads=True,  # 是否可视化梯度直方图
                                             write_images=True,  # 是否可视化参数
                                             embeddings_freq=0,
                                             embeddings_layer_names=None,
                                             embeddings_metadata=None)

    model.fit(train_x, train_y, batch_size=BATCH_SIZE, epochs=EPOCH, verbose=2, validation_data=(val_x, val_y),
              callbacks=[tbCallBack])


run()