深度推荐模型——AutoInt [CIKM 19][北大]

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

EPOCH = 10
BATCH_SIZE = 32
VEC_DIM = 10
DROPOUT_RATE = 0.5
HEAD_NUM = 4
HIDE_SIZE = 10

base, test = loadData()
# 所有的特征各个类别值个数之和
FEAT_CATE_NUM = base.shape[1] - 1
K = tf.keras.backend


def run():
    # 将所有的特征的各个类别值统一id化。x中每行为各特征的类别值的id
    val_x, val_y = getAllData(test)
    train_x, train_y = getAllData(base)
    feat_num = val_x[0].shape[0]
    print(feat_num)

    inputs_id = keras.Input((feat_num,))
    emb = keras.layers.Embedding(FEAT_CATE_NUM, VEC_DIM, input_length=feat_num)(inputs_id)
    # ( batch , cate_num, VEC_DIM)

    Qs = keras.layers.Dense(HEAD_NUM * VEC_DIM)(emb)  # ( batch , cate_num, HEAD_NUM * VEC_DIM)
    Qs = keras.layers.Dropout(DROPOUT_RATE)(Qs)
    Qs = tf.split(Qs, HEAD_NUM * [VEC_DIM], 2)  # (head_num , batch , cate_num,  VEC_DIM)

    Ks = keras.layers.Dense(HEAD_NUM * VEC_DIM)(emb)
    Ks = keras.layers.Dropout(DROPOUT_RATE)(Ks)
    Ks = tf.split(Ks, HEAD_NUM * [VEC_DIM], 2)

    Vs = keras.layers.Dense(HEAD_NUM * VEC_DIM)(emb)
    Vs = keras.layers.Dropout(DROPOUT_RATE)(Vs)
    Vs = tf.split(Vs, HEAD_NUM * [VEC_DIM], 2)

    es = []
    for num in range(HEAD_NUM):
        score = tf.nn.softmax(tf.matmul(Qs[num], Ks[num], transpose_b=True) / VEC_DIM)  # (batch , cate_num, cate_num)
        hide = tf.matmul(score, Vs[num])  # (batch , cate_num, VEC_DIM)
        hide = keras.layers.Flatten()(hide)  # (batch , cate_num * VEC_DIM)
        es.append(hide)
    self_att = keras.layers.concatenate(es)  # (batch , HEAD_NUM * cate_num * VEC_DIM)
    self_att = keras.layers.Dropout(DROPOUT_RATE)(self_att)
    self_att = keras.layers.Dense(feat_num * VEC_DIM)(self_att)

    emb = keras.layers.Flatten()(emb)
    interact_layer = keras.layers.add([self_att, emb])
    interact_layer = keras.layers.Dropout(DROPOUT_RATE)(interact_layer)

    outputs = keras.layers.Dense(1, activation='sigmoid',
                                 kernel_regularizer=keras.regularizers.l2(0.001))(interact_layer)

    model = keras.Model(inputs=inputs_id, outputs=outputs)
    model.compile(loss='binary_crossentropy', optimizer=tf.train.AdamOptimizer(0.001), metrics=[keras.metrics.AUC()])
    tbCallBack = keras.callbacks.TensorBoard(log_dir='./logs',
                                             histogram_freq=0,
                                             write_graph=True,
                                             write_grads=True,
                                             write_images=True,
                                             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()