深度推荐模型——FNN [ECIR 16]

视频讲解：https://www.yuque.com/chudi/tzqav9/ny150b#aalY8

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

EPOCH = 10
BATCH_SIZE = 32
VEC_DIM = 10
DNN_LAYERS = [64, 128, 64]
DROPOUT_RATE = 0.5

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)

    model = keras.models.Sequential()
    model.add(keras.layers.Embedding(FEAT_CATE_NUM, VEC_DIM, input_length=val_x[0].shape[0]))
    model.add(keras.layers.Flatten())
    for units in DNN_LAYERS:
        model.add(keras.layers.Dense(units, activation='relu'))
        model.add(keras.layers.Dropout(DROPOUT_RATE))
    model.add(keras.layers.Dense(1, activation='sigmoid'))

    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()

今天的文章深度推荐模型——FNN [ECIR 16]分享到此就结束了，感谢您的阅读，如果确实帮到您，您可以动动手指转发给其他人。