https://blog.csdn.net/luoyexuge/article/details/79941565?utm_source=blogxgwz8

首先是libsvm格式数据生成java代码，我用数字特征为5个，字符特征为3个，one-hot之后总计为39个特征：

package com.meituan.test;

import java.io.BufferedWriter;

import java.io.FileOutputStream;

import java.io.IOException;

import java.io.OutputStreamWriter;

import java.util.Random;

public class LibSvmUtils {

	public final static String TrainPATH = "click/train.txt";

	public final static String TestPATH = "click/test.txt";

	public final static String VAPATH = "click/va.txt";

	public static void main(String[] args) throws IOException {

		genatartelibsvm(TrainPATH, 20000);

		genatartelibsvm(TestPATH, 2000);

		genatartelibsvm(VAPATH, 200);

	}

	public static void genatartelibsvm(String trainpath, int count) throws IOException {

		StringBuffer sb = null;

		Random random = new Random();

		BufferedWriter bf = new BufferedWriter(new OutputStreamWriter(

				new FileOutputStream(trainpath)));

		/**

		 *

		 * 总共field是8个，其中数值是5个，分类形状是3个，总估计特征39个

		 * **/

		for (int i = 0; i < count; i++) {

			sb = new StringBuffer();

			int featureidx=1;

			/**

			 * 产生label

			 * */

			sb.append(random.nextInt(2)).append(" ");

			/**

			 * 产生数字特征 5个

			 * */

			for (int j = 0; j < 5; j++) {

				sb.append(featureidx+":"+Math.random()).append(" ");

				featureidx+=1;

			}

			/**

			 * 字符特征 5个, 此时featureidx等于6，从6开始算

			 * */

			int featureidxa=featureidx;

			sb.append(featureidxa+random.nextInt(18)+":1").append(" ");

			/**

			 * 前面已经有23个，特征这一步从24算起，6+18

			 * */

			int featureidxb=featureidx+18;

			sb.append(featureidxb + random.nextInt(10)+":1").append(" ");

			/**

			 * 前面已经有33个，特征这一步从34算起，总共估计39个特征

			 * */

			int featureidxc=featureidx+18+10;

			sb.append(featureidxc+ random.nextInt(5)+":1").append("\n");

			bf.write(sb.toString());

		}

		bf.close();

	}

}

　　

tensorflow的模型代码:

"""

#1 Input pipline using Dataset high level API, Support parallel and prefetch reading

#2 Train pipline using Coustom Estimator by rewriting model_fn

#3 Support distincted training using TF_CONFIG

#4 Support export_model for TensorFlow Serving

方便迁移到其他算法上，只要修改input_fn and model_fn

by lambdaji

"""

#from __future__ import absolute_import

#from __future__ import division

#from __future__ import print_function

#import argparse

import shutil

#import sys

import os

import json

import glob

from datetime import date, timedelta

from time import time

#import gc

#from multiprocessing import Process

#import math

import random

import pandas as pd

import numpy as np

import tensorflow as tf

#################### CMD Arguments ####################

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_integer("dist_mode", 0, "distribuion mode {0-loacal, 1-single_dist, 2-multi_dist}")

tf.app.flags.DEFINE_string("ps_hosts", '', "Comma-separated list of hostname:port pairs")

tf.app.flags.DEFINE_string("worker_hosts", '', "Comma-separated list of hostname:port pairs")

tf.app.flags.DEFINE_string("job_name", '', "One of 'ps', 'worker'")

tf.app.flags.DEFINE_integer("task_index", 0, "Index of task within the job")

tf.app.flags.DEFINE_integer("num_threads", 16, "Number of threads")

tf.app.flags.DEFINE_integer("feature_size", 0, "Number of features")

tf.app.flags.DEFINE_integer("field_size", 0, "Number of fields")

tf.app.flags.DEFINE_integer("embedding_size", 32, "Embedding size")

tf.app.flags.DEFINE_integer("num_epochs", 10, "Number of epochs")

tf.app.flags.DEFINE_integer("batch_size", 64, "Number of batch size")

tf.app.flags.DEFINE_integer("log_steps", 1000, "save summary every steps")

tf.app.flags.DEFINE_float("learning_rate", 0.0005, "learning rate")

tf.app.flags.DEFINE_float("l2_reg", 0.0001, "L2 regularization")

tf.app.flags.DEFINE_string("loss_type", 'log_loss', "loss type {square_loss, log_loss}")

tf.app.flags.DEFINE_string("optimizer", 'Adam', "optimizer type {Adam, Adagrad, GD, Momentum}")

tf.app.flags.DEFINE_string("deep_layers", '256,128,64', "deep layers")

tf.app.flags.DEFINE_string("dropout", '0.5,0.5,0.5', "dropout rate")

tf.app.flags.DEFINE_boolean("batch_norm", False, "perform batch normaization (True or False)")

tf.app.flags.DEFINE_float("batch_norm_decay", 0.9, "decay for the moving average(recommend trying decay=0.9)")

tf.app.flags.DEFINE_string("data_dir", '', "data dir")

tf.app.flags.DEFINE_string("dt_dir", '', "data dt partition")

tf.app.flags.DEFINE_string("model_dir", 'tensorflow/', "model check point dir")

tf.app.flags.DEFINE_string("servable_model_dir", 'servermodel/', "export servable model for TensorFlow Serving")

tf.app.flags.DEFINE_string("task_type", 'train', "task type {train, infer, eval, export}")

tf.app.flags.DEFINE_boolean("clear_existing_model", False, "clear existing model or not")

#1 1:0.5 2:0.03519 3:1 4:0.02567 7:0.03708 8:0.01705 9:0.06296 10:0.18185 11:0.02497 12:1 14:0.02565 15:0.03267 17:0.0247 18:0.03158 20:1 22:1 23:0.13169 24:0.02933 27:0.18159 31:0.0177 34:0.02888 38:1 51:1 63:1 132:1 164:1 236:1

def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):

    print('Parsing', filenames)

    def decode_libsvm(line):

        #columns = tf.decode_csv(value, record_defaults=CSV_COLUMN_DEFAULTS)

        #features = dict(zip(CSV_COLUMNS, columns))

        #labels = features.pop(LABEL_COLUMN)

        columns = tf.string_split([line], ' ')

        labels = tf.string_to_number(columns.values[0], out_type=tf.float32)

        splits = tf.string_split(columns.values[1:], ':')

        id_vals = tf.reshape(splits.values,splits.dense_shape)

        feat_ids, feat_vals = tf.split(id_vals,num_or_size_splits=2,axis=1)

        feat_ids = tf.string_to_number(feat_ids, out_type=tf.int32)

        feat_vals = tf.string_to_number(feat_vals, out_type=tf.float32)

        return {"feat_ids": feat_ids, "feat_vals": feat_vals}, labels

    dataset = tf.data.TextLineDataset(filenames).map(decode_libsvm, num_parallel_calls=10).prefetch(500000)

    if perform_shuffle:

        dataset = dataset.shuffle(buffer_size=256)

    # epochs from blending together.

    dataset = dataset.repeat(num_epochs)

    dataset = dataset.batch(batch_size) # Batch size to use

    iterator = dataset.make_one_shot_iterator()

    batch_features, batch_labels = iterator.get_next()

    #return tf.reshape(batch_ids,shape=[-1,field_size]), tf.reshape(batch_vals,shape=[-1,field_size]), batch_labels

    return batch_features, batch_labels

def model_fn(features, labels, mode, params):

    """Bulid Model function f(x) for Estimator."""

    #------hyperparameters----

    field_size = params["field_size"]

    feature_size = params["feature_size"]

    embedding_size = params["embedding_size"]

    l2_reg = params["l2_reg"]

    learning_rate = params["learning_rate"]

    #batch_norm_decay = params["batch_norm_decay"]

    #optimizer = params["optimizer"]

    layers = list(map(int, params["deep_layers"].split(',')))

    dropout = list(map(float, params["dropout"].split(',')))

    #------bulid weights------

    FM_B = tf.get_variable(name='fm_bias', shape=[1], initializer=tf.constant_initializer(0.0))

    FM_W = tf.get_variable(name='fm_w', shape=[feature_size], initializer=tf.glorot_normal_initializer())

    FM_V = tf.get_variable(name='fm_v', shape=[feature_size, embedding_size], initializer=tf.glorot_normal_initializer())

    #------build feaure-------

    feat_ids  = features['feat_ids']

    feat_ids = tf.reshape(feat_ids,shape=[-1,field_size])

    feat_vals = features['feat_vals']

    feat_vals = tf.reshape(feat_vals,shape=[-1,field_size])

    #------build f(x)------

    with tf.variable_scope("First-order"):

        feat_wgts = tf.nn.embedding_lookup(FM_W, feat_ids) # None * F * 1

        y_w = tf.reduce_sum(tf.multiply(feat_wgts, feat_vals),1)

    with tf.variable_scope("Second-order"):

        embeddings = tf.nn.embedding_lookup(FM_V, feat_ids) # None * F * K

        feat_vals = tf.reshape(feat_vals, shape=[-1, field_size, 1])

        embeddings = tf.multiply(embeddings, feat_vals) #vij*xi

        sum_square = tf.square(tf.reduce_sum(embeddings,1))

        square_sum = tf.reduce_sum(tf.square(embeddings),1)

        y_v = 0.5*tf.reduce_sum(tf.subtract(sum_square, square_sum),1)	# None * 1

    with tf.variable_scope("Deep-part"):

        if FLAGS.batch_norm:

            #normalizer_fn = tf.contrib.layers.batch_norm

            #normalizer_fn = tf.layers.batch_normalization

            if mode == tf.estimator.ModeKeys.TRAIN:

                train_phase = True

                #normalizer_params = {'decay': batch_norm_decay, 'center': True, 'scale': True, 'updates_collections': None, 'is_training': True, 'reuse': None}

            else:

                train_phase = False

                #normalizer_params = {'decay': batch_norm_decay, 'center': True, 'scale': True, 'updates_collections': None, 'is_training': False, 'reuse': True}

        else:

            normalizer_fn = None

            normalizer_params = None

        deep_inputs = tf.reshape(embeddings,shape=[-1,field_size*embedding_size]) # None * (F*K)

        for i in range(len(layers)):

            #if FLAGS.batch_norm:

            #    deep_inputs = batch_norm_layer(deep_inputs, train_phase=train_phase, scope_bn='bn_%d' %i)

                #normalizer_params.update({'scope': 'bn_%d' %i})

            deep_inputs = tf.contrib.layers.fully_connected(inputs=deep_inputs, num_outputs=layers[i], \

                #normalizer_fn=normalizer_fn, normalizer_params=normalizer_params, \

                weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg), scope='mlp%d' % i)

            if FLAGS.batch_norm:

                deep_inputs = batch_norm_layer(deep_inputs, train_phase=train_phase, scope_bn='bn_%d' %i)   #放在RELU之后 https://github.com/ducha-aiki/caffenet-benchmark/blob/master/batchnorm.md#bn----before-or-after-relu

            if mode == tf.estimator.ModeKeys.TRAIN:

                deep_inputs = tf.nn.dropout(deep_inputs, keep_prob=dropout[i])                              #Apply Dropout after all BN layers and set dropout=0.8(drop_ratio=0.2)

                #deep_inputs = tf.layers.dropout(inputs=deep_inputs, rate=dropout[i], training=mode == tf.estimator.ModeKeys.TRAIN)

        y_deep = tf.contrib.layers.fully_connected(inputs=deep_inputs, num_outputs=1, activation_fn=tf.identity, \

                weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg), scope='deep_out')

        y_d = tf.reshape(y_deep,shape=[-1])

    with tf.variable_scope("DeepFM-out"):

        y_bias = FM_B * tf.ones_like(y_d, dtype=tf.float32)     # None * 1

        y = y_bias + y_w + y_v + y_d

        pred = tf.sigmoid(y)

    predictions={"prob": pred}

    export_outputs = {tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: tf.estimator.export.PredictOutput(predictions)}

    # Provide an estimator spec for `ModeKeys.PREDICT`

    if mode == tf.estimator.ModeKeys.PREDICT:

        return tf.estimator.EstimatorSpec(

                mode=mode,

                predictions=predictions,

                export_outputs=export_outputs)

    #------bulid loss------

    loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=y, labels=labels)) + \

        l2_reg * tf.nn.l2_loss(FM_W) + \

        l2_reg * tf.nn.l2_loss(FM_V) #+ \ l2_reg * tf.nn.l2_loss(sig_wgts)

    # Provide an estimator spec for `ModeKeys.EVAL`

    eval_metric_ops = {

        "auc": tf.metrics.auc(labels, pred)

    }

    if mode == tf.estimator.ModeKeys.EVAL:

        return tf.estimator.EstimatorSpec(

                mode=mode,

                predictions=predictions,

                loss=loss,

                eval_metric_ops=eval_metric_ops)

    #------bulid optimizer------

    if FLAGS.optimizer == 'Adam':

        optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-8)

    elif FLAGS.optimizer == 'Adagrad':

        optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate, initial_accumulator_value=1e-8)

    elif FLAGS.optimizer == 'Momentum':

        optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.95)

    elif FLAGS.optimizer == 'ftrl':

        optimizer = tf.train.FtrlOptimizer(learning_rate)

    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())

    # Provide an estimator spec for `ModeKeys.TRAIN` modes

    if mode == tf.estimator.ModeKeys.TRAIN:

        return tf.estimator.EstimatorSpec(

                mode=mode,

                predictions=predictions,

                loss=loss,

                train_op=train_op)

def batch_norm_layer(x, train_phase, scope_bn):

    bn_train = tf.contrib.layers.batch_norm(x, decay=FLAGS.batch_norm_decay, center=True, scale=True, updates_collections=None, is_training=True,  reuse=None, scope=scope_bn)

    bn_infer = tf.contrib.layers.batch_norm(x, decay=FLAGS.batch_norm_decay, center=True, scale=True, updates_collections=None, is_training=False, reuse=True, scope=scope_bn)

    z = tf.cond(tf.cast(train_phase, tf.bool), lambda: bn_train, lambda: bn_infer)

    return z

def set_dist_env():

    if FLAGS.dist_mode == 1:        # 本地分布式测试模式1 chief, 1 ps, 1 evaluator

        ps_hosts = FLAGS.ps_hosts.split(',')

        chief_hosts = FLAGS.chief_hosts.split(',')

        task_index = FLAGS.task_index

        job_name = FLAGS.job_name

        print('ps_host', ps_hosts)

        print('chief_hosts', chief_hosts)

        print('job_name', job_name)

        print('task_index', str(task_index))

        # 无worker参数

        tf_config = {

            'cluster': {'chief': chief_hosts, 'ps': ps_hosts},

            'task': {'type': job_name, 'index': task_index }

        }

        print(json.dumps(tf_config))

        os.environ['TF_CONFIG'] = json.dumps(tf_config)

    elif FLAGS.dist_mode == 2:      # 集群分布式模式

        ps_hosts = FLAGS.ps_hosts.split(',')

        worker_hosts = FLAGS.worker_hosts.split(',')

        chief_hosts = worker_hosts[0:1] # get first worker as chief

        worker_hosts = worker_hosts[2:] # the rest as worker

        task_index = FLAGS.task_index

        job_name = FLAGS.job_name

        print('ps_host', ps_hosts)

        print('worker_host', worker_hosts)

        print('chief_hosts', chief_hosts)

        print('job_name', job_name)

        print('task_index', str(task_index))

        # use #worker=0 as chief

        if job_name == "worker" and task_index == 0:

            job_name = "chief"

        # use #worker=1 as evaluator

        if job_name == "worker" and task_index == 1:

            job_name = 'evaluator'

            task_index = 0

        # the others as worker

        if job_name == "worker" and task_index > 1:

            task_index -= 2

        tf_config = {

            'cluster': {'chief': chief_hosts, 'worker': worker_hosts, 'ps': ps_hosts},

            'task': {'type': job_name, 'index': task_index }

        }

        print(json.dumps(tf_config))

        os.environ['TF_CONFIG'] = json.dumps(tf_config)

def main(_):

    #tr_files = glob.glob("%s/tr*libsvm" % FLAGS.data_dir)

    tr_files = FLAGS.data_dir+"train.txt"

    #random.shuffle(tr_files)

    print("tr_files:", tr_files)

    #va_files = glob.glob("%s/va*libsvm" % FLAGS.data_dir)

    va_files = FLAGS.data_dir + "va.txt"

    print("va_files:", va_files)

    #te_files = glob.glob("%s/te*libsvm" % FLAGS.data_dir)

    te_files = FLAGS.data_dir + "test.txt"

    print("te_files:", te_files)

    if FLAGS.clear_existing_model:

        try:

            shutil.rmtree(FLAGS.model_dir)

        except Exception as e:

            print(e, "at clear_existing_model")

        else:

            print("existing model cleaned at %s" % FLAGS.model_dir)

    set_dist_env()

    model_params = {

        "field_size": FLAGS.field_size,

        "feature_size": FLAGS.feature_size,

        "embedding_size": FLAGS.embedding_size,

        "learning_rate": FLAGS.learning_rate,

        "batch_norm_decay": FLAGS.batch_norm_decay,

        "l2_reg": FLAGS.l2_reg,

        "deep_layers": FLAGS.deep_layers,

        "dropout": FLAGS.dropout

    }

    config = tf.estimator.RunConfig().replace(session_config = tf.ConfigProto(device_count={'GPU':0, 'CPU':FLAGS.num_threads}),

            log_step_count_steps=FLAGS.log_steps, save_summary_steps=FLAGS.log_steps)

    DeepFM = tf.estimator.Estimator(model_fn=model_fn, model_dir=FLAGS.model_dir, params=model_params, config=config)

    if FLAGS.task_type == 'train':

        train_spec = tf.estimator.TrainSpec(input_fn=lambda: input_fn(tr_files, num_epochs=FLAGS.num_epochs, batch_size=FLAGS.batch_size))

        eval_spec = tf.estimator.EvalSpec(input_fn=lambda: input_fn(va_files, num_epochs=1, batch_size=FLAGS.batch_size), steps=None, start_delay_secs=1000, throttle_secs=1200)

        tf.estimator.train_and_evaluate(DeepFM, train_spec, eval_spec)

    elif FLAGS.task_type == 'eval':

        DeepFM.evaluate(input_fn=lambda: input_fn(va_files, num_epochs=1, batch_size=FLAGS.batch_size))

    elif FLAGS.task_type == 'infer':

        preds = DeepFM.predict(input_fn=lambda: input_fn(te_files, num_epochs=1, batch_size=FLAGS.batch_size), predict_keys="prob")

        with open(FLAGS.data_dir+"/pred.txt", "w") as fo:

            for prob in preds:

                fo.write("%f\n" % (prob['prob']))

    elif FLAGS.task_type == 'export':

        #feature_spec = tf.feature_column.make_parse_example_spec(feature_columns)

        #feature_spec = {

        #    'feat_ids': tf.FixedLenFeature(dtype=tf.int64, shape=[None, FLAGS.field_size]),

        #    'feat_vals': tf.FixedLenFeature(dtype=tf.float32, shape=[None, FLAGS.field_size])

        #}

        #serving_input_receiver_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)

        feature_spec = {

            'feat_ids': tf.placeholder(dtype=tf.int64, shape=[None, FLAGS.field_size], name='feat_ids'),

            'feat_vals': tf.placeholder(dtype=tf.float32, shape=[None, FLAGS.field_size], name='feat_vals')

        }

        serving_input_receiver_fn = tf.estimator.export.build_raw_serving_input_receiver_fn(feature_spec)

        DeepFM.export_savedmodel(FLAGS.servable_model_dir, serving_input_receiver_fn)

if __name__ == "__main__":

    #------check Arguments------

    if FLAGS.dt_dir == "":

        FLAGS.dt_dir = (date.today() + timedelta(-1)).strftime('%Y%m%d')

    FLAGS.model_dir = FLAGS.model_dir + FLAGS.dt_dir

    #FLAGS.data_dir  = FLAGS.data_dir + FLAGS.dt_dir

    print('task_type ', FLAGS.task_type)

    print('model_dir ', FLAGS.model_dir)

    print('data_dir ', FLAGS.data_dir)

    print('dt_dir ', FLAGS.dt_dir)

    print('num_epochs ', FLAGS.num_epochs)

    print('feature_size ', FLAGS.feature_size)

    print('field_size ', FLAGS.field_size)

    print('embedding_size ', FLAGS.embedding_size)

    print('batch_size ', FLAGS.batch_size)

    print('deep_layers ', FLAGS.deep_layers)

    print('dropout ', FLAGS.dropout)

    print('loss_type ', FLAGS.loss_type)

    print('optimizer ', FLAGS.optimizer)

    print('learning_rate ', FLAGS.learning_rate)

    print('batch_norm_decay ', FLAGS.batch_norm_decay)

    print('batch_norm ', FLAGS.batch_norm)

    print('l2_reg ', FLAGS.l2_reg)

    tf.logging.set_verbosity(tf.logging.INFO)

    tf.app.run()

　　模型训练以及保存为pb格式代码，训练过程会先checkpoint，然后再保存:

python3 DeepFM.py --task_type=train --learning_rate=0.0005 --optimizer=Adam --num_epochs=1 --batch_size=256 --field_size=8 --feature_size=39 --deep_layers=400,400,400 --dropout=0.5,0.5,0.5 --log_steps=1000 --num_threads=8 --model_dir=tensorflow/  --data_dir=data/

　　

python3 DeepFM.py --task_type=export --learning_rate=0.0005 --optimizer=Adam --batch_size=256 --field_size=8 --feature_size=39 --deep_layers=400,400,400 --dropout=0.5,0.5,0.5 --log_steps=1000 --num_threads=8 --model_dir=tensorflow/ --servable_model_dir=servermodel/

　　tensorflow-serving命令：

bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --port=9000  --model_base_path=/Users/shuubiasahi/Desktop/modelserving

　　

java中调请求serving返回结果：

package org.ranksys.javafm.example;

import java.util.Arrays;

import java.util.HashMap;

import java.util.List;

import java.util.Map;

import org.tensorflow.framework.DataType;

import org.tensorflow.framework.TensorProto;

import org.tensorflow.framework.TensorShapeProto;

import io.grpc.ManagedChannel;

import io.grpc.ManagedChannelBuilder;

import tensorflow.serving.Model;

import tensorflow.serving.Predict;

import tensorflow.serving.PredictionServiceGrpc;

public class DeepFMPrediction {

	public static void main(String[] args) {

		List<Integer> ids_vec = Arrays.asList(1, 2, 3, 4, 5, 13, 31, 38);

		List<Float> vals_vec = Arrays.asList(0.076f, 0.796f, 0.4707f, 0.21f,

				0.83f, 1.0f, 1.0f, 1.0f);

		ManagedChannel channel = ManagedChannelBuilder

				.forAddress("0.0.0.0", 9000).usePlaintext(true).build();

		// 这里还是先用block模式

		PredictionServiceGrpc.PredictionServiceBlockingStub stub = PredictionServiceGrpc

				.newBlockingStub(channel);

		// 创建请求

		Predict.PredictRequest.Builder predictRequestBuilder = Predict.PredictRequest

				.newBuilder();

		// 模型名称和模型方法名预设

		Model.ModelSpec.Builder modelSpecBuilder = Model.ModelSpec.newBuilder();

		modelSpecBuilder.setName("default");

		modelSpecBuilder.setSignatureName("");

		predictRequestBuilder.setModelSpec(modelSpecBuilder);

		TensorShapeProto.Builder tensorShapeBuilder = TensorShapeProto.newBuilder();

		tensorShapeBuilder.addDim(TensorShapeProto.Dim.newBuilder().setSize(1));

		tensorShapeBuilder.addDim(TensorShapeProto.Dim.newBuilder().setSize(8));

		// 设置入参1

		TensorProto.Builder tensorids_ids = TensorProto.newBuilder();

		tensorids_ids.setDtype(DataType.DT_INT64);

		tensorids_ids.setTensorShape(tensorShapeBuilder.build());

		tensorids_ids.addAllIntVal(ids_vec);

		// 设置入参2

		TensorProto.Builder tensorids_val = TensorProto.newBuilder();

		tensorids_val.setDtype(DataType.DT_FLOAT);

		tensorids_val.setTensorShape(tensorShapeBuilder.build());

		tensorids_val.addAllFloatVal(vals_vec);

		Map<String, TensorProto>  map=new HashMap<String, TensorProto>();

		map.put("feat_ids", tensorids_ids.build());

		map.put("feat_vals", tensorids_val.build());

		predictRequestBuilder.putAllInputs(map);

		Predict.PredictResponse predictResponse = stub.predict(predictRequestBuilder.build());

		System.out.println(predictResponse);

	}

}

　　

结果：

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