tensorflow for python做模型训练、tensorflow for java做模型预测(只生产pb文件，不生产variable的情况下)

我不是女神ヾ 2021-09-28 09:06 223阅读 0赞

python脚本

#!/usr/bin/python
    # -*- coding:utf-8 -*-
    
    import tensorflow as tf
    from tensorflow import saved_model as sm
    import numpy as np
    
    x_data = np.linspace(-0.5,0.5,200)[:,np.newaxis]
    noise = np.random.normal(0,0.02,x_data.shape)
    y_data = np.square(x_data) + noise
    
    x = tf.placeholder(tf.float32,[None,1],name="x")
    y = tf.placeholder(tf.float32,[None,1])
    
    Weights_L1 = tf.Variable(tf.random_normal([1,10]))
    biases_L1 = tf.Variable(tf.zeros([1,10]))
    Wx_plus_b_L1 = tf.matmul(x,Weights_L1) + biases_L1
    L1 = tf.nn.tanh(Wx_plus_b_L1)
    
    Weights_L2 = tf.Variable(tf.random_normal([10,1]))
    biases_L2 = tf.Variable(tf.zeros([1,1]))
    Wx_plus_b_L2 = tf.matmul(L1,Weights_L2) + biases_L2
    prediction = tf.nn.tanh(Wx_plus_b_L2,name="y")
    
    loss = tf.reduce_mean(tf.square(y - prediction))
    # 使用梯度下降法训练
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
    
    with tf.Session() as sess:
        # 变量初始化
        sess.run(tf.global_variables_initializer())
        for _ in range(2000):
            sess.run(train_step, feed_dict={x: x_data, y: y_data})
    
        # 获得预测值
        prediction_value = sess.run(prediction, feed_dict={x: x_data})
        print([n.name for n in sess.graph.as_graph_def().node])
        frozen_graph_def = tf.graph_util.convert_variables_to_constants(
            sess,
            sess.graph_def,
            output_node_names=["y"])
    
        # 保存图为pb文件
        with open('model.pb', 'wb') as f:
            f.write(frozen_graph_def.SerializeToString())
    
    java脚本

package XXX;
    
    import org.apache.commons.io.IOUtils;
    import org.tensorflow.Graph;
    import org.tensorflow.Session;
    import org.tensorflow.Tensor;
    import java.util.Random;
    
    import java.io.FileInputStream;
    import java.io.IOException;
    import java.nio.FloatBuffer;
    
    public class tflinearmodel {
    
        public static void main(String[] args) throws IOException {
            String path = "xxx/model.pb";
            try (Graph graph = new Graph()) {
                //导入图
                byte[] graphBytes = IOUtils.toByteArray(new
                        FileInputStream(path));
                graph.importGraphDef(graphBytes);
                System.out.println(graphBytes);
    
                //根据图建立Session
                try(Session session = new Session(graph)){
                    System.out.println("hello");
                    float[][] input = new float[1][1];
                    input[0] = new float[]{10.0f};
                    //相当于TensorFlow Python中的sess.run(z,feed_dict = ({'x': 10.0})
                    //float z = session.runner()
                    //        .feed("x", Tensor.create(input))
                    //        .fetch("y").run().get(0).floatValue();  此脚本增加floatValue()方法后会报错   //Exception in thread "main" java.lang.IllegalStateException: Tensor is not a scalar                  //脚本修改如下则执行正确
      Tensor z = session.runner()   .feed("x", Tensor.create(input))   .fetch("y").run().get(0);                   float[][] zz = (float[][]) z.copyTo(new float[1][1]);
                       System.out.println("y = " + zz[0][0]);            }
            }
    
        }
    }