Train TF models in Python and Invoke models in Java

• Plan A

#Train in Pythonimport tensorflow as tf# good idea# https://stackoverflow.com/documentation/tensorflow/10718/save-tensorflow-model-in-python-and-load-with-java#t=201709030336395954421tf.reset_default_graph()# DO MODEL STUFF# Pretrained weighting of 2.0W = tf.get_variable('w', initializer=tf.constant(2.0), dtype=tf.float32)# Model input xx = tf.placeholder(tf.float32, name='x')# Model output y = W*xy = tf.multiply(W, x, name='y')# DO SESSION STUFFsess = tf.Session()sess.run(tf.global_variables_initializer())# SAVE THE MODELbuilder = tf.saved_model.builder.SavedModelBuilder("/tmp/model" )builder.add_meta_graph_and_variables(  sess,  [tf.saved_model.tag_constants.SERVING])builder.save()

//Invoke in Javaimport org.tensorflow.SavedModelBundle;import org.tensorflow.Session;import org.tensorflow.Tensor;import org.tensorflow.TensorFlow;import java.io.IOException;import java.nio.FloatBuffer;/** * Created by apollo on 17-9-3. * https://stackoverflow.com/documentation/tensorflow/10718/save-tensorflow-model-in-python-and-load-with-java#t=201709030336395954421 */public class LoadModel {    public static void main(String[] args) throws IOException {        // good idea to print the version number, 1.2.0 as of this writing        System.out.println(TensorFlow.version());        final int NUM_PREDICTIONS = 1;        /* load the model Bundle */        SavedModelBundle b = SavedModelBundle.load("/tmp/model", "serve");        // create the session from the Bundle        Session sess = b.session();        // create an input Tensor, value = 2.0f        Tensor x = Tensor.create(                new long[]{NUM_PREDICTIONS},                FloatBuffer.wrap(new float[]{2.0f})        );        // run the model and get the result, 4.0f.        float[] y = sess.runner()                .feed("x", x)                .fetch("y")                .run()                .get(0)                .copyTo(new float[NUM_PREDICTIONS]);        // print out the result.        System.out.println(y[0]);    }}

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||||Plan B|||| {only in python , only import}
On the Python side, Tensorflow suggests to use a Saver object to save a model to disk. It creates a .meta file that has the definition and has .data files for the weights. In Python, I use new_saver=tf.train.import_meta_graph(var_filename)
new_saver.restore(sess, model_filename) to read the model from the disk.

||||Plan C|||| {only in python, only save}
tf.train.write_graph(sess.graph_def, “./data”, “aaa.pb”);
this aaa.pb contains graph and variables , not like Plan A(that pb only contain graph)

||||Plan D|||| {only in python, only save , import and perdict have error}
//https://github.com/jiegzhan/multi-class-text-classification-cnn-rnn
saver = tf.train.Saver(tf.all_variables())

error===
saver = tf.train.import_meta_graph(“{}.meta”.format(checkpoint_file[:-5]))
saver.restore(sess, checkpoint_file)
path = saver.save(sess, checkpoint_prefix, global_step=current_step)

||||Plan E||||
https://stackoverflow.com/questions/43598953/loading-sklearn-model-in-java-model-created-with-dnnclassifier-in-python

classifier = learn.DNNClassifier(hidden_units=[10, 20, 5], n_classes=5,feature_columns=feature_columns)
A model_saved.pbtxt file is created.

SavedModelBundle bundle=SavedModelBundle.load(“/java/…/ModelSave”,”serve”);

Reference Website:

http://blog.csdn.net/michael_yt/article/details/74737489

http://blog.csdn.net/lujiandong1/article/details/53385092

https://blog.metaflow.fr/tensorflow-saving-restoring-and-mixing-multiple-models-c4c94d5d7125

http://www.cnblogs.com/nowornever-L/p/6991295.html