El objetivo es abrir en Java un modelo creado / entrenado en python contensorflow.contrib.learn.learn.DNNClassifier.

Por el momento, el problema principal es saber el nombre del "tensor" para dar en Java en el método de corredor de sesión.

Tengo este código de prueba en python:

    from __future__ import division, print_function, absolute_import
import tensorflow as tf
import pandas as pd
import tensorflow.contrib.learn as learn
import numpy as np
from sklearn import metrics
from sklearn.cross_validation import train_test_split
from tensorflow.contrib import layers
from tensorflow.contrib.learn.python.learn.utils import input_fn_utils
from tensorflow.python.ops import array_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.util.compat import as_text

print(tf.VERSION)

df = pd.read_csv('../NNNormalizeData-out.csv')

inputs = []
target = []

y=0;    
for x in df.columns:
    if y != 35 :
        #print("added %d" %y)
        inputs.append(x)
    else :
        target.append(x)
    y+=1

total_inputs,total_output = df.as_matrix(inputs).astype(np.float32),df.as_matrix([target]).astype(np.int32)

train_inputs, test_inputs, train_output, test_output = train_test_split(total_inputs, total_output, test_size=0.2, random_state=42)

feature_columns = [tf.contrib.layers.real_valued_column("", dimension=train_inputs.shape[1],dtype=tf.float32)]
#target_column = [tf.contrib.layers.real_valued_column("output", dimension=train_output.shape[1])]

classifier = learn.DNNClassifier(hidden_units=[10, 20, 5], n_classes=5
                                 ,feature_columns=feature_columns)

classifier.fit(train_inputs, train_output, steps=100)

#Save Model into saved_model.pbtxt file (possible to Load in Java)
tfrecord_serving_input_fn = tf.contrib.learn.build_parsing_serving_input_fn(layers.create_feature_spec_for_parsing(feature_columns))  
classifier.export_savedmodel(export_dir_base="test", serving_input_fn = tfrecord_serving_input_fn,as_text=True)


# Measure accuracy
pred = list(classifier.predict(test_inputs, as_iterable=True))
score = metrics.accuracy_score(test_output, pred)
print("Final score: {}".format(score))

# test individual samples 
sample_1 = np.array( [[0.37671986791414125,0.28395908337619136,-0.0966095873607713,-1.0,0.06891621389763203,-0.09716678086712205,0.726029084013637,4.984689881073479E-4,-0.30296253267499107,-0.16192917054985334,0.04820256230479658,0.4951319883569152,0.5269983894210499,-0.2560313828048315,-0.3710980821053321,-0.4845867212612598,-0.8647234314469595,-0.6491591208322198,-1.0,-0.5004549422844073,-0.9880910165770813,0.5540293108747256,0.5625990251930839,0.7420121698556554,0.5445551415657979,0.4644276850235627,0.7316976292340245,0.636690006814346,0.16486621649984112,-0.0466018967678159,0.5261100063227044,0.6256168612312738,-0.544295484930702,0.379125782517193,0.6959368575211544]], dtype=float)
sample_2 = np.array( [[1.0,0.7982741870963959,1.0,-0.46270838239235024,0.040320274521029376,0.443451913224413,-1.0,1.0,1.0,-1.0,0.36689718911339564,-0.13577379160035796,-0.5162916256414466,-0.03373651520104648,1.0,1.0,1.0,1.0,0.786999801054777,-0.43856035121103853,-0.8199093927945158,1.0,-1.0,-1.0,-0.1134921695894473,-1.0,0.6420892436196663,0.7871737734493178,1.0,0.6501788845358409,1.0,1.0,1.0,-0.17586627413625022,0.8817194210401085]], dtype=float)

pred = list(classifier.predict(sample_2, as_iterable=True))
print("Prediction for sample_1 is:{} ".format(pred))

pred = list(classifier.predict_proba(sample_2, as_iterable=True))
print("Prediction for sample_2 is:{} ".format(pred))

Se crea un archivo model_saved.pbtxt.

Intento cargar este modelo en Java con el siguiente código:

    public class HelloTF {
    public static void main(String[] args) throws Exception {
        SavedModelBundle bundle=SavedModelBundle.load("/java/workspace/APIJavaSampleCode/tfModels/dnn/ModelSave","serve");
        Session s = bundle.session();

        double[] inputDouble = {1.0,0.7982741870963959,1.0,-0.46270838239235024,0.040320274521029376,0.443451913224413,-1.0,1.0,1.0,-1.0,0.36689718911339564,-0.13577379160035796,-0.5162916256414466,-0.03373651520104648,1.0,1.0,1.0,1.0,0.786999801054777,-0.43856035121103853,-0.8199093927945158,1.0,-1.0,-1.0,-0.1134921695894473,-1.0,0.6420892436196663,0.7871737734493178,1.0,0.6501788845358409,1.0,1.0,1.0,-0.17586627413625022,0.8817194210401085};
        float [] inputfloat=new float[inputDouble.length];
        for(int i=0;i<inputfloat.length;i++)
        {
            inputfloat[i]=(float)inputDouble[i];
        }
        Tensor inputTensor = Tensor.create(new long[] {35}, FloatBuffer.wrap(inputfloat) );

        Tensor result = s.runner()
                .feed("input_example_tensor", inputTensor)
                .fetch("dnn/multi_class_head/predictions/probabilities")
                .run().get(0);


         float[] m = new float[5];
         float[] vector = result.copyTo(m);
         float maxVal = 0;
         int inc = 0;
         int predict = -1;
         for(float val : vector) 
         {
             System.out.println(val+"  ");
             if(val > maxVal) {
                 predict = inc;
                 maxVal = val;
             }
             inc++;
         }
         System.out.println(predict);



    }
} 

Me sale el error en .run (). Get (0); línea:

Exception in thread "main" org.tensorflow.TensorFlowException: Output 0 of type float does not match declared output type string for node _recv_input_example_tensor_0 = _Recv[_output_shapes=[[-1]], client_terminated=true, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=3663984897684684554, tensor_name="input_example_tensor:0", tensor_type=DT_STRING, _device="/job:localhost/replica:0/task:0/cpu:0"]()
    at org.tensorflow.Session.run(Native Method)
    at org.tensorflow.Session.access$100(Session.java:48)
    at org.tensorflow.Session$Runner.runHelper(Session.java:285)
    at org.tensorflow.Session$Runner.run(Session.java:235)
    at tensorflow.HelloTF.main(HelloTF.java:35)