All geek questions in one place

Loading sklearn model in Java. Model created using DNNClassifier in python

The goal is to open in Java a model created / prepared in python with tensorflow.contrib.learn.learn.DNNClassifier.

At the moment, the main problem is to find out the name of the "tensor", which must be passed to java using the "Session runner" method.

I have this test code in 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))

The file model_saved.pbtxt is created.

I am trying to load this model in Java with the following code:

    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);



    }
}

I get an error on .run (). get (0); line:

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)
+4
source share
4 answers

, , : java, "dnn/input_from_feature_columns/input_from_feature_columns/concat", "input_example_tensor".

, : tensorboard --logdir = D:\python\Workspace\Autoencoder\src\dnn\ModelSave

java:

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];
    }
FloatBuffer.wrap(inputfloat) );
    float[][] data= new float[1][35];
    data[0]=inputfloat;
    Tensor inputTensor=Tensor.create(data);


    Tensor result = s.runner()
            .feed("dnn/input_from_feature_columns/input_from_feature_columns/concat", inputTensor)
            //.feed("input_example_tensor", inputTensor)
            //.fetch("tensorflow/serving/classify")
            .fetch("dnn/multi_class_head/predictions/probabilities")
            //.fetch("dnn/zero_fraction_3/Cast")
            .run().get(0);


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



}

}

:

phyton side:

Prediction for sample_2 is:[3] 
Prediction for sample_2 is:[array([ 0.17157166,  0.24475774,  0.16158019,  0.24648622,  0.17560424], dtype=float32)]

Java:

0.17157166  
0.24475774  
0.16158019   
0.24648622  
0.17560424  
3
+2

: "input_example_tensor" string , float.

, , , , input_fn_utils.py. tf.parse_example() op, tf.train.Example, .

+1

feed("input_example_tensor", inputTensor) Tensorflow 1.1.

, example.proto "input_example_tensor", , , .

inputTensor.

org.tensorflow.example.Example.Builder example = org.tensorflow.example.Example.newBuilder();   
/* set some features to example... */

Tensor exampleTensor = Tensor.create(example.build().toByteArray());
// Here, the shape of exampleTensor is not specified yet.

// Set the shape to feed this as "input_example_tensor"
Graph g = bundle.graph(); 
Output examplePlaceholder =
                  g.opBuilder("Placeholder", "example")
                  .setAttr("dtype", exampleTensor.dataType())                        
                      .build().output(0);
Tensor shapeTensor = Tensor.create(new long[]{1}, IntBuffer.wrap(new int[]{1}));                      
Output shapeConst = g.opBuilder("Const", "shape")
                      .setAttr("dtype", shapeTensor.dataType())
                      .setAttr("value", shapeTensor)
                      .build().output(0);
Output shaped = g.opBuilder("Reshape", "output").addInput(examplePlaceholder).addInput(shapeConst).build().output(0);


Tensor inputTensor = s.runner().feed(examplePlaceholder, exampleTensor).fetch(shaped).run().get(0);                   
// Now, inputTensor has shape of [1] and ready to feed.
0

.feed() .fetch() .

savemodel.pbtxt. /.

,

Java

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

my savedModel.pbtxt( )

node {
  name: "ParseExample/ParseExample"
  op: "ParseExample"
  input: "input_example_tensor"
  input: "ParseExample/ParseExample/names"
  input: "ParseExample/ParseExample/dense_keys_0"
  input: "ParseExample/Const"
  attr {
    key: "Ndense"
    value {
      i: 1
    }
  }
  attr {
    key: "Nsparse"
    value {
      i: 0
    }
  }
  attr {
    key: "Tdense"
    value {
      list {
        type: DT_FLOAT
      }
    }
  }
  attr {
    key: "_output_shapes"
    value {
      list {
        shape {
          dim {
            size: -1
          }
          dim {
            size: 2
          }
        }
      }
    }
  }
  attr {
    key: "dense_shapes"
    value {
      list {
        shape {
          dim {
            size: 2
          }
        }
      }
    }
  }
  attr {
    key: "sparse_types"
    value {
      list {
      }
    }
  }
}
  outputs {
    key: "scores"
    value {
      name: "dnn/binary_logistic_head/predictions/probabilities:0"
      dtype: DT_FLOAT
      tensor_shape {
        dim {
          size: -1
        }
        dim {
          size: 2
        }
      }
    }
  }

, .

0

Source: https://habr.com/ru/post/1675571/

More articles:


All Articles