1. 技术文章
  2. Loading sklearn model in Java. Model created with DNNClassifier in python

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

由 一笑奈何 提交于 2019-12-03 15:15:40

Ok I finally Solve : the main problem was the name of the input to use in java that is ""dnn/input_from_feature_columns/input_from_feature_columns/concat" and not "input_example_tensor".

I have discover this using the graph navigation with: tensorboard --logdir=D:\python\Workspace\Autoencoder\src\dnn\ModelSave

here is the java 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];
    }
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);



}

}

I have tested the output :

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 Side :

0.17157166  
0.24475774  
0.16158019   
0.24648622  
0.17560424  
3

The error message offers a clue: the tensor named "input_example_tensor" in the model expects to have string contents, whereas you provided float values.

Judging by the name of the tensor and your code, I'd guess that the tensor you're feeding is defined in input_fn_utils.py. This tensor is passed to the tf.parse_example() op, which expects a vector of tf.train.Example protocol buffers, serialized as strings.

I got an error without feed("input_example_tensor", inputTensor) on Tensorflow 1.1.

But I found that example.proto can be fed as "input_example_tensor", although it took a lot of time to figure out how to create string tensors for serialized protocol buffer.

This is how I created 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.

Your parameters in .feed() and .fetch() should be matching with your input and output datatype.

You can look at your savedmodel.pbtxt file. There are details about your paramaters and their input/output types.

For instance,

my java code

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

my savedModel.pbtxt (part of it)

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
        }
      }
    }
  }

They both compatible with my datatype, float.

标签
java
tensorflow
tensorflow-serving
sklearn-pandas
易学教程内所有资源均来自网络或用户发布的内容，如有违反法律规定的内容欢迎反馈！
该文章没有解决你所遇到的问题?点击提问,说说你的问题,让更多的人一起探讨吧!