Predictor

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1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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13. final def notify(): Unit

    

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15. def predict(input: List[List[Float]]): List[List[Float]]

    

    Takes input as List of one dimensional arrays and creates the NDArray needed for inference The array will be reshaped based on the input descriptors.

    Takes input as List of one dimensional arrays and creates the NDArray needed for inference The array will be reshaped based on the input descriptors.

    returns

    Indexed sequence array of outputs

16. def predict(input: Array[Array[Float]]): Array[Array[Float]]

    

    Takes input as Array of one dimensional arrays and creates the NDArray needed for inference The array will be reshaped based on the input descriptors.

    Takes input as Array of one dimensional arrays and creates the NDArray needed for inference The array will be reshaped based on the input descriptors. Example of calling in Java:

    
    float tmp[][] = new float[1][224];
    for (int x = 0; x < 1; x++)
      for (int y = 0; y < 224; y++)
        tmp[x][y] = (int)(Math.random()*10);
    predictor.predict(tmp);
    
    

    returns

    Indexed sequence array of outputs

17. def predictWithNDArray(input: List[javaapi.NDArray]): List[javaapi.NDArray]

    

    Predict using NDArray as input This method is useful when the input is a batch of data Note: User is responsible for managing allocation/deallocation of input/output NDArrays.

    Predict using NDArray as input This method is useful when the input is a batch of data Note: User is responsible for managing allocation/deallocation of input/output NDArrays.

    input

    List of NDArrays

    returns

    Output of predictions as NDArrays

18. val predictor: infer.Predictor

    

19. final def synchronized[T0](arg0: ⇒ T0): T0

    

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21. final def wait(): Unit

    

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23. final def wait(arg0: Long): Unit

    

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