Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 5

Prediction

Algorithm Input

Neural network prediction algorithm in the batch processing mode accepts the following input. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.

Input ID

Input

data

Pointer to the tensor of size n1 x n2 x ... x np that stores the neural network input data. This input can be an object of any class derived from Tensor.

model

Trained model with the optimum set of weights and biases. The result can only be an object of the Model class.

Algorithm Parameters

Neural network prediction algorithm in the batch processing mode has the following parameters:

Parameter

Default Value

Description

algorithmFPType

float

The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

method

defaultDense

Performance-oriented computation method.

nIterations

1000

The number of iterations.

batchSize

1

The number of samples simultaneously used for prediction.

Algorithm Output

Neural network prediction algorithm in the batch processing mode calculates the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.

Result ID

Result

prediction

Pointer to the tensor of size n1 that stores the predicted result for each sample. This input can be an object of any class derived from Tensor.

Examples

C++: neural_net_predict_dense_batch.cpp

Java*: NeuralNetPredicDenseBatch.java

Python*: neural_net_predict_dense_batch.py

See Also