Developer Guide for Intel® Data Analytics Acceleration Library 2018
Neural network training 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. |
|
groundTruth |
Pointer to the tensor of size n1 that stores stated results associated with the input data. This input can be an object of any class derived from Tensor. |
Neural network training 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. |
|
batchSize |
1 |
The number of samples simultaneously used for training. NoteBecause the first dimension of the input data tensor represents the data samples, the library computes the number of batches by dividing n1 by the value of batchSize. After processing each batch the library updates the parameters of the model. If n1 is not a multiple of batchSize, the algorithm ignores data samples at the end of the data tensor. |
|
optimizationSolver |
SharedPtr< optimization_solver::sgd::Batch<algorithmFPType,defaultDense> > |
The optimization procedure used at the training stage. |
|
engine |
SharePtr<engines::mt19937::Batch>() |
Pointer to the engine to be used by a neural network in computations. The neural network sets this engine to each layer in topology during model initialization if the layer's engine is not set yet. |
Neural network training 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 |
|
---|---|---|
model |
Trained model with the optimum set of weights and biases. The result can only be an object of the Model class. |
C++: neural_net_dense_batch.cpp
Java*: NeuralNetDenseBatch.java
Python*: neural_net_dense_batch.py