modelBaseGenericNeuralNet
Extends from BaseNeuralNet.
Information
This is the most generic base class to include neural networks within Modelica. It can be used for any neural network. For easier handling the specialized versions BaseFeedForwardNeuralNet, BaseRecurrentNeuralNet and BaseStatefulRecurrentNeuralNet are available.
The most likely use case of this model is with a multi-layer perceptron neural network.
In order to include a neural network in Model, extend this base class in your own model. After extending you have to
- give the path of the TFLite/ONNX model
- specify the number of dimensions for in and output
- specify the vector sizes in each input and output dimension
- create input and output connectors
- connect input and output connectors to the single input and single output vector of the runInference submodel
The runInference model uses flattened vectors for input and output. The total number of elements equals the product of all input or output sizes, respectively. The user has to connect the defined input and output to the flattened vectors in the same order as they are used within the created neural network.
Parameters
| Type | Name | Default | Description |
|---|---|---|---|
| Integer | nInputElements (from BaseNeuralNet) | product(inputSizes) | |
| Integer | nOutputElements (from BaseNeuralNet) | product(outputSizes) | |
| Selected Model | |||
| String | pathToAIModel (from BaseNeuralNet) | "" | Choose path to AI model |
| Tensor sizing | |||
| Integer | inputDimensions (from BaseNeuralNet) | 1 | Number of input dimension |
| Integer | inputSizes (from BaseNeuralNet) | {1} | Vector with size of tensor in each dimension |
| Integer | outputDimensions (from BaseNeuralNet) | 1 | Number of output dimension |
| Integer | outputSizes (from BaseNeuralNet) | {1} | Vector with size of tensor in each dimension |
| RNN Timing Settings | |||
| Boolean | stateful (from BaseNeuralNet) | false | Activate state handling for RNN with state in-/outputs |
| Real | samplePeriod (from BaseNeuralNet) | 0 | Fixed sample period for RNNs |
| Advanced › Performance | |||
| Integer | numberOfThreads (from BaseNeuralNet) | 1 | Number of threads used for inference (0: Number of CPU cores) |
| Advanced › TFLite | |||
| Boolean | useFlexOps (from BaseNeuralNet) | false | Activate to allow Tensorflow Flex Ops for tflite models |
| Advanced › ONNX | |||
| Boolean | useGPU (from BaseNeuralNet) | false | Activate to use a compatible CUDA GPU for inference |
| Integer | gpuDeviceID (from BaseNeuralNet) | 0 | CUDA device ID |
| SMArtInt.Internal.Types.ExecutionMode | executionMode (from BaseNeuralNet) | SMArtInt.Internal.Types.ExecutionMode.Sequential | Execution mode for run inference |
Components
| Type | Name | Default | Description |
|---|---|---|---|
| Internal.Utilities.Dependencies.OnnxGpuDependencies | onnxGpuDependencies (from BaseNeuralNet) | ||
| Internal.Utilities.Dependencies.TFFlexOpsDependencies | tFFlexOpsDependencies (from BaseNeuralNet) | ||
| Internal.Utilities.RunInferenceFlatInput | runInference |