exportONNXNetwork — Export supported feed-forward deep-learning networks to ONNX.

exportONNXNetwork(net, filename) writes deterministic ONNX graph files for RunMat dlnetwork, SeriesNetwork, and DAGNetwork compatibility objects that use the supported sequential feed-forward execution subset.

Syntax

exportONNXNetwork(net, filename, Name, Value)

How exportONNXNetwork works

• Supported layers are featureInputLayer, fullyConnectedLayer, reluLayer, eluLayer, softmaxLayer, and terminal classificationLayer or regressionLayer objects.
• fullyConnectedLayer exports as ONNX Gemm; ReLU, ELU, and Softmax export as their corresponding ONNX operators.
• Learned parameters are serialized as ONNX DOUBLE tensor initializers using deterministic names derived from layer names.
• OpsetVersion supports versions 13 through 20; BatchSize, InputNames, OutputNames, and Verbose are accepted for single-input/single-output exports.
• Unsupported layers, options, or malformed network metadata raise deterministic errors without writing partial graphs.

GPU memory and residency

ONNX export is host graph serialization and file I/O. GPU-resident top-level values are gathered before export; no provider kernels are applicable.

Example

Export ONNX

exportONNXNetwork(net, 'model.onnx', 'OpsetVersion', 14)

Expected output:

model.onnx is written.

Using exportONNXNetwork with coding agents

Open a RunMat example with live inputs, then ask the agent to explain how exportONNXNetwork changes the result.

Related Deep Learning functions

About RunMat

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