VerifiaML

Testing & Examples

To run the tests, use cargo test.

To run the examples, use:

cargo run --example <example_name> --features "test-types"

where <example_name> is one of the following:

• simple_perceptron_mnist_inference
• simple_perceptron_mnist_proof
• two_layer_perceptron_mnist_inference
• two_layer_perceptron_mnist_proof

In order to run any tests involving python code, such as compatibility tests with TF Lite, the feature python must be activated (which automatically enables test-types).

From ndarray to QArray

In order to save a numpy ndarray (python side) as a serialised JSON which can be directly read into a QArray of ours (Rust side),

• Convert the ndarray into an OrderedDict using our custom python function tensor_to_dict (available in several of the python notebooks)
• Pass the resulting OrderedDict together with the destination path to json.dump.

The saved JSON file can be deserialised over in Rust with QArray::read(path: &str) -> QArray. If instead of a single OrderedDict, a python list of OrderedDicts is passed to json.dump, the resulting file can be deserialised with QArray::read_list(path: &str) -> Vec<QArray> .

Cf. exploring_tf_lite/training_two_layer_perceptron.ipynb for example usage.

This can be useful when bringing over to Rust some TF Lite model parameters or inputs.