Deep Learning Examples
Last updated
Last updated
These examples illustrate the basic usage of Concrete-ML to build various types of neural networks. They use simple data-sets, focusing on the syntax and usage of Concrete-ML. For examples showing how to train high-accuracy models on more complex data-sets, see the Demos and Tutorials section.
The examples listed here make use of simulation (using the Virtual Library) to perform evaluation over large test sets. Since FHE execution can be slow, only a few FHE executions can be performed. The correctness guarantees of Concrete-ML ensure that accuracy measured with simulation is the same that will be obtained during FHE execution.
Some examples constrain accumulators to 7-8 bits, which can be sufficient for simple data-sets. Up to 16-bit accumulators can be used, but this introduces a slowdown of 4-5x compared to 8-bit accumulators.
Quantization aware training example
Shows how to use Quantization Aware Training and pruning when starting out from a classical PyTorch network. This example uses a simple data-set and a small NN, which achieves good accuracy with low accumulator size.
Following the Step-by-step guide, this notebook implements a Quantization Aware Training convolutional neural network on the MNIST data-set. It uses 3-bit weights and activations, giving a 7-bit accumulator.
