Deep Learning Examples
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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 section.
The examples listed here make use of simulation (using the ) to perform evaluation over large test sets. Since FHE execution can be slow, only a few FHE executions can be performed. The 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.
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 , 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.
