file_path stringlengths 7 180 | content stringlengths 0 811k | repo stringclasses 11
values |
|---|---|---|
awesome-orion/finance/provable_multiple_linear_regression_solver/src/datasets/linear_data/feature_data.cairo | use array::ArrayTrait;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq};
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
fn x_feature_data() -> Tensor<FP16x16> {
let tensor = TensorTrait::<
FP16x1... | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/datasets/linear_data/label_data.cairo | use array::ArrayTrait;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq};
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
fn y_label_data() -> Tensor<FP16x16> {
let tensor = TensorTrait::<
FP16x16
... | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/datasets/user_inputs_data.cairo | mod user_inputs_boston_data;
mod aave_weth_revenue_data_input;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/datasets/user_inputs_data/aave_weth_revenue_data_input.cairo | use array::ArrayTrait;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq};
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
fn aave_weth_revenue_data_input() -> Tensor<FP16x16> {
let tensor = TensorTrait::<
... | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/datasets/user_inputs_data/user_inputs_boston_data.cairo | use array::ArrayTrait;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq};
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
fn user_input_boston_housing() -> Tensor<FP16x16> {
let tensor = TensorTrait::<
... | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/helper_functions.cairo | use debug::PrintTrait;
use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,
FP16x16TensorDiv, FP16x16TensorMul
};
use orion::numbers::{FP16x16, FixedTrait};
// retrieves row data by index in a 2D te... | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/lib.cairo | mod test;
mod data_preprocessing;
mod helper_functions;
mod datasets;
mod model;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/model.cairo | mod linear_regression_model;
mod multiple_linear_regression_model;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/model/linear_regression_model.cairo | use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,
FP16x16TensorDiv, FP16x16TensorMul
};
use orion::numbers::{FP16x16, FixedTrait};
use multiple_linear_regresion::data_preprocessing::{Dataset, DatasetTrait};
use multiple_linear_regres... | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/model/multiple_linear_regression_model.cairo | use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,
FP16x16TensorDiv, FP16x16TensorMul
};
use orion::numbers::{FP16x16, FixedTrait};
use multiple_linear_regresion::data_preprocessing::{Dataset, DatasetTrait};
use multiple_linear_regres... | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/test.cairo | // use traits::Into;
use debug::PrintTrait;
use array::{ArrayTrait, SpanTrait};
use multiple_linear_regresion::datasets::aave_data::aave_x_features::aave_x_features;
use multiple_linear_regresion::datasets::aave_data::aave_y_labels::aave_y_labels;
use multiple_linear_regresion::datasets::boston_data::boston_x_features... | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/gaming/tic_tac_toe/tic_tac_toe.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Importing required moduless\n",
"import numpy as np \n",
"import pandas as pd \n",
"import pprint,random\n",
"\n",
"from scipy.ndimage.interpolation import shift\n",
"... | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/gaming/tic_tac_toe/tic_tac_toe_orion/crates/sequential_1_dense_1_biasadd_readvariableop_0/src/lib.cairo | use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::FP16x16Tensor;
use orion::numbers::{FixedTrait, FP16x16};
fn tensor() -> Tensor<FP16x16> {
Tensor {
shape: array![18,].span(),
data: array![
FP16x16 { mag: 77099, sign:... | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/gaming/tic_tac_toe/tic_tac_toe_orion/crates/sequential_1_dense_1_matmul_readvariableop_0/src/lib.cairo | use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::FP16x16Tensor;
use orion::numbers::{FixedTrait, FP16x16};
fn tensor() -> Tensor<FP16x16> {
Tensor {
shape: array![9, 18,].span(),
data: array![
FP16x16 { mag: 3955, sig... | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/gaming/tic_tac_toe/tic_tac_toe_orion/crates/sequential_1_dense_2_biasadd_readvariableop_0/src/lib.cairo | use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::FP16x16Tensor;
use orion::numbers::{FixedTrait, FP16x16};
fn tensor() -> Tensor<FP16x16> {
Tensor {
shape: array![9,].span(),
data: array![
FP16x16 {mag: 1241, sign: true}, FP16x16 {... | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/gaming/tic_tac_toe/tic_tac_toe_orion/crates/sequential_1_dense_2_matmul_readvariableop_0/src/lib.cairo | use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::FP16x16Tensor;
use orion::numbers::{FixedTrait, FP16x16};
fn tensor() -> Tensor<FP16x16> {
Tensor {
shape: array![18,9,].span(),
data: array![
FP16x16 {mag: 2465, sign: false}, FP16x... | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/gaming/tic_tac_toe/tic_tac_toe_orion/crates/sequential_1_dense_3_biasadd_readvariableop_0/src/lib.cairo | use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::FP16x16Tensor;
use orion::numbers::{FixedTrait, FP16x16};
fn tensor() -> Tensor<FP16x16> {
Tensor {
shape: array![1,].span(),
data: array![
FP16x16 {mag: 17046, sign: false}, ].span(... | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/gaming/tic_tac_toe/tic_tac_toe_orion/crates/sequential_1_dense_3_matmul_readvariableop_0/src/lib.cairo | use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::FP16x16Tensor;
use orion::numbers::{FixedTrait, FP16x16};
fn tensor() -> Tensor<FP16x16> {
Tensor {
shape: array![9,1,].span(),
data: array![
FP16x16 {mag: 15010, sign: true}, FP16x1... | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/gaming/tic_tac_toe/tic_tac_toe_orion/src/inference.cairo | #[starknet::contract]
mod OrionRunner {
use core::array::SpanTrait;
use orion::operators::tensor::{TensorTrait, FP16x16Tensor, Tensor, FP16x16TensorAdd};
use orion::operators::nn::{NNTrait, FP16x16NN};
use orion::numbers::{FP16x16, FixedTrait};
use sequential_1_dense_1_matmul_readvariableop_0::tens... | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/gaming/tic_tac_toe/tic_tac_toe_orion/src/lib.cairo | mod inference;
mod test; | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/gaming/tic_tac_toe/tic_tac_toe_orion/src/test.cairo | #[cfg(test)]
mod tests {
use core::array::SpanTrait;
use orion::operators::tensor::{TensorTrait, FP16x16Tensor, Tensor, FP16x16TensorAdd};
use orion::operators::nn::{NNTrait, FP16x16NN};
use orion::numbers::{FP16x16, FixedTrait};
use sequential_1_dense_1_matmul_readvariableop_0::tensor as _sequenti... | https://github.com/gizatechxyz/Giza-Hub |
benches/accum_conv.rs | use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
use ezkl::circuit::poly::PolyOp;
use ezkl::circuit::*;
use ezkl::pfsys::create_keys;
use ezkl::pfsys::create_proof_circuit;
use ezkl::pfsys::srs::gen_srs;
use ezkl::pfsys::TranscriptType;
use ezkl::tensor::*;
use halo2_proofs::poly::k... | https://github.com/zkonduit/ezkl |
benches/accum_dot.rs | use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
use ezkl::circuit::poly::PolyOp;
use ezkl::circuit::*;
use ezkl::pfsys::create_proof_circuit;
use ezkl::pfsys::TranscriptType;
use ezkl::pfsys::{create_keys, srs::gen_srs};
use ezkl::tensor::*;
use halo2_proofs::poly::kzg::commitment:... | https://github.com/zkonduit/ezkl |
benches/accum_einsum_matmul.rs | use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
use ezkl::circuit::poly::PolyOp;
use ezkl::circuit::*;
use ezkl::pfsys::create_proof_circuit;
use ezkl::pfsys::TranscriptType;
use ezkl::pfsys::{create_keys, srs::gen_srs};
use ezkl::tensor::*;
use halo2_proofs::poly::kzg::commitment:... | https://github.com/zkonduit/ezkl |
benches/accum_matmul_relu.rs | use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
use ezkl::circuit::table::Range;
use ezkl::circuit::*;
use ezkl::circuit::lookup::LookupOp;
use ezkl::circuit::poly::PolyOp;
use ezkl::pfsys::create_proof_circuit;
use ezkl::pfsys::TranscriptType;
use ezkl::pfsys::{create_keys, srs::... | https://github.com/zkonduit/ezkl |
benches/accum_matmul_relu_overflow.rs | use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
use ezkl::circuit::*;
use ezkl::circuit::lookup::LookupOp;
use ezkl::circuit::poly::PolyOp;
use ezkl::circuit::table::Range;
use ezkl::pfsys::create_proof_circuit;
use ezkl::pfsys::TranscriptType;
use ezkl::pfsys::{create_keys, srs::... | https://github.com/zkonduit/ezkl |
benches/accum_sum.rs | use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
use ezkl::circuit::poly::PolyOp;
use ezkl::circuit::*;
use ezkl::pfsys::create_proof_circuit;
use ezkl::pfsys::TranscriptType;
use ezkl::pfsys::{create_keys, srs::gen_srs};
use ezkl::tensor::*;
use halo2_proofs::poly::kzg::commitment:... | https://github.com/zkonduit/ezkl |
benches/accum_sumpool.rs | use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
use ezkl::circuit::hybrid::HybridOp;
use ezkl::circuit::*;
use ezkl::pfsys::create_keys;
use ezkl::pfsys::create_proof_circuit;
use ezkl::pfsys::srs::gen_srs;
use ezkl::pfsys::TranscriptType;
use ezkl::tensor::*;
use halo2_proofs::pol... | https://github.com/zkonduit/ezkl |
benches/pairwise_add.rs | use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
use ezkl::circuit::poly::PolyOp;
use ezkl::circuit::*;
use ezkl::pfsys::create_proof_circuit;
use ezkl::pfsys::TranscriptType;
use ezkl::pfsys::{create_keys, srs::gen_srs};
use ezkl::tensor::*;
use halo2_proofs::poly::kzg::commitment:... | https://github.com/zkonduit/ezkl |
benches/pairwise_pow.rs | use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
use ezkl::circuit::poly::PolyOp;
use ezkl::circuit::region::RegionCtx;
use ezkl::circuit::*;
use ezkl::pfsys::create_proof_circuit;
use ezkl::pfsys::TranscriptType;
use ezkl::pfsys::{create_keys, srs::gen_srs};
use ezkl::tensor::*;
us... | https://github.com/zkonduit/ezkl |
benches/poseidon.rs | use std::collections::HashMap;
use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
use ezkl::circuit::modules::poseidon::spec::{PoseidonSpec, POSEIDON_RATE, POSEIDON_WIDTH};
use ezkl::circuit::modules::poseidon::{PoseidonChip, PoseidonConfig};
use ezkl::circuit::modules::Module;
use e... | https://github.com/zkonduit/ezkl |
benches/relu.rs | use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
use ezkl::circuit::region::RegionCtx;
use ezkl::circuit::table::Range;
use ezkl::circuit::{ops::lookup::LookupOp, BaseConfig as Config, CheckMode};
use ezkl::pfsys::create_proof_circuit;
use ezkl::pfsys::TranscriptType;
use ezkl::pfsy... | https://github.com/zkonduit/ezkl |
contracts/AttestData.sol | // SPDX-License-Identifier: MIT
pragma solidity ^0.8.20;
import './LoadInstances.sol';
// This contract serves as a Data Attestation Verifier for the EZKL model.
// It is designed to read and attest to instances of proofs generated from a specified circuit.
// It is particularly constructed to read only int256 data fr... | https://github.com/zkonduit/ezkl |
contracts/LoadInstances.sol | // SPDX-License-Identifier: MIT
pragma solidity ^0.8.20;
contract LoadInstances {
/**
* @dev Parse the instances array from the Halo2Verifier encoded calldata.
* @notice must pass encoded bytes from memory
* @param encoded - verifier calldata
*/
function getInstancesMemory(
bytes me... | https://github.com/zkonduit/ezkl |
contracts/QuantizeData.sol | // SPDX-License-Identifier: GPL-3.0
pragma solidity ^0.8.17;
contract QuantizeData {
/**
* @notice EZKL P value
* @dev In order to prevent the verifier from accepting two version of the same instance, n and the quantity (n + P), where n + P <= 2^256, we require that all instances are stricly less than ... | https://github.com/zkonduit/ezkl |
contracts/TestReads.sol | // SPDX-License-Identifier: UNLICENSED
pragma solidity ^0.8.17;
contract TestReads {
int[] public arr;
constructor(int256[] memory _numbers) {
for (uint256 i = 0; i < _numbers.length; i++) {
arr.push(_numbers[i]);
}
}
}
| https://github.com/zkonduit/ezkl |
docs/python/src/conf.py | import ezkl
project = 'ezkl'
release = '0.0.0'
version = release
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.autosummary',
'sphinx.ext.intersphinx',
'sphinx.ext.todo',
'sphinx.ext.inheritance_diagram',
'sphinx.ext.autosectionlabel',
'sphinx.ext.napoleon',
'sphinx_rtd_theme',
]
a... | https://github.com/zkonduit/ezkl |
examples/conv2d_mnist/main.rs | use ezkl::circuit::region::RegionCtx;
use ezkl::circuit::{
ops::lookup::LookupOp, ops::poly::PolyOp, BaseConfig as PolyConfig, CheckMode,
};
use ezkl::fieldutils;
use ezkl::fieldutils::i32_to_felt;
use ezkl::tensor::*;
use halo2_proofs::dev::MockProver;
use halo2_proofs::poly::commitment::Params;
use halo2_proofs::... | https://github.com/zkonduit/ezkl |
examples/conv2d_mnist/params.rs | pub struct Params {
pub kernels: Vec<Vec<Vec<Vec<f32>>>>,
pub weights: Vec<Vec<f32>>,
pub biases: Vec<f32>,
}
impl Params {
pub fn new() -> Params {
let kernels: Vec<Vec<Vec<Vec<f32>>>> = vec![
vec![
vec![vec![
-0.109_487_005,
0... | https://github.com/zkonduit/ezkl |
examples/mlp_4d_einsum.rs | use ezkl::circuit::region::RegionCtx;
use ezkl::circuit::{
ops::lookup::LookupOp, ops::poly::PolyOp, BaseConfig as PolyConfig, CheckMode,
};
use ezkl::fieldutils::i32_to_felt;
use ezkl::tensor::*;
use halo2_proofs::dev::MockProver;
use halo2_proofs::{
circuit::{Layouter, SimpleFloorPlanner, Value},
plonk::{... | https://github.com/zkonduit/ezkl |
examples/notebooks/data_attest.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# data-attest-ezkl\n",
"\n",
"Here's an example leveraging EZKL whereby the inputs to the model are read and attested to fro... | https://github.com/zkonduit/ezkl |
examples/notebooks/data_attest_hashed.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# data-attest-ezkl hashed\n",
"\n",
"Here's an example leveraging EZKL whereby the hashes of the outputs to the model are re... | https://github.com/zkonduit/ezkl |
examples/notebooks/decision_tree.ipynb | {
"cells": [
{
"attachments": {
"image-2.png": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAokAAARDCAYAAAAEdLvJAAABYmlDQ1BJQ0MgUHJvZmlsZQAAKJF1kDFLw1AUhU9stSAVHRwEHQKKUy01rdi1LSKCQxoVqlvyWlMlTR9JRNTFQRengi5uUhd/gS4OjoKDguAgIoKDP0DsoiXeNGpbxft43I/DvYfDBTrCKudGEEDJd... | https://github.com/zkonduit/ezkl |
examples/notebooks/ezkl_demo.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "n8QlFzjPRIGN"
},
"source": [
"# EZKL DEMO\n",
"\n",
"**Learning Objectives**\n",
"1. Learn some basic AI/ML techniques by training a toy model in pytorch to perform classification\n",
... | https://github.com/zkonduit/ezkl |
examples/notebooks/gcn.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "5fe9feb6-2b35-414a-be9d-771eabdbb0dc",
"metadata": {
"id": "5fe9feb6-2b35-414a-be9d-771eabdbb0dc"
},
"source": [
"## EZKL GCN Notebook"
]
},
{
"cell_type": "code",
"execution_count": null,
... | https://github.com/zkonduit/ezkl |
examples/notebooks/generalized_inverse.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
"metadata": {
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67"
},
"source": [
"\n",
"## Generalized Inverse\n",
"\n",
"We show how to use EZKL to prove that we kn... | https://github.com/zkonduit/ezkl |
examples/notebooks/gradient_boosted_trees.ipynb | {
"cells": [
{
"attachments": {
"image-2.png": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAokAAARDCAYAAAAEdLvJAAABYmlDQ1BJQ0MgUHJvZmlsZQAAKJF1kDFLw1AUhU9stSAVHRwEHQKKUy01rdi1LSKCQxoVqlvyWlMlTR9JRNTFQRengi5uUhd/gS4OjoKDguAgIoKDP0DsoiXeNGpbxft43I/DvYfDBTrCKudGEEDJd... | https://github.com/zkonduit/ezkl |
examples/notebooks/hashed_vis.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# hashed-ezkl\n",
"\n",
"Here's an example leveraging EZKL whereby the inputs to the model, and the model params themselves,... | https://github.com/zkonduit/ezkl |
examples/notebooks/keras_simple_demo.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
"metadata": {},
"source": [
"## EZKL Jupyter Notebook Demo with Keras\n"
]
},
{
"cell_t... | https://github.com/zkonduit/ezkl |
examples/notebooks/kmeans.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
"metadata": {},
"source": [
"## K-means\n",
"\n",
"\n"
]
},
{
... | https://github.com/zkonduit/ezkl |
examples/notebooks/kzg_vis.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# kzg-ezkl\n",
"\n",
"Here's an example leveraging EZKL whereby the inputs to the model, and the model params themselves, ar... | https://github.com/zkonduit/ezkl |
examples/notebooks/lightgbm.ipynb | {
"cells": [
{
"attachments": {
"image-3.png": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAywAAAV+CAYAAACUGLpbAAAMPWlDQ1BJQ0MgUHJvZmlsZQAASImVVwdYU8kWnltSIbQAAlJCb4JIDSAlhBZAercRkgChhBgIKnZ0UcG1iwVs6KqIYqfZETuLYsO+WFBR1sWCXXmTArruK9873zf3/vefM/85c+7cMgCon+CKxbmoB... | https://github.com/zkonduit/ezkl |
examples/notebooks/linear_regression.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
"metadata": {},
"source": [
"## Linear Regression\n",
"\n",
"\n",
"Sklearn based models are slightly finicky t... | https://github.com/zkonduit/ezkl |
examples/notebooks/little_transformer.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "d0a82619",
"metadata": {},
"source": [
"Credits to [geohot](https://github.com/geohot/ai-notebooks/blob/master/mnist_gan.ipynb) for most of this code\n",
"\n",
"## M... | https://github.com/zkonduit/ezkl |
examples/notebooks/lstm.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "d0a82619",
"metadata": {},
"source": [
"\n",
"## Model Architecture and training"
]
},
{
"cell_type": "code",
"execution_count": ... | https://github.com/zkonduit/ezkl |
examples/notebooks/mean_postgres.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mean of ERC20 transfer amounts\n",
"\n",
"This notebook shows how to calculate the mean of ERC20 transfer amounts, pulling data in from a Postgres database. First we install and get the necessary libr... | https://github.com/zkonduit/ezkl |
examples/notebooks/mnist_classifier.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "6ypZa6jg0rZy"
},
"source": [
"## Mnist Clan-ssifier ;)\n",
"\n",
"Here we demonstrate how to use the EZKL package to build an MNIST classifier for on-chain handrawn digit recognition.\n",
"... | https://github.com/zkonduit/ezkl |
examples/notebooks/mnist_gan.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Credits to [geohot](https://github.com/geohot/ai-notebooks/blob/master/mnist_gan.ipynb) for most of this code"
]
}... | https://github.com/zkonduit/ezkl |
examples/notebooks/mnist_gan_proof_splitting.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Proof splitting\n",
"\n",
"Here we showcase how to split a larger circuit into multiple smaller proofs. This is useful if ... | https://github.com/zkonduit/ezkl |
examples/notebooks/mnist_vae.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# what is the variational?"
]
},
{
"cell_type": "markdown",
"metadata": {},... | https://github.com/zkonduit/ezkl |
examples/notebooks/nbeats_timeseries_forecasting.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "TB8jFLoLwZ8K"
},
"source": [
"# N-BEATS Time Series Forecasting\n",
"In this tutorial we utilize the N-BEATS (Neural basis expansion analysis for interpretable time series forecasting\n",
") for fo... | https://github.com/zkonduit/ezkl |
examples/notebooks/proof_splitting.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Proof splitting\n",
"\n",
"Here we showcase how to split a larger circuit into multiple smaller proofs. This is useful if ... | https://github.com/zkonduit/ezkl |
examples/notebooks/random_forest.ipynb | {
"cells": [
{
"attachments": {
"image-2.png": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAokAAARDCAYAAAAEdLvJAAABYmlDQ1BJQ0MgUHJvZmlsZQAAKJF1kDFLw1AUhU9stSAVHRwEHQKKUy01rdi1LSKCQxoVqlvyWlMlTR9JRNTFQRengi5uUhd/gS4OjoKDguAgIoKDP0DsoiXeNGpbxft43I/DvYfDBTrCKudGEEDJd... | https://github.com/zkonduit/ezkl |
examples/notebooks/set_membership.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
"metadata": {
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67"
},
"source": [
"## Hash set membership demo"
]
},
{
"cell_type": "code",
"execution_count": null,... | https://github.com/zkonduit/ezkl |
examples/notebooks/simple_demo_aggregated_proofs.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
"metadata": {},
"source": [
"## EZKL Jupyter Notebook Demo (Aggregated Proofs) \n",
"\n",
"Demo... | https://github.com/zkonduit/ezkl |
examples/notebooks/simple_demo_all_public.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
"metadata": {},
"source": [
"## EZKL Jupyter Notebook Demo \n",
"\n",
"Here we demonstrate the ... | https://github.com/zkonduit/ezkl |
examples/notebooks/simple_demo_public_input_output.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
"metadata": {},
"source": [
"## EZKL Jupyter Notebook Demo \n",
"\n",
"Here we demonstrate how ... | https://github.com/zkonduit/ezkl |
examples/notebooks/simple_demo_public_network_output.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
"metadata": {},
"source": [
"## EZKL Jupyter Notebook Demo \n",
"\n",
"Here we demonstrate how ... | https://github.com/zkonduit/ezkl |
examples/notebooks/sklearn_mlp.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
"metadata": {},
"source": [
"## Sklearn MLP to ONNX\n",
"\n",
"\n",
"Sklearn based models are slightly finicky... | https://github.com/zkonduit/ezkl |
examples/notebooks/solvency.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
"metadata": {
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67"
},
"source": [
"## Solvency demo\n",
"\n",
"Here we create a demo of a solvency calculation in the manner o... | https://github.com/zkonduit/ezkl |
examples/notebooks/stacked_regression.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
"metadata": {},
"source": [
"## Stacked Regression\n",
"\n",
"\n",
"Sklearn based models are slightly finicky ... | https://github.com/zkonduit/ezkl |
examples/notebooks/svm.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
"metadata": {},
"source": [
"## Support Vector Machines\n",
"\n",
"\n"
]
},... | https://github.com/zkonduit/ezkl |
examples/notebooks/tictactoe_autoencoder.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Autoencoder Tic Tac Toe Verifier\n",
"\n",
"This is another approach of verifying games but using an anomaly detection approach instead of classification... | https://github.com/zkonduit/ezkl |
examples/notebooks/tictactoe_binary_classification.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Binary Classification Tic Tac Toe Verifier\n",
"\n",
"We create an ML model that verifies TicTacToe Games\n",
"\n",
"Make... | https://github.com/zkonduit/ezkl |
examples/notebooks/variance.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "uKQPd7_5ANe-"
},
"source": [
"# Calculate the variance of an asset \n",
"\n",
"In this example we will calculate the variance of an asset using ezkl. Here's a diagram of the process:\n",
"\... | https://github.com/zkonduit/ezkl |
examples/notebooks/voice_judge.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Voice judgoor\n",
"\n",
"Here we showcase a full-end-to-end flow of:\n",
"1. training a model for a specif... | https://github.com/zkonduit/ezkl |
examples/notebooks/world_rotation.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
"metadata": {},
"source": [
"## World rotation\n",
"\n",
"Here we demonstrate how to use the EZKL package to rotate an on-chain world. \n",
"\n",
"![zk-gaming-diagram-transforme... | https://github.com/zkonduit/ezkl |
examples/notebooks/xgboost.ipynb | {
"cells": [
{
"attachments": {
"image-3.png": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAywAAAV+CAYAAACUGLpbAAAMPWlDQ1BJQ0MgUHJvZmlsZQAASImVVwdYU8kWnltSIbQAAlJCb4JIDSAlhBZAercRkgChhBgIKnZ0UcG1iwVs6KqIYqfZETuLYsO+WFBR1sWCXXmTArruK9873zf3/vefM/85c+7cMgCon+CKxbmoB... | https://github.com/zkonduit/ezkl |
examples/onnx/1l_average/gen.py | from torch import nn
from ezkl import export
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.layer = nn.AvgPool2d(2, 1, (1, 1))
def forward(self, x):
return self.layer(x)[0]
circuit = Model()
export(circuit, input_shape=[3, 2, 2])
| https://github.com/zkonduit/ezkl |
examples/onnx/1l_batch_norm/gen.py | from torch import nn
from ezkl import export
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.layer = nn.BatchNorm2d(3)
def forward(self, x):
return self.layer(x)
circuit = Model()
export(circuit, input_shape=[3, 2, 2])
| https://github.com/zkonduit/ezkl |
examples/onnx/1l_concat/gen.py | import torch
from torch import nn
from ezkl import export
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
def forward(self, x):
return torch.concat([x, x], 2)
circuit = MyModel()
export(circuit, input_shape=[3, 2, 3, 2, 2])
| https://github.com/zkonduit/ezkl |
examples/onnx/1l_conv/gen.py | from torch import nn
from ezkl import export
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.layer = nn.Conv2d(3, 1, (1, 1), 1, 1)
def forward(self, x):
return self.layer(x)
circuit = Model()
export(circuit, input_shape = [3, 1, 1])
| https://github.com/zkonduit/ezkl |
examples/onnx/1l_conv_transpose/gen.py | import io
import numpy as np
from torch import nn
import torch.onnx
import torch.nn as nn
import torch.nn.init as init
import json
class Circuit(nn.Module):
def __init__(self, inplace=False):
super(Circuit, self).__init__()
self.convtranspose = nn.ConvTranspose2d(3, 3, (5, 5), stride=2, padding=2, ... | https://github.com/zkonduit/ezkl |
examples/onnx/1l_div/gen.py | from torch import nn
from ezkl import export
class Circuit(nn.Module):
def __init__(self, inplace=False):
super(Circuit, self).__init__()
def forward(self, x):
return x/ 10
circuit = Circuit()
export(circuit, input_shape = [1])
| https://github.com/zkonduit/ezkl |
examples/onnx/1l_downsample/gen.py | from torch import nn
from ezkl import export
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.layer = nn.Conv2d(3, 1, (1, 1), 2, 1)
def forward(self, x):
return self.layer(x)
circuit = Model()
export(circuit, input_shape=[3, 6, 6])
| https://github.com/zkonduit/ezkl |
examples/onnx/1l_eltwise_div/gen.py | from torch import nn
from ezkl import export
class Circuit(nn.Module):
def __init__(self, inplace=False):
super(Circuit, self).__init__()
def forward(self, x):
return x/(2*x)
circuit = Circuit()
export(circuit, input_shape=[1])
| https://github.com/zkonduit/ezkl |
examples/onnx/1l_elu/gen.py | from torch import nn
import torch
import json
class Circuit(nn.Module):
def __init__(self):
super(Circuit, self).__init__()
self.layer = nn.ELU()
def forward(self, x):
return self.layer(x)
def main():
torch_model = Circuit()
# Input to the model
shape = [3, 2, 3]
x =... | https://github.com/zkonduit/ezkl |
examples/onnx/1l_erf/gen.py | import json
import torch
from torch import nn
class Circuit(nn.Module):
def __init__(self):
super(Circuit, self).__init__()
def forward(self, x):
return torch.special.erf(x)
def main():
torch_model = Circuit()
# Input to the model
shape = [3]
x = torch.rand(1,*shape, requires... | https://github.com/zkonduit/ezkl |
examples/onnx/1l_flatten/gen.py | import io
import numpy as np
from torch import nn
import torch.onnx
import torch.nn as nn
import torch.nn.init as init
import json
class Circuit(nn.Module):
def __init__(self):
super(Circuit, self).__init__()
self.flatten = nn.Flatten()
def forward(self, x):
return self.flatten(x)
d... | https://github.com/zkonduit/ezkl |
examples/onnx/1l_gelu_noappx/gen.py | import json
import torch
from torch import nn
class Circuit(nn.Module):
def __init__(self):
super(Circuit, self).__init__()
self.layer = nn.GELU() # approximation = false in our case
def forward(self, x):
return self.layer(x)
def main():
torch_model = Circuit()
# Input to the... | https://github.com/zkonduit/ezkl |
examples/onnx/1l_gelu_tanh_appx/gen.py | import json
import torch
from torch import nn
class Circuit(nn.Module):
def __init__(self):
super(Circuit, self).__init__()
self.layer = nn.GELU('tanh') # approximation = false in our case
def forward(self, x):
return self.layer(x)
def main():
torch_model = Circuit()
# Input ... | https://github.com/zkonduit/ezkl |
examples/onnx/1l_identity/gen.py | import torch
from torch import nn
import json
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
def forward(self, x):
return x
circuit = MyModel()
x = 0.1*torch.rand(1, *[2], requires_grad=True)
# Flips the neural net into inference mode
circuit.eval()
# Expor... | https://github.com/zkonduit/ezkl |
examples/onnx/1l_instance_norm/gen.py | from torch import nn
from ezkl import export
import torch
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.layer = nn.InstanceNorm2d(3).eval()
def forward(self, x):
return [self.layer(x)]
circuit = MyModel()
export(circuit, input_shape=[3, 2, 2])
| https://github.com/zkonduit/ezkl |
examples/onnx/1l_leakyrelu/gen.py | from torch import nn
from ezkl import export
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.layer = nn.LeakyReLU(negative_slope=0.05)
def forward(self, x):
return self.layer(x)
circuit = Model()
export(circuit, input_shape = [3])
| https://github.com/zkonduit/ezkl |
examples/onnx/1l_linear/gen.py | import random
import math
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import json
model = nn.Linear(1, 1)
x = torch.randn(1, 1)
print(x)
# Flips the neural net into inference mode
model.eval()
model.to('cpu')
# Export the model
torch.onnx.export(model, # mode... | https://github.com/zkonduit/ezkl |
examples/onnx/1l_lppool/gen.py | from torch import nn
import torch
import json
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.layer = nn.LPPool2d(2, 1, (1, 1))
def forward(self, x):
return self.layer(x)[0]
circuit = Model()
x = torch.empty(1, 3, 2, 2).uniform_(0, 1)
out = circuit(x)... | https://github.com/zkonduit/ezkl |
examples/onnx/1l_max_pool/gen.py | from torch import nn
from ezkl import export
import torch
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.layer = nn.MaxPool2d(2, 1, (1, 1), 1, 1)
def forward(self, x):
return self.layer(x)[0]
circuit = Model()
# Input to the model
shape = [3, 2, 2]
... | https://github.com/zkonduit/ezkl |
examples/onnx/1l_pad/gen.py | from torch import nn
from ezkl import export
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
def forward(self, x):
return nn.functional.pad(x, (1,1))
circuit = Model()
export(circuit, input_shape = [3, 2, 2])
| https://github.com/zkonduit/ezkl |
examples/onnx/1l_powf/gen.py | from torch import nn
import torch
import json
import numpy as np
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
def forward(self, x):
return torch.pow(x, -0.1)
circuit = MyModel()
x = torch.rand(1, 4)
torch.onnx.export(circuit, (x), "network.onnx",
... | https://github.com/zkonduit/ezkl |
examples/onnx/1l_prelu/gen.py | from torch import nn
from ezkl import export
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.layer = nn.PReLU(num_parameters=3, init=0.25)
def forward(self, x):
return self.layer(x)
circuit = Model()
export(circuit, input_shape = [3, 2, 2])
| https://github.com/zkonduit/ezkl |
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