file_path stringlengths 7 180 | content stringlengths 0 811k | repo stringclasses 11
values |
|---|---|---|
src/pfsys/evm/aggregation_kzg.rs | #[cfg(not(target_arch = "wasm32"))]
use crate::graph::CircuitSize;
use crate::pfsys::{Snark, SnarkWitness};
#[cfg(not(target_arch = "wasm32"))]
use colored_json::ToColoredJson;
use halo2_proofs::circuit::AssignedCell;
use halo2_proofs::plonk::{self};
use halo2_proofs::{
circuit::{Layouter, SimpleFloorPlanner, Value... | https://github.com/zkonduit/ezkl |
src/pfsys/evm/mod.rs | use thiserror::Error;
/// Aggregate proof generation for EVM using KZG
pub mod aggregation_kzg;
#[derive(Error, Debug)]
/// Errors related to evm verification
pub enum EvmVerificationError {
/// If the Solidity verifier worked but returned false
#[error("Solidity verifier found the proof invalid")]
Invali... | https://github.com/zkonduit/ezkl |
src/pfsys/mod.rs | /// EVM related proving and verification
pub mod evm;
/// SRS generation, processing, verification and downloading
pub mod srs;
use crate::circuit::CheckMode;
use crate::graph::GraphWitness;
use crate::pfsys::evm::aggregation_kzg::PoseidonTranscript;
use crate::{Commitments, EZKL_BUF_CAPACITY, EZKL_KEY_FORMAT};
use c... | https://github.com/zkonduit/ezkl |
src/pfsys/srs.rs | use halo2_proofs::poly::commitment::CommitmentScheme;
use halo2_proofs::poly::commitment::Params;
use halo2_proofs::poly::commitment::ParamsProver;
use log::info;
use std::error::Error;
use std::fs::File;
use std::io::BufReader;
use std::path::PathBuf;
/// for now we use the urls of the powers of tau ceremony from <ht... | https://github.com/zkonduit/ezkl |
src/python.rs | use crate::circuit::modules::polycommit::PolyCommitChip;
use crate::circuit::modules::poseidon::{
spec::{PoseidonSpec, POSEIDON_RATE, POSEIDON_WIDTH},
PoseidonChip,
};
use crate::circuit::modules::Module;
use crate::circuit::{CheckMode, Tolerance};
use crate::commands::*;
use crate::fieldutils::{felt_to_i128, i... | https://github.com/zkonduit/ezkl |
src/srs_sha.rs | use lazy_static::lazy_static;
use std::collections::HashMap;
lazy_static! {
/// SRS SHA256 hashes
pub static ref PUBLIC_SRS_SHA256_HASHES: HashMap<u32, &'static str> = HashMap::from_iter([
(
1,
"cafb2aa72c200ddc4e28aacabb8066e829207e2484b8d17059a566232f8a297b",
),
... | https://github.com/zkonduit/ezkl |
src/tensor/mod.rs | /// Implementations of common operations on tensors.
pub mod ops;
/// A wrapper around a tensor of circuit variables / advices.
pub mod val;
/// A wrapper around a tensor of Halo2 Value types.
pub mod var;
use halo2curves::ff::PrimeField;
use maybe_rayon::{
prelude::{
IndexedParallelIterator, IntoParallelR... | https://github.com/zkonduit/ezkl |
src/tensor/ops.rs | use super::TensorError;
use crate::tensor::{Tensor, TensorType};
use itertools::Itertools;
use maybe_rayon::{iter::ParallelIterator, prelude::IntoParallelRefIterator};
pub use std::ops::{Add, Mul, Neg, Sub};
/// Trilu operation.
/// # Arguments
/// * `a` - Tensor
/// * `k` - i32
/// * `upper` - Boolean
/// # Examples
... | https://github.com/zkonduit/ezkl |
src/tensor/val.rs | use core::{iter::FilterMap, slice::Iter};
use crate::circuit::region::ConstantsMap;
use super::{
ops::{intercalate_values, pad, resize},
*,
};
use halo2_proofs::{arithmetic::Field, circuit::Cell, plonk::Instance};
pub(crate) fn create_constant_tensor<
F: PrimeField + TensorType + std::marker::Send + std:... | https://github.com/zkonduit/ezkl |
src/tensor/var.rs | use std::collections::HashSet;
use log::{debug, error, warn};
use crate::circuit::{region::ConstantsMap, CheckMode};
use super::*;
/// A wrapper around Halo2's `Column<Fixed>` or `Column<Advice>`.
/// Typically assign [ValTensor]s to [VarTensor]s when laying out a circuit.
#[derive(Clone, Default, Debug, PartialEq, ... | https://github.com/zkonduit/ezkl |
src/wasm.rs | use crate::circuit::modules::polycommit::PolyCommitChip;
use crate::circuit::modules::poseidon::spec::{PoseidonSpec, POSEIDON_RATE, POSEIDON_WIDTH};
use crate::circuit::modules::poseidon::PoseidonChip;
use crate::circuit::modules::Module;
use crate::fieldutils::felt_to_i128;
use crate::fieldutils::i128_to_felt;
use cra... | https://github.com/zkonduit/ezkl |
tests/integration_tests.rs | #[cfg(not(target_arch = "wasm32"))]
#[cfg(test)]
mod native_tests {
use ezkl::circuit::Tolerance;
use ezkl::fieldutils::{felt_to_i128, i128_to_felt};
// use ezkl::circuit::table::RESERVED_BLINDING_ROWS_PAD;
use ezkl::graph::input::{FileSource, FileSourceInner, GraphData};
use ezkl::graph::{DataSour... | https://github.com/zkonduit/ezkl |
tests/output_comparison.py | import ezkl
import json
import onnx
import onnxruntime
import numpy as np
import sys
def get_ezkl_output(witness_file, settings_file):
# convert the quantized ezkl output to float value
witness_output = json.load(open(witness_file))
outputs = witness_output['outputs']
with open(settings_file) as f:
... | https://github.com/zkonduit/ezkl |
tests/py_integration_tests.rs | #[cfg(not(target_arch = "wasm32"))]
#[cfg(test)]
mod py_tests {
use lazy_static::lazy_static;
use std::env::var;
use std::process::{Child, Command};
use std::sync::Once;
use tempdir::TempDir;
static COMPILE: Once = Once::new();
static ENV_SETUP: Once = Once::new();
static DOWNLOAD_VOICE... | https://github.com/zkonduit/ezkl |
tests/python/binding_tests.py | import ezkl
import os
import pytest
import json
import subprocess
import time
folder_path = os.path.abspath(
os.path.join(
os.path.dirname(__file__),
'.',
)
)
examples_path = os.path.abspath(
os.path.join(
folder_path,
'..',
'..',
'examples',
)
)
srs_pa... | https://github.com/zkonduit/ezkl |
tests/python/srs_utils.py | """
This is meant to be used locally for development.
Generating the SRS is costly so we run this instead of creating a new SRS each
time we run tests.
"""
import ezkl
import os
srs_path = os.path.abspath(
os.path.join(
os.path.dirname(__file__),
'.',
'kzg_test.params',
)
)
def gen_t... | https://github.com/zkonduit/ezkl |
tests/wasm.rs | #[cfg(all(target_arch = "wasm32", target_os = "unknown"))]
#[cfg(test)]
mod wasm32 {
use ezkl::circuit::modules::polycommit::PolyCommitChip;
use ezkl::circuit::modules::poseidon::spec::{PoseidonSpec, POSEIDON_RATE, POSEIDON_WIDTH};
use ezkl::circuit::modules::poseidon::PoseidonChip;
use ezkl::circuit::m... | https://github.com/zkonduit/ezkl |
keras2circom/circom.py | # Ref: https://github.com/zk-ml/uchikoma/blob/main/python/uchikoma/circom.py
from __future__ import annotations
import typing
import os
from os import path
import json
from dataclasses import dataclass
import re
import numpy as np
class SafeDict(dict):
def __missing__(self, key):
return '{' + key + '}'
... | https://github.com/socathie/keras2circom |
keras2circom/model.py | # Read keras model into list of parameters like op, input, output, weight, bias
from __future__ import annotations
from dataclasses import dataclass
import typing
from tensorflow.keras.models import load_model
from tensorflow.keras.layers import Layer as KerasLayer
import numpy as np
supported_ops = [
'Activation'... | https://github.com/socathie/keras2circom |
keras2circom/script.py | from .circom import Circuit, Component
# template string for circuit.py
python_template_string = '''""" Make an interger-only circuit of the corresponding CIRCOM circuit.
Usage:
circuit.py <circuit.json> <input.json> [-o <output>]
circuit.py (-h | --help)
Options:
-h --help ... | https://github.com/socathie/keras2circom |
keras2circom/transpiler.py | from .circom import *
from .model import *
from .script import *
import os
def transpile(filename: str, output_dir: str = 'output', raw: bool = False, dec: int = 18) -> Circuit:
''' Transpile a Keras model to a CIRCOM circuit.'''
model = Model(filename, raw)
circuit = Circuit()
for layer in mode... | https://github.com/socathie/keras2circom |
keras2circom/util.py | # assume all inputs are strings
def AveragePooling2DInt (nRows, nCols, nChannels, poolSize, strides, input):
out = [[[0 for _ in range(nChannels)] for _ in range((nCols-poolSize)//strides + 1)] for _ in range((nRows-poolSize)//strides + 1)]
remainder = [[[None for _ in range(nChannels)] for _ in range((nCols-po... | https://github.com/socathie/keras2circom |
main.py | """ Transpile a Keras model to a CIRCOM circuit.
Usage:
main.py <model.h5> [-o <output>] [--raw] [-d <decimals>]
main.py (-h | --help)
Options:
-h --help Show this screen.
-o <output> --output=<output> Output directory [default: output].
--raw ... | https://github.com/socathie/keras2circom |
models/model.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# list of supported layers\n",
"from tensorflow.keras.layers import (\n",
" Input,\n",
" Activation,\n",
" AveragePooling2D,\n",
" BatchNormalization,\n",
" ... | https://github.com/socathie/keras2circom |
test/accuracy.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!cd .. && python main.py models/model.h5"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n... | https://github.com/socathie/keras2circom |
test/circuit.js | const chai = require('chai');
const fs = require('fs');
const wasm_tester = require('circom_tester').wasm;
const F1Field = require('ffjavascript').F1Field;
const Scalar = require('ffjavascript').Scalar;
exports.p = Scalar.fromString('21888242871839275222246405745257275088548364400416034343698204186575808495617');
con... | https://github.com/socathie/keras2circom |
test/load_input.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.datasets import mnist\n",
"import json\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
... | https://github.com/socathie/keras2circom |
circuits/ArgMax.circom | // from 0xZKML/zk-mnist
pragma circom 2.0.0;
include "./circomlib/comparators.circom";
include "./circomlib/switcher.circom";
template ArgMax (n) {
signal input in[n];
signal input out;
assert (out < n);
component gts[n]; // store comparators
component switchers[n+1]; // switcher for com... | https://github.com/socathie/circomlib-ml |
circuits/AveragePooling2D.circom | pragma circom 2.0.0;
include "./SumPooling2D.circom";
// AveragePooling2D layer, poolSize is required to be equal for both dimensions, might lose precision compared to SumPooling2D
template AveragePooling2D (nRows, nCols, nChannels, poolSize, strides) {
signal input in[nRows][nCols][nChannels];
signal input o... | https://github.com/socathie/circomlib-ml |
circuits/BatchNormalization2D.circom | pragma circom 2.0.0;
// BatchNormalization layer for 2D inputs
// a = gamma/(moving_var+epsilon)**.5
// b = beta-gamma*moving_mean/(moving_var+epsilon)**.5
// n = 10 to the power of the number of decimal places
template BatchNormalization2D(nRows, nCols, nChannels, n) {
signal input in[nRows][nCols][nChannels];
... | https://github.com/socathie/circomlib-ml |
circuits/Conv1D.circom | pragma circom 2.0.0;
include "./circomlib-matrix/matElemMul.circom";
include "./circomlib-matrix/matElemSum.circom";
include "./util.circom";
// Conv1D layer with valid padding
// n = 10 to the power of the number of decimal places
template Conv1D (nInputs, nChannels, nFilters, kernelSize, strides, n) {
signal in... | https://github.com/socathie/circomlib-ml |
circuits/Conv2D.circom | pragma circom 2.0.0;
include "./circomlib-matrix/matElemMul.circom";
include "./circomlib-matrix/matElemSum.circom";
include "./util.circom";
// Conv2D layer with valid padding
// n = 10 to the power of the number of decimal places
template Conv2D (nRows, nCols, nChannels, nFilters, kernelSize, strides, n) {
sign... | https://github.com/socathie/circomlib-ml |
circuits/Conv2Dsame.circom | pragma circom 2.0.0;
include "./Conv2D.circom";
template Conv2Dsame (nRows, nCols, nChannels, nFilters, kernelSize, strides, n) {
signal input in[nRows][nCols][nChannels];
signal input weights[kernelSize][kernelSize][nChannels][nFilters];
signal input bias[nFilters];
var rowPadding, colPadding;
... | https://github.com/socathie/circomlib-ml |
circuits/Dense.circom | pragma circom 2.0.0;
include "./circomlib-matrix/matMul.circom";
// Dense layer
// n = 10 to the power of the number of decimal places
template Dense (nInputs, nOutputs, n) {
signal input in[nInputs];
signal input weights[nInputs][nOutputs];
signal input bias[nOutputs];
signal input out[nOutputs];
... | https://github.com/socathie/circomlib-ml |
circuits/DepthwiseConv2D.circom | pragma circom 2.1.1;
// include "./Conv2D.circom";
include "./circomlib/sign.circom";
include "./circomlib/bitify.circom";
include "./circomlib/comparators.circom";
include "./circomlib-matrix/matElemMul.circom";
include "./circomlib-matrix/matElemSum.circom";
include "./util.circom";
// Depthwise Convolution layer w... | https://github.com/socathie/circomlib-ml |
circuits/Flatten2D.circom | pragma circom 2.0.0;
// Flatten layer with that accepts a 2D input
template Flatten2D (nRows, nCols, nChannels) {
signal input in[nRows][nCols][nChannels];
signal input out[nRows*nCols*nChannels];
var idx = 0;
for (var i=0; i<nRows; i++) {
for (var j=0; j<nCols; j++) {
for (var k=... | https://github.com/socathie/circomlib-ml |
circuits/GlobalAveragePooling2D.circom | pragma circom 2.0.0;
include "./GlobalSumPooling2D.circom";
// GlobalAveragePooling2D layer, might lose precision compared to GlobalSumPooling2D
template GlobalAveragePooling2D (nRows, nCols, nChannels) {
signal input in[nRows][nCols][nChannels];
signal input out[nChannels];
signal input remainder[nChanne... | https://github.com/socathie/circomlib-ml |
circuits/GlobalMaxPooling2D.circom | pragma circom 2.0.0;
include "./util.circom";
// GlobalMaxPooling2D layer
template GlobalMaxPooling2D (nRows, nCols, nChannels) {
signal input in[nRows][nCols][nChannels];
signal input out[nChannels];
component max[nChannels];
for (var k=0; k<nChannels; k++) {
max[k] = Max(nRows*nCols);
... | https://github.com/socathie/circomlib-ml |
circuits/GlobalSumPooling2D.circom | pragma circom 2.0.0;
include "./circomlib-matrix/matElemSum.circom";
include "./util.circom";
// GlobalSumPooling2D layer, basically GlobalAveragePooling2D layer with a constant scaling, more optimized for circom
template GlobalSumPooling2D (nRows, nCols, nChannels) {
signal input in[nRows][nCols][nChannels];
... | https://github.com/socathie/circomlib-ml |
circuits/LeakyReLU.circom | pragma circom 2.0.0;
include "./util.circom";
// LeakyReLU layer
template LeakyReLU (alpha) { // alpha is 10 times the actual alpha, since usual alpha is 0.2, 0.3, etc.
signal input in;
signal input out;
signal input remainder;
component isNegative = IsNegative();
isNegative.in <== in;
sign... | https://github.com/socathie/circomlib-ml |
circuits/MaxPooling2D.circom | pragma circom 2.0.0;
include "./util.circom";
// MaxPooling2D layer
template MaxPooling2D (nRows, nCols, nChannels, poolSize, strides) {
signal input in[nRows][nCols][nChannels];
signal input out[(nRows-poolSize)\strides+1][(nCols-poolSize)\strides+1][nChannels];
component max[(nRows-poolSize)\strides+1]... | https://github.com/socathie/circomlib-ml |
circuits/MaxPooling2Dsame.circom | pragma circom 2.0.0;
include "./MaxPooling2D.circom";
template MaxPooling2Dsame (nRows, nCols, nChannels, poolSize, strides) {
signal input in[nRows][nCols][nChannels];
var rowPadding, colPadding;
if (nRows % strides == 0) {
rowPadding = (poolSize - strides) > 0 ? (poolSize - strides) : 0;
... | https://github.com/socathie/circomlib-ml |
circuits/PointwiseConv2D.circom | pragma circom 2.1.1;
// include "./Conv2D.circom";
include "./circomlib/sign.circom";
include "./circomlib/bitify.circom";
include "./circomlib/comparators.circom";
include "./circomlib-matrix/matElemMul.circom";
include "./circomlib-matrix/matElemSum.circom";
include "./util.circom";
// Pointwise Convolution layer
/... | https://github.com/socathie/circomlib-ml |
circuits/ReLU.circom | pragma circom 2.0.0;
include "./util.circom";
// ReLU layer
template ReLU () {
signal input in;
signal input out;
component isPositive = IsPositive();
isPositive.in <== in;
out === in * isPositive.out;
} | https://github.com/socathie/circomlib-ml |
circuits/Reshape2D.circom | pragma circom 2.0.0;
// Reshape layer with that accepts a 1D input
template Reshape2D (nRows, nCols, nChannels) {
signal input in[nRows*nCols*nChannels];
signal input out[nRows][nCols][nChannels];
for (var i=0; i<nRows; i++) {
for (var j=0; j<nCols; j++) {
for (var k=0; k<nChannels; k+... | https://github.com/socathie/circomlib-ml |
circuits/SeparableConv2D.circom | pragma circom 2.1.1;
include "./PointwiseConv2D.circom";
include "./DepthwiseConv2D.circom";
// Separable convolution layer with valid padding.
// Quantization is done by the caller by multiplying float values by 10**exp.
template SeparableConv2D (nRows, nCols, nChannels, nDepthFilters, nPointFilters, depthKernelSize... | https://github.com/socathie/circomlib-ml |
circuits/SumPooling2D.circom | pragma circom 2.0.0;
include "./circomlib-matrix/matElemSum.circom";
include "./util.circom";
// SumPooling2D layer, basically AveragePooling2D layer with a constant scaling, more optimized for circom
template SumPooling2D (nRows, nCols, nChannels, poolSize, strides) {
signal input in[nRows][nCols][nChannels];
... | https://github.com/socathie/circomlib-ml |
circuits/UpSampling2D.circom | pragma circom 2.0.0;
template UpSampling2D(nRows, nCols, nChannels, size) {
signal input in[nRows][nCols][nChannels];
signal input out[nRows * size][nCols * size][nChannels];
// nearest neighbor interpolation
for (var i = 0; i < nRows; i++) {
for (var j = 0; j < nCols; j++) {
for (... | https://github.com/socathie/circomlib-ml |
circuits/Zanh.circom | pragma circom 2.0.0;
// Polynomial approximation for the tanh layer
// 0.006769816 + 0.554670504 * x - 0.009411195 * x**2 - 0.014187547 * x**3
// 6769816 + 554670504 * x - 9411195 * x**2 - 14187547 * x**3
// 6769816 + x * (554670504 - 9411195 * x - 14187547 * x**2)
// n = 10 to the power of the number of decimal place... | https://github.com/socathie/circomlib-ml |
circuits/ZeLU.circom | pragma circom 2.0.0;
// Poly activation layer: https://arxiv.org/abs/2011.05530
// n = 10 to the power of the number of decimal places
// out and remainder are from division by n so out has the same number of decimal places as in
template ZeLU (n) {
signal input in;
signal input out;
signal input remainder... | https://github.com/socathie/circomlib-ml |
circuits/Zigmoid.circom | pragma circom 2.0.0;
// Polynomial approximation for the sigmoid layer
// 0.502073021 + 0.198695283 * x - 0.001570683 * x**2 - 0.004001354 * x**3
// 502073021 + 198695283 * x - 1570683 * x**2 - 4001354 * x**3
// 502073021 + x * (198695283 - 1570683 * x - 4001354 * x**2)
// n = 10 to the power of the number of decimal ... | https://github.com/socathie/circomlib-ml |
circuits/circomlib-matrix/matElemMul.circom | pragma circom 2.0.0;
// matrix multiplication by element
template matElemMul (m,n) {
signal input a[m][n];
signal input b[m][n];
signal output out[m][n];
for (var i=0; i < m; i++) {
for (var j=0; j < n; j++) {
out[i][j] <== a[i][j] * b[i][j];
}
}
} | https://github.com/socathie/circomlib-ml |
circuits/circomlib-matrix/matElemSum.circom | pragma circom 2.0.0;
// sum of all elements in a matrix
template matElemSum (m,n) {
signal input a[m][n];
signal output out;
signal sum[m*n];
sum[0] <== a[0][0];
var idx = 0;
for (var i=0; i < m; i++) {
for (var j=0; j < n; j++) {
if (idx > 0) {
sum[idx... | https://github.com/socathie/circomlib-ml |
circuits/circomlib-matrix/matMul.circom | pragma circom 2.0.0;
include "matElemMul.circom";
include "matElemSum.circom";
// matrix multiplication
template matMul (m,n,p) {
signal input a[m][n];
signal input b[n][p];
signal output out[m][p];
component matElemMulComp[m][p];
component matElemSumComp[m][p];
for (var i=0; i < m; i++)... | https://github.com/socathie/circomlib-ml |
circuits/circomlib/aliascheck.circom | /*
Copyright 2018 0KIMS association.
This file is part of circom (Zero Knowledge Circuit Compiler).
circom is a free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, o... | https://github.com/socathie/circomlib-ml |
circuits/circomlib/babyjub.circom | /*
Copyright 2018 0KIMS association.
This file is part of circom (Zero Knowledge Circuit Compiler).
circom is a free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, o... | https://github.com/socathie/circomlib-ml |
circuits/circomlib/binsum.circom | /*
Copyright 2018 0KIMS association.
This file is part of circom (Zero Knowledge Circuit Compiler).
circom is a free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, o... | https://github.com/socathie/circomlib-ml |
circuits/circomlib/bitify.circom | /*
Copyright 2018 0KIMS association.
This file is part of circom (Zero Knowledge Circuit Compiler).
circom is a free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, o... | https://github.com/socathie/circomlib-ml |
circuits/circomlib/comparators.circom | /*
Copyright 2018 0KIMS association.
This file is part of circom (Zero Knowledge Circuit Compiler).
circom is a free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, o... | https://github.com/socathie/circomlib-ml |
circuits/circomlib/compconstant.circom | /*
Copyright 2018 0KIMS association.
This file is part of circom (Zero Knowledge Circuit Compiler).
circom is a free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, o... | https://github.com/socathie/circomlib-ml |
circuits/circomlib/escalarmulany.circom | /*
Copyright 2018 0KIMS association.
This file is part of circom (Zero Knowledge Circuit Compiler).
circom is a free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, o... | https://github.com/socathie/circomlib-ml |
circuits/circomlib/escalarmulfix.circom | /*
Copyright 2018 0KIMS association.
This file is part of circom (Zero Knowledge Circuit Compiler).
circom is a free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, o... | https://github.com/socathie/circomlib-ml |
circuits/circomlib/mimc.circom | /*
Copyright 2018 0KIMS association.
This file is part of circom (Zero Knowledge Circuit Compiler).
circom is a free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, o... | https://github.com/socathie/circomlib-ml |
circuits/circomlib/montgomery.circom | /*
Copyright 2018 0KIMS association.
This file is part of circom (Zero Knowledge Circuit Compiler).
circom is a free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, o... | https://github.com/socathie/circomlib-ml |
circuits/circomlib/mux3.circom | /*
Copyright 2018 0KIMS association.
This file is part of circom (Zero Knowledge Circuit Compiler).
circom is a free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, o... | https://github.com/socathie/circomlib-ml |
circuits/circomlib/sign.circom | /*
Copyright 2018 0KIMS association.
This file is part of circom (Zero Knowledge Circuit Compiler).
circom is a free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, o... | https://github.com/socathie/circomlib-ml |
circuits/circomlib/switcher.circom | /*
Copyright 2018 0KIMS association.
This file is part of circom (Zero Knowledge Circuit Compiler).
circom is a free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, o... | https://github.com/socathie/circomlib-ml |
circuits/crypto/ecdh.circom | // from privacy-scaling-explorations/maci
pragma circom 2.0.0;
include "../circomlib/bitify.circom";
include "../circomlib/escalarmulany.circom";
template Ecdh() {
// Note: private key
// Needs to be hashed, and then pruned before
// supplying it to the circuit
signal input private_key;
signal input public... | https://github.com/socathie/circomlib-ml |
circuits/crypto/encrypt.circom | //from zk-ml/linear-regression-demo
pragma circom 2.0.0;
include "../circomlib/mimc.circom";
template EncryptBits(N) {
signal input plaintext[N];
signal input shared_key;
signal output out[N+1];
component mimc = MultiMiMC7(N, 91);
for (var i=0; i<N; i++) {
mimc.in[i] <== plaintext[i];
}
mimc.k <==... | https://github.com/socathie/circomlib-ml |
circuits/crypto/publickey_derivation.circom | // from privacy-scaling-explorations/maci
pragma circom 2.0.0;
include "../circomlib/bitify.circom";
include "../circomlib/escalarmulfix.circom";
template PublicKey() {
// Note: private key
// Needs to be hashed, and then pruned before
// supplying it to the circuit
signal input private_key;
signal output ... | https://github.com/socathie/circomlib-ml |
circuits/util.circom | pragma circom 2.0.0;
include "./circomlib/sign.circom";
include "./circomlib/bitify.circom";
include "./circomlib/comparators.circom";
include "./circomlib/switcher.circom";
template IsNegative() {
signal input in;
signal output out;
component num2Bits = Num2Bits(254);
num2Bits.in <== in;
compone... | https://github.com/socathie/circomlib-ml |
index.js | https://github.com/socathie/circomlib-ml | |
models/averagePooling2d.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"p = 21888242871839275222246405745257275088548364400416034343698204186575808495617"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": ... | https://github.com/socathie/circomlib-ml |
models/batchNormalization.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"p = 21888242871839275222246405745257275088548364400416034343698204186575808495617"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": ... | https://github.com/socathie/circomlib-ml |
models/conv1d.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"p = 21888242871839275222246405745257275088548364400416034343698204186575808495617"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": ... | https://github.com/socathie/circomlib-ml |
models/conv2d.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"p = 21888242871839275222246405745257275088548364400416034343698204186575808495617"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": ... | https://github.com/socathie/circomlib-ml |
models/conv2d_same.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import Input, Conv2D\n",
"from tensorflow.keras import Model\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metada... | https://github.com/socathie/circomlib-ml |
models/dense.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"p = 21888242871839275222246405745257275088548364400416034343698204186575808495617"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": ... | https://github.com/socathie/circomlib-ml |
models/depthwiseConv2D.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "4d60427f-21e9-41b1-a5eb-0d36d2c395ea",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import numpy as np\n",
"import json"
]
},
... | https://github.com/socathie/circomlib-ml |
models/flatten.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import Input, Flatten\n",
"from tensorflow.keras import Model\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metad... | https://github.com/socathie/circomlib-ml |
models/globalAveragePooling2D.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"p = 21888242871839275222246405745257275088548364400416034343698204186575808495617"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": ... | https://github.com/socathie/circomlib-ml |
models/globalMaxPooling2D.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"p = 21888242871839275222246405745257275088548364400416034343698204186575808495617"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": ... | https://github.com/socathie/circomlib-ml |
models/lr_zanh.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# example from https://towardsdatascience.com/replicate-a-logistic-regression-model-as-an-artificial-neural-network-in-keras-cd6f49cf4b2c"
]
},
{
"cell_type": "code",
"execution_count... | https://github.com/socathie/circomlib-ml |
models/lr_zigmoid.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# example from https://towardsdatascience.com/replicate-a-logistic-regression-model-as-an-artificial-neural-network-in-keras-cd6f49cf4b2c"
]
},
{
"cell_type": "code",
"execution_count... | https://github.com/socathie/circomlib-ml |
models/maxPooling2d.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"p = 21888242871839275222246405745257275088548364400416034343698204186575808495617"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": ... | https://github.com/socathie/circomlib-ml |
models/maxPooling2d_same.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import Input, MaxPooling2D\n",
"from tensorflow.keras import Model\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"... | https://github.com/socathie/circomlib-ml |
models/mnist.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"p = 21888242871839275222246405745257275088548364400416034343698204186575808495617"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": ... | https://github.com/socathie/circomlib-ml |
models/model1.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"p = 21888242871839275222246405745257275088548364400416034343698204186575808495617"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": ... | https://github.com/socathie/circomlib-ml |
models/pointwiseConv2D.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "2b70084b-44da-4142-9e24-c9c8231828db",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import numpy as np\n",
"import json"
]
},
... | https://github.com/socathie/circomlib-ml |
models/remez.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from mpmath import mp\n",
"import numpy as np\n",
"\n",
"# from https://github.com/DKenefake/OptimalPoly\n",
"\n",
"def bisection_search(f, low:float, high:float):\n",
" ... | https://github.com/socathie/circomlib-ml |
models/reshape2d.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import Input, Reshape\n",
"from tensorflow.keras import Model\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metad... | https://github.com/socathie/circomlib-ml |
models/separableConv2D.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "aa5be75a-ee5f-45b0-891b-da2dd340dd00",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import numpy as np\n",
"import json"
]
},
... | https://github.com/socathie/circomlib-ml |
models/sumPooling2d.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import Input, AveragePooling2D, Lambda\n",
"from tensorflow.keras import Model\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_cou... | https://github.com/socathie/circomlib-ml |
models/upSampling2d.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import Input, UpSampling2D\n",
"from tensorflow.keras import Model\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 13,
... | https://github.com/socathie/circomlib-ml |
test/AveragePooling2D.js | const chai = require("chai");
const path = require("path");
const wasm_tester = require("circom_tester").wasm;
const F1Field = require("ffjavascript").F1Field;
const Scalar = require("ffjavascript").Scalar;
exports.p = Scalar.fromString("21888242871839275222246405745257275088548364400416034343698204186575808495617");... | https://github.com/socathie/circomlib-ml |
test/BatchNormalization.js | const chai = require("chai");
const path = require("path");
const wasm_tester = require("circom_tester").wasm;
const F1Field = require("ffjavascript").F1Field;
const Scalar = require("ffjavascript").Scalar;
exports.p = Scalar.fromString("21888242871839275222246405745257275088548364400416034343698204186575808495617");... | https://github.com/socathie/circomlib-ml |
test/Conv1D.js | const chai = require("chai");
const path = require("path");
const wasm_tester = require("circom_tester").wasm;
const F1Field = require("ffjavascript").F1Field;
const Scalar = require("ffjavascript").Scalar;
exports.p = Scalar.fromString("21888242871839275222246405745257275088548364400416034343698204186575808495617");... | https://github.com/socathie/circomlib-ml |
test/Conv2D.js | const chai = require("chai");
const path = require("path");
const wasm_tester = require("circom_tester").wasm;
const F1Field = require("ffjavascript").F1Field;
const Scalar = require("ffjavascript").Scalar;
exports.p = Scalar.fromString("21888242871839275222246405745257275088548364400416034343698204186575808495617");... | https://github.com/socathie/circomlib-ml |
test/Conv2Dsame.js | const chai = require("chai");
const path = require("path");
const wasm_tester = require("circom_tester").wasm;
const F1Field = require("ffjavascript").F1Field;
const Scalar = require("ffjavascript").Scalar;
exports.p = Scalar.fromString("21888242871839275222246405745257275088548364400416034343698204186575808495617");... | https://github.com/socathie/circomlib-ml |
test/Dense.js | const chai = require("chai");
const path = require("path");
const wasm_tester = require("circom_tester").wasm;
const F1Field = require("ffjavascript").F1Field;
const Scalar = require("ffjavascript").Scalar;
exports.p = Scalar.fromString("21888242871839275222246405745257275088548364400416034343698204186575808495617");... | https://github.com/socathie/circomlib-ml |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.