jw-optalysys

jw-optalysys

jw-optalysys

jw-optalysys

jw-optalysys

jw-optalysys

jw-optalysys

# Importing relevant libraries and modules

import torch
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch.nn as nn
import torch.nn.functional as F

# Importing the Fashion MNIST Dataset as Pandas dataframes

jw-optalysys

from concrete.ml.torch.compile import compile_torch_model

# Compiling the model into a FHE circuit using Concrete ML
quantised_compiled_module = compile_torch_model(
    model,
    X_train,
    n_bits = 3  # This is the quantisation bit width. 
    #In this case only 3 bits is sufficient.
)

jw-optalysys

# Importing relevant libraries

import numpy as np
import torch
from torch import nn

# Defining the neural network using PyTorch
# It has 2 hidden layers. Both hidden layers and the output layer have 3 neurones each
# The Sklearn wine dataset is a simple one to work with, so such a small neural network
# is already sufficient for achieving high accuracy

jw-optalysys

# Parameter grid used for GridSearchCV
  
param_grid = {
  
  "max_depth": list(range(1, 5)),
  
  "n_estimators": list(range(1, 201, 20)),  
  
  "learning_rate": [0.01, 0.1, 1],