inst = 'objects/instantiations/MNT159.inst'
vector_length = 784
k = 250
classes = 10
model = 'objects/ml_models/final.mlm'
mnist = fetch_openml('mnist_784')
X, y = mnist['data'].astype('float'), mnist['target'].astype('float')
X_test, y_test = X[60000:], y[60000:]
index = random.randint(0, 9999)
X, y = X_test[index], y_test[index]
print(f'Will test on MNIST test instance #{index}, which is a {int(y)}.')
print('Importing model.')
ml = models.MLModel(source=model)
biased = np.ones(785)
biased[1:] = X
print('Done!\n')
results = ml.evaluate(biased)
print(f'Expected output:\n{results}\n')
print('Importing scheme.')
scheme = scheme.ML_DGP(inst)
print('Done!\n')
print('Loading discrete logarithm solver.')
dlog = discretelogarithm.PreCompBabyStepGiantStep(
"""
Generates the confusion matrix for the provided model using cleartext
evaluation.
"""
import setup
from core import models
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
from sklearn.datasets import fetch_mldata
model = models.MLModel(source="objects/ml_models/final.mlm")
mnist = fetch_mldata('MNIST original')
X, y = mnist["data"], mnist["target"].astype('int')
X_test_, y_test = X[60000:], y[60000:]
X_test = np.ones((10000, 785))
X_test[:, 1:] = X_test_
predictions = [np.array(model.evaluate(x)).argmax() for x in X_test]
M = [[0 for i in range(10)] for j in range(10)]
for i in range(10000):
value = y_test[i]
pred = predictions[i]
M[value][pred] += 1
conf_mat = pd.DataFrame(
M,
from utils import fix_precision, float_precision
model = SimpleNN(10)
print("Loading model ...")
path = "/home/sukhad/Workspace/GithHub/reading-in-the-dark/mnist/objects/ml_models/simple_char.pt"
model.load_state_dict(torch.load(path))
model.eval()
print("Done")
proj_prec = 7
diag_prec = 5
data_prec = 3
model.proj1.weight = fix_precision(model.proj1.weight, proj_prec, 100)
model.proj1.bias = fix_precision(model.proj1.bias, proj_prec, 100)
model.diag1.weight = fix_precision(model.diag1.weight, diag_prec, 100)
proj_param = torch.cat((model.proj1.bias.reshape((1,-1))/2**data_prec, model.proj1.weight.t())).long().tolist()
diag_param = model.diag1.weight.t().long().tolist()
print("Loading proj and forms ...")
proj = models.Projection(proj_param)
forms = models.DiagonalQuadraticForms(diag_param)
print("Done")
model_path = "/home/sukhad/Workspace/GithHub/reading-in-the-dark/mnist/objects/ml_models"
ml = models.MLModel(proj, forms)
ml.toFile(model_path, "simple_nn_quad")