youben11

import hnumpy as hnp
import numpy as np

youben11

weights = np.array([0.1, 0.2, 0.3, 0.4, 0.5])
bias = np.array([0.1])


def sigmoid(x):
    return 1 / (1 + np.exp(-x))

  
def func(x):
    return sigmoid(np.dot(x, weights) + bias)

youben11

h = hnp.compile_fhe(
    func,
    {
        'x': hnp.encrypted_ndarray(bounds=(-1, 1), shape=(5,)),
    }
)

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x = np.random.uniform(-1, 1, 5)

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print(f"Simulation result: {h.simulate(x)}")
print(f"Plain NumPy result: {func(x)}")

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ctx = h.create_context()
keys = ctx.keygen()

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print(f"Encrypted computation result: {h.encrypt_and_run(keys, x)}")

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x_enc= keys.encrypt(x)
res = h.run(keys.public_keys, x_enc)
print(f"Encrypted computation result: {keys.decrypt(res)}")

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HIDDEN_SIZE = 100


class Model(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.lstm = torch.nn.LSTM(input_size=300, hidden_size=HIDDEN_SIZE)
        self.fc = torch.nn.Linear(HIDDEN_SIZE, 1)
        self.sigmoid = torch.nn.Sigmoid()

youben11

class Inferer:
    def __init__(self, model):
        parameters = list(model.lstm.parameters())
        
        W_ii, W_if, W_ig, W_io = parameters[0].split(HIDDEN_SIZE)
        W_hi, W_hf, W_hg, W_ho = parameters[1].split(HIDDEN_SIZE)
        b_ii, b_if, b_ig, b_io = parameters[2].split(HIDDEN_SIZE)
        b_hi, b_hf, b_hg, b_ho = parameters[3].split(HIDDEN_SIZE)
        
        self.W_ii = W_ii.detach().numpy()