def __init__(self, params=None, layer_config=None, model_builder=None):
self.nn_define = layer_config
self.layer_config = layer_config
self.host_input_shape = None
self.guest_input_shape = None
self.model = None
self.rng_generator = random_number_generator.RandomNumberGenerator()
self.model_builder = model_builder
self.transfer_variable = None
self.learning_rate = params.interactive_layer_lr
self.encrypted_host_dense_output = None
self.encrypted_host_input = None
self.guest_input = None
self.guest_output = None
self.host_output = None
self.dense_output_data = None
self.guest_model = None
self.host_model = None
self.partitions = 0
self.do_backward_select_strategy = False
self.encrypted_host_input_cached = None
self.drop_out_keep_rate = params.drop_out_keep_rate
self.drop_out = None
self.fixed_point_encoder = None if params.floating_point_precision is None else FixedPointEncoder(
2**params.floating_point_precision)
self.sync_output_unit = False
def _init_model(self, param: FTLParam):
self.nn_define = param.nn_define
self.alpha = param.alpha
self.tol = param.tol
self.n_iter_no_change = param.n_iter_no_change
self.validation_freqs = param.validation_freqs
self.optimizer = param.optimizer
self.intersect_param = param.intersect_param
self.config_type = param.config_type
self.batch_size = param.batch_size
self.epochs = param.epochs
self.mode = param.mode
self.comm_eff = param.communication_efficient
self.local_round = param.local_round
assert 'learning_rate' in self.optimizer.kwargs, 'optimizer setting must contain learning_rate'
self.learning_rate = self.optimizer.kwargs['learning_rate']
if not self.comm_eff:
self.local_round = 1
LOGGER.debug(
'communication efficient mode is not enabled, local_round set as 1'
)
self.encrypted_mode_calculator_param = param.encrypted_mode_calculator_param
self.encrypter = self.generate_encrypter(param)
self.predict_param = param.predict_param
self.rng_generator = random_number_generator.RandomNumberGenerator()
def test_tensor_op(self):
arr1 = np.ones((10, 1, 3))
arr1[0] = np.array([[2, 3, 4]])
arr2 = np.ones((10, 3, 3))
arr3 = np.ones([1, 1, 3])
arr4 = np.ones([50, 1])
arr5 = np.ones([32])
pt = PaillierTensor(arr1)
pt2 = PaillierTensor(arr2)
pt3 = PaillierTensor(arr3)
pt4 = PaillierTensor(arr4)
pt5 = PaillierTensor(arr5)
encrypter = PaillierEncrypt()
encrypter.generate_key(EncryptParam().key_length)
encrypted_calculator = EncryptModeCalculator(
encrypter,
EncryptedModeCalculatorParam().mode,
EncryptedModeCalculatorParam().re_encrypted_rate)
rs1 = pt * arr2
rs2 = pt * pt2
rs3 = pt.matmul_3d(pt2)
enpt = pt2.encrypt(encrypted_calculator)
enrs = enpt.matmul_3d(arr1, multiply='right')
rng_generator = random_number_generator.RandomNumberGenerator()
enpt2 = pt4.encrypt(encrypted_calculator)
random_num = rng_generator.generate_random_number(enpt2.shape)
def __init__(self, param):
self.acc_noise = None
self.learning_rate = param.interactive_layer_lr
self.encrypted_mode_calculator_param = param.encrypted_model_calculator_param
self.encrypter = self.generate_encrypter(param)
self.train_encrypted_calculator = []
self.predict_encrypted_calculator = []
self.transfer_variable = None
self.partitions = 1
self.input_shape = None
self.output_unit = None
self.rng_generator = random_number_generator.RandomNumberGenerator()
def __init__(self, params):
self.acc_noise = None
self.learning_rate = params.interactive_layer_lr
self.encrypter = self.generate_encrypter(params)
self.transfer_variable = None
self.partitions = 1
self.input_shape = None
self.output_unit = None
self.rng_generator = random_number_generator.RandomNumberGenerator()
self.do_backward_select_strategy = False
self.drop_out_keep_rate = params.drop_out_keep_rate
self.fixed_point_encoder = None if params.floating_point_precision is None else FixedPointEncoder(
2**params.floating_point_precision)
self.mask_table = None
def __init__(self, params=None, layer_config=None, model_builder=None):
self.nn_define = layer_config
self.layer_config = layer_config
self.host_input_shape = None
self.guest_input_shape = None
self.model = None
self.rng_generator = random_number_generator.RandomNumberGenerator()
self.model_builder = model_builder
self.transfer_variable = None
self.learning_rate = params.interactive_layer_lr
self.encrypted_host_dense_output = None
self.encrypted_host_input = None
self.guest_input = None
self.guest_output = None
self.host_output = None
self.dense_output_data = None
self.guest_model = None
self.host_model = None
self.partitions = 0
