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ওভারভিউ
এই কোডল্যাবে আপনি CIFAR10 ডেটাসেটে একটি সাধারণ চিত্র শ্রেণীবিভাগের মডেলকে প্রশিক্ষণ দেবেন, এবং তারপর আক্রমণকারী প্রশিক্ষণ সেটে একটি নির্দিষ্ট নমুনা উপস্থিত ছিল কিনা তা "অনুমান" করতে সক্ষম কিনা তা মূল্যায়ন করতে এই মডেলের বিরুদ্ধে "সদস্য অনুমান আক্রমণ" ব্যবহার করুন। . আপনি একাধিক মডেল এবং মডেল চেকপয়েন্ট থেকে ফলাফল কল্পনা করতে TF গোপনীয়তা রিপোর্ট ব্যবহার করবেন।
সেটআপ
import numpy as np
from typing import Tuple
from scipy import special
from sklearn import metrics
import tensorflow as tf
import tensorflow_datasets as tfds
# Set verbosity.
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
from sklearn.exceptions import ConvergenceWarning
import warnings
warnings.simplefilter(action="ignore", category=ConvergenceWarning)
warnings.simplefilter(action="ignore", category=FutureWarning)
TensorFlow গোপনীয়তা ইনস্টল করুন।
pip install tensorflow_privacy
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import membership_inference_attack as mia
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackInputData
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackResultsCollection
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackType
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import PrivacyMetric
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import PrivacyReportMetadata
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import SlicingSpec
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import privacy_report
import tensorflow_privacy
গোপনীয়তা মেট্রিক্স সহ দুটি মডেলকে প্রশিক্ষণ দিন
এই বিভাগে একজোড়া ট্রেনের keras.Model উপর ক্লাসিফায়ার CIFAR-10 ডেটা সেটটি। প্রশিক্ষণ প্রক্রিয়া চলাকালীন এটি গোপনীয়তা মেট্রিক্স সংগ্রহ করে, যা পরবর্তী বিভাগে প্রতিবেদন তৈরি করতে ব্যবহার করা হবে।
প্রথম ধাপ হল কিছু হাইপারপ্যারামিটার সংজ্ঞায়িত করা:
dataset = 'cifar10'
num_classes = 10
activation = 'relu'
num_conv = 3
batch_size=50
epochs_per_report = 2
total_epochs = 50
lr = 0.001
এর পরে, ডেটাসেট লোড করুন। এই কোডে গোপনীয়তা-নির্দিষ্ট কিছু নেই।
print('Loading the dataset.')
train_ds = tfds.as_numpy(
tfds.load(dataset, split=tfds.Split.TRAIN, batch_size=-1))
test_ds = tfds.as_numpy(
tfds.load(dataset, split=tfds.Split.TEST, batch_size=-1))
x_train = train_ds['image'].astype('float32') / 255.
y_train_indices = train_ds['label'][:, np.newaxis]
x_test = test_ds['image'].astype('float32') / 255.
y_test_indices = test_ds['label'][:, np.newaxis]
# Convert class vectors to binary class matrices.
y_train = tf.keras.utils.to_categorical(y_train_indices, num_classes)
y_test = tf.keras.utils.to_categorical(y_test_indices, num_classes)
input_shape = x_train.shape[1:]
assert x_train.shape[0] % batch_size == 0, "The tensorflow_privacy optimizer doesn't handle partial batches"
Loading the dataset.
পরবর্তী মডেলগুলি তৈরি করার জন্য একটি ফাংশন সংজ্ঞায়িত করুন।
def small_cnn(input_shape: Tuple[int],
num_classes: int,
num_conv: int,
activation: str = 'relu') -> tf.keras.models.Sequential:
"""Setup a small CNN for image classification.
Args:
input_shape: Integer tuple for the shape of the images.
num_classes: Number of prediction classes.
num_conv: Number of convolutional layers.
activation: The activation function to use for conv and dense layers.
Returns:
The Keras model.
"""
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Input(shape=input_shape))
# Conv layers
for _ in range(num_conv):
model.add(tf.keras.layers.Conv2D(32, (3, 3), activation=activation))
model.add(tf.keras.layers.MaxPooling2D())
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(64, activation=activation))
model.add(tf.keras.layers.Dense(num_classes))
model.compile(
loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(learning_rate=lr),
metrics=['accuracy'])
return model
সেই ফাংশনটি ব্যবহার করে দুটি তিন-স্তর সিএনএন মডেল তৈরি করুন।
একটি differentially ব্যক্তিগত অপটিমাইজার (ব্যবহার করার জন্য একটি মৌলিক SGD অপটিমাইজার ব্যবহার করতে প্রথম, একটি দ্বিতীয় কনফিগার করুন tf_privacy.DPKerasAdamOptimizer ,) তাই আপনি ফলাফল তুলনা করতে পারবেন।
model_2layers = small_cnn(
input_shape, num_classes, num_conv=2, activation=activation)
model_3layers = small_cnn(
input_shape, num_classes, num_conv=3, activation=activation)
গোপনীয়তা মেট্রিক্স সংগ্রহ করতে একটি কলব্যাক সংজ্ঞায়িত করুন
পাশে একটি সংজ্ঞায়িত keras.callbacks.Callback periorically মডেল বিরুদ্ধে কিছু গোপনীয়তা হামলার চালানোর জন্য, এবং ফলাফল লগ ইন করুন।
Keras fit পদ্ধতি ডাকব on_epoch_end প্রতিটি প্রশিক্ষণ কাল পরে পদ্ধতি। n যুক্তি (0 ভিত্তিক) যুগান্তকারী সংখ্যা।
আপনি একটি লুপ যে বারবার আহ্বান লিখে এই পদ্ধতি বাস্তবায়ন হতে পারে Model.fit(..., epochs=epochs_per_report) এবং আক্রমণ কোড রান। কলব্যাকটি এখানে ব্যবহার করা হয়েছে কারণ এটি প্রশিক্ষণের যুক্তি এবং গোপনীয়তা মূল্যায়ন যুক্তির মধ্যে একটি স্পষ্ট বিচ্ছেদ দেয়৷
class PrivacyMetrics(tf.keras.callbacks.Callback):
def __init__(self, epochs_per_report, model_name):
self.epochs_per_report = epochs_per_report
self.model_name = model_name
self.attack_results = []
def on_epoch_end(self, epoch, logs=None):
epoch = epoch+1
if epoch % self.epochs_per_report != 0:
return
print(f'\nRunning privacy report for epoch: {epoch}\n')
logits_train = self.model.predict(x_train, batch_size=batch_size)
logits_test = self.model.predict(x_test, batch_size=batch_size)
prob_train = special.softmax(logits_train, axis=1)
prob_test = special.softmax(logits_test, axis=1)
# Add metadata to generate a privacy report.
privacy_report_metadata = PrivacyReportMetadata(
# Show the validation accuracy on the plot
# It's what you send to train_accuracy that gets plotted.
accuracy_train=logs['val_accuracy'],
accuracy_test=logs['val_accuracy'],
epoch_num=epoch,
model_variant_label=self.model_name)
attack_results = mia.run_attacks(
AttackInputData(
labels_train=y_train_indices[:, 0],
labels_test=y_test_indices[:, 0],
probs_train=prob_train,
probs_test=prob_test),
SlicingSpec(entire_dataset=True, by_class=True),
attack_types=(AttackType.THRESHOLD_ATTACK,
AttackType.LOGISTIC_REGRESSION),
privacy_report_metadata=privacy_report_metadata)
self.attack_results.append(attack_results)
মডেলদের প্রশিক্ষণ দিন
পরবর্তী কোড ব্লক দুটি মডেল প্রশিক্ষণ. all_reports তালিকা সমস্ত মডেলের প্রশিক্ষণ রান থেকে সমস্ত ফলাফল সংগ্রহ করতে ব্যবহৃত হয়। পৃথক প্রতিবেদন witht ট্যাগ করা model_name , তাই কোন বিভ্রান্তি যা সম্পর্কে মডেল উত্পন্ন যা রিপোর্ট আছে।
all_reports = []
callback = PrivacyMetrics(epochs_per_report, "2 Layers")
history = model_2layers.fit(
x_train,
y_train,
batch_size=batch_size,
epochs=total_epochs,
validation_data=(x_test, y_test),
callbacks=[callback],
shuffle=True)
all_reports.extend(callback.attack_results)
Epoch 1/50 1000/1000 [==============================] - 13s 4ms/step - loss: 1.5146 - accuracy: 0.4573 - val_loss: 1.2374 - val_accuracy: 0.5660 Epoch 2/50 1000/1000 [==============================] - 3s 3ms/step - loss: 1.1933 - accuracy: 0.5811 - val_loss: 1.1873 - val_accuracy: 0.5851 Running privacy report for epoch: 2 Epoch 3/50 1000/1000 [==============================] - 3s 3ms/step - loss: 1.0694 - accuracy: 0.6246 - val_loss: 1.0526 - val_accuracy: 0.6310 Epoch 4/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.9911 - accuracy: 0.6548 - val_loss: 0.9906 - val_accuracy: 0.6549 Running privacy report for epoch: 4 Epoch 5/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.9348 - accuracy: 0.6743 - val_loss: 0.9712 - val_accuracy: 0.6617 Epoch 6/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.8881 - accuracy: 0.6912 - val_loss: 0.9595 - val_accuracy: 0.6671 Running privacy report for epoch: 6 Epoch 7/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.8495 - accuracy: 0.7024 - val_loss: 0.9574 - val_accuracy: 0.6684 Epoch 8/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.8147 - accuracy: 0.7161 - val_loss: 0.9397 - val_accuracy: 0.6740 Running privacy report for epoch: 8 Epoch 9/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7820 - accuracy: 0.7263 - val_loss: 0.9325 - val_accuracy: 0.6837 Epoch 10/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7533 - accuracy: 0.7362 - val_loss: 0.9431 - val_accuracy: 0.6843 Running privacy report for epoch: 10 Epoch 11/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7169 - accuracy: 0.7477 - val_loss: 0.9310 - val_accuracy: 0.6795 Epoch 12/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6892 - accuracy: 0.7569 - val_loss: 0.9043 - val_accuracy: 0.6975 Running privacy report for epoch: 12 Epoch 13/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6677 - accuracy: 0.7663 - val_loss: 0.9401 - val_accuracy: 0.6796 Epoch 14/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6401 - accuracy: 0.7741 - val_loss: 0.9464 - val_accuracy: 0.6880 Running privacy report for epoch: 14 Epoch 15/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6177 - accuracy: 0.7821 - val_loss: 0.9359 - val_accuracy: 0.6930 Epoch 16/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5978 - accuracy: 0.7913 - val_loss: 0.9634 - val_accuracy: 0.6896 Running privacy report for epoch: 16 Epoch 17/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5745 - accuracy: 0.7973 - val_loss: 0.9941 - val_accuracy: 0.6932 Epoch 18/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5553 - accuracy: 0.8036 - val_loss: 0.9790 - val_accuracy: 0.6974 Running privacy report for epoch: 18 Epoch 19/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5376 - accuracy: 0.8103 - val_loss: 0.9989 - val_accuracy: 0.6907 Epoch 20/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5152 - accuracy: 0.8192 - val_loss: 1.0245 - val_accuracy: 0.6878 Running privacy report for epoch: 20 Epoch 21/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5048 - accuracy: 0.8208 - val_loss: 1.0223 - val_accuracy: 0.6852 Epoch 22/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.4847 - accuracy: 0.8284 - val_loss: 1.0498 - val_accuracy: 0.6866 Running privacy report for epoch: 22 Epoch 23/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.4722 - accuracy: 0.8325 - val_loss: 1.0610 - val_accuracy: 0.6899 Epoch 24/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.4562 - accuracy: 0.8387 - val_loss: 1.0973 - val_accuracy: 0.6771 Running privacy report for epoch: 24 Epoch 25/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.4392 - accuracy: 0.8447 - val_loss: 1.1141 - val_accuracy: 0.6865 Epoch 26/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.4269 - accuracy: 0.8485 - val_loss: 1.1928 - val_accuracy: 0.6771 Running privacy report for epoch: 26 Epoch 27/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.4135 - accuracy: 0.8533 - val_loss: 1.1945 - val_accuracy: 0.6758 Epoch 28/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.4053 - accuracy: 0.8569 - val_loss: 1.2244 - val_accuracy: 0.6716 Running privacy report for epoch: 28 Epoch 29/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3880 - accuracy: 0.8611 - val_loss: 1.2362 - val_accuracy: 0.6789 Epoch 30/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3805 - accuracy: 0.8630 - val_loss: 1.2815 - val_accuracy: 0.6805 Running privacy report for epoch: 30 Epoch 31/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3756 - accuracy: 0.8656 - val_loss: 1.2973 - val_accuracy: 0.6762 Epoch 32/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3565 - accuracy: 0.8719 - val_loss: 1.3022 - val_accuracy: 0.6810 Running privacy report for epoch: 32 Epoch 33/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3494 - accuracy: 0.8749 - val_loss: 1.3248 - val_accuracy: 0.6756 Epoch 34/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3371 - accuracy: 0.8790 - val_loss: 1.3941 - val_accuracy: 0.6806 Running privacy report for epoch: 34 Epoch 35/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3248 - accuracy: 0.8839 - val_loss: 1.4391 - val_accuracy: 0.6666 Epoch 36/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3233 - accuracy: 0.8833 - val_loss: 1.5060 - val_accuracy: 0.6692 Running privacy report for epoch: 36 Epoch 37/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3109 - accuracy: 0.8882 - val_loss: 1.4624 - val_accuracy: 0.6724 Epoch 38/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3057 - accuracy: 0.8900 - val_loss: 1.5133 - val_accuracy: 0.6644 Running privacy report for epoch: 38 Epoch 39/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2929 - accuracy: 0.8949 - val_loss: 1.5465 - val_accuracy: 0.6618 Epoch 40/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2868 - accuracy: 0.8970 - val_loss: 1.5882 - val_accuracy: 0.6696 Running privacy report for epoch: 40 Epoch 41/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2778 - accuracy: 0.8982 - val_loss: 1.6317 - val_accuracy: 0.6713 Epoch 42/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2782 - accuracy: 0.8999 - val_loss: 1.6993 - val_accuracy: 0.6630 Running privacy report for epoch: 42 Epoch 43/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2675 - accuracy: 0.9039 - val_loss: 1.7294 - val_accuracy: 0.6645 Epoch 44/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2587 - accuracy: 0.9068 - val_loss: 1.7614 - val_accuracy: 0.6561 Running privacy report for epoch: 44 Epoch 45/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2528 - accuracy: 0.9076 - val_loss: 1.7835 - val_accuracy: 0.6564 Epoch 46/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2410 - accuracy: 0.9129 - val_loss: 1.8550 - val_accuracy: 0.6648 Running privacy report for epoch: 46 Epoch 47/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2409 - accuracy: 0.9106 - val_loss: 1.8705 - val_accuracy: 0.6572 Epoch 48/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2328 - accuracy: 0.9146 - val_loss: 1.9110 - val_accuracy: 0.6593 Running privacy report for epoch: 48 Epoch 49/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2299 - accuracy: 0.9164 - val_loss: 1.9468 - val_accuracy: 0.6634 Epoch 50/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2250 - accuracy: 0.9178 - val_loss: 2.0154 - val_accuracy: 0.6610 Running privacy report for epoch: 50
callback = PrivacyMetrics(epochs_per_report, "3 Layers")
history = model_3layers.fit(
x_train,
y_train,
batch_size=batch_size,
epochs=total_epochs,
validation_data=(x_test, y_test),
callbacks=[callback],
shuffle=True)
all_reports.extend(callback.attack_results)
Epoch 1/50 1000/1000 [==============================] - 4s 4ms/step - loss: 1.6838 - accuracy: 0.3772 - val_loss: 1.4805 - val_accuracy: 0.4552 Epoch 2/50 1000/1000 [==============================] - 3s 3ms/step - loss: 1.3938 - accuracy: 0.4969 - val_loss: 1.3291 - val_accuracy: 0.5276 Running privacy report for epoch: 2 Epoch 3/50 1000/1000 [==============================] - 3s 3ms/step - loss: 1.2564 - accuracy: 0.5510 - val_loss: 1.2313 - val_accuracy: 0.5627 Epoch 4/50 1000/1000 [==============================] - 3s 3ms/step - loss: 1.1610 - accuracy: 0.5884 - val_loss: 1.1251 - val_accuracy: 0.6039 Running privacy report for epoch: 4 Epoch 5/50 1000/1000 [==============================] - 3s 3ms/step - loss: 1.1034 - accuracy: 0.6105 - val_loss: 1.1168 - val_accuracy: 0.6063 Epoch 6/50 1000/1000 [==============================] - 3s 3ms/step - loss: 1.0476 - accuracy: 0.6319 - val_loss: 1.0716 - val_accuracy: 0.6248 Running privacy report for epoch: 6 Epoch 7/50 1000/1000 [==============================] - 3s 3ms/step - loss: 1.0107 - accuracy: 0.6461 - val_loss: 1.0264 - val_accuracy: 0.6407 Epoch 8/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.9731 - accuracy: 0.6597 - val_loss: 1.0216 - val_accuracy: 0.6447 Running privacy report for epoch: 8 Epoch 9/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.9437 - accuracy: 0.6712 - val_loss: 1.0016 - val_accuracy: 0.6467 Epoch 10/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.9191 - accuracy: 0.6790 - val_loss: 0.9845 - val_accuracy: 0.6553 Running privacy report for epoch: 10 Epoch 11/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.8923 - accuracy: 0.6877 - val_loss: 0.9560 - val_accuracy: 0.6670 Epoch 12/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.8722 - accuracy: 0.6959 - val_loss: 0.9518 - val_accuracy: 0.6686 Running privacy report for epoch: 12 Epoch 13/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.8495 - accuracy: 0.7029 - val_loss: 0.9427 - val_accuracy: 0.6787 Epoch 14/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.8305 - accuracy: 0.7116 - val_loss: 0.9247 - val_accuracy: 0.6814 Running privacy report for epoch: 14 Epoch 15/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.8164 - accuracy: 0.7157 - val_loss: 0.9263 - val_accuracy: 0.6797 Epoch 16/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7973 - accuracy: 0.7220 - val_loss: 0.9151 - val_accuracy: 0.6850 Running privacy report for epoch: 16 Epoch 17/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7830 - accuracy: 0.7277 - val_loss: 0.9139 - val_accuracy: 0.6842 Epoch 18/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7704 - accuracy: 0.7294 - val_loss: 0.9384 - val_accuracy: 0.6774 Running privacy report for epoch: 18 Epoch 19/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7539 - accuracy: 0.7366 - val_loss: 0.9508 - val_accuracy: 0.6761 Epoch 20/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7445 - accuracy: 0.7412 - val_loss: 0.9108 - val_accuracy: 0.6908 Running privacy report for epoch: 20 Epoch 21/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7343 - accuracy: 0.7418 - val_loss: 0.9161 - val_accuracy: 0.6855 Epoch 22/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7213 - accuracy: 0.7458 - val_loss: 0.9754 - val_accuracy: 0.6724 Running privacy report for epoch: 22 Epoch 23/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7133 - accuracy: 0.7487 - val_loss: 0.8936 - val_accuracy: 0.6984 Epoch 24/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7072 - accuracy: 0.7504 - val_loss: 0.8872 - val_accuracy: 0.7002 Running privacy report for epoch: 24 Epoch 25/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6932 - accuracy: 0.7570 - val_loss: 0.9732 - val_accuracy: 0.6769 Epoch 26/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6883 - accuracy: 0.7578 - val_loss: 0.9332 - val_accuracy: 0.6798 Running privacy report for epoch: 26 Epoch 27/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6766 - accuracy: 0.7614 - val_loss: 0.9069 - val_accuracy: 0.6998 Epoch 28/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6656 - accuracy: 0.7662 - val_loss: 0.8879 - val_accuracy: 0.7011 Running privacy report for epoch: 28 Epoch 29/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6594 - accuracy: 0.7674 - val_loss: 0.8988 - val_accuracy: 0.7037 Epoch 30/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6499 - accuracy: 0.7700 - val_loss: 0.9086 - val_accuracy: 0.7001 Running privacy report for epoch: 30 Epoch 31/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6420 - accuracy: 0.7746 - val_loss: 0.8985 - val_accuracy: 0.7034 Epoch 32/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6354 - accuracy: 0.7742 - val_loss: 0.9089 - val_accuracy: 0.7018 Running privacy report for epoch: 32 Epoch 33/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6293 - accuracy: 0.7759 - val_loss: 0.9258 - val_accuracy: 0.6947 Epoch 34/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6192 - accuracy: 0.7851 - val_loss: 0.9326 - val_accuracy: 0.6976 Running privacy report for epoch: 34 Epoch 35/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6157 - accuracy: 0.7831 - val_loss: 0.9240 - val_accuracy: 0.6973 Epoch 36/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6063 - accuracy: 0.7853 - val_loss: 0.9504 - val_accuracy: 0.6971 Running privacy report for epoch: 36 Epoch 37/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6036 - accuracy: 0.7867 - val_loss: 0.9025 - val_accuracy: 0.7094 Epoch 38/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5958 - accuracy: 0.7877 - val_loss: 0.9290 - val_accuracy: 0.6976 Running privacy report for epoch: 38 Epoch 39/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5900 - accuracy: 0.7919 - val_loss: 0.9379 - val_accuracy: 0.6963 Epoch 40/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5856 - accuracy: 0.7928 - val_loss: 0.9911 - val_accuracy: 0.6896 Running privacy report for epoch: 40 Epoch 41/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5772 - accuracy: 0.7944 - val_loss: 0.9093 - val_accuracy: 0.7059 Epoch 42/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5752 - accuracy: 0.7940 - val_loss: 0.9275 - val_accuracy: 0.7061 Running privacy report for epoch: 42 Epoch 43/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5645 - accuracy: 0.7998 - val_loss: 0.9208 - val_accuracy: 0.7025 Epoch 44/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5632 - accuracy: 0.8000 - val_loss: 0.9746 - val_accuracy: 0.6976 Running privacy report for epoch: 44 Epoch 45/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5557 - accuracy: 0.8045 - val_loss: 0.9211 - val_accuracy: 0.7098 Epoch 46/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5469 - accuracy: 0.8073 - val_loss: 0.9357 - val_accuracy: 0.7055 Running privacy report for epoch: 46 Epoch 47/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5438 - accuracy: 0.8062 - val_loss: 0.9495 - val_accuracy: 0.7025 Epoch 48/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5437 - accuracy: 0.8069 - val_loss: 0.9509 - val_accuracy: 0.6994 Running privacy report for epoch: 48 Epoch 49/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5414 - accuracy: 0.8066 - val_loss: 0.9780 - val_accuracy: 0.6939 Epoch 50/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5321 - accuracy: 0.8108 - val_loss: 1.0109 - val_accuracy: 0.6846 Running privacy report for epoch: 50
যুগের প্লট
আপনি পর্যায়ক্রমে মডেলগুলি পরীক্ষা করার মাধ্যমে মডেলগুলিকে প্রশিক্ষণ দেওয়ার সময় গোপনীয়তার ঝুঁকিগুলি কীভাবে ঘটে তা আপনি কল্পনা করতে পারেন (যেমন প্রতি 5টি যুগে), আপনি সেরা পারফরম্যান্স / গোপনীয়তা ট্রেড-অফের সাথে সময়মতো পয়েন্ট বেছে নিতে পারেন।
জেনারেট করতে মেমরি গোপনীয়তা সদস্যপদ ইনফিরেনস আক্রমণ মডিউল ব্যবহার করুন AttackResults । এই AttackResults একটি একত্রিত পেতে AttackResultsCollection । মেমরি গোপনীয়তা প্রতিবেদন প্রদান করা বিশ্লেষণ করার জন্য ডিজাইন করা AttackResultsCollection ।
results = AttackResultsCollection(all_reports)
privacy_metrics = (PrivacyMetric.AUC, PrivacyMetric.ATTACKER_ADVANTAGE)
epoch_plot = privacy_report.plot_by_epochs(
results, privacy_metrics=privacy_metrics)
দেখুন যে একটি নিয়ম হিসাবে, যুগের সংখ্যা বাড়ার সাথে সাথে গোপনীয়তার দুর্বলতা বাড়তে থাকে। এটি মডেল ভেরিয়েন্টের পাশাপাশি বিভিন্ন আক্রমণকারী প্রকারের ক্ষেত্রেও সত্য।
দুটি লেয়ার মডেল (কম কনভোলিউশনাল লেয়ার সহ) সাধারণত তাদের তিন লেয়ার মডেলের সমকক্ষের তুলনায় বেশি ঝুঁকিপূর্ণ।
এখন দেখা যাক গোপনীয়তার ঝুঁকির ক্ষেত্রে মডেলের কর্মক্ষমতা কীভাবে পরিবর্তিত হয়।
গোপনীয়তা বনাম ইউটিলিটি
privacy_metrics = (PrivacyMetric.AUC, PrivacyMetric.ATTACKER_ADVANTAGE)
utility_privacy_plot = privacy_report.plot_privacy_vs_accuracy(
results, privacy_metrics=privacy_metrics)
for axis in utility_privacy_plot.axes:
axis.set_xlabel('Validation accuracy')
তিন স্তরের মডেল (সম্ভবত অনেক প্যারামিটারের কারণে) শুধুমাত্র 0.85 এর ট্রেনের নির্ভুলতা অর্জন করে। দুটি স্তরের মডেল সেই স্তরের গোপনীয়তার ঝুঁকির জন্য মোটামুটি সমান পারফরম্যান্স অর্জন করে তবে তারা আরও ভাল নির্ভুলতা পেতে থাকে।
আপনি দেখতে পারেন কিভাবে দুটি স্তর মডেলের জন্য লাইন খাড়া হয়। এর মানে হল যে ট্রেনের নির্ভুলতার অতিরিক্ত প্রান্তিক লাভ বিশাল গোপনীয়তার দুর্বলতার খরচে আসে।
এই টিউটোরিয়াল শেষ. আপনার নিজের ফলাফল বিশ্লেষণ নির্দ্বিধায়.