2024-01-14 09:23:45 +08:00
|
|
|
import tensorflow as tf
|
|
|
|
|
|
|
|
|
|
|
|
def craft_adv(X, Y, gamma, learning_rate, model, loss_fn, md = 0):
|
|
|
|
|
|
|
|
# 将测试数据转换为TensorFlow张量
|
|
|
|
X_test_tensor = tf.convert_to_tensor(X, dtype=tf.float64)
|
|
|
|
if md == 0:
|
|
|
|
Y_test_tensor = tf.convert_to_tensor(Y, dtype=tf.int32)
|
|
|
|
elif md == 1:
|
|
|
|
Y_test_tensor = tf.convert_to_tensor(Y, dtype=tf.float64)
|
|
|
|
|
|
|
|
# 使用GradientTape计算梯度
|
|
|
|
with tf.GradientTape() as tape:
|
|
|
|
tape.watch(X_test_tensor)
|
|
|
|
predictions = model(X_test_tensor)
|
|
|
|
loss = loss_fn(Y_test_tensor, predictions)
|
|
|
|
|
|
|
|
# 计算关于输入的梯度
|
|
|
|
gradients = tape.gradient(loss, X_test_tensor)
|
|
|
|
|
|
|
|
# 平坦化梯度以便进行处理
|
|
|
|
flattened_gradients = tf.reshape(gradients, [-1])
|
|
|
|
|
|
|
|
# 选择最大的γ * |X|个梯度
|
|
|
|
num_gradients_to_select = int(gamma * tf.size(flattened_gradients, out_type=tf.dtypes.float32))
|
2024-01-26 20:42:33 +08:00
|
|
|
print(num_gradients_to_select)
|
2024-01-14 09:23:45 +08:00
|
|
|
top_gradients_indices = tf.argsort(flattened_gradients, direction='DESCENDING')[:num_gradients_to_select]
|
|
|
|
|
|
|
|
# 创建新的梯度张量,初始值为原始梯度
|
|
|
|
updated_gradients = tf.identity(flattened_gradients)
|
|
|
|
|
|
|
|
# 创建布尔掩码,用于选择特定梯度
|
|
|
|
mask = tf.ones_like(updated_gradients, dtype=bool)
|
|
|
|
mask = tf.tensor_scatter_nd_update(mask, tf.expand_dims(top_gradients_indices, 1), tf.zeros_like(top_gradients_indices, dtype=bool))
|
|
|
|
|
|
|
|
# 应用掩码更新梯度
|
|
|
|
updated_gradients = tf.where(mask, tf.zeros_like(updated_gradients), updated_gradients)
|
|
|
|
|
|
|
|
# 将梯度恢复到原始形状
|
|
|
|
updated_gradients = tf.reshape(updated_gradients, tf.shape(gradients))
|
|
|
|
|
|
|
|
# 应用学习率到梯度
|
|
|
|
scaled_gradients = (learning_rate * 700) * updated_gradients
|
|
|
|
# 更新X_test_tensor
|
|
|
|
X_train_updated = tf.add(X_test_tensor, scaled_gradients)
|
|
|
|
X_train_updated = X_train_updated.numpy()
|
|
|
|
|
|
|
|
# 评估更新后的模型
|
|
|
|
if md == 1:
|
|
|
|
loss = model.evaluate(X_train_updated, Y)
|
|
|
|
print(f"Accuracy gamma: {gamma},learning:{learning_rate}", loss)
|
|
|
|
|
|
|
|
return X_train_updated, loss
|
|
|
|
elif md == 0:
|
|
|
|
loss, accuracy = model.evaluate(X_train_updated, Y)
|
|
|
|
print(f"Accuracy gamma: {gamma},learning:{learning_rate},accuracy{accuracy}" )
|
|
|
|
|
|
|
|
return X_train_updated, accuracy
|