72 lines
2.7 KiB
Python
72 lines
2.7 KiB
Python
import tensorflow as tf
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def craft_adv(X, Y, gamma, learning_rate, model, loss_fn, md = 0):
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# 将测试数据转换为TensorFlow张量
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X_test_tensor = tf.convert_to_tensor(X, dtype=tf.float64)
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if md == 0:
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Y_test_tensor = tf.convert_to_tensor(Y, dtype=tf.int32)
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elif md == 1:
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Y_test_tensor = tf.convert_to_tensor(Y, dtype=tf.float64)
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# 初始化更新后的数据集
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X_train_updated = []
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for i in range(X_test_tensor.shape[0]):
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# 对每个样本使用GradientTape
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with tf.GradientTape() as tape:
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# 监视当前样本
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current_sample = X_test_tensor[i:i+1]
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tape.watch(current_sample)
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# 对当前样本进行预测并计算损失
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predictions = model(current_sample)
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loss = loss_fn(Y_test_tensor[i:i+1], predictions)
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# 计算关于输入的梯度
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gradients = tape.gradient(loss, current_sample)
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# 平坦化梯度以便进行处理
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flattened_gradients = tf.reshape(gradients, [-1])
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# 选择最大的γ * |X|个梯度
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num_gradients_to_select = int(gamma * tf.size(flattened_gradients, out_type=tf.dtypes.float32))
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top_gradients_indices = tf.argsort(flattened_gradients, direction='DESCENDING')[:num_gradients_to_select]
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# 创建新的梯度张量，初始值为原始梯度
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updated_gradients = tf.identity(flattened_gradients)
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# 创建布尔掩码，用于选择特定梯度
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mask = tf.ones_like(updated_gradients, dtype=bool)
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mask = tf.tensor_scatter_nd_update(mask, tf.expand_dims(top_gradients_indices, 1), tf.zeros_like(top_gradients_indices, dtype=bool))
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# 应用掩码更新梯度
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updated_gradients = tf.where(mask, tf.zeros_like(updated_gradients), updated_gradients)
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# 将梯度恢复到原始形状
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updated_gradients = tf.reshape(updated_gradients, tf.shape(gradients))
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# 应用学习率到梯度
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scaled_gradients = learning_rate * updated_gradients
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# 更新当前样本
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current_sample_updated = tf.add(current_sample, scaled_gradients)
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# 将更新后的样本添加到列表中
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X_train_updated.append(current_sample_updated.numpy())
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# 将列表转换为张量
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X_train_updated = tf.concat(X_train_updated, axis=0)
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# 评估更新后的模型
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if md == 1:
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loss, mape = model.evaluate(X_train_updated, Y)
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print(f"Accuracy gamma: {gamma},learning:{learning_rate}", loss)
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return X_train_updated, loss, mape
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elif md == 0:
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loss, accuracy = model.evaluate(X_train_updated, Y)
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print(f"Accuracy gamma: {gamma},learning:{learning_rate},accuracy{accuracy}" )
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return X_train_updated, accuracy |