NLP对抗训练：PyTorch、Tensorflow_nlp学tensorflow还是pytroch

一、定义

对抗样本：对输入增加微小扰动得到的样本。旨在增加模型损失。

对抗训练：训练模型去区分样例是真实样例还是对抗样本的过程。对抗训练不仅可以提升模型对对抗样本的防御能力，还能提升对原始样本的泛化能力。

二、PyTorch版的NLP对抗训练

三、Tensorflow版的NLP对抗训练

#! -*- coding: utf-8 -*-
 
import keras
import keras.backend as K
 
 
def search_layer(inputs, name, exclude=None):
    """根据inputs和name来搜索层
    说明：inputs为某个层或某个层的输出；name为目标层的名字。
    实现：根据inputs一直往上递归搜索，直到发现名字为name的层为止；
         如果找不到，那就返回None。
    """
    if exclude is None:
        exclude = set()
 
    if isinstance(inputs, keras.layers.Layer):
        layer = inputs
    else:
        layer = inputs._keras_history[0]
 
    if layer.name == name:
        return layer
    elif layer in exclude:
        return None
    else:
        exclude.add(layer)
        inbound_layers = layer._inbound_nodes[0].inbound_layers
        if not isinstance(inbound_layers, list):
            inbound_layers = [inbound_layers]
        if len(inbound_layers) > 0:
            for layer in inbound_layers:
                layer = search_layer(layer, name, exclude)
                if layer is not None:
                    return layer
 
 
def adversarial_training(model, embedding_name, epsilon=1):
    """给模型添加对抗训练
    其中model是需要添加对抗训练的keras模型，embedding_name
    则是model里边Embedding层的名字。要在模型compile之后使用。
    """
    if model.train_function is None:  # 如果还没有训练函数
        model._make_train_function()  # 手动make
    old_train_function = model.train_function  # 备份旧的训练函数
 
    # 查找Embedding层
    for output in model.outputs:
        embedding_layer = search_layer(output, embedding_name)
        if embedding_layer is not None:
            break
    if embedding_layer is None:
        raise Exception('Embedding layer not found')
 
    # 求Embedding梯度
    embeddings = embedding_layer.embeddings  # Embedding矩阵
    gradients = K.gradients(model.total_loss, [embeddings])  # Embedding梯度
    gradients = K.zeros_like(embeddings) + gradients[0]  # 转为dense tensor
 
    # 封装为函数
    inputs = (model._feed_inputs +
              model._feed_targets +
              model._feed_sample_weights)  # 所有输入层
    embedding_gradients = K.function(
        inputs=inputs,
        outputs=[gradients],
        name='embedding_gradients',
    )  # 封装为函数
 
    def train_function(inputs):  # 重新定义训练函数
        grads = embedding_gradients(inputs)[0]  # Embedding梯度
        delta = epsilon * grads / (np.sqrt((grads**2).sum()) + 1e-8)  # 计算扰动
        K.set_value(embeddings, K.eval(embeddings) + delta)  # 注入扰动
        outputs = old_train_function(inputs)  # 梯度下降
        K.set_value(embeddings, K.eval(embeddings) - delta)  # 删除扰动
        return outputs
 
    model.train_function = train_function  # 覆盖原训练函数

案例：

https://github.com/bojone/bert4keras/blob/master/examples/task_iflytek_adversarial_training.py

#! -*- coding:utf-8 -*-
# 通过对抗训练增强模型的泛化性能
# 比CLUE榜单公开的同数据集上的BERT base的成绩高2%
# 数据集：IFLYTEK' 长文本分类 (https://github.com/CLUEbenchmark/CLUE)
# 博客：https://kexue.fm/archives/7234
# 适用于Keras 2.3.1
 
import json
import numpy as np
from bert4keras.backend import keras, search_layer, K
from bert4keras.tokenizers import Tokenizer
from bert4keras.models import build_transformer_model
from bert4keras.optimizers import Adam
from bert4keras.snippets import sequence_padding, DataGenerator
from keras.layers import Lambda, Dense
from tqdm import tqdm
 
num_classes = 119
maxlen = 128
batch_size = 32
 
# BERT base
config_path = '/root/kg/bert/chinese_L-12_H-768_A-12/bert_config.json'
checkpoint_path = '/root/kg/bert/chinese_L-12_H-768_A-12/bert_model.ckpt'
dict_path = '/root/kg/bert/chinese_L-12_H-768_A-12/vocab.txt'
 
 
def load_data(filename):
    """加载数据
    单条格式：(文本, 标签id)
    """
    D = []
    with open(filename) as f:
        for i, l in enumerate(f):
            l = json.loads(l)
            text, label = l['sentence'], l['label']
            D.append((text, int(label)))
    return D
 
 
# 加载数据集
train_data = load_data(
    '/root/CLUE-master/baselines/CLUEdataset/iflytek/train.json'
)
valid_data = load_data(
    '/root/CLUE-master/baselines/CLUEdataset/iflytek/dev.json'
)
 
# 建立分词器
tokenizer = Tokenizer(dict_path, do_lower_case=True)
 
 
class data_generator(DataGenerator):
    """数据生成器
    """
    def __iter__(self, random=False):
        batch_token_ids, batch_segment_ids, batch_labels = [], [], []
        for is_end, (text, label) in self.sample(random):
            token_ids, segment_ids = tokenizer.encode(text, maxlen=maxlen)
            batch_token_ids.append(token_ids)
            batch_segment_ids.append(segment_ids)
            batch_labels.append([label])
            if len(batch_token_ids) == self.batch_size or is_end:
                batch_token_ids = sequence_padding(batch_token_ids)
                batch_segment_ids = sequence_padding(batch_segment_ids)
                batch_labels = sequence_padding(batch_labels)
                yield [batch_token_ids, batch_segment_ids], batch_labels
                batch_token_ids, batch_segment_ids, batch_labels = [], [], []
 
 
# 转换数据集
train_generator = data_generator(train_data, batch_size)
valid_generator = data_generator(valid_data, batch_size)
 
# 加载预训练模型
bert = build_transformer_model(
    config_path=config_path,
    checkpoint_path=checkpoint_path,
    return_keras_model=False,
)
 
output = Lambda(lambda x: x[:, 0])(bert.model.output)
output = Dense(
    units=num_classes,
    activation='softmax',
    kernel_initializer=bert.initializer
)(output)
 
model = keras.models.Model(bert.model.input, output)
model.summary()
 
model.compile(
    loss='sparse_categorical_crossentropy',
    optimizer=Adam(2e-5),
    metrics=['sparse_categorical_accuracy'],
)
 
 
def adversarial_training(model, embedding_name, epsilon=1):
    """给模型添加对抗训练
    其中model是需要添加对抗训练的keras模型，embedding_name
    则是model里边Embedding层的名字。要在模型compile之后使用。
    """
    if model.train_function is None:  # 如果还没有训练函数
        model._make_train_function()  # 手动make
    old_train_function = model.train_function  # 备份旧的训练函数
 
    # 查找Embedding层
    for output in model.outputs:
        embedding_layer = search_layer(output, embedding_name)
        if embedding_layer is not None:
            break
    if embedding_layer is None:
        raise Exception('Embedding layer not found')
 
    # 求Embedding梯度
    embeddings = embedding_layer.embeddings  # Embedding矩阵
    gradients = K.gradients(model.total_loss, [embeddings])  # Embedding梯度
    gradients = K.zeros_like(embeddings) + gradients[0]  # 转为dense tensor
 
    # 封装为函数
    inputs = (
        model._feed_inputs + model._feed_targets + model._feed_sample_weights
    )  # 所有输入层
    embedding_gradients = K.function(
        inputs=inputs,
        outputs=[gradients],
        name='embedding_gradients',
    )  # 封装为函数
 
    def train_function(inputs):  # 重新定义训练函数
        grads = embedding_gradients(inputs)[0]  # Embedding梯度
        delta = epsilon * grads / (np.sqrt((grads**2).sum()) + 1e-8)  # 计算扰动
        K.set_value(embeddings, K.eval(embeddings) + delta)  # 注入扰动
        outputs = old_train_function(inputs)  # 梯度下降
        K.set_value(embeddings, K.eval(embeddings) - delta)  # 删除扰动
        return outputs
 
    model.train_function = train_function  # 覆盖原训练函数
 
 
# 写好函数后，启用对抗训练只需要一行代码
adversarial_training(model, 'Embedding-Token', 0.5)
 
 
def evaluate(data):
    total, right = 0., 0.
    for x_true, y_true in data:
        y_pred = model.predict(x_true).argmax(axis=1)
        y_true = y_true[:, 0]
        total += len(y_true)
        right += (y_true == y_pred).sum()
    return right / total
 
 
class Evaluator(keras.callbacks.Callback):
    """评估与保存
    """
    def __init__(self):
        self.best_val_acc = 0.
 
    def on_epoch_end(self, epoch, logs=None):
        val_acc = evaluate(valid_generator)
        if val_acc > self.best_val_acc:
            self.best_val_acc = val_acc
            model.save_weights('best_model.weights')
        print(
            u'val_acc: %.5f, best_val_acc: %.5f\n' %
            (val_acc, self.best_val_acc)
        )
 
 
def predict_to_file(in_file, out_file):
    """输出预测结果到文件
    结果文件可以提交到 https://www.cluebenchmarks.com 评测。
    """
    fw = open(out_file, 'w')
    with open(in_file) as fr:
        for l in tqdm(fr):
            l = json.loads(l)
            text = l['sentence']
            token_ids, segment_ids = tokenizer.encode(text, maxlen=maxlen)
            label = model.predict([[token_ids], [segment_ids]])[0].argmax()
            l = json.dumps({'id': str(l['id']), 'label': str(label)})
            fw.write(l + '\n')
    fw.close()
 
 
if __name__ == '__main__':
 
    evaluator = Evaluator()
 
    model.fit(
        train_generator.forfit(),
        steps_per_epoch=len(train_generator),
        epochs=50,
        callbacks=[evaluator]
    )
 
else:
 
    model.load_weights('best_model.weights')
    # predict_to_file('/root/CLUE-master/baselines/CLUEdataset/iflytek/test.json', 'iflytek_predict.json')

 

GitHub - bojone/keras_adversarial_training: Adversarial Training for NLP in Keras

训练技巧 | 功守道：NLP中的对抗训练 + PyTorch实现 - 灰信网（软件开发博客聚合）

【炼丹技巧】功守道：NLP中的对抗训练 + PyTorch实现 - 知乎

GitHub - bojone/bert4keras: keras implement of transformers for humans

对抗训练_Fang Suk的博客-CSDN博客_对抗训练

对抗训练浅谈：意义、方法和思考（附Keras实现） - 科学空间|Scientific Spaces 

bert4keras 文档中心