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DSSM and Multi-View DSSM

dssm 최신버젼 다운로드

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[Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/publication/learning-deep-structured-semantic-models-for-web-search-using-clickthrough-data/)以及其后续文章

[A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](http://blog.csdn.net/shine19930820/article/details/78810984)的实现Demo。

# 注意：
**\*\*\*\*2020/11/15\*\*\*\***

论文[li2020sentence](https://arxiv.org/abs/2011.05864)将normalizing flows和bert结合，在语义相似度任务上有奇效，接下来会继续进行验证。

**\*\*\*\*2020/10/27\*\*\*\***

添加底层使用bert的siamese-bert实验，见[siamese\_network.py](https://github.com/InsaneLife/dssm/blob/master/model/siamese_network.py)中类 SiamenseBert，其他和下面一样.

相比于[bert](https://github.com/InsaneLife/dssm/blob/master/model/bert_classifier.py) 直接将两句作为输入，双塔bert的优势在于：
- max sequence len会更短，训练所需要显存更小，速度会稍微快一些，对于硬件不是太好的伙伴比较友好。
- 可以训练后使用bert作为句子embedding的encoder，在一些线上匹配的时候，可以预先将需要对比的句子向量算出来，节省实时算力。
- 效果相比于直接用bert输入两句，测试集会差一个多点。
- bert可以使用[CLS]的输出或者average context embedding, 一般后者效果会更好。
```shell
# bert_siamese双塔模型
python train.py --mode=train --method=bert_siamese
# 直接使用功能bert
python train.py --mode=train --method=bert
```
参考：[reimers2019sentence](https://arxiv.org/abs/1908.10084)

**\*\*\*\*2020/10/17\*\*\*\***

由于之前数据集问题，会有不收敛问题，现更换数据集为LCQMC口语化描述的语义相似度数据集。模型也从多塔变成了双塔模型，见[siamese\_network.py](https://github.com/InsaneLife/dssm/blob/master/model/siamese_network.py), 训练入口：[train.py](https://github.com/InsaneLife/dssm/blob/master/train.py)

> 难以找到搜索点击的公开数据集，暂且用语义相似任务数据集，有点变味了，哈哈
> 目前看在此数据集上测试数据集的准确率是提升的，只有七十多，但是要达到论文的准确率，仍然还需要进行调参

训练（默认使用功LCQMC数据集）：

```shell
python train.py --mode=train
```

预测：

```shell
python train.py --mode=train --file=$predict_file$
```

测试文件格式: q1\tq2, 例如：

```
今天天气怎么样	今天温度怎么样
```

**\*\*\*\*2019/5/18\*\*\*\***

由于之前代码api过时，已更新最新代码于：[dssm\_rnn.py](https://github.com/InsaneLife/dssm/blob/master/dssm_rnn.py)

数据处理代码[data\_input.py](https://github.com/InsaneLife/dssm/blob/master/data_input.py) 和数据[data](https://github.com/InsaneLife/dssm/tree/master/data) 已经更新，由于使用了rnn，所以**输入非bag of words方式。**

![img](https://ask.qcloudimg.com/http-save/yehe-1881084/7ficv1hhqf.png?imageView2/2/w/1620)

> 来源：Palangi, Hamid, et al. "Semantic modelling with long-short-term memory for information retrieval." arXiv preprint arXiv:1412.6629 2014.
> 
> 训练损失，在45个epoch时基本不下降：
> 
> ![dssm_rnn_loss](https://raw.githubusercontent.com/InsaneLife/dssm/master/assets/dssm_rnn_loss.png)

# 1\. 数据&环境

DSSM，对于输入数据是Query对，即Query短句和相应的展示，展示中分点击和未点击，分别为正负样，同时对于点击的先后顺序，也是有不同赋值，具体可参考论文。

对于我的Query数据本人无权开放，还请自行寻找数据。
环境：

1. win, python3.5, tensorflow1.4.

# 2\. word hashing

原文使用3-grams，对于中文，我使用了uni-gram，因为中文本身字有一定代表意义（也有论文拆笔画），对于每个gram都使用one-hot编码代替，最终可以大大降低短句维度。

# 3\. 结构

结构图：

![img](https://raw.githubusercontent.com/InsaneLife/MyPicture/master/dssm2.png)

1. 把条目映射成低维向量。
2. 计算查询和文档的cosine相似度。

## 3.1 输入

这里使用了TensorBoard可视化，所以定义了name\_scope:

``` python
with tf.name_scope('input'):
    query_batch = tf.sparse_placeholder(tf.float32, shape=[None, TRIGRAM_D], name='QueryBatch')
    doc_positive_batch = tf.sparse_placeholder(tf.float32, shape=[None, TRIGRAM_D], name='DocBatch')
    doc_negative_batch = tf.sparse_placeholder(tf.float32, shape=[None, TRIGRAM_D], name='DocBatch')
    on_train = tf.placeholder(tf.bool)
```

## 3.2 全连接层

我使用三层的全连接层，对于每一层全连接层，除了神经元不一样，其他都一样，所以可以写一个函数复用。
$$
l\_n = W\_n x + b\_1
$$

``` python
def add_layer(inputs, in_size, out_size, activation_function=None):
    wlimit = np.sqrt(6.0 / (in_size + out_size))
    Weights = tf.Variable(tf.random_uniform([in_size, out_size], -wlimit, wlimit))
    biases = tf.Variable(tf.random_uniform([out_size], -wlimit, wlimit))
    Wx_plus_b = tf.matmul(inputs, Weights) + biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs
```

其中，对于权重和Bias，使用了按照论文的特定的初始化方式：

``` python
	wlimit = np.sqrt(6.0 / (in_size + out_size))
    Weights = tf.Variable(tf.random_uniform([in_size, out_size], -wlimit, wlimit))
    biases = tf.Variable(tf.random_uniform([out_size], -wlimit, wlimit))
```

### Batch Normalization

``` python
def batch_normalization(x, phase_train, out_size):
    """
    Batch normalization on convolutional maps.
    Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
    Args:
        x:           Tensor, 4D BHWD input maps
        out_size:       integer, depth of input maps
        phase_train: boolean tf.Varialbe, true indicates training phase
        scope:       string, variable scope
    Return:
        normed:      batch-normalized maps
    """
    with tf.variable_scope('bn'):
        beta = tf.Variable(tf.constant(0.0, shape=[out_size]),
                           name='beta', trainable=True)
        gamma = tf.Variable(tf.constant(1.0, shape=[out_size]),
                            name='gamma', trainable=True)
        batch_mean, batch_var = tf.nn.moments(x, [0], name='moments')
        ema = tf.train.ExponentialMovingAverage(decay=0.5)

def mean_var_with_update():
            ema_apply_op = ema.apply([batch_mean, batch_var])
            with tf.control_dependencies([ema_apply_op]):
                return tf.identity(batch_mean), tf.identity(batch_var)

mean, var = tf.cond(phase_train,
                            mean_var_with_update,
                            lambda: (ema.average(batch_mean), ema.average(batch_var)))
        normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-3)
    return normed
```

### 单层

``` python
with tf.name_scope('FC1'):
    # 激活函数在BN之后，所以此处为None
    query_l1 = add_layer(query_batch, TRIGRAM_D, L1_N, activation_function=None)
    doc_positive_l1 = add_layer(doc_positive_batch, TRIGRAM_D, L1_N, activation_function=None)
    doc_negative_l1 = add_layer(doc_negative_batch, TRIGRAM_D, L1_N, activation_function=None)

with tf.name_scope('BN1'):
    query_l1 = batch_normalization(query_l1, on_train, L1_N)
    doc_l1 = batch_normalization(tf.concat([doc_positive_l1, doc_negative_l1], axis=0), on_train, L1_N)
    doc_positive_l1 = tf.slice(doc_l1, [0, 0], [query_BS, -1])
    doc_negative_l1 = tf.slice(doc_l1, [query_BS, 0], [-1, -1])
    query_l1_out = tf.nn.relu(query_l1)
    doc_positive_l1_out = tf.nn.relu(doc_positive_l1)
    doc_negative_l1_out = tf.nn.relu(doc_negative_l1)
······
```

合并负样本

``` python
with tf.name_scope('Merge_Negative_Doc'):
    # 合并负样本，tile可选择是否扩展负样本。
    doc_y = tf.tile(doc_positive_y, [1, 1])
    for i in range(NEG):
        for j in range(query_BS):
            # slice(input_, begin, size)切片API
            doc_y = tf.concat([doc_y, tf.slice(doc_negative_y, [j * NEG + i, 0], [1, -1])], 0)
```

## 3.3 计算cos相似度

``` python
with tf.name_scope('Cosine_Similarity'):
    # Cosine similarity
    # query_norm = sqrt(sum(each x^2))
    query_norm = tf.tile(tf.sqrt(tf.reduce_sum(tf.square(query_y), 1, True)), [NEG + 1, 1])
    # doc_norm = sqrt(sum(each x^2))
    doc_norm = tf.sqrt(tf.reduce_sum(tf.square(doc_y), 1, True))

prod = tf.reduce_sum(tf.multiply(tf.tile(query_y, [NEG + 1, 1]), doc_y), 1, True)
    norm_prod = tf.multiply(query_norm, doc_norm)

# cos_sim_raw = query * doc / (||query|| * ||doc||)
    cos_sim_raw = tf.truediv(prod, norm_prod)
    # gamma = 20
    cos_sim = tf.transpose(tf.reshape(tf.transpose(cos_sim_raw), [NEG + 1, query_BS])) * 20
```

## 3.4 定义损失函数

``` python
with tf.name_scope('Loss'):
    # Train Loss
    # 转化为softmax概率矩阵。
    prob = tf.nn.softmax(cos_sim)
    # 只取第一列，即正样本列概率。
    hit_prob = tf.slice(prob, [0, 0], [-1, 1])
    loss = -tf.reduce_sum(tf.log(hit_prob))
    tf.summary.scalar('loss', loss)
```

## 3.5选择优化方法

``` python
with tf.name_scope('Training'):
    # Optimizer
    train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(loss)
```

## 3.6 开始训练

``` python
# 创建一个Saver对象，选择性保存变量或者模型。
saver = tf.train.Saver()
# with tf.Session(config=config) as sess:
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train', sess.graph)
    start = time.time()
    for step in range(FLAGS.max_steps):
        batch_id = step % FLAGS.epoch_steps
        sess.run(train_step, feed_dict=feed_dict(True, True, batch_id % FLAGS.pack_size, 0.5))
```

GitHub完整代码 [https://github.com/InsaneLife/dssm](https://github.com/InsaneLife/dssm)

Multi-view DSSM实现同理，可以参考GitHub：[multi\_view\_dssm\_v3](https://github.com/InsaneLife/dssm/blob/master/multi_view_dssm_v3.py)

CSDN原文：[http://blog.csdn.net/shine19930820/article/details/79042567](http://blog.csdn.net/shine19930820/article/details/79042567)

## 自动调参
参数搜索空间：[search_space.json](./configs/search_space.json)
配置文件：[auto_ml.yml](auto_ml.yml)
启动命令
```shell
nnictl create --config auto_ml.yml -p 8888
```
> 由于没有gpu 😂，[auto_ml.yml](auto_ml.yml)设置中没有配置gpu，有gpu同学可自行配置。

详细文档：https://nni.readthedocs.io/zh/latest/Overview.html

# Reference
- [li2020sentence](https://arxiv.org/abs/2011.05864)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084)
- nni 调参: https://nni.readthedocs.io/zh/latest/Overview.html

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