基于Seq2Seq模型的机器翻译

r囧r小猫 2022-12-04 09:26 165阅读 0赞

如标题所见，这篇博客的主题就是`基于Seq2Seq模型的机器翻译`，它的主要任务就是将一种语言翻译为另一种语言，在这里我们以**英语翻译成法语**为例子，如`I'm a student.`\---->>>`Je suis étudiant.`

这份数据是公开，可以直接下载的，下载地址为：[翻译语料下载地址][Link 1]

# 模型结构 #

首先，我们先了解一下模型的结构：

1.  首先，第一部分是编码器`Encoder`，它接收source sentence，然后Encoder将其转换为一个包含具体语义信息的中间向量；
2.  接着，是一个解码器`Decoder`，接收Encoder传递过来的中间向量，最后通过Decoder输出最终的翻译结果。

![在这里插入图片描述][20200907214247776.jpg_pic_center]

# 具体思路 #

第一步：将source input words即待英语单词，进行词向量Embedding；

第二步：词向量作为Encoder的输入，Encoder通常是一个RNN、LSTM或GRU的网络；

第三步：target input words即法语单词，同样进行进行Embedding，**在Embedding之前，需要做预处理：在句子的最前面加上开始标识，句子的最后加上结束标识，如 “Je suis étudiant” ===>>>“<s> Je suis étudiant </s>”**；

第四步：将target input words的词向量作为Decoder的输入，Decoder网络类型跟Encoder同理。不仅如此，还需要将Encoder最后一个输出和state，可对应为下图的hidden layer 1和hidden layer 2，作为Decoder的初始状态；

第五步：使用一种`"teacher forcing"`的方法，将target input words向后偏移一个timestamp，即原来的  
**“<s> Je suis étudiant </s>”**  
变为===>>>  
**“Je suis étudiant </s>”**，得到target output words，这便是模型训练样本的真实标签；

最后：根据Decoder所有时刻的输出，再通过softmax进行概率归一化，与target output words计算loss，迭代训练模型。

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NneXVhbnNoaQ_size_16_color_FFFFFF_t_70_pic_center]

## 模型推理 ##

上面提到的其实是模型的训练方法，但是在模型推理即线上翻译时是不同的，因为我们没有完整的target input words，即Decoder缺失输入，不过Encoder训练和推理时时一样的。  
（其实如果有了target input words，还需要翻译吗？这不就是翻译结果了吗！）

所以，

1.  首先，还是将待翻译文本source input words的词向量作为Encoder的输入，然后将其最后一个输出和state传递给Decoder，作为其初始状态；
2.  这时我们会将 开始标识<s> 作为Decoder的输入，即整个target input words只有一个单词；
3.  然后我们去预测下一个单词是什么；
4.  接着，预测的单词的词向量又作为下一轮Decoder的输入，上一轮Decoder的输出和state又作为下一轮Decoder的初始状态；
5.  如此循环，直到预测的单词为结束标识即</s>。
6.  最后，将所有预测的单词拼接起来即可。

# 代码实现 #

import numpy as np
    import tensorflow as tf
    from sklearn.model_selection import train_test_split
    from google.colab import drive
    import os
    
    class Seq2Seq:
    
        batch_size = 64  # Batch size for training.
        epochs = 50  # Number of epochs to train for.
        latent_dim = 256  # Latent dimensionality of the encoding space.
        num_samples = 80000  # Number of samples to train on.
        # Path to the data txt file on disk.
        # data_path = "fra.txt"
        max_seq_len = 64  # 这里是简单化处理，将encoder和decoder的最大长度设为同一个值
        embedding_size = 128  # 词向量的size
    
        num_decoder_tokens = None
        num_encoder_tokens = None
    
        # weight_initializer = tf.truncated_normal_initializer(stddev=0.02)
    
        def __init__(self, lr):
            self.lr = lr
            # colab google云盘挂载
            drive.mount('/gdrive')
            self.data_path = "/gdrive/My Drive/sequence-to-sequence/fra.txt"
    
        def get_dataset(self):
            # 将array格式的数据转化为tf的dataset格式
            def tf_dataset(encoder_input_data, decoder_input_data, decoder_target_data):
                inputs = tf.data.Dataset.from_tensor_slices((encoder_input_data, decoder_input_data))
    
                # decoder_target_data = tf.one_hot(decoder_target_data, depth=self.num_decoder_tokens)
                labels = tf.data.Dataset.from_tensor_slices(decoder_target_data)
     
                dataset = tf.data.Dataset.zip((inputs, labels)).shuffle(1024).repeat(-1).batch(self.batch_size)
    
                return dataset
    
            encoder_inputs, decoder_inputs, decoder_targets = self.data_preprocess(self.data_path)
            encoder_inputs_train, encoder_inputs_val, decoder_inputs_train, decoder_inputs_val, decoder_targets_train, decoder_targets_val = train_test_split(
                encoder_inputs, decoder_inputs, decoder_targets, test_size=0.2)
    
            train_dataset = tf_dataset(encoder_inputs_train, decoder_inputs_train, decoder_targets_train)
            val_dataset = tf_dataset(encoder_inputs_val, decoder_inputs_val, decoder_targets_val)
    
            return train_dataset, val_dataset
    
        def data_preprocess(self, data_path):
            # 数据预处理.
            encoder_input_data = []
            decoder_input_data = []
            decoder_target_data = []
            encoder_length = []
            decoder_length = []
            input_char_map = dict()
            target_char_map = dict()
            num_encoder_tokens = 1
            num_decoder_tokens = 1
            with open(data_path, "r", encoding="utf-8") as f:
                lines = f.read().split("\n")
    
            for line in lines[:self.num_samples]:
                data = line.split("\t")
                if len(data) < 2:
                    continue
                input_text, target_text = data[0], data[1]
                target_text = "\t" + target_text + "\n"
                input_index = [0] * self.max_seq_len
                target_index = [0] * self.max_seq_len
    
                encoder_length.append(len(input_text))
                decoder_length.append(len(target_text))
    
                for i, char in enumerate(input_text.split(" ")):
                    if i >= self.max_seq_len:
                        break
                    if input_char_map.get(char):
                        input_index[i] = input_char_map.get(char)
                    else:
                        input_index[i] = num_encoder_tokens
                        input_char_map[char] = num_encoder_tokens
                        num_encoder_tokens += 1
                for i, char in enumerate(target_text.split(" ")):
                    if i >= self.max_seq_len:
                        break
                    if target_char_map.get(char):
                        target_index[i] = target_char_map.get(char)
                    else:
                        target_index[i] = num_decoder_tokens
                        target_char_map[char] = num_decoder_tokens
                        num_decoder_tokens += 1
    
                encoder_input_data.append(input_index)
                decoder_input_data.append(target_index)
                decoder_target_data.append(target_index[1:] + [0])
    
            self.num_decoder_tokens = num_decoder_tokens
            self.num_encoder_tokens = num_encoder_tokens
    
            print("num_decoder_tokens: ", num_decoder_tokens)
            print("num_encoder_tokens: ", num_encoder_tokens)
            print("encoder_input_data: ", encoder_input_data[0], "......")
            print("decoder_input_data: ", decoder_input_data[0], "......")
            print("decoder_target_data: ", decoder_target_data[0], "......")
    
            return np.array(encoder_input_data), np.array(decoder_input_data), np.array(
                decoder_target_data)
    
        def build_net(self, lr):
            # 用LSTM作为encoder和decoder的网络结构
            def custom_loss(y_true, r_pred):
                # 自定义loss，加入mask机制
                mask = tf.math.logical_not(tf.math.equal(y_true, 0))
                loss_ = tf.keras.losses.sparse_categorical_crossentropy(y_true, r_pred,
                                                                        from_logits=False)
    
                mask = tf.cast(mask, dtype=loss_.dtype)
                loss_ *= mask
    
                return tf.reduce_sum(loss_) / tf.reduce_sum(mask)
    
            encoder_input = tf.keras.Input(shape=[self.max_seq_len], name='encoder_input')
            decoder_input = tf.keras.Input(shape=[self.max_seq_len], name='decoder_input')
    
            encoder = Encoder(self.num_encoder_tokens, self.embedding_size, self.latent_dim)
            _, encoder_state_h, encoder_state_c = encoder(encoder_input)
            encoder_state = [encoder_state_h, encoder_state_c]
    
            decoder = Decoder(self.num_decoder_tokens, self.embedding_size, self.latent_dim)
            decoder_output, _, _ = decoder(decoder_input, state=encoder_state)
    
            model = tf.keras.Model([encoder_input, decoder_input], decoder_output)
            model.compile(optimizer=tf.keras.optimizers.Adam(),
                          loss=custom_loss,
                          metrics=[AccuracyWithMask()]
                          # metrics="accuracy"
                          )
            return model
    
        def train(self, strategy=None):
            train_dataset, val_dataset = self.get_dataset()
            # callbacks = [
            # tf.keras.callbacks.ModelCheckpoint(
            # # Path where to save the model
            # # The two parameters below mean that we will overwrite
            # # the current checkpoint if and only if
            # # the `val_loss` score has improved.
            # # The saved model name will include the current epoch.
            # filepath="model",
            # save_best_only=True, # Only save a model if `val_loss` has improved.
            # monitor="val_loss",
            # verbose=1,
            # )
            # ]
            if strategy: # 是否使用TPU
                with strategy.scope():
                    model = self.build_net(0.01)
            else:
                model = self.build_net(0.01)
            model.fit(train_dataset, 
                      # callbacks=callbacks, 
                      # verbose=2,
                      validation_data=val_dataset, epochs=self.epochs,
                      steps_per_epoch=int(self.num_samples/self.batch_size),
                      validation_steps=self.epochs)
    
        def inference(self, text):
            pass
    
    
    class Encoder(tf.keras.Model):
        def __init__(self, vocab_size, embedding_size, hidden_size):
            super(Encoder, self).__init__()
            self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_size)
            self.lstm = tf.keras.layers.LSTM(hidden_size, return_state=True, return_sequences=True)
    
        def call(self, inputs, **kwargs):
            x = self.embedding(inputs)
            output, state_h, state_c = self.lstm(x)
    
            return output, state_h, state_c
    
    
    class Decoder(tf.keras.Model):
        def __init__(self, vocab_size, embedding_size, hidden_size):
            super(Decoder, self).__init__()
            self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_size)
            self.lstm = tf.keras.layers.LSTM(hidden_size, return_state=True, return_sequences=True)
    
            self.fc = tf.keras.layers.Dense(vocab_size, activation="softmax")
    
        def call(self, inputs, **kwargs):
            state = kwargs.get('state')
            x = self.embedding(inputs)
            output, state_h, state_c = self.lstm(x, initial_state=state)
    
            output = self.fc(output)
    
            return output, state_h, state_c
    
    
    class AccuracyWithMask(tf.keras.metrics.Metric):
        # 自定义accuracy，加入mask机制
        def __init__(self, name="accuracy", **kwargs):
            super(AccuracyWithMask, self).__init__(name=name, **kwargs)
            self.true_samples = self.add_weight(name="ctp", initializer="zeros")
            self.all_samples = self.add_weight(name="ctp2", initializer="zeros")
    
        def update_state(self, y_true, y_pred, sample_weight=None):
            mask = tf.math.logical_not(tf.math.equal(y_true, 0))
            y_pred = tf.argmax(y_pred, axis=-1)
            values = tf.cast(y_true, "int32") == tf.cast(y_pred, "int32")
            values = tf.cast(values, "float32")
            values = tf.boolean_mask(values, mask)
            # if sample_weight is not None:
            # sample_weight = tf.cast(sample_weight, "float32")
            # values = tf.multiply(values, sample_weight)
            self.true_samples.assign_add(tf.reduce_sum(values))
            self.all_samples.assign_add(tf.cast(tf.size(values), "float32"))
    
        def result(self):
            return self.true_samples / self.all_samples
    
        def reset_states(self):
            # The state of the metric will be reset at the start of each epoch.
            self.true_samples.assign(0.0)
            self.all_samples.assign(0.0)
    
    
    # class CustomLoss(tf.keras.losses.Loss):
    # def __init__(self, name="CustomLoss"):
    # super().__init__(name=name)
    #
    # def call(self, y_true, r_pred):
    # loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
    # from_logits=True, reduction='none')
    # mask = tf.math.logical_not(tf.math.equal(y_true, 0))
    # loss_ = loss_object(y_true, r_pred)
    #
    # mask = tf.cast(mask, dtype=loss_.dtype)
    # loss_ *= mask
    #
    # return tf.reduce_mean(loss_)
    
    
    if __name__ == '__main__':
        model = Seq2Seq(0.01)
        model.train(strategy)

[Link 1]: http://www.manythings.org/anki/fra-eng.zip
[20200907214247776.jpg_pic_center]: /images/20221123/92b9864b74e741f49c5402b23a772350.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NneXVhbnNoaQ_size_16_color_FFFFFF_t_70_pic_center]: /images/20221123/42c718e2cb2944e2be4f0564a7bdf927.png