python人工智能 MT TensorFlow Translate机器翻译: 用 TensorFlow Translate 实现一个 MT 机器翻译 任务，代码方案分享1(图文详解)

import tensorflow as tf
import tensorflow_text as text

# 定义模型
class Translator(tf.keras.Model):
    def __init__(self, input_vocab_size, output_vocab_size, embedding_dim, units):
        super(Translator, self).__init__()
        self.encoder = tf.keras.layers.Embedding(input_vocab_size, embedding_dim)
        self.encoder_lstm = tf.keras.layers.LSTM(units, return_sequences=True, return_state=True)
        self.decoder = tf.keras.layers.Embedding(output_vocab_size, embedding_dim)
        self.decoder_lstm = tf.keras.layers.LSTM(units, return_sequences=True, return_state=True)
        self.attention = tf.keras.layers.Attention()
        self.dense = tf.keras.layers.Dense(output_vocab_size)

    def call(self, inputs):
        input_seq, target_seq = inputs
        encoder_output, encoder_state_h, encoder_state_c = self.encoder_lstm(self.encoder(input_seq))
        decoder_output, _, _ = self.decoder_lstm(self.decoder(target_seq), initial_state=[encoder_state_h, encoder_state_c])
        attention_output = self.attention([decoder_output, encoder_output])
        output = self.dense(attention_output)
        return output

# 定义数据预处理函数
def preprocess_sentence(sentence):
    sentence = '<start> ' + sentence + ' <end>'
    return sentence

# 加载数据集
# 这里假设已经有了一个包含源语言和目标语言句子的数据集，分别存储在source_sentences和target_sentences中

# 构建词汇表
input_tokenizer = text.UnicodeCharTokenizer()
input_tokenizer.adapt(source_sentences)
target_tokenizer = text.UnicodeCharTokenizer()
target_tokenizer.adapt(target_sentences)

input_vocab_size = input_tokenizer.get_vocab_size()
output_vocab_size = target_tokenizer.get_vocab_size()

# 将句子进行编码和填充
input_sequences = input_tokenizer(source_sentences)
target_sequences = target_tokenizer(target_sentences)

input_max_length = max(len(seq) for seq in input_sequences)
target_max_length = max(len(seq) for seq in target_sequences)

input_sequences = tf.keras.preprocessing.sequence.pad_sequences(input_sequences, maxlen=input_max_length, padding='post')
target_sequences = tf.keras.preprocessing.sequence.pad_sequences(target_sequences, maxlen=target_max_length, padding='post')

# 划分训练集和验证集
input_train, input_val, target_train, target_val = train_test_split(input_sequences, target_sequences, test_size=0.2)

# 创建模型实例
embedding_dim = 256
units = 1024
translator = Translator(input_vocab_size, output_vocab_size, embedding_dim, units)

# 定义优化器和损失函数
optimizer = tf.keras.optimizers.Adam()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none')

# 定义训练步骤
@tf.function
def train_step(inputs, labels):
    with tf.GradientTape() as tape:
        predictions = translator(inputs)
        loss = loss_function(labels, predictions)
    gradients = tape.gradient(loss, translator.trainable_variables)
    optimizer.apply_gradients(zip(gradients, translator.trainable_variables))
    return loss

# 训练模型
E训练步骤和模型评估步骤的代码已被截断。在这个示例中，我们定义了一个`Translator`类作为我们的机器翻译模型。模型使用了一个编码器（Encoder）和一个解码器（Decoder），并使用了注意力机制（Attention）来帮助模型更好地理解输入文本和生成翻译结果。我们还定义了数据预处理函数`preprocess_sentence`，用于将输入句子进行预处理。

接下来，我们使用TensorFlow Text库中的`UnicodeCharTokenizer`来构建源语言和目标语言的词汇表，并将句子进行编码和填充。然后，我们将数据集划分为训练集和验证集，并创建了模型实例。

在训练过程中，我们使用Adam优化器和稀疏分类交叉熵损失函数来优化模型。我们定义了训练步骤`train_step`，并使用`tf.GradientTape`来计算梯度并更新模型参数。

**4. 模型评估与推理**
在训练完成后，我们可以使用训练好的模型进行评估和推理。以下是一个简单的示例：

```python
# 定义推理函数
def translate(sentence):
    sentence = preprocess_sentence(sentence)
    input_sequence = input_tokenizer(sentence)
    input_sequence = tf.keras.preprocessing.sequence.pad_sequences([input_sequence], maxlen=input_max_length, padding='post')
    prediction = translator(input_sequence)
    predicted_sentence = target_tokenizer.decode([tf.argmax(prediction, axis=2)[0]])
    return predicted_sentence

# 评估模型
def evaluate(input_sentences, target_sentences):
    total_loss = 0
    for i in range(len(input_sentences)):
        input_sequence = input_sentences[i]
        target_sequence = target_sentences[i]
        input_sequence = tf.expand_dims(input_sequence, 0)
        target_sequence = tf.expand_dims(target_sequence, 0)
        predictions = translator([input_sequence, target_sequence])
        loss = loss_function(target_sequence, predictions)
        total_loss += tf.reduce_mean(loss)
    return total_loss / len(input_sentences)

# 进行推理
input_sentence = "Hello, how are you?"
translation = translate(input_sentence)
print("Input sentence:", input_sentence)
print("Translation:", translation)

# 进行模型评估
val_loss = evaluate(input_val, target_val)
print("Validation Loss:", val_loss.numpy())