tensorflow.keras.layers中有没有封装transformer

是的，TensorFlow Keras中提供了Transformer的封装。您可以在`tensorflow.keras.layers`中找到`MultiHeadAttention`和`Transformer`等层。`Transformer`层将多头自注意力和前馈神经网络组合在一起，实现了Transformer的核心架构。您可以使用这些层来构建自己的Transformer模型。以下是一个简单的示例：

import tensorflow as tf

# 定义Transformer层
class TransformerBlock(tf.keras.layers.Layer):
    def __init__(self, embedding_dim, num_heads, dense_dim, rate=0.1):
        super().__init__()
        self.attention = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=embedding_dim)
        self.dense1 = tf.keras.layers.Dense(dense_dim, activation='relu')
        self.dense2 = tf.keras.layers.Dense(embedding_dim)
        self.dropout1 = tf.keras.layers.Dropout(rate)
        self.dropout2 = tf.keras.layers.Dropout(rate)
        self.layer_norm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
        self.layer_norm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)

    def call(self, inputs, training):
        attn_output = self.attention(inputs, inputs)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layer_norm1(inputs + attn_output)
        dense_output = self.dense1(out1)
        dense_output = self.dense2(dense_output)
        dense_output = self.dropout2(dense_output, training=training)
        out2 = self.layer_norm2(out1 + dense_output)
        return out2

# 定义Transformer模型
class TransformerModel(tf.keras.Model):
    def __init__(self, num_layers, embedding_dim, num_heads, dense_dim, input_vocab_size, target_vocab_size, rate=0.1):
        super().__init__()
        self.embedding = tf.keras.layers.Embedding(input_vocab_size, embedding_dim)
        self.transformer_blocks = [TransformerBlock(embedding_dim, num_heads, dense_dim, rate) for _ in range(num_layers)]
        self.dense = tf.keras.layers.Dense(target_vocab_size)

    def call(self, inputs, training):
        embed_output = self.embedding(inputs)
        for transformer_block in self.transformer_blocks:
            embed_output = transformer_block(embed_output, training)
        output = self.dense(embed_output)
        return output

在此示例中，我们定义了一个`TransformerBlock`层和一个`TransformerModel`模型。`TransformerBlock`层包含多头自注意力、前馈神经网络和残差连接，并使用层归一化进行归一化。`TransformerModel`模型包含多个`TransformerBlock`层以及嵌入层和最终的全连接层。通过这些层的组合，我们可以构建一个完整的Transformer模型。