自注意力机制代码keras

下面是一个简单的自注意力机制的代码示例，使用了Keras库：

import tensorflow as tf
from tensorflow.keras.layers import Layer, Dense, Dropout, LayerNormalization

class SelfAttention(Layer):
    def __init__(self, d_model, num_heads):
        super(SelfAttention, self).__init__()
        self.num_heads = num_heads
        self.d_model = d_model
        assert d_model % self.num_heads == 0
        self.depth = d_model // self.num_heads

        self.query_dense = Dense(d_model)
        self.key_dense = Dense(d_model)
        self.value_dense = Dense(d_model)
        self.dense = Dense(d_model)

    def split_heads(self, x, batch_size):
        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
        return tf.transpose(x, perm=[0, 2, 1, 3])

    def call(self, inputs):
        query = inputs
        key = inputs
        value = inputs
        batch_size = tf.shape(query)[0]

        query = self.query_dense(query)
        key = self.key_dense(key)
        value = self.value_dense(value)

        query = self.split_heads(query, batch_size)
        key = self.split_heads(key, batch_size)
        value = self.split_heads(value, batch_size)

        scaled_attention_logits = tf.matmul(query, key, transpose_b=True)
        scaled_attention_logits /= tf.math.sqrt(tf.cast(self.depth, tf.float32))

        attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)
        attention_output = tf.matmul(attention_weights, value)

        attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
        attention_output = tf.reshape(attention_output, (batch_size, -1, self.d_model))

        output = self.dense(attention_output)
        return output


class TransformerBlock(Layer):
    def __init__(self, d_model, num_heads, dff, rate=0.1):
        super(TransformerBlock, self).__init__()
        self.attention = SelfAttention(d_model, num_heads)
        self.ffn = tf.keras.Sequential([
            Dense(dff, activation='relu'),
            Dense(d_model)
        ])
        self.layernorm1 = LayerNormalization(epsilon=1e-6)
        self.layernorm2 = LayerNormalization(epsilon=1e-6)
        self.dropout1 = Dropout(rate)
        self.dropout2 = Dropout(rate)

    def call(self, inputs):
        attention_output = self.attention(inputs)
        attention_output = self.dropout1(attention_output)
        out1 = self.layernorm1(inputs + attention_output)

        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output)
        out2 = self.layernorm2(out1 + ffn_output)

        return out2


class Transformer(Layer):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, maximum_position_encoding, rate=0.1):
        super(Transformer, self).__init__()
        self.d_model = d_model
        self.num_layers = num_layers

        self.embedding = Embedding(input_vocab_size, d_model)
        self.pos_encoding = positional_encoding(maximum_position_encoding, self.d_model)

        self.transformer_blocks = [TransformerBlock(d_model, num_heads, dff, rate) for _ in range(num_layers)]
        self.dropout = Dropout(rate)

    def call(self, inputs):
        seq_len = tf.shape(inputs)[1]
        word_emb = self.embedding(inputs)
        word_emb *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        word_emb += self.pos_encoding[:, :seq_len, :]

        x = self.dropout(word_emb)

        for i in range(self.num_layers):
            x = self.transformer_blocks[i](x)

        return x

这是一个基本的Transformer模型，包括自注意力机制和前馈神经网络。你可以根据自己的需求进行修改和扩展。注意，这个代码示例可能需要一些其他的辅助函数和类，例如`positional_encoding`和`Embedding`，你可以根据需要自行添加。