自注意力机制tensorflow实现

自注意力机制是一种重要的深度学习模型，它可以根据输入的序列自动地为不同的位置赋予不同的权重。TensorFlow实现自注意力机制可以使用Transformer模型，以下是一个简单的实现示例：

1. 首先导入必要的库和模块：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Input, Dense, Dropout, LayerNormalization
from tensorflow.keras.layers import MultiHeadAttention, Embedding, Flatten

2. 定义一个自注意力层：

class SelfAttention(Layer):
    def __init__(self, embed_dim, num_heads):
        super(SelfAttention, self).__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.head_dim = embed_dim // num_heads
        self.query_dense = Dense(embed_dim)
        self.key_dense = Dense(embed_dim)
        self.value_dense = Dense(embed_dim)
        self.combine_heads = Dense(embed_dim)

    def call(self, inputs):
        # Split inputs into multiple heads
        query = tf.transpose(self.query_dense(inputs), perm=[0, 2, 1])
        key = tf.transpose(self.key_dense(inputs), perm=[0, 2, 1])
        value = tf.transpose(self.value_dense(inputs), perm=[0, 2, 1])
        query_heads = tf.reshape(query, (-1, self.num_heads, self.head_dim))
        key_heads = tf.reshape(key, (-1, self.num_heads, self.head_dim))
        value_heads = tf.reshape(value, (-1, self.num_heads, self.head_dim))

        # Compute attention scores
        attention_scores = tf.matmul(query_heads, key_heads, transpose_b=True)
        attention_scores = attention_scores / tf.math.sqrt(tf.cast(self.head_dim, tf.float32))
        attention_probs = keras.activations.softmax(attention_scores, axis=-1)

        # Apply attention to values
        context = tf.matmul(attention_probs, value_heads)
        context = tf.reshape(context, (-1, self.embed_dim))
        heads_combined = self.combine_heads(context)

        return heads_combined

3. 使用自注意力层搭建Transformer模型：

def transformer_model(embed_dim, num_heads, dense_dim, input_shape):
    inputs = Input(shape=input_shape)
    x = Embedding(input_dim=input_shape[0], output_dim=embed_dim)(inputs)
    x = SelfAttention(embed_dim=embed_dim, num_heads=num_heads)(x)
    x = LayerNormalization()(x)
    x = Dense(dense_dim, activation="relu")(x)
    x = Dropout(0.5)(x)
    x = Dense(input_shape, activation="softmax")(x)

    model = keras.Model(inputs=inputs, outputs=x)
    return model

4. 编译模型并训练：

model = transformer_model(embed_dim=32, num_heads=4, dense_dim=64, input_shape=(100,))
model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10)

以上是一个简单的自注意力机制的TensorFlow实现示例，希望对你有所帮助。