用keras.layers.Attention 代码实列

下面是一个使用 Keras 中的 `Attention` 层进行文本分类的示例：

import tensorflow as tf
from tensorflow.keras import layers

class Attention(layers.Layer):
    def __init__(self, units):
        super(Attention, self).__init__()
        self.W1 = layers.Dense(units)
        self.W2 = layers.Dense(units)
        self.V = layers.Dense(1)

    def call(self, features, hidden):
        hidden_with_time_axis = tf.expand_dims(hidden, 1)

        score = tf.nn.tanh(self.W1(features) + self.W2(hidden_with_time_axis))

        attention_weights = tf.nn.softmax(self.V(score), axis=1)

        context_vector = attention_weights * features
        context_vector = tf.reduce_sum(context_vector, axis=1)

        return context_vector, attention_weights

class TextClassifier(tf.keras.Model):
    def __init__(self, vocab_size, embedding_dim, rnn_units, output_dim):
        super().__init__()
        self.embedding = layers.Embedding(vocab_size, embedding_dim)
        self.lstm = layers.LSTM(rnn_units, return_sequences=True, return_state=True)
        self.attention = Attention(rnn_units)
        self.fc = layers.Dense(output_dim, activation='softmax')

    def call(self, inputs):
        x = self.embedding(inputs)
        output, state_h, state_c = self.lstm(x)
        context_vector, attention_weights = self.attention(output, state_h)
        x = self.fc(context_vector)

        return x

以上代码定义了一个继承自 `tf.keras.Model` 的文本分类模型 `TextClassifier`，其中包含了一个 `Attention` 层。在模型的 `call` 方法中，首先对输入的文本进行嵌入层的处理，然后将嵌入向量输入到 LSTM 层中进行序列建模。在 LSTM 层的输出序列上应用 `Attention` 层，得到每个时间步的注意力权重，进而对 LSTM 层的输出进行加权求和，得到上下文向量。最后，将上下文向量输入全连接层中进行分类。