多层次注意力机制python

以下是一个使用多层次注意力机制的Python代码示例：

layer3 = Dense(machine_vocab_size, activation=softmax)

def get_model(Tx, Ty, layer1_size, layer2_size, x_vocab_size, y_vocab_size):
    X = Input(shape=(Tx, x_vocab_size))
    a1 = Bidirectional(LSTM(layer1_size, return_sequences=True), merge_mode='concat')(X)
    a2 = attention_layer(a1, layer2_size, Ty)
    a3 = \[layer3(timestep) for timestep in a2\]
    model = Model(inputs=\[X\], outputs=a3)
    return model

def one_step_of_attention(h_prev, a):
    h_repeat = at_repeat(h_prev)
    i = at_concatenate(\[a, h_repeat\])
    i = at_dense1(i)
    i = at_dense2(i)
    attention = at_softmax(i)
    context = at_dot(\[attention, a\])
    return context

def attention_layer(X, n_h, Ty):
    h = Lambda(lambda X: K.zeros(shape=(K.shape(X)\[0\], n_h)))(X)
    c = Lambda(lambda X: K.zeros(shape=(K.shape(X)\[0\], n_h)))(X)
    at_LSTM = LSTM(n_h, return_state=True)
    output = \[\]
    for _ in range(Ty):
        context = one_step_of_attention(h, X)
        h, _, c = at_LSTM(context, initial_state=\[h, c\])
        output.append(h)
    return output

这段代码定义了一个多层次注意力机制的模型。其中，`get_model`函数用于创建模型，`one_step_of_attention`函数用于计算注意力权重，`attention_layer`函数用于实现多层次注意力机制。这个模型可以用于处理文档级别的任务，并能够更好地捕捉全局和局部的信息。
#### 引用[.reference_title]
- *1* *3* [Python 实现注意力机制](https://blog.csdn.net/weixin_42232219/article/details/120540288)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insert_down28v1,239^v3^insert_chatgpt"}} ] [.reference_item]
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