Keras LSTM輸入尺寸設置注意class

Question

我正在嘗試使用 keras 訓練 LSTM model 但我認為我在這里出了點問題。

我有一個錯誤

TypeError: __init__() takes 2 positional arguments but 3 were given

代碼在以下鏈接中：

https://androidkt.com/text-classification-using-attention-mechanism-in-keras/

當我嘗試使用注意 class 應用代碼時，首先它告訴我注意沒有定義，然后我使用注意“在大寫字母中表示注意”，然后它給了我錯誤。

注意class如下：

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class Attention(tf.keras.Model):
    def __init__(self, units):
        super(Attention, self).__init__()
        self.W1 = tf.keras.layers.Dense(units)
        self.W2 = tf.keras.layers.Dense(units)
        self.V = tf.keras.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

The rest of the code in the link I mentioned before.


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import os
lstm = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM
                                     (rnn_cell_size,
                                      dropout=0.3,
                                      return_sequences=True,
                                      return_state=True,
                                      recurrent_activation='relu',
                                      recurrent_initializer='glorot_uniform'), name="bi_lstm_0")(embedded_sequences)

lstm, forward_h, forward_c, backward_h, backward_c = tf.keras.layers.Bidirectional \
    (tf.keras.layers.LSTM
     (rnn_cell_size,
      dropout=0.2,
      return_sequences=True,
      return_state=True,
      recurrent_activation='relu',
      recurrent_initializer='glorot_uniform'))(lstm)



state_h = Concatenate()([forward_h, backward_h])
state_c = Concatenate()([forward_c, backward_c])

**context_vector, attention_weights = Attention(lstm, state_h)**

output = keras.layers.Dense(1, activation='sigmoid')(context_vector)

model = keras.Model(inputs=sequence_input, outputs=output)

# summarize layers
print(model.summary())

Answer 1

對不起大家，我找到了答案：

context_vector, attention_weights = Attention(32)(lstm, state_h)