Keras实现Hierarchical Attention Network时的一些坑

我会带着你远行 2021-11-13 16:06 148阅读 0赞

# Reshape #

对于的张量x，x.shape=(a, b, c, d)的情况

若调用keras.layer.Reshape(target\_shape=(-1, c, d))，  
处理后的张量形状为(?, ?, c, d)

若调用tf.reshape(x, shape=\[-1, c, d\])  
处理后的张量形状为(a\*b, c, d)

为了在keras代码中实现tf.reshape的效果，用lambda层做，  
调用Lambda(lambda x: tf.reshape(x, shape=\[-1, c, d\]))(x)

nice and cool.

# 输出Attention的打分 #

这里，我们希望attention层能够输出attention的score，而不只是计算weighted sum。  
在使用时  
score = Attention()(x)  
weighted\_sum = MyMerge()(\[score, x\])

class Attention(Layer): def __init__(self, **kwargs): super(Attention, self).__init__(**kwargs) def build(self, input_shape): assert len(input_shape) == 3 self.w = self.add_weight(name="attention_weight", shape=(input_shape[-1], input_shape[-1]), initializer='uniform', trainable=True ) self.b = self.add_weight(name="attention_bias", shape=(input_shape[-1],), initializer='uniform', trainable=True ) self.v = self.add_weight(name="attention_v", shape=(input_shape[-1], 1), initializer='uniform', trainable=True ) super(Attention, self).build(input_shape) def call(self, inputs): x = inputs att = K.tanh(K.dot(x, self.w) + self.b) att = K.softmax(K.dot(att, self.v)) print(att.shape) return att def compute_output_shape(self, input_shape): return input_shape[0], input_shape[1], 1 class MyMerge(Layer): def __init__(self, **kwargs): super(MyMerge, self).__init__(**kwargs) def call(self, inputs): att = inputs[0] x = inputs[1] att = tf.tile(att, [1, 1, x.shape[-1]]) outputs = tf.multiply(att, x) outputs = K.sum(outputs, axis=1) return outputs def compute_output_shape(self, input_shape): return input_shape[1][0], input_shape[1][2]

# keras中Model的嵌套 #

这边是转载自https://github.com/uhauha2929/examples/blob/master/Hierarchical%20Attention%20Networks%20.ipynb  
可以看到，sentEncoder是Model类型，在后面的时候通过TimeDistributed(sentEncoder)，当成一个层那样被调用。

embedding_layer = Embedding(len(word_index) + 1, EMBEDDING_DIM, input_length=MAX_SENT_LENGTH) sentence_input = Input(shape=(MAX_SENT_LENGTH,), dtype='int32') embedded_sequences = embedding_layer(sentence_input) l_lstm = Bidirectional(LSTM(100))(embedded_sequences) sentEncoder = Model(sentence_input, l_lstm) review_input = Input(shape=(MAX_SENTS,MAX_SENT_LENGTH), dtype='int32') review_encoder = TimeDistributed(sentEncoder)(review_input) l_lstm_sent = Bidirectional(LSTM(100))(review_encoder) preds = Dense(2, activation='softmax')(l_lstm_sent) model = Model(review_input, preds)

转载于:https://www.cnblogs.com/bellz/p/11153691.html