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在尝试对一维向量进行自我关注时，我一遍又一遍地遇到此错误，我真的不明白为什么会发生这种情况，任何帮助将不胜感激。

layer = layers.MultiHeadAttention(num_heads=2, key_dim=2)
target = tf.ones(shape=[1, 16])
source = tf.ones(shape=[1, 16])
output_tensor, weights = layer(target, source)

错误：

~/anaconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/keras/layers/multi_head_attention.py in _masked_softmax(self, attention_scores, attention_mask)
    399         attention_mask = array_ops.expand_dims(
    400             attention_mask, axis=mask_expansion_axes)
--> 401     return self._softmax(attention_scores, attention_mask)
    402 
    403   def _compute_attention(self,

~/anaconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
   1010         with autocast_variable.enable_auto_cast_variables(
   1011             self._compute_dtype_object):
-> 1012           outputs = call_fn(inputs, *args, **kwargs)
   1013 
   1014         if self._activity_regularizer:

~/anaconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/keras/layers/advanced_activations.py in call(self, inputs, mask)
    332             inputs, axis=self.axis, keepdims=True))
    333       else:
--> 334         return K.softmax(inputs, axis=self.axis[0])
    335     return K.softmax(inputs, axis=self.axis)
    336 

IndexError: tuple index out of range
4

1 回答 1

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您忘记了批次维度，这是必要的。此外，如果您想要输出张量和相应的权重，则必须将参数设置return_attention_scores为True. 尝试这样的事情：

import tensorflow as tf

layer = tf.keras.layers.MultiHeadAttention(num_heads=2, key_dim=2)
samples = 5
target = tf.ones(shape=[samples, 1, 16])
source = tf.ones(shape=[samples, 1, 16])
output_tensor, weights = layer(target, source, return_attention_scores=True)

同样根据文档：

query：查询形状的张量（B，T，dim）

value：形状的值张量（B，S，dim）

于 2022-01-25T14:40:48.783 回答