attn_weights_no_cls = attn_weights.sum(0)[:, 1:, 1:],attn_weights_no_cls维度是多少
时间: 2024-05-27 07:08:13 浏览: 11
假设attn_weights的维度为(batch_size, num_heads, seq_length, seq_length),其中batch_size为批大小,num_heads为头数,seq_length为序列长度,则执行attn_weights.sum(0)后得到的张量维度为(num_heads, seq_length, seq_length),然后执行[:, 1:, 1:]操作后得到的张量维度为(num_heads, seq_length-1, seq_length-1)。因此,attn_weights_no_cls的维度为(num_heads, seq_length-1, seq_length-1)。
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attn_weights_no_cls = attn_weights.sum(0)[:, 1:, 1:]
这行代码的作用是将多头注意力机制计算得到的注意力权重矩阵中除去第一行和第一列(也就是CLS token对应的位置)之外的所有元素相加,得到每个query token与其它所有token之间的注意力权重之和。其中,attn_weights是多头注意力机制计算得到的注意力权重矩阵,sum(0)表示在第0维(也就是多头数)上求和,[:, 1:, 1:]表示选取第1到最后一行和第1到最后一列的所有元素。
tf.expand_dims(soft_attn_weights, 2))
As an AI language model, I cannot provide a complete answer without the context of the code. However, I can describe what the `tf.expand_dims()` function does in general.
`tf.expand_dims()` is a function in TensorFlow that adds a new dimension to a tensor at a specified position. The function takes two arguments: the tensor to expand and the position to insert the new dimension.
In the code snippet provided, the `soft_attn_weights` tensor is being expanded by inserting a new dimension at position 2. This means that the resulting tensor will have an additional dimension of size 1 at position 2. The expanded tensor is then used in further computations.
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