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Results tagged with transformer
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user 42699
For questions related to the transformer, which is a deep machine learning model introduced in 2017 in the paper "Attention Is All You Need", used primarily in the field of natural language processing (NLP).
2
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1
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Transformers: how to get the output (keys and values) of the encoder?
I was reading the paper Attention Is All You Need.
It seems like the last step of the encoder is a LayerNorm(relu(WX + B) + X), i.e. an add + normalization. This should result in a $n$ x $d^{model}$ m …
- 1,690
5
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2
answers
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Transformers: how does the decoder final layer output the desired token?
In the paper Attention Is All You Need, this section confuses me:
In our model, we share the same weight matrix between the two embedding layers [in the encoding section] and the pre-softmax linear t …
- 1,690
8
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1
answer
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What is the cost function of a transformer?
The paper Attention Is All You Need describes the transformer architecture that has an encoder and a decoder. … What is the object function of the transformer? Is it the MSE between $x_{french}'$ and $y_{french}$? And does it have any weight regularization terms? …
- 1,690
4
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1
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What is the gradient of an attention unit?
The paper Attention Is All You Need describes the Transformer architecture, which describes attention as a function of the queries $Q = x W^Q$, keys $K = x W^K$, and values $V = x W^V$:
$\text{Attention … (Q, K, V)} = \text{softmax}\left( \frac{QK^T}{\sqrt{d_k}} \right) V \\
= \text{softmax}\left( \frac{x W^Q (W^K)^T x}{\sqrt{d_k}} \right) x W^V$
In the Transformer, there are 3 different flavors of attention …
- 1,690