Transformer
Transformer Model
性质:
1. Transformer是Seq2Seq类模型.
2. ransformer不是RNN.
3.仅依赖attention和全连接层.
准确率远高于RNN类.
各种weights:
- \(weights \space\space \alpha_{ij} = align(h_i, s_j)\).
- Compute\(k_{:i} = W_K h_i\)and\(q_{:j} = W_Q S_j\).
- Compute weights\(\alpha_{:j} = Softmax(K^T q_{:j}) \in \mathbb{R}^m\).
- Context vector:\(c_j = \sum\limits_{i=1}^{m}{\alpha_{ij}v_{:m}}\).
- Query:\(q_{:j} = W_Q s_j\)— 匹配别人.
- Key:\(k_{:i} = W_K h_i\)— 等待被匹配.
- Value:\(V_{:i} = W_V h_i\)— 待加权平均.
- \(W_Q, W_K, W_V\)皆为待学习参数.
\(Q-K-V\)的关系其实就是:\(h(P)与s(P)求对于h(P)的 attention\), 三个(P)处都是不同的可学习的W.
Attention Layer
Key:\(k_{:i} = W_K x_i\).
Value:\(v_{:i} = W_V x_i\).
- Queries are based on decoder’s inputs\(x_1^\prime, x_2^\prime, …, x_t^\prime\).
- Query:\(q_{:j} = W_Q x_j^\prime\).
符号汇总:
- Attention layer:\(C = Attn(X, X^\prime)\).
- Encoder’s inputs:\(X = [x_1, x_2, …, x_m]\).
- Decoder’s inputs:\(X^\prime = [x_1^\prime, x_2^\prime, …, x_t^\prime]\).
- parameters:\(W_Q, W_K, W_V\).
- Self-attention layer:\(C = Attn(X, X)\).
- RNN’s inputs\(X = [x_1, x_2, …, x_m]\).
- Parameters:\(W_Q, W_K, W_V\).
Summary:
- Attention 最初用于Seq2Seq的RNN模型.
- self-attention: 可用于所有RNN模型而不仅是Seq2Seq模型.
- Attention 可以不依赖于RNN使用.
Transformer 架构:
Single-head self-attention
Multi-head self-attention:
- l 个不共享权重的single-head self-attentions.
- 将所有single-head self-attentions的结果concat起来
- 假设single-head self-attention的输出为dxm的矩阵, 则对应multi-head 的输出shape为(ld)xm.
Transformer’s Encoder:
- Transformer’s encoder = 6 stacked blocks.
- 1 encoder block $\approx$1 multi-head attention layer + 1 dense layer.
Transformer’s Decoder:
- Transformer’s decoder = 6 stacked blocks.
- 1 decoder block\(\approx\)multi-head self-attention + multi-head attention + dense layer
- Input shape: (512 x m, 512 x t), output shape: 512 x t.
Stacked Attention
BERT
- BERT 是为了预训练Transformer 的 encoder.
- 预测mask掉的单词: 随即遮挡15%的单词:
- 预测下一个句子: 50%随机抽样句子或50%下一句, 给予false/true:
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