python/pytorch 基础
培训机构（Django 类似于 Transformers）
首先由一个 norm 函数
norm 里面做残差，会输入（ x 和 淡粉色z1，残差值），输出一个值紫粉色的 z1
标准化
\[y = \frac{x-E(x)}{\sqrt{Var(x)+\epsilon}}*\gamma+\beta \]
\(E(x)\)
\(Var(x)\)
\(\epsilon\)
\(\gamma\)和\(\beta\)
class LayerNorm(nn.Module):
def __init__(self, feature, eps=1e-6):
"""
:param feature: self-attention 的 x 的大小
:param eps:
"""
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(feature))
self.b_2 = nn.Parameter(torch.zeros(feature))
self.eps = eps
def forward(self, x):
mean = x.mean(-1, keepdim=True)
std = x.std(-1, keepdim=True)
return self.a_2 * (x - mean) / (std + self.eps) + self.b_2
残差+标准化
class SublayerConnection(nn.Module):
"""
这不仅仅做了残差，这是把残差和 layernorm 一起给做了
"""
def __init__(self, size, dropout=0.1):
super(SublayerConnection, self).__init__()
# 第一步做 layernorm
self.layer_norm = LayerNorm(size)
# 第二步做 dropout
self.dropout = nn.Dropout(p=dropout)
def forward(self, x, sublayer):
"""
:param x: 就是self-attention的输入
:param sublayer: self-attention层
:return:
"""
return self.dropout(self.layer_norm(x + sublayer(x)))
