From de6d9c6f3194fb822ac9291a467d5f6ea172928c Mon Sep 17 00:00:00 2001 From: "ben.guo" <909336740@qq.com> Date: Thu, 2 May 2024 15:25:52 +0800 Subject: [PATCH] =?UTF-8?q?Add.=20Softmax=E5=BD=92=E4=B8=80=E5=8C=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .DS_Store | Bin 10244 -> 10244 bytes assets/.DS_Store | Bin 22532 -> 24580 bytes assets/image-20240502143837209.png | Bin 231925 -> 0 bytes assets/image-20240502150743024.png | Bin 0 -> 244276 bytes assets/image-20240502150841107.png | Bin 0 -> 71897 bytes assets/image-20240502152336544.png | Bin 0 -> 115170 bytes assets/image-20240502152450856.png | Bin 0 -> 116487 bytes 人人都能看懂的Transformer/.DS_Store | Bin 6148 -> 6148 bytes ...—多头注意力机制——全流程.md | 17 ++++++++++++++++- 9 files changed, 16 insertions(+), 1 deletion(-) delete mode 100644 assets/image-20240502143837209.png create mode 100644 assets/image-20240502150743024.png create mode 100644 assets/image-20240502150841107.png create 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训练的过程中(语言模型场景),是从前往后训练,然后去预测后面的词(句子),所以训练过程中,我们要遮挡一部分,让模型不知道后面的信息,自动去生成然后校验生成与实际之间的差距。 +### Softmax归一化 + +将上面的值转成0到1直接的值,即百分比的概率分布。它会放大分数较高的元素,并抑制分数较低的元素。在注意力机制中,这意味着模型可以更加集中地关注那些与当前查询(Query)最相关的键(Key),从而获取对应的值(Value)。 + +image-20240502150841107 + +Softmax函数的公式如下: +$$ +\text{Softmax}(z_i) = \frac{e^{z_i}}{\sum_{j=1}^{K} e^{z_j}} +$$ +这里就不做详解,你就简单理解为:它可以将一批值,转换为一个概率分布,其中每个元素的值介于0和1之间,并且所有元素的和为1。 + +image-20240502152450856 + +可以看到`LL`的概率总和是1,`M`的概率总和也是1。以此类推。