subtitles/zh-CN/25_keras-introduction.srt

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（欢快的音乐）
(upbeat music)

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- 在这个视频中，我会给你
- In this video, I'm going to give you

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快速介绍我们的变压器模型
a very quick introduction to how our transformer models

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与 Tensorflow 和 Keras 一起工作。
work together with Tensorflow and Keras.

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非常简短的解释
The very short explanation

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是我们所有的 Tensorflow 模型
is that all of our Tensorflow models

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也是 Keras 模型对象，
are also Keras model objects,

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因此他们拥有标准的 Keras 模型 API。
and so they have the standard Keras model API.

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如果你是一位经验丰富的机器学习工程师
If you're an experienced machine learning engineer

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谁经常使用 Keras，这可能就是你需要知道的全部
who's used Keras a lot, that's probably all you need to know

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开始与他们合作。
to start working with them.

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但对其他人来说，
But for everyone else,

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包括那里的浪子 PyTorch 工程师
including the prodigal PyTorch engineers out there

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正在回归的人，
who are returning to the fold,

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我将快速介绍 Keras 模型，
I'm going to quickly introduce Keras models,

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以及我们如何与他们合作。
and how we work with them.

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在我将在下面链接的其他视频中，
In other videos, which I'll link below,

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我将更详细地介绍 Keras 模型的训练。
I'll run through training with Keras models in more detail.

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但首先，从高层次上讲，什么是 Keras 模型？
But first, at a high level, what is a Keras model?

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所以你的模型基本上包含了你的整个网络。
So your model basically contains your entire network.

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它包含层，以及这些层的权重，
It contains the layers, and the weights for those layers,

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并告诉模型如何处理它们
and also tells the model what to do with them

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所以它一路定义了整个路径
so it defines the whole path all the way

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从你的输入到你的输出。
from your inputs to your outputs.

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如果你以前使用过 Keras，
If you've used Keras before,

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你可能开始使用模型对象
you probably started using model objects

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通过手工构建它们，
by building them out by hand,

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你一层又一层地添加
you added one layer after another

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并且可能使用 model.add () 或功能方法。
and maybe using the model.add () or the functional approach.

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这并没有错。
And there's nothing wrong with that.

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许多伟大的模型都是以这种方式构建的
Lots of great models are built that way

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但你也可以预加载整个模型、权重和所有内容。
but you can also pre-load an entire model, weights and all.

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这真的很有帮助，因为如果你，
And this is really helpful, because if you,

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正如你在这里看到的，如果你尝试阅读这篇论文
as you can see here, if you try reading the paper

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或者如果你尝试查看代码，
or if you try looking at the code,

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你会看到 Transformer 的内部非常复杂，
you'll see the inside of a Transformer is pretty complex,

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从头开始把它写出来并把它做好
and writing it all out from scratch and getting it right

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即使对于有经验的人来说也很难
would be hard even for an experienced

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机器学习工程师。
machine learning engineer.

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但因为它都装在一个模型里，
But because it's all packed inside a model,

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你不需要担心那个的复杂性
you don't need to worry about that complexity on that

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如果你不想。
if you don't want to.

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如果你是一名研究人员，如果你想真正深入研究
If you're a researcher, if you want to really dig in there

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你可以，但你也可以只加载一个预训练的，
you can, but you can also just load a pre-trained,

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只需一行代码即可预配置变压器模型。
pre-configured transformer model in just one line of code.

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当我之前提到 Keras API 时，
And when I mentioned earlier about the Keras API,

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它的优点是
the advantage of it is that

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你是否从头开始编写自己的模型
whether you write your own model from scratch

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或者加载一个预训练的，你与模型交互
or load a pre-trained one, you interact with the model

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通过相同的 API，所以你使用
through that same API, so you use exactly

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同样的几种方法，你会看到它们
the same few methods and you're gonna see them

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一次又一次，这些方法就像适合，
again and again, these methods like fit,

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编译和预测，就像我提到的
compile and predict, and like I've mentioned

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我们将介绍如何使用这些方法的具体示例
we'll cover concrete examples of how to use those methods

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在我将在下面链接的视频中。
in the videos I'll link below.

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现在要从这个视频中拿走的关键是，
For now the key thing to take away from this video,

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如果你以前从未见过 Keras，
if you've never seen Keras before,

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是这种简洁的封装意味着
is that this neat encapsulation means

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一个巨大的神经网络的所有复杂性
that all the complexity of a huge neural net

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变得易于管理，因为你与它互动
becomes manageable, because you interact with it

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以完全相同的方式，使用完全相同的方法，
in exactly the same way, using exactly the same methods,

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是否是一个庞大的预训练语言模型
whether it's a huge pre-trained language model

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或你手写的简单模型。
or a simple model that you wrote out by hand.

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（欢快的音乐）
(upbeat music)