subtitles/zh-CN/26_fine-tuning-with-tensorflow.srt

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（空气呼啸）
(air whooshing)

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- 在这段视频中，我们将看到
- In this video, we're going to see

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如何加载和微调预训练模型。
how to load and fine tune a pre-trained model.

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它非常快。
It's very quick.

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如果你看过我们关于 pipeline 的视频，
And if you've watched our pipeline videos,

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我将在下面链接之，过程非常相似。
which I'll link below, the process is very similar.

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不过这一次，我们将使用迁移学习
This time, though, we're going to be using transfer learning

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并自己做一些训练，
and doing some training ourselves,

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而不是仅仅加载模型并按原样使用它。
rather than just loading a model and using it as is.

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所以要了解更多关于迁移学习的信息，
So to learn more about transfer learning,

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前往 “What is transfer learning?” 视频，
head to the 'What is transfer learning?' video,

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我们也会在下面链接它。
and we'll link that below as well.

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但现在，让我们看看这段代码。
But for now, let's look at this code.

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首先，我们选择我们想要开始的模型。
To start, we pick which model we want to start with.

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在这种情况下，我们将使用著名的，
In this case, we're going to use the famous,

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原始的 BERT，
the original BERT,

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作为我们今天训练的基础。
as the foundation for our training today.

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但这条怪物般的一行是什么，
But what is this monstrosity line,

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这个 “TFAutoModelForSequenceClassification”？
this 'TFAutoModelForSequenceClassification'?

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这意味着什么？
What does that mean?

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好吧，TF 代表 TensorFlow。
Well, the TF stands for TensorFlow.

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剩下的意味着，
And the rest means,

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采用语言模型，
take a language model,

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并在上面贴上一个序列分类头
and stick a sequence classification head onto it

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如果它还没有的话。
if it doesn't have one already.

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所以这行代码加载了 BERT，
So this line of code loads BERT,

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这是一个通用语言模型，
which is a general purpose language model,

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它加载重量、架构和所有
it loads at weights, architecture, and all

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然后在上面添加一个新的序列分类头
and then adds a new sequence classification head onto it

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具有随机初始化的权重。
with randomly initialized weights.

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所以这个方法需要知道两件事。
So this method needs to know two things.

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首先，它需要知道模型的名称
Firstly, it needs to know the name of the model

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你想要加载的架构和权重。
you wanted to load, the architecture and weights for.

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其次，它需要知道有多少类
And secondly, it needs to know how many classes

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你的问题是有的，因为这将决定大小，
your problem has, because that will determine the size,

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对输出头中的神经元数量。
the number of neurons in the output head.

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所以如果你想跟随数据
So if you want to follow along with the data

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来自我们有关数据集的视频，我将在下面链接，
from our datasets videos, which I'll link below,

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那么你将有两个类别，积极的和消极的，
then you'll have two classes, positive and negative,

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因此 num_labels 等于二。
and thus num_labels equals two.

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但是这个编译行呢？
But what about this compile line?

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好吧，如果你熟悉 Keras，
Well, if you're familiar with Keras,

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你可能已经看过了。
you've probably seen this already.

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但如果不是，这是 Keras 中的核心方法之一
But if not, this is one of the core methods in Keras

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你会一次又一次地看到。
that you're gonna see again, and again.

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你总是需要在训练之前编译你的模型。
You always need to compile your model before you train it.

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编译需要知道两件事。
And compile needs to know two things.

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首先，它需要知道损失函数，
Firstly, it needs to know the loss function,

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这就是你要优化的内容。
which is what you're trying to optimize.

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所以在这里，我们导入 SparseCategoricalCrossentropy
So here, we import the SparseCategoricalCrossentropy

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损失函数。
loss function.

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所以这是一点点，但它是标准的损失函数
So that's a mouthful, but it's the standard loss function

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对于任何正在执行分类任务的神经网络。
for any neural network that's doing a classification task.

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它基本上鼓励网络
It basically encourages the network

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为正确的类输出大的值，
to output large values for the right class,

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以及为错误的类别输出低值。
and low values for the wrong classes.

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请注意，你可以将损失函数指定为字符串，
Note that you can specify the loss function as a string,

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就像我们对优化器所做的那样。
like we did with the optimizer.

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但这里有一个问题，
But there's a risk there,

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人们容易掉入一个非常普遍的陷阱，
there's a very common trap people fall into,

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就是默认情况下，这种损失假设
which is that by default, this loss assumes

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输出是 softmax 层之后的概率。
the output is probabilities after a softmax layer.

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但是我们的模型实际输出了什么
But what our model has actually output

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是 softmax 之前的值，
is the values before the softmax,

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通常称为 logits，有时称为 logits。
often called the logits, sometimes logits.

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没有人十分确定如何发音。
No one's quite sure how to pronounce that one.

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但你可能以前见过这些
But you probably seen these before

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在关于 pipeline 的视频中。
in the video about pipelines.

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所以如果你弄错了，你的模型就不会训练
So if you get this wrong, your model won't train

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弄清楚原因会很烦人。
and it'll be very annoying to figure out why.

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在以后的视频中，我们会看到
In future videos, we're gonna see

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如何使用模型的内部损失计算，
how to use the model's internal loss computations,

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这样你就不必自己指定损失
so that you don't have to specify the loss yourself

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而且你不必担心这些细节。
and you don't have to worry about these details.

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但是现在，请记住将 from_logits 设置为 true。
But for now, remember to set from_logits to true.

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编译需要知道的第二件事
The second thing compile needs to know

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是你想要的优化器。
is the optimizer you want.

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在我们的例子中，我们使用 adam ，
In our case, we use adam,

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这是一种标准的优化器
which is sort of the standard optimizer

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用于现代深度学习。
for deep learning these days.

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你可能想要改变的一件事
The one thing you might want to change

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是学习率。
is the learning rate.

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为此，我们需要导入实际的优化器
And to do that, we'll need to import the actual optimizer

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而不仅仅是通过字符串调用它。
rather than just calling it by string.

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但我们会在另一个视频中讨论这个，
But we'll talk about that in another video,

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我将在下面链接。
which I'll link below.

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现在，让我们尝试训练模型。
For now, let's just try training the model.

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那么，你如何训练模型呢？
Well, so how do you train the model?

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同样，如果你以前使用过 Keras，
Again, if you've used Keras before,

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这一切对你来说都很熟悉。
this is all going to be very familiar to you.

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但如果没有，让我们快速看看
But if not, let's very quickly look

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我们在这里做什么。
at what we're doing here.

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fit 几乎是 Keras 模型的核心方法。
fit is pretty much the central method for Keras models.

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它告诉模型根据我们传入的数据进行训练。
It tells the model to train on the data we're passing in.

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所以这里我们传递我们制作的数据集
So here we pass the datasets we made

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在上一节中，
in the previous section,

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数据集包含我们的输入和标签。
the dataset contains both our inputs and our labels.

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所以我们不需要指定单独的标签，
So we don't need to specify separate labels,

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当我们调用 fit 时。
when we're calling fit.

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然后我们对 validation_data 做同样的事情。
Then we do the same thing with the validation_data.

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然后我们可以，如果我们愿意，我们可以指定细节，
And then we can if we want, we can specify details,

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比如训练的次数
like the number of epochs for training

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你可以传递一些其他参数给 fit 。
where there's some other arguments you can pass to fit.

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但最后，你只需将所有这些传递给 model.fit
But in the end, you just pass all of this to model.fit

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然后你让它运行。
and you let it run.

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如果一切顺利，
If everything works out,

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你应该看到一个小训练栏
you should see a little training bar

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随着损失的减少而进步。
progressing along as your loss goes down.

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就是这样。
And that's it.

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在运行时，
While that's running,

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你知道，你可以打电话给你的老板并告诉他们
you know, you can call your boss and tell them

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你现在是高级 NLP 机器学习工程师
you're a senior NLP machine learning engineer now

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你会想要下个季度的薪水审查。
and you're gonna want a salary review next quarter.

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这几行代码真的够用了
These few lines of code are really all it takes

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应用大量的力量
to apply the power of a massive

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预训练语言问题，
pre-trained language problem,

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大规模的预训练语言模型，不好意思，
massive pre-trained language model, excuse me,

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到你的 NLP 问题。
to your NLP problem.

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但我们能做得更好吗？
But could we do better than this?

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我的意思是，我们当然可以。
I mean, we certainly could.

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具有一些更高级的 Keras 功能
With a few more advanced Keras features

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就像一个经过调整的、预定的学习率，
like a tuned, scheduled learning rate,

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我们可以获得更低的损失
we can get an even lower loss

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以及一个更准确、更有用的模型。
and an even more accurate, more useful model.

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训练模型后，我们如何处理模型？
And what do we do with our model after we train it?

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所以所有这些都将包含在视频中
So all of this is going to be covered in the videos

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即将推出，敬请期待。
that are coming up, so stay tuned.

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（空气呼啸）
(air whooshing)