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			Pytorch
		</a><a class="flex justify-center flex-1 py-1.5 px-2.5 focus:outline-none !no-underline rounded-r text-gray-500 filter grayscale" href="?fw=tf"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg>
			TensorFlow
		</a></div>
<h1 class="relative group"><a id="keras" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#keras"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>使用 Keras 微调一个模型
	</span></h1>



<div class="flex space-x-1 absolute z-10 right-0 top-0"><a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/chapter3/section3_tf.ipynb" target="_blank"><img alt="Open In Colab" class="!m-0" src="https://colab.research.google.com/assets/colab-badge.svg"></a>
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<p>完成上一节中的所有数据预处理工作后，您只剩下最后的几个步骤来训练模型。 但是请注意，<code>model.fit()</code> 命令在 CPU 上运行会非常缓慢。 如果您没有GPU，则可以在 <a href="https://colab.research.google.com/" rel="nofollow">Google Colab</a> 上使用免费的 GPU 或 TPU(需要梯子)。</p>
<p>这一节的代码示例假设您已经执行了上一节中的代码示例。 下面一个简短的摘要，包含了在开始学习这一节之前您需要的执行的代码：</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, DataCollatorWithPadding
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

raw_datasets = load_dataset(<span class="hljs-string">&quot;glue&quot;</span>, <span class="hljs-string">&quot;mrpc&quot;</span>)
checkpoint = <span class="hljs-string">&quot;bert-base-uncased&quot;</span>
tokenizer = AutoTokenizer.from_pretrained(checkpoint)


<span class="hljs-keyword">def</span> <span class="hljs-title function_">tokenize_function</span>(<span class="hljs-params">example</span>):
    <span class="hljs-keyword">return</span> tokenizer(example[<span class="hljs-string">&quot;sentence1&quot;</span>], example[<span class="hljs-string">&quot;sentence2&quot;</span>], truncation=<span class="hljs-literal">True</span>)


tokenized_datasets = raw_datasets.<span class="hljs-built_in">map</span>(tokenize_function, batched=<span class="hljs-literal">True</span>)

data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)

tf_train_dataset = tokenized_datasets[<span class="hljs-string">&quot;train&quot;</span>].to_tf_dataset(
    columns=[<span class="hljs-string">&quot;attention_mask&quot;</span>, <span class="hljs-string">&quot;input_ids&quot;</span>, <span class="hljs-string">&quot;token_type_ids&quot;</span>],
    label_cols=[<span class="hljs-string">&quot;labels&quot;</span>],
    shuffle=<span class="hljs-literal">True</span>,
    collate_fn=data_collator,
    batch_size=<span class="hljs-number">8</span>,
)

tf_validation_dataset = tokenized_datasets[<span class="hljs-string">&quot;validation&quot;</span>].to_tf_dataset(
    columns=[<span class="hljs-string">&quot;attention_mask&quot;</span>, <span class="hljs-string">&quot;input_ids&quot;</span>, <span class="hljs-string">&quot;token_type_ids&quot;</span>],
    label_cols=[<span class="hljs-string">&quot;labels&quot;</span>],
    shuffle=<span class="hljs-literal">False</span>,
    collate_fn=data_collator,
    batch_size=<span class="hljs-number">8</span>,
)<!-- HTML_TAG_END --></pre></div>
<h3 id="">训练模型</h3>
<p>从🤗 Transformers 导入的 TensorFlow 模型已经是 Keras 模型。 下面的视频是对 Keras 的简短介绍。</p>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/rnTGBy2ax1c" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>这意味着，一旦我们有了数据，就需要很少的工作就可以开始对其进行训练。</p>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/AUozVp78dhk" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>和<a href="/course/chapter2">第二章</a>使用的方法一样, 我们将使用二分类的 <code>TFAutoModelForSequenceClassification</code>类: </p>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSequenceClassification

model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=<span class="hljs-number">2</span>)<!-- HTML_TAG_END --></pre></div>
<p>您会注意到，与 <a href="/course/chapter2">第二章</a> 不同的是，您在实例化此预训练模型后会收到警告。 这是因为 BERT 没有对句子对进行分类进行预训练，所以预训练模型的 head 已经被丢弃，而是插入了一个适合序列分类的新 head。 警告表明一些权重没有使用（对应于丢弃的预训练头），而其他一些权重是随机初始化的（新头的权重）。 最后鼓励您训练模型，这正是我们现在要做的。</p>
<p>要在我们的数据集上微调模型，我们只需要在我们的模型上调用 <code>compile()</code> 方法，然后将我们的数据传递给 <code>fit()</code> 方法。 这将启动微调过程（在 GPU 上应该需要几分钟）并输出训练loss，以及每个 epoch 结束时的验证loss。</p>


<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>请注意🤗 Transformers 模型具有大多数 Keras 模型所没有的特殊能力——它们可以自动使用内部计算的loss。 如果您没有在 <code>compile()</code> 中设置损失函数，他们将默认使用内部计算的损失。 请注意，要使用内部损失，您需要将标签作为输入的一部分传递，而不是作为单独的标签（这是在 Keras 模型中使用标签的正常方式）。 您将在课程的第 2 部分中看到这方面的示例，其中定义正确的损失函数可能很棘手。 然而，对于序列分类，标准的 Keras 损失函数可以正常工作，所以我们将在这里使用它。</p></div>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> tensorflow.keras.losses <span class="hljs-keyword">import</span> SparseCategoricalCrossentropy

model.<span class="hljs-built_in">compile</span>(
    optimizer=<span class="hljs-string">&quot;adam&quot;</span>,
    loss=SparseCategoricalCrossentropy(from_logits=<span class="hljs-literal">True</span>),
    metrics=[<span class="hljs-string">&quot;accuracy&quot;</span>],
)
model.fit(
    tf_train_dataset,
    validation_data=tf_validation_dataset,
)<!-- HTML_TAG_END --></pre></div>


<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>请注意这里有一个非常常见的陷阱——你只是<em>可以</em>将损失的名称作为字符串传递给 Keras，但默认情况下，Keras 会假设你已经对输出应用了 softmax。 然而，许多模型在应用 softmax 之前就输出，也称为 <em>logits</em>。 我们需要告诉损失函数我们的模型是否经过了softmax，唯一的方法是直接调用它，而不是用字符串的名称。</p></div>
<h3 id="">提升训练的效果</h3>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/cpzq6ESSM5c" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>如果您尝试上面的代码，它肯定会运行，但您会发现loss只是缓慢或零星地下降。 主要原因是<em>学习率</em>。 与loss一样，当我们将优化器的名称作为字符串传递给 Keras 时，Keras 会初始化该优化器具有所有参数的默认值，包括学习率。 但是，根据长期经验，我们知道Transformer 模型更适合使用比 Adam 的默认值（1e-3）也写成为 10 的 -3 次方，或 0.001，低得多的学习率。 5e-5 (0.00005) 比默认值大约低 20 倍，是一个更好的起点。</p>
<p>除了降低学习率，我们还有第二个技巧：我们可以慢慢降低学习率。在训练过程中。 在文献中，您有时会看到这被称为 <em>decaying</em> 或 <em>annealing</em>学习率。 在 Keras 中，最好的方法是使用 <em>learning rate scheduler</em>。 一个好用的是<code>PolynomialDecay</code>——尽管有这个名字，但在默认设置下，它只是简单地从初始值线性衰减学习率值在训练过程中的最终值，这正是我们想要的。但是， 为了正确使用调度程序，我们需要告诉它训练的次数。 我们将在下面为其计算“num_train_steps”。</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> tensorflow.keras.optimizers.schedules <span class="hljs-keyword">import</span> PolynomialDecay

batch_size = <span class="hljs-number">8</span>
num_epochs = <span class="hljs-number">3</span>
<span class="hljs-comment"># 训练步数是数据集中的样本数除以batch size再乘以 epoch。</span>
<span class="hljs-comment"># 注意这里的tf_train_dataset是一个转化为batch后的 tf.data.Dataset，</span>
<span class="hljs-comment"># 不是原来的 Hugging Face Dataset，所以它的 len() 已经是 num_samples // batch_size。</span>
num_train_steps = <span class="hljs-built_in">len</span>(tf_train_dataset) * num_epochs
lr_scheduler = PolynomialDecay(
    initial_learning_rate=<span class="hljs-number">5e-5</span>, end_learning_rate=<span class="hljs-number">0.0</span>, decay_steps=num_train_steps
)
<span class="hljs-keyword">from</span> tensorflow.keras.optimizers <span class="hljs-keyword">import</span> Adam

opt = Adam(learning_rate=lr_scheduler)<!-- HTML_TAG_END --></pre></div>


<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>🤗 Transformers 库还有一个 <code>create_optimizer()</code> 函数，它将创建一个具有学习率衰减的 <code>AdamW</code> 优化器。 这是一个便捷的方式，您将在本课程的后续部分中详细了解。</p></div>
<p>现在我们有了全新的优化器，我们可以尝试使用它进行训练。 首先，让我们重新加载模型，以重置我们刚刚进行的训练运行对权重的更改，然后我们可以使用新的优化器对其进行编译：</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=<span class="hljs-number">2</span>)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=<span class="hljs-literal">True</span>)
model.<span class="hljs-built_in">compile</span>(optimizer=opt, loss=loss, metrics=[<span class="hljs-string">&quot;accuracy&quot;</span>])<!-- HTML_TAG_END --></pre></div>
<p>现在，我们再次进行fit：</p>

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	<pre><!-- HTML_TAG_START -->model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=<span class="hljs-number">3</span>)<!-- HTML_TAG_END --></pre></div>


<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>💡 如果您想在训练期间自动将模型上传到 Hub，您可以在 <code>model.fit()</code> 方法中传递 <code>PushToHubCallback</code>。 我们将在 <a href="/course/chapter4/3">第四章</a> 中进行介绍</p></div>
<h3 id="">模型预测</h3>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/nx10eh4CoOs" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>训练和观察的loss下降都非常好，但是如果我们想从训练后的模型中获得输出，或者计算一些指标，或者在生产中使用模型呢？ 为此，我们可以使用<code>predict()</code> 方法。 这将返回模型的输出头的<em>logits</em>数值，每个类一个。</p>

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	<pre><!-- HTML_TAG_START -->preds = model.predict(tf_validation_dataset)[<span class="hljs-string">&quot;logits&quot;</span>]<!-- HTML_TAG_END --></pre></div>
<p>我们可以将这些 logit 转换为模型的类别预测，方法是使用 argmax 找到最高的 logit，它对应于最有可能的类别：</p>

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	<pre><!-- HTML_TAG_START -->class_preds = np.argmax(preds, axis=<span class="hljs-number">1</span>)
<span class="hljs-built_in">print</span>(preds.shape, class_preds.shape)<!-- HTML_TAG_END --></pre></div>

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	<pre><!-- HTML_TAG_START -->(<span class="hljs-number">408</span>, <span class="hljs-number">2</span>) (<span class="hljs-number">408</span>,)<!-- HTML_TAG_END --></pre></div>
<p>现在，让我们使用这些 <code>preds</code> 来计算一些指标！ 我们可以像加载数据集一样轻松地加载与 MRPC 数据集相关的指标，这次使用的是 <code>load_metric()</code> 函数。 返回的对象有一个 <code>compute()</code> 方法，我们可以使用它来进行度量计算：</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_metric

metric = load_metric(<span class="hljs-string">&quot;glue&quot;</span>, <span class="hljs-string">&quot;mrpc&quot;</span>)
metric.compute(predictions=class_preds, references=raw_datasets[<span class="hljs-string">&quot;validation&quot;</span>][<span class="hljs-string">&quot;label&quot;</span>])<!-- HTML_TAG_END --></pre></div>

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	<pre><!-- HTML_TAG_START -->{<span class="hljs-string">&#x27;accuracy&#x27;</span>: <span class="hljs-number">0.8578431372549019</span>, <span class="hljs-string">&#x27;f1&#x27;</span>: <span class="hljs-number">0.8996539792387542</span>}<!-- HTML_TAG_END --></pre></div>
<p>您获得的确切结果可能会有所不同，因为模型头的随机初始化可能会改变它获得的指标。 在这里，我们可以看到我们的模型在验证集上的准确率为 85.78%，F1 得分为 89.97。 这些是用于评估 GLUE 基准的 MRPC 数据集结果的两个指标。 <a href="https://arxiv.org/pdf/1810.04805.pdf" rel="nofollow">BERT 论文</a> 中的表格报告了基本模型的 F1 分数为 88.9。 那是 <code>uncased</code> 模型，而我们目前使用的是 <code>cased</code> 模型，这解释了为什么我们会获得更好的结果。</p>
<p>使用 Keras API 进行微调的介绍到此结束。 第 7 章将给出对大多数常见 NLP 任务执行此操作的示例。如果您想在 Keras API 上磨练自己的技能，请尝试使第二节所使用的的数据处理在 GLUE SST-2 数据集上微调模型。</p>


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