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<title>torchtext ๋ผ์ด๋ธ๋ฌ๋ฆฌ๋ก ํ
์คํธ ๋ถ๋ฅํ๊ธฐ — PyTorch Tutorials 1.10.2+cu102 documentation</title>
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<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ ์ํผ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../recipes/recipes_index.html">๋ชจ๋ ๋ ์ํผ ๋ณด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../prototype/prototype_index.html">๋ชจ๋ ํ๋กํ ํ์
๋ ์ํผ ๋ณด๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ์์ํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="basics/intro.html">ํ์ดํ ์น(PyTorch) ๊ธฐ๋ณธ ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/quickstart_tutorial.html">๋น ๋ฅธ ์์(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/tensorqs_tutorial.html">ํ
์(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/data_tutorial.html">Dataset๊ณผ DataLoader</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/transforms_tutorial.html">๋ณํ(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/buildmodel_tutorial.html">์ ๊ฒฝ๋ง ๋ชจ๋ธ ๊ตฌ์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/autogradqs_tutorial.html"><code class="docutils literal notranslate"><span class="pre">torch.autograd</span></code>๋ฅผ ์ฌ์ฉํ ์๋ ๋ฏธ๋ถ</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/optimization_tutorial.html">๋ชจ๋ธ ๋งค๊ฐ๋ณ์ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/saveloadrun_tutorial.html">๋ชจ๋ธ ์ ์ฅํ๊ณ ๋ถ๋ฌ์ค๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">Introduction to PyTorch on YouTube</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="introyt.html">Introduction to PyTorch - YouTube Series</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/introyt1_tutorial.html">Introduction to PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/tensors_deeper_tutorial.html">Introduction to PyTorch Tensors</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/autogradyt_tutorial.html">The Fundamentals of Autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/modelsyt_tutorial.html">Building Models with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/tensorboardyt_tutorial.html">PyTorch TensorBoard Support</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/trainingyt.html">Training with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/captumyt.html">Model Understanding with Captum</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ฐฐ์ฐ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="deep_learning_60min_blitz.html">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="pytorch_with_examples.html">์์ ๋ก ๋ฐฐ์ฐ๋ ํ์ดํ ์น(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="nn_tutorial.html"><cite>torch.nn</cite> ์ด <em>์ค์ ๋ก</em> ๋ฌด์์ธ๊ฐ์?</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_tutorial.html">TensorBoard๋ก ๋ชจ๋ธ, ๋ฐ์ดํฐ, ํ์ต ์๊ฐํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">์ด๋ฏธ์ง/๋น๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchvision_tutorial.html">TorchVision ๊ฐ์ฒด ๊ฒ์ถ ๋ฏธ์ธ์กฐ์ (Finetuning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="transfer_learning_tutorial.html">์ปดํจํฐ ๋น์ (Vision)์ ์ํ ์ ์ดํ์ต(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="fgsm_tutorial.html">์ ๋์ ์์ ์์ฑ(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="dcgan_faces_tutorial.html">DCGAN ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํ ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">์ค๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="audio_io_tutorial.html">Audio I/O</a></li>
<li class="toctree-l1"><a class="reference internal" href="audio_resampling_tutorial.html">Audio Resampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="audio_data_augmentation_tutorial.html">Audio Data Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="audio_feature_extractions_tutorial.html">Audio Feature Extractions</a></li>
<li class="toctree-l1"><a class="reference internal" href="audio_feature_augmentation_tutorial.html">Audio Feature Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="audio_datasets_tutorial.html">Audio Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/speech_recognition_pipeline_tutorial.html">Speech Recognition with Wav2Vec2</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/speech_command_classification_with_torchaudio_tutorial.html">Speech Command Classification with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/text_to_speech_with_torchaudio.html">Text-to-speech with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/forced_alignment_with_torchaudio_tutorial.html">Forced Alignment with Wav2Vec2</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ
์คํธ</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="transformer_tutorial.html">nn.Transformer ์ TorchText ๋ก ์ํ์ค-ํฌ-์ํ์ค(Sequence-to-Sequence) ๋ชจ๋ธ๋งํ๊ธฐ</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(๋ฒ ํ) ์ปดํจํฐ ๋น์ ํํ ๋ฆฌ์ผ์ ์ํ ์์ํ๋ ์ ์ดํ์ต(Quantized Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
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<p class="caption"><span class="caption-text">๋ณ๋ ฌ ๋ฐ ๋ถ์ฐ ํ์ต</span></p>
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<li class="toctree-l1"><a class="reference internal" href="dist_overview.html">PyTorch Distributed Overview</a></li>
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๊ฐ๋ฐํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_tutorial.html">Getting Started with Distributed RPC Framework</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/rpc_ddp_tutorial.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ(DDP)๊ณผ ๋ถ์ฐ RPC ํ๋ ์์ํฌ ๊ฒฐํฉ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/pipeline_tutorial.html">ํ์ดํ๋ผ์ธ ๋ณ๋ ฌํ๋ก ํธ๋์คํฌ๋จธ ๋ชจ๋ธ ํ์ต์ํค๊ธฐ</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a></li>
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<div class="sphx-glr-example-title section" id="torchtext">
<span id="sphx-glr-beginner-text-sentiment-ngrams-tutorial-py"></span><h1>torchtext ๋ผ์ด๋ธ๋ฌ๋ฆฌ๋ก ํ
์คํธ ๋ถ๋ฅํ๊ธฐ<a class="headerlink" href="#torchtext" title="Permalink to this headline">ยถ</a></h1>
<p><strong>๋ฒ์ญ</strong>: <a class="reference external" href="https://github.com/gangsss">๊น๊ฐ๋ฏผ</a> , <a class="reference external" href="https://github.com/lewha0">๊น์งํ</a></p>
<p>์ด ํํ ๋ฆฌ์ผ์์๋ torchtext ๋ผ์ด๋ธ๋ฌ๋ฆฌ๋ฅผ ์ฌ์ฉํ์ฌ ์ด๋ป๊ฒ ํ
์คํธ ๋ถ๋ฅ ๋ถ์์ ์ํ ๋ฐ์ดํฐ์
์ ๋ง๋๋์ง๋ฅผ ์ดํด๋ณด๊ฒ ์ต๋๋ค.
๋ค์๊ณผ ๊ฐ์ ๋ด์ฉ๋ค์ ์๊ฒ ๋ฉ๋๋ค:</p>
<blockquote>
<div><ul class="simple">
<li>๋ฐ๋ณต์(iterator)๋ก ๊ฐ๊ณต๋์ง ์์ ๋ฐ์ดํฐ(raw data)์ ์ ๊ทผํ๊ธฐ</li>
<li>๊ฐ๊ณต๋์ง ์์ ํ
์คํธ ๋ฌธ์ฅ๋ค์ ๋ชจ๋ธ ํ์ต์ ์ฌ์ฉํ ์ ์๋ <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> ๋ก ๋ณํํ๋ ๋ฐ์ดํฐ ์ฒ๋ฆฌ ํ์ดํ๋ผ์ธ ๋ง๋ค๊ธฐ</li>
<li><a class="reference external" href="https://pytorch.org/docs/stable/data.html?highlight=dataloader#torch.utils.data.DataLoader">torch.utils.data.DataLoader</a> ๋ฅผ ์ฌ์ฉํ์ฌ ๋ฐ์ดํฐ๋ฅผ ์๊ณ ๋ฐ๋ณตํ๊ธฐ(shuffle and iterate)</li>
</ul>
</div></blockquote>
<div class="section" id="raw-data-iterator">
<h2>๊ธฐ์ด ๋ฐ์ดํฐ์
๋ฐ๋ณต์(raw data iterator)์ ์ ๊ทผํ๊ธฐ<a class="headerlink" href="#raw-data-iterator" title="Permalink to this headline">ยถ</a></h2>
<p>torchtext ๋ผ์ด๋ธ๋ฌ๋ฆฌ๋ ๊ฐ๊ณต๋์ง ์์ ํ
์คํธ ๋ฌธ์ฅ๋ค์ ๋ง๋๋(yield) ๋ช ๊ฐ์ง ๊ธฐ์ด ๋ฐ์ดํฐ์
๋ฐ๋ณต์(raw dataset iterator)๋ฅผ ์ ๊ณตํฉ๋๋ค.
์๋ฅผ ๋ค์ด, <code class="docutils literal notranslate"><span class="pre">AG_NEWS</span></code> ๋ฐ์ดํฐ์
๋ฐ๋ณต์๋ ๋ ์ด๋ธ(label)๊ณผ ๋ฌธ์ฅ์ ํํ(tuple) ํํ๋ก ๊ฐ๊ณต๋์ง ์์ ๋ฐ์ดํฐ๋ฅผ ๋ง๋ญ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torchtext.datasets</span> <span class="kn">import</span> <span class="n">AG_NEWS</span>
<span class="n">train_iter</span> <span class="o">=</span> <span class="n">AG_NEWS</span><span class="p">(</span><span class="n">split</span><span class="o">=</span><span class="s1">'train'</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">next</span><span class="p">(</span><span class="n">train_iter</span><span class="p">)</span>
<span class="o">>>></span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="s2">"Wall St. Bears Claw Back Into the Black (Reuters) Reuters -</span>
<span class="n">Short</span><span class="o">-</span><span class="n">sellers</span><span class="p">,</span> <span class="n">Wall</span> <span class="n">Street</span><span class="s1">'s dwindling</span><span class="se">\\</span><span class="s1">band of ultra-cynics, are seeing green</span>
<span class="n">again</span><span class="o">.</span><span class="s2">")</span>
<span class="nb">next</span><span class="p">(</span><span class="n">train_iter</span><span class="p">)</span>
<span class="o">>>></span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="s1">'Carlyle Looks Toward Commercial Aerospace (Reuters) Reuters - Private</span>
<span class="n">investment</span> <span class="n">firm</span> <span class="n">Carlyle</span> <span class="n">Group</span><span class="p">,</span>\\<span class="n">which</span> <span class="n">has</span> <span class="n">a</span> <span class="n">reputation</span> <span class="k">for</span> <span class="n">making</span> <span class="n">well</span><span class="o">-</span><span class="n">timed</span>
<span class="ow">and</span> <span class="n">occasionally</span>\\<span class="n">controversial</span> <span class="n">plays</span> <span class="ow">in</span> <span class="n">the</span> <span class="n">defense</span> <span class="n">industry</span><span class="p">,</span> <span class="n">has</span> <span class="n">quietly</span>
<span class="n">placed</span>\\<span class="n">its</span> <span class="n">bets</span> <span class="n">on</span> <span class="n">another</span> <span class="n">part</span> <span class="n">of</span> <span class="n">the</span> <span class="n">market</span><span class="o">.</span><span class="s1">')</span>
<span class="nb">next</span><span class="p">(</span><span class="n">train_iter</span><span class="p">)</span>
<span class="o">>>></span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="s2">"Oil and Economy Cloud Stocks' Outlook (Reuters) Reuters - Soaring</span>
<span class="n">crude</span> <span class="n">prices</span> <span class="n">plus</span> <span class="n">worries</span>\\<span class="n">about</span> <span class="n">the</span> <span class="n">economy</span> <span class="ow">and</span> <span class="n">the</span> <span class="n">outlook</span> <span class="k">for</span> <span class="n">earnings</span> <span class="n">are</span>
<span class="n">expected</span> <span class="n">to</span>\\<span class="n">hang</span> <span class="n">over</span> <span class="n">the</span> <span class="n">stock</span> <span class="n">market</span> <span class="nb">next</span> <span class="n">week</span> <span class="n">during</span> <span class="n">the</span> <span class="n">depth</span> <span class="n">of</span>
<span class="n">the</span>\\<span class="n">summer</span> <span class="n">doldrums</span><span class="o">.</span><span class="s2">")</span>
</pre></div>
</div>
</div>
<div class="section" id="id3">
<h2>๋ฐ์ดํฐ ์ฒ๋ฆฌ ํ์ดํ๋ผ์ธ ์ค๋นํ๊ธฐ<a class="headerlink" href="#id3" title="Permalink to this headline">ยถ</a></h2>
<p>์ดํ์ง(vocab), ๋จ์ด ๋ฒกํฐ(word vector), ํ ํฌ๋์ด์ (tokenizer)๋ฅผ ํฌํจํ์ฌ torchtext ๋ผ์ด๋ธ๋ฌ๋ฆฌ์ ๊ฐ์ฅ ๊ธฐ๋ณธ์ ์ธ ๊ตฌ์ฑ์์๋ฅผ ์ฌ๊ฒํ ํ์ต๋๋ค.
์ด๋ค์ ๊ฐ๊ณต๋์ง ์์ ํ
์คํธ ๋ฌธ์์ด์ ๋ํ ๊ธฐ๋ณธ์ ์ธ ๋ฐ์ดํฐ ์ฒ๋ฆฌ ๋น๋ฉ ๋ธ๋ก(data processing building block)์
๋๋ค.</p>
<p>๋ค์์ ํ ํฌ๋์ด์ ๋ฐ ์ดํ์ง์ ์ฌ์ฉํ ์ผ๋ฐ์ ์ธ NLP ๋ฐ์ดํฐ ์ฒ๋ฆฌ์ ์์
๋๋ค.
์ฒซ๋ฒ์งธ ๋จ๊ณ๋ ๊ฐ๊ณต๋์ง ์์ ํ์ต ๋ฐ์ดํฐ์
์ผ๋ก ์ดํ์ง์ ๋ง๋๋ ๊ฒ์
๋๋ค.
์ฌ๊ธฐ์์๋ ํ ํฐ์ ๋ชฉ๋ก ๋๋ ๋ฐ๋ณต์๋ฅผ ๋ฐ๋ ๋ด์ฅ(built-in) ํฉํ ๋ฆฌ ํจ์(factory function) <cite>build_vocab_from_iterator</cite> ๋ฅผ ์ฌ์ฉํฉ๋๋ค.
์ฌ์ฉ์๋ ์ดํ์ง์ ์ถ๊ฐํ ํน์ ๊ธฐํธ(special symbol) ๊ฐ์ ๊ฒ๋ค์ ์ ๋ฌํ ์๋ ์์ต๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torchtext.data.utils</span> <span class="kn">import</span> <span class="n">get_tokenizer</span>
<span class="kn">from</span> <span class="nn">torchtext.vocab</span> <span class="kn">import</span> <span class="n">build_vocab_from_iterator</span>
<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">get_tokenizer</span><span class="p">(</span><span class="s1">'basic_english'</span><span class="p">)</span>
<span class="n">train_iter</span> <span class="o">=</span> <span class="n">AG_NEWS</span><span class="p">(</span><span class="n">split</span><span class="o">=</span><span class="s1">'train'</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">yield_tokens</span><span class="p">(</span><span class="n">data_iter</span><span class="p">):</span>
<span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">text</span> <span class="ow">in</span> <span class="n">data_iter</span><span class="p">:</span>
<span class="k">yield</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">text</span><span class="p">)</span>
<span class="n">vocab</span> <span class="o">=</span> <span class="n">build_vocab_from_iterator</span><span class="p">(</span><span class="n">yield_tokens</span><span class="p">(</span><span class="n">train_iter</span><span class="p">),</span> <span class="n">specials</span><span class="o">=</span><span class="p">[</span><span class="s2">"<unk>"</span><span class="p">])</span>
<span class="n">vocab</span><span class="o">.</span><span class="n">set_default_index</span><span class="p">(</span><span class="n">vocab</span><span class="p">[</span><span class="s2">"<unk>"</span><span class="p">])</span>
</pre></div>
</div>
<p>์ดํ์ง ๋ธ๋ก(vocabulary block)์ ํ ํฐ ๋ชฉ๋ก์ ์ ์๋ก ๋ณํํฉ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">vocab</span><span class="p">([</span><span class="s1">'here'</span><span class="p">,</span> <span class="s1">'is'</span><span class="p">,</span> <span class="s1">'an'</span><span class="p">,</span> <span class="s1">'example'</span><span class="p">])</span>
<span class="o">>>></span> <span class="p">[</span><span class="mi">475</span><span class="p">,</span> <span class="mi">21</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">5286</span><span class="p">]</span>
</pre></div>
</div>
<p>ํ ํฌ๋์ด์ ์ ์ดํ์ง์ ๊ฐ์ถ ํ
์คํธ ์ฒ๋ฆฌ ํ์ดํ๋ผ์ธ์ ์ค๋นํฉ๋๋ค.
ํ
์คํธ ํ์ดํ๋ผ์ธ๊ณผ ๋ ์ด๋ธ(label) ํ์ดํ๋ผ์ธ์ ๋ฐ์ดํฐ์
๋ฐ๋ณต์๋ก๋ถํฐ ์ป์ด์จ ๊ฐ๊ณต๋์ง ์์ ๋ฌธ์ฅ ๋ฐ์ดํฐ๋ฅผ ์ฒ๋ฆฌํ๊ธฐ ์ํด ์ฌ์ฉ๋ฉ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">text_pipeline</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">vocab</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">label_pipeline</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
</pre></div>
</div>
<p>ํ
์คํธ ํ์ดํ๋ผ์ธ์ ์ดํ์ง์ ์ ์๋ ๋ฃฉ์
ํ
์ด๋ธ(์๋ํ; lookup table)์ ๊ธฐ๋ฐํ์ฌ ํ
์คํธ ๋ฌธ์ฅ์ ์ ์ ๋ชฉ๋ก์ผ๋ก ๋ณํํฉ๋๋ค.
๋ ์ด๋ธ(label) ํ์ดํ๋ผ์ธ์ ๋ ์ด๋ธ์ ์ ์๋ก ๋ณํํฉ๋๋ค. ์๋ฅผ ๋ค์ด,</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">text_pipeline</span><span class="p">(</span><span class="s1">'here is the an example'</span><span class="p">)</span>
<span class="o">>>></span> <span class="p">[</span><span class="mi">475</span><span class="p">,</span> <span class="mi">21</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">5286</span><span class="p">]</span>
<span class="n">label_pipeline</span><span class="p">(</span><span class="s1">'10'</span><span class="p">)</span>
<span class="o">>>></span> <span class="mi">9</span>
</pre></div>
</div>
</div>
<div class="section" id="batch">
<h2>๋ฐ์ดํฐ ๋ฐฐ์น(batch)์ ๋ฐ๋ณต์ ์์ฑํ๊ธฐ<a class="headerlink" href="#batch" title="Permalink to this headline">ยถ</a></h2>
<p><a class="reference external" href="https://pytorch.org/docs/stable/data.html?highlight=dataloader#torch.utils.data.DataLoader">torch.utils.data.DataLoader</a> ๋ฅผ
๊ถ์ฅํฉ๋๋ค. (ํํ ๋ฆฌ์ผ์ <a class="reference external" href="https://tutorials.pytorch.kr/beginner/data_loading_tutorial.html">์ฌ๊ธฐ</a> ์์ต๋๋ค.)
์ด๋ <code class="docutils literal notranslate"><span class="pre">getitem()</span></code> ๊ณผ <code class="docutils literal notranslate"><span class="pre">len()</span></code> ํ๋กํ ์ฝ์ ๊ตฌํํ ๋งต ํํ(map-style)์ ๋ฐ์ดํฐ์
์ผ๋ก ๋์ํ๋ฉฐ, ๋งต(map)์ฒ๋ผ ์ธ๋ฑ์ค/ํค๋ก ๋ฐ์ดํฐ ์ํ์ ์ป์ด์ต๋๋ค.
๋ํ, ์
ํ(shuffle) ์ธ์๋ฅผ <code class="docutils literal notranslate"><span class="pre">False</span></code> ๋ก ์ค์ ํ๋ฉด ์ํ ๊ฐ๋ฅํ(iterable) ๋ฐ์ดํฐ์
์ฒ๋ผ ๋์ํฉ๋๋ค.</p>
<p>๋ชจ๋ธ๋ก ๋ณด๋ด๊ธฐ ์ , <code class="docutils literal notranslate"><span class="pre">collate_fn</span></code> ํจ์๋ <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code> ๋ก๋ถํฐ ์์ฑ๋ ์ํ ๋ฐฐ์น๋ก ๋์ํฉ๋๋ค.
<code class="docutils literal notranslate"><span class="pre">collate_fn</span></code> ์ ์
๋ ฅ์ <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code> ์ ๋ฐฐ์น ํฌ๊ธฐ(batch size)๊ฐ ์๋ ๋ฐฐ์น(batch) ๋ฐ์ดํฐ์ด๋ฉฐ,
<code class="docutils literal notranslate"><span class="pre">collate_fn</span></code> ์ ์ด๋ฅผ ๋ฏธ๋ฆฌ ์ ์ธ๋ ๋ฐ์ดํฐ ์ฒ๋ฆฌ ํ์ดํ๋ผ์ธ์ ๋ฐ๋ผ ์ฒ๋ฆฌํฉ๋๋ค.
<code class="docutils literal notranslate"><span class="pre">collate_fn</span></code> ์ด ์ต์์ ์์ค์ผ๋ก ์ ์(top level def)๋์๋์ง ํ์ธํฉ๋๋ค. ์ด๋ ๊ฒ ํ๋ฉด ๋ชจ๋ ์์ปค์์ ์ด ํจ์๋ฅผ ์ฌ์ฉํ ์ ์์ต๋๋ค.</p>
<p>์๋ ์์ ์์, ์ฃผ์ด์ง(original) ๋ฐ์ดํฐ ๋ฐฐ์น์ ํ
์คํธ ํญ๋ชฉ๋ค์ ๋ฆฌ์คํธ(list)์ ๋ด๊ธด(pack) ๋ค <code class="docutils literal notranslate"><span class="pre">nn.EmbeddingBag</span></code> ์ ์
๋ ฅ์ ์ํ ํ๋์ tensor๋ก ํฉ์ณ(concatenate)์ง๋๋ค.
์คํ์
(offset)์ ํ
์คํธ tensor์์ ๊ฐ๋ณ ์ํ์ค ์์ ์ธ๋ฑ์ค๋ฅผ ํํํ๊ธฐ ์ํ ๊ตฌ๋ถ์(delimiter) tensor์
๋๋ค.
๋ ์ด๋ธ(label)์ ๊ฐ๋ณ ํ
์คํธ ํญ๋ชฉ์ ๋ ์ด๋ธ์ ์ ์ฅํ๋ tensor์
๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
<span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"cuda"</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">"cpu"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">collate_batch</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span>
<span class="n">label_list</span><span class="p">,</span> <span class="n">text_list</span><span class="p">,</span> <span class="n">offsets</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">for</span> <span class="p">(</span><span class="n">_label</span><span class="p">,</span> <span class="n">_text</span><span class="p">)</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">:</span>
<span class="n">label_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">label_pipeline</span><span class="p">(</span><span class="n">_label</span><span class="p">))</span>
<span class="n">processed_text</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">text_pipeline</span><span class="p">(</span><span class="n">_text</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span>
<span class="n">text_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">processed_text</span><span class="p">)</span>
<span class="n">offsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">processed_text</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="n">label_list</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">label_list</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span>
<span class="n">offsets</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">offsets</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">text_list</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">text_list</span><span class="p">)</span>
<span class="k">return</span> <span class="n">label_list</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">text_list</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">offsets</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="n">train_iter</span> <span class="o">=</span> <span class="n">AG_NEWS</span><span class="p">(</span><span class="n">split</span><span class="o">=</span><span class="s1">'train'</span><span class="p">)</span>
<span class="n">dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">train_iter</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">collate_fn</span><span class="o">=</span><span class="n">collate_batch</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="id4">
<h2>๋ชจ๋ธ ์ ์ํ๊ธฐ<a class="headerlink" href="#id4" title="Permalink to this headline">ยถ</a></h2>
<p>๋ชจ๋ธ์
<a class="reference external" href="https://pytorch.org/docs/stable/nn.html?highlight=embeddingbag#torch.nn.EmbeddingBag">nn.EmbeddingBag</a>
๋ ์ด์ด์ ๋ถ๋ฅ(classification) ๋ชฉ์ ์ ์ํ ์ ํ ๋ ์ด์ด๋ก ๊ตฌ์ฑ๋ฉ๋๋ค.
๊ธฐ๋ณธ ๋ชจ๋๊ฐ โํ๊ท (mean)โ์ธ <code class="docutils literal notranslate"><span class="pre">nn.EmbeddingBag</span></code> ์ ์๋ฒ ๋ฉ๋ค์ โ๊ฐ๋ฐฉ(bag)โ์ ํ๊ท ๊ฐ์ ๊ณ์ฐํฉ๋๋ค.
์ด๋ ํ
์คํธ(text) ํญ๋ชฉ๋ค์ ๊ฐ๊ธฐ ๊ทธ ๊ธธ์ด๊ฐ ๋ค๋ฅผ ์ ์์ง๋ง, <code class="docutils literal notranslate"><span class="pre">nn.EmbeddingBag</span></code> ๋ชจ๋์ ํ
์คํธ์ ๊ธธ์ด๋ฅผ
์คํ์
(offset)์ผ๋ก ์ ์ฅํ๊ณ ์์ผ๋ฏ๋ก ํจ๋ฉ(padding)์ด ํ์ํ์ง๋ ์์ต๋๋ค.</p>
<p>๋ง๋ถ์ฌ์, <code class="docutils literal notranslate"><span class="pre">nn.EmbeddingBag</span></code> ์ ์๋ฒ ๋ฉ์ ํ๊ท ์ ์ฆ์ ๊ณ์ฐํ๊ธฐ ๋๋ฌธ์,
tensor๋ค์ ์ํ์ค๋ฅผ ์ฒ๋ฆฌํ ๋ ์ฑ๋ฅ ๋ฐ ๋ฉ๋ชจ๋ฆฌ ํจ์จ์ฑ ์ธก๋ฉด์์์ ์ฅ์ ๋
๊ฐ๊ณ ์์ต๋๋ค.</p>
<img alt="../_images/text_sentiment_ngrams_model.png" src="../_images/text_sentiment_ngrams_model.png" />
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
<span class="k">class</span> <span class="nc">TextClassificationModel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">,</span> <span class="n">num_class</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">TextClassificationModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">embedding</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">EmbeddingBag</span><span class="p">(</span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">embed_dim</span><span class="p">,</span> <span class="n">num_class</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">init_weights</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">init_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">initrange</span> <span class="o">=</span> <span class="mf">0.5</span>
<span class="bp">self</span><span class="o">.</span><span class="n">embedding</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="o">-</span><span class="n">initrange</span><span class="p">,</span> <span class="n">initrange</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="o">-</span><span class="n">initrange</span><span class="p">,</span> <span class="n">initrange</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">text</span><span class="p">,</span> <span class="n">offsets</span><span class="p">):</span>
<span class="n">embedded</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">embedding</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="n">offsets</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">embedded</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="id5">
<h2>์ธ์คํด์ค ์์ฑํ๊ธฐ<a class="headerlink" href="#id5" title="Permalink to this headline">ยถ</a></h2>
<p><code class="docutils literal notranslate"><span class="pre">AG_NEWS</span></code> ๋ฐ์ดํฐ์
์๋ 4์ข
๋ฅ์ ๋ ์ด๋ธ์ด ์กด์ฌํ๋ฏ๋ก ํด๋์ค์ ๊ฐ์๋ 4๊ฐ์
๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">1</span> <span class="p">:</span> <span class="n">World</span> <span class="p">(</span><span class="n">์ธ๊ณ</span><span class="p">)</span>
<span class="mi">2</span> <span class="p">:</span> <span class="n">Sports</span> <span class="p">(</span><span class="n">์คํฌ์ธ </span><span class="p">)</span>
<span class="mi">3</span> <span class="p">:</span> <span class="n">Business</span> <span class="p">(</span><span class="n">๊ฒฝ์ </span><span class="p">)</span>
<span class="mi">4</span> <span class="p">:</span> <span class="n">Sci</span><span class="o">/</span><span class="n">Tec</span> <span class="p">(</span><span class="n">๊ณผํ</span><span class="o">/</span><span class="n">๊ธฐ์ </span><span class="p">)</span>
</pre></div>
</div>
<p>์๋ฒ ๋ฉ ์ฐจ์์ด 64์ธ ๋ชจ๋ธ์ ๋ง๋ญ๋๋ค.
์ดํ์ง์ ํฌ๊ธฐ(Vocab size)๋ ์ดํ์ง(vocab)์ ๊ธธ์ด์ ๊ฐ์ต๋๋ค.
ํด๋์ค์ ๊ฐ์๋ ๋ ์ด๋ธ์ ๊ฐ์์ ๊ฐ์ต๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">train_iter</span> <span class="o">=</span> <span class="n">AG_NEWS</span><span class="p">(</span><span class="n">split</span><span class="o">=</span><span class="s1">'train'</span><span class="p">)</span>
<span class="n">num_class</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">label</span> <span class="k">for</span> <span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">text</span><span class="p">)</span> <span class="ow">in</span> <span class="n">train_iter</span><span class="p">]))</span>
<span class="n">vocab_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">vocab</span><span class="p">)</span>
<span class="n">emsize</span> <span class="o">=</span> <span class="mi">64</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">TextClassificationModel</span><span class="p">(</span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">emsize</span><span class="p">,</span> <span class="n">num_class</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="id6">
<h2>๋ชจ๋ธ์ ํ์ตํ๊ณ ๊ฒฐ๊ณผ๋ฅผ ํ๊ฐํ๋ ํจ์ ์ ์ํ๊ธฐ<a class="headerlink" href="#id6" title="Permalink to this headline">ยถ</a></h2>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">time</span>
<span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">dataloader</span><span class="p">):</span>
<span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
<span class="n">total_acc</span><span class="p">,</span> <span class="n">total_count</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
<span class="n">log_interval</span> <span class="o">=</span> <span class="mi">500</span>
<span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">text</span><span class="p">,</span> <span class="n">offsets</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">):</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="n">predicted_label</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="n">offsets</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">predicted_label</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">clip_grad_norm_</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="mf">0.1</span><span class="p">)</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="n">total_acc</span> <span class="o">+=</span> <span class="p">(</span><span class="n">predicted_label</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="n">label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">total_count</span> <span class="o">+=</span> <span class="n">label</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="k">if</span> <span class="n">idx</span> <span class="o">%</span> <span class="n">log_interval</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">idx</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="n">elapsed</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'| epoch </span><span class="si">{:3d}</span><span class="s1"> | </span><span class="si">{:5d}</span><span class="s1">/</span><span class="si">{:5d}</span><span class="s1"> batches '</span>
<span class="s1">'| accuracy </span><span class="si">{:8.3f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">idx</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="p">),</span>
<span class="n">total_acc</span><span class="o">/</span><span class="n">total_count</span><span class="p">))</span>
<span class="n">total_acc</span><span class="p">,</span> <span class="n">total_count</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
<span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">):</span>
<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">total_acc</span><span class="p">,</span> <span class="n">total_count</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">text</span><span class="p">,</span> <span class="n">offsets</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">):</span>
<span class="n">predicted_label</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="n">offsets</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">predicted_label</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
<span class="n">total_acc</span> <span class="o">+=</span> <span class="p">(</span><span class="n">predicted_label</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="n">label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">total_count</span> <span class="o">+=</span> <span class="n">label</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="k">return</span> <span class="n">total_acc</span><span class="o">/</span><span class="n">total_count</span>
</pre></div>
</div>
</div>
<div class="section" id="id7">
<h2>๋ฐ์ดํฐ์
์ ๋ถํ ํ๊ณ ๋ชจ๋ธ ์ํํ๊ธฐ<a class="headerlink" href="#id7" title="Permalink to this headline">ยถ</a></h2>
<p>์๋ณธ <code class="docutils literal notranslate"><span class="pre">AG_NEWS</span></code> ์๋ ๊ฒ์ฆ์ฉ ๋ฐ์ดํฐ๊ฐ ํฌํจ๋์ด ์์ง ์๊ธฐ ๋๋ฌธ์, ์ฐ๋ฆฌ๋ ํ์ต
๋ฐ์ดํฐ๋ฅผ ํ์ต ๋ฐ ๊ฒ์ฆ ๋ฐ์ดํฐ๋ก ๋ถํ ํ๋ ค ํฉ๋๋ค. ์ด๋ ๋ฐ์ดํฐ๋ฅผ ๋ถํ ํ๋
๋น์จ์ 0.95(ํ์ต)์ 0.05(๊ฒ์ฆ) ์
๋๋ค. ์ฐ๋ฆฌ๋ ์ฌ๊ธฐ์ PyTorch์
ํต์ฌ ๋ผ์ด๋ธ๋ฌ๋ฆฌ ์ค ํ๋์ธ
<a class="reference external" href="https://pytorch.org/docs/stable/data.html?highlight=random_split#torch.utils.data.random_split">torch.utils.data.dataset.random_split</a>
ํจ์๋ฅผ ์ฌ์ฉํฉ๋๋ค.</p>
<p><a class="reference external" href="https://pytorch.org/docs/stable/nn.html?highlight=crossentropyloss#torch.nn.CrossEntropyLoss">CrossEntropyLoss</a>
๊ธฐ์ค(criterion)์ ๊ฐ ํด๋์ค์ ๋ํด <code class="docutils literal notranslate"><span class="pre">nn.LogSoftmax()</span></code> ์ <code class="docutils literal notranslate"><span class="pre">nn.NLLLoss()</span></code> ๋ฅผ
ํฉ์ณ๋์ ๋ฐฉ์์
๋๋ค.
<a class="reference external" href="https://pytorch.org/docs/stable/_modules/torch/optim/sgd.html">SGD</a>
optimizer๋ ํ๋ฅ ์ ๊ฒฝ์ฌ ํ๊ฐ๋ฒ๋ฅผ ๊ตฌํํด๋์ ๊ฒ์
๋๋ค. ์ฒ์์ ํ์ต๋ฅ ์
5.0์ผ๋ก ๋์์ต๋๋ค. ๋งค ์ํญ์ ์งํํ๋ฉด์ ํ์ต๋ฅ ์ ์กฐ์ ํ ๋๋
<a class="reference external" href="https://pytorch.org/docs/master/_modules/torch/optim/lr_scheduler.html#StepLR">StepLR</a>
์ ์ฌ์ฉํฉ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.data.dataset</span> <span class="kn">import</span> <span class="n">random_split</span>
<span class="kn">from</span> <span class="nn">torchtext.data.functional</span> <span class="kn">import</span> <span class="n">to_map_style_dataset</span>
<span class="c1"># Hyperparameters</span>
<span class="n">EPOCHS</span> <span class="o">=</span> <span class="mi">10</span> <span class="c1"># epoch</span>
<span class="n">LR</span> <span class="o">=</span> <span class="mi">5</span> <span class="c1"># learning rate</span>
<span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">64</span> <span class="c1"># batch size for training</span>
<span class="n">criterion</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">LR</span><span class="p">)</span>
<span class="n">scheduler</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">StepLR</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">total_accu</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">train_iter</span><span class="p">,</span> <span class="n">test_iter</span> <span class="o">=</span> <span class="n">AG_NEWS</span><span class="p">()</span>
<span class="n">train_dataset</span> <span class="o">=</span> <span class="n">to_map_style_dataset</span><span class="p">(</span><span class="n">train_iter</span><span class="p">)</span>
<span class="n">test_dataset</span> <span class="o">=</span> <span class="n">to_map_style_dataset</span><span class="p">(</span><span class="n">test_iter</span><span class="p">)</span>
<span class="n">num_train</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.95</span><span class="p">)</span>
<span class="n">split_train_</span><span class="p">,</span> <span class="n">split_valid_</span> <span class="o">=</span> \
<span class="n">random_split</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">,</span> <span class="p">[</span><span class="n">num_train</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">)</span> <span class="o">-</span> <span class="n">num_train</span><span class="p">])</span>
<span class="n">train_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">split_train_</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">BATCH_SIZE</span><span class="p">,</span>
<span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">collate_fn</span><span class="o">=</span><span class="n">collate_batch</span><span class="p">)</span>
<span class="n">valid_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">split_valid_</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">BATCH_SIZE</span><span class="p">,</span>
<span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">collate_fn</span><span class="o">=</span><span class="n">collate_batch</span><span class="p">)</span>
<span class="n">test_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">test_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">BATCH_SIZE</span><span class="p">,</span>
<span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">collate_fn</span><span class="o">=</span><span class="n">collate_batch</span><span class="p">)</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">EPOCHS</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="n">epoch_start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">train</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">)</span>
<span class="n">accu_val</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">valid_dataloader</span><span class="p">)</span>
<span class="k">if</span> <span class="n">total_accu</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">total_accu</span> <span class="o">></span> <span class="n">accu_val</span><span class="p">:</span>
<span class="n">scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">total_accu</span> <span class="o">=</span> <span class="n">accu_val</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'-'</span> <span class="o">*</span> <span class="mi">59</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'| end of epoch </span><span class="si">{:3d}</span><span class="s1"> | time: </span><span class="si">{:5.2f}</span><span class="s1">s | '</span>
<span class="s1">'valid accuracy </span><span class="si">{:8.3f}</span><span class="s1"> '</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span><span class="p">,</span>
<span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">epoch_start_time</span><span class="p">,</span>
<span class="n">accu_val</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'-'</span> <span class="o">*</span> <span class="mi">59</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>| epoch 1 | 500/ 1782 batches | accuracy 0.688
| epoch 1 | 1000/ 1782 batches | accuracy 0.853
| epoch 1 | 1500/ 1782 batches | accuracy 0.876
-----------------------------------------------------------
| end of epoch 1 | time: 14.83s | valid accuracy 0.881
-----------------------------------------------------------
| epoch 2 | 500/ 1782 batches | accuracy 0.898
| epoch 2 | 1000/ 1782 batches | accuracy 0.902
| epoch 2 | 1500/ 1782 batches | accuracy 0.903
-----------------------------------------------------------
| end of epoch 2 | time: 13.31s | valid accuracy 0.892
-----------------------------------------------------------
| epoch 3 | 500/ 1782 batches | accuracy 0.915
| epoch 3 | 1000/ 1782 batches | accuracy 0.915
| epoch 3 | 1500/ 1782 batches | accuracy 0.915
-----------------------------------------------------------
| end of epoch 3 | time: 12.24s | valid accuracy 0.899
-----------------------------------------------------------
| epoch 4 | 500/ 1782 batches | accuracy 0.925
| epoch 4 | 1000/ 1782 batches | accuracy 0.925
| epoch 4 | 1500/ 1782 batches | accuracy 0.924
-----------------------------------------------------------
| end of epoch 4 | time: 12.54s | valid accuracy 0.901
-----------------------------------------------------------
| epoch 5 | 500/ 1782 batches | accuracy 0.931
| epoch 5 | 1000/ 1782 batches | accuracy 0.931
| epoch 5 | 1500/ 1782 batches | accuracy 0.928
-----------------------------------------------------------
| end of epoch 5 | time: 12.79s | valid accuracy 0.906
-----------------------------------------------------------
| epoch 6 | 500/ 1782 batches | accuracy 0.934
| epoch 6 | 1000/ 1782 batches | accuracy 0.935
| epoch 6 | 1500/ 1782 batches | accuracy 0.935
-----------------------------------------------------------
| end of epoch 6 | time: 12.64s | valid accuracy 0.903
-----------------------------------------------------------
| epoch 7 | 500/ 1782 batches | accuracy 0.946
| epoch 7 | 1000/ 1782 batches | accuracy 0.949
| epoch 7 | 1500/ 1782 batches | accuracy 0.949
-----------------------------------------------------------
| end of epoch 7 | time: 13.33s | valid accuracy 0.906
-----------------------------------------------------------
| epoch 8 | 500/ 1782 batches | accuracy 0.950
| epoch 8 | 1000/ 1782 batches | accuracy 0.950
| epoch 8 | 1500/ 1782 batches | accuracy 0.948
-----------------------------------------------------------
| end of epoch 8 | time: 12.80s | valid accuracy 0.907
-----------------------------------------------------------
| epoch 9 | 500/ 1782 batches | accuracy 0.950
| epoch 9 | 1000/ 1782 batches | accuracy 0.949
| epoch 9 | 1500/ 1782 batches | accuracy 0.949
-----------------------------------------------------------
| end of epoch 9 | time: 12.78s | valid accuracy 0.907
-----------------------------------------------------------
| epoch 10 | 500/ 1782 batches | accuracy 0.952
| epoch 10 | 1000/ 1782 batches | accuracy 0.948
| epoch 10 | 1500/ 1782 batches | accuracy 0.950
-----------------------------------------------------------
| end of epoch 10 | time: 13.59s | valid accuracy 0.907
-----------------------------------------------------------
</pre></div>
</div>
</div>
<div class="section" id="id8">
<h2>ํ๊ฐ ๋ฐ์ดํฐ๋ก ๋ชจ๋ธ ํ๊ฐํ๊ธฐ<a class="headerlink" href="#id8" title="Permalink to this headline">ยถ</a></h2>
<p>ํ๊ฐ ๋ฐ์ดํฐ์
์ ํตํ ๊ฒฐ๊ณผ๋ฅผ ํ์ธํฉ๋๋คโฆ</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">'Checking the results of test dataset.'</span><span class="p">)</span>
<span class="n">accu_test</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">test_dataloader</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'test accuracy </span><span class="si">{:8.3f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accu_test</span><span class="p">))</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Checking the results of test dataset.
test accuracy 0.908
</pre></div>
</div>
</div>
<div class="section" id="id9">
<h2>์์์ ๋ด์ค๋ก ํ๊ฐํ๊ธฐ<a class="headerlink" href="#id9" title="Permalink to this headline">ยถ</a></h2>
<p>ํ์ฌ๊น์ง ์ต๊ณ ์ ๋ชจ๋ธ๋ก ๊ณจํ ๋ด์ค๋ฅผ ํ
์คํธํด๋ณด๊ฒ ์ต๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ag_news_label</span> <span class="o">=</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="s2">"World"</span><span class="p">,</span>
<span class="mi">2</span><span class="p">:</span> <span class="s2">"Sports"</span><span class="p">,</span>
<span class="mi">3</span><span class="p">:</span> <span class="s2">"Business"</span><span class="p">,</span>
<span class="mi">4</span><span class="p">:</span> <span class="s2">"Sci/Tec"</span><span class="p">}</span>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="n">text_pipeline</span><span class="p">):</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="n">text</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">text_pipeline</span><span class="p">(</span><span class="n">text</span><span class="p">))</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">0</span><span class="p">]))</span>
<span class="k">return</span> <span class="n">output</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">ex_text_str</span> <span class="o">=</span> <span class="s2">"MEMPHIS, Tenn. โ Four days ago, Jon Rahm was </span><span class="se">\</span>
<span class="s2"> enduring the seasonโs worst weather conditions on Sunday at The </span><span class="se">\</span>
<span class="s2"> Open on his way to a closing 75 at Royal Portrush, which </span><span class="se">\</span>
<span class="s2"> considering the wind and the rain was a respectable showing. </span><span class="se">\</span>
<span class="s2"> Thursdayโs first round at the WGC-FedEx St. Jude Invitational </span><span class="se">\</span>
<span class="s2"> was another story. With temperatures in the mid-80s and hardly any </span><span class="se">\</span>
<span class="s2"> wind, the Spaniard was 13 strokes better in a flawless round. </span><span class="se">\</span>
<span class="s2"> Thanks to his best putting performance on the PGA Tour, Rahm </span><span class="se">\</span>
<span class="s2"> finished with an 8-under 62 for a three-stroke lead, which </span><span class="se">\</span>
<span class="s2"> was even more impressive considering heโd never played the </span><span class="se">\</span>
<span class="s2"> front nine at TPC Southwind."</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">"cpu"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"This is a </span><span class="si">%s</span><span class="s2"> news"</span> <span class="o">%</span><span class="n">ag_news_label</span><span class="p">[</span><span class="n">predict</span><span class="p">(</span><span class="n">ex_text_str</span><span class="p">,</span> <span class="n">text_pipeline</span><span class="p">)])</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>This is a Sports news
</pre></div>
</div>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 34.203 seconds)</p>
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