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              <p class="caption"><span class="caption-text">파이토치(PyTorch) 레시피</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../recipes/recipes_index.html">모든 레시피 보기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../prototype/prototype_index.html">모든 프로토타입 레시피 보기</a></li>
</ul>
<p class="caption"><span class="caption-text">파이토치(PyTorch) 시작하기</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="basics/intro.html">파이토치(PyTorch) 기본 익히기</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/quickstart_tutorial.html">빠른 시작(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/tensorqs_tutorial.html">텐서(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/data_tutorial.html">Dataset과 DataLoader</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/transforms_tutorial.html">변형(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/buildmodel_tutorial.html">신경망 모델 구성하기</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>를 사용한 자동 미분</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/optimization_tutorial.html">모델 매개변수 최적화하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/saveloadrun_tutorial.html">모델 저장하고 불러오기</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">파이토치(PyTorch) 배우기</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="deep_learning_60min_blitz.html">PyTorch로 딥러닝하기: 60분만에 끝장내기</a></li>
<li class="toctree-l1"><a class="reference internal" href="pytorch_with_examples.html">예제로 배우는 파이토치(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="nn_tutorial.html"><cite>torch.nn</cite> 이 <em>실제로</em> 무엇인가요?</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_tutorial.html">TensorBoard로 모델, 데이터, 학습 시각화하기</a></li>
</ul>
<p class="caption"><span class="caption-text">이미지/비디오</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchvision_tutorial.html">TorchVision 객체 검출 미세조정(Finetuning) 튜토리얼</a></li>
<li class="toctree-l1"><a class="reference internal" href="transfer_learning_tutorial.html">컴퓨터 비전(Vision)을 위한 전이학습(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="fgsm_tutorial.html">적대적 예제 생성(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="dcgan_faces_tutorial.html">DCGAN 튜토리얼</a></li>
<li class="toctree-l1"><a class="reference internal" href="vt_tutorial.html">배포를 위한 비전 트랜스포머(Vision Transformer) 모델 최적화하기</a></li>
</ul>
<p class="caption"><span class="caption-text">오디오</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">텍스트</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="transformer_tutorial.html">nn.Transformer 와 TorchText 로 시퀀스-투-시퀀스(Sequence-to-Sequence) 모델링하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_classification_tutorial.html">기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 분류하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_generation_tutorial.html">기초부터 시작하는 NLP:  문자-단위 RNN으로 이름 생성하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/seq2seq_translation_tutorial.html">기초부터 시작하는 NLP: Sequence to Sequence 네트워크와 Attention을 이용한 번역</a></li>
<li class="toctree-l1"><a class="reference internal" href="text_sentiment_ngrams_tutorial.html">torchtext 라이브러리로 텍스트 분류하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="translation_transformer.html">nn.Transformer와 torchtext로 언어 번역하기</a></li>
</ul>
<p class="caption"><span class="caption-text">강화학습</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/reinforcement_q_learning.html">강화 학습 (DQN) 튜토리얼</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/mario_rl_tutorial.html">Train a Mario-playing RL Agent</a></li>
</ul>
<p class="caption"><span class="caption-text">PyTorch 모델을 프로덕션 환경에 배포하기</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/flask_rest_api_tutorial.html">Flask를 사용하여 Python에서 PyTorch를 REST API로 배포하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="Intro_to_TorchScript_tutorial.html">TorchScript 소개</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_export.html">C++에서 TorchScript 모델 로딩하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/super_resolution_with_onnxruntime.html">(선택) PyTorch 모델을 ONNX으로 변환하고 ONNX 런타임에서 실행하기</a></li>
</ul>
<p class="caption"><span class="caption-text">Code Transforms with FX</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/fx_conv_bn_fuser.html">(베타) FX에서 합성곱/배치 정규화(Convolution/Batch Norm) 결합기(Fuser) 만들기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/fx_profiling_tutorial.html">(beta) Building a Simple CPU Performance Profiler with FX</a></li>
</ul>
<p class="caption"><span class="caption-text">프론트엔드 API</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(베타) PyTorch를 사용한 Channels Last 메모리 형식</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/forward_ad_usage.html">Forward-mode Automatic Differentiation (Beta)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">PyTorch C++ 프론트엔드 사용하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/torch-script-parallelism.html">TorchScript의 동적 병렬 처리(Dynamic Parallelism)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_autograd.html">C++ 프론트엔드의 자동 미분 (autograd)</a></li>
</ul>
<p class="caption"><span class="caption-text">PyTorch 확장하기</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/custom_function_double_backward_tutorial.html">Double Backward with Custom Functions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/custom_function_conv_bn_tutorial.html">Fusing Convolution and Batch Norm using Custom Function</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_classes.html">커스텀 C++ 클래스로 TorchScript 확장하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/dispatcher.html">Registering a Dispatched Operator in C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/extend_dispatcher.html">Extending dispatcher for a new backend in C++</a></li>
</ul>
<p class="caption"><span class="caption-text">모델 최적화</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="profiler.html">PyTorch 모듈 프로파일링 하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_profiler_tutorial.html">PyTorch Profiler With TensorBoard</a></li>
<li class="toctree-l1"><a class="reference internal" href="hyperparameter_tuning_tutorial.html">Hyperparameter tuning with Ray Tune</a></li>
<li class="toctree-l1"><a class="reference internal" href="vt_tutorial.html">배포를 위한 비전 트랜스포머(Vision Transformer) 모델 최적화하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/parametrizations.html">Parametrizations Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(베타) 컴퓨터 비전 튜토리얼을 위한 양자화된 전이학습(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>
</ul>
<p class="caption"><span class="caption-text">병렬 및 분산 학습</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="dist_overview.html">PyTorch Distributed Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/model_parallel_tutorial.html">단일 머신을 사용한 모델 병렬화 모범 사례</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/ddp_tutorial.html">분산 데이터 병렬 처리 시작하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dist_tuto.html">PyTorch로 분산 어플리케이션 개발하기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_tutorial.html">Getting Started with Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_param_server_tutorial.html">Implementing a Parameter Server Using Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dist_pipeline_parallel_tutorial.html">Distributed Pipeline Parallelism Using RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_async_execution.html">Implementing Batch RPC Processing Using Asynchronous Executions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/rpc_ddp_tutorial.html">분산 데이터 병렬(DDP)과 분산 RPC 프레임워크 결합</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/pipeline_tutorial.html">파이프라인 병렬화로 트랜스포머 모델 학습시키기</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/ddp_pipeline.html">분산 데이터 병렬 처리와 병렬 처리 파이프라인을 사용한 트랜스포머 모델 학습</a></li>
<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>
</ul>
<p class="caption"><span class="caption-text">Mobile</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="deeplabv3_on_ios.html">iOS에서의 이미지 분할 DeepLapV3</a></li>
<li class="toctree-l1"><a class="reference internal" href="deeplabv3_on_android.html">안드로이드에서의 이미지 분할 DeepLapV3</a></li>
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  <div class="sphx-glr-download-link-note admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Click <a class="reference internal" href="#sphx-glr-download-beginner-torchtext-translation-py"><span class="std std-ref">here</span></a> to download the full example code</p>
</div>
<div class="sphx-glr-example-title section" id="torchtext">
<span id="sphx-glr-beginner-torchtext-translation-py"></span><h1>TorchText로 언어 번역하기<a class="headerlink" href="#torchtext" title="Permalink to this headline">¶</a></h1>
<p>이 튜토리얼에서는 <code class="docutils literal notranslate"><span class="pre">torchtext</span></code> 를 사용하여 영어와 독일어 문장들이 포함된 잘 알려진 데이터셋을 전처리(preprocess)하고
이를 사용하여 독일어 문장을 영어로 번역하는 시퀀스-투-시퀀스(sequence-to-sequence, seq2seq) 모델을 학습하는 방법을
살펴보겠습니다.</p>
<p>이 튜토리얼은
PyTorch 커뮤니티 멤버인 <a class="reference external" href="https://github.com/bentrevett">Ben Trevett</a> 이 작성한
<a class="reference external" href="https://github.com/bentrevett/pytorch-seq2seq/blob/master/3%20-%20Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate.ipynb">튜토리얼</a>
에 기초하고 있으며 Ben의 허락을 받고 만들었습니다. 몇몇 기존 코드들을 제거하고 튜토리얼을 업데이트하였습니다.</p>
<p>이 튜토리얼을 통해 NLP 모델링을 위해 문장들을 텐서(tensor)로 전처리하고, 모델을 학습하고 검증하기 위해
<a class="reference external" href="https://pytorch.org/docs/stable/data.html?highlight=dataloader#torch.utils.data.DataLoader">torch.utils.data.DataLoader</a>
을 사용할 수 있게 됩니다.
데이터 처리하기
——————————–</p>
<p><code class="docutils literal notranslate"><span class="pre">torchtext</span></code> 에는 언어 변환 모델을 만들 때 쉽게 사용할 수 있는 데이터셋을 만들기 적합한 다양한 도구가 있습니다.
이 예제에서는 가공되지 않은 텍스트 문장(raw text sentence)을 토큰화(tokenize)하고, 어휘집(vocabulary)을 만들고,
토큰을 텐서로 숫자화(numericalize)하는 방법을 알아보겠습니다.</p>
<p>참고 : 이 튜토리얼에서의 토큰화(tokenization)에는 <a class="reference external" href="https://spacy.io">Spacy</a> 가 필요합니다.
Spacy는 영어 이 외의 다른 언어에 대한 강력한 토큰화 기능을 제공하기 때문에 사용합니다. <code class="docutils literal notranslate"><span class="pre">torchtext</span></code> 는
<cite>basic_english`</cite> 토크나이저를 제공할 뿐 아니라 영어에 사용할 수 있는 다른 토크나이저들(예컨데
<a class="reference external" href="https://bitbucket.org/luismsgomes/mosestokenizer/src/default/">Moses</a> )을 지원합니다만, 언어 번역을 위해서는 다양한 언어를
다루어야 하기 때문에 Spacy가 가장 적합합니다.</p>
<p>이 튜토리얼을 실행하려면, 우선 <code class="docutils literal notranslate"><span class="pre">pip</span></code> 나 <code class="docutils literal notranslate"><span class="pre">conda</span></code> 로 <code class="docutils literal notranslate"><span class="pre">spacy</span></code> 를 설치하세요. 그 다음,
Spacy 토크나이저가 쓸 영어와 독일어에 대한 데이터를 다운로드 받습니다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">spacy</span> <span class="n">download</span> <span class="n">en</span>
<span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">spacy</span> <span class="n">download</span> <span class="n">de</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchtext</span>
<span class="kn">import</span> <span class="nn">torch</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">collections</span> <span class="kn">import</span> <span class="n">Counter</span>
<span class="kn">from</span> <span class="nn">torchtext.vocab</span> <span class="kn">import</span> <span class="n">Vocab</span>
<span class="kn">from</span> <span class="nn">torchtext.utils</span> <span class="kn">import</span> <span class="n">download_from_url</span><span class="p">,</span> <span class="n">extract_archive</span>
<span class="kn">import</span> <span class="nn">io</span>

<span class="n">url_base</span> <span class="o">=</span> <span class="s1">&#39;https://raw.githubusercontent.com/multi30k/dataset/master/data/task1/raw/&#39;</span>
<span class="n">train_urls</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;train.de.gz&#39;</span><span class="p">,</span> <span class="s1">&#39;train.en.gz&#39;</span><span class="p">)</span>
<span class="n">val_urls</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;val.de.gz&#39;</span><span class="p">,</span> <span class="s1">&#39;val.en.gz&#39;</span><span class="p">)</span>
<span class="n">test_urls</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;test_2016_flickr.de.gz&#39;</span><span class="p">,</span> <span class="s1">&#39;test_2016_flickr.en.gz&#39;</span><span class="p">)</span>

<span class="n">train_filepaths</span> <span class="o">=</span> <span class="p">[</span><span class="n">extract_archive</span><span class="p">(</span><span class="n">download_from_url</span><span class="p">(</span><span class="n">url_base</span> <span class="o">+</span> <span class="n">url</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">url</span> <span class="ow">in</span> <span class="n">train_urls</span><span class="p">]</span>
<span class="n">val_filepaths</span> <span class="o">=</span> <span class="p">[</span><span class="n">extract_archive</span><span class="p">(</span><span class="n">download_from_url</span><span class="p">(</span><span class="n">url_base</span> <span class="o">+</span> <span class="n">url</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">url</span> <span class="ow">in</span> <span class="n">val_urls</span><span class="p">]</span>
<span class="n">test_filepaths</span> <span class="o">=</span> <span class="p">[</span><span class="n">extract_archive</span><span class="p">(</span><span class="n">download_from_url</span><span class="p">(</span><span class="n">url_base</span> <span class="o">+</span> <span class="n">url</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">url</span> <span class="ow">in</span> <span class="n">test_urls</span><span class="p">]</span>

<span class="n">de_tokenizer</span> <span class="o">=</span> <span class="n">get_tokenizer</span><span class="p">(</span><span class="s1">&#39;spacy&#39;</span><span class="p">,</span> <span class="n">language</span><span class="o">=</span><span class="s1">&#39;de&#39;</span><span class="p">)</span>
<span class="n">en_tokenizer</span> <span class="o">=</span> <span class="n">get_tokenizer</span><span class="p">(</span><span class="s1">&#39;spacy&#39;</span><span class="p">,</span> <span class="n">language</span><span class="o">=</span><span class="s1">&#39;en&#39;</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">build_vocab</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">):</span>
  <span class="n">counter</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">()</span>
  <span class="k">with</span> <span class="n">io</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">&quot;utf8&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="k">for</span> <span class="n">string_</span> <span class="ow">in</span> <span class="n">f</span><span class="p">:</span>
      <span class="n">counter</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">(</span><span class="n">string_</span><span class="p">))</span>
  <span class="k">return</span> <span class="n">Vocab</span><span class="p">(</span><span class="n">counter</span><span class="p">,</span> <span class="n">specials</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;&lt;unk&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;pad&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;bos&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;eos&gt;&#39;</span><span class="p">])</span>

<span class="n">de_vocab</span> <span class="o">=</span> <span class="n">build_vocab</span><span class="p">(</span><span class="n">train_filepaths</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">de_tokenizer</span><span class="p">)</span>
<span class="n">en_vocab</span> <span class="o">=</span> <span class="n">build_vocab</span><span class="p">(</span><span class="n">train_filepaths</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">en_tokenizer</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">data_process</span><span class="p">(</span><span class="n">filepaths</span><span class="p">):</span>
  <span class="n">raw_de_iter</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">io</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">filepaths</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">&quot;utf8&quot;</span><span class="p">))</span>
  <span class="n">raw_en_iter</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">io</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">filepaths</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">&quot;utf8&quot;</span><span class="p">))</span>
  <span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
  <span class="k">for</span> <span class="p">(</span><span class="n">raw_de</span><span class="p">,</span> <span class="n">raw_en</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">raw_de_iter</span><span class="p">,</span> <span class="n">raw_en_iter</span><span class="p">):</span>
    <span class="n">de_tensor_</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">de_vocab</span><span class="p">[</span><span class="n">token</span><span class="p">]</span> <span class="k">for</span> <span class="n">token</span> <span class="ow">in</span> <span class="n">de_tokenizer</span><span class="p">(</span><span class="n">raw_de</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">long</span><span class="p">)</span>
    <span class="n">en_tensor_</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">en_vocab</span><span class="p">[</span><span class="n">token</span><span class="p">]</span> <span class="k">for</span> <span class="n">token</span> <span class="ow">in</span> <span class="n">en_tokenizer</span><span class="p">(</span><span class="n">raw_en</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">long</span><span class="p">)</span>
    <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">de_tensor_</span><span class="p">,</span> <span class="n">en_tensor_</span><span class="p">))</span>
  <span class="k">return</span> <span class="n">data</span>

<span class="n">train_data</span> <span class="o">=</span> <span class="n">data_process</span><span class="p">(</span><span class="n">train_filepaths</span><span class="p">)</span>
<span class="n">val_data</span> <span class="o">=</span> <span class="n">data_process</span><span class="p">(</span><span class="n">val_filepaths</span><span class="p">)</span>
<span class="n">test_data</span> <span class="o">=</span> <span class="n">data_process</span><span class="p">(</span><span class="n">test_filepaths</span><span class="p">)</span>
</pre></div>
</div>
<div class="section" id="dataloader">
<h2><code class="docutils literal notranslate"><span class="pre">DataLoader</span></code><a class="headerlink" href="#dataloader" title="Permalink to this headline">¶</a></h2>
<p>마지막으로 사용해 볼 <code class="docutils literal notranslate"><span class="pre">torch</span></code> 에 특화된 기능은 바로 <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code> 로,
첫 번째 인자로 데이터를 전달받기 때문에 사용하기가 쉽습니다. 문서에서도 볼 수 있듯이,
<code class="docutils literal notranslate"><span class="pre">DataLoader</span> <span class="pre">는</span> <span class="pre">데이터셋과</span> <span class="pre">샘플러를</span> <span class="pre">결합하고,</span> <span class="pre">주어진</span> <span class="pre">데이터셋에</span> <span class="pre">반복</span> <span class="pre">기능을</span> <span class="pre">제공합니다.</span>
<span class="pre">``DataLoader</span></code> 는 맵 형태(map-style)과 순회 가능한 형태(iterable-style) 데이터셋을 모두 지원하며,
단일 또는 다중 프로세스로 불러오거나, 불러오는 순서를 조정(customize)하거나
선택적 자동 일괄 처리(optional automatic batching), 메모리 피닝(memory pinning)을 지원합니다.</p>
<p>샘플 목록을 병합(merge)하여 Tensor의 미니배치를 구성하는 <code class="docutils literal notranslate"><span class="pre">collate_fn</span></code> (선택 사항)을 살펴보십시오.
맵 형태(map-style) 데이터셋을 일괄로 불러올 때 사용됩니다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</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="s1">&#39;cuda&#39;</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="s1">&#39;cpu&#39;</span><span class="p">)</span>

<span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">128</span>
<span class="n">PAD_IDX</span> <span class="o">=</span> <span class="n">de_vocab</span><span class="p">[</span><span class="s1">&#39;&lt;pad&gt;&#39;</span><span class="p">]</span>
<span class="n">BOS_IDX</span> <span class="o">=</span> <span class="n">de_vocab</span><span class="p">[</span><span class="s1">&#39;&lt;bos&gt;&#39;</span><span class="p">]</span>
<span class="n">EOS_IDX</span> <span class="o">=</span> <span class="n">de_vocab</span><span class="p">[</span><span class="s1">&#39;&lt;eos&gt;&#39;</span><span class="p">]</span>

<span class="kn">from</span> <span class="nn">torch.nn.utils.rnn</span> <span class="kn">import</span> <span class="n">pad_sequence</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="k">def</span> <span class="nf">generate_batch</span><span class="p">(</span><span class="n">data_batch</span><span class="p">):</span>
  <span class="n">de_batch</span><span class="p">,</span> <span class="n">en_batch</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
  <span class="k">for</span> <span class="p">(</span><span class="n">de_item</span><span class="p">,</span> <span class="n">en_item</span><span class="p">)</span> <span class="ow">in</span> <span class="n">data_batch</span><span class="p">:</span>
    <span class="n">de_batch</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</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="n">BOS_IDX</span><span class="p">]),</span> <span class="n">de_item</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="n">EOS_IDX</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">en_batch</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</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="n">BOS_IDX</span><span class="p">]),</span> <span class="n">en_item</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="n">EOS_IDX</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">de_batch</span> <span class="o">=</span> <span class="n">pad_sequence</span><span class="p">(</span><span class="n">de_batch</span><span class="p">,</span> <span class="n">padding_value</span><span class="o">=</span><span class="n">PAD_IDX</span><span class="p">)</span>
  <span class="n">en_batch</span> <span class="o">=</span> <span class="n">pad_sequence</span><span class="p">(</span><span class="n">en_batch</span><span class="p">,</span> <span class="n">padding_value</span><span class="o">=</span><span class="n">PAD_IDX</span><span class="p">)</span>
  <span class="k">return</span> <span class="n">de_batch</span><span class="p">,</span> <span class="n">en_batch</span>

<span class="n">train_iter</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">train_data</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">generate_batch</span><span class="p">)</span>
<span class="n">valid_iter</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">val_data</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">generate_batch</span><span class="p">)</span>
<span class="n">test_iter</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">test_data</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">generate_batch</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="nn-module-optimizer">
<h2><code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> 과 <code class="docutils literal notranslate"><span class="pre">Optimizer</span></code> 정의하기<a class="headerlink" href="#nn-module-optimizer" title="Permalink to this headline">¶</a></h2>
<p>대부분은 <code class="docutils literal notranslate"><span class="pre">torchtext</span></code> 가 알아서 해줍니다 : 데이터셋이 만들어지고 반복자가 정의되면, 이 튜토리얼에서
우리가 해야 할 일이라고는 그저 <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> 와 <code class="docutils literal notranslate"><span class="pre">Optimizer</span></code> 를 모델로서 정의하고 훈련시키는 것이 전부입니다.</p>
<p>이 튜토리얼에서 사용할 모델은 <a class="reference external" href="https://arxiv.org/abs/1409.0473">이곳</a> 에서 설명하고 있는 구조를 따르고 있으며,
더 자세한 내용은 <a class="reference external" href="https://github.com/SethHWeidman/pytorch-seq2seq/blob/master/3%20-%20Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate.ipynb">여기</a>
를 참고하시기 바랍니다.</p>
<p>참고 : 이 튜토리얼에서 사용하는 모델은 언어 번역을 위해 사용할 예시 모델입니다. 이 모델을 사용하는 것은
이 작업에 적당한 표준 모델이기 때문이지, 번역에 적합한 모델이기 때문은 아닙니다. 여러분이 최신 기술 트렌드를
잘 따라가고 있다면 잘 아시겠지만, 현재 번역에서 가장 뛰어난 모델은 Transformers입니다. PyTorch가
Transformer 레이어를 구현한 내용은 <a class="reference external" href="https://pytorch.org/docs/stable/nn.html#transformer-layers">여기</a>
에서 확인할 수 있으며 이 튜토리얼의 모델이 사용하는 “attention” 은 Transformer 모델에서 제안하는
멀티 헤드 셀프 어텐션(multi-headed self-attention) 과는 다르다는 점을 알려드립니다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>

<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>


<span class="k">class</span> <span class="nc">Encoder</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">input_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                 <span class="n">emb_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                 <span class="n">enc_hid_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                 <span class="n">dec_hid_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                 <span class="n">dropout</span><span class="p">:</span> <span class="nb">float</span><span class="p">):</span>
        <span class="nb">super</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">input_dim</span> <span class="o">=</span> <span class="n">input_dim</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">emb_dim</span> <span class="o">=</span> <span class="n">emb_dim</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">enc_hid_dim</span> <span class="o">=</span> <span class="n">enc_hid_dim</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dec_hid_dim</span> <span class="o">=</span> <span class="n">dec_hid_dim</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">dropout</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">Embedding</span><span class="p">(</span><span class="n">input_dim</span><span class="p">,</span> <span class="n">emb_dim</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">GRU</span><span class="p">(</span><span class="n">emb_dim</span><span class="p">,</span> <span class="n">enc_hid_dim</span><span class="p">,</span> <span class="n">bidirectional</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">enc_hid_dim</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="n">dec_hid_dim</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</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">src</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Tensor</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">dropout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embedding</span><span class="p">(</span><span class="n">src</span><span class="p">))</span>

        <span class="n">outputs</span><span class="p">,</span> <span class="n">hidden</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span><span class="p">(</span><span class="n">embedded</span><span class="p">)</span>

        <span class="n">hidden</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">hidden</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">,:,:],</span> <span class="n">hidden</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,:,:]),</span> <span class="n">dim</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)))</span>

        <span class="k">return</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">hidden</span>


<span class="k">class</span> <span class="nc">Attention</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">enc_hid_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                 <span class="n">dec_hid_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                 <span class="n">attn_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
        <span class="nb">super</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">enc_hid_dim</span> <span class="o">=</span> <span class="n">enc_hid_dim</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dec_hid_dim</span> <span class="o">=</span> <span class="n">dec_hid_dim</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">attn_in</span> <span class="o">=</span> <span class="p">(</span><span class="n">enc_hid_dim</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">dec_hid_dim</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">attn</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="bp">self</span><span class="o">.</span><span class="n">attn_in</span><span class="p">,</span> <span class="n">attn_dim</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">decoder_hidden</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
                <span class="n">encoder_outputs</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>

        <span class="n">src_len</span> <span class="o">=</span> <span class="n">encoder_outputs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

        <span class="n">repeated_decoder_hidden</span> <span class="o">=</span> <span class="n">decoder_hidden</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">src_len</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>

        <span class="n">encoder_outputs</span> <span class="o">=</span> <span class="n">encoder_outputs</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>

        <span class="n">energy</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">attn</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span>
            <span class="n">repeated_decoder_hidden</span><span class="p">,</span>
            <span class="n">encoder_outputs</span><span class="p">),</span>
            <span class="n">dim</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)))</span>

        <span class="n">attention</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">energy</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">attention</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>


<span class="k">class</span> <span class="nc">Decoder</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">output_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                 <span class="n">emb_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                 <span class="n">enc_hid_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                 <span class="n">dec_hid_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                 <span class="n">dropout</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                 <span class="n">attention</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="nb">super</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">emb_dim</span> <span class="o">=</span> <span class="n">emb_dim</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">enc_hid_dim</span> <span class="o">=</span> <span class="n">enc_hid_dim</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dec_hid_dim</span> <span class="o">=</span> <span class="n">dec_hid_dim</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">output_dim</span> <span class="o">=</span> <span class="n">output_dim</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">dropout</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">attention</span> <span class="o">=</span> <span class="n">attention</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">Embedding</span><span class="p">(</span><span class="n">output_dim</span><span class="p">,</span> <span class="n">emb_dim</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">GRU</span><span class="p">((</span><span class="n">enc_hid_dim</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">emb_dim</span><span class="p">,</span> <span class="n">dec_hid_dim</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">out</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="bp">self</span><span class="o">.</span><span class="n">attention</span><span class="o">.</span><span class="n">attn_in</span> <span class="o">+</span> <span class="n">emb_dim</span><span class="p">,</span> <span class="n">output_dim</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>


    <span class="k">def</span> <span class="nf">_weighted_encoder_rep</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
                              <span class="n">decoder_hidden</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
                              <span class="n">encoder_outputs</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>

        <span class="n">a</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">attention</span><span class="p">(</span><span class="n">decoder_hidden</span><span class="p">,</span> <span class="n">encoder_outputs</span><span class="p">)</span>

        <span class="n">a</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>

        <span class="n">encoder_outputs</span> <span class="o">=</span> <span class="n">encoder_outputs</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>

        <span class="n">weighted_encoder_rep</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">bmm</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">encoder_outputs</span><span class="p">)</span>

        <span class="n">weighted_encoder_rep</span> <span class="o">=</span> <span class="n">weighted_encoder_rep</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">weighted_encoder_rep</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="nb">input</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
                <span class="n">decoder_hidden</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
                <span class="n">encoder_outputs</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]:</span>

        <span class="nb">input</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</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">dropout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embedding</span><span class="p">(</span><span class="nb">input</span><span class="p">))</span>

        <span class="n">weighted_encoder_rep</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_weighted_encoder_rep</span><span class="p">(</span><span class="n">decoder_hidden</span><span class="p">,</span>
                                                          <span class="n">encoder_outputs</span><span class="p">)</span>

        <span class="n">rnn_input</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">embedded</span><span class="p">,</span> <span class="n">weighted_encoder_rep</span><span class="p">),</span> <span class="n">dim</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>

        <span class="n">output</span><span class="p">,</span> <span class="n">decoder_hidden</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span><span class="p">(</span><span class="n">rnn_input</span><span class="p">,</span> <span class="n">decoder_hidden</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>

        <span class="n">embedded</span> <span class="o">=</span> <span class="n">embedded</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
        <span class="n">weighted_encoder_rep</span> <span class="o">=</span> <span class="n">weighted_encoder_rep</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>

        <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">out</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">output</span><span class="p">,</span>
                                     <span class="n">weighted_encoder_rep</span><span class="p">,</span>
                                     <span class="n">embedded</span><span class="p">),</span> <span class="n">dim</span> <span class="o">=</span> <span class="mi">1</span><span class="p">))</span>

        <span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="n">decoder_hidden</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>


<span class="k">class</span> <span class="nc">Seq2Seq</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">encoder</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="n">decoder</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="n">device</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">):</span>
        <span class="nb">super</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">encoder</span> <span class="o">=</span> <span class="n">encoder</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">decoder</span> <span class="o">=</span> <span class="n">decoder</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</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">src</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
                <span class="n">trg</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
                <span class="n">teacher_forcing_ratio</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>

        <span class="n">batch_size</span> <span class="o">=</span> <span class="n">src</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
        <span class="n">max_len</span> <span class="o">=</span> <span class="n">trg</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">trg_vocab_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">decoder</span><span class="o">.</span><span class="n">output_dim</span>

        <span class="n">outputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">max_len</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">trg_vocab_size</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>

        <span class="n">encoder_outputs</span><span class="p">,</span> <span class="n">hidden</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="n">src</span><span class="p">)</span>

        <span class="c1"># 디코더로의 첫 번째 입력은 &lt;sos&gt; 토큰입니다.</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">trg</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span>

        <span class="k">for</span> <span class="n">t</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">max_len</span><span class="p">):</span>
            <span class="n">output</span><span class="p">,</span> <span class="n">hidden</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">decoder</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">hidden</span><span class="p">,</span> <span class="n">encoder_outputs</span><span class="p">)</span>
            <span class="n">outputs</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">output</span>
            <span class="n">teacher_force</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o">&lt;</span> <span class="n">teacher_forcing_ratio</span>
            <span class="n">top1</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
            <span class="n">output</span> <span class="o">=</span> <span class="p">(</span><span class="n">trg</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="k">if</span> <span class="n">teacher_force</span> <span class="k">else</span> <span class="n">top1</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">outputs</span>


<span class="n">INPUT_DIM</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">de_vocab</span><span class="p">)</span>
<span class="n">OUTPUT_DIM</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">en_vocab</span><span class="p">)</span>
<span class="c1"># ENC_EMB_DIM = 256</span>
<span class="c1"># DEC_EMB_DIM = 256</span>
<span class="c1"># ENC_HID_DIM = 512</span>
<span class="c1"># DEC_HID_DIM = 512</span>
<span class="c1"># ATTN_DIM = 64</span>
<span class="c1"># ENC_DROPOUT = 0.5</span>
<span class="c1"># DEC_DROPOUT = 0.5</span>

<span class="n">ENC_EMB_DIM</span> <span class="o">=</span> <span class="mi">32</span>
<span class="n">DEC_EMB_DIM</span> <span class="o">=</span> <span class="mi">32</span>
<span class="n">ENC_HID_DIM</span> <span class="o">=</span> <span class="mi">64</span>
<span class="n">DEC_HID_DIM</span> <span class="o">=</span> <span class="mi">64</span>
<span class="n">ATTN_DIM</span> <span class="o">=</span> <span class="mi">8</span>
<span class="n">ENC_DROPOUT</span> <span class="o">=</span> <span class="mf">0.5</span>
<span class="n">DEC_DROPOUT</span> <span class="o">=</span> <span class="mf">0.5</span>

<span class="n">enc</span> <span class="o">=</span> <span class="n">Encoder</span><span class="p">(</span><span class="n">INPUT_DIM</span><span class="p">,</span> <span class="n">ENC_EMB_DIM</span><span class="p">,</span> <span class="n">ENC_HID_DIM</span><span class="p">,</span> <span class="n">DEC_HID_DIM</span><span class="p">,</span> <span class="n">ENC_DROPOUT</span><span class="p">)</span>

<span class="n">attn</span> <span class="o">=</span> <span class="n">Attention</span><span class="p">(</span><span class="n">ENC_HID_DIM</span><span class="p">,</span> <span class="n">DEC_HID_DIM</span><span class="p">,</span> <span class="n">ATTN_DIM</span><span class="p">)</span>

<span class="n">dec</span> <span class="o">=</span> <span class="n">Decoder</span><span class="p">(</span><span class="n">OUTPUT_DIM</span><span class="p">,</span> <span class="n">DEC_EMB_DIM</span><span class="p">,</span> <span class="n">ENC_HID_DIM</span><span class="p">,</span> <span class="n">DEC_HID_DIM</span><span class="p">,</span> <span class="n">DEC_DROPOUT</span><span class="p">,</span> <span class="n">attn</span><span class="p">)</span>

<span class="n">model</span> <span class="o">=</span> <span class="n">Seq2Seq</span><span class="p">(</span><span class="n">enc</span><span class="p">,</span> <span class="n">dec</span><span class="p">,</span> <span class="n">device</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>


<span class="k">def</span> <span class="nf">init_weights</span><span class="p">(</span><span class="n">m</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">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">():</span>
        <span class="k">if</span> <span class="s1">&#39;weight&#39;</span> <span class="ow">in</span> <span class="n">name</span><span class="p">:</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">param</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">param</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>


<span class="n">model</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">init_weights</span><span class="p">)</span>

<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">Adam</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="k">def</span> <span class="nf">count_parameters</span><span class="p">(</span><span class="n">model</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">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">()</span> <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">)</span>


<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;The model has </span><span class="si">{</span><span class="n">count_parameters</span><span class="p">(</span><span class="n">model</span><span class="p">)</span><span class="si">:</span><span class="s1">,</span><span class="si">}</span><span class="s1"> trainable parameters&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>참고 : 언어 번역의 성능 점수를 기록하려면, <code class="docutils literal notranslate"><span class="pre">nn.CrossEntropyLoss</span></code> 함수가 단순한
패딩을 추가하는 부분을 무시할 수 있도록 해당 색인들을 알려줘야 합니다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">PAD_IDX</span> <span class="o">=</span> <span class="n">en_vocab</span><span class="o">.</span><span class="n">stoi</span><span class="p">[</span><span class="s1">&#39;&lt;pad&gt;&#39;</span><span class="p">]</span>

<span class="n">criterion</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">ignore_index</span><span class="o">=</span><span class="n">PAD_IDX</span><span class="p">)</span>
</pre></div>
</div>
<p>마지막으로 이 모델을 훈련하고 평가합니다 :</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">math</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">model</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="n">iterator</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">,</span>
          <span class="n">optimizer</span><span class="p">:</span> <span class="n">optim</span><span class="o">.</span><span class="n">Optimizer</span><span class="p">,</span>
          <span class="n">criterion</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="n">clip</span><span class="p">:</span> <span class="nb">float</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">epoch_loss</span> <span class="o">=</span> <span class="mi">0</span>

    <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">trg</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">iterator</span><span class="p">):</span>
        <span class="n">src</span><span class="p">,</span> <span class="n">trg</span> <span class="o">=</span> <span class="n">src</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">trg</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">optimizer</span><span class="o">.</span><span class="n">zero_grad</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">src</span><span class="p">,</span> <span class="n">trg</span><span class="p">)</span>

        <span class="n">output</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">output</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
        <span class="n">trg</span> <span class="o">=</span> <span class="n">trg</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</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">output</span><span class="p">,</span> <span class="n">trg</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="n">clip</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">epoch_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>

    <span class="k">return</span> <span class="n">epoch_loss</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">iterator</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="n">model</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="n">iterator</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">,</span>
             <span class="n">criterion</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="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>

    <span class="n">epoch_loss</span> <span class="o">=</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">_</span><span class="p">,</span> <span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">trg</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">iterator</span><span class="p">):</span>
            <span class="n">src</span><span class="p">,</span> <span class="n">trg</span> <span class="o">=</span> <span class="n">src</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">trg</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">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">trg</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="c1">#turn off teacher forcing</span>

            <span class="n">output</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">output</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
            <span class="n">trg</span> <span class="o">=</span> <span class="n">trg</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</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">output</span><span class="p">,</span> <span class="n">trg</span><span class="p">)</span>

            <span class="n">epoch_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>

    <span class="k">return</span> <span class="n">epoch_loss</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">iterator</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">epoch_time</span><span class="p">(</span><span class="n">start_time</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
               <span class="n">end_time</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
    <span class="n">elapsed_time</span> <span class="o">=</span> <span class="n">end_time</span> <span class="o">-</span> <span class="n">start_time</span>
    <span class="n">elapsed_mins</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">elapsed_time</span> <span class="o">/</span> <span class="mi">60</span><span class="p">)</span>
    <span class="n">elapsed_secs</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">elapsed_time</span> <span class="o">-</span> <span class="p">(</span><span class="n">elapsed_mins</span> <span class="o">*</span> <span class="mi">60</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">elapsed_mins</span><span class="p">,</span> <span class="n">elapsed_secs</span>


<span class="n">N_EPOCHS</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">CLIP</span> <span class="o">=</span> <span class="mi">1</span>

<span class="n">best_valid_loss</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;inf&#39;</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="n">N_EPOCHS</span><span class="p">):</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="n">train_loss</span> <span class="o">=</span> <span class="n">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_iter</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">CLIP</span><span class="p">)</span>
    <span class="n">valid_loss</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">valid_iter</span><span class="p">,</span> <span class="n">criterion</span><span class="p">)</span>

    <span class="n">end_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">epoch_mins</span><span class="p">,</span> <span class="n">epoch_secs</span> <span class="o">=</span> <span class="n">epoch_time</span><span class="p">(</span><span class="n">start_time</span><span class="p">,</span> <span class="n">end_time</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Epoch: </span><span class="si">{</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="si">:</span><span class="s1">02</span><span class="si">}</span><span class="s1"> | Time: </span><span class="si">{</span><span class="n">epoch_mins</span><span class="si">}</span><span class="s1">m </span><span class="si">{</span><span class="n">epoch_secs</span><span class="si">}</span><span class="s1">s&#39;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">Train Loss: </span><span class="si">{</span><span class="n">train_loss</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1"> | Train PPL: </span><span class="si">{</span><span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">train_loss</span><span class="p">)</span><span class="si">:</span><span class="s1">7.3f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1"> Val. Loss: </span><span class="si">{</span><span class="n">valid_loss</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1"> |  Val. PPL: </span><span class="si">{</span><span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">valid_loss</span><span class="p">)</span><span class="si">:</span><span class="s1">7.3f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>

<span class="n">test_loss</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">test_iter</span><span class="p">,</span> <span class="n">criterion</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;| Test Loss: </span><span class="si">{</span><span class="n">test_loss</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1"> | Test PPL: </span><span class="si">{</span><span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">test_loss</span><span class="p">)</span><span class="si">:</span><span class="s1">7.3f</span><span class="si">}</span><span class="s1"> |&#39;</span><span class="p">)</span>
</pre></div>
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<h2>다음 단계<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
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<li><code class="docutils literal notranslate"><span class="pre">nn.Transformer</span></code> 와 <code class="docutils literal notranslate"><span class="pre">torchtext</span></code> 의 다른 기능들을 이용한 다음 단어 예측을 통한 언어 모델링 튜토리얼을 살펴보세요.</li>
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        $(this).closest("a").addClass("has-code");
      });
    })
  </script>
</body>
</html>