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<title>๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ๋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="../beginner/basics/intro.html">ํ์ดํ ์น(PyTorch) ๊ธฐ๋ณธ ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/quickstart_tutorial.html">๋น ๋ฅธ ์์(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/tensorqs_tutorial.html">ํ
์(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/data_tutorial.html">Dataset๊ณผ DataLoader</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/transforms_tutorial.html">๋ณํ(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/buildmodel_tutorial.html">์ ๊ฒฝ๋ง ๋ชจ๋ธ ๊ตฌ์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/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="../beginner/basics/optimization_tutorial.html">๋ชจ๋ธ ๋งค๊ฐ๋ณ์ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/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="../beginner/introyt.html">Introduction to PyTorch - YouTube Series</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/introyt1_tutorial.html">Introduction to PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensors_deeper_tutorial.html">Introduction to PyTorch Tensors</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/autogradyt_tutorial.html">The Fundamentals of Autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/modelsyt_tutorial.html">Building Models with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensorboardyt_tutorial.html">PyTorch TensorBoard Support</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/trainingyt.html">Training with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/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="../beginner/deep_learning_60min_blitz.html">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/pytorch_with_examples.html">์์ ๋ก ๋ฐฐ์ฐ๋ ํ์ดํ ์น(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/nn_tutorial.html"><cite>torch.nn</cite> ์ด <em>์ค์ ๋ก</em> ๋ฌด์์ธ๊ฐ์?</a></li>
<li class="toctree-l1"><a class="reference internal" href="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="torchvision_tutorial.html">TorchVision ๊ฐ์ฒด ๊ฒ์ถ ๋ฏธ์ธ์กฐ์ (Finetuning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transfer_learning_tutorial.html">์ปดํจํฐ ๋น์ (Vision)์ ์ํ ์ ์ดํ์ต(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/fgsm_tutorial.html">์ ๋์ ์์ ์์ฑ(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dcgan_faces_tutorial.html">DCGAN ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/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="../beginner/audio_io_tutorial.html">Audio I/O</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_resampling_tutorial.html">Audio Resampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_data_augmentation_tutorial.html">Audio Data Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_feature_extractions_tutorial.html">Audio Feature Extractions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_feature_augmentation_tutorial.html">Audio Feature Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_datasets_tutorial.html">Audio Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="speech_recognition_pipeline_tutorial.html">Speech Recognition with Wav2Vec2</a></li>
<li class="toctree-l1"><a class="reference internal" href="speech_command_classification_with_torchaudio_tutorial.html">Speech Command Classification with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="text_to_speech_with_torchaudio.html">Text-to-speech with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="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">
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<li class="toctree-l1 current"><a class="current reference internal" href="#">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="char_rnn_generation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ์์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="seq2seq_translation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: Sequence to Sequence ๋คํธ์ํฌ์ Attention์ ์ด์ฉํ ๋ฒ์ญ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/text_sentiment_ngrams_tutorial.html">torchtext ๋ผ์ด๋ธ๋ฌ๋ฆฌ๋ก ํ
์คํธ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/translation_transformer.html">nn.Transformer์ torchtext๋ก ์ธ์ด ๋ฒ์ญํ๊ธฐ</a></li>
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<p class="caption"><span class="caption-text">๊ฐํํ์ต</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="reinforcement_q_learning.html">๊ฐํ ํ์ต (DQN) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="mario_rl_tutorial.html">Train a Mario-playing RL Agent</a></li>
</ul>
<p class="caption"><span class="caption-text">PyTorch ๋ชจ๋ธ์ ํ๋ก๋์
ํ๊ฒฝ์ ๋ฐฐํฌํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="flask_rest_api_tutorial.html">Flask๋ฅผ ์ฌ์ฉํ์ฌ Python์์ PyTorch๋ฅผ REST API๋ก ๋ฐฐํฌํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/Intro_to_TorchScript_tutorial.html">TorchScript ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_export.html">C++์์ TorchScript ๋ชจ๋ธ ๋ก๋ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/super_resolution_with_onnxruntime.html">(์ ํ) PyTorch ๋ชจ๋ธ์ ONNX์ผ๋ก ๋ณํํ๊ณ ONNX ๋ฐํ์์์ ์คํํ๊ธฐ</a></li>
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<p class="caption"><span class="caption-text">Code Transforms with FX</span></p>
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<li class="toctree-l1"><a class="reference internal" href="fx_conv_bn_fuser.html">(๋ฒ ํ) FX์์ ํฉ์ฑ๊ณฑ/๋ฐฐ์น ์ ๊ทํ(Convolution/Batch Norm) ๊ฒฐํฉ๊ธฐ(Fuser) ๋ง๋ค๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="fx_profiling_tutorial.html">(beta) Building a Simple CPU Performance Profiler with FX</a></li>
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<p class="caption"><span class="caption-text">ํ๋ก ํธ์๋ API</span></p>
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<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(๋ฒ ํ) PyTorch๋ฅผ ์ฌ์ฉํ Channels Last ๋ฉ๋ชจ๋ฆฌ ํ์</a></li>
<li class="toctree-l1"><a class="reference internal" href="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++ ํ๋ก ํธ์๋ ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/torch-script-parallelism.html">TorchScript์ ๋์ ๋ณ๋ ฌ ์ฒ๋ฆฌ(Dynamic Parallelism)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_autograd.html">C++ ํ๋ก ํธ์๋์ ์๋ ๋ฏธ๋ถ (autograd)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="custom_function_double_backward_tutorial.html">Double Backward with Custom Functions</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/dispatcher.html">Registering a Dispatched Operator in C++</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">PyTorch ๋ชจ๋ ํ๋กํ์ผ๋ง ํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensorboard_profiler_tutorial.html">PyTorch Profiler With TensorBoard</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hyperparameter_tuning_tutorial.html">Hyperparameter tuning with Ray Tune</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํ ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="parametrizations.html">Parametrizations Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">๊ฐ์ง์น๊ธฐ ๊ธฐ๋ฒ(Pruning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(๋ฒ ํ) LSTM ๊ธฐ๋ฐ ๋จ์ด ๋จ์ ์ธ์ด ๋ชจ๋ธ์ ๋์ ์์ํ</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(๋ฒ ํ) BERT ๋ชจ๋ธ ๋์ ์์ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="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>
</ul>
<p class="caption"><span class="caption-text">๋ณ๋ ฌ ๋ฐ ๋ถ์ฐ ํ์ต</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dist_overview.html">PyTorch Distributed Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="model_parallel_tutorial.html">๋จ์ผ ๋จธ์ ์ ์ฌ์ฉํ ๋ชจ๋ธ ๋ณ๋ ฌํ ๋ชจ๋ฒ ์ฌ๋ก</a></li>
<li class="toctree-l1"><a class="reference internal" href="ddp_tutorial.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ ์ฒ๋ฆฌ ์์ํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="dist_tuto.html">PyTorch๋ก ๋ถ์ฐ ์ดํ๋ฆฌ์ผ์ด์
๊ฐ๋ฐํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="rpc_tutorial.html">Getting Started with Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="rpc_param_server_tutorial.html">Implementing a Parameter Server Using Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="dist_pipeline_parallel_tutorial.html">Distributed Pipeline Parallelism Using RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="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">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ(DDP)๊ณผ ๋ถ์ฐ RPC ํ๋ ์์ํฌ ๊ฒฐํฉ</a></li>
<li class="toctree-l1"><a class="reference internal" href="pipeline_tutorial.html">ํ์ดํ๋ผ์ธ ๋ณ๋ ฌํ๋ก ํธ๋์คํฌ๋จธ ๋ชจ๋ธ ํ์ต์ํค๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/ddp_pipeline.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ ์ฒ๋ฆฌ์ ๋ณ๋ ฌ ์ฒ๋ฆฌ ํ์ดํ๋ผ์ธ์ ์ฌ์ฉํ ํธ๋์คํฌ๋จธ ๋ชจ๋ธ ํ์ต</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>
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<p class="caption"><span class="caption-text">Mobile</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/deeplabv3_on_ios.html">iOS์์์ ์ด๋ฏธ์ง ๋ถํ DeepLapV3</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/deeplabv3_on_android.html">์๋๋ก์ด๋์์์ ์ด๋ฏธ์ง ๋ถํ DeepLapV3</a></li>
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<div class="sphx-glr-example-title section" id="nlp-rnn">
<span id="sphx-glr-intermediate-char-rnn-classification-tutorial-py"></span><h1>๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ๋ถ๋ฅํ๊ธฐ<a class="headerlink" href="#nlp-rnn" title="Permalink to this headline">ยถ</a></h1>
<dl class="docutils">
<dt><strong>Author</strong>: <a class="reference external" href="https://github.com/spro/practical-pytorch">Sean Robertson</a></dt>
<dd><strong>๋ฒ์ญ</strong>: <a class="reference external" href="https://github.com/adonisues">ํฉ์ฑ์</a></dd>
</dl>
<p>๋จ์ด๋ฅผ ๋ถ๋ฅํ๊ธฐ ์ํด ๊ธฐ์ด์ ์ธ ๋ฌธ์-๋จ์ RNN์ ๊ตฌ์ถํ๊ณ ํ์ต ํ ์์ ์
๋๋ค.
์ด ํํ ๋ฆฌ์ผ์์๋ (์ดํ 2๊ฐ ํํ ๋ฆฌ์ผ๊ณผ ํจ๊ป) NLP ๋ชจ๋ธ๋ง์ ์ํ ๋ฐ์ดํฐ ์ ์ฒ๋ฆฌ๋ฅผ
<cite>torchtext</cite> ์ ํธ๋ฆฌํ ๋ง์ ๊ธฐ๋ฅ๋ค์ ์ฌ์ฉํ์ง ์๊ณ ์ด๋ป๊ฒ ํ๋์ง โ๊ธฐ์ด๋ถํฐ(from scratch)โ
๋ณด์ฌ์ฃผ๊ธฐ ๋๋ฌธ์ NLP ๋ชจ๋ธ๋ง์ ์ํ ์ ์ฒ๋ฆฌ๊ฐ ์ ์์ค์์ ์ด๋ป๊ฒ ์งํ๋๋์ง๋ฅผ ์ ์ ์์ต๋๋ค.
๋ฌธ์-๋จ์ RNN์ ๋จ์ด๋ฅผ ๋ฌธ์์ ์ฐ์์ผ๋ก ์ฝ์ด ๋ค์ฌ์ ๊ฐ ๋จ๊ณ์ ์์ธก๊ณผ
โ์๋ ์ํ(Hidden State)โ ์ถ๋ ฅํ๊ณ , ๋ค์ ๋จ๊ณ์ ์ด์ ์๋ ์ํ๋ฅผ ์ ๋ฌํฉ๋๋ค.
๋จ์ด๊ฐ ์ํ ํด๋์ค๋ก ์ถ๋ ฅ์ด ๋๋๋ก ์ต์ข
์์ธก์ผ๋ก ์ ํํฉ๋๋ค.</p>
<p>๊ตฌ์ฒด์ ์ผ๋ก, 18๊ฐ ์ธ์ด๋ก ๋ ์์ฒ ๊ฐ์ ์ฑ(ๅง)์ ํ๋ จ์ํค๊ณ ,
์ฒ ์์ ๋ฐ๋ผ ์ด๋ฆ์ด ์ด๋ค ์ธ์ด์ธ์ง ์์ธกํฉ๋๋ค:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ python predict.py Hinton
(-0.47) Scottish
(-1.52) English
(-3.57) Irish
$ python predict.py Schmidhuber
(-0.19) German
(-2.48) Czech
(-2.68) Dutch
</pre></div>
</div>
<p><strong>์ถ์ฒ ์๋ฃ:</strong></p>
<p>Pytorch๋ฅผ ์ค์นํ๊ณ , Python์ ์๊ณ , Tensor๋ฅผ ์ดํดํ๋ค๊ณ ๊ฐ์ ํฉ๋๋ค:</p>
<ul class="simple">
<li><a class="reference external" href="https://pytorch.org/">https://pytorch.org/</a> ์ค์น ์๋ด</li>
<li><a class="reference internal" href="../beginner/deep_learning_60min_blitz.html"><span class="doc">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</span></a> PyTorch ์์ํ๊ธฐ</li>
<li><a class="reference internal" href="../beginner/pytorch_with_examples.html"><span class="doc">์์ ๋ก ๋ฐฐ์ฐ๋ ํ์ดํ ์น(PyTorch)</span></a> ๋๊ณ ๊น์ ํต์ฐฐ์ ์ํ ์๋ฃ</li>
<li><a class="reference internal" href="../beginner/former_torchies_tutorial.html"><span class="doc">Torch ์ฌ์ฉ์๋ฅผ ์ํ PyTorch</span></a> ์ด์ Lua Torch ์ฌ์ฉ์๋ฅผ ์ํ ์๋ฃ</li>
</ul>
<p>RNN๊ณผ ์๋ ๋ฐฉ์์ ์๋ ๊ฒ ๋ํ ์ ์ฉํฉ๋๋ค:</p>
<ul class="simple">
<li><a class="reference external" href="https://karpathy.github.io/2015/05/21/rnn-effectiveness/">The Unreasonable Effectiveness of Recurrent Neural
Networks</a>
์ค์ํ ์์ ๋ฅผ ๋ณด์ฌ ์ค๋๋ค.</li>
<li><a class="reference external" href="https://colah.github.io/posts/2015-08-Understanding-LSTMs/">Understanding LSTM
Networks</a>
LSTM์ ๊ดํ ๊ฒ์ด์ง๋ง RNN์ ๊ดํด์๋ ์ ์ตํฉ๋๋ค.</li>
</ul>
<div class="section" id="id2">
<h2>๋ฐ์ดํฐ ์ค๋น<a class="headerlink" href="#id2" title="Permalink to this headline">ยถ</a></h2>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last"><a class="reference external" href="https://download.pytorch.org/tutorial/data.zip">์ฌ๊ธฐ</a> ์์ ๋ฐ์ดํฐ๋ฅผ ๋ค์ด ๋ฐ๊ณ ,
ํ์ฌ ๋๋ ํ ๋ฆฌ์ ์์ถ์ ํธ์ญ์์ค.</p>
</div>
<p><code class="docutils literal notranslate"><span class="pre">data/names</span></code> ๋๋ ํ ๋ฆฌ์๋ โ[Language].txtโ ๋ผ๋ 18 ๊ฐ์ ํ
์คํธ ํ์ผ์ด ์์ต๋๋ค.
๊ฐ ํ์ผ์๋ ํ ์ค์ ํ๋์ ์ด๋ฆ์ด ํฌํจ๋์ด ์์ผ๋ฉฐ ๋๋ถ๋ถ ๋ก๋ง์๋ก ๋์ด ์์ต๋๋ค
(๊ทธ๋ฌ๋, ์ ๋์ฝ๋์์ ASCII๋ก ๋ณํํด์ผ ํจ).</p>
<p>๊ฐ ์ธ์ด ๋ณ๋ก ์ด๋ฆ ๋ชฉ๋ก ์ฌ์ <code class="docutils literal notranslate"><span class="pre">{language:</span> <span class="pre">[names</span> <span class="pre">...]}</span></code> ์ ๋ง๋ญ๋๋ค.
์ผ๋ฐ ๋ณ์ โcategoryโ ์ โlineโ (์ฐ๋ฆฌ์ ๊ฒฝ์ฐ ์ธ์ด์ ์ด๋ฆ)์ ์ดํ์ ํ์ฅ์ฑ์ ์ํด ์ฌ์ฉ๋ฉ๋๋ค.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">์ญ์ ์ฃผ: โlineโ ์ ์
๋ ฅ์ โcategoryโ์ ํด๋์ค๋ฅผ ์ ์ฉํ์ฌ ๋ค๋ฅธ ๋ฌธ์ ์๋ ํ์ฉ ํ ์ ์์ต๋๋ค.
์ฌ๊ธฐ์๋ โlineโ์ ์ด๋ฆ(ex. Robert )๋ฅผ ์
๋ ฅ์ผ๋ก โcategoryโ์ ํด๋์ค(ex. english)๋ก ์ฌ์ฉํฉ๋๋ค.</p>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">unicode_literals</span><span class="p">,</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
<span class="kn">from</span> <span class="nn">io</span> <span class="kn">import</span> <span class="nb">open</span>
<span class="kn">import</span> <span class="nn">glob</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="k">def</span> <span class="nf">findFiles</span><span class="p">(</span><span class="n">path</span><span class="p">):</span> <span class="k">return</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">findFiles</span><span class="p">(</span><span class="s1">'data/names/*.txt'</span><span class="p">))</span>
<span class="kn">import</span> <span class="nn">unicodedata</span>
<span class="kn">import</span> <span class="nn">string</span>
<span class="n">all_letters</span> <span class="o">=</span> <span class="n">string</span><span class="o">.</span><span class="n">ascii_letters</span> <span class="o">+</span> <span class="s2">" .,;'"</span>
<span class="n">n_letters</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_letters</span><span class="p">)</span>
<span class="c1"># ์ ๋์ฝ๋ ๋ฌธ์์ด์ ASCII๋ก ๋ณํ, https://stackoverflow.com/a/518232/2809427</span>
<span class="k">def</span> <span class="nf">unicodeToAscii</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
<span class="k">return</span> <span class="s1">''</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
<span class="n">c</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">unicodedata</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="s1">'NFD'</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>
<span class="k">if</span> <span class="n">unicodedata</span><span class="o">.</span><span class="n">category</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="o">!=</span> <span class="s1">'Mn'</span>
<span class="ow">and</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">all_letters</span>
<span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">unicodeToAscii</span><span class="p">(</span><span class="s1">'ลlusร rski'</span><span class="p">))</span>
<span class="c1"># ๊ฐ ์ธ์ด์ ์ด๋ฆ ๋ชฉ๋ก์ธ category_lines ์ฌ์ ์์ฑ</span>
<span class="n">category_lines</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">all_categories</span> <span class="o">=</span> <span class="p">[]</span>
<span class="c1"># ํ์ผ์ ์ฝ๊ณ ์ค ๋จ์๋ก ๋ถ๋ฆฌ</span>
<span class="k">def</span> <span class="nf">readLines</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
<span class="n">lines</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-8'</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">()</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
<span class="k">return</span> <span class="p">[</span><span class="n">unicodeToAscii</span><span class="p">(</span><span class="n">line</span><span class="p">)</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">]</span>
<span class="k">for</span> <span class="n">filename</span> <span class="ow">in</span> <span class="n">findFiles</span><span class="p">(</span><span class="s1">'data/names/*.txt'</span><span class="p">):</span>
<span class="n">category</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">filename</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">all_categories</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">category</span><span class="p">)</span>
<span class="n">lines</span> <span class="o">=</span> <span class="n">readLines</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
<span class="n">category_lines</span><span class="p">[</span><span class="n">category</span><span class="p">]</span> <span class="o">=</span> <span class="n">lines</span>
<span class="n">n_categories</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_categories</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>['data/names/Arabic.txt', 'data/names/Irish.txt', 'data/names/Russian.txt', 'data/names/Dutch.txt', 'data/names/Czech.txt', 'data/names/Portuguese.txt', 'data/names/German.txt', 'data/names/French.txt', 'data/names/Korean.txt', 'data/names/Japanese.txt', 'data/names/Chinese.txt', 'data/names/Polish.txt', 'data/names/Spanish.txt', 'data/names/Italian.txt', 'data/names/English.txt', 'data/names/Greek.txt', 'data/names/Vietnamese.txt', 'data/names/Scottish.txt']
Slusarski
</pre></div>
</div>
<p>์ด์ ๊ฐ <code class="docutils literal notranslate"><span class="pre">category</span></code> (์ธ์ด)๋ฅผ <code class="docutils literal notranslate"><span class="pre">line</span></code> (์ด๋ฆ)์ ๋งคํํ๋ ์ฌ์ ์ธ
<code class="docutils literal notranslate"><span class="pre">category_lines</span></code> ๋ฅผ ๋ง๋ค์์ต๋๋ค. ๋์ค์ ์ฐธ์กฐ ํ ์ ์๋๋ก
<code class="docutils literal notranslate"><span class="pre">all_categories</span></code> (์ธ์ด ๋ชฉ๋ก)์ <code class="docutils literal notranslate"><span class="pre">n_categories</span></code> ๋ ์ถ์ ํฉ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">category_lines</span><span class="p">[</span><span class="s1">'Italian'</span><span class="p">][:</span><span class="mi">5</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>['Abandonato', 'Abatangelo', 'Abatantuono', 'Abate', 'Abategiovanni']
</pre></div>
</div>
<div class="section" id="tensor">
<h3>์ด๋ฆ์ Tensor๋ก ๋ณ๊ฒฝ<a class="headerlink" href="#tensor" title="Permalink to this headline">ยถ</a></h3>
<p>์ด์ ๋ชจ๋ ์ด๋ฆ์ ์ฒด๊ณํ ํ์ผ๋ฏ๋ก, ์ด๋ฅผ ํ์ฉํ๊ธฐ ์ํด Tensor๋ก
์ ํํด์ผ ํฉ๋๋ค.</p>
<p>ํ๋์ ๋ฌธ์๋ฅผ ํํํ๊ธฐ ์ํด, ํฌ๊ธฐ๊ฐ <code class="docutils literal notranslate"><span class="pre"><1</span> <span class="pre">x</span> <span class="pre">n_letters></span></code> ์ธ
โOne-Hot ๋ฒกํฐโ ๋ฅผ ์ฌ์ฉํฉ๋๋ค. One-Hot ๋ฒกํฐ๋ ํ์ฌ ๋ฌธ์์
์ฃผ์์๋ง 1์ ๊ฐ์ผ๋ก ๊ฐ์ง๊ณ ๊ทธ์ธ์ ๋๋จธ์ง๋ 0์ผ๋ก ์ฑ์์ง๋ค.
์์ <code class="docutils literal notranslate"><span class="pre">"b"</span> <span class="pre">=</span> <span class="pre"><0</span> <span class="pre">1</span> <span class="pre">0</span> <span class="pre">0</span> <span class="pre">0</span> <span class="pre">...></span></code> .</p>
<p>๋จ์ด๋ฅผ ๋ง๋ค๊ธฐ ์ํด One-Hot ๋ฒกํฐ๋ค์ 2 ์ฐจ์ ํ๋ ฌ
<code class="docutils literal notranslate"><span class="pre"><line_length</span> <span class="pre">x</span> <span class="pre">1</span> <span class="pre">x</span> <span class="pre">n_letters></span></code> ์ ๊ฒฐํฉ์ํต๋๋ค.</p>
<p>์์์ ๋ณด์ด๋ ์ถ๊ฐ์ ์ธ 1์ฐจ์์ PyTorch์์ ๋ชจ๋ ๊ฒ์ด ๋ฐฐ์น(batch)์ ์๋ค๊ณ ๊ฐ์ ํ๊ธฐ
๋๋ฌธ์ ๋ฐ์ํฉ๋๋ค. ์ฌ๊ธฐ์๋ ๋ฐฐ์น ํฌ๊ธฐ 1์ ์ฌ์ฉํ๊ณ ์์ต๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="sd">'''</span>
<span class="sd">.. NOTE::</span>
<span class="sd">์ญ์ ์ฃผ: One-Hot ๋ฒกํฐ๋ ์ธ์ด๋ฅผ ๋ค๋ฃฐ ๋ ์์ฃผ ์ด์ฉ๋๋ฉฐ,</span>
<span class="sd">๋จ์ด,๊ธ์ ๋ฑ์ ๋ฒกํฐ๋ก ํํ ํ ๋ ๋จ์ด,๊ธ์ ์ฌ์ด์ ์๊ด ๊ด๊ณ๋ฅผ ๋ฏธ๋ฆฌ ์ ์ ์์ ๊ฒฝ์ฐ,</span>
<span class="sd">One-Hot์ผ๋ก ํํํ์ฌ ์๋ก ์ง๊ตํ๋ค๊ณ ๊ฐ์ ํ๊ณ ํ์ต์ ์์ํฉ๋๋ค.</span>
<span class="sd">๋์ผํ๊ฒ ์๊ด ๊ด๊ณ๋ฅผ ์ ์ ์๋ ๋ค๋ฅธ ๋ฐ์ดํฐ์ ๊ฒฝ์ฐ์๋ One-Hot ๋ฒกํฐ๋ฅผ ํ์ฉ ํ ์ ์์ต๋๋ค.</span>
<span class="sd">'''</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="c1"># all_letters ๋ก ๋ฌธ์์ ์ฃผ์ ์ฐพ๊ธฐ, ์์ "a" = 0</span>
<span class="k">def</span> <span class="nf">letterToIndex</span><span class="p">(</span><span class="n">letter</span><span class="p">):</span>
<span class="k">return</span> <span class="n">all_letters</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="n">letter</span><span class="p">)</span>
<span class="c1"># ๊ฒ์ฆ์ ์ํด์ ํ๊ฐ์ ๋ฌธ์๋ฅผ <1 x n_letters> Tensor๋ก ๋ณํ</span>
<span class="k">def</span> <span class="nf">letterToTensor</span><span class="p">(</span><span class="n">letter</span><span class="p">):</span>
<span class="n">tensor</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="mi">1</span><span class="p">,</span> <span class="n">n_letters</span><span class="p">)</span>
<span class="n">tensor</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">letterToIndex</span><span class="p">(</span><span class="n">letter</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">tensor</span>
<span class="c1"># ํ ์ค(์ด๋ฆ)์ <line_length x 1 x n_letters>,</span>
<span class="c1"># ๋๋ One-Hot ๋ฌธ์ ๋ฒกํฐ์ Array๋ก ๋ณ๊ฒฝ</span>
<span class="k">def</span> <span class="nf">lineToTensor</span><span class="p">(</span><span class="n">line</span><span class="p">):</span>
<span class="n">tensor</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="nb">len</span><span class="p">(</span><span class="n">line</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n_letters</span><span class="p">)</span>
<span class="k">for</span> <span class="n">li</span><span class="p">,</span> <span class="n">letter</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">line</span><span class="p">):</span>
<span class="n">tensor</span><span class="p">[</span><span class="n">li</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="n">letterToIndex</span><span class="p">(</span><span class="n">letter</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">tensor</span>
<span class="nb">print</span><span class="p">(</span><span class="n">letterToTensor</span><span class="p">(</span><span class="s1">'J'</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">lineToTensor</span><span class="p">(</span><span class="s1">'Jones'</span><span class="p">)</span><span class="o">.</span><span class="n">size</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>tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0.]])
torch.Size([5, 1, 57])
</pre></div>
</div>
</div>
</div>
<div class="section" id="id3">
<h2>๋คํธ์ํฌ ์์ฑ<a class="headerlink" href="#id3" title="Permalink to this headline">ยถ</a></h2>
<p>Autograd ์ ์, Torch์์ RNN(recurrent neural network) ์์ฑ์
์ฌ๋ฌ ์๊ฐ ๋จ๊ณ ๊ฑธ์ฒ์ ๊ณ์ธต์ ๋งค๊ฐ๋ณ์๋ฅผ ๋ณต์ ํ๋ ์์
์ ํฌํจํฉ๋๋ค.
๊ณ์ธต์ ์๋ ์ํ์ ๋ณํ๋(Gradient)๋ฅผ ๊ฐ์ง๋ฉฐ, ์ด์ ์ด๊ฒ๋ค์ ๊ทธ๋ํ ์์ฒด์์
์์ ํ ์ฒ๋ฆฌ๋๋ ๋ฉ๋๋ค. ์ด๋ feed-forward ๊ณ์ธต๊ณผ
๊ฐ์ ๋งค์ฐ โ์์ํโ ๋ฐฉ๋ฒ์ผ๋ก RNN์ ๊ตฌํํ ์ ์๋ค๋ ๊ฒ์ ์๋ฏธํฉ๋๋ค.</p>
<p>์ญ์ ์ฃผ : ์ฌ๊ธฐ์๋ ๊ต์ก๋ชฉ์ ์ผ๋ก nn.RNN ๋์ ์ง์ RNN์ ์ฌ์ฉํฉ๋๋ค.</p>
<p>์ด RNN ๋ชจ๋(๋๋ถ๋ถ <a class="reference external" href="https://tutorials.pytorch.kr/beginner/former_torchies/nn_tutorial.html#example-2-recurrent-net">Torch ์ฌ์ฉ์๋ฅผ ์ํ PyTorch ํํ ๋ฆฌ์ผ</a> ์์ ๋ณต์ฌํจ)
์ ์
๋ ฅ ๋ฐ ์๋ ์ํ๋ก ์๋ํ๋ 2๊ฐ์ ์ ํ ๊ณ์ธต์ด๋ฉฐ,
์ถ๋ ฅ ๋ค์์ LogSoftmax ๊ณ์ธต์ด ์์ต๋๋ค.</p>
<div class="figure">
<img alt="" src="https://i.imgur.com/Z2xbySO.png" />
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="k">class</span> <span class="nc">RNN</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_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">RNN</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">hidden_size</span> <span class="o">=</span> <span class="n">hidden_size</span>
<span class="bp">self</span><span class="o">.</span><span class="n">i2h</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">input_size</span> <span class="o">+</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">i2o</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">input_size</span> <span class="o">+</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">softmax</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LogSoftmax</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">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">hidden</span><span class="p">):</span>
<span class="n">combined</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="nb">input</span><span class="p">,</span> <span class="n">hidden</span><span class="p">),</span> <span class="mi">1</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">i2h</span><span class="p">(</span><span class="n">combined</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">i2o</span><span class="p">(</span><span class="n">combined</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">softmax</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
<span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="n">hidden</span>
<span class="k">def</span> <span class="nf">initHidden</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">)</span>
<span class="n">n_hidden</span> <span class="o">=</span> <span class="mi">128</span>
<span class="n">rnn</span> <span class="o">=</span> <span class="n">RNN</span><span class="p">(</span><span class="n">n_letters</span><span class="p">,</span> <span class="n">n_hidden</span><span class="p">,</span> <span class="n">n_categories</span><span class="p">)</span>
</pre></div>
</div>
<p>์ด ๋คํธ์ํฌ์ ํ ๋จ๊ณ๋ฅผ ์คํํ๋ ค๋ฉด ์
๋ ฅ(ํ์ฌ ๋ฌธ์ Tensor)๊ณผ
์ด์ ์ ์๋ ์ํ (์ฒ์์๋ 0์ผ๋ก ์ด๊ธฐํ)๋ฅผ ์ ๋ฌํด์ผ ํฉ๋๋ค.
์ถ๋ ฅ(๊ฐ ์ธ์ด์ ํ๋ฅ )๊ณผ ๋ค์ ์๋ ์ํ (๋ค์ ๋จ๊ณ๋ฅผ ์ํด ์ ์ง)๋ฅผ
๋๋ ค ๋ฐ์ต๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">input</span> <span class="o">=</span> <span class="n">letterToTensor</span><span class="p">(</span><span class="s1">'A'</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">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_hidden</span><span class="p">)</span>
<span class="n">output</span><span class="p">,</span> <span class="n">next_hidden</span> <span class="o">=</span> <span class="n">rnn</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">hidden</span><span class="p">)</span>
</pre></div>
</div>
<p>ํจ์จ์ฑ์ ์ํด์ ๋งค ๋จ๊ณ๋ง๋ค ์๋ก์ด Tensor๋ฅผ ๋ง๋ค๊ณ ์ถ์ง ์๊ธฐ ๋๋ฌธ์
<code class="docutils literal notranslate"><span class="pre">letterToTensor</span></code> ๋์ <code class="docutils literal notranslate"><span class="pre">lineToTensor</span></code> ๋ฅผ ์๋ผ์ ์ฌ์ฉํ
๊ฒ์
๋๋ค. ์ด๊ฒ์ Tensor์ ์ฌ์ ์ฐ์ฐ(pre-computing) ๋ฐฐ์น์ ์ํด
๋์ฑ ์ต์ ํ ๋ ์ ์์ต๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">input</span> <span class="o">=</span> <span class="n">lineToTensor</span><span class="p">(</span><span class="s1">'Albert'</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">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_hidden</span><span class="p">)</span>
<span class="n">output</span><span class="p">,</span> <span class="n">next_hidden</span> <span class="o">=</span> <span class="n">rnn</span><span class="p">(</span><span class="nb">input</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">hidden</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">output</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>tensor([[-2.8379, -2.9045, -2.9315, -2.8983, -2.8976, -2.8884, -2.9605, -2.8254,
-2.9040, -2.8685, -2.8511, -2.8195, -2.9614, -2.7920, -2.8799, -2.9890,
-2.8539, -2.9918]], grad_fn=<LogSoftmaxBackward>)
</pre></div>
</div>
<p>๋ณด์๋ค์ํผ ์ถ๋ ฅ์ <code class="docutils literal notranslate"><span class="pre"><1</span> <span class="pre">x</span> <span class="pre">n_categories></span></code> Tensor์ด๊ณ , ๋ชจ๋ ํญ๋ชฉ์
ํด๋น ์นดํ
๊ณ ๋ฆฌ์ ์ฐ๋(likelihood) ์
๋๋ค (๋ ๋์ ๊ฒ์ด ๋ ํ๋ฅ ๋์).</p>
</div>
<div class="section" id="id4">
<h2>ํ์ต<a class="headerlink" href="#id4" title="Permalink to this headline">ยถ</a></h2>
<div class="section" id="id5">
<h3>ํ์ต ์ค๋น<a class="headerlink" href="#id5" title="Permalink to this headline">ยถ</a></h3>
<p>ํ์ต์ผ๋ก ๋ค์ด๊ฐ๊ธฐ ์ ์ ๋ช๋ช ๋์๋๋ ํจ์๋ฅผ ๋ง๋ค์ด์ผํฉ๋๋ค.
์ฒซ์งธ๋ ์ฐ๋ฆฌ๊ฐ ์์๋ธ ๊ฐ ์นดํ
๊ณ ๋ฆฌ์ ์ฐ๋์ธ ๋คํธ์ํฌ ์ถ๋ ฅ์ ํด์ํ๋ ๊ฒ ์
๋๋ค.
๊ฐ์ฅ ํฐ ๊ฐ์ ์ฃผ์๋ฅผ ์๊ธฐ ์ํด์ <code class="docutils literal notranslate"><span class="pre">Tensor.topk</span></code> ๋ฅผ ์ฌ์ฉ ํ ์ ์์ต๋๋ค.
์ญ์ ์ฃผ: ๋คํธ์ํฌ ์ถ๋ ฅ(๊ฐ ์นดํ
๊ณ ๋ฆฌ์ ์ฐ๋)์ผ๋ก
๊ฐ์ฅ ํ๋ฅ ์ด ๋์ ์นดํ
๊ณ ๋ฆฌ ์ด๋ฆ(์ธ์ด)๊ณผ ์นดํ
๊ณ ๋ฆฌ ๋ฒํธ ๋ฐํ</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">categoryFromOutput</span><span class="p">(</span><span class="n">output</span><span class="p">):</span>
<span class="n">top_n</span><span class="p">,</span> <span class="n">top_i</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># ํ
์์ ๊ฐ์ฅ ํฐ ๊ฐ ๋ฐ ์ฃผ์</span>
<span class="n">category_i</span> <span class="o">=</span> <span class="n">top_i</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="c1"># ํ
์์์ ์ ์ ๊ฐ์ผ๋ก ๋ณ๊ฒฝ</span>
<span class="k">return</span> <span class="n">all_categories</span><span class="p">[</span><span class="n">category_i</span><span class="p">],</span> <span class="n">category_i</span>
<span class="nb">print</span><span class="p">(</span><span class="n">categoryFromOutput</span><span class="p">(</span><span class="n">output</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>('Italian', 13)
</pre></div>
</div>
<p>ํ์ต ์์(ํ๋์ ์ด๋ฆ๊ณผ ๊ทธ ์ธ์ด)๋ฅผ ์ป๋ ๋น ๋ฅธ ๋ฐฉ๋ฒ๋ ํ์ํฉ๋๋ค.:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>
<span class="k">def</span> <span class="nf">randomChoice</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
<span class="k">return</span> <span class="n">l</span><span class="p">[</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">l</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)]</span>
<span class="k">def</span> <span class="nf">randomTrainingExample</span><span class="p">():</span>
<span class="n">category</span> <span class="o">=</span> <span class="n">randomChoice</span><span class="p">(</span><span class="n">all_categories</span><span class="p">)</span>
<span class="n">line</span> <span class="o">=</span> <span class="n">randomChoice</span><span class="p">(</span><span class="n">category_lines</span><span class="p">[</span><span class="n">category</span><span class="p">])</span>
<span class="n">category_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">all_categories</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">category</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">line_tensor</span> <span class="o">=</span> <span class="n">lineToTensor</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
<span class="k">return</span> <span class="n">category</span><span class="p">,</span> <span class="n">line</span><span class="p">,</span> <span class="n">category_tensor</span><span class="p">,</span> <span class="n">line_tensor</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="n">category</span><span class="p">,</span> <span class="n">line</span><span class="p">,</span> <span class="n">category_tensor</span><span class="p">,</span> <span class="n">line_tensor</span> <span class="o">=</span> <span class="n">randomTrainingExample</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'category ='</span><span class="p">,</span> <span class="n">category</span><span class="p">,</span> <span class="s1">'/ line ='</span><span class="p">,</span> <span class="n">line</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>category = Russian / line = Egiazarov
category = Irish / line = O'Reilly
category = Greek / line = Dertilis
category = Spanish / line = Salamanca
category = Polish / line = Janowski
category = Russian / line = Hudoleev
category = Scottish / line = Gibson
category = German / line = Denzel
category = Chinese / line = Cao
category = Czech / line = Meszes
</pre></div>
</div>
</div>
<div class="section" id="id6">
<h3>๋คํธ์ํฌ ํ์ต<a class="headerlink" href="#id6" title="Permalink to this headline">ยถ</a></h3>
<p>์ด์ ์ด ๋คํธ์ํฌ๋ฅผ ํ์ตํ๋๋ฐ ํ์ํ ์์(ํ์ต ๋ฐ์ดํฐ)๋ค์ ๋ณด์ฌ์ฃผ๊ณ ์ถ์ ํฉ๋๋ค.
๋ง์ผ ํ๋ ธ๋ค๋ฉด ์๋ ค ์ค๋๋ค.</p>
<p>RNN์ ๋ง์ง๋ง ๊ณ์ธต์ด <code class="docutils literal notranslate"><span class="pre">nn.LogSoftmax</span></code> ์ด๋ฏ๋ก ์์ค ํจ์๋ก
<code class="docutils literal notranslate"><span class="pre">nn.NLLLoss</span></code> ๊ฐ ์ ํฉํฉ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span><span class="p">()</span>
</pre></div>
</div>
<p>๊ฐ ํ์ต ๋ฃจํ๋ ๋ค์๊ณผ ๊ฐ์ต๋๋ค:</p>
<ul class="simple">
<li>์
๋ ฅ๊ณผ ๋ชฉํ Tensor ์์ฑ</li>
<li>0 ๋ก ์ด๊ธฐํ๋ ์๋ ์ํ ์์ฑ</li>
<li>๊ฐ ๋ฌธ์๋ฅผ ์ฝ๊ธฐ<ul>
<li>๋ค์ ๋ฌธ์๋ฅผ ์ํ ์๋ ์ํ ์ ์ง</li>
</ul>
</li>
<li>๋ชฉํ์ ์ต์ข
์ถ๋ ฅ ๋น๊ต</li>
<li>์ญ์ ํ</li>
<li>์ถ๋ ฅ๊ณผ ์์ค ๋ฐํ</li>
</ul>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.005</span> <span class="c1"># ์ด๊ฒ์ ๋๋ฌด ๋๊ฒ ์ค์ ํ๋ฉด ๋ฐ์ฐํ ์ ์๊ณ , ๋๋ฌด ๋ฎ์ผ๋ฉด ํ์ต์ด ๋์ง ์์ ์ ์์ต๋๋ค.</span>
<span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">category_tensor</span><span class="p">,</span> <span class="n">line_tensor</span><span class="p">):</span>
<span class="n">hidden</span> <span class="o">=</span> <span class="n">rnn</span><span class="o">.</span><span class="n">initHidden</span><span class="p">()</span>
<span class="n">rnn</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">line_tensor</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">output</span><span class="p">,</span> <span class="n">hidden</span> <span class="o">=</span> <span class="n">rnn</span><span class="p">(</span><span class="n">line_tensor</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">hidden</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">category_tensor</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="c1"># ๋งค๊ฐ๋ณ์์ ๊ฒฝ์ฌ๋์ ํ์ต๋ฅ ์ ๊ณฑํด์ ๊ทธ ๋งค๊ฐ๋ณ์์ ๊ฐ์ ๋ํฉ๋๋ค.</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">rnn</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
<span class="n">p</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=-</span><span class="n">learning_rate</span><span class="p">)</span>
<span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
</pre></div>
</div>
<p>์ด์ ์์ ๋ฐ์ดํฐ๋ฅผ ์ฌ์ฉํ์ฌ ์คํํด์ผํฉ๋๋ค. <code class="docutils literal notranslate"><span class="pre">train</span></code> ํจ์๊ฐ ์ถ๋ ฅ๊ณผ ์์ค์
๋ฐํํ๊ธฐ ๋๋ฌธ์ ์ถ์ธก์ ํ๋ฉด์ ์ถ๋ ฅํ๊ณ ๋์ํ๋ฅผ ์ํ ์์ค์ ์ถ์ ํ ์
์์ต๋๋ค. 1000๊ฐ์ ์์ ๋ฐ์ดํฐ๊ฐ ์๊ธฐ ๋๋ฌธ์ <code class="docutils literal notranslate"><span class="pre">print_every</span></code> ์์ ๋ง
์ถ๋ ฅํ๊ณ , ์์ค์ ํ๊ท ์ ์ป์ต๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="n">n_iters</span> <span class="o">=</span> <span class="mi">100000</span>
<span class="n">print_every</span> <span class="o">=</span> <span class="mi">5000</span>
<span class="n">plot_every</span> <span class="o">=</span> <span class="mi">1000</span>
<span class="c1"># ๋์ํ๋ฅผ ์ํ ์์ค ์ถ์ </span>
<span class="n">current_loss</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">all_losses</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">def</span> <span class="nf">timeSince</span><span class="p">(</span><span class="n">since</span><span class="p">):</span>
<span class="n">now</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">s</span> <span class="o">=</span> <span class="n">now</span> <span class="o">-</span> <span class="n">since</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">s</span> <span class="o">/</span> <span class="mi">60</span><span class="p">)</span>
<span class="n">s</span> <span class="o">-=</span> <span class="n">m</span> <span class="o">*</span> <span class="mi">60</span>
<span class="k">return</span> <span class="s1">'</span><span class="si">%d</span><span class="s1">m </span><span class="si">%d</span><span class="s1">s'</span> <span class="o">%</span> <span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>
<span class="n">start</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="nb">iter</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">n_iters</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="n">category</span><span class="p">,</span> <span class="n">line</span><span class="p">,</span> <span class="n">category_tensor</span><span class="p">,</span> <span class="n">line_tensor</span> <span class="o">=</span> <span class="n">randomTrainingExample</span><span class="p">()</span>
<span class="n">output</span><span class="p">,</span> <span class="n">loss</span> <span class="o">=</span> <span class="n">train</span><span class="p">(</span><span class="n">category_tensor</span><span class="p">,</span> <span class="n">line_tensor</span><span class="p">)</span>
<span class="n">current_loss</span> <span class="o">+=</span> <span class="n">loss</span>
<span class="c1"># iter ์ซ์, ์์ค, ์ด๋ฆ, ์ถ์ธก ํ๋ฉด ์ถ๋ ฅ</span>
<span class="k">if</span> <span class="nb">iter</span> <span class="o">%</span> <span class="n">print_every</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">guess</span><span class="p">,</span> <span class="n">guess_i</span> <span class="o">=</span> <span class="n">categoryFromOutput</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
<span class="n">correct</span> <span class="o">=</span> <span class="s1">'โ'</span> <span class="k">if</span> <span class="n">guess</span> <span class="o">==</span> <span class="n">category</span> <span class="k">else</span> <span class="s1">'โ (</span><span class="si">%s</span><span class="s1">)'</span> <span class="o">%</span> <span class="n">category</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="si">%d</span><span class="s1"> </span><span class="si">%d%%</span><span class="s1"> (</span><span class="si">%s</span><span class="s1">) </span><span class="si">%.4f</span><span class="s1"> </span><span class="si">%s</span><span class="s1"> / </span><span class="si">%s</span><span class="s1"> </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="nb">iter</span><span class="p">,</span> <span class="nb">iter</span> <span class="o">/</span> <span class="n">n_iters</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="n">timeSince</span><span class="p">(</span><span class="n">start</span><span class="p">),</span> <span class="n">loss</span><span class="p">,</span> <span class="n">line</span><span class="p">,</span> <span class="n">guess</span><span class="p">,</span> <span class="n">correct</span><span class="p">))</span>
<span class="c1"># ํ์ฌ ํ๊ท ์์ค์ ์ ์ฒด ์์ค ๋ฆฌ์คํธ์ ์ถ๊ฐ</span>
<span class="k">if</span> <span class="nb">iter</span> <span class="o">%</span> <span class="n">plot_every</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">all_losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">current_loss</span> <span class="o">/</span> <span class="n">plot_every</span><span class="p">)</span>
<span class="n">current_loss</span> <span class="o">=</span> <span class="mi">0</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>5000 5% (1m 28s) 2.2156 Mishima / Japanese โ
10000 10% (2m 37s) 2.3854 Choshi / Italian โ (Japanese)
15000 15% (4m 21s) 2.2965 Shamshurin / Irish โ (Russian)
20000 20% (5m 42s) 4.3417 Deeb / Dutch โ (Arabic)
25000 25% (7m 29s) 4.5581 Lockwood / Scottish โ (English)
30000 30% (9m 13s) 0.6556 O'Kelly / Irish โ
35000 35% (10m 44s) 0.3256 Ferreira / Portuguese โ
40000 40% (12m 3s) 0.5009 Suk / Korean โ
45000 45% (13m 53s) 2.2802 Schermer / German โ (Dutch)
50000 50% (15m 23s) 0.7773 Mo / Korean โ
55000 55% (16m 47s) 1.0185 Luo / Vietnamese โ (Chinese)
60000 60% (18m 22s) 4.7954 Lyon / Korean โ (French)
65000 65% (19m 40s) 3.2921 Doljs / Greek โ (Czech)
70000 70% (21m 21s) 1.3794 Roma / Spanish โ
75000 75% (22m 49s) 1.1727 Jia / Chinese โ
80000 80% (24m 16s) 0.6959 Blaise / French โ
85000 85% (25m 48s) 4.2108 Maceachthighearna / Greek โ (Irish)
90000 90% (27m 44s) 0.4474 Zobnin / Russian โ
95000 95% (29m 42s) 0.3489 Amoretto / Italian โ
100000 100% (31m 20s) 1.6778 Ventura / Spanish โ (Portuguese)
</pre></div>
</div>
</div>
<div class="section" id="id7">
<h3>๊ฒฐ๊ณผ ๋์ํ<a class="headerlink" href="#id7" title="Permalink to this headline">ยถ</a></h3>
<p><code class="docutils literal notranslate"><span class="pre">all_losses</span></code> ๋ฅผ ์ด์ฉํ ์์ค ๋์ํ๋
๋คํธ์ํฌ์ ํ์ต์ ๋ณด์ฌ์ค๋ค:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">matplotlib.ticker</span> <span class="k">as</span> <span class="nn">ticker</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">all_losses</span><span class="p">)</span>
</pre></div>
</div>
<img alt="../_images/sphx_glr_char_rnn_classification_tutorial_001.png" class="sphx-glr-single-img" src="../_images/sphx_glr_char_rnn_classification_tutorial_001.png" />
</div>
</div>
<div class="section" id="id8">
<h2>๊ฒฐ๊ณผ ํ๊ฐ<a class="headerlink" href="#id8" title="Permalink to this headline">ยถ</a></h2>
<p>๋คํธ์ํฌ๊ฐ ๋ค๋ฅธ ์นดํ
๊ณ ๋ฆฌ์์ ์ผ๋ง๋ ์ ์๋ํ๋์ง ๋ณด๊ธฐ์ํด
๋ชจ๋ ์ค์ ์ธ์ด(ํ)๊ฐ ๋คํธ์ํฌ์์ ์ด๋ค ์ธ์ด๋ก ์ถ์ธก(์ด)๋๋์ง๋ฅผ ๋ํ๋ด๋
ํผ๋ ํ์ด(confusion matrix)์ ๋ง๋ญ๋๋ค. ํผ๋ ํ๋ ฌ์ ๊ณ์ฐํ๊ธฐ ์ํด
<code class="docutils literal notranslate"><span class="pre">evaluate()</span></code> ๋ก ๋ง์ ์์ ์ํ์ ๋คํธ์ํฌ์ ์คํํฉ๋๋ค.
<code class="docutils literal notranslate"><span class="pre">evaluate()</span></code> ์ <code class="docutils literal notranslate"><span class="pre">train</span> <span class="pre">()</span></code> ๊ณผ ์ญ์ ํ๋ฅผ ๋นผ๋ฉด ๋์ผํฉ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># ํผ๋ ํ๋ ฌ์์ ์ ํํ ์ถ์ธก์ ์ถ์ </span>
<span class="n">confusion</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">n_categories</span><span class="p">,</span> <span class="n">n_categories</span><span class="p">)</span>
<span class="n">n_confusion</span> <span class="o">=</span> <span class="mi">10000</span>
<span class="c1"># ์ฃผ์ด์ง ๋ผ์ธ์ ์ถ๋ ฅ ๋ฐํ</span>
<span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="n">line_tensor</span><span class="p">):</span>
<span class="n">hidden</span> <span class="o">=</span> <span class="n">rnn</span><span class="o">.</span><span class="n">initHidden</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">line_tensor</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">output</span><span class="p">,</span> <span class="n">hidden</span> <span class="o">=</span> <span class="n">rnn</span><span class="p">(</span><span class="n">line_tensor</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">hidden</span><span class="p">)</span>
<span class="k">return</span> <span class="n">output</span>
<span class="c1"># ์์๋ค ์ค์ ์ด๋ค ๊ฒ์ด ์ ํํ๊ฒ ์์ธก๋์๋์ง ๊ธฐ๋ก</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_confusion</span><span class="p">):</span>
<span class="n">category</span><span class="p">,</span> <span class="n">line</span><span class="p">,</span> <span class="n">category_tensor</span><span class="p">,</span> <span class="n">line_tensor</span> <span class="o">=</span> <span class="n">randomTrainingExample</span><span class="p">()</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">line_tensor</span><span class="p">)</span>
<span class="n">guess</span><span class="p">,</span> <span class="n">guess_i</span> <span class="o">=</span> <span class="n">categoryFromOutput</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
<span class="n">category_i</span> <span class="o">=</span> <span class="n">all_categories</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">category</span><span class="p">)</span>
<span class="n">confusion</span><span class="p">[</span><span class="n">category_i</span><span class="p">][</span><span class="n">guess_i</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="c1"># ๋ชจ๋ ํ์ ํฉ๊ณ๋ก ๋๋์ด ์ ๊ทํ</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_categories</span><span class="p">):</span>
<span class="n">confusion</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">confusion</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/</span> <span class="n">confusion</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="c1"># ๋์ ์ค์ </span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
<span class="n">cax</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">confusion</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
<span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cax</span><span class="p">)</span>
<span class="c1"># ์ถ ์ค์ </span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">([</span><span class="s1">''</span><span class="p">]</span> <span class="o">+</span> <span class="n">all_categories</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="s1">''</span><span class="p">]</span> <span class="o">+</span> <span class="n">all_categories</span><span class="p">)</span>
<span class="c1"># ๋ชจ๋ tick์์ ๋ ์ด๋ธ ์ง์ </span>
<span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">ticker</span><span class="o">.</span><span class="n">MultipleLocator</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">ticker</span><span class="o">.</span><span class="n">MultipleLocator</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="c1"># sphinx_gallery_thumbnail_number = 2</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<img alt="../_images/sphx_glr_char_rnn_classification_tutorial_002.png" class="sphx-glr-single-img" src="../_images/sphx_glr_char_rnn_classification_tutorial_002.png" />
<p>์ฃผ์ถ์์ ๋ฒ์ด๋ ๋ฐ์ ์ ์ ์ ํํ์ฌ ์๋ชป ์ถ์ธกํ ์ธ์ด๋ฅผ ํ์
ํ ์ ์์ต๋๋ค. ์๋ฅผ ๋ค์ด ํ๊ตญ์ด๋ ์ค๊ตญ์ด๋ก ์ดํ๋ฆฌ์์ด๋ก ์คํ์ธ์ด๋ก.
๊ทธ๋ฆฌ์ค์ด๋ ๋งค์ฐ ์๋๋ ๊ฒ์ผ๋ก ์์ด๋ ๋งค์ฐ ๋์๊ฒ์ผ๋ก ๋ณด์
๋๋ค.
(๋ค๋ฅธ ์ธ์ด๋ค๊ณผ ์ค์ฒฉ ๋๋ฌธ์ผ๋ก ์ถ์ )</p>
<div class="section" id="id9">
<h3>์ฌ์ฉ์ ์
๋ ฅ์ผ๋ก ์คํ<a class="headerlink" href="#id9" title="Permalink to this headline">ยถ</a></h3>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="n">input_line</span><span class="p">,</span> <span class="n">n_predictions</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">> </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="n">input_line</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">output</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">lineToTensor</span><span class="p">(</span><span class="n">input_line</span><span class="p">))</span>
<span class="c1"># Get top N categories</span>
<span class="n">topv</span><span class="p">,</span> <span class="n">topi</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">n_predictions</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_predictions</span><span class="p">):</span>
<span class="n">value</span> <span class="o">=</span> <span class="n">topv</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">category_index</span> <span class="o">=</span> <span class="n">topi</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'(</span><span class="si">%.2f</span><span class="s1">) </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">all_categories</span><span class="p">[</span><span class="n">category_index</span><span class="p">]))</span>
<span class="n">predictions</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">value</span><span class="p">,</span> <span class="n">all_categories</span><span class="p">[</span><span class="n">category_index</span><span class="p">]])</span>
<span class="n">predict</span><span class="p">(</span><span class="s1">'Dovesky'</span><span class="p">)</span>
<span class="n">predict</span><span class="p">(</span><span class="s1">'Jackson'</span><span class="p">)</span>
<span class="n">predict</span><span class="p">(</span><span class="s1">'Satoshi'</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>> Dovesky
(-0.84) Czech
(-1.10) Russian
(-2.30) English
> Jackson
(-0.74) English
(-1.35) Scottish
(-2.27) Russian
> Satoshi
(-1.09) Japanese
(-1.27) Polish
(-1.38) Arabic
</pre></div>
</div>
<p><a class="reference external" href="https://github.com/spro/practical-pytorch/tree/master/char-rnn-classification">์ค์ฉ PyTorch ์ ์ฅ์</a>
์ ์ต์ข
๋ฒ์ ์คํฌ๋ฆฝํธ๋ ์ ์ฝ๋๋ฅผ ๋ช๊ฐ์ ํ์ผ๋ก ๋ถํ ํ์ต๋๋ค.:</p>
<ul class="simple">
<li><code class="docutils literal notranslate"><span class="pre">data.py</span></code> (ํ์ผ ์ฝ๊ธฐ)</li>
<li><code class="docutils literal notranslate"><span class="pre">model.py</span></code> (RNN ์ ์)</li>
<li><code class="docutils literal notranslate"><span class="pre">train.py</span></code> (ํ์ต ์คํ)</li>
<li><code class="docutils literal notranslate"><span class="pre">predict.py</span></code> (์ปค๋ฉ๋ ๋ผ์ธ ์ธ์๋ก <code class="docutils literal notranslate"><span class="pre">predict()</span></code> ์คํ)</li>
<li><code class="docutils literal notranslate"><span class="pre">server.py</span></code> (bottle.py๋ฅผ ์ฌ์ฉํ์ฌ JSON API๋ก ์์ธก ์ ๊ณต)</li>
</ul>
<p>ํ์ต๊ณผ ๋คํธ์ํฌ ์ ์ฅ์ ์ํด <code class="docutils literal notranslate"><span class="pre">train.py</span></code> ์คํ.</p>
<p>์ด๋ฆ์ผ๋ก ์์ธก์ ๋ณด๊ธฐ ์ํด <code class="docutils literal notranslate"><span class="pre">predict.py</span></code> ์คํ:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ python predict.py Hazaki
(-0.42) Japanese
(-1.39) Polish
(-3.51) Czech
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">server.py</span></code> ๋ฅผ ์คํํ๊ณ ์์ธก์ JSON ์ถ๋ ฅ์ ์ป๊ธฐ ์ํด
<a class="reference external" href="http://localhost:5533/Yourname">http://localhost:5533/Yourname</a> ๋ฐฉ๋ฌธ.</p>
</div>
</div>
<div class="section" id="id10">
<h2>์ฐ์ต<a class="headerlink" href="#id10" title="Permalink to this headline">ยถ</a></h2>
<ul class="simple">
<li>โline -> categoryโ ์ ๋ค๋ฅธ ๋ฐ์ดํฐ ์งํฉ์ผ๋ก ์๋ํด๋ณด์ญ์์ค, ์๋ฅผ ๋ค์ด:<ul>
<li>๋จ์ด -> ์ธ์ด</li>
<li>์ด๋ฆ -> ์ฑ๋ณ</li>
<li>์บ๋ฆญํฐ ์ด๋ฆ -> ์๊ฐ</li>
<li>ํ์ด์ง ์ ๋ชฉ -> ๋ธ๋ก๊ทธ ๋๋ ์๋ธ๋ ๋ง</li>
</ul>
</li>
<li>๋ ํฌ๊ณ ๋ ๋์ ๋ชจ์์ ๋คํธ์ํฌ๋ก ๋ ๋์ ๊ฒฐ๊ณผ๋ฅผ ์ป์ผ์ญ์์ค.<ul>
<li>๋๋ง์ ์ ํ ๊ณ์ธต์ ์ถ๊ฐํด ๋ณด์ญ์์ค</li>
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<li>์ฌ๋ฌ ๊ฐ์ ์ด๋ฐ RNN์ ์์ ์์ค ๋คํธ์ํฌ๋ก ๊ฒฐํฉํด ๋ณด์ญ์์ค</li>
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