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<p class="caption" role="heading"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ ์ํผ</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../recipes/recipes_index.html">๋ชจ๋ ๋ ์ํผ ๋ณด๊ธฐ</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/intro.html">ํ์ดํ ์น(PyTorch) ๊ธฐ๋ณธ ์ตํ๊ธฐ</a></li>
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<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" role="heading"><span class="caption-text">Introduction to PyTorch on YouTube</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt.html">PyTorch ์๊ฐ - YouTube ์๋ฆฌ์ฆ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/introyt1_tutorial.html">PyTorch ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensors_deeper_tutorial.html">Pytorch Tensor ์๊ฐ</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" role="heading"><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" role="heading"><span class="caption-text">์ด๋ฏธ์ง/๋น๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="torchvision_tutorial.html">TorchVision Object Detection Finetuning Tutorial</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 ์ํด ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="tiatoolbox_tutorial.html">Whole Slide Image Classification Using PyTorch and TIAToolbox</a></li>
</ul>
<p class="caption" role="heading"><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="text_to_speech_with_torchaudio.html">Text-to-speech with Tacotron2</a></li>
<li class="toctree-l1"><a class="reference internal" href="forced_alignment_with_torchaudio_tutorial.html">wav2vec2์ ์ด์ฉํ ๊ฐ์ ์ ๋ ฌ</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">ํ
์คํธ</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="../beginner/bettertransformer_tutorial.html">Fast Transformer Inference with Better Transformer</a></li>
<li class="toctree-l1"><a class="reference internal" href="char_rnn_classification_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">๊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"><code class="docutils literal notranslate"><span class="pre">nn.Transformer</span></code> ์ torchtext๋ก ์ธ์ด ๋ฒ์ญํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/torchtext_custom_dataset_tutorial.html">Preprocess custom text dataset using Torchtext</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">๋ฐฑ์๋</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/onnx/intro_onnx.html">Introduction to ONNX</a></li>
</ul>
<p class="caption" role="heading"><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="reinforcement_ppo.html">Reinforcement Learning (PPO) with TorchRL Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="mario_rl_tutorial.html">๋ง๋ฆฌ์ค ๊ฒ์ RL ์์ด์ ํธ๋ก ํ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/pendulum.html">Pendulum: Writing your environment and transforms with TorchRL</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">PyTorch ๋ชจ๋ธ์ ํ๋ก๋์
ํ๊ฒฝ์ ๋ฐฐํฌํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/onnx/intro_onnx.html">Introduction to ONNX</a></li>
<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>
<li class="toctree-l1"><a class="reference internal" href="realtime_rpi.html">Raspberry Pi 4 ์์ ์ค์๊ฐ ์ถ๋ก (Inference) (30fps!)</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">PyTorch ํ๋กํ์ผ๋ง</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">PyTorch ๋ชจ๋ ํ๋กํ์ผ๋งํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hta_intro_tutorial.html">Introduction to Holistic Trace Analysis</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hta_trace_diff_tutorial.html">Trace Diff using Holistic Trace Analysis</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Code Transforms with FX</span></p>
<ul>
<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>
</ul>
<p class="caption" role="heading"><span class="caption-text">ํ๋ก ํธ์๋ API</span></p>
<ul>
<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="jacobians_hessians.html">Jacobians, Hessians, hvp, vhp, and more: composing function transforms</a></li>
<li class="toctree-l1"><a class="reference internal" href="ensembling.html">๋ชจ๋ธ ์์๋ธ</a></li>
<li class="toctree-l1"><a class="reference internal" href="per_sample_grads.html">Per-sample-gradients</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>
</ul>
<p class="caption" role="heading"><span class="caption-text">PyTorch ํ์ฅํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="custom_function_double_backward_tutorial.html">Double Backward with Custom Functions</a></li>
<li class="toctree-l1"><a class="reference internal" href="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">์ปค์คํ
C++ ํด๋์ค๋ก TorchScript ํ์ฅํ๊ธฐ</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>
<li class="toctree-l1"><a class="reference internal" href="../advanced/privateuseone.html">Facilitating New Backend Integration by PrivateUse1</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">๋ชจ๋ธ ์ต์ ํ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">PyTorch ๋ชจ๋ ํ๋กํ์ผ๋งํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensorboard_profiler_tutorial.html">ํ
์๋ณด๋๋ฅผ ์ด์ฉํ ํ์ดํ ์น ํ๋กํ์ผ๋ฌ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hyperparameter_tuning_tutorial.html">Ray Tune์ ์ฌ์ฉํ ํ์ดํผํ๋ผ๋ฏธํฐ ํ๋</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํด ๋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">(๋ฒ ํ) PyTorch์์ Eager Mode๋ฅผ ์ด์ฉํ ์ ์ ์์ํ</a></li>
<li class="toctree-l1"><a class="reference internal" href="torchserve_with_ipex.html">Grokking PyTorch Intel CPU performance from first principles</a></li>
<li class="toctree-l1"><a class="reference internal" href="torchserve_with_ipex_2.html">Grokking PyTorch Intel CPU performance from first principles (Part 2)</a></li>
<li class="toctree-l1"><a class="reference internal" href="nvfuser_intro_tutorial.html">Getting Started - Accelerate Your Scripts with nvFuser</a></li>
<li class="toctree-l1"><a class="reference internal" href="ax_multiobjective_nas_tutorial.html">Multi-Objective NAS with Ax</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_compile_tutorial.html">Introduction to <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="inductor_debug_cpu.html">Inductor CPU backend debugging and profiling</a></li>
<li class="toctree-l1"><a class="reference internal" href="scaled_dot_product_attention_tutorial.html">(Beta) Scaled Dot Product Attention (SDPA)๋ก ๊ณ ์ฑ๋ฅ ํธ๋์คํฌ๋จธ(Transformers) ๊ตฌํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="scaled_dot_product_attention_tutorial.html#torch-compile-sdpa"><code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> ๊ณผ ํจ๊ป SDPA ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="scaled_dot_product_attention_tutorial.html#sdpa-atteition-bias">SDPA๋ฅผ <code class="docutils literal notranslate"><span class="pre">atteition.bias</span></code> ํ์ ํด๋์ค์ ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="scaled_dot_product_attention_tutorial.html#id8">๊ฒฐ๋ก </a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/knowledge_distillation_tutorial.html">Knowledge Distillation Tutorial</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">๋ณ๋ ฌ ๋ฐ ๋ถ์ฐ ํ์ต</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../distributed/home.html">Distributed and Parallel Training Tutorials</a></li>
<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="../beginner/ddp_series_intro.html">Distributed Data Parallel in PyTorch - Video Tutorials</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="FSDP_tutorial.html">Getting Started with Fully Sharded Data Parallel(FSDP)</a></li>
<li class="toctree-l1"><a class="reference internal" href="FSDP_adavnced_tutorial.html">Advanced Model Training with Fully Sharded Data Parallel (FSDP)</a></li>
<li class="toctree-l1"><a class="reference internal" href="TP_tutorial.html">Large Scale Transformer model training with Tensor Parallel (TP)</a></li>
<li class="toctree-l1"><a class="reference internal" href="process_group_cpp_extension_tutorial.html">Cpp ํ์ฅ์ ์ฌ์ฉํ ํ๋ก์ธ์ค ๊ทธ๋ฃน ๋ฐฑ์๋ ์ฌ์ฉ์ ์ ์</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="../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>
</ul>
<p class="caption" role="heading"><span class="caption-text">Edge with ExecuTorch</span></p>
<ul>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/tutorials/export-to-executorch-tutorial.html">Exporting to ExecuTorch Tutorial</a></li>
<li class="toctree-l1"><a class="reference external" href=" https://pytorch.org/executorch/stable/running-a-model-cpp-tutorial.html">Running an ExecuTorch Model in C++ Tutorial</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/tutorials/sdk-integration-tutorial.html">Using the ExecuTorch SDK to Profile a Model</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/demo-apps-ios.html">Building an ExecuTorch iOS Demo App</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/demo-apps-android.html">Building an ExecuTorch Android Demo App</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/examples-end-to-end-to-lower-model-to-delegate.html">Lowering a Model as a Delegate</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">์ถ์ฒ ์์คํ
</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="torchrec_tutorial.html">TorchRec ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/sharding.html">Exploring TorchRec sharding</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Multimodality</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/flava_finetuning_tutorial.html">TorchMultimodal ํํ ๋ฆฌ์ผ: FLAVA ๋ฏธ์ธ์กฐ์ </a></li>
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<p class="admonition-title">์ฐธ๊ณ </p>
<p>Click <a class="reference internal" href="#sphx-glr-download-intermediate-char-rnn-generation-tutorial-py"><span class="std std-ref">here</span></a>
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<div class="sphx-glr-example-title section" id="nlp-rnn">
<span id="sphx-glr-intermediate-char-rnn-generation-tutorial-py"></span><h1>๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ์์ฑํ๊ธฐ<a class="headerlink" href="#nlp-rnn" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h1>
<dl class="simple">
<dt><strong>Author</strong>: <a class="reference external" href="https://github.com/spro">Sean Robertson</a></dt><dd><p><strong>๋ฒ์ญ</strong>: <a class="reference external" href="https://github.com/adonisues">ํฉ์ฑ์</a></p>
</dd>
</dl>
<p>์ด ํํ ๋ฆฌ์ผ์ 3๊ฐ๋ก ์ด๋ค์ง ใ๊ธฐ์ด๋ถํฐ ์์ํ๋ NLPใ์ 2๋ฒ์งธ ํํ ๋ฆฌ์ผ์
๋๋ค.
์ฒซ๋ฒ์งธ ํํ ๋ฆฌ์ผ์ธ <a class="reference internal" href="char_rnn_classification_tutorial.html"><span class="doc">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ๋ถ๋ฅํ๊ธฐ</span></a>
์์๋ RNN์ ์ฌ์ฉํ์ฌ ์ฃผ์ด์ง ์ด๋ฆ์ด ์ด๋ ํ ์ธ์ด์ธ์ง๋ฅผ ๋ถ๋ฅํ์ต๋๋ค.
์ด๋ฒ์๋ ๋ฐ๋๋ก ์ธ์ด๋ก๋ถํฐ ์ด๋ฆ์ ์์ฑํ ์์ ์
๋๋ค.</p>
<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>><span class="w"> </span>python<span class="w"> </span>sample.py<span class="w"> </span>Russian<span class="w"> </span>RUS
Rovakov
Uantov
Shavakov
><span class="w"> </span>python<span class="w"> </span>sample.py<span class="w"> </span>German<span class="w"> </span>GER
Gerren
Ereng
Rosher
><span class="w"> </span>python<span class="w"> </span>sample.py<span class="w"> </span>Spanish<span class="w"> </span>SPA
Salla
Parer
Allan
><span class="w"> </span>python<span class="w"> </span>sample.py<span class="w"> </span>Chinese<span class="w"> </span>CHI
Chan
Hang
Iun
</pre></div>
</div>
<p>์ฐ๋ฆฌ๋ ๋ช ๊ฐ์ ์ ํ ๊ณ์ธต์ผ๋ก ์์ RNN์ ์ง์ ๋ง๋ค๊ณ ์์ต๋๋ค.
์ด์ ํํ ๋ฆฌ์ผ์ธ ์ด๋ฆ์ ์ฝ์ ํ ๊ทธ ์ธ์ด๋ฅผ ์์ธกํ๋ ๊ฒ๊ณผ์ ํฐ ์ฐจ์ด์ ์
์ธ์ด๋ฅผ ์
๋ ฅํ๊ณ ํ ๋ฒ์ ํ ๊ธ์๋ฅผ ์์ฑํ์ฌ ์ถ๋ ฅํ๋ ๊ฒ์
๋๋ค.
์ธ์ด ํ์ฑ(๋จ์ด ๋๋ ๋ค๋ฅธ ๊ณ ์ฐจ์ ๊ตฌ์กฐ๋ก๋ ์ํ๋ ์ ์์)์ ์ํด
๋ฌธ์๋ฅผ ๋ฐ๋ณต์ ์ผ๋ก ์์ธกํ๋ ๊ฒ์ ใ์ธ์ด ๋ชจ๋ธใ ์ด๋ผ๊ณ ํฉ๋๋ค.</p>
<p><strong>์ถ์ฒ ์๋ฃ:</strong></p>
<p>Pytorch๋ฅผ ์ค์นํ๊ณ , Python์ ์๊ณ , Tensor๋ฅผ ์ดํดํ๋ค๊ณ ๊ฐ์ ํฉ๋๋ค:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://pytorch.org/">https://pytorch.org/</a> ์ค์น ์๋ด</p></li>
<li><p><a class="reference internal" href="../beginner/deep_learning_60min_blitz.html"><span class="doc">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</span></a> PyTorch ์์ํ๊ธฐ</p></li>
<li><p><a class="reference internal" href="../beginner/pytorch_with_examples.html"><span class="doc">์์ ๋ก ๋ฐฐ์ฐ๋ ํ์ดํ ์น(PyTorch)</span></a> ๋๊ณ ๊น์ ํต์ฐฐ์ ์ํ ์๋ฃ</p></li>
<li><p><a class="reference internal" href="../beginner/former_torchies_tutorial.html"><span class="doc">Torch ์ฌ์ฉ์๋ฅผ ์ํ PyTorch</span></a> ์ด์ Lua Torch ์ฌ์ฉ์๋ฅผ ์ํ ์๋ฃ</p></li>
</ul>
<p>RNN๊ณผ ์๋ ๋ฐฉ์์ ์๋ ๊ฒ ๋ํ ์ ์ฉํฉ๋๋ค:</p>
<ul class="simple">
<li><p><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.</p></li>
<li><p><a class="reference external" href="https://colah.github.io/posts/2015-08-Understanding-LSTMs/">Understanding LSTM
Networks</a>
LSTM์ ๊ดํ ๊ฒ์ด์ง๋ง RNN์ ๊ดํด์๋ ์ ์ตํฉ๋๋ค.</p></li>
</ul>
<p>์ด์ ํํ ๋ฆฌ์ผ๋ ์ถ์ฒํฉ๋๋ค. <a class="reference internal" href="char_rnn_classification_tutorial.html"><span class="doc">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ๋ถ๋ฅํ๊ธฐ</span></a></p>
<div class="section" id="id2">
<h2>๋ฐ์ดํฐ ์ค๋น<a class="headerlink" href="#id2" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<div class="admonition note">
<p class="admonition-title">์ฐธ๊ณ </p>
<p><a class="reference external" href="https://download.pytorch.org/tutorial/data.zip">์ฌ๊ธฐ</a>
์์ ๋ฐ์ดํฐ๋ฅผ ๋ค์ด ๋ฐ๊ณ , ํ์ฌ ๋๋ ํ ๋ฆฌ์ ์์ถ์ ํธ์ญ์์ค.</p>
</div>
<p>์ด ๊ณผ์ ์ ๋ ์์ธํ ์ฌํญ์ ์ง๋ ํํ ๋ฆฌ์ผ์ ๋ณด์ญ์์ค.
์์ฝํ๋ฉด, ์ค๋ง๋ค ์ด๋ฆ์ด ์ ํ ํ
์คํธ ํ์ผ <code class="docutils literal notranslate"><span class="pre">data/names/[Language].txt</span></code> ์์ต๋๋ค.
์ด๊ฒ์ array๋ก ๋ถ๋ฆฌํ๊ณ , Unicode๋ฅผ ASCII๋ก ๋ณ๊ฒฝํ๊ณ ,
์ฌ์ <code class="docutils literal notranslate"><span class="pre">{language:</span> <span class="pre">[names</span> <span class="pre">...]}</span></code> ์ ๋ง๋ค์ด์ ๋ง๋ฌด๋ฆฌํฉ๋๋ค.</p>
<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="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="o">+</span> <span class="mi">1</span> <span class="c1"># EOS(end of sentence) ๊ธฐํธ ์ถ๊ฐ</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="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="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="k">with</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="k">as</span> <span class="n">some_file</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="o">.</span><span class="n">strip</span><span class="p">())</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">some_file</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="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>
<span class="k">if</span> <span class="n">n_categories</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">'Data not found. Make sure that you downloaded data '</span>
<span class="s1">'from https://download.pytorch.org/tutorial/data.zip and extract it to '</span>
<span class="s1">'the current directory.'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'# categories:'</span><span class="p">,</span> <span class="n">n_categories</span><span class="p">,</span> <span class="n">all_categories</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="s2">"O'Nรฉร l"</span><span class="p">))</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span># categories: 18 ['German', 'Spanish', 'Chinese', 'Japanese', 'Scottish', 'Arabic', 'English', 'Vietnamese', 'Dutch', 'Irish', 'Czech', 'Polish', 'Russian', 'Korean', 'Italian', 'Greek', 'Portuguese', 'French']
O'Neal
</pre></div>
</div>
</div>
<div class="section" id="id4">
<h2>๋คํธ์ํฌ ์์ฑ<a class="headerlink" href="#id4" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>์ด ๋คํธ์ํฌ๋ <a class="reference external" href="#Creating-the-Network">์ง๋ ํํ ๋ฆฌ์ผ์ RNN</a> ์ด
๋ค๋ฅธ ์
๋ ฅ๋ค๊ณผ ์ฐ๊ฒฐ๋๋ category tensor๋ฅผ ์ถ๊ฐ ์ธ์๋ก ๊ฐ์ง๊ฒ ํ์ฅํฉ๋๋ค.
category tensor๋ ๋ฌธ์ ์
๋ ฅ๊ณผ ๋ง์ฐฌ๊ฐ์ง๋ก one-hot ๋ฒกํฐ์
๋๋ค.</p>
<p>์ญ์์ฃผ: ๊ธฐ์กด ์
๋ ฅ๊ณผ category tensor๋ฅผ ๊ฒฐํฉํ์ฌ ์
๋ ฅ์ผ๋ก ์ฌ์ฉํ๊ธฐ ๋๋ฌธ์
์
๋ ฅ์ ์ฌ์ด์ฆ๊ฐ n_categories ๋งํผ ์ปค์ง๋๋ค.</p>
<p>์ฐ๋ฆฌ๋ ์ถ๋ ฅ์ ๋ค์ ๋ฌธ์์ ํ๋ฅ ๋ก ํด์ํฉ๋๋ค. ์ํ๋ง ํ ๋,
๊ฐ์ฅ ํ๋ฅ ์ด ๋์ ๋ฌธ์๊ฐ ๋ค์ ์
๋ ฅ ๋ฌธ์๋ก ์ฌ์ฉ๋ฉ๋๋ค.</p>
<p>๋ ๋์ ๋์์ ์ํด ๋ ๋ฒ์งธ ์ ํ ๋ ์ด์ด
<code class="docutils literal notranslate"><span class="pre">o2o</span></code> (์๋๊ณผ ์ถ๋ ฅ์ ๊ฒฐํฉํ ํ) ๋ฅผ ์ถ๊ฐํ์ต๋๋ค .
๋ํ Drop-out ๊ณ์ธต์ด ์์ต๋๋ค. ์ด ๊ณ์ธต์ ์ฃผ์ด์ง ํ๋ฅ (์ฌ๊ธฐ์๋ 0.1)๋ก
<a class="reference external" href="https://arxiv.org/abs/1207.0580">๋ฌด์์๋ก ์
๋ ฅ์ 0 #</a> ์ผ๋ก ๋ง๋ญ๋๋ค.
์ผ๋ฐ์ ์ผ๋ก ์
๋ ฅ์ ํ๋ฆฌ๊ฒ ํด์ ๊ณผ์ ํฉ์ ๋ง๋ ๋ฐ ์ฌ์ฉ๋ฉ๋๋ค.
์ฌ๊ธฐ์ ์ฐ๋ฆฌ๋ ๊ณ ์๋ก ์ผ๋ถ ํผ๋์ ์ถ๊ฐํ๊ณ ์ํ๋ง ๋ค์์ฑ์ ๋์ด๊ธฐ
์ํด ๋คํธ์ํฌ์ ๋ง์ง๋ง์ ์ด๊ฒ์ ์ฌ์ฉํฉ๋๋ค.</p>
<div class="figure align-default">
<img alt="" src="https://i.imgur.com/jzVrf7f.png" />
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</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">n_categories</span> <span class="o">+</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">n_categories</span> <span class="o">+</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">o2o</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">hidden_size</span> <span class="o">+</span> <span class="n">output_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">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="mf">0.1</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="n">category</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">input_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="n">category</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">input_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">input_combined</span><span class="p">)</span>
<span class="n">output_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="n">hidden</span><span class="p">,</span> <span class="n">output</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="bp">self</span><span class="o">.</span><span class="n">o2o</span><span class="p">(</span><span class="n">output_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">dropout</span><span class="p">(</span><span class="n">output</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>
</pre></div>
</div>
</div>
<div class="section" id="id5">
<h2>ํ์ต<a class="headerlink" href="#id5" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<div class="section" id="id6">
<h3>ํ์ต ์ค๋น<a class="headerlink" href="#id6" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h3>
<p>์ ์ผ ๋จผ์ (category, line)์ ๋ฌด์์ ์์ ์ป๋ ํจ์:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>
<span class="c1"># ๋ชฉ๋ก์์ ๋ฌด์์ ์์ดํ
๋ฐํ</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="c1"># ์์์ category ๋ฐ ๊ทธ category์์ ๋ฌด์์ ์ค(์ด๋ฆ) ์ป๊ธฐ</span>
<span class="k">def</span> <span class="nf">randomTrainingPair</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="k">return</span> <span class="n">category</span><span class="p">,</span> <span class="n">line</span>
</pre></div>
</div>
<p>๊ฐ ์๊ฐ ๋จ๊ณ ๋ง๋ค (์ฆ, ํ์ต ๋จ์ด์ ๊ฐ ๋ฌธ์ ๋ง๋ค) ๋คํธ์ํฌ์ ์
๋ ฅ์
<code class="docutils literal notranslate"><span class="pre">(์ธ์ด,</span> <span class="pre">ํ์ฌ</span> <span class="pre">๋ฌธ์,</span> <span class="pre">์๋</span> <span class="pre">์ํ)</span></code> ๊ฐ ๋๊ณ , ์ถ๋ ฅ์
<code class="docutils literal notranslate"><span class="pre">(๋ค์</span> <span class="pre">๋ฌธ์,</span> <span class="pre">๋ค์</span> <span class="pre">์๋</span> <span class="pre">์ํ)</span></code> ๊ฐ ๋๋ค. ๋ฐ๋ผ์ ๊ฐ ํ์ต ์ธํธ ๋ง๋ค
์ธ์ด, ์
๋ ฅ ๋ฌธ์์ ์ธํธ, ์ถ๋ ฅ/๋ชฉํ ๋ฌธ์์ ์ธํธ๊ฐ ํ์ํ๋ค.</p>
<p>๊ฐ ์๊ฐ ๋จ๊ณ๋ง๋ค ํ์ฌ ๋ฌธ์์์ ๋ค์ ๋ฌธ์๋ฅผ ์์ธกํ๊ธฐ ๋๋ฌธ์,
๋ฌธ์ ์์ ํ ์ค(ํ๋์ ์ด๋ฆ)์์ ์ฐ์๋ ๋ฌธ์ ๊ทธ๋ฃน์
๋๋ค. - ์๋ฅผ ๋ค์ด <code class="docutils literal notranslate"><span class="pre">"ABCD<EOS>"</span></code> ๋
(ใAใ, ใBใ), (ใBใ, ใCใ), (ใCใ, ใDใ), (ใDใ, ใEOSใ) ๋ก ์์ฑํฉ๋๋ค.</p>
<div class="figure align-default">
<img alt="" src="https://i.imgur.com/JH58tXY.png" />
</div>
<p>Category(์ธ์ด) Tensor๋ <code class="docutils literal notranslate"><span class="pre"><1</span> <span class="pre">x</span> <span class="pre">n_categories></span></code> ํฌ๊ธฐ์ <a class="reference external" href="https://en.wikipedia.org/wiki/One-hot">One-hot
Tensor</a> ์
๋๋ค.
ํ์ต์์ ๋ชจ๋ ์๊ฐ ๋จ๊ณ์์ ๋คํธ์ํฌ์ ์ด๊ฒ์ ์ ๋ฌํฉ๋๋ค.
- ์ด๊ฒ์ ์ค๊ณ ์ ํ์ฌํญ์ผ๋ก, ์ด๊ธฐ ์๋ ์ํ ๋๋
๋ ๋ค๋ฅธ ์ ๋ต์ ๋ถ๋ถ์ผ๋ก ํฌํจ๋ ์ ์์ต๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Category๋ฅผ ์ํ One-hot ๋ฒกํฐ</span>
<span class="k">def</span> <span class="nf">categoryTensor</span><span class="p">(</span><span class="n">category</span><span class="p">):</span>
<span class="n">li</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">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_categories</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">li</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"># ์
๋ ฅ์ ์ํ ์ฒ์๋ถํฐ ๋ง์ง๋ง ๋ฌธ์(EOS ์ ์ธ)๊น์ง์ One-hot ํ๋ ฌ</span>
<span class="k">def</span> <span class="nf">inputTensor</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="ow">in</span> <span class="nb">range</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="n">letter</span> <span class="o">=</span> <span class="n">line</span><span class="p">[</span><span class="n">li</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">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="o">=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">tensor</span>
<span class="c1"># ๋ชฉํ๋ฅผ ์ํ ๋๋ฒ์งธ ๋ฌธ์ ๋ถํฐ ๋ง์ง๋ง(EOS)๊น์ง์ ``LongTensor``</span>
<span class="k">def</span> <span class="nf">targetTensor</span><span class="p">(</span><span class="n">line</span><span class="p">):</span>
<span class="n">letter_indexes</span> <span class="o">=</span> <span class="p">[</span><span class="n">all_letters</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="n">li</span><span class="p">])</span> <span class="k">for</span> <span class="n">li</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="nb">len</span><span class="p">(</span><span class="n">line</span><span class="p">))]</span>
<span class="n">letter_indexes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">n_letters</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># EOS</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">(</span><span class="n">letter_indexes</span><span class="p">)</span>
</pre></div>
</div>
<p>ํ์ต ๋์ ํธ์๋ฅผ ์ํด ๋ฌด์์๋ก (category[์ธ์ด], line[์ด๋ฆ])์ ๊ฐ์ ธ์ค๊ณ
๊ทธ๊ฒ์ ํ์ํ ํํ (category[์ธ์ด], input[ํ์ฌ ๋ฌธ์], target[๋ค์ ๋ฌธ์]) Tensor๋ก ๋ฐ๊พธ๋
<code class="docutils literal notranslate"><span class="pre">randomTrainingExample</span></code> ํจ์๋ฅผ ๋ง๋ค ์์ ์
๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># ์์์ Category์์ Category, Input, Target Tensor๋ฅผ ๋ง๋ญ๋๋ค.</span>
<span class="k">def</span> <span class="nf">randomTrainingExample</span><span class="p">():</span>
<span class="n">category</span><span class="p">,</span> <span class="n">line</span> <span class="o">=</span> <span class="n">randomTrainingPair</span><span class="p">()</span>
<span class="n">category_tensor</span> <span class="o">=</span> <span class="n">categoryTensor</span><span class="p">(</span><span class="n">category</span><span class="p">)</span>
<span class="n">input_line_tensor</span> <span class="o">=</span> <span class="n">inputTensor</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
<span class="n">target_line_tensor</span> <span class="o">=</span> <span class="n">targetTensor</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
<span class="k">return</span> <span class="n">category_tensor</span><span class="p">,</span> <span class="n">input_line_tensor</span><span class="p">,</span> <span class="n">target_line_tensor</span>
</pre></div>
</div>
</div>
<div class="section" id="id7">
<h3>๋คํธ์ํฌ ํ์ต<a class="headerlink" href="#id7" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h3>
<p>๋ง์ง๋ง ์ถ๋ ฅ๋ง ์ฌ์ฉํ๋ ๋ถ๋ฅ์ ๋ฌ๋ฆฌ, ๋ชจ๋ ๋จ๊ณ์์ ์์ธก์ ์ํํ๋ฏ๋ก
๋ชจ๋ ๋จ๊ณ์์ ์์ค์ ๊ณ์ฐํฉ๋๋ค.</p>
<p>Autograd์ ๋ง๋ฒ์ด ๊ฐ ๋จ๊ณ์ ์์ค๋ค์ ๊ฐ๋จํ๊ฒ ํฉํ๊ณ ๋ง์ง๋ง์
์ญ์ ํ๋ฅผ ํธ์ถํ๊ฒ ํด์ค๋๋ค.</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>
<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.0005</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">input_line_tensor</span><span class="p">,</span> <span class="n">target_line_tensor</span><span class="p">):</span>
<span class="n">target_line_tensor</span><span class="o">.</span><span class="n">unsqueeze_</span><span class="p">(</span><span class="o">-</span><span class="mi">1</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="n">loss</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="mi">0</span><span class="p">])</span> <span class="c1"># ๋๋ ๊ทธ๋ฅ ``loss = 0`` ์ ์ฌ์ฉํด๋ ๋ฉ๋๋ค.</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">input_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">category_tensor</span><span class="p">,</span> <span class="n">input_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">l</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">target_line_tensor</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="n">loss</span> <span class="o">+=</span> <span class="n">l</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</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> <span class="o">/</span> <span class="n">input_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>
</pre></div>
</div>
<p>ํ์ต์ ๊ฑธ๋ฆฌ๋ ์๊ฐ์ ์ถ์ ํ๊ธฐ ์ํด ์ฌ๋์ด ์ฝ์ ์ ์๋ ๋ฌธ์์ด์
๋ฐํํ๋``timeSince (timestamp)`` ํจ์๋ฅผ ์ถ๊ฐํฉ๋๋ค:</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="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>
</pre></div>
</div>
<p>ํ์ต์ ์ผ์์ ์ธ ์ผ์
๋๋ค. - ๋ช ๋ฒ train() ์ ํธ์ถํ๊ณ , ๋ช ๋ถ ์ ๋
๊ธฐ๋ค๋ ธ๋ค๊ฐ <code class="docutils literal notranslate"><span class="pre">print_every</span></code> ๋ง๋ค ํ์ฌ ์๊ฐ๊ณผ ์์ค์ ์ถ๋ ฅํ๊ณ ,
๋์ค์ ๋์ํ๋ฅผ ์ํด <code class="docutils literal notranslate"><span class="pre">plot_every</span></code> ๋ง๋ค <code class="docutils literal notranslate"><span class="pre">all_losses</span></code> ์
ํ๊ท ์์ค์ ์ ์ฅํฉ๋๋ค.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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="mi">128</span><span class="p">,</span> <span class="n">n_letters</span><span class="p">)</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">500</span>
<span class="n">all_losses</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">total_loss</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># ``plot_every`` ๋ง๋ค ์ด๊ธฐํ</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">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="o">*</span><span class="n">randomTrainingExample</span><span class="p">())</span>
<span class="n">total_loss</span> <span class="o">+=</span> <span class="n">loss</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="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="si">%s</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">%.4f</span><span class="s1">'</span> <span class="o">%</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="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">loss</span><span class="p">))</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">total_loss</span> <span class="o">/</span> <span class="n">plot_every</span><span class="p">)</span>
<span class="n">total_loss</span> <span class="o">=</span> <span class="mi">0</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0m 7s (5000 5%) 2.5547
0m 14s (10000 10%) 3.8634
0m 21s (15000 15%) 3.2839
0m 28s (20000 20%) 2.5857
0m 35s (25000 25%) 2.7410
0m 42s (30000 30%) 3.0850
0m 50s (35000 35%) 1.6788
0m 57s (40000 40%) 2.8393
1m 4s (45000 45%) 2.2868
1m 11s (50000 50%) 2.9968
1m 18s (55000 55%) 2.4167
1m 25s (60000 60%) 2.8477
1m 33s (65000 65%) 2.8144
1m 40s (70000 70%) 2.6576
1m 47s (75000 75%) 2.0202
1m 55s (80000 80%) 2.7593
2m 3s (85000 85%) 2.7914
2m 11s (90000 90%) 1.9012
2m 18s (95000 95%) 2.5127
2m 26s (100000 100%) 2.3950
</pre></div>
</div>
</div>
<div class="section" id="id8">
<h3>์์ค ๋์ํ<a class="headerlink" href="#id8" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h3>
<p>all_losses๋ฅผ ์ด์ฉํ ์์ค์ ๋์ํ๋
๋คํธ์ํฌ์ ํ์ต ์ํ๋ฅผ ๋ณด์ฌ์ค๋๋ค:</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="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 src="../_images/sphx_glr_char_rnn_generation_tutorial_001.png" srcset="../_images/sphx_glr_char_rnn_generation_tutorial_001.png" alt="char rnn generation tutorial" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[<matplotlib.lines.Line2D object at 0x7784bcb0d1b0>]
</pre></div>
</div>
</div>
</div>
<div class="section" id="id9">
<h2>๋คํธ์ํฌ ์ํ๋ง<a class="headerlink" href="#id9" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>์ํ๋ง์ ์ํด์, ๋คํธ์ํฌ์ ํ๋์ ๊ธ์๋ฅผ ์ฃผ๊ณ ๋ค์ ๋ฌธ์๋ฅผ ๋ฌผ์ด๋ณด๊ณ
์ด๊ฒ์ ๋ค์ ๋ฌธ์๋ก ์ ๋ฌํ๋ ๊ฒ์ EOS ํ ํฐ๊น์ง ๋ฐ๋ณตํฉ๋๋ค.</p>
<ul class="simple">
<li><p>์
๋ ฅ ์นดํ
๊ณ ๋ฆฌ(์ธ์ด), ์์ ๋ฌธ์, ๋น์ด ์๋ ์๋ ์ํ๋ฅผ ์ํ Tensor๋ฅผ ์์ฑํ์ญ์์ค</p></li>
<li><p>์์ ๋ฌธ์๋ก <code class="docutils literal notranslate"><span class="pre">output_name</span></code> ๋ฌธ์์ด์ ์์ฑํ์ญ์์ค</p></li>
<li><p>์ต๋ ์ถ๋ ฅ ๊ธธ์ด๊น์ง,</p>
<ul>
<li><p>ํ์ฌ ๋ฌธ์๋ฅผ ๋คํธ์ํฌ์ ์ ๋ฌํ์ญ์์ค.</p></li>
<li><p>๊ฐ์ฅ ๋์ ์ถ๋ ฅ์์ ๋ค์ ๋ฌธ์์ ๋ค์ ์๋ ์ํ๋ฅผ ์ป์ผ์ญ์์ค</p></li>
<li><p>๋ง์ผ ๋ฌธ์๊ฐ EOS๋ฉด, ์ฌ๊ธฐ์ ๋ฉ์ถ์ญ์์ค</p></li>
<li><p>๋ง์ผ ์ผ๋ฐ์ ์ธ ๋ฌธ์๋ผ๋ฉด, <code class="docutils literal notranslate"><span class="pre">output_name</span></code> ์ ์ถ๊ฐํ๊ณ ๊ณ์ํ์ญ์์ค</p></li>
</ul>
</li>
<li><p>๋ง์ง๋ง ์ด๋ฆ์ ๋ฐํํ์ญ์์ค</p></li>
</ul>
<div class="admonition note">
<p class="admonition-title">์ฐธ๊ณ </p>
<p>์์ ๋ฌธ์๋ฅผ ์ฃผ๋ ๊ฒ ์ธ์ ใ๋ฌธ์์ด ์์ใ ํ ํฐ์ ํ์ต์
ํฌํจ๋๊ฒ ํ๊ณ ๋คํธ์ํฌ๊ฐ ์์ฒด์ ์ผ๋ก ์์ ๋ฌธ์๋ฅผ ์ ํํ๊ฒ ํ๋
๋ค๋ฅธ ๋ฐฉ๋ฒ๋ ์์ต๋๋ค.</p>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">max_length</span> <span class="o">=</span> <span class="mi">20</span>
<span class="c1"># ์นดํ
๊ณ ๋ฆฌ์ ์์ ๋ฌธ์๋ก๋ถํฐ ์ํ๋ง ํ๊ธฐ</span>
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="n">category</span><span class="p">,</span> <span class="n">start_letter</span><span class="o">=</span><span class="s1">'A'</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="c1"># ์ํ๋ง์์ ํ์คํ ๋ฆฌ๋ฅผ ์ถ์ ํ ํ์ ์์</span>
<span class="n">category_tensor</span> <span class="o">=</span> <span class="n">categoryTensor</span><span class="p">(</span><span class="n">category</span><span class="p">)</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">inputTensor</span><span class="p">(</span><span class="n">start_letter</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">output_name</span> <span class="o">=</span> <span class="n">start_letter</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">max_length</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">category_tensor</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="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="mi">1</span><span class="p">)</span>
<span class="n">topi</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="mi">0</span><span class="p">]</span>
<span class="k">if</span> <span class="n">topi</span> <span class="o">==</span> <span class="n">n_letters</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">break</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">letter</span> <span class="o">=</span> <span class="n">all_letters</span><span class="p">[</span><span class="n">topi</span><span class="p">]</span>
<span class="n">output_name</span> <span class="o">+=</span> <span class="n">letter</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">inputTensor</span><span class="p">(</span><span class="n">letter</span><span class="p">)</span>
<span class="k">return</span> <span class="n">output_name</span>
<span class="c1"># ํ๋์ ์นดํ
๊ณ ๋ฆฌ์ ์ฌ๋ฌ ์์ ๋ฌธ์๋ค๋ก ์ฌ๋ฌ ๊ฐ์ ์ํ ์ป๊ธฐ</span>
<span class="k">def</span> <span class="nf">samples</span><span class="p">(</span><span class="n">category</span><span class="p">,</span> <span class="n">start_letters</span><span class="o">=</span><span class="s1">'ABC'</span><span class="p">):</span>
<span class="k">for</span> <span class="n">start_letter</span> <span class="ow">in</span> <span class="n">start_letters</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">sample</span><span class="p">(</span><span class="n">category</span><span class="p">,</span> <span class="n">start_letter</span><span class="p">))</span>
<span class="n">samples</span><span class="p">(</span><span class="s1">'Russian'</span><span class="p">,</span> <span class="s1">'RUS'</span><span class="p">)</span>
<span class="n">samples</span><span class="p">(</span><span class="s1">'German'</span><span class="p">,</span> <span class="s1">'GER'</span><span class="p">)</span>
<span class="n">samples</span><span class="p">(</span><span class="s1">'Spanish'</span><span class="p">,</span> <span class="s1">'SPA'</span><span class="p">)</span>
<span class="n">samples</span><span class="p">(</span><span class="s1">'Chinese'</span><span class="p">,</span> <span class="s1">'CHI'</span><span class="p">)</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Roveki
Uallak
Sonhako
Gares
Eren
Roun
Salla
Parana
Aller
Cha
Han
Iun
</pre></div>
</div>
</div>
<div class="section" id="exercises">
<h2>Exercises<a class="headerlink" href="#exercises" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<ul class="simple">
<li><p>Try with a different dataset of category -> line, for example:</p>
<ul>
<li><p>Fictional series -> Character name</p></li>
<li><p>Part of speech -> Word</p></li>
<li><p>Country -> City</p></li>
</ul>
</li>
<li><p>Use a ใstart of sentenceใ token so that sampling can be done without
choosing a start letter</p></li>
<li><p>Get better results with a bigger and/or better shaped network</p>
<ul>
<li><p>Try the <code class="docutils literal notranslate"><span class="pre">nn.LSTM</span></code> and <code class="docutils literal notranslate"><span class="pre">nn.GRU</span></code> layers</p></li>
<li><p>์์ ์์ค ๋คํธ์ํฌ๋ก ์ฌ๋ฌ ๊ฐ์ ์ด๋ฐ RNN์ ๊ฒฐํฉํด ๋ณด์ญ์์ค</p></li>
</ul>
</li>
</ul>
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