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<title>TensorBoard๋ก ๋ชจ๋ธ, ๋ฐ์ดํฐ, ํ์ต ์๊ฐํํ๊ธฐ — 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 class="current">
<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 current"><a class="current reference internal" href="#">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>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transformer_tutorial.html">nn.Transformer ์ TorchText ๋ก ์ํ์ค-ํฌ-์ํ์ค(Sequence-to-Sequence) ๋ชจ๋ธ๋งํ๊ธฐ</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"><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>
</ul>
<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>
<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"><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="../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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<p class="caption"><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>
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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>
</ul>
<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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<h1>TensorBoard๋ก ๋ชจ๋ธ, ๋ฐ์ดํฐ, ํ์ต ์๊ฐํํ๊ธฐ<a class="headerlink" href="#tensorboard" title="Permalink to this headline">ยถ</a></h1>
<p><a class="reference internal" href="../beginner/deep_learning_60min_blitz.html"><span class="doc">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</span></a> ์์๋ ๋ฐ์ดํฐ๋ฅผ ๋ถ๋ฌ์ค๊ณ , <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> ์
์๋ธํด๋์ค(subclass)๋ก ์ ์ํ ๋ชจ๋ธ์ ๋ฐ์ดํฐ๋ฅผ ๊ณต๊ธ(feed)ํ๊ณ , ํ์ต ๋ฐ์ดํฐ๋ก ๋ชจ๋ธ์ ํ์ตํ๊ณ
ํ
์คํธ ๋ฐ์ดํฐ๋ก ํ
์คํธ๋ฅผ ํ๋ ๋ฐฉ๋ฒ๋ค์ ์ดํด๋ดค์ต๋๋ค. ์งํ ์ํฉ์ ์ดํด๋ณด๊ธฐ ์ํด,
ํ์ต์ด ์งํ ์ค์ผ ๋ ํ์ต์ด ์ ๋๊ณ ์๋์ง๋ฅผ ์์๋ณด๊ธฐ ์ํด ๋ช๋ช ์์น๋ค(statistic)์ ์ถ๋ ฅํ์ต๋๋ค.
ํ์ง๋ง ๋ ๋์ ๋ฐฉ๋ฒ๋ ์์ต๋๋ค: PyTorch๋ ์ ๊ฒฝ๋ง ํ์ต ๋ด์ญ์ ์๊ฐํํ๋ ๋๊ตฌ์ธ TensorBoard์
ํตํฉ๋์์ต๋๋ค. ์ด ํํ ๋ฆฌ์ผ์์๋ PyTorch์ <cite>torchvision.datasets</cite> ์์ ์ฌ์ฉ ๊ฐ๋ฅํ
<a class="reference external" href="https://github.com/zalandoresearch/fashion-mnist">Fashion-MNIST dataset</a> ์ผ๋ก
์ผ๋ถ ๊ธฐ๋ฅ์ ์ค๋ช
ํ๊ฒ ์ต๋๋ค.</p>
<p>์ด ํํ ๋ฆฌ์ผ์์๋ ๋ค์์ ๋ด์ฉ๋ค์ ๋ฐฐ์๋๋ค:</p>
<blockquote>
<div><ol class="arabic simple">
<li>(์ด์ ํํ ๋ฆฌ์ผ๊ณผ ๊ฑฐ์ ๋น์ทํ๊ฒ) ๋ฐ์ดํฐ๋ฅผ ์ฝ๊ณ ์ ์ ํ ๋ณํํฉ๋๋ค.</li>
<li>TensorBoard๋ฅผ ์ค์ (set up)ํฉ๋๋ค.</li>
<li>TensorBoard์ ๊ธฐ๋ก(write)ํฉ๋๋ค.</li>
<li>TensorBoard๋ฅผ ์ฌ์ฉํ์ฌ ๋ชจ๋ธ ๊ตฌ์กฐ๋ฅผ ์ดํด๋ด
๋๋ค.</li>
<li>์ฝ๊ฐ์ ์ฝ๋๋ฅผ ์ถ๊ฐํ์ฌ TensorBoard์์ ์ด์ ํํ ๋ฆฌ์ผ์์ ๋ง๋ ์๊ฐํ์
๋ํ์(interactive) ๋ฒ์ ์ ๋ง๋ญ๋๋ค.</li>
</ol>
</div></blockquote>
<p>๊ตฌ์ฒด์ ์ผ๋ก #5์์๋ ๋ค์ ๋ด์ฉ๋ค์ ์ดํด๋ณด๋๋ก ํ๊ฒ ์ต๋๋ค:</p>
<blockquote>
<div><ul class="simple">
<li>ํ์ต ๋ฐ์ดํฐ๋ฅผ ๊ฒ์ฌ(inspect)ํ๋ ๋ช ๊ฐ์ง ๋ฐฉ๋ฒ</li>
<li>ํ์ต์ ๋ฐ๋ฅธ ๋ชจ๋ธ์ ์ฑ๋ฅ์ ์ถ์ (track)ํ๋ ๋ฐฉ๋ฒ</li>
<li>ํ์ต์ด ์๋ฃ๋ ๋ชจ๋ธ์ ์ฑ๋ฅ์ ํ๊ฐ(assess)ํ๋ ๋ฐฉ๋ฒ</li>
</ul>
</div></blockquote>
<p><a class="reference internal" href="../beginner/blitz/cifar10_tutorial.html"><span class="doc">๋ถ๋ฅ๊ธฐ(Classifier) ํ์ตํ๊ธฐ</span></a> ์ ๋น์ทํ ์ฝ๋๋ก ์์ํด๋ณด๊ฒ ์ต๋๋ค:</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># imports</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">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torchvision</span>
<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
<span class="c1"># transforms</span>
<span class="n">transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">(</span>
<span class="p">[</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,),</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,))])</span>
<span class="c1"># datasets</span>
<span class="n">trainset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">FashionMNIST</span><span class="p">(</span><span class="s1">'./data'</span><span class="p">,</span>
<span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
<span class="n">testset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">FashionMNIST</span><span class="p">(</span><span class="s1">'./data'</span><span class="p">,</span>
<span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
<span class="c1"># dataloaders</span>
<span class="n">trainloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">trainset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
<span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">testloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">testset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
<span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="c1"># ๋ถ๋ฅ ๊ฒฐ๊ณผ๋ฅผ ์ํ ์์</span>
<span class="n">classes</span> <span class="o">=</span> <span class="p">(</span><span class="s1">'T-shirt/top'</span><span class="p">,</span> <span class="s1">'Trouser'</span><span class="p">,</span> <span class="s1">'Pullover'</span><span class="p">,</span> <span class="s1">'Dress'</span><span class="p">,</span> <span class="s1">'Coat'</span><span class="p">,</span>
<span class="s1">'Sandal'</span><span class="p">,</span> <span class="s1">'Shirt'</span><span class="p">,</span> <span class="s1">'Sneaker'</span><span class="p">,</span> <span class="s1">'Bag'</span><span class="p">,</span> <span class="s1">'Ankle Boot'</span><span class="p">)</span>
<span class="c1"># ์ด๋ฏธ์ง๋ฅผ ๋ณด์ฌ์ฃผ๊ธฐ ์ํ ํฌํผ(helper) ํจ์</span>
<span class="c1"># (์๋ `plot_classes_preds` ํจ์์์ ์ฌ์ฉ)</span>
<span class="k">def</span> <span class="nf">matplotlib_imshow</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">one_channel</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">if</span> <span class="n">one_channel</span><span class="p">:</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">img</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">+</span> <span class="mf">0.5</span> <span class="c1"># unnormalize</span>
<span class="n">npimg</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
<span class="k">if</span> <span class="n">one_channel</span><span class="p">:</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">npimg</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">"Greys"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">npimg</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
</pre></div>
</div>
<p>์ด์ ํํ ๋ฆฌ์ผ๊ณผ ์ ์ฌํ ๋ชจ๋ธ ๊ตฌ์กฐ๋ฅผ ์ ์ํ๋, ์ด๋ฏธ์ง์ ์ฑ๋์ด 3๊ฐ์์ 1๊ฐ๋ก,
ํฌ๊ธฐ๊ฐ 32x32์์ 28x28๋ก ๋ณ๊ฒฝ๋ ๊ฒ์ ์ ์ฉํ ์ ์๋๋ก ์ฝ๊ฐ๋ง ์์ ํ๊ฒ ์ต๋๋ค:</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Net</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="nb">super</span><span class="p">(</span><span class="n">Net</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">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc1</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="mi">16</span> <span class="o">*</span> <span class="mi">4</span> <span class="o">*</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">120</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc2</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="mi">120</span><span class="p">,</span> <span class="mi">84</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc3</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="mi">84</span><span class="p">,</span> <span class="mi">10</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">x</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">16</span> <span class="o">*</span> <span class="mi">4</span> <span class="o">*</span> <span class="mi">4</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="n">net</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span>
</pre></div>
</div>
<p>๋์ผํ <code class="docutils literal notranslate"><span class="pre">optimizer</span></code> ์ <code class="docutils literal notranslate"><span class="pre">criterion</span></code> ์ ์ ์ํฉ๋๋ค:</p>
<div class="code python 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">CrossEntropyLoss</span><span class="p">()</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
</pre></div>
</div>
<div class="section" id="id1">
<h2>1. TensorBoard ์ค์ <a class="headerlink" href="#id1" title="Permalink to this headline">ยถ</a></h2>
<p>์ด์ <code class="docutils literal notranslate"><span class="pre">torch.utils</span></code> ์ <code class="docutils literal notranslate"><span class="pre">tensorboard</span></code> ๋ฅผ ๋ถ๋ฌ์ค๊ณ , TensorBoard์ ์ ๋ณด๋ฅผ
์ ๊ณต(write)ํ๋ <code class="docutils literal notranslate"><span class="pre">SummaryWriter</span></code> ๋ฅผ ์ฃผ์ํ ๊ฐ์ฒด์ธ <code class="docutils literal notranslate"><span class="pre">SummaryWriter</span></code> ๋ฅผ ์ ์ํ์ฌ
TensorBoard๋ฅผ ์ค์ ํฉ๋๋ค.</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="c1"># ๊ธฐ๋ณธ `log_dir` ์ "runs"์ด๋ฉฐ, ์ฌ๊ธฐ์๋ ๋ ๊ตฌ์ฒด์ ์ผ๋ก ์ง์ ํ์์ต๋๋ค</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">(</span><span class="s1">'runs/fashion_mnist_experiment_1'</span><span class="p">)</span>
</pre></div>
</div>
<p>์ ํ(line)์ <code class="docutils literal notranslate"><span class="pre">runs/fashion_mnist_experiment_1</span></code> ํด๋๋ฅผ ์์ฑํฉ๋๋ค.</p>
</div>
<div class="section" id="id2">
<h2>2. TensorBoard์ ๊ธฐ๋กํ๊ธฐ<a class="headerlink" href="#id2" title="Permalink to this headline">ยถ</a></h2>
<p>์ด์ TensorBoard์ ์ด๋ฏธ์ง(๊ตฌ์ฒด์ ์ผ๋ก๋
<a class="reference external" href="https://pytorch.org/vision/stable/utils.html#torchvision.utils.make_grid">make_grid</a>
๋ฅผ ์ฌ์ฉํ์ฌ ๊ทธ๋ฆฌ๋(grid))๋ฅผ ์จ๋ณด๊ฒ ์ต๋๋ค.</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># ์์์ ํ์ต ์ด๋ฏธ์ง๋ฅผ ๊ฐ์ ธ์ต๋๋ค</span>
<span class="n">dataiter</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">trainloader</span><span class="p">)</span>
<span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">dataiter</span><span class="o">.</span><span class="n">next</span><span class="p">()</span>
<span class="c1"># ์ด๋ฏธ์ง ๊ทธ๋ฆฌ๋๋ฅผ ๋ง๋ญ๋๋ค.</span>
<span class="n">img_grid</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">make_grid</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
<span class="c1"># ์ด๋ฏธ์ง๋ฅผ ๋ณด์ฌ์ค๋๋ค.</span>
<span class="n">matplotlib_imshow</span><span class="p">(</span><span class="n">img_grid</span><span class="p">,</span> <span class="n">one_channel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># tensorboard์ ๊ธฐ๋กํฉ๋๋ค.</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_image</span><span class="p">(</span><span class="s1">'four_fashion_mnist_images'</span><span class="p">,</span> <span class="n">img_grid</span><span class="p">)</span>
</pre></div>
</div>
<p>์ด์ ๋ช
๋ น์ค(command line)์์</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tensorboard</span> <span class="o">--</span><span class="n">logdir</span><span class="o">=</span><span class="n">runs</span>
</pre></div>
</div>
<p>๋ฅผ ์คํํ๊ณ , <a class="reference external" href="http://localhost:6006">http://localhost:6006</a> ์ ์ด์ด๋ณด๋ฉด
๋ค์๊ณผ ๊ฐ์ ํ๋ฉด์ด ๋ํ๋ฉ๋๋ค.</p>
<img alt="intermediate/../../_static/img/tensorboard_first_view.png" src="intermediate/../../_static/img/tensorboard_first_view.png" />
<p>์ง๊ธ๊น์ง TensorBoard๋ฅผ ์ด๋ป๊ฒ ์ฌ์ฉํ๋์ง๋ฅผ ์์๋ณด์์ต๋๋ค! ๊ทธ๋ฌ๋, ์ด ์์ ๋
Jupyter Notebook์์๋ ๊ฐ๋ฅํฉ๋๋ค - TensorBoard๋ ๋ํํ ์๊ฐํ๋ฅผ ๋ง๋๋๋ฐ
ํนํ ๋ฐ์ด๋ฉ๋๋ค. ๋ค์์ ๊ทธ๋ฐ ๊ธฐ๋ฅ๋ค ์ค ํ๋๋ฅผ ์ดํด๋ณด๊ณ , ํํ ๋ฆฌ์ผ์ ๋๋จธ์ง ๋ถ๋ถ์์
๋ช ๊ฐ์ง ๊ธฐ๋ฅ๋ค์ ๋ ์ดํด๋ณด๊ฒ ์ต๋๋ค.</p>
</div>
<div class="section" id="tensorboard-inspect">
<h2>3. TensorBoard๋ฅผ ์ฌ์ฉํ์ฌ ๋ชจ๋ธ ์ดํด๋ณด๊ธฐ(inspect)<a class="headerlink" href="#tensorboard-inspect" title="Permalink to this headline">ยถ</a></h2>
<p>TensorBoard์ ๊ฐ์ ์ค ํ๋๋ ๋ณต์กํ ๋ชจ๋ธ ๊ตฌ์กฐ๋ฅผ ์๊ฐํํ๋ ๊ธฐ๋ฅ์
๋๋ค.
์ง๊ธ๊น์ง ๋ง๋ ๋ชจ๋ธ์ ์๊ฐํํด๋ณด๊ฒ ์ต๋๋ค.</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">writer</span><span class="o">.</span><span class="n">add_graph</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">images</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
<p>TensorBoard๋ฅผ ์๋ก๊ณ ์นจ(refresh)ํ๋ฉด ์๋์ ๊ฐ์ด โGraphsโ ํญ์ ๋ณผ ์ ์์ต๋๋ค:</p>
<img alt="intermediate/../../_static/img/tensorboard_model_viz.png" src="intermediate/../../_static/img/tensorboard_model_viz.png" />
<p>์๋์์ โNetโ์ ๋๋ธํด๋ฆญํ์ฌ ํผ์ณ๋ณด๋ฉด, ๋ชจ๋ธ์ ๊ตฌ์ฑํ๋ ๊ฐ๋ณ ์ฐ์ฐ(operation)๋ค์
๋ํด ์์ธํ ๋ณผ ์ ์์ต๋๋ค.</p>
<p>TensorBoard๋ ์ด๋ฏธ์ง ๋ฐ์ดํฐ์ ๊ฐ์ ๊ณ ์ฐจ์ ๋ฐ์ดํฐ๋ฅผ ์ ์ฐจ์ ๊ณต๊ฐ์ ์๊ฐํํ๋๋ฐ
๋งค์ฐ ํธ๋ฆฌํ ๊ธฐ๋ฅ๋ค์ ์ ๊ณตํฉ๋๋ค; ์์ผ๋ก ์ด ๋ด์ฉ์ ์์๋ณด๊ฒ ์ต๋๋ค.</p>
</div>
<div class="section" id="tensorboard-projector">
<h2>4. TensorBoard์ โProjectorโ ์ถ๊ฐํ๊ธฐ<a class="headerlink" href="#tensorboard-projector" title="Permalink to this headline">ยถ</a></h2>
<p><a class="reference external" href="https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_embedding">add_embedding</a>
๋ฉ์๋(method)๋ฅผ ํตํด ๊ณ ์ฐจ์ ๋ฐ์ดํฐ์ ์ ์ฐจ์ ํํ(representation)์ ์๊ฐํํ ์ ์์ต๋๋ค.</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># ํฌํผ(helper) ํจ์</span>
<span class="k">def</span> <span class="nf">select_n_random</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
<span class="sd">'''</span>
<span class="sd"> ๋ฐ์ดํฐ์
์์ n๊ฐ์ ์์์ ๋ฐ์ดํฐํฌ์ธํธ(datapoint)์ ๊ทธ์ ํด๋นํ๋ ๋ผ๋ฒจ์ ์ ํํฉ๋๋ค</span>
<span class="sd"> '''</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
<span class="n">perm</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randperm</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
<span class="k">return</span> <span class="n">data</span><span class="p">[</span><span class="n">perm</span><span class="p">][:</span><span class="n">n</span><span class="p">],</span> <span class="n">labels</span><span class="p">[</span><span class="n">perm</span><span class="p">][:</span><span class="n">n</span><span class="p">]</span>
<span class="c1"># ์์์ ์ด๋ฏธ์ง๋ค๊ณผ ์ ๋ต(target) ์ธ๋ฑ์ค๋ฅผ ์ ํํฉ๋๋ค</span>
<span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">select_n_random</span><span class="p">(</span><span class="n">trainset</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">trainset</span><span class="o">.</span><span class="n">targets</span><span class="p">)</span>
<span class="c1"># ๊ฐ ์ด๋ฏธ์ง์ ๋ถ๋ฅ ๋ผ๋ฒจ(class label)์ ๊ฐ์ ธ์ต๋๋ค</span>
<span class="n">class_labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">classes</span><span class="p">[</span><span class="n">lab</span><span class="p">]</span> <span class="k">for</span> <span class="n">lab</span> <span class="ow">in</span> <span class="n">labels</span><span class="p">]</span>
<span class="c1"># ์๋ฒ ๋ฉ(embedding) ๋ด์ญ์ ๊ธฐ๋กํฉ๋๋ค</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">images</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">28</span> <span class="o">*</span> <span class="mi">28</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_embedding</span><span class="p">(</span><span class="n">features</span><span class="p">,</span>
<span class="n">metadata</span><span class="o">=</span><span class="n">class_labels</span><span class="p">,</span>
<span class="n">label_img</span><span class="o">=</span><span class="n">images</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
<p>์ด์ TensorBoard์ โProjectorโ ํญ์์ - ๊ฐ๊ฐ์ 784 ์ฐจ์์ธ - 100๊ฐ์ ์ด๋ฏธ์ง๊ฐ
3์ฐจ์ ๊ณต๊ฐ์ ํฌ์ฌ(project)๋ ๊ฒ์ ๋ณผ ์ ์์ต๋๋ค. ๋ํ, ์ด๊ฒ์ ๋ํ์์
๋๋ค:
ํด๋ฆญํ๊ณ ๋๋๊ทธ(drag)ํ์ฌ 3์ฐจ์์ผ๋ก ํฌ์๋ ๊ฒ์ ํ์ ํ ์ ์์ต๋๋ค. ๋ง์ง๋ง์ผ๋ก
์๊ฐํ๋ฅผ ๋ ํธํ ๋ณผ ์ ์๋ ๋ช ๊ฐ์ง ํ์ด ์์ต๋๋ค: ์ข์ธก ์๋จ์์ โColor by: labelโ์
์ ํํ๊ณ , โ์ผ๊ฐ๋ชจ๋(night mode)โ๋ฅผ ํ์ฑํํ๋ฉด ์ด๋ฏธ์ง ๋ฐฐ๊ฒฝ์ด ํฐ์์ด ๋์ด ๋ ํธํ๊ฒ
๋ณผ ์ ์์ต๋๋ค:</p>
<img alt="intermediate/../../_static/img/tensorboard_projector.png" src="intermediate/../../_static/img/tensorboard_projector.png" />
<p>์ง๊ธ๊น์ง ๋ฐ์ดํฐ๋ฅผ ์ถฉ๋ถํ ์ดํด๋ณด์์ผ๋ฏ๋ก, ์ด์ ํ์ต ๊ณผ์ ๋ถํฐ ์์ํ์ฌ TensorBoard๊ฐ
์ด๋ป๊ฒ ๋ชจ๋ธ ํ์ต๊ณผ ํ๊ฐ(evaluation)๋ฅผ ๋ ๋ช
ํํ ์ถ์ (track)ํ ์ ์๋์ง ์ดํด๋ณด๊ฒ ์ต๋๋ค.</p>
</div>
<div class="section" id="id3">
<h2>5. TensorBoard๋ก ๋ชจ๋ธ ํ์ต ์ถ์ ํ๊ธฐ<a class="headerlink" href="#id3" title="Permalink to this headline">ยถ</a></h2>
<p>์ด์ ์์ ์์๋ ๋จ์ํ ๋ชจ๋ธ ํ์ต ์ค ์์ค(running loss)์ 2000๋ฒ ๋ฐ๋ณตํ ๋๋ง๋ค
<em>์ถ๋ ฅ</em> ํ๊ธฐ๋ง ํ์ต๋๋ค. ์ด์ TensorBoard์ ํ์ต ์ค ์์ค์ ๊ธฐ๋กํ๋ ๊ฒ ๋์ ์
<code class="docutils literal notranslate"><span class="pre">plot_classes_preds</span></code> ํจ์๋ฅผ ํตํด ๋ชจ๋ธ์ ์์ธก ๊ฒฐ๊ณผ๋ฅผ ํจ๊ป ๋ณผ ์ ์๋๋ก ํ๊ฒ ์ต๋๋ค.</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># ํฌํผ ํจ์</span>
<span class="k">def</span> <span class="nf">images_to_probs</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">images</span><span class="p">):</span>
<span class="sd">'''</span>
<span class="sd"> ํ์ต๋ ์ ๊ฒฝ๋ง๊ณผ ์ด๋ฏธ์ง ๋ชฉ๋ก์ผ๋ก๋ถํฐ ์์ธก ๊ฒฐ๊ณผ ๋ฐ ํ๋ฅ ์ ์์ฑํฉ๋๋ค</span>
<span class="sd"> '''</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
<span class="c1"># convert output probabilities to predicted class</span>
<span class="n">_</span><span class="p">,</span> <span class="n">preds_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</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">preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">preds_tensor</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
<span class="k">return</span> <span class="n">preds</span><span class="p">,</span> <span class="p">[</span><span class="n">F</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">el</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">el</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">output</span><span class="p">)]</span>
<span class="k">def</span> <span class="nf">plot_classes_preds</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">images</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="sd">'''</span>
<span class="sd"> ํ์ต๋ ์ ๊ฒฝ๋ง๊ณผ ๋ฐฐ์น๋ก๋ถํฐ ๊ฐ์ ธ์จ ์ด๋ฏธ์ง / ๋ผ๋ฒจ์ ์ฌ์ฉํ์ฌ matplotlib</span>
<span class="sd"> Figure๋ฅผ ์์ฑํฉ๋๋ค. ์ด๋ ์ ๊ฒฝ๋ง์ ์์ธก ๊ฒฐ๊ณผ / ํ๋ฅ ๊ณผ ํจ๊ป ์ ๋ต์ ๋ณด์ฌ์ฃผ๋ฉฐ,</span>
<span class="sd"> ์์ธก ๊ฒฐ๊ณผ๊ฐ ๋ง์๋์ง ์ฌ๋ถ์ ๋ฐ๋ผ ์์ ๋ค๋ฅด๊ฒ ํ์ํฉ๋๋ค. "images_to_probs"</span>
<span class="sd"> ํจ์๋ฅผ ์ฌ์ฉํฉ๋๋ค.</span>
<span class="sd"> '''</span>
<span class="n">preds</span><span class="p">,</span> <span class="n">probs</span> <span class="o">=</span> <span class="n">images_to_probs</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">images</span><span class="p">)</span>
<span class="c1"># ๋ฐฐ์น์์ ์ด๋ฏธ์ง๋ฅผ ๊ฐ์ ธ์ ์์ธก ๊ฒฐ๊ณผ / ์ ๋ต๊ณผ ํจ๊ป ํ์(plot)ํฉ๋๋ค</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">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">48</span><span class="p">))</span>
<span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">4</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">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">idx</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">xticks</span><span class="o">=</span><span class="p">[],</span> <span class="n">yticks</span><span class="o">=</span><span class="p">[])</span>
<span class="n">matplotlib_imshow</span><span class="p">(</span><span class="n">images</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">one_channel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"</span><span class="si">{0}</span><span class="s2">, </span><span class="si">{1:.1f}</span><span class="s2">%</span><span class="se">\n</span><span class="s2">(label: </span><span class="si">{2}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">classes</span><span class="p">[</span><span class="n">preds</span><span class="p">[</span><span class="n">idx</span><span class="p">]],</span>
<span class="n">probs</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">*</span> <span class="mf">100.0</span><span class="p">,</span>
<span class="n">classes</span><span class="p">[</span><span class="n">labels</span><span class="p">[</span><span class="n">idx</span><span class="p">]]),</span>
<span class="n">color</span><span class="o">=</span><span class="p">(</span><span class="s2">"green"</span> <span class="k">if</span> <span class="n">preds</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">==</span><span class="n">labels</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="k">else</span> <span class="s2">"red"</span><span class="p">))</span>
<span class="k">return</span> <span class="n">fig</span>
</pre></div>
</div>
<p>๋ง์ง๋ง์ผ๋ก, ์ด์ ํํ ๋ฆฌ์ผ๊ณผ ๋์ผํ ๋ชจ๋ธ ํ์ต ์ฝ๋์์ 1000 ๋ฐฐ์น๋ง๋ค ์ฝ์์
์ถ๋ ฅํ๋ ๋์ ์ TensorBoard์ ๊ฒฐ๊ณผ๋ฅผ ๊ธฐ๋กํ๋๋ก ํ์ฌ ํ์ต์ ํด๋ณด๊ฒ ์ต๋๋ค;
์ด๋ <a class="reference external" href="https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_scalar">add_scalar</a>
ํจ์๋ฅผ ์ฌ์ฉํฉ๋๋ค.</p>
<p>๋ํ, ํ์ต์ ์งํํ๋ฉด์ ๋ฐฐ์น์ ํฌํจ๋ 4๊ฐ์ ์ด๋ฏธ์ง์ ๋ํ ๋ชจ๋ธ์ ์์ธก ๊ฒฐ๊ณผ์
์ ๋ต์ ๋น๊ต(versus)ํ์ฌ ๋ณด์ฌ์ฃผ๋ ์ด๋ฏธ์ง๋ฅผ ์์ฑํ๋๋ก ํ๊ฒ ์ต๋๋ค.</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">running_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">):</span> <span class="c1"># ๋ฐ์ดํฐ์
์ ์ฌ๋ฌ๋ฒ ๋ฐ๋ณต</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">trainloader</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span>
<span class="c1"># [inputs, labels]์ ๋ชฉ๋ก์ธ data๋ก๋ถํฐ ์
๋ ฅ์ ๋ฐ์ ํ;</span>
<span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">data</span>
<span class="c1"># ๋ณํ๋(Gradient) ๋งค๊ฐ๋ณ์๋ฅผ 0์ผ๋ก ๋ง๋ค๊ณ </span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="c1"># ์์ ํ + ์ญ์ ํ + ์ต์ ํ๋ฅผ ํ ํ</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">inputs</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">outputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="n">running_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">%</span> <span class="mi">1000</span> <span class="o">==</span> <span class="mi">999</span><span class="p">:</span> <span class="c1"># ๋งค 1000 ๋ฏธ๋๋ฐฐ์น๋ง๋ค...</span>
<span class="c1"># ...ํ์ต ์ค ์์ค(running loss)์ ๊ธฐ๋กํ๊ณ </span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_scalar</span><span class="p">(</span><span class="s1">'training loss'</span><span class="p">,</span>
<span class="n">running_loss</span> <span class="o">/</span> <span class="mi">1000</span><span class="p">,</span>
<span class="n">epoch</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">trainloader</span><span class="p">)</span> <span class="o">+</span> <span class="n">i</span><span class="p">)</span>
<span class="c1"># ...๋ฌด์์ ๋ฏธ๋๋ฐฐ์น(mini-batch)์ ๋ํ ๋ชจ๋ธ์ ์์ธก ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ฌ์ฃผ๋๋ก</span>
<span class="c1"># Matplotlib Figure๋ฅผ ๊ธฐ๋กํฉ๋๋ค</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_figure</span><span class="p">(</span><span class="s1">'predictions vs. actuals'</span><span class="p">,</span>
<span class="n">plot_classes_preds</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">),</span>
<span class="n">global_step</span><span class="o">=</span><span class="n">epoch</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">trainloader</span><span class="p">)</span> <span class="o">+</span> <span class="n">i</span><span class="p">)</span>
<span class="n">running_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Finished Training'</span><span class="p">)</span>
</pre></div>
</div>
<p>์ด์ โScalarsโ ํญ์์ 15,000๋ฒ ๋ฐ๋ณต ํ์ตํ ๋์ ์์ค์ ํ์ธํ ์ ์์ต๋๋ค:</p>
<img alt="intermediate/../../_static/img/tensorboard_scalar_runs.png" src="intermediate/../../_static/img/tensorboard_scalar_runs.png" />
<p>๋ํ, ํ์ต ๊ณผ์ ์ ๋ฐ์ ๊ฑธ์ณ ์์์ ๋ฐฐ์น์ ๋ํ ๋ชจ๋ธ์ ์์ธก ๊ฒฐ๊ณผ๋ฅผ ํ์ธํ ์ ์์ต๋๋ค.
โImagesโ ํญ์์ ์คํฌ๋กค์ ๋ด๋ ค โ์์ธก vs. ์ ๋ต(predictions vs. actuals)โ ์๊ฐํ ๋ถ๋ถ์์
์ด ๋ด์ฉ์ ๋ณผ ์ ์์ต๋๋ค; ์๋ฅผ ๋ค์ด ํ์ต์ ๋จ์ง 3000๋ฒ ๋ฐ๋ณตํ๊ธฐ๋ง ํด๋, ์ ๋ขฐ๋๋
๋์ง ์์ง๋ง, ๋ชจ๋ธ์ ์
์ธ ์ ์ด๋ํ(sneakers), ์ฝํธ์ ๊ฐ์ ๋ถ๋ฅ๋ค์ ๊ตฌ๋ถํ ์ ์์์ต๋๋ค:</p>
<img alt="intermediate/../../_static/img/tensorboard_images.png" src="intermediate/../../_static/img/tensorboard_images.png" />
<p>์ด์ ํํ ๋ฆฌ์ผ์์๋ ๋ชจ๋ธ์ด ํ์ต ์๋ฃ๋ ํ์ ๊ฐ ๋ถ๋ฅ๋ณ ์ ํ๋(per-class accuracy)๋ฅผ
์ดํด๋ดค์ต๋๋ค; ์ฌ๊ธฐ์๋ TensorBoard๋ฅผ ์ฌ์ฉํ์ฌ ๊ฐ ๋ถ๋ฅ๋ณ ์ ๋ฐ๋-์ฌํ์จ(precision-recall)
๊ณก์ ( <a class="reference external" href="https://www.scikit-yb.org/en/latest/api/classifier/prcurve.html">์ฌ๊ธฐ</a> ์
์ข์ ์ค๋ช
์ด ์์ต๋๋ค)์ ๊ทธ๋ ค๋ณด๊ฒ ์ต๋๋ค.</p>
</div>
<div class="section" id="id4">
<h2>6. TensorBoard๋ก ํ์ต๋ ๋ชจ๋ธ ํ๊ฐํ๊ธฐ<a class="headerlink" href="#id4" title="Permalink to this headline">ยถ</a></h2>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 1. ์์ธก ํ๋ฅ ์ test_size x num_classes ํ
์๋ก ๊ฐ์ ธ์ต๋๋ค</span>
<span class="c1"># 2. ์์ธก ๊ฒฐ๊ณผ๋ฅผ test_size ํ
์๋ก ๊ฐ์ ธ์ต๋๋ค</span>
<span class="c1"># ์คํํ๋๋ฐ 10์ด ์ดํ ์์</span>
<span class="n">class_probs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">class_label</span> <span class="o">=</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="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">testloader</span><span class="p">:</span>
<span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">data</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
<span class="n">class_probs_batch</span> <span class="o">=</span> <span class="p">[</span><span class="n">F</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">el</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">output</span><span class="p">]</span>
<span class="n">class_probs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">class_probs_batch</span><span class="p">)</span>
<span class="n">class_label</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
<span class="n">test_probs</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">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">class_probs</span><span class="p">])</span>
<span class="n">test_label</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">class_label</span><span class="p">)</span>
<span class="c1"># ํฌํผ ํจ์</span>
<span class="k">def</span> <span class="nf">add_pr_curve_tensorboard</span><span class="p">(</span><span class="n">class_index</span><span class="p">,</span> <span class="n">test_probs</span><span class="p">,</span> <span class="n">test_label</span><span class="p">,</span> <span class="n">global_step</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="sd">'''</span>
<span class="sd"> 0๋ถํฐ 9๊น์ง์ "class_index"๋ฅผ ๊ฐ์ ธ์จ ํ ํด๋น ์ ๋ฐ๋-์ฌํ์จ(precision-recall)</span>
<span class="sd"> ๊ณก์ ์ ๊ทธ๋ฆฝ๋๋ค</span>
<span class="sd"> '''</span>
<span class="n">tensorboard_truth</span> <span class="o">=</span> <span class="n">test_label</span> <span class="o">==</span> <span class="n">class_index</span>
<span class="n">tensorboard_probs</span> <span class="o">=</span> <span class="n">test_probs</span><span class="p">[:,</span> <span class="n">class_index</span><span class="p">]</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_pr_curve</span><span class="p">(</span><span class="n">classes</span><span class="p">[</span><span class="n">class_index</span><span class="p">],</span>
<span class="n">tensorboard_truth</span><span class="p">,</span>
<span class="n">tensorboard_probs</span><span class="p">,</span>
<span class="n">global_step</span><span class="o">=</span><span class="n">global_step</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="c1"># ๋ชจ๋ ์ ๋ฐ๋-์ฌํ์จ(precision-recall; pr) ๊ณก์ ์ ๊ทธ๋ฆฝ๋๋ค</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="nb">len</span><span class="p">(</span><span class="n">classes</span><span class="p">)):</span>
<span class="n">add_pr_curve_tensorboard</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">test_probs</span><span class="p">,</span> <span class="n">test_preds</span><span class="p">)</span>
</pre></div>
</div>
<p>์ด์ โPR Curvesโ ํญ์์ ๊ฐ ๋ถ๋ฅ๋ณ ์ ๋ฐ๋-์ฌํ์จ ๊ณก์ ์ ๋ณผ ์ ์์ต๋๋ค.
๋ด๋ ค๋ณด๋ฉด์ ์ดํด๋ณด์ญ์์ค; ์ผ๋ถ ๋ถ๋ฅ๋ ๊ฑฐ์ 100%์ โ์์ญ์ด ๊ณก์ ์๋โ์ ์๊ณ ,
๋ค๋ฅธ ๋ถ๋ฅ๋ค์ ์ด ์์ญ์ด ๋ ์ ์ต๋๋ค:</p>
<img alt="intermediate/../../_static/img/tensorboard_pr_curves.png" src="intermediate/../../_static/img/tensorboard_pr_curves.png" />
<p>์ฌ๊ธฐ๊น์ง TensorBoard์ PyTorch์ ํตํฉ์ ๋ํด ์๊ฐํ์ต๋๋ค. ๋ฌผ๋ก TensorBoard์์
์ ๊ณตํ๋ ๋ชจ๋ ๊ฒ๋ค์ Jupyter Notebook์์๋ ํ ์ ์์ง๋ง, TensorBoard๋ฅผ ์ฌ์ฉํ๋ฉด
๋ํํ ์๊ฐํ๊ฐ ๊ธฐ๋ณธ์ผ๋ก ์ ๊ณต๋ฉ๋๋ค.</p>
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