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doc: update docs/pytorch.md (#554) 5fc5c065df
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备忘清单为您提供了 <a href="https://pytorch.org/">Pytorch</a> 基本语法和初步应用参考</p>
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</div></header><div class="menu-tocs"><div class="menu-btn"><svg aria-hidden="true" fill="currentColor" height="1em" width="1em" viewBox="0 0 16 16" version="1.1" data-view-component="true">
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</svg></div><div class="menu-modal"><a aria-hidden="true" class="leve2 tocs-link" data-num="2" href="#入门">入门</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#介绍">介绍</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#认识-pytorch">认识 Pytorch</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#创建一个全零矩阵">创建一个全零矩阵</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#数据创建张量">数据创建张量</a><a aria-hidden="true" class="leve2 tocs-link" data-num="2" href="#pytorch-的基本语法">Pytorch 的基本语法</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#加法操作1">加法操作(1)</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#加法操作2">加法操作(2)</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#加法操作3">加法操作(3)</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#加法操作4">加法操作(4)</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#张量操作">张量操作</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#张量形状">张量形状</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#取张量元素">取张量元素</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#torch-tensor-和-numpy-array互换">Torch Tensor 和 Numpy array互换</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#torch-tensor-转换为-numpy-array">Torch Tensor 转换为 Numpy array</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#numpy-array转换为torch-tensor">Numpy array转换为Torch Tensor</a><a aria-hidden="true" class="leve2 tocs-link" data-num="2" href="#导入-imports">导入 Imports</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#一般">一般</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#神经网络-api">神经网络 API</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#torchscript-和-jit">Torchscript 和 JIT</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#onnx">ONNX</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#vision">Vision</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#分布式训练">分布式训练</a><a aria-hidden="true" class="leve2 tocs-link" data-num="2" href="#另见">另见</a></div></div><div class="h1wrap-body"><div class="wrap h2body-exist"><div class="wrap-header h2wrap"><h2 id="入门"><a aria-hidden="true" tabindex="-1" href="#入门"><span class="icon icon-link"></span></a>入门</h2><div class="wrap-body">
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</svg></div><div class="menu-modal"><a aria-hidden="true" class="leve2 tocs-link" data-num="2" href="#入门">入门</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#介绍">介绍</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#认识-pytorch">认识 Pytorch</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#创建一个全零矩阵">创建一个全零矩阵</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#数据创建张量">数据创建张量</a><a aria-hidden="true" class="leve2 tocs-link" data-num="2" href="#pytorch-的基本语法">Pytorch 的基本语法</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#加法操作1">加法操作(1)</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#加法操作2">加法操作(2)</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#加法操作3">加法操作(3)</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#加法操作4">加法操作(4)</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#张量操作">张量操作</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#张量形状">张量形状</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#取张量元素">取张量元素</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#torch-tensor-和-numpy-array互换">Torch Tensor 和 Numpy array互换</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#torch-tensor-转换为-numpy-array">Torch Tensor 转换为 Numpy array</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#numpy-array转换为torch-tensor">Numpy array转换为Torch Tensor</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#squeeze函数">squeeze函数</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#unsqueeze函数">unsqueeze函数</a><a aria-hidden="true" class="leve2 tocs-link" data-num="2" href="#导入-imports">导入 Imports</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#一般">一般</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#神经网络-api">神经网络 API</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#torchscript-和-jit">Torchscript 和 JIT</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#onnx">ONNX</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#vision">Vision</a><a aria-hidden="true" class="leve3 tocs-link" data-num="3" href="#分布式训练">分布式训练</a><a aria-hidden="true" class="leve2 tocs-link" data-num="2" href="#另见">另见</a></div></div><div class="h1wrap-body"><div class="wrap h2body-exist"><div class="wrap-header h2wrap"><h2 id="入门"><a aria-hidden="true" tabindex="-1" href="#入门"><span class="icon icon-link"></span></a>入门</h2><div class="wrap-body">
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</div></div><div class="h2wrap-body"><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="介绍"><a aria-hidden="true" tabindex="-1" href="#介绍"><span class="icon icon-link"></span></a>介绍</h3><div class="wrap-body">
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<ul>
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<li><a href="https://pytorch.org/">Pytorch 官网</a> <em>(pytorch.org)</em></li>
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</span></code></pre>
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<!--rehype:className=wrap-text-->
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<p>注意: 所有在CPU上的Tensors, 除了CharTensor, 都可以转换为Numpy array并可以反向转换.</p>
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</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="squeeze函数"><a aria-hidden="true" tabindex="-1" href="#squeeze函数"><span class="icon icon-link"></span></a>squeeze函数</h3><div class="wrap-body">
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<pre class="language-python"><code class="language-python code-highlight"><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> x <span class="token operator">=</span> torch<span class="token punctuation">.</span>rand<span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">28</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">)</span>
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</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> x<span class="token punctuation">.</span>squeeze<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">.</span>shape <span class="token comment"># squeeze不加参数，默认去除所有为1的维度</span>
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</span><span class="code-line">torch<span class="token punctuation">.</span>Size<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">28</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
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</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> x<span class="token punctuation">.</span>squeeze<span class="token punctuation">(</span>dim<span class="token operator">=</span><span class="token number">0</span><span class="token punctuation">)</span><span class="token punctuation">.</span>shape <span class="token comment"># squeeze加参数，去除指定为1的维度</span>
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</span><span class="code-line">torch<span class="token punctuation">.</span>Size<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">28</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
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</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> x<span class="token punctuation">.</span>squeeze<span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">.</span>shape <span class="token comment"># squeeze加参数，如果不为1，则不变</span>
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</span><span class="code-line">torch<span class="token punctuation">.</span>Size<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">28</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
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</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> torch<span class="token punctuation">.</span>squeeze<span class="token punctuation">(</span>x<span class="token punctuation">,</span><span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">.</span>shape <span class="token comment"># 既可以是函数，也可以是方法</span>
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</span><span class="code-line">torch<span class="token punctuation">.</span>Size<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">28</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
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</span></code></pre>
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</div></div></div><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="unsqueeze函数"><a aria-hidden="true" tabindex="-1" href="#unsqueeze函数"><span class="icon icon-link"></span></a>unsqueeze函数</h3><div class="wrap-body">
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<pre class="language-python"><code class="language-python code-highlight"><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> x <span class="token operator">=</span> torch<span class="token punctuation">.</span>rand<span class="token punctuation">(</span><span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">28</span><span class="token punctuation">)</span>
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</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> x<span class="token punctuation">.</span>unsqueeze<span class="token punctuation">(</span><span class="token number">0</span><span class="token punctuation">)</span><span class="token punctuation">.</span>shape <span class="token comment"># unsqueeze必须加参数， _ 2 _ 28 _</span>
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</span><span class="code-line">torch<span class="token punctuation">.</span>Size<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">28</span><span class="token punctuation">]</span><span class="token punctuation">)</span> <span class="token comment"># 参数代表在哪里添加维度 0 1 2</span>
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</span><span class="code-line"><span class="token operator">>></span><span class="token operator">></span> torch<span class="token punctuation">.</span>unsqueeze<span class="token punctuation">(</span>x<span class="token punctuation">,</span> <span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">.</span>shape <span class="token comment"># 既可以是函数，也可以是方法</span>
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</span><span class="code-line">torch<span class="token punctuation">.</span>Size<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">28</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
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</span></code></pre>
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</div></div></div></div></div><div class="wrap h2body-exist"><div class="wrap-header h2wrap"><h2 id="导入-imports"><a aria-hidden="true" tabindex="-1" href="#导入-imports"><span class="icon icon-link"></span></a>导入 Imports</h2><div class="wrap-body">
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</div></div><div class="h2wrap-body"><div class="wrap h3body-not-exist"><div class="wrap-header h3wrap"><h3 id="一般"><a aria-hidden="true" tabindex="-1" href="#一般"><span class="icon icon-link"></span></a>一般</h3><div class="wrap-body">
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<pre class="wrap-text"><code class="language-python code-highlight"><span class="code-line"><span class="token comment"># 根包</span>
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