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<?xml version='1.0' encoding='UTF-8'?>
<rss xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/" version="2.0">
<channel>
<title>Random Thoughts</title>
<link>https://haoxue.dev</link>
<description>Random Thoughts From Yiping</description>
<docs>http://www.rssboard.org/rss-specification</docs>
<generator>python-feedgen</generator>
<lastBuildDate>Wed, 20 Nov 2024 08:29:07 +0000</lastBuildDate>
<item>
<title>About</title>
<link>https://haoxue.dev/page/about.html</link>
<description><h2 id="about-me">About Me</h2>
<p>I'm Yiping Qi. I have been at <a href="https://www.meituan.com">Meituan Dianping</a> since 2013
working on machine learning, deep learning and operations
research. Before that, I worked at Yahoo! Beijing R&amp;D Center and
<a href="http://www.jike.com">JIKE</a>.</p>
<p>I got my Bachelor and Master degrees from <a href="http://cfins.au.tsinghua.edu.cn/en/about/index.php">Tsinghua
University</a>.</p>
<h2 id="contact">Contact</h2>
<p>You can send me an email to <a href="&#109;&#97;&#105;&#108;&#116;&#111;&#58;&#113;&#105;&#121;&#105;&#112;&#105;&#110;&#103;&#64;&#111;&#117;&#116;&#108;&#111;&#111;&#107;&#46;&#99;&#111;&#109;">&#113;&#105;&#121;&#105;&#112;&#105;&#110;&#103;&#64;&#111;&#117;&#116;&#108;&#111;&#111;&#107;&#46;&#99;&#111;&#109;</a>.</p></description>
<content:encoded><h2 id="about-me">About Me</h2>
<p>I'm Yiping Qi. I have been at <a href="https://www.meituan.com">Meituan Dianping</a> since 2013
working on machine learning, deep learning and operations
research. Before that, I worked at Yahoo! Beijing R&amp;D Center and
<a href="http://www.jike.com">JIKE</a>.</p>
<p>I got my Bachelor and Master degrees from <a href="http://cfins.au.tsinghua.edu.cn/en/about/index.php">Tsinghua
University</a>.</p>
<h2 id="contact">Contact</h2>
<p>You can send me an email to <a href="&#109;&#97;&#105;&#108;&#116;&#111;&#58;&#113;&#105;&#121;&#105;&#112;&#105;&#110;&#103;&#64;&#111;&#117;&#116;&#108;&#111;&#111;&#107;&#46;&#99;&#111;&#109;">&#113;&#105;&#121;&#105;&#112;&#105;&#110;&#103;&#64;&#111;&#117;&#116;&#108;&#111;&#111;&#107;&#46;&#99;&#111;&#109;</a>.</p></content:encoded>
<guid isPermaLink="false">https://haoxue.dev/page/about.html</guid>
</item>
<item>
<title>AutoDiff From Scratch</title>
<link>https://haoxue.dev/post/autodiff-from-scratch.html</link>
<description><div class="codehilite"><pre><span></span><code><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">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
</code></pre></div>
<h1 id="autodiff">AutoDiff学习模仿</h1>
<p>Credit:<a href="https://github.com/karpathy/micrograd">karpathy/micrograd</a></p>
<div class="codehilite"><pre><span></span><code><span class="k">def</span> <span class="nf">unbroadcast</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">shape</span><span class="p">):</span>
<span class="n">sum_axis</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</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">shape</span><span class="p">))</span> <span class="k">if</span> <span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">==</span><span class="mi">1</span> <span class="ow">and</span> <span class="n">out</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">]</span>
<span class="k">return</span> <span class="n">out</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="n">sum_axis</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">Tensor</span><span class="p">(</span><span class="nb">object</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">data</span><span class="p">,</span> <span class="n">children</span><span class="o">=</span><span class="p">(),</span> <span class="n">op</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">grad_required</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad_required</span> <span class="o">=</span> <span class="n">grad_required</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="k">if</span> <span class="n">grad_required</span> <span class="k">else</span> <span class="kc">None</span>
<span class="c1"># internal variables used for autograd graph construction</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_backward</span> <span class="o">=</span> <span class="k">lambda</span><span class="p">:</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_prev</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">children</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_op</span> <span class="o">=</span> <span class="n">op</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="n">name</span>
<span class="k">def</span> <span class="nf">zero_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c1"># topological order all of the children in the graph</span>
<span class="n">topo</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">visited</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">build_topo</span><span class="p">(</span><span class="n">v</span><span class="p">):</span>
<span class="k">if</span> <span class="n">v</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">visited</span><span class="p">:</span>
<span class="n">visited</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
<span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="n">v</span><span class="o">.</span><span class="n">_prev</span><span class="p">:</span>
<span class="n">build_topo</span><span class="p">(</span><span class="n">child</span><span class="p">)</span>
<span class="n">topo</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
<span class="n">build_topo</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="c1"># go one variable at a time and apply the chain rule to get its gradient</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">reversed</span><span class="p">(</span><span class="n">topo</span><span class="p">):</span>
<span class="n">v</span><span class="o">.</span><span class="n">_backward</span><span class="p">()</span>
<span class="c1"># operators</span>
<span class="k">def</span> <span class="nf">mean</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="p">),</span> <span class="s1">&#39;mean&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_backward</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="n">out</span><span class="o">.</span><span class="n">grad</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">grad</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">out</span><span class="o">.</span><span class="n">_backward</span> <span class="o">=</span> <span class="n">_backward</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">def</span> <span class="nf">matmul</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
<span class="n">other</span> <span class="o">=</span> <span class="n">other</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">grad_required</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">@</span> <span class="n">other</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">),</span> <span class="s1">&#39;matmul&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_backward</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="n">out</span><span class="o">.</span><span class="n">grad</span> <span class="o">@</span> <span class="n">other</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">T</span>
<span class="k">if</span> <span class="n">other</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="n">other</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">out</span><span class="o">.</span><span class="n">grad</span>
<span class="n">out</span><span class="o">.</span><span class="n">_backward</span> <span class="o">=</span> <span class="n">_backward</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">def</span> <span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
<span class="n">other</span> <span class="o">=</span> <span class="n">other</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">grad_required</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">+</span> <span class="n">other</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">),</span> <span class="s1">&#39;+&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_backward</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="n">unbroadcast</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">if</span> <span class="n">other</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="n">other</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="n">unbroadcast</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">out</span><span class="o">.</span><span class="n">_backward</span> <span class="o">=</span> <span class="n">_backward</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">def</span> <span class="fm">__mul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
<span class="n">other</span> <span class="o">=</span> <span class="n">other</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">grad_required</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">*</span> <span class="n">other</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">),</span> <span class="s1">&#39;*&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_backward</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="n">unbroadcast</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">data</span> <span class="o">*</span> <span class="n">out</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">if</span> <span class="n">other</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="n">other</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="n">unbroadcast</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">*</span> <span class="n">out</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">out</span><span class="o">.</span><span class="n">_backward</span> <span class="o">=</span> <span class="n">_backward</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">def</span> <span class="fm">__pow__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">)),</span> <span class="s2">&quot;only supporting int/float powers for now&quot;</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">**</span><span class="n">other</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="p">,),</span> <span class="sa">f</span><span class="s1">&#39;**</span><span class="si">{</span><span class="n">other</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_backward</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="n">unbroadcast</span><span class="p">((</span><span class="n">other</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">**</span><span class="p">(</span><span class="n">other</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="n">out</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">out</span><span class="o">.</span><span class="n">_backward</span> <span class="o">=</span> <span class="n">_backward</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">def</span> <span class="nf">relu</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="bp">self</span><span class="p">,),</span> <span class="s1">&#39;relu&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_backward</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">data</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">out</span><span class="o">.</span><span class="n">grad</span>
<span class="n">out</span><span class="o">.</span><span class="n">_backward</span> <span class="o">=</span> <span class="n">_backward</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">def</span> <span class="fm">__neg__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="c1"># -self</span>
<span class="k">return</span> <span class="bp">self</span> <span class="o">*</span> <span class="o">-</span><span class="mi">1</span>
<span class="k">def</span> <span class="fm">__radd__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> <span class="c1"># other + self</span>
<span class="k">return</span> <span class="bp">self</span> <span class="o">+</span> <span class="n">other</span>
<span class="k">def</span> <span class="fm">__sub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> <span class="c1"># self - other</span>
<span class="k">return</span> <span class="bp">self</span> <span class="o">+</span> <span class="p">(</span><span class="o">-</span><span class="n">other</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__rsub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> <span class="c1"># other - self</span>
<span class="k">return</span> <span class="n">other</span> <span class="o">+</span> <span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__rmul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> <span class="c1"># other * self</span>
<span class="k">return</span> <span class="bp">self</span> <span class="o">*</span> <span class="n">other</span>
<span class="k">def</span> <span class="fm">__truediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> <span class="c1"># self / other</span>
<span class="k">return</span> <span class="bp">self</span> <span class="o">*</span> <span class="n">other</span><span class="o">**-</span><span class="mi">1</span>
<span class="k">def</span> <span class="fm">__rtruediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> <span class="c1"># other / self</span>
<span class="k">return</span> <span class="n">other</span> <span class="o">*</span> <span class="bp">self</span><span class="o">**-</span><span class="mi">1</span>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;Tensor(data=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="si">}</span><span class="s2">, grad=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">grad</span><span class="si">}</span><span class="s2">, shape=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">)&quot;</span>
</code></pre></div>
<div class="codehilite"><pre><span></span><code><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_moons</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_moons</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">*</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">1</span> <span class="c1"># make y be -1 or 1</span>
<span class="c1"># visualize in 2D</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">5</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;jet&#39;</span><span class="p">)</span>
</code></pre></div>
<div class="codehilite"><pre><span></span><code><span class="o">&lt;</span><span class="n">matplotlib</span><span class="o">.</span><span class="n">collections</span><span class="o">.</span><span class="n">PathCollection</span><span class="w"> </span><span class="n">at</span><span class="w"> </span><span class="mh">0x130ba21c0</span><span class="o">&gt;</span>
<span class="o">/</span><span class="n">usr</span><span class="o">/</span><span class="n">local</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python3</span><span class="o">.</span><span class="mi">8</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">IPython</span><span class="o">/</span><span class="n">core</span><span class="o">/</span><span class="n">pylabtools</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">132</span><span class="p">:</span><span class="w"> </span><span class="n">UserWarning</span><span class="p">:</span><span class="w"> </span><span class="n">Glyph</span><span class="w"> </span><span class="mi">8722</span><span class="w"> </span><span class="p">(</span>\<span class="n">N</span><span class="p">{</span><span class="n">MINUS</span><span class="w"> </span><span class="n">SIGN</span><span class="p">})</span><span class="w"> </span><span class="n">missing</span><span class="w"> </span><span class="n">from</span><span class="w"> </span><span class="n">current</span><span class="w"> </span><span class="n">font</span><span class="o">.</span>
<span class="w"> </span><span class="n">fig</span><span class="o">.</span><span class="n">canvas</span><span class="o">.</span><span class="n">print_figure</span><span class="p">(</span><span class="n">bytes_io</span><span class="p">,</span><span class="w"> </span><span class="o">**</span><span class="n">kw</span><span class="p">)</span>
</code></pre></div>
<p><img alt="png" src="autodiff-from-scratch_files/output_3_2.png" /></p>
<div class="codehilite"><pre><span></span><code><span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="nb">object</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="bp">self</span><span class="o">.</span><span class="n">w1</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</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">16</span><span class="p">)),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;w1&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">b1</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,(</span><span class="mi">1</span><span class="p">,</span><span class="mi">16</span><span class="p">)),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;b1&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">w2</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,(</span><span class="mi">16</span><span class="p">,</span><span class="mi">16</span><span class="p">)),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;w2&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">b2</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,(</span><span class="mi">1</span><span class="p">,</span><span class="mi">16</span><span class="p">)),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;b2&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">w3</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,(</span><span class="mi">16</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;w3&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">b3</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;b3&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">w1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">b1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">w2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">b2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">w3</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">b3</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">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">grad_required</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">h1</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">w1</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">b1</span>
<span class="n">h1</span> <span class="o">=</span> <span class="n">h1</span><span class="o">.</span><span class="n">relu</span><span class="p">()</span>
<span class="n">h2</span> <span class="o">=</span> <span class="n">h1</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">w2</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">b2</span>
<span class="n">h2</span> <span class="o">=</span> <span class="n">h2</span><span class="o">.</span><span class="n">relu</span><span class="p">()</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">h2</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">w3</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">b3</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">def</span> <span class="nf">zero_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="p">:</span>
<span class="n">param</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">epochs</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">):</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">p</span><span class="o">*</span><span class="n">y</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">relu</span><span class="p">()</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">m</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">backward</span><span class="p">()</span>
<span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">params</span><span class="p">:</span>
<span class="n">param</span><span class="o">.</span><span class="n">data</span> <span class="o">-=</span> <span class="n">param</span><span class="o">.</span><span class="n">grad</span><span class="o">*</span><span class="n">learning_rate</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;epoch = </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, loss = </span><span class="si">{</span><span class="n">loss</span><span class="o">.</span><span class="n">data</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
</code></pre></div>
<div class="codehilite"><pre><span></span><code><span class="n">m</span> <span class="o">=</span> <span class="n">Model</span><span class="p">()</span>
<span class="n">train</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
</code></pre></div>
<div class="codehilite"><pre><span></span><code>epoch = 0, loss = 5.021394851113237
epoch = 1, loss = 0.7782825941374202
epoch = 2, loss = 0.6270146232229329
epoch = 3, loss = 0.4876410028357202
epoch = 4, loss = 0.4053191542103822
epoch = 5, loss = 0.3423019550262281
epoch = 6, loss = 0.2931430061698219
epoch = 7, loss = 0.2569171027719566
epoch = 8, loss = 0.2308399492192166
epoch = 9, loss = 0.21852798237118196
epoch = 10, loss = 0.21969239665876608
epoch = 11, loss = 0.27851636368934407
epoch = 12, loss = 0.2135574717023212
epoch = 13, loss = 0.21350867166968857
epoch = 14, loss = 0.15464326232380765
epoch = 15, loss = 0.14290616266990325
epoch = 16, loss = 0.14209626203214135
epoch = 17, loss = 0.20094989428044965
epoch = 18, loss = 0.2867951978027024
epoch = 19, loss = 0.2546920133599871
epoch = 20, loss = 0.13257778232970868
epoch = 21, loss = 0.09211147632647919
epoch = 22, loss = 0.08171613041260006
epoch = 23, loss = 0.07948978767704423
epoch = 24, loss = 0.08247811992422427
epoch = 25, loss = 0.09886131091399483
epoch = 26, loss = 0.14430048308374227
epoch = 27, loss = 0.1482296835375297
epoch = 28, loss = 0.12980677461452172
epoch = 29, loss = 0.061769904240643624
epoch = 30, loss = 0.0515892662074631
epoch = 31, loss = 0.05534649255008944
epoch = 32, loss = 0.059436977475827046
epoch = 33, loss = 0.08473875918029727
epoch = 34, loss = 0.07775994104308194
epoch = 35, loss = 0.0843747331678721
epoch = 36, loss = 0.0473862158926225
epoch = 37, loss = 0.05443368450641282
epoch = 38, loss = 0.043828831213784177
epoch = 39, loss = 0.055998072626177904
epoch = 40, loss = 0.039442063746032034
epoch = 41, loss = 0.05152266616488525
epoch = 42, loss = 0.032259115364274386
epoch = 43, loss = 0.034278981647031276
epoch = 44, loss = 0.027832568878344104
epoch = 45, loss = 0.0316761998187128
epoch = 46, loss = 0.028812078058045268
epoch = 47, loss = 0.03405534098431779
epoch = 48, loss = 0.02485441114089993
epoch = 49, loss = 0.02184405938300403
epoch = 50, loss = 0.02142938173224358
epoch = 51, loss = 0.021349410454665516
epoch = 52, loss = 0.021628542166114238
epoch = 53, loss = 0.021294908949209664
epoch = 54, loss = 0.02052073687766037
epoch = 55, loss = 0.021868670153477838
epoch = 56, loss = 0.019585789081038536
epoch = 57, loss = 0.020230971658711083
epoch = 58, loss = 0.017265034130387758
epoch = 59, loss = 0.01778558374470662
epoch = 60, loss = 0.015131865371237898
epoch = 61, loss = 0.015479715465340821
epoch = 62, loss = 0.014631571313666865
epoch = 63, loss = 0.01736298430711817
epoch = 64, loss = 0.013989095252392914
epoch = 65, loss = 0.014838225132201913
epoch = 66, loss = 0.013271360750078974
epoch = 67, loss = 0.014381721733929084
epoch = 68, loss = 0.013220600026557179
epoch = 69, loss = 0.014028100510799956
epoch = 70, loss = 0.012853600857515534
epoch = 71, loss = 0.014367940292109865
epoch = 72, loss = 0.012494933137005938
epoch = 73, loss = 0.013976731525393502
epoch = 74, loss = 0.012430826858013333
epoch = 75, loss = 0.013988620781950342
epoch = 76, loss = 0.012232010342182038
epoch = 77, loss = 0.012753295867025855
epoch = 78, loss = 0.012392250263641408
epoch = 79, loss = 0.013069503132597513
epoch = 80, loss = 0.012305972844835473
epoch = 81, loss = 0.012880146555037658
epoch = 82, loss = 0.011851252165411972
epoch = 83, loss = 0.011746730611191853
epoch = 84, loss = 0.011847802903334233
epoch = 85, loss = 0.01244010301465552
epoch = 86, loss = 0.011551636229685486
epoch = 87, loss = 0.011530815040508003
epoch = 88, loss = 0.0117877929465992
epoch = 89, loss = 0.01225010926631959
epoch = 90, loss = 0.011302535185644027
epoch = 91, loss = 0.011262204188776648
epoch = 92, loss = 0.010869616957885376
epoch = 93, loss = 0.010602500501416773
epoch = 94, loss = 0.010754762653457163
epoch = 95, loss = 0.010866609501553454
epoch = 96, loss = 0.010816875983494092
epoch = 97, loss = 0.0107581447080398
epoch = 98, loss = 0.010653356182413531
epoch = 99, loss = 0.010655080626714632
</code></pre></div>
<div class="codehilite"><pre><span></span><code><span class="c1"># visualize decision boundary</span>
<span class="n">h</span> <span class="o">=</span> <span class="mf">0.25</span>
<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">h</span><span class="p">),</span>
<span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
<span class="n">Xmesh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()]</span>
<span class="n">scores</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">Xmesh</span><span class="p">]</span>
<span class="n">Z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">s</span><span class="o">.</span><span class="n">data</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">scores</span><span class="p">])</span>
<span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</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">plt</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Spectral</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Spectral</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
</code></pre></div>
<div class="codehilite"><pre><span></span><code>(-1.7260398149932836, 2.2739601850067164)
</code></pre></div>
<p><img alt="png" src="autodiff-from-scratch_files/output_6_1.png" /></p>
<div class="codehilite"><pre><span></span><code><span class="kn">from</span> <span class="nn">graphviz</span> <span class="kn">import</span> <span class="n">Digraph</span>
<span class="k">def</span> <span class="nf">trace</span><span class="p">(</span><span class="n">root</span><span class="p">):</span>
<span class="n">nodes</span><span class="p">,</span> <span class="n">edges</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(),</span> <span class="nb">set</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">build</span><span class="p">(</span><span class="n">v</span><span class="p">):</span>
<span class="k">if</span> <span class="n">v</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">nodes</span><span class="p">:</span>
<span class="n">nodes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
<span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="n">v</span><span class="o">.</span><span class="n">_prev</span><span class="p">:</span>
<span class="n">edges</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">child</span><span class="p">,</span> <span class="n">v</span><span class="p">))</span>
<span class="n">build</span><span class="p">(</span><span class="n">child</span><span class="p">)</span>
<span class="n">build</span><span class="p">(</span><span class="n">root</span><span class="p">)</span>
<span class="k">return</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">edges</span>
<span class="k">def</span> <span class="nf">draw_dot</span><span class="p">(</span><span class="n">root</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;svg&#39;</span><span class="p">,</span> <span class="n">rankdir</span><span class="o">=</span><span class="s1">&#39;LR&#39;</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> format: png | svg | ...</span>
<span class="sd"> rankdir: TB (top to bottom graph) | LR (left to right)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="n">rankdir</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;LR&#39;</span><span class="p">,</span> <span class="s1">&#39;TB&#39;</span><span class="p">]</span>
<span class="n">nodes</span><span class="p">,</span> <span class="n">edges</span> <span class="o">=</span> <span class="n">trace</span><span class="p">(</span><span class="n">root</span><span class="p">)</span>
<span class="n">dot</span> <span class="o">=</span> <span class="n">Digraph</span><span class="p">(</span><span class="nb">format</span><span class="o">=</span><span class="nb">format</span><span class="p">,</span> <span class="n">graph_attr</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;rankdir&#39;</span><span class="p">:</span> <span class="n">rankdir</span><span class="p">})</span> <span class="c1">#, node_attr={&#39;rankdir&#39;: &#39;TB&#39;})</span>
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">nodes</span><span class="p">:</span>
<span class="n">label</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Tensor(</span><span class="si">{</span><span class="n">n</span><span class="o">.</span><span class="n">_name</span><span class="si">}</span><span class="s2">,shape=</span><span class="si">{</span><span class="n">n</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">)&quot;</span> <span class="k">if</span> <span class="n">n</span><span class="o">.</span><span class="n">_name</span> <span class="k">else</span> <span class="sa">f</span><span class="s2">&quot;Tensor(shape=</span><span class="si">{</span><span class="n">n</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">)&quot;</span>
<span class="n">dot</span><span class="o">.</span><span class="n">node</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">n</span><span class="p">)),</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="s2">&quot;record&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">n</span><span class="o">.</span><span class="n">_op</span><span class="p">:</span>
<span class="n">dot</span><span class="o">.</span><span class="n">node</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">n</span><span class="p">))</span> <span class="o">+</span> <span class="n">n</span><span class="o">.</span><span class="n">_op</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">n</span><span class="o">.</span><span class="n">_op</span><span class="p">)</span>
<span class="n">dot</span><span class="o">.</span><span class="n">edge</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">n</span><span class="p">))</span> <span class="o">+</span> <span class="n">n</span><span class="o">.</span><span class="n">_op</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">n</span><span class="p">)))</span>
<span class="k">for</span> <span class="n">n1</span><span class="p">,</span> <span class="n">n2</span> <span class="ow">in</span> <span class="n">edges</span><span class="p">:</span>
<span class="n">dot</span><span class="o">.</span><span class="n">edge</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">n1</span><span class="p">)),</span> <span class="nb">str</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">n2</span><span class="p">))</span> <span class="o">+</span> <span class="n">n2</span><span class="o">.</span><span class="n">_op</span><span class="p">)</span>
<span class="k">return</span> <span class="n">dot</span>
</code></pre></div>
<div class="codehilite"><pre><span></span><code><span class="n">z</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">draw_dot</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">rankdir</span><span class="o">=</span><span class="s2">&quot;TB&quot;</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">&quot;svg&quot;</span><span class="p">)</span>
</code></pre></div>
<p><img alt="svg" src="autodiff-from-scratch_files/output_8_0.svg" /></p>
<div class="codehilite"><pre><span></span><code>
</code></pre></div></description>
<content:encoded><div class="codehilite"><pre><span></span><code><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">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
</code></pre></div>
<h1 id="autodiff">AutoDiff学习模仿</h1>
<p>Credit:<a href="https://github.com/karpathy/micrograd">karpathy/micrograd</a></p>
<div class="codehilite"><pre><span></span><code><span class="k">def</span> <span class="nf">unbroadcast</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">shape</span><span class="p">):</span>
<span class="n">sum_axis</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</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">shape</span><span class="p">))</span> <span class="k">if</span> <span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">==</span><span class="mi">1</span> <span class="ow">and</span> <span class="n">out</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">]</span>
<span class="k">return</span> <span class="n">out</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="n">sum_axis</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">Tensor</span><span class="p">(</span><span class="nb">object</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">data</span><span class="p">,</span> <span class="n">children</span><span class="o">=</span><span class="p">(),</span> <span class="n">op</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">grad_required</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad_required</span> <span class="o">=</span> <span class="n">grad_required</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="k">if</span> <span class="n">grad_required</span> <span class="k">else</span> <span class="kc">None</span>
<span class="c1"># internal variables used for autograd graph construction</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_backward</span> <span class="o">=</span> <span class="k">lambda</span><span class="p">:</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_prev</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">children</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_op</span> <span class="o">=</span> <span class="n">op</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="n">name</span>
<span class="k">def</span> <span class="nf">zero_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c1"># topological order all of the children in the graph</span>
<span class="n">topo</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">visited</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">build_topo</span><span class="p">(</span><span class="n">v</span><span class="p">):</span>
<span class="k">if</span> <span class="n">v</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">visited</span><span class="p">:</span>
<span class="n">visited</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
<span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="n">v</span><span class="o">.</span><span class="n">_prev</span><span class="p">:</span>
<span class="n">build_topo</span><span class="p">(</span><span class="n">child</span><span class="p">)</span>
<span class="n">topo</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
<span class="n">build_topo</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="c1"># go one variable at a time and apply the chain rule to get its gradient</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">reversed</span><span class="p">(</span><span class="n">topo</span><span class="p">):</span>
<span class="n">v</span><span class="o">.</span><span class="n">_backward</span><span class="p">()</span>
<span class="c1"># operators</span>
<span class="k">def</span> <span class="nf">mean</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="p">),</span> <span class="s1">&#39;mean&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_backward</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="n">out</span><span class="o">.</span><span class="n">grad</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">grad</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">out</span><span class="o">.</span><span class="n">_backward</span> <span class="o">=</span> <span class="n">_backward</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">def</span> <span class="nf">matmul</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
<span class="n">other</span> <span class="o">=</span> <span class="n">other</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">grad_required</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">@</span> <span class="n">other</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">),</span> <span class="s1">&#39;matmul&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_backward</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="n">out</span><span class="o">.</span><span class="n">grad</span> <span class="o">@</span> <span class="n">other</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">T</span>
<span class="k">if</span> <span class="n">other</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="n">other</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">out</span><span class="o">.</span><span class="n">grad</span>
<span class="n">out</span><span class="o">.</span><span class="n">_backward</span> <span class="o">=</span> <span class="n">_backward</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">def</span> <span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
<span class="n">other</span> <span class="o">=</span> <span class="n">other</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">grad_required</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">+</span> <span class="n">other</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">),</span> <span class="s1">&#39;+&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_backward</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="n">unbroadcast</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">if</span> <span class="n">other</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="n">other</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="n">unbroadcast</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">out</span><span class="o">.</span><span class="n">_backward</span> <span class="o">=</span> <span class="n">_backward</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">def</span> <span class="fm">__mul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
<span class="n">other</span> <span class="o">=</span> <span class="n">other</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">grad_required</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">*</span> <span class="n">other</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">),</span> <span class="s1">&#39;*&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_backward</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="n">unbroadcast</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">data</span> <span class="o">*</span> <span class="n">out</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">if</span> <span class="n">other</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="n">other</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="n">unbroadcast</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">*</span> <span class="n">out</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">out</span><span class="o">.</span><span class="n">_backward</span> <span class="o">=</span> <span class="n">_backward</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">def</span> <span class="fm">__pow__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">)),</span> <span class="s2">&quot;only supporting int/float powers for now&quot;</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">**</span><span class="n">other</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="p">,),</span> <span class="sa">f</span><span class="s1">&#39;**</span><span class="si">{</span><span class="n">other</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_backward</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="n">unbroadcast</span><span class="p">((</span><span class="n">other</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">**</span><span class="p">(</span><span class="n">other</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="n">out</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">out</span><span class="o">.</span><span class="n">_backward</span> <span class="o">=</span> <span class="n">_backward</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">def</span> <span class="nf">relu</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="bp">self</span><span class="p">,),</span> <span class="s1">&#39;relu&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_backward</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_required</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">grad</span> <span class="o">+=</span> <span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">data</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">out</span><span class="o">.</span><span class="n">grad</span>
<span class="n">out</span><span class="o">.</span><span class="n">_backward</span> <span class="o">=</span> <span class="n">_backward</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">def</span> <span class="fm">__neg__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="c1"># -self</span>
<span class="k">return</span> <span class="bp">self</span> <span class="o">*</span> <span class="o">-</span><span class="mi">1</span>
<span class="k">def</span> <span class="fm">__radd__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> <span class="c1"># other + self</span>
<span class="k">return</span> <span class="bp">self</span> <span class="o">+</span> <span class="n">other</span>
<span class="k">def</span> <span class="fm">__sub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> <span class="c1"># self - other</span>
<span class="k">return</span> <span class="bp">self</span> <span class="o">+</span> <span class="p">(</span><span class="o">-</span><span class="n">other</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__rsub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> <span class="c1"># other - self</span>
<span class="k">return</span> <span class="n">other</span> <span class="o">+</span> <span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__rmul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> <span class="c1"># other * self</span>
<span class="k">return</span> <span class="bp">self</span> <span class="o">*</span> <span class="n">other</span>
<span class="k">def</span> <span class="fm">__truediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> <span class="c1"># self / other</span>
<span class="k">return</span> <span class="bp">self</span> <span class="o">*</span> <span class="n">other</span><span class="o">**-</span><span class="mi">1</span>
<span class="k">def</span> <span class="fm">__rtruediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> <span class="c1"># other / self</span>
<span class="k">return</span> <span class="n">other</span> <span class="o">*</span> <span class="bp">self</span><span class="o">**-</span><span class="mi">1</span>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;Tensor(data=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="si">}</span><span class="s2">, grad=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">grad</span><span class="si">}</span><span class="s2">, shape=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">)&quot;</span>
</code></pre></div>
<div class="codehilite"><pre><span></span><code><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_moons</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_moons</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">*</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">1</span> <span class="c1"># make y be -1 or 1</span>
<span class="c1"># visualize in 2D</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">5</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;jet&#39;</span><span class="p">)</span>
</code></pre></div>
<div class="codehilite"><pre><span></span><code><span class="o">&lt;</span><span class="n">matplotlib</span><span class="o">.</span><span class="n">collections</span><span class="o">.</span><span class="n">PathCollection</span><span class="w"> </span><span class="n">at</span><span class="w"> </span><span class="mh">0x130ba21c0</span><span class="o">&gt;</span>
<span class="o">/</span><span class="n">usr</span><span class="o">/</span><span class="n">local</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python3</span><span class="o">.</span><span class="mi">8</span><span class="o">/</span><span class="n">site</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">IPython</span><span class="o">/</span><span class="n">core</span><span class="o">/</span><span class="n">pylabtools</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">132</span><span class="p">:</span><span class="w"> </span><span class="n">UserWarning</span><span class="p">:</span><span class="w"> </span><span class="n">Glyph</span><span class="w"> </span><span class="mi">8722</span><span class="w"> </span><span class="p">(</span>\<span class="n">N</span><span class="p">{</span><span class="n">MINUS</span><span class="w"> </span><span class="n">SIGN</span><span class="p">})</span><span class="w"> </span><span class="n">missing</span><span class="w"> </span><span class="n">from</span><span class="w"> </span><span class="n">current</span><span class="w"> </span><span class="n">font</span><span class="o">.</span>
<span class="w"> </span><span class="n">fig</span><span class="o">.</span><span class="n">canvas</span><span class="o">.</span><span class="n">print_figure</span><span class="p">(</span><span class="n">bytes_io</span><span class="p">,</span><span class="w"> </span><span class="o">**</span><span class="n">kw</span><span class="p">)</span>
</code></pre></div>
<p><img alt="png" src="autodiff-from-scratch_files/output_3_2.png" /></p>
<div class="codehilite"><pre><span></span><code><span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="nb">object</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="bp">self</span><span class="o">.</span><span class="n">w1</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</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">16</span><span class="p">)),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;w1&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">b1</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,(</span><span class="mi">1</span><span class="p">,</span><span class="mi">16</span><span class="p">)),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;b1&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">w2</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,(</span><span class="mi">16</span><span class="p">,</span><span class="mi">16</span><span class="p">)),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;w2&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">b2</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,(</span><span class="mi">1</span><span class="p">,</span><span class="mi">16</span><span class="p">)),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;b2&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">w3</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,(</span><span class="mi">16</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;w3&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">b3</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;b3&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">w1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">b1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">w2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">b2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">w3</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">b3</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">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">grad_required</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">h1</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">w1</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">b1</span>
<span class="n">h1</span> <span class="o">=</span> <span class="n">h1</span><span class="o">.</span><span class="n">relu</span><span class="p">()</span>
<span class="n">h2</span> <span class="o">=</span> <span class="n">h1</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">w2</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">b2</span>
<span class="n">h2</span> <span class="o">=</span> <span class="n">h2</span><span class="o">.</span><span class="n">relu</span><span class="p">()</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">h2</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">w3</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">b3</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">def</span> <span class="nf">zero_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="p">:</span>
<span class="n">param</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">epochs</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">):</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">p</span><span class="o">*</span><span class="n">y</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">relu</span><span class="p">()</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">m</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">backward</span><span class="p">()</span>
<span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">params</span><span class="p">:</span>
<span class="n">param</span><span class="o">.</span><span class="n">data</span> <span class="o">-=</span> <span class="n">param</span><span class="o">.</span><span class="n">grad</span><span class="o">*</span><span class="n">learning_rate</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;epoch = </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, loss = </span><span class="si">{</span><span class="n">loss</span><span class="o">.</span><span class="n">data</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
</code></pre></div>
<div class="codehilite"><pre><span></span><code><span class="n">m</span> <span class="o">=</span> <span class="n">Model</span><span class="p">()</span>
<span class="n">train</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
</code></pre></div>
<div class="codehilite"><pre><span></span><code>epoch = 0, loss = 5.021394851113237
epoch = 1, loss = 0.7782825941374202
epoch = 2, loss = 0.6270146232229329
epoch = 3, loss = 0.4876410028357202
epoch = 4, loss = 0.4053191542103822
epoch = 5, loss = 0.3423019550262281
epoch = 6, loss = 0.2931430061698219
epoch = 7, loss = 0.2569171027719566
epoch = 8, loss = 0.2308399492192166
epoch = 9, loss = 0.21852798237118196
epoch = 10, loss = 0.21969239665876608
epoch = 11, loss = 0.27851636368934407
epoch = 12, loss = 0.2135574717023212
epoch = 13, loss = 0.21350867166968857
epoch = 14, loss = 0.15464326232380765
epoch = 15, loss = 0.14290616266990325
epoch = 16, loss = 0.14209626203214135
epoch = 17, loss = 0.20094989428044965
epoch = 18, loss = 0.2867951978027024
epoch = 19, loss = 0.2546920133599871
epoch = 20, loss = 0.13257778232970868
epoch = 21, loss = 0.09211147632647919
epoch = 22, loss = 0.08171613041260006
epoch = 23, loss = 0.07948978767704423
epoch = 24, loss = 0.08247811992422427
epoch = 25, loss = 0.09886131091399483
epoch = 26, loss = 0.14430048308374227
epoch = 27, loss = 0.1482296835375297
epoch = 28, loss = 0.12980677461452172
epoch = 29, loss = 0.061769904240643624
epoch = 30, loss = 0.0515892662074631
epoch = 31, loss = 0.05534649255008944
epoch = 32, loss = 0.059436977475827046
epoch = 33, loss = 0.08473875918029727
epoch = 34, loss = 0.07775994104308194
epoch = 35, loss = 0.0843747331678721
epoch = 36, loss = 0.0473862158926225
epoch = 37, loss = 0.05443368450641282
epoch = 38, loss = 0.043828831213784177
epoch = 39, loss = 0.055998072626177904
epoch = 40, loss = 0.039442063746032034
epoch = 41, loss = 0.05152266616488525
epoch = 42, loss = 0.032259115364274386
epoch = 43, loss = 0.034278981647031276
epoch = 44, loss = 0.027832568878344104
epoch = 45, loss = 0.0316761998187128
epoch = 46, loss = 0.028812078058045268
epoch = 47, loss = 0.03405534098431779
epoch = 48, loss = 0.02485441114089993
epoch = 49, loss = 0.02184405938300403
epoch = 50, loss = 0.02142938173224358
epoch = 51, loss = 0.021349410454665516
epoch = 52, loss = 0.021628542166114238
epoch = 53, loss = 0.021294908949209664
epoch = 54, loss = 0.02052073687766037
epoch = 55, loss = 0.021868670153477838
epoch = 56, loss = 0.019585789081038536
epoch = 57, loss = 0.020230971658711083
epoch = 58, loss = 0.017265034130387758
epoch = 59, loss = 0.01778558374470662
epoch = 60, loss = 0.015131865371237898
epoch = 61, loss = 0.015479715465340821
epoch = 62, loss = 0.014631571313666865
epoch = 63, loss = 0.01736298430711817
epoch = 64, loss = 0.013989095252392914
epoch = 65, loss = 0.014838225132201913
epoch = 66, loss = 0.013271360750078974
epoch = 67, loss = 0.014381721733929084
epoch = 68, loss = 0.013220600026557179
epoch = 69, loss = 0.014028100510799956
epoch = 70, loss = 0.012853600857515534
epoch = 71, loss = 0.014367940292109865
epoch = 72, loss = 0.012494933137005938
epoch = 73, loss = 0.013976731525393502
epoch = 74, loss = 0.012430826858013333
epoch = 75, loss = 0.013988620781950342
epoch = 76, loss = 0.012232010342182038
epoch = 77, loss = 0.012753295867025855
epoch = 78, loss = 0.012392250263641408
epoch = 79, loss = 0.013069503132597513
epoch = 80, loss = 0.012305972844835473
epoch = 81, loss = 0.012880146555037658
epoch = 82, loss = 0.011851252165411972
epoch = 83, loss = 0.011746730611191853
epoch = 84, loss = 0.011847802903334233
epoch = 85, loss = 0.01244010301465552
epoch = 86, loss = 0.011551636229685486
epoch = 87, loss = 0.011530815040508003
epoch = 88, loss = 0.0117877929465992
epoch = 89, loss = 0.01225010926631959
epoch = 90, loss = 0.011302535185644027
epoch = 91, loss = 0.011262204188776648
epoch = 92, loss = 0.010869616957885376
epoch = 93, loss = 0.010602500501416773
epoch = 94, loss = 0.010754762653457163
epoch = 95, loss = 0.010866609501553454
epoch = 96, loss = 0.010816875983494092
epoch = 97, loss = 0.0107581447080398
epoch = 98, loss = 0.010653356182413531
epoch = 99, loss = 0.010655080626714632
</code></pre></div>
<div class="codehilite"><pre><span></span><code><span class="c1"># visualize decision boundary</span>
<span class="n">h</span> <span class="o">=</span> <span class="mf">0.25</span>
<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">h</span><span class="p">),</span>
<span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
<span class="n">Xmesh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()]</span>
<span class="n">scores</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">Xmesh</span><span class="p">]</span>
<span class="n">Z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">s</span><span class="o">.</span><span class="n">data</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">scores</span><span class="p">])</span>
<span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</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">plt</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Spectral</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Spectral</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
</code></pre></div>
<div class="codehilite"><pre><span></span><code>(-1.7260398149932836, 2.2739601850067164)
</code></pre></div>
<p><img alt="png" src="autodiff-from-scratch_files/output_6_1.png" /></p>
<div class="codehilite"><pre><span></span><code><span class="kn">from</span> <span class="nn">graphviz</span> <span class="kn">import</span> <span class="n">Digraph</span>
<span class="k">def</span> <span class="nf">trace</span><span class="p">(</span><span class="n">root</span><span class="p">):</span>
<span class="n">nodes</span><span class="p">,</span> <span class="n">edges</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(),</span> <span class="nb">set</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">build</span><span class="p">(</span><span class="n">v</span><span class="p">):</span>
<span class="k">if</span> <span class="n">v</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">nodes</span><span class="p">:</span>
<span class="n">nodes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
<span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="n">v</span><span class="o">.</span><span class="n">_prev</span><span class="p">:</span>
<span class="n">edges</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">child</span><span class="p">,</span> <span class="n">v</span><span class="p">))</span>
<span class="n">build</span><span class="p">(</span><span class="n">child</span><span class="p">)</span>
<span class="n">build</span><span class="p">(</span><span class="n">root</span><span class="p">)</span>
<span class="k">return</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">edges</span>
<span class="k">def</span> <span class="nf">draw_dot</span><span class="p">(</span><span class="n">root</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;svg&#39;</span><span class="p">,</span> <span class="n">rankdir</span><span class="o">=</span><span class="s1">&#39;LR&#39;</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> format: png | svg | ...</span>
<span class="sd"> rankdir: TB (top to bottom graph) | LR (left to right)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="n">rankdir</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;LR&#39;</span><span class="p">,</span> <span class="s1">&#39;TB&#39;</span><span class="p">]</span>
<span class="n">nodes</span><span class="p">,</span> <span class="n">edges</span> <span class="o">=</span> <span class="n">trace</span><span class="p">(</span><span class="n">root</span><span class="p">)</span>
<span class="n">dot</span> <span class="o">=</span> <span class="n">Digraph</span><span class="p">(</span><span class="nb">format</span><span class="o">=</span><span class="nb">format</span><span class="p">,</span> <span class="n">graph_attr</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;rankdir&#39;</span><span class="p">:</span> <span class="n">rankdir</span><span class="p">})</span> <span class="c1">#, node_attr={&#39;rankdir&#39;: &#39;TB&#39;})</span>
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">nodes</span><span class="p">:</span>
<span class="n">label</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Tensor(</span><span class="si">{</span><span class="n">n</span><span class="o">.</span><span class="n">_name</span><span class="si">}</span><span class="s2">,shape=</span><span class="si">{</span><span class="n">n</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">)&quot;</span> <span class="k">if</span> <span class="n">n</span><span class="o">.</span><span class="n">_name</span> <span class="k">else</span> <span class="sa">f</span><span class="s2">&quot;Tensor(shape=</span><span class="si">{</span><span class="n">n</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">)&quot;</span>
<span class="n">dot</span><span class="o">.</span><span class="n">node</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">n</span><span class="p">)),</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="s2">&quot;record&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">n</span><span class="o">.</span><span class="n">_op</span><span class="p">:</span>
<span class="n">dot</span><span class="o">.</span><span class="n">node</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">n</span><span class="p">))</span> <span class="o">+</span> <span class="n">n</span><span class="o">.</span><span class="n">_op</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">n</span><span class="o">.</span><span class="n">_op</span><span class="p">)</span>
<span class="n">dot</span><span class="o">.</span><span class="n">edge</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">n</span><span class="p">))</span> <span class="o">+</span> <span class="n">n</span><span class="o">.</span><span class="n">_op</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">n</span><span class="p">)))</span>
<span class="k">for</span> <span class="n">n1</span><span class="p">,</span> <span class="n">n2</span> <span class="ow">in</span> <span class="n">edges</span><span class="p">:</span>
<span class="n">dot</span><span class="o">.</span><span class="n">edge</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">n1</span><span class="p">)),</span> <span class="nb">str</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="n">n2</span><span class="p">))</span> <span class="o">+</span> <span class="n">n2</span><span class="o">.</span><span class="n">_op</span><span class="p">)</span>
<span class="k">return</span> <span class="n">dot</span>
</code></pre></div>
<div class="codehilite"><pre><span></span><code><span class="n">z</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">draw_dot</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">rankdir</span><span class="o">=</span><span class="s2">&quot;TB&quot;</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">&quot;svg&quot;</span><span class="p">)</span>
</code></pre></div>
<p><img alt="svg" src="autodiff-from-scratch_files/output_8_0.svg" /></p>
<div class="codehilite"><pre><span></span><code>
</code></pre></div></content:encoded>
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