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<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
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<title>sklearn_NN</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
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<p>neural networks</p>
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<h1 id="%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E5%8E%9F%E7%90%86%E4%B8%8E%E4%BD%BF%E7%94%A8">神经网络的原理与使用<a class="anchor-link" href="#%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E5%8E%9F%E7%90%86%E4%B8%8E%E4%BD%BF%E7%94%A8">¶</a></h1>
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<h2 id="%E6%BF%80%E6%B4%BB%E5%87%BD%E6%95%B0---%E9%9D%9E%E7%BA%BF%E6%80%A7%E7%9F%AB%E6%AD%A3">激活函数 - 非线性矫正<a class="anchor-link" href="#%E6%BF%80%E6%B4%BB%E5%87%BD%E6%95%B0---%E9%9D%9E%E7%BA%BF%E6%80%A7%E7%9F%AB%E6%AD%A3">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="c1"># 生成一个等差数列</span>
<span class="n">x</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
<span class="c1"># 画出非线性矫正图形</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s2">"tanh"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</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="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"best"</span><span class="p">)</span>
<span class="c1">#plt.xlabel("x")</span>
<span class="c1">#plt.ylabel("Y")</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<h2 id="%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E5%8F%82%E6%95%B0%E8%AE%BE%E7%BD%AE">神经网络的参数设置<a class="anchor-link" href="#%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E5%8F%82%E6%95%B0%E8%AE%BE%E7%BD%AE">¶</a></h2>
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<p>help(MLPClassifier)</p>
<p>class MLPClassifier(sklearn.base.ClassifierMixin, BaseMultilayerPerceptron)
| MLPClassifier(hidden_layer_sizes=(100,), activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000)</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 导入多层感知机 MPL 神经网络</span>
<span class="kn">from</span> <span class="nn">sklearn.neural_network</span> <span class="kn">import</span> <span class="n">MLPClassifier</span>
<span class="c1"># 导入wine数据集</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_wine</span>
<span class="n">wine</span><span class="o">=</span><span class="n">load_wine</span><span class="p">()</span>
<span class="n">X</span><span class="o">=</span><span class="n">wine</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">]</span>
<span class="n">y</span><span class="o">=</span><span class="n">wine</span><span class="o">.</span><span class="n">target</span>
<span class="c1"># 拆分数据</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</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">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># 拟合</span>
<span class="n">mlp</span><span class="o">=</span><span class="n">MLPClassifier</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">"lbfgs"</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
<span class="n">mlp</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"training set:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">mlp</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">))</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"tesing set:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">mlp</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span> <span class="p">)</span>
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<pre>training set:0.827
tesing set:0.822
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 可视化</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">ListedColormap</span>
<span class="c1"># 使用不同色块表示不同分类</span>
<span class="n">cmap_light</span><span class="o">=</span><span class="n">ListedColormap</span><span class="p">([</span><span class="s2">"#FFAAAA"</span><span class="p">,</span> <span class="s2">"#AAFFAA"</span><span class="p">,</span> <span class="s2">"#AAAAFF"</span><span class="p">])</span>
<span class="n">cmap_bold</span><span class="o">=</span><span class="n">ListedColormap</span><span class="p">([</span><span class="s2">"FF0000"</span><span class="p">,</span> <span class="s2">"00FF00"</span><span class="p">,</span> <span class="s2">"0000FF"</span><span class="p">])</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_train</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_train</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_train</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_train</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="mf">0.02</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="mf">0.02</span><span class="p">))</span>
<span class="n">Z</span><span class="o">=</span><span class="n">mlp</span><span class="o">.</span><span class="n">predict</span><span class="p">(</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">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">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">pcolormesh</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">cmap_light</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span>
<span class="c1"># 散点图</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">edgecolor</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">60</span><span class="p">)</span>
<span class="n">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>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"MLPClassifier:solver=lbfgs"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<h2 id="%E5%A2%9E%E5%8A%A0%E6%A8%A1%E5%9E%8B%E5%A4%8D%E6%9D%82%E5%BA%A6%E7%9A%844%E7%A7%8D%E6%96%B9%E6%B3%95">增加模型复杂度的4种方法<a class="anchor-link" href="#%E5%A2%9E%E5%8A%A0%E6%A8%A1%E5%9E%8B%E5%A4%8D%E6%9D%82%E5%BA%A6%E7%9A%844%E7%A7%8D%E6%96%B9%E6%B3%95">¶</a></h2>
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<h3 id="%E5%A2%9E%E5%8A%A0%E9%9A%90%E8%97%8F%E5%B1%82node%E6%95%B0">增加隐藏层node数<a class="anchor-link" href="#%E5%A2%9E%E5%8A%A0%E9%9A%90%E8%97%8F%E5%B1%82node%E6%95%B0">¶</a></h3>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 我们验证另一面：减少node能使模型简化。</span>
<span class="c1"># 拟合</span>
<span class="n">mlp_20</span><span class="o">=</span><span class="n">MLPClassifier</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">"lbfgs"</span><span class="p">,</span> <span class="n">hidden_layer_sizes</span> <span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">800</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="n">mlp_20</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"training set:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">mlp_20</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">))</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"tesing set:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">mlp_20</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span> <span class="p">)</span>
<span class="c1"># 画图</span>
<span class="n">Z</span><span class="o">=</span><span class="n">mlp_20</span><span class="o">.</span><span class="n">predict</span><span class="p">(</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">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">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">pcolormesh</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">cmap_light</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span>
<span class="c1"># 散点图</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">edgecolor</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">60</span><span class="p">)</span>
<span class="n">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>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"MLPClassifier:nodes=10"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>training set:0.782
tesing set:0.822
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<li>决定边界丢失了很多细节。每一个隐藏层中，node数大概决定边界的最大直线数，这个值越大，边界越平滑。</li>
<li>还有2个方法可以让边界平滑<ul>
<li>增加隐藏层数量</li>
<li>激活函数改为 tanh</li>
</ul>
</li>
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<h3 id="%E9%9A%90%E8%97%8F%E5%B1%82%E5%A2%9E%E5%8A%A0%E5%88%B02%E5%B1%82">隐藏层增加到2层<a class="anchor-link" href="#%E9%9A%90%E8%97%8F%E5%B1%82%E5%A2%9E%E5%8A%A0%E5%88%B02%E5%B1%82">¶</a></h3>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 拟合</span>
<span class="n">mlp_2L</span><span class="o">=</span><span class="n">MLPClassifier</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">"lbfgs"</span><span class="p">,</span> <span class="n">hidden_layer_sizes</span> <span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">800</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="n">mlp_2L</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"training set:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">mlp_2L</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">))</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"tesing set:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">mlp_2L</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span> <span class="p">)</span>
<span class="c1"># 画图</span>
<span class="n">Z</span><span class="o">=</span><span class="n">mlp_2L</span><span class="o">.</span><span class="n">predict</span><span class="p">(</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">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">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">pcolormesh</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">cmap_light</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span>
<span class="c1"># 散点图</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">edgecolor</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">60</span><span class="p">)</span>
<span class="n">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>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"MLPClassifier:nodes=[10, 10]"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># 边界有更多细节了</span>
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<pre>training set:0.835
tesing set:0.822
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<h3 id="%E6%BF%80%E6%B4%BB%E5%87%BD%E6%95%B0-activation='tanh'">激活函数 activation='tanh'<a class="anchor-link" href="#%E6%BF%80%E6%B4%BB%E5%87%BD%E6%95%B0-activation='tanh'">¶</a></h3>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 拟合</span>
<span class="n">mlp_2L</span><span class="o">=</span><span class="n">MLPClassifier</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">"lbfgs"</span><span class="p">,</span> <span class="n">hidden_layer_sizes</span> <span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'tanh'</span><span class="p">,</span>
<span class="n">max_iter</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
<span class="n">mlp_2L</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"training set:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">mlp_2L</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">))</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"tesing set:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">mlp_2L</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span> <span class="p">)</span>
<span class="c1"># 画图</span>
<span class="n">Z</span><span class="o">=</span><span class="n">mlp_2L</span><span class="o">.</span><span class="n">predict</span><span class="p">(</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">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">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">pcolormesh</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">cmap_light</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span>
<span class="c1"># 散点图</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">edgecolor</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">60</span><span class="p">)</span>
<span class="n">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>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"MLPClassifier:nodes=[10, 10] activation='tanh'"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># 边界有更多细节了</span>
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<pre>/home/wangjl/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)
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<pre>training set:0.782
tesing set:0.844
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<h3 id="%E6%8F%90%E9%AB%98-alpha-%E5%80%BC(%E9%BB%98%E8%AE%A4-0.0001)">提高 alpha 值(默认 0.0001)<a class="anchor-link" href="#%E6%8F%90%E9%AB%98-alpha-%E5%80%BC(%E9%BB%98%E8%AE%A4-0.0001)">¶</a></h3>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 拟合</span>
<span class="n">mlp_alpha1</span><span class="o">=</span><span class="n">MLPClassifier</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">"lbfgs"</span><span class="p">,</span> <span class="n">hidden_layer_sizes</span> <span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'tanh'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">max_iter</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
<span class="n">mlp_alpha1</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"training set:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">mlp_alpha1</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">))</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"tesing set:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">mlp_alpha1</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span> <span class="p">)</span>
<span class="c1"># 画图</span>
<span class="n">Z</span><span class="o">=</span><span class="n">mlp_alpha1</span><span class="o">.</span><span class="n">predict</span><span class="p">(</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">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">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">pcolormesh</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">cmap_light</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span>
<span class="c1"># 散点图</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">edgecolor</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">60</span><span class="p">)</span>
<span class="n">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>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"MLPClassifier:nodes=[10, 10], activation='tanh',alpha=1"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># 边界有更多细节了</span>
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<pre>/home/wangjl/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)
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<pre>training set:0.805
tesing set:0.889
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<h1 id="%E6%89%8B%E5%86%99%E6%95%B0%E5%AD%97%E8%AF%86%E5%88%AB-8x8%EF%BC%88%E6%B3%9B%E5%8C%96%E5%B7%AE%EF%BC%89">手写数字识别 8x8（泛化差）<a class="anchor-link" href="#%E6%89%8B%E5%86%99%E6%95%B0%E5%AD%97%E8%AF%86%E5%88%AB-8x8%EF%BC%88%E6%B3%9B%E5%8C%96%E5%B7%AE%EF%BC%89">¶</a></h1>
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<p>MNIST 数据集手写体数字识别，就像入门程序猿写 Hello world 一样，是非常基础的必修课。</p>
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<h2 id="%E8%BD%BD%E5%85%A5%E6%95%B0%E6%8D%AE">载入数据<a class="anchor-link" href="#%E8%BD%BD%E5%85%A5%E6%95%B0%E6%8D%AE">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># https://h1ros.github.io/posts/loading-scikit-learns-mnist-dataset/</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_digits</span>
<span class="n">mnist</span> <span class="o">=</span> <span class="n">load_digits</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">mnist</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="n">mnist</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="n">mnist</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="c1"># data是1797行，每行64像素(8*8) 是一个图形每个像素的黑白值。</span>
<span class="c1"># target 是0-9的整型数字。</span>
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<pre>dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'images', 'DESCR'])
(1797, 64) (1797,)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="nb">print</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">mnist</span><span class="o">.</span><span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
<span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">mnist</span><span class="o">.</span><span class="n">target</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<pre> 0 1 2 3 4 5 6 7 8 9 ... 54 55 56 \
0 0.0 0.0 5.0 13.0 9.0 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
1 0.0 0.0 0.0 12.0 13.0 5.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
2 0.0 0.0 0.0 4.0 15.0 12.0 0.0 0.0 0.0 0.0 ... 5.0 0.0 0.0
3 0.0 0.0 7.0 15.0 13.0 1.0 0.0 0.0 0.0 8.0 ... 9.0 0.0 0.0
4 0.0 0.0 0.0 1.0 11.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
57 58 59 60 61 62 63
0 0.0 6.0 13.0 10.0 0.0 0.0 0.0
1 0.0 0.0 11.0 16.0 10.0 0.0 0.0
2 0.0 0.0 3.0 11.0 16.0 9.0 0.0
3 0.0 7.0 13.0 13.0 9.0 0.0 0.0
4 0.0 0.0 2.0 16.0 4.0 0.0 0.0
[5 rows x 64 columns]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 可视化图形</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">mnist</span><span class="o">.</span><span class="n">images</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 批量 可视化图形</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mf">2.8</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="mi">20</span><span class="p">):</span>
<span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="o">//</span><span class="mi">10</span><span class="p">,</span> <span class="n">i</span> <span class="o">%</span><span class="k">10</span>].imshow(mnist.images[i], cmap='gray');
<span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="o">//</span><span class="mi">10</span><span class="p">,</span> <span class="n">i</span> <span class="o">%</span><span class="k">10</span>].axis('off')
<span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="o">//</span><span class="mi">10</span><span class="p">,</span> <span class="n">i</span> <span class="o">%</span><span class="k">10</span>].set_title(f"target: {mnist.target[i]}")
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
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<h2 id="%E9%A2%84%E5%A4%84%E7%90%86">预处理<a class="anchor-link" href="#%E9%A2%84%E5%A4%84%E7%90%86">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X</span><span class="o">=</span><span class="n">mnist</span><span class="o">.</span><span class="n">data</span><span class="o">/</span><span class="mi">255</span>
<span class="n">y</span><span class="o">=</span><span class="n">mnist</span><span class="o">.</span><span class="n">target</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</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">train_size</span><span class="o">=</span><span class="mi">1200</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">62</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>(1200, 64) (500, 64)
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<h2 id="%E8%AE%AD%E7%BB%83-MLP-%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C">训练 MLP 神经网络<a class="anchor-link" href="#%E8%AE%AD%E7%BB%83-MLP-%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.neural_network</span> <span class="kn">import</span> <span class="n">MLPClassifier</span>
<span class="n">mlp_hw</span><span class="o">=</span><span class="n">MLPClassifier</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">"lbfgs"</span><span class="p">,</span> <span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
<span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">62</span><span class="p">)</span>
<span class="n">mlp_hw</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"training set:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">mlp_hw</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">))</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"tesing set:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">mlp_hw</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span> <span class="p">)</span>
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<pre>training set:1.000
tesing set:0.942
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<h2 id="%E6%B5%8B%E8%AF%95">测试<a class="anchor-link" href="#%E6%B5%8B%E8%AF%95">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="c1"># 新建一个 8*8 画布，黑色字体，手写一个数字，保存。</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="n">image</span><span class="o">=</span><span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s2">"data/pic4.png"</span><span class="p">)</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s2">"F"</span><span class="p">)</span>
<span class="c1"># 调整图像大小</span>
<span class="n">image</span><span class="o">=</span><span class="n">image</span><span class="o">.</span><span class="n">resize</span><span class="p">(</span> <span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">8</span><span class="p">)</span> <span class="p">)</span>
<span class="n">arr</span><span class="o">=</span><span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">8</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">8</span><span class="p">):</span>
<span class="n">pixel</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">-</span> <span class="nb">float</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">getpixel</span><span class="p">((</span><span class="n">j</span><span class="p">,</span><span class="n">i</span><span class="p">)))</span><span class="o">/</span><span class="mi">255</span>
<span class="n">arr</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pixel</span><span class="p">)</span>
<span class="n">arr1</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">arr</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">8</span><span class="p">),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'gray'</span> <span class="p">);</span>
<span class="n">mlp_hw</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">arr1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
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