testing local website build
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KarlHenrik
@@ -28,7 +28,7 @@
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<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.5.2/webfonts/fa-brands-400.woff2" />
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<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.5.2/webfonts/fa-regular-400.woff2" />
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<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
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<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=03e43079" />
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<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=eba8b062" />
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<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
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<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
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@@ -183,7 +183,7 @@
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">About the course</span></p>
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<ul class="nav bd-sidenav">
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<li class="toctree-l1"><a class="reference internal" href="schedule.html">Teaching schedule with links to material</a></li>
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<li class="toctree-l1"><a class="reference internal" href="schedule.html">Course setting</a></li>
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<li class="toctree-l1"><a class="reference internal" href="teachers.html">Teachers and Grading</a></li>
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<li class="toctree-l1"><a class="reference internal" href="textbooks.html">Textbooks</a></li>
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@@ -271,37 +271,6 @@
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<div class="dropdown dropdown-launch-buttons">
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<button class="btn dropdown-toggle" type="button" data-bs-toggle="dropdown" aria-expanded="false" aria-label="Launch interactive content">
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<i class="fas fa-rocket"></i>
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</button>
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<ul class="dropdown-menu">
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<li><a href="https://mybinder.org/v2/git/https%3A//compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/index.html/master?urlpath=tree/chapter12.ipynb" target="_blank"
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class="btn btn-sm dropdown-item"
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title="Launch on Binder"
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data-bs-placement="left" data-bs-toggle="tooltip"
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>
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<span class="btn__icon-container">
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<img alt="Binder logo" src="_static/images/logo_binder.svg">
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</span>
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<span class="btn__text-container">Binder</span>
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</a>
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</li>
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</ul>
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</div>
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<div class="dropdown dropdown-download-buttons">
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<button class="btn dropdown-toggle" type="button" data-bs-toggle="dropdown" aria-expanded="false" aria-label="Download this page">
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<i class="fas fa-download"></i>
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@@ -708,31 +677,28 @@ driven by purely sinusoidal sources.</p>
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<p>The code here shows a typical example of such a square wave generated using the functionality included in the <strong>scipy</strong> Python package. We have used a period of <span class="math notranslate nohighlight">\(\tau=0.2\)</span>.</p>
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<div class="cell docutils container">
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<div class="cell_input docutils container">
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
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<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">import</span> <span class="nn">math</span>
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<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">signal</span>
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<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
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import numpy as np
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import math
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from scipy import signal
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import matplotlib.pyplot as plt
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<span class="c1"># number of points </span>
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<span class="n">n</span> <span class="o">=</span> <span class="mi">500</span>
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<span class="c1"># start and final times </span>
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<span class="n">t0</span> <span class="o">=</span> <span class="mf">0.0</span>
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<span class="n">tn</span> <span class="o">=</span> <span class="mf">1.0</span>
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<span class="c1"># Period </span>
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<span class="n">t</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="n">t0</span><span class="p">,</span> <span class="n">tn</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<span class="n">SqrSignal</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
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<span class="n">SqrSignal</span> <span class="o">=</span> <span class="mf">1.0</span><span class="o">+</span><span class="n">signal</span><span class="o">.</span><span class="n">square</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="mi">5</span><span class="o">*</span><span class="n">t</span><span class="p">)</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">SqrSignal</span><span class="p">)</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">)</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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# number of points
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n = 500
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# start and final times
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t0 = 0.0
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tn = 1.0
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# Period
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t = np.linspace(t0, tn, n, endpoint=False)
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SqrSignal = np.zeros(n)
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SqrSignal = 1.0+signal.square(2*np.pi*5*t)
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plt.plot(t, SqrSignal)
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plt.ylim(-0.5, 2.5)
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plt.show()
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</pre></div>
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</div>
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</div>
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<div class="cell_output docutils container">
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<img alt="_images/2c4b990dde3dc58664875794563e9a8b9e2b839de5623992eb9b8540d07e1863.png" src="_images/2c4b990dde3dc58664875794563e9a8b9e2b839de5623992eb9b8540d07e1863.png" />
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</div>
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</div>
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<p>For the sinusoidal example the
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period is <span class="math notranslate nohighlight">\(\tau=2\pi/\omega\)</span>. However, higher harmonics can also
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@@ -841,41 +807,38 @@ series, the Fourier series approximation gets closer and closer to the
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square wave signal.</p>
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<div class="cell docutils container">
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<div class="cell_input docutils container">
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">import</span> <span class="nn">math</span>
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<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">signal</span>
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<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
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<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import numpy as np
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import math
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from scipy import signal
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import matplotlib.pyplot as plt
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<span class="c1"># number of points </span>
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<span class="n">n</span> <span class="o">=</span> <span class="mi">500</span>
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<span class="c1"># start and final times </span>
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<span class="n">t0</span> <span class="o">=</span> <span class="mf">0.0</span>
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<span class="n">tn</span> <span class="o">=</span> <span class="mf">1.0</span>
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<span class="c1"># Period </span>
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<span class="n">T</span> <span class="o">=</span><span class="mf">0.2</span>
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<span class="c1"># Max value of square signal </span>
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<span class="n">Fmax</span><span class="o">=</span> <span class="mf">2.0</span>
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<span class="c1"># Width of signal </span>
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<span class="n">Width</span> <span class="o">=</span> <span class="mf">0.1</span>
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<span class="n">t</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="n">t0</span><span class="p">,</span> <span class="n">tn</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<span class="n">SqrSignal</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
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<span class="n">FourierSeriesSignal</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
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<span class="n">SqrSignal</span> <span class="o">=</span> <span class="mf">1.0</span><span class="o">+</span><span class="n">signal</span><span class="o">.</span><span class="n">square</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="mi">5</span><span class="o">*</span><span class="n">t</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">Width</span><span class="o">/</span><span class="n">T</span><span class="p">)</span>
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<span class="n">a0</span> <span class="o">=</span> <span class="n">Fmax</span><span class="o">*</span><span class="n">Width</span><span class="o">/</span><span class="n">T</span>
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<span class="n">FourierSeriesSignal</span> <span class="o">=</span> <span class="n">a0</span>
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<span class="n">Factor</span> <span class="o">=</span> <span class="mf">2.0</span><span class="o">*</span><span class="n">Fmax</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span>
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<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">1</span><span class="p">,</span><span class="mi">500</span><span class="p">):</span>
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<span class="n">FourierSeriesSignal</span> <span class="o">+=</span> <span class="n">Factor</span><span class="o">/</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">i</span><span class="o">*</span><span class="n">Width</span><span class="o">/</span><span class="n">T</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">i</span><span class="o">*</span><span class="n">t</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="n">T</span><span class="p">)</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">SqrSignal</span><span class="p">)</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">FourierSeriesSignal</span><span class="p">)</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">)</span>
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<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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# number of points
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n = 500
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# start and final times
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t0 = 0.0
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tn = 1.0
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# Period
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T =0.2
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# Max value of square signal
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Fmax= 2.0
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# Width of signal
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Width = 0.1
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t = np.linspace(t0, tn, n, endpoint=False)
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SqrSignal = np.zeros(n)
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FourierSeriesSignal = np.zeros(n)
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SqrSignal = 1.0+signal.square(2*np.pi*5*t+np.pi*Width/T)
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a0 = Fmax*Width/T
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FourierSeriesSignal = a0
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Factor = 2.0*Fmax/np.pi
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for i in range(1,500):
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FourierSeriesSignal += Factor/(i)*np.sin(np.pi*i*Width/T)*np.cos(i*t*2*np.pi/T)
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plt.plot(t, SqrSignal)
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plt.plot(t, FourierSeriesSignal)
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plt.ylim(-0.5, 2.5)
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plt.show()
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</pre></div>
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</div>
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</div>
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<div class="cell_output docutils container">
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<img alt="_images/97b8a054a5ec2216c4823693b18a380d8ae96833b3e710a3a8a788904c6c8e09.png" src="_images/97b8a054a5ec2216c4823693b18a380d8ae96833b3e710a3a8a788904c6c8e09.png" />
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</div>
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</div>
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</section>
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</section>
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@@ -1003,316 +966,164 @@ classification.</p>
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<h3><span class="section-number">16.7.3. </span>Prerequisites: Collect and pre-process data<a class="headerlink" href="#prerequisites-collect-and-pre-process-data" title="Link to this heading">#</a></h3>
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<div class="cell docutils container">
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<div class="cell_input docutils container">
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># import necessary packages</span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
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<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
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<div class="highlight-none notranslate"><div class="highlight"><pre><span></span># import necessary packages
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import datasets
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<span class="c1"># ensure the same random numbers appear every time</span>
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<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
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# ensure the same random numbers appear every time
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np.random.seed(0)
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<span class="c1"># display images in notebook</span>
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<span class="o">%</span><span class="k">matplotlib</span> inline
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<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'figure.figsize'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">12</span><span class="p">)</span>
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# display images in notebook
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%matplotlib inline
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plt.rcParams['figure.figsize'] = (12,12)
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<span class="c1"># download MNIST dataset</span>
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<span class="n">digits</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">()</span>
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# download MNIST dataset
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digits = datasets.load_digits()
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<span class="c1"># define inputs and labels</span>
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<span class="n">inputs</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">images</span>
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<span class="n">labels</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">target</span>
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# define inputs and labels
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inputs = digits.images
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||||
labels = digits.target
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||||
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<span class="c1"># RGB images have a depth of 3</span>
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||||
<span class="c1"># our images are grayscale so they should have a depth of 1</span>
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||||
<span class="n">inputs</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[:,:,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
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# RGB images have a depth of 3
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||||
# our images are grayscale so they should have a depth of 1
|
||||
inputs = inputs[:,:,:,np.newaxis]
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||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"inputs = (n_inputs, pixel_width, pixel_height, depth) = "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">inputs</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
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||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"labels = (n_inputs) = "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">labels</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
|
||||
print("inputs = (n_inputs, pixel_width, pixel_height, depth) = " + str(inputs.shape))
|
||||
print("labels = (n_inputs) = " + str(labels.shape))
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||||
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||||
<span class="c1"># choose some random images to display</span>
|
||||
<span class="n">n_inputs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
|
||||
<span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)</span>
|
||||
<span class="n">random_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
|
||||
# choose some random images to display
|
||||
n_inputs = len(inputs)
|
||||
indices = np.arange(n_inputs)
|
||||
random_indices = np.random.choice(indices, size=5)
|
||||
|
||||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">image</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">digits</span><span class="o">.</span><span class="n">images</span><span class="p">[</span><span class="n">random_indices</span><span class="p">]):</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">i</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">axis</span><span class="p">(</span><span class="s1">'off'</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">image</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">gray_r</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="s1">'nearest'</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">"Label: </span><span class="si">%d</span><span class="s2">"</span> <span class="o">%</span> <span class="n">digits</span><span class="o">.</span><span class="n">target</span><span class="p">[</span><span class="n">random_indices</span><span class="p">[</span><span class="n">i</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>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>inputs = (n_inputs, pixel_width, pixel_height, depth) = (1797, 8, 8, 1)
|
||||
labels = (n_inputs) = (1797,)
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/d4e4e03fa10c1df1662631345b50b4045eb3d798a0f1e6f200922789b2e4a448.png" src="_images/d4e4e03fa10c1df1662631345b50b4045eb3d798a0f1e6f200922789b2e4a448.png" />
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">tensorflow.keras</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">models</span>
|
||||
<span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">Input</span>
|
||||
<span class="kn">from</span> <span class="nn">tensorflow.keras.models</span> <span class="kn">import</span> <span class="n">Sequential</span> <span class="c1">#This allows appending layers to existing models</span>
|
||||
<span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">Dense</span> <span class="c1">#This allows defining the characteristics of a particular layer</span>
|
||||
<span class="kn">from</span> <span class="nn">tensorflow.keras</span> <span class="kn">import</span> <span class="n">optimizers</span> <span class="c1">#This allows using whichever optimiser we want (sgd,adam,RMSprop)</span>
|
||||
<span class="kn">from</span> <span class="nn">tensorflow.keras</span> <span class="kn">import</span> <span class="n">regularizers</span> <span class="c1">#This allows using whichever regularizer we want (l1,l2,l1_l2)</span>
|
||||
<span class="kn">from</span> <span class="nn">tensorflow.keras.utils</span> <span class="kn">import</span> <span class="n">to_categorical</span> <span class="c1">#This allows using categorical cross entropy as the cost function</span>
|
||||
<span class="c1">#from tensorflow.keras import Conv2D</span>
|
||||
<span class="c1">#from tensorflow.keras import MaxPooling2D</span>
|
||||
<span class="c1">#from tensorflow.keras import Flatten</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="c1"># representation of labels</span>
|
||||
<span class="n">labels</span> <span class="o">=</span> <span class="n">to_categorical</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># split into train and test data</span>
|
||||
<span class="c1"># one-liner from scikit-learn library</span>
|
||||
<span class="n">train_size</span> <span class="o">=</span> <span class="mf">0.8</span>
|
||||
<span class="n">test_size</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">train_size</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">inputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="n">train_size</span><span class="p">,</span>
|
||||
<span class="n">test_size</span><span class="o">=</span><span class="n">test_size</span><span class="p">)</span>
|
||||
for i, image in enumerate(digits.images[random_indices]):
|
||||
plt.subplot(1, 5, i+1)
|
||||
plt.axis('off')
|
||||
plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
|
||||
plt.title("Label: %d" % digits.target[random_indices[i]])
|
||||
plt.show()
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">create_convolutional_neural_network_keras</span><span class="p">(</span><span class="n">input_shape</span><span class="p">,</span> <span class="n">receptive_field</span><span class="p">,</span>
|
||||
<span class="n">n_filters</span><span class="p">,</span> <span class="n">n_neurons_connected</span><span class="p">,</span> <span class="n">n_categories</span><span class="p">,</span>
|
||||
<span class="n">eta</span><span class="p">,</span> <span class="n">lmbd</span><span class="p">):</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">n_filters</span><span class="p">,</span> <span class="p">(</span><span class="n">receptive_field</span><span class="p">,</span> <span class="n">receptive_field</span><span class="p">),</span> <span class="n">input_shape</span><span class="o">=</span><span class="n">input_shape</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'same'</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">kernel_regularizer</span><span class="o">=</span><span class="n">regularizers</span><span class="o">.</span><span class="n">l2</span><span class="p">(</span><span class="n">lmbd</span><span class="p">)))</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Flatten</span><span class="p">())</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">n_neurons_connected</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">kernel_regularizer</span><span class="o">=</span><span class="n">regularizers</span><span class="o">.</span><span class="n">l2</span><span class="p">(</span><span class="n">lmbd</span><span class="p">)))</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">n_categories</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'softmax'</span><span class="p">,</span> <span class="n">kernel_regularizer</span><span class="o">=</span><span class="n">regularizers</span><span class="o">.</span><span class="n">l2</span><span class="p">(</span><span class="n">lmbd</span><span class="p">)))</span>
|
||||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>from tensorflow.keras import datasets, layers, models
|
||||
from tensorflow.keras.layers import Input
|
||||
from tensorflow.keras.models import Sequential #This allows appending layers to existing models
|
||||
from tensorflow.keras.layers import Dense #This allows defining the characteristics of a particular layer
|
||||
from tensorflow.keras import optimizers #This allows using whichever optimiser we want (sgd,adam,RMSprop)
|
||||
from tensorflow.keras import regularizers #This allows using whichever regularizer we want (l1,l2,l1_l2)
|
||||
from tensorflow.keras.utils import to_categorical #This allows using categorical cross entropy as the cost function
|
||||
#from tensorflow.keras import Conv2D
|
||||
#from tensorflow.keras import MaxPooling2D
|
||||
#from tensorflow.keras import Flatten
|
||||
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
# representation of labels
|
||||
labels = to_categorical(labels)
|
||||
|
||||
# split into train and test data
|
||||
# one-liner from scikit-learn library
|
||||
train_size = 0.8
|
||||
test_size = 1 - train_size
|
||||
X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=train_size,
|
||||
test_size=test_size)
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>def create_convolutional_neural_network_keras(input_shape, receptive_field,
|
||||
n_filters, n_neurons_connected, n_categories,
|
||||
eta, lmbd):
|
||||
model = Sequential()
|
||||
model.add(layers.Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',
|
||||
activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
|
||||
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
|
||||
model.add(layers.Flatten())
|
||||
model.add(layers.Dense(n_neurons_connected, activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
|
||||
model.add(layers.Dense(n_categories, activation='softmax', kernel_regularizer=regularizers.l2(lmbd)))
|
||||
|
||||
<span class="n">sgd</span> <span class="o">=</span> <span class="n">optimizers</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="n">eta</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">'categorical_crossentropy'</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">sgd</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">])</span>
|
||||
sgd = optimizers.SGD(lr=eta)
|
||||
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
|
||||
|
||||
<span class="k">return</span> <span class="n">model</span>
|
||||
return model
|
||||
|
||||
<span class="n">epochs</span> <span class="o">=</span> <span class="mi">100</span>
|
||||
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">100</span>
|
||||
<span class="n">input_shape</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span>
|
||||
<span class="n">receptive_field</span> <span class="o">=</span> <span class="mi">3</span>
|
||||
<span class="n">n_filters</span> <span class="o">=</span> <span class="mi">10</span>
|
||||
<span class="n">n_neurons_connected</span> <span class="o">=</span> <span class="mi">50</span>
|
||||
<span class="n">n_categories</span> <span class="o">=</span> <span class="mi">10</span>
|
||||
epochs = 100
|
||||
batch_size = 100
|
||||
input_shape = X_train.shape[1:4]
|
||||
receptive_field = 3
|
||||
n_filters = 10
|
||||
n_neurons_connected = 50
|
||||
n_categories = 10
|
||||
|
||||
<span class="n">eta_vals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="p">)</span>
|
||||
<span class="n">lmbd_vals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="p">)</span>
|
||||
eta_vals = np.logspace(-5, 1, 7)
|
||||
lmbd_vals = np.logspace(-5, 1, 7)
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">CNN_keras</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">eta_vals</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">)),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
|
||||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
|
||||
|
||||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">eta</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">eta_vals</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">lmbd</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">):</span>
|
||||
<span class="n">CNN</span> <span class="o">=</span> <span class="n">create_convolutional_neural_network_keras</span><span class="p">(</span><span class="n">input_shape</span><span class="p">,</span> <span class="n">receptive_field</span><span class="p">,</span>
|
||||
<span class="n">n_filters</span><span class="p">,</span> <span class="n">n_neurons_connected</span><span class="p">,</span> <span class="n">n_categories</span><span class="p">,</span>
|
||||
<span class="n">eta</span><span class="p">,</span> <span class="n">lmbd</span><span class="p">)</span>
|
||||
<span class="n">CNN</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="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">scores</span> <span class="o">=</span> <span class="n">CNN</span><span class="o">.</span><span class="n">evaluate</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>
|
||||
for i, eta in enumerate(eta_vals):
|
||||
for j, lmbd in enumerate(lmbd_vals):
|
||||
CNN = create_convolutional_neural_network_keras(input_shape, receptive_field,
|
||||
n_filters, n_neurons_connected, n_categories,
|
||||
eta, lmbd)
|
||||
CNN.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=0)
|
||||
scores = CNN.evaluate(X_test, Y_test)
|
||||
|
||||
<span class="n">CNN_keras</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">CNN</span>
|
||||
CNN_keras[i][j] = CNN
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Learning rate = "</span><span class="p">,</span> <span class="n">eta</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Lambda = "</span><span class="p">,</span> <span class="n">lmbd</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Test accuracy: </span><span class="si">%.3f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">scores</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="nb">print</span><span class="p">()</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.SGD` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.SGD`.
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.SGD.
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.SGD`.
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2025-08-05 16:50:55.150642: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 1/12 [=>............................] - ETA: 1s - loss: 0.0169 - accuracy: 1.0000
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
|
||||
6/12 [==============>...............] - ETA: 0s - loss: 0.1284 - accuracy: 0.9740
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
|
||||
11/12 [==========================>...] - ETA: 0s - loss: 0.0845 - accuracy: 0.9830
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
|
||||
12/12 [==============================] - 0s 13ms/step - loss: 0.0843 - accuracy: 0.9833
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.SGD` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.SGD`.
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.SGD.
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.SGD`.
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
|
||||
Lambda = 1e-05
|
||||
Test accuracy: 0.983
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
|
||||
<span class="ne">KeyboardInterrupt</span><span class="g g-Whitespace"> </span>Traceback (most recent call last)
|
||||
<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="n">line</span> <span class="mi">8</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">4</span> <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">lmbd</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">5</span> <span class="n">CNN</span> <span class="o">=</span> <span class="n">create_convolutional_neural_network_keras</span><span class="p">(</span><span class="n">input_shape</span><span class="p">,</span> <span class="n">receptive_field</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">6</span> <span class="n">n_filters</span><span class="p">,</span> <span class="n">n_neurons_connected</span><span class="p">,</span> <span class="n">n_categories</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">7</span> <span class="n">eta</span><span class="p">,</span> <span class="n">lmbd</span><span class="p">)</span>
|
||||
<span class="ne">----> </span><span class="mi">8</span> <span class="n">CNN</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="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">9</span> <span class="n">scores</span> <span class="o">=</span> <span class="n">CNN</span><span class="o">.</span><span class="n">evaluate</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="g g-Whitespace"> </span><span class="mi">11</span> <span class="n">CNN_keras</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">CNN</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py:65,</span> in <span class="ni">filter_traceback.<locals>.error_handler</span><span class="nt">(*args, **kwargs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">63</span> <span class="n">filtered_tb</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">64</span> <span class="k">try</span><span class="p">:</span>
|
||||
<span class="ne">---> </span><span class="mi">65</span> <span class="k">return</span> <span class="n">fn</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">66</span> <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">67</span> <span class="n">filtered_tb</span> <span class="o">=</span> <span class="n">_process_traceback_frames</span><span class="p">(</span><span class="n">e</span><span class="o">.</span><span class="n">__traceback__</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/engine/training.py:1685,</span> in <span class="ni">Model.fit</span><span class="nt">(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1677</span> <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">experimental</span><span class="o">.</span><span class="n">Trace</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1678</span> <span class="s2">"train"</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1679</span> <span class="n">epoch_num</span><span class="o">=</span><span class="n">epoch</span><span class="p">,</span>
|
||||
<span class="p">(</span><span class="o">...</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1682</span> <span class="n">_r</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1683</span> <span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1684</span> <span class="n">callbacks</span><span class="o">.</span><span class="n">on_train_batch_begin</span><span class="p">(</span><span class="n">step</span><span class="p">)</span>
|
||||
<span class="ne">-> </span><span class="mi">1685</span> <span class="n">tmp_logs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_function</span><span class="p">(</span><span class="n">iterator</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1686</span> <span class="k">if</span> <span class="n">data_handler</span><span class="o">.</span><span class="n">should_sync</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1687</span> <span class="n">context</span><span class="o">.</span><span class="n">async_wait</span><span class="p">()</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:150,</span> in <span class="ni">filter_traceback.<locals>.error_handler</span><span class="nt">(*args, **kwargs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">148</span> <span class="n">filtered_tb</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">149</span> <span class="k">try</span><span class="p">:</span>
|
||||
<span class="ne">--> </span><span class="mi">150</span> <span class="k">return</span> <span class="n">fn</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">151</span> <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">152</span> <span class="n">filtered_tb</span> <span class="o">=</span> <span class="n">_process_traceback_frames</span><span class="p">(</span><span class="n">e</span><span class="o">.</span><span class="n">__traceback__</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:894,</span> in <span class="ni">Function.__call__</span><span class="nt">(self, *args, **kwds)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">891</span> <span class="n">compiler</span> <span class="o">=</span> <span class="s2">"xla"</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jit_compile</span> <span class="k">else</span> <span class="s2">"nonXla"</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">893</span> <span class="k">with</span> <span class="n">OptionalXlaContext</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jit_compile</span><span class="p">):</span>
|
||||
<span class="ne">--> </span><span class="mi">894</span> <span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwds</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">896</span> <span class="n">new_tracing_count</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">experimental_get_tracing_count</span><span class="p">()</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">897</span> <span class="n">without_tracing</span> <span class="o">=</span> <span class="p">(</span><span class="n">tracing_count</span> <span class="o">==</span> <span class="n">new_tracing_count</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:926,</span> in <span class="ni">Function._call</span><span class="nt">(self, *args, **kwds)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">923</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lock</span><span class="o">.</span><span class="n">release</span><span class="p">()</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">924</span> <span class="c1"># In this case we have created variables on the first call, so we run the</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">925</span> <span class="c1"># defunned version which is guaranteed to never create variables.</span>
|
||||
<span class="ne">--> </span><span class="mi">926</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_no_variable_creation_fn</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwds</span><span class="p">)</span> <span class="c1"># pylint: disable=not-callable</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">927</span> <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_variable_creation_fn</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">928</span> <span class="c1"># Release the lock early so that multiple threads can perform the call</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">929</span> <span class="c1"># in parallel.</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">930</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lock</span><span class="o">.</span><span class="n">release</span><span class="p">()</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:143,</span> in <span class="ni">TracingCompiler.__call__</span><span class="nt">(self, *args, **kwargs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">140</span> <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lock</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">141</span> <span class="p">(</span><span class="n">concrete_function</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">142</span> <span class="n">filtered_flat_args</span><span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_maybe_define_function</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="ne">--> </span><span class="mi">143</span> <span class="k">return</span> <span class="n">concrete_function</span><span class="o">.</span><span class="n">_call_flat</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">144</span> <span class="n">filtered_flat_args</span><span class="p">,</span> <span class="n">captured_inputs</span><span class="o">=</span><span class="n">concrete_function</span><span class="o">.</span><span class="n">captured_inputs</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1757,</span> in <span class="ni">ConcreteFunction._call_flat</span><span class="nt">(self, args, captured_inputs, cancellation_manager)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1753</span> <span class="n">possible_gradient_type</span> <span class="o">=</span> <span class="n">gradients_util</span><span class="o">.</span><span class="n">PossibleTapeGradientTypes</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1754</span> <span class="k">if</span> <span class="p">(</span><span class="n">possible_gradient_type</span> <span class="o">==</span> <span class="n">gradients_util</span><span class="o">.</span><span class="n">POSSIBLE_GRADIENT_TYPES_NONE</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1755</span> <span class="ow">and</span> <span class="n">executing_eagerly</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1756</span> <span class="c1"># No tape is watching; skip to running the function.</span>
|
||||
<span class="ne">-> </span><span class="mi">1757</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_build_call_outputs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_inference_function</span><span class="o">.</span><span class="n">call</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1758</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">cancellation_manager</span><span class="o">=</span><span class="n">cancellation_manager</span><span class="p">))</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1759</span> <span class="n">forward_backward</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_select_forward_and_backward_functions</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1760</span> <span class="n">args</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1761</span> <span class="n">possible_gradient_type</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1762</span> <span class="n">executing_eagerly</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">1763</span> <span class="n">forward_function</span><span class="p">,</span> <span class="n">args_with_tangents</span> <span class="o">=</span> <span class="n">forward_backward</span><span class="o">.</span><span class="n">forward</span><span class="p">()</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:381,</span> in <span class="ni">_EagerDefinedFunction.call</span><span class="nt">(self, ctx, args, cancellation_manager)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">379</span> <span class="k">with</span> <span class="n">_InterpolateFunctionError</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">380</span> <span class="k">if</span> <span class="n">cancellation_manager</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="ne">--> </span><span class="mi">381</span> <span class="n">outputs</span> <span class="o">=</span> <span class="n">execute</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">382</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">signature</span><span class="o">.</span><span class="n">name</span><span class="p">),</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">383</span> <span class="n">num_outputs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_outputs</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">384</span> <span class="n">inputs</span><span class="o">=</span><span class="n">args</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">385</span> <span class="n">attrs</span><span class="o">=</span><span class="n">attrs</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">386</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">387</span> <span class="k">else</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">388</span> <span class="n">outputs</span> <span class="o">=</span> <span class="n">execute</span><span class="o">.</span><span class="n">execute_with_cancellation</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">389</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">signature</span><span class="o">.</span><span class="n">name</span><span class="p">),</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">390</span> <span class="n">num_outputs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_outputs</span><span class="p">,</span>
|
||||
<span class="p">(</span><span class="o">...</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">393</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">394</span> <span class="n">cancellation_manager</span><span class="o">=</span><span class="n">cancellation_manager</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:52,</span> in <span class="ni">quick_execute</span><span class="nt">(op_name, num_outputs, inputs, attrs, ctx, name)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">50</span> <span class="k">try</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">51</span> <span class="n">ctx</span><span class="o">.</span><span class="n">ensure_initialized</span><span class="p">()</span>
|
||||
<span class="ne">---> </span><span class="mi">52</span> <span class="n">tensors</span> <span class="o">=</span> <span class="n">pywrap_tfe</span><span class="o">.</span><span class="n">TFE_Py_Execute</span><span class="p">(</span><span class="n">ctx</span><span class="o">.</span><span class="n">_handle</span><span class="p">,</span> <span class="n">device_name</span><span class="p">,</span> <span class="n">op_name</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">53</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">attrs</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">54</span> <span class="k">except</span> <span class="n">core</span><span class="o">.</span><span class="n">_NotOkStatusException</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">55</span> <span class="k">if</span> <span class="n">name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
|
||||
<span class="ne">KeyboardInterrupt</span>:
|
||||
print("Learning rate = ", eta)
|
||||
print("Lambda = ", lmbd)
|
||||
print("Test accuracy: %.3f" % scores[1])
|
||||
print()
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># visual representation of grid search</span>
|
||||
<span class="c1"># uses seaborn heatmap, could probably do this in matplotlib</span>
|
||||
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
|
||||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span># visual representation of grid search
|
||||
# uses seaborn heatmap, could probably do this in matplotlib
|
||||
import seaborn as sns
|
||||
|
||||
<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">()</span>
|
||||
sns.set()
|
||||
|
||||
<span class="n">train_accuracy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">eta_vals</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">)))</span>
|
||||
<span class="n">test_accuracy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">eta_vals</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">)))</span>
|
||||
train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
|
||||
test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
|
||||
|
||||
<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">eta_vals</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="nb">len</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">)):</span>
|
||||
<span class="n">CNN</span> <span class="o">=</span> <span class="n">CNN_keras</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span>
|
||||
for i in range(len(eta_vals)):
|
||||
for j in range(len(lmbd_vals)):
|
||||
CNN = CNN_keras[i][j]
|
||||
|
||||
<span class="n">train_accuracy</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">CNN</span><span class="o">.</span><span class="n">evaluate</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="mi">1</span><span class="p">]</span>
|
||||
<span class="n">test_accuracy</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">CNN</span><span class="o">.</span><span class="n">evaluate</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="mi">1</span><span class="p">]</span>
|
||||
train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]
|
||||
test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
|
||||
|
||||
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</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="n">figsize</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">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">train_accuracy</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">"viridis"</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Training Accuracy"</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"$\eta$"</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"$\lambda$"</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
|
||||
fig, ax = plt.subplots(figsize = (10, 10))
|
||||
sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
|
||||
ax.set_title("Training Accuracy")
|
||||
ax.set_ylabel("$\eta$")
|
||||
ax.set_xlabel("$\lambda$")
|
||||
plt.show()
|
||||
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</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="n">figsize</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">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">test_accuracy</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">"viridis"</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Test Accuracy"</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"$\eta$"</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"$\lambda$"</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
|
||||
fig, ax = plt.subplots(figsize = (10, 10))
|
||||
sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
|
||||
ax.set_title("Test Accuracy")
|
||||
ax.set_ylabel("$\eta$")
|
||||
ax.set_xlabel("$\lambda$")
|
||||
plt.show()
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1327,16 +1138,16 @@ training images and 10,000 testing images. The classes are mutually
|
||||
exclusive and there is no overlap between them.</p>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
|
||||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import tensorflow as tf
|
||||
|
||||
<span class="kn">from</span> <span class="nn">tensorflow.keras</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">models</span>
|
||||
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||||
from tensorflow.keras import datasets, layers, models
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
<span class="c1"># We import the data set</span>
|
||||
<span class="p">(</span><span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">),</span> <span class="p">(</span><span class="n">test_images</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">cifar10</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
|
||||
# We import the data set
|
||||
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
|
||||
|
||||
<span class="c1"># Normalize pixel values to be between 0 and 1 by dividing by 255. </span>
|
||||
<span class="n">train_images</span><span class="p">,</span> <span class="n">test_images</span> <span class="o">=</span> <span class="n">train_images</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="n">test_images</span> <span class="o">/</span> <span class="mf">255.0</span>
|
||||
# Normalize pixel values to be between 0 and 1 by dividing by 255.
|
||||
train_images, test_images = train_images / 255.0, test_images / 255.0
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1344,20 +1155,20 @@ exclusive and there is no overlap between them.</p>
|
||||
<p>To verify that the dataset looks correct, let’s plot the first 25 images from the training set and display the class name below each image.</p>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">class_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'airplane'</span><span class="p">,</span> <span class="s1">'automobile'</span><span class="p">,</span> <span class="s1">'bird'</span><span class="p">,</span> <span class="s1">'cat'</span><span class="p">,</span> <span class="s1">'deer'</span><span class="p">,</span>
|
||||
<span class="s1">'dog'</span><span class="p">,</span> <span class="s1">'frog'</span><span class="p">,</span> <span class="s1">'horse'</span><span class="p">,</span> <span class="s1">'ship'</span><span class="p">,</span> <span class="s1">'truck'</span><span class="p">]</span>
|
||||
<span class="err"></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">10</span><span class="p">,</span><span class="mi">10</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">25</span><span class="p">):</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</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">i</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">xticks</span><span class="p">([])</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">([])</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">False</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">train_images</span><span class="p">[</span><span class="n">i</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">binary</span><span class="p">)</span>
|
||||
<span class="c1"># The CIFAR labels happen to be arrays, </span>
|
||||
<span class="c1"># which is why you need the extra index</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="n">class_names</span><span class="p">[</span><span class="n">train_labels</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
|
||||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
|
||||
'dog', 'frog', 'horse', 'ship', 'truck']
|
||||
|
||||
plt.figure(figsize=(10,10))
|
||||
for i in range(25):
|
||||
plt.subplot(5,5,i+1)
|
||||
plt.xticks([])
|
||||
plt.yticks([])
|
||||
plt.grid(False)
|
||||
plt.imshow(train_images[i], cmap=plt.cm.binary)
|
||||
# The CIFAR labels happen to be arrays,
|
||||
# which is why you need the extra index
|
||||
plt.xlabel(class_names[train_labels[i][0]])
|
||||
plt.show()
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1366,16 +1177,16 @@ exclusive and there is no overlap between them.</p>
|
||||
<p>As input, a CNN takes tensors of shape (image_height, image_width, color_channels), ignoring the batch size. If you are new to these dimensions, color_channels refers to (R,G,B). In this example, you will configure our CNN to process inputs of shape (32, 32, 3), which is the format of CIFAR images. You can do this by passing the argument input_shape to our first layer.</p>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</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">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">MaxPooling2D</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</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">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">MaxPooling2D</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">))</span>
|
||||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>model = models.Sequential()
|
||||
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
|
||||
model.add(layers.MaxPooling2D((2, 2)))
|
||||
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
|
||||
model.add(layers.MaxPooling2D((2, 2)))
|
||||
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
|
||||
|
||||
<span class="c1"># Let's display the architecture of our model so far.</span>
|
||||
# Let's display the architecture of our model so far.
|
||||
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
|
||||
model.summary()
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1390,12 +1201,12 @@ layers on top. CIFAR has 10 output classes, so you use a final Dense
|
||||
layer with 10 outputs and a softmax activation.</p>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Flatten</span><span class="p">())</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">64</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">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
|
||||
<span class="n">Here</span><span class="s1">'s the complete architecture of our model.</span>
|
||||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>model.add(layers.Flatten())
|
||||
model.add(layers.Dense(64, activation='relu'))
|
||||
model.add(layers.Dense(10))
|
||||
Here's the complete architecture of our model.
|
||||
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
|
||||
model.summary()
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1403,28 +1214,28 @@ layer with 10 outputs and a softmax activation.</p>
|
||||
<p>As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.</p>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">'adam'</span><span class="p">,</span>
|
||||
<span class="n">loss</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">SparseCategoricalCrossentropy</span><span class="p">(</span><span class="n">from_logits</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
|
||||
<span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">])</span>
|
||||
<span class="err"></span>
|
||||
<span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
|
||||
<span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">test_images</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">))</span>
|
||||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>model.compile(optimizer='adam',
|
||||
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
|
||||
metrics=['accuracy'])
|
||||
|
||||
history = model.fit(train_images, train_labels, epochs=10,
|
||||
validation_data=(test_images, test_labels))
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'accuracy'</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">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">'val_accuracy'</span><span class="p">],</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">'val_accuracy'</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Epoch'</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Accuracy'</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="mf">0.5</span><span class="p">,</span> <span class="mi">1</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="s1">'lower right'</span><span class="p">)</span>
|
||||
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>plt.plot(history.history['accuracy'], label='accuracy')
|
||||
plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
|
||||
plt.xlabel('Epoch')
|
||||
plt.ylabel('Accuracy')
|
||||
plt.ylim([0.5, 1])
|
||||
plt.legend(loc='lower right')
|
||||
|
||||
<span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">test_images</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||||
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">test_acc</span><span class="p">)</span>
|
||||
print(test_acc)
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
Reference in New Issue
Block a user