mhjensen
213 lines
8.8 KiB
HTML
213 lines
8.8 KiB
HTML
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<a class="navbar-brand" href="cnn-bs.html">Convolutional Neural Networks</a>
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<ul class="dropdown-menu">
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<!-- navigation toc: --> <li><a href="._cnn-bs001.html#___sec0" style="font-size: 80%;">Convolutional Neural Networks (recognizing images)</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs002.html#___sec1" style="font-size: 80%;">Regular NNs don’t scale well to full images</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs003.html#___sec2" style="font-size: 80%;">3D volumes of neurons</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs004.html#___sec3" style="font-size: 80%;">Layers used to build CNNs</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs005.html#___sec4" style="font-size: 80%;">Transforming images</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs006.html#___sec5" style="font-size: 80%;">CNNs in brief</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs007.html#___sec6" style="font-size: 80%;">CNNs in more detail, building convolutional neural networks in Tensorflow and Keras</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs008.html#___sec7" style="font-size: 80%;">Setting it up</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs009.html#___sec8" style="font-size: 80%;">The MNIST dataset again</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs010.html#___sec9" style="font-size: 80%;">Strong correlations</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs011.html#___sec10" style="font-size: 80%;">Layers of a CNN</a></li>
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<!-- navigation toc: --> <li><a href="#___sec11" style="font-size: 80%;">Systematic reduction</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs013.html#___sec12" style="font-size: 80%;">Prerequisites: Collect and pre-process data</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs014.html#___sec13" style="font-size: 80%;">Importing Keras and Tensorflow</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs015.html#___sec14" style="font-size: 80%;">Using TensorFlow backend</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs016.html#___sec15" style="font-size: 80%;">Train the model</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs017.html#___sec16" style="font-size: 80%;">Visualizing the results</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs018.html#___sec17" style="font-size: 80%;">Running with Keras</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs019.html#___sec18" style="font-size: 80%;">Final part</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs020.html#___sec19" style="font-size: 80%;">Final visualization</a></li>
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<!-- navigation toc: --> <li><a href="._cnn-bs021.html#___sec20" style="font-size: 80%;">Fun links</a></li>
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</ul>
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</li>
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</ul>
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</div>
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</div>
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<div class="container">
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<p> </p><p> </p><p> </p> <!-- add vertical space -->
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<a name="part0012"></a>
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<!-- !split -->
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<h2 id="___sec11" class="anchor">Systematic reduction </h2>
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<p>
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By systematically reducing the size of the input volume, through
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convolution and pooling, the network should create representations of
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small parts of the input, and then from them assemble representations
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of larger areas. The final pooling layer is flattened to serve as
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input to a hidden layer, such that each neuron in the final pooling
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layer is connected to every single neuron in the hidden layer. This
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then serves as input to the output layer, e.g. a softmax output for
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classification.
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<p>
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<p>
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<li><a href="._cnn-bs011.html">«</a></li>
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<li><a href="._cnn-bs000.html">1</a></li>
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<li><a href="._cnn-bs004.html">5</a></li>
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<li><a href="._cnn-bs005.html">6</a></li>
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<li><a href="._cnn-bs006.html">7</a></li>
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<li><a href="._cnn-bs007.html">8</a></li>
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<li><a href="._cnn-bs008.html">9</a></li>
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<li><a href="._cnn-bs009.html">10</a></li>
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<li><a href="._cnn-bs010.html">11</a></li>
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<li><a href="._cnn-bs011.html">12</a></li>
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<li class="active"><a href="._cnn-bs012.html">13</a></li>
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<li><a href="._cnn-bs013.html">14</a></li>
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<li><a href="._cnn-bs018.html">19</a></li>
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<li><a href="._cnn-bs019.html">20</a></li>
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<li><a href="._cnn-bs020.html">21</a></li>
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<li><a href="._cnn-bs021.html">22</a></li>
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<li><a href="._cnn-bs013.html">»</a></li>
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