mhjensen
214 lines
8.8 KiB
HTML
214 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="#___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="._cnn-bs012.html#___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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</li>
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</ul>
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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="part0005"></a>
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<!-- !split -->
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<h2 id="___sec4" class="anchor">Transforming images </h2>
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<p>
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CNNs transform the original image layer by layer from the original
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pixel values to the final class scores.
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<p>
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Observe that some layers contain
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parameters and other don’t. In particular, the CNN layers perform
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transformations that are a function of not only the activations in the
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input volume, but also of the parameters (the weights and biases of
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the neurons). On the other hand, the RELU/POOL layers will implement a
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fixed function. The parameters in the CONV/FC layers will be trained
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with gradient descent so that the class scores that the CNN computes
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are consistent with the labels in the training set for each image.
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<p>
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<p>
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<li><a href="._cnn-bs004.html">«</a></li>
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<li><a href="._cnn-bs000.html">1</a></li>
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<li><a href="._cnn-bs001.html">2</a></li>
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<li><a href="._cnn-bs002.html">3</a></li>
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<li><a href="._cnn-bs003.html">4</a></li>
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<li><a href="._cnn-bs004.html">5</a></li>
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<li class="active"><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><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-bs014.html">15</a></li>
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<li><a href="">...</a></li>
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<li><a href="._cnn-bs021.html">22</a></li>
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<li><a href="._cnn-bs006.html">»</a></li>
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