mhjensen
221 lines
9.3 KiB
HTML
221 lines
9.3 KiB
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<!-- navigation toc: --> <li><a href="#___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="._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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<a name="part0001"></a>
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<!-- !split -->
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<h2 id="___sec0" class="anchor">Convolutional Neural Networks (recognizing images) </h2>
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<p>
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Convolutional Neural Networks (CNN) are very similar to ordinary Neural Networks.
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<p>
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They are made up of neurons that have learnable weights and
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biases. Each neuron receives some inputs, performs a dot product and
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optionally follows it with a non-linearity. The whole network still
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expresses a single differentiable score function: from the raw image
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pixels on one end to class scores at the other. And they still have a
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loss function (for example Softmax) on the last (fully-connected) layer
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and all the tips/tricks we developed for learning regular Neural
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Networks still apply (back propagation, gradient descent etc etc).
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<p>
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What is the difference? <b>CNN architectures make the explicit assumption that
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the inputs are images, which allows us to encode certain properties
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into the architecture. These then make the forward function more
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efficient to implement and vastly reduce the amount of parameters in
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the network.</b>
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<p>
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Here we provide only a superficial overview, for the more interested, we recommend highly the course
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<a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/index-eng.html" target="_self">IN5400 – Machine Learning for Image Analysis</a>
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and the slides of <a href="http://cs231n.github.io/convolutional-networks/" target="_self">CS231</a>.
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<li><a href="._cnn-bs000.html">1</a></li>
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