mhjensen
327 lines
18 KiB
HTML
327 lines
18 KiB
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('Fine-tuning neural network hyperparameters',
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('The RELU function family', 2, None, '___sec35'),
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('Which activation function should we use?', 2, None, '___sec36'),
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('More on activation functions, output layers',
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None,
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('Batch Normalization', 2, None, '___sec38'),
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('Dropout', 2, None, '___sec39'),
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('3D volumes of neurons', 2, None, '___sec46'),
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('Setting it up', 2, None, '___sec51'),
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('The MNIST dataset again', 2, None, '___sec52'),
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('Strong correlations', 2, None, '___sec53'),
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('Layers of a CNN', 2, None, '___sec54'),
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('Prerequisites: Collect and pre-process data',
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('Importing Keras and Tensorflow', 2, None, '___sec57'),
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
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<ul class="dropdown-menu">
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<!-- navigation toc: --> <li><a href="._week41-bs001.html#___sec0" style="font-size: 80%;">Plan for week 41</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs002.html#___sec1" style="font-size: 80%;">Setting up the Back propagation algorithm</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs003.html#___sec2" style="font-size: 80%;">Setting up a Multi-layer perceptron model for classification</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs004.html#___sec3" style="font-size: 80%;">Defining the cost function</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs005.html#___sec4" style="font-size: 80%;">Example: binary classification problem</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs006.html#___sec5" style="font-size: 80%;">The Softmax function</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs007.html#___sec6" style="font-size: 80%;">Developing a code for doing neural networks with back propagation</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs008.html#___sec7" style="font-size: 80%;">Collect and pre-process data</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs009.html#___sec8" style="font-size: 80%;">Train and test datasets</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs010.html#___sec9" style="font-size: 80%;">Define model and architecture</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs011.html#___sec10" style="font-size: 80%;">Layers</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs012.html#___sec11" style="font-size: 80%;">Weights and biases</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs013.html#___sec12" style="font-size: 80%;">Feed-forward pass</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs014.html#___sec13" style="font-size: 80%;">Matrix multiplications</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs015.html#___sec14" style="font-size: 80%;">Choose cost function and optimizer</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs016.html#___sec15" style="font-size: 80%;">Optimizing the cost function</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs017.html#___sec16" style="font-size: 80%;">Regularization</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs018.html#___sec17" style="font-size: 80%;">Matrix multiplication</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs019.html#___sec18" style="font-size: 80%;">Improving performance</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs020.html#___sec19" style="font-size: 80%;">Full object-oriented implementation</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs021.html#___sec20" style="font-size: 80%;">Evaluate model performance on test data</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs022.html#___sec21" style="font-size: 80%;">Adjust hyperparameters</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs023.html#___sec22" style="font-size: 80%;">Visualization</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs024.html#___sec23" style="font-size: 80%;">scikit-learn implementation</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs025.html#___sec24" style="font-size: 80%;">Visualization</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs026.html#___sec25" style="font-size: 80%;">Building neural networks in Tensorflow and Keras</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs027.html#___sec26" style="font-size: 80%;">Tensorflow</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs028.html#___sec27" style="font-size: 80%;">Using Keras</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs029.html#___sec28" style="font-size: 80%;">Collect and pre-process data</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs030.html#___sec29" style="font-size: 80%;">The Breast Cancer Data, now with Keras</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs031.html#___sec30" style="font-size: 80%;">Fine-tuning neural network hyperparameters</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs032.html#___sec31" style="font-size: 80%;">Hidden layers</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs033.html#___sec32" style="font-size: 80%;">Which activation function should I use?</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs034.html#___sec33" style="font-size: 80%;">Is the Logistic activation function (Sigmoid) our choice?</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs035.html#___sec34" style="font-size: 80%;">The derivative of the Logistic funtion</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs036.html#___sec35" style="font-size: 80%;">The RELU function family</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs037.html#___sec36" style="font-size: 80%;">Which activation function should we use?</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs038.html#___sec37" style="font-size: 80%;">More on activation functions, output layers</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs039.html#___sec38" style="font-size: 80%;">Batch Normalization</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs040.html#___sec39" style="font-size: 80%;">Dropout</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs041.html#___sec40" style="font-size: 80%;">Gradient Clipping</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs042.html#___sec41" style="font-size: 80%;">A very nice website on Neural Networks</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs043.html#___sec42" style="font-size: 80%;">A top-down perspective on Neural networks</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs044.html#___sec43" style="font-size: 80%;">Limitations of supervised learning with deep networks</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs045.html#___sec44" style="font-size: 80%;">Convolutional Neural Networks (recognizing images)</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs046.html#___sec45" style="font-size: 80%;">Regular NNs don’t scale well to full images</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs047.html#___sec46" style="font-size: 80%;">3D volumes of neurons</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs048.html#___sec47" style="font-size: 80%;">Layers used to build CNNs</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs049.html#___sec48" style="font-size: 80%;">Transforming images</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs050.html#___sec49" style="font-size: 80%;">CNNs in brief</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs051.html#___sec50" 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="._week41-bs052.html#___sec51" style="font-size: 80%;">Setting it up</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs053.html#___sec52" style="font-size: 80%;">The MNIST dataset again</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs054.html#___sec53" style="font-size: 80%;">Strong correlations</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs055.html#___sec54" style="font-size: 80%;">Layers of a CNN</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs056.html#___sec55" style="font-size: 80%;">Systematic reduction</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs057.html#___sec56" style="font-size: 80%;">Prerequisites: Collect and pre-process data</a></li>
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||
<!-- navigation toc: --> <li><a href="._week41-bs058.html#___sec57" style="font-size: 80%;">Importing Keras and Tensorflow</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs059.html#___sec58" style="font-size: 80%;">Running with Keras</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs060.html#___sec59" style="font-size: 80%;">Final part</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs061.html#___sec60" style="font-size: 80%;">Final visualization</a></li>
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<!-- navigation toc: --> <li><a href="._week41-bs062.html#___sec61" style="font-size: 80%;">Fun links</a></li>
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<!-- ------------------- main content ---------------------- -->
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<center><h1>Week 41 Tensor flow and Deep Learning, Convolutional Neural Networks</h1></center> <!-- document title -->
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<p>
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<!-- author(s): Morten Hjorth-Jensen -->
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<center>
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<b>Morten Hjorth-Jensen</b> [1, 2]
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</center>
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<p>
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<!-- institution(s) -->
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<center>[1] <b>Department of Physics, University of Oslo</b></center>
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<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
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<br>
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<p>
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<center><h4>Oct 10, 2020</h4></center> <!-- date -->
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<br>
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<p>
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<p><a href="._week41-bs001.html" class="btn btn-primary btn-lg">Read »</a></p>
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<li class="active"><a href="._week41-bs000.html">1</a></li>
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<li><a href="._week41-bs001.html">2</a></li>
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<li><a href="._week41-bs002.html">3</a></li>
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<li><a href="._week41-bs003.html">4</a></li>
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<li><a href="._week41-bs004.html">5</a></li>
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<li><a href="">...</a></li>
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<li><a href="._week41-bs062.html">63</a></li>
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<!-- copyright --> © 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
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