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('Convolutional Neural Networks (recognizing images)',
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2,
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None,
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'convolutional-neural-networks-recognizing-images'),
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('Neural Networks vs CNNs', 2, None, 'neural-networks-vs-cnns'),
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'why-cnns-for-images-sound-files-medical-images-from-ct-scans-etc'),
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('3D volumes of neurons', 2, None, '3d-volumes-of-neurons'),
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('Layers used to build CNNs',
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None,
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('Transforming images', 2, None, 'transforming-images'),
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('Setting it up', 2, None, 'setting-it-up'),
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('Strong correlations', 2, None, 'strong-correlations'),
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('Layers of a CNN', 2, None, 'layers-of-a-cnn'),
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('Systematic reduction', 2, None, 'systematic-reduction'),
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('Prerequisites: Collect and pre-process data',
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2,
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None,
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'prerequisites-collect-and-pre-process-data'),
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('Importing Keras and Tensorflow',
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2,
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'importing-keras-and-tensorflow'),
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('Running with Keras', 2, None, 'running-with-keras'),
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('Final part', 2, None, 'final-part'),
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('Final visualization', 2, None, 'final-visualization'),
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('The CIFAR01 data set', 2, None, 'the-cifar01-data-set'),
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('Verifying the data set', 2, None, 'verifying-the-data-set'),
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('Set up the model', 2, None, 'set-up-the-model'),
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('Add Dense layers on top', 2, None, 'add-dense-layers-on-top'),
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('Compile and train the model',
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'compile-and-train-the-model'),
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('Finally, evaluate the model',
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2,
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None,
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'finally-evaluate-the-model'),
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('Recurrent neural networks: Overarching view',
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2,
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None,
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'recurrent-neural-networks-overarching-view'),
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('Set up of an RNN', 2, None, 'set-up-of-an-rnn'),
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('A simple example', 2, None, 'a-simple-example'),
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('An extrapolation example', 2, None, 'an-extrapolation-example'),
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('Formatting the Data', 2, None, 'formatting-the-data'),
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('Predicting New Points With A Trained Recurrent Neural Network',
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2,
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None,
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'predicting-new-points-with-a-trained-recurrent-neural-network'),
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('Other Things to Try', 2, None, 'other-things-to-try'),
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('Other Types of Recurrent Neural Networks',
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2,
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'other-types-of-recurrent-neural-networks')]}
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<a class="navbar-brand" href="week43-bs.html">Week 43: Convolutional Neural Networks and Recurrent Neural Networks</a>
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<!-- navigation toc: --> <li><a href="._week43-bs001.html#plans-for-week-43" style="font-size: 80%;">Plans for week 43</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs002.html#convolutional-neural-networks-recognizing-images" style="font-size: 80%;">Convolutional Neural Networks (recognizing images)</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs003.html#neural-networks-vs-cnns" style="font-size: 80%;">Neural Networks vs CNNs</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs004.html#why-cnns-for-images-sound-files-medical-images-from-ct-scans-etc" style="font-size: 80%;">Why CNNS for images, sound files, medical images from CT scans etc?</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs005.html#regular-nns-don-t-scale-well-to-full-images" style="font-size: 80%;">Regular NNs don’t scale well to full images</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs006.html#3d-volumes-of-neurons" style="font-size: 80%;">3D volumes of neurons</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs007.html#layers-used-to-build-cnns" style="font-size: 80%;">Layers used to build CNNs</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs008.html#transforming-images" style="font-size: 80%;">Transforming images</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs009.html#cnns-in-brief" style="font-size: 80%;">CNNs in brief</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs010.html#cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras" 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="._week43-bs011.html#setting-it-up" style="font-size: 80%;">Setting it up</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs012.html#the-mnist-dataset-again" style="font-size: 80%;">The MNIST dataset again</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs013.html#strong-correlations" style="font-size: 80%;">Strong correlations</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs014.html#layers-of-a-cnn" style="font-size: 80%;">Layers of a CNN</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs015.html#systematic-reduction" style="font-size: 80%;">Systematic reduction</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs016.html#prerequisites-collect-and-pre-process-data" style="font-size: 80%;">Prerequisites: Collect and pre-process data</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs017.html#importing-keras-and-tensorflow" style="font-size: 80%;">Importing Keras and Tensorflow</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs018.html#running-with-keras" style="font-size: 80%;">Running with Keras</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs019.html#final-part" style="font-size: 80%;">Final part</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs020.html#final-visualization" style="font-size: 80%;">Final visualization</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs021.html#the-cifar01-data-set" style="font-size: 80%;">The CIFAR01 data set</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs022.html#verifying-the-data-set" style="font-size: 80%;">Verifying the data set</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs023.html#set-up-the-model" style="font-size: 80%;">Set up the model</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs024.html#add-dense-layers-on-top" style="font-size: 80%;">Add Dense layers on top</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs025.html#compile-and-train-the-model" style="font-size: 80%;">Compile and train the model</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs026.html#finally-evaluate-the-model" style="font-size: 80%;">Finally, evaluate the model</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs027.html#recurrent-neural-networks-overarching-view" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs028.html#set-up-of-an-rnn" style="font-size: 80%;">Set up of an RNN</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs029.html#a-simple-example" style="font-size: 80%;">A simple example</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs030.html#an-extrapolation-example" style="font-size: 80%;">An extrapolation example</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs031.html#formatting-the-data" style="font-size: 80%;">Formatting the Data</a></li>
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<!-- navigation toc: --> <li><a href="#predicting-new-points-with-a-trained-recurrent-neural-network" style="font-size: 80%;">Predicting New Points With A Trained Recurrent Neural Network</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs033.html#other-things-to-try" style="font-size: 80%;">Other Things to Try</a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs034.html#other-types-of-recurrent-neural-networks" style="font-size: 80%;">Other Types of Recurrent Neural Networks</a></li>
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</ul>
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<!-- !split -->
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<h2 id="predicting-new-points-with-a-trained-recurrent-neural-network" class="anchor">Predicting New Points With A Trained Recurrent Neural Network </h2>
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<p>
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<!-- code=python (!bc pycod) typeset with pygments style "default" -->
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<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%;"><span></span><span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">test_rnn</span> (x1, y_test, plot_min, plot_max):
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<span style="color: #BA2121; font-style: italic">"""</span>
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<span style="color: #BA2121; font-style: italic"> Inputs:</span>
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<span style="color: #BA2121; font-style: italic"> x1 (a list or numpy array): The complete x component of the data set</span>
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<span style="color: #BA2121; font-style: italic"> y_test (a list or numpy array): The complete y component of the data set</span>
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<span style="color: #BA2121; font-style: italic"> plot_min (an int or float): the smallest x value used in the training data</span>
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<span style="color: #BA2121; font-style: italic"> plot_max (an int or float): the largest x valye used in the training data</span>
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<span style="color: #BA2121; font-style: italic"> Returns:</span>
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<span style="color: #BA2121; font-style: italic"> None.</span>
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<span style="color: #BA2121; font-style: italic"> Uses a trained recurrent neural network model to predict future points in the </span>
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<span style="color: #BA2121; font-style: italic"> series. Computes the MSE of the predicted data set from the true data set, saves</span>
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<span style="color: #BA2121; font-style: italic"> the predicted data set to a csv file, and plots the predicted and true data sets w</span>
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<span style="color: #BA2121; font-style: italic"> while also displaying the data range used for training.</span>
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<span style="color: #BA2121; font-style: italic"> """</span>
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<span style="color: #408080; font-style: italic"># Add the training data as the first dim points in the predicted data array as these</span>
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<span style="color: #408080; font-style: italic"># are known values.</span>
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y_pred <span style="color: #666666">=</span> y_test[:dim]<span style="color: #666666">.</span>tolist()
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<span style="color: #408080; font-style: italic"># Generate the first input to the trained recurrent neural network using the last two </span>
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<span style="color: #408080; font-style: italic"># points of the training data. Based on how the network was trained this means that it</span>
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<span style="color: #408080; font-style: italic"># will predict the first point in the data set after the training data. All of the </span>
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<span style="color: #408080; font-style: italic"># brackets are necessary for Tensorflow.</span>
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next_input <span style="color: #666666">=</span> np<span style="color: #666666">.</span>array([[[y_test[dim<span style="color: #666666">-2</span>]], [y_test[dim<span style="color: #666666">-1</span>]]]])
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<span style="color: #408080; font-style: italic"># Save the very last point in the training data set. This will be used later.</span>
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last <span style="color: #666666">=</span> [y_test[dim<span style="color: #666666">-1</span>]]
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<span style="color: #408080; font-style: italic"># Iterate until the complete data set is created.</span>
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<span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span> (dim, <span style="color: #008000">len</span>(y_test)):
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<span style="color: #408080; font-style: italic"># Predict the next point in the data set using the previous two points.</span>
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<span style="color: #008000">next</span> <span style="color: #666666">=</span> model<span style="color: #666666">.</span>predict(next_input)
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<span style="color: #408080; font-style: italic"># Append just the number of the predicted data set</span>
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y_pred<span style="color: #666666">.</span>append(<span style="color: #008000">next</span>[<span style="color: #666666">0</span>][<span style="color: #666666">0</span>])
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<span style="color: #408080; font-style: italic"># Create the input that will be used to predict the next data point in the data set.</span>
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next_input <span style="color: #666666">=</span> np<span style="color: #666666">.</span>array([[last, <span style="color: #008000">next</span>[<span style="color: #666666">0</span>]]], dtype<span style="color: #666666">=</span>np<span style="color: #666666">.</span>float64)
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last <span style="color: #666666">=</span> <span style="color: #008000">next</span>
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<span style="color: #408080; font-style: italic"># Print the mean squared error between the known data set and the predicted data set.</span>
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<span style="color: #008000">print</span>(<span style="color: #BA2121">'MSE: '</span>, np<span style="color: #666666">.</span>square(np<span style="color: #666666">.</span>subtract(y_test, y_pred))<span style="color: #666666">.</span>mean())
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<span style="color: #408080; font-style: italic"># Save the predicted data set as a csv file for later use</span>
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name <span style="color: #666666">=</span> datatype <span style="color: #666666">+</span> <span style="color: #BA2121">'Predicted'</span><span style="color: #666666">+</span><span style="color: #008000">str</span>(dim)<span style="color: #666666">+</span><span style="color: #BA2121">'.csv'</span>
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np<span style="color: #666666">.</span>savetxt(name, y_pred, delimiter<span style="color: #666666">=</span><span style="color: #BA2121">','</span>)
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<span style="color: #408080; font-style: italic"># Plot the known data set and the predicted data set. The red box represents the region that was used</span>
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<span style="color: #408080; font-style: italic"># for the training data.</span>
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fig, ax <span style="color: #666666">=</span> plt<span style="color: #666666">.</span>subplots()
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ax<span style="color: #666666">.</span>plot(x1, y_test, label<span style="color: #666666">=</span><span style="color: #BA2121">"true"</span>, linewidth<span style="color: #666666">=3</span>)
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ax<span style="color: #666666">.</span>plot(x1, y_pred, <span style="color: #BA2121">'g-.'</span>,label<span style="color: #666666">=</span><span style="color: #BA2121">"predicted"</span>, linewidth<span style="color: #666666">=4</span>)
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ax<span style="color: #666666">.</span>legend()
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<span style="color: #408080; font-style: italic"># Created a red region to represent the points used in the training data.</span>
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ax<span style="color: #666666">.</span>axvspan(plot_min, plot_max, alpha<span style="color: #666666">=0.25</span>, color<span style="color: #666666">=</span><span style="color: #BA2121">'red'</span>)
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plt<span style="color: #666666">.</span>show()
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<span style="color: #408080; font-style: italic"># Check to make sure the data set is complete</span>
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<span style="color: #008000; font-weight: bold">assert</span> <span style="color: #008000">len</span>(X_tot) <span style="color: #666666">==</span> <span style="color: #008000">len</span>(y_tot)
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<span style="color: #408080; font-style: italic"># This is the number of points that will be used in as the training data</span>
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dim<span style="color: #666666">=12</span>
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<span style="color: #408080; font-style: italic"># Separate the training data from the whole data set</span>
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X_train <span style="color: #666666">=</span> X_tot[:dim]
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y_train <span style="color: #666666">=</span> y_tot[:dim]
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<span style="color: #408080; font-style: italic"># Generate the training data for the RNN, using a sequence of 2</span>
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rnn_input, rnn_training <span style="color: #666666">=</span> format_data(y_train, <span style="color: #666666">2</span>)
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<span style="color: #408080; font-style: italic"># Create a recurrent neural network in Keras and produce a summary of the </span>
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<span style="color: #408080; font-style: italic"># machine learning model</span>
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model <span style="color: #666666">=</span> rnn(length_of_sequences <span style="color: #666666">=</span> rnn_input<span style="color: #666666">.</span>shape[<span style="color: #666666">1</span>])
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model<span style="color: #666666">.</span>summary()
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<span style="color: #408080; font-style: italic"># Start the timer. Want to time training+testing</span>
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start <span style="color: #666666">=</span> timer()
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<span style="color: #408080; font-style: italic"># Fit the model using the training data genenerated above using 150 training iterations and a 5%</span>
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<span style="color: #408080; font-style: italic"># validation split. Setting verbose to True prints information about each training iteration.</span>
|
||
hist <span style="color: #666666">=</span> model<span style="color: #666666">.</span>fit(rnn_input, rnn_training, batch_size<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">None</span>, epochs<span style="color: #666666">=150</span>,
|
||
verbose<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>,validation_split<span style="color: #666666">=0.05</span>)
|
||
|
||
<span style="color: #008000; font-weight: bold">for</span> label <span style="color: #AA22FF; font-weight: bold">in</span> [<span style="color: #BA2121">"loss"</span>,<span style="color: #BA2121">"val_loss"</span>]:
|
||
plt<span style="color: #666666">.</span>plot(hist<span style="color: #666666">.</span>history[label],label<span style="color: #666666">=</span>label)
|
||
|
||
plt<span style="color: #666666">.</span>ylabel(<span style="color: #BA2121">"loss"</span>)
|
||
plt<span style="color: #666666">.</span>xlabel(<span style="color: #BA2121">"epoch"</span>)
|
||
plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">"The final validation loss: </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121">"</span><span style="color: #666666">.</span>format(hist<span style="color: #666666">.</span>history[<span style="color: #BA2121">"val_loss"</span>][<span style="color: #666666">-1</span>]))
|
||
plt<span style="color: #666666">.</span>legend()
|
||
plt<span style="color: #666666">.</span>show()
|
||
|
||
<span style="color: #408080; font-style: italic"># Use the trained neural network to predict more points of the data set</span>
|
||
test_rnn(X_tot, y_tot, X_tot[<span style="color: #666666">0</span>], X_tot[dim<span style="color: #666666">-1</span>])
|
||
<span style="color: #408080; font-style: italic"># Stop the timer and calculate the total time needed.</span>
|
||
end <span style="color: #666666">=</span> timer()
|
||
<span style="color: #008000">print</span>(<span style="color: #BA2121">'Time: '</span>, end<span style="color: #666666">-</span>start)
|
||
</pre></div>
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