 We'll be doing a neural networks today. Just a simple example. We had something running on the website It's not working anymore Used to look like that and noisy sinusoid prediction whatever number we have a list hidden list and We will be rewriting this in python But if you want to chat about anything else to let me know Popped in and this image of what the front panel looks like into GP4 Oops didn't intend to do that. Hopefully What is this? Yeah, the scroll is barely visible We have the text from the page and And yes, we want to do a flask application, but mainly relying on JavaScript We're gonna be using a tensorflow.js It was just templates and a Anyway, the good thing about the yeah, people say it's the hallucinating and what's not we will be able to see how well it's actually performing by the end of this Yeah, I want to have sliders. So essentially what could be able to control Stuff might as well. All right Yes, we have the sliders on the HTML. Then we have training prediction chart and then the error chart as well. Oops putting all this Placeholders in the code Is it for GitHub co-pilot to sort out or yeah, currently we have this page. It used to be levy you It to begin with we don't like it We want to keep the url 26 characters This currently it's 42 any suggestions to What this should be called would be nice to have Prediction in it. Yeah, that doesn't make sense doesn't it should be something like basic neural net Too long as well. Yeah, I just call it neural net Okay, so we should have a template or Flask application what we need to set it up manually Not to the info a text file good thing about Actually share This gip4 thing can I Right good not failed to copy link to clip what could not create link I think it can clear it link if it has Images in it or something That's a bit Unfortunate Yes, you can make mistakes, but again, we will Be able to tell how good is this code in a sec. We will use a co-pilot as well Hey, can you generate the template for a flask application with all the necessary files and folders and also the Terminal commands for creating all the files and folders See if it can do Dead. Okay. Yes standard on the flask application This cold. I really forgot what it's called a city Oops and you're on it Generating the code as well We'll be able to compare this code to what the gip4 is making sometimes. It's the same sometimes. It's not But yes, we need these two folders and Yeah, we might wanna Open it in an other window. Hey, this is mainly because for example if you do X Lane on the terminal not on the terminal if you do not use code a Workspace and I don't know find It will fail. I think because it's just too many files in the whole project So we'll let it run. Yeah, it's now passing doing something with the whole all the folders and everything And That's ever index HTML stuff So the app I we want to keep it as Clean as possible Hey, because we don't actually I want to do much If possible We don't want to use the back end Then I'm using electricity like it This HTML Yeah, obviously it won't need a lot of modification and we have Js Been talking too much already. So let's let the robot read this In this JavaScript snippet the model is created compiled and trained within the client's browser The new e-updates are managed as callbacks within the training loop You could display the last in real time which would give the user feedback on how the training is progressing Regarding the addition of an accumulative performance chart It's a good idea as it helps in visualizing the performance of the model over time Especially if users to see how the model improves or fails with different settings Remember to handle edge cases where the model might not converge or the training might result in unexpected values Also test the app thoroughly to ensure that it performs well even on devices with limited processing power as not all users may Have high performance machines. Oh, yeah, that will always be a problem, but yeah, we'll take care of it Training set size in hidden layer configuration Utilize sliders or input fields to let users configure the size of the training set and the number of neurons in each hidden layer live prediction chart user javascript charting library like chart js to plot the real-time predictions versus the training data error trend chart Similarly plot the error trend as the training progresses Let's pop in the sliders into the HTML Why is there a script section there? And this kind of helps because what we can do now Let's go say javascript Hey, we have snippets of the code that we want to integrate in Info text file No, we don't want to automate that Now we don't want to automate that we want to integrate the code in the text file into the project Yes, this is where co-pilot And Can you regenerate the whole? HTML script Yeah, this is where GPT-4 Open AI website Should be better Yes, so we're using now both TensorFlow.js and Judges it added that also have our script.js It's fine We have our controls Seeing a second what it looks like Okay, this looks like a complete code to be hypothetical JavaScript clear Can we regenerate the whole javascript code? Yep Yep certainly Below is a skeleton for the script JS that handles the interaction with the HTML sliders initializes the neural network trains it with the provided data and updates the charts in real time This is an example and you would need to flesh it out with your actual data processing and neural network configuration specifics Remember that you'll need to supply the actual sinusoidal data for the get training data Function and configure the neural networks architecture as needed The training set size and layer sizes are read from the slider inputs So they're adjustable by the user This JavaScript will need to be loaded at the end of your index HTML file to ensure that the document is fully parsed before the script runs Make sure to include TensorFlow JS and chart JS in your HTML as well as the script relies on those libraries Where is the sinusoid being generated? The sinusoidal data isn't being generated in the provided snippet. It only sets up a placeholder Here's how you can generate sinusoidal data with some noise, which is typically used for training and testing in neural network scenarios We update the get training data function to generate noisy sinusoidal data based on the training set size It's called the get training data Get other generation logic will go here placeholder blah blah blah Why is GPT-4 messing up the names? Yes, just use basic math function to generate the random numbers That's for the noise and the sinusoid Where's the frequency of it being set? In this generate sinusoidal data function XS represents the input features which are the points on the x-axis evenly spaced between 0 and 2 pi XS represents the corresponding labels which are the sin, x, values with some added noise for variability Noise is added to the sin, x, values by math, random, asterisk 0.1 to 0.05 to simulate measurement or sampling noise These are then converted into TF, tensor 2D which TensorFlow JS uses for training This data can now be used to train the neural network The sin, x function provides a continuous curve that the neural network can learn from The added noise makes the problem more realistic and challenging, as the network must learn the underlying sinusoidal pattern despite the random variations in the data The script JS is not being found because it's in a static folder Can you fix that? Control I should be straight forward Right Okay, we see the charts Okay The styles, yes, we could use some styling It's always one of those things that's always left to the very end Shall we get our co-pilot to do it? Do we need the workspace operator? Generate script for styles, styles, styles Yeah, I'll have to get rid of it eventually because we have actually styles CSS code for the project itself Why am I not seeing and not found? Why? Right, because it's also in different folders Yeah, this could be leaking memory, this thing where it keeps adding stuff to the same chart Doesn't make any sense, does it? Well, first we don't see the training and prediction We have no errors Another good thing about the GPT-4 is that we can do this Any issues with the front panel Who generated the code for it? Was it GPT-4 or a co-pilot? Must have some sort of template Is there a template in this code? Not a template, a placeholder Is there a placeholder in this JavaScript code? We want to not overload the GPT-4 as well Because we still have that usage limits may apply I actually changed that, it used to be 40 prompts per 3 hours Keep changing this, this stuff I'm pretty sure there is, the co-pilot says no The front panel in the uploaded image is a graphical user interface for a neural network training application It appears to consist of sliders for adjusting parameters and areas to display charts showing training results and error trends From the provided code and the visual, it seems like the interface was designed to be functional and straightforward The JavaScript code snippet appears to set up the neural network, generate the training data and kick off the training process with updates reflected in the chart, js plots The get training data function generates sinusoidal data points with added noise, which can be used to train the neural network The noise ensures that the data isn't perfectly predictable, which is a common real world scenario Concerning your question about whether this was generated by GPT-4 or co-pilot, this code looks like something that either could have produced, given an appropriate prompt Both GPT-4 and GitHub co-pilot can generate code samples, including HTML, CSS, and JavaScript, based on user instructions The code could also have been based on a common template for machine learning in JavaScript, especially with TensorFlow, js Regarding placeholders The function get training data is complete and doesn't have a placeholder It generates noisy sinusoidal data ready for use The train model function is ready to train the neural network, but assumes that you have already set up the chart, js instances, prediction chart and error trend chart There are no explicit template placeholders in this code, it's functional as is, but you would need to make sure the HTML and CSS are set up correctly to match the JavaScript functionality, especially for displaying the values from the sliders and for canvas elements where the charts are rendered Adjusting the neural network architecture, more layers, different types of layers, etc Setting specific training options, batch size, different optimizes, learning rate, etc Enhancing the chart options for better visualization, setting access labels, title, legend details, etc Handling predictions after training, which isn't covered in this snippet You'll want to use model, predict, and display the results on the prediction chart Ensure that the front-end interface reflects the functionality that the code provides, then test the application thoroughly to work out any bugs or UX issues Ok, the main issue at the moment is that the first chart is not displaying any data Yep If the first chart isn't displaying any data, there are a few things we need to check Chart initialization, verify that the prediction chart is being initialized properly It should be tied to less than canvas greater than element in your HTML Ensure the get context, 2D, is being called on the correct element Data binding, the chart's data set should be populated with data In a provided code, there's a placeholder for the training and prediction data arrays These should be filled with the actual data you want to plot Chart updates, after the data is prepared, you need to make sure the chart is updated This typically involves pushing data into the chart's data set and then calling chart, update Here's a more concrete check for each step Step 1, ensure the less than canvas greater than element with id equals, prediction chart, exists in your HTML Step 2, populate the data set with actual data before calling chart, update For debugging purposes, you can manually insert some data to see if the chart renders Step 3, make sure to call update, on the chart after the data is set The code provided does not contain the logic for updating the training and prediction data sets after model training You need to write that part of the logic and call update, on the chart afterwards For instance, console logs, insert console.log, statements to check if the data arrays are being populated as expected Inspect element, use the browser's developer tools to inspect the less than canvas greater than element and check for any console errors that may indicate problems Version check, ensure that the version of chart, JSU using is compatible with the code you've written Version differences can sometimes cause unexpected behavior Try these steps and if the issue persists, you may need to share more specific parts of your code or error messages that you're seeing in the browser's console for a more precise diagnosis Okay, so we do have the prediction chart Have a script Okay, where does this go? Is this going into... Okay, so it's just some mock stuff that doesn't do anything Then we have this sinusoid If we get to actually implement that then We try to have it in here I'm pretty sure we didn't glue that The function is called something else, that's not cool The function is called something else LinkedIn app is really bad Why is it so bad? Suspect we're not calling this function correctly If someone thinks you can just generate your whole code with GPT-4 Well, yeah, this way you start getting into hitting walls This is where you start hitting walls What does help though? Still need to comment all this out So what is it still training if there's no data? It's a bit odd The error trend is there, but the data is not being properly displayed on the first chart The chart is working okay in general But not displaying the training and prediction and data This will be a replacement for all the tool that we had in the output If the error trend chart is displaying data, but the first chart is not It's possible that the issue lies with the way the training and prediction data are being handled or updated in the chart Let's focus on debugging the training and prediction chart Verified chart data, make sure the data for the training and predictions is being generated and formatted correctly before attempting to display it on the chart Chart data update, ensure that the training and prediction data are being updated on the chart object and that chart Update, is called afterwards Async, await, since train model is an async function, confirm that any code dependent on the results of training is inside or properly awaits the training process Given that you have a train model, function already set up to train the network, you need to insert the logic to populate the prediction chart with actual training and prediction data after the training is completed Here's how you can modify your train model, function to update the chart after training In the modified function above, after the model is trained with model.fit, is then used to predict values based on the input data excess These predictions are then formatted into an array suitable for chart, js using, array, then, since, predict, returns at ensor The predictions and actual values from the training data are set on the respective data sets of the prediction chart The chart is updated inside the, then, to ensure the asynchronous operation has completed and the data is ready After adding this logic, when you call train model, the prediction chart should be updated with the training and predicted values Lastly, it's crucial to ensure that the scales for chart are set appropriately If your prediction values are very small or very large, the chart may not display them correctly You can configure the scales in the options property of the chart configuration If you are still experiencing issues after these checks, please look fairer in the browser's developer console Thanks for joining in If you have any specific questions, comments, especially if you check the Barney Chaos website already I'll be more than glad to hear your constructive thoughts Now, let's take a look at the feedback Currently, we're trying to fix this neural net wave prediction, which we had working in the past But only videos of it left It used to be, I don't know what the quality It's defaulting to, no, it's meant to be 1080p Yes, that's what it used to look like You could set your training size, number of hidden layers And it will do the noisy sunsword And then the prediction of it And we'll show you the error trend So currently, we have the template for it and everything To get GPT4 clear Clear to fix it for us GPT4, yeah, actually released this really nice text-to-speech I'm just having trouble with it because it's reading everything instead of just the bits that I wanted to read Clear Yeah, that's what it sounds like If the error trend chart is displaying data, but the first chart is not, it's possible that the issue lies with the way the training and prediction data are being handled or updated in the chart Let's focus on debugging the training and prediction chart 1. Verify chart data Make sure the data for the training and predictions is being generated and formatted correctly before attempting to display it on the chart 2. Chart data update Ensure that the training and prediction data are being updated on the chart object and that chart.update is called afterwards 3. Async await Since train model is an async function, confirm that any code dependent on the results of training is inside or properly awaits the training process Given that you have a train model function already set up to train the network, you need to insert the logic to populate the prediction chart with actual training and prediction data After the training is completed, here's how you can modify your train model function to update the chart after training JavaScript function to train the model and update the charts Async function train model Existing setup code, slash train the model await model.access, ys epochs 50, callbacks on epoch end, async epoch logs, gorgslash Existing error trend chart update logic Use the model to do predictions on the training data, const predictions equals model.predict.access Convert tensorflow.js tensors to regular arrays for chart.js predictions.array Then predarray equals Update the prediction data set, index 1 with predicted values, prediction chart.data.datasets1, data equals predarray.flat For the training data set, index 0, we convert ys tensor to array.array Then train array equals prediction chart.data.datasets0, data equals trainarray.flat Since we're updating the chart asynchronously, make sure to call update inside then predictionchart.update In the modified function above, after the model is trained with model.fit, it's then used to predict values based on the input data access These predictions are then formatted into an array suitable for chart.js using .array Then, since .predict returns a tensor, the predictions and actual values from the training data are set on the respective data sets of the prediction chart The chart is updated inside the then to ensure the asynchronous operation has completed and the data is ready After adding this logic, when you call train model, the prediction chart should be updated with the training and predicted values Lastly, it's crucial to ensure that the scales for the chart are set appropriately If your prediction values are very small or very large, the chart may not display them correctly You can configure the scales in the options property of the chart configuration If you are still experiencing issues after these checks, please look for errors in the browser's developer console as they can give clues about what might be going wrong The problem with two robots is that they compete with one another And it's not great to remember to select the whole code with co-pilot Trying to ensure that the training prediction line is being updated I think it is Ah, no, so it is there But it's not updated Don't need that line Train model We wait for model feed Epox callbacks We need to update the prediction chart We need to do it before the error chart And do this for a sec We need to restructure this code Just pop the code and see what it says I didn't mistake this like the order of things It's not quite right I think The training and prediction update logic in the Onipok end callback is incomplete Currently, it's set up to push values to the prediction chart, but it's not specified what data should be used You need to replace the placeholders, asterisk your training data here asterisk, and asterisk your prediction data here asterisk, with the actual training and prediction values Here's how you should update the Onipok end callback The modifications above involve using the model, predict, function to generate predictions at the end of each epoch and then plotting these predictions in the chart The training data, ease, is also being used to update the training data series in the prediction chart Please note, the above code may result in a dense chart if you have many epochs since you're pushing new labels and data points after each epoch This might not be the intended behavior, as typically you'd want to plot epoch versus loss in the error trend chart and the actual versus predicted values in the prediction chart, which doesn't necessarily need to happen in each epoch, but rather after all training is done are at specified intervals Also, there's a mix of synchronous and asynchronous code that you need to be careful with, especially regarding the async, await patterns and promises Ensure the data is not being used before it is available Array, then, is used to convert the tensorflow, js tensors into a regular array suitable for chart, js This operation is asynchronous and should be handled properly to ensure the data is available when updating the chart I have the sign, so I have the prediction, prediction is totally wrong This should add more... That point, prediction is still wrong Yeah, we don't want it to... What is it like when it's free training? What did it say? Just now, that was an important bit I wish this voice, the open AI voice was... The training and prediction update logic in the onypokend callback is incomplete Currently, it's set up to push values to the prediction chart, but it's not specified what data should be used You need to replace the placeholders, slash your training data here, and search prediction data here with the actual training and prediction values Here's how you should update the onypokend callback JavaScript, function to train the model async function train model, plus Previous setup code, start training the model await model fit xs, ys, pox50, callbacks on epokend async epok logs Use the model to do predictions on the training data const preds equals model.predict, xs Convert predictions to array for plotting preds.array, then predarray equal Update prediction chart data, prediction chart.data.label's push epok Prediction chart.data.datasets1, data equals predarray.flat Update training chart data, ys.array, then trainarray equals predictionchart.data.datasets is a data equals trainarray.flat Update prediction chart prediction chart.update Update the error trend chart after each epok Error trend chart.data.label's push epok Error trend chart.data.datasets.data.push logs.loss Error trend chart.update Rest of the train model function The modifications above involve using the model.predict function to generate predictions at the end of each epok and then plotting these predictions in the chart The training data, ys, is also being used to update the training data series in the prediction chart Please note, the above code may result in a dense chart if you have many epoks since you're pushing new labels and data points after each epok This might not be the intended behavior as typically you'd want to plot epok versus loss in the error trend chart and the actual versus predicted values in the prediction chart which doesn't necessarily need to happen each epok, but rather after all training is done or at specified intervals Also, there's a mix of synchronous and asynchronous code that you need to be careful with especially regarding the async await patterns and promises Ensure that data is not being used before it is available The .array, then, is used to convert the TensorFlow.js tensors into a regular array suitable for chart.js This operation is asynchronous and should be handled properly to ensure the data is available when updating the chart Well, seems like keep hitting that train button doesn't improve anything The prediction is wrong because our model is wrong, model must be wrong So we don't have anything currently in the back end It's just around the JavaScript processing We are using TensorFlow.js and plotting with Chart.js That actually works Apache license, open source, Chart.js as well MIT license should be fine to use question is Why is this prediction so bad? Wait, this one In our previous thing we had the prediction happening after the noisy sign, so it's like the future prediction Obviously this will make you super rich because you will be able to predict the stock prices and the like I'm just joking, of course Can you explain the chart mainly what is happening with the prediction? Yep, obviously it can do everything for you By the way, this session is meant to be interactive So please comment Is it underfitting? Let's read that This is the target the neural network is trying to learn The red dots represent the neural network's predictions It seems that the predictions are not aligning well with the actual training data This discrepancy could be due to several reasons such as insufficient training, not enough epochs A model that is not complex enough to capture the underlying pattern of the data Or it could also be an issue with how the data is being fed into the model for predictions 2. Error trend chart The green line shows how the error changes over each epoch during the training of the neural network The error is measured by the loss function used during training, which in this case is mean squared error The downward trend indicates that the model is learning and improving its predictions over time as the error decreases However, the error seems to plateau, suggesting that the model may have reached its capacity for learning from this data set with the given architecture Or it might require more epochs to continue decreasing the loss For the predictions not matching the training data closely, consider the following points for troubleshooting Underfitting, the model may not have enough layers or neurons to capture the complexity of the sinusoidal pattern Especially if noise is present Epochs Training for more epochs may help if the error is still decreasing and hasn't plateaued Learning rate If the learning rate is too high, the model may be overshooting the optimal weights If it's too low, it may not be converging fast enough Data pre-processing Ensure the data is correctly normalized or pre-processed as required by the model Check Predictions Debug the prediction step to make sure the inputs to the model during prediction match the format of the training data Based on the screenshot, it's clear that the model is attempting to learn the sinusoidal pattern, but hasn't perfectly fit the data yet The error trend is useful for diagnosing training performance, and it looks like the model could benefit from further tuning and training In your latest screenshot, the first chart which seems to be plotting the neural network's training and prediction results has some noticeable characteristics 1. Training data Displayed in blue, this is the actual output from your noisy sinusoidal function It follows the typical sinusoidal wave pattern with some added noise 2. Prediction data Shown in red, this represents the neural network's predictions There are a few key points to note about the predictions in the chart The predictions seem to closely follow the actual training data at the start and end of the data range There is a significant deviation in the middle where the predictions do not match the training data well The shape of the prediction seems to somewhat resemble the shape of the training data, but is offset and not aligned 3. Potential causes for misalignment Insufficient training data With only 18 data points, the model may not have enough information to learn the underlying pattern of a noisy sinusoidal wave Model capacity The model might not be complex enough to capture the sinusoidal pattern accurately, especially given that the data includes noise You might need more neurons or layers Overfitting If the predictions match the training data too closely at certain points, it might be an indication that the model is overfitting This can be assessed with a validation set, which is not visible here Noise The noise in the data can make it harder for the model to learn the underlying sinusoidal pattern Learning rate An inappropriate learning rate could cause the model to not converge properly Epoch count If the error trend chart is showing a plateau before the last epoch, the model may not benefit from additional epochs However, if it's still decreasing, more epochs might be necessary The error trend chart below shows a sharp spike towards the end, indicating an issue during training at that particular epoch This could be a result of an anomaly in the data or learning rate that caused the loss to increase suddenly The rest of the trend suggests the model was learning and the loss was decreasing over time until that point Regarding whether the first chart is updating correctly, it's hard to definitively say without more context on the actual and expected output of the model However, the misalignment between the training and prediction data suggests there might be issues with the model training process or the way predictions are generated and plotted It's essential to confirm that the model predictions are being processed and fed into the chart correctly Debugging tips Inspect the data Log the training and prediction data to the console to make sure they are as expected Check data feeding Make sure the data used for predictions is the same as the data used for training in terms of pre-processing and normalization Monitor overfitting Implement a validation set to monitor for overfitting Adjust model parameters Experiment with the number of neurons, layers and epochs based on the performance of the model Evaluate learning rate Adjust the learning rate and see if the training improves Investigate error spike Look into what caused the spike and error in the last epochs and address it This might be due to the learning rate being too high or it could be a data specific issue Training curve in my head is super sharp at the moment No idea what's going on How do we improve it? Yeah, I don't like this existing code and stuff I would just like you to regenerate the JavaScript Could you regenerate the JavaScript code? Make sure it can be executed without errors Make sure there are no placeholders It's an example of a neural network building a neural network Yeah, when it starts thinking for a long time like that, it's not a good sign It's probably means when it starts fresh chat By the way, if you wanna check out the eye tracker that I'm using It's available on the website over here on the landing page They can try it yourself It doesn't work well on some smaller screens like mobile phone screens And while you edit to have a look at all the other tools and blogs on this site You will be supporting the project that way My RPM is currently very high So you'll be really supporting the project Just remember to turn your head block off while you do it Generated quite a bit of code I wasn't even able to do it in one go How do I get access to this text-to-speech as a standalone browser add-on or something? Labels will be dynamic based on epochs data sets Label, error trend, border color, green, fill, false Options In this code, the update slider value function updates the slider value display when the slider is moved The create model function initializes a sequential model with specified layers based on slider values The get training data function generates a noisy sinusoidal data set The train model function handles training the neural network model Updating the error trend chart after each epoch And updates the training and prediction data in the charts The initialization of the chart.js charts has been adjusted to define data sets without initial data Before running this code, make sure you have linked tensorflow.js and chart.js correctly in your html You'll also need to add a button or some trigger to call the train model function when you want to start training The model predictions are only plotted at the end of training to avoid cluttering the chart with intermediate predictions The training just didn't get any better Worse, now the error trend is not showing anything What did you do again? Certainly Here's a revised version of the javascript code intended to be used with tensorflow.js for training a neural network on a noisy sinusoidal data set This code should be ready to execute without placeholders But keep in mind that the behavior and performance will depend on your specific data and neural network architecture Check it What changes? The update slider value function updates the slider value display when the slider is moved The create model function initializes a sequential model with specified layers based on slider values The get training data function generates a noisy sinusoidal data set The train model function handles training the neural network model, updating the error trend chart after each epoch, and updates the training and prediction data in the charts The initialization of the chart, js charts has been adjusted to define datasets without initial data Before running this code, make sure you have linked tensorflow, js in chart, js correctly in your html You also need to add a button or some trigger to call the train model function when you want to start training The model predictions are only plotted at the end of training to avoid cluttering the chart with intermediate predictions There's no improvement Well, nothing major Sure, this thing actually gets updated We went back to the previous code As your recent changes didn't fix anything or didn't make anything better Now, can we start simple? On the first chart, can we make sure it is being updated after pressing the train neural network button currently that not seem to be working as expected? To ensure that the first chart updates correctly after pressing the train and end button You need to set up your training function to update the chart data once the training is complete You should also make sure that the button triggers the training process when clicked Here's an updated version of the JavaScript code with comments on the changes Key changes and points to note Before starting the training, the chart's data is cleared This makes sure you're starting fresh when you hit the train and end button After the training process, model.fit, completes, predictions are made using the same input data access The predictions and the training data are converted to regular arrays and then assigned to the data sets of the prediction chart The out of end listener is used to bind the train model function to the button click event Note that the array sync method is used to get the labels from the XS tensor If XS is large, consider using an asynchronous approach with array instead This code assumes that you have a button with the id train BTN in your HTML Make sure you also have TensorFlow, JS and chart, JS properly included in your HTML for this code to work Lastly, always check the browser's console for any errors, as they can give you specific information about what might be going wrong If you encounter errors or if the behavior is still not as expected, you may need to debug step by step Starting from verifying the data all the way to the actual chart update calls Do you want to clear or add the do it in the first chart? Do we want the predictions to continue displaying? So the new prediction added to the chart similar to the error trend Could you re-generate the whole train model function? If the goal is to continually update the prediction chart with new predictions after each training epoch, similar to how the error trend chart is updated, you need to modify the train model function accordingly Below is the revised train model function that appends new predictions to the chart after each epoch Yeah, I wanted to continue to add more predictions to the chart, we need to figure out what happened there In this updated function, the training data set, blue line, is plotted once at the beginning of training If the training data doesn't change, there's no need to re-plot it after each epoch After each epoch, the predictions added to the existing prediction data set, red line, in the prediction chart The operator is used to flatten and spread the new predictions at the end of the existing array of prediction data If you want to maintain a history of predictions from each epoch, the code above will work However, if you only want to display the latest predictions after each epoch, you would override the prediction data each time instead of appending it Now, every epoch will result in the chart being updated with new predictions, giving you a dynamic visualization of how the model's predictions evolved over time The error trend chart will continue to be updated in the same way as before, showing the loss at each epoch Now, every epoch will result in the chart being updated with new predictions, giving you a dynamic visualization of how the model's predictions evolve over time The error trend chart will continue to be updated in the same way as before, showing the loss at each epoch I thought we said we want the first chart to behave in the same way as the second one So, as we keep training, there will be more prediction data points added to the first chart To append new prediction data points to the first chart after each training epoch, you need to adjust the train model function Here's the updated train model function Yeah, the error Changes and notes The training loop is now handled manually with a for loop to enable updating the prediction chart after each epoch The error trend chart is updated as before, with the loss at each epoch For the prediction chart, after each epoch, the predictions are added to the existing data This assumes you want to keep expanding the x-axis with more data points as training progresses If your intent is to replace the prediction data points at each epoch, you would assign a new prediction array directly instead of using the push method The label array is expanded to accommodate the increasing number of prediction points With this setup, each epoch's predictions are added to the chart, providing a growing visualization of how the model's performance evolves over time Ensure your chart configuration can handle the expanding data set, as this may require adjusting axis scales and other settings Okay, we can understand what is it doing Let's give it a larger training set It is doing the prediction Yeah, I want to like down sample it Yeah, this will become unusable after a few iterations Okay, that's the prediction Okay, so I understand now what it was doing before Let's go back to it quickly I need to check how many data points there are So that red is back now It's not plotting all the predicted values Let's try Can we log how many predicted values are there after this line? Yeah, we're pushing it Keeping the previous prediction that doesn't sound right It did 50 epochs Let me try There are 100 epochs Still going down Yeah, the red chart is not displaying correctly It seems that the prediction values in the first chart are not displayed correctly Are not displayed correctly Can we check, test that? Yep In the provided code and based on the screenshot, if the prediction values on the first chart are indeed displayed correctly, they should visually track the training data but with some deviation due to the noise and the fact that the model may not perfectly fit the data However, the code seems to push the entire prediction array to the chart after each epoch This will not work as intended because it repeats the entire prediction sequence on the chart, resulting in only the last epochs predictions being properly aligned with the x-axis labels For the first chart, we typically want to update the prediction points so that they replace the previous predictions, not append to them Here's how you could modify the train model function to update the prediction points correctly after each epoch In this modified train model function Before starting the training, the data for both charts is reset, which is standard when starting a new training session We now train the model 1 epoch at a time within a loop After each epoch, we update the error-train chart with the new loss value We then predict with the model and update the prediction data on the prediction chart by replacing the previous data with the new predictions The labels for the prediction chart are reset to match the x-values of the training data for proper alignment The updated function above will maintain the latest model predictions on the chart, aligning them with the training data after each epoch The predictions will override the previous epoch's predictions, providing a clear visual of the model's current state Please ensure you test the updated code to confirm that it works as expected and review the browser's console for any potential errors during execution Certainly! Here's the updated train model function that correctly updates the prediction values in the chart after each training epoch In this updated train model function We initialize the labels for the prediction chart to match the training data size We set the training data on the chart once before starting the training loop, as it isn't changed during training We clear out the prediction data from the chart before starting training to ensure we're starting with a clean slate Inside the loop, we train one epoch at a time using model.fit After each epoch, we make predictions with the trained model and update the prediction chart immediately with these new values We calculate a simple loss value for the error-train chart by measuring the mean squared error of the predictions against the true values This is a simplistic approach and assumes the model.fit doesn't already provide a loss value you can use We update both charts after each epoch, the prediction chart gets the latest predictions, and the error-train chart gets the latest calculated loss Before running this script, make sure that TensorFlow.js is properly loaded and available as TF The HTML elements with the IDs used in the script, training set size, hidden layer 1 size, hidden layer minus 2 size, train BTN, prediction chart, and error-train chart exist in your markup The chart.js library is also properly loaded and available to create the charts Lastly, after making these changes, test the entire process to ensure that the behavior matches your expectations and there are no console errors My apologies for the oversight It seems I mentioned updating the error-train chart with a simplistic manual loss calculation, which isn't necessary since the model.fit function provides the loss value directly Also, the updated train model function should not have any dependencies on synchronous operations like ArraySync within the training loop which could cause performance issues Here's the corrected complete train model function This function includes the entire training process, updating the prediction chart with the model's predictions after each epoch, and the error-train chart with the loss from each epoch It now uses the asynchronous Array method to convert the TensorFlow.js tensors to flat arrays suitable for chart.js, and updates the charts inside the loop offer awaiting the model's predictions The error-train chart's data is sourced directly from the history object returned by model.fit This should represent the complete function without any placeholders or synchronous operations that could block the UI during training Please make sure you have the corresponding HTML elements in TensorFlow.js correctly set up to use this function We want to add a choice next time We will add a choice to specify how much noise there is in the training data We will add an option to choose how many epochs we train for I think there's a problem with that predicted waveform being displayed I'll have to fix that if you haven't checked byonikeos.com This is a really good time to do so Because you'll be supporting the project That will be greatly appreciated Don't forget to turn your end blocker off when you do so I think it's not displayed correctly The whole coding doesn't have much happening in the backend So there's not much happening in the backend, do not worry about it Now with the JavaScript that might need some fixing The main concern is that the prediction red chart, red plot in the first chart is not displaying correctly Any data points does it have is the prediction should be longer than the training set C image attached Based on your description and the provided code, the prediction line, red, on the first chart should have the same number of data points as the training data, blue, 3G pocket training, since the model is using the same XS input tensor to generate predictions If the red prediction line is not displaying correctly or is shorter than expected, there might be a few things to check Ensure the predictions are being generated for the entire range of XS data Make sure that the prediction array, pred array, is being completely flattened and assigned to the prediction data set Here's the part of the code responsible for updating the prediction line on the chart If the prediction line is shorter than the training data, there could be a mismatch in how many data points are being passed to the pred array Flat, versus the ease, array, then, data equals greater than data, flat, for the training data To diagnose this issue, we need to look at the actual lengths of these arrays You can add console.log statements right after the arrays are created to check their lengths When the prediction line is not displaying as expected, verify the following The training data array and the prediction data array lengths should match and correspond to the number of XS data points The labels for the chart should match these lengths If you're pushing new labels with each epoch, it might cause misalignment The chart configuration should not impose any constraints that might cut off data points The length of the prediction array should match the length of the XS input tensor If the model is making a prediction for each input, then the prediction should not be longer than the training set It should be exactly the same length for a one-to-one mapping on the chart Reviewing the console logs after adding the diagnostic console.log statement should provide you with the information to resolve the charting issue If the prediction line length matches the training data length and the labels are correct, but it's still not displaying correctly, then it could be an issue with how chart.js is configured or used All train arrays, that how much longer it should be? Someone's probably screaming at me Okay silly question. Why doesn't the prediction look anything like the training data? The error doesn't go down The prediction not resembling the training data could be due to several reasons Let's explore some potential causes and diagnostic steps Model capacity. If the model doesn't have enough capacity, not enough neurons or layers, it won't be able to learn the complex patterns from the data Increasing the capacity might help, although I see you've already set the hidden layer sizes to 6, which should be ample for a simple sinusoidal pattern Activation function. The choice of the activation function could affect the model's ability to learn Sigmoid functions have a tendency to saturate and kill gradients, which could hamper learning For sinusoidal data, THAN might be a better choice as it outputs values in a range that's more similar to a sinusoidal wave Learning rate and optimizer, an inappropriate learning rate can cause the model to not learn effectively or to overshoot the minimum of the loss function You might want to experiment with different learning rates or optimizers, like SGD with momentum, Adam, etc Data normalization. The input data should be normalized so that the features have a similar scale This helps with the training of neural networks. Ensure your data is properly scaled Loss function. Mean squared error should be appropriate for regression tasks like this, but ensure that it's being computed correctly TensorFlow. JS handles this for you in the model. Compile method, but it's worth double checking Epox and convergence. Maybe the model has not trained for enough epochs to converge to a good solution You might need to increase the number of epochs Overfitting or underfitting. Overfitting would result in a prediction line that fits the training data too closely, including noise Underfitting, which seems to be the case here, will result in a prediction line that doesn't capture the pattern of the data well Adding more data or changing the model structure could help Input data representation. Ensure that the input XS represents the features correctly and that the predictions are being made on the same feature scale Chart update logic. Ensure the logic that updates the chart is working as intended For each epoch, the prediction should be updated once, not appended multiple times Here is an example of how you could adjust your existing create model function to use Than instead of Sigmoi If these adjustments don't help, you may want to plot intermediate values during training to understand what the model is learning and how it updates its predictions If you continue to have issues, consider a step by step approach to verify each part of the training process and the data flow Can we focus on the way the first chart is being rendered? I suspect there is something wrong with the prediction values being displayed on the chart Can we focus on that? Troubleshoot and fix the code To troubleshoot the first chart rendering issue, let's focus on the code that handles the prediction data and how it's being plotted on the chart Since the length of the prediction array is consistently equal to the training set size, as per your console log, but the predictions don't seem to fit the training data well, it's likely an issue with either the model's ability to predict correctly or how we're presenting the data in the chart Given that the red prediction line doesn't seem to fit the blue training data well, here's what we should check and try Data normalization, ensure the input data XS and the output data ESA within a range that the activation function can handle well, especially since you're using THAN, which outputs between minus 1 and 1 If your data isn't normalized, consider applying minmax normalization or standardization Learning dynamics, the network might not have learned the pattern within the training epochs provided Inspect the loss values to see if they've plateaued, indicating that the training has converged, or if they're still high, suggesting the need for further training or hyperparameter adjustments Chart data assignment, confirm that the data is being assigned correctly to the chart data sets The code currently updates the prediction data set after each epoch, overwriting the previous data Model predictions, after training, it could be helpful to separately evaluate the model's predictions to see if they follow the sinusoidal pattern before plotting them on the chart This could be a separate test from the training loop to isolate the issue Plotting logic, the plotting logic is currently designed to override the prediction line on each epoch This is correct if we want to show the most recent model's predictions However, if there are any issues with the way chart, JS is set up or updated, it might not reflect the changes properly To address these potential issues, here's what you can do Test normalization, if you haven't already, implement and apply a normalization step to your get training data function Evaluate predictions, after training, manually check the model's predictions against the expected sinusoidal pattern Review plotting logic, double check the chart, JS is configured to handle the dynamic update of the data arrays Now, let's make sure the chart plotting logic is correct in the train model function In this snippet, I've added a console.log, predarray, statement to inspect the actual prediction values This can help you verify that the predictions make sense If they look correct in the log, but not on the chart, the issue is likely with our chart, JS is being updated or configured Run this updated code and check the console logs If the prediction values seem to fit the expected pattern in the console, but not on the chart, we need to look into the chart, JS configuration If they don't fit the expected pattern in the console, the issue lies with the model's learning and predictions, not the chart It seems like I was wrong The chart is displaying correctly There's nothing wrong with that Let's give it a bigger training set size And we're now doing 500 epochs It is saturating at about 0.1 or so Let's try increasing the number of hidden layers to 5 It's going to go below 0.1 Let's just do the max number It's going lower now Okay, it's pretty low Let's do we need a larger training set Maybe Maybe not And this is all happening in JavaScript So when you use it, you're not using my electricity, you'll be using your electricity So that's promising And we will continue improving it the next time Do make sure you go check out Bionikeos.com All the tools I make eventually appear there And it is, the website is monetized So it's running ads So yes, you can use your ad blocker That's what I'm doing at the moment It's actually blocking two ads from displaying on this page Two ads there So yeah, if you go check it out, don't forget to turn your ad blocker off So you'll be helping the project And I'll see you next time Bye