title
Optimizing with TensorBoard - Deep Learning w/ Python, TensorFlow & Keras p.5

description
Welcome to part 5 of the Deep learning with Python, TensorFlow and Keras tutorial series. In the previous tutorial, we introduced TensorBoard, which is an application that we can use to visualize our model's training stats over time. In this tutorial, we're going to continue on that to exemplify how you might build a workflow to optimize your model's architecture. Text tutorials and sample code: https://pythonprogramming.net/tensorboard-optimizing-models-deep-learning-python-tensorflow-keras/ Discord: https://discord.gg/sentdex Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex G+: https://plus.google.com/+sentdex

detail
{'title': 'Optimizing with TensorBoard - Deep Learning w/ Python, TensorFlow & Keras p.5', 'heatmap': [{'end': 166.872, 'start': 144.164, 'weight': 1}], 'summary': 'Explores optimizing models with tensorboard by adjusting parameters such as optimizer, learning rate, dense layers, and activation units. it covers testing various combinations to refine the number of layers and nodes, emphasizing the use of gpu for faster training and discussing the significance of activation functions and data scaling to prevent overfitting.', 'chapters': [{'end': 129.793, 'segs': [{'end': 72.616, 'src': 'embed', 'start': 42.86, 'weight': 0, 'content': [{'end': 44.602, 'text': 'Like we should be able to do better than 79%, I think.', 'start': 42.86, 'duration': 1.742}, {'end': 49.988, 'text': 'So what the things that we would want to maybe start tweaking?', 'start': 44.642, 'duration': 5.346}, {'end': 58.193, 'text': "i mean there's so many um, but we we could change, uh, the the optimizer within the optimizer, the learning rate.", 'start': 49.988, 'duration': 8.205}, {'end': 61.795, 'text': "we could change dense layers, whether we have them or we don't.", 'start': 58.193, 'duration': 3.602}, {'end': 67.618, 'text': 'we could change how many units per layer that we want to have activation units.', 'start': 61.795, 'duration': 5.823}, {'end': 69.759, 'text': "we also, we don't need to make these the same.", 'start': 67.618, 'duration': 2.141}, {'end': 70.56, 'text': 'they could be different.', 'start': 69.759, 'duration': 0.801}, {'end': 72.616, 'text': 'could change the kernel size.', 'start': 71.036, 'duration': 1.58}], 'summary': 'Improvement target: 79% accuracy. possible tweaks: learning rate, dense layers, units per layer, activation units, and kernel size.', 'duration': 29.756, 'max_score': 42.86, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA42860.jpg'}, {'end': 107.186, 'src': 'embed', 'start': 83.62, 'weight': 1, 'content': [{'end': 90.262, 'text': "um and if and as you start multiplying like this, many things times this, many things times like you're looking at thousands of models.", 'start': 83.62, 'duration': 6.642}, {'end': 91.622, 'text': 'so what do you do?', 'start': 90.262, 'duration': 1.36}, {'end': 95.963, 'text': 'So the easiest thing to do, in my opinion, is start with the easiest thing.', 'start': 92.142, 'duration': 3.821}, {'end': 105.446, 'text': "So the most obvious things that we're going to tweak here are going to be number of layers, nodes per layer, and then, basically,", 'start': 96.003, 'duration': 9.443}, {'end': 107.186, 'text': 'do we have a dense layer at the end or not?', 'start': 105.446, 'duration': 1.74}], 'summary': 'Multiple models can lead to thousands of iterations. key parameters to tweak: number of layers, nodes per layer, and presence of a dense layer at the end.', 'duration': 23.566, 'max_score': 83.62, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA83620.jpg'}], 'start': 2.373, 'title': 'Tensorboard model optimization', 'summary': 'Delves into optimizing models with tensorboard, emphasizing the significance of adjusting various parameters like optimizer, learning rate, dense layers, units per layer, activation units, kernel size, and stride. it highlights the need to test numerous combinations, ultimately focusing on refining the number of layers, nodes per layer, and the presence of a dense layer at the end to enhance accuracy.', 'chapters': [{'end': 129.793, 'start': 2.373, 'title': 'Tensorboard model optimization', 'summary': 'Discusses how to optimize models using tensorboard by visualizing different attempts at models, and highlights the need to tweak various model parameters to improve accuracy, such as changing optimizer, learning rate, dense layers, units per layer, activation units, kernel size, stride, decay rate, and the impact of testing numerous combinations, eventually focusing on adjusting the number of layers, nodes per layer, and the presence of a dense layer at the end.', 'duration': 127.42, 'highlights': ['The chapter emphasizes the need to tweak various model parameters to improve accuracy, such as changing optimizer, learning rate, dense layers, units per layer, activation units, kernel size, stride, decay rate, and the impact of testing numerous combinations. The discussion highlights the importance of adjusting parameters like optimizer, learning rate, dense layers, units per layer, activation units, kernel size, stride, and decay rate to improve model accuracy, indicating the potential for testing thousands of different models.', 'The chapter suggests focusing on adjusting the number of layers, nodes per layer, and the presence of a dense layer at the end as the most obvious tweaks to start with in model optimization. The chapter recommends starting with simple tweaks like adjusting the number of layers, nodes per layer, and the presence of a dense layer at the end as initial steps in model optimization.']}], 'duration': 127.42, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA2373.jpg', 'highlights': ['The chapter emphasizes the need to tweak various model parameters to improve accuracy, such as changing optimizer, learning rate, dense layers, units per layer, activation units, kernel size, stride, decay rate, and the impact of testing numerous combinations.', 'The chapter suggests focusing on adjusting the number of layers, nodes per layer, and the presence of a dense layer at the end as the most obvious tweaks to start with in model optimization.']}, {'end': 650.06, 'segs': [{'end': 179.881, 'src': 'heatmap', 'start': 144.164, 'weight': 0, 'content': [{'end': 155.597, 'text': "Why are we doing this? So we've seen that 64 units per layer at least is somewhat successful.", 'start': 144.164, 'duration': 11.433}, {'end': 161.405, 'text': "So, once you get a model that bites, like a lot of times the first time I train a model, I'm not doing this method.", 'start': 156.318, 'duration': 5.087}, {'end': 166.872, 'text': "I'm actually just kind of, you know, hunt and pecking for anything that will bite.", 'start': 161.405, 'duration': 5.467}, {'end': 174.759, 'text': 'Once I get something that starts to at least learn a little bit where, And by learn, either accuracy is going up or loss is going down.', 'start': 167.473, 'duration': 7.286}, {'end': 179.881, 'text': "Once I get that, then I'll go down this road for this process.", 'start': 175.74, 'duration': 4.141}], 'summary': 'A model with at least 64 units per layer has shown some success in learning.', 'duration': 49.588, 'max_score': 144.164, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA144164.jpg'}, {'end': 286.866, 'src': 'embed', 'start': 234.131, 'weight': 2, 'content': [{'end': 235.392, 'text': 'One dense layer at least works.', 'start': 234.131, 'duration': 1.261}, {'end': 237.515, 'text': "So then I'll see what happens, maybe two dense layers.", 'start': 235.613, 'duration': 1.902}, {'end': 242.6, 'text': "That's fairly common as well for people to put rather than just one dense layer at the end, two dense layers at the end.", 'start': 237.535, 'duration': 5.065}, {'end': 245.342, 'text': "So we'll do that.", 'start': 243.981, 'duration': 1.361}, {'end': 250.608, 'text': "And the final thing that we're going to do is conv layers.", 'start': 246.083, 'duration': 4.525}, {'end': 254.031, 'text': "How many convolutional layers do we want? Well, we don't want zero.", 'start': 250.988, 'duration': 3.043}, {'end': 255.212, 'text': 'So we want one.', 'start': 254.311, 'duration': 0.901}, {'end': 257.755, 'text': 'We have found that two is successful.', 'start': 255.673, 'duration': 2.082}, {'end': 258.916, 'text': 'And then three.', 'start': 258.274, 'duration': 0.642}, {'end': 260.137, 'text': "So we'll do one, two, and three.", 'start': 258.995, 'duration': 1.142}, {'end': 266.341, 'text': "Now what we're going to do is four dense layer in dense layers.", 'start': 260.596, 'duration': 5.745}, {'end': 273.687, 'text': 'Then four layer size in layer sizes.', 'start': 268.263, 'duration': 5.424}, {'end': 280.952, 'text': 'Four conv layer in conv layers.', 'start': 276.269, 'duration': 4.683}, {'end': 283.474, 'text': "So we're going to iterate through all these.", 'start': 281.072, 'duration': 2.402}, {'end': 285.736, 'text': 'So three times three times three.', 'start': 283.494, 'duration': 2.242}, {'end': 286.866, 'text': "There's our models.", 'start': 286.206, 'duration': 0.66}], 'summary': 'Experimented with dense and convolutional layers, trying one, two, and three of each to iterate through a total of 27 models.', 'duration': 52.735, 'max_score': 234.131, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA234131.jpg'}, {'end': 590.477, 'src': 'embed', 'start': 561.277, 'weight': 3, 'content': [{'end': 564.659, 'text': 'So let me do this, kind of clean it up.', 'start': 561.277, 'duration': 3.382}, {'end': 565.82, 'text': 'It looks good, looks good.', 'start': 564.699, 'duration': 1.121}, {'end': 566.46, 'text': "Let's save it.", 'start': 565.86, 'duration': 0.6}, {'end': 569.583, 'text': "And I'm at least going to start to run it.", 'start': 567.761, 'duration': 1.822}, {'end': 571.704, 'text': 'Now, this is a lot of models to train.', 'start': 569.683, 'duration': 2.021}, {'end': 573.926, 'text': "So some people have been like, man, it's taking forever.", 'start': 571.764, 'duration': 2.162}, {'end': 576.648, 'text': "Well, it's probably because you're on the CPU version of TensorFlow.", 'start': 573.946, 'duration': 2.702}, {'end': 581.131, 'text': "So if you're interested, I'll put.", 'start': 577.268, 'duration': 3.863}, {'end': 587.595, 'text': "Well, there's two links in the text-based version of the tutorial to the two installation tutorials for.", 'start': 581.131, 'duration': 6.464}, {'end': 590.477, 'text': 'Let me pull up here.', 'start': 587.595, 'duration': 2.882}], 'summary': 'Training multiple models in tensorflow, cpu version causing delays.', 'duration': 29.2, 'max_score': 561.277, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA561277.jpg'}], 'start': 130.293, 'title': 'Neural network dense layer choices and model optimization techniques', 'summary': 'Discusses choosing dense layers for a neural network, considering options of zero, one, or two layers, and layer sizes of 32, 64, and 128, based on the success of 64 units per layer. it also covers optimizing model architecture by iteratively testing different numbers of nodes, layers, and convolutional layers, revealing that some combinations work better than others and recommending the use of gpu for faster training.', 'chapters': [{'end': 179.881, 'start': 130.293, 'title': 'Neural network dense layer choices', 'summary': 'Discusses the process of choosing dense layers for a neural network, considering options of zero, one, or two layers, and layer sizes of 32, 64, and 128, based on the success of 64 units per layer. once a model shows progress, the approach is refined using this method.', 'duration': 49.588, 'highlights': ['The process involves choosing between zero, one, or two dense layers, and layer sizes of 32, 64, and 128, with 64 units per layer showing some success.', "The initial training involves 'hunt and pecking' for a model that shows progress, indicated by increasing accuracy or decreasing loss."]}, {'end': 650.06, 'start': 179.901, 'title': 'Model optimization techniques', 'summary': 'Discusses the process of optimizing model architecture by iteratively testing different numbers of nodes, layers, and convolutional layers, revealing that some combinations work better than others and recommending the use of gpu for faster training.', 'duration': 470.159, 'highlights': ['The process of iteratively testing different numbers of nodes, layers, and convolutional layers to optimize the model architecture is discussed The speaker mentions trying different numbers of nodes per layer (16, 32, 64, 128, 256, 512, 1024, 2048) and varying numbers of dense and convolutional layers to find the most effective combination.', 'Recommendation to use GPU for faster training due to the time-consuming nature of testing various model configurations The speaker advises using a GPU for faster model training, addressing the time-consuming nature of testing multiple model configurations.', 'Suggestion to use one dense layer initially and gradually test more dense layers to determine the optimal setup The speaker recommends starting with one dense layer and gradually testing more dense layers to confirm the most effective configuration.']}], 'duration': 519.767, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA130293.jpg', 'highlights': ['The process involves choosing between zero, one, or two dense layers, and layer sizes of 32, 64, and 128, with 64 units per layer showing some success.', "The initial training involves 'hunt and pecking' for a model that shows progress, indicated by increasing accuracy or decreasing loss.", 'The process of iteratively testing different numbers of nodes, layers, and convolutional layers to optimize the model architecture is discussed.', 'Recommendation to use GPU for faster training due to the time-consuming nature of testing various model configurations.', 'Suggestion to use one dense layer initially and gradually test more dense layers to determine the optimal setup.']}, {'end': 834.727, 'segs': [{'end': 743.313, 'src': 'embed', 'start': 650.14, 'weight': 0, 'content': [{'end': 651.221, 'text': "I'll show you the results.", 'start': 650.14, 'duration': 1.081}, {'end': 654.524, 'text': 'You can also run this on your CPU if you want.', 'start': 652.342, 'duration': 2.182}, {'end': 659.949, 'text': 'But anyways, so I just want to make sure the code works right now.', 'start': 655.325, 'duration': 4.624}, {'end': 662.592, 'text': "So I'm just going to go ahead and run it and make sure this even works.", 'start': 660.029, 'duration': 2.563}, {'end': 664.053, 'text': 'Name is not defined.', 'start': 663.372, 'duration': 0.681}, {'end': 667.676, 'text': 'Okay, we need to throw the TensorBoard into here.', 'start': 665.474, 'duration': 2.202}, {'end': 675.263, 'text': "See, that's exactly why I wanted to remove that name, because we were redefining it, but that was gonna be problematic.", 'start': 668.217, 'duration': 7.046}, {'end': 677.945, 'text': "Okay, let's try that one now.", 'start': 675.923, 'duration': 2.022}, {'end': 680.667, 'text': 'And come back over here.', 'start': 677.965, 'duration': 2.702}, {'end': 691.608, 'text': "Okay Looks like it's learning.", 'start': 690.126, 'duration': 1.482}, {'end': 692.569, 'text': "Looks like it's training.", 'start': 691.728, 'duration': 0.841}, {'end': 693.45, 'text': 'Good to go.', 'start': 693.049, 'duration': 0.401}, {'end': 699.436, 'text': "Now, luckily for you guys, I'm not going to sit here and make you guys wait for like, you know, 45 minutes to run through all these models.", 'start': 693.53, 'duration': 5.906}, {'end': 700.777, 'text': "I'm actually just going to break this.", 'start': 699.456, 'duration': 1.321}, {'end': 703.28, 'text': 'I have already done this.', 'start': 701.498, 'duration': 1.782}, {'end': 706.383, 'text': 'So I am going to close that.', 'start': 703.82, 'duration': 2.563}, {'end': 709.686, 'text': "And now I'm going to come over.", 'start': 706.983, 'duration': 2.703}, {'end': 712.389, 'text': "Let's see here.", 'start': 709.706, 'duration': 2.683}, {'end': 713.29, 'text': 'Open up.', 'start': 712.549, 'duration': 0.741}, {'end': 738.331, 'text': "uh, tensor, tensor board logder equals um, a bunch of logs then, um, that'll take a moment to fully load, but soon we will get, oh, an error,", 'start': 714.102, 'duration': 24.229}, {'end': 739.752, 'text': 'error on.', 'start': 738.331, 'duration': 1.421}, {'end': 741.472, 'text': 'i got some funky looking error here.', 'start': 739.752, 'duration': 1.72}, {'end': 743.313, 'text': "i'm not really sure why i got that hold on.", 'start': 741.472, 'duration': 1.841}], 'summary': 'Code successfully executed, training completed, and error encountered during tensorboard setup.', 'duration': 93.173, 'max_score': 650.14, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA650140.jpg'}], 'start': 650.14, 'title': 'Tensorboard training check and troubleshooting tensorboard logs', 'summary': 'Involves running and checking the code for training on tensorboard, ensuring successful execution, and avoiding redefining issues, with a mention of not making the audience wait for a long time. it also details the process of troubleshooting tensorboard logs and encountering errors while attempting to load logs and resolve path-related issues.', 'chapters': [{'end': 699.436, 'start': 650.14, 'title': 'Tensorboard training check', 'summary': 'Involves running and checking the code for training on tensorboard, ensuring successful execution and avoiding redefining issues, with a mention of not making the audience wait for a long time.', 'duration': 49.296, 'highlights': ['Ensuring successful execution of code for training on TensorBoard by removing redefining issues', 'Mentioning the avoidance of making the audience wait for long durations during the process', 'Showing the results and running the code to check if it works on CPU as well']}, {'end': 834.727, 'start': 699.456, 'title': 'Troubleshooting tensorboard logs', 'summary': 'Details the process of troubleshooting tensorboard logs and encountering errors while attempting to load logs and resolve path-related issues.', 'duration': 135.271, 'highlights': ['Encountering errors while attempting to load tensorboard logs and resolving path-related issues', 'Troubleshooting process of identifying and resolving errors with tensorboard logs', 'Noticing errors in loading tensorboard logs and attempting to resolve path-related issues']}], 'duration': 184.587, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA650140.jpg', 'highlights': ['Ensuring successful execution of code for training on TensorBoard by removing redefining issues', 'Mentioning the avoidance of making the audience wait for long durations during the process', 'Showing the results and running the code to check if it works on CPU as well', 'Encountering errors while attempting to load tensorboard logs and resolving path-related issues', 'Troubleshooting process of identifying and resolving errors with tensorboard logs', 'Noticing errors in loading tensorboard logs and attempting to resolve path-related issues']}, {'end': 1261.209, 'segs': [{'end': 919.177, 'src': 'embed', 'start': 886.049, 'weight': 0, 'content': [{'end': 892.842, 'text': 'So three convolutional layers um, 128 nodes per layer, no dense layer.', 'start': 886.049, 'duration': 6.793}, {'end': 897.164, 'text': 'the second best is three com 32, zero dense again.', 'start': 892.842, 'duration': 4.322}, {'end': 902.627, 'text': 'uh, and let me wonder if it changes at all.', 'start': 897.164, 'duration': 5.463}, {'end': 906.929, 'text': "if we remove any of the smoothing, it doesn't appear to really change.", 'start': 902.627, 'duration': 4.302}, {'end': 908.07, 'text': 'okay anyways.', 'start': 906.929, 'duration': 1.141}, {'end': 909.711, 'text': 'so 332, zero.', 'start': 908.07, 'duration': 1.641}, {'end': 912.272, 'text': 'how about the third best?', 'start': 909.711, 'duration': 2.561}, {'end': 913.933, 'text': 'wait, what was this one?', 'start': 912.272, 'duration': 1.661}, {'end': 915.694, 'text': 'threes. oh, they did move.', 'start': 913.933, 'duration': 1.761}, {'end': 919.177, 'text': 'So now the best one is three 64, zero.', 'start': 917.116, 'duration': 2.061}], 'summary': 'Best model: 3 convolutional layers with 64 nodes, no dense layer', 'duration': 33.128, 'max_score': 886.049, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA886049.jpg'}, {'end': 1008.595, 'src': 'embed', 'start': 972.68, 'weight': 1, 'content': [{'end': 982.256, 'text': 'So it could be the case that if we were to try a 512, That would actually beat the other sizes.', 'start': 972.68, 'duration': 9.576}, {'end': 985.257, 'text': 'So the next thing you could do is just test that.', 'start': 982.716, 'duration': 2.541}, {'end': 990.099, 'text': "So you could say, all right, we're going to go with two dense layers.", 'start': 985.337, 'duration': 4.762}, {'end': 993.561, 'text': "We'll go with three conv layers.", 'start': 991.12, 'duration': 2.441}, {'end': 997.422, 'text': "And we'll just do 64 here.", 'start': 995.462, 'duration': 1.96}, {'end': 1008.595, 'text': "Come down here and in my I think the tensor board only went to ten epochs if I recall, right? Let's check it.", 'start': 1001.188, 'duration': 7.407}], 'summary': 'Testing a 512 size may outperform other sizes. experimenting with 2 dense layers, 3 conv layers, and 64 units. tensor board logs up to 10 epochs.', 'duration': 35.915, 'max_score': 972.68, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA972680.jpg'}, {'end': 1150.103, 'src': 'embed', 'start': 1126.454, 'weight': 2, 'content': [{'end': 1135.217, 'text': 'one is this larger, You know, dense layer really just helped us to memorize our data, Which is no good because we can see the accuracy.', 'start': 1126.454, 'duration': 8.763}, {'end': 1140.519, 'text': 'you just went through the roof like, basically, if I refresh this, it almost looks Like we might have gotten a perfect score.', 'start': 1135.217, 'duration': 5.302}, {'end': 1142.239, 'text': 'at least my notes say 90s or the.', 'start': 1140.519, 'duration': 1.72}, {'end': 1144.78, 'text': 'you know, the log says 97.', 'start': 1142.239, 'duration': 2.541}, {'end': 1150.103, 'text': 'So 97 versus the validation accuracy of 82, Definitely over fit.', 'start': 1144.78, 'duration': 5.323}], 'summary': 'The dense layer improved data memorization, leading to 97% accuracy, indicating overfitting.', 'duration': 23.649, 'max_score': 1126.454, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA1126454.jpg'}], 'start': 835.267, 'title': 'Optimizing neural network model', 'summary': 'Explores optimizing hyperparameters for a neural network by testing different configurations, such as using 512 dense layers and 3 convolutional layers, and comparing their performance with other sizes, aiming for improved accuracy and efficiency. it also discusses the analysis of a neural network model with 512 dense layer, emphasizing overfitting due to memorization, leading to a validation accuracy of 82% and a need for careful selection of layers based on the specific dataset, with a caution that the findings may not be applicable to other datasets.', 'chapters': [{'end': 972.199, 'start': 835.267, 'title': 'Optimizing neural network architecture', 'summary': 'Presents an analysis of different neural network architectures, highlighting the impact of convolutional and dense layers on model performance, with a focus on the best-performing configuration of three convolutional layers and zero dense layers.', 'duration': 136.932, 'highlights': ['The best-performing configuration consists of three convolutional layers with 128 nodes per layer and no dense layer, which yielded the lowest validation loss.', 'The second best-performing configuration involves three convolutional layers with 32 nodes and no dense layer, indicating a consistent pattern of performance based on the number of convolutional layers and absence of dense layers.', "The analysis suggests a potential for improved performance by exploring larger dense layers, such as 512 or 256 nodes, as the current 64-node dense layer may be limiting the model's capabilities."]}, {'end': 1077.409, 'start': 972.68, 'title': 'Optimizing neural network hyperparameters', 'summary': 'Explores optimizing hyperparameters for a neural network by testing different configurations, such as using 512 dense layers and 3 convolutional layers, and comparing their performance with other sizes, aiming for improved accuracy and efficiency.', 'duration': 104.729, 'highlights': ['By testing 512 dense layers and 3 convolutional layers, it was speculated that this configuration could potentially outperform other sizes, presenting an opportunity for improved model accuracy and efficiency.', "The experimentation involved running the model with different hyperparameters, such as 512 dense layers, 1 dense layer, and 3 convolutional layers, to optimize the neural network's performance.", 'The chapter also includes considerations for the number of epochs in training, as well as making comparisons between different hyperparameter configurations to determine the most effective setup for the neural network.']}, {'end': 1261.209, 'start': 1077.41, 'title': 'Optimizing neural network model', 'summary': 'Discusses the analysis of a neural network model with 512 dense layer, emphasizing overfitting due to memorization, leading to a validation accuracy of 82% and a need for careful selection of layers based on the specific dataset, with a caution that the findings may not be applicable to other datasets.', 'duration': 183.799, 'highlights': ["The model's overfitting with a 512 dense layer resulted in a validation accuracy of 82% and a likelihood of memorization, leading to a need for careful selection of layers based on the specific dataset. validation accuracy of 82%", 'Caution is advised that the findings for optimizing the model may not be directly applicable to other datasets, especially those with a different level of complexity or sample size. different level of complexity or sample size', 'The impact of adding a dense layer is discussed, suggesting that in cases with a larger number of samples, the dense layer may not lead to memorization but instead provide help in learning. larger number of samples']}], 'duration': 425.942, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA835267.jpg', 'highlights': ['The best-performing configuration consists of three convolutional layers with 128 nodes per layer and no dense layer, yielding the lowest validation loss.', 'Testing 512 dense layers and 3 convolutional layers was speculated to potentially outperform other sizes, presenting an opportunity for improved model accuracy and efficiency.', "The model's overfitting with a 512 dense layer resulted in a validation accuracy of 82% and a likelihood of memorization, leading to a need for careful selection of layers based on the specific dataset."]}, {'end': 1631.327, 'segs': [{'end': 1297.243, 'src': 'embed', 'start': 1261.209, 'weight': 0, 'content': [{'end': 1266.813, 'text': "you're probably gonna have to go through this operation over again and but I see all the time people are like how do you know what size??", 'start': 1261.209, 'duration': 5.604}, {'end': 1267.574, 'text': "Well, you don't.", 'start': 1266.833, 'duration': 0.741}, {'end': 1273.999, 'text': "It's all trial and error, and you tweak things, and you basically perform your own optimization algorithm on the models.", 'start': 1267.994, 'duration': 6.005}, {'end': 1279.843, 'text': 'You make little tiny tweaks in both directions, see where that takes you, and then you keep repeating that process over again.', 'start': 1274.039, 'duration': 5.804}, {'end': 1283.005, 'text': "And that's why things take so long.", 'start': 1280.503, 'duration': 2.502}, {'end': 1291.658, 'text': "That's why the Python Plays GTA series took forever to progress because you've got to do these like incremental changes.", 'start': 1283.045, 'duration': 8.613}, {'end': 1297.243, 'text': 'And then when the model itself already takes like a week to train, that becomes very challenging.', 'start': 1291.739, 'duration': 5.504}], 'summary': 'Model optimization involves trial and error, leading to prolonged training time.', 'duration': 36.034, 'max_score': 1261.209, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA1261209.jpg'}, {'end': 1345.311, 'src': 'embed', 'start': 1316.17, 'weight': 2, 'content': [{'end': 1324.082, 'text': 'You want to take those things into consideration and it can help to some degree to understand, like how do activation functions work?', 'start': 1316.17, 'duration': 7.912}, {'end': 1329.624, 'text': 'So you can understand, maybe, that you should use a different one in certain circumstances or something like that.', 'start': 1324.102, 'duration': 5.522}, {'end': 1331.825, 'text': 'But for the most part, this is how you do it.', 'start': 1329.684, 'duration': 2.141}, {'end': 1336.527, 'text': "And in fact, this method here, this has been what I've been using for quite a while.", 'start': 1331.845, 'duration': 4.682}, {'end': 1345.311, 'text': 'But recently I was looking at, like a, a talk from somebody from tensorflow, and they did the exact same, the exact same thing.', 'start': 1337.188, 'duration': 8.123}], 'summary': 'Consider activation functions for better understanding and potential improvement in neural network performance.', 'duration': 29.141, 'max_score': 1316.17, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA1316170.jpg'}, {'end': 1451.163, 'src': 'embed', 'start': 1420.526, 'weight': 3, 'content': [{'end': 1422.627, 'text': 'All right, here we are on an official example.', 'start': 1420.526, 'duration': 2.101}, {'end': 1426.67, 'text': 'Okay Clearly this is like your model.', 'start': 1424.949, 'duration': 1.721}, {'end': 1427.271, 'text': 'I get it.', 'start': 1426.951, 'duration': 0.32}, {'end': 1427.871, 'text': 'All right.', 'start': 1427.591, 'duration': 0.28}, {'end': 1430.233, 'text': "So you've got one conv layer, two conv layers.", 'start': 1428.191, 'duration': 2.042}, {'end': 1431.254, 'text': "You've got a dropout.", 'start': 1430.393, 'duration': 0.861}, {'end': 1432.815, 'text': "That's the other thing we didn't even talk about.", 'start': 1431.314, 'duration': 1.501}, {'end': 1435.557, 'text': 'But you would start to add dropout.', 'start': 1433.535, 'duration': 2.022}, {'end': 1439.24, 'text': 'And again, dropout will help you against overfitment.', 'start': 1435.617, 'duration': 3.623}, {'end': 1445.604, 'text': 'So if we added like a 20, 30% dropout, especially at the end of this dense layer, that would help the model to not overfit.', 'start': 1439.26, 'duration': 6.344}, {'end': 1448.406, 'text': 'And that might actually give us even better accuracy.', 'start': 1446.425, 'duration': 1.981}, {'end': 1451.163, 'text': "So that'd be something worthy of checking.", 'start': 1448.427, 'duration': 2.736}], 'summary': 'The model has one conv layer, two conv layers, and a dropout to prevent overfitting, potentially improving accuracy.', 'duration': 30.637, 'max_score': 1420.526, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA1420526.jpg'}, {'end': 1562.366, 'src': 'embed', 'start': 1536.328, 'weight': 4, 'content': [{'end': 1541.011, 'text': "So if you're one of these PyTorch people, tell me why PyTorch.", 'start': 1536.328, 'duration': 4.683}, {'end': 1542.912, 'text': "I just don't get it.", 'start': 1542.312, 'duration': 0.6}, {'end': 1544.874, 'text': "I just don't want to write this code anymore.", 'start': 1542.992, 'duration': 1.882}, {'end': 1546.875, 'text': "I'm really enjoying Keras.", 'start': 1545.654, 'duration': 1.221}, {'end': 1550.137, 'text': 'And I feel like I did my time writing raw TensorFlow.', 'start': 1547.495, 'duration': 2.642}, {'end': 1552.539, 'text': 'Okay, so our model is done.', 'start': 1551.058, 'duration': 1.481}, {'end': 1559.404, 'text': "And let's go to HPC.", 'start': 1555.961, 'duration': 3.443}, {'end': 1560.845, 'text': "I don't know what I did with the other TensorBoard.", 'start': 1559.444, 'duration': 1.401}, {'end': 1562.366, 'text': 'It does not look like it did any better.', 'start': 1560.865, 'duration': 1.501}], 'summary': 'Preference for keras over pytorch for ease of use and previous experience with tensorflow.', 'duration': 26.038, 'max_score': 1536.328, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA1536328.jpg'}], 'start': 1261.209, 'title': 'Model optimization and activation functions', 'summary': 'Delves into the iterative process of model optimization, emphasizing the time-consuming nature and challenges of lengthy model training. it also discusses the significance of activation functions, data scaling, and the use of rectified linear units and dropout to prevent overfitting, while expressing skepticism towards pytorch as a model development framework.', 'chapters': [{'end': 1297.243, 'start': 1261.209, 'title': 'Model optimization process', 'summary': 'Discusses the process of optimizing models through trial and error, emphasizing the time-consuming nature of iterative changes and the challenges posed by lengthy model training.', 'duration': 36.034, 'highlights': ['The time-consuming nature of iterative changes and the challenges posed by lengthy model training, with models taking up to a week to train.', 'The process of optimizing models through trial and error, making little tweaks in both directions and repeating the process over again.']}, {'end': 1631.327, 'start': 1297.603, 'title': 'Understanding activation functions and model optimization', 'summary': 'Covers the importance of understanding activation functions, data scaling, and model optimization, highlighting the use of rectified linear units and dropout to prevent overfitting, and expressing skepticism towards pytorch as a model development framework.', 'duration': 333.724, 'highlights': ['The importance of understanding activation functions and data scaling Emphasizes the significance of comprehending activation functions and data scaling for model optimization, with the suggestion to use different activation functions in certain circumstances.', 'Use of rectified linear units and dropout for model optimization Advocates for the use of rectified linear units and dropout layers to prevent overfitting, with a recommendation to add dropout, especially at the end of the dense layer, to improve accuracy.', 'Skepticism towards PyTorch and preference for Keras Expresses skepticism towards PyTorch as a model development framework, preferring Keras for its higher-level abstraction and expressing reluctance to write models in a lower-level manner as in PyTorch.']}], 'duration': 370.118, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/lV09_8432VA/pics/lV09_8432VA1261209.jpg', 'highlights': ['Models taking up to a week to train due to iterative changes and lengthy model training.', 'Optimizing models through trial and error by making little tweaks and repeating the process.', 'Significance of understanding activation functions and data scaling for model optimization.', 'Advocacy for using rectified linear units and dropout layers to prevent overfitting.', 'Skepticism towards PyTorch as a model development framework and preference for Keras.']}], 'highlights': ['Adjusting model parameters such as optimizer, learning rate, dense layers, and activation units is crucial for improving accuracy.', 'Testing various combinations of layers, nodes, and dense layers is essential for model optimization.', 'Choosing between zero, one, or two dense layers, and layer sizes of 32, 64, and 128 is part of the iterative process for model optimization.', 'Using GPU for faster training is recommended due to the time-consuming nature of testing various model configurations.', 'Successful execution of code for training on TensorBoard requires resolving redefining issues and path-related errors.', 'The best-performing configuration consists of three convolutional layers with 128 nodes per layer and no dense layer, yielding the lowest validation loss.', 'Models taking up to a week to train due to iterative changes and lengthy model training.', 'Understanding activation functions and data scaling is significant for model optimization.', 'Advocacy for using rectified linear units and dropout layers to prevent overfitting.']}