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Deep neural network training, tuning and prediction

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Sequences, Time Series and Prediction

deeplearning.ai

4.6 (878 classificações) | 14K alunos inscritos

Curso 4 de 4 no TensorFlow in Practice Programa de cursos integrados

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In this fourth course, you will learn how to build time series models in TensorFlow. You’ll first implement best practices to prepare time series data. You’ll also explore how RNNs and 1D ConvNets can be used for prediction. Finally, you’ll apply everything you’ve learned throughout the Specialization to build a sunspot prediction model using real-world data! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Visualizar o programa do curso

Habilidades que você aprenderá

Forecasting, Machine Learning, Tensorflow, Time Series, prediction

Avaliações

4.6 (878 classificações)

5 stars
657 ratings
4 stars
155 ratings
3 stars
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2 stars
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1 star
12 ratings

YK

Sep 30, 2019

A step by step explanation of how to use TensorFlow 2.0 for building a Neural network for sequences and time series. With detailed examples of code and of how to choose hyper-parameters.

MK

Aug 03, 2019

This was really a beautifully designed course. They didn't focused on teaching too much of thing at once but build up the base slowly and strongly for better understanding.

Na lição

Deep Neural Networks for Time Series

Having explored time series and some of the common attributes of time series such as trend and seasonality, and then having used statistical methods for projection, let's now begin to teach neural networks to recognize and predict on time series!

A conversation with Andrew Ng1:17

Preparing features and labels4:22

Feeding windowed dataset into neural network2:14

Single layer neural network2:53

Machine learning on time windows0:37

More on single layer neural network2:10

Deep neural network training, tuning and prediction4:18

Deep neural network3:03

Ministrado por

Laurence Moroney
AI Advocate

Transcrição

Okay, so this week you've been looking at machine learning for predicting the next value in a sequence. You've learned how to break your data down into window chunks that you could train on, and then you saw a simple single layer neural network that gave you what was effectively a linear regression. Now let's take that to the next step with a DNN to see if we can improve our model accuracy. It's not that much different from the linear regression model we saw earlier. And this is a relatively simple deep neural network that has three layers. So let's unpack it line by line. First we'll have to get a data set which will generate by passing in x_train data, along with the desired window size, batch size, and shuffle buffer size. We'll then define the model. Let's keep it simple with three layers of 10, 10, and 1 neurons. The input shape is the size of the window and we'll activate each layer using a relu. We'll then compile the model as before with a mean squared error loss function and stochastic gradient descent optimizer. Finally, we'll fit the model over 100 epochs, and after a few seconds of training, we'll see results that look like this. It's pretty good still. And when we calculate the mean absolute error, we're lower than we were earlier, so it's a step in the right direction. But it's also a somewhat a stab in the dark, particularly with the optimizer function. Wouldn't it be nice if we could pick the optimal learning rate instead of the one that we chose? We might learn more efficiently and build a better model. Now let's look at a technique for that that uses callbacks that you used way back in the first course. So here's a code for the previous neural network. But I've added a callback to tweak the learning rate using a learning rate scheduler. You can see that code here. This will be called at the callback at the end of each epoch. What it will do is change the learning rates to a value based on the epoch number. So in epoch 1, it is 1 times 10 to the -8 times 10 to the power of 1 over 20. And by the time we reach the 100 epoch, it'll be 1 times 10 to the -8 times 10 to the power of 5, and that's 100 over 20. This will happen on each callback because we set it in the callbacks parameter of modeled outfit. After training with this, we can then plot the last per epoch against the learning rate per epoch by using this code, and we'll see a chart like this. The y-axis shows us the loss for that epoch and the x-axis shows us the learning rate. We can then try to pick the lowest point of the curve where it's still relatively stable like this, and that's right around 7 times 10 to the -6. So let's set that to be our learning rate and then we'll retrain. So here's the same neural network code, and we've updated the learning rate, so we'll also train it for a bit longer. Let's check the results after training for 500 epochs. Here's the codes to plot out the loss that was calculated during the training, and it will give us a chart like this. Which upon first inspection looks like we're probably wasting our time training beyond maybe only 10 epochs, but it's somewhat skewed by the fact that the earlier losses were so high. If we cropped them off and plot the loss for epochs after number 10 with code like this, then the chart will tell us a different story. We can see that the loss was continuing to decrease even after 500 epochs. And that shows that our network is learning very well indeed. And the results of the predictions overlaid against the originals looks like this. And the mean absolute error across the results is significantly lower than earlier. I'll take you through a screencast of this code in action in the next video. Using a very simple DNN, we've improved our results very nicely. But it's still just a DNN, there's no sequencing taken into account, and in a time series like this, the values that are immediately before a value are more likely to impact it than those further in the past. And that's the perfect set up to use RNS like we had in the natural language course. Now we'll look at that next week, but first, let's dig into this code.

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