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Single layer neural network

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Sequences, Time Series and Prediction

deeplearning.ai

4.6 (772 notes) | 13K étudiants inscrits

Cours 4 de 4 dans TensorFlow in Practice Spécialisation

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.

Visualiser le programme de cours

Compétences que vous apprendrez

Forecasting, Machine Learning, Tensorflow, Time Series, prediction

Avis

4.6 (772 notes)

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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.

PS

Sep 14, 2019

An excellent course on Time Series and Sequences by Laurence Moroney. Explained how to use CNN, RNN and DNN together to bring the nest out of time series prediction.

À partir de la leçon

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

Enseigné par

Laurence Moroney
AI Advocate

Transcription

Now that we have a window datasets, we can start training neural networks with it. Let's start with a super simple one that's effectively a linear regression. We'll measure its accuracy, and then we'll work from there to improve that. Before we can do a training, we have to split our dataset into training and validation sets. Here's the code to do that at time step 1000. We can see that the training data is the subset of the series called x train up to the split time. Here's the code to do a simple linear regression. Let's look at it line by line. We'll start by setting up all the constants that we want to pass to the window dataset function. These include the window size on the data, the batch size that we want for training, and the size of the shuffled buffer as we've just discussed. Then we'll create our dataset. We'll do this by taking our series, and in the notebook that you'll go through later, you'll create the same synthetic series as you did in week one. You'll pass it your series along what your desired window size, batch size, and shuffled buffer size, and it will give you back a formatted datasets that you could use for training. I'm then going to create a single dense layer with its input shape being the window size. For linear regression, that's all you need. I'm using this approach. By passing the layer to a variable called L0, because later I'm want to print out its learned weights, and it's a lot easier for me to do that if I have a variable to refer to the layer for that. Then I simply define my model as a sequential containing the sole layer just like this. Now I'll compile and fit my model with this code. I'll use the mean squared error loss function by setting loss to MSE, and my optimizer will use Stochastic Gradient Descent. I'd use this methodology instead of the raw string, so I can set parameters on it to initialize it such as the learning rate or LR and the momentum. Experiment with different values here to see if you can get your model to converge more quickly or more accurately. Next you can fit your model by just passing it the dataset, which has already been preformatted with the x and y values. I'm going to run for a 100 epochs here. Ignoring the epoch but epoch output by setting verbose to zero. Once it's done training, you can actually inspect the different weights with this code. Remember earlier when we referred to the layer with a variable called L 0? Well, here's where that's useful. The output will look like this. If you inspect it closely, you will see that the first array has 20 values in it, and the secondary has only one value. This is because the network has learned a linear regression to fit the values as best as they can. So each of the values in the first array can be seen as the weights for the 20 values in x, and the value for the second array is the b value.

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