Book Image

Hands-On Deep Learning with TensorFlow

By : Dan Van Boxel
Book Image

Hands-On Deep Learning with TensorFlow

By: Dan Van Boxel

Overview of this book

Dan Van Boxel’s Deep Learning with TensorFlow is based on Dan’s best-selling TensorFlow video course. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel will be your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data. With Dan’s guidance, you will dig deeper into the hidden layers of abstraction using raw data. Dan then shows you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. In this book, Dan shares his knowledge across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces. With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond.
Table of Contents (12 chapters)

Single hidden layer explained

In this section, we'll carefully look at the model we built. First, we'll verify the overall accuracy of our model, then we'll see where the model goes wrong. Finally, we'll visualize the weights associated with several neurons to see what they're looking for:

plt.figure(figsize=(6, 6))

Make sure that you've trained your model as we did in the previous section, if not, you might want to stop here and do that first. Because we evaluated our model accuracy every 10 training epochs and saved the result, it's now easy to explore how our model has evolved.

Using Matplotlib, we can plot both the training accuracy (the blue dots) and testing accuracy (the red dots) on the same figure:

Again, if you don't have Matplotlib, that's okay. You can just look at the array values themselves. Note that the training accuracy (blue in color) is usually a little better than the testing accuracy (red in color). This isn't surprising,...