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  • Book Overview & Buying Hands-On Deep Learning with TensorFlow
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Hands-On Deep Learning with TensorFlow

Hands-On Deep Learning with TensorFlow

By : Dan Van Boxel
2.8 (4)
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Hands-On Deep Learning with TensorFlow

Hands-On Deep Learning with TensorFlow

2.8 (4)
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 (7 chapters)
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Pooling layer application

In this section, we're going to take a look at the TensorFlow function for max pooling, then we'll talk about transitioning from a pooling layer back to a fully connected layer. Finally, we'll visually look at the pooling output to verify its reduced size.

Let's pick up in our example from where we left off in the previous section. Make sure you've executed everything up to the pound pooling layer before starting this exercise.

Recall we've put a 10x10 image through a 3x3 convolution and rectified linear activation. Now, let's add a 2x2 max pooling layer that comes after our convolutional layer.

p1 = tf.nn.max_pool(h1, ksize=[1, 2, 2, 1],
          strides=[1, 2, 2, 1], padding='VALID')

The key to this is tf.nn.max_pool. The first argument is just the output of our previous convolutional layer, h1. Next we have the strange ksize. This really just defines the window size of our pooling. In this case, 2x2. The first 1 refers...

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