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)

Convolutional layer application

Now let's implement a simple convolutional layer in TensorFlow. First, we're going to go over the explicit shapes used in this example, as that's often tricky. Then we'll walk through the implementation and TensorFlow call for convolutions. Finally, we'll visually inspect the results of our convolution by passing in a simple example image.

Exploring the convolution layer

Let's jump right into the code with a fresh IPython session.

This is just a small example to help us get familiar with using TensorFlow for convolution layers.

After importing the necessary tools, let's make a fake 10x10 image but with larger values on the diagonal:

# Make some fake data, 1 data points
image = np.random.randint(10,size=[1,10,10]) + np.eye(10)*10

Note the unusual size specified in the preceding code. The 10, 10 is just the image dimensions but the 1 refers to the number of input channels. In this case, we're using one input channel, which is like a gray scale image. If you had a...