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  3. Image Modeling with Keras
  • 1

    Image Processing With Neural Networks

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    Convolutional neural networks use the data that is represented in images to learn. In this chapter, we will probe data in images, and we will learn how to use Keras to train a neural network to classify objects that appear in images.

  • 2

    Using Convolutions

    Convolutions are the fundamental building blocks of convolutional neural networks. In this chapter, you will be introducted to convolutions and learn how they operate on image data. You will also see how you incorporate convolutions into Keras neural networks.

  • 3

    Going Deeper

    Convolutional neural networks gain a lot of power when they are constructed with multiple layers (deep networks). In this chapter, you will learn how to stack multiple convolutional layers into a deep network. You will also learn how to keep track of the number of parameters, as the network grows, and how to control this number.

  • 4

    Understanding and Improving Deep Convolutional Networks

    There are many ways to improve training by neural networks. In this chapter, we will focus on our ability to track how well a network is doing, and explore approaches towards improving convolutional neural networks.

Initializing

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Shape of the weights

A Keras neural network stores its layers in a list called model.layers. For the convolutional layers, you can get the weights using the .get_weights() method. This returns a list, and the first item in this list is an array representing the weights of the convolutional kernels. If the shape of this array is (2, 2, 1, 5), what does the first number (2) represent?

Anweisungen

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