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Hands-On Computer Vision with TensorFlow 2

Hands-On Computer Vision with TensorFlow 2

By : Benjamin Planche, Eliot Andres
3.3 (12)
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Hands-On Computer Vision with TensorFlow 2

Hands-On Computer Vision with TensorFlow 2

3.3 (12)
By: Benjamin Planche, Eliot Andres

Overview of this book

Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. This book will help you explore TensorFlow 2, the brand new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. Hands-On Computer Vision with TensorFlow 2 starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the book demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. By the end of the book, you will have both the theoretical understanding and practical skills to solve advanced computer vision problems with TensorFlow 2.0.
Table of Contents (16 chapters)
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1
Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision
2
Computer Vision and Neural Networks
5
Section 2: State-of-the-Art Solutions for Classic Recognition Problems
6
Influential Classification Tools
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9
Section 3: Advanced Concepts and New Frontiers of Computer Vision
10
Training on Complex and Scarce Datasets

Residual blocks with the Keras Functional API

As practice, let's implement a basic residual block ourselves. As shown in Figure 4.5, the residual path consists of two convolutional layers, each one followed by batch normalization. The ReLU activation function is applied directly after the first convolution. For the second, the function is only applied after merging with the other path. Using the Keras Functional API, the residual path can thus be implemented in a matter of five or six lines, as demonstrated in the following code.

The shortcut path is even simpler. It contains either no layer at all, or a single 1 × 1 convolution to reshape the input tensor when the residual path is altering its dimensions (for instance, when a larger stride is used).

Finally, the results of the two paths are added together, and the ReLU function is applied to the sum. All in all, a basic residual block can be implemented as follows:

from tf.keras.layers import Activation, Conv2D, BatchNormalization...
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