Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Antonio Gulli, Amita Kapoor, Sujit Pal
Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Antonio Gulli, Amita Kapoor, Sujit Pal

Overview of this book

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
Table of Contents (19 chapters)
17
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18
Index

Very deep convolutional networks for large-scale image recognition

During 2014, an interesting contribution to image recognition was presented with the paper, Very Deep Convolutional Networks for Large-Scale Image Recognition, K. Simonyan and A. Zisserman [4]. The paper showed that a "significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers." One model in the paper denoted as D or VGG-16 had 16 deep layers.

An implementation in Java Caffe (http://caffe.berkeleyvision.org/) was used for training the model on the ImageNet ILSVRC-2012 (http://image-net.org/challenges/LSVRC/2012/) dataset, which includes images of 1,000 classes, and is split into three sets: training (1.3 million images), validation (50,000 images), and testing (100,000 images). Each image is (224×224) on 3 channels. The model achieves 7.5% top-5 error on ILSVRC-2012-val, 7.4% top-5 error on ILSVRC-2012-test.

According to the ImageNet site...