Book Image

Deep Learning with TensorFlow - Second Edition

By : Giancarlo Zaccone, Md. Rezaul Karim
Book Image

Deep Learning with TensorFlow - Second Edition

By: Giancarlo Zaccone, Md. Rezaul Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
Table of Contents (15 chapters)
Deep Learning with TensorFlow - Second Edition
Contributors
Preface
Other Books You May Enjoy
Index

Fine-tuning implementation


Our classification task contains two categories, so the new softmax layer of the network will consist of 2 categories instead of 1,000 categories. Here is the input tensor, which is a 227×227×3 image, and the output tensor of rank 2:

n_classes = 2
train_x = zeros((1, 227,227,3)).astype(float32)
train_y = zeros((1, n_classes))

Fine-tuning implementation consists of truncating the last layer (the softmax layer) of the pre-trained network and replacing it with a new softmax layer that is relevant to our problem.

For example, the pre-trained network on ImageNet comes with a softmax layer with 1,000 categories.

The following code snippet defines the new softmax layer, fc8:

fc8W = tf.Variable(tf.random_normal\
                   ([4096, n_classes]),\
                   trainable=True, name="fc8w")
fc8b = tf.Variable(tf.random_normal\
                   ([n_classes]),\
                   trainable=True, name="fc8b")
fc8 = tf.nn.xw_plus_b(fc7, fc8W, fc8b)
prob = tf.nn.softmax...