Book Image

Practical Convolutional Neural Networks

By : Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari
Book Image

Practical Convolutional Neural Networks

By: Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari

Overview of this book

Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets.
Table of Contents (11 chapters)

Convolution and pooling operations in TensorFlow

Now that we have seen how convolutional and pooling operations are performed theoretically, let's see how we can perform these operation hands-on using TensorFlow. So let's get started.

Applying pooling operations in TensorFlow

Using TensorFlow, a subsampling layer can normally be represented by a max_pool operation by maintaining the initial parameters of the layer. For max_pool, it has the following signature in TensorFlow:

tf.nn.max_pool(value, ksize, strides, padding, data_format, name) 

Now let's learn how to create a function that utilizes the preceding signature and returns a tensor with type tf.float32, that is, the max pooled output tensor:

import tensorflow...