Batch normalization is a technique for improving the performance and stability of neural networks. The idea is to normalize the layer inputs so that they have a mean of zero and variance of 1. Batch normalization was introduced in Sergey Ioffe's and Christian Szegedy's 2015 paper, Batch Normalization is Necessary to Make DCGANs Work. The idea is that instead of just normalizing the inputs to the network, we normalize the inputs to layers within the network. It's called batch normalization because during training, we normalize each layer's input by using the mean and variance of the values in the current mini-batch.
Batch normalization
by Pradeep Pujari, Md. Rezaul Karim, Mohit Sewak
Practical Convolutional Neural Networks
- Title Page
- Copyright and Credits
- Packt Upsell
- Contributors
- Preface
- Deep Neural Networks – Overview
- Introduction to Convolutional Neural Networks
- Build Your First CNN and Performance Optimization
- CNN architectures and drawbacks of DNNs
- Convolution and pooling operations in TensorFlow
- Training a CNN
- Building, training, and evaluating our first CNN
- Dataset description
- Step 1 – Loading the required packages
- Step 2 – Loading the training/test images to generate train/test set
- Step 3- Defining CNN hyperparameters
- Step 4 – Constructing the CNN layers
- Step 5 – Preparing the TensorFlow graph
- Step 6 – Creating a CNN model
- Step 7 – Running the TensorFlow graph to train the CNN model
- Step 8 – Model evaluation
- Dataset description
- Model performance optimization
- Summary
- Popular CNN Model Architectures
- Transfer Learning
- Autoencoders for CNN
- Object Detection and Instance Segmentation with CNN
- The differences between object detection and image classification
- Traditional, nonCNN approaches to object detection
- R-CNN – Regions with CNN features
- Fast R-CNN – fast region-based CNN
- Faster R-CNN – faster region proposal network-based CNN
- Mask R-CNN – Instance segmentation with CNN
- Instance segmentation in code
- References
- Summary
- GAN: Generating New Images with CNN
- Attention Mechanism for CNN and Visual Models
- Other Books You May Enjoy
Batch normalization
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