Now we know how to optimize the layering structure in a CNN by adding dropout, BN, and biases initializers, such as Xavier. Let's try to apply these to a less complex CNN. Throughout this example, we will see how to solve a real-life classification problem. To be more specific, our CNN model will be able to classify the traffic sign from a bunch of images.
Building the second CNN by putting everything together
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
Building the second CNN by putting everything together
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