In this project, I will be deploying a convolutional neural network (CNN) for object recognition. More specifically, I will be using the All-CNN network published in the 2015 ICLR paper, "Striving For Simplicity: The All Convolutional Net". This paper can be found at the following link:
https://arxiv.org/pdf/1412.6806.pdf
This convolutional neural network obtained near state-of-the-art performance at object recognition on the CIFAR-10 image dataset in 2015. I will be building this model using Keras, a high-level neural network application programming interface (API) that supports both Theano and Tensorflow backends. You can use either backend; however, I will be using Tensorflow.
Task Breakdown
- Import datasets from Keras
- Use one-hot vectors for categorical labels
- Addlayers to a Keras model
- Load pre-trained weights
- Make predictions using a trained Keras model
The dataset we will be using is the CIFAR-10 dataset, which consists of 60,000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.