Retraining existing CNN models
Training a new image recognition from scratch requires a lot of time and computational power. If we can take a prior-trained network and retrain it with our images, it may save us computational time. For this recipe, we will show how to use a pre-trained TensorFlow image recognition model and fine-tune it to work on a different set of images.
Getting ready
The idea is to reuse the weights and structure of a prior model from the convolutional layers and retrain the fully connected layers at the top of the network.
TensorFlow has created a tutorial about training on top of existing CNN models (refer to the first bullet point in the next See also section). In this recipe, we will illustrate how to use the same methodology for CIFAR-10. The CNN network we are going to employ uses a very popular architecture called Inception. The Inception CNN model was created by Google and has performed very well on many image recognition benchmarks. For details, see the paper reference...
