Image classification models
We covered image classification in Chapter 5, Image Classification Using Convolutional Neural Networks. In that chapter, we described convolutional and pooling layers that are essential for deep learning tasks involving images. We also built a number of models on a simple dataset, the MNIST dataset. Here, we are going to look at some advanced topics in image classification. First, we will build a complete image classification model using image files as input. We will look at callbacks, which are a great aid in building complex deep learning models. A call-back function will be used to persist (save) a model to file, which will be loaded back later. We then use this model in our next example, which is transfer learning. This is where you use some of the layers in a pre-trained model on new data.
Building a complete image classification solution
We have built a few image classification models, but they used the MNIST dataset that was loaded from Keras or from CSV...
