Hands-On Image Processing with Python

Classification with dense FC layers with Keras

Let's implement the handwritten digits classification with Keras, again using dense FC layers only. This time we shall use one more hidden layer, along with a dropout layer. The next code block shows how to implement the classifier with a few lines of code using the keras.models Sequential() function. We can simply add the layers sequentially to the model. There are a couple of hidden layers introduced, with each of them having 200 nodes along with a dropout in between, with 15% dropout rate. This time, let's use the Adam optimizer (which uses momentum to accelerate SGD). Let's fit the model on the training dataset with 10 epochs (one pass over the entire input dataset). As can be seen, with ...