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Running a simple Keras net and establishing a baseline

So let's see what will happen when we run the code in the following screenshot:

First, the net architecture is dumped, and we can see the different types of layers used, their output shape, how many parameters they need to optimize, and how they are connected. Then, the network is trained on 48,000 samples, and 12,000 are reserved for validation. Once the neural model is built, it is then tested on 10,000 samples. As you can see, Keras is internally using TensorFlow as a backend system for computation. For now, we don't go into the internals on how the training happens, but we can notice that the program runs for 200 iterations, and each time, the accuracy improves. When the training ends, we test our model on the test set and achieve about 92.36% accuracy on training, 92.27% on validation, and 92.22% on the test.

This means that a bit less than one handwritten character out of ten is not correctly recognized. We can certainly do better than that. In the following screenshot, we can see the test accuracy:

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