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Neural Network Foundations with TensorFlow 2.0
Running a simple TensorFlow 2.0 net and
establishing a baseline
So let's see what happens when we run the code:
Figure 13: Code ran from our test neural network
First, the net architecture is dumped and we can see the different types of layers
used, their output shape, how many parameters (that is, how many weights) 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. For now, we won't go into the internals of
how the training happens, but we can see that the program runs for 200 iterations
and each time accuracy improves. When the training ends, we test our model on the
test set and we achieve about 89.96% accuracy on training, 90.70% on validation, and
90.71% on test:
Figure 14: Results from testing model, accuracies displayed
This means that nearly 1 in 10 images are incorrectly classified. We can certainly do
better than that.
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