pdfcoffee

Chapter 4
Figure 10: Accuracy for different models and optimizers
Understanding the power of deep learning
Another test we can run for better understanding the power of deep learning and
convnets is to reduce the size of the training set and observe the resulting decay in
performance. One way to do this is to split the training set of 50,000 examples into
two different sets:
1. The proper training set used for training our model will progressively reduce
its size through 5,900, 3,000, 1,800, 600, and 300 examples.
2. The validation set used to estimate how well our model has been trained will
consist of the remaining examples. Our test set is always fixed, and it consists
of 10,000 examples.
With this setup we compare the previously defined deep learning convnet against
the first example neural network defined in Chapter 1, Neural Network Foundations
with TensorFlow 2.0. As we can see in the following graph, our deep network always
outperforms the simple network when there is more data available. With 5,900
training examples, the deep learning net had an accuracy of 97.23% against an
accuracy of 94% for the simple net.
[ 121 ]