Practical Convolutional Neural Networks

Step 8 – Model evaluation

We have managed to finish the training. It is time to evaluate the model. Before, we start evaluating the model, let's implement some auxiliary functions for plotting the example errors and printing the validation accuracy. The plot_example_errors() takes two parameters. The first is cls_pred, which is an array of the predicted class-number for all images in the test set.

The second parameter, correct, is a boolean array to predict whether the predicted class is equal to true class for each image in the test set. At first, it gets the images from the test set that have been incorrectly classified. Then it gets the predicted and the true classes for those images, and finally it plots the first nine images with their ...