helpers.py

import matplotlib.pyplot as plt

def plot_images(images, cls_true, img_shape, cls_pred=None):
    assert len(images) == len(cls_true) == 9
    
    # Create figure with 3x3 sub-plots.
    fig, axes = plt.subplots(3, 3)
    fig.subplots_adjust(hspace=0.3, wspace=0.3)

    for i, ax in enumerate(axes.flat):
        # Plot image.
        ax.imshow(images[i].reshape(img_shape), cmap='binary')

        # Show true and predicted classes.
        if cls_pred is None:
            xlabel = "True: {0}".format(cls_true[i])
        else:
            xlabel = "True: {0}, Pred: {1}".format(cls_true[i], cls_pred[i])

        # Show the classes as the label on the x-axis.
        ax.set_xlabel(xlabel)
        
        # Remove ticks from the plot.
        ax.set_xticks([])
        ax.set_yticks([])
    
    # Ensure the plot is shown correctly with multiple plots
    # in a single Notebook cell.
    plt.show()
    
def plot_example_errors(images, cls_true, img_shape, cls_pred, data):
    # cls_pred is an array of the predicted class-number for
    # all images in the test-set.

    # Boolean array whether the predicted class is incorrect.
    incorrect = (cls_pred != data.test.cls)

    # Get the images from the test-set that have been
    # incorrectly classified.
    images = data.test.images[incorrect]
    
    # Get the predicted classes for those images.
    cls_pred = cls_pred[incorrect]

    # Get the true classes for those images.
    cls_true = data.test.cls[incorrect]
    
    # Plot the first 9 images.
    plot_images(images=images[0:9],
                img_shape=img_shape,
                cls_true=cls_true[0:9],
                cls_pred=cls_pred[0:9])