util.py

# code refactored from Magnus Erik Hvass Pedersen tutorials

import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from sklearn.metrics import confusion_matrix
import math

class Util(object):
    
    def plot_image(self, image, img_shape=(28,28)):
        plt.imshow(image.reshape(img_shape),
                   interpolation='nearest',
                   cmap='binary')

        plt.show()
    
    def plot_images(self, images, cls_true, cls_pred=None, img_size=28, img_shape=(28,28)):
        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_history(self, history, metric='acc', loc='lower right'): 
        # list all data in history
        # print(history.history.keys())
        # summarize history for accuracy
        plt.plot(history.history[metric])
        plt.plot(history.history['val_'+metric])
        if metric == 'acc': 
            metric = 'accuracy'
        plt.title('model ' + metric)
        plt.ylabel(metric)
        plt.xlabel('epoch')
        plt.legend(['train', 'test'], loc=loc)
        plt.show()

    def plot_images_2(self, images, cls_true, class_names, cls_pred=None, smooth=True):
        assert len(images) == len(cls_true) == 9

        # Create figure with sub-plots.
        fig, axes = plt.subplots(3, 3)

        # Adjust vertical spacing if we need to print ensemble and best-net.
        if cls_pred is None:
            hspace = 0.3
        else:
            hspace = 0.6
        fig.subplots_adjust(hspace=hspace, wspace=0.3)

        for i, ax in enumerate(axes.flat):
            # Interpolation type.
            if smooth:
                interpolation = 'spline16'
            else:
                interpolation = 'nearest'

            # Plot image.
            ax.imshow(images[i, :, :, :],
                      interpolation=interpolation)

            # Name of the true class.
            cls_true_name = class_names[cls_true[i]]

            # Show true and predicted classes.
            if cls_pred is None:
                xlabel = "True: {0}".format(cls_true_name)
            else:
                # Name of the predicted class.
                cls_pred_name = class_names[cls_pred[i]]

                xlabel = "True: {0}\nPred: {1}".format(cls_true_name, cls_pred_name)

            # 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 print_test_accuracy(self, session, data, x, y_true, y_pred_cls, num_classes, 
                            show_example_errors=False,
                            show_confusion_matrix=False):

        # Split the test-set into smaller batches of this size.
        test_batch_size = 256

        # Number of images in the test-set.
        num_test = len(data.test.images)

        # Allocate an array for the predicted classes which
        # will be calculated in batches and filled into this array.
        cls_pred = np.zeros(shape=num_test, dtype=np.int)

        # Now calculate the predicted classes for the batches.
        # We will just iterate through all the batches.
        # There might be a more clever and Pythonic way of doing this.

        # The starting index for the next batch is denoted i.
        i = 0

        while i < num_test:
            # The ending index for the next batch is denoted j.
            j = min(i + test_batch_size, num_test)

            # Get the images from the test-set between index i and j.
            images = data.test.images[i:j, :]

            # Get the associated labels.
            labels = data.test.labels[i:j, :]

            # Create a feed-dict with these images and labels.
            feed_dict = {x: images,
                         y_true: labels}

            # Calculate the predicted class using TensorFlow.
            cls_pred[i:j] = session.run(y_pred_cls, feed_dict=feed_dict)

            # Set the start-index for the next batch to the
            # end-index of the current batch.
            i = j

        # Convenience variable for the true class-numbers of the test-set.
        cls_true = data.test.cls

        # Create a boolean array whether each image is correctly classified.
        correct = (cls_true == cls_pred)

        # Calculate the number of correctly classified images.
        # When summing a boolean array, False means 0 and True means 1.
        correct_sum = correct.sum()

        # Classification accuracy is the number of correctly classified
        # images divided by the total number of images in the test-set.
        acc = float(correct_sum) / num_test

        # Print the accuracy.
        msg = "Accuracy on Test-Set: {0:.1%} ({1} / {2})"
        print(msg.format(acc, correct_sum, num_test))

        # Plot some examples of mis-classifications, if desired.
        if show_example_errors:
            print("Example errors:")
            self.plot_example_errors(data=data, cls_pred=cls_pred, correct=correct)

        # Plot the confusion matrix, if desired.
        if show_confusion_matrix:
            print("Confusion Matrix:")
            self.plot_confusion_matrix(data=data, num_classes=num_classes, cls_pred=cls_pred)


    def plot_confusion_matrix(self, data, num_classes, cls_pred):
        # This is called from print_test_accuracy() below.

        # cls_pred is an array of the predicted class-number for
        # all images in the test-set.

        # Get the true classifications for the test-set.
        cls_true = data.test.cls

        # Get the confusion matrix using sklearn.
        cm = confusion_matrix(y_true=cls_true,
                              y_pred=cls_pred)

        # Print the confusion matrix as text.
        print(cm)

        # Plot the confusion matrix as an image.
        plt.matshow(cm)

        # Make various adjustments to the plot.
        plt.colorbar()
        tick_marks = np.arange(num_classes)
        plt.xticks(tick_marks, range(num_classes))
        plt.yticks(tick_marks, range(num_classes))
        plt.xlabel('Predicted')
        plt.ylabel('True')

        # Ensure the plot is shown correctly with multiple plots
        # in a single Notebook cell.
        plt.show()

    def plot_example_errors(self, data, cls_pred, correct):
        # This function is called from print_test_accuracy() below.

        # cls_pred is an array of the predicted class-number for
        # all images in the test-set.

        # correct is a boolean array whether the predicted class
        # is equal to the true class for each image in the test-set.

        # Negate the boolean array.
        incorrect = (correct == False)

        # 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.
        self.plot_images(images=images[0:9],
                    cls_true=cls_true[0:9],
                    cls_pred=cls_pred[0:9])


    def plot_weights(self, session, weights, img_shape=(28,28)):
        # Get the values for the weights from the TensorFlow variable.
        w = session.run(weights)

        # Get the lowest and highest values for the weights.
        # This is used to correct the colour intensity across
        # the images so they can be compared with each other.
        w_min = np.min(w)
        w_max = np.max(w)

        # Create figure with 3x4 sub-plots,
        # where the last 2 sub-plots are unused.
        fig, axes = plt.subplots(3, 4)
        fig.subplots_adjust(hspace=0.3, wspace=0.3)

        for i, ax in enumerate(axes.flat):
            # Only use the weights for the first 10 sub-plots.
            if i<10:
                # Get the weights for the i'th digit and reshape it.
                # Note that w.shape == (img_size_flat, 10)
                image = w[:, i].reshape(img_shape)

                # Set the label for the sub-plot.
                ax.set_xlabel("Weights: {0}".format(i))

                # Plot the image.
                ax.imshow(image, vmin=w_min, vmax=w_max, cmap='seismic')

            # Remove ticks from each sub-plot.
            ax.set_xticks([])
            ax.set_yticks([])
            
    def plot_conv_weights(self, session, weights, input_channel=0):
        # Assume weights are TensorFlow ops for 4-dim variables
        # e.g. weights_conv1 or weights_conv2.

        # Retrieve the values of the weight-variables from TensorFlow.
        # A feed-dict is not necessary because nothing is calculated.
        w = session.run(weights)

        # Get the lowest and highest values for the weights.
        # This is used to correct the colour intensity across
        # the images so they can be compared with each other.
        w_min = np.min(w)
        w_max = np.max(w)

        # Number of filters used in the conv. layer.
        num_filters = w.shape[3]

        # Number of grids to plot.
        # Rounded-up, square-root of the number of filters.
        num_grids = math.ceil(math.sqrt(num_filters))

        # Create figure with a grid of sub-plots.
        fig, axes = plt.subplots(num_grids, num_grids)

        # Plot all the filter-weights.
        for i, ax in enumerate(axes.flat):
            # Only plot the valid filter-weights.
            if i<num_filters:
                # Get the weights for the i'th filter of the input channel.
                # See new_conv_layer() for details on the format
                # of this 4-dim tensor.
                img = w[:, :, input_channel, i]

                # Plot image.
                ax.imshow(img, vmin=w_min, vmax=w_max,
                          interpolation='nearest', cmap='seismic')

            # 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_conv_layer(self, session, x, layer, image):
        # Assume layer is a TensorFlow op that outputs a 4-dim tensor
        # which is the output of a convolutional layer,
        # e.g. layer_conv1 or layer_conv2.

        # Create a feed-dict containing just one image.
        # Note that we don't need to feed y_true because it is
        # not used in this calculation.
        feed_dict = {x: [image]}

        # Calculate and retrieve the output values of the layer
        # when inputting that image.
        values = session.run(layer, feed_dict=feed_dict)

        # Number of filters used in the conv. layer.
        num_filters = values.shape[3]

        # Number of grids to plot.
        # Rounded-up, square-root of the number of filters.
        num_grids = math.ceil(math.sqrt(num_filters))

        # Create figure with a grid of sub-plots.
        fig, axes = plt.subplots(num_grids, num_grids)

        # Plot the output images of all the filters.
        for i, ax in enumerate(axes.flat):
            # Only plot the images for valid filters.
            if i<num_filters:
                # Get the output image of using the i'th filter.
                # See new_conv_layer() for details on the format
                # of this 4-dim tensor.
                img = values[0, :, :, i]

                # Plot image.
                ax.imshow(img, interpolation='nearest', cmap='binary')

            # 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_transfer_values(self, i, images, transfer_values):
        print("Input image:")

        # Plot the i'th image from the test-set.
        plt.imshow(images[i], interpolation='nearest')
        plt.show()

        print("Transfer-values for the image using Inception model:")

        # Transform the transfer-values into an image.
        img = transfer_values[i]
        img = img.reshape((32, 64))

        # Plot the image for the transfer-values.
        plt.imshow(img, interpolation='nearest', cmap='Reds')
        plt.show()
        
    def plot_scatter(self, values, cls, num_classes):
        # Create a color-map with a different color for each class.
        import matplotlib.cm as cm
        cmap = cm.rainbow(np.linspace(0.0, 1.0, num_classes))

        # Get the color for each sample.
        colors = cmap[cls]

        # Extract the x- and y-values.
        x = values[:, 0]
        y = values[:, 1]

        # Plot it.
        plt.scatter(x, y, color=colors)
        plt.show()