utils.py

import numpy as np
import matplotlib.pyplot as plt
import itertools
from sklearn.metrics import confusion_matrix 

def PlotConfusionMatrix(cm, classes,
                        normalize=False,
                        title='Confusion matrix',
                        cmap=plt.cm.Blues,
                        showAcc=True):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

    #print(cm)

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
            horizontalalignment="center",
            verticalalignment="center",
            color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')

    if showAcc:
        acc = 100*(np.trace(cm) / np.sum(cm))
        title = title + " | Acc=%.2f%%" % acc

    plt.title(title)



def PlotModelEval(Model, History, y_test, y_preds, Labels):

    # Scores for each class (can be interpreted as probabilities since we use softmax output)
    S = y_preds
    # Prediction (class number) for each test image
    P = y_test
    # Calculate confusion matrix
    CM = confusion_matrix(Y,P)

    # Plot training history
    plt.figure(figsize=(16,6))
    plt.subplot(2,2,1)
    plt.semilogy(History.history['loss'], label="Training")
    if 'val_loss' in History.history:
        plt.semilogy(History.history['val_loss'], label="Validation")
    plt.title('Model loss')
    plt.ylabel('Loss')
    plt.xlabel('Epoch')
    plt.legend(loc='upper right')
    plt.grid(True, which="both")
    plt.subplot(2,2,3)
    plt.plot(100 * np.array(History.history['accuracy']), label="Training")
    if 'val_accuracy' in History.history:
        plt.plot(100 * np.array(History.history['val_accuracy']), label="Validation")
    plt.title('Model accuracy')
    plt.ylabel('Acc [%]')
    plt.xlabel('Epoch')
    plt.legend(loc='lower right')
    plt.grid(True, which="both")

    # Plot confusion matrix
    plt.subplot(2,2,(2,4))
    PlotConfusionMatrix(CM, classes=Labels, title="Confusion matrix (test)")
    plt.show()