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ID:(1785, 0)

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def plot_confusion_matrix(cm,classes,
                         normalize=False,
                         title='Confusion matrix',
                         cmap=plt.cm.Blues):
    plt.imshow(cm,interpolation='nearest',cmap=cmap)
    plt.title(title)
    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('Normalized confusion matrix')
    else:
        print('Confusion matrix, without normalization')
        
    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',
                color='white' if cm[i,j] > thresh else 'black')
        
    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predict label')

ID:(13769, 0)

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# define directories
work_directory = 'F:/go/face_scrapper/faces/work/'
test_path = work_directory+'test/'

# generate test batches
test_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input).flow_from_directory(directory=test_path, target_size=(224,224), classes=classes, batch_size=10, shuffle=False)

ID:(13770, 0)

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# erzeugen der Vorhersehung
predictions = model.predict(x=test_batches, verbose=0)

ID:(13771, 0)

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# calculate confusion matrix
cm = confusion_matrix(y_true=test_batches.classes,y_pred=np.argmax(predictions,axis=-1))

ID:(13772, 0)

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# show confusion matrix
cm_plot_labels = classes
plot_confusion_matrix(cm=cm, classes=cm_plot_labels, title='Confusion Matrix')

ID:(13773, 0)

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import numpy

# get trace
n = numpy.trace(cm)

# get sum
s = numpy.matrix(cm)
v = s.sum()

# fraction of trace (correct) to all others (
f = 2*n/(v - n)

# show result
print(str(n)+','+str(v)+','+str(f))

ID:(13774, 0)