<reponame>ivan-alles/orthogonal-convolution<gh_stars>0
import matplotlib.pyplot as plt

CLASS_NAMES = ['airplane', 'automobile', 'bird', 'cat', 'deer',
               'dog', 'frog', 'horse', 'ship', 'truck']

def show_images(images, labels):
    plt.figure(figsize=(10, 10))
    for i in range(25):
        plt.subplot(5, 5, i + 1)
        plt.xticks([])
        plt.yticks([])
        plt.grid(False)
        plt.imshow(images[i], cmap=plt.cm.binary)
        # The CIFAR labels happen to be arrays,
        # which is why you need the extra index
        plt.xlabel(CLASS_NAMES[labels[i][0]])

def show_training_history(history):
    plt.figure(figsize=(10, 10))
    plt.plot(history.history['accuracy'], label='accuracy')
    plt.plot(history.history['val_accuracy'], label='val_accuracy')
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.ylim([0.5, 1])
    plt.legend(loc='lower right')
import matplotlib.pyplot as plt

CLASS_NAMES = ['airplane', 'automobile', 'bird', 'cat', 'deer',
               'dog', 'frog', 'horse', 'ship', 'truck']

def show_images(images, labels):
    plt.figure(figsize=(10, 10))
    for i in range(25):
        plt.subplot(5, 5, i + 1)
        plt.xticks([])
        plt.yticks([])
        plt.grid(False)
        plt.imshow(images[i], cmap=plt.cm.binary)
        # The CIFAR labels happen to be arrays,
        # which is why you need the extra index
        plt.xlabel(CLASS_NAMES[labels[i][0]])

def show_training_history(history):
    plt.figure(figsize=(10, 10))
    plt.plot(history.history['accuracy'], label='accuracy')
    plt.plot(history.history['val_accuracy'], label='val_accuracy')
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.ylim([0.5, 1])
    plt.legend(loc='lower right')
# The CIFAR labels happen to be arrays, # which is why you need the extra index