test-display-sample.py

# test display
import keras
from keras.datasets import mnist
from displaysample import display_sample

(mnist_train_images, mnist_train_labels), (mnist_test_images, mnist_test_labels) = mnist.load_data()
from keras import backend as K

if K.image_data_format() == 'channels_first':
    train_images = mnist_train_images.reshape(mnist_train_images.shape[0], 1, 28, 28)
    test_images = mnist_test_images.reshape(mnist_test_images.shape[0], 1, 28, 28)
    input_shape = (1, 28, 28)
else:
    train_images = mnist_train_images.reshape(mnist_train_images.shape[0], 28, 28, 1)
    test_images = mnist_test_images.reshape(mnist_test_images.shape[0], 28, 28, 1)
    input_shape = (28, 28, 1)
    
train_images = train_images.astype('float32')
test_images = test_images.astype('float32')
train_images /= 255
test_images /= 255

train_labels = keras.utils.to_categorical(mnist_train_labels, 10)
test_labels = keras.utils.to_categorical(mnist_test_labels, 10)





num=1234
print(train_labels[num])  
#Print the label converted back to a number
label = train_labels[num].argmax(axis=0)
image = train_images[num].reshape([28,28])

display_sample(image,label)