from dataset.mnist import load_mnist
import numpy as np
from PIL import Image

# normalize : normalize image pixel, flatten: make image file 1-D, one-hot-label: one-hot-encodeing
(x_train,t_train), (x_test, t_test) = load_mnist(flatten=True, normalize=False)

#print(x_train.shape)
#print(t_train.shape)
#print(x_test.shape)
#print(t_test.shape)

def img_show(img):
    pil_img = Image.fromarray(np.uint8(img))
    pil_img.show()

img = x_train[0]
label = t_train[0]
print(label)

print(img.shape) #flattened image
img = img.reshape(28,28)
print(img.shape)

img_show(img)
