loadmnist.py

import os
import numpy as np
from keras.datasets import mnist

# load data and reshape the Tensors
(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train = X_train.astype(np.float32).reshape((X_train.shape[0],28,28)) / 255.0
X_test  = X_test.astype(np.float32).reshape((X_test.shape[0],28,28)) / 255.0

# convert class vectors to binary class matrices
from keras.utils import np_utils
Y_train = np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)

import matplotlib.pyplot as plt
for i in range(16):
  plt.subplot(4,4,i+1)
  plt.axis('off')
  plt.imshow(X_train[i].reshape((28,28)))
plt.show()