# reading the training images matrix
train =  mnist.train.images
image_dim = 28
# printing some images with their labels
plt.figure(1)

for i in range(25):
    plt.subplot(5,5,i+1)
    flatten_img = train[i]
    # recreating the 28*28 image matrix from feature vector
    img = flatten_img.reshape([image_dim,image_dim])
    # Scaling it to [0,255]  range -- greyscale
    img = np.uint8(img*255)
    plt.axis('off')
    plt.imshow(img,cmap='gray')
plt.show()