import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, LeakyReLU, Conv2D
from keras.optimizers import RMSprop
from keras import backend as K
K.tensorflow_backend._get_available_gpus()
from matplotlib.pyplot import imshow
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data

# 讀入 MNIST
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# the data, shuffled and split between train and test sets
x_train = x_train.reshape(60000 , 784)
x_test = x_test.reshape(10000, 784)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

# convert class vectors to binary class matrices
num_classes = 10
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

print(x_train.shape, 'train samples')
print(x_test.shape, 'test samples')
print(y_train.shape, 'train labels')
print(y_test.shape, 'test labels')

# modeling
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# training
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# fitting
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# testing
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