#匯入資料
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from keras.datasets import mnist

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
print(train_images.shape)
print(train_images.dtype)
print(train_labels.shape)

print(test_images.shape)
print
 
(test_labels.shape)

#影象轉換
train_images = train_images.reshape((60000, 28*28))
train_images = train_images.astype('float32')/255

test_images = test_images.reshape((10000, 28*28))
test_images = test_images.astype('float32')/ 255

#構建網路
from keras import models
from keras import layers

network = models.Sequential()
network.add(layers.Dense(
 
512, activation = 'relu', input_shape = (28*28, )))
network.add(layers.Dense(10, activation = 'softmax'))

network.compile(optimizer = 'rmsprop', loss = 'categorical_crossentropy', metrics = ['accuracy'])

#準備標籤
from keras.utils import to_categorical

train_labels = to_categorical(train_labels)
test_labels 
 
= to_categorical(test_labels)

#模型擬合
network.fit(train_images, train_labels, epochs=5, batch_size = 128)

#測試模型
test_loss, test_acc = network.evaluate(test_images, test_labels)
print('test_acc:', test_acc)