#!/home/luoqiang/anaconda3/bin/
# -*- coding:utf-8 -*-

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

mnist = input_data.read_data_sets('MNISR_data', one_hot = True)
print (mnist.train.images.shape, mnist.train.labels.shape)
print (mnist.test.images.shape, mnist.test.labels.shape)
print (mnist.validation.images.shape, mnist.validation.labels.shape)

sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, [None, 784])

W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

y = tf.nn.softmax(tf.matmul(x, W) + b)

y_ = tf.placeholder(tf.float32, [None, 10])

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices = [1]))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
tf.global_variables_initializer().run()

for i in range(1000):
	batch_xs, batch_ys = mnist.train.next_batch(100)
	train_step.run({x: batch_xs, y_:batch_ys})

	correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
	accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
	print(accuracy.eval({x: mnist.test.images, y_: mnist.test.labels}))
