#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys,os
os.environ['PATH'] = '/usr/bin/:/usr/sbin/'
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)

print 'mnist数据下载完成'

def printDataCount():
    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


def tfWorkerDo():

    sess = tf.InteractiveSession()

    in_units = 784
    h1_units = 300

    w1 = tf.Variable(tf.truncated_normal([in_units,h1_units],stddev=0.1))
    b1 = tf.Variable(tf.zeros([h1_units]))
    w2 = tf.Variable(tf.zeros([h1_units,10]))
    b2 = tf.Variable(tf.zeros([10]))

    x = tf.placeholder(tf.float32,[None,in_units])
    keep_prob = tf.placeholder(tf.float32)

    hidden1 = tf.nn.relu(tf.matmul(x,w1) + b1)
    hidden1_drop = tf.nn.dropout(hidden1, keep_prob)
    y = tf.nn.softmax(tf.matmul(hidden1_drop,w2) + b2)

    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.AdagradOptimizer(0.3).minimize(cross_entropy)

    tf.global_variables_initializer().run()

    for i in range(3000):
        batch_xs,batch_ys = mnist.train.next_batch(100)
        train_step.run({x:batch_xs,y_:batch_ys,keep_prob:0.75})

        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,keep_prob:1.0})
    
def tfWorkerNotHidden():
    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})

def main():
   
    printDataCount()
    print '开始训练数据'
    tfWorkerDo()
    # tfWorkerDo(patobj)
if __name__ == '__main__':
    main()