#classification
#2018.03.15
import tensorflow as tf
import numpy as np

import matplotlib as mpl
mpl.use("Agg")
import matplotlib.pyplot as plt

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('MNIST_data',one_hot=True)

def add_layer(inputs,in_size,out_size,activation_function = None):
    Weights = tf.Variable(tf.random_normal([in_size,out_size]))
    biases = tf.Variable(tf.zeros([1,out_size]) + 0.1)
    Wx_plus_b = tf.matmul(inputs,Weights) + biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs

def compute_accuracy(v_xs,v_ys):
    global prediction
    y_pre = sess.run(prediction,feed_dict= {xs:v_xs})
    correct_prediction = tf.equal(tf.arg_max(y_pre,1),tf.arg_max(v_ys,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    result = sess.run(accuracy,feed_dict= {xs:v_xs,ys:v_ys})
    return result

xs = tf.placeholder(tf.float32,[None,28*28])
ys = tf.placeholder(tf.float32,[None,10])

prediction = add_layer(xs,28*28,10,activation_function=tf.nn.softmax)

#loss
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy)

sess = tf.Session()

init = tf.global_variables_initializer()
sess.run(init)

fid = open('accuracy.txt','w')
fid.truncate()#清空文件

for i in range(3001):
    batch_xs,batch_ys = mnist.train.next_batch(100)
    sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys})
    if i % 50 ==0:
        acc = compute_accuracy(mnist.test.images,mnist.test.labels)
        print(i,'正确率',acc)
        fid.write(str(i) + '     ' + 'acc='  + str(acc) + '\n')


fid.close()








